program(1.0)
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3304.5.2"}, {"coremlc-version", "3304.6.2"}, {"coremltools-component-milinternal", ""}, {"coremltools-version", "7.1"}})]
{
    func main<ios16>(tensor<fp16, [2, 1024, 1, 77]> encoder_hidden_states, tensor<fp16, [2, 4, 48, 80]> sample, tensor<fp16, [2]> timestep) {
            tensor<int32, []> var_25 = const()[name = tensor<string, []>("op_25"), val = tensor<int32, []>(-1)];
            tensor<int32, [1]> var_42_axes_0 = const()[name = tensor<string, []>("op_42_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1]> var_42_cast_fp16 = expand_dims(axes = var_42_axes_0, x = timestep)[name = tensor<string, []>("op_42_cast_fp16")];
            tensor<fp16, [1, 160]> var_44_to_fp16 = const()[name = tensor<string, []>("op_44_to_fp16"), val = tensor<fp16, [1, 160]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
            tensor<fp16, [2, 160]> emb_3_cast_fp16 = mul(x = var_42_cast_fp16, y = var_44_to_fp16)[name = tensor<string, []>("emb_3_cast_fp16")];
            tensor<fp16, [2, 160]> var_49_cast_fp16 = sin(x = emb_3_cast_fp16)[name = tensor<string, []>("op_49_cast_fp16")];
            tensor<fp16, [2, 160]> var_50_cast_fp16 = cos(x = emb_3_cast_fp16)[name = tensor<string, []>("op_50_cast_fp16")];
            tensor<bool, []> emb_interleave_0 = const()[name = tensor<string, []>("emb_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 320]> emb_cast_fp16 = concat(axis = var_25, interleave = emb_interleave_0, values = (var_49_cast_fp16, var_50_cast_fp16))[name = tensor<string, []>("emb_cast_fp16")];
            tensor<int32, [2]> var_54_begin_0 = const()[name = tensor<string, []>("op_54_begin_0"), val = tensor<int32, [2]>([0, 160])];
            tensor<int32, [2]> var_54_end_0 = const()[name = tensor<string, []>("op_54_end_0"), val = tensor<int32, [2]>([2, 320])];
            tensor<bool, [2]> var_54_end_mask_0 = const()[name = tensor<string, []>("op_54_end_mask_0"), val = tensor<bool, [2]>([true, true])];
            tensor<fp16, [2, 160]> var_54_cast_fp16 = slice_by_index(begin = var_54_begin_0, end = var_54_end_0, end_mask = var_54_end_mask_0, x = emb_cast_fp16)[name = tensor<string, []>("op_54_cast_fp16")];
            tensor<int32, [2]> var_56_begin_0 = const()[name = tensor<string, []>("op_56_begin_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [2]> var_56_end_0 = const()[name = tensor<string, []>("op_56_end_0"), val = tensor<int32, [2]>([2, 160])];
            tensor<bool, [2]> var_56_end_mask_0 = const()[name = tensor<string, []>("op_56_end_mask_0"), val = tensor<bool, [2]>([true, false])];
            tensor<fp16, [2, 160]> var_56_cast_fp16 = slice_by_index(begin = var_56_begin_0, end = var_56_end_0, end_mask = var_56_end_mask_0, x = emb_cast_fp16)[name = tensor<string, []>("op_56_cast_fp16")];
            tensor<bool, []> sample_interleave_0 = const()[name = tensor<string, []>("sample_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 320]> sample_cast_fp16 = concat(axis = var_25, interleave = sample_interleave_0, values = (var_54_cast_fp16, var_56_cast_fp16))[name = tensor<string, []>("sample_cast_fp16")];
            tensor<int32, []> var_59 = const()[name = tensor<string, []>("op_59"), val = tensor<int32, []>(1)];
            tensor<int32, [1]> var_66_axes_0 = const()[name = tensor<string, []>("op_66_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [2, 320, 1]> var_66_cast_fp16 = expand_dims(axes = var_66_axes_0, x = sample_cast_fp16)[name = tensor<string, []>("op_66_cast_fp16")];
            tensor<int32, [1]> input_1_axes_0 = const()[name = tensor<string, []>("input_1_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [2, 320, 1, 1]> input_1_cast_fp16 = expand_dims(axes = input_1_axes_0, x = var_66_cast_fp16)[name = tensor<string, []>("input_1_cast_fp16")];
            tensor<int32, [2]> var_70 = const()[name = tensor<string, []>("op_70"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_72 = const()[name = tensor<string, []>("op_72"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> input_3_pad_type_0 = const()[name = tensor<string, []>("input_3_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_3_pad_0 = const()[name = tensor<string, []>("input_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 320, 1, 1]> time_embedding_linear_1_weight_to_fp16 = const()[name = tensor<string, []>("time_embedding_linear_1_weight_to_fp16"), val = tensor<fp16, [1280, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(448)))];
            tensor<fp16, [1280]> time_embedding_linear_1_bias_to_fp16 = const()[name = tensor<string, []>("time_embedding_linear_1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(819712)))];
            tensor<fp16, [2, 1280, 1, 1]> input_3_cast_fp16 = conv(bias = time_embedding_linear_1_bias_to_fp16, dilations = var_72, groups = var_59, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_70, weight = time_embedding_linear_1_weight_to_fp16, x = input_1_cast_fp16)[name = tensor<string, []>("input_3_cast_fp16")];
            tensor<fp16, [2, 1280, 1, 1]> input_5_cast_fp16 = silu(x = input_3_cast_fp16)[name = tensor<string, []>("input_5_cast_fp16")];
            tensor<int32, [2]> var_78 = const()[name = tensor<string, []>("op_78"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_80 = const()[name = tensor<string, []>("op_80"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> input_13_pad_type_0 = const()[name = tensor<string, []>("input_13_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_13_pad_0 = const()[name = tensor<string, []>("input_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> time_embedding_linear_2_weight_to_fp16 = const()[name = tensor<string, []>("time_embedding_linear_2_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(822336)))];
            tensor<fp16, [1280]> time_embedding_linear_2_bias_to_fp16 = const()[name = tensor<string, []>("time_embedding_linear_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4099200)))];
            tensor<fp16, [2, 1280, 1, 1]> input_13_cast_fp16 = conv(bias = time_embedding_linear_2_bias_to_fp16, dilations = var_80, groups = var_59, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = var_78, weight = time_embedding_linear_2_weight_to_fp16, x = input_5_cast_fp16)[name = tensor<string, []>("input_13_cast_fp16")];
            tensor<int32, []> var_86 = const()[name = tensor<string, []>("op_86"), val = tensor<int32, []>(1)];
            tensor<int32, [2]> var_89 = const()[name = tensor<string, []>("op_89"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_91 = const()[name = tensor<string, []>("op_91"), 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]>([1, 1, 1, 1])];
            tensor<fp16, [320, 4, 3, 3]> conv_in_weight_to_fp16 = const()[name = tensor<string, []>("conv_in_weight_to_fp16"), val = tensor<fp16, [320, 4, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4101824)))];
            tensor<fp16, [320]> conv_in_bias_to_fp16 = const()[name = tensor<string, []>("conv_in_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4124928)))];
            tensor<fp16, [2, 320, 48, 80]> input_7_cast_fp16_1 = conv(bias = conv_in_bias_to_fp16, dilations = var_91, groups = var_86, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = var_89, weight = conv_in_weight_to_fp16, x = sample)[name = tensor<string, []>("input_7_cast_fp16")];
            tensor<int32, []> var_95 = const()[name = tensor<string, []>("op_95"), val = tensor<int32, []>(3)];
            tensor<bool, []> var_106 = const()[name = tensor<string, []>("op_106"), val = tensor<bool, []>(true)];
            tensor<int32, []> var_111 = const()[name = tensor<string, []>("op_111"), val = tensor<int32, []>(1)];
            tensor<int32, [5]> reshape_0_shape_0 = const()[name = tensor<string, []>("reshape_0_shape_0"), val = tensor<int32, [5]>([2, 32, 10, 48, 80])];
            tensor<fp16, [2, 32, 10, 48, 80]> reshape_0_cast_fp16 = reshape(shape = reshape_0_shape_0, x = input_7_cast_fp16_1)[name = tensor<string, []>("reshape_0_cast_fp16")];
            tensor<int32, [3]> reduce_mean_0_axes_0 = const()[name = tensor<string, []>("reduce_mean_0_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_0_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_0_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_0_cast_fp16 = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = reshape_0_cast_fp16)[name = tensor<string, []>("reduce_mean_0_cast_fp16")];
            tensor<fp16, [2, 32, 10, 48, 80]> sub_0_cast_fp16 = sub(x = reshape_0_cast_fp16, y = reduce_mean_0_cast_fp16)[name = tensor<string, []>("sub_0_cast_fp16")];
            tensor<fp16, [2, 32, 10, 48, 80]> square_0_cast_fp16 = square(x = sub_0_cast_fp16)[name = tensor<string, []>("square_0_cast_fp16")];
            tensor<int32, [3]> reduce_mean_2_axes_0 = const()[name = tensor<string, []>("reduce_mean_2_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_2_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_2_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_2_cast_fp16 = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = square_0_cast_fp16)[name = tensor<string, []>("reduce_mean_2_cast_fp16")];
            tensor<fp16, []> add_0_y_0_to_fp16 = const()[name = tensor<string, []>("add_0_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_0_cast_fp16 = add(x = reduce_mean_2_cast_fp16, y = add_0_y_0_to_fp16)[name = tensor<string, []>("add_0_cast_fp16")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_0_cast_fp16 = sqrt(x = add_0_cast_fp16)[name = tensor<string, []>("sqrt_0_cast_fp16")];
            tensor<fp16, [2, 32, 10, 48, 80]> real_div_0_cast_fp16 = real_div(x = sub_0_cast_fp16, y = sqrt_0_cast_fp16)[name = tensor<string, []>("real_div_0_cast_fp16")];
            tensor<int32, [4]> reshape_1_shape_0 = const()[name = tensor<string, []>("reshape_1_shape_0"), val = tensor<int32, [4]>([2, 320, 48, 80])];
            tensor<fp16, [2, 320, 48, 80]> reshape_1_cast_fp16 = reshape(shape = reshape_1_shape_0, x = real_div_0_cast_fp16)[name = tensor<string, []>("reshape_1_cast_fp16")];
            tensor<fp16, [320]> add_1_mean_0_to_fp16 = const()[name = tensor<string, []>("add_1_mean_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4125632)))];
            tensor<fp16, [320]> add_1_variance_0_to_fp16 = const()[name = tensor<string, []>("add_1_variance_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4126336)))];
            tensor<fp16, [320]> add_1_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_1_gamma_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4127040)))];
            tensor<fp16, [320]> add_1_beta_0_to_fp16 = const()[name = tensor<string, []>("add_1_beta_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4127744)))];
            tensor<fp16, []> add_1_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_1_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 320, 48, 80]> add_1_cast_fp16 = batch_norm(beta = add_1_beta_0_to_fp16, epsilon = add_1_epsilon_0_to_fp16, gamma = add_1_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_1_cast_fp16)[name = tensor<string, []>("add_1_cast_fp16")];
            tensor<fp16, [2, 320, 48, 80]> input_11_cast_fp16 = silu(x = add_1_cast_fp16)[name = tensor<string, []>("input_11_cast_fp16")];
            tensor<int32, [2]> var_133 = const()[name = tensor<string, []>("op_133"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_135 = const()[name = tensor<string, []>("op_135"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_1_pad_type_0 = const()[name = tensor<string, []>("hidden_states_1_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_1_pad_0 = const()[name = tensor<string, []>("hidden_states_1_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [320, 320, 3, 3]> down_blocks_0_resnets_0_conv1_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_resnets_0_conv1_weight_to_fp16"), val = tensor<fp16, [320, 320, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4128448)))];
            tensor<fp16, [320]> down_blocks_0_resnets_0_conv1_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_resnets_0_conv1_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5971712)))];
            tensor<fp16, [2, 320, 48, 80]> hidden_states_1_cast_fp16 = conv(bias = down_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = var_135, groups = var_111, pad = hidden_states_1_pad_0, pad_type = hidden_states_1_pad_type_0, strides = var_133, weight = down_blocks_0_resnets_0_conv1_weight_to_fp16, x = input_11_cast_fp16)[name = tensor<string, []>("hidden_states_1_cast_fp16")];
            tensor<fp16, [2, 1280, 1, 1]> input_15_cast_fp16_1 = silu(x = input_13_cast_fp16)[name = tensor<string, []>("input_15_cast_fp16")];
            tensor<int32, [2]> var_141 = const()[name = tensor<string, []>("op_141"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_143 = const()[name = tensor<string, []>("op_143"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> temb_1_pad_type_0 = const()[name = tensor<string, []>("temb_1_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> temb_1_pad_0 = const()[name = tensor<string, []>("temb_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 1280, 1, 1]> down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor<fp16, [320, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5972416)))];
            tensor<fp16, [320]> down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6791680)))];
            tensor<fp16, [2, 320, 1, 1]> temb_1_cast_fp16 = conv(bias = down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_143, groups = var_111, pad = temb_1_pad_0, pad_type = temb_1_pad_type_0, strides = var_141, weight = down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16_1)[name = tensor<string, []>("temb_1_cast_fp16")];
            tensor<fp16, [2, 320, 48, 80]> input_17_cast_fp16 = add(x = hidden_states_1_cast_fp16, y = temb_1_cast_fp16)[name = tensor<string, []>("input_17_cast_fp16")];
            tensor<int32, [5]> reshape_4_shape_0 = const()[name = tensor<string, []>("reshape_4_shape_0"), val = tensor<int32, [5]>([2, 32, 10, 48, 80])];
            tensor<fp16, [2, 32, 10, 48, 80]> reshape_4_cast_fp16 = reshape(shape = reshape_4_shape_0, x = input_17_cast_fp16)[name = tensor<string, []>("reshape_4_cast_fp16")];
            tensor<int32, [3]> reduce_mean_3_axes_0 = const()[name = tensor<string, []>("reduce_mean_3_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_3_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_3_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_3_cast_fp16 = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = reshape_4_cast_fp16)[name = tensor<string, []>("reduce_mean_3_cast_fp16")];
            tensor<fp16, [2, 32, 10, 48, 80]> sub_2_cast_fp16 = sub(x = reshape_4_cast_fp16, y = reduce_mean_3_cast_fp16)[name = tensor<string, []>("sub_2_cast_fp16")];
            tensor<fp16, [2, 32, 10, 48, 80]> square_1_cast_fp16 = square(x = sub_2_cast_fp16)[name = tensor<string, []>("square_1_cast_fp16")];
            tensor<int32, [3]> reduce_mean_5_axes_0 = const()[name = tensor<string, []>("reduce_mean_5_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_5_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_5_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_5_cast_fp16 = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_1_cast_fp16)[name = tensor<string, []>("reduce_mean_5_cast_fp16")];
            tensor<fp16, []> add_2_y_0_to_fp16 = const()[name = tensor<string, []>("add_2_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_2_cast_fp16 = add(x = reduce_mean_5_cast_fp16, y = add_2_y_0_to_fp16)[name = tensor<string, []>("add_2_cast_fp16")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_1_cast_fp16 = sqrt(x = add_2_cast_fp16)[name = tensor<string, []>("sqrt_1_cast_fp16")];
            tensor<fp16, [2, 32, 10, 48, 80]> real_div_1_cast_fp16 = real_div(x = sub_2_cast_fp16, y = sqrt_1_cast_fp16)[name = tensor<string, []>("real_div_1_cast_fp16")];
            tensor<int32, [4]> reshape_5_shape_0 = const()[name = tensor<string, []>("reshape_5_shape_0"), val = tensor<int32, [4]>([2, 320, 48, 80])];
            tensor<fp16, [2, 320, 48, 80]> reshape_5_cast_fp16 = reshape(shape = reshape_5_shape_0, x = real_div_1_cast_fp16)[name = tensor<string, []>("reshape_5_cast_fp16")];
