diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..4b604e5e3ff7dc946080ef1717879c44bd7a1ea6 --- /dev/null +++ b/README.md @@ -0,0 +1,12 @@ +# Core ML SD-Turbo (640x384) + +This repository contains pre-converted Core ML models of SD-Turbo for use on +Apple silicon devices. I changed the resolution to make it handy for generating +widescreen images. + +- Original model: [SD-Turbo] +- Image resolution: 640x384 +- Tested on [keijiro/ml-stable-diffusion] v1.2.0 + +[SD-Turbo]: https://huggingface.co/stabilityai/sd-turbo +[keijiro/ml-stable-diffusion]: https://github.com/keijiro/ml-stable-diffusion diff --git a/original/compiled/TextEncoder.mlmodelc/analytics/coremldata.bin b/original/compiled/TextEncoder.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..126e0b36a6fb4bfbf4827b523b7324e44434771f --- /dev/null +++ b/original/compiled/TextEncoder.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:920ce4c4b8e64b36950857debdfb2c770c3cbfccdde2c7b5088411f96163e1e2 +size 243 diff --git a/original/compiled/TextEncoder.mlmodelc/coremldata.bin b/original/compiled/TextEncoder.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..958923523f0ee963510a47bf4ec92225350b3264 --- /dev/null +++ b/original/compiled/TextEncoder.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:74e23873394ad211ea22dcd3653e626f57353af8b938ffc07899daf1e44596a4 +size 933 diff --git a/original/compiled/TextEncoder.mlmodelc/metadata.json b/original/compiled/TextEncoder.mlmodelc/metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..34d3f24793dd635bed7991aa0f1f87ff133baaaa --- /dev/null +++ b/original/compiled/TextEncoder.mlmodelc/metadata.json @@ -0,0 +1,84 @@ +[ + { + "shortDescription" : "Stable Diffusion generates images conditioned on text and\/or other images as input through the diffusion process. Please refer to https:\/\/arxiv.org\/abs\/2112.10752 for details.", + "metadataOutputVersion" : "3.0", + "outputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 77 × 1024)", + "shortDescription" : "The token embeddings as encoded by the Transformer model", + "shape" : "[1, 77, 1024]", + "name" : "last_hidden_state", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 1024)", + "shortDescription" : "The version of the `last_hidden_state` output after pooling", + "shape" : "[1, 1024]", + "name" : "pooled_outputs", + "type" : "MultiArray" + } + ], + "version" : "\/Users\/keijiro\/Documents\/StableDiffusion\/sd-turbo", + "modelParameters" : [ + + ], + "author" : "Please refer to the Model Card available at huggingface.co\/\/Users\/keijiro\/Documents\/StableDiffusion\/sd-turbo", + "specificationVersion" : 7, + "storagePrecision" : "Float16", + "license" : "OpenRAIL (https:\/\/huggingface.co\/spaces\/CompVis\/stable-diffusion-license)", + "mlProgramOperationTypeHistogram" : { + "Ios16.cast" : 3, + "Ios16.mul" : 23, + "Ios16.layerNorm" : 47, + "Stack" : 1, + "Transpose" : 115, + "Ios16.linear" : 138, + "Ios16.add" : 70, + "Ios16.matmul" : 46, + "Ios16.gelu" : 23, + "Ios16.softmax" : 23, + "Ios16.gatherNd" : 1, + "Ios16.gather" : 1, + "Ios16.reshape" : 230, + "Ios16.reduceArgmax" : 1 + }, + "computePrecision" : "Mixed (Float16, Float32, Int32)", + "isUpdatable" : "0", + "availability" : { + "macOS" : "13.0", + "tvOS" : "16.0", + "visionOS" : "1.0", + "watchOS" : "9.0", + "iOS" : "16.0", + "macCatalyst" : "16.0" + }, + "modelType" : { + "name" : "MLModelType_mlProgram" + }, + "inputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 77)", + "shortDescription" : "The token ids that represent the input text", + "shape" : "[1, 77]", + "name" : "input_ids", + "type" : "MultiArray" + } + ], + "userDefinedMetadata" : { + "com.github.apple.coremltools.source_dialect" : "TorchScript", + "com.github.apple.coremltools.source" : "torch==2.1.2", + "com.github.apple.coremltools.version" : "7.1" + }, + "generatedClassName" : "Stable_Diffusion_version__Users_keijiro_Documents_StableDiffusion_sd_turbo_text_encoder", + "method" : "predict" + } +] \ No newline at end of file diff --git a/original/compiled/TextEncoder.mlmodelc/model.mil b/original/compiled/TextEncoder.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..a5b51c363921953414b7752dea42612d127de13b --- /dev/null +++ b/original/compiled/TextEncoder.mlmodelc/model.mil @@ -0,0 +1,1642 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3304.5.2"}, {"coremlc-version", "3304.6.2"}, {"coremltools-component-torch", "2.1.2"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "7.1"}})] +{ + func main(tensor input_ids) { + tensor var_5 = const()[name = tensor("op_5"), val = tensor(-1)]; + tensor var_6 = const()[name = tensor("op_6"), val = tensor(false)]; + tensor cast_1_dtype_0 = const()[name = tensor("cast_1_dtype_0"), val = tensor("int32")]; + tensor inputs_embeds_axis_0 = const()[name = tensor("inputs_embeds_axis_0"), val = tensor(0)]; + tensor inputs_embeds_batch_dims_0 = const()[name = tensor("inputs_embeds_batch_dims_0"), val = tensor(0)]; + tensor text_encoder_text_model_embeddings_token_embedding_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_embeddings_token_embedding_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor cast_239 = cast(dtype = cast_1_dtype_0, x = input_ids)[name = tensor("cast_239")]; + tensor inputs_embeds_cast_fp16 = gather(axis = inputs_embeds_axis_0, batch_dims = inputs_embeds_batch_dims_0, indices = cast_239, x = text_encoder_text_model_embeddings_token_embedding_weight_to_fp16)[name = tensor("inputs_embeds_cast_fp16")]; + tensor position_embeddings_to_fp16 = const()[name = tensor("position_embeddings_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(101187712)))]; + tensor input_3_cast_fp16 = add(x = inputs_embeds_cast_fp16, y = position_embeddings_to_fp16)[name = tensor("input_3_cast_fp16")]; + tensor hidden_states_1_axes_0 = const()[name = tensor("hidden_states_1_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_0_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(101345472)))]; + tensor text_encoder_text_model_encoder_layers_0_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(101347584)))]; + tensor var_15_to_fp16 = const()[name = tensor("op_15_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_1_cast_fp16 = layer_norm(axes = hidden_states_1_axes_0, beta = text_encoder_text_model_encoder_layers_0_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_0_layer_norm1_weight_to_fp16, x = input_3_cast_fp16)[name = tensor("hidden_states_1_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_0_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(101349696)))]; + tensor text_encoder_text_model_encoder_layers_0_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(103446912)))]; + tensor linear_0_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_0_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_0_self_attn_q_proj_weight_to_fp16, x = hidden_states_1_cast_fp16)[name = tensor("linear_0_cast_fp16")]; + tensor var_129_to_fp16 = const()[name = tensor("op_129_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_5_cast_fp16 = mul(x = linear_0_cast_fp16, y = var_129_to_fp16)[name = tensor("tensor_5_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_0_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(103449024)))]; + tensor text_encoder_text_model_encoder_layers_0_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(105546240)))]; + tensor linear_1_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_0_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_0_self_attn_k_proj_weight_to_fp16, x = hidden_states_1_cast_fp16)[name = tensor("linear_1_cast_fp16")]; + tensor var_134 = const()[name = tensor("op_134"), val = tensor([1, -1, 16, 64])]; + tensor var_135_cast_fp16 = reshape(shape = var_134, x = linear_1_cast_fp16)[name = tensor("op_135_cast_fp16")]; + tensor var_136_perm_0 = const()[name = tensor("op_136_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_0_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(105548352)))]; + tensor text_encoder_text_model_encoder_layers_0_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107645568)))]; + tensor linear_2_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_0_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_0_self_attn_v_proj_weight_to_fp16, x = hidden_states_1_cast_fp16)[name = tensor("linear_2_cast_fp16")]; + tensor var_141 = const()[name = tensor("op_141"), val = tensor([1, -1, 16, 64])]; + tensor var_142_cast_fp16 = reshape(shape = var_141, x = linear_2_cast_fp16)[name = tensor("op_142_cast_fp16")]; + tensor var_143_perm_0 = const()[name = tensor("op_143_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_150 = const()[name = tensor("op_150"), val = tensor([1, 77, 16, 64])]; + tensor var_151_cast_fp16 = reshape(shape = var_150, x = tensor_5_cast_fp16)[name = tensor("op_151_cast_fp16")]; + tensor var_152_perm_0 = const()[name = tensor("op_152_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_154 = const()[name = tensor("op_154"), val = tensor([16, -1, 64])]; + tensor transpose_113 = transpose(perm = var_152_perm_0, x = var_151_cast_fp16)[name = tensor("transpose_113")]; + tensor query_states_1_cast_fp16 = reshape(shape = var_154, x = transpose_113)[name = tensor("query_states_1_cast_fp16")]; + tensor var_156 = const()[name = tensor("op_156"), val = tensor([16, -1, 64])]; + tensor transpose_115 = transpose(perm = var_136_perm_0, x = var_135_cast_fp16)[name = tensor("transpose_115")]; + tensor key_states_3_cast_fp16 = reshape(shape = var_156, x = transpose_115)[name = tensor("key_states_3_cast_fp16")]; + tensor var_158 = const()[name = tensor("op_158"), val = tensor([16, -1, 64])]; + tensor transpose_114 = transpose(perm = var_143_perm_0, x = var_142_cast_fp16)[name = tensor("transpose_114")]; + tensor value_states_3_cast_fp16 = reshape(shape = var_158, x = transpose_114)[name = tensor("value_states_3_cast_fp16")]; + tensor var_161_perm_0 = const()[name = tensor("op_161_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_1_transpose_x_0 = const()[name = tensor("attn_weights_1_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_1_transpose_y_0 = const()[name = tensor("attn_weights_1_transpose_y_0"), val = tensor(false)]; + tensor transpose_112 = transpose(perm = var_161_perm_0, x = key_states_3_cast_fp16)[name = tensor("transpose_112")]; + tensor attn_weights_1_cast_fp16 = matmul(transpose_x = attn_weights_1_transpose_x_0, transpose_y = attn_weights_1_transpose_y_0, x = query_states_1_cast_fp16, y = transpose_112)[name = tensor("attn_weights_1_cast_fp16")]; + tensor var_163 = const()[name = tensor("op_163"), val = tensor([1, 16, 77, 77])]; + tensor var_164_cast_fp16 = reshape(shape = var_163, x = attn_weights_1_cast_fp16)[name = tensor("op_164_cast_fp16")]; + tensor var_57_to_fp16 = const()[name = tensor("op_57_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107647680)))]; + tensor attn_weights_3_cast_fp16 = add(x = var_164_cast_fp16, y = var_57_to_fp16)[name = tensor("attn_weights_3_cast_fp16")]; + tensor var_169 = const()[name = tensor("op_169"), val = tensor([16, 77, 77])]; + tensor input_5_cast_fp16 = reshape(shape = var_169, x = attn_weights_3_cast_fp16)[name = tensor("input_5_cast_fp16")]; + tensor input_7_cast_fp16 = softmax(axis = var_5, x = input_5_cast_fp16)[name = tensor("input_7_cast_fp16")]; + tensor attn_output_1_transpose_x_0 = const()[name = tensor("attn_output_1_transpose_x_0"), val = tensor(false)]; + tensor attn_output_1_transpose_y_0 = const()[name = tensor("attn_output_1_transpose_y_0"), val = tensor(false)]; + tensor attn_output_1_cast_fp16 = matmul(transpose_x = attn_output_1_transpose_x_0, transpose_y = attn_output_1_transpose_y_0, x = input_7_cast_fp16, y = value_states_3_cast_fp16)[name = tensor("attn_output_1_cast_fp16")]; + tensor var_174 = const()[name = tensor("op_174"), val = tensor([1, 16, 77, 64])]; + tensor attn_output_3_cast_fp16 = reshape(shape = var_174, x = attn_output_1_cast_fp16)[name = tensor("attn_output_3_cast_fp16")]; + tensor attn_output_5_perm_0 = const()[name = tensor("attn_output_5_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_177 = const()[name = tensor("op_177"), val = tensor([1, 77, 1024])]; + tensor transpose_111 = transpose(perm = attn_output_5_perm_0, x = attn_output_3_cast_fp16)[name = tensor("transpose_111")]; + tensor input_9_cast_fp16 = reshape(shape = var_177, x = transpose_111)[name = tensor("input_9_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_0_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107659648)))]; + tensor text_encoder_text_model_encoder_layers_0_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109756864)))]; + tensor linear_3_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_0_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_0_self_attn_out_proj_weight_to_fp16, x = input_9_cast_fp16)[name = tensor("linear_3_cast_fp16")]; + tensor input_11_cast_fp16 = add(x = input_3_cast_fp16, y = linear_3_cast_fp16)[name = tensor("input_11_cast_fp16")]; + tensor input_13_axes_0 = const()[name = tensor("input_13_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_0_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109758976)))]; + tensor text_encoder_text_model_encoder_layers_0_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109761088)))]; + tensor input_13_cast_fp16 = layer_norm(axes = input_13_axes_0, beta = text_encoder_text_model_encoder_layers_0_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_0_layer_norm2_weight_to_fp16, x = input_11_cast_fp16)[name = tensor("input_13_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_0_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109763200)))]; + tensor text_encoder_text_model_encoder_layers_0_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(118151872)))]; + tensor linear_4_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_0_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_0_mlp_fc1_weight_to_fp16, x = input_13_cast_fp16)[name = tensor("linear_4_cast_fp16")]; + tensor input_17_mode_0 = const()[name = tensor("input_17_mode_0"), val = tensor("EXACT")]; + tensor input_17_cast_fp16 = gelu(mode = input_17_mode_0, x = linear_4_cast_fp16)[name = tensor("input_17_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_0_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(118160128)))]; + tensor text_encoder_text_model_encoder_layers_0_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(126548800)))]; + tensor linear_5_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_0_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_0_mlp_fc2_weight_to_fp16, x = input_17_cast_fp16)[name = tensor("linear_5_cast_fp16")]; + tensor input_19_cast_fp16 = add(x = input_11_cast_fp16, y = linear_5_cast_fp16)[name = tensor("input_19_cast_fp16")]; + tensor hidden_states_7_axes_0 = const()[name = tensor("hidden_states_7_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_1_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(126550912)))]; + tensor text_encoder_text_model_encoder_layers_1_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(126553024)))]; + tensor hidden_states_7_cast_fp16 = layer_norm(axes = hidden_states_7_axes_0, beta = text_encoder_text_model_encoder_layers_1_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_1_layer_norm1_weight_to_fp16, x = input_19_cast_fp16)[name = tensor("hidden_states_7_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_1_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(126555136)))]; + tensor text_encoder_text_model_encoder_layers_1_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(128652352)))]; + tensor linear_6_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_1_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_1_self_attn_q_proj_weight_to_fp16, x = hidden_states_7_cast_fp16)[name = tensor("linear_6_cast_fp16")]; + tensor var_216_to_fp16 = const()[name = tensor("op_216_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_11_cast_fp16 = mul(x = linear_6_cast_fp16, y = var_216_to_fp16)[name = tensor("tensor_11_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_1_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(128654464)))]; + tensor text_encoder_text_model_encoder_layers_1_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(130751680)))]; + tensor linear_7_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_1_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_1_self_attn_k_proj_weight_to_fp16, x = hidden_states_7_cast_fp16)[name = tensor("linear_7_cast_fp16")]; + tensor var_221 = const()[name = tensor("op_221"), val = tensor([1, -1, 16, 64])]; + tensor var_222_cast_fp16 = reshape(shape = var_221, x = linear_7_cast_fp16)[name = tensor("op_222_cast_fp16")]; + tensor var_223_perm_0 = const()[name = tensor("op_223_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_1_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(130753792)))]; + tensor text_encoder_text_model_encoder_layers_1_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132851008)))]; + tensor linear_8_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_1_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_1_self_attn_v_proj_weight_to_fp16, x = hidden_states_7_cast_fp16)[name = tensor("linear_8_cast_fp16")]; + tensor var_228 = const()[name = tensor("op_228"), val = tensor([1, -1, 16, 64])]; + tensor var_229_cast_fp16 = reshape(shape = var_228, x = linear_8_cast_fp16)[name = tensor("op_229_cast_fp16")]; + tensor var_230_perm_0 = const()[name = tensor("op_230_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_237 = const()[name = tensor("op_237"), val = tensor([1, 77, 16, 64])]; + tensor var_238_cast_fp16 = reshape(shape = var_237, x = tensor_11_cast_fp16)[name = tensor("op_238_cast_fp16")]; + tensor var_239_perm_0 = const()[name = tensor("op_239_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_241 = const()[name = tensor("op_241"), val = tensor([16, -1, 64])]; + tensor transpose_108 = transpose(perm = var_239_perm_0, x = var_238_cast_fp16)[name = tensor("transpose_108")]; + tensor query_states_3_cast_fp16 = reshape(shape = var_241, x = transpose_108)[name = tensor("query_states_3_cast_fp16")]; + tensor var_243 = const()[name = tensor("op_243"), val = tensor([16, -1, 64])]; + tensor transpose_110 = transpose(perm = var_223_perm_0, x = var_222_cast_fp16)[name = tensor("transpose_110")]; + tensor key_states_7_cast_fp16 = reshape(shape = var_243, x = transpose_110)[name = tensor("key_states_7_cast_fp16")]; + tensor var_245 = const()[name = tensor("op_245"), val = tensor([16, -1, 64])]; + tensor transpose_109 = transpose(perm = var_230_perm_0, x = var_229_cast_fp16)[name = tensor("transpose_109")]; + tensor value_states_7_cast_fp16 = reshape(shape = var_245, x = transpose_109)[name = tensor("value_states_7_cast_fp16")]; + tensor var_248_perm_0 = const()[name = tensor("op_248_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_7_transpose_x_0 = const()[name = tensor("attn_weights_7_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_7_transpose_y_0 = const()[name = tensor("attn_weights_7_transpose_y_0"), val = tensor(false)]; + tensor transpose_107 = transpose(perm = var_248_perm_0, x = key_states_7_cast_fp16)[name = tensor("transpose_107")]; + tensor attn_weights_7_cast_fp16 = matmul(transpose_x = attn_weights_7_transpose_x_0, transpose_y = attn_weights_7_transpose_y_0, x = query_states_3_cast_fp16, y = transpose_107)[name = tensor("attn_weights_7_cast_fp16")]; + tensor var_250 = const()[name = tensor("op_250"), val = tensor([1, 16, 77, 77])]; + tensor var_251_cast_fp16 = reshape(shape = var_250, x = attn_weights_7_cast_fp16)[name = tensor("op_251_cast_fp16")]; + tensor attn_weights_9_cast_fp16 = add(x = var_251_cast_fp16, y = var_57_to_fp16)[name = tensor("attn_weights_9_cast_fp16")]; + tensor var_256 = const()[name = tensor("op_256"), val = tensor([16, 77, 77])]; + tensor input_21_cast_fp16 = reshape(shape = var_256, x = attn_weights_9_cast_fp16)[name = tensor("input_21_cast_fp16")]; + tensor input_23_cast_fp16 = softmax(axis = var_5, x = input_21_cast_fp16)[name = tensor("input_23_cast_fp16")]; + tensor attn_output_7_transpose_x_0 = const()[name = tensor("attn_output_7_transpose_x_0"), val = tensor(false)]; + tensor attn_output_7_transpose_y_0 = const()[name = tensor("attn_output_7_transpose_y_0"), val = tensor(false)]; + tensor attn_output_7_cast_fp16 = matmul(transpose_x = attn_output_7_transpose_x_0, transpose_y = attn_output_7_transpose_y_0, x = input_23_cast_fp16, y = value_states_7_cast_fp16)[name = tensor("attn_output_7_cast_fp16")]; + tensor var_261 = const()[name = tensor("op_261"), val = tensor([1, 16, 77, 64])]; + tensor attn_output_9_cast_fp16 = reshape(shape = var_261, x = attn_output_7_cast_fp16)[name = tensor("attn_output_9_cast_fp16")]; + tensor attn_output_11_perm_0 = const()[name = tensor("attn_output_11_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_264 = const()[name = tensor("op_264"), val = tensor([1, 77, 1024])]; + tensor transpose_106 = transpose(perm = attn_output_11_perm_0, x = attn_output_9_cast_fp16)[name = tensor("transpose_106")]; + tensor input_25_cast_fp16 = reshape(shape = var_264, x = transpose_106)[name = tensor("input_25_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_1_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132853120)))]; + tensor text_encoder_text_model_encoder_layers_1_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134950336)))]; + tensor linear_9_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_1_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_1_self_attn_out_proj_weight_to_fp16, x = input_25_cast_fp16)[name = tensor("linear_9_cast_fp16")]; + tensor input_27_cast_fp16 = add(x = input_19_cast_fp16, y = linear_9_cast_fp16)[name = tensor("input_27_cast_fp16")]; + tensor input_29_axes_0 = const()[name = tensor("input_29_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_1_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134952448)))]; + tensor text_encoder_text_model_encoder_layers_1_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134954560)))]; + tensor input_29_cast_fp16 = layer_norm(axes = input_29_axes_0, beta = text_encoder_text_model_encoder_layers_1_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_1_layer_norm2_weight_to_fp16, x = input_27_cast_fp16)[name = tensor("input_29_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_1_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134956672)))]; + tensor text_encoder_text_model_encoder_layers_1_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(143345344)))]; + tensor linear_10_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_1_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_1_mlp_fc1_weight_to_fp16, x = input_29_cast_fp16)[name = tensor("linear_10_cast_fp16")]; + tensor input_33_mode_0 = const()[name = tensor("input_33_mode_0"), val = tensor("EXACT")]; + tensor input_33_cast_fp16 = gelu(mode = input_33_mode_0, x = linear_10_cast_fp16)[name = tensor("input_33_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_1_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(143353600)))]; + tensor text_encoder_text_model_encoder_layers_1_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(151742272)))]; + tensor linear_11_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_1_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_1_mlp_fc2_weight_to_fp16, x = input_33_cast_fp16)[name = tensor("linear_11_cast_fp16")]; + tensor input_35_cast_fp16 = add(x = input_27_cast_fp16, y = linear_11_cast_fp16)[name = tensor("input_35_cast_fp16")]; + tensor hidden_states_13_axes_0 = const()[name = tensor("hidden_states_13_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_2_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(151744384)))]; + tensor text_encoder_text_model_encoder_layers_2_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(151746496)))]; + tensor hidden_states_13_cast_fp16 = layer_norm(axes = hidden_states_13_axes_0, beta = text_encoder_text_model_encoder_layers_2_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_2_layer_norm1_weight_to_fp16, x = input_35_cast_fp16)[name = tensor("hidden_states_13_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_2_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(151748608)))]; + tensor text_encoder_text_model_encoder_layers_2_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(153845824)))]; + tensor linear_12_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_2_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_2_self_attn_q_proj_weight_to_fp16, x = hidden_states_13_cast_fp16)[name = tensor("linear_12_cast_fp16")]; + tensor var_303_to_fp16 = const()[name = tensor("op_303_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_17_cast_fp16 = mul(x = linear_12_cast_fp16, y = var_303_to_fp16)[name = tensor("tensor_17_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_2_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(153847936)))]; + tensor text_encoder_text_model_encoder_layers_2_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(155945152)))]; + tensor linear_13_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_2_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_2_self_attn_k_proj_weight_to_fp16, x = hidden_states_13_cast_fp16)[name = tensor("linear_13_cast_fp16")]; + tensor var_308 = const()[name = tensor("op_308"), val = tensor([1, -1, 16, 64])]; + tensor var_309_cast_fp16 = reshape(shape = var_308, x = linear_13_cast_fp16)[name = tensor("op_309_cast_fp16")]; + tensor var_310_perm_0 = const()[name = tensor("op_310_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_2_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(155947264)))]; + tensor text_encoder_text_model_encoder_layers_2_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(158044480)))]; + tensor linear_14_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_2_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_2_self_attn_v_proj_weight_to_fp16, x = hidden_states_13_cast_fp16)[name = tensor("linear_14_cast_fp16")]; + tensor var_315 = const()[name = tensor("op_315"), val = tensor([1, -1, 16, 64])]; + tensor var_316_cast_fp16 = reshape(shape = var_315, x = linear_14_cast_fp16)[name = tensor("op_316_cast_fp16")]; + tensor var_317_perm_0 = const()[name = tensor("op_317_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_324 = const()[name = tensor("op_324"), val = tensor([1, 77, 16, 64])]; + tensor var_325_cast_fp16 = reshape(shape = var_324, x = tensor_17_cast_fp16)[name = tensor("op_325_cast_fp16")]; + tensor var_326_perm_0 = const()[name = tensor("op_326_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_328 = const()[name = tensor("op_328"), val = tensor([16, -1, 64])]; + tensor transpose_103 = transpose(perm = var_326_perm_0, x = var_325_cast_fp16)[name = tensor("transpose_103")]; + tensor query_states_5_cast_fp16 = reshape(shape = var_328, x = transpose_103)[name = tensor("query_states_5_cast_fp16")]; + tensor var_330 = const()[name = tensor("op_330"), val = tensor([16, -1, 64])]; + tensor transpose_105 = transpose(perm = var_310_perm_0, x = var_309_cast_fp16)[name = tensor("transpose_105")]; + tensor key_states_11_cast_fp16 = reshape(shape = var_330, x = transpose_105)[name = tensor("key_states_11_cast_fp16")]; + tensor var_332 = const()[name = tensor("op_332"), val = tensor([16, -1, 64])]; + tensor transpose_104 = transpose(perm = var_317_perm_0, x = var_316_cast_fp16)[name = tensor("transpose_104")]; + tensor value_states_11_cast_fp16 = reshape(shape = var_332, x = transpose_104)[name = tensor("value_states_11_cast_fp16")]; + tensor var_335_perm_0 = const()[name = tensor("op_335_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_13_transpose_x_0 = const()[name = tensor("attn_weights_13_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_13_transpose_y_0 = const()[name = tensor("attn_weights_13_transpose_y_0"), val = tensor(false)]; + tensor transpose_102 = transpose(perm = var_335_perm_0, x = key_states_11_cast_fp16)[name = tensor("transpose_102")]; + tensor attn_weights_13_cast_fp16 = matmul(transpose_x = attn_weights_13_transpose_x_0, transpose_y = attn_weights_13_transpose_y_0, x = query_states_5_cast_fp16, y = transpose_102)[name = tensor("attn_weights_13_cast_fp16")]; + tensor var_337 = const()[name = tensor("op_337"), val = tensor([1, 16, 77, 77])]; + tensor var_338_cast_fp16 = reshape(shape = var_337, x = attn_weights_13_cast_fp16)[name = tensor("op_338_cast_fp16")]; + tensor attn_weights_15_cast_fp16 = add(x = var_338_cast_fp16, y = var_57_to_fp16)[name = tensor("attn_weights_15_cast_fp16")]; + tensor var_343 = const()[name = tensor("op_343"), val = tensor([16, 77, 77])]; + tensor input_37_cast_fp16 = reshape(shape = var_343, x = attn_weights_15_cast_fp16)[name = tensor("input_37_cast_fp16")]; + tensor input_39_cast_fp16 = softmax(axis = var_5, x = input_37_cast_fp16)[name = tensor("input_39_cast_fp16")]; + tensor attn_output_13_transpose_x_0 = const()[name = tensor("attn_output_13_transpose_x_0"), val = tensor(false)]; + tensor attn_output_13_transpose_y_0 = const()[name = tensor("attn_output_13_transpose_y_0"), val = tensor(false)]; + tensor attn_output_13_cast_fp16 = matmul(transpose_x = attn_output_13_transpose_x_0, transpose_y = attn_output_13_transpose_y_0, x = input_39_cast_fp16, y = value_states_11_cast_fp16)[name = tensor("attn_output_13_cast_fp16")]; + tensor var_348 = const()[name = tensor("op_348"), val = tensor([1, 16, 77, 64])]; + tensor attn_output_15_cast_fp16 = reshape(shape = var_348, x = attn_output_13_cast_fp16)[name = tensor("attn_output_15_cast_fp16")]; + tensor attn_output_17_perm_0 = const()[name = tensor("attn_output_17_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_351 = const()[name = tensor("op_351"), val = tensor([1, 77, 1024])]; + tensor transpose_101 = transpose(perm = attn_output_17_perm_0, x = attn_output_15_cast_fp16)[name = tensor("transpose_101")]; + tensor input_41_cast_fp16 = reshape(shape = var_351, x = transpose_101)[name = tensor("input_41_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_2_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(158046592)))]; + tensor text_encoder_text_model_encoder_layers_2_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160143808)))]; + tensor linear_15_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_2_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_2_self_attn_out_proj_weight_to_fp16, x = input_41_cast_fp16)[name = tensor("linear_15_cast_fp16")]; + tensor input_43_cast_fp16 = add(x = input_35_cast_fp16, y = linear_15_cast_fp16)[name = tensor("input_43_cast_fp16")]; + tensor input_45_axes_0 = const()[name = tensor("input_45_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_2_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160145920)))]; + tensor text_encoder_text_model_encoder_layers_2_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160148032)))]; + tensor input_45_cast_fp16 = layer_norm(axes = input_45_axes_0, beta = text_encoder_text_model_encoder_layers_2_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_2_layer_norm2_weight_to_fp16, x = input_43_cast_fp16)[name = tensor("input_45_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_2_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160150144)))]; + tensor text_encoder_text_model_encoder_layers_2_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(168538816)))]; + tensor linear_16_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_2_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_2_mlp_fc1_weight_to_fp16, x = input_45_cast_fp16)[name = tensor("linear_16_cast_fp16")]; + tensor input_49_mode_0 = const()[name = tensor("input_49_mode_0"), val = tensor("EXACT")]; + tensor input_49_cast_fp16 = gelu(mode = input_49_mode_0, x = linear_16_cast_fp16)[name = tensor("input_49_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_2_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(168547072)))]; + tensor text_encoder_text_model_encoder_layers_2_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(176935744)))]; + tensor linear_17_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_2_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_2_mlp_fc2_weight_to_fp16, x = input_49_cast_fp16)[name = tensor("linear_17_cast_fp16")]; + tensor input_51_cast_fp16 = add(x = input_43_cast_fp16, y = linear_17_cast_fp16)[name = tensor("input_51_cast_fp16")]; + tensor hidden_states_19_axes_0 = const()[name = tensor("hidden_states_19_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_3_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(176937856)))]; + tensor text_encoder_text_model_encoder_layers_3_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(176939968)))]; + tensor hidden_states_19_cast_fp16 = layer_norm(axes = hidden_states_19_axes_0, beta = text_encoder_text_model_encoder_layers_3_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_3_layer_norm1_weight_to_fp16, x = input_51_cast_fp16)[name = tensor("hidden_states_19_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_3_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(176942080)))]; + tensor text_encoder_text_model_encoder_layers_3_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(179039296)))]; + tensor linear_18_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_3_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_3_self_attn_q_proj_weight_to_fp16, x = hidden_states_19_cast_fp16)[name = tensor("linear_18_cast_fp16")]; + tensor var_390_to_fp16 = const()[name = tensor("op_390_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_23_cast_fp16 = mul(x = linear_18_cast_fp16, y = var_390_to_fp16)[name = tensor("tensor_23_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_3_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(179041408)))]; + tensor text_encoder_text_model_encoder_layers_3_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181138624)))]; + tensor linear_19_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_3_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_3_self_attn_k_proj_weight_to_fp16, x = hidden_states_19_cast_fp16)[name = tensor("linear_19_cast_fp16")]; + tensor var_395 = const()[name = tensor("op_395"), val = tensor([1, -1, 16, 64])]; + tensor var_396_cast_fp16 = reshape(shape = var_395, x = linear_19_cast_fp16)[name = tensor("op_396_cast_fp16")]; + tensor var_397_perm_0 = const()[name = tensor("op_397_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_3_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181140736)))]; + tensor text_encoder_text_model_encoder_layers_3_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183237952)))]; + tensor linear_20_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_3_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_3_self_attn_v_proj_weight_to_fp16, x = hidden_states_19_cast_fp16)[name = tensor("linear_20_cast_fp16")]; + tensor var_402 = const()[name = tensor("op_402"), val = tensor([1, -1, 16, 64])]; + tensor var_403_cast_fp16 = reshape(shape = var_402, x = linear_20_cast_fp16)[name = tensor("op_403_cast_fp16")]; + tensor var_404_perm_0 = const()[name = tensor("op_404_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_411 = const()[name = tensor("op_411"), val = tensor([1, 77, 16, 64])]; + tensor var_412_cast_fp16 = reshape(shape = var_411, x = tensor_23_cast_fp16)[name = tensor("op_412_cast_fp16")]; + tensor var_413_perm_0 = const()[name = tensor("op_413_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_415 = const()[name = tensor("op_415"), val = tensor([16, -1, 64])]; + tensor transpose_98 = transpose(perm = var_413_perm_0, x = var_412_cast_fp16)[name = tensor("transpose_98")]; + tensor query_states_7_cast_fp16 = reshape(shape = var_415, x = transpose_98)[name = tensor("query_states_7_cast_fp16")]; + tensor var_417 = const()[name = tensor("op_417"), val = tensor([16, -1, 64])]; + tensor transpose_100 = transpose(perm = var_397_perm_0, x = var_396_cast_fp16)[name = tensor("transpose_100")]; + tensor key_states_15_cast_fp16 = reshape(shape = var_417, x = transpose_100)[name = tensor("key_states_15_cast_fp16")]; + tensor var_419 = const()[name = tensor("op_419"), val = tensor([16, -1, 64])]; + tensor transpose_99 = transpose(perm = var_404_perm_0, x = var_403_cast_fp16)[name = tensor("transpose_99")]; + tensor value_states_15_cast_fp16 = reshape(shape = var_419, x = transpose_99)[name = tensor("value_states_15_cast_fp16")]; + tensor var_422_perm_0 = const()[name = tensor("op_422_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_19_transpose_x_0 = const()[name = tensor("attn_weights_19_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_19_transpose_y_0 = const()[name = tensor("attn_weights_19_transpose_y_0"), val = tensor(false)]; + tensor transpose_97 = transpose(perm = var_422_perm_0, x = key_states_15_cast_fp16)[name = tensor("transpose_97")]; + tensor attn_weights_19_cast_fp16 = matmul(transpose_x = attn_weights_19_transpose_x_0, transpose_y = attn_weights_19_transpose_y_0, x = query_states_7_cast_fp16, y = transpose_97)[name = tensor("attn_weights_19_cast_fp16")]; + tensor var_424 = const()[name = tensor("op_424"), val = tensor([1, 16, 77, 77])]; + tensor var_425_cast_fp16 = reshape(shape = var_424, x = attn_weights_19_cast_fp16)[name = tensor("op_425_cast_fp16")]; + tensor attn_weights_21_cast_fp16 = add(x = var_425_cast_fp16, y = var_57_to_fp16)[name = tensor("attn_weights_21_cast_fp16")]; + tensor var_430 = const()[name = tensor("op_430"), val = tensor([16, 77, 77])]; + tensor input_53_cast_fp16 = reshape(shape = var_430, x = attn_weights_21_cast_fp16)[name = tensor("input_53_cast_fp16")]; + tensor input_55_cast_fp16 = softmax(axis = var_5, x = input_53_cast_fp16)[name = tensor("input_55_cast_fp16")]; + tensor attn_output_19_transpose_x_0 = const()[name = tensor("attn_output_19_transpose_x_0"), val = tensor(false)]; + tensor attn_output_19_transpose_y_0 = const()[name = tensor("attn_output_19_transpose_y_0"), val = tensor(false)]; + tensor attn_output_19_cast_fp16 = matmul(transpose_x = attn_output_19_transpose_x_0, transpose_y = attn_output_19_transpose_y_0, x = input_55_cast_fp16, y = value_states_15_cast_fp16)[name = tensor("attn_output_19_cast_fp16")]; + tensor var_435 = const()[name = tensor("op_435"), val = tensor([1, 16, 77, 64])]; + tensor attn_output_21_cast_fp16 = reshape(shape = var_435, x = attn_output_19_cast_fp16)[name = tensor("attn_output_21_cast_fp16")]; + tensor attn_output_23_perm_0 = const()[name = tensor("attn_output_23_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_438 = const()[name = tensor("op_438"), val = tensor([1, 77, 1024])]; + tensor transpose_96 = transpose(perm = attn_output_23_perm_0, x = attn_output_21_cast_fp16)[name = tensor("transpose_96")]; + tensor input_57_cast_fp16 = reshape(shape = var_438, x = transpose_96)[name = tensor("input_57_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_3_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(183240064)))]; + tensor text_encoder_text_model_encoder_layers_3_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185337280)))]; + tensor linear_21_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_3_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_3_self_attn_out_proj_weight_to_fp16, x = input_57_cast_fp16)[name = tensor("linear_21_cast_fp16")]; + tensor input_59_cast_fp16 = add(x = input_51_cast_fp16, y = linear_21_cast_fp16)[name = tensor("input_59_cast_fp16")]; + tensor input_61_axes_0 = const()[name = tensor("input_61_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_3_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185339392)))]; + tensor text_encoder_text_model_encoder_layers_3_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185341504)))]; + tensor input_61_cast_fp16 = layer_norm(axes = input_61_axes_0, beta = text_encoder_text_model_encoder_layers_3_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_3_layer_norm2_weight_to_fp16, x = input_59_cast_fp16)[name = tensor("input_61_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_3_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185343616)))]; + tensor text_encoder_text_model_encoder_layers_3_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(193732288)))]; + tensor linear_22_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_3_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_3_mlp_fc1_weight_to_fp16, x = input_61_cast_fp16)[name = tensor("linear_22_cast_fp16")]; + tensor input_65_mode_0 = const()[name = tensor("input_65_mode_0"), val = tensor("EXACT")]; + tensor input_65_cast_fp16 = gelu(mode = input_65_mode_0, x = linear_22_cast_fp16)[name = tensor("input_65_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_3_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(193740544)))]; + tensor text_encoder_text_model_encoder_layers_3_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202129216)))]; + tensor linear_23_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_3_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_3_mlp_fc2_weight_to_fp16, x = input_65_cast_fp16)[name = tensor("linear_23_cast_fp16")]; + tensor input_67_cast_fp16 = add(x = input_59_cast_fp16, y = linear_23_cast_fp16)[name = tensor("input_67_cast_fp16")]; + tensor hidden_states_25_axes_0 = const()[name = tensor("hidden_states_25_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_4_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202131328)))]; + tensor text_encoder_text_model_encoder_layers_4_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202133440)))]; + tensor hidden_states_25_cast_fp16 = layer_norm(axes = hidden_states_25_axes_0, beta = text_encoder_text_model_encoder_layers_4_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_4_layer_norm1_weight_to_fp16, x = input_67_cast_fp16)[name = tensor("hidden_states_25_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_4_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(202135552)))]; + tensor text_encoder_text_model_encoder_layers_4_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204232768)))]; + tensor linear_24_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_4_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_4_self_attn_q_proj_weight_to_fp16, x = hidden_states_25_cast_fp16)[name = tensor("linear_24_cast_fp16")]; + tensor var_477_to_fp16 = const()[name = tensor("op_477_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_29_cast_fp16 = mul(x = linear_24_cast_fp16, y = var_477_to_fp16)[name = tensor("tensor_29_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_4_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204234880)))]; + tensor text_encoder_text_model_encoder_layers_4_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206332096)))]; + tensor linear_25_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_4_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_4_self_attn_k_proj_weight_to_fp16, x = hidden_states_25_cast_fp16)[name = tensor("linear_25_cast_fp16")]; + tensor var_482 = const()[name = tensor("op_482"), val = tensor([1, -1, 16, 64])]; + tensor var_483_cast_fp16 = reshape(shape = var_482, x = linear_25_cast_fp16)[name = tensor("op_483_cast_fp16")]; + tensor var_484_perm_0 = const()[name = tensor("op_484_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_4_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206334208)))]; + tensor text_encoder_text_model_encoder_layers_4_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208431424)))]; + tensor linear_26_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_4_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_4_self_attn_v_proj_weight_to_fp16, x = hidden_states_25_cast_fp16)[name = tensor("linear_26_cast_fp16")]; + tensor var_489 = const()[name = tensor("op_489"), val = tensor([1, -1, 16, 64])]; + tensor var_490_cast_fp16 = reshape(shape = var_489, x = linear_26_cast_fp16)[name = tensor("op_490_cast_fp16")]; + tensor var_491_perm_0 = const()[name = tensor("op_491_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_498 = const()[name = tensor("op_498"), val = tensor([1, 77, 16, 64])]; + tensor var_499_cast_fp16 = reshape(shape = var_498, x = tensor_29_cast_fp16)[name = tensor("op_499_cast_fp16")]; + tensor var_500_perm_0 = const()[name = tensor("op_500_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_502 = const()[name = tensor("op_502"), val = tensor([16, -1, 64])]; + tensor transpose_93 = transpose(perm = var_500_perm_0, x = var_499_cast_fp16)[name = tensor("transpose_93")]; + tensor query_states_9_cast_fp16 = reshape(shape = var_502, x = transpose_93)[name = tensor("query_states_9_cast_fp16")]; + tensor var_504 = const()[name = tensor("op_504"), val = tensor([16, -1, 64])]; + tensor transpose_95 = transpose(perm = var_484_perm_0, x = var_483_cast_fp16)[name = tensor("transpose_95")]; + tensor key_states_19_cast_fp16 = reshape(shape = var_504, x = transpose_95)[name = tensor("key_states_19_cast_fp16")]; + tensor var_506 = const()[name = tensor("op_506"), val = tensor([16, -1, 64])]; + tensor transpose_94 = transpose(perm = var_491_perm_0, x = var_490_cast_fp16)[name = tensor("transpose_94")]; + tensor value_states_19_cast_fp16 = reshape(shape = var_506, x = transpose_94)[name = tensor("value_states_19_cast_fp16")]; + tensor var_509_perm_0 = const()[name = tensor("op_509_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_25_transpose_x_0 = const()[name = tensor("attn_weights_25_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_25_transpose_y_0 = const()[name = tensor("attn_weights_25_transpose_y_0"), val = tensor(false)]; + tensor transpose_92 = transpose(perm = var_509_perm_0, x = key_states_19_cast_fp16)[name = tensor("transpose_92")]; + tensor attn_weights_25_cast_fp16 = matmul(transpose_x = attn_weights_25_transpose_x_0, transpose_y = attn_weights_25_transpose_y_0, x = query_states_9_cast_fp16, y = transpose_92)[name = tensor("attn_weights_25_cast_fp16")]; + tensor var_511 = const()[name = tensor("op_511"), val = tensor([1, 16, 77, 77])]; + tensor var_512_cast_fp16 = reshape(shape = var_511, x = attn_weights_25_cast_fp16)[name = tensor("op_512_cast_fp16")]; + tensor attn_weights_27_cast_fp16 = add(x = var_512_cast_fp16, y = var_57_to_fp16)[name = tensor("attn_weights_27_cast_fp16")]; + tensor var_517 = const()[name = tensor("op_517"), val = tensor([16, 77, 77])]; + tensor input_69_cast_fp16 = reshape(shape = var_517, x = attn_weights_27_cast_fp16)[name = tensor("input_69_cast_fp16")]; + tensor input_71_cast_fp16 = softmax(axis = var_5, x = input_69_cast_fp16)[name = tensor("input_71_cast_fp16")]; + tensor attn_output_25_transpose_x_0 = const()[name = tensor("attn_output_25_transpose_x_0"), val = tensor(false)]; + tensor attn_output_25_transpose_y_0 = const()[name = tensor("attn_output_25_transpose_y_0"), val = tensor(false)]; + tensor attn_output_25_cast_fp16 = matmul(transpose_x = attn_output_25_transpose_x_0, transpose_y = attn_output_25_transpose_y_0, x = input_71_cast_fp16, y = value_states_19_cast_fp16)[name = tensor("attn_output_25_cast_fp16")]; + tensor var_522 = const()[name = tensor("op_522"), val = tensor([1, 16, 77, 64])]; + tensor attn_output_27_cast_fp16 = reshape(shape = var_522, x = attn_output_25_cast_fp16)[name = tensor("attn_output_27_cast_fp16")]; + tensor attn_output_29_perm_0 = const()[name = tensor("attn_output_29_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_525 = const()[name = tensor("op_525"), val = tensor([1, 77, 1024])]; + tensor transpose_91 = transpose(perm = attn_output_29_perm_0, x = attn_output_27_cast_fp16)[name = tensor("transpose_91")]; + tensor input_73_cast_fp16 = reshape(shape = var_525, x = transpose_91)[name = tensor("input_73_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_4_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208433536)))]; + tensor text_encoder_text_model_encoder_layers_4_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210530752)))]; + tensor linear_27_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_4_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_4_self_attn_out_proj_weight_to_fp16, x = input_73_cast_fp16)[name = tensor("linear_27_cast_fp16")]; + tensor input_75_cast_fp16 = add(x = input_67_cast_fp16, y = linear_27_cast_fp16)[name = tensor("input_75_cast_fp16")]; + tensor input_77_axes_0 = const()[name = tensor("input_77_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_4_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210532864)))]; + tensor text_encoder_text_model_encoder_layers_4_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210534976)))]; + tensor input_77_cast_fp16 = layer_norm(axes = input_77_axes_0, beta = text_encoder_text_model_encoder_layers_4_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_4_layer_norm2_weight_to_fp16, x = input_75_cast_fp16)[name = tensor("input_77_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_4_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(210537088)))]; + tensor text_encoder_text_model_encoder_layers_4_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218925760)))]; + tensor linear_28_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_4_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_4_mlp_fc1_weight_to_fp16, x = input_77_cast_fp16)[name = tensor("linear_28_cast_fp16")]; + tensor input_81_mode_0 = const()[name = tensor("input_81_mode_0"), val = tensor("EXACT")]; + tensor input_81_cast_fp16 = gelu(mode = input_81_mode_0, x = linear_28_cast_fp16)[name = tensor("input_81_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_4_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218934016)))]; + tensor text_encoder_text_model_encoder_layers_4_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(227322688)))]; + tensor linear_29_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_4_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_4_mlp_fc2_weight_to_fp16, x = input_81_cast_fp16)[name = tensor("linear_29_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = input_75_cast_fp16, y = linear_29_cast_fp16)[name = tensor("input_83_cast_fp16")]; + tensor hidden_states_31_axes_0 = const()[name = tensor("hidden_states_31_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_5_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(227324800)))]; + tensor text_encoder_text_model_encoder_layers_5_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(227326912)))]; + tensor hidden_states_31_cast_fp16 = layer_norm(axes = hidden_states_31_axes_0, beta = text_encoder_text_model_encoder_layers_5_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_5_layer_norm1_weight_to_fp16, x = input_83_cast_fp16)[name = tensor("hidden_states_31_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_5_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(227329024)))]; + tensor text_encoder_text_model_encoder_layers_5_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(229426240)))]; + tensor linear_30_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_5_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_5_self_attn_q_proj_weight_to_fp16, x = hidden_states_31_cast_fp16)[name = tensor("linear_30_cast_fp16")]; + tensor var_564_to_fp16 = const()[name = tensor("op_564_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_35_cast_fp16 = mul(x = linear_30_cast_fp16, y = var_564_to_fp16)[name = tensor("tensor_35_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_5_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(229428352)))]; + tensor text_encoder_text_model_encoder_layers_5_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(231525568)))]; + tensor linear_31_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_5_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_5_self_attn_k_proj_weight_to_fp16, x = hidden_states_31_cast_fp16)[name = tensor("linear_31_cast_fp16")]; + tensor var_569 = const()[name = tensor("op_569"), val = tensor([1, -1, 16, 64])]; + tensor var_570_cast_fp16 = reshape(shape = var_569, x = linear_31_cast_fp16)[name = tensor("op_570_cast_fp16")]; + tensor var_571_perm_0 = const()[name = tensor("op_571_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_5_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(231527680)))]; + tensor text_encoder_text_model_encoder_layers_5_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(233624896)))]; + tensor linear_32_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_5_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_5_self_attn_v_proj_weight_to_fp16, x = hidden_states_31_cast_fp16)[name = tensor("linear_32_cast_fp16")]; + tensor var_576 = const()[name = tensor("op_576"), val = tensor([1, -1, 16, 64])]; + tensor var_577_cast_fp16 = reshape(shape = var_576, x = linear_32_cast_fp16)[name = tensor("op_577_cast_fp16")]; + tensor var_578_perm_0 = const()[name = tensor("op_578_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_585 = const()[name = tensor("op_585"), val = tensor([1, 77, 16, 64])]; + tensor var_586_cast_fp16 = reshape(shape = var_585, x = tensor_35_cast_fp16)[name = tensor("op_586_cast_fp16")]; + tensor var_587_perm_0 = const()[name = tensor("op_587_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_589 = const()[name = tensor("op_589"), val = tensor([16, -1, 64])]; + tensor transpose_88 = transpose(perm = var_587_perm_0, x = var_586_cast_fp16)[name = tensor("transpose_88")]; + tensor query_states_11_cast_fp16 = reshape(shape = var_589, x = transpose_88)[name = tensor("query_states_11_cast_fp16")]; + tensor var_591 = const()[name = tensor("op_591"), val = tensor([16, -1, 64])]; + tensor transpose_90 = transpose(perm = var_571_perm_0, x = var_570_cast_fp16)[name = tensor("transpose_90")]; + tensor key_states_23_cast_fp16 = reshape(shape = var_591, x = transpose_90)[name = tensor("key_states_23_cast_fp16")]; + tensor var_593 = const()[name = tensor("op_593"), val = tensor([16, -1, 64])]; + tensor transpose_89 = transpose(perm = var_578_perm_0, x = var_577_cast_fp16)[name = tensor("transpose_89")]; + tensor value_states_23_cast_fp16 = reshape(shape = var_593, x = transpose_89)[name = tensor("value_states_23_cast_fp16")]; + tensor var_596_perm_0 = const()[name = tensor("op_596_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_31_transpose_x_0 = const()[name = tensor("attn_weights_31_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_31_transpose_y_0 = const()[name = tensor("attn_weights_31_transpose_y_0"), val = tensor(false)]; + tensor transpose_87 = transpose(perm = var_596_perm_0, x = key_states_23_cast_fp16)[name = tensor("transpose_87")]; + tensor attn_weights_31_cast_fp16 = matmul(transpose_x = attn_weights_31_transpose_x_0, transpose_y = attn_weights_31_transpose_y_0, x = query_states_11_cast_fp16, y = transpose_87)[name = tensor("attn_weights_31_cast_fp16")]; + tensor var_598 = const()[name = tensor("op_598"), val = tensor([1, 16, 77, 77])]; + tensor var_599_cast_fp16 = reshape(shape = var_598, x = attn_weights_31_cast_fp16)[name = tensor("op_599_cast_fp16")]; + tensor attn_weights_33_cast_fp16 = add(x = var_599_cast_fp16, y = var_57_to_fp16)[name = tensor("attn_weights_33_cast_fp16")]; + tensor var_604 = const()[name = tensor("op_604"), val = tensor([16, 77, 77])]; + tensor input_85_cast_fp16 = reshape(shape = var_604, x = attn_weights_33_cast_fp16)[name = tensor("input_85_cast_fp16")]; + tensor input_87_cast_fp16 = softmax(axis = var_5, x = input_85_cast_fp16)[name = tensor("input_87_cast_fp16")]; + tensor attn_output_31_transpose_x_0 = const()[name = tensor("attn_output_31_transpose_x_0"), val = tensor(false)]; + tensor attn_output_31_transpose_y_0 = const()[name = tensor("attn_output_31_transpose_y_0"), val = tensor(false)]; + tensor attn_output_31_cast_fp16 = matmul(transpose_x = attn_output_31_transpose_x_0, transpose_y = attn_output_31_transpose_y_0, x = input_87_cast_fp16, y = value_states_23_cast_fp16)[name = tensor("attn_output_31_cast_fp16")]; + tensor var_609 = const()[name = tensor("op_609"), val = tensor([1, 16, 77, 64])]; + tensor attn_output_33_cast_fp16 = reshape(shape = var_609, x = attn_output_31_cast_fp16)[name = tensor("attn_output_33_cast_fp16")]; + tensor attn_output_35_perm_0 = const()[name = tensor("attn_output_35_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_612 = const()[name = tensor("op_612"), val = tensor([1, 77, 1024])]; + tensor transpose_86 = transpose(perm = attn_output_35_perm_0, x = attn_output_33_cast_fp16)[name = tensor("transpose_86")]; + tensor input_89_cast_fp16 = reshape(shape = var_612, x = transpose_86)[name = tensor("input_89_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_5_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(233627008)))]; + tensor text_encoder_text_model_encoder_layers_5_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(235724224)))]; + tensor linear_33_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_5_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_5_self_attn_out_proj_weight_to_fp16, x = input_89_cast_fp16)[name = tensor("linear_33_cast_fp16")]; + tensor input_91_cast_fp16 = add(x = input_83_cast_fp16, y = linear_33_cast_fp16)[name = tensor("input_91_cast_fp16")]; + tensor input_93_axes_0 = const()[name = tensor("input_93_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_5_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(235726336)))]; + tensor text_encoder_text_model_encoder_layers_5_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(235728448)))]; + tensor input_93_cast_fp16 = layer_norm(axes = input_93_axes_0, beta = text_encoder_text_model_encoder_layers_5_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_5_layer_norm2_weight_to_fp16, x = input_91_cast_fp16)[name = tensor("input_93_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_5_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(235730560)))]; + tensor text_encoder_text_model_encoder_layers_5_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(244119232)))]; + tensor linear_34_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_5_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_5_mlp_fc1_weight_to_fp16, x = input_93_cast_fp16)[name = tensor("linear_34_cast_fp16")]; + tensor input_97_mode_0 = const()[name = tensor("input_97_mode_0"), val = tensor("EXACT")]; + tensor input_97_cast_fp16 = gelu(mode = input_97_mode_0, x = linear_34_cast_fp16)[name = tensor("input_97_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_5_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(244127488)))]; + tensor text_encoder_text_model_encoder_layers_5_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(252516160)))]; + tensor linear_35_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_5_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_5_mlp_fc2_weight_to_fp16, x = input_97_cast_fp16)[name = tensor("linear_35_cast_fp16")]; + tensor input_99_cast_fp16 = add(x = input_91_cast_fp16, y = linear_35_cast_fp16)[name = tensor("input_99_cast_fp16")]; + tensor hidden_states_37_axes_0 = const()[name = tensor("hidden_states_37_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_6_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(252518272)))]; + tensor text_encoder_text_model_encoder_layers_6_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(252520384)))]; + tensor hidden_states_37_cast_fp16 = layer_norm(axes = hidden_states_37_axes_0, beta = text_encoder_text_model_encoder_layers_6_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_6_layer_norm1_weight_to_fp16, x = input_99_cast_fp16)[name = tensor("hidden_states_37_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_6_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(252522496)))]; + tensor text_encoder_text_model_encoder_layers_6_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(254619712)))]; + tensor linear_36_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_6_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_6_self_attn_q_proj_weight_to_fp16, x = hidden_states_37_cast_fp16)[name = tensor("linear_36_cast_fp16")]; + tensor var_651_to_fp16 = const()[name = tensor("op_651_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_41_cast_fp16 = mul(x = linear_36_cast_fp16, y = var_651_to_fp16)[name = tensor("tensor_41_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_6_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(254621824)))]; + tensor text_encoder_text_model_encoder_layers_6_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(256719040)))]; + tensor linear_37_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_6_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_6_self_attn_k_proj_weight_to_fp16, x = hidden_states_37_cast_fp16)[name = tensor("linear_37_cast_fp16")]; + tensor var_656 = const()[name = tensor("op_656"), val = tensor([1, -1, 16, 64])]; + tensor var_657_cast_fp16 = reshape(shape = var_656, x = linear_37_cast_fp16)[name = tensor("op_657_cast_fp16")]; + tensor var_658_perm_0 = const()[name = tensor("op_658_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_6_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(256721152)))]; + tensor text_encoder_text_model_encoder_layers_6_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(258818368)))]; + tensor linear_38_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_6_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_6_self_attn_v_proj_weight_to_fp16, x = hidden_states_37_cast_fp16)[name = tensor("linear_38_cast_fp16")]; + tensor var_663 = const()[name = tensor("op_663"), val = tensor([1, -1, 16, 64])]; + tensor var_664_cast_fp16 = reshape(shape = var_663, x = linear_38_cast_fp16)[name = tensor("op_664_cast_fp16")]; + tensor var_665_perm_0 = const()[name = tensor("op_665_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_672 = const()[name = tensor("op_672"), val = tensor([1, 77, 16, 64])]; + tensor var_673_cast_fp16 = reshape(shape = var_672, x = tensor_41_cast_fp16)[name = tensor("op_673_cast_fp16")]; + tensor var_674_perm_0 = const()[name = tensor("op_674_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_676 = const()[name = tensor("op_676"), val = tensor([16, -1, 64])]; + tensor transpose_83 = transpose(perm = var_674_perm_0, x = var_673_cast_fp16)[name = tensor("transpose_83")]; + tensor query_states_13_cast_fp16 = reshape(shape = var_676, x = transpose_83)[name = tensor("query_states_13_cast_fp16")]; + tensor var_678 = const()[name = tensor("op_678"), val = tensor([16, -1, 64])]; + tensor transpose_85 = transpose(perm = var_658_perm_0, x = var_657_cast_fp16)[name = tensor("transpose_85")]; + tensor key_states_27_cast_fp16 = reshape(shape = var_678, x = transpose_85)[name = tensor("key_states_27_cast_fp16")]; + tensor var_680 = const()[name = tensor("op_680"), val = tensor([16, -1, 64])]; + tensor transpose_84 = transpose(perm = var_665_perm_0, x = var_664_cast_fp16)[name = tensor("transpose_84")]; + tensor value_states_27_cast_fp16 = reshape(shape = var_680, x = transpose_84)[name = tensor("value_states_27_cast_fp16")]; + tensor var_683_perm_0 = const()[name = tensor("op_683_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_37_transpose_x_0 = const()[name = tensor("attn_weights_37_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_37_transpose_y_0 = const()[name = tensor("attn_weights_37_transpose_y_0"), val = tensor(false)]; + tensor transpose_82 = transpose(perm = var_683_perm_0, x = key_states_27_cast_fp16)[name = tensor("transpose_82")]; + tensor attn_weights_37_cast_fp16 = matmul(transpose_x = attn_weights_37_transpose_x_0, transpose_y = attn_weights_37_transpose_y_0, x = query_states_13_cast_fp16, y = transpose_82)[name = tensor("attn_weights_37_cast_fp16")]; + tensor var_685 = const()[name = tensor("op_685"), val = tensor([1, 16, 77, 77])]; + tensor var_686_cast_fp16 = reshape(shape = var_685, x = attn_weights_37_cast_fp16)[name = tensor("op_686_cast_fp16")]; + tensor attn_weights_39_cast_fp16 = add(x = var_686_cast_fp16, y = var_57_to_fp16)[name = tensor("attn_weights_39_cast_fp16")]; + tensor var_691 = const()[name = tensor("op_691"), val = tensor([16, 77, 77])]; + tensor input_101_cast_fp16 = reshape(shape = var_691, x = attn_weights_39_cast_fp16)[name = tensor("input_101_cast_fp16")]; + tensor input_103_cast_fp16 = softmax(axis = var_5, x = input_101_cast_fp16)[name = tensor("input_103_cast_fp16")]; + tensor attn_output_37_transpose_x_0 = const()[name = tensor("attn_output_37_transpose_x_0"), val = tensor(false)]; + tensor attn_output_37_transpose_y_0 = const()[name = tensor("attn_output_37_transpose_y_0"), val = tensor(false)]; + tensor attn_output_37_cast_fp16 = matmul(transpose_x = attn_output_37_transpose_x_0, transpose_y = attn_output_37_transpose_y_0, x = input_103_cast_fp16, y = value_states_27_cast_fp16)[name = tensor("attn_output_37_cast_fp16")]; + tensor var_696 = const()[name = tensor("op_696"), val = tensor([1, 16, 77, 64])]; + tensor attn_output_39_cast_fp16 = reshape(shape = var_696, x = attn_output_37_cast_fp16)[name = tensor("attn_output_39_cast_fp16")]; + tensor attn_output_41_perm_0 = const()[name = tensor("attn_output_41_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_699 = const()[name = tensor("op_699"), val = tensor([1, 77, 1024])]; + tensor transpose_81 = transpose(perm = attn_output_41_perm_0, x = attn_output_39_cast_fp16)[name = tensor("transpose_81")]; + tensor input_105_cast_fp16 = reshape(shape = var_699, x = transpose_81)[name = tensor("input_105_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_6_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(258820480)))]; + tensor text_encoder_text_model_encoder_layers_6_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(260917696)))]; + tensor linear_39_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_6_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_6_self_attn_out_proj_weight_to_fp16, x = input_105_cast_fp16)[name = tensor("linear_39_cast_fp16")]; + tensor input_107_cast_fp16 = add(x = input_99_cast_fp16, y = linear_39_cast_fp16)[name = tensor("input_107_cast_fp16")]; + tensor input_109_axes_0 = const()[name = tensor("input_109_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_6_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(260919808)))]; + tensor text_encoder_text_model_encoder_layers_6_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(260921920)))]; + tensor input_109_cast_fp16 = layer_norm(axes = input_109_axes_0, beta = text_encoder_text_model_encoder_layers_6_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_6_layer_norm2_weight_to_fp16, x = input_107_cast_fp16)[name = tensor("input_109_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_6_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(260924032)))]; + tensor text_encoder_text_model_encoder_layers_6_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(269312704)))]; + tensor linear_40_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_6_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_6_mlp_fc1_weight_to_fp16, x = input_109_cast_fp16)[name = tensor("linear_40_cast_fp16")]; + tensor input_113_mode_0 = const()[name = tensor("input_113_mode_0"), val = tensor("EXACT")]; + tensor input_113_cast_fp16 = gelu(mode = input_113_mode_0, x = linear_40_cast_fp16)[name = tensor("input_113_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_6_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(269320960)))]; + tensor text_encoder_text_model_encoder_layers_6_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(277709632)))]; + tensor linear_41_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_6_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_6_mlp_fc2_weight_to_fp16, x = input_113_cast_fp16)[name = tensor("linear_41_cast_fp16")]; + tensor input_115_cast_fp16 = add(x = input_107_cast_fp16, y = linear_41_cast_fp16)[name = tensor("input_115_cast_fp16")]; + tensor hidden_states_43_axes_0 = const()[name = tensor("hidden_states_43_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_7_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(277711744)))]; + tensor text_encoder_text_model_encoder_layers_7_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(277713856)))]; + tensor hidden_states_43_cast_fp16 = layer_norm(axes = hidden_states_43_axes_0, beta = text_encoder_text_model_encoder_layers_7_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_7_layer_norm1_weight_to_fp16, x = input_115_cast_fp16)[name = tensor("hidden_states_43_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_7_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(277715968)))]; + tensor text_encoder_text_model_encoder_layers_7_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(279813184)))]; + tensor linear_42_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_7_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_7_self_attn_q_proj_weight_to_fp16, x = hidden_states_43_cast_fp16)[name = tensor("linear_42_cast_fp16")]; + tensor var_738_to_fp16 = const()[name = tensor("op_738_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_47_cast_fp16 = mul(x = linear_42_cast_fp16, y = var_738_to_fp16)[name = tensor("tensor_47_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_7_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(279815296)))]; + tensor text_encoder_text_model_encoder_layers_7_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(281912512)))]; + tensor linear_43_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_7_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_7_self_attn_k_proj_weight_to_fp16, x = hidden_states_43_cast_fp16)[name = tensor("linear_43_cast_fp16")]; + tensor var_743 = const()[name = tensor("op_743"), val = tensor([1, -1, 16, 64])]; + tensor var_744_cast_fp16 = reshape(shape = var_743, x = linear_43_cast_fp16)[name = tensor("op_744_cast_fp16")]; + tensor var_745_perm_0 = const()[name = tensor("op_745_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_7_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(281914624)))]; + tensor text_encoder_text_model_encoder_layers_7_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(284011840)))]; + tensor linear_44_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_7_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_7_self_attn_v_proj_weight_to_fp16, x = hidden_states_43_cast_fp16)[name = tensor("linear_44_cast_fp16")]; + tensor var_750 = const()[name = tensor("op_750"), val = tensor([1, -1, 16, 64])]; + tensor var_751_cast_fp16 = reshape(shape = var_750, x = linear_44_cast_fp16)[name = tensor("op_751_cast_fp16")]; + tensor var_752_perm_0 = const()[name = tensor("op_752_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_759 = const()[name = tensor("op_759"), val = tensor([1, 77, 16, 64])]; + tensor var_760_cast_fp16 = reshape(shape = var_759, x = tensor_47_cast_fp16)[name = tensor("op_760_cast_fp16")]; + tensor var_761_perm_0 = const()[name = tensor("op_761_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_763 = const()[name = tensor("op_763"), val = tensor([16, -1, 64])]; + tensor transpose_78 = transpose(perm = var_761_perm_0, x = var_760_cast_fp16)[name = tensor("transpose_78")]; + tensor query_states_15_cast_fp16 = reshape(shape = var_763, x = transpose_78)[name = tensor("query_states_15_cast_fp16")]; + tensor var_765 = const()[name = tensor("op_765"), val = tensor([16, -1, 64])]; + tensor transpose_80 = transpose(perm = var_745_perm_0, x = var_744_cast_fp16)[name = tensor("transpose_80")]; + tensor key_states_31_cast_fp16 = reshape(shape = var_765, x = transpose_80)[name = tensor("key_states_31_cast_fp16")]; + tensor var_767 = const()[name = tensor("op_767"), val = tensor([16, -1, 64])]; + tensor transpose_79 = transpose(perm = var_752_perm_0, x = var_751_cast_fp16)[name = tensor("transpose_79")]; + tensor value_states_31_cast_fp16 = reshape(shape = var_767, x = transpose_79)[name = tensor("value_states_31_cast_fp16")]; + tensor var_770_perm_0 = const()[name = tensor("op_770_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_43_transpose_x_0 = const()[name = tensor("attn_weights_43_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_43_transpose_y_0 = const()[name = tensor("attn_weights_43_transpose_y_0"), val = tensor(false)]; + tensor transpose_77 = transpose(perm = var_770_perm_0, x = key_states_31_cast_fp16)[name = tensor("transpose_77")]; + tensor attn_weights_43_cast_fp16 = matmul(transpose_x = attn_weights_43_transpose_x_0, transpose_y = attn_weights_43_transpose_y_0, x = query_states_15_cast_fp16, y = transpose_77)[name = tensor("attn_weights_43_cast_fp16")]; + tensor var_772 = const()[name = tensor("op_772"), val = tensor([1, 16, 77, 77])]; + tensor var_773_cast_fp16 = reshape(shape = var_772, x = attn_weights_43_cast_fp16)[name = tensor("op_773_cast_fp16")]; + tensor attn_weights_45_cast_fp16 = add(x = var_773_cast_fp16, y = var_57_to_fp16)[name = tensor("attn_weights_45_cast_fp16")]; + tensor var_778 = const()[name = tensor("op_778"), val = tensor([16, 77, 77])]; + tensor input_117_cast_fp16 = reshape(shape = var_778, x = attn_weights_45_cast_fp16)[name = tensor("input_117_cast_fp16")]; + tensor input_119_cast_fp16 = softmax(axis = var_5, x = input_117_cast_fp16)[name = tensor("input_119_cast_fp16")]; + tensor attn_output_43_transpose_x_0 = const()[name = tensor("attn_output_43_transpose_x_0"), val = tensor(false)]; + tensor attn_output_43_transpose_y_0 = const()[name = tensor("attn_output_43_transpose_y_0"), val = tensor(false)]; + tensor attn_output_43_cast_fp16 = matmul(transpose_x = attn_output_43_transpose_x_0, transpose_y = attn_output_43_transpose_y_0, x = input_119_cast_fp16, y = value_states_31_cast_fp16)[name = tensor("attn_output_43_cast_fp16")]; + tensor var_783 = const()[name = tensor("op_783"), val = tensor([1, 16, 77, 64])]; + tensor attn_output_45_cast_fp16 = reshape(shape = var_783, x = attn_output_43_cast_fp16)[name = tensor("attn_output_45_cast_fp16")]; + tensor attn_output_47_perm_0 = const()[name = tensor("attn_output_47_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_786 = const()[name = tensor("op_786"), val = tensor([1, 77, 1024])]; + tensor transpose_76 = transpose(perm = attn_output_47_perm_0, x = attn_output_45_cast_fp16)[name = tensor("transpose_76")]; + tensor input_121_cast_fp16 = reshape(shape = var_786, x = transpose_76)[name = tensor("input_121_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_7_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(284013952)))]; + tensor text_encoder_text_model_encoder_layers_7_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(286111168)))]; + tensor linear_45_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_7_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_7_self_attn_out_proj_weight_to_fp16, x = input_121_cast_fp16)[name = tensor("linear_45_cast_fp16")]; + tensor input_123_cast_fp16 = add(x = input_115_cast_fp16, y = linear_45_cast_fp16)[name = tensor("input_123_cast_fp16")]; + tensor input_125_axes_0 = const()[name = tensor("input_125_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_7_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(286113280)))]; + tensor text_encoder_text_model_encoder_layers_7_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(286115392)))]; + tensor input_125_cast_fp16 = layer_norm(axes = input_125_axes_0, beta = text_encoder_text_model_encoder_layers_7_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_7_layer_norm2_weight_to_fp16, x = input_123_cast_fp16)[name = tensor("input_125_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_7_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(286117504)))]; + tensor text_encoder_text_model_encoder_layers_7_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(294506176)))]; + tensor linear_46_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_7_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_7_mlp_fc1_weight_to_fp16, x = input_125_cast_fp16)[name = tensor("linear_46_cast_fp16")]; + tensor input_129_mode_0 = const()[name = tensor("input_129_mode_0"), val = tensor("EXACT")]; + tensor input_129_cast_fp16 = gelu(mode = input_129_mode_0, x = linear_46_cast_fp16)[name = tensor("input_129_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_7_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(294514432)))]; + tensor text_encoder_text_model_encoder_layers_7_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(302903104)))]; + tensor linear_47_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_7_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_7_mlp_fc2_weight_to_fp16, x = input_129_cast_fp16)[name = tensor("linear_47_cast_fp16")]; + tensor input_131_cast_fp16 = add(x = input_123_cast_fp16, y = linear_47_cast_fp16)[name = tensor("input_131_cast_fp16")]; + tensor hidden_states_49_axes_0 = const()[name = tensor("hidden_states_49_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_8_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(302905216)))]; + tensor text_encoder_text_model_encoder_layers_8_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(302907328)))]; + tensor hidden_states_49_cast_fp16 = layer_norm(axes = hidden_states_49_axes_0, beta = text_encoder_text_model_encoder_layers_8_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_8_layer_norm1_weight_to_fp16, x = input_131_cast_fp16)[name = tensor("hidden_states_49_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_8_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(302909440)))]; + tensor text_encoder_text_model_encoder_layers_8_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(305006656)))]; + tensor linear_48_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_8_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_8_self_attn_q_proj_weight_to_fp16, x = hidden_states_49_cast_fp16)[name = tensor("linear_48_cast_fp16")]; + tensor var_825_to_fp16 = const()[name = tensor("op_825_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_53_cast_fp16 = mul(x = linear_48_cast_fp16, y = var_825_to_fp16)[name = tensor("tensor_53_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_8_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(305008768)))]; + tensor text_encoder_text_model_encoder_layers_8_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(307105984)))]; + tensor linear_49_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_8_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_8_self_attn_k_proj_weight_to_fp16, x = hidden_states_49_cast_fp16)[name = tensor("linear_49_cast_fp16")]; + tensor var_830 = const()[name = tensor("op_830"), val = tensor([1, -1, 16, 64])]; + tensor var_831_cast_fp16 = reshape(shape = var_830, x = linear_49_cast_fp16)[name = tensor("op_831_cast_fp16")]; + tensor var_832_perm_0 = const()[name = tensor("op_832_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_8_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(307108096)))]; + tensor text_encoder_text_model_encoder_layers_8_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309205312)))]; + tensor linear_50_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_8_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_8_self_attn_v_proj_weight_to_fp16, x = hidden_states_49_cast_fp16)[name = tensor("linear_50_cast_fp16")]; + tensor var_837 = const()[name = tensor("op_837"), val = tensor([1, -1, 16, 64])]; + tensor var_838_cast_fp16 = reshape(shape = var_837, x = linear_50_cast_fp16)[name = tensor("op_838_cast_fp16")]; + tensor var_839_perm_0 = const()[name = tensor("op_839_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_846 = const()[name = tensor("op_846"), val = tensor([1, 77, 16, 64])]; + tensor var_847_cast_fp16 = reshape(shape = var_846, x = tensor_53_cast_fp16)[name = tensor("op_847_cast_fp16")]; + tensor var_848_perm_0 = const()[name = tensor("op_848_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_850 = const()[name = tensor("op_850"), val = tensor([16, -1, 64])]; + tensor transpose_73 = transpose(perm = var_848_perm_0, x = var_847_cast_fp16)[name = tensor("transpose_73")]; + tensor query_states_17_cast_fp16 = reshape(shape = var_850, x = transpose_73)[name = tensor("query_states_17_cast_fp16")]; + tensor var_852 = const()[name = tensor("op_852"), val = tensor([16, -1, 64])]; + tensor transpose_75 = transpose(perm = var_832_perm_0, x = var_831_cast_fp16)[name = tensor("transpose_75")]; + tensor key_states_35_cast_fp16 = reshape(shape = var_852, x = transpose_75)[name = tensor("key_states_35_cast_fp16")]; + tensor var_854 = const()[name = tensor("op_854"), val = tensor([16, -1, 64])]; + tensor transpose_74 = transpose(perm = var_839_perm_0, x = var_838_cast_fp16)[name = tensor("transpose_74")]; + tensor value_states_35_cast_fp16 = reshape(shape = var_854, x = transpose_74)[name = tensor("value_states_35_cast_fp16")]; + tensor var_857_perm_0 = const()[name = tensor("op_857_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_49_transpose_x_0 = const()[name = tensor("attn_weights_49_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_49_transpose_y_0 = const()[name = tensor("attn_weights_49_transpose_y_0"), val = tensor(false)]; + tensor transpose_72 = transpose(perm = var_857_perm_0, x = key_states_35_cast_fp16)[name = tensor("transpose_72")]; + tensor attn_weights_49_cast_fp16 = matmul(transpose_x = attn_weights_49_transpose_x_0, transpose_y = attn_weights_49_transpose_y_0, x = query_states_17_cast_fp16, y = transpose_72)[name = tensor("attn_weights_49_cast_fp16")]; + tensor var_859 = const()[name = tensor("op_859"), val = tensor([1, 16, 77, 77])]; + tensor var_860_cast_fp16 = reshape(shape = var_859, x = attn_weights_49_cast_fp16)[name = tensor("op_860_cast_fp16")]; + tensor attn_weights_51_cast_fp16 = add(x = var_860_cast_fp16, y = var_57_to_fp16)[name = tensor("attn_weights_51_cast_fp16")]; + tensor var_865 = const()[name = tensor("op_865"), val = tensor([16, 77, 77])]; + tensor input_133_cast_fp16 = reshape(shape = var_865, x = attn_weights_51_cast_fp16)[name = tensor("input_133_cast_fp16")]; + tensor input_135_cast_fp16 = softmax(axis = var_5, x = input_133_cast_fp16)[name = tensor("input_135_cast_fp16")]; + tensor attn_output_49_transpose_x_0 = const()[name = tensor("attn_output_49_transpose_x_0"), val = tensor(false)]; + tensor attn_output_49_transpose_y_0 = const()[name = tensor("attn_output_49_transpose_y_0"), val = tensor(false)]; + tensor attn_output_49_cast_fp16 = matmul(transpose_x = attn_output_49_transpose_x_0, transpose_y = attn_output_49_transpose_y_0, x = input_135_cast_fp16, y = value_states_35_cast_fp16)[name = tensor("attn_output_49_cast_fp16")]; + tensor var_870 = const()[name = tensor("op_870"), val = tensor([1, 16, 77, 64])]; + tensor attn_output_51_cast_fp16 = reshape(shape = var_870, x = attn_output_49_cast_fp16)[name = tensor("attn_output_51_cast_fp16")]; + tensor attn_output_53_perm_0 = const()[name = tensor("attn_output_53_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_873 = const()[name = tensor("op_873"), val = tensor([1, 77, 1024])]; + tensor transpose_71 = transpose(perm = attn_output_53_perm_0, x = attn_output_51_cast_fp16)[name = tensor("transpose_71")]; + tensor input_137_cast_fp16 = reshape(shape = var_873, x = transpose_71)[name = tensor("input_137_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_8_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309207424)))]; + tensor text_encoder_text_model_encoder_layers_8_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(311304640)))]; + tensor linear_51_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_8_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_8_self_attn_out_proj_weight_to_fp16, x = input_137_cast_fp16)[name = tensor("linear_51_cast_fp16")]; + tensor input_139_cast_fp16 = add(x = input_131_cast_fp16, y = linear_51_cast_fp16)[name = tensor("input_139_cast_fp16")]; + tensor input_141_axes_0 = const()[name = tensor("input_141_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_8_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(311306752)))]; + tensor text_encoder_text_model_encoder_layers_8_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(311308864)))]; + tensor input_141_cast_fp16 = layer_norm(axes = input_141_axes_0, beta = text_encoder_text_model_encoder_layers_8_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_8_layer_norm2_weight_to_fp16, x = input_139_cast_fp16)[name = tensor("input_141_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_8_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(311310976)))]; + tensor text_encoder_text_model_encoder_layers_8_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(319699648)))]; + tensor linear_52_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_8_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_8_mlp_fc1_weight_to_fp16, x = input_141_cast_fp16)[name = tensor("linear_52_cast_fp16")]; + tensor input_145_mode_0 = const()[name = tensor("input_145_mode_0"), val = tensor("EXACT")]; + tensor input_145_cast_fp16 = gelu(mode = input_145_mode_0, x = linear_52_cast_fp16)[name = tensor("input_145_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_8_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(319707904)))]; + tensor text_encoder_text_model_encoder_layers_8_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(328096576)))]; + tensor linear_53_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_8_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_8_mlp_fc2_weight_to_fp16, x = input_145_cast_fp16)[name = tensor("linear_53_cast_fp16")]; + tensor input_147_cast_fp16 = add(x = input_139_cast_fp16, y = linear_53_cast_fp16)[name = tensor("input_147_cast_fp16")]; + tensor hidden_states_55_axes_0 = const()[name = tensor("hidden_states_55_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_9_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(328098688)))]; + tensor text_encoder_text_model_encoder_layers_9_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(328100800)))]; + tensor hidden_states_55_cast_fp16 = layer_norm(axes = hidden_states_55_axes_0, beta = text_encoder_text_model_encoder_layers_9_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_9_layer_norm1_weight_to_fp16, x = input_147_cast_fp16)[name = tensor("hidden_states_55_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_9_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(328102912)))]; + tensor text_encoder_text_model_encoder_layers_9_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(330200128)))]; + tensor linear_54_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_9_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_9_self_attn_q_proj_weight_to_fp16, x = hidden_states_55_cast_fp16)[name = tensor("linear_54_cast_fp16")]; + tensor var_912_to_fp16 = const()[name = tensor("op_912_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_59_cast_fp16 = mul(x = linear_54_cast_fp16, y = var_912_to_fp16)[name = tensor("tensor_59_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_9_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(330202240)))]; + tensor text_encoder_text_model_encoder_layers_9_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(332299456)))]; + tensor linear_55_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_9_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_9_self_attn_k_proj_weight_to_fp16, x = hidden_states_55_cast_fp16)[name = tensor("linear_55_cast_fp16")]; + tensor var_917 = const()[name = tensor("op_917"), val = tensor([1, -1, 16, 64])]; + tensor var_918_cast_fp16 = reshape(shape = var_917, x = linear_55_cast_fp16)[name = tensor("op_918_cast_fp16")]; + tensor var_919_perm_0 = const()[name = tensor("op_919_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_9_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(332301568)))]; + tensor text_encoder_text_model_encoder_layers_9_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(334398784)))]; + tensor linear_56_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_9_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_9_self_attn_v_proj_weight_to_fp16, x = hidden_states_55_cast_fp16)[name = tensor("linear_56_cast_fp16")]; + tensor var_924 = const()[name = tensor("op_924"), val = tensor([1, -1, 16, 64])]; + tensor var_925_cast_fp16 = reshape(shape = var_924, x = linear_56_cast_fp16)[name = tensor("op_925_cast_fp16")]; + tensor var_926_perm_0 = const()[name = tensor("op_926_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_933 = const()[name = tensor("op_933"), val = tensor([1, 77, 16, 64])]; + tensor var_934_cast_fp16 = reshape(shape = var_933, x = tensor_59_cast_fp16)[name = tensor("op_934_cast_fp16")]; + tensor var_935_perm_0 = const()[name = tensor("op_935_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_937 = const()[name = tensor("op_937"), val = tensor([16, -1, 64])]; + tensor transpose_68 = transpose(perm = var_935_perm_0, x = var_934_cast_fp16)[name = tensor("transpose_68")]; + tensor query_states_19_cast_fp16 = reshape(shape = var_937, x = transpose_68)[name = tensor("query_states_19_cast_fp16")]; + tensor var_939 = const()[name = tensor("op_939"), val = tensor([16, -1, 64])]; + tensor transpose_70 = transpose(perm = var_919_perm_0, x = var_918_cast_fp16)[name = tensor("transpose_70")]; + tensor key_states_39_cast_fp16 = reshape(shape = var_939, x = transpose_70)[name = tensor("key_states_39_cast_fp16")]; + tensor var_941 = const()[name = tensor("op_941"), val = tensor([16, -1, 64])]; + tensor transpose_69 = transpose(perm = var_926_perm_0, x = var_925_cast_fp16)[name = tensor("transpose_69")]; + tensor value_states_39_cast_fp16 = reshape(shape = var_941, x = transpose_69)[name = tensor("value_states_39_cast_fp16")]; + tensor var_944_perm_0 = const()[name = tensor("op_944_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_55_transpose_x_0 = const()[name = tensor("attn_weights_55_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_55_transpose_y_0 = const()[name = tensor("attn_weights_55_transpose_y_0"), val = tensor(false)]; + tensor transpose_67 = transpose(perm = var_944_perm_0, x = key_states_39_cast_fp16)[name = tensor("transpose_67")]; + tensor attn_weights_55_cast_fp16 = matmul(transpose_x = attn_weights_55_transpose_x_0, transpose_y = attn_weights_55_transpose_y_0, x = query_states_19_cast_fp16, y = transpose_67)[name = tensor("attn_weights_55_cast_fp16")]; + tensor var_946 = const()[name = tensor("op_946"), val = tensor([1, 16, 77, 77])]; + tensor var_947_cast_fp16 = reshape(shape = var_946, x = attn_weights_55_cast_fp16)[name = tensor("op_947_cast_fp16")]; + tensor attn_weights_57_cast_fp16 = add(x = var_947_cast_fp16, y = var_57_to_fp16)[name = tensor("attn_weights_57_cast_fp16")]; + tensor var_952 = const()[name = tensor("op_952"), val = tensor([16, 77, 77])]; + tensor input_149_cast_fp16 = reshape(shape = var_952, x = attn_weights_57_cast_fp16)[name = tensor("input_149_cast_fp16")]; + tensor input_151_cast_fp16 = softmax(axis = var_5, x = input_149_cast_fp16)[name = tensor("input_151_cast_fp16")]; + tensor attn_output_55_transpose_x_0 = const()[name = tensor("attn_output_55_transpose_x_0"), val = tensor(false)]; + tensor attn_output_55_transpose_y_0 = const()[name = tensor("attn_output_55_transpose_y_0"), val = tensor(false)]; + tensor attn_output_55_cast_fp16 = matmul(transpose_x = attn_output_55_transpose_x_0, transpose_y = attn_output_55_transpose_y_0, x = input_151_cast_fp16, y = value_states_39_cast_fp16)[name = tensor("attn_output_55_cast_fp16")]; + tensor var_957 = const()[name = tensor("op_957"), val = tensor([1, 16, 77, 64])]; + tensor attn_output_57_cast_fp16 = reshape(shape = var_957, x = attn_output_55_cast_fp16)[name = tensor("attn_output_57_cast_fp16")]; + tensor attn_output_59_perm_0 = const()[name = tensor("attn_output_59_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_960 = const()[name = tensor("op_960"), val = tensor([1, 77, 1024])]; + tensor transpose_66 = transpose(perm = attn_output_59_perm_0, x = attn_output_57_cast_fp16)[name = tensor("transpose_66")]; + tensor input_153_cast_fp16 = reshape(shape = var_960, x = transpose_66)[name = tensor("input_153_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_9_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(334400896)))]; + tensor text_encoder_text_model_encoder_layers_9_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(336498112)))]; + tensor linear_57_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_9_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_9_self_attn_out_proj_weight_to_fp16, x = input_153_cast_fp16)[name = tensor("linear_57_cast_fp16")]; + tensor input_155_cast_fp16 = add(x = input_147_cast_fp16, y = linear_57_cast_fp16)[name = tensor("input_155_cast_fp16")]; + tensor input_157_axes_0 = const()[name = tensor("input_157_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_9_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(336500224)))]; + tensor text_encoder_text_model_encoder_layers_9_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(336502336)))]; + tensor input_157_cast_fp16 = layer_norm(axes = input_157_axes_0, beta = text_encoder_text_model_encoder_layers_9_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_9_layer_norm2_weight_to_fp16, x = input_155_cast_fp16)[name = tensor("input_157_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_9_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(336504448)))]; + tensor text_encoder_text_model_encoder_layers_9_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(344893120)))]; + tensor linear_58_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_9_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_9_mlp_fc1_weight_to_fp16, x = input_157_cast_fp16)[name = tensor("linear_58_cast_fp16")]; + tensor input_161_mode_0 = const()[name = tensor("input_161_mode_0"), val = tensor("EXACT")]; + tensor input_161_cast_fp16 = gelu(mode = input_161_mode_0, x = linear_58_cast_fp16)[name = tensor("input_161_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_9_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(344901376)))]; + tensor text_encoder_text_model_encoder_layers_9_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(353290048)))]; + tensor linear_59_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_9_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_9_mlp_fc2_weight_to_fp16, x = input_161_cast_fp16)[name = tensor("linear_59_cast_fp16")]; + tensor input_163_cast_fp16 = add(x = input_155_cast_fp16, y = linear_59_cast_fp16)[name = tensor("input_163_cast_fp16")]; + tensor hidden_states_61_axes_0 = const()[name = tensor("hidden_states_61_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_10_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(353292160)))]; + tensor text_encoder_text_model_encoder_layers_10_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(353294272)))]; + tensor hidden_states_61_cast_fp16 = layer_norm(axes = hidden_states_61_axes_0, beta = text_encoder_text_model_encoder_layers_10_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_10_layer_norm1_weight_to_fp16, x = input_163_cast_fp16)[name = tensor("hidden_states_61_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_10_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(353296384)))]; + tensor text_encoder_text_model_encoder_layers_10_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(355393600)))]; + tensor linear_60_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_10_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_10_self_attn_q_proj_weight_to_fp16, x = hidden_states_61_cast_fp16)[name = tensor("linear_60_cast_fp16")]; + tensor var_999_to_fp16 = const()[name = tensor("op_999_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_65_cast_fp16 = mul(x = linear_60_cast_fp16, y = var_999_to_fp16)[name = tensor("tensor_65_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_10_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(355395712)))]; + tensor text_encoder_text_model_encoder_layers_10_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(357492928)))]; + tensor linear_61_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_10_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_10_self_attn_k_proj_weight_to_fp16, x = hidden_states_61_cast_fp16)[name = tensor("linear_61_cast_fp16")]; + tensor var_1004 = const()[name = tensor("op_1004"), val = tensor([1, -1, 16, 64])]; + tensor var_1005_cast_fp16 = reshape(shape = var_1004, x = linear_61_cast_fp16)[name = tensor("op_1005_cast_fp16")]; + tensor var_1006_perm_0 = const()[name = tensor("op_1006_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_10_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(357495040)))]; + tensor text_encoder_text_model_encoder_layers_10_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(359592256)))]; + tensor linear_62_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_10_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_10_self_attn_v_proj_weight_to_fp16, x = hidden_states_61_cast_fp16)[name = tensor("linear_62_cast_fp16")]; + tensor var_1011 = const()[name = tensor("op_1011"), val = tensor([1, -1, 16, 64])]; + tensor var_1012_cast_fp16 = reshape(shape = var_1011, x = linear_62_cast_fp16)[name = tensor("op_1012_cast_fp16")]; + tensor var_1013_perm_0 = const()[name = tensor("op_1013_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1020 = const()[name = tensor("op_1020"), val = tensor([1, 77, 16, 64])]; + tensor var_1021_cast_fp16 = reshape(shape = var_1020, x = tensor_65_cast_fp16)[name = tensor("op_1021_cast_fp16")]; + tensor var_1022_perm_0 = const()[name = tensor("op_1022_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1024 = const()[name = tensor("op_1024"), val = tensor([16, -1, 64])]; + tensor transpose_63 = transpose(perm = var_1022_perm_0, x = var_1021_cast_fp16)[name = tensor("transpose_63")]; + tensor query_states_21_cast_fp16 = reshape(shape = var_1024, x = transpose_63)[name = tensor("query_states_21_cast_fp16")]; + tensor var_1026 = const()[name = tensor("op_1026"), val = tensor([16, -1, 64])]; + tensor transpose_65 = transpose(perm = var_1006_perm_0, x = var_1005_cast_fp16)[name = tensor("transpose_65")]; + tensor key_states_43_cast_fp16 = reshape(shape = var_1026, x = transpose_65)[name = tensor("key_states_43_cast_fp16")]; + tensor var_1028 = const()[name = tensor("op_1028"), val = tensor([16, -1, 64])]; + tensor transpose_64 = transpose(perm = var_1013_perm_0, x = var_1012_cast_fp16)[name = tensor("transpose_64")]; + tensor value_states_43_cast_fp16 = reshape(shape = var_1028, x = transpose_64)[name = tensor("value_states_43_cast_fp16")]; + tensor var_1031_perm_0 = const()[name = tensor("op_1031_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_61_transpose_x_0 = const()[name = tensor("attn_weights_61_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_61_transpose_y_0 = const()[name = tensor("attn_weights_61_transpose_y_0"), val = tensor(false)]; + tensor transpose_62 = transpose(perm = var_1031_perm_0, x = key_states_43_cast_fp16)[name = tensor("transpose_62")]; + tensor attn_weights_61_cast_fp16 = matmul(transpose_x = attn_weights_61_transpose_x_0, transpose_y = attn_weights_61_transpose_y_0, x = query_states_21_cast_fp16, y = transpose_62)[name = tensor("attn_weights_61_cast_fp16")]; + tensor var_1033 = const()[name = tensor("op_1033"), val = tensor([1, 16, 77, 77])]; + tensor var_1034_cast_fp16 = reshape(shape = var_1033, x = attn_weights_61_cast_fp16)[name = tensor("op_1034_cast_fp16")]; + tensor attn_weights_63_cast_fp16 = add(x = var_1034_cast_fp16, y = var_57_to_fp16)[name = tensor("attn_weights_63_cast_fp16")]; + tensor var_1039 = const()[name = tensor("op_1039"), val = tensor([16, 77, 77])]; + tensor input_165_cast_fp16 = reshape(shape = var_1039, x = attn_weights_63_cast_fp16)[name = tensor("input_165_cast_fp16")]; + tensor input_167_cast_fp16 = softmax(axis = var_5, x = input_165_cast_fp16)[name = tensor("input_167_cast_fp16")]; + tensor attn_output_61_transpose_x_0 = const()[name = tensor("attn_output_61_transpose_x_0"), val = tensor(false)]; + tensor attn_output_61_transpose_y_0 = const()[name = tensor("attn_output_61_transpose_y_0"), val = tensor(false)]; + tensor attn_output_61_cast_fp16 = matmul(transpose_x = attn_output_61_transpose_x_0, transpose_y = attn_output_61_transpose_y_0, x = input_167_cast_fp16, y = value_states_43_cast_fp16)[name = tensor("attn_output_61_cast_fp16")]; + tensor var_1044 = const()[name = tensor("op_1044"), val = tensor([1, 16, 77, 64])]; + tensor attn_output_63_cast_fp16 = reshape(shape = var_1044, x = attn_output_61_cast_fp16)[name = tensor("attn_output_63_cast_fp16")]; + tensor attn_output_65_perm_0 = const()[name = tensor("attn_output_65_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1047 = const()[name = tensor("op_1047"), val = tensor([1, 77, 1024])]; + tensor transpose_61 = transpose(perm = attn_output_65_perm_0, x = attn_output_63_cast_fp16)[name = tensor("transpose_61")]; + tensor input_169_cast_fp16 = reshape(shape = var_1047, x = transpose_61)[name = tensor("input_169_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_10_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(359594368)))]; + tensor text_encoder_text_model_encoder_layers_10_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(361691584)))]; + tensor linear_63_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_10_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_10_self_attn_out_proj_weight_to_fp16, x = input_169_cast_fp16)[name = tensor("linear_63_cast_fp16")]; + tensor input_171_cast_fp16 = add(x = input_163_cast_fp16, y = linear_63_cast_fp16)[name = tensor("input_171_cast_fp16")]; + tensor input_173_axes_0 = const()[name = tensor("input_173_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_10_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(361693696)))]; + tensor text_encoder_text_model_encoder_layers_10_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(361695808)))]; + tensor input_173_cast_fp16 = layer_norm(axes = input_173_axes_0, beta = text_encoder_text_model_encoder_layers_10_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_10_layer_norm2_weight_to_fp16, x = input_171_cast_fp16)[name = tensor("input_173_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_10_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(361697920)))]; + tensor text_encoder_text_model_encoder_layers_10_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(370086592)))]; + tensor linear_64_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_10_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_10_mlp_fc1_weight_to_fp16, x = input_173_cast_fp16)[name = tensor("linear_64_cast_fp16")]; + tensor input_177_mode_0 = const()[name = tensor("input_177_mode_0"), val = tensor("EXACT")]; + tensor input_177_cast_fp16 = gelu(mode = input_177_mode_0, x = linear_64_cast_fp16)[name = tensor("input_177_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_10_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(370094848)))]; + tensor text_encoder_text_model_encoder_layers_10_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(378483520)))]; + tensor linear_65_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_10_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_10_mlp_fc2_weight_to_fp16, x = input_177_cast_fp16)[name = tensor("linear_65_cast_fp16")]; + tensor input_179_cast_fp16 = add(x = input_171_cast_fp16, y = linear_65_cast_fp16)[name = tensor("input_179_cast_fp16")]; + tensor hidden_states_67_axes_0 = const()[name = tensor("hidden_states_67_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_11_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(378485632)))]; + tensor text_encoder_text_model_encoder_layers_11_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(378487744)))]; + tensor hidden_states_67_cast_fp16 = layer_norm(axes = hidden_states_67_axes_0, beta = text_encoder_text_model_encoder_layers_11_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_11_layer_norm1_weight_to_fp16, x = input_179_cast_fp16)[name = tensor("hidden_states_67_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_11_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(378489856)))]; + tensor text_encoder_text_model_encoder_layers_11_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(380587072)))]; + tensor linear_66_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_11_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_11_self_attn_q_proj_weight_to_fp16, x = hidden_states_67_cast_fp16)[name = tensor("linear_66_cast_fp16")]; + tensor var_1086_to_fp16 = const()[name = tensor("op_1086_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_71_cast_fp16 = mul(x = linear_66_cast_fp16, y = var_1086_to_fp16)[name = tensor("tensor_71_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_11_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(380589184)))]; + tensor text_encoder_text_model_encoder_layers_11_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(382686400)))]; + tensor linear_67_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_11_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_11_self_attn_k_proj_weight_to_fp16, x = hidden_states_67_cast_fp16)[name = tensor("linear_67_cast_fp16")]; + tensor var_1091 = const()[name = tensor("op_1091"), val = tensor([1, -1, 16, 64])]; + tensor var_1092_cast_fp16 = reshape(shape = var_1091, x = linear_67_cast_fp16)[name = tensor("op_1092_cast_fp16")]; + tensor var_1093_perm_0 = const()[name = tensor("op_1093_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_11_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(382688512)))]; + tensor text_encoder_text_model_encoder_layers_11_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(384785728)))]; + tensor linear_68_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_11_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_11_self_attn_v_proj_weight_to_fp16, x = hidden_states_67_cast_fp16)[name = tensor("linear_68_cast_fp16")]; + tensor var_1098 = const()[name = tensor("op_1098"), val = tensor([1, -1, 16, 64])]; + tensor var_1099_cast_fp16 = reshape(shape = var_1098, x = linear_68_cast_fp16)[name = tensor("op_1099_cast_fp16")]; + tensor var_1100_perm_0 = const()[name = tensor("op_1100_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1107 = const()[name = tensor("op_1107"), val = tensor([1, 77, 16, 64])]; + tensor var_1108_cast_fp16 = reshape(shape = var_1107, x = tensor_71_cast_fp16)[name = tensor("op_1108_cast_fp16")]; + tensor var_1109_perm_0 = const()[name = tensor("op_1109_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1111 = const()[name = tensor("op_1111"), val = tensor([16, -1, 64])]; + tensor transpose_58 = transpose(perm = var_1109_perm_0, x = var_1108_cast_fp16)[name = tensor("transpose_58")]; + tensor query_states_23_cast_fp16 = reshape(shape = var_1111, x = transpose_58)[name = tensor("query_states_23_cast_fp16")]; + tensor var_1113 = const()[name = tensor("op_1113"), val = tensor([16, -1, 64])]; + tensor transpose_60 = transpose(perm = var_1093_perm_0, x = var_1092_cast_fp16)[name = tensor("transpose_60")]; + tensor key_states_47_cast_fp16 = reshape(shape = var_1113, x = transpose_60)[name = tensor("key_states_47_cast_fp16")]; + tensor var_1115 = const()[name = tensor("op_1115"), val = tensor([16, -1, 64])]; + tensor transpose_59 = transpose(perm = var_1100_perm_0, x = var_1099_cast_fp16)[name = tensor("transpose_59")]; + tensor value_states_47_cast_fp16 = reshape(shape = var_1115, x = transpose_59)[name = tensor("value_states_47_cast_fp16")]; + tensor var_1118_perm_0 = const()[name = tensor("op_1118_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_67_transpose_x_0 = const()[name = tensor("attn_weights_67_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_67_transpose_y_0 = const()[name = tensor("attn_weights_67_transpose_y_0"), val = tensor(false)]; + tensor transpose_57 = transpose(perm = var_1118_perm_0, x = key_states_47_cast_fp16)[name = tensor("transpose_57")]; + tensor attn_weights_67_cast_fp16 = matmul(transpose_x = attn_weights_67_transpose_x_0, transpose_y = attn_weights_67_transpose_y_0, x = query_states_23_cast_fp16, y = transpose_57)[name = tensor("attn_weights_67_cast_fp16")]; + tensor var_1120 = const()[name = tensor("op_1120"), val = tensor([1, 16, 77, 77])]; + tensor var_1121_cast_fp16 = reshape(shape = var_1120, x = attn_weights_67_cast_fp16)[name = tensor("op_1121_cast_fp16")]; + tensor attn_weights_69_cast_fp16 = add(x = var_1121_cast_fp16, y = var_57_to_fp16)[name = tensor("attn_weights_69_cast_fp16")]; + tensor var_1126 = const()[name = tensor("op_1126"), val = tensor([16, 77, 77])]; + tensor input_181_cast_fp16 = reshape(shape = var_1126, x = attn_weights_69_cast_fp16)[name = tensor("input_181_cast_fp16")]; + tensor input_183_cast_fp16 = softmax(axis = var_5, x = input_181_cast_fp16)[name = tensor("input_183_cast_fp16")]; + tensor attn_output_67_transpose_x_0 = const()[name = tensor("attn_output_67_transpose_x_0"), val = tensor(false)]; + tensor attn_output_67_transpose_y_0 = const()[name = tensor("attn_output_67_transpose_y_0"), val = tensor(false)]; + tensor attn_output_67_cast_fp16 = matmul(transpose_x = attn_output_67_transpose_x_0, transpose_y = attn_output_67_transpose_y_0, x = input_183_cast_fp16, y = value_states_47_cast_fp16)[name = tensor("attn_output_67_cast_fp16")]; + tensor var_1131 = const()[name = tensor("op_1131"), val = tensor([1, 16, 77, 64])]; + tensor attn_output_69_cast_fp16 = reshape(shape = var_1131, x = attn_output_67_cast_fp16)[name = tensor("attn_output_69_cast_fp16")]; + tensor attn_output_71_perm_0 = const()[name = tensor("attn_output_71_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1134 = const()[name = tensor("op_1134"), val = tensor([1, 77, 1024])]; + tensor transpose_56 = transpose(perm = attn_output_71_perm_0, x = attn_output_69_cast_fp16)[name = tensor("transpose_56")]; + tensor input_185_cast_fp16 = reshape(shape = var_1134, x = transpose_56)[name = tensor("input_185_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_11_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(384787840)))]; + tensor text_encoder_text_model_encoder_layers_11_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(386885056)))]; + tensor linear_69_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_11_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_11_self_attn_out_proj_weight_to_fp16, x = input_185_cast_fp16)[name = tensor("linear_69_cast_fp16")]; + tensor input_187_cast_fp16 = add(x = input_179_cast_fp16, y = linear_69_cast_fp16)[name = tensor("input_187_cast_fp16")]; + tensor input_189_axes_0 = const()[name = tensor("input_189_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_11_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(386887168)))]; + tensor text_encoder_text_model_encoder_layers_11_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(386889280)))]; + tensor input_189_cast_fp16 = layer_norm(axes = input_189_axes_0, beta = text_encoder_text_model_encoder_layers_11_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_11_layer_norm2_weight_to_fp16, x = input_187_cast_fp16)[name = tensor("input_189_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_11_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(386891392)))]; + tensor text_encoder_text_model_encoder_layers_11_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(395280064)))]; + tensor linear_70_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_11_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_11_mlp_fc1_weight_to_fp16, x = input_189_cast_fp16)[name = tensor("linear_70_cast_fp16")]; + tensor input_193_mode_0 = const()[name = tensor("input_193_mode_0"), val = tensor("EXACT")]; + tensor input_193_cast_fp16 = gelu(mode = input_193_mode_0, x = linear_70_cast_fp16)[name = tensor("input_193_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_11_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(395288320)))]; + tensor text_encoder_text_model_encoder_layers_11_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(403676992)))]; + tensor linear_71_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_11_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_11_mlp_fc2_weight_to_fp16, x = input_193_cast_fp16)[name = tensor("linear_71_cast_fp16")]; + tensor input_195_cast_fp16 = add(x = input_187_cast_fp16, y = linear_71_cast_fp16)[name = tensor("input_195_cast_fp16")]; + tensor hidden_states_73_axes_0 = const()[name = tensor("hidden_states_73_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_12_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_12_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(403679104)))]; + tensor text_encoder_text_model_encoder_layers_12_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_12_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(403681216)))]; + tensor hidden_states_73_cast_fp16 = layer_norm(axes = hidden_states_73_axes_0, beta = text_encoder_text_model_encoder_layers_12_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_12_layer_norm1_weight_to_fp16, x = input_195_cast_fp16)[name = tensor("hidden_states_73_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_12_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_12_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(403683328)))]; + tensor text_encoder_text_model_encoder_layers_12_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_12_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(405780544)))]; + tensor linear_72_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_12_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_12_self_attn_q_proj_weight_to_fp16, x = hidden_states_73_cast_fp16)[name = tensor("linear_72_cast_fp16")]; + tensor var_1173_to_fp16 = const()[name = tensor("op_1173_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_77_cast_fp16 = mul(x = linear_72_cast_fp16, y = var_1173_to_fp16)[name = tensor("tensor_77_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_12_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_12_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(405782656)))]; + tensor text_encoder_text_model_encoder_layers_12_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_12_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(407879872)))]; + tensor linear_73_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_12_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_12_self_attn_k_proj_weight_to_fp16, x = hidden_states_73_cast_fp16)[name = tensor("linear_73_cast_fp16")]; + tensor var_1178 = const()[name = tensor("op_1178"), val = tensor([1, -1, 16, 64])]; + tensor var_1179_cast_fp16 = reshape(shape = var_1178, x = linear_73_cast_fp16)[name = tensor("op_1179_cast_fp16")]; + tensor var_1180_perm_0 = const()[name = tensor("op_1180_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_12_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_12_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(407881984)))]; + tensor text_encoder_text_model_encoder_layers_12_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_12_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(409979200)))]; + tensor linear_74_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_12_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_12_self_attn_v_proj_weight_to_fp16, x = hidden_states_73_cast_fp16)[name = tensor("linear_74_cast_fp16")]; + tensor var_1185 = const()[name = tensor("op_1185"), val = tensor([1, -1, 16, 64])]; + tensor var_1186_cast_fp16 = reshape(shape = var_1185, x = linear_74_cast_fp16)[name = tensor("op_1186_cast_fp16")]; + tensor var_1187_perm_0 = const()[name = tensor("op_1187_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1194 = const()[name = tensor("op_1194"), val = tensor([1, 77, 16, 64])]; + tensor var_1195_cast_fp16 = reshape(shape = var_1194, x = tensor_77_cast_fp16)[name = tensor("op_1195_cast_fp16")]; + tensor var_1196_perm_0 = const()[name = tensor("op_1196_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1198 = const()[name = tensor("op_1198"), val = tensor([16, -1, 64])]; + tensor transpose_53 = transpose(perm = var_1196_perm_0, x = var_1195_cast_fp16)[name = tensor("transpose_53")]; + tensor query_states_25_cast_fp16 = reshape(shape = var_1198, x = transpose_53)[name = tensor("query_states_25_cast_fp16")]; + tensor var_1200 = const()[name = tensor("op_1200"), val = tensor([16, -1, 64])]; + tensor transpose_55 = transpose(perm = var_1180_perm_0, x = var_1179_cast_fp16)[name = tensor("transpose_55")]; + tensor key_states_51_cast_fp16 = reshape(shape = var_1200, x = transpose_55)[name = tensor("key_states_51_cast_fp16")]; + tensor var_1202 = const()[name = tensor("op_1202"), val = tensor([16, -1, 64])]; + tensor transpose_54 = transpose(perm = var_1187_perm_0, x = var_1186_cast_fp16)[name = tensor("transpose_54")]; + tensor value_states_51_cast_fp16 = reshape(shape = var_1202, x = transpose_54)[name = tensor("value_states_51_cast_fp16")]; + tensor var_1205_perm_0 = const()[name = tensor("op_1205_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_73_transpose_x_0 = const()[name = tensor("attn_weights_73_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_73_transpose_y_0 = const()[name = tensor("attn_weights_73_transpose_y_0"), val = tensor(false)]; + tensor transpose_52 = transpose(perm = var_1205_perm_0, x = key_states_51_cast_fp16)[name = tensor("transpose_52")]; + tensor attn_weights_73_cast_fp16 = matmul(transpose_x = attn_weights_73_transpose_x_0, transpose_y = attn_weights_73_transpose_y_0, x = query_states_25_cast_fp16, y = transpose_52)[name = tensor("attn_weights_73_cast_fp16")]; + tensor var_1207 = const()[name = tensor("op_1207"), val = tensor([1, 16, 77, 77])]; + tensor var_1208_cast_fp16 = reshape(shape = var_1207, x = attn_weights_73_cast_fp16)[name = tensor("op_1208_cast_fp16")]; + tensor attn_weights_75_cast_fp16 = add(x = var_1208_cast_fp16, y = var_57_to_fp16)[name = tensor("attn_weights_75_cast_fp16")]; + tensor var_1213 = const()[name = tensor("op_1213"), val = tensor([16, 77, 77])]; + tensor input_197_cast_fp16 = reshape(shape = var_1213, x = attn_weights_75_cast_fp16)[name = tensor("input_197_cast_fp16")]; + tensor input_199_cast_fp16 = softmax(axis = var_5, x = input_197_cast_fp16)[name = tensor("input_199_cast_fp16")]; + tensor attn_output_73_transpose_x_0 = const()[name = tensor("attn_output_73_transpose_x_0"), val = tensor(false)]; + tensor attn_output_73_transpose_y_0 = const()[name = tensor("attn_output_73_transpose_y_0"), val = tensor(false)]; + tensor attn_output_73_cast_fp16 = matmul(transpose_x = attn_output_73_transpose_x_0, transpose_y = attn_output_73_transpose_y_0, x = input_199_cast_fp16, y = value_states_51_cast_fp16)[name = tensor("attn_output_73_cast_fp16")]; + tensor var_1218 = const()[name = tensor("op_1218"), val = tensor([1, 16, 77, 64])]; + tensor attn_output_75_cast_fp16 = reshape(shape = var_1218, x = attn_output_73_cast_fp16)[name = tensor("attn_output_75_cast_fp16")]; + tensor attn_output_77_perm_0 = const()[name = tensor("attn_output_77_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1221 = const()[name = tensor("op_1221"), val = tensor([1, 77, 1024])]; + tensor transpose_51 = transpose(perm = attn_output_77_perm_0, x = attn_output_75_cast_fp16)[name = tensor("transpose_51")]; + tensor input_201_cast_fp16 = reshape(shape = var_1221, x = transpose_51)[name = tensor("input_201_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_12_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_12_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(409981312)))]; + tensor text_encoder_text_model_encoder_layers_12_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_12_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(412078528)))]; + tensor linear_75_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_12_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_12_self_attn_out_proj_weight_to_fp16, x = input_201_cast_fp16)[name = tensor("linear_75_cast_fp16")]; + tensor input_203_cast_fp16 = add(x = input_195_cast_fp16, y = linear_75_cast_fp16)[name = tensor("input_203_cast_fp16")]; + tensor input_205_axes_0 = const()[name = tensor("input_205_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_12_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_12_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(412080640)))]; + tensor text_encoder_text_model_encoder_layers_12_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_12_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(412082752)))]; + tensor input_205_cast_fp16 = layer_norm(axes = input_205_axes_0, beta = text_encoder_text_model_encoder_layers_12_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_12_layer_norm2_weight_to_fp16, x = input_203_cast_fp16)[name = tensor("input_205_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_12_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_12_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(412084864)))]; + tensor text_encoder_text_model_encoder_layers_12_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_12_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(420473536)))]; + tensor linear_76_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_12_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_12_mlp_fc1_weight_to_fp16, x = input_205_cast_fp16)[name = tensor("linear_76_cast_fp16")]; + tensor input_209_mode_0 = const()[name = tensor("input_209_mode_0"), val = tensor("EXACT")]; + tensor input_209_cast_fp16 = gelu(mode = input_209_mode_0, x = linear_76_cast_fp16)[name = tensor("input_209_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_12_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_12_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(420481792)))]; + tensor text_encoder_text_model_encoder_layers_12_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_12_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(428870464)))]; + tensor linear_77_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_12_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_12_mlp_fc2_weight_to_fp16, x = input_209_cast_fp16)[name = tensor("linear_77_cast_fp16")]; + tensor input_211_cast_fp16 = add(x = input_203_cast_fp16, y = linear_77_cast_fp16)[name = tensor("input_211_cast_fp16")]; + tensor hidden_states_79_axes_0 = const()[name = tensor("hidden_states_79_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_13_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_13_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(428872576)))]; + tensor text_encoder_text_model_encoder_layers_13_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_13_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(428874688)))]; + tensor hidden_states_79_cast_fp16 = layer_norm(axes = hidden_states_79_axes_0, beta = text_encoder_text_model_encoder_layers_13_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_13_layer_norm1_weight_to_fp16, x = input_211_cast_fp16)[name = tensor("hidden_states_79_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_13_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_13_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(428876800)))]; + tensor text_encoder_text_model_encoder_layers_13_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_13_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(430974016)))]; + tensor linear_78_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_13_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_13_self_attn_q_proj_weight_to_fp16, x = hidden_states_79_cast_fp16)[name = tensor("linear_78_cast_fp16")]; + tensor var_1260_to_fp16 = const()[name = tensor("op_1260_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_83_cast_fp16 = mul(x = linear_78_cast_fp16, y = var_1260_to_fp16)[name = tensor("tensor_83_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_13_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_13_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(430976128)))]; + tensor text_encoder_text_model_encoder_layers_13_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_13_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(433073344)))]; + tensor linear_79_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_13_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_13_self_attn_k_proj_weight_to_fp16, x = hidden_states_79_cast_fp16)[name = tensor("linear_79_cast_fp16")]; + tensor var_1265 = const()[name = tensor("op_1265"), val = tensor([1, -1, 16, 64])]; + tensor var_1266_cast_fp16 = reshape(shape = var_1265, x = linear_79_cast_fp16)[name = tensor("op_1266_cast_fp16")]; + tensor var_1267_perm_0 = const()[name = tensor("op_1267_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_13_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_13_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(433075456)))]; + tensor text_encoder_text_model_encoder_layers_13_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_13_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(435172672)))]; + tensor linear_80_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_13_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_13_self_attn_v_proj_weight_to_fp16, x = hidden_states_79_cast_fp16)[name = tensor("linear_80_cast_fp16")]; + tensor var_1272 = const()[name = tensor("op_1272"), val = tensor([1, -1, 16, 64])]; + tensor var_1273_cast_fp16 = reshape(shape = var_1272, x = linear_80_cast_fp16)[name = tensor("op_1273_cast_fp16")]; + tensor var_1274_perm_0 = const()[name = tensor("op_1274_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1281 = const()[name = tensor("op_1281"), val = tensor([1, 77, 16, 64])]; + tensor var_1282_cast_fp16 = reshape(shape = var_1281, x = tensor_83_cast_fp16)[name = tensor("op_1282_cast_fp16")]; + tensor var_1283_perm_0 = const()[name = tensor("op_1283_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1285 = const()[name = tensor("op_1285"), val = tensor([16, -1, 64])]; + tensor transpose_48 = transpose(perm = var_1283_perm_0, x = var_1282_cast_fp16)[name = tensor("transpose_48")]; + tensor query_states_27_cast_fp16 = reshape(shape = var_1285, x = transpose_48)[name = tensor("query_states_27_cast_fp16")]; + tensor var_1287 = const()[name = tensor("op_1287"), val = tensor([16, -1, 64])]; + tensor transpose_50 = transpose(perm = var_1267_perm_0, x = var_1266_cast_fp16)[name = tensor("transpose_50")]; + tensor key_states_55_cast_fp16 = reshape(shape = var_1287, x = transpose_50)[name = tensor("key_states_55_cast_fp16")]; + tensor var_1289 = const()[name = tensor("op_1289"), val = tensor([16, -1, 64])]; + tensor transpose_49 = transpose(perm = var_1274_perm_0, x = var_1273_cast_fp16)[name = tensor("transpose_49")]; + tensor value_states_55_cast_fp16 = reshape(shape = var_1289, x = transpose_49)[name = tensor("value_states_55_cast_fp16")]; + tensor var_1292_perm_0 = const()[name = tensor("op_1292_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_79_transpose_x_0 = const()[name = tensor("attn_weights_79_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_79_transpose_y_0 = const()[name = tensor("attn_weights_79_transpose_y_0"), val = tensor(false)]; + tensor transpose_47 = transpose(perm = var_1292_perm_0, x = key_states_55_cast_fp16)[name = tensor("transpose_47")]; + tensor attn_weights_79_cast_fp16 = matmul(transpose_x = attn_weights_79_transpose_x_0, transpose_y = attn_weights_79_transpose_y_0, x = query_states_27_cast_fp16, y = transpose_47)[name = tensor("attn_weights_79_cast_fp16")]; + tensor var_1294 = const()[name = tensor("op_1294"), val = tensor([1, 16, 77, 77])]; + tensor var_1295_cast_fp16 = reshape(shape = var_1294, x = attn_weights_79_cast_fp16)[name = tensor("op_1295_cast_fp16")]; + tensor attn_weights_81_cast_fp16 = add(x = var_1295_cast_fp16, y = var_57_to_fp16)[name = tensor("attn_weights_81_cast_fp16")]; + tensor var_1300 = const()[name = tensor("op_1300"), val = tensor([16, 77, 77])]; + tensor input_213_cast_fp16 = reshape(shape = var_1300, x = attn_weights_81_cast_fp16)[name = tensor("input_213_cast_fp16")]; + tensor input_215_cast_fp16 = softmax(axis = var_5, x = input_213_cast_fp16)[name = tensor("input_215_cast_fp16")]; + tensor attn_output_79_transpose_x_0 = const()[name = tensor("attn_output_79_transpose_x_0"), val = tensor(false)]; + tensor attn_output_79_transpose_y_0 = const()[name = tensor("attn_output_79_transpose_y_0"), val = tensor(false)]; + tensor attn_output_79_cast_fp16 = matmul(transpose_x = attn_output_79_transpose_x_0, transpose_y = attn_output_79_transpose_y_0, x = input_215_cast_fp16, y = value_states_55_cast_fp16)[name = tensor("attn_output_79_cast_fp16")]; + tensor var_1305 = const()[name = tensor("op_1305"), val = tensor([1, 16, 77, 64])]; + tensor attn_output_81_cast_fp16 = reshape(shape = var_1305, x = attn_output_79_cast_fp16)[name = tensor("attn_output_81_cast_fp16")]; + tensor attn_output_83_perm_0 = const()[name = tensor("attn_output_83_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1308 = const()[name = tensor("op_1308"), val = tensor([1, 77, 1024])]; + tensor transpose_46 = transpose(perm = attn_output_83_perm_0, x = attn_output_81_cast_fp16)[name = tensor("transpose_46")]; + tensor input_217_cast_fp16 = reshape(shape = var_1308, x = transpose_46)[name = tensor("input_217_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_13_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_13_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(435174784)))]; + tensor text_encoder_text_model_encoder_layers_13_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_13_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(437272000)))]; + tensor linear_81_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_13_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_13_self_attn_out_proj_weight_to_fp16, x = input_217_cast_fp16)[name = tensor("linear_81_cast_fp16")]; + tensor input_219_cast_fp16 = add(x = input_211_cast_fp16, y = linear_81_cast_fp16)[name = tensor("input_219_cast_fp16")]; + tensor input_221_axes_0 = const()[name = tensor("input_221_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_13_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_13_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(437274112)))]; + tensor text_encoder_text_model_encoder_layers_13_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_13_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(437276224)))]; + tensor input_221_cast_fp16 = layer_norm(axes = input_221_axes_0, beta = text_encoder_text_model_encoder_layers_13_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_13_layer_norm2_weight_to_fp16, x = input_219_cast_fp16)[name = tensor("input_221_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_13_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_13_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(437278336)))]; + tensor text_encoder_text_model_encoder_layers_13_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_13_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(445667008)))]; + tensor linear_82_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_13_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_13_mlp_fc1_weight_to_fp16, x = input_221_cast_fp16)[name = tensor("linear_82_cast_fp16")]; + tensor input_225_mode_0 = const()[name = tensor("input_225_mode_0"), val = tensor("EXACT")]; + tensor input_225_cast_fp16 = gelu(mode = input_225_mode_0, x = linear_82_cast_fp16)[name = tensor("input_225_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_13_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_13_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(445675264)))]; + tensor text_encoder_text_model_encoder_layers_13_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_13_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(454063936)))]; + tensor linear_83_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_13_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_13_mlp_fc2_weight_to_fp16, x = input_225_cast_fp16)[name = tensor("linear_83_cast_fp16")]; + tensor input_227_cast_fp16 = add(x = input_219_cast_fp16, y = linear_83_cast_fp16)[name = tensor("input_227_cast_fp16")]; + tensor hidden_states_85_axes_0 = const()[name = tensor("hidden_states_85_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_14_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_14_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(454066048)))]; + tensor text_encoder_text_model_encoder_layers_14_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_14_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(454068160)))]; + tensor hidden_states_85_cast_fp16 = layer_norm(axes = hidden_states_85_axes_0, beta = text_encoder_text_model_encoder_layers_14_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_14_layer_norm1_weight_to_fp16, x = input_227_cast_fp16)[name = tensor("hidden_states_85_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_14_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_14_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(454070272)))]; + tensor text_encoder_text_model_encoder_layers_14_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_14_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(456167488)))]; + tensor linear_84_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_14_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_14_self_attn_q_proj_weight_to_fp16, x = hidden_states_85_cast_fp16)[name = tensor("linear_84_cast_fp16")]; + tensor var_1347_to_fp16 = const()[name = tensor("op_1347_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_89_cast_fp16 = mul(x = linear_84_cast_fp16, y = var_1347_to_fp16)[name = tensor("tensor_89_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_14_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_14_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(456169600)))]; + tensor text_encoder_text_model_encoder_layers_14_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_14_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(458266816)))]; + tensor linear_85_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_14_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_14_self_attn_k_proj_weight_to_fp16, x = hidden_states_85_cast_fp16)[name = tensor("linear_85_cast_fp16")]; + tensor var_1352 = const()[name = tensor("op_1352"), val = tensor([1, -1, 16, 64])]; + tensor var_1353_cast_fp16 = reshape(shape = var_1352, x = linear_85_cast_fp16)[name = tensor("op_1353_cast_fp16")]; + tensor var_1354_perm_0 = const()[name = tensor("op_1354_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_14_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_14_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(458268928)))]; + tensor text_encoder_text_model_encoder_layers_14_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_14_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(460366144)))]; + tensor linear_86_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_14_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_14_self_attn_v_proj_weight_to_fp16, x = hidden_states_85_cast_fp16)[name = tensor("linear_86_cast_fp16")]; + tensor var_1359 = const()[name = tensor("op_1359"), val = tensor([1, -1, 16, 64])]; + tensor var_1360_cast_fp16 = reshape(shape = var_1359, x = linear_86_cast_fp16)[name = tensor("op_1360_cast_fp16")]; + tensor var_1361_perm_0 = const()[name = tensor("op_1361_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1368 = const()[name = tensor("op_1368"), val = tensor([1, 77, 16, 64])]; + tensor var_1369_cast_fp16 = reshape(shape = var_1368, x = tensor_89_cast_fp16)[name = tensor("op_1369_cast_fp16")]; + tensor var_1370_perm_0 = const()[name = tensor("op_1370_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1372 = const()[name = tensor("op_1372"), val = tensor([16, -1, 64])]; + tensor transpose_43 = transpose(perm = var_1370_perm_0, x = var_1369_cast_fp16)[name = tensor("transpose_43")]; + tensor query_states_29_cast_fp16 = reshape(shape = var_1372, x = transpose_43)[name = tensor("query_states_29_cast_fp16")]; + tensor var_1374 = const()[name = tensor("op_1374"), val = tensor([16, -1, 64])]; + tensor transpose_45 = transpose(perm = var_1354_perm_0, x = var_1353_cast_fp16)[name = tensor("transpose_45")]; + tensor key_states_59_cast_fp16 = reshape(shape = var_1374, x = transpose_45)[name = tensor("key_states_59_cast_fp16")]; + tensor var_1376 = const()[name = tensor("op_1376"), val = tensor([16, -1, 64])]; + tensor transpose_44 = transpose(perm = var_1361_perm_0, x = var_1360_cast_fp16)[name = tensor("transpose_44")]; + tensor value_states_59_cast_fp16 = reshape(shape = var_1376, x = transpose_44)[name = tensor("value_states_59_cast_fp16")]; + tensor var_1379_perm_0 = const()[name = tensor("op_1379_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_85_transpose_x_0 = const()[name = tensor("attn_weights_85_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_85_transpose_y_0 = const()[name = tensor("attn_weights_85_transpose_y_0"), val = tensor(false)]; + tensor transpose_42 = transpose(perm = var_1379_perm_0, x = key_states_59_cast_fp16)[name = tensor("transpose_42")]; + tensor attn_weights_85_cast_fp16 = matmul(transpose_x = attn_weights_85_transpose_x_0, transpose_y = attn_weights_85_transpose_y_0, x = query_states_29_cast_fp16, y = transpose_42)[name = tensor("attn_weights_85_cast_fp16")]; + tensor var_1381 = const()[name = tensor("op_1381"), val = tensor([1, 16, 77, 77])]; + tensor var_1382_cast_fp16 = reshape(shape = var_1381, x = attn_weights_85_cast_fp16)[name = tensor("op_1382_cast_fp16")]; + tensor attn_weights_87_cast_fp16 = add(x = var_1382_cast_fp16, y = var_57_to_fp16)[name = tensor("attn_weights_87_cast_fp16")]; + tensor var_1387 = const()[name = tensor("op_1387"), val = tensor([16, 77, 77])]; + tensor input_229_cast_fp16 = reshape(shape = var_1387, x = attn_weights_87_cast_fp16)[name = tensor("input_229_cast_fp16")]; + tensor input_231_cast_fp16 = softmax(axis = var_5, x = input_229_cast_fp16)[name = tensor("input_231_cast_fp16")]; + tensor attn_output_85_transpose_x_0 = const()[name = tensor("attn_output_85_transpose_x_0"), val = tensor(false)]; + tensor attn_output_85_transpose_y_0 = const()[name = tensor("attn_output_85_transpose_y_0"), val = tensor(false)]; + tensor attn_output_85_cast_fp16 = matmul(transpose_x = attn_output_85_transpose_x_0, transpose_y = attn_output_85_transpose_y_0, x = input_231_cast_fp16, y = value_states_59_cast_fp16)[name = tensor("attn_output_85_cast_fp16")]; + tensor var_1392 = const()[name = tensor("op_1392"), val = tensor([1, 16, 77, 64])]; + tensor attn_output_87_cast_fp16 = reshape(shape = var_1392, x = attn_output_85_cast_fp16)[name = tensor("attn_output_87_cast_fp16")]; + tensor attn_output_89_perm_0 = const()[name = tensor("attn_output_89_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1395 = const()[name = tensor("op_1395"), val = tensor([1, 77, 1024])]; + tensor transpose_41 = transpose(perm = attn_output_89_perm_0, x = attn_output_87_cast_fp16)[name = tensor("transpose_41")]; + tensor input_233_cast_fp16 = reshape(shape = var_1395, x = transpose_41)[name = tensor("input_233_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_14_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_14_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(460368256)))]; + tensor text_encoder_text_model_encoder_layers_14_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_14_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(462465472)))]; + tensor linear_87_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_14_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_14_self_attn_out_proj_weight_to_fp16, x = input_233_cast_fp16)[name = tensor("linear_87_cast_fp16")]; + tensor input_235_cast_fp16 = add(x = input_227_cast_fp16, y = linear_87_cast_fp16)[name = tensor("input_235_cast_fp16")]; + tensor input_237_axes_0 = const()[name = tensor("input_237_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_14_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_14_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(462467584)))]; + tensor text_encoder_text_model_encoder_layers_14_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_14_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(462469696)))]; + tensor input_237_cast_fp16 = layer_norm(axes = input_237_axes_0, beta = text_encoder_text_model_encoder_layers_14_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_14_layer_norm2_weight_to_fp16, x = input_235_cast_fp16)[name = tensor("input_237_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_14_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_14_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(462471808)))]; + tensor text_encoder_text_model_encoder_layers_14_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_14_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(470860480)))]; + tensor linear_88_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_14_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_14_mlp_fc1_weight_to_fp16, x = input_237_cast_fp16)[name = tensor("linear_88_cast_fp16")]; + tensor input_241_mode_0 = const()[name = tensor("input_241_mode_0"), val = tensor("EXACT")]; + tensor input_241_cast_fp16 = gelu(mode = input_241_mode_0, x = linear_88_cast_fp16)[name = tensor("input_241_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_14_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_14_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(470868736)))]; + tensor text_encoder_text_model_encoder_layers_14_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_14_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(479257408)))]; + tensor linear_89_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_14_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_14_mlp_fc2_weight_to_fp16, x = input_241_cast_fp16)[name = tensor("linear_89_cast_fp16")]; + tensor input_243_cast_fp16 = add(x = input_235_cast_fp16, y = linear_89_cast_fp16)[name = tensor("input_243_cast_fp16")]; + tensor hidden_states_91_axes_0 = const()[name = tensor("hidden_states_91_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_15_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_15_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(479259520)))]; + tensor text_encoder_text_model_encoder_layers_15_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_15_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(479261632)))]; + tensor hidden_states_91_cast_fp16 = layer_norm(axes = hidden_states_91_axes_0, beta = text_encoder_text_model_encoder_layers_15_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_15_layer_norm1_weight_to_fp16, x = input_243_cast_fp16)[name = tensor("hidden_states_91_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_15_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_15_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(479263744)))]; + tensor text_encoder_text_model_encoder_layers_15_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_15_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(481360960)))]; + tensor linear_90_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_15_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_15_self_attn_q_proj_weight_to_fp16, x = hidden_states_91_cast_fp16)[name = tensor("linear_90_cast_fp16")]; + tensor var_1434_to_fp16 = const()[name = tensor("op_1434_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_95_cast_fp16 = mul(x = linear_90_cast_fp16, y = var_1434_to_fp16)[name = tensor("tensor_95_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_15_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_15_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(481363072)))]; + tensor text_encoder_text_model_encoder_layers_15_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_15_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(483460288)))]; + tensor linear_91_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_15_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_15_self_attn_k_proj_weight_to_fp16, x = hidden_states_91_cast_fp16)[name = tensor("linear_91_cast_fp16")]; + tensor var_1439 = const()[name = tensor("op_1439"), val = tensor([1, -1, 16, 64])]; + tensor var_1440_cast_fp16 = reshape(shape = var_1439, x = linear_91_cast_fp16)[name = tensor("op_1440_cast_fp16")]; + tensor var_1441_perm_0 = const()[name = tensor("op_1441_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_15_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_15_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(483462400)))]; + tensor text_encoder_text_model_encoder_layers_15_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_15_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(485559616)))]; + tensor linear_92_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_15_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_15_self_attn_v_proj_weight_to_fp16, x = hidden_states_91_cast_fp16)[name = tensor("linear_92_cast_fp16")]; + tensor var_1446 = const()[name = tensor("op_1446"), val = tensor([1, -1, 16, 64])]; + tensor var_1447_cast_fp16 = reshape(shape = var_1446, x = linear_92_cast_fp16)[name = tensor("op_1447_cast_fp16")]; + tensor var_1448_perm_0 = const()[name = tensor("op_1448_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1455 = const()[name = tensor("op_1455"), val = tensor([1, 77, 16, 64])]; + tensor var_1456_cast_fp16 = reshape(shape = var_1455, x = tensor_95_cast_fp16)[name = tensor("op_1456_cast_fp16")]; + tensor var_1457_perm_0 = const()[name = tensor("op_1457_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1459 = const()[name = tensor("op_1459"), val = tensor([16, -1, 64])]; + tensor transpose_38 = transpose(perm = var_1457_perm_0, x = var_1456_cast_fp16)[name = tensor("transpose_38")]; + tensor query_states_31_cast_fp16 = reshape(shape = var_1459, x = transpose_38)[name = tensor("query_states_31_cast_fp16")]; + tensor var_1461 = const()[name = tensor("op_1461"), val = tensor([16, -1, 64])]; + tensor transpose_40 = transpose(perm = var_1441_perm_0, x = var_1440_cast_fp16)[name = tensor("transpose_40")]; + tensor key_states_63_cast_fp16 = reshape(shape = var_1461, x = transpose_40)[name = tensor("key_states_63_cast_fp16")]; + tensor var_1463 = const()[name = tensor("op_1463"), val = tensor([16, -1, 64])]; + tensor transpose_39 = transpose(perm = var_1448_perm_0, x = var_1447_cast_fp16)[name = tensor("transpose_39")]; + tensor value_states_63_cast_fp16 = reshape(shape = var_1463, x = transpose_39)[name = tensor("value_states_63_cast_fp16")]; + tensor var_1466_perm_0 = const()[name = tensor("op_1466_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_91_transpose_x_0 = const()[name = tensor("attn_weights_91_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_91_transpose_y_0 = const()[name = tensor("attn_weights_91_transpose_y_0"), val = tensor(false)]; + tensor transpose_37 = transpose(perm = var_1466_perm_0, x = key_states_63_cast_fp16)[name = tensor("transpose_37")]; + tensor attn_weights_91_cast_fp16 = matmul(transpose_x = attn_weights_91_transpose_x_0, transpose_y = attn_weights_91_transpose_y_0, x = query_states_31_cast_fp16, y = transpose_37)[name = tensor("attn_weights_91_cast_fp16")]; + tensor var_1468 = const()[name = tensor("op_1468"), val = tensor([1, 16, 77, 77])]; + tensor var_1469_cast_fp16 = reshape(shape = var_1468, x = attn_weights_91_cast_fp16)[name = tensor("op_1469_cast_fp16")]; + tensor attn_weights_93_cast_fp16 = add(x = var_1469_cast_fp16, y = var_57_to_fp16)[name = tensor("attn_weights_93_cast_fp16")]; + tensor var_1474 = const()[name = tensor("op_1474"), val = tensor([16, 77, 77])]; + tensor input_245_cast_fp16 = reshape(shape = var_1474, x = attn_weights_93_cast_fp16)[name = tensor("input_245_cast_fp16")]; + tensor input_247_cast_fp16 = softmax(axis = var_5, x = input_245_cast_fp16)[name = tensor("input_247_cast_fp16")]; + tensor attn_output_91_transpose_x_0 = const()[name = tensor("attn_output_91_transpose_x_0"), val = tensor(false)]; + tensor attn_output_91_transpose_y_0 = const()[name = tensor("attn_output_91_transpose_y_0"), val = tensor(false)]; + tensor attn_output_91_cast_fp16 = matmul(transpose_x = attn_output_91_transpose_x_0, transpose_y = attn_output_91_transpose_y_0, x = input_247_cast_fp16, y = value_states_63_cast_fp16)[name = tensor("attn_output_91_cast_fp16")]; + tensor var_1479 = const()[name = tensor("op_1479"), val = tensor([1, 16, 77, 64])]; + tensor attn_output_93_cast_fp16 = reshape(shape = var_1479, x = attn_output_91_cast_fp16)[name = tensor("attn_output_93_cast_fp16")]; + tensor attn_output_95_perm_0 = const()[name = tensor("attn_output_95_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1482 = const()[name = tensor("op_1482"), val = tensor([1, 77, 1024])]; + tensor transpose_36 = transpose(perm = attn_output_95_perm_0, x = attn_output_93_cast_fp16)[name = tensor("transpose_36")]; + tensor input_249_cast_fp16 = reshape(shape = var_1482, x = transpose_36)[name = tensor("input_249_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_15_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_15_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(485561728)))]; + tensor text_encoder_text_model_encoder_layers_15_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_15_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(487658944)))]; + tensor linear_93_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_15_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_15_self_attn_out_proj_weight_to_fp16, x = input_249_cast_fp16)[name = tensor("linear_93_cast_fp16")]; + tensor input_251_cast_fp16 = add(x = input_243_cast_fp16, y = linear_93_cast_fp16)[name = tensor("input_251_cast_fp16")]; + tensor input_253_axes_0 = const()[name = tensor("input_253_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_15_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_15_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(487661056)))]; + tensor text_encoder_text_model_encoder_layers_15_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_15_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(487663168)))]; + tensor input_253_cast_fp16 = layer_norm(axes = input_253_axes_0, beta = text_encoder_text_model_encoder_layers_15_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_15_layer_norm2_weight_to_fp16, x = input_251_cast_fp16)[name = tensor("input_253_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_15_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_15_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(487665280)))]; + tensor text_encoder_text_model_encoder_layers_15_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_15_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(496053952)))]; + tensor linear_94_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_15_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_15_mlp_fc1_weight_to_fp16, x = input_253_cast_fp16)[name = tensor("linear_94_cast_fp16")]; + tensor input_257_mode_0 = const()[name = tensor("input_257_mode_0"), val = tensor("EXACT")]; + tensor input_257_cast_fp16 = gelu(mode = input_257_mode_0, x = linear_94_cast_fp16)[name = tensor("input_257_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_15_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_15_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(496062208)))]; + tensor text_encoder_text_model_encoder_layers_15_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_15_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(504450880)))]; + tensor linear_95_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_15_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_15_mlp_fc2_weight_to_fp16, x = input_257_cast_fp16)[name = tensor("linear_95_cast_fp16")]; + tensor input_259_cast_fp16 = add(x = input_251_cast_fp16, y = linear_95_cast_fp16)[name = tensor("input_259_cast_fp16")]; + tensor hidden_states_97_axes_0 = const()[name = tensor("hidden_states_97_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_16_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_16_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(504452992)))]; + tensor text_encoder_text_model_encoder_layers_16_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_16_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(504455104)))]; + tensor hidden_states_97_cast_fp16 = layer_norm(axes = hidden_states_97_axes_0, beta = text_encoder_text_model_encoder_layers_16_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_16_layer_norm1_weight_to_fp16, x = input_259_cast_fp16)[name = tensor("hidden_states_97_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_16_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_16_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(504457216)))]; + tensor text_encoder_text_model_encoder_layers_16_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_16_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(506554432)))]; + tensor linear_96_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_16_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_16_self_attn_q_proj_weight_to_fp16, x = hidden_states_97_cast_fp16)[name = tensor("linear_96_cast_fp16")]; + tensor var_1521_to_fp16 = const()[name = tensor("op_1521_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_101_cast_fp16 = mul(x = linear_96_cast_fp16, y = var_1521_to_fp16)[name = tensor("tensor_101_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_16_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_16_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(506556544)))]; + tensor text_encoder_text_model_encoder_layers_16_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_16_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(508653760)))]; + tensor linear_97_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_16_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_16_self_attn_k_proj_weight_to_fp16, x = hidden_states_97_cast_fp16)[name = tensor("linear_97_cast_fp16")]; + tensor var_1526 = const()[name = tensor("op_1526"), val = tensor([1, -1, 16, 64])]; + tensor var_1527_cast_fp16 = reshape(shape = var_1526, x = linear_97_cast_fp16)[name = tensor("op_1527_cast_fp16")]; + tensor var_1528_perm_0 = const()[name = tensor("op_1528_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_16_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_16_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(508655872)))]; + tensor text_encoder_text_model_encoder_layers_16_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_16_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(510753088)))]; + tensor linear_98_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_16_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_16_self_attn_v_proj_weight_to_fp16, x = hidden_states_97_cast_fp16)[name = tensor("linear_98_cast_fp16")]; + tensor var_1533 = const()[name = tensor("op_1533"), val = tensor([1, -1, 16, 64])]; + tensor var_1534_cast_fp16 = reshape(shape = var_1533, x = linear_98_cast_fp16)[name = tensor("op_1534_cast_fp16")]; + tensor var_1535_perm_0 = const()[name = tensor("op_1535_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1542 = const()[name = tensor("op_1542"), val = tensor([1, 77, 16, 64])]; + tensor var_1543_cast_fp16 = reshape(shape = var_1542, x = tensor_101_cast_fp16)[name = tensor("op_1543_cast_fp16")]; + tensor var_1544_perm_0 = const()[name = tensor("op_1544_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1546 = const()[name = tensor("op_1546"), val = tensor([16, -1, 64])]; + tensor transpose_33 = transpose(perm = var_1544_perm_0, x = var_1543_cast_fp16)[name = tensor("transpose_33")]; + tensor query_states_33_cast_fp16 = reshape(shape = var_1546, x = transpose_33)[name = tensor("query_states_33_cast_fp16")]; + tensor var_1548 = const()[name = tensor("op_1548"), val = tensor([16, -1, 64])]; + tensor transpose_35 = transpose(perm = var_1528_perm_0, x = var_1527_cast_fp16)[name = tensor("transpose_35")]; + tensor key_states_67_cast_fp16 = reshape(shape = var_1548, x = transpose_35)[name = tensor("key_states_67_cast_fp16")]; + tensor var_1550 = const()[name = tensor("op_1550"), val = tensor([16, -1, 64])]; + tensor transpose_34 = transpose(perm = var_1535_perm_0, x = var_1534_cast_fp16)[name = tensor("transpose_34")]; + tensor value_states_67_cast_fp16 = reshape(shape = var_1550, x = transpose_34)[name = tensor("value_states_67_cast_fp16")]; + tensor var_1553_perm_0 = const()[name = tensor("op_1553_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_97_transpose_x_0 = const()[name = tensor("attn_weights_97_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_97_transpose_y_0 = const()[name = tensor("attn_weights_97_transpose_y_0"), val = tensor(false)]; + tensor transpose_32 = transpose(perm = var_1553_perm_0, x = key_states_67_cast_fp16)[name = tensor("transpose_32")]; + tensor attn_weights_97_cast_fp16 = matmul(transpose_x = attn_weights_97_transpose_x_0, transpose_y = attn_weights_97_transpose_y_0, x = query_states_33_cast_fp16, y = transpose_32)[name = tensor("attn_weights_97_cast_fp16")]; + tensor var_1555 = const()[name = tensor("op_1555"), val = tensor([1, 16, 77, 77])]; + tensor var_1556_cast_fp16 = reshape(shape = var_1555, x = attn_weights_97_cast_fp16)[name = tensor("op_1556_cast_fp16")]; + tensor attn_weights_99_cast_fp16 = add(x = var_1556_cast_fp16, y = var_57_to_fp16)[name = tensor("attn_weights_99_cast_fp16")]; + tensor var_1561 = const()[name = tensor("op_1561"), val = tensor([16, 77, 77])]; + tensor input_261_cast_fp16 = reshape(shape = var_1561, x = attn_weights_99_cast_fp16)[name = tensor("input_261_cast_fp16")]; + tensor input_263_cast_fp16 = softmax(axis = var_5, x = input_261_cast_fp16)[name = tensor("input_263_cast_fp16")]; + tensor attn_output_97_transpose_x_0 = const()[name = tensor("attn_output_97_transpose_x_0"), val = tensor(false)]; + tensor attn_output_97_transpose_y_0 = const()[name = tensor("attn_output_97_transpose_y_0"), val = tensor(false)]; + tensor attn_output_97_cast_fp16 = matmul(transpose_x = attn_output_97_transpose_x_0, transpose_y = attn_output_97_transpose_y_0, x = input_263_cast_fp16, y = value_states_67_cast_fp16)[name = tensor("attn_output_97_cast_fp16")]; + tensor var_1566 = const()[name = tensor("op_1566"), val = tensor([1, 16, 77, 64])]; + tensor attn_output_99_cast_fp16 = reshape(shape = var_1566, x = attn_output_97_cast_fp16)[name = tensor("attn_output_99_cast_fp16")]; + tensor attn_output_101_perm_0 = const()[name = tensor("attn_output_101_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1569 = const()[name = tensor("op_1569"), val = tensor([1, 77, 1024])]; + tensor transpose_31 = transpose(perm = attn_output_101_perm_0, x = attn_output_99_cast_fp16)[name = tensor("transpose_31")]; + tensor input_265_cast_fp16 = reshape(shape = var_1569, x = transpose_31)[name = tensor("input_265_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_16_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_16_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(510755200)))]; + tensor text_encoder_text_model_encoder_layers_16_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_16_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(512852416)))]; + tensor linear_99_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_16_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_16_self_attn_out_proj_weight_to_fp16, x = input_265_cast_fp16)[name = tensor("linear_99_cast_fp16")]; + tensor input_267_cast_fp16 = add(x = input_259_cast_fp16, y = linear_99_cast_fp16)[name = tensor("input_267_cast_fp16")]; + tensor input_269_axes_0 = const()[name = tensor("input_269_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_16_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_16_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(512854528)))]; + tensor text_encoder_text_model_encoder_layers_16_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_16_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(512856640)))]; + tensor input_269_cast_fp16 = layer_norm(axes = input_269_axes_0, beta = text_encoder_text_model_encoder_layers_16_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_16_layer_norm2_weight_to_fp16, x = input_267_cast_fp16)[name = tensor("input_269_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_16_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_16_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(512858752)))]; + tensor text_encoder_text_model_encoder_layers_16_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_16_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(521247424)))]; + tensor linear_100_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_16_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_16_mlp_fc1_weight_to_fp16, x = input_269_cast_fp16)[name = tensor("linear_100_cast_fp16")]; + tensor input_273_mode_0 = const()[name = tensor("input_273_mode_0"), val = tensor("EXACT")]; + tensor input_273_cast_fp16 = gelu(mode = input_273_mode_0, x = linear_100_cast_fp16)[name = tensor("input_273_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_16_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_16_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(521255680)))]; + tensor text_encoder_text_model_encoder_layers_16_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_16_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(529644352)))]; + tensor linear_101_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_16_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_16_mlp_fc2_weight_to_fp16, x = input_273_cast_fp16)[name = tensor("linear_101_cast_fp16")]; + tensor input_275_cast_fp16 = add(x = input_267_cast_fp16, y = linear_101_cast_fp16)[name = tensor("input_275_cast_fp16")]; + tensor hidden_states_103_axes_0 = const()[name = tensor("hidden_states_103_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_17_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_17_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(529646464)))]; + tensor text_encoder_text_model_encoder_layers_17_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_17_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(529648576)))]; + tensor hidden_states_103_cast_fp16 = layer_norm(axes = hidden_states_103_axes_0, beta = text_encoder_text_model_encoder_layers_17_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_17_layer_norm1_weight_to_fp16, x = input_275_cast_fp16)[name = tensor("hidden_states_103_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_17_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_17_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(529650688)))]; + tensor text_encoder_text_model_encoder_layers_17_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_17_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(531747904)))]; + tensor linear_102_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_17_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_17_self_attn_q_proj_weight_to_fp16, x = hidden_states_103_cast_fp16)[name = tensor("linear_102_cast_fp16")]; + tensor var_1608_to_fp16 = const()[name = tensor("op_1608_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_107_cast_fp16 = mul(x = linear_102_cast_fp16, y = var_1608_to_fp16)[name = tensor("tensor_107_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_17_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_17_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(531750016)))]; + tensor text_encoder_text_model_encoder_layers_17_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_17_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(533847232)))]; + tensor linear_103_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_17_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_17_self_attn_k_proj_weight_to_fp16, x = hidden_states_103_cast_fp16)[name = tensor("linear_103_cast_fp16")]; + tensor var_1613 = const()[name = tensor("op_1613"), val = tensor([1, -1, 16, 64])]; + tensor var_1614_cast_fp16 = reshape(shape = var_1613, x = linear_103_cast_fp16)[name = tensor("op_1614_cast_fp16")]; + tensor var_1615_perm_0 = const()[name = tensor("op_1615_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_17_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_17_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(533849344)))]; + tensor text_encoder_text_model_encoder_layers_17_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_17_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(535946560)))]; + tensor linear_104_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_17_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_17_self_attn_v_proj_weight_to_fp16, x = hidden_states_103_cast_fp16)[name = tensor("linear_104_cast_fp16")]; + tensor var_1620 = const()[name = tensor("op_1620"), val = tensor([1, -1, 16, 64])]; + tensor var_1621_cast_fp16 = reshape(shape = var_1620, x = linear_104_cast_fp16)[name = tensor("op_1621_cast_fp16")]; + tensor var_1622_perm_0 = const()[name = tensor("op_1622_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1629 = const()[name = tensor("op_1629"), val = tensor([1, 77, 16, 64])]; + tensor var_1630_cast_fp16 = reshape(shape = var_1629, x = tensor_107_cast_fp16)[name = tensor("op_1630_cast_fp16")]; + tensor var_1631_perm_0 = const()[name = tensor("op_1631_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1633 = const()[name = tensor("op_1633"), val = tensor([16, -1, 64])]; + tensor transpose_28 = transpose(perm = var_1631_perm_0, x = var_1630_cast_fp16)[name = tensor("transpose_28")]; + tensor query_states_35_cast_fp16 = reshape(shape = var_1633, x = transpose_28)[name = tensor("query_states_35_cast_fp16")]; + tensor var_1635 = const()[name = tensor("op_1635"), val = tensor([16, -1, 64])]; + tensor transpose_30 = transpose(perm = var_1615_perm_0, x = var_1614_cast_fp16)[name = tensor("transpose_30")]; + tensor key_states_71_cast_fp16 = reshape(shape = var_1635, x = transpose_30)[name = tensor("key_states_71_cast_fp16")]; + tensor var_1637 = const()[name = tensor("op_1637"), val = tensor([16, -1, 64])]; + tensor transpose_29 = transpose(perm = var_1622_perm_0, x = var_1621_cast_fp16)[name = tensor("transpose_29")]; + tensor value_states_71_cast_fp16 = reshape(shape = var_1637, x = transpose_29)[name = tensor("value_states_71_cast_fp16")]; + tensor var_1640_perm_0 = const()[name = tensor("op_1640_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_103_transpose_x_0 = const()[name = tensor("attn_weights_103_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_103_transpose_y_0 = const()[name = tensor("attn_weights_103_transpose_y_0"), val = tensor(false)]; + tensor transpose_27 = transpose(perm = var_1640_perm_0, x = key_states_71_cast_fp16)[name = tensor("transpose_27")]; + tensor attn_weights_103_cast_fp16 = matmul(transpose_x = attn_weights_103_transpose_x_0, transpose_y = attn_weights_103_transpose_y_0, x = query_states_35_cast_fp16, y = transpose_27)[name = tensor("attn_weights_103_cast_fp16")]; + tensor var_1642 = const()[name = tensor("op_1642"), val = tensor([1, 16, 77, 77])]; + tensor var_1643_cast_fp16 = reshape(shape = var_1642, x = attn_weights_103_cast_fp16)[name = tensor("op_1643_cast_fp16")]; + tensor attn_weights_105_cast_fp16 = add(x = var_1643_cast_fp16, y = var_57_to_fp16)[name = tensor("attn_weights_105_cast_fp16")]; + tensor var_1648 = const()[name = tensor("op_1648"), val = tensor([16, 77, 77])]; + tensor input_277_cast_fp16 = reshape(shape = var_1648, x = attn_weights_105_cast_fp16)[name = tensor("input_277_cast_fp16")]; + tensor input_279_cast_fp16 = softmax(axis = var_5, x = input_277_cast_fp16)[name = tensor("input_279_cast_fp16")]; + tensor attn_output_103_transpose_x_0 = const()[name = tensor("attn_output_103_transpose_x_0"), val = tensor(false)]; + tensor attn_output_103_transpose_y_0 = const()[name = tensor("attn_output_103_transpose_y_0"), val = tensor(false)]; + tensor attn_output_103_cast_fp16 = matmul(transpose_x = attn_output_103_transpose_x_0, transpose_y = attn_output_103_transpose_y_0, x = input_279_cast_fp16, y = value_states_71_cast_fp16)[name = tensor("attn_output_103_cast_fp16")]; + tensor var_1653 = const()[name = tensor("op_1653"), val = tensor([1, 16, 77, 64])]; + tensor attn_output_105_cast_fp16 = reshape(shape = var_1653, x = attn_output_103_cast_fp16)[name = tensor("attn_output_105_cast_fp16")]; + tensor attn_output_107_perm_0 = const()[name = tensor("attn_output_107_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1656 = const()[name = tensor("op_1656"), val = tensor([1, 77, 1024])]; + tensor transpose_26 = transpose(perm = attn_output_107_perm_0, x = attn_output_105_cast_fp16)[name = tensor("transpose_26")]; + tensor input_281_cast_fp16 = reshape(shape = var_1656, x = transpose_26)[name = tensor("input_281_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_17_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_17_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(535948672)))]; + tensor text_encoder_text_model_encoder_layers_17_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_17_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(538045888)))]; + tensor linear_105_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_17_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_17_self_attn_out_proj_weight_to_fp16, x = input_281_cast_fp16)[name = tensor("linear_105_cast_fp16")]; + tensor input_283_cast_fp16 = add(x = input_275_cast_fp16, y = linear_105_cast_fp16)[name = tensor("input_283_cast_fp16")]; + tensor input_285_axes_0 = const()[name = tensor("input_285_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_17_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_17_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(538048000)))]; + tensor text_encoder_text_model_encoder_layers_17_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_17_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(538050112)))]; + tensor input_285_cast_fp16 = layer_norm(axes = input_285_axes_0, beta = text_encoder_text_model_encoder_layers_17_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_17_layer_norm2_weight_to_fp16, x = input_283_cast_fp16)[name = tensor("input_285_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_17_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_17_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(538052224)))]; + tensor text_encoder_text_model_encoder_layers_17_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_17_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(546440896)))]; + tensor linear_106_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_17_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_17_mlp_fc1_weight_to_fp16, x = input_285_cast_fp16)[name = tensor("linear_106_cast_fp16")]; + tensor input_289_mode_0 = const()[name = tensor("input_289_mode_0"), val = tensor("EXACT")]; + tensor input_289_cast_fp16 = gelu(mode = input_289_mode_0, x = linear_106_cast_fp16)[name = tensor("input_289_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_17_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_17_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(546449152)))]; + tensor text_encoder_text_model_encoder_layers_17_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_17_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(554837824)))]; + tensor linear_107_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_17_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_17_mlp_fc2_weight_to_fp16, x = input_289_cast_fp16)[name = tensor("linear_107_cast_fp16")]; + tensor input_291_cast_fp16 = add(x = input_283_cast_fp16, y = linear_107_cast_fp16)[name = tensor("input_291_cast_fp16")]; + tensor hidden_states_109_axes_0 = const()[name = tensor("hidden_states_109_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_18_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_18_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(554839936)))]; + tensor text_encoder_text_model_encoder_layers_18_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_18_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(554842048)))]; + tensor hidden_states_109_cast_fp16 = layer_norm(axes = hidden_states_109_axes_0, beta = text_encoder_text_model_encoder_layers_18_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_18_layer_norm1_weight_to_fp16, x = input_291_cast_fp16)[name = tensor("hidden_states_109_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_18_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_18_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(554844160)))]; + tensor text_encoder_text_model_encoder_layers_18_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_18_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(556941376)))]; + tensor linear_108_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_18_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_18_self_attn_q_proj_weight_to_fp16, x = hidden_states_109_cast_fp16)[name = tensor("linear_108_cast_fp16")]; + tensor var_1695_to_fp16 = const()[name = tensor("op_1695_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_113_cast_fp16 = mul(x = linear_108_cast_fp16, y = var_1695_to_fp16)[name = tensor("tensor_113_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_18_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_18_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(556943488)))]; + tensor text_encoder_text_model_encoder_layers_18_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_18_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(559040704)))]; + tensor linear_109_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_18_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_18_self_attn_k_proj_weight_to_fp16, x = hidden_states_109_cast_fp16)[name = tensor("linear_109_cast_fp16")]; + tensor var_1700 = const()[name = tensor("op_1700"), val = tensor([1, -1, 16, 64])]; + tensor var_1701_cast_fp16 = reshape(shape = var_1700, x = linear_109_cast_fp16)[name = tensor("op_1701_cast_fp16")]; + tensor var_1702_perm_0 = const()[name = tensor("op_1702_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_18_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_18_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(559042816)))]; + tensor text_encoder_text_model_encoder_layers_18_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_18_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(561140032)))]; + tensor linear_110_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_18_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_18_self_attn_v_proj_weight_to_fp16, x = hidden_states_109_cast_fp16)[name = tensor("linear_110_cast_fp16")]; + tensor var_1707 = const()[name = tensor("op_1707"), val = tensor([1, -1, 16, 64])]; + tensor var_1708_cast_fp16 = reshape(shape = var_1707, x = linear_110_cast_fp16)[name = tensor("op_1708_cast_fp16")]; + tensor var_1709_perm_0 = const()[name = tensor("op_1709_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1716 = const()[name = tensor("op_1716"), val = tensor([1, 77, 16, 64])]; + tensor var_1717_cast_fp16 = reshape(shape = var_1716, x = tensor_113_cast_fp16)[name = tensor("op_1717_cast_fp16")]; + tensor var_1718_perm_0 = const()[name = tensor("op_1718_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1720 = const()[name = tensor("op_1720"), val = tensor([16, -1, 64])]; + tensor transpose_23 = transpose(perm = var_1718_perm_0, x = var_1717_cast_fp16)[name = tensor("transpose_23")]; + tensor query_states_37_cast_fp16 = reshape(shape = var_1720, x = transpose_23)[name = tensor("query_states_37_cast_fp16")]; + tensor var_1722 = const()[name = tensor("op_1722"), val = tensor([16, -1, 64])]; + tensor transpose_25 = transpose(perm = var_1702_perm_0, x = var_1701_cast_fp16)[name = tensor("transpose_25")]; + tensor key_states_75_cast_fp16 = reshape(shape = var_1722, x = transpose_25)[name = tensor("key_states_75_cast_fp16")]; + tensor var_1724 = const()[name = tensor("op_1724"), val = tensor([16, -1, 64])]; + tensor transpose_24 = transpose(perm = var_1709_perm_0, x = var_1708_cast_fp16)[name = tensor("transpose_24")]; + tensor value_states_75_cast_fp16 = reshape(shape = var_1724, x = transpose_24)[name = tensor("value_states_75_cast_fp16")]; + tensor var_1727_perm_0 = const()[name = tensor("op_1727_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_109_transpose_x_0 = const()[name = tensor("attn_weights_109_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_109_transpose_y_0 = const()[name = tensor("attn_weights_109_transpose_y_0"), val = tensor(false)]; + tensor transpose_22 = transpose(perm = var_1727_perm_0, x = key_states_75_cast_fp16)[name = tensor("transpose_22")]; + tensor attn_weights_109_cast_fp16 = matmul(transpose_x = attn_weights_109_transpose_x_0, transpose_y = attn_weights_109_transpose_y_0, x = query_states_37_cast_fp16, y = transpose_22)[name = tensor("attn_weights_109_cast_fp16")]; + tensor var_1729 = const()[name = tensor("op_1729"), val = tensor([1, 16, 77, 77])]; + tensor var_1730_cast_fp16 = reshape(shape = var_1729, x = attn_weights_109_cast_fp16)[name = tensor("op_1730_cast_fp16")]; + tensor attn_weights_111_cast_fp16 = add(x = var_1730_cast_fp16, y = var_57_to_fp16)[name = tensor("attn_weights_111_cast_fp16")]; + tensor var_1735 = const()[name = tensor("op_1735"), val = tensor([16, 77, 77])]; + tensor input_293_cast_fp16 = reshape(shape = var_1735, x = attn_weights_111_cast_fp16)[name = tensor("input_293_cast_fp16")]; + tensor input_295_cast_fp16 = softmax(axis = var_5, x = input_293_cast_fp16)[name = tensor("input_295_cast_fp16")]; + tensor attn_output_109_transpose_x_0 = const()[name = tensor("attn_output_109_transpose_x_0"), val = tensor(false)]; + tensor attn_output_109_transpose_y_0 = const()[name = tensor("attn_output_109_transpose_y_0"), val = tensor(false)]; + tensor attn_output_109_cast_fp16 = matmul(transpose_x = attn_output_109_transpose_x_0, transpose_y = attn_output_109_transpose_y_0, x = input_295_cast_fp16, y = value_states_75_cast_fp16)[name = tensor("attn_output_109_cast_fp16")]; + tensor var_1740 = const()[name = tensor("op_1740"), val = tensor([1, 16, 77, 64])]; + tensor attn_output_111_cast_fp16 = reshape(shape = var_1740, x = attn_output_109_cast_fp16)[name = tensor("attn_output_111_cast_fp16")]; + tensor attn_output_113_perm_0 = const()[name = tensor("attn_output_113_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1743 = const()[name = tensor("op_1743"), val = tensor([1, 77, 1024])]; + tensor transpose_21 = transpose(perm = attn_output_113_perm_0, x = attn_output_111_cast_fp16)[name = tensor("transpose_21")]; + tensor input_297_cast_fp16 = reshape(shape = var_1743, x = transpose_21)[name = tensor("input_297_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_18_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_18_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(561142144)))]; + tensor text_encoder_text_model_encoder_layers_18_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_18_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(563239360)))]; + tensor linear_111_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_18_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_18_self_attn_out_proj_weight_to_fp16, x = input_297_cast_fp16)[name = tensor("linear_111_cast_fp16")]; + tensor input_299_cast_fp16 = add(x = input_291_cast_fp16, y = linear_111_cast_fp16)[name = tensor("input_299_cast_fp16")]; + tensor input_301_axes_0 = const()[name = tensor("input_301_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_18_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_18_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(563241472)))]; + tensor text_encoder_text_model_encoder_layers_18_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_18_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(563243584)))]; + tensor input_301_cast_fp16 = layer_norm(axes = input_301_axes_0, beta = text_encoder_text_model_encoder_layers_18_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_18_layer_norm2_weight_to_fp16, x = input_299_cast_fp16)[name = tensor("input_301_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_18_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_18_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(563245696)))]; + tensor text_encoder_text_model_encoder_layers_18_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_18_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(571634368)))]; + tensor linear_112_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_18_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_18_mlp_fc1_weight_to_fp16, x = input_301_cast_fp16)[name = tensor("linear_112_cast_fp16")]; + tensor input_305_mode_0 = const()[name = tensor("input_305_mode_0"), val = tensor("EXACT")]; + tensor input_305_cast_fp16 = gelu(mode = input_305_mode_0, x = linear_112_cast_fp16)[name = tensor("input_305_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_18_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_18_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(571642624)))]; + tensor text_encoder_text_model_encoder_layers_18_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_18_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(580031296)))]; + tensor linear_113_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_18_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_18_mlp_fc2_weight_to_fp16, x = input_305_cast_fp16)[name = tensor("linear_113_cast_fp16")]; + tensor input_307_cast_fp16 = add(x = input_299_cast_fp16, y = linear_113_cast_fp16)[name = tensor("input_307_cast_fp16")]; + tensor hidden_states_115_axes_0 = const()[name = tensor("hidden_states_115_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_19_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_19_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(580033408)))]; + tensor text_encoder_text_model_encoder_layers_19_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_19_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(580035520)))]; + tensor hidden_states_115_cast_fp16 = layer_norm(axes = hidden_states_115_axes_0, beta = text_encoder_text_model_encoder_layers_19_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_19_layer_norm1_weight_to_fp16, x = input_307_cast_fp16)[name = tensor("hidden_states_115_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_19_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_19_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(580037632)))]; + tensor text_encoder_text_model_encoder_layers_19_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_19_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(582134848)))]; + tensor linear_114_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_19_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_19_self_attn_q_proj_weight_to_fp16, x = hidden_states_115_cast_fp16)[name = tensor("linear_114_cast_fp16")]; + tensor var_1782_to_fp16 = const()[name = tensor("op_1782_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_119_cast_fp16 = mul(x = linear_114_cast_fp16, y = var_1782_to_fp16)[name = tensor("tensor_119_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_19_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_19_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(582136960)))]; + tensor text_encoder_text_model_encoder_layers_19_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_19_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(584234176)))]; + tensor linear_115_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_19_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_19_self_attn_k_proj_weight_to_fp16, x = hidden_states_115_cast_fp16)[name = tensor("linear_115_cast_fp16")]; + tensor var_1787 = const()[name = tensor("op_1787"), val = tensor([1, -1, 16, 64])]; + tensor var_1788_cast_fp16 = reshape(shape = var_1787, x = linear_115_cast_fp16)[name = tensor("op_1788_cast_fp16")]; + tensor var_1789_perm_0 = const()[name = tensor("op_1789_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_19_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_19_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(584236288)))]; + tensor text_encoder_text_model_encoder_layers_19_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_19_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(586333504)))]; + tensor linear_116_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_19_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_19_self_attn_v_proj_weight_to_fp16, x = hidden_states_115_cast_fp16)[name = tensor("linear_116_cast_fp16")]; + tensor var_1794 = const()[name = tensor("op_1794"), val = tensor([1, -1, 16, 64])]; + tensor var_1795_cast_fp16 = reshape(shape = var_1794, x = linear_116_cast_fp16)[name = tensor("op_1795_cast_fp16")]; + tensor var_1796_perm_0 = const()[name = tensor("op_1796_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1803 = const()[name = tensor("op_1803"), val = tensor([1, 77, 16, 64])]; + tensor var_1804_cast_fp16 = reshape(shape = var_1803, x = tensor_119_cast_fp16)[name = tensor("op_1804_cast_fp16")]; + tensor var_1805_perm_0 = const()[name = tensor("op_1805_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1807 = const()[name = tensor("op_1807"), val = tensor([16, -1, 64])]; + tensor transpose_18 = transpose(perm = var_1805_perm_0, x = var_1804_cast_fp16)[name = tensor("transpose_18")]; + tensor query_states_39_cast_fp16 = reshape(shape = var_1807, x = transpose_18)[name = tensor("query_states_39_cast_fp16")]; + tensor var_1809 = const()[name = tensor("op_1809"), val = tensor([16, -1, 64])]; + tensor transpose_20 = transpose(perm = var_1789_perm_0, x = var_1788_cast_fp16)[name = tensor("transpose_20")]; + tensor key_states_79_cast_fp16 = reshape(shape = var_1809, x = transpose_20)[name = tensor("key_states_79_cast_fp16")]; + tensor var_1811 = const()[name = tensor("op_1811"), val = tensor([16, -1, 64])]; + tensor transpose_19 = transpose(perm = var_1796_perm_0, x = var_1795_cast_fp16)[name = tensor("transpose_19")]; + tensor value_states_79_cast_fp16 = reshape(shape = var_1811, x = transpose_19)[name = tensor("value_states_79_cast_fp16")]; + tensor var_1814_perm_0 = const()[name = tensor("op_1814_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_115_transpose_x_0 = const()[name = tensor("attn_weights_115_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_115_transpose_y_0 = const()[name = tensor("attn_weights_115_transpose_y_0"), val = tensor(false)]; + tensor transpose_17 = transpose(perm = var_1814_perm_0, x = key_states_79_cast_fp16)[name = tensor("transpose_17")]; + tensor attn_weights_115_cast_fp16 = matmul(transpose_x = attn_weights_115_transpose_x_0, transpose_y = attn_weights_115_transpose_y_0, x = query_states_39_cast_fp16, y = transpose_17)[name = tensor("attn_weights_115_cast_fp16")]; + tensor var_1816 = const()[name = tensor("op_1816"), val = tensor([1, 16, 77, 77])]; + tensor var_1817_cast_fp16 = reshape(shape = var_1816, x = attn_weights_115_cast_fp16)[name = tensor("op_1817_cast_fp16")]; + tensor attn_weights_117_cast_fp16 = add(x = var_1817_cast_fp16, y = var_57_to_fp16)[name = tensor("attn_weights_117_cast_fp16")]; + tensor var_1822 = const()[name = tensor("op_1822"), val = tensor([16, 77, 77])]; + tensor input_309_cast_fp16 = reshape(shape = var_1822, x = attn_weights_117_cast_fp16)[name = tensor("input_309_cast_fp16")]; + tensor input_311_cast_fp16 = softmax(axis = var_5, x = input_309_cast_fp16)[name = tensor("input_311_cast_fp16")]; + tensor attn_output_115_transpose_x_0 = const()[name = tensor("attn_output_115_transpose_x_0"), val = tensor(false)]; + tensor attn_output_115_transpose_y_0 = const()[name = tensor("attn_output_115_transpose_y_0"), val = tensor(false)]; + tensor attn_output_115_cast_fp16 = matmul(transpose_x = attn_output_115_transpose_x_0, transpose_y = attn_output_115_transpose_y_0, x = input_311_cast_fp16, y = value_states_79_cast_fp16)[name = tensor("attn_output_115_cast_fp16")]; + tensor var_1827 = const()[name = tensor("op_1827"), val = tensor([1, 16, 77, 64])]; + tensor attn_output_117_cast_fp16 = reshape(shape = var_1827, x = attn_output_115_cast_fp16)[name = tensor("attn_output_117_cast_fp16")]; + tensor attn_output_119_perm_0 = const()[name = tensor("attn_output_119_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1830 = const()[name = tensor("op_1830"), val = tensor([1, 77, 1024])]; + tensor transpose_16 = transpose(perm = attn_output_119_perm_0, x = attn_output_117_cast_fp16)[name = tensor("transpose_16")]; + tensor input_313_cast_fp16 = reshape(shape = var_1830, x = transpose_16)[name = tensor("input_313_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_19_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_19_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(586335616)))]; + tensor text_encoder_text_model_encoder_layers_19_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_19_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(588432832)))]; + tensor linear_117_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_19_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_19_self_attn_out_proj_weight_to_fp16, x = input_313_cast_fp16)[name = tensor("linear_117_cast_fp16")]; + tensor input_315_cast_fp16 = add(x = input_307_cast_fp16, y = linear_117_cast_fp16)[name = tensor("input_315_cast_fp16")]; + tensor input_317_axes_0 = const()[name = tensor("input_317_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_19_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_19_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(588434944)))]; + tensor text_encoder_text_model_encoder_layers_19_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_19_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(588437056)))]; + tensor input_317_cast_fp16 = layer_norm(axes = input_317_axes_0, beta = text_encoder_text_model_encoder_layers_19_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_19_layer_norm2_weight_to_fp16, x = input_315_cast_fp16)[name = tensor("input_317_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_19_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_19_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(588439168)))]; + tensor text_encoder_text_model_encoder_layers_19_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_19_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(596827840)))]; + tensor linear_118_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_19_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_19_mlp_fc1_weight_to_fp16, x = input_317_cast_fp16)[name = tensor("linear_118_cast_fp16")]; + tensor input_321_mode_0 = const()[name = tensor("input_321_mode_0"), val = tensor("EXACT")]; + tensor input_321_cast_fp16 = gelu(mode = input_321_mode_0, x = linear_118_cast_fp16)[name = tensor("input_321_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_19_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_19_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(596836096)))]; + tensor text_encoder_text_model_encoder_layers_19_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_19_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(605224768)))]; + tensor linear_119_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_19_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_19_mlp_fc2_weight_to_fp16, x = input_321_cast_fp16)[name = tensor("linear_119_cast_fp16")]; + tensor input_323_cast_fp16 = add(x = input_315_cast_fp16, y = linear_119_cast_fp16)[name = tensor("input_323_cast_fp16")]; + tensor hidden_states_121_axes_0 = const()[name = tensor("hidden_states_121_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_20_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_20_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(605226880)))]; + tensor text_encoder_text_model_encoder_layers_20_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_20_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(605228992)))]; + tensor hidden_states_121_cast_fp16 = layer_norm(axes = hidden_states_121_axes_0, beta = text_encoder_text_model_encoder_layers_20_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_20_layer_norm1_weight_to_fp16, x = input_323_cast_fp16)[name = tensor("hidden_states_121_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_20_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_20_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(605231104)))]; + tensor text_encoder_text_model_encoder_layers_20_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_20_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(607328320)))]; + tensor linear_120_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_20_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_20_self_attn_q_proj_weight_to_fp16, x = hidden_states_121_cast_fp16)[name = tensor("linear_120_cast_fp16")]; + tensor var_1869_to_fp16 = const()[name = tensor("op_1869_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_125_cast_fp16 = mul(x = linear_120_cast_fp16, y = var_1869_to_fp16)[name = tensor("tensor_125_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_20_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_20_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(607330432)))]; + tensor text_encoder_text_model_encoder_layers_20_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_20_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(609427648)))]; + tensor linear_121_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_20_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_20_self_attn_k_proj_weight_to_fp16, x = hidden_states_121_cast_fp16)[name = tensor("linear_121_cast_fp16")]; + tensor var_1874 = const()[name = tensor("op_1874"), val = tensor([1, -1, 16, 64])]; + tensor var_1875_cast_fp16 = reshape(shape = var_1874, x = linear_121_cast_fp16)[name = tensor("op_1875_cast_fp16")]; + tensor var_1876_perm_0 = const()[name = tensor("op_1876_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_20_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_20_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(609429760)))]; + tensor text_encoder_text_model_encoder_layers_20_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_20_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(611526976)))]; + tensor linear_122_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_20_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_20_self_attn_v_proj_weight_to_fp16, x = hidden_states_121_cast_fp16)[name = tensor("linear_122_cast_fp16")]; + tensor var_1881 = const()[name = tensor("op_1881"), val = tensor([1, -1, 16, 64])]; + tensor var_1882_cast_fp16 = reshape(shape = var_1881, x = linear_122_cast_fp16)[name = tensor("op_1882_cast_fp16")]; + tensor var_1883_perm_0 = const()[name = tensor("op_1883_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1890 = const()[name = tensor("op_1890"), val = tensor([1, 77, 16, 64])]; + tensor var_1891_cast_fp16 = reshape(shape = var_1890, x = tensor_125_cast_fp16)[name = tensor("op_1891_cast_fp16")]; + tensor var_1892_perm_0 = const()[name = tensor("op_1892_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1894 = const()[name = tensor("op_1894"), val = tensor([16, -1, 64])]; + tensor transpose_13 = transpose(perm = var_1892_perm_0, x = var_1891_cast_fp16)[name = tensor("transpose_13")]; + tensor query_states_41_cast_fp16 = reshape(shape = var_1894, x = transpose_13)[name = tensor("query_states_41_cast_fp16")]; + tensor var_1896 = const()[name = tensor("op_1896"), val = tensor([16, -1, 64])]; + tensor transpose_15 = transpose(perm = var_1876_perm_0, x = var_1875_cast_fp16)[name = tensor("transpose_15")]; + tensor key_states_83_cast_fp16 = reshape(shape = var_1896, x = transpose_15)[name = tensor("key_states_83_cast_fp16")]; + tensor var_1898 = const()[name = tensor("op_1898"), val = tensor([16, -1, 64])]; + tensor transpose_14 = transpose(perm = var_1883_perm_0, x = var_1882_cast_fp16)[name = tensor("transpose_14")]; + tensor value_states_83_cast_fp16 = reshape(shape = var_1898, x = transpose_14)[name = tensor("value_states_83_cast_fp16")]; + tensor var_1901_perm_0 = const()[name = tensor("op_1901_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_121_transpose_x_0 = const()[name = tensor("attn_weights_121_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_121_transpose_y_0 = const()[name = tensor("attn_weights_121_transpose_y_0"), val = tensor(false)]; + tensor transpose_12 = transpose(perm = var_1901_perm_0, x = key_states_83_cast_fp16)[name = tensor("transpose_12")]; + tensor attn_weights_121_cast_fp16 = matmul(transpose_x = attn_weights_121_transpose_x_0, transpose_y = attn_weights_121_transpose_y_0, x = query_states_41_cast_fp16, y = transpose_12)[name = tensor("attn_weights_121_cast_fp16")]; + tensor var_1903 = const()[name = tensor("op_1903"), val = tensor([1, 16, 77, 77])]; + tensor var_1904_cast_fp16 = reshape(shape = var_1903, x = attn_weights_121_cast_fp16)[name = tensor("op_1904_cast_fp16")]; + tensor attn_weights_123_cast_fp16 = add(x = var_1904_cast_fp16, y = var_57_to_fp16)[name = tensor("attn_weights_123_cast_fp16")]; + tensor var_1909 = const()[name = tensor("op_1909"), val = tensor([16, 77, 77])]; + tensor input_325_cast_fp16 = reshape(shape = var_1909, x = attn_weights_123_cast_fp16)[name = tensor("input_325_cast_fp16")]; + tensor input_327_cast_fp16 = softmax(axis = var_5, x = input_325_cast_fp16)[name = tensor("input_327_cast_fp16")]; + tensor attn_output_121_transpose_x_0 = const()[name = tensor("attn_output_121_transpose_x_0"), val = tensor(false)]; + tensor attn_output_121_transpose_y_0 = const()[name = tensor("attn_output_121_transpose_y_0"), val = tensor(false)]; + tensor attn_output_121_cast_fp16 = matmul(transpose_x = attn_output_121_transpose_x_0, transpose_y = attn_output_121_transpose_y_0, x = input_327_cast_fp16, y = value_states_83_cast_fp16)[name = tensor("attn_output_121_cast_fp16")]; + tensor var_1914 = const()[name = tensor("op_1914"), val = tensor([1, 16, 77, 64])]; + tensor attn_output_123_cast_fp16 = reshape(shape = var_1914, x = attn_output_121_cast_fp16)[name = tensor("attn_output_123_cast_fp16")]; + tensor attn_output_125_perm_0 = const()[name = tensor("attn_output_125_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1917 = const()[name = tensor("op_1917"), val = tensor([1, 77, 1024])]; + tensor transpose_11 = transpose(perm = attn_output_125_perm_0, x = attn_output_123_cast_fp16)[name = tensor("transpose_11")]; + tensor input_329_cast_fp16 = reshape(shape = var_1917, x = transpose_11)[name = tensor("input_329_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_20_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_20_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(611529088)))]; + tensor text_encoder_text_model_encoder_layers_20_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_20_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(613626304)))]; + tensor linear_123_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_20_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_20_self_attn_out_proj_weight_to_fp16, x = input_329_cast_fp16)[name = tensor("linear_123_cast_fp16")]; + tensor input_331_cast_fp16 = add(x = input_323_cast_fp16, y = linear_123_cast_fp16)[name = tensor("input_331_cast_fp16")]; + tensor input_333_axes_0 = const()[name = tensor("input_333_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_20_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_20_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(613628416)))]; + tensor text_encoder_text_model_encoder_layers_20_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_20_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(613630528)))]; + tensor input_333_cast_fp16 = layer_norm(axes = input_333_axes_0, beta = text_encoder_text_model_encoder_layers_20_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_20_layer_norm2_weight_to_fp16, x = input_331_cast_fp16)[name = tensor("input_333_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_20_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_20_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(613632640)))]; + tensor text_encoder_text_model_encoder_layers_20_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_20_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(622021312)))]; + tensor linear_124_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_20_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_20_mlp_fc1_weight_to_fp16, x = input_333_cast_fp16)[name = tensor("linear_124_cast_fp16")]; + tensor input_337_mode_0 = const()[name = tensor("input_337_mode_0"), val = tensor("EXACT")]; + tensor input_337_cast_fp16 = gelu(mode = input_337_mode_0, x = linear_124_cast_fp16)[name = tensor("input_337_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_20_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_20_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(622029568)))]; + tensor text_encoder_text_model_encoder_layers_20_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_20_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(630418240)))]; + tensor linear_125_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_20_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_20_mlp_fc2_weight_to_fp16, x = input_337_cast_fp16)[name = tensor("linear_125_cast_fp16")]; + tensor input_339_cast_fp16 = add(x = input_331_cast_fp16, y = linear_125_cast_fp16)[name = tensor("input_339_cast_fp16")]; + tensor hidden_states_127_axes_0 = const()[name = tensor("hidden_states_127_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_21_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_21_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(630420352)))]; + tensor text_encoder_text_model_encoder_layers_21_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_21_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(630422464)))]; + tensor hidden_states_127_cast_fp16 = layer_norm(axes = hidden_states_127_axes_0, beta = text_encoder_text_model_encoder_layers_21_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_21_layer_norm1_weight_to_fp16, x = input_339_cast_fp16)[name = tensor("hidden_states_127_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_21_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_21_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(630424576)))]; + tensor text_encoder_text_model_encoder_layers_21_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_21_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(632521792)))]; + tensor linear_126_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_21_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_21_self_attn_q_proj_weight_to_fp16, x = hidden_states_127_cast_fp16)[name = tensor("linear_126_cast_fp16")]; + tensor var_1956_to_fp16 = const()[name = tensor("op_1956_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_131_cast_fp16 = mul(x = linear_126_cast_fp16, y = var_1956_to_fp16)[name = tensor("tensor_131_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_21_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_21_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(632523904)))]; + tensor text_encoder_text_model_encoder_layers_21_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_21_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634621120)))]; + tensor linear_127_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_21_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_21_self_attn_k_proj_weight_to_fp16, x = hidden_states_127_cast_fp16)[name = tensor("linear_127_cast_fp16")]; + tensor var_1961 = const()[name = tensor("op_1961"), val = tensor([1, -1, 16, 64])]; + tensor var_1962_cast_fp16 = reshape(shape = var_1961, x = linear_127_cast_fp16)[name = tensor("op_1962_cast_fp16")]; + tensor var_1963_perm_0 = const()[name = tensor("op_1963_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_21_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_21_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634623232)))]; + tensor text_encoder_text_model_encoder_layers_21_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_21_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(636720448)))]; + tensor linear_128_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_21_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_21_self_attn_v_proj_weight_to_fp16, x = hidden_states_127_cast_fp16)[name = tensor("linear_128_cast_fp16")]; + tensor var_1968 = const()[name = tensor("op_1968"), val = tensor([1, -1, 16, 64])]; + tensor var_1969_cast_fp16 = reshape(shape = var_1968, x = linear_128_cast_fp16)[name = tensor("op_1969_cast_fp16")]; + tensor var_1970_perm_0 = const()[name = tensor("op_1970_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1977 = const()[name = tensor("op_1977"), val = tensor([1, 77, 16, 64])]; + tensor var_1978_cast_fp16 = reshape(shape = var_1977, x = tensor_131_cast_fp16)[name = tensor("op_1978_cast_fp16")]; + tensor var_1979_perm_0 = const()[name = tensor("op_1979_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1981 = const()[name = tensor("op_1981"), val = tensor([16, -1, 64])]; + tensor transpose_8 = transpose(perm = var_1979_perm_0, x = var_1978_cast_fp16)[name = tensor("transpose_8")]; + tensor query_states_43_cast_fp16 = reshape(shape = var_1981, x = transpose_8)[name = tensor("query_states_43_cast_fp16")]; + tensor var_1983 = const()[name = tensor("op_1983"), val = tensor([16, -1, 64])]; + tensor transpose_10 = transpose(perm = var_1963_perm_0, x = var_1962_cast_fp16)[name = tensor("transpose_10")]; + tensor key_states_87_cast_fp16 = reshape(shape = var_1983, x = transpose_10)[name = tensor("key_states_87_cast_fp16")]; + tensor var_1985 = const()[name = tensor("op_1985"), val = tensor([16, -1, 64])]; + tensor transpose_9 = transpose(perm = var_1970_perm_0, x = var_1969_cast_fp16)[name = tensor("transpose_9")]; + tensor value_states_87_cast_fp16 = reshape(shape = var_1985, x = transpose_9)[name = tensor("value_states_87_cast_fp16")]; + tensor var_1988_perm_0 = const()[name = tensor("op_1988_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_127_transpose_x_0 = const()[name = tensor("attn_weights_127_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_127_transpose_y_0 = const()[name = tensor("attn_weights_127_transpose_y_0"), val = tensor(false)]; + tensor transpose_7 = transpose(perm = var_1988_perm_0, x = key_states_87_cast_fp16)[name = tensor("transpose_7")]; + tensor attn_weights_127_cast_fp16 = matmul(transpose_x = attn_weights_127_transpose_x_0, transpose_y = attn_weights_127_transpose_y_0, x = query_states_43_cast_fp16, y = transpose_7)[name = tensor("attn_weights_127_cast_fp16")]; + tensor var_1990 = const()[name = tensor("op_1990"), val = tensor([1, 16, 77, 77])]; + tensor var_1991_cast_fp16 = reshape(shape = var_1990, x = attn_weights_127_cast_fp16)[name = tensor("op_1991_cast_fp16")]; + tensor attn_weights_129_cast_fp16 = add(x = var_1991_cast_fp16, y = var_57_to_fp16)[name = tensor("attn_weights_129_cast_fp16")]; + tensor var_1996 = const()[name = tensor("op_1996"), val = tensor([16, 77, 77])]; + tensor input_341_cast_fp16 = reshape(shape = var_1996, x = attn_weights_129_cast_fp16)[name = tensor("input_341_cast_fp16")]; + tensor input_343_cast_fp16 = softmax(axis = var_5, x = input_341_cast_fp16)[name = tensor("input_343_cast_fp16")]; + tensor attn_output_127_transpose_x_0 = const()[name = tensor("attn_output_127_transpose_x_0"), val = tensor(false)]; + tensor attn_output_127_transpose_y_0 = const()[name = tensor("attn_output_127_transpose_y_0"), val = tensor(false)]; + tensor attn_output_127_cast_fp16 = matmul(transpose_x = attn_output_127_transpose_x_0, transpose_y = attn_output_127_transpose_y_0, x = input_343_cast_fp16, y = value_states_87_cast_fp16)[name = tensor("attn_output_127_cast_fp16")]; + tensor var_2001 = const()[name = tensor("op_2001"), val = tensor([1, 16, 77, 64])]; + tensor attn_output_129_cast_fp16 = reshape(shape = var_2001, x = attn_output_127_cast_fp16)[name = tensor("attn_output_129_cast_fp16")]; + tensor attn_output_131_perm_0 = const()[name = tensor("attn_output_131_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2004 = const()[name = tensor("op_2004"), val = tensor([1, 77, 1024])]; + tensor transpose_6 = transpose(perm = attn_output_131_perm_0, x = attn_output_129_cast_fp16)[name = tensor("transpose_6")]; + tensor input_345_cast_fp16 = reshape(shape = var_2004, x = transpose_6)[name = tensor("input_345_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_21_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_21_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(636722560)))]; + tensor text_encoder_text_model_encoder_layers_21_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_21_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638819776)))]; + tensor linear_129_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_21_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_21_self_attn_out_proj_weight_to_fp16, x = input_345_cast_fp16)[name = tensor("linear_129_cast_fp16")]; + tensor input_347_cast_fp16 = add(x = input_339_cast_fp16, y = linear_129_cast_fp16)[name = tensor("input_347_cast_fp16")]; + tensor input_349_axes_0 = const()[name = tensor("input_349_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_21_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_21_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638821888)))]; + tensor text_encoder_text_model_encoder_layers_21_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_21_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638824000)))]; + tensor input_349_cast_fp16 = layer_norm(axes = input_349_axes_0, beta = text_encoder_text_model_encoder_layers_21_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_21_layer_norm2_weight_to_fp16, x = input_347_cast_fp16)[name = tensor("input_349_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_21_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_21_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638826112)))]; + tensor text_encoder_text_model_encoder_layers_21_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_21_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(647214784)))]; + tensor linear_130_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_21_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_21_mlp_fc1_weight_to_fp16, x = input_349_cast_fp16)[name = tensor("linear_130_cast_fp16")]; + tensor input_353_mode_0 = const()[name = tensor("input_353_mode_0"), val = tensor("EXACT")]; + tensor input_353_cast_fp16 = gelu(mode = input_353_mode_0, x = linear_130_cast_fp16)[name = tensor("input_353_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_21_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_21_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(647223040)))]; + tensor text_encoder_text_model_encoder_layers_21_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_21_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(655611712)))]; + tensor linear_131_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_21_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_21_mlp_fc2_weight_to_fp16, x = input_353_cast_fp16)[name = tensor("linear_131_cast_fp16")]; + tensor input_355_cast_fp16 = add(x = input_347_cast_fp16, y = linear_131_cast_fp16)[name = tensor("input_355_cast_fp16")]; + tensor hidden_states_133_axes_0 = const()[name = tensor("hidden_states_133_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_22_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_22_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(655613824)))]; + tensor text_encoder_text_model_encoder_layers_22_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_22_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(655615936)))]; + tensor hidden_states_133_cast_fp16 = layer_norm(axes = hidden_states_133_axes_0, beta = text_encoder_text_model_encoder_layers_22_layer_norm1_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_22_layer_norm1_weight_to_fp16, x = input_355_cast_fp16)[name = tensor("hidden_states_133_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_22_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_22_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(655618048)))]; + tensor text_encoder_text_model_encoder_layers_22_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_22_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(657715264)))]; + tensor linear_132_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_22_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_22_self_attn_q_proj_weight_to_fp16, x = hidden_states_133_cast_fp16)[name = tensor("linear_132_cast_fp16")]; + tensor var_2043_to_fp16 = const()[name = tensor("op_2043_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_cast_fp16 = mul(x = linear_132_cast_fp16, y = var_2043_to_fp16)[name = tensor("tensor_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_22_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_22_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(657717376)))]; + tensor text_encoder_text_model_encoder_layers_22_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_22_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(659814592)))]; + tensor linear_133_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_22_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_22_self_attn_k_proj_weight_to_fp16, x = hidden_states_133_cast_fp16)[name = tensor("linear_133_cast_fp16")]; + tensor var_2048 = const()[name = tensor("op_2048"), val = tensor([1, -1, 16, 64])]; + tensor var_2049_cast_fp16 = reshape(shape = var_2048, x = linear_133_cast_fp16)[name = tensor("op_2049_cast_fp16")]; + tensor var_2050_perm_0 = const()[name = tensor("op_2050_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_22_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_22_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(659816704)))]; + tensor text_encoder_text_model_encoder_layers_22_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_22_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(661913920)))]; + tensor linear_134_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_22_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_22_self_attn_v_proj_weight_to_fp16, x = hidden_states_133_cast_fp16)[name = tensor("linear_134_cast_fp16")]; + tensor var_2055 = const()[name = tensor("op_2055"), val = tensor([1, -1, 16, 64])]; + tensor var_2056_cast_fp16 = reshape(shape = var_2055, x = linear_134_cast_fp16)[name = tensor("op_2056_cast_fp16")]; + tensor var_2057_perm_0 = const()[name = tensor("op_2057_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2064 = const()[name = tensor("op_2064"), val = tensor([1, 77, 16, 64])]; + tensor var_2065_cast_fp16 = reshape(shape = var_2064, x = tensor_cast_fp16)[name = tensor("op_2065_cast_fp16")]; + tensor var_2066_perm_0 = const()[name = tensor("op_2066_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2068 = const()[name = tensor("op_2068"), val = tensor([16, -1, 64])]; + tensor transpose_3 = transpose(perm = var_2066_perm_0, x = var_2065_cast_fp16)[name = tensor("transpose_3")]; + tensor query_states_cast_fp16 = reshape(shape = var_2068, x = transpose_3)[name = tensor("query_states_cast_fp16")]; + tensor var_2070 = const()[name = tensor("op_2070"), val = tensor([16, -1, 64])]; + tensor transpose_5 = transpose(perm = var_2050_perm_0, x = var_2049_cast_fp16)[name = tensor("transpose_5")]; + tensor key_states_cast_fp16 = reshape(shape = var_2070, x = transpose_5)[name = tensor("key_states_cast_fp16")]; + tensor var_2072 = const()[name = tensor("op_2072"), val = tensor([16, -1, 64])]; + tensor transpose_4 = transpose(perm = var_2057_perm_0, x = var_2056_cast_fp16)[name = tensor("transpose_4")]; + tensor value_states_cast_fp16 = reshape(shape = var_2072, x = transpose_4)[name = tensor("value_states_cast_fp16")]; + tensor var_2075_perm_0 = const()[name = tensor("op_2075_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_133_transpose_x_0 = const()[name = tensor("attn_weights_133_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_133_transpose_y_0 = const()[name = tensor("attn_weights_133_transpose_y_0"), val = tensor(false)]; + tensor transpose_2 = transpose(perm = var_2075_perm_0, x = key_states_cast_fp16)[name = tensor("transpose_2")]; + tensor attn_weights_133_cast_fp16 = matmul(transpose_x = attn_weights_133_transpose_x_0, transpose_y = attn_weights_133_transpose_y_0, x = query_states_cast_fp16, y = transpose_2)[name = tensor("attn_weights_133_cast_fp16")]; + tensor var_2077 = const()[name = tensor("op_2077"), val = tensor([1, 16, 77, 77])]; + tensor var_2078_cast_fp16 = reshape(shape = var_2077, x = attn_weights_133_cast_fp16)[name = tensor("op_2078_cast_fp16")]; + tensor attn_weights_135_cast_fp16 = add(x = var_2078_cast_fp16, y = var_57_to_fp16)[name = tensor("attn_weights_135_cast_fp16")]; + tensor var_2083 = const()[name = tensor("op_2083"), val = tensor([16, 77, 77])]; + tensor input_357_cast_fp16 = reshape(shape = var_2083, x = attn_weights_135_cast_fp16)[name = tensor("input_357_cast_fp16")]; + tensor input_359_cast_fp16 = softmax(axis = var_5, x = input_357_cast_fp16)[name = tensor("input_359_cast_fp16")]; + tensor attn_output_133_transpose_x_0 = const()[name = tensor("attn_output_133_transpose_x_0"), val = tensor(false)]; + tensor attn_output_133_transpose_y_0 = const()[name = tensor("attn_output_133_transpose_y_0"), val = tensor(false)]; + tensor attn_output_133_cast_fp16 = matmul(transpose_x = attn_output_133_transpose_x_0, transpose_y = attn_output_133_transpose_y_0, x = input_359_cast_fp16, y = value_states_cast_fp16)[name = tensor("attn_output_133_cast_fp16")]; + tensor var_2088 = const()[name = tensor("op_2088"), val = tensor([1, 16, 77, 64])]; + tensor attn_output_135_cast_fp16 = reshape(shape = var_2088, x = attn_output_133_cast_fp16)[name = tensor("attn_output_135_cast_fp16")]; + tensor attn_output_perm_0 = const()[name = tensor("attn_output_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2091 = const()[name = tensor("op_2091"), val = tensor([1, 77, 1024])]; + tensor transpose_1 = transpose(perm = attn_output_perm_0, x = attn_output_135_cast_fp16)[name = tensor("transpose_1")]; + tensor input_361_cast_fp16 = reshape(shape = var_2091, x = transpose_1)[name = tensor("input_361_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_22_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_22_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(661916032)))]; + tensor text_encoder_text_model_encoder_layers_22_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_22_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(664013248)))]; + tensor linear_135_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_22_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_22_self_attn_out_proj_weight_to_fp16, x = input_361_cast_fp16)[name = tensor("linear_135_cast_fp16")]; + tensor input_363_cast_fp16 = add(x = input_355_cast_fp16, y = linear_135_cast_fp16)[name = tensor("input_363_cast_fp16")]; + tensor input_365_axes_0 = const()[name = tensor("input_365_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_22_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_22_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(664015360)))]; + tensor text_encoder_text_model_encoder_layers_22_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_22_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(664017472)))]; + tensor input_365_cast_fp16 = layer_norm(axes = input_365_axes_0, beta = text_encoder_text_model_encoder_layers_22_layer_norm2_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_encoder_layers_22_layer_norm2_weight_to_fp16, x = input_363_cast_fp16)[name = tensor("input_365_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_22_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_22_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(664019584)))]; + tensor text_encoder_text_model_encoder_layers_22_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_22_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(672408256)))]; + tensor linear_136_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_22_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_22_mlp_fc1_weight_to_fp16, x = input_365_cast_fp16)[name = tensor("linear_136_cast_fp16")]; + tensor input_369_mode_0 = const()[name = tensor("input_369_mode_0"), val = tensor("EXACT")]; + tensor input_369_cast_fp16 = gelu(mode = input_369_mode_0, x = linear_136_cast_fp16)[name = tensor("input_369_cast_fp16")]; + tensor text_encoder_text_model_encoder_layers_22_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_22_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(672416512)))]; + tensor text_encoder_text_model_encoder_layers_22_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_22_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(680805184)))]; + tensor linear_137_cast_fp16 = linear(bias = text_encoder_text_model_encoder_layers_22_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_22_mlp_fc2_weight_to_fp16, x = input_369_cast_fp16)[name = tensor("linear_137_cast_fp16")]; + tensor input_cast_fp16 = add(x = input_363_cast_fp16, y = linear_137_cast_fp16)[name = tensor("input_cast_fp16")]; + tensor last_hidden_state_axes_0 = const()[name = tensor("last_hidden_state_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_final_layer_norm_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_final_layer_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(680807296)))]; + tensor text_encoder_text_model_final_layer_norm_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_final_layer_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(680809408)))]; + tensor last_hidden_state_cast_fp16 = layer_norm(axes = last_hidden_state_axes_0, beta = text_encoder_text_model_final_layer_norm_bias_to_fp16, epsilon = var_15_to_fp16, gamma = text_encoder_text_model_final_layer_norm_weight_to_fp16, x = input_cast_fp16)[name = tensor("last_hidden_state_cast_fp16")]; + tensor last_hidden_state_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("last_hidden_state_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor var_2116 = const()[name = tensor("op_2116"), val = tensor([0])]; + tensor var_2118 = reduce_argmax(axis = var_5, keep_dims = var_6, x = cast_239)[name = tensor("op_2118")]; + tensor stack_0_axis_0 = const()[name = tensor("stack_0_axis_0"), val = tensor(1)]; + tensor stack_0 = stack(axis = stack_0_axis_0, values = (var_2116, var_2118))[name = tensor("stack_0")]; + tensor var_2120_transpose_batch_dims_0 = const()[name = tensor("op_2120_transpose_batch_dims_0"), val = tensor(0)]; + tensor var_2120_transpose_cast_fp16 = gather_nd(batch_dims = var_2120_transpose_batch_dims_0, indices = stack_0, x = last_hidden_state_cast_fp16)[name = tensor("op_2120_transpose_cast_fp16")]; + tensor var_2120_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("op_2120_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor pooled_outputs = cast(dtype = var_2120_cast_fp16_to_fp32_dtype_0, x = var_2120_transpose_cast_fp16)[name = tensor("cast_237")]; + tensor last_hidden_state = cast(dtype = last_hidden_state_cast_fp16_to_fp32_dtype_0, x = last_hidden_state_cast_fp16)[name = tensor("cast_238")]; + } -> (last_hidden_state, pooled_outputs); +} \ No newline at end of file diff --git a/original/compiled/TextEncoder.mlmodelc/weights/weight.bin b/original/compiled/TextEncoder.