program(1.0)
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "5.33.5"}, {"coremlc-version", "1877.40.3"}, {"coremltools-component-torch", "2.2.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "7.1"}})]
{
func main<ios16>(tensor<int32, [1]> cache_length, tensor<fp16, [1, 224]> decoder_key_padding_mask, tensor<fp16, [1, 1280, 1, 1500]> encoder_output_embeds, tensor<int32, [1]> input_ids, tensor<fp16, [1, 40960, 1, 224]> key_cache, tensor<fp16, [1, 224]> kv_cache_update_mask, tensor<fp16, [1, 40960, 1, 224]> value_cache) {
tensor<int32, []> var_80_axis_0 = const()[name = tensor<string, []>("op_80_axis_0"), val = tensor<int32, []>(0)];
tensor<int32, []> var_80_batch_dims_0 = const()[name = tensor<string, []>("op_80_batch_dims_0"), val = tensor<int32, []>(0)];
tensor<fp16, [51865, 1280]> embed_tokens_weight_to_fp16 = const()[name = tensor<string, []>("embed_tokens_weight_to_fp16"), val = tensor<fp16, [51865, 1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
tensor<fp16, [1, 1280]> var_80_cast_fp16 = gather(axis = var_80_axis_0, batch_dims = var_80_batch_dims_0, indices = input_ids, x = embed_tokens_weight_to_fp16)[name = tensor<string, []>("op_80_cast_fp16")];
tensor<int32, []> var_84_axis_0 = const()[name = tensor<string, []>("op_84_axis_0"), val = tensor<int32, []>(0)];
tensor<int32, []> var_84_batch_dims_0 = const()[name = tensor<string, []>("op_84_batch_dims_0"), val = tensor<int32, []>(0)];
tensor<fp16, [448, 1280]> embed_positions_weight_to_fp16 = const()[name = tensor<string, []>("embed_positions_weight_to_fp16"), val = tensor<fp16, [448, 1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(132774528)))];
tensor<fp16, [1, 1280]> var_84_cast_fp16 = gather(axis = var_84_axis_0, batch_dims = var_84_batch_dims_0, indices = cache_length, x = embed_positions_weight_to_fp16)[name = tensor<string, []>("op_84_cast_fp16")];
tensor<fp16, [1, 1280]> hidden_states_1_cast_fp16 = add(x = var_80_cast_fp16, y = var_84_cast_fp16)[name = tensor<string, []>("hidden_states_1_cast_fp16")];
tensor<int32, [1]> var_98_axes_0 = const()[name = tensor<string, []>("op_98_axes_0"), val = tensor<int32, [1]>([2])];
tensor<fp16, [1, 1280, 1]> var_98_cast_fp16 = expand_dims(axes = var_98_axes_0, x = hidden_states_1_cast_fp16)[name = tensor<string, []>("op_98_cast_fp16")];
tensor<int32, [1]> inputs_1_axes_0 = const()[name = tensor<string, []>("inputs_1_axes_0"), val = tensor<int32, [1]>([3])];
tensor<fp16, [1, 1280, 1, 1]> inputs_1_cast_fp16 = expand_dims(axes = inputs_1_axes_0, x = var_98_cast_fp16)[name = tensor<string, []>("inputs_1_cast_fp16")];
tensor<int32, [32]> tile_0 = const()[name = tensor<string, []>("tile_0"), val = tensor<int32, [32]>([1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280])];
tensor<int32, []> var_103_axis_0 = const()[name = tensor<string, []>("op_103_axis_0"), val = tensor<int32, []>(1)];
tensor<fp16, [1, 1280, 1, 224]> var_103_cast_fp16_0, tensor<fp16, [1, 1280, 1, 224]> var_103_cast_fp16_1, tensor<fp16, [1, 1280, 1, 224]> var_103_cast_fp16_2, tensor<fp16, [1, 1280, 1, 224]> var_103_cast_fp16_3, tensor<fp16, [1, 1280, 1, 224]> var_103_cast_fp16_4, tensor<fp16, [1, 1280, 1, 224]> var_103_cast_fp16_5, tensor<fp16, [1, 1280, 1, 224]> var_103_cast_fp16_6, tensor<fp16, [1, 1280, 1, 224]> var_103_cast_fp16_7, tensor<fp16, [1, 1280, 1, 224]> var_103_cast_fp16_8, tensor<fp16, [1, 1280, 1, 224]> var_103_cast_fp16_9, tensor<fp16, [1, 1280, 1, 224]> var_103_cast_fp16_10, tensor<fp16, [1, 1280, 1, 224]> var_103_cast_fp16_11, tensor<fp16, [1, 1280, 1, 224]> var_103_cast_fp16_12, tensor<fp16, [1, 1280, 1, 224]> var_103_cast_fp16_13, tensor<fp16, [1, 1280, 1, 224]> var_103_cast_fp16_14, tensor<fp16, [1, 1280, 1, 224]> var_103_cast_fp16_15, tensor<fp16, [1, 1280, 1, 224]> var_103_cast_fp16_16, tensor<fp16, [1, 1280, 1, 224]> var_103_cast_fp16_17, tensor<fp16, [1, 1280, 1, 224]> var_103_cast_fp16_18, tensor<fp16, [1, 1280, 1, 224]> var_103_cast_fp16_19, tensor<fp16, [1, 1280, 1, 224]> var_103_cast_fp16_20, tensor<fp16, [1, 1280, 1, 224]> var_103_cast_fp16_21, tensor<fp16, [1, 1280, 1, 224]> var_103_cast_fp16_22, tensor<fp16, [1, 1280, 1, 224]> var_103_cast_fp16_23, tensor<fp16, [1, 1280, 1, 224]> var_103_cast_fp16_24, tensor<fp16, [1, 1280, 1, 224]> var_103_cast_fp16_25, tensor<fp16, [1, 1280, 1, 224]> var_103_cast_fp16_26, tensor<fp16, [1, 1280, 1, 224]> var_103_cast_fp16_27, tensor<fp16, [1, 1280, 1, 224]> var_103_cast_fp16_28, tensor<fp16, [1, 1280, 1, 224]> var_103_cast_fp16_29, tensor<fp16, [1, 1280, 1, 224]> var_103_cast_fp16_30, tensor<fp16, [1, 1280, 1, 224]> var_103_cast_fp16_31 = split(axis = var_103_axis_0, split_sizes = tile_0, x = key_cache)[name = tensor<string, []>("op_103_cast_fp16")];
tensor<int32, [32]> tile_1 = const()[name = tensor<string, []>("tile_1"), val = tensor<int32, [32]>([1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280, 1280])];
tensor<int32, []> var_138_axis_0 = const()[name = tensor<string, []>("op_138_axis_0"), val = tensor<int32, []>(1)];
tensor<fp16, [1, 1280, 1, 224]> var_138_cast_fp16_0, tensor<fp16, [1, 1280, 1, 224]> var_138_cast_fp16_1, tensor<fp16, [1, 1280, 1, 224]> var_138_cast_fp16_2, tensor<fp16, [1, 1280, 1, 224]> var_138_cast_fp16_3, tensor<fp16, [1, 1280, 1, 224]> var_138_cast_fp16_4, tensor<fp16, [1, 1280, 1, 224]> var_138_cast_fp16_5, tensor<fp16, [1, 1280, 1, 224]> var_138_cast_fp16_6, tensor<fp16, [1, 1280, 1, 224]> var_138_cast_fp16_7, tensor<fp16, [1, 1280, 1, 224]> var_138_cast_fp16_8, tensor<fp16, [1, 1280, 1, 224]> var_138_cast_fp16_9, tensor<fp16, [1, 1280, 1, 224]> var_138_cast_fp16_10, tensor<fp16, [1, 1280, 1, 224]> var_138_cast_fp16_11, tensor<fp16, [1, 1280, 1, 224]> var_138_cast_fp16_12, tensor<fp16, [1, 1280, 1, 224]> var_138_cast_fp16_13, tensor<fp16, [1, 1280, 1, 224]> var_138_cast_fp16_14, tensor<fp16, [1, 1280, 1, 224]> var_138_cast_fp16_15, tensor<fp16, [1, 1280, 1, 224]> var_138_cast_fp16_16, tensor<fp16, [1, 1280, 1, 224]> var_138_cast_fp16_17, tensor<fp16, [1, 1280, 1, 224]> var_138_cast_fp16_18, tensor<fp16, [1, 1280, 1, 224]> var_138_cast_fp16_19, tensor<fp16, [1, 1280, 1, 224]> var_138_cast_fp16_20, tensor<fp16, [1, 1280, 1, 224]> var_138_cast_fp16_21, tensor<fp16, [1, 1280, 1, 224]> var_138_cast_fp16_22, tensor<fp16, [1, 1280, 1, 224]> var_138_cast_fp16_23, tensor<fp16, [1, 1280, 1, 224]> var_138_cast_fp16_24, tensor<fp16, [1, 1280, 1, 224]> var_138_cast_fp16_25, tensor<fp16, [1, 1280, 1, 224]> var_138_cast_fp16_26, tensor<fp16, [1, 1280, 1, 224]> var_138_cast_fp16_27, tensor<fp16, [1, 1280, 1, 224]> var_138_cast_fp16_28, tensor<fp16, [1, 1280, 1, 224]> var_138_cast_fp16_29, tensor<fp16, [1, 1280, 1, 224]> var_138_cast_fp16_30, tensor<fp16, [1, 1280, 1, 224]> var_138_cast_fp16_31 = split(axis = var_138_axis_0, split_sizes = tile_1, x = value_cache)[name = tensor<string, []>("op_138_cast_fp16")];
tensor<int32, []> var_176 = const()[name = tensor<string, []>("op_176"), val = tensor<int32, []>(3)];
tensor<int32, []> var_183 = const()[name = tensor<string, []>("op_183"), val = tensor<int32, []>(1)];
tensor<bool, []> var_184 = const()[name = tensor<string, []>("op_184"), val = tensor<bool, []>(true)];
tensor<int32, [1]> var_196 = const()[name = tensor<string, []>("op_196"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_1_cast_fp16 = reduce_mean(axes = var_196, keep_dims = var_184, x = inputs_1_cast_fp16)[name = tensor<string, []>("channels_mean_1_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_1_cast_fp16 = sub(x = inputs_1_cast_fp16, y = channels_mean_1_cast_fp16)[name = tensor<string, []>("zero_mean_1_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_1_cast_fp16 = mul(x = zero_mean_1_cast_fp16, y = zero_mean_1_cast_fp16)[name = tensor<string, []>("zero_mean_sq_1_cast_fp16")];
tensor<int32, [1]> var_200 = const()[name = tensor<string, []>("op_200"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_201_cast_fp16 = reduce_mean(axes = var_200, keep_dims = var_184, x = zero_mean_sq_1_cast_fp16)[name = tensor<string, []>("op_201_cast_fp16")];
tensor<fp16, []> var_202_to_fp16 = const()[name = tensor<string, []>("op_202_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_203_cast_fp16 = add(x = var_201_cast_fp16, y = var_202_to_fp16)[name = tensor<string, []>("op_203_cast_fp16")];
tensor<fp16, []> denom_1_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_1_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_1_cast_fp16 = rsqrt(epsilon = denom_1_epsilon_0_to_fp16, x = var_203_cast_fp16)[name = tensor<string, []>("denom_1_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_1_cast_fp16 = mul(x = zero_mean_1_cast_fp16, y = denom_1_cast_fp16)[name = tensor<string, []>("out_1_cast_fp16")];
tensor<fp16, [1280]> obj_1_mean_0_to_fp16 = const()[name = tensor<string, []>("obj_1_mean_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133921472)))];
tensor<fp16, [1280]> obj_1_variance_0_to_fp16 = const()[name = tensor<string, []>("obj_1_variance_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133924096)))];
tensor<fp16, [1280]> obj_1_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_1_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133926720)))];
tensor<fp16, [1280]> obj_1_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_1_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133929344)))];
tensor<fp16, []> obj_1_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_1_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_1_cast_fp16 = batch_norm(beta = obj_1_beta_0_to_fp16, epsilon = obj_1_epsilon_0_to_fp16, gamma = obj_1_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_1_cast_fp16)[name = tensor<string, []>("obj_1_cast_fp16")];
tensor<int32, [2]> var_218 = const()[name = tensor<string, []>("op_218"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_220 = const()[name = tensor<string, []>("op_220"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_1_pad_type_0 = const()[name = tensor<string, []>("query_1_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_1_pad_0 = const()[name = tensor<string, []>("query_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_0_self_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_0_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133931968)))];
tensor<fp16, [1280]> layers_0_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_0_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(137208832)))];
tensor<fp16, [1, 1280, 1, 1]> query_1_cast_fp16 = conv(bias = layers_0_self_attn_q_proj_bias_to_fp16, dilations = var_220, groups = var_183, pad = query_1_pad_0, pad_type = query_1_pad_type_0, strides = var_218, weight = layers_0_self_attn_q_proj_weight_to_fp16, x = obj_1_cast_fp16)[name = tensor<string, []>("query_1_cast_fp16")];
tensor<int32, [2]> var_224 = const()[name = tensor<string, []>("op_224"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_226 = const()[name = tensor<string, []>("op_226"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_key_1_pad_type_0 = const()[name = tensor<string, []>("current_key_1_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_key_1_pad_0 = const()[name = tensor<string, []>("current_key_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_0_self_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_0_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(137211456)))];
tensor<fp16, [1, 1280, 1, 1]> current_key_1_cast_fp16 = conv(dilations = var_226, groups = var_183, pad = current_key_1_pad_0, pad_type = current_key_1_pad_type_0, strides = var_224, weight = layers_0_self_attn_k_proj_weight_to_fp16, x = obj_1_cast_fp16)[name = tensor<string, []>("current_key_1_cast_fp16")];
tensor<int32, [2]> var_231 = const()[name = tensor<string, []>("op_231"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_233 = const()[name = tensor<string, []>("op_233"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_value_1_pad_type_0 = const()[name = tensor<string, []>("current_value_1_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_value_1_pad_0 = const()[name = tensor<string, []>("current_value_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_0_self_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_0_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(140488320)))];
tensor<fp16, [1280]> layers_0_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_0_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(143765184)))];
tensor<fp16, [1, 1280, 1, 1]> current_value_1_cast_fp16 = conv(bias = layers_0_self_attn_v_proj_bias_to_fp16, dilations = var_233, groups = var_183, pad = current_value_1_pad_0, pad_type = current_value_1_pad_type_0, strides = var_231, weight = layers_0_self_attn_v_proj_weight_to_fp16, x = obj_1_cast_fp16)[name = tensor<string, []>("current_value_1_cast_fp16")];
tensor<int32, [1]> var_237_axes_0 = const()[name = tensor<string, []>("op_237_axes_0"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 224]> var_237_cast_fp16 = expand_dims(axes = var_237_axes_0, x = kv_cache_update_mask)[name = tensor<string, []>("op_237_cast_fp16")];
tensor<int32, [1]> var_238_axes_0 = const()[name = tensor<string, []>("op_238_axes_0"), val = tensor<int32, [1]>([2])];
tensor<fp16, [1, 1, 1, 224]> var_238_cast_fp16 = expand_dims(axes = var_238_axes_0, x = var_237_cast_fp16)[name = tensor<string, []>("op_238_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_240_cast_fp16 = mul(x = current_key_1_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_240_cast_fp16")];
tensor<fp16, []> var_177_to_fp16 = const()[name = tensor<string, []>("op_177_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 1, 1, 224]> var_241_cast_fp16 = sub(x = var_177_to_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_241_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_242_cast_fp16 = mul(x = var_103_cast_fp16_0, y = var_241_cast_fp16)[name = tensor<string, []>("op_242_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> key_1_cast_fp16 = add(x = var_240_cast_fp16, y = var_242_cast_fp16)[name = tensor<string, []>("key_1_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_244_cast_fp16 = mul(x = current_value_1_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_244_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_246_cast_fp16 = mul(x = var_138_cast_fp16_0, y = var_241_cast_fp16)[name = tensor<string, []>("op_246_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> value_1_cast_fp16 = add(x = var_244_cast_fp16, y = var_246_cast_fp16)[name = tensor<string, []>("value_1_cast_fp16")];
tensor<int32, [4]> var_249 = const()[name = tensor<string, []>("op_249"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_250_cast_fp16 = reshape(shape = var_249, x = query_1_cast_fp16)[name = tensor<string, []>("op_250_cast_fp16")];
tensor<fp16, []> var_251_to_fp16 = const()[name = tensor<string, []>("op_251_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_252_cast_fp16 = mul(x = var_250_cast_fp16, y = var_251_to_fp16)[name = tensor<string, []>("op_252_cast_fp16")];
tensor<int32, [4]> var_253 = const()[name = tensor<string, []>("op_253"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_254_cast_fp16 = reshape(shape = var_253, x = key_1_cast_fp16)[name = tensor<string, []>("op_254_cast_fp16")];
tensor<bool, []> mh_w_1_transpose_x_0 = const()[name = tensor<string, []>("mh_w_1_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_1_transpose_y_0 = const()[name = tensor<string, []>("mh_w_1_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 224]> mh_w_1_cast_fp16 = matmul(transpose_x = mh_w_1_transpose_x_0, transpose_y = mh_w_1_transpose_y_0, x = var_252_cast_fp16, y = var_254_cast_fp16)[name = tensor<string, []>("mh_w_1_cast_fp16")];
tensor<int32, [1]> var_258_axes_0 = const()[name = tensor<string, []>("op_258_axes_0"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 224]> var_258_cast_fp16 = expand_dims(axes = var_258_axes_0, x = decoder_key_padding_mask)[name = tensor<string, []>("op_258_cast_fp16")];
tensor<int32, [1]> var_259_axes_0 = const()[name = tensor<string, []>("op_259_axes_0"), val = tensor<int32, [1]>([2])];
tensor<fp16, [1, 1, 1, 224]> var_259_cast_fp16 = expand_dims(axes = var_259_axes_0, x = var_258_cast_fp16)[name = tensor<string, []>("op_259_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> mh_w_3_cast_fp16 = add(x = mh_w_1_cast_fp16, y = var_259_cast_fp16)[name = tensor<string, []>("mh_w_3_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> var_262_cast_fp16 = softmax(axis = var_176, x = mh_w_3_cast_fp16)[name = tensor<string, []>("op_262_cast_fp16")];
tensor<int32, [4]> var_263 = const()[name = tensor<string, []>("op_263"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_264_cast_fp16 = reshape(shape = var_263, x = value_1_cast_fp16)[name = tensor<string, []>("op_264_cast_fp16")];
tensor<bool, []> attn_1_transpose_x_0 = const()[name = tensor<string, []>("attn_1_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_1_transpose_y_0 = const()[name = tensor<string, []>("attn_1_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_1_cast_fp16 = matmul(transpose_x = attn_1_transpose_x_0, transpose_y = attn_1_transpose_y_0, x = var_264_cast_fp16, y = var_262_cast_fp16)[name = tensor<string, []>("attn_1_cast_fp16")];
tensor<int32, [4]> var_267 = const()[name = tensor<string, []>("op_267"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_1_cast_fp16 = reshape(shape = var_267, x = attn_1_cast_fp16)[name = tensor<string, []>("input_1_cast_fp16")];
tensor<int32, [2]> var_271 = const()[name = tensor<string, []>("op_271"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_273 = const()[name = tensor<string, []>("op_273"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_7_pad_type_0 = const()[name = tensor<string, []>("obj_7_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_7_pad_0 = const()[name = tensor<string, []>("obj_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_0_self_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_0_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(143767808)))];
tensor<fp16, [1280]> layers_0_self_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_0_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(147044672)))];
tensor<fp16, [1, 1280, 1, 1]> obj_7_cast_fp16 = conv(bias = layers_0_self_attn_o_proj_bias_to_fp16, dilations = var_273, groups = var_183, pad = obj_7_pad_0, pad_type = obj_7_pad_type_0, strides = var_271, weight = layers_0_self_attn_o_proj_weight_to_fp16, x = input_1_cast_fp16)[name = tensor<string, []>("obj_7_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_3_cast_fp16 = add(x = inputs_1_cast_fp16, y = obj_7_cast_fp16)[name = tensor<string, []>("inputs_3_cast_fp16")];
tensor<int32, [1]> var_283 = const()[name = tensor<string, []>("op_283"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_3_cast_fp16 = reduce_mean(axes = var_283, keep_dims = var_184, x = inputs_3_cast_fp16)[name = tensor<string, []>("channels_mean_3_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_3_cast_fp16 = sub(x = inputs_3_cast_fp16, y = channels_mean_3_cast_fp16)[name = tensor<string, []>("zero_mean_3_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_3_cast_fp16 = mul(x = zero_mean_3_cast_fp16, y = zero_mean_3_cast_fp16)[name = tensor<string, []>("zero_mean_sq_3_cast_fp16")];
tensor<int32, [1]> var_287 = const()[name = tensor<string, []>("op_287"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_288_cast_fp16 = reduce_mean(axes = var_287, keep_dims = var_184, x = zero_mean_sq_3_cast_fp16)[name = tensor<string, []>("op_288_cast_fp16")];
tensor<fp16, []> var_289_to_fp16 = const()[name = tensor<string, []>("op_289_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_290_cast_fp16 = add(x = var_288_cast_fp16, y = var_289_to_fp16)[name = tensor<string, []>("op_290_cast_fp16")];
tensor<fp16, []> denom_3_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_3_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_3_cast_fp16 = rsqrt(epsilon = denom_3_epsilon_0_to_fp16, x = var_290_cast_fp16)[name = tensor<string, []>("denom_3_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_3_cast_fp16 = mul(x = zero_mean_3_cast_fp16, y = denom_3_cast_fp16)[name = tensor<string, []>("out_3_cast_fp16")];
tensor<fp16, [1280]> obj_9_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_9_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(147047296)))];
tensor<fp16, [1280]> obj_9_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_9_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(147049920)))];
tensor<fp16, []> obj_9_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_9_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_9_cast_fp16 = batch_norm(beta = obj_9_beta_0_to_fp16, epsilon = obj_9_epsilon_0_to_fp16, gamma = obj_9_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_3_cast_fp16)[name = tensor<string, []>("obj_9_cast_fp16")];
tensor<int32, [2]> var_305 = const()[name = tensor<string, []>("op_305"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_307 = const()[name = tensor<string, []>("op_307"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_3_pad_type_0 = const()[name = tensor<string, []>("query_3_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_3_pad_0 = const()[name = tensor<string, []>("query_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_0_encoder_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_0_encoder_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(147052544)))];
tensor<fp16, [1280]> layers_0_encoder_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_0_encoder_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(150329408)))];
tensor<fp16, [1, 1280, 1, 1]> query_3_cast_fp16 = conv(bias = layers_0_encoder_attn_q_proj_bias_to_fp16, dilations = var_307, groups = var_183, pad = query_3_pad_0, pad_type = query_3_pad_type_0, strides = var_305, weight = layers_0_encoder_attn_q_proj_weight_to_fp16, x = obj_9_cast_fp16)[name = tensor<string, []>("query_3_cast_fp16")];
tensor<int32, [2]> var_311 = const()[name = tensor<string, []>("op_311"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_313 = const()[name = tensor<string, []>("op_313"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> key_3_pad_type_0 = const()[name = tensor<string, []>("key_3_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> key_3_pad_0 = const()[name = tensor<string, []>("key_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_0_encoder_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_0_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(150332032)))];
tensor<fp16, [1, 1280, 1, 1500]> key_3_cast_fp16 = conv(dilations = var_313, groups = var_183, pad = key_3_pad_0, pad_type = key_3_pad_type_0, strides = var_311, weight = layers_0_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("key_3_cast_fp16")];
tensor<int32, [2]> var_318 = const()[name = tensor<string, []>("op_318"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_320 = const()[name = tensor<string, []>("op_320"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> value_3_pad_type_0 = const()[name = tensor<string, []>("value_3_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> value_3_pad_0 = const()[name = tensor<string, []>("value_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_0_encoder_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_0_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(153608896)))];
tensor<fp16, [1280]> layers_0_encoder_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_0_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(156885760)))];
tensor<fp16, [1, 1280, 1, 1500]> value_3_cast_fp16 = conv(bias = layers_0_encoder_attn_v_proj_bias_to_fp16, dilations = var_320, groups = var_183, pad = value_3_pad_0, pad_type = value_3_pad_type_0, strides = var_318, weight = layers_0_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("value_3_cast_fp16")];
tensor<int32, [4]> var_324 = const()[name = tensor<string, []>("op_324"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_325_cast_fp16 = reshape(shape = var_324, x = query_3_cast_fp16)[name = tensor<string, []>("op_325_cast_fp16")];
tensor<fp16, []> var_326_to_fp16 = const()[name = tensor<string, []>("op_326_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_327_cast_fp16 = mul(x = var_325_cast_fp16, y = var_326_to_fp16)[name = tensor<string, []>("op_327_cast_fp16")];
tensor<int32, [4]> var_328 = const()[name = tensor<string, []>("op_328"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_329_cast_fp16 = reshape(shape = var_328, x = key_3_cast_fp16)[name = tensor<string, []>("op_329_cast_fp16")];
tensor<bool, []> mh_w_5_transpose_x_0 = const()[name = tensor<string, []>("mh_w_5_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_5_transpose_y_0 = const()[name = tensor<string, []>("mh_w_5_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 1500]> mh_w_5_cast_fp16 = matmul(transpose_x = mh_w_5_transpose_x_0, transpose_y = mh_w_5_transpose_y_0, x = var_327_cast_fp16, y = var_329_cast_fp16)[name = tensor<string, []>("mh_w_5_cast_fp16")];
tensor<fp16, [1, 20, 1, 1500]> obj_13_cast_fp16 = softmax(axis = var_176, x = mh_w_5_cast_fp16)[name = tensor<string, []>("obj_13_cast_fp16")];
tensor<int32, [4]> var_333 = const()[name = tensor<string, []>("op_333"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_334_cast_fp16 = reshape(shape = var_333, x = value_3_cast_fp16)[name = tensor<string, []>("op_334_cast_fp16")];
tensor<bool, []> attn_3_transpose_x_0 = const()[name = tensor<string, []>("attn_3_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_3_transpose_y_0 = const()[name = tensor<string, []>("attn_3_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_3_cast_fp16 = matmul(transpose_x = attn_3_transpose_x_0, transpose_y = attn_3_transpose_y_0, x = var_334_cast_fp16, y = obj_13_cast_fp16)[name = tensor<string, []>("attn_3_cast_fp16")];
tensor<int32, [4]> var_337 = const()[name = tensor<string, []>("op_337"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_3_cast_fp16 = reshape(shape = var_337, x = attn_3_cast_fp16)[name = tensor<string, []>("input_3_cast_fp16")];
tensor<int32, [2]> var_341 = const()[name = tensor<string, []>("op_341"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_343 = const()[name = tensor<string, []>("op_343"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_11_pad_type_0 = const()[name = tensor<string, []>("obj_11_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_11_pad_0 = const()[name = tensor<string, []>("obj_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_0_encoder_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_0_encoder_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(156888384)))];
tensor<fp16, [1280]> layers_0_encoder_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_0_encoder_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(160165248)))];
tensor<fp16, [1, 1280, 1, 1]> obj_11_cast_fp16 = conv(bias = layers_0_encoder_attn_o_proj_bias_to_fp16, dilations = var_343, groups = var_183, pad = obj_11_pad_0, pad_type = obj_11_pad_type_0, strides = var_341, weight = layers_0_encoder_attn_o_proj_weight_to_fp16, x = input_3_cast_fp16)[name = tensor<string, []>("obj_11_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_5_cast_fp16 = add(x = inputs_3_cast_fp16, y = obj_11_cast_fp16)[name = tensor<string, []>("inputs_5_cast_fp16")];
tensor<int32, [1]> var_349 = const()[name = tensor<string, []>("op_349"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_5_cast_fp16 = reduce_mean(axes = var_349, keep_dims = var_184, x = inputs_5_cast_fp16)[name = tensor<string, []>("channels_mean_5_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_5_cast_fp16 = sub(x = inputs_5_cast_fp16, y = channels_mean_5_cast_fp16)[name = tensor<string, []>("zero_mean_5_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_5_cast_fp16 = mul(x = zero_mean_5_cast_fp16, y = zero_mean_5_cast_fp16)[name = tensor<string, []>("zero_mean_sq_5_cast_fp16")];
tensor<int32, [1]> var_353 = const()[name = tensor<string, []>("op_353"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_354_cast_fp16 = reduce_mean(axes = var_353, keep_dims = var_184, x = zero_mean_sq_5_cast_fp16)[name = tensor<string, []>("op_354_cast_fp16")];
tensor<fp16, []> var_355_to_fp16 = const()[name = tensor<string, []>("op_355_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_356_cast_fp16 = add(x = var_354_cast_fp16, y = var_355_to_fp16)[name = tensor<string, []>("op_356_cast_fp16")];
tensor<fp16, []> denom_5_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_5_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_5_cast_fp16 = rsqrt(epsilon = denom_5_epsilon_0_to_fp16, x = var_356_cast_fp16)[name = tensor<string, []>("denom_5_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_5_cast_fp16 = mul(x = zero_mean_5_cast_fp16, y = denom_5_cast_fp16)[name = tensor<string, []>("out_5_cast_fp16")];
tensor<fp16, [1280]> input_5_gamma_0_to_fp16 = const()[name = tensor<string, []>("input_5_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(160167872)))];
tensor<fp16, [1280]> input_5_beta_0_to_fp16 = const()[name = tensor<string, []>("input_5_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(160170496)))];
tensor<fp16, []> input_5_epsilon_0_to_fp16 = const()[name = tensor<string, []>("input_5_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> input_5_cast_fp16 = batch_norm(beta = input_5_beta_0_to_fp16, epsilon = input_5_epsilon_0_to_fp16, gamma = input_5_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_5_cast_fp16)[name = tensor<string, []>("input_5_cast_fp16")];
tensor<int32, [2]> var_367 = const()[name = tensor<string, []>("op_367"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_369 = const()[name = tensor<string, []>("op_369"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_7_pad_type_0 = const()[name = tensor<string, []>("input_7_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_7_pad_0 = const()[name = tensor<string, []>("input_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [5120, 1280, 1, 1]> layers_0_fc1_weight_to_fp16 = const()[name = tensor<string, []>("layers_0_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(160173120)))];
tensor<fp16, [5120]> layers_0_fc1_bias_to_fp16 = const()[name = tensor<string, []>("layers_0_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(173280384)))];
tensor<fp16, [1, 5120, 1, 1]> input_7_cast_fp16 = conv(bias = layers_0_fc1_bias_to_fp16, dilations = var_369, groups = var_183, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = var_367, weight = layers_0_fc1_weight_to_fp16, x = input_5_cast_fp16)[name = tensor<string, []>("input_7_cast_fp16")];
tensor<string, []> input_9_mode_0 = const()[name = tensor<string, []>("input_9_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 5120, 1, 1]> input_9_cast_fp16 = gelu(mode = input_9_mode_0, x = input_7_cast_fp16)[name = tensor<string, []>("input_9_cast_fp16")];
tensor<int32, [2]> var_375 = const()[name = tensor<string, []>("op_375"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_377 = const()[name = tensor<string, []>("op_377"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_3_pad_type_0 = const()[name = tensor<string, []>("hidden_states_3_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_3_pad_0 = const()[name = tensor<string, []>("hidden_states_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 5120, 1, 1]> layers_0_fc2_weight_to_fp16 = const()[name = tensor<string, []>("layers_0_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(173290688)))];
tensor<fp16, [1280]> layers_0_fc2_bias_to_fp16 = const()[name = tensor<string, []>("layers_0_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(186397952)))];
tensor<fp16, [1, 1280, 1, 1]> hidden_states_3_cast_fp16 = conv(bias = layers_0_fc2_bias_to_fp16, dilations = var_377, groups = var_183, pad = hidden_states_3_pad_0, pad_type = hidden_states_3_pad_type_0, strides = var_375, weight = layers_0_fc2_weight_to_fp16, x = input_9_cast_fp16)[name = tensor<string, []>("hidden_states_3_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_7_cast_fp16 = add(x = inputs_5_cast_fp16, y = hidden_states_3_cast_fp16)[name = tensor<string, []>("inputs_7_cast_fp16")];
tensor<int32, []> var_390 = const()[name = tensor<string, []>("op_390"), val = tensor<int32, []>(3)];
tensor<int32, []> var_397 = const()[name = tensor<string, []>("op_397"), val = tensor<int32, []>(1)];
tensor<bool, []> var_398 = const()[name = tensor<string, []>("op_398"), val = tensor<bool, []>(true)];
tensor<int32, [1]> var_410 = const()[name = tensor<string, []>("op_410"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_7_cast_fp16 = reduce_mean(axes = var_410, keep_dims = var_398, x = inputs_7_cast_fp16)[name = tensor<string, []>("channels_mean_7_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_7_cast_fp16 = sub(x = inputs_7_cast_fp16, y = channels_mean_7_cast_fp16)[name = tensor<string, []>("zero_mean_7_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_7_cast_fp16 = mul(x = zero_mean_7_cast_fp16, y = zero_mean_7_cast_fp16)[name = tensor<string, []>("zero_mean_sq_7_cast_fp16")];
tensor<int32, [1]> var_414 = const()[name = tensor<string, []>("op_414"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_415_cast_fp16 = reduce_mean(axes = var_414, keep_dims = var_398, x = zero_mean_sq_7_cast_fp16)[name = tensor<string, []>("op_415_cast_fp16")];
tensor<fp16, []> var_416_to_fp16 = const()[name = tensor<string, []>("op_416_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_417_cast_fp16 = add(x = var_415_cast_fp16, y = var_416_to_fp16)[name = tensor<string, []>("op_417_cast_fp16")];
tensor<fp16, []> denom_7_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_7_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_7_cast_fp16 = rsqrt(epsilon = denom_7_epsilon_0_to_fp16, x = var_417_cast_fp16)[name = tensor<string, []>("denom_7_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_7_cast_fp16 = mul(x = zero_mean_7_cast_fp16, y = denom_7_cast_fp16)[name = tensor<string, []>("out_7_cast_fp16")];
tensor<fp16, [1280]> obj_15_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_15_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(186400576)))];
tensor<fp16, [1280]> obj_15_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_15_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(186403200)))];
tensor<fp16, []> obj_15_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_15_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_15_cast_fp16 = batch_norm(beta = obj_15_beta_0_to_fp16, epsilon = obj_15_epsilon_0_to_fp16, gamma = obj_15_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_7_cast_fp16)[name = tensor<string, []>("obj_15_cast_fp16")];
tensor<int32, [2]> var_432 = const()[name = tensor<string, []>("op_432"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_434 = const()[name = tensor<string, []>("op_434"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_5_pad_type_0 = const()[name = tensor<string, []>("query_5_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_5_pad_0 = const()[name = tensor<string, []>("query_5_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_1_self_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_1_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(186405824)))];
tensor<fp16, [1280]> layers_1_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_1_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(189682688)))];
tensor<fp16, [1, 1280, 1, 1]> query_5_cast_fp16 = conv(bias = layers_1_self_attn_q_proj_bias_to_fp16, dilations = var_434, groups = var_397, pad = query_5_pad_0, pad_type = query_5_pad_type_0, strides = var_432, weight = layers_1_self_attn_q_proj_weight_to_fp16, x = obj_15_cast_fp16)[name = tensor<string, []>("query_5_cast_fp16")];
tensor<int32, [2]> var_438 = const()[name = tensor<string, []>("op_438"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_440 = const()[name = tensor<string, []>("op_440"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_key_3_pad_type_0 = const()[name = tensor<string, []>("current_key_3_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_key_3_pad_0 = const()[name = tensor<string, []>("current_key_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_1_self_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_1_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(189685312)))];
tensor<fp16, [1, 1280, 1, 1]> current_key_3_cast_fp16 = conv(dilations = var_440, groups = var_397, pad = current_key_3_pad_0, pad_type = current_key_3_pad_type_0, strides = var_438, weight = layers_1_self_attn_k_proj_weight_to_fp16, x = obj_15_cast_fp16)[name = tensor<string, []>("current_key_3_cast_fp16")];
tensor<int32, [2]> var_445 = const()[name = tensor<string, []>("op_445"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_447 = const()[name = tensor<string, []>("op_447"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_value_3_pad_type_0 = const()[name = tensor<string, []>("current_value_3_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_value_3_pad_0 = const()[name = tensor<string, []>("current_value_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_1_self_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_1_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(192962176)))];
tensor<fp16, [1280]> layers_1_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_1_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(196239040)))];
tensor<fp16, [1, 1280, 1, 1]> current_value_3_cast_fp16 = conv(bias = layers_1_self_attn_v_proj_bias_to_fp16, dilations = var_447, groups = var_397, pad = current_value_3_pad_0, pad_type = current_value_3_pad_type_0, strides = var_445, weight = layers_1_self_attn_v_proj_weight_to_fp16, x = obj_15_cast_fp16)[name = tensor<string, []>("current_value_3_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_454_cast_fp16 = mul(x = current_key_3_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_454_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_456_cast_fp16 = mul(x = var_103_cast_fp16_1, y = var_241_cast_fp16)[name = tensor<string, []>("op_456_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> key_5_cast_fp16 = add(x = var_454_cast_fp16, y = var_456_cast_fp16)[name = tensor<string, []>("key_5_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_458_cast_fp16 = mul(x = current_value_3_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_458_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_460_cast_fp16 = mul(x = var_138_cast_fp16_1, y = var_241_cast_fp16)[name = tensor<string, []>("op_460_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> value_5_cast_fp16 = add(x = var_458_cast_fp16, y = var_460_cast_fp16)[name = tensor<string, []>("value_5_cast_fp16")];
tensor<int32, [4]> var_463 = const()[name = tensor<string, []>("op_463"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_464_cast_fp16 = reshape(shape = var_463, x = query_5_cast_fp16)[name = tensor<string, []>("op_464_cast_fp16")];
tensor<fp16, []> var_465_to_fp16 = const()[name = tensor<string, []>("op_465_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_466_cast_fp16 = mul(x = var_464_cast_fp16, y = var_465_to_fp16)[name = tensor<string, []>("op_466_cast_fp16")];
tensor<int32, [4]> var_467 = const()[name = tensor<string, []>("op_467"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_468_cast_fp16 = reshape(shape = var_467, x = key_5_cast_fp16)[name = tensor<string, []>("op_468_cast_fp16")];
tensor<bool, []> mh_w_7_transpose_x_0 = const()[name = tensor<string, []>("mh_w_7_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_7_transpose_y_0 = const()[name = tensor<string, []>("mh_w_7_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 224]> mh_w_7_cast_fp16 = matmul(transpose_x = mh_w_7_transpose_x_0, transpose_y = mh_w_7_transpose_y_0, x = var_466_cast_fp16, y = var_468_cast_fp16)[name = tensor<string, []>("mh_w_7_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> mh_w_9_cast_fp16 = add(x = mh_w_7_cast_fp16, y = var_259_cast_fp16)[name = tensor<string, []>("mh_w_9_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> var_476_cast_fp16 = softmax(axis = var_390, x = mh_w_9_cast_fp16)[name = tensor<string, []>("op_476_cast_fp16")];
tensor<int32, [4]> var_477 = const()[name = tensor<string, []>("op_477"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_478_cast_fp16 = reshape(shape = var_477, x = value_5_cast_fp16)[name = tensor<string, []>("op_478_cast_fp16")];
tensor<bool, []> attn_5_transpose_x_0 = const()[name = tensor<string, []>("attn_5_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_5_transpose_y_0 = const()[name = tensor<string, []>("attn_5_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_5_cast_fp16 = matmul(transpose_x = attn_5_transpose_x_0, transpose_y = attn_5_transpose_y_0, x = var_478_cast_fp16, y = var_476_cast_fp16)[name = tensor<string, []>("attn_5_cast_fp16")];
tensor<int32, [4]> var_481 = const()[name = tensor<string, []>("op_481"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_11_cast_fp16 = reshape(shape = var_481, x = attn_5_cast_fp16)[name = tensor<string, []>("input_11_cast_fp16")];
tensor<int32, [2]> var_485 = const()[name = tensor<string, []>("op_485"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_487 = const()[name = tensor<string, []>("op_487"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_21_pad_type_0 = const()[name = tensor<string, []>("obj_21_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_21_pad_0 = const()[name = tensor<string, []>("obj_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_1_self_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_1_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(196241664)))];
tensor<fp16, [1280]> layers_1_self_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_1_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(199518528)))];
tensor<fp16, [1, 1280, 1, 1]> obj_21_cast_fp16 = conv(bias = layers_1_self_attn_o_proj_bias_to_fp16, dilations = var_487, groups = var_397, pad = obj_21_pad_0, pad_type = obj_21_pad_type_0, strides = var_485, weight = layers_1_self_attn_o_proj_weight_to_fp16, x = input_11_cast_fp16)[name = tensor<string, []>("obj_21_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_9_cast_fp16 = add(x = inputs_7_cast_fp16, y = obj_21_cast_fp16)[name = tensor<string, []>("inputs_9_cast_fp16")];
tensor<int32, [1]> var_497 = const()[name = tensor<string, []>("op_497"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_9_cast_fp16 = reduce_mean(axes = var_497, keep_dims = var_398, x = inputs_9_cast_fp16)[name = tensor<string, []>("channels_mean_9_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_9_cast_fp16 = sub(x = inputs_9_cast_fp16, y = channels_mean_9_cast_fp16)[name = tensor<string, []>("zero_mean_9_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_9_cast_fp16 = mul(x = zero_mean_9_cast_fp16, y = zero_mean_9_cast_fp16)[name = tensor<string, []>("zero_mean_sq_9_cast_fp16")];
tensor<int32, [1]> var_501 = const()[name = tensor<string, []>("op_501"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_502_cast_fp16 = reduce_mean(axes = var_501, keep_dims = var_398, x = zero_mean_sq_9_cast_fp16)[name = tensor<string, []>("op_502_cast_fp16")];
tensor<fp16, []> var_503_to_fp16 = const()[name = tensor<string, []>("op_503_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_504_cast_fp16 = add(x = var_502_cast_fp16, y = var_503_to_fp16)[name = tensor<string, []>("op_504_cast_fp16")];
tensor<fp16, []> denom_9_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_9_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_9_cast_fp16 = rsqrt(epsilon = denom_9_epsilon_0_to_fp16, x = var_504_cast_fp16)[name = tensor<string, []>("denom_9_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_9_cast_fp16 = mul(x = zero_mean_9_cast_fp16, y = denom_9_cast_fp16)[name = tensor<string, []>("out_9_cast_fp16")];
tensor<fp16, [1280]> obj_23_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_23_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(199521152)))];
tensor<fp16, [1280]> obj_23_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_23_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(199523776)))];
tensor<fp16, []> obj_23_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_23_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_23_cast_fp16 = batch_norm(beta = obj_23_beta_0_to_fp16, epsilon = obj_23_epsilon_0_to_fp16, gamma = obj_23_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_9_cast_fp16)[name = tensor<string, []>("obj_23_cast_fp16")];
tensor<int32, [2]> var_519 = const()[name = tensor<string, []>("op_519"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_521 = const()[name = tensor<string, []>("op_521"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_7_pad_type_0 = const()[name = tensor<string, []>("query_7_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_7_pad_0 = const()[name = tensor<string, []>("query_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_1_encoder_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_1_encoder_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(199526400)))];
tensor<fp16, [1280]> layers_1_encoder_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_1_encoder_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(202803264)))];
tensor<fp16, [1, 1280, 1, 1]> query_7_cast_fp16 = conv(bias = layers_1_encoder_attn_q_proj_bias_to_fp16, dilations = var_521, groups = var_397, pad = query_7_pad_0, pad_type = query_7_pad_type_0, strides = var_519, weight = layers_1_encoder_attn_q_proj_weight_to_fp16, x = obj_23_cast_fp16)[name = tensor<string, []>("query_7_cast_fp16")];
tensor<int32, [2]> var_525 = const()[name = tensor<string, []>("op_525"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_527 = const()[name = tensor<string, []>("op_527"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> key_7_pad_type_0 = const()[name = tensor<string, []>("key_7_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> key_7_pad_0 = const()[name = tensor<string, []>("key_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_1_encoder_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_1_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(202805888)))];
tensor<fp16, [1, 1280, 1, 1500]> key_7_cast_fp16 = conv(dilations = var_527, groups = var_397, pad = key_7_pad_0, pad_type = key_7_pad_type_0, strides = var_525, weight = layers_1_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("key_7_cast_fp16")];
tensor<int32, [2]> var_532 = const()[name = tensor<string, []>("op_532"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_534 = const()[name = tensor<string, []>("op_534"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> value_7_pad_type_0 = const()[name = tensor<string, []>("value_7_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> value_7_pad_0 = const()[name = tensor<string, []>("value_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_1_encoder_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_1_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(206082752)))];
tensor<fp16, [1280]> layers_1_encoder_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_1_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(209359616)))];
tensor<fp16, [1, 1280, 1, 1500]> value_7_cast_fp16 = conv(bias = layers_1_encoder_attn_v_proj_bias_to_fp16, dilations = var_534, groups = var_397, pad = value_7_pad_0, pad_type = value_7_pad_type_0, strides = var_532, weight = layers_1_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("value_7_cast_fp16")];
tensor<int32, [4]> var_538 = const()[name = tensor<string, []>("op_538"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_539_cast_fp16 = reshape(shape = var_538, x = query_7_cast_fp16)[name = tensor<string, []>("op_539_cast_fp16")];
tensor<fp16, []> var_540_to_fp16 = const()[name = tensor<string, []>("op_540_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_541_cast_fp16 = mul(x = var_539_cast_fp16, y = var_540_to_fp16)[name = tensor<string, []>("op_541_cast_fp16")];
tensor<int32, [4]> var_542 = const()[name = tensor<string, []>("op_542"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_543_cast_fp16 = reshape(shape = var_542, x = key_7_cast_fp16)[name = tensor<string, []>("op_543_cast_fp16")];
tensor<bool, []> mh_w_11_transpose_x_0 = const()[name = tensor<string, []>("mh_w_11_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_11_transpose_y_0 = const()[name = tensor<string, []>("mh_w_11_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 1500]> mh_w_11_cast_fp16 = matmul(transpose_x = mh_w_11_transpose_x_0, transpose_y = mh_w_11_transpose_y_0, x = var_541_cast_fp16, y = var_543_cast_fp16)[name = tensor<string, []>("mh_w_11_cast_fp16")];
tensor<fp16, [1, 20, 1, 1500]> obj_27_cast_fp16 = softmax(axis = var_390, x = mh_w_11_cast_fp16)[name = tensor<string, []>("obj_27_cast_fp16")];
tensor<int32, [4]> var_547 = const()[name = tensor<string, []>("op_547"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_548_cast_fp16 = reshape(shape = var_547, x = value_7_cast_fp16)[name = tensor<string, []>("op_548_cast_fp16")];
tensor<bool, []> attn_7_transpose_x_0 = const()[name = tensor<string, []>("attn_7_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_7_transpose_y_0 = const()[name = tensor<string, []>("attn_7_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_7_cast_fp16 = matmul(transpose_x = attn_7_transpose_x_0, transpose_y = attn_7_transpose_y_0, x = var_548_cast_fp16, y = obj_27_cast_fp16)[name = tensor<string, []>("attn_7_cast_fp16")];
tensor<int32, [4]> var_551 = const()[name = tensor<string, []>("op_551"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_13_cast_fp16 = reshape(shape = var_551, x = attn_7_cast_fp16)[name = tensor<string, []>("input_13_cast_fp16")];
tensor<int32, [2]> var_555 = const()[name = tensor<string, []>("op_555"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_557 = const()[name = tensor<string, []>("op_557"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_25_pad_type_0 = const()[name = tensor<string, []>("obj_25_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_25_pad_0 = const()[name = tensor<string, []>("obj_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_1_encoder_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_1_encoder_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(209362240)))];
tensor<fp16, [1280]> layers_1_encoder_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_1_encoder_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(212639104)))];
tensor<fp16, [1, 1280, 1, 1]> obj_25_cast_fp16 = conv(bias = layers_1_encoder_attn_o_proj_bias_to_fp16, dilations = var_557, groups = var_397, pad = obj_25_pad_0, pad_type = obj_25_pad_type_0, strides = var_555, weight = layers_1_encoder_attn_o_proj_weight_to_fp16, x = input_13_cast_fp16)[name = tensor<string, []>("obj_25_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_11_cast_fp16 = add(x = inputs_9_cast_fp16, y = obj_25_cast_fp16)[name = tensor<string, []>("inputs_11_cast_fp16")];
tensor<int32, [1]> var_563 = const()[name = tensor<string, []>("op_563"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_11_cast_fp16 = reduce_mean(axes = var_563, keep_dims = var_398, x = inputs_11_cast_fp16)[name = tensor<string, []>("channels_mean_11_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_11_cast_fp16 = sub(x = inputs_11_cast_fp16, y = channels_mean_11_cast_fp16)[name = tensor<string, []>("zero_mean_11_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_11_cast_fp16 = mul(x = zero_mean_11_cast_fp16, y = zero_mean_11_cast_fp16)[name = tensor<string, []>("zero_mean_sq_11_cast_fp16")];
tensor<int32, [1]> var_567 = const()[name = tensor<string, []>("op_567"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_568_cast_fp16 = reduce_mean(axes = var_567, keep_dims = var_398, x = zero_mean_sq_11_cast_fp16)[name = tensor<string, []>("op_568_cast_fp16")];
tensor<fp16, []> var_569_to_fp16 = const()[name = tensor<string, []>("op_569_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_570_cast_fp16 = add(x = var_568_cast_fp16, y = var_569_to_fp16)[name = tensor<string, []>("op_570_cast_fp16")];
tensor<fp16, []> denom_11_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_11_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_11_cast_fp16 = rsqrt(epsilon = denom_11_epsilon_0_to_fp16, x = var_570_cast_fp16)[name = tensor<string, []>("denom_11_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_11_cast_fp16 = mul(x = zero_mean_11_cast_fp16, y = denom_11_cast_fp16)[name = tensor<string, []>("out_11_cast_fp16")];
tensor<fp16, [1280]> input_15_gamma_0_to_fp16 = const()[name = tensor<string, []>("input_15_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(212641728)))];
tensor<fp16, [1280]> input_15_beta_0_to_fp16 = const()[name = tensor<string, []>("input_15_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(212644352)))];
tensor<fp16, []> input_15_epsilon_0_to_fp16 = const()[name = tensor<string, []>("input_15_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> input_15_cast_fp16 = batch_norm(beta = input_15_beta_0_to_fp16, epsilon = input_15_epsilon_0_to_fp16, gamma = input_15_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_11_cast_fp16)[name = tensor<string, []>("input_15_cast_fp16")];
tensor<int32, [2]> var_581 = const()[name = tensor<string, []>("op_581"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_583 = const()[name = tensor<string, []>("op_583"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_17_pad_type_0 = const()[name = tensor<string, []>("input_17_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_17_pad_0 = const()[name = tensor<string, []>("input_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [5120, 1280, 1, 1]> layers_1_fc1_weight_to_fp16 = const()[name = tensor<string, []>("layers_1_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(212646976)))];
tensor<fp16, [5120]> layers_1_fc1_bias_to_fp16 = const()[name = tensor<string, []>("layers_1_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(225754240)))];
tensor<fp16, [1, 5120, 1, 1]> input_17_cast_fp16 = conv(bias = layers_1_fc1_bias_to_fp16, dilations = var_583, groups = var_397, pad = input_17_pad_0, pad_type = input_17_pad_type_0, strides = var_581, weight = layers_1_fc1_weight_to_fp16, x = input_15_cast_fp16)[name = tensor<string, []>("input_17_cast_fp16")];
tensor<string, []> input_19_mode_0 = const()[name = tensor<string, []>("input_19_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 5120, 1, 1]> input_19_cast_fp16 = gelu(mode = input_19_mode_0, x = input_17_cast_fp16)[name = tensor<string, []>("input_19_cast_fp16")];
tensor<int32, [2]> var_589 = const()[name = tensor<string, []>("op_589"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_591 = const()[name = tensor<string, []>("op_591"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_5_pad_type_0 = const()[name = tensor<string, []>("hidden_states_5_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_5_pad_0 = const()[name = tensor<string, []>("hidden_states_5_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 5120, 1, 1]> layers_1_fc2_weight_to_fp16 = const()[name = tensor<string, []>("layers_1_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(225764544)))];
tensor<fp16, [1280]> layers_1_fc2_bias_to_fp16 = const()[name = tensor<string, []>("layers_1_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(238871808)))];
tensor<fp16, [1, 1280, 1, 1]> hidden_states_5_cast_fp16 = conv(bias = layers_1_fc2_bias_to_fp16, dilations = var_591, groups = var_397, pad = hidden_states_5_pad_0, pad_type = hidden_states_5_pad_type_0, strides = var_589, weight = layers_1_fc2_weight_to_fp16, x = input_19_cast_fp16)[name = tensor<string, []>("hidden_states_5_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_13_cast_fp16 = add(x = inputs_11_cast_fp16, y = hidden_states_5_cast_fp16)[name = tensor<string, []>("inputs_13_cast_fp16")];
tensor<int32, []> var_604 = const()[name = tensor<string, []>("op_604"), val = tensor<int32, []>(3)];
tensor<int32, []> var_611 = const()[name = tensor<string, []>("op_611"), val = tensor<int32, []>(1)];
tensor<bool, []> var_612 = const()[name = tensor<string, []>("op_612"), val = tensor<bool, []>(true)];
tensor<int32, [1]> var_624 = const()[name = tensor<string, []>("op_624"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_13_cast_fp16 = reduce_mean(axes = var_624, keep_dims = var_612, x = inputs_13_cast_fp16)[name = tensor<string, []>("channels_mean_13_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_13_cast_fp16 = sub(x = inputs_13_cast_fp16, y = channels_mean_13_cast_fp16)[name = tensor<string, []>("zero_mean_13_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_13_cast_fp16 = mul(x = zero_mean_13_cast_fp16, y = zero_mean_13_cast_fp16)[name = tensor<string, []>("zero_mean_sq_13_cast_fp16")];
tensor<int32, [1]> var_628 = const()[name = tensor<string, []>("op_628"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_629_cast_fp16 = reduce_mean(axes = var_628, keep_dims = var_612, x = zero_mean_sq_13_cast_fp16)[name = tensor<string, []>("op_629_cast_fp16")];
tensor<fp16, []> var_630_to_fp16 = const()[name = tensor<string, []>("op_630_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_631_cast_fp16 = add(x = var_629_cast_fp16, y = var_630_to_fp16)[name = tensor<string, []>("op_631_cast_fp16")];
tensor<fp16, []> denom_13_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_13_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_13_cast_fp16 = rsqrt(epsilon = denom_13_epsilon_0_to_fp16, x = var_631_cast_fp16)[name = tensor<string, []>("denom_13_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_13_cast_fp16 = mul(x = zero_mean_13_cast_fp16, y = denom_13_cast_fp16)[name = tensor<string, []>("out_13_cast_fp16")];
tensor<fp16, [1280]> obj_29_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_29_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(238874432)))];
tensor<fp16, [1280]> obj_29_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_29_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(238877056)))];
tensor<fp16, []> obj_29_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_29_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_29_cast_fp16 = batch_norm(beta = obj_29_beta_0_to_fp16, epsilon = obj_29_epsilon_0_to_fp16, gamma = obj_29_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_13_cast_fp16)[name = tensor<string, []>("obj_29_cast_fp16")];
tensor<int32, [2]> var_646 = const()[name = tensor<string, []>("op_646"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_648 = const()[name = tensor<string, []>("op_648"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_9_pad_type_0 = const()[name = tensor<string, []>("query_9_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_9_pad_0 = const()[name = tensor<string, []>("query_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_2_self_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_2_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(238879680)))];
tensor<fp16, [1280]> layers_2_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_2_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(242156544)))];
tensor<fp16, [1, 1280, 1, 1]> query_9_cast_fp16 = conv(bias = layers_2_self_attn_q_proj_bias_to_fp16, dilations = var_648, groups = var_611, pad = query_9_pad_0, pad_type = query_9_pad_type_0, strides = var_646, weight = layers_2_self_attn_q_proj_weight_to_fp16, x = obj_29_cast_fp16)[name = tensor<string, []>("query_9_cast_fp16")];
tensor<int32, [2]> var_652 = const()[name = tensor<string, []>("op_652"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_654 = const()[name = tensor<string, []>("op_654"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_key_5_pad_type_0 = const()[name = tensor<string, []>("current_key_5_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_key_5_pad_0 = const()[name = tensor<string, []>("current_key_5_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_2_self_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_2_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(242159168)))];
tensor<fp16, [1, 1280, 1, 1]> current_key_5_cast_fp16 = conv(dilations = var_654, groups = var_611, pad = current_key_5_pad_0, pad_type = current_key_5_pad_type_0, strides = var_652, weight = layers_2_self_attn_k_proj_weight_to_fp16, x = obj_29_cast_fp16)[name = tensor<string, []>("current_key_5_cast_fp16")];
tensor<int32, [2]> var_659 = const()[name = tensor<string, []>("op_659"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_661 = const()[name = tensor<string, []>("op_661"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_value_5_pad_type_0 = const()[name = tensor<string, []>("current_value_5_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_value_5_pad_0 = const()[name = tensor<string, []>("current_value_5_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_2_self_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_2_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(245436032)))];
tensor<fp16, [1280]> layers_2_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_2_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(248712896)))];
tensor<fp16, [1, 1280, 1, 1]> current_value_5_cast_fp16 = conv(bias = layers_2_self_attn_v_proj_bias_to_fp16, dilations = var_661, groups = var_611, pad = current_value_5_pad_0, pad_type = current_value_5_pad_type_0, strides = var_659, weight = layers_2_self_attn_v_proj_weight_to_fp16, x = obj_29_cast_fp16)[name = tensor<string, []>("current_value_5_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_668_cast_fp16 = mul(x = current_key_5_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_668_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_670_cast_fp16 = mul(x = var_103_cast_fp16_2, y = var_241_cast_fp16)[name = tensor<string, []>("op_670_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> key_9_cast_fp16 = add(x = var_668_cast_fp16, y = var_670_cast_fp16)[name = tensor<string, []>("key_9_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_672_cast_fp16 = mul(x = current_value_5_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_672_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_674_cast_fp16 = mul(x = var_138_cast_fp16_2, y = var_241_cast_fp16)[name = tensor<string, []>("op_674_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> value_9_cast_fp16 = add(x = var_672_cast_fp16, y = var_674_cast_fp16)[name = tensor<string, []>("value_9_cast_fp16")];
tensor<int32, [4]> var_677 = const()[name = tensor<string, []>("op_677"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_678_cast_fp16 = reshape(shape = var_677, x = query_9_cast_fp16)[name = tensor<string, []>("op_678_cast_fp16")];
tensor<fp16, []> var_679_to_fp16 = const()[name = tensor<string, []>("op_679_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_680_cast_fp16 = mul(x = var_678_cast_fp16, y = var_679_to_fp16)[name = tensor<string, []>("op_680_cast_fp16")];
tensor<int32, [4]> var_681 = const()[name = tensor<string, []>("op_681"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_682_cast_fp16 = reshape(shape = var_681, x = key_9_cast_fp16)[name = tensor<string, []>("op_682_cast_fp16")];
tensor<bool, []> mh_w_13_transpose_x_0 = const()[name = tensor<string, []>("mh_w_13_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_13_transpose_y_0 = const()[name = tensor<string, []>("mh_w_13_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 224]> mh_w_13_cast_fp16 = matmul(transpose_x = mh_w_13_transpose_x_0, transpose_y = mh_w_13_transpose_y_0, x = var_680_cast_fp16, y = var_682_cast_fp16)[name = tensor<string, []>("mh_w_13_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> mh_w_15_cast_fp16 = add(x = mh_w_13_cast_fp16, y = var_259_cast_fp16)[name = tensor<string, []>("mh_w_15_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> var_690_cast_fp16 = softmax(axis = var_604, x = mh_w_15_cast_fp16)[name = tensor<string, []>("op_690_cast_fp16")];
tensor<int32, [4]> var_691 = const()[name = tensor<string, []>("op_691"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_692_cast_fp16 = reshape(shape = var_691, x = value_9_cast_fp16)[name = tensor<string, []>("op_692_cast_fp16")];
tensor<bool, []> attn_9_transpose_x_0 = const()[name = tensor<string, []>("attn_9_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_9_transpose_y_0 = const()[name = tensor<string, []>("attn_9_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_9_cast_fp16 = matmul(transpose_x = attn_9_transpose_x_0, transpose_y = attn_9_transpose_y_0, x = var_692_cast_fp16, y = var_690_cast_fp16)[name = tensor<string, []>("attn_9_cast_fp16")];
tensor<int32, [4]> var_695 = const()[name = tensor<string, []>("op_695"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_21_cast_fp16 = reshape(shape = var_695, x = attn_9_cast_fp16)[name = tensor<string, []>("input_21_cast_fp16")];
tensor<int32, [2]> var_699 = const()[name = tensor<string, []>("op_699"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_701 = const()[name = tensor<string, []>("op_701"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_35_pad_type_0 = const()[name = tensor<string, []>("obj_35_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_35_pad_0 = const()[name = tensor<string, []>("obj_35_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_2_self_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_2_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(248715520)))];
tensor<fp16, [1280]> layers_2_self_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_2_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(251992384)))];
tensor<fp16, [1, 1280, 1, 1]> obj_35_cast_fp16 = conv(bias = layers_2_self_attn_o_proj_bias_to_fp16, dilations = var_701, groups = var_611, pad = obj_35_pad_0, pad_type = obj_35_pad_type_0, strides = var_699, weight = layers_2_self_attn_o_proj_weight_to_fp16, x = input_21_cast_fp16)[name = tensor<string, []>("obj_35_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_15_cast_fp16 = add(x = inputs_13_cast_fp16, y = obj_35_cast_fp16)[name = tensor<string, []>("inputs_15_cast_fp16")];
tensor<int32, [1]> var_711 = const()[name = tensor<string, []>("op_711"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_15_cast_fp16 = reduce_mean(axes = var_711, keep_dims = var_612, x = inputs_15_cast_fp16)[name = tensor<string, []>("channels_mean_15_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_15_cast_fp16 = sub(x = inputs_15_cast_fp16, y = channels_mean_15_cast_fp16)[name = tensor<string, []>("zero_mean_15_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_15_cast_fp16 = mul(x = zero_mean_15_cast_fp16, y = zero_mean_15_cast_fp16)[name = tensor<string, []>("zero_mean_sq_15_cast_fp16")];
tensor<int32, [1]> var_715 = const()[name = tensor<string, []>("op_715"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_716_cast_fp16 = reduce_mean(axes = var_715, keep_dims = var_612, x = zero_mean_sq_15_cast_fp16)[name = tensor<string, []>("op_716_cast_fp16")];
tensor<fp16, []> var_717_to_fp16 = const()[name = tensor<string, []>("op_717_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_718_cast_fp16 = add(x = var_716_cast_fp16, y = var_717_to_fp16)[name = tensor<string, []>("op_718_cast_fp16")];
tensor<fp16, []> denom_15_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_15_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_15_cast_fp16 = rsqrt(epsilon = denom_15_epsilon_0_to_fp16, x = var_718_cast_fp16)[name = tensor<string, []>("denom_15_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_15_cast_fp16 = mul(x = zero_mean_15_cast_fp16, y = denom_15_cast_fp16)[name = tensor<string, []>("out_15_cast_fp16")];
tensor<fp16, [1280]> obj_37_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_37_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(251995008)))];
tensor<fp16, [1280]> obj_37_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_37_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(251997632)))];
tensor<fp16, []> obj_37_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_37_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_37_cast_fp16 = batch_norm(beta = obj_37_beta_0_to_fp16, epsilon = obj_37_epsilon_0_to_fp16, gamma = obj_37_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_15_cast_fp16)[name = tensor<string, []>("obj_37_cast_fp16")];
tensor<int32, [2]> var_733 = const()[name = tensor<string, []>("op_733"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_735 = const()[name = tensor<string, []>("op_735"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_11_pad_type_0 = const()[name = tensor<string, []>("query_11_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_11_pad_0 = const()[name = tensor<string, []>("query_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_2_encoder_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_2_encoder_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(252000256)))];
tensor<fp16, [1280]> layers_2_encoder_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_2_encoder_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(255277120)))];
tensor<fp16, [1, 1280, 1, 1]> query_11_cast_fp16 = conv(bias = layers_2_encoder_attn_q_proj_bias_to_fp16, dilations = var_735, groups = var_611, pad = query_11_pad_0, pad_type = query_11_pad_type_0, strides = var_733, weight = layers_2_encoder_attn_q_proj_weight_to_fp16, x = obj_37_cast_fp16)[name = tensor<string, []>("query_11_cast_fp16")];
tensor<int32, [2]> var_739 = const()[name = tensor<string, []>("op_739"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_741 = const()[name = tensor<string, []>("op_741"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> key_11_pad_type_0 = const()[name = tensor<string, []>("key_11_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> key_11_pad_0 = const()[name = tensor<string, []>("key_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_2_encoder_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_2_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(255279744)))];
tensor<fp16, [1, 1280, 1, 1500]> key_11_cast_fp16 = conv(dilations = var_741, groups = var_611, pad = key_11_pad_0, pad_type = key_11_pad_type_0, strides = var_739, weight = layers_2_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("key_11_cast_fp16")];
tensor<int32, [2]> var_746 = const()[name = tensor<string, []>("op_746"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_748 = const()[name = tensor<string, []>("op_748"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> value_11_pad_type_0 = const()[name = tensor<string, []>("value_11_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> value_11_pad_0 = const()[name = tensor<string, []>("value_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_2_encoder_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_2_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(258556608)))];
tensor<fp16, [1280]> layers_2_encoder_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_2_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(261833472)))];
tensor<fp16, [1, 1280, 1, 1500]> value_11_cast_fp16 = conv(bias = layers_2_encoder_attn_v_proj_bias_to_fp16, dilations = var_748, groups = var_611, pad = value_11_pad_0, pad_type = value_11_pad_type_0, strides = var_746, weight = layers_2_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("value_11_cast_fp16")];
tensor<int32, [4]> var_752 = const()[name = tensor<string, []>("op_752"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_753_cast_fp16 = reshape(shape = var_752, x = query_11_cast_fp16)[name = tensor<string, []>("op_753_cast_fp16")];
tensor<fp16, []> var_754_to_fp16 = const()[name = tensor<string, []>("op_754_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_755_cast_fp16 = mul(x = var_753_cast_fp16, y = var_754_to_fp16)[name = tensor<string, []>("op_755_cast_fp16")];
tensor<int32, [4]> var_756 = const()[name = tensor<string, []>("op_756"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_757_cast_fp16 = reshape(shape = var_756, x = key_11_cast_fp16)[name = tensor<string, []>("op_757_cast_fp16")];
tensor<bool, []> mh_w_17_transpose_x_0 = const()[name = tensor<string, []>("mh_w_17_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_17_transpose_y_0 = const()[name = tensor<string, []>("mh_w_17_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 1500]> mh_w_17_cast_fp16 = matmul(transpose_x = mh_w_17_transpose_x_0, transpose_y = mh_w_17_transpose_y_0, x = var_755_cast_fp16, y = var_757_cast_fp16)[name = tensor<string, []>("mh_w_17_cast_fp16")];
tensor<fp16, [1, 20, 1, 1500]> obj_41_cast_fp16 = softmax(axis = var_604, x = mh_w_17_cast_fp16)[name = tensor<string, []>("obj_41_cast_fp16")];
tensor<int32, [4]> var_761 = const()[name = tensor<string, []>("op_761"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_762_cast_fp16 = reshape(shape = var_761, x = value_11_cast_fp16)[name = tensor<string, []>("op_762_cast_fp16")];
tensor<bool, []> attn_11_transpose_x_0 = const()[name = tensor<string, []>("attn_11_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_11_transpose_y_0 = const()[name = tensor<string, []>("attn_11_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_11_cast_fp16 = matmul(transpose_x = attn_11_transpose_x_0, transpose_y = attn_11_transpose_y_0, x = var_762_cast_fp16, y = obj_41_cast_fp16)[name = tensor<string, []>("attn_11_cast_fp16")];
tensor<int32, [4]> var_765 = const()[name = tensor<string, []>("op_765"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_23_cast_fp16 = reshape(shape = var_765, x = attn_11_cast_fp16)[name = tensor<string, []>("input_23_cast_fp16")];
tensor<int32, [2]> var_769 = const()[name = tensor<string, []>("op_769"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_771 = const()[name = tensor<string, []>("op_771"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_39_pad_type_0 = const()[name = tensor<string, []>("obj_39_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_39_pad_0 = const()[name = tensor<string, []>("obj_39_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_2_encoder_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_2_encoder_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(261836096)))];
tensor<fp16, [1280]> layers_2_encoder_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_2_encoder_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(265112960)))];
tensor<fp16, [1, 1280, 1, 1]> obj_39_cast_fp16 = conv(bias = layers_2_encoder_attn_o_proj_bias_to_fp16, dilations = var_771, groups = var_611, pad = obj_39_pad_0, pad_type = obj_39_pad_type_0, strides = var_769, weight = layers_2_encoder_attn_o_proj_weight_to_fp16, x = input_23_cast_fp16)[name = tensor<string, []>("obj_39_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_17_cast_fp16 = add(x = inputs_15_cast_fp16, y = obj_39_cast_fp16)[name = tensor<string, []>("inputs_17_cast_fp16")];
tensor<int32, [1]> var_777 = const()[name = tensor<string, []>("op_777"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_17_cast_fp16 = reduce_mean(axes = var_777, keep_dims = var_612, x = inputs_17_cast_fp16)[name = tensor<string, []>("channels_mean_17_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_17_cast_fp16 = sub(x = inputs_17_cast_fp16, y = channels_mean_17_cast_fp16)[name = tensor<string, []>("zero_mean_17_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_17_cast_fp16 = mul(x = zero_mean_17_cast_fp16, y = zero_mean_17_cast_fp16)[name = tensor<string, []>("zero_mean_sq_17_cast_fp16")];
tensor<int32, [1]> var_781 = const()[name = tensor<string, []>("op_781"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_782_cast_fp16 = reduce_mean(axes = var_781, keep_dims = var_612, x = zero_mean_sq_17_cast_fp16)[name = tensor<string, []>("op_782_cast_fp16")];
tensor<fp16, []> var_783_to_fp16 = const()[name = tensor<string, []>("op_783_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_784_cast_fp16 = add(x = var_782_cast_fp16, y = var_783_to_fp16)[name = tensor<string, []>("op_784_cast_fp16")];
tensor<fp16, []> denom_17_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_17_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_17_cast_fp16 = rsqrt(epsilon = denom_17_epsilon_0_to_fp16, x = var_784_cast_fp16)[name = tensor<string, []>("denom_17_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_17_cast_fp16 = mul(x = zero_mean_17_cast_fp16, y = denom_17_cast_fp16)[name = tensor<string, []>("out_17_cast_fp16")];
tensor<fp16, [1280]> input_25_gamma_0_to_fp16 = const()[name = tensor<string, []>("input_25_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(265115584)))];
tensor<fp16, [1280]> input_25_beta_0_to_fp16 = const()[name = tensor<string, []>("input_25_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(265118208)))];
tensor<fp16, []> input_25_epsilon_0_to_fp16 = const()[name = tensor<string, []>("input_25_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> input_25_cast_fp16 = batch_norm(beta = input_25_beta_0_to_fp16, epsilon = input_25_epsilon_0_to_fp16, gamma = input_25_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_17_cast_fp16)[name = tensor<string, []>("input_25_cast_fp16")];
tensor<int32, [2]> var_795 = const()[name = tensor<string, []>("op_795"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_797 = const()[name = tensor<string, []>("op_797"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_27_pad_type_0 = const()[name = tensor<string, []>("input_27_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_27_pad_0 = const()[name = tensor<string, []>("input_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [5120, 1280, 1, 1]> layers_2_fc1_weight_to_fp16 = const()[name = tensor<string, []>("layers_2_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(265120832)))];
tensor<fp16, [5120]> layers_2_fc1_bias_to_fp16 = const()[name = tensor<string, []>("layers_2_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(278228096)))];
tensor<fp16, [1, 5120, 1, 1]> input_27_cast_fp16 = conv(bias = layers_2_fc1_bias_to_fp16, dilations = var_797, groups = var_611, pad = input_27_pad_0, pad_type = input_27_pad_type_0, strides = var_795, weight = layers_2_fc1_weight_to_fp16, x = input_25_cast_fp16)[name = tensor<string, []>("input_27_cast_fp16")];
tensor<string, []> input_29_mode_0 = const()[name = tensor<string, []>("input_29_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 5120, 1, 1]> input_29_cast_fp16 = gelu(mode = input_29_mode_0, x = input_27_cast_fp16)[name = tensor<string, []>("input_29_cast_fp16")];
tensor<int32, [2]> var_803 = const()[name = tensor<string, []>("op_803"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_805 = const()[name = tensor<string, []>("op_805"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_7_pad_type_0 = const()[name = tensor<string, []>("hidden_states_7_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_7_pad_0 = const()[name = tensor<string, []>("hidden_states_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 5120, 1, 1]> layers_2_fc2_weight_to_fp16 = const()[name = tensor<string, []>("layers_2_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(278238400)))];
tensor<fp16, [1280]> layers_2_fc2_bias_to_fp16 = const()[name = tensor<string, []>("layers_2_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(291345664)))];
tensor<fp16, [1, 1280, 1, 1]> hidden_states_7_cast_fp16 = conv(bias = layers_2_fc2_bias_to_fp16, dilations = var_805, groups = var_611, pad = hidden_states_7_pad_0, pad_type = hidden_states_7_pad_type_0, strides = var_803, weight = layers_2_fc2_weight_to_fp16, x = input_29_cast_fp16)[name = tensor<string, []>("hidden_states_7_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_19_cast_fp16 = add(x = inputs_17_cast_fp16, y = hidden_states_7_cast_fp16)[name = tensor<string, []>("inputs_19_cast_fp16")];
tensor<int32, []> var_818 = const()[name = tensor<string, []>("op_818"), val = tensor<int32, []>(3)];
tensor<int32, []> var_825 = const()[name = tensor<string, []>("op_825"), val = tensor<int32, []>(1)];
tensor<bool, []> var_826 = const()[name = tensor<string, []>("op_826"), val = tensor<bool, []>(true)];
tensor<int32, [1]> var_838 = const()[name = tensor<string, []>("op_838"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_19_cast_fp16 = reduce_mean(axes = var_838, keep_dims = var_826, x = inputs_19_cast_fp16)[name = tensor<string, []>("channels_mean_19_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_19_cast_fp16 = sub(x = inputs_19_cast_fp16, y = channels_mean_19_cast_fp16)[name = tensor<string, []>("zero_mean_19_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_19_cast_fp16 = mul(x = zero_mean_19_cast_fp16, y = zero_mean_19_cast_fp16)[name = tensor<string, []>("zero_mean_sq_19_cast_fp16")];
tensor<int32, [1]> var_842 = const()[name = tensor<string, []>("op_842"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_843_cast_fp16 = reduce_mean(axes = var_842, keep_dims = var_826, x = zero_mean_sq_19_cast_fp16)[name = tensor<string, []>("op_843_cast_fp16")];
tensor<fp16, []> var_844_to_fp16 = const()[name = tensor<string, []>("op_844_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_845_cast_fp16 = add(x = var_843_cast_fp16, y = var_844_to_fp16)[name = tensor<string, []>("op_845_cast_fp16")];
tensor<fp16, []> denom_19_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_19_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_19_cast_fp16 = rsqrt(epsilon = denom_19_epsilon_0_to_fp16, x = var_845_cast_fp16)[name = tensor<string, []>("denom_19_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_19_cast_fp16 = mul(x = zero_mean_19_cast_fp16, y = denom_19_cast_fp16)[name = tensor<string, []>("out_19_cast_fp16")];
tensor<fp16, [1280]> obj_43_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_43_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(291348288)))];
tensor<fp16, [1280]> obj_43_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_43_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(291350912)))];
tensor<fp16, []> obj_43_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_43_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_43_cast_fp16 = batch_norm(beta = obj_43_beta_0_to_fp16, epsilon = obj_43_epsilon_0_to_fp16, gamma = obj_43_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_19_cast_fp16)[name = tensor<string, []>("obj_43_cast_fp16")];
tensor<int32, [2]> var_860 = const()[name = tensor<string, []>("op_860"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_862 = const()[name = tensor<string, []>("op_862"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_13_pad_type_0 = const()[name = tensor<string, []>("query_13_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_13_pad_0 = const()[name = tensor<string, []>("query_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_3_self_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_3_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(291353536)))];
tensor<fp16, [1280]> layers_3_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_3_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(294630400)))];
tensor<fp16, [1, 1280, 1, 1]> query_13_cast_fp16 = conv(bias = layers_3_self_attn_q_proj_bias_to_fp16, dilations = var_862, groups = var_825, pad = query_13_pad_0, pad_type = query_13_pad_type_0, strides = var_860, weight = layers_3_self_attn_q_proj_weight_to_fp16, x = obj_43_cast_fp16)[name = tensor<string, []>("query_13_cast_fp16")];
tensor<int32, [2]> var_866 = const()[name = tensor<string, []>("op_866"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_868 = const()[name = tensor<string, []>("op_868"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_key_7_pad_type_0 = const()[name = tensor<string, []>("current_key_7_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_key_7_pad_0 = const()[name = tensor<string, []>("current_key_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_3_self_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_3_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(294633024)))];
tensor<fp16, [1, 1280, 1, 1]> current_key_7_cast_fp16 = conv(dilations = var_868, groups = var_825, pad = current_key_7_pad_0, pad_type = current_key_7_pad_type_0, strides = var_866, weight = layers_3_self_attn_k_proj_weight_to_fp16, x = obj_43_cast_fp16)[name = tensor<string, []>("current_key_7_cast_fp16")];
tensor<int32, [2]> var_873 = const()[name = tensor<string, []>("op_873"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_875 = const()[name = tensor<string, []>("op_875"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_value_7_pad_type_0 = const()[name = tensor<string, []>("current_value_7_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_value_7_pad_0 = const()[name = tensor<string, []>("current_value_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_3_self_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_3_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(297909888)))];
tensor<fp16, [1280]> layers_3_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_3_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(301186752)))];
tensor<fp16, [1, 1280, 1, 1]> current_value_7_cast_fp16 = conv(bias = layers_3_self_attn_v_proj_bias_to_fp16, dilations = var_875, groups = var_825, pad = current_value_7_pad_0, pad_type = current_value_7_pad_type_0, strides = var_873, weight = layers_3_self_attn_v_proj_weight_to_fp16, x = obj_43_cast_fp16)[name = tensor<string, []>("current_value_7_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_882_cast_fp16 = mul(x = current_key_7_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_882_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_884_cast_fp16 = mul(x = var_103_cast_fp16_3, y = var_241_cast_fp16)[name = tensor<string, []>("op_884_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> key_13_cast_fp16 = add(x = var_882_cast_fp16, y = var_884_cast_fp16)[name = tensor<string, []>("key_13_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_886_cast_fp16 = mul(x = current_value_7_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_886_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_888_cast_fp16 = mul(x = var_138_cast_fp16_3, y = var_241_cast_fp16)[name = tensor<string, []>("op_888_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> value_13_cast_fp16 = add(x = var_886_cast_fp16, y = var_888_cast_fp16)[name = tensor<string, []>("value_13_cast_fp16")];
tensor<int32, [4]> var_891 = const()[name = tensor<string, []>("op_891"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_892_cast_fp16 = reshape(shape = var_891, x = query_13_cast_fp16)[name = tensor<string, []>("op_892_cast_fp16")];
tensor<fp16, []> var_893_to_fp16 = const()[name = tensor<string, []>("op_893_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_894_cast_fp16 = mul(x = var_892_cast_fp16, y = var_893_to_fp16)[name = tensor<string, []>("op_894_cast_fp16")];
tensor<int32, [4]> var_895 = const()[name = tensor<string, []>("op_895"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_896_cast_fp16 = reshape(shape = var_895, x = key_13_cast_fp16)[name = tensor<string, []>("op_896_cast_fp16")];
tensor<bool, []> mh_w_19_transpose_x_0 = const()[name = tensor<string, []>("mh_w_19_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_19_transpose_y_0 = const()[name = tensor<string, []>("mh_w_19_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 224]> mh_w_19_cast_fp16 = matmul(transpose_x = mh_w_19_transpose_x_0, transpose_y = mh_w_19_transpose_y_0, x = var_894_cast_fp16, y = var_896_cast_fp16)[name = tensor<string, []>("mh_w_19_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> mh_w_21_cast_fp16 = add(x = mh_w_19_cast_fp16, y = var_259_cast_fp16)[name = tensor<string, []>("mh_w_21_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> var_904_cast_fp16 = softmax(axis = var_818, x = mh_w_21_cast_fp16)[name = tensor<string, []>("op_904_cast_fp16")];
tensor<int32, [4]> var_905 = const()[name = tensor<string, []>("op_905"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_906_cast_fp16 = reshape(shape = var_905, x = value_13_cast_fp16)[name = tensor<string, []>("op_906_cast_fp16")];
tensor<bool, []> attn_13_transpose_x_0 = const()[name = tensor<string, []>("attn_13_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_13_transpose_y_0 = const()[name = tensor<string, []>("attn_13_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_13_cast_fp16 = matmul(transpose_x = attn_13_transpose_x_0, transpose_y = attn_13_transpose_y_0, x = var_906_cast_fp16, y = var_904_cast_fp16)[name = tensor<string, []>("attn_13_cast_fp16")];
tensor<int32, [4]> var_909 = const()[name = tensor<string, []>("op_909"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_31_cast_fp16 = reshape(shape = var_909, x = attn_13_cast_fp16)[name = tensor<string, []>("input_31_cast_fp16")];
tensor<int32, [2]> var_913 = const()[name = tensor<string, []>("op_913"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_915 = const()[name = tensor<string, []>("op_915"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_49_pad_type_0 = const()[name = tensor<string, []>("obj_49_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_49_pad_0 = const()[name = tensor<string, []>("obj_49_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_3_self_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_3_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(301189376)))];
tensor<fp16, [1280]> layers_3_self_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_3_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(304466240)))];
tensor<fp16, [1, 1280, 1, 1]> obj_49_cast_fp16 = conv(bias = layers_3_self_attn_o_proj_bias_to_fp16, dilations = var_915, groups = var_825, pad = obj_49_pad_0, pad_type = obj_49_pad_type_0, strides = var_913, weight = layers_3_self_attn_o_proj_weight_to_fp16, x = input_31_cast_fp16)[name = tensor<string, []>("obj_49_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_21_cast_fp16 = add(x = inputs_19_cast_fp16, y = obj_49_cast_fp16)[name = tensor<string, []>("inputs_21_cast_fp16")];
tensor<int32, [1]> var_925 = const()[name = tensor<string, []>("op_925"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_21_cast_fp16 = reduce_mean(axes = var_925, keep_dims = var_826, x = inputs_21_cast_fp16)[name = tensor<string, []>("channels_mean_21_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_21_cast_fp16 = sub(x = inputs_21_cast_fp16, y = channels_mean_21_cast_fp16)[name = tensor<string, []>("zero_mean_21_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_21_cast_fp16 = mul(x = zero_mean_21_cast_fp16, y = zero_mean_21_cast_fp16)[name = tensor<string, []>("zero_mean_sq_21_cast_fp16")];
tensor<int32, [1]> var_929 = const()[name = tensor<string, []>("op_929"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_930_cast_fp16 = reduce_mean(axes = var_929, keep_dims = var_826, x = zero_mean_sq_21_cast_fp16)[name = tensor<string, []>("op_930_cast_fp16")];
tensor<fp16, []> var_931_to_fp16 = const()[name = tensor<string, []>("op_931_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_932_cast_fp16 = add(x = var_930_cast_fp16, y = var_931_to_fp16)[name = tensor<string, []>("op_932_cast_fp16")];
tensor<fp16, []> denom_21_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_21_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_21_cast_fp16 = rsqrt(epsilon = denom_21_epsilon_0_to_fp16, x = var_932_cast_fp16)[name = tensor<string, []>("denom_21_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_21_cast_fp16 = mul(x = zero_mean_21_cast_fp16, y = denom_21_cast_fp16)[name = tensor<string, []>("out_21_cast_fp16")];
tensor<fp16, [1280]> obj_51_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_51_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(304468864)))];
tensor<fp16, [1280]> obj_51_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_51_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(304471488)))];
tensor<fp16, []> obj_51_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_51_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_51_cast_fp16 = batch_norm(beta = obj_51_beta_0_to_fp16, epsilon = obj_51_epsilon_0_to_fp16, gamma = obj_51_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_21_cast_fp16)[name = tensor<string, []>("obj_51_cast_fp16")];
tensor<int32, [2]> var_947 = const()[name = tensor<string, []>("op_947"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_949 = const()[name = tensor<string, []>("op_949"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_15_pad_type_0 = const()[name = tensor<string, []>("query_15_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_15_pad_0 = const()[name = tensor<string, []>("query_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_3_encoder_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_3_encoder_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(304474112)))];
tensor<fp16, [1280]> layers_3_encoder_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_3_encoder_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(307750976)))];
tensor<fp16, [1, 1280, 1, 1]> query_15_cast_fp16 = conv(bias = layers_3_encoder_attn_q_proj_bias_to_fp16, dilations = var_949, groups = var_825, pad = query_15_pad_0, pad_type = query_15_pad_type_0, strides = var_947, weight = layers_3_encoder_attn_q_proj_weight_to_fp16, x = obj_51_cast_fp16)[name = tensor<string, []>("query_15_cast_fp16")];
tensor<int32, [2]> var_953 = const()[name = tensor<string, []>("op_953"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_955 = const()[name = tensor<string, []>("op_955"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> key_15_pad_type_0 = const()[name = tensor<string, []>("key_15_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> key_15_pad_0 = const()[name = tensor<string, []>("key_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_3_encoder_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_3_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(307753600)))];
tensor<fp16, [1, 1280, 1, 1500]> key_15_cast_fp16 = conv(dilations = var_955, groups = var_825, pad = key_15_pad_0, pad_type = key_15_pad_type_0, strides = var_953, weight = layers_3_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("key_15_cast_fp16")];
tensor<int32, [2]> var_960 = const()[name = tensor<string, []>("op_960"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_962 = const()[name = tensor<string, []>("op_962"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> value_15_pad_type_0 = const()[name = tensor<string, []>("value_15_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> value_15_pad_0 = const()[name = tensor<string, []>("value_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_3_encoder_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_3_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(311030464)))];
tensor<fp16, [1280]> layers_3_encoder_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_3_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(314307328)))];
tensor<fp16, [1, 1280, 1, 1500]> value_15_cast_fp16 = conv(bias = layers_3_encoder_attn_v_proj_bias_to_fp16, dilations = var_962, groups = var_825, pad = value_15_pad_0, pad_type = value_15_pad_type_0, strides = var_960, weight = layers_3_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("value_15_cast_fp16")];
tensor<int32, [4]> var_966 = const()[name = tensor<string, []>("op_966"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_967_cast_fp16 = reshape(shape = var_966, x = query_15_cast_fp16)[name = tensor<string, []>("op_967_cast_fp16")];
tensor<fp16, []> var_968_to_fp16 = const()[name = tensor<string, []>("op_968_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_969_cast_fp16 = mul(x = var_967_cast_fp16, y = var_968_to_fp16)[name = tensor<string, []>("op_969_cast_fp16")];
tensor<int32, [4]> var_970 = const()[name = tensor<string, []>("op_970"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_971_cast_fp16 = reshape(shape = var_970, x = key_15_cast_fp16)[name = tensor<string, []>("op_971_cast_fp16")];
tensor<bool, []> mh_w_23_transpose_x_0 = const()[name = tensor<string, []>("mh_w_23_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_23_transpose_y_0 = const()[name = tensor<string, []>("mh_w_23_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 1500]> mh_w_23_cast_fp16 = matmul(transpose_x = mh_w_23_transpose_x_0, transpose_y = mh_w_23_transpose_y_0, x = var_969_cast_fp16, y = var_971_cast_fp16)[name = tensor<string, []>("mh_w_23_cast_fp16")];
tensor<fp16, [1, 20, 1, 1500]> obj_55_cast_fp16 = softmax(axis = var_818, x = mh_w_23_cast_fp16)[name = tensor<string, []>("obj_55_cast_fp16")];
tensor<int32, [4]> var_975 = const()[name = tensor<string, []>("op_975"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_976_cast_fp16 = reshape(shape = var_975, x = value_15_cast_fp16)[name = tensor<string, []>("op_976_cast_fp16")];
tensor<bool, []> attn_15_transpose_x_0 = const()[name = tensor<string, []>("attn_15_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_15_transpose_y_0 = const()[name = tensor<string, []>("attn_15_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_15_cast_fp16 = matmul(transpose_x = attn_15_transpose_x_0, transpose_y = attn_15_transpose_y_0, x = var_976_cast_fp16, y = obj_55_cast_fp16)[name = tensor<string, []>("attn_15_cast_fp16")];
tensor<int32, [4]> var_979 = const()[name = tensor<string, []>("op_979"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_33_cast_fp16 = reshape(shape = var_979, x = attn_15_cast_fp16)[name = tensor<string, []>("input_33_cast_fp16")];
tensor<int32, [2]> var_983 = const()[name = tensor<string, []>("op_983"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_985 = const()[name = tensor<string, []>("op_985"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_53_pad_type_0 = const()[name = tensor<string, []>("obj_53_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_53_pad_0 = const()[name = tensor<string, []>("obj_53_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_3_encoder_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_3_encoder_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(314309952)))];
tensor<fp16, [1280]> layers_3_encoder_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_3_encoder_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(317586816)))];
tensor<fp16, [1, 1280, 1, 1]> obj_53_cast_fp16 = conv(bias = layers_3_encoder_attn_o_proj_bias_to_fp16, dilations = var_985, groups = var_825, pad = obj_53_pad_0, pad_type = obj_53_pad_type_0, strides = var_983, weight = layers_3_encoder_attn_o_proj_weight_to_fp16, x = input_33_cast_fp16)[name = tensor<string, []>("obj_53_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_23_cast_fp16 = add(x = inputs_21_cast_fp16, y = obj_53_cast_fp16)[name = tensor<string, []>("inputs_23_cast_fp16")];
tensor<int32, [1]> var_991 = const()[name = tensor<string, []>("op_991"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_23_cast_fp16 = reduce_mean(axes = var_991, keep_dims = var_826, x = inputs_23_cast_fp16)[name = tensor<string, []>("channels_mean_23_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_23_cast_fp16 = sub(x = inputs_23_cast_fp16, y = channels_mean_23_cast_fp16)[name = tensor<string, []>("zero_mean_23_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_23_cast_fp16 = mul(x = zero_mean_23_cast_fp16, y = zero_mean_23_cast_fp16)[name = tensor<string, []>("zero_mean_sq_23_cast_fp16")];
tensor<int32, [1]> var_995 = const()[name = tensor<string, []>("op_995"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_996_cast_fp16 = reduce_mean(axes = var_995, keep_dims = var_826, x = zero_mean_sq_23_cast_fp16)[name = tensor<string, []>("op_996_cast_fp16")];
tensor<fp16, []> var_997_to_fp16 = const()[name = tensor<string, []>("op_997_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_998_cast_fp16 = add(x = var_996_cast_fp16, y = var_997_to_fp16)[name = tensor<string, []>("op_998_cast_fp16")];
tensor<fp16, []> denom_23_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_23_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_23_cast_fp16 = rsqrt(epsilon = denom_23_epsilon_0_to_fp16, x = var_998_cast_fp16)[name = tensor<string, []>("denom_23_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_23_cast_fp16 = mul(x = zero_mean_23_cast_fp16, y = denom_23_cast_fp16)[name = tensor<string, []>("out_23_cast_fp16")];
tensor<fp16, [1280]> input_35_gamma_0_to_fp16 = const()[name = tensor<string, []>("input_35_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(317589440)))];
tensor<fp16, [1280]> input_35_beta_0_to_fp16 = const()[name = tensor<string, []>("input_35_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(317592064)))];
tensor<fp16, []> input_35_epsilon_0_to_fp16 = const()[name = tensor<string, []>("input_35_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> input_35_cast_fp16 = batch_norm(beta = input_35_beta_0_to_fp16, epsilon = input_35_epsilon_0_to_fp16, gamma = input_35_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_23_cast_fp16)[name = tensor<string, []>("input_35_cast_fp16")];
tensor<int32, [2]> var_1009 = const()[name = tensor<string, []>("op_1009"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1011 = const()[name = tensor<string, []>("op_1011"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_37_pad_type_0 = const()[name = tensor<string, []>("input_37_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_37_pad_0 = const()[name = tensor<string, []>("input_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [5120, 1280, 1, 1]> layers_3_fc1_weight_to_fp16 = const()[name = tensor<string, []>("layers_3_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(317594688)))];
tensor<fp16, [5120]> layers_3_fc1_bias_to_fp16 = const()[name = tensor<string, []>("layers_3_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(330701952)))];
tensor<fp16, [1, 5120, 1, 1]> input_37_cast_fp16 = conv(bias = layers_3_fc1_bias_to_fp16, dilations = var_1011, groups = var_825, pad = input_37_pad_0, pad_type = input_37_pad_type_0, strides = var_1009, weight = layers_3_fc1_weight_to_fp16, x = input_35_cast_fp16)[name = tensor<string, []>("input_37_cast_fp16")];
tensor<string, []> input_39_mode_0 = const()[name = tensor<string, []>("input_39_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 5120, 1, 1]> input_39_cast_fp16 = gelu(mode = input_39_mode_0, x = input_37_cast_fp16)[name = tensor<string, []>("input_39_cast_fp16")];
tensor<int32, [2]> var_1017 = const()[name = tensor<string, []>("op_1017"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1019 = const()[name = tensor<string, []>("op_1019"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_9_pad_type_0 = const()[name = tensor<string, []>("hidden_states_9_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_9_pad_0 = const()[name = tensor<string, []>("hidden_states_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 5120, 1, 1]> layers_3_fc2_weight_to_fp16 = const()[name = tensor<string, []>("layers_3_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(330712256)))];