            tensor<fp16, [320]> add_3_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_3_gamma_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6792384)))];
            tensor<fp16, [320]> add_3_beta_0_to_fp16 = const()[name = tensor<string, []>("add_3_beta_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6793088)))];
            tensor<fp16, []> add_3_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_3_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 320, 48, 80]> add_3_cast_fp16 = batch_norm(beta = add_3_beta_0_to_fp16, epsilon = add_3_epsilon_0_to_fp16, gamma = add_3_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_5_cast_fp16)[name = tensor<string, []>("add_3_cast_fp16")];
            tensor<fp16, [2, 320, 48, 80]> input_21_cast_fp16 = silu(x = add_3_cast_fp16)[name = tensor<string, []>("input_21_cast_fp16")];
            tensor<int32, [2]> var_153 = const()[name = tensor<string, []>("op_153"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_155 = const()[name = tensor<string, []>("op_155"), 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]>([1, 1, 1, 1])];
            tensor<fp16, [320, 320, 3, 3]> down_blocks_0_resnets_0_conv2_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_resnets_0_conv2_weight_to_fp16"), val = tensor<fp16, [320, 320, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6793792)))];
            tensor<fp16, [320]> down_blocks_0_resnets_0_conv2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_resnets_0_conv2_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8637056)))];
            tensor<fp16, [2, 320, 48, 80]> hidden_states_3_cast_fp16 = conv(bias = down_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = var_155, groups = var_111, pad = hidden_states_3_pad_0, pad_type = hidden_states_3_pad_type_0, strides = var_153, weight = down_blocks_0_resnets_0_conv2_weight_to_fp16, x = input_21_cast_fp16)[name = tensor<string, []>("hidden_states_3_cast_fp16")];
            tensor<fp16, [2, 320, 48, 80]> hidden_states_5_cast_fp16 = add(x = input_7_cast_fp16_1, y = hidden_states_3_cast_fp16)[name = tensor<string, []>("hidden_states_5_cast_fp16")];
            tensor<int32, [5]> reshape_8_shape_0 = const()[name = tensor<string, []>("reshape_8_shape_0"), val = tensor<int32, [5]>([2, 32, 10, 48, 80])];
            tensor<fp16, [2, 32, 10, 48, 80]> reshape_8_cast_fp16 = reshape(shape = reshape_8_shape_0, x = hidden_states_5_cast_fp16)[name = tensor<string, []>("reshape_8_cast_fp16")];
            tensor<int32, [3]> reduce_mean_6_axes_0 = const()[name = tensor<string, []>("reduce_mean_6_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_6_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_6_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_6_cast_fp16 = reduce_mean(axes = reduce_mean_6_axes_0, keep_dims = reduce_mean_6_keep_dims_0, x = reshape_8_cast_fp16)[name = tensor<string, []>("reduce_mean_6_cast_fp16")];
            tensor<fp16, [2, 32, 10, 48, 80]> sub_4_cast_fp16 = sub(x = reshape_8_cast_fp16, y = reduce_mean_6_cast_fp16)[name = tensor<string, []>("sub_4_cast_fp16")];
            tensor<fp16, [2, 32, 10, 48, 80]> square_2_cast_fp16 = square(x = sub_4_cast_fp16)[name = tensor<string, []>("square_2_cast_fp16")];
            tensor<int32, [3]> reduce_mean_8_axes_0 = const()[name = tensor<string, []>("reduce_mean_8_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_8_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_8_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_8_cast_fp16 = reduce_mean(axes = reduce_mean_8_axes_0, keep_dims = reduce_mean_8_keep_dims_0, x = square_2_cast_fp16)[name = tensor<string, []>("reduce_mean_8_cast_fp16")];
            tensor<fp16, []> add_4_y_0_to_fp16 = const()[name = tensor<string, []>("add_4_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_4_cast_fp16 = add(x = reduce_mean_8_cast_fp16, y = add_4_y_0_to_fp16)[name = tensor<string, []>("add_4_cast_fp16")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_2_cast_fp16 = sqrt(x = add_4_cast_fp16)[name = tensor<string, []>("sqrt_2_cast_fp16")];
            tensor<fp16, [2, 32, 10, 48, 80]> real_div_2_cast_fp16 = real_div(x = sub_4_cast_fp16, y = sqrt_2_cast_fp16)[name = tensor<string, []>("real_div_2_cast_fp16")];
            tensor<int32, [4]> reshape_9_shape_0 = const()[name = tensor<string, []>("reshape_9_shape_0"), val = tensor<int32, [4]>([2, 320, 48, 80])];
            tensor<fp16, [2, 320, 48, 80]> reshape_9_cast_fp16 = reshape(shape = reshape_9_shape_0, x = real_div_2_cast_fp16)[name = tensor<string, []>("reshape_9_cast_fp16")];
            tensor<fp16, [320]> add_5_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_5_gamma_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8637760)))];
            tensor<fp16, [320]> add_5_beta_0_to_fp16 = const()[name = tensor<string, []>("add_5_beta_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8638464)))];
            tensor<fp16, []> add_5_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_5_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 320, 48, 80]> add_5_cast_fp16 = batch_norm(beta = add_5_beta_0_to_fp16, epsilon = add_5_epsilon_0_to_fp16, gamma = add_5_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_9_cast_fp16)[name = tensor<string, []>("add_5_cast_fp16")];
            tensor<int32, [2]> var_175 = const()[name = tensor<string, []>("op_175"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_177 = const()[name = tensor<string, []>("op_177"), 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, [320, 320, 1, 1]> down_blocks_0_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_0_proj_in_weight_to_fp16"), val = tensor<fp16, [320, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8639168)))];
            tensor<fp16, [320]> down_blocks_0_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_0_proj_in_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8844032)))];
            tensor<fp16, [2, 320, 48, 80]> hidden_states_7_cast_fp16 = conv(bias = down_blocks_0_attentions_0_proj_in_bias_to_fp16, dilations = var_177, groups = var_111, pad = hidden_states_7_pad_0, pad_type = hidden_states_7_pad_type_0, strides = var_175, weight = down_blocks_0_attentions_0_proj_in_weight_to_fp16, x = add_5_cast_fp16)[name = tensor<string, []>("hidden_states_7_cast_fp16")];
            tensor<int32, [4]> var_182 = const()[name = tensor<string, []>("op_182"), val = tensor<int32, [4]>([2, 320, 1, 3840])];
            tensor<fp16, [2, 320, 1, 3840]> inputs_1_cast_fp16 = reshape(shape = var_182, x = hidden_states_7_cast_fp16)[name = tensor<string, []>("inputs_1_cast_fp16")];
            tensor<int32, [1]> var_192 = const()[name = tensor<string, []>("op_192"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 3840]> channels_mean_1_cast_fp16 = reduce_mean(axes = var_192, keep_dims = var_106, x = inputs_1_cast_fp16)[name = tensor<string, []>("channels_mean_1_cast_fp16")];
            tensor<fp16, [2, 320, 1, 3840]> 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, [2, 320, 1, 3840]> 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_196 = const()[name = tensor<string, []>("op_196"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 3840]> var_197_cast_fp16 = reduce_mean(axes = var_196, keep_dims = var_106, x = zero_mean_sq_1_cast_fp16)[name = tensor<string, []>("op_197_cast_fp16")];
            tensor<fp16, []> var_198_to_fp16 = const()[name = tensor<string, []>("op_198_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 3840]> var_199_cast_fp16 = add(x = var_197_cast_fp16, y = var_198_to_fp16)[name = tensor<string, []>("op_199_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, [2, 1, 1, 3840]> denom_1_cast_fp16 = rsqrt(epsilon = denom_1_epsilon_0_to_fp16, x = var_199_cast_fp16)[name = tensor<string, []>("denom_1_cast_fp16")];
            tensor<fp16, [2, 320, 1, 3840]> 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, [1, 320, 1, 1]> var_203_to_fp16 = const()[name = tensor<string, []>("op_203_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8844736)))];
            tensor<fp16, [2, 320, 1, 3840]> var_204_cast_fp16 = add(x = out_1_cast_fp16, y = var_203_to_fp16)[name = tensor<string, []>("op_204_cast_fp16")];
            tensor<fp16, [1, 320, 1, 1]> var_206_to_fp16 = const()[name = tensor<string, []>("op_206_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8845440)))];
            tensor<fp16, [2, 320, 1, 3840]> hidden_states_9_cast_fp16 = mul(x = var_204_cast_fp16, y = var_206_to_fp16)[name = tensor<string, []>("hidden_states_9_cast_fp16")];
            tensor<int32, [2]> var_213 = const()[name = tensor<string, []>("op_213"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_215 = const()[name = tensor<string, []>("op_215"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_1_pad_type_0 = const()[name = tensor<string, []>("q_1_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_1_pad_0 = const()[name = tensor<string, []>("q_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 320, 1, 1]> down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor<fp16, [320, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8846144)))];
            tensor<fp16, [2, 320, 1, 3840]> q_1_cast_fp16 = conv(dilations = var_215, groups = var_111, pad = q_1_pad_0, pad_type = q_1_pad_type_0, strides = var_213, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_9_cast_fp16)[name = tensor<string, []>("q_1_cast_fp16")];
            tensor<int32, [2]> var_219 = const()[name = tensor<string, []>("op_219"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_221 = const()[name = tensor<string, []>("op_221"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_1_pad_type_0 = const()[name = tensor<string, []>("k_1_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_1_pad_0 = const()[name = tensor<string, []>("k_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 320, 1, 1]> down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor<fp16, [320, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9051008)))];
            tensor<fp16, [2, 320, 1, 3840]> k_1_cast_fp16 = conv(dilations = var_221, groups = var_111, pad = k_1_pad_0, pad_type = k_1_pad_type_0, strides = var_219, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_9_cast_fp16)[name = tensor<string, []>("k_1_cast_fp16")];
            tensor<int32, [2]> var_225 = const()[name = tensor<string, []>("op_225"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_227 = const()[name = tensor<string, []>("op_227"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_1_pad_type_0 = const()[name = tensor<string, []>("v_1_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_1_pad_0 = const()[name = tensor<string, []>("v_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 320, 1, 1]> down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor<fp16, [320, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9255872)))];
            tensor<fp16, [2, 320, 1, 3840]> v_1_cast_fp16 = conv(dilations = var_227, groups = var_111, pad = v_1_pad_0, pad_type = v_1_pad_type_0, strides = var_225, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_9_cast_fp16)[name = tensor<string, []>("v_1_cast_fp16")];
            tensor<int32, [4]> var_231 = const()[name = tensor<string, []>("op_231"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 3840]> var_232_cast_fp16 = reshape(shape = var_231, x = q_1_cast_fp16)[name = tensor<string, []>("op_232_cast_fp16")];
            tensor<int32, [4]> var_233 = const()[name = tensor<string, []>("op_233"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 3840]> var_234_cast_fp16 = reshape(shape = var_233, x = k_1_cast_fp16)[name = tensor<string, []>("op_234_cast_fp16")];
            tensor<int32, [4]> var_235 = const()[name = tensor<string, []>("op_235"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 3840]> var_236_cast_fp16 = reshape(shape = var_235, x = v_1_cast_fp16)[name = tensor<string, []>("op_236_cast_fp16")];
            tensor<bool, []> attn_weights_1_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_1_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_1_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_1_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 5, 3840, 3840]> attn_weights_1_cast_fp16 = matmul(transpose_x = attn_weights_1_transpose_x_0, transpose_y = attn_weights_1_transpose_y_0, x = var_232_cast_fp16, y = var_234_cast_fp16)[name = tensor<string, []>("attn_weights_1_cast_fp16")];
            tensor<fp16, []> var_102_to_fp16 = const()[name = tensor<string, []>("op_102_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [2, 5, 3840, 3840]> attn_weights_3_cast_fp16 = mul(x = attn_weights_1_cast_fp16, y = var_102_to_fp16)[name = tensor<string, []>("attn_weights_3_cast_fp16")];
            tensor<fp16, [2, 5, 3840, 3840]> var_240_cast_fp16 = softmax(axis = var_95, x = attn_weights_3_cast_fp16)[name = tensor<string, []>("op_240_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, [2, 5, 64, 3840]> attn_1_cast_fp16 = matmul(transpose_x = attn_1_transpose_x_0, transpose_y = attn_1_transpose_y_0, x = var_236_cast_fp16, y = var_240_cast_fp16)[name = tensor<string, []>("attn_1_cast_fp16")];
            tensor<int32, [4]> var_244 = const()[name = tensor<string, []>("op_244"), val = tensor<int32, [4]>([2, 320, 1, -1])];
            tensor<fp16, [2, 320, 1, 3840]> input_25_cast_fp16 = reshape(shape = var_244, x = attn_1_cast_fp16)[name = tensor<string, []>("input_25_cast_fp16")];
            tensor<int32, [2]> var_249 = const()[name = tensor<string, []>("op_249"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_251 = const()[name = tensor<string, []>("op_251"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_253_pad_type_0 = const()[name = tensor<string, []>("op_253_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_253_pad_0 = const()[name = tensor<string, []>("op_253_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 320, 1, 1]> down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor<fp16, [320, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9460736)))];
            tensor<fp16, [320]> down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9665600)))];
            tensor<fp16, [2, 320, 1, 3840]> var_253_cast_fp16 = conv(bias = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_251, groups = var_111, pad = var_253_pad_0, pad_type = var_253_pad_type_0, strides = var_249, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_25_cast_fp16)[name = tensor<string, []>("op_253_cast_fp16")];
            tensor<fp16, [2, 320, 1, 3840]> inputs_3_cast_fp16 = add(x = var_253_cast_fp16, y = inputs_1_cast_fp16)[name = tensor<string, []>("inputs_3_cast_fp16")];
            tensor<int32, [1]> var_257 = const()[name = tensor<string, []>("op_257"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 3840]> channels_mean_3_cast_fp16 = reduce_mean(axes = var_257, keep_dims = var_106, x = inputs_3_cast_fp16)[name = tensor<string, []>("channels_mean_3_cast_fp16")];
            tensor<fp16, [2, 320, 1, 3840]> 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, [2, 320, 1, 3840]> 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_261 = const()[name = tensor<string, []>("op_261"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 3840]> var_262_cast_fp16 = reduce_mean(axes = var_261, keep_dims = var_106, x = zero_mean_sq_3_cast_fp16)[name = tensor<string, []>("op_262_cast_fp16")];
            tensor<fp16, []> var_263_to_fp16 = const()[name = tensor<string, []>("op_263_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 3840]> var_264_cast_fp16 = add(x = var_262_cast_fp16, y = var_263_to_fp16)[name = tensor<string, []>("op_264_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, [2, 1, 1, 3840]> denom_3_cast_fp16 = rsqrt(epsilon = denom_3_epsilon_0_to_fp16, x = var_264_cast_fp16)[name = tensor<string, []>("denom_3_cast_fp16")];
            tensor<fp16, [2, 320, 1, 3840]> 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, [1, 320, 1, 1]> var_268_to_fp16 = const()[name = tensor<string, []>("op_268_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9666304)))];
            tensor<fp16, [2, 320, 1, 3840]> var_269_cast_fp16 = add(x = out_3_cast_fp16, y = var_268_to_fp16)[name = tensor<string, []>("op_269_cast_fp16")];
            tensor<fp16, [1, 320, 1, 1]> var_271_to_fp16 = const()[name = tensor<string, []>("op_271_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9667008)))];