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..b157510ae9b06ecaf07eff34eff4a10319a940eb --- /dev/null +++ b/original/compiled/TextEncoder.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4b462f569021a6c18123a53ace80df1bf3ebb7d76c830b5394e37463af5a3969 +size 680811520 diff --git a/original/compiled/Unet.mlmodelc/analytics/coremldata.bin b/original/compiled/Unet.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..a04747f3096a9ac9d607b18796e1525e384938e7 --- /dev/null +++ b/original/compiled/Unet.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2240ec844eced7b3018d6fd983d1779ea2376085ca080a8a3faa34f4f9515286 +size 243 diff --git a/original/compiled/Unet.mlmodelc/coremldata.bin b/original/compiled/Unet.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..4492a0b6a317579100fa36e96cbc16b34ff6edb5 --- /dev/null +++ b/original/compiled/Unet.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9f3ab6e861de0e74e94260a3a09196826b8652e0d3b15e6d17e743e2d5e05e0e +size 1366 diff --git a/original/compiled/Unet.mlmodelc/metadata.json b/original/compiled/Unet.mlmodelc/metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..1e425c05116a2c0f98d914af5414dcdb0f2ace6f --- /dev/null +++ b/original/compiled/Unet.mlmodelc/metadata.json @@ -0,0 +1,104 @@ +[ + { + "shortDescription" : "Stable Diffusion generates images conditioned on text or other images as input through the diffusion process. Please refer to https:\/\/arxiv.org\/abs\/2112.10752 for details.", + "metadataOutputVersion" : "3.0", + "outputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 2 × 4 × 48 × 80)", + "shortDescription" : "Same shape and dtype as the `sample` input. The predicted noise to facilitate the reverse diffusion (denoising) process", + "shape" : "[2, 4, 48, 80]", + "name" : "noise_pred", + "type" : "MultiArray" + } + ], + "version" : "\/Users\/keijiro\/Documents\/StableDiffusion\/sd-turbo", + "modelParameters" : [ + + ], + "author" : "Please refer to the Model Card available at huggingface.co\/\/Users\/keijiro\/Documents\/StableDiffusion\/sd-turbo", + "specificationVersion" : 7, + "storagePrecision" : "Float16", + "license" : "OpenRAIL (https:\/\/huggingface.co\/spaces\/CompVis\/stable-diffusion-license)", + "mlProgramOperationTypeHistogram" : { + "UpsampleNearestNeighbor" : 3, + "Ios16.reduceMean" : 218, + "Ios16.sin" : 1, + "Ios16.softmax" : 32, + "Split" : 16, + "Ios16.add" : 265, + "Concat" : 14, + "Ios16.realDiv" : 61, + "Ios16.square" : 61, + "ExpandDims" : 3, + "Ios16.sub" : 109, + "Ios16.cast" : 1, + "Ios16.conv" : 282, + "Ios16.gelu" : 16, + "Ios16.matmul" : 64, + "Ios16.reshape" : 282, + "Ios16.batchNorm" : 61, + "Ios16.rsqrt" : 48, + "Ios16.silu" : 47, + "Ios16.sqrt" : 61, + "SliceByIndex" : 2, + "Ios16.mul" : 193, + "Ios16.cos" : 1 + }, + "computePrecision" : "Mixed (Float16, Float32, Int32)", + "isUpdatable" : "0", + "availability" : { + "macOS" : "13.0", + "tvOS" : "16.0", + "visionOS" : "1.0", + "watchOS" : "9.0", + "iOS" : "16.0", + "macCatalyst" : "16.0" + }, + "modelType" : { + "name" : "MLModelType_mlProgram" + }, + "inputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 2 × 4 × 48 × 80)", + "shortDescription" : "The low resolution latent feature maps being denoised through reverse diffusion", + "shape" : "[2, 4, 48, 80]", + "name" : "sample", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 2)", + "shortDescription" : "A value emitted by the associated scheduler object to condition the model on a given noise schedule", + "shape" : "[2]", + "name" : "timestep", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 2 × 1024 × 1 × 77)", + "shortDescription" : "Output embeddings from the associated text_encoder model to condition to generated image on text. A maximum of 77 tokens (~40 words) are allowed. Longer text is truncated. Shorter text does not reduce computation.", + "shape" : "[2, 1024, 1, 77]", + "name" : "encoder_hidden_states", + "type" : "MultiArray" + } + ], + "userDefinedMetadata" : { + "com.github.apple.ml-stable-diffusion.version" : "1.1.0", + "com.github.apple.coremltools.source" : "torch==2.1.2", + "com.github.apple.coremltools.version" : "7.1", + "com.github.apple.coremltools.source_dialect" : "TorchScript" + }, + "generatedClassName" : "Stable_Diffusion_version__Users_keijiro_Documents_StableDiffusion_sd_turbo_unet", + "method" : "predict" + } +] \ No newline at end of file diff --git a/original/compiled/Unet.mlmodelc/model.mil b/original/compiled/Unet.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..52c471093caf237c3de59e9fefb3f575be3e9694 --- /dev/null +++ b/original/compiled/Unet.mlmodelc/model.mil @@ -0,0 +1,4754 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3304.5.2"}, {"coremlc-version", "3304.6.2"}, {"coremltools-component-torch", "2.1.2"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "7.1"}})] +{ + func main(tensor encoder_hidden_states, tensor sample, tensor timestep) { + tensor var_25 = const()[name = tensor("op_25"), val = tensor(-1)]; + tensor var_42_axes_0 = const()[name = tensor("op_42_axes_0"), val = tensor([1])]; + tensor var_42_cast_fp16 = expand_dims(axes = var_42_axes_0, x = timestep)[name = tensor("op_42_cast_fp16")]; + tensor var_44_to_fp16 = const()[name = tensor("op_44_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor emb_3_cast_fp16 = mul(x = var_42_cast_fp16, y = var_44_to_fp16)[name = tensor("emb_3_cast_fp16")]; + tensor var_49_cast_fp16 = sin(x = emb_3_cast_fp16)[name = tensor("op_49_cast_fp16")]; + tensor var_50_cast_fp16 = cos(x = emb_3_cast_fp16)[name = tensor("op_50_cast_fp16")]; + tensor emb_interleave_0 = const()[name = tensor("emb_interleave_0"), val = tensor(false)]; + tensor emb_cast_fp16 = concat(axis = var_25, interleave = emb_interleave_0, values = (var_49_cast_fp16, var_50_cast_fp16))[name = tensor("emb_cast_fp16")]; + tensor var_54_begin_0 = const()[name = tensor("op_54_begin_0"), val = tensor([0, 160])]; + tensor var_54_end_0 = const()[name = tensor("op_54_end_0"), val = tensor([2, 320])]; + tensor var_54_end_mask_0 = const()[name = tensor("op_54_end_mask_0"), val = tensor([true, true])]; + tensor var_54_cast_fp16 = slice_by_index(begin = var_54_begin_0, end = var_54_end_0, end_mask = var_54_end_mask_0, x = emb_cast_fp16)[name = tensor("op_54_cast_fp16")]; + tensor var_56_begin_0 = const()[name = tensor("op_56_begin_0"), val = tensor([0, 0])]; + tensor var_56_end_0 = const()[name = tensor("op_56_end_0"), val = tensor([2, 160])]; + tensor var_56_end_mask_0 = const()[name = tensor("op_56_end_mask_0"), val = tensor([true, false])]; + tensor var_56_cast_fp16 = slice_by_index(begin = var_56_begin_0, end = var_56_end_0, end_mask = var_56_end_mask_0, x = emb_cast_fp16)[name = tensor("op_56_cast_fp16")]; + tensor sample_interleave_0 = const()[name = tensor("sample_interleave_0"), val = tensor(false)]; + tensor sample_cast_fp16 = concat(axis = var_25, interleave = sample_interleave_0, values = (var_54_cast_fp16, var_56_cast_fp16))[name = tensor("sample_cast_fp16")]; + tensor var_59 = const()[name = tensor("op_59"), val = tensor(1)]; + tensor var_66_axes_0 = const()[name = tensor("op_66_axes_0"), val = tensor([-1])]; + tensor var_66_cast_fp16 = expand_dims(axes = var_66_axes_0, x = sample_cast_fp16)[name = tensor("op_66_cast_fp16")]; + tensor input_1_axes_0 = const()[name = tensor("input_1_axes_0"), val = tensor([-1])]; + tensor input_1_cast_fp16 = expand_dims(axes = input_1_axes_0, x = var_66_cast_fp16)[name = tensor("input_1_cast_fp16")]; + tensor var_70 = const()[name = tensor("op_70"), val = tensor([1, 1])]; + tensor var_72 = const()[name = tensor("op_72"), val = tensor([1, 1])]; + tensor input_3_pad_type_0 = const()[name = tensor("input_3_pad_type_0"), val = tensor("custom")]; + tensor input_3_pad_0 = const()[name = tensor("input_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor time_embedding_linear_1_weight_to_fp16 = const()[name = tensor("time_embedding_linear_1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(448)))]; + tensor time_embedding_linear_1_bias_to_fp16 = const()[name = tensor("time_embedding_linear_1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(819712)))]; + tensor input_3_cast_fp16 = conv(bias = time_embedding_linear_1_bias_to_fp16, dilations = var_72, groups = var_59, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_70, weight = time_embedding_linear_1_weight_to_fp16, x = input_1_cast_fp16)[name = tensor("input_3_cast_fp16")]; + tensor input_5_cast_fp16 = silu(x = input_3_cast_fp16)[name = tensor("input_5_cast_fp16")]; + tensor var_78 = const()[name = tensor("op_78"), val = tensor([1, 1])]; + tensor var_80 = const()[name = tensor("op_80"), val = tensor([1, 1])]; + tensor input_13_pad_type_0 = const()[name = tensor("input_13_pad_type_0"), val = tensor("custom")]; + tensor input_13_pad_0 = const()[name = tensor("input_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor time_embedding_linear_2_weight_to_fp16 = const()[name = tensor("time_embedding_linear_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(822336)))]; + tensor time_embedding_linear_2_bias_to_fp16 = const()[name = tensor("time_embedding_linear_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4099200)))]; + tensor input_13_cast_fp16 = conv(bias = time_embedding_linear_2_bias_to_fp16, dilations = var_80, groups = var_59, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = var_78, weight = time_embedding_linear_2_weight_to_fp16, x = input_5_cast_fp16)[name = tensor("input_13_cast_fp16")]; + tensor var_86 = const()[name = tensor("op_86"), val = tensor(1)]; + tensor var_89 = const()[name = tensor("op_89"), val = tensor([1, 1])]; + tensor var_91 = const()[name = tensor("op_91"), val = tensor([1, 1])]; + tensor input_7_pad_type_0 = const()[name = tensor("input_7_pad_type_0"), val = tensor("custom")]; + tensor input_7_pad_0 = const()[name = tensor("input_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv_in_weight_to_fp16 = const()[name = tensor("conv_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4101824)))]; + tensor conv_in_bias_to_fp16 = const()[name = tensor("conv_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4124928)))]; + tensor input_7_cast_fp16 = conv(bias = conv_in_bias_to_fp16, dilations = var_91, groups = var_86, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = var_89, weight = conv_in_weight_to_fp16, x = sample)[name = tensor("input_7_cast_fp16")]; + tensor var_95 = const()[name = tensor("op_95"), val = tensor(3)]; + tensor var_106 = const()[name = tensor("op_106"), val = tensor(true)]; + tensor var_111 = const()[name = tensor("op_111"), val = tensor(1)]; + tensor reshape_0_shape_0 = const()[name = tensor("reshape_0_shape_0"), val = tensor([2, 32, 10, 48, 80])]; + tensor reshape_0_cast_fp16 = reshape(shape = reshape_0_shape_0, x = input_7_cast_fp16)[name = tensor("reshape_0_cast_fp16")]; + tensor reduce_mean_0_axes_0 = const()[name = tensor("reduce_mean_0_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_0_keep_dims_0 = const()[name = tensor("reduce_mean_0_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_0_cast_fp16 = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = reshape_0_cast_fp16)[name = tensor("reduce_mean_0_cast_fp16")]; + tensor sub_0_cast_fp16 = sub(x = reshape_0_cast_fp16, y = reduce_mean_0_cast_fp16)[name = tensor("sub_0_cast_fp16")]; + tensor square_0_cast_fp16 = square(x = sub_0_cast_fp16)[name = tensor("square_0_cast_fp16")]; + tensor reduce_mean_2_axes_0 = const()[name = tensor("reduce_mean_2_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_2_keep_dims_0 = const()[name = tensor("reduce_mean_2_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_2_cast_fp16 = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = square_0_cast_fp16)[name = tensor("reduce_mean_2_cast_fp16")]; + tensor add_0_y_0_to_fp16 = const()[name = tensor("add_0_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_0_cast_fp16 = add(x = reduce_mean_2_cast_fp16, y = add_0_y_0_to_fp16)[name = tensor("add_0_cast_fp16")]; + tensor sqrt_0_cast_fp16 = sqrt(x = add_0_cast_fp16)[name = tensor("sqrt_0_cast_fp16")]; + tensor real_div_0_cast_fp16 = real_div(x = sub_0_cast_fp16, y = sqrt_0_cast_fp16)[name = tensor("real_div_0_cast_fp16")]; + tensor reshape_1_shape_0 = const()[name = tensor("reshape_1_shape_0"), val = tensor([2, 320, 48, 80])]; + tensor reshape_1_cast_fp16 = reshape(shape = reshape_1_shape_0, x = real_div_0_cast_fp16)[name = tensor("reshape_1_cast_fp16")]; + tensor add_1_mean_0_to_fp16 = const()[name = tensor("add_1_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4125632)))]; + tensor add_1_variance_0_to_fp16 = const()[name = tensor("add_1_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4126336)))]; + tensor add_1_gamma_0_to_fp16 = const()[name = tensor("add_1_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4127040)))]; + tensor add_1_beta_0_to_fp16 = const()[name = tensor("add_1_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4127744)))]; + tensor add_1_epsilon_0_to_fp16 = const()[name = tensor("add_1_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_1_cast_fp16 = batch_norm(beta = add_1_beta_0_to_fp16, epsilon = add_1_epsilon_0_to_fp16, gamma = add_1_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_1_cast_fp16)[name = tensor("add_1_cast_fp16")]; + tensor input_11_cast_fp16 = silu(x = add_1_cast_fp16)[name = tensor("input_11_cast_fp16")]; + tensor var_133 = const()[name = tensor("op_133"), val = tensor([1, 1])]; + tensor var_135 = const()[name = tensor("op_135"), val = tensor([1, 1])]; + tensor hidden_states_1_pad_type_0 = const()[name = tensor("hidden_states_1_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_1_pad_0 = const()[name = tensor("hidden_states_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4128448)))]; + tensor down_blocks_0_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5971712)))]; + tensor hidden_states_1_cast_fp16 = conv(bias = down_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = var_135, groups = var_111, pad = hidden_states_1_pad_0, pad_type = hidden_states_1_pad_type_0, strides = var_133, weight = down_blocks_0_resnets_0_conv1_weight_to_fp16, x = input_11_cast_fp16)[name = tensor("hidden_states_1_cast_fp16")]; + tensor input_15_cast_fp16 = silu(x = input_13_cast_fp16)[name = tensor("input_15_cast_fp16")]; + tensor var_141 = const()[name = tensor("op_141"), val = tensor([1, 1])]; + tensor var_143 = const()[name = tensor("op_143"), val = tensor([1, 1])]; + tensor temb_1_pad_type_0 = const()[name = tensor("temb_1_pad_type_0"), val = tensor("custom")]; + tensor temb_1_pad_0 = const()[name = tensor("temb_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5972416)))]; + tensor down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6791680)))]; + tensor temb_1_cast_fp16 = conv(bias = down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_143, groups = var_111, pad = temb_1_pad_0, pad_type = temb_1_pad_type_0, strides = var_141, weight = down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16)[name = tensor("temb_1_cast_fp16")]; + tensor input_17_cast_fp16 = add(x = hidden_states_1_cast_fp16, y = temb_1_cast_fp16)[name = tensor("input_17_cast_fp16")]; + tensor reshape_4_shape_0 = const()[name = tensor("reshape_4_shape_0"), val = tensor([2, 32, 10, 48, 80])]; + tensor reshape_4_cast_fp16 = reshape(shape = reshape_4_shape_0, x = input_17_cast_fp16)[name = tensor("reshape_4_cast_fp16")]; + tensor reduce_mean_3_axes_0 = const()[name = tensor("reduce_mean_3_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_3_keep_dims_0 = const()[name = tensor("reduce_mean_3_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_3_cast_fp16 = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = reshape_4_cast_fp16)[name = tensor("reduce_mean_3_cast_fp16")]; + tensor sub_2_cast_fp16 = sub(x = reshape_4_cast_fp16, y = reduce_mean_3_cast_fp16)[name = tensor("sub_2_cast_fp16")]; + tensor square_1_cast_fp16 = square(x = sub_2_cast_fp16)[name = tensor("square_1_cast_fp16")]; + tensor reduce_mean_5_axes_0 = const()[name = tensor("reduce_mean_5_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_5_keep_dims_0 = const()[name = tensor("reduce_mean_5_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_5_cast_fp16 = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_1_cast_fp16)[name = tensor("reduce_mean_5_cast_fp16")]; + tensor add_2_y_0_to_fp16 = const()[name = tensor("add_2_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_2_cast_fp16 = add(x = reduce_mean_5_cast_fp16, y = add_2_y_0_to_fp16)[name = tensor("add_2_cast_fp16")]; + tensor sqrt_1_cast_fp16 = sqrt(x = add_2_cast_fp16)[name = tensor("sqrt_1_cast_fp16")]; + tensor real_div_1_cast_fp16 = real_div(x = sub_2_cast_fp16, y = sqrt_1_cast_fp16)[name = tensor("real_div_1_cast_fp16")]; + tensor reshape_5_shape_0 = const()[name = tensor("reshape_5_shape_0"), val = tensor([2, 320, 48, 80])]; + tensor reshape_5_cast_fp16 = reshape(shape = reshape_5_shape_0, x = real_div_1_cast_fp16)[name = tensor("reshape_5_cast_fp16")]; + tensor add_3_gamma_0_to_fp16 = const()[name = tensor("add_3_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6792384)))]; + tensor add_3_beta_0_to_fp16 = const()[name = tensor("add_3_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6793088)))]; + tensor add_3_epsilon_0_to_fp16 = const()[name = tensor("add_3_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_3_cast_fp16 = batch_norm(beta = add_3_beta_0_to_fp16, epsilon = add_3_epsilon_0_to_fp16, gamma = add_3_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_5_cast_fp16)[name = tensor("add_3_cast_fp16")]; + tensor input_21_cast_fp16 = silu(x = add_3_cast_fp16)[name = tensor("input_21_cast_fp16")]; + tensor var_153 = const()[name = tensor("op_153"), val = tensor([1, 1])]; + tensor var_155 = const()[name = tensor("op_155"), val = tensor([1, 1])]; + tensor hidden_states_3_pad_type_0 = const()[name = tensor("hidden_states_3_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_3_pad_0 = const()[name = tensor("hidden_states_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6793792)))]; + tensor down_blocks_0_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8637056)))]; + tensor hidden_states_3_cast_fp16 = conv(bias = down_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = var_155, groups = var_111, pad = hidden_states_3_pad_0, pad_type = hidden_states_3_pad_type_0, strides = var_153, weight = down_blocks_0_resnets_0_conv2_weight_to_fp16, x = input_21_cast_fp16)[name = tensor("hidden_states_3_cast_fp16")]; + tensor hidden_states_5_cast_fp16 = add(x = input_7_cast_fp16, y = hidden_states_3_cast_fp16)[name = tensor("hidden_states_5_cast_fp16")]; + tensor reshape_8_shape_0 = const()[name = tensor("reshape_8_shape_0"), val = tensor([2, 32, 10, 48, 80])]; + tensor reshape_8_cast_fp16 = reshape(shape = reshape_8_shape_0, x = hidden_states_5_cast_fp16)[name = tensor("reshape_8_cast_fp16")]; + tensor reduce_mean_6_axes_0 = const()[name = tensor("reduce_mean_6_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_6_keep_dims_0 = const()[name = tensor("reduce_mean_6_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_6_cast_fp16 = reduce_mean(axes = reduce_mean_6_axes_0, keep_dims = reduce_mean_6_keep_dims_0, x = reshape_8_cast_fp16)[name = tensor("reduce_mean_6_cast_fp16")]; + tensor sub_4_cast_fp16 = sub(x = reshape_8_cast_fp16, y = reduce_mean_6_cast_fp16)[name = tensor("sub_4_cast_fp16")]; + tensor square_2_cast_fp16 = square(x = sub_4_cast_fp16)[name = tensor("square_2_cast_fp16")]; + tensor reduce_mean_8_axes_0 = const()[name = tensor("reduce_mean_8_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_8_keep_dims_0 = const()[name = tensor("reduce_mean_8_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_8_cast_fp16 = reduce_mean(axes = reduce_mean_8_axes_0, keep_dims = reduce_mean_8_keep_dims_0, x = square_2_cast_fp16)[name = tensor("reduce_mean_8_cast_fp16")]; + tensor add_4_y_0_to_fp16 = const()[name = tensor("add_4_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_4_cast_fp16 = add(x = reduce_mean_8_cast_fp16, y = add_4_y_0_to_fp16)[name = tensor("add_4_cast_fp16")]; + tensor sqrt_2_cast_fp16 = sqrt(x = add_4_cast_fp16)[name = tensor("sqrt_2_cast_fp16")]; + tensor real_div_2_cast_fp16 = real_div(x = sub_4_cast_fp16, y = sqrt_2_cast_fp16)[name = tensor("real_div_2_cast_fp16")]; + tensor reshape_9_shape_0 = const()[name = tensor("reshape_9_shape_0"), val = tensor([2, 320, 48, 80])]; + tensor reshape_9_cast_fp16 = reshape(shape = reshape_9_shape_0, x = real_div_2_cast_fp16)[name = tensor("reshape_9_cast_fp16")]; + tensor add_5_gamma_0_to_fp16 = const()[name = tensor("add_5_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8637760)))]; + tensor add_5_beta_0_to_fp16 = const()[name = tensor("add_5_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8638464)))]; + tensor add_5_epsilon_0_to_fp16 = const()[name = tensor("add_5_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_5_cast_fp16 = batch_norm(beta = add_5_beta_0_to_fp16, epsilon = add_5_epsilon_0_to_fp16, gamma = add_5_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_9_cast_fp16)[name = tensor("add_5_cast_fp16")]; + tensor var_175 = const()[name = tensor("op_175"), val = tensor([1, 1])]; + tensor var_177 = const()[name = tensor("op_177"), val = tensor([1, 1])]; + tensor hidden_states_7_pad_type_0 = const()[name = tensor("hidden_states_7_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_7_pad_0 = const()[name = tensor("hidden_states_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8639168)))]; + tensor down_blocks_0_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8844032)))]; + tensor hidden_states_7_cast_fp16 = conv(bias = down_blocks_0_attentions_0_proj_in_bias_to_fp16, dilations = var_177, groups = var_111, pad = hidden_states_7_pad_0, pad_type = hidden_states_7_pad_type_0, strides = var_175, weight = down_blocks_0_attentions_0_proj_in_weight_to_fp16, x = add_5_cast_fp16)[name = tensor("hidden_states_7_cast_fp16")]; + tensor var_182 = const()[name = tensor("op_182"), val = tensor([2, 320, 1, 3840])]; + tensor inputs_1_cast_fp16 = reshape(shape = var_182, x = hidden_states_7_cast_fp16)[name = tensor("inputs_1_cast_fp16")]; + tensor var_192 = const()[name = tensor("op_192"), val = tensor([1])]; + tensor channels_mean_1_cast_fp16 = reduce_mean(axes = var_192, keep_dims = var_106, x = inputs_1_cast_fp16)[name = tensor("channels_mean_1_cast_fp16")]; + tensor zero_mean_1_cast_fp16 = sub(x = inputs_1_cast_fp16, y = channels_mean_1_cast_fp16)[name = tensor("zero_mean_1_cast_fp16")]; + tensor zero_mean_sq_1_cast_fp16 = mul(x = zero_mean_1_cast_fp16, y = zero_mean_1_cast_fp16)[name = tensor("zero_mean_sq_1_cast_fp16")]; + tensor var_196 = const()[name = tensor("op_196"), val = tensor([1])]; + tensor var_197_cast_fp16 = reduce_mean(axes = var_196, keep_dims = var_106, x = zero_mean_sq_1_cast_fp16)[name = tensor("op_197_cast_fp16")]; + tensor var_198_to_fp16 = const()[name = tensor("op_198_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_199_cast_fp16 = add(x = var_197_cast_fp16, y = var_198_to_fp16)[name = tensor("op_199_cast_fp16")]; + tensor denom_1_epsilon_0_to_fp16 = const()[name = tensor("denom_1_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_1_cast_fp16 = rsqrt(epsilon = denom_1_epsilon_0_to_fp16, x = var_199_cast_fp16)[name = tensor("denom_1_cast_fp16")]; + tensor out_1_cast_fp16 = mul(x = zero_mean_1_cast_fp16, y = denom_1_cast_fp16)[name = tensor("out_1_cast_fp16")]; + tensor var_203_to_fp16 = const()[name = tensor("op_203_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8844736)))]; + tensor var_204_cast_fp16 = add(x = out_1_cast_fp16, y = var_203_to_fp16)[name = tensor("op_204_cast_fp16")]; + tensor var_206_to_fp16 = const()[name = tensor("op_206_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8845440)))]; + tensor hidden_states_9_cast_fp16 = mul(x = var_204_cast_fp16, y = var_206_to_fp16)[name = tensor("hidden_states_9_cast_fp16")]; + tensor var_213 = const()[name = tensor("op_213"), val = tensor([1, 1])]; + tensor var_215 = const()[name = tensor("op_215"), val = tensor([1, 1])]; + tensor q_1_pad_type_0 = const()[name = tensor("q_1_pad_type_0"), val = tensor("custom")]; + tensor q_1_pad_0 = const()[name = tensor("q_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8846144)))]; + tensor q_1_cast_fp16 = conv(dilations = var_215, groups = var_111, pad = q_1_pad_0, pad_type = q_1_pad_type_0, strides = var_213, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_9_cast_fp16)[name = tensor("q_1_cast_fp16")]; + tensor var_219 = const()[name = tensor("op_219"), val = tensor([1, 1])]; + tensor var_221 = const()[name = tensor("op_221"), val = tensor([1, 1])]; + tensor k_1_pad_type_0 = const()[name = tensor("k_1_pad_type_0"), val = tensor("custom")]; + tensor k_1_pad_0 = const()[name = tensor("k_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9051008)))]; + tensor k_1_cast_fp16 = conv(dilations = var_221, groups = var_111, pad = k_1_pad_0, pad_type = k_1_pad_type_0, strides = var_219, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_9_cast_fp16)[name = tensor("k_1_cast_fp16")]; + tensor var_225 = const()[name = tensor("op_225"), val = tensor([1, 1])]; + tensor var_227 = const()[name = tensor("op_227"), val = tensor([1, 1])]; + tensor v_1_pad_type_0 = const()[name = tensor("v_1_pad_type_0"), val = tensor("custom")]; + tensor v_1_pad_0 = const()[name = tensor("v_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9255872)))]; + tensor v_1_cast_fp16 = conv(dilations = var_227, groups = var_111, pad = v_1_pad_0, pad_type = v_1_pad_type_0, strides = var_225, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_9_cast_fp16)[name = tensor("v_1_cast_fp16")]; + tensor var_231 = const()[name = tensor("op_231"), val = tensor([2, 5, 64, -1])]; + tensor var_232_cast_fp16 = reshape(shape = var_231, x = q_1_cast_fp16)[name = tensor("op_232_cast_fp16")]; + tensor var_233 = const()[name = tensor("op_233"), val = tensor([2, 5, 64, -1])]; + tensor var_234_cast_fp16 = reshape(shape = var_233, x = k_1_cast_fp16)[name = tensor("op_234_cast_fp16")]; + tensor var_235 = const()[name = tensor("op_235"), val = tensor([2, 5, 64, -1])]; + tensor var_236_cast_fp16 = reshape(shape = var_235, x = v_1_cast_fp16)[name = tensor("op_236_cast_fp16")]; + tensor attn_weights_1_transpose_x_0 = const()[name = tensor("attn_weights_1_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_1_transpose_y_0 = const()[name = tensor("attn_weights_1_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_1_cast_fp16 = matmul(transpose_x = attn_weights_1_transpose_x_0, transpose_y = attn_weights_1_transpose_y_0, x = var_232_cast_fp16, y = var_234_cast_fp16)[name = tensor("attn_weights_1_cast_fp16")]; + tensor var_102_to_fp16 = const()[name = tensor("op_102_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_3_cast_fp16 = mul(x = attn_weights_1_cast_fp16, y = var_102_to_fp16)[name = tensor("attn_weights_3_cast_fp16")]; + tensor var_240_cast_fp16 = softmax(axis = var_95, x = attn_weights_3_cast_fp16)[name = tensor("op_240_cast_fp16")]; + tensor attn_1_transpose_x_0 = const()[name = tensor("attn_1_transpose_x_0"), val = tensor(false)]; + tensor attn_1_transpose_y_0 = const()[name = tensor("attn_1_transpose_y_0"), val = tensor(true)]; + tensor attn_1_cast_fp16 = matmul(transpose_x = attn_1_transpose_x_0, transpose_y = attn_1_transpose_y_0, x = var_236_cast_fp16, y = var_240_cast_fp16)[name = tensor("attn_1_cast_fp16")]; + tensor var_244 = const()[name = tensor("op_244"), val = tensor([2, 320, 1, -1])]; + tensor input_25_cast_fp16 = reshape(shape = var_244, x = attn_1_cast_fp16)[name = tensor("input_25_cast_fp16")]; + tensor var_249 = const()[name = tensor("op_249"), val = tensor([1, 1])]; + tensor var_251 = const()[name = tensor("op_251"), val = tensor([1, 1])]; + tensor var_253_pad_type_0 = const()[name = tensor("op_253_pad_type_0"), val = tensor("custom")]; + tensor var_253_pad_0 = const()[name = tensor("op_253_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9460736)))]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9665600)))]; + tensor var_253_cast_fp16 = conv(bias = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_251, groups = var_111, pad = var_253_pad_0, pad_type = var_253_pad_type_0, strides = var_249, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_25_cast_fp16)[name = tensor("op_253_cast_fp16")]; + tensor inputs_3_cast_fp16 = add(x = var_253_cast_fp16, y = inputs_1_cast_fp16)[name = tensor("inputs_3_cast_fp16")]; + tensor var_257 = const()[name = tensor("op_257"), val = tensor([1])]; + tensor channels_mean_3_cast_fp16 = reduce_mean(axes = var_257, keep_dims = var_106, x = inputs_3_cast_fp16)[name = tensor("channels_mean_3_cast_fp16")]; + tensor zero_mean_3_cast_fp16 = sub(x = inputs_3_cast_fp16, y = channels_mean_3_cast_fp16)[name = tensor("zero_mean_3_cast_fp16")]; + tensor zero_mean_sq_3_cast_fp16 = mul(x = zero_mean_3_cast_fp16, y = zero_mean_3_cast_fp16)[name = tensor("zero_mean_sq_3_cast_fp16")]; + tensor var_261 = const()[name = tensor("op_261"), val = tensor([1])]; + tensor var_262_cast_fp16 = reduce_mean(axes = var_261, keep_dims = var_106, x = zero_mean_sq_3_cast_fp16)[name = tensor("op_262_cast_fp16")]; + tensor var_263_to_fp16 = const()[name = tensor("op_263_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_264_cast_fp16 = add(x = var_262_cast_fp16, y = var_263_to_fp16)[name = tensor("op_264_cast_fp16")]; + tensor denom_3_epsilon_0_to_fp16 = const()[name = tensor("denom_3_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_3_cast_fp16 = rsqrt(epsilon = denom_3_epsilon_0_to_fp16, x = var_264_cast_fp16)[name = tensor("denom_3_cast_fp16")]; + tensor out_3_cast_fp16 = mul(x = zero_mean_3_cast_fp16, y = denom_3_cast_fp16)[name = tensor("out_3_cast_fp16")]; + tensor var_268_to_fp16 = const()[name = tensor("op_268_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9666304)))]; + tensor var_269_cast_fp16 = add(x = out_3_cast_fp16, y = var_268_to_fp16)[name = tensor("op_269_cast_fp16")]; + tensor var_271_to_fp16 = const()[name = tensor("op_271_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9667008)))]; + tensor hidden_states_11_cast_fp16 = mul(x = var_269_cast_fp16, y = var_271_to_fp16)[name = tensor("hidden_states_11_cast_fp16")]; + tensor var_278 = const()[name = tensor("op_278"), val = tensor([1, 1])]; + tensor var_280 = const()[name = tensor("op_280"), val = tensor([1, 1])]; + tensor q_3_pad_type_0 = const()[name = tensor("q_3_pad_type_0"), val = tensor("custom")]; + tensor q_3_pad_0 = const()[name = tensor("q_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9667712)))]; + tensor q_3_cast_fp16 = conv(dilations = var_280, groups = var_111, pad = q_3_pad_0, pad_type = q_3_pad_type_0, strides = var_278, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_11_cast_fp16)[name = tensor("q_3_cast_fp16")]; + tensor var_284 = const()[name = tensor("op_284"), val = tensor([1, 1])]; + tensor var_286 = const()[name = tensor("op_286"), val = tensor([1, 1])]; + tensor k_3_pad_type_0 = const()[name = tensor("k_3_pad_type_0"), val = tensor("custom")]; + tensor k_3_pad_0 = const()[name = tensor("k_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9872576)))]; + tensor k_3_cast_fp16 = conv(dilations = var_286, groups = var_111, pad = k_3_pad_0, pad_type = k_3_pad_type_0, strides = var_284, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_3_cast_fp16")]; + tensor var_290 = const()[name = tensor("op_290"), val = tensor([1, 1])]; + tensor var_292 = const()[name = tensor("op_292"), val = tensor([1, 1])]; + tensor v_3_pad_type_0 = const()[name = tensor("v_3_pad_type_0"), val = tensor("custom")]; + tensor v_3_pad_0 = const()[name = tensor("v_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10528000)))]; + tensor v_3_cast_fp16 = conv(dilations = var_292, groups = var_111, pad = v_3_pad_0, pad_type = v_3_pad_type_0, strides = var_290, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_3_cast_fp16")]; + tensor var_296 = const()[name = tensor("op_296"), val = tensor([2, 5, 64, -1])]; + tensor var_297_cast_fp16 = reshape(shape = var_296, x = q_3_cast_fp16)[name = tensor("op_297_cast_fp16")]; + tensor var_298 = const()[name = tensor("op_298"), val = tensor([2, 5, 64, -1])]; + tensor var_299_cast_fp16 = reshape(shape = var_298, x = k_3_cast_fp16)[name = tensor("op_299_cast_fp16")]; + tensor var_300 = const()[name = tensor("op_300"), val = tensor([2, 5, 64, -1])]; + tensor var_301_cast_fp16 = reshape(shape = var_300, x = v_3_cast_fp16)[name = tensor("op_301_cast_fp16")]; + tensor attn_weights_5_transpose_x_0 = const()[name = tensor("attn_weights_5_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_5_transpose_y_0 = const()[name = tensor("attn_weights_5_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_5_cast_fp16 = matmul(transpose_x = attn_weights_5_transpose_x_0, transpose_y = attn_weights_5_transpose_y_0, x = var_297_cast_fp16, y = var_299_cast_fp16)[name = tensor("attn_weights_5_cast_fp16")]; + tensor attn_weights_7_cast_fp16 = mul(x = attn_weights_5_cast_fp16, y = var_102_to_fp16)[name = tensor("attn_weights_7_cast_fp16")]; + tensor var_305_cast_fp16 = softmax(axis = var_95, x = attn_weights_7_cast_fp16)[name = tensor("op_305_cast_fp16")]; + tensor attn_3_transpose_x_0 = const()[name = tensor("attn_3_transpose_x_0"), val = tensor(false)]; + tensor attn_3_transpose_y_0 = const()[name = tensor("attn_3_transpose_y_0"), val = tensor(true)]; + tensor attn_3_cast_fp16 = matmul(transpose_x = attn_3_transpose_x_0, transpose_y = attn_3_transpose_y_0, x = var_301_cast_fp16, y = var_305_cast_fp16)[name = tensor("attn_3_cast_fp16")]; + tensor var_309 = const()[name = tensor("op_309"), val = tensor([2, 320, 1, -1])]; + tensor input_27_cast_fp16 = reshape(shape = var_309, x = attn_3_cast_fp16)[name = tensor("input_27_cast_fp16")]; + tensor var_314 = const()[name = tensor("op_314"), val = tensor([1, 1])]; + tensor var_316 = const()[name = tensor("op_316"), val = tensor([1, 1])]; + tensor var_318_pad_type_0 = const()[name = tensor("op_318_pad_type_0"), val = tensor("custom")]; + tensor var_318_pad_0 = const()[name = tensor("op_318_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11183424)))]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11388288)))]; + tensor var_318_cast_fp16 = conv(bias = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_316, groups = var_111, pad = var_318_pad_0, pad_type = var_318_pad_type_0, strides = var_314, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_27_cast_fp16)[name = tensor("op_318_cast_fp16")]; + tensor inputs_5_cast_fp16 = add(x = var_318_cast_fp16, y = inputs_3_cast_fp16)[name = tensor("inputs_5_cast_fp16")]; + tensor var_322 = const()[name = tensor("op_322"), val = tensor([1])]; + tensor channels_mean_5_cast_fp16 = reduce_mean(axes = var_322, keep_dims = var_106, x = inputs_5_cast_fp16)[name = tensor("channels_mean_5_cast_fp16")]; + tensor zero_mean_5_cast_fp16 = sub(x = inputs_5_cast_fp16, y = channels_mean_5_cast_fp16)[name = tensor("zero_mean_5_cast_fp16")]; + tensor zero_mean_sq_5_cast_fp16 = mul(x = zero_mean_5_cast_fp16, y = zero_mean_5_cast_fp16)[name = tensor("zero_mean_sq_5_cast_fp16")]; + tensor var_326 = const()[name = tensor("op_326"), val = tensor([1])]; + tensor var_327_cast_fp16 = reduce_mean(axes = var_326, keep_dims = var_106, x = zero_mean_sq_5_cast_fp16)[name = tensor("op_327_cast_fp16")]; + tensor var_328_to_fp16 = const()[name = tensor("op_328_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_329_cast_fp16 = add(x = var_327_cast_fp16, y = var_328_to_fp16)[name = tensor("op_329_cast_fp16")]; + tensor denom_5_epsilon_0_to_fp16 = const()[name = tensor("denom_5_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_5_cast_fp16 = rsqrt(epsilon = denom_5_epsilon_0_to_fp16, x = var_329_cast_fp16)[name = tensor("denom_5_cast_fp16")]; + tensor out_5_cast_fp16 = mul(x = zero_mean_5_cast_fp16, y = denom_5_cast_fp16)[name = tensor("out_5_cast_fp16")]; + tensor var_333_to_fp16 = const()[name = tensor("op_333_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11388992)))]; + tensor var_334_cast_fp16 = add(x = out_5_cast_fp16, y = var_333_to_fp16)[name = tensor("op_334_cast_fp16")]; + tensor var_336_to_fp16 = const()[name = tensor("op_336_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11389696)))]; + tensor input_29_cast_fp16 = mul(x = var_334_cast_fp16, y = var_336_to_fp16)[name = tensor("input_29_cast_fp16")]; + tensor var_344 = const()[name = tensor("op_344"), val = tensor([1, 1])]; + tensor var_346 = const()[name = tensor("op_346"), val = tensor([1, 1])]; + tensor var_348_pad_type_0 = const()[name = tensor("op_348_pad_type_0"), val = tensor("custom")]; + tensor var_348_pad_0 = const()[name = tensor("op_348_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11390400)))]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13028864)))]; + tensor var_348_cast_fp16 = conv(bias = down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_346, groups = var_111, pad = var_348_pad_0, pad_type = var_348_pad_type_0, strides = var_344, weight = down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_29_cast_fp16)[name = tensor("op_348_cast_fp16")]; + tensor var_349_split_sizes_0 = const()[name = tensor("op_349_split_sizes_0"), val = tensor([1280, 1280])]; + tensor var_349_axis_0 = const()[name = tensor("op_349_axis_0"), val = tensor(1)]; + tensor var_349_cast_fp16_0, tensor var_349_cast_fp16_1 = split(axis = var_349_axis_0, split_sizes = var_349_split_sizes_0, x = var_348_cast_fp16)[name = tensor("op_349_cast_fp16")]; + tensor var_351_mode_0 = const()[name = tensor("op_351_mode_0"), val = tensor("EXACT")]; + tensor var_351_cast_fp16 = gelu(mode = var_351_mode_0, x = var_349_cast_fp16_1)[name = tensor("op_351_cast_fp16")]; + tensor input_31_cast_fp16 = mul(x = var_349_cast_fp16_0, y = var_351_cast_fp16)[name = tensor("input_31_cast_fp16")]; + tensor var_355 = const()[name = tensor("op_355"), val = tensor([1, 1])]; + tensor var_357 = const()[name = tensor("op_357"), val = tensor([1, 1])]; + tensor var_359_pad_type_0 = const()[name = tensor("op_359_pad_type_0"), val = tensor("custom")]; + tensor var_359_pad_0 = const()[name = tensor("op_359_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13034048)))]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13853312)))]; + tensor var_359_cast_fp16 = conv(bias = down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_357, groups = var_111, pad = var_359_pad_0, pad_type = var_359_pad_type_0, strides = var_355, weight = down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_31_cast_fp16)[name = tensor("op_359_cast_fp16")]; + tensor hidden_states_15_cast_fp16 = add(x = var_359_cast_fp16, y = inputs_5_cast_fp16)[name = tensor("hidden_states_15_cast_fp16")]; + tensor var_361 = const()[name = tensor("op_361"), val = tensor([2, 320, 48, 80])]; + tensor input_33_cast_fp16 = reshape(shape = var_361, x = hidden_states_15_cast_fp16)[name = tensor("input_33_cast_fp16")]; + tensor var_365 = const()[name = tensor("op_365"), val = tensor([1, 1])]; + tensor var_367 = const()[name = tensor("op_367"), val = tensor([1, 1])]; + tensor hidden_states_17_pad_type_0 = const()[name = tensor("hidden_states_17_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_17_pad_0 = const()[name = tensor("hidden_states_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13854016)))]; + tensor down_blocks_0_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14058880)))]; + tensor hidden_states_17_cast_fp16 = conv(bias = down_blocks_0_attentions_0_proj_out_bias_to_fp16, dilations = var_367, groups = var_111, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = var_365, weight = down_blocks_0_attentions_0_proj_out_weight_to_fp16, x = input_33_cast_fp16)[name = tensor("hidden_states_17_cast_fp16")]; + tensor input_35_cast_fp16 = add(x = hidden_states_17_cast_fp16, y = hidden_states_5_cast_fp16)[name = tensor("input_35_cast_fp16")]; + tensor reshape_12_shape_0 = const()[name = tensor("reshape_12_shape_0"), val = tensor([2, 32, 10, 48, 80])]; + tensor reshape_12_cast_fp16 = reshape(shape = reshape_12_shape_0, x = input_35_cast_fp16)[name = tensor("reshape_12_cast_fp16")]; + tensor reduce_mean_9_axes_0 = const()[name = tensor("reduce_mean_9_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_9_keep_dims_0 = const()[name = tensor("reduce_mean_9_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_9_cast_fp16 = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = reshape_12_cast_fp16)[name = tensor("reduce_mean_9_cast_fp16")]; + tensor sub_6_cast_fp16 = sub(x = reshape_12_cast_fp16, y = reduce_mean_9_cast_fp16)[name = tensor("sub_6_cast_fp16")]; + tensor square_3_cast_fp16 = square(x = sub_6_cast_fp16)[name = tensor("square_3_cast_fp16")]; + tensor reduce_mean_11_axes_0 = const()[name = tensor("reduce_mean_11_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_11_keep_dims_0 = const()[name = tensor("reduce_mean_11_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_11_cast_fp16 = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_3_cast_fp16)[name = tensor("reduce_mean_11_cast_fp16")]; + tensor add_6_y_0_to_fp16 = const()[name = tensor("add_6_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_6_cast_fp16 = add(x = reduce_mean_11_cast_fp16, y = add_6_y_0_to_fp16)[name = tensor("add_6_cast_fp16")]; + tensor sqrt_3_cast_fp16 = sqrt(x = add_6_cast_fp16)[name = tensor("sqrt_3_cast_fp16")]; + tensor real_div_3_cast_fp16 = real_div(x = sub_6_cast_fp16, y = sqrt_3_cast_fp16)[name = tensor("real_div_3_cast_fp16")]; + tensor reshape_13_shape_0 = const()[name = tensor("reshape_13_shape_0"), val = tensor([2, 320, 48, 80])]; + tensor reshape_13_cast_fp16 = reshape(shape = reshape_13_shape_0, x = real_div_3_cast_fp16)[name = tensor("reshape_13_cast_fp16")]; + tensor add_7_gamma_0_to_fp16 = const()[name = tensor("add_7_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14059584)))]; + tensor add_7_beta_0_to_fp16 = const()[name = tensor("add_7_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14060288)))]; + tensor add_7_epsilon_0_to_fp16 = const()[name = tensor("add_7_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_7_cast_fp16 = batch_norm(beta = add_7_beta_0_to_fp16, epsilon = add_7_epsilon_0_to_fp16, gamma = add_7_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_13_cast_fp16)[name = tensor("add_7_cast_fp16")]; + tensor input_39_cast_fp16 = silu(x = add_7_cast_fp16)[name = tensor("input_39_cast_fp16")]; + tensor var_382 = const()[name = tensor("op_382"), val = tensor([1, 1])]; + tensor var_384 = const()[name = tensor("op_384"), val = tensor([1, 1])]; + tensor hidden_states_19_pad_type_0 = const()[name = tensor("hidden_states_19_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_19_pad_0 = const()[name = tensor("hidden_states_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14060992)))]; + tensor down_blocks_0_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15904256)))]; + tensor hidden_states_19_cast_fp16 = conv(bias = down_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = var_384, groups = var_111, pad = hidden_states_19_pad_0, pad_type = hidden_states_19_pad_type_0, strides = var_382, weight = down_blocks_0_resnets_1_conv1_weight_to_fp16, x = input_39_cast_fp16)[name = tensor("hidden_states_19_cast_fp16")]; + tensor var_390 = const()[name = tensor("op_390"), val = tensor([1, 1])]; + tensor var_392 = const()[name = tensor("op_392"), val = tensor([1, 1])]; + tensor temb_3_pad_type_0 = const()[name = tensor("temb_3_pad_type_0"), val = tensor("custom")]; + tensor temb_3_pad_0 = const()[name = tensor("temb_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15904960)))]; + tensor down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16724224)))]; + tensor temb_3_cast_fp16 = conv(bias = down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_392, groups = var_111, pad = temb_3_pad_0, pad_type = temb_3_pad_type_0, strides = var_390, weight = down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16)[name = tensor("temb_3_cast_fp16")]; + tensor input_43_cast_fp16 = add(x = hidden_states_19_cast_fp16, y = temb_3_cast_fp16)[name = tensor("input_43_cast_fp16")]; + tensor reshape_16_shape_0 = const()[name = tensor("reshape_16_shape_0"), val = tensor([2, 32, 10, 48, 80])]; + tensor reshape_16_cast_fp16 = reshape(shape = reshape_16_shape_0, x = input_43_cast_fp16)[name = tensor("reshape_16_cast_fp16")]; + tensor reduce_mean_12_axes_0 = const()[name = tensor("reduce_mean_12_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_12_keep_dims_0 = const()[name = tensor("reduce_mean_12_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_12_cast_fp16 = reduce_mean(axes = reduce_mean_12_axes_0, keep_dims = reduce_mean_12_keep_dims_0, x = reshape_16_cast_fp16)[name = tensor("reduce_mean_12_cast_fp16")]; + tensor sub_8_cast_fp16 = sub(x = reshape_16_cast_fp16, y = reduce_mean_12_cast_fp16)[name = tensor("sub_8_cast_fp16")]; + tensor square_4_cast_fp16 = square(x = sub_8_cast_fp16)[name = tensor("square_4_cast_fp16")]; + tensor reduce_mean_14_axes_0 = const()[name = tensor("reduce_mean_14_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_14_keep_dims_0 = const()[name = tensor("reduce_mean_14_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_14_cast_fp16 = reduce_mean(axes = reduce_mean_14_axes_0, keep_dims = reduce_mean_14_keep_dims_0, x = square_4_cast_fp16)[name = tensor("reduce_mean_14_cast_fp16")]; + tensor add_8_y_0_to_fp16 = const()[name = tensor("add_8_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_8_cast_fp16 = add(x = reduce_mean_14_cast_fp16, y = add_8_y_0_to_fp16)[name = tensor("add_8_cast_fp16")]; + tensor sqrt_4_cast_fp16 = sqrt(x = add_8_cast_fp16)[name = tensor("sqrt_4_cast_fp16")]; + tensor real_div_4_cast_fp16 = real_div(x = sub_8_cast_fp16, y = sqrt_4_cast_fp16)[name = tensor("real_div_4_cast_fp16")]; + tensor reshape_17_shape_0 = const()[name = tensor("reshape_17_shape_0"), val = tensor([2, 320, 48, 80])]; + tensor reshape_17_cast_fp16 = reshape(shape = reshape_17_shape_0, x = real_div_4_cast_fp16)[name = tensor("reshape_17_cast_fp16")]; + tensor add_9_gamma_0_to_fp16 = const()[name = tensor("add_9_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16724928)))]; + tensor add_9_beta_0_to_fp16 = const()[name = tensor("add_9_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16725632)))]; + tensor add_9_epsilon_0_to_fp16 = const()[name = tensor("add_9_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_9_cast_fp16 = batch_norm(beta = add_9_beta_0_to_fp16, epsilon = add_9_epsilon_0_to_fp16, gamma = add_9_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_17_cast_fp16)[name = tensor("add_9_cast_fp16")]; + tensor input_47_cast_fp16 = silu(x = add_9_cast_fp16)[name = tensor("input_47_cast_fp16")]; + tensor var_402 = const()[name = tensor("op_402"), val = tensor([1, 1])]; + tensor var_404 = const()[name = tensor("op_404"), val = tensor([1, 1])]; + tensor hidden_states_21_pad_type_0 = const()[name = tensor("hidden_states_21_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_21_pad_0 = const()[name = tensor("hidden_states_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16726336)))]; + tensor down_blocks_0_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18569600)))]; + tensor hidden_states_21_cast_fp16 = conv(bias = down_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = var_404, groups = var_111, pad = hidden_states_21_pad_0, pad_type = hidden_states_21_pad_type_0, strides = var_402, weight = down_blocks_0_resnets_1_conv2_weight_to_fp16, x = input_47_cast_fp16)[name = tensor("hidden_states_21_cast_fp16")]; + tensor hidden_states_23_cast_fp16 = add(x = input_35_cast_fp16, y = hidden_states_21_cast_fp16)[name = tensor("hidden_states_23_cast_fp16")]; + tensor reshape_20_shape_0 = const()[name = tensor("reshape_20_shape_0"), val = tensor([2, 32, 10, 48, 80])]; + tensor reshape_20_cast_fp16 = reshape(shape = reshape_20_shape_0, x = hidden_states_23_cast_fp16)[name = tensor("reshape_20_cast_fp16")]; + tensor reduce_mean_15_axes_0 = const()[name = tensor("reduce_mean_15_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_15_keep_dims_0 = const()[name = tensor("reduce_mean_15_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_15_cast_fp16 = reduce_mean(axes = reduce_mean_15_axes_0, keep_dims = reduce_mean_15_keep_dims_0, x = reshape_20_cast_fp16)[name = tensor("reduce_mean_15_cast_fp16")]; + tensor sub_10_cast_fp16 = sub(x = reshape_20_cast_fp16, y = reduce_mean_15_cast_fp16)[name = tensor("sub_10_cast_fp16")]; + tensor square_5_cast_fp16 = square(x = sub_10_cast_fp16)[name = tensor("square_5_cast_fp16")]; + tensor reduce_mean_17_axes_0 = const()[name = tensor("reduce_mean_17_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_17_keep_dims_0 = const()[name = tensor("reduce_mean_17_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_17_cast_fp16 = reduce_mean(axes = reduce_mean_17_axes_0, keep_dims = reduce_mean_17_keep_dims_0, x = square_5_cast_fp16)[name = tensor("reduce_mean_17_cast_fp16")]; + tensor add_10_y_0_to_fp16 = const()[name = tensor("add_10_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_10_cast_fp16 = add(x = reduce_mean_17_cast_fp16, y = add_10_y_0_to_fp16)[name = tensor("add_10_cast_fp16")]; + tensor sqrt_5_cast_fp16 = sqrt(x = add_10_cast_fp16)[name = tensor("sqrt_5_cast_fp16")]; + tensor real_div_5_cast_fp16 = real_div(x = sub_10_cast_fp16, y = sqrt_5_cast_fp16)[name = tensor("real_div_5_cast_fp16")]; + tensor reshape_21_shape_0 = const()[name = tensor("reshape_21_shape_0"), val = tensor([2, 320, 48, 80])]; + tensor reshape_21_cast_fp16 = reshape(shape = reshape_21_shape_0, x = real_div_5_cast_fp16)[name = tensor("reshape_21_cast_fp16")]; + tensor add_11_gamma_0_to_fp16 = const()[name = tensor("add_11_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18570304)))]; + tensor add_11_beta_0_to_fp16 = const()[name = tensor("add_11_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18571008)))]; + tensor add_11_epsilon_0_to_fp16 = const()[name = tensor("add_11_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_11_cast_fp16 = batch_norm(beta = add_11_beta_0_to_fp16, epsilon = add_11_epsilon_0_to_fp16, gamma = add_11_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_21_cast_fp16)[name = tensor("add_11_cast_fp16")]; + tensor var_424 = const()[name = tensor("op_424"), val = tensor([1, 1])]; + tensor var_426 = const()[name = tensor("op_426"), val = tensor([1, 1])]; + tensor hidden_states_25_pad_type_0 = const()[name = tensor("hidden_states_25_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_25_pad_0 = const()[name = tensor("hidden_states_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_proj_in_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18571712)))]; + tensor down_blocks_0_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18776576)))]; + tensor hidden_states_25_cast_fp16 = conv(bias = down_blocks_0_attentions_1_proj_in_bias_to_fp16, dilations = var_426, groups = var_111, pad = hidden_states_25_pad_0, pad_type = hidden_states_25_pad_type_0, strides = var_424, weight = down_blocks_0_attentions_1_proj_in_weight_to_fp16, x = add_11_cast_fp16)[name = tensor("hidden_states_25_cast_fp16")]; + tensor var_431 = const()[name = tensor("op_431"), val = tensor([2, 320, 1, 3840])]; + tensor inputs_7_cast_fp16 = reshape(shape = var_431, x = hidden_states_25_cast_fp16)[name = tensor("inputs_7_cast_fp16")]; + tensor var_441 = const()[name = tensor("op_441"), val = tensor([1])]; + tensor channels_mean_7_cast_fp16 = reduce_mean(axes = var_441, keep_dims = var_106, x = inputs_7_cast_fp16)[name = tensor("channels_mean_7_cast_fp16")]; + tensor zero_mean_7_cast_fp16 = sub(x = inputs_7_cast_fp16, y = channels_mean_7_cast_fp16)[name = tensor("zero_mean_7_cast_fp16")]; + tensor zero_mean_sq_7_cast_fp16 = mul(x = zero_mean_7_cast_fp16, y = zero_mean_7_cast_fp16)[name = tensor("zero_mean_sq_7_cast_fp16")]; + tensor var_445 = const()[name = tensor("op_445"), val = tensor([1])]; + tensor var_446_cast_fp16 = reduce_mean(axes = var_445, keep_dims = var_106, x = zero_mean_sq_7_cast_fp16)[name = tensor("op_446_cast_fp16")]; + tensor var_447_to_fp16 = const()[name = tensor("op_447_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_448_cast_fp16 = add(x = var_446_cast_fp16, y = var_447_to_fp16)[name = tensor("op_448_cast_fp16")]; + tensor denom_7_epsilon_0_to_fp16 = const()[name = tensor("denom_7_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_7_cast_fp16 = rsqrt(epsilon = denom_7_epsilon_0_to_fp16, x = var_448_cast_fp16)[name = tensor("denom_7_cast_fp16")]; + tensor out_7_cast_fp16 = mul(x = zero_mean_7_cast_fp16, y = denom_7_cast_fp16)[name = tensor("out_7_cast_fp16")]; + tensor var_452_to_fp16 = const()[name = tensor("op_452_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18777280)))]; + tensor var_453_cast_fp16 = add(x = out_7_cast_fp16, y = var_452_to_fp16)[name = tensor("op_453_cast_fp16")]; + tensor var_455_to_fp16 = const()[name = tensor("op_455_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18777984)))]; + tensor hidden_states_27_cast_fp16 = mul(x = var_453_cast_fp16, y = var_455_to_fp16)[name = tensor("hidden_states_27_cast_fp16")]; + tensor var_462 = const()[name = tensor("op_462"), val = tensor([1, 1])]; + tensor var_464 = const()[name = tensor("op_464"), val = tensor([1, 1])]; + tensor q_5_pad_type_0 = const()[name = tensor("q_5_pad_type_0"), val = tensor("custom")]; + tensor q_5_pad_0 = const()[name = tensor("q_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18778688)))]; + tensor q_5_cast_fp16 = conv(dilations = var_464, groups = var_111, pad = q_5_pad_0, pad_type = q_5_pad_type_0, strides = var_462, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_27_cast_fp16)[name = tensor("q_5_cast_fp16")]; + tensor var_468 = const()[name = tensor("op_468"), val = tensor([1, 1])]; + tensor var_470 = const()[name = tensor("op_470"), val = tensor([1, 1])]; + tensor k_5_pad_type_0 = const()[name = tensor("k_5_pad_type_0"), val = tensor("custom")]; + tensor k_5_pad_0 = const()[name = tensor("k_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18983552)))]; + tensor k_5_cast_fp16 = conv(dilations = var_470, groups = var_111, pad = k_5_pad_0, pad_type = k_5_pad_type_0, strides = var_468, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_27_cast_fp16)[name = tensor("k_5_cast_fp16")]; + tensor var_474 = const()[name = tensor("op_474"), val = tensor([1, 1])]; + tensor var_476 = const()[name = tensor("op_476"), val = tensor([1, 1])]; + tensor v_5_pad_type_0 = const()[name = tensor("v_5_pad_type_0"), val = tensor("custom")]; + tensor v_5_pad_0 = const()[name = tensor("v_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19188416)))]; + tensor v_5_cast_fp16 = conv(dilations = var_476, groups = var_111, pad = v_5_pad_0, pad_type = v_5_pad_type_0, strides = var_474, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_27_cast_fp16)[name = tensor("v_5_cast_fp16")]; + tensor var_480 = const()[name = tensor("op_480"), val = tensor([2, 5, 64, -1])]; + tensor var_481_cast_fp16 = reshape(shape = var_480, x = q_5_cast_fp16)[name = tensor("op_481_cast_fp16")]; + tensor var_482 = const()[name = tensor("op_482"), val = tensor([2, 5, 64, -1])]; + tensor var_483_cast_fp16 = reshape(shape = var_482, x = k_5_cast_fp16)[name = tensor("op_483_cast_fp16")]; + tensor var_484 = const()[name = tensor("op_484"), val = tensor([2, 5, 64, -1])]; + tensor var_485_cast_fp16 = reshape(shape = var_484, x = v_5_cast_fp16)[name = tensor("op_485_cast_fp16")]; + tensor attn_weights_9_transpose_x_0 = const()[name = tensor("attn_weights_9_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_9_transpose_y_0 = const()[name = tensor("attn_weights_9_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_9_cast_fp16 = matmul(transpose_x = attn_weights_9_transpose_x_0, transpose_y = attn_weights_9_transpose_y_0, x = var_481_cast_fp16, y = var_483_cast_fp16)[name = tensor("attn_weights_9_cast_fp16")]; + tensor attn_weights_11_cast_fp16 = mul(x = attn_weights_9_cast_fp16, y = var_102_to_fp16)[name = tensor("attn_weights_11_cast_fp16")]; + tensor var_489_cast_fp16 = softmax(axis = var_95, x = attn_weights_11_cast_fp16)[name = tensor("op_489_cast_fp16")]; + tensor attn_5_transpose_x_0 = const()[name = tensor("attn_5_transpose_x_0"), val = tensor(false)]; + tensor attn_5_transpose_y_0 = const()[name = tensor("attn_5_transpose_y_0"), val = tensor(true)]; + tensor attn_5_cast_fp16 = matmul(transpose_x = attn_5_transpose_x_0, transpose_y = attn_5_transpose_y_0, x = var_485_cast_fp16, y = var_489_cast_fp16)[name = tensor("attn_5_cast_fp16")]; + tensor var_493 = const()[name = tensor("op_493"), val = tensor([2, 320, 1, -1])]; + tensor input_51_cast_fp16 = reshape(shape = var_493, x = attn_5_cast_fp16)[name = tensor("input_51_cast_fp16")]; + tensor var_498 = const()[name = tensor("op_498"), val = tensor([1, 1])]; + tensor var_500 = const()[name = tensor("op_500"), val = tensor([1, 1])]; + tensor var_502_pad_type_0 = const()[name = tensor("op_502_pad_type_0"), val = tensor("custom")]; + tensor var_502_pad_0 = const()[name = tensor("op_502_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19393280)))]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19598144)))]; + tensor var_502_cast_fp16 = conv(bias = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_500, groups = var_111, pad = var_502_pad_0, pad_type = var_502_pad_type_0, strides = var_498, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_51_cast_fp16)[name = tensor("op_502_cast_fp16")]; + tensor inputs_9_cast_fp16 = add(x = var_502_cast_fp16, y = inputs_7_cast_fp16)[name = tensor("inputs_9_cast_fp16")]; + tensor var_506 = const()[name = tensor("op_506"), val = tensor([1])]; + tensor channels_mean_9_cast_fp16 = reduce_mean(axes = var_506, keep_dims = var_106, x = inputs_9_cast_fp16)[name = tensor("channels_mean_9_cast_fp16")]; + tensor zero_mean_9_cast_fp16 = sub(x = inputs_9_cast_fp16, y = channels_mean_9_cast_fp16)[name = tensor("zero_mean_9_cast_fp16")]; + tensor zero_mean_sq_9_cast_fp16 = mul(x = zero_mean_9_cast_fp16, y = zero_mean_9_cast_fp16)[name = tensor("zero_mean_sq_9_cast_fp16")]; + tensor var_510 = const()[name = tensor("op_510"), val = tensor([1])]; + tensor var_511_cast_fp16 = reduce_mean(axes = var_510, keep_dims = var_106, x = zero_mean_sq_9_cast_fp16)[name = tensor("op_511_cast_fp16")]; + tensor var_512_to_fp16 = const()[name = tensor("op_512_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_513_cast_fp16 = add(x = var_511_cast_fp16, y = var_512_to_fp16)[name = tensor("op_513_cast_fp16")]; + tensor denom_9_epsilon_0_to_fp16 = const()[name = tensor("denom_9_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_9_cast_fp16 = rsqrt(epsilon = denom_9_epsilon_0_to_fp16, x = var_513_cast_fp16)[name = tensor("denom_9_cast_fp16")]; + tensor out_9_cast_fp16 = mul(x = zero_mean_9_cast_fp16, y = denom_9_cast_fp16)[name = tensor("out_9_cast_fp16")]; + tensor var_517_to_fp16 = const()[name = tensor("op_517_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19598848)))]; + tensor var_518_cast_fp16 = add(x = out_9_cast_fp16, y = var_517_to_fp16)[name = tensor("op_518_cast_fp16")]; + tensor var_520_to_fp16 = const()[name = tensor("op_520_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19599552)))]; + tensor hidden_states_29_cast_fp16 = mul(x = var_518_cast_fp16, y = var_520_to_fp16)[name = tensor("hidden_states_29_cast_fp16")]; + tensor var_527 = const()[name = tensor("op_527"), val = tensor([1, 1])]; + tensor var_529 = const()[name = tensor("op_529"), val = tensor([1, 1])]; + tensor q_7_pad_type_0 = const()[name = tensor("q_7_pad_type_0"), val = tensor("custom")]; + tensor q_7_pad_0 = const()[name = tensor("q_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19600256)))]; + tensor q_7_cast_fp16 = conv(dilations = var_529, groups = var_111, pad = q_7_pad_0, pad_type = q_7_pad_type_0, strides = var_527, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_29_cast_fp16)[name = tensor("q_7_cast_fp16")]; + tensor var_533 = const()[name = tensor("op_533"), val = tensor([1, 1])]; + tensor var_535 = const()[name = tensor("op_535"), val = tensor([1, 1])]; + tensor k_7_pad_type_0 = const()[name = tensor("k_7_pad_type_0"), val = tensor("custom")]; + tensor k_7_pad_0 = const()[name = tensor("k_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19805120)))]; + tensor k_7_cast_fp16 = conv(dilations = var_535, groups = var_111, pad = k_7_pad_0, pad_type = k_7_pad_type_0, strides = var_533, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_7_cast_fp16")]; + tensor var_539 = const()[name = tensor("op_539"), val = tensor([1, 1])]; + tensor var_541 = const()[name = tensor("op_541"), val = tensor([1, 1])]; + tensor v_7_pad_type_0 = const()[name = tensor("v_7_pad_type_0"), val = tensor("custom")]; + tensor v_7_pad_0 = const()[name = tensor("v_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20460544)))]; + tensor v_7_cast_fp16 = conv(dilations = var_541, groups = var_111, pad = v_7_pad_0, pad_type = v_7_pad_type_0, strides = var_539, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_7_cast_fp16")]; + tensor var_545 = const()[name = tensor("op_545"), val = tensor([2, 5, 64, -1])]; + tensor var_546_cast_fp16 = reshape(shape = var_545, x = q_7_cast_fp16)[name = tensor("op_546_cast_fp16")]; + tensor var_547 = const()[name = tensor("op_547"), val = tensor([2, 5, 64, -1])]; + tensor var_548_cast_fp16 = reshape(shape = var_547, x = k_7_cast_fp16)[name = tensor("op_548_cast_fp16")]; + tensor var_549 = const()[name = tensor("op_549"), val = tensor([2, 5, 64, -1])]; + tensor var_550_cast_fp16 = reshape(shape = var_549, x = v_7_cast_fp16)[name = tensor("op_550_cast_fp16")]; + tensor attn_weights_13_transpose_x_0 = const()[name = tensor("attn_weights_13_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_13_transpose_y_0 = const()[name = tensor("attn_weights_13_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_13_cast_fp16 = matmul(transpose_x = attn_weights_13_transpose_x_0, transpose_y = attn_weights_13_transpose_y_0, x = var_546_cast_fp16, y = var_548_cast_fp16)[name = tensor("attn_weights_13_cast_fp16")]; + tensor attn_weights_15_cast_fp16 = mul(x = attn_weights_13_cast_fp16, y = var_102_to_fp16)[name = tensor("attn_weights_15_cast_fp16")]; + tensor var_554_cast_fp16 = softmax(axis = var_95, x = attn_weights_15_cast_fp16)[name = tensor("op_554_cast_fp16")]; + tensor attn_7_transpose_x_0 = const()[name = tensor("attn_7_transpose_x_0"), val = tensor(false)]; + tensor attn_7_transpose_y_0 = const()[name = tensor("attn_7_transpose_y_0"), val = tensor(true)]; + tensor attn_7_cast_fp16 = matmul(transpose_x = attn_7_transpose_x_0, transpose_y = attn_7_transpose_y_0, x = var_550_cast_fp16, y = var_554_cast_fp16)[name = tensor("attn_7_cast_fp16")]; + tensor var_558 = const()[name = tensor("op_558"), val = tensor([2, 320, 1, -1])]; + tensor input_53_cast_fp16 = reshape(shape = var_558, x = attn_7_cast_fp16)[name = tensor("input_53_cast_fp16")]; + tensor var_563 = const()[name = tensor("op_563"), val = tensor([1, 1])]; + tensor var_565 = const()[name = tensor("op_565"), val = tensor([1, 1])]; + tensor var_567_pad_type_0 = const()[name = tensor("op_567_pad_type_0"), val = tensor("custom")]; + tensor var_567_pad_0 = const()[name = tensor("op_567_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21115968)))]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21320832)))]; + tensor var_567_cast_fp16 = conv(bias = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_565, groups = var_111, pad = var_567_pad_0, pad_type = var_567_pad_type_0, strides = var_563, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_53_cast_fp16)[name = tensor("op_567_cast_fp16")]; + tensor inputs_11_cast_fp16 = add(x = var_567_cast_fp16, y = inputs_9_cast_fp16)[name = tensor("inputs_11_cast_fp16")]; + tensor var_571 = const()[name = tensor("op_571"), val = tensor([1])]; + tensor channels_mean_11_cast_fp16 = reduce_mean(axes = var_571, keep_dims = var_106, x = inputs_11_cast_fp16)[name = tensor("channels_mean_11_cast_fp16")]; + tensor zero_mean_11_cast_fp16 = sub(x = inputs_11_cast_fp16, y = channels_mean_11_cast_fp16)[name = tensor("zero_mean_11_cast_fp16")]; + tensor zero_mean_sq_11_cast_fp16 = mul(x = zero_mean_11_cast_fp16, y = zero_mean_11_cast_fp16)[name = tensor("zero_mean_sq_11_cast_fp16")]; + tensor var_575 = const()[name = tensor("op_575"), val = tensor([1])]; + tensor var_576_cast_fp16 = reduce_mean(axes = var_575, keep_dims = var_106, x = zero_mean_sq_11_cast_fp16)[name = tensor("op_576_cast_fp16")]; + tensor var_577_to_fp16 = const()[name = tensor("op_577_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_578_cast_fp16 = add(x = var_576_cast_fp16, y = var_577_to_fp16)[name = tensor("op_578_cast_fp16")]; + tensor denom_11_epsilon_0_to_fp16 = const()[name = tensor("denom_11_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_11_cast_fp16 = rsqrt(epsilon = denom_11_epsilon_0_to_fp16, x = var_578_cast_fp16)[name = tensor("denom_11_cast_fp16")]; + tensor out_11_cast_fp16 = mul(x = zero_mean_11_cast_fp16, y = denom_11_cast_fp16)[name = tensor("out_11_cast_fp16")]; + tensor var_582_to_fp16 = const()[name = tensor("op_582_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21321536)))]; + tensor var_583_cast_fp16 = add(x = out_11_cast_fp16, y = var_582_to_fp16)[name = tensor("op_583_cast_fp16")]; + tensor var_585_to_fp16 = const()[name = tensor("op_585_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21322240)))]; + tensor input_55_cast_fp16 = mul(x = var_583_cast_fp16, y = var_585_to_fp16)[name = tensor("input_55_cast_fp16")]; + tensor var_593 = const()[name = tensor("op_593"), val = tensor([1, 1])]; + tensor var_595 = const()[name = tensor("op_595"), val = tensor([1, 1])]; + tensor var_597_pad_type_0 = const()[name = tensor("op_597_pad_type_0"), val = tensor("custom")]; + tensor var_597_pad_0 = const()[name = tensor("op_597_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21322944)))]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22961408)))]; + tensor var_597_cast_fp16 = conv(bias = down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_595, groups = var_111, pad = var_597_pad_0, pad_type = var_597_pad_type_0, strides = var_593, weight = down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_55_cast_fp16)[name = tensor("op_597_cast_fp16")]; + tensor var_598_split_sizes_0 = const()[name = tensor("op_598_split_sizes_0"), val = tensor([1280, 1280])]; + tensor var_598_axis_0 = const()[name = tensor("op_598_axis_0"), val = tensor(1)]; + tensor var_598_cast_fp16_0, tensor var_598_cast_fp16_1 = split(axis = var_598_axis_0, split_sizes = var_598_split_sizes_0, x = var_597_cast_fp16)[name = tensor("op_598_cast_fp16")]; + tensor var_600_mode_0 = const()[name = tensor("op_600_mode_0"), val = tensor("EXACT")]; + tensor var_600_cast_fp16 = gelu(mode = var_600_mode_0, x = var_598_cast_fp16_1)[name = tensor("op_600_cast_fp16")]; + tensor input_57_cast_fp16 = mul(x = var_598_cast_fp16_0, y = var_600_cast_fp16)[name = tensor("input_57_cast_fp16")]; + tensor var_604 = const()[name = tensor("op_604"), val = tensor([1, 1])]; + tensor var_606 = const()[name = tensor("op_606"), val = tensor([1, 1])]; + tensor var_608_pad_type_0 = const()[name = tensor("op_608_pad_type_0"), val = tensor("custom")]; + tensor var_608_pad_0 = const()[name = tensor("op_608_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22966592)))]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23785856)))]; + tensor var_608_cast_fp16 = conv(bias = down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_606, groups = var_111, pad = var_608_pad_0, pad_type = var_608_pad_type_0, strides = var_604, weight = down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_57_cast_fp16)[name = tensor("op_608_cast_fp16")]; + tensor hidden_states_33_cast_fp16 = add(x = var_608_cast_fp16, y = inputs_11_cast_fp16)[name = tensor("hidden_states_33_cast_fp16")]; + tensor var_610 = const()[name = tensor("op_610"), val = tensor([2, 320, 48, 80])]; + tensor input_59_cast_fp16 = reshape(shape = var_610, x = hidden_states_33_cast_fp16)[name = tensor("input_59_cast_fp16")]; + tensor var_614 = const()[name = tensor("op_614"), val = tensor([1, 1])]; + tensor var_616 = const()[name = tensor("op_616"), val = tensor([1, 1])]; + tensor hidden_states_35_pad_type_0 = const()[name = tensor("hidden_states_35_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_35_pad_0 = const()[name = tensor("hidden_states_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_proj_out_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23786560)))]; + tensor down_blocks_0_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23991424)))]; + tensor hidden_states_35_cast_fp16 = conv(bias = down_blocks_0_attentions_1_proj_out_bias_to_fp16, dilations = var_616, groups = var_111, pad = hidden_states_35_pad_0, pad_type = hidden_states_35_pad_type_0, strides = var_614, weight = down_blocks_0_attentions_1_proj_out_weight_to_fp16, x = input_59_cast_fp16)[name = tensor("hidden_states_35_cast_fp16")]; + tensor input_61_cast_fp16 = add(x = hidden_states_35_cast_fp16, y = hidden_states_23_cast_fp16)[name = tensor("input_61_cast_fp16")]; + tensor var_623 = const()[name = tensor("op_623"), val = tensor([2, 2])]; + tensor var_625 = const()[name = tensor("op_625"), val = tensor([1, 1])]; + tensor input_63_pad_type_0 = const()[name = tensor("input_63_pad_type_0"), val = tensor("custom")]; + tensor input_63_pad_0 = const()[name = tensor("input_63_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_downsamplers_0_conv_weight_to_fp16 = const()[name = tensor("down_blocks_0_downsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23992128)))]; + tensor down_blocks_0_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor("down_blocks_0_downsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25835392)))]; + tensor input_63_cast_fp16 = conv(bias = down_blocks_0_downsamplers_0_conv_bias_to_fp16, dilations = var_625, groups = var_111, pad = input_63_pad_0, pad_type = input_63_pad_type_0, strides = var_623, weight = down_blocks_0_downsamplers_0_conv_weight_to_fp16, x = input_61_cast_fp16)[name = tensor("input_63_cast_fp16")]; + tensor var_633 = const()[name = tensor("op_633"), val = tensor(3)]; + tensor var_644 = const()[name = tensor("op_644"), val = tensor(true)]; + tensor var_649 = const()[name = tensor("op_649"), val = tensor(1)]; + tensor reshape_24_shape_0 = const()[name = tensor("reshape_24_shape_0"), val = tensor([2, 32, 10, 24, 40])]; + tensor reshape_24_cast_fp16 = reshape(shape = reshape_24_shape_0, x = input_63_cast_fp16)[name = tensor("reshape_24_cast_fp16")]; + tensor reduce_mean_18_axes_0 = const()[name = tensor("reduce_mean_18_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_18_keep_dims_0 = const()[name = tensor("reduce_mean_18_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_18_cast_fp16 = reduce_mean(axes = reduce_mean_18_axes_0, keep_dims = reduce_mean_18_keep_dims_0, x = reshape_24_cast_fp16)[name = tensor("reduce_mean_18_cast_fp16")]; + tensor sub_12_cast_fp16 = sub(x = reshape_24_cast_fp16, y = reduce_mean_18_cast_fp16)[name = tensor("sub_12_cast_fp16")]; + tensor square_6_cast_fp16 = square(x = sub_12_cast_fp16)[name = tensor("square_6_cast_fp16")]; + tensor reduce_mean_20_axes_0 = const()[name = tensor("reduce_mean_20_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_20_keep_dims_0 = const()[name = tensor("reduce_mean_20_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_20_cast_fp16 = reduce_mean(axes = reduce_mean_20_axes_0, keep_dims = reduce_mean_20_keep_dims_0, x = square_6_cast_fp16)[name = tensor("reduce_mean_20_cast_fp16")]; + tensor add_12_y_0_to_fp16 = const()[name = tensor("add_12_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_12_cast_fp16 = add(x = reduce_mean_20_cast_fp16, y = add_12_y_0_to_fp16)[name = tensor("add_12_cast_fp16")]; + tensor sqrt_6_cast_fp16 = sqrt(x = add_12_cast_fp16)[name = tensor("sqrt_6_cast_fp16")]; + tensor real_div_6_cast_fp16 = real_div(x = sub_12_cast_fp16, y = sqrt_6_cast_fp16)[name = tensor("real_div_6_cast_fp16")]; + tensor reshape_25_shape_0 = const()[name = tensor("reshape_25_shape_0"), val = tensor([2, 320, 24, 40])]; + tensor reshape_25_cast_fp16 = reshape(shape = reshape_25_shape_0, x = real_div_6_cast_fp16)[name = tensor("reshape_25_cast_fp16")]; + tensor add_13_gamma_0_to_fp16 = const()[name = tensor("add_13_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25836096)))]; + tensor add_13_beta_0_to_fp16 = const()[name = tensor("add_13_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25836800)))]; + tensor add_13_epsilon_0_to_fp16 = const()[name = tensor("add_13_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_13_cast_fp16 = batch_norm(beta = add_13_beta_0_to_fp16, epsilon = add_13_epsilon_0_to_fp16, gamma = add_13_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_25_cast_fp16)[name = tensor("add_13_cast_fp16")]; + tensor input_67_cast_fp16 = silu(x = add_13_cast_fp16)[name = tensor("input_67_cast_fp16")]; + tensor var_672 = const()[name = tensor("op_672"), val = tensor([1, 1])]; + tensor var_674 = const()[name = tensor("op_674"), val = tensor([1, 1])]; + tensor hidden_states_37_pad_type_0 = const()[name = tensor("hidden_states_37_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_37_pad_0 = const()[name = tensor("hidden_states_37_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25837504)))]; + tensor down_blocks_1_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29523968)))]; + tensor hidden_states_37_cast_fp16 = conv(bias = down_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = var_674, groups = var_649, pad = hidden_states_37_pad_0, pad_type = hidden_states_37_pad_type_0, strides = var_672, weight = down_blocks_1_resnets_0_conv1_weight_to_fp16, x = input_67_cast_fp16)[name = tensor("hidden_states_37_cast_fp16")]; + tensor var_680 = const()[name = tensor("op_680"), val = tensor([1, 1])]; + tensor var_682 = const()[name = tensor("op_682"), val = tensor([1, 1])]; + tensor temb_5_pad_type_0 = const()[name = tensor("temb_5_pad_type_0"), val = tensor("custom")]; + tensor temb_5_pad_0 = const()[name = tensor("temb_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29525312)))]; + tensor down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31163776)))]; + tensor temb_5_cast_fp16 = conv(bias = down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_682, groups = var_649, pad = temb_5_pad_0, pad_type = temb_5_pad_type_0, strides = var_680, weight = down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16)[name = tensor("temb_5_cast_fp16")]; + tensor input_71_cast_fp16 = add(x = hidden_states_37_cast_fp16, y = temb_5_cast_fp16)[name = tensor("input_71_cast_fp16")]; + tensor reshape_28_shape_0 = const()[name = tensor("reshape_28_shape_0"), val = tensor([2, 32, 20, 24, 40])]; + tensor reshape_28_cast_fp16 = reshape(shape = reshape_28_shape_0, x = input_71_cast_fp16)[name = tensor("reshape_28_cast_fp16")]; + tensor reduce_mean_21_axes_0 = const()[name = tensor("reduce_mean_21_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_21_keep_dims_0 = const()[name = tensor("reduce_mean_21_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_21_cast_fp16 = reduce_mean(axes = reduce_mean_21_axes_0, keep_dims = reduce_mean_21_keep_dims_0, x = reshape_28_cast_fp16)[name = tensor("reduce_mean_21_cast_fp16")]; + tensor sub_14_cast_fp16 = sub(x = reshape_28_cast_fp16, y = reduce_mean_21_cast_fp16)[name = tensor("sub_14_cast_fp16")]; + tensor square_7_cast_fp16 = square(x = sub_14_cast_fp16)[name = tensor("square_7_cast_fp16")]; + tensor reduce_mean_23_axes_0 = const()[name = tensor("reduce_mean_23_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_23_keep_dims_0 = const()[name = tensor("reduce_mean_23_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_23_cast_fp16 = reduce_mean(axes = reduce_mean_23_axes_0, keep_dims = reduce_mean_23_keep_dims_0, x = square_7_cast_fp16)[name = tensor("reduce_mean_23_cast_fp16")]; + tensor add_14_y_0_to_fp16 = const()[name = tensor("add_14_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_14_cast_fp16 = add(x = reduce_mean_23_cast_fp16, y = add_14_y_0_to_fp16)[name = tensor("add_14_cast_fp16")]; + tensor sqrt_7_cast_fp16 = sqrt(x = add_14_cast_fp16)[name = tensor("sqrt_7_cast_fp16")]; + tensor real_div_7_cast_fp16 = real_div(x = sub_14_cast_fp16, y = sqrt_7_cast_fp16)[name = tensor("real_div_7_cast_fp16")]; + tensor reshape_29_shape_0 = const()[name = tensor("reshape_29_shape_0"), val = tensor([2, 640, 24, 40])]; + tensor reshape_29_cast_fp16 = reshape(shape = reshape_29_shape_0, x = real_div_7_cast_fp16)[name = tensor("reshape_29_cast_fp16")]; + tensor add_15_mean_0_to_fp16 = const()[name = tensor("add_15_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31165120)))]; + tensor add_15_variance_0_to_fp16 = const()[name = tensor("add_15_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31166464)))]; + tensor add_15_gamma_0_to_fp16 = const()[name = tensor("add_15_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31167808)))]; + tensor add_15_beta_0_to_fp16 = const()[name = tensor("add_15_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31169152)))]; + tensor add_15_epsilon_0_to_fp16 = const()[name = tensor("add_15_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_15_cast_fp16 = batch_norm(beta = add_15_beta_0_to_fp16, epsilon = add_15_epsilon_0_to_fp16, gamma = add_15_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_29_cast_fp16)[name = tensor("add_15_cast_fp16")]; + tensor input_75_cast_fp16 = silu(x = add_15_cast_fp16)[name = tensor("input_75_cast_fp16")]; + tensor var_692 = const()[name = tensor("op_692"), val = tensor([1, 1])]; + tensor var_694 = const()[name = tensor("op_694"), val = tensor([1, 1])]; + tensor hidden_states_39_pad_type_0 = const()[name = tensor("hidden_states_39_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_39_pad_0 = const()[name = tensor("hidden_states_39_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31170496)))]; + tensor down_blocks_1_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38543360)))]; + tensor hidden_states_39_cast_fp16 = conv(bias = down_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = var_694, groups = var_649, pad = hidden_states_39_pad_0, pad_type = hidden_states_39_pad_type_0, strides = var_692, weight = down_blocks_1_resnets_0_conv2_weight_to_fp16, x = input_75_cast_fp16)[name = tensor("hidden_states_39_cast_fp16")]; + tensor var_699 = const()[name = tensor("op_699"), val = tensor([1, 1])]; + tensor var_701 = const()[name = tensor("op_701"), val = tensor([1, 1])]; + tensor x_1_pad_type_0 = const()[name = tensor("x_1_pad_type_0"), val = tensor("custom")]; + tensor x_1_pad_0 = const()[name = tensor("x_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38544704)))]; + tensor down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38954368)))]; + tensor x_1_cast_fp16 = conv(bias = down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_701, groups = var_649, pad = x_1_pad_0, pad_type = x_1_pad_type_0, strides = var_699, weight = down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16, x = input_63_cast_fp16)[name = tensor("x_1_cast_fp16")]; + tensor hidden_states_41_cast_fp16 = add(x = x_1_cast_fp16, y = hidden_states_39_cast_fp16)[name = tensor("hidden_states_41_cast_fp16")]; + tensor reshape_32_shape_0 = const()[name = tensor("reshape_32_shape_0"), val = tensor([2, 32, 20, 24, 40])]; + tensor reshape_32_cast_fp16 = reshape(shape = reshape_32_shape_0, x = hidden_states_41_cast_fp16)[name = tensor("reshape_32_cast_fp16")]; + tensor reduce_mean_24_axes_0 = const()[name = tensor("reduce_mean_24_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_24_keep_dims_0 = const()[name = tensor("reduce_mean_24_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_24_cast_fp16 = reduce_mean(axes = reduce_mean_24_axes_0, keep_dims = reduce_mean_24_keep_dims_0, x = reshape_32_cast_fp16)[name = tensor("reduce_mean_24_cast_fp16")]; + tensor sub_16_cast_fp16 = sub(x = reshape_32_cast_fp16, y = reduce_mean_24_cast_fp16)[name = tensor("sub_16_cast_fp16")]; + tensor square_8_cast_fp16 = square(x = sub_16_cast_fp16)[name = tensor("square_8_cast_fp16")]; + tensor reduce_mean_26_axes_0 = const()[name = tensor("reduce_mean_26_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_26_keep_dims_0 = const()[name = tensor("reduce_mean_26_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_26_cast_fp16 = reduce_mean(axes = reduce_mean_26_axes_0, keep_dims = reduce_mean_26_keep_dims_0, x = square_8_cast_fp16)[name = tensor("reduce_mean_26_cast_fp16")]; + tensor add_16_y_0_to_fp16 = const()[name = tensor("add_16_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_16_cast_fp16 = add(x = reduce_mean_26_cast_fp16, y = add_16_y_0_to_fp16)[name = tensor("add_16_cast_fp16")]; + tensor sqrt_8_cast_fp16 = sqrt(x = add_16_cast_fp16)[name = tensor("sqrt_8_cast_fp16")]; + tensor real_div_8_cast_fp16 = real_div(x = sub_16_cast_fp16, y = sqrt_8_cast_fp16)[name = tensor("real_div_8_cast_fp16")]; + tensor reshape_33_shape_0 = const()[name = tensor("reshape_33_shape_0"), val = tensor([2, 640, 24, 40])]; + tensor reshape_33_cast_fp16 = reshape(shape = reshape_33_shape_0, x = real_div_8_cast_fp16)[name = tensor("reshape_33_cast_fp16")]; + tensor add_17_gamma_0_to_fp16 = const()[name = tensor("add_17_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38955712)))]; + tensor add_17_beta_0_to_fp16 = const()[name = tensor("add_17_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38957056)))]; + tensor add_17_epsilon_0_to_fp16 = const()[name = tensor("add_17_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_17_cast_fp16 = batch_norm(beta = add_17_beta_0_to_fp16, epsilon = add_17_epsilon_0_to_fp16, gamma = add_17_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_33_cast_fp16)[name = tensor("add_17_cast_fp16")]; + tensor var_721 = const()[name = tensor("op_721"), val = tensor([1, 1])]; + tensor var_723 = const()[name = tensor("op_723"), val = tensor([1, 1])]; + tensor hidden_states_43_pad_type_0 = const()[name = tensor("hidden_states_43_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_43_pad_0 = const()[name = tensor("hidden_states_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38958400)))]; + tensor down_blocks_1_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39777664)))]; + tensor hidden_states_43_cast_fp16 = conv(bias = down_blocks_1_attentions_0_proj_in_bias_to_fp16, dilations = var_723, groups = var_649, pad = hidden_states_43_pad_0, pad_type = hidden_states_43_pad_type_0, strides = var_721, weight = down_blocks_1_attentions_0_proj_in_weight_to_fp16, x = add_17_cast_fp16)[name = tensor("hidden_states_43_cast_fp16")]; + tensor var_728 = const()[name = tensor("op_728"), val = tensor([2, 640, 1, 960])]; + tensor inputs_13_cast_fp16 = reshape(shape = var_728, x = hidden_states_43_cast_fp16)[name = tensor("inputs_13_cast_fp16")]; + tensor var_738 = const()[name = tensor("op_738"), val = tensor([1])]; + tensor channels_mean_13_cast_fp16 = reduce_mean(axes = var_738, keep_dims = var_644, x = inputs_13_cast_fp16)[name = tensor("channels_mean_13_cast_fp16")]; + tensor zero_mean_13_cast_fp16 = sub(x = inputs_13_cast_fp16, y = channels_mean_13_cast_fp16)[name = tensor("zero_mean_13_cast_fp16")]; + tensor zero_mean_sq_13_cast_fp16 = mul(x = zero_mean_13_cast_fp16, y = zero_mean_13_cast_fp16)[name = tensor("zero_mean_sq_13_cast_fp16")]; + tensor var_742 = const()[name = tensor("op_742"), val = tensor([1])]; + tensor var_743_cast_fp16 = reduce_mean(axes = var_742, keep_dims = var_644, x = zero_mean_sq_13_cast_fp16)[name = tensor("op_743_cast_fp16")]; + tensor var_744_to_fp16 = const()[name = tensor("op_744_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_745_cast_fp16 = add(x = var_743_cast_fp16, y = var_744_to_fp16)[name = tensor("op_745_cast_fp16")]; + tensor denom_13_epsilon_0_to_fp16 = const()[name = tensor("denom_13_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_13_cast_fp16 = rsqrt(epsilon = denom_13_epsilon_0_to_fp16, x = var_745_cast_fp16)[name = tensor("denom_13_cast_fp16")]; + tensor out_13_cast_fp16 = mul(x = zero_mean_13_cast_fp16, y = denom_13_cast_fp16)[name = tensor("out_13_cast_fp16")]; + tensor var_749_to_fp16 = const()[name = tensor("op_749_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39779008)))]; + tensor var_750_cast_fp16 = add(x = out_13_cast_fp16, y = var_749_to_fp16)[name = tensor("op_750_cast_fp16")]; + tensor var_752_to_fp16 = const()[name = tensor("op_752_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39780352)))]; + tensor hidden_states_45_cast_fp16 = mul(x = var_750_cast_fp16, y = var_752_to_fp16)[name = tensor("hidden_states_45_cast_fp16")]; + tensor var_759 = const()[name = tensor("op_759"), val = tensor([1, 1])]; + tensor var_761 = const()[name = tensor("op_761"), val = tensor([1, 1])]; + tensor q_9_pad_type_0 = const()[name = tensor("q_9_pad_type_0"), val = tensor("custom")]; + tensor q_9_pad_0 = const()[name = tensor("q_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39781696)))]; + tensor q_9_cast_fp16 = conv(dilations = var_761, groups = var_649, pad = q_9_pad_0, pad_type = q_9_pad_type_0, strides = var_759, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_45_cast_fp16)[name = tensor("q_9_cast_fp16")]; + tensor var_765 = const()[name = tensor("op_765"), val = tensor([1, 1])]; + tensor var_767 = const()[name = tensor("op_767"), val = tensor([1, 1])]; + tensor k_9_pad_type_0 = const()[name = tensor("k_9_pad_type_0"), val = tensor("custom")]; + tensor k_9_pad_0 = const()[name = tensor("k_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40600960)))]; + tensor k_9_cast_fp16 = conv(dilations = var_767, groups = var_649, pad = k_9_pad_0, pad_type = k_9_pad_type_0, strides = var_765, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_45_cast_fp16)[name = tensor("k_9_cast_fp16")]; + tensor var_771 = const()[name = tensor("op_771"), val = tensor([1, 1])]; + tensor var_773 = const()[name = tensor("op_773"), val = tensor([1, 1])]; + tensor v_9_pad_type_0 = const()[name = tensor("v_9_pad_type_0"), val = tensor("custom")]; + tensor v_9_pad_0 = const()[name = tensor("v_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41420224)))]; + tensor v_9_cast_fp16 = conv(dilations = var_773, groups = var_649, pad = v_9_pad_0, pad_type = v_9_pad_type_0, strides = var_771, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_45_cast_fp16)[name = tensor("v_9_cast_fp16")]; + tensor var_777 = const()[name = tensor("op_777"), val = tensor([2, 10, 64, -1])]; + tensor var_778_cast_fp16 = reshape(shape = var_777, x = q_9_cast_fp16)[name = tensor("op_778_cast_fp16")]; + tensor var_779 = const()[name = tensor("op_779"), val = tensor([2, 10, 64, -1])]; + tensor var_780_cast_fp16 = reshape(shape = var_779, x = k_9_cast_fp16)[name = tensor("op_780_cast_fp16")]; + tensor var_781 = const()[name = tensor("op_781"), val = tensor([2, 10, 64, -1])]; + tensor var_782_cast_fp16 = reshape(shape = var_781, x = v_9_cast_fp16)[name = tensor("op_782_cast_fp16")]; + tensor attn_weights_17_transpose_x_0 = const()[name = tensor("attn_weights_17_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_17_transpose_y_0 = const()[name = tensor("attn_weights_17_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_17_cast_fp16 = matmul(transpose_x = attn_weights_17_transpose_x_0, transpose_y = attn_weights_17_transpose_y_0, x = var_778_cast_fp16, y = var_780_cast_fp16)[name = tensor("attn_weights_17_cast_fp16")]; + tensor var_640_to_fp16 = const()[name = tensor("op_640_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_19_cast_fp16 = mul(x = attn_weights_17_cast_fp16, y = var_640_to_fp16)[name = tensor("attn_weights_19_cast_fp16")]; + tensor var_786_cast_fp16 = softmax(axis = var_633, x = attn_weights_19_cast_fp16)[name = tensor("op_786_cast_fp16")]; + tensor attn_9_transpose_x_0 = const()[name = tensor("attn_9_transpose_x_0"), val = tensor(false)]; + tensor attn_9_transpose_y_0 = const()[name = tensor("attn_9_transpose_y_0"), val = tensor(true)]; + tensor attn_9_cast_fp16 = matmul(transpose_x = attn_9_transpose_x_0, transpose_y = attn_9_transpose_y_0, x = var_782_cast_fp16, y = var_786_cast_fp16)[name = tensor("attn_9_cast_fp16")]; + tensor var_790 = const()[name = tensor("op_790"), val = tensor([2, 640, 1, -1])]; + tensor input_79_cast_fp16 = reshape(shape = var_790, x = attn_9_cast_fp16)[name = tensor("input_79_cast_fp16")]; + tensor var_795 = const()[name = tensor("op_795"), val = tensor([1, 1])]; + tensor var_797 = const()[name = tensor("op_797"), val = tensor([1, 1])]; + tensor var_799_pad_type_0 = const()[name = tensor("op_799_pad_type_0"), val = tensor("custom")]; + tensor var_799_pad_0 = const()[name = tensor("op_799_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42239488)))]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43058752)))]; + tensor var_799_cast_fp16 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_797, groups = var_649, pad = var_799_pad_0, pad_type = var_799_pad_type_0, strides = var_795, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_79_cast_fp16)[name = tensor("op_799_cast_fp16")]; + tensor inputs_15_cast_fp16 = add(x = var_799_cast_fp16, y = inputs_13_cast_fp16)[name = tensor("inputs_15_cast_fp16")]; + tensor var_803 = const()[name = tensor("op_803"), val = tensor([1])]; + tensor channels_mean_15_cast_fp16 = reduce_mean(axes = var_803, keep_dims = var_644, x = inputs_15_cast_fp16)[name = tensor("channels_mean_15_cast_fp16")]; + tensor zero_mean_15_cast_fp16 = sub(x = inputs_15_cast_fp16, y = channels_mean_15_cast_fp16)[name = tensor("zero_mean_15_cast_fp16")]; + tensor zero_mean_sq_15_cast_fp16 = mul(x = zero_mean_15_cast_fp16, y = zero_mean_15_cast_fp16)[name = tensor("zero_mean_sq_15_cast_fp16")]; + tensor var_807 = const()[name = tensor("op_807"), val = tensor([1])]; + tensor var_808_cast_fp16 = reduce_mean(axes = var_807, keep_dims = var_644, x = zero_mean_sq_15_cast_fp16)[name = tensor("op_808_cast_fp16")]; + tensor var_809_to_fp16 = const()[name = tensor("op_809_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_810_cast_fp16 = add(x = var_808_cast_fp16, y = var_809_to_fp16)[name = tensor("op_810_cast_fp16")]; + tensor denom_15_epsilon_0_to_fp16 = const()[name = tensor("denom_15_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_15_cast_fp16 = rsqrt(epsilon = denom_15_epsilon_0_to_fp16, x = var_810_cast_fp16)[name = tensor("denom_15_cast_fp16")]; + tensor out_15_cast_fp16 = mul(x = zero_mean_15_cast_fp16, y = denom_15_cast_fp16)[name = tensor("out_15_cast_fp16")]; + tensor var_814_to_fp16 = const()[name = tensor("op_814_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43060096)))]; + tensor var_815_cast_fp16 = add(x = out_15_cast_fp16, y = var_814_to_fp16)[name = tensor("op_815_cast_fp16")]; + tensor var_817_to_fp16 = const()[name = tensor("op_817_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43061440)))]; + tensor hidden_states_47_cast_fp16 = mul(x = var_815_cast_fp16, y = var_817_to_fp16)[name = tensor("hidden_states_47_cast_fp16")]; + tensor var_824 = const()[name = tensor("op_824"), val = tensor([1, 1])]; + tensor var_826 = const()[name = tensor("op_826"), val = tensor([1, 1])]; + tensor q_11_pad_type_0 = const()[name = tensor("q_11_pad_type_0"), val = tensor("custom")]; + tensor q_11_pad_0 = const()[name = tensor("q_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43062784)))]; + tensor q_11_cast_fp16 = conv(dilations = var_826, groups = var_649, pad = q_11_pad_0, pad_type = q_11_pad_type_0, strides = var_824, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_47_cast_fp16)[name = tensor("q_11_cast_fp16")]; + tensor var_830 = const()[name = tensor("op_830"), val = tensor([1, 1])]; + tensor var_832 = const()[name = tensor("op_832"), val = tensor([1, 1])]; + tensor k_11_pad_type_0 = const()[name = tensor("k_11_pad_type_0"), val = tensor("custom")]; + tensor k_11_pad_0 = const()[name = tensor("k_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43882048)))]; + tensor k_11_cast_fp16 = conv(dilations = var_832, groups = var_649, pad = k_11_pad_0, pad_type = k_11_pad_type_0, strides = var_830, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_11_cast_fp16")]; + tensor var_836 = const()[name = tensor("op_836"), val = tensor([1, 1])]; + tensor var_838 = const()[name = tensor("op_838"), val = tensor([1, 1])]; + tensor v_11_pad_type_0 = const()[name = tensor("v_11_pad_type_0"), val = tensor("custom")]; + tensor v_11_pad_0 = const()[name = tensor("v_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45192832)))]; + tensor v_11_cast_fp16 = conv(dilations = var_838, groups = var_649, pad = v_11_pad_0, pad_type = v_11_pad_type_0, strides = var_836, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_11_cast_fp16")]; + tensor var_842 = const()[name = tensor("op_842"), val = tensor([2, 10, 64, -1])]; + tensor var_843_cast_fp16 = reshape(shape = var_842, x = q_11_cast_fp16)[name = tensor("op_843_cast_fp16")]; + tensor var_844 = const()[name = tensor("op_844"), val = tensor([2, 10, 64, -1])]; + tensor var_845_cast_fp16 = reshape(shape = var_844, x = k_11_cast_fp16)[name = tensor("op_845_cast_fp16")]; + tensor var_846 = const()[name = tensor("op_846"), val = tensor([2, 10, 64, -1])]; + tensor var_847_cast_fp16 = reshape(shape = var_846, x = v_11_cast_fp16)[name = tensor("op_847_cast_fp16")]; + tensor attn_weights_21_transpose_x_0 = const()[name = tensor("attn_weights_21_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_21_transpose_y_0 = const()[name = tensor("attn_weights_21_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_21_cast_fp16 = matmul(transpose_x = attn_weights_21_transpose_x_0, transpose_y = attn_weights_21_transpose_y_0, x = var_843_cast_fp16, y = var_845_cast_fp16)[name = tensor("attn_weights_21_cast_fp16")]; + tensor attn_weights_23_cast_fp16 = mul(x = attn_weights_21_cast_fp16, y = var_640_to_fp16)[name = tensor("attn_weights_23_cast_fp16")]; + tensor var_851_cast_fp16 = softmax(axis = var_633, x = attn_weights_23_cast_fp16)[name = tensor("op_851_cast_fp16")]; + tensor attn_11_transpose_x_0 = const()[name = tensor("attn_11_transpose_x_0"), val = tensor(false)]; + tensor attn_11_transpose_y_0 = const()[name = tensor("attn_11_transpose_y_0"), val = tensor(true)]; + tensor attn_11_cast_fp16 = matmul(transpose_x = attn_11_transpose_x_0, transpose_y = attn_11_transpose_y_0, x = var_847_cast_fp16, y = var_851_cast_fp16)[name = tensor("attn_11_cast_fp16")]; + tensor var_855 = const()[name = tensor("op_855"), val = tensor([2, 640, 1, -1])]; + tensor input_81_cast_fp16 = reshape(shape = var_855, x = attn_11_cast_fp16)[name = tensor("input_81_cast_fp16")]; + tensor var_860 = const()[name = tensor("op_860"), val = tensor([1, 1])]; + tensor var_862 = const()[name = tensor("op_862"), val = tensor([1, 1])]; + tensor var_864_pad_type_0 = const()[name = tensor("op_864_pad_type_0"), val = tensor("custom")]; + tensor var_864_pad_0 = const()[name = tensor("op_864_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46503616)))]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47322880)))]; + tensor var_864_cast_fp16 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_862, groups = var_649, pad = var_864_pad_0, pad_type = var_864_pad_type_0, strides = var_860, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_81_cast_fp16)[name = tensor("op_864_cast_fp16")]; + tensor inputs_17_cast_fp16 = add(x = var_864_cast_fp16, y = inputs_15_cast_fp16)[name = tensor("inputs_17_cast_fp16")]; + tensor var_868 = const()[name = tensor("op_868"), val = tensor([1])]; + tensor channels_mean_17_cast_fp16 = reduce_mean(axes = var_868, keep_dims = var_644, x = inputs_17_cast_fp16)[name = tensor("channels_mean_17_cast_fp16")]; + tensor zero_mean_17_cast_fp16 = sub(x = inputs_17_cast_fp16, y = channels_mean_17_cast_fp16)[name = tensor("zero_mean_17_cast_fp16")]; + tensor zero_mean_sq_17_cast_fp16 = mul(x = zero_mean_17_cast_fp16, y = zero_mean_17_cast_fp16)[name = tensor("zero_mean_sq_17_cast_fp16")]; + tensor var_872 = const()[name = tensor("op_872"), val = tensor([1])]; + tensor var_873_cast_fp16 = reduce_mean(axes = var_872, keep_dims = var_644, x = zero_mean_sq_17_cast_fp16)[name = tensor("op_873_cast_fp16")]; + tensor var_874_to_fp16 = const()[name = tensor("op_874_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_875_cast_fp16 = add(x = var_873_cast_fp16, y = var_874_to_fp16)[name = tensor("op_875_cast_fp16")]; + tensor denom_17_epsilon_0_to_fp16 = const()[name = tensor("denom_17_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_17_cast_fp16 = rsqrt(epsilon = denom_17_epsilon_0_to_fp16, x = var_875_cast_fp16)[name = tensor("denom_17_cast_fp16")]; + tensor out_17_cast_fp16 = mul(x = zero_mean_17_cast_fp16, y = denom_17_cast_fp16)[name = tensor("out_17_cast_fp16")]; + tensor var_879_to_fp16 = const()[name = tensor("op_879_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47324224)))]; + tensor var_880_cast_fp16 = add(x = out_17_cast_fp16, y = var_879_to_fp16)[name = tensor("op_880_cast_fp16")]; + tensor var_882_to_fp16 = const()[name = tensor("op_882_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47325568)))]; + tensor input_83_cast_fp16 = mul(x = var_880_cast_fp16, y = var_882_to_fp16)[name = tensor("input_83_cast_fp16")]; + tensor var_890 = const()[name = tensor("op_890"), val = tensor([1, 1])]; + tensor var_892 = const()[name = tensor("op_892"), val = tensor([1, 1])]; + tensor var_894_pad_type_0 = const()[name = tensor("op_894_pad_type_0"), val = tensor("custom")]; + tensor var_894_pad_0 = const()[name = tensor("op_894_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47326912)))]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(53880576)))]; + tensor var_894_cast_fp16 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_892, groups = var_649, pad = var_894_pad_0, pad_type = var_894_pad_type_0, strides = var_890, weight = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_83_cast_fp16)[name = tensor("op_894_cast_fp16")]; + tensor var_895_split_sizes_0 = const()[name = tensor("op_895_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_895_axis_0 = const()[name = tensor("op_895_axis_0"), val = tensor(1)]; + tensor var_895_cast_fp16_0, tensor var_895_cast_fp16_1 = split(axis = var_895_axis_0, split_sizes = var_895_split_sizes_0, x = var_894_cast_fp16)[name = tensor("op_895_cast_fp16")]; + tensor var_897_mode_0 = const()[name = tensor("op_897_mode_0"), val = tensor("EXACT")]; + tensor var_897_cast_fp16 = gelu(mode = var_897_mode_0, x = var_895_cast_fp16_1)[name = tensor("op_897_cast_fp16")]; + tensor input_85_cast_fp16 = mul(x = var_895_cast_fp16_0, y = var_897_cast_fp16)[name = tensor("input_85_cast_fp16")]; + tensor var_901 = const()[name = tensor("op_901"), val = tensor([1, 1])]; + tensor var_903 = const()[name = tensor("op_903"), val = tensor([1, 1])]; + tensor var_905_pad_type_0 = const()[name = tensor("op_905_pad_type_0"), val = tensor("custom")]; + tensor var_905_pad_0 = const()[name = tensor("op_905_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(53890880)))]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57167744)))]; + tensor var_905_cast_fp16 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_903, groups = var_649, pad = var_905_pad_0, pad_type = var_905_pad_type_0, strides = var_901, weight = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_85_cast_fp16)[name = tensor("op_905_cast_fp16")]; + tensor hidden_states_51_cast_fp16 = add(x = var_905_cast_fp16, y = inputs_17_cast_fp16)[name = tensor("hidden_states_51_cast_fp16")]; + tensor var_907 = const()[name = tensor("op_907"), val = tensor([2, 640, 24, 40])]; + tensor input_87_cast_fp16 = reshape(shape = var_907, x = hidden_states_51_cast_fp16)[name = tensor("input_87_cast_fp16")]; + tensor var_911 = const()[name = tensor("op_911"), val = tensor([1, 1])]; + tensor var_913 = const()[name = tensor("op_913"), val = tensor([1, 1])]; + tensor hidden_states_53_pad_type_0 = const()[name = tensor("hidden_states_53_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_53_pad_0 = const()[name = tensor("hidden_states_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57169088)))]; + tensor down_blocks_1_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57988352)))]; + tensor hidden_states_53_cast_fp16 = conv(bias = down_blocks_1_attentions_0_proj_out_bias_to_fp16, dilations = var_913, groups = var_649, pad = hidden_states_53_pad_0, pad_type = hidden_states_53_pad_type_0, strides = var_911, weight = down_blocks_1_attentions_0_proj_out_weight_to_fp16, x = input_87_cast_fp16)[name = tensor("hidden_states_53_cast_fp16")]; + tensor input_89_cast_fp16 = add(x = hidden_states_53_cast_fp16, y = hidden_states_41_cast_fp16)[name = tensor("input_89_cast_fp16")]; + tensor reshape_36_shape_0 = const()[name = tensor("reshape_36_shape_0"), val = tensor([2, 32, 20, 24, 40])]; + tensor reshape_36_cast_fp16 = reshape(shape = reshape_36_shape_0, x = input_89_cast_fp16)[name = tensor("reshape_36_cast_fp16")]; + tensor reduce_mean_27_axes_0 = const()[name = tensor("reduce_mean_27_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_27_keep_dims_0 = const()[name = tensor("reduce_mean_27_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_27_cast_fp16 = reduce_mean(axes = reduce_mean_27_axes_0, keep_dims = reduce_mean_27_keep_dims_0, x = reshape_36_cast_fp16)[name = tensor("reduce_mean_27_cast_fp16")]; + tensor sub_18_cast_fp16 = sub(x = reshape_36_cast_fp16, y = reduce_mean_27_cast_fp16)[name = tensor("sub_18_cast_fp16")]; + tensor square_9_cast_fp16 = square(x = sub_18_cast_fp16)[name = tensor("square_9_cast_fp16")]; + tensor reduce_mean_29_axes_0 = const()[name = tensor("reduce_mean_29_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_29_keep_dims_0 = const()[name = tensor("reduce_mean_29_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_29_cast_fp16 = reduce_mean(axes = reduce_mean_29_axes_0, keep_dims = reduce_mean_29_keep_dims_0, x = square_9_cast_fp16)[name = tensor("reduce_mean_29_cast_fp16")]; + tensor add_18_y_0_to_fp16 = const()[name = tensor("add_18_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_18_cast_fp16 = add(x = reduce_mean_29_cast_fp16, y = add_18_y_0_to_fp16)[name = tensor("add_18_cast_fp16")]; + tensor sqrt_9_cast_fp16 = sqrt(x = add_18_cast_fp16)[name = tensor("sqrt_9_cast_fp16")]; + tensor real_div_9_cast_fp16 = real_div(x = sub_18_cast_fp16, y = sqrt_9_cast_fp16)[name = tensor("real_div_9_cast_fp16")]; + tensor reshape_37_shape_0 = const()[name = tensor("reshape_37_shape_0"), val = tensor([2, 640, 24, 40])]; + tensor reshape_37_cast_fp16 = reshape(shape = reshape_37_shape_0, x = real_div_9_cast_fp16)[name = tensor("reshape_37_cast_fp16")]; + tensor add_19_gamma_0_to_fp16 = const()[name = tensor("add_19_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57989696)))]; + tensor add_19_beta_0_to_fp16 = const()[name = tensor("add_19_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57991040)))]; + tensor add_19_epsilon_0_to_fp16 = const()[name = tensor("add_19_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_19_cast_fp16 = batch_norm(beta = add_19_beta_0_to_fp16, epsilon = add_19_epsilon_0_to_fp16, gamma = add_19_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_37_cast_fp16)[name = tensor("add_19_cast_fp16")]; + tensor input_93_cast_fp16 = silu(x = add_19_cast_fp16)[name = tensor("input_93_cast_fp16")]; + tensor var_928 = const()[name = tensor("op_928"), val = tensor([1, 1])]; + tensor var_930 = const()[name = tensor("op_930"), val = tensor([1, 1])]; + tensor hidden_states_55_pad_type_0 = const()[name = tensor("hidden_states_55_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_55_pad_0 = const()[name = tensor("hidden_states_55_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57992384)))]; + tensor down_blocks_1_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65365248)))]; + tensor hidden_states_55_cast_fp16 = conv(bias = down_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = var_930, groups = var_649, pad = hidden_states_55_pad_0, pad_type = hidden_states_55_pad_type_0, strides = var_928, weight = down_blocks_1_resnets_1_conv1_weight_to_fp16, x = input_93_cast_fp16)[name = tensor("hidden_states_55_cast_fp16")]; + tensor var_936 = const()[name = tensor("op_936"), val = tensor([1, 1])]; + tensor var_938 = const()[name = tensor("op_938"), val = tensor([1, 1])]; + tensor temb_7_pad_type_0 = const()[name = tensor("temb_7_pad_type_0"), val = tensor("custom")]; + tensor temb_7_pad_0 = const()[name = tensor("temb_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65366592)))]; + tensor down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67005056)))]; + tensor temb_7_cast_fp16 = conv(bias = down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_938, groups = var_649, pad = temb_7_pad_0, pad_type = temb_7_pad_type_0, strides = var_936, weight = down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16)[name = tensor("temb_7_cast_fp16")]; + tensor input_97_cast_fp16 = add(x = hidden_states_55_cast_fp16, y = temb_7_cast_fp16)[name = tensor("input_97_cast_fp16")]; + tensor reshape_40_shape_0 = const()[name = tensor("reshape_40_shape_0"), val = tensor([2, 32, 20, 24, 40])]; + tensor reshape_40_cast_fp16 = reshape(shape = reshape_40_shape_0, x = input_97_cast_fp16)[name = tensor("reshape_40_cast_fp16")]; + tensor reduce_mean_30_axes_0 = const()[name = tensor("reduce_mean_30_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_30_keep_dims_0 = const()[name = tensor("reduce_mean_30_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_30_cast_fp16 = reduce_mean(axes = reduce_mean_30_axes_0, keep_dims = reduce_mean_30_keep_dims_0, x = reshape_40_cast_fp16)[name = tensor("reduce_mean_30_cast_fp16")]; + tensor sub_20_cast_fp16 = sub(x = reshape_40_cast_fp16, y = reduce_mean_30_cast_fp16)[name = tensor("sub_20_cast_fp16")]; + tensor square_10_cast_fp16 = square(x = sub_20_cast_fp16)[name = tensor("square_10_cast_fp16")]; + tensor reduce_mean_32_axes_0 = const()[name = tensor("reduce_mean_32_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_32_keep_dims_0 = const()[name = tensor("reduce_mean_32_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_32_cast_fp16 = reduce_mean(axes = reduce_mean_32_axes_0, keep_dims = reduce_mean_32_keep_dims_0, x = square_10_cast_fp16)[name = tensor("reduce_mean_32_cast_fp16")]; + tensor add_20_y_0_to_fp16 = const()[name = tensor("add_20_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_20_cast_fp16 = add(x = reduce_mean_32_cast_fp16, y = add_20_y_0_to_fp16)[name = tensor("add_20_cast_fp16")]; + tensor sqrt_10_cast_fp16 = sqrt(x = add_20_cast_fp16)[name = tensor("sqrt_10_cast_fp16")]; + tensor real_div_10_cast_fp16 = real_div(x = sub_20_cast_fp16, y = sqrt_10_cast_fp16)[name = tensor("real_div_10_cast_fp16")]; + tensor reshape_41_shape_0 = const()[name = tensor("reshape_41_shape_0"), val = tensor([2, 640, 24, 40])]; + tensor reshape_41_cast_fp16 = reshape(shape = reshape_41_shape_0, x = real_div_10_cast_fp16)[name = tensor("reshape_41_cast_fp16")]; + tensor add_21_gamma_0_to_fp16 = const()[name = tensor("add_21_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67006400)))]; + tensor add_21_beta_0_to_fp16 = const()[name = tensor("add_21_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67007744)))]; + tensor add_21_epsilon_0_to_fp16 = const()[name = tensor("add_21_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_21_cast_fp16 = batch_norm(beta = add_21_beta_0_to_fp16, epsilon = add_21_epsilon_0_to_fp16, gamma = add_21_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_41_cast_fp16)[name = tensor("add_21_cast_fp16")]; + tensor input_101_cast_fp16 = silu(x = add_21_cast_fp16)[name = tensor("input_101_cast_fp16")]; + tensor var_948 = const()[name = tensor("op_948"), val = tensor([1, 1])]; + tensor var_950 = const()[name = tensor("op_950"), val = tensor([1, 1])]; + tensor hidden_states_57_pad_type_0 = const()[name = tensor("hidden_states_57_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_57_pad_0 = const()[name = tensor("hidden_states_57_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67009088)))]; + tensor down_blocks_1_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74381952)))]; + tensor hidden_states_57_cast_fp16 = conv(bias = down_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = var_950, groups = var_649, pad = hidden_states_57_pad_0, pad_type = hidden_states_57_pad_type_0, strides = var_948, weight = down_blocks_1_resnets_1_conv2_weight_to_fp16, x = input_101_cast_fp16)[name = tensor("hidden_states_57_cast_fp16")]; + tensor hidden_states_59_cast_fp16 = add(x = input_89_cast_fp16, y = hidden_states_57_cast_fp16)[name = tensor("hidden_states_59_cast_fp16")]; + tensor reshape_44_shape_0 = const()[name = tensor("reshape_44_shape_0"), val = tensor([2, 32, 20, 24, 40])]; + tensor reshape_44_cast_fp16 = reshape(shape = reshape_44_shape_0, x = hidden_states_59_cast_fp16)[name = tensor("reshape_44_cast_fp16")]; + tensor reduce_mean_33_axes_0 = const()[name = tensor("reduce_mean_33_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_33_keep_dims_0 = const()[name = tensor("reduce_mean_33_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_33_cast_fp16 = reduce_mean(axes = reduce_mean_33_axes_0, keep_dims = reduce_mean_33_keep_dims_0, x = reshape_44_cast_fp16)[name = tensor("reduce_mean_33_cast_fp16")]; + tensor sub_22_cast_fp16 = sub(x = reshape_44_cast_fp16, y = reduce_mean_33_cast_fp16)[name = tensor("sub_22_cast_fp16")]; + tensor square_11_cast_fp16 = square(x = sub_22_cast_fp16)[name = tensor("square_11_cast_fp16")]; + tensor reduce_mean_35_axes_0 = const()[name = tensor("reduce_mean_35_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_35_keep_dims_0 = const()[name = tensor("reduce_mean_35_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_35_cast_fp16 = reduce_mean(axes = reduce_mean_35_axes_0, keep_dims = reduce_mean_35_keep_dims_0, x = square_11_cast_fp16)[name = tensor("reduce_mean_35_cast_fp16")]; + tensor add_22_y_0_to_fp16 = const()[name = tensor("add_22_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_22_cast_fp16 = add(x = reduce_mean_35_cast_fp16, y = add_22_y_0_to_fp16)[name = tensor("add_22_cast_fp16")]; + tensor sqrt_11_cast_fp16 = sqrt(x = add_22_cast_fp16)[name = tensor("sqrt_11_cast_fp16")]; + tensor real_div_11_cast_fp16 = real_div(x = sub_22_cast_fp16, y = sqrt_11_cast_fp16)[name = tensor("real_div_11_cast_fp16")]; + tensor reshape_45_shape_0 = const()[name = tensor("reshape_45_shape_0"), val = tensor([2, 640, 24, 40])]; + tensor reshape_45_cast_fp16 = reshape(shape = reshape_45_shape_0, x = real_div_11_cast_fp16)[name = tensor("reshape_45_cast_fp16")]; + tensor add_23_gamma_0_to_fp16 = const()[name = tensor("add_23_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74383296)))]; + tensor add_23_beta_0_to_fp16 = const()[name = tensor("add_23_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74384640)))]; + tensor add_23_epsilon_0_to_fp16 = const()[name = tensor("add_23_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_23_cast_fp16 = batch_norm(beta = add_23_beta_0_to_fp16, epsilon = add_23_epsilon_0_to_fp16, gamma = add_23_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_45_cast_fp16)[name = tensor("add_23_cast_fp16")]; + tensor var_970 = const()[name = tensor("op_970"), val = tensor([1, 1])]; + tensor var_972 = const()[name = tensor("op_972"), val = tensor([1, 1])]; + tensor hidden_states_61_pad_type_0 = const()[name = tensor("hidden_states_61_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_61_pad_0 = const()[name = tensor("hidden_states_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_proj_in_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74385984)))]; + tensor down_blocks_1_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75205248)))]; + tensor hidden_states_61_cast_fp16 = conv(bias = down_blocks_1_attentions_1_proj_in_bias_to_fp16, dilations = var_972, groups = var_649, pad = hidden_states_61_pad_0, pad_type = hidden_states_61_pad_type_0, strides = var_970, weight = down_blocks_1_attentions_1_proj_in_weight_to_fp16, x = add_23_cast_fp16)[name = tensor("hidden_states_61_cast_fp16")]; + tensor var_977 = const()[name = tensor("op_977"), val = tensor([2, 640, 1, 960])]; + tensor inputs_19_cast_fp16 = reshape(shape = var_977, x = hidden_states_61_cast_fp16)[name = tensor("inputs_19_cast_fp16")]; + tensor var_987 = const()[name = tensor("op_987"), val = tensor([1])]; + tensor channels_mean_19_cast_fp16 = reduce_mean(axes = var_987, keep_dims = var_644, x = inputs_19_cast_fp16)[name = tensor("channels_mean_19_cast_fp16")]; + tensor zero_mean_19_cast_fp16 = sub(x = inputs_19_cast_fp16, y = channels_mean_19_cast_fp16)[name = tensor("zero_mean_19_cast_fp16")]; + tensor zero_mean_sq_19_cast_fp16 = mul(x = zero_mean_19_cast_fp16, y = zero_mean_19_cast_fp16)[name = tensor("zero_mean_sq_19_cast_fp16")]; + tensor var_991 = const()[name = tensor("op_991"), val = tensor([1])]; + tensor var_992_cast_fp16 = reduce_mean(axes = var_991, keep_dims = var_644, x = zero_mean_sq_19_cast_fp16)[name = tensor("op_992_cast_fp16")]; + tensor var_993_to_fp16 = const()[name = tensor("op_993_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_994_cast_fp16 = add(x = var_992_cast_fp16, y = var_993_to_fp16)[name = tensor("op_994_cast_fp16")]; + tensor denom_19_epsilon_0_to_fp16 = const()[name = tensor("denom_19_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_19_cast_fp16 = rsqrt(epsilon = denom_19_epsilon_0_to_fp16, x = var_994_cast_fp16)[name = tensor("denom_19_cast_fp16")]; + tensor out_19_cast_fp16 = mul(x = zero_mean_19_cast_fp16, y = denom_19_cast_fp16)[name = tensor("out_19_cast_fp16")]; + tensor var_998_to_fp16 = const()[name = tensor("op_998_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75206592)))]; + tensor var_999_cast_fp16 = add(x = out_19_cast_fp16, y = var_998_to_fp16)[name = tensor("op_999_cast_fp16")]; + tensor var_1001_to_fp16 = const()[name = tensor("op_1001_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75207936)))]; + tensor hidden_states_63_cast_fp16 = mul(x = var_999_cast_fp16, y = var_1001_to_fp16)[name = tensor("hidden_states_63_cast_fp16")]; + tensor var_1008 = const()[name = tensor("op_1008"), val = tensor([1, 1])]; + tensor var_1010 = const()[name = tensor("op_1010"), val = tensor([1, 1])]; + tensor q_13_pad_type_0 = const()[name = tensor("q_13_pad_type_0"), val = tensor("custom")]; + tensor q_13_pad_0 = const()[name = tensor("q_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75209280)))]; + tensor q_13_cast_fp16 = conv(dilations = var_1010, groups = var_649, pad = q_13_pad_0, pad_type = q_13_pad_type_0, strides = var_1008, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_63_cast_fp16)[name = tensor("q_13_cast_fp16")]; + tensor var_1014 = const()[name = tensor("op_1014"), val = tensor([1, 1])]; + tensor var_1016 = const()[name = tensor("op_1016"), val = tensor([1, 1])]; + tensor k_13_pad_type_0 = const()[name = tensor("k_13_pad_type_0"), val = tensor("custom")]; + tensor k_13_pad_0 = const()[name = tensor("k_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76028544)))]; + tensor k_13_cast_fp16 = conv(dilations = var_1016, groups = var_649, pad = k_13_pad_0, pad_type = k_13_pad_type_0, strides = var_1014, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_63_cast_fp16)[name = tensor("k_13_cast_fp16")]; + tensor var_1020 = const()[name = tensor("op_1020"), val = tensor([1, 1])]; + tensor var_1022 = const()[name = tensor("op_1022"), val = tensor([1, 1])]; + tensor v_13_pad_type_0 = const()[name = tensor("v_13_pad_type_0"), val = tensor("custom")]; + tensor v_13_pad_0 = const()[name = tensor("v_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76847808)))]; + tensor v_13_cast_fp16 = conv(dilations = var_1022, groups = var_649, pad = v_13_pad_0, pad_type = v_13_pad_type_0, strides = var_1020, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_63_cast_fp16)[name = tensor("v_13_cast_fp16")]; + tensor var_1026 = const()[name = tensor("op_1026"), val = tensor([2, 10, 64, -1])]; + tensor var_1027_cast_fp16 = reshape(shape = var_1026, x = q_13_cast_fp16)[name = tensor("op_1027_cast_fp16")]; + tensor var_1028 = const()[name = tensor("op_1028"), val = tensor([2, 10, 64, -1])]; + tensor var_1029_cast_fp16 = reshape(shape = var_1028, x = k_13_cast_fp16)[name = tensor("op_1029_cast_fp16")]; + tensor var_1030 = const()[name = tensor("op_1030"), val = tensor([2, 10, 64, -1])]; + tensor var_1031_cast_fp16 = reshape(shape = var_1030, x = v_13_cast_fp16)[name = tensor("op_1031_cast_fp16")]; + tensor attn_weights_25_transpose_x_0 = const()[name = tensor("attn_weights_25_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_25_transpose_y_0 = const()[name = tensor("attn_weights_25_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_25_cast_fp16 = matmul(transpose_x = attn_weights_25_transpose_x_0, transpose_y = attn_weights_25_transpose_y_0, x = var_1027_cast_fp16, y = var_1029_cast_fp16)[name = tensor("attn_weights_25_cast_fp16")]; + tensor attn_weights_27_cast_fp16 = mul(x = attn_weights_25_cast_fp16, y = var_640_to_fp16)[name = tensor("attn_weights_27_cast_fp16")]; + tensor var_1035_cast_fp16 = softmax(axis = var_633, x = attn_weights_27_cast_fp16)[name = tensor("op_1035_cast_fp16")]; + tensor attn_13_transpose_x_0 = const()[name = tensor("attn_13_transpose_x_0"), val = tensor(false)]; + tensor attn_13_transpose_y_0 = const()[name = tensor("attn_13_transpose_y_0"), val = tensor(true)]; + tensor attn_13_cast_fp16 = matmul(transpose_x = attn_13_transpose_x_0, transpose_y = attn_13_transpose_y_0, x = var_1031_cast_fp16, y = var_1035_cast_fp16)[name = tensor("attn_13_cast_fp16")]; + tensor var_1039 = const()[name = tensor("op_1039"), val = tensor([2, 640, 1, -1])]; + tensor input_105_cast_fp16 = reshape(shape = var_1039, x = attn_13_cast_fp16)[name = tensor("input_105_cast_fp16")]; + tensor var_1044 = const()[name = tensor("op_1044"), val = tensor([1, 1])]; + tensor var_1046 = const()[name = tensor("op_1046"), val = tensor([1, 1])]; + tensor var_1048_pad_type_0 = const()[name = tensor("op_1048_pad_type_0"), val = tensor("custom")]; + tensor var_1048_pad_0 = const()[name = tensor("op_1048_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77667072)))]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(78486336)))]; + tensor var_1048_cast_fp16 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_1046, groups = var_649, pad = var_1048_pad_0, pad_type = var_1048_pad_type_0, strides = var_1044, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_105_cast_fp16)[name = tensor("op_1048_cast_fp16")]; + tensor inputs_21_cast_fp16 = add(x = var_1048_cast_fp16, y = inputs_19_cast_fp16)[name = tensor("inputs_21_cast_fp16")]; + tensor var_1052 = const()[name = tensor("op_1052"), val = tensor([1])]; + tensor channels_mean_21_cast_fp16 = reduce_mean(axes = var_1052, keep_dims = var_644, x = inputs_21_cast_fp16)[name = tensor("channels_mean_21_cast_fp16")]; + tensor zero_mean_21_cast_fp16 = sub(x = inputs_21_cast_fp16, y = channels_mean_21_cast_fp16)[name = tensor("zero_mean_21_cast_fp16")]; + tensor zero_mean_sq_21_cast_fp16 = mul(x = zero_mean_21_cast_fp16, y = zero_mean_21_cast_fp16)[name = tensor("zero_mean_sq_21_cast_fp16")]; + tensor var_1056 = const()[name = tensor("op_1056"), val = tensor([1])]; + tensor var_1057_cast_fp16 = reduce_mean(axes = var_1056, keep_dims = var_644, x = zero_mean_sq_21_cast_fp16)[name = tensor("op_1057_cast_fp16")]; + tensor var_1058_to_fp16 = const()[name = tensor("op_1058_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1059_cast_fp16 = add(x = var_1057_cast_fp16, y = var_1058_to_fp16)[name = tensor("op_1059_cast_fp16")]; + tensor denom_21_epsilon_0_to_fp16 = const()[name = tensor("denom_21_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_21_cast_fp16 = rsqrt(epsilon = denom_21_epsilon_0_to_fp16, x = var_1059_cast_fp16)[name = tensor("denom_21_cast_fp16")]; + tensor out_21_cast_fp16 = mul(x = zero_mean_21_cast_fp16, y = denom_21_cast_fp16)[name = tensor("out_21_cast_fp16")]; + tensor var_1063_to_fp16 = const()[name = tensor("op_1063_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(78487680)))]; + tensor var_1064_cast_fp16 = add(x = out_21_cast_fp16, y = var_1063_to_fp16)[name = tensor("op_1064_cast_fp16")]; + tensor var_1066_to_fp16 = const()[name = tensor("op_1066_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(78489024)))]; + tensor hidden_states_65_cast_fp16 = mul(x = var_1064_cast_fp16, y = var_1066_to_fp16)[name = tensor("hidden_states_65_cast_fp16")]; + tensor var_1073 = const()[name = tensor("op_1073"), val = tensor([1, 1])]; + tensor var_1075 = const()[name = tensor("op_1075"), val = tensor([1, 1])]; + tensor q_15_pad_type_0 = const()[name = tensor("q_15_pad_type_0"), val = tensor("custom")]; + tensor q_15_pad_0 = const()[name = tensor("q_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(78490368)))]; + tensor q_15_cast_fp16 = conv(dilations = var_1075, groups = var_649, pad = q_15_pad_0, pad_type = q_15_pad_type_0, strides = var_1073, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_65_cast_fp16)[name = tensor("q_15_cast_fp16")]; + tensor var_1079 = const()[name = tensor("op_1079"), val = tensor([1, 1])]; + tensor var_1081 = const()[name = tensor("op_1081"), val = tensor([1, 1])]; + tensor k_15_pad_type_0 = const()[name = tensor("k_15_pad_type_0"), val = tensor("custom")]; + tensor k_15_pad_0 = const()[name = tensor("k_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79309632)))]; + tensor k_15_cast_fp16 = conv(dilations = var_1081, groups = var_649, pad = k_15_pad_0, pad_type = k_15_pad_type_0, strides = var_1079, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_15_cast_fp16")]; + tensor var_1085 = const()[name = tensor("op_1085"), val = tensor([1, 1])]; + tensor var_1087 = const()[name = tensor("op_1087"), val = tensor([1, 1])]; + tensor v_15_pad_type_0 = const()[name = tensor("v_15_pad_type_0"), val = tensor("custom")]; + tensor v_15_pad_0 = const()[name = tensor("v_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80620416)))]; + tensor v_15_cast_fp16 = conv(dilations = var_1087, groups = var_649, pad = v_15_pad_0, pad_type = v_15_pad_type_0, strides = var_1085, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_15_cast_fp16")]; + tensor var_1091 = const()[name = tensor("op_1091"), val = tensor([2, 10, 64, -1])]; + tensor var_1092_cast_fp16 = reshape(shape = var_1091, x = q_15_cast_fp16)[name = tensor("op_1092_cast_fp16")]; + tensor var_1093 = const()[name = tensor("op_1093"), val = tensor([2, 10, 64, -1])]; + tensor var_1094_cast_fp16 = reshape(shape = var_1093, x = k_15_cast_fp16)[name = tensor("op_1094_cast_fp16")]; + tensor var_1095 = const()[name = tensor("op_1095"), val = tensor([2, 10, 64, -1])]; + tensor var_1096_cast_fp16 = reshape(shape = var_1095, x = v_15_cast_fp16)[name = tensor("op_1096_cast_fp16")]; + tensor attn_weights_29_transpose_x_0 = const()[name = tensor("attn_weights_29_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_29_transpose_y_0 = const()[name = tensor("attn_weights_29_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_29_cast_fp16 = matmul(transpose_x = attn_weights_29_transpose_x_0, transpose_y = attn_weights_29_transpose_y_0, x = var_1092_cast_fp16, y = var_1094_cast_fp16)[name = tensor("attn_weights_29_cast_fp16")]; + tensor attn_weights_31_cast_fp16 = mul(x = attn_weights_29_cast_fp16, y = var_640_to_fp16)[name = tensor("attn_weights_31_cast_fp16")]; + tensor var_1100_cast_fp16 = softmax(axis = var_633, x = attn_weights_31_cast_fp16)[name = tensor("op_1100_cast_fp16")]; + tensor attn_15_transpose_x_0 = const()[name = tensor("attn_15_transpose_x_0"), val = tensor(false)]; + tensor attn_15_transpose_y_0 = const()[name = tensor("attn_15_transpose_y_0"), val = tensor(true)]; + tensor attn_15_cast_fp16 = matmul(transpose_x = attn_15_transpose_x_0, transpose_y = attn_15_transpose_y_0, x = var_1096_cast_fp16, y = var_1100_cast_fp16)[name = tensor("attn_15_cast_fp16")]; + tensor var_1104 = const()[name = tensor("op_1104"), val = tensor([2, 640, 1, -1])]; + tensor input_107_cast_fp16 = reshape(shape = var_1104, x = attn_15_cast_fp16)[name = tensor("input_107_cast_fp16")]; + tensor var_1109 = const()[name = tensor("op_1109"), val = tensor([1, 1])]; + tensor var_1111 = const()[name = tensor("op_1111"), val = tensor([1, 1])]; + tensor var_1113_pad_type_0 = const()[name = tensor("op_1113_pad_type_0"), val = tensor("custom")]; + tensor var_1113_pad_0 = const()[name = tensor("op_1113_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(81931200)))]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82750464)))]; + tensor var_1113_cast_fp16 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_1111, groups = var_649, pad = var_1113_pad_0, pad_type = var_1113_pad_type_0, strides = var_1109, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_107_cast_fp16)[name = tensor("op_1113_cast_fp16")]; + tensor inputs_23_cast_fp16 = add(x = var_1113_cast_fp16, y = inputs_21_cast_fp16)[name = tensor("inputs_23_cast_fp16")]; + tensor var_1117 = const()[name = tensor("op_1117"), val = tensor([1])]; + tensor channels_mean_23_cast_fp16 = reduce_mean(axes = var_1117, keep_dims = var_644, x = inputs_23_cast_fp16)[name = tensor("channels_mean_23_cast_fp16")]; + tensor zero_mean_23_cast_fp16 = sub(x = inputs_23_cast_fp16, y = channels_mean_23_cast_fp16)[name = tensor("zero_mean_23_cast_fp16")]; + tensor zero_mean_sq_23_cast_fp16 = mul(x = zero_mean_23_cast_fp16, y = zero_mean_23_cast_fp16)[name = tensor("zero_mean_sq_23_cast_fp16")]; + tensor var_1121 = const()[name = tensor("op_1121"), val = tensor([1])]; + tensor var_1122_cast_fp16 = reduce_mean(axes = var_1121, keep_dims = var_644, x = zero_mean_sq_23_cast_fp16)[name = tensor("op_1122_cast_fp16")]; + tensor var_1123_to_fp16 = const()[name = tensor("op_1123_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1124_cast_fp16 = add(x = var_1122_cast_fp16, y = var_1123_to_fp16)[name = tensor("op_1124_cast_fp16")]; + tensor denom_23_epsilon_0_to_fp16 = const()[name = tensor("denom_23_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_23_cast_fp16 = rsqrt(epsilon = denom_23_epsilon_0_to_fp16, x = var_1124_cast_fp16)[name = tensor("denom_23_cast_fp16")]; + tensor out_23_cast_fp16 = mul(x = zero_mean_23_cast_fp16, y = denom_23_cast_fp16)[name = tensor("out_23_cast_fp16")]; + tensor var_1128_to_fp16 = const()[name = tensor("op_1128_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82751808)))]; + tensor var_1129_cast_fp16 = add(x = out_23_cast_fp16, y = var_1128_to_fp16)[name = tensor("op_1129_cast_fp16")]; + tensor var_1131_to_fp16 = const()[name = tensor("op_1131_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82753152)))]; + tensor input_109_cast_fp16 = mul(x = var_1129_cast_fp16, y = var_1131_to_fp16)[name = tensor("input_109_cast_fp16")]; + tensor var_1139 = const()[name = tensor("op_1139"), val = tensor([1, 1])]; + tensor var_1141 = const()[name = tensor("op_1141"), val = tensor([1, 1])]; + tensor var_1143_pad_type_0 = const()[name = tensor("op_1143_pad_type_0"), val = tensor("custom")]; + tensor var_1143_pad_0 = const()[name = tensor("op_1143_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82754496)))]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89308160)))]; + tensor var_1143_cast_fp16 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_1141, groups = var_649, pad = var_1143_pad_0, pad_type = var_1143_pad_type_0, strides = var_1139, weight = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_109_cast_fp16)[name = tensor("op_1143_cast_fp16")]; + tensor var_1144_split_sizes_0 = const()[name = tensor("op_1144_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_1144_axis_0 = const()[name = tensor("op_1144_axis_0"), val = tensor(1)]; + tensor var_1144_cast_fp16_0, tensor var_1144_cast_fp16_1 = split(axis = var_1144_axis_0, split_sizes = var_1144_split_sizes_0, x = var_1143_cast_fp16)[name = tensor("op_1144_cast_fp16")]; + tensor var_1146_mode_0 = const()[name = tensor("op_1146_mode_0"), val = tensor("EXACT")]; + tensor var_1146_cast_fp16 = gelu(mode = var_1146_mode_0, x = var_1144_cast_fp16_1)[name = tensor("op_1146_cast_fp16")]; + tensor input_111_cast_fp16 = mul(x = var_1144_cast_fp16_0, y = var_1146_cast_fp16)[name = tensor("input_111_cast_fp16")]; + tensor var_1150 = const()[name = tensor("op_1150"), val = tensor([1, 1])]; + tensor var_1152 = const()[name = tensor("op_1152"), val = tensor([1, 1])]; + tensor var_1154_pad_type_0 = const()[name = tensor("op_1154_pad_type_0"), val = tensor("custom")]; + tensor var_1154_pad_0 = const()[name = tensor("op_1154_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89318464)))]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92595328)))]; + tensor var_1154_cast_fp16 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_1152, groups = var_649, pad = var_1154_pad_0, pad_type = var_1154_pad_type_0, strides = var_1150, weight = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_111_cast_fp16)[name = tensor("op_1154_cast_fp16")]; + tensor hidden_states_69_cast_fp16 = add(x = var_1154_cast_fp16, y = inputs_23_cast_fp16)[name = tensor("hidden_states_69_cast_fp16")]; + tensor var_1156 = const()[name = tensor("op_1156"), val = tensor([2, 640, 24, 40])]; + tensor input_113_cast_fp16 = reshape(shape = var_1156, x = hidden_states_69_cast_fp16)[name = tensor("input_113_cast_fp16")]; + tensor var_1160 = const()[name = tensor("op_1160"), val = tensor([1, 1])]; + tensor var_1162 = const()[name = tensor("op_1162"), val = tensor([1, 1])]; + tensor hidden_states_71_pad_type_0 = const()[name = tensor("hidden_states_71_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_71_pad_0 = const()[name = tensor("hidden_states_71_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_proj_out_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92596672)))]; + tensor down_blocks_1_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93415936)))]; + tensor hidden_states_71_cast_fp16 = conv(bias = down_blocks_1_attentions_1_proj_out_bias_to_fp16, dilations = var_1162, groups = var_649, pad = hidden_states_71_pad_0, pad_type = hidden_states_71_pad_type_0, strides = var_1160, weight = down_blocks_1_attentions_1_proj_out_weight_to_fp16, x = input_113_cast_fp16)[name = tensor("hidden_states_71_cast_fp16")]; + tensor input_115_cast_fp16 = add(x = hidden_states_71_cast_fp16, y = hidden_states_59_cast_fp16)[name = tensor("input_115_cast_fp16")]; + tensor var_1169 = const()[name = tensor("op_1169"), val = tensor([2, 2])]; + tensor var_1171 = const()[name = tensor("op_1171"), val = tensor([1, 1])]; + tensor input_117_pad_type_0 = const()[name = tensor("input_117_pad_type_0"), val = tensor("custom")]; + tensor input_117_pad_0 = const()[name = tensor("input_117_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_downsamplers_0_conv_weight_to_fp16 = const()[name = tensor("down_blocks_1_downsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93417280)))]; + tensor down_blocks_1_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor("down_blocks_1_downsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100790144)))]; + tensor input_117_cast_fp16 = conv(bias = down_blocks_1_downsamplers_0_conv_bias_to_fp16, dilations = var_1171, groups = var_649, pad = input_117_pad_0, pad_type = input_117_pad_type_0, strides = var_1169, weight = down_blocks_1_downsamplers_0_conv_weight_to_fp16, x = input_115_cast_fp16)[name = tensor("input_117_cast_fp16")]; + tensor var_1179 = const()[name = tensor("op_1179"), val = tensor(3)]; + tensor var_1190 = const()[name = tensor("op_1190"), val = tensor(true)]; + tensor var_1195 = const()[name = tensor("op_1195"), val = tensor(1)]; + tensor reshape_48_shape_0 = const()[name = tensor("reshape_48_shape_0"), val = tensor([2, 32, 20, 12, 20])]; + tensor reshape_48_cast_fp16 = reshape(shape = reshape_48_shape_0, x = input_117_cast_fp16)[name = tensor("reshape_48_cast_fp16")]; + tensor reduce_mean_36_axes_0 = const()[name = tensor("reduce_mean_36_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_36_keep_dims_0 = const()[name = tensor("reduce_mean_36_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_36_cast_fp16 = reduce_mean(axes = reduce_mean_36_axes_0, keep_dims = reduce_mean_36_keep_dims_0, x = reshape_48_cast_fp16)[name = tensor("reduce_mean_36_cast_fp16")]; + tensor sub_24_cast_fp16 = sub(x = reshape_48_cast_fp16, y = reduce_mean_36_cast_fp16)[name = tensor("sub_24_cast_fp16")]; + tensor square_12_cast_fp16 = square(x = sub_24_cast_fp16)[name = tensor("square_12_cast_fp16")]; + tensor reduce_mean_38_axes_0 = const()[name = tensor("reduce_mean_38_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_38_keep_dims_0 = const()[name = tensor("reduce_mean_38_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_38_cast_fp16 = reduce_mean(axes = reduce_mean_38_axes_0, keep_dims = reduce_mean_38_keep_dims_0, x = square_12_cast_fp16)[name = tensor("reduce_mean_38_cast_fp16")]; + tensor add_24_y_0_to_fp16 = const()[name = tensor("add_24_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_24_cast_fp16 = add(x = reduce_mean_38_cast_fp16, y = add_24_y_0_to_fp16)[name = tensor("add_24_cast_fp16")]; + tensor sqrt_12_cast_fp16 = sqrt(x = add_24_cast_fp16)[name = tensor("sqrt_12_cast_fp16")]; + tensor real_div_12_cast_fp16 = real_div(x = sub_24_cast_fp16, y = sqrt_12_cast_fp16)[name = tensor("real_div_12_cast_fp16")]; + tensor reshape_49_shape_0 = const()[name = tensor("reshape_49_shape_0"), val = tensor([2, 640, 12, 20])]; + tensor reshape_49_cast_fp16 = reshape(shape = reshape_49_shape_0, x = real_div_12_cast_fp16)[name = tensor("reshape_49_cast_fp16")]; + tensor add_25_gamma_0_to_fp16 = const()[name = tensor("add_25_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100791488)))]; + tensor add_25_beta_0_to_fp16 = const()[name = tensor("add_25_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100792832)))]; + tensor add_25_epsilon_0_to_fp16 = const()[name = tensor("add_25_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_25_cast_fp16 = batch_norm(beta = add_25_beta_0_to_fp16, epsilon = add_25_epsilon_0_to_fp16, gamma = add_25_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_49_cast_fp16)[name = tensor("add_25_cast_fp16")]; + tensor input_121_cast_fp16 = silu(x = add_25_cast_fp16)[name = tensor("input_121_cast_fp16")]; + tensor var_1218 = const()[name = tensor("op_1218"), val = tensor([1, 1])]; + tensor var_1220 = const()[name = tensor("op_1220"), val = tensor([1, 1])]; + tensor hidden_states_73_pad_type_0 = const()[name = tensor("hidden_states_73_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_73_pad_0 = const()[name = tensor("hidden_states_73_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100794176)))]; + tensor down_blocks_2_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115539840)))]; + tensor hidden_states_73_cast_fp16 = conv(bias = down_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = var_1220, groups = var_1195, pad = hidden_states_73_pad_0, pad_type = hidden_states_73_pad_type_0, strides = var_1218, weight = down_blocks_2_resnets_0_conv1_weight_to_fp16, x = input_121_cast_fp16)[name = tensor("hidden_states_73_cast_fp16")]; + tensor var_1226 = const()[name = tensor("op_1226"), val = tensor([1, 1])]; + tensor var_1228 = const()[name = tensor("op_1228"), val = tensor([1, 1])]; + tensor temb_9_pad_type_0 = const()[name = tensor("temb_9_pad_type_0"), val = tensor("custom")]; + tensor temb_9_pad_0 = const()[name = tensor("temb_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115542464)))]; + tensor down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(118819328)))]; + tensor temb_9_cast_fp16 = conv(bias = down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_1228, groups = var_1195, pad = temb_9_pad_0, pad_type = temb_9_pad_type_0, strides = var_1226, weight = down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16)[name = tensor("temb_9_cast_fp16")]; + tensor input_125_cast_fp16 = add(x = hidden_states_73_cast_fp16, y = temb_9_cast_fp16)[name = tensor("input_125_cast_fp16")]; + tensor reshape_52_shape_0 = const()[name = tensor("reshape_52_shape_0"), val = tensor([2, 32, 40, 12, 20])]; + tensor reshape_52_cast_fp16 = reshape(shape = reshape_52_shape_0, x = input_125_cast_fp16)[name = tensor("reshape_52_cast_fp16")]; + tensor reduce_mean_39_axes_0 = const()[name = tensor("reduce_mean_39_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_39_keep_dims_0 = const()[name = tensor("reduce_mean_39_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_39_cast_fp16 = reduce_mean(axes = reduce_mean_39_axes_0, keep_dims = reduce_mean_39_keep_dims_0, x = reshape_52_cast_fp16)[name = tensor("reduce_mean_39_cast_fp16")]; + tensor sub_26_cast_fp16 = sub(x = reshape_52_cast_fp16, y = reduce_mean_39_cast_fp16)[name = tensor("sub_26_cast_fp16")]; + tensor square_13_cast_fp16 = square(x = sub_26_cast_fp16)[name = tensor("square_13_cast_fp16")]; + tensor reduce_mean_41_axes_0 = const()[name = tensor("reduce_mean_41_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_41_keep_dims_0 = const()[name = tensor("reduce_mean_41_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_41_cast_fp16 = reduce_mean(axes = reduce_mean_41_axes_0, keep_dims = reduce_mean_41_keep_dims_0, x = square_13_cast_fp16)[name = tensor("reduce_mean_41_cast_fp16")]; + tensor add_26_y_0_to_fp16 = const()[name = tensor("add_26_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_26_cast_fp16 = add(x = reduce_mean_41_cast_fp16, y = add_26_y_0_to_fp16)[name = tensor("add_26_cast_fp16")]; + tensor sqrt_13_cast_fp16 = sqrt(x = add_26_cast_fp16)[name = tensor("sqrt_13_cast_fp16")]; + tensor real_div_13_cast_fp16 = real_div(x = sub_26_cast_fp16, y = sqrt_13_cast_fp16)[name = tensor("real_div_13_cast_fp16")]; + tensor reshape_53_shape_0 = const()[name = tensor("reshape_53_shape_0"), val = tensor([2, 1280, 12, 20])]; + tensor reshape_53_cast_fp16 = reshape(shape = reshape_53_shape_0, x = real_div_13_cast_fp16)[name = tensor("reshape_53_cast_fp16")]; + tensor add_27_mean_0_to_fp16 = const()[name = tensor("add_27_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(118821952)))]; + tensor add_27_variance_0_to_fp16 = const()[name = tensor("add_27_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(118824576)))]; + tensor add_27_gamma_0_to_fp16 = const()[name = tensor("add_27_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(118827200)))]; + tensor add_27_beta_0_to_fp16 = const()[name = tensor("add_27_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(118829824)))]; + tensor add_27_epsilon_0_to_fp16 = const()[name = tensor("add_27_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_27_cast_fp16 = batch_norm(beta = add_27_beta_0_to_fp16, epsilon = add_27_epsilon_0_to_fp16, gamma = add_27_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_53_cast_fp16)[name = tensor("add_27_cast_fp16")]; + tensor input_129_cast_fp16 = silu(x = add_27_cast_fp16)[name = tensor("input_129_cast_fp16")]; + tensor var_1238 = const()[name = tensor("op_1238"), val = tensor([1, 1])]; + tensor var_1240 = const()[name = tensor("op_1240"), val = tensor([1, 1])]; + tensor hidden_states_75_pad_type_0 = const()[name = tensor("hidden_states_75_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_75_pad_0 = const()[name = tensor("hidden_states_75_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(118832448)))]; + tensor down_blocks_2_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(148323712)))]; + tensor hidden_states_75_cast_fp16 = conv(bias = down_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = var_1240, groups = var_1195, pad = hidden_states_75_pad_0, pad_type = hidden_states_75_pad_type_0, strides = var_1238, weight = down_blocks_2_resnets_0_conv2_weight_to_fp16, x = input_129_cast_fp16)[name = tensor("hidden_states_75_cast_fp16")]; + tensor var_1245 = const()[name = tensor("op_1245"), val = tensor([1, 1])]; + tensor var_1247 = const()[name = tensor("op_1247"), val = tensor([1, 1])]; + tensor x_3_pad_type_0 = const()[name = tensor("x_3_pad_type_0"), val = tensor("custom")]; + tensor x_3_pad_0 = const()[name = tensor("x_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(148326336)))]; + tensor down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(149964800)))]; + tensor x_3_cast_fp16 = conv(bias = down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_1247, groups = var_1195, pad = x_3_pad_0, pad_type = x_3_pad_type_0, strides = var_1245, weight = down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16, x = input_117_cast_fp16)[name = tensor("x_3_cast_fp16")]; + tensor hidden_states_77_cast_fp16 = add(x = x_3_cast_fp16, y = hidden_states_75_cast_fp16)[name = tensor("hidden_states_77_cast_fp16")]; + tensor reshape_56_shape_0 = const()[name = tensor("reshape_56_shape_0"), val = tensor([2, 32, 40, 12, 20])]; + tensor reshape_56_cast_fp16 = reshape(shape = reshape_56_shape_0, x = hidden_states_77_cast_fp16)[name = tensor("reshape_56_cast_fp16")]; + tensor reduce_mean_42_axes_0 = const()[name = tensor("reduce_mean_42_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_42_keep_dims_0 = const()[name = tensor("reduce_mean_42_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_42_cast_fp16 = reduce_mean(axes = reduce_mean_42_axes_0, keep_dims = reduce_mean_42_keep_dims_0, x = reshape_56_cast_fp16)[name = tensor("reduce_mean_42_cast_fp16")]; + tensor sub_28_cast_fp16 = sub(x = reshape_56_cast_fp16, y = reduce_mean_42_cast_fp16)[name = tensor("sub_28_cast_fp16")]; + tensor square_14_cast_fp16 = square(x = sub_28_cast_fp16)[name = tensor("square_14_cast_fp16")]; + tensor reduce_mean_44_axes_0 = const()[name = tensor("reduce_mean_44_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_44_keep_dims_0 = const()[name = tensor("reduce_mean_44_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_44_cast_fp16 = reduce_mean(axes = reduce_mean_44_axes_0, keep_dims = reduce_mean_44_keep_dims_0, x = square_14_cast_fp16)[name = tensor("reduce_mean_44_cast_fp16")]; + tensor add_28_y_0_to_fp16 = const()[name = tensor("add_28_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_28_cast_fp16 = add(x = reduce_mean_44_cast_fp16, y = add_28_y_0_to_fp16)[name = tensor("add_28_cast_fp16")]; + tensor sqrt_14_cast_fp16 = sqrt(x = add_28_cast_fp16)[name = tensor("sqrt_14_cast_fp16")]; + tensor real_div_14_cast_fp16 = real_div(x = sub_28_cast_fp16, y = sqrt_14_cast_fp16)[name = tensor("real_div_14_cast_fp16")]; + tensor reshape_57_shape_0 = const()[name = tensor("reshape_57_shape_0"), val = tensor([2, 1280, 12, 20])]; + tensor reshape_57_cast_fp16 = reshape(shape = reshape_57_shape_0, x = real_div_14_cast_fp16)[name = tensor("reshape_57_cast_fp16")]; + tensor add_29_gamma_0_to_fp16 = const()[name = tensor("add_29_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(149967424)))]; + tensor add_29_beta_0_to_fp16 = const()[name = tensor("add_29_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(149970048)))]; + tensor add_29_epsilon_0_to_fp16 = const()[name = tensor("add_29_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_29_cast_fp16 = batch_norm(beta = add_29_beta_0_to_fp16, epsilon = add_29_epsilon_0_to_fp16, gamma = add_29_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_57_cast_fp16)[name = tensor("add_29_cast_fp16")]; + tensor var_1267 = const()[name = tensor("op_1267"), val = tensor([1, 1])]; + tensor var_1269 = const()[name = tensor("op_1269"), val = tensor([1, 1])]; + tensor hidden_states_79_pad_type_0 = const()[name = tensor("hidden_states_79_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_79_pad_0 = const()[name = tensor("hidden_states_79_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(149972672)))]; + tensor down_blocks_2_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(153249536)))]; + tensor hidden_states_79_cast_fp16 = conv(bias = down_blocks_2_attentions_0_proj_in_bias_to_fp16, dilations = var_1269, groups = var_1195, pad = hidden_states_79_pad_0, pad_type = hidden_states_79_pad_type_0, strides = var_1267, weight = down_blocks_2_attentions_0_proj_in_weight_to_fp16, x = add_29_cast_fp16)[name = tensor("hidden_states_79_cast_fp16")]; + tensor var_1274 = const()[name = tensor("op_1274"), val = tensor([2, 1280, 1, 240])]; + tensor inputs_25_cast_fp16 = reshape(shape = var_1274, x = hidden_states_79_cast_fp16)[name = tensor("inputs_25_cast_fp16")]; + tensor var_1284 = const()[name = tensor("op_1284"), val = tensor([1])]; + tensor channels_mean_25_cast_fp16 = reduce_mean(axes = var_1284, keep_dims = var_1190, x = inputs_25_cast_fp16)[name = tensor("channels_mean_25_cast_fp16")]; + tensor zero_mean_25_cast_fp16 = sub(x = inputs_25_cast_fp16, y = channels_mean_25_cast_fp16)[name = tensor("zero_mean_25_cast_fp16")]; + tensor zero_mean_sq_25_cast_fp16 = mul(x = zero_mean_25_cast_fp16, y = zero_mean_25_cast_fp16)[name = tensor("zero_mean_sq_25_cast_fp16")]; + tensor var_1288 = const()[name = tensor("op_1288"), val = tensor([1])]; + tensor var_1289_cast_fp16 = reduce_mean(axes = var_1288, keep_dims = var_1190, x = zero_mean_sq_25_cast_fp16)[name = tensor("op_1289_cast_fp16")]; + tensor var_1290_to_fp16 = const()[name = tensor("op_1290_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1291_cast_fp16 = add(x = var_1289_cast_fp16, y = var_1290_to_fp16)[name = tensor("op_1291_cast_fp16")]; + tensor denom_25_epsilon_0_to_fp16 = const()[name = tensor("denom_25_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_25_cast_fp16 = rsqrt(epsilon = denom_25_epsilon_0_to_fp16, x = var_1291_cast_fp16)[name = tensor("denom_25_cast_fp16")]; + tensor out_25_cast_fp16 = mul(x = zero_mean_25_cast_fp16, y = denom_25_cast_fp16)[name = tensor("out_25_cast_fp16")]; + tensor var_1295_to_fp16 = const()[name = tensor("op_1295_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(153252160)))]; + tensor var_1296_cast_fp16 = add(x = out_25_cast_fp16, y = var_1295_to_fp16)[name = tensor("op_1296_cast_fp16")]; + tensor var_1298_to_fp16 = const()[name = tensor("op_1298_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(153254784)))]; + tensor hidden_states_81_cast_fp16 = mul(x = var_1296_cast_fp16, y = var_1298_to_fp16)[name = tensor("hidden_states_81_cast_fp16")]; + tensor var_1305 = const()[name = tensor("op_1305"), val = tensor([1, 1])]; + tensor var_1307 = const()[name = tensor("op_1307"), val = tensor([1, 1])]; + tensor q_17_pad_type_0 = const()[name = tensor("q_17_pad_type_0"), val = tensor("custom")]; + tensor q_17_pad_0 = const()[name = tensor("q_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(153257408)))]; + tensor q_17_cast_fp16 = conv(dilations = var_1307, groups = var_1195, pad = q_17_pad_0, pad_type = q_17_pad_type_0, strides = var_1305, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_81_cast_fp16)[name = tensor("q_17_cast_fp16")]; + tensor var_1311 = const()[name = tensor("op_1311"), val = tensor([1, 1])]; + tensor var_1313 = const()[name = tensor("op_1313"), val = tensor([1, 1])]; + tensor k_17_pad_type_0 = const()[name = tensor("k_17_pad_type_0"), val = tensor("custom")]; + tensor k_17_pad_0 = const()[name = tensor("k_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156534272)))]; + tensor k_17_cast_fp16 = conv(dilations = var_1313, groups = var_1195, pad = k_17_pad_0, pad_type = k_17_pad_type_0, strides = var_1311, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_81_cast_fp16)[name = tensor("k_17_cast_fp16")]; + tensor var_1317 = const()[name = tensor("op_1317"), val = tensor([1, 1])]; + tensor var_1319 = const()[name = tensor("op_1319"), val = tensor([1, 1])]; + tensor v_17_pad_type_0 = const()[name = tensor("v_17_pad_type_0"), val = tensor("custom")]; + tensor v_17_pad_0 = const()[name = tensor("v_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159811136)))]; + tensor v_17_cast_fp16 = conv(dilations = var_1319, groups = var_1195, pad = v_17_pad_0, pad_type = v_17_pad_type_0, strides = var_1317, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_81_cast_fp16)[name = tensor("v_17_cast_fp16")]; + tensor var_1323 = const()[name = tensor("op_1323"), val = tensor([2, 20, 64, -1])]; + tensor var_1324_cast_fp16 = reshape(shape = var_1323, x = q_17_cast_fp16)[name = tensor("op_1324_cast_fp16")]; + tensor var_1325 = const()[name = tensor("op_1325"), val = tensor([2, 20, 64, -1])]; + tensor var_1326_cast_fp16 = reshape(shape = var_1325, x = k_17_cast_fp16)[name = tensor("op_1326_cast_fp16")]; + tensor var_1327 = const()[name = tensor("op_1327"), val = tensor([2, 20, 64, -1])]; + tensor var_1328_cast_fp16 = reshape(shape = var_1327, x = v_17_cast_fp16)[name = tensor("op_1328_cast_fp16")]; + tensor attn_weights_33_transpose_x_0 = const()[name = tensor("attn_weights_33_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_33_transpose_y_0 = const()[name = tensor("attn_weights_33_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_33_cast_fp16 = matmul(transpose_x = attn_weights_33_transpose_x_0, transpose_y = attn_weights_33_transpose_y_0, x = var_1324_cast_fp16, y = var_1326_cast_fp16)[name = tensor("attn_weights_33_cast_fp16")]; + tensor var_1186_to_fp16 = const()[name = tensor("op_1186_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_35_cast_fp16 = mul(x = attn_weights_33_cast_fp16, y = var_1186_to_fp16)[name = tensor("attn_weights_35_cast_fp16")]; + tensor var_1332_cast_fp16 = softmax(axis = var_1179, x = attn_weights_35_cast_fp16)[name = tensor("op_1332_cast_fp16")]; + tensor attn_17_transpose_x_0 = const()[name = tensor("attn_17_transpose_x_0"), val = tensor(false)]; + tensor attn_17_transpose_y_0 = const()[name = tensor("attn_17_transpose_y_0"), val = tensor(true)]; + tensor attn_17_cast_fp16 = matmul(transpose_x = attn_17_transpose_x_0, transpose_y = attn_17_transpose_y_0, x = var_1328_cast_fp16, y = var_1332_cast_fp16)[name = tensor("attn_17_cast_fp16")]; + tensor var_1336 = const()[name = tensor("op_1336"), val = tensor([2, 1280, 1, -1])]; + tensor input_133_cast_fp16 = reshape(shape = var_1336, x = attn_17_cast_fp16)[name = tensor("input_133_cast_fp16")]; + tensor var_1341 = const()[name = tensor("op_1341"), val = tensor([1, 1])]; + tensor var_1343 = const()[name = tensor("op_1343"), val = tensor([1, 1])]; + tensor var_1345_pad_type_0 = const()[name = tensor("op_1345_pad_type_0"), val = tensor("custom")]; + tensor var_1345_pad_0 = const()[name = tensor("op_1345_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163088000)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(166364864)))]; + tensor var_1345_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_1343, groups = var_1195, pad = var_1345_pad_0, pad_type = var_1345_pad_type_0, strides = var_1341, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_133_cast_fp16)[name = tensor("op_1345_cast_fp16")]; + tensor inputs_27_cast_fp16 = add(x = var_1345_cast_fp16, y = inputs_25_cast_fp16)[name = tensor("inputs_27_cast_fp16")]; + tensor var_1349 = const()[name = tensor("op_1349"), val = tensor([1])]; + tensor channels_mean_27_cast_fp16 = reduce_mean(axes = var_1349, keep_dims = var_1190, x = inputs_27_cast_fp16)[name = tensor("channels_mean_27_cast_fp16")]; + tensor zero_mean_27_cast_fp16 = sub(x = inputs_27_cast_fp16, y = channels_mean_27_cast_fp16)[name = tensor("zero_mean_27_cast_fp16")]; + tensor zero_mean_sq_27_cast_fp16 = mul(x = zero_mean_27_cast_fp16, y = zero_mean_27_cast_fp16)[name = tensor("zero_mean_sq_27_cast_fp16")]; + tensor var_1353 = const()[name = tensor("op_1353"), val = tensor([1])]; + tensor var_1354_cast_fp16 = reduce_mean(axes = var_1353, keep_dims = var_1190, x = zero_mean_sq_27_cast_fp16)[name = tensor("op_1354_cast_fp16")]; + tensor var_1355_to_fp16 = const()[name = tensor("op_1355_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1356_cast_fp16 = add(x = var_1354_cast_fp16, y = var_1355_to_fp16)[name = tensor("op_1356_cast_fp16")]; + tensor denom_27_epsilon_0_to_fp16 = const()[name = tensor("denom_27_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_27_cast_fp16 = rsqrt(epsilon = denom_27_epsilon_0_to_fp16, x = var_1356_cast_fp16)[name = tensor("denom_27_cast_fp16")]; + tensor out_27_cast_fp16 = mul(x = zero_mean_27_cast_fp16, y = denom_27_cast_fp16)[name = tensor("out_27_cast_fp16")]; + tensor var_1360_to_fp16 = const()[name = tensor("op_1360_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(166367488)))]; + tensor var_1361_cast_fp16 = add(x = out_27_cast_fp16, y = var_1360_to_fp16)[name = tensor("op_1361_cast_fp16")]; + tensor var_1363_to_fp16 = const()[name = tensor("op_1363_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(166370112)))]; + tensor hidden_states_83_cast_fp16 = mul(x = var_1361_cast_fp16, y = var_1363_to_fp16)[name = tensor("hidden_states_83_cast_fp16")]; + tensor var_1370 = const()[name = tensor("op_1370"), val = tensor([1, 1])]; + tensor var_1372 = const()[name = tensor("op_1372"), val = tensor([1, 1])]; + tensor q_19_pad_type_0 = const()[name = tensor("q_19_pad_type_0"), val = tensor("custom")]; + tensor q_19_pad_0 = const()[name = tensor("q_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(166372736)))]; + tensor q_19_cast_fp16 = conv(dilations = var_1372, groups = var_1195, pad = q_19_pad_0, pad_type = q_19_pad_type_0, strides = var_1370, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_83_cast_fp16)[name = tensor("q_19_cast_fp16")]; + tensor var_1376 = const()[name = tensor("op_1376"), val = tensor([1, 1])]; + tensor var_1378 = const()[name = tensor("op_1378"), val = tensor([1, 1])]; + tensor k_19_pad_type_0 = const()[name = tensor("k_19_pad_type_0"), val = tensor("custom")]; + tensor k_19_pad_0 = const()[name = tensor("k_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169649600)))]; + tensor k_19_cast_fp16 = conv(dilations = var_1378, groups = var_1195, pad = k_19_pad_0, pad_type = k_19_pad_type_0, strides = var_1376, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_19_cast_fp16")]; + tensor var_1382 = const()[name = tensor("op_1382"), val = tensor([1, 1])]; + tensor var_1384 = const()[name = tensor("op_1384"), val = tensor([1, 1])]; + tensor v_19_pad_type_0 = const()[name = tensor("v_19_pad_type_0"), val = tensor("custom")]; + tensor v_19_pad_0 = const()[name = tensor("v_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(172271104)))]; + tensor v_19_cast_fp16 = conv(dilations = var_1384, groups = var_1195, pad = v_19_pad_0, pad_type = v_19_pad_type_0, strides = var_1382, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_19_cast_fp16")]; + tensor var_1388 = const()[name = tensor("op_1388"), val = tensor([2, 20, 64, -1])]; + tensor var_1389_cast_fp16 = reshape(shape = var_1388, x = q_19_cast_fp16)[name = tensor("op_1389_cast_fp16")]; + tensor var_1390 = const()[name = tensor("op_1390"), val = tensor([2, 20, 64, -1])]; + tensor var_1391_cast_fp16 = reshape(shape = var_1390, x = k_19_cast_fp16)[name = tensor("op_1391_cast_fp16")]; + tensor var_1392 = const()[name = tensor("op_1392"), val = tensor([2, 20, 64, -1])]; + tensor var_1393_cast_fp16 = reshape(shape = var_1392, x = v_19_cast_fp16)[name = tensor("op_1393_cast_fp16")]; + tensor attn_weights_37_transpose_x_0 = const()[name = tensor("attn_weights_37_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_37_transpose_y_0 = const()[name = tensor("attn_weights_37_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_37_cast_fp16 = matmul(transpose_x = attn_weights_37_transpose_x_0, transpose_y = attn_weights_37_transpose_y_0, x = var_1389_cast_fp16, y = var_1391_cast_fp16)[name = tensor("attn_weights_37_cast_fp16")]; + tensor attn_weights_39_cast_fp16 = mul(x = attn_weights_37_cast_fp16, y = var_1186_to_fp16)[name = tensor("attn_weights_39_cast_fp16")]; + tensor var_1397_cast_fp16 = softmax(axis = var_1179, x = attn_weights_39_cast_fp16)[name = tensor("op_1397_cast_fp16")]; + tensor attn_19_transpose_x_0 = const()[name = tensor("attn_19_transpose_x_0"), val = tensor(false)]; + tensor attn_19_transpose_y_0 = const()[name = tensor("attn_19_transpose_y_0"), val = tensor(true)]; + tensor attn_19_cast_fp16 = matmul(transpose_x = attn_19_transpose_x_0, transpose_y = attn_19_transpose_y_0, x = var_1393_cast_fp16, y = var_1397_cast_fp16)[name = tensor("attn_19_cast_fp16")]; + tensor var_1401 = const()[name = tensor("op_1401"), val = tensor([2, 1280, 1, -1])]; + tensor input_135_cast_fp16 = reshape(shape = var_1401, x = attn_19_cast_fp16)[name = tensor("input_135_cast_fp16")]; + tensor var_1406 = const()[name = tensor("op_1406"), val = tensor([1, 1])]; + tensor var_1408 = const()[name = tensor("op_1408"), val = tensor([1, 1])]; + tensor var_1410_pad_type_0 = const()[name = tensor("op_1410_pad_type_0"), val = tensor("custom")]; + tensor var_1410_pad_0 = const()[name = tensor("op_1410_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(174892608)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178169472)))]; + tensor var_1410_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_1408, groups = var_1195, pad = var_1410_pad_0, pad_type = var_1410_pad_type_0, strides = var_1406, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_135_cast_fp16)[name = tensor("op_1410_cast_fp16")]; + tensor inputs_29_cast_fp16 = add(x = var_1410_cast_fp16, y = inputs_27_cast_fp16)[name = tensor("inputs_29_cast_fp16")]; + tensor var_1414 = const()[name = tensor("op_1414"), val = tensor([1])]; + tensor channels_mean_29_cast_fp16 = reduce_mean(axes = var_1414, keep_dims = var_1190, x = inputs_29_cast_fp16)[name = tensor("channels_mean_29_cast_fp16")]; + tensor zero_mean_29_cast_fp16 = sub(x = inputs_29_cast_fp16, y = channels_mean_29_cast_fp16)[name = tensor("zero_mean_29_cast_fp16")]; + tensor zero_mean_sq_29_cast_fp16 = mul(x = zero_mean_29_cast_fp16, y = zero_mean_29_cast_fp16)[name = tensor("zero_mean_sq_29_cast_fp16")]; + tensor var_1418 = const()[name = tensor("op_1418"), val = tensor([1])]; + tensor var_1419_cast_fp16 = reduce_mean(axes = var_1418, keep_dims = var_1190, x = zero_mean_sq_29_cast_fp16)[name = tensor("op_1419_cast_fp16")]; + tensor var_1420_to_fp16 = const()[name = tensor("op_1420_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1421_cast_fp16 = add(x = var_1419_cast_fp16, y = var_1420_to_fp16)[name = tensor("op_1421_cast_fp16")]; + tensor denom_29_epsilon_0_to_fp16 = const()[name = tensor("denom_29_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_29_cast_fp16 = rsqrt(epsilon = denom_29_epsilon_0_to_fp16, x = var_1421_cast_fp16)[name = tensor("denom_29_cast_fp16")]; + tensor out_29_cast_fp16 = mul(x = zero_mean_29_cast_fp16, y = denom_29_cast_fp16)[name = tensor("out_29_cast_fp16")]; + tensor var_1425_to_fp16 = const()[name = tensor("op_1425_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178172096)))]; + tensor var_1426_cast_fp16 = add(x = out_29_cast_fp16, y = var_1425_to_fp16)[name = tensor("op_1426_cast_fp16")]; + tensor var_1428_to_fp16 = const()[name = tensor("op_1428_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178174720)))]; + tensor input_137_cast_fp16 = mul(x = var_1426_cast_fp16, y = var_1428_to_fp16)[name = tensor("input_137_cast_fp16")]; + tensor var_1436 = const()[name = tensor("op_1436"), val = tensor([1, 1])]; + tensor var_1438 = const()[name = tensor("op_1438"), val = tensor([1, 1])]; + tensor var_1440_pad_type_0 = const()[name = tensor("op_1440_pad_type_0"), val = tensor("custom")]; + tensor var_1440_pad_0 = const()[name = tensor("op_1440_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178177344)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204391808)))]; + tensor var_1440_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_1438, groups = var_1195, pad = var_1440_pad_0, pad_type = var_1440_pad_type_0, strides = var_1436, weight = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_137_cast_fp16)[name = tensor("op_1440_cast_fp16")]; + tensor var_1441_split_sizes_0 = const()[name = tensor("op_1441_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_1441_axis_0 = const()[name = tensor("op_1441_axis_0"), val = tensor(1)]; + tensor var_1441_cast_fp16_0, tensor var_1441_cast_fp16_1 = split(axis = var_1441_axis_0, split_sizes = var_1441_split_sizes_0, x = var_1440_cast_fp16)[name = tensor("op_1441_cast_fp16")]; + tensor var_1443_mode_0 = const()[name = tensor("op_1443_mode_0"), val = tensor("EXACT")]; + tensor var_1443_cast_fp16 = gelu(mode = var_1443_mode_0, x = var_1441_cast_fp16_1)[name = tensor("op_1443_cast_fp16")]; + tensor input_139_cast_fp16 = mul(x = var_1441_cast_fp16_0, y = var_1443_cast_fp16)[name = tensor("input_139_cast_fp16")]; + tensor var_1447 = const()[name = tensor("op_1447"), val = tensor([1, 1])]; + tensor var_1449 = const()[name = tensor("op_1449"), val = tensor([1, 1])]; + tensor var_1451_pad_type_0 = const()[name = tensor("op_1451_pad_type_0"), val = tensor("custom")]; + tensor var_1451_pad_0 = const()[name = tensor("op_1451_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204412352)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217519616)))]; + tensor var_1451_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_1449, groups = var_1195, pad = var_1451_pad_0, pad_type = var_1451_pad_type_0, strides = var_1447, weight = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_139_cast_fp16)[name = tensor("op_1451_cast_fp16")]; + tensor hidden_states_87_cast_fp16 = add(x = var_1451_cast_fp16, y = inputs_29_cast_fp16)[name = tensor("hidden_states_87_cast_fp16")]; + tensor var_1453 = const()[name = tensor("op_1453"), val = tensor([2, 1280, 12, 20])]; + tensor input_141_cast_fp16 = reshape(shape = var_1453, x = hidden_states_87_cast_fp16)[name = tensor("input_141_cast_fp16")]; + tensor var_1457 = const()[name = tensor("op_1457"), val = tensor([1, 1])]; + tensor var_1459 = const()[name = tensor("op_1459"), val = tensor([1, 1])]; + tensor hidden_states_89_pad_type_0 = const()[name = tensor("hidden_states_89_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_89_pad_0 = const()[name = tensor("hidden_states_89_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217522240)))]; + tensor down_blocks_2_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(220799104)))]; + tensor hidden_states_89_cast_fp16 = conv(bias = down_blocks_2_attentions_0_proj_out_bias_to_fp16, dilations = var_1459, groups = var_1195, pad = hidden_states_89_pad_0, pad_type = hidden_states_89_pad_type_0, strides = var_1457, weight = down_blocks_2_attentions_0_proj_out_weight_to_fp16, x = input_141_cast_fp16)[name = tensor("hidden_states_89_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = hidden_states_89_cast_fp16, y = hidden_states_77_cast_fp16)[name = tensor("input_143_cast_fp16")]; + tensor reshape_60_shape_0 = const()[name = tensor("reshape_60_shape_0"), val = tensor([2, 32, 40, 12, 20])]; + tensor reshape_60_cast_fp16 = reshape(shape = reshape_60_shape_0, x = input_143_cast_fp16)[name = tensor("reshape_60_cast_fp16")]; + tensor reduce_mean_45_axes_0 = const()[name = tensor("reduce_mean_45_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_45_keep_dims_0 = const()[name = tensor("reduce_mean_45_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_45_cast_fp16 = reduce_mean(axes = reduce_mean_45_axes_0, keep_dims = reduce_mean_45_keep_dims_0, x = reshape_60_cast_fp16)[name = tensor("reduce_mean_45_cast_fp16")]; + tensor sub_30_cast_fp16 = sub(x = reshape_60_cast_fp16, y = reduce_mean_45_cast_fp16)[name = tensor("sub_30_cast_fp16")]; + tensor square_15_cast_fp16 = square(x = sub_30_cast_fp16)[name = tensor("square_15_cast_fp16")]; + tensor reduce_mean_47_axes_0 = const()[name = tensor("reduce_mean_47_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_47_keep_dims_0 = const()[name = tensor("reduce_mean_47_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_47_cast_fp16 = reduce_mean(axes = reduce_mean_47_axes_0, keep_dims = reduce_mean_47_keep_dims_0, x = square_15_cast_fp16)[name = tensor("reduce_mean_47_cast_fp16")]; + tensor add_30_y_0_to_fp16 = const()[name = tensor("add_30_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_30_cast_fp16 = add(x = reduce_mean_47_cast_fp16, y = add_30_y_0_to_fp16)[name = tensor("add_30_cast_fp16")]; + tensor sqrt_15_cast_fp16 = sqrt(x = add_30_cast_fp16)[name = tensor("sqrt_15_cast_fp16")]; + tensor real_div_15_cast_fp16 = real_div(x = sub_30_cast_fp16, y = sqrt_15_cast_fp16)[name = tensor("real_div_15_cast_fp16")]; + tensor reshape_61_shape_0 = const()[name = tensor("reshape_61_shape_0"), val = tensor([2, 1280, 12, 20])]; + tensor reshape_61_cast_fp16 = reshape(shape = reshape_61_shape_0, x = real_div_15_cast_fp16)[name = tensor("reshape_61_cast_fp16")]; + tensor add_31_gamma_0_to_fp16 = const()[name = tensor("add_31_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(220801728)))]; + tensor add_31_beta_0_to_fp16 = const()[name = tensor("add_31_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(220804352)))]; + tensor add_31_epsilon_0_to_fp16 = const()[name = tensor("add_31_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_31_cast_fp16 = batch_norm(beta = add_31_beta_0_to_fp16, epsilon = add_31_epsilon_0_to_fp16, gamma = add_31_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_61_cast_fp16)[name = tensor("add_31_cast_fp16")]; + tensor input_147_cast_fp16 = silu(x = add_31_cast_fp16)[name = tensor("input_147_cast_fp16")]; + tensor var_1474 = const()[name = tensor("op_1474"), val = tensor([1, 1])]; + tensor var_1476 = const()[name = tensor("op_1476"), val = tensor([1, 1])]; + tensor hidden_states_91_pad_type_0 = const()[name = tensor("hidden_states_91_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_91_pad_0 = const()[name = tensor("hidden_states_91_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(220806976)))]; + tensor down_blocks_2_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(250298240)))]; + tensor hidden_states_91_cast_fp16 = conv(bias = down_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = var_1476, groups = var_1195, pad = hidden_states_91_pad_0, pad_type = hidden_states_91_pad_type_0, strides = var_1474, weight = down_blocks_2_resnets_1_conv1_weight_to_fp16, x = input_147_cast_fp16)[name = tensor("hidden_states_91_cast_fp16")]; + tensor var_1482 = const()[name = tensor("op_1482"), val = tensor([1, 1])]; + tensor var_1484 = const()[name = tensor("op_1484"), val = tensor([1, 1])]; + tensor temb_11_pad_type_0 = const()[name = tensor("temb_11_pad_type_0"), val = tensor("custom")]; + tensor temb_11_pad_0 = const()[name = tensor("temb_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(250300864)))]; + tensor down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(253577728)))]; + tensor temb_11_cast_fp16 = conv(bias = down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_1484, groups = var_1195, pad = temb_11_pad_0, pad_type = temb_11_pad_type_0, strides = var_1482, weight = down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16)[name = tensor("temb_11_cast_fp16")]; + tensor input_151_cast_fp16 = add(x = hidden_states_91_cast_fp16, y = temb_11_cast_fp16)[name = tensor("input_151_cast_fp16")]; + tensor reshape_64_shape_0 = const()[name = tensor("reshape_64_shape_0"), val = tensor([2, 32, 40, 12, 20])]; + tensor reshape_64_cast_fp16 = reshape(shape = reshape_64_shape_0, x = input_151_cast_fp16)[name = tensor("reshape_64_cast_fp16")]; + tensor reduce_mean_48_axes_0 = const()[name = tensor("reduce_mean_48_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_48_keep_dims_0 = const()[name = tensor("reduce_mean_48_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_48_cast_fp16 = reduce_mean(axes = reduce_mean_48_axes_0, keep_dims = reduce_mean_48_keep_dims_0, x = reshape_64_cast_fp16)[name = tensor("reduce_mean_48_cast_fp16")]; + tensor sub_32_cast_fp16 = sub(x = reshape_64_cast_fp16, y = reduce_mean_48_cast_fp16)[name = tensor("sub_32_cast_fp16")]; + tensor square_16_cast_fp16 = square(x = sub_32_cast_fp16)[name = tensor("square_16_cast_fp16")]; + tensor reduce_mean_50_axes_0 = const()[name = tensor("reduce_mean_50_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_50_keep_dims_0 = const()[name = tensor("reduce_mean_50_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_50_cast_fp16 = reduce_mean(axes = reduce_mean_50_axes_0, keep_dims = reduce_mean_50_keep_dims_0, x = square_16_cast_fp16)[name = tensor("reduce_mean_50_cast_fp16")]; + tensor add_32_y_0_to_fp16 = const()[name = tensor("add_32_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_32_cast_fp16 = add(x = reduce_mean_50_cast_fp16, y = add_32_y_0_to_fp16)[name = tensor("add_32_cast_fp16")]; + tensor sqrt_16_cast_fp16 = sqrt(x = add_32_cast_fp16)[name = tensor("sqrt_16_cast_fp16")]; + tensor real_div_16_cast_fp16 = real_div(x = sub_32_cast_fp16, y = sqrt_16_cast_fp16)[name = tensor("real_div_16_cast_fp16")]; + tensor reshape_65_shape_0 = const()[name = tensor("reshape_65_shape_0"), val = tensor([2, 1280, 12, 20])]; + tensor reshape_65_cast_fp16 = reshape(shape = reshape_65_shape_0, x = real_div_16_cast_fp16)[name = tensor("reshape_65_cast_fp16")]; + tensor add_33_gamma_0_to_fp16 = const()[name = tensor("add_33_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(253580352)))]; + tensor add_33_beta_0_to_fp16 = const()[name = tensor("add_33_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(253582976)))]; + tensor add_33_epsilon_0_to_fp16 = const()[name = tensor("add_33_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_33_cast_fp16 = batch_norm(beta = add_33_beta_0_to_fp16, epsilon = add_33_epsilon_0_to_fp16, gamma = add_33_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_65_cast_fp16)[name = tensor("add_33_cast_fp16")]; + tensor input_155_cast_fp16 = silu(x = add_33_cast_fp16)[name = tensor("input_155_cast_fp16")]; + tensor var_1494 = const()[name = tensor("op_1494"), val = tensor([1, 1])]; + tensor var_1496 = const()[name = tensor("op_1496"), val = tensor([1, 1])]; + tensor hidden_states_93_pad_type_0 = const()[name = tensor("hidden_states_93_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_93_pad_0 = const()[name = tensor("hidden_states_93_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(253585600)))]; + tensor down_blocks_2_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283076864)))]; + tensor hidden_states_93_cast_fp16 = conv(bias = down_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = var_1496, groups = var_1195, pad = hidden_states_93_pad_0, pad_type = hidden_states_93_pad_type_0, strides = var_1494, weight = down_blocks_2_resnets_1_conv2_weight_to_fp16, x = input_155_cast_fp16)[name = tensor("hidden_states_93_cast_fp16")]; + tensor hidden_states_95_cast_fp16 = add(x = input_143_cast_fp16, y = hidden_states_93_cast_fp16)[name = tensor("hidden_states_95_cast_fp16")]; + tensor reshape_68_shape_0 = const()[name = tensor("reshape_68_shape_0"), val = tensor([2, 32, 40, 12, 20])]; + tensor reshape_68_cast_fp16 = reshape(shape = reshape_68_shape_0, x = hidden_states_95_cast_fp16)[name = tensor("reshape_68_cast_fp16")]; + tensor reduce_mean_51_axes_0 = const()[name = tensor("reduce_mean_51_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_51_keep_dims_0 = const()[name = tensor("reduce_mean_51_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_51_cast_fp16 = reduce_mean(axes = reduce_mean_51_axes_0, keep_dims = reduce_mean_51_keep_dims_0, x = reshape_68_cast_fp16)[name = tensor("reduce_mean_51_cast_fp16")]; + tensor sub_34_cast_fp16 = sub(x = reshape_68_cast_fp16, y = reduce_mean_51_cast_fp16)[name = tensor("sub_34_cast_fp16")]; + tensor square_17_cast_fp16 = square(x = sub_34_cast_fp16)[name = tensor("square_17_cast_fp16")]; + tensor reduce_mean_53_axes_0 = const()[name = tensor("reduce_mean_53_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_53_keep_dims_0 = const()[name = tensor("reduce_mean_53_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_53_cast_fp16 = reduce_mean(axes = reduce_mean_53_axes_0, keep_dims = reduce_mean_53_keep_dims_0, x = square_17_cast_fp16)[name = tensor("reduce_mean_53_cast_fp16")]; + tensor add_34_y_0_to_fp16 = const()[name = tensor("add_34_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_34_cast_fp16 = add(x = reduce_mean_53_cast_fp16, y = add_34_y_0_to_fp16)[name = tensor("add_34_cast_fp16")]; + tensor sqrt_17_cast_fp16 = sqrt(x = add_34_cast_fp16)[name = tensor("sqrt_17_cast_fp16")]; + tensor real_div_17_cast_fp16 = real_div(x = sub_34_cast_fp16, y = sqrt_17_cast_fp16)[name = tensor("real_div_17_cast_fp16")]; + tensor reshape_69_shape_0 = const()[name = tensor("reshape_69_shape_0"), val = tensor([2, 1280, 12, 20])]; + tensor reshape_69_cast_fp16 = reshape(shape = reshape_69_shape_0, x = real_div_17_cast_fp16)[name = tensor("reshape_69_cast_fp16")]; + tensor add_35_gamma_0_to_fp16 = const()[name = tensor("add_35_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283079488)))]; + tensor add_35_beta_0_to_fp16 = const()[name = tensor("add_35_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283082112)))]; + tensor add_35_epsilon_0_to_fp16 = const()[name = tensor("add_35_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_35_cast_fp16 = batch_norm(beta = add_35_beta_0_to_fp16, epsilon = add_35_epsilon_0_to_fp16, gamma = add_35_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_69_cast_fp16)[name = tensor("add_35_cast_fp16")]; + tensor var_1516 = const()[name = tensor("op_1516"), val = tensor([1, 1])]; + tensor var_1518 = const()[name = tensor("op_1518"), val = tensor([1, 1])]; + tensor hidden_states_97_pad_type_0 = const()[name = tensor("hidden_states_97_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_97_pad_0 = const()[name = tensor("hidden_states_97_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_proj_in_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283084736)))]; + tensor down_blocks_2_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(286361600)))]; + tensor hidden_states_97_cast_fp16 = conv(bias = down_blocks_2_attentions_1_proj_in_bias_to_fp16, dilations = var_1518, groups = var_1195, pad = hidden_states_97_pad_0, pad_type = hidden_states_97_pad_type_0, strides = var_1516, weight = down_blocks_2_attentions_1_proj_in_weight_to_fp16, x = add_35_cast_fp16)[name = tensor("hidden_states_97_cast_fp16")]; + tensor var_1523 = const()[name = tensor("op_1523"), val = tensor([2, 1280, 1, 240])]; + tensor inputs_31_cast_fp16 = reshape(shape = var_1523, x = hidden_states_97_cast_fp16)[name = tensor("inputs_31_cast_fp16")]; + tensor var_1533 = const()[name = tensor("op_1533"), val = tensor([1])]; + tensor channels_mean_31_cast_fp16 = reduce_mean(axes = var_1533, keep_dims = var_1190, x = inputs_31_cast_fp16)[name = tensor("channels_mean_31_cast_fp16")]; + tensor zero_mean_31_cast_fp16 = sub(x = inputs_31_cast_fp16, y = channels_mean_31_cast_fp16)[name = tensor("zero_mean_31_cast_fp16")]; + tensor zero_mean_sq_31_cast_fp16 = mul(x = zero_mean_31_cast_fp16, y = zero_mean_31_cast_fp16)[name = tensor("zero_mean_sq_31_cast_fp16")]; + tensor var_1537 = const()[name = tensor("op_1537"), val = tensor([1])]; + tensor var_1538_cast_fp16 = reduce_mean(axes = var_1537, keep_dims = var_1190, x = zero_mean_sq_31_cast_fp16)[name = tensor("op_1538_cast_fp16")]; + tensor var_1539_to_fp16 = const()[name = tensor("op_1539_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1540_cast_fp16 = add(x = var_1538_cast_fp16, y = var_1539_to_fp16)[name = tensor("op_1540_cast_fp16")]; + tensor denom_31_epsilon_0_to_fp16 = const()[name = tensor("denom_31_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_31_cast_fp16 = rsqrt(epsilon = denom_31_epsilon_0_to_fp16, x = var_1540_cast_fp16)[name = tensor("denom_31_cast_fp16")]; + tensor out_31_cast_fp16 = mul(x = zero_mean_31_cast_fp16, y = denom_31_cast_fp16)[name = tensor("out_31_cast_fp16")]; + tensor var_1544_to_fp16 = const()[name = tensor("op_1544_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(286364224)))]; + tensor var_1545_cast_fp16 = add(x = out_31_cast_fp16, y = var_1544_to_fp16)[name = tensor("op_1545_cast_fp16")]; + tensor var_1547_to_fp16 = const()[name = tensor("op_1547_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(286366848)))]; + tensor hidden_states_99_cast_fp16 = mul(x = var_1545_cast_fp16, y = var_1547_to_fp16)[name = tensor("hidden_states_99_cast_fp16")]; + tensor var_1554 = const()[name = tensor("op_1554"), val = tensor([1, 1])]; + tensor var_1556 = const()[name = tensor("op_1556"), val = tensor([1, 1])]; + tensor q_21_pad_type_0 = const()[name = tensor("q_21_pad_type_0"), val = tensor("custom")]; + tensor q_21_pad_0 = const()[name = tensor("q_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(286369472)))]; + tensor q_21_cast_fp16 = conv(dilations = var_1556, groups = var_1195, pad = q_21_pad_0, pad_type = q_21_pad_type_0, strides = var_1554, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_99_cast_fp16)[name = tensor("q_21_cast_fp16")]; + tensor var_1560 = const()[name = tensor("op_1560"), val = tensor([1, 1])]; + tensor var_1562 = const()[name = tensor("op_1562"), val = tensor([1, 1])]; + tensor k_21_pad_type_0 = const()[name = tensor("k_21_pad_type_0"), val = tensor("custom")]; + tensor k_21_pad_0 = const()[name = tensor("k_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(289646336)))]; + tensor k_21_cast_fp16 = conv(dilations = var_1562, groups = var_1195, pad = k_21_pad_0, pad_type = k_21_pad_type_0, strides = var_1560, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_99_cast_fp16)[name = tensor("k_21_cast_fp16")]; + tensor var_1566 = const()[name = tensor("op_1566"), val = tensor([1, 1])]; + tensor var_1568 = const()[name = tensor("op_1568"), val = tensor([1, 1])]; + tensor v_21_pad_type_0 = const()[name = tensor("v_21_pad_type_0"), val = tensor("custom")]; + tensor v_21_pad_0 = const()[name = tensor("v_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(292923200)))]; + tensor v_21_cast_fp16 = conv(dilations = var_1568, groups = var_1195, pad = v_21_pad_0, pad_type = v_21_pad_type_0, strides = var_1566, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_99_cast_fp16)[name = tensor("v_21_cast_fp16")]; + tensor var_1572 = const()[name = tensor("op_1572"), val = tensor([2, 20, 64, -1])]; + tensor var_1573_cast_fp16 = reshape(shape = var_1572, x = q_21_cast_fp16)[name = tensor("op_1573_cast_fp16")]; + tensor var_1574 = const()[name = tensor("op_1574"), val = tensor([2, 20, 64, -1])]; + tensor var_1575_cast_fp16 = reshape(shape = var_1574, x = k_21_cast_fp16)[name = tensor("op_1575_cast_fp16")]; + tensor var_1576 = const()[name = tensor("op_1576"), val = tensor([2, 20, 64, -1])]; + tensor var_1577_cast_fp16 = reshape(shape = var_1576, x = v_21_cast_fp16)[name = tensor("op_1577_cast_fp16")]; + tensor attn_weights_41_transpose_x_0 = const()[name = tensor("attn_weights_41_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_41_transpose_y_0 = const()[name = tensor("attn_weights_41_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_41_cast_fp16 = matmul(transpose_x = attn_weights_41_transpose_x_0, transpose_y = attn_weights_41_transpose_y_0, x = var_1573_cast_fp16, y = var_1575_cast_fp16)[name = tensor("attn_weights_41_cast_fp16")]; + tensor attn_weights_43_cast_fp16 = mul(x = attn_weights_41_cast_fp16, y = var_1186_to_fp16)[name = tensor("attn_weights_43_cast_fp16")]; + tensor var_1581_cast_fp16 = softmax(axis = var_1179, x = attn_weights_43_cast_fp16)[name = tensor("op_1581_cast_fp16")]; + tensor attn_21_transpose_x_0 = const()[name = tensor("attn_21_transpose_x_0"), val = tensor(false)]; + tensor attn_21_transpose_y_0 = const()[name = tensor("attn_21_transpose_y_0"), val = tensor(true)]; + tensor attn_21_cast_fp16 = matmul(transpose_x = attn_21_transpose_x_0, transpose_y = attn_21_transpose_y_0, x = var_1577_cast_fp16, y = var_1581_cast_fp16)[name = tensor("attn_21_cast_fp16")]; + tensor var_1585 = const()[name = tensor("op_1585"), val = tensor([2, 1280, 1, -1])]; + tensor input_159_cast_fp16 = reshape(shape = var_1585, x = attn_21_cast_fp16)[name = tensor("input_159_cast_fp16")]; + tensor var_1590 = const()[name = tensor("op_1590"), val = tensor([1, 1])]; + tensor var_1592 = const()[name = tensor("op_1592"), val = tensor([1, 1])]; + tensor var_1594_pad_type_0 = const()[name = tensor("op_1594_pad_type_0"), val = tensor("custom")]; + tensor var_1594_pad_0 = const()[name = tensor("op_1594_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(296200064)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(299476928)))]; + tensor var_1594_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_1592, groups = var_1195, pad = var_1594_pad_0, pad_type = var_1594_pad_type_0, strides = var_1590, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_159_cast_fp16)[name = tensor("op_1594_cast_fp16")]; + tensor inputs_33_cast_fp16 = add(x = var_1594_cast_fp16, y = inputs_31_cast_fp16)[name = tensor("inputs_33_cast_fp16")]; + tensor var_1598 = const()[name = tensor("op_1598"), val = tensor([1])]; + tensor channels_mean_33_cast_fp16 = reduce_mean(axes = var_1598, keep_dims = var_1190, x = inputs_33_cast_fp16)[name = tensor("channels_mean_33_cast_fp16")]; + tensor zero_mean_33_cast_fp16 = sub(x = inputs_33_cast_fp16, y = channels_mean_33_cast_fp16)[name = tensor("zero_mean_33_cast_fp16")]; + tensor zero_mean_sq_33_cast_fp16 = mul(x = zero_mean_33_cast_fp16, y = zero_mean_33_cast_fp16)[name = tensor("zero_mean_sq_33_cast_fp16")]; + tensor var_1602 = const()[name = tensor("op_1602"), val = tensor([1])]; + tensor var_1603_cast_fp16 = reduce_mean(axes = var_1602, keep_dims = var_1190, x = zero_mean_sq_33_cast_fp16)[name = tensor("op_1603_cast_fp16")]; + tensor var_1604_to_fp16 = const()[name = tensor("op_1604_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1605_cast_fp16 = add(x = var_1603_cast_fp16, y = var_1604_to_fp16)[name = tensor("op_1605_cast_fp16")]; + tensor denom_33_epsilon_0_to_fp16 = const()[name = tensor("denom_33_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_33_cast_fp16 = rsqrt(epsilon = denom_33_epsilon_0_to_fp16, x = var_1605_cast_fp16)[name = tensor("denom_33_cast_fp16")]; + tensor out_33_cast_fp16 = mul(x = zero_mean_33_cast_fp16, y = denom_33_cast_fp16)[name = tensor("out_33_cast_fp16")]; + tensor var_1609_to_fp16 = const()[name = tensor("op_1609_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(299479552)))]; + tensor var_1610_cast_fp16 = add(x = out_33_cast_fp16, y = var_1609_to_fp16)[name = tensor("op_1610_cast_fp16")]; + tensor var_1612_to_fp16 = const()[name = tensor("op_1612_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(299482176)))]; + tensor hidden_states_101_cast_fp16 = mul(x = var_1610_cast_fp16, y = var_1612_to_fp16)[name = tensor("hidden_states_101_cast_fp16")]; + tensor var_1619 = const()[name = tensor("op_1619"), val = tensor([1, 1])]; + tensor var_1621 = const()[name = tensor("op_1621"), val = tensor([1, 1])]; + tensor q_23_pad_type_0 = const()[name = tensor("q_23_pad_type_0"), val = tensor("custom")]; + tensor q_23_pad_0 = const()[name = tensor("q_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(299484800)))]; + tensor q_23_cast_fp16 = conv(dilations = var_1621, groups = var_1195, pad = q_23_pad_0, pad_type = q_23_pad_type_0, strides = var_1619, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_101_cast_fp16)[name = tensor("q_23_cast_fp16")]; + tensor var_1625 = const()[name = tensor("op_1625"), val = tensor([1, 1])]; + tensor var_1627 = const()[name = tensor("op_1627"), val = tensor([1, 1])]; + tensor k_23_pad_type_0 = const()[name = tensor("k_23_pad_type_0"), val = tensor("custom")]; + tensor k_23_pad_0 = const()[name = tensor("k_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(302761664)))]; + tensor k_23_cast_fp16 = conv(dilations = var_1627, groups = var_1195, pad = k_23_pad_0, pad_type = k_23_pad_type_0, strides = var_1625, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_23_cast_fp16")]; + tensor var_1631 = const()[name = tensor("op_1631"), val = tensor([1, 1])]; + tensor var_1633 = const()[name = tensor("op_1633"), val = tensor([1, 1])]; + tensor v_23_pad_type_0 = const()[name = tensor("v_23_pad_type_0"), val = tensor("custom")]; + tensor v_23_pad_0 = const()[name = tensor("v_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(305383168)))]; + tensor v_23_cast_fp16 = conv(dilations = var_1633, groups = var_1195, pad = v_23_pad_0, pad_type = v_23_pad_type_0, strides = var_1631, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_23_cast_fp16")]; + tensor var_1637 = const()[name = tensor("op_1637"), val = tensor([2, 20, 64, -1])]; + tensor var_1638_cast_fp16 = reshape(shape = var_1637, x = q_23_cast_fp16)[name = tensor("op_1638_cast_fp16")]; + tensor var_1639 = const()[name = tensor("op_1639"), val = tensor([2, 20, 64, -1])]; + tensor var_1640_cast_fp16 = reshape(shape = var_1639, x = k_23_cast_fp16)[name = tensor("op_1640_cast_fp16")]; + tensor var_1641 = const()[name = tensor("op_1641"), val = tensor([2, 20, 64, -1])]; + tensor var_1642_cast_fp16 = reshape(shape = var_1641, x = v_23_cast_fp16)[name = tensor("op_1642_cast_fp16")]; + tensor attn_weights_45_transpose_x_0 = const()[name = tensor("attn_weights_45_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_45_transpose_y_0 = const()[name = tensor("attn_weights_45_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_45_cast_fp16 = matmul(transpose_x = attn_weights_45_transpose_x_0, transpose_y = attn_weights_45_transpose_y_0, x = var_1638_cast_fp16, y = var_1640_cast_fp16)[name = tensor("attn_weights_45_cast_fp16")]; + tensor attn_weights_47_cast_fp16 = mul(x = attn_weights_45_cast_fp16, y = var_1186_to_fp16)[name = tensor("attn_weights_47_cast_fp16")]; + tensor var_1646_cast_fp16 = softmax(axis = var_1179, x = attn_weights_47_cast_fp16)[name = tensor("op_1646_cast_fp16")]; + tensor attn_23_transpose_x_0 = const()[name = tensor("attn_23_transpose_x_0"), val = tensor(false)]; + tensor attn_23_transpose_y_0 = const()[name = tensor("attn_23_transpose_y_0"), val = tensor(true)]; + tensor attn_23_cast_fp16 = matmul(transpose_x = attn_23_transpose_x_0, transpose_y = attn_23_transpose_y_0, x = var_1642_cast_fp16, y = var_1646_cast_fp16)[name = tensor("attn_23_cast_fp16")]; + tensor var_1650 = const()[name = tensor("op_1650"), val = tensor([2, 1280, 1, -1])]; + tensor input_161_cast_fp16 = reshape(shape = var_1650, x = attn_23_cast_fp16)[name = tensor("input_161_cast_fp16")]; + tensor var_1655 = const()[name = tensor("op_1655"), val = tensor([1, 1])]; + tensor var_1657 = const()[name = tensor("op_1657"), val = tensor([1, 1])]; + tensor var_1659_pad_type_0 = const()[name = tensor("op_1659_pad_type_0"), val = tensor("custom")]; + tensor var_1659_pad_0 = const()[name = tensor("op_1659_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(308004672)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(311281536)))]; + tensor var_1659_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_1657, groups = var_1195, pad = var_1659_pad_0, pad_type = var_1659_pad_type_0, strides = var_1655, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_161_cast_fp16)[name = tensor("op_1659_cast_fp16")]; + tensor inputs_35_cast_fp16 = add(x = var_1659_cast_fp16, y = inputs_33_cast_fp16)[name = tensor("inputs_35_cast_fp16")]; + tensor var_1663 = const()[name = tensor("op_1663"), val = tensor([1])]; + tensor channels_mean_35_cast_fp16 = reduce_mean(axes = var_1663, keep_dims = var_1190, x = inputs_35_cast_fp16)[name = tensor("channels_mean_35_cast_fp16")]; + tensor zero_mean_35_cast_fp16 = sub(x = inputs_35_cast_fp16, y = channels_mean_35_cast_fp16)[name = tensor("zero_mean_35_cast_fp16")]; + tensor zero_mean_sq_35_cast_fp16 = mul(x = zero_mean_35_cast_fp16, y = zero_mean_35_cast_fp16)[name = tensor("zero_mean_sq_35_cast_fp16")]; + tensor var_1667 = const()[name = tensor("op_1667"), val = tensor([1])]; + tensor var_1668_cast_fp16 = reduce_mean(axes = var_1667, keep_dims = var_1190, x = zero_mean_sq_35_cast_fp16)[name = tensor("op_1668_cast_fp16")]; + tensor var_1669_to_fp16 = const()[name = tensor("op_1669_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1670_cast_fp16 = add(x = var_1668_cast_fp16, y = var_1669_to_fp16)[name = tensor("op_1670_cast_fp16")]; + tensor denom_35_epsilon_0_to_fp16 = const()[name = tensor("denom_35_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_35_cast_fp16 = rsqrt(epsilon = denom_35_epsilon_0_to_fp16, x = var_1670_cast_fp16)[name = tensor("denom_35_cast_fp16")]; + tensor out_35_cast_fp16 = mul(x = zero_mean_35_cast_fp16, y = denom_35_cast_fp16)[name = tensor("out_35_cast_fp16")]; + tensor var_1674_to_fp16 = const()[name = tensor("op_1674_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(311284160)))]; + tensor var_1675_cast_fp16 = add(x = out_35_cast_fp16, y = var_1674_to_fp16)[name = tensor("op_1675_cast_fp16")]; + tensor var_1677_to_fp16 = const()[name = tensor("op_1677_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(311286784)))]; + tensor input_163_cast_fp16 = mul(x = var_1675_cast_fp16, y = var_1677_to_fp16)[name = tensor("input_163_cast_fp16")]; + tensor var_1685 = const()[name = tensor("op_1685"), val = tensor([1, 1])]; + tensor var_1687 = const()[name = tensor("op_1687"), val = tensor([1, 1])]; + tensor var_1689_pad_type_0 = const()[name = tensor("op_1689_pad_type_0"), val = tensor("custom")]; + tensor var_1689_pad_0 = const()[name = tensor("op_1689_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(311289408)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(337503872)))]; + tensor var_1689_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_1687, groups = var_1195, pad = var_1689_pad_0, pad_type = var_1689_pad_type_0, strides = var_1685, weight = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_163_cast_fp16)[name = tensor("op_1689_cast_fp16")]; + tensor var_1690_split_sizes_0 = const()[name = tensor("op_1690_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_1690_axis_0 = const()[name = tensor("op_1690_axis_0"), val = tensor(1)]; + tensor var_1690_cast_fp16_0, tensor var_1690_cast_fp16_1 = split(axis = var_1690_axis_0, split_sizes = var_1690_split_sizes_0, x = var_1689_cast_fp16)[name = tensor("op_1690_cast_fp16")]; + tensor var_1692_mode_0 = const()[name = tensor("op_1692_mode_0"), val = tensor("EXACT")]; + tensor var_1692_cast_fp16 = gelu(mode = var_1692_mode_0, x = var_1690_cast_fp16_1)[name = tensor("op_1692_cast_fp16")]; + tensor input_165_cast_fp16 = mul(x = var_1690_cast_fp16_0, y = var_1692_cast_fp16)[name = tensor("input_165_cast_fp16")]; + tensor var_1696 = const()[name = tensor("op_1696"), val = tensor([1, 1])]; + tensor var_1698 = const()[name = tensor("op_1698"), val = tensor([1, 1])]; + tensor var_1700_pad_type_0 = const()[name = tensor("op_1700_pad_type_0"), val = tensor("custom")]; + tensor var_1700_pad_0 = const()[name = tensor("op_1700_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(337524416)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(350631680)))]; + tensor var_1700_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_1698, groups = var_1195, pad = var_1700_pad_0, pad_type = var_1700_pad_type_0, strides = var_1696, weight = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_165_cast_fp16)[name = tensor("op_1700_cast_fp16")]; + tensor hidden_states_105_cast_fp16 = add(x = var_1700_cast_fp16, y = inputs_35_cast_fp16)[name = tensor("hidden_states_105_cast_fp16")]; + tensor var_1702 = const()[name = tensor("op_1702"), val = tensor([2, 1280, 12, 20])]; + tensor input_167_cast_fp16 = reshape(shape = var_1702, x = hidden_states_105_cast_fp16)[name = tensor("input_167_cast_fp16")]; + tensor var_1706 = const()[name = tensor("op_1706"), val = tensor([1, 1])]; + tensor var_1708 = const()[name = tensor("op_1708"), val = tensor([1, 1])]; + tensor hidden_states_107_pad_type_0 = const()[name = tensor("hidden_states_107_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_107_pad_0 = const()[name = tensor("hidden_states_107_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_proj_out_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(350634304)))]; + tensor down_blocks_2_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(353911168)))]; + tensor hidden_states_107_cast_fp16 = conv(bias = down_blocks_2_attentions_1_proj_out_bias_to_fp16, dilations = var_1708, groups = var_1195, pad = hidden_states_107_pad_0, pad_type = hidden_states_107_pad_type_0, strides = var_1706, weight = down_blocks_2_attentions_1_proj_out_weight_to_fp16, x = input_167_cast_fp16)[name = tensor("hidden_states_107_cast_fp16")]; + tensor input_169_cast_fp16 = add(x = hidden_states_107_cast_fp16, y = hidden_states_95_cast_fp16)[name = tensor("input_169_cast_fp16")]; + tensor var_1715 = const()[name = tensor("op_1715"), val = tensor([2, 2])]; + tensor var_1717 = const()[name = tensor("op_1717"), val = tensor([1, 1])]; + tensor input_171_pad_type_0 = const()[name = tensor("input_171_pad_type_0"), val = tensor("custom")]; + tensor input_171_pad_0 = const()[name = tensor("input_171_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_downsamplers_0_conv_weight_to_fp16 = const()[name = tensor("down_blocks_2_downsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(353913792)))]; + tensor down_blocks_2_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor("down_blocks_2_downsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(383405056)))]; + tensor input_171_cast_fp16 = conv(bias = down_blocks_2_downsamplers_0_conv_bias_to_fp16, dilations = var_1717, groups = var_1195, pad = input_171_pad_0, pad_type = input_171_pad_type_0, strides = var_1715, weight = down_blocks_2_downsamplers_0_conv_weight_to_fp16, x = input_169_cast_fp16)[name = tensor("input_171_cast_fp16")]; + tensor var_1729 = const()[name = tensor("op_1729"), val = tensor(1)]; + tensor reshape_72_shape_0 = const()[name = tensor("reshape_72_shape_0"), val = tensor([2, 32, 40, 6, 10])]; + tensor reshape_72_cast_fp16 = reshape(shape = reshape_72_shape_0, x = input_171_cast_fp16)[name = tensor("reshape_72_cast_fp16")]; + tensor reduce_mean_54_axes_0 = const()[name = tensor("reduce_mean_54_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_54_keep_dims_0 = const()[name = tensor("reduce_mean_54_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_54_cast_fp16 = reduce_mean(axes = reduce_mean_54_axes_0, keep_dims = reduce_mean_54_keep_dims_0, x = reshape_72_cast_fp16)[name = tensor("reduce_mean_54_cast_fp16")]; + tensor sub_36_cast_fp16 = sub(x = reshape_72_cast_fp16, y = reduce_mean_54_cast_fp16)[name = tensor("sub_36_cast_fp16")]; + tensor square_18_cast_fp16 = square(x = sub_36_cast_fp16)[name = tensor("square_18_cast_fp16")]; + tensor reduce_mean_56_axes_0 = const()[name = tensor("reduce_mean_56_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_56_keep_dims_0 = const()[name = tensor("reduce_mean_56_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_56_cast_fp16 = reduce_mean(axes = reduce_mean_56_axes_0, keep_dims = reduce_mean_56_keep_dims_0, x = square_18_cast_fp16)[name = tensor("reduce_mean_56_cast_fp16")]; + tensor add_36_y_0_to_fp16 = const()[name = tensor("add_36_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_36_cast_fp16 = add(x = reduce_mean_56_cast_fp16, y = add_36_y_0_to_fp16)[name = tensor("add_36_cast_fp16")]; + tensor sqrt_18_cast_fp16 = sqrt(x = add_36_cast_fp16)[name = tensor("sqrt_18_cast_fp16")]; + tensor real_div_18_cast_fp16 = real_div(x = sub_36_cast_fp16, y = sqrt_18_cast_fp16)[name = tensor("real_div_18_cast_fp16")]; + tensor reshape_73_shape_0 = const()[name = tensor("reshape_73_shape_0"), val = tensor([2, 1280, 6, 10])]; + tensor reshape_73_cast_fp16 = reshape(shape = reshape_73_shape_0, x = real_div_18_cast_fp16)[name = tensor("reshape_73_cast_fp16")]; + tensor add_37_gamma_0_to_fp16 = const()[name = tensor("add_37_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(383407680)))]; + tensor add_37_beta_0_to_fp16 = const()[name = tensor("add_37_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(383410304)))]; + tensor add_37_epsilon_0_to_fp16 = const()[name = tensor("add_37_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_37_cast_fp16 = batch_norm(beta = add_37_beta_0_to_fp16, epsilon = add_37_epsilon_0_to_fp16, gamma = add_37_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_73_cast_fp16)[name = tensor("add_37_cast_fp16")]; + tensor input_175_cast_fp16 = silu(x = add_37_cast_fp16)[name = tensor("input_175_cast_fp16")]; + tensor var_1745 = const()[name = tensor("op_1745"), val = tensor([1, 1])]; + tensor var_1747 = const()[name = tensor("op_1747"), val = tensor([1, 1])]; + tensor hidden_states_109_pad_type_0 = const()[name = tensor("hidden_states_109_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_109_pad_0 = const()[name = tensor("hidden_states_109_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_3_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("down_blocks_3_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(383412928)))]; + tensor down_blocks_3_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(412904192)))]; + tensor hidden_states_109_cast_fp16 = conv(bias = down_blocks_3_resnets_0_conv1_bias_to_fp16, dilations = var_1747, groups = var_1729, pad = hidden_states_109_pad_0, pad_type = hidden_states_109_pad_type_0, strides = var_1745, weight = down_blocks_3_resnets_0_conv1_weight_to_fp16, x = input_175_cast_fp16)[name = tensor("hidden_states_109_cast_fp16")]; + tensor var_1753 = const()[name = tensor("op_1753"), val = tensor([1, 1])]; + tensor var_1755 = const()[name = tensor("op_1755"), val = tensor([1, 1])]; + tensor temb_13_pad_type_0 = const()[name = tensor("temb_13_pad_type_0"), val = tensor("custom")]; + tensor temb_13_pad_0 = const()[name = tensor("temb_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_3_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("down_blocks_3_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(412906816)))]; + tensor down_blocks_3_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(416183680)))]; + tensor temb_13_cast_fp16 = conv(bias = down_blocks_3_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_1755, groups = var_1729, pad = temb_13_pad_0, pad_type = temb_13_pad_type_0, strides = var_1753, weight = down_blocks_3_resnets_0_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16)[name = tensor("temb_13_cast_fp16")]; + tensor input_179_cast_fp16 = add(x = hidden_states_109_cast_fp16, y = temb_13_cast_fp16)[name = tensor("input_179_cast_fp16")]; + tensor reshape_76_shape_0 = const()[name = tensor("reshape_76_shape_0"), val = tensor([2, 32, 40, 6, 10])]; + tensor reshape_76_cast_fp16 = reshape(shape = reshape_76_shape_0, x = input_179_cast_fp16)[name = tensor("reshape_76_cast_fp16")]; + tensor reduce_mean_57_axes_0 = const()[name = tensor("reduce_mean_57_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_57_keep_dims_0 = const()[name = tensor("reduce_mean_57_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_57_cast_fp16 = reduce_mean(axes = reduce_mean_57_axes_0, keep_dims = reduce_mean_57_keep_dims_0, x = reshape_76_cast_fp16)[name = tensor("reduce_mean_57_cast_fp16")]; + tensor sub_38_cast_fp16 = sub(x = reshape_76_cast_fp16, y = reduce_mean_57_cast_fp16)[name = tensor("sub_38_cast_fp16")]; + tensor square_19_cast_fp16 = square(x = sub_38_cast_fp16)[name = tensor("square_19_cast_fp16")]; + tensor reduce_mean_59_axes_0 = const()[name = tensor("reduce_mean_59_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_59_keep_dims_0 = const()[name = tensor("reduce_mean_59_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_59_cast_fp16 = reduce_mean(axes = reduce_mean_59_axes_0, keep_dims = reduce_mean_59_keep_dims_0, x = square_19_cast_fp16)[name = tensor("reduce_mean_59_cast_fp16")]; + tensor add_38_y_0_to_fp16 = const()[name = tensor("add_38_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_38_cast_fp16 = add(x = reduce_mean_59_cast_fp16, y = add_38_y_0_to_fp16)[name = tensor("add_38_cast_fp16")]; + tensor sqrt_19_cast_fp16 = sqrt(x = add_38_cast_fp16)[name = tensor("sqrt_19_cast_fp16")]; + tensor real_div_19_cast_fp16 = real_div(x = sub_38_cast_fp16, y = sqrt_19_cast_fp16)[name = tensor("real_div_19_cast_fp16")]; + tensor reshape_77_shape_0 = const()[name = tensor("reshape_77_shape_0"), val = tensor([2, 1280, 6, 10])]; + tensor reshape_77_cast_fp16 = reshape(shape = reshape_77_shape_0, x = real_div_19_cast_fp16)[name = tensor("reshape_77_cast_fp16")]; + tensor add_39_gamma_0_to_fp16 = const()[name = tensor("add_39_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(416186304)))]; + tensor add_39_beta_0_to_fp16 = const()[name = tensor("add_39_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(416188928)))]; + tensor add_39_epsilon_0_to_fp16 = const()[name = tensor("add_39_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_39_cast_fp16 = batch_norm(beta = add_39_beta_0_to_fp16, epsilon = add_39_epsilon_0_to_fp16, gamma = add_39_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_77_cast_fp16)[name = tensor("add_39_cast_fp16")]; + tensor input_183_cast_fp16 = silu(x = add_39_cast_fp16)[name = tensor("input_183_cast_fp16")]; + tensor var_1765 = const()[name = tensor("op_1765"), val = tensor([1, 1])]; + tensor var_1767 = const()[name = tensor("op_1767"), val = tensor([1, 1])]; + tensor hidden_states_111_pad_type_0 = const()[name = tensor("hidden_states_111_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_111_pad_0 = const()[name = tensor("hidden_states_111_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_3_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("down_blocks_3_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(416191552)))]; + tensor down_blocks_3_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(445682816)))]; + tensor hidden_states_111_cast_fp16 = conv(bias = down_blocks_3_resnets_0_conv2_bias_to_fp16, dilations = var_1767, groups = var_1729, pad = hidden_states_111_pad_0, pad_type = hidden_states_111_pad_type_0, strides = var_1765, weight = down_blocks_3_resnets_0_conv2_weight_to_fp16, x = input_183_cast_fp16)[name = tensor("hidden_states_111_cast_fp16")]; + tensor input_185_cast_fp16 = add(x = input_171_cast_fp16, y = hidden_states_111_cast_fp16)[name = tensor("input_185_cast_fp16")]; + tensor reshape_80_shape_0 = const()[name = tensor("reshape_80_shape_0"), val = tensor([2, 32, 40, 6, 10])]; + tensor reshape_80_cast_fp16 = reshape(shape = reshape_80_shape_0, x = input_185_cast_fp16)[name = tensor("reshape_80_cast_fp16")]; + tensor reduce_mean_60_axes_0 = const()[name = tensor("reduce_mean_60_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_60_keep_dims_0 = const()[name = tensor("reduce_mean_60_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_60_cast_fp16 = reduce_mean(axes = reduce_mean_60_axes_0, keep_dims = reduce_mean_60_keep_dims_0, x = reshape_80_cast_fp16)[name = tensor("reduce_mean_60_cast_fp16")]; + tensor sub_40_cast_fp16 = sub(x = reshape_80_cast_fp16, y = reduce_mean_60_cast_fp16)[name = tensor("sub_40_cast_fp16")]; + tensor square_20_cast_fp16 = square(x = sub_40_cast_fp16)[name = tensor("square_20_cast_fp16")]; + tensor reduce_mean_62_axes_0 = const()[name = tensor("reduce_mean_62_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_62_keep_dims_0 = const()[name = tensor("reduce_mean_62_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_62_cast_fp16 = reduce_mean(axes = reduce_mean_62_axes_0, keep_dims = reduce_mean_62_keep_dims_0, x = square_20_cast_fp16)[name = tensor("reduce_mean_62_cast_fp16")]; + tensor add_40_y_0_to_fp16 = const()[name = tensor("add_40_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_40_cast_fp16 = add(x = reduce_mean_62_cast_fp16, y = add_40_y_0_to_fp16)[name = tensor("add_40_cast_fp16")]; + tensor sqrt_20_cast_fp16 = sqrt(x = add_40_cast_fp16)[name = tensor("sqrt_20_cast_fp16")]; + tensor real_div_20_cast_fp16 = real_div(x = sub_40_cast_fp16, y = sqrt_20_cast_fp16)[name = tensor("real_div_20_cast_fp16")]; + tensor reshape_81_shape_0 = const()[name = tensor("reshape_81_shape_0"), val = tensor([2, 1280, 6, 10])]; + tensor reshape_81_cast_fp16 = reshape(shape = reshape_81_shape_0, x = real_div_20_cast_fp16)[name = tensor("reshape_81_cast_fp16")]; + tensor add_41_gamma_0_to_fp16 = const()[name = tensor("add_41_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(445685440)))]; + tensor add_41_beta_0_to_fp16 = const()[name = tensor("add_41_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(445688064)))]; + tensor add_41_epsilon_0_to_fp16 = const()[name = tensor("add_41_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_41_cast_fp16 = batch_norm(beta = add_41_beta_0_to_fp16, epsilon = add_41_epsilon_0_to_fp16, gamma = add_41_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_81_cast_fp16)[name = tensor("add_41_cast_fp16")]; + tensor input_189_cast_fp16 = silu(x = add_41_cast_fp16)[name = tensor("input_189_cast_fp16")]; + tensor var_1782 = const()[name = tensor("op_1782"), val = tensor([1, 1])]; + tensor var_1784 = const()[name = tensor("op_1784"), val = tensor([1, 1])]; + tensor hidden_states_113_pad_type_0 = const()[name = tensor("hidden_states_113_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_113_pad_0 = const()[name = tensor("hidden_states_113_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_3_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("down_blocks_3_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(445690688)))]; + tensor down_blocks_3_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(475181952)))]; + tensor hidden_states_113_cast_fp16 = conv(bias = down_blocks_3_resnets_1_conv1_bias_to_fp16, dilations = var_1784, groups = var_1729, pad = hidden_states_113_pad_0, pad_type = hidden_states_113_pad_type_0, strides = var_1782, weight = down_blocks_3_resnets_1_conv1_weight_to_fp16, x = input_189_cast_fp16)[name = tensor("hidden_states_113_cast_fp16")]; + tensor var_1790 = const()[name = tensor("op_1790"), val = tensor([1, 1])]; + tensor var_1792 = const()[name = tensor("op_1792"), val = tensor([1, 1])]; + tensor temb_15_pad_type_0 = const()[name = tensor("temb_15_pad_type_0"), val = tensor("custom")]; + tensor temb_15_pad_0 = const()[name = tensor("temb_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_3_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("down_blocks_3_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(475184576)))]; + tensor down_blocks_3_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(478461440)))]; + tensor temb_15_cast_fp16 = conv(bias = down_blocks_3_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_1792, groups = var_1729, pad = temb_15_pad_0, pad_type = temb_15_pad_type_0, strides = var_1790, weight = down_blocks_3_resnets_1_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16)[name = tensor("temb_15_cast_fp16")]; + tensor input_193_cast_fp16 = add(x = hidden_states_113_cast_fp16, y = temb_15_cast_fp16)[name = tensor("input_193_cast_fp16")]; + tensor reshape_84_shape_0 = const()[name = tensor("reshape_84_shape_0"), val = tensor([2, 32, 40, 6, 10])]; + tensor reshape_84_cast_fp16 = reshape(shape = reshape_84_shape_0, x = input_193_cast_fp16)[name = tensor("reshape_84_cast_fp16")]; + tensor reduce_mean_63_axes_0 = const()[name = tensor("reduce_mean_63_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_63_keep_dims_0 = const()[name = tensor("reduce_mean_63_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_63_cast_fp16 = reduce_mean(axes = reduce_mean_63_axes_0, keep_dims = reduce_mean_63_keep_dims_0, x = reshape_84_cast_fp16)[name = tensor("reduce_mean_63_cast_fp16")]; + tensor sub_42_cast_fp16 = sub(x = reshape_84_cast_fp16, y = reduce_mean_63_cast_fp16)[name = tensor("sub_42_cast_fp16")]; + tensor square_21_cast_fp16 = square(x = sub_42_cast_fp16)[name = tensor("square_21_cast_fp16")]; + tensor reduce_mean_65_axes_0 = const()[name = tensor("reduce_mean_65_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_65_keep_dims_0 = const()[name = tensor("reduce_mean_65_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_65_cast_fp16 = reduce_mean(axes = reduce_mean_65_axes_0, keep_dims = reduce_mean_65_keep_dims_0, x = square_21_cast_fp16)[name = tensor("reduce_mean_65_cast_fp16")]; + tensor add_42_y_0_to_fp16 = const()[name = tensor("add_42_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_42_cast_fp16 = add(x = reduce_mean_65_cast_fp16, y = add_42_y_0_to_fp16)[name = tensor("add_42_cast_fp16")]; + tensor sqrt_21_cast_fp16 = sqrt(x = add_42_cast_fp16)[name = tensor("sqrt_21_cast_fp16")]; + tensor real_div_21_cast_fp16 = real_div(x = sub_42_cast_fp16, y = sqrt_21_cast_fp16)[name = tensor("real_div_21_cast_fp16")]; + tensor reshape_85_shape_0 = const()[name = tensor("reshape_85_shape_0"), val = tensor([2, 1280, 6, 10])]; + tensor reshape_85_cast_fp16 = reshape(shape = reshape_85_shape_0, x = real_div_21_cast_fp16)[name = tensor("reshape_85_cast_fp16")]; + tensor add_43_gamma_0_to_fp16 = const()[name = tensor("add_43_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(478464064)))]; + tensor add_43_beta_0_to_fp16 = const()[name = tensor("add_43_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(478466688)))]; + tensor add_43_epsilon_0_to_fp16 = const()[name = tensor("add_43_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_43_cast_fp16 = batch_norm(beta = add_43_beta_0_to_fp16, epsilon = add_43_epsilon_0_to_fp16, gamma = add_43_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_85_cast_fp16)[name = tensor("add_43_cast_fp16")]; + tensor input_197_cast_fp16 = silu(x = add_43_cast_fp16)[name = tensor("input_197_cast_fp16")]; + tensor var_1802 = const()[name = tensor("op_1802"), val = tensor([1, 1])]; + tensor var_1804 = const()[name = tensor("op_1804"), val = tensor([1, 1])]; + tensor hidden_states_115_pad_type_0 = const()[name = tensor("hidden_states_115_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_115_pad_0 = const()[name = tensor("hidden_states_115_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_3_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("down_blocks_3_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(478469312)))]; + tensor down_blocks_3_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(507960576)))]; + tensor hidden_states_115_cast_fp16 = conv(bias = down_blocks_3_resnets_1_conv2_bias_to_fp16, dilations = var_1804, groups = var_1729, pad = hidden_states_115_pad_0, pad_type = hidden_states_115_pad_type_0, strides = var_1802, weight = down_blocks_3_resnets_1_conv2_weight_to_fp16, x = input_197_cast_fp16)[name = tensor("hidden_states_115_cast_fp16")]; + tensor input_199_cast_fp16 = add(x = input_185_cast_fp16, y = hidden_states_115_cast_fp16)[name = tensor("input_199_cast_fp16")]; + tensor var_1812 = const()[name = tensor("op_1812"), val = tensor(3)]; + tensor var_1823 = const()[name = tensor("op_1823"), val = tensor(true)]; + tensor var_1828 = const()[name = tensor("op_1828"), val = tensor(1)]; + tensor reshape_88_shape_0 = const()[name = tensor("reshape_88_shape_0"), val = tensor([2, 32, 40, 6, 10])]; + tensor reshape_88_cast_fp16 = reshape(shape = reshape_88_shape_0, x = input_199_cast_fp16)[name = tensor("reshape_88_cast_fp16")]; + tensor reduce_mean_66_axes_0 = const()[name = tensor("reduce_mean_66_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_66_keep_dims_0 = const()[name = tensor("reduce_mean_66_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_66_cast_fp16 = reduce_mean(axes = reduce_mean_66_axes_0, keep_dims = reduce_mean_66_keep_dims_0, x = reshape_88_cast_fp16)[name = tensor("reduce_mean_66_cast_fp16")]; + tensor sub_44_cast_fp16 = sub(x = reshape_88_cast_fp16, y = reduce_mean_66_cast_fp16)[name = tensor("sub_44_cast_fp16")]; + tensor square_22_cast_fp16 = square(x = sub_44_cast_fp16)[name = tensor("square_22_cast_fp16")]; + tensor reduce_mean_68_axes_0 = const()[name = tensor("reduce_mean_68_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_68_keep_dims_0 = const()[name = tensor("reduce_mean_68_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_68_cast_fp16 = reduce_mean(axes = reduce_mean_68_axes_0, keep_dims = reduce_mean_68_keep_dims_0, x = square_22_cast_fp16)[name = tensor("reduce_mean_68_cast_fp16")]; + tensor add_44_y_0_to_fp16 = const()[name = tensor("add_44_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_44_cast_fp16 = add(x = reduce_mean_68_cast_fp16, y = add_44_y_0_to_fp16)[name = tensor("add_44_cast_fp16")]; + tensor sqrt_22_cast_fp16 = sqrt(x = add_44_cast_fp16)[name = tensor("sqrt_22_cast_fp16")]; + tensor real_div_22_cast_fp16 = real_div(x = sub_44_cast_fp16, y = sqrt_22_cast_fp16)[name = tensor("real_div_22_cast_fp16")]; + tensor reshape_89_shape_0 = const()[name = tensor("reshape_89_shape_0"), val = tensor([2, 1280, 6, 10])]; + tensor reshape_89_cast_fp16 = reshape(shape = reshape_89_shape_0, x = real_div_22_cast_fp16)[name = tensor("reshape_89_cast_fp16")]; + tensor add_45_gamma_0_to_fp16 = const()[name = tensor("add_45_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(507963200)))]; + tensor add_45_beta_0_to_fp16 = const()[name = tensor("add_45_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(507965824)))]; + tensor add_45_epsilon_0_to_fp16 = const()[name = tensor("add_45_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_45_cast_fp16 = batch_norm(beta = add_45_beta_0_to_fp16, epsilon = add_45_epsilon_0_to_fp16, gamma = add_45_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_89_cast_fp16)[name = tensor("add_45_cast_fp16")]; + tensor input_203_cast_fp16 = silu(x = add_45_cast_fp16)[name = tensor("input_203_cast_fp16")]; + tensor var_1846 = const()[name = tensor("op_1846"), val = tensor([1, 1])]; + tensor var_1848 = const()[name = tensor("op_1848"), val = tensor([1, 1])]; + tensor hidden_states_117_pad_type_0 = const()[name = tensor("hidden_states_117_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_117_pad_0 = const()[name = tensor("hidden_states_117_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor mid_block_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("mid_block_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(507968448)))]; + tensor mid_block_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("mid_block_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(537459712)))]; + tensor hidden_states_117_cast_fp16 = conv(bias = mid_block_resnets_0_conv1_bias_to_fp16, dilations = var_1848, groups = var_1828, pad = hidden_states_117_pad_0, pad_type = hidden_states_117_pad_type_0, strides = var_1846, weight = mid_block_resnets_0_conv1_weight_to_fp16, x = input_203_cast_fp16)[name = tensor("hidden_states_117_cast_fp16")]; + tensor var_1854 = const()[name = tensor("op_1854"), val = tensor([1, 1])]; + tensor var_1856 = const()[name = tensor("op_1856"), val = tensor([1, 1])]; + tensor temb_17_pad_type_0 = const()[name = tensor("temb_17_pad_type_0"), val = tensor("custom")]; + tensor temb_17_pad_0 = const()[name = tensor("temb_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("mid_block_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(537462336)))]; + tensor mid_block_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("mid_block_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(540739200)))]; + tensor temb_17_cast_fp16 = conv(bias = mid_block_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_1856, groups = var_1828, pad = temb_17_pad_0, pad_type = temb_17_pad_type_0, strides = var_1854, weight = mid_block_resnets_0_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16)[name = tensor("temb_17_cast_fp16")]; + tensor input_207_cast_fp16 = add(x = hidden_states_117_cast_fp16, y = temb_17_cast_fp16)[name = tensor("input_207_cast_fp16")]; + tensor reshape_92_shape_0 = const()[name = tensor("reshape_92_shape_0"), val = tensor([2, 32, 40, 6, 10])]; + tensor reshape_92_cast_fp16 = reshape(shape = reshape_92_shape_0, x = input_207_cast_fp16)[name = tensor("reshape_92_cast_fp16")]; + tensor reduce_mean_69_axes_0 = const()[name = tensor("reduce_mean_69_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_69_keep_dims_0 = const()[name = tensor("reduce_mean_69_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_69_cast_fp16 = reduce_mean(axes = reduce_mean_69_axes_0, keep_dims = reduce_mean_69_keep_dims_0, x = reshape_92_cast_fp16)[name = tensor("reduce_mean_69_cast_fp16")]; + tensor sub_46_cast_fp16 = sub(x = reshape_92_cast_fp16, y = reduce_mean_69_cast_fp16)[name = tensor("sub_46_cast_fp16")]; + tensor square_23_cast_fp16 = square(x = sub_46_cast_fp16)[name = tensor("square_23_cast_fp16")]; + tensor reduce_mean_71_axes_0 = const()[name = tensor("reduce_mean_71_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_71_keep_dims_0 = const()[name = tensor("reduce_mean_71_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_71_cast_fp16 = reduce_mean(axes = reduce_mean_71_axes_0, keep_dims = reduce_mean_71_keep_dims_0, x = square_23_cast_fp16)[name = tensor("reduce_mean_71_cast_fp16")]; + tensor add_46_y_0_to_fp16 = const()[name = tensor("add_46_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_46_cast_fp16 = add(x = reduce_mean_71_cast_fp16, y = add_46_y_0_to_fp16)[name = tensor("add_46_cast_fp16")]; + tensor sqrt_23_cast_fp16 = sqrt(x = add_46_cast_fp16)[name = tensor("sqrt_23_cast_fp16")]; + tensor real_div_23_cast_fp16 = real_div(x = sub_46_cast_fp16, y = sqrt_23_cast_fp16)[name = tensor("real_div_23_cast_fp16")]; + tensor reshape_93_shape_0 = const()[name = tensor("reshape_93_shape_0"), val = tensor([2, 1280, 6, 10])]; + tensor reshape_93_cast_fp16 = reshape(shape = reshape_93_shape_0, x = real_div_23_cast_fp16)[name = tensor("reshape_93_cast_fp16")]; + tensor add_47_gamma_0_to_fp16 = const()[name = tensor("add_47_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(540741824)))]; + tensor add_47_beta_0_to_fp16 = const()[name = tensor("add_47_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(540744448)))]; + tensor add_47_epsilon_0_to_fp16 = const()[name = tensor("add_47_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_47_cast_fp16 = batch_norm(beta = add_47_beta_0_to_fp16, epsilon = add_47_epsilon_0_to_fp16, gamma = add_47_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_93_cast_fp16)[name = tensor("add_47_cast_fp16")]; + tensor input_211_cast_fp16 = silu(x = add_47_cast_fp16)[name = tensor("input_211_cast_fp16")]; + tensor var_1866 = const()[name = tensor("op_1866"), val = tensor([1, 1])]; + tensor var_1868 = const()[name = tensor("op_1868"), val = tensor([1, 1])]; + tensor hidden_states_119_pad_type_0 = const()[name = tensor("hidden_states_119_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_119_pad_0 = const()[name = tensor("hidden_states_119_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor mid_block_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("mid_block_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(540747072)))]; + tensor mid_block_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("mid_block_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(570238336)))]; + tensor hidden_states_119_cast_fp16 = conv(bias = mid_block_resnets_0_conv2_bias_to_fp16, dilations = var_1868, groups = var_1828, pad = hidden_states_119_pad_0, pad_type = hidden_states_119_pad_type_0, strides = var_1866, weight = mid_block_resnets_0_conv2_weight_to_fp16, x = input_211_cast_fp16)[name = tensor("hidden_states_119_cast_fp16")]; + tensor hidden_states_121_cast_fp16 = add(x = input_199_cast_fp16, y = hidden_states_119_cast_fp16)[name = tensor("hidden_states_121_cast_fp16")]; + tensor reshape_96_shape_0 = const()[name = tensor("reshape_96_shape_0"), val = tensor([2, 32, 40, 6, 10])]; + tensor reshape_96_cast_fp16 = reshape(shape = reshape_96_shape_0, x = hidden_states_121_cast_fp16)[name = tensor("reshape_96_cast_fp16")]; + tensor reduce_mean_72_axes_0 = const()[name = tensor("reduce_mean_72_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_72_keep_dims_0 = const()[name = tensor("reduce_mean_72_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_72_cast_fp16 = reduce_mean(axes = reduce_mean_72_axes_0, keep_dims = reduce_mean_72_keep_dims_0, x = reshape_96_cast_fp16)[name = tensor("reduce_mean_72_cast_fp16")]; + tensor sub_48_cast_fp16 = sub(x = reshape_96_cast_fp16, y = reduce_mean_72_cast_fp16)[name = tensor("sub_48_cast_fp16")]; + tensor square_24_cast_fp16 = square(x = sub_48_cast_fp16)[name = tensor("square_24_cast_fp16")]; + tensor reduce_mean_74_axes_0 = const()[name = tensor("reduce_mean_74_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_74_keep_dims_0 = const()[name = tensor("reduce_mean_74_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_74_cast_fp16 = reduce_mean(axes = reduce_mean_74_axes_0, keep_dims = reduce_mean_74_keep_dims_0, x = square_24_cast_fp16)[name = tensor("reduce_mean_74_cast_fp16")]; + tensor add_48_y_0_to_fp16 = const()[name = tensor("add_48_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_48_cast_fp16 = add(x = reduce_mean_74_cast_fp16, y = add_48_y_0_to_fp16)[name = tensor("add_48_cast_fp16")]; + tensor sqrt_24_cast_fp16 = sqrt(x = add_48_cast_fp16)[name = tensor("sqrt_24_cast_fp16")]; + tensor real_div_24_cast_fp16 = real_div(x = sub_48_cast_fp16, y = sqrt_24_cast_fp16)[name = tensor("real_div_24_cast_fp16")]; + tensor reshape_97_shape_0 = const()[name = tensor("reshape_97_shape_0"), val = tensor([2, 1280, 6, 10])]; + tensor reshape_97_cast_fp16 = reshape(shape = reshape_97_shape_0, x = real_div_24_cast_fp16)[name = tensor("reshape_97_cast_fp16")]; + tensor add_49_gamma_0_to_fp16 = const()[name = tensor("add_49_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(570240960)))]; + tensor add_49_beta_0_to_fp16 = const()[name = tensor("add_49_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(570243584)))]; + tensor add_49_epsilon_0_to_fp16 = const()[name = tensor("add_49_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_49_cast_fp16 = batch_norm(beta = add_49_beta_0_to_fp16, epsilon = add_49_epsilon_0_to_fp16, gamma = add_49_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_97_cast_fp16)[name = tensor("add_49_cast_fp16")]; + tensor var_1888 = const()[name = tensor("op_1888"), val = tensor([1, 1])]; + tensor var_1890 = const()[name = tensor("op_1890"), val = tensor([1, 1])]; + tensor hidden_states_123_pad_type_0 = const()[name = tensor("hidden_states_123_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_123_pad_0 = const()[name = tensor("hidden_states_123_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(570246208)))]; + tensor mid_block_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(573523072)))]; + tensor hidden_states_123_cast_fp16 = conv(bias = mid_block_attentions_0_proj_in_bias_to_fp16, dilations = var_1890, groups = var_1828, pad = hidden_states_123_pad_0, pad_type = hidden_states_123_pad_type_0, strides = var_1888, weight = mid_block_attentions_0_proj_in_weight_to_fp16, x = add_49_cast_fp16)[name = tensor("hidden_states_123_cast_fp16")]; + tensor var_1895 = const()[name = tensor("op_1895"), val = tensor([2, 1280, 1, 60])]; + tensor inputs_37_cast_fp16 = reshape(shape = var_1895, x = hidden_states_123_cast_fp16)[name = tensor("inputs_37_cast_fp16")]; + tensor var_1905 = const()[name = tensor("op_1905"), val = tensor([1])]; + tensor channels_mean_37_cast_fp16 = reduce_mean(axes = var_1905, keep_dims = var_1823, x = inputs_37_cast_fp16)[name = tensor("channels_mean_37_cast_fp16")]; + tensor zero_mean_37_cast_fp16 = sub(x = inputs_37_cast_fp16, y = channels_mean_37_cast_fp16)[name = tensor("zero_mean_37_cast_fp16")]; + tensor zero_mean_sq_37_cast_fp16 = mul(x = zero_mean_37_cast_fp16, y = zero_mean_37_cast_fp16)[name = tensor("zero_mean_sq_37_cast_fp16")]; + tensor var_1909 = const()[name = tensor("op_1909"), val = tensor([1])]; + tensor var_1910_cast_fp16 = reduce_mean(axes = var_1909, keep_dims = var_1823, x = zero_mean_sq_37_cast_fp16)[name = tensor("op_1910_cast_fp16")]; + tensor var_1911_to_fp16 = const()[name = tensor("op_1911_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1912_cast_fp16 = add(x = var_1910_cast_fp16, y = var_1911_to_fp16)[name = tensor("op_1912_cast_fp16")]; + tensor denom_37_epsilon_0_to_fp16 = const()[name = tensor("denom_37_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_37_cast_fp16 = rsqrt(epsilon = denom_37_epsilon_0_to_fp16, x = var_1912_cast_fp16)[name = tensor("denom_37_cast_fp16")]; + tensor out_37_cast_fp16 = mul(x = zero_mean_37_cast_fp16, y = denom_37_cast_fp16)[name = tensor("out_37_cast_fp16")]; + tensor var_1916_to_fp16 = const()[name = tensor("op_1916_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(573525696)))]; + tensor var_1917_cast_fp16 = add(x = out_37_cast_fp16, y = var_1916_to_fp16)[name = tensor("op_1917_cast_fp16")]; + tensor var_1919_to_fp16 = const()[name = tensor("op_1919_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(573528320)))]; + tensor hidden_states_125_cast_fp16 = mul(x = var_1917_cast_fp16, y = var_1919_to_fp16)[name = tensor("hidden_states_125_cast_fp16")]; + tensor var_1926 = const()[name = tensor("op_1926"), val = tensor([1, 1])]; + tensor var_1928 = const()[name = tensor("op_1928"), val = tensor([1, 1])]; + tensor q_25_pad_type_0 = const()[name = tensor("q_25_pad_type_0"), val = tensor("custom")]; + tensor q_25_pad_0 = const()[name = tensor("q_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(573530944)))]; + tensor q_25_cast_fp16 = conv(dilations = var_1928, groups = var_1828, pad = q_25_pad_0, pad_type = q_25_pad_type_0, strides = var_1926, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_125_cast_fp16)[name = tensor("q_25_cast_fp16")]; + tensor var_1932 = const()[name = tensor("op_1932"), val = tensor([1, 1])]; + tensor var_1934 = const()[name = tensor("op_1934"), val = tensor([1, 1])]; + tensor k_25_pad_type_0 = const()[name = tensor("k_25_pad_type_0"), val = tensor("custom")]; + tensor k_25_pad_0 = const()[name = tensor("k_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(576807808)))]; + tensor k_25_cast_fp16 = conv(dilations = var_1934, groups = var_1828, pad = k_25_pad_0, pad_type = k_25_pad_type_0, strides = var_1932, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_125_cast_fp16)[name = tensor("k_25_cast_fp16")]; + tensor var_1938 = const()[name = tensor("op_1938"), val = tensor([1, 1])]; + tensor var_1940 = const()[name = tensor("op_1940"), val = tensor([1, 1])]; + tensor v_25_pad_type_0 = const()[name = tensor("v_25_pad_type_0"), val = tensor("custom")]; + tensor v_25_pad_0 = const()[name = tensor("v_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(580084672)))]; + tensor v_25_cast_fp16 = conv(dilations = var_1940, groups = var_1828, pad = v_25_pad_0, pad_type = v_25_pad_type_0, strides = var_1938, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_125_cast_fp16)[name = tensor("v_25_cast_fp16")]; + tensor var_1944 = const()[name = tensor("op_1944"), val = tensor([2, 20, 64, -1])]; + tensor var_1945_cast_fp16 = reshape(shape = var_1944, x = q_25_cast_fp16)[name = tensor("op_1945_cast_fp16")]; + tensor var_1946 = const()[name = tensor("op_1946"), val = tensor([2, 20, 64, -1])]; + tensor var_1947_cast_fp16 = reshape(shape = var_1946, x = k_25_cast_fp16)[name = tensor("op_1947_cast_fp16")]; + tensor var_1948 = const()[name = tensor("op_1948"), val = tensor([2, 20, 64, -1])]; + tensor var_1949_cast_fp16 = reshape(shape = var_1948, x = v_25_cast_fp16)[name = tensor("op_1949_cast_fp16")]; + tensor attn_weights_49_transpose_x_0 = const()[name = tensor("attn_weights_49_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_49_transpose_y_0 = const()[name = tensor("attn_weights_49_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_49_cast_fp16 = matmul(transpose_x = attn_weights_49_transpose_x_0, transpose_y = attn_weights_49_transpose_y_0, x = var_1945_cast_fp16, y = var_1947_cast_fp16)[name = tensor("attn_weights_49_cast_fp16")]; + tensor var_1819_to_fp16 = const()[name = tensor("op_1819_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_51_cast_fp16 = mul(x = attn_weights_49_cast_fp16, y = var_1819_to_fp16)[name = tensor("attn_weights_51_cast_fp16")]; + tensor var_1953_cast_fp16 = softmax(axis = var_1812, x = attn_weights_51_cast_fp16)[name = tensor("op_1953_cast_fp16")]; + tensor attn_25_transpose_x_0 = const()[name = tensor("attn_25_transpose_x_0"), val = tensor(false)]; + tensor attn_25_transpose_y_0 = const()[name = tensor("attn_25_transpose_y_0"), val = tensor(true)]; + tensor attn_25_cast_fp16 = matmul(transpose_x = attn_25_transpose_x_0, transpose_y = attn_25_transpose_y_0, x = var_1949_cast_fp16, y = var_1953_cast_fp16)[name = tensor("attn_25_cast_fp16")]; + tensor var_1957 = const()[name = tensor("op_1957"), val = tensor([2, 1280, 1, -1])]; + tensor input_215_cast_fp16 = reshape(shape = var_1957, x = attn_25_cast_fp16)[name = tensor("input_215_cast_fp16")]; + tensor var_1962 = const()[name = tensor("op_1962"), val = tensor([1, 1])]; + tensor var_1964 = const()[name = tensor("op_1964"), val = tensor([1, 1])]; + tensor var_1966_pad_type_0 = const()[name = tensor("op_1966_pad_type_0"), val = tensor("custom")]; + tensor var_1966_pad_0 = const()[name = tensor("op_1966_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(583361536)))]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(586638400)))]; + tensor var_1966_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_1964, groups = var_1828, pad = var_1966_pad_0, pad_type = var_1966_pad_type_0, strides = var_1962, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_215_cast_fp16)[name = tensor("op_1966_cast_fp16")]; + tensor inputs_39_cast_fp16 = add(x = var_1966_cast_fp16, y = inputs_37_cast_fp16)[name = tensor("inputs_39_cast_fp16")]; + tensor var_1970 = const()[name = tensor("op_1970"), val = tensor([1])]; + tensor channels_mean_39_cast_fp16 = reduce_mean(axes = var_1970, keep_dims = var_1823, x = inputs_39_cast_fp16)[name = tensor("channels_mean_39_cast_fp16")]; + tensor zero_mean_39_cast_fp16 = sub(x = inputs_39_cast_fp16, y = channels_mean_39_cast_fp16)[name = tensor("zero_mean_39_cast_fp16")]; + tensor zero_mean_sq_39_cast_fp16 = mul(x = zero_mean_39_cast_fp16, y = zero_mean_39_cast_fp16)[name = tensor("zero_mean_sq_39_cast_fp16")]; + tensor var_1974 = const()[name = tensor("op_1974"), val = tensor([1])]; + tensor var_1975_cast_fp16 = reduce_mean(axes = var_1974, keep_dims = var_1823, x = zero_mean_sq_39_cast_fp16)[name = tensor("op_1975_cast_fp16")]; + tensor var_1976_to_fp16 = const()[name = tensor("op_1976_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1977_cast_fp16 = add(x = var_1975_cast_fp16, y = var_1976_to_fp16)[name = tensor("op_1977_cast_fp16")]; + tensor denom_39_epsilon_0_to_fp16 = const()[name = tensor("denom_39_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_39_cast_fp16 = rsqrt(epsilon = denom_39_epsilon_0_to_fp16, x = var_1977_cast_fp16)[name = tensor("denom_39_cast_fp16")]; + tensor out_39_cast_fp16 = mul(x = zero_mean_39_cast_fp16, y = denom_39_cast_fp16)[name = tensor("out_39_cast_fp16")]; + tensor var_1981_to_fp16 = const()[name = tensor("op_1981_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(586641024)))]; + tensor var_1982_cast_fp16 = add(x = out_39_cast_fp16, y = var_1981_to_fp16)[name = tensor("op_1982_cast_fp16")]; + tensor var_1984_to_fp16 = const()[name = tensor("op_1984_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(586643648)))]; + tensor hidden_states_127_cast_fp16 = mul(x = var_1982_cast_fp16, y = var_1984_to_fp16)[name = tensor("hidden_states_127_cast_fp16")]; + tensor var_1991 = const()[name = tensor("op_1991"), val = tensor([1, 1])]; + tensor var_1993 = const()[name = tensor("op_1993"), val = tensor([1, 1])]; + tensor q_27_pad_type_0 = const()[name = tensor("q_27_pad_type_0"), val = tensor("custom")]; + tensor q_27_pad_0 = const()[name = tensor("q_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(586646272)))]; + tensor q_27_cast_fp16 = conv(dilations = var_1993, groups = var_1828, pad = q_27_pad_0, pad_type = q_27_pad_type_0, strides = var_1991, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_127_cast_fp16)[name = tensor("q_27_cast_fp16")]; + tensor var_1997 = const()[name = tensor("op_1997"), val = tensor([1, 1])]; + tensor var_1999 = const()[name = tensor("op_1999"), val = tensor([1, 1])]; + tensor k_27_pad_type_0 = const()[name = tensor("k_27_pad_type_0"), val = tensor("custom")]; + tensor k_27_pad_0 = const()[name = tensor("k_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(589923136)))]; + tensor k_27_cast_fp16 = conv(dilations = var_1999, groups = var_1828, pad = k_27_pad_0, pad_type = k_27_pad_type_0, strides = var_1997, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_27_cast_fp16")]; + tensor var_2003 = const()[name = tensor("op_2003"), val = tensor([1, 1])]; + tensor var_2005 = const()[name = tensor("op_2005"), val = tensor([1, 1])]; + tensor v_27_pad_type_0 = const()[name = tensor("v_27_pad_type_0"), val = tensor("custom")]; + tensor v_27_pad_0 = const()[name = tensor("v_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(592544640)))]; + tensor v_27_cast_fp16 = conv(dilations = var_2005, groups = var_1828, pad = v_27_pad_0, pad_type = v_27_pad_type_0, strides = var_2003, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_27_cast_fp16")]; + tensor var_2009 = const()[name = tensor("op_2009"), val = tensor([2, 20, 64, -1])]; + tensor var_2010_cast_fp16 = reshape(shape = var_2009, x = q_27_cast_fp16)[name = tensor("op_2010_cast_fp16")]; + tensor var_2011 = const()[name = tensor("op_2011"), val = tensor([2, 20, 64, -1])]; + tensor var_2012_cast_fp16 = reshape(shape = var_2011, x = k_27_cast_fp16)[name = tensor("op_2012_cast_fp16")]; + tensor var_2013 = const()[name = tensor("op_2013"), val = tensor([2, 20, 64, -1])]; + tensor var_2014_cast_fp16 = reshape(shape = var_2013, x = v_27_cast_fp16)[name = tensor("op_2014_cast_fp16")]; + tensor attn_weights_53_transpose_x_0 = const()[name = tensor("attn_weights_53_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_53_transpose_y_0 = const()[name = tensor("attn_weights_53_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_53_cast_fp16 = matmul(transpose_x = attn_weights_53_transpose_x_0, transpose_y = attn_weights_53_transpose_y_0, x = var_2010_cast_fp16, y = var_2012_cast_fp16)[name = tensor("attn_weights_53_cast_fp16")]; + tensor attn_weights_55_cast_fp16 = mul(x = attn_weights_53_cast_fp16, y = var_1819_to_fp16)[name = tensor("attn_weights_55_cast_fp16")]; + tensor var_2018_cast_fp16 = softmax(axis = var_1812, x = attn_weights_55_cast_fp16)[name = tensor("op_2018_cast_fp16")]; + tensor attn_27_transpose_x_0 = const()[name = tensor("attn_27_transpose_x_0"), val = tensor(false)]; + tensor attn_27_transpose_y_0 = const()[name = tensor("attn_27_transpose_y_0"), val = tensor(true)]; + tensor attn_27_cast_fp16 = matmul(transpose_x = attn_27_transpose_x_0, transpose_y = attn_27_transpose_y_0, x = var_2014_cast_fp16, y = var_2018_cast_fp16)[name = tensor("attn_27_cast_fp16")]; + tensor var_2022 = const()[name = tensor("op_2022"), val = tensor([2, 1280, 1, -1])]; + tensor input_217_cast_fp16 = reshape(shape = var_2022, x = attn_27_cast_fp16)[name = tensor("input_217_cast_fp16")]; + tensor var_2027 = const()[name = tensor("op_2027"), val = tensor([1, 1])]; + tensor var_2029 = const()[name = tensor("op_2029"), val = tensor([1, 1])]; + tensor var_2031_pad_type_0 = const()[name = tensor("op_2031_pad_type_0"), val = tensor("custom")]; + tensor var_2031_pad_0 = const()[name = tensor("op_2031_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(595166144)))]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(598443008)))]; + tensor var_2031_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_2029, groups = var_1828, pad = var_2031_pad_0, pad_type = var_2031_pad_type_0, strides = var_2027, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_217_cast_fp16)[name = tensor("op_2031_cast_fp16")]; + tensor inputs_41_cast_fp16 = add(x = var_2031_cast_fp16, y = inputs_39_cast_fp16)[name = tensor("inputs_41_cast_fp16")]; + tensor var_2035 = const()[name = tensor("op_2035"), val = tensor([1])]; + tensor channels_mean_41_cast_fp16 = reduce_mean(axes = var_2035, keep_dims = var_1823, x = inputs_41_cast_fp16)[name = tensor("channels_mean_41_cast_fp16")]; + tensor zero_mean_41_cast_fp16 = sub(x = inputs_41_cast_fp16, y = channels_mean_41_cast_fp16)[name = tensor("zero_mean_41_cast_fp16")]; + tensor zero_mean_sq_41_cast_fp16 = mul(x = zero_mean_41_cast_fp16, y = zero_mean_41_cast_fp16)[name = tensor("zero_mean_sq_41_cast_fp16")]; + tensor var_2039 = const()[name = tensor("op_2039"), val = tensor([1])]; + tensor var_2040_cast_fp16 = reduce_mean(axes = var_2039, keep_dims = var_1823, x = zero_mean_sq_41_cast_fp16)[name = tensor("op_2040_cast_fp16")]; + tensor var_2041_to_fp16 = const()[name = tensor("op_2041_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2042_cast_fp16 = add(x = var_2040_cast_fp16, y = var_2041_to_fp16)[name = tensor("op_2042_cast_fp16")]; + tensor denom_41_epsilon_0_to_fp16 = const()[name = tensor("denom_41_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_41_cast_fp16 = rsqrt(epsilon = denom_41_epsilon_0_to_fp16, x = var_2042_cast_fp16)[name = tensor("denom_41_cast_fp16")]; + tensor out_41_cast_fp16 = mul(x = zero_mean_41_cast_fp16, y = denom_41_cast_fp16)[name = tensor("out_41_cast_fp16")]; + tensor var_2046_to_fp16 = const()[name = tensor("op_2046_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(598445632)))]; + tensor var_2047_cast_fp16 = add(x = out_41_cast_fp16, y = var_2046_to_fp16)[name = tensor("op_2047_cast_fp16")]; + tensor var_2049_to_fp16 = const()[name = tensor("op_2049_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(598448256)))]; + tensor input_219_cast_fp16 = mul(x = var_2047_cast_fp16, y = var_2049_to_fp16)[name = tensor("input_219_cast_fp16")]; + tensor var_2057 = const()[name = tensor("op_2057"), val = tensor([1, 1])]; + tensor var_2059 = const()[name = tensor("op_2059"), val = tensor([1, 1])]; + tensor var_2061_pad_type_0 = const()[name = tensor("op_2061_pad_type_0"), val = tensor("custom")]; + tensor var_2061_pad_0 = const()[name = tensor("op_2061_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(598450880)))]; + tensor mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(624665344)))]; + tensor var_2061_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_2059, groups = var_1828, pad = var_2061_pad_0, pad_type = var_2061_pad_type_0, strides = var_2057, weight = mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_219_cast_fp16)[name = tensor("op_2061_cast_fp16")]; + tensor var_2062_split_sizes_0 = const()[name = tensor("op_2062_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2062_axis_0 = const()[name = tensor("op_2062_axis_0"), val = tensor(1)]; + tensor var_2062_cast_fp16_0, tensor var_2062_cast_fp16_1 = split(axis = var_2062_axis_0, split_sizes = var_2062_split_sizes_0, x = var_2061_cast_fp16)[name = tensor("op_2062_cast_fp16")]; + tensor var_2064_mode_0 = const()[name = tensor("op_2064_mode_0"), val = tensor("EXACT")]; + tensor var_2064_cast_fp16 = gelu(mode = var_2064_mode_0, x = var_2062_cast_fp16_1)[name = tensor("op_2064_cast_fp16")]; + tensor input_221_cast_fp16 = mul(x = var_2062_cast_fp16_0, y = var_2064_cast_fp16)[name = tensor("input_221_cast_fp16")]; + tensor var_2068 = const()[name = tensor("op_2068"), val = tensor([1, 1])]; + tensor var_2070 = const()[name = tensor("op_2070"), val = tensor([1, 1])]; + tensor var_2072_pad_type_0 = const()[name = tensor("op_2072_pad_type_0"), val = tensor("custom")]; + tensor var_2072_pad_0 = const()[name = tensor("op_2072_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(624685888)))]; + tensor mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(637793152)))]; + tensor var_2072_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_2070, groups = var_1828, pad = var_2072_pad_0, pad_type = var_2072_pad_type_0, strides = var_2068, weight = mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_221_cast_fp16)[name = tensor("op_2072_cast_fp16")]; + tensor hidden_states_131_cast_fp16 = add(x = var_2072_cast_fp16, y = inputs_41_cast_fp16)[name = tensor("hidden_states_131_cast_fp16")]; + tensor var_2074 = const()[name = tensor("op_2074"), val = tensor([2, 1280, 6, 10])]; + tensor input_223_cast_fp16 = reshape(shape = var_2074, x = hidden_states_131_cast_fp16)[name = tensor("input_223_cast_fp16")]; + tensor var_2078 = const()[name = tensor("op_2078"), val = tensor([1, 1])]; + tensor var_2080 = const()[name = tensor("op_2080"), val = tensor([1, 1])]; + tensor hidden_states_133_pad_type_0 = const()[name = tensor("hidden_states_133_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_133_pad_0 = const()[name = tensor("hidden_states_133_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(637795776)))]; + tensor mid_block_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(641072640)))]; + tensor hidden_states_133_cast_fp16 = conv(bias = mid_block_attentions_0_proj_out_bias_to_fp16, dilations = var_2080, groups = var_1828, pad = hidden_states_133_pad_0, pad_type = hidden_states_133_pad_type_0, strides = var_2078, weight = mid_block_attentions_0_proj_out_weight_to_fp16, x = input_223_cast_fp16)[name = tensor("hidden_states_133_cast_fp16")]; + tensor input_225_cast_fp16 = add(x = hidden_states_133_cast_fp16, y = hidden_states_121_cast_fp16)[name = tensor("input_225_cast_fp16")]; + tensor reshape_100_shape_0 = const()[name = tensor("reshape_100_shape_0"), val = tensor([2, 32, 40, 6, 10])]; + tensor reshape_100_cast_fp16 = reshape(shape = reshape_100_shape_0, x = input_225_cast_fp16)[name = tensor("reshape_100_cast_fp16")]; + tensor reduce_mean_75_axes_0 = const()[name = tensor("reduce_mean_75_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_75_keep_dims_0 = const()[name = tensor("reduce_mean_75_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_75_cast_fp16 = reduce_mean(axes = reduce_mean_75_axes_0, keep_dims = reduce_mean_75_keep_dims_0, x = reshape_100_cast_fp16)[name = tensor("reduce_mean_75_cast_fp16")]; + tensor sub_50_cast_fp16 = sub(x = reshape_100_cast_fp16, y = reduce_mean_75_cast_fp16)[name = tensor("sub_50_cast_fp16")]; + tensor square_25_cast_fp16 = square(x = sub_50_cast_fp16)[name = tensor("square_25_cast_fp16")]; + tensor reduce_mean_77_axes_0 = const()[name = tensor("reduce_mean_77_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_77_keep_dims_0 = const()[name = tensor("reduce_mean_77_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_77_cast_fp16 = reduce_mean(axes = reduce_mean_77_axes_0, keep_dims = reduce_mean_77_keep_dims_0, x = square_25_cast_fp16)[name = tensor("reduce_mean_77_cast_fp16")]; + tensor add_50_y_0_to_fp16 = const()[name = tensor("add_50_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_50_cast_fp16 = add(x = reduce_mean_77_cast_fp16, y = add_50_y_0_to_fp16)[name = tensor("add_50_cast_fp16")]; + tensor sqrt_25_cast_fp16 = sqrt(x = add_50_cast_fp16)[name = tensor("sqrt_25_cast_fp16")]; + tensor real_div_25_cast_fp16 = real_div(x = sub_50_cast_fp16, y = sqrt_25_cast_fp16)[name = tensor("real_div_25_cast_fp16")]; + tensor reshape_101_shape_0 = const()[name = tensor("reshape_101_shape_0"), val = tensor([2, 1280, 6, 10])]; + tensor reshape_101_cast_fp16 = reshape(shape = reshape_101_shape_0, x = real_div_25_cast_fp16)[name = tensor("reshape_101_cast_fp16")]; + tensor add_51_gamma_0_to_fp16 = const()[name = tensor("add_51_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(641075264)))]; + tensor add_51_beta_0_to_fp16 = const()[name = tensor("add_51_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(641077888)))]; + tensor add_51_epsilon_0_to_fp16 = const()[name = tensor("add_51_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_51_cast_fp16 = batch_norm(beta = add_51_beta_0_to_fp16, epsilon = add_51_epsilon_0_to_fp16, gamma = add_51_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_101_cast_fp16)[name = tensor("add_51_cast_fp16")]; + tensor input_229_cast_fp16 = silu(x = add_51_cast_fp16)[name = tensor("input_229_cast_fp16")]; + tensor var_2095 = const()[name = tensor("op_2095"), val = tensor([1, 1])]; + tensor var_2097 = const()[name = tensor("op_2097"), val = tensor([1, 1])]; + tensor hidden_states_135_pad_type_0 = const()[name = tensor("hidden_states_135_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_135_pad_0 = const()[name = tensor("hidden_states_135_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor mid_block_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("mid_block_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(641080512)))]; + tensor mid_block_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("mid_block_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(670571776)))]; + tensor hidden_states_135_cast_fp16 = conv(bias = mid_block_resnets_1_conv1_bias_to_fp16, dilations = var_2097, groups = var_1828, pad = hidden_states_135_pad_0, pad_type = hidden_states_135_pad_type_0, strides = var_2095, weight = mid_block_resnets_1_conv1_weight_to_fp16, x = input_229_cast_fp16)[name = tensor("hidden_states_135_cast_fp16")]; + tensor var_2103 = const()[name = tensor("op_2103"), val = tensor([1, 1])]; + tensor var_2105 = const()[name = tensor("op_2105"), val = tensor([1, 1])]; + tensor temb_19_pad_type_0 = const()[name = tensor("temb_19_pad_type_0"), val = tensor("custom")]; + tensor temb_19_pad_0 = const()[name = tensor("temb_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("mid_block_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(670574400)))]; + tensor mid_block_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("mid_block_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(673851264)))]; + tensor temb_19_cast_fp16 = conv(bias = mid_block_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_2105, groups = var_1828, pad = temb_19_pad_0, pad_type = temb_19_pad_type_0, strides = var_2103, weight = mid_block_resnets_1_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16)[name = tensor("temb_19_cast_fp16")]; + tensor input_233_cast_fp16 = add(x = hidden_states_135_cast_fp16, y = temb_19_cast_fp16)[name = tensor("input_233_cast_fp16")]; + tensor reshape_104_shape_0 = const()[name = tensor("reshape_104_shape_0"), val = tensor([2, 32, 40, 6, 10])]; + tensor reshape_104_cast_fp16 = reshape(shape = reshape_104_shape_0, x = input_233_cast_fp16)[name = tensor("reshape_104_cast_fp16")]; + tensor reduce_mean_78_axes_0 = const()[name = tensor("reduce_mean_78_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_78_keep_dims_0 = const()[name = tensor("reduce_mean_78_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_78_cast_fp16 = reduce_mean(axes = reduce_mean_78_axes_0, keep_dims = reduce_mean_78_keep_dims_0, x = reshape_104_cast_fp16)[name = tensor("reduce_mean_78_cast_fp16")]; + tensor sub_52_cast_fp16 = sub(x = reshape_104_cast_fp16, y = reduce_mean_78_cast_fp16)[name = tensor("sub_52_cast_fp16")]; + tensor square_26_cast_fp16 = square(x = sub_52_cast_fp16)[name = tensor("square_26_cast_fp16")]; + tensor reduce_mean_80_axes_0 = const()[name = tensor("reduce_mean_80_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_80_keep_dims_0 = const()[name = tensor("reduce_mean_80_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_80_cast_fp16 = reduce_mean(axes = reduce_mean_80_axes_0, keep_dims = reduce_mean_80_keep_dims_0, x = square_26_cast_fp16)[name = tensor("reduce_mean_80_cast_fp16")]; + tensor add_52_y_0_to_fp16 = const()[name = tensor("add_52_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_52_cast_fp16 = add(x = reduce_mean_80_cast_fp16, y = add_52_y_0_to_fp16)[name = tensor("add_52_cast_fp16")]; + tensor sqrt_26_cast_fp16 = sqrt(x = add_52_cast_fp16)[name = tensor("sqrt_26_cast_fp16")]; + tensor real_div_26_cast_fp16 = real_div(x = sub_52_cast_fp16, y = sqrt_26_cast_fp16)[name = tensor("real_div_26_cast_fp16")]; + tensor reshape_105_shape_0 = const()[name = tensor("reshape_105_shape_0"), val = tensor([2, 1280, 6, 10])]; + tensor reshape_105_cast_fp16 = reshape(shape = reshape_105_shape_0, x = real_div_26_cast_fp16)[name = tensor("reshape_105_cast_fp16")]; + tensor add_53_gamma_0_to_fp16 = const()[name = tensor("add_53_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(673853888)))]; + tensor add_53_beta_0_to_fp16 = const()[name = tensor("add_53_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(673856512)))]; + tensor add_53_epsilon_0_to_fp16 = const()[name = tensor("add_53_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_53_cast_fp16 = batch_norm(beta = add_53_beta_0_to_fp16, epsilon = add_53_epsilon_0_to_fp16, gamma = add_53_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_105_cast_fp16)[name = tensor("add_53_cast_fp16")]; + tensor input_237_cast_fp16 = silu(x = add_53_cast_fp16)[name = tensor("input_237_cast_fp16")]; + tensor var_2115 = const()[name = tensor("op_2115"), val = tensor([1, 1])]; + tensor var_2117 = const()[name = tensor("op_2117"), val = tensor([1, 1])]; + tensor hidden_states_137_pad_type_0 = const()[name = tensor("hidden_states_137_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_137_pad_0 = const()[name = tensor("hidden_states_137_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor mid_block_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("mid_block_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(673859136)))]; + tensor mid_block_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("mid_block_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(703350400)))]; + tensor hidden_states_137_cast_fp16 = conv(bias = mid_block_resnets_1_conv2_bias_to_fp16, dilations = var_2117, groups = var_1828, pad = hidden_states_137_pad_0, pad_type = hidden_states_137_pad_type_0, strides = var_2115, weight = mid_block_resnets_1_conv2_weight_to_fp16, x = input_237_cast_fp16)[name = tensor("hidden_states_137_cast_fp16")]; + tensor hidden_states_139_cast_fp16 = add(x = input_225_cast_fp16, y = hidden_states_137_cast_fp16)[name = tensor("hidden_states_139_cast_fp16")]; + tensor var_2128 = const()[name = tensor("op_2128"), val = tensor(1)]; + tensor input_239_interleave_0 = const()[name = tensor("input_239_interleave_0"), val = tensor(false)]; + tensor input_239_cast_fp16 = concat(axis = var_2128, interleave = input_239_interleave_0, values = (hidden_states_139_cast_fp16, input_199_cast_fp16))[name = tensor("input_239_cast_fp16")]; + tensor reshape_108_shape_0 = const()[name = tensor("reshape_108_shape_0"), val = tensor([2, 32, 80, 6, 10])]; + tensor reshape_108_cast_fp16 = reshape(shape = reshape_108_shape_0, x = input_239_cast_fp16)[name = tensor("reshape_108_cast_fp16")]; + tensor reduce_mean_81_axes_0 = const()[name = tensor("reduce_mean_81_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_81_keep_dims_0 = const()[name = tensor("reduce_mean_81_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_81_cast_fp16 = reduce_mean(axes = reduce_mean_81_axes_0, keep_dims = reduce_mean_81_keep_dims_0, x = reshape_108_cast_fp16)[name = tensor("reduce_mean_81_cast_fp16")]; + tensor sub_54_cast_fp16 = sub(x = reshape_108_cast_fp16, y = reduce_mean_81_cast_fp16)[name = tensor("sub_54_cast_fp16")]; + tensor square_27_cast_fp16 = square(x = sub_54_cast_fp16)[name = tensor("square_27_cast_fp16")]; + tensor reduce_mean_83_axes_0 = const()[name = tensor("reduce_mean_83_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_83_keep_dims_0 = const()[name = tensor("reduce_mean_83_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_83_cast_fp16 = reduce_mean(axes = reduce_mean_83_axes_0, keep_dims = reduce_mean_83_keep_dims_0, x = square_27_cast_fp16)[name = tensor("reduce_mean_83_cast_fp16")]; + tensor add_54_y_0_to_fp16 = const()[name = tensor("add_54_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_54_cast_fp16 = add(x = reduce_mean_83_cast_fp16, y = add_54_y_0_to_fp16)[name = tensor("add_54_cast_fp16")]; + tensor sqrt_27_cast_fp16 = sqrt(x = add_54_cast_fp16)[name = tensor("sqrt_27_cast_fp16")]; + tensor real_div_27_cast_fp16 = real_div(x = sub_54_cast_fp16, y = sqrt_27_cast_fp16)[name = tensor("real_div_27_cast_fp16")]; + tensor reshape_109_shape_0 = const()[name = tensor("reshape_109_shape_0"), val = tensor([2, 2560, 6, 10])]; + tensor reshape_109_cast_fp16 = reshape(shape = reshape_109_shape_0, x = real_div_27_cast_fp16)[name = tensor("reshape_109_cast_fp16")]; + tensor add_55_mean_0_to_fp16 = const()[name = tensor("add_55_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(703353024)))]; + tensor add_55_variance_0_to_fp16 = const()[name = tensor("add_55_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(703358208)))]; + tensor add_55_gamma_0_to_fp16 = const()[name = tensor("add_55_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(703363392)))]; + tensor add_55_beta_0_to_fp16 = const()[name = tensor("add_55_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(703368576)))]; + tensor add_55_epsilon_0_to_fp16 = const()[name = tensor("add_55_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_55_cast_fp16 = batch_norm(beta = add_55_beta_0_to_fp16, epsilon = add_55_epsilon_0_to_fp16, gamma = add_55_gamma_0_to_fp16, mean = add_55_mean_0_to_fp16, variance = add_55_variance_0_to_fp16, x = reshape_109_cast_fp16)[name = tensor("add_55_cast_fp16")]; + tensor input_243_cast_fp16 = silu(x = add_55_cast_fp16)[name = tensor("input_243_cast_fp16")]; + tensor var_2151 = const()[name = tensor("op_2151"), val = tensor([1, 1])]; + tensor var_2153 = const()[name = tensor("op_2153"), val = tensor([1, 1])]; + tensor hidden_states_141_pad_type_0 = const()[name = tensor("hidden_states_141_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_141_pad_0 = const()[name = tensor("hidden_states_141_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(703373760)))]; + tensor up_blocks_0_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(762356224)))]; + tensor hidden_states_141_cast_fp16 = conv(bias = up_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = var_2153, groups = var_2128, pad = hidden_states_141_pad_0, pad_type = hidden_states_141_pad_type_0, strides = var_2151, weight = up_blocks_0_resnets_0_conv1_weight_to_fp16, x = input_243_cast_fp16)[name = tensor("hidden_states_141_cast_fp16")]; + tensor var_2159 = const()[name = tensor("op_2159"), val = tensor([1, 1])]; + tensor var_2161 = const()[name = tensor("op_2161"), val = tensor([1, 1])]; + tensor temb_21_pad_type_0 = const()[name = tensor("temb_21_pad_type_0"), val = tensor("custom")]; + tensor temb_21_pad_0 = const()[name = tensor("temb_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(762358848)))]; + tensor up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(765635712)))]; + tensor temb_21_cast_fp16 = conv(bias = up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_2161, groups = var_2128, pad = temb_21_pad_0, pad_type = temb_21_pad_type_0, strides = var_2159, weight = up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16)[name = tensor("temb_21_cast_fp16")]; + tensor input_247_cast_fp16 = add(x = hidden_states_141_cast_fp16, y = temb_21_cast_fp16)[name = tensor("input_247_cast_fp16")]; + tensor reshape_112_shape_0 = const()[name = tensor("reshape_112_shape_0"), val = tensor([2, 32, 40, 6, 10])]; + tensor reshape_112_cast_fp16 = reshape(shape = reshape_112_shape_0, x = input_247_cast_fp16)[name = tensor("reshape_112_cast_fp16")]; + tensor reduce_mean_84_axes_0 = const()[name = tensor("reduce_mean_84_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_84_keep_dims_0 = const()[name = tensor("reduce_mean_84_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_84_cast_fp16 = reduce_mean(axes = reduce_mean_84_axes_0, keep_dims = reduce_mean_84_keep_dims_0, x = reshape_112_cast_fp16)[name = tensor("reduce_mean_84_cast_fp16")]; + tensor sub_56_cast_fp16 = sub(x = reshape_112_cast_fp16, y = reduce_mean_84_cast_fp16)[name = tensor("sub_56_cast_fp16")]; + tensor square_28_cast_fp16 = square(x = sub_56_cast_fp16)[name = tensor("square_28_cast_fp16")]; + tensor reduce_mean_86_axes_0 = const()[name = tensor("reduce_mean_86_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_86_keep_dims_0 = const()[name = tensor("reduce_mean_86_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_86_cast_fp16 = reduce_mean(axes = reduce_mean_86_axes_0, keep_dims = reduce_mean_86_keep_dims_0, x = square_28_cast_fp16)[name = tensor("reduce_mean_86_cast_fp16")]; + tensor add_56_y_0_to_fp16 = const()[name = tensor("add_56_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_56_cast_fp16 = add(x = reduce_mean_86_cast_fp16, y = add_56_y_0_to_fp16)[name = tensor("add_56_cast_fp16")]; + tensor sqrt_28_cast_fp16 = sqrt(x = add_56_cast_fp16)[name = tensor("sqrt_28_cast_fp16")]; + tensor real_div_28_cast_fp16 = real_div(x = sub_56_cast_fp16, y = sqrt_28_cast_fp16)[name = tensor("real_div_28_cast_fp16")]; + tensor reshape_113_shape_0 = const()[name = tensor("reshape_113_shape_0"), val = tensor([2, 1280, 6, 10])]; + tensor reshape_113_cast_fp16 = reshape(shape = reshape_113_shape_0, x = real_div_28_cast_fp16)[name = tensor("reshape_113_cast_fp16")]; + tensor add_57_gamma_0_to_fp16 = const()[name = tensor("add_57_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(765638336)))]; + tensor add_57_beta_0_to_fp16 = const()[name = tensor("add_57_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(765640960)))]; + tensor add_57_epsilon_0_to_fp16 = const()[name = tensor("add_57_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_57_cast_fp16 = batch_norm(beta = add_57_beta_0_to_fp16, epsilon = add_57_epsilon_0_to_fp16, gamma = add_57_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_113_cast_fp16)[name = tensor("add_57_cast_fp16")]; + tensor input_251_cast_fp16 = silu(x = add_57_cast_fp16)[name = tensor("input_251_cast_fp16")]; + tensor var_2171 = const()[name = tensor("op_2171"), val = tensor([1, 1])]; + tensor var_2173 = const()[name = tensor("op_2173"), val = tensor([1, 1])]; + tensor hidden_states_143_pad_type_0 = const()[name = tensor("hidden_states_143_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_143_pad_0 = const()[name = tensor("hidden_states_143_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(765643584)))]; + tensor up_blocks_0_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(795134848)))]; + tensor hidden_states_143_cast_fp16 = conv(bias = up_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = var_2173, groups = var_2128, pad = hidden_states_143_pad_0, pad_type = hidden_states_143_pad_type_0, strides = var_2171, weight = up_blocks_0_resnets_0_conv2_weight_to_fp16, x = input_251_cast_fp16)[name = tensor("hidden_states_143_cast_fp16")]; + tensor var_2178 = const()[name = tensor("op_2178"), val = tensor([1, 1])]; + tensor var_2180 = const()[name = tensor("op_2180"), val = tensor([1, 1])]; + tensor x_5_pad_type_0 = const()[name = tensor("x_5_pad_type_0"), val = tensor("custom")]; + tensor x_5_pad_0 = const()[name = tensor("x_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(795137472)))]; + tensor up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(801691136)))]; + tensor x_5_cast_fp16 = conv(bias = up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_2180, groups = var_2128, pad = x_5_pad_0, pad_type = x_5_pad_type_0, strides = var_2178, weight = up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16, x = input_239_cast_fp16)[name = tensor("x_5_cast_fp16")]; + tensor hidden_states_145_cast_fp16 = add(x = x_5_cast_fp16, y = hidden_states_143_cast_fp16)[name = tensor("hidden_states_145_cast_fp16")]; + tensor input_253_interleave_0 = const()[name = tensor("input_253_interleave_0"), val = tensor(false)]; + tensor input_253_cast_fp16 = concat(axis = var_2128, interleave = input_253_interleave_0, values = (hidden_states_145_cast_fp16, input_185_cast_fp16))[name = tensor("input_253_cast_fp16")]; + tensor reshape_116_shape_0 = const()[name = tensor("reshape_116_shape_0"), val = tensor([2, 32, 80, 6, 10])]; + tensor reshape_116_cast_fp16 = reshape(shape = reshape_116_shape_0, x = input_253_cast_fp16)[name = tensor("reshape_116_cast_fp16")]; + tensor reduce_mean_87_axes_0 = const()[name = tensor("reduce_mean_87_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_87_keep_dims_0 = const()[name = tensor("reduce_mean_87_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_87_cast_fp16 = reduce_mean(axes = reduce_mean_87_axes_0, keep_dims = reduce_mean_87_keep_dims_0, x = reshape_116_cast_fp16)[name = tensor("reduce_mean_87_cast_fp16")]; + tensor sub_58_cast_fp16 = sub(x = reshape_116_cast_fp16, y = reduce_mean_87_cast_fp16)[name = tensor("sub_58_cast_fp16")]; + tensor square_29_cast_fp16 = square(x = sub_58_cast_fp16)[name = tensor("square_29_cast_fp16")]; + tensor reduce_mean_89_axes_0 = const()[name = tensor("reduce_mean_89_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_89_keep_dims_0 = const()[name = tensor("reduce_mean_89_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_89_cast_fp16 = reduce_mean(axes = reduce_mean_89_axes_0, keep_dims = reduce_mean_89_keep_dims_0, x = square_29_cast_fp16)[name = tensor("reduce_mean_89_cast_fp16")]; + tensor add_58_y_0_to_fp16 = const()[name = tensor("add_58_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_58_cast_fp16 = add(x = reduce_mean_89_cast_fp16, y = add_58_y_0_to_fp16)[name = tensor("add_58_cast_fp16")]; + tensor sqrt_29_cast_fp16 = sqrt(x = add_58_cast_fp16)[name = tensor("sqrt_29_cast_fp16")]; + tensor real_div_29_cast_fp16 = real_div(x = sub_58_cast_fp16, y = sqrt_29_cast_fp16)[name = tensor("real_div_29_cast_fp16")]; + tensor reshape_117_shape_0 = const()[name = tensor("reshape_117_shape_0"), val = tensor([2, 2560, 6, 10])]; + tensor reshape_117_cast_fp16 = reshape(shape = reshape_117_shape_0, x = real_div_29_cast_fp16)[name = tensor("reshape_117_cast_fp16")]; + tensor add_59_gamma_0_to_fp16 = const()[name = tensor("add_59_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(801693760)))]; + tensor add_59_beta_0_to_fp16 = const()[name = tensor("add_59_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(801698944)))]; + tensor add_59_epsilon_0_to_fp16 = const()[name = tensor("add_59_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_59_cast_fp16 = batch_norm(beta = add_59_beta_0_to_fp16, epsilon = add_59_epsilon_0_to_fp16, gamma = add_59_gamma_0_to_fp16, mean = add_55_mean_0_to_fp16, variance = add_55_variance_0_to_fp16, x = reshape_117_cast_fp16)[name = tensor("add_59_cast_fp16")]; + tensor input_257_cast_fp16 = silu(x = add_59_cast_fp16)[name = tensor("input_257_cast_fp16")]; + tensor var_2198 = const()[name = tensor("op_2198"), val = tensor([1, 1])]; + tensor var_2200 = const()[name = tensor("op_2200"), val = tensor([1, 1])]; + tensor hidden_states_147_pad_type_0 = const()[name = tensor("hidden_states_147_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_147_pad_0 = const()[name = tensor("hidden_states_147_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(801704128)))]; + tensor up_blocks_0_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(860686592)))]; + tensor hidden_states_147_cast_fp16 = conv(bias = up_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = var_2200, groups = var_2128, pad = hidden_states_147_pad_0, pad_type = hidden_states_147_pad_type_0, strides = var_2198, weight = up_blocks_0_resnets_1_conv1_weight_to_fp16, x = input_257_cast_fp16)[name = tensor("hidden_states_147_cast_fp16")]; + tensor var_2206 = const()[name = tensor("op_2206"), val = tensor([1, 1])]; + tensor var_2208 = const()[name = tensor("op_2208"), val = tensor([1, 1])]; + tensor temb_23_pad_type_0 = const()[name = tensor("temb_23_pad_type_0"), val = tensor("custom")]; + tensor temb_23_pad_0 = const()[name = tensor("temb_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(860689216)))]; + tensor up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(863966080)))]; + tensor temb_23_cast_fp16 = conv(bias = up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_2208, groups = var_2128, pad = temb_23_pad_0, pad_type = temb_23_pad_type_0, strides = var_2206, weight = up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16)[name = tensor("temb_23_cast_fp16")]; + tensor input_261_cast_fp16 = add(x = hidden_states_147_cast_fp16, y = temb_23_cast_fp16)[name = tensor("input_261_cast_fp16")]; + tensor reshape_120_shape_0 = const()[name = tensor("reshape_120_shape_0"), val = tensor([2, 32, 40, 6, 10])]; + tensor reshape_120_cast_fp16 = reshape(shape = reshape_120_shape_0, x = input_261_cast_fp16)[name = tensor("reshape_120_cast_fp16")]; + tensor reduce_mean_90_axes_0 = const()[name = tensor("reduce_mean_90_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_90_keep_dims_0 = const()[name = tensor("reduce_mean_90_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_90_cast_fp16 = reduce_mean(axes = reduce_mean_90_axes_0, keep_dims = reduce_mean_90_keep_dims_0, x = reshape_120_cast_fp16)[name = tensor("reduce_mean_90_cast_fp16")]; + tensor sub_60_cast_fp16 = sub(x = reshape_120_cast_fp16, y = reduce_mean_90_cast_fp16)[name = tensor("sub_60_cast_fp16")]; + tensor square_30_cast_fp16 = square(x = sub_60_cast_fp16)[name = tensor("square_30_cast_fp16")]; + tensor reduce_mean_92_axes_0 = const()[name = tensor("reduce_mean_92_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_92_keep_dims_0 = const()[name = tensor("reduce_mean_92_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_92_cast_fp16 = reduce_mean(axes = reduce_mean_92_axes_0, keep_dims = reduce_mean_92_keep_dims_0, x = square_30_cast_fp16)[name = tensor("reduce_mean_92_cast_fp16")]; + tensor add_60_y_0_to_fp16 = const()[name = tensor("add_60_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_60_cast_fp16 = add(x = reduce_mean_92_cast_fp16, y = add_60_y_0_to_fp16)[name = tensor("add_60_cast_fp16")]; + tensor sqrt_30_cast_fp16 = sqrt(x = add_60_cast_fp16)[name = tensor("sqrt_30_cast_fp16")]; + tensor real_div_30_cast_fp16 = real_div(x = sub_60_cast_fp16, y = sqrt_30_cast_fp16)[name = tensor("real_div_30_cast_fp16")]; + tensor reshape_121_shape_0 = const()[name = tensor("reshape_121_shape_0"), val = tensor([2, 1280, 6, 10])]; + tensor reshape_121_cast_fp16 = reshape(shape = reshape_121_shape_0, x = real_div_30_cast_fp16)[name = tensor("reshape_121_cast_fp16")]; + tensor add_61_gamma_0_to_fp16 = const()[name = tensor("add_61_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(863968704)))]; + tensor add_61_beta_0_to_fp16 = const()[name = tensor("add_61_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(863971328)))]; + tensor add_61_epsilon_0_to_fp16 = const()[name = tensor("add_61_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_61_cast_fp16 = batch_norm(beta = add_61_beta_0_to_fp16, epsilon = add_61_epsilon_0_to_fp16, gamma = add_61_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_121_cast_fp16)[name = tensor("add_61_cast_fp16")]; + tensor input_265_cast_fp16 = silu(x = add_61_cast_fp16)[name = tensor("input_265_cast_fp16")]; + tensor var_2218 = const()[name = tensor("op_2218"), val = tensor([1, 1])]; + tensor var_2220 = const()[name = tensor("op_2220"), val = tensor([1, 1])]; + tensor hidden_states_149_pad_type_0 = const()[name = tensor("hidden_states_149_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_149_pad_0 = const()[name = tensor("hidden_states_149_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(863973952)))]; + tensor up_blocks_0_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(893465216)))]; + tensor hidden_states_149_cast_fp16 = conv(bias = up_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = var_2220, groups = var_2128, pad = hidden_states_149_pad_0, pad_type = hidden_states_149_pad_type_0, strides = var_2218, weight = up_blocks_0_resnets_1_conv2_weight_to_fp16, x = input_265_cast_fp16)[name = tensor("hidden_states_149_cast_fp16")]; + tensor var_2225 = const()[name = tensor("op_2225"), val = tensor([1, 1])]; + tensor var_2227 = const()[name = tensor("op_2227"), val = tensor([1, 1])]; + tensor x_7_pad_type_0 = const()[name = tensor("x_7_pad_type_0"), val = tensor("custom")]; + tensor x_7_pad_0 = const()[name = tensor("x_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_1_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(893467840)))]; + tensor up_blocks_0_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(900021504)))]; + tensor x_7_cast_fp16 = conv(bias = up_blocks_0_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_2227, groups = var_2128, pad = x_7_pad_0, pad_type = x_7_pad_type_0, strides = var_2225, weight = up_blocks_0_resnets_1_conv_shortcut_weight_to_fp16, x = input_253_cast_fp16)[name = tensor("x_7_cast_fp16")]; + tensor hidden_states_151_cast_fp16 = add(x = x_7_cast_fp16, y = hidden_states_149_cast_fp16)[name = tensor("hidden_states_151_cast_fp16")]; + tensor input_267_interleave_0 = const()[name = tensor("input_267_interleave_0"), val = tensor(false)]; + tensor input_267_cast_fp16 = concat(axis = var_2128, interleave = input_267_interleave_0, values = (hidden_states_151_cast_fp16, input_171_cast_fp16))[name = tensor("input_267_cast_fp16")]; + tensor reshape_124_shape_0 = const()[name = tensor("reshape_124_shape_0"), val = tensor([2, 32, 80, 6, 10])]; + tensor reshape_124_cast_fp16 = reshape(shape = reshape_124_shape_0, x = input_267_cast_fp16)[name = tensor("reshape_124_cast_fp16")]; + tensor reduce_mean_93_axes_0 = const()[name = tensor("reduce_mean_93_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_93_keep_dims_0 = const()[name = tensor("reduce_mean_93_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_93_cast_fp16 = reduce_mean(axes = reduce_mean_93_axes_0, keep_dims = reduce_mean_93_keep_dims_0, x = reshape_124_cast_fp16)[name = tensor("reduce_mean_93_cast_fp16")]; + tensor sub_62_cast_fp16 = sub(x = reshape_124_cast_fp16, y = reduce_mean_93_cast_fp16)[name = tensor("sub_62_cast_fp16")]; + tensor square_31_cast_fp16 = square(x = sub_62_cast_fp16)[name = tensor("square_31_cast_fp16")]; + tensor reduce_mean_95_axes_0 = const()[name = tensor("reduce_mean_95_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_95_keep_dims_0 = const()[name = tensor("reduce_mean_95_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_95_cast_fp16 = reduce_mean(axes = reduce_mean_95_axes_0, keep_dims = reduce_mean_95_keep_dims_0, x = square_31_cast_fp16)[name = tensor("reduce_mean_95_cast_fp16")]; + tensor add_62_y_0_to_fp16 = const()[name = tensor("add_62_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_62_cast_fp16 = add(x = reduce_mean_95_cast_fp16, y = add_62_y_0_to_fp16)[name = tensor("add_62_cast_fp16")]; + tensor sqrt_31_cast_fp16 = sqrt(x = add_62_cast_fp16)[name = tensor("sqrt_31_cast_fp16")]; + tensor real_div_31_cast_fp16 = real_div(x = sub_62_cast_fp16, y = sqrt_31_cast_fp16)[name = tensor("real_div_31_cast_fp16")]; + tensor reshape_125_shape_0 = const()[name = tensor("reshape_125_shape_0"), val = tensor([2, 2560, 6, 10])]; + tensor reshape_125_cast_fp16 = reshape(shape = reshape_125_shape_0, x = real_div_31_cast_fp16)[name = tensor("reshape_125_cast_fp16")]; + tensor add_63_gamma_0_to_fp16 = const()[name = tensor("add_63_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(900024128)))]; + tensor add_63_beta_0_to_fp16 = const()[name = tensor("add_63_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(900029312)))]; + tensor add_63_epsilon_0_to_fp16 = const()[name = tensor("add_63_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_63_cast_fp16 = batch_norm(beta = add_63_beta_0_to_fp16, epsilon = add_63_epsilon_0_to_fp16, gamma = add_63_gamma_0_to_fp16, mean = add_55_mean_0_to_fp16, variance = add_55_variance_0_to_fp16, x = reshape_125_cast_fp16)[name = tensor("add_63_cast_fp16")]; + tensor input_271_cast_fp16 = silu(x = add_63_cast_fp16)[name = tensor("input_271_cast_fp16")]; + tensor var_2245 = const()[name = tensor("op_2245"), val = tensor([1, 1])]; + tensor var_2247 = const()[name = tensor("op_2247"), val = tensor([1, 1])]; + tensor hidden_states_153_pad_type_0 = const()[name = tensor("hidden_states_153_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_153_pad_0 = const()[name = tensor("hidden_states_153_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_2_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(900034496)))]; + tensor up_blocks_0_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(959016960)))]; + tensor hidden_states_153_cast_fp16 = conv(bias = up_blocks_0_resnets_2_conv1_bias_to_fp16, dilations = var_2247, groups = var_2128, pad = hidden_states_153_pad_0, pad_type = hidden_states_153_pad_type_0, strides = var_2245, weight = up_blocks_0_resnets_2_conv1_weight_to_fp16, x = input_271_cast_fp16)[name = tensor("hidden_states_153_cast_fp16")]; + tensor var_2253 = const()[name = tensor("op_2253"), val = tensor([1, 1])]; + tensor var_2255 = const()[name = tensor("op_2255"), val = tensor([1, 1])]; + tensor temb_25_pad_type_0 = const()[name = tensor("temb_25_pad_type_0"), val = tensor("custom")]; + tensor temb_25_pad_0 = const()[name = tensor("temb_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_2_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(959019584)))]; + tensor up_blocks_0_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(962296448)))]; + tensor temb_25_cast_fp16 = conv(bias = up_blocks_0_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_2255, groups = var_2128, pad = temb_25_pad_0, pad_type = temb_25_pad_type_0, strides = var_2253, weight = up_blocks_0_resnets_2_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16)[name = tensor("temb_25_cast_fp16")]; + tensor input_275_cast_fp16 = add(x = hidden_states_153_cast_fp16, y = temb_25_cast_fp16)[name = tensor("input_275_cast_fp16")]; + tensor reshape_128_shape_0 = const()[name = tensor("reshape_128_shape_0"), val = tensor([2, 32, 40, 6, 10])]; + tensor reshape_128_cast_fp16 = reshape(shape = reshape_128_shape_0, x = input_275_cast_fp16)[name = tensor("reshape_128_cast_fp16")]; + tensor reduce_mean_96_axes_0 = const()[name = tensor("reduce_mean_96_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_96_keep_dims_0 = const()[name = tensor("reduce_mean_96_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_96_cast_fp16 = reduce_mean(axes = reduce_mean_96_axes_0, keep_dims = reduce_mean_96_keep_dims_0, x = reshape_128_cast_fp16)[name = tensor("reduce_mean_96_cast_fp16")]; + tensor sub_64_cast_fp16 = sub(x = reshape_128_cast_fp16, y = reduce_mean_96_cast_fp16)[name = tensor("sub_64_cast_fp16")]; + tensor square_32_cast_fp16 = square(x = sub_64_cast_fp16)[name = tensor("square_32_cast_fp16")]; + tensor reduce_mean_98_axes_0 = const()[name = tensor("reduce_mean_98_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_98_keep_dims_0 = const()[name = tensor("reduce_mean_98_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_98_cast_fp16 = reduce_mean(axes = reduce_mean_98_axes_0, keep_dims = reduce_mean_98_keep_dims_0, x = square_32_cast_fp16)[name = tensor("reduce_mean_98_cast_fp16")]; + tensor add_64_y_0_to_fp16 = const()[name = tensor("add_64_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_64_cast_fp16 = add(x = reduce_mean_98_cast_fp16, y = add_64_y_0_to_fp16)[name = tensor("add_64_cast_fp16")]; + tensor sqrt_32_cast_fp16 = sqrt(x = add_64_cast_fp16)[name = tensor("sqrt_32_cast_fp16")]; + tensor real_div_32_cast_fp16 = real_div(x = sub_64_cast_fp16, y = sqrt_32_cast_fp16)[name = tensor("real_div_32_cast_fp16")]; + tensor reshape_129_shape_0 = const()[name = tensor("reshape_129_shape_0"), val = tensor([2, 1280, 6, 10])]; + tensor reshape_129_cast_fp16 = reshape(shape = reshape_129_shape_0, x = real_div_32_cast_fp16)[name = tensor("reshape_129_cast_fp16")]; + tensor add_65_gamma_0_to_fp16 = const()[name = tensor("add_65_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(962299072)))]; + tensor add_65_beta_0_to_fp16 = const()[name = tensor("add_65_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(962301696)))]; + tensor add_65_epsilon_0_to_fp16 = const()[name = tensor("add_65_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_65_cast_fp16 = batch_norm(beta = add_65_beta_0_to_fp16, epsilon = add_65_epsilon_0_to_fp16, gamma = add_65_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_129_cast_fp16)[name = tensor("add_65_cast_fp16")]; + tensor input_279_cast_fp16 = silu(x = add_65_cast_fp16)[name = tensor("input_279_cast_fp16")]; + tensor var_2265 = const()[name = tensor("op_2265"), val = tensor([1, 1])]; + tensor var_2267 = const()[name = tensor("op_2267"), val = tensor([1, 1])]; + tensor hidden_states_155_pad_type_0 = const()[name = tensor("hidden_states_155_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_155_pad_0 = const()[name = tensor("hidden_states_155_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_2_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(962304320)))]; + tensor up_blocks_0_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(991795584)))]; + tensor hidden_states_155_cast_fp16 = conv(bias = up_blocks_0_resnets_2_conv2_bias_to_fp16, dilations = var_2267, groups = var_2128, pad = hidden_states_155_pad_0, pad_type = hidden_states_155_pad_type_0, strides = var_2265, weight = up_blocks_0_resnets_2_conv2_weight_to_fp16, x = input_279_cast_fp16)[name = tensor("hidden_states_155_cast_fp16")]; + tensor var_2272 = const()[name = tensor("op_2272"), val = tensor([1, 1])]; + tensor var_2274 = const()[name = tensor("op_2274"), val = tensor([1, 1])]; + tensor x_9_pad_type_0 = const()[name = tensor("x_9_pad_type_0"), val = tensor("custom")]; + tensor x_9_pad_0 = const()[name = tensor("x_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(991798208)))]; + tensor up_blocks_0_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(998351872)))]; + tensor x_9_cast_fp16 = conv(bias = up_blocks_0_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_2274, groups = var_2128, pad = x_9_pad_0, pad_type = x_9_pad_type_0, strides = var_2272, weight = up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16, x = input_267_cast_fp16)[name = tensor("x_9_cast_fp16")]; + tensor input_281_cast_fp16 = add(x = x_9_cast_fp16, y = hidden_states_155_cast_fp16)[name = tensor("input_281_cast_fp16")]; + tensor input_283_scale_factor_height_0 = const()[name = tensor("input_283_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_283_scale_factor_width_0 = const()[name = tensor("input_283_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor input_283_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = input_283_scale_factor_height_0, scale_factor_width = input_283_scale_factor_width_0, x = input_281_cast_fp16)[name = tensor("input_283_cast_fp16")]; + tensor var_2283 = const()[name = tensor("op_2283"), val = tensor([1, 1])]; + tensor var_2285 = const()[name = tensor("op_2285"), val = tensor([1, 1])]; + tensor hidden_states_157_pad_type_0 = const()[name = tensor("hidden_states_157_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_157_pad_0 = const()[name = tensor("hidden_states_157_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_upsamplers_0_conv_weight_to_fp16 = const()[name = tensor("up_blocks_0_upsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(998354496)))]; + tensor up_blocks_0_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("up_blocks_0_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1027845760)))]; + tensor hidden_states_157_cast_fp16 = conv(bias = up_blocks_0_upsamplers_0_conv_bias_to_fp16, dilations = var_2285, groups = var_2128, pad = hidden_states_157_pad_0, pad_type = hidden_states_157_pad_type_0, strides = var_2283, weight = up_blocks_0_upsamplers_0_conv_weight_to_fp16, x = input_283_cast_fp16)[name = tensor("hidden_states_157_cast_fp16")]; + tensor var_2290 = const()[name = tensor("op_2290"), val = tensor(3)]; + tensor var_2301 = const()[name = tensor("op_2301"), val = tensor(true)]; + tensor var_2306 = const()[name = tensor("op_2306"), val = tensor(1)]; + tensor input_285_interleave_0 = const()[name = tensor("input_285_interleave_0"), val = tensor(false)]; + tensor input_285_cast_fp16 = concat(axis = var_2306, interleave = input_285_interleave_0, values = (hidden_states_157_cast_fp16, input_169_cast_fp16))[name = tensor("input_285_cast_fp16")]; + tensor reshape_132_shape_0 = const()[name = tensor("reshape_132_shape_0"), val = tensor([2, 32, 80, 12, 20])]; + tensor reshape_132_cast_fp16 = reshape(shape = reshape_132_shape_0, x = input_285_cast_fp16)[name = tensor("reshape_132_cast_fp16")]; + tensor reduce_mean_99_axes_0 = const()[name = tensor("reduce_mean_99_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_99_keep_dims_0 = const()[name = tensor("reduce_mean_99_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_99_cast_fp16 = reduce_mean(axes = reduce_mean_99_axes_0, keep_dims = reduce_mean_99_keep_dims_0, x = reshape_132_cast_fp16)[name = tensor("reduce_mean_99_cast_fp16")]; + tensor sub_66_cast_fp16 = sub(x = reshape_132_cast_fp16, y = reduce_mean_99_cast_fp16)[name = tensor("sub_66_cast_fp16")]; + tensor square_33_cast_fp16 = square(x = sub_66_cast_fp16)[name = tensor("square_33_cast_fp16")]; + tensor reduce_mean_101_axes_0 = const()[name = tensor("reduce_mean_101_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_101_keep_dims_0 = const()[name = tensor("reduce_mean_101_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_101_cast_fp16 = reduce_mean(axes = reduce_mean_101_axes_0, keep_dims = reduce_mean_101_keep_dims_0, x = square_33_cast_fp16)[name = tensor("reduce_mean_101_cast_fp16")]; + tensor add_66_y_0_to_fp16 = const()[name = tensor("add_66_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_66_cast_fp16 = add(x = reduce_mean_101_cast_fp16, y = add_66_y_0_to_fp16)[name = tensor("add_66_cast_fp16")]; + tensor sqrt_33_cast_fp16 = sqrt(x = add_66_cast_fp16)[name = tensor("sqrt_33_cast_fp16")]; + tensor real_div_33_cast_fp16 = real_div(x = sub_66_cast_fp16, y = sqrt_33_cast_fp16)[name = tensor("real_div_33_cast_fp16")]; + tensor reshape_133_shape_0 = const()[name = tensor("reshape_133_shape_0"), val = tensor([2, 2560, 12, 20])]; + tensor reshape_133_cast_fp16 = reshape(shape = reshape_133_shape_0, x = real_div_33_cast_fp16)[name = tensor("reshape_133_cast_fp16")]; + tensor add_67_gamma_0_to_fp16 = const()[name = tensor("add_67_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1027848384)))]; + tensor add_67_beta_0_to_fp16 = const()[name = tensor("add_67_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1027853568)))]; + tensor add_67_epsilon_0_to_fp16 = const()[name = tensor("add_67_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_67_cast_fp16 = batch_norm(beta = add_67_beta_0_to_fp16, epsilon = add_67_epsilon_0_to_fp16, gamma = add_67_gamma_0_to_fp16, mean = add_55_mean_0_to_fp16, variance = add_55_variance_0_to_fp16, x = reshape_133_cast_fp16)[name = tensor("add_67_cast_fp16")]; + tensor input_289_cast_fp16 = silu(x = add_67_cast_fp16)[name = tensor("input_289_cast_fp16")]; + tensor var_2335 = const()[name = tensor("op_2335"), val = tensor([1, 1])]; + tensor var_2337 = const()[name = tensor("op_2337"), val = tensor([1, 1])]; + tensor hidden_states_159_pad_type_0 = const()[name = tensor("hidden_states_159_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_159_pad_0 = const()[name = tensor("hidden_states_159_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1027858752)))]; + tensor up_blocks_1_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1086841216)))]; + tensor hidden_states_159_cast_fp16 = conv(bias = up_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = var_2337, groups = var_2306, pad = hidden_states_159_pad_0, pad_type = hidden_states_159_pad_type_0, strides = var_2335, weight = up_blocks_1_resnets_0_conv1_weight_to_fp16, x = input_289_cast_fp16)[name = tensor("hidden_states_159_cast_fp16")]; + tensor var_2343 = const()[name = tensor("op_2343"), val = tensor([1, 1])]; + tensor var_2345 = const()[name = tensor("op_2345"), val = tensor([1, 1])]; + tensor temb_27_pad_type_0 = const()[name = tensor("temb_27_pad_type_0"), val = tensor("custom")]; + tensor temb_27_pad_0 = const()[name = tensor("temb_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1086843840)))]; + tensor up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1090120704)))]; + tensor temb_27_cast_fp16 = conv(bias = up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_2345, groups = var_2306, pad = temb_27_pad_0, pad_type = temb_27_pad_type_0, strides = var_2343, weight = up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16)[name = tensor("temb_27_cast_fp16")]; + tensor input_293_cast_fp16 = add(x = hidden_states_159_cast_fp16, y = temb_27_cast_fp16)[name = tensor("input_293_cast_fp16")]; + tensor reshape_136_shape_0 = const()[name = tensor("reshape_136_shape_0"), val = tensor([2, 32, 40, 12, 20])]; + tensor reshape_136_cast_fp16 = reshape(shape = reshape_136_shape_0, x = input_293_cast_fp16)[name = tensor("reshape_136_cast_fp16")]; + tensor reduce_mean_102_axes_0 = const()[name = tensor("reduce_mean_102_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_102_keep_dims_0 = const()[name = tensor("reduce_mean_102_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_102_cast_fp16 = reduce_mean(axes = reduce_mean_102_axes_0, keep_dims = reduce_mean_102_keep_dims_0, x = reshape_136_cast_fp16)[name = tensor("reduce_mean_102_cast_fp16")]; + tensor sub_68_cast_fp16 = sub(x = reshape_136_cast_fp16, y = reduce_mean_102_cast_fp16)[name = tensor("sub_68_cast_fp16")]; + tensor square_34_cast_fp16 = square(x = sub_68_cast_fp16)[name = tensor("square_34_cast_fp16")]; + tensor reduce_mean_104_axes_0 = const()[name = tensor("reduce_mean_104_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_104_keep_dims_0 = const()[name = tensor("reduce_mean_104_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_104_cast_fp16 = reduce_mean(axes = reduce_mean_104_axes_0, keep_dims = reduce_mean_104_keep_dims_0, x = square_34_cast_fp16)[name = tensor("reduce_mean_104_cast_fp16")]; + tensor add_68_y_0_to_fp16 = const()[name = tensor("add_68_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_68_cast_fp16 = add(x = reduce_mean_104_cast_fp16, y = add_68_y_0_to_fp16)[name = tensor("add_68_cast_fp16")]; + tensor sqrt_34_cast_fp16 = sqrt(x = add_68_cast_fp16)[name = tensor("sqrt_34_cast_fp16")]; + tensor real_div_34_cast_fp16 = real_div(x = sub_68_cast_fp16, y = sqrt_34_cast_fp16)[name = tensor("real_div_34_cast_fp16")]; + tensor reshape_137_shape_0 = const()[name = tensor("reshape_137_shape_0"), val = tensor([2, 1280, 12, 20])]; + tensor reshape_137_cast_fp16 = reshape(shape = reshape_137_shape_0, x = real_div_34_cast_fp16)[name = tensor("reshape_137_cast_fp16")]; + tensor add_69_gamma_0_to_fp16 = const()[name = tensor("add_69_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1090123328)))]; + tensor add_69_beta_0_to_fp16 = const()[name = tensor("add_69_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1090125952)))]; + tensor add_69_epsilon_0_to_fp16 = const()[name = tensor("add_69_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_69_cast_fp16 = batch_norm(beta = add_69_beta_0_to_fp16, epsilon = add_69_epsilon_0_to_fp16, gamma = add_69_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_137_cast_fp16)[name = tensor("add_69_cast_fp16")]; + tensor input_297_cast_fp16 = silu(x = add_69_cast_fp16)[name = tensor("input_297_cast_fp16")]; + tensor var_2355 = const()[name = tensor("op_2355"), val = tensor([1, 1])]; + tensor var_2357 = const()[name = tensor("op_2357"), val = tensor([1, 1])]; + tensor hidden_states_161_pad_type_0 = const()[name = tensor("hidden_states_161_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_161_pad_0 = const()[name = tensor("hidden_states_161_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1090128576)))]; + tensor up_blocks_1_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1119619840)))]; + tensor hidden_states_161_cast_fp16 = conv(bias = up_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = var_2357, groups = var_2306, pad = hidden_states_161_pad_0, pad_type = hidden_states_161_pad_type_0, strides = var_2355, weight = up_blocks_1_resnets_0_conv2_weight_to_fp16, x = input_297_cast_fp16)[name = tensor("hidden_states_161_cast_fp16")]; + tensor var_2362 = const()[name = tensor("op_2362"), val = tensor([1, 1])]; + tensor var_2364 = const()[name = tensor("op_2364"), val = tensor([1, 1])]; + tensor x_11_pad_type_0 = const()[name = tensor("x_11_pad_type_0"), val = tensor("custom")]; + tensor x_11_pad_0 = const()[name = tensor("x_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1119622464)))]; + tensor up_blocks_1_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1126176128)))]; + tensor x_11_cast_fp16 = conv(bias = up_blocks_1_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_2364, groups = var_2306, pad = x_11_pad_0, pad_type = x_11_pad_type_0, strides = var_2362, weight = up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16, x = input_285_cast_fp16)[name = tensor("x_11_cast_fp16")]; + tensor hidden_states_163_cast_fp16 = add(x = x_11_cast_fp16, y = hidden_states_161_cast_fp16)[name = tensor("hidden_states_163_cast_fp16")]; + tensor reshape_140_shape_0 = const()[name = tensor("reshape_140_shape_0"), val = tensor([2, 32, 40, 12, 20])]; + tensor reshape_140_cast_fp16 = reshape(shape = reshape_140_shape_0, x = hidden_states_163_cast_fp16)[name = tensor("reshape_140_cast_fp16")]; + tensor reduce_mean_105_axes_0 = const()[name = tensor("reduce_mean_105_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_105_keep_dims_0 = const()[name = tensor("reduce_mean_105_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_105_cast_fp16 = reduce_mean(axes = reduce_mean_105_axes_0, keep_dims = reduce_mean_105_keep_dims_0, x = reshape_140_cast_fp16)[name = tensor("reduce_mean_105_cast_fp16")]; + tensor sub_70_cast_fp16 = sub(x = reshape_140_cast_fp16, y = reduce_mean_105_cast_fp16)[name = tensor("sub_70_cast_fp16")]; + tensor square_35_cast_fp16 = square(x = sub_70_cast_fp16)[name = tensor("square_35_cast_fp16")]; + tensor reduce_mean_107_axes_0 = const()[name = tensor("reduce_mean_107_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_107_keep_dims_0 = const()[name = tensor("reduce_mean_107_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_107_cast_fp16 = reduce_mean(axes = reduce_mean_107_axes_0, keep_dims = reduce_mean_107_keep_dims_0, x = square_35_cast_fp16)[name = tensor("reduce_mean_107_cast_fp16")]; + tensor add_70_y_0_to_fp16 = const()[name = tensor("add_70_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_70_cast_fp16 = add(x = reduce_mean_107_cast_fp16, y = add_70_y_0_to_fp16)[name = tensor("add_70_cast_fp16")]; + tensor sqrt_35_cast_fp16 = sqrt(x = add_70_cast_fp16)[name = tensor("sqrt_35_cast_fp16")]; + tensor real_div_35_cast_fp16 = real_div(x = sub_70_cast_fp16, y = sqrt_35_cast_fp16)[name = tensor("real_div_35_cast_fp16")]; + tensor reshape_141_shape_0 = const()[name = tensor("reshape_141_shape_0"), val = tensor([2, 1280, 12, 20])]; + tensor reshape_141_cast_fp16 = reshape(shape = reshape_141_shape_0, x = real_div_35_cast_fp16)[name = tensor("reshape_141_cast_fp16")]; + tensor add_71_gamma_0_to_fp16 = const()[name = tensor("add_71_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1126178752)))]; + tensor add_71_beta_0_to_fp16 = const()[name = tensor("add_71_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1126181376)))]; + tensor add_71_epsilon_0_to_fp16 = const()[name = tensor("add_71_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_71_cast_fp16 = batch_norm(beta = add_71_beta_0_to_fp16, epsilon = add_71_epsilon_0_to_fp16, gamma = add_71_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_141_cast_fp16)[name = tensor("add_71_cast_fp16")]; + tensor var_2384 = const()[name = tensor("op_2384"), val = tensor([1, 1])]; + tensor var_2386 = const()[name = tensor("op_2386"), val = tensor([1, 1])]; + tensor hidden_states_165_pad_type_0 = const()[name = tensor("hidden_states_165_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_165_pad_0 = const()[name = tensor("hidden_states_165_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1126184000)))]; + tensor up_blocks_1_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1129460864)))]; + tensor hidden_states_165_cast_fp16 = conv(bias = up_blocks_1_attentions_0_proj_in_bias_to_fp16, dilations = var_2386, groups = var_2306, pad = hidden_states_165_pad_0, pad_type = hidden_states_165_pad_type_0, strides = var_2384, weight = up_blocks_1_attentions_0_proj_in_weight_to_fp16, x = add_71_cast_fp16)[name = tensor("hidden_states_165_cast_fp16")]; + tensor var_2391 = const()[name = tensor("op_2391"), val = tensor([2, 1280, 1, 240])]; + tensor inputs_43_cast_fp16 = reshape(shape = var_2391, x = hidden_states_165_cast_fp16)[name = tensor("inputs_43_cast_fp16")]; + tensor var_2401 = const()[name = tensor("op_2401"), val = tensor([1])]; + tensor channels_mean_43_cast_fp16 = reduce_mean(axes = var_2401, keep_dims = var_2301, x = inputs_43_cast_fp16)[name = tensor("channels_mean_43_cast_fp16")]; + tensor zero_mean_43_cast_fp16 = sub(x = inputs_43_cast_fp16, y = channels_mean_43_cast_fp16)[name = tensor("zero_mean_43_cast_fp16")]; + tensor zero_mean_sq_43_cast_fp16 = mul(x = zero_mean_43_cast_fp16, y = zero_mean_43_cast_fp16)[name = tensor("zero_mean_sq_43_cast_fp16")]; + tensor var_2405 = const()[name = tensor("op_2405"), val = tensor([1])]; + tensor var_2406_cast_fp16 = reduce_mean(axes = var_2405, keep_dims = var_2301, x = zero_mean_sq_43_cast_fp16)[name = tensor("op_2406_cast_fp16")]; + tensor var_2407_to_fp16 = const()[name = tensor("op_2407_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2408_cast_fp16 = add(x = var_2406_cast_fp16, y = var_2407_to_fp16)[name = tensor("op_2408_cast_fp16")]; + tensor denom_43_epsilon_0_to_fp16 = const()[name = tensor("denom_43_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_43_cast_fp16 = rsqrt(epsilon = denom_43_epsilon_0_to_fp16, x = var_2408_cast_fp16)[name = tensor("denom_43_cast_fp16")]; + tensor out_43_cast_fp16 = mul(x = zero_mean_43_cast_fp16, y = denom_43_cast_fp16)[name = tensor("out_43_cast_fp16")]; + tensor var_2412_to_fp16 = const()[name = tensor("op_2412_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1129463488)))]; + tensor var_2413_cast_fp16 = add(x = out_43_cast_fp16, y = var_2412_to_fp16)[name = tensor("op_2413_cast_fp16")]; + tensor var_2415_to_fp16 = const()[name = tensor("op_2415_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1129466112)))]; + tensor hidden_states_167_cast_fp16 = mul(x = var_2413_cast_fp16, y = var_2415_to_fp16)[name = tensor("hidden_states_167_cast_fp16")]; + tensor var_2422 = const()[name = tensor("op_2422"), val = tensor([1, 1])]; + tensor var_2424 = const()[name = tensor("op_2424"), val = tensor([1, 1])]; + tensor q_29_pad_type_0 = const()[name = tensor("q_29_pad_type_0"), val = tensor("custom")]; + tensor q_29_pad_0 = const()[name = tensor("q_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1129468736)))]; + tensor q_29_cast_fp16 = conv(dilations = var_2424, groups = var_2306, pad = q_29_pad_0, pad_type = q_29_pad_type_0, strides = var_2422, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_167_cast_fp16)[name = tensor("q_29_cast_fp16")]; + tensor var_2428 = const()[name = tensor("op_2428"), val = tensor([1, 1])]; + tensor var_2430 = const()[name = tensor("op_2430"), val = tensor([1, 1])]; + tensor k_29_pad_type_0 = const()[name = tensor("k_29_pad_type_0"), val = tensor("custom")]; + tensor k_29_pad_0 = const()[name = tensor("k_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1132745600)))]; + tensor k_29_cast_fp16 = conv(dilations = var_2430, groups = var_2306, pad = k_29_pad_0, pad_type = k_29_pad_type_0, strides = var_2428, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_167_cast_fp16)[name = tensor("k_29_cast_fp16")]; + tensor var_2434 = const()[name = tensor("op_2434"), val = tensor([1, 1])]; + tensor var_2436 = const()[name = tensor("op_2436"), val = tensor([1, 1])]; + tensor v_29_pad_type_0 = const()[name = tensor("v_29_pad_type_0"), val = tensor("custom")]; + tensor v_29_pad_0 = const()[name = tensor("v_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1136022464)))]; + tensor v_29_cast_fp16 = conv(dilations = var_2436, groups = var_2306, pad = v_29_pad_0, pad_type = v_29_pad_type_0, strides = var_2434, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_167_cast_fp16)[name = tensor("v_29_cast_fp16")]; + tensor var_2440 = const()[name = tensor("op_2440"), val = tensor([2, 20, 64, -1])]; + tensor var_2441_cast_fp16 = reshape(shape = var_2440, x = q_29_cast_fp16)[name = tensor("op_2441_cast_fp16")]; + tensor var_2442 = const()[name = tensor("op_2442"), val = tensor([2, 20, 64, -1])]; + tensor var_2443_cast_fp16 = reshape(shape = var_2442, x = k_29_cast_fp16)[name = tensor("op_2443_cast_fp16")]; + tensor var_2444 = const()[name = tensor("op_2444"), val = tensor([2, 20, 64, -1])]; + tensor var_2445_cast_fp16 = reshape(shape = var_2444, x = v_29_cast_fp16)[name = tensor("op_2445_cast_fp16")]; + tensor attn_weights_57_transpose_x_0 = const()[name = tensor("attn_weights_57_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_57_transpose_y_0 = const()[name = tensor("attn_weights_57_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_57_cast_fp16 = matmul(transpose_x = attn_weights_57_transpose_x_0, transpose_y = attn_weights_57_transpose_y_0, x = var_2441_cast_fp16, y = var_2443_cast_fp16)[name = tensor("attn_weights_57_cast_fp16")]; + tensor var_2297_to_fp16 = const()[name = tensor("op_2297_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_59_cast_fp16 = mul(x = attn_weights_57_cast_fp16, y = var_2297_to_fp16)[name = tensor("attn_weights_59_cast_fp16")]; + tensor var_2449_cast_fp16 = softmax(axis = var_2290, x = attn_weights_59_cast_fp16)[name = tensor("op_2449_cast_fp16")]; + tensor attn_29_transpose_x_0 = const()[name = tensor("attn_29_transpose_x_0"), val = tensor(false)]; + tensor attn_29_transpose_y_0 = const()[name = tensor("attn_29_transpose_y_0"), val = tensor(true)]; + tensor attn_29_cast_fp16 = matmul(transpose_x = attn_29_transpose_x_0, transpose_y = attn_29_transpose_y_0, x = var_2445_cast_fp16, y = var_2449_cast_fp16)[name = tensor("attn_29_cast_fp16")]; + tensor var_2453 = const()[name = tensor("op_2453"), val = tensor([2, 1280, 1, -1])]; + tensor input_301_cast_fp16 = reshape(shape = var_2453, x = attn_29_cast_fp16)[name = tensor("input_301_cast_fp16")]; + tensor var_2458 = const()[name = tensor("op_2458"), val = tensor([1, 1])]; + tensor var_2460 = const()[name = tensor("op_2460"), val = tensor([1, 1])]; + tensor var_2462_pad_type_0 = const()[name = tensor("op_2462_pad_type_0"), val = tensor("custom")]; + tensor var_2462_pad_0 = const()[name = tensor("op_2462_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1139299328)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1142576192)))]; + tensor var_2462_cast_fp16 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_2460, groups = var_2306, pad = var_2462_pad_0, pad_type = var_2462_pad_type_0, strides = var_2458, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_301_cast_fp16)[name = tensor("op_2462_cast_fp16")]; + tensor inputs_45_cast_fp16 = add(x = var_2462_cast_fp16, y = inputs_43_cast_fp16)[name = tensor("inputs_45_cast_fp16")]; + tensor var_2466 = const()[name = tensor("op_2466"), val = tensor([1])]; + tensor channels_mean_45_cast_fp16 = reduce_mean(axes = var_2466, keep_dims = var_2301, x = inputs_45_cast_fp16)[name = tensor("channels_mean_45_cast_fp16")]; + tensor zero_mean_45_cast_fp16 = sub(x = inputs_45_cast_fp16, y = channels_mean_45_cast_fp16)[name = tensor("zero_mean_45_cast_fp16")]; + tensor zero_mean_sq_45_cast_fp16 = mul(x = zero_mean_45_cast_fp16, y = zero_mean_45_cast_fp16)[name = tensor("zero_mean_sq_45_cast_fp16")]; + tensor var_2470 = const()[name = tensor("op_2470"), val = tensor([1])]; + tensor var_2471_cast_fp16 = reduce_mean(axes = var_2470, keep_dims = var_2301, x = zero_mean_sq_45_cast_fp16)[name = tensor("op_2471_cast_fp16")]; + tensor var_2472_to_fp16 = const()[name = tensor("op_2472_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2473_cast_fp16 = add(x = var_2471_cast_fp16, y = var_2472_to_fp16)[name = tensor("op_2473_cast_fp16")]; + tensor denom_45_epsilon_0_to_fp16 = const()[name = tensor("denom_45_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_45_cast_fp16 = rsqrt(epsilon = denom_45_epsilon_0_to_fp16, x = var_2473_cast_fp16)[name = tensor("denom_45_cast_fp16")]; + tensor out_45_cast_fp16 = mul(x = zero_mean_45_cast_fp16, y = denom_45_cast_fp16)[name = tensor("out_45_cast_fp16")]; + tensor var_2477_to_fp16 = const()[name = tensor("op_2477_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1142578816)))]; + tensor var_2478_cast_fp16 = add(x = out_45_cast_fp16, y = var_2477_to_fp16)[name = tensor("op_2478_cast_fp16")]; + tensor var_2480_to_fp16 = const()[name = tensor("op_2480_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1142581440)))]; + tensor hidden_states_169_cast_fp16 = mul(x = var_2478_cast_fp16, y = var_2480_to_fp16)[name = tensor("hidden_states_169_cast_fp16")]; + tensor var_2487 = const()[name = tensor("op_2487"), val = tensor([1, 1])]; + tensor var_2489 = const()[name = tensor("op_2489"), val = tensor([1, 1])]; + tensor q_31_pad_type_0 = const()[name = tensor("q_31_pad_type_0"), val = tensor("custom")]; + tensor q_31_pad_0 = const()[name = tensor("q_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1142584064)))]; + tensor q_31_cast_fp16 = conv(dilations = var_2489, groups = var_2306, pad = q_31_pad_0, pad_type = q_31_pad_type_0, strides = var_2487, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_169_cast_fp16)[name = tensor("q_31_cast_fp16")]; + tensor var_2493 = const()[name = tensor("op_2493"), val = tensor([1, 1])]; + tensor var_2495 = const()[name = tensor("op_2495"), val = tensor([1, 1])]; + tensor k_31_pad_type_0 = const()[name = tensor("k_31_pad_type_0"), val = tensor("custom")]; + tensor k_31_pad_0 = const()[name = tensor("k_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1145860928)))]; + tensor k_31_cast_fp16 = conv(dilations = var_2495, groups = var_2306, pad = k_31_pad_0, pad_type = k_31_pad_type_0, strides = var_2493, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_31_cast_fp16")]; + tensor var_2499 = const()[name = tensor("op_2499"), val = tensor([1, 1])]; + tensor var_2501 = const()[name = tensor("op_2501"), val = tensor([1, 1])]; + tensor v_31_pad_type_0 = const()[name = tensor("v_31_pad_type_0"), val = tensor("custom")]; + tensor v_31_pad_0 = const()[name = tensor("v_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1148482432)))]; + tensor v_31_cast_fp16 = conv(dilations = var_2501, groups = var_2306, pad = v_31_pad_0, pad_type = v_31_pad_type_0, strides = var_2499, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_31_cast_fp16")]; + tensor var_2505 = const()[name = tensor("op_2505"), val = tensor([2, 20, 64, -1])]; + tensor var_2506_cast_fp16 = reshape(shape = var_2505, x = q_31_cast_fp16)[name = tensor("op_2506_cast_fp16")]; + tensor var_2507 = const()[name = tensor("op_2507"), val = tensor([2, 20, 64, -1])]; + tensor var_2508_cast_fp16 = reshape(shape = var_2507, x = k_31_cast_fp16)[name = tensor("op_2508_cast_fp16")]; + tensor var_2509 = const()[name = tensor("op_2509"), val = tensor([2, 20, 64, -1])]; + tensor var_2510_cast_fp16 = reshape(shape = var_2509, x = v_31_cast_fp16)[name = tensor("op_2510_cast_fp16")]; + tensor attn_weights_61_transpose_x_0 = const()[name = tensor("attn_weights_61_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_61_transpose_y_0 = const()[name = tensor("attn_weights_61_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_61_cast_fp16 = matmul(transpose_x = attn_weights_61_transpose_x_0, transpose_y = attn_weights_61_transpose_y_0, x = var_2506_cast_fp16, y = var_2508_cast_fp16)[name = tensor("attn_weights_61_cast_fp16")]; + tensor attn_weights_63_cast_fp16 = mul(x = attn_weights_61_cast_fp16, y = var_2297_to_fp16)[name = tensor("attn_weights_63_cast_fp16")]; + tensor var_2514_cast_fp16 = softmax(axis = var_2290, x = attn_weights_63_cast_fp16)[name = tensor("op_2514_cast_fp16")]; + tensor attn_31_transpose_x_0 = const()[name = tensor("attn_31_transpose_x_0"), val = tensor(false)]; + tensor attn_31_transpose_y_0 = const()[name = tensor("attn_31_transpose_y_0"), val = tensor(true)]; + tensor attn_31_cast_fp16 = matmul(transpose_x = attn_31_transpose_x_0, transpose_y = attn_31_transpose_y_0, x = var_2510_cast_fp16, y = var_2514_cast_fp16)[name = tensor("attn_31_cast_fp16")]; + tensor var_2518 = const()[name = tensor("op_2518"), val = tensor([2, 1280, 1, -1])]; + tensor input_303_cast_fp16 = reshape(shape = var_2518, x = attn_31_cast_fp16)[name = tensor("input_303_cast_fp16")]; + tensor var_2523 = const()[name = tensor("op_2523"), val = tensor([1, 1])]; + tensor var_2525 = const()[name = tensor("op_2525"), val = tensor([1, 1])]; + tensor var_2527_pad_type_0 = const()[name = tensor("op_2527_pad_type_0"), val = tensor("custom")]; + tensor var_2527_pad_0 = const()[name = tensor("op_2527_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1151103936)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1154380800)))]; + tensor var_2527_cast_fp16 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_2525, groups = var_2306, pad = var_2527_pad_0, pad_type = var_2527_pad_type_0, strides = var_2523, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_303_cast_fp16)[name = tensor("op_2527_cast_fp16")]; + tensor inputs_47_cast_fp16 = add(x = var_2527_cast_fp16, y = inputs_45_cast_fp16)[name = tensor("inputs_47_cast_fp16")]; + tensor var_2531 = const()[name = tensor("op_2531"), val = tensor([1])]; + tensor channels_mean_47_cast_fp16 = reduce_mean(axes = var_2531, keep_dims = var_2301, x = inputs_47_cast_fp16)[name = tensor("channels_mean_47_cast_fp16")]; + tensor zero_mean_47_cast_fp16 = sub(x = inputs_47_cast_fp16, y = channels_mean_47_cast_fp16)[name = tensor("zero_mean_47_cast_fp16")]; + tensor zero_mean_sq_47_cast_fp16 = mul(x = zero_mean_47_cast_fp16, y = zero_mean_47_cast_fp16)[name = tensor("zero_mean_sq_47_cast_fp16")]; + tensor var_2535 = const()[name = tensor("op_2535"), val = tensor([1])]; + tensor var_2536_cast_fp16 = reduce_mean(axes = var_2535, keep_dims = var_2301, x = zero_mean_sq_47_cast_fp16)[name = tensor("op_2536_cast_fp16")]; + tensor var_2537_to_fp16 = const()[name = tensor("op_2537_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2538_cast_fp16 = add(x = var_2536_cast_fp16, y = var_2537_to_fp16)[name = tensor("op_2538_cast_fp16")]; + tensor denom_47_epsilon_0_to_fp16 = const()[name = tensor("denom_47_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_47_cast_fp16 = rsqrt(epsilon = denom_47_epsilon_0_to_fp16, x = var_2538_cast_fp16)[name = tensor("denom_47_cast_fp16")]; + tensor out_47_cast_fp16 = mul(x = zero_mean_47_cast_fp16, y = denom_47_cast_fp16)[name = tensor("out_47_cast_fp16")]; + tensor var_2542_to_fp16 = const()[name = tensor("op_2542_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1154383424)))]; + tensor var_2543_cast_fp16 = add(x = out_47_cast_fp16, y = var_2542_to_fp16)[name = tensor("op_2543_cast_fp16")]; + tensor var_2545_to_fp16 = const()[name = tensor("op_2545_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1154386048)))]; + tensor input_305_cast_fp16 = mul(x = var_2543_cast_fp16, y = var_2545_to_fp16)[name = tensor("input_305_cast_fp16")]; + tensor var_2553 = const()[name = tensor("op_2553"), val = tensor([1, 1])]; + tensor var_2555 = const()[name = tensor("op_2555"), val = tensor([1, 1])]; + tensor var_2557_pad_type_0 = const()[name = tensor("op_2557_pad_type_0"), val = tensor("custom")]; + tensor var_2557_pad_0 = const()[name = tensor("op_2557_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1154388672)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1180603136)))]; + tensor var_2557_cast_fp16 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_2555, groups = var_2306, pad = var_2557_pad_0, pad_type = var_2557_pad_type_0, strides = var_2553, weight = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_305_cast_fp16)[name = tensor("op_2557_cast_fp16")]; + tensor var_2558_split_sizes_0 = const()[name = tensor("op_2558_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2558_axis_0 = const()[name = tensor("op_2558_axis_0"), val = tensor(1)]; + tensor var_2558_cast_fp16_0, tensor var_2558_cast_fp16_1 = split(axis = var_2558_axis_0, split_sizes = var_2558_split_sizes_0, x = var_2557_cast_fp16)[name = tensor("op_2558_cast_fp16")]; + tensor var_2560_mode_0 = const()[name = tensor("op_2560_mode_0"), val = tensor("EXACT")]; + tensor var_2560_cast_fp16 = gelu(mode = var_2560_mode_0, x = var_2558_cast_fp16_1)[name = tensor("op_2560_cast_fp16")]; + tensor input_307_cast_fp16 = mul(x = var_2558_cast_fp16_0, y = var_2560_cast_fp16)[name = tensor("input_307_cast_fp16")]; + tensor var_2564 = const()[name = tensor("op_2564"), val = tensor([1, 1])]; + tensor var_2566 = const()[name = tensor("op_2566"), val = tensor([1, 1])]; + tensor var_2568_pad_type_0 = const()[name = tensor("op_2568_pad_type_0"), val = tensor("custom")]; + tensor var_2568_pad_0 = const()[name = tensor("op_2568_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1180623680)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1193730944)))]; + tensor var_2568_cast_fp16 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_2566, groups = var_2306, pad = var_2568_pad_0, pad_type = var_2568_pad_type_0, strides = var_2564, weight = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_307_cast_fp16)[name = tensor("op_2568_cast_fp16")]; + tensor hidden_states_173_cast_fp16 = add(x = var_2568_cast_fp16, y = inputs_47_cast_fp16)[name = tensor("hidden_states_173_cast_fp16")]; + tensor var_2570 = const()[name = tensor("op_2570"), val = tensor([2, 1280, 12, 20])]; + tensor input_309_cast_fp16 = reshape(shape = var_2570, x = hidden_states_173_cast_fp16)[name = tensor("input_309_cast_fp16")]; + tensor var_2574 = const()[name = tensor("op_2574"), val = tensor([1, 1])]; + tensor var_2576 = const()[name = tensor("op_2576"), val = tensor([1, 1])]; + tensor hidden_states_175_pad_type_0 = const()[name = tensor("hidden_states_175_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_175_pad_0 = const()[name = tensor("hidden_states_175_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1193733568)))]; + tensor up_blocks_1_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1197010432)))]; + tensor hidden_states_175_cast_fp16 = conv(bias = up_blocks_1_attentions_0_proj_out_bias_to_fp16, dilations = var_2576, groups = var_2306, pad = hidden_states_175_pad_0, pad_type = hidden_states_175_pad_type_0, strides = var_2574, weight = up_blocks_1_attentions_0_proj_out_weight_to_fp16, x = input_309_cast_fp16)[name = tensor("hidden_states_175_cast_fp16")]; + tensor hidden_states_177_cast_fp16 = add(x = hidden_states_175_cast_fp16, y = hidden_states_163_cast_fp16)[name = tensor("hidden_states_177_cast_fp16")]; + tensor input_311_interleave_0 = const()[name = tensor("input_311_interleave_0"), val = tensor(false)]; + tensor input_311_cast_fp16 = concat(axis = var_2306, interleave = input_311_interleave_0, values = (hidden_states_177_cast_fp16, input_143_cast_fp16))[name = tensor("input_311_cast_fp16")]; + tensor reshape_144_shape_0 = const()[name = tensor("reshape_144_shape_0"), val = tensor([2, 32, 80, 12, 20])]; + tensor reshape_144_cast_fp16 = reshape(shape = reshape_144_shape_0, x = input_311_cast_fp16)[name = tensor("reshape_144_cast_fp16")]; + tensor reduce_mean_108_axes_0 = const()[name = tensor("reduce_mean_108_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_108_keep_dims_0 = const()[name = tensor("reduce_mean_108_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_108_cast_fp16 = reduce_mean(axes = reduce_mean_108_axes_0, keep_dims = reduce_mean_108_keep_dims_0, x = reshape_144_cast_fp16)[name = tensor("reduce_mean_108_cast_fp16")]; + tensor sub_72_cast_fp16 = sub(x = reshape_144_cast_fp16, y = reduce_mean_108_cast_fp16)[name = tensor("sub_72_cast_fp16")]; + tensor square_36_cast_fp16 = square(x = sub_72_cast_fp16)[name = tensor("square_36_cast_fp16")]; + tensor reduce_mean_110_axes_0 = const()[name = tensor("reduce_mean_110_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_110_keep_dims_0 = const()[name = tensor("reduce_mean_110_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_110_cast_fp16 = reduce_mean(axes = reduce_mean_110_axes_0, keep_dims = reduce_mean_110_keep_dims_0, x = square_36_cast_fp16)[name = tensor("reduce_mean_110_cast_fp16")]; + tensor add_72_y_0_to_fp16 = const()[name = tensor("add_72_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_72_cast_fp16 = add(x = reduce_mean_110_cast_fp16, y = add_72_y_0_to_fp16)[name = tensor("add_72_cast_fp16")]; + tensor sqrt_36_cast_fp16 = sqrt(x = add_72_cast_fp16)[name = tensor("sqrt_36_cast_fp16")]; + tensor real_div_36_cast_fp16 = real_div(x = sub_72_cast_fp16, y = sqrt_36_cast_fp16)[name = tensor("real_div_36_cast_fp16")]; + tensor reshape_145_shape_0 = const()[name = tensor("reshape_145_shape_0"), val = tensor([2, 2560, 12, 20])]; + tensor reshape_145_cast_fp16 = reshape(shape = reshape_145_shape_0, x = real_div_36_cast_fp16)[name = tensor("reshape_145_cast_fp16")]; + tensor add_73_gamma_0_to_fp16 = const()[name = tensor("add_73_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1197013056)))]; + tensor add_73_beta_0_to_fp16 = const()[name = tensor("add_73_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1197018240)))]; + tensor add_73_epsilon_0_to_fp16 = const()[name = tensor("add_73_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_73_cast_fp16 = batch_norm(beta = add_73_beta_0_to_fp16, epsilon = add_73_epsilon_0_to_fp16, gamma = add_73_gamma_0_to_fp16, mean = add_55_mean_0_to_fp16, variance = add_55_variance_0_to_fp16, x = reshape_145_cast_fp16)[name = tensor("add_73_cast_fp16")]; + tensor input_315_cast_fp16 = silu(x = add_73_cast_fp16)[name = tensor("input_315_cast_fp16")]; + tensor var_2594 = const()[name = tensor("op_2594"), val = tensor([1, 1])]; + tensor var_2596 = const()[name = tensor("op_2596"), val = tensor([1, 1])]; + tensor hidden_states_179_pad_type_0 = const()[name = tensor("hidden_states_179_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_179_pad_0 = const()[name = tensor("hidden_states_179_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1197023424)))]; + tensor up_blocks_1_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1256005888)))]; + tensor hidden_states_179_cast_fp16 = conv(bias = up_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = var_2596, groups = var_2306, pad = hidden_states_179_pad_0, pad_type = hidden_states_179_pad_type_0, strides = var_2594, weight = up_blocks_1_resnets_1_conv1_weight_to_fp16, x = input_315_cast_fp16)[name = tensor("hidden_states_179_cast_fp16")]; + tensor var_2602 = const()[name = tensor("op_2602"), val = tensor([1, 1])]; + tensor var_2604 = const()[name = tensor("op_2604"), val = tensor([1, 1])]; + tensor temb_29_pad_type_0 = const()[name = tensor("temb_29_pad_type_0"), val = tensor("custom")]; + tensor temb_29_pad_0 = const()[name = tensor("temb_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1256008512)))]; + tensor up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1259285376)))]; + tensor temb_29_cast_fp16 = conv(bias = up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_2604, groups = var_2306, pad = temb_29_pad_0, pad_type = temb_29_pad_type_0, strides = var_2602, weight = up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16)[name = tensor("temb_29_cast_fp16")]; + tensor input_319_cast_fp16 = add(x = hidden_states_179_cast_fp16, y = temb_29_cast_fp16)[name = tensor("input_319_cast_fp16")]; + tensor reshape_148_shape_0 = const()[name = tensor("reshape_148_shape_0"), val = tensor([2, 32, 40, 12, 20])]; + tensor reshape_148_cast_fp16 = reshape(shape = reshape_148_shape_0, x = input_319_cast_fp16)[name = tensor("reshape_148_cast_fp16")]; + tensor reduce_mean_111_axes_0 = const()[name = tensor("reduce_mean_111_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_111_keep_dims_0 = const()[name = tensor("reduce_mean_111_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_111_cast_fp16 = reduce_mean(axes = reduce_mean_111_axes_0, keep_dims = reduce_mean_111_keep_dims_0, x = reshape_148_cast_fp16)[name = tensor("reduce_mean_111_cast_fp16")]; + tensor sub_74_cast_fp16 = sub(x = reshape_148_cast_fp16, y = reduce_mean_111_cast_fp16)[name = tensor("sub_74_cast_fp16")]; + tensor square_37_cast_fp16 = square(x = sub_74_cast_fp16)[name = tensor("square_37_cast_fp16")]; + tensor reduce_mean_113_axes_0 = const()[name = tensor("reduce_mean_113_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_113_keep_dims_0 = const()[name = tensor("reduce_mean_113_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_113_cast_fp16 = reduce_mean(axes = reduce_mean_113_axes_0, keep_dims = reduce_mean_113_keep_dims_0, x = square_37_cast_fp16)[name = tensor("reduce_mean_113_cast_fp16")]; + tensor add_74_y_0_to_fp16 = const()[name = tensor("add_74_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_74_cast_fp16 = add(x = reduce_mean_113_cast_fp16, y = add_74_y_0_to_fp16)[name = tensor("add_74_cast_fp16")]; + tensor sqrt_37_cast_fp16 = sqrt(x = add_74_cast_fp16)[name = tensor("sqrt_37_cast_fp16")]; + tensor real_div_37_cast_fp16 = real_div(x = sub_74_cast_fp16, y = sqrt_37_cast_fp16)[name = tensor("real_div_37_cast_fp16")]; + tensor reshape_149_shape_0 = const()[name = tensor("reshape_149_shape_0"), val = tensor([2, 1280, 12, 20])]; + tensor reshape_149_cast_fp16 = reshape(shape = reshape_149_shape_0, x = real_div_37_cast_fp16)[name = tensor("reshape_149_cast_fp16")]; + tensor add_75_gamma_0_to_fp16 = const()[name = tensor("add_75_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1259288000)))]; + tensor add_75_beta_0_to_fp16 = const()[name = tensor("add_75_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1259290624)))]; + tensor add_75_epsilon_0_to_fp16 = const()[name = tensor("add_75_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_75_cast_fp16 = batch_norm(beta = add_75_beta_0_to_fp16, epsilon = add_75_epsilon_0_to_fp16, gamma = add_75_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_149_cast_fp16)[name = tensor("add_75_cast_fp16")]; + tensor input_323_cast_fp16 = silu(x = add_75_cast_fp16)[name = tensor("input_323_cast_fp16")]; + tensor var_2614 = const()[name = tensor("op_2614"), val = tensor([1, 1])]; + tensor var_2616 = const()[name = tensor("op_2616"), val = tensor([1, 1])]; + tensor hidden_states_181_pad_type_0 = const()[name = tensor("hidden_states_181_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_181_pad_0 = const()[name = tensor("hidden_states_181_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1259293248)))]; + tensor up_blocks_1_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1288784512)))]; + tensor hidden_states_181_cast_fp16 = conv(bias = up_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = var_2616, groups = var_2306, pad = hidden_states_181_pad_0, pad_type = hidden_states_181_pad_type_0, strides = var_2614, weight = up_blocks_1_resnets_1_conv2_weight_to_fp16, x = input_323_cast_fp16)[name = tensor("hidden_states_181_cast_fp16")]; + tensor var_2621 = const()[name = tensor("op_2621"), val = tensor([1, 1])]; + tensor var_2623 = const()[name = tensor("op_2623"), val = tensor([1, 1])]; + tensor x_13_pad_type_0 = const()[name = tensor("x_13_pad_type_0"), val = tensor("custom")]; + tensor x_13_pad_0 = const()[name = tensor("x_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1288787136)))]; + tensor up_blocks_1_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1295340800)))]; + tensor x_13_cast_fp16 = conv(bias = up_blocks_1_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_2623, groups = var_2306, pad = x_13_pad_0, pad_type = x_13_pad_type_0, strides = var_2621, weight = up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16, x = input_311_cast_fp16)[name = tensor("x_13_cast_fp16")]; + tensor hidden_states_183_cast_fp16 = add(x = x_13_cast_fp16, y = hidden_states_181_cast_fp16)[name = tensor("hidden_states_183_cast_fp16")]; + tensor reshape_152_shape_0 = const()[name = tensor("reshape_152_shape_0"), val = tensor([2, 32, 40, 12, 20])]; + tensor reshape_152_cast_fp16 = reshape(shape = reshape_152_shape_0, x = hidden_states_183_cast_fp16)[name = tensor("reshape_152_cast_fp16")]; + tensor reduce_mean_114_axes_0 = const()[name = tensor("reduce_mean_114_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_114_keep_dims_0 = const()[name = tensor("reduce_mean_114_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_114_cast_fp16 = reduce_mean(axes = reduce_mean_114_axes_0, keep_dims = reduce_mean_114_keep_dims_0, x = reshape_152_cast_fp16)[name = tensor("reduce_mean_114_cast_fp16")]; + tensor sub_76_cast_fp16 = sub(x = reshape_152_cast_fp16, y = reduce_mean_114_cast_fp16)[name = tensor("sub_76_cast_fp16")]; + tensor square_38_cast_fp16 = square(x = sub_76_cast_fp16)[name = tensor("square_38_cast_fp16")]; + tensor reduce_mean_116_axes_0 = const()[name = tensor("reduce_mean_116_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_116_keep_dims_0 = const()[name = tensor("reduce_mean_116_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_116_cast_fp16 = reduce_mean(axes = reduce_mean_116_axes_0, keep_dims = reduce_mean_116_keep_dims_0, x = square_38_cast_fp16)[name = tensor("reduce_mean_116_cast_fp16")]; + tensor add_76_y_0_to_fp16 = const()[name = tensor("add_76_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_76_cast_fp16 = add(x = reduce_mean_116_cast_fp16, y = add_76_y_0_to_fp16)[name = tensor("add_76_cast_fp16")]; + tensor sqrt_38_cast_fp16 = sqrt(x = add_76_cast_fp16)[name = tensor("sqrt_38_cast_fp16")]; + tensor real_div_38_cast_fp16 = real_div(x = sub_76_cast_fp16, y = sqrt_38_cast_fp16)[name = tensor("real_div_38_cast_fp16")]; + tensor reshape_153_shape_0 = const()[name = tensor("reshape_153_shape_0"), val = tensor([2, 1280, 12, 20])]; + tensor reshape_153_cast_fp16 = reshape(shape = reshape_153_shape_0, x = real_div_38_cast_fp16)[name = tensor("reshape_153_cast_fp16")]; + tensor add_77_gamma_0_to_fp16 = const()[name = tensor("add_77_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1295343424)))]; + tensor add_77_beta_0_to_fp16 = const()[name = tensor("add_77_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1295346048)))]; + tensor add_77_epsilon_0_to_fp16 = const()[name = tensor("add_77_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_77_cast_fp16 = batch_norm(beta = add_77_beta_0_to_fp16, epsilon = add_77_epsilon_0_to_fp16, gamma = add_77_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_153_cast_fp16)[name = tensor("add_77_cast_fp16")]; + tensor var_2643 = const()[name = tensor("op_2643"), val = tensor([1, 1])]; + tensor var_2645 = const()[name = tensor("op_2645"), val = tensor([1, 1])]; + tensor hidden_states_185_pad_type_0 = const()[name = tensor("hidden_states_185_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_185_pad_0 = const()[name = tensor("hidden_states_185_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1295348672)))]; + tensor up_blocks_1_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1298625536)))]; + tensor hidden_states_185_cast_fp16 = conv(bias = up_blocks_1_attentions_1_proj_in_bias_to_fp16, dilations = var_2645, groups = var_2306, pad = hidden_states_185_pad_0, pad_type = hidden_states_185_pad_type_0, strides = var_2643, weight = up_blocks_1_attentions_1_proj_in_weight_to_fp16, x = add_77_cast_fp16)[name = tensor("hidden_states_185_cast_fp16")]; + tensor var_2650 = const()[name = tensor("op_2650"), val = tensor([2, 1280, 1, 240])]; + tensor inputs_49_cast_fp16 = reshape(shape = var_2650, x = hidden_states_185_cast_fp16)[name = tensor("inputs_49_cast_fp16")]; + tensor var_2660 = const()[name = tensor("op_2660"), val = tensor([1])]; + tensor channels_mean_49_cast_fp16 = reduce_mean(axes = var_2660, keep_dims = var_2301, x = inputs_49_cast_fp16)[name = tensor("channels_mean_49_cast_fp16")]; + tensor zero_mean_49_cast_fp16 = sub(x = inputs_49_cast_fp16, y = channels_mean_49_cast_fp16)[name = tensor("zero_mean_49_cast_fp16")]; + tensor zero_mean_sq_49_cast_fp16 = mul(x = zero_mean_49_cast_fp16, y = zero_mean_49_cast_fp16)[name = tensor("zero_mean_sq_49_cast_fp16")]; + tensor var_2664 = const()[name = tensor("op_2664"), val = tensor([1])]; + tensor var_2665_cast_fp16 = reduce_mean(axes = var_2664, keep_dims = var_2301, x = zero_mean_sq_49_cast_fp16)[name = tensor("op_2665_cast_fp16")]; + tensor var_2666_to_fp16 = const()[name = tensor("op_2666_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2667_cast_fp16 = add(x = var_2665_cast_fp16, y = var_2666_to_fp16)[name = tensor("op_2667_cast_fp16")]; + tensor denom_49_epsilon_0_to_fp16 = const()[name = tensor("denom_49_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_49_cast_fp16 = rsqrt(epsilon = denom_49_epsilon_0_to_fp16, x = var_2667_cast_fp16)[name = tensor("denom_49_cast_fp16")]; + tensor out_49_cast_fp16 = mul(x = zero_mean_49_cast_fp16, y = denom_49_cast_fp16)[name = tensor("out_49_cast_fp16")]; + tensor var_2671_to_fp16 = const()[name = tensor("op_2671_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1298628160)))]; + tensor var_2672_cast_fp16 = add(x = out_49_cast_fp16, y = var_2671_to_fp16)[name = tensor("op_2672_cast_fp16")]; + tensor var_2674_to_fp16 = const()[name = tensor("op_2674_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1298630784)))]; + tensor hidden_states_187_cast_fp16 = mul(x = var_2672_cast_fp16, y = var_2674_to_fp16)[name = tensor("hidden_states_187_cast_fp16")]; + tensor var_2681 = const()[name = tensor("op_2681"), val = tensor([1, 1])]; + tensor var_2683 = const()[name = tensor("op_2683"), val = tensor([1, 1])]; + tensor q_33_pad_type_0 = const()[name = tensor("q_33_pad_type_0"), val = tensor("custom")]; + tensor q_33_pad_0 = const()[name = tensor("q_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1298633408)))]; + tensor q_33_cast_fp16 = conv(dilations = var_2683, groups = var_2306, pad = q_33_pad_0, pad_type = q_33_pad_type_0, strides = var_2681, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_187_cast_fp16)[name = tensor("q_33_cast_fp16")]; + tensor var_2687 = const()[name = tensor("op_2687"), val = tensor([1, 1])]; + tensor var_2689 = const()[name = tensor("op_2689"), val = tensor([1, 1])]; + tensor k_33_pad_type_0 = const()[name = tensor("k_33_pad_type_0"), val = tensor("custom")]; + tensor k_33_pad_0 = const()[name = tensor("k_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1301910272)))]; + tensor k_33_cast_fp16 = conv(dilations = var_2689, groups = var_2306, pad = k_33_pad_0, pad_type = k_33_pad_type_0, strides = var_2687, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_187_cast_fp16)[name = tensor("k_33_cast_fp16")]; + tensor var_2693 = const()[name = tensor("op_2693"), val = tensor([1, 1])]; + tensor var_2695 = const()[name = tensor("op_2695"), val = tensor([1, 1])]; + tensor v_33_pad_type_0 = const()[name = tensor("v_33_pad_type_0"), val = tensor("custom")]; + tensor v_33_pad_0 = const()[name = tensor("v_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1305187136)))]; + tensor v_33_cast_fp16 = conv(dilations = var_2695, groups = var_2306, pad = v_33_pad_0, pad_type = v_33_pad_type_0, strides = var_2693, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_187_cast_fp16)[name = tensor("v_33_cast_fp16")]; + tensor var_2699 = const()[name = tensor("op_2699"), val = tensor([2, 20, 64, -1])]; + tensor var_2700_cast_fp16 = reshape(shape = var_2699, x = q_33_cast_fp16)[name = tensor("op_2700_cast_fp16")]; + tensor var_2701 = const()[name = tensor("op_2701"), val = tensor([2, 20, 64, -1])]; + tensor var_2702_cast_fp16 = reshape(shape = var_2701, x = k_33_cast_fp16)[name = tensor("op_2702_cast_fp16")]; + tensor var_2703 = const()[name = tensor("op_2703"), val = tensor([2, 20, 64, -1])]; + tensor var_2704_cast_fp16 = reshape(shape = var_2703, x = v_33_cast_fp16)[name = tensor("op_2704_cast_fp16")]; + tensor attn_weights_65_transpose_x_0 = const()[name = tensor("attn_weights_65_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_65_transpose_y_0 = const()[name = tensor("attn_weights_65_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_65_cast_fp16 = matmul(transpose_x = attn_weights_65_transpose_x_0, transpose_y = attn_weights_65_transpose_y_0, x = var_2700_cast_fp16, y = var_2702_cast_fp16)[name = tensor("attn_weights_65_cast_fp16")]; + tensor attn_weights_67_cast_fp16 = mul(x = attn_weights_65_cast_fp16, y = var_2297_to_fp16)[name = tensor("attn_weights_67_cast_fp16")]; + tensor var_2708_cast_fp16 = softmax(axis = var_2290, x = attn_weights_67_cast_fp16)[name = tensor("op_2708_cast_fp16")]; + tensor attn_33_transpose_x_0 = const()[name = tensor("attn_33_transpose_x_0"), val = tensor(false)]; + tensor attn_33_transpose_y_0 = const()[name = tensor("attn_33_transpose_y_0"), val = tensor(true)]; + tensor attn_33_cast_fp16 = matmul(transpose_x = attn_33_transpose_x_0, transpose_y = attn_33_transpose_y_0, x = var_2704_cast_fp16, y = var_2708_cast_fp16)[name = tensor("attn_33_cast_fp16")]; + tensor var_2712 = const()[name = tensor("op_2712"), val = tensor([2, 1280, 1, -1])]; + tensor input_327_cast_fp16 = reshape(shape = var_2712, x = attn_33_cast_fp16)[name = tensor("input_327_cast_fp16")]; + tensor var_2717 = const()[name = tensor("op_2717"), val = tensor([1, 1])]; + tensor var_2719 = const()[name = tensor("op_2719"), val = tensor([1, 1])]; + tensor var_2721_pad_type_0 = const()[name = tensor("op_2721_pad_type_0"), val = tensor("custom")]; + tensor var_2721_pad_0 = const()[name = tensor("op_2721_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1308464000)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1311740864)))]; + tensor var_2721_cast_fp16 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_2719, groups = var_2306, pad = var_2721_pad_0, pad_type = var_2721_pad_type_0, strides = var_2717, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_327_cast_fp16)[name = tensor("op_2721_cast_fp16")]; + tensor inputs_51_cast_fp16 = add(x = var_2721_cast_fp16, y = inputs_49_cast_fp16)[name = tensor("inputs_51_cast_fp16")]; + tensor var_2725 = const()[name = tensor("op_2725"), val = tensor([1])]; + tensor channels_mean_51_cast_fp16 = reduce_mean(axes = var_2725, keep_dims = var_2301, x = inputs_51_cast_fp16)[name = tensor("channels_mean_51_cast_fp16")]; + tensor zero_mean_51_cast_fp16 = sub(x = inputs_51_cast_fp16, y = channels_mean_51_cast_fp16)[name = tensor("zero_mean_51_cast_fp16")]; + tensor zero_mean_sq_51_cast_fp16 = mul(x = zero_mean_51_cast_fp16, y = zero_mean_51_cast_fp16)[name = tensor("zero_mean_sq_51_cast_fp16")]; + tensor var_2729 = const()[name = tensor("op_2729"), val = tensor([1])]; + tensor var_2730_cast_fp16 = reduce_mean(axes = var_2729, keep_dims = var_2301, x = zero_mean_sq_51_cast_fp16)[name = tensor("op_2730_cast_fp16")]; + tensor var_2731_to_fp16 = const()[name = tensor("op_2731_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2732_cast_fp16 = add(x = var_2730_cast_fp16, y = var_2731_to_fp16)[name = tensor("op_2732_cast_fp16")]; + tensor denom_51_epsilon_0_to_fp16 = const()[name = tensor("denom_51_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_51_cast_fp16 = rsqrt(epsilon = denom_51_epsilon_0_to_fp16, x = var_2732_cast_fp16)[name = tensor("denom_51_cast_fp16")]; + tensor out_51_cast_fp16 = mul(x = zero_mean_51_cast_fp16, y = denom_51_cast_fp16)[name = tensor("out_51_cast_fp16")]; + tensor var_2736_to_fp16 = const()[name = tensor("op_2736_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1311743488)))]; + tensor var_2737_cast_fp16 = add(x = out_51_cast_fp16, y = var_2736_to_fp16)[name = tensor("op_2737_cast_fp16")]; + tensor var_2739_to_fp16 = const()[name = tensor("op_2739_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1311746112)))]; + tensor hidden_states_189_cast_fp16 = mul(x = var_2737_cast_fp16, y = var_2739_to_fp16)[name = tensor("hidden_states_189_cast_fp16")]; + tensor var_2746 = const()[name = tensor("op_2746"), val = tensor([1, 1])]; + tensor var_2748 = const()[name = tensor("op_2748"), val = tensor([1, 1])]; + tensor q_35_pad_type_0 = const()[name = tensor("q_35_pad_type_0"), val = tensor("custom")]; + tensor q_35_pad_0 = const()[name = tensor("q_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1311748736)))]; + tensor q_35_cast_fp16 = conv(dilations = var_2748, groups = var_2306, pad = q_35_pad_0, pad_type = q_35_pad_type_0, strides = var_2746, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_189_cast_fp16)[name = tensor("q_35_cast_fp16")]; + tensor var_2752 = const()[name = tensor("op_2752"), val = tensor([1, 1])]; + tensor var_2754 = const()[name = tensor("op_2754"), val = tensor([1, 1])]; + tensor k_35_pad_type_0 = const()[name = tensor("k_35_pad_type_0"), val = tensor("custom")]; + tensor k_35_pad_0 = const()[name = tensor("k_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1315025600)))]; + tensor k_35_cast_fp16 = conv(dilations = var_2754, groups = var_2306, pad = k_35_pad_0, pad_type = k_35_pad_type_0, strides = var_2752, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_35_cast_fp16")]; + tensor var_2758 = const()[name = tensor("op_2758"), val = tensor([1, 1])]; + tensor var_2760 = const()[name = tensor("op_2760"), val = tensor([1, 1])]; + tensor v_35_pad_type_0 = const()[name = tensor("v_35_pad_type_0"), val = tensor("custom")]; + tensor v_35_pad_0 = const()[name = tensor("v_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1317647104)))]; + tensor v_35_cast_fp16 = conv(dilations = var_2760, groups = var_2306, pad = v_35_pad_0, pad_type = v_35_pad_type_0, strides = var_2758, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_35_cast_fp16")]; + tensor var_2764 = const()[name = tensor("op_2764"), val = tensor([2, 20, 64, -1])]; + tensor var_2765_cast_fp16 = reshape(shape = var_2764, x = q_35_cast_fp16)[name = tensor("op_2765_cast_fp16")]; + tensor var_2766 = const()[name = tensor("op_2766"), val = tensor([2, 20, 64, -1])]; + tensor var_2767_cast_fp16 = reshape(shape = var_2766, x = k_35_cast_fp16)[name = tensor("op_2767_cast_fp16")]; + tensor var_2768 = const()[name = tensor("op_2768"), val = tensor([2, 20, 64, -1])]; + tensor var_2769_cast_fp16 = reshape(shape = var_2768, x = v_35_cast_fp16)[name = tensor("op_2769_cast_fp16")]; + tensor attn_weights_69_transpose_x_0 = const()[name = tensor("attn_weights_69_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_69_transpose_y_0 = const()[name = tensor("attn_weights_69_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_69_cast_fp16 = matmul(transpose_x = attn_weights_69_transpose_x_0, transpose_y = attn_weights_69_transpose_y_0, x = var_2765_cast_fp16, y = var_2767_cast_fp16)[name = tensor("attn_weights_69_cast_fp16")]; + tensor attn_weights_71_cast_fp16 = mul(x = attn_weights_69_cast_fp16, y = var_2297_to_fp16)[name = tensor("attn_weights_71_cast_fp16")]; + tensor var_2773_cast_fp16 = softmax(axis = var_2290, x = attn_weights_71_cast_fp16)[name = tensor("op_2773_cast_fp16")]; + tensor attn_35_transpose_x_0 = const()[name = tensor("attn_35_transpose_x_0"), val = tensor(false)]; + tensor attn_35_transpose_y_0 = const()[name = tensor("attn_35_transpose_y_0"), val = tensor(true)]; + tensor attn_35_cast_fp16 = matmul(transpose_x = attn_35_transpose_x_0, transpose_y = attn_35_transpose_y_0, x = var_2769_cast_fp16, y = var_2773_cast_fp16)[name = tensor("attn_35_cast_fp16")]; + tensor var_2777 = const()[name = tensor("op_2777"), val = tensor([2, 1280, 1, -1])]; + tensor input_329_cast_fp16 = reshape(shape = var_2777, x = attn_35_cast_fp16)[name = tensor("input_329_cast_fp16")]; + tensor var_2782 = const()[name = tensor("op_2782"), val = tensor([1, 1])]; + tensor var_2784 = const()[name = tensor("op_2784"), val = tensor([1, 1])]; + tensor var_2786_pad_type_0 = const()[name = tensor("op_2786_pad_type_0"), val = tensor("custom")]; + tensor var_2786_pad_0 = const()[name = tensor("op_2786_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1320268608)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1323545472)))]; + tensor var_2786_cast_fp16 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_2784, groups = var_2306, pad = var_2786_pad_0, pad_type = var_2786_pad_type_0, strides = var_2782, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_329_cast_fp16)[name = tensor("op_2786_cast_fp16")]; + tensor inputs_53_cast_fp16 = add(x = var_2786_cast_fp16, y = inputs_51_cast_fp16)[name = tensor("inputs_53_cast_fp16")]; + tensor var_2790 = const()[name = tensor("op_2790"), val = tensor([1])]; + tensor channels_mean_53_cast_fp16 = reduce_mean(axes = var_2790, keep_dims = var_2301, x = inputs_53_cast_fp16)[name = tensor("channels_mean_53_cast_fp16")]; + tensor zero_mean_53_cast_fp16 = sub(x = inputs_53_cast_fp16, y = channels_mean_53_cast_fp16)[name = tensor("zero_mean_53_cast_fp16")]; + tensor zero_mean_sq_53_cast_fp16 = mul(x = zero_mean_53_cast_fp16, y = zero_mean_53_cast_fp16)[name = tensor("zero_mean_sq_53_cast_fp16")]; + tensor var_2794 = const()[name = tensor("op_2794"), val = tensor([1])]; + tensor var_2795_cast_fp16 = reduce_mean(axes = var_2794, keep_dims = var_2301, x = zero_mean_sq_53_cast_fp16)[name = tensor("op_2795_cast_fp16")]; + tensor var_2796_to_fp16 = const()[name = tensor("op_2796_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2797_cast_fp16 = add(x = var_2795_cast_fp16, y = var_2796_to_fp16)[name = tensor("op_2797_cast_fp16")]; + tensor denom_53_epsilon_0_to_fp16 = const()[name = tensor("denom_53_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_53_cast_fp16 = rsqrt(epsilon = denom_53_epsilon_0_to_fp16, x = var_2797_cast_fp16)[name = tensor("denom_53_cast_fp16")]; + tensor out_53_cast_fp16 = mul(x = zero_mean_53_cast_fp16, y = denom_53_cast_fp16)[name = tensor("out_53_cast_fp16")]; + tensor var_2801_to_fp16 = const()[name = tensor("op_2801_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1323548096)))]; + tensor var_2802_cast_fp16 = add(x = out_53_cast_fp16, y = var_2801_to_fp16)[name = tensor("op_2802_cast_fp16")]; + tensor var_2804_to_fp16 = const()[name = tensor("op_2804_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1323550720)))]; + tensor input_331_cast_fp16 = mul(x = var_2802_cast_fp16, y = var_2804_to_fp16)[name = tensor("input_331_cast_fp16")]; + tensor var_2812 = const()[name = tensor("op_2812"), val = tensor([1, 1])]; + tensor var_2814 = const()[name = tensor("op_2814"), val = tensor([1, 1])]; + tensor var_2816_pad_type_0 = const()[name = tensor("op_2816_pad_type_0"), val = tensor("custom")]; + tensor var_2816_pad_0 = const()[name = tensor("op_2816_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1323553344)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1349767808)))]; + tensor var_2816_cast_fp16 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_2814, groups = var_2306, pad = var_2816_pad_0, pad_type = var_2816_pad_type_0, strides = var_2812, weight = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_331_cast_fp16)[name = tensor("op_2816_cast_fp16")]; + tensor var_2817_split_sizes_0 = const()[name = tensor("op_2817_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2817_axis_0 = const()[name = tensor("op_2817_axis_0"), val = tensor(1)]; + tensor var_2817_cast_fp16_0, tensor var_2817_cast_fp16_1 = split(axis = var_2817_axis_0, split_sizes = var_2817_split_sizes_0, x = var_2816_cast_fp16)[name = tensor("op_2817_cast_fp16")]; + tensor var_2819_mode_0 = const()[name = tensor("op_2819_mode_0"), val = tensor("EXACT")]; + tensor var_2819_cast_fp16 = gelu(mode = var_2819_mode_0, x = var_2817_cast_fp16_1)[name = tensor("op_2819_cast_fp16")]; + tensor input_333_cast_fp16 = mul(x = var_2817_cast_fp16_0, y = var_2819_cast_fp16)[name = tensor("input_333_cast_fp16")]; + tensor var_2823 = const()[name = tensor("op_2823"), val = tensor([1, 1])]; + tensor var_2825 = const()[name = tensor("op_2825"), val = tensor([1, 1])]; + tensor var_2827_pad_type_0 = const()[name = tensor("op_2827_pad_type_0"), val = tensor("custom")]; + tensor var_2827_pad_0 = const()[name = tensor("op_2827_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1349788352)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1362895616)))]; + tensor var_2827_cast_fp16 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_2825, groups = var_2306, pad = var_2827_pad_0, pad_type = var_2827_pad_type_0, strides = var_2823, weight = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_333_cast_fp16)[name = tensor("op_2827_cast_fp16")]; + tensor hidden_states_193_cast_fp16 = add(x = var_2827_cast_fp16, y = inputs_53_cast_fp16)[name = tensor("hidden_states_193_cast_fp16")]; + tensor var_2829 = const()[name = tensor("op_2829"), val = tensor([2, 1280, 12, 20])]; + tensor input_335_cast_fp16 = reshape(shape = var_2829, x = hidden_states_193_cast_fp16)[name = tensor("input_335_cast_fp16")]; + tensor var_2833 = const()[name = tensor("op_2833"), val = tensor([1, 1])]; + tensor var_2835 = const()[name = tensor("op_2835"), val = tensor([1, 1])]; + tensor hidden_states_195_pad_type_0 = const()[name = tensor("hidden_states_195_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_195_pad_0 = const()[name = tensor("hidden_states_195_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1362898240)))]; + tensor up_blocks_1_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1366175104)))]; + tensor hidden_states_195_cast_fp16 = conv(bias = up_blocks_1_attentions_1_proj_out_bias_to_fp16, dilations = var_2835, groups = var_2306, pad = hidden_states_195_pad_0, pad_type = hidden_states_195_pad_type_0, strides = var_2833, weight = up_blocks_1_attentions_1_proj_out_weight_to_fp16, x = input_335_cast_fp16)[name = tensor("hidden_states_195_cast_fp16")]; + tensor hidden_states_197_cast_fp16 = add(x = hidden_states_195_cast_fp16, y = hidden_states_183_cast_fp16)[name = tensor("hidden_states_197_cast_fp16")]; + tensor input_337_interleave_0 = const()[name = tensor("input_337_interleave_0"), val = tensor(false)]; + tensor input_337_cast_fp16 = concat(axis = var_2306, interleave = input_337_interleave_0, values = (hidden_states_197_cast_fp16, input_117_cast_fp16))[name = tensor("input_337_cast_fp16")]; + tensor reshape_156_shape_0 = const()[name = tensor("reshape_156_shape_0"), val = tensor([2, 32, 60, 12, 20])]; + tensor reshape_156_cast_fp16 = reshape(shape = reshape_156_shape_0, x = input_337_cast_fp16)[name = tensor("reshape_156_cast_fp16")]; + tensor reduce_mean_117_axes_0 = const()[name = tensor("reduce_mean_117_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_117_keep_dims_0 = const()[name = tensor("reduce_mean_117_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_117_cast_fp16 = reduce_mean(axes = reduce_mean_117_axes_0, keep_dims = reduce_mean_117_keep_dims_0, x = reshape_156_cast_fp16)[name = tensor("reduce_mean_117_cast_fp16")]; + tensor sub_78_cast_fp16 = sub(x = reshape_156_cast_fp16, y = reduce_mean_117_cast_fp16)[name = tensor("sub_78_cast_fp16")]; + tensor square_39_cast_fp16 = square(x = sub_78_cast_fp16)[name = tensor("square_39_cast_fp16")]; + tensor reduce_mean_119_axes_0 = const()[name = tensor("reduce_mean_119_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_119_keep_dims_0 = const()[name = tensor("reduce_mean_119_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_119_cast_fp16 = reduce_mean(axes = reduce_mean_119_axes_0, keep_dims = reduce_mean_119_keep_dims_0, x = square_39_cast_fp16)[name = tensor("reduce_mean_119_cast_fp16")]; + tensor add_78_y_0_to_fp16 = const()[name = tensor("add_78_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_78_cast_fp16 = add(x = reduce_mean_119_cast_fp16, y = add_78_y_0_to_fp16)[name = tensor("add_78_cast_fp16")]; + tensor sqrt_39_cast_fp16 = sqrt(x = add_78_cast_fp16)[name = tensor("sqrt_39_cast_fp16")]; + tensor real_div_39_cast_fp16 = real_div(x = sub_78_cast_fp16, y = sqrt_39_cast_fp16)[name = tensor("real_div_39_cast_fp16")]; + tensor reshape_157_shape_0 = const()[name = tensor("reshape_157_shape_0"), val = tensor([2, 1920, 12, 20])]; + tensor reshape_157_cast_fp16 = reshape(shape = reshape_157_shape_0, x = real_div_39_cast_fp16)[name = tensor("reshape_157_cast_fp16")]; + tensor add_79_mean_0_to_fp16 = const()[name = tensor("add_79_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1366177728)))]; + tensor add_79_variance_0_to_fp16 = const()[name = tensor("add_79_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1366181632)))]; + tensor add_79_gamma_0_to_fp16 = const()[name = tensor("add_79_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1366185536)))]; + tensor add_79_beta_0_to_fp16 = const()[name = tensor("add_79_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1366189440)))]; + tensor add_79_epsilon_0_to_fp16 = const()[name = tensor("add_79_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_79_cast_fp16 = batch_norm(beta = add_79_beta_0_to_fp16, epsilon = add_79_epsilon_0_to_fp16, gamma = add_79_gamma_0_to_fp16, mean = add_79_mean_0_to_fp16, variance = add_79_variance_0_to_fp16, x = reshape_157_cast_fp16)[name = tensor("add_79_cast_fp16")]; + tensor input_341_cast_fp16 = silu(x = add_79_cast_fp16)[name = tensor("input_341_cast_fp16")]; + tensor var_2853 = const()[name = tensor("op_2853"), val = tensor([1, 1])]; + tensor var_2855 = const()[name = tensor("op_2855"), val = tensor([1, 1])]; + tensor hidden_states_199_pad_type_0 = const()[name = tensor("hidden_states_199_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_199_pad_0 = const()[name = tensor("hidden_states_199_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_2_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1366193344)))]; + tensor up_blocks_1_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1410430208)))]; + tensor hidden_states_199_cast_fp16 = conv(bias = up_blocks_1_resnets_2_conv1_bias_to_fp16, dilations = var_2855, groups = var_2306, pad = hidden_states_199_pad_0, pad_type = hidden_states_199_pad_type_0, strides = var_2853, weight = up_blocks_1_resnets_2_conv1_weight_to_fp16, x = input_341_cast_fp16)[name = tensor("hidden_states_199_cast_fp16")]; + tensor var_2861 = const()[name = tensor("op_2861"), val = tensor([1, 1])]; + tensor var_2863 = const()[name = tensor("op_2863"), val = tensor([1, 1])]; + tensor temb_31_pad_type_0 = const()[name = tensor("temb_31_pad_type_0"), val = tensor("custom")]; + tensor temb_31_pad_0 = const()[name = tensor("temb_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1410432832)))]; + tensor up_blocks_1_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1413709696)))]; + tensor temb_31_cast_fp16 = conv(bias = up_blocks_1_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_2863, groups = var_2306, pad = temb_31_pad_0, pad_type = temb_31_pad_type_0, strides = var_2861, weight = up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16)[name = tensor("temb_31_cast_fp16")]; + tensor input_345_cast_fp16 = add(x = hidden_states_199_cast_fp16, y = temb_31_cast_fp16)[name = tensor("input_345_cast_fp16")]; + tensor reshape_160_shape_0 = const()[name = tensor("reshape_160_shape_0"), val = tensor([2, 32, 40, 12, 20])]; + tensor reshape_160_cast_fp16 = reshape(shape = reshape_160_shape_0, x = input_345_cast_fp16)[name = tensor("reshape_160_cast_fp16")]; + tensor reduce_mean_120_axes_0 = const()[name = tensor("reduce_mean_120_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_120_keep_dims_0 = const()[name = tensor("reduce_mean_120_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_120_cast_fp16 = reduce_mean(axes = reduce_mean_120_axes_0, keep_dims = reduce_mean_120_keep_dims_0, x = reshape_160_cast_fp16)[name = tensor("reduce_mean_120_cast_fp16")]; + tensor sub_80_cast_fp16 = sub(x = reshape_160_cast_fp16, y = reduce_mean_120_cast_fp16)[name = tensor("sub_80_cast_fp16")]; + tensor square_40_cast_fp16 = square(x = sub_80_cast_fp16)[name = tensor("square_40_cast_fp16")]; + tensor reduce_mean_122_axes_0 = const()[name = tensor("reduce_mean_122_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_122_keep_dims_0 = const()[name = tensor("reduce_mean_122_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_122_cast_fp16 = reduce_mean(axes = reduce_mean_122_axes_0, keep_dims = reduce_mean_122_keep_dims_0, x = square_40_cast_fp16)[name = tensor("reduce_mean_122_cast_fp16")]; + tensor add_80_y_0_to_fp16 = const()[name = tensor("add_80_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_80_cast_fp16 = add(x = reduce_mean_122_cast_fp16, y = add_80_y_0_to_fp16)[name = tensor("add_80_cast_fp16")]; + tensor sqrt_40_cast_fp16 = sqrt(x = add_80_cast_fp16)[name = tensor("sqrt_40_cast_fp16")]; + tensor real_div_40_cast_fp16 = real_div(x = sub_80_cast_fp16, y = sqrt_40_cast_fp16)[name = tensor("real_div_40_cast_fp16")]; + tensor reshape_161_shape_0 = const()[name = tensor("reshape_161_shape_0"), val = tensor([2, 1280, 12, 20])]; + tensor reshape_161_cast_fp16 = reshape(shape = reshape_161_shape_0, x = real_div_40_cast_fp16)[name = tensor("reshape_161_cast_fp16")]; + tensor add_81_gamma_0_to_fp16 = const()[name = tensor("add_81_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1413712320)))]; + tensor add_81_beta_0_to_fp16 = const()[name = tensor("add_81_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1413714944)))]; + tensor add_81_epsilon_0_to_fp16 = const()[name = tensor("add_81_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_81_cast_fp16 = batch_norm(beta = add_81_beta_0_to_fp16, epsilon = add_81_epsilon_0_to_fp16, gamma = add_81_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_161_cast_fp16)[name = tensor("add_81_cast_fp16")]; + tensor input_349_cast_fp16 = silu(x = add_81_cast_fp16)[name = tensor("input_349_cast_fp16")]; + tensor var_2873 = const()[name = tensor("op_2873"), val = tensor([1, 1])]; + tensor var_2875 = const()[name = tensor("op_2875"), val = tensor([1, 1])]; + tensor hidden_states_201_pad_type_0 = const()[name = tensor("hidden_states_201_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_201_pad_0 = const()[name = tensor("hidden_states_201_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_2_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1413717568)))]; + tensor up_blocks_1_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1443208832)))]; + tensor hidden_states_201_cast_fp16 = conv(bias = up_blocks_1_resnets_2_conv2_bias_to_fp16, dilations = var_2875, groups = var_2306, pad = hidden_states_201_pad_0, pad_type = hidden_states_201_pad_type_0, strides = var_2873, weight = up_blocks_1_resnets_2_conv2_weight_to_fp16, x = input_349_cast_fp16)[name = tensor("hidden_states_201_cast_fp16")]; + tensor var_2880 = const()[name = tensor("op_2880"), val = tensor([1, 1])]; + tensor var_2882 = const()[name = tensor("op_2882"), val = tensor([1, 1])]; + tensor x_15_pad_type_0 = const()[name = tensor("x_15_pad_type_0"), val = tensor("custom")]; + tensor x_15_pad_0 = const()[name = tensor("x_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1443211456)))]; + tensor up_blocks_1_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1448126720)))]; + tensor x_15_cast_fp16 = conv(bias = up_blocks_1_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_2882, groups = var_2306, pad = x_15_pad_0, pad_type = x_15_pad_type_0, strides = var_2880, weight = up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16, x = input_337_cast_fp16)[name = tensor("x_15_cast_fp16")]; + tensor hidden_states_203_cast_fp16 = add(x = x_15_cast_fp16, y = hidden_states_201_cast_fp16)[name = tensor("hidden_states_203_cast_fp16")]; + tensor reshape_164_shape_0 = const()[name = tensor("reshape_164_shape_0"), val = tensor([2, 32, 40, 12, 20])]; + tensor reshape_164_cast_fp16 = reshape(shape = reshape_164_shape_0, x = hidden_states_203_cast_fp16)[name = tensor("reshape_164_cast_fp16")]; + tensor reduce_mean_123_axes_0 = const()[name = tensor("reduce_mean_123_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_123_keep_dims_0 = const()[name = tensor("reduce_mean_123_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_123_cast_fp16 = reduce_mean(axes = reduce_mean_123_axes_0, keep_dims = reduce_mean_123_keep_dims_0, x = reshape_164_cast_fp16)[name = tensor("reduce_mean_123_cast_fp16")]; + tensor sub_82_cast_fp16 = sub(x = reshape_164_cast_fp16, y = reduce_mean_123_cast_fp16)[name = tensor("sub_82_cast_fp16")]; + tensor square_41_cast_fp16 = square(x = sub_82_cast_fp16)[name = tensor("square_41_cast_fp16")]; + tensor reduce_mean_125_axes_0 = const()[name = tensor("reduce_mean_125_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_125_keep_dims_0 = const()[name = tensor("reduce_mean_125_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_125_cast_fp16 = reduce_mean(axes = reduce_mean_125_axes_0, keep_dims = reduce_mean_125_keep_dims_0, x = square_41_cast_fp16)[name = tensor("reduce_mean_125_cast_fp16")]; + tensor add_82_y_0_to_fp16 = const()[name = tensor("add_82_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_82_cast_fp16 = add(x = reduce_mean_125_cast_fp16, y = add_82_y_0_to_fp16)[name = tensor("add_82_cast_fp16")]; + tensor sqrt_41_cast_fp16 = sqrt(x = add_82_cast_fp16)[name = tensor("sqrt_41_cast_fp16")]; + tensor real_div_41_cast_fp16 = real_div(x = sub_82_cast_fp16, y = sqrt_41_cast_fp16)[name = tensor("real_div_41_cast_fp16")]; + tensor reshape_165_shape_0 = const()[name = tensor("reshape_165_shape_0"), val = tensor([2, 1280, 12, 20])]; + tensor reshape_165_cast_fp16 = reshape(shape = reshape_165_shape_0, x = real_div_41_cast_fp16)[name = tensor("reshape_165_cast_fp16")]; + tensor add_83_gamma_0_to_fp16 = const()[name = tensor("add_83_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1448129344)))]; + tensor add_83_beta_0_to_fp16 = const()[name = tensor("add_83_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1448131968)))]; + tensor add_83_epsilon_0_to_fp16 = const()[name = tensor("add_83_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_83_cast_fp16 = batch_norm(beta = add_83_beta_0_to_fp16, epsilon = add_83_epsilon_0_to_fp16, gamma = add_83_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_165_cast_fp16)[name = tensor("add_83_cast_fp16")]; + tensor var_2902 = const()[name = tensor("op_2902"), val = tensor([1, 1])]; + tensor var_2904 = const()[name = tensor("op_2904"), val = tensor([1, 1])]; + tensor hidden_states_205_pad_type_0 = const()[name = tensor("hidden_states_205_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_205_pad_0 = const()[name = tensor("hidden_states_205_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1448134592)))]; + tensor up_blocks_1_attentions_2_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1451411456)))]; + tensor hidden_states_205_cast_fp16 = conv(bias = up_blocks_1_attentions_2_proj_in_bias_to_fp16, dilations = var_2904, groups = var_2306, pad = hidden_states_205_pad_0, pad_type = hidden_states_205_pad_type_0, strides = var_2902, weight = up_blocks_1_attentions_2_proj_in_weight_to_fp16, x = add_83_cast_fp16)[name = tensor("hidden_states_205_cast_fp16")]; + tensor var_2909 = const()[name = tensor("op_2909"), val = tensor([2, 1280, 1, 240])]; + tensor inputs_55_cast_fp16 = reshape(shape = var_2909, x = hidden_states_205_cast_fp16)[name = tensor("inputs_55_cast_fp16")]; + tensor var_2919 = const()[name = tensor("op_2919"), val = tensor([1])]; + tensor channels_mean_55_cast_fp16 = reduce_mean(axes = var_2919, keep_dims = var_2301, x = inputs_55_cast_fp16)[name = tensor("channels_mean_55_cast_fp16")]; + tensor zero_mean_55_cast_fp16 = sub(x = inputs_55_cast_fp16, y = channels_mean_55_cast_fp16)[name = tensor("zero_mean_55_cast_fp16")]; + tensor zero_mean_sq_55_cast_fp16 = mul(x = zero_mean_55_cast_fp16, y = zero_mean_55_cast_fp16)[name = tensor("zero_mean_sq_55_cast_fp16")]; + tensor var_2923 = const()[name = tensor("op_2923"), val = tensor([1])]; + tensor var_2924_cast_fp16 = reduce_mean(axes = var_2923, keep_dims = var_2301, x = zero_mean_sq_55_cast_fp16)[name = tensor("op_2924_cast_fp16")]; + tensor var_2925_to_fp16 = const()[name = tensor("op_2925_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2926_cast_fp16 = add(x = var_2924_cast_fp16, y = var_2925_to_fp16)[name = tensor("op_2926_cast_fp16")]; + tensor denom_55_epsilon_0_to_fp16 = const()[name = tensor("denom_55_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_55_cast_fp16 = rsqrt(epsilon = denom_55_epsilon_0_to_fp16, x = var_2926_cast_fp16)[name = tensor("denom_55_cast_fp16")]; + tensor out_55_cast_fp16 = mul(x = zero_mean_55_cast_fp16, y = denom_55_cast_fp16)[name = tensor("out_55_cast_fp16")]; + tensor var_2930_to_fp16 = const()[name = tensor("op_2930_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1451414080)))]; + tensor var_2931_cast_fp16 = add(x = out_55_cast_fp16, y = var_2930_to_fp16)[name = tensor("op_2931_cast_fp16")]; + tensor var_2933_to_fp16 = const()[name = tensor("op_2933_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1451416704)))]; + tensor hidden_states_207_cast_fp16 = mul(x = var_2931_cast_fp16, y = var_2933_to_fp16)[name = tensor("hidden_states_207_cast_fp16")]; + tensor var_2940 = const()[name = tensor("op_2940"), val = tensor([1, 1])]; + tensor var_2942 = const()[name = tensor("op_2942"), val = tensor([1, 1])]; + tensor q_37_pad_type_0 = const()[name = tensor("q_37_pad_type_0"), val = tensor("custom")]; + tensor q_37_pad_0 = const()[name = tensor("q_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1451419328)))]; + tensor q_37_cast_fp16 = conv(dilations = var_2942, groups = var_2306, pad = q_37_pad_0, pad_type = q_37_pad_type_0, strides = var_2940, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_207_cast_fp16)[name = tensor("q_37_cast_fp16")]; + tensor var_2946 = const()[name = tensor("op_2946"), val = tensor([1, 1])]; + tensor var_2948 = const()[name = tensor("op_2948"), val = tensor([1, 1])]; + tensor k_37_pad_type_0 = const()[name = tensor("k_37_pad_type_0"), val = tensor("custom")]; + tensor k_37_pad_0 = const()[name = tensor("k_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1454696192)))]; + tensor k_37_cast_fp16 = conv(dilations = var_2948, groups = var_2306, pad = k_37_pad_0, pad_type = k_37_pad_type_0, strides = var_2946, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_207_cast_fp16)[name = tensor("k_37_cast_fp16")]; + tensor var_2952 = const()[name = tensor("op_2952"), val = tensor([1, 1])]; + tensor var_2954 = const()[name = tensor("op_2954"), val = tensor([1, 1])]; + tensor v_37_pad_type_0 = const()[name = tensor("v_37_pad_type_0"), val = tensor("custom")]; + tensor v_37_pad_0 = const()[name = tensor("v_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1457973056)))]; + tensor v_37_cast_fp16 = conv(dilations = var_2954, groups = var_2306, pad = v_37_pad_0, pad_type = v_37_pad_type_0, strides = var_2952, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_207_cast_fp16)[name = tensor("v_37_cast_fp16")]; + tensor var_2958 = const()[name = tensor("op_2958"), val = tensor([2, 20, 64, -1])]; + tensor var_2959_cast_fp16 = reshape(shape = var_2958, x = q_37_cast_fp16)[name = tensor("op_2959_cast_fp16")]; + tensor var_2960 = const()[name = tensor("op_2960"), val = tensor([2, 20, 64, -1])]; + tensor var_2961_cast_fp16 = reshape(shape = var_2960, x = k_37_cast_fp16)[name = tensor("op_2961_cast_fp16")]; + tensor var_2962 = const()[name = tensor("op_2962"), val = tensor([2, 20, 64, -1])]; + tensor var_2963_cast_fp16 = reshape(shape = var_2962, x = v_37_cast_fp16)[name = tensor("op_2963_cast_fp16")]; + tensor attn_weights_73_transpose_x_0 = const()[name = tensor("attn_weights_73_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_73_transpose_y_0 = const()[name = tensor("attn_weights_73_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_73_cast_fp16 = matmul(transpose_x = attn_weights_73_transpose_x_0, transpose_y = attn_weights_73_transpose_y_0, x = var_2959_cast_fp16, y = var_2961_cast_fp16)[name = tensor("attn_weights_73_cast_fp16")]; + tensor attn_weights_75_cast_fp16 = mul(x = attn_weights_73_cast_fp16, y = var_2297_to_fp16)[name = tensor("attn_weights_75_cast_fp16")]; + tensor var_2967_cast_fp16 = softmax(axis = var_2290, x = attn_weights_75_cast_fp16)[name = tensor("op_2967_cast_fp16")]; + tensor attn_37_transpose_x_0 = const()[name = tensor("attn_37_transpose_x_0"), val = tensor(false)]; + tensor attn_37_transpose_y_0 = const()[name = tensor("attn_37_transpose_y_0"), val = tensor(true)]; + tensor attn_37_cast_fp16 = matmul(transpose_x = attn_37_transpose_x_0, transpose_y = attn_37_transpose_y_0, x = var_2963_cast_fp16, y = var_2967_cast_fp16)[name = tensor("attn_37_cast_fp16")]; + tensor var_2971 = const()[name = tensor("op_2971"), val = tensor([2, 1280, 1, -1])]; + tensor input_353_cast_fp16 = reshape(shape = var_2971, x = attn_37_cast_fp16)[name = tensor("input_353_cast_fp16")]; + tensor var_2976 = const()[name = tensor("op_2976"), val = tensor([1, 1])]; + tensor var_2978 = const()[name = tensor("op_2978"), val = tensor([1, 1])]; + tensor var_2980_pad_type_0 = const()[name = tensor("op_2980_pad_type_0"), val = tensor("custom")]; + tensor var_2980_pad_0 = const()[name = tensor("op_2980_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1461249920)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1464526784)))]; + tensor var_2980_cast_fp16 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_2978, groups = var_2306, pad = var_2980_pad_0, pad_type = var_2980_pad_type_0, strides = var_2976, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_353_cast_fp16)[name = tensor("op_2980_cast_fp16")]; + tensor inputs_57_cast_fp16 = add(x = var_2980_cast_fp16, y = inputs_55_cast_fp16)[name = tensor("inputs_57_cast_fp16")]; + tensor var_2984 = const()[name = tensor("op_2984"), val = tensor([1])]; + tensor channels_mean_57_cast_fp16 = reduce_mean(axes = var_2984, keep_dims = var_2301, x = inputs_57_cast_fp16)[name = tensor("channels_mean_57_cast_fp16")]; + tensor zero_mean_57_cast_fp16 = sub(x = inputs_57_cast_fp16, y = channels_mean_57_cast_fp16)[name = tensor("zero_mean_57_cast_fp16")]; + tensor zero_mean_sq_57_cast_fp16 = mul(x = zero_mean_57_cast_fp16, y = zero_mean_57_cast_fp16)[name = tensor("zero_mean_sq_57_cast_fp16")]; + tensor var_2988 = const()[name = tensor("op_2988"), val = tensor([1])]; + tensor var_2989_cast_fp16 = reduce_mean(axes = var_2988, keep_dims = var_2301, x = zero_mean_sq_57_cast_fp16)[name = tensor("op_2989_cast_fp16")]; + tensor var_2990_to_fp16 = const()[name = tensor("op_2990_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2991_cast_fp16 = add(x = var_2989_cast_fp16, y = var_2990_to_fp16)[name = tensor("op_2991_cast_fp16")]; + tensor denom_57_epsilon_0_to_fp16 = const()[name = tensor("denom_57_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_57_cast_fp16 = rsqrt(epsilon = denom_57_epsilon_0_to_fp16, x = var_2991_cast_fp16)[name = tensor("denom_57_cast_fp16")]; + tensor out_57_cast_fp16 = mul(x = zero_mean_57_cast_fp16, y = denom_57_cast_fp16)[name = tensor("out_57_cast_fp16")]; + tensor var_2995_to_fp16 = const()[name = tensor("op_2995_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1464529408)))]; + tensor var_2996_cast_fp16 = add(x = out_57_cast_fp16, y = var_2995_to_fp16)[name = tensor("op_2996_cast_fp16")]; + tensor var_2998_to_fp16 = const()[name = tensor("op_2998_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1464532032)))]; + tensor hidden_states_209_cast_fp16 = mul(x = var_2996_cast_fp16, y = var_2998_to_fp16)[name = tensor("hidden_states_209_cast_fp16")]; + tensor var_3005 = const()[name = tensor("op_3005"), val = tensor([1, 1])]; + tensor var_3007 = const()[name = tensor("op_3007"), val = tensor([1, 1])]; + tensor q_39_pad_type_0 = const()[name = tensor("q_39_pad_type_0"), val = tensor("custom")]; + tensor q_39_pad_0 = const()[name = tensor("q_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1464534656)))]; + tensor q_39_cast_fp16 = conv(dilations = var_3007, groups = var_2306, pad = q_39_pad_0, pad_type = q_39_pad_type_0, strides = var_3005, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_209_cast_fp16)[name = tensor("q_39_cast_fp16")]; + tensor var_3011 = const()[name = tensor("op_3011"), val = tensor([1, 1])]; + tensor var_3013 = const()[name = tensor("op_3013"), val = tensor([1, 1])]; + tensor k_39_pad_type_0 = const()[name = tensor("k_39_pad_type_0"), val = tensor("custom")]; + tensor k_39_pad_0 = const()[name = tensor("k_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1467811520)))]; + tensor k_39_cast_fp16 = conv(dilations = var_3013, groups = var_2306, pad = k_39_pad_0, pad_type = k_39_pad_type_0, strides = var_3011, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_39_cast_fp16")]; + tensor var_3017 = const()[name = tensor("op_3017"), val = tensor([1, 1])]; + tensor var_3019 = const()[name = tensor("op_3019"), val = tensor([1, 1])]; + tensor v_39_pad_type_0 = const()[name = tensor("v_39_pad_type_0"), val = tensor("custom")]; + tensor v_39_pad_0 = const()[name = tensor("v_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1470433024)))]; + tensor v_39_cast_fp16 = conv(dilations = var_3019, groups = var_2306, pad = v_39_pad_0, pad_type = v_39_pad_type_0, strides = var_3017, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_39_cast_fp16")]; + tensor var_3023 = const()[name = tensor("op_3023"), val = tensor([2, 20, 64, -1])]; + tensor var_3024_cast_fp16 = reshape(shape = var_3023, x = q_39_cast_fp16)[name = tensor("op_3024_cast_fp16")]; + tensor var_3025 = const()[name = tensor("op_3025"), val = tensor([2, 20, 64, -1])]; + tensor var_3026_cast_fp16 = reshape(shape = var_3025, x = k_39_cast_fp16)[name = tensor("op_3026_cast_fp16")]; + tensor var_3027 = const()[name = tensor("op_3027"), val = tensor([2, 20, 64, -1])]; + tensor var_3028_cast_fp16 = reshape(shape = var_3027, x = v_39_cast_fp16)[name = tensor("op_3028_cast_fp16")]; + tensor attn_weights_77_transpose_x_0 = const()[name = tensor("attn_weights_77_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_77_transpose_y_0 = const()[name = tensor("attn_weights_77_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_77_cast_fp16 = matmul(transpose_x = attn_weights_77_transpose_x_0, transpose_y = attn_weights_77_transpose_y_0, x = var_3024_cast_fp16, y = var_3026_cast_fp16)[name = tensor("attn_weights_77_cast_fp16")]; + tensor attn_weights_79_cast_fp16 = mul(x = attn_weights_77_cast_fp16, y = var_2297_to_fp16)[name = tensor("attn_weights_79_cast_fp16")]; + tensor var_3032_cast_fp16 = softmax(axis = var_2290, x = attn_weights_79_cast_fp16)[name = tensor("op_3032_cast_fp16")]; + tensor attn_39_transpose_x_0 = const()[name = tensor("attn_39_transpose_x_0"), val = tensor(false)]; + tensor attn_39_transpose_y_0 = const()[name = tensor("attn_39_transpose_y_0"), val = tensor(true)]; + tensor attn_39_cast_fp16 = matmul(transpose_x = attn_39_transpose_x_0, transpose_y = attn_39_transpose_y_0, x = var_3028_cast_fp16, y = var_3032_cast_fp16)[name = tensor("attn_39_cast_fp16")]; + tensor var_3036 = const()[name = tensor("op_3036"), val = tensor([2, 1280, 1, -1])]; + tensor input_355_cast_fp16 = reshape(shape = var_3036, x = attn_39_cast_fp16)[name = tensor("input_355_cast_fp16")]; + tensor var_3041 = const()[name = tensor("op_3041"), val = tensor([1, 1])]; + tensor var_3043 = const()[name = tensor("op_3043"), val = tensor([1, 1])]; + tensor var_3045_pad_type_0 = const()[name = tensor("op_3045_pad_type_0"), val = tensor("custom")]; + tensor var_3045_pad_0 = const()[name = tensor("op_3045_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1473054528)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1476331392)))]; + tensor var_3045_cast_fp16 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_3043, groups = var_2306, pad = var_3045_pad_0, pad_type = var_3045_pad_type_0, strides = var_3041, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_355_cast_fp16)[name = tensor("op_3045_cast_fp16")]; + tensor inputs_59_cast_fp16 = add(x = var_3045_cast_fp16, y = inputs_57_cast_fp16)[name = tensor("inputs_59_cast_fp16")]; + tensor var_3049 = const()[name = tensor("op_3049"), val = tensor([1])]; + tensor channels_mean_59_cast_fp16 = reduce_mean(axes = var_3049, keep_dims = var_2301, x = inputs_59_cast_fp16)[name = tensor("channels_mean_59_cast_fp16")]; + tensor zero_mean_59_cast_fp16 = sub(x = inputs_59_cast_fp16, y = channels_mean_59_cast_fp16)[name = tensor("zero_mean_59_cast_fp16")]; + tensor zero_mean_sq_59_cast_fp16 = mul(x = zero_mean_59_cast_fp16, y = zero_mean_59_cast_fp16)[name = tensor("zero_mean_sq_59_cast_fp16")]; + tensor var_3053 = const()[name = tensor("op_3053"), val = tensor([1])]; + tensor var_3054_cast_fp16 = reduce_mean(axes = var_3053, keep_dims = var_2301, x = zero_mean_sq_59_cast_fp16)[name = tensor("op_3054_cast_fp16")]; + tensor var_3055_to_fp16 = const()[name = tensor("op_3055_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3056_cast_fp16 = add(x = var_3054_cast_fp16, y = var_3055_to_fp16)[name = tensor("op_3056_cast_fp16")]; + tensor denom_59_epsilon_0_to_fp16 = const()[name = tensor("denom_59_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_59_cast_fp16 = rsqrt(epsilon = denom_59_epsilon_0_to_fp16, x = var_3056_cast_fp16)[name = tensor("denom_59_cast_fp16")]; + tensor out_59_cast_fp16 = mul(x = zero_mean_59_cast_fp16, y = denom_59_cast_fp16)[name = tensor("out_59_cast_fp16")]; + tensor var_3060_to_fp16 = const()[name = tensor("op_3060_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1476334016)))]; + tensor var_3061_cast_fp16 = add(x = out_59_cast_fp16, y = var_3060_to_fp16)[name = tensor("op_3061_cast_fp16")]; + tensor var_3063_to_fp16 = const()[name = tensor("op_3063_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1476336640)))]; + tensor input_357_cast_fp16 = mul(x = var_3061_cast_fp16, y = var_3063_to_fp16)[name = tensor("input_357_cast_fp16")]; + tensor var_3071 = const()[name = tensor("op_3071"), val = tensor([1, 1])]; + tensor var_3073 = const()[name = tensor("op_3073"), val = tensor([1, 1])]; + tensor var_3075_pad_type_0 = const()[name = tensor("op_3075_pad_type_0"), val = tensor("custom")]; + tensor var_3075_pad_0 = const()[name = tensor("op_3075_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1476339264)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1502553728)))]; + tensor var_3075_cast_fp16 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_3073, groups = var_2306, pad = var_3075_pad_0, pad_type = var_3075_pad_type_0, strides = var_3071, weight = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_357_cast_fp16)[name = tensor("op_3075_cast_fp16")]; + tensor var_3076_split_sizes_0 = const()[name = tensor("op_3076_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_3076_axis_0 = const()[name = tensor("op_3076_axis_0"), val = tensor(1)]; + tensor var_3076_cast_fp16_0, tensor var_3076_cast_fp16_1 = split(axis = var_3076_axis_0, split_sizes = var_3076_split_sizes_0, x = var_3075_cast_fp16)[name = tensor("op_3076_cast_fp16")]; + tensor var_3078_mode_0 = const()[name = tensor("op_3078_mode_0"), val = tensor("EXACT")]; + tensor var_3078_cast_fp16 = gelu(mode = var_3078_mode_0, x = var_3076_cast_fp16_1)[name = tensor("op_3078_cast_fp16")]; + tensor input_359_cast_fp16 = mul(x = var_3076_cast_fp16_0, y = var_3078_cast_fp16)[name = tensor("input_359_cast_fp16")]; + tensor var_3082 = const()[name = tensor("op_3082"), val = tensor([1, 1])]; + tensor var_3084 = const()[name = tensor("op_3084"), val = tensor([1, 1])]; + tensor var_3086_pad_type_0 = const()[name = tensor("op_3086_pad_type_0"), val = tensor("custom")]; + tensor var_3086_pad_0 = const()[name = tensor("op_3086_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1502574272)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1515681536)))]; + tensor var_3086_cast_fp16 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_3084, groups = var_2306, pad = var_3086_pad_0, pad_type = var_3086_pad_type_0, strides = var_3082, weight = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_359_cast_fp16)[name = tensor("op_3086_cast_fp16")]; + tensor hidden_states_213_cast_fp16 = add(x = var_3086_cast_fp16, y = inputs_59_cast_fp16)[name = tensor("hidden_states_213_cast_fp16")]; + tensor var_3088 = const()[name = tensor("op_3088"), val = tensor([2, 1280, 12, 20])]; + tensor input_361_cast_fp16 = reshape(shape = var_3088, x = hidden_states_213_cast_fp16)[name = tensor("input_361_cast_fp16")]; + tensor var_3092 = const()[name = tensor("op_3092"), val = tensor([1, 1])]; + tensor var_3094 = const()[name = tensor("op_3094"), val = tensor([1, 1])]; + tensor hidden_states_215_pad_type_0 = const()[name = tensor("hidden_states_215_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_215_pad_0 = const()[name = tensor("hidden_states_215_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1515684160)))]; + tensor up_blocks_1_attentions_2_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1518961024)))]; + tensor hidden_states_215_cast_fp16 = conv(bias = up_blocks_1_attentions_2_proj_out_bias_to_fp16, dilations = var_3094, groups = var_2306, pad = hidden_states_215_pad_0, pad_type = hidden_states_215_pad_type_0, strides = var_3092, weight = up_blocks_1_attentions_2_proj_out_weight_to_fp16, x = input_361_cast_fp16)[name = tensor("hidden_states_215_cast_fp16")]; + tensor input_363_cast_fp16 = add(x = hidden_states_215_cast_fp16, y = hidden_states_203_cast_fp16)[name = tensor("input_363_cast_fp16")]; + tensor input_365_scale_factor_height_0 = const()[name = tensor("input_365_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_365_scale_factor_width_0 = const()[name = tensor("input_365_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor input_365_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = input_365_scale_factor_height_0, scale_factor_width = input_365_scale_factor_width_0, x = input_363_cast_fp16)[name = tensor("input_365_cast_fp16")]; + tensor var_3103 = const()[name = tensor("op_3103"), val = tensor([1, 1])]; + tensor var_3105 = const()[name = tensor("op_3105"), val = tensor([1, 1])]; + tensor hidden_states_217_pad_type_0 = const()[name = tensor("hidden_states_217_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_217_pad_0 = const()[name = tensor("hidden_states_217_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_upsamplers_0_conv_weight_to_fp16 = const()[name = tensor("up_blocks_1_upsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1518963648)))]; + tensor up_blocks_1_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("up_blocks_1_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1548454912)))]; + tensor hidden_states_217_cast_fp16 = conv(bias = up_blocks_1_upsamplers_0_conv_bias_to_fp16, dilations = var_3105, groups = var_2306, pad = hidden_states_217_pad_0, pad_type = hidden_states_217_pad_type_0, strides = var_3103, weight = up_blocks_1_upsamplers_0_conv_weight_to_fp16, x = input_365_cast_fp16)[name = tensor("hidden_states_217_cast_fp16")]; + tensor var_3110 = const()[name = tensor("op_3110"), val = tensor(3)]; + tensor var_3121 = const()[name = tensor("op_3121"), val = tensor(true)]; + tensor var_3126 = const()[name = tensor("op_3126"), val = tensor(1)]; + tensor input_367_interleave_0 = const()[name = tensor("input_367_interleave_0"), val = tensor(false)]; + tensor input_367_cast_fp16 = concat(axis = var_3126, interleave = input_367_interleave_0, values = (hidden_states_217_cast_fp16, input_115_cast_fp16))[name = tensor("input_367_cast_fp16")]; + tensor reshape_168_shape_0 = const()[name = tensor("reshape_168_shape_0"), val = tensor([2, 32, 60, 24, 40])]; + tensor reshape_168_cast_fp16 = reshape(shape = reshape_168_shape_0, x = input_367_cast_fp16)[name = tensor("reshape_168_cast_fp16")]; + tensor reduce_mean_126_axes_0 = const()[name = tensor("reduce_mean_126_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_126_keep_dims_0 = const()[name = tensor("reduce_mean_126_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_126_cast_fp16 = reduce_mean(axes = reduce_mean_126_axes_0, keep_dims = reduce_mean_126_keep_dims_0, x = reshape_168_cast_fp16)[name = tensor("reduce_mean_126_cast_fp16")]; + tensor sub_84_cast_fp16 = sub(x = reshape_168_cast_fp16, y = reduce_mean_126_cast_fp16)[name = tensor("sub_84_cast_fp16")]; + tensor square_42_cast_fp16 = square(x = sub_84_cast_fp16)[name = tensor("square_42_cast_fp16")]; + tensor reduce_mean_128_axes_0 = const()[name = tensor("reduce_mean_128_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_128_keep_dims_0 = const()[name = tensor("reduce_mean_128_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_128_cast_fp16 = reduce_mean(axes = reduce_mean_128_axes_0, keep_dims = reduce_mean_128_keep_dims_0, x = square_42_cast_fp16)[name = tensor("reduce_mean_128_cast_fp16")]; + tensor add_84_y_0_to_fp16 = const()[name = tensor("add_84_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_84_cast_fp16 = add(x = reduce_mean_128_cast_fp16, y = add_84_y_0_to_fp16)[name = tensor("add_84_cast_fp16")]; + tensor sqrt_42_cast_fp16 = sqrt(x = add_84_cast_fp16)[name = tensor("sqrt_42_cast_fp16")]; + tensor real_div_42_cast_fp16 = real_div(x = sub_84_cast_fp16, y = sqrt_42_cast_fp16)[name = tensor("real_div_42_cast_fp16")]; + tensor reshape_169_shape_0 = const()[name = tensor("reshape_169_shape_0"), val = tensor([2, 1920, 24, 40])]; + tensor reshape_169_cast_fp16 = reshape(shape = reshape_169_shape_0, x = real_div_42_cast_fp16)[name = tensor("reshape_169_cast_fp16")]; + tensor add_85_gamma_0_to_fp16 = const()[name = tensor("add_85_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1548457536)))]; + tensor add_85_beta_0_to_fp16 = const()[name = tensor("add_85_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1548461440)))]; + tensor add_85_epsilon_0_to_fp16 = const()[name = tensor("add_85_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_85_cast_fp16 = batch_norm(beta = add_85_beta_0_to_fp16, epsilon = add_85_epsilon_0_to_fp16, gamma = add_85_gamma_0_to_fp16, mean = add_79_mean_0_to_fp16, variance = add_79_variance_0_to_fp16, x = reshape_169_cast_fp16)[name = tensor("add_85_cast_fp16")]; + tensor input_371_cast_fp16 = silu(x = add_85_cast_fp16)[name = tensor("input_371_cast_fp16")]; + tensor var_3155 = const()[name = tensor("op_3155"), val = tensor([1, 1])]; + tensor var_3157 = const()[name = tensor("op_3157"), val = tensor([1, 1])]; + tensor hidden_states_219_pad_type_0 = const()[name = tensor("hidden_states_219_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_219_pad_0 = const()[name = tensor("hidden_states_219_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1548465344)))]; + tensor up_blocks_2_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1570583808)))]; + tensor hidden_states_219_cast_fp16 = conv(bias = up_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = var_3157, groups = var_3126, pad = hidden_states_219_pad_0, pad_type = hidden_states_219_pad_type_0, strides = var_3155, weight = up_blocks_2_resnets_0_conv1_weight_to_fp16, x = input_371_cast_fp16)[name = tensor("hidden_states_219_cast_fp16")]; + tensor var_3163 = const()[name = tensor("op_3163"), val = tensor([1, 1])]; + tensor var_3165 = const()[name = tensor("op_3165"), val = tensor([1, 1])]; + tensor temb_33_pad_type_0 = const()[name = tensor("temb_33_pad_type_0"), val = tensor("custom")]; + tensor temb_33_pad_0 = const()[name = tensor("temb_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1570585152)))]; + tensor up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1572223616)))]; + tensor temb_33_cast_fp16 = conv(bias = up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_3165, groups = var_3126, pad = temb_33_pad_0, pad_type = temb_33_pad_type_0, strides = var_3163, weight = up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16)[name = tensor("temb_33_cast_fp16")]; + tensor input_375_cast_fp16 = add(x = hidden_states_219_cast_fp16, y = temb_33_cast_fp16)[name = tensor("input_375_cast_fp16")]; + tensor reshape_172_shape_0 = const()[name = tensor("reshape_172_shape_0"), val = tensor([2, 32, 20, 24, 40])]; + tensor reshape_172_cast_fp16 = reshape(shape = reshape_172_shape_0, x = input_375_cast_fp16)[name = tensor("reshape_172_cast_fp16")]; + tensor reduce_mean_129_axes_0 = const()[name = tensor("reduce_mean_129_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_129_keep_dims_0 = const()[name = tensor("reduce_mean_129_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_129_cast_fp16 = reduce_mean(axes = reduce_mean_129_axes_0, keep_dims = reduce_mean_129_keep_dims_0, x = reshape_172_cast_fp16)[name = tensor("reduce_mean_129_cast_fp16")]; + tensor sub_86_cast_fp16 = sub(x = reshape_172_cast_fp16, y = reduce_mean_129_cast_fp16)[name = tensor("sub_86_cast_fp16")]; + tensor square_43_cast_fp16 = square(x = sub_86_cast_fp16)[name = tensor("square_43_cast_fp16")]; + tensor reduce_mean_131_axes_0 = const()[name = tensor("reduce_mean_131_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_131_keep_dims_0 = const()[name = tensor("reduce_mean_131_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_131_cast_fp16 = reduce_mean(axes = reduce_mean_131_axes_0, keep_dims = reduce_mean_131_keep_dims_0, x = square_43_cast_fp16)[name = tensor("reduce_mean_131_cast_fp16")]; + tensor add_86_y_0_to_fp16 = const()[name = tensor("add_86_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_86_cast_fp16 = add(x = reduce_mean_131_cast_fp16, y = add_86_y_0_to_fp16)[name = tensor("add_86_cast_fp16")]; + tensor sqrt_43_cast_fp16 = sqrt(x = add_86_cast_fp16)[name = tensor("sqrt_43_cast_fp16")]; + tensor real_div_43_cast_fp16 = real_div(x = sub_86_cast_fp16, y = sqrt_43_cast_fp16)[name = tensor("real_div_43_cast_fp16")]; + tensor reshape_173_shape_0 = const()[name = tensor("reshape_173_shape_0"), val = tensor([2, 640, 24, 40])]; + tensor reshape_173_cast_fp16 = reshape(shape = reshape_173_shape_0, x = real_div_43_cast_fp16)[name = tensor("reshape_173_cast_fp16")]; + tensor add_87_gamma_0_to_fp16 = const()[name = tensor("add_87_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1572224960)))]; + tensor add_87_beta_0_to_fp16 = const()[name = tensor("add_87_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1572226304)))]; + tensor add_87_epsilon_0_to_fp16 = const()[name = tensor("add_87_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_87_cast_fp16 = batch_norm(beta = add_87_beta_0_to_fp16, epsilon = add_87_epsilon_0_to_fp16, gamma = add_87_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_173_cast_fp16)[name = tensor("add_87_cast_fp16")]; + tensor input_379_cast_fp16 = silu(x = add_87_cast_fp16)[name = tensor("input_379_cast_fp16")]; + tensor var_3175 = const()[name = tensor("op_3175"), val = tensor([1, 1])]; + tensor var_3177 = const()[name = tensor("op_3177"), val = tensor([1, 1])]; + tensor hidden_states_221_pad_type_0 = const()[name = tensor("hidden_states_221_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_221_pad_0 = const()[name = tensor("hidden_states_221_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1572227648)))]; + tensor up_blocks_2_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1579600512)))]; + tensor hidden_states_221_cast_fp16 = conv(bias = up_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = var_3177, groups = var_3126, pad = hidden_states_221_pad_0, pad_type = hidden_states_221_pad_type_0, strides = var_3175, weight = up_blocks_2_resnets_0_conv2_weight_to_fp16, x = input_379_cast_fp16)[name = tensor("hidden_states_221_cast_fp16")]; + tensor var_3182 = const()[name = tensor("op_3182"), val = tensor([1, 1])]; + tensor var_3184 = const()[name = tensor("op_3184"), val = tensor([1, 1])]; + tensor x_17_pad_type_0 = const()[name = tensor("x_17_pad_type_0"), val = tensor("custom")]; + tensor x_17_pad_0 = const()[name = tensor("x_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1579601856)))]; + tensor up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1582059520)))]; + tensor x_17_cast_fp16 = conv(bias = up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_3184, groups = var_3126, pad = x_17_pad_0, pad_type = x_17_pad_type_0, strides = var_3182, weight = up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16, x = input_367_cast_fp16)[name = tensor("x_17_cast_fp16")]; + tensor hidden_states_223_cast_fp16 = add(x = x_17_cast_fp16, y = hidden_states_221_cast_fp16)[name = tensor("hidden_states_223_cast_fp16")]; + tensor reshape_176_shape_0 = const()[name = tensor("reshape_176_shape_0"), val = tensor([2, 32, 20, 24, 40])]; + tensor reshape_176_cast_fp16 = reshape(shape = reshape_176_shape_0, x = hidden_states_223_cast_fp16)[name = tensor("reshape_176_cast_fp16")]; + tensor reduce_mean_132_axes_0 = const()[name = tensor("reduce_mean_132_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_132_keep_dims_0 = const()[name = tensor("reduce_mean_132_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_132_cast_fp16 = reduce_mean(axes = reduce_mean_132_axes_0, keep_dims = reduce_mean_132_keep_dims_0, x = reshape_176_cast_fp16)[name = tensor("reduce_mean_132_cast_fp16")]; + tensor sub_88_cast_fp16 = sub(x = reshape_176_cast_fp16, y = reduce_mean_132_cast_fp16)[name = tensor("sub_88_cast_fp16")]; + tensor square_44_cast_fp16 = square(x = sub_88_cast_fp16)[name = tensor("square_44_cast_fp16")]; + tensor reduce_mean_134_axes_0 = const()[name = tensor("reduce_mean_134_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_134_keep_dims_0 = const()[name = tensor("reduce_mean_134_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_134_cast_fp16 = reduce_mean(axes = reduce_mean_134_axes_0, keep_dims = reduce_mean_134_keep_dims_0, x = square_44_cast_fp16)[name = tensor("reduce_mean_134_cast_fp16")]; + tensor add_88_y_0_to_fp16 = const()[name = tensor("add_88_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_88_cast_fp16 = add(x = reduce_mean_134_cast_fp16, y = add_88_y_0_to_fp16)[name = tensor("add_88_cast_fp16")]; + tensor sqrt_44_cast_fp16 = sqrt(x = add_88_cast_fp16)[name = tensor("sqrt_44_cast_fp16")]; + tensor real_div_44_cast_fp16 = real_div(x = sub_88_cast_fp16, y = sqrt_44_cast_fp16)[name = tensor("real_div_44_cast_fp16")]; + tensor reshape_177_shape_0 = const()[name = tensor("reshape_177_shape_0"), val = tensor([2, 640, 24, 40])]; + tensor reshape_177_cast_fp16 = reshape(shape = reshape_177_shape_0, x = real_div_44_cast_fp16)[name = tensor("reshape_177_cast_fp16")]; + tensor add_89_gamma_0_to_fp16 = const()[name = tensor("add_89_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1582060864)))]; + tensor add_89_beta_0_to_fp16 = const()[name = tensor("add_89_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1582062208)))]; + tensor add_89_epsilon_0_to_fp16 = const()[name = tensor("add_89_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_89_cast_fp16 = batch_norm(beta = add_89_beta_0_to_fp16, epsilon = add_89_epsilon_0_to_fp16, gamma = add_89_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_177_cast_fp16)[name = tensor("add_89_cast_fp16")]; + tensor var_3204 = const()[name = tensor("op_3204"), val = tensor([1, 1])]; + tensor var_3206 = const()[name = tensor("op_3206"), val = tensor([1, 1])]; + tensor hidden_states_225_pad_type_0 = const()[name = tensor("hidden_states_225_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_225_pad_0 = const()[name = tensor("hidden_states_225_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1582063552)))]; + tensor up_blocks_2_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1582882816)))]; + tensor hidden_states_225_cast_fp16 = conv(bias = up_blocks_2_attentions_0_proj_in_bias_to_fp16, dilations = var_3206, groups = var_3126, pad = hidden_states_225_pad_0, pad_type = hidden_states_225_pad_type_0, strides = var_3204, weight = up_blocks_2_attentions_0_proj_in_weight_to_fp16, x = add_89_cast_fp16)[name = tensor("hidden_states_225_cast_fp16")]; + tensor var_3211 = const()[name = tensor("op_3211"), val = tensor([2, 640, 1, 960])]; + tensor inputs_61_cast_fp16 = reshape(shape = var_3211, x = hidden_states_225_cast_fp16)[name = tensor("inputs_61_cast_fp16")]; + tensor var_3221 = const()[name = tensor("op_3221"), val = tensor([1])]; + tensor channels_mean_61_cast_fp16 = reduce_mean(axes = var_3221, keep_dims = var_3121, x = inputs_61_cast_fp16)[name = tensor("channels_mean_61_cast_fp16")]; + tensor zero_mean_61_cast_fp16 = sub(x = inputs_61_cast_fp16, y = channels_mean_61_cast_fp16)[name = tensor("zero_mean_61_cast_fp16")]; + tensor zero_mean_sq_61_cast_fp16 = mul(x = zero_mean_61_cast_fp16, y = zero_mean_61_cast_fp16)[name = tensor("zero_mean_sq_61_cast_fp16")]; + tensor var_3225 = const()[name = tensor("op_3225"), val = tensor([1])]; + tensor var_3226_cast_fp16 = reduce_mean(axes = var_3225, keep_dims = var_3121, x = zero_mean_sq_61_cast_fp16)[name = tensor("op_3226_cast_fp16")]; + tensor var_3227_to_fp16 = const()[name = tensor("op_3227_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3228_cast_fp16 = add(x = var_3226_cast_fp16, y = var_3227_to_fp16)[name = tensor("op_3228_cast_fp16")]; + tensor denom_61_epsilon_0_to_fp16 = const()[name = tensor("denom_61_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_61_cast_fp16 = rsqrt(epsilon = denom_61_epsilon_0_to_fp16, x = var_3228_cast_fp16)[name = tensor("denom_61_cast_fp16")]; + tensor out_61_cast_fp16 = mul(x = zero_mean_61_cast_fp16, y = denom_61_cast_fp16)[name = tensor("out_61_cast_fp16")]; + tensor var_3232_to_fp16 = const()[name = tensor("op_3232_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1582884160)))]; + tensor var_3233_cast_fp16 = add(x = out_61_cast_fp16, y = var_3232_to_fp16)[name = tensor("op_3233_cast_fp16")]; + tensor var_3235_to_fp16 = const()[name = tensor("op_3235_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1582885504)))]; + tensor hidden_states_227_cast_fp16 = mul(x = var_3233_cast_fp16, y = var_3235_to_fp16)[name = tensor("hidden_states_227_cast_fp16")]; + tensor var_3242 = const()[name = tensor("op_3242"), val = tensor([1, 1])]; + tensor var_3244 = const()[name = tensor("op_3244"), val = tensor([1, 1])]; + tensor q_41_pad_type_0 = const()[name = tensor("q_41_pad_type_0"), val = tensor("custom")]; + tensor q_41_pad_0 = const()[name = tensor("q_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1582886848)))]; + tensor q_41_cast_fp16 = conv(dilations = var_3244, groups = var_3126, pad = q_41_pad_0, pad_type = q_41_pad_type_0, strides = var_3242, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_227_cast_fp16)[name = tensor("q_41_cast_fp16")]; + tensor var_3248 = const()[name = tensor("op_3248"), val = tensor([1, 1])]; + tensor var_3250 = const()[name = tensor("op_3250"), val = tensor([1, 1])]; + tensor k_41_pad_type_0 = const()[name = tensor("k_41_pad_type_0"), val = tensor("custom")]; + tensor k_41_pad_0 = const()[name = tensor("k_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1583706112)))]; + tensor k_41_cast_fp16 = conv(dilations = var_3250, groups = var_3126, pad = k_41_pad_0, pad_type = k_41_pad_type_0, strides = var_3248, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_227_cast_fp16)[name = tensor("k_41_cast_fp16")]; + tensor var_3254 = const()[name = tensor("op_3254"), val = tensor([1, 1])]; + tensor var_3256 = const()[name = tensor("op_3256"), val = tensor([1, 1])]; + tensor v_41_pad_type_0 = const()[name = tensor("v_41_pad_type_0"), val = tensor("custom")]; + tensor v_41_pad_0 = const()[name = tensor("v_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1584525376)))]; + tensor v_41_cast_fp16 = conv(dilations = var_3256, groups = var_3126, pad = v_41_pad_0, pad_type = v_41_pad_type_0, strides = var_3254, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_227_cast_fp16)[name = tensor("v_41_cast_fp16")]; + tensor var_3260 = const()[name = tensor("op_3260"), val = tensor([2, 10, 64, -1])]; + tensor var_3261_cast_fp16 = reshape(shape = var_3260, x = q_41_cast_fp16)[name = tensor("op_3261_cast_fp16")]; + tensor var_3262 = const()[name = tensor("op_3262"), val = tensor([2, 10, 64, -1])]; + tensor var_3263_cast_fp16 = reshape(shape = var_3262, x = k_41_cast_fp16)[name = tensor("op_3263_cast_fp16")]; + tensor var_3264 = const()[name = tensor("op_3264"), val = tensor([2, 10, 64, -1])]; + tensor var_3265_cast_fp16 = reshape(shape = var_3264, x = v_41_cast_fp16)[name = tensor("op_3265_cast_fp16")]; + tensor attn_weights_81_transpose_x_0 = const()[name = tensor("attn_weights_81_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_81_transpose_y_0 = const()[name = tensor("attn_weights_81_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_81_cast_fp16 = matmul(transpose_x = attn_weights_81_transpose_x_0, transpose_y = attn_weights_81_transpose_y_0, x = var_3261_cast_fp16, y = var_3263_cast_fp16)[name = tensor("attn_weights_81_cast_fp16")]; + tensor var_3117_to_fp16 = const()[name = tensor("op_3117_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_83_cast_fp16 = mul(x = attn_weights_81_cast_fp16, y = var_3117_to_fp16)[name = tensor("attn_weights_83_cast_fp16")]; + tensor var_3269_cast_fp16 = softmax(axis = var_3110, x = attn_weights_83_cast_fp16)[name = tensor("op_3269_cast_fp16")]; + tensor attn_41_transpose_x_0 = const()[name = tensor("attn_41_transpose_x_0"), val = tensor(false)]; + tensor attn_41_transpose_y_0 = const()[name = tensor("attn_41_transpose_y_0"), val = tensor(true)]; + tensor attn_41_cast_fp16 = matmul(transpose_x = attn_41_transpose_x_0, transpose_y = attn_41_transpose_y_0, x = var_3265_cast_fp16, y = var_3269_cast_fp16)[name = tensor("attn_41_cast_fp16")]; + tensor var_3273 = const()[name = tensor("op_3273"), val = tensor([2, 640, 1, -1])]; + tensor input_383_cast_fp16 = reshape(shape = var_3273, x = attn_41_cast_fp16)[name = tensor("input_383_cast_fp16")]; + tensor var_3278 = const()[name = tensor("op_3278"), val = tensor([1, 1])]; + tensor var_3280 = const()[name = tensor("op_3280"), val = tensor([1, 1])]; + tensor var_3282_pad_type_0 = const()[name = tensor("op_3282_pad_type_0"), val = tensor("custom")]; + tensor var_3282_pad_0 = const()[name = tensor("op_3282_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1585344640)))]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1586163904)))]; + tensor var_3282_cast_fp16 = conv(bias = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_3280, groups = var_3126, pad = var_3282_pad_0, pad_type = var_3282_pad_type_0, strides = var_3278, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_383_cast_fp16)[name = tensor("op_3282_cast_fp16")]; + tensor inputs_63_cast_fp16 = add(x = var_3282_cast_fp16, y = inputs_61_cast_fp16)[name = tensor("inputs_63_cast_fp16")]; + tensor var_3286 = const()[name = tensor("op_3286"), val = tensor([1])]; + tensor channels_mean_63_cast_fp16 = reduce_mean(axes = var_3286, keep_dims = var_3121, x = inputs_63_cast_fp16)[name = tensor("channels_mean_63_cast_fp16")]; + tensor zero_mean_63_cast_fp16 = sub(x = inputs_63_cast_fp16, y = channels_mean_63_cast_fp16)[name = tensor("zero_mean_63_cast_fp16")]; + tensor zero_mean_sq_63_cast_fp16 = mul(x = zero_mean_63_cast_fp16, y = zero_mean_63_cast_fp16)[name = tensor("zero_mean_sq_63_cast_fp16")]; + tensor var_3290 = const()[name = tensor("op_3290"), val = tensor([1])]; + tensor var_3291_cast_fp16 = reduce_mean(axes = var_3290, keep_dims = var_3121, x = zero_mean_sq_63_cast_fp16)[name = tensor("op_3291_cast_fp16")]; + tensor var_3292_to_fp16 = const()[name = tensor("op_3292_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3293_cast_fp16 = add(x = var_3291_cast_fp16, y = var_3292_to_fp16)[name = tensor("op_3293_cast_fp16")]; + tensor denom_63_epsilon_0_to_fp16 = const()[name = tensor("denom_63_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_63_cast_fp16 = rsqrt(epsilon = denom_63_epsilon_0_to_fp16, x = var_3293_cast_fp16)[name = tensor("denom_63_cast_fp16")]; + tensor out_63_cast_fp16 = mul(x = zero_mean_63_cast_fp16, y = denom_63_cast_fp16)[name = tensor("out_63_cast_fp16")]; + tensor var_3297_to_fp16 = const()[name = tensor("op_3297_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1586165248)))]; + tensor var_3298_cast_fp16 = add(x = out_63_cast_fp16, y = var_3297_to_fp16)[name = tensor("op_3298_cast_fp16")]; + tensor var_3300_to_fp16 = const()[name = tensor("op_3300_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1586166592)))]; + tensor hidden_states_229_cast_fp16 = mul(x = var_3298_cast_fp16, y = var_3300_to_fp16)[name = tensor("hidden_states_229_cast_fp16")]; + tensor var_3307 = const()[name = tensor("op_3307"), val = tensor([1, 1])]; + tensor var_3309 = const()[name = tensor("op_3309"), val = tensor([1, 1])]; + tensor q_43_pad_type_0 = const()[name = tensor("q_43_pad_type_0"), val = tensor("custom")]; + tensor q_43_pad_0 = const()[name = tensor("q_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1586167936)))]; + tensor q_43_cast_fp16 = conv(dilations = var_3309, groups = var_3126, pad = q_43_pad_0, pad_type = q_43_pad_type_0, strides = var_3307, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_229_cast_fp16)[name = tensor("q_43_cast_fp16")]; + tensor var_3313 = const()[name = tensor("op_3313"), val = tensor([1, 1])]; + tensor var_3315 = const()[name = tensor("op_3315"), val = tensor([1, 1])]; + tensor k_43_pad_type_0 = const()[name = tensor("k_43_pad_type_0"), val = tensor("custom")]; + tensor k_43_pad_0 = const()[name = tensor("k_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1586987200)))]; + tensor k_43_cast_fp16 = conv(dilations = var_3315, groups = var_3126, pad = k_43_pad_0, pad_type = k_43_pad_type_0, strides = var_3313, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_43_cast_fp16")]; + tensor var_3319 = const()[name = tensor("op_3319"), val = tensor([1, 1])]; + tensor var_3321 = const()[name = tensor("op_3321"), val = tensor([1, 1])]; + tensor v_43_pad_type_0 = const()[name = tensor("v_43_pad_type_0"), val = tensor("custom")]; + tensor v_43_pad_0 = const()[name = tensor("v_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1588297984)))]; + tensor v_43_cast_fp16 = conv(dilations = var_3321, groups = var_3126, pad = v_43_pad_0, pad_type = v_43_pad_type_0, strides = var_3319, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_43_cast_fp16")]; + tensor var_3325 = const()[name = tensor("op_3325"), val = tensor([2, 10, 64, -1])]; + tensor var_3326_cast_fp16 = reshape(shape = var_3325, x = q_43_cast_fp16)[name = tensor("op_3326_cast_fp16")]; + tensor var_3327 = const()[name = tensor("op_3327"), val = tensor([2, 10, 64, -1])]; + tensor var_3328_cast_fp16 = reshape(shape = var_3327, x = k_43_cast_fp16)[name = tensor("op_3328_cast_fp16")]; + tensor var_3329 = const()[name = tensor("op_3329"), val = tensor([2, 10, 64, -1])]; + tensor var_3330_cast_fp16 = reshape(shape = var_3329, x = v_43_cast_fp16)[name = tensor("op_3330_cast_fp16")]; + tensor attn_weights_85_transpose_x_0 = const()[name = tensor("attn_weights_85_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_85_transpose_y_0 = const()[name = tensor("attn_weights_85_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_85_cast_fp16 = matmul(transpose_x = attn_weights_85_transpose_x_0, transpose_y = attn_weights_85_transpose_y_0, x = var_3326_cast_fp16, y = var_3328_cast_fp16)[name = tensor("attn_weights_85_cast_fp16")]; + tensor attn_weights_87_cast_fp16 = mul(x = attn_weights_85_cast_fp16, y = var_3117_to_fp16)[name = tensor("attn_weights_87_cast_fp16")]; + tensor var_3334_cast_fp16 = softmax(axis = var_3110, x = attn_weights_87_cast_fp16)[name = tensor("op_3334_cast_fp16")]; + tensor attn_43_transpose_x_0 = const()[name = tensor("attn_43_transpose_x_0"), val = tensor(false)]; + tensor attn_43_transpose_y_0 = const()[name = tensor("attn_43_transpose_y_0"), val = tensor(true)]; + tensor attn_43_cast_fp16 = matmul(transpose_x = attn_43_transpose_x_0, transpose_y = attn_43_transpose_y_0, x = var_3330_cast_fp16, y = var_3334_cast_fp16)[name = tensor("attn_43_cast_fp16")]; + tensor var_3338 = const()[name = tensor("op_3338"), val = tensor([2, 640, 1, -1])]; + tensor input_385_cast_fp16 = reshape(shape = var_3338, x = attn_43_cast_fp16)[name = tensor("input_385_cast_fp16")]; + tensor var_3343 = const()[name = tensor("op_3343"), val = tensor([1, 1])]; + tensor var_3345 = const()[name = tensor("op_3345"), val = tensor([1, 1])]; + tensor var_3347_pad_type_0 = const()[name = tensor("op_3347_pad_type_0"), val = tensor("custom")]; + tensor var_3347_pad_0 = const()[name = tensor("op_3347_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1589608768)))]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1590428032)))]; + tensor var_3347_cast_fp16 = conv(bias = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_3345, groups = var_3126, pad = var_3347_pad_0, pad_type = var_3347_pad_type_0, strides = var_3343, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_385_cast_fp16)[name = tensor("op_3347_cast_fp16")]; + tensor inputs_65_cast_fp16 = add(x = var_3347_cast_fp16, y = inputs_63_cast_fp16)[name = tensor("inputs_65_cast_fp16")]; + tensor var_3351 = const()[name = tensor("op_3351"), val = tensor([1])]; + tensor channels_mean_65_cast_fp16 = reduce_mean(axes = var_3351, keep_dims = var_3121, x = inputs_65_cast_fp16)[name = tensor("channels_mean_65_cast_fp16")]; + tensor zero_mean_65_cast_fp16 = sub(x = inputs_65_cast_fp16, y = channels_mean_65_cast_fp16)[name = tensor("zero_mean_65_cast_fp16")]; + tensor zero_mean_sq_65_cast_fp16 = mul(x = zero_mean_65_cast_fp16, y = zero_mean_65_cast_fp16)[name = tensor("zero_mean_sq_65_cast_fp16")]; + tensor var_3355 = const()[name = tensor("op_3355"), val = tensor([1])]; + tensor var_3356_cast_fp16 = reduce_mean(axes = var_3355, keep_dims = var_3121, x = zero_mean_sq_65_cast_fp16)[name = tensor("op_3356_cast_fp16")]; + tensor var_3357_to_fp16 = const()[name = tensor("op_3357_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3358_cast_fp16 = add(x = var_3356_cast_fp16, y = var_3357_to_fp16)[name = tensor("op_3358_cast_fp16")]; + tensor denom_65_epsilon_0_to_fp16 = const()[name = tensor("denom_65_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_65_cast_fp16 = rsqrt(epsilon = denom_65_epsilon_0_to_fp16, x = var_3358_cast_fp16)[name = tensor("denom_65_cast_fp16")]; + tensor out_65_cast_fp16 = mul(x = zero_mean_65_cast_fp16, y = denom_65_cast_fp16)[name = tensor("out_65_cast_fp16")]; + tensor var_3362_to_fp16 = const()[name = tensor("op_3362_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1590429376)))]; + tensor var_3363_cast_fp16 = add(x = out_65_cast_fp16, y = var_3362_to_fp16)[name = tensor("op_3363_cast_fp16")]; + tensor var_3365_to_fp16 = const()[name = tensor("op_3365_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1590430720)))]; + tensor input_387_cast_fp16 = mul(x = var_3363_cast_fp16, y = var_3365_to_fp16)[name = tensor("input_387_cast_fp16")]; + tensor var_3373 = const()[name = tensor("op_3373"), val = tensor([1, 1])]; + tensor var_3375 = const()[name = tensor("op_3375"), val = tensor([1, 1])]; + tensor var_3377_pad_type_0 = const()[name = tensor("op_3377_pad_type_0"), val = tensor("custom")]; + tensor var_3377_pad_0 = const()[name = tensor("op_3377_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1590432064)))]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1596985728)))]; + tensor var_3377_cast_fp16 = conv(bias = up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_3375, groups = var_3126, pad = var_3377_pad_0, pad_type = var_3377_pad_type_0, strides = var_3373, weight = up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_387_cast_fp16)[name = tensor("op_3377_cast_fp16")]; + tensor var_3378_split_sizes_0 = const()[name = tensor("op_3378_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_3378_axis_0 = const()[name = tensor("op_3378_axis_0"), val = tensor(1)]; + tensor var_3378_cast_fp16_0, tensor var_3378_cast_fp16_1 = split(axis = var_3378_axis_0, split_sizes = var_3378_split_sizes_0, x = var_3377_cast_fp16)[name = tensor("op_3378_cast_fp16")]; + tensor var_3380_mode_0 = const()[name = tensor("op_3380_mode_0"), val = tensor("EXACT")]; + tensor var_3380_cast_fp16 = gelu(mode = var_3380_mode_0, x = var_3378_cast_fp16_1)[name = tensor("op_3380_cast_fp16")]; + tensor input_389_cast_fp16 = mul(x = var_3378_cast_fp16_0, y = var_3380_cast_fp16)[name = tensor("input_389_cast_fp16")]; + tensor var_3384 = const()[name = tensor("op_3384"), val = tensor([1, 1])]; + tensor var_3386 = const()[name = tensor("op_3386"), val = tensor([1, 1])]; + tensor var_3388_pad_type_0 = const()[name = tensor("op_3388_pad_type_0"), val = tensor("custom")]; + tensor var_3388_pad_0 = const()[name = tensor("op_3388_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1596996032)))]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1600272896)))]; + tensor var_3388_cast_fp16 = conv(bias = up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_3386, groups = var_3126, pad = var_3388_pad_0, pad_type = var_3388_pad_type_0, strides = var_3384, weight = up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_389_cast_fp16)[name = tensor("op_3388_cast_fp16")]; + tensor hidden_states_233_cast_fp16 = add(x = var_3388_cast_fp16, y = inputs_65_cast_fp16)[name = tensor("hidden_states_233_cast_fp16")]; + tensor var_3390 = const()[name = tensor("op_3390"), val = tensor([2, 640, 24, 40])]; + tensor input_391_cast_fp16 = reshape(shape = var_3390, x = hidden_states_233_cast_fp16)[name = tensor("input_391_cast_fp16")]; + tensor var_3394 = const()[name = tensor("op_3394"), val = tensor([1, 1])]; + tensor var_3396 = const()[name = tensor("op_3396"), val = tensor([1, 1])]; + tensor hidden_states_235_pad_type_0 = const()[name = tensor("hidden_states_235_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_235_pad_0 = const()[name = tensor("hidden_states_235_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1600274240)))]; + tensor up_blocks_2_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1601093504)))]; + tensor hidden_states_235_cast_fp16 = conv(bias = up_blocks_2_attentions_0_proj_out_bias_to_fp16, dilations = var_3396, groups = var_3126, pad = hidden_states_235_pad_0, pad_type = hidden_states_235_pad_type_0, strides = var_3394, weight = up_blocks_2_attentions_0_proj_out_weight_to_fp16, x = input_391_cast_fp16)[name = tensor("hidden_states_235_cast_fp16")]; + tensor hidden_states_237_cast_fp16 = add(x = hidden_states_235_cast_fp16, y = hidden_states_223_cast_fp16)[name = tensor("hidden_states_237_cast_fp16")]; + tensor input_393_interleave_0 = const()[name = tensor("input_393_interleave_0"), val = tensor(false)]; + tensor input_393_cast_fp16 = concat(axis = var_3126, interleave = input_393_interleave_0, values = (hidden_states_237_cast_fp16, input_89_cast_fp16))[name = tensor("input_393_cast_fp16")]; + tensor reshape_180_shape_0 = const()[name = tensor("reshape_180_shape_0"), val = tensor([2, 32, 40, 24, 40])]; + tensor reshape_180_cast_fp16 = reshape(shape = reshape_180_shape_0, x = input_393_cast_fp16)[name = tensor("reshape_180_cast_fp16")]; + tensor reduce_mean_135_axes_0 = const()[name = tensor("reduce_mean_135_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_135_keep_dims_0 = const()[name = tensor("reduce_mean_135_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_135_cast_fp16 = reduce_mean(axes = reduce_mean_135_axes_0, keep_dims = reduce_mean_135_keep_dims_0, x = reshape_180_cast_fp16)[name = tensor("reduce_mean_135_cast_fp16")]; + tensor sub_90_cast_fp16 = sub(x = reshape_180_cast_fp16, y = reduce_mean_135_cast_fp16)[name = tensor("sub_90_cast_fp16")]; + tensor square_45_cast_fp16 = square(x = sub_90_cast_fp16)[name = tensor("square_45_cast_fp16")]; + tensor reduce_mean_137_axes_0 = const()[name = tensor("reduce_mean_137_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_137_keep_dims_0 = const()[name = tensor("reduce_mean_137_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_137_cast_fp16 = reduce_mean(axes = reduce_mean_137_axes_0, keep_dims = reduce_mean_137_keep_dims_0, x = square_45_cast_fp16)[name = tensor("reduce_mean_137_cast_fp16")]; + tensor add_90_y_0_to_fp16 = const()[name = tensor("add_90_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_90_cast_fp16 = add(x = reduce_mean_137_cast_fp16, y = add_90_y_0_to_fp16)[name = tensor("add_90_cast_fp16")]; + tensor sqrt_45_cast_fp16 = sqrt(x = add_90_cast_fp16)[name = tensor("sqrt_45_cast_fp16")]; + tensor real_div_45_cast_fp16 = real_div(x = sub_90_cast_fp16, y = sqrt_45_cast_fp16)[name = tensor("real_div_45_cast_fp16")]; + tensor reshape_181_shape_0 = const()[name = tensor("reshape_181_shape_0"), val = tensor([2, 1280, 24, 40])]; + tensor reshape_181_cast_fp16 = reshape(shape = reshape_181_shape_0, x = real_div_45_cast_fp16)[name = tensor("reshape_181_cast_fp16")]; + tensor add_91_gamma_0_to_fp16 = const()[name = tensor("add_91_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1601094848)))]; + tensor add_91_beta_0_to_fp16 = const()[name = tensor("add_91_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1601097472)))]; + tensor add_91_epsilon_0_to_fp16 = const()[name = tensor("add_91_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_91_cast_fp16 = batch_norm(beta = add_91_beta_0_to_fp16, epsilon = add_91_epsilon_0_to_fp16, gamma = add_91_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_181_cast_fp16)[name = tensor("add_91_cast_fp16")]; + tensor input_397_cast_fp16 = silu(x = add_91_cast_fp16)[name = tensor("input_397_cast_fp16")]; + tensor var_3414 = const()[name = tensor("op_3414"), val = tensor([1, 1])]; + tensor var_3416 = const()[name = tensor("op_3416"), val = tensor([1, 1])]; + tensor hidden_states_239_pad_type_0 = const()[name = tensor("hidden_states_239_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_239_pad_0 = const()[name = tensor("hidden_states_239_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1601100096)))]; + tensor up_blocks_2_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1615845760)))]; + tensor hidden_states_239_cast_fp16 = conv(bias = up_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = var_3416, groups = var_3126, pad = hidden_states_239_pad_0, pad_type = hidden_states_239_pad_type_0, strides = var_3414, weight = up_blocks_2_resnets_1_conv1_weight_to_fp16, x = input_397_cast_fp16)[name = tensor("hidden_states_239_cast_fp16")]; + tensor var_3422 = const()[name = tensor("op_3422"), val = tensor([1, 1])]; + tensor var_3424 = const()[name = tensor("op_3424"), val = tensor([1, 1])]; + tensor temb_35_pad_type_0 = const()[name = tensor("temb_35_pad_type_0"), val = tensor("custom")]; + tensor temb_35_pad_0 = const()[name = tensor("temb_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1615847104)))]; + tensor up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1617485568)))]; + tensor temb_35_cast_fp16 = conv(bias = up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_3424, groups = var_3126, pad = temb_35_pad_0, pad_type = temb_35_pad_type_0, strides = var_3422, weight = up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16)[name = tensor("temb_35_cast_fp16")]; + tensor input_401_cast_fp16 = add(x = hidden_states_239_cast_fp16, y = temb_35_cast_fp16)[name = tensor("input_401_cast_fp16")]; + tensor reshape_184_shape_0 = const()[name = tensor("reshape_184_shape_0"), val = tensor([2, 32, 20, 24, 40])]; + tensor reshape_184_cast_fp16 = reshape(shape = reshape_184_shape_0, x = input_401_cast_fp16)[name = tensor("reshape_184_cast_fp16")]; + tensor reduce_mean_138_axes_0 = const()[name = tensor("reduce_mean_138_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_138_keep_dims_0 = const()[name = tensor("reduce_mean_138_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_138_cast_fp16 = reduce_mean(axes = reduce_mean_138_axes_0, keep_dims = reduce_mean_138_keep_dims_0, x = reshape_184_cast_fp16)[name = tensor("reduce_mean_138_cast_fp16")]; + tensor sub_92_cast_fp16 = sub(x = reshape_184_cast_fp16, y = reduce_mean_138_cast_fp16)[name = tensor("sub_92_cast_fp16")]; + tensor square_46_cast_fp16 = square(x = sub_92_cast_fp16)[name = tensor("square_46_cast_fp16")]; + tensor reduce_mean_140_axes_0 = const()[name = tensor("reduce_mean_140_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_140_keep_dims_0 = const()[name = tensor("reduce_mean_140_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_140_cast_fp16 = reduce_mean(axes = reduce_mean_140_axes_0, keep_dims = reduce_mean_140_keep_dims_0, x = square_46_cast_fp16)[name = tensor("reduce_mean_140_cast_fp16")]; + tensor add_92_y_0_to_fp16 = const()[name = tensor("add_92_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_92_cast_fp16 = add(x = reduce_mean_140_cast_fp16, y = add_92_y_0_to_fp16)[name = tensor("add_92_cast_fp16")]; + tensor sqrt_46_cast_fp16 = sqrt(x = add_92_cast_fp16)[name = tensor("sqrt_46_cast_fp16")]; + tensor real_div_46_cast_fp16 = real_div(x = sub_92_cast_fp16, y = sqrt_46_cast_fp16)[name = tensor("real_div_46_cast_fp16")]; + tensor reshape_185_shape_0 = const()[name = tensor("reshape_185_shape_0"), val = tensor([2, 640, 24, 40])]; + tensor reshape_185_cast_fp16 = reshape(shape = reshape_185_shape_0, x = real_div_46_cast_fp16)[name = tensor("reshape_185_cast_fp16")]; + tensor add_93_gamma_0_to_fp16 = const()[name = tensor("add_93_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1617486912)))]; + tensor add_93_beta_0_to_fp16 = const()[name = tensor("add_93_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1617488256)))]; + tensor add_93_epsilon_0_to_fp16 = const()[name = tensor("add_93_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_93_cast_fp16 = batch_norm(beta = add_93_beta_0_to_fp16, epsilon = add_93_epsilon_0_to_fp16, gamma = add_93_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_185_cast_fp16)[name = tensor("add_93_cast_fp16")]; + tensor input_405_cast_fp16 = silu(x = add_93_cast_fp16)[name = tensor("input_405_cast_fp16")]; + tensor var_3434 = const()[name = tensor("op_3434"), val = tensor([1, 1])]; + tensor var_3436 = const()[name = tensor("op_3436"), val = tensor([1, 1])]; + tensor hidden_states_241_pad_type_0 = const()[name = tensor("hidden_states_241_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_241_pad_0 = const()[name = tensor("hidden_states_241_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1617489600)))]; + tensor up_blocks_2_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1624862464)))]; + tensor hidden_states_241_cast_fp16 = conv(bias = up_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = var_3436, groups = var_3126, pad = hidden_states_241_pad_0, pad_type = hidden_states_241_pad_type_0, strides = var_3434, weight = up_blocks_2_resnets_1_conv2_weight_to_fp16, x = input_405_cast_fp16)[name = tensor("hidden_states_241_cast_fp16")]; + tensor var_3441 = const()[name = tensor("op_3441"), val = tensor([1, 1])]; + tensor var_3443 = const()[name = tensor("op_3443"), val = tensor([1, 1])]; + tensor x_19_pad_type_0 = const()[name = tensor("x_19_pad_type_0"), val = tensor("custom")]; + tensor x_19_pad_0 = const()[name = tensor("x_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1624863808)))]; + tensor up_blocks_2_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1626502272)))]; + tensor x_19_cast_fp16 = conv(bias = up_blocks_2_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_3443, groups = var_3126, pad = x_19_pad_0, pad_type = x_19_pad_type_0, strides = var_3441, weight = up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16, x = input_393_cast_fp16)[name = tensor("x_19_cast_fp16")]; + tensor hidden_states_243_cast_fp16 = add(x = x_19_cast_fp16, y = hidden_states_241_cast_fp16)[name = tensor("hidden_states_243_cast_fp16")]; + tensor reshape_188_shape_0 = const()[name = tensor("reshape_188_shape_0"), val = tensor([2, 32, 20, 24, 40])]; + tensor reshape_188_cast_fp16 = reshape(shape = reshape_188_shape_0, x = hidden_states_243_cast_fp16)[name = tensor("reshape_188_cast_fp16")]; + tensor reduce_mean_141_axes_0 = const()[name = tensor("reduce_mean_141_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_141_keep_dims_0 = const()[name = tensor("reduce_mean_141_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_141_cast_fp16 = reduce_mean(axes = reduce_mean_141_axes_0, keep_dims = reduce_mean_141_keep_dims_0, x = reshape_188_cast_fp16)[name = tensor("reduce_mean_141_cast_fp16")]; + tensor sub_94_cast_fp16 = sub(x = reshape_188_cast_fp16, y = reduce_mean_141_cast_fp16)[name = tensor("sub_94_cast_fp16")]; + tensor square_47_cast_fp16 = square(x = sub_94_cast_fp16)[name = tensor("square_47_cast_fp16")]; + tensor reduce_mean_143_axes_0 = const()[name = tensor("reduce_mean_143_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_143_keep_dims_0 = const()[name = tensor("reduce_mean_143_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_143_cast_fp16 = reduce_mean(axes = reduce_mean_143_axes_0, keep_dims = reduce_mean_143_keep_dims_0, x = square_47_cast_fp16)[name = tensor("reduce_mean_143_cast_fp16")]; + tensor add_94_y_0_to_fp16 = const()[name = tensor("add_94_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_94_cast_fp16 = add(x = reduce_mean_143_cast_fp16, y = add_94_y_0_to_fp16)[name = tensor("add_94_cast_fp16")]; + tensor sqrt_47_cast_fp16 = sqrt(x = add_94_cast_fp16)[name = tensor("sqrt_47_cast_fp16")]; + tensor real_div_47_cast_fp16 = real_div(x = sub_94_cast_fp16, y = sqrt_47_cast_fp16)[name = tensor("real_div_47_cast_fp16")]; + tensor reshape_189_shape_0 = const()[name = tensor("reshape_189_shape_0"), val = tensor([2, 640, 24, 40])]; + tensor reshape_189_cast_fp16 = reshape(shape = reshape_189_shape_0, x = real_div_47_cast_fp16)[name = tensor("reshape_189_cast_fp16")]; + tensor add_95_gamma_0_to_fp16 = const()[name = tensor("add_95_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1626503616)))]; + tensor add_95_beta_0_to_fp16 = const()[name = tensor("add_95_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1626504960)))]; + tensor add_95_epsilon_0_to_fp16 = const()[name = tensor("add_95_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_95_cast_fp16 = batch_norm(beta = add_95_beta_0_to_fp16, epsilon = add_95_epsilon_0_to_fp16, gamma = add_95_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_189_cast_fp16)[name = tensor("add_95_cast_fp16")]; + tensor var_3463 = const()[name = tensor("op_3463"), val = tensor([1, 1])]; + tensor var_3465 = const()[name = tensor("op_3465"), val = tensor([1, 1])]; + tensor hidden_states_245_pad_type_0 = const()[name = tensor("hidden_states_245_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_245_pad_0 = const()[name = tensor("hidden_states_245_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1626506304)))]; + tensor up_blocks_2_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1627325568)))]; + tensor hidden_states_245_cast_fp16 = conv(bias = up_blocks_2_attentions_1_proj_in_bias_to_fp16, dilations = var_3465, groups = var_3126, pad = hidden_states_245_pad_0, pad_type = hidden_states_245_pad_type_0, strides = var_3463, weight = up_blocks_2_attentions_1_proj_in_weight_to_fp16, x = add_95_cast_fp16)[name = tensor("hidden_states_245_cast_fp16")]; + tensor var_3470 = const()[name = tensor("op_3470"), val = tensor([2, 640, 1, 960])]; + tensor inputs_67_cast_fp16 = reshape(shape = var_3470, x = hidden_states_245_cast_fp16)[name = tensor("inputs_67_cast_fp16")]; + tensor var_3480 = const()[name = tensor("op_3480"), val = tensor([1])]; + tensor channels_mean_67_cast_fp16 = reduce_mean(axes = var_3480, keep_dims = var_3121, x = inputs_67_cast_fp16)[name = tensor("channels_mean_67_cast_fp16")]; + tensor zero_mean_67_cast_fp16 = sub(x = inputs_67_cast_fp16, y = channels_mean_67_cast_fp16)[name = tensor("zero_mean_67_cast_fp16")]; + tensor zero_mean_sq_67_cast_fp16 = mul(x = zero_mean_67_cast_fp16, y = zero_mean_67_cast_fp16)[name = tensor("zero_mean_sq_67_cast_fp16")]; + tensor var_3484 = const()[name = tensor("op_3484"), val = tensor([1])]; + tensor var_3485_cast_fp16 = reduce_mean(axes = var_3484, keep_dims = var_3121, x = zero_mean_sq_67_cast_fp16)[name = tensor("op_3485_cast_fp16")]; + tensor var_3486_to_fp16 = const()[name = tensor("op_3486_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3487_cast_fp16 = add(x = var_3485_cast_fp16, y = var_3486_to_fp16)[name = tensor("op_3487_cast_fp16")]; + tensor denom_67_epsilon_0_to_fp16 = const()[name = tensor("denom_67_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_67_cast_fp16 = rsqrt(epsilon = denom_67_epsilon_0_to_fp16, x = var_3487_cast_fp16)[name = tensor("denom_67_cast_fp16")]; + tensor out_67_cast_fp16 = mul(x = zero_mean_67_cast_fp16, y = denom_67_cast_fp16)[name = tensor("out_67_cast_fp16")]; + tensor var_3491_to_fp16 = const()[name = tensor("op_3491_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1627326912)))]; + tensor var_3492_cast_fp16 = add(x = out_67_cast_fp16, y = var_3491_to_fp16)[name = tensor("op_3492_cast_fp16")]; + tensor var_3494_to_fp16 = const()[name = tensor("op_3494_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1627328256)))]; + tensor hidden_states_247_cast_fp16 = mul(x = var_3492_cast_fp16, y = var_3494_to_fp16)[name = tensor("hidden_states_247_cast_fp16")]; + tensor var_3501 = const()[name = tensor("op_3501"), val = tensor([1, 1])]; + tensor var_3503 = const()[name = tensor("op_3503"), val = tensor([1, 1])]; + tensor q_45_pad_type_0 = const()[name = tensor("q_45_pad_type_0"), val = tensor("custom")]; + tensor q_45_pad_0 = const()[name = tensor("q_45_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1627329600)))]; + tensor q_45_cast_fp16 = conv(dilations = var_3503, groups = var_3126, pad = q_45_pad_0, pad_type = q_45_pad_type_0, strides = var_3501, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_247_cast_fp16)[name = tensor("q_45_cast_fp16")]; + tensor var_3507 = const()[name = tensor("op_3507"), val = tensor([1, 1])]; + tensor var_3509 = const()[name = tensor("op_3509"), val = tensor([1, 1])]; + tensor k_45_pad_type_0 = const()[name = tensor("k_45_pad_type_0"), val = tensor("custom")]; + tensor k_45_pad_0 = const()[name = tensor("k_45_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1628148864)))]; + tensor k_45_cast_fp16 = conv(dilations = var_3509, groups = var_3126, pad = k_45_pad_0, pad_type = k_45_pad_type_0, strides = var_3507, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_247_cast_fp16)[name = tensor("k_45_cast_fp16")]; + tensor var_3513 = const()[name = tensor("op_3513"), val = tensor([1, 1])]; + tensor var_3515 = const()[name = tensor("op_3515"), val = tensor([1, 1])]; + tensor v_45_pad_type_0 = const()[name = tensor("v_45_pad_type_0"), val = tensor("custom")]; + tensor v_45_pad_0 = const()[name = tensor("v_45_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1628968128)))]; + tensor v_45_cast_fp16 = conv(dilations = var_3515, groups = var_3126, pad = v_45_pad_0, pad_type = v_45_pad_type_0, strides = var_3513, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_247_cast_fp16)[name = tensor("v_45_cast_fp16")]; + tensor var_3519 = const()[name = tensor("op_3519"), val = tensor([2, 10, 64, -1])]; + tensor var_3520_cast_fp16 = reshape(shape = var_3519, x = q_45_cast_fp16)[name = tensor("op_3520_cast_fp16")]; + tensor var_3521 = const()[name = tensor("op_3521"), val = tensor([2, 10, 64, -1])]; + tensor var_3522_cast_fp16 = reshape(shape = var_3521, x = k_45_cast_fp16)[name = tensor("op_3522_cast_fp16")]; + tensor var_3523 = const()[name = tensor("op_3523"), val = tensor([2, 10, 64, -1])]; + tensor var_3524_cast_fp16 = reshape(shape = var_3523, x = v_45_cast_fp16)[name = tensor("op_3524_cast_fp16")]; + tensor attn_weights_89_transpose_x_0 = const()[name = tensor("attn_weights_89_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_89_transpose_y_0 = const()[name = tensor("attn_weights_89_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_89_cast_fp16 = matmul(transpose_x = attn_weights_89_transpose_x_0, transpose_y = attn_weights_89_transpose_y_0, x = var_3520_cast_fp16, y = var_3522_cast_fp16)[name = tensor("attn_weights_89_cast_fp16")]; + tensor attn_weights_91_cast_fp16 = mul(x = attn_weights_89_cast_fp16, y = var_3117_to_fp16)[name = tensor("attn_weights_91_cast_fp16")]; + tensor var_3528_cast_fp16 = softmax(axis = var_3110, x = attn_weights_91_cast_fp16)[name = tensor("op_3528_cast_fp16")]; + tensor attn_45_transpose_x_0 = const()[name = tensor("attn_45_transpose_x_0"), val = tensor(false)]; + tensor attn_45_transpose_y_0 = const()[name = tensor("attn_45_transpose_y_0"), val = tensor(true)]; + tensor attn_45_cast_fp16 = matmul(transpose_x = attn_45_transpose_x_0, transpose_y = attn_45_transpose_y_0, x = var_3524_cast_fp16, y = var_3528_cast_fp16)[name = tensor("attn_45_cast_fp16")]; + tensor var_3532 = const()[name = tensor("op_3532"), val = tensor([2, 640, 1, -1])]; + tensor input_409_cast_fp16 = reshape(shape = var_3532, x = attn_45_cast_fp16)[name = tensor("input_409_cast_fp16")]; + tensor var_3537 = const()[name = tensor("op_3537"), val = tensor([1, 1])]; + tensor var_3539 = const()[name = tensor("op_3539"), val = tensor([1, 1])]; + tensor var_3541_pad_type_0 = const()[name = tensor("op_3541_pad_type_0"), val = tensor("custom")]; + tensor var_3541_pad_0 = const()[name = tensor("op_3541_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1629787392)))]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1630606656)))]; + tensor var_3541_cast_fp16 = conv(bias = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_3539, groups = var_3126, pad = var_3541_pad_0, pad_type = var_3541_pad_type_0, strides = var_3537, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_409_cast_fp16)[name = tensor("op_3541_cast_fp16")]; + tensor inputs_69_cast_fp16 = add(x = var_3541_cast_fp16, y = inputs_67_cast_fp16)[name = tensor("inputs_69_cast_fp16")]; + tensor var_3545 = const()[name = tensor("op_3545"), val = tensor([1])]; + tensor channels_mean_69_cast_fp16 = reduce_mean(axes = var_3545, keep_dims = var_3121, x = inputs_69_cast_fp16)[name = tensor("channels_mean_69_cast_fp16")]; + tensor zero_mean_69_cast_fp16 = sub(x = inputs_69_cast_fp16, y = channels_mean_69_cast_fp16)[name = tensor("zero_mean_69_cast_fp16")]; + tensor zero_mean_sq_69_cast_fp16 = mul(x = zero_mean_69_cast_fp16, y = zero_mean_69_cast_fp16)[name = tensor("zero_mean_sq_69_cast_fp16")]; + tensor var_3549 = const()[name = tensor("op_3549"), val = tensor([1])]; + tensor var_3550_cast_fp16 = reduce_mean(axes = var_3549, keep_dims = var_3121, x = zero_mean_sq_69_cast_fp16)[name = tensor("op_3550_cast_fp16")]; + tensor var_3551_to_fp16 = const()[name = tensor("op_3551_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3552_cast_fp16 = add(x = var_3550_cast_fp16, y = var_3551_to_fp16)[name = tensor("op_3552_cast_fp16")]; + tensor denom_69_epsilon_0_to_fp16 = const()[name = tensor("denom_69_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_69_cast_fp16 = rsqrt(epsilon = denom_69_epsilon_0_to_fp16, x = var_3552_cast_fp16)[name = tensor("denom_69_cast_fp16")]; + tensor out_69_cast_fp16 = mul(x = zero_mean_69_cast_fp16, y = denom_69_cast_fp16)[name = tensor("out_69_cast_fp16")]; + tensor var_3556_to_fp16 = const()[name = tensor("op_3556_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1630608000)))]; + tensor var_3557_cast_fp16 = add(x = out_69_cast_fp16, y = var_3556_to_fp16)[name = tensor("op_3557_cast_fp16")]; + tensor var_3559_to_fp16 = const()[name = tensor("op_3559_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1630609344)))]; + tensor hidden_states_249_cast_fp16 = mul(x = var_3557_cast_fp16, y = var_3559_to_fp16)[name = tensor("hidden_states_249_cast_fp16")]; + tensor var_3566 = const()[name = tensor("op_3566"), val = tensor([1, 1])]; + tensor var_3568 = const()[name = tensor("op_3568"), val = tensor([1, 1])]; + tensor q_47_pad_type_0 = const()[name = tensor("q_47_pad_type_0"), val = tensor("custom")]; + tensor q_47_pad_0 = const()[name = tensor("q_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1630610688)))]; + tensor q_47_cast_fp16 = conv(dilations = var_3568, groups = var_3126, pad = q_47_pad_0, pad_type = q_47_pad_type_0, strides = var_3566, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_249_cast_fp16)[name = tensor("q_47_cast_fp16")]; + tensor var_3572 = const()[name = tensor("op_3572"), val = tensor([1, 1])]; + tensor var_3574 = const()[name = tensor("op_3574"), val = tensor([1, 1])]; + tensor k_47_pad_type_0 = const()[name = tensor("k_47_pad_type_0"), val = tensor("custom")]; + tensor k_47_pad_0 = const()[name = tensor("k_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1631429952)))]; + tensor k_47_cast_fp16 = conv(dilations = var_3574, groups = var_3126, pad = k_47_pad_0, pad_type = k_47_pad_type_0, strides = var_3572, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_47_cast_fp16")]; + tensor var_3578 = const()[name = tensor("op_3578"), val = tensor([1, 1])]; + tensor var_3580 = const()[name = tensor("op_3580"), val = tensor([1, 1])]; + tensor v_47_pad_type_0 = const()[name = tensor("v_47_pad_type_0"), val = tensor("custom")]; + tensor v_47_pad_0 = const()[name = tensor("v_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1632740736)))]; + tensor v_47_cast_fp16 = conv(dilations = var_3580, groups = var_3126, pad = v_47_pad_0, pad_type = v_47_pad_type_0, strides = var_3578, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_47_cast_fp16")]; + tensor var_3584 = const()[name = tensor("op_3584"), val = tensor([2, 10, 64, -1])]; + tensor var_3585_cast_fp16 = reshape(shape = var_3584, x = q_47_cast_fp16)[name = tensor("op_3585_cast_fp16")]; + tensor var_3586 = const()[name = tensor("op_3586"), val = tensor([2, 10, 64, -1])]; + tensor var_3587_cast_fp16 = reshape(shape = var_3586, x = k_47_cast_fp16)[name = tensor("op_3587_cast_fp16")]; + tensor var_3588 = const()[name = tensor("op_3588"), val = tensor([2, 10, 64, -1])]; + tensor var_3589_cast_fp16 = reshape(shape = var_3588, x = v_47_cast_fp16)[name = tensor("op_3589_cast_fp16")]; + tensor attn_weights_93_transpose_x_0 = const()[name = tensor("attn_weights_93_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_93_transpose_y_0 = const()[name = tensor("attn_weights_93_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_93_cast_fp16 = matmul(transpose_x = attn_weights_93_transpose_x_0, transpose_y = attn_weights_93_transpose_y_0, x = var_3585_cast_fp16, y = var_3587_cast_fp16)[name = tensor("attn_weights_93_cast_fp16")]; + tensor attn_weights_95_cast_fp16 = mul(x = attn_weights_93_cast_fp16, y = var_3117_to_fp16)[name = tensor("attn_weights_95_cast_fp16")]; + tensor var_3593_cast_fp16 = softmax(axis = var_3110, x = attn_weights_95_cast_fp16)[name = tensor("op_3593_cast_fp16")]; + tensor attn_47_transpose_x_0 = const()[name = tensor("attn_47_transpose_x_0"), val = tensor(false)]; + tensor attn_47_transpose_y_0 = const()[name = tensor("attn_47_transpose_y_0"), val = tensor(true)]; + tensor attn_47_cast_fp16 = matmul(transpose_x = attn_47_transpose_x_0, transpose_y = attn_47_transpose_y_0, x = var_3589_cast_fp16, y = var_3593_cast_fp16)[name = tensor("attn_47_cast_fp16")]; + tensor var_3597 = const()[name = tensor("op_3597"), val = tensor([2, 640, 1, -1])]; + tensor input_411_cast_fp16 = reshape(shape = var_3597, x = attn_47_cast_fp16)[name = tensor("input_411_cast_fp16")]; + tensor var_3602 = const()[name = tensor("op_3602"), val = tensor([1, 1])]; + tensor var_3604 = const()[name = tensor("op_3604"), val = tensor([1, 1])]; + tensor var_3606_pad_type_0 = const()[name = tensor("op_3606_pad_type_0"), val = tensor("custom")]; + tensor var_3606_pad_0 = const()[name = tensor("op_3606_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1634051520)))]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1634870784)))]; + tensor var_3606_cast_fp16 = conv(bias = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_3604, groups = var_3126, pad = var_3606_pad_0, pad_type = var_3606_pad_type_0, strides = var_3602, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_411_cast_fp16)[name = tensor("op_3606_cast_fp16")]; + tensor inputs_71_cast_fp16 = add(x = var_3606_cast_fp16, y = inputs_69_cast_fp16)[name = tensor("inputs_71_cast_fp16")]; + tensor var_3610 = const()[name = tensor("op_3610"), val = tensor([1])]; + tensor channels_mean_71_cast_fp16 = reduce_mean(axes = var_3610, keep_dims = var_3121, x = inputs_71_cast_fp16)[name = tensor("channels_mean_71_cast_fp16")]; + tensor zero_mean_71_cast_fp16 = sub(x = inputs_71_cast_fp16, y = channels_mean_71_cast_fp16)[name = tensor("zero_mean_71_cast_fp16")]; + tensor zero_mean_sq_71_cast_fp16 = mul(x = zero_mean_71_cast_fp16, y = zero_mean_71_cast_fp16)[name = tensor("zero_mean_sq_71_cast_fp16")]; + tensor var_3614 = const()[name = tensor("op_3614"), val = tensor([1])]; + tensor var_3615_cast_fp16 = reduce_mean(axes = var_3614, keep_dims = var_3121, x = zero_mean_sq_71_cast_fp16)[name = tensor("op_3615_cast_fp16")]; + tensor var_3616_to_fp16 = const()[name = tensor("op_3616_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3617_cast_fp16 = add(x = var_3615_cast_fp16, y = var_3616_to_fp16)[name = tensor("op_3617_cast_fp16")]; + tensor denom_71_epsilon_0_to_fp16 = const()[name = tensor("denom_71_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_71_cast_fp16 = rsqrt(epsilon = denom_71_epsilon_0_to_fp16, x = var_3617_cast_fp16)[name = tensor("denom_71_cast_fp16")]; + tensor out_71_cast_fp16 = mul(x = zero_mean_71_cast_fp16, y = denom_71_cast_fp16)[name = tensor("out_71_cast_fp16")]; + tensor var_3621_to_fp16 = const()[name = tensor("op_3621_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1634872128)))]; + tensor var_3622_cast_fp16 = add(x = out_71_cast_fp16, y = var_3621_to_fp16)[name = tensor("op_3622_cast_fp16")]; + tensor var_3624_to_fp16 = const()[name = tensor("op_3624_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1634873472)))]; + tensor input_413_cast_fp16 = mul(x = var_3622_cast_fp16, y = var_3624_to_fp16)[name = tensor("input_413_cast_fp16")]; + tensor var_3632 = const()[name = tensor("op_3632"), val = tensor([1, 1])]; + tensor var_3634 = const()[name = tensor("op_3634"), val = tensor([1, 1])]; + tensor var_3636_pad_type_0 = const()[name = tensor("op_3636_pad_type_0"), val = tensor("custom")]; + tensor var_3636_pad_0 = const()[name = tensor("op_3636_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1634874816)))]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1641428480)))]; + tensor var_3636_cast_fp16 = conv(bias = up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_3634, groups = var_3126, pad = var_3636_pad_0, pad_type = var_3636_pad_type_0, strides = var_3632, weight = up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_413_cast_fp16)[name = tensor("op_3636_cast_fp16")]; + tensor var_3637_split_sizes_0 = const()[name = tensor("op_3637_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_3637_axis_0 = const()[name = tensor("op_3637_axis_0"), val = tensor(1)]; + tensor var_3637_cast_fp16_0, tensor var_3637_cast_fp16_1 = split(axis = var_3637_axis_0, split_sizes = var_3637_split_sizes_0, x = var_3636_cast_fp16)[name = tensor("op_3637_cast_fp16")]; + tensor var_3639_mode_0 = const()[name = tensor("op_3639_mode_0"), val = tensor("EXACT")]; + tensor var_3639_cast_fp16 = gelu(mode = var_3639_mode_0, x = var_3637_cast_fp16_1)[name = tensor("op_3639_cast_fp16")]; + tensor input_415_cast_fp16 = mul(x = var_3637_cast_fp16_0, y = var_3639_cast_fp16)[name = tensor("input_415_cast_fp16")]; + tensor var_3643 = const()[name = tensor("op_3643"), val = tensor([1, 1])]; + tensor var_3645 = const()[name = tensor("op_3645"), val = tensor([1, 1])]; + tensor var_3647_pad_type_0 = const()[name = tensor("op_3647_pad_type_0"), val = tensor("custom")]; + tensor var_3647_pad_0 = const()[name = tensor("op_3647_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1641438784)))]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1644715648)))]; + tensor var_3647_cast_fp16 = conv(bias = up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_3645, groups = var_3126, pad = var_3647_pad_0, pad_type = var_3647_pad_type_0, strides = var_3643, weight = up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_415_cast_fp16)[name = tensor("op_3647_cast_fp16")]; + tensor hidden_states_253_cast_fp16 = add(x = var_3647_cast_fp16, y = inputs_71_cast_fp16)[name = tensor("hidden_states_253_cast_fp16")]; + tensor var_3649 = const()[name = tensor("op_3649"), val = tensor([2, 640, 24, 40])]; + tensor input_417_cast_fp16 = reshape(shape = var_3649, x = hidden_states_253_cast_fp16)[name = tensor("input_417_cast_fp16")]; + tensor var_3653 = const()[name = tensor("op_3653"), val = tensor([1, 1])]; + tensor var_3655 = const()[name = tensor("op_3655"), val = tensor([1, 1])]; + tensor hidden_states_255_pad_type_0 = const()[name = tensor("hidden_states_255_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_255_pad_0 = const()[name = tensor("hidden_states_255_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1644716992)))]; + tensor up_blocks_2_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1645536256)))]; + tensor hidden_states_255_cast_fp16 = conv(bias = up_blocks_2_attentions_1_proj_out_bias_to_fp16, dilations = var_3655, groups = var_3126, pad = hidden_states_255_pad_0, pad_type = hidden_states_255_pad_type_0, strides = var_3653, weight = up_blocks_2_attentions_1_proj_out_weight_to_fp16, x = input_417_cast_fp16)[name = tensor("hidden_states_255_cast_fp16")]; + tensor hidden_states_257_cast_fp16 = add(x = hidden_states_255_cast_fp16, y = hidden_states_243_cast_fp16)[name = tensor("hidden_states_257_cast_fp16")]; + tensor input_419_interleave_0 = const()[name = tensor("input_419_interleave_0"), val = tensor(false)]; + tensor input_419_cast_fp16 = concat(axis = var_3126, interleave = input_419_interleave_0, values = (hidden_states_257_cast_fp16, input_63_cast_fp16))[name = tensor("input_419_cast_fp16")]; + tensor reshape_192_shape_0 = const()[name = tensor("reshape_192_shape_0"), val = tensor([2, 32, 30, 24, 40])]; + tensor reshape_192_cast_fp16 = reshape(shape = reshape_192_shape_0, x = input_419_cast_fp16)[name = tensor("reshape_192_cast_fp16")]; + tensor reduce_mean_144_axes_0 = const()[name = tensor("reduce_mean_144_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_144_keep_dims_0 = const()[name = tensor("reduce_mean_144_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_144_cast_fp16 = reduce_mean(axes = reduce_mean_144_axes_0, keep_dims = reduce_mean_144_keep_dims_0, x = reshape_192_cast_fp16)[name = tensor("reduce_mean_144_cast_fp16")]; + tensor sub_96_cast_fp16 = sub(x = reshape_192_cast_fp16, y = reduce_mean_144_cast_fp16)[name = tensor("sub_96_cast_fp16")]; + tensor square_48_cast_fp16 = square(x = sub_96_cast_fp16)[name = tensor("square_48_cast_fp16")]; + tensor reduce_mean_146_axes_0 = const()[name = tensor("reduce_mean_146_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_146_keep_dims_0 = const()[name = tensor("reduce_mean_146_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_146_cast_fp16 = reduce_mean(axes = reduce_mean_146_axes_0, keep_dims = reduce_mean_146_keep_dims_0, x = square_48_cast_fp16)[name = tensor("reduce_mean_146_cast_fp16")]; + tensor add_96_y_0_to_fp16 = const()[name = tensor("add_96_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_96_cast_fp16 = add(x = reduce_mean_146_cast_fp16, y = add_96_y_0_to_fp16)[name = tensor("add_96_cast_fp16")]; + tensor sqrt_48_cast_fp16 = sqrt(x = add_96_cast_fp16)[name = tensor("sqrt_48_cast_fp16")]; + tensor real_div_48_cast_fp16 = real_div(x = sub_96_cast_fp16, y = sqrt_48_cast_fp16)[name = tensor("real_div_48_cast_fp16")]; + tensor reshape_193_shape_0 = const()[name = tensor("reshape_193_shape_0"), val = tensor([2, 960, 24, 40])]; + tensor reshape_193_cast_fp16 = reshape(shape = reshape_193_shape_0, x = real_div_48_cast_fp16)[name = tensor("reshape_193_cast_fp16")]; + tensor add_97_mean_0_to_fp16 = const()[name = tensor("add_97_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1645537600)))]; + tensor add_97_variance_0_to_fp16 = const()[name = tensor("add_97_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1645539584)))]; + tensor add_97_gamma_0_to_fp16 = const()[name = tensor("add_97_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1645541568)))]; + tensor add_97_beta_0_to_fp16 = const()[name = tensor("add_97_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1645543552)))]; + tensor add_97_epsilon_0_to_fp16 = const()[name = tensor("add_97_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_97_cast_fp16 = batch_norm(beta = add_97_beta_0_to_fp16, epsilon = add_97_epsilon_0_to_fp16, gamma = add_97_gamma_0_to_fp16, mean = add_97_mean_0_to_fp16, variance = add_97_variance_0_to_fp16, x = reshape_193_cast_fp16)[name = tensor("add_97_cast_fp16")]; + tensor input_423_cast_fp16 = silu(x = add_97_cast_fp16)[name = tensor("input_423_cast_fp16")]; + tensor var_3673 = const()[name = tensor("op_3673"), val = tensor([1, 1])]; + tensor var_3675 = const()[name = tensor("op_3675"), val = tensor([1, 1])]; + tensor hidden_states_259_pad_type_0 = const()[name = tensor("hidden_states_259_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_259_pad_0 = const()[name = tensor("hidden_states_259_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_2_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1645545536)))]; + tensor up_blocks_2_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1656604800)))]; + tensor hidden_states_259_cast_fp16 = conv(bias = up_blocks_2_resnets_2_conv1_bias_to_fp16, dilations = var_3675, groups = var_3126, pad = hidden_states_259_pad_0, pad_type = hidden_states_259_pad_type_0, strides = var_3673, weight = up_blocks_2_resnets_2_conv1_weight_to_fp16, x = input_423_cast_fp16)[name = tensor("hidden_states_259_cast_fp16")]; + tensor var_3681 = const()[name = tensor("op_3681"), val = tensor([1, 1])]; + tensor var_3683 = const()[name = tensor("op_3683"), val = tensor([1, 1])]; + tensor temb_37_pad_type_0 = const()[name = tensor("temb_37_pad_type_0"), val = tensor("custom")]; + tensor temb_37_pad_0 = const()[name = tensor("temb_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1656606144)))]; + tensor up_blocks_2_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1658244608)))]; + tensor temb_37_cast_fp16 = conv(bias = up_blocks_2_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_3683, groups = var_3126, pad = temb_37_pad_0, pad_type = temb_37_pad_type_0, strides = var_3681, weight = up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16)[name = tensor("temb_37_cast_fp16")]; + tensor input_427_cast_fp16 = add(x = hidden_states_259_cast_fp16, y = temb_37_cast_fp16)[name = tensor("input_427_cast_fp16")]; + tensor reshape_196_shape_0 = const()[name = tensor("reshape_196_shape_0"), val = tensor([2, 32, 20, 24, 40])]; + tensor reshape_196_cast_fp16 = reshape(shape = reshape_196_shape_0, x = input_427_cast_fp16)[name = tensor("reshape_196_cast_fp16")]; + tensor reduce_mean_147_axes_0 = const()[name = tensor("reduce_mean_147_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_147_keep_dims_0 = const()[name = tensor("reduce_mean_147_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_147_cast_fp16 = reduce_mean(axes = reduce_mean_147_axes_0, keep_dims = reduce_mean_147_keep_dims_0, x = reshape_196_cast_fp16)[name = tensor("reduce_mean_147_cast_fp16")]; + tensor sub_98_cast_fp16 = sub(x = reshape_196_cast_fp16, y = reduce_mean_147_cast_fp16)[name = tensor("sub_98_cast_fp16")]; + tensor square_49_cast_fp16 = square(x = sub_98_cast_fp16)[name = tensor("square_49_cast_fp16")]; + tensor reduce_mean_149_axes_0 = const()[name = tensor("reduce_mean_149_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_149_keep_dims_0 = const()[name = tensor("reduce_mean_149_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_149_cast_fp16 = reduce_mean(axes = reduce_mean_149_axes_0, keep_dims = reduce_mean_149_keep_dims_0, x = square_49_cast_fp16)[name = tensor("reduce_mean_149_cast_fp16")]; + tensor add_98_y_0_to_fp16 = const()[name = tensor("add_98_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_98_cast_fp16 = add(x = reduce_mean_149_cast_fp16, y = add_98_y_0_to_fp16)[name = tensor("add_98_cast_fp16")]; + tensor sqrt_49_cast_fp16 = sqrt(x = add_98_cast_fp16)[name = tensor("sqrt_49_cast_fp16")]; + tensor real_div_49_cast_fp16 = real_div(x = sub_98_cast_fp16, y = sqrt_49_cast_fp16)[name = tensor("real_div_49_cast_fp16")]; + tensor reshape_197_shape_0 = const()[name = tensor("reshape_197_shape_0"), val = tensor([2, 640, 24, 40])]; + tensor reshape_197_cast_fp16 = reshape(shape = reshape_197_shape_0, x = real_div_49_cast_fp16)[name = tensor("reshape_197_cast_fp16")]; + tensor add_99_gamma_0_to_fp16 = const()[name = tensor("add_99_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1658245952)))]; + tensor add_99_beta_0_to_fp16 = const()[name = tensor("add_99_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1658247296)))]; + tensor add_99_epsilon_0_to_fp16 = const()[name = tensor("add_99_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_99_cast_fp16 = batch_norm(beta = add_99_beta_0_to_fp16, epsilon = add_99_epsilon_0_to_fp16, gamma = add_99_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_197_cast_fp16)[name = tensor("add_99_cast_fp16")]; + tensor input_431_cast_fp16 = silu(x = add_99_cast_fp16)[name = tensor("input_431_cast_fp16")]; + tensor var_3693 = const()[name = tensor("op_3693"), val = tensor([1, 1])]; + tensor var_3695 = const()[name = tensor("op_3695"), val = tensor([1, 1])]; + tensor hidden_states_261_pad_type_0 = const()[name = tensor("hidden_states_261_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_261_pad_0 = const()[name = tensor("hidden_states_261_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_2_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1658248640)))]; + tensor up_blocks_2_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1665621504)))]; + tensor hidden_states_261_cast_fp16 = conv(bias = up_blocks_2_resnets_2_conv2_bias_to_fp16, dilations = var_3695, groups = var_3126, pad = hidden_states_261_pad_0, pad_type = hidden_states_261_pad_type_0, strides = var_3693, weight = up_blocks_2_resnets_2_conv2_weight_to_fp16, x = input_431_cast_fp16)[name = tensor("hidden_states_261_cast_fp16")]; + tensor var_3700 = const()[name = tensor("op_3700"), val = tensor([1, 1])]; + tensor var_3702 = const()[name = tensor("op_3702"), val = tensor([1, 1])]; + tensor x_21_pad_type_0 = const()[name = tensor("x_21_pad_type_0"), val = tensor("custom")]; + tensor x_21_pad_0 = const()[name = tensor("x_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1665622848)))]; + tensor up_blocks_2_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1666851712)))]; + tensor x_21_cast_fp16 = conv(bias = up_blocks_2_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_3702, groups = var_3126, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = var_3700, weight = up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16, x = input_419_cast_fp16)[name = tensor("x_21_cast_fp16")]; + tensor hidden_states_263_cast_fp16 = add(x = x_21_cast_fp16, y = hidden_states_261_cast_fp16)[name = tensor("hidden_states_263_cast_fp16")]; + tensor reshape_200_shape_0 = const()[name = tensor("reshape_200_shape_0"), val = tensor([2, 32, 20, 24, 40])]; + tensor reshape_200_cast_fp16 = reshape(shape = reshape_200_shape_0, x = hidden_states_263_cast_fp16)[name = tensor("reshape_200_cast_fp16")]; + tensor reduce_mean_150_axes_0 = const()[name = tensor("reduce_mean_150_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_150_keep_dims_0 = const()[name = tensor("reduce_mean_150_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_150_cast_fp16 = reduce_mean(axes = reduce_mean_150_axes_0, keep_dims = reduce_mean_150_keep_dims_0, x = reshape_200_cast_fp16)[name = tensor("reduce_mean_150_cast_fp16")]; + tensor sub_100_cast_fp16 = sub(x = reshape_200_cast_fp16, y = reduce_mean_150_cast_fp16)[name = tensor("sub_100_cast_fp16")]; + tensor square_50_cast_fp16 = square(x = sub_100_cast_fp16)[name = tensor("square_50_cast_fp16")]; + tensor reduce_mean_152_axes_0 = const()[name = tensor("reduce_mean_152_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_152_keep_dims_0 = const()[name = tensor("reduce_mean_152_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_152_cast_fp16 = reduce_mean(axes = reduce_mean_152_axes_0, keep_dims = reduce_mean_152_keep_dims_0, x = square_50_cast_fp16)[name = tensor("reduce_mean_152_cast_fp16")]; + tensor add_100_y_0_to_fp16 = const()[name = tensor("add_100_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_100_cast_fp16 = add(x = reduce_mean_152_cast_fp16, y = add_100_y_0_to_fp16)[name = tensor("add_100_cast_fp16")]; + tensor sqrt_50_cast_fp16 = sqrt(x = add_100_cast_fp16)[name = tensor("sqrt_50_cast_fp16")]; + tensor real_div_50_cast_fp16 = real_div(x = sub_100_cast_fp16, y = sqrt_50_cast_fp16)[name = tensor("real_div_50_cast_fp16")]; + tensor reshape_201_shape_0 = const()[name = tensor("reshape_201_shape_0"), val = tensor([2, 640, 24, 40])]; + tensor reshape_201_cast_fp16 = reshape(shape = reshape_201_shape_0, x = real_div_50_cast_fp16)[name = tensor("reshape_201_cast_fp16")]; + tensor add_101_gamma_0_to_fp16 = const()[name = tensor("add_101_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1666853056)))]; + tensor add_101_beta_0_to_fp16 = const()[name = tensor("add_101_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1666854400)))]; + tensor add_101_epsilon_0_to_fp16 = const()[name = tensor("add_101_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_101_cast_fp16 = batch_norm(beta = add_101_beta_0_to_fp16, epsilon = add_101_epsilon_0_to_fp16, gamma = add_101_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_201_cast_fp16)[name = tensor("add_101_cast_fp16")]; + tensor var_3722 = const()[name = tensor("op_3722"), val = tensor([1, 1])]; + tensor var_3724 = const()[name = tensor("op_3724"), val = tensor([1, 1])]; + tensor hidden_states_265_pad_type_0 = const()[name = tensor("hidden_states_265_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_265_pad_0 = const()[name = tensor("hidden_states_265_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1666855744)))]; + tensor up_blocks_2_attentions_2_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1667675008)))]; + tensor hidden_states_265_cast_fp16 = conv(bias = up_blocks_2_attentions_2_proj_in_bias_to_fp16, dilations = var_3724, groups = var_3126, pad = hidden_states_265_pad_0, pad_type = hidden_states_265_pad_type_0, strides = var_3722, weight = up_blocks_2_attentions_2_proj_in_weight_to_fp16, x = add_101_cast_fp16)[name = tensor("hidden_states_265_cast_fp16")]; + tensor var_3729 = const()[name = tensor("op_3729"), val = tensor([2, 640, 1, 960])]; + tensor inputs_73_cast_fp16 = reshape(shape = var_3729, x = hidden_states_265_cast_fp16)[name = tensor("inputs_73_cast_fp16")]; + tensor var_3739 = const()[name = tensor("op_3739"), val = tensor([1])]; + tensor channels_mean_73_cast_fp16 = reduce_mean(axes = var_3739, keep_dims = var_3121, x = inputs_73_cast_fp16)[name = tensor("channels_mean_73_cast_fp16")]; + tensor zero_mean_73_cast_fp16 = sub(x = inputs_73_cast_fp16, y = channels_mean_73_cast_fp16)[name = tensor("zero_mean_73_cast_fp16")]; + tensor zero_mean_sq_73_cast_fp16 = mul(x = zero_mean_73_cast_fp16, y = zero_mean_73_cast_fp16)[name = tensor("zero_mean_sq_73_cast_fp16")]; + tensor var_3743 = const()[name = tensor("op_3743"), val = tensor([1])]; + tensor var_3744_cast_fp16 = reduce_mean(axes = var_3743, keep_dims = var_3121, x = zero_mean_sq_73_cast_fp16)[name = tensor("op_3744_cast_fp16")]; + tensor var_3745_to_fp16 = const()[name = tensor("op_3745_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3746_cast_fp16 = add(x = var_3744_cast_fp16, y = var_3745_to_fp16)[name = tensor("op_3746_cast_fp16")]; + tensor denom_73_epsilon_0_to_fp16 = const()[name = tensor("denom_73_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_73_cast_fp16 = rsqrt(epsilon = denom_73_epsilon_0_to_fp16, x = var_3746_cast_fp16)[name = tensor("denom_73_cast_fp16")]; + tensor out_73_cast_fp16 = mul(x = zero_mean_73_cast_fp16, y = denom_73_cast_fp16)[name = tensor("out_73_cast_fp16")]; + tensor var_3750_to_fp16 = const()[name = tensor("op_3750_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1667676352)))]; + tensor var_3751_cast_fp16 = add(x = out_73_cast_fp16, y = var_3750_to_fp16)[name = tensor("op_3751_cast_fp16")]; + tensor var_3753_to_fp16 = const()[name = tensor("op_3753_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1667677696)))]; + tensor hidden_states_267_cast_fp16 = mul(x = var_3751_cast_fp16, y = var_3753_to_fp16)[name = tensor("hidden_states_267_cast_fp16")]; + tensor var_3760 = const()[name = tensor("op_3760"), val = tensor([1, 1])]; + tensor var_3762 = const()[name = tensor("op_3762"), val = tensor([1, 1])]; + tensor q_49_pad_type_0 = const()[name = tensor("q_49_pad_type_0"), val = tensor("custom")]; + tensor q_49_pad_0 = const()[name = tensor("q_49_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1667679040)))]; + tensor q_49_cast_fp16 = conv(dilations = var_3762, groups = var_3126, pad = q_49_pad_0, pad_type = q_49_pad_type_0, strides = var_3760, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_267_cast_fp16)[name = tensor("q_49_cast_fp16")]; + tensor var_3766 = const()[name = tensor("op_3766"), val = tensor([1, 1])]; + tensor var_3768 = const()[name = tensor("op_3768"), val = tensor([1, 1])]; + tensor k_49_pad_type_0 = const()[name = tensor("k_49_pad_type_0"), val = tensor("custom")]; + tensor k_49_pad_0 = const()[name = tensor("k_49_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1668498304)))]; + tensor k_49_cast_fp16 = conv(dilations = var_3768, groups = var_3126, pad = k_49_pad_0, pad_type = k_49_pad_type_0, strides = var_3766, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_267_cast_fp16)[name = tensor("k_49_cast_fp16")]; + tensor var_3772 = const()[name = tensor("op_3772"), val = tensor([1, 1])]; + tensor var_3774 = const()[name = tensor("op_3774"), val = tensor([1, 1])]; + tensor v_49_pad_type_0 = const()[name = tensor("v_49_pad_type_0"), val = tensor("custom")]; + tensor v_49_pad_0 = const()[name = tensor("v_49_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1669317568)))]; + tensor v_49_cast_fp16 = conv(dilations = var_3774, groups = var_3126, pad = v_49_pad_0, pad_type = v_49_pad_type_0, strides = var_3772, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_267_cast_fp16)[name = tensor("v_49_cast_fp16")]; + tensor var_3778 = const()[name = tensor("op_3778"), val = tensor([2, 10, 64, -1])]; + tensor var_3779_cast_fp16 = reshape(shape = var_3778, x = q_49_cast_fp16)[name = tensor("op_3779_cast_fp16")]; + tensor var_3780 = const()[name = tensor("op_3780"), val = tensor([2, 10, 64, -1])]; + tensor var_3781_cast_fp16 = reshape(shape = var_3780, x = k_49_cast_fp16)[name = tensor("op_3781_cast_fp16")]; + tensor var_3782 = const()[name = tensor("op_3782"), val = tensor([2, 10, 64, -1])]; + tensor var_3783_cast_fp16 = reshape(shape = var_3782, x = v_49_cast_fp16)[name = tensor("op_3783_cast_fp16")]; + tensor attn_weights_97_transpose_x_0 = const()[name = tensor("attn_weights_97_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_97_transpose_y_0 = const()[name = tensor("attn_weights_97_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_97_cast_fp16 = matmul(transpose_x = attn_weights_97_transpose_x_0, transpose_y = attn_weights_97_transpose_y_0, x = var_3779_cast_fp16, y = var_3781_cast_fp16)[name = tensor("attn_weights_97_cast_fp16")]; + tensor attn_weights_99_cast_fp16 = mul(x = attn_weights_97_cast_fp16, y = var_3117_to_fp16)[name = tensor("attn_weights_99_cast_fp16")]; + tensor var_3787_cast_fp16 = softmax(axis = var_3110, x = attn_weights_99_cast_fp16)[name = tensor("op_3787_cast_fp16")]; + tensor attn_49_transpose_x_0 = const()[name = tensor("attn_49_transpose_x_0"), val = tensor(false)]; + tensor attn_49_transpose_y_0 = const()[name = tensor("attn_49_transpose_y_0"), val = tensor(true)]; + tensor attn_49_cast_fp16 = matmul(transpose_x = attn_49_transpose_x_0, transpose_y = attn_49_transpose_y_0, x = var_3783_cast_fp16, y = var_3787_cast_fp16)[name = tensor("attn_49_cast_fp16")]; + tensor var_3791 = const()[name = tensor("op_3791"), val = tensor([2, 640, 1, -1])]; + tensor input_435_cast_fp16 = reshape(shape = var_3791, x = attn_49_cast_fp16)[name = tensor("input_435_cast_fp16")]; + tensor var_3796 = const()[name = tensor("op_3796"), val = tensor([1, 1])]; + tensor var_3798 = const()[name = tensor("op_3798"), val = tensor([1, 1])]; + tensor var_3800_pad_type_0 = const()[name = tensor("op_3800_pad_type_0"), val = tensor("custom")]; + tensor var_3800_pad_0 = const()[name = tensor("op_3800_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1670136832)))]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1670956096)))]; + tensor var_3800_cast_fp16 = conv(bias = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_3798, groups = var_3126, pad = var_3800_pad_0, pad_type = var_3800_pad_type_0, strides = var_3796, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_435_cast_fp16)[name = tensor("op_3800_cast_fp16")]; + tensor inputs_75_cast_fp16 = add(x = var_3800_cast_fp16, y = inputs_73_cast_fp16)[name = tensor("inputs_75_cast_fp16")]; + tensor var_3804 = const()[name = tensor("op_3804"), val = tensor([1])]; + tensor channels_mean_75_cast_fp16 = reduce_mean(axes = var_3804, keep_dims = var_3121, x = inputs_75_cast_fp16)[name = tensor("channels_mean_75_cast_fp16")]; + tensor zero_mean_75_cast_fp16 = sub(x = inputs_75_cast_fp16, y = channels_mean_75_cast_fp16)[name = tensor("zero_mean_75_cast_fp16")]; + tensor zero_mean_sq_75_cast_fp16 = mul(x = zero_mean_75_cast_fp16, y = zero_mean_75_cast_fp16)[name = tensor("zero_mean_sq_75_cast_fp16")]; + tensor var_3808 = const()[name = tensor("op_3808"), val = tensor([1])]; + tensor var_3809_cast_fp16 = reduce_mean(axes = var_3808, keep_dims = var_3121, x = zero_mean_sq_75_cast_fp16)[name = tensor("op_3809_cast_fp16")]; + tensor var_3810_to_fp16 = const()[name = tensor("op_3810_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3811_cast_fp16 = add(x = var_3809_cast_fp16, y = var_3810_to_fp16)[name = tensor("op_3811_cast_fp16")]; + tensor denom_75_epsilon_0_to_fp16 = const()[name = tensor("denom_75_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_75_cast_fp16 = rsqrt(epsilon = denom_75_epsilon_0_to_fp16, x = var_3811_cast_fp16)[name = tensor("denom_75_cast_fp16")]; + tensor out_75_cast_fp16 = mul(x = zero_mean_75_cast_fp16, y = denom_75_cast_fp16)[name = tensor("out_75_cast_fp16")]; + tensor var_3815_to_fp16 = const()[name = tensor("op_3815_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1670957440)))]; + tensor var_3816_cast_fp16 = add(x = out_75_cast_fp16, y = var_3815_to_fp16)[name = tensor("op_3816_cast_fp16")]; + tensor var_3818_to_fp16 = const()[name = tensor("op_3818_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1670958784)))]; + tensor hidden_states_269_cast_fp16 = mul(x = var_3816_cast_fp16, y = var_3818_to_fp16)[name = tensor("hidden_states_269_cast_fp16")]; + tensor var_3825 = const()[name = tensor("op_3825"), val = tensor([1, 1])]; + tensor var_3827 = const()[name = tensor("op_3827"), val = tensor([1, 1])]; + tensor q_51_pad_type_0 = const()[name = tensor("q_51_pad_type_0"), val = tensor("custom")]; + tensor q_51_pad_0 = const()[name = tensor("q_51_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1670960128)))]; + tensor q_51_cast_fp16 = conv(dilations = var_3827, groups = var_3126, pad = q_51_pad_0, pad_type = q_51_pad_type_0, strides = var_3825, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_269_cast_fp16)[name = tensor("q_51_cast_fp16")]; + tensor var_3831 = const()[name = tensor("op_3831"), val = tensor([1, 1])]; + tensor var_3833 = const()[name = tensor("op_3833"), val = tensor([1, 1])]; + tensor k_51_pad_type_0 = const()[name = tensor("k_51_pad_type_0"), val = tensor("custom")]; + tensor k_51_pad_0 = const()[name = tensor("k_51_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1671779392)))]; + tensor k_51_cast_fp16 = conv(dilations = var_3833, groups = var_3126, pad = k_51_pad_0, pad_type = k_51_pad_type_0, strides = var_3831, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_51_cast_fp16")]; + tensor var_3837 = const()[name = tensor("op_3837"), val = tensor([1, 1])]; + tensor var_3839 = const()[name = tensor("op_3839"), val = tensor([1, 1])]; + tensor v_51_pad_type_0 = const()[name = tensor("v_51_pad_type_0"), val = tensor("custom")]; + tensor v_51_pad_0 = const()[name = tensor("v_51_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1673090176)))]; + tensor v_51_cast_fp16 = conv(dilations = var_3839, groups = var_3126, pad = v_51_pad_0, pad_type = v_51_pad_type_0, strides = var_3837, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_51_cast_fp16")]; + tensor var_3843 = const()[name = tensor("op_3843"), val = tensor([2, 10, 64, -1])]; + tensor var_3844_cast_fp16 = reshape(shape = var_3843, x = q_51_cast_fp16)[name = tensor("op_3844_cast_fp16")]; + tensor var_3845 = const()[name = tensor("op_3845"), val = tensor([2, 10, 64, -1])]; + tensor var_3846_cast_fp16 = reshape(shape = var_3845, x = k_51_cast_fp16)[name = tensor("op_3846_cast_fp16")]; + tensor var_3847 = const()[name = tensor("op_3847"), val = tensor([2, 10, 64, -1])]; + tensor var_3848_cast_fp16 = reshape(shape = var_3847, x = v_51_cast_fp16)[name = tensor("op_3848_cast_fp16")]; + tensor attn_weights_101_transpose_x_0 = const()[name = tensor("attn_weights_101_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_101_transpose_y_0 = const()[name = tensor("attn_weights_101_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_101_cast_fp16 = matmul(transpose_x = attn_weights_101_transpose_x_0, transpose_y = attn_weights_101_transpose_y_0, x = var_3844_cast_fp16, y = var_3846_cast_fp16)[name = tensor("attn_weights_101_cast_fp16")]; + tensor attn_weights_103_cast_fp16 = mul(x = attn_weights_101_cast_fp16, y = var_3117_to_fp16)[name = tensor("attn_weights_103_cast_fp16")]; + tensor var_3852_cast_fp16 = softmax(axis = var_3110, x = attn_weights_103_cast_fp16)[name = tensor("op_3852_cast_fp16")]; + tensor attn_51_transpose_x_0 = const()[name = tensor("attn_51_transpose_x_0"), val = tensor(false)]; + tensor attn_51_transpose_y_0 = const()[name = tensor("attn_51_transpose_y_0"), val = tensor(true)]; + tensor attn_51_cast_fp16 = matmul(transpose_x = attn_51_transpose_x_0, transpose_y = attn_51_transpose_y_0, x = var_3848_cast_fp16, y = var_3852_cast_fp16)[name = tensor("attn_51_cast_fp16")]; + tensor var_3856 = const()[name = tensor("op_3856"), val = tensor([2, 640, 1, -1])]; + tensor input_437_cast_fp16 = reshape(shape = var_3856, x = attn_51_cast_fp16)[name = tensor("input_437_cast_fp16")]; + tensor var_3861 = const()[name = tensor("op_3861"), val = tensor([1, 1])]; + tensor var_3863 = const()[name = tensor("op_3863"), val = tensor([1, 1])]; + tensor var_3865_pad_type_0 = const()[name = tensor("op_3865_pad_type_0"), val = tensor("custom")]; + tensor var_3865_pad_0 = const()[name = tensor("op_3865_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1674400960)))]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1675220224)))]; + tensor var_3865_cast_fp16 = conv(bias = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_3863, groups = var_3126, pad = var_3865_pad_0, pad_type = var_3865_pad_type_0, strides = var_3861, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_437_cast_fp16)[name = tensor("op_3865_cast_fp16")]; + tensor inputs_77_cast_fp16 = add(x = var_3865_cast_fp16, y = inputs_75_cast_fp16)[name = tensor("inputs_77_cast_fp16")]; + tensor var_3869 = const()[name = tensor("op_3869"), val = tensor([1])]; + tensor channels_mean_77_cast_fp16 = reduce_mean(axes = var_3869, keep_dims = var_3121, x = inputs_77_cast_fp16)[name = tensor("channels_mean_77_cast_fp16")]; + tensor zero_mean_77_cast_fp16 = sub(x = inputs_77_cast_fp16, y = channels_mean_77_cast_fp16)[name = tensor("zero_mean_77_cast_fp16")]; + tensor zero_mean_sq_77_cast_fp16 = mul(x = zero_mean_77_cast_fp16, y = zero_mean_77_cast_fp16)[name = tensor("zero_mean_sq_77_cast_fp16")]; + tensor var_3873 = const()[name = tensor("op_3873"), val = tensor([1])]; + tensor var_3874_cast_fp16 = reduce_mean(axes = var_3873, keep_dims = var_3121, x = zero_mean_sq_77_cast_fp16)[name = tensor("op_3874_cast_fp16")]; + tensor var_3875_to_fp16 = const()[name = tensor("op_3875_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3876_cast_fp16 = add(x = var_3874_cast_fp16, y = var_3875_to_fp16)[name = tensor("op_3876_cast_fp16")]; + tensor denom_77_epsilon_0_to_fp16 = const()[name = tensor("denom_77_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_77_cast_fp16 = rsqrt(epsilon = denom_77_epsilon_0_to_fp16, x = var_3876_cast_fp16)[name = tensor("denom_77_cast_fp16")]; + tensor out_77_cast_fp16 = mul(x = zero_mean_77_cast_fp16, y = denom_77_cast_fp16)[name = tensor("out_77_cast_fp16")]; + tensor var_3880_to_fp16 = const()[name = tensor("op_3880_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1675221568)))]; + tensor var_3881_cast_fp16 = add(x = out_77_cast_fp16, y = var_3880_to_fp16)[name = tensor("op_3881_cast_fp16")]; + tensor var_3883_to_fp16 = const()[name = tensor("op_3883_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1675222912)))]; + tensor input_439_cast_fp16 = mul(x = var_3881_cast_fp16, y = var_3883_to_fp16)[name = tensor("input_439_cast_fp16")]; + tensor var_3891 = const()[name = tensor("op_3891"), val = tensor([1, 1])]; + tensor var_3893 = const()[name = tensor("op_3893"), val = tensor([1, 1])]; + tensor var_3895_pad_type_0 = const()[name = tensor("op_3895_pad_type_0"), val = tensor("custom")]; + tensor var_3895_pad_0 = const()[name = tensor("op_3895_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1675224256)))]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1681777920)))]; + tensor var_3895_cast_fp16 = conv(bias = up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_3893, groups = var_3126, pad = var_3895_pad_0, pad_type = var_3895_pad_type_0, strides = var_3891, weight = up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_439_cast_fp16)[name = tensor("op_3895_cast_fp16")]; + tensor var_3896_split_sizes_0 = const()[name = tensor("op_3896_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_3896_axis_0 = const()[name = tensor("op_3896_axis_0"), val = tensor(1)]; + tensor var_3896_cast_fp16_0, tensor var_3896_cast_fp16_1 = split(axis = var_3896_axis_0, split_sizes = var_3896_split_sizes_0, x = var_3895_cast_fp16)[name = tensor("op_3896_cast_fp16")]; + tensor var_3898_mode_0 = const()[name = tensor("op_3898_mode_0"), val = tensor("EXACT")]; + tensor var_3898_cast_fp16 = gelu(mode = var_3898_mode_0, x = var_3896_cast_fp16_1)[name = tensor("op_3898_cast_fp16")]; + tensor input_441_cast_fp16 = mul(x = var_3896_cast_fp16_0, y = var_3898_cast_fp16)[name = tensor("input_441_cast_fp16")]; + tensor var_3902 = const()[name = tensor("op_3902"), val = tensor([1, 1])]; + tensor var_3904 = const()[name = tensor("op_3904"), val = tensor([1, 1])]; + tensor var_3906_pad_type_0 = const()[name = tensor("op_3906_pad_type_0"), val = tensor("custom")]; + tensor var_3906_pad_0 = const()[name = tensor("op_3906_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1681788224)))]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1685065088)))]; + tensor var_3906_cast_fp16 = conv(bias = up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_3904, groups = var_3126, pad = var_3906_pad_0, pad_type = var_3906_pad_type_0, strides = var_3902, weight = up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_441_cast_fp16)[name = tensor("op_3906_cast_fp16")]; + tensor hidden_states_273_cast_fp16 = add(x = var_3906_cast_fp16, y = inputs_77_cast_fp16)[name = tensor("hidden_states_273_cast_fp16")]; + tensor var_3908 = const()[name = tensor("op_3908"), val = tensor([2, 640, 24, 40])]; + tensor input_443_cast_fp16 = reshape(shape = var_3908, x = hidden_states_273_cast_fp16)[name = tensor("input_443_cast_fp16")]; + tensor var_3912 = const()[name = tensor("op_3912"), val = tensor([1, 1])]; + tensor var_3914 = const()[name = tensor("op_3914"), val = tensor([1, 1])]; + tensor hidden_states_275_pad_type_0 = const()[name = tensor("hidden_states_275_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_275_pad_0 = const()[name = tensor("hidden_states_275_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1685066432)))]; + tensor up_blocks_2_attentions_2_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1685885696)))]; + tensor hidden_states_275_cast_fp16 = conv(bias = up_blocks_2_attentions_2_proj_out_bias_to_fp16, dilations = var_3914, groups = var_3126, pad = hidden_states_275_pad_0, pad_type = hidden_states_275_pad_type_0, strides = var_3912, weight = up_blocks_2_attentions_2_proj_out_weight_to_fp16, x = input_443_cast_fp16)[name = tensor("hidden_states_275_cast_fp16")]; + tensor input_445_cast_fp16 = add(x = hidden_states_275_cast_fp16, y = hidden_states_263_cast_fp16)[name = tensor("input_445_cast_fp16")]; + tensor input_447_scale_factor_height_0 = const()[name = tensor("input_447_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_447_scale_factor_width_0 = const()[name = tensor("input_447_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor input_447_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = input_447_scale_factor_height_0, scale_factor_width = input_447_scale_factor_width_0, x = input_445_cast_fp16)[name = tensor("input_447_cast_fp16")]; + tensor var_3923 = const()[name = tensor("op_3923"), val = tensor([1, 1])]; + tensor var_3925 = const()[name = tensor("op_3925"), val = tensor([1, 1])]; + tensor hidden_states_277_pad_type_0 = const()[name = tensor("hidden_states_277_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_277_pad_0 = const()[name = tensor("hidden_states_277_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_upsamplers_0_conv_weight_to_fp16 = const()[name = tensor("up_blocks_2_upsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1685887040)))]; + tensor up_blocks_2_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("up_blocks_2_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1693259904)))]; + tensor hidden_states_277_cast_fp16 = conv(bias = up_blocks_2_upsamplers_0_conv_bias_to_fp16, dilations = var_3925, groups = var_3126, pad = hidden_states_277_pad_0, pad_type = hidden_states_277_pad_type_0, strides = var_3923, weight = up_blocks_2_upsamplers_0_conv_weight_to_fp16, x = input_447_cast_fp16)[name = tensor("hidden_states_277_cast_fp16")]; + tensor var_3929 = const()[name = tensor("op_3929"), val = tensor(3)]; + tensor var_3940 = const()[name = tensor("op_3940"), val = tensor(true)]; + tensor var_3945 = const()[name = tensor("op_3945"), val = tensor(1)]; + tensor input_449_interleave_0 = const()[name = tensor("input_449_interleave_0"), val = tensor(false)]; + tensor input_449_cast_fp16 = concat(axis = var_3945, interleave = input_449_interleave_0, values = (hidden_states_277_cast_fp16, input_61_cast_fp16))[name = tensor("input_449_cast_fp16")]; + tensor reshape_204_shape_0 = const()[name = tensor("reshape_204_shape_0"), val = tensor([2, 32, 30, 48, 80])]; + tensor reshape_204_cast_fp16 = reshape(shape = reshape_204_shape_0, x = input_449_cast_fp16)[name = tensor("reshape_204_cast_fp16")]; + tensor reduce_mean_153_axes_0 = const()[name = tensor("reduce_mean_153_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_153_keep_dims_0 = const()[name = tensor("reduce_mean_153_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_153_cast_fp16 = reduce_mean(axes = reduce_mean_153_axes_0, keep_dims = reduce_mean_153_keep_dims_0, x = reshape_204_cast_fp16)[name = tensor("reduce_mean_153_cast_fp16")]; + tensor sub_102_cast_fp16 = sub(x = reshape_204_cast_fp16, y = reduce_mean_153_cast_fp16)[name = tensor("sub_102_cast_fp16")]; + tensor square_51_cast_fp16 = square(x = sub_102_cast_fp16)[name = tensor("square_51_cast_fp16")]; + tensor reduce_mean_155_axes_0 = const()[name = tensor("reduce_mean_155_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_155_keep_dims_0 = const()[name = tensor("reduce_mean_155_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_155_cast_fp16 = reduce_mean(axes = reduce_mean_155_axes_0, keep_dims = reduce_mean_155_keep_dims_0, x = square_51_cast_fp16)[name = tensor("reduce_mean_155_cast_fp16")]; + tensor add_102_y_0_to_fp16 = const()[name = tensor("add_102_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_102_cast_fp16 = add(x = reduce_mean_155_cast_fp16, y = add_102_y_0_to_fp16)[name = tensor("add_102_cast_fp16")]; + tensor sqrt_51_cast_fp16 = sqrt(x = add_102_cast_fp16)[name = tensor("sqrt_51_cast_fp16")]; + tensor real_div_51_cast_fp16 = real_div(x = sub_102_cast_fp16, y = sqrt_51_cast_fp16)[name = tensor("real_div_51_cast_fp16")]; + tensor reshape_205_shape_0 = const()[name = tensor("reshape_205_shape_0"), val = tensor([2, 960, 48, 80])]; + tensor reshape_205_cast_fp16 = reshape(shape = reshape_205_shape_0, x = real_div_51_cast_fp16)[name = tensor("reshape_205_cast_fp16")]; + tensor add_103_gamma_0_to_fp16 = const()[name = tensor("add_103_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1693261248)))]; + tensor add_103_beta_0_to_fp16 = const()[name = tensor("add_103_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1693263232)))]; + tensor add_103_epsilon_0_to_fp16 = const()[name = tensor("add_103_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_103_cast_fp16 = batch_norm(beta = add_103_beta_0_to_fp16, epsilon = add_103_epsilon_0_to_fp16, gamma = add_103_gamma_0_to_fp16, mean = add_97_mean_0_to_fp16, variance = add_97_variance_0_to_fp16, x = reshape_205_cast_fp16)[name = tensor("add_103_cast_fp16")]; + tensor input_453_cast_fp16 = silu(x = add_103_cast_fp16)[name = tensor("input_453_cast_fp16")]; + tensor var_3972 = const()[name = tensor("op_3972"), val = tensor([1, 1])]; + tensor var_3974 = const()[name = tensor("op_3974"), val = tensor([1, 1])]; + tensor hidden_states_279_pad_type_0 = const()[name = tensor("hidden_states_279_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_279_pad_0 = const()[name = tensor("hidden_states_279_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1693265216)))]; + tensor up_blocks_3_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1698794880)))]; + tensor hidden_states_279_cast_fp16 = conv(bias = up_blocks_3_resnets_0_conv1_bias_to_fp16, dilations = var_3974, groups = var_3945, pad = hidden_states_279_pad_0, pad_type = hidden_states_279_pad_type_0, strides = var_3972, weight = up_blocks_3_resnets_0_conv1_weight_to_fp16, x = input_453_cast_fp16)[name = tensor("hidden_states_279_cast_fp16")]; + tensor var_3980 = const()[name = tensor("op_3980"), val = tensor([1, 1])]; + tensor var_3982 = const()[name = tensor("op_3982"), val = tensor([1, 1])]; + tensor temb_39_pad_type_0 = const()[name = tensor("temb_39_pad_type_0"), val = tensor("custom")]; + tensor temb_39_pad_0 = const()[name = tensor("temb_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1698795584)))]; + tensor up_blocks_3_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1699614848)))]; + tensor temb_39_cast_fp16 = conv(bias = up_blocks_3_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_3982, groups = var_3945, pad = temb_39_pad_0, pad_type = temb_39_pad_type_0, strides = var_3980, weight = up_blocks_3_resnets_0_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16)[name = tensor("temb_39_cast_fp16")]; + tensor input_457_cast_fp16 = add(x = hidden_states_279_cast_fp16, y = temb_39_cast_fp16)[name = tensor("input_457_cast_fp16")]; + tensor reshape_208_shape_0 = const()[name = tensor("reshape_208_shape_0"), val = tensor([2, 32, 10, 48, 80])]; + tensor reshape_208_cast_fp16 = reshape(shape = reshape_208_shape_0, x = input_457_cast_fp16)[name = tensor("reshape_208_cast_fp16")]; + tensor reduce_mean_156_axes_0 = const()[name = tensor("reduce_mean_156_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_156_keep_dims_0 = const()[name = tensor("reduce_mean_156_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_156_cast_fp16 = reduce_mean(axes = reduce_mean_156_axes_0, keep_dims = reduce_mean_156_keep_dims_0, x = reshape_208_cast_fp16)[name = tensor("reduce_mean_156_cast_fp16")]; + tensor sub_104_cast_fp16 = sub(x = reshape_208_cast_fp16, y = reduce_mean_156_cast_fp16)[name = tensor("sub_104_cast_fp16")]; + tensor square_52_cast_fp16 = square(x = sub_104_cast_fp16)[name = tensor("square_52_cast_fp16")]; + tensor reduce_mean_158_axes_0 = const()[name = tensor("reduce_mean_158_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_158_keep_dims_0 = const()[name = tensor("reduce_mean_158_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_158_cast_fp16 = reduce_mean(axes = reduce_mean_158_axes_0, keep_dims = reduce_mean_158_keep_dims_0, x = square_52_cast_fp16)[name = tensor("reduce_mean_158_cast_fp16")]; + tensor add_104_y_0_to_fp16 = const()[name = tensor("add_104_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_104_cast_fp16 = add(x = reduce_mean_158_cast_fp16, y = add_104_y_0_to_fp16)[name = tensor("add_104_cast_fp16")]; + tensor sqrt_52_cast_fp16 = sqrt(x = add_104_cast_fp16)[name = tensor("sqrt_52_cast_fp16")]; + tensor real_div_52_cast_fp16 = real_div(x = sub_104_cast_fp16, y = sqrt_52_cast_fp16)[name = tensor("real_div_52_cast_fp16")]; + tensor reshape_209_shape_0 = const()[name = tensor("reshape_209_shape_0"), val = tensor([2, 320, 48, 80])]; + tensor reshape_209_cast_fp16 = reshape(shape = reshape_209_shape_0, x = real_div_52_cast_fp16)[name = tensor("reshape_209_cast_fp16")]; + tensor add_105_gamma_0_to_fp16 = const()[name = tensor("add_105_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1699615552)))]; + tensor add_105_beta_0_to_fp16 = const()[name = tensor("add_105_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1699616256)))]; + tensor add_105_epsilon_0_to_fp16 = const()[name = tensor("add_105_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_105_cast_fp16 = batch_norm(beta = add_105_beta_0_to_fp16, epsilon = add_105_epsilon_0_to_fp16, gamma = add_105_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_209_cast_fp16)[name = tensor("add_105_cast_fp16")]; + tensor input_461_cast_fp16 = silu(x = add_105_cast_fp16)[name = tensor("input_461_cast_fp16")]; + tensor var_3992 = const()[name = tensor("op_3992"), val = tensor([1, 1])]; + tensor var_3994 = const()[name = tensor("op_3994"), val = tensor([1, 1])]; + tensor hidden_states_281_pad_type_0 = const()[name = tensor("hidden_states_281_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_281_pad_0 = const()[name = tensor("hidden_states_281_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1699616960)))]; + tensor up_blocks_3_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1701460224)))]; + tensor hidden_states_281_cast_fp16 = conv(bias = up_blocks_3_resnets_0_conv2_bias_to_fp16, dilations = var_3994, groups = var_3945, pad = hidden_states_281_pad_0, pad_type = hidden_states_281_pad_type_0, strides = var_3992, weight = up_blocks_3_resnets_0_conv2_weight_to_fp16, x = input_461_cast_fp16)[name = tensor("hidden_states_281_cast_fp16")]; + tensor var_3999 = const()[name = tensor("op_3999"), val = tensor([1, 1])]; + tensor var_4001 = const()[name = tensor("op_4001"), val = tensor([1, 1])]; + tensor x_23_pad_type_0 = const()[name = tensor("x_23_pad_type_0"), val = tensor("custom")]; + tensor x_23_pad_0 = const()[name = tensor("x_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1701460928)))]; + tensor up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1702075392)))]; + tensor x_23_cast_fp16 = conv(bias = up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_4001, groups = var_3945, pad = x_23_pad_0, pad_type = x_23_pad_type_0, strides = var_3999, weight = up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16, x = input_449_cast_fp16)[name = tensor("x_23_cast_fp16")]; + tensor hidden_states_283_cast_fp16 = add(x = x_23_cast_fp16, y = hidden_states_281_cast_fp16)[name = tensor("hidden_states_283_cast_fp16")]; + tensor reshape_212_shape_0 = const()[name = tensor("reshape_212_shape_0"), val = tensor([2, 32, 10, 48, 80])]; + tensor reshape_212_cast_fp16 = reshape(shape = reshape_212_shape_0, x = hidden_states_283_cast_fp16)[name = tensor("reshape_212_cast_fp16")]; + tensor reduce_mean_159_axes_0 = const()[name = tensor("reduce_mean_159_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_159_keep_dims_0 = const()[name = tensor("reduce_mean_159_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_159_cast_fp16 = reduce_mean(axes = reduce_mean_159_axes_0, keep_dims = reduce_mean_159_keep_dims_0, x = reshape_212_cast_fp16)[name = tensor("reduce_mean_159_cast_fp16")]; + tensor sub_106_cast_fp16 = sub(x = reshape_212_cast_fp16, y = reduce_mean_159_cast_fp16)[name = tensor("sub_106_cast_fp16")]; + tensor square_53_cast_fp16 = square(x = sub_106_cast_fp16)[name = tensor("square_53_cast_fp16")]; + tensor reduce_mean_161_axes_0 = const()[name = tensor("reduce_mean_161_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_161_keep_dims_0 = const()[name = tensor("reduce_mean_161_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_161_cast_fp16 = reduce_mean(axes = reduce_mean_161_axes_0, keep_dims = reduce_mean_161_keep_dims_0, x = square_53_cast_fp16)[name = tensor("reduce_mean_161_cast_fp16")]; + tensor add_106_y_0_to_fp16 = const()[name = tensor("add_106_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_106_cast_fp16 = add(x = reduce_mean_161_cast_fp16, y = add_106_y_0_to_fp16)[name = tensor("add_106_cast_fp16")]; + tensor sqrt_53_cast_fp16 = sqrt(x = add_106_cast_fp16)[name = tensor("sqrt_53_cast_fp16")]; + tensor real_div_53_cast_fp16 = real_div(x = sub_106_cast_fp16, y = sqrt_53_cast_fp16)[name = tensor("real_div_53_cast_fp16")]; + tensor reshape_213_shape_0 = const()[name = tensor("reshape_213_shape_0"), val = tensor([2, 320, 48, 80])]; + tensor reshape_213_cast_fp16 = reshape(shape = reshape_213_shape_0, x = real_div_53_cast_fp16)[name = tensor("reshape_213_cast_fp16")]; + tensor add_107_gamma_0_to_fp16 = const()[name = tensor("add_107_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1702076096)))]; + tensor add_107_beta_0_to_fp16 = const()[name = tensor("add_107_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1702076800)))]; + tensor add_107_epsilon_0_to_fp16 = const()[name = tensor("add_107_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_107_cast_fp16 = batch_norm(beta = add_107_beta_0_to_fp16, epsilon = add_107_epsilon_0_to_fp16, gamma = add_107_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_213_cast_fp16)[name = tensor("add_107_cast_fp16")]; + tensor var_4021 = const()[name = tensor("op_4021"), val = tensor([1, 1])]; + tensor var_4023 = const()[name = tensor("op_4023"), val = tensor([1, 1])]; + tensor hidden_states_285_pad_type_0 = const()[name = tensor("hidden_states_285_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_285_pad_0 = const()[name = tensor("hidden_states_285_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1702077504)))]; + tensor up_blocks_3_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1702282368)))]; + tensor hidden_states_285_cast_fp16 = conv(bias = up_blocks_3_attentions_0_proj_in_bias_to_fp16, dilations = var_4023, groups = var_3945, pad = hidden_states_285_pad_0, pad_type = hidden_states_285_pad_type_0, strides = var_4021, weight = up_blocks_3_attentions_0_proj_in_weight_to_fp16, x = add_107_cast_fp16)[name = tensor("hidden_states_285_cast_fp16")]; + tensor var_4028 = const()[name = tensor("op_4028"), val = tensor([2, 320, 1, 3840])]; + tensor inputs_79_cast_fp16 = reshape(shape = var_4028, x = hidden_states_285_cast_fp16)[name = tensor("inputs_79_cast_fp16")]; + tensor var_4038 = const()[name = tensor("op_4038"), val = tensor([1])]; + tensor channels_mean_79_cast_fp16 = reduce_mean(axes = var_4038, keep_dims = var_3940, x = inputs_79_cast_fp16)[name = tensor("channels_mean_79_cast_fp16")]; + tensor zero_mean_79_cast_fp16 = sub(x = inputs_79_cast_fp16, y = channels_mean_79_cast_fp16)[name = tensor("zero_mean_79_cast_fp16")]; + tensor zero_mean_sq_79_cast_fp16 = mul(x = zero_mean_79_cast_fp16, y = zero_mean_79_cast_fp16)[name = tensor("zero_mean_sq_79_cast_fp16")]; + tensor var_4042 = const()[name = tensor("op_4042"), val = tensor([1])]; + tensor var_4043_cast_fp16 = reduce_mean(axes = var_4042, keep_dims = var_3940, x = zero_mean_sq_79_cast_fp16)[name = tensor("op_4043_cast_fp16")]; + tensor var_4044_to_fp16 = const()[name = tensor("op_4044_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4045_cast_fp16 = add(x = var_4043_cast_fp16, y = var_4044_to_fp16)[name = tensor("op_4045_cast_fp16")]; + tensor denom_79_epsilon_0_to_fp16 = const()[name = tensor("denom_79_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_79_cast_fp16 = rsqrt(epsilon = denom_79_epsilon_0_to_fp16, x = var_4045_cast_fp16)[name = tensor("denom_79_cast_fp16")]; + tensor out_79_cast_fp16 = mul(x = zero_mean_79_cast_fp16, y = denom_79_cast_fp16)[name = tensor("out_79_cast_fp16")]; + tensor var_4049_to_fp16 = const()[name = tensor("op_4049_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1702283072)))]; + tensor var_4050_cast_fp16 = add(x = out_79_cast_fp16, y = var_4049_to_fp16)[name = tensor("op_4050_cast_fp16")]; + tensor var_4052_to_fp16 = const()[name = tensor("op_4052_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1702283776)))]; + tensor hidden_states_287_cast_fp16 = mul(x = var_4050_cast_fp16, y = var_4052_to_fp16)[name = tensor("hidden_states_287_cast_fp16")]; + tensor var_4059 = const()[name = tensor("op_4059"), val = tensor([1, 1])]; + tensor var_4061 = const()[name = tensor("op_4061"), val = tensor([1, 1])]; + tensor q_53_pad_type_0 = const()[name = tensor("q_53_pad_type_0"), val = tensor("custom")]; + tensor q_53_pad_0 = const()[name = tensor("q_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1702284480)))]; + tensor q_53_cast_fp16 = conv(dilations = var_4061, groups = var_3945, pad = q_53_pad_0, pad_type = q_53_pad_type_0, strides = var_4059, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_287_cast_fp16)[name = tensor("q_53_cast_fp16")]; + tensor var_4065 = const()[name = tensor("op_4065"), val = tensor([1, 1])]; + tensor var_4067 = const()[name = tensor("op_4067"), val = tensor([1, 1])]; + tensor k_53_pad_type_0 = const()[name = tensor("k_53_pad_type_0"), val = tensor("custom")]; + tensor k_53_pad_0 = const()[name = tensor("k_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1702489344)))]; + tensor k_53_cast_fp16 = conv(dilations = var_4067, groups = var_3945, pad = k_53_pad_0, pad_type = k_53_pad_type_0, strides = var_4065, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_287_cast_fp16)[name = tensor("k_53_cast_fp16")]; + tensor var_4071 = const()[name = tensor("op_4071"), val = tensor([1, 1])]; + tensor var_4073 = const()[name = tensor("op_4073"), val = tensor([1, 1])]; + tensor v_53_pad_type_0 = const()[name = tensor("v_53_pad_type_0"), val = tensor("custom")]; + tensor v_53_pad_0 = const()[name = tensor("v_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1702694208)))]; + tensor v_53_cast_fp16 = conv(dilations = var_4073, groups = var_3945, pad = v_53_pad_0, pad_type = v_53_pad_type_0, strides = var_4071, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_287_cast_fp16)[name = tensor("v_53_cast_fp16")]; + tensor var_4077 = const()[name = tensor("op_4077"), val = tensor([2, 5, 64, -1])]; + tensor var_4078_cast_fp16 = reshape(shape = var_4077, x = q_53_cast_fp16)[name = tensor("op_4078_cast_fp16")]; + tensor var_4079 = const()[name = tensor("op_4079"), val = tensor([2, 5, 64, -1])]; + tensor var_4080_cast_fp16 = reshape(shape = var_4079, x = k_53_cast_fp16)[name = tensor("op_4080_cast_fp16")]; + tensor var_4081 = const()[name = tensor("op_4081"), val = tensor([2, 5, 64, -1])]; + tensor var_4082_cast_fp16 = reshape(shape = var_4081, x = v_53_cast_fp16)[name = tensor("op_4082_cast_fp16")]; + tensor attn_weights_105_transpose_x_0 = const()[name = tensor("attn_weights_105_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_105_transpose_y_0 = const()[name = tensor("attn_weights_105_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_105_cast_fp16 = matmul(transpose_x = attn_weights_105_transpose_x_0, transpose_y = attn_weights_105_transpose_y_0, x = var_4078_cast_fp16, y = var_4080_cast_fp16)[name = tensor("attn_weights_105_cast_fp16")]; + tensor var_3936_to_fp16 = const()[name = tensor("op_3936_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_107_cast_fp16 = mul(x = attn_weights_105_cast_fp16, y = var_3936_to_fp16)[name = tensor("attn_weights_107_cast_fp16")]; + tensor var_4086_cast_fp16 = softmax(axis = var_3929, x = attn_weights_107_cast_fp16)[name = tensor("op_4086_cast_fp16")]; + tensor attn_53_transpose_x_0 = const()[name = tensor("attn_53_transpose_x_0"), val = tensor(false)]; + tensor attn_53_transpose_y_0 = const()[name = tensor("attn_53_transpose_y_0"), val = tensor(true)]; + tensor attn_53_cast_fp16 = matmul(transpose_x = attn_53_transpose_x_0, transpose_y = attn_53_transpose_y_0, x = var_4082_cast_fp16, y = var_4086_cast_fp16)[name = tensor("attn_53_cast_fp16")]; + tensor var_4090 = const()[name = tensor("op_4090"), val = tensor([2, 320, 1, -1])]; + tensor input_465_cast_fp16 = reshape(shape = var_4090, x = attn_53_cast_fp16)[name = tensor("input_465_cast_fp16")]; + tensor var_4095 = const()[name = tensor("op_4095"), val = tensor([1, 1])]; + tensor var_4097 = const()[name = tensor("op_4097"), val = tensor([1, 1])]; + tensor var_4099_pad_type_0 = const()[name = tensor("op_4099_pad_type_0"), val = tensor("custom")]; + tensor var_4099_pad_0 = const()[name = tensor("op_4099_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1702899072)))]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1703103936)))]; + tensor var_4099_cast_fp16 = conv(bias = up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_4097, groups = var_3945, pad = var_4099_pad_0, pad_type = var_4099_pad_type_0, strides = var_4095, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_465_cast_fp16)[name = tensor("op_4099_cast_fp16")]; + tensor inputs_81_cast_fp16 = add(x = var_4099_cast_fp16, y = inputs_79_cast_fp16)[name = tensor("inputs_81_cast_fp16")]; + tensor var_4103 = const()[name = tensor("op_4103"), val = tensor([1])]; + tensor channels_mean_81_cast_fp16 = reduce_mean(axes = var_4103, keep_dims = var_3940, x = inputs_81_cast_fp16)[name = tensor("channels_mean_81_cast_fp16")]; + tensor zero_mean_81_cast_fp16 = sub(x = inputs_81_cast_fp16, y = channels_mean_81_cast_fp16)[name = tensor("zero_mean_81_cast_fp16")]; + tensor zero_mean_sq_81_cast_fp16 = mul(x = zero_mean_81_cast_fp16, y = zero_mean_81_cast_fp16)[name = tensor("zero_mean_sq_81_cast_fp16")]; + tensor var_4107 = const()[name = tensor("op_4107"), val = tensor([1])]; + tensor var_4108_cast_fp16 = reduce_mean(axes = var_4107, keep_dims = var_3940, x = zero_mean_sq_81_cast_fp16)[name = tensor("op_4108_cast_fp16")]; + tensor var_4109_to_fp16 = const()[name = tensor("op_4109_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4110_cast_fp16 = add(x = var_4108_cast_fp16, y = var_4109_to_fp16)[name = tensor("op_4110_cast_fp16")]; + tensor denom_81_epsilon_0_to_fp16 = const()[name = tensor("denom_81_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_81_cast_fp16 = rsqrt(epsilon = denom_81_epsilon_0_to_fp16, x = var_4110_cast_fp16)[name = tensor("denom_81_cast_fp16")]; + tensor out_81_cast_fp16 = mul(x = zero_mean_81_cast_fp16, y = denom_81_cast_fp16)[name = tensor("out_81_cast_fp16")]; + tensor var_4114_to_fp16 = const()[name = tensor("op_4114_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1703104640)))]; + tensor var_4115_cast_fp16 = add(x = out_81_cast_fp16, y = var_4114_to_fp16)[name = tensor("op_4115_cast_fp16")]; + tensor var_4117_to_fp16 = const()[name = tensor("op_4117_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1703105344)))]; + tensor hidden_states_289_cast_fp16 = mul(x = var_4115_cast_fp16, y = var_4117_to_fp16)[name = tensor("hidden_states_289_cast_fp16")]; + tensor var_4124 = const()[name = tensor("op_4124"), val = tensor([1, 1])]; + tensor var_4126 = const()[name = tensor("op_4126"), val = tensor([1, 1])]; + tensor q_55_pad_type_0 = const()[name = tensor("q_55_pad_type_0"), val = tensor("custom")]; + tensor q_55_pad_0 = const()[name = tensor("q_55_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1703106048)))]; + tensor q_55_cast_fp16 = conv(dilations = var_4126, groups = var_3945, pad = q_55_pad_0, pad_type = q_55_pad_type_0, strides = var_4124, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_289_cast_fp16)[name = tensor("q_55_cast_fp16")]; + tensor var_4130 = const()[name = tensor("op_4130"), val = tensor([1, 1])]; + tensor var_4132 = const()[name = tensor("op_4132"), val = tensor([1, 1])]; + tensor k_55_pad_type_0 = const()[name = tensor("k_55_pad_type_0"), val = tensor("custom")]; + tensor k_55_pad_0 = const()[name = tensor("k_55_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1703310912)))]; + tensor k_55_cast_fp16 = conv(dilations = var_4132, groups = var_3945, pad = k_55_pad_0, pad_type = k_55_pad_type_0, strides = var_4130, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_55_cast_fp16")]; + tensor var_4136 = const()[name = tensor("op_4136"), val = tensor([1, 1])]; + tensor var_4138 = const()[name = tensor("op_4138"), val = tensor([1, 1])]; + tensor v_55_pad_type_0 = const()[name = tensor("v_55_pad_type_0"), val = tensor("custom")]; + tensor v_55_pad_0 = const()[name = tensor("v_55_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1703966336)))]; + tensor v_55_cast_fp16 = conv(dilations = var_4138, groups = var_3945, pad = v_55_pad_0, pad_type = v_55_pad_type_0, strides = var_4136, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_55_cast_fp16")]; + tensor var_4142 = const()[name = tensor("op_4142"), val = tensor([2, 5, 64, -1])]; + tensor var_4143_cast_fp16 = reshape(shape = var_4142, x = q_55_cast_fp16)[name = tensor("op_4143_cast_fp16")]; + tensor var_4144 = const()[name = tensor("op_4144"), val = tensor([2, 5, 64, -1])]; + tensor var_4145_cast_fp16 = reshape(shape = var_4144, x = k_55_cast_fp16)[name = tensor("op_4145_cast_fp16")]; + tensor var_4146 = const()[name = tensor("op_4146"), val = tensor([2, 5, 64, -1])]; + tensor var_4147_cast_fp16 = reshape(shape = var_4146, x = v_55_cast_fp16)[name = tensor("op_4147_cast_fp16")]; + tensor attn_weights_109_transpose_x_0 = const()[name = tensor("attn_weights_109_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_109_transpose_y_0 = const()[name = tensor("attn_weights_109_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_109_cast_fp16 = matmul(transpose_x = attn_weights_109_transpose_x_0, transpose_y = attn_weights_109_transpose_y_0, x = var_4143_cast_fp16, y = var_4145_cast_fp16)[name = tensor("attn_weights_109_cast_fp16")]; + tensor attn_weights_111_cast_fp16 = mul(x = attn_weights_109_cast_fp16, y = var_3936_to_fp16)[name = tensor("attn_weights_111_cast_fp16")]; + tensor var_4151_cast_fp16 = softmax(axis = var_3929, x = attn_weights_111_cast_fp16)[name = tensor("op_4151_cast_fp16")]; + tensor attn_55_transpose_x_0 = const()[name = tensor("attn_55_transpose_x_0"), val = tensor(false)]; + tensor attn_55_transpose_y_0 = const()[name = tensor("attn_55_transpose_y_0"), val = tensor(true)]; + tensor attn_55_cast_fp16 = matmul(transpose_x = attn_55_transpose_x_0, transpose_y = attn_55_transpose_y_0, x = var_4147_cast_fp16, y = var_4151_cast_fp16)[name = tensor("attn_55_cast_fp16")]; + tensor var_4155 = const()[name = tensor("op_4155"), val = tensor([2, 320, 1, -1])]; + tensor input_467_cast_fp16 = reshape(shape = var_4155, x = attn_55_cast_fp16)[name = tensor("input_467_cast_fp16")]; + tensor var_4160 = const()[name = tensor("op_4160"), val = tensor([1, 1])]; + tensor var_4162 = const()[name = tensor("op_4162"), val = tensor([1, 1])]; + tensor var_4164_pad_type_0 = const()[name = tensor("op_4164_pad_type_0"), val = tensor("custom")]; + tensor var_4164_pad_0 = const()[name = tensor("op_4164_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1704621760)))]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1704826624)))]; + tensor var_4164_cast_fp16 = conv(bias = up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_4162, groups = var_3945, pad = var_4164_pad_0, pad_type = var_4164_pad_type_0, strides = var_4160, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_467_cast_fp16)[name = tensor("op_4164_cast_fp16")]; + tensor inputs_83_cast_fp16 = add(x = var_4164_cast_fp16, y = inputs_81_cast_fp16)[name = tensor("inputs_83_cast_fp16")]; + tensor var_4168 = const()[name = tensor("op_4168"), val = tensor([1])]; + tensor channels_mean_83_cast_fp16 = reduce_mean(axes = var_4168, keep_dims = var_3940, x = inputs_83_cast_fp16)[name = tensor("channels_mean_83_cast_fp16")]; + tensor zero_mean_83_cast_fp16 = sub(x = inputs_83_cast_fp16, y = channels_mean_83_cast_fp16)[name = tensor("zero_mean_83_cast_fp16")]; + tensor zero_mean_sq_83_cast_fp16 = mul(x = zero_mean_83_cast_fp16, y = zero_mean_83_cast_fp16)[name = tensor("zero_mean_sq_83_cast_fp16")]; + tensor var_4172 = const()[name = tensor("op_4172"), val = tensor([1])]; + tensor var_4173_cast_fp16 = reduce_mean(axes = var_4172, keep_dims = var_3940, x = zero_mean_sq_83_cast_fp16)[name = tensor("op_4173_cast_fp16")]; + tensor var_4174_to_fp16 = const()[name = tensor("op_4174_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4175_cast_fp16 = add(x = var_4173_cast_fp16, y = var_4174_to_fp16)[name = tensor("op_4175_cast_fp16")]; + tensor denom_83_epsilon_0_to_fp16 = const()[name = tensor("denom_83_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_83_cast_fp16 = rsqrt(epsilon = denom_83_epsilon_0_to_fp16, x = var_4175_cast_fp16)[name = tensor("denom_83_cast_fp16")]; + tensor out_83_cast_fp16 = mul(x = zero_mean_83_cast_fp16, y = denom_83_cast_fp16)[name = tensor("out_83_cast_fp16")]; + tensor var_4179_to_fp16 = const()[name = tensor("op_4179_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1704827328)))]; + tensor var_4180_cast_fp16 = add(x = out_83_cast_fp16, y = var_4179_to_fp16)[name = tensor("op_4180_cast_fp16")]; + tensor var_4182_to_fp16 = const()[name = tensor("op_4182_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1704828032)))]; + tensor input_469_cast_fp16 = mul(x = var_4180_cast_fp16, y = var_4182_to_fp16)[name = tensor("input_469_cast_fp16")]; + tensor var_4190 = const()[name = tensor("op_4190"), val = tensor([1, 1])]; + tensor var_4192 = const()[name = tensor("op_4192"), val = tensor([1, 1])]; + tensor var_4194_pad_type_0 = const()[name = tensor("op_4194_pad_type_0"), val = tensor("custom")]; + tensor var_4194_pad_0 = const()[name = tensor("op_4194_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1704828736)))]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1706467200)))]; + tensor var_4194_cast_fp16 = conv(bias = up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_4192, groups = var_3945, pad = var_4194_pad_0, pad_type = var_4194_pad_type_0, strides = var_4190, weight = up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_469_cast_fp16)[name = tensor("op_4194_cast_fp16")]; + tensor var_4195_split_sizes_0 = const()[name = tensor("op_4195_split_sizes_0"), val = tensor([1280, 1280])]; + tensor var_4195_axis_0 = const()[name = tensor("op_4195_axis_0"), val = tensor(1)]; + tensor var_4195_cast_fp16_0, tensor var_4195_cast_fp16_1 = split(axis = var_4195_axis_0, split_sizes = var_4195_split_sizes_0, x = var_4194_cast_fp16)[name = tensor("op_4195_cast_fp16")]; + tensor var_4197_mode_0 = const()[name = tensor("op_4197_mode_0"), val = tensor("EXACT")]; + tensor var_4197_cast_fp16 = gelu(mode = var_4197_mode_0, x = var_4195_cast_fp16_1)[name = tensor("op_4197_cast_fp16")]; + tensor input_471_cast_fp16 = mul(x = var_4195_cast_fp16_0, y = var_4197_cast_fp16)[name = tensor("input_471_cast_fp16")]; + tensor var_4201 = const()[name = tensor("op_4201"), val = tensor([1, 1])]; + tensor var_4203 = const()[name = tensor("op_4203"), val = tensor([1, 1])]; + tensor var_4205_pad_type_0 = const()[name = tensor("op_4205_pad_type_0"), val = tensor("custom")]; + tensor var_4205_pad_0 = const()[name = tensor("op_4205_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1706472384)))]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1707291648)))]; + tensor var_4205_cast_fp16 = conv(bias = up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_4203, groups = var_3945, pad = var_4205_pad_0, pad_type = var_4205_pad_type_0, strides = var_4201, weight = up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_471_cast_fp16)[name = tensor("op_4205_cast_fp16")]; + tensor hidden_states_293_cast_fp16 = add(x = var_4205_cast_fp16, y = inputs_83_cast_fp16)[name = tensor("hidden_states_293_cast_fp16")]; + tensor var_4207 = const()[name = tensor("op_4207"), val = tensor([2, 320, 48, 80])]; + tensor input_473_cast_fp16 = reshape(shape = var_4207, x = hidden_states_293_cast_fp16)[name = tensor("input_473_cast_fp16")]; + tensor var_4211 = const()[name = tensor("op_4211"), val = tensor([1, 1])]; + tensor var_4213 = const()[name = tensor("op_4213"), val = tensor([1, 1])]; + tensor hidden_states_295_pad_type_0 = const()[name = tensor("hidden_states_295_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_295_pad_0 = const()[name = tensor("hidden_states_295_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1707292352)))]; + tensor up_blocks_3_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1707497216)))]; + tensor hidden_states_295_cast_fp16 = conv(bias = up_blocks_3_attentions_0_proj_out_bias_to_fp16, dilations = var_4213, groups = var_3945, pad = hidden_states_295_pad_0, pad_type = hidden_states_295_pad_type_0, strides = var_4211, weight = up_blocks_3_attentions_0_proj_out_weight_to_fp16, x = input_473_cast_fp16)[name = tensor("hidden_states_295_cast_fp16")]; + tensor hidden_states_297_cast_fp16 = add(x = hidden_states_295_cast_fp16, y = hidden_states_283_cast_fp16)[name = tensor("hidden_states_297_cast_fp16")]; + tensor input_475_interleave_0 = const()[name = tensor("input_475_interleave_0"), val = tensor(false)]; + tensor input_475_cast_fp16 = concat(axis = var_3945, interleave = input_475_interleave_0, values = (hidden_states_297_cast_fp16, input_35_cast_fp16))[name = tensor("input_475_cast_fp16")]; + tensor reshape_216_shape_0 = const()[name = tensor("reshape_216_shape_0"), val = tensor([2, 32, 20, 48, 80])]; + tensor reshape_216_cast_fp16 = reshape(shape = reshape_216_shape_0, x = input_475_cast_fp16)[name = tensor("reshape_216_cast_fp16")]; + tensor reduce_mean_162_axes_0 = const()[name = tensor("reduce_mean_162_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_162_keep_dims_0 = const()[name = tensor("reduce_mean_162_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_162_cast_fp16 = reduce_mean(axes = reduce_mean_162_axes_0, keep_dims = reduce_mean_162_keep_dims_0, x = reshape_216_cast_fp16)[name = tensor("reduce_mean_162_cast_fp16")]; + tensor sub_108_cast_fp16 = sub(x = reshape_216_cast_fp16, y = reduce_mean_162_cast_fp16)[name = tensor("sub_108_cast_fp16")]; + tensor square_54_cast_fp16 = square(x = sub_108_cast_fp16)[name = tensor("square_54_cast_fp16")]; + tensor reduce_mean_164_axes_0 = const()[name = tensor("reduce_mean_164_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_164_keep_dims_0 = const()[name = tensor("reduce_mean_164_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_164_cast_fp16 = reduce_mean(axes = reduce_mean_164_axes_0, keep_dims = reduce_mean_164_keep_dims_0, x = square_54_cast_fp16)[name = tensor("reduce_mean_164_cast_fp16")]; + tensor add_108_y_0_to_fp16 = const()[name = tensor("add_108_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_108_cast_fp16 = add(x = reduce_mean_164_cast_fp16, y = add_108_y_0_to_fp16)[name = tensor("add_108_cast_fp16")]; + tensor sqrt_54_cast_fp16 = sqrt(x = add_108_cast_fp16)[name = tensor("sqrt_54_cast_fp16")]; + tensor real_div_54_cast_fp16 = real_div(x = sub_108_cast_fp16, y = sqrt_54_cast_fp16)[name = tensor("real_div_54_cast_fp16")]; + tensor reshape_217_shape_0 = const()[name = tensor("reshape_217_shape_0"), val = tensor([2, 640, 48, 80])]; + tensor reshape_217_cast_fp16 = reshape(shape = reshape_217_shape_0, x = real_div_54_cast_fp16)[name = tensor("reshape_217_cast_fp16")]; + tensor add_109_gamma_0_to_fp16 = const()[name = tensor("add_109_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1707497920)))]; + tensor add_109_beta_0_to_fp16 = const()[name = tensor("add_109_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1707499264)))]; + tensor add_109_epsilon_0_to_fp16 = const()[name = tensor("add_109_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_109_cast_fp16 = batch_norm(beta = add_109_beta_0_to_fp16, epsilon = add_109_epsilon_0_to_fp16, gamma = add_109_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_217_cast_fp16)[name = tensor("add_109_cast_fp16")]; + tensor input_479_cast_fp16 = silu(x = add_109_cast_fp16)[name = tensor("input_479_cast_fp16")]; + tensor var_4231 = const()[name = tensor("op_4231"), val = tensor([1, 1])]; + tensor var_4233 = const()[name = tensor("op_4233"), val = tensor([1, 1])]; + tensor hidden_states_299_pad_type_0 = const()[name = tensor("hidden_states_299_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_299_pad_0 = const()[name = tensor("hidden_states_299_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1707500608)))]; + tensor up_blocks_3_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1711187072)))]; + tensor hidden_states_299_cast_fp16 = conv(bias = up_blocks_3_resnets_1_conv1_bias_to_fp16, dilations = var_4233, groups = var_3945, pad = hidden_states_299_pad_0, pad_type = hidden_states_299_pad_type_0, strides = var_4231, weight = up_blocks_3_resnets_1_conv1_weight_to_fp16, x = input_479_cast_fp16)[name = tensor("hidden_states_299_cast_fp16")]; + tensor var_4239 = const()[name = tensor("op_4239"), val = tensor([1, 1])]; + tensor var_4241 = const()[name = tensor("op_4241"), val = tensor([1, 1])]; + tensor temb_41_pad_type_0 = const()[name = tensor("temb_41_pad_type_0"), val = tensor("custom")]; + tensor temb_41_pad_0 = const()[name = tensor("temb_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1711187776)))]; + tensor up_blocks_3_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1712007040)))]; + tensor temb_41_cast_fp16 = conv(bias = up_blocks_3_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_4241, groups = var_3945, pad = temb_41_pad_0, pad_type = temb_41_pad_type_0, strides = var_4239, weight = up_blocks_3_resnets_1_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16)[name = tensor("temb_41_cast_fp16")]; + tensor input_483_cast_fp16 = add(x = hidden_states_299_cast_fp16, y = temb_41_cast_fp16)[name = tensor("input_483_cast_fp16")]; + tensor reshape_220_shape_0 = const()[name = tensor("reshape_220_shape_0"), val = tensor([2, 32, 10, 48, 80])]; + tensor reshape_220_cast_fp16 = reshape(shape = reshape_220_shape_0, x = input_483_cast_fp16)[name = tensor("reshape_220_cast_fp16")]; + tensor reduce_mean_165_axes_0 = const()[name = tensor("reduce_mean_165_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_165_keep_dims_0 = const()[name = tensor("reduce_mean_165_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_165_cast_fp16 = reduce_mean(axes = reduce_mean_165_axes_0, keep_dims = reduce_mean_165_keep_dims_0, x = reshape_220_cast_fp16)[name = tensor("reduce_mean_165_cast_fp16")]; + tensor sub_110_cast_fp16 = sub(x = reshape_220_cast_fp16, y = reduce_mean_165_cast_fp16)[name = tensor("sub_110_cast_fp16")]; + tensor square_55_cast_fp16 = square(x = sub_110_cast_fp16)[name = tensor("square_55_cast_fp16")]; + tensor reduce_mean_167_axes_0 = const()[name = tensor("reduce_mean_167_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_167_keep_dims_0 = const()[name = tensor("reduce_mean_167_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_167_cast_fp16 = reduce_mean(axes = reduce_mean_167_axes_0, keep_dims = reduce_mean_167_keep_dims_0, x = square_55_cast_fp16)[name = tensor("reduce_mean_167_cast_fp16")]; + tensor add_110_y_0_to_fp16 = const()[name = tensor("add_110_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_110_cast_fp16 = add(x = reduce_mean_167_cast_fp16, y = add_110_y_0_to_fp16)[name = tensor("add_110_cast_fp16")]; + tensor sqrt_55_cast_fp16 = sqrt(x = add_110_cast_fp16)[name = tensor("sqrt_55_cast_fp16")]; + tensor real_div_55_cast_fp16 = real_div(x = sub_110_cast_fp16, y = sqrt_55_cast_fp16)[name = tensor("real_div_55_cast_fp16")]; + tensor reshape_221_shape_0 = const()[name = tensor("reshape_221_shape_0"), val = tensor([2, 320, 48, 80])]; + tensor reshape_221_cast_fp16 = reshape(shape = reshape_221_shape_0, x = real_div_55_cast_fp16)[name = tensor("reshape_221_cast_fp16")]; + tensor add_111_gamma_0_to_fp16 = const()[name = tensor("add_111_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1712007744)))]; + tensor add_111_beta_0_to_fp16 = const()[name = tensor("add_111_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1712008448)))]; + tensor add_111_epsilon_0_to_fp16 = const()[name = tensor("add_111_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_111_cast_fp16 = batch_norm(beta = add_111_beta_0_to_fp16, epsilon = add_111_epsilon_0_to_fp16, gamma = add_111_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_221_cast_fp16)[name = tensor("add_111_cast_fp16")]; + tensor input_487_cast_fp16 = silu(x = add_111_cast_fp16)[name = tensor("input_487_cast_fp16")]; + tensor var_4251 = const()[name = tensor("op_4251"), val = tensor([1, 1])]; + tensor var_4253 = const()[name = tensor("op_4253"), val = tensor([1, 1])]; + tensor hidden_states_301_pad_type_0 = const()[name = tensor("hidden_states_301_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_301_pad_0 = const()[name = tensor("hidden_states_301_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1712009152)))]; + tensor up_blocks_3_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1713852416)))]; + tensor hidden_states_301_cast_fp16 = conv(bias = up_blocks_3_resnets_1_conv2_bias_to_fp16, dilations = var_4253, groups = var_3945, pad = hidden_states_301_pad_0, pad_type = hidden_states_301_pad_type_0, strides = var_4251, weight = up_blocks_3_resnets_1_conv2_weight_to_fp16, x = input_487_cast_fp16)[name = tensor("hidden_states_301_cast_fp16")]; + tensor var_4258 = const()[name = tensor("op_4258"), val = tensor([1, 1])]; + tensor var_4260 = const()[name = tensor("op_4260"), val = tensor([1, 1])]; + tensor x_25_pad_type_0 = const()[name = tensor("x_25_pad_type_0"), val = tensor("custom")]; + tensor x_25_pad_0 = const()[name = tensor("x_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_1_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1713853120)))]; + tensor up_blocks_3_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1714262784)))]; + tensor x_25_cast_fp16 = conv(bias = up_blocks_3_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_4260, groups = var_3945, pad = x_25_pad_0, pad_type = x_25_pad_type_0, strides = var_4258, weight = up_blocks_3_resnets_1_conv_shortcut_weight_to_fp16, x = input_475_cast_fp16)[name = tensor("x_25_cast_fp16")]; + tensor hidden_states_303_cast_fp16 = add(x = x_25_cast_fp16, y = hidden_states_301_cast_fp16)[name = tensor("hidden_states_303_cast_fp16")]; + tensor reshape_224_shape_0 = const()[name = tensor("reshape_224_shape_0"), val = tensor([2, 32, 10, 48, 80])]; + tensor reshape_224_cast_fp16 = reshape(shape = reshape_224_shape_0, x = hidden_states_303_cast_fp16)[name = tensor("reshape_224_cast_fp16")]; + tensor reduce_mean_168_axes_0 = const()[name = tensor("reduce_mean_168_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_168_keep_dims_0 = const()[name = tensor("reduce_mean_168_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_168_cast_fp16 = reduce_mean(axes = reduce_mean_168_axes_0, keep_dims = reduce_mean_168_keep_dims_0, x = reshape_224_cast_fp16)[name = tensor("reduce_mean_168_cast_fp16")]; + tensor sub_112_cast_fp16 = sub(x = reshape_224_cast_fp16, y = reduce_mean_168_cast_fp16)[name = tensor("sub_112_cast_fp16")]; + tensor square_56_cast_fp16 = square(x = sub_112_cast_fp16)[name = tensor("square_56_cast_fp16")]; + tensor reduce_mean_170_axes_0 = const()[name = tensor("reduce_mean_170_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_170_keep_dims_0 = const()[name = tensor("reduce_mean_170_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_170_cast_fp16 = reduce_mean(axes = reduce_mean_170_axes_0, keep_dims = reduce_mean_170_keep_dims_0, x = square_56_cast_fp16)[name = tensor("reduce_mean_170_cast_fp16")]; + tensor add_112_y_0_to_fp16 = const()[name = tensor("add_112_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_112_cast_fp16 = add(x = reduce_mean_170_cast_fp16, y = add_112_y_0_to_fp16)[name = tensor("add_112_cast_fp16")]; + tensor sqrt_56_cast_fp16 = sqrt(x = add_112_cast_fp16)[name = tensor("sqrt_56_cast_fp16")]; + tensor real_div_56_cast_fp16 = real_div(x = sub_112_cast_fp16, y = sqrt_56_cast_fp16)[name = tensor("real_div_56_cast_fp16")]; + tensor reshape_225_shape_0 = const()[name = tensor("reshape_225_shape_0"), val = tensor([2, 320, 48, 80])]; + tensor reshape_225_cast_fp16 = reshape(shape = reshape_225_shape_0, x = real_div_56_cast_fp16)[name = tensor("reshape_225_cast_fp16")]; + tensor add_113_gamma_0_to_fp16 = const()[name = tensor("add_113_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1714263488)))]; + tensor add_113_beta_0_to_fp16 = const()[name = tensor("add_113_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1714264192)))]; + tensor add_113_epsilon_0_to_fp16 = const()[name = tensor("add_113_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_113_cast_fp16 = batch_norm(beta = add_113_beta_0_to_fp16, epsilon = add_113_epsilon_0_to_fp16, gamma = add_113_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_225_cast_fp16)[name = tensor("add_113_cast_fp16")]; + tensor var_4280 = const()[name = tensor("op_4280"), val = tensor([1, 1])]; + tensor var_4282 = const()[name = tensor("op_4282"), val = tensor([1, 1])]; + tensor hidden_states_305_pad_type_0 = const()[name = tensor("hidden_states_305_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_305_pad_0 = const()[name = tensor("hidden_states_305_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1714264896)))]; + tensor up_blocks_3_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1714469760)))]; + tensor hidden_states_305_cast_fp16 = conv(bias = up_blocks_3_attentions_1_proj_in_bias_to_fp16, dilations = var_4282, groups = var_3945, pad = hidden_states_305_pad_0, pad_type = hidden_states_305_pad_type_0, strides = var_4280, weight = up_blocks_3_attentions_1_proj_in_weight_to_fp16, x = add_113_cast_fp16)[name = tensor("hidden_states_305_cast_fp16")]; + tensor var_4287 = const()[name = tensor("op_4287"), val = tensor([2, 320, 1, 3840])]; + tensor inputs_85_cast_fp16 = reshape(shape = var_4287, x = hidden_states_305_cast_fp16)[name = tensor("inputs_85_cast_fp16")]; + tensor var_4297 = const()[name = tensor("op_4297"), val = tensor([1])]; + tensor channels_mean_85_cast_fp16 = reduce_mean(axes = var_4297, keep_dims = var_3940, x = inputs_85_cast_fp16)[name = tensor("channels_mean_85_cast_fp16")]; + tensor zero_mean_85_cast_fp16 = sub(x = inputs_85_cast_fp16, y = channels_mean_85_cast_fp16)[name = tensor("zero_mean_85_cast_fp16")]; + tensor zero_mean_sq_85_cast_fp16 = mul(x = zero_mean_85_cast_fp16, y = zero_mean_85_cast_fp16)[name = tensor("zero_mean_sq_85_cast_fp16")]; + tensor var_4301 = const()[name = tensor("op_4301"), val = tensor([1])]; + tensor var_4302_cast_fp16 = reduce_mean(axes = var_4301, keep_dims = var_3940, x = zero_mean_sq_85_cast_fp16)[name = tensor("op_4302_cast_fp16")]; + tensor var_4303_to_fp16 = const()[name = tensor("op_4303_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4304_cast_fp16 = add(x = var_4302_cast_fp16, y = var_4303_to_fp16)[name = tensor("op_4304_cast_fp16")]; + tensor denom_85_epsilon_0_to_fp16 = const()[name = tensor("denom_85_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_85_cast_fp16 = rsqrt(epsilon = denom_85_epsilon_0_to_fp16, x = var_4304_cast_fp16)[name = tensor("denom_85_cast_fp16")]; + tensor out_85_cast_fp16 = mul(x = zero_mean_85_cast_fp16, y = denom_85_cast_fp16)[name = tensor("out_85_cast_fp16")]; + tensor var_4308_to_fp16 = const()[name = tensor("op_4308_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1714470464)))]; + tensor var_4309_cast_fp16 = add(x = out_85_cast_fp16, y = var_4308_to_fp16)[name = tensor("op_4309_cast_fp16")]; + tensor var_4311_to_fp16 = const()[name = tensor("op_4311_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1714471168)))]; + tensor hidden_states_307_cast_fp16 = mul(x = var_4309_cast_fp16, y = var_4311_to_fp16)[name = tensor("hidden_states_307_cast_fp16")]; + tensor var_4318 = const()[name = tensor("op_4318"), val = tensor([1, 1])]; + tensor var_4320 = const()[name = tensor("op_4320"), val = tensor([1, 1])]; + tensor q_57_pad_type_0 = const()[name = tensor("q_57_pad_type_0"), val = tensor("custom")]; + tensor q_57_pad_0 = const()[name = tensor("q_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1714471872)))]; + tensor q_57_cast_fp16 = conv(dilations = var_4320, groups = var_3945, pad = q_57_pad_0, pad_type = q_57_pad_type_0, strides = var_4318, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_307_cast_fp16)[name = tensor("q_57_cast_fp16")]; + tensor var_4324 = const()[name = tensor("op_4324"), val = tensor([1, 1])]; + tensor var_4326 = const()[name = tensor("op_4326"), val = tensor([1, 1])]; + tensor k_57_pad_type_0 = const()[name = tensor("k_57_pad_type_0"), val = tensor("custom")]; + tensor k_57_pad_0 = const()[name = tensor("k_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1714676736)))]; + tensor k_57_cast_fp16 = conv(dilations = var_4326, groups = var_3945, pad = k_57_pad_0, pad_type = k_57_pad_type_0, strides = var_4324, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_307_cast_fp16)[name = tensor("k_57_cast_fp16")]; + tensor var_4330 = const()[name = tensor("op_4330"), val = tensor([1, 1])]; + tensor var_4332 = const()[name = tensor("op_4332"), val = tensor([1, 1])]; + tensor v_57_pad_type_0 = const()[name = tensor("v_57_pad_type_0"), val = tensor("custom")]; + tensor v_57_pad_0 = const()[name = tensor("v_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1714881600)))]; + tensor v_57_cast_fp16 = conv(dilations = var_4332, groups = var_3945, pad = v_57_pad_0, pad_type = v_57_pad_type_0, strides = var_4330, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_307_cast_fp16)[name = tensor("v_57_cast_fp16")]; + tensor var_4336 = const()[name = tensor("op_4336"), val = tensor([2, 5, 64, -1])]; + tensor var_4337_cast_fp16 = reshape(shape = var_4336, x = q_57_cast_fp16)[name = tensor("op_4337_cast_fp16")]; + tensor var_4338 = const()[name = tensor("op_4338"), val = tensor([2, 5, 64, -1])]; + tensor var_4339_cast_fp16 = reshape(shape = var_4338, x = k_57_cast_fp16)[name = tensor("op_4339_cast_fp16")]; + tensor var_4340 = const()[name = tensor("op_4340"), val = tensor([2, 5, 64, -1])]; + tensor var_4341_cast_fp16 = reshape(shape = var_4340, x = v_57_cast_fp16)[name = tensor("op_4341_cast_fp16")]; + tensor attn_weights_113_transpose_x_0 = const()[name = tensor("attn_weights_113_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_113_transpose_y_0 = const()[name = tensor("attn_weights_113_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_113_cast_fp16 = matmul(transpose_x = attn_weights_113_transpose_x_0, transpose_y = attn_weights_113_transpose_y_0, x = var_4337_cast_fp16, y = var_4339_cast_fp16)[name = tensor("attn_weights_113_cast_fp16")]; + tensor attn_weights_115_cast_fp16 = mul(x = attn_weights_113_cast_fp16, y = var_3936_to_fp16)[name = tensor("attn_weights_115_cast_fp16")]; + tensor var_4345_cast_fp16 = softmax(axis = var_3929, x = attn_weights_115_cast_fp16)[name = tensor("op_4345_cast_fp16")]; + tensor attn_57_transpose_x_0 = const()[name = tensor("attn_57_transpose_x_0"), val = tensor(false)]; + tensor attn_57_transpose_y_0 = const()[name = tensor("attn_57_transpose_y_0"), val = tensor(true)]; + tensor attn_57_cast_fp16 = matmul(transpose_x = attn_57_transpose_x_0, transpose_y = attn_57_transpose_y_0, x = var_4341_cast_fp16, y = var_4345_cast_fp16)[name = tensor("attn_57_cast_fp16")]; + tensor var_4349 = const()[name = tensor("op_4349"), val = tensor([2, 320, 1, -1])]; + tensor input_491_cast_fp16 = reshape(shape = var_4349, x = attn_57_cast_fp16)[name = tensor("input_491_cast_fp16")]; + tensor var_4354 = const()[name = tensor("op_4354"), val = tensor([1, 1])]; + tensor var_4356 = const()[name = tensor("op_4356"), val = tensor([1, 1])]; + tensor var_4358_pad_type_0 = const()[name = tensor("op_4358_pad_type_0"), val = tensor("custom")]; + tensor var_4358_pad_0 = const()[name = tensor("op_4358_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1715086464)))]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1715291328)))]; + tensor var_4358_cast_fp16 = conv(bias = up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_4356, groups = var_3945, pad = var_4358_pad_0, pad_type = var_4358_pad_type_0, strides = var_4354, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_491_cast_fp16)[name = tensor("op_4358_cast_fp16")]; + tensor inputs_87_cast_fp16 = add(x = var_4358_cast_fp16, y = inputs_85_cast_fp16)[name = tensor("inputs_87_cast_fp16")]; + tensor var_4362 = const()[name = tensor("op_4362"), val = tensor([1])]; + tensor channels_mean_87_cast_fp16 = reduce_mean(axes = var_4362, keep_dims = var_3940, x = inputs_87_cast_fp16)[name = tensor("channels_mean_87_cast_fp16")]; + tensor zero_mean_87_cast_fp16 = sub(x = inputs_87_cast_fp16, y = channels_mean_87_cast_fp16)[name = tensor("zero_mean_87_cast_fp16")]; + tensor zero_mean_sq_87_cast_fp16 = mul(x = zero_mean_87_cast_fp16, y = zero_mean_87_cast_fp16)[name = tensor("zero_mean_sq_87_cast_fp16")]; + tensor var_4366 = const()[name = tensor("op_4366"), val = tensor([1])]; + tensor var_4367_cast_fp16 = reduce_mean(axes = var_4366, keep_dims = var_3940, x = zero_mean_sq_87_cast_fp16)[name = tensor("op_4367_cast_fp16")]; + tensor var_4368_to_fp16 = const()[name = tensor("op_4368_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4369_cast_fp16 = add(x = var_4367_cast_fp16, y = var_4368_to_fp16)[name = tensor("op_4369_cast_fp16")]; + tensor denom_87_epsilon_0_to_fp16 = const()[name = tensor("denom_87_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_87_cast_fp16 = rsqrt(epsilon = denom_87_epsilon_0_to_fp16, x = var_4369_cast_fp16)[name = tensor("denom_87_cast_fp16")]; + tensor out_87_cast_fp16 = mul(x = zero_mean_87_cast_fp16, y = denom_87_cast_fp16)[name = tensor("out_87_cast_fp16")]; + tensor var_4373_to_fp16 = const()[name = tensor("op_4373_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1715292032)))]; + tensor var_4374_cast_fp16 = add(x = out_87_cast_fp16, y = var_4373_to_fp16)[name = tensor("op_4374_cast_fp16")]; + tensor var_4376_to_fp16 = const()[name = tensor("op_4376_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1715292736)))]; + tensor hidden_states_309_cast_fp16 = mul(x = var_4374_cast_fp16, y = var_4376_to_fp16)[name = tensor("hidden_states_309_cast_fp16")]; + tensor var_4383 = const()[name = tensor("op_4383"), val = tensor([1, 1])]; + tensor var_4385 = const()[name = tensor("op_4385"), val = tensor([1, 1])]; + tensor q_59_pad_type_0 = const()[name = tensor("q_59_pad_type_0"), val = tensor("custom")]; + tensor q_59_pad_0 = const()[name = tensor("q_59_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1715293440)))]; + tensor q_59_cast_fp16 = conv(dilations = var_4385, groups = var_3945, pad = q_59_pad_0, pad_type = q_59_pad_type_0, strides = var_4383, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_309_cast_fp16)[name = tensor("q_59_cast_fp16")]; + tensor var_4389 = const()[name = tensor("op_4389"), val = tensor([1, 1])]; + tensor var_4391 = const()[name = tensor("op_4391"), val = tensor([1, 1])]; + tensor k_59_pad_type_0 = const()[name = tensor("k_59_pad_type_0"), val = tensor("custom")]; + tensor k_59_pad_0 = const()[name = tensor("k_59_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1715498304)))]; + tensor k_59_cast_fp16 = conv(dilations = var_4391, groups = var_3945, pad = k_59_pad_0, pad_type = k_59_pad_type_0, strides = var_4389, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_59_cast_fp16")]; + tensor var_4395 = const()[name = tensor("op_4395"), val = tensor([1, 1])]; + tensor var_4397 = const()[name = tensor("op_4397"), val = tensor([1, 1])]; + tensor v_59_pad_type_0 = const()[name = tensor("v_59_pad_type_0"), val = tensor("custom")]; + tensor v_59_pad_0 = const()[name = tensor("v_59_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1716153728)))]; + tensor v_59_cast_fp16 = conv(dilations = var_4397, groups = var_3945, pad = v_59_pad_0, pad_type = v_59_pad_type_0, strides = var_4395, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_59_cast_fp16")]; + tensor var_4401 = const()[name = tensor("op_4401"), val = tensor([2, 5, 64, -1])]; + tensor var_4402_cast_fp16 = reshape(shape = var_4401, x = q_59_cast_fp16)[name = tensor("op_4402_cast_fp16")]; + tensor var_4403 = const()[name = tensor("op_4403"), val = tensor([2, 5, 64, -1])]; + tensor var_4404_cast_fp16 = reshape(shape = var_4403, x = k_59_cast_fp16)[name = tensor("op_4404_cast_fp16")]; + tensor var_4405 = const()[name = tensor("op_4405"), val = tensor([2, 5, 64, -1])]; + tensor var_4406_cast_fp16 = reshape(shape = var_4405, x = v_59_cast_fp16)[name = tensor("op_4406_cast_fp16")]; + tensor attn_weights_117_transpose_x_0 = const()[name = tensor("attn_weights_117_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_117_transpose_y_0 = const()[name = tensor("attn_weights_117_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_117_cast_fp16 = matmul(transpose_x = attn_weights_117_transpose_x_0, transpose_y = attn_weights_117_transpose_y_0, x = var_4402_cast_fp16, y = var_4404_cast_fp16)[name = tensor("attn_weights_117_cast_fp16")]; + tensor attn_weights_119_cast_fp16 = mul(x = attn_weights_117_cast_fp16, y = var_3936_to_fp16)[name = tensor("attn_weights_119_cast_fp16")]; + tensor var_4410_cast_fp16 = softmax(axis = var_3929, x = attn_weights_119_cast_fp16)[name = tensor("op_4410_cast_fp16")]; + tensor attn_59_transpose_x_0 = const()[name = tensor("attn_59_transpose_x_0"), val = tensor(false)]; + tensor attn_59_transpose_y_0 = const()[name = tensor("attn_59_transpose_y_0"), val = tensor(true)]; + tensor attn_59_cast_fp16 = matmul(transpose_x = attn_59_transpose_x_0, transpose_y = attn_59_transpose_y_0, x = var_4406_cast_fp16, y = var_4410_cast_fp16)[name = tensor("attn_59_cast_fp16")]; + tensor var_4414 = const()[name = tensor("op_4414"), val = tensor([2, 320, 1, -1])]; + tensor input_493_cast_fp16 = reshape(shape = var_4414, x = attn_59_cast_fp16)[name = tensor("input_493_cast_fp16")]; + tensor var_4419 = const()[name = tensor("op_4419"), val = tensor([1, 1])]; + tensor var_4421 = const()[name = tensor("op_4421"), val = tensor([1, 1])]; + tensor var_4423_pad_type_0 = const()[name = tensor("op_4423_pad_type_0"), val = tensor("custom")]; + tensor var_4423_pad_0 = const()[name = tensor("op_4423_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1716809152)))]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1717014016)))]; + tensor var_4423_cast_fp16 = conv(bias = up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_4421, groups = var_3945, pad = var_4423_pad_0, pad_type = var_4423_pad_type_0, strides = var_4419, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_493_cast_fp16)[name = tensor("op_4423_cast_fp16")]; + tensor inputs_89_cast_fp16 = add(x = var_4423_cast_fp16, y = inputs_87_cast_fp16)[name = tensor("inputs_89_cast_fp16")]; + tensor var_4427 = const()[name = tensor("op_4427"), val = tensor([1])]; + tensor channels_mean_89_cast_fp16 = reduce_mean(axes = var_4427, keep_dims = var_3940, x = inputs_89_cast_fp16)[name = tensor("channels_mean_89_cast_fp16")]; + tensor zero_mean_89_cast_fp16 = sub(x = inputs_89_cast_fp16, y = channels_mean_89_cast_fp16)[name = tensor("zero_mean_89_cast_fp16")]; + tensor zero_mean_sq_89_cast_fp16 = mul(x = zero_mean_89_cast_fp16, y = zero_mean_89_cast_fp16)[name = tensor("zero_mean_sq_89_cast_fp16")]; + tensor var_4431 = const()[name = tensor("op_4431"), val = tensor([1])]; + tensor var_4432_cast_fp16 = reduce_mean(axes = var_4431, keep_dims = var_3940, x = zero_mean_sq_89_cast_fp16)[name = tensor("op_4432_cast_fp16")]; + tensor var_4433_to_fp16 = const()[name = tensor("op_4433_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4434_cast_fp16 = add(x = var_4432_cast_fp16, y = var_4433_to_fp16)[name = tensor("op_4434_cast_fp16")]; + tensor denom_89_epsilon_0_to_fp16 = const()[name = tensor("denom_89_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_89_cast_fp16 = rsqrt(epsilon = denom_89_epsilon_0_to_fp16, x = var_4434_cast_fp16)[name = tensor("denom_89_cast_fp16")]; + tensor out_89_cast_fp16 = mul(x = zero_mean_89_cast_fp16, y = denom_89_cast_fp16)[name = tensor("out_89_cast_fp16")]; + tensor var_4438_to_fp16 = const()[name = tensor("op_4438_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1717014720)))]; + tensor var_4439_cast_fp16 = add(x = out_89_cast_fp16, y = var_4438_to_fp16)[name = tensor("op_4439_cast_fp16")]; + tensor var_4441_to_fp16 = const()[name = tensor("op_4441_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1717015424)))]; + tensor input_495_cast_fp16 = mul(x = var_4439_cast_fp16, y = var_4441_to_fp16)[name = tensor("input_495_cast_fp16")]; + tensor var_4449 = const()[name = tensor("op_4449"), val = tensor([1, 1])]; + tensor var_4451 = const()[name = tensor("op_4451"), val = tensor([1, 1])]; + tensor var_4453_pad_type_0 = const()[name = tensor("op_4453_pad_type_0"), val = tensor("custom")]; + tensor var_4453_pad_0 = const()[name = tensor("op_4453_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1717016128)))]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1718654592)))]; + tensor var_4453_cast_fp16 = conv(bias = up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_4451, groups = var_3945, pad = var_4453_pad_0, pad_type = var_4453_pad_type_0, strides = var_4449, weight = up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_495_cast_fp16)[name = tensor("op_4453_cast_fp16")]; + tensor var_4454_split_sizes_0 = const()[name = tensor("op_4454_split_sizes_0"), val = tensor([1280, 1280])]; + tensor var_4454_axis_0 = const()[name = tensor("op_4454_axis_0"), val = tensor(1)]; + tensor var_4454_cast_fp16_0, tensor var_4454_cast_fp16_1 = split(axis = var_4454_axis_0, split_sizes = var_4454_split_sizes_0, x = var_4453_cast_fp16)[name = tensor("op_4454_cast_fp16")]; + tensor var_4456_mode_0 = const()[name = tensor("op_4456_mode_0"), val = tensor("EXACT")]; + tensor var_4456_cast_fp16 = gelu(mode = var_4456_mode_0, x = var_4454_cast_fp16_1)[name = tensor("op_4456_cast_fp16")]; + tensor input_497_cast_fp16 = mul(x = var_4454_cast_fp16_0, y = var_4456_cast_fp16)[name = tensor("input_497_cast_fp16")]; + tensor var_4460 = const()[name = tensor("op_4460"), val = tensor([1, 1])]; + tensor var_4462 = const()[name = tensor("op_4462"), val = tensor([1, 1])]; + tensor var_4464_pad_type_0 = const()[name = tensor("op_4464_pad_type_0"), val = tensor("custom")]; + tensor var_4464_pad_0 = const()[name = tensor("op_4464_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1718659776)))]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1719479040)))]; + tensor var_4464_cast_fp16 = conv(bias = up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_4462, groups = var_3945, pad = var_4464_pad_0, pad_type = var_4464_pad_type_0, strides = var_4460, weight = up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_497_cast_fp16)[name = tensor("op_4464_cast_fp16")]; + tensor hidden_states_313_cast_fp16 = add(x = var_4464_cast_fp16, y = inputs_89_cast_fp16)[name = tensor("hidden_states_313_cast_fp16")]; + tensor var_4466 = const()[name = tensor("op_4466"), val = tensor([2, 320, 48, 80])]; + tensor input_499_cast_fp16 = reshape(shape = var_4466, x = hidden_states_313_cast_fp16)[name = tensor("input_499_cast_fp16")]; + tensor var_4470 = const()[name = tensor("op_4470"), val = tensor([1, 1])]; + tensor var_4472 = const()[name = tensor("op_4472"), val = tensor([1, 1])]; + tensor hidden_states_315_pad_type_0 = const()[name = tensor("hidden_states_315_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_315_pad_0 = const()[name = tensor("hidden_states_315_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1719479744)))]; + tensor up_blocks_3_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1719684608)))]; + tensor hidden_states_315_cast_fp16 = conv(bias = up_blocks_3_attentions_1_proj_out_bias_to_fp16, dilations = var_4472, groups = var_3945, pad = hidden_states_315_pad_0, pad_type = hidden_states_315_pad_type_0, strides = var_4470, weight = up_blocks_3_attentions_1_proj_out_weight_to_fp16, x = input_499_cast_fp16)[name = tensor("hidden_states_315_cast_fp16")]; + tensor hidden_states_317_cast_fp16 = add(x = hidden_states_315_cast_fp16, y = hidden_states_303_cast_fp16)[name = tensor("hidden_states_317_cast_fp16")]; + tensor input_501_interleave_0 = const()[name = tensor("input_501_interleave_0"), val = tensor(false)]; + tensor input_501_cast_fp16 = concat(axis = var_3945, interleave = input_501_interleave_0, values = (hidden_states_317_cast_fp16, input_7_cast_fp16))[name = tensor("input_501_cast_fp16")]; + tensor reshape_228_shape_0 = const()[name = tensor("reshape_228_shape_0"), val = tensor([2, 32, 20, 48, 80])]; + tensor reshape_228_cast_fp16 = reshape(shape = reshape_228_shape_0, x = input_501_cast_fp16)[name = tensor("reshape_228_cast_fp16")]; + tensor reduce_mean_171_axes_0 = const()[name = tensor("reduce_mean_171_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_171_keep_dims_0 = const()[name = tensor("reduce_mean_171_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_171_cast_fp16 = reduce_mean(axes = reduce_mean_171_axes_0, keep_dims = reduce_mean_171_keep_dims_0, x = reshape_228_cast_fp16)[name = tensor("reduce_mean_171_cast_fp16")]; + tensor sub_114_cast_fp16 = sub(x = reshape_228_cast_fp16, y = reduce_mean_171_cast_fp16)[name = tensor("sub_114_cast_fp16")]; + tensor square_57_cast_fp16 = square(x = sub_114_cast_fp16)[name = tensor("square_57_cast_fp16")]; + tensor reduce_mean_173_axes_0 = const()[name = tensor("reduce_mean_173_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_173_keep_dims_0 = const()[name = tensor("reduce_mean_173_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_173_cast_fp16 = reduce_mean(axes = reduce_mean_173_axes_0, keep_dims = reduce_mean_173_keep_dims_0, x = square_57_cast_fp16)[name = tensor("reduce_mean_173_cast_fp16")]; + tensor add_114_y_0_to_fp16 = const()[name = tensor("add_114_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_114_cast_fp16 = add(x = reduce_mean_173_cast_fp16, y = add_114_y_0_to_fp16)[name = tensor("add_114_cast_fp16")]; + tensor sqrt_57_cast_fp16 = sqrt(x = add_114_cast_fp16)[name = tensor("sqrt_57_cast_fp16")]; + tensor real_div_57_cast_fp16 = real_div(x = sub_114_cast_fp16, y = sqrt_57_cast_fp16)[name = tensor("real_div_57_cast_fp16")]; + tensor reshape_229_shape_0 = const()[name = tensor("reshape_229_shape_0"), val = tensor([2, 640, 48, 80])]; + tensor reshape_229_cast_fp16 = reshape(shape = reshape_229_shape_0, x = real_div_57_cast_fp16)[name = tensor("reshape_229_cast_fp16")]; + tensor add_115_gamma_0_to_fp16 = const()[name = tensor("add_115_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1719685312)))]; + tensor add_115_beta_0_to_fp16 = const()[name = tensor("add_115_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1719686656)))]; + tensor add_115_epsilon_0_to_fp16 = const()[name = tensor("add_115_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_115_cast_fp16 = batch_norm(beta = add_115_beta_0_to_fp16, epsilon = add_115_epsilon_0_to_fp16, gamma = add_115_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_229_cast_fp16)[name = tensor("add_115_cast_fp16")]; + tensor input_505_cast_fp16 = silu(x = add_115_cast_fp16)[name = tensor("input_505_cast_fp16")]; + tensor var_4490 = const()[name = tensor("op_4490"), val = tensor([1, 1])]; + tensor var_4492 = const()[name = tensor("op_4492"), val = tensor([1, 1])]; + tensor hidden_states_319_pad_type_0 = const()[name = tensor("hidden_states_319_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_319_pad_0 = const()[name = tensor("hidden_states_319_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_2_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1719688000)))]; + tensor up_blocks_3_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1723374464)))]; + tensor hidden_states_319_cast_fp16 = conv(bias = up_blocks_3_resnets_2_conv1_bias_to_fp16, dilations = var_4492, groups = var_3945, pad = hidden_states_319_pad_0, pad_type = hidden_states_319_pad_type_0, strides = var_4490, weight = up_blocks_3_resnets_2_conv1_weight_to_fp16, x = input_505_cast_fp16)[name = tensor("hidden_states_319_cast_fp16")]; + tensor var_4498 = const()[name = tensor("op_4498"), val = tensor([1, 1])]; + tensor var_4500 = const()[name = tensor("op_4500"), val = tensor([1, 1])]; + tensor temb_pad_type_0 = const()[name = tensor("temb_pad_type_0"), val = tensor("custom")]; + tensor temb_pad_0 = const()[name = tensor("temb_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_2_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1723375168)))]; + tensor up_blocks_3_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1724194432)))]; + tensor temb_cast_fp16 = conv(bias = up_blocks_3_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_4500, groups = var_3945, pad = temb_pad_0, pad_type = temb_pad_type_0, strides = var_4498, weight = up_blocks_3_resnets_2_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16)[name = tensor("temb_cast_fp16")]; + tensor input_509_cast_fp16 = add(x = hidden_states_319_cast_fp16, y = temb_cast_fp16)[name = tensor("input_509_cast_fp16")]; + tensor reshape_232_shape_0 = const()[name = tensor("reshape_232_shape_0"), val = tensor([2, 32, 10, 48, 80])]; + tensor reshape_232_cast_fp16 = reshape(shape = reshape_232_shape_0, x = input_509_cast_fp16)[name = tensor("reshape_232_cast_fp16")]; + tensor reduce_mean_174_axes_0 = const()[name = tensor("reduce_mean_174_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_174_keep_dims_0 = const()[name = tensor("reduce_mean_174_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_174_cast_fp16 = reduce_mean(axes = reduce_mean_174_axes_0, keep_dims = reduce_mean_174_keep_dims_0, x = reshape_232_cast_fp16)[name = tensor("reduce_mean_174_cast_fp16")]; + tensor sub_116_cast_fp16 = sub(x = reshape_232_cast_fp16, y = reduce_mean_174_cast_fp16)[name = tensor("sub_116_cast_fp16")]; + tensor square_58_cast_fp16 = square(x = sub_116_cast_fp16)[name = tensor("square_58_cast_fp16")]; + tensor reduce_mean_176_axes_0 = const()[name = tensor("reduce_mean_176_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_176_keep_dims_0 = const()[name = tensor("reduce_mean_176_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_176_cast_fp16 = reduce_mean(axes = reduce_mean_176_axes_0, keep_dims = reduce_mean_176_keep_dims_0, x = square_58_cast_fp16)[name = tensor("reduce_mean_176_cast_fp16")]; + tensor add_116_y_0_to_fp16 = const()[name = tensor("add_116_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_116_cast_fp16 = add(x = reduce_mean_176_cast_fp16, y = add_116_y_0_to_fp16)[name = tensor("add_116_cast_fp16")]; + tensor sqrt_58_cast_fp16 = sqrt(x = add_116_cast_fp16)[name = tensor("sqrt_58_cast_fp16")]; + tensor real_div_58_cast_fp16 = real_div(x = sub_116_cast_fp16, y = sqrt_58_cast_fp16)[name = tensor("real_div_58_cast_fp16")]; + tensor reshape_233_shape_0 = const()[name = tensor("reshape_233_shape_0"), val = tensor([2, 320, 48, 80])]; + tensor reshape_233_cast_fp16 = reshape(shape = reshape_233_shape_0, x = real_div_58_cast_fp16)[name = tensor("reshape_233_cast_fp16")]; + tensor add_117_gamma_0_to_fp16 = const()[name = tensor("add_117_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1724195136)))]; + tensor add_117_beta_0_to_fp16 = const()[name = tensor("add_117_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1724195840)))]; + tensor add_117_epsilon_0_to_fp16 = const()[name = tensor("add_117_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_117_cast_fp16 = batch_norm(beta = add_117_beta_0_to_fp16, epsilon = add_117_epsilon_0_to_fp16, gamma = add_117_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_233_cast_fp16)[name = tensor("add_117_cast_fp16")]; + tensor input_513_cast_fp16 = silu(x = add_117_cast_fp16)[name = tensor("input_513_cast_fp16")]; + tensor var_4510 = const()[name = tensor("op_4510"), val = tensor([1, 1])]; + tensor var_4512 = const()[name = tensor("op_4512"), val = tensor([1, 1])]; + tensor hidden_states_321_pad_type_0 = const()[name = tensor("hidden_states_321_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_321_pad_0 = const()[name = tensor("hidden_states_321_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_2_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1724196544)))]; + tensor up_blocks_3_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1726039808)))]; + tensor hidden_states_321_cast_fp16 = conv(bias = up_blocks_3_resnets_2_conv2_bias_to_fp16, dilations = var_4512, groups = var_3945, pad = hidden_states_321_pad_0, pad_type = hidden_states_321_pad_type_0, strides = var_4510, weight = up_blocks_3_resnets_2_conv2_weight_to_fp16, x = input_513_cast_fp16)[name = tensor("hidden_states_321_cast_fp16")]; + tensor var_4517 = const()[name = tensor("op_4517"), val = tensor([1, 1])]; + tensor var_4519 = const()[name = tensor("op_4519"), val = tensor([1, 1])]; + tensor x_pad_type_0 = const()[name = tensor("x_pad_type_0"), val = tensor("custom")]; + tensor x_pad_0 = const()[name = tensor("x_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_2_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1726040512)))]; + tensor up_blocks_3_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1726450176)))]; + tensor x_cast_fp16 = conv(bias = up_blocks_3_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_4519, groups = var_3945, pad = x_pad_0, pad_type = x_pad_type_0, strides = var_4517, weight = up_blocks_3_resnets_2_conv_shortcut_weight_to_fp16, x = input_501_cast_fp16)[name = tensor("x_cast_fp16")]; + tensor hidden_states_323_cast_fp16 = add(x = x_cast_fp16, y = hidden_states_321_cast_fp16)[name = tensor("hidden_states_323_cast_fp16")]; + tensor reshape_236_shape_0 = const()[name = tensor("reshape_236_shape_0"), val = tensor([2, 32, 10, 48, 80])]; + tensor reshape_236_cast_fp16 = reshape(shape = reshape_236_shape_0, x = hidden_states_323_cast_fp16)[name = tensor("reshape_236_cast_fp16")]; + tensor reduce_mean_177_axes_0 = const()[name = tensor("reduce_mean_177_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_177_keep_dims_0 = const()[name = tensor("reduce_mean_177_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_177_cast_fp16 = reduce_mean(axes = reduce_mean_177_axes_0, keep_dims = reduce_mean_177_keep_dims_0, x = reshape_236_cast_fp16)[name = tensor("reduce_mean_177_cast_fp16")]; + tensor sub_118_cast_fp16 = sub(x = reshape_236_cast_fp16, y = reduce_mean_177_cast_fp16)[name = tensor("sub_118_cast_fp16")]; + tensor square_59_cast_fp16 = square(x = sub_118_cast_fp16)[name = tensor("square_59_cast_fp16")]; + tensor reduce_mean_179_axes_0 = const()[name = tensor("reduce_mean_179_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_179_keep_dims_0 = const()[name = tensor("reduce_mean_179_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_179_cast_fp16 = reduce_mean(axes = reduce_mean_179_axes_0, keep_dims = reduce_mean_179_keep_dims_0, x = square_59_cast_fp16)[name = tensor("reduce_mean_179_cast_fp16")]; + tensor add_118_y_0_to_fp16 = const()[name = tensor("add_118_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_118_cast_fp16 = add(x = reduce_mean_179_cast_fp16, y = add_118_y_0_to_fp16)[name = tensor("add_118_cast_fp16")]; + tensor sqrt_59_cast_fp16 = sqrt(x = add_118_cast_fp16)[name = tensor("sqrt_59_cast_fp16")]; + tensor real_div_59_cast_fp16 = real_div(x = sub_118_cast_fp16, y = sqrt_59_cast_fp16)[name = tensor("real_div_59_cast_fp16")]; + tensor reshape_237_shape_0 = const()[name = tensor("reshape_237_shape_0"), val = tensor([2, 320, 48, 80])]; + tensor reshape_237_cast_fp16 = reshape(shape = reshape_237_shape_0, x = real_div_59_cast_fp16)[name = tensor("reshape_237_cast_fp16")]; + tensor add_119_gamma_0_to_fp16 = const()[name = tensor("add_119_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1726450880)))]; + tensor add_119_beta_0_to_fp16 = const()[name = tensor("add_119_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1726451584)))]; + tensor add_119_epsilon_0_to_fp16 = const()[name = tensor("add_119_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_119_cast_fp16 = batch_norm(beta = add_119_beta_0_to_fp16, epsilon = add_119_epsilon_0_to_fp16, gamma = add_119_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_237_cast_fp16)[name = tensor("add_119_cast_fp16")]; + tensor var_4539 = const()[name = tensor("op_4539"), val = tensor([1, 1])]; + tensor var_4541 = const()[name = tensor("op_4541"), val = tensor([1, 1])]; + tensor hidden_states_325_pad_type_0 = const()[name = tensor("hidden_states_325_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_325_pad_0 = const()[name = tensor("hidden_states_325_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1726452288)))]; + tensor up_blocks_3_attentions_2_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1726657152)))]; + tensor hidden_states_325_cast_fp16 = conv(bias = up_blocks_3_attentions_2_proj_in_bias_to_fp16, dilations = var_4541, groups = var_3945, pad = hidden_states_325_pad_0, pad_type = hidden_states_325_pad_type_0, strides = var_4539, weight = up_blocks_3_attentions_2_proj_in_weight_to_fp16, x = add_119_cast_fp16)[name = tensor("hidden_states_325_cast_fp16")]; + tensor var_4546 = const()[name = tensor("op_4546"), val = tensor([2, 320, 1, 3840])]; + tensor inputs_91_cast_fp16 = reshape(shape = var_4546, x = hidden_states_325_cast_fp16)[name = tensor("inputs_91_cast_fp16")]; + tensor var_4556 = const()[name = tensor("op_4556"), val = tensor([1])]; + tensor channels_mean_91_cast_fp16 = reduce_mean(axes = var_4556, keep_dims = var_3940, x = inputs_91_cast_fp16)[name = tensor("channels_mean_91_cast_fp16")]; + tensor zero_mean_91_cast_fp16 = sub(x = inputs_91_cast_fp16, y = channels_mean_91_cast_fp16)[name = tensor("zero_mean_91_cast_fp16")]; + tensor zero_mean_sq_91_cast_fp16 = mul(x = zero_mean_91_cast_fp16, y = zero_mean_91_cast_fp16)[name = tensor("zero_mean_sq_91_cast_fp16")]; + tensor var_4560 = const()[name = tensor("op_4560"), val = tensor([1])]; + tensor var_4561_cast_fp16 = reduce_mean(axes = var_4560, keep_dims = var_3940, x = zero_mean_sq_91_cast_fp16)[name = tensor("op_4561_cast_fp16")]; + tensor var_4562_to_fp16 = const()[name = tensor("op_4562_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4563_cast_fp16 = add(x = var_4561_cast_fp16, y = var_4562_to_fp16)[name = tensor("op_4563_cast_fp16")]; + tensor denom_91_epsilon_0_to_fp16 = const()[name = tensor("denom_91_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_91_cast_fp16 = rsqrt(epsilon = denom_91_epsilon_0_to_fp16, x = var_4563_cast_fp16)[name = tensor("denom_91_cast_fp16")]; + tensor out_91_cast_fp16 = mul(x = zero_mean_91_cast_fp16, y = denom_91_cast_fp16)[name = tensor("out_91_cast_fp16")]; + tensor var_4567_to_fp16 = const()[name = tensor("op_4567_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1726657856)))]; + tensor var_4568_cast_fp16 = add(x = out_91_cast_fp16, y = var_4567_to_fp16)[name = tensor("op_4568_cast_fp16")]; + tensor var_4570_to_fp16 = const()[name = tensor("op_4570_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1726658560)))]; + tensor hidden_states_327_cast_fp16 = mul(x = var_4568_cast_fp16, y = var_4570_to_fp16)[name = tensor("hidden_states_327_cast_fp16")]; + tensor var_4577 = const()[name = tensor("op_4577"), val = tensor([1, 1])]; + tensor var_4579 = const()[name = tensor("op_4579"), val = tensor([1, 1])]; + tensor q_61_pad_type_0 = const()[name = tensor("q_61_pad_type_0"), val = tensor("custom")]; + tensor q_61_pad_0 = const()[name = tensor("q_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1726659264)))]; + tensor q_61_cast_fp16 = conv(dilations = var_4579, groups = var_3945, pad = q_61_pad_0, pad_type = q_61_pad_type_0, strides = var_4577, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_327_cast_fp16)[name = tensor("q_61_cast_fp16")]; + tensor var_4583 = const()[name = tensor("op_4583"), val = tensor([1, 1])]; + tensor var_4585 = const()[name = tensor("op_4585"), val = tensor([1, 1])]; + tensor k_61_pad_type_0 = const()[name = tensor("k_61_pad_type_0"), val = tensor("custom")]; + tensor k_61_pad_0 = const()[name = tensor("k_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1726864128)))]; + tensor k_61_cast_fp16 = conv(dilations = var_4585, groups = var_3945, pad = k_61_pad_0, pad_type = k_61_pad_type_0, strides = var_4583, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_327_cast_fp16)[name = tensor("k_61_cast_fp16")]; + tensor var_4589 = const()[name = tensor("op_4589"), val = tensor([1, 1])]; + tensor var_4591 = const()[name = tensor("op_4591"), val = tensor([1, 1])]; + tensor v_61_pad_type_0 = const()[name = tensor("v_61_pad_type_0"), val = tensor("custom")]; + tensor v_61_pad_0 = const()[name = tensor("v_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1727068992)))]; + tensor v_61_cast_fp16 = conv(dilations = var_4591, groups = var_3945, pad = v_61_pad_0, pad_type = v_61_pad_type_0, strides = var_4589, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_327_cast_fp16)[name = tensor("v_61_cast_fp16")]; + tensor var_4595 = const()[name = tensor("op_4595"), val = tensor([2, 5, 64, -1])]; + tensor var_4596_cast_fp16 = reshape(shape = var_4595, x = q_61_cast_fp16)[name = tensor("op_4596_cast_fp16")]; + tensor var_4597 = const()[name = tensor("op_4597"), val = tensor([2, 5, 64, -1])]; + tensor var_4598_cast_fp16 = reshape(shape = var_4597, x = k_61_cast_fp16)[name = tensor("op_4598_cast_fp16")]; + tensor var_4599 = const()[name = tensor("op_4599"), val = tensor([2, 5, 64, -1])]; + tensor var_4600_cast_fp16 = reshape(shape = var_4599, x = v_61_cast_fp16)[name = tensor("op_4600_cast_fp16")]; + tensor attn_weights_121_transpose_x_0 = const()[name = tensor("attn_weights_121_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_121_transpose_y_0 = const()[name = tensor("attn_weights_121_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_121_cast_fp16 = matmul(transpose_x = attn_weights_121_transpose_x_0, transpose_y = attn_weights_121_transpose_y_0, x = var_4596_cast_fp16, y = var_4598_cast_fp16)[name = tensor("attn_weights_121_cast_fp16")]; + tensor attn_weights_123_cast_fp16 = mul(x = attn_weights_121_cast_fp16, y = var_3936_to_fp16)[name = tensor("attn_weights_123_cast_fp16")]; + tensor var_4604_cast_fp16 = softmax(axis = var_3929, x = attn_weights_123_cast_fp16)[name = tensor("op_4604_cast_fp16")]; + tensor attn_61_transpose_x_0 = const()[name = tensor("attn_61_transpose_x_0"), val = tensor(false)]; + tensor attn_61_transpose_y_0 = const()[name = tensor("attn_61_transpose_y_0"), val = tensor(true)]; + tensor attn_61_cast_fp16 = matmul(transpose_x = attn_61_transpose_x_0, transpose_y = attn_61_transpose_y_0, x = var_4600_cast_fp16, y = var_4604_cast_fp16)[name = tensor("attn_61_cast_fp16")]; + tensor var_4608 = const()[name = tensor("op_4608"), val = tensor([2, 320, 1, -1])]; + tensor input_517_cast_fp16 = reshape(shape = var_4608, x = attn_61_cast_fp16)[name = tensor("input_517_cast_fp16")]; + tensor var_4613 = const()[name = tensor("op_4613"), val = tensor([1, 1])]; + tensor var_4615 = const()[name = tensor("op_4615"), val = tensor([1, 1])]; + tensor var_4617_pad_type_0 = const()[name = tensor("op_4617_pad_type_0"), val = tensor("custom")]; + tensor var_4617_pad_0 = const()[name = tensor("op_4617_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1727273856)))]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1727478720)))]; + tensor var_4617_cast_fp16 = conv(bias = up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_4615, groups = var_3945, pad = var_4617_pad_0, pad_type = var_4617_pad_type_0, strides = var_4613, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_517_cast_fp16)[name = tensor("op_4617_cast_fp16")]; + tensor inputs_93_cast_fp16 = add(x = var_4617_cast_fp16, y = inputs_91_cast_fp16)[name = tensor("inputs_93_cast_fp16")]; + tensor var_4621 = const()[name = tensor("op_4621"), val = tensor([1])]; + tensor channels_mean_93_cast_fp16 = reduce_mean(axes = var_4621, keep_dims = var_3940, x = inputs_93_cast_fp16)[name = tensor("channels_mean_93_cast_fp16")]; + tensor zero_mean_93_cast_fp16 = sub(x = inputs_93_cast_fp16, y = channels_mean_93_cast_fp16)[name = tensor("zero_mean_93_cast_fp16")]; + tensor zero_mean_sq_93_cast_fp16 = mul(x = zero_mean_93_cast_fp16, y = zero_mean_93_cast_fp16)[name = tensor("zero_mean_sq_93_cast_fp16")]; + tensor var_4625 = const()[name = tensor("op_4625"), val = tensor([1])]; + tensor var_4626_cast_fp16 = reduce_mean(axes = var_4625, keep_dims = var_3940, x = zero_mean_sq_93_cast_fp16)[name = tensor("op_4626_cast_fp16")]; + tensor var_4627_to_fp16 = const()[name = tensor("op_4627_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4628_cast_fp16 = add(x = var_4626_cast_fp16, y = var_4627_to_fp16)[name = tensor("op_4628_cast_fp16")]; + tensor denom_93_epsilon_0_to_fp16 = const()[name = tensor("denom_93_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_93_cast_fp16 = rsqrt(epsilon = denom_93_epsilon_0_to_fp16, x = var_4628_cast_fp16)[name = tensor("denom_93_cast_fp16")]; + tensor out_93_cast_fp16 = mul(x = zero_mean_93_cast_fp16, y = denom_93_cast_fp16)[name = tensor("out_93_cast_fp16")]; + tensor var_4632_to_fp16 = const()[name = tensor("op_4632_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1727479424)))]; + tensor var_4633_cast_fp16 = add(x = out_93_cast_fp16, y = var_4632_to_fp16)[name = tensor("op_4633_cast_fp16")]; + tensor var_4635_to_fp16 = const()[name = tensor("op_4635_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1727480128)))]; + tensor hidden_states_329_cast_fp16 = mul(x = var_4633_cast_fp16, y = var_4635_to_fp16)[name = tensor("hidden_states_329_cast_fp16")]; + tensor var_4642 = const()[name = tensor("op_4642"), val = tensor([1, 1])]; + tensor var_4644 = const()[name = tensor("op_4644"), val = tensor([1, 1])]; + tensor q_pad_type_0 = const()[name = tensor("q_pad_type_0"), val = tensor("custom")]; + tensor q_pad_0 = const()[name = tensor("q_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1727480832)))]; + tensor q_cast_fp16 = conv(dilations = var_4644, groups = var_3945, pad = q_pad_0, pad_type = q_pad_type_0, strides = var_4642, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_329_cast_fp16)[name = tensor("q_cast_fp16")]; + tensor var_4648 = const()[name = tensor("op_4648"), val = tensor([1, 1])]; + tensor var_4650 = const()[name = tensor("op_4650"), val = tensor([1, 1])]; + tensor k_pad_type_0 = const()[name = tensor("k_pad_type_0"), val = tensor("custom")]; + tensor k_pad_0 = const()[name = tensor("k_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1727685696)))]; + tensor k_cast_fp16 = conv(dilations = var_4650, groups = var_3945, pad = k_pad_0, pad_type = k_pad_type_0, strides = var_4648, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_cast_fp16")]; + tensor var_4654 = const()[name = tensor("op_4654"), val = tensor([1, 1])]; + tensor var_4656 = const()[name = tensor("op_4656"), val = tensor([1, 1])]; + tensor v_pad_type_0 = const()[name = tensor("v_pad_type_0"), val = tensor("custom")]; + tensor v_pad_0 = const()[name = tensor("v_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1728341120)))]; + tensor v_cast_fp16 = conv(dilations = var_4656, groups = var_3945, pad = v_pad_0, pad_type = v_pad_type_0, strides = var_4654, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_cast_fp16")]; + tensor var_4660 = const()[name = tensor("op_4660"), val = tensor([2, 5, 64, -1])]; + tensor var_4661_cast_fp16 = reshape(shape = var_4660, x = q_cast_fp16)[name = tensor("op_4661_cast_fp16")]; + tensor var_4662 = const()[name = tensor("op_4662"), val = tensor([2, 5, 64, -1])]; + tensor var_4663_cast_fp16 = reshape(shape = var_4662, x = k_cast_fp16)[name = tensor("op_4663_cast_fp16")]; + tensor var_4664 = const()[name = tensor("op_4664"), val = tensor([2, 5, 64, -1])]; + tensor var_4665_cast_fp16 = reshape(shape = var_4664, x = v_cast_fp16)[name = tensor("op_4665_cast_fp16")]; + tensor attn_weights_125_transpose_x_0 = const()[name = tensor("attn_weights_125_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_125_transpose_y_0 = const()[name = tensor("attn_weights_125_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_125_cast_fp16 = matmul(transpose_x = attn_weights_125_transpose_x_0, transpose_y = attn_weights_125_transpose_y_0, x = var_4661_cast_fp16, y = var_4663_cast_fp16)[name = tensor("attn_weights_125_cast_fp16")]; + tensor attn_weights_cast_fp16 = mul(x = attn_weights_125_cast_fp16, y = var_3936_to_fp16)[name = tensor("attn_weights_cast_fp16")]; + tensor var_4669_cast_fp16 = softmax(axis = var_3929, x = attn_weights_cast_fp16)[name = tensor("op_4669_cast_fp16")]; + tensor attn_transpose_x_0 = const()[name = tensor("attn_transpose_x_0"), val = tensor(false)]; + tensor attn_transpose_y_0 = const()[name = tensor("attn_transpose_y_0"), val = tensor(true)]; + tensor attn_cast_fp16 = matmul(transpose_x = attn_transpose_x_0, transpose_y = attn_transpose_y_0, x = var_4665_cast_fp16, y = var_4669_cast_fp16)[name = tensor("attn_cast_fp16")]; + tensor var_4673 = const()[name = tensor("op_4673"), val = tensor([2, 320, 1, -1])]; + tensor input_519_cast_fp16 = reshape(shape = var_4673, x = attn_cast_fp16)[name = tensor("input_519_cast_fp16")]; + tensor var_4678 = const()[name = tensor("op_4678"), val = tensor([1, 1])]; + tensor var_4680 = const()[name = tensor("op_4680"), val = tensor([1, 1])]; + tensor var_4682_pad_type_0 = const()[name = tensor("op_4682_pad_type_0"), val = tensor("custom")]; + tensor var_4682_pad_0 = const()[name = tensor("op_4682_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1728996544)))]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1729201408)))]; + tensor var_4682_cast_fp16 = conv(bias = up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_4680, groups = var_3945, pad = var_4682_pad_0, pad_type = var_4682_pad_type_0, strides = var_4678, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_519_cast_fp16)[name = tensor("op_4682_cast_fp16")]; + tensor inputs_cast_fp16 = add(x = var_4682_cast_fp16, y = inputs_93_cast_fp16)[name = tensor("inputs_cast_fp16")]; + tensor var_4686 = const()[name = tensor("op_4686"), val = tensor([1])]; + tensor channels_mean_cast_fp16 = reduce_mean(axes = var_4686, keep_dims = var_3940, x = inputs_cast_fp16)[name = tensor("channels_mean_cast_fp16")]; + tensor zero_mean_cast_fp16 = sub(x = inputs_cast_fp16, y = channels_mean_cast_fp16)[name = tensor("zero_mean_cast_fp16")]; + tensor zero_mean_sq_cast_fp16 = mul(x = zero_mean_cast_fp16, y = zero_mean_cast_fp16)[name = tensor("zero_mean_sq_cast_fp16")]; + tensor var_4690 = const()[name = tensor("op_4690"), val = tensor([1])]; + tensor var_4691_cast_fp16 = reduce_mean(axes = var_4690, keep_dims = var_3940, x = zero_mean_sq_cast_fp16)[name = tensor("op_4691_cast_fp16")]; + tensor var_4692_to_fp16 = const()[name = tensor("op_4692_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4693_cast_fp16 = add(x = var_4691_cast_fp16, y = var_4692_to_fp16)[name = tensor("op_4693_cast_fp16")]; + tensor denom_epsilon_0_to_fp16 = const()[name = tensor("denom_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_cast_fp16 = rsqrt(epsilon = denom_epsilon_0_to_fp16, x = var_4693_cast_fp16)[name = tensor("denom_cast_fp16")]; + tensor out_cast_fp16 = mul(x = zero_mean_cast_fp16, y = denom_cast_fp16)[name = tensor("out_cast_fp16")]; + tensor var_4697_to_fp16 = const()[name = tensor("op_4697_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1729202112)))]; + tensor var_4698_cast_fp16 = add(x = out_cast_fp16, y = var_4697_to_fp16)[name = tensor("op_4698_cast_fp16")]; + tensor var_4700_to_fp16 = const()[name = tensor("op_4700_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1729202816)))]; + tensor input_521_cast_fp16 = mul(x = var_4698_cast_fp16, y = var_4700_to_fp16)[name = tensor("input_521_cast_fp16")]; + tensor var_4708 = const()[name = tensor("op_4708"), val = tensor([1, 1])]; + tensor var_4710 = const()[name = tensor("op_4710"), val = tensor([1, 1])]; + tensor var_4712_pad_type_0 = const()[name = tensor("op_4712_pad_type_0"), val = tensor("custom")]; + tensor var_4712_pad_0 = const()[name = tensor("op_4712_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1729203520)))]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1730841984)))]; + tensor var_4712_cast_fp16 = conv(bias = up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_4710, groups = var_3945, pad = var_4712_pad_0, pad_type = var_4712_pad_type_0, strides = var_4708, weight = up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_521_cast_fp16)[name = tensor("op_4712_cast_fp16")]; + tensor var_4713_split_sizes_0 = const()[name = tensor("op_4713_split_sizes_0"), val = tensor([1280, 1280])]; + tensor var_4713_axis_0 = const()[name = tensor("op_4713_axis_0"), val = tensor(1)]; + tensor var_4713_cast_fp16_0, tensor var_4713_cast_fp16_1 = split(axis = var_4713_axis_0, split_sizes = var_4713_split_sizes_0, x = var_4712_cast_fp16)[name = tensor("op_4713_cast_fp16")]; + tensor var_4715_mode_0 = const()[name = tensor("op_4715_mode_0"), val = tensor("EXACT")]; + tensor var_4715_cast_fp16 = gelu(mode = var_4715_mode_0, x = var_4713_cast_fp16_1)[name = tensor("op_4715_cast_fp16")]; + tensor input_523_cast_fp16 = mul(x = var_4713_cast_fp16_0, y = var_4715_cast_fp16)[name = tensor("input_523_cast_fp16")]; + tensor var_4719 = const()[name = tensor("op_4719"), val = tensor([1, 1])]; + tensor var_4721 = const()[name = tensor("op_4721"), val = tensor([1, 1])]; + tensor var_4723_pad_type_0 = const()[name = tensor("op_4723_pad_type_0"), val = tensor("custom")]; + tensor var_4723_pad_0 = const()[name = tensor("op_4723_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1730847168)))]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1731666432)))]; + tensor var_4723_cast_fp16 = conv(bias = up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_4721, groups = var_3945, pad = var_4723_pad_0, pad_type = var_4723_pad_type_0, strides = var_4719, weight = up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_523_cast_fp16)[name = tensor("op_4723_cast_fp16")]; + tensor hidden_states_333_cast_fp16 = add(x = var_4723_cast_fp16, y = inputs_cast_fp16)[name = tensor("hidden_states_333_cast_fp16")]; + tensor var_4725 = const()[name = tensor("op_4725"), val = tensor([2, 320, 48, 80])]; + tensor input_525_cast_fp16 = reshape(shape = var_4725, x = hidden_states_333_cast_fp16)[name = tensor("input_525_cast_fp16")]; + tensor var_4729 = const()[name = tensor("op_4729"), val = tensor([1, 1])]; + tensor var_4731 = const()[name = tensor("op_4731"), val = tensor([1, 1])]; + tensor hidden_states_pad_type_0 = const()[name = tensor("hidden_states_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_pad_0 = const()[name = tensor("hidden_states_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1731667136)))]; + tensor up_blocks_3_attentions_2_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1731872000)))]; + tensor hidden_states_cast_fp16 = conv(bias = up_blocks_3_attentions_2_proj_out_bias_to_fp16, dilations = var_4731, groups = var_3945, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = var_4729, weight = up_blocks_3_attentions_2_proj_out_weight_to_fp16, x = input_525_cast_fp16)[name = tensor("hidden_states_cast_fp16")]; + tensor input_527_cast_fp16 = add(x = hidden_states_cast_fp16, y = hidden_states_323_cast_fp16)[name = tensor("input_527_cast_fp16")]; + tensor reshape_240_shape_0 = const()[name = tensor("reshape_240_shape_0"), val = tensor([2, 32, 10, 48, 80])]; + tensor reshape_240_cast_fp16 = reshape(shape = reshape_240_shape_0, x = input_527_cast_fp16)[name = tensor("reshape_240_cast_fp16")]; + tensor reduce_mean_180_axes_0 = const()[name = tensor("reduce_mean_180_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_180_keep_dims_0 = const()[name = tensor("reduce_mean_180_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_180_cast_fp16 = reduce_mean(axes = reduce_mean_180_axes_0, keep_dims = reduce_mean_180_keep_dims_0, x = reshape_240_cast_fp16)[name = tensor("reduce_mean_180_cast_fp16")]; + tensor sub_120_cast_fp16 = sub(x = reshape_240_cast_fp16, y = reduce_mean_180_cast_fp16)[name = tensor("sub_120_cast_fp16")]; + tensor square_60_cast_fp16 = square(x = sub_120_cast_fp16)[name = tensor("square_60_cast_fp16")]; + tensor reduce_mean_182_axes_0 = const()[name = tensor("reduce_mean_182_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_182_keep_dims_0 = const()[name = tensor("reduce_mean_182_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_182_cast_fp16 = reduce_mean(axes = reduce_mean_182_axes_0, keep_dims = reduce_mean_182_keep_dims_0, x = square_60_cast_fp16)[name = tensor("reduce_mean_182_cast_fp16")]; + tensor add_120_y_0_to_fp16 = const()[name = tensor("add_120_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_120_cast_fp16 = add(x = reduce_mean_182_cast_fp16, y = add_120_y_0_to_fp16)[name = tensor("add_120_cast_fp16")]; + tensor sqrt_60_cast_fp16 = sqrt(x = add_120_cast_fp16)[name = tensor("sqrt_60_cast_fp16")]; + tensor real_div_60_cast_fp16 = real_div(x = sub_120_cast_fp16, y = sqrt_60_cast_fp16)[name = tensor("real_div_60_cast_fp16")]; + tensor reshape_241_shape_0 = const()[name = tensor("reshape_241_shape_0"), val = tensor([2, 320, 48, 80])]; + tensor reshape_241_cast_fp16 = reshape(shape = reshape_241_shape_0, x = real_div_60_cast_fp16)[name = tensor("reshape_241_cast_fp16")]; + tensor add_121_gamma_0_to_fp16 = const()[name = tensor("add_121_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1731872704)))]; + tensor add_121_beta_0_to_fp16 = const()[name = tensor("add_121_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1731873408)))]; + tensor add_121_epsilon_0_to_fp16 = const()[name = tensor("add_121_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_121_cast_fp16 = batch_norm(beta = add_121_beta_0_to_fp16, epsilon = add_121_epsilon_0_to_fp16, gamma = add_121_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_241_cast_fp16)[name = tensor("add_121_cast_fp16")]; + tensor input_cast_fp16 = silu(x = add_121_cast_fp16)[name = tensor("input_cast_fp16")]; + tensor var_4745 = const()[name = tensor("op_4745"), val = tensor(1)]; + tensor var_4748 = const()[name = tensor("op_4748"), val = tensor([1, 1])]; + tensor var_4750 = const()[name = tensor("op_4750"), val = tensor([1, 1])]; + tensor var_4752_pad_type_0 = const()[name = tensor("op_4752_pad_type_0"), val = tensor("custom")]; + tensor var_4752_pad_0 = const()[name = tensor("op_4752_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv_out_weight_to_fp16 = const()[name = tensor("conv_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1731874112)))]; + tensor conv_out_bias_to_fp16 = const()[name = tensor("conv_out_bias_to_fp16"), val = tensor([-0x1.4b4p-9, 0x1.6f4p-9, 0x1.9ap-12, 0x1.04p-9])]; + tensor var_4752_cast_fp16 = conv(bias = conv_out_bias_to_fp16, dilations = var_4750, groups = var_4745, pad = var_4752_pad_0, pad_type = var_4752_pad_type_0, strides = var_4748, weight = conv_out_weight_to_fp16, x = input_cast_fp16)[name = tensor("op_4752_cast_fp16")]; + tensor var_4752_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("op_4752_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor noise_pred = cast(dtype = var_4752_cast_fp16_to_fp32_dtype_0, x = var_4752_cast_fp16)[name = tensor("cast_261")]; + } -> (noise_pred); +} \ No newline at end of file diff --git a/original/compiled/Unet.mlmodelc/weights/weight.bin b/original/compiled/Unet.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..469524f2870f7ad707e9f788704cf02d0d4320ac --- /dev/null +++ b/original/compiled/Unet.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8679eecc6eb9650d756f5eccbe737bea7c170edeff4dd7b29e056895edea8e69 +size 1731897216 diff --git a/original/compiled/UnetChunk1.mlmodelc/analytics/coremldata.bin b/original/compiled/UnetChunk1.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..c44e329eec247aa59e4337e1f72e7863ac887fa7 --- /dev/null +++ b/original/compiled/UnetChunk1.