tensor<fp16, [1280]> layers_3_fc2_bias_to_fp16 = const()[name = tensor<string, []>("layers_3_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(343819520)))];
tensor<fp16, [1, 1280, 1, 1]> hidden_states_9_cast_fp16 = conv(bias = layers_3_fc2_bias_to_fp16, dilations = var_1019, groups = var_825, pad = hidden_states_9_pad_0, pad_type = hidden_states_9_pad_type_0, strides = var_1017, weight = layers_3_fc2_weight_to_fp16, x = input_39_cast_fp16)[name = tensor<string, []>("hidden_states_9_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_25_cast_fp16 = add(x = inputs_23_cast_fp16, y = hidden_states_9_cast_fp16)[name = tensor<string, []>("inputs_25_cast_fp16")];
tensor<int32, []> var_1032 = const()[name = tensor<string, []>("op_1032"), val = tensor<int32, []>(3)];
tensor<int32, []> var_1039 = const()[name = tensor<string, []>("op_1039"), val = tensor<int32, []>(1)];
tensor<bool, []> var_1040 = const()[name = tensor<string, []>("op_1040"), val = tensor<bool, []>(true)];
tensor<int32, [1]> var_1052 = const()[name = tensor<string, []>("op_1052"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_25_cast_fp16 = reduce_mean(axes = var_1052, keep_dims = var_1040, x = inputs_25_cast_fp16)[name = tensor<string, []>("channels_mean_25_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_25_cast_fp16 = sub(x = inputs_25_cast_fp16, y = channels_mean_25_cast_fp16)[name = tensor<string, []>("zero_mean_25_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_25_cast_fp16 = mul(x = zero_mean_25_cast_fp16, y = zero_mean_25_cast_fp16)[name = tensor<string, []>("zero_mean_sq_25_cast_fp16")];
tensor<int32, [1]> var_1056 = const()[name = tensor<string, []>("op_1056"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_1057_cast_fp16 = reduce_mean(axes = var_1056, keep_dims = var_1040, x = zero_mean_sq_25_cast_fp16)[name = tensor<string, []>("op_1057_cast_fp16")];
tensor<fp16, []> var_1058_to_fp16 = const()[name = tensor<string, []>("op_1058_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_1059_cast_fp16 = add(x = var_1057_cast_fp16, y = var_1058_to_fp16)[name = tensor<string, []>("op_1059_cast_fp16")];
tensor<fp16, []> denom_25_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_25_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_25_cast_fp16 = rsqrt(epsilon = denom_25_epsilon_0_to_fp16, x = var_1059_cast_fp16)[name = tensor<string, []>("denom_25_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_25_cast_fp16 = mul(x = zero_mean_25_cast_fp16, y = denom_25_cast_fp16)[name = tensor<string, []>("out_25_cast_fp16")];
tensor<fp16, [1280]> obj_57_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_57_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(343822144)))];
tensor<fp16, [1280]> obj_57_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_57_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(343824768)))];
tensor<fp16, []> obj_57_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_57_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_57_cast_fp16 = batch_norm(beta = obj_57_beta_0_to_fp16, epsilon = obj_57_epsilon_0_to_fp16, gamma = obj_57_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_25_cast_fp16)[name = tensor<string, []>("obj_57_cast_fp16")];
tensor<int32, [2]> var_1074 = const()[name = tensor<string, []>("op_1074"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1076 = const()[name = tensor<string, []>("op_1076"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_17_pad_type_0 = const()[name = tensor<string, []>("query_17_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_17_pad_0 = const()[name = tensor<string, []>("query_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_4_self_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_4_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(343827392)))];
tensor<fp16, [1280]> layers_4_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_4_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(347104256)))];
tensor<fp16, [1, 1280, 1, 1]> query_17_cast_fp16 = conv(bias = layers_4_self_attn_q_proj_bias_to_fp16, dilations = var_1076, groups = var_1039, pad = query_17_pad_0, pad_type = query_17_pad_type_0, strides = var_1074, weight = layers_4_self_attn_q_proj_weight_to_fp16, x = obj_57_cast_fp16)[name = tensor<string, []>("query_17_cast_fp16")];
tensor<int32, [2]> var_1080 = const()[name = tensor<string, []>("op_1080"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1082 = const()[name = tensor<string, []>("op_1082"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_key_9_pad_type_0 = const()[name = tensor<string, []>("current_key_9_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_key_9_pad_0 = const()[name = tensor<string, []>("current_key_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_4_self_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_4_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(347106880)))];
tensor<fp16, [1, 1280, 1, 1]> current_key_9_cast_fp16 = conv(dilations = var_1082, groups = var_1039, pad = current_key_9_pad_0, pad_type = current_key_9_pad_type_0, strides = var_1080, weight = layers_4_self_attn_k_proj_weight_to_fp16, x = obj_57_cast_fp16)[name = tensor<string, []>("current_key_9_cast_fp16")];
tensor<int32, [2]> var_1087 = const()[name = tensor<string, []>("op_1087"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1089 = const()[name = tensor<string, []>("op_1089"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_value_9_pad_type_0 = const()[name = tensor<string, []>("current_value_9_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_value_9_pad_0 = const()[name = tensor<string, []>("current_value_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_4_self_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_4_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(350383744)))];
tensor<fp16, [1280]> layers_4_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_4_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(353660608)))];
tensor<fp16, [1, 1280, 1, 1]> current_value_9_cast_fp16 = conv(bias = layers_4_self_attn_v_proj_bias_to_fp16, dilations = var_1089, groups = var_1039, pad = current_value_9_pad_0, pad_type = current_value_9_pad_type_0, strides = var_1087, weight = layers_4_self_attn_v_proj_weight_to_fp16, x = obj_57_cast_fp16)[name = tensor<string, []>("current_value_9_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_1096_cast_fp16 = mul(x = current_key_9_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_1096_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_1098_cast_fp16 = mul(x = var_103_cast_fp16_4, y = var_241_cast_fp16)[name = tensor<string, []>("op_1098_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> key_17_cast_fp16 = add(x = var_1096_cast_fp16, y = var_1098_cast_fp16)[name = tensor<string, []>("key_17_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_1100_cast_fp16 = mul(x = current_value_9_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_1100_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_1102_cast_fp16 = mul(x = var_138_cast_fp16_4, y = var_241_cast_fp16)[name = tensor<string, []>("op_1102_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> value_17_cast_fp16 = add(x = var_1100_cast_fp16, y = var_1102_cast_fp16)[name = tensor<string, []>("value_17_cast_fp16")];
tensor<int32, [4]> var_1105 = const()[name = tensor<string, []>("op_1105"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_1106_cast_fp16 = reshape(shape = var_1105, x = query_17_cast_fp16)[name = tensor<string, []>("op_1106_cast_fp16")];
tensor<fp16, []> var_1107_to_fp16 = const()[name = tensor<string, []>("op_1107_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_1108_cast_fp16 = mul(x = var_1106_cast_fp16, y = var_1107_to_fp16)[name = tensor<string, []>("op_1108_cast_fp16")];
tensor<int32, [4]> var_1109 = const()[name = tensor<string, []>("op_1109"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_1110_cast_fp16 = reshape(shape = var_1109, x = key_17_cast_fp16)[name = tensor<string, []>("op_1110_cast_fp16")];
tensor<bool, []> mh_w_25_transpose_x_0 = const()[name = tensor<string, []>("mh_w_25_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_25_transpose_y_0 = const()[name = tensor<string, []>("mh_w_25_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 224]> mh_w_25_cast_fp16 = matmul(transpose_x = mh_w_25_transpose_x_0, transpose_y = mh_w_25_transpose_y_0, x = var_1108_cast_fp16, y = var_1110_cast_fp16)[name = tensor<string, []>("mh_w_25_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> mh_w_27_cast_fp16 = add(x = mh_w_25_cast_fp16, y = var_259_cast_fp16)[name = tensor<string, []>("mh_w_27_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> var_1118_cast_fp16 = softmax(axis = var_1032, x = mh_w_27_cast_fp16)[name = tensor<string, []>("op_1118_cast_fp16")];
tensor<int32, [4]> var_1119 = const()[name = tensor<string, []>("op_1119"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_1120_cast_fp16 = reshape(shape = var_1119, x = value_17_cast_fp16)[name = tensor<string, []>("op_1120_cast_fp16")];
tensor<bool, []> attn_17_transpose_x_0 = const()[name = tensor<string, []>("attn_17_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_17_transpose_y_0 = const()[name = tensor<string, []>("attn_17_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_17_cast_fp16 = matmul(transpose_x = attn_17_transpose_x_0, transpose_y = attn_17_transpose_y_0, x = var_1120_cast_fp16, y = var_1118_cast_fp16)[name = tensor<string, []>("attn_17_cast_fp16")];
tensor<int32, [4]> var_1123 = const()[name = tensor<string, []>("op_1123"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_41_cast_fp16 = reshape(shape = var_1123, x = attn_17_cast_fp16)[name = tensor<string, []>("input_41_cast_fp16")];
tensor<int32, [2]> var_1127 = const()[name = tensor<string, []>("op_1127"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1129 = const()[name = tensor<string, []>("op_1129"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_63_pad_type_0 = const()[name = tensor<string, []>("obj_63_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_63_pad_0 = const()[name = tensor<string, []>("obj_63_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_4_self_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_4_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(353663232)))];
tensor<fp16, [1280]> layers_4_self_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_4_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(356940096)))];
tensor<fp16, [1, 1280, 1, 1]> obj_63_cast_fp16 = conv(bias = layers_4_self_attn_o_proj_bias_to_fp16, dilations = var_1129, groups = var_1039, pad = obj_63_pad_0, pad_type = obj_63_pad_type_0, strides = var_1127, weight = layers_4_self_attn_o_proj_weight_to_fp16, x = input_41_cast_fp16)[name = tensor<string, []>("obj_63_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_27_cast_fp16 = add(x = inputs_25_cast_fp16, y = obj_63_cast_fp16)[name = tensor<string, []>("inputs_27_cast_fp16")];
tensor<int32, [1]> var_1139 = const()[name = tensor<string, []>("op_1139"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_27_cast_fp16 = reduce_mean(axes = var_1139, keep_dims = var_1040, x = inputs_27_cast_fp16)[name = tensor<string, []>("channels_mean_27_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_27_cast_fp16 = sub(x = inputs_27_cast_fp16, y = channels_mean_27_cast_fp16)[name = tensor<string, []>("zero_mean_27_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_27_cast_fp16 = mul(x = zero_mean_27_cast_fp16, y = zero_mean_27_cast_fp16)[name = tensor<string, []>("zero_mean_sq_27_cast_fp16")];
tensor<int32, [1]> var_1143 = const()[name = tensor<string, []>("op_1143"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_1144_cast_fp16 = reduce_mean(axes = var_1143, keep_dims = var_1040, x = zero_mean_sq_27_cast_fp16)[name = tensor<string, []>("op_1144_cast_fp16")];
tensor<fp16, []> var_1145_to_fp16 = const()[name = tensor<string, []>("op_1145_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_1146_cast_fp16 = add(x = var_1144_cast_fp16, y = var_1145_to_fp16)[name = tensor<string, []>("op_1146_cast_fp16")];
tensor<fp16, []> denom_27_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_27_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_27_cast_fp16 = rsqrt(epsilon = denom_27_epsilon_0_to_fp16, x = var_1146_cast_fp16)[name = tensor<string, []>("denom_27_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_27_cast_fp16 = mul(x = zero_mean_27_cast_fp16, y = denom_27_cast_fp16)[name = tensor<string, []>("out_27_cast_fp16")];
tensor<fp16, [1280]> obj_65_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_65_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(356942720)))];
tensor<fp16, [1280]> obj_65_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_65_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(356945344)))];
tensor<fp16, []> obj_65_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_65_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_65_cast_fp16 = batch_norm(beta = obj_65_beta_0_to_fp16, epsilon = obj_65_epsilon_0_to_fp16, gamma = obj_65_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_27_cast_fp16)[name = tensor<string, []>("obj_65_cast_fp16")];
tensor<int32, [2]> var_1161 = const()[name = tensor<string, []>("op_1161"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1163 = const()[name = tensor<string, []>("op_1163"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_19_pad_type_0 = const()[name = tensor<string, []>("query_19_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_19_pad_0 = const()[name = tensor<string, []>("query_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_4_encoder_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_4_encoder_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(356947968)))];
tensor<fp16, [1280]> layers_4_encoder_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_4_encoder_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(360224832)))];
tensor<fp16, [1, 1280, 1, 1]> query_19_cast_fp16 = conv(bias = layers_4_encoder_attn_q_proj_bias_to_fp16, dilations = var_1163, groups = var_1039, pad = query_19_pad_0, pad_type = query_19_pad_type_0, strides = var_1161, weight = layers_4_encoder_attn_q_proj_weight_to_fp16, x = obj_65_cast_fp16)[name = tensor<string, []>("query_19_cast_fp16")];
tensor<int32, [2]> var_1167 = const()[name = tensor<string, []>("op_1167"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1169 = const()[name = tensor<string, []>("op_1169"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> key_19_pad_type_0 = const()[name = tensor<string, []>("key_19_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> key_19_pad_0 = const()[name = tensor<string, []>("key_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_4_encoder_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_4_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(360227456)))];
tensor<fp16, [1, 1280, 1, 1500]> key_19_cast_fp16 = conv(dilations = var_1169, groups = var_1039, pad = key_19_pad_0, pad_type = key_19_pad_type_0, strides = var_1167, weight = layers_4_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("key_19_cast_fp16")];
tensor<int32, [2]> var_1174 = const()[name = tensor<string, []>("op_1174"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1176 = const()[name = tensor<string, []>("op_1176"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> value_19_pad_type_0 = const()[name = tensor<string, []>("value_19_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> value_19_pad_0 = const()[name = tensor<string, []>("value_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_4_encoder_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_4_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(363504320)))];
tensor<fp16, [1280]> layers_4_encoder_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_4_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(366781184)))];
tensor<fp16, [1, 1280, 1, 1500]> value_19_cast_fp16 = conv(bias = layers_4_encoder_attn_v_proj_bias_to_fp16, dilations = var_1176, groups = var_1039, pad = value_19_pad_0, pad_type = value_19_pad_type_0, strides = var_1174, weight = layers_4_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("value_19_cast_fp16")];
tensor<int32, [4]> var_1180 = const()[name = tensor<string, []>("op_1180"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_1181_cast_fp16 = reshape(shape = var_1180, x = query_19_cast_fp16)[name = tensor<string, []>("op_1181_cast_fp16")];
tensor<fp16, []> var_1182_to_fp16 = const()[name = tensor<string, []>("op_1182_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_1183_cast_fp16 = mul(x = var_1181_cast_fp16, y = var_1182_to_fp16)[name = tensor<string, []>("op_1183_cast_fp16")];
tensor<int32, [4]> var_1184 = const()[name = tensor<string, []>("op_1184"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_1185_cast_fp16 = reshape(shape = var_1184, x = key_19_cast_fp16)[name = tensor<string, []>("op_1185_cast_fp16")];
tensor<bool, []> mh_w_29_transpose_x_0 = const()[name = tensor<string, []>("mh_w_29_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_29_transpose_y_0 = const()[name = tensor<string, []>("mh_w_29_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 1500]> mh_w_29_cast_fp16 = matmul(transpose_x = mh_w_29_transpose_x_0, transpose_y = mh_w_29_transpose_y_0, x = var_1183_cast_fp16, y = var_1185_cast_fp16)[name = tensor<string, []>("mh_w_29_cast_fp16")];
tensor<fp16, [1, 20, 1, 1500]> obj_69_cast_fp16 = softmax(axis = var_1032, x = mh_w_29_cast_fp16)[name = tensor<string, []>("obj_69_cast_fp16")];
tensor<int32, [4]> var_1189 = const()[name = tensor<string, []>("op_1189"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_1190_cast_fp16 = reshape(shape = var_1189, x = value_19_cast_fp16)[name = tensor<string, []>("op_1190_cast_fp16")];
tensor<bool, []> attn_19_transpose_x_0 = const()[name = tensor<string, []>("attn_19_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_19_transpose_y_0 = const()[name = tensor<string, []>("attn_19_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_19_cast_fp16 = matmul(transpose_x = attn_19_transpose_x_0, transpose_y = attn_19_transpose_y_0, x = var_1190_cast_fp16, y = obj_69_cast_fp16)[name = tensor<string, []>("attn_19_cast_fp16")];
tensor<int32, [4]> var_1193 = const()[name = tensor<string, []>("op_1193"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_43_cast_fp16 = reshape(shape = var_1193, x = attn_19_cast_fp16)[name = tensor<string, []>("input_43_cast_fp16")];
tensor<int32, [2]> var_1197 = const()[name = tensor<string, []>("op_1197"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1199 = const()[name = tensor<string, []>("op_1199"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_67_pad_type_0 = const()[name = tensor<string, []>("obj_67_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_67_pad_0 = const()[name = tensor<string, []>("obj_67_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_4_encoder_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_4_encoder_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(366783808)))];
tensor<fp16, [1280]> layers_4_encoder_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_4_encoder_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(370060672)))];
tensor<fp16, [1, 1280, 1, 1]> obj_67_cast_fp16 = conv(bias = layers_4_encoder_attn_o_proj_bias_to_fp16, dilations = var_1199, groups = var_1039, pad = obj_67_pad_0, pad_type = obj_67_pad_type_0, strides = var_1197, weight = layers_4_encoder_attn_o_proj_weight_to_fp16, x = input_43_cast_fp16)[name = tensor<string, []>("obj_67_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_29_cast_fp16 = add(x = inputs_27_cast_fp16, y = obj_67_cast_fp16)[name = tensor<string, []>("inputs_29_cast_fp16")];
tensor<int32, [1]> var_1205 = const()[name = tensor<string, []>("op_1205"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_29_cast_fp16 = reduce_mean(axes = var_1205, keep_dims = var_1040, x = inputs_29_cast_fp16)[name = tensor<string, []>("channels_mean_29_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_29_cast_fp16 = sub(x = inputs_29_cast_fp16, y = channels_mean_29_cast_fp16)[name = tensor<string, []>("zero_mean_29_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_29_cast_fp16 = mul(x = zero_mean_29_cast_fp16, y = zero_mean_29_cast_fp16)[name = tensor<string, []>("zero_mean_sq_29_cast_fp16")];
tensor<int32, [1]> var_1209 = const()[name = tensor<string, []>("op_1209"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_1210_cast_fp16 = reduce_mean(axes = var_1209, keep_dims = var_1040, x = zero_mean_sq_29_cast_fp16)[name = tensor<string, []>("op_1210_cast_fp16")];
tensor<fp16, []> var_1211_to_fp16 = const()[name = tensor<string, []>("op_1211_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_1212_cast_fp16 = add(x = var_1210_cast_fp16, y = var_1211_to_fp16)[name = tensor<string, []>("op_1212_cast_fp16")];
tensor<fp16, []> denom_29_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_29_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_29_cast_fp16 = rsqrt(epsilon = denom_29_epsilon_0_to_fp16, x = var_1212_cast_fp16)[name = tensor<string, []>("denom_29_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_29_cast_fp16 = mul(x = zero_mean_29_cast_fp16, y = denom_29_cast_fp16)[name = tensor<string, []>("out_29_cast_fp16")];
tensor<fp16, [1280]> input_45_gamma_0_to_fp16 = const()[name = tensor<string, []>("input_45_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(370063296)))];
tensor<fp16, [1280]> input_45_beta_0_to_fp16 = const()[name = tensor<string, []>("input_45_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(370065920)))];
tensor<fp16, []> input_45_epsilon_0_to_fp16 = const()[name = tensor<string, []>("input_45_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> input_45_cast_fp16 = batch_norm(beta = input_45_beta_0_to_fp16, epsilon = input_45_epsilon_0_to_fp16, gamma = input_45_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_29_cast_fp16)[name = tensor<string, []>("input_45_cast_fp16")];
tensor<int32, [2]> var_1223 = const()[name = tensor<string, []>("op_1223"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1225 = const()[name = tensor<string, []>("op_1225"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_47_pad_type_0 = const()[name = tensor<string, []>("input_47_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_47_pad_0 = const()[name = tensor<string, []>("input_47_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [5120, 1280, 1, 1]> layers_4_fc1_weight_to_fp16 = const()[name = tensor<string, []>("layers_4_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(370068544)))];
tensor<fp16, [5120]> layers_4_fc1_bias_to_fp16 = const()[name = tensor<string, []>("layers_4_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(383175808)))];
tensor<fp16, [1, 5120, 1, 1]> input_47_cast_fp16 = conv(bias = layers_4_fc1_bias_to_fp16, dilations = var_1225, groups = var_1039, pad = input_47_pad_0, pad_type = input_47_pad_type_0, strides = var_1223, weight = layers_4_fc1_weight_to_fp16, x = input_45_cast_fp16)[name = tensor<string, []>("input_47_cast_fp16")];
tensor<string, []> input_49_mode_0 = const()[name = tensor<string, []>("input_49_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 5120, 1, 1]> input_49_cast_fp16 = gelu(mode = input_49_mode_0, x = input_47_cast_fp16)[name = tensor<string, []>("input_49_cast_fp16")];
tensor<int32, [2]> var_1231 = const()[name = tensor<string, []>("op_1231"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1233 = const()[name = tensor<string, []>("op_1233"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_11_pad_type_0 = const()[name = tensor<string, []>("hidden_states_11_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_11_pad_0 = const()[name = tensor<string, []>("hidden_states_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 5120, 1, 1]> layers_4_fc2_weight_to_fp16 = const()[name = tensor<string, []>("layers_4_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(383186112)))];
tensor<fp16, [1280]> layers_4_fc2_bias_to_fp16 = const()[name = tensor<string, []>("layers_4_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(396293376)))];
tensor<fp16, [1, 1280, 1, 1]> hidden_states_11_cast_fp16 = conv(bias = layers_4_fc2_bias_to_fp16, dilations = var_1233, groups = var_1039, pad = hidden_states_11_pad_0, pad_type = hidden_states_11_pad_type_0, strides = var_1231, weight = layers_4_fc2_weight_to_fp16, x = input_49_cast_fp16)[name = tensor<string, []>("hidden_states_11_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_31_cast_fp16 = add(x = inputs_29_cast_fp16, y = hidden_states_11_cast_fp16)[name = tensor<string, []>("inputs_31_cast_fp16")];
tensor<int32, []> var_1246 = const()[name = tensor<string, []>("op_1246"), val = tensor<int32, []>(3)];
tensor<int32, []> var_1253 = const()[name = tensor<string, []>("op_1253"), val = tensor<int32, []>(1)];
tensor<bool, []> var_1254 = const()[name = tensor<string, []>("op_1254"), val = tensor<bool, []>(true)];
tensor<int32, [1]> var_1266 = const()[name = tensor<string, []>("op_1266"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_31_cast_fp16 = reduce_mean(axes = var_1266, keep_dims = var_1254, x = inputs_31_cast_fp16)[name = tensor<string, []>("channels_mean_31_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_31_cast_fp16 = sub(x = inputs_31_cast_fp16, y = channels_mean_31_cast_fp16)[name = tensor<string, []>("zero_mean_31_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_31_cast_fp16 = mul(x = zero_mean_31_cast_fp16, y = zero_mean_31_cast_fp16)[name = tensor<string, []>("zero_mean_sq_31_cast_fp16")];
tensor<int32, [1]> var_1270 = const()[name = tensor<string, []>("op_1270"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_1271_cast_fp16 = reduce_mean(axes = var_1270, keep_dims = var_1254, x = zero_mean_sq_31_cast_fp16)[name = tensor<string, []>("op_1271_cast_fp16")];
tensor<fp16, []> var_1272_to_fp16 = const()[name = tensor<string, []>("op_1272_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_1273_cast_fp16 = add(x = var_1271_cast_fp16, y = var_1272_to_fp16)[name = tensor<string, []>("op_1273_cast_fp16")];
tensor<fp16, []> denom_31_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_31_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_31_cast_fp16 = rsqrt(epsilon = denom_31_epsilon_0_to_fp16, x = var_1273_cast_fp16)[name = tensor<string, []>("denom_31_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_31_cast_fp16 = mul(x = zero_mean_31_cast_fp16, y = denom_31_cast_fp16)[name = tensor<string, []>("out_31_cast_fp16")];
tensor<fp16, [1280]> obj_71_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_71_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(396296000)))];
tensor<fp16, [1280]> obj_71_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_71_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(396298624)))];
tensor<fp16, []> obj_71_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_71_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_71_cast_fp16 = batch_norm(beta = obj_71_beta_0_to_fp16, epsilon = obj_71_epsilon_0_to_fp16, gamma = obj_71_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_31_cast_fp16)[name = tensor<string, []>("obj_71_cast_fp16")];
tensor<int32, [2]> var_1288 = const()[name = tensor<string, []>("op_1288"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1290 = const()[name = tensor<string, []>("op_1290"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_21_pad_type_0 = const()[name = tensor<string, []>("query_21_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_21_pad_0 = const()[name = tensor<string, []>("query_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_5_self_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_5_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(396301248)))];
tensor<fp16, [1280]> layers_5_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_5_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(399578112)))];
tensor<fp16, [1, 1280, 1, 1]> query_21_cast_fp16 = conv(bias = layers_5_self_attn_q_proj_bias_to_fp16, dilations = var_1290, groups = var_1253, pad = query_21_pad_0, pad_type = query_21_pad_type_0, strides = var_1288, weight = layers_5_self_attn_q_proj_weight_to_fp16, x = obj_71_cast_fp16)[name = tensor<string, []>("query_21_cast_fp16")];
tensor<int32, [2]> var_1294 = const()[name = tensor<string, []>("op_1294"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1296 = const()[name = tensor<string, []>("op_1296"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_key_11_pad_type_0 = const()[name = tensor<string, []>("current_key_11_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_key_11_pad_0 = const()[name = tensor<string, []>("current_key_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_5_self_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_5_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(399580736)))];
tensor<fp16, [1, 1280, 1, 1]> current_key_11_cast_fp16 = conv(dilations = var_1296, groups = var_1253, pad = current_key_11_pad_0, pad_type = current_key_11_pad_type_0, strides = var_1294, weight = layers_5_self_attn_k_proj_weight_to_fp16, x = obj_71_cast_fp16)[name = tensor<string, []>("current_key_11_cast_fp16")];
tensor<int32, [2]> var_1301 = const()[name = tensor<string, []>("op_1301"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1303 = const()[name = tensor<string, []>("op_1303"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_value_11_pad_type_0 = const()[name = tensor<string, []>("current_value_11_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_value_11_pad_0 = const()[name = tensor<string, []>("current_value_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_5_self_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_5_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(402857600)))];
tensor<fp16, [1280]> layers_5_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_5_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(406134464)))];
tensor<fp16, [1, 1280, 1, 1]> current_value_11_cast_fp16 = conv(bias = layers_5_self_attn_v_proj_bias_to_fp16, dilations = var_1303, groups = var_1253, pad = current_value_11_pad_0, pad_type = current_value_11_pad_type_0, strides = var_1301, weight = layers_5_self_attn_v_proj_weight_to_fp16, x = obj_71_cast_fp16)[name = tensor<string, []>("current_value_11_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_1310_cast_fp16 = mul(x = current_key_11_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_1310_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_1312_cast_fp16 = mul(x = var_103_cast_fp16_5, y = var_241_cast_fp16)[name = tensor<string, []>("op_1312_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> key_21_cast_fp16 = add(x = var_1310_cast_fp16, y = var_1312_cast_fp16)[name = tensor<string, []>("key_21_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_1314_cast_fp16 = mul(x = current_value_11_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_1314_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_1316_cast_fp16 = mul(x = var_138_cast_fp16_5, y = var_241_cast_fp16)[name = tensor<string, []>("op_1316_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> value_21_cast_fp16 = add(x = var_1314_cast_fp16, y = var_1316_cast_fp16)[name = tensor<string, []>("value_21_cast_fp16")];
tensor<int32, [4]> var_1319 = const()[name = tensor<string, []>("op_1319"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_1320_cast_fp16 = reshape(shape = var_1319, x = query_21_cast_fp16)[name = tensor<string, []>("op_1320_cast_fp16")];
tensor<fp16, []> var_1321_to_fp16 = const()[name = tensor<string, []>("op_1321_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_1322_cast_fp16 = mul(x = var_1320_cast_fp16, y = var_1321_to_fp16)[name = tensor<string, []>("op_1322_cast_fp16")];
tensor<int32, [4]> var_1323 = const()[name = tensor<string, []>("op_1323"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_1324_cast_fp16 = reshape(shape = var_1323, x = key_21_cast_fp16)[name = tensor<string, []>("op_1324_cast_fp16")];
tensor<bool, []> mh_w_31_transpose_x_0 = const()[name = tensor<string, []>("mh_w_31_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_31_transpose_y_0 = const()[name = tensor<string, []>("mh_w_31_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 224]> mh_w_31_cast_fp16 = matmul(transpose_x = mh_w_31_transpose_x_0, transpose_y = mh_w_31_transpose_y_0, x = var_1322_cast_fp16, y = var_1324_cast_fp16)[name = tensor<string, []>("mh_w_31_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> mh_w_33_cast_fp16 = add(x = mh_w_31_cast_fp16, y = var_259_cast_fp16)[name = tensor<string, []>("mh_w_33_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> var_1332_cast_fp16 = softmax(axis = var_1246, x = mh_w_33_cast_fp16)[name = tensor<string, []>("op_1332_cast_fp16")];
tensor<int32, [4]> var_1333 = const()[name = tensor<string, []>("op_1333"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_1334_cast_fp16 = reshape(shape = var_1333, x = value_21_cast_fp16)[name = tensor<string, []>("op_1334_cast_fp16")];
tensor<bool, []> attn_21_transpose_x_0 = const()[name = tensor<string, []>("attn_21_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_21_transpose_y_0 = const()[name = tensor<string, []>("attn_21_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_21_cast_fp16 = matmul(transpose_x = attn_21_transpose_x_0, transpose_y = attn_21_transpose_y_0, x = var_1334_cast_fp16, y = var_1332_cast_fp16)[name = tensor<string, []>("attn_21_cast_fp16")];
tensor<int32, [4]> var_1337 = const()[name = tensor<string, []>("op_1337"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_51_cast_fp16 = reshape(shape = var_1337, x = attn_21_cast_fp16)[name = tensor<string, []>("input_51_cast_fp16")];
tensor<int32, [2]> var_1341 = const()[name = tensor<string, []>("op_1341"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1343 = const()[name = tensor<string, []>("op_1343"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_77_pad_type_0 = const()[name = tensor<string, []>("obj_77_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_77_pad_0 = const()[name = tensor<string, []>("obj_77_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_5_self_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_5_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(406137088)))];
tensor<fp16, [1280]> layers_5_self_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_5_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(409413952)))];
tensor<fp16, [1, 1280, 1, 1]> obj_77_cast_fp16 = conv(bias = layers_5_self_attn_o_proj_bias_to_fp16, dilations = var_1343, groups = var_1253, pad = obj_77_pad_0, pad_type = obj_77_pad_type_0, strides = var_1341, weight = layers_5_self_attn_o_proj_weight_to_fp16, x = input_51_cast_fp16)[name = tensor<string, []>("obj_77_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_33_cast_fp16 = add(x = inputs_31_cast_fp16, y = obj_77_cast_fp16)[name = tensor<string, []>("inputs_33_cast_fp16")];
tensor<int32, [1]> var_1353 = const()[name = tensor<string, []>("op_1353"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_33_cast_fp16 = reduce_mean(axes = var_1353, keep_dims = var_1254, x = inputs_33_cast_fp16)[name = tensor<string, []>("channels_mean_33_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_33_cast_fp16 = sub(x = inputs_33_cast_fp16, y = channels_mean_33_cast_fp16)[name = tensor<string, []>("zero_mean_33_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_33_cast_fp16 = mul(x = zero_mean_33_cast_fp16, y = zero_mean_33_cast_fp16)[name = tensor<string, []>("zero_mean_sq_33_cast_fp16")];
tensor<int32, [1]> var_1357 = const()[name = tensor<string, []>("op_1357"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_1358_cast_fp16 = reduce_mean(axes = var_1357, keep_dims = var_1254, x = zero_mean_sq_33_cast_fp16)[name = tensor<string, []>("op_1358_cast_fp16")];
tensor<fp16, []> var_1359_to_fp16 = const()[name = tensor<string, []>("op_1359_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_1360_cast_fp16 = add(x = var_1358_cast_fp16, y = var_1359_to_fp16)[name = tensor<string, []>("op_1360_cast_fp16")];
tensor<fp16, []> denom_33_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_33_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_33_cast_fp16 = rsqrt(epsilon = denom_33_epsilon_0_to_fp16, x = var_1360_cast_fp16)[name = tensor<string, []>("denom_33_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_33_cast_fp16 = mul(x = zero_mean_33_cast_fp16, y = denom_33_cast_fp16)[name = tensor<string, []>("out_33_cast_fp16")];
tensor<fp16, [1280]> obj_79_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_79_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(409416576)))];
tensor<fp16, [1280]> obj_79_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_79_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(409419200)))];
tensor<fp16, []> obj_79_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_79_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_79_cast_fp16 = batch_norm(beta = obj_79_beta_0_to_fp16, epsilon = obj_79_epsilon_0_to_fp16, gamma = obj_79_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_33_cast_fp16)[name = tensor<string, []>("obj_79_cast_fp16")];
tensor<int32, [2]> var_1375 = const()[name = tensor<string, []>("op_1375"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1377 = const()[name = tensor<string, []>("op_1377"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_23_pad_type_0 = const()[name = tensor<string, []>("query_23_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_23_pad_0 = const()[name = tensor<string, []>("query_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_5_encoder_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_5_encoder_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(409421824)))];
tensor<fp16, [1280]> layers_5_encoder_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_5_encoder_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(412698688)))];
tensor<fp16, [1, 1280, 1, 1]> query_23_cast_fp16 = conv(bias = layers_5_encoder_attn_q_proj_bias_to_fp16, dilations = var_1377, groups = var_1253, pad = query_23_pad_0, pad_type = query_23_pad_type_0, strides = var_1375, weight = layers_5_encoder_attn_q_proj_weight_to_fp16, x = obj_79_cast_fp16)[name = tensor<string, []>("query_23_cast_fp16")];
tensor<int32, [2]> var_1381 = const()[name = tensor<string, []>("op_1381"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1383 = const()[name = tensor<string, []>("op_1383"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> key_23_pad_type_0 = const()[name = tensor<string, []>("key_23_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> key_23_pad_0 = const()[name = tensor<string, []>("key_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_5_encoder_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_5_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(412701312)))];
tensor<fp16, [1, 1280, 1, 1500]> key_23_cast_fp16 = conv(dilations = var_1383, groups = var_1253, pad = key_23_pad_0, pad_type = key_23_pad_type_0, strides = var_1381, weight = layers_5_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("key_23_cast_fp16")];
tensor<int32, [2]> var_1388 = const()[name = tensor<string, []>("op_1388"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1390 = const()[name = tensor<string, []>("op_1390"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> value_23_pad_type_0 = const()[name = tensor<string, []>("value_23_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> value_23_pad_0 = const()[name = tensor<string, []>("value_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_5_encoder_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_5_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(415978176)))];
tensor<fp16, [1280]> layers_5_encoder_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_5_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(419255040)))];
tensor<fp16, [1, 1280, 1, 1500]> value_23_cast_fp16 = conv(bias = layers_5_encoder_attn_v_proj_bias_to_fp16, dilations = var_1390, groups = var_1253, pad = value_23_pad_0, pad_type = value_23_pad_type_0, strides = var_1388, weight = layers_5_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("value_23_cast_fp16")];
tensor<int32, [4]> var_1394 = const()[name = tensor<string, []>("op_1394"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_1395_cast_fp16 = reshape(shape = var_1394, x = query_23_cast_fp16)[name = tensor<string, []>("op_1395_cast_fp16")];
tensor<fp16, []> var_1396_to_fp16 = const()[name = tensor<string, []>("op_1396_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_1397_cast_fp16 = mul(x = var_1395_cast_fp16, y = var_1396_to_fp16)[name = tensor<string, []>("op_1397_cast_fp16")];
tensor<int32, [4]> var_1398 = const()[name = tensor<string, []>("op_1398"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_1399_cast_fp16 = reshape(shape = var_1398, x = key_23_cast_fp16)[name = tensor<string, []>("op_1399_cast_fp16")];
tensor<bool, []> mh_w_35_transpose_x_0 = const()[name = tensor<string, []>("mh_w_35_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_35_transpose_y_0 = const()[name = tensor<string, []>("mh_w_35_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 1500]> mh_w_35_cast_fp16 = matmul(transpose_x = mh_w_35_transpose_x_0, transpose_y = mh_w_35_transpose_y_0, x = var_1397_cast_fp16, y = var_1399_cast_fp16)[name = tensor<string, []>("mh_w_35_cast_fp16")];
tensor<fp16, [1, 20, 1, 1500]> obj_83_cast_fp16 = softmax(axis = var_1246, x = mh_w_35_cast_fp16)[name = tensor<string, []>("obj_83_cast_fp16")];
tensor<int32, [4]> var_1403 = const()[name = tensor<string, []>("op_1403"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_1404_cast_fp16 = reshape(shape = var_1403, x = value_23_cast_fp16)[name = tensor<string, []>("op_1404_cast_fp16")];
tensor<bool, []> attn_23_transpose_x_0 = const()[name = tensor<string, []>("attn_23_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_23_transpose_y_0 = const()[name = tensor<string, []>("attn_23_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_23_cast_fp16 = matmul(transpose_x = attn_23_transpose_x_0, transpose_y = attn_23_transpose_y_0, x = var_1404_cast_fp16, y = obj_83_cast_fp16)[name = tensor<string, []>("attn_23_cast_fp16")];
tensor<int32, [4]> var_1407 = const()[name = tensor<string, []>("op_1407"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_53_cast_fp16 = reshape(shape = var_1407, x = attn_23_cast_fp16)[name = tensor<string, []>("input_53_cast_fp16")];
tensor<int32, [2]> var_1411 = const()[name = tensor<string, []>("op_1411"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1413 = const()[name = tensor<string, []>("op_1413"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_81_pad_type_0 = const()[name = tensor<string, []>("obj_81_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_81_pad_0 = const()[name = tensor<string, []>("obj_81_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_5_encoder_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_5_encoder_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(419257664)))];
tensor<fp16, [1280]> layers_5_encoder_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_5_encoder_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(422534528)))];
tensor<fp16, [1, 1280, 1, 1]> obj_81_cast_fp16 = conv(bias = layers_5_encoder_attn_o_proj_bias_to_fp16, dilations = var_1413, groups = var_1253, pad = obj_81_pad_0, pad_type = obj_81_pad_type_0, strides = var_1411, weight = layers_5_encoder_attn_o_proj_weight_to_fp16, x = input_53_cast_fp16)[name = tensor<string, []>("obj_81_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_35_cast_fp16 = add(x = inputs_33_cast_fp16, y = obj_81_cast_fp16)[name = tensor<string, []>("inputs_35_cast_fp16")];
tensor<int32, [1]> var_1419 = const()[name = tensor<string, []>("op_1419"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_35_cast_fp16 = reduce_mean(axes = var_1419, keep_dims = var_1254, x = inputs_35_cast_fp16)[name = tensor<string, []>("channels_mean_35_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_35_cast_fp16 = sub(x = inputs_35_cast_fp16, y = channels_mean_35_cast_fp16)[name = tensor<string, []>("zero_mean_35_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_35_cast_fp16 = mul(x = zero_mean_35_cast_fp16, y = zero_mean_35_cast_fp16)[name = tensor<string, []>("zero_mean_sq_35_cast_fp16")];
tensor<int32, [1]> var_1423 = const()[name = tensor<string, []>("op_1423"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_1424_cast_fp16 = reduce_mean(axes = var_1423, keep_dims = var_1254, x = zero_mean_sq_35_cast_fp16)[name = tensor<string, []>("op_1424_cast_fp16")];
tensor<fp16, []> var_1425_to_fp16 = const()[name = tensor<string, []>("op_1425_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_1426_cast_fp16 = add(x = var_1424_cast_fp16, y = var_1425_to_fp16)[name = tensor<string, []>("op_1426_cast_fp16")];
tensor<fp16, []> denom_35_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_35_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_35_cast_fp16 = rsqrt(epsilon = denom_35_epsilon_0_to_fp16, x = var_1426_cast_fp16)[name = tensor<string, []>("denom_35_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_35_cast_fp16 = mul(x = zero_mean_35_cast_fp16, y = denom_35_cast_fp16)[name = tensor<string, []>("out_35_cast_fp16")];
tensor<fp16, [1280]> input_55_gamma_0_to_fp16 = const()[name = tensor<string, []>("input_55_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(422537152)))];
tensor<fp16, [1280]> input_55_beta_0_to_fp16 = const()[name = tensor<string, []>("input_55_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(422539776)))];
tensor<fp16, []> input_55_epsilon_0_to_fp16 = const()[name = tensor<string, []>("input_55_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> input_55_cast_fp16 = batch_norm(beta = input_55_beta_0_to_fp16, epsilon = input_55_epsilon_0_to_fp16, gamma = input_55_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_35_cast_fp16)[name = tensor<string, []>("input_55_cast_fp16")];
tensor<int32, [2]> var_1437 = const()[name = tensor<string, []>("op_1437"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1439 = const()[name = tensor<string, []>("op_1439"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_57_pad_type_0 = const()[name = tensor<string, []>("input_57_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_57_pad_0 = const()[name = tensor<string, []>("input_57_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [5120, 1280, 1, 1]> layers_5_fc1_weight_to_fp16 = const()[name = tensor<string, []>("layers_5_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(422542400)))];
tensor<fp16, [5120]> layers_5_fc1_bias_to_fp16 = const()[name = tensor<string, []>("layers_5_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(435649664)))];
tensor<fp16, [1, 5120, 1, 1]> input_57_cast_fp16 = conv(bias = layers_5_fc1_bias_to_fp16, dilations = var_1439, groups = var_1253, pad = input_57_pad_0, pad_type = input_57_pad_type_0, strides = var_1437, weight = layers_5_fc1_weight_to_fp16, x = input_55_cast_fp16)[name = tensor<string, []>("input_57_cast_fp16")];
tensor<string, []> input_59_mode_0 = const()[name = tensor<string, []>("input_59_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 5120, 1, 1]> input_59_cast_fp16 = gelu(mode = input_59_mode_0, x = input_57_cast_fp16)[name = tensor<string, []>("input_59_cast_fp16")];
tensor<int32, [2]> var_1445 = const()[name = tensor<string, []>("op_1445"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1447 = const()[name = tensor<string, []>("op_1447"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_13_pad_type_0 = const()[name = tensor<string, []>("hidden_states_13_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_13_pad_0 = const()[name = tensor<string, []>("hidden_states_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 5120, 1, 1]> layers_5_fc2_weight_to_fp16 = const()[name = tensor<string, []>("layers_5_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(435659968)))];
tensor<fp16, [1280]> layers_5_fc2_bias_to_fp16 = const()[name = tensor<string, []>("layers_5_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(448767232)))];
tensor<fp16, [1, 1280, 1, 1]> hidden_states_13_cast_fp16 = conv(bias = layers_5_fc2_bias_to_fp16, dilations = var_1447, groups = var_1253, pad = hidden_states_13_pad_0, pad_type = hidden_states_13_pad_type_0, strides = var_1445, weight = layers_5_fc2_weight_to_fp16, x = input_59_cast_fp16)[name = tensor<string, []>("hidden_states_13_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_37_cast_fp16 = add(x = inputs_35_cast_fp16, y = hidden_states_13_cast_fp16)[name = tensor<string, []>("inputs_37_cast_fp16")];
tensor<int32, []> var_1460 = const()[name = tensor<string, []>("op_1460"), val = tensor<int32, []>(3)];
tensor<int32, []> var_1467 = const()[name = tensor<string, []>("op_1467"), val = tensor<int32, []>(1)];
tensor<bool, []> var_1468 = const()[name = tensor<string, []>("op_1468"), val = tensor<bool, []>(true)];
tensor<int32, [1]> var_1480 = const()[name = tensor<string, []>("op_1480"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_37_cast_fp16 = reduce_mean(axes = var_1480, keep_dims = var_1468, x = inputs_37_cast_fp16)[name = tensor<string, []>("channels_mean_37_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_37_cast_fp16 = sub(x = inputs_37_cast_fp16, y = channels_mean_37_cast_fp16)[name = tensor<string, []>("zero_mean_37_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_37_cast_fp16 = mul(x = zero_mean_37_cast_fp16, y = zero_mean_37_cast_fp16)[name = tensor<string, []>("zero_mean_sq_37_cast_fp16")];
tensor<int32, [1]> var_1484 = const()[name = tensor<string, []>("op_1484"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_1485_cast_fp16 = reduce_mean(axes = var_1484, keep_dims = var_1468, x = zero_mean_sq_37_cast_fp16)[name = tensor<string, []>("op_1485_cast_fp16")];
tensor<fp16, []> var_1486_to_fp16 = const()[name = tensor<string, []>("op_1486_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_1487_cast_fp16 = add(x = var_1485_cast_fp16, y = var_1486_to_fp16)[name = tensor<string, []>("op_1487_cast_fp16")];
tensor<fp16, []> denom_37_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_37_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_37_cast_fp16 = rsqrt(epsilon = denom_37_epsilon_0_to_fp16, x = var_1487_cast_fp16)[name = tensor<string, []>("denom_37_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_37_cast_fp16 = mul(x = zero_mean_37_cast_fp16, y = denom_37_cast_fp16)[name = tensor<string, []>("out_37_cast_fp16")];
tensor<fp16, [1280]> obj_85_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_85_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(448769856)))];
tensor<fp16, [1280]> obj_85_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_85_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(448772480)))];
tensor<fp16, []> obj_85_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_85_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_85_cast_fp16 = batch_norm(beta = obj_85_beta_0_to_fp16, epsilon = obj_85_epsilon_0_to_fp16, gamma = obj_85_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_37_cast_fp16)[name = tensor<string, []>("obj_85_cast_fp16")];
tensor<int32, [2]> var_1502 = const()[name = tensor<string, []>("op_1502"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1504 = const()[name = tensor<string, []>("op_1504"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_25_pad_type_0 = const()[name = tensor<string, []>("query_25_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_25_pad_0 = const()[name = tensor<string, []>("query_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_6_self_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_6_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(448775104)))];
tensor<fp16, [1280]> layers_6_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_6_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(452051968)))];
tensor<fp16, [1, 1280, 1, 1]> query_25_cast_fp16 = conv(bias = layers_6_self_attn_q_proj_bias_to_fp16, dilations = var_1504, groups = var_1467, pad = query_25_pad_0, pad_type = query_25_pad_type_0, strides = var_1502, weight = layers_6_self_attn_q_proj_weight_to_fp16, x = obj_85_cast_fp16)[name = tensor<string, []>("query_25_cast_fp16")];
tensor<int32, [2]> var_1508 = const()[name = tensor<string, []>("op_1508"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1510 = const()[name = tensor<string, []>("op_1510"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_key_13_pad_type_0 = const()[name = tensor<string, []>("current_key_13_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_key_13_pad_0 = const()[name = tensor<string, []>("current_key_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_6_self_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_6_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(452054592)))];
tensor<fp16, [1, 1280, 1, 1]> current_key_13_cast_fp16 = conv(dilations = var_1510, groups = var_1467, pad = current_key_13_pad_0, pad_type = current_key_13_pad_type_0, strides = var_1508, weight = layers_6_self_attn_k_proj_weight_to_fp16, x = obj_85_cast_fp16)[name = tensor<string, []>("current_key_13_cast_fp16")];
tensor<int32, [2]> var_1515 = const()[name = tensor<string, []>("op_1515"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1517 = const()[name = tensor<string, []>("op_1517"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_value_13_pad_type_0 = const()[name = tensor<string, []>("current_value_13_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_value_13_pad_0 = const()[name = tensor<string, []>("current_value_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_6_self_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_6_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(455331456)))];
tensor<fp16, [1280]> layers_6_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_6_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(458608320)))];
tensor<fp16, [1, 1280, 1, 1]> current_value_13_cast_fp16 = conv(bias = layers_6_self_attn_v_proj_bias_to_fp16, dilations = var_1517, groups = var_1467, pad = current_value_13_pad_0, pad_type = current_value_13_pad_type_0, strides = var_1515, weight = layers_6_self_attn_v_proj_weight_to_fp16, x = obj_85_cast_fp16)[name = tensor<string, []>("current_value_13_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_1524_cast_fp16 = mul(x = current_key_13_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_1524_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_1526_cast_fp16 = mul(x = var_103_cast_fp16_6, y = var_241_cast_fp16)[name = tensor<string, []>("op_1526_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> key_25_cast_fp16 = add(x = var_1524_cast_fp16, y = var_1526_cast_fp16)[name = tensor<string, []>("key_25_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_1528_cast_fp16 = mul(x = current_value_13_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_1528_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_1530_cast_fp16 = mul(x = var_138_cast_fp16_6, y = var_241_cast_fp16)[name = tensor<string, []>("op_1530_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> value_25_cast_fp16 = add(x = var_1528_cast_fp16, y = var_1530_cast_fp16)[name = tensor<string, []>("value_25_cast_fp16")];
tensor<int32, [4]> var_1533 = const()[name = tensor<string, []>("op_1533"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_1534_cast_fp16 = reshape(shape = var_1533, x = query_25_cast_fp16)[name = tensor<string, []>("op_1534_cast_fp16")];
tensor<fp16, []> var_1535_to_fp16 = const()[name = tensor<string, []>("op_1535_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_1536_cast_fp16 = mul(x = var_1534_cast_fp16, y = var_1535_to_fp16)[name = tensor<string, []>("op_1536_cast_fp16")];
tensor<int32, [4]> var_1537 = const()[name = tensor<string, []>("op_1537"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_1538_cast_fp16 = reshape(shape = var_1537, x = key_25_cast_fp16)[name = tensor<string, []>("op_1538_cast_fp16")];
tensor<bool, []> mh_w_37_transpose_x_0 = const()[name = tensor<string, []>("mh_w_37_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_37_transpose_y_0 = const()[name = tensor<string, []>("mh_w_37_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 224]> mh_w_37_cast_fp16 = matmul(transpose_x = mh_w_37_transpose_x_0, transpose_y = mh_w_37_transpose_y_0, x = var_1536_cast_fp16, y = var_1538_cast_fp16)[name = tensor<string, []>("mh_w_37_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> mh_w_39_cast_fp16 = add(x = mh_w_37_cast_fp16, y = var_259_cast_fp16)[name = tensor<string, []>("mh_w_39_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> var_1546_cast_fp16 = softmax(axis = var_1460, x = mh_w_39_cast_fp16)[name = tensor<string, []>("op_1546_cast_fp16")];
tensor<int32, [4]> var_1547 = const()[name = tensor<string, []>("op_1547"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_1548_cast_fp16 = reshape(shape = var_1547, x = value_25_cast_fp16)[name = tensor<string, []>("op_1548_cast_fp16")];
tensor<bool, []> attn_25_transpose_x_0 = const()[name = tensor<string, []>("attn_25_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_25_transpose_y_0 = const()[name = tensor<string, []>("attn_25_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_25_cast_fp16 = matmul(transpose_x = attn_25_transpose_x_0, transpose_y = attn_25_transpose_y_0, x = var_1548_cast_fp16, y = var_1546_cast_fp16)[name = tensor<string, []>("attn_25_cast_fp16")];
tensor<int32, [4]> var_1551 = const()[name = tensor<string, []>("op_1551"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_61_cast_fp16 = reshape(shape = var_1551, x = attn_25_cast_fp16)[name = tensor<string, []>("input_61_cast_fp16")];
tensor<int32, [2]> var_1555 = const()[name = tensor<string, []>("op_1555"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1557 = const()[name = tensor<string, []>("op_1557"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_91_pad_type_0 = const()[name = tensor<string, []>("obj_91_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_91_pad_0 = const()[name = tensor<string, []>("obj_91_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_6_self_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_6_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(458610944)))];
tensor<fp16, [1280]> layers_6_self_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_6_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(461887808)))];
tensor<fp16, [1, 1280, 1, 1]> obj_91_cast_fp16 = conv(bias = layers_6_self_attn_o_proj_bias_to_fp16, dilations = var_1557, groups = var_1467, pad = obj_91_pad_0, pad_type = obj_91_pad_type_0, strides = var_1555, weight = layers_6_self_attn_o_proj_weight_to_fp16, x = input_61_cast_fp16)[name = tensor<string, []>("obj_91_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_39_cast_fp16 = add(x = inputs_37_cast_fp16, y = obj_91_cast_fp16)[name = tensor<string, []>("inputs_39_cast_fp16")];
tensor<int32, [1]> var_1567 = const()[name = tensor<string, []>("op_1567"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_39_cast_fp16 = reduce_mean(axes = var_1567, keep_dims = var_1468, x = inputs_39_cast_fp16)[name = tensor<string, []>("channels_mean_39_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_39_cast_fp16 = sub(x = inputs_39_cast_fp16, y = channels_mean_39_cast_fp16)[name = tensor<string, []>("zero_mean_39_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_39_cast_fp16 = mul(x = zero_mean_39_cast_fp16, y = zero_mean_39_cast_fp16)[name = tensor<string, []>("zero_mean_sq_39_cast_fp16")];
tensor<int32, [1]> var_1571 = const()[name = tensor<string, []>("op_1571"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_1572_cast_fp16 = reduce_mean(axes = var_1571, keep_dims = var_1468, x = zero_mean_sq_39_cast_fp16)[name = tensor<string, []>("op_1572_cast_fp16")];
tensor<fp16, []> var_1573_to_fp16 = const()[name = tensor<string, []>("op_1573_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_1574_cast_fp16 = add(x = var_1572_cast_fp16, y = var_1573_to_fp16)[name = tensor<string, []>("op_1574_cast_fp16")];
tensor<fp16, []> denom_39_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_39_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_39_cast_fp16 = rsqrt(epsilon = denom_39_epsilon_0_to_fp16, x = var_1574_cast_fp16)[name = tensor<string, []>("denom_39_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_39_cast_fp16 = mul(x = zero_mean_39_cast_fp16, y = denom_39_cast_fp16)[name = tensor<string, []>("out_39_cast_fp16")];
tensor<fp16, [1280]> obj_93_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_93_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(461890432)))];
tensor<fp16, [1280]> obj_93_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_93_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(461893056)))];
tensor<fp16, []> obj_93_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_93_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_93_cast_fp16 = batch_norm(beta = obj_93_beta_0_to_fp16, epsilon = obj_93_epsilon_0_to_fp16, gamma = obj_93_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_39_cast_fp16)[name = tensor<string, []>("obj_93_cast_fp16")];
tensor<int32, [2]> var_1589 = const()[name = tensor<string, []>("op_1589"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1591 = const()[name = tensor<string, []>("op_1591"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_27_pad_type_0 = const()[name = tensor<string, []>("query_27_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_27_pad_0 = const()[name = tensor<string, []>("query_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_6_encoder_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_6_encoder_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(461895680)))];
tensor<fp16, [1280]> layers_6_encoder_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_6_encoder_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(465172544)))];
tensor<fp16, [1, 1280, 1, 1]> query_27_cast_fp16 = conv(bias = layers_6_encoder_attn_q_proj_bias_to_fp16, dilations = var_1591, groups = var_1467, pad = query_27_pad_0, pad_type = query_27_pad_type_0, strides = var_1589, weight = layers_6_encoder_attn_q_proj_weight_to_fp16, x = obj_93_cast_fp16)[name = tensor<string, []>("query_27_cast_fp16")];
tensor<int32, [2]> var_1595 = const()[name = tensor<string, []>("op_1595"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1597 = const()[name = tensor<string, []>("op_1597"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> key_27_pad_type_0 = const()[name = tensor<string, []>("key_27_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> key_27_pad_0 = const()[name = tensor<string, []>("key_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_6_encoder_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_6_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(465175168)))];
tensor<fp16, [1, 1280, 1, 1500]> key_27_cast_fp16 = conv(dilations = var_1597, groups = var_1467, pad = key_27_pad_0, pad_type = key_27_pad_type_0, strides = var_1595, weight = layers_6_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("key_27_cast_fp16")];
tensor<int32, [2]> var_1602 = const()[name = tensor<string, []>("op_1602"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1604 = const()[name = tensor<string, []>("op_1604"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> value_27_pad_type_0 = const()[name = tensor<string, []>("value_27_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> value_27_pad_0 = const()[name = tensor<string, []>("value_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_6_encoder_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_6_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(468452032)))];
tensor<fp16, [1280]> layers_6_encoder_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_6_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(471728896)))];
tensor<fp16, [1, 1280, 1, 1500]> value_27_cast_fp16 = conv(bias = layers_6_encoder_attn_v_proj_bias_to_fp16, dilations = var_1604, groups = var_1467, pad = value_27_pad_0, pad_type = value_27_pad_type_0, strides = var_1602, weight = layers_6_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("value_27_cast_fp16")];
tensor<int32, [4]> var_1608 = const()[name = tensor<string, []>("op_1608"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_1609_cast_fp16 = reshape(shape = var_1608, x = query_27_cast_fp16)[name = tensor<string, []>("op_1609_cast_fp16")];
tensor<fp16, []> var_1610_to_fp16 = const()[name = tensor<string, []>("op_1610_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_1611_cast_fp16 = mul(x = var_1609_cast_fp16, y = var_1610_to_fp16)[name = tensor<string, []>("op_1611_cast_fp16")];
tensor<int32, [4]> var_1612 = const()[name = tensor<string, []>("op_1612"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_1613_cast_fp16 = reshape(shape = var_1612, x = key_27_cast_fp16)[name = tensor<string, []>("op_1613_cast_fp16")];
tensor<bool, []> mh_w_41_transpose_x_0 = const()[name = tensor<string, []>("mh_w_41_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_41_transpose_y_0 = const()[name = tensor<string, []>("mh_w_41_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 1500]> mh_w_41_cast_fp16 = matmul(transpose_x = mh_w_41_transpose_x_0, transpose_y = mh_w_41_transpose_y_0, x = var_1611_cast_fp16, y = var_1613_cast_fp16)[name = tensor<string, []>("mh_w_41_cast_fp16")];
tensor<fp16, [1, 20, 1, 1500]> obj_97_cast_fp16 = softmax(axis = var_1460, x = mh_w_41_cast_fp16)[name = tensor<string, []>("obj_97_cast_fp16")];
tensor<int32, [4]> var_1617 = const()[name = tensor<string, []>("op_1617"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_1618_cast_fp16 = reshape(shape = var_1617, x = value_27_cast_fp16)[name = tensor<string, []>("op_1618_cast_fp16")];
tensor<bool, []> attn_27_transpose_x_0 = const()[name = tensor<string, []>("attn_27_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_27_transpose_y_0 = const()[name = tensor<string, []>("attn_27_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_27_cast_fp16 = matmul(transpose_x = attn_27_transpose_x_0, transpose_y = attn_27_transpose_y_0, x = var_1618_cast_fp16, y = obj_97_cast_fp16)[name = tensor<string, []>("attn_27_cast_fp16")];
tensor<int32, [4]> var_1621 = const()[name = tensor<string, []>("op_1621"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_63_cast_fp16 = reshape(shape = var_1621, x = attn_27_cast_fp16)[name = tensor<string, []>("input_63_cast_fp16")];
tensor<int32, [2]> var_1625 = const()[name = tensor<string, []>("op_1625"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1627 = const()[name = tensor<string, []>("op_1627"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_95_pad_type_0 = const()[name = tensor<string, []>("obj_95_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_95_pad_0 = const()[name = tensor<string, []>("obj_95_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_6_encoder_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_6_encoder_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(471731520)))];
tensor<fp16, [1280]> layers_6_encoder_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_6_encoder_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(475008384)))];
tensor<fp16, [1, 1280, 1, 1]> obj_95_cast_fp16 = conv(bias = layers_6_encoder_attn_o_proj_bias_to_fp16, dilations = var_1627, groups = var_1467, pad = obj_95_pad_0, pad_type = obj_95_pad_type_0, strides = var_1625, weight = layers_6_encoder_attn_o_proj_weight_to_fp16, x = input_63_cast_fp16)[name = tensor<string, []>("obj_95_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_41_cast_fp16 = add(x = inputs_39_cast_fp16, y = obj_95_cast_fp16)[name = tensor<string, []>("inputs_41_cast_fp16")];
tensor<int32, [1]> var_1633 = const()[name = tensor<string, []>("op_1633"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_41_cast_fp16 = reduce_mean(axes = var_1633, keep_dims = var_1468, x = inputs_41_cast_fp16)[name = tensor<string, []>("channels_mean_41_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_41_cast_fp16 = sub(x = inputs_41_cast_fp16, y = channels_mean_41_cast_fp16)[name = tensor<string, []>("zero_mean_41_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_41_cast_fp16 = mul(x = zero_mean_41_cast_fp16, y = zero_mean_41_cast_fp16)[name = tensor<string, []>("zero_mean_sq_41_cast_fp16")];
tensor<int32, [1]> var_1637 = const()[name = tensor<string, []>("op_1637"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_1638_cast_fp16 = reduce_mean(axes = var_1637, keep_dims = var_1468, x = zero_mean_sq_41_cast_fp16)[name = tensor<string, []>("op_1638_cast_fp16")];
tensor<fp16, []> var_1639_to_fp16 = const()[name = tensor<string, []>("op_1639_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_1640_cast_fp16 = add(x = var_1638_cast_fp16, y = var_1639_to_fp16)[name = tensor<string, []>("op_1640_cast_fp16")];
tensor<fp16, []> denom_41_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_41_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_41_cast_fp16 = rsqrt(epsilon = denom_41_epsilon_0_to_fp16, x = var_1640_cast_fp16)[name = tensor<string, []>("denom_41_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_41_cast_fp16 = mul(x = zero_mean_41_cast_fp16, y = denom_41_cast_fp16)[name = tensor<string, []>("out_41_cast_fp16")];
tensor<fp16, [1280]> input_65_gamma_0_to_fp16 = const()[name = tensor<string, []>("input_65_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(475011008)))];
tensor<fp16, [1280]> input_65_beta_0_to_fp16 = const()[name = tensor<string, []>("input_65_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(475013632)))];
tensor<fp16, []> input_65_epsilon_0_to_fp16 = const()[name = tensor<string, []>("input_65_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> input_65_cast_fp16 = batch_norm(beta = input_65_beta_0_to_fp16, epsilon = input_65_epsilon_0_to_fp16, gamma = input_65_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_41_cast_fp16)[name = tensor<string, []>("input_65_cast_fp16")];
tensor<int32, [2]> var_1651 = const()[name = tensor<string, []>("op_1651"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1653 = const()[name = tensor<string, []>("op_1653"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_67_pad_type_0 = const()[name = tensor<string, []>("input_67_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_67_pad_0 = const()[name = tensor<string, []>("input_67_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [5120, 1280, 1, 1]> layers_6_fc1_weight_to_fp16 = const()[name = tensor<string, []>("layers_6_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(475016256)))];
tensor<fp16, [5120]> layers_6_fc1_bias_to_fp16 = const()[name = tensor<string, []>("layers_6_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(488123520)))];
tensor<fp16, [1, 5120, 1, 1]> input_67_cast_fp16 = conv(bias = layers_6_fc1_bias_to_fp16, dilations = var_1653, groups = var_1467, pad = input_67_pad_0, pad_type = input_67_pad_type_0, strides = var_1651, weight = layers_6_fc1_weight_to_fp16, x = input_65_cast_fp16)[name = tensor<string, []>("input_67_cast_fp16")];
tensor<string, []> input_69_mode_0 = const()[name = tensor<string, []>("input_69_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 5120, 1, 1]> input_69_cast_fp16 = gelu(mode = input_69_mode_0, x = input_67_cast_fp16)[name = tensor<string, []>("input_69_cast_fp16")];
tensor<int32, [2]> var_1659 = const()[name = tensor<string, []>("op_1659"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1661 = const()[name = tensor<string, []>("op_1661"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_15_pad_type_0 = const()[name = tensor<string, []>("hidden_states_15_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_15_pad_0 = const()[name = tensor<string, []>("hidden_states_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 5120, 1, 1]> layers_6_fc2_weight_to_fp16 = const()[name = tensor<string, []>("layers_6_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(488133824)))];
tensor<fp16, [1280]> layers_6_fc2_bias_to_fp16 = const()[name = tensor<string, []>("layers_6_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(501241088)))];
tensor<fp16, [1, 1280, 1, 1]> hidden_states_15_cast_fp16 = conv(bias = layers_6_fc2_bias_to_fp16, dilations = var_1661, groups = var_1467, pad = hidden_states_15_pad_0, pad_type = hidden_states_15_pad_type_0, strides = var_1659, weight = layers_6_fc2_weight_to_fp16, x = input_69_cast_fp16)[name = tensor<string, []>("hidden_states_15_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_43_cast_fp16 = add(x = inputs_41_cast_fp16, y = hidden_states_15_cast_fp16)[name = tensor<string, []>("inputs_43_cast_fp16")];
tensor<int32, []> var_1674 = const()[name = tensor<string, []>("op_1674"), val = tensor<int32, []>(3)];
tensor<int32, []> var_1681 = const()[name = tensor<string, []>("op_1681"), val = tensor<int32, []>(1)];
tensor<bool, []> var_1682 = const()[name = tensor<string, []>("op_1682"), val = tensor<bool, []>(true)];
tensor<int32, [1]> var_1694 = const()[name = tensor<string, []>("op_1694"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_43_cast_fp16 = reduce_mean(axes = var_1694, keep_dims = var_1682, x = inputs_43_cast_fp16)[name = tensor<string, []>("channels_mean_43_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_43_cast_fp16 = sub(x = inputs_43_cast_fp16, y = channels_mean_43_cast_fp16)[name = tensor<string, []>("zero_mean_43_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_43_cast_fp16 = mul(x = zero_mean_43_cast_fp16, y = zero_mean_43_cast_fp16)[name = tensor<string, []>("zero_mean_sq_43_cast_fp16")];
tensor<int32, [1]> var_1698 = const()[name = tensor<string, []>("op_1698"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_1699_cast_fp16 = reduce_mean(axes = var_1698, keep_dims = var_1682, x = zero_mean_sq_43_cast_fp16)[name = tensor<string, []>("op_1699_cast_fp16")];
tensor<fp16, []> var_1700_to_fp16 = const()[name = tensor<string, []>("op_1700_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_1701_cast_fp16 = add(x = var_1699_cast_fp16, y = var_1700_to_fp16)[name = tensor<string, []>("op_1701_cast_fp16")];
tensor<fp16, []> denom_43_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_43_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_43_cast_fp16 = rsqrt(epsilon = denom_43_epsilon_0_to_fp16, x = var_1701_cast_fp16)[name = tensor<string, []>("denom_43_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_43_cast_fp16 = mul(x = zero_mean_43_cast_fp16, y = denom_43_cast_fp16)[name = tensor<string, []>("out_43_cast_fp16")];
tensor<fp16, [1280]> obj_99_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_99_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(501243712)))];
tensor<fp16, [1280]> obj_99_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_99_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(501246336)))];
tensor<fp16, []> obj_99_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_99_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_99_cast_fp16 = batch_norm(beta = obj_99_beta_0_to_fp16, epsilon = obj_99_epsilon_0_to_fp16, gamma = obj_99_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_43_cast_fp16)[name = tensor<string, []>("obj_99_cast_fp16")];
tensor<int32, [2]> var_1716 = const()[name = tensor<string, []>("op_1716"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1718 = const()[name = tensor<string, []>("op_1718"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_29_pad_type_0 = const()[name = tensor<string, []>("query_29_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_29_pad_0 = const()[name = tensor<string, []>("query_29_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_7_self_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_7_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(501248960)))];
tensor<fp16, [1280]> layers_7_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_7_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(504525824)))];
tensor<fp16, [1, 1280, 1, 1]> query_29_cast_fp16 = conv(bias = layers_7_self_attn_q_proj_bias_to_fp16, dilations = var_1718, groups = var_1681, pad = query_29_pad_0, pad_type = query_29_pad_type_0, strides = var_1716, weight = layers_7_self_attn_q_proj_weight_to_fp16, x = obj_99_cast_fp16)[name = tensor<string, []>("query_29_cast_fp16")];
tensor<int32, [2]> var_1722 = const()[name = tensor<string, []>("op_1722"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1724 = const()[name = tensor<string, []>("op_1724"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_key_15_pad_type_0 = const()[name = tensor<string, []>("current_key_15_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_key_15_pad_0 = const()[name = tensor<string, []>("current_key_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_7_self_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_7_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(504528448)))];
tensor<fp16, [1, 1280, 1, 1]> current_key_15_cast_fp16 = conv(dilations = var_1724, groups = var_1681, pad = current_key_15_pad_0, pad_type = current_key_15_pad_type_0, strides = var_1722, weight = layers_7_self_attn_k_proj_weight_to_fp16, x = obj_99_cast_fp16)[name = tensor<string, []>("current_key_15_cast_fp16")];
tensor<int32, [2]> var_1729 = const()[name = tensor<string, []>("op_1729"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1731 = const()[name = tensor<string, []>("op_1731"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_value_15_pad_type_0 = const()[name = tensor<string, []>("current_value_15_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_value_15_pad_0 = const()[name = tensor<string, []>("current_value_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_7_self_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_7_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(507805312)))];
tensor<fp16, [1280]> layers_7_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_7_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(511082176)))];
tensor<fp16, [1, 1280, 1, 1]> current_value_15_cast_fp16 = conv(bias = layers_7_self_attn_v_proj_bias_to_fp16, dilations = var_1731, groups = var_1681, pad = current_value_15_pad_0, pad_type = current_value_15_pad_type_0, strides = var_1729, weight = layers_7_self_attn_v_proj_weight_to_fp16, x = obj_99_cast_fp16)[name = tensor<string, []>("current_value_15_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_1738_cast_fp16 = mul(x = current_key_15_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_1738_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_1740_cast_fp16 = mul(x = var_103_cast_fp16_7, y = var_241_cast_fp16)[name = tensor<string, []>("op_1740_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> key_29_cast_fp16 = add(x = var_1738_cast_fp16, y = var_1740_cast_fp16)[name = tensor<string, []>("key_29_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_1742_cast_fp16 = mul(x = current_value_15_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_1742_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_1744_cast_fp16 = mul(x = var_138_cast_fp16_7, y = var_241_cast_fp16)[name = tensor<string, []>("op_1744_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> value_29_cast_fp16 = add(x = var_1742_cast_fp16, y = var_1744_cast_fp16)[name = tensor<string, []>("value_29_cast_fp16")];
tensor<int32, [4]> var_1747 = const()[name = tensor<string, []>("op_1747"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_1748_cast_fp16 = reshape(shape = var_1747, x = query_29_cast_fp16)[name = tensor<string, []>("op_1748_cast_fp16")];
tensor<fp16, []> var_1749_to_fp16 = const()[name = tensor<string, []>("op_1749_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_1750_cast_fp16 = mul(x = var_1748_cast_fp16, y = var_1749_to_fp16)[name = tensor<string, []>("op_1750_cast_fp16")];
tensor<int32, [4]> var_1751 = const()[name = tensor<string, []>("op_1751"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_1752_cast_fp16 = reshape(shape = var_1751, x = key_29_cast_fp16)[name = tensor<string, []>("op_1752_cast_fp16")];
tensor<bool, []> mh_w_43_transpose_x_0 = const()[name = tensor<string, []>("mh_w_43_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_43_transpose_y_0 = const()[name = tensor<string, []>("mh_w_43_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 224]> mh_w_43_cast_fp16 = matmul(transpose_x = mh_w_43_transpose_x_0, transpose_y = mh_w_43_transpose_y_0, x = var_1750_cast_fp16, y = var_1752_cast_fp16)[name = tensor<string, []>("mh_w_43_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> mh_w_45_cast_fp16 = add(x = mh_w_43_cast_fp16, y = var_259_cast_fp16)[name = tensor<string, []>("mh_w_45_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> var_1760_cast_fp16 = softmax(axis = var_1674, x = mh_w_45_cast_fp16)[name = tensor<string, []>("op_1760_cast_fp16")];
tensor<int32, [4]> var_1761 = const()[name = tensor<string, []>("op_1761"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_1762_cast_fp16 = reshape(shape = var_1761, x = value_29_cast_fp16)[name = tensor<string, []>("op_1762_cast_fp16")];
tensor<bool, []> attn_29_transpose_x_0 = const()[name = tensor<string, []>("attn_29_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_29_transpose_y_0 = const()[name = tensor<string, []>("attn_29_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_29_cast_fp16 = matmul(transpose_x = attn_29_transpose_x_0, transpose_y = attn_29_transpose_y_0, x = var_1762_cast_fp16, y = var_1760_cast_fp16)[name = tensor<string, []>("attn_29_cast_fp16")];
tensor<int32, [4]> var_1765 = const()[name = tensor<string, []>("op_1765"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_71_cast_fp16 = reshape(shape = var_1765, x = attn_29_cast_fp16)[name = tensor<string, []>("input_71_cast_fp16")];
tensor<int32, [2]> var_1769 = const()[name = tensor<string, []>("op_1769"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1771 = const()[name = tensor<string, []>("op_1771"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_105_pad_type_0 = const()[name = tensor<string, []>("obj_105_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_105_pad_0 = const()[name = tensor<string, []>("obj_105_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_7_self_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_7_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(511084800)))];
tensor<fp16, [1280]> layers_7_self_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_7_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(514361664)))];
tensor<fp16, [1, 1280, 1, 1]> obj_105_cast_fp16 = conv(bias = layers_7_self_attn_o_proj_bias_to_fp16, dilations = var_1771, groups = var_1681, pad = obj_105_pad_0, pad_type = obj_105_pad_type_0, strides = var_1769, weight = layers_7_self_attn_o_proj_weight_to_fp16, x = input_71_cast_fp16)[name = tensor<string, []>("obj_105_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_45_cast_fp16 = add(x = inputs_43_cast_fp16, y = obj_105_cast_fp16)[name = tensor<string, []>("inputs_45_cast_fp16")];
tensor<int32, [1]> var_1781 = const()[name = tensor<string, []>("op_1781"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_45_cast_fp16 = reduce_mean(axes = var_1781, keep_dims = var_1682, x = inputs_45_cast_fp16)[name = tensor<string, []>("channels_mean_45_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_45_cast_fp16 = sub(x = inputs_45_cast_fp16, y = channels_mean_45_cast_fp16)[name = tensor<string, []>("zero_mean_45_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_45_cast_fp16 = mul(x = zero_mean_45_cast_fp16, y = zero_mean_45_cast_fp16)[name = tensor<string, []>("zero_mean_sq_45_cast_fp16")];
tensor<int32, [1]> var_1785 = const()[name = tensor<string, []>("op_1785"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_1786_cast_fp16 = reduce_mean(axes = var_1785, keep_dims = var_1682, x = zero_mean_sq_45_cast_fp16)[name = tensor<string, []>("op_1786_cast_fp16")];
tensor<fp16, []> var_1787_to_fp16 = const()[name = tensor<string, []>("op_1787_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_1788_cast_fp16 = add(x = var_1786_cast_fp16, y = var_1787_to_fp16)[name = tensor<string, []>("op_1788_cast_fp16")];
tensor<fp16, []> denom_45_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_45_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_45_cast_fp16 = rsqrt(epsilon = denom_45_epsilon_0_to_fp16, x = var_1788_cast_fp16)[name = tensor<string, []>("denom_45_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_45_cast_fp16 = mul(x = zero_mean_45_cast_fp16, y = denom_45_cast_fp16)[name = tensor<string, []>("out_45_cast_fp16")];
tensor<fp16, [1280]> obj_107_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_107_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(514364288)))];
tensor<fp16, [1280]> obj_107_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_107_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(514366912)))];
tensor<fp16, []> obj_107_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_107_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_107_cast_fp16 = batch_norm(beta = obj_107_beta_0_to_fp16, epsilon = obj_107_epsilon_0_to_fp16, gamma = obj_107_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_45_cast_fp16)[name = tensor<string, []>("obj_107_cast_fp16")];
tensor<int32, [2]> var_1803 = const()[name = tensor<string, []>("op_1803"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1805 = const()[name = tensor<string, []>("op_1805"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_31_pad_type_0 = const()[name = tensor<string, []>("query_31_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_31_pad_0 = const()[name = tensor<string, []>("query_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_7_encoder_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_7_encoder_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(514369536)))];
tensor<fp16, [1280]> layers_7_encoder_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_7_encoder_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(517646400)))];
tensor<fp16, [1, 1280, 1, 1]> query_31_cast_fp16 = conv(bias = layers_7_encoder_attn_q_proj_bias_to_fp16, dilations = var_1805, groups = var_1681, pad = query_31_pad_0, pad_type = query_31_pad_type_0, strides = var_1803, weight = layers_7_encoder_attn_q_proj_weight_to_fp16, x = obj_107_cast_fp16)[name = tensor<string, []>("query_31_cast_fp16")];
tensor<int32, [2]> var_1809 = const()[name = tensor<string, []>("op_1809"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1811 = const()[name = tensor<string, []>("op_1811"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> key_31_pad_type_0 = const()[name = tensor<string, []>("key_31_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> key_31_pad_0 = const()[name = tensor<string, []>("key_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_7_encoder_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_7_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(517649024)))];
tensor<fp16, [1, 1280, 1, 1500]> key_31_cast_fp16 = conv(dilations = var_1811, groups = var_1681, pad = key_31_pad_0, pad_type = key_31_pad_type_0, strides = var_1809, weight = layers_7_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("key_31_cast_fp16")];
tensor<int32, [2]> var_1816 = const()[name = tensor<string, []>("op_1816"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1818 = const()[name = tensor<string, []>("op_1818"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> value_31_pad_type_0 = const()[name = tensor<string, []>("value_31_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> value_31_pad_0 = const()[name = tensor<string, []>("value_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_7_encoder_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_7_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(520925888)))];
tensor<fp16, [1280]> layers_7_encoder_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_7_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(524202752)))];
tensor<fp16, [1, 1280, 1, 1500]> value_31_cast_fp16 = conv(bias = layers_7_encoder_attn_v_proj_bias_to_fp16, dilations = var_1818, groups = var_1681, pad = value_31_pad_0, pad_type = value_31_pad_type_0, strides = var_1816, weight = layers_7_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("value_31_cast_fp16")];
tensor<int32, [4]> var_1822 = const()[name = tensor<string, []>("op_1822"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_1823_cast_fp16 = reshape(shape = var_1822, x = query_31_cast_fp16)[name = tensor<string, []>("op_1823_cast_fp16")];
tensor<fp16, []> var_1824_to_fp16 = const()[name = tensor<string, []>("op_1824_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_1825_cast_fp16 = mul(x = var_1823_cast_fp16, y = var_1824_to_fp16)[name = tensor<string, []>("op_1825_cast_fp16")];
tensor<int32, [4]> var_1826 = const()[name = tensor<string, []>("op_1826"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_1827_cast_fp16 = reshape(shape = var_1826, x = key_31_cast_fp16)[name = tensor<string, []>("op_1827_cast_fp16")];
tensor<bool, []> mh_w_47_transpose_x_0 = const()[name = tensor<string, []>("mh_w_47_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_47_transpose_y_0 = const()[name = tensor<string, []>("mh_w_47_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 1500]> mh_w_47_cast_fp16 = matmul(transpose_x = mh_w_47_transpose_x_0, transpose_y = mh_w_47_transpose_y_0, x = var_1825_cast_fp16, y = var_1827_cast_fp16)[name = tensor<string, []>("mh_w_47_cast_fp16")];
tensor<fp16, [1, 20, 1, 1500]> obj_111_cast_fp16 = softmax(axis = var_1674, x = mh_w_47_cast_fp16)[name = tensor<string, []>("obj_111_cast_fp16")];
tensor<int32, [4]> var_1831 = const()[name = tensor<string, []>("op_1831"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_1832_cast_fp16 = reshape(shape = var_1831, x = value_31_cast_fp16)[name = tensor<string, []>("op_1832_cast_fp16")];
tensor<bool, []> attn_31_transpose_x_0 = const()[name = tensor<string, []>("attn_31_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_31_transpose_y_0 = const()[name = tensor<string, []>("attn_31_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_31_cast_fp16 = matmul(transpose_x = attn_31_transpose_x_0, transpose_y = attn_31_transpose_y_0, x = var_1832_cast_fp16, y = obj_111_cast_fp16)[name = tensor<string, []>("attn_31_cast_fp16")];
tensor<int32, [4]> var_1835 = const()[name = tensor<string, []>("op_1835"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_73_cast_fp16 = reshape(shape = var_1835, x = attn_31_cast_fp16)[name = tensor<string, []>("input_73_cast_fp16")];
tensor<int32, [2]> var_1839 = const()[name = tensor<string, []>("op_1839"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1841 = const()[name = tensor<string, []>("op_1841"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_109_pad_type_0 = const()[name = tensor<string, []>("obj_109_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_109_pad_0 = const()[name = tensor<string, []>("obj_109_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_7_encoder_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_7_encoder_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(524205376)))];
tensor<fp16, [1280]> layers_7_encoder_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_7_encoder_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(527482240)))];
tensor<fp16, [1, 1280, 1, 1]> obj_109_cast_fp16 = conv(bias = layers_7_encoder_attn_o_proj_bias_to_fp16, dilations = var_1841, groups = var_1681, pad = obj_109_pad_0, pad_type = obj_109_pad_type_0, strides = var_1839, weight = layers_7_encoder_attn_o_proj_weight_to_fp16, x = input_73_cast_fp16)[name = tensor<string, []>("obj_109_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_47_cast_fp16 = add(x = inputs_45_cast_fp16, y = obj_109_cast_fp16)[name = tensor<string, []>("inputs_47_cast_fp16")];
tensor<int32, [1]> var_1847 = const()[name = tensor<string, []>("op_1847"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_47_cast_fp16 = reduce_mean(axes = var_1847, keep_dims = var_1682, x = inputs_47_cast_fp16)[name = tensor<string, []>("channels_mean_47_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_47_cast_fp16 = sub(x = inputs_47_cast_fp16, y = channels_mean_47_cast_fp16)[name = tensor<string, []>("zero_mean_47_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_47_cast_fp16 = mul(x = zero_mean_47_cast_fp16, y = zero_mean_47_cast_fp16)[name = tensor<string, []>("zero_mean_sq_47_cast_fp16")];
tensor<int32, [1]> var_1851 = const()[name = tensor<string, []>("op_1851"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_1852_cast_fp16 = reduce_mean(axes = var_1851, keep_dims = var_1682, x = zero_mean_sq_47_cast_fp16)[name = tensor<string, []>("op_1852_cast_fp16")];
tensor<fp16, []> var_1853_to_fp16 = const()[name = tensor<string, []>("op_1853_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_1854_cast_fp16 = add(x = var_1852_cast_fp16, y = var_1853_to_fp16)[name = tensor<string, []>("op_1854_cast_fp16")];
tensor<fp16, []> denom_47_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_47_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_47_cast_fp16 = rsqrt(epsilon = denom_47_epsilon_0_to_fp16, x = var_1854_cast_fp16)[name = tensor<string, []>("denom_47_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_47_cast_fp16 = mul(x = zero_mean_47_cast_fp16, y = denom_47_cast_fp16)[name = tensor<string, []>("out_47_cast_fp16")];
tensor<fp16, [1280]> input_75_gamma_0_to_fp16 = const()[name = tensor<string, []>("input_75_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(527484864)))];
tensor<fp16, [1280]> input_75_beta_0_to_fp16 = const()[name = tensor<string, []>("input_75_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(527487488)))];
tensor<fp16, []> input_75_epsilon_0_to_fp16 = const()[name = tensor<string, []>("input_75_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> input_75_cast_fp16 = batch_norm(beta = input_75_beta_0_to_fp16, epsilon = input_75_epsilon_0_to_fp16, gamma = input_75_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_47_cast_fp16)[name = tensor<string, []>("input_75_cast_fp16")];
tensor<int32, [2]> var_1865 = const()[name = tensor<string, []>("op_1865"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1867 = const()[name = tensor<string, []>("op_1867"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_77_pad_type_0 = const()[name = tensor<string, []>("input_77_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_77_pad_0 = const()[name = tensor<string, []>("input_77_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [5120, 1280, 1, 1]> layers_7_fc1_weight_to_fp16 = const()[name = tensor<string, []>("layers_7_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(527490112)))];
tensor<fp16, [5120]> layers_7_fc1_bias_to_fp16 = const()[name = tensor<string, []>("layers_7_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(540597376)))];
tensor<fp16, [1, 5120, 1, 1]> input_77_cast_fp16 = conv(bias = layers_7_fc1_bias_to_fp16, dilations = var_1867, groups = var_1681, pad = input_77_pad_0, pad_type = input_77_pad_type_0, strides = var_1865, weight = layers_7_fc1_weight_to_fp16, x = input_75_cast_fp16)[name = tensor<string, []>("input_77_cast_fp16")];
tensor<string, []> input_79_mode_0 = const()[name = tensor<string, []>("input_79_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 5120, 1, 1]> input_79_cast_fp16 = gelu(mode = input_79_mode_0, x = input_77_cast_fp16)[name = tensor<string, []>("input_79_cast_fp16")];
tensor<int32, [2]> var_1873 = const()[name = tensor<string, []>("op_1873"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1875 = const()[name = tensor<string, []>("op_1875"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_17_pad_type_0 = const()[name = tensor<string, []>("hidden_states_17_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_17_pad_0 = const()[name = tensor<string, []>("hidden_states_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 5120, 1, 1]> layers_7_fc2_weight_to_fp16 = const()[name = tensor<string, []>("layers_7_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(540607680)))];
tensor<fp16, [1280]> layers_7_fc2_bias_to_fp16 = const()[name = tensor<string, []>("layers_7_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(553714944)))];
tensor<fp16, [1, 1280, 1, 1]> hidden_states_17_cast_fp16 = conv(bias = layers_7_fc2_bias_to_fp16, dilations = var_1875, groups = var_1681, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = var_1873, weight = layers_7_fc2_weight_to_fp16, x = input_79_cast_fp16)[name = tensor<string, []>("hidden_states_17_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_49_cast_fp16 = add(x = inputs_47_cast_fp16, y = hidden_states_17_cast_fp16)[name = tensor<string, []>("inputs_49_cast_fp16")];
tensor<int32, []> var_1888 = const()[name = tensor<string, []>("op_1888"), val = tensor<int32, []>(3)];
tensor<int32, []> var_1895 = const()[name = tensor<string, []>("op_1895"), val = tensor<int32, []>(1)];
tensor<bool, []> var_1896 = const()[name = tensor<string, []>("op_1896"), val = tensor<bool, []>(true)];
tensor<int32, [1]> var_1908 = const()[name = tensor<string, []>("op_1908"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_49_cast_fp16 = reduce_mean(axes = var_1908, keep_dims = var_1896, x = inputs_49_cast_fp16)[name = tensor<string, []>("channels_mean_49_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_49_cast_fp16 = sub(x = inputs_49_cast_fp16, y = channels_mean_49_cast_fp16)[name = tensor<string, []>("zero_mean_49_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_49_cast_fp16 = mul(x = zero_mean_49_cast_fp16, y = zero_mean_49_cast_fp16)[name = tensor<string, []>("zero_mean_sq_49_cast_fp16")];
tensor<int32, [1]> var_1912 = const()[name = tensor<string, []>("op_1912"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_1913_cast_fp16 = reduce_mean(axes = var_1912, keep_dims = var_1896, x = zero_mean_sq_49_cast_fp16)[name = tensor<string, []>("op_1913_cast_fp16")];
tensor<fp16, []> var_1914_to_fp16 = const()[name = tensor<string, []>("op_1914_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_1915_cast_fp16 = add(x = var_1913_cast_fp16, y = var_1914_to_fp16)[name = tensor<string, []>("op_1915_cast_fp16")];
tensor<fp16, []> denom_49_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_49_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_49_cast_fp16 = rsqrt(epsilon = denom_49_epsilon_0_to_fp16, x = var_1915_cast_fp16)[name = tensor<string, []>("denom_49_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_49_cast_fp16 = mul(x = zero_mean_49_cast_fp16, y = denom_49_cast_fp16)[name = tensor<string, []>("out_49_cast_fp16")];
tensor<fp16, [1280]> obj_113_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_113_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(553717568)))];
tensor<fp16, [1280]> obj_113_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_113_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(553720192)))];
tensor<fp16, []> obj_113_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_113_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_113_cast_fp16 = batch_norm(beta = obj_113_beta_0_to_fp16, epsilon = obj_113_epsilon_0_to_fp16, gamma = obj_113_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_49_cast_fp16)[name = tensor<string, []>("obj_113_cast_fp16")];
tensor<int32, [2]> var_1930 = const()[name = tensor<string, []>("op_1930"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1932 = const()[name = tensor<string, []>("op_1932"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_33_pad_type_0 = const()[name = tensor<string, []>("query_33_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_33_pad_0 = const()[name = tensor<string, []>("query_33_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_8_self_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_8_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(553722816)))];
tensor<fp16, [1280]> layers_8_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_8_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(556999680)))];
tensor<fp16, [1, 1280, 1, 1]> query_33_cast_fp16 = conv(bias = layers_8_self_attn_q_proj_bias_to_fp16, dilations = var_1932, groups = var_1895, pad = query_33_pad_0, pad_type = query_33_pad_type_0, strides = var_1930, weight = layers_8_self_attn_q_proj_weight_to_fp16, x = obj_113_cast_fp16)[name = tensor<string, []>("query_33_cast_fp16")];
tensor<int32, [2]> var_1936 = const()[name = tensor<string, []>("op_1936"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1938 = const()[name = tensor<string, []>("op_1938"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_key_17_pad_type_0 = const()[name = tensor<string, []>("current_key_17_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_key_17_pad_0 = const()[name = tensor<string, []>("current_key_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_8_self_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_8_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(557002304)))];
tensor<fp16, [1, 1280, 1, 1]> current_key_17_cast_fp16 = conv(dilations = var_1938, groups = var_1895, pad = current_key_17_pad_0, pad_type = current_key_17_pad_type_0, strides = var_1936, weight = layers_8_self_attn_k_proj_weight_to_fp16, x = obj_113_cast_fp16)[name = tensor<string, []>("current_key_17_cast_fp16")];
tensor<int32, [2]> var_1943 = const()[name = tensor<string, []>("op_1943"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1945 = const()[name = tensor<string, []>("op_1945"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_value_17_pad_type_0 = const()[name = tensor<string, []>("current_value_17_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_value_17_pad_0 = const()[name = tensor<string, []>("current_value_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_8_self_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_8_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(560279168)))];
tensor<fp16, [1280]> layers_8_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_8_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(563556032)))];