            tensor<fp16, [2, 320, 1, 3840]> hidden_states_11_cast_fp16 = mul(x = var_269_cast_fp16, y = var_271_to_fp16)[name = tensor<string, []>("hidden_states_11_cast_fp16")];
            tensor<int32, [2]> var_278 = const()[name = tensor<string, []>("op_278"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_280 = const()[name = tensor<string, []>("op_280"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_3_pad_type_0 = const()[name = tensor<string, []>("q_3_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_3_pad_0 = const()[name = tensor<string, []>("q_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 320, 1, 1]> down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor<fp16, [320, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9667712)))];
            tensor<fp16, [2, 320, 1, 3840]> q_3_cast_fp16 = conv(dilations = var_280, groups = var_111, pad = q_3_pad_0, pad_type = q_3_pad_type_0, strides = var_278, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_11_cast_fp16)[name = tensor<string, []>("q_3_cast_fp16")];
            tensor<int32, [2]> var_284 = const()[name = tensor<string, []>("op_284"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_286 = const()[name = tensor<string, []>("op_286"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_3_pad_type_0 = const()[name = tensor<string, []>("k_3_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_3_pad_0 = const()[name = tensor<string, []>("k_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 1024, 1, 1]> down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor<fp16, [320, 1024, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9872576)))];
            tensor<fp16, [2, 320, 1, 77]> k_3_cast_fp16 = conv(dilations = var_286, groups = var_111, pad = k_3_pad_0, pad_type = k_3_pad_type_0, strides = var_284, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor<string, []>("k_3_cast_fp16")];
            tensor<int32, [2]> var_290 = const()[name = tensor<string, []>("op_290"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_292 = const()[name = tensor<string, []>("op_292"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_3_pad_type_0 = const()[name = tensor<string, []>("v_3_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_3_pad_0 = const()[name = tensor<string, []>("v_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 1024, 1, 1]> down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor<fp16, [320, 1024, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10528000)))];
            tensor<fp16, [2, 320, 1, 77]> v_3_cast_fp16 = conv(dilations = var_292, groups = var_111, pad = v_3_pad_0, pad_type = v_3_pad_type_0, strides = var_290, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor<string, []>("v_3_cast_fp16")];
            tensor<int32, [4]> var_296 = const()[name = tensor<string, []>("op_296"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 3840]> var_297_cast_fp16 = reshape(shape = var_296, x = q_3_cast_fp16)[name = tensor<string, []>("op_297_cast_fp16")];
            tensor<int32, [4]> var_298 = const()[name = tensor<string, []>("op_298"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 77]> var_299_cast_fp16 = reshape(shape = var_298, x = k_3_cast_fp16)[name = tensor<string, []>("op_299_cast_fp16")];
            tensor<int32, [4]> var_300 = const()[name = tensor<string, []>("op_300"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 77]> var_301_cast_fp16 = reshape(shape = var_300, x = v_3_cast_fp16)[name = tensor<string, []>("op_301_cast_fp16")];
            tensor<bool, []> attn_weights_5_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_5_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_5_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_5_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 5, 3840, 77]> attn_weights_5_cast_fp16 = matmul(transpose_x = attn_weights_5_transpose_x_0, transpose_y = attn_weights_5_transpose_y_0, x = var_297_cast_fp16, y = var_299_cast_fp16)[name = tensor<string, []>("attn_weights_5_cast_fp16")];
            tensor<fp16, [2, 5, 3840, 77]> attn_weights_7_cast_fp16 = mul(x = attn_weights_5_cast_fp16, y = var_102_to_fp16)[name = tensor<string, []>("attn_weights_7_cast_fp16")];
            tensor<fp16, [2, 5, 3840, 77]> var_305_cast_fp16 = softmax(axis = var_95, x = attn_weights_7_cast_fp16)[name = tensor<string, []>("op_305_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, [2, 5, 64, 3840]> attn_3_cast_fp16 = matmul(transpose_x = attn_3_transpose_x_0, transpose_y = attn_3_transpose_y_0, x = var_301_cast_fp16, y = var_305_cast_fp16)[name = tensor<string, []>("attn_3_cast_fp16")];
            tensor<int32, [4]> var_309 = const()[name = tensor<string, []>("op_309"), val = tensor<int32, [4]>([2, 320, 1, -1])];
            tensor<fp16, [2, 320, 1, 3840]> input_27_cast_fp16 = reshape(shape = var_309, x = attn_3_cast_fp16)[name = tensor<string, []>("input_27_cast_fp16")];
            tensor<int32, [2]> var_314 = const()[name = tensor<string, []>("op_314"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_316 = const()[name = tensor<string, []>("op_316"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_318_pad_type_0 = const()[name = tensor<string, []>("op_318_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_318_pad_0 = const()[name = tensor<string, []>("op_318_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 320, 1, 1]> down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor<fp16, [320, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11183424)))];
            tensor<fp16, [320]> down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11388288)))];
            tensor<fp16, [2, 320, 1, 3840]> var_318_cast_fp16 = conv(bias = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_316, groups = var_111, pad = var_318_pad_0, pad_type = var_318_pad_type_0, strides = var_314, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_27_cast_fp16)[name = tensor<string, []>("op_318_cast_fp16")];
            tensor<fp16, [2, 320, 1, 3840]> inputs_5_cast_fp16 = add(x = var_318_cast_fp16, y = inputs_3_cast_fp16)[name = tensor<string, []>("inputs_5_cast_fp16")];
            tensor<int32, [1]> var_322 = const()[name = tensor<string, []>("op_322"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 3840]> channels_mean_5_cast_fp16 = reduce_mean(axes = var_322, keep_dims = var_106, x = inputs_5_cast_fp16)[name = tensor<string, []>("channels_mean_5_cast_fp16")];
            tensor<fp16, [2, 320, 1, 3840]> 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, [2, 320, 1, 3840]> 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_326 = const()[name = tensor<string, []>("op_326"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 3840]> var_327_cast_fp16 = reduce_mean(axes = var_326, keep_dims = var_106, x = zero_mean_sq_5_cast_fp16)[name = tensor<string, []>("op_327_cast_fp16")];
            tensor<fp16, []> var_328_to_fp16 = const()[name = tensor<string, []>("op_328_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 3840]> var_329_cast_fp16 = add(x = var_327_cast_fp16, y = var_328_to_fp16)[name = tensor<string, []>("op_329_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, [2, 1, 1, 3840]> denom_5_cast_fp16 = rsqrt(epsilon = denom_5_epsilon_0_to_fp16, x = var_329_cast_fp16)[name = tensor<string, []>("denom_5_cast_fp16")];
            tensor<fp16, [2, 320, 1, 3840]> 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, [1, 320, 1, 1]> var_333_to_fp16 = const()[name = tensor<string, []>("op_333_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11388992)))];
            tensor<fp16, [2, 320, 1, 3840]> var_334_cast_fp16 = add(x = out_5_cast_fp16, y = var_333_to_fp16)[name = tensor<string, []>("op_334_cast_fp16")];
            tensor<fp16, [1, 320, 1, 1]> var_336_to_fp16 = const()[name = tensor<string, []>("op_336_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11389696)))];
            tensor<fp16, [2, 320, 1, 3840]> input_29_cast_fp16 = mul(x = var_334_cast_fp16, y = var_336_to_fp16)[name = tensor<string, []>("input_29_cast_fp16")];
            tensor<int32, [2]> var_344 = const()[name = tensor<string, []>("op_344"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_346 = const()[name = tensor<string, []>("op_346"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_348_pad_type_0 = const()[name = tensor<string, []>("op_348_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_348_pad_0 = const()[name = tensor<string, []>("op_348_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [2560, 320, 1, 1]> down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor<fp16, [2560, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11390400)))];
            tensor<fp16, [2560]> down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [2560]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13028864)))];
            tensor<fp16, [2, 2560, 1, 3840]> var_348_cast_fp16 = conv(bias = down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_346, groups = var_111, pad = var_348_pad_0, pad_type = var_348_pad_type_0, strides = var_344, weight = down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_29_cast_fp16)[name = tensor<string, []>("op_348_cast_fp16")];
            tensor<int32, [2]> var_349_split_sizes_0 = const()[name = tensor<string, []>("op_349_split_sizes_0"), val = tensor<int32, [2]>([1280, 1280])];
            tensor<int32, []> var_349_axis_0 = const()[name = tensor<string, []>("op_349_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 1280, 1, 3840]> var_349_cast_fp16_0, tensor<fp16, [2, 1280, 1, 3840]> var_349_cast_fp16_1 = split(axis = var_349_axis_0, split_sizes = var_349_split_sizes_0, x = var_348_cast_fp16)[name = tensor<string, []>("op_349_cast_fp16")];
            tensor<string, []> var_351_mode_0 = const()[name = tensor<string, []>("op_351_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 1280, 1, 3840]> var_351_cast_fp16 = gelu(mode = var_351_mode_0, x = var_349_cast_fp16_1)[name = tensor<string, []>("op_351_cast_fp16")];
            tensor<fp16, [2, 1280, 1, 3840]> input_31_cast_fp16 = mul(x = var_349_cast_fp16_0, y = var_351_cast_fp16)[name = tensor<string, []>("input_31_cast_fp16")];
            tensor<int32, [2]> var_355 = const()[name = tensor<string, []>("op_355"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_357 = const()[name = tensor<string, []>("op_357"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_359_pad_type_0 = const()[name = tensor<string, []>("op_359_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_359_pad_0 = const()[name = tensor<string, []>("op_359_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 1280, 1, 1]> down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor<fp16, [320, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13034048)))];
            tensor<fp16, [320]> down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13853312)))];
            tensor<fp16, [2, 320, 1, 3840]> var_359_cast_fp16 = conv(bias = down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_357, groups = var_111, pad = var_359_pad_0, pad_type = var_359_pad_type_0, strides = var_355, weight = down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_31_cast_fp16)[name = tensor<string, []>("op_359_cast_fp16")];
            tensor<fp16, [2, 320, 1, 3840]> hidden_states_15_cast_fp16 = add(x = var_359_cast_fp16, y = inputs_5_cast_fp16)[name = tensor<string, []>("hidden_states_15_cast_fp16")];
            tensor<int32, [4]> var_361 = const()[name = tensor<string, []>("op_361"), val = tensor<int32, [4]>([2, 320, 48, 80])];
            tensor<fp16, [2, 320, 48, 80]> input_33_cast_fp16 = reshape(shape = var_361, x = hidden_states_15_cast_fp16)[name = tensor<string, []>("input_33_cast_fp16")];
            tensor<int32, [2]> var_365 = const()[name = tensor<string, []>("op_365"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_367 = const()[name = tensor<string, []>("op_367"), 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, [320, 320, 1, 1]> down_blocks_0_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_0_proj_out_weight_to_fp16"), val = tensor<fp16, [320, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13854016)))];
            tensor<fp16, [320]> down_blocks_0_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_0_proj_out_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14058880)))];
            tensor<fp16, [2, 320, 48, 80]> hidden_states_17_cast_fp16 = conv(bias = down_blocks_0_attentions_0_proj_out_bias_to_fp16, dilations = var_367, groups = var_111, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = var_365, weight = down_blocks_0_attentions_0_proj_out_weight_to_fp16, x = input_33_cast_fp16)[name = tensor<string, []>("hidden_states_17_cast_fp16")];
            tensor<fp16, [2, 320, 48, 80]> input_35_cast_fp16_1 = add(x = hidden_states_17_cast_fp16, y = hidden_states_5_cast_fp16)[name = tensor<string, []>("input_35_cast_fp16")];
            tensor<int32, [5]> reshape_12_shape_0 = const()[name = tensor<string, []>("reshape_12_shape_0"), val = tensor<int32, [5]>([2, 32, 10, 48, 80])];
            tensor<fp16, [2, 32, 10, 48, 80]> reshape_12_cast_fp16 = reshape(shape = reshape_12_shape_0, x = input_35_cast_fp16_1)[name = tensor<string, []>("reshape_12_cast_fp16")];
            tensor<int32, [3]> reduce_mean_9_axes_0 = const()[name = tensor<string, []>("reduce_mean_9_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_9_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_9_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_9_cast_fp16 = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = reshape_12_cast_fp16)[name = tensor<string, []>("reduce_mean_9_cast_fp16")];
            tensor<fp16, [2, 32, 10, 48, 80]> sub_6_cast_fp16 = sub(x = reshape_12_cast_fp16, y = reduce_mean_9_cast_fp16)[name = tensor<string, []>("sub_6_cast_fp16")];
            tensor<fp16, [2, 32, 10, 48, 80]> square_3_cast_fp16 = square(x = sub_6_cast_fp16)[name = tensor<string, []>("square_3_cast_fp16")];
            tensor<int32, [3]> reduce_mean_11_axes_0 = const()[name = tensor<string, []>("reduce_mean_11_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_11_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_11_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_11_cast_fp16 = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_3_cast_fp16)[name = tensor<string, []>("reduce_mean_11_cast_fp16")];
            tensor<fp16, []> add_6_y_0_to_fp16 = const()[name = tensor<string, []>("add_6_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_6_cast_fp16 = add(x = reduce_mean_11_cast_fp16, y = add_6_y_0_to_fp16)[name = tensor<string, []>("add_6_cast_fp16")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_3_cast_fp16 = sqrt(x = add_6_cast_fp16)[name = tensor<string, []>("sqrt_3_cast_fp16")];
            tensor<fp16, [2, 32, 10, 48, 80]> real_div_3_cast_fp16 = real_div(x = sub_6_cast_fp16, y = sqrt_3_cast_fp16)[name = tensor<string, []>("real_div_3_cast_fp16")];
            tensor<int32, [4]> reshape_13_shape_0 = const()[name = tensor<string, []>("reshape_13_shape_0"), val = tensor<int32, [4]>([2, 320, 48, 80])];
            tensor<fp16, [2, 320, 48, 80]> reshape_13_cast_fp16 = reshape(shape = reshape_13_shape_0, x = real_div_3_cast_fp16)[name = tensor<string, []>("reshape_13_cast_fp16")];
            tensor<fp16, [320]> add_7_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_7_gamma_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14059584)))];
            tensor<fp16, [320]> add_7_beta_0_to_fp16 = const()[name = tensor<string, []>("add_7_beta_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14060288)))];
            tensor<fp16, []> add_7_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_7_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 320, 48, 80]> add_7_cast_fp16 = batch_norm(beta = add_7_beta_0_to_fp16, epsilon = add_7_epsilon_0_to_fp16, gamma = add_7_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_13_cast_fp16)[name = tensor<string, []>("add_7_cast_fp16")];
            tensor<fp16, [2, 320, 48, 80]> input_39_cast_fp16 = silu(x = add_7_cast_fp16)[name = tensor<string, []>("input_39_cast_fp16")];