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 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tensor var_25 = const()[name = tensor("op_25"), val = tensor(-1)]; + tensor var_42_axes_0 = const()[name = tensor("op_42_axes_0"), val = tensor([1])]; + tensor var_42_cast_fp16 = expand_dims(axes = var_42_axes_0, x = timestep)[name = tensor("op_42_cast_fp16")]; + tensor var_44_to_fp16 = const()[name = tensor("op_44_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor emb_3_cast_fp16 = mul(x = var_42_cast_fp16, y = var_44_to_fp16)[name = tensor("emb_3_cast_fp16")]; + tensor var_49_cast_fp16 = sin(x = emb_3_cast_fp16)[name = tensor("op_49_cast_fp16")]; + tensor var_50_cast_fp16 = cos(x = emb_3_cast_fp16)[name = tensor("op_50_cast_fp16")]; + tensor emb_interleave_0 = const()[name = tensor("emb_interleave_0"), val = tensor(false)]; + tensor emb_cast_fp16 = concat(axis = var_25, interleave = emb_interleave_0, values = (var_49_cast_fp16, var_50_cast_fp16))[name = tensor("emb_cast_fp16")]; + tensor var_54_begin_0 = const()[name = tensor("op_54_begin_0"), val = tensor([0, 160])]; + tensor var_54_end_0 = const()[name = tensor("op_54_end_0"), val = tensor([2, 320])]; + tensor var_54_end_mask_0 = const()[name = tensor("op_54_end_mask_0"), val = tensor([true, true])]; + tensor var_54_cast_fp16 = slice_by_index(begin = var_54_begin_0, end = var_54_end_0, end_mask = var_54_end_mask_0, x = emb_cast_fp16)[name = tensor("op_54_cast_fp16")]; + tensor var_56_begin_0 = const()[name = tensor("op_56_begin_0"), val = tensor([0, 0])]; + tensor var_56_end_0 = const()[name = tensor("op_56_end_0"), val = tensor([2, 160])]; + tensor var_56_end_mask_0 = const()[name = tensor("op_56_end_mask_0"), val = tensor([true, false])]; + tensor var_56_cast_fp16 = slice_by_index(begin = var_56_begin_0, end = var_56_end_0, end_mask = var_56_end_mask_0, x = emb_cast_fp16)[name = tensor("op_56_cast_fp16")]; + tensor sample_interleave_0 = const()[name = tensor("sample_interleave_0"), val = tensor(false)]; + tensor sample_cast_fp16 = concat(axis = var_25, interleave = sample_interleave_0, values = (var_54_cast_fp16, var_56_cast_fp16))[name = tensor("sample_cast_fp16")]; + tensor var_59 = const()[name = tensor("op_59"), val = tensor(1)]; + tensor var_66_axes_0 = const()[name = tensor("op_66_axes_0"), val = tensor([-1])]; + tensor var_66_cast_fp16 = expand_dims(axes = var_66_axes_0, x = sample_cast_fp16)[name = tensor("op_66_cast_fp16")]; + tensor input_1_axes_0 = const()[name = tensor("input_1_axes_0"), val = tensor([-1])]; + tensor input_1_cast_fp16 = expand_dims(axes = input_1_axes_0, x = var_66_cast_fp16)[name = tensor("input_1_cast_fp16")]; + tensor var_70 = const()[name = tensor("op_70"), val = tensor([1, 1])]; + tensor var_72 = const()[name = tensor("op_72"), val = tensor([1, 1])]; + tensor input_3_pad_type_0 = const()[name = tensor("input_3_pad_type_0"), val = tensor("custom")]; + tensor input_3_pad_0 = const()[name = tensor("input_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor time_embedding_linear_1_weight_to_fp16 = const()[name = tensor("time_embedding_linear_1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(448)))]; + tensor time_embedding_linear_1_bias_to_fp16 = const()[name = tensor("time_embedding_linear_1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(819712)))]; + tensor input_3_cast_fp16 = conv(bias = time_embedding_linear_1_bias_to_fp16, dilations = var_72, groups = var_59, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_70, weight = time_embedding_linear_1_weight_to_fp16, x = input_1_cast_fp16)[name = tensor("input_3_cast_fp16")]; + tensor input_5_cast_fp16 = silu(x = input_3_cast_fp16)[name = tensor("input_5_cast_fp16")]; + tensor var_78 = const()[name = tensor("op_78"), val = tensor([1, 1])]; + tensor var_80 = const()[name = tensor("op_80"), val = tensor([1, 1])]; + tensor input_13_pad_type_0 = const()[name = tensor("input_13_pad_type_0"), val = tensor("custom")]; + tensor input_13_pad_0 = const()[name = tensor("input_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor time_embedding_linear_2_weight_to_fp16 = const()[name = tensor("time_embedding_linear_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(822336)))]; + tensor time_embedding_linear_2_bias_to_fp16 = const()[name = tensor("time_embedding_linear_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4099200)))]; + tensor input_13_cast_fp16 = conv(bias = time_embedding_linear_2_bias_to_fp16, dilations = var_80, groups = var_59, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = var_78, weight = time_embedding_linear_2_weight_to_fp16, x = input_5_cast_fp16)[name = tensor("input_13_cast_fp16")]; + tensor var_86 = const()[name = tensor("op_86"), val = tensor(1)]; + tensor var_89 = const()[name = tensor("op_89"), val = tensor([1, 1])]; + tensor var_91 = const()[name = tensor("op_91"), val = tensor([1, 1])]; + tensor input_7_pad_type_0 = const()[name = tensor("input_7_pad_type_0"), val = tensor("custom")]; + tensor input_7_pad_0 = const()[name = tensor("input_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv_in_weight_to_fp16 = const()[name = tensor("conv_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4101824)))]; + tensor conv_in_bias_to_fp16 = const()[name = tensor("conv_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4124928)))]; + tensor input_7_cast_fp16_1 = conv(bias = conv_in_bias_to_fp16, dilations = var_91, groups = var_86, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = var_89, weight = conv_in_weight_to_fp16, x = sample)[name = tensor("input_7_cast_fp16")]; + tensor var_95 = const()[name = tensor("op_95"), val = tensor(3)]; + tensor var_106 = const()[name = tensor("op_106"), val = tensor(true)]; + tensor var_111 = const()[name = tensor("op_111"), val = tensor(1)]; + tensor reshape_0_shape_0 = const()[name = tensor("reshape_0_shape_0"), val = tensor([2, 32, 10, 48, 80])]; + tensor reshape_0_cast_fp16 = reshape(shape = reshape_0_shape_0, x = input_7_cast_fp16_1)[name = tensor("reshape_0_cast_fp16")]; + tensor reduce_mean_0_axes_0 = const()[name = tensor("reduce_mean_0_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_0_keep_dims_0 = const()[name = tensor("reduce_mean_0_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_0_cast_fp16 = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = reshape_0_cast_fp16)[name = tensor("reduce_mean_0_cast_fp16")]; + tensor sub_0_cast_fp16 = sub(x = reshape_0_cast_fp16, y = reduce_mean_0_cast_fp16)[name = tensor("sub_0_cast_fp16")]; + tensor square_0_cast_fp16 = square(x = sub_0_cast_fp16)[name = tensor("square_0_cast_fp16")]; + tensor reduce_mean_2_axes_0 = const()[name = tensor("reduce_mean_2_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_2_keep_dims_0 = const()[name = tensor("reduce_mean_2_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_2_cast_fp16 = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = square_0_cast_fp16)[name = tensor("reduce_mean_2_cast_fp16")]; + tensor add_0_y_0_to_fp16 = const()[name = tensor("add_0_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_0_cast_fp16 = add(x = reduce_mean_2_cast_fp16, y = add_0_y_0_to_fp16)[name = tensor("add_0_cast_fp16")]; + tensor sqrt_0_cast_fp16 = sqrt(x = add_0_cast_fp16)[name = tensor("sqrt_0_cast_fp16")]; + tensor real_div_0_cast_fp16 = real_div(x = sub_0_cast_fp16, y = sqrt_0_cast_fp16)[name = tensor("real_div_0_cast_fp16")]; + tensor reshape_1_shape_0 = const()[name = tensor("reshape_1_shape_0"), val = tensor([2, 320, 48, 80])]; + tensor reshape_1_cast_fp16 = reshape(shape = reshape_1_shape_0, x = real_div_0_cast_fp16)[name = tensor("reshape_1_cast_fp16")]; + tensor add_1_mean_0_to_fp16 = const()[name = tensor("add_1_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4125632)))]; + tensor add_1_variance_0_to_fp16 = const()[name = tensor("add_1_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4126336)))]; + tensor add_1_gamma_0_to_fp16 = const()[name = tensor("add_1_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4127040)))]; + tensor add_1_beta_0_to_fp16 = const()[name = tensor("add_1_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4127744)))]; + tensor add_1_epsilon_0_to_fp16 = const()[name = tensor("add_1_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_1_cast_fp16 = batch_norm(beta = add_1_beta_0_to_fp16, epsilon = add_1_epsilon_0_to_fp16, gamma = add_1_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_1_cast_fp16)[name = tensor("add_1_cast_fp16")]; + tensor input_11_cast_fp16 = silu(x = add_1_cast_fp16)[name = tensor("input_11_cast_fp16")]; + tensor var_133 = const()[name = tensor("op_133"), val = tensor([1, 1])]; + tensor var_135 = const()[name = tensor("op_135"), val = tensor([1, 1])]; + tensor hidden_states_1_pad_type_0 = const()[name = tensor("hidden_states_1_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_1_pad_0 = const()[name = tensor("hidden_states_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4128448)))]; + tensor down_blocks_0_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5971712)))]; + tensor hidden_states_1_cast_fp16 = conv(bias = down_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = var_135, groups = var_111, pad = hidden_states_1_pad_0, pad_type = hidden_states_1_pad_type_0, strides = var_133, weight = down_blocks_0_resnets_0_conv1_weight_to_fp16, x = input_11_cast_fp16)[name = tensor("hidden_states_1_cast_fp16")]; + tensor input_15_cast_fp16_1 = silu(x = input_13_cast_fp16)[name = tensor("input_15_cast_fp16")]; + tensor var_141 = const()[name = tensor("op_141"), val = tensor([1, 1])]; + tensor var_143 = const()[name = tensor("op_143"), val = tensor([1, 1])]; + tensor temb_1_pad_type_0 = const()[name = tensor("temb_1_pad_type_0"), val = tensor("custom")]; + tensor temb_1_pad_0 = const()[name = tensor("temb_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5972416)))]; + tensor down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6791680)))]; + tensor temb_1_cast_fp16 = conv(bias = down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_143, groups = var_111, pad = temb_1_pad_0, pad_type = temb_1_pad_type_0, strides = var_141, weight = down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16_1)[name = tensor("temb_1_cast_fp16")]; + tensor input_17_cast_fp16 = add(x = hidden_states_1_cast_fp16, y = temb_1_cast_fp16)[name = tensor("input_17_cast_fp16")]; + tensor reshape_4_shape_0 = const()[name = tensor("reshape_4_shape_0"), val = tensor([2, 32, 10, 48, 80])]; + tensor reshape_4_cast_fp16 = reshape(shape = reshape_4_shape_0, x = input_17_cast_fp16)[name = tensor("reshape_4_cast_fp16")]; + tensor reduce_mean_3_axes_0 = const()[name = tensor("reduce_mean_3_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_3_keep_dims_0 = const()[name = tensor("reduce_mean_3_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_3_cast_fp16 = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = reshape_4_cast_fp16)[name = tensor("reduce_mean_3_cast_fp16")]; + tensor sub_2_cast_fp16 = sub(x = reshape_4_cast_fp16, y = reduce_mean_3_cast_fp16)[name = tensor("sub_2_cast_fp16")]; + tensor square_1_cast_fp16 = square(x = sub_2_cast_fp16)[name = tensor("square_1_cast_fp16")]; + tensor reduce_mean_5_axes_0 = const()[name = tensor("reduce_mean_5_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_5_keep_dims_0 = const()[name = tensor("reduce_mean_5_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_5_cast_fp16 = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_1_cast_fp16)[name = tensor("reduce_mean_5_cast_fp16")]; + tensor add_2_y_0_to_fp16 = const()[name = tensor("add_2_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_2_cast_fp16 = add(x = reduce_mean_5_cast_fp16, y = add_2_y_0_to_fp16)[name = tensor("add_2_cast_fp16")]; + tensor sqrt_1_cast_fp16 = sqrt(x = add_2_cast_fp16)[name = tensor("sqrt_1_cast_fp16")]; + tensor real_div_1_cast_fp16 = real_div(x = sub_2_cast_fp16, y = sqrt_1_cast_fp16)[name = tensor("real_div_1_cast_fp16")]; + tensor reshape_5_shape_0 = const()[name = tensor("reshape_5_shape_0"), val = tensor([2, 320, 48, 80])]; + tensor reshape_5_cast_fp16 = reshape(shape = reshape_5_shape_0, x = real_div_1_cast_fp16)[name = tensor("reshape_5_cast_fp16")]; + tensor add_3_gamma_0_to_fp16 = const()[name = tensor("add_3_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6792384)))]; + tensor add_3_beta_0_to_fp16 = const()[name = tensor("add_3_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6793088)))]; + tensor add_3_epsilon_0_to_fp16 = const()[name = tensor("add_3_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_3_cast_fp16 = batch_norm(beta = add_3_beta_0_to_fp16, epsilon = add_3_epsilon_0_to_fp16, gamma = add_3_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_5_cast_fp16)[name = tensor("add_3_cast_fp16")]; + tensor input_21_cast_fp16 = silu(x = add_3_cast_fp16)[name = tensor("input_21_cast_fp16")]; + tensor var_153 = const()[name = tensor("op_153"), val = tensor([1, 1])]; + tensor var_155 = const()[name = tensor("op_155"), val = tensor([1, 1])]; + tensor hidden_states_3_pad_type_0 = const()[name = tensor("hidden_states_3_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_3_pad_0 = const()[name = tensor("hidden_states_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6793792)))]; + tensor down_blocks_0_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8637056)))]; + tensor hidden_states_3_cast_fp16 = conv(bias = down_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = var_155, groups = var_111, pad = hidden_states_3_pad_0, pad_type = hidden_states_3_pad_type_0, strides = var_153, weight = down_blocks_0_resnets_0_conv2_weight_to_fp16, x = input_21_cast_fp16)[name = tensor("hidden_states_3_cast_fp16")]; + tensor hidden_states_5_cast_fp16 = add(x = input_7_cast_fp16_1, y = hidden_states_3_cast_fp16)[name = tensor("hidden_states_5_cast_fp16")]; + tensor reshape_8_shape_0 = const()[name = tensor("reshape_8_shape_0"), val = tensor([2, 32, 10, 48, 80])]; + tensor reshape_8_cast_fp16 = reshape(shape = reshape_8_shape_0, x = hidden_states_5_cast_fp16)[name = tensor("reshape_8_cast_fp16")]; + tensor reduce_mean_6_axes_0 = const()[name = tensor("reduce_mean_6_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_6_keep_dims_0 = const()[name = tensor("reduce_mean_6_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_6_cast_fp16 = reduce_mean(axes = reduce_mean_6_axes_0, keep_dims = reduce_mean_6_keep_dims_0, x = reshape_8_cast_fp16)[name = tensor("reduce_mean_6_cast_fp16")]; + tensor sub_4_cast_fp16 = sub(x = reshape_8_cast_fp16, y = reduce_mean_6_cast_fp16)[name = tensor("sub_4_cast_fp16")]; + tensor square_2_cast_fp16 = square(x = sub_4_cast_fp16)[name = tensor("square_2_cast_fp16")]; + tensor reduce_mean_8_axes_0 = const()[name = tensor("reduce_mean_8_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_8_keep_dims_0 = const()[name = tensor("reduce_mean_8_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_8_cast_fp16 = reduce_mean(axes = reduce_mean_8_axes_0, keep_dims = reduce_mean_8_keep_dims_0, x = square_2_cast_fp16)[name = tensor("reduce_mean_8_cast_fp16")]; + tensor add_4_y_0_to_fp16 = const()[name = tensor("add_4_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_4_cast_fp16 = add(x = reduce_mean_8_cast_fp16, y = add_4_y_0_to_fp16)[name = tensor("add_4_cast_fp16")]; + tensor sqrt_2_cast_fp16 = sqrt(x = add_4_cast_fp16)[name = tensor("sqrt_2_cast_fp16")]; + tensor real_div_2_cast_fp16 = real_div(x = sub_4_cast_fp16, y = sqrt_2_cast_fp16)[name = tensor("real_div_2_cast_fp16")]; + tensor reshape_9_shape_0 = const()[name = tensor("reshape_9_shape_0"), val = tensor([2, 320, 48, 80])]; + tensor reshape_9_cast_fp16 = reshape(shape = reshape_9_shape_0, x = real_div_2_cast_fp16)[name = tensor("reshape_9_cast_fp16")]; + tensor add_5_gamma_0_to_fp16 = const()[name = tensor("add_5_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8637760)))]; + tensor add_5_beta_0_to_fp16 = const()[name = tensor("add_5_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8638464)))]; + tensor add_5_epsilon_0_to_fp16 = const()[name = tensor("add_5_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_5_cast_fp16 = batch_norm(beta = add_5_beta_0_to_fp16, epsilon = add_5_epsilon_0_to_fp16, gamma = add_5_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_9_cast_fp16)[name = tensor("add_5_cast_fp16")]; + tensor var_175 = const()[name = tensor("op_175"), val = tensor([1, 1])]; + tensor var_177 = const()[name = tensor("op_177"), val = tensor([1, 1])]; + tensor hidden_states_7_pad_type_0 = const()[name = tensor("hidden_states_7_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_7_pad_0 = const()[name = tensor("hidden_states_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8639168)))]; + tensor down_blocks_0_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8844032)))]; + tensor hidden_states_7_cast_fp16 = conv(bias = down_blocks_0_attentions_0_proj_in_bias_to_fp16, dilations = var_177, groups = var_111, pad = hidden_states_7_pad_0, pad_type = hidden_states_7_pad_type_0, strides = var_175, weight = down_blocks_0_attentions_0_proj_in_weight_to_fp16, x = add_5_cast_fp16)[name = tensor("hidden_states_7_cast_fp16")]; + tensor var_182 = const()[name = tensor("op_182"), val = tensor([2, 320, 1, 3840])]; + tensor inputs_1_cast_fp16 = reshape(shape = var_182, x = hidden_states_7_cast_fp16)[name = tensor("inputs_1_cast_fp16")]; + tensor var_192 = const()[name = tensor("op_192"), val = tensor([1])]; + tensor channels_mean_1_cast_fp16 = reduce_mean(axes = var_192, keep_dims = var_106, x = inputs_1_cast_fp16)[name = tensor("channels_mean_1_cast_fp16")]; + tensor zero_mean_1_cast_fp16 = sub(x = inputs_1_cast_fp16, y = channels_mean_1_cast_fp16)[name = tensor("zero_mean_1_cast_fp16")]; + tensor zero_mean_sq_1_cast_fp16 = mul(x = zero_mean_1_cast_fp16, y = zero_mean_1_cast_fp16)[name = tensor("zero_mean_sq_1_cast_fp16")]; + tensor var_196 = const()[name = tensor("op_196"), val = tensor([1])]; + tensor var_197_cast_fp16 = reduce_mean(axes = var_196, keep_dims = var_106, x = zero_mean_sq_1_cast_fp16)[name = tensor("op_197_cast_fp16")]; + tensor var_198_to_fp16 = const()[name = tensor("op_198_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_199_cast_fp16 = add(x = var_197_cast_fp16, y = var_198_to_fp16)[name = tensor("op_199_cast_fp16")]; + tensor denom_1_epsilon_0_to_fp16 = const()[name = tensor("denom_1_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_1_cast_fp16 = rsqrt(epsilon = denom_1_epsilon_0_to_fp16, x = var_199_cast_fp16)[name = tensor("denom_1_cast_fp16")]; + tensor out_1_cast_fp16 = mul(x = zero_mean_1_cast_fp16, y = denom_1_cast_fp16)[name = tensor("out_1_cast_fp16")]; + tensor var_203_to_fp16 = const()[name = tensor("op_203_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8844736)))]; + tensor var_204_cast_fp16 = add(x = out_1_cast_fp16, y = var_203_to_fp16)[name = tensor("op_204_cast_fp16")]; + tensor var_206_to_fp16 = const()[name = tensor("op_206_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8845440)))]; + tensor hidden_states_9_cast_fp16 = mul(x = var_204_cast_fp16, y = var_206_to_fp16)[name = tensor("hidden_states_9_cast_fp16")]; + tensor var_213 = const()[name = tensor("op_213"), val = tensor([1, 1])]; + tensor var_215 = const()[name = tensor("op_215"), val = tensor([1, 1])]; + tensor q_1_pad_type_0 = const()[name = tensor("q_1_pad_type_0"), val = tensor("custom")]; + tensor q_1_pad_0 = const()[name = tensor("q_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8846144)))]; + tensor q_1_cast_fp16 = conv(dilations = var_215, groups = var_111, pad = q_1_pad_0, pad_type = q_1_pad_type_0, strides = var_213, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_9_cast_fp16)[name = tensor("q_1_cast_fp16")]; + tensor var_219 = const()[name = tensor("op_219"), val = tensor([1, 1])]; + tensor var_221 = const()[name = tensor("op_221"), val = tensor([1, 1])]; + tensor k_1_pad_type_0 = const()[name = tensor("k_1_pad_type_0"), val = tensor("custom")]; + tensor k_1_pad_0 = const()[name = tensor("k_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9051008)))]; + tensor k_1_cast_fp16 = conv(dilations = var_221, groups = var_111, pad = k_1_pad_0, pad_type = k_1_pad_type_0, strides = var_219, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_9_cast_fp16)[name = tensor("k_1_cast_fp16")]; + tensor var_225 = const()[name = tensor("op_225"), val = tensor([1, 1])]; + tensor var_227 = const()[name = tensor("op_227"), val = tensor([1, 1])]; + tensor v_1_pad_type_0 = const()[name = tensor("v_1_pad_type_0"), val = tensor("custom")]; + tensor v_1_pad_0 = const()[name = tensor("v_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9255872)))]; + tensor v_1_cast_fp16 = conv(dilations = var_227, groups = var_111, pad = v_1_pad_0, pad_type = v_1_pad_type_0, strides = var_225, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_9_cast_fp16)[name = tensor("v_1_cast_fp16")]; + tensor var_231 = const()[name = tensor("op_231"), val = tensor([2, 5, 64, -1])]; + tensor var_232_cast_fp16 = reshape(shape = var_231, x = q_1_cast_fp16)[name = tensor("op_232_cast_fp16")]; + tensor var_233 = const()[name = tensor("op_233"), val = tensor([2, 5, 64, -1])]; + tensor var_234_cast_fp16 = reshape(shape = var_233, x = k_1_cast_fp16)[name = tensor("op_234_cast_fp16")]; + tensor var_235 = const()[name = tensor("op_235"), val = tensor([2, 5, 64, -1])]; + tensor var_236_cast_fp16 = reshape(shape = var_235, x = v_1_cast_fp16)[name = tensor("op_236_cast_fp16")]; + tensor attn_weights_1_transpose_x_0 = const()[name = tensor("attn_weights_1_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_1_transpose_y_0 = const()[name = tensor("attn_weights_1_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_1_cast_fp16 = matmul(transpose_x = attn_weights_1_transpose_x_0, transpose_y = attn_weights_1_transpose_y_0, x = var_232_cast_fp16, y = var_234_cast_fp16)[name = tensor("attn_weights_1_cast_fp16")]; + tensor var_102_to_fp16 = const()[name = tensor("op_102_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_3_cast_fp16 = mul(x = attn_weights_1_cast_fp16, y = var_102_to_fp16)[name = tensor("attn_weights_3_cast_fp16")]; + tensor var_240_cast_fp16 = softmax(axis = var_95, x = attn_weights_3_cast_fp16)[name = tensor("op_240_cast_fp16")]; + tensor attn_1_transpose_x_0 = const()[name = tensor("attn_1_transpose_x_0"), val = tensor(false)]; + tensor attn_1_transpose_y_0 = const()[name = tensor("attn_1_transpose_y_0"), val = tensor(true)]; + tensor attn_1_cast_fp16 = matmul(transpose_x = attn_1_transpose_x_0, transpose_y = attn_1_transpose_y_0, x = var_236_cast_fp16, y = var_240_cast_fp16)[name = tensor("attn_1_cast_fp16")]; + tensor var_244 = const()[name = tensor("op_244"), val = tensor([2, 320, 1, -1])]; + tensor input_25_cast_fp16 = reshape(shape = var_244, x = attn_1_cast_fp16)[name = tensor("input_25_cast_fp16")]; + tensor var_249 = const()[name = tensor("op_249"), val = tensor([1, 1])]; + tensor var_251 = const()[name = tensor("op_251"), val = tensor([1, 1])]; + tensor var_253_pad_type_0 = const()[name = tensor("op_253_pad_type_0"), val = tensor("custom")]; + tensor var_253_pad_0 = const()[name = tensor("op_253_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9460736)))]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9665600)))]; + tensor var_253_cast_fp16 = conv(bias = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_251, groups = var_111, pad = var_253_pad_0, pad_type = var_253_pad_type_0, strides = var_249, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_25_cast_fp16)[name = tensor("op_253_cast_fp16")]; + tensor inputs_3_cast_fp16 = add(x = var_253_cast_fp16, y = inputs_1_cast_fp16)[name = tensor("inputs_3_cast_fp16")]; + tensor var_257 = const()[name = tensor("op_257"), val = tensor([1])]; + tensor channels_mean_3_cast_fp16 = reduce_mean(axes = var_257, keep_dims = var_106, x = inputs_3_cast_fp16)[name = tensor("channels_mean_3_cast_fp16")]; + tensor zero_mean_3_cast_fp16 = sub(x = inputs_3_cast_fp16, y = channels_mean_3_cast_fp16)[name = tensor("zero_mean_3_cast_fp16")]; + tensor zero_mean_sq_3_cast_fp16 = mul(x = zero_mean_3_cast_fp16, y = zero_mean_3_cast_fp16)[name = tensor("zero_mean_sq_3_cast_fp16")]; + tensor var_261 = const()[name = tensor("op_261"), val = tensor([1])]; + tensor var_262_cast_fp16 = reduce_mean(axes = var_261, keep_dims = var_106, x = zero_mean_sq_3_cast_fp16)[name = tensor("op_262_cast_fp16")]; + tensor var_263_to_fp16 = const()[name = tensor("op_263_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_264_cast_fp16 = add(x = var_262_cast_fp16, y = var_263_to_fp16)[name = tensor("op_264_cast_fp16")]; + tensor denom_3_epsilon_0_to_fp16 = const()[name = tensor("denom_3_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_3_cast_fp16 = rsqrt(epsilon = denom_3_epsilon_0_to_fp16, x = var_264_cast_fp16)[name = tensor("denom_3_cast_fp16")]; + tensor out_3_cast_fp16 = mul(x = zero_mean_3_cast_fp16, y = denom_3_cast_fp16)[name = tensor("out_3_cast_fp16")]; + tensor var_268_to_fp16 = const()[name = tensor("op_268_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9666304)))]; + tensor var_269_cast_fp16 = add(x = out_3_cast_fp16, y = var_268_to_fp16)[name = tensor("op_269_cast_fp16")]; + tensor var_271_to_fp16 = const()[name = tensor("op_271_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9667008)))]; + tensor hidden_states_11_cast_fp16 = mul(x = var_269_cast_fp16, y = var_271_to_fp16)[name = tensor("hidden_states_11_cast_fp16")]; + tensor var_278 = const()[name = tensor("op_278"), val = tensor([1, 1])]; + tensor var_280 = const()[name = tensor("op_280"), val = tensor([1, 1])]; + tensor q_3_pad_type_0 = const()[name = tensor("q_3_pad_type_0"), val = tensor("custom")]; + tensor q_3_pad_0 = const()[name = tensor("q_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9667712)))]; + tensor q_3_cast_fp16 = conv(dilations = var_280, groups = var_111, pad = q_3_pad_0, pad_type = q_3_pad_type_0, strides = var_278, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_11_cast_fp16)[name = tensor("q_3_cast_fp16")]; + tensor var_284 = const()[name = tensor("op_284"), val = tensor([1, 1])]; + tensor var_286 = const()[name = tensor("op_286"), val = tensor([1, 1])]; + tensor k_3_pad_type_0 = const()[name = tensor("k_3_pad_type_0"), val = tensor("custom")]; + tensor k_3_pad_0 = const()[name = tensor("k_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9872576)))]; + tensor k_3_cast_fp16 = conv(dilations = var_286, groups = var_111, pad = k_3_pad_0, pad_type = k_3_pad_type_0, strides = var_284, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_3_cast_fp16")]; + tensor var_290 = const()[name = tensor("op_290"), val = tensor([1, 1])]; + tensor var_292 = const()[name = tensor("op_292"), val = tensor([1, 1])]; + tensor v_3_pad_type_0 = const()[name = tensor("v_3_pad_type_0"), val = tensor("custom")]; + tensor v_3_pad_0 = const()[name = tensor("v_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10528000)))]; + tensor v_3_cast_fp16 = conv(dilations = var_292, groups = var_111, pad = v_3_pad_0, pad_type = v_3_pad_type_0, strides = var_290, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_3_cast_fp16")]; + tensor var_296 = const()[name = tensor("op_296"), val = tensor([2, 5, 64, -1])]; + tensor var_297_cast_fp16 = reshape(shape = var_296, x = q_3_cast_fp16)[name = tensor("op_297_cast_fp16")]; + tensor var_298 = const()[name = tensor("op_298"), val = tensor([2, 5, 64, -1])]; + tensor var_299_cast_fp16 = reshape(shape = var_298, x = k_3_cast_fp16)[name = tensor("op_299_cast_fp16")]; + tensor var_300 = const()[name = tensor("op_300"), val = tensor([2, 5, 64, -1])]; + tensor var_301_cast_fp16 = reshape(shape = var_300, x = v_3_cast_fp16)[name = tensor("op_301_cast_fp16")]; + tensor attn_weights_5_transpose_x_0 = const()[name = tensor("attn_weights_5_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_5_transpose_y_0 = const()[name = tensor("attn_weights_5_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_5_cast_fp16 = matmul(transpose_x = attn_weights_5_transpose_x_0, transpose_y = attn_weights_5_transpose_y_0, x = var_297_cast_fp16, y = var_299_cast_fp16)[name = tensor("attn_weights_5_cast_fp16")]; + tensor attn_weights_7_cast_fp16 = mul(x = attn_weights_5_cast_fp16, y = var_102_to_fp16)[name = tensor("attn_weights_7_cast_fp16")]; + tensor var_305_cast_fp16 = softmax(axis = var_95, x = attn_weights_7_cast_fp16)[name = tensor("op_305_cast_fp16")]; + tensor attn_3_transpose_x_0 = const()[name = tensor("attn_3_transpose_x_0"), val = tensor(false)]; + tensor attn_3_transpose_y_0 = const()[name = tensor("attn_3_transpose_y_0"), val = tensor(true)]; + tensor attn_3_cast_fp16 = matmul(transpose_x = attn_3_transpose_x_0, transpose_y = attn_3_transpose_y_0, x = var_301_cast_fp16, y = var_305_cast_fp16)[name = tensor("attn_3_cast_fp16")]; + tensor var_309 = const()[name = tensor("op_309"), val = tensor([2, 320, 1, -1])]; + tensor input_27_cast_fp16 = reshape(shape = var_309, x = attn_3_cast_fp16)[name = tensor("input_27_cast_fp16")]; + tensor var_314 = const()[name = tensor("op_314"), val = tensor([1, 1])]; + tensor var_316 = const()[name = tensor("op_316"), val = tensor([1, 1])]; + tensor var_318_pad_type_0 = const()[name = tensor("op_318_pad_type_0"), val = tensor("custom")]; + tensor var_318_pad_0 = const()[name = tensor("op_318_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11183424)))]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11388288)))]; + tensor var_318_cast_fp16 = conv(bias = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_316, groups = var_111, pad = var_318_pad_0, pad_type = var_318_pad_type_0, strides = var_314, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_27_cast_fp16)[name = tensor("op_318_cast_fp16")]; + tensor inputs_5_cast_fp16 = add(x = var_318_cast_fp16, y = inputs_3_cast_fp16)[name = tensor("inputs_5_cast_fp16")]; + tensor var_322 = const()[name = tensor("op_322"), val = tensor([1])]; + tensor channels_mean_5_cast_fp16 = reduce_mean(axes = var_322, keep_dims = var_106, x = inputs_5_cast_fp16)[name = tensor("channels_mean_5_cast_fp16")]; + tensor zero_mean_5_cast_fp16 = sub(x = inputs_5_cast_fp16, y = channels_mean_5_cast_fp16)[name = tensor("zero_mean_5_cast_fp16")]; + tensor zero_mean_sq_5_cast_fp16 = mul(x = zero_mean_5_cast_fp16, y = zero_mean_5_cast_fp16)[name = tensor("zero_mean_sq_5_cast_fp16")]; + tensor var_326 = const()[name = tensor("op_326"), val = tensor([1])]; + tensor var_327_cast_fp16 = reduce_mean(axes = var_326, keep_dims = var_106, x = zero_mean_sq_5_cast_fp16)[name = tensor("op_327_cast_fp16")]; + tensor var_328_to_fp16 = const()[name = tensor("op_328_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_329_cast_fp16 = add(x = var_327_cast_fp16, y = var_328_to_fp16)[name = tensor("op_329_cast_fp16")]; + tensor denom_5_epsilon_0_to_fp16 = const()[name = tensor("denom_5_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_5_cast_fp16 = rsqrt(epsilon = denom_5_epsilon_0_to_fp16, x = var_329_cast_fp16)[name = tensor("denom_5_cast_fp16")]; + tensor out_5_cast_fp16 = mul(x = zero_mean_5_cast_fp16, y = denom_5_cast_fp16)[name = tensor("out_5_cast_fp16")]; + tensor var_333_to_fp16 = const()[name = tensor("op_333_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11388992)))]; + tensor var_334_cast_fp16 = add(x = out_5_cast_fp16, y = var_333_to_fp16)[name = tensor("op_334_cast_fp16")]; + tensor var_336_to_fp16 = const()[name = tensor("op_336_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11389696)))]; + tensor input_29_cast_fp16 = mul(x = var_334_cast_fp16, y = var_336_to_fp16)[name = tensor("input_29_cast_fp16")]; + tensor var_344 = const()[name = tensor("op_344"), val = tensor([1, 1])]; + tensor var_346 = const()[name = tensor("op_346"), val = tensor([1, 1])]; + tensor var_348_pad_type_0 = const()[name = tensor("op_348_pad_type_0"), val = tensor("custom")]; + tensor var_348_pad_0 = const()[name = tensor("op_348_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11390400)))]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13028864)))]; + tensor var_348_cast_fp16 = conv(bias = down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_346, groups = var_111, pad = var_348_pad_0, pad_type = var_348_pad_type_0, strides = var_344, weight = down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_29_cast_fp16)[name = tensor("op_348_cast_fp16")]; + tensor var_349_split_sizes_0 = const()[name = tensor("op_349_split_sizes_0"), val = tensor([1280, 1280])]; + tensor var_349_axis_0 = const()[name = tensor("op_349_axis_0"), val = tensor(1)]; + tensor var_349_cast_fp16_0, tensor var_349_cast_fp16_1 = split(axis = var_349_axis_0, split_sizes = var_349_split_sizes_0, x = var_348_cast_fp16)[name = tensor("op_349_cast_fp16")]; + tensor var_351_mode_0 = const()[name = tensor("op_351_mode_0"), val = tensor("EXACT")]; + tensor var_351_cast_fp16 = gelu(mode = var_351_mode_0, x = var_349_cast_fp16_1)[name = tensor("op_351_cast_fp16")]; + tensor input_31_cast_fp16 = mul(x = var_349_cast_fp16_0, y = var_351_cast_fp16)[name = tensor("input_31_cast_fp16")]; + tensor var_355 = const()[name = tensor("op_355"), val = tensor([1, 1])]; + tensor var_357 = const()[name = tensor("op_357"), val = tensor([1, 1])]; + tensor var_359_pad_type_0 = const()[name = tensor("op_359_pad_type_0"), val = tensor("custom")]; + tensor var_359_pad_0 = const()[name = tensor("op_359_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13034048)))]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13853312)))]; + tensor var_359_cast_fp16 = conv(bias = down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_357, groups = var_111, pad = var_359_pad_0, pad_type = var_359_pad_type_0, strides = var_355, weight = down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_31_cast_fp16)[name = tensor("op_359_cast_fp16")]; + tensor hidden_states_15_cast_fp16 = add(x = var_359_cast_fp16, y = inputs_5_cast_fp16)[name = tensor("hidden_states_15_cast_fp16")]; + tensor var_361 = const()[name = tensor("op_361"), val = tensor([2, 320, 48, 80])]; + tensor input_33_cast_fp16 = reshape(shape = var_361, x = hidden_states_15_cast_fp16)[name = tensor("input_33_cast_fp16")]; + tensor var_365 = const()[name = tensor("op_365"), val = tensor([1, 1])]; + tensor var_367 = const()[name = tensor("op_367"), val = tensor([1, 1])]; + tensor hidden_states_17_pad_type_0 = const()[name = tensor("hidden_states_17_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_17_pad_0 = const()[name = tensor("hidden_states_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13854016)))]; + tensor down_blocks_0_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14058880)))]; + tensor hidden_states_17_cast_fp16 = conv(bias = down_blocks_0_attentions_0_proj_out_bias_to_fp16, dilations = var_367, groups = var_111, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = var_365, weight = down_blocks_0_attentions_0_proj_out_weight_to_fp16, x = input_33_cast_fp16)[name = tensor("hidden_states_17_cast_fp16")]; + tensor input_35_cast_fp16_1 = add(x = hidden_states_17_cast_fp16, y = hidden_states_5_cast_fp16)[name = tensor("input_35_cast_fp16")]; + tensor reshape_12_shape_0 = const()[name = tensor("reshape_12_shape_0"), val = tensor([2, 32, 10, 48, 80])]; + tensor reshape_12_cast_fp16 = reshape(shape = reshape_12_shape_0, x = input_35_cast_fp16_1)[name = tensor("reshape_12_cast_fp16")]; + tensor reduce_mean_9_axes_0 = const()[name = tensor("reduce_mean_9_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_9_keep_dims_0 = const()[name = tensor("reduce_mean_9_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_9_cast_fp16 = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = reshape_12_cast_fp16)[name = tensor("reduce_mean_9_cast_fp16")]; + tensor sub_6_cast_fp16 = sub(x = reshape_12_cast_fp16, y = reduce_mean_9_cast_fp16)[name = tensor("sub_6_cast_fp16")]; + tensor square_3_cast_fp16 = square(x = sub_6_cast_fp16)[name = tensor("square_3_cast_fp16")]; + tensor reduce_mean_11_axes_0 = const()[name = tensor("reduce_mean_11_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_11_keep_dims_0 = const()[name = tensor("reduce_mean_11_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_11_cast_fp16 = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_3_cast_fp16)[name = tensor("reduce_mean_11_cast_fp16")]; + tensor add_6_y_0_to_fp16 = const()[name = tensor("add_6_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_6_cast_fp16 = add(x = reduce_mean_11_cast_fp16, y = add_6_y_0_to_fp16)[name = tensor("add_6_cast_fp16")]; + tensor sqrt_3_cast_fp16 = sqrt(x = add_6_cast_fp16)[name = tensor("sqrt_3_cast_fp16")]; + tensor real_div_3_cast_fp16 = real_div(x = sub_6_cast_fp16, y = sqrt_3_cast_fp16)[name = tensor("real_div_3_cast_fp16")]; + tensor reshape_13_shape_0 = const()[name = tensor("reshape_13_shape_0"), val = tensor([2, 320, 48, 80])]; + tensor reshape_13_cast_fp16 = reshape(shape = reshape_13_shape_0, x = real_div_3_cast_fp16)[name = tensor("reshape_13_cast_fp16")]; + tensor add_7_gamma_0_to_fp16 = const()[name = tensor("add_7_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14059584)))]; + tensor add_7_beta_0_to_fp16 = const()[name = tensor("add_7_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14060288)))]; + tensor add_7_epsilon_0_to_fp16 = const()[name = tensor("add_7_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_7_cast_fp16 = batch_norm(beta = add_7_beta_0_to_fp16, epsilon = add_7_epsilon_0_to_fp16, gamma = add_7_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_13_cast_fp16)[name = tensor("add_7_cast_fp16")]; + tensor input_39_cast_fp16 = silu(x = add_7_cast_fp16)[name = tensor("input_39_cast_fp16")]; + tensor var_382 = const()[name = tensor("op_382"), val = tensor([1, 1])]; + tensor var_384 = const()[name = tensor("op_384"), val = tensor([1, 1])]; + tensor hidden_states_19_pad_type_0 = const()[name = tensor("hidden_states_19_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_19_pad_0 = const()[name = tensor("hidden_states_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14060992)))]; + tensor down_blocks_0_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15904256)))]; + tensor hidden_states_19_cast_fp16 = conv(bias = down_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = var_384, groups = var_111, pad = hidden_states_19_pad_0, pad_type = hidden_states_19_pad_type_0, strides = var_382, weight = down_blocks_0_resnets_1_conv1_weight_to_fp16, x = input_39_cast_fp16)[name = tensor("hidden_states_19_cast_fp16")]; + tensor var_390 = const()[name = tensor("op_390"), val = tensor([1, 1])]; + tensor var_392 = const()[name = tensor("op_392"), val = tensor([1, 1])]; + tensor temb_3_pad_type_0 = const()[name = tensor("temb_3_pad_type_0"), val = tensor("custom")]; + tensor temb_3_pad_0 = const()[name = tensor("temb_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15904960)))]; + tensor down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16724224)))]; + tensor temb_3_cast_fp16 = conv(bias = down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_392, groups = var_111, pad = temb_3_pad_0, pad_type = temb_3_pad_type_0, strides = var_390, weight = down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16_1)[name = tensor("temb_3_cast_fp16")]; + tensor input_43_cast_fp16 = add(x = hidden_states_19_cast_fp16, y = temb_3_cast_fp16)[name = tensor("input_43_cast_fp16")]; + tensor reshape_16_shape_0 = const()[name = tensor("reshape_16_shape_0"), val = tensor([2, 32, 10, 48, 80])]; + tensor reshape_16_cast_fp16 = reshape(shape = reshape_16_shape_0, x = input_43_cast_fp16)[name = tensor("reshape_16_cast_fp16")]; + tensor reduce_mean_12_axes_0 = const()[name = tensor("reduce_mean_12_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_12_keep_dims_0 = const()[name = tensor("reduce_mean_12_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_12_cast_fp16 = reduce_mean(axes = reduce_mean_12_axes_0, keep_dims = reduce_mean_12_keep_dims_0, x = reshape_16_cast_fp16)[name = tensor("reduce_mean_12_cast_fp16")]; + tensor sub_8_cast_fp16 = sub(x = reshape_16_cast_fp16, y = reduce_mean_12_cast_fp16)[name = tensor("sub_8_cast_fp16")]; + tensor square_4_cast_fp16 = square(x = sub_8_cast_fp16)[name = tensor("square_4_cast_fp16")]; + tensor reduce_mean_14_axes_0 = const()[name = tensor("reduce_mean_14_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_14_keep_dims_0 = const()[name = tensor("reduce_mean_14_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_14_cast_fp16 = reduce_mean(axes = reduce_mean_14_axes_0, keep_dims = reduce_mean_14_keep_dims_0, x = square_4_cast_fp16)[name = tensor("reduce_mean_14_cast_fp16")]; + tensor add_8_y_0_to_fp16 = const()[name = tensor("add_8_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_8_cast_fp16 = add(x = reduce_mean_14_cast_fp16, y = add_8_y_0_to_fp16)[name = tensor("add_8_cast_fp16")]; + tensor sqrt_4_cast_fp16 = sqrt(x = add_8_cast_fp16)[name = tensor("sqrt_4_cast_fp16")]; + tensor real_div_4_cast_fp16 = real_div(x = sub_8_cast_fp16, y = sqrt_4_cast_fp16)[name = tensor("real_div_4_cast_fp16")]; + tensor reshape_17_shape_0 = const()[name = tensor("reshape_17_shape_0"), val = tensor([2, 320, 48, 80])]; + tensor reshape_17_cast_fp16 = reshape(shape = reshape_17_shape_0, x = real_div_4_cast_fp16)[name = tensor("reshape_17_cast_fp16")]; + tensor add_9_gamma_0_to_fp16 = const()[name = tensor("add_9_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16724928)))]; + tensor add_9_beta_0_to_fp16 = const()[name = tensor("add_9_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16725632)))]; + tensor add_9_epsilon_0_to_fp16 = const()[name = tensor("add_9_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_9_cast_fp16 = batch_norm(beta = add_9_beta_0_to_fp16, epsilon = add_9_epsilon_0_to_fp16, gamma = add_9_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_17_cast_fp16)[name = tensor("add_9_cast_fp16")]; + tensor input_47_cast_fp16 = silu(x = add_9_cast_fp16)[name = tensor("input_47_cast_fp16")]; + tensor var_402 = const()[name = tensor("op_402"), val = tensor([1, 1])]; + tensor var_404 = const()[name = tensor("op_404"), val = tensor([1, 1])]; + tensor hidden_states_21_pad_type_0 = const()[name = tensor("hidden_states_21_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_21_pad_0 = const()[name = tensor("hidden_states_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16726336)))]; + tensor down_blocks_0_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18569600)))]; + tensor hidden_states_21_cast_fp16 = conv(bias = down_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = var_404, groups = var_111, pad = hidden_states_21_pad_0, pad_type = hidden_states_21_pad_type_0, strides = var_402, weight = down_blocks_0_resnets_1_conv2_weight_to_fp16, x = input_47_cast_fp16)[name = tensor("hidden_states_21_cast_fp16")]; + tensor hidden_states_23_cast_fp16 = add(x = input_35_cast_fp16_1, y = hidden_states_21_cast_fp16)[name = tensor("hidden_states_23_cast_fp16")]; + tensor reshape_20_shape_0 = const()[name = tensor("reshape_20_shape_0"), val = tensor([2, 32, 10, 48, 80])]; + tensor reshape_20_cast_fp16 = reshape(shape = reshape_20_shape_0, x = hidden_states_23_cast_fp16)[name = tensor("reshape_20_cast_fp16")]; + tensor reduce_mean_15_axes_0 = const()[name = tensor("reduce_mean_15_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_15_keep_dims_0 = const()[name = tensor("reduce_mean_15_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_15_cast_fp16 = reduce_mean(axes = reduce_mean_15_axes_0, keep_dims = reduce_mean_15_keep_dims_0, x = reshape_20_cast_fp16)[name = tensor("reduce_mean_15_cast_fp16")]; + tensor sub_10_cast_fp16 = sub(x = reshape_20_cast_fp16, y = reduce_mean_15_cast_fp16)[name = tensor("sub_10_cast_fp16")]; + tensor square_5_cast_fp16 = square(x = sub_10_cast_fp16)[name = tensor("square_5_cast_fp16")]; + tensor reduce_mean_17_axes_0 = const()[name = tensor("reduce_mean_17_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_17_keep_dims_0 = const()[name = tensor("reduce_mean_17_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_17_cast_fp16 = reduce_mean(axes = reduce_mean_17_axes_0, keep_dims = reduce_mean_17_keep_dims_0, x = square_5_cast_fp16)[name = tensor("reduce_mean_17_cast_fp16")]; + tensor add_10_y_0_to_fp16 = const()[name = tensor("add_10_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_10_cast_fp16 = add(x = reduce_mean_17_cast_fp16, y = add_10_y_0_to_fp16)[name = tensor("add_10_cast_fp16")]; + tensor sqrt_5_cast_fp16 = sqrt(x = add_10_cast_fp16)[name = tensor("sqrt_5_cast_fp16")]; + tensor real_div_5_cast_fp16 = real_div(x = sub_10_cast_fp16, y = sqrt_5_cast_fp16)[name = tensor("real_div_5_cast_fp16")]; + tensor reshape_21_shape_0 = const()[name = tensor("reshape_21_shape_0"), val = tensor([2, 320, 48, 80])]; + tensor reshape_21_cast_fp16 = reshape(shape = reshape_21_shape_0, x = real_div_5_cast_fp16)[name = tensor("reshape_21_cast_fp16")]; + tensor add_11_gamma_0_to_fp16 = const()[name = tensor("add_11_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18570304)))]; + tensor add_11_beta_0_to_fp16 = const()[name = tensor("add_11_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18571008)))]; + tensor add_11_epsilon_0_to_fp16 = const()[name = tensor("add_11_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_11_cast_fp16 = batch_norm(beta = add_11_beta_0_to_fp16, epsilon = add_11_epsilon_0_to_fp16, gamma = add_11_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_21_cast_fp16)[name = tensor("add_11_cast_fp16")]; + tensor var_424 = const()[name = tensor("op_424"), val = tensor([1, 1])]; + tensor var_426 = const()[name = tensor("op_426"), val = tensor([1, 1])]; + tensor hidden_states_25_pad_type_0 = const()[name = tensor("hidden_states_25_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_25_pad_0 = const()[name = tensor("hidden_states_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_proj_in_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18571712)))]; + tensor down_blocks_0_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18776576)))]; + tensor hidden_states_25_cast_fp16 = conv(bias = down_blocks_0_attentions_1_proj_in_bias_to_fp16, dilations = var_426, groups = var_111, pad = hidden_states_25_pad_0, pad_type = hidden_states_25_pad_type_0, strides = var_424, weight = down_blocks_0_attentions_1_proj_in_weight_to_fp16, x = add_11_cast_fp16)[name = tensor("hidden_states_25_cast_fp16")]; + tensor var_431 = const()[name = tensor("op_431"), val = tensor([2, 320, 1, 3840])]; + tensor inputs_7_cast_fp16 = reshape(shape = var_431, x = hidden_states_25_cast_fp16)[name = tensor("inputs_7_cast_fp16")]; + tensor var_441 = const()[name = tensor("op_441"), val = tensor([1])]; + tensor channels_mean_7_cast_fp16 = reduce_mean(axes = var_441, keep_dims = var_106, x = inputs_7_cast_fp16)[name = tensor("channels_mean_7_cast_fp16")]; + tensor zero_mean_7_cast_fp16 = sub(x = inputs_7_cast_fp16, y = channels_mean_7_cast_fp16)[name = tensor("zero_mean_7_cast_fp16")]; + tensor zero_mean_sq_7_cast_fp16 = mul(x = zero_mean_7_cast_fp16, y = zero_mean_7_cast_fp16)[name = tensor("zero_mean_sq_7_cast_fp16")]; + tensor var_445 = const()[name = tensor("op_445"), val = tensor([1])]; + tensor var_446_cast_fp16 = reduce_mean(axes = var_445, keep_dims = var_106, x = zero_mean_sq_7_cast_fp16)[name = tensor("op_446_cast_fp16")]; + tensor var_447_to_fp16 = const()[name = tensor("op_447_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_448_cast_fp16 = add(x = var_446_cast_fp16, y = var_447_to_fp16)[name = tensor("op_448_cast_fp16")]; + tensor denom_7_epsilon_0_to_fp16 = const()[name = tensor("denom_7_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_7_cast_fp16 = rsqrt(epsilon = denom_7_epsilon_0_to_fp16, x = var_448_cast_fp16)[name = tensor("denom_7_cast_fp16")]; + tensor out_7_cast_fp16 = mul(x = zero_mean_7_cast_fp16, y = denom_7_cast_fp16)[name = tensor("out_7_cast_fp16")]; + tensor var_452_to_fp16 = const()[name = tensor("op_452_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18777280)))]; + tensor var_453_cast_fp16 = add(x = out_7_cast_fp16, y = var_452_to_fp16)[name = tensor("op_453_cast_fp16")]; + tensor var_455_to_fp16 = const()[name = tensor("op_455_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18777984)))]; + tensor hidden_states_27_cast_fp16 = mul(x = var_453_cast_fp16, y = var_455_to_fp16)[name = tensor("hidden_states_27_cast_fp16")]; + tensor var_462 = const()[name = tensor("op_462"), val = tensor([1, 1])]; + tensor var_464 = const()[name = tensor("op_464"), val = tensor([1, 1])]; + tensor q_5_pad_type_0 = const()[name = tensor("q_5_pad_type_0"), val = tensor("custom")]; + tensor q_5_pad_0 = const()[name = tensor("q_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18778688)))]; + tensor q_5_cast_fp16 = conv(dilations = var_464, groups = var_111, pad = q_5_pad_0, pad_type = q_5_pad_type_0, strides = var_462, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_27_cast_fp16)[name = tensor("q_5_cast_fp16")]; + tensor var_468 = const()[name = tensor("op_468"), val = tensor([1, 1])]; + tensor var_470 = const()[name = tensor("op_470"), val = tensor([1, 1])]; + tensor k_5_pad_type_0 = const()[name = tensor("k_5_pad_type_0"), val = tensor("custom")]; + tensor k_5_pad_0 = const()[name = tensor("k_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18983552)))]; + tensor k_5_cast_fp16 = conv(dilations = var_470, groups = var_111, pad = k_5_pad_0, pad_type = k_5_pad_type_0, strides = var_468, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_27_cast_fp16)[name = tensor("k_5_cast_fp16")]; + tensor var_474 = const()[name = tensor("op_474"), val = tensor([1, 1])]; + tensor var_476 = const()[name = tensor("op_476"), val = tensor([1, 1])]; + tensor v_5_pad_type_0 = const()[name = tensor("v_5_pad_type_0"), val = tensor("custom")]; + tensor v_5_pad_0 = const()[name = tensor("v_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19188416)))]; + tensor v_5_cast_fp16 = conv(dilations = var_476, groups = var_111, pad = v_5_pad_0, pad_type = v_5_pad_type_0, strides = var_474, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_27_cast_fp16)[name = tensor("v_5_cast_fp16")]; + tensor var_480 = const()[name = tensor("op_480"), val = tensor([2, 5, 64, -1])]; + tensor var_481_cast_fp16 = reshape(shape = var_480, x = q_5_cast_fp16)[name = tensor("op_481_cast_fp16")]; + tensor var_482 = const()[name = tensor("op_482"), val = tensor([2, 5, 64, -1])]; + tensor var_483_cast_fp16 = reshape(shape = var_482, x = k_5_cast_fp16)[name = tensor("op_483_cast_fp16")]; + tensor var_484 = const()[name = tensor("op_484"), val = tensor([2, 5, 64, -1])]; + tensor var_485_cast_fp16 = reshape(shape = var_484, x = v_5_cast_fp16)[name = tensor("op_485_cast_fp16")]; + tensor attn_weights_9_transpose_x_0 = const()[name = tensor("attn_weights_9_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_9_transpose_y_0 = const()[name = tensor("attn_weights_9_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_9_cast_fp16 = matmul(transpose_x = attn_weights_9_transpose_x_0, transpose_y = attn_weights_9_transpose_y_0, x = var_481_cast_fp16, y = var_483_cast_fp16)[name = tensor("attn_weights_9_cast_fp16")]; + tensor attn_weights_11_cast_fp16 = mul(x = attn_weights_9_cast_fp16, y = var_102_to_fp16)[name = tensor("attn_weights_11_cast_fp16")]; + tensor var_489_cast_fp16 = softmax(axis = var_95, x = attn_weights_11_cast_fp16)[name = tensor("op_489_cast_fp16")]; + tensor attn_5_transpose_x_0 = const()[name = tensor("attn_5_transpose_x_0"), val = tensor(false)]; + tensor attn_5_transpose_y_0 = const()[name = tensor("attn_5_transpose_y_0"), val = tensor(true)]; + tensor attn_5_cast_fp16 = matmul(transpose_x = attn_5_transpose_x_0, transpose_y = attn_5_transpose_y_0, x = var_485_cast_fp16, y = var_489_cast_fp16)[name = tensor("attn_5_cast_fp16")]; + tensor var_493 = const()[name = tensor("op_493"), val = tensor([2, 320, 1, -1])]; + tensor input_51_cast_fp16 = reshape(shape = var_493, x = attn_5_cast_fp16)[name = tensor("input_51_cast_fp16")]; + tensor var_498 = const()[name = tensor("op_498"), val = tensor([1, 1])]; + tensor var_500 = const()[name = tensor("op_500"), val = tensor([1, 1])]; + tensor var_502_pad_type_0 = const()[name = tensor("op_502_pad_type_0"), val = tensor("custom")]; + tensor var_502_pad_0 = const()[name = tensor("op_502_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19393280)))]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19598144)))]; + tensor var_502_cast_fp16 = conv(bias = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_500, groups = var_111, pad = var_502_pad_0, pad_type = var_502_pad_type_0, strides = var_498, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_51_cast_fp16)[name = tensor("op_502_cast_fp16")]; + tensor inputs_9_cast_fp16 = add(x = var_502_cast_fp16, y = inputs_7_cast_fp16)[name = tensor("inputs_9_cast_fp16")]; + tensor var_506 = const()[name = tensor("op_506"), val = tensor([1])]; + tensor channels_mean_9_cast_fp16 = reduce_mean(axes = var_506, keep_dims = var_106, x = inputs_9_cast_fp16)[name = tensor("channels_mean_9_cast_fp16")]; + tensor zero_mean_9_cast_fp16 = sub(x = inputs_9_cast_fp16, y = channels_mean_9_cast_fp16)[name = tensor("zero_mean_9_cast_fp16")]; + tensor zero_mean_sq_9_cast_fp16 = mul(x = zero_mean_9_cast_fp16, y = zero_mean_9_cast_fp16)[name = tensor("zero_mean_sq_9_cast_fp16")]; + tensor var_510 = const()[name = tensor("op_510"), val = tensor([1])]; + tensor var_511_cast_fp16 = reduce_mean(axes = var_510, keep_dims = var_106, x = zero_mean_sq_9_cast_fp16)[name = tensor("op_511_cast_fp16")]; + tensor var_512_to_fp16 = const()[name = tensor("op_512_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_513_cast_fp16 = add(x = var_511_cast_fp16, y = var_512_to_fp16)[name = tensor("op_513_cast_fp16")]; + tensor denom_9_epsilon_0_to_fp16 = const()[name = tensor("denom_9_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_9_cast_fp16 = rsqrt(epsilon = denom_9_epsilon_0_to_fp16, x = var_513_cast_fp16)[name = tensor("denom_9_cast_fp16")]; + tensor out_9_cast_fp16 = mul(x = zero_mean_9_cast_fp16, y = denom_9_cast_fp16)[name = tensor("out_9_cast_fp16")]; + tensor var_517_to_fp16 = const()[name = tensor("op_517_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19598848)))]; + tensor var_518_cast_fp16 = add(x = out_9_cast_fp16, y = var_517_to_fp16)[name = tensor("op_518_cast_fp16")]; + tensor var_520_to_fp16 = const()[name = tensor("op_520_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19599552)))]; + tensor hidden_states_29_cast_fp16 = mul(x = var_518_cast_fp16, y = var_520_to_fp16)[name = tensor("hidden_states_29_cast_fp16")]; + tensor var_527 = const()[name = tensor("op_527"), val = tensor([1, 1])]; + tensor var_529 = const()[name = tensor("op_529"), val = tensor([1, 1])]; + tensor q_7_pad_type_0 = const()[name = tensor("q_7_pad_type_0"), val = tensor("custom")]; + tensor q_7_pad_0 = const()[name = tensor("q_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19600256)))]; + tensor q_7_cast_fp16 = conv(dilations = var_529, groups = var_111, pad = q_7_pad_0, pad_type = q_7_pad_type_0, strides = var_527, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_29_cast_fp16)[name = tensor("q_7_cast_fp16")]; + tensor var_533 = const()[name = tensor("op_533"), val = tensor([1, 1])]; + tensor var_535 = const()[name = tensor("op_535"), val = tensor([1, 1])]; + tensor k_7_pad_type_0 = const()[name = tensor("k_7_pad_type_0"), val = tensor("custom")]; + tensor k_7_pad_0 = const()[name = tensor("k_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19805120)))]; + tensor k_7_cast_fp16 = conv(dilations = var_535, groups = var_111, pad = k_7_pad_0, pad_type = k_7_pad_type_0, strides = var_533, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_7_cast_fp16")]; + tensor var_539 = const()[name = tensor("op_539"), val = tensor([1, 1])]; + tensor var_541 = const()[name = tensor("op_541"), val = tensor([1, 1])]; + tensor v_7_pad_type_0 = const()[name = tensor("v_7_pad_type_0"), val = tensor("custom")]; + tensor v_7_pad_0 = const()[name = tensor("v_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20460544)))]; + tensor v_7_cast_fp16 = conv(dilations = var_541, groups = var_111, pad = v_7_pad_0, pad_type = v_7_pad_type_0, strides = var_539, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_7_cast_fp16")]; + tensor var_545 = const()[name = tensor("op_545"), val = tensor([2, 5, 64, -1])]; + tensor var_546_cast_fp16 = reshape(shape = var_545, x = q_7_cast_fp16)[name = tensor("op_546_cast_fp16")]; + tensor var_547 = const()[name = tensor("op_547"), val = tensor([2, 5, 64, -1])]; + tensor var_548_cast_fp16 = reshape(shape = var_547, x = k_7_cast_fp16)[name = tensor("op_548_cast_fp16")]; + tensor var_549 = const()[name = tensor("op_549"), val = tensor([2, 5, 64, -1])]; + tensor var_550_cast_fp16 = reshape(shape = var_549, x = v_7_cast_fp16)[name = tensor("op_550_cast_fp16")]; + tensor attn_weights_13_transpose_x_0 = const()[name = tensor("attn_weights_13_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_13_transpose_y_0 = const()[name = tensor("attn_weights_13_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_13_cast_fp16 = matmul(transpose_x = attn_weights_13_transpose_x_0, transpose_y = attn_weights_13_transpose_y_0, x = var_546_cast_fp16, y = var_548_cast_fp16)[name = tensor("attn_weights_13_cast_fp16")]; + tensor attn_weights_15_cast_fp16 = mul(x = attn_weights_13_cast_fp16, y = var_102_to_fp16)[name = tensor("attn_weights_15_cast_fp16")]; + tensor var_554_cast_fp16 = softmax(axis = var_95, x = attn_weights_15_cast_fp16)[name = tensor("op_554_cast_fp16")]; + tensor attn_7_transpose_x_0 = const()[name = tensor("attn_7_transpose_x_0"), val = tensor(false)]; + tensor attn_7_transpose_y_0 = const()[name = tensor("attn_7_transpose_y_0"), val = tensor(true)]; + tensor attn_7_cast_fp16 = matmul(transpose_x = attn_7_transpose_x_0, transpose_y = attn_7_transpose_y_0, x = var_550_cast_fp16, y = var_554_cast_fp16)[name = tensor("attn_7_cast_fp16")]; + tensor var_558 = const()[name = tensor("op_558"), val = tensor([2, 320, 1, -1])]; + tensor input_53_cast_fp16 = reshape(shape = var_558, x = attn_7_cast_fp16)[name = tensor("input_53_cast_fp16")]; + tensor var_563 = const()[name = tensor("op_563"), val = tensor([1, 1])]; + tensor var_565 = const()[name = tensor("op_565"), val = tensor([1, 1])]; + tensor var_567_pad_type_0 = const()[name = tensor("op_567_pad_type_0"), val = tensor("custom")]; + tensor var_567_pad_0 = const()[name = tensor("op_567_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21115968)))]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21320832)))]; + tensor var_567_cast_fp16 = conv(bias = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_565, groups = var_111, pad = var_567_pad_0, pad_type = var_567_pad_type_0, strides = var_563, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_53_cast_fp16)[name = tensor("op_567_cast_fp16")]; + tensor inputs_11_cast_fp16 = add(x = var_567_cast_fp16, y = inputs_9_cast_fp16)[name = tensor("inputs_11_cast_fp16")]; + tensor var_571 = const()[name = tensor("op_571"), val = tensor([1])]; + tensor channels_mean_11_cast_fp16 = reduce_mean(axes = var_571, keep_dims = var_106, x = inputs_11_cast_fp16)[name = tensor("channels_mean_11_cast_fp16")]; + tensor zero_mean_11_cast_fp16 = sub(x = inputs_11_cast_fp16, y = channels_mean_11_cast_fp16)[name = tensor("zero_mean_11_cast_fp16")]; + tensor zero_mean_sq_11_cast_fp16 = mul(x = zero_mean_11_cast_fp16, y = zero_mean_11_cast_fp16)[name = tensor("zero_mean_sq_11_cast_fp16")]; + tensor var_575 = const()[name = tensor("op_575"), val = tensor([1])]; + tensor var_576_cast_fp16 = reduce_mean(axes = var_575, keep_dims = var_106, x = zero_mean_sq_11_cast_fp16)[name = tensor("op_576_cast_fp16")]; + tensor var_577_to_fp16 = const()[name = tensor("op_577_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_578_cast_fp16 = add(x = var_576_cast_fp16, y = var_577_to_fp16)[name = tensor("op_578_cast_fp16")]; + tensor denom_11_epsilon_0_to_fp16 = const()[name = tensor("denom_11_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_11_cast_fp16 = rsqrt(epsilon = denom_11_epsilon_0_to_fp16, x = var_578_cast_fp16)[name = tensor("denom_11_cast_fp16")]; + tensor out_11_cast_fp16 = mul(x = zero_mean_11_cast_fp16, y = denom_11_cast_fp16)[name = tensor("out_11_cast_fp16")]; + tensor var_582_to_fp16 = const()[name = tensor("op_582_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21321536)))]; + tensor var_583_cast_fp16 = add(x = out_11_cast_fp16, y = var_582_to_fp16)[name = tensor("op_583_cast_fp16")]; + tensor var_585_to_fp16 = const()[name = tensor("op_585_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21322240)))]; + tensor input_55_cast_fp16 = mul(x = var_583_cast_fp16, y = var_585_to_fp16)[name = tensor("input_55_cast_fp16")]; + tensor var_593 = const()[name = tensor("op_593"), val = tensor([1, 1])]; + tensor var_595 = const()[name = tensor("op_595"), val = tensor([1, 1])]; + tensor var_597_pad_type_0 = const()[name = tensor("op_597_pad_type_0"), val = tensor("custom")]; + tensor var_597_pad_0 = const()[name = tensor("op_597_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21322944)))]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22961408)))]; + tensor var_597_cast_fp16 = conv(bias = down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_595, groups = var_111, pad = var_597_pad_0, pad_type = var_597_pad_type_0, strides = var_593, weight = down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_55_cast_fp16)[name = tensor("op_597_cast_fp16")]; + tensor var_598_split_sizes_0 = const()[name = tensor("op_598_split_sizes_0"), val = tensor([1280, 1280])]; + tensor var_598_axis_0 = const()[name = tensor("op_598_axis_0"), val = tensor(1)]; + tensor var_598_cast_fp16_0, tensor var_598_cast_fp16_1 = split(axis = var_598_axis_0, split_sizes = var_598_split_sizes_0, x = var_597_cast_fp16)[name = tensor("op_598_cast_fp16")]; + tensor var_600_mode_0 = const()[name = tensor("op_600_mode_0"), val = tensor("EXACT")]; + tensor var_600_cast_fp16 = gelu(mode = var_600_mode_0, x = var_598_cast_fp16_1)[name = tensor("op_600_cast_fp16")]; + tensor input_57_cast_fp16 = mul(x = var_598_cast_fp16_0, y = var_600_cast_fp16)[name = tensor("input_57_cast_fp16")]; + tensor var_604 = const()[name = tensor("op_604"), val = tensor([1, 1])]; + tensor var_606 = const()[name = tensor("op_606"), val = tensor([1, 1])]; + tensor var_608_pad_type_0 = const()[name = tensor("op_608_pad_type_0"), val = tensor("custom")]; + tensor var_608_pad_0 = const()[name = tensor("op_608_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22966592)))]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23785856)))]; + tensor var_608_cast_fp16 = conv(bias = down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_606, groups = var_111, pad = var_608_pad_0, pad_type = var_608_pad_type_0, strides = var_604, weight = down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_57_cast_fp16)[name = tensor("op_608_cast_fp16")]; + tensor hidden_states_33_cast_fp16 = add(x = var_608_cast_fp16, y = inputs_11_cast_fp16)[name = tensor("hidden_states_33_cast_fp16")]; + tensor var_610 = const()[name = tensor("op_610"), val = tensor([2, 320, 48, 80])]; + tensor input_59_cast_fp16 = reshape(shape = var_610, x = hidden_states_33_cast_fp16)[name = tensor("input_59_cast_fp16")]; + tensor var_614 = const()[name = tensor("op_614"), val = tensor([1, 1])]; + tensor var_616 = const()[name = tensor("op_616"), val = tensor([1, 1])]; + tensor hidden_states_35_pad_type_0 = const()[name = tensor("hidden_states_35_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_35_pad_0 = const()[name = tensor("hidden_states_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_proj_out_weight_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23786560)))]; + tensor down_blocks_0_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23991424)))]; + tensor hidden_states_35_cast_fp16 = conv(bias = down_blocks_0_attentions_1_proj_out_bias_to_fp16, dilations = var_616, groups = var_111, pad = hidden_states_35_pad_0, pad_type = hidden_states_35_pad_type_0, strides = var_614, weight = down_blocks_0_attentions_1_proj_out_weight_to_fp16, x = input_59_cast_fp16)[name = tensor("hidden_states_35_cast_fp16")]; + tensor input_61_cast_fp16_1 = add(x = hidden_states_35_cast_fp16, y = hidden_states_23_cast_fp16)[name = tensor("input_61_cast_fp16")]; + tensor var_623 = const()[name = tensor("op_623"), val = tensor([2, 2])]; + tensor var_625 = const()[name = tensor("op_625"), val = tensor([1, 1])]; + tensor input_63_pad_type_0 = const()[name = tensor("input_63_pad_type_0"), val = tensor("custom")]; + tensor input_63_pad_0 = const()[name = tensor("input_63_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_downsamplers_0_conv_weight_to_fp16 = const()[name = tensor("down_blocks_0_downsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23992128)))]; + tensor down_blocks_0_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor("down_blocks_0_downsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25835392)))]; + tensor input_63_cast_fp16_1 = conv(bias = down_blocks_0_downsamplers_0_conv_bias_to_fp16, dilations = var_625, groups = var_111, pad = input_63_pad_0, pad_type = input_63_pad_type_0, strides = var_623, weight = down_blocks_0_downsamplers_0_conv_weight_to_fp16, x = input_61_cast_fp16_1)[name = tensor("input_63_cast_fp16")]; + tensor var_633 = const()[name = tensor("op_633"), val = tensor(3)]; + tensor var_644 = const()[name = tensor("op_644"), val = tensor(true)]; + tensor var_649 = const()[name = tensor("op_649"), val = tensor(1)]; + tensor reshape_24_shape_0 = const()[name = tensor("reshape_24_shape_0"), val = tensor([2, 32, 10, 24, 40])]; + tensor reshape_24_cast_fp16 = reshape(shape = reshape_24_shape_0, x = input_63_cast_fp16_1)[name = tensor("reshape_24_cast_fp16")]; + tensor reduce_mean_18_axes_0 = const()[name = tensor("reduce_mean_18_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_18_keep_dims_0 = const()[name = tensor("reduce_mean_18_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_18_cast_fp16 = reduce_mean(axes = reduce_mean_18_axes_0, keep_dims = reduce_mean_18_keep_dims_0, x = reshape_24_cast_fp16)[name = tensor("reduce_mean_18_cast_fp16")]; + tensor sub_12_cast_fp16 = sub(x = reshape_24_cast_fp16, y = reduce_mean_18_cast_fp16)[name = tensor("sub_12_cast_fp16")]; + tensor square_6_cast_fp16 = square(x = sub_12_cast_fp16)[name = tensor("square_6_cast_fp16")]; + tensor reduce_mean_20_axes_0 = const()[name = tensor("reduce_mean_20_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_20_keep_dims_0 = const()[name = tensor("reduce_mean_20_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_20_cast_fp16 = reduce_mean(axes = reduce_mean_20_axes_0, keep_dims = reduce_mean_20_keep_dims_0, x = square_6_cast_fp16)[name = tensor("reduce_mean_20_cast_fp16")]; + tensor add_12_y_0_to_fp16 = const()[name = tensor("add_12_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_12_cast_fp16 = add(x = reduce_mean_20_cast_fp16, y = add_12_y_0_to_fp16)[name = tensor("add_12_cast_fp16")]; + tensor sqrt_6_cast_fp16 = sqrt(x = add_12_cast_fp16)[name = tensor("sqrt_6_cast_fp16")]; + tensor real_div_6_cast_fp16 = real_div(x = sub_12_cast_fp16, y = sqrt_6_cast_fp16)[name = tensor("real_div_6_cast_fp16")]; + tensor reshape_25_shape_0 = const()[name = tensor("reshape_25_shape_0"), val = tensor([2, 320, 24, 40])]; + tensor reshape_25_cast_fp16 = reshape(shape = reshape_25_shape_0, x = real_div_6_cast_fp16)[name = tensor("reshape_25_cast_fp16")]; + tensor add_13_gamma_0_to_fp16 = const()[name = tensor("add_13_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25836096)))]; + tensor add_13_beta_0_to_fp16 = const()[name = tensor("add_13_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25836800)))]; + tensor add_13_epsilon_0_to_fp16 = const()[name = tensor("add_13_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_13_cast_fp16 = batch_norm(beta = add_13_beta_0_to_fp16, epsilon = add_13_epsilon_0_to_fp16, gamma = add_13_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_25_cast_fp16)[name = tensor("add_13_cast_fp16")]; + tensor input_67_cast_fp16 = silu(x = add_13_cast_fp16)[name = tensor("input_67_cast_fp16")]; + tensor var_672 = const()[name = tensor("op_672"), val = tensor([1, 1])]; + tensor var_674 = const()[name = tensor("op_674"), val = tensor([1, 1])]; + tensor hidden_states_37_pad_type_0 = const()[name = tensor("hidden_states_37_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_37_pad_0 = const()[name = tensor("hidden_states_37_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25837504)))]; + tensor down_blocks_1_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29523968)))]; + tensor hidden_states_37_cast_fp16 = conv(bias = down_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = var_674, groups = var_649, pad = hidden_states_37_pad_0, pad_type = hidden_states_37_pad_type_0, strides = var_672, weight = down_blocks_1_resnets_0_conv1_weight_to_fp16, x = input_67_cast_fp16)[name = tensor("hidden_states_37_cast_fp16")]; + tensor var_680 = const()[name = tensor("op_680"), val = tensor([1, 1])]; + tensor var_682 = const()[name = tensor("op_682"), val = tensor([1, 1])]; + tensor temb_5_pad_type_0 = const()[name = tensor("temb_5_pad_type_0"), val = tensor("custom")]; + tensor temb_5_pad_0 = const()[name = tensor("temb_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29525312)))]; + tensor down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31163776)))]; + tensor temb_5_cast_fp16 = conv(bias = down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_682, groups = var_649, pad = temb_5_pad_0, pad_type = temb_5_pad_type_0, strides = var_680, weight = down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16_1)[name = tensor("temb_5_cast_fp16")]; + tensor input_71_cast_fp16 = add(x = hidden_states_37_cast_fp16, y = temb_5_cast_fp16)[name = tensor("input_71_cast_fp16")]; + tensor reshape_28_shape_0 = const()[name = tensor("reshape_28_shape_0"), val = tensor([2, 32, 20, 24, 40])]; + tensor reshape_28_cast_fp16 = reshape(shape = reshape_28_shape_0, x = input_71_cast_fp16)[name = tensor("reshape_28_cast_fp16")]; + tensor reduce_mean_21_axes_0 = const()[name = tensor("reduce_mean_21_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_21_keep_dims_0 = const()[name = tensor("reduce_mean_21_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_21_cast_fp16 = reduce_mean(axes = reduce_mean_21_axes_0, keep_dims = reduce_mean_21_keep_dims_0, x = reshape_28_cast_fp16)[name = tensor("reduce_mean_21_cast_fp16")]; + tensor sub_14_cast_fp16 = sub(x = reshape_28_cast_fp16, y = reduce_mean_21_cast_fp16)[name = tensor("sub_14_cast_fp16")]; + tensor square_7_cast_fp16 = square(x = sub_14_cast_fp16)[name = tensor("square_7_cast_fp16")]; + tensor reduce_mean_23_axes_0 = const()[name = tensor("reduce_mean_23_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_23_keep_dims_0 = const()[name = tensor("reduce_mean_23_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_23_cast_fp16 = reduce_mean(axes = reduce_mean_23_axes_0, keep_dims = reduce_mean_23_keep_dims_0, x = square_7_cast_fp16)[name = tensor("reduce_mean_23_cast_fp16")]; + tensor add_14_y_0_to_fp16 = const()[name = tensor("add_14_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_14_cast_fp16 = add(x = reduce_mean_23_cast_fp16, y = add_14_y_0_to_fp16)[name = tensor("add_14_cast_fp16")]; + tensor sqrt_7_cast_fp16 = sqrt(x = add_14_cast_fp16)[name = tensor("sqrt_7_cast_fp16")]; + tensor real_div_7_cast_fp16 = real_div(x = sub_14_cast_fp16, y = sqrt_7_cast_fp16)[name = tensor("real_div_7_cast_fp16")]; + tensor reshape_29_shape_0 = const()[name = tensor("reshape_29_shape_0"), val = tensor([2, 640, 24, 40])]; + tensor reshape_29_cast_fp16 = reshape(shape = reshape_29_shape_0, x = real_div_7_cast_fp16)[name = tensor("reshape_29_cast_fp16")]; + tensor add_15_mean_0_to_fp16 = const()[name = tensor("add_15_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31165120)))]; + tensor add_15_variance_0_to_fp16 = const()[name = tensor("add_15_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31166464)))]; + tensor add_15_gamma_0_to_fp16 = const()[name = tensor("add_15_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31167808)))]; + tensor add_15_beta_0_to_fp16 = const()[name = tensor("add_15_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31169152)))]; + tensor add_15_epsilon_0_to_fp16 = const()[name = tensor("add_15_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_15_cast_fp16 = batch_norm(beta = add_15_beta_0_to_fp16, epsilon = add_15_epsilon_0_to_fp16, gamma = add_15_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_29_cast_fp16)[name = tensor("add_15_cast_fp16")]; + tensor input_75_cast_fp16 = silu(x = add_15_cast_fp16)[name = tensor("input_75_cast_fp16")]; + tensor var_692 = const()[name = tensor("op_692"), val = tensor([1, 1])]; + tensor var_694 = const()[name = tensor("op_694"), val = tensor([1, 1])]; + tensor hidden_states_39_pad_type_0 = const()[name = tensor("hidden_states_39_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_39_pad_0 = const()[name = tensor("hidden_states_39_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31170496)))]; + tensor down_blocks_1_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38543360)))]; + tensor hidden_states_39_cast_fp16 = conv(bias = down_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = var_694, groups = var_649, pad = hidden_states_39_pad_0, pad_type = hidden_states_39_pad_type_0, strides = var_692, weight = down_blocks_1_resnets_0_conv2_weight_to_fp16, x = input_75_cast_fp16)[name = tensor("hidden_states_39_cast_fp16")]; + tensor var_699 = const()[name = tensor("op_699"), val = tensor([1, 1])]; + tensor var_701 = const()[name = tensor("op_701"), val = tensor([1, 1])]; + tensor x_1_pad_type_0 = const()[name = tensor("x_1_pad_type_0"), val = tensor("custom")]; + tensor x_1_pad_0 = const()[name = tensor("x_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38544704)))]; + tensor down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38954368)))]; + tensor x_1_cast_fp16 = conv(bias = down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_701, groups = var_649, pad = x_1_pad_0, pad_type = x_1_pad_type_0, strides = var_699, weight = down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16, x = input_63_cast_fp16_1)[name = tensor("x_1_cast_fp16")]; + tensor hidden_states_41_cast_fp16 = add(x = x_1_cast_fp16, y = hidden_states_39_cast_fp16)[name = tensor("hidden_states_41_cast_fp16")]; + tensor reshape_32_shape_0 = const()[name = tensor("reshape_32_shape_0"), val = tensor([2, 32, 20, 24, 40])]; + tensor reshape_32_cast_fp16 = reshape(shape = reshape_32_shape_0, x = hidden_states_41_cast_fp16)[name = tensor("reshape_32_cast_fp16")]; + tensor reduce_mean_24_axes_0 = const()[name = tensor("reduce_mean_24_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_24_keep_dims_0 = const()[name = tensor("reduce_mean_24_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_24_cast_fp16 = reduce_mean(axes = reduce_mean_24_axes_0, keep_dims = reduce_mean_24_keep_dims_0, x = reshape_32_cast_fp16)[name = tensor("reduce_mean_24_cast_fp16")]; + tensor sub_16_cast_fp16 = sub(x = reshape_32_cast_fp16, y = reduce_mean_24_cast_fp16)[name = tensor("sub_16_cast_fp16")]; + tensor square_8_cast_fp16 = square(x = sub_16_cast_fp16)[name = tensor("square_8_cast_fp16")]; + tensor reduce_mean_26_axes_0 = const()[name = tensor("reduce_mean_26_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_26_keep_dims_0 = const()[name = tensor("reduce_mean_26_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_26_cast_fp16 = reduce_mean(axes = reduce_mean_26_axes_0, keep_dims = reduce_mean_26_keep_dims_0, x = square_8_cast_fp16)[name = tensor("reduce_mean_26_cast_fp16")]; + tensor add_16_y_0_to_fp16 = const()[name = tensor("add_16_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_16_cast_fp16 = add(x = reduce_mean_26_cast_fp16, y = add_16_y_0_to_fp16)[name = tensor("add_16_cast_fp16")]; + tensor sqrt_8_cast_fp16 = sqrt(x = add_16_cast_fp16)[name = tensor("sqrt_8_cast_fp16")]; + tensor real_div_8_cast_fp16 = real_div(x = sub_16_cast_fp16, y = sqrt_8_cast_fp16)[name = tensor("real_div_8_cast_fp16")]; + tensor reshape_33_shape_0 = const()[name = tensor("reshape_33_shape_0"), val = tensor([2, 640, 24, 40])]; + tensor reshape_33_cast_fp16 = reshape(shape = reshape_33_shape_0, x = real_div_8_cast_fp16)[name = tensor("reshape_33_cast_fp16")]; + tensor add_17_gamma_0_to_fp16 = const()[name = tensor("add_17_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38955712)))]; + tensor add_17_beta_0_to_fp16 = const()[name = tensor("add_17_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38957056)))]; + tensor add_17_epsilon_0_to_fp16 = const()[name = tensor("add_17_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_17_cast_fp16 = batch_norm(beta = add_17_beta_0_to_fp16, epsilon = add_17_epsilon_0_to_fp16, gamma = add_17_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_33_cast_fp16)[name = tensor("add_17_cast_fp16")]; + tensor var_721 = const()[name = tensor("op_721"), val = tensor([1, 1])]; + tensor var_723 = const()[name = tensor("op_723"), val = tensor([1, 1])]; + tensor hidden_states_43_pad_type_0 = const()[name = tensor("hidden_states_43_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_43_pad_0 = const()[name = tensor("hidden_states_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38958400)))]; + tensor down_blocks_1_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39777664)))]; + tensor hidden_states_43_cast_fp16 = conv(bias = down_blocks_1_attentions_0_proj_in_bias_to_fp16, dilations = var_723, groups = var_649, pad = hidden_states_43_pad_0, pad_type = hidden_states_43_pad_type_0, strides = var_721, weight = down_blocks_1_attentions_0_proj_in_weight_to_fp16, x = add_17_cast_fp16)[name = tensor("hidden_states_43_cast_fp16")]; + tensor var_728 = const()[name = tensor("op_728"), val = tensor([2, 640, 1, 960])]; + tensor inputs_13_cast_fp16 = reshape(shape = var_728, x = hidden_states_43_cast_fp16)[name = tensor("inputs_13_cast_fp16")]; + tensor var_738 = const()[name = tensor("op_738"), val = tensor([1])]; + tensor channels_mean_13_cast_fp16 = reduce_mean(axes = var_738, keep_dims = var_644, x = inputs_13_cast_fp16)[name = tensor("channels_mean_13_cast_fp16")]; + tensor zero_mean_13_cast_fp16 = sub(x = inputs_13_cast_fp16, y = channels_mean_13_cast_fp16)[name = tensor("zero_mean_13_cast_fp16")]; + tensor zero_mean_sq_13_cast_fp16 = mul(x = zero_mean_13_cast_fp16, y = zero_mean_13_cast_fp16)[name = tensor("zero_mean_sq_13_cast_fp16")]; + tensor var_742 = const()[name = tensor("op_742"), val = tensor([1])]; + tensor var_743_cast_fp16 = reduce_mean(axes = var_742, keep_dims = var_644, x = zero_mean_sq_13_cast_fp16)[name = tensor("op_743_cast_fp16")]; + tensor var_744_to_fp16 = const()[name = tensor("op_744_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_745_cast_fp16 = add(x = var_743_cast_fp16, y = var_744_to_fp16)[name = tensor("op_745_cast_fp16")]; + tensor denom_13_epsilon_0_to_fp16 = const()[name = tensor("denom_13_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_13_cast_fp16 = rsqrt(epsilon = denom_13_epsilon_0_to_fp16, x = var_745_cast_fp16)[name = tensor("denom_13_cast_fp16")]; + tensor out_13_cast_fp16 = mul(x = zero_mean_13_cast_fp16, y = denom_13_cast_fp16)[name = tensor("out_13_cast_fp16")]; + tensor var_749_to_fp16 = const()[name = tensor("op_749_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39779008)))]; + tensor var_750_cast_fp16 = add(x = out_13_cast_fp16, y = var_749_to_fp16)[name = tensor("op_750_cast_fp16")]; + tensor var_752_to_fp16 = const()[name = tensor("op_752_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39780352)))]; + tensor hidden_states_45_cast_fp16 = mul(x = var_750_cast_fp16, y = var_752_to_fp16)[name = tensor("hidden_states_45_cast_fp16")]; + tensor var_759 = const()[name = tensor("op_759"), val = tensor([1, 1])]; + tensor var_761 = const()[name = tensor("op_761"), val = tensor([1, 1])]; + tensor q_9_pad_type_0 = const()[name = tensor("q_9_pad_type_0"), val = tensor("custom")]; + tensor q_9_pad_0 = const()[name = tensor("q_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39781696)))]; + tensor q_9_cast_fp16 = conv(dilations = var_761, groups = var_649, pad = q_9_pad_0, pad_type = q_9_pad_type_0, strides = var_759, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_45_cast_fp16)[name = tensor("q_9_cast_fp16")]; + tensor var_765 = const()[name = tensor("op_765"), val = tensor([1, 1])]; + tensor var_767 = const()[name = tensor("op_767"), val = tensor([1, 1])]; + tensor k_9_pad_type_0 = const()[name = tensor("k_9_pad_type_0"), val = tensor("custom")]; + tensor k_9_pad_0 = const()[name = tensor("k_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40600960)))]; + tensor k_9_cast_fp16 = conv(dilations = var_767, groups = var_649, pad = k_9_pad_0, pad_type = k_9_pad_type_0, strides = var_765, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_45_cast_fp16)[name = tensor("k_9_cast_fp16")]; + tensor var_771 = const()[name = tensor("op_771"), val = tensor([1, 1])]; + tensor var_773 = const()[name = tensor("op_773"), val = tensor([1, 1])]; + tensor v_9_pad_type_0 = const()[name = tensor("v_9_pad_type_0"), val = tensor("custom")]; + tensor v_9_pad_0 = const()[name = tensor("v_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41420224)))]; + tensor v_9_cast_fp16 = conv(dilations = var_773, groups = var_649, pad = v_9_pad_0, pad_type = v_9_pad_type_0, strides = var_771, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_45_cast_fp16)[name = tensor("v_9_cast_fp16")]; + tensor var_777 = const()[name = tensor("op_777"), val = tensor([2, 10, 64, -1])]; + tensor var_778_cast_fp16 = reshape(shape = var_777, x = q_9_cast_fp16)[name = tensor("op_778_cast_fp16")]; + tensor var_779 = const()[name = tensor("op_779"), val = tensor([2, 10, 64, -1])]; + tensor var_780_cast_fp16 = reshape(shape = var_779, x = k_9_cast_fp16)[name = tensor("op_780_cast_fp16")]; + tensor var_781 = const()[name = tensor("op_781"), val = tensor([2, 10, 64, -1])]; + tensor var_782_cast_fp16 = reshape(shape = var_781, x = v_9_cast_fp16)[name = tensor("op_782_cast_fp16")]; + tensor attn_weights_17_transpose_x_0 = const()[name = tensor("attn_weights_17_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_17_transpose_y_0 = const()[name = tensor("attn_weights_17_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_17_cast_fp16 = matmul(transpose_x = attn_weights_17_transpose_x_0, transpose_y = attn_weights_17_transpose_y_0, x = var_778_cast_fp16, y = var_780_cast_fp16)[name = tensor("attn_weights_17_cast_fp16")]; + tensor var_640_to_fp16 = const()[name = tensor("op_640_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_19_cast_fp16 = mul(x = attn_weights_17_cast_fp16, y = var_640_to_fp16)[name = tensor("attn_weights_19_cast_fp16")]; + tensor var_786_cast_fp16 = softmax(axis = var_633, x = attn_weights_19_cast_fp16)[name = tensor("op_786_cast_fp16")]; + tensor attn_9_transpose_x_0 = const()[name = tensor("attn_9_transpose_x_0"), val = tensor(false)]; + tensor attn_9_transpose_y_0 = const()[name = tensor("attn_9_transpose_y_0"), val = tensor(true)]; + tensor attn_9_cast_fp16 = matmul(transpose_x = attn_9_transpose_x_0, transpose_y = attn_9_transpose_y_0, x = var_782_cast_fp16, y = var_786_cast_fp16)[name = tensor("attn_9_cast_fp16")]; + tensor var_790 = const()[name = tensor("op_790"), val = tensor([2, 640, 1, -1])]; + tensor input_79_cast_fp16 = reshape(shape = var_790, x = attn_9_cast_fp16)[name = tensor("input_79_cast_fp16")]; + tensor var_795 = const()[name = tensor("op_795"), val = tensor([1, 1])]; + tensor var_797 = const()[name = tensor("op_797"), val = tensor([1, 1])]; + tensor var_799_pad_type_0 = const()[name = tensor("op_799_pad_type_0"), val = tensor("custom")]; + tensor var_799_pad_0 = const()[name = tensor("op_799_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42239488)))]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43058752)))]; + tensor var_799_cast_fp16 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_797, groups = var_649, pad = var_799_pad_0, pad_type = var_799_pad_type_0, strides = var_795, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_79_cast_fp16)[name = tensor("op_799_cast_fp16")]; + tensor inputs_15_cast_fp16 = add(x = var_799_cast_fp16, y = inputs_13_cast_fp16)[name = tensor("inputs_15_cast_fp16")]; + tensor var_803 = const()[name = tensor("op_803"), val = tensor([1])]; + tensor channels_mean_15_cast_fp16 = reduce_mean(axes = var_803, keep_dims = var_644, x = inputs_15_cast_fp16)[name = tensor("channels_mean_15_cast_fp16")]; + tensor zero_mean_15_cast_fp16 = sub(x = inputs_15_cast_fp16, y = channels_mean_15_cast_fp16)[name = tensor("zero_mean_15_cast_fp16")]; + tensor zero_mean_sq_15_cast_fp16 = mul(x = zero_mean_15_cast_fp16, y = zero_mean_15_cast_fp16)[name = tensor("zero_mean_sq_15_cast_fp16")]; + tensor var_807 = const()[name = tensor("op_807"), val = tensor([1])]; + tensor var_808_cast_fp16 = reduce_mean(axes = var_807, keep_dims = var_644, x = zero_mean_sq_15_cast_fp16)[name = tensor("op_808_cast_fp16")]; + tensor var_809_to_fp16 = const()[name = tensor("op_809_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_810_cast_fp16 = add(x = var_808_cast_fp16, y = var_809_to_fp16)[name = tensor("op_810_cast_fp16")]; + tensor denom_15_epsilon_0_to_fp16 = const()[name = tensor("denom_15_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_15_cast_fp16 = rsqrt(epsilon = denom_15_epsilon_0_to_fp16, x = var_810_cast_fp16)[name = tensor("denom_15_cast_fp16")]; + tensor out_15_cast_fp16 = mul(x = zero_mean_15_cast_fp16, y = denom_15_cast_fp16)[name = tensor("out_15_cast_fp16")]; + tensor var_814_to_fp16 = const()[name = tensor("op_814_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43060096)))]; + tensor var_815_cast_fp16 = add(x = out_15_cast_fp16, y = var_814_to_fp16)[name = tensor("op_815_cast_fp16")]; + tensor var_817_to_fp16 = const()[name = tensor("op_817_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43061440)))]; + tensor hidden_states_47_cast_fp16 = mul(x = var_815_cast_fp16, y = var_817_to_fp16)[name = tensor("hidden_states_47_cast_fp16")]; + tensor var_824 = const()[name = tensor("op_824"), val = tensor([1, 1])]; + tensor var_826 = const()[name = tensor("op_826"), val = tensor([1, 1])]; + tensor q_11_pad_type_0 = const()[name = tensor("q_11_pad_type_0"), val = tensor("custom")]; + tensor q_11_pad_0 = const()[name = tensor("q_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43062784)))]; + tensor q_11_cast_fp16 = conv(dilations = var_826, groups = var_649, pad = q_11_pad_0, pad_type = q_11_pad_type_0, strides = var_824, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_47_cast_fp16)[name = tensor("q_11_cast_fp16")]; + tensor var_830 = const()[name = tensor("op_830"), val = tensor([1, 1])]; + tensor var_832 = const()[name = tensor("op_832"), val = tensor([1, 1])]; + tensor k_11_pad_type_0 = const()[name = tensor("k_11_pad_type_0"), val = tensor("custom")]; + tensor k_11_pad_0 = const()[name = tensor("k_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43882048)))]; + tensor k_11_cast_fp16 = conv(dilations = var_832, groups = var_649, pad = k_11_pad_0, pad_type = k_11_pad_type_0, strides = var_830, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_11_cast_fp16")]; + tensor var_836 = const()[name = tensor("op_836"), val = tensor([1, 1])]; + tensor var_838 = const()[name = tensor("op_838"), val = tensor([1, 1])]; + tensor v_11_pad_type_0 = const()[name = tensor("v_11_pad_type_0"), val = tensor("custom")]; + tensor v_11_pad_0 = const()[name = tensor("v_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45192832)))]; + tensor v_11_cast_fp16 = conv(dilations = var_838, groups = var_649, pad = v_11_pad_0, pad_type = v_11_pad_type_0, strides = var_836, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_11_cast_fp16")]; + tensor var_842 = const()[name = tensor("op_842"), val = tensor([2, 10, 64, -1])]; + tensor var_843_cast_fp16 = reshape(shape = var_842, x = q_11_cast_fp16)[name = tensor("op_843_cast_fp16")]; + tensor var_844 = const()[name = tensor("op_844"), val = tensor([2, 10, 64, -1])]; + tensor var_845_cast_fp16 = reshape(shape = var_844, x = k_11_cast_fp16)[name = tensor("op_845_cast_fp16")]; + tensor var_846 = const()[name = tensor("op_846"), val = tensor([2, 10, 64, -1])]; + tensor var_847_cast_fp16 = reshape(shape = var_846, x = v_11_cast_fp16)[name = tensor("op_847_cast_fp16")]; + tensor attn_weights_21_transpose_x_0 = const()[name = tensor("attn_weights_21_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_21_transpose_y_0 = const()[name = tensor("attn_weights_21_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_21_cast_fp16 = matmul(transpose_x = attn_weights_21_transpose_x_0, transpose_y = attn_weights_21_transpose_y_0, x = var_843_cast_fp16, y = var_845_cast_fp16)[name = tensor("attn_weights_21_cast_fp16")]; + tensor attn_weights_23_cast_fp16 = mul(x = attn_weights_21_cast_fp16, y = var_640_to_fp16)[name = tensor("attn_weights_23_cast_fp16")]; + tensor var_851_cast_fp16 = softmax(axis = var_633, x = attn_weights_23_cast_fp16)[name = tensor("op_851_cast_fp16")]; + tensor attn_11_transpose_x_0 = const()[name = tensor("attn_11_transpose_x_0"), val = tensor(false)]; + tensor attn_11_transpose_y_0 = const()[name = tensor("attn_11_transpose_y_0"), val = tensor(true)]; + tensor attn_11_cast_fp16 = matmul(transpose_x = attn_11_transpose_x_0, transpose_y = attn_11_transpose_y_0, x = var_847_cast_fp16, y = var_851_cast_fp16)[name = tensor("attn_11_cast_fp16")]; + tensor var_855 = const()[name = tensor("op_855"), val = tensor([2, 640, 1, -1])]; + tensor input_81_cast_fp16 = reshape(shape = var_855, x = attn_11_cast_fp16)[name = tensor("input_81_cast_fp16")]; + tensor var_860 = const()[name = tensor("op_860"), val = tensor([1, 1])]; + tensor var_862 = const()[name = tensor("op_862"), val = tensor([1, 1])]; + tensor var_864_pad_type_0 = const()[name = tensor("op_864_pad_type_0"), val = tensor("custom")]; + tensor var_864_pad_0 = const()[name = tensor("op_864_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46503616)))]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47322880)))]; + tensor var_864_cast_fp16 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_862, groups = var_649, pad = var_864_pad_0, pad_type = var_864_pad_type_0, strides = var_860, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_81_cast_fp16)[name = tensor("op_864_cast_fp16")]; + tensor inputs_17_cast_fp16 = add(x = var_864_cast_fp16, y = inputs_15_cast_fp16)[name = tensor("inputs_17_cast_fp16")]; + tensor var_868 = const()[name = tensor("op_868"), val = tensor([1])]; + tensor channels_mean_17_cast_fp16 = reduce_mean(axes = var_868, keep_dims = var_644, x = inputs_17_cast_fp16)[name = tensor("channels_mean_17_cast_fp16")]; + tensor zero_mean_17_cast_fp16 = sub(x = inputs_17_cast_fp16, y = channels_mean_17_cast_fp16)[name = tensor("zero_mean_17_cast_fp16")]; + tensor zero_mean_sq_17_cast_fp16 = mul(x = zero_mean_17_cast_fp16, y = zero_mean_17_cast_fp16)[name = tensor("zero_mean_sq_17_cast_fp16")]; + tensor var_872 = const()[name = tensor("op_872"), val = tensor([1])]; + tensor var_873_cast_fp16 = reduce_mean(axes = var_872, keep_dims = var_644, x = zero_mean_sq_17_cast_fp16)[name = tensor("op_873_cast_fp16")]; + tensor var_874_to_fp16 = const()[name = tensor("op_874_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_875_cast_fp16 = add(x = var_873_cast_fp16, y = var_874_to_fp16)[name = tensor("op_875_cast_fp16")]; + tensor denom_17_epsilon_0_to_fp16 = const()[name = tensor("denom_17_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_17_cast_fp16 = rsqrt(epsilon = denom_17_epsilon_0_to_fp16, x = var_875_cast_fp16)[name = tensor("denom_17_cast_fp16")]; + tensor out_17_cast_fp16 = mul(x = zero_mean_17_cast_fp16, y = denom_17_cast_fp16)[name = tensor("out_17_cast_fp16")]; + tensor var_879_to_fp16 = const()[name = tensor("op_879_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47324224)))]; + tensor var_880_cast_fp16 = add(x = out_17_cast_fp16, y = var_879_to_fp16)[name = tensor("op_880_cast_fp16")]; + tensor var_882_to_fp16 = const()[name = tensor("op_882_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47325568)))]; + tensor input_83_cast_fp16 = mul(x = var_880_cast_fp16, y = var_882_to_fp16)[name = tensor("input_83_cast_fp16")]; + tensor var_890 = const()[name = tensor("op_890"), val = tensor([1, 1])]; + tensor var_892 = const()[name = tensor("op_892"), val = tensor([1, 1])]; + tensor var_894_pad_type_0 = const()[name = tensor("op_894_pad_type_0"), val = tensor("custom")]; + tensor var_894_pad_0 = const()[name = tensor("op_894_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47326912)))]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(53880576)))]; + tensor var_894_cast_fp16 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_892, groups = var_649, pad = var_894_pad_0, pad_type = var_894_pad_type_0, strides = var_890, weight = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_83_cast_fp16)[name = tensor("op_894_cast_fp16")]; + tensor var_895_split_sizes_0 = const()[name = tensor("op_895_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_895_axis_0 = const()[name = tensor("op_895_axis_0"), val = tensor(1)]; + tensor var_895_cast_fp16_0, tensor var_895_cast_fp16_1 = split(axis = var_895_axis_0, split_sizes = var_895_split_sizes_0, x = var_894_cast_fp16)[name = tensor("op_895_cast_fp16")]; + tensor var_897_mode_0 = const()[name = tensor("op_897_mode_0"), val = tensor("EXACT")]; + tensor var_897_cast_fp16 = gelu(mode = var_897_mode_0, x = var_895_cast_fp16_1)[name = tensor("op_897_cast_fp16")]; + tensor input_85_cast_fp16 = mul(x = var_895_cast_fp16_0, y = var_897_cast_fp16)[name = tensor("input_85_cast_fp16")]; + tensor var_901 = const()[name = tensor("op_901"), val = tensor([1, 1])]; + tensor var_903 = const()[name = tensor("op_903"), val = tensor([1, 1])]; + tensor var_905_pad_type_0 = const()[name = tensor("op_905_pad_type_0"), val = tensor("custom")]; + tensor var_905_pad_0 = const()[name = tensor("op_905_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(53890880)))]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57167744)))]; + tensor var_905_cast_fp16 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_903, groups = var_649, pad = var_905_pad_0, pad_type = var_905_pad_type_0, strides = var_901, weight = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_85_cast_fp16)[name = tensor("op_905_cast_fp16")]; + tensor hidden_states_51_cast_fp16 = add(x = var_905_cast_fp16, y = inputs_17_cast_fp16)[name = tensor("hidden_states_51_cast_fp16")]; + tensor var_907 = const()[name = tensor("op_907"), val = tensor([2, 640, 24, 40])]; + tensor input_87_cast_fp16 = reshape(shape = var_907, x = hidden_states_51_cast_fp16)[name = tensor("input_87_cast_fp16")]; + tensor var_911 = const()[name = tensor("op_911"), val = tensor([1, 1])]; + tensor var_913 = const()[name = tensor("op_913"), val = tensor([1, 1])]; + tensor hidden_states_53_pad_type_0 = const()[name = tensor("hidden_states_53_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_53_pad_0 = const()[name = tensor("hidden_states_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57169088)))]; + tensor down_blocks_1_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57988352)))]; + tensor hidden_states_53_cast_fp16 = conv(bias = down_blocks_1_attentions_0_proj_out_bias_to_fp16, dilations = var_913, groups = var_649, pad = hidden_states_53_pad_0, pad_type = hidden_states_53_pad_type_0, strides = var_911, weight = down_blocks_1_attentions_0_proj_out_weight_to_fp16, x = input_87_cast_fp16)[name = tensor("hidden_states_53_cast_fp16")]; + tensor input_89_cast_fp16_1 = add(x = hidden_states_53_cast_fp16, y = hidden_states_41_cast_fp16)[name = tensor("input_89_cast_fp16")]; + tensor reshape_36_shape_0 = const()[name = tensor("reshape_36_shape_0"), val = tensor([2, 32, 20, 24, 40])]; + tensor reshape_36_cast_fp16 = reshape(shape = reshape_36_shape_0, x = input_89_cast_fp16_1)[name = tensor("reshape_36_cast_fp16")]; + tensor reduce_mean_27_axes_0 = const()[name = tensor("reduce_mean_27_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_27_keep_dims_0 = const()[name = tensor("reduce_mean_27_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_27_cast_fp16 = reduce_mean(axes = reduce_mean_27_axes_0, keep_dims = reduce_mean_27_keep_dims_0, x = reshape_36_cast_fp16)[name = tensor("reduce_mean_27_cast_fp16")]; + tensor sub_18_cast_fp16 = sub(x = reshape_36_cast_fp16, y = reduce_mean_27_cast_fp16)[name = tensor("sub_18_cast_fp16")]; + tensor square_9_cast_fp16 = square(x = sub_18_cast_fp16)[name = tensor("square_9_cast_fp16")]; + tensor reduce_mean_29_axes_0 = const()[name = tensor("reduce_mean_29_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_29_keep_dims_0 = const()[name = tensor("reduce_mean_29_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_29_cast_fp16 = reduce_mean(axes = reduce_mean_29_axes_0, keep_dims = reduce_mean_29_keep_dims_0, x = square_9_cast_fp16)[name = tensor("reduce_mean_29_cast_fp16")]; + tensor add_18_y_0_to_fp16 = const()[name = tensor("add_18_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_18_cast_fp16 = add(x = reduce_mean_29_cast_fp16, y = add_18_y_0_to_fp16)[name = tensor("add_18_cast_fp16")]; + tensor sqrt_9_cast_fp16 = sqrt(x = add_18_cast_fp16)[name = tensor("sqrt_9_cast_fp16")]; + tensor real_div_9_cast_fp16 = real_div(x = sub_18_cast_fp16, y = sqrt_9_cast_fp16)[name = tensor("real_div_9_cast_fp16")]; + tensor reshape_37_shape_0 = const()[name = tensor("reshape_37_shape_0"), val = tensor([2, 640, 24, 40])]; + tensor reshape_37_cast_fp16 = reshape(shape = reshape_37_shape_0, x = real_div_9_cast_fp16)[name = tensor("reshape_37_cast_fp16")]; + tensor add_19_gamma_0_to_fp16 = const()[name = tensor("add_19_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57989696)))]; + tensor add_19_beta_0_to_fp16 = const()[name = tensor("add_19_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57991040)))]; + tensor add_19_epsilon_0_to_fp16 = const()[name = tensor("add_19_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_19_cast_fp16 = batch_norm(beta = add_19_beta_0_to_fp16, epsilon = add_19_epsilon_0_to_fp16, gamma = add_19_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_37_cast_fp16)[name = tensor("add_19_cast_fp16")]; + tensor input_93_cast_fp16 = silu(x = add_19_cast_fp16)[name = tensor("input_93_cast_fp16")]; + tensor var_928 = const()[name = tensor("op_928"), val = tensor([1, 1])]; + tensor var_930 = const()[name = tensor("op_930"), val = tensor([1, 1])]; + tensor hidden_states_55_pad_type_0 = const()[name = tensor("hidden_states_55_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_55_pad_0 = const()[name = tensor("hidden_states_55_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57992384)))]; + tensor down_blocks_1_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65365248)))]; + tensor hidden_states_55_cast_fp16 = conv(bias = down_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = var_930, groups = var_649, pad = hidden_states_55_pad_0, pad_type = hidden_states_55_pad_type_0, strides = var_928, weight = down_blocks_1_resnets_1_conv1_weight_to_fp16, x = input_93_cast_fp16)[name = tensor("hidden_states_55_cast_fp16")]; + tensor var_936 = const()[name = tensor("op_936"), val = tensor([1, 1])]; + tensor var_938 = const()[name = tensor("op_938"), val = tensor([1, 1])]; + tensor temb_7_pad_type_0 = const()[name = tensor("temb_7_pad_type_0"), val = tensor("custom")]; + tensor temb_7_pad_0 = const()[name = tensor("temb_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65366592)))]; + tensor down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67005056)))]; + tensor temb_7_cast_fp16 = conv(bias = down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_938, groups = var_649, pad = temb_7_pad_0, pad_type = temb_7_pad_type_0, strides = var_936, weight = down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16_1)[name = tensor("temb_7_cast_fp16")]; + tensor input_97_cast_fp16 = add(x = hidden_states_55_cast_fp16, y = temb_7_cast_fp16)[name = tensor("input_97_cast_fp16")]; + tensor reshape_40_shape_0 = const()[name = tensor("reshape_40_shape_0"), val = tensor([2, 32, 20, 24, 40])]; + tensor reshape_40_cast_fp16 = reshape(shape = reshape_40_shape_0, x = input_97_cast_fp16)[name = tensor("reshape_40_cast_fp16")]; + tensor reduce_mean_30_axes_0 = const()[name = tensor("reduce_mean_30_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_30_keep_dims_0 = const()[name = tensor("reduce_mean_30_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_30_cast_fp16 = reduce_mean(axes = reduce_mean_30_axes_0, keep_dims = reduce_mean_30_keep_dims_0, x = reshape_40_cast_fp16)[name = tensor("reduce_mean_30_cast_fp16")]; + tensor sub_20_cast_fp16 = sub(x = reshape_40_cast_fp16, y = reduce_mean_30_cast_fp16)[name = tensor("sub_20_cast_fp16")]; + tensor square_10_cast_fp16 = square(x = sub_20_cast_fp16)[name = tensor("square_10_cast_fp16")]; + tensor reduce_mean_32_axes_0 = const()[name = tensor("reduce_mean_32_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_32_keep_dims_0 = const()[name = tensor("reduce_mean_32_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_32_cast_fp16 = reduce_mean(axes = reduce_mean_32_axes_0, keep_dims = reduce_mean_32_keep_dims_0, x = square_10_cast_fp16)[name = tensor("reduce_mean_32_cast_fp16")]; + tensor add_20_y_0_to_fp16 = const()[name = tensor("add_20_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_20_cast_fp16 = add(x = reduce_mean_32_cast_fp16, y = add_20_y_0_to_fp16)[name = tensor("add_20_cast_fp16")]; + tensor sqrt_10_cast_fp16 = sqrt(x = add_20_cast_fp16)[name = tensor("sqrt_10_cast_fp16")]; + tensor real_div_10_cast_fp16 = real_div(x = sub_20_cast_fp16, y = sqrt_10_cast_fp16)[name = tensor("real_div_10_cast_fp16")]; + tensor reshape_41_shape_0 = const()[name = tensor("reshape_41_shape_0"), val = tensor([2, 640, 24, 40])]; + tensor reshape_41_cast_fp16 = reshape(shape = reshape_41_shape_0, x = real_div_10_cast_fp16)[name = tensor("reshape_41_cast_fp16")]; + tensor add_21_gamma_0_to_fp16 = const()[name = tensor("add_21_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67006400)))]; + tensor add_21_beta_0_to_fp16 = const()[name = tensor("add_21_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67007744)))]; + tensor add_21_epsilon_0_to_fp16 = const()[name = tensor("add_21_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_21_cast_fp16 = batch_norm(beta = add_21_beta_0_to_fp16, epsilon = add_21_epsilon_0_to_fp16, gamma = add_21_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_41_cast_fp16)[name = tensor("add_21_cast_fp16")]; + tensor input_101_cast_fp16 = silu(x = add_21_cast_fp16)[name = tensor("input_101_cast_fp16")]; + tensor var_948 = const()[name = tensor("op_948"), val = tensor([1, 1])]; + tensor var_950 = const()[name = tensor("op_950"), val = tensor([1, 1])]; + tensor hidden_states_57_pad_type_0 = const()[name = tensor("hidden_states_57_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_57_pad_0 = const()[name = tensor("hidden_states_57_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67009088)))]; + tensor down_blocks_1_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74381952)))]; + tensor hidden_states_57_cast_fp16 = conv(bias = down_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = var_950, groups = var_649, pad = hidden_states_57_pad_0, pad_type = hidden_states_57_pad_type_0, strides = var_948, weight = down_blocks_1_resnets_1_conv2_weight_to_fp16, x = input_101_cast_fp16)[name = tensor("hidden_states_57_cast_fp16")]; + tensor hidden_states_59_cast_fp16 = add(x = input_89_cast_fp16_1, y = hidden_states_57_cast_fp16)[name = tensor("hidden_states_59_cast_fp16")]; + tensor reshape_44_shape_0 = const()[name = tensor("reshape_44_shape_0"), val = tensor([2, 32, 20, 24, 40])]; + tensor reshape_44_cast_fp16 = reshape(shape = reshape_44_shape_0, x = hidden_states_59_cast_fp16)[name = tensor("reshape_44_cast_fp16")]; + tensor reduce_mean_33_axes_0 = const()[name = tensor("reduce_mean_33_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_33_keep_dims_0 = const()[name = tensor("reduce_mean_33_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_33_cast_fp16 = reduce_mean(axes = reduce_mean_33_axes_0, keep_dims = reduce_mean_33_keep_dims_0, x = reshape_44_cast_fp16)[name = tensor("reduce_mean_33_cast_fp16")]; + tensor sub_22_cast_fp16 = sub(x = reshape_44_cast_fp16, y = reduce_mean_33_cast_fp16)[name = tensor("sub_22_cast_fp16")]; + tensor square_11_cast_fp16 = square(x = sub_22_cast_fp16)[name = tensor("square_11_cast_fp16")]; + tensor reduce_mean_35_axes_0 = const()[name = tensor("reduce_mean_35_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_35_keep_dims_0 = const()[name = tensor("reduce_mean_35_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_35_cast_fp16 = reduce_mean(axes = reduce_mean_35_axes_0, keep_dims = reduce_mean_35_keep_dims_0, x = square_11_cast_fp16)[name = tensor("reduce_mean_35_cast_fp16")]; + tensor add_22_y_0_to_fp16 = const()[name = tensor("add_22_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_22_cast_fp16 = add(x = reduce_mean_35_cast_fp16, y = add_22_y_0_to_fp16)[name = tensor("add_22_cast_fp16")]; + tensor sqrt_11_cast_fp16 = sqrt(x = add_22_cast_fp16)[name = tensor("sqrt_11_cast_fp16")]; + tensor real_div_11_cast_fp16 = real_div(x = sub_22_cast_fp16, y = sqrt_11_cast_fp16)[name = tensor("real_div_11_cast_fp16")]; + tensor reshape_45_shape_0 = const()[name = tensor("reshape_45_shape_0"), val = tensor([2, 640, 24, 40])]; + tensor reshape_45_cast_fp16 = reshape(shape = reshape_45_shape_0, x = real_div_11_cast_fp16)[name = tensor("reshape_45_cast_fp16")]; + tensor add_23_gamma_0_to_fp16 = const()[name = tensor("add_23_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74383296)))]; + tensor add_23_beta_0_to_fp16 = const()[name = tensor("add_23_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74384640)))]; + tensor add_23_epsilon_0_to_fp16 = const()[name = tensor("add_23_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_23_cast_fp16 = batch_norm(beta = add_23_beta_0_to_fp16, epsilon = add_23_epsilon_0_to_fp16, gamma = add_23_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_45_cast_fp16)[name = tensor("add_23_cast_fp16")]; + tensor var_970 = const()[name = tensor("op_970"), val = tensor([1, 1])]; + tensor var_972 = const()[name = tensor("op_972"), val = tensor([1, 1])]; + tensor hidden_states_61_pad_type_0 = const()[name = tensor("hidden_states_61_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_61_pad_0 = const()[name = tensor("hidden_states_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_proj_in_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74385984)))]; + tensor down_blocks_1_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75205248)))]; + tensor hidden_states_61_cast_fp16 = conv(bias = down_blocks_1_attentions_1_proj_in_bias_to_fp16, dilations = var_972, groups = var_649, pad = hidden_states_61_pad_0, pad_type = hidden_states_61_pad_type_0, strides = var_970, weight = down_blocks_1_attentions_1_proj_in_weight_to_fp16, x = add_23_cast_fp16)[name = tensor("hidden_states_61_cast_fp16")]; + tensor var_977 = const()[name = tensor("op_977"), val = tensor([2, 640, 1, 960])]; + tensor inputs_19_cast_fp16 = reshape(shape = var_977, x = hidden_states_61_cast_fp16)[name = tensor("inputs_19_cast_fp16")]; + tensor var_987 = const()[name = tensor("op_987"), val = tensor([1])]; + tensor channels_mean_19_cast_fp16 = reduce_mean(axes = var_987, keep_dims = var_644, x = inputs_19_cast_fp16)[name = tensor("channels_mean_19_cast_fp16")]; + tensor zero_mean_19_cast_fp16 = sub(x = inputs_19_cast_fp16, y = channels_mean_19_cast_fp16)[name = tensor("zero_mean_19_cast_fp16")]; + tensor zero_mean_sq_19_cast_fp16 = mul(x = zero_mean_19_cast_fp16, y = zero_mean_19_cast_fp16)[name = tensor("zero_mean_sq_19_cast_fp16")]; + tensor var_991 = const()[name = tensor("op_991"), val = tensor([1])]; + tensor var_992_cast_fp16 = reduce_mean(axes = var_991, keep_dims = var_644, x = zero_mean_sq_19_cast_fp16)[name = tensor("op_992_cast_fp16")]; + tensor var_993_to_fp16 = const()[name = tensor("op_993_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_994_cast_fp16 = add(x = var_992_cast_fp16, y = var_993_to_fp16)[name = tensor("op_994_cast_fp16")]; + tensor denom_19_epsilon_0_to_fp16 = const()[name = tensor("denom_19_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_19_cast_fp16 = rsqrt(epsilon = denom_19_epsilon_0_to_fp16, x = var_994_cast_fp16)[name = tensor("denom_19_cast_fp16")]; + tensor out_19_cast_fp16 = mul(x = zero_mean_19_cast_fp16, y = denom_19_cast_fp16)[name = tensor("out_19_cast_fp16")]; + tensor var_998_to_fp16 = const()[name = tensor("op_998_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75206592)))]; + tensor var_999_cast_fp16 = add(x = out_19_cast_fp16, y = var_998_to_fp16)[name = tensor("op_999_cast_fp16")]; + tensor var_1001_to_fp16 = const()[name = tensor("op_1001_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75207936)))]; + tensor hidden_states_63_cast_fp16 = mul(x = var_999_cast_fp16, y = var_1001_to_fp16)[name = tensor("hidden_states_63_cast_fp16")]; + tensor var_1008 = const()[name = tensor("op_1008"), val = tensor([1, 1])]; + tensor var_1010 = const()[name = tensor("op_1010"), val = tensor([1, 1])]; + tensor q_13_pad_type_0 = const()[name = tensor("q_13_pad_type_0"), val = tensor("custom")]; + tensor q_13_pad_0 = const()[name = tensor("q_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75209280)))]; + tensor q_13_cast_fp16 = conv(dilations = var_1010, groups = var_649, pad = q_13_pad_0, pad_type = q_13_pad_type_0, strides = var_1008, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_63_cast_fp16)[name = tensor("q_13_cast_fp16")]; + tensor var_1014 = const()[name = tensor("op_1014"), val = tensor([1, 1])]; + tensor var_1016 = const()[name = tensor("op_1016"), val = tensor([1, 1])]; + tensor k_13_pad_type_0 = const()[name = tensor("k_13_pad_type_0"), val = tensor("custom")]; + tensor k_13_pad_0 = const()[name = tensor("k_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76028544)))]; + tensor k_13_cast_fp16 = conv(dilations = var_1016, groups = var_649, pad = k_13_pad_0, pad_type = k_13_pad_type_0, strides = var_1014, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_63_cast_fp16)[name = tensor("k_13_cast_fp16")]; + tensor var_1020 = const()[name = tensor("op_1020"), val = tensor([1, 1])]; + tensor var_1022 = const()[name = tensor("op_1022"), val = tensor([1, 1])]; + tensor v_13_pad_type_0 = const()[name = tensor("v_13_pad_type_0"), val = tensor("custom")]; + tensor v_13_pad_0 = const()[name = tensor("v_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76847808)))]; + tensor v_13_cast_fp16 = conv(dilations = var_1022, groups = var_649, pad = v_13_pad_0, pad_type = v_13_pad_type_0, strides = var_1020, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_63_cast_fp16)[name = tensor("v_13_cast_fp16")]; + tensor var_1026 = const()[name = tensor("op_1026"), val = tensor([2, 10, 64, -1])]; + tensor var_1027_cast_fp16 = reshape(shape = var_1026, x = q_13_cast_fp16)[name = tensor("op_1027_cast_fp16")]; + tensor var_1028 = const()[name = tensor("op_1028"), val = tensor([2, 10, 64, -1])]; + tensor var_1029_cast_fp16 = reshape(shape = var_1028, x = k_13_cast_fp16)[name = tensor("op_1029_cast_fp16")]; + tensor var_1030 = const()[name = tensor("op_1030"), val = tensor([2, 10, 64, -1])]; + tensor var_1031_cast_fp16 = reshape(shape = var_1030, x = v_13_cast_fp16)[name = tensor("op_1031_cast_fp16")]; + tensor attn_weights_25_transpose_x_0 = const()[name = tensor("attn_weights_25_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_25_transpose_y_0 = const()[name = tensor("attn_weights_25_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_25_cast_fp16 = matmul(transpose_x = attn_weights_25_transpose_x_0, transpose_y = attn_weights_25_transpose_y_0, x = var_1027_cast_fp16, y = var_1029_cast_fp16)[name = tensor("attn_weights_25_cast_fp16")]; + tensor attn_weights_27_cast_fp16 = mul(x = attn_weights_25_cast_fp16, y = var_640_to_fp16)[name = tensor("attn_weights_27_cast_fp16")]; + tensor var_1035_cast_fp16 = softmax(axis = var_633, x = attn_weights_27_cast_fp16)[name = tensor("op_1035_cast_fp16")]; + tensor attn_13_transpose_x_0 = const()[name = tensor("attn_13_transpose_x_0"), val = tensor(false)]; + tensor attn_13_transpose_y_0 = const()[name = tensor("attn_13_transpose_y_0"), val = tensor(true)]; + tensor attn_13_cast_fp16 = matmul(transpose_x = attn_13_transpose_x_0, transpose_y = attn_13_transpose_y_0, x = var_1031_cast_fp16, y = var_1035_cast_fp16)[name = tensor("attn_13_cast_fp16")]; + tensor var_1039 = const()[name = tensor("op_1039"), val = tensor([2, 640, 1, -1])]; + tensor input_105_cast_fp16 = reshape(shape = var_1039, x = attn_13_cast_fp16)[name = tensor("input_105_cast_fp16")]; + tensor var_1044 = const()[name = tensor("op_1044"), val = tensor([1, 1])]; + tensor var_1046 = const()[name = tensor("op_1046"), val = tensor([1, 1])]; + tensor var_1048_pad_type_0 = const()[name = tensor("op_1048_pad_type_0"), val = tensor("custom")]; + tensor var_1048_pad_0 = const()[name = tensor("op_1048_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77667072)))]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(78486336)))]; + tensor var_1048_cast_fp16 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_1046, groups = var_649, pad = var_1048_pad_0, pad_type = var_1048_pad_type_0, strides = var_1044, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_105_cast_fp16)[name = tensor("op_1048_cast_fp16")]; + tensor inputs_21_cast_fp16 = add(x = var_1048_cast_fp16, y = inputs_19_cast_fp16)[name = tensor("inputs_21_cast_fp16")]; + tensor var_1052 = const()[name = tensor("op_1052"), val = tensor([1])]; + tensor channels_mean_21_cast_fp16 = reduce_mean(axes = var_1052, keep_dims = var_644, x = inputs_21_cast_fp16)[name = tensor("channels_mean_21_cast_fp16")]; + tensor zero_mean_21_cast_fp16 = sub(x = inputs_21_cast_fp16, y = channels_mean_21_cast_fp16)[name = tensor("zero_mean_21_cast_fp16")]; + tensor zero_mean_sq_21_cast_fp16 = mul(x = zero_mean_21_cast_fp16, y = zero_mean_21_cast_fp16)[name = tensor("zero_mean_sq_21_cast_fp16")]; + tensor var_1056 = const()[name = tensor("op_1056"), val = tensor([1])]; + tensor var_1057_cast_fp16 = reduce_mean(axes = var_1056, keep_dims = var_644, x = zero_mean_sq_21_cast_fp16)[name = tensor("op_1057_cast_fp16")]; + tensor var_1058_to_fp16 = const()[name = tensor("op_1058_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1059_cast_fp16 = add(x = var_1057_cast_fp16, y = var_1058_to_fp16)[name = tensor("op_1059_cast_fp16")]; + tensor denom_21_epsilon_0_to_fp16 = const()[name = tensor("denom_21_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_21_cast_fp16 = rsqrt(epsilon = denom_21_epsilon_0_to_fp16, x = var_1059_cast_fp16)[name = tensor("denom_21_cast_fp16")]; + tensor out_21_cast_fp16 = mul(x = zero_mean_21_cast_fp16, y = denom_21_cast_fp16)[name = tensor("out_21_cast_fp16")]; + tensor var_1063_to_fp16 = const()[name = tensor("op_1063_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(78487680)))]; + tensor var_1064_cast_fp16 = add(x = out_21_cast_fp16, y = var_1063_to_fp16)[name = tensor("op_1064_cast_fp16")]; + tensor var_1066_to_fp16 = const()[name = tensor("op_1066_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(78489024)))]; + tensor hidden_states_65_cast_fp16 = mul(x = var_1064_cast_fp16, y = var_1066_to_fp16)[name = tensor("hidden_states_65_cast_fp16")]; + tensor var_1073 = const()[name = tensor("op_1073"), val = tensor([1, 1])]; + tensor var_1075 = const()[name = tensor("op_1075"), val = tensor([1, 1])]; + tensor q_15_pad_type_0 = const()[name = tensor("q_15_pad_type_0"), val = tensor("custom")]; + tensor q_15_pad_0 = const()[name = tensor("q_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(78490368)))]; + tensor q_15_cast_fp16 = conv(dilations = var_1075, groups = var_649, pad = q_15_pad_0, pad_type = q_15_pad_type_0, strides = var_1073, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_65_cast_fp16)[name = tensor("q_15_cast_fp16")]; + tensor var_1079 = const()[name = tensor("op_1079"), val = tensor([1, 1])]; + tensor var_1081 = const()[name = tensor("op_1081"), val = tensor([1, 1])]; + tensor k_15_pad_type_0 = const()[name = tensor("k_15_pad_type_0"), val = tensor("custom")]; + tensor k_15_pad_0 = const()[name = tensor("k_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79309632)))]; + tensor k_15_cast_fp16 = conv(dilations = var_1081, groups = var_649, pad = k_15_pad_0, pad_type = k_15_pad_type_0, strides = var_1079, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_15_cast_fp16")]; + tensor var_1085 = const()[name = tensor("op_1085"), val = tensor([1, 1])]; + tensor var_1087 = const()[name = tensor("op_1087"), val = tensor([1, 1])]; + tensor v_15_pad_type_0 = const()[name = tensor("v_15_pad_type_0"), val = tensor("custom")]; + tensor v_15_pad_0 = const()[name = tensor("v_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80620416)))]; + tensor v_15_cast_fp16 = conv(dilations = var_1087, groups = var_649, pad = v_15_pad_0, pad_type = v_15_pad_type_0, strides = var_1085, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_15_cast_fp16")]; + tensor var_1091 = const()[name = tensor("op_1091"), val = tensor([2, 10, 64, -1])]; + tensor var_1092_cast_fp16 = reshape(shape = var_1091, x = q_15_cast_fp16)[name = tensor("op_1092_cast_fp16")]; + tensor var_1093 = const()[name = tensor("op_1093"), val = tensor([2, 10, 64, -1])]; + tensor var_1094_cast_fp16 = reshape(shape = var_1093, x = k_15_cast_fp16)[name = tensor("op_1094_cast_fp16")]; + tensor var_1095 = const()[name = tensor("op_1095"), val = tensor([2, 10, 64, -1])]; + tensor var_1096_cast_fp16 = reshape(shape = var_1095, x = v_15_cast_fp16)[name = tensor("op_1096_cast_fp16")]; + tensor attn_weights_29_transpose_x_0 = const()[name = tensor("attn_weights_29_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_29_transpose_y_0 = const()[name = tensor("attn_weights_29_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_29_cast_fp16 = matmul(transpose_x = attn_weights_29_transpose_x_0, transpose_y = attn_weights_29_transpose_y_0, x = var_1092_cast_fp16, y = var_1094_cast_fp16)[name = tensor("attn_weights_29_cast_fp16")]; + tensor attn_weights_31_cast_fp16 = mul(x = attn_weights_29_cast_fp16, y = var_640_to_fp16)[name = tensor("attn_weights_31_cast_fp16")]; + tensor var_1100_cast_fp16 = softmax(axis = var_633, x = attn_weights_31_cast_fp16)[name = tensor("op_1100_cast_fp16")]; + tensor attn_15_transpose_x_0 = const()[name = tensor("attn_15_transpose_x_0"), val = tensor(false)]; + tensor attn_15_transpose_y_0 = const()[name = tensor("attn_15_transpose_y_0"), val = tensor(true)]; + tensor attn_15_cast_fp16 = matmul(transpose_x = attn_15_transpose_x_0, transpose_y = attn_15_transpose_y_0, x = var_1096_cast_fp16, y = var_1100_cast_fp16)[name = tensor("attn_15_cast_fp16")]; + tensor var_1104 = const()[name = tensor("op_1104"), val = tensor([2, 640, 1, -1])]; + tensor input_107_cast_fp16 = reshape(shape = var_1104, x = attn_15_cast_fp16)[name = tensor("input_107_cast_fp16")]; + tensor var_1109 = const()[name = tensor("op_1109"), val = tensor([1, 1])]; + tensor var_1111 = const()[name = tensor("op_1111"), val = tensor([1, 1])]; + tensor var_1113_pad_type_0 = const()[name = tensor("op_1113_pad_type_0"), val = tensor("custom")]; + tensor var_1113_pad_0 = const()[name = tensor("op_1113_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(81931200)))]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82750464)))]; + tensor var_1113_cast_fp16 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_1111, groups = var_649, pad = var_1113_pad_0, pad_type = var_1113_pad_type_0, strides = var_1109, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_107_cast_fp16)[name = tensor("op_1113_cast_fp16")]; + tensor inputs_23_cast_fp16 = add(x = var_1113_cast_fp16, y = inputs_21_cast_fp16)[name = tensor("inputs_23_cast_fp16")]; + tensor var_1117 = const()[name = tensor("op_1117"), val = tensor([1])]; + tensor channels_mean_23_cast_fp16 = reduce_mean(axes = var_1117, keep_dims = var_644, x = inputs_23_cast_fp16)[name = tensor("channels_mean_23_cast_fp16")]; + tensor zero_mean_23_cast_fp16 = sub(x = inputs_23_cast_fp16, y = channels_mean_23_cast_fp16)[name = tensor("zero_mean_23_cast_fp16")]; + tensor zero_mean_sq_23_cast_fp16 = mul(x = zero_mean_23_cast_fp16, y = zero_mean_23_cast_fp16)[name = tensor("zero_mean_sq_23_cast_fp16")]; + tensor var_1121 = const()[name = tensor("op_1121"), val = tensor([1])]; + tensor var_1122_cast_fp16 = reduce_mean(axes = var_1121, keep_dims = var_644, x = zero_mean_sq_23_cast_fp16)[name = tensor("op_1122_cast_fp16")]; + tensor var_1123_to_fp16 = const()[name = tensor("op_1123_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1124_cast_fp16 = add(x = var_1122_cast_fp16, y = var_1123_to_fp16)[name = tensor("op_1124_cast_fp16")]; + tensor denom_23_epsilon_0_to_fp16 = const()[name = tensor("denom_23_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_23_cast_fp16 = rsqrt(epsilon = denom_23_epsilon_0_to_fp16, x = var_1124_cast_fp16)[name = tensor("denom_23_cast_fp16")]; + tensor out_23_cast_fp16 = mul(x = zero_mean_23_cast_fp16, y = denom_23_cast_fp16)[name = tensor("out_23_cast_fp16")]; + tensor var_1128_to_fp16 = const()[name = tensor("op_1128_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82751808)))]; + tensor var_1129_cast_fp16 = add(x = out_23_cast_fp16, y = var_1128_to_fp16)[name = tensor("op_1129_cast_fp16")]; + tensor var_1131_to_fp16 = const()[name = tensor("op_1131_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82753152)))]; + tensor input_109_cast_fp16 = mul(x = var_1129_cast_fp16, y = var_1131_to_fp16)[name = tensor("input_109_cast_fp16")]; + tensor var_1139 = const()[name = tensor("op_1139"), val = tensor([1, 1])]; + tensor var_1141 = const()[name = tensor("op_1141"), val = tensor([1, 1])]; + tensor var_1143_pad_type_0 = const()[name = tensor("op_1143_pad_type_0"), val = tensor("custom")]; + tensor var_1143_pad_0 = const()[name = tensor("op_1143_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82754496)))]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89308160)))]; + tensor var_1143_cast_fp16 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_1141, groups = var_649, pad = var_1143_pad_0, pad_type = var_1143_pad_type_0, strides = var_1139, weight = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_109_cast_fp16)[name = tensor("op_1143_cast_fp16")]; + tensor var_1144_split_sizes_0 = const()[name = tensor("op_1144_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_1144_axis_0 = const()[name = tensor("op_1144_axis_0"), val = tensor(1)]; + tensor var_1144_cast_fp16_0, tensor var_1144_cast_fp16_1 = split(axis = var_1144_axis_0, split_sizes = var_1144_split_sizes_0, x = var_1143_cast_fp16)[name = tensor("op_1144_cast_fp16")]; + tensor var_1146_mode_0 = const()[name = tensor("op_1146_mode_0"), val = tensor("EXACT")]; + tensor var_1146_cast_fp16 = gelu(mode = var_1146_mode_0, x = var_1144_cast_fp16_1)[name = tensor("op_1146_cast_fp16")]; + tensor input_111_cast_fp16 = mul(x = var_1144_cast_fp16_0, y = var_1146_cast_fp16)[name = tensor("input_111_cast_fp16")]; + tensor var_1150 = const()[name = tensor("op_1150"), val = tensor([1, 1])]; + tensor var_1152 = const()[name = tensor("op_1152"), val = tensor([1, 1])]; + tensor var_1154_pad_type_0 = const()[name = tensor("op_1154_pad_type_0"), val = tensor("custom")]; + tensor var_1154_pad_0 = const()[name = tensor("op_1154_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89318464)))]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92595328)))]; + tensor var_1154_cast_fp16 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_1152, groups = var_649, pad = var_1154_pad_0, pad_type = var_1154_pad_type_0, strides = var_1150, weight = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_111_cast_fp16)[name = tensor("op_1154_cast_fp16")]; + tensor hidden_states_69_cast_fp16 = add(x = var_1154_cast_fp16, y = inputs_23_cast_fp16)[name = tensor("hidden_states_69_cast_fp16")]; + tensor var_1156 = const()[name = tensor("op_1156"), val = tensor([2, 640, 24, 40])]; + tensor input_113_cast_fp16 = reshape(shape = var_1156, x = hidden_states_69_cast_fp16)[name = tensor("input_113_cast_fp16")]; + tensor var_1160 = const()[name = tensor("op_1160"), val = tensor([1, 1])]; + tensor var_1162 = const()[name = tensor("op_1162"), val = tensor([1, 1])]; + tensor hidden_states_71_pad_type_0 = const()[name = tensor("hidden_states_71_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_71_pad_0 = const()[name = tensor("hidden_states_71_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_proj_out_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92596672)))]; + tensor down_blocks_1_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93415936)))]; + tensor hidden_states_71_cast_fp16 = conv(bias = down_blocks_1_attentions_1_proj_out_bias_to_fp16, dilations = var_1162, groups = var_649, pad = hidden_states_71_pad_0, pad_type = hidden_states_71_pad_type_0, strides = var_1160, weight = down_blocks_1_attentions_1_proj_out_weight_to_fp16, x = input_113_cast_fp16)[name = tensor("hidden_states_71_cast_fp16")]; + tensor input_115_cast_fp16_1 = add(x = hidden_states_71_cast_fp16, y = hidden_states_59_cast_fp16)[name = tensor("input_115_cast_fp16")]; + tensor var_1169 = const()[name = tensor("op_1169"), val = tensor([2, 2])]; + tensor var_1171 = const()[name = tensor("op_1171"), val = tensor([1, 1])]; + tensor input_117_pad_type_0 = const()[name = tensor("input_117_pad_type_0"), val = tensor("custom")]; + tensor input_117_pad_0 = const()[name = tensor("input_117_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_downsamplers_0_conv_weight_to_fp16 = const()[name = tensor("down_blocks_1_downsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93417280)))]; + tensor down_blocks_1_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor("down_blocks_1_downsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100790144)))]; + tensor input_117_cast_fp16_1 = conv(bias = down_blocks_1_downsamplers_0_conv_bias_to_fp16, dilations = var_1171, groups = var_649, pad = input_117_pad_0, pad_type = input_117_pad_type_0, strides = var_1169, weight = down_blocks_1_downsamplers_0_conv_weight_to_fp16, x = input_115_cast_fp16_1)[name = tensor("input_117_cast_fp16")]; + tensor var_1179 = const()[name = tensor("op_1179"), val = tensor(3)]; + tensor var_1190 = const()[name = tensor("op_1190"), val = tensor(true)]; + tensor var_1195 = const()[name = tensor("op_1195"), val = tensor(1)]; + tensor reshape_48_shape_0 = const()[name = tensor("reshape_48_shape_0"), val = tensor([2, 32, 20, 12, 20])]; + tensor reshape_48_cast_fp16 = reshape(shape = reshape_48_shape_0, x = input_117_cast_fp16_1)[name = tensor("reshape_48_cast_fp16")]; + tensor reduce_mean_36_axes_0 = const()[name = tensor("reduce_mean_36_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_36_keep_dims_0 = const()[name = tensor("reduce_mean_36_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_36_cast_fp16 = reduce_mean(axes = reduce_mean_36_axes_0, keep_dims = reduce_mean_36_keep_dims_0, x = reshape_48_cast_fp16)[name = tensor("reduce_mean_36_cast_fp16")]; + tensor sub_24_cast_fp16 = sub(x = reshape_48_cast_fp16, y = reduce_mean_36_cast_fp16)[name = tensor("sub_24_cast_fp16")]; + tensor square_12_cast_fp16 = square(x = sub_24_cast_fp16)[name = tensor("square_12_cast_fp16")]; + tensor reduce_mean_38_axes_0 = const()[name = tensor("reduce_mean_38_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_38_keep_dims_0 = const()[name = tensor("reduce_mean_38_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_38_cast_fp16 = reduce_mean(axes = reduce_mean_38_axes_0, keep_dims = reduce_mean_38_keep_dims_0, x = square_12_cast_fp16)[name = tensor("reduce_mean_38_cast_fp16")]; + tensor add_24_y_0_to_fp16 = const()[name = tensor("add_24_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_24_cast_fp16 = add(x = reduce_mean_38_cast_fp16, y = add_24_y_0_to_fp16)[name = tensor("add_24_cast_fp16")]; + tensor sqrt_12_cast_fp16 = sqrt(x = add_24_cast_fp16)[name = tensor("sqrt_12_cast_fp16")]; + tensor real_div_12_cast_fp16 = real_div(x = sub_24_cast_fp16, y = sqrt_12_cast_fp16)[name = tensor("real_div_12_cast_fp16")]; + tensor reshape_49_shape_0 = const()[name = tensor("reshape_49_shape_0"), val = tensor([2, 640, 12, 20])]; + tensor reshape_49_cast_fp16 = reshape(shape = reshape_49_shape_0, x = real_div_12_cast_fp16)[name = tensor("reshape_49_cast_fp16")]; + tensor add_25_gamma_0_to_fp16 = const()[name = tensor("add_25_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100791488)))]; + tensor add_25_beta_0_to_fp16 = const()[name = tensor("add_25_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100792832)))]; + tensor add_25_epsilon_0_to_fp16 = const()[name = tensor("add_25_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_25_cast_fp16 = batch_norm(beta = add_25_beta_0_to_fp16, epsilon = add_25_epsilon_0_to_fp16, gamma = add_25_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_49_cast_fp16)[name = tensor("add_25_cast_fp16")]; + tensor input_121_cast_fp16 = silu(x = add_25_cast_fp16)[name = tensor("input_121_cast_fp16")]; + tensor var_1218 = const()[name = tensor("op_1218"), val = tensor([1, 1])]; + tensor var_1220 = const()[name = tensor("op_1220"), val = tensor([1, 1])]; + tensor hidden_states_73_pad_type_0 = const()[name = tensor("hidden_states_73_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_73_pad_0 = const()[name = tensor("hidden_states_73_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100794176)))]; + tensor down_blocks_2_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115539840)))]; + tensor hidden_states_73_cast_fp16 = conv(bias = down_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = var_1220, groups = var_1195, pad = hidden_states_73_pad_0, pad_type = hidden_states_73_pad_type_0, strides = var_1218, weight = down_blocks_2_resnets_0_conv1_weight_to_fp16, x = input_121_cast_fp16)[name = tensor("hidden_states_73_cast_fp16")]; + tensor var_1226 = const()[name = tensor("op_1226"), val = tensor([1, 1])]; + tensor var_1228 = const()[name = tensor("op_1228"), val = tensor([1, 1])]; + tensor temb_9_pad_type_0 = const()[name = tensor("temb_9_pad_type_0"), val = tensor("custom")]; + tensor temb_9_pad_0 = const()[name = tensor("temb_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115542464)))]; + tensor down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(118819328)))]; + tensor temb_9_cast_fp16 = conv(bias = down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_1228, groups = var_1195, pad = temb_9_pad_0, pad_type = temb_9_pad_type_0, strides = var_1226, weight = down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16_1)[name = tensor("temb_9_cast_fp16")]; + tensor input_125_cast_fp16 = add(x = hidden_states_73_cast_fp16, y = temb_9_cast_fp16)[name = tensor("input_125_cast_fp16")]; + tensor reshape_52_shape_0 = const()[name = tensor("reshape_52_shape_0"), val = tensor([2, 32, 40, 12, 20])]; + tensor reshape_52_cast_fp16 = reshape(shape = reshape_52_shape_0, x = input_125_cast_fp16)[name = tensor("reshape_52_cast_fp16")]; + tensor reduce_mean_39_axes_0 = const()[name = tensor("reduce_mean_39_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_39_keep_dims_0 = const()[name = tensor("reduce_mean_39_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_39_cast_fp16 = reduce_mean(axes = reduce_mean_39_axes_0, keep_dims = reduce_mean_39_keep_dims_0, x = reshape_52_cast_fp16)[name = tensor("reduce_mean_39_cast_fp16")]; + tensor sub_26_cast_fp16 = sub(x = reshape_52_cast_fp16, y = reduce_mean_39_cast_fp16)[name = tensor("sub_26_cast_fp16")]; + tensor square_13_cast_fp16 = square(x = sub_26_cast_fp16)[name = tensor("square_13_cast_fp16")]; + tensor reduce_mean_41_axes_0 = const()[name = tensor("reduce_mean_41_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_41_keep_dims_0 = const()[name = tensor("reduce_mean_41_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_41_cast_fp16 = reduce_mean(axes = reduce_mean_41_axes_0, keep_dims = reduce_mean_41_keep_dims_0, x = square_13_cast_fp16)[name = tensor("reduce_mean_41_cast_fp16")]; + tensor add_26_y_0_to_fp16 = const()[name = tensor("add_26_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_26_cast_fp16 = add(x = reduce_mean_41_cast_fp16, y = add_26_y_0_to_fp16)[name = tensor("add_26_cast_fp16")]; + tensor sqrt_13_cast_fp16 = sqrt(x = add_26_cast_fp16)[name = tensor("sqrt_13_cast_fp16")]; + tensor real_div_13_cast_fp16 = real_div(x = sub_26_cast_fp16, y = sqrt_13_cast_fp16)[name = tensor("real_div_13_cast_fp16")]; + tensor reshape_53_shape_0 = const()[name = tensor("reshape_53_shape_0"), val = tensor([2, 1280, 12, 20])]; + tensor reshape_53_cast_fp16 = reshape(shape = reshape_53_shape_0, x = real_div_13_cast_fp16)[name = tensor("reshape_53_cast_fp16")]; + tensor add_27_mean_0_to_fp16 = const()[name = tensor("add_27_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(118821952)))]; + tensor add_27_variance_0_to_fp16 = const()[name = tensor("add_27_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(118824576)))]; + tensor add_27_gamma_0_to_fp16 = const()[name = tensor("add_27_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(118827200)))]; + tensor add_27_beta_0_to_fp16 = const()[name = tensor("add_27_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(118829824)))]; + tensor add_27_epsilon_0_to_fp16 = const()[name = tensor("add_27_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_27_cast_fp16 = batch_norm(beta = add_27_beta_0_to_fp16, epsilon = add_27_epsilon_0_to_fp16, gamma = add_27_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_53_cast_fp16)[name = tensor("add_27_cast_fp16")]; + tensor input_129_cast_fp16 = silu(x = add_27_cast_fp16)[name = tensor("input_129_cast_fp16")]; + tensor var_1238 = const()[name = tensor("op_1238"), val = tensor([1, 1])]; + tensor var_1240 = const()[name = tensor("op_1240"), val = tensor([1, 1])]; + tensor hidden_states_75_pad_type_0 = const()[name = tensor("hidden_states_75_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_75_pad_0 = const()[name = tensor("hidden_states_75_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(118832448)))]; + tensor down_blocks_2_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(148323712)))]; + tensor hidden_states_75_cast_fp16 = conv(bias = down_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = var_1240, groups = var_1195, pad = hidden_states_75_pad_0, pad_type = hidden_states_75_pad_type_0, strides = var_1238, weight = down_blocks_2_resnets_0_conv2_weight_to_fp16, x = input_129_cast_fp16)[name = tensor("hidden_states_75_cast_fp16")]; + tensor var_1245 = const()[name = tensor("op_1245"), val = tensor([1, 1])]; + tensor var_1247 = const()[name = tensor("op_1247"), val = tensor([1, 1])]; + tensor x_3_pad_type_0 = const()[name = tensor("x_3_pad_type_0"), val = tensor("custom")]; + tensor x_3_pad_0 = const()[name = tensor("x_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(148326336)))]; + tensor down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(149964800)))]; + tensor x_3_cast_fp16 = conv(bias = down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_1247, groups = var_1195, pad = x_3_pad_0, pad_type = x_3_pad_type_0, strides = var_1245, weight = down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16, x = input_117_cast_fp16_1)[name = tensor("x_3_cast_fp16")]; + tensor hidden_states_77_cast_fp16 = add(x = x_3_cast_fp16, y = hidden_states_75_cast_fp16)[name = tensor("hidden_states_77_cast_fp16")]; + tensor reshape_56_shape_0 = const()[name = tensor("reshape_56_shape_0"), val = tensor([2, 32, 40, 12, 20])]; + tensor reshape_56_cast_fp16 = reshape(shape = reshape_56_shape_0, x = hidden_states_77_cast_fp16)[name = tensor("reshape_56_cast_fp16")]; + tensor reduce_mean_42_axes_0 = const()[name = tensor("reduce_mean_42_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_42_keep_dims_0 = const()[name = tensor("reduce_mean_42_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_42_cast_fp16 = reduce_mean(axes = reduce_mean_42_axes_0, keep_dims = reduce_mean_42_keep_dims_0, x = reshape_56_cast_fp16)[name = tensor("reduce_mean_42_cast_fp16")]; + tensor sub_28_cast_fp16 = sub(x = reshape_56_cast_fp16, y = reduce_mean_42_cast_fp16)[name = tensor("sub_28_cast_fp16")]; + tensor square_14_cast_fp16 = square(x = sub_28_cast_fp16)[name = tensor("square_14_cast_fp16")]; + tensor reduce_mean_44_axes_0 = const()[name = tensor("reduce_mean_44_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_44_keep_dims_0 = const()[name = tensor("reduce_mean_44_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_44_cast_fp16 = reduce_mean(axes = reduce_mean_44_axes_0, keep_dims = reduce_mean_44_keep_dims_0, x = square_14_cast_fp16)[name = tensor("reduce_mean_44_cast_fp16")]; + tensor add_28_y_0_to_fp16 = const()[name = tensor("add_28_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_28_cast_fp16 = add(x = reduce_mean_44_cast_fp16, y = add_28_y_0_to_fp16)[name = tensor("add_28_cast_fp16")]; + tensor sqrt_14_cast_fp16 = sqrt(x = add_28_cast_fp16)[name = tensor("sqrt_14_cast_fp16")]; + tensor real_div_14_cast_fp16 = real_div(x = sub_28_cast_fp16, y = sqrt_14_cast_fp16)[name = tensor("real_div_14_cast_fp16")]; + tensor reshape_57_shape_0 = const()[name = tensor("reshape_57_shape_0"), val = tensor([2, 1280, 12, 20])]; + tensor reshape_57_cast_fp16 = reshape(shape = reshape_57_shape_0, x = real_div_14_cast_fp16)[name = tensor("reshape_57_cast_fp16")]; + tensor add_29_gamma_0_to_fp16 = const()[name = tensor("add_29_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(149967424)))]; + tensor add_29_beta_0_to_fp16 = const()[name = tensor("add_29_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(149970048)))]; + tensor add_29_epsilon_0_to_fp16 = const()[name = tensor("add_29_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_29_cast_fp16 = batch_norm(beta = add_29_beta_0_to_fp16, epsilon = add_29_epsilon_0_to_fp16, gamma = add_29_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_57_cast_fp16)[name = tensor("add_29_cast_fp16")]; + tensor var_1267 = const()[name = tensor("op_1267"), val = tensor([1, 1])]; + tensor var_1269 = const()[name = tensor("op_1269"), val = tensor([1, 1])]; + tensor hidden_states_79_pad_type_0 = const()[name = tensor("hidden_states_79_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_79_pad_0 = const()[name = tensor("hidden_states_79_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(149972672)))]; + tensor down_blocks_2_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(153249536)))]; + tensor hidden_states_79_cast_fp16 = conv(bias = down_blocks_2_attentions_0_proj_in_bias_to_fp16, dilations = var_1269, groups = var_1195, pad = hidden_states_79_pad_0, pad_type = hidden_states_79_pad_type_0, strides = var_1267, weight = down_blocks_2_attentions_0_proj_in_weight_to_fp16, x = add_29_cast_fp16)[name = tensor("hidden_states_79_cast_fp16")]; + tensor var_1274 = const()[name = tensor("op_1274"), val = tensor([2, 1280, 1, 240])]; + tensor inputs_25_cast_fp16 = reshape(shape = var_1274, x = hidden_states_79_cast_fp16)[name = tensor("inputs_25_cast_fp16")]; + tensor var_1284 = const()[name = tensor("op_1284"), val = tensor([1])]; + tensor channels_mean_25_cast_fp16 = reduce_mean(axes = var_1284, keep_dims = var_1190, x = inputs_25_cast_fp16)[name = tensor("channels_mean_25_cast_fp16")]; + tensor zero_mean_25_cast_fp16 = sub(x = inputs_25_cast_fp16, y = channels_mean_25_cast_fp16)[name = tensor("zero_mean_25_cast_fp16")]; + tensor zero_mean_sq_25_cast_fp16 = mul(x = zero_mean_25_cast_fp16, y = zero_mean_25_cast_fp16)[name = tensor("zero_mean_sq_25_cast_fp16")]; + tensor var_1288 = const()[name = tensor("op_1288"), val = tensor([1])]; + tensor var_1289_cast_fp16 = reduce_mean(axes = var_1288, keep_dims = var_1190, x = zero_mean_sq_25_cast_fp16)[name = tensor("op_1289_cast_fp16")]; + tensor var_1290_to_fp16 = const()[name = tensor("op_1290_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1291_cast_fp16 = add(x = var_1289_cast_fp16, y = var_1290_to_fp16)[name = tensor("op_1291_cast_fp16")]; + tensor denom_25_epsilon_0_to_fp16 = const()[name = tensor("denom_25_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_25_cast_fp16 = rsqrt(epsilon = denom_25_epsilon_0_to_fp16, x = var_1291_cast_fp16)[name = tensor("denom_25_cast_fp16")]; + tensor out_25_cast_fp16 = mul(x = zero_mean_25_cast_fp16, y = denom_25_cast_fp16)[name = tensor("out_25_cast_fp16")]; + tensor var_1295_to_fp16 = const()[name = tensor("op_1295_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(153252160)))]; + tensor var_1296_cast_fp16 = add(x = out_25_cast_fp16, y = var_1295_to_fp16)[name = tensor("op_1296_cast_fp16")]; + tensor var_1298_to_fp16 = const()[name = tensor("op_1298_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(153254784)))]; + tensor hidden_states_81_cast_fp16 = mul(x = var_1296_cast_fp16, y = var_1298_to_fp16)[name = tensor("hidden_states_81_cast_fp16")]; + tensor var_1305 = const()[name = tensor("op_1305"), val = tensor([1, 1])]; + tensor var_1307 = const()[name = tensor("op_1307"), val = tensor([1, 1])]; + tensor q_17_pad_type_0 = const()[name = tensor("q_17_pad_type_0"), val = tensor("custom")]; + tensor q_17_pad_0 = const()[name = tensor("q_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(153257408)))]; + tensor q_17_cast_fp16 = conv(dilations = var_1307, groups = var_1195, pad = q_17_pad_0, pad_type = q_17_pad_type_0, strides = var_1305, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_81_cast_fp16)[name = tensor("q_17_cast_fp16")]; + tensor var_1311 = const()[name = tensor("op_1311"), val = tensor([1, 1])]; + tensor var_1313 = const()[name = tensor("op_1313"), val = tensor([1, 1])]; + tensor k_17_pad_type_0 = const()[name = tensor("k_17_pad_type_0"), val = tensor("custom")]; + tensor k_17_pad_0 = const()[name = tensor("k_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156534272)))]; + tensor k_17_cast_fp16 = conv(dilations = var_1313, groups = var_1195, pad = k_17_pad_0, pad_type = k_17_pad_type_0, strides = var_1311, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_81_cast_fp16)[name = tensor("k_17_cast_fp16")]; + tensor var_1317 = const()[name = tensor("op_1317"), val = tensor([1, 1])]; + tensor var_1319 = const()[name = tensor("op_1319"), val = tensor([1, 1])]; + tensor v_17_pad_type_0 = const()[name = tensor("v_17_pad_type_0"), val = tensor("custom")]; + tensor v_17_pad_0 = const()[name = tensor("v_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159811136)))]; + tensor v_17_cast_fp16 = conv(dilations = var_1319, groups = var_1195, pad = v_17_pad_0, pad_type = v_17_pad_type_0, strides = var_1317, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_81_cast_fp16)[name = tensor("v_17_cast_fp16")]; + tensor var_1323 = const()[name = tensor("op_1323"), val = tensor([2, 20, 64, -1])]; + tensor var_1324_cast_fp16 = reshape(shape = var_1323, x = q_17_cast_fp16)[name = tensor("op_1324_cast_fp16")]; + tensor var_1325 = const()[name = tensor("op_1325"), val = tensor([2, 20, 64, -1])]; + tensor var_1326_cast_fp16 = reshape(shape = var_1325, x = k_17_cast_fp16)[name = tensor("op_1326_cast_fp16")]; + tensor var_1327 = const()[name = tensor("op_1327"), val = tensor([2, 20, 64, -1])]; + tensor var_1328_cast_fp16 = reshape(shape = var_1327, x = v_17_cast_fp16)[name = tensor("op_1328_cast_fp16")]; + tensor attn_weights_33_transpose_x_0 = const()[name = tensor("attn_weights_33_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_33_transpose_y_0 = const()[name = tensor("attn_weights_33_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_33_cast_fp16 = matmul(transpose_x = attn_weights_33_transpose_x_0, transpose_y = attn_weights_33_transpose_y_0, x = var_1324_cast_fp16, y = var_1326_cast_fp16)[name = tensor("attn_weights_33_cast_fp16")]; + tensor var_1186_to_fp16 = const()[name = tensor("op_1186_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_35_cast_fp16 = mul(x = attn_weights_33_cast_fp16, y = var_1186_to_fp16)[name = tensor("attn_weights_35_cast_fp16")]; + tensor var_1332_cast_fp16 = softmax(axis = var_1179, x = attn_weights_35_cast_fp16)[name = tensor("op_1332_cast_fp16")]; + tensor attn_17_transpose_x_0 = const()[name = tensor("attn_17_transpose_x_0"), val = tensor(false)]; + tensor attn_17_transpose_y_0 = const()[name = tensor("attn_17_transpose_y_0"), val = tensor(true)]; + tensor attn_17_cast_fp16 = matmul(transpose_x = attn_17_transpose_x_0, transpose_y = attn_17_transpose_y_0, x = var_1328_cast_fp16, y = var_1332_cast_fp16)[name = tensor("attn_17_cast_fp16")]; + tensor var_1336 = const()[name = tensor("op_1336"), val = tensor([2, 1280, 1, -1])]; + tensor input_133_cast_fp16 = reshape(shape = var_1336, x = attn_17_cast_fp16)[name = tensor("input_133_cast_fp16")]; + tensor var_1341 = const()[name = tensor("op_1341"), val = tensor([1, 1])]; + tensor var_1343 = const()[name = tensor("op_1343"), val = tensor([1, 1])]; + tensor var_1345_pad_type_0 = const()[name = tensor("op_1345_pad_type_0"), val = tensor("custom")]; + tensor var_1345_pad_0 = const()[name = tensor("op_1345_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163088000)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(166364864)))]; + tensor var_1345_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_1343, groups = var_1195, pad = var_1345_pad_0, pad_type = var_1345_pad_type_0, strides = var_1341, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_133_cast_fp16)[name = tensor("op_1345_cast_fp16")]; + tensor inputs_27_cast_fp16 = add(x = var_1345_cast_fp16, y = inputs_25_cast_fp16)[name = tensor("inputs_27_cast_fp16")]; + tensor var_1349 = const()[name = tensor("op_1349"), val = tensor([1])]; + tensor channels_mean_27_cast_fp16 = reduce_mean(axes = var_1349, keep_dims = var_1190, x = inputs_27_cast_fp16)[name = tensor("channels_mean_27_cast_fp16")]; + tensor zero_mean_27_cast_fp16 = sub(x = inputs_27_cast_fp16, y = channels_mean_27_cast_fp16)[name = tensor("zero_mean_27_cast_fp16")]; + tensor zero_mean_sq_27_cast_fp16 = mul(x = zero_mean_27_cast_fp16, y = zero_mean_27_cast_fp16)[name = tensor("zero_mean_sq_27_cast_fp16")]; + tensor var_1353 = const()[name = tensor("op_1353"), val = tensor([1])]; + tensor var_1354_cast_fp16 = reduce_mean(axes = var_1353, keep_dims = var_1190, x = zero_mean_sq_27_cast_fp16)[name = tensor("op_1354_cast_fp16")]; + tensor var_1355_to_fp16 = const()[name = tensor("op_1355_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1356_cast_fp16 = add(x = var_1354_cast_fp16, y = var_1355_to_fp16)[name = tensor("op_1356_cast_fp16")]; + tensor denom_27_epsilon_0_to_fp16 = const()[name = tensor("denom_27_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_27_cast_fp16 = rsqrt(epsilon = denom_27_epsilon_0_to_fp16, x = var_1356_cast_fp16)[name = tensor("denom_27_cast_fp16")]; + tensor out_27_cast_fp16 = mul(x = zero_mean_27_cast_fp16, y = denom_27_cast_fp16)[name = tensor("out_27_cast_fp16")]; + tensor var_1360_to_fp16 = const()[name = tensor("op_1360_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(166367488)))]; + tensor var_1361_cast_fp16 = add(x = out_27_cast_fp16, y = var_1360_to_fp16)[name = tensor("op_1361_cast_fp16")]; + tensor var_1363_to_fp16 = const()[name = tensor("op_1363_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(166370112)))]; + tensor hidden_states_83_cast_fp16 = mul(x = var_1361_cast_fp16, y = var_1363_to_fp16)[name = tensor("hidden_states_83_cast_fp16")]; + tensor var_1370 = const()[name = tensor("op_1370"), val = tensor([1, 1])]; + tensor var_1372 = const()[name = tensor("op_1372"), val = tensor([1, 1])]; + tensor q_19_pad_type_0 = const()[name = tensor("q_19_pad_type_0"), val = tensor("custom")]; + tensor q_19_pad_0 = const()[name = tensor("q_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(166372736)))]; + tensor q_19_cast_fp16 = conv(dilations = var_1372, groups = var_1195, pad = q_19_pad_0, pad_type = q_19_pad_type_0, strides = var_1370, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_83_cast_fp16)[name = tensor("q_19_cast_fp16")]; + tensor var_1376 = const()[name = tensor("op_1376"), val = tensor([1, 1])]; + tensor var_1378 = const()[name = tensor("op_1378"), val = tensor([1, 1])]; + tensor k_19_pad_type_0 = const()[name = tensor("k_19_pad_type_0"), val = tensor("custom")]; + tensor k_19_pad_0 = const()[name = tensor("k_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169649600)))]; + tensor k_19_cast_fp16 = conv(dilations = var_1378, groups = var_1195, pad = k_19_pad_0, pad_type = k_19_pad_type_0, strides = var_1376, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_19_cast_fp16")]; + tensor var_1382 = const()[name = tensor("op_1382"), val = tensor([1, 1])]; + tensor var_1384 = const()[name = tensor("op_1384"), val = tensor([1, 1])]; + tensor v_19_pad_type_0 = const()[name = tensor("v_19_pad_type_0"), val = tensor("custom")]; + tensor v_19_pad_0 = const()[name = tensor("v_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(172271104)))]; + tensor v_19_cast_fp16 = conv(dilations = var_1384, groups = var_1195, pad = v_19_pad_0, pad_type = v_19_pad_type_0, strides = var_1382, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_19_cast_fp16")]; + tensor var_1388 = const()[name = tensor("op_1388"), val = tensor([2, 20, 64, -1])]; + tensor var_1389_cast_fp16 = reshape(shape = var_1388, x = q_19_cast_fp16)[name = tensor("op_1389_cast_fp16")]; + tensor var_1390 = const()[name = tensor("op_1390"), val = tensor([2, 20, 64, -1])]; + tensor var_1391_cast_fp16 = reshape(shape = var_1390, x = k_19_cast_fp16)[name = tensor("op_1391_cast_fp16")]; + tensor var_1392 = const()[name = tensor("op_1392"), val = tensor([2, 20, 64, -1])]; + tensor var_1393_cast_fp16 = reshape(shape = var_1392, x = v_19_cast_fp16)[name = tensor("op_1393_cast_fp16")]; + tensor attn_weights_37_transpose_x_0 = const()[name = tensor("attn_weights_37_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_37_transpose_y_0 = const()[name = tensor("attn_weights_37_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_37_cast_fp16 = matmul(transpose_x = attn_weights_37_transpose_x_0, transpose_y = attn_weights_37_transpose_y_0, x = var_1389_cast_fp16, y = var_1391_cast_fp16)[name = tensor("attn_weights_37_cast_fp16")]; + tensor attn_weights_39_cast_fp16 = mul(x = attn_weights_37_cast_fp16, y = var_1186_to_fp16)[name = tensor("attn_weights_39_cast_fp16")]; + tensor var_1397_cast_fp16 = softmax(axis = var_1179, x = attn_weights_39_cast_fp16)[name = tensor("op_1397_cast_fp16")]; + tensor attn_19_transpose_x_0 = const()[name = tensor("attn_19_transpose_x_0"), val = tensor(false)]; + tensor attn_19_transpose_y_0 = const()[name = tensor("attn_19_transpose_y_0"), val = tensor(true)]; + tensor attn_19_cast_fp16 = matmul(transpose_x = attn_19_transpose_x_0, transpose_y = attn_19_transpose_y_0, x = var_1393_cast_fp16, y = var_1397_cast_fp16)[name = tensor("attn_19_cast_fp16")]; + tensor var_1401 = const()[name = tensor("op_1401"), val = tensor([2, 1280, 1, -1])]; + tensor input_135_cast_fp16 = reshape(shape = var_1401, x = attn_19_cast_fp16)[name = tensor("input_135_cast_fp16")]; + tensor var_1406 = const()[name = tensor("op_1406"), val = tensor([1, 1])]; + tensor var_1408 = const()[name = tensor("op_1408"), val = tensor([1, 1])]; + tensor var_1410_pad_type_0 = const()[name = tensor("op_1410_pad_type_0"), val = tensor("custom")]; + tensor var_1410_pad_0 = const()[name = tensor("op_1410_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(174892608)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178169472)))]; + tensor var_1410_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_1408, groups = var_1195, pad = var_1410_pad_0, pad_type = var_1410_pad_type_0, strides = var_1406, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_135_cast_fp16)[name = tensor("op_1410_cast_fp16")]; + tensor inputs_29_cast_fp16 = add(x = var_1410_cast_fp16, y = inputs_27_cast_fp16)[name = tensor("inputs_29_cast_fp16")]; + tensor var_1414 = const()[name = tensor("op_1414"), val = tensor([1])]; + tensor channels_mean_29_cast_fp16 = reduce_mean(axes = var_1414, keep_dims = var_1190, x = inputs_29_cast_fp16)[name = tensor("channels_mean_29_cast_fp16")]; + tensor zero_mean_29_cast_fp16 = sub(x = inputs_29_cast_fp16, y = channels_mean_29_cast_fp16)[name = tensor("zero_mean_29_cast_fp16")]; + tensor zero_mean_sq_29_cast_fp16 = mul(x = zero_mean_29_cast_fp16, y = zero_mean_29_cast_fp16)[name = tensor("zero_mean_sq_29_cast_fp16")]; + tensor var_1418 = const()[name = tensor("op_1418"), val = tensor([1])]; + tensor var_1419_cast_fp16 = reduce_mean(axes = var_1418, keep_dims = var_1190, x = zero_mean_sq_29_cast_fp16)[name = tensor("op_1419_cast_fp16")]; + tensor var_1420_to_fp16 = const()[name = tensor("op_1420_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1421_cast_fp16 = add(x = var_1419_cast_fp16, y = var_1420_to_fp16)[name = tensor("op_1421_cast_fp16")]; + tensor denom_29_epsilon_0_to_fp16 = const()[name = tensor("denom_29_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_29_cast_fp16 = rsqrt(epsilon = denom_29_epsilon_0_to_fp16, x = var_1421_cast_fp16)[name = tensor("denom_29_cast_fp16")]; + tensor out_29_cast_fp16 = mul(x = zero_mean_29_cast_fp16, y = denom_29_cast_fp16)[name = tensor("out_29_cast_fp16")]; + tensor var_1425_to_fp16 = const()[name = tensor("op_1425_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178172096)))]; + tensor var_1426_cast_fp16 = add(x = out_29_cast_fp16, y = var_1425_to_fp16)[name = tensor("op_1426_cast_fp16")]; + tensor var_1428_to_fp16 = const()[name = tensor("op_1428_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178174720)))]; + tensor input_137_cast_fp16 = mul(x = var_1426_cast_fp16, y = var_1428_to_fp16)[name = tensor("input_137_cast_fp16")]; + tensor var_1436 = const()[name = tensor("op_1436"), val = tensor([1, 1])]; + tensor var_1438 = const()[name = tensor("op_1438"), val = tensor([1, 1])]; + tensor var_1440_pad_type_0 = const()[name = tensor("op_1440_pad_type_0"), val = tensor("custom")]; + tensor var_1440_pad_0 = const()[name = tensor("op_1440_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178177344)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204391808)))]; + tensor var_1440_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_1438, groups = var_1195, pad = var_1440_pad_0, pad_type = var_1440_pad_type_0, strides = var_1436, weight = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_137_cast_fp16)[name = tensor("op_1440_cast_fp16")]; + tensor var_1441_split_sizes_0 = const()[name = tensor("op_1441_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_1441_axis_0 = const()[name = tensor("op_1441_axis_0"), val = tensor(1)]; + tensor var_1441_cast_fp16_0, tensor var_1441_cast_fp16_1 = split(axis = var_1441_axis_0, split_sizes = var_1441_split_sizes_0, x = var_1440_cast_fp16)[name = tensor("op_1441_cast_fp16")]; + tensor var_1443_mode_0 = const()[name = tensor("op_1443_mode_0"), val = tensor("EXACT")]; + tensor var_1443_cast_fp16 = gelu(mode = var_1443_mode_0, x = var_1441_cast_fp16_1)[name = tensor("op_1443_cast_fp16")]; + tensor input_139_cast_fp16 = mul(x = var_1441_cast_fp16_0, y = var_1443_cast_fp16)[name = tensor("input_139_cast_fp16")]; + tensor var_1447 = const()[name = tensor("op_1447"), val = tensor([1, 1])]; + tensor var_1449 = const()[name = tensor("op_1449"), val = tensor([1, 1])]; + tensor var_1451_pad_type_0 = const()[name = tensor("op_1451_pad_type_0"), val = tensor("custom")]; + tensor var_1451_pad_0 = const()[name = tensor("op_1451_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204412352)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217519616)))]; + tensor var_1451_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_1449, groups = var_1195, pad = var_1451_pad_0, pad_type = var_1451_pad_type_0, strides = var_1447, weight = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_139_cast_fp16)[name = tensor("op_1451_cast_fp16")]; + tensor hidden_states_87_cast_fp16 = add(x = var_1451_cast_fp16, y = inputs_29_cast_fp16)[name = tensor("hidden_states_87_cast_fp16")]; + tensor var_1453 = const()[name = tensor("op_1453"), val = tensor([2, 1280, 12, 20])]; + tensor input_141_cast_fp16 = reshape(shape = var_1453, x = hidden_states_87_cast_fp16)[name = tensor("input_141_cast_fp16")]; + tensor var_1457 = const()[name = tensor("op_1457"), val = tensor([1, 1])]; + tensor var_1459 = const()[name = tensor("op_1459"), val = tensor([1, 1])]; + tensor hidden_states_89_pad_type_0 = const()[name = tensor("hidden_states_89_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_89_pad_0 = const()[name = tensor("hidden_states_89_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217522240)))]; + tensor down_blocks_2_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(220799104)))]; + tensor hidden_states_89_cast_fp16 = conv(bias = down_blocks_2_attentions_0_proj_out_bias_to_fp16, dilations = var_1459, groups = var_1195, pad = hidden_states_89_pad_0, pad_type = hidden_states_89_pad_type_0, strides = var_1457, weight = down_blocks_2_attentions_0_proj_out_weight_to_fp16, x = input_141_cast_fp16)[name = tensor("hidden_states_89_cast_fp16")]; + tensor input_143_cast_fp16_1 = add(x = hidden_states_89_cast_fp16, y = hidden_states_77_cast_fp16)[name = tensor("input_143_cast_fp16")]; + tensor reshape_60_shape_0 = const()[name = tensor("reshape_60_shape_0"), val = tensor([2, 32, 40, 12, 20])]; + tensor reshape_60_cast_fp16 = reshape(shape = reshape_60_shape_0, x = input_143_cast_fp16_1)[name = tensor("reshape_60_cast_fp16")]; + tensor reduce_mean_45_axes_0 = const()[name = tensor("reduce_mean_45_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_45_keep_dims_0 = const()[name = tensor("reduce_mean_45_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_45_cast_fp16 = reduce_mean(axes = reduce_mean_45_axes_0, keep_dims = reduce_mean_45_keep_dims_0, x = reshape_60_cast_fp16)[name = tensor("reduce_mean_45_cast_fp16")]; + tensor sub_30_cast_fp16 = sub(x = reshape_60_cast_fp16, y = reduce_mean_45_cast_fp16)[name = tensor("sub_30_cast_fp16")]; + tensor square_15_cast_fp16 = square(x = sub_30_cast_fp16)[name = tensor("square_15_cast_fp16")]; + tensor reduce_mean_47_axes_0 = const()[name = tensor("reduce_mean_47_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_47_keep_dims_0 = const()[name = tensor("reduce_mean_47_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_47_cast_fp16 = reduce_mean(axes = reduce_mean_47_axes_0, keep_dims = reduce_mean_47_keep_dims_0, x = square_15_cast_fp16)[name = tensor("reduce_mean_47_cast_fp16")]; + tensor add_30_y_0_to_fp16 = const()[name = tensor("add_30_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_30_cast_fp16 = add(x = reduce_mean_47_cast_fp16, y = add_30_y_0_to_fp16)[name = tensor("add_30_cast_fp16")]; + tensor sqrt_15_cast_fp16 = sqrt(x = add_30_cast_fp16)[name = tensor("sqrt_15_cast_fp16")]; + tensor real_div_15_cast_fp16 = real_div(x = sub_30_cast_fp16, y = sqrt_15_cast_fp16)[name = tensor("real_div_15_cast_fp16")]; + tensor reshape_61_shape_0 = const()[name = tensor("reshape_61_shape_0"), val = tensor([2, 1280, 12, 20])]; + tensor reshape_61_cast_fp16 = reshape(shape = reshape_61_shape_0, x = real_div_15_cast_fp16)[name = tensor("reshape_61_cast_fp16")]; + tensor add_31_gamma_0_to_fp16 = const()[name = tensor("add_31_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(220801728)))]; + tensor add_31_beta_0_to_fp16 = const()[name = tensor("add_31_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(220804352)))]; + tensor add_31_epsilon_0_to_fp16 = const()[name = tensor("add_31_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_31_cast_fp16 = batch_norm(beta = add_31_beta_0_to_fp16, epsilon = add_31_epsilon_0_to_fp16, gamma = add_31_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_61_cast_fp16)[name = tensor("add_31_cast_fp16")]; + tensor input_147_cast_fp16 = silu(x = add_31_cast_fp16)[name = tensor("input_147_cast_fp16")]; + tensor var_1474 = const()[name = tensor("op_1474"), val = tensor([1, 1])]; + tensor var_1476 = const()[name = tensor("op_1476"), val = tensor([1, 1])]; + tensor hidden_states_91_pad_type_0 = const()[name = tensor("hidden_states_91_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_91_pad_0 = const()[name = tensor("hidden_states_91_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(220806976)))]; + tensor down_blocks_2_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(250298240)))]; + tensor hidden_states_91_cast_fp16 = conv(bias = down_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = var_1476, groups = var_1195, pad = hidden_states_91_pad_0, pad_type = hidden_states_91_pad_type_0, strides = var_1474, weight = down_blocks_2_resnets_1_conv1_weight_to_fp16, x = input_147_cast_fp16)[name = tensor("hidden_states_91_cast_fp16")]; + tensor var_1482 = const()[name = tensor("op_1482"), val = tensor([1, 1])]; + tensor var_1484 = const()[name = tensor("op_1484"), val = tensor([1, 1])]; + tensor temb_11_pad_type_0 = const()[name = tensor("temb_11_pad_type_0"), val = tensor("custom")]; + tensor temb_11_pad_0 = const()[name = tensor("temb_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(250300864)))]; + tensor down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(253577728)))]; + tensor temb_11_cast_fp16 = conv(bias = down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_1484, groups = var_1195, pad = temb_11_pad_0, pad_type = temb_11_pad_type_0, strides = var_1482, weight = down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16_1)[name = tensor("temb_11_cast_fp16")]; + tensor input_151_cast_fp16 = add(x = hidden_states_91_cast_fp16, y = temb_11_cast_fp16)[name = tensor("input_151_cast_fp16")]; + tensor reshape_64_shape_0 = const()[name = tensor("reshape_64_shape_0"), val = tensor([2, 32, 40, 12, 20])]; + tensor reshape_64_cast_fp16 = reshape(shape = reshape_64_shape_0, x = input_151_cast_fp16)[name = tensor("reshape_64_cast_fp16")]; + tensor reduce_mean_48_axes_0 = const()[name = tensor("reduce_mean_48_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_48_keep_dims_0 = const()[name = tensor("reduce_mean_48_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_48_cast_fp16 = reduce_mean(axes = reduce_mean_48_axes_0, keep_dims = reduce_mean_48_keep_dims_0, x = reshape_64_cast_fp16)[name = tensor("reduce_mean_48_cast_fp16")]; + tensor sub_32_cast_fp16 = sub(x = reshape_64_cast_fp16, y = reduce_mean_48_cast_fp16)[name = tensor("sub_32_cast_fp16")]; + tensor square_16_cast_fp16 = square(x = sub_32_cast_fp16)[name = tensor("square_16_cast_fp16")]; + tensor reduce_mean_50_axes_0 = const()[name = tensor("reduce_mean_50_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_50_keep_dims_0 = const()[name = tensor("reduce_mean_50_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_50_cast_fp16 = reduce_mean(axes = reduce_mean_50_axes_0, keep_dims = reduce_mean_50_keep_dims_0, x = square_16_cast_fp16)[name = tensor("reduce_mean_50_cast_fp16")]; + tensor add_32_y_0_to_fp16 = const()[name = tensor("add_32_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_32_cast_fp16 = add(x = reduce_mean_50_cast_fp16, y = add_32_y_0_to_fp16)[name = tensor("add_32_cast_fp16")]; + tensor sqrt_16_cast_fp16 = sqrt(x = add_32_cast_fp16)[name = tensor("sqrt_16_cast_fp16")]; + tensor real_div_16_cast_fp16 = real_div(x = sub_32_cast_fp16, y = sqrt_16_cast_fp16)[name = tensor("real_div_16_cast_fp16")]; + tensor reshape_65_shape_0 = const()[name = tensor("reshape_65_shape_0"), val = tensor([2, 1280, 12, 20])]; + tensor reshape_65_cast_fp16 = reshape(shape = reshape_65_shape_0, x = real_div_16_cast_fp16)[name = tensor("reshape_65_cast_fp16")]; + tensor add_33_gamma_0_to_fp16 = const()[name = tensor("add_33_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(253580352)))]; + tensor add_33_beta_0_to_fp16 = const()[name = tensor("add_33_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(253582976)))]; + tensor add_33_epsilon_0_to_fp16 = const()[name = tensor("add_33_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_33_cast_fp16 = batch_norm(beta = add_33_beta_0_to_fp16, epsilon = add_33_epsilon_0_to_fp16, gamma = add_33_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_65_cast_fp16)[name = tensor("add_33_cast_fp16")]; + tensor input_155_cast_fp16 = silu(x = add_33_cast_fp16)[name = tensor("input_155_cast_fp16")]; + tensor var_1494 = const()[name = tensor("op_1494"), val = tensor([1, 1])]; + tensor var_1496 = const()[name = tensor("op_1496"), val = tensor([1, 1])]; + tensor hidden_states_93_pad_type_0 = const()[name = tensor("hidden_states_93_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_93_pad_0 = const()[name = tensor("hidden_states_93_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(253585600)))]; + tensor down_blocks_2_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283076864)))]; + tensor hidden_states_93_cast_fp16 = conv(bias = down_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = var_1496, groups = var_1195, pad = hidden_states_93_pad_0, pad_type = hidden_states_93_pad_type_0, strides = var_1494, weight = down_blocks_2_resnets_1_conv2_weight_to_fp16, x = input_155_cast_fp16)[name = tensor("hidden_states_93_cast_fp16")]; + tensor hidden_states_95_cast_fp16 = add(x = input_143_cast_fp16_1, y = hidden_states_93_cast_fp16)[name = tensor("hidden_states_95_cast_fp16")]; + tensor reshape_68_shape_0 = const()[name = tensor("reshape_68_shape_0"), val = tensor([2, 32, 40, 12, 20])]; + tensor reshape_68_cast_fp16 = reshape(shape = reshape_68_shape_0, x = hidden_states_95_cast_fp16)[name = tensor("reshape_68_cast_fp16")]; + tensor reduce_mean_51_axes_0 = const()[name = tensor("reduce_mean_51_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_51_keep_dims_0 = const()[name = tensor("reduce_mean_51_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_51_cast_fp16 = reduce_mean(axes = reduce_mean_51_axes_0, keep_dims = reduce_mean_51_keep_dims_0, x = reshape_68_cast_fp16)[name = tensor("reduce_mean_51_cast_fp16")]; + tensor sub_34_cast_fp16 = sub(x = reshape_68_cast_fp16, y = reduce_mean_51_cast_fp16)[name = tensor("sub_34_cast_fp16")]; + tensor square_17_cast_fp16 = square(x = sub_34_cast_fp16)[name = tensor("square_17_cast_fp16")]; + tensor reduce_mean_53_axes_0 = const()[name = tensor("reduce_mean_53_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_53_keep_dims_0 = const()[name = tensor("reduce_mean_53_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_53_cast_fp16 = reduce_mean(axes = reduce_mean_53_axes_0, keep_dims = reduce_mean_53_keep_dims_0, x = square_17_cast_fp16)[name = tensor("reduce_mean_53_cast_fp16")]; + tensor add_34_y_0_to_fp16 = const()[name = tensor("add_34_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_34_cast_fp16 = add(x = reduce_mean_53_cast_fp16, y = add_34_y_0_to_fp16)[name = tensor("add_34_cast_fp16")]; + tensor sqrt_17_cast_fp16 = sqrt(x = add_34_cast_fp16)[name = tensor("sqrt_17_cast_fp16")]; + tensor real_div_17_cast_fp16 = real_div(x = sub_34_cast_fp16, y = sqrt_17_cast_fp16)[name = tensor("real_div_17_cast_fp16")]; + tensor reshape_69_shape_0 = const()[name = tensor("reshape_69_shape_0"), val = tensor([2, 1280, 12, 20])]; + tensor reshape_69_cast_fp16 = reshape(shape = reshape_69_shape_0, x = real_div_17_cast_fp16)[name = tensor("reshape_69_cast_fp16")]; + tensor add_35_gamma_0_to_fp16 = const()[name = tensor("add_35_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283079488)))]; + tensor add_35_beta_0_to_fp16 = const()[name = tensor("add_35_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283082112)))]; + tensor add_35_epsilon_0_to_fp16 = const()[name = tensor("add_35_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_35_cast_fp16 = batch_norm(beta = add_35_beta_0_to_fp16, epsilon = add_35_epsilon_0_to_fp16, gamma = add_35_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_69_cast_fp16)[name = tensor("add_35_cast_fp16")]; + tensor var_1516 = const()[name = tensor("op_1516"), val = tensor([1, 1])]; + tensor var_1518 = const()[name = tensor("op_1518"), val = tensor([1, 1])]; + tensor hidden_states_97_pad_type_0 = const()[name = tensor("hidden_states_97_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_97_pad_0 = const()[name = tensor("hidden_states_97_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_proj_in_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283084736)))]; + tensor down_blocks_2_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(286361600)))]; + tensor hidden_states_97_cast_fp16 = conv(bias = down_blocks_2_attentions_1_proj_in_bias_to_fp16, dilations = var_1518, groups = var_1195, pad = hidden_states_97_pad_0, pad_type = hidden_states_97_pad_type_0, strides = var_1516, weight = down_blocks_2_attentions_1_proj_in_weight_to_fp16, x = add_35_cast_fp16)[name = tensor("hidden_states_97_cast_fp16")]; + tensor var_1523 = const()[name = tensor("op_1523"), val = tensor([2, 1280, 1, 240])]; + tensor inputs_31_cast_fp16 = reshape(shape = var_1523, x = hidden_states_97_cast_fp16)[name = tensor("inputs_31_cast_fp16")]; + tensor var_1533 = const()[name = tensor("op_1533"), val = tensor([1])]; + tensor channels_mean_31_cast_fp16 = reduce_mean(axes = var_1533, keep_dims = var_1190, x = inputs_31_cast_fp16)[name = tensor("channels_mean_31_cast_fp16")]; + tensor zero_mean_31_cast_fp16 = sub(x = inputs_31_cast_fp16, y = channels_mean_31_cast_fp16)[name = tensor("zero_mean_31_cast_fp16")]; + tensor zero_mean_sq_31_cast_fp16 = mul(x = zero_mean_31_cast_fp16, y = zero_mean_31_cast_fp16)[name = tensor("zero_mean_sq_31_cast_fp16")]; + tensor var_1537 = const()[name = tensor("op_1537"), val = tensor([1])]; + tensor var_1538_cast_fp16 = reduce_mean(axes = var_1537, keep_dims = var_1190, x = zero_mean_sq_31_cast_fp16)[name = tensor("op_1538_cast_fp16")]; + tensor var_1539_to_fp16 = const()[name = tensor("op_1539_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1540_cast_fp16 = add(x = var_1538_cast_fp16, y = var_1539_to_fp16)[name = tensor("op_1540_cast_fp16")]; + tensor denom_31_epsilon_0_to_fp16 = const()[name = tensor("denom_31_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_31_cast_fp16 = rsqrt(epsilon = denom_31_epsilon_0_to_fp16, x = var_1540_cast_fp16)[name = tensor("denom_31_cast_fp16")]; + tensor out_31_cast_fp16 = mul(x = zero_mean_31_cast_fp16, y = denom_31_cast_fp16)[name = tensor("out_31_cast_fp16")]; + tensor var_1544_to_fp16 = const()[name = tensor("op_1544_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(286364224)))]; + tensor var_1545_cast_fp16 = add(x = out_31_cast_fp16, y = var_1544_to_fp16)[name = tensor("op_1545_cast_fp16")]; + tensor var_1547_to_fp16 = const()[name = tensor("op_1547_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(286366848)))]; + tensor hidden_states_99_cast_fp16 = mul(x = var_1545_cast_fp16, y = var_1547_to_fp16)[name = tensor("hidden_states_99_cast_fp16")]; + tensor var_1554 = const()[name = tensor("op_1554"), val = tensor([1, 1])]; + tensor var_1556 = const()[name = tensor("op_1556"), val = tensor([1, 1])]; + tensor q_21_pad_type_0 = const()[name = tensor("q_21_pad_type_0"), val = tensor("custom")]; + tensor q_21_pad_0 = const()[name = tensor("q_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(286369472)))]; + tensor q_21_cast_fp16 = conv(dilations = var_1556, groups = var_1195, pad = q_21_pad_0, pad_type = q_21_pad_type_0, strides = var_1554, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_99_cast_fp16)[name = tensor("q_21_cast_fp16")]; + tensor var_1560 = const()[name = tensor("op_1560"), val = tensor([1, 1])]; + tensor var_1562 = const()[name = tensor("op_1562"), val = tensor([1, 1])]; + tensor k_21_pad_type_0 = const()[name = tensor("k_21_pad_type_0"), val = tensor("custom")]; + tensor k_21_pad_0 = const()[name = tensor("k_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(289646336)))]; + tensor k_21_cast_fp16 = conv(dilations = var_1562, groups = var_1195, pad = k_21_pad_0, pad_type = k_21_pad_type_0, strides = var_1560, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_99_cast_fp16)[name = tensor("k_21_cast_fp16")]; + tensor var_1566 = const()[name = tensor("op_1566"), val = tensor([1, 1])]; + tensor var_1568 = const()[name = tensor("op_1568"), val = tensor([1, 1])]; + tensor v_21_pad_type_0 = const()[name = tensor("v_21_pad_type_0"), val = tensor("custom")]; + tensor v_21_pad_0 = const()[name = tensor("v_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(292923200)))]; + tensor v_21_cast_fp16 = conv(dilations = var_1568, groups = var_1195, pad = v_21_pad_0, pad_type = v_21_pad_type_0, strides = var_1566, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_99_cast_fp16)[name = tensor("v_21_cast_fp16")]; + tensor var_1572 = const()[name = tensor("op_1572"), val = tensor([2, 20, 64, -1])]; + tensor var_1573_cast_fp16 = reshape(shape = var_1572, x = q_21_cast_fp16)[name = tensor("op_1573_cast_fp16")]; + tensor var_1574 = const()[name = tensor("op_1574"), val = tensor([2, 20, 64, -1])]; + tensor var_1575_cast_fp16 = reshape(shape = var_1574, x = k_21_cast_fp16)[name = tensor("op_1575_cast_fp16")]; + tensor var_1576 = const()[name = tensor("op_1576"), val = tensor([2, 20, 64, -1])]; + tensor var_1577_cast_fp16 = reshape(shape = var_1576, x = v_21_cast_fp16)[name = tensor("op_1577_cast_fp16")]; + tensor attn_weights_41_transpose_x_0 = const()[name = tensor("attn_weights_41_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_41_transpose_y_0 = const()[name = tensor("attn_weights_41_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_41_cast_fp16 = matmul(transpose_x = attn_weights_41_transpose_x_0, transpose_y = attn_weights_41_transpose_y_0, x = var_1573_cast_fp16, y = var_1575_cast_fp16)[name = tensor("attn_weights_41_cast_fp16")]; + tensor attn_weights_43_cast_fp16 = mul(x = attn_weights_41_cast_fp16, y = var_1186_to_fp16)[name = tensor("attn_weights_43_cast_fp16")]; + tensor var_1581_cast_fp16 = softmax(axis = var_1179, x = attn_weights_43_cast_fp16)[name = tensor("op_1581_cast_fp16")]; + tensor attn_21_transpose_x_0 = const()[name = tensor("attn_21_transpose_x_0"), val = tensor(false)]; + tensor attn_21_transpose_y_0 = const()[name = tensor("attn_21_transpose_y_0"), val = tensor(true)]; + tensor attn_21_cast_fp16 = matmul(transpose_x = attn_21_transpose_x_0, transpose_y = attn_21_transpose_y_0, x = var_1577_cast_fp16, y = var_1581_cast_fp16)[name = tensor("attn_21_cast_fp16")]; + tensor var_1585 = const()[name = tensor("op_1585"), val = tensor([2, 1280, 1, -1])]; + tensor input_159_cast_fp16 = reshape(shape = var_1585, x = attn_21_cast_fp16)[name = tensor("input_159_cast_fp16")]; + tensor var_1590 = const()[name = tensor("op_1590"), val = tensor([1, 1])]; + tensor var_1592 = const()[name = tensor("op_1592"), val = tensor([1, 1])]; + tensor var_1594_pad_type_0 = const()[name = tensor("op_1594_pad_type_0"), val = tensor("custom")]; + tensor var_1594_pad_0 = const()[name = tensor("op_1594_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(296200064)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(299476928)))]; + tensor var_1594_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_1592, groups = var_1195, pad = var_1594_pad_0, pad_type = var_1594_pad_type_0, strides = var_1590, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_159_cast_fp16)[name = tensor("op_1594_cast_fp16")]; + tensor inputs_33_cast_fp16 = add(x = var_1594_cast_fp16, y = inputs_31_cast_fp16)[name = tensor("inputs_33_cast_fp16")]; + tensor var_1598 = const()[name = tensor("op_1598"), val = tensor([1])]; + tensor channels_mean_33_cast_fp16 = reduce_mean(axes = var_1598, keep_dims = var_1190, x = inputs_33_cast_fp16)[name = tensor("channels_mean_33_cast_fp16")]; + tensor zero_mean_33_cast_fp16 = sub(x = inputs_33_cast_fp16, y = channels_mean_33_cast_fp16)[name = tensor("zero_mean_33_cast_fp16")]; + tensor zero_mean_sq_33_cast_fp16 = mul(x = zero_mean_33_cast_fp16, y = zero_mean_33_cast_fp16)[name = tensor("zero_mean_sq_33_cast_fp16")]; + tensor var_1602 = const()[name = tensor("op_1602"), val = tensor([1])]; + tensor var_1603_cast_fp16 = reduce_mean(axes = var_1602, keep_dims = var_1190, x = zero_mean_sq_33_cast_fp16)[name = tensor("op_1603_cast_fp16")]; + tensor var_1604_to_fp16 = const()[name = tensor("op_1604_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1605_cast_fp16 = add(x = var_1603_cast_fp16, y = var_1604_to_fp16)[name = tensor("op_1605_cast_fp16")]; + tensor denom_33_epsilon_0_to_fp16 = const()[name = tensor("denom_33_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_33_cast_fp16 = rsqrt(epsilon = denom_33_epsilon_0_to_fp16, x = var_1605_cast_fp16)[name = tensor("denom_33_cast_fp16")]; + tensor out_33_cast_fp16 = mul(x = zero_mean_33_cast_fp16, y = denom_33_cast_fp16)[name = tensor("out_33_cast_fp16")]; + tensor var_1609_to_fp16 = const()[name = tensor("op_1609_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(299479552)))]; + tensor var_1610_cast_fp16 = add(x = out_33_cast_fp16, y = var_1609_to_fp16)[name = tensor("op_1610_cast_fp16")]; + tensor var_1612_to_fp16 = const()[name = tensor("op_1612_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(299482176)))]; + tensor hidden_states_101_cast_fp16 = mul(x = var_1610_cast_fp16, y = var_1612_to_fp16)[name = tensor("hidden_states_101_cast_fp16")]; + tensor var_1619 = const()[name = tensor("op_1619"), val = tensor([1, 1])]; + tensor var_1621 = const()[name = tensor("op_1621"), val = tensor([1, 1])]; + tensor q_23_pad_type_0 = const()[name = tensor("q_23_pad_type_0"), val = tensor("custom")]; + tensor q_23_pad_0 = const()[name = tensor("q_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(299484800)))]; + tensor q_23_cast_fp16 = conv(dilations = var_1621, groups = var_1195, pad = q_23_pad_0, pad_type = q_23_pad_type_0, strides = var_1619, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_101_cast_fp16)[name = tensor("q_23_cast_fp16")]; + tensor var_1625 = const()[name = tensor("op_1625"), val = tensor([1, 1])]; + tensor var_1627 = const()[name = tensor("op_1627"), val = tensor([1, 1])]; + tensor k_23_pad_type_0 = const()[name = tensor("k_23_pad_type_0"), val = tensor("custom")]; + tensor k_23_pad_0 = const()[name = tensor("k_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(302761664)))]; + tensor k_23_cast_fp16 = conv(dilations = var_1627, groups = var_1195, pad = k_23_pad_0, pad_type = k_23_pad_type_0, strides = var_1625, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_23_cast_fp16")]; + tensor var_1631 = const()[name = tensor("op_1631"), val = tensor([1, 1])]; + tensor var_1633 = const()[name = tensor("op_1633"), val = tensor([1, 1])]; + tensor v_23_pad_type_0 = const()[name = tensor("v_23_pad_type_0"), val = tensor("custom")]; + tensor v_23_pad_0 = const()[name = tensor("v_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(305383168)))]; + tensor v_23_cast_fp16 = conv(dilations = var_1633, groups = var_1195, pad = v_23_pad_0, pad_type = v_23_pad_type_0, strides = var_1631, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_23_cast_fp16")]; + tensor var_1637 = const()[name = tensor("op_1637"), val = tensor([2, 20, 64, -1])]; + tensor var_1638_cast_fp16 = reshape(shape = var_1637, x = q_23_cast_fp16)[name = tensor("op_1638_cast_fp16")]; + tensor var_1639 = const()[name = tensor("op_1639"), val = tensor([2, 20, 64, -1])]; + tensor var_1640_cast_fp16 = reshape(shape = var_1639, x = k_23_cast_fp16)[name = tensor("op_1640_cast_fp16")]; + tensor var_1641 = const()[name = tensor("op_1641"), val = tensor([2, 20, 64, -1])]; + tensor var_1642_cast_fp16 = reshape(shape = var_1641, x = v_23_cast_fp16)[name = tensor("op_1642_cast_fp16")]; + tensor attn_weights_45_transpose_x_0 = const()[name = tensor("attn_weights_45_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_45_transpose_y_0 = const()[name = tensor("attn_weights_45_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_45_cast_fp16 = matmul(transpose_x = attn_weights_45_transpose_x_0, transpose_y = attn_weights_45_transpose_y_0, x = var_1638_cast_fp16, y = var_1640_cast_fp16)[name = tensor("attn_weights_45_cast_fp16")]; + tensor attn_weights_47_cast_fp16 = mul(x = attn_weights_45_cast_fp16, y = var_1186_to_fp16)[name = tensor("attn_weights_47_cast_fp16")]; + tensor var_1646_cast_fp16 = softmax(axis = var_1179, x = attn_weights_47_cast_fp16)[name = tensor("op_1646_cast_fp16")]; + tensor attn_23_transpose_x_0 = const()[name = tensor("attn_23_transpose_x_0"), val = tensor(false)]; + tensor attn_23_transpose_y_0 = const()[name = tensor("attn_23_transpose_y_0"), val = tensor(true)]; + tensor attn_23_cast_fp16 = matmul(transpose_x = attn_23_transpose_x_0, transpose_y = attn_23_transpose_y_0, x = var_1642_cast_fp16, y = var_1646_cast_fp16)[name = tensor("attn_23_cast_fp16")]; + tensor var_1650 = const()[name = tensor("op_1650"), val = tensor([2, 1280, 1, -1])]; + tensor input_161_cast_fp16 = reshape(shape = var_1650, x = attn_23_cast_fp16)[name = tensor("input_161_cast_fp16")]; + tensor var_1655 = const()[name = tensor("op_1655"), val = tensor([1, 1])]; + tensor var_1657 = const()[name = tensor("op_1657"), val = tensor([1, 1])]; + tensor var_1659_pad_type_0 = const()[name = tensor("op_1659_pad_type_0"), val = tensor("custom")]; + tensor var_1659_pad_0 = const()[name = tensor("op_1659_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(308004672)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(311281536)))]; + tensor var_1659_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_1657, groups = var_1195, pad = var_1659_pad_0, pad_type = var_1659_pad_type_0, strides = var_1655, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_161_cast_fp16)[name = tensor("op_1659_cast_fp16")]; + tensor inputs_35_cast_fp16 = add(x = var_1659_cast_fp16, y = inputs_33_cast_fp16)[name = tensor("inputs_35_cast_fp16")]; + tensor var_1663 = const()[name = tensor("op_1663"), val = tensor([1])]; + tensor channels_mean_35_cast_fp16 = reduce_mean(axes = var_1663, keep_dims = var_1190, x = inputs_35_cast_fp16)[name = tensor("channels_mean_35_cast_fp16")]; + tensor zero_mean_35_cast_fp16 = sub(x = inputs_35_cast_fp16, y = channels_mean_35_cast_fp16)[name = tensor("zero_mean_35_cast_fp16")]; + tensor zero_mean_sq_35_cast_fp16 = mul(x = zero_mean_35_cast_fp16, y = zero_mean_35_cast_fp16)[name = tensor("zero_mean_sq_35_cast_fp16")]; + tensor var_1667 = const()[name = tensor("op_1667"), val = tensor([1])]; + tensor var_1668_cast_fp16 = reduce_mean(axes = var_1667, keep_dims = var_1190, x = zero_mean_sq_35_cast_fp16)[name = tensor("op_1668_cast_fp16")]; + tensor var_1669_to_fp16 = const()[name = tensor("op_1669_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1670_cast_fp16 = add(x = var_1668_cast_fp16, y = var_1669_to_fp16)[name = tensor("op_1670_cast_fp16")]; + tensor denom_35_epsilon_0_to_fp16 = const()[name = tensor("denom_35_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_35_cast_fp16 = rsqrt(epsilon = denom_35_epsilon_0_to_fp16, x = var_1670_cast_fp16)[name = tensor("denom_35_cast_fp16")]; + tensor out_35_cast_fp16 = mul(x = zero_mean_35_cast_fp16, y = denom_35_cast_fp16)[name = tensor("out_35_cast_fp16")]; + tensor var_1674_to_fp16 = const()[name = tensor("op_1674_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(311284160)))]; + tensor var_1675_cast_fp16 = add(x = out_35_cast_fp16, y = var_1674_to_fp16)[name = tensor("op_1675_cast_fp16")]; + tensor var_1677_to_fp16 = const()[name = tensor("op_1677_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(311286784)))]; + tensor input_163_cast_fp16 = mul(x = var_1675_cast_fp16, y = var_1677_to_fp16)[name = tensor("input_163_cast_fp16")]; + tensor var_1685 = const()[name = tensor("op_1685"), val = tensor([1, 1])]; + tensor var_1687 = const()[name = tensor("op_1687"), val = tensor([1, 1])]; + tensor var_1689_pad_type_0 = const()[name = tensor("op_1689_pad_type_0"), val = tensor("custom")]; + tensor var_1689_pad_0 = const()[name = tensor("op_1689_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(311289408)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(337503872)))]; + tensor var_1689_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_1687, groups = var_1195, pad = var_1689_pad_0, pad_type = var_1689_pad_type_0, strides = var_1685, weight = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_163_cast_fp16)[name = tensor("op_1689_cast_fp16")]; + tensor var_1690_split_sizes_0 = const()[name = tensor("op_1690_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_1690_axis_0 = const()[name = tensor("op_1690_axis_0"), val = tensor(1)]; + tensor var_1690_cast_fp16_0, tensor var_1690_cast_fp16_1 = split(axis = var_1690_axis_0, split_sizes = var_1690_split_sizes_0, x = var_1689_cast_fp16)[name = tensor("op_1690_cast_fp16")]; + tensor var_1692_mode_0 = const()[name = tensor("op_1692_mode_0"), val = tensor("EXACT")]; + tensor var_1692_cast_fp16 = gelu(mode = var_1692_mode_0, x = var_1690_cast_fp16_1)[name = tensor("op_1692_cast_fp16")]; + tensor input_165_cast_fp16 = mul(x = var_1690_cast_fp16_0, y = var_1692_cast_fp16)[name = tensor("input_165_cast_fp16")]; + tensor var_1696 = const()[name = tensor("op_1696"), val = tensor([1, 1])]; + tensor var_1698 = const()[name = tensor("op_1698"), val = tensor([1, 1])]; + tensor var_1700_pad_type_0 = const()[name = tensor("op_1700_pad_type_0"), val = tensor("custom")]; + tensor var_1700_pad_0 = const()[name = tensor("op_1700_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(337524416)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(350631680)))]; + tensor var_1700_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_1698, groups = var_1195, pad = var_1700_pad_0, pad_type = var_1700_pad_type_0, strides = var_1696, weight = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_165_cast_fp16)[name = tensor("op_1700_cast_fp16")]; + tensor hidden_states_105_cast_fp16 = add(x = var_1700_cast_fp16, y = inputs_35_cast_fp16)[name = tensor("hidden_states_105_cast_fp16")]; + tensor var_1702 = const()[name = tensor("op_1702"), val = tensor([2, 1280, 12, 20])]; + tensor input_167_cast_fp16 = reshape(shape = var_1702, x = hidden_states_105_cast_fp16)[name = tensor("input_167_cast_fp16")]; + tensor var_1706 = const()[name = tensor("op_1706"), val = tensor([1, 1])]; + tensor var_1708 = const()[name = tensor("op_1708"), val = tensor([1, 1])]; + tensor hidden_states_107_pad_type_0 = const()[name = tensor("hidden_states_107_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_107_pad_0 = const()[name = tensor("hidden_states_107_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_proj_out_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(350634304)))]; + tensor down_blocks_2_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(353911168)))]; + tensor hidden_states_107_cast_fp16 = conv(bias = down_blocks_2_attentions_1_proj_out_bias_to_fp16, dilations = var_1708, groups = var_1195, pad = hidden_states_107_pad_0, pad_type = hidden_states_107_pad_type_0, strides = var_1706, weight = down_blocks_2_attentions_1_proj_out_weight_to_fp16, x = input_167_cast_fp16)[name = tensor("hidden_states_107_cast_fp16")]; + tensor input_169_cast_fp16_1 = add(x = hidden_states_107_cast_fp16, y = hidden_states_95_cast_fp16)[name = tensor("input_169_cast_fp16")]; + tensor var_1715 = const()[name = tensor("op_1715"), val = tensor([2, 2])]; + tensor var_1717 = const()[name = tensor("op_1717"), val = tensor([1, 1])]; + tensor input_171_pad_type_0 = const()[name = tensor("input_171_pad_type_0"), val = tensor("custom")]; + tensor input_171_pad_0 = const()[name = tensor("input_171_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_downsamplers_0_conv_weight_to_fp16 = const()[name = tensor("down_blocks_2_downsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(353913792)))]; + tensor down_blocks_2_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor("down_blocks_2_downsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(383405056)))]; + tensor input_171_cast_fp16_1 = conv(bias = down_blocks_2_downsamplers_0_conv_bias_to_fp16, dilations = var_1717, groups = var_1195, pad = input_171_pad_0, pad_type = input_171_pad_type_0, strides = var_1715, weight = down_blocks_2_downsamplers_0_conv_weight_to_fp16, x = input_169_cast_fp16_1)[name = tensor("input_171_cast_fp16")]; + tensor var_1729 = const()[name = tensor("op_1729"), val = tensor(1)]; + tensor reshape_72_shape_0 = const()[name = tensor("reshape_72_shape_0"), val = tensor([2, 32, 40, 6, 10])]; + tensor reshape_72_cast_fp16 = reshape(shape = reshape_72_shape_0, x = input_171_cast_fp16_1)[name = tensor("reshape_72_cast_fp16")]; + tensor reduce_mean_54_axes_0 = const()[name = tensor("reduce_mean_54_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_54_keep_dims_0 = const()[name = tensor("reduce_mean_54_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_54_cast_fp16 = reduce_mean(axes = reduce_mean_54_axes_0, keep_dims = reduce_mean_54_keep_dims_0, x = reshape_72_cast_fp16)[name = tensor("reduce_mean_54_cast_fp16")]; + tensor sub_36_cast_fp16 = sub(x = reshape_72_cast_fp16, y = reduce_mean_54_cast_fp16)[name = tensor("sub_36_cast_fp16")]; + tensor square_18_cast_fp16 = square(x = sub_36_cast_fp16)[name = tensor("square_18_cast_fp16")]; + tensor reduce_mean_56_axes_0 = const()[name = tensor("reduce_mean_56_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_56_keep_dims_0 = const()[name = tensor("reduce_mean_56_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_56_cast_fp16 = reduce_mean(axes = reduce_mean_56_axes_0, keep_dims = reduce_mean_56_keep_dims_0, x = square_18_cast_fp16)[name = tensor("reduce_mean_56_cast_fp16")]; + tensor add_36_y_0_to_fp16 = const()[name = tensor("add_36_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_36_cast_fp16 = add(x = reduce_mean_56_cast_fp16, y = add_36_y_0_to_fp16)[name = tensor("add_36_cast_fp16")]; + tensor sqrt_18_cast_fp16 = sqrt(x = add_36_cast_fp16)[name = tensor("sqrt_18_cast_fp16")]; + tensor real_div_18_cast_fp16 = real_div(x = sub_36_cast_fp16, y = sqrt_18_cast_fp16)[name = tensor("real_div_18_cast_fp16")]; + tensor reshape_73_shape_0 = const()[name = tensor("reshape_73_shape_0"), val = tensor([2, 1280, 6, 10])]; + tensor reshape_73_cast_fp16 = reshape(shape = reshape_73_shape_0, x = real_div_18_cast_fp16)[name = tensor("reshape_73_cast_fp16")]; + tensor add_37_gamma_0_to_fp16 = const()[name = tensor("add_37_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(383407680)))]; + tensor add_37_beta_0_to_fp16 = const()[name = tensor("add_37_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(383410304)))]; + tensor add_37_epsilon_0_to_fp16 = const()[name = tensor("add_37_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_37_cast_fp16 = batch_norm(beta = add_37_beta_0_to_fp16, epsilon = add_37_epsilon_0_to_fp16, gamma = add_37_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_73_cast_fp16)[name = tensor("add_37_cast_fp16")]; + tensor input_175_cast_fp16 = silu(x = add_37_cast_fp16)[name = tensor("input_175_cast_fp16")]; + tensor var_1745 = const()[name = tensor("op_1745"), val = tensor([1, 1])]; + tensor var_1747 = const()[name = tensor("op_1747"), val = tensor([1, 1])]; + tensor hidden_states_109_pad_type_0 = const()[name = tensor("hidden_states_109_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_109_pad_0 = const()[name = tensor("hidden_states_109_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_3_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("down_blocks_3_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(383412928)))]; + tensor down_blocks_3_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(412904192)))]; + tensor hidden_states_109_cast_fp16 = conv(bias = down_blocks_3_resnets_0_conv1_bias_to_fp16, dilations = var_1747, groups = var_1729, pad = hidden_states_109_pad_0, pad_type = hidden_states_109_pad_type_0, strides = var_1745, weight = down_blocks_3_resnets_0_conv1_weight_to_fp16, x = input_175_cast_fp16)[name = tensor("hidden_states_109_cast_fp16")]; + tensor var_1753 = const()[name = tensor("op_1753"), val = tensor([1, 1])]; + tensor var_1755 = const()[name = tensor("op_1755"), val = tensor([1, 1])]; + tensor temb_13_pad_type_0 = const()[name = tensor("temb_13_pad_type_0"), val = tensor("custom")]; + tensor temb_13_pad_0 = const()[name = tensor("temb_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_3_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("down_blocks_3_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(412906816)))]; + tensor down_blocks_3_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(416183680)))]; + tensor temb_13_cast_fp16 = conv(bias = down_blocks_3_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_1755, groups = var_1729, pad = temb_13_pad_0, pad_type = temb_13_pad_type_0, strides = var_1753, weight = down_blocks_3_resnets_0_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16_1)[name = tensor("temb_13_cast_fp16")]; + tensor input_179_cast_fp16 = add(x = hidden_states_109_cast_fp16, y = temb_13_cast_fp16)[name = tensor("input_179_cast_fp16")]; + tensor reshape_76_shape_0 = const()[name = tensor("reshape_76_shape_0"), val = tensor([2, 32, 40, 6, 10])]; + tensor reshape_76_cast_fp16 = reshape(shape = reshape_76_shape_0, x = input_179_cast_fp16)[name = tensor("reshape_76_cast_fp16")]; + tensor reduce_mean_57_axes_0 = const()[name = tensor("reduce_mean_57_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_57_keep_dims_0 = const()[name = tensor("reduce_mean_57_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_57_cast_fp16 = reduce_mean(axes = reduce_mean_57_axes_0, keep_dims = reduce_mean_57_keep_dims_0, x = reshape_76_cast_fp16)[name = tensor("reduce_mean_57_cast_fp16")]; + tensor sub_38_cast_fp16 = sub(x = reshape_76_cast_fp16, y = reduce_mean_57_cast_fp16)[name = tensor("sub_38_cast_fp16")]; + tensor square_19_cast_fp16 = square(x = sub_38_cast_fp16)[name = tensor("square_19_cast_fp16")]; + tensor reduce_mean_59_axes_0 = const()[name = tensor("reduce_mean_59_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_59_keep_dims_0 = const()[name = tensor("reduce_mean_59_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_59_cast_fp16 = reduce_mean(axes = reduce_mean_59_axes_0, keep_dims = reduce_mean_59_keep_dims_0, x = square_19_cast_fp16)[name = tensor("reduce_mean_59_cast_fp16")]; + tensor add_38_y_0_to_fp16 = const()[name = tensor("add_38_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_38_cast_fp16 = add(x = reduce_mean_59_cast_fp16, y = add_38_y_0_to_fp16)[name = tensor("add_38_cast_fp16")]; + tensor sqrt_19_cast_fp16 = sqrt(x = add_38_cast_fp16)[name = tensor("sqrt_19_cast_fp16")]; + tensor real_div_19_cast_fp16 = real_div(x = sub_38_cast_fp16, y = sqrt_19_cast_fp16)[name = tensor("real_div_19_cast_fp16")]; + tensor reshape_77_shape_0 = const()[name = tensor("reshape_77_shape_0"), val = tensor([2, 1280, 6, 10])]; + tensor reshape_77_cast_fp16 = reshape(shape = reshape_77_shape_0, x = real_div_19_cast_fp16)[name = tensor("reshape_77_cast_fp16")]; + tensor add_39_gamma_0_to_fp16 = const()[name = tensor("add_39_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(416186304)))]; + tensor add_39_beta_0_to_fp16 = const()[name = tensor("add_39_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(416188928)))]; + tensor add_39_epsilon_0_to_fp16 = const()[name = tensor("add_39_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_39_cast_fp16 = batch_norm(beta = add_39_beta_0_to_fp16, epsilon = add_39_epsilon_0_to_fp16, gamma = add_39_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_77_cast_fp16)[name = tensor("add_39_cast_fp16")]; + tensor input_183_cast_fp16 = silu(x = add_39_cast_fp16)[name = tensor("input_183_cast_fp16")]; + tensor var_1765 = const()[name = tensor("op_1765"), val = tensor([1, 1])]; + tensor var_1767 = const()[name = tensor("op_1767"), val = tensor([1, 1])]; + tensor hidden_states_111_pad_type_0 = const()[name = tensor("hidden_states_111_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_111_pad_0 = const()[name = tensor("hidden_states_111_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_3_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("down_blocks_3_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(416191552)))]; + tensor down_blocks_3_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(445682816)))]; + tensor hidden_states_111_cast_fp16 = conv(bias = down_blocks_3_resnets_0_conv2_bias_to_fp16, dilations = var_1767, groups = var_1729, pad = hidden_states_111_pad_0, pad_type = hidden_states_111_pad_type_0, strides = var_1765, weight = down_blocks_3_resnets_0_conv2_weight_to_fp16, x = input_183_cast_fp16)[name = tensor("hidden_states_111_cast_fp16")]; + tensor input_185_cast_fp16 = add(x = input_171_cast_fp16_1, y = hidden_states_111_cast_fp16)[name = tensor("input_185_cast_fp16")]; + tensor reshape_80_shape_0 = const()[name = tensor("reshape_80_shape_0"), val = tensor([2, 32, 40, 6, 10])]; + tensor reshape_80_cast_fp16 = reshape(shape = reshape_80_shape_0, x = input_185_cast_fp16)[name = tensor("reshape_80_cast_fp16")]; + tensor reduce_mean_60_axes_0 = const()[name = tensor("reduce_mean_60_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_60_keep_dims_0 = const()[name = tensor("reduce_mean_60_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_60_cast_fp16 = reduce_mean(axes = reduce_mean_60_axes_0, keep_dims = reduce_mean_60_keep_dims_0, x = reshape_80_cast_fp16)[name = tensor("reduce_mean_60_cast_fp16")]; + tensor sub_40_cast_fp16 = sub(x = reshape_80_cast_fp16, y = reduce_mean_60_cast_fp16)[name = tensor("sub_40_cast_fp16")]; + tensor square_20_cast_fp16 = square(x = sub_40_cast_fp16)[name = tensor("square_20_cast_fp16")]; + tensor reduce_mean_62_axes_0 = const()[name = tensor("reduce_mean_62_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_62_keep_dims_0 = const()[name = tensor("reduce_mean_62_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_62_cast_fp16 = reduce_mean(axes = reduce_mean_62_axes_0, keep_dims = reduce_mean_62_keep_dims_0, x = square_20_cast_fp16)[name = tensor("reduce_mean_62_cast_fp16")]; + tensor add_40_y_0_to_fp16 = const()[name = tensor("add_40_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_40_cast_fp16 = add(x = reduce_mean_62_cast_fp16, y = add_40_y_0_to_fp16)[name = tensor("add_40_cast_fp16")]; + tensor sqrt_20_cast_fp16 = sqrt(x = add_40_cast_fp16)[name = tensor("sqrt_20_cast_fp16")]; + tensor real_div_20_cast_fp16 = real_div(x = sub_40_cast_fp16, y = sqrt_20_cast_fp16)[name = tensor("real_div_20_cast_fp16")]; + tensor reshape_81_shape_0 = const()[name = tensor("reshape_81_shape_0"), val = tensor([2, 1280, 6, 10])]; + tensor reshape_81_cast_fp16 = reshape(shape = reshape_81_shape_0, x = real_div_20_cast_fp16)[name = tensor("reshape_81_cast_fp16")]; + tensor add_41_gamma_0_to_fp16 = const()[name = tensor("add_41_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(445685440)))]; + tensor add_41_beta_0_to_fp16 = const()[name = tensor("add_41_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(445688064)))]; + tensor add_41_epsilon_0_to_fp16 = const()[name = tensor("add_41_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_41_cast_fp16 = batch_norm(beta = add_41_beta_0_to_fp16, epsilon = add_41_epsilon_0_to_fp16, gamma = add_41_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_81_cast_fp16)[name = tensor("add_41_cast_fp16")]; + tensor input_189_cast_fp16 = silu(x = add_41_cast_fp16)[name = tensor("input_189_cast_fp16")]; + tensor var_1782 = const()[name = tensor("op_1782"), val = tensor([1, 1])]; + tensor var_1784 = const()[name = tensor("op_1784"), val = tensor([1, 1])]; + tensor hidden_states_113_pad_type_0 = const()[name = tensor("hidden_states_113_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_113_pad_0 = const()[name = tensor("hidden_states_113_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_3_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("down_blocks_3_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(445690688)))]; + tensor down_blocks_3_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(475181952)))]; + tensor hidden_states_113_cast_fp16 = conv(bias = down_blocks_3_resnets_1_conv1_bias_to_fp16, dilations = var_1784, groups = var_1729, pad = hidden_states_113_pad_0, pad_type = hidden_states_113_pad_type_0, strides = var_1782, weight = down_blocks_3_resnets_1_conv1_weight_to_fp16, x = input_189_cast_fp16)[name = tensor("hidden_states_113_cast_fp16")]; + tensor var_1790 = const()[name = tensor("op_1790"), val = tensor([1, 1])]; + tensor var_1792 = const()[name = tensor("op_1792"), val = tensor([1, 1])]; + tensor temb_15_pad_type_0 = const()[name = tensor("temb_15_pad_type_0"), val = tensor("custom")]; + tensor temb_15_pad_0 = const()[name = tensor("temb_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_3_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("down_blocks_3_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(475184576)))]; + tensor down_blocks_3_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(478461440)))]; + tensor temb_15_cast_fp16 = conv(bias = down_blocks_3_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_1792, groups = var_1729, pad = temb_15_pad_0, pad_type = temb_15_pad_type_0, strides = var_1790, weight = down_blocks_3_resnets_1_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16_1)[name = tensor("temb_15_cast_fp16")]; + tensor input_193_cast_fp16 = add(x = hidden_states_113_cast_fp16, y = temb_15_cast_fp16)[name = tensor("input_193_cast_fp16")]; + tensor reshape_84_shape_0 = const()[name = tensor("reshape_84_shape_0"), val = tensor([2, 32, 40, 6, 10])]; + tensor reshape_84_cast_fp16 = reshape(shape = reshape_84_shape_0, x = input_193_cast_fp16)[name = tensor("reshape_84_cast_fp16")]; + tensor reduce_mean_63_axes_0 = const()[name = tensor("reduce_mean_63_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_63_keep_dims_0 = const()[name = tensor("reduce_mean_63_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_63_cast_fp16 = reduce_mean(axes = reduce_mean_63_axes_0, keep_dims = reduce_mean_63_keep_dims_0, x = reshape_84_cast_fp16)[name = tensor("reduce_mean_63_cast_fp16")]; + tensor sub_42_cast_fp16 = sub(x = reshape_84_cast_fp16, y = reduce_mean_63_cast_fp16)[name = tensor("sub_42_cast_fp16")]; + tensor square_21_cast_fp16 = square(x = sub_42_cast_fp16)[name = tensor("square_21_cast_fp16")]; + tensor reduce_mean_65_axes_0 = const()[name = tensor("reduce_mean_65_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_65_keep_dims_0 = const()[name = tensor("reduce_mean_65_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_65_cast_fp16 = reduce_mean(axes = reduce_mean_65_axes_0, keep_dims = reduce_mean_65_keep_dims_0, x = square_21_cast_fp16)[name = tensor("reduce_mean_65_cast_fp16")]; + tensor add_42_y_0_to_fp16 = const()[name = tensor("add_42_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_42_cast_fp16 = add(x = reduce_mean_65_cast_fp16, y = add_42_y_0_to_fp16)[name = tensor("add_42_cast_fp16")]; + tensor sqrt_21_cast_fp16 = sqrt(x = add_42_cast_fp16)[name = tensor("sqrt_21_cast_fp16")]; + tensor real_div_21_cast_fp16 = real_div(x = sub_42_cast_fp16, y = sqrt_21_cast_fp16)[name = tensor("real_div_21_cast_fp16")]; + tensor reshape_85_shape_0 = const()[name = tensor("reshape_85_shape_0"), val = tensor([2, 1280, 6, 10])]; + tensor reshape_85_cast_fp16 = reshape(shape = reshape_85_shape_0, x = real_div_21_cast_fp16)[name = tensor("reshape_85_cast_fp16")]; + tensor add_43_gamma_0_to_fp16 = const()[name = tensor("add_43_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(478464064)))]; + tensor add_43_beta_0_to_fp16 = const()[name = tensor("add_43_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(478466688)))]; + tensor add_43_epsilon_0_to_fp16 = const()[name = tensor("add_43_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_43_cast_fp16 = batch_norm(beta = add_43_beta_0_to_fp16, epsilon = add_43_epsilon_0_to_fp16, gamma = add_43_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_85_cast_fp16)[name = tensor("add_43_cast_fp16")]; + tensor input_197_cast_fp16 = silu(x = add_43_cast_fp16)[name = tensor("input_197_cast_fp16")]; + tensor