tensor<fp16, [1, 1280, 1, 1]> current_value_17_cast_fp16 = conv(bias = layers_8_self_attn_v_proj_bias_to_fp16, dilations = var_1945, groups = var_1895, pad = current_value_17_pad_0, pad_type = current_value_17_pad_type_0, strides = var_1943, weight = layers_8_self_attn_v_proj_weight_to_fp16, x = obj_113_cast_fp16)[name = tensor<string, []>("current_value_17_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_1952_cast_fp16 = mul(x = current_key_17_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_1952_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_1954_cast_fp16 = mul(x = var_103_cast_fp16_8, y = var_241_cast_fp16)[name = tensor<string, []>("op_1954_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> key_33_cast_fp16 = add(x = var_1952_cast_fp16, y = var_1954_cast_fp16)[name = tensor<string, []>("key_33_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_1956_cast_fp16 = mul(x = current_value_17_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_1956_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_1958_cast_fp16 = mul(x = var_138_cast_fp16_8, y = var_241_cast_fp16)[name = tensor<string, []>("op_1958_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> value_33_cast_fp16 = add(x = var_1956_cast_fp16, y = var_1958_cast_fp16)[name = tensor<string, []>("value_33_cast_fp16")];
tensor<int32, [4]> var_1961 = const()[name = tensor<string, []>("op_1961"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_1962_cast_fp16 = reshape(shape = var_1961, x = query_33_cast_fp16)[name = tensor<string, []>("op_1962_cast_fp16")];
tensor<fp16, []> var_1963_to_fp16 = const()[name = tensor<string, []>("op_1963_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_1964_cast_fp16 = mul(x = var_1962_cast_fp16, y = var_1963_to_fp16)[name = tensor<string, []>("op_1964_cast_fp16")];
tensor<int32, [4]> var_1965 = const()[name = tensor<string, []>("op_1965"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_1966_cast_fp16 = reshape(shape = var_1965, x = key_33_cast_fp16)[name = tensor<string, []>("op_1966_cast_fp16")];
tensor<bool, []> mh_w_49_transpose_x_0 = const()[name = tensor<string, []>("mh_w_49_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_49_transpose_y_0 = const()[name = tensor<string, []>("mh_w_49_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 224]> mh_w_49_cast_fp16 = matmul(transpose_x = mh_w_49_transpose_x_0, transpose_y = mh_w_49_transpose_y_0, x = var_1964_cast_fp16, y = var_1966_cast_fp16)[name = tensor<string, []>("mh_w_49_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> mh_w_51_cast_fp16 = add(x = mh_w_49_cast_fp16, y = var_259_cast_fp16)[name = tensor<string, []>("mh_w_51_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> var_1974_cast_fp16 = softmax(axis = var_1888, x = mh_w_51_cast_fp16)[name = tensor<string, []>("op_1974_cast_fp16")];
tensor<int32, [4]> var_1975 = const()[name = tensor<string, []>("op_1975"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_1976_cast_fp16 = reshape(shape = var_1975, x = value_33_cast_fp16)[name = tensor<string, []>("op_1976_cast_fp16")];
tensor<bool, []> attn_33_transpose_x_0 = const()[name = tensor<string, []>("attn_33_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_33_transpose_y_0 = const()[name = tensor<string, []>("attn_33_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_33_cast_fp16 = matmul(transpose_x = attn_33_transpose_x_0, transpose_y = attn_33_transpose_y_0, x = var_1976_cast_fp16, y = var_1974_cast_fp16)[name = tensor<string, []>("attn_33_cast_fp16")];
tensor<int32, [4]> var_1979 = const()[name = tensor<string, []>("op_1979"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_81_cast_fp16 = reshape(shape = var_1979, x = attn_33_cast_fp16)[name = tensor<string, []>("input_81_cast_fp16")];
tensor<int32, [2]> var_1983 = const()[name = tensor<string, []>("op_1983"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_1985 = const()[name = tensor<string, []>("op_1985"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_119_pad_type_0 = const()[name = tensor<string, []>("obj_119_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_119_pad_0 = const()[name = tensor<string, []>("obj_119_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_8_self_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_8_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(563558656)))];
tensor<fp16, [1280]> layers_8_self_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_8_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(566835520)))];
tensor<fp16, [1, 1280, 1, 1]> obj_119_cast_fp16 = conv(bias = layers_8_self_attn_o_proj_bias_to_fp16, dilations = var_1985, groups = var_1895, pad = obj_119_pad_0, pad_type = obj_119_pad_type_0, strides = var_1983, weight = layers_8_self_attn_o_proj_weight_to_fp16, x = input_81_cast_fp16)[name = tensor<string, []>("obj_119_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_51_cast_fp16 = add(x = inputs_49_cast_fp16, y = obj_119_cast_fp16)[name = tensor<string, []>("inputs_51_cast_fp16")];
tensor<int32, [1]> var_1995 = const()[name = tensor<string, []>("op_1995"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_51_cast_fp16 = reduce_mean(axes = var_1995, keep_dims = var_1896, x = inputs_51_cast_fp16)[name = tensor<string, []>("channels_mean_51_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_51_cast_fp16 = sub(x = inputs_51_cast_fp16, y = channels_mean_51_cast_fp16)[name = tensor<string, []>("zero_mean_51_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_51_cast_fp16 = mul(x = zero_mean_51_cast_fp16, y = zero_mean_51_cast_fp16)[name = tensor<string, []>("zero_mean_sq_51_cast_fp16")];
tensor<int32, [1]> var_1999 = const()[name = tensor<string, []>("op_1999"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_2000_cast_fp16 = reduce_mean(axes = var_1999, keep_dims = var_1896, x = zero_mean_sq_51_cast_fp16)[name = tensor<string, []>("op_2000_cast_fp16")];
tensor<fp16, []> var_2001_to_fp16 = const()[name = tensor<string, []>("op_2001_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_2002_cast_fp16 = add(x = var_2000_cast_fp16, y = var_2001_to_fp16)[name = tensor<string, []>("op_2002_cast_fp16")];
tensor<fp16, []> denom_51_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_51_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_51_cast_fp16 = rsqrt(epsilon = denom_51_epsilon_0_to_fp16, x = var_2002_cast_fp16)[name = tensor<string, []>("denom_51_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_51_cast_fp16 = mul(x = zero_mean_51_cast_fp16, y = denom_51_cast_fp16)[name = tensor<string, []>("out_51_cast_fp16")];
tensor<fp16, [1280]> obj_121_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_121_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(566838144)))];
tensor<fp16, [1280]> obj_121_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_121_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(566840768)))];
tensor<fp16, []> obj_121_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_121_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_121_cast_fp16 = batch_norm(beta = obj_121_beta_0_to_fp16, epsilon = obj_121_epsilon_0_to_fp16, gamma = obj_121_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_51_cast_fp16)[name = tensor<string, []>("obj_121_cast_fp16")];
tensor<int32, [2]> var_2017 = const()[name = tensor<string, []>("op_2017"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2019 = const()[name = tensor<string, []>("op_2019"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_35_pad_type_0 = const()[name = tensor<string, []>("query_35_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_35_pad_0 = const()[name = tensor<string, []>("query_35_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_8_encoder_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_8_encoder_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(566843392)))];
tensor<fp16, [1280]> layers_8_encoder_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_8_encoder_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(570120256)))];
tensor<fp16, [1, 1280, 1, 1]> query_35_cast_fp16 = conv(bias = layers_8_encoder_attn_q_proj_bias_to_fp16, dilations = var_2019, groups = var_1895, pad = query_35_pad_0, pad_type = query_35_pad_type_0, strides = var_2017, weight = layers_8_encoder_attn_q_proj_weight_to_fp16, x = obj_121_cast_fp16)[name = tensor<string, []>("query_35_cast_fp16")];
tensor<int32, [2]> var_2023 = const()[name = tensor<string, []>("op_2023"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2025 = const()[name = tensor<string, []>("op_2025"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> key_35_pad_type_0 = const()[name = tensor<string, []>("key_35_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> key_35_pad_0 = const()[name = tensor<string, []>("key_35_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_8_encoder_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_8_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(570122880)))];
tensor<fp16, [1, 1280, 1, 1500]> key_35_cast_fp16 = conv(dilations = var_2025, groups = var_1895, pad = key_35_pad_0, pad_type = key_35_pad_type_0, strides = var_2023, weight = layers_8_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("key_35_cast_fp16")];
tensor<int32, [2]> var_2030 = const()[name = tensor<string, []>("op_2030"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2032 = const()[name = tensor<string, []>("op_2032"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> value_35_pad_type_0 = const()[name = tensor<string, []>("value_35_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> value_35_pad_0 = const()[name = tensor<string, []>("value_35_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_8_encoder_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_8_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(573399744)))];
tensor<fp16, [1280]> layers_8_encoder_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_8_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(576676608)))];
tensor<fp16, [1, 1280, 1, 1500]> value_35_cast_fp16 = conv(bias = layers_8_encoder_attn_v_proj_bias_to_fp16, dilations = var_2032, groups = var_1895, pad = value_35_pad_0, pad_type = value_35_pad_type_0, strides = var_2030, weight = layers_8_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("value_35_cast_fp16")];
tensor<int32, [4]> var_2036 = const()[name = tensor<string, []>("op_2036"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_2037_cast_fp16 = reshape(shape = var_2036, x = query_35_cast_fp16)[name = tensor<string, []>("op_2037_cast_fp16")];
tensor<fp16, []> var_2038_to_fp16 = const()[name = tensor<string, []>("op_2038_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_2039_cast_fp16 = mul(x = var_2037_cast_fp16, y = var_2038_to_fp16)[name = tensor<string, []>("op_2039_cast_fp16")];
tensor<int32, [4]> var_2040 = const()[name = tensor<string, []>("op_2040"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_2041_cast_fp16 = reshape(shape = var_2040, x = key_35_cast_fp16)[name = tensor<string, []>("op_2041_cast_fp16")];
tensor<bool, []> mh_w_53_transpose_x_0 = const()[name = tensor<string, []>("mh_w_53_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_53_transpose_y_0 = const()[name = tensor<string, []>("mh_w_53_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 1500]> mh_w_53_cast_fp16 = matmul(transpose_x = mh_w_53_transpose_x_0, transpose_y = mh_w_53_transpose_y_0, x = var_2039_cast_fp16, y = var_2041_cast_fp16)[name = tensor<string, []>("mh_w_53_cast_fp16")];
tensor<fp16, [1, 20, 1, 1500]> obj_125_cast_fp16 = softmax(axis = var_1888, x = mh_w_53_cast_fp16)[name = tensor<string, []>("obj_125_cast_fp16")];
tensor<int32, [4]> var_2045 = const()[name = tensor<string, []>("op_2045"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_2046_cast_fp16 = reshape(shape = var_2045, x = value_35_cast_fp16)[name = tensor<string, []>("op_2046_cast_fp16")];
tensor<bool, []> attn_35_transpose_x_0 = const()[name = tensor<string, []>("attn_35_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_35_transpose_y_0 = const()[name = tensor<string, []>("attn_35_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_35_cast_fp16 = matmul(transpose_x = attn_35_transpose_x_0, transpose_y = attn_35_transpose_y_0, x = var_2046_cast_fp16, y = obj_125_cast_fp16)[name = tensor<string, []>("attn_35_cast_fp16")];
tensor<int32, [4]> var_2049 = const()[name = tensor<string, []>("op_2049"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_83_cast_fp16 = reshape(shape = var_2049, x = attn_35_cast_fp16)[name = tensor<string, []>("input_83_cast_fp16")];
tensor<int32, [2]> var_2053 = const()[name = tensor<string, []>("op_2053"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2055 = const()[name = tensor<string, []>("op_2055"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_123_pad_type_0 = const()[name = tensor<string, []>("obj_123_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_123_pad_0 = const()[name = tensor<string, []>("obj_123_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_8_encoder_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_8_encoder_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(576679232)))];
tensor<fp16, [1280]> layers_8_encoder_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_8_encoder_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(579956096)))];
tensor<fp16, [1, 1280, 1, 1]> obj_123_cast_fp16 = conv(bias = layers_8_encoder_attn_o_proj_bias_to_fp16, dilations = var_2055, groups = var_1895, pad = obj_123_pad_0, pad_type = obj_123_pad_type_0, strides = var_2053, weight = layers_8_encoder_attn_o_proj_weight_to_fp16, x = input_83_cast_fp16)[name = tensor<string, []>("obj_123_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_53_cast_fp16 = add(x = inputs_51_cast_fp16, y = obj_123_cast_fp16)[name = tensor<string, []>("inputs_53_cast_fp16")];
tensor<int32, [1]> var_2061 = const()[name = tensor<string, []>("op_2061"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_53_cast_fp16 = reduce_mean(axes = var_2061, keep_dims = var_1896, x = inputs_53_cast_fp16)[name = tensor<string, []>("channels_mean_53_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_53_cast_fp16 = sub(x = inputs_53_cast_fp16, y = channels_mean_53_cast_fp16)[name = tensor<string, []>("zero_mean_53_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_53_cast_fp16 = mul(x = zero_mean_53_cast_fp16, y = zero_mean_53_cast_fp16)[name = tensor<string, []>("zero_mean_sq_53_cast_fp16")];
tensor<int32, [1]> var_2065 = const()[name = tensor<string, []>("op_2065"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_2066_cast_fp16 = reduce_mean(axes = var_2065, keep_dims = var_1896, x = zero_mean_sq_53_cast_fp16)[name = tensor<string, []>("op_2066_cast_fp16")];
tensor<fp16, []> var_2067_to_fp16 = const()[name = tensor<string, []>("op_2067_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_2068_cast_fp16 = add(x = var_2066_cast_fp16, y = var_2067_to_fp16)[name = tensor<string, []>("op_2068_cast_fp16")];
tensor<fp16, []> denom_53_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_53_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_53_cast_fp16 = rsqrt(epsilon = denom_53_epsilon_0_to_fp16, x = var_2068_cast_fp16)[name = tensor<string, []>("denom_53_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_53_cast_fp16 = mul(x = zero_mean_53_cast_fp16, y = denom_53_cast_fp16)[name = tensor<string, []>("out_53_cast_fp16")];
tensor<fp16, [1280]> input_85_gamma_0_to_fp16 = const()[name = tensor<string, []>("input_85_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(579958720)))];
tensor<fp16, [1280]> input_85_beta_0_to_fp16 = const()[name = tensor<string, []>("input_85_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(579961344)))];
tensor<fp16, []> input_85_epsilon_0_to_fp16 = const()[name = tensor<string, []>("input_85_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> input_85_cast_fp16 = batch_norm(beta = input_85_beta_0_to_fp16, epsilon = input_85_epsilon_0_to_fp16, gamma = input_85_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_53_cast_fp16)[name = tensor<string, []>("input_85_cast_fp16")];
tensor<int32, [2]> var_2079 = const()[name = tensor<string, []>("op_2079"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2081 = const()[name = tensor<string, []>("op_2081"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_87_pad_type_0 = const()[name = tensor<string, []>("input_87_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_87_pad_0 = const()[name = tensor<string, []>("input_87_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [5120, 1280, 1, 1]> layers_8_fc1_weight_to_fp16 = const()[name = tensor<string, []>("layers_8_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(579963968)))];
tensor<fp16, [5120]> layers_8_fc1_bias_to_fp16 = const()[name = tensor<string, []>("layers_8_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(593071232)))];
tensor<fp16, [1, 5120, 1, 1]> input_87_cast_fp16 = conv(bias = layers_8_fc1_bias_to_fp16, dilations = var_2081, groups = var_1895, pad = input_87_pad_0, pad_type = input_87_pad_type_0, strides = var_2079, weight = layers_8_fc1_weight_to_fp16, x = input_85_cast_fp16)[name = tensor<string, []>("input_87_cast_fp16")];
tensor<string, []> input_89_mode_0 = const()[name = tensor<string, []>("input_89_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 5120, 1, 1]> input_89_cast_fp16 = gelu(mode = input_89_mode_0, x = input_87_cast_fp16)[name = tensor<string, []>("input_89_cast_fp16")];
tensor<int32, [2]> var_2087 = const()[name = tensor<string, []>("op_2087"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2089 = const()[name = tensor<string, []>("op_2089"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_19_pad_type_0 = const()[name = tensor<string, []>("hidden_states_19_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_19_pad_0 = const()[name = tensor<string, []>("hidden_states_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 5120, 1, 1]> layers_8_fc2_weight_to_fp16 = const()[name = tensor<string, []>("layers_8_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(593081536)))];
tensor<fp16, [1280]> layers_8_fc2_bias_to_fp16 = const()[name = tensor<string, []>("layers_8_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(606188800)))];
tensor<fp16, [1, 1280, 1, 1]> hidden_states_19_cast_fp16 = conv(bias = layers_8_fc2_bias_to_fp16, dilations = var_2089, groups = var_1895, pad = hidden_states_19_pad_0, pad_type = hidden_states_19_pad_type_0, strides = var_2087, weight = layers_8_fc2_weight_to_fp16, x = input_89_cast_fp16)[name = tensor<string, []>("hidden_states_19_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_55_cast_fp16 = add(x = inputs_53_cast_fp16, y = hidden_states_19_cast_fp16)[name = tensor<string, []>("inputs_55_cast_fp16")];
tensor<int32, []> var_2102 = const()[name = tensor<string, []>("op_2102"), val = tensor<int32, []>(3)];
tensor<int32, []> var_2109 = const()[name = tensor<string, []>("op_2109"), val = tensor<int32, []>(1)];
tensor<bool, []> var_2110 = const()[name = tensor<string, []>("op_2110"), val = tensor<bool, []>(true)];
tensor<int32, [1]> var_2122 = const()[name = tensor<string, []>("op_2122"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_55_cast_fp16 = reduce_mean(axes = var_2122, keep_dims = var_2110, x = inputs_55_cast_fp16)[name = tensor<string, []>("channels_mean_55_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_55_cast_fp16 = sub(x = inputs_55_cast_fp16, y = channels_mean_55_cast_fp16)[name = tensor<string, []>("zero_mean_55_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_55_cast_fp16 = mul(x = zero_mean_55_cast_fp16, y = zero_mean_55_cast_fp16)[name = tensor<string, []>("zero_mean_sq_55_cast_fp16")];
tensor<int32, [1]> var_2126 = const()[name = tensor<string, []>("op_2126"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_2127_cast_fp16 = reduce_mean(axes = var_2126, keep_dims = var_2110, x = zero_mean_sq_55_cast_fp16)[name = tensor<string, []>("op_2127_cast_fp16")];
tensor<fp16, []> var_2128_to_fp16 = const()[name = tensor<string, []>("op_2128_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_2129_cast_fp16 = add(x = var_2127_cast_fp16, y = var_2128_to_fp16)[name = tensor<string, []>("op_2129_cast_fp16")];
tensor<fp16, []> denom_55_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_55_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_55_cast_fp16 = rsqrt(epsilon = denom_55_epsilon_0_to_fp16, x = var_2129_cast_fp16)[name = tensor<string, []>("denom_55_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_55_cast_fp16 = mul(x = zero_mean_55_cast_fp16, y = denom_55_cast_fp16)[name = tensor<string, []>("out_55_cast_fp16")];
tensor<fp16, [1280]> obj_127_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_127_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(606191424)))];
tensor<fp16, [1280]> obj_127_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_127_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(606194048)))];
tensor<fp16, []> obj_127_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_127_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_127_cast_fp16 = batch_norm(beta = obj_127_beta_0_to_fp16, epsilon = obj_127_epsilon_0_to_fp16, gamma = obj_127_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_55_cast_fp16)[name = tensor<string, []>("obj_127_cast_fp16")];
tensor<int32, [2]> var_2144 = const()[name = tensor<string, []>("op_2144"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2146 = const()[name = tensor<string, []>("op_2146"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_37_pad_type_0 = const()[name = tensor<string, []>("query_37_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_37_pad_0 = const()[name = tensor<string, []>("query_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_9_self_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_9_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(606196672)))];
tensor<fp16, [1280]> layers_9_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_9_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(609473536)))];
tensor<fp16, [1, 1280, 1, 1]> query_37_cast_fp16 = conv(bias = layers_9_self_attn_q_proj_bias_to_fp16, dilations = var_2146, groups = var_2109, pad = query_37_pad_0, pad_type = query_37_pad_type_0, strides = var_2144, weight = layers_9_self_attn_q_proj_weight_to_fp16, x = obj_127_cast_fp16)[name = tensor<string, []>("query_37_cast_fp16")];
tensor<int32, [2]> var_2150 = const()[name = tensor<string, []>("op_2150"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2152 = const()[name = tensor<string, []>("op_2152"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_key_19_pad_type_0 = const()[name = tensor<string, []>("current_key_19_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_key_19_pad_0 = const()[name = tensor<string, []>("current_key_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_9_self_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_9_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(609476160)))];
tensor<fp16, [1, 1280, 1, 1]> current_key_19_cast_fp16 = conv(dilations = var_2152, groups = var_2109, pad = current_key_19_pad_0, pad_type = current_key_19_pad_type_0, strides = var_2150, weight = layers_9_self_attn_k_proj_weight_to_fp16, x = obj_127_cast_fp16)[name = tensor<string, []>("current_key_19_cast_fp16")];
tensor<int32, [2]> var_2157 = const()[name = tensor<string, []>("op_2157"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2159 = const()[name = tensor<string, []>("op_2159"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_value_19_pad_type_0 = const()[name = tensor<string, []>("current_value_19_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_value_19_pad_0 = const()[name = tensor<string, []>("current_value_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_9_self_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_9_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(612753024)))];
tensor<fp16, [1280]> layers_9_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_9_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(616029888)))];
tensor<fp16, [1, 1280, 1, 1]> current_value_19_cast_fp16 = conv(bias = layers_9_self_attn_v_proj_bias_to_fp16, dilations = var_2159, groups = var_2109, pad = current_value_19_pad_0, pad_type = current_value_19_pad_type_0, strides = var_2157, weight = layers_9_self_attn_v_proj_weight_to_fp16, x = obj_127_cast_fp16)[name = tensor<string, []>("current_value_19_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_2166_cast_fp16 = mul(x = current_key_19_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_2166_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_2168_cast_fp16 = mul(x = var_103_cast_fp16_9, y = var_241_cast_fp16)[name = tensor<string, []>("op_2168_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> key_37_cast_fp16 = add(x = var_2166_cast_fp16, y = var_2168_cast_fp16)[name = tensor<string, []>("key_37_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_2170_cast_fp16 = mul(x = current_value_19_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_2170_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_2172_cast_fp16 = mul(x = var_138_cast_fp16_9, y = var_241_cast_fp16)[name = tensor<string, []>("op_2172_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> value_37_cast_fp16 = add(x = var_2170_cast_fp16, y = var_2172_cast_fp16)[name = tensor<string, []>("value_37_cast_fp16")];
tensor<int32, [4]> var_2175 = const()[name = tensor<string, []>("op_2175"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_2176_cast_fp16 = reshape(shape = var_2175, x = query_37_cast_fp16)[name = tensor<string, []>("op_2176_cast_fp16")];
tensor<fp16, []> var_2177_to_fp16 = const()[name = tensor<string, []>("op_2177_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_2178_cast_fp16 = mul(x = var_2176_cast_fp16, y = var_2177_to_fp16)[name = tensor<string, []>("op_2178_cast_fp16")];
tensor<int32, [4]> var_2179 = const()[name = tensor<string, []>("op_2179"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_2180_cast_fp16 = reshape(shape = var_2179, x = key_37_cast_fp16)[name = tensor<string, []>("op_2180_cast_fp16")];
tensor<bool, []> mh_w_55_transpose_x_0 = const()[name = tensor<string, []>("mh_w_55_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_55_transpose_y_0 = const()[name = tensor<string, []>("mh_w_55_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 224]> mh_w_55_cast_fp16 = matmul(transpose_x = mh_w_55_transpose_x_0, transpose_y = mh_w_55_transpose_y_0, x = var_2178_cast_fp16, y = var_2180_cast_fp16)[name = tensor<string, []>("mh_w_55_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> mh_w_57_cast_fp16 = add(x = mh_w_55_cast_fp16, y = var_259_cast_fp16)[name = tensor<string, []>("mh_w_57_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> var_2188_cast_fp16 = softmax(axis = var_2102, x = mh_w_57_cast_fp16)[name = tensor<string, []>("op_2188_cast_fp16")];
tensor<int32, [4]> var_2189 = const()[name = tensor<string, []>("op_2189"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_2190_cast_fp16 = reshape(shape = var_2189, x = value_37_cast_fp16)[name = tensor<string, []>("op_2190_cast_fp16")];
tensor<bool, []> attn_37_transpose_x_0 = const()[name = tensor<string, []>("attn_37_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_37_transpose_y_0 = const()[name = tensor<string, []>("attn_37_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_37_cast_fp16 = matmul(transpose_x = attn_37_transpose_x_0, transpose_y = attn_37_transpose_y_0, x = var_2190_cast_fp16, y = var_2188_cast_fp16)[name = tensor<string, []>("attn_37_cast_fp16")];
tensor<int32, [4]> var_2193 = const()[name = tensor<string, []>("op_2193"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_91_cast_fp16 = reshape(shape = var_2193, x = attn_37_cast_fp16)[name = tensor<string, []>("input_91_cast_fp16")];
tensor<int32, [2]> var_2197 = const()[name = tensor<string, []>("op_2197"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2199 = const()[name = tensor<string, []>("op_2199"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_133_pad_type_0 = const()[name = tensor<string, []>("obj_133_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_133_pad_0 = const()[name = tensor<string, []>("obj_133_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_9_self_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_9_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(616032512)))];
tensor<fp16, [1280]> layers_9_self_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_9_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(619309376)))];
tensor<fp16, [1, 1280, 1, 1]> obj_133_cast_fp16 = conv(bias = layers_9_self_attn_o_proj_bias_to_fp16, dilations = var_2199, groups = var_2109, pad = obj_133_pad_0, pad_type = obj_133_pad_type_0, strides = var_2197, weight = layers_9_self_attn_o_proj_weight_to_fp16, x = input_91_cast_fp16)[name = tensor<string, []>("obj_133_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_57_cast_fp16 = add(x = inputs_55_cast_fp16, y = obj_133_cast_fp16)[name = tensor<string, []>("inputs_57_cast_fp16")];
tensor<int32, [1]> var_2209 = const()[name = tensor<string, []>("op_2209"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_57_cast_fp16 = reduce_mean(axes = var_2209, keep_dims = var_2110, x = inputs_57_cast_fp16)[name = tensor<string, []>("channels_mean_57_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_57_cast_fp16 = sub(x = inputs_57_cast_fp16, y = channels_mean_57_cast_fp16)[name = tensor<string, []>("zero_mean_57_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_57_cast_fp16 = mul(x = zero_mean_57_cast_fp16, y = zero_mean_57_cast_fp16)[name = tensor<string, []>("zero_mean_sq_57_cast_fp16")];
tensor<int32, [1]> var_2213 = const()[name = tensor<string, []>("op_2213"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_2214_cast_fp16 = reduce_mean(axes = var_2213, keep_dims = var_2110, x = zero_mean_sq_57_cast_fp16)[name = tensor<string, []>("op_2214_cast_fp16")];
tensor<fp16, []> var_2215_to_fp16 = const()[name = tensor<string, []>("op_2215_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_2216_cast_fp16 = add(x = var_2214_cast_fp16, y = var_2215_to_fp16)[name = tensor<string, []>("op_2216_cast_fp16")];
tensor<fp16, []> denom_57_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_57_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_57_cast_fp16 = rsqrt(epsilon = denom_57_epsilon_0_to_fp16, x = var_2216_cast_fp16)[name = tensor<string, []>("denom_57_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_57_cast_fp16 = mul(x = zero_mean_57_cast_fp16, y = denom_57_cast_fp16)[name = tensor<string, []>("out_57_cast_fp16")];
tensor<fp16, [1280]> obj_135_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_135_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(619312000)))];
tensor<fp16, [1280]> obj_135_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_135_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(619314624)))];
tensor<fp16, []> obj_135_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_135_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_135_cast_fp16 = batch_norm(beta = obj_135_beta_0_to_fp16, epsilon = obj_135_epsilon_0_to_fp16, gamma = obj_135_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_57_cast_fp16)[name = tensor<string, []>("obj_135_cast_fp16")];
tensor<int32, [2]> var_2231 = const()[name = tensor<string, []>("op_2231"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2233 = const()[name = tensor<string, []>("op_2233"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_39_pad_type_0 = const()[name = tensor<string, []>("query_39_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_39_pad_0 = const()[name = tensor<string, []>("query_39_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_9_encoder_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_9_encoder_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(619317248)))];
tensor<fp16, [1280]> layers_9_encoder_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_9_encoder_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(622594112)))];
tensor<fp16, [1, 1280, 1, 1]> query_39_cast_fp16 = conv(bias = layers_9_encoder_attn_q_proj_bias_to_fp16, dilations = var_2233, groups = var_2109, pad = query_39_pad_0, pad_type = query_39_pad_type_0, strides = var_2231, weight = layers_9_encoder_attn_q_proj_weight_to_fp16, x = obj_135_cast_fp16)[name = tensor<string, []>("query_39_cast_fp16")];
tensor<int32, [2]> var_2237 = const()[name = tensor<string, []>("op_2237"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2239 = const()[name = tensor<string, []>("op_2239"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> key_39_pad_type_0 = const()[name = tensor<string, []>("key_39_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> key_39_pad_0 = const()[name = tensor<string, []>("key_39_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_9_encoder_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_9_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(622596736)))];
tensor<fp16, [1, 1280, 1, 1500]> key_39_cast_fp16 = conv(dilations = var_2239, groups = var_2109, pad = key_39_pad_0, pad_type = key_39_pad_type_0, strides = var_2237, weight = layers_9_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("key_39_cast_fp16")];
tensor<int32, [2]> var_2244 = const()[name = tensor<string, []>("op_2244"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2246 = const()[name = tensor<string, []>("op_2246"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> value_39_pad_type_0 = const()[name = tensor<string, []>("value_39_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> value_39_pad_0 = const()[name = tensor<string, []>("value_39_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_9_encoder_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_9_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(625873600)))];
tensor<fp16, [1280]> layers_9_encoder_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_9_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(629150464)))];
tensor<fp16, [1, 1280, 1, 1500]> value_39_cast_fp16 = conv(bias = layers_9_encoder_attn_v_proj_bias_to_fp16, dilations = var_2246, groups = var_2109, pad = value_39_pad_0, pad_type = value_39_pad_type_0, strides = var_2244, weight = layers_9_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("value_39_cast_fp16")];
tensor<int32, [4]> var_2250 = const()[name = tensor<string, []>("op_2250"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_2251_cast_fp16 = reshape(shape = var_2250, x = query_39_cast_fp16)[name = tensor<string, []>("op_2251_cast_fp16")];
tensor<fp16, []> var_2252_to_fp16 = const()[name = tensor<string, []>("op_2252_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_2253_cast_fp16 = mul(x = var_2251_cast_fp16, y = var_2252_to_fp16)[name = tensor<string, []>("op_2253_cast_fp16")];
tensor<int32, [4]> var_2254 = const()[name = tensor<string, []>("op_2254"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_2255_cast_fp16 = reshape(shape = var_2254, x = key_39_cast_fp16)[name = tensor<string, []>("op_2255_cast_fp16")];
tensor<bool, []> mh_w_59_transpose_x_0 = const()[name = tensor<string, []>("mh_w_59_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_59_transpose_y_0 = const()[name = tensor<string, []>("mh_w_59_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 1500]> mh_w_59_cast_fp16 = matmul(transpose_x = mh_w_59_transpose_x_0, transpose_y = mh_w_59_transpose_y_0, x = var_2253_cast_fp16, y = var_2255_cast_fp16)[name = tensor<string, []>("mh_w_59_cast_fp16")];
tensor<fp16, [1, 20, 1, 1500]> obj_139_cast_fp16 = softmax(axis = var_2102, x = mh_w_59_cast_fp16)[name = tensor<string, []>("obj_139_cast_fp16")];
tensor<int32, [4]> var_2259 = const()[name = tensor<string, []>("op_2259"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_2260_cast_fp16 = reshape(shape = var_2259, x = value_39_cast_fp16)[name = tensor<string, []>("op_2260_cast_fp16")];
tensor<bool, []> attn_39_transpose_x_0 = const()[name = tensor<string, []>("attn_39_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_39_transpose_y_0 = const()[name = tensor<string, []>("attn_39_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_39_cast_fp16 = matmul(transpose_x = attn_39_transpose_x_0, transpose_y = attn_39_transpose_y_0, x = var_2260_cast_fp16, y = obj_139_cast_fp16)[name = tensor<string, []>("attn_39_cast_fp16")];
tensor<int32, [4]> var_2263 = const()[name = tensor<string, []>("op_2263"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_93_cast_fp16 = reshape(shape = var_2263, x = attn_39_cast_fp16)[name = tensor<string, []>("input_93_cast_fp16")];
tensor<int32, [2]> var_2267 = const()[name = tensor<string, []>("op_2267"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2269 = const()[name = tensor<string, []>("op_2269"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_137_pad_type_0 = const()[name = tensor<string, []>("obj_137_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_137_pad_0 = const()[name = tensor<string, []>("obj_137_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_9_encoder_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_9_encoder_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(629153088)))];
tensor<fp16, [1280]> layers_9_encoder_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_9_encoder_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(632429952)))];
tensor<fp16, [1, 1280, 1, 1]> obj_137_cast_fp16 = conv(bias = layers_9_encoder_attn_o_proj_bias_to_fp16, dilations = var_2269, groups = var_2109, pad = obj_137_pad_0, pad_type = obj_137_pad_type_0, strides = var_2267, weight = layers_9_encoder_attn_o_proj_weight_to_fp16, x = input_93_cast_fp16)[name = tensor<string, []>("obj_137_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_59_cast_fp16 = add(x = inputs_57_cast_fp16, y = obj_137_cast_fp16)[name = tensor<string, []>("inputs_59_cast_fp16")];
tensor<int32, [1]> var_2275 = const()[name = tensor<string, []>("op_2275"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_59_cast_fp16 = reduce_mean(axes = var_2275, keep_dims = var_2110, x = inputs_59_cast_fp16)[name = tensor<string, []>("channels_mean_59_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_59_cast_fp16 = sub(x = inputs_59_cast_fp16, y = channels_mean_59_cast_fp16)[name = tensor<string, []>("zero_mean_59_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_59_cast_fp16 = mul(x = zero_mean_59_cast_fp16, y = zero_mean_59_cast_fp16)[name = tensor<string, []>("zero_mean_sq_59_cast_fp16")];
tensor<int32, [1]> var_2279 = const()[name = tensor<string, []>("op_2279"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_2280_cast_fp16 = reduce_mean(axes = var_2279, keep_dims = var_2110, x = zero_mean_sq_59_cast_fp16)[name = tensor<string, []>("op_2280_cast_fp16")];
tensor<fp16, []> var_2281_to_fp16 = const()[name = tensor<string, []>("op_2281_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_2282_cast_fp16 = add(x = var_2280_cast_fp16, y = var_2281_to_fp16)[name = tensor<string, []>("op_2282_cast_fp16")];
tensor<fp16, []> denom_59_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_59_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_59_cast_fp16 = rsqrt(epsilon = denom_59_epsilon_0_to_fp16, x = var_2282_cast_fp16)[name = tensor<string, []>("denom_59_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_59_cast_fp16 = mul(x = zero_mean_59_cast_fp16, y = denom_59_cast_fp16)[name = tensor<string, []>("out_59_cast_fp16")];
tensor<fp16, [1280]> input_95_gamma_0_to_fp16 = const()[name = tensor<string, []>("input_95_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(632432576)))];
tensor<fp16, [1280]> input_95_beta_0_to_fp16 = const()[name = tensor<string, []>("input_95_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(632435200)))];
tensor<fp16, []> input_95_epsilon_0_to_fp16 = const()[name = tensor<string, []>("input_95_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> input_95_cast_fp16 = batch_norm(beta = input_95_beta_0_to_fp16, epsilon = input_95_epsilon_0_to_fp16, gamma = input_95_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_59_cast_fp16)[name = tensor<string, []>("input_95_cast_fp16")];
tensor<int32, [2]> var_2293 = const()[name = tensor<string, []>("op_2293"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2295 = const()[name = tensor<string, []>("op_2295"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_97_pad_type_0 = const()[name = tensor<string, []>("input_97_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_97_pad_0 = const()[name = tensor<string, []>("input_97_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [5120, 1280, 1, 1]> layers_9_fc1_weight_to_fp16 = const()[name = tensor<string, []>("layers_9_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(632437824)))];
tensor<fp16, [5120]> layers_9_fc1_bias_to_fp16 = const()[name = tensor<string, []>("layers_9_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(645545088)))];
tensor<fp16, [1, 5120, 1, 1]> input_97_cast_fp16 = conv(bias = layers_9_fc1_bias_to_fp16, dilations = var_2295, groups = var_2109, pad = input_97_pad_0, pad_type = input_97_pad_type_0, strides = var_2293, weight = layers_9_fc1_weight_to_fp16, x = input_95_cast_fp16)[name = tensor<string, []>("input_97_cast_fp16")];
tensor<string, []> input_99_mode_0 = const()[name = tensor<string, []>("input_99_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 5120, 1, 1]> input_99_cast_fp16 = gelu(mode = input_99_mode_0, x = input_97_cast_fp16)[name = tensor<string, []>("input_99_cast_fp16")];
tensor<int32, [2]> var_2301 = const()[name = tensor<string, []>("op_2301"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2303 = const()[name = tensor<string, []>("op_2303"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_21_pad_type_0 = const()[name = tensor<string, []>("hidden_states_21_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_21_pad_0 = const()[name = tensor<string, []>("hidden_states_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 5120, 1, 1]> layers_9_fc2_weight_to_fp16 = const()[name = tensor<string, []>("layers_9_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(645555392)))];
tensor<fp16, [1280]> layers_9_fc2_bias_to_fp16 = const()[name = tensor<string, []>("layers_9_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(658662656)))];
tensor<fp16, [1, 1280, 1, 1]> hidden_states_21_cast_fp16 = conv(bias = layers_9_fc2_bias_to_fp16, dilations = var_2303, groups = var_2109, pad = hidden_states_21_pad_0, pad_type = hidden_states_21_pad_type_0, strides = var_2301, weight = layers_9_fc2_weight_to_fp16, x = input_99_cast_fp16)[name = tensor<string, []>("hidden_states_21_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_61_cast_fp16 = add(x = inputs_59_cast_fp16, y = hidden_states_21_cast_fp16)[name = tensor<string, []>("inputs_61_cast_fp16")];
tensor<int32, []> var_2316 = const()[name = tensor<string, []>("op_2316"), val = tensor<int32, []>(3)];
tensor<int32, []> var_2323 = const()[name = tensor<string, []>("op_2323"), val = tensor<int32, []>(1)];
tensor<bool, []> var_2324 = const()[name = tensor<string, []>("op_2324"), val = tensor<bool, []>(true)];
tensor<int32, [1]> var_2336 = const()[name = tensor<string, []>("op_2336"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_61_cast_fp16 = reduce_mean(axes = var_2336, keep_dims = var_2324, x = inputs_61_cast_fp16)[name = tensor<string, []>("channels_mean_61_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_61_cast_fp16 = sub(x = inputs_61_cast_fp16, y = channels_mean_61_cast_fp16)[name = tensor<string, []>("zero_mean_61_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_61_cast_fp16 = mul(x = zero_mean_61_cast_fp16, y = zero_mean_61_cast_fp16)[name = tensor<string, []>("zero_mean_sq_61_cast_fp16")];
tensor<int32, [1]> var_2340 = const()[name = tensor<string, []>("op_2340"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_2341_cast_fp16 = reduce_mean(axes = var_2340, keep_dims = var_2324, x = zero_mean_sq_61_cast_fp16)[name = tensor<string, []>("op_2341_cast_fp16")];
tensor<fp16, []> var_2342_to_fp16 = const()[name = tensor<string, []>("op_2342_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_2343_cast_fp16 = add(x = var_2341_cast_fp16, y = var_2342_to_fp16)[name = tensor<string, []>("op_2343_cast_fp16")];
tensor<fp16, []> denom_61_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_61_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_61_cast_fp16 = rsqrt(epsilon = denom_61_epsilon_0_to_fp16, x = var_2343_cast_fp16)[name = tensor<string, []>("denom_61_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_61_cast_fp16 = mul(x = zero_mean_61_cast_fp16, y = denom_61_cast_fp16)[name = tensor<string, []>("out_61_cast_fp16")];
tensor<fp16, [1280]> obj_141_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_141_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(658665280)))];
tensor<fp16, [1280]> obj_141_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_141_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(658667904)))];
tensor<fp16, []> obj_141_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_141_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_141_cast_fp16 = batch_norm(beta = obj_141_beta_0_to_fp16, epsilon = obj_141_epsilon_0_to_fp16, gamma = obj_141_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_61_cast_fp16)[name = tensor<string, []>("obj_141_cast_fp16")];
tensor<int32, [2]> var_2358 = const()[name = tensor<string, []>("op_2358"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2360 = const()[name = tensor<string, []>("op_2360"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_41_pad_type_0 = const()[name = tensor<string, []>("query_41_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_41_pad_0 = const()[name = tensor<string, []>("query_41_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_10_self_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_10_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(658670528)))];
tensor<fp16, [1280]> layers_10_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_10_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(661947392)))];
tensor<fp16, [1, 1280, 1, 1]> query_41_cast_fp16 = conv(bias = layers_10_self_attn_q_proj_bias_to_fp16, dilations = var_2360, groups = var_2323, pad = query_41_pad_0, pad_type = query_41_pad_type_0, strides = var_2358, weight = layers_10_self_attn_q_proj_weight_to_fp16, x = obj_141_cast_fp16)[name = tensor<string, []>("query_41_cast_fp16")];
tensor<int32, [2]> var_2364 = const()[name = tensor<string, []>("op_2364"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2366 = const()[name = tensor<string, []>("op_2366"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_key_21_pad_type_0 = const()[name = tensor<string, []>("current_key_21_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_key_21_pad_0 = const()[name = tensor<string, []>("current_key_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_10_self_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_10_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(661950016)))];
tensor<fp16, [1, 1280, 1, 1]> current_key_21_cast_fp16 = conv(dilations = var_2366, groups = var_2323, pad = current_key_21_pad_0, pad_type = current_key_21_pad_type_0, strides = var_2364, weight = layers_10_self_attn_k_proj_weight_to_fp16, x = obj_141_cast_fp16)[name = tensor<string, []>("current_key_21_cast_fp16")];
tensor<int32, [2]> var_2371 = const()[name = tensor<string, []>("op_2371"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2373 = const()[name = tensor<string, []>("op_2373"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_value_21_pad_type_0 = const()[name = tensor<string, []>("current_value_21_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_value_21_pad_0 = const()[name = tensor<string, []>("current_value_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_10_self_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_10_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(665226880)))];
tensor<fp16, [1280]> layers_10_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_10_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(668503744)))];
tensor<fp16, [1, 1280, 1, 1]> current_value_21_cast_fp16 = conv(bias = layers_10_self_attn_v_proj_bias_to_fp16, dilations = var_2373, groups = var_2323, pad = current_value_21_pad_0, pad_type = current_value_21_pad_type_0, strides = var_2371, weight = layers_10_self_attn_v_proj_weight_to_fp16, x = obj_141_cast_fp16)[name = tensor<string, []>("current_value_21_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_2380_cast_fp16 = mul(x = current_key_21_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_2380_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_2382_cast_fp16 = mul(x = var_103_cast_fp16_10, y = var_241_cast_fp16)[name = tensor<string, []>("op_2382_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> key_41_cast_fp16 = add(x = var_2380_cast_fp16, y = var_2382_cast_fp16)[name = tensor<string, []>("key_41_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_2384_cast_fp16 = mul(x = current_value_21_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_2384_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_2386_cast_fp16 = mul(x = var_138_cast_fp16_10, y = var_241_cast_fp16)[name = tensor<string, []>("op_2386_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> value_41_cast_fp16 = add(x = var_2384_cast_fp16, y = var_2386_cast_fp16)[name = tensor<string, []>("value_41_cast_fp16")];
tensor<int32, [4]> var_2389 = const()[name = tensor<string, []>("op_2389"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_2390_cast_fp16 = reshape(shape = var_2389, x = query_41_cast_fp16)[name = tensor<string, []>("op_2390_cast_fp16")];
tensor<fp16, []> var_2391_to_fp16 = const()[name = tensor<string, []>("op_2391_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_2392_cast_fp16 = mul(x = var_2390_cast_fp16, y = var_2391_to_fp16)[name = tensor<string, []>("op_2392_cast_fp16")];
tensor<int32, [4]> var_2393 = const()[name = tensor<string, []>("op_2393"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_2394_cast_fp16 = reshape(shape = var_2393, x = key_41_cast_fp16)[name = tensor<string, []>("op_2394_cast_fp16")];
tensor<bool, []> mh_w_61_transpose_x_0 = const()[name = tensor<string, []>("mh_w_61_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_61_transpose_y_0 = const()[name = tensor<string, []>("mh_w_61_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 224]> mh_w_61_cast_fp16 = matmul(transpose_x = mh_w_61_transpose_x_0, transpose_y = mh_w_61_transpose_y_0, x = var_2392_cast_fp16, y = var_2394_cast_fp16)[name = tensor<string, []>("mh_w_61_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> mh_w_63_cast_fp16 = add(x = mh_w_61_cast_fp16, y = var_259_cast_fp16)[name = tensor<string, []>("mh_w_63_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> var_2402_cast_fp16 = softmax(axis = var_2316, x = mh_w_63_cast_fp16)[name = tensor<string, []>("op_2402_cast_fp16")];
tensor<int32, [4]> var_2403 = const()[name = tensor<string, []>("op_2403"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_2404_cast_fp16 = reshape(shape = var_2403, x = value_41_cast_fp16)[name = tensor<string, []>("op_2404_cast_fp16")];
tensor<bool, []> attn_41_transpose_x_0 = const()[name = tensor<string, []>("attn_41_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_41_transpose_y_0 = const()[name = tensor<string, []>("attn_41_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_41_cast_fp16 = matmul(transpose_x = attn_41_transpose_x_0, transpose_y = attn_41_transpose_y_0, x = var_2404_cast_fp16, y = var_2402_cast_fp16)[name = tensor<string, []>("attn_41_cast_fp16")];
tensor<int32, [4]> var_2407 = const()[name = tensor<string, []>("op_2407"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_101_cast_fp16 = reshape(shape = var_2407, x = attn_41_cast_fp16)[name = tensor<string, []>("input_101_cast_fp16")];
tensor<int32, [2]> var_2411 = const()[name = tensor<string, []>("op_2411"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2413 = const()[name = tensor<string, []>("op_2413"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_147_pad_type_0 = const()[name = tensor<string, []>("obj_147_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_147_pad_0 = const()[name = tensor<string, []>("obj_147_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_10_self_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_10_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(668506368)))];
tensor<fp16, [1280]> layers_10_self_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_10_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(671783232)))];
tensor<fp16, [1, 1280, 1, 1]> obj_147_cast_fp16 = conv(bias = layers_10_self_attn_o_proj_bias_to_fp16, dilations = var_2413, groups = var_2323, pad = obj_147_pad_0, pad_type = obj_147_pad_type_0, strides = var_2411, weight = layers_10_self_attn_o_proj_weight_to_fp16, x = input_101_cast_fp16)[name = tensor<string, []>("obj_147_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_63_cast_fp16 = add(x = inputs_61_cast_fp16, y = obj_147_cast_fp16)[name = tensor<string, []>("inputs_63_cast_fp16")];
tensor<int32, [1]> var_2423 = const()[name = tensor<string, []>("op_2423"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_63_cast_fp16 = reduce_mean(axes = var_2423, keep_dims = var_2324, x = inputs_63_cast_fp16)[name = tensor<string, []>("channels_mean_63_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_63_cast_fp16 = sub(x = inputs_63_cast_fp16, y = channels_mean_63_cast_fp16)[name = tensor<string, []>("zero_mean_63_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_63_cast_fp16 = mul(x = zero_mean_63_cast_fp16, y = zero_mean_63_cast_fp16)[name = tensor<string, []>("zero_mean_sq_63_cast_fp16")];
tensor<int32, [1]> var_2427 = const()[name = tensor<string, []>("op_2427"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_2428_cast_fp16 = reduce_mean(axes = var_2427, keep_dims = var_2324, x = zero_mean_sq_63_cast_fp16)[name = tensor<string, []>("op_2428_cast_fp16")];
tensor<fp16, []> var_2429_to_fp16 = const()[name = tensor<string, []>("op_2429_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_2430_cast_fp16 = add(x = var_2428_cast_fp16, y = var_2429_to_fp16)[name = tensor<string, []>("op_2430_cast_fp16")];
tensor<fp16, []> denom_63_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_63_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_63_cast_fp16 = rsqrt(epsilon = denom_63_epsilon_0_to_fp16, x = var_2430_cast_fp16)[name = tensor<string, []>("denom_63_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_63_cast_fp16 = mul(x = zero_mean_63_cast_fp16, y = denom_63_cast_fp16)[name = tensor<string, []>("out_63_cast_fp16")];
tensor<fp16, [1280]> obj_149_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_149_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(671785856)))];
tensor<fp16, [1280]> obj_149_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_149_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(671788480)))];
tensor<fp16, []> obj_149_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_149_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_149_cast_fp16 = batch_norm(beta = obj_149_beta_0_to_fp16, epsilon = obj_149_epsilon_0_to_fp16, gamma = obj_149_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_63_cast_fp16)[name = tensor<string, []>("obj_149_cast_fp16")];
tensor<int32, [2]> var_2445 = const()[name = tensor<string, []>("op_2445"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2447 = const()[name = tensor<string, []>("op_2447"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_43_pad_type_0 = const()[name = tensor<string, []>("query_43_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_43_pad_0 = const()[name = tensor<string, []>("query_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_10_encoder_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_10_encoder_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(671791104)))];
tensor<fp16, [1280]> layers_10_encoder_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_10_encoder_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(675067968)))];
tensor<fp16, [1, 1280, 1, 1]> query_43_cast_fp16 = conv(bias = layers_10_encoder_attn_q_proj_bias_to_fp16, dilations = var_2447, groups = var_2323, pad = query_43_pad_0, pad_type = query_43_pad_type_0, strides = var_2445, weight = layers_10_encoder_attn_q_proj_weight_to_fp16, x = obj_149_cast_fp16)[name = tensor<string, []>("query_43_cast_fp16")];
tensor<int32, [2]> var_2451 = const()[name = tensor<string, []>("op_2451"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2453 = const()[name = tensor<string, []>("op_2453"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> key_43_pad_type_0 = const()[name = tensor<string, []>("key_43_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> key_43_pad_0 = const()[name = tensor<string, []>("key_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_10_encoder_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_10_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(675070592)))];
tensor<fp16, [1, 1280, 1, 1500]> key_43_cast_fp16 = conv(dilations = var_2453, groups = var_2323, pad = key_43_pad_0, pad_type = key_43_pad_type_0, strides = var_2451, weight = layers_10_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("key_43_cast_fp16")];
tensor<int32, [2]> var_2458 = const()[name = tensor<string, []>("op_2458"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2460 = const()[name = tensor<string, []>("op_2460"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> value_43_pad_type_0 = const()[name = tensor<string, []>("value_43_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> value_43_pad_0 = const()[name = tensor<string, []>("value_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_10_encoder_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_10_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(678347456)))];
tensor<fp16, [1280]> layers_10_encoder_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_10_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(681624320)))];
tensor<fp16, [1, 1280, 1, 1500]> value_43_cast_fp16 = conv(bias = layers_10_encoder_attn_v_proj_bias_to_fp16, dilations = var_2460, groups = var_2323, pad = value_43_pad_0, pad_type = value_43_pad_type_0, strides = var_2458, weight = layers_10_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("value_43_cast_fp16")];
tensor<int32, [4]> var_2464 = const()[name = tensor<string, []>("op_2464"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_2465_cast_fp16 = reshape(shape = var_2464, x = query_43_cast_fp16)[name = tensor<string, []>("op_2465_cast_fp16")];
tensor<fp16, []> var_2466_to_fp16 = const()[name = tensor<string, []>("op_2466_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_2467_cast_fp16 = mul(x = var_2465_cast_fp16, y = var_2466_to_fp16)[name = tensor<string, []>("op_2467_cast_fp16")];
tensor<int32, [4]> var_2468 = const()[name = tensor<string, []>("op_2468"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_2469_cast_fp16 = reshape(shape = var_2468, x = key_43_cast_fp16)[name = tensor<string, []>("op_2469_cast_fp16")];
tensor<bool, []> mh_w_65_transpose_x_0 = const()[name = tensor<string, []>("mh_w_65_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_65_transpose_y_0 = const()[name = tensor<string, []>("mh_w_65_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 1500]> mh_w_65_cast_fp16 = matmul(transpose_x = mh_w_65_transpose_x_0, transpose_y = mh_w_65_transpose_y_0, x = var_2467_cast_fp16, y = var_2469_cast_fp16)[name = tensor<string, []>("mh_w_65_cast_fp16")];
tensor<fp16, [1, 20, 1, 1500]> obj_153_cast_fp16 = softmax(axis = var_2316, x = mh_w_65_cast_fp16)[name = tensor<string, []>("obj_153_cast_fp16")];
tensor<int32, [4]> var_2473 = const()[name = tensor<string, []>("op_2473"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_2474_cast_fp16 = reshape(shape = var_2473, x = value_43_cast_fp16)[name = tensor<string, []>("op_2474_cast_fp16")];
tensor<bool, []> attn_43_transpose_x_0 = const()[name = tensor<string, []>("attn_43_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_43_transpose_y_0 = const()[name = tensor<string, []>("attn_43_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_43_cast_fp16 = matmul(transpose_x = attn_43_transpose_x_0, transpose_y = attn_43_transpose_y_0, x = var_2474_cast_fp16, y = obj_153_cast_fp16)[name = tensor<string, []>("attn_43_cast_fp16")];
tensor<int32, [4]> var_2477 = const()[name = tensor<string, []>("op_2477"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_103_cast_fp16 = reshape(shape = var_2477, x = attn_43_cast_fp16)[name = tensor<string, []>("input_103_cast_fp16")];
tensor<int32, [2]> var_2481 = const()[name = tensor<string, []>("op_2481"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2483 = const()[name = tensor<string, []>("op_2483"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_151_pad_type_0 = const()[name = tensor<string, []>("obj_151_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_151_pad_0 = const()[name = tensor<string, []>("obj_151_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_10_encoder_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_10_encoder_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(681626944)))];
tensor<fp16, [1280]> layers_10_encoder_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_10_encoder_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(684903808)))];
tensor<fp16, [1, 1280, 1, 1]> obj_151_cast_fp16 = conv(bias = layers_10_encoder_attn_o_proj_bias_to_fp16, dilations = var_2483, groups = var_2323, pad = obj_151_pad_0, pad_type = obj_151_pad_type_0, strides = var_2481, weight = layers_10_encoder_attn_o_proj_weight_to_fp16, x = input_103_cast_fp16)[name = tensor<string, []>("obj_151_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_65_cast_fp16 = add(x = inputs_63_cast_fp16, y = obj_151_cast_fp16)[name = tensor<string, []>("inputs_65_cast_fp16")];
tensor<int32, [1]> var_2492 = const()[name = tensor<string, []>("op_2492"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_65_cast_fp16 = reduce_mean(axes = var_2492, keep_dims = var_2324, x = inputs_65_cast_fp16)[name = tensor<string, []>("channels_mean_65_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_65_cast_fp16 = sub(x = inputs_65_cast_fp16, y = channels_mean_65_cast_fp16)[name = tensor<string, []>("zero_mean_65_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_65_cast_fp16 = mul(x = zero_mean_65_cast_fp16, y = zero_mean_65_cast_fp16)[name = tensor<string, []>("zero_mean_sq_65_cast_fp16")];
tensor<int32, [1]> var_2496 = const()[name = tensor<string, []>("op_2496"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_2497_cast_fp16 = reduce_mean(axes = var_2496, keep_dims = var_2324, x = zero_mean_sq_65_cast_fp16)[name = tensor<string, []>("op_2497_cast_fp16")];
tensor<fp16, []> var_2498_to_fp16 = const()[name = tensor<string, []>("op_2498_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_2499_cast_fp16 = add(x = var_2497_cast_fp16, y = var_2498_to_fp16)[name = tensor<string, []>("op_2499_cast_fp16")];
tensor<fp16, []> denom_65_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_65_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_65_cast_fp16 = rsqrt(epsilon = denom_65_epsilon_0_to_fp16, x = var_2499_cast_fp16)[name = tensor<string, []>("denom_65_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_65_cast_fp16 = mul(x = zero_mean_65_cast_fp16, y = denom_65_cast_fp16)[name = tensor<string, []>("out_65_cast_fp16")];
tensor<fp16, [1280]> input_105_gamma_0_to_fp16 = const()[name = tensor<string, []>("input_105_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(684906432)))];
tensor<fp16, [1280]> input_105_beta_0_to_fp16 = const()[name = tensor<string, []>("input_105_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(684909056)))];
tensor<fp16, []> input_105_epsilon_0_to_fp16 = const()[name = tensor<string, []>("input_105_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> input_105_cast_fp16 = batch_norm(beta = input_105_beta_0_to_fp16, epsilon = input_105_epsilon_0_to_fp16, gamma = input_105_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_65_cast_fp16)[name = tensor<string, []>("input_105_cast_fp16")];
tensor<int32, [2]> var_2510 = const()[name = tensor<string, []>("op_2510"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2512 = const()[name = tensor<string, []>("op_2512"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_107_pad_type_0 = const()[name = tensor<string, []>("input_107_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_107_pad_0 = const()[name = tensor<string, []>("input_107_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [5120, 1280, 1, 1]> layers_10_fc1_weight_to_fp16 = const()[name = tensor<string, []>("layers_10_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(684911680)))];
tensor<fp16, [5120]> layers_10_fc1_bias_to_fp16 = const()[name = tensor<string, []>("layers_10_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(698018944)))];
tensor<fp16, [1, 5120, 1, 1]> input_107_cast_fp16 = conv(bias = layers_10_fc1_bias_to_fp16, dilations = var_2512, groups = var_2323, pad = input_107_pad_0, pad_type = input_107_pad_type_0, strides = var_2510, weight = layers_10_fc1_weight_to_fp16, x = input_105_cast_fp16)[name = tensor<string, []>("input_107_cast_fp16")];
tensor<string, []> input_109_mode_0 = const()[name = tensor<string, []>("input_109_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 5120, 1, 1]> input_109_cast_fp16 = gelu(mode = input_109_mode_0, x = input_107_cast_fp16)[name = tensor<string, []>("input_109_cast_fp16")];
tensor<int32, [2]> var_2518 = const()[name = tensor<string, []>("op_2518"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2520 = const()[name = tensor<string, []>("op_2520"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_23_pad_type_0 = const()[name = tensor<string, []>("hidden_states_23_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_23_pad_0 = const()[name = tensor<string, []>("hidden_states_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 5120, 1, 1]> layers_10_fc2_weight_to_fp16 = const()[name = tensor<string, []>("layers_10_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(698029248)))];
tensor<fp16, [1280]> layers_10_fc2_bias_to_fp16 = const()[name = tensor<string, []>("layers_10_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(711136512)))];
tensor<fp16, [1, 1280, 1, 1]> hidden_states_23_cast_fp16 = conv(bias = layers_10_fc2_bias_to_fp16, dilations = var_2520, groups = var_2323, pad = hidden_states_23_pad_0, pad_type = hidden_states_23_pad_type_0, strides = var_2518, weight = layers_10_fc2_weight_to_fp16, x = input_109_cast_fp16)[name = tensor<string, []>("hidden_states_23_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_67_cast_fp16 = add(x = inputs_65_cast_fp16, y = hidden_states_23_cast_fp16)[name = tensor<string, []>("inputs_67_cast_fp16")];
tensor<int32, []> var_2534 = const()[name = tensor<string, []>("op_2534"), val = tensor<int32, []>(3)];
tensor<int32, []> var_2541 = const()[name = tensor<string, []>("op_2541"), val = tensor<int32, []>(1)];
tensor<bool, []> var_2542 = const()[name = tensor<string, []>("op_2542"), val = tensor<bool, []>(true)];
tensor<int32, [1]> var_2554 = const()[name = tensor<string, []>("op_2554"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_67_cast_fp16 = reduce_mean(axes = var_2554, keep_dims = var_2542, x = inputs_67_cast_fp16)[name = tensor<string, []>("channels_mean_67_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_67_cast_fp16 = sub(x = inputs_67_cast_fp16, y = channels_mean_67_cast_fp16)[name = tensor<string, []>("zero_mean_67_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_67_cast_fp16 = mul(x = zero_mean_67_cast_fp16, y = zero_mean_67_cast_fp16)[name = tensor<string, []>("zero_mean_sq_67_cast_fp16")];
tensor<int32, [1]> var_2558 = const()[name = tensor<string, []>("op_2558"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_2559_cast_fp16 = reduce_mean(axes = var_2558, keep_dims = var_2542, x = zero_mean_sq_67_cast_fp16)[name = tensor<string, []>("op_2559_cast_fp16")];
tensor<fp16, []> var_2560_to_fp16 = const()[name = tensor<string, []>("op_2560_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_2561_cast_fp16 = add(x = var_2559_cast_fp16, y = var_2560_to_fp16)[name = tensor<string, []>("op_2561_cast_fp16")];
tensor<fp16, []> denom_67_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_67_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_67_cast_fp16 = rsqrt(epsilon = denom_67_epsilon_0_to_fp16, x = var_2561_cast_fp16)[name = tensor<string, []>("denom_67_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_67_cast_fp16 = mul(x = zero_mean_67_cast_fp16, y = denom_67_cast_fp16)[name = tensor<string, []>("out_67_cast_fp16")];
tensor<fp16, [1280]> obj_155_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_155_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(711139136)))];
tensor<fp16, [1280]> obj_155_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_155_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(711141760)))];
tensor<fp16, []> obj_155_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_155_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_155_cast_fp16 = batch_norm(beta = obj_155_beta_0_to_fp16, epsilon = obj_155_epsilon_0_to_fp16, gamma = obj_155_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_67_cast_fp16)[name = tensor<string, []>("obj_155_cast_fp16")];
tensor<int32, [2]> var_2576 = const()[name = tensor<string, []>("op_2576"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2578 = const()[name = tensor<string, []>("op_2578"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_45_pad_type_0 = const()[name = tensor<string, []>("query_45_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_45_pad_0 = const()[name = tensor<string, []>("query_45_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_11_self_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_11_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(711144384)))];
tensor<fp16, [1280]> layers_11_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_11_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(714421248)))];
tensor<fp16, [1, 1280, 1, 1]> query_45_cast_fp16 = conv(bias = layers_11_self_attn_q_proj_bias_to_fp16, dilations = var_2578, groups = var_2541, pad = query_45_pad_0, pad_type = query_45_pad_type_0, strides = var_2576, weight = layers_11_self_attn_q_proj_weight_to_fp16, x = obj_155_cast_fp16)[name = tensor<string, []>("query_45_cast_fp16")];
tensor<int32, [2]> var_2582 = const()[name = tensor<string, []>("op_2582"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2584 = const()[name = tensor<string, []>("op_2584"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_key_23_pad_type_0 = const()[name = tensor<string, []>("current_key_23_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_key_23_pad_0 = const()[name = tensor<string, []>("current_key_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_11_self_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_11_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(714423872)))];
tensor<fp16, [1, 1280, 1, 1]> current_key_23_cast_fp16 = conv(dilations = var_2584, groups = var_2541, pad = current_key_23_pad_0, pad_type = current_key_23_pad_type_0, strides = var_2582, weight = layers_11_self_attn_k_proj_weight_to_fp16, x = obj_155_cast_fp16)[name = tensor<string, []>("current_key_23_cast_fp16")];
tensor<int32, [2]> var_2589 = const()[name = tensor<string, []>("op_2589"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2591 = const()[name = tensor<string, []>("op_2591"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_value_23_pad_type_0 = const()[name = tensor<string, []>("current_value_23_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_value_23_pad_0 = const()[name = tensor<string, []>("current_value_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_11_self_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_11_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(717700736)))];
tensor<fp16, [1280]> layers_11_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_11_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(720977600)))];
tensor<fp16, [1, 1280, 1, 1]> current_value_23_cast_fp16 = conv(bias = layers_11_self_attn_v_proj_bias_to_fp16, dilations = var_2591, groups = var_2541, pad = current_value_23_pad_0, pad_type = current_value_23_pad_type_0, strides = var_2589, weight = layers_11_self_attn_v_proj_weight_to_fp16, x = obj_155_cast_fp16)[name = tensor<string, []>("current_value_23_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_2598_cast_fp16 = mul(x = current_key_23_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_2598_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_2600_cast_fp16 = mul(x = var_103_cast_fp16_11, y = var_241_cast_fp16)[name = tensor<string, []>("op_2600_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> key_45_cast_fp16 = add(x = var_2598_cast_fp16, y = var_2600_cast_fp16)[name = tensor<string, []>("key_45_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_2602_cast_fp16 = mul(x = current_value_23_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_2602_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_2604_cast_fp16 = mul(x = var_138_cast_fp16_11, y = var_241_cast_fp16)[name = tensor<string, []>("op_2604_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> value_45_cast_fp16 = add(x = var_2602_cast_fp16, y = var_2604_cast_fp16)[name = tensor<string, []>("value_45_cast_fp16")];
tensor<int32, [4]> var_2607 = const()[name = tensor<string, []>("op_2607"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_2608_cast_fp16 = reshape(shape = var_2607, x = query_45_cast_fp16)[name = tensor<string, []>("op_2608_cast_fp16")];
tensor<fp16, []> var_2609_to_fp16 = const()[name = tensor<string, []>("op_2609_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_2610_cast_fp16 = mul(x = var_2608_cast_fp16, y = var_2609_to_fp16)[name = tensor<string, []>("op_2610_cast_fp16")];
tensor<int32, [4]> var_2611 = const()[name = tensor<string, []>("op_2611"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_2612_cast_fp16 = reshape(shape = var_2611, x = key_45_cast_fp16)[name = tensor<string, []>("op_2612_cast_fp16")];
tensor<bool, []> mh_w_67_transpose_x_0 = const()[name = tensor<string, []>("mh_w_67_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_67_transpose_y_0 = const()[name = tensor<string, []>("mh_w_67_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 224]> mh_w_67_cast_fp16 = matmul(transpose_x = mh_w_67_transpose_x_0, transpose_y = mh_w_67_transpose_y_0, x = var_2610_cast_fp16, y = var_2612_cast_fp16)[name = tensor<string, []>("mh_w_67_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> mh_w_69_cast_fp16 = add(x = mh_w_67_cast_fp16, y = var_259_cast_fp16)[name = tensor<string, []>("mh_w_69_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> var_2620_cast_fp16 = softmax(axis = var_2534, x = mh_w_69_cast_fp16)[name = tensor<string, []>("op_2620_cast_fp16")];
tensor<int32, [4]> var_2621 = const()[name = tensor<string, []>("op_2621"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_2622_cast_fp16 = reshape(shape = var_2621, x = value_45_cast_fp16)[name = tensor<string, []>("op_2622_cast_fp16")];
tensor<bool, []> attn_45_transpose_x_0 = const()[name = tensor<string, []>("attn_45_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_45_transpose_y_0 = const()[name = tensor<string, []>("attn_45_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_45_cast_fp16 = matmul(transpose_x = attn_45_transpose_x_0, transpose_y = attn_45_transpose_y_0, x = var_2622_cast_fp16, y = var_2620_cast_fp16)[name = tensor<string, []>("attn_45_cast_fp16")];
tensor<int32, [4]> var_2625 = const()[name = tensor<string, []>("op_2625"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_111_cast_fp16 = reshape(shape = var_2625, x = attn_45_cast_fp16)[name = tensor<string, []>("input_111_cast_fp16")];
tensor<int32, [2]> var_2629 = const()[name = tensor<string, []>("op_2629"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2631 = const()[name = tensor<string, []>("op_2631"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_161_pad_type_0 = const()[name = tensor<string, []>("obj_161_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_161_pad_0 = const()[name = tensor<string, []>("obj_161_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_11_self_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_11_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(720980224)))];
tensor<fp16, [1280]> layers_11_self_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_11_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(724257088)))];
tensor<fp16, [1, 1280, 1, 1]> obj_161_cast_fp16 = conv(bias = layers_11_self_attn_o_proj_bias_to_fp16, dilations = var_2631, groups = var_2541, pad = obj_161_pad_0, pad_type = obj_161_pad_type_0, strides = var_2629, weight = layers_11_self_attn_o_proj_weight_to_fp16, x = input_111_cast_fp16)[name = tensor<string, []>("obj_161_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_69_cast_fp16 = add(x = inputs_67_cast_fp16, y = obj_161_cast_fp16)[name = tensor<string, []>("inputs_69_cast_fp16")];
tensor<int32, [1]> var_2641 = const()[name = tensor<string, []>("op_2641"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_69_cast_fp16 = reduce_mean(axes = var_2641, keep_dims = var_2542, x = inputs_69_cast_fp16)[name = tensor<string, []>("channels_mean_69_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_69_cast_fp16 = sub(x = inputs_69_cast_fp16, y = channels_mean_69_cast_fp16)[name = tensor<string, []>("zero_mean_69_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_69_cast_fp16 = mul(x = zero_mean_69_cast_fp16, y = zero_mean_69_cast_fp16)[name = tensor<string, []>("zero_mean_sq_69_cast_fp16")];
tensor<int32, [1]> var_2645 = const()[name = tensor<string, []>("op_2645"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_2646_cast_fp16 = reduce_mean(axes = var_2645, keep_dims = var_2542, x = zero_mean_sq_69_cast_fp16)[name = tensor<string, []>("op_2646_cast_fp16")];
tensor<fp16, []> var_2647_to_fp16 = const()[name = tensor<string, []>("op_2647_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_2648_cast_fp16 = add(x = var_2646_cast_fp16, y = var_2647_to_fp16)[name = tensor<string, []>("op_2648_cast_fp16")];
tensor<fp16, []> denom_69_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_69_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_69_cast_fp16 = rsqrt(epsilon = denom_69_epsilon_0_to_fp16, x = var_2648_cast_fp16)[name = tensor<string, []>("denom_69_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_69_cast_fp16 = mul(x = zero_mean_69_cast_fp16, y = denom_69_cast_fp16)[name = tensor<string, []>("out_69_cast_fp16")];
tensor<fp16, [1280]> obj_163_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_163_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(724259712)))];
tensor<fp16, [1280]> obj_163_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_163_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(724262336)))];
tensor<fp16, []> obj_163_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_163_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_163_cast_fp16 = batch_norm(beta = obj_163_beta_0_to_fp16, epsilon = obj_163_epsilon_0_to_fp16, gamma = obj_163_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_69_cast_fp16)[name = tensor<string, []>("obj_163_cast_fp16")];
tensor<int32, [2]> var_2663 = const()[name = tensor<string, []>("op_2663"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2665 = const()[name = tensor<string, []>("op_2665"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_47_pad_type_0 = const()[name = tensor<string, []>("query_47_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_47_pad_0 = const()[name = tensor<string, []>("query_47_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_11_encoder_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_11_encoder_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(724264960)))];
tensor<fp16, [1280]> layers_11_encoder_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_11_encoder_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(727541824)))];
tensor<fp16, [1, 1280, 1, 1]> query_47_cast_fp16 = conv(bias = layers_11_encoder_attn_q_proj_bias_to_fp16, dilations = var_2665, groups = var_2541, pad = query_47_pad_0, pad_type = query_47_pad_type_0, strides = var_2663, weight = layers_11_encoder_attn_q_proj_weight_to_fp16, x = obj_163_cast_fp16)[name = tensor<string, []>("query_47_cast_fp16")];
tensor<int32, [2]> var_2669 = const()[name = tensor<string, []>("op_2669"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2671 = const()[name = tensor<string, []>("op_2671"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> key_47_pad_type_0 = const()[name = tensor<string, []>("key_47_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> key_47_pad_0 = const()[name = tensor<string, []>("key_47_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_11_encoder_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_11_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(727544448)))];
tensor<fp16, [1, 1280, 1, 1500]> key_47_cast_fp16 = conv(dilations = var_2671, groups = var_2541, pad = key_47_pad_0, pad_type = key_47_pad_type_0, strides = var_2669, weight = layers_11_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("key_47_cast_fp16")];
tensor<int32, [2]> var_2676 = const()[name = tensor<string, []>("op_2676"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2678 = const()[name = tensor<string, []>("op_2678"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> value_47_pad_type_0 = const()[name = tensor<string, []>("value_47_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> value_47_pad_0 = const()[name = tensor<string, []>("value_47_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_11_encoder_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_11_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(730821312)))];
tensor<fp16, [1280]> layers_11_encoder_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_11_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(734098176)))];
tensor<fp16, [1, 1280, 1, 1500]> value_47_cast_fp16 = conv(bias = layers_11_encoder_attn_v_proj_bias_to_fp16, dilations = var_2678, groups = var_2541, pad = value_47_pad_0, pad_type = value_47_pad_type_0, strides = var_2676, weight = layers_11_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("value_47_cast_fp16")];
tensor<int32, [4]> var_2682 = const()[name = tensor<string, []>("op_2682"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_2683_cast_fp16 = reshape(shape = var_2682, x = query_47_cast_fp16)[name = tensor<string, []>("op_2683_cast_fp16")];
tensor<fp16, []> var_2684_to_fp16 = const()[name = tensor<string, []>("op_2684_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_2685_cast_fp16 = mul(x = var_2683_cast_fp16, y = var_2684_to_fp16)[name = tensor<string, []>("op_2685_cast_fp16")];
tensor<int32, [4]> var_2686 = const()[name = tensor<string, []>("op_2686"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_2687_cast_fp16 = reshape(shape = var_2686, x = key_47_cast_fp16)[name = tensor<string, []>("op_2687_cast_fp16")];
tensor<bool, []> mh_w_71_transpose_x_0 = const()[name = tensor<string, []>("mh_w_71_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_71_transpose_y_0 = const()[name = tensor<string, []>("mh_w_71_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 1500]> mh_w_71_cast_fp16 = matmul(transpose_x = mh_w_71_transpose_x_0, transpose_y = mh_w_71_transpose_y_0, x = var_2685_cast_fp16, y = var_2687_cast_fp16)[name = tensor<string, []>("mh_w_71_cast_fp16")];
tensor<fp16, [1, 20, 1, 1500]> obj_167_cast_fp16 = softmax(axis = var_2534, x = mh_w_71_cast_fp16)[name = tensor<string, []>("obj_167_cast_fp16")];
tensor<int32, [4]> var_2691 = const()[name = tensor<string, []>("op_2691"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_2692_cast_fp16 = reshape(shape = var_2691, x = value_47_cast_fp16)[name = tensor<string, []>("op_2692_cast_fp16")];
tensor<bool, []> attn_47_transpose_x_0 = const()[name = tensor<string, []>("attn_47_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_47_transpose_y_0 = const()[name = tensor<string, []>("attn_47_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_47_cast_fp16 = matmul(transpose_x = attn_47_transpose_x_0, transpose_y = attn_47_transpose_y_0, x = var_2692_cast_fp16, y = obj_167_cast_fp16)[name = tensor<string, []>("attn_47_cast_fp16")];
tensor<int32, [4]> var_2695 = const()[name = tensor<string, []>("op_2695"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_113_cast_fp16 = reshape(shape = var_2695, x = attn_47_cast_fp16)[name = tensor<string, []>("input_113_cast_fp16")];
tensor<int32, [2]> var_2699 = const()[name = tensor<string, []>("op_2699"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2701 = const()[name = tensor<string, []>("op_2701"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_165_pad_type_0 = const()[name = tensor<string, []>("obj_165_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_165_pad_0 = const()[name = tensor<string, []>("obj_165_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_11_encoder_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_11_encoder_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(734100800)))];
tensor<fp16, [1280]> layers_11_encoder_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_11_encoder_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(737377664)))];
tensor<fp16, [1, 1280, 1, 1]> obj_165_cast_fp16 = conv(bias = layers_11_encoder_attn_o_proj_bias_to_fp16, dilations = var_2701, groups = var_2541, pad = obj_165_pad_0, pad_type = obj_165_pad_type_0, strides = var_2699, weight = layers_11_encoder_attn_o_proj_weight_to_fp16, x = input_113_cast_fp16)[name = tensor<string, []>("obj_165_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_71_cast_fp16 = add(x = inputs_69_cast_fp16, y = obj_165_cast_fp16)[name = tensor<string, []>("inputs_71_cast_fp16")];
tensor<int32, [1]> var_2707 = const()[name = tensor<string, []>("op_2707"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_71_cast_fp16 = reduce_mean(axes = var_2707, keep_dims = var_2542, x = inputs_71_cast_fp16)[name = tensor<string, []>("channels_mean_71_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_71_cast_fp16 = sub(x = inputs_71_cast_fp16, y = channels_mean_71_cast_fp16)[name = tensor<string, []>("zero_mean_71_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_71_cast_fp16 = mul(x = zero_mean_71_cast_fp16, y = zero_mean_71_cast_fp16)[name = tensor<string, []>("zero_mean_sq_71_cast_fp16")];
tensor<int32, [1]> var_2711 = const()[name = tensor<string, []>("op_2711"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_2712_cast_fp16 = reduce_mean(axes = var_2711, keep_dims = var_2542, x = zero_mean_sq_71_cast_fp16)[name = tensor<string, []>("op_2712_cast_fp16")];
tensor<fp16, []> var_2713_to_fp16 = const()[name = tensor<string, []>("op_2713_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_2714_cast_fp16 = add(x = var_2712_cast_fp16, y = var_2713_to_fp16)[name = tensor<string, []>("op_2714_cast_fp16")];
tensor<fp16, []> denom_71_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_71_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_71_cast_fp16 = rsqrt(epsilon = denom_71_epsilon_0_to_fp16, x = var_2714_cast_fp16)[name = tensor<string, []>("denom_71_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_71_cast_fp16 = mul(x = zero_mean_71_cast_fp16, y = denom_71_cast_fp16)[name = tensor<string, []>("out_71_cast_fp16")];
tensor<fp16, [1280]> input_115_gamma_0_to_fp16 = const()[name = tensor<string, []>("input_115_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(737380288)))];
tensor<fp16, [1280]> input_115_beta_0_to_fp16 = const()[name = tensor<string, []>("input_115_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(737382912)))];
tensor<fp16, []> input_115_epsilon_0_to_fp16 = const()[name = tensor<string, []>("input_115_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> input_115_cast_fp16 = batch_norm(beta = input_115_beta_0_to_fp16, epsilon = input_115_epsilon_0_to_fp16, gamma = input_115_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_71_cast_fp16)[name = tensor<string, []>("input_115_cast_fp16")];
tensor<int32, [2]> var_2725 = const()[name = tensor<string, []>("op_2725"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2727 = const()[name = tensor<string, []>("op_2727"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_117_pad_type_0 = const()[name = tensor<string, []>("input_117_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_117_pad_0 = const()[name = tensor<string, []>("input_117_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [5120, 1280, 1, 1]> layers_11_fc1_weight_to_fp16 = const()[name = tensor<string, []>("layers_11_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(737385536)))];
tensor<fp16, [5120]> layers_11_fc1_bias_to_fp16 = const()[name = tensor<string, []>("layers_11_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(750492800)))];
tensor<fp16, [1, 5120, 1, 1]> input_117_cast_fp16 = conv(bias = layers_11_fc1_bias_to_fp16, dilations = var_2727, groups = var_2541, pad = input_117_pad_0, pad_type = input_117_pad_type_0, strides = var_2725, weight = layers_11_fc1_weight_to_fp16, x = input_115_cast_fp16)[name = tensor<string, []>("input_117_cast_fp16")];
tensor<string, []> input_119_mode_0 = const()[name = tensor<string, []>("input_119_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 5120, 1, 1]> input_119_cast_fp16 = gelu(mode = input_119_mode_0, x = input_117_cast_fp16)[name = tensor<string, []>("input_119_cast_fp16")];
tensor<int32, [2]> var_2733 = const()[name = tensor<string, []>("op_2733"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2735 = const()[name = tensor<string, []>("op_2735"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_25_pad_type_0 = const()[name = tensor<string, []>("hidden_states_25_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_25_pad_0 = const()[name = tensor<string, []>("hidden_states_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 5120, 1, 1]> layers_11_fc2_weight_to_fp16 = const()[name = tensor<string, []>("layers_11_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(750503104)))];
tensor<fp16, [1280]> layers_11_fc2_bias_to_fp16 = const()[name = tensor<string, []>("layers_11_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(763610368)))];
tensor<fp16, [1, 1280, 1, 1]> hidden_states_25_cast_fp16 = conv(bias = layers_11_fc2_bias_to_fp16, dilations = var_2735, groups = var_2541, pad = hidden_states_25_pad_0, pad_type = hidden_states_25_pad_type_0, strides = var_2733, weight = layers_11_fc2_weight_to_fp16, x = input_119_cast_fp16)[name = tensor<string, []>("hidden_states_25_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_73_cast_fp16 = add(x = inputs_71_cast_fp16, y = hidden_states_25_cast_fp16)[name = tensor<string, []>("inputs_73_cast_fp16")];
tensor<int32, []> var_2748 = const()[name = tensor<string, []>("op_2748"), val = tensor<int32, []>(3)];
tensor<int32, []> var_2755 = const()[name = tensor<string, []>("op_2755"), val = tensor<int32, []>(1)];
tensor<bool, []> var_2756 = const()[name = tensor<string, []>("op_2756"), val = tensor<bool, []>(true)];
tensor<int32, [1]> var_2768 = const()[name = tensor<string, []>("op_2768"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_73_cast_fp16 = reduce_mean(axes = var_2768, keep_dims = var_2756, x = inputs_73_cast_fp16)[name = tensor<string, []>("channels_mean_73_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_73_cast_fp16 = sub(x = inputs_73_cast_fp16, y = channels_mean_73_cast_fp16)[name = tensor<string, []>("zero_mean_73_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_73_cast_fp16 = mul(x = zero_mean_73_cast_fp16, y = zero_mean_73_cast_fp16)[name = tensor<string, []>("zero_mean_sq_73_cast_fp16")];
tensor<int32, [1]> var_2772 = const()[name = tensor<string, []>("op_2772"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_2773_cast_fp16 = reduce_mean(axes = var_2772, keep_dims = var_2756, x = zero_mean_sq_73_cast_fp16)[name = tensor<string, []>("op_2773_cast_fp16")];
tensor<fp16, []> var_2774_to_fp16 = const()[name = tensor<string, []>("op_2774_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_2775_cast_fp16 = add(x = var_2773_cast_fp16, y = var_2774_to_fp16)[name = tensor<string, []>("op_2775_cast_fp16")];
tensor<fp16, []> denom_73_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_73_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_73_cast_fp16 = rsqrt(epsilon = denom_73_epsilon_0_to_fp16, x = var_2775_cast_fp16)[name = tensor<string, []>("denom_73_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_73_cast_fp16 = mul(x = zero_mean_73_cast_fp16, y = denom_73_cast_fp16)[name = tensor<string, []>("out_73_cast_fp16")];
tensor<fp16, [1280]> obj_169_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_169_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(763612992)))];
tensor<fp16, [1280]> obj_169_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_169_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(763615616)))];
tensor<fp16, []> obj_169_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_169_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_169_cast_fp16 = batch_norm(beta = obj_169_beta_0_to_fp16, epsilon = obj_169_epsilon_0_to_fp16, gamma = obj_169_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_73_cast_fp16)[name = tensor<string, []>("obj_169_cast_fp16")];
tensor<int32, [2]> var_2790 = const()[name = tensor<string, []>("op_2790"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2792 = const()[name = tensor<string, []>("op_2792"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_49_pad_type_0 = const()[name = tensor<string, []>("query_49_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_49_pad_0 = const()[name = tensor<string, []>("query_49_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_12_self_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_12_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(763618240)))];
tensor<fp16, [1280]> layers_12_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_12_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(766895104)))];
tensor<fp16, [1, 1280, 1, 1]> query_49_cast_fp16 = conv(bias = layers_12_self_attn_q_proj_bias_to_fp16, dilations = var_2792, groups = var_2755, pad = query_49_pad_0, pad_type = query_49_pad_type_0, strides = var_2790, weight = layers_12_self_attn_q_proj_weight_to_fp16, x = obj_169_cast_fp16)[name = tensor<string, []>("query_49_cast_fp16")];
tensor<int32, [2]> var_2796 = const()[name = tensor<string, []>("op_2796"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2798 = const()[name = tensor<string, []>("op_2798"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_key_25_pad_type_0 = const()[name = tensor<string, []>("current_key_25_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_key_25_pad_0 = const()[name = tensor<string, []>("current_key_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_12_self_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_12_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(766897728)))];
tensor<fp16, [1, 1280, 1, 1]> current_key_25_cast_fp16 = conv(dilations = var_2798, groups = var_2755, pad = current_key_25_pad_0, pad_type = current_key_25_pad_type_0, strides = var_2796, weight = layers_12_self_attn_k_proj_weight_to_fp16, x = obj_169_cast_fp16)[name = tensor<string, []>("current_key_25_cast_fp16")];
tensor<int32, [2]> var_2803 = const()[name = tensor<string, []>("op_2803"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2805 = const()[name = tensor<string, []>("op_2805"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_value_25_pad_type_0 = const()[name = tensor<string, []>("current_value_25_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_value_25_pad_0 = const()[name = tensor<string, []>("current_value_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_12_self_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_12_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(770174592)))];
tensor<fp16, [1280]> layers_12_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_12_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(773451456)))];
tensor<fp16, [1, 1280, 1, 1]> current_value_25_cast_fp16 = conv(bias = layers_12_self_attn_v_proj_bias_to_fp16, dilations = var_2805, groups = var_2755, pad = current_value_25_pad_0, pad_type = current_value_25_pad_type_0, strides = var_2803, weight = layers_12_self_attn_v_proj_weight_to_fp16, x = obj_169_cast_fp16)[name = tensor<string, []>("current_value_25_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_2812_cast_fp16 = mul(x = current_key_25_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_2812_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_2814_cast_fp16 = mul(x = var_103_cast_fp16_12, y = var_241_cast_fp16)[name = tensor<string, []>("op_2814_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> key_49_cast_fp16 = add(x = var_2812_cast_fp16, y = var_2814_cast_fp16)[name = tensor<string, []>("key_49_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_2816_cast_fp16 = mul(x = current_value_25_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_2816_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_2818_cast_fp16 = mul(x = var_138_cast_fp16_12, y = var_241_cast_fp16)[name = tensor<string, []>("op_2818_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> value_49_cast_fp16 = add(x = var_2816_cast_fp16, y = var_2818_cast_fp16)[name = tensor<string, []>("value_49_cast_fp16")];
tensor<int32, [4]> var_2821 = const()[name = tensor<string, []>("op_2821"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_2822_cast_fp16 = reshape(shape = var_2821, x = query_49_cast_fp16)[name = tensor<string, []>("op_2822_cast_fp16")];
tensor<fp16, []> var_2823_to_fp16 = const()[name = tensor<string, []>("op_2823_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_2824_cast_fp16 = mul(x = var_2822_cast_fp16, y = var_2823_to_fp16)[name = tensor<string, []>("op_2824_cast_fp16")];
tensor<int32, [4]> var_2825 = const()[name = tensor<string, []>("op_2825"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_2826_cast_fp16 = reshape(shape = var_2825, x = key_49_cast_fp16)[name = tensor<string, []>("op_2826_cast_fp16")];
tensor<bool, []> mh_w_73_transpose_x_0 = const()[name = tensor<string, []>("mh_w_73_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_73_transpose_y_0 = const()[name = tensor<string, []>("mh_w_73_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 224]> mh_w_73_cast_fp16 = matmul(transpose_x = mh_w_73_transpose_x_0, transpose_y = mh_w_73_transpose_y_0, x = var_2824_cast_fp16, y = var_2826_cast_fp16)[name = tensor<string, []>("mh_w_73_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> mh_w_75_cast_fp16 = add(x = mh_w_73_cast_fp16, y = var_259_cast_fp16)[name = tensor<string, []>("mh_w_75_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> var_2834_cast_fp16 = softmax(axis = var_2748, x = mh_w_75_cast_fp16)[name = tensor<string, []>("op_2834_cast_fp16")];
tensor<int32, [4]> var_2835 = const()[name = tensor<string, []>("op_2835"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_2836_cast_fp16 = reshape(shape = var_2835, x = value_49_cast_fp16)[name = tensor<string, []>("op_2836_cast_fp16")];
tensor<bool, []> attn_49_transpose_x_0 = const()[name = tensor<string, []>("attn_49_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_49_transpose_y_0 = const()[name = tensor<string, []>("attn_49_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_49_cast_fp16 = matmul(transpose_x = attn_49_transpose_x_0, transpose_y = attn_49_transpose_y_0, x = var_2836_cast_fp16, y = var_2834_cast_fp16)[name = tensor<string, []>("attn_49_cast_fp16")];
tensor<int32, [4]> var_2839 = const()[name = tensor<string, []>("op_2839"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_121_cast_fp16 = reshape(shape = var_2839, x = attn_49_cast_fp16)[name = tensor<string, []>("input_121_cast_fp16")];
tensor<int32, [2]> var_2843 = const()[name = tensor<string, []>("op_2843"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2845 = const()[name = tensor<string, []>("op_2845"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_175_pad_type_0 = const()[name = tensor<string, []>("obj_175_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_175_pad_0 = const()[name = tensor<string, []>("obj_175_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_12_self_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_12_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(773454080)))];
tensor<fp16, [1280]> layers_12_self_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_12_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(776730944)))];
tensor<fp16, [1, 1280, 1, 1]> obj_175_cast_fp16 = conv(bias = layers_12_self_attn_o_proj_bias_to_fp16, dilations = var_2845, groups = var_2755, pad = obj_175_pad_0, pad_type = obj_175_pad_type_0, strides = var_2843, weight = layers_12_self_attn_o_proj_weight_to_fp16, x = input_121_cast_fp16)[name = tensor<string, []>("obj_175_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_75_cast_fp16 = add(x = inputs_73_cast_fp16, y = obj_175_cast_fp16)[name = tensor<string, []>("inputs_75_cast_fp16")];
tensor<int32, [1]> var_2855 = const()[name = tensor<string, []>("op_2855"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_75_cast_fp16 = reduce_mean(axes = var_2855, keep_dims = var_2756, x = inputs_75_cast_fp16)[name = tensor<string, []>("channels_mean_75_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_75_cast_fp16 = sub(x = inputs_75_cast_fp16, y = channels_mean_75_cast_fp16)[name = tensor<string, []>("zero_mean_75_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_75_cast_fp16 = mul(x = zero_mean_75_cast_fp16, y = zero_mean_75_cast_fp16)[name = tensor<string, []>("zero_mean_sq_75_cast_fp16")];
tensor<int32, [1]> var_2859 = const()[name = tensor<string, []>("op_2859"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_2860_cast_fp16 = reduce_mean(axes = var_2859, keep_dims = var_2756, x = zero_mean_sq_75_cast_fp16)[name = tensor<string, []>("op_2860_cast_fp16")];
tensor<fp16, []> var_2861_to_fp16 = const()[name = tensor<string, []>("op_2861_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_2862_cast_fp16 = add(x = var_2860_cast_fp16, y = var_2861_to_fp16)[name = tensor<string, []>("op_2862_cast_fp16")];
tensor<fp16, []> denom_75_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_75_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_75_cast_fp16 = rsqrt(epsilon = denom_75_epsilon_0_to_fp16, x = var_2862_cast_fp16)[name = tensor<string, []>("denom_75_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_75_cast_fp16 = mul(x = zero_mean_75_cast_fp16, y = denom_75_cast_fp16)[name = tensor<string, []>("out_75_cast_fp16")];
tensor<fp16, [1280]> obj_177_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_177_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(776733568)))];
tensor<fp16, [1280]> obj_177_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_177_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(776736192)))];