            tensor<int32, [2]> var_382 = const()[name = tensor<string, []>("op_382"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_384 = const()[name = tensor<string, []>("op_384"), 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]>([1, 1, 1, 1])];
            tensor<fp16, [320, 320, 3, 3]> down_blocks_0_resnets_1_conv1_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_resnets_1_conv1_weight_to_fp16"), val = tensor<fp16, [320, 320, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14060992)))];
            tensor<fp16, [320]> down_blocks_0_resnets_1_conv1_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_resnets_1_conv1_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15904256)))];
            tensor<fp16, [2, 320, 48, 80]> hidden_states_19_cast_fp16 = conv(bias = down_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = var_384, groups = var_111, pad = hidden_states_19_pad_0, pad_type = hidden_states_19_pad_type_0, strides = var_382, weight = down_blocks_0_resnets_1_conv1_weight_to_fp16, x = input_39_cast_fp16)[name = tensor<string, []>("hidden_states_19_cast_fp16")];
            tensor<int32, [2]> var_390 = const()[name = tensor<string, []>("op_390"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_392 = const()[name = tensor<string, []>("op_392"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> temb_3_pad_type_0 = const()[name = tensor<string, []>("temb_3_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> temb_3_pad_0 = const()[name = tensor<string, []>("temb_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 1280, 1, 1]> down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor<fp16, [320, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15904960)))];
            tensor<fp16, [320]> down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16724224)))];
            tensor<fp16, [2, 320, 1, 1]> temb_3_cast_fp16 = conv(bias = down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_392, groups = var_111, pad = temb_3_pad_0, pad_type = temb_3_pad_type_0, strides = var_390, weight = down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16_1)[name = tensor<string, []>("temb_3_cast_fp16")];
            tensor<fp16, [2, 320, 48, 80]> input_43_cast_fp16 = add(x = hidden_states_19_cast_fp16, y = temb_3_cast_fp16)[name = tensor<string, []>("input_43_cast_fp16")];
            tensor<int32, [5]> reshape_16_shape_0 = const()[name = tensor<string, []>("reshape_16_shape_0"), val = tensor<int32, [5]>([2, 32, 10, 48, 80])];
            tensor<fp16, [2, 32, 10, 48, 80]> reshape_16_cast_fp16 = reshape(shape = reshape_16_shape_0, x = input_43_cast_fp16)[name = tensor<string, []>("reshape_16_cast_fp16")];
            tensor<int32, [3]> reduce_mean_12_axes_0 = const()[name = tensor<string, []>("reduce_mean_12_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_12_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_12_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_12_cast_fp16 = reduce_mean(axes = reduce_mean_12_axes_0, keep_dims = reduce_mean_12_keep_dims_0, x = reshape_16_cast_fp16)[name = tensor<string, []>("reduce_mean_12_cast_fp16")];
            tensor<fp16, [2, 32, 10, 48, 80]> sub_8_cast_fp16 = sub(x = reshape_16_cast_fp16, y = reduce_mean_12_cast_fp16)[name = tensor<string, []>("sub_8_cast_fp16")];
            tensor<fp16, [2, 32, 10, 48, 80]> square_4_cast_fp16 = square(x = sub_8_cast_fp16)[name = tensor<string, []>("square_4_cast_fp16")];
            tensor<int32, [3]> reduce_mean_14_axes_0 = const()[name = tensor<string, []>("reduce_mean_14_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_14_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_14_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_14_cast_fp16 = reduce_mean(axes = reduce_mean_14_axes_0, keep_dims = reduce_mean_14_keep_dims_0, x = square_4_cast_fp16)[name = tensor<string, []>("reduce_mean_14_cast_fp16")];
            tensor<fp16, []> add_8_y_0_to_fp16 = const()[name = tensor<string, []>("add_8_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_8_cast_fp16 = add(x = reduce_mean_14_cast_fp16, y = add_8_y_0_to_fp16)[name = tensor<string, []>("add_8_cast_fp16")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_4_cast_fp16 = sqrt(x = add_8_cast_fp16)[name = tensor<string, []>("sqrt_4_cast_fp16")];
            tensor<fp16, [2, 32, 10, 48, 80]> real_div_4_cast_fp16 = real_div(x = sub_8_cast_fp16, y = sqrt_4_cast_fp16)[name = tensor<string, []>("real_div_4_cast_fp16")];
            tensor<int32, [4]> reshape_17_shape_0 = const()[name = tensor<string, []>("reshape_17_shape_0"), val = tensor<int32, [4]>([2, 320, 48, 80])];
            tensor<fp16, [2, 320, 48, 80]> reshape_17_cast_fp16 = reshape(shape = reshape_17_shape_0, x = real_div_4_cast_fp16)[name = tensor<string, []>("reshape_17_cast_fp16")];
            tensor<fp16, [320]> add_9_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_9_gamma_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16724928)))];
            tensor<fp16, [320]> add_9_beta_0_to_fp16 = const()[name = tensor<string, []>("add_9_beta_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16725632)))];
            tensor<fp16, []> add_9_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_9_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 320, 48, 80]> add_9_cast_fp16 = batch_norm(beta = add_9_beta_0_to_fp16, epsilon = add_9_epsilon_0_to_fp16, gamma = add_9_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_17_cast_fp16)[name = tensor<string, []>("add_9_cast_fp16")];
            tensor<fp16, [2, 320, 48, 80]> input_47_cast_fp16 = silu(x = add_9_cast_fp16)[name = tensor<string, []>("input_47_cast_fp16")];
            tensor<int32, [2]> var_402 = const()[name = tensor<string, []>("op_402"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_404 = const()[name = tensor<string, []>("op_404"), 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]>([1, 1, 1, 1])];
            tensor<fp16, [320, 320, 3, 3]> down_blocks_0_resnets_1_conv2_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_resnets_1_conv2_weight_to_fp16"), val = tensor<fp16, [320, 320, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16726336)))];
            tensor<fp16, [320]> down_blocks_0_resnets_1_conv2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_resnets_1_conv2_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18569600)))];
            tensor<fp16, [2, 320, 48, 80]> hidden_states_21_cast_fp16 = conv(bias = down_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = var_404, groups = var_111, pad = hidden_states_21_pad_0, pad_type = hidden_states_21_pad_type_0, strides = var_402, weight = down_blocks_0_resnets_1_conv2_weight_to_fp16, x = input_47_cast_fp16)[name = tensor<string, []>("hidden_states_21_cast_fp16")];
            tensor<fp16, [2, 320, 48, 80]> hidden_states_23_cast_fp16 = add(x = input_35_cast_fp16_1, y = hidden_states_21_cast_fp16)[name = tensor<string, []>("hidden_states_23_cast_fp16")];
            tensor<int32, [5]> reshape_20_shape_0 = const()[name = tensor<string, []>("reshape_20_shape_0"), val = tensor<int32, [5]>([2, 32, 10, 48, 80])];
            tensor<fp16, [2, 32, 10, 48, 80]> reshape_20_cast_fp16 = reshape(shape = reshape_20_shape_0, x = hidden_states_23_cast_fp16)[name = tensor<string, []>("reshape_20_cast_fp16")];
            tensor<int32, [3]> reduce_mean_15_axes_0 = const()[name = tensor<string, []>("reduce_mean_15_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_15_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_15_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_15_cast_fp16 = reduce_mean(axes = reduce_mean_15_axes_0, keep_dims = reduce_mean_15_keep_dims_0, x = reshape_20_cast_fp16)[name = tensor<string, []>("reduce_mean_15_cast_fp16")];
            tensor<fp16, [2, 32, 10, 48, 80]> sub_10_cast_fp16 = sub(x = reshape_20_cast_fp16, y = reduce_mean_15_cast_fp16)[name = tensor<string, []>("sub_10_cast_fp16")];
            tensor<fp16, [2, 32, 10, 48, 80]> square_5_cast_fp16 = square(x = sub_10_cast_fp16)[name = tensor<string, []>("square_5_cast_fp16")];
            tensor<int32, [3]> reduce_mean_17_axes_0 = const()[name = tensor<string, []>("reduce_mean_17_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_17_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_17_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_17_cast_fp16 = reduce_mean(axes = reduce_mean_17_axes_0, keep_dims = reduce_mean_17_keep_dims_0, x = square_5_cast_fp16)[name = tensor<string, []>("reduce_mean_17_cast_fp16")];
            tensor<fp16, []> add_10_y_0_to_fp16 = const()[name = tensor<string, []>("add_10_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_10_cast_fp16 = add(x = reduce_mean_17_cast_fp16, y = add_10_y_0_to_fp16)[name = tensor<string, []>("add_10_cast_fp16")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_5_cast_fp16 = sqrt(x = add_10_cast_fp16)[name = tensor<string, []>("sqrt_5_cast_fp16")];
            tensor<fp16, [2, 32, 10, 48, 80]> real_div_5_cast_fp16 = real_div(x = sub_10_cast_fp16, y = sqrt_5_cast_fp16)[name = tensor<string, []>("real_div_5_cast_fp16")];
            tensor<int32, [4]> reshape_21_shape_0 = const()[name = tensor<string, []>("reshape_21_shape_0"), val = tensor<int32, [4]>([2, 320, 48, 80])];
            tensor<fp16, [2, 320, 48, 80]> reshape_21_cast_fp16 = reshape(shape = reshape_21_shape_0, x = real_div_5_cast_fp16)[name = tensor<string, []>("reshape_21_cast_fp16")];
            tensor<fp16, [320]> add_11_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_11_gamma_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18570304)))];
            tensor<fp16, [320]> add_11_beta_0_to_fp16 = const()[name = tensor<string, []>("add_11_beta_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18571008)))];
            tensor<fp16, []> add_11_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_11_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 320, 48, 80]> add_11_cast_fp16 = batch_norm(beta = add_11_beta_0_to_fp16, epsilon = add_11_epsilon_0_to_fp16, gamma = add_11_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_21_cast_fp16)[name = tensor<string, []>("add_11_cast_fp16")];
            tensor<int32, [2]> var_424 = const()[name = tensor<string, []>("op_424"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_426 = const()[name = tensor<string, []>("op_426"), 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, [320, 320, 1, 1]> down_blocks_0_attentions_1_proj_in_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_1_proj_in_weight_to_fp16"), val = tensor<fp16, [320, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18571712)))];
            tensor<fp16, [320]> down_blocks_0_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_1_proj_in_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18776576)))];
            tensor<fp16, [2, 320, 48, 80]> hidden_states_25_cast_fp16 = conv(bias = down_blocks_0_attentions_1_proj_in_bias_to_fp16, dilations = var_426, groups = var_111, pad = hidden_states_25_pad_0, pad_type = hidden_states_25_pad_type_0, strides = var_424, weight = down_blocks_0_attentions_1_proj_in_weight_to_fp16, x = add_11_cast_fp16)[name = tensor<string, []>("hidden_states_25_cast_fp16")];
            tensor<int32, [4]> var_431 = const()[name = tensor<string, []>("op_431"), val = tensor<int32, [4]>([2, 320, 1, 3840])];
            tensor<fp16, [2, 320, 1, 3840]> inputs_7_cast_fp16 = reshape(shape = var_431, x = hidden_states_25_cast_fp16)[name = tensor<string, []>("inputs_7_cast_fp16")];
            tensor<int32, [1]> var_441 = const()[name = tensor<string, []>("op_441"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 3840]> channels_mean_7_cast_fp16 = reduce_mean(axes = var_441, keep_dims = var_106, x = inputs_7_cast_fp16)[name = tensor<string, []>("channels_mean_7_cast_fp16")];
            tensor<fp16, [2, 320, 1, 3840]> 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, [2, 320, 1, 3840]> 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_445 = const()[name = tensor<string, []>("op_445"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 3840]> var_446_cast_fp16 = reduce_mean(axes = var_445, keep_dims = var_106, x = zero_mean_sq_7_cast_fp16)[name = tensor<string, []>("op_446_cast_fp16")];
            tensor<fp16, []> var_447_to_fp16 = const()[name = tensor<string, []>("op_447_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 3840]> var_448_cast_fp16 = add(x = var_446_cast_fp16, y = var_447_to_fp16)[name = tensor<string, []>("op_448_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, [2, 1, 1, 3840]> denom_7_cast_fp16 = rsqrt(epsilon = denom_7_epsilon_0_to_fp16, x = var_448_cast_fp16)[name = tensor<string, []>("denom_7_cast_fp16")];
            tensor<fp16, [2, 320, 1, 3840]> 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, [1, 320, 1, 1]> var_452_to_fp16 = const()[name = tensor<string, []>("op_452_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18777280)))];
            tensor<fp16, [2, 320, 1, 3840]> var_453_cast_fp16 = add(x = out_7_cast_fp16, y = var_452_to_fp16)[name = tensor<string, []>("op_453_cast_fp16")];
            tensor<fp16, [1, 320, 1, 1]> var_455_to_fp16 = const()[name = tensor<string, []>("op_455_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18777984)))];
            tensor<fp16, [2, 320, 1, 3840]> hidden_states_27_cast_fp16 = mul(x = var_453_cast_fp16, y = var_455_to_fp16)[name = tensor<string, []>("hidden_states_27_cast_fp16")];
            tensor<int32, [2]> var_462 = const()[name = tensor<string, []>("op_462"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_464 = const()[name = tensor<string, []>("op_464"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_5_pad_type_0 = const()[name = tensor<string, []>("q_5_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_5_pad_0 = const()[name = tensor<string, []>("q_5_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 320, 1, 1]> down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor<fp16, [320, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18778688)))];
            tensor<fp16, [2, 320, 1, 3840]> q_5_cast_fp16 = conv(dilations = var_464, groups = var_111, pad = q_5_pad_0, pad_type = q_5_pad_type_0, strides = var_462, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_27_cast_fp16)[name = tensor<string, []>("q_5_cast_fp16")];
            tensor<int32, [2]> var_468 = const()[name = tensor<string, []>("op_468"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_470 = const()[name = tensor<string, []>("op_470"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_5_pad_type_0 = const()[name = tensor<string, []>("k_5_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_5_pad_0 = const()[name = tensor<string, []>("k_5_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 320, 1, 1]> down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor<fp16, [320, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18983552)))];
            tensor<fp16, [2, 320, 1, 3840]> k_5_cast_fp16 = conv(dilations = var_470, groups = var_111, pad = k_5_pad_0, pad_type = k_5_pad_type_0, strides = var_468, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_27_cast_fp16)[name = tensor<string, []>("k_5_cast_fp16")];
            tensor<int32, [2]> var_474 = const()[name = tensor<string, []>("op_474"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_476 = const()[name = tensor<string, []>("op_476"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_5_pad_type_0 = const()[name = tensor<string, []>("v_5_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_5_pad_0 = const()[name = tensor<string, []>("v_5_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 320, 1, 1]> down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor<fp16, [320, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19188416)))];
            tensor<fp16, [2, 320, 1, 3840]> v_5_cast_fp16 = conv(dilations = var_476, groups = var_111, pad = v_5_pad_0, pad_type = v_5_pad_type_0, strides = var_474, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_27_cast_fp16)[name = tensor<string, []>("v_5_cast_fp16")];
            tensor<int32, [4]> var_480 = const()[name = tensor<string, []>("op_480"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 3840]> var_481_cast_fp16 = reshape(shape = var_480, x = q_5_cast_fp16)[name = tensor<string, []>("op_481_cast_fp16")];