var_1802 = const()[name = tensor("op_1802"), val = tensor([1, 1])]; + tensor var_1804 = const()[name = tensor("op_1804"), val = tensor([1, 1])]; + tensor hidden_states_115_pad_type_0 = const()[name = tensor("hidden_states_115_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_115_pad_0 = const()[name = tensor("hidden_states_115_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_3_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("down_blocks_3_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(478469312)))]; + tensor down_blocks_3_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(507960576)))]; + tensor hidden_states_115_cast_fp16 = conv(bias = down_blocks_3_resnets_1_conv2_bias_to_fp16, dilations = var_1804, groups = var_1729, pad = hidden_states_115_pad_0, pad_type = hidden_states_115_pad_type_0, strides = var_1802, weight = down_blocks_3_resnets_1_conv2_weight_to_fp16, x = input_197_cast_fp16)[name = tensor("hidden_states_115_cast_fp16")]; + tensor input_199_cast_fp16 = add(x = input_185_cast_fp16, y = hidden_states_115_cast_fp16)[name = tensor("input_199_cast_fp16")]; + tensor var_1812 = const()[name = tensor("op_1812"), val = tensor(3)]; + tensor var_1823 = const()[name = tensor("op_1823"), val = tensor(true)]; + tensor var_1828 = const()[name = tensor("op_1828"), val = tensor(1)]; + tensor reshape_88_shape_0 = const()[name = tensor("reshape_88_shape_0"), val = tensor([2, 32, 40, 6, 10])]; + tensor reshape_88_cast_fp16 = reshape(shape = reshape_88_shape_0, x = input_199_cast_fp16)[name = tensor("reshape_88_cast_fp16")]; + tensor reduce_mean_66_axes_0 = const()[name = tensor("reduce_mean_66_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_66_keep_dims_0 = const()[name = tensor("reduce_mean_66_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_66_cast_fp16 = reduce_mean(axes = reduce_mean_66_axes_0, keep_dims = reduce_mean_66_keep_dims_0, x = reshape_88_cast_fp16)[name = tensor("reduce_mean_66_cast_fp16")]; + tensor sub_44_cast_fp16 = sub(x = reshape_88_cast_fp16, y = reduce_mean_66_cast_fp16)[name = tensor("sub_44_cast_fp16")]; + tensor square_22_cast_fp16 = square(x = sub_44_cast_fp16)[name = tensor("square_22_cast_fp16")]; + tensor reduce_mean_68_axes_0 = const()[name = tensor("reduce_mean_68_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_68_keep_dims_0 = const()[name = tensor("reduce_mean_68_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_68_cast_fp16 = reduce_mean(axes = reduce_mean_68_axes_0, keep_dims = reduce_mean_68_keep_dims_0, x = square_22_cast_fp16)[name = tensor("reduce_mean_68_cast_fp16")]; + tensor add_44_y_0_to_fp16 = const()[name = tensor("add_44_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_44_cast_fp16 = add(x = reduce_mean_68_cast_fp16, y = add_44_y_0_to_fp16)[name = tensor("add_44_cast_fp16")]; + tensor sqrt_22_cast_fp16 = sqrt(x = add_44_cast_fp16)[name = tensor("sqrt_22_cast_fp16")]; + tensor real_div_22_cast_fp16 = real_div(x = sub_44_cast_fp16, y = sqrt_22_cast_fp16)[name = tensor("real_div_22_cast_fp16")]; + tensor reshape_89_shape_0 = const()[name = tensor("reshape_89_shape_0"), val = tensor([2, 1280, 6, 10])]; + tensor reshape_89_cast_fp16 = reshape(shape = reshape_89_shape_0, x = real_div_22_cast_fp16)[name = tensor("reshape_89_cast_fp16")]; + tensor add_45_gamma_0_to_fp16 = const()[name = tensor("add_45_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(507963200)))]; + tensor add_45_beta_0_to_fp16 = const()[name = tensor("add_45_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(507965824)))]; + tensor add_45_epsilon_0_to_fp16 = const()[name = tensor("add_45_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_45_cast_fp16 = batch_norm(beta = add_45_beta_0_to_fp16, epsilon = add_45_epsilon_0_to_fp16, gamma = add_45_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_89_cast_fp16)[name = tensor("add_45_cast_fp16")]; + tensor input_203_cast_fp16 = silu(x = add_45_cast_fp16)[name = tensor("input_203_cast_fp16")]; + tensor var_1846 = const()[name = tensor("op_1846"), val = tensor([1, 1])]; + tensor var_1848 = const()[name = tensor("op_1848"), val = tensor([1, 1])]; + tensor hidden_states_117_pad_type_0 = const()[name = tensor("hidden_states_117_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_117_pad_0 = const()[name = tensor("hidden_states_117_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor mid_block_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("mid_block_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(507968448)))]; + tensor mid_block_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("mid_block_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(537459712)))]; + tensor hidden_states_117_cast_fp16 = conv(bias = mid_block_resnets_0_conv1_bias_to_fp16, dilations = var_1848, groups = var_1828, pad = hidden_states_117_pad_0, pad_type = hidden_states_117_pad_type_0, strides = var_1846, weight = mid_block_resnets_0_conv1_weight_to_fp16, x = input_203_cast_fp16)[name = tensor("hidden_states_117_cast_fp16")]; + tensor var_1854 = const()[name = tensor("op_1854"), val = tensor([1, 1])]; + tensor var_1856 = const()[name = tensor("op_1856"), val = tensor([1, 1])]; + tensor temb_17_pad_type_0 = const()[name = tensor("temb_17_pad_type_0"), val = tensor("custom")]; + tensor temb_17_pad_0 = const()[name = tensor("temb_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("mid_block_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(537462336)))]; + tensor mid_block_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("mid_block_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(540739200)))]; + tensor temb_17_cast_fp16 = conv(bias = mid_block_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_1856, groups = var_1828, pad = temb_17_pad_0, pad_type = temb_17_pad_type_0, strides = var_1854, weight = mid_block_resnets_0_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16_1)[name = tensor("temb_17_cast_fp16")]; + tensor input_207_cast_fp16 = add(x = hidden_states_117_cast_fp16, y = temb_17_cast_fp16)[name = tensor("input_207_cast_fp16")]; + tensor reshape_92_shape_0 = const()[name = tensor("reshape_92_shape_0"), val = tensor([2, 32, 40, 6, 10])]; + tensor reshape_92_cast_fp16 = reshape(shape = reshape_92_shape_0, x = input_207_cast_fp16)[name = tensor("reshape_92_cast_fp16")]; + tensor reduce_mean_69_axes_0 = const()[name = tensor("reduce_mean_69_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_69_keep_dims_0 = const()[name = tensor("reduce_mean_69_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_69_cast_fp16 = reduce_mean(axes = reduce_mean_69_axes_0, keep_dims = reduce_mean_69_keep_dims_0, x = reshape_92_cast_fp16)[name = tensor("reduce_mean_69_cast_fp16")]; + tensor sub_46_cast_fp16 = sub(x = reshape_92_cast_fp16, y = reduce_mean_69_cast_fp16)[name = tensor("sub_46_cast_fp16")]; + tensor square_23_cast_fp16 = square(x = sub_46_cast_fp16)[name = tensor("square_23_cast_fp16")]; + tensor reduce_mean_71_axes_0 = const()[name = tensor("reduce_mean_71_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_71_keep_dims_0 = const()[name = tensor("reduce_mean_71_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_71_cast_fp16 = reduce_mean(axes = reduce_mean_71_axes_0, keep_dims = reduce_mean_71_keep_dims_0, x = square_23_cast_fp16)[name = tensor("reduce_mean_71_cast_fp16")]; + tensor add_46_y_0_to_fp16 = const()[name = tensor("add_46_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_46_cast_fp16 = add(x = reduce_mean_71_cast_fp16, y = add_46_y_0_to_fp16)[name = tensor("add_46_cast_fp16")]; + tensor sqrt_23_cast_fp16 = sqrt(x = add_46_cast_fp16)[name = tensor("sqrt_23_cast_fp16")]; + tensor real_div_23_cast_fp16 = real_div(x = sub_46_cast_fp16, y = sqrt_23_cast_fp16)[name = tensor("real_div_23_cast_fp16")]; + tensor reshape_93_shape_0 = const()[name = tensor("reshape_93_shape_0"), val = tensor([2, 1280, 6, 10])]; + tensor reshape_93_cast_fp16 = reshape(shape = reshape_93_shape_0, x = real_div_23_cast_fp16)[name = tensor("reshape_93_cast_fp16")]; + tensor add_47_gamma_0_to_fp16 = const()[name = tensor("add_47_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(540741824)))]; + tensor add_47_beta_0_to_fp16 = const()[name = tensor("add_47_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(540744448)))]; + tensor add_47_epsilon_0_to_fp16 = const()[name = tensor("add_47_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_47_cast_fp16 = batch_norm(beta = add_47_beta_0_to_fp16, epsilon = add_47_epsilon_0_to_fp16, gamma = add_47_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_93_cast_fp16)[name = tensor("add_47_cast_fp16")]; + tensor input_211_cast_fp16 = silu(x = add_47_cast_fp16)[name = tensor("input_211_cast_fp16")]; + tensor var_1866 = const()[name = tensor("op_1866"), val = tensor([1, 1])]; + tensor var_1868 = const()[name = tensor("op_1868"), val = tensor([1, 1])]; + tensor hidden_states_119_pad_type_0 = const()[name = tensor("hidden_states_119_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_119_pad_0 = const()[name = tensor("hidden_states_119_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor mid_block_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("mid_block_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(540747072)))]; + tensor mid_block_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("mid_block_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(570238336)))]; + tensor hidden_states_119_cast_fp16 = conv(bias = mid_block_resnets_0_conv2_bias_to_fp16, dilations = var_1868, groups = var_1828, pad = hidden_states_119_pad_0, pad_type = hidden_states_119_pad_type_0, strides = var_1866, weight = mid_block_resnets_0_conv2_weight_to_fp16, x = input_211_cast_fp16)[name = tensor("hidden_states_119_cast_fp16")]; + tensor hidden_states_121_cast_fp16 = add(x = input_199_cast_fp16, y = hidden_states_119_cast_fp16)[name = tensor("hidden_states_121_cast_fp16")]; + tensor reshape_96_shape_0 = const()[name = tensor("reshape_96_shape_0"), val = tensor([2, 32, 40, 6, 10])]; + tensor reshape_96_cast_fp16 = reshape(shape = reshape_96_shape_0, x = hidden_states_121_cast_fp16)[name = tensor("reshape_96_cast_fp16")]; + tensor reduce_mean_72_axes_0 = const()[name = tensor("reduce_mean_72_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_72_keep_dims_0 = const()[name = tensor("reduce_mean_72_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_72_cast_fp16 = reduce_mean(axes = reduce_mean_72_axes_0, keep_dims = reduce_mean_72_keep_dims_0, x = reshape_96_cast_fp16)[name = tensor("reduce_mean_72_cast_fp16")]; + tensor sub_48_cast_fp16 = sub(x = reshape_96_cast_fp16, y = reduce_mean_72_cast_fp16)[name = tensor("sub_48_cast_fp16")]; + tensor square_24_cast_fp16 = square(x = sub_48_cast_fp16)[name = tensor("square_24_cast_fp16")]; + tensor reduce_mean_74_axes_0 = const()[name = tensor("reduce_mean_74_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_74_keep_dims_0 = const()[name = tensor("reduce_mean_74_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_74_cast_fp16 = reduce_mean(axes = reduce_mean_74_axes_0, keep_dims = reduce_mean_74_keep_dims_0, x = square_24_cast_fp16)[name = tensor("reduce_mean_74_cast_fp16")]; + tensor add_48_y_0_to_fp16 = const()[name = tensor("add_48_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_48_cast_fp16 = add(x = reduce_mean_74_cast_fp16, y = add_48_y_0_to_fp16)[name = tensor("add_48_cast_fp16")]; + tensor sqrt_24_cast_fp16 = sqrt(x = add_48_cast_fp16)[name = tensor("sqrt_24_cast_fp16")]; + tensor real_div_24_cast_fp16 = real_div(x = sub_48_cast_fp16, y = sqrt_24_cast_fp16)[name = tensor("real_div_24_cast_fp16")]; + tensor reshape_97_shape_0 = const()[name = tensor("reshape_97_shape_0"), val = tensor([2, 1280, 6, 10])]; + tensor reshape_97_cast_fp16 = reshape(shape = reshape_97_shape_0, x = real_div_24_cast_fp16)[name = tensor("reshape_97_cast_fp16")]; + tensor add_49_gamma_0_to_fp16 = const()[name = tensor("add_49_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(570240960)))]; + tensor add_49_beta_0_to_fp16 = const()[name = tensor("add_49_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(570243584)))]; + tensor add_49_epsilon_0_to_fp16 = const()[name = tensor("add_49_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_49_cast_fp16 = batch_norm(beta = add_49_beta_0_to_fp16, epsilon = add_49_epsilon_0_to_fp16, gamma = add_49_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_97_cast_fp16)[name = tensor("add_49_cast_fp16")]; + tensor var_1888 = const()[name = tensor("op_1888"), val = tensor([1, 1])]; + tensor var_1890 = const()[name = tensor("op_1890"), val = tensor([1, 1])]; + tensor hidden_states_123_pad_type_0 = const()[name = tensor("hidden_states_123_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_123_pad_0 = const()[name = tensor("hidden_states_123_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(570246208)))]; + tensor mid_block_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(573523072)))]; + tensor hidden_states_123_cast_fp16 = conv(bias = mid_block_attentions_0_proj_in_bias_to_fp16, dilations = var_1890, groups = var_1828, pad = hidden_states_123_pad_0, pad_type = hidden_states_123_pad_type_0, strides = var_1888, weight = mid_block_attentions_0_proj_in_weight_to_fp16, x = add_49_cast_fp16)[name = tensor("hidden_states_123_cast_fp16")]; + tensor var_1895 = const()[name = tensor("op_1895"), val = tensor([2, 1280, 1, 60])]; + tensor inputs_37_cast_fp16 = reshape(shape = var_1895, x = hidden_states_123_cast_fp16)[name = tensor("inputs_37_cast_fp16")]; + tensor var_1905 = const()[name = tensor("op_1905"), val = tensor([1])]; + tensor channels_mean_37_cast_fp16 = reduce_mean(axes = var_1905, keep_dims = var_1823, x = inputs_37_cast_fp16)[name = tensor("channels_mean_37_cast_fp16")]; + tensor zero_mean_37_cast_fp16 = sub(x = inputs_37_cast_fp16, y = channels_mean_37_cast_fp16)[name = tensor("zero_mean_37_cast_fp16")]; + tensor zero_mean_sq_37_cast_fp16 = mul(x = zero_mean_37_cast_fp16, y = zero_mean_37_cast_fp16)[name = tensor("zero_mean_sq_37_cast_fp16")]; + tensor var_1909 = const()[name = tensor("op_1909"), val = tensor([1])]; + tensor var_1910_cast_fp16 = reduce_mean(axes = var_1909, keep_dims = var_1823, x = zero_mean_sq_37_cast_fp16)[name = tensor("op_1910_cast_fp16")]; + tensor var_1911_to_fp16 = const()[name = tensor("op_1911_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1912_cast_fp16 = add(x = var_1910_cast_fp16, y = var_1911_to_fp16)[name = tensor("op_1912_cast_fp16")]; + tensor denom_37_epsilon_0_to_fp16 = const()[name = tensor("denom_37_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_37_cast_fp16 = rsqrt(epsilon = denom_37_epsilon_0_to_fp16, x = var_1912_cast_fp16)[name = tensor("denom_37_cast_fp16")]; + tensor out_37_cast_fp16 = mul(x = zero_mean_37_cast_fp16, y = denom_37_cast_fp16)[name = tensor("out_37_cast_fp16")]; + tensor var_1916_to_fp16 = const()[name = tensor("op_1916_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(573525696)))]; + tensor var_1917_cast_fp16 = add(x = out_37_cast_fp16, y = var_1916_to_fp16)[name = tensor("op_1917_cast_fp16")]; + tensor var_1919_to_fp16 = const()[name = tensor("op_1919_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(573528320)))]; + tensor hidden_states_125_cast_fp16 = mul(x = var_1917_cast_fp16, y = var_1919_to_fp16)[name = tensor("hidden_states_125_cast_fp16")]; + tensor var_1926 = const()[name = tensor("op_1926"), val = tensor([1, 1])]; + tensor var_1928 = const()[name = tensor("op_1928"), val = tensor([1, 1])]; + tensor q_25_pad_type_0 = const()[name = tensor("q_25_pad_type_0"), val = tensor("custom")]; + tensor q_25_pad_0 = const()[name = tensor("q_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(573530944)))]; + tensor q_25_cast_fp16 = conv(dilations = var_1928, groups = var_1828, pad = q_25_pad_0, pad_type = q_25_pad_type_0, strides = var_1926, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_125_cast_fp16)[name = tensor("q_25_cast_fp16")]; + tensor var_1932 = const()[name = tensor("op_1932"), val = tensor([1, 1])]; + tensor var_1934 = const()[name = tensor("op_1934"), val = tensor([1, 1])]; + tensor k_25_pad_type_0 = const()[name = tensor("k_25_pad_type_0"), val = tensor("custom")]; + tensor k_25_pad_0 = const()[name = tensor("k_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(576807808)))]; + tensor k_25_cast_fp16 = conv(dilations = var_1934, groups = var_1828, pad = k_25_pad_0, pad_type = k_25_pad_type_0, strides = var_1932, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_125_cast_fp16)[name = tensor("k_25_cast_fp16")]; + tensor var_1938 = const()[name = tensor("op_1938"), val = tensor([1, 1])]; + tensor var_1940 = const()[name = tensor("op_1940"), val = tensor([1, 1])]; + tensor v_25_pad_type_0 = const()[name = tensor("v_25_pad_type_0"), val = tensor("custom")]; + tensor v_25_pad_0 = const()[name = tensor("v_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(580084672)))]; + tensor v_25_cast_fp16 = conv(dilations = var_1940, groups = var_1828, pad = v_25_pad_0, pad_type = v_25_pad_type_0, strides = var_1938, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_125_cast_fp16)[name = tensor("v_25_cast_fp16")]; + tensor var_1944 = const()[name = tensor("op_1944"), val = tensor([2, 20, 64, -1])]; + tensor var_1945_cast_fp16 = reshape(shape = var_1944, x = q_25_cast_fp16)[name = tensor("op_1945_cast_fp16")]; + tensor var_1946 = const()[name = tensor("op_1946"), val = tensor([2, 20, 64, -1])]; + tensor var_1947_cast_fp16 = reshape(shape = var_1946, x = k_25_cast_fp16)[name = tensor("op_1947_cast_fp16")]; + tensor var_1948 = const()[name = tensor("op_1948"), val = tensor([2, 20, 64, -1])]; + tensor var_1949_cast_fp16 = reshape(shape = var_1948, x = v_25_cast_fp16)[name = tensor("op_1949_cast_fp16")]; + tensor attn_weights_49_transpose_x_0 = const()[name = tensor("attn_weights_49_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_49_transpose_y_0 = const()[name = tensor("attn_weights_49_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_49_cast_fp16 = matmul(transpose_x = attn_weights_49_transpose_x_0, transpose_y = attn_weights_49_transpose_y_0, x = var_1945_cast_fp16, y = var_1947_cast_fp16)[name = tensor("attn_weights_49_cast_fp16")]; + tensor var_1819_to_fp16 = const()[name = tensor("op_1819_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_51_cast_fp16 = mul(x = attn_weights_49_cast_fp16, y = var_1819_to_fp16)[name = tensor("attn_weights_51_cast_fp16")]; + tensor var_1953_cast_fp16 = softmax(axis = var_1812, x = attn_weights_51_cast_fp16)[name = tensor("op_1953_cast_fp16")]; + tensor attn_25_transpose_x_0 = const()[name = tensor("attn_25_transpose_x_0"), val = tensor(false)]; + tensor attn_25_transpose_y_0 = const()[name = tensor("attn_25_transpose_y_0"), val = tensor(true)]; + tensor attn_25_cast_fp16 = matmul(transpose_x = attn_25_transpose_x_0, transpose_y = attn_25_transpose_y_0, x = var_1949_cast_fp16, y = var_1953_cast_fp16)[name = tensor("attn_25_cast_fp16")]; + tensor var_1957 = const()[name = tensor("op_1957"), val = tensor([2, 1280, 1, -1])]; + tensor input_215_cast_fp16 = reshape(shape = var_1957, x = attn_25_cast_fp16)[name = tensor("input_215_cast_fp16")]; + tensor var_1962 = const()[name = tensor("op_1962"), val = tensor([1, 1])]; + tensor var_1964 = const()[name = tensor("op_1964"), val = tensor([1, 1])]; + tensor var_1966_pad_type_0 = const()[name = tensor("op_1966_pad_type_0"), val = tensor("custom")]; + tensor var_1966_pad_0 = const()[name = tensor("op_1966_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(583361536)))]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(586638400)))]; + tensor var_1966_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_1964, groups = var_1828, pad = var_1966_pad_0, pad_type = var_1966_pad_type_0, strides = var_1962, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_215_cast_fp16)[name = tensor("op_1966_cast_fp16")]; + tensor inputs_39_cast_fp16 = add(x = var_1966_cast_fp16, y = inputs_37_cast_fp16)[name = tensor("inputs_39_cast_fp16")]; + tensor var_1970 = const()[name = tensor("op_1970"), val = tensor([1])]; + tensor channels_mean_39_cast_fp16 = reduce_mean(axes = var_1970, keep_dims = var_1823, x = inputs_39_cast_fp16)[name = tensor("channels_mean_39_cast_fp16")]; + tensor zero_mean_39_cast_fp16 = sub(x = inputs_39_cast_fp16, y = channels_mean_39_cast_fp16)[name = tensor("zero_mean_39_cast_fp16")]; + tensor zero_mean_sq_39_cast_fp16 = mul(x = zero_mean_39_cast_fp16, y = zero_mean_39_cast_fp16)[name = tensor("zero_mean_sq_39_cast_fp16")]; + tensor var_1974 = const()[name = tensor("op_1974"), val = tensor([1])]; + tensor var_1975_cast_fp16 = reduce_mean(axes = var_1974, keep_dims = var_1823, x = zero_mean_sq_39_cast_fp16)[name = tensor("op_1975_cast_fp16")]; + tensor var_1976_to_fp16 = const()[name = tensor("op_1976_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1977_cast_fp16 = add(x = var_1975_cast_fp16, y = var_1976_to_fp16)[name = tensor("op_1977_cast_fp16")]; + tensor denom_39_epsilon_0_to_fp16 = const()[name = tensor("denom_39_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_39_cast_fp16 = rsqrt(epsilon = denom_39_epsilon_0_to_fp16, x = var_1977_cast_fp16)[name = tensor("denom_39_cast_fp16")]; + tensor out_39_cast_fp16 = mul(x = zero_mean_39_cast_fp16, y = denom_39_cast_fp16)[name = tensor("out_39_cast_fp16")]; + tensor var_1981_to_fp16 = const()[name = tensor("op_1981_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(586641024)))]; + tensor var_1982_cast_fp16 = add(x = out_39_cast_fp16, y = var_1981_to_fp16)[name = tensor("op_1982_cast_fp16")]; + tensor var_1984_to_fp16 = const()[name = tensor("op_1984_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(586643648)))]; + tensor hidden_states_127_cast_fp16 = mul(x = var_1982_cast_fp16, y = var_1984_to_fp16)[name = tensor("hidden_states_127_cast_fp16")]; + tensor var_1991 = const()[name = tensor("op_1991"), val = tensor([1, 1])]; + tensor var_1993 = const()[name = tensor("op_1993"), val = tensor([1, 1])]; + tensor q_27_pad_type_0 = const()[name = tensor("q_27_pad_type_0"), val = tensor("custom")]; + tensor q_27_pad_0 = const()[name = tensor("q_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(586646272)))]; + tensor q_27_cast_fp16 = conv(dilations = var_1993, groups = var_1828, pad = q_27_pad_0, pad_type = q_27_pad_type_0, strides = var_1991, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_127_cast_fp16)[name = tensor("q_27_cast_fp16")]; + tensor var_1997 = const()[name = tensor("op_1997"), val = tensor([1, 1])]; + tensor var_1999 = const()[name = tensor("op_1999"), val = tensor([1, 1])]; + tensor k_27_pad_type_0 = const()[name = tensor("k_27_pad_type_0"), val = tensor("custom")]; + tensor k_27_pad_0 = const()[name = tensor("k_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(589923136)))]; + tensor k_27_cast_fp16 = conv(dilations = var_1999, groups = var_1828, pad = k_27_pad_0, pad_type = k_27_pad_type_0, strides = var_1997, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_27_cast_fp16")]; + tensor var_2003 = const()[name = tensor("op_2003"), val = tensor([1, 1])]; + tensor var_2005 = const()[name = tensor("op_2005"), val = tensor([1, 1])]; + tensor v_27_pad_type_0 = const()[name = tensor("v_27_pad_type_0"), val = tensor("custom")]; + tensor v_27_pad_0 = const()[name = tensor("v_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(592544640)))]; + tensor v_27_cast_fp16 = conv(dilations = var_2005, groups = var_1828, pad = v_27_pad_0, pad_type = v_27_pad_type_0, strides = var_2003, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_27_cast_fp16")]; + tensor var_2009 = const()[name = tensor("op_2009"), val = tensor([2, 20, 64, -1])]; + tensor var_2010_cast_fp16 = reshape(shape = var_2009, x = q_27_cast_fp16)[name = tensor("op_2010_cast_fp16")]; + tensor var_2011 = const()[name = tensor("op_2011"), val = tensor([2, 20, 64, -1])]; + tensor var_2012_cast_fp16 = reshape(shape = var_2011, x = k_27_cast_fp16)[name = tensor("op_2012_cast_fp16")]; + tensor var_2013 = const()[name = tensor("op_2013"), val = tensor([2, 20, 64, -1])]; + tensor var_2014_cast_fp16 = reshape(shape = var_2013, x = v_27_cast_fp16)[name = tensor("op_2014_cast_fp16")]; + tensor attn_weights_53_transpose_x_0 = const()[name = tensor("attn_weights_53_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_53_transpose_y_0 = const()[name = tensor("attn_weights_53_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_53_cast_fp16 = matmul(transpose_x = attn_weights_53_transpose_x_0, transpose_y = attn_weights_53_transpose_y_0, x = var_2010_cast_fp16, y = var_2012_cast_fp16)[name = tensor("attn_weights_53_cast_fp16")]; + tensor attn_weights_55_cast_fp16 = mul(x = attn_weights_53_cast_fp16, y = var_1819_to_fp16)[name = tensor("attn_weights_55_cast_fp16")]; + tensor var_2018_cast_fp16 = softmax(axis = var_1812, x = attn_weights_55_cast_fp16)[name = tensor("op_2018_cast_fp16")]; + tensor attn_27_transpose_x_0 = const()[name = tensor("attn_27_transpose_x_0"), val = tensor(false)]; + tensor attn_27_transpose_y_0 = const()[name = tensor("attn_27_transpose_y_0"), val = tensor(true)]; + tensor attn_27_cast_fp16 = matmul(transpose_x = attn_27_transpose_x_0, transpose_y = attn_27_transpose_y_0, x = var_2014_cast_fp16, y = var_2018_cast_fp16)[name = tensor("attn_27_cast_fp16")]; + tensor var_2022 = const()[name = tensor("op_2022"), val = tensor([2, 1280, 1, -1])]; + tensor input_217_cast_fp16 = reshape(shape = var_2022, x = attn_27_cast_fp16)[name = tensor("input_217_cast_fp16")]; + tensor var_2027 = const()[name = tensor("op_2027"), val = tensor([1, 1])]; + tensor var_2029 = const()[name = tensor("op_2029"), val = tensor([1, 1])]; + tensor var_2031_pad_type_0 = const()[name = tensor("op_2031_pad_type_0"), val = tensor("custom")]; + tensor var_2031_pad_0 = const()[name = tensor("op_2031_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(595166144)))]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(598443008)))]; + tensor var_2031_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_2029, groups = var_1828, pad = var_2031_pad_0, pad_type = var_2031_pad_type_0, strides = var_2027, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_217_cast_fp16)[name = tensor("op_2031_cast_fp16")]; + tensor inputs_41_cast_fp16 = add(x = var_2031_cast_fp16, y = inputs_39_cast_fp16)[name = tensor("inputs_41_cast_fp16")]; + tensor var_2035 = const()[name = tensor("op_2035"), val = tensor([1])]; + tensor channels_mean_41_cast_fp16 = reduce_mean(axes = var_2035, keep_dims = var_1823, x = inputs_41_cast_fp16)[name = tensor("channels_mean_41_cast_fp16")]; + tensor zero_mean_41_cast_fp16 = sub(x = inputs_41_cast_fp16, y = channels_mean_41_cast_fp16)[name = tensor("zero_mean_41_cast_fp16")]; + tensor zero_mean_sq_41_cast_fp16 = mul(x = zero_mean_41_cast_fp16, y = zero_mean_41_cast_fp16)[name = tensor("zero_mean_sq_41_cast_fp16")]; + tensor var_2039 = const()[name = tensor("op_2039"), val = tensor([1])]; + tensor var_2040_cast_fp16 = reduce_mean(axes = var_2039, keep_dims = var_1823, x = zero_mean_sq_41_cast_fp16)[name = tensor("op_2040_cast_fp16")]; + tensor var_2041_to_fp16 = const()[name = tensor("op_2041_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2042_cast_fp16 = add(x = var_2040_cast_fp16, y = var_2041_to_fp16)[name = tensor("op_2042_cast_fp16")]; + tensor denom_41_epsilon_0_to_fp16 = const()[name = tensor("denom_41_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_41_cast_fp16 = rsqrt(epsilon = denom_41_epsilon_0_to_fp16, x = var_2042_cast_fp16)[name = tensor("denom_41_cast_fp16")]; + tensor out_41_cast_fp16 = mul(x = zero_mean_41_cast_fp16, y = denom_41_cast_fp16)[name = tensor("out_41_cast_fp16")]; + tensor var_2046_to_fp16 = const()[name = tensor("op_2046_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(598445632)))]; + tensor var_2047_cast_fp16 = add(x = out_41_cast_fp16, y = var_2046_to_fp16)[name = tensor("op_2047_cast_fp16")]; + tensor var_2049_to_fp16 = const()[name = tensor("op_2049_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(598448256)))]; + tensor input_219_cast_fp16 = mul(x = var_2047_cast_fp16, y = var_2049_to_fp16)[name = tensor("input_219_cast_fp16")]; + tensor var_2057 = const()[name = tensor("op_2057"), val = tensor([1, 1])]; + tensor var_2059 = const()[name = tensor("op_2059"), val = tensor([1, 1])]; + tensor var_2061_pad_type_0 = const()[name = tensor("op_2061_pad_type_0"), val = tensor("custom")]; + tensor var_2061_pad_0 = const()[name = tensor("op_2061_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(598450880)))]; + tensor mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(624665344)))]; + tensor var_2061_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_2059, groups = var_1828, pad = var_2061_pad_0, pad_type = var_2061_pad_type_0, strides = var_2057, weight = mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_219_cast_fp16)[name = tensor("op_2061_cast_fp16")]; + tensor var_2062_split_sizes_0 = const()[name = tensor("op_2062_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2062_axis_0 = const()[name = tensor("op_2062_axis_0"), val = tensor(1)]; + tensor var_2062_cast_fp16_0, tensor var_2062_cast_fp16_1 = split(axis = var_2062_axis_0, split_sizes = var_2062_split_sizes_0, x = var_2061_cast_fp16)[name = tensor("op_2062_cast_fp16")]; + tensor var_2064_mode_0 = const()[name = tensor("op_2064_mode_0"), val = tensor("EXACT")]; + tensor var_2064_cast_fp16 = gelu(mode = var_2064_mode_0, x = var_2062_cast_fp16_1)[name = tensor("op_2064_cast_fp16")]; + tensor input_221_cast_fp16 = mul(x = var_2062_cast_fp16_0, y = var_2064_cast_fp16)[name = tensor("input_221_cast_fp16")]; + tensor var_2068 = const()[name = tensor("op_2068"), val = tensor([1, 1])]; + tensor var_2070 = const()[name = tensor("op_2070"), val = tensor([1, 1])]; + tensor var_2072_pad_type_0 = const()[name = tensor("op_2072_pad_type_0"), val = tensor("custom")]; + tensor var_2072_pad_0 = const()[name = tensor("op_2072_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(624685888)))]; + tensor mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(637793152)))]; + tensor var_2072_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_2070, groups = var_1828, pad = var_2072_pad_0, pad_type = var_2072_pad_type_0, strides = var_2068, weight = mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_221_cast_fp16)[name = tensor("op_2072_cast_fp16")]; + tensor hidden_states_131_cast_fp16 = add(x = var_2072_cast_fp16, y = inputs_41_cast_fp16)[name = tensor("hidden_states_131_cast_fp16")]; + tensor var_2074 = const()[name = tensor("op_2074"), val = tensor([2, 1280, 6, 10])]; + tensor input_223_cast_fp16 = reshape(shape = var_2074, x = hidden_states_131_cast_fp16)[name = tensor("input_223_cast_fp16")]; + tensor var_2078 = const()[name = tensor("op_2078"), val = tensor([1, 1])]; + tensor var_2080 = const()[name = tensor("op_2080"), val = tensor([1, 1])]; + tensor hidden_states_133_pad_type_0 = const()[name = tensor("hidden_states_133_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_133_pad_0 = const()[name = tensor("hidden_states_133_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(637795776)))]; + tensor mid_block_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(641072640)))]; + tensor hidden_states_133_cast_fp16 = conv(bias = mid_block_attentions_0_proj_out_bias_to_fp16, dilations = var_2080, groups = var_1828, pad = hidden_states_133_pad_0, pad_type = hidden_states_133_pad_type_0, strides = var_2078, weight = mid_block_attentions_0_proj_out_weight_to_fp16, x = input_223_cast_fp16)[name = tensor("hidden_states_133_cast_fp16")]; + tensor input_225_cast_fp16 = add(x = hidden_states_133_cast_fp16, y = hidden_states_121_cast_fp16)[name = tensor("input_225_cast_fp16")]; + tensor reshape_100_shape_0 = const()[name = tensor("reshape_100_shape_0"), val = tensor([2, 32, 40, 6, 10])]; + tensor reshape_100_cast_fp16 = reshape(shape = reshape_100_shape_0, x = input_225_cast_fp16)[name = tensor("reshape_100_cast_fp16")]; + tensor reduce_mean_75_axes_0 = const()[name = tensor("reduce_mean_75_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_75_keep_dims_0 = const()[name = tensor("reduce_mean_75_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_75_cast_fp16 = reduce_mean(axes = reduce_mean_75_axes_0, keep_dims = reduce_mean_75_keep_dims_0, x = reshape_100_cast_fp16)[name = tensor("reduce_mean_75_cast_fp16")]; + tensor sub_50_cast_fp16 = sub(x = reshape_100_cast_fp16, y = reduce_mean_75_cast_fp16)[name = tensor("sub_50_cast_fp16")]; + tensor square_25_cast_fp16 = square(x = sub_50_cast_fp16)[name = tensor("square_25_cast_fp16")]; + tensor reduce_mean_77_axes_0 = const()[name = tensor("reduce_mean_77_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_77_keep_dims_0 = const()[name = tensor("reduce_mean_77_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_77_cast_fp16 = reduce_mean(axes = reduce_mean_77_axes_0, keep_dims = reduce_mean_77_keep_dims_0, x = square_25_cast_fp16)[name = tensor("reduce_mean_77_cast_fp16")]; + tensor add_50_y_0_to_fp16 = const()[name = tensor("add_50_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_50_cast_fp16 = add(x = reduce_mean_77_cast_fp16, y = add_50_y_0_to_fp16)[name = tensor("add_50_cast_fp16")]; + tensor sqrt_25_cast_fp16 = sqrt(x = add_50_cast_fp16)[name = tensor("sqrt_25_cast_fp16")]; + tensor real_div_25_cast_fp16 = real_div(x = sub_50_cast_fp16, y = sqrt_25_cast_fp16)[name = tensor("real_div_25_cast_fp16")]; + tensor reshape_101_shape_0 = const()[name = tensor("reshape_101_shape_0"), val = tensor([2, 1280, 6, 10])]; + tensor reshape_101_cast_fp16 = reshape(shape = reshape_101_shape_0, x = real_div_25_cast_fp16)[name = tensor("reshape_101_cast_fp16")]; + tensor add_51_gamma_0_to_fp16 = const()[name = tensor("add_51_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(641075264)))]; + tensor add_51_beta_0_to_fp16 = const()[name = tensor("add_51_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(641077888)))]; + tensor add_51_epsilon_0_to_fp16 = const()[name = tensor("add_51_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_51_cast_fp16 = batch_norm(beta = add_51_beta_0_to_fp16, epsilon = add_51_epsilon_0_to_fp16, gamma = add_51_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_101_cast_fp16)[name = tensor("add_51_cast_fp16")]; + tensor input_229_cast_fp16 = silu(x = add_51_cast_fp16)[name = tensor("input_229_cast_fp16")]; + tensor var_2095 = const()[name = tensor("op_2095"), val = tensor([1, 1])]; + tensor var_2097 = const()[name = tensor("op_2097"), val = tensor([1, 1])]; + tensor hidden_states_135_pad_type_0 = const()[name = tensor("hidden_states_135_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_135_pad_0 = const()[name = tensor("hidden_states_135_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor mid_block_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("mid_block_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(641080512)))]; + tensor mid_block_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("mid_block_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(670571776)))]; + tensor hidden_states_135_cast_fp16 = conv(bias = mid_block_resnets_1_conv1_bias_to_fp16, dilations = var_2097, groups = var_1828, pad = hidden_states_135_pad_0, pad_type = hidden_states_135_pad_type_0, strides = var_2095, weight = mid_block_resnets_1_conv1_weight_to_fp16, x = input_229_cast_fp16)[name = tensor("hidden_states_135_cast_fp16")]; + tensor var_2103 = const()[name = tensor("op_2103"), val = tensor([1, 1])]; + tensor var_2105 = const()[name = tensor("op_2105"), val = tensor([1, 1])]; + tensor temb_19_pad_type_0 = const()[name = tensor("temb_19_pad_type_0"), val = tensor("custom")]; + tensor temb_19_pad_0 = const()[name = tensor("temb_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("mid_block_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(670574400)))]; + tensor mid_block_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("mid_block_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(673851264)))]; + tensor temb_19_cast_fp16 = conv(bias = mid_block_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_2105, groups = var_1828, pad = temb_19_pad_0, pad_type = temb_19_pad_type_0, strides = var_2103, weight = mid_block_resnets_1_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16_1)[name = tensor("temb_19_cast_fp16")]; + tensor input_233_cast_fp16 = add(x = hidden_states_135_cast_fp16, y = temb_19_cast_fp16)[name = tensor("input_233_cast_fp16")]; + tensor reshape_104_shape_0 = const()[name = tensor("reshape_104_shape_0"), val = tensor([2, 32, 40, 6, 10])]; + tensor reshape_104_cast_fp16 = reshape(shape = reshape_104_shape_0, x = input_233_cast_fp16)[name = tensor("reshape_104_cast_fp16")]; + tensor reduce_mean_78_axes_0 = const()[name = tensor("reduce_mean_78_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_78_keep_dims_0 = const()[name = tensor("reduce_mean_78_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_78_cast_fp16 = reduce_mean(axes = reduce_mean_78_axes_0, keep_dims = reduce_mean_78_keep_dims_0, x = reshape_104_cast_fp16)[name = tensor("reduce_mean_78_cast_fp16")]; + tensor sub_52_cast_fp16 = sub(x = reshape_104_cast_fp16, y = reduce_mean_78_cast_fp16)[name = tensor("sub_52_cast_fp16")]; + tensor square_26_cast_fp16 = square(x = sub_52_cast_fp16)[name = tensor("square_26_cast_fp16")]; + tensor reduce_mean_80_axes_0 = const()[name = tensor("reduce_mean_80_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_80_keep_dims_0 = const()[name = tensor("reduce_mean_80_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_80_cast_fp16 = reduce_mean(axes = reduce_mean_80_axes_0, keep_dims = reduce_mean_80_keep_dims_0, x = square_26_cast_fp16)[name = tensor("reduce_mean_80_cast_fp16")]; + tensor add_52_y_0_to_fp16 = const()[name = tensor("add_52_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_52_cast_fp16 = add(x = reduce_mean_80_cast_fp16, y = add_52_y_0_to_fp16)[name = tensor("add_52_cast_fp16")]; + tensor sqrt_26_cast_fp16 = sqrt(x = add_52_cast_fp16)[name = tensor("sqrt_26_cast_fp16")]; + tensor real_div_26_cast_fp16 = real_div(x = sub_52_cast_fp16, y = sqrt_26_cast_fp16)[name = tensor("real_div_26_cast_fp16")]; + tensor reshape_105_shape_0 = const()[name = tensor("reshape_105_shape_0"), val = tensor([2, 1280, 6, 10])]; + tensor reshape_105_cast_fp16 = reshape(shape = reshape_105_shape_0, x = real_div_26_cast_fp16)[name = tensor("reshape_105_cast_fp16")]; + tensor add_53_gamma_0_to_fp16 = const()[name = tensor("add_53_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(673853888)))]; + tensor add_53_beta_0_to_fp16 = const()[name = tensor("add_53_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(673856512)))]; + tensor add_53_epsilon_0_to_fp16 = const()[name = tensor("add_53_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_53_cast_fp16 = batch_norm(beta = add_53_beta_0_to_fp16, epsilon = add_53_epsilon_0_to_fp16, gamma = add_53_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_105_cast_fp16)[name = tensor("add_53_cast_fp16")]; + tensor input_237_cast_fp16 = silu(x = add_53_cast_fp16)[name = tensor("input_237_cast_fp16")]; + tensor var_2115 = const()[name = tensor("op_2115"), val = tensor([1, 1])]; + tensor var_2117 = const()[name = tensor("op_2117"), val = tensor([1, 1])]; + tensor hidden_states_137_pad_type_0 = const()[name = tensor("hidden_states_137_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_137_pad_0 = const()[name = tensor("hidden_states_137_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor mid_block_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("mid_block_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(673859136)))]; + tensor mid_block_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("mid_block_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(703350400)))]; + tensor hidden_states_137_cast_fp16 = conv(bias = mid_block_resnets_1_conv2_bias_to_fp16, dilations = var_2117, groups = var_1828, pad = hidden_states_137_pad_0, pad_type = hidden_states_137_pad_type_0, strides = var_2115, weight = mid_block_resnets_1_conv2_weight_to_fp16, x = input_237_cast_fp16)[name = tensor("hidden_states_137_cast_fp16")]; + tensor hidden_states_139_cast_fp16 = add(x = input_225_cast_fp16, y = hidden_states_137_cast_fp16)[name = tensor("hidden_states_139_cast_fp16")]; + tensor var_2128 = const()[name = tensor("op_2128"), val = tensor(1)]; + tensor input_239_interleave_0 = const()[name = tensor("input_239_interleave_0"), val = tensor(false)]; + tensor input_239_cast_fp16 = concat(axis = var_2128, interleave = input_239_interleave_0, values = (hidden_states_139_cast_fp16, input_199_cast_fp16))[name = tensor("input_239_cast_fp16")]; + tensor reshape_108_shape_0 = const()[name = tensor("reshape_108_shape_0"), val = tensor([2, 32, 80, 6, 10])]; + tensor reshape_108_cast_fp16 = reshape(shape = reshape_108_shape_0, x = input_239_cast_fp16)[name = tensor("reshape_108_cast_fp16")]; + tensor reduce_mean_81_axes_0 = const()[name = tensor("reduce_mean_81_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_81_keep_dims_0 = const()[name = tensor("reduce_mean_81_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_81_cast_fp16 = reduce_mean(axes = reduce_mean_81_axes_0, keep_dims = reduce_mean_81_keep_dims_0, x = reshape_108_cast_fp16)[name = tensor("reduce_mean_81_cast_fp16")]; + tensor sub_54_cast_fp16 = sub(x = reshape_108_cast_fp16, y = reduce_mean_81_cast_fp16)[name = tensor("sub_54_cast_fp16")]; + tensor square_27_cast_fp16 = square(x = sub_54_cast_fp16)[name = tensor("square_27_cast_fp16")]; + tensor reduce_mean_83_axes_0 = const()[name = tensor("reduce_mean_83_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_83_keep_dims_0 = const()[name = tensor("reduce_mean_83_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_83_cast_fp16 = reduce_mean(axes = reduce_mean_83_axes_0, keep_dims = reduce_mean_83_keep_dims_0, x = square_27_cast_fp16)[name = tensor("reduce_mean_83_cast_fp16")]; + tensor add_54_y_0_to_fp16 = const()[name = tensor("add_54_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_54_cast_fp16 = add(x = reduce_mean_83_cast_fp16, y = add_54_y_0_to_fp16)[name = tensor("add_54_cast_fp16")]; + tensor sqrt_27_cast_fp16 = sqrt(x = add_54_cast_fp16)[name = tensor("sqrt_27_cast_fp16")]; + tensor real_div_27_cast_fp16 = real_div(x = sub_54_cast_fp16, y = sqrt_27_cast_fp16)[name = tensor("real_div_27_cast_fp16")]; + tensor reshape_109_shape_0 = const()[name = tensor("reshape_109_shape_0"), val = tensor([2, 2560, 6, 10])]; + tensor reshape_109_cast_fp16 = reshape(shape = reshape_109_shape_0, x = real_div_27_cast_fp16)[name = tensor("reshape_109_cast_fp16")]; + tensor add_55_mean_0_to_fp16 = const()[name = tensor("add_55_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(703353024)))]; + tensor add_55_variance_0_to_fp16 = const()[name = tensor("add_55_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(703358208)))]; + tensor add_55_gamma_0_to_fp16 = const()[name = tensor("add_55_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(703363392)))]; + tensor add_55_beta_0_to_fp16 = const()[name = tensor("add_55_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(703368576)))]; + tensor add_55_epsilon_0_to_fp16 = const()[name = tensor("add_55_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_55_cast_fp16 = batch_norm(beta = add_55_beta_0_to_fp16, epsilon = add_55_epsilon_0_to_fp16, gamma = add_55_gamma_0_to_fp16, mean = add_55_mean_0_to_fp16, variance = add_55_variance_0_to_fp16, x = reshape_109_cast_fp16)[name = tensor("add_55_cast_fp16")]; + tensor input_243_cast_fp16 = silu(x = add_55_cast_fp16)[name = tensor("input_243_cast_fp16")]; + tensor var_2151 = const()[name = tensor("op_2151"), val = tensor([1, 1])]; + tensor var_2153 = const()[name = tensor("op_2153"), val = tensor([1, 1])]; + tensor hidden_states_141_pad_type_0 = const()[name = tensor("hidden_states_141_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_141_pad_0 = const()[name = tensor("hidden_states_141_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(703373760)))]; + tensor up_blocks_0_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(762356224)))]; + tensor hidden_states_141_cast_fp16 = conv(bias = up_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = var_2153, groups = var_2128, pad = hidden_states_141_pad_0, pad_type = hidden_states_141_pad_type_0, strides = var_2151, weight = up_blocks_0_resnets_0_conv1_weight_to_fp16, x = input_243_cast_fp16)[name = tensor("hidden_states_141_cast_fp16")]; + tensor var_2159 = const()[name = tensor("op_2159"), val = tensor([1, 1])]; + tensor var_2161 = const()[name = tensor("op_2161"), val = tensor([1, 1])]; + tensor temb_21_pad_type_0 = const()[name = tensor("temb_21_pad_type_0"), val = tensor("custom")]; + tensor temb_21_pad_0 = const()[name = tensor("temb_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(762358848)))]; + tensor up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(765635712)))]; + tensor temb_21_cast_fp16 = conv(bias = up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_2161, groups = var_2128, pad = temb_21_pad_0, pad_type = temb_21_pad_type_0, strides = var_2159, weight = up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16_1)[name = tensor("temb_21_cast_fp16")]; + tensor input_247_cast_fp16 = add(x = hidden_states_141_cast_fp16, y = temb_21_cast_fp16)[name = tensor("input_247_cast_fp16")]; + tensor reshape_112_shape_0 = const()[name = tensor("reshape_112_shape_0"), val = tensor([2, 32, 40, 6, 10])]; + tensor reshape_112_cast_fp16 = reshape(shape = reshape_112_shape_0, x = input_247_cast_fp16)[name = tensor("reshape_112_cast_fp16")]; + tensor reduce_mean_84_axes_0 = const()[name = tensor("reduce_mean_84_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_84_keep_dims_0 = const()[name = tensor("reduce_mean_84_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_84_cast_fp16 = reduce_mean(axes = reduce_mean_84_axes_0, keep_dims = reduce_mean_84_keep_dims_0, x = reshape_112_cast_fp16)[name = tensor("reduce_mean_84_cast_fp16")]; + tensor sub_56_cast_fp16 = sub(x = reshape_112_cast_fp16, y = reduce_mean_84_cast_fp16)[name = tensor("sub_56_cast_fp16")]; + tensor square_28_cast_fp16 = square(x = sub_56_cast_fp16)[name = tensor("square_28_cast_fp16")]; + tensor reduce_mean_86_axes_0 = const()[name = tensor("reduce_mean_86_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_86_keep_dims_0 = const()[name = tensor("reduce_mean_86_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_86_cast_fp16 = reduce_mean(axes = reduce_mean_86_axes_0, keep_dims = reduce_mean_86_keep_dims_0, x = square_28_cast_fp16)[name = tensor("reduce_mean_86_cast_fp16")]; + tensor add_56_y_0_to_fp16 = const()[name = tensor("add_56_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_56_cast_fp16 = add(x = reduce_mean_86_cast_fp16, y = add_56_y_0_to_fp16)[name = tensor("add_56_cast_fp16")]; + tensor sqrt_28_cast_fp16 = sqrt(x = add_56_cast_fp16)[name = tensor("sqrt_28_cast_fp16")]; + tensor real_div_28_cast_fp16 = real_div(x = sub_56_cast_fp16, y = sqrt_28_cast_fp16)[name = tensor("real_div_28_cast_fp16")]; + tensor reshape_113_shape_0 = const()[name = tensor("reshape_113_shape_0"), val = tensor([2, 1280, 6, 10])]; + tensor reshape_113_cast_fp16 = reshape(shape = reshape_113_shape_0, x = real_div_28_cast_fp16)[name = tensor("reshape_113_cast_fp16")]; + tensor add_57_gamma_0_to_fp16 = const()[name = tensor("add_57_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(765638336)))]; + tensor add_57_beta_0_to_fp16 = const()[name = tensor("add_57_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(765640960)))]; + tensor add_57_epsilon_0_to_fp16 = const()[name = tensor("add_57_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_57_cast_fp16 = batch_norm(beta = add_57_beta_0_to_fp16, epsilon = add_57_epsilon_0_to_fp16, gamma = add_57_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_113_cast_fp16)[name = tensor("add_57_cast_fp16")]; + tensor input_251_cast_fp16 = silu(x = add_57_cast_fp16)[name = tensor("input_251_cast_fp16")]; + tensor var_2171 = const()[name = tensor("op_2171"), val = tensor([1, 1])]; + tensor var_2173 = const()[name = tensor("op_2173"), val = tensor([1, 1])]; + tensor hidden_states_143_pad_type_0 = const()[name = tensor("hidden_states_143_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_143_pad_0 = const()[name = tensor("hidden_states_143_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(765643584)))]; + tensor up_blocks_0_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(795134848)))]; + tensor hidden_states_143_cast_fp16 = conv(bias = up_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = var_2173, groups = var_2128, pad = hidden_states_143_pad_0, pad_type = hidden_states_143_pad_type_0, strides = var_2171, weight = up_blocks_0_resnets_0_conv2_weight_to_fp16, x = input_251_cast_fp16)[name = tensor("hidden_states_143_cast_fp16")]; + tensor var_2178 = const()[name = tensor("op_2178"), val = tensor([1, 1])]; + tensor var_2180 = const()[name = tensor("op_2180"), val = tensor([1, 1])]; + tensor x_5_pad_type_0 = const()[name = tensor("x_5_pad_type_0"), val = tensor("custom")]; + tensor x_5_pad_0 = const()[name = tensor("x_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(795137472)))]; + tensor up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(801691136)))]; + tensor x_5_cast_fp16 = conv(bias = up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_2180, groups = var_2128, pad = x_5_pad_0, pad_type = x_5_pad_type_0, strides = var_2178, weight = up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16, x = input_239_cast_fp16)[name = tensor("x_5_cast_fp16")]; + tensor hidden_states_145_cast_fp16 = add(x = x_5_cast_fp16, y = hidden_states_143_cast_fp16)[name = tensor("hidden_states_145_cast_fp16")]; + tensor input_253_interleave_0 = const()[name = tensor("input_253_interleave_0"), val = tensor(false)]; + tensor input_253_cast_fp16_1 = concat(axis = var_2128, interleave = input_253_interleave_0, values = (hidden_states_145_cast_fp16, input_185_cast_fp16))[name = tensor("input_253_cast_fp16")]; + tensor reshape_116_shape_0 = const()[name = tensor("reshape_116_shape_0"), val = tensor([2, 32, 80, 6, 10])]; + tensor reshape_116_cast_fp16 = reshape(shape = reshape_116_shape_0, x = input_253_cast_fp16_1)[name = tensor("reshape_116_cast_fp16")]; + tensor reduce_mean_87_axes_0 = const()[name = tensor("reduce_mean_87_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_87_keep_dims_0 = const()[name = tensor("reduce_mean_87_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_87_cast_fp16 = reduce_mean(axes = reduce_mean_87_axes_0, keep_dims = reduce_mean_87_keep_dims_0, x = reshape_116_cast_fp16)[name = tensor("reduce_mean_87_cast_fp16")]; + tensor sub_58_cast_fp16 = sub(x = reshape_116_cast_fp16, y = reduce_mean_87_cast_fp16)[name = tensor("sub_58_cast_fp16")]; + tensor square_29_cast_fp16 = square(x = sub_58_cast_fp16)[name = tensor("square_29_cast_fp16")]; + tensor reduce_mean_89_axes_0 = const()[name = tensor("reduce_mean_89_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_89_keep_dims_0 = const()[name = tensor("reduce_mean_89_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_89_cast_fp16 = reduce_mean(axes = reduce_mean_89_axes_0, keep_dims = reduce_mean_89_keep_dims_0, x = square_29_cast_fp16)[name = tensor("reduce_mean_89_cast_fp16")]; + tensor add_58_y_0_to_fp16 = const()[name = tensor("add_58_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_58_cast_fp16 = add(x = reduce_mean_89_cast_fp16, y = add_58_y_0_to_fp16)[name = tensor("add_58_cast_fp16")]; + tensor sqrt_29_cast_fp16 = sqrt(x = add_58_cast_fp16)[name = tensor("sqrt_29_cast_fp16")]; + tensor real_div_29_cast_fp16 = real_div(x = sub_58_cast_fp16, y = sqrt_29_cast_fp16)[name = tensor("real_div_29_cast_fp16")]; + tensor reshape_117_shape_0 = const()[name = tensor("reshape_117_shape_0"), val = tensor([2, 2560, 6, 10])]; + tensor reshape_117_cast_fp16 = reshape(shape = reshape_117_shape_0, x = real_div_29_cast_fp16)[name = tensor("reshape_117_cast_fp16")]; + tensor add_59_gamma_0_to_fp16 = const()[name = tensor("add_59_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(801693760)))]; + tensor add_59_beta_0_to_fp16 = const()[name = tensor("add_59_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(801698944)))]; + tensor add_59_epsilon_0_to_fp16 = const()[name = tensor("add_59_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_59_cast_fp16 = batch_norm(beta = add_59_beta_0_to_fp16, epsilon = add_59_epsilon_0_to_fp16, gamma = add_59_gamma_0_to_fp16, mean = add_55_mean_0_to_fp16, variance = add_55_variance_0_to_fp16, x = reshape_117_cast_fp16)[name = tensor("add_59_cast_fp16")]; + tensor input_257_cast_fp16 = silu(x = add_59_cast_fp16)[name = tensor("input_257_cast_fp16")]; + tensor var_2198 = const()[name = tensor("op_2198"), val = tensor([1, 1])]; + tensor var_2200 = const()[name = tensor("op_2200"), val = tensor([1, 1])]; + tensor hidden_states_147_pad_type_0 = const()[name = tensor("hidden_states_147_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_147_pad_0 = const()[name = tensor("hidden_states_147_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(801704128)))]; + tensor up_blocks_0_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(860686592)))]; + tensor hidden_states_147_cast_fp16 = conv(bias = up_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = var_2200, groups = var_2128, pad = hidden_states_147_pad_0, pad_type = hidden_states_147_pad_type_0, strides = var_2198, weight = up_blocks_0_resnets_1_conv1_weight_to_fp16, x = input_257_cast_fp16)[name = tensor("hidden_states_147_cast_fp16")]; + tensor var_2206 = const()[name = tensor("op_2206"), val = tensor([1, 1])]; + tensor var_2208 = const()[name = tensor("op_2208"), val = tensor([1, 1])]; + tensor temb_23_pad_type_0 = const()[name = tensor("temb_23_pad_type_0"), val = tensor("custom")]; + tensor temb_23_pad_0 = const()[name = tensor("temb_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(860689216)))]; + tensor up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(863966080)))]; + tensor temb_23_cast_fp16 = conv(bias = up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_2208, groups = var_2128, pad = temb_23_pad_0, pad_type = temb_23_pad_type_0, strides = var_2206, weight = up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16_1)[name = tensor("temb_23_cast_fp16")]; + tensor input_261_cast_fp16 = add(x = hidden_states_147_cast_fp16, y = temb_23_cast_fp16)[name = tensor("input_261_cast_fp16")]; + tensor reshape_120_shape_0 = const()[name = tensor("reshape_120_shape_0"), val = tensor([2, 32, 40, 6, 10])]; + tensor reshape_120_cast_fp16 = reshape(shape = reshape_120_shape_0, x = input_261_cast_fp16)[name = tensor("reshape_120_cast_fp16")]; + tensor reduce_mean_90_axes_0 = const()[name = tensor("reduce_mean_90_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_90_keep_dims_0 = const()[name = tensor("reduce_mean_90_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_90_cast_fp16 = reduce_mean(axes = reduce_mean_90_axes_0, keep_dims = reduce_mean_90_keep_dims_0, x = reshape_120_cast_fp16)[name = tensor("reduce_mean_90_cast_fp16")]; + tensor sub_60_cast_fp16 = sub(x = reshape_120_cast_fp16, y = reduce_mean_90_cast_fp16)[name = tensor("sub_60_cast_fp16")]; + tensor square_30_cast_fp16 = square(x = sub_60_cast_fp16)[name = tensor("square_30_cast_fp16")]; + tensor reduce_mean_92_axes_0 = const()[name = tensor("reduce_mean_92_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_92_keep_dims_0 = const()[name = tensor("reduce_mean_92_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_92_cast_fp16 = reduce_mean(axes = reduce_mean_92_axes_0, keep_dims = reduce_mean_92_keep_dims_0, x = square_30_cast_fp16)[name = tensor("reduce_mean_92_cast_fp16")]; + tensor add_60_y_0_to_fp16 = const()[name = tensor("add_60_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_60_cast_fp16 = add(x = reduce_mean_92_cast_fp16, y = add_60_y_0_to_fp16)[name = tensor("add_60_cast_fp16")]; + tensor sqrt_30_cast_fp16 = sqrt(x = add_60_cast_fp16)[name = tensor("sqrt_30_cast_fp16")]; + tensor real_div_30_cast_fp16 = real_div(x = sub_60_cast_fp16, y = sqrt_30_cast_fp16)[name = tensor("real_div_30_cast_fp16")]; + tensor reshape_121_shape_0 = const()[name = tensor("reshape_121_shape_0"), val = tensor([2, 1280, 6, 10])]; + tensor reshape_121_cast_fp16 = reshape(shape = reshape_121_shape_0, x = real_div_30_cast_fp16)[name = tensor("reshape_121_cast_fp16")]; + tensor add_61_gamma_0_to_fp16 = const()[name = tensor("add_61_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(863968704)))]; + tensor add_61_beta_0_to_fp16 = const()[name = tensor("add_61_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(863971328)))]; + tensor add_61_epsilon_0_to_fp16 = const()[name = tensor("add_61_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_61_cast_fp16 = batch_norm(beta = add_61_beta_0_to_fp16, epsilon = add_61_epsilon_0_to_fp16, gamma = add_61_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_121_cast_fp16)[name = tensor("add_61_cast_fp16")]; + tensor input_265_cast_fp16 = silu(x = add_61_cast_fp16)[name = tensor("input_265_cast_fp16")]; + tensor var_2218 = const()[name = tensor("op_2218"), val = tensor([1, 1])]; + tensor var_2220 = const()[name = tensor("op_2220"), val = tensor([1, 1])]; + tensor hidden_states_149_pad_type_0 = const()[name = tensor("hidden_states_149_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_149_pad_0 = const()[name = tensor("hidden_states_149_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(863973952)))]; + tensor up_blocks_0_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(893465216)))]; + tensor hidden_states_149_cast_fp16_1 = conv(bias = up_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = var_2220, groups = var_2128, pad = hidden_states_149_pad_0, pad_type = hidden_states_149_pad_type_0, strides = var_2218, weight = up_blocks_0_resnets_1_conv2_weight_to_fp16, x = input_265_cast_fp16)[name = tensor("hidden_states_149_cast_fp16")]; + tensor input_61_cast_fp16_dtype_0 = const()[name = tensor("input_61_cast_fp16_dtype_0"), val = tensor("fp32")]; + tensor hidden_states_149_cast_fp16_dtype_0 = const()[name = tensor("hidden_states_149_cast_fp16_dtype_0"), val = tensor("fp32")]; + tensor input_15_cast_fp16_dtype_0 = const()[name = tensor("input_15_cast_fp16_dtype_0"), val = tensor("fp32")]; + tensor input_117_cast_fp16_dtype_0 = const()[name = tensor("input_117_cast_fp16_dtype_0"), val = tensor("fp32")]; + tensor input_115_cast_fp16_dtype_0 = const()[name = tensor("input_115_cast_fp16_dtype_0"), val = tensor("fp32")]; + tensor input_7_cast_fp16_dtype_0 = const()[name = tensor("input_7_cast_fp16_dtype_0"), val = tensor("fp32")]; + tensor input_171_cast_fp16_dtype_0 = const()[name = tensor("input_171_cast_fp16_dtype_0"), val = tensor("fp32")]; + tensor input_169_cast_fp16_dtype_0 = const()[name = tensor("input_169_cast_fp16_dtype_0"), val = tensor("fp32")]; + tensor input_253_cast_fp16_dtype_0 = const()[name = tensor("input_253_cast_fp16_dtype_0"), val = tensor("fp32")]; + tensor input_35_cast_fp16_dtype_0 = const()[name = tensor("input_35_cast_fp16_dtype_0"), val = tensor("fp32")]; + tensor input_89_cast_fp16_dtype_0 = const()[name = tensor("input_89_cast_fp16_dtype_0"), val = tensor("fp32")]; + tensor input_143_cast_fp16_dtype_0 = const()[name = tensor("input_143_cast_fp16_dtype_0"), val = tensor("fp32")]; + tensor input_63_cast_fp16_dtype_0 = const()[name = tensor("input_63_cast_fp16_dtype_0"), val = tensor("fp32")]; + tensor input_63_cast_fp16 = cast(dtype = input_63_cast_fp16_dtype_0, x = input_63_cast_fp16_1)[name = tensor("cast_13")]; + tensor input_143_cast_fp16 = cast(dtype = input_143_cast_fp16_dtype_0, x = input_143_cast_fp16_1)[name = tensor("cast_14")]; + tensor input_89_cast_fp16 = cast(dtype = input_89_cast_fp16_dtype_0, x = input_89_cast_fp16_1)[name = tensor("cast_15")]; + tensor input_35_cast_fp16 = cast(dtype = input_35_cast_fp16_dtype_0, x = input_35_cast_fp16_1)[name = tensor("cast_16")]; + tensor input_253_cast_fp16 = cast(dtype = input_253_cast_fp16_dtype_0, x = input_253_cast_fp16_1)[name = tensor("cast_17")]; + tensor input_169_cast_fp16 = cast(dtype = input_169_cast_fp16_dtype_0, x = input_169_cast_fp16_1)[name = tensor("cast_18")]; + tensor input_171_cast_fp16 = cast(dtype = input_171_cast_fp16_dtype_0, x = input_171_cast_fp16_1)[name = tensor("cast_19")]; + tensor input_7_cast_fp16 = cast(dtype = input_7_cast_fp16_dtype_0, x = input_7_cast_fp16_1)[name = tensor("cast_20")]; + tensor input_115_cast_fp16 = cast(dtype = input_115_cast_fp16_dtype_0, x = input_115_cast_fp16_1)[name = tensor("cast_21")]; + tensor input_117_cast_fp16 = cast(dtype = input_117_cast_fp16_dtype_0, x = input_117_cast_fp16_1)[name = tensor("cast_22")]; + tensor input_15_cast_fp16 = cast(dtype = input_15_cast_fp16_dtype_0, x = input_15_cast_fp16_1)[name = tensor("cast_23")]; + tensor hidden_states_149_cast_fp16 = cast(dtype = hidden_states_149_cast_fp16_dtype_0, x = hidden_states_149_cast_fp16_1)[name = tensor("cast_24")]; + tensor input_61_cast_fp16 = cast(dtype = input_61_cast_fp16_dtype_0, x = input_61_cast_fp16_1)[name = tensor("cast_25")]; + } -> (input_61_cast_fp16, hidden_states_149_cast_fp16, input_15_cast_fp16, input_117_cast_fp16, input_115_cast_fp16, input_7_cast_fp16, input_171_cast_fp16, input_169_cast_fp16, input_253_cast_fp16, input_35_cast_fp16, input_89_cast_fp16, input_143_cast_fp16, input_63_cast_fp16); +} \ No newline at end of file diff --git a/original/compiled/UnetChunk1.mlmodelc/weights/weight.bin b/original/compiled/UnetChunk1.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..0b611cf28c20eff0dd642b0c853df0ab34bfd81b --- /dev/null +++ b/original/compiled/UnetChunk1.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8cc3a9500cf3bae788e238558bbd67c60636ef1d05bbee12d37b821633e3a50d +size 893467840 diff --git a/original/compiled/UnetChunk2.mlmodelc/analytics/coremldata.bin b/original/compiled/UnetChunk2.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..3ee8aa39204ba09976a58325851690bb688404c3 --- /dev/null +++ b/original/compiled/UnetChunk2.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ec93d1b49276c8e187d539a2ef823cf6d424ed1eeef7948d262dede80162b7f9 +size 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: "predict" + } +] \ No newline at end of file diff --git a/original/compiled/UnetChunk2.mlmodelc/model.mil b/original/compiled/UnetChunk2.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..bd357251ae0bc538576c28da600b3513c061c7a2 --- /dev/null +++ b/original/compiled/UnetChunk2.mlmodelc/model.mil @@ -0,0 +1,2567 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3304.5.2"}, {"coremlc-version", "3304.6.2"}, {"coremltools-component-milinternal", ""}, {"coremltools-version", "7.1"}})] +{ + func main(tensor encoder_hidden_states, tensor hidden_states_149_cast_fp16, tensor input_115_cast_fp16, tensor input_117_cast_fp16, tensor input_143_cast_fp16, tensor input_15_cast_fp16, tensor input_169_cast_fp16, tensor input_171_cast_fp16, tensor input_253_cast_fp16, tensor input_35_cast_fp16, tensor input_61_cast_fp16, tensor input_63_cast_fp16, tensor input_7_cast_fp16, tensor input_89_cast_fp16) { + tensor cast_0_dtype_0 = const()[name = tensor("cast_0_dtype_0"), val = tensor("fp16")]; + tensor add_1_mean_0_to_fp16 = const()[name = tensor("add_1_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor add_1_variance_0_to_fp16 = const()[name = tensor("add_1_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(768)))]; + tensor cast_10_dtype_0 = const()[name = tensor("cast_10_dtype_0"), val = tensor("fp16")]; + tensor cast_5_dtype_0 = const()[name = tensor("cast_5_dtype_0"), val = tensor("fp16")]; + tensor cast_9_dtype_0 = const()[name = tensor("cast_9_dtype_0"), val = tensor("fp16")]; + tensor cast_8_dtype_0 = const()[name = tensor("cast_8_dtype_0"), val = tensor("fp16")]; + tensor add_15_mean_0_to_fp16 = const()[name = tensor("add_15_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1472)))]; + tensor add_15_variance_0_to_fp16 = const()[name = tensor("add_15_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2816)))]; + tensor cast_7_dtype_0 = const()[name = tensor("cast_7_dtype_0"), val = tensor("fp16")]; + tensor cast_12_dtype_0 = const()[name = tensor("cast_12_dtype_0"), val = tensor("fp16")]; + tensor cast_11_dtype_0 = const()[name = tensor("cast_11_dtype_0"), val = tensor("fp16")]; + tensor add_27_mean_0_to_fp16 = const()[name = tensor("add_27_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4160)))]; + tensor add_27_variance_0_to_fp16 = const()[name = tensor("add_27_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6784)))]; + tensor cast_2_dtype_0 = const()[name = tensor("cast_2_dtype_0"), val = tensor("fp16")]; + tensor cast_3_dtype_0 = const()[name = tensor("cast_3_dtype_0"), val = tensor("fp16")]; + tensor cast_4_dtype_0 = const()[name = tensor("cast_4_dtype_0"), val = tensor("fp16")]; + tensor var_2128 = const()[name = tensor("op_2128"), val = tensor(1)]; + tensor add_55_mean_0_to_fp16 = const()[name = tensor("add_55_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9408)))]; + tensor add_55_variance_0_to_fp16 = const()[name = tensor("add_55_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14592)))]; + tensor cast_1_dtype_0 = const()[name = tensor("cast_1_dtype_0"), val = tensor("fp16")]; + tensor cast_6_dtype_0 = const()[name = tensor("cast_6_dtype_0"), val = tensor("fp16")]; + tensor var_2225 = const()[name = tensor("op_2225"), val = tensor([1, 1])]; + tensor var_2227 = const()[name = tensor("op_2227"), val = tensor([1, 1])]; + tensor x_7_pad_type_0 = const()[name = tensor("x_7_pad_type_0"), val = tensor("custom")]; + tensor x_7_pad_0 = const()[name = tensor("x_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_1_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19776)))]; + tensor up_blocks_0_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6573440)))]; + tensor cast_2 = cast(dtype = cast_1_dtype_0, x = input_253_cast_fp16)[name = tensor("cast_2")]; + tensor x_7_cast_fp16 = conv(bias = up_blocks_0_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_2227, groups = var_2128, pad = x_7_pad_0, pad_type = x_7_pad_type_0, strides = var_2225, weight = up_blocks_0_resnets_1_conv_shortcut_weight_to_fp16, x = cast_2)[name = tensor("x_7_cast_fp16")]; + tensor cast_1 = cast(dtype = cast_6_dtype_0, x = hidden_states_149_cast_fp16)[name = tensor("cast_1")]; + tensor hidden_states_151_cast_fp16 = add(x = x_7_cast_fp16, y = cast_1)[name = tensor("hidden_states_151_cast_fp16")]; + tensor input_267_interleave_0 = const()[name = tensor("input_267_interleave_0"), val = tensor(false)]; + tensor cast_3 = cast(dtype = cast_4_dtype_0, x = input_171_cast_fp16)[name = tensor("cast_3")]; + tensor input_267_cast_fp16 = concat(axis = var_2128, interleave = input_267_interleave_0, values = (hidden_states_151_cast_fp16, cast_3))[name = tensor("input_267_cast_fp16")]; + tensor reshape_124_shape_0 = const()[name = tensor("reshape_124_shape_0"), val = tensor([2, 32, 80, 6, 10])]; + tensor reshape_124_cast_fp16 = reshape(shape = reshape_124_shape_0, x = input_267_cast_fp16)[name = tensor("reshape_124_cast_fp16")]; + tensor reduce_mean_93_axes_0 = const()[name = tensor("reduce_mean_93_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_93_keep_dims_0 = const()[name = tensor("reduce_mean_93_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_93_cast_fp16 = reduce_mean(axes = reduce_mean_93_axes_0, keep_dims = reduce_mean_93_keep_dims_0, x = reshape_124_cast_fp16)[name = tensor("reduce_mean_93_cast_fp16")]; + tensor sub_62_cast_fp16 = sub(x = reshape_124_cast_fp16, y = reduce_mean_93_cast_fp16)[name = tensor("sub_62_cast_fp16")]; + tensor square_31_cast_fp16 = square(x = sub_62_cast_fp16)[name = tensor("square_31_cast_fp16")]; + tensor reduce_mean_95_axes_0 = const()[name = tensor("reduce_mean_95_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_95_keep_dims_0 = const()[name = tensor("reduce_mean_95_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_95_cast_fp16 = reduce_mean(axes = reduce_mean_95_axes_0, keep_dims = reduce_mean_95_keep_dims_0, x = square_31_cast_fp16)[name = tensor("reduce_mean_95_cast_fp16")]; + tensor add_62_y_0_to_fp16 = const()[name = tensor("add_62_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_62_cast_fp16 = add(x = reduce_mean_95_cast_fp16, y = add_62_y_0_to_fp16)[name = tensor("add_62_cast_fp16")]; + tensor sqrt_31_cast_fp16 = sqrt(x = add_62_cast_fp16)[name = tensor("sqrt_31_cast_fp16")]; + tensor real_div_31_cast_fp16 = real_div(x = sub_62_cast_fp16, y = sqrt_31_cast_fp16)[name = tensor("real_div_31_cast_fp16")]; + tensor reshape_125_shape_0 = const()[name = tensor("reshape_125_shape_0"), val = tensor([2, 2560, 6, 10])]; + tensor reshape_125_cast_fp16 = reshape(shape = reshape_125_shape_0, x = real_div_31_cast_fp16)[name = tensor("reshape_125_cast_fp16")]; + tensor add_63_gamma_0_to_fp16 = const()[name = tensor("add_63_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6576064)))]; + tensor add_63_beta_0_to_fp16 = const()[name = tensor("add_63_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6581248)))]; + tensor add_63_epsilon_0_to_fp16 = const()[name = tensor("add_63_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_63_cast_fp16 = batch_norm(beta = add_63_beta_0_to_fp16, epsilon = add_63_epsilon_0_to_fp16, gamma = add_63_gamma_0_to_fp16, mean = add_55_mean_0_to_fp16, variance = add_55_variance_0_to_fp16, x = reshape_125_cast_fp16)[name = tensor("add_63_cast_fp16")]; + tensor input_271_cast_fp16 = silu(x = add_63_cast_fp16)[name = tensor("input_271_cast_fp16")]; + tensor var_2245 = const()[name = tensor("op_2245"), val = tensor([1, 1])]; + tensor var_2247 = const()[name = tensor("op_2247"), val = tensor([1, 1])]; + tensor hidden_states_153_pad_type_0 = const()[name = tensor("hidden_states_153_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_153_pad_0 = const()[name = tensor("hidden_states_153_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_2_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6586432)))]; + tensor up_blocks_0_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65568896)))]; + tensor hidden_states_153_cast_fp16 = conv(bias = up_blocks_0_resnets_2_conv1_bias_to_fp16, dilations = var_2247, groups = var_2128, pad = hidden_states_153_pad_0, pad_type = hidden_states_153_pad_type_0, strides = var_2245, weight = up_blocks_0_resnets_2_conv1_weight_to_fp16, x = input_271_cast_fp16)[name = tensor("hidden_states_153_cast_fp16")]; + tensor var_2253 = const()[name = tensor("op_2253"), val = tensor([1, 1])]; + tensor var_2255 = const()[name = tensor("op_2255"), val = tensor([1, 1])]; + tensor temb_25_pad_type_0 = const()[name = tensor("temb_25_pad_type_0"), val = tensor("custom")]; + tensor temb_25_pad_0 = const()[name = tensor("temb_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_2_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65571520)))]; + tensor up_blocks_0_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68848384)))]; + tensor cast_12 = cast(dtype = cast_10_dtype_0, x = input_15_cast_fp16)[name = tensor("cast_12")]; + tensor temb_25_cast_fp16 = conv(bias = up_blocks_0_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_2255, groups = var_2128, pad = temb_25_pad_0, pad_type = temb_25_pad_type_0, strides = var_2253, weight = up_blocks_0_resnets_2_time_emb_proj_weight_to_fp16, x = cast_12)[name = tensor("temb_25_cast_fp16")]; + tensor input_275_cast_fp16 = add(x = hidden_states_153_cast_fp16, y = temb_25_cast_fp16)[name = tensor("input_275_cast_fp16")]; + tensor reshape_128_shape_0 = const()[name = tensor("reshape_128_shape_0"), val = tensor([2, 32, 40, 6, 10])]; + tensor reshape_128_cast_fp16 = reshape(shape = reshape_128_shape_0, x = input_275_cast_fp16)[name = tensor("reshape_128_cast_fp16")]; + tensor reduce_mean_96_axes_0 = const()[name = tensor("reduce_mean_96_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_96_keep_dims_0 = const()[name = tensor("reduce_mean_96_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_96_cast_fp16 = reduce_mean(axes = reduce_mean_96_axes_0, keep_dims = reduce_mean_96_keep_dims_0, x = reshape_128_cast_fp16)[name = tensor("reduce_mean_96_cast_fp16")]; + tensor sub_64_cast_fp16 = sub(x = reshape_128_cast_fp16, y = reduce_mean_96_cast_fp16)[name = tensor("sub_64_cast_fp16")]; + tensor square_32_cast_fp16 = square(x = sub_64_cast_fp16)[name = tensor("square_32_cast_fp16")]; + tensor reduce_mean_98_axes_0 = const()[name = tensor("reduce_mean_98_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_98_keep_dims_0 = const()[name = tensor("reduce_mean_98_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_98_cast_fp16 = reduce_mean(axes = reduce_mean_98_axes_0, keep_dims = reduce_mean_98_keep_dims_0, x = square_32_cast_fp16)[name = tensor("reduce_mean_98_cast_fp16")]; + tensor add_64_y_0_to_fp16 = const()[name = tensor("add_64_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_64_cast_fp16 = add(x = reduce_mean_98_cast_fp16, y = add_64_y_0_to_fp16)[name = tensor("add_64_cast_fp16")]; + tensor sqrt_32_cast_fp16 = sqrt(x = add_64_cast_fp16)[name = tensor("sqrt_32_cast_fp16")]; + tensor real_div_32_cast_fp16 = real_div(x = sub_64_cast_fp16, y = sqrt_32_cast_fp16)[name = tensor("real_div_32_cast_fp16")]; + tensor reshape_129_shape_0 = const()[name = tensor("reshape_129_shape_0"), val = tensor([2, 1280, 6, 10])]; + tensor reshape_129_cast_fp16 = reshape(shape = reshape_129_shape_0, x = real_div_32_cast_fp16)[name = tensor("reshape_129_cast_fp16")]; + tensor add_65_gamma_0_to_fp16 = const()[name = tensor("add_65_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68851008)))]; + tensor add_65_beta_0_to_fp16 = const()[name = tensor("add_65_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68853632)))]; + tensor add_65_epsilon_0_to_fp16 = const()[name = tensor("add_65_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_65_cast_fp16 = batch_norm(beta = add_65_beta_0_to_fp16, epsilon = add_65_epsilon_0_to_fp16, gamma = add_65_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_129_cast_fp16)[name = tensor("add_65_cast_fp16")]; + tensor input_279_cast_fp16 = silu(x = add_65_cast_fp16)[name = tensor("input_279_cast_fp16")]; + tensor var_2265 = const()[name = tensor("op_2265"), val = tensor([1, 1])]; + tensor var_2267 = const()[name = tensor("op_2267"), val = tensor([1, 1])]; + tensor hidden_states_155_pad_type_0 = const()[name = tensor("hidden_states_155_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_155_pad_0 = const()[name = tensor("hidden_states_155_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_2_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68856256)))]; + tensor up_blocks_0_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98347520)))]; + tensor hidden_states_155_cast_fp16 = conv(bias = up_blocks_0_resnets_2_conv2_bias_to_fp16, dilations = var_2267, groups = var_2128, pad = hidden_states_155_pad_0, pad_type = hidden_states_155_pad_type_0, strides = var_2265, weight = up_blocks_0_resnets_2_conv2_weight_to_fp16, x = input_279_cast_fp16)[name = tensor("hidden_states_155_cast_fp16")]; + tensor var_2272 = const()[name = tensor("op_2272"), val = tensor([1, 1])]; + tensor var_2274 = const()[name = tensor("op_2274"), val = tensor([1, 1])]; + tensor x_9_pad_type_0 = const()[name = tensor("x_9_pad_type_0"), val = tensor("custom")]; + tensor x_9_pad_0 = const()[name = tensor("x_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98350144)))]; + tensor up_blocks_0_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(104903808)))]; + tensor x_9_cast_fp16 = conv(bias = up_blocks_0_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_2274, groups = var_2128, pad = x_9_pad_0, pad_type = x_9_pad_type_0, strides = var_2272, weight = up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16, x = input_267_cast_fp16)[name = tensor("x_9_cast_fp16")]; + tensor input_281_cast_fp16 = add(x = x_9_cast_fp16, y = hidden_states_155_cast_fp16)[name = tensor("input_281_cast_fp16")]; + tensor input_283_scale_factor_height_0 = const()[name = tensor("input_283_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_283_scale_factor_width_0 = const()[name = tensor("input_283_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor input_283_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = input_283_scale_factor_height_0, scale_factor_width = input_283_scale_factor_width_0, x = input_281_cast_fp16)[name = tensor("input_283_cast_fp16")]; + tensor var_2283 = const()[name = tensor("op_2283"), val = tensor([1, 1])]; + tensor var_2285 = const()[name = tensor("op_2285"), val = tensor([1, 1])]; + tensor hidden_states_157_pad_type_0 = const()[name = tensor("hidden_states_157_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_157_pad_0 = const()[name = tensor("hidden_states_157_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_upsamplers_0_conv_weight_to_fp16 = const()[name = tensor("up_blocks_0_upsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(104906432)))]; + tensor up_blocks_0_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("up_blocks_0_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134397696)))]; + tensor hidden_states_157_cast_fp16 = conv(bias = up_blocks_0_upsamplers_0_conv_bias_to_fp16, dilations = var_2285, groups = var_2128, pad = hidden_states_157_pad_0, pad_type = hidden_states_157_pad_type_0, strides = var_2283, weight = up_blocks_0_upsamplers_0_conv_weight_to_fp16, x = input_283_cast_fp16)[name = tensor("hidden_states_157_cast_fp16")]; + tensor var_2290 = const()[name = tensor("op_2290"), val = tensor(3)]; + tensor var_2301 = const()[name = tensor("op_2301"), val = tensor(true)]; + tensor var_2306 = const()[name = tensor("op_2306"), val = tensor(1)]; + tensor input_285_interleave_0 = const()[name = tensor("input_285_interleave_0"), val = tensor(false)]; + tensor cast_4 = cast(dtype = cast_3_dtype_0, x = input_169_cast_fp16)[name = tensor("cast_4")]; + tensor input_285_cast_fp16 = concat(axis = var_2306, interleave = input_285_interleave_0, values = (hidden_states_157_cast_fp16, cast_4))[name = tensor("input_285_cast_fp16")]; + tensor reshape_132_shape_0 = const()[name = tensor("reshape_132_shape_0"), val = tensor([2, 32, 80, 12, 20])]; + tensor reshape_132_cast_fp16 = reshape(shape = reshape_132_shape_0, x = input_285_cast_fp16)[name = tensor("reshape_132_cast_fp16")]; + tensor reduce_mean_99_axes_0 = const()[name = tensor("reduce_mean_99_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_99_keep_dims_0 = const()[name = tensor("reduce_mean_99_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_99_cast_fp16 = reduce_mean(axes = reduce_mean_99_axes_0, keep_dims = reduce_mean_99_keep_dims_0, x = reshape_132_cast_fp16)[name = tensor("reduce_mean_99_cast_fp16")]; + tensor sub_66_cast_fp16 = sub(x = reshape_132_cast_fp16, y = reduce_mean_99_cast_fp16)[name = tensor("sub_66_cast_fp16")]; + tensor square_33_cast_fp16 = square(x = sub_66_cast_fp16)[name = tensor("square_33_cast_fp16")]; + tensor reduce_mean_101_axes_0 = const()[name = tensor("reduce_mean_101_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_101_keep_dims_0 = const()[name = tensor("reduce_mean_101_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_101_cast_fp16 = reduce_mean(axes = reduce_mean_101_axes_0, keep_dims = reduce_mean_101_keep_dims_0, x = square_33_cast_fp16)[name = tensor("reduce_mean_101_cast_fp16")]; + tensor add_66_y_0_to_fp16 = const()[name = tensor("add_66_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_66_cast_fp16 = add(x = reduce_mean_101_cast_fp16, y = add_66_y_0_to_fp16)[name = tensor("add_66_cast_fp16")]; + tensor sqrt_33_cast_fp16 = sqrt(x = add_66_cast_fp16)[name = tensor("sqrt_33_cast_fp16")]; + tensor real_div_33_cast_fp16 = real_div(x = sub_66_cast_fp16, y = sqrt_33_cast_fp16)[name = tensor("real_div_33_cast_fp16")]; + tensor reshape_133_shape_0 = const()[name = tensor("reshape_133_shape_0"), val = tensor([2, 2560, 12, 20])]; + tensor reshape_133_cast_fp16 = reshape(shape = reshape_133_shape_0, x = real_div_33_cast_fp16)[name = tensor("reshape_133_cast_fp16")]; + tensor add_67_gamma_0_to_fp16 = const()[name = tensor("add_67_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134400320)))]; + tensor add_67_beta_0_to_fp16 = const()[name = tensor("add_67_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134405504)))]; + tensor add_67_epsilon_0_to_fp16 = const()[name = tensor("add_67_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_67_cast_fp16 = batch_norm(beta = add_67_beta_0_to_fp16, epsilon = add_67_epsilon_0_to_fp16, gamma = add_67_gamma_0_to_fp16, mean = add_55_mean_0_to_fp16, variance = add_55_variance_0_to_fp16, x = reshape_133_cast_fp16)[name = tensor("add_67_cast_fp16")]; + tensor input_289_cast_fp16 = silu(x = add_67_cast_fp16)[name = tensor("input_289_cast_fp16")]; + tensor var_2335 = const()[name = tensor("op_2335"), val = tensor([1, 1])]; + tensor var_2337 = const()[name = tensor("op_2337"), val = tensor([1, 1])]; + tensor hidden_states_159_pad_type_0 = const()[name = tensor("hidden_states_159_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_159_pad_0 = const()[name = tensor("hidden_states_159_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134410688)))]; + tensor up_blocks_1_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(193393152)))]; + tensor hidden_states_159_cast_fp16 = conv(bias = up_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = var_2337, groups = var_2306, pad = hidden_states_159_pad_0, pad_type = hidden_states_159_pad_type_0, strides = var_2335, weight = up_blocks_1_resnets_0_conv1_weight_to_fp16, x = input_289_cast_fp16)[name = tensor("hidden_states_159_cast_fp16")]; + tensor var_2343 = const()[name = tensor("op_2343"), val = tensor([1, 1])]; + tensor var_2345 = const()[name = tensor("op_2345"), val = tensor([1, 1])]; + tensor temb_27_pad_type_0 = const()[name = tensor("temb_27_pad_type_0"), val = tensor("custom")]; + tensor temb_27_pad_0 = const()[name = tensor("temb_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(193395776)))]; + tensor up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196672640)))]; + tensor temb_27_cast_fp16 = conv(bias = up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_2345, groups = var_2306, pad = temb_27_pad_0, pad_type = temb_27_pad_type_0, strides = var_2343, weight = up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16, x = cast_12)[name = tensor("temb_27_cast_fp16")]; + tensor input_293_cast_fp16 = add(x = hidden_states_159_cast_fp16, y = temb_27_cast_fp16)[name = tensor("input_293_cast_fp16")]; + tensor reshape_136_shape_0 = const()[name = tensor("reshape_136_shape_0"), val = tensor([2, 32, 40, 12, 20])]; + tensor reshape_136_cast_fp16 = reshape(shape = reshape_136_shape_0, x = input_293_cast_fp16)[name = tensor("reshape_136_cast_fp16")]; + tensor reduce_mean_102_axes_0 = const()[name = tensor("reduce_mean_102_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_102_keep_dims_0 = const()[name = tensor("reduce_mean_102_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_102_cast_fp16 = reduce_mean(axes = reduce_mean_102_axes_0, keep_dims = reduce_mean_102_keep_dims_0, x = reshape_136_cast_fp16)[name = tensor("reduce_mean_102_cast_fp16")]; + tensor sub_68_cast_fp16 = sub(x = reshape_136_cast_fp16, y = reduce_mean_102_cast_fp16)[name = tensor("sub_68_cast_fp16")]; + tensor square_34_cast_fp16 = square(x = sub_68_cast_fp16)[name = tensor("square_34_cast_fp16")]; + tensor reduce_mean_104_axes_0 = const()[name = tensor("reduce_mean_104_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_104_keep_dims_0 = const()[name = tensor("reduce_mean_104_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_104_cast_fp16 = reduce_mean(axes = reduce_mean_104_axes_0, keep_dims = reduce_mean_104_keep_dims_0, x = square_34_cast_fp16)[name = tensor("reduce_mean_104_cast_fp16")]; + tensor add_68_y_0_to_fp16 = const()[name = tensor("add_68_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_68_cast_fp16 = add(x = reduce_mean_104_cast_fp16, y = add_68_y_0_to_fp16)[name = tensor("add_68_cast_fp16")]; + tensor sqrt_34_cast_fp16 = sqrt(x = add_68_cast_fp16)[name = tensor("sqrt_34_cast_fp16")]; + tensor real_div_34_cast_fp16 = real_div(x = sub_68_cast_fp16, y = sqrt_34_cast_fp16)[name = tensor("real_div_34_cast_fp16")]; + tensor reshape_137_shape_0 = const()[name = tensor("reshape_137_shape_0"), val = tensor([2, 1280, 12, 20])]; + tensor reshape_137_cast_fp16 = reshape(shape = reshape_137_shape_0, x = real_div_34_cast_fp16)[name = tensor("reshape_137_cast_fp16")]; + tensor add_69_gamma_0_to_fp16 = const()[name = tensor("add_69_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196675264)))]; + tensor add_69_beta_0_to_fp16 = const()[name = tensor("add_69_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196677888)))]; + tensor add_69_epsilon_0_to_fp16 = const()[name = tensor("add_69_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_69_cast_fp16 = batch_norm(beta = add_69_beta_0_to_fp16, epsilon = add_69_epsilon_0_to_fp16, gamma = add_69_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_137_cast_fp16)[name = tensor("add_69_cast_fp16")]; + tensor input_297_cast_fp16 = silu(x = add_69_cast_fp16)[name = tensor("input_297_cast_fp16")]; + tensor var_2355 = const()[name = tensor("op_2355"), val = tensor([1, 1])]; + tensor var_2357 = const()[name = tensor("op_2357"), val = tensor([1, 1])]; + tensor hidden_states_161_pad_type_0 = const()[name = tensor("hidden_states_161_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_161_pad_0 = const()[name = tensor("hidden_states_161_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196680512)))]; + tensor up_blocks_1_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(226171776)))]; + tensor hidden_states_161_cast_fp16 = conv(bias = up_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = var_2357, groups = var_2306, pad = hidden_states_161_pad_0, pad_type = hidden_states_161_pad_type_0, strides = var_2355, weight = up_blocks_1_resnets_0_conv2_weight_to_fp16, x = input_297_cast_fp16)[name = tensor("hidden_states_161_cast_fp16")]; + tensor var_2362 = const()[name = tensor("op_2362"), val = tensor([1, 1])]; + tensor var_2364 = const()[name = tensor("op_2364"), val = tensor([1, 1])]; + tensor x_11_pad_type_0 = const()[name = tensor("x_11_pad_type_0"), val = tensor("custom")]; + tensor x_11_pad_0 = const()[name = tensor("x_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(226174400)))]; + tensor up_blocks_1_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(232728064)))]; + tensor x_11_cast_fp16 = conv(bias = up_blocks_1_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_2364, groups = var_2306, pad = x_11_pad_0, pad_type = x_11_pad_type_0, strides = var_2362, weight = up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16, x = input_285_cast_fp16)[name = tensor("x_11_cast_fp16")]; + tensor hidden_states_163_cast_fp16 = add(x = x_11_cast_fp16, y = hidden_states_161_cast_fp16)[name = tensor("hidden_states_163_cast_fp16")]; + tensor reshape_140_shape_0 = const()[name = tensor("reshape_140_shape_0"), val = tensor([2, 32, 40, 12, 20])]; + tensor reshape_140_cast_fp16 = reshape(shape = reshape_140_shape_0, x = hidden_states_163_cast_fp16)[name = tensor("reshape_140_cast_fp16")]; + tensor reduce_mean_105_axes_0 = const()[name = tensor("reduce_mean_105_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_105_keep_dims_0 = const()[name = tensor("reduce_mean_105_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_105_cast_fp16 = reduce_mean(axes = reduce_mean_105_axes_0, keep_dims = reduce_mean_105_keep_dims_0, x = reshape_140_cast_fp16)[name = tensor("reduce_mean_105_cast_fp16")]; + tensor sub_70_cast_fp16 = sub(x = reshape_140_cast_fp16, y = reduce_mean_105_cast_fp16)[name = tensor("sub_70_cast_fp16")]; + tensor square_35_cast_fp16 = square(x = sub_70_cast_fp16)[name = tensor("square_35_cast_fp16")]; + tensor reduce_mean_107_axes_0 = const()[name = tensor("reduce_mean_107_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_107_keep_dims_0 = const()[name = tensor("reduce_mean_107_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_107_cast_fp16 = reduce_mean(axes = reduce_mean_107_axes_0, keep_dims = reduce_mean_107_keep_dims_0, x = square_35_cast_fp16)[name = tensor("reduce_mean_107_cast_fp16")]; + tensor add_70_y_0_to_fp16 = const()[name = tensor("add_70_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_70_cast_fp16 = add(x = reduce_mean_107_cast_fp16, y = add_70_y_0_to_fp16)[name = tensor("add_70_cast_fp16")]; + tensor sqrt_35_cast_fp16 = sqrt(x = add_70_cast_fp16)[name = tensor("sqrt_35_cast_fp16")]; + tensor real_div_35_cast_fp16 = real_div(x = sub_70_cast_fp16, y = sqrt_35_cast_fp16)[name = tensor("real_div_35_cast_fp16")]; + tensor reshape_141_shape_0 = const()[name = tensor("reshape_141_shape_0"), val = tensor([2, 1280, 12, 20])]; + tensor reshape_141_cast_fp16 = reshape(shape = reshape_141_shape_0, x = real_div_35_cast_fp16)[name = tensor("reshape_141_cast_fp16")]; + tensor add_71_gamma_0_to_fp16 = const()[name = tensor("add_71_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(232730688)))]; + tensor add_71_beta_0_to_fp16 = const()[name = tensor("add_71_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(232733312)))]; + tensor add_71_epsilon_0_to_fp16 = const()[name = tensor("add_71_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_71_cast_fp16 = batch_norm(beta = add_71_beta_0_to_fp16, epsilon = add_71_epsilon_0_to_fp16, gamma = add_71_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_141_cast_fp16)[name = tensor("add_71_cast_fp16")]; + tensor var_2384 = const()[name = tensor("op_2384"), val = tensor([1, 1])]; + tensor var_2386 = const()[name = tensor("op_2386"), val = tensor([1, 1])]; + tensor hidden_states_165_pad_type_0 = const()[name = tensor("hidden_states_165_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_165_pad_0 = const()[name = tensor("hidden_states_165_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(232735936)))]; + tensor up_blocks_1_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(236012800)))]; + tensor hidden_states_165_cast_fp16 = conv(bias = up_blocks_1_attentions_0_proj_in_bias_to_fp16, dilations = var_2386, groups = var_2306, pad = hidden_states_165_pad_0, pad_type = hidden_states_165_pad_type_0, strides = var_2384, weight = up_blocks_1_attentions_0_proj_in_weight_to_fp16, x = add_71_cast_fp16)[name = tensor("hidden_states_165_cast_fp16")]; + tensor var_2391 = const()[name = tensor("op_2391"), val = tensor([2, 1280, 1, 240])]; + tensor inputs_43_cast_fp16 = reshape(shape = var_2391, x = hidden_states_165_cast_fp16)[name = tensor("inputs_43_cast_fp16")]; + tensor var_2401 = const()[name = tensor("op_2401"), val = tensor([1])]; + tensor channels_mean_43_cast_fp16 = reduce_mean(axes = var_2401, keep_dims = var_2301, x = inputs_43_cast_fp16)[name = tensor("channels_mean_43_cast_fp16")]; + tensor zero_mean_43_cast_fp16 = sub(x = inputs_43_cast_fp16, y = channels_mean_43_cast_fp16)[name = tensor("zero_mean_43_cast_fp16")]; + tensor zero_mean_sq_43_cast_fp16 = mul(x = zero_mean_43_cast_fp16, y = zero_mean_43_cast_fp16)[name = tensor("zero_mean_sq_43_cast_fp16")]; + tensor var_2405 = const()[name = tensor("op_2405"), val = tensor([1])]; + tensor var_2406_cast_fp16 = reduce_mean(axes = var_2405, keep_dims = var_2301, x = zero_mean_sq_43_cast_fp16)[name = tensor("op_2406_cast_fp16")]; + tensor var_2407_to_fp16 = const()[name = tensor("op_2407_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2408_cast_fp16 = add(x = var_2406_cast_fp16, y = var_2407_to_fp16)[name = tensor("op_2408_cast_fp16")]; + tensor denom_43_epsilon_0_to_fp16 = const()[name = tensor("denom_43_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_43_cast_fp16 = rsqrt(epsilon = denom_43_epsilon_0_to_fp16, x = var_2408_cast_fp16)[name = tensor("denom_43_cast_fp16")]; + tensor out_43_cast_fp16 = mul(x = zero_mean_43_cast_fp16, y = denom_43_cast_fp16)[name = tensor("out_43_cast_fp16")]; + tensor var_2412_to_fp16 = const()[name = tensor("op_2412_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(236015424)))]; + tensor var_2413_cast_fp16 = add(x = out_43_cast_fp16, y = var_2412_to_fp16)[name = tensor("op_2413_cast_fp16")]; + tensor var_2415_to_fp16 = const()[name = tensor("op_2415_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(236018048)))]; + tensor hidden_states_167_cast_fp16 = mul(x = var_2413_cast_fp16, y = var_2415_to_fp16)[name = tensor("hidden_states_167_cast_fp16")]; + tensor var_2422 = const()[name = tensor("op_2422"), val = tensor([1, 1])]; + tensor var_2424 = const()[name = tensor("op_2424"), val = tensor([1, 1])]; + tensor q_29_pad_type_0 = const()[name = tensor("q_29_pad_type_0"), val = tensor("custom")]; + tensor q_29_pad_0 = const()[name = tensor("q_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(236020672)))]; + tensor q_29_cast_fp16 = conv(dilations = var_2424, groups = var_2306, pad = q_29_pad_0, pad_type = q_29_pad_type_0, strides = var_2422, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_167_cast_fp16)[name = tensor("q_29_cast_fp16")]; + tensor var_2428 = const()[name = tensor("op_2428"), val = tensor([1, 1])]; + tensor var_2430 = const()[name = tensor("op_2430"), val = tensor([1, 1])]; + tensor k_29_pad_type_0 = const()[name = tensor("k_29_pad_type_0"), val = tensor("custom")]; + tensor k_29_pad_0 = const()[name = tensor("k_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(239297536)))]; + tensor k_29_cast_fp16 = conv(dilations = var_2430, groups = var_2306, pad = k_29_pad_0, pad_type = k_29_pad_type_0, strides = var_2428, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_167_cast_fp16)[name = tensor("k_29_cast_fp16")]; + tensor var_2434 = const()[name = tensor("op_2434"), val = tensor([1, 1])]; + tensor var_2436 = const()[name = tensor("op_2436"), val = tensor([1, 1])]; + tensor v_29_pad_type_0 = const()[name = tensor("v_29_pad_type_0"), val = tensor("custom")]; + tensor v_29_pad_0 = const()[name = tensor("v_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(242574400)))]; + tensor v_29_cast_fp16 = conv(dilations = var_2436, groups = var_2306, pad = v_29_pad_0, pad_type = v_29_pad_type_0, strides = var_2434, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_167_cast_fp16)[name = tensor("v_29_cast_fp16")]; + tensor var_2440 = const()[name = tensor("op_2440"), val = tensor([2, 20, 64, -1])]; + tensor var_2441_cast_fp16 = reshape(shape = var_2440, x = q_29_cast_fp16)[name = tensor("op_2441_cast_fp16")]; + tensor var_2442 = const()[name = tensor("op_2442"), val = tensor([2, 20, 64, -1])]; + tensor var_2443_cast_fp16 = reshape(shape = var_2442, x = k_29_cast_fp16)[name = tensor("op_2443_cast_fp16")]; + tensor var_2444 = const()[name = tensor("op_2444"), val = tensor([2, 20, 64, -1])]; + tensor var_2445_cast_fp16 = reshape(shape = var_2444, x = v_29_cast_fp16)[name = tensor("op_2445_cast_fp16")]; + tensor attn_weights_57_transpose_x_0 = const()[name = tensor("attn_weights_57_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_57_transpose_y_0 = const()[name = tensor("attn_weights_57_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_57_cast_fp16 = matmul(transpose_x = attn_weights_57_transpose_x_0, transpose_y = attn_weights_57_transpose_y_0, x = var_2441_cast_fp16, y = var_2443_cast_fp16)[name = tensor("attn_weights_57_cast_fp16")]; + tensor var_2297_to_fp16 = const()[name = tensor("op_2297_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_59_cast_fp16 = mul(x = attn_weights_57_cast_fp16, y = var_2297_to_fp16)[name = tensor("attn_weights_59_cast_fp16")]; + tensor var_2449_cast_fp16 = softmax(axis = var_2290, x = attn_weights_59_cast_fp16)[name = tensor("op_2449_cast_fp16")]; + tensor attn_29_transpose_x_0 = const()[name = tensor("attn_29_transpose_x_0"), val = tensor(false)]; + tensor attn_29_transpose_y_0 = const()[name = tensor("attn_29_transpose_y_0"), val = tensor(true)]; + tensor attn_29_cast_fp16 = matmul(transpose_x = attn_29_transpose_x_0, transpose_y = attn_29_transpose_y_0, x = var_2445_cast_fp16, y = var_2449_cast_fp16)[name = tensor("attn_29_cast_fp16")]; + tensor var_2453 = const()[name = tensor("op_2453"), val = tensor([2, 1280, 1, -1])]; + tensor input_301_cast_fp16 = reshape(shape = var_2453, x = attn_29_cast_fp16)[name = tensor("input_301_cast_fp16")]; + tensor var_2458 = const()[name = tensor("op_2458"), val = tensor([1, 1])]; + tensor var_2460 = const()[name = tensor("op_2460"), val = tensor([1, 1])]; + tensor var_2462_pad_type_0 = const()[name = tensor("op_2462_pad_type_0"), val = tensor("custom")]; + tensor var_2462_pad_0 = const()[name = tensor("op_2462_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(245851264)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(249128128)))]; + tensor var_2462_cast_fp16 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_2460, groups = var_2306, pad = var_2462_pad_0, pad_type = var_2462_pad_type_0, strides = var_2458, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_301_cast_fp16)[name = tensor("op_2462_cast_fp16")]; + tensor inputs_45_cast_fp16 = add(x = var_2462_cast_fp16, y = inputs_43_cast_fp16)[name = tensor("inputs_45_cast_fp16")]; + tensor var_2466 = const()[name = tensor("op_2466"), val = tensor([1])]; + tensor channels_mean_45_cast_fp16 = reduce_mean(axes = var_2466, keep_dims = var_2301, x = inputs_45_cast_fp16)[name = tensor("channels_mean_45_cast_fp16")]; + tensor zero_mean_45_cast_fp16 = sub(x = inputs_45_cast_fp16, y = channels_mean_45_cast_fp16)[name = tensor("zero_mean_45_cast_fp16")]; + tensor zero_mean_sq_45_cast_fp16 = mul(x = zero_mean_45_cast_fp16, y = zero_mean_45_cast_fp16)[name = tensor("zero_mean_sq_45_cast_fp16")]; + tensor var_2470 = const()[name = tensor("op_2470"), val = tensor([1])]; + tensor var_2471_cast_fp16 = reduce_mean(axes = var_2470, keep_dims = var_2301, x = zero_mean_sq_45_cast_fp16)[name = tensor("op_2471_cast_fp16")]; + tensor var_2472_to_fp16 = const()[name = tensor("op_2472_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2473_cast_fp16 = add(x = var_2471_cast_fp16, y = var_2472_to_fp16)[name = tensor("op_2473_cast_fp16")]; + tensor denom_45_epsilon_0_to_fp16 = const()[name = tensor("denom_45_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_45_cast_fp16 = rsqrt(epsilon = denom_45_epsilon_0_to_fp16, x = var_2473_cast_fp16)[name = tensor("denom_45_cast_fp16")]; + tensor out_45_cast_fp16 = mul(x = zero_mean_45_cast_fp16, y = denom_45_cast_fp16)[name = tensor("out_45_cast_fp16")]; + tensor var_2477_to_fp16 = const()[name = tensor("op_2477_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(249130752)))]; + tensor var_2478_cast_fp16 = add(x = out_45_cast_fp16, y = var_2477_to_fp16)[name = tensor("op_2478_cast_fp16")]; + tensor var_2480_to_fp16 = const()[name = tensor("op_2480_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(249133376)))]; + tensor hidden_states_169_cast_fp16 = mul(x = var_2478_cast_fp16, y = var_2480_to_fp16)[name = tensor("hidden_states_169_cast_fp16")]; + tensor var_2487 = const()[name = tensor("op_2487"), val = tensor([1, 1])]; + tensor var_2489 = const()[name = tensor("op_2489"), val = tensor([1, 1])]; + tensor q_31_pad_type_0 = const()[name = tensor("q_31_pad_type_0"), val = tensor("custom")]; + tensor q_31_pad_0 = const()[name = tensor("q_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(249136000)))]; + tensor q_31_cast_fp16 = conv(dilations = var_2489, groups = var_2306, pad = q_31_pad_0, pad_type = q_31_pad_type_0, strides = var_2487, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_169_cast_fp16)[name = tensor("q_31_cast_fp16")]; + tensor var_2493 = const()[name = tensor("op_2493"), val = tensor([1, 1])]; + tensor var_2495 = const()[name = tensor("op_2495"), val = tensor([1, 1])]; + tensor k_31_pad_type_0 = const()[name = tensor("k_31_pad_type_0"), val = tensor("custom")]; + tensor k_31_pad_0 = const()[name = tensor("k_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(252412864)))]; + tensor k_31_cast_fp16 = conv(dilations = var_2495, groups = var_2306, pad = k_31_pad_0, pad_type = k_31_pad_type_0, strides = var_2493, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_31_cast_fp16")]; + tensor var_2499 = const()[name = tensor("op_2499"), val = tensor([1, 1])]; + tensor var_2501 = const()[name = tensor("op_2501"), val = tensor([1, 1])]; + tensor v_31_pad_type_0 = const()[name = tensor("v_31_pad_type_0"), val = tensor("custom")]; + tensor v_31_pad_0 = const()[name = tensor("v_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(255034368)))]; + tensor v_31_cast_fp16 = conv(dilations = var_2501, groups = var_2306, pad = v_31_pad_0, pad_type = v_31_pad_type_0, strides = var_2499, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_31_cast_fp16")]; + tensor var_2505 = const()[name = tensor("op_2505"), val = tensor([2, 20, 64, -1])]; + tensor var_2506_cast_fp16 = reshape(shape = var_2505, x = q_31_cast_fp16)[name = tensor("op_2506_cast_fp16")]; + tensor var_2507 = const()[name = tensor("op_2507"), val = tensor([2, 20, 64, -1])]; + tensor var_2508_cast_fp16 = reshape(shape = var_2507, x = k_31_cast_fp16)[name = tensor("op_2508_cast_fp16")]; + tensor var_2509 = const()[name = tensor("op_2509"), val = tensor([2, 20, 64, -1])]; + tensor var_2510_cast_fp16 = reshape(shape = var_2509, x = v_31_cast_fp16)[name = tensor("op_2510_cast_fp16")]; + tensor attn_weights_61_transpose_x_0 = const()[name = tensor("attn_weights_61_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_61_transpose_y_0 = const()[name = tensor("attn_weights_61_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_61_cast_fp16 = matmul(transpose_x = attn_weights_61_transpose_x_0, transpose_y = attn_weights_61_transpose_y_0, x = var_2506_cast_fp16, y = var_2508_cast_fp16)[name = tensor("attn_weights_61_cast_fp16")]; + tensor attn_weights_63_cast_fp16 = mul(x = attn_weights_61_cast_fp16, y = var_2297_to_fp16)[name = tensor("attn_weights_63_cast_fp16")]; + tensor var_2514_cast_fp16 = softmax(axis = var_2290, x = attn_weights_63_cast_fp16)[name = tensor("op_2514_cast_fp16")]; + tensor attn_31_transpose_x_0 = const()[name = tensor("attn_31_transpose_x_0"), val = tensor(false)]; + tensor attn_31_transpose_y_0 = const()[name = tensor("attn_31_transpose_y_0"), val = tensor(true)]; + tensor attn_31_cast_fp16 = matmul(transpose_x = attn_31_transpose_x_0, transpose_y = attn_31_transpose_y_0, x = var_2510_cast_fp16, y = var_2514_cast_fp16)[name = tensor("attn_31_cast_fp16")]; + tensor var_2518 = const()[name = tensor("op_2518"), val = tensor([2, 1280, 1, -1])]; + tensor input_303_cast_fp16 = reshape(shape = var_2518, x = attn_31_cast_fp16)[name = tensor("input_303_cast_fp16")]; + tensor var_2523 = const()[name = tensor("op_2523"), val = tensor([1, 1])]; + tensor var_2525 = const()[name = tensor("op_2525"), val = tensor([1, 1])]; + tensor var_2527_pad_type_0 = const()[name = tensor("op_2527_pad_type_0"), val = tensor("custom")]; + tensor var_2527_pad_0 = const()[name = tensor("op_2527_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(257655872)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(260932736)))]; + tensor var_2527_cast_fp16 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_2525, groups = var_2306, pad = var_2527_pad_0, pad_type = var_2527_pad_type_0, strides = var_2523, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_303_cast_fp16)[name = tensor("op_2527_cast_fp16")]; + tensor inputs_47_cast_fp16 = add(x = var_2527_cast_fp16, y = inputs_45_cast_fp16)[name = tensor("inputs_47_cast_fp16")]; + tensor var_2531 = const()[name = tensor("op_2531"), val = tensor([1])]; + tensor channels_mean_47_cast_fp16 = reduce_mean(axes = var_2531, keep_dims = var_2301, x = inputs_47_cast_fp16)[name = tensor("channels_mean_47_cast_fp16")]; + tensor zero_mean_47_cast_fp16 = sub(x = inputs_47_cast_fp16, y = channels_mean_47_cast_fp16)[name = tensor("zero_mean_47_cast_fp16")]; + tensor zero_mean_sq_47_cast_fp16 = mul(x = zero_mean_47_cast_fp16, y = zero_mean_47_cast_fp16)[name = tensor("zero_mean_sq_47_cast_fp16")]; + tensor var_2535 = const()[name = tensor("op_2535"), val = tensor([1])]; + tensor var_2536_cast_fp16 = reduce_mean(axes = var_2535, keep_dims = var_2301, x = zero_mean_sq_47_cast_fp16)[name = tensor("op_2536_cast_fp16")]; + tensor var_2537_to_fp16 = const()[name = tensor("op_2537_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2538_cast_fp16 = add(x = var_2536_cast_fp16, y = var_2537_to_fp16)[name = tensor("op_2538_cast_fp16")]; + tensor denom_47_epsilon_0_to_fp16 = const()[name = tensor("denom_47_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_47_cast_fp16 = rsqrt(epsilon = denom_47_epsilon_0_to_fp16, x = var_2538_cast_fp16)[name = tensor("denom_47_cast_fp16")]; + tensor out_47_cast_fp16 = mul(x = zero_mean_47_cast_fp16, y = denom_47_cast_fp16)[name = tensor("out_47_cast_fp16")]; + tensor var_2542_to_fp16 = const()[name = tensor("op_2542_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(260935360)))]; + tensor var_2543_cast_fp16 = add(x = out_47_cast_fp16, y = var_2542_to_fp16)[name = tensor("op_2543_cast_fp16")]; + tensor var_2545_to_fp16 = const()[name = tensor("op_2545_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(260937984)))]; + tensor input_305_cast_fp16 = mul(x = var_2543_cast_fp16, y = var_2545_to_fp16)[name = tensor("input_305_cast_fp16")]; + tensor var_2553 = const()[name = tensor("op_2553"), val = tensor([1, 1])]; + tensor var_2555 = const()[name = tensor("op_2555"), val = tensor([1, 1])]; + tensor var_2557_pad_type_0 = const()[name = tensor("op_2557_pad_type_0"), val = tensor("custom")]; + tensor var_2557_pad_0 = const()[name = tensor("op_2557_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(260940608)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(287155072)))]; + tensor var_2557_cast_fp16 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_2555, groups = var_2306, pad = var_2557_pad_0, pad_type = var_2557_pad_type_0, strides = var_2553, weight = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_305_cast_fp16)[name = tensor("op_2557_cast_fp16")]; + tensor var_2558_split_sizes_0 = const()[name = tensor("op_2558_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2558_axis_0 = const()[name = tensor("op_2558_axis_0"), val = tensor(1)]; + tensor var_2558_cast_fp16_0, tensor var_2558_cast_fp16_1 = split(axis = var_2558_axis_0, split_sizes = var_2558_split_sizes_0, x = var_2557_cast_fp16)[name = tensor("op_2558_cast_fp16")]; + tensor var_2560_mode_0 = const()[name = tensor("op_2560_mode_0"), val = tensor("EXACT")]; + tensor var_2560_cast_fp16 = gelu(mode = var_2560_mode_0, x = var_2558_cast_fp16_1)[name = tensor("op_2560_cast_fp16")]; + tensor input_307_cast_fp16 = mul(x = var_2558_cast_fp16_0, y = var_2560_cast_fp16)[name = tensor("input_307_cast_fp16")]; + tensor var_2564 = const()[name = tensor("op_2564"), val = tensor([1, 1])]; + tensor var_2566 = const()[name = tensor("op_2566"), val = tensor([1, 1])]; + tensor var_2568_pad_type_0 = const()[name = tensor("op_2568_pad_type_0"), val = tensor("custom")]; + tensor var_2568_pad_0 = const()[name = tensor("op_2568_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(287175616)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(300282880)))]; + tensor var_2568_cast_fp16 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_2566, groups = var_2306, pad = var_2568_pad_0, pad_type = var_2568_pad_type_0, strides = var_2564, weight = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_307_cast_fp16)[name = tensor("op_2568_cast_fp16")]; + tensor hidden_states_173_cast_fp16 = add(x = var_2568_cast_fp16, y = inputs_47_cast_fp16)[name = tensor("hidden_states_173_cast_fp16")]; + tensor var_2570 = const()[name = tensor("op_2570"), val = tensor([2, 1280, 12, 20])]; + tensor input_309_cast_fp16 = reshape(shape = var_2570, x = hidden_states_173_cast_fp16)[name = tensor("input_309_cast_fp16")]; + tensor var_2574 = const()[name = tensor("op_2574"), val = tensor([1, 1])]; + tensor var_2576 = const()[name = tensor("op_2576"), val = tensor([1, 1])]; + tensor hidden_states_175_pad_type_0 = const()[name = tensor("hidden_states_175_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_175_pad_0 = const()[name = tensor("hidden_states_175_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(300285504)))]; + tensor up_blocks_1_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(303562368)))]; + tensor hidden_states_175_cast_fp16 = conv(bias = up_blocks_1_attentions_0_proj_out_bias_to_fp16, dilations = var_2576, groups = var_2306, pad = hidden_states_175_pad_0, pad_type = hidden_states_175_pad_type_0, strides = var_2574, weight = up_blocks_1_attentions_0_proj_out_weight_to_fp16, x = input_309_cast_fp16)[name = tensor("hidden_states_175_cast_fp16")]; + tensor hidden_states_177_cast_fp16 = add(x = hidden_states_175_cast_fp16, y = hidden_states_163_cast_fp16)[name = tensor("hidden_states_177_cast_fp16")]; + tensor input_311_interleave_0 = const()[name = tensor("input_311_interleave_0"), val = tensor(false)]; + tensor cast_5 = cast(dtype = cast_2_dtype_0, x = input_143_cast_fp16)[name = tensor("cast_5")]; + tensor input_311_cast_fp16 = concat(axis = var_2306, interleave = input_311_interleave_0, values = (hidden_states_177_cast_fp16, cast_5))[name = tensor("input_311_cast_fp16")]; + tensor reshape_144_shape_0 = const()[name = tensor("reshape_144_shape_0"), val = tensor([2, 32, 80, 12, 20])]; + tensor reshape_144_cast_fp16 = reshape(shape = reshape_144_shape_0, x = input_311_cast_fp16)[name = tensor("reshape_144_cast_fp16")]; + tensor reduce_mean_108_axes_0 = const()[name = tensor("reduce_mean_108_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_108_keep_dims_0 = const()[name = tensor("reduce_mean_108_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_108_cast_fp16 = reduce_mean(axes = reduce_mean_108_axes_0, keep_dims = reduce_mean_108_keep_dims_0, x = reshape_144_cast_fp16)[name = tensor("reduce_mean_108_cast_fp16")]; + tensor sub_72_cast_fp16 = sub(x = reshape_144_cast_fp16, y = reduce_mean_108_cast_fp16)[name = tensor("sub_72_cast_fp16")]; + tensor square_36_cast_fp16 = square(x = sub_72_cast_fp16)[name = tensor("square_36_cast_fp16")]; + tensor reduce_mean_110_axes_0 = const()[name = tensor("reduce_mean_110_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_110_keep_dims_0 = const()[name = tensor("reduce_mean_110_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_110_cast_fp16 = reduce_mean(axes = reduce_mean_110_axes_0, keep_dims = reduce_mean_110_keep_dims_0, x = square_36_cast_fp16)[name = tensor("reduce_mean_110_cast_fp16")]; + tensor add_72_y_0_to_fp16 = const()[name = tensor("add_72_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_72_cast_fp16 = add(x = reduce_mean_110_cast_fp16, y = add_72_y_0_to_fp16)[name = tensor("add_72_cast_fp16")]; + tensor sqrt_36_cast_fp16 = sqrt(x = add_72_cast_fp16)[name = tensor("sqrt_36_cast_fp16")]; + tensor real_div_36_cast_fp16 = real_div(x = sub_72_cast_fp16, y = sqrt_36_cast_fp16)[name = tensor("real_div_36_cast_fp16")]; + tensor reshape_145_shape_0 = const()[name = tensor("reshape_145_shape_0"), val = tensor([2, 2560, 12, 20])]; + tensor reshape_145_cast_fp16 = reshape(shape = reshape_145_shape_0, x = real_div_36_cast_fp16)[name = tensor("reshape_145_cast_fp16")]; + tensor add_73_gamma_0_to_fp16 = const()[name = tensor("add_73_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(303564992)))]; + tensor add_73_beta_0_to_fp16 = const()[name = tensor("add_73_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(303570176)))]; + tensor add_73_epsilon_0_to_fp16 = const()[name = tensor("add_73_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_73_cast_fp16 = batch_norm(beta = add_73_beta_0_to_fp16, epsilon = add_73_epsilon_0_to_fp16, gamma = add_73_gamma_0_to_fp16, mean = add_55_mean_0_to_fp16, variance = add_55_variance_0_to_fp16, x = reshape_145_cast_fp16)[name = tensor("add_73_cast_fp16")]; + tensor input_315_cast_fp16 = silu(x = add_73_cast_fp16)[name = tensor("input_315_cast_fp16")]; + tensor var_2594 = const()[name = tensor("op_2594"), val = tensor([1, 1])]; + tensor var_2596 = const()[name = tensor("op_2596"), val = tensor([1, 1])]; + tensor hidden_states_179_pad_type_0 = const()[name = tensor("hidden_states_179_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_179_pad_0 = const()[name = tensor("hidden_states_179_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(303575360)))]; + tensor up_blocks_1_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(362557824)))]; + tensor hidden_states_179_cast_fp16 = conv(bias = up_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = var_2596, groups = var_2306, pad = hidden_states_179_pad_0, pad_type = hidden_states_179_pad_type_0, strides = var_2594, weight = up_blocks_1_resnets_1_conv1_weight_to_fp16, x = input_315_cast_fp16)[name = tensor("hidden_states_179_cast_fp16")]; + tensor var_2602 = const()[name = tensor("op_2602"), val = tensor([1, 1])]; + tensor var_2604 = const()[name = tensor("op_2604"), val = tensor([1, 1])]; + tensor temb_29_pad_type_0 = const()[name = tensor("temb_29_pad_type_0"), val = tensor("custom")]; + tensor temb_29_pad_0 = const()[name = tensor("temb_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(362560448)))]; + tensor up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(365837312)))]; + tensor temb_29_cast_fp16 = conv(bias = up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_2604, groups = var_2306, pad = temb_29_pad_0, pad_type = temb_29_pad_type_0, strides = var_2602, weight = up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16, x = cast_12)[name = tensor("temb_29_cast_fp16")]; + tensor input_319_cast_fp16 = add(x = hidden_states_179_cast_fp16, y = temb_29_cast_fp16)[name = tensor("input_319_cast_fp16")]; + tensor reshape_148_shape_0 = const()[name = tensor("reshape_148_shape_0"), val = tensor([2, 32, 40, 12, 20])]; + tensor reshape_148_cast_fp16 = reshape(shape = reshape_148_shape_0, x = input_319_cast_fp16)[name = tensor("reshape_148_cast_fp16")]; + tensor reduce_mean_111_axes_0 = const()[name = tensor("reduce_mean_111_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_111_keep_dims_0 = const()[name = tensor("reduce_mean_111_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_111_cast_fp16 = reduce_mean(axes = reduce_mean_111_axes_0, keep_dims = reduce_mean_111_keep_dims_0, x = reshape_148_cast_fp16)[name = tensor("reduce_mean_111_cast_fp16")]; + tensor sub_74_cast_fp16 = sub(x = reshape_148_cast_fp16, y = reduce_mean_111_cast_fp16)[name = tensor("sub_74_cast_fp16")]; + tensor square_37_cast_fp16 = square(x = sub_74_cast_fp16)[name = tensor("square_37_cast_fp16")]; + tensor reduce_mean_113_axes_0 = const()[name = tensor("reduce_mean_113_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_113_keep_dims_0 = const()[name = tensor("reduce_mean_113_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_113_cast_fp16 = reduce_mean(axes = reduce_mean_113_axes_0, keep_dims = reduce_mean_113_keep_dims_0, x = square_37_cast_fp16)[name = tensor("reduce_mean_113_cast_fp16")]; + tensor add_74_y_0_to_fp16 = const()[name = tensor("add_74_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_74_cast_fp16 = add(x = reduce_mean_113_cast_fp16, y = add_74_y_0_to_fp16)[name = tensor("add_74_cast_fp16")]; + tensor sqrt_37_cast_fp16 = sqrt(x = add_74_cast_fp16)[name = tensor("sqrt_37_cast_fp16")]; + tensor real_div_37_cast_fp16 = real_div(x = sub_74_cast_fp16, y = sqrt_37_cast_fp16)[name = tensor("real_div_37_cast_fp16")]; + tensor reshape_149_shape_0 = const()[name = tensor("reshape_149_shape_0"), val = tensor([2, 1280, 12, 20])]; + tensor reshape_149_cast_fp16 = reshape(shape = reshape_149_shape_0, x = real_div_37_cast_fp16)[name = tensor("reshape_149_cast_fp16")]; + tensor add_75_gamma_0_to_fp16 = const()[name = tensor("add_75_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(365839936)))]; + tensor add_75_beta_0_to_fp16 = const()[name = tensor("add_75_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(365842560)))]; + tensor add_75_epsilon_0_to_fp16 = const()[name = tensor("add_75_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_75_cast_fp16 = batch_norm(beta = add_75_beta_0_to_fp16, epsilon = add_75_epsilon_0_to_fp16, gamma = add_75_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_149_cast_fp16)[name = tensor("add_75_cast_fp16")]; + tensor input_323_cast_fp16 = silu(x = add_75_cast_fp16)[name = tensor("input_323_cast_fp16")]; + tensor var_2614 = const()[name = tensor("op_2614"), val = tensor([1, 1])]; + tensor var_2616 = const()[name = tensor("op_2616"), val = tensor([1, 1])]; + tensor hidden_states_181_pad_type_0 = const()[name = tensor("hidden_states_181_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_181_pad_0 = const()[name = tensor("hidden_states_181_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(365845184)))]; + tensor up_blocks_1_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(395336448)))]; + tensor hidden_states_181_cast_fp16 = conv(bias = up_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = var_2616, groups = var_2306, pad = hidden_states_181_pad_0, pad_type = hidden_states_181_pad_type_0, strides = var_2614, weight = up_blocks_1_resnets_1_conv2_weight_to_fp16, x = input_323_cast_fp16)[name = tensor("hidden_states_181_cast_fp16")]; + tensor var_2621 = const()[name = tensor("op_2621"), val = tensor([1, 1])]; + tensor var_2623 = const()[name = tensor("op_2623"), val = tensor([1, 1])]; + tensor x_13_pad_type_0 = const()[name = tensor("x_13_pad_type_0"), val = tensor("custom")]; + tensor x_13_pad_0 = const()[name = tensor("x_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(395339072)))]; + tensor up_blocks_1_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(401892736)))]; + tensor x_13_cast_fp16 = conv(bias = up_blocks_1_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_2623, groups = var_2306, pad = x_13_pad_0, pad_type = x_13_pad_type_0, strides = var_2621, weight = up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16, x = input_311_cast_fp16)[name = tensor("x_13_cast_fp16")]; + tensor hidden_states_183_cast_fp16 = add(x = x_13_cast_fp16, y = hidden_states_181_cast_fp16)[name = tensor("hidden_states_183_cast_fp16")]; + tensor reshape_152_shape_0 = const()[name = tensor("reshape_152_shape_0"), val = tensor([2, 32, 40, 12, 20])]; + tensor reshape_152_cast_fp16 = reshape(shape = reshape_152_shape_0, x = hidden_states_183_cast_fp16)[name = tensor("reshape_152_cast_fp16")]; + tensor reduce_mean_114_axes_0 = const()[name = tensor("reduce_mean_114_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_114_keep_dims_0 = const()[name = tensor("reduce_mean_114_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_114_cast_fp16 = reduce_mean(axes = reduce_mean_114_axes_0, keep_dims = reduce_mean_114_keep_dims_0, x = reshape_152_cast_fp16)[name = tensor("reduce_mean_114_cast_fp16")]; + tensor sub_76_cast_fp16 = sub(x = reshape_152_cast_fp16, y = reduce_mean_114_cast_fp16)[name = tensor("sub_76_cast_fp16")]; + tensor square_38_cast_fp16 = square(x = sub_76_cast_fp16)[name = tensor("square_38_cast_fp16")]; + tensor reduce_mean_116_axes_0 = const()[name = tensor("reduce_mean_116_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_116_keep_dims_0 = const()[name = tensor("reduce_mean_116_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_116_cast_fp16 = reduce_mean(axes = reduce_mean_116_axes_0, keep_dims = reduce_mean_116_keep_dims_0, x = square_38_cast_fp16)[name = tensor("reduce_mean_116_cast_fp16")]; + tensor add_76_y_0_to_fp16 = const()[name = tensor("add_76_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_76_cast_fp16 = add(x = reduce_mean_116_cast_fp16, y = add_76_y_0_to_fp16)[name = tensor("add_76_cast_fp16")]; + tensor sqrt_38_cast_fp16 = sqrt(x = add_76_cast_fp16)[name = tensor("sqrt_38_cast_fp16")]; + tensor real_div_38_cast_fp16 = real_div(x = sub_76_cast_fp16, y = sqrt_38_cast_fp16)[name = tensor("real_div_38_cast_fp16")]; + tensor reshape_153_shape_0 = const()[name = tensor("reshape_153_shape_0"), val = tensor([2, 1280, 12, 20])]; + tensor reshape_153_cast_fp16 = reshape(shape = reshape_153_shape_0, x = real_div_38_cast_fp16)[name = tensor("reshape_153_cast_fp16")]; + tensor add_77_gamma_0_to_fp16 = const()[name = tensor("add_77_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(401895360)))]; + tensor add_77_beta_0_to_fp16 = const()[name = tensor("add_77_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(401897984)))]; + tensor add_77_epsilon_0_to_fp16 = const()[name = tensor("add_77_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_77_cast_fp16 = batch_norm(beta = add_77_beta_0_to_fp16, epsilon = add_77_epsilon_0_to_fp16, gamma = add_77_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_153_cast_fp16)[name = tensor("add_77_cast_fp16")]; + tensor var_2643 = const()[name = tensor("op_2643"), val = tensor([1, 1])]; + tensor var_2645 = const()[name = tensor("op_2645"), val = tensor([1, 1])]; + tensor hidden_states_185_pad_type_0 = const()[name = tensor("hidden_states_185_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_185_pad_0 = const()[name = tensor("hidden_states_185_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(401900608)))]; + tensor up_blocks_1_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(405177472)))]; + tensor hidden_states_185_cast_fp16 = conv(bias = up_blocks_1_attentions_1_proj_in_bias_to_fp16, dilations = var_2645, groups = var_2306, pad = hidden_states_185_pad_0, pad_type = hidden_states_185_pad_type_0, strides = var_2643, weight = up_blocks_1_attentions_1_proj_in_weight_to_fp16, x = add_77_cast_fp16)[name = tensor("hidden_states_185_cast_fp16")]; + tensor var_2650 = const()[name = tensor("op_2650"), val = tensor([2, 1280, 1, 240])]; + tensor inputs_49_cast_fp16 = reshape(shape = var_2650, x = hidden_states_185_cast_fp16)[name = tensor("inputs_49_cast_fp16")]; + tensor var_2660 = const()[name = tensor("op_2660"), val = tensor([1])]; + tensor channels_mean_49_cast_fp16 = reduce_mean(axes = var_2660, keep_dims = var_2301, x = inputs_49_cast_fp16)[name = tensor("channels_mean_49_cast_fp16")]; + tensor zero_mean_49_cast_fp16 = sub(x = inputs_49_cast_fp16, y = channels_mean_49_cast_fp16)[name = tensor("zero_mean_49_cast_fp16")]; + tensor zero_mean_sq_49_cast_fp16 = mul(x = zero_mean_49_cast_fp16, y = zero_mean_49_cast_fp16)[name = tensor("zero_mean_sq_49_cast_fp16")]; + tensor var_2664 = const()[name = tensor("op_2664"), val = tensor([1])]; + tensor var_2665_cast_fp16 = reduce_mean(axes = var_2664, keep_dims = var_2301, x = zero_mean_sq_49_cast_fp16)[name = tensor("op_2665_cast_fp16")]; + tensor var_2666_to_fp16 = const()[name = tensor("op_2666_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2667_cast_fp16 = add(x = var_2665_cast_fp16, y = var_2666_to_fp16)[name = tensor("op_2667_cast_fp16")]; + tensor denom_49_epsilon_0_to_fp16 = const()[name = tensor("denom_49_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_49_cast_fp16 = rsqrt(epsilon = denom_49_epsilon_0_to_fp16, x = var_2667_cast_fp16)[name = tensor("denom_49_cast_fp16")]; + tensor out_49_cast_fp16 = mul(x = zero_mean_49_cast_fp16, y = denom_49_cast_fp16)[name = tensor("out_49_cast_fp16")]; + tensor var_2671_to_fp16 = const()[name = tensor("op_2671_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(405180096)))]; + tensor var_2672_cast_fp16 = add(x = out_49_cast_fp16, y = var_2671_to_fp16)[name = tensor("op_2672_cast_fp16")]; + tensor var_2674_to_fp16 = const()[name = tensor("op_2674_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(405182720)))]; + tensor hidden_states_187_cast_fp16 = mul(x = var_2672_cast_fp16, y = var_2674_to_fp16)[name = tensor("hidden_states_187_cast_fp16")]; + tensor var_2681 = const()[name = tensor("op_2681"), val = tensor([1, 1])]; + tensor var_2683 = const()[name = tensor("op_2683"), val = tensor([1, 1])]; + tensor q_33_pad_type_0 = const()[name = tensor("q_33_pad_type_0"), val = tensor("custom")]; + tensor q_33_pad_0 = const()[name = tensor("q_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(405185344)))]; + tensor q_33_cast_fp16 = conv(dilations = var_2683, groups = var_2306, pad = q_33_pad_0, pad_type = q_33_pad_type_0, strides = var_2681, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_187_cast_fp16)[name = tensor("q_33_cast_fp16")]; + tensor var_2687 = const()[name = tensor("op_2687"), val = tensor([1, 1])]; + tensor var_2689 = const()[name = tensor("op_2689"), val = tensor([1, 1])]; + tensor k_33_pad_type_0 = const()[name = tensor("k_33_pad_type_0"), val = tensor("custom")]; + tensor k_33_pad_0 = const()[name = tensor("k_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(408462208)))]; + tensor k_33_cast_fp16 = conv(dilations = var_2689, groups = var_2306, pad = k_33_pad_0, pad_type = k_33_pad_type_0, strides = var_2687, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_187_cast_fp16)[name = tensor("k_33_cast_fp16")]; + tensor var_2693 = const()[name = tensor("op_2693"), val = tensor([1, 1])]; + tensor var_2695 = const()[name = tensor("op_2695"), val = tensor([1, 1])]; + tensor v_33_pad_type_0 = const()[name = tensor("v_33_pad_type_0"), val = tensor("custom")]; + tensor v_33_pad_0 = const()[name = tensor("v_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(411739072)))]; + tensor v_33_cast_fp16 = conv(dilations = var_2695, groups = var_2306, pad = v_33_pad_0, pad_type = v_33_pad_type_0, strides = var_2693, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_187_cast_fp16)[name = tensor("v_33_cast_fp16")]; + tensor var_2699 = const()[name = tensor("op_2699"), val = tensor([2, 20, 64, -1])]; + tensor var_2700_cast_fp16 = reshape(shape = var_2699, x = q_33_cast_fp16)[name = tensor("op_2700_cast_fp16")]; + tensor var_2701 = const()[name = tensor("op_2701"), val = tensor([2, 20, 64, -1])]; + tensor var_2702_cast_fp16 = reshape(shape = var_2701, x = k_33_cast_fp16)[name = tensor("op_2702_cast_fp16")]; + tensor var_2703 = const()[name = tensor("op_2703"), val = tensor([2, 20, 64, -1])]; + tensor var_2704_cast_fp16 = reshape(shape = var_2703, x = v_33_cast_fp16)[name = tensor("op_2704_cast_fp16")]; + tensor attn_weights_65_transpose_x_0 = const()[name = tensor("attn_weights_65_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_65_transpose_y_0 = const()[name = tensor("attn_weights_65_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_65_cast_fp16 = matmul(transpose_x = attn_weights_65_transpose_x_0, transpose_y = attn_weights_65_transpose_y_0, x = var_2700_cast_fp16, y = var_2702_cast_fp16)[name = tensor("attn_weights_65_cast_fp16")]; + tensor attn_weights_67_cast_fp16 = mul(x = attn_weights_65_cast_fp16, y = var_2297_to_fp16)[name = tensor("attn_weights_67_cast_fp16")]; + tensor var_2708_cast_fp16 = softmax(axis = var_2290, x = attn_weights_67_cast_fp16)[name = tensor("op_2708_cast_fp16")]; + tensor attn_33_transpose_x_0 = const()[name = tensor("attn_33_transpose_x_0"), val = tensor(false)]; + tensor attn_33_transpose_y_0 = const()[name = tensor("attn_33_transpose_y_0"), val = tensor(true)]; + tensor attn_33_cast_fp16 = matmul(transpose_x = attn_33_transpose_x_0, transpose_y = attn_33_transpose_y_0, x = var_2704_cast_fp16, y = var_2708_cast_fp16)[name = tensor("attn_33_cast_fp16")]; + tensor var_2712 = const()[name = tensor("op_2712"), val = tensor([2, 1280, 1, -1])]; + tensor input_327_cast_fp16 = reshape(shape = var_2712, x = attn_33_cast_fp16)[name = tensor("input_327_cast_fp16")]; + tensor var_2717 = const()[name = tensor("op_2717"), val = tensor([1, 1])]; + tensor var_2719 = const()[name = tensor("op_2719"), val = tensor([1, 1])]; + tensor var_2721_pad_type_0 = const()[name = tensor("op_2721_pad_type_0"), val = tensor("custom")]; + tensor var_2721_pad_0 = const()[name = tensor("op_2721_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(415015936)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(418292800)))]; + tensor var_2721_cast_fp16 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_2719, groups = var_2306, pad = var_2721_pad_0, pad_type = var_2721_pad_type_0, strides = var_2717, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_327_cast_fp16)[name = tensor("op_2721_cast_fp16")]; + tensor inputs_51_cast_fp16 = add(x = var_2721_cast_fp16, y = inputs_49_cast_fp16)[name = tensor("inputs_51_cast_fp16")]; + tensor var_2725 = const()[name = tensor("op_2725"), val = tensor([1])]; + tensor channels_mean_51_cast_fp16 = reduce_mean(axes = var_2725, keep_dims = var_2301, x = inputs_51_cast_fp16)[name = tensor("channels_mean_51_cast_fp16")]; + tensor zero_mean_51_cast_fp16 = sub(x = inputs_51_cast_fp16, y = channels_mean_51_cast_fp16)[name = tensor("zero_mean_51_cast_fp16")]; + tensor zero_mean_sq_51_cast_fp16 = mul(x = zero_mean_51_cast_fp16, y = zero_mean_51_cast_fp16)[name = tensor("zero_mean_sq_51_cast_fp16")]; + tensor var_2729 = const()[name = tensor("op_2729"), val = tensor([1])]; + tensor var_2730_cast_fp16 = reduce_mean(axes = var_2729, keep_dims = var_2301, x = zero_mean_sq_51_cast_fp16)[name = tensor("op_2730_cast_fp16")]; + tensor var_2731_to_fp16 = const()[name = tensor("op_2731_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2732_cast_fp16 = add(x = var_2730_cast_fp16, y = var_2731_to_fp16)[name = tensor("op_2732_cast_fp16")]; + tensor denom_51_epsilon_0_to_fp16 = const()[name = tensor("denom_51_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_51_cast_fp16 = rsqrt(epsilon = denom_51_epsilon_0_to_fp16, x = var_2732_cast_fp16)[name = tensor("denom_51_cast_fp16")]; + tensor out_51_cast_fp16 = mul(x = zero_mean_51_cast_fp16, y = denom_51_cast_fp16)[name = tensor("out_51_cast_fp16")]; + tensor var_2736_to_fp16 = const()[name = tensor("op_2736_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(418295424)))]; + tensor var_2737_cast_fp16 = add(x = out_51_cast_fp16, y = var_2736_to_fp16)[name = tensor("op_2737_cast_fp16")]; + tensor var_2739_to_fp16 = const()[name = tensor("op_2739_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(418298048)))]; + tensor hidden_states_189_cast_fp16 = mul(x = var_2737_cast_fp16, y = var_2739_to_fp16)[name = tensor("hidden_states_189_cast_fp16")]; + tensor var_2746 = const()[name = tensor("op_2746"), val = tensor([1, 1])]; + tensor var_2748 = const()[name = tensor("op_2748"), val = tensor([1, 1])]; + tensor q_35_pad_type_0 = const()[name = tensor("q_35_pad_type_0"), val = tensor("custom")]; + tensor q_35_pad_0 = const()[name = tensor("q_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(418300672)))]; + tensor q_35_cast_fp16 = conv(dilations = var_2748, groups = var_2306, pad = q_35_pad_0, pad_type = q_35_pad_type_0, strides = var_2746, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_189_cast_fp16)[name = tensor("q_35_cast_fp16")]; + tensor var_2752 = const()[name = tensor("op_2752"), val = tensor([1, 1])]; + tensor var_2754 = const()[name = tensor("op_2754"), val = tensor([1, 1])]; + tensor k_35_pad_type_0 = const()[name = tensor("k_35_pad_type_0"), val = tensor("custom")]; + tensor k_35_pad_0 = const()[name = tensor("k_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(421577536)))]; + tensor k_35_cast_fp16 = conv(dilations = var_2754, groups = var_2306, pad = k_35_pad_0, pad_type = k_35_pad_type_0, strides = var_2752, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_35_cast_fp16")]; + tensor var_2758 = const()[name = tensor("op_2758"), val = tensor([1, 1])]; + tensor var_2760 = const()[name = tensor("op_2760"), val = tensor([1, 1])]; + tensor v_35_pad_type_0 = const()[name = tensor("v_35_pad_type_0"), val = tensor("custom")]; + tensor v_35_pad_0 = const()[name = tensor("v_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(424199040)))]; + tensor v_35_cast_fp16 = conv(dilations = var_2760, groups = var_2306, pad = v_35_pad_0, pad_type = v_35_pad_type_0, strides = var_2758, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_35_cast_fp16")]; + tensor var_2764 = const()[name = tensor("op_2764"), val = tensor([2, 20, 64, -1])]; + tensor var_2765_cast_fp16 = reshape(shape = var_2764, x = q_35_cast_fp16)[name = tensor("op_2765_cast_fp16")]; + tensor var_2766 = const()[name = tensor("op_2766"), val = tensor([2, 20, 64, -1])]; + tensor var_2767_cast_fp16 = reshape(shape = var_2766, x = k_35_cast_fp16)[name = tensor("op_2767_cast_fp16")]; + tensor var_2768 = const()[name = tensor("op_2768"), val = tensor([2, 20, 64, -1])]; + tensor var_2769_cast_fp16 = reshape(shape = var_2768, x = v_35_cast_fp16)[name = tensor("op_2769_cast_fp16")]; + tensor attn_weights_69_transpose_x_0 = const()[name = tensor("attn_weights_69_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_69_transpose_y_0 = const()[name = tensor("attn_weights_69_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_69_cast_fp16 = matmul(transpose_x = attn_weights_69_transpose_x_0, transpose_y = attn_weights_69_transpose_y_0, x = var_2765_cast_fp16, y = var_2767_cast_fp16)[name = tensor("attn_weights_69_cast_fp16")]; + tensor attn_weights_71_cast_fp16 = mul(x = attn_weights_69_cast_fp16, y = var_2297_to_fp16)[name = tensor("attn_weights_71_cast_fp16")]; + tensor var_2773_cast_fp16 = softmax(axis = var_2290, x = attn_weights_71_cast_fp16)[name = tensor("op_2773_cast_fp16")]; + tensor attn_35_transpose_x_0 = const()[name = tensor("attn_35_transpose_x_0"), val = tensor(false)]; + tensor attn_35_transpose_y_0 = const()[name = tensor("attn_35_transpose_y_0"), val = tensor(true)]; + tensor attn_35_cast_fp16 = matmul(transpose_x = attn_35_transpose_x_0, transpose_y = attn_35_transpose_y_0, x = var_2769_cast_fp16, y = var_2773_cast_fp16)[name = tensor("attn_35_cast_fp16")]; + tensor var_2777 = const()[name = tensor("op_2777"), val = tensor([2, 1280, 1, -1])]; + tensor input_329_cast_fp16 = reshape(shape = var_2777, x = attn_35_cast_fp16)[name = tensor("input_329_cast_fp16")]; + tensor var_2782 = const()[name = tensor("op_2782"), val = tensor([1, 1])]; + tensor var_2784 = const()[name = tensor("op_2784"), val = tensor([1, 1])]; + tensor var_2786_pad_type_0 = const()[name = tensor("op_2786_pad_type_0"), val = tensor("custom")]; + tensor var_2786_pad_0 = const()[name = tensor("op_2786_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(426820544)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(430097408)))]; + tensor var_2786_cast_fp16 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_2784, groups = var_2306, pad = var_2786_pad_0, pad_type = var_2786_pad_type_0, strides = var_2782, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_329_cast_fp16)[name = tensor("op_2786_cast_fp16")]; + tensor inputs_53_cast_fp16 = add(x = var_2786_cast_fp16, y = inputs_51_cast_fp16)[name = tensor("inputs_53_cast_fp16")]; + tensor var_2790 = const()[name = tensor("op_2790"), val = tensor([1])]; + tensor channels_mean_53_cast_fp16 = reduce_mean(axes = var_2790, keep_dims = var_2301, x = inputs_53_cast_fp16)[name = tensor("channels_mean_53_cast_fp16")]; + tensor zero_mean_53_cast_fp16 = sub(x = inputs_53_cast_fp16, y = channels_mean_53_cast_fp16)[name = tensor("zero_mean_53_cast_fp16")]; + tensor zero_mean_sq_53_cast_fp16 = mul(x = zero_mean_53_cast_fp16, y = zero_mean_53_cast_fp16)[name = tensor("zero_mean_sq_53_cast_fp16")]; + tensor var_2794 = const()[name = tensor("op_2794"), val = tensor([1])]; + tensor var_2795_cast_fp16 = reduce_mean(axes = var_2794, keep_dims = var_2301, x = zero_mean_sq_53_cast_fp16)[name = tensor("op_2795_cast_fp16")]; + tensor var_2796_to_fp16 = const()[name = tensor("op_2796_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2797_cast_fp16 = add(x = var_2795_cast_fp16, y = var_2796_to_fp16)[name = tensor("op_2797_cast_fp16")]; + tensor denom_53_epsilon_0_to_fp16 = const()[name = tensor("denom_53_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_53_cast_fp16 = rsqrt(epsilon = denom_53_epsilon_0_to_fp16, x = var_2797_cast_fp16)[name = tensor("denom_53_cast_fp16")]; + tensor out_53_cast_fp16 = mul(x = zero_mean_53_cast_fp16, y = denom_53_cast_fp16)[name = tensor("out_53_cast_fp16")]; + tensor var_2801_to_fp16 = const()[name = tensor("op_2801_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(430100032)))]; + tensor var_2802_cast_fp16 = add(x = out_53_cast_fp16, y = var_2801_to_fp16)[name = tensor("op_2802_cast_fp16")]; + tensor var_2804_to_fp16 = const()[name = tensor("op_2804_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(430102656)))]; + tensor input_331_cast_fp16 = mul(x = var_2802_cast_fp16, y = var_2804_to_fp16)[name = tensor("input_331_cast_fp16")]; + tensor var_2812 = const()[name = tensor("op_2812"), val = tensor([1, 1])]; + tensor var_2814 = const()[name = tensor("op_2814"), val = tensor([1, 1])]; + tensor var_2816_pad_type_0 = const()[name = tensor("op_2816_pad_type_0"), val = tensor("custom")]; + tensor var_2816_pad_0 = const()[name = tensor("op_2816_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(430105280)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(456319744)))]; + tensor var_2816_cast_fp16 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_2814, groups = var_2306, pad = var_2816_pad_0, pad_type = var_2816_pad_type_0, strides = var_2812, weight = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_331_cast_fp16)[name = tensor("op_2816_cast_fp16")]; + tensor var_2817_split_sizes_0 = const()[name = tensor("op_2817_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2817_axis_0 = const()[name = tensor("op_2817_axis_0"), val = tensor(1)]; + tensor var_2817_cast_fp16_0, tensor var_2817_cast_fp16_1 = split(axis = var_2817_axis_0, split_sizes = var_2817_split_sizes_0, x = var_2816_cast_fp16)[name = tensor("op_2817_cast_fp16")]; + tensor var_2819_mode_0 = const()[name = tensor("op_2819_mode_0"), val = tensor("EXACT")]; + tensor var_2819_cast_fp16 = gelu(mode = var_2819_mode_0, x = var_2817_cast_fp16_1)[name = tensor("op_2819_cast_fp16")]; + tensor input_333_cast_fp16 = mul(x = var_2817_cast_fp16_0, y = var_2819_cast_fp16)[name = tensor("input_333_cast_fp16")]; + tensor var_2823 = const()[name = tensor("op_2823"), val = tensor([1, 1])]; + tensor var_2825 = const()[name = tensor("op_2825"), val = tensor([1, 1])]; + tensor var_2827_pad_type_0 = const()[name = tensor("op_2827_pad_type_0"), val = tensor("custom")]; + tensor var_2827_pad_0 = const()[name = tensor("op_2827_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(456340288)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(469447552)))]; + tensor var_2827_cast_fp16 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_2825, groups = var_2306, pad = var_2827_pad_0, pad_type = var_2827_pad_type_0, strides = var_2823, weight = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_333_cast_fp16)[name = tensor("op_2827_cast_fp16")]; + tensor hidden_states_193_cast_fp16 = add(x = var_2827_cast_fp16, y = inputs_53_cast_fp16)[name = tensor("hidden_states_193_cast_fp16")]; + tensor var_2829 = const()[name = tensor("op_2829"), val = tensor([2, 1280, 12, 20])]; + tensor input_335_cast_fp16 = reshape(shape = var_2829, x = hidden_states_193_cast_fp16)[name = tensor("input_335_cast_fp16")]; + tensor var_2833 = const()[name = tensor("op_2833"), val = tensor([1, 1])]; + tensor var_2835 = const()[name = tensor("op_2835"), val = tensor([1, 1])]; + tensor hidden_states_195_pad_type_0 = const()[name = tensor("hidden_states_195_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_195_pad_0 = const()[name = tensor("hidden_states_195_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(469450176)))]; + tensor up_blocks_1_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(472727040)))]; + tensor hidden_states_195_cast_fp16 = conv(bias = up_blocks_1_attentions_1_proj_out_bias_to_fp16, dilations = var_2835, groups = var_2306, pad = hidden_states_195_pad_0, pad_type = hidden_states_195_pad_type_0, strides = var_2833, weight = up_blocks_1_attentions_1_proj_out_weight_to_fp16, x = input_335_cast_fp16)[name = tensor("hidden_states_195_cast_fp16")]; + tensor hidden_states_197_cast_fp16 = add(x = hidden_states_195_cast_fp16, y = hidden_states_183_cast_fp16)[name = tensor("hidden_states_197_cast_fp16")]; + tensor input_337_interleave_0 = const()[name = tensor("input_337_interleave_0"), val = tensor(false)]; + tensor cast_6 = cast(dtype = cast_11_dtype_0, x = input_117_cast_fp16)[name = tensor("cast_6")]; + tensor input_337_cast_fp16 = concat(axis = var_2306, interleave = input_337_interleave_0, values = (hidden_states_197_cast_fp16, cast_6))[name = tensor("input_337_cast_fp16")]; + tensor reshape_156_shape_0 = const()[name = tensor("reshape_156_shape_0"), val = tensor([2, 32, 60, 12, 20])]; + tensor reshape_156_cast_fp16 = reshape(shape = reshape_156_shape_0, x = input_337_cast_fp16)[name = tensor("reshape_156_cast_fp16")]; + tensor reduce_mean_117_axes_0 = const()[name = tensor("reduce_mean_117_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_117_keep_dims_0 = const()[name = tensor("reduce_mean_117_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_117_cast_fp16 = reduce_mean(axes = reduce_mean_117_axes_0, keep_dims = reduce_mean_117_keep_dims_0, x = reshape_156_cast_fp16)[name = tensor("reduce_mean_117_cast_fp16")]; + tensor sub_78_cast_fp16 = sub(x = reshape_156_cast_fp16, y = reduce_mean_117_cast_fp16)[name = tensor("sub_78_cast_fp16")]; + tensor square_39_cast_fp16 = square(x = sub_78_cast_fp16)[name = tensor("square_39_cast_fp16")]; + tensor reduce_mean_119_axes_0 = const()[name = tensor("reduce_mean_119_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_119_keep_dims_0 = const()[name = tensor("reduce_mean_119_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_119_cast_fp16 = reduce_mean(axes = reduce_mean_119_axes_0, keep_dims = reduce_mean_119_keep_dims_0, x = square_39_cast_fp16)[name = tensor("reduce_mean_119_cast_fp16")]; + tensor add_78_y_0_to_fp16 = const()[name = tensor("add_78_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_78_cast_fp16 = add(x = reduce_mean_119_cast_fp16, y = add_78_y_0_to_fp16)[name = tensor("add_78_cast_fp16")]; + tensor sqrt_39_cast_fp16 = sqrt(x = add_78_cast_fp16)[name = tensor("sqrt_39_cast_fp16")]; + tensor real_div_39_cast_fp16 = real_div(x = sub_78_cast_fp16, y = sqrt_39_cast_fp16)[name = tensor("real_div_39_cast_fp16")]; + tensor reshape_157_shape_0 = const()[name = tensor("reshape_157_shape_0"), val = tensor([2, 1920, 12, 20])]; + tensor reshape_157_cast_fp16 = reshape(shape = reshape_157_shape_0, x = real_div_39_cast_fp16)[name = tensor("reshape_157_cast_fp16")]; + tensor add_79_mean_0_to_fp16 = const()[name = tensor("add_79_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(472729664)))]; + tensor add_79_variance_0_to_fp16 = const()[name = tensor("add_79_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(472733568)))]; + tensor add_79_gamma_0_to_fp16 = const()[name = tensor("add_79_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(472737472)))]; + tensor add_79_beta_0_to_fp16 = const()[name = tensor("add_79_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(472741376)))]; + tensor add_79_epsilon_0_to_fp16 = const()[name = tensor("add_79_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_79_cast_fp16 = batch_norm(beta = add_79_beta_0_to_fp16, epsilon = add_79_epsilon_0_to_fp16, gamma = add_79_gamma_0_to_fp16, mean = add_79_mean_0_to_fp16, variance = add_79_variance_0_to_fp16, x = reshape_157_cast_fp16)[name = tensor("add_79_cast_fp16")]; + tensor input_341_cast_fp16 = silu(x = add_79_cast_fp16)[name = tensor("input_341_cast_fp16")]; + tensor var_2853 = const()[name = tensor("op_2853"), val = tensor([1, 1])]; + tensor var_2855 = const()[name = tensor("op_2855"), val = tensor([1, 1])]; + tensor hidden_states_199_pad_type_0 = const()[name = tensor("hidden_states_199_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_199_pad_0 = const()[name = tensor("hidden_states_199_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_2_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(472745280)))]; + tensor up_blocks_1_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(516982144)))]; + tensor hidden_states_199_cast_fp16 = conv(bias = up_blocks_1_resnets_2_conv1_bias_to_fp16, dilations = var_2855, groups = var_2306, pad = hidden_states_199_pad_0, pad_type = hidden_states_199_pad_type_0, strides = var_2853, weight = up_blocks_1_resnets_2_conv1_weight_to_fp16, x = input_341_cast_fp16)[name = tensor("hidden_states_199_cast_fp16")]; + tensor var_2861 = const()[name = tensor("op_2861"), val = tensor([1, 1])]; + tensor var_2863 = const()[name = tensor("op_2863"), val = tensor([1, 1])]; + tensor temb_31_pad_type_0 = const()[name = tensor("temb_31_pad_type_0"), val = tensor("custom")]; + tensor temb_31_pad_0 = const()[name = tensor("temb_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(516984768)))]; + tensor up_blocks_1_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(520261632)))]; + tensor temb_31_cast_fp16 = conv(bias = up_blocks_1_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_2863, groups = var_2306, pad = temb_31_pad_0, pad_type = temb_31_pad_type_0, strides = var_2861, weight = up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16, x = cast_12)[name = tensor("temb_31_cast_fp16")]; + tensor input_345_cast_fp16 = add(x = hidden_states_199_cast_fp16, y = temb_31_cast_fp16)[name = tensor("input_345_cast_fp16")]; + tensor reshape_160_shape_0 = const()[name = tensor("reshape_160_shape_0"), val = tensor([2, 32, 40, 12, 20])]; + tensor reshape_160_cast_fp16 = reshape(shape = reshape_160_shape_0, x = input_345_cast_fp16)[name = tensor("reshape_160_cast_fp16")]; + tensor reduce_mean_120_axes_0 = const()[name = tensor("reduce_mean_120_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_120_keep_dims_0 = const()[name = tensor("reduce_mean_120_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_120_cast_fp16 = reduce_mean(axes = reduce_mean_120_axes_0, keep_dims = reduce_mean_120_keep_dims_0, x = reshape_160_cast_fp16)[name = tensor("reduce_mean_120_cast_fp16")]; + tensor sub_80_cast_fp16 = sub(x = reshape_160_cast_fp16, y = reduce_mean_120_cast_fp16)[name = tensor("sub_80_cast_fp16")]; + tensor square_40_cast_fp16 = square(x = sub_80_cast_fp16)[name = tensor("square_40_cast_fp16")]; + tensor reduce_mean_122_axes_0 = const()[name = tensor("reduce_mean_122_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_122_keep_dims_0 = const()[name = tensor("reduce_mean_122_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_122_cast_fp16 = reduce_mean(axes = reduce_mean_122_axes_0, keep_dims = reduce_mean_122_keep_dims_0, x = square_40_cast_fp16)[name = tensor("reduce_mean_122_cast_fp16")]; + tensor add_80_y_0_to_fp16 = const()[name = tensor("add_80_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_80_cast_fp16 = add(x = reduce_mean_122_cast_fp16, y = add_80_y_0_to_fp16)[name = tensor("add_80_cast_fp16")]; + tensor sqrt_40_cast_fp16 = sqrt(x = add_80_cast_fp16)[name = tensor("sqrt_40_cast_fp16")]; + tensor real_div_40_cast_fp16 = real_div(x = sub_80_cast_fp16, y = sqrt_40_cast_fp16)[name = tensor("real_div_40_cast_fp16")]; + tensor reshape_161_shape_0 = const()[name = tensor("reshape_161_shape_0"), val = tensor([2, 1280, 12, 20])]; + tensor reshape_161_cast_fp16 = reshape(shape = reshape_161_shape_0, x = real_div_40_cast_fp16)[name = tensor("reshape_161_cast_fp16")]; + tensor add_81_gamma_0_to_fp16 = const()[name = tensor("add_81_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(520264256)))]; + tensor add_81_beta_0_to_fp16 = const()[name = tensor("add_81_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(520266880)))]; + tensor add_81_epsilon_0_to_fp16 = const()[name = tensor("add_81_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_81_cast_fp16 = batch_norm(beta = add_81_beta_0_to_fp16, epsilon = add_81_epsilon_0_to_fp16, gamma = add_81_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_161_cast_fp16)[name = tensor("add_81_cast_fp16")]; + tensor input_349_cast_fp16 = silu(x = add_81_cast_fp16)[name = tensor("input_349_cast_fp16")]; + tensor var_2873 = const()[name = tensor("op_2873"), val = tensor([1, 1])]; + tensor var_2875 = const()[name = tensor("op_2875"), val = tensor([1, 1])]; + tensor hidden_states_201_pad_type_0 = const()[name = tensor("hidden_states_201_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_201_pad_0 = const()[name = tensor("hidden_states_201_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_2_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(520269504)))]; + tensor up_blocks_1_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(549760768)))]; + tensor hidden_states_201_cast_fp16 = conv(bias = up_blocks_1_resnets_2_conv2_bias_to_fp16, dilations = var_2875, groups = var_2306, pad = hidden_states_201_pad_0, pad_type = hidden_states_201_pad_type_0, strides = var_2873, weight = up_blocks_1_resnets_2_conv2_weight_to_fp16, x = input_349_cast_fp16)[name = tensor("hidden_states_201_cast_fp16")]; + tensor var_2880 = const()[name = tensor("op_2880"), val = tensor([1, 1])]; + tensor var_2882 = const()[name = tensor("op_2882"), val = tensor([1, 1])]; + tensor x_15_pad_type_0 = const()[name = tensor("x_15_pad_type_0"), val = tensor("custom")]; + tensor x_15_pad_0 = const()[name = tensor("x_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(549763392)))]; + tensor up_blocks_1_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(554678656)))]; + tensor x_15_cast_fp16 = conv(bias = up_blocks_1_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_2882, groups = var_2306, pad = x_15_pad_0, pad_type = x_15_pad_type_0, strides = var_2880, weight = up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16, x = input_337_cast_fp16)[name = tensor("x_15_cast_fp16")]; + tensor hidden_states_203_cast_fp16 = add(x = x_15_cast_fp16, y = hidden_states_201_cast_fp16)[name = tensor("hidden_states_203_cast_fp16")]; + tensor reshape_164_shape_0 = const()[name = tensor("reshape_164_shape_0"), val = tensor([2, 32, 40, 12, 20])]; + tensor reshape_164_cast_fp16 = reshape(shape = reshape_164_shape_0, x = hidden_states_203_cast_fp16)[name = tensor("reshape_164_cast_fp16")]; + tensor reduce_mean_123_axes_0 = const()[name = tensor("reduce_mean_123_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_123_keep_dims_0 = const()[name = tensor("reduce_mean_123_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_123_cast_fp16 = reduce_mean(axes = reduce_mean_123_axes_0, keep_dims = reduce_mean_123_keep_dims_0, x = reshape_164_cast_fp16)[name = tensor("reduce_mean_123_cast_fp16")]; + tensor sub_82_cast_fp16 = sub(x = reshape_164_cast_fp16, y = reduce_mean_123_cast_fp16)[name = tensor("sub_82_cast_fp16")]; + tensor square_41_cast_fp16 = square(x = sub_82_cast_fp16)[name = tensor("square_41_cast_fp16")]; + tensor reduce_mean_125_axes_0 = const()[name = tensor("reduce_mean_125_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_125_keep_dims_0 = const()[name = tensor("reduce_mean_125_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_125_cast_fp16 = reduce_mean(axes = reduce_mean_125_axes_0, keep_dims = reduce_mean_125_keep_dims_0, x = square_41_cast_fp16)[name = tensor("reduce_mean_125_cast_fp16")]; + tensor add_82_y_0_to_fp16 = const()[name = tensor("add_82_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_82_cast_fp16 = add(x = reduce_mean_125_cast_fp16, y = add_82_y_0_to_fp16)[name = tensor("add_82_cast_fp16")]; + tensor sqrt_41_cast_fp16 = sqrt(x = add_82_cast_fp16)[name = tensor("sqrt_41_cast_fp16")]; + tensor real_div_41_cast_fp16 = real_div(x = sub_82_cast_fp16, y = sqrt_41_cast_fp16)[name = tensor("real_div_41_cast_fp16")]; + tensor reshape_165_shape_0 = const()[name = tensor("reshape_165_shape_0"), val = tensor([2, 1280, 12, 20])]; + tensor reshape_165_cast_fp16 = reshape(shape = reshape_165_shape_0, x = real_div_41_cast_fp16)[name = tensor("reshape_165_cast_fp16")]; + tensor add_83_gamma_0_to_fp16 = const()[name = tensor("add_83_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(554681280)))]; + tensor add_83_beta_0_to_fp16 = const()[name = tensor("add_83_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(554683904)))]; + tensor add_83_epsilon_0_to_fp16 = const()[name = tensor("add_83_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_83_cast_fp16 = batch_norm(beta = add_83_beta_0_to_fp16, epsilon = add_83_epsilon_0_to_fp16, gamma = add_83_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_165_cast_fp16)[name = tensor("add_83_cast_fp16")]; + tensor var_2902 = const()[name = tensor("op_2902"), val = tensor([1, 1])]; + tensor var_2904 = const()[name = tensor("op_2904"), val = tensor([1, 1])]; + tensor hidden_states_205_pad_type_0 = const()[name = tensor("hidden_states_205_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_205_pad_0 = const()[name = tensor("hidden_states_205_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(554686528)))]; + tensor up_blocks_1_attentions_2_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(557963392)))]; + tensor hidden_states_205_cast_fp16 = conv(bias = up_blocks_1_attentions_2_proj_in_bias_to_fp16, dilations = var_2904, groups = var_2306, pad = hidden_states_205_pad_0, pad_type = hidden_states_205_pad_type_0, strides = var_2902, weight = up_blocks_1_attentions_2_proj_in_weight_to_fp16, x = add_83_cast_fp16)[name = tensor("hidden_states_205_cast_fp16")]; + tensor var_2909 = const()[name = tensor("op_2909"), val = tensor([2, 1280, 1, 240])]; + tensor inputs_55_cast_fp16 = reshape(shape = var_2909, x = hidden_states_205_cast_fp16)[name = tensor("inputs_55_cast_fp16")]; + tensor var_2919 = const()[name = tensor("op_2919"), val = tensor([1])]; + tensor channels_mean_55_cast_fp16 = reduce_mean(axes = var_2919, keep_dims = var_2301, x = inputs_55_cast_fp16)[name = tensor("channels_mean_55_cast_fp16")]; + tensor zero_mean_55_cast_fp16 = sub(x = inputs_55_cast_fp16, y = channels_mean_55_cast_fp16)[name = tensor("zero_mean_55_cast_fp16")]; + tensor zero_mean_sq_55_cast_fp16 = mul(x = zero_mean_55_cast_fp16, y = zero_mean_55_cast_fp16)[name = tensor("zero_mean_sq_55_cast_fp16")]; + tensor var_2923 = const()[name = tensor("op_2923"), val = tensor([1])]; + tensor var_2924_cast_fp16 = reduce_mean(axes = var_2923, keep_dims = var_2301, x = zero_mean_sq_55_cast_fp16)[name = tensor("op_2924_cast_fp16")]; + tensor var_2925_to_fp16 = const()[name = tensor("op_2925_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2926_cast_fp16 = add(x = var_2924_cast_fp16, y = var_2925_to_fp16)[name = tensor("op_2926_cast_fp16")]; + tensor denom_55_epsilon_0_to_fp16 = const()[name = tensor("denom_55_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_55_cast_fp16 = rsqrt(epsilon = denom_55_epsilon_0_to_fp16, x = var_2926_cast_fp16)[name = tensor("denom_55_cast_fp16")]; + tensor out_55_cast_fp16 = mul(x = zero_mean_55_cast_fp16, y = denom_55_cast_fp16)[name = tensor("out_55_cast_fp16")]; + tensor var_2930_to_fp16 = const()[name = tensor("op_2930_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(557966016)))]; + tensor var_2931_cast_fp16 = add(x = out_55_cast_fp16, y = var_2930_to_fp16)[name = tensor("op_2931_cast_fp16")]; + tensor var_2933_to_fp16 = const()[name = tensor("op_2933_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(557968640)))]; + tensor hidden_states_207_cast_fp16 = mul(x = var_2931_cast_fp16, y = var_2933_to_fp16)[name = tensor("hidden_states_207_cast_fp16")]; + tensor var_2940 = const()[name = tensor("op_2940"), val = tensor([1, 1])]; + tensor var_2942 = const()[name = tensor("op_2942"), val = tensor([1, 1])]; + tensor q_37_pad_type_0 = const()[name = tensor("q_37_pad_type_0"), val = tensor("custom")]; + tensor q_37_pad_0 = const()[name = tensor("q_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(557971264)))]; + tensor q_37_cast_fp16 = conv(dilations = var_2942, groups = var_2306, pad = q_37_pad_0, pad_type = q_37_pad_type_0, strides = var_2940, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_207_cast_fp16)[name = tensor("q_37_cast_fp16")]; + tensor var_2946 = const()[name = tensor("op_2946"), val = tensor([1, 1])]; + tensor var_2948 = const()[name = tensor("op_2948"), val = tensor([1, 1])]; + tensor k_37_pad_type_0 = const()[name = tensor("k_37_pad_type_0"), val = tensor("custom")]; + tensor k_37_pad_0 = const()[name = tensor("k_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(561248128)))]; + tensor k_37_cast_fp16 = conv(dilations = var_2948, groups = var_2306, pad = k_37_pad_0, pad_type = k_37_pad_type_0, strides = var_2946, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_207_cast_fp16)[name = tensor("k_37_cast_fp16")]; + tensor var_2952 = const()[name = tensor("op_2952"), val = tensor([1, 1])]; + tensor var_2954 = const()[name = tensor("op_2954"), val = tensor([1, 1])]; + tensor v_37_pad_type_0 = const()[name = tensor("v_37_pad_type_0"), val = tensor("custom")]; + tensor v_37_pad_0 = const()[name = tensor("v_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(564524992)))]; + tensor v_37_cast_fp16 = conv(dilations = var_2954, groups = var_2306, pad = v_37_pad_0, pad_type = v_37_pad_type_0, strides = var_2952, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_207_cast_fp16)[name = tensor("v_37_cast_fp16")]; + tensor var_2958 = const()[name = tensor("op_2958"), val = tensor([2, 20, 64, -1])]; + tensor var_2959_cast_fp16 = reshape(shape = var_2958, x = q_37_cast_fp16)[name = tensor("op_2959_cast_fp16")]; + tensor var_2960 = const()[name = tensor("op_2960"), val = tensor([2, 20, 64, -1])]; + tensor var_2961_cast_fp16 = reshape(shape = var_2960, x = k_37_cast_fp16)[name = tensor("op_2961_cast_fp16")]; + tensor var_2962 = const()[name = tensor("op_2962"), val = tensor([2, 20, 64, -1])]; + tensor var_2963_cast_fp16 = reshape(shape = var_2962, x = v_37_cast_fp16)[name = tensor("op_2963_cast_fp16")]; + tensor attn_weights_73_transpose_x_0 = const()[name = tensor("attn_weights_73_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_73_transpose_y_0 = const()[name = tensor("attn_weights_73_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_73_cast_fp16 = matmul(transpose_x = attn_weights_73_transpose_x_0, transpose_y = attn_weights_73_transpose_y_0, x = var_2959_cast_fp16, y = var_2961_cast_fp16)[name = tensor("attn_weights_73_cast_fp16")]; + tensor attn_weights_75_cast_fp16 = mul(x = attn_weights_73_cast_fp16, y = var_2297_to_fp16)[name = tensor("attn_weights_75_cast_fp16")]; + tensor var_2967_cast_fp16 = softmax(axis = var_2290, x = attn_weights_75_cast_fp16)[name = tensor("op_2967_cast_fp16")]; + tensor attn_37_transpose_x_0 = const()[name = tensor("attn_37_transpose_x_0"), val = tensor(false)]; + tensor attn_37_transpose_y_0 = const()[name = tensor("attn_37_transpose_y_0"), val = tensor(true)]; + tensor attn_37_cast_fp16 = matmul(transpose_x = attn_37_transpose_x_0, transpose_y = attn_37_transpose_y_0, x = var_2963_cast_fp16, y = var_2967_cast_fp16)[name = tensor("attn_37_cast_fp16")]; + tensor var_2971 = const()[name = tensor("op_2971"), val = tensor([2, 1280, 1, -1])]; + tensor input_353_cast_fp16 = reshape(shape = var_2971, x = attn_37_cast_fp16)[name = tensor("input_353_cast_fp16")]; + tensor var_2976 = const()[name = tensor("op_2976"), val = tensor([1, 1])]; + tensor var_2978 = const()[name = tensor("op_2978"), val = tensor([1, 1])]; + tensor var_2980_pad_type_0 = const()[name = tensor("op_2980_pad_type_0"), val = tensor("custom")]; + tensor var_2980_pad_0 = const()[name = tensor("op_2980_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(567801856)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(571078720)))]; + tensor var_2980_cast_fp16 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_2978, groups = var_2306, pad = var_2980_pad_0, pad_type = var_2980_pad_type_0, strides = var_2976, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_353_cast_fp16)[name = tensor("op_2980_cast_fp16")]; + tensor inputs_57_cast_fp16 = add(x = var_2980_cast_fp16, y = inputs_55_cast_fp16)[name = tensor("inputs_57_cast_fp16")]; + tensor var_2984 = const()[name = tensor("op_2984"), val = tensor([1])]; + tensor channels_mean_57_cast_fp16 = reduce_mean(axes = var_2984, keep_dims = var_2301, x = inputs_57_cast_fp16)[name = tensor("channels_mean_57_cast_fp16")]; + tensor zero_mean_57_cast_fp16 = sub(x = inputs_57_cast_fp16, y = channels_mean_57_cast_fp16)[name = tensor("zero_mean_57_cast_fp16")]; + tensor zero_mean_sq_57_cast_fp16 = mul(x = zero_mean_57_cast_fp16, y = zero_mean_57_cast_fp16)[name = tensor("zero_mean_sq_57_cast_fp16")]; + tensor var_2988 = const()[name = tensor("op_2988"), val = tensor([1])]; + tensor var_2989_cast_fp16 = reduce_mean(axes = var_2988, keep_dims = var_2301, x = zero_mean_sq_57_cast_fp16)[name = tensor("op_2989_cast_fp16")]; + tensor var_2990_to_fp16 = const()[name = tensor("op_2990_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2991_cast_fp16 = add(x = var_2989_cast_fp16, y = var_2990_to_fp16)[name = tensor("op_2991_cast_fp16")]; + tensor denom_57_epsilon_0_to_fp16 = const()[name = tensor("denom_57_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_57_cast_fp16 = rsqrt(epsilon = denom_57_epsilon_0_to_fp16, x = var_2991_cast_fp16)[name = tensor("denom_57_cast_fp16")]; + tensor out_57_cast_fp16 = mul(x = zero_mean_57_cast_fp16, y = denom_57_cast_fp16)[name = tensor("out_57_cast_fp16")]; + tensor var_2995_to_fp16 = const()[name = tensor("op_2995_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(571081344)))]; + tensor var_2996_cast_fp16 = add(x = out_57_cast_fp16, y = var_2995_to_fp16)[name = tensor("op_2996_cast_fp16")]; + tensor var_2998_to_fp16 = const()[name = tensor("op_2998_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(571083968)))]; + tensor hidden_states_209_cast_fp16 = mul(x = var_2996_cast_fp16, y = var_2998_to_fp16)[name = tensor("hidden_states_209_cast_fp16")]; + tensor var_3005 = const()[name = tensor("op_3005"), val = tensor([1, 1])]; + tensor var_3007 = const()[name = tensor("op_3007"), val = tensor([1, 1])]; + tensor q_39_pad_type_0 = const()[name = tensor("q_39_pad_type_0"), val = tensor("custom")]; + tensor q_39_pad_0 = const()[name = tensor("q_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(571086592)))]; + tensor q_39_cast_fp16 = conv(dilations = var_3007, groups = var_2306, pad = q_39_pad_0, pad_type = q_39_pad_type_0, strides = var_3005, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_209_cast_fp16)[name = tensor("q_39_cast_fp16")]; + tensor var_3011 = const()[name = tensor("op_3011"), val = tensor([1, 1])]; + tensor var_3013 = const()[name = tensor("op_3013"), val = tensor([1, 1])]; + tensor k_39_pad_type_0 = const()[name = tensor("k_39_pad_type_0"), val = tensor("custom")]; + tensor k_39_pad_0 = const()[name = tensor("k_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(574363456)))]; + tensor k_39_cast_fp16 = conv(dilations = var_3013, groups = var_2306, pad = k_39_pad_0, pad_type = k_39_pad_type_0, strides = var_3011, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_39_cast_fp16")]; + tensor var_3017 = const()[name = tensor("op_3017"), val = tensor([1, 1])]; + tensor var_3019 = const()[name = tensor("op_3019"), val = tensor([1, 1])]; + tensor v_39_pad_type_0 = const()[name = tensor("v_39_pad_type_0"), val = tensor("custom")]; + tensor v_39_pad_0 = const()[name = tensor("v_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(576984960)))]; + tensor v_39_cast_fp16 = conv(dilations = var_3019, groups = var_2306, pad = v_39_pad_0, pad_type = v_39_pad_type_0, strides = var_3017, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_39_cast_fp16")]; + tensor var_3023 = const()[name = tensor("op_3023"), val = tensor([2, 20, 64, -1])]; + tensor var_3024_cast_fp16 = reshape(shape = var_3023, x = q_39_cast_fp16)[name = tensor("op_3024_cast_fp16")]; + tensor var_3025 = const()[name = tensor("op_3025"), val = tensor([2, 20, 64, -1])]; + tensor var_3026_cast_fp16 = reshape(shape = var_3025, x = k_39_cast_fp16)[name = tensor("op_3026_cast_fp16")]; + tensor var_3027 = const()[name = tensor("op_3027"), val = tensor([2, 20, 64, -1])]; + tensor var_3028_cast_fp16 = reshape(shape = var_3027, x = v_39_cast_fp16)[name = tensor("op_3028_cast_fp16")]; + tensor attn_weights_77_transpose_x_0 = const()[name = tensor("attn_weights_77_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_77_transpose_y_0 = const()[name = tensor("attn_weights_77_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_77_cast_fp16 = matmul(transpose_x = attn_weights_77_transpose_x_0, transpose_y = attn_weights_77_transpose_y_0, x = var_3024_cast_fp16, y = var_3026_cast_fp16)[name = tensor("attn_weights_77_cast_fp16")]; + tensor attn_weights_79_cast_fp16 = mul(x = attn_weights_77_cast_fp16, y = var_2297_to_fp16)[name = tensor("attn_weights_79_cast_fp16")]; + tensor var_3032_cast_fp16 = softmax(axis = var_2290, x = attn_weights_79_cast_fp16)[name = tensor("op_3032_cast_fp16")]; + tensor attn_39_transpose_x_0 = const()[name = tensor("attn_39_transpose_x_0"), val = tensor(false)]; + tensor attn_39_transpose_y_0 = const()[name = tensor("attn_39_transpose_y_0"), val = tensor(true)]; + tensor attn_39_cast_fp16 = matmul(transpose_x = attn_39_transpose_x_0, transpose_y = attn_39_transpose_y_0, x = var_3028_cast_fp16, y = var_3032_cast_fp16)[name = tensor("attn_39_cast_fp16")]; + tensor var_3036 = const()[name = tensor("op_3036"), val = tensor([2, 1280, 1, -1])]; + tensor input_355_cast_fp16 = reshape(shape = var_3036, x = attn_39_cast_fp16)[name = tensor("input_355_cast_fp16")]; + tensor var_3041 = const()[name = tensor("op_3041"), val = tensor([1, 1])]; + tensor var_3043 = const()[name = tensor("op_3043"), val = tensor([1, 1])]; + tensor var_3045_pad_type_0 = const()[name = tensor("op_3045_pad_type_0"), val = tensor("custom")]; + tensor var_3045_pad_0 = const()[name = tensor("op_3045_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(579606464)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(582883328)))]; + tensor var_3045_cast_fp16 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_3043, groups = var_2306, pad = var_3045_pad_0, pad_type = var_3045_pad_type_0, strides = var_3041, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_355_cast_fp16)[name = tensor("op_3045_cast_fp16")]; + tensor inputs_59_cast_fp16 = add(x = var_3045_cast_fp16, y = inputs_57_cast_fp16)[name = tensor("inputs_59_cast_fp16")]; + tensor var_3049 = const()[name = tensor("op_3049"), val = tensor([1])]; + tensor channels_mean_59_cast_fp16 = reduce_mean(axes = var_3049, keep_dims = var_2301, x = inputs_59_cast_fp16)[name = tensor("channels_mean_59_cast_fp16")]; + tensor zero_mean_59_cast_fp16 = sub(x = inputs_59_cast_fp16, y = channels_mean_59_cast_fp16)[name = tensor("zero_mean_59_cast_fp16")]; + tensor zero_mean_sq_59_cast_fp16 = mul(x = zero_mean_59_cast_fp16, y = zero_mean_59_cast_fp16)[name = tensor("zero_mean_sq_59_cast_fp16")]; + tensor var_3053 = const()[name = tensor("op_3053"), val = tensor([1])]; + tensor var_3054_cast_fp16 = reduce_mean(axes = var_3053, keep_dims = var_2301, x = zero_mean_sq_59_cast_fp16)[name = tensor("op_3054_cast_fp16")]; + tensor var_3055_to_fp16 = const()[name = tensor("op_3055_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3056_cast_fp16 = add(x = var_3054_cast_fp16, y = var_3055_to_fp16)[name = tensor("op_3056_cast_fp16")]; + tensor denom_59_epsilon_0_to_fp16 = const()[name = tensor("denom_59_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_59_cast_fp16 = rsqrt(epsilon = denom_59_epsilon_0_to_fp16, x = var_3056_cast_fp16)[name = tensor("denom_59_cast_fp16")]; + tensor out_59_cast_fp16 = mul(x = zero_mean_59_cast_fp16, y = denom_59_cast_fp16)[name = tensor("out_59_cast_fp16")]; + tensor var_3060_to_fp16 = const()[name = tensor("op_3060_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(582885952)))]; + tensor var_3061_cast_fp16 = add(x = out_59_cast_fp16, y = var_3060_to_fp16)[name = tensor("op_3061_cast_fp16")]; + tensor var_3063_to_fp16 = const()[name = tensor("op_3063_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(582888576)))]; + tensor input_357_cast_fp16 = mul(x = var_3061_cast_fp16, y = var_3063_to_fp16)[name = tensor("input_357_cast_fp16")]; + tensor var_3071 = const()[name = tensor("op_3071"), val = tensor([1, 1])]; + tensor var_3073 = const()[name = tensor("op_3073"), val = tensor([1, 1])]; + tensor var_3075_pad_type_0 = const()[name = tensor("op_3075_pad_type_0"), val = tensor("custom")]; + tensor var_3075_pad_0 = const()[name = tensor("op_3075_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(582891200)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(609105664)))]; + tensor var_3075_cast_fp16 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_3073, groups = var_2306, pad = var_3075_pad_0, pad_type = var_3075_pad_type_0, strides = var_3071, weight = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_357_cast_fp16)[name = tensor("op_3075_cast_fp16")]; + tensor var_3076_split_sizes_0 = const()[name = tensor("op_3076_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_3076_axis_0 = const()[name = tensor("op_3076_axis_0"), val = tensor(1)]; + tensor var_3076_cast_fp16_0, tensor var_3076_cast_fp16_1 = split(axis = var_3076_axis_0, split_sizes = var_3076_split_sizes_0, x = var_3075_cast_fp16)[name = tensor("op_3076_cast_fp16")]; + tensor var_3078_mode_0 = const()[name = tensor("op_3078_mode_0"), val = tensor("EXACT")]; + tensor var_3078_cast_fp16 = gelu(mode = var_3078_mode_0, x = var_3076_cast_fp16_1)[name = tensor("op_3078_cast_fp16")]; + tensor input_359_cast_fp16 = mul(x = var_3076_cast_fp16_0, y = var_3078_cast_fp16)[name = tensor("input_359_cast_fp16")]; + tensor var_3082 = const()[name = tensor("op_3082"), val = tensor([1, 1])]; + tensor var_3084 = const()[name = tensor("op_3084"), val = tensor([1, 1])]; + tensor var_3086_pad_type_0 = const()[name = tensor("op_3086_pad_type_0"), val = tensor("custom")]; + tensor var_3086_pad_0 = const()[name = tensor("op_3086_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(609126208)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(622233472)))]; + tensor var_3086_cast_fp16 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_3084, groups = var_2306, pad = var_3086_pad_0, pad_type = var_3086_pad_type_0, strides = var_3082, weight = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_359_cast_fp16)[name = tensor("op_3086_cast_fp16")]; + tensor hidden_states_213_cast_fp16 = add(x = var_3086_cast_fp16, y = inputs_59_cast_fp16)[name = tensor("hidden_states_213_cast_fp16")]; + tensor var_3088 = const()[name = tensor("op_3088"), val = tensor([2, 1280, 12, 20])]; + tensor input_361_cast_fp16 = reshape(shape = var_3088, x = hidden_states_213_cast_fp16)[name = tensor("input_361_cast_fp16")]; + tensor var_3092 = const()[name = tensor("op_3092"), val = tensor([1, 1])]; + tensor var_3094 = const()[name = tensor("op_3094"), val = tensor([1, 1])]; + tensor hidden_states_215_pad_type_0 = const()[name = tensor("hidden_states_215_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_215_pad_0 = const()[name = tensor("hidden_states_215_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(622236096)))]; + tensor up_blocks_1_attentions_2_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(625512960)))]; + tensor hidden_states_215_cast_fp16 = conv(bias = up_blocks_1_attentions_2_proj_out_bias_to_fp16, dilations = var_3094, groups = var_2306, pad = hidden_states_215_pad_0, pad_type = hidden_states_215_pad_type_0, strides = var_3092, weight = up_blocks_1_attentions_2_proj_out_weight_to_fp16, x = input_361_cast_fp16)[name = tensor("hidden_states_215_cast_fp16")]; + tensor input_363_cast_fp16 = add(x = hidden_states_215_cast_fp16, y = hidden_states_203_cast_fp16)[name = tensor("input_363_cast_fp16")]; + tensor input_365_scale_factor_height_0 = const()[name = tensor("input_365_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_365_scale_factor_width_0 = const()[name = tensor("input_365_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor input_365_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = input_365_scale_factor_height_0, scale_factor_width = input_365_scale_factor_width_0, x = input_363_cast_fp16)[name = tensor("input_365_cast_fp16")]; + tensor var_3103 = const()[name = tensor("op_3103"), val = tensor([1, 1])]; + tensor var_3105 = const()[name = tensor("op_3105"), val = tensor([1, 1])]; + tensor hidden_states_217_pad_type_0 = const()[name = tensor("hidden_states_217_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_217_pad_0 = const()[name = tensor("hidden_states_217_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_upsamplers_0_conv_weight_to_fp16 = const()[name = tensor("up_blocks_1_upsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(625515584)))]; + tensor up_blocks_1_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("up_blocks_1_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(655006848)))]; + tensor hidden_states_217_cast_fp16 = conv(bias = up_blocks_1_upsamplers_0_conv_bias_to_fp16, dilations = var_3105, groups = var_2306, pad = hidden_states_217_pad_0, pad_type = hidden_states_217_pad_type_0, strides = var_3103, weight = up_blocks_1_upsamplers_0_conv_weight_to_fp16, x = input_365_cast_fp16)[name = tensor("hidden_states_217_cast_fp16")]; + tensor var_3110 = const()[name = tensor("op_3110"), val = tensor(3)]; + tensor var_3121 = const()[name = tensor("op_3121"), val = tensor(true)]; + tensor var_3126 = const()[name = tensor("op_3126"), val = tensor(1)]; + tensor input_367_interleave_0 = const()[name = tensor("input_367_interleave_0"), val = tensor(false)]; + tensor cast_7 = cast(dtype = cast_12_dtype_0, x = input_115_cast_fp16)[name = tensor("cast_7")]; + tensor input_367_cast_fp16 = concat(axis = var_3126, interleave = input_367_interleave_0, values = (hidden_states_217_cast_fp16, cast_7))[name = tensor("input_367_cast_fp16")]; + tensor reshape_168_shape_0 = const()[name = tensor("reshape_168_shape_0"), val = tensor([2, 32, 60, 24, 40])]; + tensor reshape_168_cast_fp16 = reshape(shape = reshape_168_shape_0, x = input_367_cast_fp16)[name = tensor("reshape_168_cast_fp16")]; + tensor reduce_mean_126_axes_0 = const()[name = tensor("reduce_mean_126_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_126_keep_dims_0 = const()[name = tensor("reduce_mean_126_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_126_cast_fp16 = reduce_mean(axes = reduce_mean_126_axes_0, keep_dims = reduce_mean_126_keep_dims_0, x = reshape_168_cast_fp16)[name = tensor("reduce_mean_126_cast_fp16")]; + tensor sub_84_cast_fp16 = sub(x = reshape_168_cast_fp16, y = reduce_mean_126_cast_fp16)[name = tensor("sub_84_cast_fp16")]; + tensor square_42_cast_fp16 = square(x = sub_84_cast_fp16)[name = tensor("square_42_cast_fp16")]; + tensor reduce_mean_128_axes_0 = const()[name = tensor("reduce_mean_128_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_128_keep_dims_0 = const()[name = tensor("reduce_mean_128_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_128_cast_fp16 = reduce_mean(axes = reduce_mean_128_axes_0, keep_dims = reduce_mean_128_keep_dims_0, x = square_42_cast_fp16)[name = tensor("reduce_mean_128_cast_fp16")]; + tensor add_84_y_0_to_fp16 = const()[name = tensor("add_84_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_84_cast_fp16 = add(x = reduce_mean_128_cast_fp16, y = add_84_y_0_to_fp16)[name = tensor("add_84_cast_fp16")]; + tensor sqrt_42_cast_fp16 = sqrt(x = add_84_cast_fp16)[name = tensor("sqrt_42_cast_fp16")]; + tensor real_div_42_cast_fp16 = real_div(x = sub_84_cast_fp16, y = sqrt_42_cast_fp16)[name = tensor("real_div_42_cast_fp16")]; + tensor reshape_169_shape_0 = const()[name = tensor("reshape_169_shape_0"), val = tensor([2, 1920, 24, 40])]; + tensor reshape_169_cast_fp16 = reshape(shape = reshape_169_shape_0, x = real_div_42_cast_fp16)[name = tensor("reshape_169_cast_fp16")]; + tensor add_85_gamma_0_to_fp16 = const()[name = tensor("add_85_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(655009472)))]; + tensor add_85_beta_0_to_fp16 = const()[name = tensor("add_85_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(655013376)))]; + tensor add_85_epsilon_0_to_fp16 = const()[name = tensor("add_85_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_85_cast_fp16 = batch_norm(beta = add_85_beta_0_to_fp16, epsilon = add_85_epsilon_0_to_fp16, gamma = add_85_gamma_0_to_fp16, mean = add_79_mean_0_to_fp16, variance = add_79_variance_0_to_fp16, x = reshape_169_cast_fp16)[name = tensor("add_85_cast_fp16")]; + tensor input_371_cast_fp16 = silu(x = add_85_cast_fp16)[name = tensor("input_371_cast_fp16")]; + tensor var_3155 = const()[name = tensor("op_3155"), val = tensor([1, 1])]; + tensor var_3157 = const()[name = tensor("op_3157"), val = tensor([1, 1])]; + tensor hidden_states_219_pad_type_0 = const()[name = tensor("hidden_states_219_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_219_pad_0 = const()[name = tensor("hidden_states_219_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(655017280)))]; + tensor up_blocks_2_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(677135744)))]; + tensor hidden_states_219_cast_fp16 = conv(bias = up_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = var_3157, groups = var_3126, pad = hidden_states_219_pad_0, pad_type = hidden_states_219_pad_type_0, strides = var_3155, weight = up_blocks_2_resnets_0_conv1_weight_to_fp16, x = input_371_cast_fp16)[name = tensor("hidden_states_219_cast_fp16")]; + tensor var_3163 = const()[name = tensor("op_3163"), val = tensor([1, 1])]; + tensor var_3165 = const()[name = tensor("op_3165"), val = tensor([1, 1])]; + tensor temb_33_pad_type_0 = const()[name = tensor("temb_33_pad_type_0"), val = tensor("custom")]; + tensor temb_33_pad_0 = const()[name = tensor("temb_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(677137088)))]; + tensor up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(678775552)))]; + tensor temb_33_cast_fp16 = conv(bias = up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_3165, groups = var_3126, pad = temb_33_pad_0, pad_type = temb_33_pad_type_0, strides = var_3163, weight = up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16, x = cast_12)[name = tensor("temb_33_cast_fp16")]; + tensor input_375_cast_fp16 = add(x = hidden_states_219_cast_fp16, y = temb_33_cast_fp16)[name = tensor("input_375_cast_fp16")]; + tensor reshape_172_shape_0 = const()[name = tensor("reshape_172_shape_0"), val = tensor([2, 32, 20, 24, 40])]; + tensor reshape_172_cast_fp16 = reshape(shape = reshape_172_shape_0, x = input_375_cast_fp16)[name = tensor("reshape_172_cast_fp16")]; + tensor reduce_mean_129_axes_0 = const()[name = tensor("reduce_mean_129_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_129_keep_dims_0 = const()[name = tensor("reduce_mean_129_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_129_cast_fp16 = reduce_mean(axes = reduce_mean_129_axes_0, keep_dims = reduce_mean_129_keep_dims_0, x = reshape_172_cast_fp16)[name = tensor("reduce_mean_129_cast_fp16")]; + tensor sub_86_cast_fp16 = sub(x = reshape_172_cast_fp16, y = reduce_mean_129_cast_fp16)[name = tensor("sub_86_cast_fp16")]; + tensor square_43_cast_fp16 = square(x = sub_86_cast_fp16)[name = tensor("square_43_cast_fp16")]; + tensor reduce_mean_131_axes_0 = const()[name = tensor("reduce_mean_131_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_131_keep_dims_0 = const()[name = tensor("reduce_mean_131_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_131_cast_fp16 = reduce_mean(axes = reduce_mean_131_axes_0, keep_dims = reduce_mean_131_keep_dims_0, x = square_43_cast_fp16)[name = tensor("reduce_mean_131_cast_fp16")]; + tensor add_86_y_0_to_fp16 = const()[name = tensor("add_86_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_86_cast_fp16 = add(x = reduce_mean_131_cast_fp16, y = add_86_y_0_to_fp16)[name = tensor("add_86_cast_fp16")]; + tensor sqrt_43_cast_fp16 = sqrt(x = add_86_cast_fp16)[name = tensor("sqrt_43_cast_fp16")]; + tensor real_div_43_cast_fp16 = real_div(x = sub_86_cast_fp16, y = sqrt_43_cast_fp16)[name = tensor("real_div_43_cast_fp16")]; + tensor reshape_173_shape_0 = const()[name = tensor("reshape_173_shape_0"), val = tensor([2, 640, 24, 40])]; + tensor reshape_173_cast_fp16 = reshape(shape = reshape_173_shape_0, x = real_div_43_cast_fp16)[name = tensor("reshape_173_cast_fp16")]; + tensor add_87_gamma_0_to_fp16 = const()[name = tensor("add_87_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(678776896)))]; + tensor add_87_beta_0_to_fp16 = const()[name = tensor("add_87_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(678778240)))]; + tensor add_87_epsilon_0_to_fp16 = const()[name = tensor("add_87_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_87_cast_fp16 = batch_norm(beta = add_87_beta_0_to_fp16, epsilon = add_87_epsilon_0_to_fp16, gamma = add_87_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_173_cast_fp16)[name = tensor("add_87_cast_fp16")]; + tensor input_379_cast_fp16 = silu(x = add_87_cast_fp16)[name = tensor("input_379_cast_fp16")]; + tensor var_3175 = const()[name = tensor("op_3175"), val = tensor([1, 1])]; + tensor var_3177 = const()[name = tensor("op_3177"), val = tensor([1, 1])]; + tensor hidden_states_221_pad_type_0 = const()[name = tensor("hidden_states_221_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_221_pad_0 = const()[name = tensor("hidden_states_221_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(678779584)))]; + tensor up_blocks_2_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(686152448)))]; + tensor hidden_states_221_cast_fp16 = conv(bias = up_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = var_3177, groups = var_3126, pad = hidden_states_221_pad_0, pad_type = hidden_states_221_pad_type_0, strides = var_3175, weight = up_blocks_2_resnets_0_conv2_weight_to_fp16, x = input_379_cast_fp16)[name = tensor("hidden_states_221_cast_fp16")]; + tensor var_3182 = const()[name = tensor("op_3182"), val = tensor([1, 1])]; + tensor var_3184 = const()[name = tensor("op_3184"), val = tensor([1, 1])]; + tensor x_17_pad_type_0 = const()[name = tensor("x_17_pad_type_0"), val = tensor("custom")]; + tensor x_17_pad_0 = const()[name = tensor("x_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(686153792)))]; + tensor up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(688611456)))]; + tensor x_17_cast_fp16 = conv(bias = up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_3184, groups = var_3126, pad = x_17_pad_0, pad_type = x_17_pad_type_0, strides = var_3182, weight = up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16, x = input_367_cast_fp16)[name = tensor("x_17_cast_fp16")]; + tensor hidden_states_223_cast_fp16 = add(x = x_17_cast_fp16, y = hidden_states_221_cast_fp16)[name = tensor("hidden_states_223_cast_fp16")]; + tensor reshape_176_shape_0 = const()[name = tensor("reshape_176_shape_0"), val = tensor([2, 32, 20, 24, 40])]; + tensor reshape_176_cast_fp16 = reshape(shape = reshape_176_shape_0, x = hidden_states_223_cast_fp16)[name = tensor("reshape_176_cast_fp16")]; + tensor reduce_mean_132_axes_0 = const()[name = tensor("reduce_mean_132_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_132_keep_dims_0 = const()[name = tensor("reduce_mean_132_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_132_cast_fp16 = reduce_mean(axes = reduce_mean_132_axes_0, keep_dims = reduce_mean_132_keep_dims_0, x = reshape_176_cast_fp16)[name = tensor("reduce_mean_132_cast_fp16")]; + tensor sub_88_cast_fp16 = sub(x = reshape_176_cast_fp16, y = reduce_mean_132_cast_fp16)[name = tensor("sub_88_cast_fp16")]; + tensor square_44_cast_fp16 = square(x = sub_88_cast_fp16)[name = tensor("square_44_cast_fp16")]; + tensor reduce_mean_134_axes_0 = const()[name = tensor("reduce_mean_134_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_134_keep_dims_0 = const()[name = tensor("reduce_mean_134_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_134_cast_fp16 = reduce_mean(axes = reduce_mean_134_axes_0, keep_dims = reduce_mean_134_keep_dims_0, x = square_44_cast_fp16)[name = tensor("reduce_mean_134_cast_fp16")]; + tensor add_88_y_0_to_fp16 = const()[name = tensor("add_88_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_88_cast_fp16 = add(x = reduce_mean_134_cast_fp16, y = add_88_y_0_to_fp16)[name = tensor("add_88_cast_fp16")]; + tensor sqrt_44_cast_fp16 = sqrt(x = add_88_cast_fp16)[name = tensor("sqrt_44_cast_fp16")]; + tensor real_div_44_cast_fp16 = real_div(x = sub_88_cast_fp16, y = sqrt_44_cast_fp16)[name = tensor("real_div_44_cast_fp16")]; + tensor reshape_177_shape_0 = const()[name = tensor("reshape_177_shape_0"), val = tensor([2, 640, 24, 40])]; + tensor reshape_177_cast_fp16 = reshape(shape = reshape_177_shape_0, x = real_div_44_cast_fp16)[name = tensor("reshape_177_cast_fp16")]; + tensor add_89_gamma_0_to_fp16 = const()[name = tensor("add_89_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(688612800)))]; + tensor add_89_beta_0_to_fp16 = const()[name = tensor("add_89_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(688614144)))]; + tensor add_89_epsilon_0_to_fp16 = const()[name = tensor("add_89_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_89_cast_fp16 = batch_norm(beta = add_89_beta_0_to_fp16, epsilon = add_89_epsilon_0_to_fp16, gamma = add_89_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_177_cast_fp16)[name = tensor("add_89_cast_fp16")]; + tensor var_3204 = const()[name = tensor("op_3204"), val = tensor([1, 1])]; + tensor var_3206 = const()[name = tensor("op_3206"), val = tensor([1, 1])]; + tensor hidden_states_225_pad_type_0 = const()[name = tensor("hidden_states_225_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_225_pad_0 = const()[name = tensor("hidden_states_225_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(688615488)))]; + tensor up_blocks_2_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(689434752)))]; + tensor hidden_states_225_cast_fp16 = conv(bias = up_blocks_2_attentions_0_proj_in_bias_to_fp16, dilations = var_3206, groups = var_3126, pad = hidden_states_225_pad_0, pad_type = hidden_states_225_pad_type_0, strides = var_3204, weight = up_blocks_2_attentions_0_proj_in_weight_to_fp16, x = add_89_cast_fp16)[name = tensor("hidden_states_225_cast_fp16")]; + tensor var_3211 = const()[name = tensor("op_3211"), val = tensor([2, 640, 1, 960])]; + tensor inputs_61_cast_fp16 = reshape(shape = var_3211, x = hidden_states_225_cast_fp16)[name = tensor("inputs_61_cast_fp16")]; + tensor var_3221 = const()[name = tensor("op_3221"), val = tensor([1])]; + tensor channels_mean_61_cast_fp16 = reduce_mean(axes = var_3221, keep_dims = var_3121, x = inputs_61_cast_fp16)[name = tensor("channels_mean_61_cast_fp16")]; + tensor zero_mean_61_cast_fp16 = sub(x = inputs_61_cast_fp16, y = channels_mean_61_cast_fp16)[name = tensor("zero_mean_61_cast_fp16")]; + tensor zero_mean_sq_61_cast_fp16 = mul(x = zero_mean_61_cast_fp16, y = zero_mean_61_cast_fp16)[name = tensor("zero_mean_sq_61_cast_fp16")]; + tensor var_3225 = const()[name = tensor("op_3225"), val = tensor([1])]; + tensor var_3226_cast_fp16 = reduce_mean(axes = var_3225, keep_dims = var_3121, x = zero_mean_sq_61_cast_fp16)[name = tensor("op_3226_cast_fp16")]; + tensor var_3227_to_fp16 = const()[name = tensor("op_3227_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3228_cast_fp16 = add(x = var_3226_cast_fp16, y = var_3227_to_fp16)[name = tensor("op_3228_cast_fp16")]; + tensor denom_61_epsilon_0_to_fp16 = const()[name = tensor("denom_61_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_61_cast_fp16 = rsqrt(epsilon = denom_61_epsilon_0_to_fp16, x = var_3228_cast_fp16)[name = tensor("denom_61_cast_fp16")]; + tensor out_61_cast_fp16 = mul(x = zero_mean_61_cast_fp16, y = denom_61_cast_fp16)[name = tensor("out_61_cast_fp16")]; + tensor var_3232_to_fp16 = const()[name = tensor("op_3232_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(689436096)))]; + tensor var_3233_cast_fp16 = add(x = out_61_cast_fp16, y = var_3232_to_fp16)[name = tensor("op_3233_cast_fp16")]; + tensor var_3235_to_fp16 = const()[name = tensor("op_3235_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(689437440)))]; + tensor hidden_states_227_cast_fp16 = mul(x = var_3233_cast_fp16, y = var_3235_to_fp16)[name = tensor("hidden_states_227_cast_fp16")]; + tensor var_3242 = const()[name = tensor("op_3242"), val = tensor([1, 1])]; + tensor var_3244 = const()[name = tensor("op_3244"), val = tensor([1, 1])]; + tensor q_41_pad_type_0 = const()[name = tensor("q_41_pad_type_0"), val = tensor("custom")]; + tensor q_41_pad_0 = const()[name = tensor("q_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(689438784)))]; + tensor q_41_cast_fp16 = conv(dilations = var_3244, groups = var_3126, pad = q_41_pad_0, pad_type = q_41_pad_type_0, strides = var_3242, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_227_cast_fp16)[name = tensor("q_41_cast_fp16")]; + tensor var_3248 = const()[name = tensor("op_3248"), val = tensor([1, 1])]; + tensor var_3250 = const()[name = tensor("op_3250"), val = tensor([1, 1])]; + tensor k_41_pad_type_0 = const()[name = tensor("k_41_pad_type_0"), val = tensor("custom")]; + tensor k_41_pad_0 = const()[name = tensor("k_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(690258048)))]; + tensor k_41_cast_fp16 = conv(dilations = var_3250, groups = var_3126, pad = k_41_pad_0, pad_type = k_41_pad_type_0, strides = var_3248, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_227_cast_fp16)[name = tensor("k_41_cast_fp16")]; + tensor var_3254 = const()[name = tensor("op_3254"), val = tensor([1, 1])]; + tensor var_3256 = const()[name = tensor("op_3256"), val = tensor([1, 1])]; + tensor v_41_pad_type_0 = const()[name = tensor("v_41_pad_type_0"), val = tensor("custom")]; + tensor v_41_pad_0 = const()[name = tensor("v_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(691077312)))]; + tensor v_41_cast_fp16 = conv(dilations = var_3256, groups = var_3126, pad = v_41_pad_0, pad_type = v_41_pad_type_0, strides = var_3254, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_227_cast_fp16)[name = tensor("v_41_cast_fp16")]; + tensor var_3260 = const()[name = tensor("op_3260"), val = tensor([2, 10, 64, -1])]; + tensor var_3261_cast_fp16 = reshape(shape = var_3260, x = q_41_cast_fp16)[name = tensor("op_3261_cast_fp16")]; + tensor var_3262 = const()[name = tensor("op_3262"), val = tensor([2, 10, 64, -1])]; + tensor var_3263_cast_fp16 = reshape(shape = var_3262, x = k_41_cast_fp16)[name = tensor("op_3263_cast_fp16")]; + tensor var_3264 = const()[name = tensor("op_3264"), val = tensor([2, 10, 64, -1])]; + tensor var_3265_cast_fp16 = reshape(shape = var_3264, x = v_41_cast_fp16)[name = tensor("op_3265_cast_fp16")]; + tensor attn_weights_81_transpose_x_0 = const()[name = tensor("attn_weights_81_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_81_transpose_y_0 = const()[name = tensor("attn_weights_81_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_81_cast_fp16 = matmul(transpose_x = attn_weights_81_transpose_x_0, transpose_y = attn_weights_81_transpose_y_0, x = var_3261_cast_fp16, y = var_3263_cast_fp16)[name = tensor("attn_weights_81_cast_fp16")]; + tensor var_3117_to_fp16 = const()[name = tensor("op_3117_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_83_cast_fp16 = mul(x = attn_weights_81_cast_fp16, y = var_3117_to_fp16)[name = tensor("attn_weights_83_cast_fp16")]; + tensor var_3269_cast_fp16 = softmax(axis = var_3110, x = attn_weights_83_cast_fp16)[name = tensor("op_3269_cast_fp16")]; + tensor attn_41_transpose_x_0 = const()[name = tensor("attn_41_transpose_x_0"), val = tensor(false)]; + tensor attn_41_transpose_y_0 = const()[name = tensor("attn_41_transpose_y_0"), val = tensor(true)]; + tensor attn_41_cast_fp16 = matmul(transpose_x = attn_41_transpose_x_0, transpose_y = attn_41_transpose_y_0, x = var_3265_cast_fp16, y = var_3269_cast_fp16)[name = tensor("attn_41_cast_fp16")]; + tensor var_3273 = const()[name = tensor("op_3273"), val = tensor([2, 640, 1, -1])]; + tensor input_383_cast_fp16 = reshape(shape = var_3273, x = attn_41_cast_fp16)[name = tensor("input_383_cast_fp16")]; + tensor var_3278 = const()[name = tensor("op_3278"), val = tensor([1, 1])]; + tensor var_3280 = const()[name = tensor("op_3280"), val = tensor([1, 1])]; + tensor var_3282_pad_type_0 = const()[name = tensor("op_3282_pad_type_0"), val = tensor("custom")]; + tensor var_3282_pad_0 = const()[name = tensor("op_3282_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(691896576)))]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(692715840)))]; + tensor var_3282_cast_fp16 = conv(bias = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_3280, groups = var_3126, pad = var_3282_pad_0, pad_type = var_3282_pad_type_0, strides = var_3278, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_383_cast_fp16)[name = tensor("op_3282_cast_fp16")]; + tensor inputs_63_cast_fp16 = add(x = var_3282_cast_fp16, y = inputs_61_cast_fp16)[name = tensor("inputs_63_cast_fp16")]; + tensor var_3286 = const()[name = tensor("op_3286"), val = tensor([1])]; + tensor channels_mean_63_cast_fp16 = reduce_mean(axes = var_3286, keep_dims = var_3121, x = inputs_63_cast_fp16)[name = tensor("channels_mean_63_cast_fp16")]; + tensor zero_mean_63_cast_fp16 = sub(x = inputs_63_cast_fp16, y = channels_mean_63_cast_fp16)[name = tensor("zero_mean_63_cast_fp16")]; + tensor zero_mean_sq_63_cast_fp16 = mul(x = zero_mean_63_cast_fp16, y = zero_mean_63_cast_fp16)[name = tensor("zero_mean_sq_63_cast_fp16")]; + tensor var_3290 = const()[name = tensor("op_3290"), val = tensor([1])]; + tensor var_3291_cast_fp16 = reduce_mean(axes = var_3290, keep_dims = var_3121, x = zero_mean_sq_63_cast_fp16)[name = tensor("op_3291_cast_fp16")]; + tensor var_3292_to_fp16 = const()[name = tensor("op_3292_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3293_cast_fp16 = add(x = var_3291_cast_fp16, y = var_3292_to_fp16)[name = tensor("op_3293_cast_fp16")]; + tensor denom_63_epsilon_0_to_fp16 = const()[name = tensor("denom_63_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_63_cast_fp16 = rsqrt(epsilon = denom_63_epsilon_0_to_fp16, x = var_3293_cast_fp16)[name = tensor("denom_63_cast_fp16")]; + tensor out_63_cast_fp16 = mul(x = zero_mean_63_cast_fp16, y = denom_63_cast_fp16)[name = tensor("out_63_cast_fp16")]; + tensor var_3297_to_fp16 = const()[name = tensor("op_3297_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(692717184)))]; + tensor var_3298_cast_fp16 = add(x = out_63_cast_fp16, y = var_3297_to_fp16)[name = tensor("op_3298_cast_fp16")]; + tensor var_3300_to_fp16 = const()[name = tensor("op_3300_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(692718528)))]; + tensor hidden_states_229_cast_fp16 = mul(x = var_3298_cast_fp16, y = var_3300_to_fp16)[name = tensor("hidden_states_229_cast_fp16")]; + tensor var_3307 = const()[name = tensor("op_3307"), val = tensor([1, 1])]; + tensor var_3309 = const()[name = tensor("op_3309"), val = tensor([1, 1])]; + tensor q_43_pad_type_0 = const()[name = tensor("q_43_pad_type_0"), val = tensor("custom")]; + tensor q_43_pad_0 = const()[name = tensor("q_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(692719872)))]; + tensor q_43_cast_fp16 = conv(dilations = var_3309, groups = var_3126, pad = q_43_pad_0, pad_type = q_43_pad_type_0, strides = var_3307, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_229_cast_fp16)[name = tensor("q_43_cast_fp16")]; + tensor var_3313 = const()[name = tensor("op_3313"), val = tensor([1, 1])]; + tensor var_3315 = const()[name = tensor("op_3315"), val = tensor([1, 1])]; + tensor k_43_pad_type_0 = const()[name = tensor("k_43_pad_type_0"), val = tensor("custom")]; + tensor k_43_pad_0 = const()[name = tensor("k_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(693539136)))]; + tensor k_43_cast_fp16 = conv(dilations = var_3315, groups = var_3126, pad = k_43_pad_0, pad_type = k_43_pad_type_0, strides = var_3313, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_43_cast_fp16")]; + tensor var_3319 = const()[name = tensor("op_3319"), val = tensor([1, 1])]; + tensor var_3321 = const()[name = tensor("op_3321"), val = tensor([1, 1])]; + tensor v_43_pad_type_0 = const()[name = tensor("v_43_pad_type_0"), val = tensor("custom")]; + tensor v_43_pad_0 = const()[name = tensor("v_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(694849920)))]; + tensor v_43_cast_fp16 = conv(dilations = var_3321, groups = var_3126, pad = v_43_pad_0, pad_type = v_43_pad_type_0, strides = var_3319, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_43_cast_fp16")]; + tensor var_3325 = const()[name = tensor("op_3325"), val = tensor([2, 10, 64, -1])]; + tensor var_3326_cast_fp16 = reshape(shape = var_3325, x = q_43_cast_fp16)[name = tensor("op_3326_cast_fp16")]; + tensor var_3327 = const()[name = tensor("op_3327"), val = tensor([2, 10, 64, -1])]; + tensor var_3328_cast_fp16 = reshape(shape = var_3327, x = k_43_cast_fp16)[name = tensor("op_3328_cast_fp16")]; + tensor var_3329 = const()[name = tensor("op_3329"), val = tensor([2, 10, 64, -1])]; + tensor var_3330_cast_fp16 = reshape(shape = var_3329, x = v_43_cast_fp16)[name = tensor("op_3330_cast_fp16")]; + tensor attn_weights_85_transpose_x_0 = const()[name = tensor("attn_weights_85_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_85_transpose_y_0 = const()[name = tensor("attn_weights_85_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_85_cast_fp16 = matmul(transpose_x = attn_weights_85_transpose_x_0, transpose_y = attn_weights_85_transpose_y_0, x = var_3326_cast_fp16, y = var_3328_cast_fp16)[name = tensor("attn_weights_85_cast_fp16")]; + tensor attn_weights_87_cast_fp16 = mul(x = attn_weights_85_cast_fp16, y = var_3117_to_fp16)[name = tensor("attn_weights_87_cast_fp16")]; + tensor var_3334_cast_fp16 = softmax(axis = var_3110, x = attn_weights_87_cast_fp16)[name = tensor("op_3334_cast_fp16")]; + tensor attn_43_transpose_x_0 = const()[name = tensor("attn_43_transpose_x_0"), val = tensor(false)]; + tensor attn_43_transpose_y_0 = const()[name = tensor("attn_43_transpose_y_0"), val = tensor(true)]; + tensor attn_43_cast_fp16 = matmul(transpose_x = attn_43_transpose_x_0, transpose_y = attn_43_transpose_y_0, x = var_3330_cast_fp16, y = var_3334_cast_fp16)[name = tensor("attn_43_cast_fp16")]; + tensor var_3338 = const()[name = tensor("op_3338"), val = tensor([2, 640, 1, -1])]; + tensor input_385_cast_fp16 = reshape(shape = var_3338, x = attn_43_cast_fp16)[name = tensor("input_385_cast_fp16")]; + tensor var_3343 = const()[name = tensor("op_3343"), val = tensor([1, 1])]; + tensor var_3345 = const()[name = tensor("op_3345"), val = tensor([1, 1])]; + tensor var_3347_pad_type_0 = const()[name = tensor("op_3347_pad_type_0"), val = tensor("custom")]; + tensor var_3347_pad_0 = const()[name = tensor("op_3347_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(696160704)))]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(696979968)))]; + tensor var_3347_cast_fp16 = conv(bias = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_3345, groups = var_3126, pad = var_3347_pad_0, pad_type = var_3347_pad_type_0, strides = var_3343, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_385_cast_fp16)[name = tensor("op_3347_cast_fp16")]; + tensor inputs_65_cast_fp16 = add(x = var_3347_cast_fp16, y = inputs_63_cast_fp16)[name = tensor("inputs_65_cast_fp16")]; + tensor var_3351 = const()[name = tensor("op_3351"), val = tensor([1])]; + tensor channels_mean_65_cast_fp16 = reduce_mean(axes = var_3351, keep_dims = var_3121, x = inputs_65_cast_fp16)[name = tensor("channels_mean_65_cast_fp16")]; + tensor zero_mean_65_cast_fp16 = sub(x = inputs_65_cast_fp16, y = channels_mean_65_cast_fp16)[name = tensor("zero_mean_65_cast_fp16")]; + tensor zero_mean_sq_65_cast_fp16 = mul(x = zero_mean_65_cast_fp16, y = zero_mean_65_cast_fp16)[name = tensor("zero_mean_sq_65_cast_fp16")]; + tensor var_3355 = const()[name = tensor("op_3355"), val = tensor([1])]; + tensor var_3356_cast_fp16 = reduce_mean(axes = var_3355, keep_dims = var_3121, x = zero_mean_sq_65_cast_fp16)[name = tensor("op_3356_cast_fp16")]; + tensor var_3357_to_fp16 = const()[name = tensor("op_3357_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3358_cast_fp16 = add(x = var_3356_cast_fp16, y = var_3357_to_fp16)[name = tensor("op_3358_cast_fp16")]; + tensor denom_65_epsilon_0_to_fp16 = const()[name = tensor("denom_65_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_65_cast_fp16 = rsqrt(epsilon = denom_65_epsilon_0_to_fp16, x = var_3358_cast_fp16)[name = tensor("denom_65_cast_fp16")]; + tensor out_65_cast_fp16 = mul(x = zero_mean_65_cast_fp16, y = denom_65_cast_fp16)[name = tensor("out_65_cast_fp16")]; + tensor var_3362_to_fp16 = const()[name = tensor("op_3362_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(696981312)))]; + tensor var_3363_cast_fp16 = add(x = out_65_cast_fp16, y = var_3362_to_fp16)[name = tensor("op_3363_cast_fp16")]; + tensor var_3365_to_fp16 = const()[name = tensor("op_3365_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(696982656)))]; + tensor input_387_cast_fp16 = mul(x = var_3363_cast_fp16, y = var_3365_to_fp16)[name = tensor("input_387_cast_fp16")]; + tensor var_3373 = const()[name = tensor("op_3373"), val = tensor([1, 1])]; + tensor var_3375 = const()[name = tensor("op_3375"), val = tensor([1, 1])]; + tensor var_3377_pad_type_0 = const()[name = tensor("op_3377_pad_type_0"), val = tensor("custom")]; + tensor var_3377_pad_0 = const()[name = tensor("op_3377_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(696984000)))]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(703537664)))]; + tensor var_3377_cast_fp16 = conv(bias = up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_3375, groups = var_3126, pad = var_3377_pad_0, pad_type = var_3377_pad_type_0, strides = var_3373, weight = up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_387_cast_fp16)[name = tensor("op_3377_cast_fp16")]; + tensor var_3378_split_sizes_0 = const()[name = tensor("op_3378_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_3378_axis_0 = const()[name = tensor("op_3378_axis_0"), val = tensor(1)]; + tensor var_3378_cast_fp16_0, tensor var_3378_cast_fp16_1 = split(axis = var_3378_axis_0, split_sizes = var_3378_split_sizes_0, x = var_3377_cast_fp16)[name = tensor("op_3378_cast_fp16")]; + tensor var_3380_mode_0 = const()[name = tensor("op_3380_mode_0"), val = tensor("EXACT")]; + tensor var_3380_cast_fp16 = gelu(mode = var_3380_mode_0, x = var_3378_cast_fp16_1)[name = tensor("op_3380_cast_fp16")]; + tensor input_389_cast_fp16 = mul(x = var_3378_cast_fp16_0, y = var_3380_cast_fp16)[name = tensor("input_389_cast_fp16")]; + tensor var_3384 = const()[name = tensor("op_3384"), val = tensor([1, 1])]; + tensor var_3386 = const()[name = tensor("op_3386"), val = tensor([1, 1])]; + tensor var_3388_pad_type_0 = const()[name = tensor("op_3388_pad_type_0"), val = tensor("custom")]; + tensor var_3388_pad_0 = const()[name = tensor("op_3388_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(703547968)))]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(706824832)))]; + tensor var_3388_cast_fp16 = conv(bias = up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_3386, groups = var_3126, pad = var_3388_pad_0, pad_type = var_3388_pad_type_0, strides = var_3384, weight = up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_389_cast_fp16)[name = tensor("op_3388_cast_fp16")]; + tensor hidden_states_233_cast_fp16 = add(x = var_3388_cast_fp16, y = inputs_65_cast_fp16)[name = tensor("hidden_states_233_cast_fp16")]; + tensor var_3390 = const()[name = tensor("op_3390"), val = tensor([2, 640, 24, 40])]; + tensor input_391_cast_fp16 = reshape(shape = var_3390, x = hidden_states_233_cast_fp16)[name = tensor("input_391_cast_fp16")]; + tensor var_3394 = const()[name = tensor("op_3394"), val = tensor([1, 1])]; + tensor var_3396 = const()[name = tensor("op_3396"), val = tensor([1, 1])]; + tensor hidden_states_235_pad_type_0 = const()[name = tensor("hidden_states_235_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_235_pad_0 = const()[name = tensor("hidden_states_235_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(706826176)))]; + tensor up_blocks_2_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(707645440)))]; + tensor hidden_states_235_cast_fp16 = conv(bias = up_blocks_2_attentions_0_proj_out_bias_to_fp16, dilations = var_3396, groups = var_3126, pad = hidden_states_235_pad_0, pad_type = hidden_states_235_pad_type_0, strides = var_3394, weight = up_blocks_2_attentions_0_proj_out_weight_to_fp16, x = input_391_cast_fp16)[name = tensor("hidden_states_235_cast_fp16")]; + tensor hidden_states_237_cast_fp16 = add(x = hidden_states_235_cast_fp16, y = hidden_states_223_cast_fp16)[name = tensor("hidden_states_237_cast_fp16")]; + tensor input_393_interleave_0 = const()[name = tensor("input_393_interleave_0"), val = tensor(false)]; + tensor cast_8 = cast(dtype = cast_7_dtype_0, x = input_89_cast_fp16)[name = tensor("cast_8")]; + tensor input_393_cast_fp16 = concat(axis = var_3126, interleave = input_393_interleave_0, values = (hidden_states_237_cast_fp16, cast_8))[name = tensor("input_393_cast_fp16")]; + tensor reshape_180_shape_0 = const()[name = tensor("reshape_180_shape_0"), val = tensor([2, 32, 40, 24, 40])]; + tensor reshape_180_cast_fp16 = reshape(shape = reshape_180_shape_0, x = input_393_cast_fp16)[name = tensor("reshape_180_cast_fp16")]; + tensor reduce_mean_135_axes_0 = const()[name = tensor("reduce_mean_135_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_135_keep_dims_0 = const()[name = tensor("reduce_mean_135_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_135_cast_fp16 = reduce_mean(axes = reduce_mean_135_axes_0, keep_dims = reduce_mean_135_keep_dims_0, x = reshape_180_cast_fp16)[name = tensor("reduce_mean_135_cast_fp16")]; + tensor sub_90_cast_fp16 = sub(x = reshape_180_cast_fp16, y = reduce_mean_135_cast_fp16)[name = tensor("sub_90_cast_fp16")]; + tensor square_45_cast_fp16 = square(x = sub_90_cast_fp16)[name = tensor("square_45_cast_fp16")]; + tensor reduce_mean_137_axes_0 = const()[name = tensor("reduce_mean_137_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_137_keep_dims_0 = const()[name = tensor("reduce_mean_137_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_137_cast_fp16 = reduce_mean(axes = reduce_mean_137_axes_0, keep_dims = reduce_mean_137_keep_dims_0, x = square_45_cast_fp16)[name = tensor("reduce_mean_137_cast_fp16")]; + tensor add_90_y_0_to_fp16 = const()[name = tensor("add_90_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_90_cast_fp16 = add(x = reduce_mean_137_cast_fp16, y = add_90_y_0_to_fp16)[name = tensor("add_90_cast_fp16")]; + tensor sqrt_45_cast_fp16 = sqrt(x = add_90_cast_fp16)[name = tensor("sqrt_45_cast_fp16")]; + tensor real_div_45_cast_fp16 = real_div(x = sub_90_cast_fp16, y = sqrt_45_cast_fp16)[name = tensor("real_div_45_cast_fp16")]; + tensor reshape_181_shape_0 = const()[name = tensor("reshape_181_shape_0"), val = tensor([2, 1280, 24, 40])]; + tensor reshape_181_cast_fp16 = reshape(shape = reshape_181_shape_0, x = real_div_45_cast_fp16)[name = tensor("reshape_181_cast_fp16")]; + tensor add_91_gamma_0_to_fp16 = const()[name = tensor("add_91_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(707646784)))]; + tensor add_91_beta_0_to_fp16 = const()[name = tensor("add_91_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(707649408)))]; + tensor add_91_epsilon_0_to_fp16 = const()[name = tensor("add_91_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_91_cast_fp16 = batch_norm(beta = add_91_beta_0_to_fp16, epsilon = add_91_epsilon_0_to_fp16, gamma = add_91_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_181_cast_fp16)[name = tensor("add_91_cast_fp16")]; + tensor input_397_cast_fp16 = silu(x = add_91_cast_fp16)[name = tensor("input_397_cast_fp16")]; + tensor var_3414 = const()[name = tensor("op_3414"), val = tensor([1, 1])]; + tensor var_3416 = const()[name = tensor("op_3416"), val = tensor([1, 1])]; + tensor hidden_states_239_pad_type_0 = const()[name = tensor("hidden_states_239_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_239_pad_0 = const()[name = tensor("hidden_states_239_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(707652032)))]; + tensor up_blocks_2_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(722397696)))]; + tensor hidden_states_239_cast_fp16 = conv(bias = up_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = var_3416, groups = var_3126, pad = hidden_states_239_pad_0, pad_type = hidden_states_239_pad_type_0, strides = var_3414, weight = up_blocks_2_resnets_1_conv1_weight_to_fp16, x = input_397_cast_fp16)[name = tensor("hidden_states_239_cast_fp16")]; + tensor var_3422 = const()[name = tensor("op_3422"), val = tensor([1, 1])]; + tensor var_3424 = const()[name = tensor("op_3424"), val = tensor([1, 1])]; + tensor temb_35_pad_type_0 = const()[name = tensor("temb_35_pad_type_0"), val = tensor("custom")]; + tensor temb_35_pad_0 = const()[name = tensor("temb_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(722399040)))]; + tensor up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(724037504)))]; + tensor temb_35_cast_fp16 = conv(bias = up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_3424, groups = var_3126, pad = temb_35_pad_0, pad_type = temb_35_pad_type_0, strides = var_3422, weight = up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16, x = cast_12)[name = tensor("temb_35_cast_fp16")]; + tensor input_401_cast_fp16 = add(x = hidden_states_239_cast_fp16, y = temb_35_cast_fp16)[name = tensor("input_401_cast_fp16")]; + tensor reshape_184_shape_0 = const()[name = tensor("reshape_184_shape_0"), val = tensor([2, 32, 20, 24, 40])]; + tensor reshape_184_cast_fp16 = reshape(shape = reshape_184_shape_0, x = input_401_cast_fp16)[name = tensor("reshape_184_cast_fp16")]; + tensor reduce_mean_138_axes_0 = const()[name = tensor("reduce_mean_138_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_138_keep_dims_0 = const()[name = tensor("reduce_mean_138_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_138_cast_fp16 = reduce_mean(axes = reduce_mean_138_axes_0, keep_dims = reduce_mean_138_keep_dims_0, x = reshape_184_cast_fp16)[name = tensor("reduce_mean_138_cast_fp16")]; + tensor sub_92_cast_fp16 = sub(x = reshape_184_cast_fp16, y = reduce_mean_138_cast_fp16)[name = tensor("sub_92_cast_fp16")]; + tensor square_46_cast_fp16 = square(x = sub_92_cast_fp16)[name = tensor("square_46_cast_fp16")]; + tensor reduce_mean_140_axes_0 = const()[name = tensor("reduce_mean_140_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_140_keep_dims_0 = const()[name = tensor("reduce_mean_140_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_140_cast_fp16 = reduce_mean(axes = reduce_mean_140_axes_0, keep_dims = reduce_mean_140_keep_dims_0, x = square_46_cast_fp16)[name = tensor("reduce_mean_140_cast_fp16")]; + tensor add_92_y_0_to_fp16 = const()[name = tensor("add_92_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_92_cast_fp16 = add(x = reduce_mean_140_cast_fp16, y = add_92_y_0_to_fp16)[name = tensor("add_92_cast_fp16")]; + tensor sqrt_46_cast_fp16 = sqrt(x = add_92_cast_fp16)[name = tensor("sqrt_46_cast_fp16")]; + tensor real_div_46_cast_fp16 = real_div(x = sub_92_cast_fp16, y = sqrt_46_cast_fp16)[name = tensor("real_div_46_cast_fp16")]; + tensor reshape_185_shape_0 = const()[name = tensor("reshape_185_shape_0"), val = tensor([2, 640, 24, 40])]; + tensor reshape_185_cast_fp16 = reshape(shape = reshape_185_shape_0, x = real_div_46_cast_fp16)[name = tensor("reshape_185_cast_fp16")]; + tensor add_93_gamma_0_to_fp16 = const()[name = tensor("add_93_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(724038848)))]; + tensor add_93_beta_0_to_fp16 = const()[name = tensor("add_93_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(724040192)))]; + tensor add_93_epsilon_0_to_fp16 = const()[name = tensor("add_93_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_93_cast_fp16 = batch_norm(beta = add_93_beta_0_to_fp16, epsilon = add_93_epsilon_0_to_fp16, gamma = add_93_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_185_cast_fp16)[name = tensor("add_93_cast_fp16")]; + tensor input_405_cast_fp16 = silu(x = add_93_cast_fp16)[name = tensor("input_405_cast_fp16")]; + tensor var_3434 = const()[name = tensor("op_3434"), val = tensor([1, 1])]; + tensor var_3436 = const()[name = tensor("op_3436"), val = tensor([1, 1])]; + tensor hidden_states_241_pad_type_0 = const()[name = tensor("hidden_states_241_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_241_pad_0 = const()[name = tensor("hidden_states_241_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(724041536)))]; + tensor up_blocks_2_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(731414400)))]; + tensor hidden_states_241_cast_fp16 = conv(bias = up_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = var_3436, groups = var_3126, pad = hidden_states_241_pad_0, pad_type = hidden_states_241_pad_type_0, strides = var_3434, weight = up_blocks_2_resnets_1_conv2_weight_to_fp16, x = input_405_cast_fp16)[name = tensor("hidden_states_241_cast_fp16")]; + tensor var_3441 = const()[name = tensor("op_3441"), val = tensor([1, 1])]; + tensor var_3443 = const()[name = tensor("op_3443"), val = tensor([1, 1])]; + tensor x_19_pad_type_0 = const()[name = tensor("x_19_pad_type_0"), val = tensor("custom")]; + tensor x_19_pad_0 = const()[name = tensor("x_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(731415744)))]; + tensor up_blocks_2_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(733054208)))]; + tensor x_19_cast_fp16 = conv(bias = up_blocks_2_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_3443, groups = var_3126, pad = x_19_pad_0, pad_type = x_19_pad_type_0, strides = var_3441, weight = up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16, x = input_393_cast_fp16)[name = tensor("x_19_cast_fp16")]; + tensor hidden_states_243_cast_fp16 = add(x = x_19_cast_fp16, y = hidden_states_241_cast_fp16)[name = tensor("hidden_states_243_cast_fp16")]; + tensor reshape_188_shape_0 = const()[name = tensor("reshape_188_shape_0"), val = tensor([2, 32, 20, 24, 40])]; + tensor reshape_188_cast_fp16 = reshape(shape = reshape_188_shape_0, x = hidden_states_243_cast_fp16)[name = tensor("reshape_188_cast_fp16")]; + tensor reduce_mean_141_axes_0 = const()[name = tensor("reduce_mean_141_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_141_keep_dims_0 = const()[name = tensor("reduce_mean_141_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_141_cast_fp16 = reduce_mean(axes = reduce_mean_141_axes_0, keep_dims = reduce_mean_141_keep_dims_0, x = reshape_188_cast_fp16)[name = tensor("reduce_mean_141_cast_fp16")]; + tensor sub_94_cast_fp16 = sub(x = reshape_188_cast_fp16, y = reduce_mean_141_cast_fp16)[name = tensor("sub_94_cast_fp16")]; + tensor square_47_cast_fp16 = square(x = sub_94_cast_fp16)[name = tensor("square_47_cast_fp16")]; + tensor reduce_mean_143_axes_0 = const()[name = tensor("reduce_mean_143_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_143_keep_dims_0 = const()[name = tensor("reduce_mean_143_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_143_cast_fp16 = reduce_mean(axes = reduce_mean_143_axes_0, keep_dims = reduce_mean_143_keep_dims_0, x = square_47_cast_fp16)[name = tensor("reduce_mean_143_cast_fp16")]; + tensor add_94_y_0_to_fp16 = const()[name = tensor("add_94_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_94_cast_fp16 = add(x = reduce_mean_143_cast_fp16, y = add_94_y_0_to_fp16)[name = tensor("add_94_cast_fp16")]; + tensor sqrt_47_cast_fp16 = sqrt(x = add_94_cast_fp16)[name = tensor("sqrt_47_cast_fp16")]; + tensor real_div_47_cast_fp16 = real_div(x = sub_94_cast_fp16, y = sqrt_47_cast_fp16)[name = tensor("real_div_47_cast_fp16")]; + tensor reshape_189_shape_0 = const()[name = tensor("reshape_189_shape_0"), val = tensor([2, 640, 24, 40])]; + tensor reshape_189_cast_fp16 = reshape(shape = reshape_189_shape_0, x = real_div_47_cast_fp16)[name = tensor("reshape_189_cast_fp16")]; + tensor add_95_gamma_0_to_fp16 = const()[name = tensor("add_95_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(733055552)))]; + tensor add_95_beta_0_to_fp16 = const()[name = tensor("add_95_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(733056896)))]; + tensor add_95_epsilon_0_to_fp16 = const()[name = tensor("add_95_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_95_cast_fp16 = batch_norm(beta = add_95_beta_0_to_fp16, epsilon = add_95_epsilon_0_to_fp16, gamma = add_95_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_189_cast_fp16)[name = tensor("add_95_cast_fp16")]; + tensor var_3463 = const()[name = tensor("op_3463"), val = tensor([1, 1])]; + tensor var_3465 = const()[name = tensor("op_3465"), val = tensor([1, 1])]; + tensor hidden_states_245_pad_type_0 = const()[name = tensor("hidden_states_245_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_245_pad_0 = const()[name = tensor("hidden_states_245_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(733058240)))]; + tensor up_blocks_2_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(733877504)))]; + tensor hidden_states_245_cast_fp16 = conv(bias = up_blocks_2_attentions_1_proj_in_bias_to_fp16, dilations = var_3465, groups = var_3126, pad = hidden_states_245_pad_0, pad_type = hidden_states_245_pad_type_0, strides = var_3463, weight = up_blocks_2_attentions_1_proj_in_weight_to_fp16, x = add_95_cast_fp16)[name = tensor("hidden_states_245_cast_fp16")]; + tensor var_3470 = const()[name = tensor("op_3470"), val = tensor([2, 640, 1, 960])]; + tensor inputs_67_cast_fp16 = reshape(shape = var_3470, x = hidden_states_245_cast_fp16)[name = tensor("inputs_67_cast_fp16")]; + tensor var_3480 = const()[name = tensor("op_3480"), val = tensor([1])]; + tensor channels_mean_67_cast_fp16 = reduce_mean(axes = var_3480, keep_dims = var_3121, x = inputs_67_cast_fp16)[name = tensor("channels_mean_67_cast_fp16")]; + tensor zero_mean_67_cast_fp16 = sub(x = inputs_67_cast_fp16, y = channels_mean_67_cast_fp16)[name = tensor("zero_mean_67_cast_fp16")]; + tensor zero_mean_sq_67_cast_fp16 = mul(x = zero_mean_67_cast_fp16, y = zero_mean_67_cast_fp16)[name = tensor("zero_mean_sq_67_cast_fp16")]; + tensor var_3484 = const()[name = tensor("op_3484"), val = tensor([1])]; + tensor var_3485_cast_fp16 = reduce_mean(axes = var_3484, keep_dims = var_3121, x = zero_mean_sq_67_cast_fp16)[name = tensor("op_3485_cast_fp16")]; + tensor var_3486_to_fp16 = const()[name = tensor("op_3486_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3487_cast_fp16 = add(x = var_3485_cast_fp16, y = var_3486_to_fp16)[name = tensor("op_3487_cast_fp16")]; + tensor denom_67_epsilon_0_to_fp16 = const()[name = tensor("denom_67_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_67_cast_fp16 = rsqrt(epsilon = denom_67_epsilon_0_to_fp16, x = var_3487_cast_fp16)[name = tensor("denom_67_cast_fp16")]; + tensor out_67_cast_fp16 = mul(x = zero_mean_67_cast_fp16, y = denom_67_cast_fp16)[name = tensor("out_67_cast_fp16")]; + tensor var_3491_to_fp16 = const()[name = tensor("op_3491_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(733878848)))]; + tensor var_3492_cast_fp16 = add(x = out_67_cast_fp16, y = var_3491_to_fp16)[name = tensor("op_3492_cast_fp16")]; + tensor var_3494_to_fp16 = const()[name = tensor("op_3494_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(733880192)))]; + tensor hidden_states_247_cast_fp16 = mul(x = var_3492_cast_fp16, y = var_3494_to_fp16)[name = tensor("hidden_states_247_cast_fp16")]; + tensor var_3501 = const()[name = tensor("op_3501"), val = tensor([1, 1])]; + tensor var_3503 = const()[name = tensor("op_3503"), val = tensor([1, 1])]; + tensor q_45_pad_type_0 = const()[name = tensor("q_45_pad_type_0"), val = tensor("custom")]; + tensor q_45_pad_0 = const()[name = tensor("q_45_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(733881536)))]; + tensor q_45_cast_fp16 = conv(dilations = var_3503, groups = var_3126, pad = q_45_pad_0, pad_type = q_45_pad_type_0, strides = var_3501, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_247_cast_fp16)[name = tensor("q_45_cast_fp16")]; + tensor var_3507 = const()[name = tensor("op_3507"), val = tensor([1, 1])]; + tensor var_3509 = const()[name = tensor("op_3509"), val = tensor([1, 1])]; + tensor k_45_pad_type_0 = const()[name = tensor("k_45_pad_type_0"), val = tensor("custom")]; + tensor k_45_pad_0 = const()[name = tensor("k_45_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(734700800)))]; + tensor k_45_cast_fp16 = conv(dilations = var_3509, groups = var_3126, pad = k_45_pad_0, pad_type = k_45_pad_type_0, strides = var_3507, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_247_cast_fp16)[name = tensor("k_45_cast_fp16")]; + tensor var_3513 = const()[name = tensor("op_3513"), val = tensor([1, 1])]; + tensor var_3515 = const()[name = tensor("op_3515"), val = tensor([1, 1])]; + tensor v_45_pad_type_0 = const()[name = tensor("v_45_pad_type_0"), val = tensor("custom")]; + tensor v_45_pad_0 = const()[name = tensor("v_45_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(735520064)))]; + tensor v_45_cast_fp16 = conv(dilations = var_3515, groups = var_3126, pad = v_45_pad_0, pad_type = v_45_pad_type_0, strides = var_3513, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_247_cast_fp16)[name = tensor("v_45_cast_fp16")]; + tensor var_3519 = const()[name = tensor("op_3519"), val = tensor([2, 10, 64, -1])]; + tensor var_3520_cast_fp16 = reshape(shape = var_3519, x = q_45_cast_fp16)[name = tensor("op_3520_cast_fp16")]; + tensor var_3521 = const()[name = tensor("op_3521"), val = tensor([2, 10, 64, -1])]; + tensor var_3522_cast_fp16 = reshape(shape = var_3521, x = k_45_cast_fp16)[name = tensor("op_3522_cast_fp16")]; + tensor var_3523 = const()[name = tensor("op_3523"), val = tensor([2, 10, 64, -1])]; + tensor var_3524_cast_fp16 = reshape(shape = var_3523, x = v_45_cast_fp16)[name = tensor("op_3524_cast_fp16")]; + tensor attn_weights_89_transpose_x_0 = const()[name = tensor("attn_weights_89_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_89_transpose_y_0 = const()[name = tensor("attn_weights_89_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_89_cast_fp16 = matmul(transpose_x = attn_weights_89_transpose_x_0, transpose_y = attn_weights_89_transpose_y_0, x = var_3520_cast_fp16, y = var_3522_cast_fp16)[name = tensor("attn_weights_89_cast_fp16")]; + tensor attn_weights_91_cast_fp16 = mul(x = attn_weights_89_cast_fp16, y = var_3117_to_fp16)[name = tensor("attn_weights_91_cast_fp16")]; + tensor var_3528_cast_fp16 = softmax(axis = var_3110, x = attn_weights_91_cast_fp16)[name = tensor("op_3528_cast_fp16")]; + tensor attn_45_transpose_x_0 = const()[name = tensor("attn_45_transpose_x_0"), val = tensor(false)]; + tensor attn_45_transpose_y_0 = const()[name = tensor("attn_45_transpose_y_0"), val = tensor(true)]; + tensor attn_45_cast_fp16 = matmul(transpose_x = attn_45_transpose_x_0, transpose_y = attn_45_transpose_y_0, x = var_3524_cast_fp16, y = var_3528_cast_fp16)[name = tensor("attn_45_cast_fp16")]; + tensor var_3532 = const()[name = tensor("op_3532"), val = tensor([2, 640, 1, -1])]; + tensor input_409_cast_fp16 = reshape(shape = var_3532, x = attn_45_cast_fp16)[name = tensor("input_409_cast_fp16")]; + tensor var_3537 = const()[name = tensor("op_3537"), val = tensor([1, 1])]; + tensor var_3539 = const()[name = tensor("op_3539"), val = tensor([1, 1])]; + tensor var_3541_pad_type_0 = const()[name = tensor("op_3541_pad_type_0"), val = tensor("custom")]; + tensor var_3541_pad_0 = const()[name = tensor("op_3541_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(736339328)))]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(737158592)))]; + tensor var_3541_cast_fp16 = conv(bias = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_3539, groups = var_3126, pad = var_3541_pad_0, pad_type = var_3541_pad_type_0, strides = var_3537, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_409_cast_fp16)[name = tensor("op_3541_cast_fp16")]; + tensor inputs_69_cast_fp16 = add(x = var_3541_cast_fp16, y = inputs_67_cast_fp16)[name = tensor("inputs_69_cast_fp16")]; + tensor var_3545 = const()[name = tensor("op_3545"), val = tensor([1])]; + tensor channels_mean_69_cast_fp16 = reduce_mean(axes = var_3545, keep_dims = var_3121, x = inputs_69_cast_fp16)[name = tensor("channels_mean_69_cast_fp16")]; + tensor zero_mean_69_cast_fp16 = sub(x = inputs_69_cast_fp16, y = channels_mean_69_cast_fp16)[name = tensor("zero_mean_69_cast_fp16")]; + tensor zero_mean_sq_69_cast_fp16 = mul(x = zero_mean_69_cast_fp16, y = zero_mean_69_cast_fp16)[name = tensor("zero_mean_sq_69_cast_fp16")]; + tensor var_3549 = const()[name = tensor("op_3549"), val = tensor([1])]; + tensor var_3550_cast_fp16 = reduce_mean(axes = var_3549, keep_dims = var_3121, x = zero_mean_sq_69_cast_fp16)[name = tensor("op_3550_cast_fp16")]; + tensor var_3551_to_fp16 = const()[name = tensor("op_3551_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3552_cast_fp16 = add(x = var_3550_cast_fp16, y = var_3551_to_fp16)[name = tensor("op_3552_cast_fp16")]; + tensor denom_69_epsilon_0_to_fp16 = const()[name = tensor("denom_69_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_69_cast_fp16 = rsqrt(epsilon = denom_69_epsilon_0_to_fp16, x = var_3552_cast_fp16)[name = tensor("denom_69_cast_fp16")]; + tensor out_69_cast_fp16 = mul(x = zero_mean_69_cast_fp16, y = denom_69_cast_fp16)[name = tensor("out_69_cast_fp16")]; + tensor var_3556_to_fp16 = const()[name = tensor("op_3556_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(737159936)))]; + tensor var_3557_cast_fp16 = add(x = out_69_cast_fp16, y = var_3556_to_fp16)[name = tensor("op_3557_cast_fp16")]; + tensor var_3559_to_fp16 = const()[name = tensor("op_3559_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(737161280)))]; + tensor hidden_states_249_cast_fp16 = mul(x = var_3557_cast_fp16, y = var_3559_to_fp16)[name = tensor("hidden_states_249_cast_fp16")]; + tensor var_3566 = const()[name = tensor("op_3566"), val = tensor([1, 1])]; + tensor var_3568 = const()[name = tensor("op_3568"), val = tensor([1, 1])]; + tensor q_47_pad_type_0 = const()[name = tensor("q_47_pad_type_0"), val = tensor("custom")]; + tensor q_47_pad_0 = const()[name = tensor("q_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(737162624)))]; + tensor q_47_cast_fp16 = conv(dilations = var_3568, groups = var_3126, pad = q_47_pad_0, pad_type = q_47_pad_type_0, strides = var_3566, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_249_cast_fp16)[name = tensor("q_47_cast_fp16")]; + tensor var_3572 = const()[name = tensor("op_3572"), val = tensor([1, 1])]; + tensor var_3574 = const()[name = tensor("op_3574"), val = tensor([1, 1])]; + tensor k_47_pad_type_0 = const()[name = tensor("k_47_pad_type_0"), val = tensor("custom")]; + tensor k_47_pad_0 = const()[name = tensor("k_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(737981888)))]; + tensor k_47_cast_fp16 = conv(dilations = var_3574, groups = var_3126, pad = k_47_pad_0, pad_type = k_47_pad_type_0, strides = var_3572, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_47_cast_fp16")]; + tensor var_3578 = const()[name = tensor("op_3578"), val = tensor([1, 1])]; + tensor var_3580 = const()[name = tensor("op_3580"), val = tensor([1, 1])]; + tensor v_47_pad_type_0 = const()[name = tensor("v_47_pad_type_0"), val = tensor("custom")]; + tensor v_47_pad_0 = const()[name = tensor("v_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(739292672)))]; + tensor v_47_cast_fp16 = conv(dilations = var_3580, groups = var_3126, pad = v_47_pad_0, pad_type = v_47_pad_type_0, strides = var_3578, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_47_cast_fp16")]; + tensor var_3584 = const()[name = tensor("op_3584"), val = tensor([2, 10, 64, -1])]; + tensor var_3585_cast_fp16 = reshape(shape = var_3584, x = q_47_cast_fp16)[name = tensor("op_3585_cast_fp16")]; + tensor var_3586 = const()[name = tensor("op_3586"), val = tensor([2, 10, 64, -1])]; + tensor var_3587_cast_fp16 = reshape(shape = var_3586, x = k_47_cast_fp16)[name = tensor("op_3587_cast_fp16")]; + tensor var_3588 = const()[name = tensor("op_3588"), val = tensor([2, 10, 64, -1])]; + tensor var_3589_cast_fp16 = reshape(shape = var_3588, x = v_47_cast_fp16)[name = tensor("op_3589_cast_fp16")]; + tensor attn_weights_93_transpose_x_0 = const()[name = tensor("attn_weights_93_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_93_transpose_y_0 = const()[name = tensor("attn_weights_93_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_93_cast_fp16 = matmul(transpose_x = attn_weights_93_transpose_x_0, transpose_y = attn_weights_93_transpose_y_0, x = var_3585_cast_fp16, y = var_3587_cast_fp16)[name = tensor("attn_weights_93_cast_fp16")]; + tensor attn_weights_95_cast_fp16 = mul(x = attn_weights_93_cast_fp16, y = var_3117_to_fp16)[name = tensor("attn_weights_95_cast_fp16")]; + tensor var_3593_cast_fp16 = softmax(axis = var_3110, x = attn_weights_95_cast_fp16)[name = tensor("op_3593_cast_fp16")]; + tensor attn_47_transpose_x_0 = const()[name = tensor("attn_47_transpose_x_0"), val = tensor(false)]; + tensor attn_47_transpose_y_0 = const()[name = tensor("attn_47_transpose_y_0"), val = tensor(true)]; + tensor attn_47_cast_fp16 = matmul(transpose_x = attn_47_transpose_x_0, transpose_y = attn_47_transpose_y_0, x = var_3589_cast_fp16, y = var_3593_cast_fp16)[name = tensor("attn_47_cast_fp16")]; + tensor var_3597 = const()[name = tensor("op_3597"), val = tensor([2, 640, 1, -1])]; + tensor input_411_cast_fp16 = reshape(shape = var_3597, x = attn_47_cast_fp16)[name = tensor("input_411_cast_fp16")]; + tensor var_3602 = const()[name = tensor("op_3602"), val = tensor([1, 1])]; + tensor var_3604 = const()[name = tensor("op_3604"), val = tensor([1, 1])]; + tensor var_3606_pad_type_0 = const()[name = tensor("op_3606_pad_type_0"), val = tensor("custom")]; + tensor var_3606_pad_0 = const()[name = tensor("op_3606_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(740603456)))]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(741422720)))]; + tensor var_3606_cast_fp16 = conv(bias = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_3604, groups = var_3126, pad = var_3606_pad_0, pad_type = var_3606_pad_type_0, strides = var_3602, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_411_cast_fp16)[name = tensor("op_3606_cast_fp16")]; + tensor inputs_71_cast_fp16 = add(x = var_3606_cast_fp16, y = inputs_69_cast_fp16)[name = tensor("inputs_71_cast_fp16")]; + tensor var_3610 = const()[name = tensor("op_3610"), val = tensor([1])]; + tensor channels_mean_71_cast_fp16 = reduce_mean(axes = var_3610, keep_dims = var_3121, x = inputs_71_cast_fp16)[name = tensor("channels_mean_71_cast_fp16")]; + tensor zero_mean_71_cast_fp16 = sub(x = inputs_71_cast_fp16, y = channels_mean_71_cast_fp16)[name = tensor("zero_mean_71_cast_fp16")]; + tensor zero_mean_sq_71_cast_fp16 = mul(x = zero_mean_71_cast_fp16, y = zero_mean_71_cast_fp16)[name = tensor("zero_mean_sq_71_cast_fp16")]; + tensor var_3614 = const()[name = tensor("op_3614"), val = tensor([1])]; + tensor var_3615_cast_fp16 = reduce_mean(axes = var_3614, keep_dims = var_3121, x = zero_mean_sq_71_cast_fp16)[name = tensor("op_3615_cast_fp16")]; + tensor var_3616_to_fp16 = const()[name = tensor("op_3616_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3617_cast_fp16 = add(x = var_3615_cast_fp16, y = var_3616_to_fp16)[name = tensor("op_3617_cast_fp16")]; + tensor denom_71_epsilon_0_to_fp16 = const()[name = tensor("denom_71_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_71_cast_fp16 = rsqrt(epsilon = denom_71_epsilon_0_to_fp16, x = var_3617_cast_fp16)[name = tensor("denom_71_cast_fp16")]; + tensor out_71_cast_fp16 = mul(x = zero_mean_71_cast_fp16, y = denom_71_cast_fp16)[name = tensor("out_71_cast_fp16")]; + tensor var_3621_to_fp16 = const()[name = tensor("op_3621_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(741424064)))]; + tensor var_3622_cast_fp16 = add(x = out_71_cast_fp16, y = var_3621_to_fp16)[name = tensor("op_3622_cast_fp16")]; + tensor var_3624_to_fp16 = const()[name = tensor("op_3624_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(741425408)))]; + tensor input_413_cast_fp16 = mul(x = var_3622_cast_fp16, y = var_3624_to_fp16)[name = tensor("input_413_cast_fp16")]; + tensor var_3632 = const()[name = tensor("op_3632"), val = tensor([1, 1])]; + tensor var_3634 = const()[name = tensor("op_3634"), val = tensor([1, 1])]; + tensor var_3636_pad_type_0 = const()[name = tensor("op_3636_pad_type_0"), val = tensor("custom")]; + tensor var_3636_pad_0 = const()[name = tensor("op_3636_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(741426752)))]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(747980416)))]; + tensor var_3636_cast_fp16 = conv(bias = up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_3634, groups = var_3126, pad = var_3636_pad_0, pad_type = var_3636_pad_type_0, strides = var_3632, weight = up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_413_cast_fp16)[name = tensor("op_3636_cast_fp16")]; + tensor var_3637_split_sizes_0 = const()[name = tensor("op_3637_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_3637_axis_0 = const()[name = tensor("op_3637_axis_0"), val = tensor(1)]; + tensor var_3637_cast_fp16_0, tensor var_3637_cast_fp16_1 = split(axis = var_3637_axis_0, split_sizes = var_3637_split_sizes_0, x = var_3636_cast_fp16)[name = tensor("op_3637_cast_fp16")]; + tensor var_3639_mode_0 = const()[name = tensor("op_3639_mode_0"), val = tensor("EXACT")]; + tensor var_3639_cast_fp16 = gelu(mode = var_3639_mode_0, x = var_3637_cast_fp16_1)[name = tensor("op_3639_cast_fp16")]; + tensor input_415_cast_fp16 = mul(x = var_3637_cast_fp16_0, y = var_3639_cast_fp16)[name = tensor("input_415_cast_fp16")]; + tensor var_3643 = const()[name = tensor("op_3643"), val = tensor([1, 1])]; + tensor var_3645 = const()[name = tensor("op_3645"), val = tensor([1, 1])]; + tensor var_3647_pad_type_0 = const()[name = tensor("op_3647_pad_type_0"), val = tensor("custom")]; + tensor var_3647_pad_0 = const()[name = tensor("op_3647_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(747990720)))]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(751267584)))]; + tensor var_3647_cast_fp16 = conv(bias = up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_3645, groups = var_3126, pad = var_3647_pad_0, pad_type = var_3647_pad_type_0, strides = var_3643, weight = up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_415_cast_fp16)[name = tensor("op_3647_cast_fp16")]; + tensor hidden_states_253_cast_fp16 = add(x = var_3647_cast_fp16, y = inputs_71_cast_fp16)[name = tensor("hidden_states_253_cast_fp16")]; + tensor var_3649 = const()[name = tensor("op_3649"), val = tensor([2, 640, 24, 40])]; + tensor input_417_cast_fp16 = reshape(shape = var_3649, x = hidden_states_253_cast_fp16)[name = tensor("input_417_cast_fp16")]; + tensor var_3653 = const()[name = tensor("op_3653"), val = tensor([1, 1])]; + tensor var_3655 = const()[name = tensor("op_3655"), val = tensor([1, 1])]; + tensor hidden_states_255_pad_type_0 = const()[name = tensor("hidden_states_255_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_255_pad_0 = const()[name = tensor("hidden_states_255_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(751268928)))]; + tensor up_blocks_2_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(752088192)))]; + tensor hidden_states_255_cast_fp16 = conv(bias = up_blocks_2_attentions_1_proj_out_bias_to_fp16, dilations = var_3655, groups = var_3126, pad = hidden_states_255_pad_0, pad_type = hidden_states_255_pad_type_0, strides = var_3653, weight = up_blocks_2_attentions_1_proj_out_weight_to_fp16, x = input_417_cast_fp16)[name = tensor("hidden_states_255_cast_fp16")]; + tensor hidden_states_257_cast_fp16 = add(x = hidden_states_255_cast_fp16, y = hidden_states_243_cast_fp16)[name = tensor("hidden_states_257_cast_fp16")]; + tensor input_419_interleave_0 = const()[name = tensor("input_419_interleave_0"), val = tensor(false)]; + tensor cast_9 = cast(dtype = cast_8_dtype_0, x = input_63_cast_fp16)[name = tensor("cast_9")]; + tensor input_419_cast_fp16 = concat(axis = var_3126, interleave = input_419_interleave_0, values = (hidden_states_257_cast_fp16, cast_9))[name = tensor("input_419_cast_fp16")]; + tensor reshape_192_shape_0 = const()[name = tensor("reshape_192_shape_0"), val = tensor([2, 32, 30, 24, 40])]; + tensor reshape_192_cast_fp16 = reshape(shape = reshape_192_shape_0, x = input_419_cast_fp16)[name = tensor("reshape_192_cast_fp16")]; + tensor reduce_mean_144_axes_0 = const()[name = tensor("reduce_mean_144_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_144_keep_dims_0 = const()[name = tensor("reduce_mean_144_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_144_cast_fp16 = reduce_mean(axes = reduce_mean_144_axes_0, keep_dims = reduce_mean_144_keep_dims_0, x = reshape_192_cast_fp16)[name = tensor("reduce_mean_144_cast_fp16")]; + tensor sub_96_cast_fp16 = sub(x = reshape_192_cast_fp16, y = reduce_mean_144_cast_fp16)[name = tensor("sub_96_cast_fp16")]; + tensor square_48_cast_fp16 = square(x = sub_96_cast_fp16)[name = tensor("square_48_cast_fp16")]; + tensor reduce_mean_146_axes_0 = const()[name = tensor("reduce_mean_146_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_146_keep_dims_0 = const()[name = tensor("reduce_mean_146_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_146_cast_fp16 = reduce_mean(axes = reduce_mean_146_axes_0, keep_dims = reduce_mean_146_keep_dims_0, x = square_48_cast_fp16)[name = tensor("reduce_mean_146_cast_fp16")]; + tensor add_96_y_0_to_fp16 = const()[name = tensor("add_96_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_96_cast_fp16 = add(x = reduce_mean_146_cast_fp16, y = add_96_y_0_to_fp16)[name = tensor("add_96_cast_fp16")]; + tensor sqrt_48_cast_fp16 = sqrt(x = add_96_cast_fp16)[name = tensor("sqrt_48_cast_fp16")]; + tensor real_div_48_cast_fp16 = real_div(x = sub_96_cast_fp16, y = sqrt_48_cast_fp16)[name = tensor("real_div_48_cast_fp16")]; + tensor reshape_193_shape_0 = const()[name = tensor("reshape_193_shape_0"), val = tensor([2, 960, 24, 40])]; + tensor reshape_193_cast_fp16 = reshape(shape = reshape_193_shape_0, x = real_div_48_cast_fp16)[name = tensor("reshape_193_cast_fp16")]; + tensor add_97_mean_0_to_fp16 = const()[name = tensor("add_97_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(752089536)))]; + tensor add_97_variance_0_to_fp16 = const()[name = tensor("add_97_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(752091520)))]; + tensor add_97_gamma_0_to_fp16 = const()[name = tensor("add_97_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(752093504)))]; + tensor add_97_beta_0_to_fp16 = const()[name = tensor("add_97_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(752095488)))]; + tensor add_97_epsilon_0_to_fp16 = const()[name = tensor("add_97_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_97_cast_fp16 = batch_norm(beta = add_97_beta_0_to_fp16, epsilon = add_97_epsilon_0_to_fp16, gamma = add_97_gamma_0_to_fp16, mean = add_97_mean_0_to_fp16, variance = add_97_variance_0_to_fp16, x = reshape_193_cast_fp16)[name = tensor("add_97_cast_fp16")]; + tensor input_423_cast_fp16 = silu(x = add_97_cast_fp16)[name = tensor("input_423_cast_fp16")]; + tensor var_3673 = const()[name = tensor("op_3673"), val = tensor([1, 1])]; + tensor var_3675 = const()[name = tensor("op_3675"), val = tensor([1, 1])]; + tensor hidden_states_259_pad_type_0 = const()[name = tensor("hidden_states_259_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_259_pad_0 = const()[name = tensor("hidden_states_259_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_2_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(752097472)))]; + tensor up_blocks_2_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(763156736)))]; + tensor hidden_states_259_cast_fp16 = conv(bias = up_blocks_2_resnets_2_conv1_bias_to_fp16, dilations = var_3675, groups = var_3126, pad = hidden_states_259_pad_0, pad_type = hidden_states_259_pad_type_0, strides = var_3673, weight = up_blocks_2_resnets_2_conv1_weight_to_fp16, x = input_423_cast_fp16)[name = tensor("hidden_states_259_cast_fp16")]; + tensor var_3681 = const()[name = tensor("op_3681"), val = tensor([1, 1])]; + tensor var_3683 = const()[name = tensor("op_3683"), val = tensor([1, 1])]; + tensor temb_37_pad_type_0 = const()[name = tensor("temb_37_pad_type_0"), val = tensor("custom")]; + tensor temb_37_pad_0 = const()[name = tensor("temb_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(763158080)))]; + tensor up_blocks_2_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(764796544)))]; + tensor temb_37_cast_fp16 = conv(bias = up_blocks_2_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_3683, groups = var_3126, pad = temb_37_pad_0, pad_type = temb_37_pad_type_0, strides = var_3681, weight = up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16, x = cast_12)[name = tensor("temb_37_cast_fp16")]; + tensor input_427_cast_fp16 = add(x = hidden_states_259_cast_fp16, y = temb_37_cast_fp16)[name = tensor("input_427_cast_fp16")]; + tensor reshape_196_shape_0 = const()[name = tensor("reshape_196_shape_0"), val = tensor([2, 32, 20, 24, 40])]; + tensor reshape_196_cast_fp16 = reshape(shape = reshape_196_shape_0, x = input_427_cast_fp16)[name = tensor("reshape_196_cast_fp16")]; + tensor reduce_mean_147_axes_0 = const()[name = tensor("reduce_mean_147_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_147_keep_dims_0 = const()[name = tensor("reduce_mean_147_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_147_cast_fp16 = reduce_mean(axes = reduce_mean_147_axes_0, keep_dims = reduce_mean_147_keep_dims_0, x = reshape_196_cast_fp16)[name = tensor("reduce_mean_147_cast_fp16")]; + tensor sub_98_cast_fp16 = sub(x = reshape_196_cast_fp16, y = reduce_mean_147_cast_fp16)[name = tensor("sub_98_cast_fp16")]; + tensor square_49_cast_fp16 = square(x = sub_98_cast_fp16)[name = tensor("square_49_cast_fp16")]; + tensor reduce_mean_149_axes_0 = const()[name = tensor("reduce_mean_149_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_149_keep_dims_0 = const()[name = tensor("reduce_mean_149_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_149_cast_fp16 = reduce_mean(axes = reduce_mean_149_axes_0, keep_dims = reduce_mean_149_keep_dims_0, x = square_49_cast_fp16)[name = tensor("reduce_mean_149_cast_fp16")]; + tensor add_98_y_0_to_fp16 = const()[name = tensor("add_98_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_98_cast_fp16 = add(x = reduce_mean_149_cast_fp16, y = add_98_y_0_to_fp16)[name = tensor("add_98_cast_fp16")]; + tensor sqrt_49_cast_fp16 = sqrt(x = add_98_cast_fp16)[name = tensor("sqrt_49_cast_fp16")]; + tensor real_div_49_cast_fp16 = real_div(x = sub_98_cast_fp16, y = sqrt_49_cast_fp16)[name = tensor("real_div_49_cast_fp16")]; + tensor reshape_197_shape_0 = const()[name = tensor("reshape_197_shape_0"), val = tensor([2, 640, 24, 40])]; + tensor reshape_197_cast_fp16 = reshape(shape = reshape_197_shape_0, x = real_div_49_cast_fp16)[name = tensor("reshape_197_cast_fp16")]; + tensor add_99_gamma_0_to_fp16 = const()[name = tensor("add_99_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(764797888)))]; + tensor add_99_beta_0_to_fp16 = const()[name = tensor("add_99_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(764799232)))]; + tensor add_99_epsilon_0_to_fp16 = const()[name = tensor("add_99_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_99_cast_fp16 = batch_norm(beta = add_99_beta_0_to_fp16, epsilon = add_99_epsilon_0_to_fp16, gamma = add_99_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_197_cast_fp16)[name = tensor("add_99_cast_fp16")]; + tensor input_431_cast_fp16 = silu(x = add_99_cast_fp16)[name = tensor("input_431_cast_fp16")]; + tensor var_3693 = const()[name = tensor("op_3693"), val = tensor([1, 1])]; + tensor var_3695 = const()[name = tensor("op_3695"), val = tensor([1, 1])]; + tensor hidden_states_261_pad_type_0 = const()[name = tensor("hidden_states_261_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_261_pad_0 = const()[name = tensor("hidden_states_261_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_2_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(764800576)))]; + tensor up_blocks_2_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(772173440)))]; + tensor hidden_states_261_cast_fp16 = conv(bias = up_blocks_2_resnets_2_conv2_bias_to_fp16, dilations = var_3695, groups = var_3126, pad = hidden_states_261_pad_0, pad_type = hidden_states_261_pad_type_0, strides = var_3693, weight = up_blocks_2_resnets_2_conv2_weight_to_fp16, x = input_431_cast_fp16)[name = tensor("hidden_states_261_cast_fp16")]; + tensor var_3700 = const()[name = tensor("op_3700"), val = tensor([1, 1])]; + tensor var_3702 = const()[name = tensor("op_3702"), val = tensor([1, 1])]; + tensor x_21_pad_type_0 = const()[name = tensor("x_21_pad_type_0"), val = tensor("custom")]; + tensor x_21_pad_0 = const()[name = tensor("x_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(772174784)))]; + tensor up_blocks_2_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(773403648)))]; + tensor x_21_cast_fp16 = conv(bias = up_blocks_2_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_3702, groups = var_3126, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = var_3700, weight = up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16, x = input_419_cast_fp16)[name = tensor("x_21_cast_fp16")]; + tensor hidden_states_263_cast_fp16 = add(x = x_21_cast_fp16, y = hidden_states_261_cast_fp16)[name = tensor("hidden_states_263_cast_fp16")]; + tensor reshape_200_shape_0 = const()[name = tensor("reshape_200_shape_0"), val = tensor([2, 32, 20, 24, 40])]; + tensor reshape_200_cast_fp16 = reshape(shape = reshape_200_shape_0, x = hidden_states_263_cast_fp16)[name = tensor("reshape_200_cast_fp16")]; + tensor reduce_mean_150_axes_0 = const()[name = tensor("reduce_mean_150_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_150_keep_dims_0 = const()[name = tensor("reduce_mean_150_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_150_cast_fp16 = reduce_mean(axes = reduce_mean_150_axes_0, keep_dims = reduce_mean_150_keep_dims_0, x = reshape_200_cast_fp16)[name = tensor("reduce_mean_150_cast_fp16")]; + tensor sub_100_cast_fp16 = sub(x = reshape_200_cast_fp16, y = reduce_mean_150_cast_fp16)[name = tensor("sub_100_cast_fp16")]; + tensor square_50_cast_fp16 = square(x = sub_100_cast_fp16)[name = tensor("square_50_cast_fp16")]; + tensor reduce_mean_152_axes_0 = const()[name = tensor("reduce_mean_152_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_152_keep_dims_0 = const()[name = tensor("reduce_mean_152_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_152_cast_fp16 = reduce_mean(axes = reduce_mean_152_axes_0, keep_dims = reduce_mean_152_keep_dims_0, x = square_50_cast_fp16)[name = tensor("reduce_mean_152_cast_fp16")]; + tensor add_100_y_0_to_fp16 = const()[name = tensor("add_100_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_100_cast_fp16 = add(x = reduce_mean_152_cast_fp16, y = add_100_y_0_to_fp16)[name = tensor("add_100_cast_fp16")]; + tensor sqrt_50_cast_fp16 = sqrt(x = add_100_cast_fp16)[name = tensor("sqrt_50_cast_fp16")]; + tensor real_div_50_cast_fp16 = real_div(x = sub_100_cast_fp16, y = sqrt_50_cast_fp16)[name = tensor("real_div_50_cast_fp16")]; + tensor reshape_201_shape_0 = const()[name = tensor("reshape_201_shape_0"), val = tensor([2, 640, 24, 40])]; + tensor reshape_201_cast_fp16 = reshape(shape = reshape_201_shape_0, x = real_div_50_cast_fp16)[name = tensor("reshape_201_cast_fp16")]; + tensor add_101_gamma_0_to_fp16 = const()[name = tensor("add_101_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(773404992)))]; + tensor add_101_beta_0_to_fp16 = const()[name = tensor("add_101_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(773406336)))]; + tensor add_101_epsilon_0_to_fp16 = const()[name = tensor("add_101_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_101_cast_fp16 = batch_norm(beta = add_101_beta_0_to_fp16, epsilon = add_101_epsilon_0_to_fp16, gamma = add_101_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_201_cast_fp16)[name = tensor("add_101_cast_fp16")]; + tensor var_3722 = const()[name = tensor("op_3722"), val = tensor([1, 1])]; + tensor var_3724 = const()[name = tensor("op_3724"), val = tensor([1, 1])]; + tensor hidden_states_265_pad_type_0 = const()[name = tensor("hidden_states_265_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_265_pad_0 = const()[name = tensor("hidden_states_265_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(773407680)))]; + tensor up_blocks_2_attentions_2_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(774226944)))]; + tensor hidden_states_265_cast_fp16 = conv(bias = up_blocks_2_attentions_2_proj_in_bias_to_fp16, dilations = var_3724, groups = var_3126, pad = hidden_states_265_pad_0, pad_type = hidden_states_265_pad_type_0, strides = var_3722, weight = up_blocks_2_attentions_2_proj_in_weight_to_fp16, x = add_101_cast_fp16)[name = tensor("hidden_states_265_cast_fp16")]; + tensor var_3729 = const()[name = tensor("op_3729"), val = tensor([2, 640, 1, 960])]; + tensor inputs_73_cast_fp16 = reshape(shape = var_3729, x = hidden_states_265_cast_fp16)[name = tensor("inputs_73_cast_fp16")]; + tensor var_3739 = const()[name = tensor("op_3739"), val = tensor([1])]; + tensor channels_mean_73_cast_fp16 = reduce_mean(axes = var_3739, keep_dims = var_3121, x = inputs_73_cast_fp16)[name = tensor("channels_mean_73_cast_fp16")]; + tensor zero_mean_73_cast_fp16 = sub(x = inputs_73_cast_fp16, y = channels_mean_73_cast_fp16)[name = tensor("zero_mean_73_cast_fp16")]; + tensor zero_mean_sq_73_cast_fp16 = mul(x = zero_mean_73_cast_fp16, y = zero_mean_73_cast_fp16)[name = tensor("zero_mean_sq_73_cast_fp16")]; + tensor var_3743 = const()[name = tensor("op_3743"), val = tensor([1])]; + tensor var_3744_cast_fp16 = reduce_mean(axes = var_3743, keep_dims = var_3121, x = zero_mean_sq_73_cast_fp16)[name = tensor("op_3744_cast_fp16")]; + tensor var_3745_to_fp16 = const()[name = tensor("op_3745_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3746_cast_fp16 = add(x = var_3744_cast_fp16, y = var_3745_to_fp16)[name = tensor("op_3746_cast_fp16")]; + tensor denom_73_epsilon_0_to_fp16 = const()[name = tensor("denom_73_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_73_cast_fp16 = rsqrt(epsilon = denom_73_epsilon_0_to_fp16, x = var_3746_cast_fp16)[name = tensor("denom_73_cast_fp16")]; + tensor out_73_cast_fp16 = mul(x = zero_mean_73_cast_fp16, y = denom_73_cast_fp16)[name = tensor("out_73_cast_fp16")]; + tensor var_3750_to_fp16 = const()[name = tensor("op_3750_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(774228288)))]; + tensor var_3751_cast_fp16 = add(x = out_73_cast_fp16, y = var_3750_to_fp16)[name = tensor("op_3751_cast_fp16")]; + tensor var_3753_to_fp16 = const()[name = tensor("op_3753_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(774229632)))]; + tensor hidden_states_267_cast_fp16 = mul(x = var_3751_cast_fp16, y = var_3753_to_fp16)[name = tensor("hidden_states_267_cast_fp16")]; + tensor var_3760 = const()[name = tensor("op_3760"), val = tensor([1, 1])]; + tensor var_3762 = const()[name = tensor("op_3762"), val = tensor([1, 1])]; + tensor q_49_pad_type_0 = const()[name = tensor("q_49_pad_type_0"), val = tensor("custom")]; + tensor q_49_pad_0 = const()[name = tensor("q_49_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(774230976)))]; + tensor q_49_cast_fp16 = conv(dilations = var_3762, groups = var_3126, pad = q_49_pad_0, pad_type = q_49_pad_type_0, strides = var_3760, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_267_cast_fp16)[name = tensor("q_49_cast_fp16")]; + tensor var_3766 = const()[name = tensor("op_3766"), val = tensor([1, 1])]; + tensor var_3768 = const()[name = tensor("op_3768"), val = tensor([1, 1])]; + tensor k_49_pad_type_0 = const()[name = tensor("k_49_pad_type_0"), val = tensor("custom")]; + tensor k_49_pad_0 = const()[name = tensor("k_49_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(775050240)))]; + tensor k_49_cast_fp16 = conv(dilations = var_3768, groups = var_3126, pad = k_49_pad_0, pad_type = k_49_pad_type_0, strides = var_3766, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_267_cast_fp16)[name = tensor("k_49_cast_fp16")]; + tensor var_3772 = const()[name = tensor("op_3772"), val = tensor([1, 1])]; + tensor var_3774 = const()[name = tensor("op_3774"), val = tensor([1, 1])]; + tensor v_49_pad_type_0 = const()[name = tensor("v_49_pad_type_0"), val = tensor("custom")]; + tensor v_49_pad_0 = const()[name = tensor("v_49_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(775869504)))]; + tensor v_49_cast_fp16 = conv(dilations = var_3774, groups = var_3126, pad = v_49_pad_0, pad_type = v_49_pad_type_0, strides = var_3772, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_267_cast_fp16)[name = tensor("v_49_cast_fp16")]; + tensor var_3778 = const()[name = tensor("op_3778"), val = tensor([2, 10, 64, -1])]; + tensor var_3779_cast_fp16 = reshape(shape = var_3778, x = q_49_cast_fp16)[name = tensor("op_3779_cast_fp16")]; + tensor var_3780 = const()[name = tensor("op_3780"), val = tensor([2, 10, 64, -1])]; + tensor var_3781_cast_fp16 = reshape(shape = var_3780, x = k_49_cast_fp16)[name = tensor("op_3781_cast_fp16")]; + tensor var_3782 = const()[name = tensor("op_3782"), val = tensor([2, 10, 64, -1])]; + tensor var_3783_cast_fp16 = reshape(shape = var_3782, x = v_49_cast_fp16)[name = tensor("op_3783_cast_fp16")]; + tensor attn_weights_97_transpose_x_0 = const()[name = tensor("attn_weights_97_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_97_transpose_y_0 = const()[name = tensor("attn_weights_97_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_97_cast_fp16 = matmul(transpose_x = attn_weights_97_transpose_x_0, transpose_y = attn_weights_97_transpose_y_0, x = var_3779_cast_fp16, y = var_3781_cast_fp16)[name = tensor("attn_weights_97_cast_fp16")]; + tensor attn_weights_99_cast_fp16 = mul(x = attn_weights_97_cast_fp16, y = var_3117_to_fp16)[name = tensor("attn_weights_99_cast_fp16")]; + tensor var_3787_cast_fp16 = softmax(axis = var_3110, x = attn_weights_99_cast_fp16)[name = tensor("op_3787_cast_fp16")]; + tensor attn_49_transpose_x_0 = const()[name = tensor("attn_49_transpose_x_0"), val = tensor(false)]; + tensor attn_49_transpose_y_0 = const()[name = tensor("attn_49_transpose_y_0"), val = tensor(true)]; + tensor attn_49_cast_fp16 = matmul(transpose_x = attn_49_transpose_x_0, transpose_y = attn_49_transpose_y_0, x = var_3783_cast_fp16, y = var_3787_cast_fp16)[name = tensor("attn_49_cast_fp16")]; + tensor var_3791 = const()[name = tensor("op_3791"), val = tensor([2, 640, 1, -1])]; + tensor input_435_cast_fp16 = reshape(shape = var_3791, x = attn_49_cast_fp16)[name = tensor("input_435_cast_fp16")]; + tensor var_3796 = const()[name = tensor("op_3796"), val = tensor([1, 1])]; + tensor var_3798 = const()[name = tensor("op_3798"), val = tensor([1, 1])]; + tensor var_3800_pad_type_0 = const()[name = tensor("op_3800_pad_type_0"), val = tensor("custom")]; + tensor var_3800_pad_0 = const()[name = tensor("op_3800_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(776688768)))]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(777508032)))]; + tensor var_3800_cast_fp16 = conv(bias = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_3798, groups = var_3126, pad = var_3800_pad_0, pad_type = var_3800_pad_type_0, strides = var_3796, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_435_cast_fp16)[name = tensor("op_3800_cast_fp16")]; + tensor inputs_75_cast_fp16 = add(x = var_3800_cast_fp16, y = inputs_73_cast_fp16)[name = tensor("inputs_75_cast_fp16")]; + tensor var_3804 = const()[name = tensor("op_3804"), val = tensor([1])]; + tensor channels_mean_75_cast_fp16 = reduce_mean(axes = var_3804, keep_dims = var_3121, x = inputs_75_cast_fp16)[name = tensor("channels_mean_75_cast_fp16")]; + tensor zero_mean_75_cast_fp16 = sub(x = inputs_75_cast_fp16, y = channels_mean_75_cast_fp16)[name = tensor("zero_mean_75_cast_fp16")]; + tensor zero_mean_sq_75_cast_fp16 = mul(x = zero_mean_75_cast_fp16, y = zero_mean_75_cast_fp16)[name = tensor("zero_mean_sq_75_cast_fp16")]; + tensor var_3808 = const()[name = tensor("op_3808"), val = tensor([1])]; + tensor var_3809_cast_fp16 = reduce_mean(axes = var_3808, keep_dims = var_3121, x = zero_mean_sq_75_cast_fp16)[name = tensor("op_3809_cast_fp16")]; + tensor var_3810_to_fp16 = const()[name = tensor("op_3810_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3811_cast_fp16 = add(x = var_3809_cast_fp16, y = var_3810_to_fp16)[name = tensor("op_3811_cast_fp16")]; + tensor denom_75_epsilon_0_to_fp16 = const()[name = tensor("denom_75_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_75_cast_fp16 = rsqrt(epsilon = denom_75_epsilon_0_to_fp16, x = var_3811_cast_fp16)[name = tensor("denom_75_cast_fp16")]; + tensor out_75_cast_fp16 = mul(x = zero_mean_75_cast_fp16, y = denom_75_cast_fp16)[name = tensor("out_75_cast_fp16")]; + tensor var_3815_to_fp16 = const()[name = tensor("op_3815_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(777509376)))]; + tensor var_3816_cast_fp16 = add(x = out_75_cast_fp16, y = var_3815_to_fp16)[name = tensor("op_3816_cast_fp16")]; + tensor var_3818_to_fp16 = const()[name = tensor("op_3818_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(777510720)))]; + tensor hidden_states_269_cast_fp16 = mul(x = var_3816_cast_fp16, y = var_3818_to_fp16)[name = tensor("hidden_states_269_cast_fp16")]; + tensor var_3825 = const()[name = tensor("op_3825"), val = tensor([1, 1])]; + tensor var_3827 = const()[name = tensor("op_3827"), val = tensor([1, 1])]; + tensor q_51_pad_type_0 = const()[name = tensor("q_51_pad_type_0"), val = tensor("custom")]; + tensor q_51_pad_0 = const()[name = tensor("q_51_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(777512064)))]; + tensor q_51_cast_fp16 = conv(dilations = var_3827, groups = var_3126, pad = q_51_pad_0, pad_type = q_51_pad_type_0, strides = var_3825, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_269_cast_fp16)[name = tensor("q_51_cast_fp16")]; + tensor var_3831 = const()[name = tensor("op_3831"), val = tensor([1, 1])]; + tensor var_3833 = const()[name = tensor("op_3833"), val = tensor([1, 1])]; + tensor k_51_pad_type_0 = const()[name = tensor("k_51_pad_type_0"), val = tensor("custom")]; + tensor k_51_pad_0 = const()[name = tensor("k_51_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(778331328)))]; + tensor k_51_cast_fp16 = conv(dilations = var_3833, groups = var_3126, pad = k_51_pad_0, pad_type = k_51_pad_type_0, strides = var_3831, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_51_cast_fp16")]; + tensor var_3837 = const()[name = tensor("op_3837"), val = tensor([1, 1])]; + tensor var_3839 = const()[name = tensor("op_3839"), val = tensor([1, 1])]; + tensor v_51_pad_type_0 = const()[name = tensor("v_51_pad_type_0"), val = tensor("custom")]; + tensor v_51_pad_0 = const()[name = tensor("v_51_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(779642112)))]; + tensor v_51_cast_fp16 = conv(dilations = var_3839, groups = var_3126, pad = v_51_pad_0, pad_type = v_51_pad_type_0, strides = var_3837, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_51_cast_fp16")]; + tensor var_3843 = const()[name = tensor("op_3843"), val = tensor([2, 10, 64, -1])]; + tensor var_3844_cast_fp16 = reshape(shape = var_3843, x = q_51_cast_fp16)[name = tensor("op_3844_cast_fp16")]; + tensor var_3845 = const()[name = tensor("op_3845"), val = tensor([2, 10, 64, -1])]; + tensor var_3846_cast_fp16 = reshape(shape = var_3845, x = k_51_cast_fp16)[name = tensor("op_3846_cast_fp16")]; + tensor var_3847 = const()[name = tensor("op_3847"), val = tensor([2, 10, 64, -1])]; + tensor var_3848_cast_fp16 = reshape(shape = var_3847, x = v_51_cast_fp16)[name = tensor("op_3848_cast_fp16")]; + tensor attn_weights_101_transpose_x_0 = const()[name = tensor("attn_weights_101_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_101_transpose_y_0 = const()[name = tensor("attn_weights_101_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_101_cast_fp16 = matmul(transpose_x = attn_weights_101_transpose_x_0, transpose_y = attn_weights_101_transpose_y_0, x = var_3844_cast_fp16, y = var_3846_cast_fp16)[name = tensor("attn_weights_101_cast_fp16")]; + tensor attn_weights_103_cast_fp16 = mul(x = attn_weights_101_cast_fp16, y = var_3117_to_fp16)[name = tensor("attn_weights_103_cast_fp16")]; + tensor var_3852_cast_fp16 = softmax(axis = var_3110, x = attn_weights_103_cast_fp16)[name = tensor("op_3852_cast_fp16")]; + tensor attn_51_transpose_x_0 = const()[name = tensor("attn_51_transpose_x_0"), val = tensor(false)]; + tensor attn_51_transpose_y_0 = const()[name = tensor("attn_51_transpose_y_0"), val = tensor(true)]; + tensor attn_51_cast_fp16 = matmul(transpose_x = attn_51_transpose_x_0, transpose_y = attn_51_transpose_y_0, x = var_3848_cast_fp16, y = var_3852_cast_fp16)[name = tensor("attn_51_cast_fp16")]; + tensor var_3856 = const()[name = tensor("op_3856"), val = tensor([2, 640, 1, -1])]; + tensor input_437_cast_fp16 = reshape(shape = var_3856, x = attn_51_cast_fp16)[name = tensor("input_437_cast_fp16")]; + tensor var_3861 = const()[name = tensor("op_3861"), val = tensor([1, 1])]; + tensor var_3863 = const()[name = tensor("op_3863"), val = tensor([1, 1])]; + tensor var_3865_pad_type_0 = const()[name = tensor("op_3865_pad_type_0"), val = tensor("custom")]; + tensor var_3865_pad_0 = const()[name = tensor("op_3865_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(780952896)))]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(781772160)))]; + tensor var_3865_cast_fp16 = conv(bias = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_3863, groups = var_3126, pad = var_3865_pad_0, pad_type = var_3865_pad_type_0, strides = var_3861, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_437_cast_fp16)[name = tensor("op_3865_cast_fp16")]; + tensor inputs_77_cast_fp16 = add(x = var_3865_cast_fp16, y = inputs_75_cast_fp16)[name = tensor("inputs_77_cast_fp16")]; + tensor var_3869 = const()[name = tensor("op_3869"), val = tensor([1])]; + tensor channels_mean_77_cast_fp16 = reduce_mean(axes = var_3869, keep_dims = var_3121, x = inputs_77_cast_fp16)[name = tensor("channels_mean_77_cast_fp16")]; + tensor zero_mean_77_cast_fp16 = sub(x = inputs_77_cast_fp16, y = channels_mean_77_cast_fp16)[name = tensor("zero_mean_77_cast_fp16")]; + tensor zero_mean_sq_77_cast_fp16 = mul(x = zero_mean_77_cast_fp16, y = zero_mean_77_cast_fp16)[name = tensor("zero_mean_sq_77_cast_fp16")]; + tensor var_3873 = const()[name = tensor("op_3873"), val = tensor([1])]; + tensor var_3874_cast_fp16 = reduce_mean(axes = var_3873, keep_dims = var_3121, x = zero_mean_sq_77_cast_fp16)[name = tensor("op_3874_cast_fp16")]; + tensor var_3875_to_fp16 = const()[name = tensor("op_3875_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3876_cast_fp16 = add(x = var_3874_cast_fp16, y = var_3875_to_fp16)[name = tensor("op_3876_cast_fp16")]; + tensor denom_77_epsilon_0_to_fp16 = const()[name = tensor("denom_77_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_77_cast_fp16 = rsqrt(epsilon = denom_77_epsilon_0_to_fp16, x = var_3876_cast_fp16)[name = tensor("denom_77_cast_fp16")]; + tensor out_77_cast_fp16 = mul(x = zero_mean_77_cast_fp16, y = denom_77_cast_fp16)[name = tensor("out_77_cast_fp16")]; + tensor var_3880_to_fp16 = const()[name = tensor("op_3880_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(781773504)))]; + tensor var_3881_cast_fp16 = add(x = out_77_cast_fp16, y = var_3880_to_fp16)[name = tensor("op_3881_cast_fp16")]; + tensor var_3883_to_fp16 = const()[name = tensor("op_3883_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(781774848)))]; + tensor input_439_cast_fp16 = mul(x = var_3881_cast_fp16, y = var_3883_to_fp16)[name = tensor("input_439_cast_fp16")]; + tensor var_3891 = const()[name = tensor("op_3891"), val = tensor([1, 1])]; + tensor var_3893 = const()[name = tensor("op_3893"), val = tensor([1, 1])]; + tensor var_3895_pad_type_0 = const()[name = tensor("op_3895_pad_type_0"), val = tensor("custom")]; + tensor var_3895_pad_0 = const()[name = tensor("op_3895_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(781776192)))]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(788329856)))]; + tensor var_3895_cast_fp16 = conv(bias = up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_3893, groups = var_3126, pad = var_3895_pad_0, pad_type = var_3895_pad_type_0, strides = var_3891, weight = up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_439_cast_fp16)[name = tensor("op_3895_cast_fp16")]; + tensor var_3896_split_sizes_0 = const()[name = tensor("op_3896_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_3896_axis_0 = const()[name = tensor("op_3896_axis_0"), val = tensor(1)]; + tensor var_3896_cast_fp16_0, tensor var_3896_cast_fp16_1 = split(axis = var_3896_axis_0, split_sizes = var_3896_split_sizes_0, x = var_3895_cast_fp16)[name = tensor("op_3896_cast_fp16")]; + tensor var_3898_mode_0 = const()[name = tensor("op_3898_mode_0"), val = tensor("EXACT")]; + tensor var_3898_cast_fp16 = gelu(mode = var_3898_mode_0, x = var_3896_cast_fp16_1)[name = tensor("op_3898_cast_fp16")]; + tensor input_441_cast_fp16 = mul(x = var_3896_cast_fp16_0, y = var_3898_cast_fp16)[name = tensor("input_441_cast_fp16")]; + tensor var_3902 = const()[name = tensor("op_3902"), val = tensor([1, 1])]; + tensor var_3904 = const()[name = tensor("op_3904"), val = tensor([1, 1])]; + tensor var_3906_pad_type_0 = const()[name = tensor("op_3906_pad_type_0"), val = tensor("custom")]; + tensor var_3906_pad_0 = const()[name = tensor("op_3906_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(788340160)))]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(791617024)))]; + tensor var_3906_cast_fp16 = conv(bias = up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_3904, groups = var_3126, pad = var_3906_pad_0, pad_type = var_3906_pad_type_0, strides = var_3902, weight = up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_441_cast_fp16)[name = tensor("op_3906_cast_fp16")]; + tensor hidden_states_273_cast_fp16 = add(x = var_3906_cast_fp16, y = inputs_77_cast_fp16)[name = tensor("hidden_states_273_cast_fp16")]; + tensor var_3908 = const()[name = tensor("op_3908"), val = tensor([2, 640, 24, 40])]; + tensor input_443_cast_fp16 = reshape(shape = var_3908, x = hidden_states_273_cast_fp16)[name = tensor("input_443_cast_fp16")]; + tensor var_3912 = const()[name = tensor("op_3912"), val = tensor([1, 1])]; + tensor var_3914 = const()[name = tensor("op_3914"), val = tensor([1, 1])]; + tensor hidden_states_275_pad_type_0 = const()[name = tensor("hidden_states_275_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_275_pad_0 = const()[name = tensor("hidden_states_275_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(791618368)))]; + tensor up_blocks_2_attentions_2_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(792437632)))]; + tensor hidden_states_275_cast_fp16 = conv(bias = up_blocks_2_attentions_2_proj_out_bias_to_fp16, dilations = var_3914, groups = var_3126, pad = hidden_states_275_pad_0, pad_type = hidden_states_275_pad_type_0, strides = var_3912, weight = up_blocks_2_attentions_2_proj_out_weight_to_fp16, x = input_443_cast_fp16)[name = tensor("hidden_states_275_cast_fp16")]; + tensor input_445_cast_fp16 = add(x = hidden_states_275_cast_fp16, y = hidden_states_263_cast_fp16)[name = tensor("input_445_cast_fp16")]; + tensor input_447_scale_factor_height_0 = const()[name = tensor("input_447_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_447_scale_factor_width_0 = const()[name = tensor("input_447_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor input_447_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = input_447_scale_factor_height_0, scale_factor_width = input_447_scale_factor_width_0, x = input_445_cast_fp16)[name = tensor("input_447_cast_fp16")]; + tensor var_3923 = const()[name = tensor("op_3923"), val = tensor([1, 1])]; + tensor var_3925 = const()[name = tensor("op_3925"), val = tensor([1, 1])]; + tensor hidden_states_277_pad_type_0 = const()[name = tensor("hidden_states_277_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_277_pad_0 = const()[name = tensor("hidden_states_277_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_upsamplers_0_conv_weight_to_fp16 = const()[name = tensor("up_blocks_2_upsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(792438976)))]; + tensor up_blocks_2_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("up_blocks_2_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(799811840)))]; + tensor hidden_states_277_cast_fp16 = conv(bias = up_blocks_2_upsamplers_0_conv_bias_to_fp16, dilations = var_3925, groups = var_3126, pad = hidden_states_277_pad_0, pad_type = hidden_states_277_pad_type_0, strides = var_3923, weight = up_blocks_2_upsamplers_0_conv_weight_to_fp16, x = input_447_cast_fp16)[name = tensor("hidden_states_277_cast_fp16")]; + tensor var_3929 = const()[name = tensor("op_3929"), val = tensor(3)]; + tensor var_3940 = const()[name = tensor("op_3940"), val = tensor(true)]; + tensor var_3945 = const()[name = tensor("op_3945"), val = tensor(1)]; + tensor input_449_interleave_0 = const()[name = tensor("input_449_interleave_0"), val = tensor(false)]; + tensor cast_10 = cast(dtype = cast_9_dtype_0, x = input_61_cast_fp16)[name = tensor("cast_10")]; + tensor input_449_cast_fp16 = concat(axis = var_3945, interleave = input_449_interleave_0, values = (hidden_states_277_cast_fp16, cast_10))[name = tensor("input_449_cast_fp16")]; + tensor reshape_204_shape_0 = const()[name = tensor("reshape_204_shape_0"), val = tensor([2, 32, 30, 48, 80])]; + tensor reshape_204_cast_fp16 = reshape(shape = reshape_204_shape_0, x = input_449_cast_fp16)[name = tensor("reshape_204_cast_fp16")]; + tensor reduce_mean_153_axes_0 = const()[name = tensor("reduce_mean_153_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_153_keep_dims_0 = const()[name = tensor("reduce_mean_153_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_153_cast_fp16 = reduce_mean(axes = reduce_mean_153_axes_0, keep_dims = reduce_mean_153_keep_dims_0, x = reshape_204_cast_fp16)[name = tensor("reduce_mean_153_cast_fp16")]; + tensor sub_102_cast_fp16 = sub(x = reshape_204_cast_fp16, y = reduce_mean_153_cast_fp16)[name = tensor("sub_102_cast_fp16")]; + tensor square_51_cast_fp16 = square(x = sub_102_cast_fp16)[name = tensor("square_51_cast_fp16")]; + tensor reduce_mean_155_axes_0 = const()[name = tensor("reduce_mean_155_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_155_keep_dims_0 = const()[name = tensor("reduce_mean_155_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_155_cast_fp16 = reduce_mean(axes = reduce_mean_155_axes_0, keep_dims = reduce_mean_155_keep_dims_0, x = square_51_cast_fp16)[name = tensor("reduce_mean_155_cast_fp16")]; + tensor add_102_y_0_to_fp16 = const()[name = tensor("add_102_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_102_cast_fp16 = add(x = reduce_mean_155_cast_fp16, y = add_102_y_0_to_fp16)[name = tensor("add_102_cast_fp16")]; + tensor sqrt_51_cast_fp16 = sqrt(x = add_102_cast_fp16)[name = tensor("sqrt_51_cast_fp16")]; + tensor real_div_51_cast_fp16 = real_div(x = sub_102_cast_fp16, y = sqrt_51_cast_fp16)[name = tensor("real_div_51_cast_fp16")]; + tensor reshape_205_shape_0 = const()[name = tensor("reshape_205_shape_0"), val = tensor([2, 960, 48, 80])]; + tensor reshape_205_cast_fp16 = reshape(shape = reshape_205_shape_0, x = real_div_51_cast_fp16)[name = tensor("reshape_205_cast_fp16")]; + tensor add_103_gamma_0_to_fp16 = const()[name = tensor("add_103_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(799813184)))]; + tensor add_103_beta_0_to_fp16 = const()[name = tensor("add_103_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(799815168)))]; + tensor add_103_epsilon_0_to_fp16 = const()[name = tensor("add_103_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_103_cast_fp16 = batch_norm(beta = add_103_beta_0_to_fp16, epsilon = add_103_epsilon_0_to_fp16, gamma = add_103_gamma_0_to_fp16, mean = add_97_mean_0_to_fp16, variance = add_97_variance_0_to_fp16, x = reshape_205_cast_fp16)[name = tensor("add_103_cast_fp16")]; + tensor input_453_cast_fp16 = silu(x = add_103_cast_fp16)[name = tensor("input_453_cast_fp16")]; + tensor var_3972 = const()[name = tensor("op_3972"), val = tensor([1, 1])]; + tensor var_3974 = const()[name = tensor("op_3974"), val = tensor([1, 1])]; + tensor hidden_states_279_pad_type_0 = const()[name = tensor("hidden_states_279_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_279_pad_0 = const()[name = tensor("hidden_states_279_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(799817152)))]; + tensor up_blocks_3_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(805346816)))]; + tensor hidden_states_279_cast_fp16 = conv(bias = up_blocks_3_resnets_0_conv1_bias_to_fp16, dilations = var_3974, groups = var_3945, pad = hidden_states_279_pad_0, pad_type = hidden_states_279_pad_type_0, strides = var_3972, weight = up_blocks_3_resnets_0_conv1_weight_to_fp16, x = input_453_cast_fp16)[name = tensor("hidden_states_279_cast_fp16")]; + tensor var_3980 = const()[name = tensor("op_3980"), val = tensor([1, 1])]; + tensor var_3982 = const()[name = tensor("op_3982"), val = tensor([1, 1])]; + tensor temb_39_pad_type_0 = const()[name = tensor("temb_39_pad_type_0"), val = tensor("custom")]; + tensor temb_39_pad_0 = const()[name = tensor("temb_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(805347520)))]; + tensor up_blocks_3_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(806166784)))]; + tensor temb_39_cast_fp16 = conv(bias = up_blocks_3_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_3982, groups = var_3945, pad = temb_39_pad_0, pad_type = temb_39_pad_type_0, strides = var_3980, weight = up_blocks_3_resnets_0_time_emb_proj_weight_to_fp16, x = cast_12)[name = tensor("temb_39_cast_fp16")]; + tensor input_457_cast_fp16 = add(x = hidden_states_279_cast_fp16, y = temb_39_cast_fp16)[name = tensor("input_457_cast_fp16")]; + tensor reshape_208_shape_0 = const()[name = tensor("reshape_208_shape_0"), val = tensor([2, 32, 10, 48, 80])]; + tensor reshape_208_cast_fp16 = reshape(shape = reshape_208_shape_0, x = input_457_cast_fp16)[name = tensor("reshape_208_cast_fp16")]; + tensor reduce_mean_156_axes_0 = const()[name = tensor("reduce_mean_156_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_156_keep_dims_0 = const()[name = tensor("reduce_mean_156_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_156_cast_fp16 = reduce_mean(axes = reduce_mean_156_axes_0, keep_dims = reduce_mean_156_keep_dims_0, x = reshape_208_cast_fp16)[name = tensor("reduce_mean_156_cast_fp16")]; + tensor sub_104_cast_fp16 = sub(x = reshape_208_cast_fp16, y = reduce_mean_156_cast_fp16)[name = tensor("sub_104_cast_fp16")]; + tensor square_52_cast_fp16 = square(x = sub_104_cast_fp16)[name = tensor("square_52_cast_fp16")]; + tensor reduce_mean_158_axes_0 = const()[name = tensor("reduce_mean_158_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_158_keep_dims_0 = const()[name = tensor("reduce_mean_158_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_158_cast_fp16 = reduce_mean(axes = reduce_mean_158_axes_0, keep_dims = reduce_mean_158_keep_dims_0, x = square_52_cast_fp16)[name = tensor("reduce_mean_158_cast_fp16")]; + tensor add_104_y_0_to_fp16 = const()[name = tensor("add_104_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_104_cast_fp16 = add(x = reduce_mean_158_cast_fp16, y = add_104_y_0_to_fp16)[name = tensor("add_104_cast_fp16")]; + tensor sqrt_52_cast_fp16 = sqrt(x = add_104_cast_fp16)[name = tensor("sqrt_52_cast_fp16")]; + tensor real_div_52_cast_fp16 = real_div(x = sub_104_cast_fp16, y = sqrt_52_cast_fp16)[name = tensor("real_div_52_cast_fp16")]; + tensor reshape_209_shape_0 = const()[name = tensor("reshape_209_shape_0"), val = tensor([2, 320, 48, 80])]; + tensor reshape_209_cast_fp16 = reshape(shape = reshape_209_shape_0, x = real_div_52_cast_fp16)[name = tensor("reshape_209_cast_fp16")]; + tensor add_105_gamma_0_to_fp16 = const()[name = tensor("add_105_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(806167488)))]; + tensor add_105_beta_0_to_fp16 = const()[name = tensor("add_105_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(806168192)))]; + tensor add_105_epsilon_0_to_fp16 = const()[name = tensor("add_105_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_105_cast_fp16 = batch_norm(beta = add_105_beta_0_to_fp16, epsilon = add_105_epsilon_0_to_fp16, gamma = add_105_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_209_cast_fp16)[name = tensor("add_105_cast_fp16")]; + tensor input_461_cast_fp16 = silu(x = add_105_cast_fp16)[name = tensor("input_461_cast_fp16")]; + tensor var_3992 = const()[name = tensor("op_3992"), val = tensor([1, 1])]; + tensor var_3994 = const()[name = tensor("op_3994"), val = tensor([1, 1])]; + tensor hidden_states_281_pad_type_0 = const()[name = tensor("hidden_states_281_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_281_pad_0 = const()[name = tensor("hidden_states_281_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(806168896)))]; + tensor up_blocks_3_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(808012160)))]; + tensor hidden_states_281_cast_fp16 = conv(bias = up_blocks_3_resnets_0_conv2_bias_to_fp16, dilations = var_3994, groups = var_3945, pad = hidden_states_281_pad_0, pad_type = hidden_states_281_pad_type_0, strides = var_3992, weight = up_blocks_3_resnets_0_conv2_weight_to_fp16, x = input_461_cast_fp16)[name = tensor("hidden_states_281_cast_fp16")]; + tensor var_3999 = const()[name = tensor("op_3999"), val = tensor([1, 1])]; + tensor var_4001 = const()[name = tensor("op_4001"), val = tensor([1, 1])]; + tensor x_23_pad_type_0 = const()[name = tensor("x_23_pad_type_0"), val = tensor("custom")]; + tensor x_23_pad_0 = const()[name = tensor("x_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(808012864)))]; + tensor up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(808627328)))]; + tensor x_23_cast_fp16 = conv(bias = up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_4001, groups = var_3945, pad = x_23_pad_0, pad_type = x_23_pad_type_0, strides = var_3999, weight = up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16, x = input_449_cast_fp16)[name = tensor("x_23_cast_fp16")]; + tensor hidden_states_283_cast_fp16 = add(x = x_23_cast_fp16, y = hidden_states_281_cast_fp16)[name = tensor("hidden_states_283_cast_fp16")]; + tensor reshape_212_shape_0 = const()[name = tensor("reshape_212_shape_0"), val = tensor([2, 32, 10, 48, 80])]; + tensor reshape_212_cast_fp16 = reshape(shape = reshape_212_shape_0, x = hidden_states_283_cast_fp16)[name = tensor("reshape_212_cast_fp16")]; + tensor reduce_mean_159_axes_0 = const()[name = tensor("reduce_mean_159_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_159_keep_dims_0 = const()[name = tensor("reduce_mean_159_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_159_cast_fp16 = reduce_mean(axes = reduce_mean_159_axes_0, keep_dims = reduce_mean_159_keep_dims_0, x = reshape_212_cast_fp16)[name = tensor("reduce_mean_159_cast_fp16")]; + tensor sub_106_cast_fp16 = sub(x = reshape_212_cast_fp16, y = reduce_mean_159_cast_fp16)[name = tensor("sub_106_cast_fp16")]; + tensor square_53_cast_fp16 = square(x = sub_106_cast_fp16)[name = tensor("square_53_cast_fp16")]; + tensor reduce_mean_161_axes_0 = const()[name = tensor("reduce_mean_161_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_161_keep_dims_0 = const()[name = tensor("reduce_mean_161_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_161_cast_fp16 = reduce_mean(axes = reduce_mean_161_axes_0, keep_dims = reduce_mean_161_keep_dims_0, x = square_53_cast_fp16)[name = tensor("reduce_mean_161_cast_fp16")]; + tensor add_106_y_0_to_fp16 = const()[name = tensor("add_106_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_106_cast_fp16 = add(x = reduce_mean_161_cast_fp16, y = add_106_y_0_to_fp16)[name = tensor("add_106_cast_fp16")]; + tensor sqrt_53_cast_fp16 = sqrt(x = add_106_cast_fp16)[name = tensor("sqrt_53_cast_fp16")]; + tensor real_div_53_cast_fp16 = real_div(x = sub_106_cast_fp16, y = sqrt_53_cast_fp16)[name = tensor("real_div_53_cast_fp16")]; + tensor reshape_213_shape_0 = const()[name = tensor("reshape_213_shape_0"), val = tensor([2, 320, 48, 80])]; + tensor reshape_213_cast_fp16 = reshape(shape = reshape_213_shape_0, x = real_div_53_cast_fp16)[name = tensor("reshape_213_cast_fp16")]; + tensor add_107_gamma_0_to_fp16 = const()[name = tensor("add_107_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(808628032)))]; + tensor add_107_beta_0_to_fp16 = const()[name = tensor("add_107_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(808628736)))]; + tensor add_107_epsilon_0_to_fp16 = const()[name = tensor("add_107_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_107_cast_fp16 = batch_norm(beta = add_107_beta_0_to_fp16, epsilon = add_107_epsilon_0_to_fp16, gamma = add_107_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_213_cast_fp16)[name = tensor("add_107_cast_fp16")]; + tensor var_4021 = const()[name = tensor("op_4021"), val = tensor([1, 1])]; + tensor var_4023 = const()[name = tensor("op_4023"), val = tensor([1, 1])]; + tensor hidden_states_285_pad_type_0 = const()[name = tensor("hidden_states_285_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_285_pad_0 = const()[name = tensor("hidden_states_285_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(808629440)))]; + tensor up_blocks_3_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(808834304)))]; + tensor hidden_states_285_cast_fp16 = conv(bias = up_blocks_3_attentions_0_proj_in_bias_to_fp16, dilations = var_4023, groups = var_3945, pad = hidden_states_285_pad_0, pad_type = hidden_states_285_pad_type_0, strides = var_4021, weight = up_blocks_3_attentions_0_proj_in_weight_to_fp16, x = add_107_cast_fp16)[name = tensor("hidden_states_285_cast_fp16")]; + tensor var_4028 = const()[name = tensor("op_4028"), val = tensor([2, 320, 1, 3840])]; + tensor inputs_79_cast_fp16 = reshape(shape = var_4028, x = hidden_states_285_cast_fp16)[name = tensor("inputs_79_cast_fp16")]; + tensor var_4038 = const()[name = tensor("op_4038"), val = tensor([1])]; + tensor channels_mean_79_cast_fp16 = reduce_mean(axes = var_4038, keep_dims = var_3940, x = inputs_79_cast_fp16)[name = tensor("channels_mean_79_cast_fp16")]; + tensor zero_mean_79_cast_fp16 = sub(x = inputs_79_cast_fp16, y = channels_mean_79_cast_fp16)[name = tensor("zero_mean_79_cast_fp16")]; + tensor zero_mean_sq_79_cast_fp16 = mul(x = zero_mean_79_cast_fp16, y = zero_mean_79_cast_fp16)[name = tensor("zero_mean_sq_79_cast_fp16")]; + tensor var_4042 = const()[name = tensor("op_4042"), val = tensor([1])]; + tensor var_4043_cast_fp16 = reduce_mean(axes = var_4042, keep_dims = var_3940, x = zero_mean_sq_79_cast_fp16)[name = tensor("op_4043_cast_fp16")]; + tensor var_4044_to_fp16 = const()[name = tensor("op_4044_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4045_cast_fp16 = add(x = var_4043_cast_fp16, y = var_4044_to_fp16)[name = tensor("op_4045_cast_fp16")]; + tensor denom_79_epsilon_0_to_fp16 = const()[name = tensor("denom_79_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_79_cast_fp16 = rsqrt(epsilon = denom_79_epsilon_0_to_fp16, x = var_4045_cast_fp16)[name = tensor("denom_79_cast_fp16")]; + tensor out_79_cast_fp16 = mul(x = zero_mean_79_cast_fp16, y = denom_79_cast_fp16)[name = tensor("out_79_cast_fp16")]; + tensor var_4049_to_fp16 = const()[name = tensor("op_4049_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(808835008)))]; + tensor var_4050_cast_fp16 = add(x = out_79_cast_fp16, y = var_4049_to_fp16)[name = tensor("op_4050_cast_fp16")]; + tensor var_4052_to_fp16 = const()[name = tensor("op_4052_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(808835712)))]; + tensor hidden_states_287_cast_fp16 = mul(x = var_4050_cast_fp16, y = var_4052_to_fp16)[name = tensor("hidden_states_287_cast_fp16")]; + tensor var_4059 = const()[name = tensor("op_4059"), val = tensor([1, 1])]; + tensor var_4061 = const()[name = tensor("op_4061"), val = tensor([1, 1])]; + tensor q_53_pad_type_0 = const()[name = tensor("q_53_pad_type_0"), val = tensor("custom")]; + tensor q_53_pad_0 = const()[name = tensor("q_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(808836416)))]; + tensor q_53_cast_fp16 = conv(dilations = var_4061, groups = var_3945, pad = q_53_pad_0, pad_type = q_53_pad_type_0, strides = var_4059, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_287_cast_fp16)[name = tensor("q_53_cast_fp16")]; + tensor var_4065 = const()[name = tensor("op_4065"), val = tensor([1, 1])]; + tensor var_4067 = const()[name = tensor("op_4067"), val = tensor([1, 1])]; + tensor k_53_pad_type_0 = const()[name = tensor("k_53_pad_type_0"), val = tensor("custom")]; + tensor k_53_pad_0 = const()[name = tensor("k_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(809041280)))]; + tensor k_53_cast_fp16 = conv(dilations = var_4067, groups = var_3945, pad = k_53_pad_0, pad_type = k_53_pad_type_0, strides = var_4065, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_287_cast_fp16)[name = tensor("k_53_cast_fp16")]; + tensor var_4071 = const()[name = tensor("op_4071"), val = tensor([1, 1])]; + tensor var_4073 = const()[name = tensor("op_4073"), val = tensor([1, 1])]; + tensor v_53_pad_type_0 = const()[name = tensor("v_53_pad_type_0"), val = tensor("custom")]; + tensor v_53_pad_0 = const()[name = tensor("v_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(809246144)))]; + tensor v_53_cast_fp16 = conv(dilations = var_4073, groups = var_3945, pad = v_53_pad_0, pad_type = v_53_pad_type_0, strides = var_4071, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_287_cast_fp16)[name = tensor("v_53_cast_fp16")]; + tensor var_4077 = const()[name = tensor("op_4077"), val = tensor([2, 5, 64, -1])]; + tensor var_4078_cast_fp16 = reshape(shape = var_4077, x = q_53_cast_fp16)[name = tensor("op_4078_cast_fp16")]; + tensor var_4079 = const()[name = tensor("op_4079"), val = tensor([2, 5, 64, -1])]; + tensor var_4080_cast_fp16 = reshape(shape = var_4079, x = k_53_cast_fp16)[name = tensor("op_4080_cast_fp16")]; + tensor var_4081 = const()[name = tensor("op_4081"), val = tensor([2, 5, 64, -1])]; + tensor var_4082_cast_fp16 = reshape(shape = var_4081, x = v_53_cast_fp16)[name = tensor("op_4082_cast_fp16")]; + tensor attn_weights_105_transpose_x_0 = const()[name = tensor("attn_weights_105_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_105_transpose_y_0 = const()[name = tensor("attn_weights_105_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_105_cast_fp16 = matmul(transpose_x = attn_weights_105_transpose_x_0, transpose_y = attn_weights_105_transpose_y_0, x = var_4078_cast_fp16, y = var_4080_cast_fp16)[name = tensor("attn_weights_105_cast_fp16")]; + tensor var_3936_to_fp16 = const()[name = tensor("op_3936_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_107_cast_fp16 = mul(x = attn_weights_105_cast_fp16, y = var_3936_to_fp16)[name = tensor("attn_weights_107_cast_fp16")]; + tensor var_4086_cast_fp16 = softmax(axis = var_3929, x = attn_weights_107_cast_fp16)[name = tensor("op_4086_cast_fp16")]; + tensor attn_53_transpose_x_0 = const()[name = tensor("attn_53_transpose_x_0"), val = tensor(false)]; + tensor attn_53_transpose_y_0 = const()[name = tensor("attn_53_transpose_y_0"), val = tensor(true)]; + tensor attn_53_cast_fp16 = matmul(transpose_x = attn_53_transpose_x_0, transpose_y = attn_53_transpose_y_0, x = var_4082_cast_fp16, y = var_4086_cast_fp16)[name = tensor("attn_53_cast_fp16")]; + tensor var_4090 = const()[name = tensor("op_4090"), val = tensor([2, 320, 1, -1])]; + tensor input_465_cast_fp16 = reshape(shape = var_4090, x = attn_53_cast_fp16)[name = tensor("input_465_cast_fp16")]; + tensor var_4095 = const()[name = tensor("op_4095"), val = tensor([1, 1])]; + tensor var_4097 = const()[name = tensor("op_4097"), val = tensor([1, 1])]; + tensor var_4099_pad_type_0 = const()[name = tensor("op_4099_pad_type_0"), val = tensor("custom")]; + tensor var_4099_pad_0 = const()[name = tensor("op_4099_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(809451008)))]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(809655872)))]; + tensor var_4099_cast_fp16 = conv(bias = up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_4097, groups = var_3945, pad = var_4099_pad_0, pad_type = var_4099_pad_type_0, strides = var_4095, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_465_cast_fp16)[name = tensor("op_4099_cast_fp16")]; + tensor inputs_81_cast_fp16 = add(x = var_4099_cast_fp16, y = inputs_79_cast_fp16)[name = tensor("inputs_81_cast_fp16")]; + tensor var_4103 = const()[name = tensor("op_4103"), val = tensor([1])]; + tensor channels_mean_81_cast_fp16 = reduce_mean(axes = var_4103, keep_dims = var_3940, x = inputs_81_cast_fp16)[name = tensor("channels_mean_81_cast_fp16")]; + tensor zero_mean_81_cast_fp16 = sub(x = inputs_81_cast_fp16, y = channels_mean_81_cast_fp16)[name = tensor("zero_mean_81_cast_fp16")]; + tensor zero_mean_sq_81_cast_fp16 = mul(x = zero_mean_81_cast_fp16, y = zero_mean_81_cast_fp16)[name = tensor("zero_mean_sq_81_cast_fp16")]; + tensor var_4107 = const()[name = tensor("op_4107"), val = tensor([1])]; + tensor var_4108_cast_fp16 = reduce_mean(axes = var_4107, keep_dims = var_3940, x = zero_mean_sq_81_cast_fp16)[name = tensor("op_4108_cast_fp16")]; + tensor var_4109_to_fp16 = const()[name = tensor("op_4109_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4110_cast_fp16 = add(x = var_4108_cast_fp16, y = var_4109_to_fp16)[name = tensor("op_4110_cast_fp16")]; + tensor denom_81_epsilon_0_to_fp16 = const()[name = tensor("denom_81_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_81_cast_fp16 = rsqrt(epsilon = denom_81_epsilon_0_to_fp16, x = var_4110_cast_fp16)[name = tensor("denom_81_cast_fp16")]; + tensor out_81_cast_fp16 = mul(x = zero_mean_81_cast_fp16, y = denom_81_cast_fp16)[name = tensor("out_81_cast_fp16")]; + tensor var_4114_to_fp16 = const()[name = tensor("op_4114_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(809656576)))]; + tensor var_4115_cast_fp16 = add(x = out_81_cast_fp16, y = var_4114_to_fp16)[name = tensor("op_4115_cast_fp16")]; + tensor var_4117_to_fp16 = const()[name = tensor("op_4117_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(809657280)))]; + tensor hidden_states_289_cast_fp16 = mul(x = var_4115_cast_fp16, y = var_4117_to_fp16)[name = tensor("hidden_states_289_cast_fp16")]; + tensor var_4124 = const()[name = tensor("op_4124"), val = tensor([1, 1])]; + tensor var_4126 = const()[name = tensor("op_4126"), val = tensor([1, 1])]; + tensor q_55_pad_type_0 = const()[name = tensor("q_55_pad_type_0"), val = tensor("custom")]; + tensor q_55_pad_0 = const()[name = tensor("q_55_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(809657984)))]; + tensor q_55_cast_fp16 = conv(dilations = var_4126, groups = var_3945, pad = q_55_pad_0, pad_type = q_55_pad_type_0, strides = var_4124, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_289_cast_fp16)[name = tensor("q_55_cast_fp16")]; + tensor var_4130 = const()[name = tensor("op_4130"), val = tensor([1, 1])]; + tensor var_4132 = const()[name = tensor("op_4132"), val = tensor([1, 1])]; + tensor k_55_pad_type_0 = const()[name = tensor("k_55_pad_type_0"), val = tensor("custom")]; + tensor k_55_pad_0 = const()[name = tensor("k_55_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(809862848)))]; + tensor k_55_cast_fp16 = conv(dilations = var_4132, groups = var_3945, pad = k_55_pad_0, pad_type = k_55_pad_type_0, strides = var_4130, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_55_cast_fp16")]; + tensor var_4136 = const()[name = tensor("op_4136"), val = tensor([1, 1])]; + tensor var_4138 = const()[name = tensor("op_4138"), val = tensor([1, 1])]; + tensor v_55_pad_type_0 = const()[name = tensor("v_55_pad_type_0"), val = tensor("custom")]; + tensor v_55_pad_0 = const()[name = tensor("v_55_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(810518272)))]; + tensor v_55_cast_fp16 = conv(dilations = var_4138, groups = var_3945, pad = v_55_pad_0, pad_type = v_55_pad_type_0, strides = var_4136, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_55_cast_fp16")]; + tensor var_4142 = const()[name = tensor("op_4142"), val = tensor([2, 5, 64, -1])]; + tensor var_4143_cast_fp16 = reshape(shape = var_4142, x = q_55_cast_fp16)[name = tensor("op_4143_cast_fp16")]; + tensor var_4144 = const()[name = tensor("op_4144"), val = tensor([2, 5, 64, -1])]; + tensor var_4145_cast_fp16 = reshape(shape = var_4144, x = k_55_cast_fp16)[name = tensor("op_4145_cast_fp16")]; + tensor var_4146 = const()[name = tensor("op_4146"), val = tensor([2, 5, 64, -1])]; + tensor var_4147_cast_fp16 = reshape(shape = var_4146, x = v_55_cast_fp16)[name = tensor("op_4147_cast_fp16")]; + tensor attn_weights_109_transpose_x_0 = const()[name = tensor("attn_weights_109_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_109_transpose_y_0 = const()[name = tensor("attn_weights_109_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_109_cast_fp16 = matmul(transpose_x = attn_weights_109_transpose_x_0, transpose_y = attn_weights_109_transpose_y_0, x = var_4143_cast_fp16, y = var_4145_cast_fp16)[name = tensor("attn_weights_109_cast_fp16")]; + tensor attn_weights_111_cast_fp16 = mul(x = attn_weights_109_cast_fp16, y = var_3936_to_fp16)[name = tensor("attn_weights_111_cast_fp16")]; + tensor var_4151_cast_fp16 = softmax(axis = var_3929, x = attn_weights_111_cast_fp16)[name = tensor("op_4151_cast_fp16")]; + tensor attn_55_transpose_x_0 = const()[name = tensor("attn_55_transpose_x_0"), val = tensor(false)]; + tensor attn_55_transpose_y_0 = const()[name = tensor("attn_55_transpose_y_0"), val = tensor(true)]; + tensor attn_55_cast_fp16 = matmul(transpose_x = attn_55_transpose_x_0, transpose_y = attn_55_transpose_y_0, x = var_4147_cast_fp16, y = var_4151_cast_fp16)[name = tensor("attn_55_cast_fp16")]; + tensor var_4155 = const()[name = tensor("op_4155"), val = tensor([2, 320, 1, -1])]; + tensor input_467_cast_fp16 = reshape(shape = var_4155, x = attn_55_cast_fp16)[name = tensor("input_467_cast_fp16")]; + tensor var_4160 = const()[name = tensor("op_4160"), val = tensor([1, 1])]; + tensor var_4162 = const()[name = tensor("op_4162"), val = tensor([1, 1])]; + tensor var_4164_pad_type_0 = const()[name = tensor("op_4164_pad_type_0"), val = tensor("custom")]; + tensor var_4164_pad_0 = const()[name = tensor("op_4164_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(811173696)))]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(811378560)))]; + tensor var_4164_cast_fp16 = conv(bias = up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_4162, groups = var_3945, pad = var_4164_pad_0, pad_type = var_4164_pad_type_0, strides = var_4160, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_467_cast_fp16)[name = tensor("op_4164_cast_fp16")]; + tensor inputs_83_cast_fp16 = add(x = var_4164_cast_fp16, y = inputs_81_cast_fp16)[name = tensor("inputs_83_cast_fp16")]; + tensor var_4168 = const()[name = tensor("op_4168"), val = tensor([1])]; + tensor channels_mean_83_cast_fp16 = reduce_mean(axes = var_4168, keep_dims = var_3940, x = inputs_83_cast_fp16)[name = tensor("channels_mean_83_cast_fp16")]; + tensor zero_mean_83_cast_fp16 = sub(x = inputs_83_cast_fp16, y = channels_mean_83_cast_fp16)[name = tensor("zero_mean_83_cast_fp16")]; + tensor zero_mean_sq_83_cast_fp16 = mul(x = zero_mean_83_cast_fp16, y = zero_mean_83_cast_fp16)[name = tensor("zero_mean_sq_83_cast_fp16")]; + tensor var_4172 = const()[name = tensor("op_4172"), val = tensor([1])]; + tensor var_4173_cast_fp16 = reduce_mean(axes = var_4172, keep_dims = var_3940, x = zero_mean_sq_83_cast_fp16)[name = tensor("op_4173_cast_fp16")]; + tensor var_4174_to_fp16 = const()[name = tensor("op_4174_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4175_cast_fp16 = add(x = var_4173_cast_fp16, y = var_4174_to_fp16)[name = tensor("op_4175_cast_fp16")]; + tensor denom_83_epsilon_0_to_fp16 = const()[name = tensor("denom_83_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_83_cast_fp16 = rsqrt(epsilon = denom_83_epsilon_0_to_fp16, x = var_4175_cast_fp16)[name = tensor("denom_83_cast_fp16")]; + tensor out_83_cast_fp16 = mul(x = zero_mean_83_cast_fp16, y = denom_83_cast_fp16)[name = tensor("out_83_cast_fp16")]; + tensor var_4179_to_fp16 = const()[name = tensor("op_4179_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(811379264)))]; + tensor var_4180_cast_fp16 = add(x = out_83_cast_fp16, y = var_4179_to_fp16)[name = tensor("op_4180_cast_fp16")]; + tensor var_4182_to_fp16 = const()[name = tensor("op_4182_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(811379968)))]; + tensor input_469_cast_fp16 = mul(x = var_4180_cast_fp16, y = var_4182_to_fp16)[name = tensor("input_469_cast_fp16")]; + tensor var_4190 = const()[name = tensor("op_4190"), val = tensor([1, 1])]; + tensor var_4192 = const()[name = tensor("op_4192"), val = tensor([1, 1])]; + tensor var_4194_pad_type_0 = const()[name = tensor("op_4194_pad_type_0"), val = tensor("custom")]; + tensor var_4194_pad_0 = const()[name = tensor("op_4194_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(811380672)))]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(813019136)))]; + tensor var_4194_cast_fp16 = conv(bias = up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_4192, groups = var_3945, pad = var_4194_pad_0, pad_type = var_4194_pad_type_0, strides = var_4190, weight = up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_469_cast_fp16)[name = tensor("op_4194_cast_fp16")]; + tensor var_4195_split_sizes_0 = const()[name = tensor("op_4195_split_sizes_0"), val = tensor([1280, 1280])]; + tensor var_4195_axis_0 = const()[name = tensor("op_4195_axis_0"), val = tensor(1)]; + tensor var_4195_cast_fp16_0, tensor var_4195_cast_fp16_1 = split(axis = var_4195_axis_0, split_sizes = var_4195_split_sizes_0, x = var_4194_cast_fp16)[name = tensor("op_4195_cast_fp16")]; + tensor var_4197_mode_0 = const()[name = tensor("op_4197_mode_0"), val = tensor("EXACT")]; + tensor var_4197_cast_fp16 = gelu(mode = var_4197_mode_0, x = var_4195_cast_fp16_1)[name = tensor("op_4197_cast_fp16")]; + tensor input_471_cast_fp16 = mul(x = var_4195_cast_fp16_0, y = var_4197_cast_fp16)[name = tensor("input_471_cast_fp16")]; + tensor var_4201 = const()[name = tensor("op_4201"), val = tensor([1, 1])]; + tensor var_4203 = const()[name = tensor("op_4203"), val = tensor([1, 1])]; + tensor var_4205_pad_type_0 = const()[name = tensor("op_4205_pad_type_0"), val = tensor("custom")]; + tensor var_4205_pad_0 = const()[name = tensor("op_4205_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(813024320)))]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(813843584)))]; + tensor var_4205_cast_fp16 = conv(bias = up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_4203, groups = var_3945, pad = var_4205_pad_0, pad_type = var_4205_pad_type_0, strides = var_4201, weight = up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_471_cast_fp16)[name = tensor("op_4205_cast_fp16")]; + tensor hidden_states_293_cast_fp16 = add(x = var_4205_cast_fp16, y = inputs_83_cast_fp16)[name = tensor("hidden_states_293_cast_fp16")]; + tensor var_4207 = const()[name = tensor("op_4207"), val = tensor([2, 320, 48, 80])]; + tensor input_473_cast_fp16 = reshape(shape = var_4207, x = hidden_states_293_cast_fp16)[name = tensor("input_473_cast_fp16")]; + tensor var_4211 = const()[name = tensor("op_4211"), val = tensor([1, 1])]; + tensor var_4213 = const()[name = tensor("op_4213"), val = tensor([1, 1])]; + tensor hidden_states_295_pad_type_0 = const()[name = tensor("hidden_states_295_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_295_pad_0 = const()[name = tensor("hidden_states_295_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(813844288)))]; + tensor up_blocks_3_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(814049152)))]; + tensor hidden_states_295_cast_fp16 = conv(bias = up_blocks_3_attentions_0_proj_out_bias_to_fp16, dilations = var_4213, groups = var_3945, pad = hidden_states_295_pad_0, pad_type = hidden_states_295_pad_type_0, strides = var_4211, weight = up_blocks_3_attentions_0_proj_out_weight_to_fp16, x = input_473_cast_fp16)[name = tensor("hidden_states_295_cast_fp16")]; + tensor hidden_states_297_cast_fp16 = add(x = hidden_states_295_cast_fp16, y = hidden_states_283_cast_fp16)[name = tensor("hidden_states_297_cast_fp16")]; + tensor input_475_interleave_0 = const()[name = tensor("input_475_interleave_0"), val = tensor(false)]; + tensor cast_11 = cast(dtype = cast_5_dtype_0, x = input_35_cast_fp16)[name = tensor("cast_11")]; + tensor input_475_cast_fp16 = concat(axis = var_3945, interleave = input_475_interleave_0, values = (hidden_states_297_cast_fp16, cast_11))[name = tensor("input_475_cast_fp16")]; + tensor reshape_216_shape_0 = const()[name = tensor("reshape_216_shape_0"), val = tensor([2, 32, 20, 48, 80])]; + tensor reshape_216_cast_fp16 = reshape(shape = reshape_216_shape_0, x = input_475_cast_fp16)[name = tensor("reshape_216_cast_fp16")]; + tensor reduce_mean_162_axes_0 = const()[name = tensor("reduce_mean_162_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_162_keep_dims_0 = const()[name = tensor("reduce_mean_162_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_162_cast_fp16 = reduce_mean(axes = reduce_mean_162_axes_0, keep_dims = reduce_mean_162_keep_dims_0, x = reshape_216_cast_fp16)[name = tensor("reduce_mean_162_cast_fp16")]; + tensor sub_108_cast_fp16 = sub(x = reshape_216_cast_fp16, y = reduce_mean_162_cast_fp16)[name = tensor("sub_108_cast_fp16")]; + tensor square_54_cast_fp16 = square(x = sub_108_cast_fp16)[name = tensor("square_54_cast_fp16")]; + tensor reduce_mean_164_axes_0 = const()[name = tensor("reduce_mean_164_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_164_keep_dims_0 = const()[name = tensor("reduce_mean_164_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_164_cast_fp16 = reduce_mean(axes = reduce_mean_164_axes_0, keep_dims = reduce_mean_164_keep_dims_0, x = square_54_cast_fp16)[name = tensor("reduce_mean_164_cast_fp16")]; + tensor add_108_y_0_to_fp16 = const()[name = tensor("add_108_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_108_cast_fp16 = add(x = reduce_mean_164_cast_fp16, y = add_108_y_0_to_fp16)[name = tensor("add_108_cast_fp16")]; + tensor sqrt_54_cast_fp16 = sqrt(x = add_108_cast_fp16)[name = tensor("sqrt_54_cast_fp16")]; + tensor real_div_54_cast_fp16 = real_div(x = sub_108_cast_fp16, y = sqrt_54_cast_fp16)[name = tensor("real_div_54_cast_fp16")]; + tensor reshape_217_shape_0 = const()[name = tensor("reshape_217_shape_0"), val = tensor([2, 640, 48, 80])]; + tensor reshape_217_cast_fp16 = reshape(shape = reshape_217_shape_0, x = real_div_54_cast_fp16)[name = tensor("reshape_217_cast_fp16")]; + tensor add_109_gamma_0_to_fp16 = const()[name = tensor("add_109_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(814049856)))]; + tensor add_109_beta_0_to_fp16 = const()[name = tensor("add_109_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(814051200)))]; + tensor add_109_epsilon_0_to_fp16 = const()[name = tensor("add_109_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_109_cast_fp16 = batch_norm(beta = add_109_beta_0_to_fp16, epsilon = add_109_epsilon_0_to_fp16, gamma = add_109_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_217_cast_fp16)[name = tensor("add_109_cast_fp16")]; + tensor input_479_cast_fp16 = silu(x = add_109_cast_fp16)[name = tensor("input_479_cast_fp16")]; + tensor var_4231 = const()[name = tensor("op_4231"), val = tensor([1, 1])]; + tensor var_4233 = const()[name = tensor("op_4233"), val = tensor([1, 1])]; + tensor hidden_states_299_pad_type_0 = const()[name = tensor("hidden_states_299_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_299_pad_0 = const()[name = tensor("hidden_states_299_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(814052544)))]; + tensor up_blocks_3_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(817739008)))]; + tensor hidden_states_299_cast_fp16 = conv(bias = up_blocks_3_resnets_1_conv1_bias_to_fp16, dilations = var_4233, groups = var_3945, pad = hidden_states_299_pad_0, pad_type = hidden_states_299_pad_type_0, strides = var_4231, weight = up_blocks_3_resnets_1_conv1_weight_to_fp16, x = input_479_cast_fp16)[name = tensor("hidden_states_299_cast_fp16")]; + tensor var_4239 = const()[name = tensor("op_4239"), val = tensor([1, 1])]; + tensor var_4241 = const()[name = tensor("op_4241"), val = tensor([1, 1])]; + tensor temb_41_pad_type_0 = const()[name = tensor("temb_41_pad_type_0"), val = tensor("custom")]; + tensor temb_41_pad_0 = const()[name = tensor("temb_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(817739712)))]; + tensor up_blocks_3_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(818558976)))]; + tensor temb_41_cast_fp16 = conv(bias = up_blocks_3_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_4241, groups = var_3945, pad = temb_41_pad_0, pad_type = temb_41_pad_type_0, strides = var_4239, weight = up_blocks_3_resnets_1_time_emb_proj_weight_to_fp16, x = cast_12)[name = tensor("temb_41_cast_fp16")]; + tensor input_483_cast_fp16 = add(x = hidden_states_299_cast_fp16, y = temb_41_cast_fp16)[name = tensor("input_483_cast_fp16")]; + tensor reshape_220_shape_0 = const()[name = tensor("reshape_220_shape_0"), val = tensor([2, 32, 10, 48, 80])]; + tensor reshape_220_cast_fp16 = reshape(shape = reshape_220_shape_0, x = input_483_cast_fp16)[name = tensor("reshape_220_cast_fp16")]; + tensor reduce_mean_165_axes_0 = const()[name = tensor("reduce_mean_165_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_165_keep_dims_0 = const()[name = tensor("reduce_mean_165_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_165_cast_fp16 = reduce_mean(axes = reduce_mean_165_axes_0, keep_dims = reduce_mean_165_keep_dims_0, x = reshape_220_cast_fp16)[name = tensor("reduce_mean_165_cast_fp16")]; + tensor sub_110_cast_fp16 = sub(x = reshape_220_cast_fp16, y = reduce_mean_165_cast_fp16)[name = tensor("sub_110_cast_fp16")]; + tensor square_55_cast_fp16 = square(x = sub_110_cast_fp16)[name = tensor("square_55_cast_fp16")]; + tensor reduce_mean_167_axes_0 = const()[name = tensor("reduce_mean_167_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_167_keep_dims_0 = const()[name = tensor("reduce_mean_167_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_167_cast_fp16 = reduce_mean(axes = reduce_mean_167_axes_0, keep_dims = reduce_mean_167_keep_dims_0, x = square_55_cast_fp16)[name = tensor("reduce_mean_167_cast_fp16")]; + tensor add_110_y_0_to_fp16 = const()[name = tensor("add_110_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_110_cast_fp16 = add(x = reduce_mean_167_cast_fp16, y = add_110_y_0_to_fp16)[name = tensor("add_110_cast_fp16")]; + tensor sqrt_55_cast_fp16 = sqrt(x = add_110_cast_fp16)[name = tensor("sqrt_55_cast_fp16")]; + tensor real_div_55_cast_fp16 = real_div(x = sub_110_cast_fp16, y = sqrt_55_cast_fp16)[name = tensor("real_div_55_cast_fp16")]; + tensor reshape_221_shape_0 = const()[name = tensor("reshape_221_shape_0"), val = tensor([2, 320, 48, 80])]; + tensor reshape_221_cast_fp16 = reshape(shape = reshape_221_shape_0, x = real_div_55_cast_fp16)[name = tensor("reshape_221_cast_fp16")]; + tensor add_111_gamma_0_to_fp16 = const()[name = tensor("add_111_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(818559680)))]; + tensor add_111_beta_0_to_fp16 = const()[name = tensor("add_111_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(818560384)))]; + tensor add_111_epsilon_0_to_fp16 = const()[name = tensor("add_111_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_111_cast_fp16 = batch_norm(beta = add_111_beta_0_to_fp16, epsilon = add_111_epsilon_0_to_fp16, gamma = add_111_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_221_cast_fp16)[name = tensor("add_111_cast_fp16")]; + tensor input_487_cast_fp16 = silu(x = add_111_cast_fp16)[name = tensor("input_487_cast_fp16")]; + tensor var_4251 = const()[name = tensor("op_4251"), val = tensor([1, 1])]; + tensor var_4253 = const()[name = tensor("op_4253"), val = tensor([1, 1])]; + tensor hidden_states_301_pad_type_0 = const()[name = tensor("hidden_states_301_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_301_pad_0 = const()[name = tensor("hidden_states_301_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(818561088)))]; + tensor up_blocks_3_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(820404352)))]; + tensor hidden_states_301_cast_fp16 = conv(bias = up_blocks_3_resnets_1_conv2_bias_to_fp16, dilations = var_4253, groups = var_3945, pad = hidden_states_301_pad_0, pad_type = hidden_states_301_pad_type_0, strides = var_4251, weight = up_blocks_3_resnets_1_conv2_weight_to_fp16, x = input_487_cast_fp16)[name = tensor("hidden_states_301_cast_fp16")]; + tensor var_4258 = const()[name = tensor("op_4258"), val = tensor([1, 1])]; + tensor var_4260 = const()[name = tensor("op_4260"), val = tensor([1, 1])]; + tensor x_25_pad_type_0 = const()[name = tensor("x_25_pad_type_0"), val = tensor("custom")]; + tensor x_25_pad_0 = const()[name = tensor("x_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_1_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(820405056)))]; + tensor up_blocks_3_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(820814720)))]; + tensor x_25_cast_fp16 = conv(bias = up_blocks_3_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_4260, groups = var_3945, pad = x_25_pad_0, pad_type = x_25_pad_type_0, strides = var_4258, weight = up_blocks_3_resnets_1_conv_shortcut_weight_to_fp16, x = input_475_cast_fp16)[name = tensor("x_25_cast_fp16")]; + tensor hidden_states_303_cast_fp16 = add(x = x_25_cast_fp16, y = hidden_states_301_cast_fp16)[name = tensor("hidden_states_303_cast_fp16")]; + tensor reshape_224_shape_0 = const()[name = tensor("reshape_224_shape_0"), val = tensor([2, 32, 10, 48, 80])]; + tensor reshape_224_cast_fp16 = reshape(shape = reshape_224_shape_0, x = hidden_states_303_cast_fp16)[name = tensor("reshape_224_cast_fp16")]; + tensor reduce_mean_168_axes_0 = const()[name = tensor("reduce_mean_168_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_168_keep_dims_0 = const()[name = tensor("reduce_mean_168_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_168_cast_fp16 = reduce_mean(axes = reduce_mean_168_axes_0, keep_dims = reduce_mean_168_keep_dims_0, x = reshape_224_cast_fp16)[name = tensor("reduce_mean_168_cast_fp16")]; + tensor sub_112_cast_fp16 = sub(x = reshape_224_cast_fp16, y = reduce_mean_168_cast_fp16)[name = tensor("sub_112_cast_fp16")]; + tensor square_56_cast_fp16 = square(x = sub_112_cast_fp16)[name = tensor("square_56_cast_fp16")]; + tensor reduce_mean_170_axes_0 = const()[name = tensor("reduce_mean_170_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_170_keep_dims_0 = const()[name = tensor("reduce_mean_170_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_170_cast_fp16 = reduce_mean(axes = reduce_mean_170_axes_0, keep_dims = reduce_mean_170_keep_dims_0, x = square_56_cast_fp16)[name = tensor("reduce_mean_170_cast_fp16")]; + tensor add_112_y_0_to_fp16 = const()[name = tensor("add_112_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_112_cast_fp16 = add(x = reduce_mean_170_cast_fp16, y = add_112_y_0_to_fp16)[name = tensor("add_112_cast_fp16")]; + tensor sqrt_56_cast_fp16 = sqrt(x = add_112_cast_fp16)[name = tensor("sqrt_56_cast_fp16")]; + tensor real_div_56_cast_fp16 = real_div(x = sub_112_cast_fp16, y = sqrt_56_cast_fp16)[name = tensor("real_div_56_cast_fp16")]; + tensor reshape_225_shape_0 = const()[name = tensor("reshape_225_shape_0"), val = tensor([2, 320, 48, 80])]; + tensor reshape_225_cast_fp16 = reshape(shape = reshape_225_shape_0, x = real_div_56_cast_fp16)[name = tensor("reshape_225_cast_fp16")]; + tensor add_113_gamma_0_to_fp16 = const()[name = tensor("add_113_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(820815424)))]; + tensor add_113_beta_0_to_fp16 = const()[name = tensor("add_113_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(820816128)))]; + tensor add_113_epsilon_0_to_fp16 = const()[name = tensor("add_113_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_113_cast_fp16 = batch_norm(beta = add_113_beta_0_to_fp16, epsilon = add_113_epsilon_0_to_fp16, gamma = add_113_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_225_cast_fp16)[name = tensor("add_113_cast_fp16")]; + tensor var_4280 = const()[name = tensor("op_4280"), val = tensor([1, 1])]; + tensor var_4282 = const()[name = tensor("op_4282"), val = tensor([1, 1])]; + tensor hidden_states_305_pad_type_0 = const()[name = tensor("hidden_states_305_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_305_pad_0 = const()[name = tensor("hidden_states_305_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(820816832)))]; + tensor up_blocks_3_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(821021696)))]; + tensor hidden_states_305_cast_fp16 = conv(bias = up_blocks_3_attentions_1_proj_in_bias_to_fp16, dilations = var_4282, groups = var_3945, pad = hidden_states_305_pad_0, pad_type = hidden_states_305_pad_type_0, strides = var_4280, weight = up_blocks_3_attentions_1_proj_in_weight_to_fp16, x = add_113_cast_fp16)[name = tensor("hidden_states_305_cast_fp16")]; + tensor var_4287 = const()[name = tensor("op_4287"), val = tensor([2, 320, 1, 3840])]; + tensor inputs_85_cast_fp16 = reshape(shape = var_4287, x = hidden_states_305_cast_fp16)[name = tensor("inputs_85_cast_fp16")]; + tensor var_4297 = const()[name = tensor("op_4297"), val = tensor([1])]; + tensor channels_mean_85_cast_fp16 = reduce_mean(axes = var_4297, keep_dims = var_3940, x = inputs_85_cast_fp16)[name = tensor("channels_mean_85_cast_fp16")]; + tensor zero_mean_85_cast_fp16 = sub(x = inputs_85_cast_fp16, y = channels_mean_85_cast_fp16)[name = tensor("zero_mean_85_cast_fp16")]; + tensor zero_mean_sq_85_cast_fp16 = mul(x = zero_mean_85_cast_fp16, y = zero_mean_85_cast_fp16)[name = tensor("zero_mean_sq_85_cast_fp16")]; + tensor var_4301 = const()[name = tensor("op_4301"), val = tensor([1])]; + tensor var_4302_cast_fp16 = reduce_mean(axes = var_4301, keep_dims = var_3940, x = zero_mean_sq_85_cast_fp16)[name = tensor("op_4302_cast_fp16")]; + tensor var_4303_to_fp16 = const()[name = tensor("op_4303_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4304_cast_fp16 = add(x = var_4302_cast_fp16, y = var_4303_to_fp16)[name = tensor("op_4304_cast_fp16")]; + tensor denom_85_epsilon_0_to_fp16 = const()[name = tensor("denom_85_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_85_cast_fp16 = rsqrt(epsilon = denom_85_epsilon_0_to_fp16, x = var_4304_cast_fp16)[name = tensor("denom_85_cast_fp16")]; + tensor out_85_cast_fp16 = mul(x = zero_mean_85_cast_fp16, y = denom_85_cast_fp16)[name = tensor("out_85_cast_fp16")]; + tensor var_4308_to_fp16 = const()[name = tensor("op_4308_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(821022400)))]; + tensor var_4309_cast_fp16 = add(x = out_85_cast_fp16, y = var_4308_to_fp16)[name = tensor("op_4309_cast_fp16")]; + tensor var_4311_to_fp16 = const()[name = tensor("op_4311_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(821023104)))]; + tensor hidden_states_307_cast_fp16 = mul(x = var_4309_cast_fp16, y = var_4311_to_fp16)[name = tensor("hidden_states_307_cast_fp16")]; + tensor var_4318 = const()[name = tensor("op_4318"), val = tensor([1, 1])]; + tensor var_4320 = const()[name = tensor("op_4320"), val = tensor([1, 1])]; + tensor q_57_pad_type_0 = const()[name = tensor("q_57_pad_type_0"), val = tensor("custom")]; + tensor q_57_pad_0 = const()[name = tensor("q_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(821023808)))]; + tensor q_57_cast_fp16 = conv(dilations = var_4320, groups = var_3945, pad = q_57_pad_0, pad_type = q_57_pad_type_0, strides = var_4318, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_307_cast_fp16)[name = tensor("q_57_cast_fp16")]; + tensor var_4324 = const()[name = tensor("op_4324"), val = tensor([1, 1])]; + tensor var_4326 = const()[name = tensor("op_4326"), val = tensor([1, 1])]; + tensor k_57_pad_type_0 = const()[name = tensor("k_57_pad_type_0"), val = tensor("custom")]; + tensor k_57_pad_0 = const()[name = tensor("k_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(821228672)))]; + tensor k_57_cast_fp16 = conv(dilations = var_4326, groups = var_3945, pad = k_57_pad_0, pad_type = k_57_pad_type_0, strides = var_4324, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_307_cast_fp16)[name = tensor("k_57_cast_fp16")]; + tensor var_4330 = const()[name = tensor("op_4330"), val = tensor([1, 1])]; + tensor var_4332 = const()[name = tensor("op_4332"), val = tensor([1, 1])]; + tensor v_57_pad_type_0 = const()[name = tensor("v_57_pad_type_0"), val = tensor("custom")]; + tensor v_57_pad_0 = const()[name = tensor("v_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(821433536)))]; + tensor v_57_cast_fp16 = conv(dilations = var_4332, groups = var_3945, pad = v_57_pad_0, pad_type = v_57_pad_type_0, strides = var_4330, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_307_cast_fp16)[name = tensor("v_57_cast_fp16")]; + tensor var_4336 = const()[name = tensor("op_4336"), val = tensor([2, 5, 64, -1])]; + tensor var_4337_cast_fp16 = reshape(shape = var_4336, x = q_57_cast_fp16)[name = tensor("op_4337_cast_fp16")]; + tensor var_4338 = const()[name = tensor("op_4338"), val = tensor([2, 5, 64, -1])]; + tensor var_4339_cast_fp16 = reshape(shape = var_4338, x = k_57_cast_fp16)[name = tensor("op_4339_cast_fp16")]; + tensor var_4340 = const()[name = tensor("op_4340"), val = tensor([2, 5, 64, -1])]; + tensor var_4341_cast_fp16 = reshape(shape = var_4340, x = v_57_cast_fp16)[name = tensor("op_4341_cast_fp16")]; + tensor attn_weights_113_transpose_x_0 = const()[name = tensor("attn_weights_113_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_113_transpose_y_0 = const()[name = tensor("attn_weights_113_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_113_cast_fp16 = matmul(transpose_x = attn_weights_113_transpose_x_0, transpose_y = attn_weights_113_transpose_y_0, x = var_4337_cast_fp16, y = var_4339_cast_fp16)[name = tensor("attn_weights_113_cast_fp16")]; + tensor attn_weights_115_cast_fp16 = mul(x = attn_weights_113_cast_fp16, y = var_3936_to_fp16)[name = tensor("attn_weights_115_cast_fp16")]; + tensor var_4345_cast_fp16 = softmax(axis = var_3929, x = attn_weights_115_cast_fp16)[name = tensor("op_4345_cast_fp16")]; + tensor attn_57_transpose_x_0 = const()[name = tensor("attn_57_transpose_x_0"), val = tensor(false)]; + tensor attn_57_transpose_y_0 = const()[name = tensor("attn_57_transpose_y_0"), val = tensor(true)]; + tensor attn_57_cast_fp16 = matmul(transpose_x = attn_57_transpose_x_0, transpose_y = attn_57_transpose_y_0, x = var_4341_cast_fp16, y = var_4345_cast_fp16)[name = tensor("attn_57_cast_fp16")]; + tensor var_4349 = const()[name = tensor("op_4349"), val = tensor([2, 320, 1, -1])]; + tensor input_491_cast_fp16 = reshape(shape = var_4349, x = attn_57_cast_fp16)[name = tensor("input_491_cast_fp16")]; + tensor var_4354 = const()[name = tensor("op_4354"), val = tensor([1, 1])]; + tensor var_4356 = const()[name = tensor("op_4356"), val = tensor([1, 1])]; + tensor var_4358_pad_type_0 = const()[name = tensor("op_4358_pad_type_0"), val = tensor("custom")]; + tensor var_4358_pad_0 = const()[name = tensor("op_4358_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(821638400)))]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(821843264)))]; + tensor var_4358_cast_fp16 = conv(bias = up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_4356, groups = var_3945, pad = var_4358_pad_0, pad_type = var_4358_pad_type_0, strides = var_4354, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_491_cast_fp16)[name = tensor("op_4358_cast_fp16")]; + tensor inputs_87_cast_fp16 = add(x = var_4358_cast_fp16, y = inputs_85_cast_fp16)[name = tensor("inputs_87_cast_fp16")]; + tensor var_4362 = const()[name = tensor("op_4362"), val = tensor([1])]; + tensor channels_mean_87_cast_fp16 = reduce_mean(axes = var_4362, keep_dims = var_3940, x = inputs_87_cast_fp16)[name = tensor("channels_mean_87_cast_fp16")]; + tensor zero_mean_87_cast_fp16 = sub(x = inputs_87_cast_fp16, y = channels_mean_87_cast_fp16)[name = tensor("zero_mean_87_cast_fp16")]; + tensor zero_mean_sq_87_cast_fp16 = mul(x = zero_mean_87_cast_fp16, y = zero_mean_87_cast_fp16)[name = tensor("zero_mean_sq_87_cast_fp16")]; + tensor var_4366 = const()[name = tensor("op_4366"), val = tensor([1])]; + tensor var_4367_cast_fp16 = reduce_mean(axes = var_4366, keep_dims = var_3940, x = zero_mean_sq_87_cast_fp16)[name = tensor("op_4367_cast_fp16")]; + tensor var_4368_to_fp16 = const()[name = tensor("op_4368_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4369_cast_fp16 = add(x = var_4367_cast_fp16, y = var_4368_to_fp16)[name = tensor("op_4369_cast_fp16")]; + tensor denom_87_epsilon_0_to_fp16 = const()[name = tensor("denom_87_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_87_cast_fp16 = rsqrt(epsilon = denom_87_epsilon_0_to_fp16, x = var_4369_cast_fp16)[name = tensor("denom_87_cast_fp16")]; + tensor out_87_cast_fp16 = mul(x = zero_mean_87_cast_fp16, y = denom_87_cast_fp16)[name = tensor("out_87_cast_fp16")]; + tensor var_4373_to_fp16 = const()[name = tensor("op_4373_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(821843968)))]; + tensor var_4374_cast_fp16 = add(x = out_87_cast_fp16, y = var_4373_to_fp16)[name = tensor("op_4374_cast_fp16")]; + tensor var_4376_to_fp16 = const()[name = tensor("op_4376_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(821844672)))]; + tensor hidden_states_309_cast_fp16 = mul(x = var_4374_cast_fp16, y = var_4376_to_fp16)[name = tensor("hidden_states_309_cast_fp16")]; + tensor var_4383 = const()[name = tensor("op_4383"), val = tensor([1, 1])]; + tensor var_4385 = const()[name = tensor("op_4385"), val = tensor([1, 1])]; + tensor q_59_pad_type_0 = const()[name = tensor("q_59_pad_type_0"), val = tensor("custom")]; + tensor q_59_pad_0 = const()[name = tensor("q_59_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(821845376)))]; + tensor q_59_cast_fp16 = conv(dilations = var_4385, groups = var_3945, pad = q_59_pad_0, pad_type = q_59_pad_type_0, strides = var_4383, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_309_cast_fp16)[name = tensor("q_59_cast_fp16")]; + tensor var_4389 = const()[name = tensor("op_4389"), val = tensor([1, 1])]; + tensor var_4391 = const()[name = tensor("op_4391"), val = tensor([1, 1])]; + tensor k_59_pad_type_0 = const()[name = tensor("k_59_pad_type_0"), val = tensor("custom")]; + tensor k_59_pad_0 = const()[name = tensor("k_59_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(822050240)))]; + tensor k_59_cast_fp16 = conv(dilations = var_4391, groups = var_3945, pad = k_59_pad_0, pad_type = k_59_pad_type_0, strides = var_4389, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_59_cast_fp16")]; + tensor var_4395 = const()[name = tensor("op_4395"), val = tensor([1, 1])]; + tensor var_4397 = const()[name = tensor("op_4397"), val = tensor([1, 1])]; + tensor v_59_pad_type_0 = const()[name = tensor("v_59_pad_type_0"), val = tensor("custom")]; + tensor v_59_pad_0 = const()[name = tensor("v_59_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(822705664)))]; + tensor v_59_cast_fp16 = conv(dilations = var_4397, groups = var_3945, pad = v_59_pad_0, pad_type = v_59_pad_type_0, strides = var_4395, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_59_cast_fp16")]; + tensor var_4401 = const()[name = tensor("op_4401"), val = tensor([2, 5, 64, -1])]; + tensor var_4402_cast_fp16 = reshape(shape = var_4401, x = q_59_cast_fp16)[name = tensor("op_4402_cast_fp16")]; + tensor var_4403 = const()[name = tensor("op_4403"), val = tensor([2, 5, 64, -1])]; + tensor var_4404_cast_fp16 = reshape(shape = var_4403, x = k_59_cast_fp16)[name = tensor("op_4404_cast_fp16")]; + tensor var_4405 = const()[name = tensor("op_4405"), val = tensor([2, 5, 64, -1])]; + tensor var_4406_cast_fp16 = reshape(shape = var_4405, x = v_59_cast_fp16)[name = tensor("op_4406_cast_fp16")]; + tensor attn_weights_117_transpose_x_0 = const()[name = tensor("attn_weights_117_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_117_transpose_y_0 = const()[name = tensor("attn_weights_117_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_117_cast_fp16 = matmul(transpose_x = attn_weights_117_transpose_x_0, transpose_y = attn_weights_117_transpose_y_0, x = var_4402_cast_fp16, y = var_4404_cast_fp16)[name = tensor("attn_weights_117_cast_fp16")]; + tensor attn_weights_119_cast_fp16 = mul(x = attn_weights_117_cast_fp16, y = var_3936_to_fp16)[name = tensor("attn_weights_119_cast_fp16")]; + tensor var_4410_cast_fp16 = softmax(axis = var_3929, x = attn_weights_119_cast_fp16)[name = tensor("op_4410_cast_fp16")]; + tensor attn_59_transpose_x_0 = const()[name = tensor("attn_59_transpose_x_0"), val = tensor(false)]; + tensor attn_59_transpose_y_0 = const()[name = tensor("attn_59_transpose_y_0"), val = tensor(true)]; + tensor attn_59_cast_fp16 = matmul(transpose_x = attn_59_transpose_x_0, transpose_y = attn_59_transpose_y_0, x = var_4406_cast_fp16, y = var_4410_cast_fp16)[name = tensor("attn_59_cast_fp16")]; + tensor var_4414 = const()[name = tensor("op_4414"), val = tensor([2, 320, 1, -1])]; + tensor input_493_cast_fp16 = reshape(shape = var_4414, x = attn_59_cast_fp16)[name = tensor("input_493_cast_fp16")]; + tensor var_4419 = const()[name = tensor("op_4419"), val = tensor([1, 1])]; + tensor var_4421 = const()[name = tensor("op_4421"), val = tensor([1, 1])]; + tensor var_4423_pad_type_0 = const()[name = tensor("op_4423_pad_type_0"), val = tensor("custom")]; + tensor var_4423_pad_0 = const()[name = tensor("op_4423_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(823361088)))]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(823565952)))]; + tensor var_4423_cast_fp16 = conv(bias = up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_4421, groups = var_3945, pad = var_4423_pad_0, pad_type = var_4423_pad_type_0, strides = var_4419, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_493_cast_fp16)[name = tensor("op_4423_cast_fp16")]; + tensor inputs_89_cast_fp16 = add(x = var_4423_cast_fp16, y = inputs_87_cast_fp16)[name = tensor("inputs_89_cast_fp16")]; + tensor var_4427 = const()[name = tensor("op_4427"), val = tensor([1])]; + tensor channels_mean_89_cast_fp16 = reduce_mean(axes = var_4427, keep_dims = var_3940, x = inputs_89_cast_fp16)[name = tensor("channels_mean_89_cast_fp16")]; + tensor zero_mean_89_cast_fp16 = sub(x = inputs_89_cast_fp16, y = channels_mean_89_cast_fp16)[name = tensor("zero_mean_89_cast_fp16")]; + tensor zero_mean_sq_89_cast_fp16 = mul(x = zero_mean_89_cast_fp16, y = zero_mean_89_cast_fp16)[name = tensor("zero_mean_sq_89_cast_fp16")]; + tensor var_4431 = const()[name = tensor("op_4431"), val = tensor([1])]; + tensor var_4432_cast_fp16 = reduce_mean(axes = var_4431, keep_dims = var_3940, x = zero_mean_sq_89_cast_fp16)[name = tensor("op_4432_cast_fp16")]; + tensor var_4433_to_fp16 = const()[name = tensor("op_4433_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4434_cast_fp16 = add(x = var_4432_cast_fp16, y = var_4433_to_fp16)[name = tensor("op_4434_cast_fp16")]; + tensor denom_89_epsilon_0_to_fp16 = const()[name = tensor("denom_89_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_89_cast_fp16 = rsqrt(epsilon = denom_89_epsilon_0_to_fp16, x = var_4434_cast_fp16)[name = tensor("denom_89_cast_fp16")]; + tensor out_89_cast_fp16 = mul(x = zero_mean_89_cast_fp16, y = denom_89_cast_fp16)[name = tensor("out_89_cast_fp16")]; + tensor var_4438_to_fp16 = const()[name = tensor("op_4438_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(823566656)))]; + tensor var_4439_cast_fp16 = add(x = out_89_cast_fp16, y = var_4438_to_fp16)[name = tensor("op_4439_cast_fp16")]; + tensor var_4441_to_fp16 = const()[name = tensor("op_4441_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(823567360)))]; + tensor input_495_cast_fp16 = mul(x = var_4439_cast_fp16, y = var_4441_to_fp16)[name = tensor("input_495_cast_fp16")]; + tensor var_4449 = const()[name = tensor("op_4449"), val = tensor([1, 1])]; + tensor var_4451 = const()[name = tensor("op_4451"), val = tensor([1, 1])]; + tensor var_4453_pad_type_0 = const()[name = tensor("op_4453_pad_type_0"), val = tensor("custom")]; + tensor var_4453_pad_0 = const()[name = tensor("op_4453_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(823568064)))]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(825206528)))]; + tensor var_4453_cast_fp16 = conv(bias = up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_4451, groups = var_3945, pad = var_4453_pad_0, pad_type = var_4453_pad_type_0, strides = var_4449, weight = up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_495_cast_fp16)[name = tensor("op_4453_cast_fp16")]; + tensor var_4454_split_sizes_0 = const()[name = tensor("op_4454_split_sizes_0"), val = tensor([1280, 1280])]; + tensor var_4454_axis_0 = const()[name = tensor("op_4454_axis_0"), val = tensor(1)]; + tensor var_4454_cast_fp16_0, tensor var_4454_cast_fp16_1 = split(axis = var_4454_axis_0, split_sizes = var_4454_split_sizes_0, x = var_4453_cast_fp16)[name = tensor("op_4454_cast_fp16")]; + tensor var_4456_mode_0 = const()[name = tensor("op_4456_mode_0"), val = tensor("EXACT")]; + tensor var_4456_cast_fp16 = gelu(mode = var_4456_mode_0, x = var_4454_cast_fp16_1)[name = tensor("op_4456_cast_fp16")]; + tensor input_497_cast_fp16 = mul(x = var_4454_cast_fp16_0, y = var_4456_cast_fp16)[name = tensor("input_497_cast_fp16")]; + tensor var_4460 = const()[name = tensor("op_4460"), val = tensor([1, 1])]; + tensor var_4462 = const()[name = tensor("op_4462"), val = tensor([1, 1])]; + tensor var_4464_pad_type_0 = const()[name = tensor("op_4464_pad_type_0"), val = tensor("custom")]; + tensor var_4464_pad_0 = const()[name = tensor("op_4464_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(825211712)))]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(826030976)))]; + tensor var_4464_cast_fp16 = conv(bias = up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_4462, groups = var_3945, pad = var_4464_pad_0, pad_type = var_4464_pad_type_0, strides = var_4460, weight = up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_497_cast_fp16)[name = tensor("op_4464_cast_fp16")]; + tensor hidden_states_313_cast_fp16 = add(x = var_4464_cast_fp16, y = inputs_89_cast_fp16)[name = tensor("hidden_states_313_cast_fp16")]; + tensor var_4466 = const()[name = tensor("op_4466"), val = tensor([2, 320, 48, 80])]; + tensor input_499_cast_fp16 = reshape(shape = var_4466, x = hidden_states_313_cast_fp16)[name = tensor("input_499_cast_fp16")]; + tensor var_4470 = const()[name = tensor("op_4470"), val = tensor([1, 1])]; + tensor var_4472 = const()[name = tensor("op_4472"), val = tensor([1, 1])]; + tensor hidden_states_315_pad_type_0 = const()[name = tensor("hidden_states_315_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_315_pad_0 = const()[name = tensor("hidden_states_315_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(826031680)))]; + tensor up_blocks_3_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(826236544)))]; + tensor hidden_states_315_cast_fp16 = conv(bias = up_blocks_3_attentions_1_proj_out_bias_to_fp16, dilations = var_4472, groups = var_3945, pad = hidden_states_315_pad_0, pad_type = hidden_states_315_pad_type_0, strides = var_4470, weight = up_blocks_3_attentions_1_proj_out_weight_to_fp16, x = input_499_cast_fp16)[name = tensor("hidden_states_315_cast_fp16")]; + tensor hidden_states_317_cast_fp16 = add(x = hidden_states_315_cast_fp16, y = hidden_states_303_cast_fp16)[name = tensor("hidden_states_317_cast_fp16")]; + tensor input_501_interleave_0 = const()[name = tensor("input_501_interleave_0"), val = tensor(false)]; + tensor cast_13 = cast(dtype = cast_0_dtype_0, x = input_7_cast_fp16)[name = tensor("cast_13")]; + tensor input_501_cast_fp16 = concat(axis = var_3945, interleave = input_501_interleave_0, values = (hidden_states_317_cast_fp16, cast_13))[name = tensor("input_501_cast_fp16")]; + tensor reshape_228_shape_0 = const()[name = tensor("reshape_228_shape_0"), val = tensor([2, 32, 20, 48, 80])]; + tensor reshape_228_cast_fp16 = reshape(shape = reshape_228_shape_0, x = input_501_cast_fp16)[name = tensor("reshape_228_cast_fp16")]; + tensor reduce_mean_171_axes_0 = const()[name = tensor("reduce_mean_171_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_171_keep_dims_0 = const()[name = tensor("reduce_mean_171_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_171_cast_fp16 = reduce_mean(axes = reduce_mean_171_axes_0, keep_dims = reduce_mean_171_keep_dims_0, x = reshape_228_cast_fp16)[name = tensor("reduce_mean_171_cast_fp16")]; + tensor sub_114_cast_fp16 = sub(x = reshape_228_cast_fp16, y = reduce_mean_171_cast_fp16)[name = tensor("sub_114_cast_fp16")]; + tensor square_57_cast_fp16 = square(x = sub_114_cast_fp16)[name = tensor("square_57_cast_fp16")]; + tensor reduce_mean_173_axes_0 = const()[name = tensor("reduce_mean_173_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_173_keep_dims_0 = const()[name = tensor("reduce_mean_173_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_173_cast_fp16 = reduce_mean(axes = reduce_mean_173_axes_0, keep_dims = reduce_mean_173_keep_dims_0, x = square_57_cast_fp16)[name = tensor("reduce_mean_173_cast_fp16")]; + tensor add_114_y_0_to_fp16 = const()[name = tensor("add_114_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_114_cast_fp16 = add(x = reduce_mean_173_cast_fp16, y = add_114_y_0_to_fp16)[name = tensor("add_114_cast_fp16")]; + tensor sqrt_57_cast_fp16 = sqrt(x = add_114_cast_fp16)[name = tensor("sqrt_57_cast_fp16")]; + tensor real_div_57_cast_fp16 = real_div(x = sub_114_cast_fp16, y = sqrt_57_cast_fp16)[name = tensor("real_div_57_cast_fp16")]; + tensor reshape_229_shape_0 = const()[name = tensor("reshape_229_shape_0"), val = tensor([2, 640, 48, 80])]; + tensor reshape_229_cast_fp16 = reshape(shape = reshape_229_shape_0, x = real_div_57_cast_fp16)[name = tensor("reshape_229_cast_fp16")]; + tensor add_115_gamma_0_to_fp16 = const()[name = tensor("add_115_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(826237248)))]; + tensor add_115_beta_0_to_fp16 = const()[name = tensor("add_115_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(826238592)))]; + tensor add_115_epsilon_0_to_fp16 = const()[name = tensor("add_115_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_115_cast_fp16 = batch_norm(beta = add_115_beta_0_to_fp16, epsilon = add_115_epsilon_0_to_fp16, gamma = add_115_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_229_cast_fp16)[name = tensor("add_115_cast_fp16")]; + tensor input_505_cast_fp16 = silu(x = add_115_cast_fp16)[name = tensor("input_505_cast_fp16")]; + tensor var_4490 = const()[name = tensor("op_4490"), val = tensor([1, 1])]; + tensor var_4492 = const()[name = tensor("op_4492"), val = tensor([1, 1])]; + tensor hidden_states_319_pad_type_0 = const()[name = tensor("hidden_states_319_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_319_pad_0 = const()[name = tensor("hidden_states_319_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_2_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(826239936)))]; + tensor up_blocks_3_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(829926400)))]; + tensor hidden_states_319_cast_fp16 = conv(bias = up_blocks_3_resnets_2_conv1_bias_to_fp16, dilations = var_4492, groups = var_3945, pad = hidden_states_319_pad_0, pad_type = hidden_states_319_pad_type_0, strides = var_4490, weight = up_blocks_3_resnets_2_conv1_weight_to_fp16, x = input_505_cast_fp16)[name = tensor("hidden_states_319_cast_fp16")]; + tensor var_4498 = const()[name = tensor("op_4498"), val = tensor([1, 1])]; + tensor var_4500 = const()[name = tensor("op_4500"), val = tensor([1, 1])]; + tensor temb_pad_type_0 = const()[name = tensor("temb_pad_type_0"), val = tensor("custom")]; + tensor temb_pad_0 = const()[name = tensor("temb_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_2_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(829927104)))]; + tensor up_blocks_3_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(830746368)))]; + tensor temb_cast_fp16 = conv(bias = up_blocks_3_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_4500, groups = var_3945, pad = temb_pad_0, pad_type = temb_pad_type_0, strides = var_4498, weight = up_blocks_3_resnets_2_time_emb_proj_weight_to_fp16, x = cast_12)[name = tensor("temb_cast_fp16")]; + tensor input_509_cast_fp16 = add(x = hidden_states_319_cast_fp16, y = temb_cast_fp16)[name = tensor("input_509_cast_fp16")]; + tensor reshape_232_shape_0 = const()[name = tensor("reshape_232_shape_0"), val = tensor([2, 32, 10, 48, 80])]; + tensor reshape_232_cast_fp16 = reshape(shape = reshape_232_shape_0, x = input_509_cast_fp16)[name = tensor("reshape_232_cast_fp16")]; + tensor reduce_mean_174_axes_0 = const()[name = tensor("reduce_mean_174_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_174_keep_dims_0 = const()[name = tensor("reduce_mean_174_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_174_cast_fp16 = reduce_mean(axes = reduce_mean_174_axes_0, keep_dims = reduce_mean_174_keep_dims_0, x = reshape_232_cast_fp16)[name = tensor("reduce_mean_174_cast_fp16")]; + tensor sub_116_cast_fp16 = sub(x = reshape_232_cast_fp16, y = reduce_mean_174_cast_fp16)[name = tensor("sub_116_cast_fp16")]; + tensor square_58_cast_fp16 = square(x = sub_116_cast_fp16)[name = tensor("square_58_cast_fp16")]; + tensor reduce_mean_176_axes_0 = const()[name = tensor("reduce_mean_176_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_176_keep_dims_0 = const()[name = tensor("reduce_mean_176_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_176_cast_fp16 = reduce_mean(axes = reduce_mean_176_axes_0, keep_dims = reduce_mean_176_keep_dims_0, x = square_58_cast_fp16)[name = tensor("reduce_mean_176_cast_fp16")]; + tensor add_116_y_0_to_fp16 = const()[name = tensor("add_116_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_116_cast_fp16 = add(x = reduce_mean_176_cast_fp16, y = add_116_y_0_to_fp16)[name = tensor("add_116_cast_fp16")]; + tensor sqrt_58_cast_fp16 = sqrt(x = add_116_cast_fp16)[name = tensor("sqrt_58_cast_fp16")]; + tensor real_div_58_cast_fp16 = real_div(x = sub_116_cast_fp16, y = sqrt_58_cast_fp16)[name = tensor("real_div_58_cast_fp16")]; + tensor reshape_233_shape_0 = const()[name = tensor("reshape_233_shape_0"), val = tensor([2, 320, 48, 80])]; + tensor reshape_233_cast_fp16 = reshape(shape = reshape_233_shape_0, x = real_div_58_cast_fp16)[name = tensor("reshape_233_cast_fp16")]; + tensor add_117_gamma_0_to_fp16 = const()[name = tensor("add_117_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(830747072)))]; + tensor add_117_beta_0_to_fp16 = const()[name = tensor("add_117_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(830747776)))]; + tensor add_117_epsilon_0_to_fp16 = const()[name = tensor("add_117_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_117_cast_fp16 = batch_norm(beta = add_117_beta_0_to_fp16, epsilon = add_117_epsilon_0_to_fp16, gamma = add_117_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_233_cast_fp16)[name = tensor("add_117_cast_fp16")]; + tensor input_513_cast_fp16 = silu(x = add_117_cast_fp16)[name = tensor("input_513_cast_fp16")]; + tensor var_4510 = const()[name = tensor("op_4510"), val = tensor([1, 1])]; + tensor var_4512 = const()[name = tensor("op_4512"), val = tensor([1, 1])]; + tensor hidden_states_321_pad_type_0 = const()[name = tensor("hidden_states_321_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_321_pad_0 = const()[name = tensor("hidden_states_321_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_2_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(830748480)))]; + tensor up_blocks_3_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(832591744)))]; + tensor hidden_states_321_cast_fp16 = conv(bias = up_blocks_3_resnets_2_conv2_bias_to_fp16, dilations = var_4512, groups = var_3945, pad = hidden_states_321_pad_0, pad_type = hidden_states_321_pad_type_0, strides = var_4510, weight = up_blocks_3_resnets_2_conv2_weight_to_fp16, x = input_513_cast_fp16)[name = tensor("hidden_states_321_cast_fp16")]; + tensor var_4517 = const()[name = tensor("op_4517"), val = tensor([1, 1])]; + tensor var_4519 = const()[name = tensor("op_4519"), val = tensor([1, 1])]; + tensor x_pad_type_0 = const()[name = tensor("x_pad_type_0"), val = tensor("custom")]; + tensor x_pad_0 = const()[name = tensor("x_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_2_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(832592448)))]; + tensor up_blocks_3_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(833002112)))]; + tensor x_cast_fp16 = conv(bias = up_blocks_3_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_4519, groups = var_3945, pad = x_pad_0, pad_type = x_pad_type_0, strides = var_4517, weight = up_blocks_3_resnets_2_conv_shortcut_weight_to_fp16, x = input_501_cast_fp16)[name = tensor("x_cast_fp16")]; + tensor hidden_states_323_cast_fp16 = add(x = x_cast_fp16, y = hidden_states_321_cast_fp16)[name = tensor("hidden_states_323_cast_fp16")]; + tensor reshape_236_shape_0 = const()[name = tensor("reshape_236_shape_0"), val = tensor([2, 32, 10, 48, 80])]; + tensor reshape_236_cast_fp16 = reshape(shape = reshape_236_shape_0, x = hidden_states_323_cast_fp16)[name = tensor("reshape_236_cast_fp16")]; + tensor reduce_mean_177_axes_0 = const()[name = tensor("reduce_mean_177_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_177_keep_dims_0 = const()[name = tensor("reduce_mean_177_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_177_cast_fp16 = reduce_mean(axes = reduce_mean_177_axes_0, keep_dims = reduce_mean_177_keep_dims_0, x = reshape_236_cast_fp16)[name = tensor("reduce_mean_177_cast_fp16")]; + tensor sub_118_cast_fp16 = sub(x = reshape_236_cast_fp16, y = reduce_mean_177_cast_fp16)[name = tensor("sub_118_cast_fp16")]; + tensor square_59_cast_fp16 = square(x = sub_118_cast_fp16)[name = tensor("square_59_cast_fp16")]; + tensor reduce_mean_179_axes_0 = const()[name = tensor("reduce_mean_179_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_179_keep_dims_0 = const()[name = tensor("reduce_mean_179_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_179_cast_fp16 = reduce_mean(axes = reduce_mean_179_axes_0, keep_dims = reduce_mean_179_keep_dims_0, x = square_59_cast_fp16)[name = tensor("reduce_mean_179_cast_fp16")]; + tensor add_118_y_0_to_fp16 = const()[name = tensor("add_118_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_118_cast_fp16 = add(x = reduce_mean_179_cast_fp16, y = add_118_y_0_to_fp16)[name = tensor("add_118_cast_fp16")]; + tensor sqrt_59_cast_fp16 = sqrt(x = add_118_cast_fp16)[name = tensor("sqrt_59_cast_fp16")]; + tensor real_div_59_cast_fp16 = real_div(x = sub_118_cast_fp16, y = sqrt_59_cast_fp16)[name = tensor("real_div_59_cast_fp16")]; + tensor reshape_237_shape_0 = const()[name = tensor("reshape_237_shape_0"), val = tensor([2, 320, 48, 80])]; + tensor reshape_237_cast_fp16 = reshape(shape = reshape_237_shape_0, x = real_div_59_cast_fp16)[name = tensor("reshape_237_cast_fp16")]; + tensor add_119_gamma_0_to_fp16 = const()[name = tensor("add_119_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(833002816)))]; + tensor add_119_beta_0_to_fp16 = const()[name = tensor("add_119_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(833003520)))]; + tensor add_119_epsilon_0_to_fp16 = const()[name = tensor("add_119_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_119_cast_fp16 = batch_norm(beta = add_119_beta_0_to_fp16, epsilon = add_119_epsilon_0_to_fp16, gamma = add_119_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_237_cast_fp16)[name = tensor("add_119_cast_fp16")]; + tensor var_4539 = const()[name = tensor("op_4539"), val = tensor([1, 1])]; + tensor var_4541 = const()[name = tensor("op_4541"), val = tensor([1, 1])]; + tensor hidden_states_325_pad_type_0 = const()[name = tensor("hidden_states_325_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_325_pad_0 = const()[name = tensor("hidden_states_325_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(833004224)))]; + tensor up_blocks_3_attentions_2_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(833209088)))]; + tensor hidden_states_325_cast_fp16 = conv(bias = up_blocks_3_attentions_2_proj_in_bias_to_fp16, dilations = var_4541, groups = var_3945, pad = hidden_states_325_pad_0, pad_type = hidden_states_325_pad_type_0, strides = var_4539, weight = up_blocks_3_attentions_2_proj_in_weight_to_fp16, x = add_119_cast_fp16)[name = tensor("hidden_states_325_cast_fp16")]; + tensor var_4546 = const()[name = tensor("op_4546"), val = tensor([2, 320, 1, 3840])]; + tensor inputs_91_cast_fp16 = reshape(shape = var_4546, x = hidden_states_325_cast_fp16)[name = tensor("inputs_91_cast_fp16")]; + tensor var_4556 = const()[name = tensor("op_4556"), val = tensor([1])]; + tensor channels_mean_91_cast_fp16 = reduce_mean(axes = var_4556, keep_dims = var_3940, x = inputs_91_cast_fp16)[name = tensor("channels_mean_91_cast_fp16")]; + tensor zero_mean_91_cast_fp16 = sub(x = inputs_91_cast_fp16, y = channels_mean_91_cast_fp16)[name = tensor("zero_mean_91_cast_fp16")]; + tensor zero_mean_sq_91_cast_fp16 = mul(x = zero_mean_91_cast_fp16, y = zero_mean_91_cast_fp16)[name = tensor("zero_mean_sq_91_cast_fp16")]; + tensor var_4560 = const()[name = tensor("op_4560"), val = tensor([1])]; + tensor var_4561_cast_fp16 = reduce_mean(axes = var_4560, keep_dims = var_3940, x = zero_mean_sq_91_cast_fp16)[name = tensor("op_4561_cast_fp16")]; + tensor var_4562_to_fp16 = const()[name = tensor("op_4562_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4563_cast_fp16 = add(x = var_4561_cast_fp16, y = var_4562_to_fp16)[name = tensor("op_4563_cast_fp16")]; + tensor denom_91_epsilon_0_to_fp16 = const()[name = tensor("denom_91_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_91_cast_fp16 = rsqrt(epsilon = denom_91_epsilon_0_to_fp16, x = var_4563_cast_fp16)[name = tensor("denom_91_cast_fp16")]; + tensor out_91_cast_fp16 = mul(x = zero_mean_91_cast_fp16, y = denom_91_cast_fp16)[name = tensor("out_91_cast_fp16")]; + tensor var_4567_to_fp16 = const()[name = tensor("op_4567_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(833209792)))]; + tensor var_4568_cast_fp16 = add(x = out_91_cast_fp16, y = var_4567_to_fp16)[name = tensor("op_4568_cast_fp16")]; + tensor var_4570_to_fp16 = const()[name = tensor("op_4570_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(833210496)))]; + tensor hidden_states_327_cast_fp16 = mul(x = var_4568_cast_fp16, y = var_4570_to_fp16)[name = tensor("hidden_states_327_cast_fp16")]; + tensor var_4577 = const()[name = tensor("op_4577"), val = tensor([1, 1])]; + tensor var_4579 = const()[name = tensor("op_4579"), val = tensor([1, 1])]; + tensor q_61_pad_type_0 = const()[name = tensor("q_61_pad_type_0"), val = tensor("custom")]; + tensor q_61_pad_0 = const()[name = tensor("q_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(833211200)))]; + tensor q_61_cast_fp16 = conv(dilations = var_4579, groups = var_3945, pad = q_61_pad_0, pad_type = q_61_pad_type_0, strides = var_4577, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_327_cast_fp16)[name = tensor("q_61_cast_fp16")]; + tensor var_4583 = const()[name = tensor("op_4583"), val = tensor([1, 1])]; + tensor var_4585 = const()[name = tensor("op_4585"), val = tensor([1, 1])]; + tensor k_61_pad_type_0 = const()[name = tensor("k_61_pad_type_0"), val = tensor("custom")]; + tensor k_61_pad_0 = const()[name = tensor("k_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(833416064)))]; + tensor k_61_cast_fp16 = conv(dilations = var_4585, groups = var_3945, pad = k_61_pad_0, pad_type = k_61_pad_type_0, strides = var_4583, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_327_cast_fp16)[name = tensor("k_61_cast_fp16")]; + tensor var_4589 = const()[name = tensor("op_4589"), val = tensor([1, 1])]; + tensor var_4591 = const()[name = tensor("op_4591"), val = tensor([1, 1])]; + tensor v_61_pad_type_0 = const()[name = tensor("v_61_pad_type_0"), val = tensor("custom")]; + tensor v_61_pad_0 = const()[name = tensor("v_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(833620928)))]; + tensor v_61_cast_fp16 = conv(dilations = var_4591, groups = var_3945, pad = v_61_pad_0, pad_type = v_61_pad_type_0, strides = var_4589, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_327_cast_fp16)[name = tensor("v_61_cast_fp16")]; + tensor var_4595 = const()[name = tensor("op_4595"), val = tensor([2, 5, 64, -1])]; + tensor var_4596_cast_fp16 = reshape(shape = var_4595, x = q_61_cast_fp16)[name = tensor("op_4596_cast_fp16")]; + tensor var_4597 = const()[name = tensor("op_4597"), val = tensor([2, 5, 64, -1])]; + tensor var_4598_cast_fp16 = reshape(shape = var_4597, x = k_61_cast_fp16)[name = tensor("op_4598_cast_fp16")]; + tensor var_4599 = const()[name = tensor("op_4599"), val = tensor([2, 5, 64, -1])]; + tensor var_4600_cast_fp16 = reshape(shape = var_4599, x = v_61_cast_fp16)[name = tensor("op_4600_cast_fp16")]; + tensor attn_weights_121_transpose_x_0 = const()[name = tensor("attn_weights_121_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_121_transpose_y_0 = const()[name = tensor("attn_weights_121_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_121_cast_fp16 = matmul(transpose_x = attn_weights_121_transpose_x_0, transpose_y = attn_weights_121_transpose_y_0, x = var_4596_cast_fp16, y = var_4598_cast_fp16)[name = tensor("attn_weights_121_cast_fp16")]; + tensor attn_weights_123_cast_fp16 = mul(x = attn_weights_121_cast_fp16, y = var_3936_to_fp16)[name = tensor("attn_weights_123_cast_fp16")]; + tensor var_4604_cast_fp16 = softmax(axis = var_3929, x = attn_weights_123_cast_fp16)[name = tensor("op_4604_cast_fp16")]; + tensor attn_61_transpose_x_0 = const()[name = tensor("attn_61_transpose_x_0"), val = tensor(false)]; + tensor attn_61_transpose_y_0 = const()[name = tensor("attn_61_transpose_y_0"), val = tensor(true)]; + tensor attn_61_cast_fp16 = matmul(transpose_x = attn_61_transpose_x_0, transpose_y = attn_61_transpose_y_0, x = var_4600_cast_fp16, y = var_4604_cast_fp16)[name = tensor("attn_61_cast_fp16")]; + tensor var_4608 = const()[name = tensor("op_4608"), val = tensor([2, 320, 1, -1])]; + tensor input_517_cast_fp16 = reshape(shape = var_4608, x = attn_61_cast_fp16)[name = tensor("input_517_cast_fp16")]; + tensor var_4613 = const()[name = tensor("op_4613"), val = tensor([1, 1])]; + tensor var_4615 = const()[name = tensor("op_4615"), val = tensor([1, 1])]; + tensor var_4617_pad_type_0 = const()[name = tensor("op_4617_pad_type_0"), val = tensor("custom")]; + tensor var_4617_pad_0 = const()[name = tensor("op_4617_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(833825792)))]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(834030656)))]; + tensor var_4617_cast_fp16 = conv(bias = up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_4615, groups = var_3945, pad = var_4617_pad_0, pad_type = var_4617_pad_type_0, strides = var_4613, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_517_cast_fp16)[name = tensor("op_4617_cast_fp16")]; + tensor inputs_93_cast_fp16 = add(x = var_4617_cast_fp16, y = inputs_91_cast_fp16)[name = tensor("inputs_93_cast_fp16")]; + tensor var_4621 = const()[name = tensor("op_4621"), val = tensor([1])]; + tensor channels_mean_93_cast_fp16 = reduce_mean(axes = var_4621, keep_dims = var_3940, x = inputs_93_cast_fp16)[name = tensor("channels_mean_93_cast_fp16")]; + tensor zero_mean_93_cast_fp16 = sub(x = inputs_93_cast_fp16, y = channels_mean_93_cast_fp16)[name = tensor("zero_mean_93_cast_fp16")]; + tensor zero_mean_sq_93_cast_fp16 = mul(x = zero_mean_93_cast_fp16, y = zero_mean_93_cast_fp16)[name = tensor("zero_mean_sq_93_cast_fp16")]; + tensor var_4625 = const()[name = tensor("op_4625"), val = tensor([1])]; + tensor var_4626_cast_fp16 = reduce_mean(axes = var_4625, keep_dims = var_3940, x = zero_mean_sq_93_cast_fp16)[name = tensor("op_4626_cast_fp16")]; + tensor var_4627_to_fp16 = const()[name = tensor("op_4627_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4628_cast_fp16 = add(x = var_4626_cast_fp16, y = var_4627_to_fp16)[name = tensor("op_4628_cast_fp16")]; + tensor denom_93_epsilon_0_to_fp16 = const()[name = tensor("denom_93_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_93_cast_fp16 = rsqrt(epsilon = denom_93_epsilon_0_to_fp16, x = var_4628_cast_fp16)[name = tensor("denom_93_cast_fp16")]; + tensor out_93_cast_fp16 = mul(x = zero_mean_93_cast_fp16, y = denom_93_cast_fp16)[name = tensor("out_93_cast_fp16")]; + tensor var_4632_to_fp16 = const()[name = tensor("op_4632_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(834031360)))]; + tensor var_4633_cast_fp16 = add(x = out_93_cast_fp16, y = var_4632_to_fp16)[name = tensor("op_4633_cast_fp16")]; + tensor var_4635_to_fp16 = const()[name = tensor("op_4635_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(834032064)))]; + tensor hidden_states_329_cast_fp16 = mul(x = var_4633_cast_fp16, y = var_4635_to_fp16)[name = tensor("hidden_states_329_cast_fp16")]; + tensor var_4642 = const()[name = tensor("op_4642"), val = tensor([1, 1])]; + tensor var_4644 = const()[name = tensor("op_4644"), val = tensor([1, 1])]; + tensor q_pad_type_0 = const()[name = tensor("q_pad_type_0"), val = tensor("custom")]; + tensor q_pad_0 = const()[name = tensor("q_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(834032768)))]; + tensor q_cast_fp16 = conv(dilations = var_4644, groups = var_3945, pad = q_pad_0, pad_type = q_pad_type_0, strides = var_4642, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_329_cast_fp16)[name = tensor("q_cast_fp16")]; + tensor var_4648 = const()[name = tensor("op_4648"), val = tensor([1, 1])]; + tensor var_4650 = const()[name = tensor("op_4650"), val = tensor([1, 1])]; + tensor k_pad_type_0 = const()[name = tensor("k_pad_type_0"), val = tensor("custom")]; + tensor k_pad_0 = const()[name = tensor("k_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(834237632)))]; + tensor k_cast_fp16 = conv(dilations = var_4650, groups = var_3945, pad = k_pad_0, pad_type = k_pad_type_0, strides = var_4648, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_cast_fp16")]; + tensor var_4654 = const()[name = tensor("op_4654"), val = tensor([1, 1])]; + tensor var_4656 = const()[name = tensor("op_4656"), val = tensor([1, 1])]; + tensor v_pad_type_0 = const()[name = tensor("v_pad_type_0"), val = tensor("custom")]; + tensor v_pad_0 = const()[name = tensor("v_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(834893056)))]; + tensor v_cast_fp16 = conv(dilations = var_4656, groups = var_3945, pad = v_pad_0, pad_type = v_pad_type_0, strides = var_4654, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_cast_fp16")]; + tensor var_4660 = const()[name = tensor("op_4660"), val = tensor([2, 5, 64, -1])]; + tensor var_4661_cast_fp16 = reshape(shape = var_4660, x = q_cast_fp16)[name = tensor("op_4661_cast_fp16")]; + tensor var_4662 = const()[name = tensor("op_4662"), val = tensor([2, 5, 64, -1])]; + tensor var_4663_cast_fp16 = reshape(shape = var_4662, x = k_cast_fp16)[name = tensor("op_4663_cast_fp16")]; + tensor var_4664 = const()[name = tensor("op_4664"), val = tensor([2, 5, 64, -1])]; + tensor var_4665_cast_fp16 = reshape(shape = var_4664, x = v_cast_fp16)[name = tensor("op_4665_cast_fp16")]; + tensor attn_weights_125_transpose_x_0 = const()[name = tensor("attn_weights_125_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_125_transpose_y_0 = const()[name = tensor("attn_weights_125_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_125_cast_fp16 = matmul(transpose_x = attn_weights_125_transpose_x_0, transpose_y = attn_weights_125_transpose_y_0, x = var_4661_cast_fp16, y = var_4663_cast_fp16)[name = tensor("attn_weights_125_cast_fp16")]; + tensor attn_weights_cast_fp16 = mul(x = attn_weights_125_cast_fp16, y = var_3936_to_fp16)[name = tensor("attn_weights_cast_fp16")]; + tensor var_4669_cast_fp16 = softmax(axis = var_3929, x = attn_weights_cast_fp16)[name = tensor("op_4669_cast_fp16")]; + tensor attn_transpose_x_0 = const()[name = tensor("attn_transpose_x_0"), val = tensor(false)]; + tensor attn_transpose_y_0 = const()[name = tensor("attn_transpose_y_0"), val = tensor(true)]; + tensor attn_cast_fp16 = matmul(transpose_x = attn_transpose_x_0, transpose_y = attn_transpose_y_0, x = var_4665_cast_fp16, y = var_4669_cast_fp16)[name = tensor("attn_cast_fp16")]; + tensor var_4673 = const()[name = tensor("op_4673"), val = tensor([2, 320, 1, -1])]; + tensor input_519_cast_fp16 = reshape(shape = var_4673, x = attn_cast_fp16)[name = tensor("input_519_cast_fp16")]; + tensor var_4678 = const()[name = tensor("op_4678"), val = tensor([1, 1])]; + tensor var_4680 = const()[name = tensor("op_4680"), val = tensor([1, 1])]; + tensor var_4682_pad_type_0 = const()[name = tensor("op_4682_pad_type_0"), val = tensor("custom")]; + tensor var_4682_pad_0 = const()[name = tensor("op_4682_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(835548480)))]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(835753344)))]; + tensor var_4682_cast_fp16 = conv(bias = up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_4680, groups = var_3945, pad = var_4682_pad_0, pad_type = var_4682_pad_type_0, strides = var_4678, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_519_cast_fp16)[name = tensor("op_4682_cast_fp16")]; + tensor inputs_cast_fp16 = add(x = var_4682_cast_fp16, y = inputs_93_cast_fp16)[name = tensor("inputs_cast_fp16")]; + tensor var_4686 = const()[name = tensor("op_4686"), val = tensor([1])]; + tensor channels_mean_cast_fp16 = reduce_mean(axes = var_4686, keep_dims = var_3940, x = inputs_cast_fp16)[name = tensor("channels_mean_cast_fp16")]; + tensor zero_mean_cast_fp16 = sub(x = inputs_cast_fp16, y = channels_mean_cast_fp16)[name = tensor("zero_mean_cast_fp16")]; + tensor zero_mean_sq_cast_fp16 = mul(x = zero_mean_cast_fp16, y = zero_mean_cast_fp16)[name = tensor("zero_mean_sq_cast_fp16")]; + tensor var_4690 = const()[name = tensor("op_4690"), val = tensor([1])]; + tensor var_4691_cast_fp16 = reduce_mean(axes = var_4690, keep_dims = var_3940, x = zero_mean_sq_cast_fp16)[name = tensor("op_4691_cast_fp16")]; + tensor var_4692_to_fp16 = const()[name = tensor("op_4692_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4693_cast_fp16 = add(x = var_4691_cast_fp16, y = var_4692_to_fp16)[name = tensor("op_4693_cast_fp16")]; + tensor denom_epsilon_0_to_fp16 = const()[name = tensor("denom_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_cast_fp16 = rsqrt(epsilon = denom_epsilon_0_to_fp16, x = var_4693_cast_fp16)[name = tensor("denom_cast_fp16")]; + tensor out_cast_fp16 = mul(x = zero_mean_cast_fp16, y = denom_cast_fp16)[name = tensor("out_cast_fp16")]; + tensor var_4697_to_fp16 = const()[name = tensor("op_4697_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(835754048)))]; + tensor var_4698_cast_fp16 = add(x = out_cast_fp16, y = var_4697_to_fp16)[name = tensor("op_4698_cast_fp16")]; + tensor var_4700_to_fp16 = const()[name = tensor("op_4700_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(835754752)))]; + tensor input_521_cast_fp16 = mul(x = var_4698_cast_fp16, y = var_4700_to_fp16)[name = tensor("input_521_cast_fp16")]; + tensor var_4708 = const()[name = tensor("op_4708"), val = tensor([1, 1])]; + tensor var_4710 = const()[name = tensor("op_4710"), val = tensor([1, 1])]; + tensor var_4712_pad_type_0 = const()[name = tensor("op_4712_pad_type_0"), val = tensor("custom")]; + tensor var_4712_pad_0 = const()[name = tensor("op_4712_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(835755456)))]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(837393920)))]; + tensor var_4712_cast_fp16 = conv(bias = up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_4710, groups = var_3945, pad = var_4712_pad_0, pad_type = var_4712_pad_type_0, strides = var_4708, weight = up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_521_cast_fp16)[name = tensor("op_4712_cast_fp16")]; + tensor var_4713_split_sizes_0 = const()[name = tensor("op_4713_split_sizes_0"), val = tensor([1280, 1280])]; + tensor var_4713_axis_0 = const()[name = tensor("op_4713_axis_0"), val = tensor(1)]; + tensor var_4713_cast_fp16_0, tensor var_4713_cast_fp16_1 = split(axis = var_4713_axis_0, split_sizes = var_4713_split_sizes_0, x = var_4712_cast_fp16)[name = tensor("op_4713_cast_fp16")]; + tensor var_4715_mode_0 = const()[name = tensor("op_4715_mode_0"), val = tensor("EXACT")]; + tensor var_4715_cast_fp16 = gelu(mode = var_4715_mode_0, x = var_4713_cast_fp16_1)[name = tensor("op_4715_cast_fp16")]; + tensor input_523_cast_fp16 = mul(x = var_4713_cast_fp16_0, y = var_4715_cast_fp16)[name = tensor("input_523_cast_fp16")]; + tensor var_4719 = const()[name = tensor("op_4719"), val = tensor([1, 1])]; + tensor var_4721 = const()[name = tensor("op_4721"), val = tensor([1, 1])]; + tensor var_4723_pad_type_0 = const()[name = tensor("op_4723_pad_type_0"), val = tensor("custom")]; + tensor var_4723_pad_0 = const()[name = tensor("op_4723_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(837399104)))]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(838218368)))]; + tensor var_4723_cast_fp16 = conv(bias = up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_4721, groups = var_3945, pad = var_4723_pad_0, pad_type = var_4723_pad_type_0, strides = var_4719, weight = up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_523_cast_fp16)[name = tensor("op_4723_cast_fp16")]; + tensor hidden_states_333_cast_fp16 = add(x = var_4723_cast_fp16, y = inputs_cast_fp16)[name = tensor("hidden_states_333_cast_fp16")]; + tensor var_4725 = const()[name = tensor("op_4725"), val = tensor([2, 320, 48, 80])]; + tensor input_525_cast_fp16 = reshape(shape = var_4725, x = hidden_states_333_cast_fp16)[name = tensor("input_525_cast_fp16")]; + tensor var_4729 = const()[name = tensor("op_4729"), val = tensor([1, 1])]; + tensor var_4731 = const()[name = tensor("op_4731"), val = tensor([1, 1])]; + tensor hidden_states_pad_type_0 = const()[name = tensor("hidden_states_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_pad_0 = const()[name = tensor("hidden_states_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(838219072)))]; + tensor up_blocks_3_attentions_2_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(838423936)))]; + tensor hidden_states_cast_fp16 = conv(bias = up_blocks_3_attentions_2_proj_out_bias_to_fp16, dilations = var_4731, groups = var_3945, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = var_4729, weight = up_blocks_3_attentions_2_proj_out_weight_to_fp16, x = input_525_cast_fp16)[name = tensor("hidden_states_cast_fp16")]; + tensor input_527_cast_fp16 = add(x = hidden_states_cast_fp16, y = hidden_states_323_cast_fp16)[name = tensor("input_527_cast_fp16")]; + tensor reshape_240_shape_0 = const()[name = tensor("reshape_240_shape_0"), val = tensor([2, 32, 10, 48, 80])]; + tensor reshape_240_cast_fp16 = reshape(shape = reshape_240_shape_0, x = input_527_cast_fp16)[name = tensor("reshape_240_cast_fp16")]; + tensor reduce_mean_180_axes_0 = const()[name = tensor("reduce_mean_180_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_180_keep_dims_0 = const()[name = tensor("reduce_mean_180_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_180_cast_fp16 = reduce_mean(axes = reduce_mean_180_axes_0, keep_dims = reduce_mean_180_keep_dims_0, x = reshape_240_cast_fp16)[name = tensor("reduce_mean_180_cast_fp16")]; + tensor sub_120_cast_fp16 = sub(x = reshape_240_cast_fp16, y = reduce_mean_180_cast_fp16)[name = tensor("sub_120_cast_fp16")]; + tensor square_60_cast_fp16 = square(x = sub_120_cast_fp16)[name = tensor("square_60_cast_fp16")]; + tensor reduce_mean_182_axes_0 = const()[name = tensor("reduce_mean_182_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_182_keep_dims_0 = const()[name = tensor("reduce_mean_182_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_182_cast_fp16 = reduce_mean(axes = reduce_mean_182_axes_0, keep_dims = reduce_mean_182_keep_dims_0, x = square_60_cast_fp16)[name = tensor("reduce_mean_182_cast_fp16")]; + tensor add_120_y_0_to_fp16 = const()[name = tensor("add_120_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_120_cast_fp16 = add(x = reduce_mean_182_cast_fp16, y = add_120_y_0_to_fp16)[name = tensor("add_120_cast_fp16")]; + tensor sqrt_60_cast_fp16 = sqrt(x = add_120_cast_fp16)[name = tensor("sqrt_60_cast_fp16")]; + tensor real_div_60_cast_fp16 = real_div(x = sub_120_cast_fp16, y = sqrt_60_cast_fp16)[name = tensor("real_div_60_cast_fp16")]; + tensor reshape_241_shape_0 = const()[name = tensor("reshape_241_shape_0"), val = tensor([2, 320, 48, 80])]; + tensor reshape_241_cast_fp16 = reshape(shape = reshape_241_shape_0, x = real_div_60_cast_fp16)[name = tensor("reshape_241_cast_fp16")]; + tensor add_121_gamma_0_to_fp16 = const()[name = tensor("add_121_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(838424640)))]; + tensor add_121_beta_0_to_fp16 = const()[name = tensor("add_121_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(838425344)))]; + tensor add_121_epsilon_0_to_fp16 = const()[name = tensor("add_121_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_121_cast_fp16 = batch_norm(beta = add_121_beta_0_to_fp16, epsilon = add_121_epsilon_0_to_fp16, gamma = add_121_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_241_cast_fp16)[name = tensor("add_121_cast_fp16")]; + tensor input_cast_fp16 = silu(x = add_121_cast_fp16)[name = tensor("input_cast_fp16")]; + tensor var_4745 = const()[name = tensor("op_4745"), val = tensor(1)]; + tensor var_4748 = const()[name = tensor("op_4748"), val = tensor([1, 1])]; + tensor var_4750 = const()[name = tensor("op_4750"), val = tensor([1, 1])]; + tensor var_4752_pad_type_0 = const()[name = tensor("op_4752_pad_type_0"), val = tensor("custom")]; + tensor var_4752_pad_0 = const()[name = tensor("op_4752_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv_out_weight_to_fp16 = const()[name = tensor("conv_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(838426048)))]; + tensor conv_out_bias_to_fp16 = const()[name = tensor("conv_out_bias_to_fp16"), val = tensor([-0x1.4b4p-9, 0x1.6f4p-9, 0x1.9ap-12, 0x1.04p-9])]; + tensor var_4752_cast_fp16 = conv(bias = conv_out_bias_to_fp16, dilations = var_4750, groups = var_4745, pad = var_4752_pad_0, pad_type = var_4752_pad_type_0, strides = var_4748, weight = conv_out_weight_to_fp16, x = input_cast_fp16)[name = tensor("op_4752_cast_fp16")]; + tensor var_4752_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("op_4752_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor noise_pred = cast(dtype = var_4752_cast_fp16_to_fp32_dtype_0, x = var_4752_cast_fp16)[name = tensor("cast_0")]; + } -> (noise_pred); +} \ No newline at end of file diff --git a/original/compiled/UnetChunk2.mlmodelc/weights/weight.bin b/original/compiled/UnetChunk2.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..96e004f272f8565f2aafbb804be6053d39f8ac18 --- /dev/null +++ b/original/compiled/UnetChunk2.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c73b29d6f4f6869e8bd429ecc623ddb942c09a58ddaf28c45c29bd980f91dd2f +size 838449152 diff --git a/original/compiled/VAEDecoder.mlmodelc/analytics/coremldata.bin b/original/compiled/VAEDecoder.mlmodelc/analytics/coremldata.bin new 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0000000000000000000000000000000000000000..c24fc6c662ebd24e78f559ec65a2536839f17336 --- /dev/null +++ b/original/compiled/VAEDecoder.mlmodelc/metadata.json @@ -0,0 +1,77 @@ +[ + { + "shortDescription" : "Stable Diffusion generates images conditioned on text and\/or other images as input through the diffusion process. Please refer to https:\/\/arxiv.org\/abs\/2112.10752 for details.", + "metadataOutputVersion" : "3.0", + "outputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 3 × 384 × 640)", + "shortDescription" : "Generated image normalized to range [-1, 1]", + "shape" : "[1, 3, 384, 640]", + "name" : "image", + "type" : "MultiArray" + } + ], + "version" : "\/Users\/keijiro\/Documents\/StableDiffusion\/sd-turbo", + "modelParameters" : [ + + ], + "author" : "Please refer to the Model Card available at huggingface.co\/\/Users\/keijiro\/Documents\/StableDiffusion\/sd-turbo", + "specificationVersion" : 7, + "storagePrecision" : "Float16", + "license" : "OpenRAIL (https:\/\/huggingface.co\/spaces\/CompVis\/stable-diffusion-license)", + "mlProgramOperationTypeHistogram" : { + "Ios16.cast" : 1, + "Ios16.mul" : 2, + "Ios16.sqrt" : 30, + "Ios16.sub" : 30, + "Transpose" : 6, + "UpsampleNearestNeighbor" : 3, + "Ios16.conv" : 36, + "Ios16.add" : 46, + "Ios16.linear" : 4, + "Ios16.matmul" : 2, + "Ios16.realDiv" : 30, + "Ios16.reduceMean" : 60, + "Ios16.softmax" : 1, + "Ios16.batchNorm" : 29, + "Ios16.square" : 30, + "Ios16.reshape" : 65, + "Ios16.silu" : 29 + }, + "computePrecision" : "Mixed (Float16, Float32, Int32)", + "isUpdatable" : "0", + "availability" : { + "macOS" : "13.0", + "tvOS" : "16.0", + "visionOS" : "1.0", + "watchOS" : "9.0", + "iOS" : "16.0", + "macCatalyst" : "16.0" + }, + "modelType" : { + "name" : "MLModelType_mlProgram" + }, + "inputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 1 × 4 × 48 × 80)", + "shortDescription" : "The denoised latent embeddings from the unet model after the last step of reverse diffusion", + "shape" : "[1, 4, 48, 80]", + "name" : "z", + "type" : "MultiArray" + } + ], + "userDefinedMetadata" : { + "com.github.apple.coremltools.source_dialect" : "TorchScript", + "com.github.apple.coremltools.version" : "7.1", + "com.github.apple.coremltools.source" : "torch==2.1.2" + }, + "generatedClassName" : "Stable_Diffusion_version__Users_keijiro_Documents_StableDiffusion_sd_turbo_vae_decoder", + "method" : "predict" + } +] \ No newline at end of file diff --git a/original/compiled/VAEDecoder.mlmodelc/model.mil b/original/compiled/VAEDecoder.