tensor<fp16, []> obj_177_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_177_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_177_cast_fp16 = batch_norm(beta = obj_177_beta_0_to_fp16, epsilon = obj_177_epsilon_0_to_fp16, gamma = obj_177_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_75_cast_fp16)[name = tensor<string, []>("obj_177_cast_fp16")];
tensor<int32, [2]> var_2877 = const()[name = tensor<string, []>("op_2877"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2879 = const()[name = tensor<string, []>("op_2879"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_51_pad_type_0 = const()[name = tensor<string, []>("query_51_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_51_pad_0 = const()[name = tensor<string, []>("query_51_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_12_encoder_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_12_encoder_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(776738816)))];
tensor<fp16, [1280]> layers_12_encoder_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_12_encoder_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(780015680)))];
tensor<fp16, [1, 1280, 1, 1]> query_51_cast_fp16 = conv(bias = layers_12_encoder_attn_q_proj_bias_to_fp16, dilations = var_2879, groups = var_2755, pad = query_51_pad_0, pad_type = query_51_pad_type_0, strides = var_2877, weight = layers_12_encoder_attn_q_proj_weight_to_fp16, x = obj_177_cast_fp16)[name = tensor<string, []>("query_51_cast_fp16")];
tensor<int32, [2]> var_2883 = const()[name = tensor<string, []>("op_2883"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2885 = const()[name = tensor<string, []>("op_2885"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> key_51_pad_type_0 = const()[name = tensor<string, []>("key_51_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> key_51_pad_0 = const()[name = tensor<string, []>("key_51_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_12_encoder_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_12_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(780018304)))];
tensor<fp16, [1, 1280, 1, 1500]> key_51_cast_fp16 = conv(dilations = var_2885, groups = var_2755, pad = key_51_pad_0, pad_type = key_51_pad_type_0, strides = var_2883, weight = layers_12_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("key_51_cast_fp16")];
tensor<int32, [2]> var_2890 = const()[name = tensor<string, []>("op_2890"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2892 = const()[name = tensor<string, []>("op_2892"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> value_51_pad_type_0 = const()[name = tensor<string, []>("value_51_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> value_51_pad_0 = const()[name = tensor<string, []>("value_51_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_12_encoder_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_12_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(783295168)))];
tensor<fp16, [1280]> layers_12_encoder_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_12_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(786572032)))];
tensor<fp16, [1, 1280, 1, 1500]> value_51_cast_fp16 = conv(bias = layers_12_encoder_attn_v_proj_bias_to_fp16, dilations = var_2892, groups = var_2755, pad = value_51_pad_0, pad_type = value_51_pad_type_0, strides = var_2890, weight = layers_12_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("value_51_cast_fp16")];
tensor<int32, [4]> var_2896 = const()[name = tensor<string, []>("op_2896"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_2897_cast_fp16 = reshape(shape = var_2896, x = query_51_cast_fp16)[name = tensor<string, []>("op_2897_cast_fp16")];
tensor<fp16, []> var_2898_to_fp16 = const()[name = tensor<string, []>("op_2898_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_2899_cast_fp16 = mul(x = var_2897_cast_fp16, y = var_2898_to_fp16)[name = tensor<string, []>("op_2899_cast_fp16")];
tensor<int32, [4]> var_2900 = const()[name = tensor<string, []>("op_2900"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_2901_cast_fp16 = reshape(shape = var_2900, x = key_51_cast_fp16)[name = tensor<string, []>("op_2901_cast_fp16")];
tensor<bool, []> mh_w_77_transpose_x_0 = const()[name = tensor<string, []>("mh_w_77_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_77_transpose_y_0 = const()[name = tensor<string, []>("mh_w_77_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 1500]> mh_w_77_cast_fp16 = matmul(transpose_x = mh_w_77_transpose_x_0, transpose_y = mh_w_77_transpose_y_0, x = var_2899_cast_fp16, y = var_2901_cast_fp16)[name = tensor<string, []>("mh_w_77_cast_fp16")];
tensor<fp16, [1, 20, 1, 1500]> obj_181_cast_fp16 = softmax(axis = var_2748, x = mh_w_77_cast_fp16)[name = tensor<string, []>("obj_181_cast_fp16")];
tensor<int32, [4]> var_2905 = const()[name = tensor<string, []>("op_2905"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_2906_cast_fp16 = reshape(shape = var_2905, x = value_51_cast_fp16)[name = tensor<string, []>("op_2906_cast_fp16")];
tensor<bool, []> attn_51_transpose_x_0 = const()[name = tensor<string, []>("attn_51_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_51_transpose_y_0 = const()[name = tensor<string, []>("attn_51_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_51_cast_fp16 = matmul(transpose_x = attn_51_transpose_x_0, transpose_y = attn_51_transpose_y_0, x = var_2906_cast_fp16, y = obj_181_cast_fp16)[name = tensor<string, []>("attn_51_cast_fp16")];
tensor<int32, [4]> var_2909 = const()[name = tensor<string, []>("op_2909"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_123_cast_fp16 = reshape(shape = var_2909, x = attn_51_cast_fp16)[name = tensor<string, []>("input_123_cast_fp16")];
tensor<int32, [2]> var_2913 = const()[name = tensor<string, []>("op_2913"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2915 = const()[name = tensor<string, []>("op_2915"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_179_pad_type_0 = const()[name = tensor<string, []>("obj_179_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_179_pad_0 = const()[name = tensor<string, []>("obj_179_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_12_encoder_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_12_encoder_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(786574656)))];
tensor<fp16, [1280]> layers_12_encoder_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_12_encoder_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(789851520)))];
tensor<fp16, [1, 1280, 1, 1]> obj_179_cast_fp16 = conv(bias = layers_12_encoder_attn_o_proj_bias_to_fp16, dilations = var_2915, groups = var_2755, pad = obj_179_pad_0, pad_type = obj_179_pad_type_0, strides = var_2913, weight = layers_12_encoder_attn_o_proj_weight_to_fp16, x = input_123_cast_fp16)[name = tensor<string, []>("obj_179_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_77_cast_fp16 = add(x = inputs_75_cast_fp16, y = obj_179_cast_fp16)[name = tensor<string, []>("inputs_77_cast_fp16")];
tensor<int32, [1]> var_2921 = const()[name = tensor<string, []>("op_2921"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_77_cast_fp16 = reduce_mean(axes = var_2921, keep_dims = var_2756, x = inputs_77_cast_fp16)[name = tensor<string, []>("channels_mean_77_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_77_cast_fp16 = sub(x = inputs_77_cast_fp16, y = channels_mean_77_cast_fp16)[name = tensor<string, []>("zero_mean_77_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_77_cast_fp16 = mul(x = zero_mean_77_cast_fp16, y = zero_mean_77_cast_fp16)[name = tensor<string, []>("zero_mean_sq_77_cast_fp16")];
tensor<int32, [1]> var_2925 = const()[name = tensor<string, []>("op_2925"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_2926_cast_fp16 = reduce_mean(axes = var_2925, keep_dims = var_2756, x = zero_mean_sq_77_cast_fp16)[name = tensor<string, []>("op_2926_cast_fp16")];
tensor<fp16, []> var_2927_to_fp16 = const()[name = tensor<string, []>("op_2927_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_2928_cast_fp16 = add(x = var_2926_cast_fp16, y = var_2927_to_fp16)[name = tensor<string, []>("op_2928_cast_fp16")];
tensor<fp16, []> denom_77_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_77_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_77_cast_fp16 = rsqrt(epsilon = denom_77_epsilon_0_to_fp16, x = var_2928_cast_fp16)[name = tensor<string, []>("denom_77_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_77_cast_fp16 = mul(x = zero_mean_77_cast_fp16, y = denom_77_cast_fp16)[name = tensor<string, []>("out_77_cast_fp16")];
tensor<fp16, [1280]> input_125_gamma_0_to_fp16 = const()[name = tensor<string, []>("input_125_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(789854144)))];
tensor<fp16, [1280]> input_125_beta_0_to_fp16 = const()[name = tensor<string, []>("input_125_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(789856768)))];
tensor<fp16, []> input_125_epsilon_0_to_fp16 = const()[name = tensor<string, []>("input_125_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> input_125_cast_fp16 = batch_norm(beta = input_125_beta_0_to_fp16, epsilon = input_125_epsilon_0_to_fp16, gamma = input_125_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_77_cast_fp16)[name = tensor<string, []>("input_125_cast_fp16")];
tensor<int32, [2]> var_2939 = const()[name = tensor<string, []>("op_2939"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2941 = const()[name = tensor<string, []>("op_2941"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_127_pad_type_0 = const()[name = tensor<string, []>("input_127_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_127_pad_0 = const()[name = tensor<string, []>("input_127_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [5120, 1280, 1, 1]> layers_12_fc1_weight_to_fp16 = const()[name = tensor<string, []>("layers_12_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(789859392)))];
tensor<fp16, [5120]> layers_12_fc1_bias_to_fp16 = const()[name = tensor<string, []>("layers_12_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(802966656)))];
tensor<fp16, [1, 5120, 1, 1]> input_127_cast_fp16 = conv(bias = layers_12_fc1_bias_to_fp16, dilations = var_2941, groups = var_2755, pad = input_127_pad_0, pad_type = input_127_pad_type_0, strides = var_2939, weight = layers_12_fc1_weight_to_fp16, x = input_125_cast_fp16)[name = tensor<string, []>("input_127_cast_fp16")];
tensor<string, []> input_129_mode_0 = const()[name = tensor<string, []>("input_129_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 5120, 1, 1]> input_129_cast_fp16 = gelu(mode = input_129_mode_0, x = input_127_cast_fp16)[name = tensor<string, []>("input_129_cast_fp16")];
tensor<int32, [2]> var_2947 = const()[name = tensor<string, []>("op_2947"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_2949 = const()[name = tensor<string, []>("op_2949"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_27_pad_type_0 = const()[name = tensor<string, []>("hidden_states_27_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_27_pad_0 = const()[name = tensor<string, []>("hidden_states_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 5120, 1, 1]> layers_12_fc2_weight_to_fp16 = const()[name = tensor<string, []>("layers_12_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(802976960)))];
tensor<fp16, [1280]> layers_12_fc2_bias_to_fp16 = const()[name = tensor<string, []>("layers_12_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(816084224)))];
tensor<fp16, [1, 1280, 1, 1]> hidden_states_27_cast_fp16 = conv(bias = layers_12_fc2_bias_to_fp16, dilations = var_2949, groups = var_2755, pad = hidden_states_27_pad_0, pad_type = hidden_states_27_pad_type_0, strides = var_2947, weight = layers_12_fc2_weight_to_fp16, x = input_129_cast_fp16)[name = tensor<string, []>("hidden_states_27_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_79_cast_fp16 = add(x = inputs_77_cast_fp16, y = hidden_states_27_cast_fp16)[name = tensor<string, []>("inputs_79_cast_fp16")];
tensor<int32, []> var_2962 = const()[name = tensor<string, []>("op_2962"), val = tensor<int32, []>(3)];
tensor<int32, []> var_2969 = const()[name = tensor<string, []>("op_2969"), val = tensor<int32, []>(1)];
tensor<bool, []> var_2970 = const()[name = tensor<string, []>("op_2970"), val = tensor<bool, []>(true)];
tensor<int32, [1]> var_2982 = const()[name = tensor<string, []>("op_2982"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_79_cast_fp16 = reduce_mean(axes = var_2982, keep_dims = var_2970, x = inputs_79_cast_fp16)[name = tensor<string, []>("channels_mean_79_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_79_cast_fp16 = sub(x = inputs_79_cast_fp16, y = channels_mean_79_cast_fp16)[name = tensor<string, []>("zero_mean_79_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_79_cast_fp16 = mul(x = zero_mean_79_cast_fp16, y = zero_mean_79_cast_fp16)[name = tensor<string, []>("zero_mean_sq_79_cast_fp16")];
tensor<int32, [1]> var_2986 = const()[name = tensor<string, []>("op_2986"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_2987_cast_fp16 = reduce_mean(axes = var_2986, keep_dims = var_2970, x = zero_mean_sq_79_cast_fp16)[name = tensor<string, []>("op_2987_cast_fp16")];
tensor<fp16, []> var_2988_to_fp16 = const()[name = tensor<string, []>("op_2988_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_2989_cast_fp16 = add(x = var_2987_cast_fp16, y = var_2988_to_fp16)[name = tensor<string, []>("op_2989_cast_fp16")];
tensor<fp16, []> denom_79_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_79_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_79_cast_fp16 = rsqrt(epsilon = denom_79_epsilon_0_to_fp16, x = var_2989_cast_fp16)[name = tensor<string, []>("denom_79_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_79_cast_fp16 = mul(x = zero_mean_79_cast_fp16, y = denom_79_cast_fp16)[name = tensor<string, []>("out_79_cast_fp16")];
tensor<fp16, [1280]> obj_183_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_183_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(816086848)))];
tensor<fp16, [1280]> obj_183_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_183_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(816089472)))];
tensor<fp16, []> obj_183_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_183_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_183_cast_fp16 = batch_norm(beta = obj_183_beta_0_to_fp16, epsilon = obj_183_epsilon_0_to_fp16, gamma = obj_183_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_79_cast_fp16)[name = tensor<string, []>("obj_183_cast_fp16")];
tensor<int32, [2]> var_3004 = const()[name = tensor<string, []>("op_3004"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3006 = const()[name = tensor<string, []>("op_3006"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_53_pad_type_0 = const()[name = tensor<string, []>("query_53_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_53_pad_0 = const()[name = tensor<string, []>("query_53_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_13_self_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_13_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(816092096)))];
tensor<fp16, [1280]> layers_13_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_13_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(819368960)))];
tensor<fp16, [1, 1280, 1, 1]> query_53_cast_fp16 = conv(bias = layers_13_self_attn_q_proj_bias_to_fp16, dilations = var_3006, groups = var_2969, pad = query_53_pad_0, pad_type = query_53_pad_type_0, strides = var_3004, weight = layers_13_self_attn_q_proj_weight_to_fp16, x = obj_183_cast_fp16)[name = tensor<string, []>("query_53_cast_fp16")];
tensor<int32, [2]> var_3010 = const()[name = tensor<string, []>("op_3010"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3012 = const()[name = tensor<string, []>("op_3012"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_key_27_pad_type_0 = const()[name = tensor<string, []>("current_key_27_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_key_27_pad_0 = const()[name = tensor<string, []>("current_key_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_13_self_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_13_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(819371584)))];
tensor<fp16, [1, 1280, 1, 1]> current_key_27_cast_fp16 = conv(dilations = var_3012, groups = var_2969, pad = current_key_27_pad_0, pad_type = current_key_27_pad_type_0, strides = var_3010, weight = layers_13_self_attn_k_proj_weight_to_fp16, x = obj_183_cast_fp16)[name = tensor<string, []>("current_key_27_cast_fp16")];
tensor<int32, [2]> var_3017 = const()[name = tensor<string, []>("op_3017"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3019 = const()[name = tensor<string, []>("op_3019"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_value_27_pad_type_0 = const()[name = tensor<string, []>("current_value_27_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_value_27_pad_0 = const()[name = tensor<string, []>("current_value_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_13_self_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_13_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(822648448)))];
tensor<fp16, [1280]> layers_13_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_13_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(825925312)))];
tensor<fp16, [1, 1280, 1, 1]> current_value_27_cast_fp16 = conv(bias = layers_13_self_attn_v_proj_bias_to_fp16, dilations = var_3019, groups = var_2969, pad = current_value_27_pad_0, pad_type = current_value_27_pad_type_0, strides = var_3017, weight = layers_13_self_attn_v_proj_weight_to_fp16, x = obj_183_cast_fp16)[name = tensor<string, []>("current_value_27_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_3026_cast_fp16 = mul(x = current_key_27_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_3026_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_3028_cast_fp16 = mul(x = var_103_cast_fp16_13, y = var_241_cast_fp16)[name = tensor<string, []>("op_3028_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> key_53_cast_fp16 = add(x = var_3026_cast_fp16, y = var_3028_cast_fp16)[name = tensor<string, []>("key_53_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_3030_cast_fp16 = mul(x = current_value_27_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_3030_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_3032_cast_fp16 = mul(x = var_138_cast_fp16_13, y = var_241_cast_fp16)[name = tensor<string, []>("op_3032_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> value_53_cast_fp16 = add(x = var_3030_cast_fp16, y = var_3032_cast_fp16)[name = tensor<string, []>("value_53_cast_fp16")];
tensor<int32, [4]> var_3035 = const()[name = tensor<string, []>("op_3035"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_3036_cast_fp16 = reshape(shape = var_3035, x = query_53_cast_fp16)[name = tensor<string, []>("op_3036_cast_fp16")];
tensor<fp16, []> var_3037_to_fp16 = const()[name = tensor<string, []>("op_3037_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_3038_cast_fp16 = mul(x = var_3036_cast_fp16, y = var_3037_to_fp16)[name = tensor<string, []>("op_3038_cast_fp16")];
tensor<int32, [4]> var_3039 = const()[name = tensor<string, []>("op_3039"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_3040_cast_fp16 = reshape(shape = var_3039, x = key_53_cast_fp16)[name = tensor<string, []>("op_3040_cast_fp16")];
tensor<bool, []> mh_w_79_transpose_x_0 = const()[name = tensor<string, []>("mh_w_79_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_79_transpose_y_0 = const()[name = tensor<string, []>("mh_w_79_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 224]> mh_w_79_cast_fp16 = matmul(transpose_x = mh_w_79_transpose_x_0, transpose_y = mh_w_79_transpose_y_0, x = var_3038_cast_fp16, y = var_3040_cast_fp16)[name = tensor<string, []>("mh_w_79_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> mh_w_81_cast_fp16 = add(x = mh_w_79_cast_fp16, y = var_259_cast_fp16)[name = tensor<string, []>("mh_w_81_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> var_3048_cast_fp16 = softmax(axis = var_2962, x = mh_w_81_cast_fp16)[name = tensor<string, []>("op_3048_cast_fp16")];
tensor<int32, [4]> var_3049 = const()[name = tensor<string, []>("op_3049"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_3050_cast_fp16 = reshape(shape = var_3049, x = value_53_cast_fp16)[name = tensor<string, []>("op_3050_cast_fp16")];
tensor<bool, []> attn_53_transpose_x_0 = const()[name = tensor<string, []>("attn_53_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_53_transpose_y_0 = const()[name = tensor<string, []>("attn_53_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_53_cast_fp16 = matmul(transpose_x = attn_53_transpose_x_0, transpose_y = attn_53_transpose_y_0, x = var_3050_cast_fp16, y = var_3048_cast_fp16)[name = tensor<string, []>("attn_53_cast_fp16")];
tensor<int32, [4]> var_3053 = const()[name = tensor<string, []>("op_3053"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_131_cast_fp16 = reshape(shape = var_3053, x = attn_53_cast_fp16)[name = tensor<string, []>("input_131_cast_fp16")];
tensor<int32, [2]> var_3057 = const()[name = tensor<string, []>("op_3057"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3059 = const()[name = tensor<string, []>("op_3059"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_189_pad_type_0 = const()[name = tensor<string, []>("obj_189_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_189_pad_0 = const()[name = tensor<string, []>("obj_189_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_13_self_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_13_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(825927936)))];
tensor<fp16, [1280]> layers_13_self_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_13_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(829204800)))];
tensor<fp16, [1, 1280, 1, 1]> obj_189_cast_fp16 = conv(bias = layers_13_self_attn_o_proj_bias_to_fp16, dilations = var_3059, groups = var_2969, pad = obj_189_pad_0, pad_type = obj_189_pad_type_0, strides = var_3057, weight = layers_13_self_attn_o_proj_weight_to_fp16, x = input_131_cast_fp16)[name = tensor<string, []>("obj_189_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_81_cast_fp16 = add(x = inputs_79_cast_fp16, y = obj_189_cast_fp16)[name = tensor<string, []>("inputs_81_cast_fp16")];
tensor<int32, [1]> var_3069 = const()[name = tensor<string, []>("op_3069"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_81_cast_fp16 = reduce_mean(axes = var_3069, keep_dims = var_2970, x = inputs_81_cast_fp16)[name = tensor<string, []>("channels_mean_81_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_81_cast_fp16 = sub(x = inputs_81_cast_fp16, y = channels_mean_81_cast_fp16)[name = tensor<string, []>("zero_mean_81_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_81_cast_fp16 = mul(x = zero_mean_81_cast_fp16, y = zero_mean_81_cast_fp16)[name = tensor<string, []>("zero_mean_sq_81_cast_fp16")];
tensor<int32, [1]> var_3073 = const()[name = tensor<string, []>("op_3073"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_3074_cast_fp16 = reduce_mean(axes = var_3073, keep_dims = var_2970, x = zero_mean_sq_81_cast_fp16)[name = tensor<string, []>("op_3074_cast_fp16")];
tensor<fp16, []> var_3075_to_fp16 = const()[name = tensor<string, []>("op_3075_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_3076_cast_fp16 = add(x = var_3074_cast_fp16, y = var_3075_to_fp16)[name = tensor<string, []>("op_3076_cast_fp16")];
tensor<fp16, []> denom_81_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_81_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_81_cast_fp16 = rsqrt(epsilon = denom_81_epsilon_0_to_fp16, x = var_3076_cast_fp16)[name = tensor<string, []>("denom_81_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_81_cast_fp16 = mul(x = zero_mean_81_cast_fp16, y = denom_81_cast_fp16)[name = tensor<string, []>("out_81_cast_fp16")];
tensor<fp16, [1280]> obj_191_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_191_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(829207424)))];
tensor<fp16, [1280]> obj_191_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_191_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(829210048)))];
tensor<fp16, []> obj_191_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_191_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_191_cast_fp16 = batch_norm(beta = obj_191_beta_0_to_fp16, epsilon = obj_191_epsilon_0_to_fp16, gamma = obj_191_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_81_cast_fp16)[name = tensor<string, []>("obj_191_cast_fp16")];
tensor<int32, [2]> var_3091 = const()[name = tensor<string, []>("op_3091"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3093 = const()[name = tensor<string, []>("op_3093"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_55_pad_type_0 = const()[name = tensor<string, []>("query_55_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_55_pad_0 = const()[name = tensor<string, []>("query_55_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_13_encoder_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_13_encoder_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(829212672)))];
tensor<fp16, [1280]> layers_13_encoder_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_13_encoder_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(832489536)))];
tensor<fp16, [1, 1280, 1, 1]> query_55_cast_fp16 = conv(bias = layers_13_encoder_attn_q_proj_bias_to_fp16, dilations = var_3093, groups = var_2969, pad = query_55_pad_0, pad_type = query_55_pad_type_0, strides = var_3091, weight = layers_13_encoder_attn_q_proj_weight_to_fp16, x = obj_191_cast_fp16)[name = tensor<string, []>("query_55_cast_fp16")];
tensor<int32, [2]> var_3097 = const()[name = tensor<string, []>("op_3097"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3099 = const()[name = tensor<string, []>("op_3099"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> key_55_pad_type_0 = const()[name = tensor<string, []>("key_55_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> key_55_pad_0 = const()[name = tensor<string, []>("key_55_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_13_encoder_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_13_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(832492160)))];
tensor<fp16, [1, 1280, 1, 1500]> key_55_cast_fp16 = conv(dilations = var_3099, groups = var_2969, pad = key_55_pad_0, pad_type = key_55_pad_type_0, strides = var_3097, weight = layers_13_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("key_55_cast_fp16")];
tensor<int32, [2]> var_3104 = const()[name = tensor<string, []>("op_3104"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3106 = const()[name = tensor<string, []>("op_3106"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> value_55_pad_type_0 = const()[name = tensor<string, []>("value_55_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> value_55_pad_0 = const()[name = tensor<string, []>("value_55_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_13_encoder_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_13_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(835769024)))];
tensor<fp16, [1280]> layers_13_encoder_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_13_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(839045888)))];
tensor<fp16, [1, 1280, 1, 1500]> value_55_cast_fp16 = conv(bias = layers_13_encoder_attn_v_proj_bias_to_fp16, dilations = var_3106, groups = var_2969, pad = value_55_pad_0, pad_type = value_55_pad_type_0, strides = var_3104, weight = layers_13_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("value_55_cast_fp16")];
tensor<int32, [4]> var_3110 = const()[name = tensor<string, []>("op_3110"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_3111_cast_fp16 = reshape(shape = var_3110, x = query_55_cast_fp16)[name = tensor<string, []>("op_3111_cast_fp16")];
tensor<fp16, []> var_3112_to_fp16 = const()[name = tensor<string, []>("op_3112_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_3113_cast_fp16 = mul(x = var_3111_cast_fp16, y = var_3112_to_fp16)[name = tensor<string, []>("op_3113_cast_fp16")];
tensor<int32, [4]> var_3114 = const()[name = tensor<string, []>("op_3114"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_3115_cast_fp16 = reshape(shape = var_3114, x = key_55_cast_fp16)[name = tensor<string, []>("op_3115_cast_fp16")];
tensor<bool, []> mh_w_83_transpose_x_0 = const()[name = tensor<string, []>("mh_w_83_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_83_transpose_y_0 = const()[name = tensor<string, []>("mh_w_83_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 1500]> mh_w_83_cast_fp16 = matmul(transpose_x = mh_w_83_transpose_x_0, transpose_y = mh_w_83_transpose_y_0, x = var_3113_cast_fp16, y = var_3115_cast_fp16)[name = tensor<string, []>("mh_w_83_cast_fp16")];
tensor<fp16, [1, 20, 1, 1500]> obj_195_cast_fp16 = softmax(axis = var_2962, x = mh_w_83_cast_fp16)[name = tensor<string, []>("obj_195_cast_fp16")];
tensor<int32, [4]> var_3119 = const()[name = tensor<string, []>("op_3119"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_3120_cast_fp16 = reshape(shape = var_3119, x = value_55_cast_fp16)[name = tensor<string, []>("op_3120_cast_fp16")];
tensor<bool, []> attn_55_transpose_x_0 = const()[name = tensor<string, []>("attn_55_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_55_transpose_y_0 = const()[name = tensor<string, []>("attn_55_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_55_cast_fp16 = matmul(transpose_x = attn_55_transpose_x_0, transpose_y = attn_55_transpose_y_0, x = var_3120_cast_fp16, y = obj_195_cast_fp16)[name = tensor<string, []>("attn_55_cast_fp16")];
tensor<int32, [4]> var_3123 = const()[name = tensor<string, []>("op_3123"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_133_cast_fp16 = reshape(shape = var_3123, x = attn_55_cast_fp16)[name = tensor<string, []>("input_133_cast_fp16")];
tensor<int32, [2]> var_3127 = const()[name = tensor<string, []>("op_3127"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3129 = const()[name = tensor<string, []>("op_3129"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_193_pad_type_0 = const()[name = tensor<string, []>("obj_193_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_193_pad_0 = const()[name = tensor<string, []>("obj_193_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_13_encoder_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_13_encoder_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(839048512)))];
tensor<fp16, [1280]> layers_13_encoder_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_13_encoder_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(842325376)))];
tensor<fp16, [1, 1280, 1, 1]> obj_193_cast_fp16 = conv(bias = layers_13_encoder_attn_o_proj_bias_to_fp16, dilations = var_3129, groups = var_2969, pad = obj_193_pad_0, pad_type = obj_193_pad_type_0, strides = var_3127, weight = layers_13_encoder_attn_o_proj_weight_to_fp16, x = input_133_cast_fp16)[name = tensor<string, []>("obj_193_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_83_cast_fp16 = add(x = inputs_81_cast_fp16, y = obj_193_cast_fp16)[name = tensor<string, []>("inputs_83_cast_fp16")];
tensor<int32, [1]> var_3138 = const()[name = tensor<string, []>("op_3138"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_83_cast_fp16 = reduce_mean(axes = var_3138, keep_dims = var_2970, x = inputs_83_cast_fp16)[name = tensor<string, []>("channels_mean_83_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_83_cast_fp16 = sub(x = inputs_83_cast_fp16, y = channels_mean_83_cast_fp16)[name = tensor<string, []>("zero_mean_83_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_83_cast_fp16 = mul(x = zero_mean_83_cast_fp16, y = zero_mean_83_cast_fp16)[name = tensor<string, []>("zero_mean_sq_83_cast_fp16")];
tensor<int32, [1]> var_3142 = const()[name = tensor<string, []>("op_3142"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_3143_cast_fp16 = reduce_mean(axes = var_3142, keep_dims = var_2970, x = zero_mean_sq_83_cast_fp16)[name = tensor<string, []>("op_3143_cast_fp16")];
tensor<fp16, []> var_3144_to_fp16 = const()[name = tensor<string, []>("op_3144_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_3145_cast_fp16 = add(x = var_3143_cast_fp16, y = var_3144_to_fp16)[name = tensor<string, []>("op_3145_cast_fp16")];
tensor<fp16, []> denom_83_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_83_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_83_cast_fp16 = rsqrt(epsilon = denom_83_epsilon_0_to_fp16, x = var_3145_cast_fp16)[name = tensor<string, []>("denom_83_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_83_cast_fp16 = mul(x = zero_mean_83_cast_fp16, y = denom_83_cast_fp16)[name = tensor<string, []>("out_83_cast_fp16")];
tensor<fp16, [1280]> input_135_gamma_0_to_fp16 = const()[name = tensor<string, []>("input_135_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(842328000)))];
tensor<fp16, [1280]> input_135_beta_0_to_fp16 = const()[name = tensor<string, []>("input_135_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(842330624)))];
tensor<fp16, []> input_135_epsilon_0_to_fp16 = const()[name = tensor<string, []>("input_135_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> input_135_cast_fp16 = batch_norm(beta = input_135_beta_0_to_fp16, epsilon = input_135_epsilon_0_to_fp16, gamma = input_135_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_83_cast_fp16)[name = tensor<string, []>("input_135_cast_fp16")];
tensor<int32, [2]> var_3156 = const()[name = tensor<string, []>("op_3156"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3158 = const()[name = tensor<string, []>("op_3158"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_137_pad_type_0 = const()[name = tensor<string, []>("input_137_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_137_pad_0 = const()[name = tensor<string, []>("input_137_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [5120, 1280, 1, 1]> layers_13_fc1_weight_to_fp16 = const()[name = tensor<string, []>("layers_13_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(842333248)))];
tensor<fp16, [5120]> layers_13_fc1_bias_to_fp16 = const()[name = tensor<string, []>("layers_13_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(855440512)))];
tensor<fp16, [1, 5120, 1, 1]> input_137_cast_fp16 = conv(bias = layers_13_fc1_bias_to_fp16, dilations = var_3158, groups = var_2969, pad = input_137_pad_0, pad_type = input_137_pad_type_0, strides = var_3156, weight = layers_13_fc1_weight_to_fp16, x = input_135_cast_fp16)[name = tensor<string, []>("input_137_cast_fp16")];
tensor<string, []> input_139_mode_0 = const()[name = tensor<string, []>("input_139_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 5120, 1, 1]> input_139_cast_fp16 = gelu(mode = input_139_mode_0, x = input_137_cast_fp16)[name = tensor<string, []>("input_139_cast_fp16")];
tensor<int32, [2]> var_3164 = const()[name = tensor<string, []>("op_3164"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3166 = const()[name = tensor<string, []>("op_3166"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_29_pad_type_0 = const()[name = tensor<string, []>("hidden_states_29_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_29_pad_0 = const()[name = tensor<string, []>("hidden_states_29_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 5120, 1, 1]> layers_13_fc2_weight_to_fp16 = const()[name = tensor<string, []>("layers_13_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(855450816)))];
tensor<fp16, [1280]> layers_13_fc2_bias_to_fp16 = const()[name = tensor<string, []>("layers_13_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(868558080)))];
tensor<fp16, [1, 1280, 1, 1]> hidden_states_29_cast_fp16 = conv(bias = layers_13_fc2_bias_to_fp16, dilations = var_3166, groups = var_2969, pad = hidden_states_29_pad_0, pad_type = hidden_states_29_pad_type_0, strides = var_3164, weight = layers_13_fc2_weight_to_fp16, x = input_139_cast_fp16)[name = tensor<string, []>("hidden_states_29_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_85_cast_fp16 = add(x = inputs_83_cast_fp16, y = hidden_states_29_cast_fp16)[name = tensor<string, []>("inputs_85_cast_fp16")];
tensor<int32, []> var_3180 = const()[name = tensor<string, []>("op_3180"), val = tensor<int32, []>(3)];
tensor<int32, []> var_3187 = const()[name = tensor<string, []>("op_3187"), val = tensor<int32, []>(1)];
tensor<bool, []> var_3188 = const()[name = tensor<string, []>("op_3188"), val = tensor<bool, []>(true)];
tensor<int32, [1]> var_3200 = const()[name = tensor<string, []>("op_3200"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_85_cast_fp16 = reduce_mean(axes = var_3200, keep_dims = var_3188, x = inputs_85_cast_fp16)[name = tensor<string, []>("channels_mean_85_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_85_cast_fp16 = sub(x = inputs_85_cast_fp16, y = channels_mean_85_cast_fp16)[name = tensor<string, []>("zero_mean_85_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_85_cast_fp16 = mul(x = zero_mean_85_cast_fp16, y = zero_mean_85_cast_fp16)[name = tensor<string, []>("zero_mean_sq_85_cast_fp16")];
tensor<int32, [1]> var_3204 = const()[name = tensor<string, []>("op_3204"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_3205_cast_fp16 = reduce_mean(axes = var_3204, keep_dims = var_3188, x = zero_mean_sq_85_cast_fp16)[name = tensor<string, []>("op_3205_cast_fp16")];
tensor<fp16, []> var_3206_to_fp16 = const()[name = tensor<string, []>("op_3206_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_3207_cast_fp16 = add(x = var_3205_cast_fp16, y = var_3206_to_fp16)[name = tensor<string, []>("op_3207_cast_fp16")];
tensor<fp16, []> denom_85_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_85_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_85_cast_fp16 = rsqrt(epsilon = denom_85_epsilon_0_to_fp16, x = var_3207_cast_fp16)[name = tensor<string, []>("denom_85_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_85_cast_fp16 = mul(x = zero_mean_85_cast_fp16, y = denom_85_cast_fp16)[name = tensor<string, []>("out_85_cast_fp16")];
tensor<fp16, [1280]> obj_197_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_197_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(868560704)))];
tensor<fp16, [1280]> obj_197_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_197_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(868563328)))];
tensor<fp16, []> obj_197_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_197_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_197_cast_fp16 = batch_norm(beta = obj_197_beta_0_to_fp16, epsilon = obj_197_epsilon_0_to_fp16, gamma = obj_197_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_85_cast_fp16)[name = tensor<string, []>("obj_197_cast_fp16")];
tensor<int32, [2]> var_3222 = const()[name = tensor<string, []>("op_3222"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3224 = const()[name = tensor<string, []>("op_3224"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_57_pad_type_0 = const()[name = tensor<string, []>("query_57_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_57_pad_0 = const()[name = tensor<string, []>("query_57_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_14_self_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_14_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(868565952)))];
tensor<fp16, [1280]> layers_14_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_14_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(871842816)))];
tensor<fp16, [1, 1280, 1, 1]> query_57_cast_fp16 = conv(bias = layers_14_self_attn_q_proj_bias_to_fp16, dilations = var_3224, groups = var_3187, pad = query_57_pad_0, pad_type = query_57_pad_type_0, strides = var_3222, weight = layers_14_self_attn_q_proj_weight_to_fp16, x = obj_197_cast_fp16)[name = tensor<string, []>("query_57_cast_fp16")];
tensor<int32, [2]> var_3228 = const()[name = tensor<string, []>("op_3228"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3230 = const()[name = tensor<string, []>("op_3230"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_key_29_pad_type_0 = const()[name = tensor<string, []>("current_key_29_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_key_29_pad_0 = const()[name = tensor<string, []>("current_key_29_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_14_self_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_14_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(871845440)))];
tensor<fp16, [1, 1280, 1, 1]> current_key_29_cast_fp16 = conv(dilations = var_3230, groups = var_3187, pad = current_key_29_pad_0, pad_type = current_key_29_pad_type_0, strides = var_3228, weight = layers_14_self_attn_k_proj_weight_to_fp16, x = obj_197_cast_fp16)[name = tensor<string, []>("current_key_29_cast_fp16")];
tensor<int32, [2]> var_3235 = const()[name = tensor<string, []>("op_3235"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3237 = const()[name = tensor<string, []>("op_3237"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_value_29_pad_type_0 = const()[name = tensor<string, []>("current_value_29_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_value_29_pad_0 = const()[name = tensor<string, []>("current_value_29_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_14_self_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_14_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(875122304)))];
tensor<fp16, [1280]> layers_14_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_14_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(878399168)))];
tensor<fp16, [1, 1280, 1, 1]> current_value_29_cast_fp16 = conv(bias = layers_14_self_attn_v_proj_bias_to_fp16, dilations = var_3237, groups = var_3187, pad = current_value_29_pad_0, pad_type = current_value_29_pad_type_0, strides = var_3235, weight = layers_14_self_attn_v_proj_weight_to_fp16, x = obj_197_cast_fp16)[name = tensor<string, []>("current_value_29_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_3244_cast_fp16 = mul(x = current_key_29_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_3244_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_3246_cast_fp16 = mul(x = var_103_cast_fp16_14, y = var_241_cast_fp16)[name = tensor<string, []>("op_3246_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> key_57_cast_fp16 = add(x = var_3244_cast_fp16, y = var_3246_cast_fp16)[name = tensor<string, []>("key_57_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_3248_cast_fp16 = mul(x = current_value_29_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_3248_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_3250_cast_fp16 = mul(x = var_138_cast_fp16_14, y = var_241_cast_fp16)[name = tensor<string, []>("op_3250_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> value_57_cast_fp16 = add(x = var_3248_cast_fp16, y = var_3250_cast_fp16)[name = tensor<string, []>("value_57_cast_fp16")];
tensor<int32, [4]> var_3253 = const()[name = tensor<string, []>("op_3253"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_3254_cast_fp16 = reshape(shape = var_3253, x = query_57_cast_fp16)[name = tensor<string, []>("op_3254_cast_fp16")];
tensor<fp16, []> var_3255_to_fp16 = const()[name = tensor<string, []>("op_3255_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_3256_cast_fp16 = mul(x = var_3254_cast_fp16, y = var_3255_to_fp16)[name = tensor<string, []>("op_3256_cast_fp16")];
tensor<int32, [4]> var_3257 = const()[name = tensor<string, []>("op_3257"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_3258_cast_fp16 = reshape(shape = var_3257, x = key_57_cast_fp16)[name = tensor<string, []>("op_3258_cast_fp16")];
tensor<bool, []> mh_w_85_transpose_x_0 = const()[name = tensor<string, []>("mh_w_85_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_85_transpose_y_0 = const()[name = tensor<string, []>("mh_w_85_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 224]> mh_w_85_cast_fp16 = matmul(transpose_x = mh_w_85_transpose_x_0, transpose_y = mh_w_85_transpose_y_0, x = var_3256_cast_fp16, y = var_3258_cast_fp16)[name = tensor<string, []>("mh_w_85_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> mh_w_87_cast_fp16 = add(x = mh_w_85_cast_fp16, y = var_259_cast_fp16)[name = tensor<string, []>("mh_w_87_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> var_3266_cast_fp16 = softmax(axis = var_3180, x = mh_w_87_cast_fp16)[name = tensor<string, []>("op_3266_cast_fp16")];
tensor<int32, [4]> var_3267 = const()[name = tensor<string, []>("op_3267"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_3268_cast_fp16 = reshape(shape = var_3267, x = value_57_cast_fp16)[name = tensor<string, []>("op_3268_cast_fp16")];
tensor<bool, []> attn_57_transpose_x_0 = const()[name = tensor<string, []>("attn_57_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_57_transpose_y_0 = const()[name = tensor<string, []>("attn_57_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_57_cast_fp16 = matmul(transpose_x = attn_57_transpose_x_0, transpose_y = attn_57_transpose_y_0, x = var_3268_cast_fp16, y = var_3266_cast_fp16)[name = tensor<string, []>("attn_57_cast_fp16")];
tensor<int32, [4]> var_3271 = const()[name = tensor<string, []>("op_3271"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_141_cast_fp16 = reshape(shape = var_3271, x = attn_57_cast_fp16)[name = tensor<string, []>("input_141_cast_fp16")];
tensor<int32, [2]> var_3275 = const()[name = tensor<string, []>("op_3275"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3277 = const()[name = tensor<string, []>("op_3277"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_203_pad_type_0 = const()[name = tensor<string, []>("obj_203_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_203_pad_0 = const()[name = tensor<string, []>("obj_203_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_14_self_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_14_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(878401792)))];
tensor<fp16, [1280]> layers_14_self_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_14_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(881678656)))];
tensor<fp16, [1, 1280, 1, 1]> obj_203_cast_fp16 = conv(bias = layers_14_self_attn_o_proj_bias_to_fp16, dilations = var_3277, groups = var_3187, pad = obj_203_pad_0, pad_type = obj_203_pad_type_0, strides = var_3275, weight = layers_14_self_attn_o_proj_weight_to_fp16, x = input_141_cast_fp16)[name = tensor<string, []>("obj_203_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_87_cast_fp16 = add(x = inputs_85_cast_fp16, y = obj_203_cast_fp16)[name = tensor<string, []>("inputs_87_cast_fp16")];
tensor<int32, [1]> var_3287 = const()[name = tensor<string, []>("op_3287"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_87_cast_fp16 = reduce_mean(axes = var_3287, keep_dims = var_3188, x = inputs_87_cast_fp16)[name = tensor<string, []>("channels_mean_87_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_87_cast_fp16 = sub(x = inputs_87_cast_fp16, y = channels_mean_87_cast_fp16)[name = tensor<string, []>("zero_mean_87_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_87_cast_fp16 = mul(x = zero_mean_87_cast_fp16, y = zero_mean_87_cast_fp16)[name = tensor<string, []>("zero_mean_sq_87_cast_fp16")];
tensor<int32, [1]> var_3291 = const()[name = tensor<string, []>("op_3291"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_3292_cast_fp16 = reduce_mean(axes = var_3291, keep_dims = var_3188, x = zero_mean_sq_87_cast_fp16)[name = tensor<string, []>("op_3292_cast_fp16")];
tensor<fp16, []> var_3293_to_fp16 = const()[name = tensor<string, []>("op_3293_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_3294_cast_fp16 = add(x = var_3292_cast_fp16, y = var_3293_to_fp16)[name = tensor<string, []>("op_3294_cast_fp16")];
tensor<fp16, []> denom_87_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_87_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_87_cast_fp16 = rsqrt(epsilon = denom_87_epsilon_0_to_fp16, x = var_3294_cast_fp16)[name = tensor<string, []>("denom_87_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_87_cast_fp16 = mul(x = zero_mean_87_cast_fp16, y = denom_87_cast_fp16)[name = tensor<string, []>("out_87_cast_fp16")];
tensor<fp16, [1280]> obj_205_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_205_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(881681280)))];
tensor<fp16, [1280]> obj_205_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_205_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(881683904)))];
tensor<fp16, []> obj_205_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_205_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_205_cast_fp16 = batch_norm(beta = obj_205_beta_0_to_fp16, epsilon = obj_205_epsilon_0_to_fp16, gamma = obj_205_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_87_cast_fp16)[name = tensor<string, []>("obj_205_cast_fp16")];
tensor<int32, [2]> var_3309 = const()[name = tensor<string, []>("op_3309"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3311 = const()[name = tensor<string, []>("op_3311"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_59_pad_type_0 = const()[name = tensor<string, []>("query_59_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_59_pad_0 = const()[name = tensor<string, []>("query_59_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_14_encoder_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_14_encoder_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(881686528)))];
tensor<fp16, [1280]> layers_14_encoder_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_14_encoder_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(884963392)))];
tensor<fp16, [1, 1280, 1, 1]> query_59_cast_fp16 = conv(bias = layers_14_encoder_attn_q_proj_bias_to_fp16, dilations = var_3311, groups = var_3187, pad = query_59_pad_0, pad_type = query_59_pad_type_0, strides = var_3309, weight = layers_14_encoder_attn_q_proj_weight_to_fp16, x = obj_205_cast_fp16)[name = tensor<string, []>("query_59_cast_fp16")];
tensor<int32, [2]> var_3315 = const()[name = tensor<string, []>("op_3315"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3317 = const()[name = tensor<string, []>("op_3317"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> key_59_pad_type_0 = const()[name = tensor<string, []>("key_59_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> key_59_pad_0 = const()[name = tensor<string, []>("key_59_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_14_encoder_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_14_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(884966016)))];
tensor<fp16, [1, 1280, 1, 1500]> key_59_cast_fp16 = conv(dilations = var_3317, groups = var_3187, pad = key_59_pad_0, pad_type = key_59_pad_type_0, strides = var_3315, weight = layers_14_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("key_59_cast_fp16")];
tensor<int32, [2]> var_3322 = const()[name = tensor<string, []>("op_3322"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3324 = const()[name = tensor<string, []>("op_3324"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> value_59_pad_type_0 = const()[name = tensor<string, []>("value_59_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> value_59_pad_0 = const()[name = tensor<string, []>("value_59_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_14_encoder_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_14_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(888242880)))];
tensor<fp16, [1280]> layers_14_encoder_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_14_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(891519744)))];
tensor<fp16, [1, 1280, 1, 1500]> value_59_cast_fp16 = conv(bias = layers_14_encoder_attn_v_proj_bias_to_fp16, dilations = var_3324, groups = var_3187, pad = value_59_pad_0, pad_type = value_59_pad_type_0, strides = var_3322, weight = layers_14_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("value_59_cast_fp16")];
tensor<int32, [4]> var_3328 = const()[name = tensor<string, []>("op_3328"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_3329_cast_fp16 = reshape(shape = var_3328, x = query_59_cast_fp16)[name = tensor<string, []>("op_3329_cast_fp16")];
tensor<fp16, []> var_3330_to_fp16 = const()[name = tensor<string, []>("op_3330_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_3331_cast_fp16 = mul(x = var_3329_cast_fp16, y = var_3330_to_fp16)[name = tensor<string, []>("op_3331_cast_fp16")];
tensor<int32, [4]> var_3332 = const()[name = tensor<string, []>("op_3332"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_3333_cast_fp16 = reshape(shape = var_3332, x = key_59_cast_fp16)[name = tensor<string, []>("op_3333_cast_fp16")];
tensor<bool, []> mh_w_89_transpose_x_0 = const()[name = tensor<string, []>("mh_w_89_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_89_transpose_y_0 = const()[name = tensor<string, []>("mh_w_89_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 1500]> mh_w_89_cast_fp16 = matmul(transpose_x = mh_w_89_transpose_x_0, transpose_y = mh_w_89_transpose_y_0, x = var_3331_cast_fp16, y = var_3333_cast_fp16)[name = tensor<string, []>("mh_w_89_cast_fp16")];
tensor<fp16, [1, 20, 1, 1500]> obj_209_cast_fp16 = softmax(axis = var_3180, x = mh_w_89_cast_fp16)[name = tensor<string, []>("obj_209_cast_fp16")];
tensor<int32, [4]> var_3337 = const()[name = tensor<string, []>("op_3337"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_3338_cast_fp16 = reshape(shape = var_3337, x = value_59_cast_fp16)[name = tensor<string, []>("op_3338_cast_fp16")];
tensor<bool, []> attn_59_transpose_x_0 = const()[name = tensor<string, []>("attn_59_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_59_transpose_y_0 = const()[name = tensor<string, []>("attn_59_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_59_cast_fp16 = matmul(transpose_x = attn_59_transpose_x_0, transpose_y = attn_59_transpose_y_0, x = var_3338_cast_fp16, y = obj_209_cast_fp16)[name = tensor<string, []>("attn_59_cast_fp16")];
tensor<int32, [4]> var_3341 = const()[name = tensor<string, []>("op_3341"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_143_cast_fp16 = reshape(shape = var_3341, x = attn_59_cast_fp16)[name = tensor<string, []>("input_143_cast_fp16")];
tensor<int32, [2]> var_3345 = const()[name = tensor<string, []>("op_3345"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3347 = const()[name = tensor<string, []>("op_3347"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_207_pad_type_0 = const()[name = tensor<string, []>("obj_207_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_207_pad_0 = const()[name = tensor<string, []>("obj_207_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_14_encoder_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_14_encoder_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(891522368)))];
tensor<fp16, [1280]> layers_14_encoder_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_14_encoder_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(894799232)))];
tensor<fp16, [1, 1280, 1, 1]> obj_207_cast_fp16 = conv(bias = layers_14_encoder_attn_o_proj_bias_to_fp16, dilations = var_3347, groups = var_3187, pad = obj_207_pad_0, pad_type = obj_207_pad_type_0, strides = var_3345, weight = layers_14_encoder_attn_o_proj_weight_to_fp16, x = input_143_cast_fp16)[name = tensor<string, []>("obj_207_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_89_cast_fp16 = add(x = inputs_87_cast_fp16, y = obj_207_cast_fp16)[name = tensor<string, []>("inputs_89_cast_fp16")];
tensor<int32, [1]> var_3353 = const()[name = tensor<string, []>("op_3353"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_89_cast_fp16 = reduce_mean(axes = var_3353, keep_dims = var_3188, x = inputs_89_cast_fp16)[name = tensor<string, []>("channels_mean_89_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_89_cast_fp16 = sub(x = inputs_89_cast_fp16, y = channels_mean_89_cast_fp16)[name = tensor<string, []>("zero_mean_89_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_89_cast_fp16 = mul(x = zero_mean_89_cast_fp16, y = zero_mean_89_cast_fp16)[name = tensor<string, []>("zero_mean_sq_89_cast_fp16")];
tensor<int32, [1]> var_3357 = const()[name = tensor<string, []>("op_3357"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_3358_cast_fp16 = reduce_mean(axes = var_3357, keep_dims = var_3188, x = zero_mean_sq_89_cast_fp16)[name = tensor<string, []>("op_3358_cast_fp16")];
tensor<fp16, []> var_3359_to_fp16 = const()[name = tensor<string, []>("op_3359_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_3360_cast_fp16 = add(x = var_3358_cast_fp16, y = var_3359_to_fp16)[name = tensor<string, []>("op_3360_cast_fp16")];
tensor<fp16, []> denom_89_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_89_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_89_cast_fp16 = rsqrt(epsilon = denom_89_epsilon_0_to_fp16, x = var_3360_cast_fp16)[name = tensor<string, []>("denom_89_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_89_cast_fp16 = mul(x = zero_mean_89_cast_fp16, y = denom_89_cast_fp16)[name = tensor<string, []>("out_89_cast_fp16")];
tensor<fp16, [1280]> input_145_gamma_0_to_fp16 = const()[name = tensor<string, []>("input_145_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(894801856)))];
tensor<fp16, [1280]> input_145_beta_0_to_fp16 = const()[name = tensor<string, []>("input_145_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(894804480)))];
tensor<fp16, []> input_145_epsilon_0_to_fp16 = const()[name = tensor<string, []>("input_145_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> input_145_cast_fp16 = batch_norm(beta = input_145_beta_0_to_fp16, epsilon = input_145_epsilon_0_to_fp16, gamma = input_145_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_89_cast_fp16)[name = tensor<string, []>("input_145_cast_fp16")];
tensor<int32, [2]> var_3371 = const()[name = tensor<string, []>("op_3371"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3373 = const()[name = tensor<string, []>("op_3373"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_147_pad_type_0 = const()[name = tensor<string, []>("input_147_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_147_pad_0 = const()[name = tensor<string, []>("input_147_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [5120, 1280, 1, 1]> layers_14_fc1_weight_to_fp16 = const()[name = tensor<string, []>("layers_14_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(894807104)))];
tensor<fp16, [5120]> layers_14_fc1_bias_to_fp16 = const()[name = tensor<string, []>("layers_14_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(907914368)))];
tensor<fp16, [1, 5120, 1, 1]> input_147_cast_fp16 = conv(bias = layers_14_fc1_bias_to_fp16, dilations = var_3373, groups = var_3187, pad = input_147_pad_0, pad_type = input_147_pad_type_0, strides = var_3371, weight = layers_14_fc1_weight_to_fp16, x = input_145_cast_fp16)[name = tensor<string, []>("input_147_cast_fp16")];
tensor<string, []> input_149_mode_0 = const()[name = tensor<string, []>("input_149_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 5120, 1, 1]> input_149_cast_fp16 = gelu(mode = input_149_mode_0, x = input_147_cast_fp16)[name = tensor<string, []>("input_149_cast_fp16")];
tensor<int32, [2]> var_3379 = const()[name = tensor<string, []>("op_3379"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3381 = const()[name = tensor<string, []>("op_3381"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_31_pad_type_0 = const()[name = tensor<string, []>("hidden_states_31_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_31_pad_0 = const()[name = tensor<string, []>("hidden_states_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 5120, 1, 1]> layers_14_fc2_weight_to_fp16 = const()[name = tensor<string, []>("layers_14_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(907924672)))];
tensor<fp16, [1280]> layers_14_fc2_bias_to_fp16 = const()[name = tensor<string, []>("layers_14_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(921031936)))];
tensor<fp16, [1, 1280, 1, 1]> hidden_states_31_cast_fp16 = conv(bias = layers_14_fc2_bias_to_fp16, dilations = var_3381, groups = var_3187, pad = hidden_states_31_pad_0, pad_type = hidden_states_31_pad_type_0, strides = var_3379, weight = layers_14_fc2_weight_to_fp16, x = input_149_cast_fp16)[name = tensor<string, []>("hidden_states_31_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_91_cast_fp16 = add(x = inputs_89_cast_fp16, y = hidden_states_31_cast_fp16)[name = tensor<string, []>("inputs_91_cast_fp16")];
tensor<int32, []> var_3394 = const()[name = tensor<string, []>("op_3394"), val = tensor<int32, []>(3)];
tensor<int32, []> var_3401 = const()[name = tensor<string, []>("op_3401"), val = tensor<int32, []>(1)];
tensor<bool, []> var_3402 = const()[name = tensor<string, []>("op_3402"), val = tensor<bool, []>(true)];
tensor<int32, [1]> var_3414 = const()[name = tensor<string, []>("op_3414"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_91_cast_fp16 = reduce_mean(axes = var_3414, keep_dims = var_3402, x = inputs_91_cast_fp16)[name = tensor<string, []>("channels_mean_91_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_91_cast_fp16 = sub(x = inputs_91_cast_fp16, y = channels_mean_91_cast_fp16)[name = tensor<string, []>("zero_mean_91_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_91_cast_fp16 = mul(x = zero_mean_91_cast_fp16, y = zero_mean_91_cast_fp16)[name = tensor<string, []>("zero_mean_sq_91_cast_fp16")];
tensor<int32, [1]> var_3418 = const()[name = tensor<string, []>("op_3418"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_3419_cast_fp16 = reduce_mean(axes = var_3418, keep_dims = var_3402, x = zero_mean_sq_91_cast_fp16)[name = tensor<string, []>("op_3419_cast_fp16")];
tensor<fp16, []> var_3420_to_fp16 = const()[name = tensor<string, []>("op_3420_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_3421_cast_fp16 = add(x = var_3419_cast_fp16, y = var_3420_to_fp16)[name = tensor<string, []>("op_3421_cast_fp16")];
tensor<fp16, []> denom_91_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_91_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_91_cast_fp16 = rsqrt(epsilon = denom_91_epsilon_0_to_fp16, x = var_3421_cast_fp16)[name = tensor<string, []>("denom_91_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_91_cast_fp16 = mul(x = zero_mean_91_cast_fp16, y = denom_91_cast_fp16)[name = tensor<string, []>("out_91_cast_fp16")];
tensor<fp16, [1280]> obj_211_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_211_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(921034560)))];
tensor<fp16, [1280]> obj_211_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_211_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(921037184)))];
tensor<fp16, []> obj_211_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_211_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_211_cast_fp16 = batch_norm(beta = obj_211_beta_0_to_fp16, epsilon = obj_211_epsilon_0_to_fp16, gamma = obj_211_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_91_cast_fp16)[name = tensor<string, []>("obj_211_cast_fp16")];
tensor<int32, [2]> var_3436 = const()[name = tensor<string, []>("op_3436"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3438 = const()[name = tensor<string, []>("op_3438"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_61_pad_type_0 = const()[name = tensor<string, []>("query_61_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_61_pad_0 = const()[name = tensor<string, []>("query_61_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_15_self_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_15_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(921039808)))];
tensor<fp16, [1280]> layers_15_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_15_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(924316672)))];
tensor<fp16, [1, 1280, 1, 1]> query_61_cast_fp16 = conv(bias = layers_15_self_attn_q_proj_bias_to_fp16, dilations = var_3438, groups = var_3401, pad = query_61_pad_0, pad_type = query_61_pad_type_0, strides = var_3436, weight = layers_15_self_attn_q_proj_weight_to_fp16, x = obj_211_cast_fp16)[name = tensor<string, []>("query_61_cast_fp16")];
tensor<int32, [2]> var_3442 = const()[name = tensor<string, []>("op_3442"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3444 = const()[name = tensor<string, []>("op_3444"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_key_31_pad_type_0 = const()[name = tensor<string, []>("current_key_31_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_key_31_pad_0 = const()[name = tensor<string, []>("current_key_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_15_self_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_15_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(924319296)))];
tensor<fp16, [1, 1280, 1, 1]> current_key_31_cast_fp16 = conv(dilations = var_3444, groups = var_3401, pad = current_key_31_pad_0, pad_type = current_key_31_pad_type_0, strides = var_3442, weight = layers_15_self_attn_k_proj_weight_to_fp16, x = obj_211_cast_fp16)[name = tensor<string, []>("current_key_31_cast_fp16")];
tensor<int32, [2]> var_3449 = const()[name = tensor<string, []>("op_3449"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3451 = const()[name = tensor<string, []>("op_3451"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_value_31_pad_type_0 = const()[name = tensor<string, []>("current_value_31_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_value_31_pad_0 = const()[name = tensor<string, []>("current_value_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_15_self_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_15_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(927596160)))];
tensor<fp16, [1280]> layers_15_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_15_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(930873024)))];
tensor<fp16, [1, 1280, 1, 1]> current_value_31_cast_fp16 = conv(bias = layers_15_self_attn_v_proj_bias_to_fp16, dilations = var_3451, groups = var_3401, pad = current_value_31_pad_0, pad_type = current_value_31_pad_type_0, strides = var_3449, weight = layers_15_self_attn_v_proj_weight_to_fp16, x = obj_211_cast_fp16)[name = tensor<string, []>("current_value_31_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_3458_cast_fp16 = mul(x = current_key_31_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_3458_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_3460_cast_fp16 = mul(x = var_103_cast_fp16_15, y = var_241_cast_fp16)[name = tensor<string, []>("op_3460_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> key_61_cast_fp16 = add(x = var_3458_cast_fp16, y = var_3460_cast_fp16)[name = tensor<string, []>("key_61_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_3462_cast_fp16 = mul(x = current_value_31_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_3462_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_3464_cast_fp16 = mul(x = var_138_cast_fp16_15, y = var_241_cast_fp16)[name = tensor<string, []>("op_3464_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> value_61_cast_fp16 = add(x = var_3462_cast_fp16, y = var_3464_cast_fp16)[name = tensor<string, []>("value_61_cast_fp16")];
tensor<int32, [4]> var_3467 = const()[name = tensor<string, []>("op_3467"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_3468_cast_fp16 = reshape(shape = var_3467, x = query_61_cast_fp16)[name = tensor<string, []>("op_3468_cast_fp16")];
tensor<fp16, []> var_3469_to_fp16 = const()[name = tensor<string, []>("op_3469_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_3470_cast_fp16 = mul(x = var_3468_cast_fp16, y = var_3469_to_fp16)[name = tensor<string, []>("op_3470_cast_fp16")];
tensor<int32, [4]> var_3471 = const()[name = tensor<string, []>("op_3471"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_3472_cast_fp16 = reshape(shape = var_3471, x = key_61_cast_fp16)[name = tensor<string, []>("op_3472_cast_fp16")];
tensor<bool, []> mh_w_91_transpose_x_0 = const()[name = tensor<string, []>("mh_w_91_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_91_transpose_y_0 = const()[name = tensor<string, []>("mh_w_91_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 224]> mh_w_91_cast_fp16 = matmul(transpose_x = mh_w_91_transpose_x_0, transpose_y = mh_w_91_transpose_y_0, x = var_3470_cast_fp16, y = var_3472_cast_fp16)[name = tensor<string, []>("mh_w_91_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> mh_w_93_cast_fp16 = add(x = mh_w_91_cast_fp16, y = var_259_cast_fp16)[name = tensor<string, []>("mh_w_93_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> var_3480_cast_fp16 = softmax(axis = var_3394, x = mh_w_93_cast_fp16)[name = tensor<string, []>("op_3480_cast_fp16")];
tensor<int32, [4]> var_3481 = const()[name = tensor<string, []>("op_3481"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_3482_cast_fp16 = reshape(shape = var_3481, x = value_61_cast_fp16)[name = tensor<string, []>("op_3482_cast_fp16")];
tensor<bool, []> attn_61_transpose_x_0 = const()[name = tensor<string, []>("attn_61_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_61_transpose_y_0 = const()[name = tensor<string, []>("attn_61_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_61_cast_fp16 = matmul(transpose_x = attn_61_transpose_x_0, transpose_y = attn_61_transpose_y_0, x = var_3482_cast_fp16, y = var_3480_cast_fp16)[name = tensor<string, []>("attn_61_cast_fp16")];
tensor<int32, [4]> var_3485 = const()[name = tensor<string, []>("op_3485"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_151_cast_fp16 = reshape(shape = var_3485, x = attn_61_cast_fp16)[name = tensor<string, []>("input_151_cast_fp16")];
tensor<int32, [2]> var_3489 = const()[name = tensor<string, []>("op_3489"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3491 = const()[name = tensor<string, []>("op_3491"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_217_pad_type_0 = const()[name = tensor<string, []>("obj_217_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_217_pad_0 = const()[name = tensor<string, []>("obj_217_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_15_self_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_15_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(930875648)))];
tensor<fp16, [1280]> layers_15_self_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_15_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(934152512)))];
tensor<fp16, [1, 1280, 1, 1]> obj_217_cast_fp16 = conv(bias = layers_15_self_attn_o_proj_bias_to_fp16, dilations = var_3491, groups = var_3401, pad = obj_217_pad_0, pad_type = obj_217_pad_type_0, strides = var_3489, weight = layers_15_self_attn_o_proj_weight_to_fp16, x = input_151_cast_fp16)[name = tensor<string, []>("obj_217_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_93_cast_fp16 = add(x = inputs_91_cast_fp16, y = obj_217_cast_fp16)[name = tensor<string, []>("inputs_93_cast_fp16")];
tensor<int32, [1]> var_3501 = const()[name = tensor<string, []>("op_3501"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_93_cast_fp16 = reduce_mean(axes = var_3501, keep_dims = var_3402, x = inputs_93_cast_fp16)[name = tensor<string, []>("channels_mean_93_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_93_cast_fp16 = sub(x = inputs_93_cast_fp16, y = channels_mean_93_cast_fp16)[name = tensor<string, []>("zero_mean_93_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_93_cast_fp16 = mul(x = zero_mean_93_cast_fp16, y = zero_mean_93_cast_fp16)[name = tensor<string, []>("zero_mean_sq_93_cast_fp16")];
tensor<int32, [1]> var_3505 = const()[name = tensor<string, []>("op_3505"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_3506_cast_fp16 = reduce_mean(axes = var_3505, keep_dims = var_3402, x = zero_mean_sq_93_cast_fp16)[name = tensor<string, []>("op_3506_cast_fp16")];
tensor<fp16, []> var_3507_to_fp16 = const()[name = tensor<string, []>("op_3507_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_3508_cast_fp16 = add(x = var_3506_cast_fp16, y = var_3507_to_fp16)[name = tensor<string, []>("op_3508_cast_fp16")];
tensor<fp16, []> denom_93_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_93_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_93_cast_fp16 = rsqrt(epsilon = denom_93_epsilon_0_to_fp16, x = var_3508_cast_fp16)[name = tensor<string, []>("denom_93_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_93_cast_fp16 = mul(x = zero_mean_93_cast_fp16, y = denom_93_cast_fp16)[name = tensor<string, []>("out_93_cast_fp16")];
tensor<fp16, [1280]> obj_219_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_219_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(934155136)))];
tensor<fp16, [1280]> obj_219_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_219_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(934157760)))];
tensor<fp16, []> obj_219_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_219_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_219_cast_fp16 = batch_norm(beta = obj_219_beta_0_to_fp16, epsilon = obj_219_epsilon_0_to_fp16, gamma = obj_219_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_93_cast_fp16)[name = tensor<string, []>("obj_219_cast_fp16")];
tensor<int32, [2]> var_3523 = const()[name = tensor<string, []>("op_3523"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3525 = const()[name = tensor<string, []>("op_3525"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_63_pad_type_0 = const()[name = tensor<string, []>("query_63_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_63_pad_0 = const()[name = tensor<string, []>("query_63_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_15_encoder_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_15_encoder_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(934160384)))];
tensor<fp16, [1280]> layers_15_encoder_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_15_encoder_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(937437248)))];
tensor<fp16, [1, 1280, 1, 1]> query_63_cast_fp16 = conv(bias = layers_15_encoder_attn_q_proj_bias_to_fp16, dilations = var_3525, groups = var_3401, pad = query_63_pad_0, pad_type = query_63_pad_type_0, strides = var_3523, weight = layers_15_encoder_attn_q_proj_weight_to_fp16, x = obj_219_cast_fp16)[name = tensor<string, []>("query_63_cast_fp16")];
tensor<int32, [2]> var_3529 = const()[name = tensor<string, []>("op_3529"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3531 = const()[name = tensor<string, []>("op_3531"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> key_63_pad_type_0 = const()[name = tensor<string, []>("key_63_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> key_63_pad_0 = const()[name = tensor<string, []>("key_63_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_15_encoder_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_15_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(937439872)))];
tensor<fp16, [1, 1280, 1, 1500]> key_63_cast_fp16 = conv(dilations = var_3531, groups = var_3401, pad = key_63_pad_0, pad_type = key_63_pad_type_0, strides = var_3529, weight = layers_15_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("key_63_cast_fp16")];
tensor<int32, [2]> var_3536 = const()[name = tensor<string, []>("op_3536"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3538 = const()[name = tensor<string, []>("op_3538"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> value_63_pad_type_0 = const()[name = tensor<string, []>("value_63_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> value_63_pad_0 = const()[name = tensor<string, []>("value_63_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_15_encoder_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_15_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(940716736)))];
tensor<fp16, [1280]> layers_15_encoder_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_15_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(943993600)))];
tensor<fp16, [1, 1280, 1, 1500]> value_63_cast_fp16 = conv(bias = layers_15_encoder_attn_v_proj_bias_to_fp16, dilations = var_3538, groups = var_3401, pad = value_63_pad_0, pad_type = value_63_pad_type_0, strides = var_3536, weight = layers_15_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("value_63_cast_fp16")];
tensor<int32, [4]> var_3542 = const()[name = tensor<string, []>("op_3542"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_3543_cast_fp16 = reshape(shape = var_3542, x = query_63_cast_fp16)[name = tensor<string, []>("op_3543_cast_fp16")];
tensor<fp16, []> var_3544_to_fp16 = const()[name = tensor<string, []>("op_3544_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_3545_cast_fp16 = mul(x = var_3543_cast_fp16, y = var_3544_to_fp16)[name = tensor<string, []>("op_3545_cast_fp16")];
tensor<int32, [4]> var_3546 = const()[name = tensor<string, []>("op_3546"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_3547_cast_fp16 = reshape(shape = var_3546, x = key_63_cast_fp16)[name = tensor<string, []>("op_3547_cast_fp16")];
tensor<bool, []> mh_w_95_transpose_x_0 = const()[name = tensor<string, []>("mh_w_95_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_95_transpose_y_0 = const()[name = tensor<string, []>("mh_w_95_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 1500]> mh_w_95_cast_fp16 = matmul(transpose_x = mh_w_95_transpose_x_0, transpose_y = mh_w_95_transpose_y_0, x = var_3545_cast_fp16, y = var_3547_cast_fp16)[name = tensor<string, []>("mh_w_95_cast_fp16")];
tensor<fp16, [1, 20, 1, 1500]> obj_223_cast_fp16 = softmax(axis = var_3394, x = mh_w_95_cast_fp16)[name = tensor<string, []>("obj_223_cast_fp16")];
tensor<int32, [4]> var_3551 = const()[name = tensor<string, []>("op_3551"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_3552_cast_fp16 = reshape(shape = var_3551, x = value_63_cast_fp16)[name = tensor<string, []>("op_3552_cast_fp16")];
tensor<bool, []> attn_63_transpose_x_0 = const()[name = tensor<string, []>("attn_63_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_63_transpose_y_0 = const()[name = tensor<string, []>("attn_63_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_63_cast_fp16 = matmul(transpose_x = attn_63_transpose_x_0, transpose_y = attn_63_transpose_y_0, x = var_3552_cast_fp16, y = obj_223_cast_fp16)[name = tensor<string, []>("attn_63_cast_fp16")];
tensor<int32, [4]> var_3555 = const()[name = tensor<string, []>("op_3555"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_153_cast_fp16 = reshape(shape = var_3555, x = attn_63_cast_fp16)[name = tensor<string, []>("input_153_cast_fp16")];
tensor<int32, [2]> var_3559 = const()[name = tensor<string, []>("op_3559"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3561 = const()[name = tensor<string, []>("op_3561"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_221_pad_type_0 = const()[name = tensor<string, []>("obj_221_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_221_pad_0 = const()[name = tensor<string, []>("obj_221_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_15_encoder_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_15_encoder_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(943996224)))];
tensor<fp16, [1280]> layers_15_encoder_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_15_encoder_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(947273088)))];
tensor<fp16, [1, 1280, 1, 1]> obj_221_cast_fp16 = conv(bias = layers_15_encoder_attn_o_proj_bias_to_fp16, dilations = var_3561, groups = var_3401, pad = obj_221_pad_0, pad_type = obj_221_pad_type_0, strides = var_3559, weight = layers_15_encoder_attn_o_proj_weight_to_fp16, x = input_153_cast_fp16)[name = tensor<string, []>("obj_221_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_95_cast_fp16 = add(x = inputs_93_cast_fp16, y = obj_221_cast_fp16)[name = tensor<string, []>("inputs_95_cast_fp16")];
tensor<int32, [1]> var_3567 = const()[name = tensor<string, []>("op_3567"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_95_cast_fp16 = reduce_mean(axes = var_3567, keep_dims = var_3402, x = inputs_95_cast_fp16)[name = tensor<string, []>("channels_mean_95_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_95_cast_fp16 = sub(x = inputs_95_cast_fp16, y = channels_mean_95_cast_fp16)[name = tensor<string, []>("zero_mean_95_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_95_cast_fp16 = mul(x = zero_mean_95_cast_fp16, y = zero_mean_95_cast_fp16)[name = tensor<string, []>("zero_mean_sq_95_cast_fp16")];
tensor<int32, [1]> var_3571 = const()[name = tensor<string, []>("op_3571"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_3572_cast_fp16 = reduce_mean(axes = var_3571, keep_dims = var_3402, x = zero_mean_sq_95_cast_fp16)[name = tensor<string, []>("op_3572_cast_fp16")];
tensor<fp16, []> var_3573_to_fp16 = const()[name = tensor<string, []>("op_3573_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_3574_cast_fp16 = add(x = var_3572_cast_fp16, y = var_3573_to_fp16)[name = tensor<string, []>("op_3574_cast_fp16")];
tensor<fp16, []> denom_95_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_95_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_95_cast_fp16 = rsqrt(epsilon = denom_95_epsilon_0_to_fp16, x = var_3574_cast_fp16)[name = tensor<string, []>("denom_95_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_95_cast_fp16 = mul(x = zero_mean_95_cast_fp16, y = denom_95_cast_fp16)[name = tensor<string, []>("out_95_cast_fp16")];
tensor<fp16, [1280]> input_155_gamma_0_to_fp16 = const()[name = tensor<string, []>("input_155_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(947275712)))];
tensor<fp16, [1280]> input_155_beta_0_to_fp16 = const()[name = tensor<string, []>("input_155_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(947278336)))];
tensor<fp16, []> input_155_epsilon_0_to_fp16 = const()[name = tensor<string, []>("input_155_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> input_155_cast_fp16 = batch_norm(beta = input_155_beta_0_to_fp16, epsilon = input_155_epsilon_0_to_fp16, gamma = input_155_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_95_cast_fp16)[name = tensor<string, []>("input_155_cast_fp16")];
tensor<int32, [2]> var_3585 = const()[name = tensor<string, []>("op_3585"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3587 = const()[name = tensor<string, []>("op_3587"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_157_pad_type_0 = const()[name = tensor<string, []>("input_157_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_157_pad_0 = const()[name = tensor<string, []>("input_157_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [5120, 1280, 1, 1]> layers_15_fc1_weight_to_fp16 = const()[name = tensor<string, []>("layers_15_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(947280960)))];
tensor<fp16, [5120]> layers_15_fc1_bias_to_fp16 = const()[name = tensor<string, []>("layers_15_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(960388224)))];
tensor<fp16, [1, 5120, 1, 1]> input_157_cast_fp16 = conv(bias = layers_15_fc1_bias_to_fp16, dilations = var_3587, groups = var_3401, pad = input_157_pad_0, pad_type = input_157_pad_type_0, strides = var_3585, weight = layers_15_fc1_weight_to_fp16, x = input_155_cast_fp16)[name = tensor<string, []>("input_157_cast_fp16")];
tensor<string, []> input_159_mode_0 = const()[name = tensor<string, []>("input_159_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 5120, 1, 1]> input_159_cast_fp16 = gelu(mode = input_159_mode_0, x = input_157_cast_fp16)[name = tensor<string, []>("input_159_cast_fp16")];
tensor<int32, [2]> var_3593 = const()[name = tensor<string, []>("op_3593"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3595 = const()[name = tensor<string, []>("op_3595"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_33_pad_type_0 = const()[name = tensor<string, []>("hidden_states_33_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_33_pad_0 = const()[name = tensor<string, []>("hidden_states_33_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 5120, 1, 1]> layers_15_fc2_weight_to_fp16 = const()[name = tensor<string, []>("layers_15_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(960398528)))];
tensor<fp16, [1280]> layers_15_fc2_bias_to_fp16 = const()[name = tensor<string, []>("layers_15_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(973505792)))];
tensor<fp16, [1, 1280, 1, 1]> hidden_states_33_cast_fp16 = conv(bias = layers_15_fc2_bias_to_fp16, dilations = var_3595, groups = var_3401, pad = hidden_states_33_pad_0, pad_type = hidden_states_33_pad_type_0, strides = var_3593, weight = layers_15_fc2_weight_to_fp16, x = input_159_cast_fp16)[name = tensor<string, []>("hidden_states_33_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_97_cast_fp16 = add(x = inputs_95_cast_fp16, y = hidden_states_33_cast_fp16)[name = tensor<string, []>("inputs_97_cast_fp16")];
tensor<int32, []> var_3608 = const()[name = tensor<string, []>("op_3608"), val = tensor<int32, []>(3)];
tensor<int32, []> var_3615 = const()[name = tensor<string, []>("op_3615"), val = tensor<int32, []>(1)];
tensor<bool, []> var_3616 = const()[name = tensor<string, []>("op_3616"), val = tensor<bool, []>(true)];
tensor<int32, [1]> var_3628 = const()[name = tensor<string, []>("op_3628"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_97_cast_fp16 = reduce_mean(axes = var_3628, keep_dims = var_3616, x = inputs_97_cast_fp16)[name = tensor<string, []>("channels_mean_97_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_97_cast_fp16 = sub(x = inputs_97_cast_fp16, y = channels_mean_97_cast_fp16)[name = tensor<string, []>("zero_mean_97_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_97_cast_fp16 = mul(x = zero_mean_97_cast_fp16, y = zero_mean_97_cast_fp16)[name = tensor<string, []>("zero_mean_sq_97_cast_fp16")];
tensor<int32, [1]> var_3632 = const()[name = tensor<string, []>("op_3632"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_3633_cast_fp16 = reduce_mean(axes = var_3632, keep_dims = var_3616, x = zero_mean_sq_97_cast_fp16)[name = tensor<string, []>("op_3633_cast_fp16")];
tensor<fp16, []> var_3634_to_fp16 = const()[name = tensor<string, []>("op_3634_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_3635_cast_fp16 = add(x = var_3633_cast_fp16, y = var_3634_to_fp16)[name = tensor<string, []>("op_3635_cast_fp16")];
tensor<fp16, []> denom_97_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_97_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_97_cast_fp16 = rsqrt(epsilon = denom_97_epsilon_0_to_fp16, x = var_3635_cast_fp16)[name = tensor<string, []>("denom_97_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_97_cast_fp16 = mul(x = zero_mean_97_cast_fp16, y = denom_97_cast_fp16)[name = tensor<string, []>("out_97_cast_fp16")];
tensor<fp16, [1280]> obj_225_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_225_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(973508416)))];
tensor<fp16, [1280]> obj_225_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_225_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(973511040)))];
tensor<fp16, []> obj_225_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_225_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_225_cast_fp16 = batch_norm(beta = obj_225_beta_0_to_fp16, epsilon = obj_225_epsilon_0_to_fp16, gamma = obj_225_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_97_cast_fp16)[name = tensor<string, []>("obj_225_cast_fp16")];
tensor<int32, [2]> var_3650 = const()[name = tensor<string, []>("op_3650"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3652 = const()[name = tensor<string, []>("op_3652"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_65_pad_type_0 = const()[name = tensor<string, []>("query_65_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_65_pad_0 = const()[name = tensor<string, []>("query_65_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_16_self_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_16_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(973513664)))];
tensor<fp16, [1280]> layers_16_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_16_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(976790528)))];
tensor<fp16, [1, 1280, 1, 1]> query_65_cast_fp16 = conv(bias = layers_16_self_attn_q_proj_bias_to_fp16, dilations = var_3652, groups = var_3615, pad = query_65_pad_0, pad_type = query_65_pad_type_0, strides = var_3650, weight = layers_16_self_attn_q_proj_weight_to_fp16, x = obj_225_cast_fp16)[name = tensor<string, []>("query_65_cast_fp16")];
tensor<int32, [2]> var_3656 = const()[name = tensor<string, []>("op_3656"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3658 = const()[name = tensor<string, []>("op_3658"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_key_33_pad_type_0 = const()[name = tensor<string, []>("current_key_33_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_key_33_pad_0 = const()[name = tensor<string, []>("current_key_33_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_16_self_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_16_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(976793152)))];
tensor<fp16, [1, 1280, 1, 1]> current_key_33_cast_fp16 = conv(dilations = var_3658, groups = var_3615, pad = current_key_33_pad_0, pad_type = current_key_33_pad_type_0, strides = var_3656, weight = layers_16_self_attn_k_proj_weight_to_fp16, x = obj_225_cast_fp16)[name = tensor<string, []>("current_key_33_cast_fp16")];
tensor<int32, [2]> var_3663 = const()[name = tensor<string, []>("op_3663"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3665 = const()[name = tensor<string, []>("op_3665"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_value_33_pad_type_0 = const()[name = tensor<string, []>("current_value_33_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_value_33_pad_0 = const()[name = tensor<string, []>("current_value_33_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_16_self_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_16_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(980070016)))];
tensor<fp16, [1280]> layers_16_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_16_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(983346880)))];
tensor<fp16, [1, 1280, 1, 1]> current_value_33_cast_fp16 = conv(bias = layers_16_self_attn_v_proj_bias_to_fp16, dilations = var_3665, groups = var_3615, pad = current_value_33_pad_0, pad_type = current_value_33_pad_type_0, strides = var_3663, weight = layers_16_self_attn_v_proj_weight_to_fp16, x = obj_225_cast_fp16)[name = tensor<string, []>("current_value_33_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_3672_cast_fp16 = mul(x = current_key_33_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_3672_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_3674_cast_fp16 = mul(x = var_103_cast_fp16_16, y = var_241_cast_fp16)[name = tensor<string, []>("op_3674_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> key_65_cast_fp16 = add(x = var_3672_cast_fp16, y = var_3674_cast_fp16)[name = tensor<string, []>("key_65_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_3676_cast_fp16 = mul(x = current_value_33_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_3676_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_3678_cast_fp16 = mul(x = var_138_cast_fp16_16, y = var_241_cast_fp16)[name = tensor<string, []>("op_3678_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> value_65_cast_fp16 = add(x = var_3676_cast_fp16, y = var_3678_cast_fp16)[name = tensor<string, []>("value_65_cast_fp16")];
tensor<int32, [4]> var_3681 = const()[name = tensor<string, []>("op_3681"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_3682_cast_fp16 = reshape(shape = var_3681, x = query_65_cast_fp16)[name = tensor<string, []>("op_3682_cast_fp16")];
tensor<fp16, []> var_3683_to_fp16 = const()[name = tensor<string, []>("op_3683_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_3684_cast_fp16 = mul(x = var_3682_cast_fp16, y = var_3683_to_fp16)[name = tensor<string, []>("op_3684_cast_fp16")];
tensor<int32, [4]> var_3685 = const()[name = tensor<string, []>("op_3685"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_3686_cast_fp16 = reshape(shape = var_3685, x = key_65_cast_fp16)[name = tensor<string, []>("op_3686_cast_fp16")];
tensor<bool, []> mh_w_97_transpose_x_0 = const()[name = tensor<string, []>("mh_w_97_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_97_transpose_y_0 = const()[name = tensor<string, []>("mh_w_97_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 224]> mh_w_97_cast_fp16 = matmul(transpose_x = mh_w_97_transpose_x_0, transpose_y = mh_w_97_transpose_y_0, x = var_3684_cast_fp16, y = var_3686_cast_fp16)[name = tensor<string, []>("mh_w_97_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> mh_w_99_cast_fp16 = add(x = mh_w_97_cast_fp16, y = var_259_cast_fp16)[name = tensor<string, []>("mh_w_99_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> var_3694_cast_fp16 = softmax(axis = var_3608, x = mh_w_99_cast_fp16)[name = tensor<string, []>("op_3694_cast_fp16")];
tensor<int32, [4]> var_3695 = const()[name = tensor<string, []>("op_3695"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_3696_cast_fp16 = reshape(shape = var_3695, x = value_65_cast_fp16)[name = tensor<string, []>("op_3696_cast_fp16")];
tensor<bool, []> attn_65_transpose_x_0 = const()[name = tensor<string, []>("attn_65_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_65_transpose_y_0 = const()[name = tensor<string, []>("attn_65_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_65_cast_fp16 = matmul(transpose_x = attn_65_transpose_x_0, transpose_y = attn_65_transpose_y_0, x = var_3696_cast_fp16, y = var_3694_cast_fp16)[name = tensor<string, []>("attn_65_cast_fp16")];
tensor<int32, [4]> var_3699 = const()[name = tensor<string, []>("op_3699"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_161_cast_fp16 = reshape(shape = var_3699, x = attn_65_cast_fp16)[name = tensor<string, []>("input_161_cast_fp16")];
tensor<int32, [2]> var_3703 = const()[name = tensor<string, []>("op_3703"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3705 = const()[name = tensor<string, []>("op_3705"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_231_pad_type_0 = const()[name = tensor<string, []>("obj_231_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_231_pad_0 = const()[name = tensor<string, []>("obj_231_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_16_self_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_16_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(983349504)))];
tensor<fp16, [1280]> layers_16_self_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_16_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(986626368)))];
tensor<fp16, [1, 1280, 1, 1]> obj_231_cast_fp16 = conv(bias = layers_16_self_attn_o_proj_bias_to_fp16, dilations = var_3705, groups = var_3615, pad = obj_231_pad_0, pad_type = obj_231_pad_type_0, strides = var_3703, weight = layers_16_self_attn_o_proj_weight_to_fp16, x = input_161_cast_fp16)[name = tensor<string, []>("obj_231_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_99_cast_fp16 = add(x = inputs_97_cast_fp16, y = obj_231_cast_fp16)[name = tensor<string, []>("inputs_99_cast_fp16")];
tensor<int32, [1]> var_3715 = const()[name = tensor<string, []>("op_3715"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_99_cast_fp16 = reduce_mean(axes = var_3715, keep_dims = var_3616, x = inputs_99_cast_fp16)[name = tensor<string, []>("channels_mean_99_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_99_cast_fp16 = sub(x = inputs_99_cast_fp16, y = channels_mean_99_cast_fp16)[name = tensor<string, []>("zero_mean_99_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_99_cast_fp16 = mul(x = zero_mean_99_cast_fp16, y = zero_mean_99_cast_fp16)[name = tensor<string, []>("zero_mean_sq_99_cast_fp16")];
tensor<int32, [1]> var_3719 = const()[name = tensor<string, []>("op_3719"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_3720_cast_fp16 = reduce_mean(axes = var_3719, keep_dims = var_3616, x = zero_mean_sq_99_cast_fp16)[name = tensor<string, []>("op_3720_cast_fp16")];
tensor<fp16, []> var_3721_to_fp16 = const()[name = tensor<string, []>("op_3721_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_3722_cast_fp16 = add(x = var_3720_cast_fp16, y = var_3721_to_fp16)[name = tensor<string, []>("op_3722_cast_fp16")];
tensor<fp16, []> denom_99_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_99_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_99_cast_fp16 = rsqrt(epsilon = denom_99_epsilon_0_to_fp16, x = var_3722_cast_fp16)[name = tensor<string, []>("denom_99_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_99_cast_fp16 = mul(x = zero_mean_99_cast_fp16, y = denom_99_cast_fp16)[name = tensor<string, []>("out_99_cast_fp16")];
tensor<fp16, [1280]> obj_233_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_233_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(986628992)))];
tensor<fp16, [1280]> obj_233_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_233_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(986631616)))];
tensor<fp16, []> obj_233_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_233_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_233_cast_fp16 = batch_norm(beta = obj_233_beta_0_to_fp16, epsilon = obj_233_epsilon_0_to_fp16, gamma = obj_233_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_99_cast_fp16)[name = tensor<string, []>("obj_233_cast_fp16")];
tensor<int32, [2]> var_3737 = const()[name = tensor<string, []>("op_3737"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3739 = const()[name = tensor<string, []>("op_3739"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_67_pad_type_0 = const()[name = tensor<string, []>("query_67_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_67_pad_0 = const()[name = tensor<string, []>("query_67_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_16_encoder_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_16_encoder_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(986634240)))];
tensor<fp16, [1280]> layers_16_encoder_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_16_encoder_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(989911104)))];
tensor<fp16, [1, 1280, 1, 1]> query_67_cast_fp16 = conv(bias = layers_16_encoder_attn_q_proj_bias_to_fp16, dilations = var_3739, groups = var_3615, pad = query_67_pad_0, pad_type = query_67_pad_type_0, strides = var_3737, weight = layers_16_encoder_attn_q_proj_weight_to_fp16, x = obj_233_cast_fp16)[name = tensor<string, []>("query_67_cast_fp16")];
tensor<int32, [2]> var_3743 = const()[name = tensor<string, []>("op_3743"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3745 = const()[name = tensor<string, []>("op_3745"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> key_67_pad_type_0 = const()[name = tensor<string, []>("key_67_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> key_67_pad_0 = const()[name = tensor<string, []>("key_67_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_16_encoder_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_16_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(989913728)))];
tensor<fp16, [1, 1280, 1, 1500]> key_67_cast_fp16 = conv(dilations = var_3745, groups = var_3615, pad = key_67_pad_0, pad_type = key_67_pad_type_0, strides = var_3743, weight = layers_16_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("key_67_cast_fp16")];
tensor<int32, [2]> var_3750 = const()[name = tensor<string, []>("op_3750"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3752 = const()[name = tensor<string, []>("op_3752"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> value_67_pad_type_0 = const()[name = tensor<string, []>("value_67_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> value_67_pad_0 = const()[name = tensor<string, []>("value_67_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_16_encoder_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_16_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(993190592)))];
tensor<fp16, [1280]> layers_16_encoder_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_16_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(996467456)))];
tensor<fp16, [1, 1280, 1, 1500]> value_67_cast_fp16 = conv(bias = layers_16_encoder_attn_v_proj_bias_to_fp16, dilations = var_3752, groups = var_3615, pad = value_67_pad_0, pad_type = value_67_pad_type_0, strides = var_3750, weight = layers_16_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("value_67_cast_fp16")];
tensor<int32, [4]> var_3756 = const()[name = tensor<string, []>("op_3756"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_3757_cast_fp16 = reshape(shape = var_3756, x = query_67_cast_fp16)[name = tensor<string, []>("op_3757_cast_fp16")];
tensor<fp16, []> var_3758_to_fp16 = const()[name = tensor<string, []>("op_3758_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_3759_cast_fp16 = mul(x = var_3757_cast_fp16, y = var_3758_to_fp16)[name = tensor<string, []>("op_3759_cast_fp16")];
tensor<int32, [4]> var_3760 = const()[name = tensor<string, []>("op_3760"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_3761_cast_fp16 = reshape(shape = var_3760, x = key_67_cast_fp16)[name = tensor<string, []>("op_3761_cast_fp16")];
tensor<bool, []> mh_w_101_transpose_x_0 = const()[name = tensor<string, []>("mh_w_101_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_101_transpose_y_0 = const()[name = tensor<string, []>("mh_w_101_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 1500]> mh_w_101_cast_fp16 = matmul(transpose_x = mh_w_101_transpose_x_0, transpose_y = mh_w_101_transpose_y_0, x = var_3759_cast_fp16, y = var_3761_cast_fp16)[name = tensor<string, []>("mh_w_101_cast_fp16")];
tensor<fp16, [1, 20, 1, 1500]> obj_237_cast_fp16 = softmax(axis = var_3608, x = mh_w_101_cast_fp16)[name = tensor<string, []>("obj_237_cast_fp16")];
tensor<int32, [4]> var_3765 = const()[name = tensor<string, []>("op_3765"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_3766_cast_fp16 = reshape(shape = var_3765, x = value_67_cast_fp16)[name = tensor<string, []>("op_3766_cast_fp16")];
tensor<bool, []> attn_67_transpose_x_0 = const()[name = tensor<string, []>("attn_67_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_67_transpose_y_0 = const()[name = tensor<string, []>("attn_67_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_67_cast_fp16 = matmul(transpose_x = attn_67_transpose_x_0, transpose_y = attn_67_transpose_y_0, x = var_3766_cast_fp16, y = obj_237_cast_fp16)[name = tensor<string, []>("attn_67_cast_fp16")];
tensor<int32, [4]> var_3769 = const()[name = tensor<string, []>("op_3769"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_163_cast_fp16 = reshape(shape = var_3769, x = attn_67_cast_fp16)[name = tensor<string, []>("input_163_cast_fp16")];