            tensor<int32, [4]> var_482 = const()[name = tensor<string, []>("op_482"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 3840]> var_483_cast_fp16 = reshape(shape = var_482, x = k_5_cast_fp16)[name = tensor<string, []>("op_483_cast_fp16")];
            tensor<int32, [4]> var_484 = const()[name = tensor<string, []>("op_484"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 3840]> var_485_cast_fp16 = reshape(shape = var_484, x = v_5_cast_fp16)[name = tensor<string, []>("op_485_cast_fp16")];
            tensor<bool, []> attn_weights_9_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_9_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_9_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_9_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 5, 3840, 3840]> attn_weights_9_cast_fp16 = matmul(transpose_x = attn_weights_9_transpose_x_0, transpose_y = attn_weights_9_transpose_y_0, x = var_481_cast_fp16, y = var_483_cast_fp16)[name = tensor<string, []>("attn_weights_9_cast_fp16")];
            tensor<fp16, [2, 5, 3840, 3840]> attn_weights_11_cast_fp16 = mul(x = attn_weights_9_cast_fp16, y = var_102_to_fp16)[name = tensor<string, []>("attn_weights_11_cast_fp16")];
            tensor<fp16, [2, 5, 3840, 3840]> var_489_cast_fp16 = softmax(axis = var_95, x = attn_weights_11_cast_fp16)[name = tensor<string, []>("op_489_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, [2, 5, 64, 3840]> attn_5_cast_fp16 = matmul(transpose_x = attn_5_transpose_x_0, transpose_y = attn_5_transpose_y_0, x = var_485_cast_fp16, y = var_489_cast_fp16)[name = tensor<string, []>("attn_5_cast_fp16")];
            tensor<int32, [4]> var_493 = const()[name = tensor<string, []>("op_493"), val = tensor<int32, [4]>([2, 320, 1, -1])];
            tensor<fp16, [2, 320, 1, 3840]> input_51_cast_fp16 = reshape(shape = var_493, x = attn_5_cast_fp16)[name = tensor<string, []>("input_51_cast_fp16")];
            tensor<int32, [2]> var_498 = const()[name = tensor<string, []>("op_498"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_500 = const()[name = tensor<string, []>("op_500"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_502_pad_type_0 = const()[name = tensor<string, []>("op_502_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_502_pad_0 = const()[name = tensor<string, []>("op_502_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 320, 1, 1]> down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor<fp16, [320, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19393280)))];
            tensor<fp16, [320]> down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19598144)))];
            tensor<fp16, [2, 320, 1, 3840]> var_502_cast_fp16 = conv(bias = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_500, groups = var_111, pad = var_502_pad_0, pad_type = var_502_pad_type_0, strides = var_498, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_51_cast_fp16)[name = tensor<string, []>("op_502_cast_fp16")];
            tensor<fp16, [2, 320, 1, 3840]> inputs_9_cast_fp16 = add(x = var_502_cast_fp16, y = inputs_7_cast_fp16)[name = tensor<string, []>("inputs_9_cast_fp16")];
            tensor<int32, [1]> var_506 = const()[name = tensor<string, []>("op_506"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 3840]> channels_mean_9_cast_fp16 = reduce_mean(axes = var_506, keep_dims = var_106, x = inputs_9_cast_fp16)[name = tensor<string, []>("channels_mean_9_cast_fp16")];
            tensor<fp16, [2, 320, 1, 3840]> 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, [2, 320, 1, 3840]> 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_510 = const()[name = tensor<string, []>("op_510"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 3840]> var_511_cast_fp16 = reduce_mean(axes = var_510, keep_dims = var_106, x = zero_mean_sq_9_cast_fp16)[name = tensor<string, []>("op_511_cast_fp16")];
            tensor<fp16, []> var_512_to_fp16 = const()[name = tensor<string, []>("op_512_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 3840]> var_513_cast_fp16 = add(x = var_511_cast_fp16, y = var_512_to_fp16)[name = tensor<string, []>("op_513_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, [2, 1, 1, 3840]> denom_9_cast_fp16 = rsqrt(epsilon = denom_9_epsilon_0_to_fp16, x = var_513_cast_fp16)[name = tensor<string, []>("denom_9_cast_fp16")];
            tensor<fp16, [2, 320, 1, 3840]> 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, [1, 320, 1, 1]> var_517_to_fp16 = const()[name = tensor<string, []>("op_517_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19598848)))];
            tensor<fp16, [2, 320, 1, 3840]> var_518_cast_fp16 = add(x = out_9_cast_fp16, y = var_517_to_fp16)[name = tensor<string, []>("op_518_cast_fp16")];
            tensor<fp16, [1, 320, 1, 1]> var_520_to_fp16 = const()[name = tensor<string, []>("op_520_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19599552)))];
            tensor<fp16, [2, 320, 1, 3840]> hidden_states_29_cast_fp16 = mul(x = var_518_cast_fp16, y = var_520_to_fp16)[name = tensor<string, []>("hidden_states_29_cast_fp16")];
            tensor<int32, [2]> var_527 = const()[name = tensor<string, []>("op_527"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_529 = const()[name = tensor<string, []>("op_529"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_7_pad_type_0 = const()[name = tensor<string, []>("q_7_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_7_pad_0 = const()[name = tensor<string, []>("q_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 320, 1, 1]> down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor<fp16, [320, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19600256)))];
            tensor<fp16, [2, 320, 1, 3840]> q_7_cast_fp16 = conv(dilations = var_529, groups = var_111, pad = q_7_pad_0, pad_type = q_7_pad_type_0, strides = var_527, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_29_cast_fp16)[name = tensor<string, []>("q_7_cast_fp16")];
            tensor<int32, [2]> var_533 = const()[name = tensor<string, []>("op_533"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_535 = const()[name = tensor<string, []>("op_535"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_7_pad_type_0 = const()[name = tensor<string, []>("k_7_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_7_pad_0 = const()[name = tensor<string, []>("k_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 1024, 1, 1]> down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor<fp16, [320, 1024, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19805120)))];
            tensor<fp16, [2, 320, 1, 77]> k_7_cast_fp16 = conv(dilations = var_535, groups = var_111, pad = k_7_pad_0, pad_type = k_7_pad_type_0, strides = var_533, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor<string, []>("k_7_cast_fp16")];
            tensor<int32, [2]> var_539 = const()[name = tensor<string, []>("op_539"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_541 = const()[name = tensor<string, []>("op_541"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_7_pad_type_0 = const()[name = tensor<string, []>("v_7_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_7_pad_0 = const()[name = tensor<string, []>("v_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 1024, 1, 1]> down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor<fp16, [320, 1024, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20460544)))];
            tensor<fp16, [2, 320, 1, 77]> v_7_cast_fp16 = conv(dilations = var_541, groups = var_111, pad = v_7_pad_0, pad_type = v_7_pad_type_0, strides = var_539, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor<string, []>("v_7_cast_fp16")];
            tensor<int32, [4]> var_545 = const()[name = tensor<string, []>("op_545"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 3840]> var_546_cast_fp16 = reshape(shape = var_545, x = q_7_cast_fp16)[name = tensor<string, []>("op_546_cast_fp16")];
            tensor<int32, [4]> var_547 = const()[name = tensor<string, []>("op_547"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 77]> var_548_cast_fp16 = reshape(shape = var_547, x = k_7_cast_fp16)[name = tensor<string, []>("op_548_cast_fp16")];
            tensor<int32, [4]> var_549 = const()[name = tensor<string, []>("op_549"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 77]> var_550_cast_fp16 = reshape(shape = var_549, x = v_7_cast_fp16)[name = tensor<string, []>("op_550_cast_fp16")];
            tensor<bool, []> attn_weights_13_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_13_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_13_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_13_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 5, 3840, 77]> attn_weights_13_cast_fp16 = matmul(transpose_x = attn_weights_13_transpose_x_0, transpose_y = attn_weights_13_transpose_y_0, x = var_546_cast_fp16, y = var_548_cast_fp16)[name = tensor<string, []>("attn_weights_13_cast_fp16")];
            tensor<fp16, [2, 5, 3840, 77]> attn_weights_15_cast_fp16 = mul(x = attn_weights_13_cast_fp16, y = var_102_to_fp16)[name = tensor<string, []>("attn_weights_15_cast_fp16")];
            tensor<fp16, [2, 5, 3840, 77]> var_554_cast_fp16 = softmax(axis = var_95, x = attn_weights_15_cast_fp16)[name = tensor<string, []>("op_554_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, [2, 5, 64, 3840]> attn_7_cast_fp16 = matmul(transpose_x = attn_7_transpose_x_0, transpose_y = attn_7_transpose_y_0, x = var_550_cast_fp16, y = var_554_cast_fp16)[name = tensor<string, []>("attn_7_cast_fp16")];
            tensor<int32, [4]> var_558 = const()[name = tensor<string, []>("op_558"), val = tensor<int32, [4]>([2, 320, 1, -1])];
            tensor<fp16, [2, 320, 1, 3840]> input_53_cast_fp16 = reshape(shape = var_558, x = attn_7_cast_fp16)[name = tensor<string, []>("input_53_cast_fp16")];
            tensor<int32, [2]> var_563 = const()[name = tensor<string, []>("op_563"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_565 = const()[name = tensor<string, []>("op_565"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_567_pad_type_0 = const()[name = tensor<string, []>("op_567_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_567_pad_0 = const()[name = tensor<string, []>("op_567_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 320, 1, 1]> down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor<fp16, [320, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21115968)))];
            tensor<fp16, [320]> down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21320832)))];
            tensor<fp16, [2, 320, 1, 3840]> var_567_cast_fp16 = conv(bias = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_565, groups = var_111, pad = var_567_pad_0, pad_type = var_567_pad_type_0, strides = var_563, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_53_cast_fp16)[name = tensor<string, []>("op_567_cast_fp16")];
            tensor<fp16, [2, 320, 1, 3840]> inputs_11_cast_fp16 = add(x = var_567_cast_fp16, y = inputs_9_cast_fp16)[name = tensor<string, []>("inputs_11_cast_fp16")];
            tensor<int32, [1]> var_571 = const()[name = tensor<string, []>("op_571"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 3840]> channels_mean_11_cast_fp16 = reduce_mean(axes = var_571, keep_dims = var_106, x = inputs_11_cast_fp16)[name = tensor<string, []>("channels_mean_11_cast_fp16")];
            tensor<fp16, [2, 320, 1, 3840]> 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, [2, 320, 1, 3840]> 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_575 = const()[name = tensor<string, []>("op_575"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 3840]> var_576_cast_fp16 = reduce_mean(axes = var_575, keep_dims = var_106, x = zero_mean_sq_11_cast_fp16)[name = tensor<string, []>("op_576_cast_fp16")];
            tensor<fp16, []> var_577_to_fp16 = const()[name = tensor<string, []>("op_577_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 3840]> var_578_cast_fp16 = add(x = var_576_cast_fp16, y = var_577_to_fp16)[name = tensor<string, []>("op_578_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, [2, 1, 1, 3840]> denom_11_cast_fp16 = rsqrt(epsilon = denom_11_epsilon_0_to_fp16, x = var_578_cast_fp16)[name = tensor<string, []>("denom_11_cast_fp16")];
            tensor<fp16, [2, 320, 1, 3840]> 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, [1, 320, 1, 1]> var_582_to_fp16 = const()[name = tensor<string, []>("op_582_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21321536)))];
            tensor<fp16, [2, 320, 1, 3840]> var_583_cast_fp16 = add(x = out_11_cast_fp16, y = var_582_to_fp16)[name = tensor<string, []>("op_583_cast_fp16")];
            tensor<fp16, [1, 320, 1, 1]> var_585_to_fp16 = const()[name = tensor<string, []>("op_585_to_fp16"), val = tensor<fp16, [1, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21322240)))];
            tensor<fp16, [2, 320, 1, 3840]> input_55_cast_fp16 = mul(x = var_583_cast_fp16, y = var_585_to_fp16)[name = tensor<string, []>("input_55_cast_fp16")];
            tensor<int32, [2]> var_593 = const()[name = tensor<string, []>("op_593"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_595 = const()[name = tensor<string, []>("op_595"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_597_pad_type_0 = const()[name = tensor<string, []>("op_597_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_597_pad_0 = const()[name = tensor<string, []>("op_597_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [2560, 320, 1, 1]> down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor<fp16, [2560, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21322944)))];
            tensor<fp16, [2560]> down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [2560]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22961408)))];
            tensor<fp16, [2, 2560, 1, 3840]> var_597_cast_fp16 = conv(bias = down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_595, groups = var_111, pad = var_597_pad_0, pad_type = var_597_pad_type_0, strides = var_593, weight = down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_55_cast_fp16)[name = tensor<string, []>("op_597_cast_fp16")];
            tensor<int32, [2]> var_598_split_sizes_0 = const()[name = tensor<string, []>("op_598_split_sizes_0"), val = tensor<int32, [2]>([1280, 1280])];
            tensor<int32, []> var_598_axis_0 = const()[name = tensor<string, []>("op_598_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 1280, 1, 3840]> var_598_cast_fp16_0, tensor<fp16, [2, 1280, 1, 3840]> var_598_cast_fp16_1 = split(axis = var_598_axis_0, split_sizes = var_598_split_sizes_0, x = var_597_cast_fp16)[name = tensor<string, []>("op_598_cast_fp16")];
            tensor<string, []> var_600_mode_0 = const()[name = tensor<string, []>("op_600_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 1280, 1, 3840]> var_600_cast_fp16 = gelu(mode = var_600_mode_0, x = var_598_cast_fp16_1)[name = tensor<string, []>("op_600_cast_fp16")];
            tensor<fp16, [2, 1280, 1, 3840]> input_57_cast_fp16 = mul(x = var_598_cast_fp16_0, y = var_600_cast_fp16)[name = tensor<string, []>("input_57_cast_fp16")];
            tensor<int32, [2]> var_604 = const()[name = tensor<string, []>("op_604"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_606 = const()[name = tensor<string, []>("op_606"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_608_pad_type_0 = const()[name = tensor<string, []>("op_608_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_608_pad_0 = const()[name = tensor<string, []>("op_608_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [320, 1280, 1, 1]> down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor<fp16, [320, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22966592)))];
            tensor<fp16, [320]> down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(23785856)))];
            tensor<fp16, [2, 320, 1, 3840]> var_608_cast_fp16 = conv(bias = down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_606, groups = var_111, pad = var_608_pad_0, pad_type = var_608_pad_type_0, strides = var_604, weight = down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_57_cast_fp16)[name = tensor<string, []>("op_608_cast_fp16")];
            tensor<fp16, [2, 320, 1, 3840]> hidden_states_33_cast_fp16 = add(x = var_608_cast_fp16, y = inputs_11_cast_fp16)[name = tensor<string, []>("hidden_states_33_cast_fp16")];