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..363b39813a58dd6bc424761aececd367fafc522b --- /dev/null +++ b/original/compiled/VAEDecoder.mlmodelc/model.mil @@ -0,0 +1,965 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3304.5.2"}, {"coremlc-version", "3304.6.2"}, {"coremltools-component-torch", "2.1.2"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "7.1"}})] +{ + func main(tensor z) { + tensor var_7 = const()[name = tensor("op_7"), val = tensor(1)]; + tensor var_10 = const()[name = tensor("op_10"), val = tensor([1, 1])]; + tensor var_12 = const()[name = tensor("op_12"), val = tensor([1, 1])]; + tensor input_1_pad_type_0 = const()[name = tensor("input_1_pad_type_0"), val = tensor("custom")]; + tensor input_1_pad_0 = const()[name = tensor("input_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor post_quant_conv_weight_to_fp16 = const()[name = tensor("post_quant_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor post_quant_conv_bias_to_fp16 = const()[name = tensor("post_quant_conv_bias_to_fp16"), val = tensor([0x1.06cp-5, -0x1.594p-4, -0x1.f24p-3, 0x1.0d8p-3])]; + tensor input_1_cast_fp16 = conv(bias = post_quant_conv_bias_to_fp16, dilations = var_12, groups = var_7, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = var_10, weight = post_quant_conv_weight_to_fp16, x = z)[name = tensor("input_1_cast_fp16")]; + tensor var_26 = const()[name = tensor("op_26"), val = tensor(1)]; + tensor var_44 = const()[name = tensor("op_44"), val = tensor([1, 1])]; + tensor var_46 = const()[name = tensor("op_46"), val = tensor([1, 1])]; + tensor input_3_pad_type_0 = const()[name = tensor("input_3_pad_type_0"), val = tensor("custom")]; + tensor input_3_pad_0 = const()[name = tensor("input_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_conv_in_weight_to_fp16 = const()[name = tensor("decoder_conv_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(192)))]; + tensor decoder_conv_in_bias_to_fp16 = const()[name = tensor("decoder_conv_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37120)))]; + tensor input_3_cast_fp16 = conv(bias = decoder_conv_in_bias_to_fp16, dilations = var_46, groups = var_26, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_44, weight = decoder_conv_in_weight_to_fp16, x = input_1_cast_fp16)[name = tensor("input_3_cast_fp16")]; + tensor reshape_0_shape_0 = const()[name = tensor("reshape_0_shape_0"), val = tensor([1, 32, 16, 48, 80])]; + tensor reshape_0_cast_fp16 = reshape(shape = reshape_0_shape_0, x = input_3_cast_fp16)[name = tensor("reshape_0_cast_fp16")]; + tensor reduce_mean_0_axes_0 = const()[name = tensor("reduce_mean_0_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_0_keep_dims_0 = const()[name = tensor("reduce_mean_0_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_0_cast_fp16 = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = reshape_0_cast_fp16)[name = tensor("reduce_mean_0_cast_fp16")]; + tensor sub_0_cast_fp16 = sub(x = reshape_0_cast_fp16, y = reduce_mean_0_cast_fp16)[name = tensor("sub_0_cast_fp16")]; + tensor square_0_cast_fp16 = square(x = sub_0_cast_fp16)[name = tensor("square_0_cast_fp16")]; + tensor reduce_mean_2_axes_0 = const()[name = tensor("reduce_mean_2_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_2_keep_dims_0 = const()[name = tensor("reduce_mean_2_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_2_cast_fp16 = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = square_0_cast_fp16)[name = tensor("reduce_mean_2_cast_fp16")]; + tensor add_0_y_0_to_fp16 = const()[name = tensor("add_0_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_0_cast_fp16 = add(x = reduce_mean_2_cast_fp16, y = add_0_y_0_to_fp16)[name = tensor("add_0_cast_fp16")]; + tensor sqrt_0_cast_fp16 = sqrt(x = add_0_cast_fp16)[name = tensor("sqrt_0_cast_fp16")]; + tensor real_div_0_cast_fp16 = real_div(x = sub_0_cast_fp16, y = sqrt_0_cast_fp16)[name = tensor("real_div_0_cast_fp16")]; + tensor reshape_1_shape_0 = const()[name = tensor("reshape_1_shape_0"), val = tensor([1, 512, 48, 80])]; + tensor reshape_1_cast_fp16 = reshape(shape = reshape_1_shape_0, x = real_div_0_cast_fp16)[name = tensor("reshape_1_cast_fp16")]; + tensor add_1_mean_0_to_fp16 = const()[name = tensor("add_1_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38208)))]; + tensor add_1_variance_0_to_fp16 = const()[name = tensor("add_1_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39296)))]; + tensor add_1_gamma_0_to_fp16 = const()[name = tensor("add_1_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40384)))]; + tensor add_1_beta_0_to_fp16 = const()[name = tensor("add_1_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41472)))]; + tensor add_1_epsilon_0_to_fp16 = const()[name = tensor("add_1_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_1_cast_fp16 = batch_norm(beta = add_1_beta_0_to_fp16, epsilon = add_1_epsilon_0_to_fp16, gamma = add_1_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_1_cast_fp16)[name = tensor("add_1_cast_fp16")]; + tensor hidden_states_1_cast_fp16 = silu(x = add_1_cast_fp16)[name = tensor("hidden_states_1_cast_fp16")]; + tensor var_65 = const()[name = tensor("op_65"), val = tensor([1, 1])]; + tensor var_67 = const()[name = tensor("op_67"), val = tensor([1, 1])]; + tensor input_7_pad_type_0 = const()[name = tensor("input_7_pad_type_0"), val = tensor("custom")]; + tensor input_7_pad_0 = const()[name = tensor("input_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_mid_block_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("decoder_mid_block_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42560)))]; + tensor decoder_mid_block_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("decoder_mid_block_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4761216)))]; + tensor input_7_cast_fp16 = conv(bias = decoder_mid_block_resnets_0_conv1_bias_to_fp16, dilations = var_67, groups = var_26, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = var_65, weight = decoder_mid_block_resnets_0_conv1_weight_to_fp16, x = hidden_states_1_cast_fp16)[name = tensor("input_7_cast_fp16")]; + tensor reshape_4_shape_0 = const()[name = tensor("reshape_4_shape_0"), val = tensor([1, 32, 16, 48, 80])]; + tensor reshape_4_cast_fp16 = reshape(shape = reshape_4_shape_0, x = input_7_cast_fp16)[name = tensor("reshape_4_cast_fp16")]; + tensor reduce_mean_3_axes_0 = const()[name = tensor("reduce_mean_3_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_3_keep_dims_0 = const()[name = tensor("reduce_mean_3_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_3_cast_fp16 = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = reshape_4_cast_fp16)[name = tensor("reduce_mean_3_cast_fp16")]; + tensor sub_2_cast_fp16 = sub(x = reshape_4_cast_fp16, y = reduce_mean_3_cast_fp16)[name = tensor("sub_2_cast_fp16")]; + tensor square_1_cast_fp16 = square(x = sub_2_cast_fp16)[name = tensor("square_1_cast_fp16")]; + tensor reduce_mean_5_axes_0 = const()[name = tensor("reduce_mean_5_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_5_keep_dims_0 = const()[name = tensor("reduce_mean_5_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_5_cast_fp16 = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_1_cast_fp16)[name = tensor("reduce_mean_5_cast_fp16")]; + tensor add_2_y_0_to_fp16 = const()[name = tensor("add_2_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_2_cast_fp16 = add(x = reduce_mean_5_cast_fp16, y = add_2_y_0_to_fp16)[name = tensor("add_2_cast_fp16")]; + tensor sqrt_1_cast_fp16 = sqrt(x = add_2_cast_fp16)[name = tensor("sqrt_1_cast_fp16")]; + tensor real_div_1_cast_fp16 = real_div(x = sub_2_cast_fp16, y = sqrt_1_cast_fp16)[name = tensor("real_div_1_cast_fp16")]; + tensor reshape_5_shape_0 = const()[name = tensor("reshape_5_shape_0"), val = tensor([1, 512, 48, 80])]; + tensor reshape_5_cast_fp16 = reshape(shape = reshape_5_shape_0, x = real_div_1_cast_fp16)[name = tensor("reshape_5_cast_fp16")]; + tensor add_3_gamma_0_to_fp16 = const()[name = tensor("add_3_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4762304)))]; + tensor add_3_beta_0_to_fp16 = const()[name = tensor("add_3_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4763392)))]; + tensor add_3_epsilon_0_to_fp16 = const()[name = tensor("add_3_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_3_cast_fp16 = batch_norm(beta = add_3_beta_0_to_fp16, epsilon = add_3_epsilon_0_to_fp16, gamma = add_3_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_5_cast_fp16)[name = tensor("add_3_cast_fp16")]; + tensor input_11_cast_fp16 = silu(x = add_3_cast_fp16)[name = tensor("input_11_cast_fp16")]; + tensor var_77 = const()[name = tensor("op_77"), val = tensor([1, 1])]; + tensor var_79 = const()[name = tensor("op_79"), val = tensor([1, 1])]; + tensor hidden_states_5_pad_type_0 = const()[name = tensor("hidden_states_5_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_5_pad_0 = const()[name = tensor("hidden_states_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_mid_block_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("decoder_mid_block_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4764480)))]; + tensor decoder_mid_block_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("decoder_mid_block_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9483136)))]; + tensor hidden_states_5_cast_fp16 = conv(bias = decoder_mid_block_resnets_0_conv2_bias_to_fp16, dilations = var_79, groups = var_26, pad = hidden_states_5_pad_0, pad_type = hidden_states_5_pad_type_0, strides = var_77, weight = decoder_mid_block_resnets_0_conv2_weight_to_fp16, x = input_11_cast_fp16)[name = tensor("hidden_states_5_cast_fp16")]; + tensor var_82_cast_fp16 = add(x = input_3_cast_fp16, y = hidden_states_5_cast_fp16)[name = tensor("op_82_cast_fp16")]; + tensor reshape_8_shape_0 = const()[name = tensor("reshape_8_shape_0"), val = tensor([1, 32, 16, 3840])]; + tensor reshape_8_cast_fp16 = reshape(shape = reshape_8_shape_0, x = var_82_cast_fp16)[name = tensor("reshape_8_cast_fp16")]; + tensor reduce_mean_6_axes_0 = const()[name = tensor("reduce_mean_6_axes_0"), val = tensor([2, 3])]; + tensor reduce_mean_6_keep_dims_0 = const()[name = tensor("reduce_mean_6_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_6_cast_fp16 = reduce_mean(axes = reduce_mean_6_axes_0, keep_dims = reduce_mean_6_keep_dims_0, x = reshape_8_cast_fp16)[name = tensor("reduce_mean_6_cast_fp16")]; + tensor sub_4_cast_fp16 = sub(x = reshape_8_cast_fp16, y = reduce_mean_6_cast_fp16)[name = tensor("sub_4_cast_fp16")]; + tensor square_2_cast_fp16 = square(x = sub_4_cast_fp16)[name = tensor("square_2_cast_fp16")]; + tensor reduce_mean_8_axes_0 = const()[name = tensor("reduce_mean_8_axes_0"), val = tensor([2, 3])]; + tensor reduce_mean_8_keep_dims_0 = const()[name = tensor("reduce_mean_8_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_8_cast_fp16 = reduce_mean(axes = reduce_mean_8_axes_0, keep_dims = reduce_mean_8_keep_dims_0, x = square_2_cast_fp16)[name = tensor("reduce_mean_8_cast_fp16")]; + tensor add_4_y_0_to_fp16 = const()[name = tensor("add_4_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_4_cast_fp16 = add(x = reduce_mean_8_cast_fp16, y = add_4_y_0_to_fp16)[name = tensor("add_4_cast_fp16")]; + tensor sqrt_2_cast_fp16 = sqrt(x = add_4_cast_fp16)[name = tensor("sqrt_2_cast_fp16")]; + tensor real_div_2_cast_fp16 = real_div(x = sub_4_cast_fp16, y = sqrt_2_cast_fp16)[name = tensor("real_div_2_cast_fp16")]; + tensor reshape_9_shape_0 = const()[name = tensor("reshape_9_shape_0"), val = tensor([1, 512, 3840])]; + tensor reshape_9_cast_fp16 = reshape(shape = reshape_9_shape_0, x = real_div_2_cast_fp16)[name = tensor("reshape_9_cast_fp16")]; + tensor reshape_10_to_fp16 = const()[name = tensor("reshape_10_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9484224)))]; + tensor mul_2_cast_fp16 = mul(x = reshape_9_cast_fp16, y = reshape_10_to_fp16)[name = tensor("mul_2_cast_fp16")]; + tensor reshape_11_to_fp16 = const()[name = tensor("reshape_11_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9485312)))]; + tensor add_5_cast_fp16 = add(x = mul_2_cast_fp16, y = reshape_11_to_fp16)[name = tensor("add_5_cast_fp16")]; + tensor input_15_perm_0 = const()[name = tensor("input_15_perm_0"), val = tensor([0, 2, 1])]; + tensor decoder_mid_block_attentions_0_to_q_weight_to_fp16 = const()[name = tensor("decoder_mid_block_attentions_0_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9486400)))]; + tensor decoder_mid_block_attentions_0_to_q_bias_to_fp16 = const()[name = tensor("decoder_mid_block_attentions_0_to_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10010752)))]; + tensor transpose_11 = transpose(perm = input_15_perm_0, x = add_5_cast_fp16)[name = tensor("transpose_11")]; + tensor linear_0_cast_fp16 = linear(bias = decoder_mid_block_attentions_0_to_q_bias_to_fp16, weight = decoder_mid_block_attentions_0_to_q_weight_to_fp16, x = transpose_11)[name = tensor("linear_0_cast_fp16")]; + tensor decoder_mid_block_attentions_0_to_k_weight_to_fp16 = const()[name = tensor("decoder_mid_block_attentions_0_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10011840)))]; + tensor decoder_mid_block_attentions_0_to_k_bias_to_fp16 = const()[name = tensor("decoder_mid_block_attentions_0_to_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10536192)))]; + tensor linear_1_cast_fp16 = linear(bias = decoder_mid_block_attentions_0_to_k_bias_to_fp16, weight = decoder_mid_block_attentions_0_to_k_weight_to_fp16, x = transpose_11)[name = tensor("linear_1_cast_fp16")]; + tensor decoder_mid_block_attentions_0_to_v_weight_to_fp16 = const()[name = tensor("decoder_mid_block_attentions_0_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10537280)))]; + tensor decoder_mid_block_attentions_0_to_v_bias_to_fp16 = const()[name = tensor("decoder_mid_block_attentions_0_to_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11061632)))]; + tensor linear_2_cast_fp16 = linear(bias = decoder_mid_block_attentions_0_to_v_bias_to_fp16, weight = decoder_mid_block_attentions_0_to_v_weight_to_fp16, x = transpose_11)[name = tensor("linear_2_cast_fp16")]; + tensor var_123 = const()[name = tensor("op_123"), val = tensor([1, -1, 1, 512])]; + tensor var_124_cast_fp16 = reshape(shape = var_123, x = linear_0_cast_fp16)[name = tensor("op_124_cast_fp16")]; + tensor var_126 = const()[name = tensor("op_126"), val = tensor([1, -1, 1, 512])]; + tensor var_127_cast_fp16 = reshape(shape = var_126, x = linear_1_cast_fp16)[name = tensor("op_127_cast_fp16")]; + tensor var_129 = const()[name = tensor("op_129"), val = tensor([1, -1, 1, 512])]; + tensor var_130_cast_fp16 = reshape(shape = var_129, x = linear_2_cast_fp16)[name = tensor("op_130_cast_fp16")]; + tensor value_perm_0 = const()[name = tensor("value_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor mul_3_y_0_to_fp16 = const()[name = tensor("mul_3_y_0_to_fp16"), val = tensor(0x1.6ap-5)]; + tensor mul_3_cast_fp16 = mul(x = var_124_cast_fp16, y = mul_3_y_0_to_fp16)[name = tensor("mul_3_cast_fp16")]; + tensor matmul_0_transpose_y_0 = const()[name = tensor("matmul_0_transpose_y_0"), val = tensor(true)]; + tensor matmul_0_transpose_x_0 = const()[name = tensor("matmul_0_transpose_x_0"), val = tensor(false)]; + tensor transpose_4_perm_0 = const()[name = tensor("transpose_4_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_5_perm_0 = const()[name = tensor("transpose_5_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_8 = transpose(perm = transpose_5_perm_0, x = var_127_cast_fp16)[name = tensor("transpose_8")]; + tensor transpose_9 = transpose(perm = transpose_4_perm_0, x = mul_3_cast_fp16)[name = tensor("transpose_9")]; + tensor matmul_0_cast_fp16 = matmul(transpose_x = matmul_0_transpose_x_0, transpose_y = matmul_0_transpose_y_0, x = transpose_9, y = transpose_8)[name = tensor("matmul_0_cast_fp16")]; + tensor softmax_0_axis_0 = const()[name = tensor("softmax_0_axis_0"), val = tensor(-1)]; + tensor softmax_0_cast_fp16 = softmax(axis = softmax_0_axis_0, x = matmul_0_cast_fp16)[name = tensor("softmax_0_cast_fp16")]; + tensor hidden_states_11_transpose_x_0 = const()[name = tensor("hidden_states_11_transpose_x_0"), val = tensor(false)]; + tensor hidden_states_11_transpose_y_0 = const()[name = tensor("hidden_states_11_transpose_y_0"), val = tensor(false)]; + tensor transpose_10 = transpose(perm = value_perm_0, x = var_130_cast_fp16)[name = tensor("transpose_10")]; + tensor hidden_states_11_cast_fp16 = matmul(transpose_x = hidden_states_11_transpose_x_0, transpose_y = hidden_states_11_transpose_y_0, x = softmax_0_cast_fp16, y = transpose_10)[name = tensor("hidden_states_11_cast_fp16")]; + tensor var_133_perm_0 = const()[name = tensor("op_133_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_137 = const()[name = tensor("op_137"), val = tensor([1, -1, 512])]; + tensor transpose_7 = transpose(perm = var_133_perm_0, x = hidden_states_11_cast_fp16)[name = tensor("transpose_7")]; + tensor hidden_states_13_cast_fp16 = reshape(shape = var_137, x = transpose_7)[name = tensor("hidden_states_13_cast_fp16")]; + tensor decoder_mid_block_attentions_0_to_out_0_weight_to_fp16 = const()[name = tensor("decoder_mid_block_attentions_0_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11062720)))]; + tensor decoder_mid_block_attentions_0_to_out_0_bias_to_fp16 = const()[name = tensor("decoder_mid_block_attentions_0_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11587072)))]; + tensor linear_3_cast_fp16 = linear(bias = decoder_mid_block_attentions_0_to_out_0_bias_to_fp16, weight = decoder_mid_block_attentions_0_to_out_0_weight_to_fp16, x = hidden_states_13_cast_fp16)[name = tensor("linear_3_cast_fp16")]; + tensor var_144_perm_0 = const()[name = tensor("op_144_perm_0"), val = tensor([0, -1, -2])]; + tensor var_145 = const()[name = tensor("op_145"), val = tensor([1, 512, 48, 80])]; + tensor transpose_6 = transpose(perm = var_144_perm_0, x = linear_3_cast_fp16)[name = tensor("transpose_6")]; + tensor hidden_states_17_cast_fp16 = reshape(shape = var_145, x = transpose_6)[name = tensor("hidden_states_17_cast_fp16")]; + tensor hidden_states_19_cast_fp16 = add(x = hidden_states_17_cast_fp16, y = var_82_cast_fp16)[name = tensor("hidden_states_19_cast_fp16")]; + tensor reshape_12_shape_0 = const()[name = tensor("reshape_12_shape_0"), val = tensor([1, 32, 16, 48, 80])]; + tensor reshape_12_cast_fp16 = reshape(shape = reshape_12_shape_0, x = hidden_states_19_cast_fp16)[name = tensor("reshape_12_cast_fp16")]; + tensor reduce_mean_9_axes_0 = const()[name = tensor("reduce_mean_9_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_9_keep_dims_0 = const()[name = tensor("reduce_mean_9_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_9_cast_fp16 = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = reshape_12_cast_fp16)[name = tensor("reduce_mean_9_cast_fp16")]; + tensor sub_6_cast_fp16 = sub(x = reshape_12_cast_fp16, y = reduce_mean_9_cast_fp16)[name = tensor("sub_6_cast_fp16")]; + tensor square_3_cast_fp16 = square(x = sub_6_cast_fp16)[name = tensor("square_3_cast_fp16")]; + tensor reduce_mean_11_axes_0 = const()[name = tensor("reduce_mean_11_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_11_keep_dims_0 = const()[name = tensor("reduce_mean_11_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_11_cast_fp16 = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_3_cast_fp16)[name = tensor("reduce_mean_11_cast_fp16")]; + tensor add_6_y_0_to_fp16 = const()[name = tensor("add_6_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_6_cast_fp16 = add(x = reduce_mean_11_cast_fp16, y = add_6_y_0_to_fp16)[name = tensor("add_6_cast_fp16")]; + tensor sqrt_3_cast_fp16 = sqrt(x = add_6_cast_fp16)[name = tensor("sqrt_3_cast_fp16")]; + tensor real_div_3_cast_fp16 = real_div(x = sub_6_cast_fp16, y = sqrt_3_cast_fp16)[name = tensor("real_div_3_cast_fp16")]; + tensor reshape_13_shape_0 = const()[name = tensor("reshape_13_shape_0"), val = tensor([1, 512, 48, 80])]; + tensor reshape_13_cast_fp16 = reshape(shape = reshape_13_shape_0, x = real_div_3_cast_fp16)[name = tensor("reshape_13_cast_fp16")]; + tensor add_7_gamma_0_to_fp16 = const()[name = tensor("add_7_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11588160)))]; + tensor add_7_beta_0_to_fp16 = const()[name = tensor("add_7_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11589248)))]; + tensor add_7_epsilon_0_to_fp16 = const()[name = tensor("add_7_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_7_cast_fp16 = batch_norm(beta = add_7_beta_0_to_fp16, epsilon = add_7_epsilon_0_to_fp16, gamma = add_7_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_13_cast_fp16)[name = tensor("add_7_cast_fp16")]; + tensor hidden_states_21_cast_fp16 = silu(x = add_7_cast_fp16)[name = tensor("hidden_states_21_cast_fp16")]; + tensor var_160 = const()[name = tensor("op_160"), val = tensor([1, 1])]; + tensor var_162 = const()[name = tensor("op_162"), val = tensor([1, 1])]; + tensor input_25_pad_type_0 = const()[name = tensor("input_25_pad_type_0"), val = tensor("custom")]; + tensor input_25_pad_0 = const()[name = tensor("input_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_mid_block_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("decoder_mid_block_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11590336)))]; + tensor decoder_mid_block_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("decoder_mid_block_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16308992)))]; + tensor input_25_cast_fp16 = conv(bias = decoder_mid_block_resnets_1_conv1_bias_to_fp16, dilations = var_162, groups = var_26, pad = input_25_pad_0, pad_type = input_25_pad_type_0, strides = var_160, weight = decoder_mid_block_resnets_1_conv1_weight_to_fp16, x = hidden_states_21_cast_fp16)[name = tensor("input_25_cast_fp16")]; + tensor reshape_16_shape_0 = const()[name = tensor("reshape_16_shape_0"), val = tensor([1, 32, 16, 48, 80])]; + tensor reshape_16_cast_fp16 = reshape(shape = reshape_16_shape_0, x = input_25_cast_fp16)[name = tensor("reshape_16_cast_fp16")]; + tensor reduce_mean_12_axes_0 = const()[name = tensor("reduce_mean_12_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_12_keep_dims_0 = const()[name = tensor("reduce_mean_12_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_12_cast_fp16 = reduce_mean(axes = reduce_mean_12_axes_0, keep_dims = reduce_mean_12_keep_dims_0, x = reshape_16_cast_fp16)[name = tensor("reduce_mean_12_cast_fp16")]; + tensor sub_8_cast_fp16 = sub(x = reshape_16_cast_fp16, y = reduce_mean_12_cast_fp16)[name = tensor("sub_8_cast_fp16")]; + tensor square_4_cast_fp16 = square(x = sub_8_cast_fp16)[name = tensor("square_4_cast_fp16")]; + tensor reduce_mean_14_axes_0 = const()[name = tensor("reduce_mean_14_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_14_keep_dims_0 = const()[name = tensor("reduce_mean_14_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_14_cast_fp16 = reduce_mean(axes = reduce_mean_14_axes_0, keep_dims = reduce_mean_14_keep_dims_0, x = square_4_cast_fp16)[name = tensor("reduce_mean_14_cast_fp16")]; + tensor add_8_y_0_to_fp16 = const()[name = tensor("add_8_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_8_cast_fp16 = add(x = reduce_mean_14_cast_fp16, y = add_8_y_0_to_fp16)[name = tensor("add_8_cast_fp16")]; + tensor sqrt_4_cast_fp16 = sqrt(x = add_8_cast_fp16)[name = tensor("sqrt_4_cast_fp16")]; + tensor real_div_4_cast_fp16 = real_div(x = sub_8_cast_fp16, y = sqrt_4_cast_fp16)[name = tensor("real_div_4_cast_fp16")]; + tensor reshape_17_shape_0 = const()[name = tensor("reshape_17_shape_0"), val = tensor([1, 512, 48, 80])]; + tensor reshape_17_cast_fp16 = reshape(shape = reshape_17_shape_0, x = real_div_4_cast_fp16)[name = tensor("reshape_17_cast_fp16")]; + tensor add_9_gamma_0_to_fp16 = const()[name = tensor("add_9_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16310080)))]; + tensor add_9_beta_0_to_fp16 = const()[name = tensor("add_9_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16311168)))]; + tensor add_9_epsilon_0_to_fp16 = const()[name = tensor("add_9_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_9_cast_fp16 = batch_norm(beta = add_9_beta_0_to_fp16, epsilon = add_9_epsilon_0_to_fp16, gamma = add_9_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_17_cast_fp16)[name = tensor("add_9_cast_fp16")]; + tensor input_29_cast_fp16 = silu(x = add_9_cast_fp16)[name = tensor("input_29_cast_fp16")]; + tensor var_172 = const()[name = tensor("op_172"), val = tensor([1, 1])]; + tensor var_174 = const()[name = tensor("op_174"), val = tensor([1, 1])]; + tensor hidden_states_25_pad_type_0 = const()[name = tensor("hidden_states_25_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_25_pad_0 = const()[name = tensor("hidden_states_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_mid_block_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("decoder_mid_block_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16312256)))]; + tensor decoder_mid_block_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("decoder_mid_block_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21030912)))]; + tensor hidden_states_25_cast_fp16 = conv(bias = decoder_mid_block_resnets_1_conv2_bias_to_fp16, dilations = var_174, groups = var_26, pad = hidden_states_25_pad_0, pad_type = hidden_states_25_pad_type_0, strides = var_172, weight = decoder_mid_block_resnets_1_conv2_weight_to_fp16, x = input_29_cast_fp16)[name = tensor("hidden_states_25_cast_fp16")]; + tensor var_177_cast_fp16 = add(x = hidden_states_19_cast_fp16, y = hidden_states_25_cast_fp16)[name = tensor("op_177_cast_fp16")]; + tensor reshape_20_shape_0 = const()[name = tensor("reshape_20_shape_0"), val = tensor([1, 32, 16, 48, 80])]; + tensor reshape_20_cast_fp16 = reshape(shape = reshape_20_shape_0, x = var_177_cast_fp16)[name = tensor("reshape_20_cast_fp16")]; + tensor reduce_mean_15_axes_0 = const()[name = tensor("reduce_mean_15_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_15_keep_dims_0 = const()[name = tensor("reduce_mean_15_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_15_cast_fp16 = reduce_mean(axes = reduce_mean_15_axes_0, keep_dims = reduce_mean_15_keep_dims_0, x = reshape_20_cast_fp16)[name = tensor("reduce_mean_15_cast_fp16")]; + tensor sub_10_cast_fp16 = sub(x = reshape_20_cast_fp16, y = reduce_mean_15_cast_fp16)[name = tensor("sub_10_cast_fp16")]; + tensor square_5_cast_fp16 = square(x = sub_10_cast_fp16)[name = tensor("square_5_cast_fp16")]; + tensor reduce_mean_17_axes_0 = const()[name = tensor("reduce_mean_17_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_17_keep_dims_0 = const()[name = tensor("reduce_mean_17_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_17_cast_fp16 = reduce_mean(axes = reduce_mean_17_axes_0, keep_dims = reduce_mean_17_keep_dims_0, x = square_5_cast_fp16)[name = tensor("reduce_mean_17_cast_fp16")]; + tensor add_10_y_0_to_fp16 = const()[name = tensor("add_10_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_10_cast_fp16 = add(x = reduce_mean_17_cast_fp16, y = add_10_y_0_to_fp16)[name = tensor("add_10_cast_fp16")]; + tensor sqrt_5_cast_fp16 = sqrt(x = add_10_cast_fp16)[name = tensor("sqrt_5_cast_fp16")]; + tensor real_div_5_cast_fp16 = real_div(x = sub_10_cast_fp16, y = sqrt_5_cast_fp16)[name = tensor("real_div_5_cast_fp16")]; + tensor reshape_21_shape_0 = const()[name = tensor("reshape_21_shape_0"), val = tensor([1, 512, 48, 80])]; + tensor reshape_21_cast_fp16 = reshape(shape = reshape_21_shape_0, x = real_div_5_cast_fp16)[name = tensor("reshape_21_cast_fp16")]; + tensor add_11_gamma_0_to_fp16 = const()[name = tensor("add_11_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21032000)))]; + tensor add_11_beta_0_to_fp16 = const()[name = tensor("add_11_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21033088)))]; + tensor add_11_epsilon_0_to_fp16 = const()[name = tensor("add_11_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_11_cast_fp16 = batch_norm(beta = add_11_beta_0_to_fp16, epsilon = add_11_epsilon_0_to_fp16, gamma = add_11_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_21_cast_fp16)[name = tensor("add_11_cast_fp16")]; + tensor hidden_states_27_cast_fp16 = silu(x = add_11_cast_fp16)[name = tensor("hidden_states_27_cast_fp16")]; + tensor var_199 = const()[name = tensor("op_199"), val = tensor([1, 1])]; + tensor var_201 = const()[name = tensor("op_201"), val = tensor([1, 1])]; + tensor input_35_pad_type_0 = const()[name = tensor("input_35_pad_type_0"), val = tensor("custom")]; + tensor input_35_pad_0 = const()[name = tensor("input_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_0_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21034176)))]; + tensor decoder_up_blocks_0_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25752832)))]; + tensor input_35_cast_fp16 = conv(bias = decoder_up_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = var_201, groups = var_26, pad = input_35_pad_0, pad_type = input_35_pad_type_0, strides = var_199, weight = decoder_up_blocks_0_resnets_0_conv1_weight_to_fp16, x = hidden_states_27_cast_fp16)[name = tensor("input_35_cast_fp16")]; + tensor reshape_24_shape_0 = const()[name = tensor("reshape_24_shape_0"), val = tensor([1, 32, 16, 48, 80])]; + tensor reshape_24_cast_fp16 = reshape(shape = reshape_24_shape_0, x = input_35_cast_fp16)[name = tensor("reshape_24_cast_fp16")]; + tensor reduce_mean_18_axes_0 = const()[name = tensor("reduce_mean_18_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_18_keep_dims_0 = const()[name = tensor("reduce_mean_18_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_18_cast_fp16 = reduce_mean(axes = reduce_mean_18_axes_0, keep_dims = reduce_mean_18_keep_dims_0, x = reshape_24_cast_fp16)[name = tensor("reduce_mean_18_cast_fp16")]; + tensor sub_12_cast_fp16 = sub(x = reshape_24_cast_fp16, y = reduce_mean_18_cast_fp16)[name = tensor("sub_12_cast_fp16")]; + tensor square_6_cast_fp16 = square(x = sub_12_cast_fp16)[name = tensor("square_6_cast_fp16")]; + tensor reduce_mean_20_axes_0 = const()[name = tensor("reduce_mean_20_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_20_keep_dims_0 = const()[name = tensor("reduce_mean_20_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_20_cast_fp16 = reduce_mean(axes = reduce_mean_20_axes_0, keep_dims = reduce_mean_20_keep_dims_0, x = square_6_cast_fp16)[name = tensor("reduce_mean_20_cast_fp16")]; + tensor add_12_y_0_to_fp16 = const()[name = tensor("add_12_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_12_cast_fp16 = add(x = reduce_mean_20_cast_fp16, y = add_12_y_0_to_fp16)[name = tensor("add_12_cast_fp16")]; + tensor sqrt_6_cast_fp16 = sqrt(x = add_12_cast_fp16)[name = tensor("sqrt_6_cast_fp16")]; + tensor real_div_6_cast_fp16 = real_div(x = sub_12_cast_fp16, y = sqrt_6_cast_fp16)[name = tensor("real_div_6_cast_fp16")]; + tensor reshape_25_shape_0 = const()[name = tensor("reshape_25_shape_0"), val = tensor([1, 512, 48, 80])]; + tensor reshape_25_cast_fp16 = reshape(shape = reshape_25_shape_0, x = real_div_6_cast_fp16)[name = tensor("reshape_25_cast_fp16")]; + tensor add_13_gamma_0_to_fp16 = const()[name = tensor("add_13_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25753920)))]; + tensor add_13_beta_0_to_fp16 = const()[name = tensor("add_13_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25755008)))]; + tensor add_13_epsilon_0_to_fp16 = const()[name = tensor("add_13_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_13_cast_fp16 = batch_norm(beta = add_13_beta_0_to_fp16, epsilon = add_13_epsilon_0_to_fp16, gamma = add_13_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_25_cast_fp16)[name = tensor("add_13_cast_fp16")]; + tensor input_39_cast_fp16 = silu(x = add_13_cast_fp16)[name = tensor("input_39_cast_fp16")]; + tensor var_211 = const()[name = tensor("op_211"), val = tensor([1, 1])]; + tensor var_213 = const()[name = tensor("op_213"), val = tensor([1, 1])]; + tensor hidden_states_31_pad_type_0 = const()[name = tensor("hidden_states_31_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_31_pad_0 = const()[name = tensor("hidden_states_31_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_0_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25756096)))]; + tensor decoder_up_blocks_0_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30474752)))]; + tensor hidden_states_31_cast_fp16 = conv(bias = decoder_up_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = var_213, groups = var_26, pad = hidden_states_31_pad_0, pad_type = hidden_states_31_pad_type_0, strides = var_211, weight = decoder_up_blocks_0_resnets_0_conv2_weight_to_fp16, x = input_39_cast_fp16)[name = tensor("hidden_states_31_cast_fp16")]; + tensor var_216_cast_fp16 = add(x = var_177_cast_fp16, y = hidden_states_31_cast_fp16)[name = tensor("op_216_cast_fp16")]; + tensor reshape_28_shape_0 = const()[name = tensor("reshape_28_shape_0"), val = tensor([1, 32, 16, 48, 80])]; + tensor reshape_28_cast_fp16 = reshape(shape = reshape_28_shape_0, x = var_216_cast_fp16)[name = tensor("reshape_28_cast_fp16")]; + tensor reduce_mean_21_axes_0 = const()[name = tensor("reduce_mean_21_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_21_keep_dims_0 = const()[name = tensor("reduce_mean_21_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_21_cast_fp16 = reduce_mean(axes = reduce_mean_21_axes_0, keep_dims = reduce_mean_21_keep_dims_0, x = reshape_28_cast_fp16)[name = tensor("reduce_mean_21_cast_fp16")]; + tensor sub_14_cast_fp16 = sub(x = reshape_28_cast_fp16, y = reduce_mean_21_cast_fp16)[name = tensor("sub_14_cast_fp16")]; + tensor square_7_cast_fp16 = square(x = sub_14_cast_fp16)[name = tensor("square_7_cast_fp16")]; + tensor reduce_mean_23_axes_0 = const()[name = tensor("reduce_mean_23_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_23_keep_dims_0 = const()[name = tensor("reduce_mean_23_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_23_cast_fp16 = reduce_mean(axes = reduce_mean_23_axes_0, keep_dims = reduce_mean_23_keep_dims_0, x = square_7_cast_fp16)[name = tensor("reduce_mean_23_cast_fp16")]; + tensor add_14_y_0_to_fp16 = const()[name = tensor("add_14_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_14_cast_fp16 = add(x = reduce_mean_23_cast_fp16, y = add_14_y_0_to_fp16)[name = tensor("add_14_cast_fp16")]; + tensor sqrt_7_cast_fp16 = sqrt(x = add_14_cast_fp16)[name = tensor("sqrt_7_cast_fp16")]; + tensor real_div_7_cast_fp16 = real_div(x = sub_14_cast_fp16, y = sqrt_7_cast_fp16)[name = tensor("real_div_7_cast_fp16")]; + tensor reshape_29_shape_0 = const()[name = tensor("reshape_29_shape_0"), val = tensor([1, 512, 48, 80])]; + tensor reshape_29_cast_fp16 = reshape(shape = reshape_29_shape_0, x = real_div_7_cast_fp16)[name = tensor("reshape_29_cast_fp16")]; + tensor add_15_gamma_0_to_fp16 = const()[name = tensor("add_15_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30475840)))]; + tensor add_15_beta_0_to_fp16 = const()[name = tensor("add_15_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30476928)))]; + tensor add_15_epsilon_0_to_fp16 = const()[name = tensor("add_15_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_15_cast_fp16 = batch_norm(beta = add_15_beta_0_to_fp16, epsilon = add_15_epsilon_0_to_fp16, gamma = add_15_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_29_cast_fp16)[name = tensor("add_15_cast_fp16")]; + tensor hidden_states_33_cast_fp16 = silu(x = add_15_cast_fp16)[name = tensor("hidden_states_33_cast_fp16")]; + tensor var_229 = const()[name = tensor("op_229"), val = tensor([1, 1])]; + tensor var_231 = const()[name = tensor("op_231"), val = tensor([1, 1])]; + tensor input_45_pad_type_0 = const()[name = tensor("input_45_pad_type_0"), val = tensor("custom")]; + tensor input_45_pad_0 = const()[name = tensor("input_45_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_0_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30478016)))]; + tensor decoder_up_blocks_0_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35196672)))]; + tensor input_45_cast_fp16 = conv(bias = decoder_up_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = var_231, groups = var_26, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = var_229, weight = decoder_up_blocks_0_resnets_1_conv1_weight_to_fp16, x = hidden_states_33_cast_fp16)[name = tensor("input_45_cast_fp16")]; + tensor reshape_32_shape_0 = const()[name = tensor("reshape_32_shape_0"), val = tensor([1, 32, 16, 48, 80])]; + tensor reshape_32_cast_fp16 = reshape(shape = reshape_32_shape_0, x = input_45_cast_fp16)[name = tensor("reshape_32_cast_fp16")]; + tensor reduce_mean_24_axes_0 = const()[name = tensor("reduce_mean_24_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_24_keep_dims_0 = const()[name = tensor("reduce_mean_24_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_24_cast_fp16 = reduce_mean(axes = reduce_mean_24_axes_0, keep_dims = reduce_mean_24_keep_dims_0, x = reshape_32_cast_fp16)[name = tensor("reduce_mean_24_cast_fp16")]; + tensor sub_16_cast_fp16 = sub(x = reshape_32_cast_fp16, y = reduce_mean_24_cast_fp16)[name = tensor("sub_16_cast_fp16")]; + tensor square_8_cast_fp16 = square(x = sub_16_cast_fp16)[name = tensor("square_8_cast_fp16")]; + tensor reduce_mean_26_axes_0 = const()[name = tensor("reduce_mean_26_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_26_keep_dims_0 = const()[name = tensor("reduce_mean_26_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_26_cast_fp16 = reduce_mean(axes = reduce_mean_26_axes_0, keep_dims = reduce_mean_26_keep_dims_0, x = square_8_cast_fp16)[name = tensor("reduce_mean_26_cast_fp16")]; + tensor add_16_y_0_to_fp16 = const()[name = tensor("add_16_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_16_cast_fp16 = add(x = reduce_mean_26_cast_fp16, y = add_16_y_0_to_fp16)[name = tensor("add_16_cast_fp16")]; + tensor sqrt_8_cast_fp16 = sqrt(x = add_16_cast_fp16)[name = tensor("sqrt_8_cast_fp16")]; + tensor real_div_8_cast_fp16 = real_div(x = sub_16_cast_fp16, y = sqrt_8_cast_fp16)[name = tensor("real_div_8_cast_fp16")]; + tensor reshape_33_shape_0 = const()[name = tensor("reshape_33_shape_0"), val = tensor([1, 512, 48, 80])]; + tensor reshape_33_cast_fp16 = reshape(shape = reshape_33_shape_0, x = real_div_8_cast_fp16)[name = tensor("reshape_33_cast_fp16")]; + tensor add_17_gamma_0_to_fp16 = const()[name = tensor("add_17_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35197760)))]; + tensor add_17_beta_0_to_fp16 = const()[name = tensor("add_17_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35198848)))]; + tensor add_17_epsilon_0_to_fp16 = const()[name = tensor("add_17_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_17_cast_fp16 = batch_norm(beta = add_17_beta_0_to_fp16, epsilon = add_17_epsilon_0_to_fp16, gamma = add_17_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_33_cast_fp16)[name = tensor("add_17_cast_fp16")]; + tensor input_49_cast_fp16 = silu(x = add_17_cast_fp16)[name = tensor("input_49_cast_fp16")]; + tensor var_241 = const()[name = tensor("op_241"), val = tensor([1, 1])]; + tensor var_243 = const()[name = tensor("op_243"), val = tensor([1, 1])]; + tensor hidden_states_37_pad_type_0 = const()[name = tensor("hidden_states_37_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_37_pad_0 = const()[name = tensor("hidden_states_37_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_0_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35199936)))]; + tensor decoder_up_blocks_0_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39918592)))]; + tensor hidden_states_37_cast_fp16 = conv(bias = decoder_up_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = var_243, groups = var_26, pad = hidden_states_37_pad_0, pad_type = hidden_states_37_pad_type_0, strides = var_241, weight = decoder_up_blocks_0_resnets_1_conv2_weight_to_fp16, x = input_49_cast_fp16)[name = tensor("hidden_states_37_cast_fp16")]; + tensor var_246_cast_fp16 = add(x = var_216_cast_fp16, y = hidden_states_37_cast_fp16)[name = tensor("op_246_cast_fp16")]; + tensor reshape_36_shape_0 = const()[name = tensor("reshape_36_shape_0"), val = tensor([1, 32, 16, 48, 80])]; + tensor reshape_36_cast_fp16 = reshape(shape = reshape_36_shape_0, x = var_246_cast_fp16)[name = tensor("reshape_36_cast_fp16")]; + tensor reduce_mean_27_axes_0 = const()[name = tensor("reduce_mean_27_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_27_keep_dims_0 = const()[name = tensor("reduce_mean_27_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_27_cast_fp16 = reduce_mean(axes = reduce_mean_27_axes_0, keep_dims = reduce_mean_27_keep_dims_0, x = reshape_36_cast_fp16)[name = tensor("reduce_mean_27_cast_fp16")]; + tensor sub_18_cast_fp16 = sub(x = reshape_36_cast_fp16, y = reduce_mean_27_cast_fp16)[name = tensor("sub_18_cast_fp16")]; + tensor square_9_cast_fp16 = square(x = sub_18_cast_fp16)[name = tensor("square_9_cast_fp16")]; + tensor reduce_mean_29_axes_0 = const()[name = tensor("reduce_mean_29_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_29_keep_dims_0 = const()[name = tensor("reduce_mean_29_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_29_cast_fp16 = reduce_mean(axes = reduce_mean_29_axes_0, keep_dims = reduce_mean_29_keep_dims_0, x = square_9_cast_fp16)[name = tensor("reduce_mean_29_cast_fp16")]; + tensor add_18_y_0_to_fp16 = const()[name = tensor("add_18_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_18_cast_fp16 = add(x = reduce_mean_29_cast_fp16, y = add_18_y_0_to_fp16)[name = tensor("add_18_cast_fp16")]; + tensor sqrt_9_cast_fp16 = sqrt(x = add_18_cast_fp16)[name = tensor("sqrt_9_cast_fp16")]; + tensor real_div_9_cast_fp16 = real_div(x = sub_18_cast_fp16, y = sqrt_9_cast_fp16)[name = tensor("real_div_9_cast_fp16")]; + tensor reshape_37_shape_0 = const()[name = tensor("reshape_37_shape_0"), val = tensor([1, 512, 48, 80])]; + tensor reshape_37_cast_fp16 = reshape(shape = reshape_37_shape_0, x = real_div_9_cast_fp16)[name = tensor("reshape_37_cast_fp16")]; + tensor add_19_gamma_0_to_fp16 = const()[name = tensor("add_19_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39919680)))]; + tensor add_19_beta_0_to_fp16 = const()[name = tensor("add_19_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39920768)))]; + tensor add_19_epsilon_0_to_fp16 = const()[name = tensor("add_19_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_19_cast_fp16 = batch_norm(beta = add_19_beta_0_to_fp16, epsilon = add_19_epsilon_0_to_fp16, gamma = add_19_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_37_cast_fp16)[name = tensor("add_19_cast_fp16")]; + tensor hidden_states_39_cast_fp16 = silu(x = add_19_cast_fp16)[name = tensor("hidden_states_39_cast_fp16")]; + tensor var_259 = const()[name = tensor("op_259"), val = tensor([1, 1])]; + tensor var_261 = const()[name = tensor("op_261"), val = tensor([1, 1])]; + tensor input_55_pad_type_0 = const()[name = tensor("input_55_pad_type_0"), val = tensor("custom")]; + tensor input_55_pad_0 = const()[name = tensor("input_55_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_0_resnets_2_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_2_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39921856)))]; + tensor decoder_up_blocks_0_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44640512)))]; + tensor input_55_cast_fp16 = conv(bias = decoder_up_blocks_0_resnets_2_conv1_bias_to_fp16, dilations = var_261, groups = var_26, pad = input_55_pad_0, pad_type = input_55_pad_type_0, strides = var_259, weight = decoder_up_blocks_0_resnets_2_conv1_weight_to_fp16, x = hidden_states_39_cast_fp16)[name = tensor("input_55_cast_fp16")]; + tensor reshape_40_shape_0 = const()[name = tensor("reshape_40_shape_0"), val = tensor([1, 32, 16, 48, 80])]; + tensor reshape_40_cast_fp16 = reshape(shape = reshape_40_shape_0, x = input_55_cast_fp16)[name = tensor("reshape_40_cast_fp16")]; + tensor reduce_mean_30_axes_0 = const()[name = tensor("reduce_mean_30_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_30_keep_dims_0 = const()[name = tensor("reduce_mean_30_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_30_cast_fp16 = reduce_mean(axes = reduce_mean_30_axes_0, keep_dims = reduce_mean_30_keep_dims_0, x = reshape_40_cast_fp16)[name = tensor("reduce_mean_30_cast_fp16")]; + tensor sub_20_cast_fp16 = sub(x = reshape_40_cast_fp16, y = reduce_mean_30_cast_fp16)[name = tensor("sub_20_cast_fp16")]; + tensor square_10_cast_fp16 = square(x = sub_20_cast_fp16)[name = tensor("square_10_cast_fp16")]; + tensor reduce_mean_32_axes_0 = const()[name = tensor("reduce_mean_32_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_32_keep_dims_0 = const()[name = tensor("reduce_mean_32_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_32_cast_fp16 = reduce_mean(axes = reduce_mean_32_axes_0, keep_dims = reduce_mean_32_keep_dims_0, x = square_10_cast_fp16)[name = tensor("reduce_mean_32_cast_fp16")]; + tensor add_20_y_0_to_fp16 = const()[name = tensor("add_20_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_20_cast_fp16 = add(x = reduce_mean_32_cast_fp16, y = add_20_y_0_to_fp16)[name = tensor("add_20_cast_fp16")]; + tensor sqrt_10_cast_fp16 = sqrt(x = add_20_cast_fp16)[name = tensor("sqrt_10_cast_fp16")]; + tensor real_div_10_cast_fp16 = real_div(x = sub_20_cast_fp16, y = sqrt_10_cast_fp16)[name = tensor("real_div_10_cast_fp16")]; + tensor reshape_41_shape_0 = const()[name = tensor("reshape_41_shape_0"), val = tensor([1, 512, 48, 80])]; + tensor reshape_41_cast_fp16 = reshape(shape = reshape_41_shape_0, x = real_div_10_cast_fp16)[name = tensor("reshape_41_cast_fp16")]; + tensor add_21_gamma_0_to_fp16 = const()[name = tensor("add_21_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44641600)))]; + tensor add_21_beta_0_to_fp16 = const()[name = tensor("add_21_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44642688)))]; + tensor add_21_epsilon_0_to_fp16 = const()[name = tensor("add_21_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_21_cast_fp16 = batch_norm(beta = add_21_beta_0_to_fp16, epsilon = add_21_epsilon_0_to_fp16, gamma = add_21_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_41_cast_fp16)[name = tensor("add_21_cast_fp16")]; + tensor input_59_cast_fp16 = silu(x = add_21_cast_fp16)[name = tensor("input_59_cast_fp16")]; + tensor var_271 = const()[name = tensor("op_271"), val = tensor([1, 1])]; + tensor var_273 = const()[name = tensor("op_273"), val = tensor([1, 1])]; + tensor hidden_states_43_pad_type_0 = const()[name = tensor("hidden_states_43_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_43_pad_0 = const()[name = tensor("hidden_states_43_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_0_resnets_2_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_2_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44643776)))]; + tensor decoder_up_blocks_0_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49362432)))]; + tensor hidden_states_43_cast_fp16 = conv(bias = decoder_up_blocks_0_resnets_2_conv2_bias_to_fp16, dilations = var_273, groups = var_26, pad = hidden_states_43_pad_0, pad_type = hidden_states_43_pad_type_0, strides = var_271, weight = decoder_up_blocks_0_resnets_2_conv2_weight_to_fp16, x = input_59_cast_fp16)[name = tensor("hidden_states_43_cast_fp16")]; + tensor var_276_cast_fp16 = add(x = var_246_cast_fp16, y = hidden_states_43_cast_fp16)[name = tensor("op_276_cast_fp16")]; + tensor hidden_states_47_scale_factor_height_0 = const()[name = tensor("hidden_states_47_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor hidden_states_47_scale_factor_width_0 = const()[name = tensor("hidden_states_47_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor hidden_states_47_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = hidden_states_47_scale_factor_height_0, scale_factor_width = hidden_states_47_scale_factor_width_0, x = var_276_cast_fp16)[name = tensor("hidden_states_47_cast_fp16")]; + tensor var_284 = const()[name = tensor("op_284"), val = tensor([1, 1])]; + tensor var_286 = const()[name = tensor("op_286"), val = tensor([1, 1])]; + tensor input_61_pad_type_0 = const()[name = tensor("input_61_pad_type_0"), val = tensor("custom")]; + tensor input_61_pad_0 = const()[name = tensor("input_61_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_0_upsamplers_0_conv_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_0_upsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49363520)))]; + tensor decoder_up_blocks_0_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_0_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54082176)))]; + tensor input_61_cast_fp16 = conv(bias = decoder_up_blocks_0_upsamplers_0_conv_bias_to_fp16, dilations = var_286, groups = var_26, pad = input_61_pad_0, pad_type = input_61_pad_type_0, strides = var_284, weight = decoder_up_blocks_0_upsamplers_0_conv_weight_to_fp16, x = hidden_states_47_cast_fp16)[name = tensor("input_61_cast_fp16")]; + tensor reshape_44_shape_0 = const()[name = tensor("reshape_44_shape_0"), val = tensor([1, 32, 16, 96, 160])]; + tensor reshape_44_cast_fp16 = reshape(shape = reshape_44_shape_0, x = input_61_cast_fp16)[name = tensor("reshape_44_cast_fp16")]; + tensor reduce_mean_33_axes_0 = const()[name = tensor("reduce_mean_33_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_33_keep_dims_0 = const()[name = tensor("reduce_mean_33_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_33_cast_fp16 = reduce_mean(axes = reduce_mean_33_axes_0, keep_dims = reduce_mean_33_keep_dims_0, x = reshape_44_cast_fp16)[name = tensor("reduce_mean_33_cast_fp16")]; + tensor sub_22_cast_fp16 = sub(x = reshape_44_cast_fp16, y = reduce_mean_33_cast_fp16)[name = tensor("sub_22_cast_fp16")]; + tensor square_11_cast_fp16 = square(x = sub_22_cast_fp16)[name = tensor("square_11_cast_fp16")]; + tensor reduce_mean_35_axes_0 = const()[name = tensor("reduce_mean_35_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_35_keep_dims_0 = const()[name = tensor("reduce_mean_35_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_35_cast_fp16 = reduce_mean(axes = reduce_mean_35_axes_0, keep_dims = reduce_mean_35_keep_dims_0, x = square_11_cast_fp16)[name = tensor("reduce_mean_35_cast_fp16")]; + tensor add_22_y_0_to_fp16 = const()[name = tensor("add_22_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_22_cast_fp16 = add(x = reduce_mean_35_cast_fp16, y = add_22_y_0_to_fp16)[name = tensor("add_22_cast_fp16")]; + tensor sqrt_11_cast_fp16 = sqrt(x = add_22_cast_fp16)[name = tensor("sqrt_11_cast_fp16")]; + tensor real_div_11_cast_fp16 = real_div(x = sub_22_cast_fp16, y = sqrt_11_cast_fp16)[name = tensor("real_div_11_cast_fp16")]; + tensor reshape_45_shape_0 = const()[name = tensor("reshape_45_shape_0"), val = tensor([1, 512, 96, 160])]; + tensor reshape_45_cast_fp16 = reshape(shape = reshape_45_shape_0, x = real_div_11_cast_fp16)[name = tensor("reshape_45_cast_fp16")]; + tensor add_23_gamma_0_to_fp16 = const()[name = tensor("add_23_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54083264)))]; + tensor add_23_beta_0_to_fp16 = const()[name = tensor("add_23_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54084352)))]; + tensor add_23_epsilon_0_to_fp16 = const()[name = tensor("add_23_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_23_cast_fp16 = batch_norm(beta = add_23_beta_0_to_fp16, epsilon = add_23_epsilon_0_to_fp16, gamma = add_23_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_45_cast_fp16)[name = tensor("add_23_cast_fp16")]; + tensor hidden_states_49_cast_fp16 = silu(x = add_23_cast_fp16)[name = tensor("hidden_states_49_cast_fp16")]; + tensor var_307 = const()[name = tensor("op_307"), val = tensor([1, 1])]; + tensor var_309 = const()[name = tensor("op_309"), val = tensor([1, 1])]; + tensor input_65_pad_type_0 = const()[name = tensor("input_65_pad_type_0"), val = tensor("custom")]; + tensor input_65_pad_0 = const()[name = tensor("input_65_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_1_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54085440)))]; + tensor decoder_up_blocks_1_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58804096)))]; + tensor input_65_cast_fp16 = conv(bias = decoder_up_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = var_309, groups = var_26, pad = input_65_pad_0, pad_type = input_65_pad_type_0, strides = var_307, weight = decoder_up_blocks_1_resnets_0_conv1_weight_to_fp16, x = hidden_states_49_cast_fp16)[name = tensor("input_65_cast_fp16")]; + tensor reshape_48_shape_0 = const()[name = tensor("reshape_48_shape_0"), val = tensor([1, 32, 16, 96, 160])]; + tensor reshape_48_cast_fp16 = reshape(shape = reshape_48_shape_0, x = input_65_cast_fp16)[name = tensor("reshape_48_cast_fp16")]; + tensor reduce_mean_36_axes_0 = const()[name = tensor("reduce_mean_36_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_36_keep_dims_0 = const()[name = tensor("reduce_mean_36_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_36_cast_fp16 = reduce_mean(axes = reduce_mean_36_axes_0, keep_dims = reduce_mean_36_keep_dims_0, x = reshape_48_cast_fp16)[name = tensor("reduce_mean_36_cast_fp16")]; + tensor sub_24_cast_fp16 = sub(x = reshape_48_cast_fp16, y = reduce_mean_36_cast_fp16)[name = tensor("sub_24_cast_fp16")]; + tensor square_12_cast_fp16 = square(x = sub_24_cast_fp16)[name = tensor("square_12_cast_fp16")]; + tensor reduce_mean_38_axes_0 = const()[name = tensor("reduce_mean_38_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_38_keep_dims_0 = const()[name = tensor("reduce_mean_38_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_38_cast_fp16 = reduce_mean(axes = reduce_mean_38_axes_0, keep_dims = reduce_mean_38_keep_dims_0, x = square_12_cast_fp16)[name = tensor("reduce_mean_38_cast_fp16")]; + tensor add_24_y_0_to_fp16 = const()[name = tensor("add_24_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_24_cast_fp16 = add(x = reduce_mean_38_cast_fp16, y = add_24_y_0_to_fp16)[name = tensor("add_24_cast_fp16")]; + tensor sqrt_12_cast_fp16 = sqrt(x = add_24_cast_fp16)[name = tensor("sqrt_12_cast_fp16")]; + tensor real_div_12_cast_fp16 = real_div(x = sub_24_cast_fp16, y = sqrt_12_cast_fp16)[name = tensor("real_div_12_cast_fp16")]; + tensor reshape_49_shape_0 = const()[name = tensor("reshape_49_shape_0"), val = tensor([1, 512, 96, 160])]; + tensor reshape_49_cast_fp16 = reshape(shape = reshape_49_shape_0, x = real_div_12_cast_fp16)[name = tensor("reshape_49_cast_fp16")]; + tensor add_25_gamma_0_to_fp16 = const()[name = tensor("add_25_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58805184)))]; + tensor add_25_beta_0_to_fp16 = const()[name = tensor("add_25_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58806272)))]; + tensor add_25_epsilon_0_to_fp16 = const()[name = tensor("add_25_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_25_cast_fp16 = batch_norm(beta = add_25_beta_0_to_fp16, epsilon = add_25_epsilon_0_to_fp16, gamma = add_25_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_49_cast_fp16)[name = tensor("add_25_cast_fp16")]; + tensor input_69_cast_fp16 = silu(x = add_25_cast_fp16)[name = tensor("input_69_cast_fp16")]; + tensor var_319 = const()[name = tensor("op_319"), val = tensor([1, 1])]; + tensor var_321 = const()[name = tensor("op_321"), val = tensor([1, 1])]; + tensor hidden_states_53_pad_type_0 = const()[name = tensor("hidden_states_53_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_53_pad_0 = const()[name = tensor("hidden_states_53_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_1_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58807360)))]; + tensor decoder_up_blocks_1_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63526016)))]; + tensor hidden_states_53_cast_fp16 = conv(bias = decoder_up_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = var_321, groups = var_26, pad = hidden_states_53_pad_0, pad_type = hidden_states_53_pad_type_0, strides = var_319, weight = decoder_up_blocks_1_resnets_0_conv2_weight_to_fp16, x = input_69_cast_fp16)[name = tensor("hidden_states_53_cast_fp16")]; + tensor var_324_cast_fp16 = add(x = input_61_cast_fp16, y = hidden_states_53_cast_fp16)[name = tensor("op_324_cast_fp16")]; + tensor reshape_52_shape_0 = const()[name = tensor("reshape_52_shape_0"), val = tensor([1, 32, 16, 96, 160])]; + tensor reshape_52_cast_fp16 = reshape(shape = reshape_52_shape_0, x = var_324_cast_fp16)[name = tensor("reshape_52_cast_fp16")]; + tensor reduce_mean_39_axes_0 = const()[name = tensor("reduce_mean_39_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_39_keep_dims_0 = const()[name = tensor("reduce_mean_39_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_39_cast_fp16 = reduce_mean(axes = reduce_mean_39_axes_0, keep_dims = reduce_mean_39_keep_dims_0, x = reshape_52_cast_fp16)[name = tensor("reduce_mean_39_cast_fp16")]; + tensor sub_26_cast_fp16 = sub(x = reshape_52_cast_fp16, y = reduce_mean_39_cast_fp16)[name = tensor("sub_26_cast_fp16")]; + tensor square_13_cast_fp16 = square(x = sub_26_cast_fp16)[name = tensor("square_13_cast_fp16")]; + tensor reduce_mean_41_axes_0 = const()[name = tensor("reduce_mean_41_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_41_keep_dims_0 = const()[name = tensor("reduce_mean_41_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_41_cast_fp16 = reduce_mean(axes = reduce_mean_41_axes_0, keep_dims = reduce_mean_41_keep_dims_0, x = square_13_cast_fp16)[name = tensor("reduce_mean_41_cast_fp16")]; + tensor add_26_y_0_to_fp16 = const()[name = tensor("add_26_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_26_cast_fp16 = add(x = reduce_mean_41_cast_fp16, y = add_26_y_0_to_fp16)[name = tensor("add_26_cast_fp16")]; + tensor sqrt_13_cast_fp16 = sqrt(x = add_26_cast_fp16)[name = tensor("sqrt_13_cast_fp16")]; + tensor real_div_13_cast_fp16 = real_div(x = sub_26_cast_fp16, y = sqrt_13_cast_fp16)[name = tensor("real_div_13_cast_fp16")]; + tensor reshape_53_shape_0 = const()[name = tensor("reshape_53_shape_0"), val = tensor([1, 512, 96, 160])]; + tensor reshape_53_cast_fp16 = reshape(shape = reshape_53_shape_0, x = real_div_13_cast_fp16)[name = tensor("reshape_53_cast_fp16")]; + tensor add_27_gamma_0_to_fp16 = const()[name = tensor("add_27_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63527104)))]; + tensor add_27_beta_0_to_fp16 = const()[name = tensor("add_27_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63528192)))]; + tensor add_27_epsilon_0_to_fp16 = const()[name = tensor("add_27_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_27_cast_fp16 = batch_norm(beta = add_27_beta_0_to_fp16, epsilon = add_27_epsilon_0_to_fp16, gamma = add_27_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_53_cast_fp16)[name = tensor("add_27_cast_fp16")]; + tensor hidden_states_55_cast_fp16 = silu(x = add_27_cast_fp16)[name = tensor("hidden_states_55_cast_fp16")]; + tensor var_337 = const()[name = tensor("op_337"), val = tensor([1, 1])]; + tensor var_339 = const()[name = tensor("op_339"), val = tensor([1, 1])]; + tensor input_75_pad_type_0 = const()[name = tensor("input_75_pad_type_0"), val = tensor("custom")]; + tensor input_75_pad_0 = const()[name = tensor("input_75_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_1_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63529280)))]; + tensor decoder_up_blocks_1_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68247936)))]; + tensor input_75_cast_fp16 = conv(bias = decoder_up_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = var_339, groups = var_26, pad = input_75_pad_0, pad_type = input_75_pad_type_0, strides = var_337, weight = decoder_up_blocks_1_resnets_1_conv1_weight_to_fp16, x = hidden_states_55_cast_fp16)[name = tensor("input_75_cast_fp16")]; + tensor reshape_56_shape_0 = const()[name = tensor("reshape_56_shape_0"), val = tensor([1, 32, 16, 96, 160])]; + tensor reshape_56_cast_fp16 = reshape(shape = reshape_56_shape_0, x = input_75_cast_fp16)[name = tensor("reshape_56_cast_fp16")]; + tensor reduce_mean_42_axes_0 = const()[name = tensor("reduce_mean_42_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_42_keep_dims_0 = const()[name = tensor("reduce_mean_42_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_42_cast_fp16 = reduce_mean(axes = reduce_mean_42_axes_0, keep_dims = reduce_mean_42_keep_dims_0, x = reshape_56_cast_fp16)[name = tensor("reduce_mean_42_cast_fp16")]; + tensor sub_28_cast_fp16 = sub(x = reshape_56_cast_fp16, y = reduce_mean_42_cast_fp16)[name = tensor("sub_28_cast_fp16")]; + tensor square_14_cast_fp16 = square(x = sub_28_cast_fp16)[name = tensor("square_14_cast_fp16")]; + tensor reduce_mean_44_axes_0 = const()[name = tensor("reduce_mean_44_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_44_keep_dims_0 = const()[name = tensor("reduce_mean_44_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_44_cast_fp16 = reduce_mean(axes = reduce_mean_44_axes_0, keep_dims = reduce_mean_44_keep_dims_0, x = square_14_cast_fp16)[name = tensor("reduce_mean_44_cast_fp16")]; + tensor add_28_y_0_to_fp16 = const()[name = tensor("add_28_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_28_cast_fp16 = add(x = reduce_mean_44_cast_fp16, y = add_28_y_0_to_fp16)[name = tensor("add_28_cast_fp16")]; + tensor sqrt_14_cast_fp16 = sqrt(x = add_28_cast_fp16)[name = tensor("sqrt_14_cast_fp16")]; + tensor real_div_14_cast_fp16 = real_div(x = sub_28_cast_fp16, y = sqrt_14_cast_fp16)[name = tensor("real_div_14_cast_fp16")]; + tensor reshape_57_shape_0 = const()[name = tensor("reshape_57_shape_0"), val = tensor([1, 512, 96, 160])]; + tensor reshape_57_cast_fp16 = reshape(shape = reshape_57_shape_0, x = real_div_14_cast_fp16)[name = tensor("reshape_57_cast_fp16")]; + tensor add_29_gamma_0_to_fp16 = const()[name = tensor("add_29_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68249024)))]; + tensor add_29_beta_0_to_fp16 = const()[name = tensor("add_29_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68250112)))]; + tensor add_29_epsilon_0_to_fp16 = const()[name = tensor("add_29_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_29_cast_fp16 = batch_norm(beta = add_29_beta_0_to_fp16, epsilon = add_29_epsilon_0_to_fp16, gamma = add_29_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_57_cast_fp16)[name = tensor("add_29_cast_fp16")]; + tensor input_79_cast_fp16 = silu(x = add_29_cast_fp16)[name = tensor("input_79_cast_fp16")]; + tensor var_349 = const()[name = tensor("op_349"), val = tensor([1, 1])]; + tensor var_351 = const()[name = tensor("op_351"), val = tensor([1, 1])]; + tensor hidden_states_59_pad_type_0 = const()[name = tensor("hidden_states_59_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_59_pad_0 = const()[name = tensor("hidden_states_59_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_1_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68251200)))]; + tensor decoder_up_blocks_1_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72969856)))]; + tensor hidden_states_59_cast_fp16 = conv(bias = decoder_up_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = var_351, groups = var_26, pad = hidden_states_59_pad_0, pad_type = hidden_states_59_pad_type_0, strides = var_349, weight = decoder_up_blocks_1_resnets_1_conv2_weight_to_fp16, x = input_79_cast_fp16)[name = tensor("hidden_states_59_cast_fp16")]; + tensor var_354_cast_fp16 = add(x = var_324_cast_fp16, y = hidden_states_59_cast_fp16)[name = tensor("op_354_cast_fp16")]; + tensor reshape_60_shape_0 = const()[name = tensor("reshape_60_shape_0"), val = tensor([1, 32, 16, 96, 160])]; + tensor reshape_60_cast_fp16 = reshape(shape = reshape_60_shape_0, x = var_354_cast_fp16)[name = tensor("reshape_60_cast_fp16")]; + tensor reduce_mean_45_axes_0 = const()[name = tensor("reduce_mean_45_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_45_keep_dims_0 = const()[name = tensor("reduce_mean_45_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_45_cast_fp16 = reduce_mean(axes = reduce_mean_45_axes_0, keep_dims = reduce_mean_45_keep_dims_0, x = reshape_60_cast_fp16)[name = tensor("reduce_mean_45_cast_fp16")]; + tensor sub_30_cast_fp16 = sub(x = reshape_60_cast_fp16, y = reduce_mean_45_cast_fp16)[name = tensor("sub_30_cast_fp16")]; + tensor square_15_cast_fp16 = square(x = sub_30_cast_fp16)[name = tensor("square_15_cast_fp16")]; + tensor reduce_mean_47_axes_0 = const()[name = tensor("reduce_mean_47_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_47_keep_dims_0 = const()[name = tensor("reduce_mean_47_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_47_cast_fp16 = reduce_mean(axes = reduce_mean_47_axes_0, keep_dims = reduce_mean_47_keep_dims_0, x = square_15_cast_fp16)[name = tensor("reduce_mean_47_cast_fp16")]; + tensor add_30_y_0_to_fp16 = const()[name = tensor("add_30_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_30_cast_fp16 = add(x = reduce_mean_47_cast_fp16, y = add_30_y_0_to_fp16)[name = tensor("add_30_cast_fp16")]; + tensor sqrt_15_cast_fp16 = sqrt(x = add_30_cast_fp16)[name = tensor("sqrt_15_cast_fp16")]; + tensor real_div_15_cast_fp16 = real_div(x = sub_30_cast_fp16, y = sqrt_15_cast_fp16)[name = tensor("real_div_15_cast_fp16")]; + tensor reshape_61_shape_0 = const()[name = tensor("reshape_61_shape_0"), val = tensor([1, 512, 96, 160])]; + tensor reshape_61_cast_fp16 = reshape(shape = reshape_61_shape_0, x = real_div_15_cast_fp16)[name = tensor("reshape_61_cast_fp16")]; + tensor add_31_gamma_0_to_fp16 = const()[name = tensor("add_31_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72970944)))]; + tensor add_31_beta_0_to_fp16 = const()[name = tensor("add_31_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72972032)))]; + tensor add_31_epsilon_0_to_fp16 = const()[name = tensor("add_31_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_31_cast_fp16 = batch_norm(beta = add_31_beta_0_to_fp16, epsilon = add_31_epsilon_0_to_fp16, gamma = add_31_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_61_cast_fp16)[name = tensor("add_31_cast_fp16")]; + tensor hidden_states_61_cast_fp16 = silu(x = add_31_cast_fp16)[name = tensor("hidden_states_61_cast_fp16")]; + tensor var_367 = const()[name = tensor("op_367"), val = tensor([1, 1])]; + tensor var_369 = const()[name = tensor("op_369"), val = tensor([1, 1])]; + tensor input_85_pad_type_0 = const()[name = tensor("input_85_pad_type_0"), val = tensor("custom")]; + tensor input_85_pad_0 = const()[name = tensor("input_85_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_1_resnets_2_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_2_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72973120)))]; + tensor decoder_up_blocks_1_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77691776)))]; + tensor input_85_cast_fp16 = conv(bias = decoder_up_blocks_1_resnets_2_conv1_bias_to_fp16, dilations = var_369, groups = var_26, pad = input_85_pad_0, pad_type = input_85_pad_type_0, strides = var_367, weight = decoder_up_blocks_1_resnets_2_conv1_weight_to_fp16, x = hidden_states_61_cast_fp16)[name = tensor("input_85_cast_fp16")]; + tensor reshape_64_shape_0 = const()[name = tensor("reshape_64_shape_0"), val = tensor([1, 32, 16, 96, 160])]; + tensor reshape_64_cast_fp16 = reshape(shape = reshape_64_shape_0, x = input_85_cast_fp16)[name = tensor("reshape_64_cast_fp16")]; + tensor reduce_mean_48_axes_0 = const()[name = tensor("reduce_mean_48_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_48_keep_dims_0 = const()[name = tensor("reduce_mean_48_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_48_cast_fp16 = reduce_mean(axes = reduce_mean_48_axes_0, keep_dims = reduce_mean_48_keep_dims_0, x = reshape_64_cast_fp16)[name = tensor("reduce_mean_48_cast_fp16")]; + tensor sub_32_cast_fp16 = sub(x = reshape_64_cast_fp16, y = reduce_mean_48_cast_fp16)[name = tensor("sub_32_cast_fp16")]; + tensor square_16_cast_fp16 = square(x = sub_32_cast_fp16)[name = tensor("square_16_cast_fp16")]; + tensor reduce_mean_50_axes_0 = const()[name = tensor("reduce_mean_50_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_50_keep_dims_0 = const()[name = tensor("reduce_mean_50_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_50_cast_fp16 = reduce_mean(axes = reduce_mean_50_axes_0, keep_dims = reduce_mean_50_keep_dims_0, x = square_16_cast_fp16)[name = tensor("reduce_mean_50_cast_fp16")]; + tensor add_32_y_0_to_fp16 = const()[name = tensor("add_32_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_32_cast_fp16 = add(x = reduce_mean_50_cast_fp16, y = add_32_y_0_to_fp16)[name = tensor("add_32_cast_fp16")]; + tensor sqrt_16_cast_fp16 = sqrt(x = add_32_cast_fp16)[name = tensor("sqrt_16_cast_fp16")]; + tensor real_div_16_cast_fp16 = real_div(x = sub_32_cast_fp16, y = sqrt_16_cast_fp16)[name = tensor("real_div_16_cast_fp16")]; + tensor reshape_65_shape_0 = const()[name = tensor("reshape_65_shape_0"), val = tensor([1, 512, 96, 160])]; + tensor reshape_65_cast_fp16 = reshape(shape = reshape_65_shape_0, x = real_div_16_cast_fp16)[name = tensor("reshape_65_cast_fp16")]; + tensor add_33_gamma_0_to_fp16 = const()[name = tensor("add_33_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77692864)))]; + tensor add_33_beta_0_to_fp16 = const()[name = tensor("add_33_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77693952)))]; + tensor add_33_epsilon_0_to_fp16 = const()[name = tensor("add_33_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_33_cast_fp16 = batch_norm(beta = add_33_beta_0_to_fp16, epsilon = add_33_epsilon_0_to_fp16, gamma = add_33_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_65_cast_fp16)[name = tensor("add_33_cast_fp16")]; + tensor input_89_cast_fp16 = silu(x = add_33_cast_fp16)[name = tensor("input_89_cast_fp16")]; + tensor var_379 = const()[name = tensor("op_379"), val = tensor([1, 1])]; + tensor var_381 = const()[name = tensor("op_381"), val = tensor([1, 1])]; + tensor hidden_states_65_pad_type_0 = const()[name = tensor("hidden_states_65_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_65_pad_0 = const()[name = tensor("hidden_states_65_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_1_resnets_2_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_2_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77695040)))]; + tensor decoder_up_blocks_1_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82413696)))]; + tensor hidden_states_65_cast_fp16 = conv(bias = decoder_up_blocks_1_resnets_2_conv2_bias_to_fp16, dilations = var_381, groups = var_26, pad = hidden_states_65_pad_0, pad_type = hidden_states_65_pad_type_0, strides = var_379, weight = decoder_up_blocks_1_resnets_2_conv2_weight_to_fp16, x = input_89_cast_fp16)[name = tensor("hidden_states_65_cast_fp16")]; + tensor var_384_cast_fp16 = add(x = var_354_cast_fp16, y = hidden_states_65_cast_fp16)[name = tensor("op_384_cast_fp16")]; + tensor hidden_states_69_scale_factor_height_0 = const()[name = tensor("hidden_states_69_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor hidden_states_69_scale_factor_width_0 = const()[name = tensor("hidden_states_69_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor hidden_states_69_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = hidden_states_69_scale_factor_height_0, scale_factor_width = hidden_states_69_scale_factor_width_0, x = var_384_cast_fp16)[name = tensor("hidden_states_69_cast_fp16")]; + tensor var_392 = const()[name = tensor("op_392"), val = tensor([1, 1])]; + tensor var_394 = const()[name = tensor("op_394"), val = tensor([1, 1])]; + tensor input_91_pad_type_0 = const()[name = tensor("input_91_pad_type_0"), val = tensor("custom")]; + tensor input_91_pad_0 = const()[name = tensor("input_91_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_1_upsamplers_0_conv_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_1_upsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82414784)))]; + tensor decoder_up_blocks_1_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_1_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(87133440)))]; + tensor input_91_cast_fp16 = conv(bias = decoder_up_blocks_1_upsamplers_0_conv_bias_to_fp16, dilations = var_394, groups = var_26, pad = input_91_pad_0, pad_type = input_91_pad_type_0, strides = var_392, weight = decoder_up_blocks_1_upsamplers_0_conv_weight_to_fp16, x = hidden_states_69_cast_fp16)[name = tensor("input_91_cast_fp16")]; + tensor reshape_68_shape_0 = const()[name = tensor("reshape_68_shape_0"), val = tensor([1, 32, 16, 192, 320])]; + tensor reshape_68_cast_fp16 = reshape(shape = reshape_68_shape_0, x = input_91_cast_fp16)[name = tensor("reshape_68_cast_fp16")]; + tensor reduce_mean_51_axes_0 = const()[name = tensor("reduce_mean_51_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_51_keep_dims_0 = const()[name = tensor("reduce_mean_51_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_51_cast_fp16 = reduce_mean(axes = reduce_mean_51_axes_0, keep_dims = reduce_mean_51_keep_dims_0, x = reshape_68_cast_fp16)[name = tensor("reduce_mean_51_cast_fp16")]; + tensor sub_34_cast_fp16 = sub(x = reshape_68_cast_fp16, y = reduce_mean_51_cast_fp16)[name = tensor("sub_34_cast_fp16")]; + tensor square_17_cast_fp16 = square(x = sub_34_cast_fp16)[name = tensor("square_17_cast_fp16")]; + tensor reduce_mean_53_axes_0 = const()[name = tensor("reduce_mean_53_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_53_keep_dims_0 = const()[name = tensor("reduce_mean_53_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_53_cast_fp16 = reduce_mean(axes = reduce_mean_53_axes_0, keep_dims = reduce_mean_53_keep_dims_0, x = square_17_cast_fp16)[name = tensor("reduce_mean_53_cast_fp16")]; + tensor add_34_y_0_to_fp16 = const()[name = tensor("add_34_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_34_cast_fp16 = add(x = reduce_mean_53_cast_fp16, y = add_34_y_0_to_fp16)[name = tensor("add_34_cast_fp16")]; + tensor sqrt_17_cast_fp16 = sqrt(x = add_34_cast_fp16)[name = tensor("sqrt_17_cast_fp16")]; + tensor real_div_17_cast_fp16 = real_div(x = sub_34_cast_fp16, y = sqrt_17_cast_fp16)[name = tensor("real_div_17_cast_fp16")]; + tensor reshape_69_shape_0 = const()[name = tensor("reshape_69_shape_0"), val = tensor([1, 512, 192, 320])]; + tensor reshape_69_cast_fp16 = reshape(shape = reshape_69_shape_0, x = real_div_17_cast_fp16)[name = tensor("reshape_69_cast_fp16")]; + tensor add_35_gamma_0_to_fp16 = const()[name = tensor("add_35_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(87134528)))]; + tensor add_35_beta_0_to_fp16 = const()[name = tensor("add_35_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(87135616)))]; + tensor add_35_epsilon_0_to_fp16 = const()[name = tensor("add_35_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_35_cast_fp16 = batch_norm(beta = add_35_beta_0_to_fp16, epsilon = add_35_epsilon_0_to_fp16, gamma = add_35_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_69_cast_fp16)[name = tensor("add_35_cast_fp16")]; + tensor hidden_states_71_cast_fp16 = silu(x = add_35_cast_fp16)[name = tensor("hidden_states_71_cast_fp16")]; + tensor var_416 = const()[name = tensor("op_416"), val = tensor([1, 1])]; + tensor var_418 = const()[name = tensor("op_418"), val = tensor([1, 1])]; + tensor input_95_pad_type_0 = const()[name = tensor("input_95_pad_type_0"), val = tensor("custom")]; + tensor input_95_pad_0 = const()[name = tensor("input_95_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_2_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(87136704)))]; + tensor decoder_up_blocks_2_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89496064)))]; + tensor input_95_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = var_418, groups = var_26, pad = input_95_pad_0, pad_type = input_95_pad_type_0, strides = var_416, weight = decoder_up_blocks_2_resnets_0_conv1_weight_to_fp16, x = hidden_states_71_cast_fp16)[name = tensor("input_95_cast_fp16")]; + tensor reshape_72_shape_0 = const()[name = tensor("reshape_72_shape_0"), val = tensor([1, 32, 8, 192, 320])]; + tensor reshape_72_cast_fp16 = reshape(shape = reshape_72_shape_0, x = input_95_cast_fp16)[name = tensor("reshape_72_cast_fp16")]; + tensor reduce_mean_54_axes_0 = const()[name = tensor("reduce_mean_54_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_54_keep_dims_0 = const()[name = tensor("reduce_mean_54_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_54_cast_fp16 = reduce_mean(axes = reduce_mean_54_axes_0, keep_dims = reduce_mean_54_keep_dims_0, x = reshape_72_cast_fp16)[name = tensor("reduce_mean_54_cast_fp16")]; + tensor sub_36_cast_fp16 = sub(x = reshape_72_cast_fp16, y = reduce_mean_54_cast_fp16)[name = tensor("sub_36_cast_fp16")]; + tensor square_18_cast_fp16 = square(x = sub_36_cast_fp16)[name = tensor("square_18_cast_fp16")]; + tensor reduce_mean_56_axes_0 = const()[name = tensor("reduce_mean_56_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_56_keep_dims_0 = const()[name = tensor("reduce_mean_56_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_56_cast_fp16 = reduce_mean(axes = reduce_mean_56_axes_0, keep_dims = reduce_mean_56_keep_dims_0, x = square_18_cast_fp16)[name = tensor("reduce_mean_56_cast_fp16")]; + tensor add_36_y_0_to_fp16 = const()[name = tensor("add_36_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_36_cast_fp16 = add(x = reduce_mean_56_cast_fp16, y = add_36_y_0_to_fp16)[name = tensor("add_36_cast_fp16")]; + tensor sqrt_18_cast_fp16 = sqrt(x = add_36_cast_fp16)[name = tensor("sqrt_18_cast_fp16")]; + tensor real_div_18_cast_fp16 = real_div(x = sub_36_cast_fp16, y = sqrt_18_cast_fp16)[name = tensor("real_div_18_cast_fp16")]; + tensor reshape_73_shape_0 = const()[name = tensor("reshape_73_shape_0"), val = tensor([1, 256, 192, 320])]; + tensor reshape_73_cast_fp16 = reshape(shape = reshape_73_shape_0, x = real_div_18_cast_fp16)[name = tensor("reshape_73_cast_fp16")]; + tensor add_37_mean_0_to_fp16 = const()[name = tensor("add_37_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89496640)))]; + tensor add_37_variance_0_to_fp16 = const()[name = tensor("add_37_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89497216)))]; + tensor add_37_gamma_0_to_fp16 = const()[name = tensor("add_37_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89497792)))]; + tensor add_37_beta_0_to_fp16 = const()[name = tensor("add_37_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89498368)))]; + tensor add_37_epsilon_0_to_fp16 = const()[name = tensor("add_37_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_37_cast_fp16 = batch_norm(beta = add_37_beta_0_to_fp16, epsilon = add_37_epsilon_0_to_fp16, gamma = add_37_gamma_0_to_fp16, mean = add_37_mean_0_to_fp16, variance = add_37_variance_0_to_fp16, x = reshape_73_cast_fp16)[name = tensor("add_37_cast_fp16")]; + tensor input_99_cast_fp16 = silu(x = add_37_cast_fp16)[name = tensor("input_99_cast_fp16")]; + tensor var_428 = const()[name = tensor("op_428"), val = tensor([1, 1])]; + tensor var_430 = const()[name = tensor("op_430"), val = tensor([1, 1])]; + tensor hidden_states_75_pad_type_0 = const()[name = tensor("hidden_states_75_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_75_pad_0 = const()[name = tensor("hidden_states_75_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_2_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89498944)))]; + tensor decoder_up_blocks_2_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90678656)))]; + tensor hidden_states_75_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = var_430, groups = var_26, pad = hidden_states_75_pad_0, pad_type = hidden_states_75_pad_type_0, strides = var_428, weight = decoder_up_blocks_2_resnets_0_conv2_weight_to_fp16, x = input_99_cast_fp16)[name = tensor("hidden_states_75_cast_fp16")]; + tensor var_435 = const()[name = tensor("op_435"), val = tensor([1, 1])]; + tensor var_437 = const()[name = tensor("op_437"), val = tensor([1, 1])]; + tensor input_tensor_1_pad_type_0 = const()[name = tensor("input_tensor_1_pad_type_0"), val = tensor("custom")]; + tensor input_tensor_1_pad_0 = const()[name = tensor("input_tensor_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor decoder_up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90679232)))]; + tensor decoder_up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90941440)))]; + tensor input_tensor_1_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_437, groups = var_26, pad = input_tensor_1_pad_0, pad_type = input_tensor_1_pad_type_0, strides = var_435, weight = decoder_up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16, x = input_91_cast_fp16)[name = tensor("input_tensor_1_cast_fp16")]; + tensor var_440_cast_fp16 = add(x = input_tensor_1_cast_fp16, y = hidden_states_75_cast_fp16)[name = tensor("op_440_cast_fp16")]; + tensor reshape_76_shape_0 = const()[name = tensor("reshape_76_shape_0"), val = tensor([1, 32, 8, 192, 320])]; + tensor reshape_76_cast_fp16 = reshape(shape = reshape_76_shape_0, x = var_440_cast_fp16)[name = tensor("reshape_76_cast_fp16")]; + tensor reduce_mean_57_axes_0 = const()[name = tensor("reduce_mean_57_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_57_keep_dims_0 = const()[name = tensor("reduce_mean_57_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_57_cast_fp16 = reduce_mean(axes = reduce_mean_57_axes_0, keep_dims = reduce_mean_57_keep_dims_0, x = reshape_76_cast_fp16)[name = tensor("reduce_mean_57_cast_fp16")]; + tensor sub_38_cast_fp16 = sub(x = reshape_76_cast_fp16, y = reduce_mean_57_cast_fp16)[name = tensor("sub_38_cast_fp16")]; + tensor square_19_cast_fp16 = square(x = sub_38_cast_fp16)[name = tensor("square_19_cast_fp16")]; + tensor reduce_mean_59_axes_0 = const()[name = tensor("reduce_mean_59_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_59_keep_dims_0 = const()[name = tensor("reduce_mean_59_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_59_cast_fp16 = reduce_mean(axes = reduce_mean_59_axes_0, keep_dims = reduce_mean_59_keep_dims_0, x = square_19_cast_fp16)[name = tensor("reduce_mean_59_cast_fp16")]; + tensor add_38_y_0_to_fp16 = const()[name = tensor("add_38_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_38_cast_fp16 = add(x = reduce_mean_59_cast_fp16, y = add_38_y_0_to_fp16)[name = tensor("add_38_cast_fp16")]; + tensor sqrt_19_cast_fp16 = sqrt(x = add_38_cast_fp16)[name = tensor("sqrt_19_cast_fp16")]; + tensor real_div_19_cast_fp16 = real_div(x = sub_38_cast_fp16, y = sqrt_19_cast_fp16)[name = tensor("real_div_19_cast_fp16")]; + tensor reshape_77_shape_0 = const()[name = tensor("reshape_77_shape_0"), val = tensor([1, 256, 192, 320])]; + tensor reshape_77_cast_fp16 = reshape(shape = reshape_77_shape_0, x = real_div_19_cast_fp16)[name = tensor("reshape_77_cast_fp16")]; + tensor add_39_gamma_0_to_fp16 = const()[name = tensor("add_39_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90942016)))]; + tensor add_39_beta_0_to_fp16 = const()[name = tensor("add_39_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90942592)))]; + tensor add_39_epsilon_0_to_fp16 = const()[name = tensor("add_39_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_39_cast_fp16 = batch_norm(beta = add_39_beta_0_to_fp16, epsilon = add_39_epsilon_0_to_fp16, gamma = add_39_gamma_0_to_fp16, mean = add_37_mean_0_to_fp16, variance = add_37_variance_0_to_fp16, x = reshape_77_cast_fp16)[name = tensor("add_39_cast_fp16")]; + tensor hidden_states_77_cast_fp16 = silu(x = add_39_cast_fp16)[name = tensor("hidden_states_77_cast_fp16")]; + tensor var_453 = const()[name = tensor("op_453"), val = tensor([1, 1])]; + tensor var_455 = const()[name = tensor("op_455"), val = tensor([1, 1])]; + tensor input_105_pad_type_0 = const()[name = tensor("input_105_pad_type_0"), val = tensor("custom")]; + tensor input_105_pad_0 = const()[name = tensor("input_105_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_2_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90943168)))]; + tensor decoder_up_blocks_2_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92122880)))]; + tensor input_105_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = var_455, groups = var_26, pad = input_105_pad_0, pad_type = input_105_pad_type_0, strides = var_453, weight = decoder_up_blocks_2_resnets_1_conv1_weight_to_fp16, x = hidden_states_77_cast_fp16)[name = tensor("input_105_cast_fp16")]; + tensor reshape_80_shape_0 = const()[name = tensor("reshape_80_shape_0"), val = tensor([1, 32, 8, 192, 320])]; + tensor reshape_80_cast_fp16 = reshape(shape = reshape_80_shape_0, x = input_105_cast_fp16)[name = tensor("reshape_80_cast_fp16")]; + tensor reduce_mean_60_axes_0 = const()[name = tensor("reduce_mean_60_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_60_keep_dims_0 = const()[name = tensor("reduce_mean_60_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_60_cast_fp16 = reduce_mean(axes = reduce_mean_60_axes_0, keep_dims = reduce_mean_60_keep_dims_0, x = reshape_80_cast_fp16)[name = tensor("reduce_mean_60_cast_fp16")]; + tensor sub_40_cast_fp16 = sub(x = reshape_80_cast_fp16, y = reduce_mean_60_cast_fp16)[name = tensor("sub_40_cast_fp16")]; + tensor square_20_cast_fp16 = square(x = sub_40_cast_fp16)[name = tensor("square_20_cast_fp16")]; + tensor reduce_mean_62_axes_0 = const()[name = tensor("reduce_mean_62_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_62_keep_dims_0 = const()[name = tensor("reduce_mean_62_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_62_cast_fp16 = reduce_mean(axes = reduce_mean_62_axes_0, keep_dims = reduce_mean_62_keep_dims_0, x = square_20_cast_fp16)[name = tensor("reduce_mean_62_cast_fp16")]; + tensor add_40_y_0_to_fp16 = const()[name = tensor("add_40_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_40_cast_fp16 = add(x = reduce_mean_62_cast_fp16, y = add_40_y_0_to_fp16)[name = tensor("add_40_cast_fp16")]; + tensor sqrt_20_cast_fp16 = sqrt(x = add_40_cast_fp16)[name = tensor("sqrt_20_cast_fp16")]; + tensor real_div_20_cast_fp16 = real_div(x = sub_40_cast_fp16, y = sqrt_20_cast_fp16)[name = tensor("real_div_20_cast_fp16")]; + tensor reshape_81_shape_0 = const()[name = tensor("reshape_81_shape_0"), val = tensor([1, 256, 192, 320])]; + tensor reshape_81_cast_fp16 = reshape(shape = reshape_81_shape_0, x = real_div_20_cast_fp16)[name = tensor("reshape_81_cast_fp16")]; + tensor add_41_gamma_0_to_fp16 = const()[name = tensor("add_41_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92123456)))]; + tensor add_41_beta_0_to_fp16 = const()[name = tensor("add_41_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92124032)))]; + tensor add_41_epsilon_0_to_fp16 = const()[name = tensor("add_41_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_41_cast_fp16 = batch_norm(beta = add_41_beta_0_to_fp16, epsilon = add_41_epsilon_0_to_fp16, gamma = add_41_gamma_0_to_fp16, mean = add_37_mean_0_to_fp16, variance = add_37_variance_0_to_fp16, x = reshape_81_cast_fp16)[name = tensor("add_41_cast_fp16")]; + tensor input_109_cast_fp16 = silu(x = add_41_cast_fp16)[name = tensor("input_109_cast_fp16")]; + tensor var_465 = const()[name = tensor("op_465"), val = tensor([1, 1])]; + tensor var_467 = const()[name = tensor("op_467"), val = tensor([1, 1])]; + tensor hidden_states_81_pad_type_0 = const()[name = tensor("hidden_states_81_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_81_pad_0 = const()[name = tensor("hidden_states_81_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_2_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92124608)))]; + tensor decoder_up_blocks_2_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93304320)))]; + tensor hidden_states_81_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = var_467, groups = var_26, pad = hidden_states_81_pad_0, pad_type = hidden_states_81_pad_type_0, strides = var_465, weight = decoder_up_blocks_2_resnets_1_conv2_weight_to_fp16, x = input_109_cast_fp16)[name = tensor("hidden_states_81_cast_fp16")]; + tensor var_470_cast_fp16 = add(x = var_440_cast_fp16, y = hidden_states_81_cast_fp16)[name = tensor("op_470_cast_fp16")]; + tensor reshape_84_shape_0 = const()[name = tensor("reshape_84_shape_0"), val = tensor([1, 32, 8, 192, 320])]; + tensor reshape_84_cast_fp16 = reshape(shape = reshape_84_shape_0, x = var_470_cast_fp16)[name = tensor("reshape_84_cast_fp16")]; + tensor reduce_mean_63_axes_0 = const()[name = tensor("reduce_mean_63_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_63_keep_dims_0 = const()[name = tensor("reduce_mean_63_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_63_cast_fp16 = reduce_mean(axes = reduce_mean_63_axes_0, keep_dims = reduce_mean_63_keep_dims_0, x = reshape_84_cast_fp16)[name = tensor("reduce_mean_63_cast_fp16")]; + tensor sub_42_cast_fp16 = sub(x = reshape_84_cast_fp16, y = reduce_mean_63_cast_fp16)[name = tensor("sub_42_cast_fp16")]; + tensor square_21_cast_fp16 = square(x = sub_42_cast_fp16)[name = tensor("square_21_cast_fp16")]; + tensor reduce_mean_65_axes_0 = const()[name = tensor("reduce_mean_65_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_65_keep_dims_0 = const()[name = tensor("reduce_mean_65_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_65_cast_fp16 = reduce_mean(axes = reduce_mean_65_axes_0, keep_dims = reduce_mean_65_keep_dims_0, x = square_21_cast_fp16)[name = tensor("reduce_mean_65_cast_fp16")]; + tensor add_42_y_0_to_fp16 = const()[name = tensor("add_42_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_42_cast_fp16 = add(x = reduce_mean_65_cast_fp16, y = add_42_y_0_to_fp16)[name = tensor("add_42_cast_fp16")]; + tensor sqrt_21_cast_fp16 = sqrt(x = add_42_cast_fp16)[name = tensor("sqrt_21_cast_fp16")]; + tensor real_div_21_cast_fp16 = real_div(x = sub_42_cast_fp16, y = sqrt_21_cast_fp16)[name = tensor("real_div_21_cast_fp16")]; + tensor reshape_85_shape_0 = const()[name = tensor("reshape_85_shape_0"), val = tensor([1, 256, 192, 320])]; + tensor reshape_85_cast_fp16 = reshape(shape = reshape_85_shape_0, x = real_div_21_cast_fp16)[name = tensor("reshape_85_cast_fp16")]; + tensor add_43_gamma_0_to_fp16 = const()[name = tensor("add_43_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93304896)))]; + tensor add_43_beta_0_to_fp16 = const()[name = tensor("add_43_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93305472)))]; + tensor add_43_epsilon_0_to_fp16 = const()[name = tensor("add_43_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_43_cast_fp16 = batch_norm(beta = add_43_beta_0_to_fp16, epsilon = add_43_epsilon_0_to_fp16, gamma = add_43_gamma_0_to_fp16, mean = add_37_mean_0_to_fp16, variance = add_37_variance_0_to_fp16, x = reshape_85_cast_fp16)[name = tensor("add_43_cast_fp16")]; + tensor hidden_states_83_cast_fp16 = silu(x = add_43_cast_fp16)[name = tensor("hidden_states_83_cast_fp16")]; + tensor var_483 = const()[name = tensor("op_483"), val = tensor([1, 1])]; + tensor var_485 = const()[name = tensor("op_485"), val = tensor([1, 1])]; + tensor input_115_pad_type_0 = const()[name = tensor("input_115_pad_type_0"), val = tensor("custom")]; + tensor input_115_pad_0 = const()[name = tensor("input_115_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_2_resnets_2_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_2_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93306048)))]; + tensor decoder_up_blocks_2_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94485760)))]; + tensor input_115_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_2_conv1_bias_to_fp16, dilations = var_485, groups = var_26, pad = input_115_pad_0, pad_type = input_115_pad_type_0, strides = var_483, weight = decoder_up_blocks_2_resnets_2_conv1_weight_to_fp16, x = hidden_states_83_cast_fp16)[name = tensor("input_115_cast_fp16")]; + tensor reshape_88_shape_0 = const()[name = tensor("reshape_88_shape_0"), val = tensor([1, 32, 8, 192, 320])]; + tensor reshape_88_cast_fp16 = reshape(shape = reshape_88_shape_0, x = input_115_cast_fp16)[name = tensor("reshape_88_cast_fp16")]; + tensor reduce_mean_66_axes_0 = const()[name = tensor("reduce_mean_66_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_66_keep_dims_0 = const()[name = tensor("reduce_mean_66_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_66_cast_fp16 = reduce_mean(axes = reduce_mean_66_axes_0, keep_dims = reduce_mean_66_keep_dims_0, x = reshape_88_cast_fp16)[name = tensor("reduce_mean_66_cast_fp16")]; + tensor sub_44_cast_fp16 = sub(x = reshape_88_cast_fp16, y = reduce_mean_66_cast_fp16)[name = tensor("sub_44_cast_fp16")]; + tensor square_22_cast_fp16 = square(x = sub_44_cast_fp16)[name = tensor("square_22_cast_fp16")]; + tensor reduce_mean_68_axes_0 = const()[name = tensor("reduce_mean_68_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_68_keep_dims_0 = const()[name = tensor("reduce_mean_68_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_68_cast_fp16 = reduce_mean(axes = reduce_mean_68_axes_0, keep_dims = reduce_mean_68_keep_dims_0, x = square_22_cast_fp16)[name = tensor("reduce_mean_68_cast_fp16")]; + tensor add_44_y_0_to_fp16 = const()[name = tensor("add_44_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_44_cast_fp16 = add(x = reduce_mean_68_cast_fp16, y = add_44_y_0_to_fp16)[name = tensor("add_44_cast_fp16")]; + tensor sqrt_22_cast_fp16 = sqrt(x = add_44_cast_fp16)[name = tensor("sqrt_22_cast_fp16")]; + tensor real_div_22_cast_fp16 = real_div(x = sub_44_cast_fp16, y = sqrt_22_cast_fp16)[name = tensor("real_div_22_cast_fp16")]; + tensor reshape_89_shape_0 = const()[name = tensor("reshape_89_shape_0"), val = tensor([1, 256, 192, 320])]; + tensor reshape_89_cast_fp16 = reshape(shape = reshape_89_shape_0, x = real_div_22_cast_fp16)[name = tensor("reshape_89_cast_fp16")]; + tensor add_45_gamma_0_to_fp16 = const()[name = tensor("add_45_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94486336)))]; + tensor add_45_beta_0_to_fp16 = const()[name = tensor("add_45_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94486912)))]; + tensor add_45_epsilon_0_to_fp16 = const()[name = tensor("add_45_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_45_cast_fp16 = batch_norm(beta = add_45_beta_0_to_fp16, epsilon = add_45_epsilon_0_to_fp16, gamma = add_45_gamma_0_to_fp16, mean = add_37_mean_0_to_fp16, variance = add_37_variance_0_to_fp16, x = reshape_89_cast_fp16)[name = tensor("add_45_cast_fp16")]; + tensor input_119_cast_fp16 = silu(x = add_45_cast_fp16)[name = tensor("input_119_cast_fp16")]; + tensor var_495 = const()[name = tensor("op_495"), val = tensor([1, 1])]; + tensor var_497 = const()[name = tensor("op_497"), val = tensor([1, 1])]; + tensor hidden_states_87_pad_type_0 = const()[name = tensor("hidden_states_87_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_87_pad_0 = const()[name = tensor("hidden_states_87_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_2_resnets_2_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_2_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94487488)))]; + tensor decoder_up_blocks_2_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95667200)))]; + tensor hidden_states_87_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_2_conv2_bias_to_fp16, dilations = var_497, groups = var_26, pad = hidden_states_87_pad_0, pad_type = hidden_states_87_pad_type_0, strides = var_495, weight = decoder_up_blocks_2_resnets_2_conv2_weight_to_fp16, x = input_119_cast_fp16)[name = tensor("hidden_states_87_cast_fp16")]; + tensor var_500_cast_fp16 = add(x = var_470_cast_fp16, y = hidden_states_87_cast_fp16)[name = tensor("op_500_cast_fp16")]; + tensor hidden_states_91_scale_factor_height_0 = const()[name = tensor("hidden_states_91_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor hidden_states_91_scale_factor_width_0 = const()[name = tensor("hidden_states_91_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor hidden_states_91_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = hidden_states_91_scale_factor_height_0, scale_factor_width = hidden_states_91_scale_factor_width_0, x = var_500_cast_fp16)[name = tensor("hidden_states_91_cast_fp16")]; + tensor var_508 = const()[name = tensor("op_508"), val = tensor([1, 1])]; + tensor var_510 = const()[name = tensor("op_510"), val = tensor([1, 1])]; + tensor input_121_pad_type_0 = const()[name = tensor("input_121_pad_type_0"), val = tensor("custom")]; + tensor input_121_pad_0 = const()[name = tensor("input_121_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_2_upsamplers_0_conv_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_2_upsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95667776)))]; + tensor decoder_up_blocks_2_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_2_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96847488)))]; + tensor input_121_cast_fp16 = conv(bias = decoder_up_blocks_2_upsamplers_0_conv_bias_to_fp16, dilations = var_510, groups = var_26, pad = input_121_pad_0, pad_type = input_121_pad_type_0, strides = var_508, weight = decoder_up_blocks_2_upsamplers_0_conv_weight_to_fp16, x = hidden_states_91_cast_fp16)[name = tensor("input_121_cast_fp16")]; + tensor reshape_92_shape_0 = const()[name = tensor("reshape_92_shape_0"), val = tensor([1, 32, 8, 384, 640])]; + tensor reshape_92_cast_fp16 = reshape(shape = reshape_92_shape_0, x = input_121_cast_fp16)[name = tensor("reshape_92_cast_fp16")]; + tensor reduce_mean_69_axes_0 = const()[name = tensor("reduce_mean_69_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_69_keep_dims_0 = const()[name = tensor("reduce_mean_69_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_69_cast_fp16 = reduce_mean(axes = reduce_mean_69_axes_0, keep_dims = reduce_mean_69_keep_dims_0, x = reshape_92_cast_fp16)[name = tensor("reduce_mean_69_cast_fp16")]; + tensor sub_46_cast_fp16 = sub(x = reshape_92_cast_fp16, y = reduce_mean_69_cast_fp16)[name = tensor("sub_46_cast_fp16")]; + tensor square_23_cast_fp16 = square(x = sub_46_cast_fp16)[name = tensor("square_23_cast_fp16")]; + tensor reduce_mean_71_axes_0 = const()[name = tensor("reduce_mean_71_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_71_keep_dims_0 = const()[name = tensor("reduce_mean_71_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_71_cast_fp16 = reduce_mean(axes = reduce_mean_71_axes_0, keep_dims = reduce_mean_71_keep_dims_0, x = square_23_cast_fp16)[name = tensor("reduce_mean_71_cast_fp16")]; + tensor add_46_y_0_to_fp16 = const()[name = tensor("add_46_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_46_cast_fp16 = add(x = reduce_mean_71_cast_fp16, y = add_46_y_0_to_fp16)[name = tensor("add_46_cast_fp16")]; + tensor sqrt_23_cast_fp16 = sqrt(x = add_46_cast_fp16)[name = tensor("sqrt_23_cast_fp16")]; + tensor real_div_23_cast_fp16 = real_div(x = sub_46_cast_fp16, y = sqrt_23_cast_fp16)[name = tensor("real_div_23_cast_fp16")]; + tensor reshape_93_shape_0 = const()[name = tensor("reshape_93_shape_0"), val = tensor([1, 256, 384, 640])]; + tensor reshape_93_cast_fp16 = reshape(shape = reshape_93_shape_0, x = real_div_23_cast_fp16)[name = tensor("reshape_93_cast_fp16")]; + tensor add_47_gamma_0_to_fp16 = const()[name = tensor("add_47_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96848064)))]; + tensor add_47_beta_0_to_fp16 = const()[name = tensor("add_47_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96848640)))]; + tensor add_47_epsilon_0_to_fp16 = const()[name = tensor("add_47_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_47_cast_fp16 = batch_norm(beta = add_47_beta_0_to_fp16, epsilon = add_47_epsilon_0_to_fp16, gamma = add_47_gamma_0_to_fp16, mean = add_37_mean_0_to_fp16, variance = add_37_variance_0_to_fp16, x = reshape_93_cast_fp16)[name = tensor("add_47_cast_fp16")]; + tensor hidden_states_93_cast_fp16 = silu(x = add_47_cast_fp16)[name = tensor("hidden_states_93_cast_fp16")]; + tensor var_530 = const()[name = tensor("op_530"), val = tensor([1, 1])]; + tensor var_532 = const()[name = tensor("op_532"), val = tensor([1, 1])]; + tensor input_125_pad_type_0 = const()[name = tensor("input_125_pad_type_0"), val = tensor("custom")]; + tensor input_125_pad_0 = const()[name = tensor("input_125_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_3_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96849216)))]; + tensor decoder_up_blocks_3_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97439104)))]; + tensor input_125_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_0_conv1_bias_to_fp16, dilations = var_532, groups = var_26, pad = input_125_pad_0, pad_type = input_125_pad_type_0, strides = var_530, weight = decoder_up_blocks_3_resnets_0_conv1_weight_to_fp16, x = hidden_states_93_cast_fp16)[name = tensor("input_125_cast_fp16")]; + tensor reshape_96_shape_0 = const()[name = tensor("reshape_96_shape_0"), val = tensor([1, 32, 4, 384, 640])]; + tensor reshape_96_cast_fp16 = reshape(shape = reshape_96_shape_0, x = input_125_cast_fp16)[name = tensor("reshape_96_cast_fp16")]; + tensor reduce_mean_72_axes_0 = const()[name = tensor("reduce_mean_72_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_72_keep_dims_0 = const()[name = tensor("reduce_mean_72_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_72_cast_fp16 = reduce_mean(axes = reduce_mean_72_axes_0, keep_dims = reduce_mean_72_keep_dims_0, x = reshape_96_cast_fp16)[name = tensor("reduce_mean_72_cast_fp16")]; + tensor sub_48_cast_fp16 = sub(x = reshape_96_cast_fp16, y = reduce_mean_72_cast_fp16)[name = tensor("sub_48_cast_fp16")]; + tensor square_24_cast_fp16 = square(x = sub_48_cast_fp16)[name = tensor("square_24_cast_fp16")]; + tensor reduce_mean_74_axes_0 = const()[name = tensor("reduce_mean_74_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_74_keep_dims_0 = const()[name = tensor("reduce_mean_74_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_74_cast_fp16 = reduce_mean(axes = reduce_mean_74_axes_0, keep_dims = reduce_mean_74_keep_dims_0, x = square_24_cast_fp16)[name = tensor("reduce_mean_74_cast_fp16")]; + tensor add_48_y_0_to_fp16 = const()[name = tensor("add_48_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_48_cast_fp16 = add(x = reduce_mean_74_cast_fp16, y = add_48_y_0_to_fp16)[name = tensor("add_48_cast_fp16")]; + tensor sqrt_24_cast_fp16 = sqrt(x = add_48_cast_fp16)[name = tensor("sqrt_24_cast_fp16")]; + tensor real_div_24_cast_fp16 = real_div(x = sub_48_cast_fp16, y = sqrt_24_cast_fp16)[name = tensor("real_div_24_cast_fp16")]; + tensor reshape_97_shape_0 = const()[name = tensor("reshape_97_shape_0"), val = tensor([1, 128, 384, 640])]; + tensor reshape_97_cast_fp16 = reshape(shape = reshape_97_shape_0, x = real_div_24_cast_fp16)[name = tensor("reshape_97_cast_fp16")]; + tensor add_49_mean_0_to_fp16 = const()[name = tensor("add_49_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97439424)))]; + tensor add_49_variance_0_to_fp16 = const()[name = tensor("add_49_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97439744)))]; + tensor add_49_gamma_0_to_fp16 = const()[name = tensor("add_49_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97440064)))]; + tensor add_49_beta_0_to_fp16 = const()[name = tensor("add_49_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97440384)))]; + tensor add_49_epsilon_0_to_fp16 = const()[name = tensor("add_49_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_49_cast_fp16 = batch_norm(beta = add_49_beta_0_to_fp16, epsilon = add_49_epsilon_0_to_fp16, gamma = add_49_gamma_0_to_fp16, mean = add_49_mean_0_to_fp16, variance = add_49_variance_0_to_fp16, x = reshape_97_cast_fp16)[name = tensor("add_49_cast_fp16")]; + tensor input_129_cast_fp16 = silu(x = add_49_cast_fp16)[name = tensor("input_129_cast_fp16")]; + tensor var_542 = const()[name = tensor("op_542"), val = tensor([1, 1])]; + tensor var_544 = const()[name = tensor("op_544"), val = tensor([1, 1])]; + tensor hidden_states_97_pad_type_0 = const()[name = tensor("hidden_states_97_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_97_pad_0 = const()[name = tensor("hidden_states_97_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_3_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97440704)))]; + tensor decoder_up_blocks_3_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97735680)))]; + tensor hidden_states_97_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_0_conv2_bias_to_fp16, dilations = var_544, groups = var_26, pad = hidden_states_97_pad_0, pad_type = hidden_states_97_pad_type_0, strides = var_542, weight = decoder_up_blocks_3_resnets_0_conv2_weight_to_fp16, x = input_129_cast_fp16)[name = tensor("hidden_states_97_cast_fp16")]; + tensor var_549 = const()[name = tensor("op_549"), val = tensor([1, 1])]; + tensor var_551 = const()[name = tensor("op_551"), val = tensor([1, 1])]; + tensor input_tensor_pad_type_0 = const()[name = tensor("input_tensor_pad_type_0"), val = tensor("custom")]; + tensor input_tensor_pad_0 = const()[name = tensor("input_tensor_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor decoder_up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97736000)))]; + tensor decoder_up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97801600)))]; + tensor input_tensor_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_551, groups = var_26, pad = input_tensor_pad_0, pad_type = input_tensor_pad_type_0, strides = var_549, weight = decoder_up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16, x = input_121_cast_fp16)[name = tensor("input_tensor_cast_fp16")]; + tensor var_554_cast_fp16 = add(x = input_tensor_cast_fp16, y = hidden_states_97_cast_fp16)[name = tensor("op_554_cast_fp16")]; + tensor reshape_100_shape_0 = const()[name = tensor("reshape_100_shape_0"), val = tensor([1, 32, 4, 384, 640])]; + tensor reshape_100_cast_fp16 = reshape(shape = reshape_100_shape_0, x = var_554_cast_fp16)[name = tensor("reshape_100_cast_fp16")]; + tensor reduce_mean_75_axes_0 = const()[name = tensor("reduce_mean_75_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_75_keep_dims_0 = const()[name = tensor("reduce_mean_75_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_75_cast_fp16 = reduce_mean(axes = reduce_mean_75_axes_0, keep_dims = reduce_mean_75_keep_dims_0, x = reshape_100_cast_fp16)[name = tensor("reduce_mean_75_cast_fp16")]; + tensor sub_50_cast_fp16 = sub(x = reshape_100_cast_fp16, y = reduce_mean_75_cast_fp16)[name = tensor("sub_50_cast_fp16")]; + tensor square_25_cast_fp16 = square(x = sub_50_cast_fp16)[name = tensor("square_25_cast_fp16")]; + tensor reduce_mean_77_axes_0 = const()[name = tensor("reduce_mean_77_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_77_keep_dims_0 = const()[name = tensor("reduce_mean_77_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_77_cast_fp16 = reduce_mean(axes = reduce_mean_77_axes_0, keep_dims = reduce_mean_77_keep_dims_0, x = square_25_cast_fp16)[name = tensor("reduce_mean_77_cast_fp16")]; + tensor add_50_y_0_to_fp16 = const()[name = tensor("add_50_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_50_cast_fp16 = add(x = reduce_mean_77_cast_fp16, y = add_50_y_0_to_fp16)[name = tensor("add_50_cast_fp16")]; + tensor sqrt_25_cast_fp16 = sqrt(x = add_50_cast_fp16)[name = tensor("sqrt_25_cast_fp16")]; + tensor real_div_25_cast_fp16 = real_div(x = sub_50_cast_fp16, y = sqrt_25_cast_fp16)[name = tensor("real_div_25_cast_fp16")]; + tensor reshape_101_shape_0 = const()[name = tensor("reshape_101_shape_0"), val = tensor([1, 128, 384, 640])]; + tensor reshape_101_cast_fp16 = reshape(shape = reshape_101_shape_0, x = real_div_25_cast_fp16)[name = tensor("reshape_101_cast_fp16")]; + tensor add_51_gamma_0_to_fp16 = const()[name = tensor("add_51_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97801920)))]; + tensor add_51_beta_0_to_fp16 = const()[name = tensor("add_51_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97802240)))]; + tensor add_51_epsilon_0_to_fp16 = const()[name = tensor("add_51_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_51_cast_fp16 = batch_norm(beta = add_51_beta_0_to_fp16, epsilon = add_51_epsilon_0_to_fp16, gamma = add_51_gamma_0_to_fp16, mean = add_49_mean_0_to_fp16, variance = add_49_variance_0_to_fp16, x = reshape_101_cast_fp16)[name = tensor("add_51_cast_fp16")]; + tensor hidden_states_99_cast_fp16 = silu(x = add_51_cast_fp16)[name = tensor("hidden_states_99_cast_fp16")]; + tensor var_567 = const()[name = tensor("op_567"), val = tensor([1, 1])]; + tensor var_569 = const()[name = tensor("op_569"), val = tensor([1, 1])]; + tensor input_135_pad_type_0 = const()[name = tensor("input_135_pad_type_0"), val = tensor("custom")]; + tensor input_135_pad_0 = const()[name = tensor("input_135_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_3_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97802560)))]; + tensor decoder_up_blocks_3_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98097536)))]; + tensor input_135_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_1_conv1_bias_to_fp16, dilations = var_569, groups = var_26, pad = input_135_pad_0, pad_type = input_135_pad_type_0, strides = var_567, weight = decoder_up_blocks_3_resnets_1_conv1_weight_to_fp16, x = hidden_states_99_cast_fp16)[name = tensor("input_135_cast_fp16")]; + tensor reshape_104_shape_0 = const()[name = tensor("reshape_104_shape_0"), val = tensor([1, 32, 4, 384, 640])]; + tensor reshape_104_cast_fp16 = reshape(shape = reshape_104_shape_0, x = input_135_cast_fp16)[name = tensor("reshape_104_cast_fp16")]; + tensor reduce_mean_78_axes_0 = const()[name = tensor("reduce_mean_78_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_78_keep_dims_0 = const()[name = tensor("reduce_mean_78_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_78_cast_fp16 = reduce_mean(axes = reduce_mean_78_axes_0, keep_dims = reduce_mean_78_keep_dims_0, x = reshape_104_cast_fp16)[name = tensor("reduce_mean_78_cast_fp16")]; + tensor sub_52_cast_fp16 = sub(x = reshape_104_cast_fp16, y = reduce_mean_78_cast_fp16)[name = tensor("sub_52_cast_fp16")]; + tensor square_26_cast_fp16 = square(x = sub_52_cast_fp16)[name = tensor("square_26_cast_fp16")]; + tensor reduce_mean_80_axes_0 = const()[name = tensor("reduce_mean_80_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_80_keep_dims_0 = const()[name = tensor("reduce_mean_80_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_80_cast_fp16 = reduce_mean(axes = reduce_mean_80_axes_0, keep_dims = reduce_mean_80_keep_dims_0, x = square_26_cast_fp16)[name = tensor("reduce_mean_80_cast_fp16")]; + tensor add_52_y_0_to_fp16 = const()[name = tensor("add_52_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_52_cast_fp16 = add(x = reduce_mean_80_cast_fp16, y = add_52_y_0_to_fp16)[name = tensor("add_52_cast_fp16")]; + tensor sqrt_26_cast_fp16 = sqrt(x = add_52_cast_fp16)[name = tensor("sqrt_26_cast_fp16")]; + tensor real_div_26_cast_fp16 = real_div(x = sub_52_cast_fp16, y = sqrt_26_cast_fp16)[name = tensor("real_div_26_cast_fp16")]; + tensor reshape_105_shape_0 = const()[name = tensor("reshape_105_shape_0"), val = tensor([1, 128, 384, 640])]; + tensor reshape_105_cast_fp16 = reshape(shape = reshape_105_shape_0, x = real_div_26_cast_fp16)[name = tensor("reshape_105_cast_fp16")]; + tensor add_53_gamma_0_to_fp16 = const()[name = tensor("add_53_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98097856)))]; + tensor add_53_beta_0_to_fp16 = const()[name = tensor("add_53_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98098176)))]; + tensor add_53_epsilon_0_to_fp16 = const()[name = tensor("add_53_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_53_cast_fp16 = batch_norm(beta = add_53_beta_0_to_fp16, epsilon = add_53_epsilon_0_to_fp16, gamma = add_53_gamma_0_to_fp16, mean = add_49_mean_0_to_fp16, variance = add_49_variance_0_to_fp16, x = reshape_105_cast_fp16)[name = tensor("add_53_cast_fp16")]; + tensor input_139_cast_fp16 = silu(x = add_53_cast_fp16)[name = tensor("input_139_cast_fp16")]; + tensor var_579 = const()[name = tensor("op_579"), val = tensor([1, 1])]; + tensor var_581 = const()[name = tensor("op_581"), val = tensor([1, 1])]; + tensor hidden_states_103_pad_type_0 = const()[name = tensor("hidden_states_103_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_103_pad_0 = const()[name = tensor("hidden_states_103_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_3_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98098496)))]; + tensor decoder_up_blocks_3_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98393472)))]; + tensor hidden_states_103_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_1_conv2_bias_to_fp16, dilations = var_581, groups = var_26, pad = hidden_states_103_pad_0, pad_type = hidden_states_103_pad_type_0, strides = var_579, weight = decoder_up_blocks_3_resnets_1_conv2_weight_to_fp16, x = input_139_cast_fp16)[name = tensor("hidden_states_103_cast_fp16")]; + tensor var_584_cast_fp16 = add(x = var_554_cast_fp16, y = hidden_states_103_cast_fp16)[name = tensor("op_584_cast_fp16")]; + tensor reshape_108_shape_0 = const()[name = tensor("reshape_108_shape_0"), val = tensor([1, 32, 4, 384, 640])]; + tensor reshape_108_cast_fp16 = reshape(shape = reshape_108_shape_0, x = var_584_cast_fp16)[name = tensor("reshape_108_cast_fp16")]; + tensor reduce_mean_81_axes_0 = const()[name = tensor("reduce_mean_81_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_81_keep_dims_0 = const()[name = tensor("reduce_mean_81_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_81_cast_fp16 = reduce_mean(axes = reduce_mean_81_axes_0, keep_dims = reduce_mean_81_keep_dims_0, x = reshape_108_cast_fp16)[name = tensor("reduce_mean_81_cast_fp16")]; + tensor sub_54_cast_fp16 = sub(x = reshape_108_cast_fp16, y = reduce_mean_81_cast_fp16)[name = tensor("sub_54_cast_fp16")]; + tensor square_27_cast_fp16 = square(x = sub_54_cast_fp16)[name = tensor("square_27_cast_fp16")]; + tensor reduce_mean_83_axes_0 = const()[name = tensor("reduce_mean_83_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_83_keep_dims_0 = const()[name = tensor("reduce_mean_83_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_83_cast_fp16 = reduce_mean(axes = reduce_mean_83_axes_0, keep_dims = reduce_mean_83_keep_dims_0, x = square_27_cast_fp16)[name = tensor("reduce_mean_83_cast_fp16")]; + tensor add_54_y_0_to_fp16 = const()[name = tensor("add_54_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_54_cast_fp16 = add(x = reduce_mean_83_cast_fp16, y = add_54_y_0_to_fp16)[name = tensor("add_54_cast_fp16")]; + tensor sqrt_27_cast_fp16 = sqrt(x = add_54_cast_fp16)[name = tensor("sqrt_27_cast_fp16")]; + tensor real_div_27_cast_fp16 = real_div(x = sub_54_cast_fp16, y = sqrt_27_cast_fp16)[name = tensor("real_div_27_cast_fp16")]; + tensor reshape_109_shape_0 = const()[name = tensor("reshape_109_shape_0"), val = tensor([1, 128, 384, 640])]; + tensor reshape_109_cast_fp16 = reshape(shape = reshape_109_shape_0, x = real_div_27_cast_fp16)[name = tensor("reshape_109_cast_fp16")]; + tensor add_55_gamma_0_to_fp16 = const()[name = tensor("add_55_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98393792)))]; + tensor add_55_beta_0_to_fp16 = const()[name = tensor("add_55_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98394112)))]; + tensor add_55_epsilon_0_to_fp16 = const()[name = tensor("add_55_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_55_cast_fp16 = batch_norm(beta = add_55_beta_0_to_fp16, epsilon = add_55_epsilon_0_to_fp16, gamma = add_55_gamma_0_to_fp16, mean = add_49_mean_0_to_fp16, variance = add_49_variance_0_to_fp16, x = reshape_109_cast_fp16)[name = tensor("add_55_cast_fp16")]; + tensor hidden_states_105_cast_fp16 = silu(x = add_55_cast_fp16)[name = tensor("hidden_states_105_cast_fp16")]; + tensor var_597 = const()[name = tensor("op_597"), val = tensor([1, 1])]; + tensor var_599 = const()[name = tensor("op_599"), val = tensor([1, 1])]; + tensor input_145_pad_type_0 = const()[name = tensor("input_145_pad_type_0"), val = tensor("custom")]; + tensor input_145_pad_0 = const()[name = tensor("input_145_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_3_resnets_2_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_2_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98394432)))]; + tensor decoder_up_blocks_3_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98689408)))]; + tensor input_145_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_2_conv1_bias_to_fp16, dilations = var_599, groups = var_26, pad = input_145_pad_0, pad_type = input_145_pad_type_0, strides = var_597, weight = decoder_up_blocks_3_resnets_2_conv1_weight_to_fp16, x = hidden_states_105_cast_fp16)[name = tensor("input_145_cast_fp16")]; + tensor reshape_112_shape_0 = const()[name = tensor("reshape_112_shape_0"), val = tensor([1, 32, 4, 384, 640])]; + tensor reshape_112_cast_fp16 = reshape(shape = reshape_112_shape_0, x = input_145_cast_fp16)[name = tensor("reshape_112_cast_fp16")]; + tensor reduce_mean_84_axes_0 = const()[name = tensor("reduce_mean_84_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_84_keep_dims_0 = const()[name = tensor("reduce_mean_84_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_84_cast_fp16 = reduce_mean(axes = reduce_mean_84_axes_0, keep_dims = reduce_mean_84_keep_dims_0, x = reshape_112_cast_fp16)[name = tensor("reduce_mean_84_cast_fp16")]; + tensor sub_56_cast_fp16 = sub(x = reshape_112_cast_fp16, y = reduce_mean_84_cast_fp16)[name = tensor("sub_56_cast_fp16")]; + tensor square_28_cast_fp16 = square(x = sub_56_cast_fp16)[name = tensor("square_28_cast_fp16")]; + tensor reduce_mean_86_axes_0 = const()[name = tensor("reduce_mean_86_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_86_keep_dims_0 = const()[name = tensor("reduce_mean_86_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_86_cast_fp16 = reduce_mean(axes = reduce_mean_86_axes_0, keep_dims = reduce_mean_86_keep_dims_0, x = square_28_cast_fp16)[name = tensor("reduce_mean_86_cast_fp16")]; + tensor add_56_y_0_to_fp16 = const()[name = tensor("add_56_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_56_cast_fp16 = add(x = reduce_mean_86_cast_fp16, y = add_56_y_0_to_fp16)[name = tensor("add_56_cast_fp16")]; + tensor sqrt_28_cast_fp16 = sqrt(x = add_56_cast_fp16)[name = tensor("sqrt_28_cast_fp16")]; + tensor real_div_28_cast_fp16 = real_div(x = sub_56_cast_fp16, y = sqrt_28_cast_fp16)[name = tensor("real_div_28_cast_fp16")]; + tensor reshape_113_shape_0 = const()[name = tensor("reshape_113_shape_0"), val = tensor([1, 128, 384, 640])]; + tensor reshape_113_cast_fp16 = reshape(shape = reshape_113_shape_0, x = real_div_28_cast_fp16)[name = tensor("reshape_113_cast_fp16")]; + tensor add_57_gamma_0_to_fp16 = const()[name = tensor("add_57_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98689728)))]; + tensor add_57_beta_0_to_fp16 = const()[name = tensor("add_57_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98690048)))]; + tensor add_57_epsilon_0_to_fp16 = const()[name = tensor("add_57_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_57_cast_fp16 = batch_norm(beta = add_57_beta_0_to_fp16, epsilon = add_57_epsilon_0_to_fp16, gamma = add_57_gamma_0_to_fp16, mean = add_49_mean_0_to_fp16, variance = add_49_variance_0_to_fp16, x = reshape_113_cast_fp16)[name = tensor("add_57_cast_fp16")]; + tensor input_149_cast_fp16 = silu(x = add_57_cast_fp16)[name = tensor("input_149_cast_fp16")]; + tensor var_609 = const()[name = tensor("op_609"), val = tensor([1, 1])]; + tensor var_611 = const()[name = tensor("op_611"), val = tensor([1, 1])]; + tensor hidden_states_pad_type_0 = const()[name = tensor("hidden_states_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_pad_0 = const()[name = tensor("hidden_states_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_3_resnets_2_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_2_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98690368)))]; + tensor decoder_up_blocks_3_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98985344)))]; + tensor hidden_states_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_2_conv2_bias_to_fp16, dilations = var_611, groups = var_26, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = var_609, weight = decoder_up_blocks_3_resnets_2_conv2_weight_to_fp16, x = input_149_cast_fp16)[name = tensor("hidden_states_cast_fp16")]; + tensor var_614_cast_fp16 = add(x = var_584_cast_fp16, y = hidden_states_cast_fp16)[name = tensor("op_614_cast_fp16")]; + tensor reshape_116_shape_0 = const()[name = tensor("reshape_116_shape_0"), val = tensor([1, 32, 4, 384, 640])]; + tensor reshape_116_cast_fp16 = reshape(shape = reshape_116_shape_0, x = var_614_cast_fp16)[name = tensor("reshape_116_cast_fp16")]; + tensor reduce_mean_87_axes_0 = const()[name = tensor("reduce_mean_87_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_87_keep_dims_0 = const()[name = tensor("reduce_mean_87_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_87_cast_fp16 = reduce_mean(axes = reduce_mean_87_axes_0, keep_dims = reduce_mean_87_keep_dims_0, x = reshape_116_cast_fp16)[name = tensor("reduce_mean_87_cast_fp16")]; + tensor sub_58_cast_fp16 = sub(x = reshape_116_cast_fp16, y = reduce_mean_87_cast_fp16)[name = tensor("sub_58_cast_fp16")]; + tensor square_29_cast_fp16 = square(x = sub_58_cast_fp16)[name = tensor("square_29_cast_fp16")]; + tensor reduce_mean_89_axes_0 = const()[name = tensor("reduce_mean_89_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_89_keep_dims_0 = const()[name = tensor("reduce_mean_89_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_89_cast_fp16 = reduce_mean(axes = reduce_mean_89_axes_0, keep_dims = reduce_mean_89_keep_dims_0, x = square_29_cast_fp16)[name = tensor("reduce_mean_89_cast_fp16")]; + tensor add_58_y_0_to_fp16 = const()[name = tensor("add_58_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_58_cast_fp16 = add(x = reduce_mean_89_cast_fp16, y = add_58_y_0_to_fp16)[name = tensor("add_58_cast_fp16")]; + tensor sqrt_29_cast_fp16 = sqrt(x = add_58_cast_fp16)[name = tensor("sqrt_29_cast_fp16")]; + tensor real_div_29_cast_fp16 = real_div(x = sub_58_cast_fp16, y = sqrt_29_cast_fp16)[name = tensor("real_div_29_cast_fp16")]; + tensor reshape_117_shape_0 = const()[name = tensor("reshape_117_shape_0"), val = tensor([1, 128, 384, 640])]; + tensor reshape_117_cast_fp16 = reshape(shape = reshape_117_shape_0, x = real_div_29_cast_fp16)[name = tensor("reshape_117_cast_fp16")]; + tensor add_59_gamma_0_to_fp16 = const()[name = tensor("add_59_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98985664)))]; + tensor add_59_beta_0_to_fp16 = const()[name = tensor("add_59_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98985984)))]; + tensor add_59_epsilon_0_to_fp16 = const()[name = tensor("add_59_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_59_cast_fp16 = batch_norm(beta = add_59_beta_0_to_fp16, epsilon = add_59_epsilon_0_to_fp16, gamma = add_59_gamma_0_to_fp16, mean = add_49_mean_0_to_fp16, variance = add_49_variance_0_to_fp16, x = reshape_117_cast_fp16)[name = tensor("add_59_cast_fp16")]; + tensor input_cast_fp16 = silu(x = add_59_cast_fp16)[name = tensor("input_cast_fp16")]; + tensor var_623 = const()[name = tensor("op_623"), val = tensor([1, 1])]; + tensor var_625 = const()[name = tensor("op_625"), val = tensor([1, 1])]; + tensor var_627_pad_type_0 = const()[name = tensor("op_627_pad_type_0"), val = tensor("custom")]; + tensor var_627_pad_0 = const()[name = tensor("op_627_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_conv_out_weight_to_fp16 = const()[name = tensor("decoder_conv_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98986304)))]; + tensor decoder_conv_out_bias_to_fp16 = const()[name = tensor("decoder_conv_out_bias_to_fp16"), val = tensor([0x1.02p-6, -0x1.4ccp-6, -0x1.7bcp-5])]; + tensor var_627_cast_fp16 = conv(bias = decoder_conv_out_bias_to_fp16, dilations = var_625, groups = var_26, pad = var_627_pad_0, pad_type = var_627_pad_type_0, strides = var_623, weight = decoder_conv_out_weight_to_fp16, x = input_cast_fp16)[name = tensor("op_627_cast_fp16")]; + tensor var_627_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("op_627_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor image = cast(dtype = var_627_cast_fp16_to_fp32_dtype_0, x = var_627_cast_fp16)[name = tensor("cast_37")]; + } -> (image); +} \ No newline at end of file diff --git a/original/compiled/VAEDecoder.mlmodelc/weights/weight.bin b/original/compiled/VAEDecoder.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..499c8a61a7a476c152fbb7b078f4c196a3c90a1d --- /dev/null +++ b/original/compiled/VAEDecoder.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b5dd566219d197b1c1529f562512ad3d62938d69f9d677ba2ccbab2115fa1b17 +size 98993280 diff --git a/original/compiled/VAEEncoder.mlmodelc/analytics/coremldata.bin b/original/compiled/VAEEncoder.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..2dbfe49dc7c8d5af546cb82bed95a8ec5d7c85c4 --- /dev/null +++ b/original/compiled/VAEEncoder.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6af42e2250346e84df8eb6b54e2b3ed32baf91ee09f45a7988e0d61b76e1529d +size 243 diff --git a/original/compiled/VAEEncoder.mlmodelc/coremldata.bin b/original/compiled/VAEEncoder.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..42beb07152387b382d448feead45f844e2676bca --- /dev/null +++ b/original/compiled/VAEEncoder.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:54e9255078ca27443e6e6462e3e66cdeb07e1605ce4903e43374e519de342225 +size 864 diff --git a/original/compiled/VAEEncoder.mlmodelc/metadata.json b/original/compiled/VAEEncoder.mlmodelc/metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..2b9c2e305d33c0c2915e5e00941398a00d0365d3 --- /dev/null +++ b/original/compiled/VAEEncoder.mlmodelc/metadata.json @@ -0,0 +1,77 @@ +[ + { + "shortDescription" : "Stable Diffusion generates images conditioned on text and\/or other images as input through the diffusion process. Please refer to https:\/\/arxiv.org\/abs\/2112.10752 for details.", + "metadataOutputVersion" : "3.0", + "outputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 8 × 48 × 80)", + "shortDescription" : "The latent embeddings from the unet model from the input image.", + "shape" : "[1, 8, 48, 80]", + "name" : "latent", + "type" : "MultiArray" + } + ], + "version" : "\/Users\/keijiro\/Documents\/StableDiffusion\/sd-turbo", + "modelParameters" : [ + + ], + "author" : "Please refer to the Model Card available at huggingface.co\/\/Users\/keijiro\/Documents\/StableDiffusion\/sd-turbo", + "specificationVersion" : 7, + "storagePrecision" : "Float16", + "license" : "OpenRAIL (https:\/\/huggingface.co\/spaces\/CompVis\/stable-diffusion-license)", + "mlProgramOperationTypeHistogram" : { + "Pad" : 3, + "Ios16.cast" : 1, + "Ios16.mul" : 2, + "Ios16.sqrt" : 22, + "Ios16.sub" : 22, + "Transpose" : 6, + "Ios16.conv" : 28, + "Ios16.add" : 34, + "Ios16.linear" : 4, + "Ios16.matmul" : 2, + "Ios16.realDiv" : 22, + "Ios16.reduceMean" : 44, + "Ios16.softmax" : 1, + "Ios16.batchNorm" : 21, + "Ios16.square" : 22, + "Ios16.reshape" : 49, + "Ios16.silu" : 21 + }, + "computePrecision" : "Mixed (Float16, Float32, Int32)", + "isUpdatable" : "0", + "availability" : { + "macOS" : "13.0", + "tvOS" : "16.0", + "visionOS" : "1.0", + "watchOS" : "9.0", + "iOS" : "16.0", + "macCatalyst" : "16.0" + }, + "modelType" : { + "name" : "MLModelType_mlProgram" + }, + "inputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 1 × 3 × 384 × 640)", + "shortDescription" : "The input image to base the initial latents on normalized to range [-1, 1]", + "shape" : "[1, 3, 384, 640]", + "name" : "x", + "type" : "MultiArray" + } + ], + "userDefinedMetadata" : { + "com.github.apple.coremltools.source_dialect" : "TorchScript", + "com.github.apple.coremltools.source" : "torch==2.1.2", + "com.github.apple.coremltools.version" : "7.1" + }, + "generatedClassName" : "Stable_Diffusion_version__Users_keijiro_Documents_StableDiffusion_sd_turbo_vae_encoder", + "method" : "predict" + } +] \ No newline at end of file diff --git a/original/compiled/VAEEncoder.mlmodelc/model.mil b/original/compiled/VAEEncoder.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..6679054be8a766f601ece571c2ca9df2b9104fd6 --- /dev/null +++ b/original/compiled/VAEEncoder.mlmodelc/model.mil @@ -0,0 +1,740 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3304.5.2"}, {"coremlc-version", "3304.6.2"}, {"coremltools-component-torch", "2.1.2"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "7.1"}})] +{ + func main(tensor x) { + tensor var_15 = const()[name = tensor("op_15"), val = tensor(1)]; + tensor var_33 = const()[name = tensor("op_33"), val = tensor([1, 1])]; + tensor var_35 = const()[name = tensor("op_35"), val = tensor([1, 1])]; + tensor input_1_pad_type_0 = const()[name = tensor("input_1_pad_type_0"), val = tensor("custom")]; + tensor input_1_pad_0 = const()[name = tensor("input_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_conv_in_weight_to_fp16 = const()[name = tensor("encoder_conv_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor encoder_conv_in_bias_to_fp16 = const()[name = tensor("encoder_conv_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7040)))]; + tensor input_1_cast_fp16 = conv(bias = encoder_conv_in_bias_to_fp16, dilations = var_35, groups = var_15, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = var_33, weight = encoder_conv_in_weight_to_fp16, x = x)[name = tensor("input_1_cast_fp16")]; + tensor reshape_0_shape_0 = const()[name = tensor("reshape_0_shape_0"), val = tensor([1, 32, 4, 384, 640])]; + tensor reshape_0_cast_fp16 = reshape(shape = reshape_0_shape_0, x = input_1_cast_fp16)[name = tensor("reshape_0_cast_fp16")]; + tensor reduce_mean_0_axes_0 = const()[name = tensor("reduce_mean_0_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_0_keep_dims_0 = const()[name = tensor("reduce_mean_0_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_0_cast_fp16 = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = reshape_0_cast_fp16)[name = tensor("reduce_mean_0_cast_fp16")]; + tensor sub_0_cast_fp16 = sub(x = reshape_0_cast_fp16, y = reduce_mean_0_cast_fp16)[name = tensor("sub_0_cast_fp16")]; + tensor square_0_cast_fp16 = square(x = sub_0_cast_fp16)[name = tensor("square_0_cast_fp16")]; + tensor reduce_mean_2_axes_0 = const()[name = tensor("reduce_mean_2_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_2_keep_dims_0 = const()[name = tensor("reduce_mean_2_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_2_cast_fp16 = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = square_0_cast_fp16)[name = tensor("reduce_mean_2_cast_fp16")]; + tensor add_0_y_0_to_fp16 = const()[name = tensor("add_0_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_0_cast_fp16 = add(x = reduce_mean_2_cast_fp16, y = add_0_y_0_to_fp16)[name = tensor("add_0_cast_fp16")]; + tensor sqrt_0_cast_fp16 = sqrt(x = add_0_cast_fp16)[name = tensor("sqrt_0_cast_fp16")]; + tensor real_div_0_cast_fp16 = real_div(x = sub_0_cast_fp16, y = sqrt_0_cast_fp16)[name = tensor("real_div_0_cast_fp16")]; + tensor reshape_1_shape_0 = const()[name = tensor("reshape_1_shape_0"), val = tensor([1, 128, 384, 640])]; + tensor reshape_1_cast_fp16 = reshape(shape = reshape_1_shape_0, x = real_div_0_cast_fp16)[name = tensor("reshape_1_cast_fp16")]; + tensor add_1_mean_0_to_fp16 = const()[name = tensor("add_1_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7360)))]; + tensor add_1_variance_0_to_fp16 = const()[name = tensor("add_1_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7680)))]; + tensor add_1_gamma_0_to_fp16 = const()[name = tensor("add_1_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8000)))]; + tensor add_1_beta_0_to_fp16 = const()[name = tensor("add_1_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8320)))]; + tensor add_1_epsilon_0_to_fp16 = const()[name = tensor("add_1_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_1_cast_fp16 = batch_norm(beta = add_1_beta_0_to_fp16, epsilon = add_1_epsilon_0_to_fp16, gamma = add_1_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_1_cast_fp16)[name = tensor("add_1_cast_fp16")]; + tensor hidden_states_1_cast_fp16 = silu(x = add_1_cast_fp16)[name = tensor("hidden_states_1_cast_fp16")]; + tensor var_54 = const()[name = tensor("op_54"), val = tensor([1, 1])]; + tensor var_56 = const()[name = tensor("op_56"), val = tensor([1, 1])]; + tensor input_5_pad_type_0 = const()[name = tensor("input_5_pad_type_0"), val = tensor("custom")]; + tensor input_5_pad_0 = const()[name = tensor("input_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_0_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_0_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8640)))]; + tensor encoder_down_blocks_0_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_0_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(303616)))]; + tensor input_5_cast_fp16 = conv(bias = encoder_down_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = var_56, groups = var_15, pad = input_5_pad_0, pad_type = input_5_pad_type_0, strides = var_54, weight = encoder_down_blocks_0_resnets_0_conv1_weight_to_fp16, x = hidden_states_1_cast_fp16)[name = tensor("input_5_cast_fp16")]; + tensor reshape_4_shape_0 = const()[name = tensor("reshape_4_shape_0"), val = tensor([1, 32, 4, 384, 640])]; + tensor reshape_4_cast_fp16 = reshape(shape = reshape_4_shape_0, x = input_5_cast_fp16)[name = tensor("reshape_4_cast_fp16")]; + tensor reduce_mean_3_axes_0 = const()[name = tensor("reduce_mean_3_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_3_keep_dims_0 = const()[name = tensor("reduce_mean_3_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_3_cast_fp16 = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = reshape_4_cast_fp16)[name = tensor("reduce_mean_3_cast_fp16")]; + tensor sub_2_cast_fp16 = sub(x = reshape_4_cast_fp16, y = reduce_mean_3_cast_fp16)[name = tensor("sub_2_cast_fp16")]; + tensor square_1_cast_fp16 = square(x = sub_2_cast_fp16)[name = tensor("square_1_cast_fp16")]; + tensor reduce_mean_5_axes_0 = const()[name = tensor("reduce_mean_5_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_5_keep_dims_0 = const()[name = tensor("reduce_mean_5_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_5_cast_fp16 = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_1_cast_fp16)[name = tensor("reduce_mean_5_cast_fp16")]; + tensor add_2_y_0_to_fp16 = const()[name = tensor("add_2_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_2_cast_fp16 = add(x = reduce_mean_5_cast_fp16, y = add_2_y_0_to_fp16)[name = tensor("add_2_cast_fp16")]; + tensor sqrt_1_cast_fp16 = sqrt(x = add_2_cast_fp16)[name = tensor("sqrt_1_cast_fp16")]; + tensor real_div_1_cast_fp16 = real_div(x = sub_2_cast_fp16, y = sqrt_1_cast_fp16)[name = tensor("real_div_1_cast_fp16")]; + tensor reshape_5_shape_0 = const()[name = tensor("reshape_5_shape_0"), val = tensor([1, 128, 384, 640])]; + tensor reshape_5_cast_fp16 = reshape(shape = reshape_5_shape_0, x = real_div_1_cast_fp16)[name = tensor("reshape_5_cast_fp16")]; + tensor add_3_gamma_0_to_fp16 = const()[name = tensor("add_3_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(303936)))]; + tensor add_3_beta_0_to_fp16 = const()[name = tensor("add_3_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304256)))]; + tensor add_3_epsilon_0_to_fp16 = const()[name = tensor("add_3_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_3_cast_fp16 = batch_norm(beta = add_3_beta_0_to_fp16, epsilon = add_3_epsilon_0_to_fp16, gamma = add_3_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_5_cast_fp16)[name = tensor("add_3_cast_fp16")]; + tensor input_9_cast_fp16 = silu(x = add_3_cast_fp16)[name = tensor("input_9_cast_fp16")]; + tensor var_66 = const()[name = tensor("op_66"), val = tensor([1, 1])]; + tensor var_68 = const()[name = tensor("op_68"), val = tensor([1, 1])]; + tensor hidden_states_5_pad_type_0 = const()[name = tensor("hidden_states_5_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_5_pad_0 = const()[name = tensor("hidden_states_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_0_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_0_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304576)))]; + tensor encoder_down_blocks_0_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_0_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(599552)))]; + tensor hidden_states_5_cast_fp16 = conv(bias = encoder_down_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = var_68, groups = var_15, pad = hidden_states_5_pad_0, pad_type = hidden_states_5_pad_type_0, strides = var_66, weight = encoder_down_blocks_0_resnets_0_conv2_weight_to_fp16, x = input_9_cast_fp16)[name = tensor("hidden_states_5_cast_fp16")]; + tensor var_71_cast_fp16 = add(x = input_1_cast_fp16, y = hidden_states_5_cast_fp16)[name = tensor("op_71_cast_fp16")]; + tensor reshape_8_shape_0 = const()[name = tensor("reshape_8_shape_0"), val = tensor([1, 32, 4, 384, 640])]; + tensor reshape_8_cast_fp16 = reshape(shape = reshape_8_shape_0, x = var_71_cast_fp16)[name = tensor("reshape_8_cast_fp16")]; + tensor reduce_mean_6_axes_0 = const()[name = tensor("reduce_mean_6_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_6_keep_dims_0 = const()[name = tensor("reduce_mean_6_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_6_cast_fp16 = reduce_mean(axes = reduce_mean_6_axes_0, keep_dims = reduce_mean_6_keep_dims_0, x = reshape_8_cast_fp16)[name = tensor("reduce_mean_6_cast_fp16")]; + tensor sub_4_cast_fp16 = sub(x = reshape_8_cast_fp16, y = reduce_mean_6_cast_fp16)[name = tensor("sub_4_cast_fp16")]; + tensor square_2_cast_fp16 = square(x = sub_4_cast_fp16)[name = tensor("square_2_cast_fp16")]; + tensor reduce_mean_8_axes_0 = const()[name = tensor("reduce_mean_8_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_8_keep_dims_0 = const()[name = tensor("reduce_mean_8_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_8_cast_fp16 = reduce_mean(axes = reduce_mean_8_axes_0, keep_dims = reduce_mean_8_keep_dims_0, x = square_2_cast_fp16)[name = tensor("reduce_mean_8_cast_fp16")]; + tensor add_4_y_0_to_fp16 = const()[name = tensor("add_4_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_4_cast_fp16 = add(x = reduce_mean_8_cast_fp16, y = add_4_y_0_to_fp16)[name = tensor("add_4_cast_fp16")]; + tensor sqrt_2_cast_fp16 = sqrt(x = add_4_cast_fp16)[name = tensor("sqrt_2_cast_fp16")]; + tensor real_div_2_cast_fp16 = real_div(x = sub_4_cast_fp16, y = sqrt_2_cast_fp16)[name = tensor("real_div_2_cast_fp16")]; + tensor reshape_9_shape_0 = const()[name = tensor("reshape_9_shape_0"), val = tensor([1, 128, 384, 640])]; + tensor reshape_9_cast_fp16 = reshape(shape = reshape_9_shape_0, x = real_div_2_cast_fp16)[name = tensor("reshape_9_cast_fp16")]; + tensor add_5_gamma_0_to_fp16 = const()[name = tensor("add_5_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(599872)))]; + tensor add_5_beta_0_to_fp16 = const()[name = tensor("add_5_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(600192)))]; + tensor add_5_epsilon_0_to_fp16 = const()[name = tensor("add_5_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_5_cast_fp16 = batch_norm(beta = add_5_beta_0_to_fp16, epsilon = add_5_epsilon_0_to_fp16, gamma = add_5_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_9_cast_fp16)[name = tensor("add_5_cast_fp16")]; + tensor hidden_states_7_cast_fp16 = silu(x = add_5_cast_fp16)[name = tensor("hidden_states_7_cast_fp16")]; + tensor var_84 = const()[name = tensor("op_84"), val = tensor([1, 1])]; + tensor var_86 = const()[name = tensor("op_86"), val = tensor([1, 1])]; + tensor input_15_pad_type_0 = const()[name = tensor("input_15_pad_type_0"), val = tensor("custom")]; + tensor input_15_pad_0 = const()[name = tensor("input_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_0_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_0_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(600512)))]; + tensor encoder_down_blocks_0_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_0_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(895488)))]; + tensor input_15_cast_fp16 = conv(bias = encoder_down_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = var_86, groups = var_15, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = var_84, weight = encoder_down_blocks_0_resnets_1_conv1_weight_to_fp16, x = hidden_states_7_cast_fp16)[name = tensor("input_15_cast_fp16")]; + tensor reshape_12_shape_0 = const()[name = tensor("reshape_12_shape_0"), val = tensor([1, 32, 4, 384, 640])]; + tensor reshape_12_cast_fp16 = reshape(shape = reshape_12_shape_0, x = input_15_cast_fp16)[name = tensor("reshape_12_cast_fp16")]; + tensor reduce_mean_9_axes_0 = const()[name = tensor("reduce_mean_9_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_9_keep_dims_0 = const()[name = tensor("reduce_mean_9_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_9_cast_fp16 = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = reshape_12_cast_fp16)[name = tensor("reduce_mean_9_cast_fp16")]; + tensor sub_6_cast_fp16 = sub(x = reshape_12_cast_fp16, y = reduce_mean_9_cast_fp16)[name = tensor("sub_6_cast_fp16")]; + tensor square_3_cast_fp16 = square(x = sub_6_cast_fp16)[name = tensor("square_3_cast_fp16")]; + tensor reduce_mean_11_axes_0 = const()[name = tensor("reduce_mean_11_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_11_keep_dims_0 = const()[name = tensor("reduce_mean_11_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_11_cast_fp16 = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_3_cast_fp16)[name = tensor("reduce_mean_11_cast_fp16")]; + tensor add_6_y_0_to_fp16 = const()[name = tensor("add_6_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_6_cast_fp16 = add(x = reduce_mean_11_cast_fp16, y = add_6_y_0_to_fp16)[name = tensor("add_6_cast_fp16")]; + tensor sqrt_3_cast_fp16 = sqrt(x = add_6_cast_fp16)[name = tensor("sqrt_3_cast_fp16")]; + tensor real_div_3_cast_fp16 = real_div(x = sub_6_cast_fp16, y = sqrt_3_cast_fp16)[name = tensor("real_div_3_cast_fp16")]; + tensor reshape_13_shape_0 = const()[name = tensor("reshape_13_shape_0"), val = tensor([1, 128, 384, 640])]; + tensor reshape_13_cast_fp16 = reshape(shape = reshape_13_shape_0, x = real_div_3_cast_fp16)[name = tensor("reshape_13_cast_fp16")]; + tensor add_7_gamma_0_to_fp16 = const()[name = tensor("add_7_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(895808)))]; + tensor add_7_beta_0_to_fp16 = const()[name = tensor("add_7_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(896128)))]; + tensor add_7_epsilon_0_to_fp16 = const()[name = tensor("add_7_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_7_cast_fp16 = batch_norm(beta = add_7_beta_0_to_fp16, epsilon = add_7_epsilon_0_to_fp16, gamma = add_7_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_13_cast_fp16)[name = tensor("add_7_cast_fp16")]; + tensor input_19_cast_fp16 = silu(x = add_7_cast_fp16)[name = tensor("input_19_cast_fp16")]; + tensor var_96 = const()[name = tensor("op_96"), val = tensor([1, 1])]; + tensor var_98 = const()[name = tensor("op_98"), val = tensor([1, 1])]; + tensor hidden_states_11_pad_type_0 = const()[name = tensor("hidden_states_11_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_11_pad_0 = const()[name = tensor("hidden_states_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_0_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_0_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(896448)))]; + tensor encoder_down_blocks_0_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_0_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1191424)))]; + tensor hidden_states_11_cast_fp16 = conv(bias = encoder_down_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = var_98, groups = var_15, pad = hidden_states_11_pad_0, pad_type = hidden_states_11_pad_type_0, strides = var_96, weight = encoder_down_blocks_0_resnets_1_conv2_weight_to_fp16, x = input_19_cast_fp16)[name = tensor("hidden_states_11_cast_fp16")]; + tensor var_101_cast_fp16 = add(x = var_71_cast_fp16, y = hidden_states_11_cast_fp16)[name = tensor("op_101_cast_fp16")]; + tensor hidden_states_15_pad_0 = const()[name = tensor("hidden_states_15_pad_0"), val = tensor([0, 0, 0, 0, 0, 1, 0, 1])]; + tensor hidden_states_15_mode_0 = const()[name = tensor("hidden_states_15_mode_0"), val = tensor("constant")]; + tensor hidden_states_15_constant_val_0_to_fp16 = const()[name = tensor("hidden_states_15_constant_val_0_to_fp16"), val = tensor(0x0p+0)]; + tensor hidden_states_15_cast_fp16 = pad(constant_val = hidden_states_15_constant_val_0_to_fp16, mode = hidden_states_15_mode_0, pad = hidden_states_15_pad_0, x = var_101_cast_fp16)[name = tensor("hidden_states_15_cast_fp16")]; + tensor var_109 = const()[name = tensor("op_109"), val = tensor([2, 2])]; + tensor var_111 = const()[name = tensor("op_111"), val = tensor([1, 1])]; + tensor input_21_pad_type_0 = const()[name = tensor("input_21_pad_type_0"), val = tensor("custom")]; + tensor input_21_pad_0 = const()[name = tensor("input_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor encoder_down_blocks_0_downsamplers_0_conv_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_0_downsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1191744)))]; + tensor encoder_down_blocks_0_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_0_downsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1486720)))]; + tensor input_21_cast_fp16 = conv(bias = encoder_down_blocks_0_downsamplers_0_conv_bias_to_fp16, dilations = var_111, groups = var_15, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = var_109, weight = encoder_down_blocks_0_downsamplers_0_conv_weight_to_fp16, x = hidden_states_15_cast_fp16)[name = tensor("input_21_cast_fp16")]; + tensor reshape_16_shape_0 = const()[name = tensor("reshape_16_shape_0"), val = tensor([1, 32, 4, 192, 320])]; + tensor reshape_16_cast_fp16 = reshape(shape = reshape_16_shape_0, x = input_21_cast_fp16)[name = tensor("reshape_16_cast_fp16")]; + tensor reduce_mean_12_axes_0 = const()[name = tensor("reduce_mean_12_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_12_keep_dims_0 = const()[name = tensor("reduce_mean_12_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_12_cast_fp16 = reduce_mean(axes = reduce_mean_12_axes_0, keep_dims = reduce_mean_12_keep_dims_0, x = reshape_16_cast_fp16)[name = tensor("reduce_mean_12_cast_fp16")]; + tensor sub_8_cast_fp16 = sub(x = reshape_16_cast_fp16, y = reduce_mean_12_cast_fp16)[name = tensor("sub_8_cast_fp16")]; + tensor square_4_cast_fp16 = square(x = sub_8_cast_fp16)[name = tensor("square_4_cast_fp16")]; + tensor reduce_mean_14_axes_0 = const()[name = tensor("reduce_mean_14_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_14_keep_dims_0 = const()[name = tensor("reduce_mean_14_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_14_cast_fp16 = reduce_mean(axes = reduce_mean_14_axes_0, keep_dims = reduce_mean_14_keep_dims_0, x = square_4_cast_fp16)[name = tensor("reduce_mean_14_cast_fp16")]; + tensor add_8_y_0_to_fp16 = const()[name = tensor("add_8_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_8_cast_fp16 = add(x = reduce_mean_14_cast_fp16, y = add_8_y_0_to_fp16)[name = tensor("add_8_cast_fp16")]; + tensor sqrt_4_cast_fp16 = sqrt(x = add_8_cast_fp16)[name = tensor("sqrt_4_cast_fp16")]; + tensor real_div_4_cast_fp16 = real_div(x = sub_8_cast_fp16, y = sqrt_4_cast_fp16)[name = tensor("real_div_4_cast_fp16")]; + tensor reshape_17_shape_0 = const()[name = tensor("reshape_17_shape_0"), val = tensor([1, 128, 192, 320])]; + tensor reshape_17_cast_fp16 = reshape(shape = reshape_17_shape_0, x = real_div_4_cast_fp16)[name = tensor("reshape_17_cast_fp16")]; + tensor add_9_gamma_0_to_fp16 = const()[name = tensor("add_9_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1487040)))]; + tensor add_9_beta_0_to_fp16 = const()[name = tensor("add_9_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1487360)))]; + tensor add_9_epsilon_0_to_fp16 = const()[name = tensor("add_9_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_9_cast_fp16 = batch_norm(beta = add_9_beta_0_to_fp16, epsilon = add_9_epsilon_0_to_fp16, gamma = add_9_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_17_cast_fp16)[name = tensor("add_9_cast_fp16")]; + tensor hidden_states_17_cast_fp16 = silu(x = add_9_cast_fp16)[name = tensor("hidden_states_17_cast_fp16")]; + tensor var_131 = const()[name = tensor("op_131"), val = tensor([1, 1])]; + tensor var_133 = const()[name = tensor("op_133"), val = tensor([1, 1])]; + tensor input_25_pad_type_0 = const()[name = tensor("input_25_pad_type_0"), val = tensor("custom")]; + tensor input_25_pad_0 = const()[name = tensor("input_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_1_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_1_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1487680)))]; + tensor encoder_down_blocks_1_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_1_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2077568)))]; + tensor input_25_cast_fp16 = conv(bias = encoder_down_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = var_133, groups = var_15, pad = input_25_pad_0, pad_type = input_25_pad_type_0, strides = var_131, weight = encoder_down_blocks_1_resnets_0_conv1_weight_to_fp16, x = hidden_states_17_cast_fp16)[name = tensor("input_25_cast_fp16")]; + tensor reshape_20_shape_0 = const()[name = tensor("reshape_20_shape_0"), val = tensor([1, 32, 8, 192, 320])]; + tensor reshape_20_cast_fp16 = reshape(shape = reshape_20_shape_0, x = input_25_cast_fp16)[name = tensor("reshape_20_cast_fp16")]; + tensor reduce_mean_15_axes_0 = const()[name = tensor("reduce_mean_15_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_15_keep_dims_0 = const()[name = tensor("reduce_mean_15_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_15_cast_fp16 = reduce_mean(axes = reduce_mean_15_axes_0, keep_dims = reduce_mean_15_keep_dims_0, x = reshape_20_cast_fp16)[name = tensor("reduce_mean_15_cast_fp16")]; + tensor sub_10_cast_fp16 = sub(x = reshape_20_cast_fp16, y = reduce_mean_15_cast_fp16)[name = tensor("sub_10_cast_fp16")]; + tensor square_5_cast_fp16 = square(x = sub_10_cast_fp16)[name = tensor("square_5_cast_fp16")]; + tensor reduce_mean_17_axes_0 = const()[name = tensor("reduce_mean_17_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_17_keep_dims_0 = const()[name = tensor("reduce_mean_17_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_17_cast_fp16 = reduce_mean(axes = reduce_mean_17_axes_0, keep_dims = reduce_mean_17_keep_dims_0, x = square_5_cast_fp16)[name = tensor("reduce_mean_17_cast_fp16")]; + tensor add_10_y_0_to_fp16 = const()[name = tensor("add_10_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_10_cast_fp16 = add(x = reduce_mean_17_cast_fp16, y = add_10_y_0_to_fp16)[name = tensor("add_10_cast_fp16")]; + tensor sqrt_5_cast_fp16 = sqrt(x = add_10_cast_fp16)[name = tensor("sqrt_5_cast_fp16")]; + tensor real_div_5_cast_fp16 = real_div(x = sub_10_cast_fp16, y = sqrt_5_cast_fp16)[name = tensor("real_div_5_cast_fp16")]; + tensor reshape_21_shape_0 = const()[name = tensor("reshape_21_shape_0"), val = tensor([1, 256, 192, 320])]; + tensor reshape_21_cast_fp16 = reshape(shape = reshape_21_shape_0, x = real_div_5_cast_fp16)[name = tensor("reshape_21_cast_fp16")]; + tensor add_11_mean_0_to_fp16 = const()[name = tensor("add_11_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2078144)))]; + tensor add_11_variance_0_to_fp16 = const()[name = tensor("add_11_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2078720)))]; + tensor add_11_gamma_0_to_fp16 = const()[name = tensor("add_11_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2079296)))]; + tensor add_11_beta_0_to_fp16 = const()[name = tensor("add_11_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2079872)))]; + tensor add_11_epsilon_0_to_fp16 = const()[name = tensor("add_11_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_11_cast_fp16 = batch_norm(beta = add_11_beta_0_to_fp16, epsilon = add_11_epsilon_0_to_fp16, gamma = add_11_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_21_cast_fp16)[name = tensor("add_11_cast_fp16")]; + tensor input_29_cast_fp16 = silu(x = add_11_cast_fp16)[name = tensor("input_29_cast_fp16")]; + tensor var_143 = const()[name = tensor("op_143"), val = tensor([1, 1])]; + tensor var_145 = const()[name = tensor("op_145"), val = tensor([1, 1])]; + tensor hidden_states_21_pad_type_0 = const()[name = tensor("hidden_states_21_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_21_pad_0 = const()[name = tensor("hidden_states_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_1_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_1_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2080448)))]; + tensor encoder_down_blocks_1_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_1_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3260160)))]; + tensor hidden_states_21_cast_fp16 = conv(bias = encoder_down_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = var_145, groups = var_15, pad = hidden_states_21_pad_0, pad_type = hidden_states_21_pad_type_0, strides = var_143, weight = encoder_down_blocks_1_resnets_0_conv2_weight_to_fp16, x = input_29_cast_fp16)[name = tensor("hidden_states_21_cast_fp16")]; + tensor var_150 = const()[name = tensor("op_150"), val = tensor([1, 1])]; + tensor var_152 = const()[name = tensor("op_152"), val = tensor([1, 1])]; + tensor input_tensor_1_pad_type_0 = const()[name = tensor("input_tensor_1_pad_type_0"), val = tensor("custom")]; + tensor input_tensor_1_pad_0 = const()[name = tensor("input_tensor_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor encoder_down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3260736)))]; + tensor encoder_down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3326336)))]; + tensor input_tensor_1_cast_fp16 = conv(bias = encoder_down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_152, groups = var_15, pad = input_tensor_1_pad_0, pad_type = input_tensor_1_pad_type_0, strides = var_150, weight = encoder_down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16, x = input_21_cast_fp16)[name = tensor("input_tensor_1_cast_fp16")]; + tensor var_155_cast_fp16 = add(x = input_tensor_1_cast_fp16, y = hidden_states_21_cast_fp16)[name = tensor("op_155_cast_fp16")]; + tensor reshape_24_shape_0 = const()[name = tensor("reshape_24_shape_0"), val = tensor([1, 32, 8, 192, 320])]; + tensor reshape_24_cast_fp16 = reshape(shape = reshape_24_shape_0, x = var_155_cast_fp16)[name = tensor("reshape_24_cast_fp16")]; + tensor reduce_mean_18_axes_0 = const()[name = tensor("reduce_mean_18_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_18_keep_dims_0 = const()[name = tensor("reduce_mean_18_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_18_cast_fp16 = reduce_mean(axes = reduce_mean_18_axes_0, keep_dims = reduce_mean_18_keep_dims_0, x = reshape_24_cast_fp16)[name = tensor("reduce_mean_18_cast_fp16")]; + tensor sub_12_cast_fp16 = sub(x = reshape_24_cast_fp16, y = reduce_mean_18_cast_fp16)[name = tensor("sub_12_cast_fp16")]; + tensor square_6_cast_fp16 = square(x = sub_12_cast_fp16)[name = tensor("square_6_cast_fp16")]; + tensor reduce_mean_20_axes_0 = const()[name = tensor("reduce_mean_20_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_20_keep_dims_0 = const()[name = tensor("reduce_mean_20_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_20_cast_fp16 = reduce_mean(axes = reduce_mean_20_axes_0, keep_dims = reduce_mean_20_keep_dims_0, x = square_6_cast_fp16)[name = tensor("reduce_mean_20_cast_fp16")]; + tensor add_12_y_0_to_fp16 = const()[name = tensor("add_12_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_12_cast_fp16 = add(x = reduce_mean_20_cast_fp16, y = add_12_y_0_to_fp16)[name = tensor("add_12_cast_fp16")]; + tensor sqrt_6_cast_fp16 = sqrt(x = add_12_cast_fp16)[name = tensor("sqrt_6_cast_fp16")]; + tensor real_div_6_cast_fp16 = real_div(x = sub_12_cast_fp16, y = sqrt_6_cast_fp16)[name = tensor("real_div_6_cast_fp16")]; + tensor reshape_25_shape_0 = const()[name = tensor("reshape_25_shape_0"), val = tensor([1, 256, 192, 320])]; + tensor reshape_25_cast_fp16 = reshape(shape = reshape_25_shape_0, x = real_div_6_cast_fp16)[name = tensor("reshape_25_cast_fp16")]; + tensor add_13_gamma_0_to_fp16 = const()[name = tensor("add_13_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3326912)))]; + tensor add_13_beta_0_to_fp16 = const()[name = tensor("add_13_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3327488)))]; + tensor add_13_epsilon_0_to_fp16 = const()[name = tensor("add_13_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_13_cast_fp16 = batch_norm(beta = add_13_beta_0_to_fp16, epsilon = add_13_epsilon_0_to_fp16, gamma = add_13_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_25_cast_fp16)[name = tensor("add_13_cast_fp16")]; + tensor hidden_states_23_cast_fp16 = silu(x = add_13_cast_fp16)[name = tensor("hidden_states_23_cast_fp16")]; + tensor var_168 = const()[name = tensor("op_168"), val = tensor([1, 1])]; + tensor var_170 = const()[name = tensor("op_170"), val = tensor([1, 1])]; + tensor input_35_pad_type_0 = const()[name = tensor("input_35_pad_type_0"), val = tensor("custom")]; + tensor input_35_pad_0 = const()[name = tensor("input_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_1_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_1_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3328064)))]; + tensor encoder_down_blocks_1_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_1_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4507776)))]; + tensor input_35_cast_fp16 = conv(bias = encoder_down_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = var_170, groups = var_15, pad = input_35_pad_0, pad_type = input_35_pad_type_0, strides = var_168, weight = encoder_down_blocks_1_resnets_1_conv1_weight_to_fp16, x = hidden_states_23_cast_fp16)[name = tensor("input_35_cast_fp16")]; + tensor reshape_28_shape_0 = const()[name = tensor("reshape_28_shape_0"), val = tensor([1, 32, 8, 192, 320])]; + tensor reshape_28_cast_fp16 = reshape(shape = reshape_28_shape_0, x = input_35_cast_fp16)[name = tensor("reshape_28_cast_fp16")]; + tensor reduce_mean_21_axes_0 = const()[name = tensor("reduce_mean_21_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_21_keep_dims_0 = const()[name = tensor("reduce_mean_21_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_21_cast_fp16 = reduce_mean(axes = reduce_mean_21_axes_0, keep_dims = reduce_mean_21_keep_dims_0, x = reshape_28_cast_fp16)[name = tensor("reduce_mean_21_cast_fp16")]; + tensor sub_14_cast_fp16 = sub(x = reshape_28_cast_fp16, y = reduce_mean_21_cast_fp16)[name = tensor("sub_14_cast_fp16")]; + tensor square_7_cast_fp16 = square(x = sub_14_cast_fp16)[name = tensor("square_7_cast_fp16")]; + tensor reduce_mean_23_axes_0 = const()[name = tensor("reduce_mean_23_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_23_keep_dims_0 = const()[name = tensor("reduce_mean_23_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_23_cast_fp16 = reduce_mean(axes = reduce_mean_23_axes_0, keep_dims = reduce_mean_23_keep_dims_0, x = square_7_cast_fp16)[name = tensor("reduce_mean_23_cast_fp16")]; + tensor add_14_y_0_to_fp16 = const()[name = tensor("add_14_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_14_cast_fp16 = add(x = reduce_mean_23_cast_fp16, y = add_14_y_0_to_fp16)[name = tensor("add_14_cast_fp16")]; + tensor sqrt_7_cast_fp16 = sqrt(x = add_14_cast_fp16)[name = tensor("sqrt_7_cast_fp16")]; + tensor real_div_7_cast_fp16 = real_div(x = sub_14_cast_fp16, y = sqrt_7_cast_fp16)[name = tensor("real_div_7_cast_fp16")]; + tensor reshape_29_shape_0 = const()[name = tensor("reshape_29_shape_0"), val = tensor([1, 256, 192, 320])]; + tensor reshape_29_cast_fp16 = reshape(shape = reshape_29_shape_0, x = real_div_7_cast_fp16)[name = tensor("reshape_29_cast_fp16")]; + tensor add_15_gamma_0_to_fp16 = const()[name = tensor("add_15_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4508352)))]; + tensor add_15_beta_0_to_fp16 = const()[name = tensor("add_15_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4508928)))]; + tensor add_15_epsilon_0_to_fp16 = const()[name = tensor("add_15_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_15_cast_fp16 = batch_norm(beta = add_15_beta_0_to_fp16, epsilon = add_15_epsilon_0_to_fp16, gamma = add_15_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_29_cast_fp16)[name = tensor("add_15_cast_fp16")]; + tensor input_39_cast_fp16 = silu(x = add_15_cast_fp16)[name = tensor("input_39_cast_fp16")]; + tensor var_180 = const()[name = tensor("op_180"), val = tensor([1, 1])]; + tensor var_182 = const()[name = tensor("op_182"), val = tensor([1, 1])]; + tensor hidden_states_27_pad_type_0 = const()[name = tensor("hidden_states_27_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_27_pad_0 = const()[name = tensor("hidden_states_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_1_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_1_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4509504)))]; + tensor encoder_down_blocks_1_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_1_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5689216)))]; + tensor hidden_states_27_cast_fp16 = conv(bias = encoder_down_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = var_182, groups = var_15, pad = hidden_states_27_pad_0, pad_type = hidden_states_27_pad_type_0, strides = var_180, weight = encoder_down_blocks_1_resnets_1_conv2_weight_to_fp16, x = input_39_cast_fp16)[name = tensor("hidden_states_27_cast_fp16")]; + tensor var_185_cast_fp16 = add(x = var_155_cast_fp16, y = hidden_states_27_cast_fp16)[name = tensor("op_185_cast_fp16")]; + tensor hidden_states_31_pad_0 = const()[name = tensor("hidden_states_31_pad_0"), val = tensor([0, 0, 0, 0, 0, 1, 0, 1])]; + tensor hidden_states_31_mode_0 = const()[name = tensor("hidden_states_31_mode_0"), val = tensor("constant")]; + tensor hidden_states_31_constant_val_0_to_fp16 = const()[name = tensor("hidden_states_31_constant_val_0_to_fp16"), val = tensor(0x0p+0)]; + tensor hidden_states_31_cast_fp16 = pad(constant_val = hidden_states_31_constant_val_0_to_fp16, mode = hidden_states_31_mode_0, pad = hidden_states_31_pad_0, x = var_185_cast_fp16)[name = tensor("hidden_states_31_cast_fp16")]; + tensor var_193 = const()[name = tensor("op_193"), val = tensor([2, 2])]; + tensor var_195 = const()[name = tensor("op_195"), val = tensor([1, 1])]; + tensor input_41_pad_type_0 = const()[name = tensor("input_41_pad_type_0"), val = tensor("custom")]; + tensor input_41_pad_0 = const()[name = tensor("input_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor encoder_down_blocks_1_downsamplers_0_conv_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_1_downsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5689792)))]; + tensor encoder_down_blocks_1_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_1_downsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6869504)))]; + tensor input_41_cast_fp16 = conv(bias = encoder_down_blocks_1_downsamplers_0_conv_bias_to_fp16, dilations = var_195, groups = var_15, pad = input_41_pad_0, pad_type = input_41_pad_type_0, strides = var_193, weight = encoder_down_blocks_1_downsamplers_0_conv_weight_to_fp16, x = hidden_states_31_cast_fp16)[name = tensor("input_41_cast_fp16")]; + tensor reshape_32_shape_0 = const()[name = tensor("reshape_32_shape_0"), val = tensor([1, 32, 8, 96, 160])]; + tensor reshape_32_cast_fp16 = reshape(shape = reshape_32_shape_0, x = input_41_cast_fp16)[name = tensor("reshape_32_cast_fp16")]; + tensor reduce_mean_24_axes_0 = const()[name = tensor("reduce_mean_24_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_24_keep_dims_0 = const()[name = tensor("reduce_mean_24_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_24_cast_fp16 = reduce_mean(axes = reduce_mean_24_axes_0, keep_dims = reduce_mean_24_keep_dims_0, x = reshape_32_cast_fp16)[name = tensor("reduce_mean_24_cast_fp16")]; + tensor sub_16_cast_fp16 = sub(x = reshape_32_cast_fp16, y = reduce_mean_24_cast_fp16)[name = tensor("sub_16_cast_fp16")]; + tensor square_8_cast_fp16 = square(x = sub_16_cast_fp16)[name = tensor("square_8_cast_fp16")]; + tensor reduce_mean_26_axes_0 = const()[name = tensor("reduce_mean_26_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_26_keep_dims_0 = const()[name = tensor("reduce_mean_26_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_26_cast_fp16 = reduce_mean(axes = reduce_mean_26_axes_0, keep_dims = reduce_mean_26_keep_dims_0, x = square_8_cast_fp16)[name = tensor("reduce_mean_26_cast_fp16")]; + tensor add_16_y_0_to_fp16 = const()[name = tensor("add_16_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_16_cast_fp16 = add(x = reduce_mean_26_cast_fp16, y = add_16_y_0_to_fp16)[name = tensor("add_16_cast_fp16")]; + tensor sqrt_8_cast_fp16 = sqrt(x = add_16_cast_fp16)[name = tensor("sqrt_8_cast_fp16")]; + tensor real_div_8_cast_fp16 = real_div(x = sub_16_cast_fp16, y = sqrt_8_cast_fp16)[name = tensor("real_div_8_cast_fp16")]; + tensor reshape_33_shape_0 = const()[name = tensor("reshape_33_shape_0"), val = tensor([1, 256, 96, 160])]; + tensor reshape_33_cast_fp16 = reshape(shape = reshape_33_shape_0, x = real_div_8_cast_fp16)[name = tensor("reshape_33_cast_fp16")]; + tensor add_17_gamma_0_to_fp16 = const()[name = tensor("add_17_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6870080)))]; + tensor add_17_beta_0_to_fp16 = const()[name = tensor("add_17_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6870656)))]; + tensor add_17_epsilon_0_to_fp16 = const()[name = tensor("add_17_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_17_cast_fp16 = batch_norm(beta = add_17_beta_0_to_fp16, epsilon = add_17_epsilon_0_to_fp16, gamma = add_17_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_33_cast_fp16)[name = tensor("add_17_cast_fp16")]; + tensor hidden_states_33_cast_fp16 = silu(x = add_17_cast_fp16)[name = tensor("hidden_states_33_cast_fp16")]; + tensor var_215 = const()[name = tensor("op_215"), val = tensor([1, 1])]; + tensor var_217 = const()[name = tensor("op_217"), val = tensor([1, 1])]; + tensor input_45_pad_type_0 = const()[name = tensor("input_45_pad_type_0"), val = tensor("custom")]; + tensor input_45_pad_0 = const()[name = tensor("input_45_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_2_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_2_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6871232)))]; + tensor encoder_down_blocks_2_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_2_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9230592)))]; + tensor input_45_cast_fp16 = conv(bias = encoder_down_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = var_217, groups = var_15, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = var_215, weight = encoder_down_blocks_2_resnets_0_conv1_weight_to_fp16, x = hidden_states_33_cast_fp16)[name = tensor("input_45_cast_fp16")]; + tensor reshape_36_shape_0 = const()[name = tensor("reshape_36_shape_0"), val = tensor([1, 32, 16, 96, 160])]; + tensor reshape_36_cast_fp16 = reshape(shape = reshape_36_shape_0, x = input_45_cast_fp16)[name = tensor("reshape_36_cast_fp16")]; + tensor reduce_mean_27_axes_0 = const()[name = tensor("reduce_mean_27_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_27_keep_dims_0 = const()[name = tensor("reduce_mean_27_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_27_cast_fp16 = reduce_mean(axes = reduce_mean_27_axes_0, keep_dims = reduce_mean_27_keep_dims_0, x = reshape_36_cast_fp16)[name = tensor("reduce_mean_27_cast_fp16")]; + tensor sub_18_cast_fp16 = sub(x = reshape_36_cast_fp16, y = reduce_mean_27_cast_fp16)[name = tensor("sub_18_cast_fp16")]; + tensor square_9_cast_fp16 = square(x = sub_18_cast_fp16)[name = tensor("square_9_cast_fp16")]; + tensor reduce_mean_29_axes_0 = const()[name = tensor("reduce_mean_29_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_29_keep_dims_0 = const()[name = tensor("reduce_mean_29_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_29_cast_fp16 = reduce_mean(axes = reduce_mean_29_axes_0, keep_dims = reduce_mean_29_keep_dims_0, x = square_9_cast_fp16)[name = tensor("reduce_mean_29_cast_fp16")]; + tensor add_18_y_0_to_fp16 = const()[name = tensor("add_18_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_18_cast_fp16 = add(x = reduce_mean_29_cast_fp16, y = add_18_y_0_to_fp16)[name = tensor("add_18_cast_fp16")]; + tensor sqrt_9_cast_fp16 = sqrt(x = add_18_cast_fp16)[name = tensor("sqrt_9_cast_fp16")]; + tensor real_div_9_cast_fp16 = real_div(x = sub_18_cast_fp16, y = sqrt_9_cast_fp16)[name = tensor("real_div_9_cast_fp16")]; + tensor reshape_37_shape_0 = const()[name = tensor("reshape_37_shape_0"), val = tensor([1, 512, 96, 160])]; + tensor reshape_37_cast_fp16 = reshape(shape = reshape_37_shape_0, x = real_div_9_cast_fp16)[name = tensor("reshape_37_cast_fp16")]; + tensor add_19_mean_0_to_fp16 = const()[name = tensor("add_19_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9231680)))]; + tensor add_19_variance_0_to_fp16 = const()[name = tensor("add_19_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9232768)))]; + tensor add_19_gamma_0_to_fp16 = const()[name = tensor("add_19_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9233856)))]; + tensor add_19_beta_0_to_fp16 = const()[name = tensor("add_19_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9234944)))]; + tensor add_19_epsilon_0_to_fp16 = const()[name = tensor("add_19_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_19_cast_fp16 = batch_norm(beta = add_19_beta_0_to_fp16, epsilon = add_19_epsilon_0_to_fp16, gamma = add_19_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_37_cast_fp16)[name = tensor("add_19_cast_fp16")]; + tensor input_49_cast_fp16 = silu(x = add_19_cast_fp16)[name = tensor("input_49_cast_fp16")]; + tensor var_227 = const()[name = tensor("op_227"), val = tensor([1, 1])]; + tensor var_229 = const()[name = tensor("op_229"), val = tensor([1, 1])]; + tensor hidden_states_37_pad_type_0 = const()[name = tensor("hidden_states_37_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_37_pad_0 = const()[name = tensor("hidden_states_37_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_2_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_2_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9236032)))]; + tensor encoder_down_blocks_2_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_2_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13954688)))]; + tensor hidden_states_37_cast_fp16 = conv(bias = encoder_down_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = var_229, groups = var_15, pad = hidden_states_37_pad_0, pad_type = hidden_states_37_pad_type_0, strides = var_227, weight = encoder_down_blocks_2_resnets_0_conv2_weight_to_fp16, x = input_49_cast_fp16)[name = tensor("hidden_states_37_cast_fp16")]; + tensor var_234 = const()[name = tensor("op_234"), val = tensor([1, 1])]; + tensor var_236 = const()[name = tensor("op_236"), val = tensor([1, 1])]; + tensor input_tensor_pad_type_0 = const()[name = tensor("input_tensor_pad_type_0"), val = tensor("custom")]; + tensor input_tensor_pad_0 = const()[name = tensor("input_tensor_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor encoder_down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13955776)))]; + tensor encoder_down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14217984)))]; + tensor input_tensor_cast_fp16 = conv(bias = encoder_down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_236, groups = var_15, pad = input_tensor_pad_0, pad_type = input_tensor_pad_type_0, strides = var_234, weight = encoder_down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16, x = input_41_cast_fp16)[name = tensor("input_tensor_cast_fp16")]; + tensor var_239_cast_fp16 = add(x = input_tensor_cast_fp16, y = hidden_states_37_cast_fp16)[name = tensor("op_239_cast_fp16")]; + tensor reshape_40_shape_0 = const()[name = tensor("reshape_40_shape_0"), val = tensor([1, 32, 16, 96, 160])]; + tensor reshape_40_cast_fp16 = reshape(shape = reshape_40_shape_0, x = var_239_cast_fp16)[name = tensor("reshape_40_cast_fp16")]; + tensor reduce_mean_30_axes_0 = const()[name = tensor("reduce_mean_30_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_30_keep_dims_0 = const()[name = tensor("reduce_mean_30_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_30_cast_fp16 = reduce_mean(axes = reduce_mean_30_axes_0, keep_dims = reduce_mean_30_keep_dims_0, x = reshape_40_cast_fp16)[name = tensor("reduce_mean_30_cast_fp16")]; + tensor sub_20_cast_fp16 = sub(x = reshape_40_cast_fp16, y = reduce_mean_30_cast_fp16)[name = tensor("sub_20_cast_fp16")]; + tensor square_10_cast_fp16 = square(x = sub_20_cast_fp16)[name = tensor("square_10_cast_fp16")]; + tensor reduce_mean_32_axes_0 = const()[name = tensor("reduce_mean_32_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_32_keep_dims_0 = const()[name = tensor("reduce_mean_32_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_32_cast_fp16 = reduce_mean(axes = reduce_mean_32_axes_0, keep_dims = reduce_mean_32_keep_dims_0, x = square_10_cast_fp16)[name = tensor("reduce_mean_32_cast_fp16")]; + tensor add_20_y_0_to_fp16 = const()[name = tensor("add_20_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_20_cast_fp16 = add(x = reduce_mean_32_cast_fp16, y = add_20_y_0_to_fp16)[name = tensor("add_20_cast_fp16")]; + tensor sqrt_10_cast_fp16 = sqrt(x = add_20_cast_fp16)[name = tensor("sqrt_10_cast_fp16")]; + tensor real_div_10_cast_fp16 = real_div(x = sub_20_cast_fp16, y = sqrt_10_cast_fp16)[name = tensor("real_div_10_cast_fp16")]; + tensor reshape_41_shape_0 = const()[name = tensor("reshape_41_shape_0"), val = tensor([1, 512, 96, 160])]; + tensor reshape_41_cast_fp16 = reshape(shape = reshape_41_shape_0, x = real_div_10_cast_fp16)[name = tensor("reshape_41_cast_fp16")]; + tensor add_21_gamma_0_to_fp16 = const()[name = tensor("add_21_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14219072)))]; + tensor add_21_beta_0_to_fp16 = const()[name = tensor("add_21_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14220160)))]; + tensor add_21_epsilon_0_to_fp16 = const()[name = tensor("add_21_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_21_cast_fp16 = batch_norm(beta = add_21_beta_0_to_fp16, epsilon = add_21_epsilon_0_to_fp16, gamma = add_21_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_41_cast_fp16)[name = tensor("add_21_cast_fp16")]; + tensor hidden_states_39_cast_fp16 = silu(x = add_21_cast_fp16)[name = tensor("hidden_states_39_cast_fp16")]; + tensor var_252 = const()[name = tensor("op_252"), val = tensor([1, 1])]; + tensor var_254 = const()[name = tensor("op_254"), val = tensor([1, 1])]; + tensor input_55_pad_type_0 = const()[name = tensor("input_55_pad_type_0"), val = tensor("custom")]; + tensor input_55_pad_0 = const()[name = tensor("input_55_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_2_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_2_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14221248)))]; + tensor encoder_down_blocks_2_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_2_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18939904)))]; + tensor input_55_cast_fp16 = conv(bias = encoder_down_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = var_254, groups = var_15, pad = input_55_pad_0, pad_type = input_55_pad_type_0, strides = var_252, weight = encoder_down_blocks_2_resnets_1_conv1_weight_to_fp16, x = hidden_states_39_cast_fp16)[name = tensor("input_55_cast_fp16")]; + tensor reshape_44_shape_0 = const()[name = tensor("reshape_44_shape_0"), val = tensor([1, 32, 16, 96, 160])]; + tensor reshape_44_cast_fp16 = reshape(shape = reshape_44_shape_0, x = input_55_cast_fp16)[name = tensor("reshape_44_cast_fp16")]; + tensor reduce_mean_33_axes_0 = const()[name = tensor("reduce_mean_33_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_33_keep_dims_0 = const()[name = tensor("reduce_mean_33_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_33_cast_fp16 = reduce_mean(axes = reduce_mean_33_axes_0, keep_dims = reduce_mean_33_keep_dims_0, x = reshape_44_cast_fp16)[name = tensor("reduce_mean_33_cast_fp16")]; + tensor sub_22_cast_fp16 = sub(x = reshape_44_cast_fp16, y = reduce_mean_33_cast_fp16)[name = tensor("sub_22_cast_fp16")]; + tensor square_11_cast_fp16 = square(x = sub_22_cast_fp16)[name = tensor("square_11_cast_fp16")]; + tensor reduce_mean_35_axes_0 = const()[name = tensor("reduce_mean_35_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_35_keep_dims_0 = const()[name = tensor("reduce_mean_35_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_35_cast_fp16 = reduce_mean(axes = reduce_mean_35_axes_0, keep_dims = reduce_mean_35_keep_dims_0, x = square_11_cast_fp16)[name = tensor("reduce_mean_35_cast_fp16")]; + tensor add_22_y_0_to_fp16 = const()[name = tensor("add_22_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_22_cast_fp16 = add(x = reduce_mean_35_cast_fp16, y = add_22_y_0_to_fp16)[name = tensor("add_22_cast_fp16")]; + tensor sqrt_11_cast_fp16 = sqrt(x = add_22_cast_fp16)[name = tensor("sqrt_11_cast_fp16")]; + tensor real_div_11_cast_fp16 = real_div(x = sub_22_cast_fp16, y = sqrt_11_cast_fp16)[name = tensor("real_div_11_cast_fp16")]; + tensor reshape_45_shape_0 = const()[name = tensor("reshape_45_shape_0"), val = tensor([1, 512, 96, 160])]; + tensor reshape_45_cast_fp16 = reshape(shape = reshape_45_shape_0, x = real_div_11_cast_fp16)[name = tensor("reshape_45_cast_fp16")]; + tensor add_23_gamma_0_to_fp16 = const()[name = tensor("add_23_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18940992)))]; + tensor add_23_beta_0_to_fp16 = const()[name = tensor("add_23_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18942080)))]; + tensor add_23_epsilon_0_to_fp16 = const()[name = tensor("add_23_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_23_cast_fp16 = batch_norm(beta = add_23_beta_0_to_fp16, epsilon = add_23_epsilon_0_to_fp16, gamma = add_23_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_45_cast_fp16)[name = tensor("add_23_cast_fp16")]; + tensor input_59_cast_fp16 = silu(x = add_23_cast_fp16)[name = tensor("input_59_cast_fp16")]; + tensor var_264 = const()[name = tensor("op_264"), val = tensor([1, 1])]; + tensor var_266 = const()[name = tensor("op_266"), val = tensor([1, 1])]; + tensor hidden_states_43_pad_type_0 = const()[name = tensor("hidden_states_43_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_43_pad_0 = const()[name = tensor("hidden_states_43_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_2_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_2_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18943168)))]; + tensor encoder_down_blocks_2_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_2_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23661824)))]; + tensor hidden_states_43_cast_fp16 = conv(bias = encoder_down_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = var_266, groups = var_15, pad = hidden_states_43_pad_0, pad_type = hidden_states_43_pad_type_0, strides = var_264, weight = encoder_down_blocks_2_resnets_1_conv2_weight_to_fp16, x = input_59_cast_fp16)[name = tensor("hidden_states_43_cast_fp16")]; + tensor var_269_cast_fp16 = add(x = var_239_cast_fp16, y = hidden_states_43_cast_fp16)[name = tensor("op_269_cast_fp16")]; + tensor hidden_states_47_pad_0 = const()[name = tensor("hidden_states_47_pad_0"), val = tensor([0, 0, 0, 0, 0, 1, 0, 1])]; + tensor hidden_states_47_mode_0 = const()[name = tensor("hidden_states_47_mode_0"), val = tensor("constant")]; + tensor hidden_states_47_constant_val_0_to_fp16 = const()[name = tensor("hidden_states_47_constant_val_0_to_fp16"), val = tensor(0x0p+0)]; + tensor hidden_states_47_cast_fp16 = pad(constant_val = hidden_states_47_constant_val_0_to_fp16, mode = hidden_states_47_mode_0, pad = hidden_states_47_pad_0, x = var_269_cast_fp16)[name = tensor("hidden_states_47_cast_fp16")]; + tensor var_277 = const()[name = tensor("op_277"), val = tensor([2, 2])]; + tensor var_279 = const()[name = tensor("op_279"), val = tensor([1, 1])]; + tensor input_61_pad_type_0 = const()[name = tensor("input_61_pad_type_0"), val = tensor("custom")]; + tensor input_61_pad_0 = const()[name = tensor("input_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor encoder_down_blocks_2_downsamplers_0_conv_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_2_downsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23662912)))]; + tensor encoder_down_blocks_2_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_2_downsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28381568)))]; + tensor input_61_cast_fp16 = conv(bias = encoder_down_blocks_2_downsamplers_0_conv_bias_to_fp16, dilations = var_279, groups = var_15, pad = input_61_pad_0, pad_type = input_61_pad_type_0, strides = var_277, weight = encoder_down_blocks_2_downsamplers_0_conv_weight_to_fp16, x = hidden_states_47_cast_fp16)[name = tensor("input_61_cast_fp16")]; + tensor reshape_48_shape_0 = const()[name = tensor("reshape_48_shape_0"), val = tensor([1, 32, 16, 48, 80])]; + tensor reshape_48_cast_fp16 = reshape(shape = reshape_48_shape_0, x = input_61_cast_fp16)[name = tensor("reshape_48_cast_fp16")]; + tensor reduce_mean_36_axes_0 = const()[name = tensor("reduce_mean_36_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_36_keep_dims_0 = const()[name = tensor("reduce_mean_36_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_36_cast_fp16 = reduce_mean(axes = reduce_mean_36_axes_0, keep_dims = reduce_mean_36_keep_dims_0, x = reshape_48_cast_fp16)[name = tensor("reduce_mean_36_cast_fp16")]; + tensor sub_24_cast_fp16 = sub(x = reshape_48_cast_fp16, y = reduce_mean_36_cast_fp16)[name = tensor("sub_24_cast_fp16")]; + tensor square_12_cast_fp16 = square(x = sub_24_cast_fp16)[name = tensor("square_12_cast_fp16")]; + tensor reduce_mean_38_axes_0 = const()[name = tensor("reduce_mean_38_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_38_keep_dims_0 = const()[name = tensor("reduce_mean_38_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_38_cast_fp16 = reduce_mean(axes = reduce_mean_38_axes_0, keep_dims = reduce_mean_38_keep_dims_0, x = square_12_cast_fp16)[name = tensor("reduce_mean_38_cast_fp16")]; + tensor add_24_y_0_to_fp16 = const()[name = tensor("add_24_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_24_cast_fp16 = add(x = reduce_mean_38_cast_fp16, y = add_24_y_0_to_fp16)[name = tensor("add_24_cast_fp16")]; + tensor sqrt_12_cast_fp16 = sqrt(x = add_24_cast_fp16)[name = tensor("sqrt_12_cast_fp16")]; + tensor real_div_12_cast_fp16 = real_div(x = sub_24_cast_fp16, y = sqrt_12_cast_fp16)[name = tensor("real_div_12_cast_fp16")]; + tensor reshape_49_shape_0 = const()[name = tensor("reshape_49_shape_0"), val = tensor([1, 512, 48, 80])]; + tensor reshape_49_cast_fp16 = reshape(shape = reshape_49_shape_0, x = real_div_12_cast_fp16)[name = tensor("reshape_49_cast_fp16")]; + tensor add_25_gamma_0_to_fp16 = const()[name = tensor("add_25_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28382656)))]; + tensor add_25_beta_0_to_fp16 = const()[name = tensor("add_25_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28383744)))]; + tensor add_25_epsilon_0_to_fp16 = const()[name = tensor("add_25_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_25_cast_fp16 = batch_norm(beta = add_25_beta_0_to_fp16, epsilon = add_25_epsilon_0_to_fp16, gamma = add_25_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_49_cast_fp16)[name = tensor("add_25_cast_fp16")]; + tensor hidden_states_49_cast_fp16 = silu(x = add_25_cast_fp16)[name = tensor("hidden_states_49_cast_fp16")]; + tensor var_296 = const()[name = tensor("op_296"), val = tensor([1, 1])]; + tensor var_298 = const()[name = tensor("op_298"), val = tensor([1, 1])]; + tensor input_65_pad_type_0 = const()[name = tensor("input_65_pad_type_0"), val = tensor("custom")]; + tensor input_65_pad_0 = const()[name = tensor("input_65_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_3_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_3_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28384832)))]; + tensor encoder_down_blocks_3_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_3_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33103488)))]; + tensor input_65_cast_fp16 = conv(bias = encoder_down_blocks_3_resnets_0_conv1_bias_to_fp16, dilations = var_298, groups = var_15, pad = input_65_pad_0, pad_type = input_65_pad_type_0, strides = var_296, weight = encoder_down_blocks_3_resnets_0_conv1_weight_to_fp16, x = hidden_states_49_cast_fp16)[name = tensor("input_65_cast_fp16")]; + tensor reshape_52_shape_0 = const()[name = tensor("reshape_52_shape_0"), val = tensor([1, 32, 16, 48, 80])]; + tensor reshape_52_cast_fp16 = reshape(shape = reshape_52_shape_0, x = input_65_cast_fp16)[name = tensor("reshape_52_cast_fp16")]; + tensor reduce_mean_39_axes_0 = const()[name = tensor("reduce_mean_39_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_39_keep_dims_0 = const()[name = tensor("reduce_mean_39_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_39_cast_fp16 = reduce_mean(axes = reduce_mean_39_axes_0, keep_dims = reduce_mean_39_keep_dims_0, x = reshape_52_cast_fp16)[name = tensor("reduce_mean_39_cast_fp16")]; + tensor sub_26_cast_fp16 = sub(x = reshape_52_cast_fp16, y = reduce_mean_39_cast_fp16)[name = tensor("sub_26_cast_fp16")]; + tensor square_13_cast_fp16 = square(x = sub_26_cast_fp16)[name = tensor("square_13_cast_fp16")]; + tensor reduce_mean_41_axes_0 = const()[name = tensor("reduce_mean_41_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_41_keep_dims_0 = const()[name = tensor("reduce_mean_41_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_41_cast_fp16 = reduce_mean(axes = reduce_mean_41_axes_0, keep_dims = reduce_mean_41_keep_dims_0, x = square_13_cast_fp16)[name = tensor("reduce_mean_41_cast_fp16")]; + tensor add_26_y_0_to_fp16 = const()[name = tensor("add_26_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_26_cast_fp16 = add(x = reduce_mean_41_cast_fp16, y = add_26_y_0_to_fp16)[name = tensor("add_26_cast_fp16")]; + tensor sqrt_13_cast_fp16 = sqrt(x = add_26_cast_fp16)[name = tensor("sqrt_13_cast_fp16")]; + tensor real_div_13_cast_fp16 = real_div(x = sub_26_cast_fp16, y = sqrt_13_cast_fp16)[name = tensor("real_div_13_cast_fp16")]; + tensor reshape_53_shape_0 = const()[name = tensor("reshape_53_shape_0"), val = tensor([1, 512, 48, 80])]; + tensor reshape_53_cast_fp16 = reshape(shape = reshape_53_shape_0, x = real_div_13_cast_fp16)[name = tensor("reshape_53_cast_fp16")]; + tensor add_27_gamma_0_to_fp16 = const()[name = tensor("add_27_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33104576)))]; + tensor add_27_beta_0_to_fp16 = const()[name = tensor("add_27_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33105664)))]; + tensor add_27_epsilon_0_to_fp16 = const()[name = tensor("add_27_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_27_cast_fp16 = batch_norm(beta = add_27_beta_0_to_fp16, epsilon = add_27_epsilon_0_to_fp16, gamma = add_27_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_53_cast_fp16)[name = tensor("add_27_cast_fp16")]; + tensor input_69_cast_fp16 = silu(x = add_27_cast_fp16)[name = tensor("input_69_cast_fp16")]; + tensor var_308 = const()[name = tensor("op_308"), val = tensor([1, 1])]; + tensor var_310 = const()[name = tensor("op_310"), val = tensor([1, 1])]; + tensor hidden_states_53_pad_type_0 = const()[name = tensor("hidden_states_53_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_53_pad_0 = const()[name = tensor("hidden_states_53_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_3_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_3_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33106752)))]; + tensor encoder_down_blocks_3_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_3_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37825408)))]; + tensor hidden_states_53_cast_fp16 = conv(bias = encoder_down_blocks_3_resnets_0_conv2_bias_to_fp16, dilations = var_310, groups = var_15, pad = hidden_states_53_pad_0, pad_type = hidden_states_53_pad_type_0, strides = var_308, weight = encoder_down_blocks_3_resnets_0_conv2_weight_to_fp16, x = input_69_cast_fp16)[name = tensor("hidden_states_53_cast_fp16")]; + tensor var_313_cast_fp16 = add(x = input_61_cast_fp16, y = hidden_states_53_cast_fp16)[name = tensor("op_313_cast_fp16")]; + tensor reshape_56_shape_0 = const()[name = tensor("reshape_56_shape_0"), val = tensor([1, 32, 16, 48, 80])]; + tensor reshape_56_cast_fp16 = reshape(shape = reshape_56_shape_0, x = var_313_cast_fp16)[name = tensor("reshape_56_cast_fp16")]; + tensor reduce_mean_42_axes_0 = const()[name = tensor("reduce_mean_42_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_42_keep_dims_0 = const()[name = tensor("reduce_mean_42_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_42_cast_fp16 = reduce_mean(axes = reduce_mean_42_axes_0, keep_dims = reduce_mean_42_keep_dims_0, x = reshape_56_cast_fp16)[name = tensor("reduce_mean_42_cast_fp16")]; + tensor sub_28_cast_fp16 = sub(x = reshape_56_cast_fp16, y = reduce_mean_42_cast_fp16)[name = tensor("sub_28_cast_fp16")]; + tensor square_14_cast_fp16 = square(x = sub_28_cast_fp16)[name = tensor("square_14_cast_fp16")]; + tensor reduce_mean_44_axes_0 = const()[name = tensor("reduce_mean_44_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_44_keep_dims_0 = const()[name = tensor("reduce_mean_44_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_44_cast_fp16 = reduce_mean(axes = reduce_mean_44_axes_0, keep_dims = reduce_mean_44_keep_dims_0, x = square_14_cast_fp16)[name = tensor("reduce_mean_44_cast_fp16")]; + tensor add_28_y_0_to_fp16 = const()[name = tensor("add_28_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_28_cast_fp16 = add(x = reduce_mean_44_cast_fp16, y = add_28_y_0_to_fp16)[name = tensor("add_28_cast_fp16")]; + tensor sqrt_14_cast_fp16 = sqrt(x = add_28_cast_fp16)[name = tensor("sqrt_14_cast_fp16")]; + tensor real_div_14_cast_fp16 = real_div(x = sub_28_cast_fp16, y = sqrt_14_cast_fp16)[name = tensor("real_div_14_cast_fp16")]; + tensor reshape_57_shape_0 = const()[name = tensor("reshape_57_shape_0"), val = tensor([1, 512, 48, 80])]; + tensor reshape_57_cast_fp16 = reshape(shape = reshape_57_shape_0, x = real_div_14_cast_fp16)[name = tensor("reshape_57_cast_fp16")]; + tensor add_29_gamma_0_to_fp16 = const()[name = tensor("add_29_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37826496)))]; + tensor add_29_beta_0_to_fp16 = const()[name = tensor("add_29_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37827584)))]; + tensor add_29_epsilon_0_to_fp16 = const()[name = tensor("add_29_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_29_cast_fp16 = batch_norm(beta = add_29_beta_0_to_fp16, epsilon = add_29_epsilon_0_to_fp16, gamma = add_29_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_57_cast_fp16)[name = tensor("add_29_cast_fp16")]; + tensor hidden_states_55_cast_fp16 = silu(x = add_29_cast_fp16)[name = tensor("hidden_states_55_cast_fp16")]; + tensor var_326 = const()[name = tensor("op_326"), val = tensor([1, 1])]; + tensor var_328 = const()[name = tensor("op_328"), val = tensor([1, 1])]; + tensor input_75_pad_type_0 = const()[name = tensor("input_75_pad_type_0"), val = tensor("custom")]; + tensor input_75_pad_0 = const()[name = tensor("input_75_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_3_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_3_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37828672)))]; + tensor encoder_down_blocks_3_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_3_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42547328)))]; + tensor input_75_cast_fp16 = conv(bias = encoder_down_blocks_3_resnets_1_conv1_bias_to_fp16, dilations = var_328, groups = var_15, pad = input_75_pad_0, pad_type = input_75_pad_type_0, strides = var_326, weight = encoder_down_blocks_3_resnets_1_conv1_weight_to_fp16, x = hidden_states_55_cast_fp16)[name = tensor("input_75_cast_fp16")]; + tensor reshape_60_shape_0 = const()[name = tensor("reshape_60_shape_0"), val = tensor([1, 32, 16, 48, 80])]; + tensor reshape_60_cast_fp16 = reshape(shape = reshape_60_shape_0, x = input_75_cast_fp16)[name = tensor("reshape_60_cast_fp16")]; + tensor reduce_mean_45_axes_0 = const()[name = tensor("reduce_mean_45_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_45_keep_dims_0 = const()[name = tensor("reduce_mean_45_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_45_cast_fp16 = reduce_mean(axes = reduce_mean_45_axes_0, keep_dims = reduce_mean_45_keep_dims_0, x = reshape_60_cast_fp16)[name = tensor("reduce_mean_45_cast_fp16")]; + tensor sub_30_cast_fp16 = sub(x = reshape_60_cast_fp16, y = reduce_mean_45_cast_fp16)[name = tensor("sub_30_cast_fp16")]; + tensor square_15_cast_fp16 = square(x = sub_30_cast_fp16)[name = tensor("square_15_cast_fp16")]; + tensor reduce_mean_47_axes_0 = const()[name = tensor("reduce_mean_47_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_47_keep_dims_0 = const()[name = tensor("reduce_mean_47_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_47_cast_fp16 = reduce_mean(axes = reduce_mean_47_axes_0, keep_dims = reduce_mean_47_keep_dims_0, x = square_15_cast_fp16)[name = tensor("reduce_mean_47_cast_fp16")]; + tensor add_30_y_0_to_fp16 = const()[name = tensor("add_30_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_30_cast_fp16 = add(x = reduce_mean_47_cast_fp16, y = add_30_y_0_to_fp16)[name = tensor("add_30_cast_fp16")]; + tensor sqrt_15_cast_fp16 = sqrt(x = add_30_cast_fp16)[name = tensor("sqrt_15_cast_fp16")]; + tensor real_div_15_cast_fp16 = real_div(x = sub_30_cast_fp16, y = sqrt_15_cast_fp16)[name = tensor("real_div_15_cast_fp16")]; + tensor reshape_61_shape_0 = const()[name = tensor("reshape_61_shape_0"), val = tensor([1, 512, 48, 80])]; + tensor reshape_61_cast_fp16 = reshape(shape = reshape_61_shape_0, x = real_div_15_cast_fp16)[name = tensor("reshape_61_cast_fp16")]; + tensor add_31_gamma_0_to_fp16 = const()[name = tensor("add_31_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42548416)))]; + tensor add_31_beta_0_to_fp16 = const()[name = tensor("add_31_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42549504)))]; + tensor add_31_epsilon_0_to_fp16 = const()[name = tensor("add_31_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_31_cast_fp16 = batch_norm(beta = add_31_beta_0_to_fp16, epsilon = add_31_epsilon_0_to_fp16, gamma = add_31_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_61_cast_fp16)[name = tensor("add_31_cast_fp16")]; + tensor input_79_cast_fp16 = silu(x = add_31_cast_fp16)[name = tensor("input_79_cast_fp16")]; + tensor var_338 = const()[name = tensor("op_338"), val = tensor([1, 1])]; + tensor var_340 = const()[name = tensor("op_340"), val = tensor([1, 1])]; + tensor hidden_states_59_pad_type_0 = const()[name = tensor("hidden_states_59_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_59_pad_0 = const()[name = tensor("hidden_states_59_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_3_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_3_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42550592)))]; + tensor encoder_down_blocks_3_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_3_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47269248)))]; + tensor hidden_states_59_cast_fp16 = conv(bias = encoder_down_blocks_3_resnets_1_conv2_bias_to_fp16, dilations = var_340, groups = var_15, pad = hidden_states_59_pad_0, pad_type = hidden_states_59_pad_type_0, strides = var_338, weight = encoder_down_blocks_3_resnets_1_conv2_weight_to_fp16, x = input_79_cast_fp16)[name = tensor("hidden_states_59_cast_fp16")]; + tensor var_343_cast_fp16 = add(x = var_313_cast_fp16, y = hidden_states_59_cast_fp16)[name = tensor("op_343_cast_fp16")]; + tensor reshape_64_shape_0 = const()[name = tensor("reshape_64_shape_0"), val = tensor([1, 32, 16, 48, 80])]; + tensor reshape_64_cast_fp16 = reshape(shape = reshape_64_shape_0, x = var_343_cast_fp16)[name = tensor("reshape_64_cast_fp16")]; + tensor reduce_mean_48_axes_0 = const()[name = tensor("reduce_mean_48_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_48_keep_dims_0 = const()[name = tensor("reduce_mean_48_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_48_cast_fp16 = reduce_mean(axes = reduce_mean_48_axes_0, keep_dims = reduce_mean_48_keep_dims_0, x = reshape_64_cast_fp16)[name = tensor("reduce_mean_48_cast_fp16")]; + tensor sub_32_cast_fp16 = sub(x = reshape_64_cast_fp16, y = reduce_mean_48_cast_fp16)[name = tensor("sub_32_cast_fp16")]; + tensor square_16_cast_fp16 = square(x = sub_32_cast_fp16)[name = tensor("square_16_cast_fp16")]; + tensor reduce_mean_50_axes_0 = const()[name = tensor("reduce_mean_50_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_50_keep_dims_0 = const()[name = tensor("reduce_mean_50_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_50_cast_fp16 = reduce_mean(axes = reduce_mean_50_axes_0, keep_dims = reduce_mean_50_keep_dims_0, x = square_16_cast_fp16)[name = tensor("reduce_mean_50_cast_fp16")]; + tensor add_32_y_0_to_fp16 = const()[name = tensor("add_32_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_32_cast_fp16 = add(x = reduce_mean_50_cast_fp16, y = add_32_y_0_to_fp16)[name = tensor("add_32_cast_fp16")]; + tensor sqrt_16_cast_fp16 = sqrt(x = add_32_cast_fp16)[name = tensor("sqrt_16_cast_fp16")]; + tensor real_div_16_cast_fp16 = real_div(x = sub_32_cast_fp16, y = sqrt_16_cast_fp16)[name = tensor("real_div_16_cast_fp16")]; + tensor reshape_65_shape_0 = const()[name = tensor("reshape_65_shape_0"), val = tensor([1, 512, 48, 80])]; + tensor reshape_65_cast_fp16 = reshape(shape = reshape_65_shape_0, x = real_div_16_cast_fp16)[name = tensor("reshape_65_cast_fp16")]; + tensor add_33_gamma_0_to_fp16 = const()[name = tensor("add_33_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47270336)))]; + tensor add_33_beta_0_to_fp16 = const()[name = tensor("add_33_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47271424)))]; + tensor add_33_epsilon_0_to_fp16 = const()[name = tensor("add_33_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_33_cast_fp16 = batch_norm(beta = add_33_beta_0_to_fp16, epsilon = add_33_epsilon_0_to_fp16, gamma = add_33_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_65_cast_fp16)[name = tensor("add_33_cast_fp16")]; + tensor hidden_states_61_cast_fp16 = silu(x = add_33_cast_fp16)[name = tensor("hidden_states_61_cast_fp16")]; + tensor var_362 = const()[name = tensor("op_362"), val = tensor([1, 1])]; + tensor var_364 = const()[name = tensor("op_364"), val = tensor([1, 1])]; + tensor input_85_pad_type_0 = const()[name = tensor("input_85_pad_type_0"), val = tensor("custom")]; + tensor input_85_pad_0 = const()[name = tensor("input_85_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_mid_block_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("encoder_mid_block_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47272512)))]; + tensor encoder_mid_block_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("encoder_mid_block_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51991168)))]; + tensor input_85_cast_fp16 = conv(bias = encoder_mid_block_resnets_0_conv1_bias_to_fp16, dilations = var_364, groups = var_15, pad = input_85_pad_0, pad_type = input_85_pad_type_0, strides = var_362, weight = encoder_mid_block_resnets_0_conv1_weight_to_fp16, x = hidden_states_61_cast_fp16)[name = tensor("input_85_cast_fp16")]; + tensor reshape_68_shape_0 = const()[name = tensor("reshape_68_shape_0"), val = tensor([1, 32, 16, 48, 80])]; + tensor reshape_68_cast_fp16 = reshape(shape = reshape_68_shape_0, x = input_85_cast_fp16)[name = tensor("reshape_68_cast_fp16")]; + tensor reduce_mean_51_axes_0 = const()[name = tensor("reduce_mean_51_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_51_keep_dims_0 = const()[name = tensor("reduce_mean_51_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_51_cast_fp16 = reduce_mean(axes = reduce_mean_51_axes_0, keep_dims = reduce_mean_51_keep_dims_0, x = reshape_68_cast_fp16)[name = tensor("reduce_mean_51_cast_fp16")]; + tensor sub_34_cast_fp16 = sub(x = reshape_68_cast_fp16, y = reduce_mean_51_cast_fp16)[name = tensor("sub_34_cast_fp16")]; + tensor square_17_cast_fp16 = square(x = sub_34_cast_fp16)[name = tensor("square_17_cast_fp16")]; + tensor reduce_mean_53_axes_0 = const()[name = tensor("reduce_mean_53_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_53_keep_dims_0 = const()[name = tensor("reduce_mean_53_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_53_cast_fp16 = reduce_mean(axes = reduce_mean_53_axes_0, keep_dims = reduce_mean_53_keep_dims_0, x = square_17_cast_fp16)[name = tensor("reduce_mean_53_cast_fp16")]; + tensor add_34_y_0_to_fp16 = const()[name = tensor("add_34_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_34_cast_fp16 = add(x = reduce_mean_53_cast_fp16, y = add_34_y_0_to_fp16)[name = tensor("add_34_cast_fp16")]; + tensor sqrt_17_cast_fp16 = sqrt(x = add_34_cast_fp16)[name = tensor("sqrt_17_cast_fp16")]; + tensor real_div_17_cast_fp16 = real_div(x = sub_34_cast_fp16, y = sqrt_17_cast_fp16)[name = tensor("real_div_17_cast_fp16")]; + tensor reshape_69_shape_0 = const()[name = tensor("reshape_69_shape_0"), val = tensor([1, 512, 48, 80])]; + tensor reshape_69_cast_fp16 = reshape(shape = reshape_69_shape_0, x = real_div_17_cast_fp16)[name = tensor("reshape_69_cast_fp16")]; + tensor add_35_gamma_0_to_fp16 = const()[name = tensor("add_35_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51992256)))]; + tensor add_35_beta_0_to_fp16 = const()[name = tensor("add_35_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51993344)))]; + tensor add_35_epsilon_0_to_fp16 = const()[name = tensor("add_35_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_35_cast_fp16 = batch_norm(beta = add_35_beta_0_to_fp16, epsilon = add_35_epsilon_0_to_fp16, gamma = add_35_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_69_cast_fp16)[name = tensor("add_35_cast_fp16")]; + tensor input_89_cast_fp16 = silu(x = add_35_cast_fp16)[name = tensor("input_89_cast_fp16")]; + tensor var_374 = const()[name = tensor("op_374"), val = tensor([1, 1])]; + tensor var_376 = const()[name = tensor("op_376"), val = tensor([1, 1])]; + tensor hidden_states_65_pad_type_0 = const()[name = tensor("hidden_states_65_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_65_pad_0 = const()[name = tensor("hidden_states_65_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_mid_block_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("encoder_mid_block_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51994432)))]; + tensor encoder_mid_block_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("encoder_mid_block_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56713088)))]; + tensor hidden_states_65_cast_fp16 = conv(bias = encoder_mid_block_resnets_0_conv2_bias_to_fp16, dilations = var_376, groups = var_15, pad = hidden_states_65_pad_0, pad_type = hidden_states_65_pad_type_0, strides = var_374, weight = encoder_mid_block_resnets_0_conv2_weight_to_fp16, x = input_89_cast_fp16)[name = tensor("hidden_states_65_cast_fp16")]; + tensor var_379_cast_fp16 = add(x = var_343_cast_fp16, y = hidden_states_65_cast_fp16)[name = tensor("op_379_cast_fp16")]; + tensor reshape_72_shape_0 = const()[name = tensor("reshape_72_shape_0"), val = tensor([1, 32, 16, 3840])]; + tensor reshape_72_cast_fp16 = reshape(shape = reshape_72_shape_0, x = var_379_cast_fp16)[name = tensor("reshape_72_cast_fp16")]; + tensor reduce_mean_54_axes_0 = const()[name = tensor("reduce_mean_54_axes_0"), val = tensor([2, 3])]; + tensor reduce_mean_54_keep_dims_0 = const()[name = tensor("reduce_mean_54_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_54_cast_fp16 = reduce_mean(axes = reduce_mean_54_axes_0, keep_dims = reduce_mean_54_keep_dims_0, x = reshape_72_cast_fp16)[name = tensor("reduce_mean_54_cast_fp16")]; + tensor sub_36_cast_fp16 = sub(x = reshape_72_cast_fp16, y = reduce_mean_54_cast_fp16)[name = tensor("sub_36_cast_fp16")]; + tensor square_18_cast_fp16 = square(x = sub_36_cast_fp16)[name = tensor("square_18_cast_fp16")]; + tensor reduce_mean_56_axes_0 = const()[name = tensor("reduce_mean_56_axes_0"), val = tensor([2, 3])]; + tensor reduce_mean_56_keep_dims_0 = const()[name = tensor("reduce_mean_56_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_56_cast_fp16 = reduce_mean(axes = reduce_mean_56_axes_0, keep_dims = reduce_mean_56_keep_dims_0, x = square_18_cast_fp16)[name = tensor("reduce_mean_56_cast_fp16")]; + tensor add_36_y_0_to_fp16 = const()[name = tensor("add_36_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_36_cast_fp16 = add(x = reduce_mean_56_cast_fp16, y = add_36_y_0_to_fp16)[name = tensor("add_36_cast_fp16")]; + tensor sqrt_18_cast_fp16 = sqrt(x = add_36_cast_fp16)[name = tensor("sqrt_18_cast_fp16")]; + tensor real_div_18_cast_fp16 = real_div(x = sub_36_cast_fp16, y = sqrt_18_cast_fp16)[name = tensor("real_div_18_cast_fp16")]; + tensor reshape_73_shape_0 = const()[name = tensor("reshape_73_shape_0"), val = tensor([1, 512, 3840])]; + tensor reshape_73_cast_fp16 = reshape(shape = reshape_73_shape_0, x = real_div_18_cast_fp16)[name = tensor("reshape_73_cast_fp16")]; + tensor reshape_74_to_fp16 = const()[name = tensor("reshape_74_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56714176)))]; + tensor mul_18_cast_fp16 = mul(x = reshape_73_cast_fp16, y = reshape_74_to_fp16)[name = tensor("mul_18_cast_fp16")]; + tensor reshape_75_to_fp16 = const()[name = tensor("reshape_75_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56715264)))]; + tensor add_37_cast_fp16 = add(x = mul_18_cast_fp16, y = reshape_75_to_fp16)[name = tensor("add_37_cast_fp16")]; + tensor input_93_perm_0 = const()[name = tensor("input_93_perm_0"), val = tensor([0, 2, 1])]; + tensor encoder_mid_block_attentions_0_to_q_weight_to_fp16 = const()[name = tensor("encoder_mid_block_attentions_0_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56716352)))]; + tensor encoder_mid_block_attentions_0_to_q_bias_to_fp16 = const()[name = tensor("encoder_mid_block_attentions_0_to_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57240704)))]; + tensor transpose_11 = transpose(perm = input_93_perm_0, x = add_37_cast_fp16)[name = tensor("transpose_11")]; + tensor linear_0_cast_fp16 = linear(bias = encoder_mid_block_attentions_0_to_q_bias_to_fp16, weight = encoder_mid_block_attentions_0_to_q_weight_to_fp16, x = transpose_11)[name = tensor("linear_0_cast_fp16")]; + tensor encoder_mid_block_attentions_0_to_k_weight_to_fp16 = const()[name = tensor("encoder_mid_block_attentions_0_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57241792)))]; + tensor encoder_mid_block_attentions_0_to_k_bias_to_fp16 = const()[name = tensor("encoder_mid_block_attentions_0_to_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57766144)))]; + tensor linear_1_cast_fp16 = linear(bias = encoder_mid_block_attentions_0_to_k_bias_to_fp16, weight = encoder_mid_block_attentions_0_to_k_weight_to_fp16, x = transpose_11)[name = tensor("linear_1_cast_fp16")]; + tensor encoder_mid_block_attentions_0_to_v_weight_to_fp16 = const()[name = tensor("encoder_mid_block_attentions_0_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57767232)))]; + tensor encoder_mid_block_attentions_0_to_v_bias_to_fp16 = const()[name = tensor("encoder_mid_block_attentions_0_to_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58291584)))]; + tensor linear_2_cast_fp16 = linear(bias = encoder_mid_block_attentions_0_to_v_bias_to_fp16, weight = encoder_mid_block_attentions_0_to_v_weight_to_fp16, x = transpose_11)[name = tensor("linear_2_cast_fp16")]; + tensor var_420 = const()[name = tensor("op_420"), val = tensor([1, -1, 1, 512])]; + tensor var_421_cast_fp16 = reshape(shape = var_420, x = linear_0_cast_fp16)[name = tensor("op_421_cast_fp16")]; + tensor var_423 = const()[name = tensor("op_423"), val = tensor([1, -1, 1, 512])]; + tensor var_424_cast_fp16 = reshape(shape = var_423, x = linear_1_cast_fp16)[name = tensor("op_424_cast_fp16")]; + tensor var_426 = const()[name = tensor("op_426"), val = tensor([1, -1, 1, 512])]; + tensor var_427_cast_fp16 = reshape(shape = var_426, x = linear_2_cast_fp16)[name = tensor("op_427_cast_fp16")]; + tensor value_perm_0 = const()[name = tensor("value_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor mul_19_y_0_to_fp16 = const()[name = tensor("mul_19_y_0_to_fp16"), val = tensor(0x1.6ap-5)]; + tensor mul_19_cast_fp16 = mul(x = var_421_cast_fp16, y = mul_19_y_0_to_fp16)[name = tensor("mul_19_cast_fp16")]; + tensor matmul_0_transpose_y_0 = const()[name = tensor("matmul_0_transpose_y_0"), val = tensor(true)]; + tensor matmul_0_transpose_x_0 = const()[name = tensor("matmul_0_transpose_x_0"), val = tensor(false)]; + tensor transpose_4_perm_0 = const()[name = tensor("transpose_4_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_5_perm_0 = const()[name = tensor("transpose_5_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_8 = transpose(perm = transpose_5_perm_0, x = var_424_cast_fp16)[name = tensor("transpose_8")]; + tensor transpose_9 = transpose(perm = transpose_4_perm_0, x = mul_19_cast_fp16)[name = tensor("transpose_9")]; + tensor matmul_0_cast_fp16 = matmul(transpose_x = matmul_0_transpose_x_0, transpose_y = matmul_0_transpose_y_0, x = transpose_9, y = transpose_8)[name = tensor("matmul_0_cast_fp16")]; + tensor softmax_0_axis_0 = const()[name = tensor("softmax_0_axis_0"), val = tensor(-1)]; + tensor softmax_0_cast_fp16 = softmax(axis = softmax_0_axis_0, x = matmul_0_cast_fp16)[name = tensor("softmax_0_cast_fp16")]; + tensor hidden_states_71_transpose_x_0 = const()[name = tensor("hidden_states_71_transpose_x_0"), val = tensor(false)]; + tensor hidden_states_71_transpose_y_0 = const()[name = tensor("hidden_states_71_transpose_y_0"), val = tensor(false)]; + tensor transpose_10 = transpose(perm = value_perm_0, x = var_427_cast_fp16)[name = tensor("transpose_10")]; + tensor hidden_states_71_cast_fp16 = matmul(transpose_x = hidden_states_71_transpose_x_0, transpose_y = hidden_states_71_transpose_y_0, x = softmax_0_cast_fp16, y = transpose_10)[name = tensor("hidden_states_71_cast_fp16")]; + tensor var_430_perm_0 = const()[name = tensor("op_430_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_434 = const()[name = tensor("op_434"), val = tensor([1, -1, 512])]; + tensor transpose_7 = transpose(perm = var_430_perm_0, x = hidden_states_71_cast_fp16)[name = tensor("transpose_7")]; + tensor hidden_states_73_cast_fp16 = reshape(shape = var_434, x = transpose_7)[name = tensor("hidden_states_73_cast_fp16")]; + tensor encoder_mid_block_attentions_0_to_out_0_weight_to_fp16 = const()[name = tensor("encoder_mid_block_attentions_0_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58292672)))]; + tensor encoder_mid_block_attentions_0_to_out_0_bias_to_fp16 = const()[name = tensor("encoder_mid_block_attentions_0_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58817024)))]; + tensor linear_3_cast_fp16 = linear(bias = encoder_mid_block_attentions_0_to_out_0_bias_to_fp16, weight = encoder_mid_block_attentions_0_to_out_0_weight_to_fp16, x = hidden_states_73_cast_fp16)[name = tensor("linear_3_cast_fp16")]; + tensor var_441_perm_0 = const()[name = tensor("op_441_perm_0"), val = tensor([0, -1, -2])]; + tensor var_442 = const()[name = tensor("op_442"), val = tensor([1, 512, 48, 80])]; + tensor transpose_6 = transpose(perm = var_441_perm_0, x = linear_3_cast_fp16)[name = tensor("transpose_6")]; + tensor hidden_states_77_cast_fp16 = reshape(shape = var_442, x = transpose_6)[name = tensor("hidden_states_77_cast_fp16")]; + tensor hidden_states_79_cast_fp16 = add(x = hidden_states_77_cast_fp16, y = var_379_cast_fp16)[name = tensor("hidden_states_79_cast_fp16")]; + tensor reshape_76_shape_0 = const()[name = tensor("reshape_76_shape_0"), val = tensor([1, 32, 16, 48, 80])]; + tensor reshape_76_cast_fp16 = reshape(shape = reshape_76_shape_0, x = hidden_states_79_cast_fp16)[name = tensor("reshape_76_cast_fp16")]; + tensor reduce_mean_57_axes_0 = const()[name = tensor("reduce_mean_57_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_57_keep_dims_0 = const()[name = tensor("reduce_mean_57_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_57_cast_fp16 = reduce_mean(axes = reduce_mean_57_axes_0, keep_dims = reduce_mean_57_keep_dims_0, x = reshape_76_cast_fp16)[name = tensor("reduce_mean_57_cast_fp16")]; + tensor sub_38_cast_fp16 = sub(x = reshape_76_cast_fp16, y = reduce_mean_57_cast_fp16)[name = tensor("sub_38_cast_fp16")]; + tensor square_19_cast_fp16 = square(x = sub_38_cast_fp16)[name = tensor("square_19_cast_fp16")]; + tensor reduce_mean_59_axes_0 = const()[name = tensor("reduce_mean_59_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_59_keep_dims_0 = const()[name = tensor("reduce_mean_59_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_59_cast_fp16 = reduce_mean(axes = reduce_mean_59_axes_0, keep_dims = reduce_mean_59_keep_dims_0, x = square_19_cast_fp16)[name = tensor("reduce_mean_59_cast_fp16")]; + tensor add_38_y_0_to_fp16 = const()[name = tensor("add_38_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_38_cast_fp16 = add(x = reduce_mean_59_cast_fp16, y = add_38_y_0_to_fp16)[name = tensor("add_38_cast_fp16")]; + tensor sqrt_19_cast_fp16 = sqrt(x = add_38_cast_fp16)[name = tensor("sqrt_19_cast_fp16")]; + tensor real_div_19_cast_fp16 = real_div(x = sub_38_cast_fp16, y = sqrt_19_cast_fp16)[name = tensor("real_div_19_cast_fp16")]; + tensor reshape_77_shape_0 = const()[name = tensor("reshape_77_shape_0"), val = tensor([1, 512, 48, 80])]; + tensor reshape_77_cast_fp16 = reshape(shape = reshape_77_shape_0, x = real_div_19_cast_fp16)[name = tensor("reshape_77_cast_fp16")]; + tensor add_39_gamma_0_to_fp16 = const()[name = tensor("add_39_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58818112)))]; + tensor add_39_beta_0_to_fp16 = const()[name = tensor("add_39_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58819200)))]; + tensor add_39_epsilon_0_to_fp16 = const()[name = tensor("add_39_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_39_cast_fp16 = batch_norm(beta = add_39_beta_0_to_fp16, epsilon = add_39_epsilon_0_to_fp16, gamma = add_39_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_77_cast_fp16)[name = tensor("add_39_cast_fp16")]; + tensor hidden_states_81_cast_fp16 = silu(x = add_39_cast_fp16)[name = tensor("hidden_states_81_cast_fp16")]; + tensor var_457 = const()[name = tensor("op_457"), val = tensor([1, 1])]; + tensor var_459 = const()[name = tensor("op_459"), val = tensor([1, 1])]; + tensor input_103_pad_type_0 = const()[name = tensor("input_103_pad_type_0"), val = tensor("custom")]; + tensor input_103_pad_0 = const()[name = tensor("input_103_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_mid_block_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("encoder_mid_block_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58820288)))]; + tensor encoder_mid_block_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("encoder_mid_block_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63538944)))]; + tensor input_103_cast_fp16 = conv(bias = encoder_mid_block_resnets_1_conv1_bias_to_fp16, dilations = var_459, groups = var_15, pad = input_103_pad_0, pad_type = input_103_pad_type_0, strides = var_457, weight = encoder_mid_block_resnets_1_conv1_weight_to_fp16, x = hidden_states_81_cast_fp16)[name = tensor("input_103_cast_fp16")]; + tensor reshape_80_shape_0 = const()[name = tensor("reshape_80_shape_0"), val = tensor([1, 32, 16, 48, 80])]; + tensor reshape_80_cast_fp16 = reshape(shape = reshape_80_shape_0, x = input_103_cast_fp16)[name = tensor("reshape_80_cast_fp16")]; + tensor reduce_mean_60_axes_0 = const()[name = tensor("reduce_mean_60_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_60_keep_dims_0 = const()[name = tensor("reduce_mean_60_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_60_cast_fp16 = reduce_mean(axes = reduce_mean_60_axes_0, keep_dims = reduce_mean_60_keep_dims_0, x = reshape_80_cast_fp16)[name = tensor("reduce_mean_60_cast_fp16")]; + tensor sub_40_cast_fp16 = sub(x = reshape_80_cast_fp16, y = reduce_mean_60_cast_fp16)[name = tensor("sub_40_cast_fp16")]; + tensor square_20_cast_fp16 = square(x = sub_40_cast_fp16)[name = tensor("square_20_cast_fp16")]; + tensor reduce_mean_62_axes_0 = const()[name = tensor("reduce_mean_62_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_62_keep_dims_0 = const()[name = tensor("reduce_mean_62_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_62_cast_fp16 = reduce_mean(axes = reduce_mean_62_axes_0, keep_dims = reduce_mean_62_keep_dims_0, x = square_20_cast_fp16)[name = tensor("reduce_mean_62_cast_fp16")]; + tensor add_40_y_0_to_fp16 = const()[name = tensor("add_40_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_40_cast_fp16 = add(x = reduce_mean_62_cast_fp16, y = add_40_y_0_to_fp16)[name = tensor("add_40_cast_fp16")]; + tensor sqrt_20_cast_fp16 = sqrt(x = add_40_cast_fp16)[name = tensor("sqrt_20_cast_fp16")]; + tensor real_div_20_cast_fp16 = real_div(x = sub_40_cast_fp16, y = sqrt_20_cast_fp16)[name = tensor("real_div_20_cast_fp16")]; + tensor reshape_81_shape_0 = const()[name = tensor("reshape_81_shape_0"), val = tensor([1, 512, 48, 80])]; + tensor reshape_81_cast_fp16 = reshape(shape = reshape_81_shape_0, x = real_div_20_cast_fp16)[name = tensor("reshape_81_cast_fp16")]; + tensor add_41_gamma_0_to_fp16 = const()[name = tensor("add_41_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63540032)))]; + tensor add_41_beta_0_to_fp16 = const()[name = tensor("add_41_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63541120)))]; + tensor add_41_epsilon_0_to_fp16 = const()[name = tensor("add_41_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_41_cast_fp16 = batch_norm(beta = add_41_beta_0_to_fp16, epsilon = add_41_epsilon_0_to_fp16, gamma = add_41_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_81_cast_fp16)[name = tensor("add_41_cast_fp16")]; + tensor input_107_cast_fp16 = silu(x = add_41_cast_fp16)[name = tensor("input_107_cast_fp16")]; + tensor var_469 = const()[name = tensor("op_469"), val = tensor([1, 1])]; + tensor var_471 = const()[name = tensor("op_471"), val = tensor([1, 1])]; + tensor hidden_states_pad_type_0 = const()[name = tensor("hidden_states_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_pad_0 = const()[name = tensor("hidden_states_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_mid_block_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("encoder_mid_block_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63542208)))]; + tensor encoder_mid_block_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("encoder_mid_block_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68260864)))]; + tensor hidden_states_cast_fp16 = conv(bias = encoder_mid_block_resnets_1_conv2_bias_to_fp16, dilations = var_471, groups = var_15, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = var_469, weight = encoder_mid_block_resnets_1_conv2_weight_to_fp16, x = input_107_cast_fp16)[name = tensor("hidden_states_cast_fp16")]; + tensor var_474_cast_fp16 = add(x = hidden_states_79_cast_fp16, y = hidden_states_cast_fp16)[name = tensor("op_474_cast_fp16")]; + tensor reshape_84_shape_0 = const()[name = tensor("reshape_84_shape_0"), val = tensor([1, 32, 16, 48, 80])]; + tensor reshape_84_cast_fp16 = reshape(shape = reshape_84_shape_0, x = var_474_cast_fp16)[name = tensor("reshape_84_cast_fp16")]; + tensor reduce_mean_63_axes_0 = const()[name = tensor("reduce_mean_63_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_63_keep_dims_0 = const()[name = tensor("reduce_mean_63_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_63_cast_fp16 = reduce_mean(axes = reduce_mean_63_axes_0, keep_dims = reduce_mean_63_keep_dims_0, x = reshape_84_cast_fp16)[name = tensor("reduce_mean_63_cast_fp16")]; + tensor sub_42_cast_fp16 = sub(x = reshape_84_cast_fp16, y = reduce_mean_63_cast_fp16)[name = tensor("sub_42_cast_fp16")]; + tensor square_21_cast_fp16 = square(x = sub_42_cast_fp16)[name = tensor("square_21_cast_fp16")]; + tensor reduce_mean_65_axes_0 = const()[name = tensor("reduce_mean_65_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_65_keep_dims_0 = const()[name = tensor("reduce_mean_65_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_65_cast_fp16 = reduce_mean(axes = reduce_mean_65_axes_0, keep_dims = reduce_mean_65_keep_dims_0, x = square_21_cast_fp16)[name = tensor("reduce_mean_65_cast_fp16")]; + tensor add_42_y_0_to_fp16 = const()[name = tensor("add_42_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_42_cast_fp16 = add(x = reduce_mean_65_cast_fp16, y = add_42_y_0_to_fp16)[name = tensor("add_42_cast_fp16")]; + tensor sqrt_21_cast_fp16 = sqrt(x = add_42_cast_fp16)[name = tensor("sqrt_21_cast_fp16")]; + tensor real_div_21_cast_fp16 = real_div(x = sub_42_cast_fp16, y = sqrt_21_cast_fp16)[name = tensor("real_div_21_cast_fp16")]; + tensor reshape_85_shape_0 = const()[name = tensor("reshape_85_shape_0"), val = tensor([1, 512, 48, 80])]; + tensor reshape_85_cast_fp16 = reshape(shape = reshape_85_shape_0, x = real_div_21_cast_fp16)[name = tensor("reshape_85_cast_fp16")]; + tensor add_43_gamma_0_to_fp16 = const()[name = tensor("add_43_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68261952)))]; + tensor add_43_beta_0_to_fp16 = const()[name = tensor("add_43_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68263040)))]; + tensor add_43_epsilon_0_to_fp16 = const()[name = tensor("add_43_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_43_cast_fp16 = batch_norm(beta = add_43_beta_0_to_fp16, epsilon = add_43_epsilon_0_to_fp16, gamma = add_43_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_85_cast_fp16)[name = tensor("add_43_cast_fp16")]; + tensor input_113_cast_fp16 = silu(x = add_43_cast_fp16)[name = tensor("input_113_cast_fp16")]; + tensor var_483 = const()[name = tensor("op_483"), val = tensor([1, 1])]; + tensor var_485 = const()[name = tensor("op_485"), val = tensor([1, 1])]; + tensor input_pad_type_0 = const()[name = tensor("input_pad_type_0"), val = tensor("custom")]; + tensor input_pad_0 = const()[name = tensor("input_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_conv_out_weight_to_fp16 = const()[name = tensor("encoder_conv_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68264128)))]; + tensor encoder_conv_out_bias_to_fp16 = const()[name = tensor("encoder_conv_out_bias_to_fp16"), val = tensor([-0x1.734p-9, 0x1.0f4p-8, 0x1.afp-6, -0x1.494p-7, -0x1.ep-9, -0x1.924p-8, -0x1.1dp-10, -0x1.4b8p-8])]; + tensor input_cast_fp16 = conv(bias = encoder_conv_out_bias_to_fp16, dilations = var_485, groups = var_15, pad = input_pad_0, pad_type = input_pad_type_0, strides = var_483, weight = encoder_conv_out_weight_to_fp16, x = input_113_cast_fp16)[name = tensor("input_cast_fp16")]; + tensor var_491 = const()[name = tensor("op_491"), val = tensor(1)]; + tensor var_494 = const()[name = tensor("op_494"), val = tensor([1, 1])]; + tensor var_496 = const()[name = tensor("op_496"), val = tensor([1, 1])]; + tensor var_498_pad_type_0 = const()[name = tensor("op_498_pad_type_0"), val = tensor("custom")]; + tensor var_498_pad_0 = const()[name = tensor("op_498_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor quant_conv_weight_to_fp16 = const()[name = tensor("quant_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68337920)))]; + tensor quant_conv_bias_to_fp16 = const()[name = tensor("quant_conv_bias_to_fp16"), val = tensor([0x1.8cp-3, 0x1.d68p-4, -0x1.b8cp-4, -0x1.5fp-2, -0x1.284p+1, -0x1.09cp+1, -0x1.178p+1, -0x1.1d8p+1])]; + tensor var_498_cast_fp16 = conv(bias = quant_conv_bias_to_fp16, dilations = var_496, groups = var_491, pad = var_498_pad_0, pad_type = var_498_pad_type_0, strides = var_494, weight = quant_conv_weight_to_fp16, x = input_cast_fp16)[name = tensor("op_498_cast_fp16")]; + tensor var_498_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("op_498_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor latent = cast(dtype = var_498_cast_fp16_to_fp32_dtype_0, x = var_498_cast_fp16)[name = tensor("cast_29")]; + } -> (latent); +} \ No newline at end of file diff --git a/original/compiled/VAEEncoder.mlmodelc/weights/weight.bin b/original/compiled/VAEEncoder.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..9f0bc17bc128cbaf2b561440d23cb5679a3a846b --- /dev/null +++ b/original/compiled/VAEEncoder.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dcdc23bf9f08f08d6f0ab3e7ea9754d6f141a9e6a6793754ec5345dc0a1d26bc +size 68338112 diff --git a/original/compiled/merges.txt b/original/compiled/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..bbfec752c9a675946c6dce106def6f35c882dcc2 --- /dev/null +++ b/original/compiled/merges.txt @@ -0,0 +1,48895 @@ +#version: 0.2 - Trained by `huggingface/tokenizers` +i n +t h +a n +r e +a r +e r +th e +in g +o u +o n +s t +o r +e n +o n +a l +a t +e r +i t +i n +t o +r o +i s +l e +i c +a t +an d +e d +o f +c h +o r +e s +i l +e l +s t +a c +o m +a m +l o +a n +a y +s h +r i +l i +t i +f or +n e +ð Ł +r a +h a +d e +o l +v e +s i +u r +a l +s e +' s +u n +d i +b e +l a +w h +o o +d ay +e n +m a +n o +l e +t o +ou r +i r +g h +w it +i t +y o +a s +s p +th is +t s +at i +yo u +wit h +a d +i s +a b +l y +w e +th e +t e +a s +a g +v i +p p +s u +h o +m y +. . +b u +c om +s e +er s +m e +m e +al l +c on +m o +k e +g e +ou t +en t +c o +f e +v er +a r +f ro +a u +p o +c e +gh t +ar e +s s +fro m +c h +t r +ou n +on e +b y +d o +t h +w or +er e +k e +p ro +f or +d s +b o +t a +w e +g o +h e +t er +in g +d e +b e +ati on +m or +a y +e x +il l +p e +k s +s c +l u +f u +q u +v er +ðŁ ĺ +j u +m u +at e +an d +v e +k ing +m ar +o p +h i +.. . +p re +a d +r u +th at +j o +o f +c e +ne w +a m +a p +g re +s s +d u +no w +y e +t ing +y our +it y +n i +c i +p ar +g u +f i +a f +p er +t er +u p +s o +g i +on s +g r +g e +b r +p l +' t +m i +in e +we e +b i +u s +sh o +ha ve +to day +a v +m an +en t +ac k +ur e +ou r +â Ģ +c u +l d +lo o +i m +ic e +s om +f in +re d +re n +oo d +w as +ti on +p i +i r +th er +t y +p h +ar d +e c +! ! +m on +mor e +w ill +t ra +c an +c ol +p u +t e +w n +m b +s o +it i +ju st +n ing +h ere +t u +p a +p r +bu t +wh at +al ly +f ir +m in +c a +an t +s a +t ed +e v +m ent +f a +ge t +am e +ab out +g ra +no t +ha pp +ay s +m an +h is +ti me +li ke +g h +ha s +th an +lo ve +ar t +st e +d ing +h e +c re +w s +w at +d er +it e +s er +ac e +ag e +en d +st r +a w +st or +r e +c ar +el l +al l +p s +f ri +p ho +p or +d o +a k +w i +f re +wh o +sh i +b oo +s on +el l +wh en +il l +ho w +gre at +w in +e l +b l +s si +al i +som e +ðŁ Ĵ +t on +d er +le s +p la +ï ¸ +e d +s ch +h u +on g +d on +k i +s h +an n +c or +. . +oun d +a z +in e +ar y +fu l +st u +ou ld +st i +g o +se e +ab le +ar s +l l +m is +b er +c k +w a +en ts +n o +si g +f e +fir st +e t +sp e +ac k +i f +ou s +' m +st er +a pp +an g +an ce +an s +g ood +b re +e ver +the y +t ic +com e +of f +b ack +as e +ing s +ol d +i ght +f o +h er +happ y +p ic +it s +v ing +u s +m at +h om +d y +e m +s k +y ing +the ir +le d +r y +u l +h ar +c k +t on +on al +h el +r ic +b ir +vi e +w ay +t ri +d a +p le +b ro +st o +oo l +ni ght +tr u +b a +re ad +re s +ye ar +f r +t or +al s +c oun +c la +t ure +v el +at ed +le c +en d +th ing +v o +ic i +be st +c an +wor k +la st +af ter +en ce +p ri +p e +e s +i l +âĢ ¦ +d re +y s +o ver +i es +ðŁ ij +com m +t w +in k +s un +c l +li fe +t t +a ch +l and +s y +t re +t al +p ol +s m +du c +s al +f t +' re +ch e +w ar +t ur +ati ons +ac h +m s +il e +p m +ou gh +at e +st ar +wee k +! !! +c lu +th ere +n er +t om +s el +ï¸ ı +wor ld +v es +c am +go t +in ter +of f +u m +ton ight +o ther +h ou +loo k +j e +i d +si on +be au +at t +el i +or t +re c +f f +st er +su pp +g en +be en +il y +te am +m m +i c +pe op +it t +at s +on ly +mb er +en g +b ri +m p +k now +b ur +b ar +in s +lo w +sh e +ro w +â Ŀ +t ro +peop le +vi a +lo w +ag a +be t +x t +f ac +ch ar +e ar +w al +s en +f am +b le +n ati +is h +n or +g ame +li ve +s co +le y +d on +ic k +b all +ver y +the se +p an +i a +at ing +c r +a re +g ir +ma ke +st re +sho w +. 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