tensor<int32, [2]> var_3773 = const()[name = tensor<string, []>("op_3773"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3775 = const()[name = tensor<string, []>("op_3775"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_235_pad_type_0 = const()[name = tensor<string, []>("obj_235_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_235_pad_0 = const()[name = tensor<string, []>("obj_235_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_16_encoder_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_16_encoder_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(996470080)))];
tensor<fp16, [1280]> layers_16_encoder_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_16_encoder_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(999746944)))];
tensor<fp16, [1, 1280, 1, 1]> obj_235_cast_fp16 = conv(bias = layers_16_encoder_attn_o_proj_bias_to_fp16, dilations = var_3775, groups = var_3615, pad = obj_235_pad_0, pad_type = obj_235_pad_type_0, strides = var_3773, weight = layers_16_encoder_attn_o_proj_weight_to_fp16, x = input_163_cast_fp16)[name = tensor<string, []>("obj_235_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_101_cast_fp16 = add(x = inputs_99_cast_fp16, y = obj_235_cast_fp16)[name = tensor<string, []>("inputs_101_cast_fp16")];
tensor<int32, [1]> var_3784 = const()[name = tensor<string, []>("op_3784"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_101_cast_fp16 = reduce_mean(axes = var_3784, keep_dims = var_3616, x = inputs_101_cast_fp16)[name = tensor<string, []>("channels_mean_101_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_101_cast_fp16 = sub(x = inputs_101_cast_fp16, y = channels_mean_101_cast_fp16)[name = tensor<string, []>("zero_mean_101_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_101_cast_fp16 = mul(x = zero_mean_101_cast_fp16, y = zero_mean_101_cast_fp16)[name = tensor<string, []>("zero_mean_sq_101_cast_fp16")];
tensor<int32, [1]> var_3788 = const()[name = tensor<string, []>("op_3788"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_3789_cast_fp16 = reduce_mean(axes = var_3788, keep_dims = var_3616, x = zero_mean_sq_101_cast_fp16)[name = tensor<string, []>("op_3789_cast_fp16")];
tensor<fp16, []> var_3790_to_fp16 = const()[name = tensor<string, []>("op_3790_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_3791_cast_fp16 = add(x = var_3789_cast_fp16, y = var_3790_to_fp16)[name = tensor<string, []>("op_3791_cast_fp16")];
tensor<fp16, []> denom_101_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_101_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_101_cast_fp16 = rsqrt(epsilon = denom_101_epsilon_0_to_fp16, x = var_3791_cast_fp16)[name = tensor<string, []>("denom_101_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_101_cast_fp16 = mul(x = zero_mean_101_cast_fp16, y = denom_101_cast_fp16)[name = tensor<string, []>("out_101_cast_fp16")];
tensor<fp16, [1280]> input_165_gamma_0_to_fp16 = const()[name = tensor<string, []>("input_165_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(999749568)))];
tensor<fp16, [1280]> input_165_beta_0_to_fp16 = const()[name = tensor<string, []>("input_165_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(999752192)))];
tensor<fp16, []> input_165_epsilon_0_to_fp16 = const()[name = tensor<string, []>("input_165_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> input_165_cast_fp16 = batch_norm(beta = input_165_beta_0_to_fp16, epsilon = input_165_epsilon_0_to_fp16, gamma = input_165_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_101_cast_fp16)[name = tensor<string, []>("input_165_cast_fp16")];
tensor<int32, [2]> var_3802 = const()[name = tensor<string, []>("op_3802"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3804 = const()[name = tensor<string, []>("op_3804"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_167_pad_type_0 = const()[name = tensor<string, []>("input_167_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_167_pad_0 = const()[name = tensor<string, []>("input_167_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [5120, 1280, 1, 1]> layers_16_fc1_weight_to_fp16 = const()[name = tensor<string, []>("layers_16_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(999754816)))];
tensor<fp16, [5120]> layers_16_fc1_bias_to_fp16 = const()[name = tensor<string, []>("layers_16_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1012862080)))];
tensor<fp16, [1, 5120, 1, 1]> input_167_cast_fp16 = conv(bias = layers_16_fc1_bias_to_fp16, dilations = var_3804, groups = var_3615, pad = input_167_pad_0, pad_type = input_167_pad_type_0, strides = var_3802, weight = layers_16_fc1_weight_to_fp16, x = input_165_cast_fp16)[name = tensor<string, []>("input_167_cast_fp16")];
tensor<string, []> input_169_mode_0 = const()[name = tensor<string, []>("input_169_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 5120, 1, 1]> input_169_cast_fp16 = gelu(mode = input_169_mode_0, x = input_167_cast_fp16)[name = tensor<string, []>("input_169_cast_fp16")];
tensor<int32, [2]> var_3810 = const()[name = tensor<string, []>("op_3810"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3812 = const()[name = tensor<string, []>("op_3812"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_35_pad_type_0 = const()[name = tensor<string, []>("hidden_states_35_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_35_pad_0 = const()[name = tensor<string, []>("hidden_states_35_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 5120, 1, 1]> layers_16_fc2_weight_to_fp16 = const()[name = tensor<string, []>("layers_16_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1012872384)))];
tensor<fp16, [1280]> layers_16_fc2_bias_to_fp16 = const()[name = tensor<string, []>("layers_16_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1025979648)))];
tensor<fp16, [1, 1280, 1, 1]> hidden_states_35_cast_fp16 = conv(bias = layers_16_fc2_bias_to_fp16, dilations = var_3812, groups = var_3615, pad = hidden_states_35_pad_0, pad_type = hidden_states_35_pad_type_0, strides = var_3810, weight = layers_16_fc2_weight_to_fp16, x = input_169_cast_fp16)[name = tensor<string, []>("hidden_states_35_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_103_cast_fp16 = add(x = inputs_101_cast_fp16, y = hidden_states_35_cast_fp16)[name = tensor<string, []>("inputs_103_cast_fp16")];
tensor<int32, []> var_3826 = const()[name = tensor<string, []>("op_3826"), val = tensor<int32, []>(3)];
tensor<int32, []> var_3833 = const()[name = tensor<string, []>("op_3833"), val = tensor<int32, []>(1)];
tensor<bool, []> var_3834 = const()[name = tensor<string, []>("op_3834"), val = tensor<bool, []>(true)];
tensor<int32, [1]> var_3846 = const()[name = tensor<string, []>("op_3846"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_103_cast_fp16 = reduce_mean(axes = var_3846, keep_dims = var_3834, x = inputs_103_cast_fp16)[name = tensor<string, []>("channels_mean_103_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_103_cast_fp16 = sub(x = inputs_103_cast_fp16, y = channels_mean_103_cast_fp16)[name = tensor<string, []>("zero_mean_103_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_103_cast_fp16 = mul(x = zero_mean_103_cast_fp16, y = zero_mean_103_cast_fp16)[name = tensor<string, []>("zero_mean_sq_103_cast_fp16")];
tensor<int32, [1]> var_3850 = const()[name = tensor<string, []>("op_3850"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_3851_cast_fp16 = reduce_mean(axes = var_3850, keep_dims = var_3834, x = zero_mean_sq_103_cast_fp16)[name = tensor<string, []>("op_3851_cast_fp16")];
tensor<fp16, []> var_3852_to_fp16 = const()[name = tensor<string, []>("op_3852_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_3853_cast_fp16 = add(x = var_3851_cast_fp16, y = var_3852_to_fp16)[name = tensor<string, []>("op_3853_cast_fp16")];
tensor<fp16, []> denom_103_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_103_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_103_cast_fp16 = rsqrt(epsilon = denom_103_epsilon_0_to_fp16, x = var_3853_cast_fp16)[name = tensor<string, []>("denom_103_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_103_cast_fp16 = mul(x = zero_mean_103_cast_fp16, y = denom_103_cast_fp16)[name = tensor<string, []>("out_103_cast_fp16")];
tensor<fp16, [1280]> obj_239_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_239_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1025982272)))];
tensor<fp16, [1280]> obj_239_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_239_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1025984896)))];
tensor<fp16, []> obj_239_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_239_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_239_cast_fp16 = batch_norm(beta = obj_239_beta_0_to_fp16, epsilon = obj_239_epsilon_0_to_fp16, gamma = obj_239_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_103_cast_fp16)[name = tensor<string, []>("obj_239_cast_fp16")];
tensor<int32, [2]> var_3868 = const()[name = tensor<string, []>("op_3868"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3870 = const()[name = tensor<string, []>("op_3870"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_69_pad_type_0 = const()[name = tensor<string, []>("query_69_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_69_pad_0 = const()[name = tensor<string, []>("query_69_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_17_self_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_17_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1025987520)))];
tensor<fp16, [1280]> layers_17_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_17_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1029264384)))];
tensor<fp16, [1, 1280, 1, 1]> query_69_cast_fp16 = conv(bias = layers_17_self_attn_q_proj_bias_to_fp16, dilations = var_3870, groups = var_3833, pad = query_69_pad_0, pad_type = query_69_pad_type_0, strides = var_3868, weight = layers_17_self_attn_q_proj_weight_to_fp16, x = obj_239_cast_fp16)[name = tensor<string, []>("query_69_cast_fp16")];
tensor<int32, [2]> var_3874 = const()[name = tensor<string, []>("op_3874"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3876 = const()[name = tensor<string, []>("op_3876"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_key_35_pad_type_0 = const()[name = tensor<string, []>("current_key_35_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_key_35_pad_0 = const()[name = tensor<string, []>("current_key_35_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_17_self_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_17_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1029267008)))];
tensor<fp16, [1, 1280, 1, 1]> current_key_35_cast_fp16 = conv(dilations = var_3876, groups = var_3833, pad = current_key_35_pad_0, pad_type = current_key_35_pad_type_0, strides = var_3874, weight = layers_17_self_attn_k_proj_weight_to_fp16, x = obj_239_cast_fp16)[name = tensor<string, []>("current_key_35_cast_fp16")];
tensor<int32, [2]> var_3881 = const()[name = tensor<string, []>("op_3881"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3883 = const()[name = tensor<string, []>("op_3883"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_value_35_pad_type_0 = const()[name = tensor<string, []>("current_value_35_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_value_35_pad_0 = const()[name = tensor<string, []>("current_value_35_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_17_self_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_17_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1032543872)))];
tensor<fp16, [1280]> layers_17_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_17_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1035820736)))];
tensor<fp16, [1, 1280, 1, 1]> current_value_35_cast_fp16 = conv(bias = layers_17_self_attn_v_proj_bias_to_fp16, dilations = var_3883, groups = var_3833, pad = current_value_35_pad_0, pad_type = current_value_35_pad_type_0, strides = var_3881, weight = layers_17_self_attn_v_proj_weight_to_fp16, x = obj_239_cast_fp16)[name = tensor<string, []>("current_value_35_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_3890_cast_fp16 = mul(x = current_key_35_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_3890_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_3892_cast_fp16 = mul(x = var_103_cast_fp16_17, y = var_241_cast_fp16)[name = tensor<string, []>("op_3892_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> key_69_cast_fp16 = add(x = var_3890_cast_fp16, y = var_3892_cast_fp16)[name = tensor<string, []>("key_69_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_3894_cast_fp16 = mul(x = current_value_35_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_3894_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_3896_cast_fp16 = mul(x = var_138_cast_fp16_17, y = var_241_cast_fp16)[name = tensor<string, []>("op_3896_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> value_69_cast_fp16 = add(x = var_3894_cast_fp16, y = var_3896_cast_fp16)[name = tensor<string, []>("value_69_cast_fp16")];
tensor<int32, [4]> var_3899 = const()[name = tensor<string, []>("op_3899"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_3900_cast_fp16 = reshape(shape = var_3899, x = query_69_cast_fp16)[name = tensor<string, []>("op_3900_cast_fp16")];
tensor<fp16, []> var_3901_to_fp16 = const()[name = tensor<string, []>("op_3901_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_3902_cast_fp16 = mul(x = var_3900_cast_fp16, y = var_3901_to_fp16)[name = tensor<string, []>("op_3902_cast_fp16")];
tensor<int32, [4]> var_3903 = const()[name = tensor<string, []>("op_3903"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_3904_cast_fp16 = reshape(shape = var_3903, x = key_69_cast_fp16)[name = tensor<string, []>("op_3904_cast_fp16")];
tensor<bool, []> mh_w_103_transpose_x_0 = const()[name = tensor<string, []>("mh_w_103_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_103_transpose_y_0 = const()[name = tensor<string, []>("mh_w_103_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 224]> mh_w_103_cast_fp16 = matmul(transpose_x = mh_w_103_transpose_x_0, transpose_y = mh_w_103_transpose_y_0, x = var_3902_cast_fp16, y = var_3904_cast_fp16)[name = tensor<string, []>("mh_w_103_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> mh_w_105_cast_fp16 = add(x = mh_w_103_cast_fp16, y = var_259_cast_fp16)[name = tensor<string, []>("mh_w_105_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> var_3912_cast_fp16 = softmax(axis = var_3826, x = mh_w_105_cast_fp16)[name = tensor<string, []>("op_3912_cast_fp16")];
tensor<int32, [4]> var_3913 = const()[name = tensor<string, []>("op_3913"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_3914_cast_fp16 = reshape(shape = var_3913, x = value_69_cast_fp16)[name = tensor<string, []>("op_3914_cast_fp16")];
tensor<bool, []> attn_69_transpose_x_0 = const()[name = tensor<string, []>("attn_69_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_69_transpose_y_0 = const()[name = tensor<string, []>("attn_69_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_69_cast_fp16 = matmul(transpose_x = attn_69_transpose_x_0, transpose_y = attn_69_transpose_y_0, x = var_3914_cast_fp16, y = var_3912_cast_fp16)[name = tensor<string, []>("attn_69_cast_fp16")];
tensor<int32, [4]> var_3917 = const()[name = tensor<string, []>("op_3917"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_171_cast_fp16 = reshape(shape = var_3917, x = attn_69_cast_fp16)[name = tensor<string, []>("input_171_cast_fp16")];
tensor<int32, [2]> var_3921 = const()[name = tensor<string, []>("op_3921"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3923 = const()[name = tensor<string, []>("op_3923"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_245_pad_type_0 = const()[name = tensor<string, []>("obj_245_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_245_pad_0 = const()[name = tensor<string, []>("obj_245_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_17_self_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_17_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1035823360)))];
tensor<fp16, [1280]> layers_17_self_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_17_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1039100224)))];
tensor<fp16, [1, 1280, 1, 1]> obj_245_cast_fp16 = conv(bias = layers_17_self_attn_o_proj_bias_to_fp16, dilations = var_3923, groups = var_3833, pad = obj_245_pad_0, pad_type = obj_245_pad_type_0, strides = var_3921, weight = layers_17_self_attn_o_proj_weight_to_fp16, x = input_171_cast_fp16)[name = tensor<string, []>("obj_245_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_105_cast_fp16 = add(x = inputs_103_cast_fp16, y = obj_245_cast_fp16)[name = tensor<string, []>("inputs_105_cast_fp16")];
tensor<int32, [1]> var_3933 = const()[name = tensor<string, []>("op_3933"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_105_cast_fp16 = reduce_mean(axes = var_3933, keep_dims = var_3834, x = inputs_105_cast_fp16)[name = tensor<string, []>("channels_mean_105_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_105_cast_fp16 = sub(x = inputs_105_cast_fp16, y = channels_mean_105_cast_fp16)[name = tensor<string, []>("zero_mean_105_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_105_cast_fp16 = mul(x = zero_mean_105_cast_fp16, y = zero_mean_105_cast_fp16)[name = tensor<string, []>("zero_mean_sq_105_cast_fp16")];
tensor<int32, [1]> var_3937 = const()[name = tensor<string, []>("op_3937"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_3938_cast_fp16 = reduce_mean(axes = var_3937, keep_dims = var_3834, x = zero_mean_sq_105_cast_fp16)[name = tensor<string, []>("op_3938_cast_fp16")];
tensor<fp16, []> var_3939_to_fp16 = const()[name = tensor<string, []>("op_3939_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_3940_cast_fp16 = add(x = var_3938_cast_fp16, y = var_3939_to_fp16)[name = tensor<string, []>("op_3940_cast_fp16")];
tensor<fp16, []> denom_105_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_105_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_105_cast_fp16 = rsqrt(epsilon = denom_105_epsilon_0_to_fp16, x = var_3940_cast_fp16)[name = tensor<string, []>("denom_105_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_105_cast_fp16 = mul(x = zero_mean_105_cast_fp16, y = denom_105_cast_fp16)[name = tensor<string, []>("out_105_cast_fp16")];
tensor<fp16, [1280]> obj_247_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_247_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1039102848)))];
tensor<fp16, [1280]> obj_247_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_247_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1039105472)))];
tensor<fp16, []> obj_247_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_247_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_247_cast_fp16 = batch_norm(beta = obj_247_beta_0_to_fp16, epsilon = obj_247_epsilon_0_to_fp16, gamma = obj_247_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_105_cast_fp16)[name = tensor<string, []>("obj_247_cast_fp16")];
tensor<int32, [2]> var_3955 = const()[name = tensor<string, []>("op_3955"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3957 = const()[name = tensor<string, []>("op_3957"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_71_pad_type_0 = const()[name = tensor<string, []>("query_71_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_71_pad_0 = const()[name = tensor<string, []>("query_71_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_17_encoder_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_17_encoder_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1039108096)))];
tensor<fp16, [1280]> layers_17_encoder_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_17_encoder_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1042384960)))];
tensor<fp16, [1, 1280, 1, 1]> query_71_cast_fp16 = conv(bias = layers_17_encoder_attn_q_proj_bias_to_fp16, dilations = var_3957, groups = var_3833, pad = query_71_pad_0, pad_type = query_71_pad_type_0, strides = var_3955, weight = layers_17_encoder_attn_q_proj_weight_to_fp16, x = obj_247_cast_fp16)[name = tensor<string, []>("query_71_cast_fp16")];
tensor<int32, [2]> var_3961 = const()[name = tensor<string, []>("op_3961"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3963 = const()[name = tensor<string, []>("op_3963"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> key_71_pad_type_0 = const()[name = tensor<string, []>("key_71_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> key_71_pad_0 = const()[name = tensor<string, []>("key_71_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_17_encoder_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_17_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1042387584)))];
tensor<fp16, [1, 1280, 1, 1500]> key_71_cast_fp16 = conv(dilations = var_3963, groups = var_3833, pad = key_71_pad_0, pad_type = key_71_pad_type_0, strides = var_3961, weight = layers_17_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("key_71_cast_fp16")];
tensor<int32, [2]> var_3968 = const()[name = tensor<string, []>("op_3968"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3970 = const()[name = tensor<string, []>("op_3970"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> value_71_pad_type_0 = const()[name = tensor<string, []>("value_71_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> value_71_pad_0 = const()[name = tensor<string, []>("value_71_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_17_encoder_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_17_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1045664448)))];
tensor<fp16, [1280]> layers_17_encoder_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_17_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1048941312)))];
tensor<fp16, [1, 1280, 1, 1500]> value_71_cast_fp16 = conv(bias = layers_17_encoder_attn_v_proj_bias_to_fp16, dilations = var_3970, groups = var_3833, pad = value_71_pad_0, pad_type = value_71_pad_type_0, strides = var_3968, weight = layers_17_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("value_71_cast_fp16")];
tensor<int32, [4]> var_3974 = const()[name = tensor<string, []>("op_3974"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_3975_cast_fp16 = reshape(shape = var_3974, x = query_71_cast_fp16)[name = tensor<string, []>("op_3975_cast_fp16")];
tensor<fp16, []> var_3976_to_fp16 = const()[name = tensor<string, []>("op_3976_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_3977_cast_fp16 = mul(x = var_3975_cast_fp16, y = var_3976_to_fp16)[name = tensor<string, []>("op_3977_cast_fp16")];
tensor<int32, [4]> var_3978 = const()[name = tensor<string, []>("op_3978"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_3979_cast_fp16 = reshape(shape = var_3978, x = key_71_cast_fp16)[name = tensor<string, []>("op_3979_cast_fp16")];
tensor<bool, []> mh_w_107_transpose_x_0 = const()[name = tensor<string, []>("mh_w_107_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_107_transpose_y_0 = const()[name = tensor<string, []>("mh_w_107_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 1500]> mh_w_107_cast_fp16 = matmul(transpose_x = mh_w_107_transpose_x_0, transpose_y = mh_w_107_transpose_y_0, x = var_3977_cast_fp16, y = var_3979_cast_fp16)[name = tensor<string, []>("mh_w_107_cast_fp16")];
tensor<fp16, [1, 20, 1, 1500]> obj_251_cast_fp16 = softmax(axis = var_3826, x = mh_w_107_cast_fp16)[name = tensor<string, []>("obj_251_cast_fp16")];
tensor<int32, [4]> var_3983 = const()[name = tensor<string, []>("op_3983"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_3984_cast_fp16 = reshape(shape = var_3983, x = value_71_cast_fp16)[name = tensor<string, []>("op_3984_cast_fp16")];
tensor<bool, []> attn_71_transpose_x_0 = const()[name = tensor<string, []>("attn_71_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_71_transpose_y_0 = const()[name = tensor<string, []>("attn_71_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_71_cast_fp16 = matmul(transpose_x = attn_71_transpose_x_0, transpose_y = attn_71_transpose_y_0, x = var_3984_cast_fp16, y = obj_251_cast_fp16)[name = tensor<string, []>("attn_71_cast_fp16")];
tensor<int32, [4]> var_3987 = const()[name = tensor<string, []>("op_3987"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_173_cast_fp16 = reshape(shape = var_3987, x = attn_71_cast_fp16)[name = tensor<string, []>("input_173_cast_fp16")];
tensor<int32, [2]> var_3991 = const()[name = tensor<string, []>("op_3991"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_3993 = const()[name = tensor<string, []>("op_3993"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_249_pad_type_0 = const()[name = tensor<string, []>("obj_249_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_249_pad_0 = const()[name = tensor<string, []>("obj_249_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_17_encoder_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_17_encoder_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1048943936)))];
tensor<fp16, [1280]> layers_17_encoder_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_17_encoder_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1052220800)))];
tensor<fp16, [1, 1280, 1, 1]> obj_249_cast_fp16 = conv(bias = layers_17_encoder_attn_o_proj_bias_to_fp16, dilations = var_3993, groups = var_3833, pad = obj_249_pad_0, pad_type = obj_249_pad_type_0, strides = var_3991, weight = layers_17_encoder_attn_o_proj_weight_to_fp16, x = input_173_cast_fp16)[name = tensor<string, []>("obj_249_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_107_cast_fp16 = add(x = inputs_105_cast_fp16, y = obj_249_cast_fp16)[name = tensor<string, []>("inputs_107_cast_fp16")];
tensor<int32, [1]> var_4002 = const()[name = tensor<string, []>("op_4002"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_107_cast_fp16 = reduce_mean(axes = var_4002, keep_dims = var_3834, x = inputs_107_cast_fp16)[name = tensor<string, []>("channels_mean_107_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_107_cast_fp16 = sub(x = inputs_107_cast_fp16, y = channels_mean_107_cast_fp16)[name = tensor<string, []>("zero_mean_107_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_107_cast_fp16 = mul(x = zero_mean_107_cast_fp16, y = zero_mean_107_cast_fp16)[name = tensor<string, []>("zero_mean_sq_107_cast_fp16")];
tensor<int32, [1]> var_4006 = const()[name = tensor<string, []>("op_4006"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_4007_cast_fp16 = reduce_mean(axes = var_4006, keep_dims = var_3834, x = zero_mean_sq_107_cast_fp16)[name = tensor<string, []>("op_4007_cast_fp16")];
tensor<fp16, []> var_4008_to_fp16 = const()[name = tensor<string, []>("op_4008_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_4009_cast_fp16 = add(x = var_4007_cast_fp16, y = var_4008_to_fp16)[name = tensor<string, []>("op_4009_cast_fp16")];
tensor<fp16, []> denom_107_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_107_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_107_cast_fp16 = rsqrt(epsilon = denom_107_epsilon_0_to_fp16, x = var_4009_cast_fp16)[name = tensor<string, []>("denom_107_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_107_cast_fp16 = mul(x = zero_mean_107_cast_fp16, y = denom_107_cast_fp16)[name = tensor<string, []>("out_107_cast_fp16")];
tensor<fp16, [1280]> input_175_gamma_0_to_fp16 = const()[name = tensor<string, []>("input_175_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1052223424)))];
tensor<fp16, [1280]> input_175_beta_0_to_fp16 = const()[name = tensor<string, []>("input_175_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1052226048)))];
tensor<fp16, []> input_175_epsilon_0_to_fp16 = const()[name = tensor<string, []>("input_175_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> input_175_cast_fp16 = batch_norm(beta = input_175_beta_0_to_fp16, epsilon = input_175_epsilon_0_to_fp16, gamma = input_175_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_107_cast_fp16)[name = tensor<string, []>("input_175_cast_fp16")];
tensor<int32, [2]> var_4020 = const()[name = tensor<string, []>("op_4020"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4022 = const()[name = tensor<string, []>("op_4022"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_177_pad_type_0 = const()[name = tensor<string, []>("input_177_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_177_pad_0 = const()[name = tensor<string, []>("input_177_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [5120, 1280, 1, 1]> layers_17_fc1_weight_to_fp16 = const()[name = tensor<string, []>("layers_17_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1052228672)))];
tensor<fp16, [5120]> layers_17_fc1_bias_to_fp16 = const()[name = tensor<string, []>("layers_17_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1065335936)))];
tensor<fp16, [1, 5120, 1, 1]> input_177_cast_fp16 = conv(bias = layers_17_fc1_bias_to_fp16, dilations = var_4022, groups = var_3833, pad = input_177_pad_0, pad_type = input_177_pad_type_0, strides = var_4020, weight = layers_17_fc1_weight_to_fp16, x = input_175_cast_fp16)[name = tensor<string, []>("input_177_cast_fp16")];
tensor<string, []> input_179_mode_0 = const()[name = tensor<string, []>("input_179_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 5120, 1, 1]> input_179_cast_fp16 = gelu(mode = input_179_mode_0, x = input_177_cast_fp16)[name = tensor<string, []>("input_179_cast_fp16")];
tensor<int32, [2]> var_4028 = const()[name = tensor<string, []>("op_4028"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4030 = const()[name = tensor<string, []>("op_4030"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_37_pad_type_0 = const()[name = tensor<string, []>("hidden_states_37_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_37_pad_0 = const()[name = tensor<string, []>("hidden_states_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 5120, 1, 1]> layers_17_fc2_weight_to_fp16 = const()[name = tensor<string, []>("layers_17_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1065346240)))];
tensor<fp16, [1280]> layers_17_fc2_bias_to_fp16 = const()[name = tensor<string, []>("layers_17_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1078453504)))];
tensor<fp16, [1, 1280, 1, 1]> hidden_states_37_cast_fp16 = conv(bias = layers_17_fc2_bias_to_fp16, dilations = var_4030, groups = var_3833, pad = hidden_states_37_pad_0, pad_type = hidden_states_37_pad_type_0, strides = var_4028, weight = layers_17_fc2_weight_to_fp16, x = input_179_cast_fp16)[name = tensor<string, []>("hidden_states_37_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_109_cast_fp16 = add(x = inputs_107_cast_fp16, y = hidden_states_37_cast_fp16)[name = tensor<string, []>("inputs_109_cast_fp16")];
tensor<int32, []> var_4044 = const()[name = tensor<string, []>("op_4044"), val = tensor<int32, []>(3)];
tensor<int32, []> var_4051 = const()[name = tensor<string, []>("op_4051"), val = tensor<int32, []>(1)];
tensor<bool, []> var_4052 = const()[name = tensor<string, []>("op_4052"), val = tensor<bool, []>(true)];
tensor<int32, [1]> var_4064 = const()[name = tensor<string, []>("op_4064"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_109_cast_fp16 = reduce_mean(axes = var_4064, keep_dims = var_4052, x = inputs_109_cast_fp16)[name = tensor<string, []>("channels_mean_109_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_109_cast_fp16 = sub(x = inputs_109_cast_fp16, y = channels_mean_109_cast_fp16)[name = tensor<string, []>("zero_mean_109_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_109_cast_fp16 = mul(x = zero_mean_109_cast_fp16, y = zero_mean_109_cast_fp16)[name = tensor<string, []>("zero_mean_sq_109_cast_fp16")];
tensor<int32, [1]> var_4068 = const()[name = tensor<string, []>("op_4068"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_4069_cast_fp16 = reduce_mean(axes = var_4068, keep_dims = var_4052, x = zero_mean_sq_109_cast_fp16)[name = tensor<string, []>("op_4069_cast_fp16")];
tensor<fp16, []> var_4070_to_fp16 = const()[name = tensor<string, []>("op_4070_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_4071_cast_fp16 = add(x = var_4069_cast_fp16, y = var_4070_to_fp16)[name = tensor<string, []>("op_4071_cast_fp16")];
tensor<fp16, []> denom_109_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_109_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_109_cast_fp16 = rsqrt(epsilon = denom_109_epsilon_0_to_fp16, x = var_4071_cast_fp16)[name = tensor<string, []>("denom_109_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_109_cast_fp16 = mul(x = zero_mean_109_cast_fp16, y = denom_109_cast_fp16)[name = tensor<string, []>("out_109_cast_fp16")];
tensor<fp16, [1280]> obj_253_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_253_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1078456128)))];
tensor<fp16, [1280]> obj_253_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_253_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1078458752)))];
tensor<fp16, []> obj_253_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_253_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_253_cast_fp16 = batch_norm(beta = obj_253_beta_0_to_fp16, epsilon = obj_253_epsilon_0_to_fp16, gamma = obj_253_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_109_cast_fp16)[name = tensor<string, []>("obj_253_cast_fp16")];
tensor<int32, [2]> var_4086 = const()[name = tensor<string, []>("op_4086"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4088 = const()[name = tensor<string, []>("op_4088"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_73_pad_type_0 = const()[name = tensor<string, []>("query_73_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_73_pad_0 = const()[name = tensor<string, []>("query_73_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_18_self_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_18_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1078461376)))];
tensor<fp16, [1280]> layers_18_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_18_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1081738240)))];
tensor<fp16, [1, 1280, 1, 1]> query_73_cast_fp16 = conv(bias = layers_18_self_attn_q_proj_bias_to_fp16, dilations = var_4088, groups = var_4051, pad = query_73_pad_0, pad_type = query_73_pad_type_0, strides = var_4086, weight = layers_18_self_attn_q_proj_weight_to_fp16, x = obj_253_cast_fp16)[name = tensor<string, []>("query_73_cast_fp16")];
tensor<int32, [2]> var_4092 = const()[name = tensor<string, []>("op_4092"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4094 = const()[name = tensor<string, []>("op_4094"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_key_37_pad_type_0 = const()[name = tensor<string, []>("current_key_37_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_key_37_pad_0 = const()[name = tensor<string, []>("current_key_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_18_self_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_18_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1081740864)))];
tensor<fp16, [1, 1280, 1, 1]> current_key_37_cast_fp16 = conv(dilations = var_4094, groups = var_4051, pad = current_key_37_pad_0, pad_type = current_key_37_pad_type_0, strides = var_4092, weight = layers_18_self_attn_k_proj_weight_to_fp16, x = obj_253_cast_fp16)[name = tensor<string, []>("current_key_37_cast_fp16")];
tensor<int32, [2]> var_4099 = const()[name = tensor<string, []>("op_4099"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4101 = const()[name = tensor<string, []>("op_4101"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_value_37_pad_type_0 = const()[name = tensor<string, []>("current_value_37_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_value_37_pad_0 = const()[name = tensor<string, []>("current_value_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_18_self_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_18_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1085017728)))];
tensor<fp16, [1280]> layers_18_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_18_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1088294592)))];
tensor<fp16, [1, 1280, 1, 1]> current_value_37_cast_fp16 = conv(bias = layers_18_self_attn_v_proj_bias_to_fp16, dilations = var_4101, groups = var_4051, pad = current_value_37_pad_0, pad_type = current_value_37_pad_type_0, strides = var_4099, weight = layers_18_self_attn_v_proj_weight_to_fp16, x = obj_253_cast_fp16)[name = tensor<string, []>("current_value_37_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_4108_cast_fp16 = mul(x = current_key_37_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_4108_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_4110_cast_fp16 = mul(x = var_103_cast_fp16_18, y = var_241_cast_fp16)[name = tensor<string, []>("op_4110_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> key_73_cast_fp16 = add(x = var_4108_cast_fp16, y = var_4110_cast_fp16)[name = tensor<string, []>("key_73_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_4112_cast_fp16 = mul(x = current_value_37_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_4112_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_4114_cast_fp16 = mul(x = var_138_cast_fp16_18, y = var_241_cast_fp16)[name = tensor<string, []>("op_4114_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> value_73_cast_fp16 = add(x = var_4112_cast_fp16, y = var_4114_cast_fp16)[name = tensor<string, []>("value_73_cast_fp16")];
tensor<int32, [4]> var_4117 = const()[name = tensor<string, []>("op_4117"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_4118_cast_fp16 = reshape(shape = var_4117, x = query_73_cast_fp16)[name = tensor<string, []>("op_4118_cast_fp16")];
tensor<fp16, []> var_4119_to_fp16 = const()[name = tensor<string, []>("op_4119_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_4120_cast_fp16 = mul(x = var_4118_cast_fp16, y = var_4119_to_fp16)[name = tensor<string, []>("op_4120_cast_fp16")];
tensor<int32, [4]> var_4121 = const()[name = tensor<string, []>("op_4121"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_4122_cast_fp16 = reshape(shape = var_4121, x = key_73_cast_fp16)[name = tensor<string, []>("op_4122_cast_fp16")];
tensor<bool, []> mh_w_109_transpose_x_0 = const()[name = tensor<string, []>("mh_w_109_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_109_transpose_y_0 = const()[name = tensor<string, []>("mh_w_109_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 224]> mh_w_109_cast_fp16 = matmul(transpose_x = mh_w_109_transpose_x_0, transpose_y = mh_w_109_transpose_y_0, x = var_4120_cast_fp16, y = var_4122_cast_fp16)[name = tensor<string, []>("mh_w_109_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> mh_w_111_cast_fp16 = add(x = mh_w_109_cast_fp16, y = var_259_cast_fp16)[name = tensor<string, []>("mh_w_111_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> var_4130_cast_fp16 = softmax(axis = var_4044, x = mh_w_111_cast_fp16)[name = tensor<string, []>("op_4130_cast_fp16")];
tensor<int32, [4]> var_4131 = const()[name = tensor<string, []>("op_4131"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_4132_cast_fp16 = reshape(shape = var_4131, x = value_73_cast_fp16)[name = tensor<string, []>("op_4132_cast_fp16")];
tensor<bool, []> attn_73_transpose_x_0 = const()[name = tensor<string, []>("attn_73_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_73_transpose_y_0 = const()[name = tensor<string, []>("attn_73_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_73_cast_fp16 = matmul(transpose_x = attn_73_transpose_x_0, transpose_y = attn_73_transpose_y_0, x = var_4132_cast_fp16, y = var_4130_cast_fp16)[name = tensor<string, []>("attn_73_cast_fp16")];
tensor<int32, [4]> var_4135 = const()[name = tensor<string, []>("op_4135"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_181_cast_fp16 = reshape(shape = var_4135, x = attn_73_cast_fp16)[name = tensor<string, []>("input_181_cast_fp16")];
tensor<int32, [2]> var_4139 = const()[name = tensor<string, []>("op_4139"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4141 = const()[name = tensor<string, []>("op_4141"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_259_pad_type_0 = const()[name = tensor<string, []>("obj_259_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_259_pad_0 = const()[name = tensor<string, []>("obj_259_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_18_self_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_18_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1088297216)))];
tensor<fp16, [1280]> layers_18_self_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_18_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1091574080)))];
tensor<fp16, [1, 1280, 1, 1]> obj_259_cast_fp16 = conv(bias = layers_18_self_attn_o_proj_bias_to_fp16, dilations = var_4141, groups = var_4051, pad = obj_259_pad_0, pad_type = obj_259_pad_type_0, strides = var_4139, weight = layers_18_self_attn_o_proj_weight_to_fp16, x = input_181_cast_fp16)[name = tensor<string, []>("obj_259_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_111_cast_fp16 = add(x = inputs_109_cast_fp16, y = obj_259_cast_fp16)[name = tensor<string, []>("inputs_111_cast_fp16")];
tensor<int32, [1]> var_4151 = const()[name = tensor<string, []>("op_4151"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_111_cast_fp16 = reduce_mean(axes = var_4151, keep_dims = var_4052, x = inputs_111_cast_fp16)[name = tensor<string, []>("channels_mean_111_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_111_cast_fp16 = sub(x = inputs_111_cast_fp16, y = channels_mean_111_cast_fp16)[name = tensor<string, []>("zero_mean_111_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_111_cast_fp16 = mul(x = zero_mean_111_cast_fp16, y = zero_mean_111_cast_fp16)[name = tensor<string, []>("zero_mean_sq_111_cast_fp16")];
tensor<int32, [1]> var_4155 = const()[name = tensor<string, []>("op_4155"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_4156_cast_fp16 = reduce_mean(axes = var_4155, keep_dims = var_4052, x = zero_mean_sq_111_cast_fp16)[name = tensor<string, []>("op_4156_cast_fp16")];
tensor<fp16, []> var_4157_to_fp16 = const()[name = tensor<string, []>("op_4157_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_4158_cast_fp16 = add(x = var_4156_cast_fp16, y = var_4157_to_fp16)[name = tensor<string, []>("op_4158_cast_fp16")];
tensor<fp16, []> denom_111_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_111_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_111_cast_fp16 = rsqrt(epsilon = denom_111_epsilon_0_to_fp16, x = var_4158_cast_fp16)[name = tensor<string, []>("denom_111_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_111_cast_fp16 = mul(x = zero_mean_111_cast_fp16, y = denom_111_cast_fp16)[name = tensor<string, []>("out_111_cast_fp16")];
tensor<fp16, [1280]> obj_261_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_261_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1091576704)))];
tensor<fp16, [1280]> obj_261_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_261_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1091579328)))];
tensor<fp16, []> obj_261_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_261_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_261_cast_fp16 = batch_norm(beta = obj_261_beta_0_to_fp16, epsilon = obj_261_epsilon_0_to_fp16, gamma = obj_261_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_111_cast_fp16)[name = tensor<string, []>("obj_261_cast_fp16")];
tensor<int32, [2]> var_4173 = const()[name = tensor<string, []>("op_4173"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4175 = const()[name = tensor<string, []>("op_4175"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_75_pad_type_0 = const()[name = tensor<string, []>("query_75_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_75_pad_0 = const()[name = tensor<string, []>("query_75_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_18_encoder_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_18_encoder_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1091581952)))];
tensor<fp16, [1280]> layers_18_encoder_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_18_encoder_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1094858816)))];
tensor<fp16, [1, 1280, 1, 1]> query_75_cast_fp16 = conv(bias = layers_18_encoder_attn_q_proj_bias_to_fp16, dilations = var_4175, groups = var_4051, pad = query_75_pad_0, pad_type = query_75_pad_type_0, strides = var_4173, weight = layers_18_encoder_attn_q_proj_weight_to_fp16, x = obj_261_cast_fp16)[name = tensor<string, []>("query_75_cast_fp16")];
tensor<int32, [2]> var_4179 = const()[name = tensor<string, []>("op_4179"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4181 = const()[name = tensor<string, []>("op_4181"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> key_75_pad_type_0 = const()[name = tensor<string, []>("key_75_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> key_75_pad_0 = const()[name = tensor<string, []>("key_75_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_18_encoder_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_18_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1094861440)))];
tensor<fp16, [1, 1280, 1, 1500]> key_75_cast_fp16 = conv(dilations = var_4181, groups = var_4051, pad = key_75_pad_0, pad_type = key_75_pad_type_0, strides = var_4179, weight = layers_18_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("key_75_cast_fp16")];
tensor<int32, [2]> var_4186 = const()[name = tensor<string, []>("op_4186"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4188 = const()[name = tensor<string, []>("op_4188"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> value_75_pad_type_0 = const()[name = tensor<string, []>("value_75_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> value_75_pad_0 = const()[name = tensor<string, []>("value_75_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_18_encoder_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_18_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1098138304)))];
tensor<fp16, [1280]> layers_18_encoder_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_18_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1101415168)))];
tensor<fp16, [1, 1280, 1, 1500]> value_75_cast_fp16 = conv(bias = layers_18_encoder_attn_v_proj_bias_to_fp16, dilations = var_4188, groups = var_4051, pad = value_75_pad_0, pad_type = value_75_pad_type_0, strides = var_4186, weight = layers_18_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("value_75_cast_fp16")];
tensor<int32, [4]> var_4192 = const()[name = tensor<string, []>("op_4192"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_4193_cast_fp16 = reshape(shape = var_4192, x = query_75_cast_fp16)[name = tensor<string, []>("op_4193_cast_fp16")];
tensor<fp16, []> var_4194_to_fp16 = const()[name = tensor<string, []>("op_4194_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_4195_cast_fp16 = mul(x = var_4193_cast_fp16, y = var_4194_to_fp16)[name = tensor<string, []>("op_4195_cast_fp16")];
tensor<int32, [4]> var_4196 = const()[name = tensor<string, []>("op_4196"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_4197_cast_fp16 = reshape(shape = var_4196, x = key_75_cast_fp16)[name = tensor<string, []>("op_4197_cast_fp16")];
tensor<bool, []> mh_w_113_transpose_x_0 = const()[name = tensor<string, []>("mh_w_113_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_113_transpose_y_0 = const()[name = tensor<string, []>("mh_w_113_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 1500]> mh_w_113_cast_fp16 = matmul(transpose_x = mh_w_113_transpose_x_0, transpose_y = mh_w_113_transpose_y_0, x = var_4195_cast_fp16, y = var_4197_cast_fp16)[name = tensor<string, []>("mh_w_113_cast_fp16")];
tensor<fp16, [1, 20, 1, 1500]> obj_265_cast_fp16 = softmax(axis = var_4044, x = mh_w_113_cast_fp16)[name = tensor<string, []>("obj_265_cast_fp16")];
tensor<int32, [4]> var_4201 = const()[name = tensor<string, []>("op_4201"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_4202_cast_fp16 = reshape(shape = var_4201, x = value_75_cast_fp16)[name = tensor<string, []>("op_4202_cast_fp16")];
tensor<bool, []> attn_75_transpose_x_0 = const()[name = tensor<string, []>("attn_75_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_75_transpose_y_0 = const()[name = tensor<string, []>("attn_75_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_75_cast_fp16 = matmul(transpose_x = attn_75_transpose_x_0, transpose_y = attn_75_transpose_y_0, x = var_4202_cast_fp16, y = obj_265_cast_fp16)[name = tensor<string, []>("attn_75_cast_fp16")];
tensor<int32, [4]> var_4205 = const()[name = tensor<string, []>("op_4205"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_183_cast_fp16 = reshape(shape = var_4205, x = attn_75_cast_fp16)[name = tensor<string, []>("input_183_cast_fp16")];
tensor<int32, [2]> var_4209 = const()[name = tensor<string, []>("op_4209"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4211 = const()[name = tensor<string, []>("op_4211"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_263_pad_type_0 = const()[name = tensor<string, []>("obj_263_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_263_pad_0 = const()[name = tensor<string, []>("obj_263_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_18_encoder_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_18_encoder_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1101417792)))];
tensor<fp16, [1280]> layers_18_encoder_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_18_encoder_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1104694656)))];
tensor<fp16, [1, 1280, 1, 1]> obj_263_cast_fp16 = conv(bias = layers_18_encoder_attn_o_proj_bias_to_fp16, dilations = var_4211, groups = var_4051, pad = obj_263_pad_0, pad_type = obj_263_pad_type_0, strides = var_4209, weight = layers_18_encoder_attn_o_proj_weight_to_fp16, x = input_183_cast_fp16)[name = tensor<string, []>("obj_263_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_113_cast_fp16 = add(x = inputs_111_cast_fp16, y = obj_263_cast_fp16)[name = tensor<string, []>("inputs_113_cast_fp16")];
tensor<int32, [1]> var_4220 = const()[name = tensor<string, []>("op_4220"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_113_cast_fp16 = reduce_mean(axes = var_4220, keep_dims = var_4052, x = inputs_113_cast_fp16)[name = tensor<string, []>("channels_mean_113_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_113_cast_fp16 = sub(x = inputs_113_cast_fp16, y = channels_mean_113_cast_fp16)[name = tensor<string, []>("zero_mean_113_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_113_cast_fp16 = mul(x = zero_mean_113_cast_fp16, y = zero_mean_113_cast_fp16)[name = tensor<string, []>("zero_mean_sq_113_cast_fp16")];
tensor<int32, [1]> var_4224 = const()[name = tensor<string, []>("op_4224"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_4225_cast_fp16 = reduce_mean(axes = var_4224, keep_dims = var_4052, x = zero_mean_sq_113_cast_fp16)[name = tensor<string, []>("op_4225_cast_fp16")];
tensor<fp16, []> var_4226_to_fp16 = const()[name = tensor<string, []>("op_4226_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_4227_cast_fp16 = add(x = var_4225_cast_fp16, y = var_4226_to_fp16)[name = tensor<string, []>("op_4227_cast_fp16")];
tensor<fp16, []> denom_113_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_113_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_113_cast_fp16 = rsqrt(epsilon = denom_113_epsilon_0_to_fp16, x = var_4227_cast_fp16)[name = tensor<string, []>("denom_113_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_113_cast_fp16 = mul(x = zero_mean_113_cast_fp16, y = denom_113_cast_fp16)[name = tensor<string, []>("out_113_cast_fp16")];
tensor<fp16, [1280]> input_185_gamma_0_to_fp16 = const()[name = tensor<string, []>("input_185_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1104697280)))];
tensor<fp16, [1280]> input_185_beta_0_to_fp16 = const()[name = tensor<string, []>("input_185_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1104699904)))];
tensor<fp16, []> input_185_epsilon_0_to_fp16 = const()[name = tensor<string, []>("input_185_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> input_185_cast_fp16 = batch_norm(beta = input_185_beta_0_to_fp16, epsilon = input_185_epsilon_0_to_fp16, gamma = input_185_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_113_cast_fp16)[name = tensor<string, []>("input_185_cast_fp16")];
tensor<int32, [2]> var_4238 = const()[name = tensor<string, []>("op_4238"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4240 = const()[name = tensor<string, []>("op_4240"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_187_pad_type_0 = const()[name = tensor<string, []>("input_187_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_187_pad_0 = const()[name = tensor<string, []>("input_187_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [5120, 1280, 1, 1]> layers_18_fc1_weight_to_fp16 = const()[name = tensor<string, []>("layers_18_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1104702528)))];
tensor<fp16, [5120]> layers_18_fc1_bias_to_fp16 = const()[name = tensor<string, []>("layers_18_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1117809792)))];
tensor<fp16, [1, 5120, 1, 1]> input_187_cast_fp16 = conv(bias = layers_18_fc1_bias_to_fp16, dilations = var_4240, groups = var_4051, pad = input_187_pad_0, pad_type = input_187_pad_type_0, strides = var_4238, weight = layers_18_fc1_weight_to_fp16, x = input_185_cast_fp16)[name = tensor<string, []>("input_187_cast_fp16")];
tensor<string, []> input_189_mode_0 = const()[name = tensor<string, []>("input_189_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 5120, 1, 1]> input_189_cast_fp16 = gelu(mode = input_189_mode_0, x = input_187_cast_fp16)[name = tensor<string, []>("input_189_cast_fp16")];
tensor<int32, [2]> var_4246 = const()[name = tensor<string, []>("op_4246"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4248 = const()[name = tensor<string, []>("op_4248"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_39_pad_type_0 = const()[name = tensor<string, []>("hidden_states_39_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_39_pad_0 = const()[name = tensor<string, []>("hidden_states_39_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 5120, 1, 1]> layers_18_fc2_weight_to_fp16 = const()[name = tensor<string, []>("layers_18_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1117820096)))];
tensor<fp16, [1280]> layers_18_fc2_bias_to_fp16 = const()[name = tensor<string, []>("layers_18_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1130927360)))];
tensor<fp16, [1, 1280, 1, 1]> hidden_states_39_cast_fp16 = conv(bias = layers_18_fc2_bias_to_fp16, dilations = var_4248, groups = var_4051, pad = hidden_states_39_pad_0, pad_type = hidden_states_39_pad_type_0, strides = var_4246, weight = layers_18_fc2_weight_to_fp16, x = input_189_cast_fp16)[name = tensor<string, []>("hidden_states_39_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_115_cast_fp16 = add(x = inputs_113_cast_fp16, y = hidden_states_39_cast_fp16)[name = tensor<string, []>("inputs_115_cast_fp16")];
tensor<int32, []> var_4262 = const()[name = tensor<string, []>("op_4262"), val = tensor<int32, []>(3)];
tensor<int32, []> var_4269 = const()[name = tensor<string, []>("op_4269"), val = tensor<int32, []>(1)];
tensor<bool, []> var_4270 = const()[name = tensor<string, []>("op_4270"), val = tensor<bool, []>(true)];
tensor<int32, [1]> var_4282 = const()[name = tensor<string, []>("op_4282"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_115_cast_fp16 = reduce_mean(axes = var_4282, keep_dims = var_4270, x = inputs_115_cast_fp16)[name = tensor<string, []>("channels_mean_115_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_115_cast_fp16 = sub(x = inputs_115_cast_fp16, y = channels_mean_115_cast_fp16)[name = tensor<string, []>("zero_mean_115_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_115_cast_fp16 = mul(x = zero_mean_115_cast_fp16, y = zero_mean_115_cast_fp16)[name = tensor<string, []>("zero_mean_sq_115_cast_fp16")];
tensor<int32, [1]> var_4286 = const()[name = tensor<string, []>("op_4286"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_4287_cast_fp16 = reduce_mean(axes = var_4286, keep_dims = var_4270, x = zero_mean_sq_115_cast_fp16)[name = tensor<string, []>("op_4287_cast_fp16")];
tensor<fp16, []> var_4288_to_fp16 = const()[name = tensor<string, []>("op_4288_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_4289_cast_fp16 = add(x = var_4287_cast_fp16, y = var_4288_to_fp16)[name = tensor<string, []>("op_4289_cast_fp16")];
tensor<fp16, []> denom_115_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_115_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_115_cast_fp16 = rsqrt(epsilon = denom_115_epsilon_0_to_fp16, x = var_4289_cast_fp16)[name = tensor<string, []>("denom_115_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_115_cast_fp16 = mul(x = zero_mean_115_cast_fp16, y = denom_115_cast_fp16)[name = tensor<string, []>("out_115_cast_fp16")];
tensor<fp16, [1280]> obj_267_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_267_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1130929984)))];
tensor<fp16, [1280]> obj_267_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_267_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1130932608)))];
tensor<fp16, []> obj_267_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_267_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_267_cast_fp16 = batch_norm(beta = obj_267_beta_0_to_fp16, epsilon = obj_267_epsilon_0_to_fp16, gamma = obj_267_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_115_cast_fp16)[name = tensor<string, []>("obj_267_cast_fp16")];
tensor<int32, [2]> var_4304 = const()[name = tensor<string, []>("op_4304"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4306 = const()[name = tensor<string, []>("op_4306"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_77_pad_type_0 = const()[name = tensor<string, []>("query_77_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_77_pad_0 = const()[name = tensor<string, []>("query_77_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_19_self_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_19_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1130935232)))];
tensor<fp16, [1280]> layers_19_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_19_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1134212096)))];
tensor<fp16, [1, 1280, 1, 1]> query_77_cast_fp16 = conv(bias = layers_19_self_attn_q_proj_bias_to_fp16, dilations = var_4306, groups = var_4269, pad = query_77_pad_0, pad_type = query_77_pad_type_0, strides = var_4304, weight = layers_19_self_attn_q_proj_weight_to_fp16, x = obj_267_cast_fp16)[name = tensor<string, []>("query_77_cast_fp16")];
tensor<int32, [2]> var_4310 = const()[name = tensor<string, []>("op_4310"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4312 = const()[name = tensor<string, []>("op_4312"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_key_39_pad_type_0 = const()[name = tensor<string, []>("current_key_39_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_key_39_pad_0 = const()[name = tensor<string, []>("current_key_39_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_19_self_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_19_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1134214720)))];
tensor<fp16, [1, 1280, 1, 1]> current_key_39_cast_fp16 = conv(dilations = var_4312, groups = var_4269, pad = current_key_39_pad_0, pad_type = current_key_39_pad_type_0, strides = var_4310, weight = layers_19_self_attn_k_proj_weight_to_fp16, x = obj_267_cast_fp16)[name = tensor<string, []>("current_key_39_cast_fp16")];
tensor<int32, [2]> var_4317 = const()[name = tensor<string, []>("op_4317"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4319 = const()[name = tensor<string, []>("op_4319"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_value_39_pad_type_0 = const()[name = tensor<string, []>("current_value_39_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_value_39_pad_0 = const()[name = tensor<string, []>("current_value_39_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_19_self_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_19_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1137491584)))];
tensor<fp16, [1280]> layers_19_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_19_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1140768448)))];
tensor<fp16, [1, 1280, 1, 1]> current_value_39_cast_fp16 = conv(bias = layers_19_self_attn_v_proj_bias_to_fp16, dilations = var_4319, groups = var_4269, pad = current_value_39_pad_0, pad_type = current_value_39_pad_type_0, strides = var_4317, weight = layers_19_self_attn_v_proj_weight_to_fp16, x = obj_267_cast_fp16)[name = tensor<string, []>("current_value_39_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_4326_cast_fp16 = mul(x = current_key_39_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_4326_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_4328_cast_fp16 = mul(x = var_103_cast_fp16_19, y = var_241_cast_fp16)[name = tensor<string, []>("op_4328_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> key_77_cast_fp16 = add(x = var_4326_cast_fp16, y = var_4328_cast_fp16)[name = tensor<string, []>("key_77_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_4330_cast_fp16 = mul(x = current_value_39_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_4330_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_4332_cast_fp16 = mul(x = var_138_cast_fp16_19, y = var_241_cast_fp16)[name = tensor<string, []>("op_4332_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> value_77_cast_fp16 = add(x = var_4330_cast_fp16, y = var_4332_cast_fp16)[name = tensor<string, []>("value_77_cast_fp16")];
tensor<int32, [4]> var_4335 = const()[name = tensor<string, []>("op_4335"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_4336_cast_fp16 = reshape(shape = var_4335, x = query_77_cast_fp16)[name = tensor<string, []>("op_4336_cast_fp16")];
tensor<fp16, []> var_4337_to_fp16 = const()[name = tensor<string, []>("op_4337_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_4338_cast_fp16 = mul(x = var_4336_cast_fp16, y = var_4337_to_fp16)[name = tensor<string, []>("op_4338_cast_fp16")];
tensor<int32, [4]> var_4339 = const()[name = tensor<string, []>("op_4339"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_4340_cast_fp16 = reshape(shape = var_4339, x = key_77_cast_fp16)[name = tensor<string, []>("op_4340_cast_fp16")];
tensor<bool, []> mh_w_115_transpose_x_0 = const()[name = tensor<string, []>("mh_w_115_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_115_transpose_y_0 = const()[name = tensor<string, []>("mh_w_115_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 224]> mh_w_115_cast_fp16 = matmul(transpose_x = mh_w_115_transpose_x_0, transpose_y = mh_w_115_transpose_y_0, x = var_4338_cast_fp16, y = var_4340_cast_fp16)[name = tensor<string, []>("mh_w_115_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> mh_w_117_cast_fp16 = add(x = mh_w_115_cast_fp16, y = var_259_cast_fp16)[name = tensor<string, []>("mh_w_117_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> var_4348_cast_fp16 = softmax(axis = var_4262, x = mh_w_117_cast_fp16)[name = tensor<string, []>("op_4348_cast_fp16")];
tensor<int32, [4]> var_4349 = const()[name = tensor<string, []>("op_4349"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_4350_cast_fp16 = reshape(shape = var_4349, x = value_77_cast_fp16)[name = tensor<string, []>("op_4350_cast_fp16")];
tensor<bool, []> attn_77_transpose_x_0 = const()[name = tensor<string, []>("attn_77_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_77_transpose_y_0 = const()[name = tensor<string, []>("attn_77_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_77_cast_fp16 = matmul(transpose_x = attn_77_transpose_x_0, transpose_y = attn_77_transpose_y_0, x = var_4350_cast_fp16, y = var_4348_cast_fp16)[name = tensor<string, []>("attn_77_cast_fp16")];
tensor<int32, [4]> var_4353 = const()[name = tensor<string, []>("op_4353"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_191_cast_fp16 = reshape(shape = var_4353, x = attn_77_cast_fp16)[name = tensor<string, []>("input_191_cast_fp16")];
tensor<int32, [2]> var_4357 = const()[name = tensor<string, []>("op_4357"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4359 = const()[name = tensor<string, []>("op_4359"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_273_pad_type_0 = const()[name = tensor<string, []>("obj_273_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_273_pad_0 = const()[name = tensor<string, []>("obj_273_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_19_self_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_19_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1140771072)))];
tensor<fp16, [1280]> layers_19_self_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_19_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1144047936)))];
tensor<fp16, [1, 1280, 1, 1]> obj_273_cast_fp16 = conv(bias = layers_19_self_attn_o_proj_bias_to_fp16, dilations = var_4359, groups = var_4269, pad = obj_273_pad_0, pad_type = obj_273_pad_type_0, strides = var_4357, weight = layers_19_self_attn_o_proj_weight_to_fp16, x = input_191_cast_fp16)[name = tensor<string, []>("obj_273_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_117_cast_fp16 = add(x = inputs_115_cast_fp16, y = obj_273_cast_fp16)[name = tensor<string, []>("inputs_117_cast_fp16")];
tensor<int32, [1]> var_4369 = const()[name = tensor<string, []>("op_4369"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_117_cast_fp16 = reduce_mean(axes = var_4369, keep_dims = var_4270, x = inputs_117_cast_fp16)[name = tensor<string, []>("channels_mean_117_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_117_cast_fp16 = sub(x = inputs_117_cast_fp16, y = channels_mean_117_cast_fp16)[name = tensor<string, []>("zero_mean_117_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_117_cast_fp16 = mul(x = zero_mean_117_cast_fp16, y = zero_mean_117_cast_fp16)[name = tensor<string, []>("zero_mean_sq_117_cast_fp16")];
tensor<int32, [1]> var_4373 = const()[name = tensor<string, []>("op_4373"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_4374_cast_fp16 = reduce_mean(axes = var_4373, keep_dims = var_4270, x = zero_mean_sq_117_cast_fp16)[name = tensor<string, []>("op_4374_cast_fp16")];
tensor<fp16, []> var_4375_to_fp16 = const()[name = tensor<string, []>("op_4375_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_4376_cast_fp16 = add(x = var_4374_cast_fp16, y = var_4375_to_fp16)[name = tensor<string, []>("op_4376_cast_fp16")];
tensor<fp16, []> denom_117_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_117_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_117_cast_fp16 = rsqrt(epsilon = denom_117_epsilon_0_to_fp16, x = var_4376_cast_fp16)[name = tensor<string, []>("denom_117_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_117_cast_fp16 = mul(x = zero_mean_117_cast_fp16, y = denom_117_cast_fp16)[name = tensor<string, []>("out_117_cast_fp16")];
tensor<fp16, [1280]> obj_275_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_275_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1144050560)))];
tensor<fp16, [1280]> obj_275_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_275_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1144053184)))];
tensor<fp16, []> obj_275_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_275_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_275_cast_fp16 = batch_norm(beta = obj_275_beta_0_to_fp16, epsilon = obj_275_epsilon_0_to_fp16, gamma = obj_275_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_117_cast_fp16)[name = tensor<string, []>("obj_275_cast_fp16")];
tensor<int32, [2]> var_4391 = const()[name = tensor<string, []>("op_4391"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4393 = const()[name = tensor<string, []>("op_4393"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_79_pad_type_0 = const()[name = tensor<string, []>("query_79_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_79_pad_0 = const()[name = tensor<string, []>("query_79_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_19_encoder_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_19_encoder_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1144055808)))];
tensor<fp16, [1280]> layers_19_encoder_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_19_encoder_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1147332672)))];
tensor<fp16, [1, 1280, 1, 1]> query_79_cast_fp16 = conv(bias = layers_19_encoder_attn_q_proj_bias_to_fp16, dilations = var_4393, groups = var_4269, pad = query_79_pad_0, pad_type = query_79_pad_type_0, strides = var_4391, weight = layers_19_encoder_attn_q_proj_weight_to_fp16, x = obj_275_cast_fp16)[name = tensor<string, []>("query_79_cast_fp16")];
tensor<int32, [2]> var_4397 = const()[name = tensor<string, []>("op_4397"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4399 = const()[name = tensor<string, []>("op_4399"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> key_79_pad_type_0 = const()[name = tensor<string, []>("key_79_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> key_79_pad_0 = const()[name = tensor<string, []>("key_79_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_19_encoder_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_19_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1147335296)))];
tensor<fp16, [1, 1280, 1, 1500]> key_79_cast_fp16 = conv(dilations = var_4399, groups = var_4269, pad = key_79_pad_0, pad_type = key_79_pad_type_0, strides = var_4397, weight = layers_19_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("key_79_cast_fp16")];
tensor<int32, [2]> var_4404 = const()[name = tensor<string, []>("op_4404"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4406 = const()[name = tensor<string, []>("op_4406"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> value_79_pad_type_0 = const()[name = tensor<string, []>("value_79_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> value_79_pad_0 = const()[name = tensor<string, []>("value_79_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_19_encoder_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_19_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1150612160)))];
tensor<fp16, [1280]> layers_19_encoder_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_19_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1153889024)))];
tensor<fp16, [1, 1280, 1, 1500]> value_79_cast_fp16 = conv(bias = layers_19_encoder_attn_v_proj_bias_to_fp16, dilations = var_4406, groups = var_4269, pad = value_79_pad_0, pad_type = value_79_pad_type_0, strides = var_4404, weight = layers_19_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("value_79_cast_fp16")];
tensor<int32, [4]> var_4410 = const()[name = tensor<string, []>("op_4410"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_4411_cast_fp16 = reshape(shape = var_4410, x = query_79_cast_fp16)[name = tensor<string, []>("op_4411_cast_fp16")];
tensor<fp16, []> var_4412_to_fp16 = const()[name = tensor<string, []>("op_4412_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_4413_cast_fp16 = mul(x = var_4411_cast_fp16, y = var_4412_to_fp16)[name = tensor<string, []>("op_4413_cast_fp16")];
tensor<int32, [4]> var_4414 = const()[name = tensor<string, []>("op_4414"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_4415_cast_fp16 = reshape(shape = var_4414, x = key_79_cast_fp16)[name = tensor<string, []>("op_4415_cast_fp16")];
tensor<bool, []> mh_w_119_transpose_x_0 = const()[name = tensor<string, []>("mh_w_119_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_119_transpose_y_0 = const()[name = tensor<string, []>("mh_w_119_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 1500]> mh_w_119_cast_fp16 = matmul(transpose_x = mh_w_119_transpose_x_0, transpose_y = mh_w_119_transpose_y_0, x = var_4413_cast_fp16, y = var_4415_cast_fp16)[name = tensor<string, []>("mh_w_119_cast_fp16")];
tensor<fp16, [1, 20, 1, 1500]> obj_279_cast_fp16 = softmax(axis = var_4262, x = mh_w_119_cast_fp16)[name = tensor<string, []>("obj_279_cast_fp16")];
tensor<int32, [4]> var_4419 = const()[name = tensor<string, []>("op_4419"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_4420_cast_fp16 = reshape(shape = var_4419, x = value_79_cast_fp16)[name = tensor<string, []>("op_4420_cast_fp16")];
tensor<bool, []> attn_79_transpose_x_0 = const()[name = tensor<string, []>("attn_79_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_79_transpose_y_0 = const()[name = tensor<string, []>("attn_79_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_79_cast_fp16 = matmul(transpose_x = attn_79_transpose_x_0, transpose_y = attn_79_transpose_y_0, x = var_4420_cast_fp16, y = obj_279_cast_fp16)[name = tensor<string, []>("attn_79_cast_fp16")];
tensor<int32, [4]> var_4423 = const()[name = tensor<string, []>("op_4423"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_193_cast_fp16 = reshape(shape = var_4423, x = attn_79_cast_fp16)[name = tensor<string, []>("input_193_cast_fp16")];
tensor<int32, [2]> var_4427 = const()[name = tensor<string, []>("op_4427"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4429 = const()[name = tensor<string, []>("op_4429"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_277_pad_type_0 = const()[name = tensor<string, []>("obj_277_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_277_pad_0 = const()[name = tensor<string, []>("obj_277_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_19_encoder_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_19_encoder_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1153891648)))];
tensor<fp16, [1280]> layers_19_encoder_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_19_encoder_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1157168512)))];
tensor<fp16, [1, 1280, 1, 1]> obj_277_cast_fp16 = conv(bias = layers_19_encoder_attn_o_proj_bias_to_fp16, dilations = var_4429, groups = var_4269, pad = obj_277_pad_0, pad_type = obj_277_pad_type_0, strides = var_4427, weight = layers_19_encoder_attn_o_proj_weight_to_fp16, x = input_193_cast_fp16)[name = tensor<string, []>("obj_277_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_119_cast_fp16 = add(x = inputs_117_cast_fp16, y = obj_277_cast_fp16)[name = tensor<string, []>("inputs_119_cast_fp16")];
tensor<int32, [1]> var_4438 = const()[name = tensor<string, []>("op_4438"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_119_cast_fp16 = reduce_mean(axes = var_4438, keep_dims = var_4270, x = inputs_119_cast_fp16)[name = tensor<string, []>("channels_mean_119_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_119_cast_fp16 = sub(x = inputs_119_cast_fp16, y = channels_mean_119_cast_fp16)[name = tensor<string, []>("zero_mean_119_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_119_cast_fp16 = mul(x = zero_mean_119_cast_fp16, y = zero_mean_119_cast_fp16)[name = tensor<string, []>("zero_mean_sq_119_cast_fp16")];
tensor<int32, [1]> var_4442 = const()[name = tensor<string, []>("op_4442"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_4443_cast_fp16 = reduce_mean(axes = var_4442, keep_dims = var_4270, x = zero_mean_sq_119_cast_fp16)[name = tensor<string, []>("op_4443_cast_fp16")];
tensor<fp16, []> var_4444_to_fp16 = const()[name = tensor<string, []>("op_4444_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_4445_cast_fp16 = add(x = var_4443_cast_fp16, y = var_4444_to_fp16)[name = tensor<string, []>("op_4445_cast_fp16")];
tensor<fp16, []> denom_119_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_119_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_119_cast_fp16 = rsqrt(epsilon = denom_119_epsilon_0_to_fp16, x = var_4445_cast_fp16)[name = tensor<string, []>("denom_119_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_119_cast_fp16 = mul(x = zero_mean_119_cast_fp16, y = denom_119_cast_fp16)[name = tensor<string, []>("out_119_cast_fp16")];
tensor<fp16, [1280]> input_195_gamma_0_to_fp16 = const()[name = tensor<string, []>("input_195_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1157171136)))];
tensor<fp16, [1280]> input_195_beta_0_to_fp16 = const()[name = tensor<string, []>("input_195_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1157173760)))];
tensor<fp16, []> input_195_epsilon_0_to_fp16 = const()[name = tensor<string, []>("input_195_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> input_195_cast_fp16 = batch_norm(beta = input_195_beta_0_to_fp16, epsilon = input_195_epsilon_0_to_fp16, gamma = input_195_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_119_cast_fp16)[name = tensor<string, []>("input_195_cast_fp16")];
tensor<int32, [2]> var_4456 = const()[name = tensor<string, []>("op_4456"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4458 = const()[name = tensor<string, []>("op_4458"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_197_pad_type_0 = const()[name = tensor<string, []>("input_197_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_197_pad_0 = const()[name = tensor<string, []>("input_197_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [5120, 1280, 1, 1]> layers_19_fc1_weight_to_fp16 = const()[name = tensor<string, []>("layers_19_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1157176384)))];
tensor<fp16, [5120]> layers_19_fc1_bias_to_fp16 = const()[name = tensor<string, []>("layers_19_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1170283648)))];
tensor<fp16, [1, 5120, 1, 1]> input_197_cast_fp16 = conv(bias = layers_19_fc1_bias_to_fp16, dilations = var_4458, groups = var_4269, pad = input_197_pad_0, pad_type = input_197_pad_type_0, strides = var_4456, weight = layers_19_fc1_weight_to_fp16, x = input_195_cast_fp16)[name = tensor<string, []>("input_197_cast_fp16")];
tensor<string, []> input_199_mode_0 = const()[name = tensor<string, []>("input_199_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 5120, 1, 1]> input_199_cast_fp16 = gelu(mode = input_199_mode_0, x = input_197_cast_fp16)[name = tensor<string, []>("input_199_cast_fp16")];
tensor<int32, [2]> var_4464 = const()[name = tensor<string, []>("op_4464"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4466 = const()[name = tensor<string, []>("op_4466"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_41_pad_type_0 = const()[name = tensor<string, []>("hidden_states_41_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_41_pad_0 = const()[name = tensor<string, []>("hidden_states_41_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 5120, 1, 1]> layers_19_fc2_weight_to_fp16 = const()[name = tensor<string, []>("layers_19_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1170293952)))];
tensor<fp16, [1280]> layers_19_fc2_bias_to_fp16 = const()[name = tensor<string, []>("layers_19_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1183401216)))];
tensor<fp16, [1, 1280, 1, 1]> hidden_states_41_cast_fp16 = conv(bias = layers_19_fc2_bias_to_fp16, dilations = var_4466, groups = var_4269, pad = hidden_states_41_pad_0, pad_type = hidden_states_41_pad_type_0, strides = var_4464, weight = layers_19_fc2_weight_to_fp16, x = input_199_cast_fp16)[name = tensor<string, []>("hidden_states_41_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_121_cast_fp16 = add(x = inputs_119_cast_fp16, y = hidden_states_41_cast_fp16)[name = tensor<string, []>("inputs_121_cast_fp16")];
tensor<int32, []> var_4480 = const()[name = tensor<string, []>("op_4480"), val = tensor<int32, []>(3)];
tensor<int32, []> var_4487 = const()[name = tensor<string, []>("op_4487"), val = tensor<int32, []>(1)];
tensor<bool, []> var_4488 = const()[name = tensor<string, []>("op_4488"), val = tensor<bool, []>(true)];
tensor<int32, [1]> var_4500 = const()[name = tensor<string, []>("op_4500"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_121_cast_fp16 = reduce_mean(axes = var_4500, keep_dims = var_4488, x = inputs_121_cast_fp16)[name = tensor<string, []>("channels_mean_121_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_121_cast_fp16 = sub(x = inputs_121_cast_fp16, y = channels_mean_121_cast_fp16)[name = tensor<string, []>("zero_mean_121_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_121_cast_fp16 = mul(x = zero_mean_121_cast_fp16, y = zero_mean_121_cast_fp16)[name = tensor<string, []>("zero_mean_sq_121_cast_fp16")];
tensor<int32, [1]> var_4504 = const()[name = tensor<string, []>("op_4504"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_4505_cast_fp16 = reduce_mean(axes = var_4504, keep_dims = var_4488, x = zero_mean_sq_121_cast_fp16)[name = tensor<string, []>("op_4505_cast_fp16")];
tensor<fp16, []> var_4506_to_fp16 = const()[name = tensor<string, []>("op_4506_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_4507_cast_fp16 = add(x = var_4505_cast_fp16, y = var_4506_to_fp16)[name = tensor<string, []>("op_4507_cast_fp16")];
tensor<fp16, []> denom_121_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_121_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_121_cast_fp16 = rsqrt(epsilon = denom_121_epsilon_0_to_fp16, x = var_4507_cast_fp16)[name = tensor<string, []>("denom_121_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_121_cast_fp16 = mul(x = zero_mean_121_cast_fp16, y = denom_121_cast_fp16)[name = tensor<string, []>("out_121_cast_fp16")];
tensor<fp16, [1280]> obj_281_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_281_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1183403840)))];
tensor<fp16, [1280]> obj_281_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_281_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1183406464)))];
tensor<fp16, []> obj_281_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_281_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_281_cast_fp16 = batch_norm(beta = obj_281_beta_0_to_fp16, epsilon = obj_281_epsilon_0_to_fp16, gamma = obj_281_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_121_cast_fp16)[name = tensor<string, []>("obj_281_cast_fp16")];
tensor<int32, [2]> var_4522 = const()[name = tensor<string, []>("op_4522"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4524 = const()[name = tensor<string, []>("op_4524"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_81_pad_type_0 = const()[name = tensor<string, []>("query_81_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_81_pad_0 = const()[name = tensor<string, []>("query_81_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_20_self_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_20_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1183409088)))];
tensor<fp16, [1280]> layers_20_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_20_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1186685952)))];
tensor<fp16, [1, 1280, 1, 1]> query_81_cast_fp16 = conv(bias = layers_20_self_attn_q_proj_bias_to_fp16, dilations = var_4524, groups = var_4487, pad = query_81_pad_0, pad_type = query_81_pad_type_0, strides = var_4522, weight = layers_20_self_attn_q_proj_weight_to_fp16, x = obj_281_cast_fp16)[name = tensor<string, []>("query_81_cast_fp16")];
tensor<int32, [2]> var_4528 = const()[name = tensor<string, []>("op_4528"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4530 = const()[name = tensor<string, []>("op_4530"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_key_41_pad_type_0 = const()[name = tensor<string, []>("current_key_41_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_key_41_pad_0 = const()[name = tensor<string, []>("current_key_41_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_20_self_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_20_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1186688576)))];
tensor<fp16, [1, 1280, 1, 1]> current_key_41_cast_fp16 = conv(dilations = var_4530, groups = var_4487, pad = current_key_41_pad_0, pad_type = current_key_41_pad_type_0, strides = var_4528, weight = layers_20_self_attn_k_proj_weight_to_fp16, x = obj_281_cast_fp16)[name = tensor<string, []>("current_key_41_cast_fp16")];
tensor<int32, [2]> var_4535 = const()[name = tensor<string, []>("op_4535"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4537 = const()[name = tensor<string, []>("op_4537"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_value_41_pad_type_0 = const()[name = tensor<string, []>("current_value_41_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_value_41_pad_0 = const()[name = tensor<string, []>("current_value_41_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_20_self_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_20_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1189965440)))];
tensor<fp16, [1280]> layers_20_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_20_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1193242304)))];
tensor<fp16, [1, 1280, 1, 1]> current_value_41_cast_fp16 = conv(bias = layers_20_self_attn_v_proj_bias_to_fp16, dilations = var_4537, groups = var_4487, pad = current_value_41_pad_0, pad_type = current_value_41_pad_type_0, strides = var_4535, weight = layers_20_self_attn_v_proj_weight_to_fp16, x = obj_281_cast_fp16)[name = tensor<string, []>("current_value_41_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_4544_cast_fp16 = mul(x = current_key_41_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_4544_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_4546_cast_fp16 = mul(x = var_103_cast_fp16_20, y = var_241_cast_fp16)[name = tensor<string, []>("op_4546_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> key_81_cast_fp16 = add(x = var_4544_cast_fp16, y = var_4546_cast_fp16)[name = tensor<string, []>("key_81_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_4548_cast_fp16 = mul(x = current_value_41_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_4548_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_4550_cast_fp16 = mul(x = var_138_cast_fp16_20, y = var_241_cast_fp16)[name = tensor<string, []>("op_4550_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> value_81_cast_fp16 = add(x = var_4548_cast_fp16, y = var_4550_cast_fp16)[name = tensor<string, []>("value_81_cast_fp16")];
tensor<int32, [4]> var_4553 = const()[name = tensor<string, []>("op_4553"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_4554_cast_fp16 = reshape(shape = var_4553, x = query_81_cast_fp16)[name = tensor<string, []>("op_4554_cast_fp16")];
tensor<fp16, []> var_4555_to_fp16 = const()[name = tensor<string, []>("op_4555_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_4556_cast_fp16 = mul(x = var_4554_cast_fp16, y = var_4555_to_fp16)[name = tensor<string, []>("op_4556_cast_fp16")];
tensor<int32, [4]> var_4557 = const()[name = tensor<string, []>("op_4557"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_4558_cast_fp16 = reshape(shape = var_4557, x = key_81_cast_fp16)[name = tensor<string, []>("op_4558_cast_fp16")];
tensor<bool, []> mh_w_121_transpose_x_0 = const()[name = tensor<string, []>("mh_w_121_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_121_transpose_y_0 = const()[name = tensor<string, []>("mh_w_121_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 224]> mh_w_121_cast_fp16 = matmul(transpose_x = mh_w_121_transpose_x_0, transpose_y = mh_w_121_transpose_y_0, x = var_4556_cast_fp16, y = var_4558_cast_fp16)[name = tensor<string, []>("mh_w_121_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> mh_w_123_cast_fp16 = add(x = mh_w_121_cast_fp16, y = var_259_cast_fp16)[name = tensor<string, []>("mh_w_123_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> var_4566_cast_fp16 = softmax(axis = var_4480, x = mh_w_123_cast_fp16)[name = tensor<string, []>("op_4566_cast_fp16")];
tensor<int32, [4]> var_4567 = const()[name = tensor<string, []>("op_4567"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_4568_cast_fp16 = reshape(shape = var_4567, x = value_81_cast_fp16)[name = tensor<string, []>("op_4568_cast_fp16")];
tensor<bool, []> attn_81_transpose_x_0 = const()[name = tensor<string, []>("attn_81_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_81_transpose_y_0 = const()[name = tensor<string, []>("attn_81_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_81_cast_fp16 = matmul(transpose_x = attn_81_transpose_x_0, transpose_y = attn_81_transpose_y_0, x = var_4568_cast_fp16, y = var_4566_cast_fp16)[name = tensor<string, []>("attn_81_cast_fp16")];
tensor<int32, [4]> var_4571 = const()[name = tensor<string, []>("op_4571"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_201_cast_fp16 = reshape(shape = var_4571, x = attn_81_cast_fp16)[name = tensor<string, []>("input_201_cast_fp16")];
tensor<int32, [2]> var_4575 = const()[name = tensor<string, []>("op_4575"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4577 = const()[name = tensor<string, []>("op_4577"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_287_pad_type_0 = const()[name = tensor<string, []>("obj_287_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_287_pad_0 = const()[name = tensor<string, []>("obj_287_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_20_self_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_20_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1193244928)))];
tensor<fp16, [1280]> layers_20_self_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_20_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1196521792)))];
tensor<fp16, [1, 1280, 1, 1]> obj_287_cast_fp16 = conv(bias = layers_20_self_attn_o_proj_bias_to_fp16, dilations = var_4577, groups = var_4487, pad = obj_287_pad_0, pad_type = obj_287_pad_type_0, strides = var_4575, weight = layers_20_self_attn_o_proj_weight_to_fp16, x = input_201_cast_fp16)[name = tensor<string, []>("obj_287_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_123_cast_fp16 = add(x = inputs_121_cast_fp16, y = obj_287_cast_fp16)[name = tensor<string, []>("inputs_123_cast_fp16")];
tensor<int32, [1]> var_4587 = const()[name = tensor<string, []>("op_4587"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_123_cast_fp16 = reduce_mean(axes = var_4587, keep_dims = var_4488, x = inputs_123_cast_fp16)[name = tensor<string, []>("channels_mean_123_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_123_cast_fp16 = sub(x = inputs_123_cast_fp16, y = channels_mean_123_cast_fp16)[name = tensor<string, []>("zero_mean_123_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_123_cast_fp16 = mul(x = zero_mean_123_cast_fp16, y = zero_mean_123_cast_fp16)[name = tensor<string, []>("zero_mean_sq_123_cast_fp16")];
tensor<int32, [1]> var_4591 = const()[name = tensor<string, []>("op_4591"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_4592_cast_fp16 = reduce_mean(axes = var_4591, keep_dims = var_4488, x = zero_mean_sq_123_cast_fp16)[name = tensor<string, []>("op_4592_cast_fp16")];
tensor<fp16, []> var_4593_to_fp16 = const()[name = tensor<string, []>("op_4593_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_4594_cast_fp16 = add(x = var_4592_cast_fp16, y = var_4593_to_fp16)[name = tensor<string, []>("op_4594_cast_fp16")];
tensor<fp16, []> denom_123_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_123_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_123_cast_fp16 = rsqrt(epsilon = denom_123_epsilon_0_to_fp16, x = var_4594_cast_fp16)[name = tensor<string, []>("denom_123_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_123_cast_fp16 = mul(x = zero_mean_123_cast_fp16, y = denom_123_cast_fp16)[name = tensor<string, []>("out_123_cast_fp16")];
tensor<fp16, [1280]> obj_289_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_289_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1196524416)))];
tensor<fp16, [1280]> obj_289_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_289_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1196527040)))];
tensor<fp16, []> obj_289_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_289_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_289_cast_fp16 = batch_norm(beta = obj_289_beta_0_to_fp16, epsilon = obj_289_epsilon_0_to_fp16, gamma = obj_289_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_123_cast_fp16)[name = tensor<string, []>("obj_289_cast_fp16")];
tensor<int32, [2]> var_4609 = const()[name = tensor<string, []>("op_4609"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4611 = const()[name = tensor<string, []>("op_4611"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_83_pad_type_0 = const()[name = tensor<string, []>("query_83_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_83_pad_0 = const()[name = tensor<string, []>("query_83_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_20_encoder_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_20_encoder_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1196529664)))];
tensor<fp16, [1280]> layers_20_encoder_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_20_encoder_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1199806528)))];
tensor<fp16, [1, 1280, 1, 1]> query_83_cast_fp16 = conv(bias = layers_20_encoder_attn_q_proj_bias_to_fp16, dilations = var_4611, groups = var_4487, pad = query_83_pad_0, pad_type = query_83_pad_type_0, strides = var_4609, weight = layers_20_encoder_attn_q_proj_weight_to_fp16, x = obj_289_cast_fp16)[name = tensor<string, []>("query_83_cast_fp16")];
tensor<int32, [2]> var_4615 = const()[name = tensor<string, []>("op_4615"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4617 = const()[name = tensor<string, []>("op_4617"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> key_83_pad_type_0 = const()[name = tensor<string, []>("key_83_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> key_83_pad_0 = const()[name = tensor<string, []>("key_83_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_20_encoder_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_20_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1199809152)))];
tensor<fp16, [1, 1280, 1, 1500]> key_83_cast_fp16 = conv(dilations = var_4617, groups = var_4487, pad = key_83_pad_0, pad_type = key_83_pad_type_0, strides = var_4615, weight = layers_20_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("key_83_cast_fp16")];
tensor<int32, [2]> var_4622 = const()[name = tensor<string, []>("op_4622"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4624 = const()[name = tensor<string, []>("op_4624"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> value_83_pad_type_0 = const()[name = tensor<string, []>("value_83_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> value_83_pad_0 = const()[name = tensor<string, []>("value_83_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_20_encoder_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_20_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1203086016)))];
tensor<fp16, [1280]> layers_20_encoder_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_20_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1206362880)))];
tensor<fp16, [1, 1280, 1, 1500]> value_83_cast_fp16 = conv(bias = layers_20_encoder_attn_v_proj_bias_to_fp16, dilations = var_4624, groups = var_4487, pad = value_83_pad_0, pad_type = value_83_pad_type_0, strides = var_4622, weight = layers_20_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("value_83_cast_fp16")];
tensor<int32, [4]> var_4628 = const()[name = tensor<string, []>("op_4628"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_4629_cast_fp16 = reshape(shape = var_4628, x = query_83_cast_fp16)[name = tensor<string, []>("op_4629_cast_fp16")];
tensor<fp16, []> var_4630_to_fp16 = const()[name = tensor<string, []>("op_4630_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_4631_cast_fp16 = mul(x = var_4629_cast_fp16, y = var_4630_to_fp16)[name = tensor<string, []>("op_4631_cast_fp16")];
tensor<int32, [4]> var_4632 = const()[name = tensor<string, []>("op_4632"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_4633_cast_fp16 = reshape(shape = var_4632, x = key_83_cast_fp16)[name = tensor<string, []>("op_4633_cast_fp16")];
tensor<bool, []> mh_w_125_transpose_x_0 = const()[name = tensor<string, []>("mh_w_125_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_125_transpose_y_0 = const()[name = tensor<string, []>("mh_w_125_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 1500]> mh_w_125_cast_fp16 = matmul(transpose_x = mh_w_125_transpose_x_0, transpose_y = mh_w_125_transpose_y_0, x = var_4631_cast_fp16, y = var_4633_cast_fp16)[name = tensor<string, []>("mh_w_125_cast_fp16")];
tensor<fp16, [1, 20, 1, 1500]> obj_293_cast_fp16 = softmax(axis = var_4480, x = mh_w_125_cast_fp16)[name = tensor<string, []>("obj_293_cast_fp16")];
tensor<int32, [4]> var_4637 = const()[name = tensor<string, []>("op_4637"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_4638_cast_fp16 = reshape(shape = var_4637, x = value_83_cast_fp16)[name = tensor<string, []>("op_4638_cast_fp16")];
tensor<bool, []> attn_83_transpose_x_0 = const()[name = tensor<string, []>("attn_83_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_83_transpose_y_0 = const()[name = tensor<string, []>("attn_83_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_83_cast_fp16 = matmul(transpose_x = attn_83_transpose_x_0, transpose_y = attn_83_transpose_y_0, x = var_4638_cast_fp16, y = obj_293_cast_fp16)[name = tensor<string, []>("attn_83_cast_fp16")];
tensor<int32, [4]> var_4641 = const()[name = tensor<string, []>("op_4641"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_203_cast_fp16 = reshape(shape = var_4641, x = attn_83_cast_fp16)[name = tensor<string, []>("input_203_cast_fp16")];
tensor<int32, [2]> var_4645 = const()[name = tensor<string, []>("op_4645"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4647 = const()[name = tensor<string, []>("op_4647"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_291_pad_type_0 = const()[name = tensor<string, []>("obj_291_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_291_pad_0 = const()[name = tensor<string, []>("obj_291_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_20_encoder_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_20_encoder_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1206365504)))];
tensor<fp16, [1280]> layers_20_encoder_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_20_encoder_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1209642368)))];
tensor<fp16, [1, 1280, 1, 1]> obj_291_cast_fp16 = conv(bias = layers_20_encoder_attn_o_proj_bias_to_fp16, dilations = var_4647, groups = var_4487, pad = obj_291_pad_0, pad_type = obj_291_pad_type_0, strides = var_4645, weight = layers_20_encoder_attn_o_proj_weight_to_fp16, x = input_203_cast_fp16)[name = tensor<string, []>("obj_291_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_125_cast_fp16 = add(x = inputs_123_cast_fp16, y = obj_291_cast_fp16)[name = tensor<string, []>("inputs_125_cast_fp16")];
tensor<int32, [1]> var_4653 = const()[name = tensor<string, []>("op_4653"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_125_cast_fp16 = reduce_mean(axes = var_4653, keep_dims = var_4488, x = inputs_125_cast_fp16)[name = tensor<string, []>("channels_mean_125_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_125_cast_fp16 = sub(x = inputs_125_cast_fp16, y = channels_mean_125_cast_fp16)[name = tensor<string, []>("zero_mean_125_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_125_cast_fp16 = mul(x = zero_mean_125_cast_fp16, y = zero_mean_125_cast_fp16)[name = tensor<string, []>("zero_mean_sq_125_cast_fp16")];
tensor<int32, [1]> var_4657 = const()[name = tensor<string, []>("op_4657"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_4658_cast_fp16 = reduce_mean(axes = var_4657, keep_dims = var_4488, x = zero_mean_sq_125_cast_fp16)[name = tensor<string, []>("op_4658_cast_fp16")];
tensor<fp16, []> var_4659_to_fp16 = const()[name = tensor<string, []>("op_4659_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_4660_cast_fp16 = add(x = var_4658_cast_fp16, y = var_4659_to_fp16)[name = tensor<string, []>("op_4660_cast_fp16")];
tensor<fp16, []> denom_125_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_125_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_125_cast_fp16 = rsqrt(epsilon = denom_125_epsilon_0_to_fp16, x = var_4660_cast_fp16)[name = tensor<string, []>("denom_125_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_125_cast_fp16 = mul(x = zero_mean_125_cast_fp16, y = denom_125_cast_fp16)[name = tensor<string, []>("out_125_cast_fp16")];
tensor<fp16, [1280]> input_205_gamma_0_to_fp16 = const()[name = tensor<string, []>("input_205_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1209644992)))];
tensor<fp16, [1280]> input_205_beta_0_to_fp16 = const()[name = tensor<string, []>("input_205_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1209647616)))];
tensor<fp16, []> input_205_epsilon_0_to_fp16 = const()[name = tensor<string, []>("input_205_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> input_205_cast_fp16 = batch_norm(beta = input_205_beta_0_to_fp16, epsilon = input_205_epsilon_0_to_fp16, gamma = input_205_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_125_cast_fp16)[name = tensor<string, []>("input_205_cast_fp16")];
tensor<int32, [2]> var_4671 = const()[name = tensor<string, []>("op_4671"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4673 = const()[name = tensor<string, []>("op_4673"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_207_pad_type_0 = const()[name = tensor<string, []>("input_207_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_207_pad_0 = const()[name = tensor<string, []>("input_207_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [5120, 1280, 1, 1]> layers_20_fc1_weight_to_fp16 = const()[name = tensor<string, []>("layers_20_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1209650240)))];
tensor<fp16, [5120]> layers_20_fc1_bias_to_fp16 = const()[name = tensor<string, []>("layers_20_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1222757504)))];
tensor<fp16, [1, 5120, 1, 1]> input_207_cast_fp16 = conv(bias = layers_20_fc1_bias_to_fp16, dilations = var_4673, groups = var_4487, pad = input_207_pad_0, pad_type = input_207_pad_type_0, strides = var_4671, weight = layers_20_fc1_weight_to_fp16, x = input_205_cast_fp16)[name = tensor<string, []>("input_207_cast_fp16")];
tensor<string, []> input_209_mode_0 = const()[name = tensor<string, []>("input_209_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 5120, 1, 1]> input_209_cast_fp16 = gelu(mode = input_209_mode_0, x = input_207_cast_fp16)[name = tensor<string, []>("input_209_cast_fp16")];
tensor<int32, [2]> var_4679 = const()[name = tensor<string, []>("op_4679"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4681 = const()[name = tensor<string, []>("op_4681"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_43_pad_type_0 = const()[name = tensor<string, []>("hidden_states_43_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_43_pad_0 = const()[name = tensor<string, []>("hidden_states_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 5120, 1, 1]> layers_20_fc2_weight_to_fp16 = const()[name = tensor<string, []>("layers_20_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1222767808)))];
tensor<fp16, [1280]> layers_20_fc2_bias_to_fp16 = const()[name = tensor<string, []>("layers_20_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1235875072)))];