            tensor<int32, [4]> var_610 = const()[name = tensor<string, []>("op_610"), val = tensor<int32, [4]>([2, 320, 48, 80])];
            tensor<fp16, [2, 320, 48, 80]> input_59_cast_fp16 = reshape(shape = var_610, x = hidden_states_33_cast_fp16)[name = tensor<string, []>("input_59_cast_fp16")];
            tensor<int32, [2]> var_614 = const()[name = tensor<string, []>("op_614"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_616 = const()[name = tensor<string, []>("op_616"), 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, [320, 320, 1, 1]> down_blocks_0_attentions_1_proj_out_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_1_proj_out_weight_to_fp16"), val = tensor<fp16, [320, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(23786560)))];
            tensor<fp16, [320]> down_blocks_0_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_attentions_1_proj_out_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(23991424)))];
            tensor<fp16, [2, 320, 48, 80]> hidden_states_35_cast_fp16 = conv(bias = down_blocks_0_attentions_1_proj_out_bias_to_fp16, dilations = var_616, groups = var_111, pad = hidden_states_35_pad_0, pad_type = hidden_states_35_pad_type_0, strides = var_614, weight = down_blocks_0_attentions_1_proj_out_weight_to_fp16, x = input_59_cast_fp16)[name = tensor<string, []>("hidden_states_35_cast_fp16")];
            tensor<fp16, [2, 320, 48, 80]> input_61_cast_fp16_1 = add(x = hidden_states_35_cast_fp16, y = hidden_states_23_cast_fp16)[name = tensor<string, []>("input_61_cast_fp16")];
            tensor<int32, [2]> var_623 = const()[name = tensor<string, []>("op_623"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, [2]> var_625 = const()[name = tensor<string, []>("op_625"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> input_63_pad_type_0 = const()[name = tensor<string, []>("input_63_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_63_pad_0 = const()[name = tensor<string, []>("input_63_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [320, 320, 3, 3]> down_blocks_0_downsamplers_0_conv_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_downsamplers_0_conv_weight_to_fp16"), val = tensor<fp16, [320, 320, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(23992128)))];
            tensor<fp16, [320]> down_blocks_0_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_0_downsamplers_0_conv_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25835392)))];
            tensor<fp16, [2, 320, 24, 40]> input_63_cast_fp16_1 = conv(bias = down_blocks_0_downsamplers_0_conv_bias_to_fp16, dilations = var_625, groups = var_111, pad = input_63_pad_0, pad_type = input_63_pad_type_0, strides = var_623, weight = down_blocks_0_downsamplers_0_conv_weight_to_fp16, x = input_61_cast_fp16_1)[name = tensor<string, []>("input_63_cast_fp16")];
            tensor<int32, []> var_633 = const()[name = tensor<string, []>("op_633"), val = tensor<int32, []>(3)];
            tensor<bool, []> var_644 = const()[name = tensor<string, []>("op_644"), val = tensor<bool, []>(true)];
            tensor<int32, []> var_649 = const()[name = tensor<string, []>("op_649"), val = tensor<int32, []>(1)];
            tensor<int32, [5]> reshape_24_shape_0 = const()[name = tensor<string, []>("reshape_24_shape_0"), val = tensor<int32, [5]>([2, 32, 10, 24, 40])];
            tensor<fp16, [2, 32, 10, 24, 40]> reshape_24_cast_fp16 = reshape(shape = reshape_24_shape_0, x = input_63_cast_fp16_1)[name = tensor<string, []>("reshape_24_cast_fp16")];
            tensor<int32, [3]> reduce_mean_18_axes_0 = const()[name = tensor<string, []>("reduce_mean_18_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_18_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_18_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_18_cast_fp16 = reduce_mean(axes = reduce_mean_18_axes_0, keep_dims = reduce_mean_18_keep_dims_0, x = reshape_24_cast_fp16)[name = tensor<string, []>("reduce_mean_18_cast_fp16")];
            tensor<fp16, [2, 32, 10, 24, 40]> sub_12_cast_fp16 = sub(x = reshape_24_cast_fp16, y = reduce_mean_18_cast_fp16)[name = tensor<string, []>("sub_12_cast_fp16")];
            tensor<fp16, [2, 32, 10, 24, 40]> square_6_cast_fp16 = square(x = sub_12_cast_fp16)[name = tensor<string, []>("square_6_cast_fp16")];
            tensor<int32, [3]> reduce_mean_20_axes_0 = const()[name = tensor<string, []>("reduce_mean_20_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_20_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_20_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_20_cast_fp16 = reduce_mean(axes = reduce_mean_20_axes_0, keep_dims = reduce_mean_20_keep_dims_0, x = square_6_cast_fp16)[name = tensor<string, []>("reduce_mean_20_cast_fp16")];
            tensor<fp16, []> add_12_y_0_to_fp16 = const()[name = tensor<string, []>("add_12_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_12_cast_fp16 = add(x = reduce_mean_20_cast_fp16, y = add_12_y_0_to_fp16)[name = tensor<string, []>("add_12_cast_fp16")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_6_cast_fp16 = sqrt(x = add_12_cast_fp16)[name = tensor<string, []>("sqrt_6_cast_fp16")];
            tensor<fp16, [2, 32, 10, 24, 40]> real_div_6_cast_fp16 = real_div(x = sub_12_cast_fp16, y = sqrt_6_cast_fp16)[name = tensor<string, []>("real_div_6_cast_fp16")];
            tensor<int32, [4]> reshape_25_shape_0 = const()[name = tensor<string, []>("reshape_25_shape_0"), val = tensor<int32, [4]>([2, 320, 24, 40])];
            tensor<fp16, [2, 320, 24, 40]> reshape_25_cast_fp16 = reshape(shape = reshape_25_shape_0, x = real_div_6_cast_fp16)[name = tensor<string, []>("reshape_25_cast_fp16")];
            tensor<fp16, [320]> add_13_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_13_gamma_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25836096)))];
            tensor<fp16, [320]> add_13_beta_0_to_fp16 = const()[name = tensor<string, []>("add_13_beta_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25836800)))];
            tensor<fp16, []> add_13_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_13_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 320, 24, 40]> add_13_cast_fp16 = batch_norm(beta = add_13_beta_0_to_fp16, epsilon = add_13_epsilon_0_to_fp16, gamma = add_13_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_25_cast_fp16)[name = tensor<string, []>("add_13_cast_fp16")];
            tensor<fp16, [2, 320, 24, 40]> input_67_cast_fp16 = silu(x = add_13_cast_fp16)[name = tensor<string, []>("input_67_cast_fp16")];
            tensor<int32, [2]> var_672 = const()[name = tensor<string, []>("op_672"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_674 = const()[name = tensor<string, []>("op_674"), 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]>([1, 1, 1, 1])];
            tensor<fp16, [640, 320, 3, 3]> down_blocks_1_resnets_0_conv1_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_resnets_0_conv1_weight_to_fp16"), val = tensor<fp16, [640, 320, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25837504)))];
            tensor<fp16, [640]> down_blocks_1_resnets_0_conv1_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_resnets_0_conv1_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29523968)))];
            tensor<fp16, [2, 640, 24, 40]> hidden_states_37_cast_fp16 = conv(bias = down_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = var_674, groups = var_649, pad = hidden_states_37_pad_0, pad_type = hidden_states_37_pad_type_0, strides = var_672, weight = down_blocks_1_resnets_0_conv1_weight_to_fp16, x = input_67_cast_fp16)[name = tensor<string, []>("hidden_states_37_cast_fp16")];
            tensor<int32, [2]> var_680 = const()[name = tensor<string, []>("op_680"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_682 = const()[name = tensor<string, []>("op_682"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> temb_5_pad_type_0 = const()[name = tensor<string, []>("temb_5_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> temb_5_pad_0 = const()[name = tensor<string, []>("temb_5_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 1280, 1, 1]> down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor<fp16, [640, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29525312)))];
            tensor<fp16, [640]> down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(31163776)))];
            tensor<fp16, [2, 640, 1, 1]> temb_5_cast_fp16 = conv(bias = down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_682, groups = var_649, pad = temb_5_pad_0, pad_type = temb_5_pad_type_0, strides = var_680, weight = down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16_1)[name = tensor<string, []>("temb_5_cast_fp16")];
            tensor<fp16, [2, 640, 24, 40]> input_71_cast_fp16 = add(x = hidden_states_37_cast_fp16, y = temb_5_cast_fp16)[name = tensor<string, []>("input_71_cast_fp16")];
            tensor<int32, [5]> reshape_28_shape_0 = const()[name = tensor<string, []>("reshape_28_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 24, 40])];
            tensor<fp16, [2, 32, 20, 24, 40]> reshape_28_cast_fp16 = reshape(shape = reshape_28_shape_0, x = input_71_cast_fp16)[name = tensor<string, []>("reshape_28_cast_fp16")];
            tensor<int32, [3]> reduce_mean_21_axes_0 = const()[name = tensor<string, []>("reduce_mean_21_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_21_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_21_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_21_cast_fp16 = reduce_mean(axes = reduce_mean_21_axes_0, keep_dims = reduce_mean_21_keep_dims_0, x = reshape_28_cast_fp16)[name = tensor<string, []>("reduce_mean_21_cast_fp16")];
            tensor<fp16, [2, 32, 20, 24, 40]> sub_14_cast_fp16 = sub(x = reshape_28_cast_fp16, y = reduce_mean_21_cast_fp16)[name = tensor<string, []>("sub_14_cast_fp16")];
            tensor<fp16, [2, 32, 20, 24, 40]> square_7_cast_fp16 = square(x = sub_14_cast_fp16)[name = tensor<string, []>("square_7_cast_fp16")];
            tensor<int32, [3]> reduce_mean_23_axes_0 = const()[name = tensor<string, []>("reduce_mean_23_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_23_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_23_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_23_cast_fp16 = reduce_mean(axes = reduce_mean_23_axes_0, keep_dims = reduce_mean_23_keep_dims_0, x = square_7_cast_fp16)[name = tensor<string, []>("reduce_mean_23_cast_fp16")];
            tensor<fp16, []> add_14_y_0_to_fp16 = const()[name = tensor<string, []>("add_14_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_14_cast_fp16 = add(x = reduce_mean_23_cast_fp16, y = add_14_y_0_to_fp16)[name = tensor<string, []>("add_14_cast_fp16")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_7_cast_fp16 = sqrt(x = add_14_cast_fp16)[name = tensor<string, []>("sqrt_7_cast_fp16")];
            tensor<fp16, [2, 32, 20, 24, 40]> real_div_7_cast_fp16 = real_div(x = sub_14_cast_fp16, y = sqrt_7_cast_fp16)[name = tensor<string, []>("real_div_7_cast_fp16")];
            tensor<int32, [4]> reshape_29_shape_0 = const()[name = tensor<string, []>("reshape_29_shape_0"), val = tensor<int32, [4]>([2, 640, 24, 40])];
            tensor<fp16, [2, 640, 24, 40]> reshape_29_cast_fp16 = reshape(shape = reshape_29_shape_0, x = real_div_7_cast_fp16)[name = tensor<string, []>("reshape_29_cast_fp16")];
            tensor<fp16, [640]> add_15_mean_0_to_fp16 = const()[name = tensor<string, []>("add_15_mean_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(31165120)))];
            tensor<fp16, [640]> add_15_variance_0_to_fp16 = const()[name = tensor<string, []>("add_15_variance_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(31166464)))];
            tensor<fp16, [640]> add_15_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_15_gamma_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(31167808)))];
            tensor<fp16, [640]> add_15_beta_0_to_fp16 = const()[name = tensor<string, []>("add_15_beta_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(31169152)))];
            tensor<fp16, []> add_15_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_15_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 640, 24, 40]> add_15_cast_fp16 = batch_norm(beta = add_15_beta_0_to_fp16, epsilon = add_15_epsilon_0_to_fp16, gamma = add_15_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_29_cast_fp16)[name = tensor<string, []>("add_15_cast_fp16")];
            tensor<fp16, [2, 640, 24, 40]> input_75_cast_fp16 = silu(x = add_15_cast_fp16)[name = tensor<string, []>("input_75_cast_fp16")];
            tensor<int32, [2]> var_692 = const()[name = tensor<string, []>("op_692"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_694 = const()[name = tensor<string, []>("op_694"), 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]>([1, 1, 1, 1])];
            tensor<fp16, [640, 640, 3, 3]> down_blocks_1_resnets_0_conv2_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_resnets_0_conv2_weight_to_fp16"), val = tensor<fp16, [640, 640, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(31170496)))];
            tensor<fp16, [640]> down_blocks_1_resnets_0_conv2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_resnets_0_conv2_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38543360)))];
            tensor<fp16, [2, 640, 24, 40]> hidden_states_39_cast_fp16 = conv(bias = down_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = var_694, groups = var_649, pad = hidden_states_39_pad_0, pad_type = hidden_states_39_pad_type_0, strides = var_692, weight = down_blocks_1_resnets_0_conv2_weight_to_fp16, x = input_75_cast_fp16)[name = tensor<string, []>("hidden_states_39_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, []> x_1_pad_type_0 = const()[name = tensor<string, []>("x_1_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> x_1_pad_0 = const()[name = tensor<string, []>("x_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 320, 1, 1]> down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor<fp16, [640, 320, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38544704)))];
            tensor<fp16, [640]> down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38954368)))];
            tensor<fp16, [2, 640, 24, 40]> x_1_cast_fp16 = conv(bias = down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_701, groups = var_649, pad = x_1_pad_0, pad_type = x_1_pad_type_0, strides = var_699, weight = down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16, x = input_63_cast_fp16_1)[name = tensor<string, []>("x_1_cast_fp16")];
            tensor<fp16, [2, 640, 24, 40]> hidden_states_41_cast_fp16 = add(x = x_1_cast_fp16, y = hidden_states_39_cast_fp16)[name = tensor<string, []>("hidden_states_41_cast_fp16")];
            tensor<int32, [5]> reshape_32_shape_0 = const()[name = tensor<string, []>("reshape_32_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 24, 40])];
            tensor<fp16, [2, 32, 20, 24, 40]> reshape_32_cast_fp16 = reshape(shape = reshape_32_shape_0, x = hidden_states_41_cast_fp16)[name = tensor<string, []>("reshape_32_cast_fp16")];
            tensor<int32, [3]> reduce_mean_24_axes_0 = const()[name = tensor<string, []>("reduce_mean_24_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_24_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_24_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_24_cast_fp16 = reduce_mean(axes = reduce_mean_24_axes_0, keep_dims = reduce_mean_24_keep_dims_0, x = reshape_32_cast_fp16)[name = tensor<string, []>("reduce_mean_24_cast_fp16")];
            tensor<fp16, [2, 32, 20, 24, 40]> sub_16_cast_fp16 = sub(x = reshape_32_cast_fp16, y = reduce_mean_24_cast_fp16)[name = tensor<string, []>("sub_16_cast_fp16")];
            tensor<fp16, [2, 32, 20, 24, 40]> square_8_cast_fp16 = square(x = sub_16_cast_fp16)[name = tensor<string, []>("square_8_cast_fp16")];
            tensor<int32, [3]> reduce_mean_26_axes_0 = const()[name = tensor<string, []>("reduce_mean_26_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_26_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_26_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_26_cast_fp16 = reduce_mean(axes = reduce_mean_26_axes_0, keep_dims = reduce_mean_26_keep_dims_0, x = square_8_cast_fp16)[name = tensor<string, []>("reduce_mean_26_cast_fp16")];