tensor<fp16, [1, 1280, 1, 1]> hidden_states_43_cast_fp16 = conv(bias = layers_20_fc2_bias_to_fp16, dilations = var_4681, groups = var_4487, pad = hidden_states_43_pad_0, pad_type = hidden_states_43_pad_type_0, strides = var_4679, weight = layers_20_fc2_weight_to_fp16, x = input_209_cast_fp16)[name = tensor<string, []>("hidden_states_43_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_127_cast_fp16 = add(x = inputs_125_cast_fp16, y = hidden_states_43_cast_fp16)[name = tensor<string, []>("inputs_127_cast_fp16")];
tensor<int32, []> var_4694 = const()[name = tensor<string, []>("op_4694"), val = tensor<int32, []>(3)];
tensor<int32, []> var_4701 = const()[name = tensor<string, []>("op_4701"), val = tensor<int32, []>(1)];
tensor<bool, []> var_4702 = const()[name = tensor<string, []>("op_4702"), val = tensor<bool, []>(true)];
tensor<int32, [1]> var_4714 = const()[name = tensor<string, []>("op_4714"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_127_cast_fp16 = reduce_mean(axes = var_4714, keep_dims = var_4702, x = inputs_127_cast_fp16)[name = tensor<string, []>("channels_mean_127_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_127_cast_fp16 = sub(x = inputs_127_cast_fp16, y = channels_mean_127_cast_fp16)[name = tensor<string, []>("zero_mean_127_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_127_cast_fp16 = mul(x = zero_mean_127_cast_fp16, y = zero_mean_127_cast_fp16)[name = tensor<string, []>("zero_mean_sq_127_cast_fp16")];
tensor<int32, [1]> var_4718 = const()[name = tensor<string, []>("op_4718"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_4719_cast_fp16 = reduce_mean(axes = var_4718, keep_dims = var_4702, x = zero_mean_sq_127_cast_fp16)[name = tensor<string, []>("op_4719_cast_fp16")];
tensor<fp16, []> var_4720_to_fp16 = const()[name = tensor<string, []>("op_4720_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_4721_cast_fp16 = add(x = var_4719_cast_fp16, y = var_4720_to_fp16)[name = tensor<string, []>("op_4721_cast_fp16")];
tensor<fp16, []> denom_127_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_127_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_127_cast_fp16 = rsqrt(epsilon = denom_127_epsilon_0_to_fp16, x = var_4721_cast_fp16)[name = tensor<string, []>("denom_127_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_127_cast_fp16 = mul(x = zero_mean_127_cast_fp16, y = denom_127_cast_fp16)[name = tensor<string, []>("out_127_cast_fp16")];
tensor<fp16, [1280]> obj_295_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_295_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1235877696)))];
tensor<fp16, [1280]> obj_295_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_295_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1235880320)))];
tensor<fp16, []> obj_295_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_295_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_295_cast_fp16 = batch_norm(beta = obj_295_beta_0_to_fp16, epsilon = obj_295_epsilon_0_to_fp16, gamma = obj_295_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_127_cast_fp16)[name = tensor<string, []>("obj_295_cast_fp16")];
tensor<int32, [2]> var_4736 = const()[name = tensor<string, []>("op_4736"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4738 = const()[name = tensor<string, []>("op_4738"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_85_pad_type_0 = const()[name = tensor<string, []>("query_85_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_85_pad_0 = const()[name = tensor<string, []>("query_85_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_21_self_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_21_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1235882944)))];
tensor<fp16, [1280]> layers_21_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_21_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1239159808)))];
tensor<fp16, [1, 1280, 1, 1]> query_85_cast_fp16 = conv(bias = layers_21_self_attn_q_proj_bias_to_fp16, dilations = var_4738, groups = var_4701, pad = query_85_pad_0, pad_type = query_85_pad_type_0, strides = var_4736, weight = layers_21_self_attn_q_proj_weight_to_fp16, x = obj_295_cast_fp16)[name = tensor<string, []>("query_85_cast_fp16")];
tensor<int32, [2]> var_4742 = const()[name = tensor<string, []>("op_4742"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4744 = const()[name = tensor<string, []>("op_4744"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_key_43_pad_type_0 = const()[name = tensor<string, []>("current_key_43_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_key_43_pad_0 = const()[name = tensor<string, []>("current_key_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_21_self_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_21_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1239162432)))];
tensor<fp16, [1, 1280, 1, 1]> current_key_43_cast_fp16 = conv(dilations = var_4744, groups = var_4701, pad = current_key_43_pad_0, pad_type = current_key_43_pad_type_0, strides = var_4742, weight = layers_21_self_attn_k_proj_weight_to_fp16, x = obj_295_cast_fp16)[name = tensor<string, []>("current_key_43_cast_fp16")];
tensor<int32, [2]> var_4749 = const()[name = tensor<string, []>("op_4749"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4751 = const()[name = tensor<string, []>("op_4751"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_value_43_pad_type_0 = const()[name = tensor<string, []>("current_value_43_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_value_43_pad_0 = const()[name = tensor<string, []>("current_value_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_21_self_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_21_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1242439296)))];
tensor<fp16, [1280]> layers_21_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_21_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1245716160)))];
tensor<fp16, [1, 1280, 1, 1]> current_value_43_cast_fp16 = conv(bias = layers_21_self_attn_v_proj_bias_to_fp16, dilations = var_4751, groups = var_4701, pad = current_value_43_pad_0, pad_type = current_value_43_pad_type_0, strides = var_4749, weight = layers_21_self_attn_v_proj_weight_to_fp16, x = obj_295_cast_fp16)[name = tensor<string, []>("current_value_43_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_4758_cast_fp16 = mul(x = current_key_43_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_4758_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_4760_cast_fp16 = mul(x = var_103_cast_fp16_21, y = var_241_cast_fp16)[name = tensor<string, []>("op_4760_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> key_85_cast_fp16 = add(x = var_4758_cast_fp16, y = var_4760_cast_fp16)[name = tensor<string, []>("key_85_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_4762_cast_fp16 = mul(x = current_value_43_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_4762_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_4764_cast_fp16 = mul(x = var_138_cast_fp16_21, y = var_241_cast_fp16)[name = tensor<string, []>("op_4764_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> value_85_cast_fp16 = add(x = var_4762_cast_fp16, y = var_4764_cast_fp16)[name = tensor<string, []>("value_85_cast_fp16")];
tensor<int32, [4]> var_4767 = const()[name = tensor<string, []>("op_4767"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_4768_cast_fp16 = reshape(shape = var_4767, x = query_85_cast_fp16)[name = tensor<string, []>("op_4768_cast_fp16")];
tensor<fp16, []> var_4769_to_fp16 = const()[name = tensor<string, []>("op_4769_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_4770_cast_fp16 = mul(x = var_4768_cast_fp16, y = var_4769_to_fp16)[name = tensor<string, []>("op_4770_cast_fp16")];
tensor<int32, [4]> var_4771 = const()[name = tensor<string, []>("op_4771"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_4772_cast_fp16 = reshape(shape = var_4771, x = key_85_cast_fp16)[name = tensor<string, []>("op_4772_cast_fp16")];
tensor<bool, []> mh_w_127_transpose_x_0 = const()[name = tensor<string, []>("mh_w_127_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_127_transpose_y_0 = const()[name = tensor<string, []>("mh_w_127_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 224]> mh_w_127_cast_fp16 = matmul(transpose_x = mh_w_127_transpose_x_0, transpose_y = mh_w_127_transpose_y_0, x = var_4770_cast_fp16, y = var_4772_cast_fp16)[name = tensor<string, []>("mh_w_127_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> mh_w_129_cast_fp16 = add(x = mh_w_127_cast_fp16, y = var_259_cast_fp16)[name = tensor<string, []>("mh_w_129_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> var_4780_cast_fp16 = softmax(axis = var_4694, x = mh_w_129_cast_fp16)[name = tensor<string, []>("op_4780_cast_fp16")];
tensor<int32, [4]> var_4781 = const()[name = tensor<string, []>("op_4781"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_4782_cast_fp16 = reshape(shape = var_4781, x = value_85_cast_fp16)[name = tensor<string, []>("op_4782_cast_fp16")];
tensor<bool, []> attn_85_transpose_x_0 = const()[name = tensor<string, []>("attn_85_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_85_transpose_y_0 = const()[name = tensor<string, []>("attn_85_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_85_cast_fp16 = matmul(transpose_x = attn_85_transpose_x_0, transpose_y = attn_85_transpose_y_0, x = var_4782_cast_fp16, y = var_4780_cast_fp16)[name = tensor<string, []>("attn_85_cast_fp16")];
tensor<int32, [4]> var_4785 = const()[name = tensor<string, []>("op_4785"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_211_cast_fp16 = reshape(shape = var_4785, x = attn_85_cast_fp16)[name = tensor<string, []>("input_211_cast_fp16")];
tensor<int32, [2]> var_4789 = const()[name = tensor<string, []>("op_4789"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4791 = const()[name = tensor<string, []>("op_4791"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_301_pad_type_0 = const()[name = tensor<string, []>("obj_301_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_301_pad_0 = const()[name = tensor<string, []>("obj_301_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_21_self_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_21_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1245718784)))];
tensor<fp16, [1280]> layers_21_self_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_21_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1248995648)))];
tensor<fp16, [1, 1280, 1, 1]> obj_301_cast_fp16 = conv(bias = layers_21_self_attn_o_proj_bias_to_fp16, dilations = var_4791, groups = var_4701, pad = obj_301_pad_0, pad_type = obj_301_pad_type_0, strides = var_4789, weight = layers_21_self_attn_o_proj_weight_to_fp16, x = input_211_cast_fp16)[name = tensor<string, []>("obj_301_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_129_cast_fp16 = add(x = inputs_127_cast_fp16, y = obj_301_cast_fp16)[name = tensor<string, []>("inputs_129_cast_fp16")];
tensor<int32, [1]> var_4801 = const()[name = tensor<string, []>("op_4801"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_129_cast_fp16 = reduce_mean(axes = var_4801, keep_dims = var_4702, x = inputs_129_cast_fp16)[name = tensor<string, []>("channels_mean_129_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_129_cast_fp16 = sub(x = inputs_129_cast_fp16, y = channels_mean_129_cast_fp16)[name = tensor<string, []>("zero_mean_129_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_129_cast_fp16 = mul(x = zero_mean_129_cast_fp16, y = zero_mean_129_cast_fp16)[name = tensor<string, []>("zero_mean_sq_129_cast_fp16")];
tensor<int32, [1]> var_4805 = const()[name = tensor<string, []>("op_4805"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_4806_cast_fp16 = reduce_mean(axes = var_4805, keep_dims = var_4702, x = zero_mean_sq_129_cast_fp16)[name = tensor<string, []>("op_4806_cast_fp16")];
tensor<fp16, []> var_4807_to_fp16 = const()[name = tensor<string, []>("op_4807_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_4808_cast_fp16 = add(x = var_4806_cast_fp16, y = var_4807_to_fp16)[name = tensor<string, []>("op_4808_cast_fp16")];
tensor<fp16, []> denom_129_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_129_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_129_cast_fp16 = rsqrt(epsilon = denom_129_epsilon_0_to_fp16, x = var_4808_cast_fp16)[name = tensor<string, []>("denom_129_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_129_cast_fp16 = mul(x = zero_mean_129_cast_fp16, y = denom_129_cast_fp16)[name = tensor<string, []>("out_129_cast_fp16")];
tensor<fp16, [1280]> obj_303_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_303_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1248998272)))];
tensor<fp16, [1280]> obj_303_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_303_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1249000896)))];
tensor<fp16, []> obj_303_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_303_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_303_cast_fp16 = batch_norm(beta = obj_303_beta_0_to_fp16, epsilon = obj_303_epsilon_0_to_fp16, gamma = obj_303_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_129_cast_fp16)[name = tensor<string, []>("obj_303_cast_fp16")];
tensor<int32, [2]> var_4823 = const()[name = tensor<string, []>("op_4823"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4825 = const()[name = tensor<string, []>("op_4825"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_87_pad_type_0 = const()[name = tensor<string, []>("query_87_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_87_pad_0 = const()[name = tensor<string, []>("query_87_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_21_encoder_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_21_encoder_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1249003520)))];
tensor<fp16, [1280]> layers_21_encoder_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_21_encoder_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1252280384)))];
tensor<fp16, [1, 1280, 1, 1]> query_87_cast_fp16 = conv(bias = layers_21_encoder_attn_q_proj_bias_to_fp16, dilations = var_4825, groups = var_4701, pad = query_87_pad_0, pad_type = query_87_pad_type_0, strides = var_4823, weight = layers_21_encoder_attn_q_proj_weight_to_fp16, x = obj_303_cast_fp16)[name = tensor<string, []>("query_87_cast_fp16")];
tensor<int32, [2]> var_4829 = const()[name = tensor<string, []>("op_4829"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4831 = const()[name = tensor<string, []>("op_4831"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> key_87_pad_type_0 = const()[name = tensor<string, []>("key_87_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> key_87_pad_0 = const()[name = tensor<string, []>("key_87_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_21_encoder_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_21_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1252283008)))];
tensor<fp16, [1, 1280, 1, 1500]> key_87_cast_fp16 = conv(dilations = var_4831, groups = var_4701, pad = key_87_pad_0, pad_type = key_87_pad_type_0, strides = var_4829, weight = layers_21_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("key_87_cast_fp16")];
tensor<int32, [2]> var_4836 = const()[name = tensor<string, []>("op_4836"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4838 = const()[name = tensor<string, []>("op_4838"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> value_87_pad_type_0 = const()[name = tensor<string, []>("value_87_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> value_87_pad_0 = const()[name = tensor<string, []>("value_87_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_21_encoder_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_21_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1255559872)))];
tensor<fp16, [1280]> layers_21_encoder_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_21_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1258836736)))];
tensor<fp16, [1, 1280, 1, 1500]> value_87_cast_fp16 = conv(bias = layers_21_encoder_attn_v_proj_bias_to_fp16, dilations = var_4838, groups = var_4701, pad = value_87_pad_0, pad_type = value_87_pad_type_0, strides = var_4836, weight = layers_21_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("value_87_cast_fp16")];
tensor<int32, [4]> var_4842 = const()[name = tensor<string, []>("op_4842"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_4843_cast_fp16 = reshape(shape = var_4842, x = query_87_cast_fp16)[name = tensor<string, []>("op_4843_cast_fp16")];
tensor<fp16, []> var_4844_to_fp16 = const()[name = tensor<string, []>("op_4844_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_4845_cast_fp16 = mul(x = var_4843_cast_fp16, y = var_4844_to_fp16)[name = tensor<string, []>("op_4845_cast_fp16")];
tensor<int32, [4]> var_4846 = const()[name = tensor<string, []>("op_4846"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_4847_cast_fp16 = reshape(shape = var_4846, x = key_87_cast_fp16)[name = tensor<string, []>("op_4847_cast_fp16")];
tensor<bool, []> mh_w_131_transpose_x_0 = const()[name = tensor<string, []>("mh_w_131_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_131_transpose_y_0 = const()[name = tensor<string, []>("mh_w_131_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 1500]> mh_w_131_cast_fp16 = matmul(transpose_x = mh_w_131_transpose_x_0, transpose_y = mh_w_131_transpose_y_0, x = var_4845_cast_fp16, y = var_4847_cast_fp16)[name = tensor<string, []>("mh_w_131_cast_fp16")];
tensor<fp16, [1, 20, 1, 1500]> obj_307_cast_fp16 = softmax(axis = var_4694, x = mh_w_131_cast_fp16)[name = tensor<string, []>("obj_307_cast_fp16")];
tensor<int32, [4]> var_4851 = const()[name = tensor<string, []>("op_4851"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_4852_cast_fp16 = reshape(shape = var_4851, x = value_87_cast_fp16)[name = tensor<string, []>("op_4852_cast_fp16")];
tensor<bool, []> attn_87_transpose_x_0 = const()[name = tensor<string, []>("attn_87_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_87_transpose_y_0 = const()[name = tensor<string, []>("attn_87_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_87_cast_fp16 = matmul(transpose_x = attn_87_transpose_x_0, transpose_y = attn_87_transpose_y_0, x = var_4852_cast_fp16, y = obj_307_cast_fp16)[name = tensor<string, []>("attn_87_cast_fp16")];
tensor<int32, [4]> var_4855 = const()[name = tensor<string, []>("op_4855"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_213_cast_fp16 = reshape(shape = var_4855, x = attn_87_cast_fp16)[name = tensor<string, []>("input_213_cast_fp16")];
tensor<int32, [2]> var_4859 = const()[name = tensor<string, []>("op_4859"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4861 = const()[name = tensor<string, []>("op_4861"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_305_pad_type_0 = const()[name = tensor<string, []>("obj_305_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_305_pad_0 = const()[name = tensor<string, []>("obj_305_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_21_encoder_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_21_encoder_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1258839360)))];
tensor<fp16, [1280]> layers_21_encoder_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_21_encoder_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1262116224)))];
tensor<fp16, [1, 1280, 1, 1]> obj_305_cast_fp16 = conv(bias = layers_21_encoder_attn_o_proj_bias_to_fp16, dilations = var_4861, groups = var_4701, pad = obj_305_pad_0, pad_type = obj_305_pad_type_0, strides = var_4859, weight = layers_21_encoder_attn_o_proj_weight_to_fp16, x = input_213_cast_fp16)[name = tensor<string, []>("obj_305_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_131_cast_fp16 = add(x = inputs_129_cast_fp16, y = obj_305_cast_fp16)[name = tensor<string, []>("inputs_131_cast_fp16")];
tensor<int32, [1]> var_4870 = const()[name = tensor<string, []>("op_4870"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_131_cast_fp16 = reduce_mean(axes = var_4870, keep_dims = var_4702, x = inputs_131_cast_fp16)[name = tensor<string, []>("channels_mean_131_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_131_cast_fp16 = sub(x = inputs_131_cast_fp16, y = channels_mean_131_cast_fp16)[name = tensor<string, []>("zero_mean_131_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_131_cast_fp16 = mul(x = zero_mean_131_cast_fp16, y = zero_mean_131_cast_fp16)[name = tensor<string, []>("zero_mean_sq_131_cast_fp16")];
tensor<int32, [1]> var_4874 = const()[name = tensor<string, []>("op_4874"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_4875_cast_fp16 = reduce_mean(axes = var_4874, keep_dims = var_4702, x = zero_mean_sq_131_cast_fp16)[name = tensor<string, []>("op_4875_cast_fp16")];
tensor<fp16, []> var_4876_to_fp16 = const()[name = tensor<string, []>("op_4876_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_4877_cast_fp16 = add(x = var_4875_cast_fp16, y = var_4876_to_fp16)[name = tensor<string, []>("op_4877_cast_fp16")];
tensor<fp16, []> denom_131_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_131_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_131_cast_fp16 = rsqrt(epsilon = denom_131_epsilon_0_to_fp16, x = var_4877_cast_fp16)[name = tensor<string, []>("denom_131_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_131_cast_fp16 = mul(x = zero_mean_131_cast_fp16, y = denom_131_cast_fp16)[name = tensor<string, []>("out_131_cast_fp16")];
tensor<fp16, [1280]> input_215_gamma_0_to_fp16 = const()[name = tensor<string, []>("input_215_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1262118848)))];
tensor<fp16, [1280]> input_215_beta_0_to_fp16 = const()[name = tensor<string, []>("input_215_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1262121472)))];
tensor<fp16, []> input_215_epsilon_0_to_fp16 = const()[name = tensor<string, []>("input_215_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> input_215_cast_fp16 = batch_norm(beta = input_215_beta_0_to_fp16, epsilon = input_215_epsilon_0_to_fp16, gamma = input_215_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_131_cast_fp16)[name = tensor<string, []>("input_215_cast_fp16")];
tensor<int32, [2]> var_4888 = const()[name = tensor<string, []>("op_4888"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4890 = const()[name = tensor<string, []>("op_4890"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_217_pad_type_0 = const()[name = tensor<string, []>("input_217_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_217_pad_0 = const()[name = tensor<string, []>("input_217_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [5120, 1280, 1, 1]> layers_21_fc1_weight_to_fp16 = const()[name = tensor<string, []>("layers_21_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1262124096)))];
tensor<fp16, [5120]> layers_21_fc1_bias_to_fp16 = const()[name = tensor<string, []>("layers_21_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1275231360)))];
tensor<fp16, [1, 5120, 1, 1]> input_217_cast_fp16 = conv(bias = layers_21_fc1_bias_to_fp16, dilations = var_4890, groups = var_4701, pad = input_217_pad_0, pad_type = input_217_pad_type_0, strides = var_4888, weight = layers_21_fc1_weight_to_fp16, x = input_215_cast_fp16)[name = tensor<string, []>("input_217_cast_fp16")];
tensor<string, []> input_219_mode_0 = const()[name = tensor<string, []>("input_219_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 5120, 1, 1]> input_219_cast_fp16 = gelu(mode = input_219_mode_0, x = input_217_cast_fp16)[name = tensor<string, []>("input_219_cast_fp16")];
tensor<int32, [2]> var_4896 = const()[name = tensor<string, []>("op_4896"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4898 = const()[name = tensor<string, []>("op_4898"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_45_pad_type_0 = const()[name = tensor<string, []>("hidden_states_45_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_45_pad_0 = const()[name = tensor<string, []>("hidden_states_45_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 5120, 1, 1]> layers_21_fc2_weight_to_fp16 = const()[name = tensor<string, []>("layers_21_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1275241664)))];
tensor<fp16, [1280]> layers_21_fc2_bias_to_fp16 = const()[name = tensor<string, []>("layers_21_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1288348928)))];
tensor<fp16, [1, 1280, 1, 1]> hidden_states_45_cast_fp16 = conv(bias = layers_21_fc2_bias_to_fp16, dilations = var_4898, groups = var_4701, pad = hidden_states_45_pad_0, pad_type = hidden_states_45_pad_type_0, strides = var_4896, weight = layers_21_fc2_weight_to_fp16, x = input_219_cast_fp16)[name = tensor<string, []>("hidden_states_45_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_133_cast_fp16 = add(x = inputs_131_cast_fp16, y = hidden_states_45_cast_fp16)[name = tensor<string, []>("inputs_133_cast_fp16")];
tensor<int32, []> var_4912 = const()[name = tensor<string, []>("op_4912"), val = tensor<int32, []>(3)];
tensor<int32, []> var_4919 = const()[name = tensor<string, []>("op_4919"), val = tensor<int32, []>(1)];
tensor<bool, []> var_4920 = const()[name = tensor<string, []>("op_4920"), val = tensor<bool, []>(true)];
tensor<int32, [1]> var_4932 = const()[name = tensor<string, []>("op_4932"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_133_cast_fp16 = reduce_mean(axes = var_4932, keep_dims = var_4920, x = inputs_133_cast_fp16)[name = tensor<string, []>("channels_mean_133_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_133_cast_fp16 = sub(x = inputs_133_cast_fp16, y = channels_mean_133_cast_fp16)[name = tensor<string, []>("zero_mean_133_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_133_cast_fp16 = mul(x = zero_mean_133_cast_fp16, y = zero_mean_133_cast_fp16)[name = tensor<string, []>("zero_mean_sq_133_cast_fp16")];
tensor<int32, [1]> var_4936 = const()[name = tensor<string, []>("op_4936"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_4937_cast_fp16 = reduce_mean(axes = var_4936, keep_dims = var_4920, x = zero_mean_sq_133_cast_fp16)[name = tensor<string, []>("op_4937_cast_fp16")];
tensor<fp16, []> var_4938_to_fp16 = const()[name = tensor<string, []>("op_4938_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_4939_cast_fp16 = add(x = var_4937_cast_fp16, y = var_4938_to_fp16)[name = tensor<string, []>("op_4939_cast_fp16")];
tensor<fp16, []> denom_133_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_133_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_133_cast_fp16 = rsqrt(epsilon = denom_133_epsilon_0_to_fp16, x = var_4939_cast_fp16)[name = tensor<string, []>("denom_133_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_133_cast_fp16 = mul(x = zero_mean_133_cast_fp16, y = denom_133_cast_fp16)[name = tensor<string, []>("out_133_cast_fp16")];
tensor<fp16, [1280]> obj_309_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_309_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1288351552)))];
tensor<fp16, [1280]> obj_309_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_309_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1288354176)))];
tensor<fp16, []> obj_309_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_309_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_309_cast_fp16 = batch_norm(beta = obj_309_beta_0_to_fp16, epsilon = obj_309_epsilon_0_to_fp16, gamma = obj_309_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_133_cast_fp16)[name = tensor<string, []>("obj_309_cast_fp16")];
tensor<int32, [2]> var_4954 = const()[name = tensor<string, []>("op_4954"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4956 = const()[name = tensor<string, []>("op_4956"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_89_pad_type_0 = const()[name = tensor<string, []>("query_89_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_89_pad_0 = const()[name = tensor<string, []>("query_89_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_22_self_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_22_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1288356800)))];
tensor<fp16, [1280]> layers_22_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_22_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1291633664)))];
tensor<fp16, [1, 1280, 1, 1]> query_89_cast_fp16 = conv(bias = layers_22_self_attn_q_proj_bias_to_fp16, dilations = var_4956, groups = var_4919, pad = query_89_pad_0, pad_type = query_89_pad_type_0, strides = var_4954, weight = layers_22_self_attn_q_proj_weight_to_fp16, x = obj_309_cast_fp16)[name = tensor<string, []>("query_89_cast_fp16")];
tensor<int32, [2]> var_4960 = const()[name = tensor<string, []>("op_4960"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4962 = const()[name = tensor<string, []>("op_4962"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_key_45_pad_type_0 = const()[name = tensor<string, []>("current_key_45_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_key_45_pad_0 = const()[name = tensor<string, []>("current_key_45_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_22_self_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_22_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1291636288)))];
tensor<fp16, [1, 1280, 1, 1]> current_key_45_cast_fp16 = conv(dilations = var_4962, groups = var_4919, pad = current_key_45_pad_0, pad_type = current_key_45_pad_type_0, strides = var_4960, weight = layers_22_self_attn_k_proj_weight_to_fp16, x = obj_309_cast_fp16)[name = tensor<string, []>("current_key_45_cast_fp16")];
tensor<int32, [2]> var_4967 = const()[name = tensor<string, []>("op_4967"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_4969 = const()[name = tensor<string, []>("op_4969"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_value_45_pad_type_0 = const()[name = tensor<string, []>("current_value_45_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_value_45_pad_0 = const()[name = tensor<string, []>("current_value_45_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_22_self_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_22_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1294913152)))];
tensor<fp16, [1280]> layers_22_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_22_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1298190016)))];
tensor<fp16, [1, 1280, 1, 1]> current_value_45_cast_fp16 = conv(bias = layers_22_self_attn_v_proj_bias_to_fp16, dilations = var_4969, groups = var_4919, pad = current_value_45_pad_0, pad_type = current_value_45_pad_type_0, strides = var_4967, weight = layers_22_self_attn_v_proj_weight_to_fp16, x = obj_309_cast_fp16)[name = tensor<string, []>("current_value_45_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_4976_cast_fp16 = mul(x = current_key_45_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_4976_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_4978_cast_fp16 = mul(x = var_103_cast_fp16_22, y = var_241_cast_fp16)[name = tensor<string, []>("op_4978_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> key_89_cast_fp16 = add(x = var_4976_cast_fp16, y = var_4978_cast_fp16)[name = tensor<string, []>("key_89_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_4980_cast_fp16 = mul(x = current_value_45_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_4980_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_4982_cast_fp16 = mul(x = var_138_cast_fp16_22, y = var_241_cast_fp16)[name = tensor<string, []>("op_4982_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> value_89_cast_fp16 = add(x = var_4980_cast_fp16, y = var_4982_cast_fp16)[name = tensor<string, []>("value_89_cast_fp16")];
tensor<int32, [4]> var_4985 = const()[name = tensor<string, []>("op_4985"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_4986_cast_fp16 = reshape(shape = var_4985, x = query_89_cast_fp16)[name = tensor<string, []>("op_4986_cast_fp16")];
tensor<fp16, []> var_4987_to_fp16 = const()[name = tensor<string, []>("op_4987_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_4988_cast_fp16 = mul(x = var_4986_cast_fp16, y = var_4987_to_fp16)[name = tensor<string, []>("op_4988_cast_fp16")];
tensor<int32, [4]> var_4989 = const()[name = tensor<string, []>("op_4989"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_4990_cast_fp16 = reshape(shape = var_4989, x = key_89_cast_fp16)[name = tensor<string, []>("op_4990_cast_fp16")];
tensor<bool, []> mh_w_133_transpose_x_0 = const()[name = tensor<string, []>("mh_w_133_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_133_transpose_y_0 = const()[name = tensor<string, []>("mh_w_133_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 224]> mh_w_133_cast_fp16 = matmul(transpose_x = mh_w_133_transpose_x_0, transpose_y = mh_w_133_transpose_y_0, x = var_4988_cast_fp16, y = var_4990_cast_fp16)[name = tensor<string, []>("mh_w_133_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> mh_w_135_cast_fp16 = add(x = mh_w_133_cast_fp16, y = var_259_cast_fp16)[name = tensor<string, []>("mh_w_135_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> var_4998_cast_fp16 = softmax(axis = var_4912, x = mh_w_135_cast_fp16)[name = tensor<string, []>("op_4998_cast_fp16")];
tensor<int32, [4]> var_4999 = const()[name = tensor<string, []>("op_4999"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_5000_cast_fp16 = reshape(shape = var_4999, x = value_89_cast_fp16)[name = tensor<string, []>("op_5000_cast_fp16")];
tensor<bool, []> attn_89_transpose_x_0 = const()[name = tensor<string, []>("attn_89_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_89_transpose_y_0 = const()[name = tensor<string, []>("attn_89_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_89_cast_fp16 = matmul(transpose_x = attn_89_transpose_x_0, transpose_y = attn_89_transpose_y_0, x = var_5000_cast_fp16, y = var_4998_cast_fp16)[name = tensor<string, []>("attn_89_cast_fp16")];
tensor<int32, [4]> var_5003 = const()[name = tensor<string, []>("op_5003"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_221_cast_fp16 = reshape(shape = var_5003, x = attn_89_cast_fp16)[name = tensor<string, []>("input_221_cast_fp16")];
tensor<int32, [2]> var_5007 = const()[name = tensor<string, []>("op_5007"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5009 = const()[name = tensor<string, []>("op_5009"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_315_pad_type_0 = const()[name = tensor<string, []>("obj_315_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_315_pad_0 = const()[name = tensor<string, []>("obj_315_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_22_self_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_22_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1298192640)))];
tensor<fp16, [1280]> layers_22_self_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_22_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1301469504)))];
tensor<fp16, [1, 1280, 1, 1]> obj_315_cast_fp16 = conv(bias = layers_22_self_attn_o_proj_bias_to_fp16, dilations = var_5009, groups = var_4919, pad = obj_315_pad_0, pad_type = obj_315_pad_type_0, strides = var_5007, weight = layers_22_self_attn_o_proj_weight_to_fp16, x = input_221_cast_fp16)[name = tensor<string, []>("obj_315_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_135_cast_fp16 = add(x = inputs_133_cast_fp16, y = obj_315_cast_fp16)[name = tensor<string, []>("inputs_135_cast_fp16")];
tensor<int32, [1]> var_5019 = const()[name = tensor<string, []>("op_5019"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_135_cast_fp16 = reduce_mean(axes = var_5019, keep_dims = var_4920, x = inputs_135_cast_fp16)[name = tensor<string, []>("channels_mean_135_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_135_cast_fp16 = sub(x = inputs_135_cast_fp16, y = channels_mean_135_cast_fp16)[name = tensor<string, []>("zero_mean_135_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_135_cast_fp16 = mul(x = zero_mean_135_cast_fp16, y = zero_mean_135_cast_fp16)[name = tensor<string, []>("zero_mean_sq_135_cast_fp16")];
tensor<int32, [1]> var_5023 = const()[name = tensor<string, []>("op_5023"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_5024_cast_fp16 = reduce_mean(axes = var_5023, keep_dims = var_4920, x = zero_mean_sq_135_cast_fp16)[name = tensor<string, []>("op_5024_cast_fp16")];
tensor<fp16, []> var_5025_to_fp16 = const()[name = tensor<string, []>("op_5025_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_5026_cast_fp16 = add(x = var_5024_cast_fp16, y = var_5025_to_fp16)[name = tensor<string, []>("op_5026_cast_fp16")];
tensor<fp16, []> denom_135_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_135_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_135_cast_fp16 = rsqrt(epsilon = denom_135_epsilon_0_to_fp16, x = var_5026_cast_fp16)[name = tensor<string, []>("denom_135_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_135_cast_fp16 = mul(x = zero_mean_135_cast_fp16, y = denom_135_cast_fp16)[name = tensor<string, []>("out_135_cast_fp16")];
tensor<fp16, [1280]> obj_317_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_317_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1301472128)))];
tensor<fp16, [1280]> obj_317_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_317_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1301474752)))];
tensor<fp16, []> obj_317_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_317_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_317_cast_fp16 = batch_norm(beta = obj_317_beta_0_to_fp16, epsilon = obj_317_epsilon_0_to_fp16, gamma = obj_317_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_135_cast_fp16)[name = tensor<string, []>("obj_317_cast_fp16")];
tensor<int32, [2]> var_5041 = const()[name = tensor<string, []>("op_5041"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5043 = const()[name = tensor<string, []>("op_5043"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_91_pad_type_0 = const()[name = tensor<string, []>("query_91_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_91_pad_0 = const()[name = tensor<string, []>("query_91_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_22_encoder_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_22_encoder_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1301477376)))];
tensor<fp16, [1280]> layers_22_encoder_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_22_encoder_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1304754240)))];
tensor<fp16, [1, 1280, 1, 1]> query_91_cast_fp16 = conv(bias = layers_22_encoder_attn_q_proj_bias_to_fp16, dilations = var_5043, groups = var_4919, pad = query_91_pad_0, pad_type = query_91_pad_type_0, strides = var_5041, weight = layers_22_encoder_attn_q_proj_weight_to_fp16, x = obj_317_cast_fp16)[name = tensor<string, []>("query_91_cast_fp16")];
tensor<int32, [2]> var_5047 = const()[name = tensor<string, []>("op_5047"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5049 = const()[name = tensor<string, []>("op_5049"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> key_91_pad_type_0 = const()[name = tensor<string, []>("key_91_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> key_91_pad_0 = const()[name = tensor<string, []>("key_91_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_22_encoder_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_22_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1304756864)))];
tensor<fp16, [1, 1280, 1, 1500]> key_91_cast_fp16 = conv(dilations = var_5049, groups = var_4919, pad = key_91_pad_0, pad_type = key_91_pad_type_0, strides = var_5047, weight = layers_22_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("key_91_cast_fp16")];
tensor<int32, [2]> var_5054 = const()[name = tensor<string, []>("op_5054"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5056 = const()[name = tensor<string, []>("op_5056"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> value_91_pad_type_0 = const()[name = tensor<string, []>("value_91_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> value_91_pad_0 = const()[name = tensor<string, []>("value_91_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_22_encoder_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_22_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1308033728)))];
tensor<fp16, [1280]> layers_22_encoder_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_22_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1311310592)))];
tensor<fp16, [1, 1280, 1, 1500]> value_91_cast_fp16 = conv(bias = layers_22_encoder_attn_v_proj_bias_to_fp16, dilations = var_5056, groups = var_4919, pad = value_91_pad_0, pad_type = value_91_pad_type_0, strides = var_5054, weight = layers_22_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("value_91_cast_fp16")];
tensor<int32, [4]> var_5060 = const()[name = tensor<string, []>("op_5060"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_5061_cast_fp16 = reshape(shape = var_5060, x = query_91_cast_fp16)[name = tensor<string, []>("op_5061_cast_fp16")];
tensor<fp16, []> var_5062_to_fp16 = const()[name = tensor<string, []>("op_5062_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_5063_cast_fp16 = mul(x = var_5061_cast_fp16, y = var_5062_to_fp16)[name = tensor<string, []>("op_5063_cast_fp16")];
tensor<int32, [4]> var_5064 = const()[name = tensor<string, []>("op_5064"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_5065_cast_fp16 = reshape(shape = var_5064, x = key_91_cast_fp16)[name = tensor<string, []>("op_5065_cast_fp16")];
tensor<bool, []> mh_w_137_transpose_x_0 = const()[name = tensor<string, []>("mh_w_137_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_137_transpose_y_0 = const()[name = tensor<string, []>("mh_w_137_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 1500]> mh_w_137_cast_fp16 = matmul(transpose_x = mh_w_137_transpose_x_0, transpose_y = mh_w_137_transpose_y_0, x = var_5063_cast_fp16, y = var_5065_cast_fp16)[name = tensor<string, []>("mh_w_137_cast_fp16")];
tensor<fp16, [1, 20, 1, 1500]> obj_321_cast_fp16 = softmax(axis = var_4912, x = mh_w_137_cast_fp16)[name = tensor<string, []>("obj_321_cast_fp16")];
tensor<int32, [4]> var_5069 = const()[name = tensor<string, []>("op_5069"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_5070_cast_fp16 = reshape(shape = var_5069, x = value_91_cast_fp16)[name = tensor<string, []>("op_5070_cast_fp16")];
tensor<bool, []> attn_91_transpose_x_0 = const()[name = tensor<string, []>("attn_91_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_91_transpose_y_0 = const()[name = tensor<string, []>("attn_91_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_91_cast_fp16 = matmul(transpose_x = attn_91_transpose_x_0, transpose_y = attn_91_transpose_y_0, x = var_5070_cast_fp16, y = obj_321_cast_fp16)[name = tensor<string, []>("attn_91_cast_fp16")];
tensor<int32, [4]> var_5073 = const()[name = tensor<string, []>("op_5073"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_223_cast_fp16 = reshape(shape = var_5073, x = attn_91_cast_fp16)[name = tensor<string, []>("input_223_cast_fp16")];
tensor<int32, [2]> var_5077 = const()[name = tensor<string, []>("op_5077"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5079 = const()[name = tensor<string, []>("op_5079"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_319_pad_type_0 = const()[name = tensor<string, []>("obj_319_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_319_pad_0 = const()[name = tensor<string, []>("obj_319_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_22_encoder_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_22_encoder_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1311313216)))];
tensor<fp16, [1280]> layers_22_encoder_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_22_encoder_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1314590080)))];
tensor<fp16, [1, 1280, 1, 1]> obj_319_cast_fp16 = conv(bias = layers_22_encoder_attn_o_proj_bias_to_fp16, dilations = var_5079, groups = var_4919, pad = obj_319_pad_0, pad_type = obj_319_pad_type_0, strides = var_5077, weight = layers_22_encoder_attn_o_proj_weight_to_fp16, x = input_223_cast_fp16)[name = tensor<string, []>("obj_319_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_137_cast_fp16 = add(x = inputs_135_cast_fp16, y = obj_319_cast_fp16)[name = tensor<string, []>("inputs_137_cast_fp16")];
tensor<int32, [1]> var_5088 = const()[name = tensor<string, []>("op_5088"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_137_cast_fp16 = reduce_mean(axes = var_5088, keep_dims = var_4920, x = inputs_137_cast_fp16)[name = tensor<string, []>("channels_mean_137_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_137_cast_fp16 = sub(x = inputs_137_cast_fp16, y = channels_mean_137_cast_fp16)[name = tensor<string, []>("zero_mean_137_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_137_cast_fp16 = mul(x = zero_mean_137_cast_fp16, y = zero_mean_137_cast_fp16)[name = tensor<string, []>("zero_mean_sq_137_cast_fp16")];
tensor<int32, [1]> var_5092 = const()[name = tensor<string, []>("op_5092"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_5093_cast_fp16 = reduce_mean(axes = var_5092, keep_dims = var_4920, x = zero_mean_sq_137_cast_fp16)[name = tensor<string, []>("op_5093_cast_fp16")];
tensor<fp16, []> var_5094_to_fp16 = const()[name = tensor<string, []>("op_5094_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_5095_cast_fp16 = add(x = var_5093_cast_fp16, y = var_5094_to_fp16)[name = tensor<string, []>("op_5095_cast_fp16")];
tensor<fp16, []> denom_137_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_137_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_137_cast_fp16 = rsqrt(epsilon = denom_137_epsilon_0_to_fp16, x = var_5095_cast_fp16)[name = tensor<string, []>("denom_137_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_137_cast_fp16 = mul(x = zero_mean_137_cast_fp16, y = denom_137_cast_fp16)[name = tensor<string, []>("out_137_cast_fp16")];
tensor<fp16, [1280]> input_225_gamma_0_to_fp16 = const()[name = tensor<string, []>("input_225_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1314592704)))];
tensor<fp16, [1280]> input_225_beta_0_to_fp16 = const()[name = tensor<string, []>("input_225_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1314595328)))];
tensor<fp16, []> input_225_epsilon_0_to_fp16 = const()[name = tensor<string, []>("input_225_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> input_225_cast_fp16 = batch_norm(beta = input_225_beta_0_to_fp16, epsilon = input_225_epsilon_0_to_fp16, gamma = input_225_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_137_cast_fp16)[name = tensor<string, []>("input_225_cast_fp16")];
tensor<int32, [2]> var_5106 = const()[name = tensor<string, []>("op_5106"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5108 = const()[name = tensor<string, []>("op_5108"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_227_pad_type_0 = const()[name = tensor<string, []>("input_227_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_227_pad_0 = const()[name = tensor<string, []>("input_227_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [5120, 1280, 1, 1]> layers_22_fc1_weight_to_fp16 = const()[name = tensor<string, []>("layers_22_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1314597952)))];
tensor<fp16, [5120]> layers_22_fc1_bias_to_fp16 = const()[name = tensor<string, []>("layers_22_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1327705216)))];
tensor<fp16, [1, 5120, 1, 1]> input_227_cast_fp16 = conv(bias = layers_22_fc1_bias_to_fp16, dilations = var_5108, groups = var_4919, pad = input_227_pad_0, pad_type = input_227_pad_type_0, strides = var_5106, weight = layers_22_fc1_weight_to_fp16, x = input_225_cast_fp16)[name = tensor<string, []>("input_227_cast_fp16")];
tensor<string, []> input_229_mode_0 = const()[name = tensor<string, []>("input_229_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 5120, 1, 1]> input_229_cast_fp16 = gelu(mode = input_229_mode_0, x = input_227_cast_fp16)[name = tensor<string, []>("input_229_cast_fp16")];
tensor<int32, [2]> var_5114 = const()[name = tensor<string, []>("op_5114"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5116 = const()[name = tensor<string, []>("op_5116"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_47_pad_type_0 = const()[name = tensor<string, []>("hidden_states_47_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_47_pad_0 = const()[name = tensor<string, []>("hidden_states_47_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 5120, 1, 1]> layers_22_fc2_weight_to_fp16 = const()[name = tensor<string, []>("layers_22_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1327715520)))];
tensor<fp16, [1280]> layers_22_fc2_bias_to_fp16 = const()[name = tensor<string, []>("layers_22_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1340822784)))];
tensor<fp16, [1, 1280, 1, 1]> hidden_states_47_cast_fp16 = conv(bias = layers_22_fc2_bias_to_fp16, dilations = var_5116, groups = var_4919, pad = hidden_states_47_pad_0, pad_type = hidden_states_47_pad_type_0, strides = var_5114, weight = layers_22_fc2_weight_to_fp16, x = input_229_cast_fp16)[name = tensor<string, []>("hidden_states_47_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_139_cast_fp16 = add(x = inputs_137_cast_fp16, y = hidden_states_47_cast_fp16)[name = tensor<string, []>("inputs_139_cast_fp16")];
tensor<int32, []> var_5130 = const()[name = tensor<string, []>("op_5130"), val = tensor<int32, []>(3)];
tensor<int32, []> var_5137 = const()[name = tensor<string, []>("op_5137"), val = tensor<int32, []>(1)];
tensor<bool, []> var_5138 = const()[name = tensor<string, []>("op_5138"), val = tensor<bool, []>(true)];
tensor<int32, [1]> var_5150 = const()[name = tensor<string, []>("op_5150"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_139_cast_fp16 = reduce_mean(axes = var_5150, keep_dims = var_5138, x = inputs_139_cast_fp16)[name = tensor<string, []>("channels_mean_139_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_139_cast_fp16 = sub(x = inputs_139_cast_fp16, y = channels_mean_139_cast_fp16)[name = tensor<string, []>("zero_mean_139_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_139_cast_fp16 = mul(x = zero_mean_139_cast_fp16, y = zero_mean_139_cast_fp16)[name = tensor<string, []>("zero_mean_sq_139_cast_fp16")];
tensor<int32, [1]> var_5154 = const()[name = tensor<string, []>("op_5154"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_5155_cast_fp16 = reduce_mean(axes = var_5154, keep_dims = var_5138, x = zero_mean_sq_139_cast_fp16)[name = tensor<string, []>("op_5155_cast_fp16")];
tensor<fp16, []> var_5156_to_fp16 = const()[name = tensor<string, []>("op_5156_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_5157_cast_fp16 = add(x = var_5155_cast_fp16, y = var_5156_to_fp16)[name = tensor<string, []>("op_5157_cast_fp16")];
tensor<fp16, []> denom_139_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_139_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_139_cast_fp16 = rsqrt(epsilon = denom_139_epsilon_0_to_fp16, x = var_5157_cast_fp16)[name = tensor<string, []>("denom_139_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_139_cast_fp16 = mul(x = zero_mean_139_cast_fp16, y = denom_139_cast_fp16)[name = tensor<string, []>("out_139_cast_fp16")];
tensor<fp16, [1280]> obj_323_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_323_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1340825408)))];
tensor<fp16, [1280]> obj_323_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_323_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1340828032)))];
tensor<fp16, []> obj_323_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_323_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_323_cast_fp16 = batch_norm(beta = obj_323_beta_0_to_fp16, epsilon = obj_323_epsilon_0_to_fp16, gamma = obj_323_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_139_cast_fp16)[name = tensor<string, []>("obj_323_cast_fp16")];
tensor<int32, [2]> var_5172 = const()[name = tensor<string, []>("op_5172"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5174 = const()[name = tensor<string, []>("op_5174"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_93_pad_type_0 = const()[name = tensor<string, []>("query_93_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_93_pad_0 = const()[name = tensor<string, []>("query_93_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_23_self_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_23_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1340830656)))];
tensor<fp16, [1280]> layers_23_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_23_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1344107520)))];
tensor<fp16, [1, 1280, 1, 1]> query_93_cast_fp16 = conv(bias = layers_23_self_attn_q_proj_bias_to_fp16, dilations = var_5174, groups = var_5137, pad = query_93_pad_0, pad_type = query_93_pad_type_0, strides = var_5172, weight = layers_23_self_attn_q_proj_weight_to_fp16, x = obj_323_cast_fp16)[name = tensor<string, []>("query_93_cast_fp16")];
tensor<int32, [2]> var_5178 = const()[name = tensor<string, []>("op_5178"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5180 = const()[name = tensor<string, []>("op_5180"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_key_47_pad_type_0 = const()[name = tensor<string, []>("current_key_47_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_key_47_pad_0 = const()[name = tensor<string, []>("current_key_47_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_23_self_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_23_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1344110144)))];
tensor<fp16, [1, 1280, 1, 1]> current_key_47_cast_fp16 = conv(dilations = var_5180, groups = var_5137, pad = current_key_47_pad_0, pad_type = current_key_47_pad_type_0, strides = var_5178, weight = layers_23_self_attn_k_proj_weight_to_fp16, x = obj_323_cast_fp16)[name = tensor<string, []>("current_key_47_cast_fp16")];
tensor<int32, [2]> var_5185 = const()[name = tensor<string, []>("op_5185"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5187 = const()[name = tensor<string, []>("op_5187"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_value_47_pad_type_0 = const()[name = tensor<string, []>("current_value_47_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_value_47_pad_0 = const()[name = tensor<string, []>("current_value_47_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_23_self_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_23_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1347387008)))];
tensor<fp16, [1280]> layers_23_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_23_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1350663872)))];
tensor<fp16, [1, 1280, 1, 1]> current_value_47_cast_fp16 = conv(bias = layers_23_self_attn_v_proj_bias_to_fp16, dilations = var_5187, groups = var_5137, pad = current_value_47_pad_0, pad_type = current_value_47_pad_type_0, strides = var_5185, weight = layers_23_self_attn_v_proj_weight_to_fp16, x = obj_323_cast_fp16)[name = tensor<string, []>("current_value_47_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_5194_cast_fp16 = mul(x = current_key_47_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_5194_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_5196_cast_fp16 = mul(x = var_103_cast_fp16_23, y = var_241_cast_fp16)[name = tensor<string, []>("op_5196_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> key_93_cast_fp16 = add(x = var_5194_cast_fp16, y = var_5196_cast_fp16)[name = tensor<string, []>("key_93_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_5198_cast_fp16 = mul(x = current_value_47_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_5198_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_5200_cast_fp16 = mul(x = var_138_cast_fp16_23, y = var_241_cast_fp16)[name = tensor<string, []>("op_5200_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> value_93_cast_fp16 = add(x = var_5198_cast_fp16, y = var_5200_cast_fp16)[name = tensor<string, []>("value_93_cast_fp16")];
tensor<int32, [4]> var_5203 = const()[name = tensor<string, []>("op_5203"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_5204_cast_fp16 = reshape(shape = var_5203, x = query_93_cast_fp16)[name = tensor<string, []>("op_5204_cast_fp16")];
tensor<fp16, []> var_5205_to_fp16 = const()[name = tensor<string, []>("op_5205_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_5206_cast_fp16 = mul(x = var_5204_cast_fp16, y = var_5205_to_fp16)[name = tensor<string, []>("op_5206_cast_fp16")];
tensor<int32, [4]> var_5207 = const()[name = tensor<string, []>("op_5207"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_5208_cast_fp16 = reshape(shape = var_5207, x = key_93_cast_fp16)[name = tensor<string, []>("op_5208_cast_fp16")];
tensor<bool, []> mh_w_139_transpose_x_0 = const()[name = tensor<string, []>("mh_w_139_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_139_transpose_y_0 = const()[name = tensor<string, []>("mh_w_139_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 224]> mh_w_139_cast_fp16 = matmul(transpose_x = mh_w_139_transpose_x_0, transpose_y = mh_w_139_transpose_y_0, x = var_5206_cast_fp16, y = var_5208_cast_fp16)[name = tensor<string, []>("mh_w_139_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> mh_w_141_cast_fp16 = add(x = mh_w_139_cast_fp16, y = var_259_cast_fp16)[name = tensor<string, []>("mh_w_141_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> var_5216_cast_fp16 = softmax(axis = var_5130, x = mh_w_141_cast_fp16)[name = tensor<string, []>("op_5216_cast_fp16")];
tensor<int32, [4]> var_5217 = const()[name = tensor<string, []>("op_5217"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_5218_cast_fp16 = reshape(shape = var_5217, x = value_93_cast_fp16)[name = tensor<string, []>("op_5218_cast_fp16")];
tensor<bool, []> attn_93_transpose_x_0 = const()[name = tensor<string, []>("attn_93_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_93_transpose_y_0 = const()[name = tensor<string, []>("attn_93_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_93_cast_fp16 = matmul(transpose_x = attn_93_transpose_x_0, transpose_y = attn_93_transpose_y_0, x = var_5218_cast_fp16, y = var_5216_cast_fp16)[name = tensor<string, []>("attn_93_cast_fp16")];
tensor<int32, [4]> var_5221 = const()[name = tensor<string, []>("op_5221"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_231_cast_fp16 = reshape(shape = var_5221, x = attn_93_cast_fp16)[name = tensor<string, []>("input_231_cast_fp16")];
tensor<int32, [2]> var_5225 = const()[name = tensor<string, []>("op_5225"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5227 = const()[name = tensor<string, []>("op_5227"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_329_pad_type_0 = const()[name = tensor<string, []>("obj_329_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_329_pad_0 = const()[name = tensor<string, []>("obj_329_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_23_self_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_23_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1350666496)))];
tensor<fp16, [1280]> layers_23_self_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_23_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1353943360)))];
tensor<fp16, [1, 1280, 1, 1]> obj_329_cast_fp16 = conv(bias = layers_23_self_attn_o_proj_bias_to_fp16, dilations = var_5227, groups = var_5137, pad = obj_329_pad_0, pad_type = obj_329_pad_type_0, strides = var_5225, weight = layers_23_self_attn_o_proj_weight_to_fp16, x = input_231_cast_fp16)[name = tensor<string, []>("obj_329_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_141_cast_fp16 = add(x = inputs_139_cast_fp16, y = obj_329_cast_fp16)[name = tensor<string, []>("inputs_141_cast_fp16")];
tensor<int32, [1]> var_5237 = const()[name = tensor<string, []>("op_5237"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_141_cast_fp16 = reduce_mean(axes = var_5237, keep_dims = var_5138, x = inputs_141_cast_fp16)[name = tensor<string, []>("channels_mean_141_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_141_cast_fp16 = sub(x = inputs_141_cast_fp16, y = channels_mean_141_cast_fp16)[name = tensor<string, []>("zero_mean_141_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_141_cast_fp16 = mul(x = zero_mean_141_cast_fp16, y = zero_mean_141_cast_fp16)[name = tensor<string, []>("zero_mean_sq_141_cast_fp16")];
tensor<int32, [1]> var_5241 = const()[name = tensor<string, []>("op_5241"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_5242_cast_fp16 = reduce_mean(axes = var_5241, keep_dims = var_5138, x = zero_mean_sq_141_cast_fp16)[name = tensor<string, []>("op_5242_cast_fp16")];
tensor<fp16, []> var_5243_to_fp16 = const()[name = tensor<string, []>("op_5243_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_5244_cast_fp16 = add(x = var_5242_cast_fp16, y = var_5243_to_fp16)[name = tensor<string, []>("op_5244_cast_fp16")];
tensor<fp16, []> denom_141_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_141_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_141_cast_fp16 = rsqrt(epsilon = denom_141_epsilon_0_to_fp16, x = var_5244_cast_fp16)[name = tensor<string, []>("denom_141_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_141_cast_fp16 = mul(x = zero_mean_141_cast_fp16, y = denom_141_cast_fp16)[name = tensor<string, []>("out_141_cast_fp16")];
tensor<fp16, [1280]> obj_331_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_331_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1353945984)))];
tensor<fp16, [1280]> obj_331_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_331_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1353948608)))];
tensor<fp16, []> obj_331_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_331_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_331_cast_fp16 = batch_norm(beta = obj_331_beta_0_to_fp16, epsilon = obj_331_epsilon_0_to_fp16, gamma = obj_331_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_141_cast_fp16)[name = tensor<string, []>("obj_331_cast_fp16")];
tensor<int32, [2]> var_5259 = const()[name = tensor<string, []>("op_5259"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5261 = const()[name = tensor<string, []>("op_5261"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_95_pad_type_0 = const()[name = tensor<string, []>("query_95_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_95_pad_0 = const()[name = tensor<string, []>("query_95_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_23_encoder_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_23_encoder_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1353951232)))];
tensor<fp16, [1280]> layers_23_encoder_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_23_encoder_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1357228096)))];
tensor<fp16, [1, 1280, 1, 1]> query_95_cast_fp16 = conv(bias = layers_23_encoder_attn_q_proj_bias_to_fp16, dilations = var_5261, groups = var_5137, pad = query_95_pad_0, pad_type = query_95_pad_type_0, strides = var_5259, weight = layers_23_encoder_attn_q_proj_weight_to_fp16, x = obj_331_cast_fp16)[name = tensor<string, []>("query_95_cast_fp16")];
tensor<int32, [2]> var_5265 = const()[name = tensor<string, []>("op_5265"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5267 = const()[name = tensor<string, []>("op_5267"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> key_95_pad_type_0 = const()[name = tensor<string, []>("key_95_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> key_95_pad_0 = const()[name = tensor<string, []>("key_95_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_23_encoder_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_23_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1357230720)))];
tensor<fp16, [1, 1280, 1, 1500]> key_95_cast_fp16 = conv(dilations = var_5267, groups = var_5137, pad = key_95_pad_0, pad_type = key_95_pad_type_0, strides = var_5265, weight = layers_23_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("key_95_cast_fp16")];
tensor<int32, [2]> var_5272 = const()[name = tensor<string, []>("op_5272"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5274 = const()[name = tensor<string, []>("op_5274"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> value_95_pad_type_0 = const()[name = tensor<string, []>("value_95_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> value_95_pad_0 = const()[name = tensor<string, []>("value_95_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_23_encoder_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_23_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1360507584)))];
tensor<fp16, [1280]> layers_23_encoder_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_23_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1363784448)))];
tensor<fp16, [1, 1280, 1, 1500]> value_95_cast_fp16 = conv(bias = layers_23_encoder_attn_v_proj_bias_to_fp16, dilations = var_5274, groups = var_5137, pad = value_95_pad_0, pad_type = value_95_pad_type_0, strides = var_5272, weight = layers_23_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("value_95_cast_fp16")];
tensor<int32, [4]> var_5278 = const()[name = tensor<string, []>("op_5278"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_5279_cast_fp16 = reshape(shape = var_5278, x = query_95_cast_fp16)[name = tensor<string, []>("op_5279_cast_fp16")];
tensor<fp16, []> var_5280_to_fp16 = const()[name = tensor<string, []>("op_5280_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_5281_cast_fp16 = mul(x = var_5279_cast_fp16, y = var_5280_to_fp16)[name = tensor<string, []>("op_5281_cast_fp16")];
tensor<int32, [4]> var_5282 = const()[name = tensor<string, []>("op_5282"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_5283_cast_fp16 = reshape(shape = var_5282, x = key_95_cast_fp16)[name = tensor<string, []>("op_5283_cast_fp16")];
tensor<bool, []> mh_w_143_transpose_x_0 = const()[name = tensor<string, []>("mh_w_143_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_143_transpose_y_0 = const()[name = tensor<string, []>("mh_w_143_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 1500]> mh_w_143_cast_fp16 = matmul(transpose_x = mh_w_143_transpose_x_0, transpose_y = mh_w_143_transpose_y_0, x = var_5281_cast_fp16, y = var_5283_cast_fp16)[name = tensor<string, []>("mh_w_143_cast_fp16")];
tensor<fp16, [1, 20, 1, 1500]> obj_335_cast_fp16 = softmax(axis = var_5130, x = mh_w_143_cast_fp16)[name = tensor<string, []>("obj_335_cast_fp16")];
tensor<int32, [4]> var_5287 = const()[name = tensor<string, []>("op_5287"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_5288_cast_fp16 = reshape(shape = var_5287, x = value_95_cast_fp16)[name = tensor<string, []>("op_5288_cast_fp16")];
tensor<bool, []> attn_95_transpose_x_0 = const()[name = tensor<string, []>("attn_95_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_95_transpose_y_0 = const()[name = tensor<string, []>("attn_95_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_95_cast_fp16 = matmul(transpose_x = attn_95_transpose_x_0, transpose_y = attn_95_transpose_y_0, x = var_5288_cast_fp16, y = obj_335_cast_fp16)[name = tensor<string, []>("attn_95_cast_fp16")];
tensor<int32, [4]> var_5291 = const()[name = tensor<string, []>("op_5291"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_233_cast_fp16 = reshape(shape = var_5291, x = attn_95_cast_fp16)[name = tensor<string, []>("input_233_cast_fp16")];
tensor<int32, [2]> var_5295 = const()[name = tensor<string, []>("op_5295"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5297 = const()[name = tensor<string, []>("op_5297"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_333_pad_type_0 = const()[name = tensor<string, []>("obj_333_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_333_pad_0 = const()[name = tensor<string, []>("obj_333_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_23_encoder_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_23_encoder_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1363787072)))];
tensor<fp16, [1280]> layers_23_encoder_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_23_encoder_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1367063936)))];
tensor<fp16, [1, 1280, 1, 1]> obj_333_cast_fp16 = conv(bias = layers_23_encoder_attn_o_proj_bias_to_fp16, dilations = var_5297, groups = var_5137, pad = obj_333_pad_0, pad_type = obj_333_pad_type_0, strides = var_5295, weight = layers_23_encoder_attn_o_proj_weight_to_fp16, x = input_233_cast_fp16)[name = tensor<string, []>("obj_333_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_143_cast_fp16 = add(x = inputs_141_cast_fp16, y = obj_333_cast_fp16)[name = tensor<string, []>("inputs_143_cast_fp16")];
tensor<int32, [1]> var_5306 = const()[name = tensor<string, []>("op_5306"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_143_cast_fp16 = reduce_mean(axes = var_5306, keep_dims = var_5138, x = inputs_143_cast_fp16)[name = tensor<string, []>("channels_mean_143_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_143_cast_fp16 = sub(x = inputs_143_cast_fp16, y = channels_mean_143_cast_fp16)[name = tensor<string, []>("zero_mean_143_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_143_cast_fp16 = mul(x = zero_mean_143_cast_fp16, y = zero_mean_143_cast_fp16)[name = tensor<string, []>("zero_mean_sq_143_cast_fp16")];
tensor<int32, [1]> var_5310 = const()[name = tensor<string, []>("op_5310"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_5311_cast_fp16 = reduce_mean(axes = var_5310, keep_dims = var_5138, x = zero_mean_sq_143_cast_fp16)[name = tensor<string, []>("op_5311_cast_fp16")];
tensor<fp16, []> var_5312_to_fp16 = const()[name = tensor<string, []>("op_5312_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_5313_cast_fp16 = add(x = var_5311_cast_fp16, y = var_5312_to_fp16)[name = tensor<string, []>("op_5313_cast_fp16")];
tensor<fp16, []> denom_143_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_143_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_143_cast_fp16 = rsqrt(epsilon = denom_143_epsilon_0_to_fp16, x = var_5313_cast_fp16)[name = tensor<string, []>("denom_143_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_143_cast_fp16 = mul(x = zero_mean_143_cast_fp16, y = denom_143_cast_fp16)[name = tensor<string, []>("out_143_cast_fp16")];
tensor<fp16, [1280]> input_235_gamma_0_to_fp16 = const()[name = tensor<string, []>("input_235_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1367066560)))];
tensor<fp16, [1280]> input_235_beta_0_to_fp16 = const()[name = tensor<string, []>("input_235_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1367069184)))];
tensor<fp16, []> input_235_epsilon_0_to_fp16 = const()[name = tensor<string, []>("input_235_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> input_235_cast_fp16 = batch_norm(beta = input_235_beta_0_to_fp16, epsilon = input_235_epsilon_0_to_fp16, gamma = input_235_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_143_cast_fp16)[name = tensor<string, []>("input_235_cast_fp16")];
tensor<int32, [2]> var_5324 = const()[name = tensor<string, []>("op_5324"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5326 = const()[name = tensor<string, []>("op_5326"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_237_pad_type_0 = const()[name = tensor<string, []>("input_237_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_237_pad_0 = const()[name = tensor<string, []>("input_237_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [5120, 1280, 1, 1]> layers_23_fc1_weight_to_fp16 = const()[name = tensor<string, []>("layers_23_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1367071808)))];
tensor<fp16, [5120]> layers_23_fc1_bias_to_fp16 = const()[name = tensor<string, []>("layers_23_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1380179072)))];
tensor<fp16, [1, 5120, 1, 1]> input_237_cast_fp16 = conv(bias = layers_23_fc1_bias_to_fp16, dilations = var_5326, groups = var_5137, pad = input_237_pad_0, pad_type = input_237_pad_type_0, strides = var_5324, weight = layers_23_fc1_weight_to_fp16, x = input_235_cast_fp16)[name = tensor<string, []>("input_237_cast_fp16")];
tensor<string, []> input_239_mode_0 = const()[name = tensor<string, []>("input_239_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 5120, 1, 1]> input_239_cast_fp16 = gelu(mode = input_239_mode_0, x = input_237_cast_fp16)[name = tensor<string, []>("input_239_cast_fp16")];
tensor<int32, [2]> var_5332 = const()[name = tensor<string, []>("op_5332"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5334 = const()[name = tensor<string, []>("op_5334"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_49_pad_type_0 = const()[name = tensor<string, []>("hidden_states_49_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_49_pad_0 = const()[name = tensor<string, []>("hidden_states_49_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 5120, 1, 1]> layers_23_fc2_weight_to_fp16 = const()[name = tensor<string, []>("layers_23_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1380189376)))];
tensor<fp16, [1280]> layers_23_fc2_bias_to_fp16 = const()[name = tensor<string, []>("layers_23_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1393296640)))];
tensor<fp16, [1, 1280, 1, 1]> hidden_states_49_cast_fp16 = conv(bias = layers_23_fc2_bias_to_fp16, dilations = var_5334, groups = var_5137, pad = hidden_states_49_pad_0, pad_type = hidden_states_49_pad_type_0, strides = var_5332, weight = layers_23_fc2_weight_to_fp16, x = input_239_cast_fp16)[name = tensor<string, []>("hidden_states_49_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_145_cast_fp16 = add(x = inputs_143_cast_fp16, y = hidden_states_49_cast_fp16)[name = tensor<string, []>("inputs_145_cast_fp16")];
tensor<int32, []> var_5348 = const()[name = tensor<string, []>("op_5348"), val = tensor<int32, []>(3)];
tensor<int32, []> var_5355 = const()[name = tensor<string, []>("op_5355"), val = tensor<int32, []>(1)];
tensor<bool, []> var_5356 = const()[name = tensor<string, []>("op_5356"), val = tensor<bool, []>(true)];
tensor<int32, [1]> var_5368 = const()[name = tensor<string, []>("op_5368"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_145_cast_fp16 = reduce_mean(axes = var_5368, keep_dims = var_5356, x = inputs_145_cast_fp16)[name = tensor<string, []>("channels_mean_145_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_145_cast_fp16 = sub(x = inputs_145_cast_fp16, y = channels_mean_145_cast_fp16)[name = tensor<string, []>("zero_mean_145_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_145_cast_fp16 = mul(x = zero_mean_145_cast_fp16, y = zero_mean_145_cast_fp16)[name = tensor<string, []>("zero_mean_sq_145_cast_fp16")];
tensor<int32, [1]> var_5372 = const()[name = tensor<string, []>("op_5372"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_5373_cast_fp16 = reduce_mean(axes = var_5372, keep_dims = var_5356, x = zero_mean_sq_145_cast_fp16)[name = tensor<string, []>("op_5373_cast_fp16")];
tensor<fp16, []> var_5374_to_fp16 = const()[name = tensor<string, []>("op_5374_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_5375_cast_fp16 = add(x = var_5373_cast_fp16, y = var_5374_to_fp16)[name = tensor<string, []>("op_5375_cast_fp16")];
tensor<fp16, []> denom_145_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_145_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_145_cast_fp16 = rsqrt(epsilon = denom_145_epsilon_0_to_fp16, x = var_5375_cast_fp16)[name = tensor<string, []>("denom_145_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_145_cast_fp16 = mul(x = zero_mean_145_cast_fp16, y = denom_145_cast_fp16)[name = tensor<string, []>("out_145_cast_fp16")];
tensor<fp16, [1280]> obj_337_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_337_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1393299264)))];
tensor<fp16, [1280]> obj_337_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_337_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1393301888)))];
tensor<fp16, []> obj_337_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_337_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_337_cast_fp16 = batch_norm(beta = obj_337_beta_0_to_fp16, epsilon = obj_337_epsilon_0_to_fp16, gamma = obj_337_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_145_cast_fp16)[name = tensor<string, []>("obj_337_cast_fp16")];
tensor<int32, [2]> var_5390 = const()[name = tensor<string, []>("op_5390"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5392 = const()[name = tensor<string, []>("op_5392"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_97_pad_type_0 = const()[name = tensor<string, []>("query_97_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_97_pad_0 = const()[name = tensor<string, []>("query_97_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_24_self_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_24_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1393304512)))];
tensor<fp16, [1280]> layers_24_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_24_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1396581376)))];
tensor<fp16, [1, 1280, 1, 1]> query_97_cast_fp16 = conv(bias = layers_24_self_attn_q_proj_bias_to_fp16, dilations = var_5392, groups = var_5355, pad = query_97_pad_0, pad_type = query_97_pad_type_0, strides = var_5390, weight = layers_24_self_attn_q_proj_weight_to_fp16, x = obj_337_cast_fp16)[name = tensor<string, []>("query_97_cast_fp16")];
tensor<int32, [2]> var_5396 = const()[name = tensor<string, []>("op_5396"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5398 = const()[name = tensor<string, []>("op_5398"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_key_49_pad_type_0 = const()[name = tensor<string, []>("current_key_49_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_key_49_pad_0 = const()[name = tensor<string, []>("current_key_49_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_24_self_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_24_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1396584000)))];
tensor<fp16, [1, 1280, 1, 1]> current_key_49_cast_fp16 = conv(dilations = var_5398, groups = var_5355, pad = current_key_49_pad_0, pad_type = current_key_49_pad_type_0, strides = var_5396, weight = layers_24_self_attn_k_proj_weight_to_fp16, x = obj_337_cast_fp16)[name = tensor<string, []>("current_key_49_cast_fp16")];
tensor<int32, [2]> var_5403 = const()[name = tensor<string, []>("op_5403"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5405 = const()[name = tensor<string, []>("op_5405"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_value_49_pad_type_0 = const()[name = tensor<string, []>("current_value_49_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_value_49_pad_0 = const()[name = tensor<string, []>("current_value_49_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_24_self_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_24_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1399860864)))];
tensor<fp16, [1280]> layers_24_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_24_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1403137728)))];
tensor<fp16, [1, 1280, 1, 1]> current_value_49_cast_fp16 = conv(bias = layers_24_self_attn_v_proj_bias_to_fp16, dilations = var_5405, groups = var_5355, pad = current_value_49_pad_0, pad_type = current_value_49_pad_type_0, strides = var_5403, weight = layers_24_self_attn_v_proj_weight_to_fp16, x = obj_337_cast_fp16)[name = tensor<string, []>("current_value_49_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_5412_cast_fp16 = mul(x = current_key_49_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_5412_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_5414_cast_fp16 = mul(x = var_103_cast_fp16_24, y = var_241_cast_fp16)[name = tensor<string, []>("op_5414_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> key_97_cast_fp16 = add(x = var_5412_cast_fp16, y = var_5414_cast_fp16)[name = tensor<string, []>("key_97_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_5416_cast_fp16 = mul(x = current_value_49_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_5416_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_5418_cast_fp16 = mul(x = var_138_cast_fp16_24, y = var_241_cast_fp16)[name = tensor<string, []>("op_5418_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> value_97_cast_fp16 = add(x = var_5416_cast_fp16, y = var_5418_cast_fp16)[name = tensor<string, []>("value_97_cast_fp16")];
tensor<int32, [4]> var_5421 = const()[name = tensor<string, []>("op_5421"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_5422_cast_fp16 = reshape(shape = var_5421, x = query_97_cast_fp16)[name = tensor<string, []>("op_5422_cast_fp16")];
tensor<fp16, []> var_5423_to_fp16 = const()[name = tensor<string, []>("op_5423_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_5424_cast_fp16 = mul(x = var_5422_cast_fp16, y = var_5423_to_fp16)[name = tensor<string, []>("op_5424_cast_fp16")];
tensor<int32, [4]> var_5425 = const()[name = tensor<string, []>("op_5425"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_5426_cast_fp16 = reshape(shape = var_5425, x = key_97_cast_fp16)[name = tensor<string, []>("op_5426_cast_fp16")];
tensor<bool, []> mh_w_145_transpose_x_0 = const()[name = tensor<string, []>("mh_w_145_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_145_transpose_y_0 = const()[name = tensor<string, []>("mh_w_145_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 224]> mh_w_145_cast_fp16 = matmul(transpose_x = mh_w_145_transpose_x_0, transpose_y = mh_w_145_transpose_y_0, x = var_5424_cast_fp16, y = var_5426_cast_fp16)[name = tensor<string, []>("mh_w_145_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> mh_w_147_cast_fp16 = add(x = mh_w_145_cast_fp16, y = var_259_cast_fp16)[name = tensor<string, []>("mh_w_147_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> var_5434_cast_fp16 = softmax(axis = var_5348, x = mh_w_147_cast_fp16)[name = tensor<string, []>("op_5434_cast_fp16")];
tensor<int32, [4]> var_5435 = const()[name = tensor<string, []>("op_5435"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_5436_cast_fp16 = reshape(shape = var_5435, x = value_97_cast_fp16)[name = tensor<string, []>("op_5436_cast_fp16")];
tensor<bool, []> attn_97_transpose_x_0 = const()[name = tensor<string, []>("attn_97_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_97_transpose_y_0 = const()[name = tensor<string, []>("attn_97_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_97_cast_fp16 = matmul(transpose_x = attn_97_transpose_x_0, transpose_y = attn_97_transpose_y_0, x = var_5436_cast_fp16, y = var_5434_cast_fp16)[name = tensor<string, []>("attn_97_cast_fp16")];
tensor<int32, [4]> var_5439 = const()[name = tensor<string, []>("op_5439"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_241_cast_fp16 = reshape(shape = var_5439, x = attn_97_cast_fp16)[name = tensor<string, []>("input_241_cast_fp16")];
tensor<int32, [2]> var_5443 = const()[name = tensor<string, []>("op_5443"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5445 = const()[name = tensor<string, []>("op_5445"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_343_pad_type_0 = const()[name = tensor<string, []>("obj_343_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_343_pad_0 = const()[name = tensor<string, []>("obj_343_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_24_self_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_24_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1403140352)))];
tensor<fp16, [1280]> layers_24_self_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_24_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1406417216)))];
tensor<fp16, [1, 1280, 1, 1]> obj_343_cast_fp16 = conv(bias = layers_24_self_attn_o_proj_bias_to_fp16, dilations = var_5445, groups = var_5355, pad = obj_343_pad_0, pad_type = obj_343_pad_type_0, strides = var_5443, weight = layers_24_self_attn_o_proj_weight_to_fp16, x = input_241_cast_fp16)[name = tensor<string, []>("obj_343_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_147_cast_fp16 = add(x = inputs_145_cast_fp16, y = obj_343_cast_fp16)[name = tensor<string, []>("inputs_147_cast_fp16")];
tensor<int32, [1]> var_5455 = const()[name = tensor<string, []>("op_5455"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_147_cast_fp16 = reduce_mean(axes = var_5455, keep_dims = var_5356, x = inputs_147_cast_fp16)[name = tensor<string, []>("channels_mean_147_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_147_cast_fp16 = sub(x = inputs_147_cast_fp16, y = channels_mean_147_cast_fp16)[name = tensor<string, []>("zero_mean_147_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_147_cast_fp16 = mul(x = zero_mean_147_cast_fp16, y = zero_mean_147_cast_fp16)[name = tensor<string, []>("zero_mean_sq_147_cast_fp16")];
tensor<int32, [1]> var_5459 = const()[name = tensor<string, []>("op_5459"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_5460_cast_fp16 = reduce_mean(axes = var_5459, keep_dims = var_5356, x = zero_mean_sq_147_cast_fp16)[name = tensor<string, []>("op_5460_cast_fp16")];
tensor<fp16, []> var_5461_to_fp16 = const()[name = tensor<string, []>("op_5461_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_5462_cast_fp16 = add(x = var_5460_cast_fp16, y = var_5461_to_fp16)[name = tensor<string, []>("op_5462_cast_fp16")];
tensor<fp16, []> denom_147_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_147_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_147_cast_fp16 = rsqrt(epsilon = denom_147_epsilon_0_to_fp16, x = var_5462_cast_fp16)[name = tensor<string, []>("denom_147_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_147_cast_fp16 = mul(x = zero_mean_147_cast_fp16, y = denom_147_cast_fp16)[name = tensor<string, []>("out_147_cast_fp16")];
tensor<fp16, [1280]> obj_345_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_345_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1406419840)))];
tensor<fp16, [1280]> obj_345_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_345_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1406422464)))];
tensor<fp16, []> obj_345_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_345_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_345_cast_fp16 = batch_norm(beta = obj_345_beta_0_to_fp16, epsilon = obj_345_epsilon_0_to_fp16, gamma = obj_345_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_147_cast_fp16)[name = tensor<string, []>("obj_345_cast_fp16")];
tensor<int32, [2]> var_5477 = const()[name = tensor<string, []>("op_5477"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5479 = const()[name = tensor<string, []>("op_5479"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_99_pad_type_0 = const()[name = tensor<string, []>("query_99_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_99_pad_0 = const()[name = tensor<string, []>("query_99_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_24_encoder_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_24_encoder_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1406425088)))];
tensor<fp16, [1280]> layers_24_encoder_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_24_encoder_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1409701952)))];
tensor<fp16, [1, 1280, 1, 1]> query_99_cast_fp16 = conv(bias = layers_24_encoder_attn_q_proj_bias_to_fp16, dilations = var_5479, groups = var_5355, pad = query_99_pad_0, pad_type = query_99_pad_type_0, strides = var_5477, weight = layers_24_encoder_attn_q_proj_weight_to_fp16, x = obj_345_cast_fp16)[name = tensor<string, []>("query_99_cast_fp16")];
tensor<int32, [2]> var_5483 = const()[name = tensor<string, []>("op_5483"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5485 = const()[name = tensor<string, []>("op_5485"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> key_99_pad_type_0 = const()[name = tensor<string, []>("key_99_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> key_99_pad_0 = const()[name = tensor<string, []>("key_99_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_24_encoder_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_24_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1409704576)))];
tensor<fp16, [1, 1280, 1, 1500]> key_99_cast_fp16 = conv(dilations = var_5485, groups = var_5355, pad = key_99_pad_0, pad_type = key_99_pad_type_0, strides = var_5483, weight = layers_24_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("key_99_cast_fp16")];
tensor<int32, [2]> var_5490 = const()[name = tensor<string, []>("op_5490"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5492 = const()[name = tensor<string, []>("op_5492"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> value_99_pad_type_0 = const()[name = tensor<string, []>("value_99_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> value_99_pad_0 = const()[name = tensor<string, []>("value_99_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_24_encoder_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_24_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1412981440)))];
tensor<fp16, [1280]> layers_24_encoder_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_24_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1416258304)))];
tensor<fp16, [1, 1280, 1, 1500]> value_99_cast_fp16 = conv(bias = layers_24_encoder_attn_v_proj_bias_to_fp16, dilations = var_5492, groups = var_5355, pad = value_99_pad_0, pad_type = value_99_pad_type_0, strides = var_5490, weight = layers_24_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("value_99_cast_fp16")];
tensor<int32, [4]> var_5496 = const()[name = tensor<string, []>("op_5496"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_5497_cast_fp16 = reshape(shape = var_5496, x = query_99_cast_fp16)[name = tensor<string, []>("op_5497_cast_fp16")];
tensor<fp16, []> var_5498_to_fp16 = const()[name = tensor<string, []>("op_5498_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_5499_cast_fp16 = mul(x = var_5497_cast_fp16, y = var_5498_to_fp16)[name = tensor<string, []>("op_5499_cast_fp16")];
tensor<int32, [4]> var_5500 = const()[name = tensor<string, []>("op_5500"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_5501_cast_fp16 = reshape(shape = var_5500, x = key_99_cast_fp16)[name = tensor<string, []>("op_5501_cast_fp16")];
tensor<bool, []> mh_w_149_transpose_x_0 = const()[name = tensor<string, []>("mh_w_149_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_149_transpose_y_0 = const()[name = tensor<string, []>("mh_w_149_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 1500]> mh_w_149_cast_fp16 = matmul(transpose_x = mh_w_149_transpose_x_0, transpose_y = mh_w_149_transpose_y_0, x = var_5499_cast_fp16, y = var_5501_cast_fp16)[name = tensor<string, []>("mh_w_149_cast_fp16")];
tensor<fp16, [1, 20, 1, 1500]> obj_349_cast_fp16 = softmax(axis = var_5348, x = mh_w_149_cast_fp16)[name = tensor<string, []>("obj_349_cast_fp16")];
tensor<int32, [4]> var_5505 = const()[name = tensor<string, []>("op_5505"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_5506_cast_fp16 = reshape(shape = var_5505, x = value_99_cast_fp16)[name = tensor<string, []>("op_5506_cast_fp16")];
tensor<bool, []> attn_99_transpose_x_0 = const()[name = tensor<string, []>("attn_99_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_99_transpose_y_0 = const()[name = tensor<string, []>("attn_99_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_99_cast_fp16 = matmul(transpose_x = attn_99_transpose_x_0, transpose_y = attn_99_transpose_y_0, x = var_5506_cast_fp16, y = obj_349_cast_fp16)[name = tensor<string, []>("attn_99_cast_fp16")];
tensor<int32, [4]> var_5509 = const()[name = tensor<string, []>("op_5509"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_243_cast_fp16 = reshape(shape = var_5509, x = attn_99_cast_fp16)[name = tensor<string, []>("input_243_cast_fp16")];
tensor<int32, [2]> var_5513 = const()[name = tensor<string, []>("op_5513"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5515 = const()[name = tensor<string, []>("op_5515"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_347_pad_type_0 = const()[name = tensor<string, []>("obj_347_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_347_pad_0 = const()[name = tensor<string, []>("obj_347_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_24_encoder_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_24_encoder_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1416260928)))];
tensor<fp16, [1280]> layers_24_encoder_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_24_encoder_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1419537792)))];
tensor<fp16, [1, 1280, 1, 1]> obj_347_cast_fp16 = conv(bias = layers_24_encoder_attn_o_proj_bias_to_fp16, dilations = var_5515, groups = var_5355, pad = obj_347_pad_0, pad_type = obj_347_pad_type_0, strides = var_5513, weight = layers_24_encoder_attn_o_proj_weight_to_fp16, x = input_243_cast_fp16)[name = tensor<string, []>("obj_347_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_149_cast_fp16 = add(x = inputs_147_cast_fp16, y = obj_347_cast_fp16)[name = tensor<string, []>("inputs_149_cast_fp16")];
tensor<int32, [1]> var_5521 = const()[name = tensor<string, []>("op_5521"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_149_cast_fp16 = reduce_mean(axes = var_5521, keep_dims = var_5356, x = inputs_149_cast_fp16)[name = tensor<string, []>("channels_mean_149_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_149_cast_fp16 = sub(x = inputs_149_cast_fp16, y = channels_mean_149_cast_fp16)[name = tensor<string, []>("zero_mean_149_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_149_cast_fp16 = mul(x = zero_mean_149_cast_fp16, y = zero_mean_149_cast_fp16)[name = tensor<string, []>("zero_mean_sq_149_cast_fp16")];
tensor<int32, [1]> var_5525 = const()[name = tensor<string, []>("op_5525"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_5526_cast_fp16 = reduce_mean(axes = var_5525, keep_dims = var_5356, x = zero_mean_sq_149_cast_fp16)[name = tensor<string, []>("op_5526_cast_fp16")];
tensor<fp16, []> var_5527_to_fp16 = const()[name = tensor<string, []>("op_5527_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_5528_cast_fp16 = add(x = var_5526_cast_fp16, y = var_5527_to_fp16)[name = tensor<string, []>("op_5528_cast_fp16")];
tensor<fp16, []> denom_149_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_149_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_149_cast_fp16 = rsqrt(epsilon = denom_149_epsilon_0_to_fp16, x = var_5528_cast_fp16)[name = tensor<string, []>("denom_149_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_149_cast_fp16 = mul(x = zero_mean_149_cast_fp16, y = denom_149_cast_fp16)[name = tensor<string, []>("out_149_cast_fp16")];
tensor<fp16, [1280]> input_245_gamma_0_to_fp16 = const()[name = tensor<string, []>("input_245_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1419540416)))];
tensor<fp16, [1280]> input_245_beta_0_to_fp16 = const()[name = tensor<string, []>("input_245_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1419543040)))];
tensor<fp16, []> input_245_epsilon_0_to_fp16 = const()[name = tensor<string, []>("input_245_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> input_245_cast_fp16 = batch_norm(beta = input_245_beta_0_to_fp16, epsilon = input_245_epsilon_0_to_fp16, gamma = input_245_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_149_cast_fp16)[name = tensor<string, []>("input_245_cast_fp16")];
tensor<int32, [2]> var_5539 = const()[name = tensor<string, []>("op_5539"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5541 = const()[name = tensor<string, []>("op_5541"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_247_pad_type_0 = const()[name = tensor<string, []>("input_247_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_247_pad_0 = const()[name = tensor<string, []>("input_247_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [5120, 1280, 1, 1]> layers_24_fc1_weight_to_fp16 = const()[name = tensor<string, []>("layers_24_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1419545664)))];
tensor<fp16, [5120]> layers_24_fc1_bias_to_fp16 = const()[name = tensor<string, []>("layers_24_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1432652928)))];
tensor<fp16, [1, 5120, 1, 1]> input_247_cast_fp16 = conv(bias = layers_24_fc1_bias_to_fp16, dilations = var_5541, groups = var_5355, pad = input_247_pad_0, pad_type = input_247_pad_type_0, strides = var_5539, weight = layers_24_fc1_weight_to_fp16, x = input_245_cast_fp16)[name = tensor<string, []>("input_247_cast_fp16")];
tensor<string, []> input_249_mode_0 = const()[name = tensor<string, []>("input_249_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 5120, 1, 1]> input_249_cast_fp16 = gelu(mode = input_249_mode_0, x = input_247_cast_fp16)[name = tensor<string, []>("input_249_cast_fp16")];
tensor<int32, [2]> var_5547 = const()[name = tensor<string, []>("op_5547"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5549 = const()[name = tensor<string, []>("op_5549"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_51_pad_type_0 = const()[name = tensor<string, []>("hidden_states_51_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_51_pad_0 = const()[name = tensor<string, []>("hidden_states_51_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 5120, 1, 1]> layers_24_fc2_weight_to_fp16 = const()[name = tensor<string, []>("layers_24_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1432663232)))];
tensor<fp16, [1280]> layers_24_fc2_bias_to_fp16 = const()[name = tensor<string, []>("layers_24_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1445770496)))];
tensor<fp16, [1, 1280, 1, 1]> hidden_states_51_cast_fp16 = conv(bias = layers_24_fc2_bias_to_fp16, dilations = var_5549, groups = var_5355, pad = hidden_states_51_pad_0, pad_type = hidden_states_51_pad_type_0, strides = var_5547, weight = layers_24_fc2_weight_to_fp16, x = input_249_cast_fp16)[name = tensor<string, []>("hidden_states_51_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_151_cast_fp16 = add(x = inputs_149_cast_fp16, y = hidden_states_51_cast_fp16)[name = tensor<string, []>("inputs_151_cast_fp16")];
tensor<int32, []> var_5562 = const()[name = tensor<string, []>("op_5562"), val = tensor<int32, []>(3)];
tensor<int32, []> var_5569 = const()[name = tensor<string, []>("op_5569"), val = tensor<int32, []>(1)];
tensor<bool, []> var_5570 = const()[name = tensor<string, []>("op_5570"), val = tensor<bool, []>(true)];
tensor<int32, [1]> var_5582 = const()[name = tensor<string, []>("op_5582"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_151_cast_fp16 = reduce_mean(axes = var_5582, keep_dims = var_5570, x = inputs_151_cast_fp16)[name = tensor<string, []>("channels_mean_151_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_151_cast_fp16 = sub(x = inputs_151_cast_fp16, y = channels_mean_151_cast_fp16)[name = tensor<string, []>("zero_mean_151_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_151_cast_fp16 = mul(x = zero_mean_151_cast_fp16, y = zero_mean_151_cast_fp16)[name = tensor<string, []>("zero_mean_sq_151_cast_fp16")];
tensor<int32, [1]> var_5586 = const()[name = tensor<string, []>("op_5586"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_5587_cast_fp16 = reduce_mean(axes = var_5586, keep_dims = var_5570, x = zero_mean_sq_151_cast_fp16)[name = tensor<string, []>("op_5587_cast_fp16")];
tensor<fp16, []> var_5588_to_fp16 = const()[name = tensor<string, []>("op_5588_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_5589_cast_fp16 = add(x = var_5587_cast_fp16, y = var_5588_to_fp16)[name = tensor<string, []>("op_5589_cast_fp16")];
tensor<fp16, []> denom_151_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_151_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_151_cast_fp16 = rsqrt(epsilon = denom_151_epsilon_0_to_fp16, x = var_5589_cast_fp16)[name = tensor<string, []>("denom_151_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_151_cast_fp16 = mul(x = zero_mean_151_cast_fp16, y = denom_151_cast_fp16)[name = tensor<string, []>("out_151_cast_fp16")];
tensor<fp16, [1280]> obj_351_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_351_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1445773120)))];
tensor<fp16, [1280]> obj_351_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_351_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1445775744)))];
tensor<fp16, []> obj_351_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_351_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_351_cast_fp16 = batch_norm(beta = obj_351_beta_0_to_fp16, epsilon = obj_351_epsilon_0_to_fp16, gamma = obj_351_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_151_cast_fp16)[name = tensor<string, []>("obj_351_cast_fp16")];
tensor<int32, [2]> var_5604 = const()[name = tensor<string, []>("op_5604"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5606 = const()[name = tensor<string, []>("op_5606"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_101_pad_type_0 = const()[name = tensor<string, []>("query_101_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_101_pad_0 = const()[name = tensor<string, []>("query_101_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_25_self_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_25_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1445778368)))];
tensor<fp16, [1280]> layers_25_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_25_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1449055232)))];
tensor<fp16, [1, 1280, 1, 1]> query_101_cast_fp16 = conv(bias = layers_25_self_attn_q_proj_bias_to_fp16, dilations = var_5606, groups = var_5569, pad = query_101_pad_0, pad_type = query_101_pad_type_0, strides = var_5604, weight = layers_25_self_attn_q_proj_weight_to_fp16, x = obj_351_cast_fp16)[name = tensor<string, []>("query_101_cast_fp16")];
tensor<int32, [2]> var_5610 = const()[name = tensor<string, []>("op_5610"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5612 = const()[name = tensor<string, []>("op_5612"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_key_51_pad_type_0 = const()[name = tensor<string, []>("current_key_51_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_key_51_pad_0 = const()[name = tensor<string, []>("current_key_51_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_25_self_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_25_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1449057856)))];
tensor<fp16, [1, 1280, 1, 1]> current_key_51_cast_fp16 = conv(dilations = var_5612, groups = var_5569, pad = current_key_51_pad_0, pad_type = current_key_51_pad_type_0, strides = var_5610, weight = layers_25_self_attn_k_proj_weight_to_fp16, x = obj_351_cast_fp16)[name = tensor<string, []>("current_key_51_cast_fp16")];
tensor<int32, [2]> var_5617 = const()[name = tensor<string, []>("op_5617"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5619 = const()[name = tensor<string, []>("op_5619"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_value_51_pad_type_0 = const()[name = tensor<string, []>("current_value_51_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_value_51_pad_0 = const()[name = tensor<string, []>("current_value_51_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_25_self_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_25_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1452334720)))];
tensor<fp16, [1280]> layers_25_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_25_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1455611584)))];
tensor<fp16, [1, 1280, 1, 1]> current_value_51_cast_fp16 = conv(bias = layers_25_self_attn_v_proj_bias_to_fp16, dilations = var_5619, groups = var_5569, pad = current_value_51_pad_0, pad_type = current_value_51_pad_type_0, strides = var_5617, weight = layers_25_self_attn_v_proj_weight_to_fp16, x = obj_351_cast_fp16)[name = tensor<string, []>("current_value_51_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_5626_cast_fp16 = mul(x = current_key_51_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_5626_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_5628_cast_fp16 = mul(x = var_103_cast_fp16_25, y = var_241_cast_fp16)[name = tensor<string, []>("op_5628_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> key_101_cast_fp16 = add(x = var_5626_cast_fp16, y = var_5628_cast_fp16)[name = tensor<string, []>("key_101_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_5630_cast_fp16 = mul(x = current_value_51_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_5630_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_5632_cast_fp16 = mul(x = var_138_cast_fp16_25, y = var_241_cast_fp16)[name = tensor<string, []>("op_5632_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> value_101_cast_fp16 = add(x = var_5630_cast_fp16, y = var_5632_cast_fp16)[name = tensor<string, []>("value_101_cast_fp16")];
tensor<int32, [4]> var_5635 = const()[name = tensor<string, []>("op_5635"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_5636_cast_fp16 = reshape(shape = var_5635, x = query_101_cast_fp16)[name = tensor<string, []>("op_5636_cast_fp16")];
tensor<fp16, []> var_5637_to_fp16 = const()[name = tensor<string, []>("op_5637_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_5638_cast_fp16 = mul(x = var_5636_cast_fp16, y = var_5637_to_fp16)[name = tensor<string, []>("op_5638_cast_fp16")];
tensor<int32, [4]> var_5639 = const()[name = tensor<string, []>("op_5639"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_5640_cast_fp16 = reshape(shape = var_5639, x = key_101_cast_fp16)[name = tensor<string, []>("op_5640_cast_fp16")];
tensor<bool, []> mh_w_151_transpose_x_0 = const()[name = tensor<string, []>("mh_w_151_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_151_transpose_y_0 = const()[name = tensor<string, []>("mh_w_151_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 224]> mh_w_151_cast_fp16 = matmul(transpose_x = mh_w_151_transpose_x_0, transpose_y = mh_w_151_transpose_y_0, x = var_5638_cast_fp16, y = var_5640_cast_fp16)[name = tensor<string, []>("mh_w_151_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> mh_w_153_cast_fp16 = add(x = mh_w_151_cast_fp16, y = var_259_cast_fp16)[name = tensor<string, []>("mh_w_153_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> var_5648_cast_fp16 = softmax(axis = var_5562, x = mh_w_153_cast_fp16)[name = tensor<string, []>("op_5648_cast_fp16")];
tensor<int32, [4]> var_5649 = const()[name = tensor<string, []>("op_5649"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_5650_cast_fp16 = reshape(shape = var_5649, x = value_101_cast_fp16)[name = tensor<string, []>("op_5650_cast_fp16")];
tensor<bool, []> attn_101_transpose_x_0 = const()[name = tensor<string, []>("attn_101_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_101_transpose_y_0 = const()[name = tensor<string, []>("attn_101_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_101_cast_fp16 = matmul(transpose_x = attn_101_transpose_x_0, transpose_y = attn_101_transpose_y_0, x = var_5650_cast_fp16, y = var_5648_cast_fp16)[name = tensor<string, []>("attn_101_cast_fp16")];
tensor<int32, [4]> var_5653 = const()[name = tensor<string, []>("op_5653"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_251_cast_fp16 = reshape(shape = var_5653, x = attn_101_cast_fp16)[name = tensor<string, []>("input_251_cast_fp16")];
tensor<int32, [2]> var_5657 = const()[name = tensor<string, []>("op_5657"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5659 = const()[name = tensor<string, []>("op_5659"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_357_pad_type_0 = const()[name = tensor<string, []>("obj_357_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_357_pad_0 = const()[name = tensor<string, []>("obj_357_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_25_self_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_25_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1455614208)))];
tensor<fp16, [1280]> layers_25_self_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_25_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1458891072)))];
tensor<fp16, [1, 1280, 1, 1]> obj_357_cast_fp16 = conv(bias = layers_25_self_attn_o_proj_bias_to_fp16, dilations = var_5659, groups = var_5569, pad = obj_357_pad_0, pad_type = obj_357_pad_type_0, strides = var_5657, weight = layers_25_self_attn_o_proj_weight_to_fp16, x = input_251_cast_fp16)[name = tensor<string, []>("obj_357_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_153_cast_fp16 = add(x = inputs_151_cast_fp16, y = obj_357_cast_fp16)[name = tensor<string, []>("inputs_153_cast_fp16")];
tensor<int32, [1]> var_5669 = const()[name = tensor<string, []>("op_5669"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_153_cast_fp16 = reduce_mean(axes = var_5669, keep_dims = var_5570, x = inputs_153_cast_fp16)[name = tensor<string, []>("channels_mean_153_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_153_cast_fp16 = sub(x = inputs_153_cast_fp16, y = channels_mean_153_cast_fp16)[name = tensor<string, []>("zero_mean_153_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_153_cast_fp16 = mul(x = zero_mean_153_cast_fp16, y = zero_mean_153_cast_fp16)[name = tensor<string, []>("zero_mean_sq_153_cast_fp16")];
tensor<int32, [1]> var_5673 = const()[name = tensor<string, []>("op_5673"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_5674_cast_fp16 = reduce_mean(axes = var_5673, keep_dims = var_5570, x = zero_mean_sq_153_cast_fp16)[name = tensor<string, []>("op_5674_cast_fp16")];
tensor<fp16, []> var_5675_to_fp16 = const()[name = tensor<string, []>("op_5675_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_5676_cast_fp16 = add(x = var_5674_cast_fp16, y = var_5675_to_fp16)[name = tensor<string, []>("op_5676_cast_fp16")];
tensor<fp16, []> denom_153_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_153_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_153_cast_fp16 = rsqrt(epsilon = denom_153_epsilon_0_to_fp16, x = var_5676_cast_fp16)[name = tensor<string, []>("denom_153_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_153_cast_fp16 = mul(x = zero_mean_153_cast_fp16, y = denom_153_cast_fp16)[name = tensor<string, []>("out_153_cast_fp16")];
tensor<fp16, [1280]> obj_359_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_359_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1458893696)))];
tensor<fp16, [1280]> obj_359_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_359_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1458896320)))];
tensor<fp16, []> obj_359_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_359_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_359_cast_fp16 = batch_norm(beta = obj_359_beta_0_to_fp16, epsilon = obj_359_epsilon_0_to_fp16, gamma = obj_359_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_153_cast_fp16)[name = tensor<string, []>("obj_359_cast_fp16")];
tensor<int32, [2]> var_5691 = const()[name = tensor<string, []>("op_5691"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5693 = const()[name = tensor<string, []>("op_5693"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_103_pad_type_0 = const()[name = tensor<string, []>("query_103_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_103_pad_0 = const()[name = tensor<string, []>("query_103_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_25_encoder_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_25_encoder_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1458898944)))];
tensor<fp16, [1280]> layers_25_encoder_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_25_encoder_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1462175808)))];
tensor<fp16, [1, 1280, 1, 1]> query_103_cast_fp16 = conv(bias = layers_25_encoder_attn_q_proj_bias_to_fp16, dilations = var_5693, groups = var_5569, pad = query_103_pad_0, pad_type = query_103_pad_type_0, strides = var_5691, weight = layers_25_encoder_attn_q_proj_weight_to_fp16, x = obj_359_cast_fp16)[name = tensor<string, []>("query_103_cast_fp16")];
tensor<int32, [2]> var_5697 = const()[name = tensor<string, []>("op_5697"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5699 = const()[name = tensor<string, []>("op_5699"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> key_103_pad_type_0 = const()[name = tensor<string, []>("key_103_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> key_103_pad_0 = const()[name = tensor<string, []>("key_103_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_25_encoder_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_25_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1462178432)))];
tensor<fp16, [1, 1280, 1, 1500]> key_103_cast_fp16 = conv(dilations = var_5699, groups = var_5569, pad = key_103_pad_0, pad_type = key_103_pad_type_0, strides = var_5697, weight = layers_25_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("key_103_cast_fp16")];
tensor<int32, [2]> var_5704 = const()[name = tensor<string, []>("op_5704"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5706 = const()[name = tensor<string, []>("op_5706"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> value_103_pad_type_0 = const()[name = tensor<string, []>("value_103_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> value_103_pad_0 = const()[name = tensor<string, []>("value_103_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_25_encoder_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_25_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1465455296)))];
tensor<fp16, [1280]> layers_25_encoder_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_25_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1468732160)))];
tensor<fp16, [1, 1280, 1, 1500]> value_103_cast_fp16 = conv(bias = layers_25_encoder_attn_v_proj_bias_to_fp16, dilations = var_5706, groups = var_5569, pad = value_103_pad_0, pad_type = value_103_pad_type_0, strides = var_5704, weight = layers_25_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("value_103_cast_fp16")];
tensor<int32, [4]> var_5710 = const()[name = tensor<string, []>("op_5710"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_5711_cast_fp16 = reshape(shape = var_5710, x = query_103_cast_fp16)[name = tensor<string, []>("op_5711_cast_fp16")];
tensor<fp16, []> var_5712_to_fp16 = const()[name = tensor<string, []>("op_5712_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_5713_cast_fp16 = mul(x = var_5711_cast_fp16, y = var_5712_to_fp16)[name = tensor<string, []>("op_5713_cast_fp16")];
tensor<int32, [4]> var_5714 = const()[name = tensor<string, []>("op_5714"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_5715_cast_fp16 = reshape(shape = var_5714, x = key_103_cast_fp16)[name = tensor<string, []>("op_5715_cast_fp16")];
tensor<bool, []> mh_w_155_transpose_x_0 = const()[name = tensor<string, []>("mh_w_155_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_155_transpose_y_0 = const()[name = tensor<string, []>("mh_w_155_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 1500]> mh_w_155_cast_fp16 = matmul(transpose_x = mh_w_155_transpose_x_0, transpose_y = mh_w_155_transpose_y_0, x = var_5713_cast_fp16, y = var_5715_cast_fp16)[name = tensor<string, []>("mh_w_155_cast_fp16")];
tensor<fp16, [1, 20, 1, 1500]> obj_363_cast_fp16 = softmax(axis = var_5562, x = mh_w_155_cast_fp16)[name = tensor<string, []>("obj_363_cast_fp16")];
tensor<int32, [4]> var_5719 = const()[name = tensor<string, []>("op_5719"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_5720_cast_fp16 = reshape(shape = var_5719, x = value_103_cast_fp16)[name = tensor<string, []>("op_5720_cast_fp16")];
tensor<bool, []> attn_103_transpose_x_0 = const()[name = tensor<string, []>("attn_103_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_103_transpose_y_0 = const()[name = tensor<string, []>("attn_103_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_103_cast_fp16 = matmul(transpose_x = attn_103_transpose_x_0, transpose_y = attn_103_transpose_y_0, x = var_5720_cast_fp16, y = obj_363_cast_fp16)[name = tensor<string, []>("attn_103_cast_fp16")];
tensor<int32, [4]> var_5723 = const()[name = tensor<string, []>("op_5723"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_253_cast_fp16 = reshape(shape = var_5723, x = attn_103_cast_fp16)[name = tensor<string, []>("input_253_cast_fp16")];
tensor<int32, [2]> var_5727 = const()[name = tensor<string, []>("op_5727"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5729 = const()[name = tensor<string, []>("op_5729"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_361_pad_type_0 = const()[name = tensor<string, []>("obj_361_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_361_pad_0 = const()[name = tensor<string, []>("obj_361_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_25_encoder_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_25_encoder_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1468734784)))];
tensor<fp16, [1280]> layers_25_encoder_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_25_encoder_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1472011648)))];
tensor<fp16, [1, 1280, 1, 1]> obj_361_cast_fp16 = conv(bias = layers_25_encoder_attn_o_proj_bias_to_fp16, dilations = var_5729, groups = var_5569, pad = obj_361_pad_0, pad_type = obj_361_pad_type_0, strides = var_5727, weight = layers_25_encoder_attn_o_proj_weight_to_fp16, x = input_253_cast_fp16)[name = tensor<string, []>("obj_361_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_155_cast_fp16 = add(x = inputs_153_cast_fp16, y = obj_361_cast_fp16)[name = tensor<string, []>("inputs_155_cast_fp16")];
tensor<int32, [1]> var_5738 = const()[name = tensor<string, []>("op_5738"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_155_cast_fp16 = reduce_mean(axes = var_5738, keep_dims = var_5570, x = inputs_155_cast_fp16)[name = tensor<string, []>("channels_mean_155_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_155_cast_fp16 = sub(x = inputs_155_cast_fp16, y = channels_mean_155_cast_fp16)[name = tensor<string, []>("zero_mean_155_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_155_cast_fp16 = mul(x = zero_mean_155_cast_fp16, y = zero_mean_155_cast_fp16)[name = tensor<string, []>("zero_mean_sq_155_cast_fp16")];
tensor<int32, [1]> var_5742 = const()[name = tensor<string, []>("op_5742"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_5743_cast_fp16 = reduce_mean(axes = var_5742, keep_dims = var_5570, x = zero_mean_sq_155_cast_fp16)[name = tensor<string, []>("op_5743_cast_fp16")];
tensor<fp16, []> var_5744_to_fp16 = const()[name = tensor<string, []>("op_5744_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_5745_cast_fp16 = add(x = var_5743_cast_fp16, y = var_5744_to_fp16)[name = tensor<string, []>("op_5745_cast_fp16")];
tensor<fp16, []> denom_155_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_155_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_155_cast_fp16 = rsqrt(epsilon = denom_155_epsilon_0_to_fp16, x = var_5745_cast_fp16)[name = tensor<string, []>("denom_155_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_155_cast_fp16 = mul(x = zero_mean_155_cast_fp16, y = denom_155_cast_fp16)[name = tensor<string, []>("out_155_cast_fp16")];
tensor<fp16, [1280]> input_255_gamma_0_to_fp16 = const()[name = tensor<string, []>("input_255_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1472014272)))];
tensor<fp16, [1280]> input_255_beta_0_to_fp16 = const()[name = tensor<string, []>("input_255_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1472016896)))];
tensor<fp16, []> input_255_epsilon_0_to_fp16 = const()[name = tensor<string, []>("input_255_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> input_255_cast_fp16 = batch_norm(beta = input_255_beta_0_to_fp16, epsilon = input_255_epsilon_0_to_fp16, gamma = input_255_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_155_cast_fp16)[name = tensor<string, []>("input_255_cast_fp16")];
tensor<int32, [2]> var_5756 = const()[name = tensor<string, []>("op_5756"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5758 = const()[name = tensor<string, []>("op_5758"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_257_pad_type_0 = const()[name = tensor<string, []>("input_257_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_257_pad_0 = const()[name = tensor<string, []>("input_257_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [5120, 1280, 1, 1]> layers_25_fc1_weight_to_fp16 = const()[name = tensor<string, []>("layers_25_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1472019520)))];
tensor<fp16, [5120]> layers_25_fc1_bias_to_fp16 = const()[name = tensor<string, []>("layers_25_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1485126784)))];
tensor<fp16, [1, 5120, 1, 1]> input_257_cast_fp16 = conv(bias = layers_25_fc1_bias_to_fp16, dilations = var_5758, groups = var_5569, pad = input_257_pad_0, pad_type = input_257_pad_type_0, strides = var_5756, weight = layers_25_fc1_weight_to_fp16, x = input_255_cast_fp16)[name = tensor<string, []>("input_257_cast_fp16")];
tensor<string, []> input_259_mode_0 = const()[name = tensor<string, []>("input_259_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 5120, 1, 1]> input_259_cast_fp16 = gelu(mode = input_259_mode_0, x = input_257_cast_fp16)[name = tensor<string, []>("input_259_cast_fp16")];
tensor<int32, [2]> var_5764 = const()[name = tensor<string, []>("op_5764"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5766 = const()[name = tensor<string, []>("op_5766"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_53_pad_type_0 = const()[name = tensor<string, []>("hidden_states_53_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_53_pad_0 = const()[name = tensor<string, []>("hidden_states_53_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 5120, 1, 1]> layers_25_fc2_weight_to_fp16 = const()[name = tensor<string, []>("layers_25_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1485137088)))];
tensor<fp16, [1280]> layers_25_fc2_bias_to_fp16 = const()[name = tensor<string, []>("layers_25_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1498244352)))];
tensor<fp16, [1, 1280, 1, 1]> hidden_states_53_cast_fp16 = conv(bias = layers_25_fc2_bias_to_fp16, dilations = var_5766, groups = var_5569, pad = hidden_states_53_pad_0, pad_type = hidden_states_53_pad_type_0, strides = var_5764, weight = layers_25_fc2_weight_to_fp16, x = input_259_cast_fp16)[name = tensor<string, []>("hidden_states_53_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_157_cast_fp16 = add(x = inputs_155_cast_fp16, y = hidden_states_53_cast_fp16)[name = tensor<string, []>("inputs_157_cast_fp16")];
tensor<int32, []> var_5780 = const()[name = tensor<string, []>("op_5780"), val = tensor<int32, []>(3)];
tensor<int32, []> var_5787 = const()[name = tensor<string, []>("op_5787"), val = tensor<int32, []>(1)];
tensor<bool, []> var_5788 = const()[name = tensor<string, []>("op_5788"), val = tensor<bool, []>(true)];
tensor<int32, [1]> var_5800 = const()[name = tensor<string, []>("op_5800"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_157_cast_fp16 = reduce_mean(axes = var_5800, keep_dims = var_5788, x = inputs_157_cast_fp16)[name = tensor<string, []>("channels_mean_157_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_157_cast_fp16 = sub(x = inputs_157_cast_fp16, y = channels_mean_157_cast_fp16)[name = tensor<string, []>("zero_mean_157_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_157_cast_fp16 = mul(x = zero_mean_157_cast_fp16, y = zero_mean_157_cast_fp16)[name = tensor<string, []>("zero_mean_sq_157_cast_fp16")];
tensor<int32, [1]> var_5804 = const()[name = tensor<string, []>("op_5804"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_5805_cast_fp16 = reduce_mean(axes = var_5804, keep_dims = var_5788, x = zero_mean_sq_157_cast_fp16)[name = tensor<string, []>("op_5805_cast_fp16")];
tensor<fp16, []> var_5806_to_fp16 = const()[name = tensor<string, []>("op_5806_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_5807_cast_fp16 = add(x = var_5805_cast_fp16, y = var_5806_to_fp16)[name = tensor<string, []>("op_5807_cast_fp16")];
tensor<fp16, []> denom_157_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_157_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_157_cast_fp16 = rsqrt(epsilon = denom_157_epsilon_0_to_fp16, x = var_5807_cast_fp16)[name = tensor<string, []>("denom_157_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_157_cast_fp16 = mul(x = zero_mean_157_cast_fp16, y = denom_157_cast_fp16)[name = tensor<string, []>("out_157_cast_fp16")];
tensor<fp16, [1280]> obj_365_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_365_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1498246976)))];
tensor<fp16, [1280]> obj_365_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_365_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1498249600)))];
tensor<fp16, []> obj_365_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_365_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_365_cast_fp16 = batch_norm(beta = obj_365_beta_0_to_fp16, epsilon = obj_365_epsilon_0_to_fp16, gamma = obj_365_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_157_cast_fp16)[name = tensor<string, []>("obj_365_cast_fp16")];
tensor<int32, [2]> var_5822 = const()[name = tensor<string, []>("op_5822"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5824 = const()[name = tensor<string, []>("op_5824"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_105_pad_type_0 = const()[name = tensor<string, []>("query_105_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_105_pad_0 = const()[name = tensor<string, []>("query_105_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_26_self_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_26_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1498252224)))];
tensor<fp16, [1280]> layers_26_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_26_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1501529088)))];
tensor<fp16, [1, 1280, 1, 1]> query_105_cast_fp16 = conv(bias = layers_26_self_attn_q_proj_bias_to_fp16, dilations = var_5824, groups = var_5787, pad = query_105_pad_0, pad_type = query_105_pad_type_0, strides = var_5822, weight = layers_26_self_attn_q_proj_weight_to_fp16, x = obj_365_cast_fp16)[name = tensor<string, []>("query_105_cast_fp16")];
tensor<int32, [2]> var_5828 = const()[name = tensor<string, []>("op_5828"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5830 = const()[name = tensor<string, []>("op_5830"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_key_53_pad_type_0 = const()[name = tensor<string, []>("current_key_53_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_key_53_pad_0 = const()[name = tensor<string, []>("current_key_53_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_26_self_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_26_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1501531712)))];
tensor<fp16, [1, 1280, 1, 1]> current_key_53_cast_fp16 = conv(dilations = var_5830, groups = var_5787, pad = current_key_53_pad_0, pad_type = current_key_53_pad_type_0, strides = var_5828, weight = layers_26_self_attn_k_proj_weight_to_fp16, x = obj_365_cast_fp16)[name = tensor<string, []>("current_key_53_cast_fp16")];
tensor<int32, [2]> var_5835 = const()[name = tensor<string, []>("op_5835"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5837 = const()[name = tensor<string, []>("op_5837"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_value_53_pad_type_0 = const()[name = tensor<string, []>("current_value_53_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_value_53_pad_0 = const()[name = tensor<string, []>("current_value_53_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_26_self_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_26_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1504808576)))];
tensor<fp16, [1280]> layers_26_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_26_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1508085440)))];
tensor<fp16, [1, 1280, 1, 1]> current_value_53_cast_fp16 = conv(bias = layers_26_self_attn_v_proj_bias_to_fp16, dilations = var_5837, groups = var_5787, pad = current_value_53_pad_0, pad_type = current_value_53_pad_type_0, strides = var_5835, weight = layers_26_self_attn_v_proj_weight_to_fp16, x = obj_365_cast_fp16)[name = tensor<string, []>("current_value_53_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_5844_cast_fp16 = mul(x = current_key_53_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_5844_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_5846_cast_fp16 = mul(x = var_103_cast_fp16_26, y = var_241_cast_fp16)[name = tensor<string, []>("op_5846_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> key_105_cast_fp16 = add(x = var_5844_cast_fp16, y = var_5846_cast_fp16)[name = tensor<string, []>("key_105_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_5848_cast_fp16 = mul(x = current_value_53_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_5848_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_5850_cast_fp16 = mul(x = var_138_cast_fp16_26, y = var_241_cast_fp16)[name = tensor<string, []>("op_5850_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> value_105_cast_fp16 = add(x = var_5848_cast_fp16, y = var_5850_cast_fp16)[name = tensor<string, []>("value_105_cast_fp16")];
tensor<int32, [4]> var_5853 = const()[name = tensor<string, []>("op_5853"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_5854_cast_fp16 = reshape(shape = var_5853, x = query_105_cast_fp16)[name = tensor<string, []>("op_5854_cast_fp16")];
tensor<fp16, []> var_5855_to_fp16 = const()[name = tensor<string, []>("op_5855_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_5856_cast_fp16 = mul(x = var_5854_cast_fp16, y = var_5855_to_fp16)[name = tensor<string, []>("op_5856_cast_fp16")];
tensor<int32, [4]> var_5857 = const()[name = tensor<string, []>("op_5857"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_5858_cast_fp16 = reshape(shape = var_5857, x = key_105_cast_fp16)[name = tensor<string, []>("op_5858_cast_fp16")];
tensor<bool, []> mh_w_157_transpose_x_0 = const()[name = tensor<string, []>("mh_w_157_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_157_transpose_y_0 = const()[name = tensor<string, []>("mh_w_157_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 224]> mh_w_157_cast_fp16 = matmul(transpose_x = mh_w_157_transpose_x_0, transpose_y = mh_w_157_transpose_y_0, x = var_5856_cast_fp16, y = var_5858_cast_fp16)[name = tensor<string, []>("mh_w_157_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> mh_w_159_cast_fp16 = add(x = mh_w_157_cast_fp16, y = var_259_cast_fp16)[name = tensor<string, []>("mh_w_159_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> var_5866_cast_fp16 = softmax(axis = var_5780, x = mh_w_159_cast_fp16)[name = tensor<string, []>("op_5866_cast_fp16")];
tensor<int32, [4]> var_5867 = const()[name = tensor<string, []>("op_5867"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_5868_cast_fp16 = reshape(shape = var_5867, x = value_105_cast_fp16)[name = tensor<string, []>("op_5868_cast_fp16")];
tensor<bool, []> attn_105_transpose_x_0 = const()[name = tensor<string, []>("attn_105_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_105_transpose_y_0 = const()[name = tensor<string, []>("attn_105_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_105_cast_fp16 = matmul(transpose_x = attn_105_transpose_x_0, transpose_y = attn_105_transpose_y_0, x = var_5868_cast_fp16, y = var_5866_cast_fp16)[name = tensor<string, []>("attn_105_cast_fp16")];
tensor<int32, [4]> var_5871 = const()[name = tensor<string, []>("op_5871"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_261_cast_fp16 = reshape(shape = var_5871, x = attn_105_cast_fp16)[name = tensor<string, []>("input_261_cast_fp16")];
tensor<int32, [2]> var_5875 = const()[name = tensor<string, []>("op_5875"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5877 = const()[name = tensor<string, []>("op_5877"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_371_pad_type_0 = const()[name = tensor<string, []>("obj_371_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_371_pad_0 = const()[name = tensor<string, []>("obj_371_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_26_self_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_26_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1508088064)))];
tensor<fp16, [1280]> layers_26_self_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_26_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1511364928)))];
tensor<fp16, [1, 1280, 1, 1]> obj_371_cast_fp16 = conv(bias = layers_26_self_attn_o_proj_bias_to_fp16, dilations = var_5877, groups = var_5787, pad = obj_371_pad_0, pad_type = obj_371_pad_type_0, strides = var_5875, weight = layers_26_self_attn_o_proj_weight_to_fp16, x = input_261_cast_fp16)[name = tensor<string, []>("obj_371_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_159_cast_fp16 = add(x = inputs_157_cast_fp16, y = obj_371_cast_fp16)[name = tensor<string, []>("inputs_159_cast_fp16")];
tensor<int32, [1]> var_5887 = const()[name = tensor<string, []>("op_5887"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_159_cast_fp16 = reduce_mean(axes = var_5887, keep_dims = var_5788, x = inputs_159_cast_fp16)[name = tensor<string, []>("channels_mean_159_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_159_cast_fp16 = sub(x = inputs_159_cast_fp16, y = channels_mean_159_cast_fp16)[name = tensor<string, []>("zero_mean_159_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_159_cast_fp16 = mul(x = zero_mean_159_cast_fp16, y = zero_mean_159_cast_fp16)[name = tensor<string, []>("zero_mean_sq_159_cast_fp16")];
tensor<int32, [1]> var_5891 = const()[name = tensor<string, []>("op_5891"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_5892_cast_fp16 = reduce_mean(axes = var_5891, keep_dims = var_5788, x = zero_mean_sq_159_cast_fp16)[name = tensor<string, []>("op_5892_cast_fp16")];
tensor<fp16, []> var_5893_to_fp16 = const()[name = tensor<string, []>("op_5893_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_5894_cast_fp16 = add(x = var_5892_cast_fp16, y = var_5893_to_fp16)[name = tensor<string, []>("op_5894_cast_fp16")];
tensor<fp16, []> denom_159_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_159_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_159_cast_fp16 = rsqrt(epsilon = denom_159_epsilon_0_to_fp16, x = var_5894_cast_fp16)[name = tensor<string, []>("denom_159_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_159_cast_fp16 = mul(x = zero_mean_159_cast_fp16, y = denom_159_cast_fp16)[name = tensor<string, []>("out_159_cast_fp16")];
tensor<fp16, [1280]> obj_373_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_373_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1511367552)))];
tensor<fp16, [1280]> obj_373_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_373_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1511370176)))];
tensor<fp16, []> obj_373_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_373_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_373_cast_fp16 = batch_norm(beta = obj_373_beta_0_to_fp16, epsilon = obj_373_epsilon_0_to_fp16, gamma = obj_373_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_159_cast_fp16)[name = tensor<string, []>("obj_373_cast_fp16")];
tensor<int32, [2]> var_5909 = const()[name = tensor<string, []>("op_5909"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5911 = const()[name = tensor<string, []>("op_5911"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_107_pad_type_0 = const()[name = tensor<string, []>("query_107_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_107_pad_0 = const()[name = tensor<string, []>("query_107_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_26_encoder_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_26_encoder_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1511372800)))];
tensor<fp16, [1280]> layers_26_encoder_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_26_encoder_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1514649664)))];
tensor<fp16, [1, 1280, 1, 1]> query_107_cast_fp16 = conv(bias = layers_26_encoder_attn_q_proj_bias_to_fp16, dilations = var_5911, groups = var_5787, pad = query_107_pad_0, pad_type = query_107_pad_type_0, strides = var_5909, weight = layers_26_encoder_attn_q_proj_weight_to_fp16, x = obj_373_cast_fp16)[name = tensor<string, []>("query_107_cast_fp16")];
tensor<int32, [2]> var_5915 = const()[name = tensor<string, []>("op_5915"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5917 = const()[name = tensor<string, []>("op_5917"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> key_107_pad_type_0 = const()[name = tensor<string, []>("key_107_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> key_107_pad_0 = const()[name = tensor<string, []>("key_107_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_26_encoder_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_26_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1514652288)))];
tensor<fp16, [1, 1280, 1, 1500]> key_107_cast_fp16 = conv(dilations = var_5917, groups = var_5787, pad = key_107_pad_0, pad_type = key_107_pad_type_0, strides = var_5915, weight = layers_26_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("key_107_cast_fp16")];
tensor<int32, [2]> var_5922 = const()[name = tensor<string, []>("op_5922"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5924 = const()[name = tensor<string, []>("op_5924"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> value_107_pad_type_0 = const()[name = tensor<string, []>("value_107_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> value_107_pad_0 = const()[name = tensor<string, []>("value_107_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_26_encoder_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_26_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1517929152)))];
tensor<fp16, [1280]> layers_26_encoder_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_26_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1521206016)))];
tensor<fp16, [1, 1280, 1, 1500]> value_107_cast_fp16 = conv(bias = layers_26_encoder_attn_v_proj_bias_to_fp16, dilations = var_5924, groups = var_5787, pad = value_107_pad_0, pad_type = value_107_pad_type_0, strides = var_5922, weight = layers_26_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("value_107_cast_fp16")];
tensor<int32, [4]> var_5928 = const()[name = tensor<string, []>("op_5928"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_5929_cast_fp16 = reshape(shape = var_5928, x = query_107_cast_fp16)[name = tensor<string, []>("op_5929_cast_fp16")];
tensor<fp16, []> var_5930_to_fp16 = const()[name = tensor<string, []>("op_5930_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_5931_cast_fp16 = mul(x = var_5929_cast_fp16, y = var_5930_to_fp16)[name = tensor<string, []>("op_5931_cast_fp16")];
tensor<int32, [4]> var_5932 = const()[name = tensor<string, []>("op_5932"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_5933_cast_fp16 = reshape(shape = var_5932, x = key_107_cast_fp16)[name = tensor<string, []>("op_5933_cast_fp16")];
tensor<bool, []> mh_w_161_transpose_x_0 = const()[name = tensor<string, []>("mh_w_161_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_161_transpose_y_0 = const()[name = tensor<string, []>("mh_w_161_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 1500]> mh_w_161_cast_fp16 = matmul(transpose_x = mh_w_161_transpose_x_0, transpose_y = mh_w_161_transpose_y_0, x = var_5931_cast_fp16, y = var_5933_cast_fp16)[name = tensor<string, []>("mh_w_161_cast_fp16")];
tensor<fp16, [1, 20, 1, 1500]> obj_377_cast_fp16 = softmax(axis = var_5780, x = mh_w_161_cast_fp16)[name = tensor<string, []>("obj_377_cast_fp16")];
tensor<int32, [4]> var_5937 = const()[name = tensor<string, []>("op_5937"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_5938_cast_fp16 = reshape(shape = var_5937, x = value_107_cast_fp16)[name = tensor<string, []>("op_5938_cast_fp16")];
tensor<bool, []> attn_107_transpose_x_0 = const()[name = tensor<string, []>("attn_107_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_107_transpose_y_0 = const()[name = tensor<string, []>("attn_107_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_107_cast_fp16 = matmul(transpose_x = attn_107_transpose_x_0, transpose_y = attn_107_transpose_y_0, x = var_5938_cast_fp16, y = obj_377_cast_fp16)[name = tensor<string, []>("attn_107_cast_fp16")];
tensor<int32, [4]> var_5941 = const()[name = tensor<string, []>("op_5941"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_263_cast_fp16 = reshape(shape = var_5941, x = attn_107_cast_fp16)[name = tensor<string, []>("input_263_cast_fp16")];
tensor<int32, [2]> var_5945 = const()[name = tensor<string, []>("op_5945"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5947 = const()[name = tensor<string, []>("op_5947"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_375_pad_type_0 = const()[name = tensor<string, []>("obj_375_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_375_pad_0 = const()[name = tensor<string, []>("obj_375_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_26_encoder_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_26_encoder_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1521208640)))];
tensor<fp16, [1280]> layers_26_encoder_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_26_encoder_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1524485504)))];
tensor<fp16, [1, 1280, 1, 1]> obj_375_cast_fp16 = conv(bias = layers_26_encoder_attn_o_proj_bias_to_fp16, dilations = var_5947, groups = var_5787, pad = obj_375_pad_0, pad_type = obj_375_pad_type_0, strides = var_5945, weight = layers_26_encoder_attn_o_proj_weight_to_fp16, x = input_263_cast_fp16)[name = tensor<string, []>("obj_375_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_161_cast_fp16 = add(x = inputs_159_cast_fp16, y = obj_375_cast_fp16)[name = tensor<string, []>("inputs_161_cast_fp16")];
tensor<int32, [1]> var_5956 = const()[name = tensor<string, []>("op_5956"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_161_cast_fp16 = reduce_mean(axes = var_5956, keep_dims = var_5788, x = inputs_161_cast_fp16)[name = tensor<string, []>("channels_mean_161_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_161_cast_fp16 = sub(x = inputs_161_cast_fp16, y = channels_mean_161_cast_fp16)[name = tensor<string, []>("zero_mean_161_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_161_cast_fp16 = mul(x = zero_mean_161_cast_fp16, y = zero_mean_161_cast_fp16)[name = tensor<string, []>("zero_mean_sq_161_cast_fp16")];
tensor<int32, [1]> var_5960 = const()[name = tensor<string, []>("op_5960"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_5961_cast_fp16 = reduce_mean(axes = var_5960, keep_dims = var_5788, x = zero_mean_sq_161_cast_fp16)[name = tensor<string, []>("op_5961_cast_fp16")];
tensor<fp16, []> var_5962_to_fp16 = const()[name = tensor<string, []>("op_5962_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_5963_cast_fp16 = add(x = var_5961_cast_fp16, y = var_5962_to_fp16)[name = tensor<string, []>("op_5963_cast_fp16")];
tensor<fp16, []> denom_161_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_161_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_161_cast_fp16 = rsqrt(epsilon = denom_161_epsilon_0_to_fp16, x = var_5963_cast_fp16)[name = tensor<string, []>("denom_161_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_161_cast_fp16 = mul(x = zero_mean_161_cast_fp16, y = denom_161_cast_fp16)[name = tensor<string, []>("out_161_cast_fp16")];
tensor<fp16, [1280]> input_265_gamma_0_to_fp16 = const()[name = tensor<string, []>("input_265_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1524488128)))];
tensor<fp16, [1280]> input_265_beta_0_to_fp16 = const()[name = tensor<string, []>("input_265_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1524490752)))];
tensor<fp16, []> input_265_epsilon_0_to_fp16 = const()[name = tensor<string, []>("input_265_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> input_265_cast_fp16 = batch_norm(beta = input_265_beta_0_to_fp16, epsilon = input_265_epsilon_0_to_fp16, gamma = input_265_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_161_cast_fp16)[name = tensor<string, []>("input_265_cast_fp16")];
tensor<int32, [2]> var_5974 = const()[name = tensor<string, []>("op_5974"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5976 = const()[name = tensor<string, []>("op_5976"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_267_pad_type_0 = const()[name = tensor<string, []>("input_267_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_267_pad_0 = const()[name = tensor<string, []>("input_267_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [5120, 1280, 1, 1]> layers_26_fc1_weight_to_fp16 = const()[name = tensor<string, []>("layers_26_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1524493376)))];
tensor<fp16, [5120]> layers_26_fc1_bias_to_fp16 = const()[name = tensor<string, []>("layers_26_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1537600640)))];
tensor<fp16, [1, 5120, 1, 1]> input_267_cast_fp16 = conv(bias = layers_26_fc1_bias_to_fp16, dilations = var_5976, groups = var_5787, pad = input_267_pad_0, pad_type = input_267_pad_type_0, strides = var_5974, weight = layers_26_fc1_weight_to_fp16, x = input_265_cast_fp16)[name = tensor<string, []>("input_267_cast_fp16")];
tensor<string, []> input_269_mode_0 = const()[name = tensor<string, []>("input_269_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 5120, 1, 1]> input_269_cast_fp16 = gelu(mode = input_269_mode_0, x = input_267_cast_fp16)[name = tensor<string, []>("input_269_cast_fp16")];
tensor<int32, [2]> var_5982 = const()[name = tensor<string, []>("op_5982"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_5984 = const()[name = tensor<string, []>("op_5984"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_55_pad_type_0 = const()[name = tensor<string, []>("hidden_states_55_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_55_pad_0 = const()[name = tensor<string, []>("hidden_states_55_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 5120, 1, 1]> layers_26_fc2_weight_to_fp16 = const()[name = tensor<string, []>("layers_26_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1537610944)))];
tensor<fp16, [1280]> layers_26_fc2_bias_to_fp16 = const()[name = tensor<string, []>("layers_26_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1550718208)))];
tensor<fp16, [1, 1280, 1, 1]> hidden_states_55_cast_fp16 = conv(bias = layers_26_fc2_bias_to_fp16, dilations = var_5984, groups = var_5787, pad = hidden_states_55_pad_0, pad_type = hidden_states_55_pad_type_0, strides = var_5982, weight = layers_26_fc2_weight_to_fp16, x = input_269_cast_fp16)[name = tensor<string, []>("hidden_states_55_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_163_cast_fp16 = add(x = inputs_161_cast_fp16, y = hidden_states_55_cast_fp16)[name = tensor<string, []>("inputs_163_cast_fp16")];
tensor<int32, []> var_5998 = const()[name = tensor<string, []>("op_5998"), val = tensor<int32, []>(3)];
tensor<int32, []> var_6005 = const()[name = tensor<string, []>("op_6005"), val = tensor<int32, []>(1)];
tensor<bool, []> var_6006 = const()[name = tensor<string, []>("op_6006"), val = tensor<bool, []>(true)];
tensor<int32, [1]> var_6018 = const()[name = tensor<string, []>("op_6018"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_163_cast_fp16 = reduce_mean(axes = var_6018, keep_dims = var_6006, x = inputs_163_cast_fp16)[name = tensor<string, []>("channels_mean_163_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_163_cast_fp16 = sub(x = inputs_163_cast_fp16, y = channels_mean_163_cast_fp16)[name = tensor<string, []>("zero_mean_163_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_163_cast_fp16 = mul(x = zero_mean_163_cast_fp16, y = zero_mean_163_cast_fp16)[name = tensor<string, []>("zero_mean_sq_163_cast_fp16")];
tensor<int32, [1]> var_6022 = const()[name = tensor<string, []>("op_6022"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_6023_cast_fp16 = reduce_mean(axes = var_6022, keep_dims = var_6006, x = zero_mean_sq_163_cast_fp16)[name = tensor<string, []>("op_6023_cast_fp16")];
tensor<fp16, []> var_6024_to_fp16 = const()[name = tensor<string, []>("op_6024_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_6025_cast_fp16 = add(x = var_6023_cast_fp16, y = var_6024_to_fp16)[name = tensor<string, []>("op_6025_cast_fp16")];
tensor<fp16, []> denom_163_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_163_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_163_cast_fp16 = rsqrt(epsilon = denom_163_epsilon_0_to_fp16, x = var_6025_cast_fp16)[name = tensor<string, []>("denom_163_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_163_cast_fp16 = mul(x = zero_mean_163_cast_fp16, y = denom_163_cast_fp16)[name = tensor<string, []>("out_163_cast_fp16")];
tensor<fp16, [1280]> obj_379_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_379_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1550720832)))];
tensor<fp16, [1280]> obj_379_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_379_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1550723456)))];
tensor<fp16, []> obj_379_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_379_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_379_cast_fp16 = batch_norm(beta = obj_379_beta_0_to_fp16, epsilon = obj_379_epsilon_0_to_fp16, gamma = obj_379_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_163_cast_fp16)[name = tensor<string, []>("obj_379_cast_fp16")];
tensor<int32, [2]> var_6040 = const()[name = tensor<string, []>("op_6040"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6042 = const()[name = tensor<string, []>("op_6042"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_109_pad_type_0 = const()[name = tensor<string, []>("query_109_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_109_pad_0 = const()[name = tensor<string, []>("query_109_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_27_self_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_27_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1550726080)))];
tensor<fp16, [1280]> layers_27_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_27_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1554002944)))];
tensor<fp16, [1, 1280, 1, 1]> query_109_cast_fp16 = conv(bias = layers_27_self_attn_q_proj_bias_to_fp16, dilations = var_6042, groups = var_6005, pad = query_109_pad_0, pad_type = query_109_pad_type_0, strides = var_6040, weight = layers_27_self_attn_q_proj_weight_to_fp16, x = obj_379_cast_fp16)[name = tensor<string, []>("query_109_cast_fp16")];
tensor<int32, [2]> var_6046 = const()[name = tensor<string, []>("op_6046"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6048 = const()[name = tensor<string, []>("op_6048"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_key_55_pad_type_0 = const()[name = tensor<string, []>("current_key_55_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_key_55_pad_0 = const()[name = tensor<string, []>("current_key_55_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_27_self_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_27_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1554005568)))];
tensor<fp16, [1, 1280, 1, 1]> current_key_55_cast_fp16 = conv(dilations = var_6048, groups = var_6005, pad = current_key_55_pad_0, pad_type = current_key_55_pad_type_0, strides = var_6046, weight = layers_27_self_attn_k_proj_weight_to_fp16, x = obj_379_cast_fp16)[name = tensor<string, []>("current_key_55_cast_fp16")];
tensor<int32, [2]> var_6053 = const()[name = tensor<string, []>("op_6053"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6055 = const()[name = tensor<string, []>("op_6055"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_value_55_pad_type_0 = const()[name = tensor<string, []>("current_value_55_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_value_55_pad_0 = const()[name = tensor<string, []>("current_value_55_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_27_self_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_27_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1557282432)))];
tensor<fp16, [1280]> layers_27_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_27_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1560559296)))];
tensor<fp16, [1, 1280, 1, 1]> current_value_55_cast_fp16 = conv(bias = layers_27_self_attn_v_proj_bias_to_fp16, dilations = var_6055, groups = var_6005, pad = current_value_55_pad_0, pad_type = current_value_55_pad_type_0, strides = var_6053, weight = layers_27_self_attn_v_proj_weight_to_fp16, x = obj_379_cast_fp16)[name = tensor<string, []>("current_value_55_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_6062_cast_fp16 = mul(x = current_key_55_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_6062_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_6064_cast_fp16 = mul(x = var_103_cast_fp16_27, y = var_241_cast_fp16)[name = tensor<string, []>("op_6064_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> key_109_cast_fp16 = add(x = var_6062_cast_fp16, y = var_6064_cast_fp16)[name = tensor<string, []>("key_109_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_6066_cast_fp16 = mul(x = current_value_55_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_6066_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_6068_cast_fp16 = mul(x = var_138_cast_fp16_27, y = var_241_cast_fp16)[name = tensor<string, []>("op_6068_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> value_109_cast_fp16 = add(x = var_6066_cast_fp16, y = var_6068_cast_fp16)[name = tensor<string, []>("value_109_cast_fp16")];
tensor<int32, [4]> var_6071 = const()[name = tensor<string, []>("op_6071"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_6072_cast_fp16 = reshape(shape = var_6071, x = query_109_cast_fp16)[name = tensor<string, []>("op_6072_cast_fp16")];
tensor<fp16, []> var_6073_to_fp16 = const()[name = tensor<string, []>("op_6073_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_6074_cast_fp16 = mul(x = var_6072_cast_fp16, y = var_6073_to_fp16)[name = tensor<string, []>("op_6074_cast_fp16")];
tensor<int32, [4]> var_6075 = const()[name = tensor<string, []>("op_6075"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_6076_cast_fp16 = reshape(shape = var_6075, x = key_109_cast_fp16)[name = tensor<string, []>("op_6076_cast_fp16")];
tensor<bool, []> mh_w_163_transpose_x_0 = const()[name = tensor<string, []>("mh_w_163_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_163_transpose_y_0 = const()[name = tensor<string, []>("mh_w_163_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 224]> mh_w_163_cast_fp16 = matmul(transpose_x = mh_w_163_transpose_x_0, transpose_y = mh_w_163_transpose_y_0, x = var_6074_cast_fp16, y = var_6076_cast_fp16)[name = tensor<string, []>("mh_w_163_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> mh_w_165_cast_fp16 = add(x = mh_w_163_cast_fp16, y = var_259_cast_fp16)[name = tensor<string, []>("mh_w_165_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> var_6084_cast_fp16 = softmax(axis = var_5998, x = mh_w_165_cast_fp16)[name = tensor<string, []>("op_6084_cast_fp16")];
tensor<int32, [4]> var_6085 = const()[name = tensor<string, []>("op_6085"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_6086_cast_fp16 = reshape(shape = var_6085, x = value_109_cast_fp16)[name = tensor<string, []>("op_6086_cast_fp16")];
tensor<bool, []> attn_109_transpose_x_0 = const()[name = tensor<string, []>("attn_109_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_109_transpose_y_0 = const()[name = tensor<string, []>("attn_109_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_109_cast_fp16 = matmul(transpose_x = attn_109_transpose_x_0, transpose_y = attn_109_transpose_y_0, x = var_6086_cast_fp16, y = var_6084_cast_fp16)[name = tensor<string, []>("attn_109_cast_fp16")];
tensor<int32, [4]> var_6089 = const()[name = tensor<string, []>("op_6089"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_271_cast_fp16 = reshape(shape = var_6089, x = attn_109_cast_fp16)[name = tensor<string, []>("input_271_cast_fp16")];
tensor<int32, [2]> var_6093 = const()[name = tensor<string, []>("op_6093"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6095 = const()[name = tensor<string, []>("op_6095"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_385_pad_type_0 = const()[name = tensor<string, []>("obj_385_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_385_pad_0 = const()[name = tensor<string, []>("obj_385_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_27_self_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_27_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1560561920)))];
tensor<fp16, [1280]> layers_27_self_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_27_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1563838784)))];
tensor<fp16, [1, 1280, 1, 1]> obj_385_cast_fp16 = conv(bias = layers_27_self_attn_o_proj_bias_to_fp16, dilations = var_6095, groups = var_6005, pad = obj_385_pad_0, pad_type = obj_385_pad_type_0, strides = var_6093, weight = layers_27_self_attn_o_proj_weight_to_fp16, x = input_271_cast_fp16)[name = tensor<string, []>("obj_385_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_165_cast_fp16 = add(x = inputs_163_cast_fp16, y = obj_385_cast_fp16)[name = tensor<string, []>("inputs_165_cast_fp16")];
tensor<int32, [1]> var_6105 = const()[name = tensor<string, []>("op_6105"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_165_cast_fp16 = reduce_mean(axes = var_6105, keep_dims = var_6006, x = inputs_165_cast_fp16)[name = tensor<string, []>("channels_mean_165_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_165_cast_fp16 = sub(x = inputs_165_cast_fp16, y = channels_mean_165_cast_fp16)[name = tensor<string, []>("zero_mean_165_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_165_cast_fp16 = mul(x = zero_mean_165_cast_fp16, y = zero_mean_165_cast_fp16)[name = tensor<string, []>("zero_mean_sq_165_cast_fp16")];
tensor<int32, [1]> var_6109 = const()[name = tensor<string, []>("op_6109"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_6110_cast_fp16 = reduce_mean(axes = var_6109, keep_dims = var_6006, x = zero_mean_sq_165_cast_fp16)[name = tensor<string, []>("op_6110_cast_fp16")];
tensor<fp16, []> var_6111_to_fp16 = const()[name = tensor<string, []>("op_6111_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_6112_cast_fp16 = add(x = var_6110_cast_fp16, y = var_6111_to_fp16)[name = tensor<string, []>("op_6112_cast_fp16")];
tensor<fp16, []> denom_165_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_165_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_165_cast_fp16 = rsqrt(epsilon = denom_165_epsilon_0_to_fp16, x = var_6112_cast_fp16)[name = tensor<string, []>("denom_165_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_165_cast_fp16 = mul(x = zero_mean_165_cast_fp16, y = denom_165_cast_fp16)[name = tensor<string, []>("out_165_cast_fp16")];
tensor<fp16, [1280]> obj_387_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_387_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1563841408)))];
tensor<fp16, [1280]> obj_387_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_387_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1563844032)))];
tensor<fp16, []> obj_387_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_387_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_387_cast_fp16 = batch_norm(beta = obj_387_beta_0_to_fp16, epsilon = obj_387_epsilon_0_to_fp16, gamma = obj_387_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_165_cast_fp16)[name = tensor<string, []>("obj_387_cast_fp16")];
tensor<int32, [2]> var_6127 = const()[name = tensor<string, []>("op_6127"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6129 = const()[name = tensor<string, []>("op_6129"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_111_pad_type_0 = const()[name = tensor<string, []>("query_111_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_111_pad_0 = const()[name = tensor<string, []>("query_111_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_27_encoder_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_27_encoder_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1563846656)))];
tensor<fp16, [1280]> layers_27_encoder_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_27_encoder_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1567123520)))];
tensor<fp16, [1, 1280, 1, 1]> query_111_cast_fp16 = conv(bias = layers_27_encoder_attn_q_proj_bias_to_fp16, dilations = var_6129, groups = var_6005, pad = query_111_pad_0, pad_type = query_111_pad_type_0, strides = var_6127, weight = layers_27_encoder_attn_q_proj_weight_to_fp16, x = obj_387_cast_fp16)[name = tensor<string, []>("query_111_cast_fp16")];
tensor<int32, [2]> var_6133 = const()[name = tensor<string, []>("op_6133"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6135 = const()[name = tensor<string, []>("op_6135"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> key_111_pad_type_0 = const()[name = tensor<string, []>("key_111_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> key_111_pad_0 = const()[name = tensor<string, []>("key_111_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_27_encoder_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_27_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1567126144)))];
tensor<fp16, [1, 1280, 1, 1500]> key_111_cast_fp16 = conv(dilations = var_6135, groups = var_6005, pad = key_111_pad_0, pad_type = key_111_pad_type_0, strides = var_6133, weight = layers_27_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("key_111_cast_fp16")];
tensor<int32, [2]> var_6140 = const()[name = tensor<string, []>("op_6140"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6142 = const()[name = tensor<string, []>("op_6142"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> value_111_pad_type_0 = const()[name = tensor<string, []>("value_111_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> value_111_pad_0 = const()[name = tensor<string, []>("value_111_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_27_encoder_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_27_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1570403008)))];
tensor<fp16, [1280]> layers_27_encoder_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_27_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1573679872)))];
tensor<fp16, [1, 1280, 1, 1500]> value_111_cast_fp16 = conv(bias = layers_27_encoder_attn_v_proj_bias_to_fp16, dilations = var_6142, groups = var_6005, pad = value_111_pad_0, pad_type = value_111_pad_type_0, strides = var_6140, weight = layers_27_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("value_111_cast_fp16")];
tensor<int32, [4]> var_6146 = const()[name = tensor<string, []>("op_6146"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_6147_cast_fp16 = reshape(shape = var_6146, x = query_111_cast_fp16)[name = tensor<string, []>("op_6147_cast_fp16")];
tensor<fp16, []> var_6148_to_fp16 = const()[name = tensor<string, []>("op_6148_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_6149_cast_fp16 = mul(x = var_6147_cast_fp16, y = var_6148_to_fp16)[name = tensor<string, []>("op_6149_cast_fp16")];
tensor<int32, [4]> var_6150 = const()[name = tensor<string, []>("op_6150"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_6151_cast_fp16 = reshape(shape = var_6150, x = key_111_cast_fp16)[name = tensor<string, []>("op_6151_cast_fp16")];
tensor<bool, []> mh_w_167_transpose_x_0 = const()[name = tensor<string, []>("mh_w_167_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_167_transpose_y_0 = const()[name = tensor<string, []>("mh_w_167_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 1500]> mh_w_167_cast_fp16 = matmul(transpose_x = mh_w_167_transpose_x_0, transpose_y = mh_w_167_transpose_y_0, x = var_6149_cast_fp16, y = var_6151_cast_fp16)[name = tensor<string, []>("mh_w_167_cast_fp16")];
tensor<fp16, [1, 20, 1, 1500]> obj_391_cast_fp16 = softmax(axis = var_5998, x = mh_w_167_cast_fp16)[name = tensor<string, []>("obj_391_cast_fp16")];
tensor<int32, [4]> var_6155 = const()[name = tensor<string, []>("op_6155"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_6156_cast_fp16 = reshape(shape = var_6155, x = value_111_cast_fp16)[name = tensor<string, []>("op_6156_cast_fp16")];
tensor<bool, []> attn_111_transpose_x_0 = const()[name = tensor<string, []>("attn_111_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_111_transpose_y_0 = const()[name = tensor<string, []>("attn_111_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_111_cast_fp16 = matmul(transpose_x = attn_111_transpose_x_0, transpose_y = attn_111_transpose_y_0, x = var_6156_cast_fp16, y = obj_391_cast_fp16)[name = tensor<string, []>("attn_111_cast_fp16")];
tensor<int32, [4]> var_6159 = const()[name = tensor<string, []>("op_6159"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_273_cast_fp16 = reshape(shape = var_6159, x = attn_111_cast_fp16)[name = tensor<string, []>("input_273_cast_fp16")];
tensor<int32, [2]> var_6163 = const()[name = tensor<string, []>("op_6163"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6165 = const()[name = tensor<string, []>("op_6165"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_389_pad_type_0 = const()[name = tensor<string, []>("obj_389_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_389_pad_0 = const()[name = tensor<string, []>("obj_389_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_27_encoder_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_27_encoder_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1573682496)))];
tensor<fp16, [1280]> layers_27_encoder_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_27_encoder_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1576959360)))];
tensor<fp16, [1, 1280, 1, 1]> obj_389_cast_fp16 = conv(bias = layers_27_encoder_attn_o_proj_bias_to_fp16, dilations = var_6165, groups = var_6005, pad = obj_389_pad_0, pad_type = obj_389_pad_type_0, strides = var_6163, weight = layers_27_encoder_attn_o_proj_weight_to_fp16, x = input_273_cast_fp16)[name = tensor<string, []>("obj_389_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_167_cast_fp16 = add(x = inputs_165_cast_fp16, y = obj_389_cast_fp16)[name = tensor<string, []>("inputs_167_cast_fp16")];
tensor<int32, [1]> var_6174 = const()[name = tensor<string, []>("op_6174"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_167_cast_fp16 = reduce_mean(axes = var_6174, keep_dims = var_6006, x = inputs_167_cast_fp16)[name = tensor<string, []>("channels_mean_167_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_167_cast_fp16 = sub(x = inputs_167_cast_fp16, y = channels_mean_167_cast_fp16)[name = tensor<string, []>("zero_mean_167_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_167_cast_fp16 = mul(x = zero_mean_167_cast_fp16, y = zero_mean_167_cast_fp16)[name = tensor<string, []>("zero_mean_sq_167_cast_fp16")];
tensor<int32, [1]> var_6178 = const()[name = tensor<string, []>("op_6178"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_6179_cast_fp16 = reduce_mean(axes = var_6178, keep_dims = var_6006, x = zero_mean_sq_167_cast_fp16)[name = tensor<string, []>("op_6179_cast_fp16")];
tensor<fp16, []> var_6180_to_fp16 = const()[name = tensor<string, []>("op_6180_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_6181_cast_fp16 = add(x = var_6179_cast_fp16, y = var_6180_to_fp16)[name = tensor<string, []>("op_6181_cast_fp16")];
tensor<fp16, []> denom_167_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_167_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_167_cast_fp16 = rsqrt(epsilon = denom_167_epsilon_0_to_fp16, x = var_6181_cast_fp16)[name = tensor<string, []>("denom_167_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_167_cast_fp16 = mul(x = zero_mean_167_cast_fp16, y = denom_167_cast_fp16)[name = tensor<string, []>("out_167_cast_fp16")];
tensor<fp16, [1280]> input_275_gamma_0_to_fp16 = const()[name = tensor<string, []>("input_275_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1576961984)))];
tensor<fp16, [1280]> input_275_beta_0_to_fp16 = const()[name = tensor<string, []>("input_275_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1576964608)))];
tensor<fp16, []> input_275_epsilon_0_to_fp16 = const()[name = tensor<string, []>("input_275_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> input_275_cast_fp16 = batch_norm(beta = input_275_beta_0_to_fp16, epsilon = input_275_epsilon_0_to_fp16, gamma = input_275_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_167_cast_fp16)[name = tensor<string, []>("input_275_cast_fp16")];
tensor<int32, [2]> var_6192 = const()[name = tensor<string, []>("op_6192"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6194 = const()[name = tensor<string, []>("op_6194"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_277_pad_type_0 = const()[name = tensor<string, []>("input_277_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_277_pad_0 = const()[name = tensor<string, []>("input_277_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [5120, 1280, 1, 1]> layers_27_fc1_weight_to_fp16 = const()[name = tensor<string, []>("layers_27_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1576967232)))];
tensor<fp16, [5120]> layers_27_fc1_bias_to_fp16 = const()[name = tensor<string, []>("layers_27_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1590074496)))];
tensor<fp16, [1, 5120, 1, 1]> input_277_cast_fp16 = conv(bias = layers_27_fc1_bias_to_fp16, dilations = var_6194, groups = var_6005, pad = input_277_pad_0, pad_type = input_277_pad_type_0, strides = var_6192, weight = layers_27_fc1_weight_to_fp16, x = input_275_cast_fp16)[name = tensor<string, []>("input_277_cast_fp16")];
tensor<string, []> input_279_mode_0 = const()[name = tensor<string, []>("input_279_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 5120, 1, 1]> input_279_cast_fp16 = gelu(mode = input_279_mode_0, x = input_277_cast_fp16)[name = tensor<string, []>("input_279_cast_fp16")];
tensor<int32, [2]> var_6200 = const()[name = tensor<string, []>("op_6200"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6202 = const()[name = tensor<string, []>("op_6202"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_57_pad_type_0 = const()[name = tensor<string, []>("hidden_states_57_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_57_pad_0 = const()[name = tensor<string, []>("hidden_states_57_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 5120, 1, 1]> layers_27_fc2_weight_to_fp16 = const()[name = tensor<string, []>("layers_27_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1590084800)))];
tensor<fp16, [1280]> layers_27_fc2_bias_to_fp16 = const()[name = tensor<string, []>("layers_27_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1603192064)))];
tensor<fp16, [1, 1280, 1, 1]> hidden_states_57_cast_fp16 = conv(bias = layers_27_fc2_bias_to_fp16, dilations = var_6202, groups = var_6005, pad = hidden_states_57_pad_0, pad_type = hidden_states_57_pad_type_0, strides = var_6200, weight = layers_27_fc2_weight_to_fp16, x = input_279_cast_fp16)[name = tensor<string, []>("hidden_states_57_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_169_cast_fp16 = add(x = inputs_167_cast_fp16, y = hidden_states_57_cast_fp16)[name = tensor<string, []>("inputs_169_cast_fp16")];
tensor<int32, []> var_6216 = const()[name = tensor<string, []>("op_6216"), val = tensor<int32, []>(3)];
tensor<int32, []> var_6223 = const()[name = tensor<string, []>("op_6223"), val = tensor<int32, []>(1)];
tensor<bool, []> var_6224 = const()[name = tensor<string, []>("op_6224"), val = tensor<bool, []>(true)];
tensor<int32, [1]> var_6236 = const()[name = tensor<string, []>("op_6236"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_169_cast_fp16 = reduce_mean(axes = var_6236, keep_dims = var_6224, x = inputs_169_cast_fp16)[name = tensor<string, []>("channels_mean_169_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_169_cast_fp16 = sub(x = inputs_169_cast_fp16, y = channels_mean_169_cast_fp16)[name = tensor<string, []>("zero_mean_169_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_169_cast_fp16 = mul(x = zero_mean_169_cast_fp16, y = zero_mean_169_cast_fp16)[name = tensor<string, []>("zero_mean_sq_169_cast_fp16")];
tensor<int32, [1]> var_6240 = const()[name = tensor<string, []>("op_6240"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_6241_cast_fp16 = reduce_mean(axes = var_6240, keep_dims = var_6224, x = zero_mean_sq_169_cast_fp16)[name = tensor<string, []>("op_6241_cast_fp16")];
tensor<fp16, []> var_6242_to_fp16 = const()[name = tensor<string, []>("op_6242_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_6243_cast_fp16 = add(x = var_6241_cast_fp16, y = var_6242_to_fp16)[name = tensor<string, []>("op_6243_cast_fp16")];
tensor<fp16, []> denom_169_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_169_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_169_cast_fp16 = rsqrt(epsilon = denom_169_epsilon_0_to_fp16, x = var_6243_cast_fp16)[name = tensor<string, []>("denom_169_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_169_cast_fp16 = mul(x = zero_mean_169_cast_fp16, y = denom_169_cast_fp16)[name = tensor<string, []>("out_169_cast_fp16")];
tensor<fp16, [1280]> obj_393_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_393_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1603194688)))];
tensor<fp16, [1280]> obj_393_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_393_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1603197312)))];
tensor<fp16, []> obj_393_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_393_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_393_cast_fp16 = batch_norm(beta = obj_393_beta_0_to_fp16, epsilon = obj_393_epsilon_0_to_fp16, gamma = obj_393_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_169_cast_fp16)[name = tensor<string, []>("obj_393_cast_fp16")];
tensor<int32, [2]> var_6258 = const()[name = tensor<string, []>("op_6258"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6260 = const()[name = tensor<string, []>("op_6260"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_113_pad_type_0 = const()[name = tensor<string, []>("query_113_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_113_pad_0 = const()[name = tensor<string, []>("query_113_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_28_self_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_28_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1603199936)))];
tensor<fp16, [1280]> layers_28_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_28_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1606476800)))];
tensor<fp16, [1, 1280, 1, 1]> query_113_cast_fp16 = conv(bias = layers_28_self_attn_q_proj_bias_to_fp16, dilations = var_6260, groups = var_6223, pad = query_113_pad_0, pad_type = query_113_pad_type_0, strides = var_6258, weight = layers_28_self_attn_q_proj_weight_to_fp16, x = obj_393_cast_fp16)[name = tensor<string, []>("query_113_cast_fp16")];
tensor<int32, [2]> var_6264 = const()[name = tensor<string, []>("op_6264"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6266 = const()[name = tensor<string, []>("op_6266"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_key_57_pad_type_0 = const()[name = tensor<string, []>("current_key_57_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_key_57_pad_0 = const()[name = tensor<string, []>("current_key_57_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_28_self_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_28_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1606479424)))];
tensor<fp16, [1, 1280, 1, 1]> current_key_57_cast_fp16 = conv(dilations = var_6266, groups = var_6223, pad = current_key_57_pad_0, pad_type = current_key_57_pad_type_0, strides = var_6264, weight = layers_28_self_attn_k_proj_weight_to_fp16, x = obj_393_cast_fp16)[name = tensor<string, []>("current_key_57_cast_fp16")];
tensor<int32, [2]> var_6271 = const()[name = tensor<string, []>("op_6271"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6273 = const()[name = tensor<string, []>("op_6273"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_value_57_pad_type_0 = const()[name = tensor<string, []>("current_value_57_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_value_57_pad_0 = const()[name = tensor<string, []>("current_value_57_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_28_self_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_28_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1609756288)))];
tensor<fp16, [1280]> layers_28_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_28_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1613033152)))];
tensor<fp16, [1, 1280, 1, 1]> current_value_57_cast_fp16 = conv(bias = layers_28_self_attn_v_proj_bias_to_fp16, dilations = var_6273, groups = var_6223, pad = current_value_57_pad_0, pad_type = current_value_57_pad_type_0, strides = var_6271, weight = layers_28_self_attn_v_proj_weight_to_fp16, x = obj_393_cast_fp16)[name = tensor<string, []>("current_value_57_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_6280_cast_fp16 = mul(x = current_key_57_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_6280_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_6282_cast_fp16 = mul(x = var_103_cast_fp16_28, y = var_241_cast_fp16)[name = tensor<string, []>("op_6282_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> key_113_cast_fp16 = add(x = var_6280_cast_fp16, y = var_6282_cast_fp16)[name = tensor<string, []>("key_113_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_6284_cast_fp16 = mul(x = current_value_57_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_6284_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_6286_cast_fp16 = mul(x = var_138_cast_fp16_28, y = var_241_cast_fp16)[name = tensor<string, []>("op_6286_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> value_113_cast_fp16 = add(x = var_6284_cast_fp16, y = var_6286_cast_fp16)[name = tensor<string, []>("value_113_cast_fp16")];
tensor<int32, [4]> var_6289 = const()[name = tensor<string, []>("op_6289"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_6290_cast_fp16 = reshape(shape = var_6289, x = query_113_cast_fp16)[name = tensor<string, []>("op_6290_cast_fp16")];
tensor<fp16, []> var_6291_to_fp16 = const()[name = tensor<string, []>("op_6291_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_6292_cast_fp16 = mul(x = var_6290_cast_fp16, y = var_6291_to_fp16)[name = tensor<string, []>("op_6292_cast_fp16")];
tensor<int32, [4]> var_6293 = const()[name = tensor<string, []>("op_6293"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_6294_cast_fp16 = reshape(shape = var_6293, x = key_113_cast_fp16)[name = tensor<string, []>("op_6294_cast_fp16")];
tensor<bool, []> mh_w_169_transpose_x_0 = const()[name = tensor<string, []>("mh_w_169_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_169_transpose_y_0 = const()[name = tensor<string, []>("mh_w_169_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 224]> mh_w_169_cast_fp16 = matmul(transpose_x = mh_w_169_transpose_x_0, transpose_y = mh_w_169_transpose_y_0, x = var_6292_cast_fp16, y = var_6294_cast_fp16)[name = tensor<string, []>("mh_w_169_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> mh_w_171_cast_fp16 = add(x = mh_w_169_cast_fp16, y = var_259_cast_fp16)[name = tensor<string, []>("mh_w_171_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> var_6302_cast_fp16 = softmax(axis = var_6216, x = mh_w_171_cast_fp16)[name = tensor<string, []>("op_6302_cast_fp16")];
tensor<int32, [4]> var_6303 = const()[name = tensor<string, []>("op_6303"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_6304_cast_fp16 = reshape(shape = var_6303, x = value_113_cast_fp16)[name = tensor<string, []>("op_6304_cast_fp16")];
tensor<bool, []> attn_113_transpose_x_0 = const()[name = tensor<string, []>("attn_113_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_113_transpose_y_0 = const()[name = tensor<string, []>("attn_113_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_113_cast_fp16 = matmul(transpose_x = attn_113_transpose_x_0, transpose_y = attn_113_transpose_y_0, x = var_6304_cast_fp16, y = var_6302_cast_fp16)[name = tensor<string, []>("attn_113_cast_fp16")];
tensor<int32, [4]> var_6307 = const()[name = tensor<string, []>("op_6307"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_281_cast_fp16 = reshape(shape = var_6307, x = attn_113_cast_fp16)[name = tensor<string, []>("input_281_cast_fp16")];
tensor<int32, [2]> var_6311 = const()[name = tensor<string, []>("op_6311"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6313 = const()[name = tensor<string, []>("op_6313"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_399_pad_type_0 = const()[name = tensor<string, []>("obj_399_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_399_pad_0 = const()[name = tensor<string, []>("obj_399_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_28_self_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_28_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1613035776)))];
tensor<fp16, [1280]> layers_28_self_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_28_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1616312640)))];
tensor<fp16, [1, 1280, 1, 1]> obj_399_cast_fp16 = conv(bias = layers_28_self_attn_o_proj_bias_to_fp16, dilations = var_6313, groups = var_6223, pad = obj_399_pad_0, pad_type = obj_399_pad_type_0, strides = var_6311, weight = layers_28_self_attn_o_proj_weight_to_fp16, x = input_281_cast_fp16)[name = tensor<string, []>("obj_399_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_171_cast_fp16 = add(x = inputs_169_cast_fp16, y = obj_399_cast_fp16)[name = tensor<string, []>("inputs_171_cast_fp16")];
tensor<int32, [1]> var_6323 = const()[name = tensor<string, []>("op_6323"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_171_cast_fp16 = reduce_mean(axes = var_6323, keep_dims = var_6224, x = inputs_171_cast_fp16)[name = tensor<string, []>("channels_mean_171_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_171_cast_fp16 = sub(x = inputs_171_cast_fp16, y = channels_mean_171_cast_fp16)[name = tensor<string, []>("zero_mean_171_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_171_cast_fp16 = mul(x = zero_mean_171_cast_fp16, y = zero_mean_171_cast_fp16)[name = tensor<string, []>("zero_mean_sq_171_cast_fp16")];
tensor<int32, [1]> var_6327 = const()[name = tensor<string, []>("op_6327"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_6328_cast_fp16 = reduce_mean(axes = var_6327, keep_dims = var_6224, x = zero_mean_sq_171_cast_fp16)[name = tensor<string, []>("op_6328_cast_fp16")];
tensor<fp16, []> var_6329_to_fp16 = const()[name = tensor<string, []>("op_6329_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_6330_cast_fp16 = add(x = var_6328_cast_fp16, y = var_6329_to_fp16)[name = tensor<string, []>("op_6330_cast_fp16")];
tensor<fp16, []> denom_171_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_171_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_171_cast_fp16 = rsqrt(epsilon = denom_171_epsilon_0_to_fp16, x = var_6330_cast_fp16)[name = tensor<string, []>("denom_171_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_171_cast_fp16 = mul(x = zero_mean_171_cast_fp16, y = denom_171_cast_fp16)[name = tensor<string, []>("out_171_cast_fp16")];
tensor<fp16, [1280]> obj_401_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_401_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1616315264)))];
tensor<fp16, [1280]> obj_401_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_401_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1616317888)))];
tensor<fp16, []> obj_401_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_401_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_401_cast_fp16 = batch_norm(beta = obj_401_beta_0_to_fp16, epsilon = obj_401_epsilon_0_to_fp16, gamma = obj_401_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_171_cast_fp16)[name = tensor<string, []>("obj_401_cast_fp16")];
tensor<int32, [2]> var_6345 = const()[name = tensor<string, []>("op_6345"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6347 = const()[name = tensor<string, []>("op_6347"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_115_pad_type_0 = const()[name = tensor<string, []>("query_115_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_115_pad_0 = const()[name = tensor<string, []>("query_115_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_28_encoder_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_28_encoder_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1616320512)))];
tensor<fp16, [1280]> layers_28_encoder_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_28_encoder_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1619597376)))];
tensor<fp16, [1, 1280, 1, 1]> query_115_cast_fp16 = conv(bias = layers_28_encoder_attn_q_proj_bias_to_fp16, dilations = var_6347, groups = var_6223, pad = query_115_pad_0, pad_type = query_115_pad_type_0, strides = var_6345, weight = layers_28_encoder_attn_q_proj_weight_to_fp16, x = obj_401_cast_fp16)[name = tensor<string, []>("query_115_cast_fp16")];
tensor<int32, [2]> var_6351 = const()[name = tensor<string, []>("op_6351"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6353 = const()[name = tensor<string, []>("op_6353"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> key_115_pad_type_0 = const()[name = tensor<string, []>("key_115_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> key_115_pad_0 = const()[name = tensor<string, []>("key_115_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_28_encoder_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_28_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1619600000)))];
tensor<fp16, [1, 1280, 1, 1500]> key_115_cast_fp16 = conv(dilations = var_6353, groups = var_6223, pad = key_115_pad_0, pad_type = key_115_pad_type_0, strides = var_6351, weight = layers_28_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("key_115_cast_fp16")];
tensor<int32, [2]> var_6358 = const()[name = tensor<string, []>("op_6358"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6360 = const()[name = tensor<string, []>("op_6360"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> value_115_pad_type_0 = const()[name = tensor<string, []>("value_115_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> value_115_pad_0 = const()[name = tensor<string, []>("value_115_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_28_encoder_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_28_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1622876864)))];
tensor<fp16, [1280]> layers_28_encoder_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_28_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1626153728)))];
tensor<fp16, [1, 1280, 1, 1500]> value_115_cast_fp16 = conv(bias = layers_28_encoder_attn_v_proj_bias_to_fp16, dilations = var_6360, groups = var_6223, pad = value_115_pad_0, pad_type = value_115_pad_type_0, strides = var_6358, weight = layers_28_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("value_115_cast_fp16")];
tensor<int32, [4]> var_6364 = const()[name = tensor<string, []>("op_6364"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_6365_cast_fp16 = reshape(shape = var_6364, x = query_115_cast_fp16)[name = tensor<string, []>("op_6365_cast_fp16")];
tensor<fp16, []> var_6366_to_fp16 = const()[name = tensor<string, []>("op_6366_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_6367_cast_fp16 = mul(x = var_6365_cast_fp16, y = var_6366_to_fp16)[name = tensor<string, []>("op_6367_cast_fp16")];
tensor<int32, [4]> var_6368 = const()[name = tensor<string, []>("op_6368"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_6369_cast_fp16 = reshape(shape = var_6368, x = key_115_cast_fp16)[name = tensor<string, []>("op_6369_cast_fp16")];
tensor<bool, []> mh_w_173_transpose_x_0 = const()[name = tensor<string, []>("mh_w_173_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_173_transpose_y_0 = const()[name = tensor<string, []>("mh_w_173_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 1500]> mh_w_173_cast_fp16 = matmul(transpose_x = mh_w_173_transpose_x_0, transpose_y = mh_w_173_transpose_y_0, x = var_6367_cast_fp16, y = var_6369_cast_fp16)[name = tensor<string, []>("mh_w_173_cast_fp16")];
tensor<fp16, [1, 20, 1, 1500]> obj_405_cast_fp16 = softmax(axis = var_6216, x = mh_w_173_cast_fp16)[name = tensor<string, []>("obj_405_cast_fp16")];
tensor<int32, [4]> var_6373 = const()[name = tensor<string, []>("op_6373"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_6374_cast_fp16 = reshape(shape = var_6373, x = value_115_cast_fp16)[name = tensor<string, []>("op_6374_cast_fp16")];
tensor<bool, []> attn_115_transpose_x_0 = const()[name = tensor<string, []>("attn_115_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_115_transpose_y_0 = const()[name = tensor<string, []>("attn_115_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_115_cast_fp16 = matmul(transpose_x = attn_115_transpose_x_0, transpose_y = attn_115_transpose_y_0, x = var_6374_cast_fp16, y = obj_405_cast_fp16)[name = tensor<string, []>("attn_115_cast_fp16")];
tensor<int32, [4]> var_6377 = const()[name = tensor<string, []>("op_6377"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_283_cast_fp16 = reshape(shape = var_6377, x = attn_115_cast_fp16)[name = tensor<string, []>("input_283_cast_fp16")];
tensor<int32, [2]> var_6381 = const()[name = tensor<string, []>("op_6381"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6383 = const()[name = tensor<string, []>("op_6383"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_403_pad_type_0 = const()[name = tensor<string, []>("obj_403_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_403_pad_0 = const()[name = tensor<string, []>("obj_403_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_28_encoder_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_28_encoder_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1626156352)))];
tensor<fp16, [1280]> layers_28_encoder_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_28_encoder_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1629433216)))];
tensor<fp16, [1, 1280, 1, 1]> obj_403_cast_fp16 = conv(bias = layers_28_encoder_attn_o_proj_bias_to_fp16, dilations = var_6383, groups = var_6223, pad = obj_403_pad_0, pad_type = obj_403_pad_type_0, strides = var_6381, weight = layers_28_encoder_attn_o_proj_weight_to_fp16, x = input_283_cast_fp16)[name = tensor<string, []>("obj_403_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_173_cast_fp16 = add(x = inputs_171_cast_fp16, y = obj_403_cast_fp16)[name = tensor<string, []>("inputs_173_cast_fp16")];
tensor<int32, [1]> var_6389 = const()[name = tensor<string, []>("op_6389"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_173_cast_fp16 = reduce_mean(axes = var_6389, keep_dims = var_6224, x = inputs_173_cast_fp16)[name = tensor<string, []>("channels_mean_173_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_173_cast_fp16 = sub(x = inputs_173_cast_fp16, y = channels_mean_173_cast_fp16)[name = tensor<string, []>("zero_mean_173_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_173_cast_fp16 = mul(x = zero_mean_173_cast_fp16, y = zero_mean_173_cast_fp16)[name = tensor<string, []>("zero_mean_sq_173_cast_fp16")];
tensor<int32, [1]> var_6393 = const()[name = tensor<string, []>("op_6393"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_6394_cast_fp16 = reduce_mean(axes = var_6393, keep_dims = var_6224, x = zero_mean_sq_173_cast_fp16)[name = tensor<string, []>("op_6394_cast_fp16")];
tensor<fp16, []> var_6395_to_fp16 = const()[name = tensor<string, []>("op_6395_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_6396_cast_fp16 = add(x = var_6394_cast_fp16, y = var_6395_to_fp16)[name = tensor<string, []>("op_6396_cast_fp16")];
tensor<fp16, []> denom_173_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_173_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_173_cast_fp16 = rsqrt(epsilon = denom_173_epsilon_0_to_fp16, x = var_6396_cast_fp16)[name = tensor<string, []>("denom_173_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_173_cast_fp16 = mul(x = zero_mean_173_cast_fp16, y = denom_173_cast_fp16)[name = tensor<string, []>("out_173_cast_fp16")];
tensor<fp16, [1280]> input_285_gamma_0_to_fp16 = const()[name = tensor<string, []>("input_285_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1629435840)))];
tensor<fp16, [1280]> input_285_beta_0_to_fp16 = const()[name = tensor<string, []>("input_285_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1629438464)))];
tensor<fp16, []> input_285_epsilon_0_to_fp16 = const()[name = tensor<string, []>("input_285_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> input_285_cast_fp16 = batch_norm(beta = input_285_beta_0_to_fp16, epsilon = input_285_epsilon_0_to_fp16, gamma = input_285_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_173_cast_fp16)[name = tensor<string, []>("input_285_cast_fp16")];
tensor<int32, [2]> var_6407 = const()[name = tensor<string, []>("op_6407"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6409 = const()[name = tensor<string, []>("op_6409"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_287_pad_type_0 = const()[name = tensor<string, []>("input_287_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_287_pad_0 = const()[name = tensor<string, []>("input_287_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [5120, 1280, 1, 1]> layers_28_fc1_weight_to_fp16 = const()[name = tensor<string, []>("layers_28_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1629441088)))];
tensor<fp16, [5120]> layers_28_fc1_bias_to_fp16 = const()[name = tensor<string, []>("layers_28_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1642548352)))];
tensor<fp16, [1, 5120, 1, 1]> input_287_cast_fp16 = conv(bias = layers_28_fc1_bias_to_fp16, dilations = var_6409, groups = var_6223, pad = input_287_pad_0, pad_type = input_287_pad_type_0, strides = var_6407, weight = layers_28_fc1_weight_to_fp16, x = input_285_cast_fp16)[name = tensor<string, []>("input_287_cast_fp16")];
tensor<string, []> input_289_mode_0 = const()[name = tensor<string, []>("input_289_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 5120, 1, 1]> input_289_cast_fp16 = gelu(mode = input_289_mode_0, x = input_287_cast_fp16)[name = tensor<string, []>("input_289_cast_fp16")];
tensor<int32, [2]> var_6415 = const()[name = tensor<string, []>("op_6415"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6417 = const()[name = tensor<string, []>("op_6417"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_59_pad_type_0 = const()[name = tensor<string, []>("hidden_states_59_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_59_pad_0 = const()[name = tensor<string, []>("hidden_states_59_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 5120, 1, 1]> layers_28_fc2_weight_to_fp16 = const()[name = tensor<string, []>("layers_28_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1642558656)))];
tensor<fp16, [1280]> layers_28_fc2_bias_to_fp16 = const()[name = tensor<string, []>("layers_28_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1655665920)))];
tensor<fp16, [1, 1280, 1, 1]> hidden_states_59_cast_fp16 = conv(bias = layers_28_fc2_bias_to_fp16, dilations = var_6417, groups = var_6223, pad = hidden_states_59_pad_0, pad_type = hidden_states_59_pad_type_0, strides = var_6415, weight = layers_28_fc2_weight_to_fp16, x = input_289_cast_fp16)[name = tensor<string, []>("hidden_states_59_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_175_cast_fp16 = add(x = inputs_173_cast_fp16, y = hidden_states_59_cast_fp16)[name = tensor<string, []>("inputs_175_cast_fp16")];
tensor<int32, []> var_6430 = const()[name = tensor<string, []>("op_6430"), val = tensor<int32, []>(3)];
tensor<int32, []> var_6437 = const()[name = tensor<string, []>("op_6437"), val = tensor<int32, []>(1)];
tensor<bool, []> var_6438 = const()[name = tensor<string, []>("op_6438"), val = tensor<bool, []>(true)];
tensor<int32, [1]> var_6450 = const()[name = tensor<string, []>("op_6450"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_175_cast_fp16 = reduce_mean(axes = var_6450, keep_dims = var_6438, x = inputs_175_cast_fp16)[name = tensor<string, []>("channels_mean_175_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_175_cast_fp16 = sub(x = inputs_175_cast_fp16, y = channels_mean_175_cast_fp16)[name = tensor<string, []>("zero_mean_175_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_175_cast_fp16 = mul(x = zero_mean_175_cast_fp16, y = zero_mean_175_cast_fp16)[name = tensor<string, []>("zero_mean_sq_175_cast_fp16")];
tensor<int32, [1]> var_6454 = const()[name = tensor<string, []>("op_6454"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_6455_cast_fp16 = reduce_mean(axes = var_6454, keep_dims = var_6438, x = zero_mean_sq_175_cast_fp16)[name = tensor<string, []>("op_6455_cast_fp16")];
tensor<fp16, []> var_6456_to_fp16 = const()[name = tensor<string, []>("op_6456_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_6457_cast_fp16 = add(x = var_6455_cast_fp16, y = var_6456_to_fp16)[name = tensor<string, []>("op_6457_cast_fp16")];
tensor<fp16, []> denom_175_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_175_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_175_cast_fp16 = rsqrt(epsilon = denom_175_epsilon_0_to_fp16, x = var_6457_cast_fp16)[name = tensor<string, []>("denom_175_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_175_cast_fp16 = mul(x = zero_mean_175_cast_fp16, y = denom_175_cast_fp16)[name = tensor<string, []>("out_175_cast_fp16")];
tensor<fp16, [1280]> obj_407_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_407_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1655668544)))];
tensor<fp16, [1280]> obj_407_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_407_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1655671168)))];
tensor<fp16, []> obj_407_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_407_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_407_cast_fp16 = batch_norm(beta = obj_407_beta_0_to_fp16, epsilon = obj_407_epsilon_0_to_fp16, gamma = obj_407_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_175_cast_fp16)[name = tensor<string, []>("obj_407_cast_fp16")];
tensor<int32, [2]> var_6472 = const()[name = tensor<string, []>("op_6472"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6474 = const()[name = tensor<string, []>("op_6474"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_117_pad_type_0 = const()[name = tensor<string, []>("query_117_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_117_pad_0 = const()[name = tensor<string, []>("query_117_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_29_self_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_29_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1655673792)))];
tensor<fp16, [1280]> layers_29_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_29_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1658950656)))];
tensor<fp16, [1, 1280, 1, 1]> query_117_cast_fp16 = conv(bias = layers_29_self_attn_q_proj_bias_to_fp16, dilations = var_6474, groups = var_6437, pad = query_117_pad_0, pad_type = query_117_pad_type_0, strides = var_6472, weight = layers_29_self_attn_q_proj_weight_to_fp16, x = obj_407_cast_fp16)[name = tensor<string, []>("query_117_cast_fp16")];
tensor<int32, [2]> var_6478 = const()[name = tensor<string, []>("op_6478"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6480 = const()[name = tensor<string, []>("op_6480"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_key_59_pad_type_0 = const()[name = tensor<string, []>("current_key_59_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_key_59_pad_0 = const()[name = tensor<string, []>("current_key_59_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_29_self_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_29_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1658953280)))];
tensor<fp16, [1, 1280, 1, 1]> current_key_59_cast_fp16 = conv(dilations = var_6480, groups = var_6437, pad = current_key_59_pad_0, pad_type = current_key_59_pad_type_0, strides = var_6478, weight = layers_29_self_attn_k_proj_weight_to_fp16, x = obj_407_cast_fp16)[name = tensor<string, []>("current_key_59_cast_fp16")];
tensor<int32, [2]> var_6485 = const()[name = tensor<string, []>("op_6485"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6487 = const()[name = tensor<string, []>("op_6487"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_value_59_pad_type_0 = const()[name = tensor<string, []>("current_value_59_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_value_59_pad_0 = const()[name = tensor<string, []>("current_value_59_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_29_self_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_29_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1662230144)))];
tensor<fp16, [1280]> layers_29_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_29_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1665507008)))];
tensor<fp16, [1, 1280, 1, 1]> current_value_59_cast_fp16 = conv(bias = layers_29_self_attn_v_proj_bias_to_fp16, dilations = var_6487, groups = var_6437, pad = current_value_59_pad_0, pad_type = current_value_59_pad_type_0, strides = var_6485, weight = layers_29_self_attn_v_proj_weight_to_fp16, x = obj_407_cast_fp16)[name = tensor<string, []>("current_value_59_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_6494_cast_fp16 = mul(x = current_key_59_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_6494_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_6496_cast_fp16 = mul(x = var_103_cast_fp16_29, y = var_241_cast_fp16)[name = tensor<string, []>("op_6496_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> key_117_cast_fp16 = add(x = var_6494_cast_fp16, y = var_6496_cast_fp16)[name = tensor<string, []>("key_117_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_6498_cast_fp16 = mul(x = current_value_59_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_6498_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_6500_cast_fp16 = mul(x = var_138_cast_fp16_29, y = var_241_cast_fp16)[name = tensor<string, []>("op_6500_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> value_117_cast_fp16 = add(x = var_6498_cast_fp16, y = var_6500_cast_fp16)[name = tensor<string, []>("value_117_cast_fp16")];
tensor<int32, [4]> var_6503 = const()[name = tensor<string, []>("op_6503"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_6504_cast_fp16 = reshape(shape = var_6503, x = query_117_cast_fp16)[name = tensor<string, []>("op_6504_cast_fp16")];
tensor<fp16, []> var_6505_to_fp16 = const()[name = tensor<string, []>("op_6505_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_6506_cast_fp16 = mul(x = var_6504_cast_fp16, y = var_6505_to_fp16)[name = tensor<string, []>("op_6506_cast_fp16")];
tensor<int32, [4]> var_6507 = const()[name = tensor<string, []>("op_6507"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_6508_cast_fp16 = reshape(shape = var_6507, x = key_117_cast_fp16)[name = tensor<string, []>("op_6508_cast_fp16")];
tensor<bool, []> mh_w_175_transpose_x_0 = const()[name = tensor<string, []>("mh_w_175_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_175_transpose_y_0 = const()[name = tensor<string, []>("mh_w_175_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 224]> mh_w_175_cast_fp16 = matmul(transpose_x = mh_w_175_transpose_x_0, transpose_y = mh_w_175_transpose_y_0, x = var_6506_cast_fp16, y = var_6508_cast_fp16)[name = tensor<string, []>("mh_w_175_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> mh_w_177_cast_fp16 = add(x = mh_w_175_cast_fp16, y = var_259_cast_fp16)[name = tensor<string, []>("mh_w_177_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> var_6516_cast_fp16 = softmax(axis = var_6430, x = mh_w_177_cast_fp16)[name = tensor<string, []>("op_6516_cast_fp16")];
tensor<int32, [4]> var_6517 = const()[name = tensor<string, []>("op_6517"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_6518_cast_fp16 = reshape(shape = var_6517, x = value_117_cast_fp16)[name = tensor<string, []>("op_6518_cast_fp16")];
tensor<bool, []> attn_117_transpose_x_0 = const()[name = tensor<string, []>("attn_117_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_117_transpose_y_0 = const()[name = tensor<string, []>("attn_117_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_117_cast_fp16 = matmul(transpose_x = attn_117_transpose_x_0, transpose_y = attn_117_transpose_y_0, x = var_6518_cast_fp16, y = var_6516_cast_fp16)[name = tensor<string, []>("attn_117_cast_fp16")];
tensor<int32, [4]> var_6521 = const()[name = tensor<string, []>("op_6521"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_291_cast_fp16 = reshape(shape = var_6521, x = attn_117_cast_fp16)[name = tensor<string, []>("input_291_cast_fp16")];
tensor<int32, [2]> var_6525 = const()[name = tensor<string, []>("op_6525"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6527 = const()[name = tensor<string, []>("op_6527"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_413_pad_type_0 = const()[name = tensor<string, []>("obj_413_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_413_pad_0 = const()[name = tensor<string, []>("obj_413_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_29_self_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_29_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1665509632)))];
tensor<fp16, [1280]> layers_29_self_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_29_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1668786496)))];
tensor<fp16, [1, 1280, 1, 1]> obj_413_cast_fp16 = conv(bias = layers_29_self_attn_o_proj_bias_to_fp16, dilations = var_6527, groups = var_6437, pad = obj_413_pad_0, pad_type = obj_413_pad_type_0, strides = var_6525, weight = layers_29_self_attn_o_proj_weight_to_fp16, x = input_291_cast_fp16)[name = tensor<string, []>("obj_413_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_177_cast_fp16 = add(x = inputs_175_cast_fp16, y = obj_413_cast_fp16)[name = tensor<string, []>("inputs_177_cast_fp16")];