            tensor<fp16, []> add_16_y_0_to_fp16 = const()[name = tensor<string, []>("add_16_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_16_cast_fp16 = add(x = reduce_mean_26_cast_fp16, y = add_16_y_0_to_fp16)[name = tensor<string, []>("add_16_cast_fp16")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_8_cast_fp16 = sqrt(x = add_16_cast_fp16)[name = tensor<string, []>("sqrt_8_cast_fp16")];
            tensor<fp16, [2, 32, 20, 24, 40]> real_div_8_cast_fp16 = real_div(x = sub_16_cast_fp16, y = sqrt_8_cast_fp16)[name = tensor<string, []>("real_div_8_cast_fp16")];
            tensor<int32, [4]> reshape_33_shape_0 = const()[name = tensor<string, []>("reshape_33_shape_0"), val = tensor<int32, [4]>([2, 640, 24, 40])];
            tensor<fp16, [2, 640, 24, 40]> reshape_33_cast_fp16 = reshape(shape = reshape_33_shape_0, x = real_div_8_cast_fp16)[name = tensor<string, []>("reshape_33_cast_fp16")];
            tensor<fp16, [640]> add_17_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_17_gamma_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38955712)))];
            tensor<fp16, [640]> add_17_beta_0_to_fp16 = const()[name = tensor<string, []>("add_17_beta_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38957056)))];
            tensor<fp16, []> add_17_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_17_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 640, 24, 40]> add_17_cast_fp16 = batch_norm(beta = add_17_beta_0_to_fp16, epsilon = add_17_epsilon_0_to_fp16, gamma = add_17_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_33_cast_fp16)[name = tensor<string, []>("add_17_cast_fp16")];
            tensor<int32, [2]> var_721 = const()[name = tensor<string, []>("op_721"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_723 = const()[name = tensor<string, []>("op_723"), 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, [640, 640, 1, 1]> down_blocks_1_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_0_proj_in_weight_to_fp16"), val = tensor<fp16, [640, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38958400)))];
            tensor<fp16, [640]> down_blocks_1_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_0_proj_in_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39777664)))];
            tensor<fp16, [2, 640, 24, 40]> hidden_states_43_cast_fp16 = conv(bias = down_blocks_1_attentions_0_proj_in_bias_to_fp16, dilations = var_723, groups = var_649, pad = hidden_states_43_pad_0, pad_type = hidden_states_43_pad_type_0, strides = var_721, weight = down_blocks_1_attentions_0_proj_in_weight_to_fp16, x = add_17_cast_fp16)[name = tensor<string, []>("hidden_states_43_cast_fp16")];
            tensor<int32, [4]> var_728 = const()[name = tensor<string, []>("op_728"), val = tensor<int32, [4]>([2, 640, 1, 960])];
            tensor<fp16, [2, 640, 1, 960]> inputs_13_cast_fp16 = reshape(shape = var_728, x = hidden_states_43_cast_fp16)[name = tensor<string, []>("inputs_13_cast_fp16")];
            tensor<int32, [1]> var_738 = const()[name = tensor<string, []>("op_738"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 960]> channels_mean_13_cast_fp16 = reduce_mean(axes = var_738, keep_dims = var_644, x = inputs_13_cast_fp16)[name = tensor<string, []>("channels_mean_13_cast_fp16")];
            tensor<fp16, [2, 640, 1, 960]> 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, [2, 640, 1, 960]> 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_742 = const()[name = tensor<string, []>("op_742"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 960]> var_743_cast_fp16 = reduce_mean(axes = var_742, keep_dims = var_644, x = zero_mean_sq_13_cast_fp16)[name = tensor<string, []>("op_743_cast_fp16")];
            tensor<fp16, []> var_744_to_fp16 = const()[name = tensor<string, []>("op_744_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 960]> var_745_cast_fp16 = add(x = var_743_cast_fp16, y = var_744_to_fp16)[name = tensor<string, []>("op_745_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, [2, 1, 1, 960]> denom_13_cast_fp16 = rsqrt(epsilon = denom_13_epsilon_0_to_fp16, x = var_745_cast_fp16)[name = tensor<string, []>("denom_13_cast_fp16")];
            tensor<fp16, [2, 640, 1, 960]> 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, [1, 640, 1, 1]> var_749_to_fp16 = const()[name = tensor<string, []>("op_749_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39779008)))];
            tensor<fp16, [2, 640, 1, 960]> var_750_cast_fp16 = add(x = out_13_cast_fp16, y = var_749_to_fp16)[name = tensor<string, []>("op_750_cast_fp16")];
            tensor<fp16, [1, 640, 1, 1]> var_752_to_fp16 = const()[name = tensor<string, []>("op_752_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39780352)))];
            tensor<fp16, [2, 640, 1, 960]> hidden_states_45_cast_fp16 = mul(x = var_750_cast_fp16, y = var_752_to_fp16)[name = tensor<string, []>("hidden_states_45_cast_fp16")];
            tensor<int32, [2]> var_759 = const()[name = tensor<string, []>("op_759"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_761 = const()[name = tensor<string, []>("op_761"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_9_pad_type_0 = const()[name = tensor<string, []>("q_9_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_9_pad_0 = const()[name = tensor<string, []>("q_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor<fp16, [640, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39781696)))];
            tensor<fp16, [2, 640, 1, 960]> q_9_cast_fp16 = conv(dilations = var_761, groups = var_649, pad = q_9_pad_0, pad_type = q_9_pad_type_0, strides = var_759, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_45_cast_fp16)[name = tensor<string, []>("q_9_cast_fp16")];
            tensor<int32, [2]> var_765 = const()[name = tensor<string, []>("op_765"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_767 = const()[name = tensor<string, []>("op_767"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_9_pad_type_0 = const()[name = tensor<string, []>("k_9_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_9_pad_0 = const()[name = tensor<string, []>("k_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor<fp16, [640, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40600960)))];
            tensor<fp16, [2, 640, 1, 960]> k_9_cast_fp16 = conv(dilations = var_767, groups = var_649, pad = k_9_pad_0, pad_type = k_9_pad_type_0, strides = var_765, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_45_cast_fp16)[name = tensor<string, []>("k_9_cast_fp16")];
            tensor<int32, [2]> var_771 = const()[name = tensor<string, []>("op_771"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_773 = const()[name = tensor<string, []>("op_773"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_9_pad_type_0 = const()[name = tensor<string, []>("v_9_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_9_pad_0 = const()[name = tensor<string, []>("v_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor<fp16, [640, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41420224)))];
            tensor<fp16, [2, 640, 1, 960]> v_9_cast_fp16 = conv(dilations = var_773, groups = var_649, pad = v_9_pad_0, pad_type = v_9_pad_type_0, strides = var_771, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_45_cast_fp16)[name = tensor<string, []>("v_9_cast_fp16")];
            tensor<int32, [4]> var_777 = const()[name = tensor<string, []>("op_777"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 960]> var_778_cast_fp16 = reshape(shape = var_777, x = q_9_cast_fp16)[name = tensor<string, []>("op_778_cast_fp16")];
            tensor<int32, [4]> var_779 = const()[name = tensor<string, []>("op_779"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 960]> var_780_cast_fp16 = reshape(shape = var_779, x = k_9_cast_fp16)[name = tensor<string, []>("op_780_cast_fp16")];
            tensor<int32, [4]> var_781 = const()[name = tensor<string, []>("op_781"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 960]> var_782_cast_fp16 = reshape(shape = var_781, x = v_9_cast_fp16)[name = tensor<string, []>("op_782_cast_fp16")];
            tensor<bool, []> attn_weights_17_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_17_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_17_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_17_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 10, 960, 960]> attn_weights_17_cast_fp16 = matmul(transpose_x = attn_weights_17_transpose_x_0, transpose_y = attn_weights_17_transpose_y_0, x = var_778_cast_fp16, y = var_780_cast_fp16)[name = tensor<string, []>("attn_weights_17_cast_fp16")];
            tensor<fp16, []> var_640_to_fp16 = const()[name = tensor<string, []>("op_640_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [2, 10, 960, 960]> attn_weights_19_cast_fp16 = mul(x = attn_weights_17_cast_fp16, y = var_640_to_fp16)[name = tensor<string, []>("attn_weights_19_cast_fp16")];
            tensor<fp16, [2, 10, 960, 960]> var_786_cast_fp16 = softmax(axis = var_633, x = attn_weights_19_cast_fp16)[name = tensor<string, []>("op_786_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, [2, 10, 64, 960]> attn_9_cast_fp16 = matmul(transpose_x = attn_9_transpose_x_0, transpose_y = attn_9_transpose_y_0, x = var_782_cast_fp16, y = var_786_cast_fp16)[name = tensor<string, []>("attn_9_cast_fp16")];
            tensor<int32, [4]> var_790 = const()[name = tensor<string, []>("op_790"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 960]> input_79_cast_fp16 = reshape(shape = var_790, x = attn_9_cast_fp16)[name = tensor<string, []>("input_79_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, []> var_799_pad_type_0 = const()[name = tensor<string, []>("op_799_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_799_pad_0 = const()[name = tensor<string, []>("op_799_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor<fp16, [640, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42239488)))];
            tensor<fp16, [640]> down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43058752)))];
            tensor<fp16, [2, 640, 1, 960]> var_799_cast_fp16 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_797, groups = var_649, pad = var_799_pad_0, pad_type = var_799_pad_type_0, strides = var_795, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_79_cast_fp16)[name = tensor<string, []>("op_799_cast_fp16")];
            tensor<fp16, [2, 640, 1, 960]> inputs_15_cast_fp16 = add(x = var_799_cast_fp16, y = inputs_13_cast_fp16)[name = tensor<string, []>("inputs_15_cast_fp16")];
            tensor<int32, [1]> var_803 = const()[name = tensor<string, []>("op_803"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 960]> channels_mean_15_cast_fp16 = reduce_mean(axes = var_803, keep_dims = var_644, x = inputs_15_cast_fp16)[name = tensor<string, []>("channels_mean_15_cast_fp16")];
            tensor<fp16, [2, 640, 1, 960]> 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, [2, 640, 1, 960]> 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_807 = const()[name = tensor<string, []>("op_807"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 960]> var_808_cast_fp16 = reduce_mean(axes = var_807, keep_dims = var_644, x = zero_mean_sq_15_cast_fp16)[name = tensor<string, []>("op_808_cast_fp16")];
            tensor<fp16, []> var_809_to_fp16 = const()[name = tensor<string, []>("op_809_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 960]> var_810_cast_fp16 = add(x = var_808_cast_fp16, y = var_809_to_fp16)[name = tensor<string, []>("op_810_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, [2, 1, 1, 960]> denom_15_cast_fp16 = rsqrt(epsilon = denom_15_epsilon_0_to_fp16, x = var_810_cast_fp16)[name = tensor<string, []>("denom_15_cast_fp16")];
            tensor<fp16, [2, 640, 1, 960]> 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, [1, 640, 1, 1]> var_814_to_fp16 = const()[name = tensor<string, []>("op_814_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43060096)))];
            tensor<fp16, [2, 640, 1, 960]> var_815_cast_fp16 = add(x = out_15_cast_fp16, y = var_814_to_fp16)[name = tensor<string, []>("op_815_cast_fp16")];
            tensor<fp16, [1, 640, 1, 1]> var_817_to_fp16 = const()[name = tensor<string, []>("op_817_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43061440)))];
            tensor<fp16, [2, 640, 1, 960]> hidden_states_47_cast_fp16 = mul(x = var_815_cast_fp16, y = var_817_to_fp16)[name = tensor<string, []>("hidden_states_47_cast_fp16")];
            tensor<int32, [2]> var_824 = const()[name = tensor<string, []>("op_824"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_826 = const()[name = tensor<string, []>("op_826"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_11_pad_type_0 = const()[name = tensor<string, []>("q_11_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_11_pad_0 = const()[name = tensor<string, []>("q_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor<fp16, [640, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43062784)))];
            tensor<fp16, [2, 640, 1, 960]> q_11_cast_fp16 = conv(dilations = var_826, groups = var_649, pad = q_11_pad_0, pad_type = q_11_pad_type_0, strides = var_824, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_47_cast_fp16)[name = tensor<string, []>("q_11_cast_fp16")];
            tensor<int32, [2]> var_830 = const()[name = tensor<string, []>("op_830"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_832 = const()[name = tensor<string, []>("op_832"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_11_pad_type_0 = const()[name = tensor<string, []>("k_11_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_11_pad_0 = const()[name = tensor<string, []>("k_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 1024, 1, 1]> down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor<fp16, [640, 1024, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43882048)))];
            tensor<fp16, [2, 640, 1, 77]> k_11_cast_fp16 = conv(dilations = var_832, groups = var_649, pad = k_11_pad_0, pad_type = k_11_pad_type_0, strides = var_830, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor<string, []>("k_11_cast_fp16")];
            tensor<int32, [2]> var_836 = const()[name = tensor<string, []>("op_836"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_838 = const()[name = tensor<string, []>("op_838"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_11_pad_type_0 = const()[name = tensor<string, []>("v_11_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_11_pad_0 = const()[name = tensor<string, []>("v_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 1024, 1, 1]> down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor<fp16, [640, 1024, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(45192832)))];
            tensor<fp16, [2, 640, 1, 77]> v_11_cast_fp16 = conv(dilations = var_838, groups = var_649, pad = v_11_pad_0, pad_type = v_11_pad_type_0, strides = var_836, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor<string, []>("v_11_cast_fp16")];
            tensor<int32, [4]> var_842 = const()[name = tensor<string, []>("op_842"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 960]> var_843_cast_fp16 = reshape(shape = var_842, x = q_11_cast_fp16)[name = tensor<string, []>("op_843_cast_fp16")];
            tensor<int32, [4]> var_844 = const()[name = tensor<string, []>("op_844"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 77]> var_845_cast_fp16 = reshape(shape = var_844, x = k_11_cast_fp16)[name = tensor<string, []>("op_845_cast_fp16")];
            tensor<int32, [4]> var_846 = const()[name = tensor<string, []>("op_846"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 77]> var_847_cast_fp16 = reshape(shape = var_846, x = v_11_cast_fp16)[name = tensor<string, []>("op_847_cast_fp16")];
            tensor<bool, []> attn_weights_21_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_21_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_21_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_21_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 10, 960, 77]> attn_weights_21_cast_fp16 = matmul(transpose_x = attn_weights_21_transpose_x_0, transpose_y = attn_weights_21_transpose_y_0, x = var_843_cast_fp16, y = var_845_cast_fp16)[name = tensor<string, []>("attn_weights_21_cast_fp16")];