tensor<int32, [1]> var_6537 = const()[name = tensor<string, []>("op_6537"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_177_cast_fp16 = reduce_mean(axes = var_6537, keep_dims = var_6438, x = inputs_177_cast_fp16)[name = tensor<string, []>("channels_mean_177_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_177_cast_fp16 = sub(x = inputs_177_cast_fp16, y = channels_mean_177_cast_fp16)[name = tensor<string, []>("zero_mean_177_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_177_cast_fp16 = mul(x = zero_mean_177_cast_fp16, y = zero_mean_177_cast_fp16)[name = tensor<string, []>("zero_mean_sq_177_cast_fp16")];
tensor<int32, [1]> var_6541 = const()[name = tensor<string, []>("op_6541"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_6542_cast_fp16 = reduce_mean(axes = var_6541, keep_dims = var_6438, x = zero_mean_sq_177_cast_fp16)[name = tensor<string, []>("op_6542_cast_fp16")];
tensor<fp16, []> var_6543_to_fp16 = const()[name = tensor<string, []>("op_6543_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_6544_cast_fp16 = add(x = var_6542_cast_fp16, y = var_6543_to_fp16)[name = tensor<string, []>("op_6544_cast_fp16")];
tensor<fp16, []> denom_177_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_177_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_177_cast_fp16 = rsqrt(epsilon = denom_177_epsilon_0_to_fp16, x = var_6544_cast_fp16)[name = tensor<string, []>("denom_177_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_177_cast_fp16 = mul(x = zero_mean_177_cast_fp16, y = denom_177_cast_fp16)[name = tensor<string, []>("out_177_cast_fp16")];
tensor<fp16, [1280]> obj_415_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_415_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1668789120)))];
tensor<fp16, [1280]> obj_415_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_415_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1668791744)))];
tensor<fp16, []> obj_415_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_415_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_415_cast_fp16 = batch_norm(beta = obj_415_beta_0_to_fp16, epsilon = obj_415_epsilon_0_to_fp16, gamma = obj_415_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_177_cast_fp16)[name = tensor<string, []>("obj_415_cast_fp16")];
tensor<int32, [2]> var_6559 = const()[name = tensor<string, []>("op_6559"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6561 = const()[name = tensor<string, []>("op_6561"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_119_pad_type_0 = const()[name = tensor<string, []>("query_119_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_119_pad_0 = const()[name = tensor<string, []>("query_119_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_29_encoder_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_29_encoder_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1668794368)))];
tensor<fp16, [1280]> layers_29_encoder_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_29_encoder_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1672071232)))];
tensor<fp16, [1, 1280, 1, 1]> query_119_cast_fp16 = conv(bias = layers_29_encoder_attn_q_proj_bias_to_fp16, dilations = var_6561, groups = var_6437, pad = query_119_pad_0, pad_type = query_119_pad_type_0, strides = var_6559, weight = layers_29_encoder_attn_q_proj_weight_to_fp16, x = obj_415_cast_fp16)[name = tensor<string, []>("query_119_cast_fp16")];
tensor<int32, [2]> var_6565 = const()[name = tensor<string, []>("op_6565"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6567 = const()[name = tensor<string, []>("op_6567"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> key_119_pad_type_0 = const()[name = tensor<string, []>("key_119_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> key_119_pad_0 = const()[name = tensor<string, []>("key_119_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_29_encoder_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_29_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1672073856)))];
tensor<fp16, [1, 1280, 1, 1500]> key_119_cast_fp16 = conv(dilations = var_6567, groups = var_6437, pad = key_119_pad_0, pad_type = key_119_pad_type_0, strides = var_6565, weight = layers_29_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("key_119_cast_fp16")];
tensor<int32, [2]> var_6572 = const()[name = tensor<string, []>("op_6572"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6574 = const()[name = tensor<string, []>("op_6574"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> value_119_pad_type_0 = const()[name = tensor<string, []>("value_119_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> value_119_pad_0 = const()[name = tensor<string, []>("value_119_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_29_encoder_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_29_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1675350720)))];
tensor<fp16, [1280]> layers_29_encoder_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_29_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1678627584)))];
tensor<fp16, [1, 1280, 1, 1500]> value_119_cast_fp16 = conv(bias = layers_29_encoder_attn_v_proj_bias_to_fp16, dilations = var_6574, groups = var_6437, pad = value_119_pad_0, pad_type = value_119_pad_type_0, strides = var_6572, weight = layers_29_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("value_119_cast_fp16")];
tensor<int32, [4]> var_6578 = const()[name = tensor<string, []>("op_6578"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_6579_cast_fp16 = reshape(shape = var_6578, x = query_119_cast_fp16)[name = tensor<string, []>("op_6579_cast_fp16")];
tensor<fp16, []> var_6580_to_fp16 = const()[name = tensor<string, []>("op_6580_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_6581_cast_fp16 = mul(x = var_6579_cast_fp16, y = var_6580_to_fp16)[name = tensor<string, []>("op_6581_cast_fp16")];
tensor<int32, [4]> var_6582 = const()[name = tensor<string, []>("op_6582"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_6583_cast_fp16 = reshape(shape = var_6582, x = key_119_cast_fp16)[name = tensor<string, []>("op_6583_cast_fp16")];
tensor<bool, []> mh_w_179_transpose_x_0 = const()[name = tensor<string, []>("mh_w_179_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_179_transpose_y_0 = const()[name = tensor<string, []>("mh_w_179_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 1500]> mh_w_179_cast_fp16 = matmul(transpose_x = mh_w_179_transpose_x_0, transpose_y = mh_w_179_transpose_y_0, x = var_6581_cast_fp16, y = var_6583_cast_fp16)[name = tensor<string, []>("mh_w_179_cast_fp16")];
tensor<fp16, [1, 20, 1, 1500]> obj_419_cast_fp16 = softmax(axis = var_6430, x = mh_w_179_cast_fp16)[name = tensor<string, []>("obj_419_cast_fp16")];
tensor<int32, [4]> var_6587 = const()[name = tensor<string, []>("op_6587"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_6588_cast_fp16 = reshape(shape = var_6587, x = value_119_cast_fp16)[name = tensor<string, []>("op_6588_cast_fp16")];
tensor<bool, []> attn_119_transpose_x_0 = const()[name = tensor<string, []>("attn_119_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_119_transpose_y_0 = const()[name = tensor<string, []>("attn_119_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_119_cast_fp16 = matmul(transpose_x = attn_119_transpose_x_0, transpose_y = attn_119_transpose_y_0, x = var_6588_cast_fp16, y = obj_419_cast_fp16)[name = tensor<string, []>("attn_119_cast_fp16")];
tensor<int32, [4]> var_6591 = const()[name = tensor<string, []>("op_6591"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_293_cast_fp16 = reshape(shape = var_6591, x = attn_119_cast_fp16)[name = tensor<string, []>("input_293_cast_fp16")];
tensor<int32, [2]> var_6595 = const()[name = tensor<string, []>("op_6595"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6597 = const()[name = tensor<string, []>("op_6597"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_417_pad_type_0 = const()[name = tensor<string, []>("obj_417_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_417_pad_0 = const()[name = tensor<string, []>("obj_417_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_29_encoder_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_29_encoder_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1678630208)))];
tensor<fp16, [1280]> layers_29_encoder_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_29_encoder_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1681907072)))];
tensor<fp16, [1, 1280, 1, 1]> obj_417_cast_fp16 = conv(bias = layers_29_encoder_attn_o_proj_bias_to_fp16, dilations = var_6597, groups = var_6437, pad = obj_417_pad_0, pad_type = obj_417_pad_type_0, strides = var_6595, weight = layers_29_encoder_attn_o_proj_weight_to_fp16, x = input_293_cast_fp16)[name = tensor<string, []>("obj_417_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_179_cast_fp16 = add(x = inputs_177_cast_fp16, y = obj_417_cast_fp16)[name = tensor<string, []>("inputs_179_cast_fp16")];
tensor<int32, [1]> var_6603 = const()[name = tensor<string, []>("op_6603"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_179_cast_fp16 = reduce_mean(axes = var_6603, keep_dims = var_6438, x = inputs_179_cast_fp16)[name = tensor<string, []>("channels_mean_179_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_179_cast_fp16 = sub(x = inputs_179_cast_fp16, y = channels_mean_179_cast_fp16)[name = tensor<string, []>("zero_mean_179_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_179_cast_fp16 = mul(x = zero_mean_179_cast_fp16, y = zero_mean_179_cast_fp16)[name = tensor<string, []>("zero_mean_sq_179_cast_fp16")];
tensor<int32, [1]> var_6607 = const()[name = tensor<string, []>("op_6607"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_6608_cast_fp16 = reduce_mean(axes = var_6607, keep_dims = var_6438, x = zero_mean_sq_179_cast_fp16)[name = tensor<string, []>("op_6608_cast_fp16")];
tensor<fp16, []> var_6609_to_fp16 = const()[name = tensor<string, []>("op_6609_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_6610_cast_fp16 = add(x = var_6608_cast_fp16, y = var_6609_to_fp16)[name = tensor<string, []>("op_6610_cast_fp16")];
tensor<fp16, []> denom_179_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_179_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_179_cast_fp16 = rsqrt(epsilon = denom_179_epsilon_0_to_fp16, x = var_6610_cast_fp16)[name = tensor<string, []>("denom_179_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_179_cast_fp16 = mul(x = zero_mean_179_cast_fp16, y = denom_179_cast_fp16)[name = tensor<string, []>("out_179_cast_fp16")];
tensor<fp16, [1280]> input_295_gamma_0_to_fp16 = const()[name = tensor<string, []>("input_295_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1681909696)))];
tensor<fp16, [1280]> input_295_beta_0_to_fp16 = const()[name = tensor<string, []>("input_295_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1681912320)))];
tensor<fp16, []> input_295_epsilon_0_to_fp16 = const()[name = tensor<string, []>("input_295_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> input_295_cast_fp16 = batch_norm(beta = input_295_beta_0_to_fp16, epsilon = input_295_epsilon_0_to_fp16, gamma = input_295_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_179_cast_fp16)[name = tensor<string, []>("input_295_cast_fp16")];
tensor<int32, [2]> var_6621 = const()[name = tensor<string, []>("op_6621"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6623 = const()[name = tensor<string, []>("op_6623"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_297_pad_type_0 = const()[name = tensor<string, []>("input_297_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_297_pad_0 = const()[name = tensor<string, []>("input_297_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [5120, 1280, 1, 1]> layers_29_fc1_weight_to_fp16 = const()[name = tensor<string, []>("layers_29_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1681914944)))];
tensor<fp16, [5120]> layers_29_fc1_bias_to_fp16 = const()[name = tensor<string, []>("layers_29_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1695022208)))];
tensor<fp16, [1, 5120, 1, 1]> input_297_cast_fp16 = conv(bias = layers_29_fc1_bias_to_fp16, dilations = var_6623, groups = var_6437, pad = input_297_pad_0, pad_type = input_297_pad_type_0, strides = var_6621, weight = layers_29_fc1_weight_to_fp16, x = input_295_cast_fp16)[name = tensor<string, []>("input_297_cast_fp16")];
tensor<string, []> input_299_mode_0 = const()[name = tensor<string, []>("input_299_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 5120, 1, 1]> input_299_cast_fp16 = gelu(mode = input_299_mode_0, x = input_297_cast_fp16)[name = tensor<string, []>("input_299_cast_fp16")];
tensor<int32, [2]> var_6629 = const()[name = tensor<string, []>("op_6629"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6631 = const()[name = tensor<string, []>("op_6631"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_61_pad_type_0 = const()[name = tensor<string, []>("hidden_states_61_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_61_pad_0 = const()[name = tensor<string, []>("hidden_states_61_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 5120, 1, 1]> layers_29_fc2_weight_to_fp16 = const()[name = tensor<string, []>("layers_29_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1695032512)))];
tensor<fp16, [1280]> layers_29_fc2_bias_to_fp16 = const()[name = tensor<string, []>("layers_29_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1708139776)))];
tensor<fp16, [1, 1280, 1, 1]> hidden_states_61_cast_fp16 = conv(bias = layers_29_fc2_bias_to_fp16, dilations = var_6631, groups = var_6437, pad = hidden_states_61_pad_0, pad_type = hidden_states_61_pad_type_0, strides = var_6629, weight = layers_29_fc2_weight_to_fp16, x = input_299_cast_fp16)[name = tensor<string, []>("hidden_states_61_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_181_cast_fp16 = add(x = inputs_179_cast_fp16, y = hidden_states_61_cast_fp16)[name = tensor<string, []>("inputs_181_cast_fp16")];
tensor<int32, []> var_6644 = const()[name = tensor<string, []>("op_6644"), val = tensor<int32, []>(3)];
tensor<int32, []> var_6651 = const()[name = tensor<string, []>("op_6651"), val = tensor<int32, []>(1)];
tensor<bool, []> var_6652 = const()[name = tensor<string, []>("op_6652"), val = tensor<bool, []>(true)];
tensor<int32, [1]> var_6664 = const()[name = tensor<string, []>("op_6664"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_181_cast_fp16 = reduce_mean(axes = var_6664, keep_dims = var_6652, x = inputs_181_cast_fp16)[name = tensor<string, []>("channels_mean_181_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_181_cast_fp16 = sub(x = inputs_181_cast_fp16, y = channels_mean_181_cast_fp16)[name = tensor<string, []>("zero_mean_181_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_181_cast_fp16 = mul(x = zero_mean_181_cast_fp16, y = zero_mean_181_cast_fp16)[name = tensor<string, []>("zero_mean_sq_181_cast_fp16")];
tensor<int32, [1]> var_6668 = const()[name = tensor<string, []>("op_6668"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_6669_cast_fp16 = reduce_mean(axes = var_6668, keep_dims = var_6652, x = zero_mean_sq_181_cast_fp16)[name = tensor<string, []>("op_6669_cast_fp16")];
tensor<fp16, []> var_6670_to_fp16 = const()[name = tensor<string, []>("op_6670_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_6671_cast_fp16 = add(x = var_6669_cast_fp16, y = var_6670_to_fp16)[name = tensor<string, []>("op_6671_cast_fp16")];
tensor<fp16, []> denom_181_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_181_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_181_cast_fp16 = rsqrt(epsilon = denom_181_epsilon_0_to_fp16, x = var_6671_cast_fp16)[name = tensor<string, []>("denom_181_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_181_cast_fp16 = mul(x = zero_mean_181_cast_fp16, y = denom_181_cast_fp16)[name = tensor<string, []>("out_181_cast_fp16")];
tensor<fp16, [1280]> obj_421_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_421_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1708142400)))];
tensor<fp16, [1280]> obj_421_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_421_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1708145024)))];
tensor<fp16, []> obj_421_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_421_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_421_cast_fp16 = batch_norm(beta = obj_421_beta_0_to_fp16, epsilon = obj_421_epsilon_0_to_fp16, gamma = obj_421_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_181_cast_fp16)[name = tensor<string, []>("obj_421_cast_fp16")];
tensor<int32, [2]> var_6686 = const()[name = tensor<string, []>("op_6686"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6688 = const()[name = tensor<string, []>("op_6688"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_121_pad_type_0 = const()[name = tensor<string, []>("query_121_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_121_pad_0 = const()[name = tensor<string, []>("query_121_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_30_self_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_30_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1708147648)))];
tensor<fp16, [1280]> layers_30_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_30_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1711424512)))];
tensor<fp16, [1, 1280, 1, 1]> query_121_cast_fp16 = conv(bias = layers_30_self_attn_q_proj_bias_to_fp16, dilations = var_6688, groups = var_6651, pad = query_121_pad_0, pad_type = query_121_pad_type_0, strides = var_6686, weight = layers_30_self_attn_q_proj_weight_to_fp16, x = obj_421_cast_fp16)[name = tensor<string, []>("query_121_cast_fp16")];
tensor<int32, [2]> var_6692 = const()[name = tensor<string, []>("op_6692"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6694 = const()[name = tensor<string, []>("op_6694"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_key_61_pad_type_0 = const()[name = tensor<string, []>("current_key_61_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_key_61_pad_0 = const()[name = tensor<string, []>("current_key_61_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_30_self_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_30_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1711427136)))];
tensor<fp16, [1, 1280, 1, 1]> current_key_61_cast_fp16 = conv(dilations = var_6694, groups = var_6651, pad = current_key_61_pad_0, pad_type = current_key_61_pad_type_0, strides = var_6692, weight = layers_30_self_attn_k_proj_weight_to_fp16, x = obj_421_cast_fp16)[name = tensor<string, []>("current_key_61_cast_fp16")];
tensor<int32, [2]> var_6699 = const()[name = tensor<string, []>("op_6699"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6701 = const()[name = tensor<string, []>("op_6701"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_value_61_pad_type_0 = const()[name = tensor<string, []>("current_value_61_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_value_61_pad_0 = const()[name = tensor<string, []>("current_value_61_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_30_self_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_30_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1714704000)))];
tensor<fp16, [1280]> layers_30_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_30_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1717980864)))];
tensor<fp16, [1, 1280, 1, 1]> current_value_61_cast_fp16 = conv(bias = layers_30_self_attn_v_proj_bias_to_fp16, dilations = var_6701, groups = var_6651, pad = current_value_61_pad_0, pad_type = current_value_61_pad_type_0, strides = var_6699, weight = layers_30_self_attn_v_proj_weight_to_fp16, x = obj_421_cast_fp16)[name = tensor<string, []>("current_value_61_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_6708_cast_fp16 = mul(x = current_key_61_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_6708_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_6710_cast_fp16 = mul(x = var_103_cast_fp16_30, y = var_241_cast_fp16)[name = tensor<string, []>("op_6710_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> key_121_cast_fp16 = add(x = var_6708_cast_fp16, y = var_6710_cast_fp16)[name = tensor<string, []>("key_121_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_6712_cast_fp16 = mul(x = current_value_61_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_6712_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_6714_cast_fp16 = mul(x = var_138_cast_fp16_30, y = var_241_cast_fp16)[name = tensor<string, []>("op_6714_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> value_121_cast_fp16 = add(x = var_6712_cast_fp16, y = var_6714_cast_fp16)[name = tensor<string, []>("value_121_cast_fp16")];
tensor<int32, [4]> var_6717 = const()[name = tensor<string, []>("op_6717"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_6718_cast_fp16 = reshape(shape = var_6717, x = query_121_cast_fp16)[name = tensor<string, []>("op_6718_cast_fp16")];
tensor<fp16, []> var_6719_to_fp16 = const()[name = tensor<string, []>("op_6719_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_6720_cast_fp16 = mul(x = var_6718_cast_fp16, y = var_6719_to_fp16)[name = tensor<string, []>("op_6720_cast_fp16")];
tensor<int32, [4]> var_6721 = const()[name = tensor<string, []>("op_6721"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_6722_cast_fp16 = reshape(shape = var_6721, x = key_121_cast_fp16)[name = tensor<string, []>("op_6722_cast_fp16")];
tensor<bool, []> mh_w_181_transpose_x_0 = const()[name = tensor<string, []>("mh_w_181_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_181_transpose_y_0 = const()[name = tensor<string, []>("mh_w_181_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 224]> mh_w_181_cast_fp16 = matmul(transpose_x = mh_w_181_transpose_x_0, transpose_y = mh_w_181_transpose_y_0, x = var_6720_cast_fp16, y = var_6722_cast_fp16)[name = tensor<string, []>("mh_w_181_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> mh_w_183_cast_fp16 = add(x = mh_w_181_cast_fp16, y = var_259_cast_fp16)[name = tensor<string, []>("mh_w_183_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> var_6730_cast_fp16 = softmax(axis = var_6644, x = mh_w_183_cast_fp16)[name = tensor<string, []>("op_6730_cast_fp16")];
tensor<int32, [4]> var_6731 = const()[name = tensor<string, []>("op_6731"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_6732_cast_fp16 = reshape(shape = var_6731, x = value_121_cast_fp16)[name = tensor<string, []>("op_6732_cast_fp16")];
tensor<bool, []> attn_121_transpose_x_0 = const()[name = tensor<string, []>("attn_121_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_121_transpose_y_0 = const()[name = tensor<string, []>("attn_121_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_121_cast_fp16 = matmul(transpose_x = attn_121_transpose_x_0, transpose_y = attn_121_transpose_y_0, x = var_6732_cast_fp16, y = var_6730_cast_fp16)[name = tensor<string, []>("attn_121_cast_fp16")];
tensor<int32, [4]> var_6735 = const()[name = tensor<string, []>("op_6735"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_301_cast_fp16 = reshape(shape = var_6735, x = attn_121_cast_fp16)[name = tensor<string, []>("input_301_cast_fp16")];
tensor<int32, [2]> var_6739 = const()[name = tensor<string, []>("op_6739"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6741 = const()[name = tensor<string, []>("op_6741"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_427_pad_type_0 = const()[name = tensor<string, []>("obj_427_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_427_pad_0 = const()[name = tensor<string, []>("obj_427_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_30_self_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_30_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1717983488)))];
tensor<fp16, [1280]> layers_30_self_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_30_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1721260352)))];
tensor<fp16, [1, 1280, 1, 1]> obj_427_cast_fp16 = conv(bias = layers_30_self_attn_o_proj_bias_to_fp16, dilations = var_6741, groups = var_6651, pad = obj_427_pad_0, pad_type = obj_427_pad_type_0, strides = var_6739, weight = layers_30_self_attn_o_proj_weight_to_fp16, x = input_301_cast_fp16)[name = tensor<string, []>("obj_427_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_183_cast_fp16 = add(x = inputs_181_cast_fp16, y = obj_427_cast_fp16)[name = tensor<string, []>("inputs_183_cast_fp16")];
tensor<int32, [1]> var_6751 = const()[name = tensor<string, []>("op_6751"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_183_cast_fp16 = reduce_mean(axes = var_6751, keep_dims = var_6652, x = inputs_183_cast_fp16)[name = tensor<string, []>("channels_mean_183_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_183_cast_fp16 = sub(x = inputs_183_cast_fp16, y = channels_mean_183_cast_fp16)[name = tensor<string, []>("zero_mean_183_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_183_cast_fp16 = mul(x = zero_mean_183_cast_fp16, y = zero_mean_183_cast_fp16)[name = tensor<string, []>("zero_mean_sq_183_cast_fp16")];
tensor<int32, [1]> var_6755 = const()[name = tensor<string, []>("op_6755"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_6756_cast_fp16 = reduce_mean(axes = var_6755, keep_dims = var_6652, x = zero_mean_sq_183_cast_fp16)[name = tensor<string, []>("op_6756_cast_fp16")];
tensor<fp16, []> var_6757_to_fp16 = const()[name = tensor<string, []>("op_6757_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_6758_cast_fp16 = add(x = var_6756_cast_fp16, y = var_6757_to_fp16)[name = tensor<string, []>("op_6758_cast_fp16")];
tensor<fp16, []> denom_183_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_183_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_183_cast_fp16 = rsqrt(epsilon = denom_183_epsilon_0_to_fp16, x = var_6758_cast_fp16)[name = tensor<string, []>("denom_183_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_183_cast_fp16 = mul(x = zero_mean_183_cast_fp16, y = denom_183_cast_fp16)[name = tensor<string, []>("out_183_cast_fp16")];
tensor<fp16, [1280]> obj_429_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_429_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1721262976)))];
tensor<fp16, [1280]> obj_429_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_429_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1721265600)))];
tensor<fp16, []> obj_429_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_429_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_429_cast_fp16 = batch_norm(beta = obj_429_beta_0_to_fp16, epsilon = obj_429_epsilon_0_to_fp16, gamma = obj_429_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_183_cast_fp16)[name = tensor<string, []>("obj_429_cast_fp16")];
tensor<int32, [2]> var_6773 = const()[name = tensor<string, []>("op_6773"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6775 = const()[name = tensor<string, []>("op_6775"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_123_pad_type_0 = const()[name = tensor<string, []>("query_123_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_123_pad_0 = const()[name = tensor<string, []>("query_123_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_30_encoder_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_30_encoder_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1721268224)))];
tensor<fp16, [1280]> layers_30_encoder_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_30_encoder_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1724545088)))];
tensor<fp16, [1, 1280, 1, 1]> query_123_cast_fp16 = conv(bias = layers_30_encoder_attn_q_proj_bias_to_fp16, dilations = var_6775, groups = var_6651, pad = query_123_pad_0, pad_type = query_123_pad_type_0, strides = var_6773, weight = layers_30_encoder_attn_q_proj_weight_to_fp16, x = obj_429_cast_fp16)[name = tensor<string, []>("query_123_cast_fp16")];
tensor<int32, [2]> var_6779 = const()[name = tensor<string, []>("op_6779"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6781 = const()[name = tensor<string, []>("op_6781"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> key_123_pad_type_0 = const()[name = tensor<string, []>("key_123_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> key_123_pad_0 = const()[name = tensor<string, []>("key_123_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_30_encoder_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_30_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1724547712)))];
tensor<fp16, [1, 1280, 1, 1500]> key_123_cast_fp16 = conv(dilations = var_6781, groups = var_6651, pad = key_123_pad_0, pad_type = key_123_pad_type_0, strides = var_6779, weight = layers_30_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("key_123_cast_fp16")];
tensor<int32, [2]> var_6786 = const()[name = tensor<string, []>("op_6786"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6788 = const()[name = tensor<string, []>("op_6788"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> value_123_pad_type_0 = const()[name = tensor<string, []>("value_123_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> value_123_pad_0 = const()[name = tensor<string, []>("value_123_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_30_encoder_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_30_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1727824576)))];
tensor<fp16, [1280]> layers_30_encoder_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_30_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1731101440)))];
tensor<fp16, [1, 1280, 1, 1500]> value_123_cast_fp16 = conv(bias = layers_30_encoder_attn_v_proj_bias_to_fp16, dilations = var_6788, groups = var_6651, pad = value_123_pad_0, pad_type = value_123_pad_type_0, strides = var_6786, weight = layers_30_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("value_123_cast_fp16")];
tensor<int32, [4]> var_6792 = const()[name = tensor<string, []>("op_6792"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_6793_cast_fp16 = reshape(shape = var_6792, x = query_123_cast_fp16)[name = tensor<string, []>("op_6793_cast_fp16")];
tensor<fp16, []> var_6794_to_fp16 = const()[name = tensor<string, []>("op_6794_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_6795_cast_fp16 = mul(x = var_6793_cast_fp16, y = var_6794_to_fp16)[name = tensor<string, []>("op_6795_cast_fp16")];
tensor<int32, [4]> var_6796 = const()[name = tensor<string, []>("op_6796"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_6797_cast_fp16 = reshape(shape = var_6796, x = key_123_cast_fp16)[name = tensor<string, []>("op_6797_cast_fp16")];
tensor<bool, []> mh_w_185_transpose_x_0 = const()[name = tensor<string, []>("mh_w_185_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_185_transpose_y_0 = const()[name = tensor<string, []>("mh_w_185_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 1500]> mh_w_185_cast_fp16 = matmul(transpose_x = mh_w_185_transpose_x_0, transpose_y = mh_w_185_transpose_y_0, x = var_6795_cast_fp16, y = var_6797_cast_fp16)[name = tensor<string, []>("mh_w_185_cast_fp16")];
tensor<fp16, [1, 20, 1, 1500]> obj_433_cast_fp16 = softmax(axis = var_6644, x = mh_w_185_cast_fp16)[name = tensor<string, []>("obj_433_cast_fp16")];
tensor<int32, [4]> var_6801 = const()[name = tensor<string, []>("op_6801"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_6802_cast_fp16 = reshape(shape = var_6801, x = value_123_cast_fp16)[name = tensor<string, []>("op_6802_cast_fp16")];
tensor<bool, []> attn_123_transpose_x_0 = const()[name = tensor<string, []>("attn_123_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_123_transpose_y_0 = const()[name = tensor<string, []>("attn_123_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_123_cast_fp16 = matmul(transpose_x = attn_123_transpose_x_0, transpose_y = attn_123_transpose_y_0, x = var_6802_cast_fp16, y = obj_433_cast_fp16)[name = tensor<string, []>("attn_123_cast_fp16")];
tensor<int32, [4]> var_6805 = const()[name = tensor<string, []>("op_6805"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_303_cast_fp16 = reshape(shape = var_6805, x = attn_123_cast_fp16)[name = tensor<string, []>("input_303_cast_fp16")];
tensor<int32, [2]> var_6809 = const()[name = tensor<string, []>("op_6809"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6811 = const()[name = tensor<string, []>("op_6811"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_431_pad_type_0 = const()[name = tensor<string, []>("obj_431_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_431_pad_0 = const()[name = tensor<string, []>("obj_431_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_30_encoder_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_30_encoder_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1731104064)))];
tensor<fp16, [1280]> layers_30_encoder_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_30_encoder_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1734380928)))];
tensor<fp16, [1, 1280, 1, 1]> obj_431_cast_fp16 = conv(bias = layers_30_encoder_attn_o_proj_bias_to_fp16, dilations = var_6811, groups = var_6651, pad = obj_431_pad_0, pad_type = obj_431_pad_type_0, strides = var_6809, weight = layers_30_encoder_attn_o_proj_weight_to_fp16, x = input_303_cast_fp16)[name = tensor<string, []>("obj_431_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_185_cast_fp16 = add(x = inputs_183_cast_fp16, y = obj_431_cast_fp16)[name = tensor<string, []>("inputs_185_cast_fp16")];
tensor<int32, [1]> var_6817 = const()[name = tensor<string, []>("op_6817"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_185_cast_fp16 = reduce_mean(axes = var_6817, keep_dims = var_6652, x = inputs_185_cast_fp16)[name = tensor<string, []>("channels_mean_185_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_185_cast_fp16 = sub(x = inputs_185_cast_fp16, y = channels_mean_185_cast_fp16)[name = tensor<string, []>("zero_mean_185_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_185_cast_fp16 = mul(x = zero_mean_185_cast_fp16, y = zero_mean_185_cast_fp16)[name = tensor<string, []>("zero_mean_sq_185_cast_fp16")];
tensor<int32, [1]> var_6821 = const()[name = tensor<string, []>("op_6821"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_6822_cast_fp16 = reduce_mean(axes = var_6821, keep_dims = var_6652, x = zero_mean_sq_185_cast_fp16)[name = tensor<string, []>("op_6822_cast_fp16")];
tensor<fp16, []> var_6823_to_fp16 = const()[name = tensor<string, []>("op_6823_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_6824_cast_fp16 = add(x = var_6822_cast_fp16, y = var_6823_to_fp16)[name = tensor<string, []>("op_6824_cast_fp16")];
tensor<fp16, []> denom_185_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_185_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_185_cast_fp16 = rsqrt(epsilon = denom_185_epsilon_0_to_fp16, x = var_6824_cast_fp16)[name = tensor<string, []>("denom_185_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_185_cast_fp16 = mul(x = zero_mean_185_cast_fp16, y = denom_185_cast_fp16)[name = tensor<string, []>("out_185_cast_fp16")];
tensor<fp16, [1280]> input_305_gamma_0_to_fp16 = const()[name = tensor<string, []>("input_305_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1734383552)))];
tensor<fp16, [1280]> input_305_beta_0_to_fp16 = const()[name = tensor<string, []>("input_305_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1734386176)))];
tensor<fp16, []> input_305_epsilon_0_to_fp16 = const()[name = tensor<string, []>("input_305_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> input_305_cast_fp16 = batch_norm(beta = input_305_beta_0_to_fp16, epsilon = input_305_epsilon_0_to_fp16, gamma = input_305_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_185_cast_fp16)[name = tensor<string, []>("input_305_cast_fp16")];
tensor<int32, [2]> var_6835 = const()[name = tensor<string, []>("op_6835"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6837 = const()[name = tensor<string, []>("op_6837"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_307_pad_type_0 = const()[name = tensor<string, []>("input_307_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_307_pad_0 = const()[name = tensor<string, []>("input_307_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [5120, 1280, 1, 1]> layers_30_fc1_weight_to_fp16 = const()[name = tensor<string, []>("layers_30_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1734388800)))];
tensor<fp16, [5120]> layers_30_fc1_bias_to_fp16 = const()[name = tensor<string, []>("layers_30_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1747496064)))];
tensor<fp16, [1, 5120, 1, 1]> input_307_cast_fp16 = conv(bias = layers_30_fc1_bias_to_fp16, dilations = var_6837, groups = var_6651, pad = input_307_pad_0, pad_type = input_307_pad_type_0, strides = var_6835, weight = layers_30_fc1_weight_to_fp16, x = input_305_cast_fp16)[name = tensor<string, []>("input_307_cast_fp16")];
tensor<string, []> input_309_mode_0 = const()[name = tensor<string, []>("input_309_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 5120, 1, 1]> input_309_cast_fp16 = gelu(mode = input_309_mode_0, x = input_307_cast_fp16)[name = tensor<string, []>("input_309_cast_fp16")];
tensor<int32, [2]> var_6843 = const()[name = tensor<string, []>("op_6843"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6845 = const()[name = tensor<string, []>("op_6845"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_63_pad_type_0 = const()[name = tensor<string, []>("hidden_states_63_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_63_pad_0 = const()[name = tensor<string, []>("hidden_states_63_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 5120, 1, 1]> layers_30_fc2_weight_to_fp16 = const()[name = tensor<string, []>("layers_30_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1747506368)))];
tensor<fp16, [1280]> layers_30_fc2_bias_to_fp16 = const()[name = tensor<string, []>("layers_30_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1760613632)))];
tensor<fp16, [1, 1280, 1, 1]> hidden_states_63_cast_fp16 = conv(bias = layers_30_fc2_bias_to_fp16, dilations = var_6845, groups = var_6651, pad = hidden_states_63_pad_0, pad_type = hidden_states_63_pad_type_0, strides = var_6843, weight = layers_30_fc2_weight_to_fp16, x = input_309_cast_fp16)[name = tensor<string, []>("hidden_states_63_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_187_cast_fp16 = add(x = inputs_185_cast_fp16, y = hidden_states_63_cast_fp16)[name = tensor<string, []>("inputs_187_cast_fp16")];
tensor<int32, []> var_6858 = const()[name = tensor<string, []>("op_6858"), val = tensor<int32, []>(3)];
tensor<int32, []> var_6865 = const()[name = tensor<string, []>("op_6865"), val = tensor<int32, []>(1)];
tensor<bool, []> var_6866 = const()[name = tensor<string, []>("op_6866"), val = tensor<bool, []>(true)];
tensor<int32, [1]> var_6878 = const()[name = tensor<string, []>("op_6878"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_187_cast_fp16 = reduce_mean(axes = var_6878, keep_dims = var_6866, x = inputs_187_cast_fp16)[name = tensor<string, []>("channels_mean_187_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_187_cast_fp16 = sub(x = inputs_187_cast_fp16, y = channels_mean_187_cast_fp16)[name = tensor<string, []>("zero_mean_187_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_187_cast_fp16 = mul(x = zero_mean_187_cast_fp16, y = zero_mean_187_cast_fp16)[name = tensor<string, []>("zero_mean_sq_187_cast_fp16")];
tensor<int32, [1]> var_6882 = const()[name = tensor<string, []>("op_6882"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_6883_cast_fp16 = reduce_mean(axes = var_6882, keep_dims = var_6866, x = zero_mean_sq_187_cast_fp16)[name = tensor<string, []>("op_6883_cast_fp16")];
tensor<fp16, []> var_6884_to_fp16 = const()[name = tensor<string, []>("op_6884_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_6885_cast_fp16 = add(x = var_6883_cast_fp16, y = var_6884_to_fp16)[name = tensor<string, []>("op_6885_cast_fp16")];
tensor<fp16, []> denom_187_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_187_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_187_cast_fp16 = rsqrt(epsilon = denom_187_epsilon_0_to_fp16, x = var_6885_cast_fp16)[name = tensor<string, []>("denom_187_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_187_cast_fp16 = mul(x = zero_mean_187_cast_fp16, y = denom_187_cast_fp16)[name = tensor<string, []>("out_187_cast_fp16")];
tensor<fp16, [1280]> obj_435_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_435_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1760616256)))];
tensor<fp16, [1280]> obj_435_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_435_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1760618880)))];
tensor<fp16, []> obj_435_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_435_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_435_cast_fp16 = batch_norm(beta = obj_435_beta_0_to_fp16, epsilon = obj_435_epsilon_0_to_fp16, gamma = obj_435_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_187_cast_fp16)[name = tensor<string, []>("obj_435_cast_fp16")];
tensor<int32, [2]> var_6900 = const()[name = tensor<string, []>("op_6900"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6902 = const()[name = tensor<string, []>("op_6902"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_125_pad_type_0 = const()[name = tensor<string, []>("query_125_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_125_pad_0 = const()[name = tensor<string, []>("query_125_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_31_self_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_31_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1760621504)))];
tensor<fp16, [1280]> layers_31_self_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_31_self_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1763898368)))];
tensor<fp16, [1, 1280, 1, 1]> query_125_cast_fp16 = conv(bias = layers_31_self_attn_q_proj_bias_to_fp16, dilations = var_6902, groups = var_6865, pad = query_125_pad_0, pad_type = query_125_pad_type_0, strides = var_6900, weight = layers_31_self_attn_q_proj_weight_to_fp16, x = obj_435_cast_fp16)[name = tensor<string, []>("query_125_cast_fp16")];
tensor<int32, [2]> var_6906 = const()[name = tensor<string, []>("op_6906"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6908 = const()[name = tensor<string, []>("op_6908"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_key_pad_type_0 = const()[name = tensor<string, []>("current_key_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_key_pad_0 = const()[name = tensor<string, []>("current_key_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_31_self_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_31_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1763900992)))];
tensor<fp16, [1, 1280, 1, 1]> current_key_cast_fp16 = conv(dilations = var_6908, groups = var_6865, pad = current_key_pad_0, pad_type = current_key_pad_type_0, strides = var_6906, weight = layers_31_self_attn_k_proj_weight_to_fp16, x = obj_435_cast_fp16)[name = tensor<string, []>("current_key_cast_fp16")];
tensor<int32, [2]> var_6913 = const()[name = tensor<string, []>("op_6913"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6915 = const()[name = tensor<string, []>("op_6915"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> current_value_pad_type_0 = const()[name = tensor<string, []>("current_value_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> current_value_pad_0 = const()[name = tensor<string, []>("current_value_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_31_self_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_31_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1767177856)))];
tensor<fp16, [1280]> layers_31_self_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_31_self_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1770454720)))];
tensor<fp16, [1, 1280, 1, 1]> current_value_cast_fp16 = conv(bias = layers_31_self_attn_v_proj_bias_to_fp16, dilations = var_6915, groups = var_6865, pad = current_value_pad_0, pad_type = current_value_pad_type_0, strides = var_6913, weight = layers_31_self_attn_v_proj_weight_to_fp16, x = obj_435_cast_fp16)[name = tensor<string, []>("current_value_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_6922_cast_fp16 = mul(x = current_key_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_6922_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_6924_cast_fp16 = mul(x = var_103_cast_fp16_31, y = var_241_cast_fp16)[name = tensor<string, []>("op_6924_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> key_125_cast_fp16 = add(x = var_6922_cast_fp16, y = var_6924_cast_fp16)[name = tensor<string, []>("key_125_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_6926_cast_fp16 = mul(x = current_value_cast_fp16, y = var_238_cast_fp16)[name = tensor<string, []>("op_6926_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> var_6928_cast_fp16 = mul(x = var_138_cast_fp16_31, y = var_241_cast_fp16)[name = tensor<string, []>("op_6928_cast_fp16")];
tensor<fp16, [1, 1280, 1, 224]> value_125_cast_fp16 = add(x = var_6926_cast_fp16, y = var_6928_cast_fp16)[name = tensor<string, []>("value_125_cast_fp16")];
tensor<int32, [4]> var_6931 = const()[name = tensor<string, []>("op_6931"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_6932_cast_fp16 = reshape(shape = var_6931, x = query_125_cast_fp16)[name = tensor<string, []>("op_6932_cast_fp16")];
tensor<fp16, []> var_6933_to_fp16 = const()[name = tensor<string, []>("op_6933_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_6934_cast_fp16 = mul(x = var_6932_cast_fp16, y = var_6933_to_fp16)[name = tensor<string, []>("op_6934_cast_fp16")];
tensor<int32, [4]> var_6935 = const()[name = tensor<string, []>("op_6935"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_6936_cast_fp16 = reshape(shape = var_6935, x = key_125_cast_fp16)[name = tensor<string, []>("op_6936_cast_fp16")];
tensor<bool, []> mh_w_187_transpose_x_0 = const()[name = tensor<string, []>("mh_w_187_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_187_transpose_y_0 = const()[name = tensor<string, []>("mh_w_187_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 224]> mh_w_187_cast_fp16 = matmul(transpose_x = mh_w_187_transpose_x_0, transpose_y = mh_w_187_transpose_y_0, x = var_6934_cast_fp16, y = var_6936_cast_fp16)[name = tensor<string, []>("mh_w_187_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> mh_w_189_cast_fp16 = add(x = mh_w_187_cast_fp16, y = var_259_cast_fp16)[name = tensor<string, []>("mh_w_189_cast_fp16")];
tensor<fp16, [1, 20, 1, 224]> var_6944_cast_fp16 = softmax(axis = var_6858, x = mh_w_189_cast_fp16)[name = tensor<string, []>("op_6944_cast_fp16")];
tensor<int32, [4]> var_6945 = const()[name = tensor<string, []>("op_6945"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 224]> var_6946_cast_fp16 = reshape(shape = var_6945, x = value_125_cast_fp16)[name = tensor<string, []>("op_6946_cast_fp16")];
tensor<bool, []> attn_125_transpose_x_0 = const()[name = tensor<string, []>("attn_125_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_125_transpose_y_0 = const()[name = tensor<string, []>("attn_125_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_125_cast_fp16 = matmul(transpose_x = attn_125_transpose_x_0, transpose_y = attn_125_transpose_y_0, x = var_6946_cast_fp16, y = var_6944_cast_fp16)[name = tensor<string, []>("attn_125_cast_fp16")];
tensor<int32, [4]> var_6949 = const()[name = tensor<string, []>("op_6949"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_311_cast_fp16 = reshape(shape = var_6949, x = attn_125_cast_fp16)[name = tensor<string, []>("input_311_cast_fp16")];
tensor<int32, [2]> var_6953 = const()[name = tensor<string, []>("op_6953"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6955 = const()[name = tensor<string, []>("op_6955"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_441_pad_type_0 = const()[name = tensor<string, []>("obj_441_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_441_pad_0 = const()[name = tensor<string, []>("obj_441_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_31_self_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_31_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1770457344)))];
tensor<fp16, [1280]> layers_31_self_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_31_self_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1773734208)))];
tensor<fp16, [1, 1280, 1, 1]> obj_441_cast_fp16 = conv(bias = layers_31_self_attn_o_proj_bias_to_fp16, dilations = var_6955, groups = var_6865, pad = obj_441_pad_0, pad_type = obj_441_pad_type_0, strides = var_6953, weight = layers_31_self_attn_o_proj_weight_to_fp16, x = input_311_cast_fp16)[name = tensor<string, []>("obj_441_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_189_cast_fp16 = add(x = inputs_187_cast_fp16, y = obj_441_cast_fp16)[name = tensor<string, []>("inputs_189_cast_fp16")];
tensor<int32, [1]> var_6965 = const()[name = tensor<string, []>("op_6965"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_189_cast_fp16 = reduce_mean(axes = var_6965, keep_dims = var_6866, x = inputs_189_cast_fp16)[name = tensor<string, []>("channels_mean_189_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_189_cast_fp16 = sub(x = inputs_189_cast_fp16, y = channels_mean_189_cast_fp16)[name = tensor<string, []>("zero_mean_189_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_189_cast_fp16 = mul(x = zero_mean_189_cast_fp16, y = zero_mean_189_cast_fp16)[name = tensor<string, []>("zero_mean_sq_189_cast_fp16")];
tensor<int32, [1]> var_6969 = const()[name = tensor<string, []>("op_6969"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_6970_cast_fp16 = reduce_mean(axes = var_6969, keep_dims = var_6866, x = zero_mean_sq_189_cast_fp16)[name = tensor<string, []>("op_6970_cast_fp16")];
tensor<fp16, []> var_6971_to_fp16 = const()[name = tensor<string, []>("op_6971_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_6972_cast_fp16 = add(x = var_6970_cast_fp16, y = var_6971_to_fp16)[name = tensor<string, []>("op_6972_cast_fp16")];
tensor<fp16, []> denom_189_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_189_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_189_cast_fp16 = rsqrt(epsilon = denom_189_epsilon_0_to_fp16, x = var_6972_cast_fp16)[name = tensor<string, []>("denom_189_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_189_cast_fp16 = mul(x = zero_mean_189_cast_fp16, y = denom_189_cast_fp16)[name = tensor<string, []>("out_189_cast_fp16")];
tensor<fp16, [1280]> obj_443_gamma_0_to_fp16 = const()[name = tensor<string, []>("obj_443_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1773736832)))];
tensor<fp16, [1280]> obj_443_beta_0_to_fp16 = const()[name = tensor<string, []>("obj_443_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1773739456)))];
tensor<fp16, []> obj_443_epsilon_0_to_fp16 = const()[name = tensor<string, []>("obj_443_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> obj_443_cast_fp16 = batch_norm(beta = obj_443_beta_0_to_fp16, epsilon = obj_443_epsilon_0_to_fp16, gamma = obj_443_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_189_cast_fp16)[name = tensor<string, []>("obj_443_cast_fp16")];
tensor<int32, [2]> var_6987 = const()[name = tensor<string, []>("op_6987"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6989 = const()[name = tensor<string, []>("op_6989"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> query_pad_type_0 = const()[name = tensor<string, []>("query_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> query_pad_0 = const()[name = tensor<string, []>("query_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_31_encoder_attn_q_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_31_encoder_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1773742080)))];
tensor<fp16, [1280]> layers_31_encoder_attn_q_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_31_encoder_attn_q_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1777018944)))];
tensor<fp16, [1, 1280, 1, 1]> query_cast_fp16 = conv(bias = layers_31_encoder_attn_q_proj_bias_to_fp16, dilations = var_6989, groups = var_6865, pad = query_pad_0, pad_type = query_pad_type_0, strides = var_6987, weight = layers_31_encoder_attn_q_proj_weight_to_fp16, x = obj_443_cast_fp16)[name = tensor<string, []>("query_cast_fp16")];
tensor<int32, [2]> var_6993 = const()[name = tensor<string, []>("op_6993"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_6995 = const()[name = tensor<string, []>("op_6995"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> key_pad_type_0 = const()[name = tensor<string, []>("key_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> key_pad_0 = const()[name = tensor<string, []>("key_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_31_encoder_attn_k_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_31_encoder_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1777021568)))];
tensor<fp16, [1, 1280, 1, 1500]> key_cast_fp16 = conv(dilations = var_6995, groups = var_6865, pad = key_pad_0, pad_type = key_pad_type_0, strides = var_6993, weight = layers_31_encoder_attn_k_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("key_cast_fp16")];
tensor<int32, [2]> var_7000 = const()[name = tensor<string, []>("op_7000"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_7002 = const()[name = tensor<string, []>("op_7002"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> value_pad_type_0 = const()[name = tensor<string, []>("value_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> value_pad_0 = const()[name = tensor<string, []>("value_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_31_encoder_attn_v_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_31_encoder_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1780298432)))];
tensor<fp16, [1280]> layers_31_encoder_attn_v_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_31_encoder_attn_v_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1783575296)))];
tensor<fp16, [1, 1280, 1, 1500]> value_cast_fp16 = conv(bias = layers_31_encoder_attn_v_proj_bias_to_fp16, dilations = var_7002, groups = var_6865, pad = value_pad_0, pad_type = value_pad_type_0, strides = var_7000, weight = layers_31_encoder_attn_v_proj_weight_to_fp16, x = encoder_output_embeds)[name = tensor<string, []>("value_cast_fp16")];
tensor<int32, [4]> var_7006 = const()[name = tensor<string, []>("op_7006"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1]> var_7007_cast_fp16 = reshape(shape = var_7006, x = query_cast_fp16)[name = tensor<string, []>("op_7007_cast_fp16")];
tensor<fp16, []> var_7008_to_fp16 = const()[name = tensor<string, []>("op_7008_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 20, 64, 1]> var_7009_cast_fp16 = mul(x = var_7007_cast_fp16, y = var_7008_to_fp16)[name = tensor<string, []>("op_7009_cast_fp16")];
tensor<int32, [4]> var_7010 = const()[name = tensor<string, []>("op_7010"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_7011_cast_fp16 = reshape(shape = var_7010, x = key_cast_fp16)[name = tensor<string, []>("op_7011_cast_fp16")];
tensor<bool, []> mh_w_transpose_x_0 = const()[name = tensor<string, []>("mh_w_transpose_x_0"), val = tensor<bool, []>(true)];
tensor<bool, []> mh_w_transpose_y_0 = const()[name = tensor<string, []>("mh_w_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 20, 1, 1500]> mh_w_cast_fp16 = matmul(transpose_x = mh_w_transpose_x_0, transpose_y = mh_w_transpose_y_0, x = var_7009_cast_fp16, y = var_7011_cast_fp16)[name = tensor<string, []>("mh_w_cast_fp16")];
tensor<fp16, [1, 20, 1, 1500]> obj_447_cast_fp16 = softmax(axis = var_6858, x = mh_w_cast_fp16)[name = tensor<string, []>("obj_447_cast_fp16")];
tensor<int32, [4]> var_7015 = const()[name = tensor<string, []>("op_7015"), val = tensor<int32, [4]>([1, 20, 64, -1])];
tensor<fp16, [1, 20, 64, 1500]> var_7016_cast_fp16 = reshape(shape = var_7015, x = value_cast_fp16)[name = tensor<string, []>("op_7016_cast_fp16")];
tensor<bool, []> attn_transpose_x_0 = const()[name = tensor<string, []>("attn_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attn_transpose_y_0 = const()[name = tensor<string, []>("attn_transpose_y_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 20, 64, 1]> attn_cast_fp16 = matmul(transpose_x = attn_transpose_x_0, transpose_y = attn_transpose_y_0, x = var_7016_cast_fp16, y = obj_447_cast_fp16)[name = tensor<string, []>("attn_cast_fp16")];
tensor<int32, [4]> var_7019 = const()[name = tensor<string, []>("op_7019"), val = tensor<int32, [4]>([1, 1280, 1, -1])];
tensor<fp16, [1, 1280, 1, 1]> input_313_cast_fp16 = reshape(shape = var_7019, x = attn_cast_fp16)[name = tensor<string, []>("input_313_cast_fp16")];
tensor<int32, [2]> var_7023 = const()[name = tensor<string, []>("op_7023"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_7025 = const()[name = tensor<string, []>("op_7025"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> obj_445_pad_type_0 = const()[name = tensor<string, []>("obj_445_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> obj_445_pad_0 = const()[name = tensor<string, []>("obj_445_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 1280, 1, 1]> layers_31_encoder_attn_o_proj_weight_to_fp16 = const()[name = tensor<string, []>("layers_31_encoder_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1783577920)))];
tensor<fp16, [1280]> layers_31_encoder_attn_o_proj_bias_to_fp16 = const()[name = tensor<string, []>("layers_31_encoder_attn_o_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1786854784)))];
tensor<fp16, [1, 1280, 1, 1]> obj_445_cast_fp16 = conv(bias = layers_31_encoder_attn_o_proj_bias_to_fp16, dilations = var_7025, groups = var_6865, pad = obj_445_pad_0, pad_type = obj_445_pad_type_0, strides = var_7023, weight = layers_31_encoder_attn_o_proj_weight_to_fp16, x = input_313_cast_fp16)[name = tensor<string, []>("obj_445_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_191_cast_fp16 = add(x = inputs_189_cast_fp16, y = obj_445_cast_fp16)[name = tensor<string, []>("inputs_191_cast_fp16")];
tensor<int32, [1]> var_7031 = const()[name = tensor<string, []>("op_7031"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_191_cast_fp16 = reduce_mean(axes = var_7031, keep_dims = var_6866, x = inputs_191_cast_fp16)[name = tensor<string, []>("channels_mean_191_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_191_cast_fp16 = sub(x = inputs_191_cast_fp16, y = channels_mean_191_cast_fp16)[name = tensor<string, []>("zero_mean_191_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_191_cast_fp16 = mul(x = zero_mean_191_cast_fp16, y = zero_mean_191_cast_fp16)[name = tensor<string, []>("zero_mean_sq_191_cast_fp16")];
tensor<int32, [1]> var_7035 = const()[name = tensor<string, []>("op_7035"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_7036_cast_fp16 = reduce_mean(axes = var_7035, keep_dims = var_6866, x = zero_mean_sq_191_cast_fp16)[name = tensor<string, []>("op_7036_cast_fp16")];
tensor<fp16, []> var_7037_to_fp16 = const()[name = tensor<string, []>("op_7037_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_7038_cast_fp16 = add(x = var_7036_cast_fp16, y = var_7037_to_fp16)[name = tensor<string, []>("op_7038_cast_fp16")];
tensor<fp16, []> denom_191_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_191_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_191_cast_fp16 = rsqrt(epsilon = denom_191_epsilon_0_to_fp16, x = var_7038_cast_fp16)[name = tensor<string, []>("denom_191_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_191_cast_fp16 = mul(x = zero_mean_191_cast_fp16, y = denom_191_cast_fp16)[name = tensor<string, []>("out_191_cast_fp16")];
tensor<fp16, [1280]> input_315_gamma_0_to_fp16 = const()[name = tensor<string, []>("input_315_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1786857408)))];
tensor<fp16, [1280]> input_315_beta_0_to_fp16 = const()[name = tensor<string, []>("input_315_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1786860032)))];
tensor<fp16, []> input_315_epsilon_0_to_fp16 = const()[name = tensor<string, []>("input_315_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> input_315_cast_fp16 = batch_norm(beta = input_315_beta_0_to_fp16, epsilon = input_315_epsilon_0_to_fp16, gamma = input_315_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_191_cast_fp16)[name = tensor<string, []>("input_315_cast_fp16")];
tensor<int32, [2]> var_7049 = const()[name = tensor<string, []>("op_7049"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_7051 = const()[name = tensor<string, []>("op_7051"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_317_pad_type_0 = const()[name = tensor<string, []>("input_317_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_317_pad_0 = const()[name = tensor<string, []>("input_317_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [5120, 1280, 1, 1]> layers_31_fc1_weight_to_fp16 = const()[name = tensor<string, []>("layers_31_fc1_weight_to_fp16"), val = tensor<fp16, [5120, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1786862656)))];
tensor<fp16, [5120]> layers_31_fc1_bias_to_fp16 = const()[name = tensor<string, []>("layers_31_fc1_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1799969920)))];
tensor<fp16, [1, 5120, 1, 1]> input_317_cast_fp16 = conv(bias = layers_31_fc1_bias_to_fp16, dilations = var_7051, groups = var_6865, pad = input_317_pad_0, pad_type = input_317_pad_type_0, strides = var_7049, weight = layers_31_fc1_weight_to_fp16, x = input_315_cast_fp16)[name = tensor<string, []>("input_317_cast_fp16")];
tensor<string, []> input_mode_0 = const()[name = tensor<string, []>("input_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp16, [1, 5120, 1, 1]> input_cast_fp16 = gelu(mode = input_mode_0, x = input_317_cast_fp16)[name = tensor<string, []>("input_cast_fp16")];
tensor<int32, [2]> var_7057 = const()[name = tensor<string, []>("op_7057"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_7059 = const()[name = tensor<string, []>("op_7059"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_65_pad_type_0 = const()[name = tensor<string, []>("hidden_states_65_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_65_pad_0 = const()[name = tensor<string, []>("hidden_states_65_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [1280, 5120, 1, 1]> layers_31_fc2_weight_to_fp16 = const()[name = tensor<string, []>("layers_31_fc2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1799980224)))];
tensor<fp16, [1280]> layers_31_fc2_bias_to_fp16 = const()[name = tensor<string, []>("layers_31_fc2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1813087488)))];
tensor<fp16, [1, 1280, 1, 1]> hidden_states_65_cast_fp16 = conv(bias = layers_31_fc2_bias_to_fp16, dilations = var_7059, groups = var_6865, pad = hidden_states_65_pad_0, pad_type = hidden_states_65_pad_type_0, strides = var_7057, weight = layers_31_fc2_weight_to_fp16, x = input_cast_fp16)[name = tensor<string, []>("hidden_states_65_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> inputs_cast_fp16 = add(x = inputs_191_cast_fp16, y = hidden_states_65_cast_fp16)[name = tensor<string, []>("inputs_cast_fp16")];
tensor<bool, []> var_7069 = const()[name = tensor<string, []>("op_7069"), val = tensor<bool, []>(true)];
tensor<int32, [1]> var_7073 = const()[name = tensor<string, []>("op_7073"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> channels_mean_cast_fp16 = reduce_mean(axes = var_7073, keep_dims = var_7069, x = inputs_cast_fp16)[name = tensor<string, []>("channels_mean_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_cast_fp16 = sub(x = inputs_cast_fp16, y = channels_mean_cast_fp16)[name = tensor<string, []>("zero_mean_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> zero_mean_sq_cast_fp16 = mul(x = zero_mean_cast_fp16, y = zero_mean_cast_fp16)[name = tensor<string, []>("zero_mean_sq_cast_fp16")];
tensor<int32, [1]> var_7077 = const()[name = tensor<string, []>("op_7077"), val = tensor<int32, [1]>([1])];
tensor<fp16, [1, 1, 1, 1]> var_7078_cast_fp16 = reduce_mean(axes = var_7077, keep_dims = var_7069, x = zero_mean_sq_cast_fp16)[name = tensor<string, []>("op_7078_cast_fp16")];
tensor<fp16, []> var_7079_to_fp16 = const()[name = tensor<string, []>("op_7079_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1, 1, 1]> var_7080_cast_fp16 = add(x = var_7078_cast_fp16, y = var_7079_to_fp16)[name = tensor<string, []>("op_7080_cast_fp16")];
tensor<fp16, []> denom_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
tensor<fp16, [1, 1, 1, 1]> denom_cast_fp16 = rsqrt(epsilon = denom_epsilon_0_to_fp16, x = var_7080_cast_fp16)[name = tensor<string, []>("denom_cast_fp16")];
tensor<fp16, [1, 1280, 1, 1]> out_cast_fp16 = mul(x = zero_mean_cast_fp16, y = denom_cast_fp16)[name = tensor<string, []>("out_cast_fp16")];
tensor<fp16, [1280]> hidden_states_gamma_0_to_fp16 = const()[name = tensor<string, []>("hidden_states_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1813090112)))];
tensor<fp16, [1280]> hidden_states_beta_0_to_fp16 = const()[name = tensor<string, []>("hidden_states_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1813092736)))];
tensor<fp16, []> hidden_states_epsilon_0_to_fp16 = const()[name = tensor<string, []>("hidden_states_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1280, 1, 1]> hidden_states_cast_fp16 = batch_norm(beta = hidden_states_beta_0_to_fp16, epsilon = hidden_states_epsilon_0_to_fp16, gamma = hidden_states_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_cast_fp16)[name = tensor<string, []>("hidden_states_cast_fp16")];
tensor<int32, [1]> var_7090_axes_0 = const()[name = tensor<string, []>("op_7090_axes_0"), val = tensor<int32, [1]>([2])];
tensor<fp16, [1, 1280, 1]> var_7090_cast_fp16 = squeeze(axes = var_7090_axes_0, x = hidden_states_cast_fp16)[name = tensor<string, []>("op_7090_cast_fp16")];
tensor<int32, [3]> var_7093_perm_0 = const()[name = tensor<string, []>("op_7093_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<fp16, [51865]> linear_0_bias_0_to_fp16 = const()[name = tensor<string, []>("linear_0_bias_0_to_fp16"), val = tensor<fp16, [51865]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1813095360)))];
tensor<fp16, [1, 1, 1280]> transpose_0 = transpose(perm = var_7093_perm_0, x = var_7090_cast_fp16)[name = tensor<string, []>("transpose_0")];
tensor<fp16, [1, 1, 51865]> logits = linear(bias = linear_0_bias_0_to_fp16, weight = embed_tokens_weight_to_fp16, x = transpose_0)[name = tensor<string, []>("linear_0_cast_fp16")];
tensor<int32, []> var_7097 = const()[name = tensor<string, []>("op_7097"), val = tensor<int32, []>(1)];
tensor<bool, []> obj_451_interleave_0 = const()[name = tensor<string, []>("obj_451_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 40960, 1, 1]> key_cache_updates = concat(axis = var_7097, interleave = obj_451_interleave_0, values = (current_key_1_cast_fp16, current_key_3_cast_fp16, current_key_5_cast_fp16, current_key_7_cast_fp16, current_key_9_cast_fp16, current_key_11_cast_fp16, current_key_13_cast_fp16, current_key_15_cast_fp16, current_key_17_cast_fp16, current_key_19_cast_fp16, current_key_21_cast_fp16, current_key_23_cast_fp16, current_key_25_cast_fp16, current_key_27_cast_fp16, current_key_29_cast_fp16, current_key_31_cast_fp16, current_key_33_cast_fp16, current_key_35_cast_fp16, current_key_37_cast_fp16, current_key_39_cast_fp16, current_key_41_cast_fp16, current_key_43_cast_fp16, current_key_45_cast_fp16, current_key_47_cast_fp16, current_key_49_cast_fp16, current_key_51_cast_fp16, current_key_53_cast_fp16, current_key_55_cast_fp16, current_key_57_cast_fp16, current_key_59_cast_fp16, current_key_61_cast_fp16, current_key_cast_fp16))[name = tensor<string, []>("obj_451_cast_fp16")];
tensor<int32, []> var_7100 = const()[name = tensor<string, []>("op_7100"), val = tensor<int32, []>(1)];
tensor<bool, []> obj_453_interleave_0 = const()[name = tensor<string, []>("obj_453_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 40960, 1, 1]> value_cache_updates = concat(axis = var_7100, interleave = obj_453_interleave_0, values = (current_value_1_cast_fp16, current_value_3_cast_fp16, current_value_5_cast_fp16, current_value_7_cast_fp16, current_value_9_cast_fp16, current_value_11_cast_fp16, current_value_13_cast_fp16, current_value_15_cast_fp16, current_value_17_cast_fp16, current_value_19_cast_fp16, current_value_21_cast_fp16, current_value_23_cast_fp16, current_value_25_cast_fp16, current_value_27_cast_fp16, current_value_29_cast_fp16, current_value_31_cast_fp16, current_value_33_cast_fp16, current_value_35_cast_fp16, current_value_37_cast_fp16, current_value_39_cast_fp16, current_value_41_cast_fp16, current_value_43_cast_fp16, current_value_45_cast_fp16, current_value_47_cast_fp16, current_value_49_cast_fp16, current_value_51_cast_fp16, current_value_53_cast_fp16, current_value_55_cast_fp16, current_value_57_cast_fp16, current_value_59_cast_fp16, current_value_61_cast_fp16, current_value_cast_fp16))[name = tensor<string, []>("obj_453_cast_fp16")];
tensor<int32, [4]> var_7111_begin_0 = const()[name = tensor<string, []>("op_7111_begin_0"), val = tensor<int32, [4]>([0, 12, 0, 0])];
tensor<int32, [4]> var_7111_end_0 = const()[name = tensor<string, []>("op_7111_end_0"), val = tensor<int32, [4]>([1, 13, 1, 1500])];
tensor<bool, [4]> var_7111_end_mask_0 = const()[name = tensor<string, []>("op_7111_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
tensor<fp16, [1, 1, 1, 1500]> var_7111_cast_fp16 = slice_by_index(begin = var_7111_begin_0, end = var_7111_end_0, end_mask = var_7111_end_mask_0, x = obj_153_cast_fp16)[name = tensor<string, []>("op_7111_cast_fp16")];
tensor<int32, [4]> var_7114_begin_0 = const()[name = tensor<string, []>("op_7114_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_7114_end_0 = const()[name = tensor<string, []>("op_7114_end_0"), val = tensor<int32, [4]>([1, 1, 1, 1500])];
tensor<bool, [4]> var_7114_end_mask_0 = const()[name = tensor<string, []>("op_7114_end_mask_0"), val = tensor<bool, [4]>([true, true, false, true])];
tensor<bool, [4]> var_7114_squeeze_mask_0 = const()[name = tensor<string, []>("op_7114_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, true, false])];
tensor<fp16, [1, 1, 1500]> var_7114_cast_fp16 = slice_by_index(begin = var_7114_begin_0, end = var_7114_end_0, end_mask = var_7114_end_mask_0, squeeze_mask = var_7114_squeeze_mask_0, x = var_7111_cast_fp16)[name = tensor<string, []>("op_7114_cast_fp16")];
tensor<int32, [4]> var_7129_begin_0 = const()[name = tensor<string, []>("op_7129_begin_0"), val = tensor<int32, [4]>([0, 17, 0, 0])];
tensor<int32, [4]> var_7129_end_0 = const()[name = tensor<string, []>("op_7129_end_0"), val = tensor<int32, [4]>([1, 18, 1, 1500])];
tensor<bool, [4]> var_7129_end_mask_0 = const()[name = tensor<string, []>("op_7129_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
tensor<fp16, [1, 1, 1, 1500]> var_7129_cast_fp16 = slice_by_index(begin = var_7129_begin_0, end = var_7129_end_0, end_mask = var_7129_end_mask_0, x = obj_195_cast_fp16)[name = tensor<string, []>("op_7129_cast_fp16")];
tensor<int32, [4]> var_7132_begin_0 = const()[name = tensor<string, []>("op_7132_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_7132_end_0 = const()[name = tensor<string, []>("op_7132_end_0"), val = tensor<int32, [4]>([1, 1, 1, 1500])];
tensor<bool, [4]> var_7132_end_mask_0 = const()[name = tensor<string, []>("op_7132_end_mask_0"), val = tensor<bool, [4]>([true, true, false, true])];
tensor<bool, [4]> var_7132_squeeze_mask_0 = const()[name = tensor<string, []>("op_7132_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, true, false])];
tensor<fp16, [1, 1, 1500]> var_7132_cast_fp16 = slice_by_index(begin = var_7132_begin_0, end = var_7132_end_0, end_mask = var_7132_end_mask_0, squeeze_mask = var_7132_squeeze_mask_0, x = var_7129_cast_fp16)[name = tensor<string, []>("op_7132_cast_fp16")];
tensor<int32, [4]> var_7147_begin_0 = const()[name = tensor<string, []>("op_7147_begin_0"), val = tensor<int32, [4]>([0, 11, 0, 0])];
tensor<int32, [4]> var_7147_end_0 = const()[name = tensor<string, []>("op_7147_end_0"), val = tensor<int32, [4]>([1, 12, 1, 1500])];
tensor<bool, [4]> var_7147_end_mask_0 = const()[name = tensor<string, []>("op_7147_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
tensor<fp16, [1, 1, 1, 1500]> var_7147_cast_fp16 = slice_by_index(begin = var_7147_begin_0, end = var_7147_end_0, end_mask = var_7147_end_mask_0, x = obj_237_cast_fp16)[name = tensor<string, []>("op_7147_cast_fp16")];
tensor<int32, [4]> var_7150_begin_0 = const()[name = tensor<string, []>("op_7150_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_7150_end_0 = const()[name = tensor<string, []>("op_7150_end_0"), val = tensor<int32, [4]>([1, 1, 1, 1500])];
tensor<bool, [4]> var_7150_end_mask_0 = const()[name = tensor<string, []>("op_7150_end_mask_0"), val = tensor<bool, [4]>([true, true, false, true])];
tensor<bool, [4]> var_7150_squeeze_mask_0 = const()[name = tensor<string, []>("op_7150_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, true, false])];
tensor<fp16, [1, 1, 1500]> var_7150_cast_fp16 = slice_by_index(begin = var_7150_begin_0, end = var_7150_end_0, end_mask = var_7150_end_mask_0, squeeze_mask = var_7150_squeeze_mask_0, x = var_7147_cast_fp16)[name = tensor<string, []>("op_7150_cast_fp16")];
tensor<int32, [4]> var_7165_begin_0 = const()[name = tensor<string, []>("op_7165_begin_0"), val = tensor<int32, [4]>([0, 12, 0, 0])];
tensor<int32, [4]> var_7165_end_0 = const()[name = tensor<string, []>("op_7165_end_0"), val = tensor<int32, [4]>([1, 13, 1, 1500])];
tensor<bool, [4]> var_7165_end_mask_0 = const()[name = tensor<string, []>("op_7165_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
tensor<fp16, [1, 1, 1, 1500]> var_7165_cast_fp16 = slice_by_index(begin = var_7165_begin_0, end = var_7165_end_0, end_mask = var_7165_end_mask_0, x = obj_237_cast_fp16)[name = tensor<string, []>("op_7165_cast_fp16")];
tensor<int32, [4]> var_7168_begin_0 = const()[name = tensor<string, []>("op_7168_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_7168_end_0 = const()[name = tensor<string, []>("op_7168_end_0"), val = tensor<int32, [4]>([1, 1, 1, 1500])];
tensor<bool, [4]> var_7168_end_mask_0 = const()[name = tensor<string, []>("op_7168_end_mask_0"), val = tensor<bool, [4]>([true, true, false, true])];
tensor<bool, [4]> var_7168_squeeze_mask_0 = const()[name = tensor<string, []>("op_7168_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, true, false])];
tensor<fp16, [1, 1, 1500]> var_7168_cast_fp16 = slice_by_index(begin = var_7168_begin_0, end = var_7168_end_0, end_mask = var_7168_end_mask_0, squeeze_mask = var_7168_squeeze_mask_0, x = var_7165_cast_fp16)[name = tensor<string, []>("op_7168_cast_fp16")];
tensor<int32, [4]> var_7183_begin_0 = const()[name = tensor<string, []>("op_7183_begin_0"), val = tensor<int32, [4]>([0, 13, 0, 0])];
tensor<int32, [4]> var_7183_end_0 = const()[name = tensor<string, []>("op_7183_end_0"), val = tensor<int32, [4]>([1, 14, 1, 1500])];
tensor<bool, [4]> var_7183_end_mask_0 = const()[name = tensor<string, []>("op_7183_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
tensor<fp16, [1, 1, 1, 1500]> var_7183_cast_fp16 = slice_by_index(begin = var_7183_begin_0, end = var_7183_end_0, end_mask = var_7183_end_mask_0, x = obj_237_cast_fp16)[name = tensor<string, []>("op_7183_cast_fp16")];
tensor<int32, [4]> var_7186_begin_0 = const()[name = tensor<string, []>("op_7186_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_7186_end_0 = const()[name = tensor<string, []>("op_7186_end_0"), val = tensor<int32, [4]>([1, 1, 1, 1500])];
tensor<bool, [4]> var_7186_end_mask_0 = const()[name = tensor<string, []>("op_7186_end_mask_0"), val = tensor<bool, [4]>([true, true, false, true])];
tensor<bool, [4]> var_7186_squeeze_mask_0 = const()[name = tensor<string, []>("op_7186_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, true, false])];
tensor<fp16, [1, 1, 1500]> var_7186_cast_fp16 = slice_by_index(begin = var_7186_begin_0, end = var_7186_end_0, end_mask = var_7186_end_mask_0, squeeze_mask = var_7186_squeeze_mask_0, x = var_7183_cast_fp16)[name = tensor<string, []>("op_7186_cast_fp16")];
tensor<int32, [4]> var_7201_begin_0 = const()[name = tensor<string, []>("op_7201_begin_0"), val = tensor<int32, [4]>([0, 15, 0, 0])];
tensor<int32, [4]> var_7201_end_0 = const()[name = tensor<string, []>("op_7201_end_0"), val = tensor<int32, [4]>([1, 16, 1, 1500])];
tensor<bool, [4]> var_7201_end_mask_0 = const()[name = tensor<string, []>("op_7201_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
tensor<fp16, [1, 1, 1, 1500]> var_7201_cast_fp16 = slice_by_index(begin = var_7201_begin_0, end = var_7201_end_0, end_mask = var_7201_end_mask_0, x = obj_251_cast_fp16)[name = tensor<string, []>("op_7201_cast_fp16")];
tensor<int32, [4]> var_7204_begin_0 = const()[name = tensor<string, []>("op_7204_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_7204_end_0 = const()[name = tensor<string, []>("op_7204_end_0"), val = tensor<int32, [4]>([1, 1, 1, 1500])];
tensor<bool, [4]> var_7204_end_mask_0 = const()[name = tensor<string, []>("op_7204_end_mask_0"), val = tensor<bool, [4]>([true, true, false, true])];
tensor<bool, [4]> var_7204_squeeze_mask_0 = const()[name = tensor<string, []>("op_7204_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, true, false])];
tensor<fp16, [1, 1, 1500]> var_7204_cast_fp16 = slice_by_index(begin = var_7204_begin_0, end = var_7204_end_0, end_mask = var_7204_end_mask_0, squeeze_mask = var_7204_squeeze_mask_0, x = var_7201_cast_fp16)[name = tensor<string, []>("op_7204_cast_fp16")];
tensor<int32, [4]> var_7219_begin_0 = const()[name = tensor<string, []>("op_7219_begin_0"), val = tensor<int32, [4]>([0, 16, 0, 0])];
tensor<int32, [4]> var_7219_end_0 = const()[name = tensor<string, []>("op_7219_end_0"), val = tensor<int32, [4]>([1, 17, 1, 1500])];
tensor<bool, [4]> var_7219_end_mask_0 = const()[name = tensor<string, []>("op_7219_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
tensor<fp16, [1, 1, 1, 1500]> var_7219_cast_fp16 = slice_by_index(begin = var_7219_begin_0, end = var_7219_end_0, end_mask = var_7219_end_mask_0, x = obj_251_cast_fp16)[name = tensor<string, []>("op_7219_cast_fp16")];
tensor<int32, [4]> var_7222_begin_0 = const()[name = tensor<string, []>("op_7222_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_7222_end_0 = const()[name = tensor<string, []>("op_7222_end_0"), val = tensor<int32, [4]>([1, 1, 1, 1500])];
tensor<bool, [4]> var_7222_end_mask_0 = const()[name = tensor<string, []>("op_7222_end_mask_0"), val = tensor<bool, [4]>([true, true, false, true])];
tensor<bool, [4]> var_7222_squeeze_mask_0 = const()[name = tensor<string, []>("op_7222_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, true, false])];
tensor<fp16, [1, 1, 1500]> var_7222_cast_fp16 = slice_by_index(begin = var_7222_begin_0, end = var_7222_end_0, end_mask = var_7222_end_mask_0, squeeze_mask = var_7222_squeeze_mask_0, x = var_7219_cast_fp16)[name = tensor<string, []>("op_7222_cast_fp16")];
tensor<int32, [4]> var_7237_begin_0 = const()[name = tensor<string, []>("op_7237_begin_0"), val = tensor<int32, [4]>([0, 4, 0, 0])];
tensor<int32, [4]> var_7237_end_0 = const()[name = tensor<string, []>("op_7237_end_0"), val = tensor<int32, [4]>([1, 5, 1, 1500])];
tensor<bool, [4]> var_7237_end_mask_0 = const()[name = tensor<string, []>("op_7237_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
tensor<fp16, [1, 1, 1, 1500]> var_7237_cast_fp16 = slice_by_index(begin = var_7237_begin_0, end = var_7237_end_0, end_mask = var_7237_end_mask_0, x = obj_265_cast_fp16)[name = tensor<string, []>("op_7237_cast_fp16")];
tensor<int32, [4]> var_7240_begin_0 = const()[name = tensor<string, []>("op_7240_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_7240_end_0 = const()[name = tensor<string, []>("op_7240_end_0"), val = tensor<int32, [4]>([1, 1, 1, 1500])];
tensor<bool, [4]> var_7240_end_mask_0 = const()[name = tensor<string, []>("op_7240_end_mask_0"), val = tensor<bool, [4]>([true, true, false, true])];
tensor<bool, [4]> var_7240_squeeze_mask_0 = const()[name = tensor<string, []>("op_7240_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, true, false])];
tensor<fp16, [1, 1, 1500]> var_7240_cast_fp16 = slice_by_index(begin = var_7240_begin_0, end = var_7240_end_0, end_mask = var_7240_end_mask_0, squeeze_mask = var_7240_squeeze_mask_0, x = var_7237_cast_fp16)[name = tensor<string, []>("op_7240_cast_fp16")];
tensor<int32, [4]> var_7255_begin_0 = const()[name = tensor<string, []>("op_7255_begin_0"), val = tensor<int32, [4]>([0, 11, 0, 0])];
tensor<int32, [4]> var_7255_end_0 = const()[name = tensor<string, []>("op_7255_end_0"), val = tensor<int32, [4]>([1, 12, 1, 1500])];
tensor<bool, [4]> var_7255_end_mask_0 = const()[name = tensor<string, []>("op_7255_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
tensor<fp16, [1, 1, 1, 1500]> var_7255_cast_fp16 = slice_by_index(begin = var_7255_begin_0, end = var_7255_end_0, end_mask = var_7255_end_mask_0, x = obj_265_cast_fp16)[name = tensor<string, []>("op_7255_cast_fp16")];
tensor<int32, [4]> var_7258_begin_0 = const()[name = tensor<string, []>("op_7258_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_7258_end_0 = const()[name = tensor<string, []>("op_7258_end_0"), val = tensor<int32, [4]>([1, 1, 1, 1500])];
tensor<bool, [4]> var_7258_end_mask_0 = const()[name = tensor<string, []>("op_7258_end_mask_0"), val = tensor<bool, [4]>([true, true, false, true])];
tensor<bool, [4]> var_7258_squeeze_mask_0 = const()[name = tensor<string, []>("op_7258_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, true, false])];
tensor<fp16, [1, 1, 1500]> var_7258_cast_fp16 = slice_by_index(begin = var_7258_begin_0, end = var_7258_end_0, end_mask = var_7258_end_mask_0, squeeze_mask = var_7258_squeeze_mask_0, x = var_7255_cast_fp16)[name = tensor<string, []>("op_7258_cast_fp16")];
tensor<int32, [4]> var_7273_begin_0 = const()[name = tensor<string, []>("op_7273_begin_0"), val = tensor<int32, [4]>([0, 19, 0, 0])];
tensor<int32, [4]> var_7273_end_0 = const()[name = tensor<string, []>("op_7273_end_0"), val = tensor<int32, [4]>([1, 20, 1, 1500])];
tensor<bool, [4]> var_7273_end_mask_0 = const()[name = tensor<string, []>("op_7273_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
tensor<fp16, [1, 1, 1, 1500]> var_7273_cast_fp16 = slice_by_index(begin = var_7273_begin_0, end = var_7273_end_0, end_mask = var_7273_end_mask_0, x = obj_265_cast_fp16)[name = tensor<string, []>("op_7273_cast_fp16")];
tensor<int32, [4]> var_7276_begin_0 = const()[name = tensor<string, []>("op_7276_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_7276_end_0 = const()[name = tensor<string, []>("op_7276_end_0"), val = tensor<int32, [4]>([1, 1, 1, 1500])];
tensor<bool, [4]> var_7276_end_mask_0 = const()[name = tensor<string, []>("op_7276_end_mask_0"), val = tensor<bool, [4]>([true, true, false, true])];
tensor<bool, [4]> var_7276_squeeze_mask_0 = const()[name = tensor<string, []>("op_7276_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, true, false])];
tensor<fp16, [1, 1, 1500]> var_7276_cast_fp16 = slice_by_index(begin = var_7276_begin_0, end = var_7276_end_0, end_mask = var_7276_end_mask_0, squeeze_mask = var_7276_squeeze_mask_0, x = var_7273_cast_fp16)[name = tensor<string, []>("op_7276_cast_fp16")];
tensor<int32, [4]> var_7291_begin_0 = const()[name = tensor<string, []>("op_7291_begin_0"), val = tensor<int32, [4]>([0, 11, 0, 0])];
tensor<int32, [4]> var_7291_end_0 = const()[name = tensor<string, []>("op_7291_end_0"), val = tensor<int32, [4]>([1, 12, 1, 1500])];
tensor<bool, [4]> var_7291_end_mask_0 = const()[name = tensor<string, []>("op_7291_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
tensor<fp16, [1, 1, 1, 1500]> var_7291_cast_fp16 = slice_by_index(begin = var_7291_begin_0, end = var_7291_end_0, end_mask = var_7291_end_mask_0, x = obj_279_cast_fp16)[name = tensor<string, []>("op_7291_cast_fp16")];
tensor<int32, [4]> var_7294_begin_0 = const()[name = tensor<string, []>("op_7294_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_7294_end_0 = const()[name = tensor<string, []>("op_7294_end_0"), val = tensor<int32, [4]>([1, 1, 1, 1500])];
tensor<bool, [4]> var_7294_end_mask_0 = const()[name = tensor<string, []>("op_7294_end_mask_0"), val = tensor<bool, [4]>([true, true, false, true])];
tensor<bool, [4]> var_7294_squeeze_mask_0 = const()[name = tensor<string, []>("op_7294_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, true, false])];
tensor<fp16, [1, 1, 1500]> var_7294_cast_fp16 = slice_by_index(begin = var_7294_begin_0, end = var_7294_end_0, end_mask = var_7294_end_mask_0, squeeze_mask = var_7294_squeeze_mask_0, x = var_7291_cast_fp16)[name = tensor<string, []>("op_7294_cast_fp16")];
tensor<int32, [4]> var_7309_begin_0 = const()[name = tensor<string, []>("op_7309_begin_0"), val = tensor<int32, [4]>([0, 2, 0, 0])];
tensor<int32, [4]> var_7309_end_0 = const()[name = tensor<string, []>("op_7309_end_0"), val = tensor<int32, [4]>([1, 3, 1, 1500])];
tensor<bool, [4]> var_7309_end_mask_0 = const()[name = tensor<string, []>("op_7309_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
tensor<fp16, [1, 1, 1, 1500]> var_7309_cast_fp16 = slice_by_index(begin = var_7309_begin_0, end = var_7309_end_0, end_mask = var_7309_end_mask_0, x = obj_307_cast_fp16)[name = tensor<string, []>("op_7309_cast_fp16")];
tensor<int32, [4]> var_7312_begin_0 = const()[name = tensor<string, []>("op_7312_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_7312_end_0 = const()[name = tensor<string, []>("op_7312_end_0"), val = tensor<int32, [4]>([1, 1, 1, 1500])];
tensor<bool, [4]> var_7312_end_mask_0 = const()[name = tensor<string, []>("op_7312_end_mask_0"), val = tensor<bool, [4]>([true, true, false, true])];
tensor<bool, [4]> var_7312_squeeze_mask_0 = const()[name = tensor<string, []>("op_7312_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, true, false])];
tensor<fp16, [1, 1, 1500]> var_7312_cast_fp16 = slice_by_index(begin = var_7312_begin_0, end = var_7312_end_0, end_mask = var_7312_end_mask_0, squeeze_mask = var_7312_squeeze_mask_0, x = var_7309_cast_fp16)[name = tensor<string, []>("op_7312_cast_fp16")];
tensor<int32, [4]> var_7327_begin_0 = const()[name = tensor<string, []>("op_7327_begin_0"), val = tensor<int32, [4]>([0, 3, 0, 0])];
tensor<int32, [4]> var_7327_end_0 = const()[name = tensor<string, []>("op_7327_end_0"), val = tensor<int32, [4]>([1, 4, 1, 1500])];
tensor<bool, [4]> var_7327_end_mask_0 = const()[name = tensor<string, []>("op_7327_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
tensor<fp16, [1, 1, 1, 1500]> var_7327_cast_fp16 = slice_by_index(begin = var_7327_begin_0, end = var_7327_end_0, end_mask = var_7327_end_mask_0, x = obj_307_cast_fp16)[name = tensor<string, []>("op_7327_cast_fp16")];
tensor<int32, [4]> var_7330_begin_0 = const()[name = tensor<string, []>("op_7330_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_7330_end_0 = const()[name = tensor<string, []>("op_7330_end_0"), val = tensor<int32, [4]>([1, 1, 1, 1500])];
tensor<bool, [4]> var_7330_end_mask_0 = const()[name = tensor<string, []>("op_7330_end_mask_0"), val = tensor<bool, [4]>([true, true, false, true])];
tensor<bool, [4]> var_7330_squeeze_mask_0 = const()[name = tensor<string, []>("op_7330_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, true, false])];
tensor<fp16, [1, 1, 1500]> var_7330_cast_fp16 = slice_by_index(begin = var_7330_begin_0, end = var_7330_end_0, end_mask = var_7330_end_mask_0, squeeze_mask = var_7330_squeeze_mask_0, x = var_7327_cast_fp16)[name = tensor<string, []>("op_7330_cast_fp16")];
tensor<int32, [4]> var_7345_begin_0 = const()[name = tensor<string, []>("op_7345_begin_0"), val = tensor<int32, [4]>([0, 3, 0, 0])];
tensor<int32, [4]> var_7345_end_0 = const()[name = tensor<string, []>("op_7345_end_0"), val = tensor<int32, [4]>([1, 4, 1, 1500])];
tensor<bool, [4]> var_7345_end_mask_0 = const()[name = tensor<string, []>("op_7345_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
tensor<fp16, [1, 1, 1, 1500]> var_7345_cast_fp16 = slice_by_index(begin = var_7345_begin_0, end = var_7345_end_0, end_mask = var_7345_end_mask_0, x = obj_321_cast_fp16)[name = tensor<string, []>("op_7345_cast_fp16")];
tensor<int32, [4]> var_7348_begin_0 = const()[name = tensor<string, []>("op_7348_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_7348_end_0 = const()[name = tensor<string, []>("op_7348_end_0"), val = tensor<int32, [4]>([1, 1, 1, 1500])];
tensor<bool, [4]> var_7348_end_mask_0 = const()[name = tensor<string, []>("op_7348_end_mask_0"), val = tensor<bool, [4]>([true, true, false, true])];
tensor<bool, [4]> var_7348_squeeze_mask_0 = const()[name = tensor<string, []>("op_7348_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, true, false])];
tensor<fp16, [1, 1, 1500]> var_7348_cast_fp16 = slice_by_index(begin = var_7348_begin_0, end = var_7348_end_0, end_mask = var_7348_end_mask_0, squeeze_mask = var_7348_squeeze_mask_0, x = var_7345_cast_fp16)[name = tensor<string, []>("op_7348_cast_fp16")];
tensor<int32, [4]> var_7363_begin_0 = const()[name = tensor<string, []>("op_7363_begin_0"), val = tensor<int32, [4]>([0, 9, 0, 0])];
tensor<int32, [4]> var_7363_end_0 = const()[name = tensor<string, []>("op_7363_end_0"), val = tensor<int32, [4]>([1, 10, 1, 1500])];
tensor<bool, [4]> var_7363_end_mask_0 = const()[name = tensor<string, []>("op_7363_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
tensor<fp16, [1, 1, 1, 1500]> var_7363_cast_fp16 = slice_by_index(begin = var_7363_begin_0, end = var_7363_end_0, end_mask = var_7363_end_mask_0, x = obj_321_cast_fp16)[name = tensor<string, []>("op_7363_cast_fp16")];
tensor<int32, [4]> var_7366_begin_0 = const()[name = tensor<string, []>("op_7366_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_7366_end_0 = const()[name = tensor<string, []>("op_7366_end_0"), val = tensor<int32, [4]>([1, 1, 1, 1500])];
tensor<bool, [4]> var_7366_end_mask_0 = const()[name = tensor<string, []>("op_7366_end_mask_0"), val = tensor<bool, [4]>([true, true, false, true])];
tensor<bool, [4]> var_7366_squeeze_mask_0 = const()[name = tensor<string, []>("op_7366_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, true, false])];
tensor<fp16, [1, 1, 1500]> var_7366_cast_fp16 = slice_by_index(begin = var_7366_begin_0, end = var_7366_end_0, end_mask = var_7366_end_mask_0, squeeze_mask = var_7366_squeeze_mask_0, x = var_7363_cast_fp16)[name = tensor<string, []>("op_7366_cast_fp16")];
tensor<int32, [4]> var_7381_begin_0 = const()[name = tensor<string, []>("op_7381_begin_0"), val = tensor<int32, [4]>([0, 12, 0, 0])];
tensor<int32, [4]> var_7381_end_0 = const()[name = tensor<string, []>("op_7381_end_0"), val = tensor<int32, [4]>([1, 13, 1, 1500])];
tensor<bool, [4]> var_7381_end_mask_0 = const()[name = tensor<string, []>("op_7381_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
tensor<fp16, [1, 1, 1, 1500]> var_7381_cast_fp16 = slice_by_index(begin = var_7381_begin_0, end = var_7381_end_0, end_mask = var_7381_end_mask_0, x = obj_321_cast_fp16)[name = tensor<string, []>("op_7381_cast_fp16")];
tensor<int32, [4]> var_7384_begin_0 = const()[name = tensor<string, []>("op_7384_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_7384_end_0 = const()[name = tensor<string, []>("op_7384_end_0"), val = tensor<int32, [4]>([1, 1, 1, 1500])];
tensor<bool, [4]> var_7384_end_mask_0 = const()[name = tensor<string, []>("op_7384_end_mask_0"), val = tensor<bool, [4]>([true, true, false, true])];
tensor<bool, [4]> var_7384_squeeze_mask_0 = const()[name = tensor<string, []>("op_7384_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, true, false])];
tensor<fp16, [1, 1, 1500]> var_7384_cast_fp16 = slice_by_index(begin = var_7384_begin_0, end = var_7384_end_0, end_mask = var_7384_end_mask_0, squeeze_mask = var_7384_squeeze_mask_0, x = var_7381_cast_fp16)[name = tensor<string, []>("op_7384_cast_fp16")];
tensor<int32, [4]> var_7399_begin_0 = const()[name = tensor<string, []>("op_7399_begin_0"), val = tensor<int32, [4]>([0, 5, 0, 0])];
tensor<int32, [4]> var_7399_end_0 = const()[name = tensor<string, []>("op_7399_end_0"), val = tensor<int32, [4]>([1, 6, 1, 1500])];
tensor<bool, [4]> var_7399_end_mask_0 = const()[name = tensor<string, []>("op_7399_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
tensor<fp16, [1, 1, 1, 1500]> var_7399_cast_fp16 = slice_by_index(begin = var_7399_begin_0, end = var_7399_end_0, end_mask = var_7399_end_mask_0, x = obj_335_cast_fp16)[name = tensor<string, []>("op_7399_cast_fp16")];
tensor<int32, [4]> var_7402_begin_0 = const()[name = tensor<string, []>("op_7402_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_7402_end_0 = const()[name = tensor<string, []>("op_7402_end_0"), val = tensor<int32, [4]>([1, 1, 1, 1500])];
tensor<bool, [4]> var_7402_end_mask_0 = const()[name = tensor<string, []>("op_7402_end_mask_0"), val = tensor<bool, [4]>([true, true, false, true])];
tensor<bool, [4]> var_7402_squeeze_mask_0 = const()[name = tensor<string, []>("op_7402_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, true, false])];
tensor<fp16, [1, 1, 1500]> var_7402_cast_fp16 = slice_by_index(begin = var_7402_begin_0, end = var_7402_end_0, end_mask = var_7402_end_mask_0, squeeze_mask = var_7402_squeeze_mask_0, x = var_7399_cast_fp16)[name = tensor<string, []>("op_7402_cast_fp16")];
tensor<int32, [4]> var_7417_begin_0 = const()[name = tensor<string, []>("op_7417_begin_0"), val = tensor<int32, [4]>([0, 7, 0, 0])];
tensor<int32, [4]> var_7417_end_0 = const()[name = tensor<string, []>("op_7417_end_0"), val = tensor<int32, [4]>([1, 8, 1, 1500])];
tensor<bool, [4]> var_7417_end_mask_0 = const()[name = tensor<string, []>("op_7417_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
tensor<fp16, [1, 1, 1, 1500]> var_7417_cast_fp16 = slice_by_index(begin = var_7417_begin_0, end = var_7417_end_0, end_mask = var_7417_end_mask_0, x = obj_335_cast_fp16)[name = tensor<string, []>("op_7417_cast_fp16")];
tensor<int32, [4]> var_7420_begin_0 = const()[name = tensor<string, []>("op_7420_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_7420_end_0 = const()[name = tensor<string, []>("op_7420_end_0"), val = tensor<int32, [4]>([1, 1, 1, 1500])];
tensor<bool, [4]> var_7420_end_mask_0 = const()[name = tensor<string, []>("op_7420_end_mask_0"), val = tensor<bool, [4]>([true, true, false, true])];
tensor<bool, [4]> var_7420_squeeze_mask_0 = const()[name = tensor<string, []>("op_7420_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, true, false])];
tensor<fp16, [1, 1, 1500]> var_7420_cast_fp16 = slice_by_index(begin = var_7420_begin_0, end = var_7420_end_0, end_mask = var_7420_end_mask_0, squeeze_mask = var_7420_squeeze_mask_0, x = var_7417_cast_fp16)[name = tensor<string, []>("op_7420_cast_fp16")];
tensor<int32, [4]> var_7435_begin_0 = const()[name = tensor<string, []>("op_7435_begin_0"), val = tensor<int32, [4]>([0, 13, 0, 0])];
tensor<int32, [4]> var_7435_end_0 = const()[name = tensor<string, []>("op_7435_end_0"), val = tensor<int32, [4]>([1, 14, 1, 1500])];
tensor<bool, [4]> var_7435_end_mask_0 = const()[name = tensor<string, []>("op_7435_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
tensor<fp16, [1, 1, 1, 1500]> var_7435_cast_fp16 = slice_by_index(begin = var_7435_begin_0, end = var_7435_end_0, end_mask = var_7435_end_mask_0, x = obj_335_cast_fp16)[name = tensor<string, []>("op_7435_cast_fp16")];
tensor<int32, [4]> var_7438_begin_0 = const()[name = tensor<string, []>("op_7438_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_7438_end_0 = const()[name = tensor<string, []>("op_7438_end_0"), val = tensor<int32, [4]>([1, 1, 1, 1500])];
tensor<bool, [4]> var_7438_end_mask_0 = const()[name = tensor<string, []>("op_7438_end_mask_0"), val = tensor<bool, [4]>([true, true, false, true])];
tensor<bool, [4]> var_7438_squeeze_mask_0 = const()[name = tensor<string, []>("op_7438_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, true, false])];
tensor<fp16, [1, 1, 1500]> var_7438_cast_fp16 = slice_by_index(begin = var_7438_begin_0, end = var_7438_end_0, end_mask = var_7438_end_mask_0, squeeze_mask = var_7438_squeeze_mask_0, x = var_7435_cast_fp16)[name = tensor<string, []>("op_7438_cast_fp16")];
tensor<int32, [4]> var_7453_begin_0 = const()[name = tensor<string, []>("op_7453_begin_0"), val = tensor<int32, [4]>([0, 5, 0, 0])];
tensor<int32, [4]> var_7453_end_0 = const()[name = tensor<string, []>("op_7453_end_0"), val = tensor<int32, [4]>([1, 6, 1, 1500])];
tensor<bool, [4]> var_7453_end_mask_0 = const()[name = tensor<string, []>("op_7453_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
tensor<fp16, [1, 1, 1, 1500]> var_7453_cast_fp16 = slice_by_index(begin = var_7453_begin_0, end = var_7453_end_0, end_mask = var_7453_end_mask_0, x = obj_363_cast_fp16)[name = tensor<string, []>("op_7453_cast_fp16")];
tensor<int32, [4]> var_7456_begin_0 = const()[name = tensor<string, []>("op_7456_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_7456_end_0 = const()[name = tensor<string, []>("op_7456_end_0"), val = tensor<int32, [4]>([1, 1, 1, 1500])];
tensor<bool, [4]> var_7456_end_mask_0 = const()[name = tensor<string, []>("op_7456_end_mask_0"), val = tensor<bool, [4]>([true, true, false, true])];
tensor<bool, [4]> var_7456_squeeze_mask_0 = const()[name = tensor<string, []>("op_7456_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, true, false])];
tensor<fp16, [1, 1, 1500]> var_7456_cast_fp16 = slice_by_index(begin = var_7456_begin_0, end = var_7456_end_0, end_mask = var_7456_end_mask_0, squeeze_mask = var_7456_squeeze_mask_0, x = var_7453_cast_fp16)[name = tensor<string, []>("op_7456_cast_fp16")];
tensor<int32, [4]> var_7471_begin_0 = const()[name = tensor<string, []>("op_7471_begin_0"), val = tensor<int32, [4]>([0, 1, 0, 0])];
tensor<int32, [4]> var_7471_end_0 = const()[name = tensor<string, []>("op_7471_end_0"), val = tensor<int32, [4]>([1, 2, 1, 1500])];
tensor<bool, [4]> var_7471_end_mask_0 = const()[name = tensor<string, []>("op_7471_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
tensor<fp16, [1, 1, 1, 1500]> var_7471_cast_fp16 = slice_by_index(begin = var_7471_begin_0, end = var_7471_end_0, end_mask = var_7471_end_mask_0, x = obj_377_cast_fp16)[name = tensor<string, []>("op_7471_cast_fp16")];
tensor<int32, [4]> var_7474_begin_0 = const()[name = tensor<string, []>("op_7474_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_7474_end_0 = const()[name = tensor<string, []>("op_7474_end_0"), val = tensor<int32, [4]>([1, 1, 1, 1500])];
tensor<bool, [4]> var_7474_end_mask_0 = const()[name = tensor<string, []>("op_7474_end_mask_0"), val = tensor<bool, [4]>([true, true, false, true])];
tensor<bool, [4]> var_7474_squeeze_mask_0 = const()[name = tensor<string, []>("op_7474_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, true, false])];
tensor<fp16, [1, 1, 1500]> var_7474_cast_fp16 = slice_by_index(begin = var_7474_begin_0, end = var_7474_end_0, end_mask = var_7474_end_mask_0, squeeze_mask = var_7474_squeeze_mask_0, x = var_7471_cast_fp16)[name = tensor<string, []>("op_7474_cast_fp16")];
tensor<int32, [4]> var_7489_begin_0 = const()[name = tensor<string, []>("op_7489_begin_0"), val = tensor<int32, [4]>([0, 12, 0, 0])];
tensor<int32, [4]> var_7489_end_0 = const()[name = tensor<string, []>("op_7489_end_0"), val = tensor<int32, [4]>([1, 13, 1, 1500])];
tensor<bool, [4]> var_7489_end_mask_0 = const()[name = tensor<string, []>("op_7489_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
tensor<fp16, [1, 1, 1, 1500]> var_7489_cast_fp16 = slice_by_index(begin = var_7489_begin_0, end = var_7489_end_0, end_mask = var_7489_end_mask_0, x = obj_377_cast_fp16)[name = tensor<string, []>("op_7489_cast_fp16")];
tensor<int32, [4]> var_7492_begin_0 = const()[name = tensor<string, []>("op_7492_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_7492_end_0 = const()[name = tensor<string, []>("op_7492_end_0"), val = tensor<int32, [4]>([1, 1, 1, 1500])];
tensor<bool, [4]> var_7492_end_mask_0 = const()[name = tensor<string, []>("op_7492_end_mask_0"), val = tensor<bool, [4]>([true, true, false, true])];
tensor<bool, [4]> var_7492_squeeze_mask_0 = const()[name = tensor<string, []>("op_7492_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, true, false])];
tensor<fp16, [1, 1, 1500]> var_7492_cast_fp16 = slice_by_index(begin = var_7492_begin_0, end = var_7492_end_0, end_mask = var_7492_end_mask_0, squeeze_mask = var_7492_squeeze_mask_0, x = var_7489_cast_fp16)[name = tensor<string, []>("op_7492_cast_fp16")];
tensor<int32, [4]> var_7507_begin_0 = const()[name = tensor<string, []>("op_7507_begin_0"), val = tensor<int32, [4]>([0, 15, 0, 0])];
tensor<int32, [4]> var_7507_end_0 = const()[name = tensor<string, []>("op_7507_end_0"), val = tensor<int32, [4]>([1, 16, 1, 1500])];
tensor<bool, [4]> var_7507_end_mask_0 = const()[name = tensor<string, []>("op_7507_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])];
tensor<fp16, [1, 1, 1, 1500]> var_7507_cast_fp16 = slice_by_index(begin = var_7507_begin_0, end = var_7507_end_0, end_mask = var_7507_end_mask_0, x = obj_391_cast_fp16)[name = tensor<string, []>("op_7507_cast_fp16")];
tensor<int32, [4]> var_7510_begin_0 = const()[name = tensor<string, []>("op_7510_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_7510_end_0 = const()[name = tensor<string, []>("op_7510_end_0"), val = tensor<int32, [4]>([1, 1, 1, 1500])];
tensor<bool, [4]> var_7510_end_mask_0 = const()[name = tensor<string, []>("op_7510_end_mask_0"), val = tensor<bool, [4]>([true, true, false, true])];
tensor<bool, [4]> var_7510_squeeze_mask_0 = const()[name = tensor<string, []>("op_7510_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, true, false])];
tensor<fp16, [1, 1, 1500]> var_7510_cast_fp16 = slice_by_index(begin = var_7510_begin_0, end = var_7510_end_0, end_mask = var_7510_end_mask_0, squeeze_mask = var_7510_squeeze_mask_0, x = var_7507_cast_fp16)[name = tensor<string, []>("op_7510_cast_fp16")];
tensor<int32, []> var_7517 = const()[name = tensor<string, []>("op_7517"), val = tensor<int32, []>(1)];
tensor<bool, []> var_7518_interleave_0 = const()[name = tensor<string, []>("op_7518_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 23, 1500]> var_7518_cast_fp16 = concat(axis = var_7517, interleave = var_7518_interleave_0, values = (var_7114_cast_fp16, var_7132_cast_fp16, var_7150_cast_fp16, var_7168_cast_fp16, var_7186_cast_fp16, var_7204_cast_fp16, var_7222_cast_fp16, var_7240_cast_fp16, var_7258_cast_fp16, var_7276_cast_fp16, var_7294_cast_fp16, var_7312_cast_fp16, var_7330_cast_fp16, var_7348_cast_fp16, var_7366_cast_fp16, var_7384_cast_fp16, var_7402_cast_fp16, var_7420_cast_fp16, var_7438_cast_fp16, var_7456_cast_fp16, var_7474_cast_fp16, var_7492_cast_fp16, var_7510_cast_fp16))[name = tensor<string, []>("op_7518_cast_fp16")];
tensor<int32, [1]> var_7520 = const()[name = tensor<string, []>("op_7520"), val = tensor<int32, [1]>([1])];
tensor<bool, []> var_7521 = const()[name = tensor<string, []>("op_7521"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 1500]> alignment_heads_weights = reduce_mean(axes = var_7520, keep_dims = var_7521, x = var_7518_cast_fp16)[name = tensor<string, []>("obj_cast_fp16")];
} -> (logits, key_cache_updates, value_cache_updates, alignment_heads_weights);
}