            tensor<fp16, [2, 10, 960, 77]> attn_weights_23_cast_fp16 = mul(x = attn_weights_21_cast_fp16, y = var_640_to_fp16)[name = tensor<string, []>("attn_weights_23_cast_fp16")];
            tensor<fp16, [2, 10, 960, 77]> var_851_cast_fp16 = softmax(axis = var_633, x = attn_weights_23_cast_fp16)[name = tensor<string, []>("op_851_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, [2, 10, 64, 960]> attn_11_cast_fp16 = matmul(transpose_x = attn_11_transpose_x_0, transpose_y = attn_11_transpose_y_0, x = var_847_cast_fp16, y = var_851_cast_fp16)[name = tensor<string, []>("attn_11_cast_fp16")];
            tensor<int32, [4]> var_855 = const()[name = tensor<string, []>("op_855"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 960]> input_81_cast_fp16 = reshape(shape = var_855, x = attn_11_cast_fp16)[name = tensor<string, []>("input_81_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, []> var_864_pad_type_0 = const()[name = tensor<string, []>("op_864_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_864_pad_0 = const()[name = tensor<string, []>("op_864_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor<fp16, [640, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46503616)))];
            tensor<fp16, [640]> down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47322880)))];
            tensor<fp16, [2, 640, 1, 960]> var_864_cast_fp16 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_862, groups = var_649, pad = var_864_pad_0, pad_type = var_864_pad_type_0, strides = var_860, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_81_cast_fp16)[name = tensor<string, []>("op_864_cast_fp16")];
            tensor<fp16, [2, 640, 1, 960]> inputs_17_cast_fp16 = add(x = var_864_cast_fp16, y = inputs_15_cast_fp16)[name = tensor<string, []>("inputs_17_cast_fp16")];
            tensor<int32, [1]> var_868 = const()[name = tensor<string, []>("op_868"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 960]> channels_mean_17_cast_fp16 = reduce_mean(axes = var_868, keep_dims = var_644, x = inputs_17_cast_fp16)[name = tensor<string, []>("channels_mean_17_cast_fp16")];
            tensor<fp16, [2, 640, 1, 960]> 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, [2, 640, 1, 960]> 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_872 = const()[name = tensor<string, []>("op_872"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 960]> var_873_cast_fp16 = reduce_mean(axes = var_872, keep_dims = var_644, x = zero_mean_sq_17_cast_fp16)[name = tensor<string, []>("op_873_cast_fp16")];
            tensor<fp16, []> var_874_to_fp16 = const()[name = tensor<string, []>("op_874_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 960]> var_875_cast_fp16 = add(x = var_873_cast_fp16, y = var_874_to_fp16)[name = tensor<string, []>("op_875_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, [2, 1, 1, 960]> denom_17_cast_fp16 = rsqrt(epsilon = denom_17_epsilon_0_to_fp16, x = var_875_cast_fp16)[name = tensor<string, []>("denom_17_cast_fp16")];
            tensor<fp16, [2, 640, 1, 960]> 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, [1, 640, 1, 1]> var_879_to_fp16 = const()[name = tensor<string, []>("op_879_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47324224)))];
            tensor<fp16, [2, 640, 1, 960]> var_880_cast_fp16 = add(x = out_17_cast_fp16, y = var_879_to_fp16)[name = tensor<string, []>("op_880_cast_fp16")];
            tensor<fp16, [1, 640, 1, 1]> var_882_to_fp16 = const()[name = tensor<string, []>("op_882_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47325568)))];
            tensor<fp16, [2, 640, 1, 960]> input_83_cast_fp16 = mul(x = var_880_cast_fp16, y = var_882_to_fp16)[name = tensor<string, []>("input_83_cast_fp16")];
            tensor<int32, [2]> var_890 = const()[name = tensor<string, []>("op_890"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_892 = const()[name = tensor<string, []>("op_892"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_894_pad_type_0 = const()[name = tensor<string, []>("op_894_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_894_pad_0 = const()[name = tensor<string, []>("op_894_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [5120, 640, 1, 1]> down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor<fp16, [5120, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47326912)))];
            tensor<fp16, [5120]> down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(53880576)))];
            tensor<fp16, [2, 5120, 1, 960]> var_894_cast_fp16 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_892, groups = var_649, pad = var_894_pad_0, pad_type = var_894_pad_type_0, strides = var_890, weight = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_83_cast_fp16)[name = tensor<string, []>("op_894_cast_fp16")];
            tensor<int32, [2]> var_895_split_sizes_0 = const()[name = tensor<string, []>("op_895_split_sizes_0"), val = tensor<int32, [2]>([2560, 2560])];
            tensor<int32, []> var_895_axis_0 = const()[name = tensor<string, []>("op_895_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 2560, 1, 960]> var_895_cast_fp16_0, tensor<fp16, [2, 2560, 1, 960]> var_895_cast_fp16_1 = split(axis = var_895_axis_0, split_sizes = var_895_split_sizes_0, x = var_894_cast_fp16)[name = tensor<string, []>("op_895_cast_fp16")];
            tensor<string, []> var_897_mode_0 = const()[name = tensor<string, []>("op_897_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 2560, 1, 960]> var_897_cast_fp16 = gelu(mode = var_897_mode_0, x = var_895_cast_fp16_1)[name = tensor<string, []>("op_897_cast_fp16")];
            tensor<fp16, [2, 2560, 1, 960]> input_85_cast_fp16 = mul(x = var_895_cast_fp16_0, y = var_897_cast_fp16)[name = tensor<string, []>("input_85_cast_fp16")];
            tensor<int32, [2]> var_901 = const()[name = tensor<string, []>("op_901"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_903 = const()[name = tensor<string, []>("op_903"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_905_pad_type_0 = const()[name = tensor<string, []>("op_905_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_905_pad_0 = const()[name = tensor<string, []>("op_905_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 2560, 1, 1]> down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor<fp16, [640, 2560, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(53890880)))];
            tensor<fp16, [640]> down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(57167744)))];
            tensor<fp16, [2, 640, 1, 960]> var_905_cast_fp16 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_903, groups = var_649, pad = var_905_pad_0, pad_type = var_905_pad_type_0, strides = var_901, weight = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_85_cast_fp16)[name = tensor<string, []>("op_905_cast_fp16")];
            tensor<fp16, [2, 640, 1, 960]> hidden_states_51_cast_fp16 = add(x = var_905_cast_fp16, y = inputs_17_cast_fp16)[name = tensor<string, []>("hidden_states_51_cast_fp16")];
            tensor<int32, [4]> var_907 = const()[name = tensor<string, []>("op_907"), val = tensor<int32, [4]>([2, 640, 24, 40])];
            tensor<fp16, [2, 640, 24, 40]> input_87_cast_fp16 = reshape(shape = var_907, x = hidden_states_51_cast_fp16)[name = tensor<string, []>("input_87_cast_fp16")];
            tensor<int32, [2]> var_911 = const()[name = tensor<string, []>("op_911"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_913 = const()[name = tensor<string, []>("op_913"), 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, [640, 640, 1, 1]> down_blocks_1_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_0_proj_out_weight_to_fp16"), val = tensor<fp16, [640, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(57169088)))];
            tensor<fp16, [640]> down_blocks_1_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_0_proj_out_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(57988352)))];
            tensor<fp16, [2, 640, 24, 40]> hidden_states_53_cast_fp16 = conv(bias = down_blocks_1_attentions_0_proj_out_bias_to_fp16, dilations = var_913, groups = var_649, pad = hidden_states_53_pad_0, pad_type = hidden_states_53_pad_type_0, strides = var_911, weight = down_blocks_1_attentions_0_proj_out_weight_to_fp16, x = input_87_cast_fp16)[name = tensor<string, []>("hidden_states_53_cast_fp16")];
            tensor<fp16, [2, 640, 24, 40]> input_89_cast_fp16_1 = add(x = hidden_states_53_cast_fp16, y = hidden_states_41_cast_fp16)[name = tensor<string, []>("input_89_cast_fp16")];
            tensor<int32, [5]> reshape_36_shape_0 = const()[name = tensor<string, []>("reshape_36_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 24, 40])];
            tensor<fp16, [2, 32, 20, 24, 40]> reshape_36_cast_fp16 = reshape(shape = reshape_36_shape_0, x = input_89_cast_fp16_1)[name = tensor<string, []>("reshape_36_cast_fp16")];
            tensor<int32, [3]> reduce_mean_27_axes_0 = const()[name = tensor<string, []>("reduce_mean_27_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_27_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_27_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_27_cast_fp16 = reduce_mean(axes = reduce_mean_27_axes_0, keep_dims = reduce_mean_27_keep_dims_0, x = reshape_36_cast_fp16)[name = tensor<string, []>("reduce_mean_27_cast_fp16")];
            tensor<fp16, [2, 32, 20, 24, 40]> sub_18_cast_fp16 = sub(x = reshape_36_cast_fp16, y = reduce_mean_27_cast_fp16)[name = tensor<string, []>("sub_18_cast_fp16")];
            tensor<fp16, [2, 32, 20, 24, 40]> square_9_cast_fp16 = square(x = sub_18_cast_fp16)[name = tensor<string, []>("square_9_cast_fp16")];
            tensor<int32, [3]> reduce_mean_29_axes_0 = const()[name = tensor<string, []>("reduce_mean_29_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_29_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_29_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_29_cast_fp16 = reduce_mean(axes = reduce_mean_29_axes_0, keep_dims = reduce_mean_29_keep_dims_0, x = square_9_cast_fp16)[name = tensor<string, []>("reduce_mean_29_cast_fp16")];
            tensor<fp16, []> add_18_y_0_to_fp16 = const()[name = tensor<string, []>("add_18_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_18_cast_fp16 = add(x = reduce_mean_29_cast_fp16, y = add_18_y_0_to_fp16)[name = tensor<string, []>("add_18_cast_fp16")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_9_cast_fp16 = sqrt(x = add_18_cast_fp16)[name = tensor<string, []>("sqrt_9_cast_fp16")];
            tensor<fp16, [2, 32, 20, 24, 40]> real_div_9_cast_fp16 = real_div(x = sub_18_cast_fp16, y = sqrt_9_cast_fp16)[name = tensor<string, []>("real_div_9_cast_fp16")];
            tensor<int32, [4]> reshape_37_shape_0 = const()[name = tensor<string, []>("reshape_37_shape_0"), val = tensor<int32, [4]>([2, 640, 24, 40])];
            tensor<fp16, [2, 640, 24, 40]> reshape_37_cast_fp16 = reshape(shape = reshape_37_shape_0, x = real_div_9_cast_fp16)[name = tensor<string, []>("reshape_37_cast_fp16")];
            tensor<fp16, [640]> add_19_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_19_gamma_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(57989696)))];
            tensor<fp16, [640]> add_19_beta_0_to_fp16 = const()[name = tensor<string, []>("add_19_beta_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(57991040)))];
            tensor<fp16, []> add_19_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_19_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 640, 24, 40]> add_19_cast_fp16 = batch_norm(beta = add_19_beta_0_to_fp16, epsilon = add_19_epsilon_0_to_fp16, gamma = add_19_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_37_cast_fp16)[name = tensor<string, []>("add_19_cast_fp16")];
            tensor<fp16, [2, 640, 24, 40]> input_93_cast_fp16 = silu(x = add_19_cast_fp16)[name = tensor<string, []>("input_93_cast_fp16")];
            tensor<int32, [2]> var_928 = const()[name = tensor<string, []>("op_928"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_930 = const()[name = tensor<string, []>("op_930"), 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]>([1, 1, 1, 1])];
            tensor<fp16, [640, 640, 3, 3]> down_blocks_1_resnets_1_conv1_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_resnets_1_conv1_weight_to_fp16"), val = tensor<fp16, [640, 640, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(57992384)))];
            tensor<fp16, [640]> down_blocks_1_resnets_1_conv1_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_resnets_1_conv1_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(65365248)))];
            tensor<fp16, [2, 640, 24, 40]> hidden_states_55_cast_fp16 = conv(bias = down_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = var_930, groups = var_649, pad = hidden_states_55_pad_0, pad_type = hidden_states_55_pad_type_0, strides = var_928, weight = down_blocks_1_resnets_1_conv1_weight_to_fp16, x = input_93_cast_fp16)[name = tensor<string, []>("hidden_states_55_cast_fp16")];
            tensor<int32, [2]> var_936 = const()[name = tensor<string, []>("op_936"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_938 = const()[name = tensor<string, []>("op_938"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> temb_7_pad_type_0 = const()[name = tensor<string, []>("temb_7_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> temb_7_pad_0 = const()[name = tensor<string, []>("temb_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 1280, 1, 1]> down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor<fp16, [640, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(65366592)))];
            tensor<fp16, [640]> down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(67005056)))];
            tensor<fp16, [2, 640, 1, 1]> temb_7_cast_fp16 = conv(bias = down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_938, groups = var_649, pad = temb_7_pad_0, pad_type = temb_7_pad_type_0, strides = var_936, weight = down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16_1)[name = tensor<string, []>("temb_7_cast_fp16")];
            tensor<fp16, [2, 640, 24, 40]> input_97_cast_fp16 = add(x = hidden_states_55_cast_fp16, y = temb_7_cast_fp16)[name = tensor<string, []>("input_97_cast_fp16")];
            tensor<int32, [5]> reshape_40_shape_0 = const()[name = tensor<string, []>("reshape_40_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 24, 40])];
            tensor<fp16, [2, 32, 20, 24, 40]> reshape_40_cast_fp16 = reshape(shape = reshape_40_shape_0, x = input_97_cast_fp16)[name = tensor<string, []>("reshape_40_cast_fp16")];
            tensor<int32, [3]> reduce_mean_30_axes_0 = const()[name = tensor<string, []>("reduce_mean_30_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_30_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_30_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_30_cast_fp16 = reduce_mean(axes = reduce_mean_30_axes_0, keep_dims = reduce_mean_30_keep_dims_0, x = reshape_40_cast_fp16)[name = tensor<string, []>("reduce_mean_30_cast_fp16")];
            tensor<fp16, [2, 32, 20, 24, 40]> sub_20_cast_fp16 = sub(x = reshape_40_cast_fp16, y = reduce_mean_30_cast_fp16)[name = tensor<string, []>("sub_20_cast_fp16")];
            tensor<fp16, [2, 32, 20, 24, 40]> square_10_cast_fp16 = square(x = sub_20_cast_fp16)[name = tensor<string, []>("square_10_cast_fp16")];
            tensor<int32, [3]> reduce_mean_32_axes_0 = const()[name = tensor<string, []>("reduce_mean_32_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_32_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_32_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_32_cast_fp16 = reduce_mean(axes = reduce_mean_32_axes_0, keep_dims = reduce_mean_32_keep_dims_0, x = square_10_cast_fp16)[name = tensor<string, []>("reduce_mean_32_cast_fp16")];
            tensor<fp16, []> add_20_y_0_to_fp16 = const()[name = tensor<string, []>("add_20_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_20_cast_fp16 = add(x = reduce_mean_32_cast_fp16, y = add_20_y_0_to_fp16)[name = tensor<string, []>("add_20_cast_fp16")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_10_cast_fp16 = sqrt(x = add_20_cast_fp16)[name = tensor<string, []>("sqrt_10_cast_fp16")];
            tensor<fp16, [2, 32, 20, 24, 40]> real_div_10_cast_fp16 = real_div(x = sub_20_cast_fp16, y = sqrt_10_cast_fp16)[name = tensor<string, []>("real_div_10_cast_fp16")];
            tensor<int32, [4]> reshape_41_shape_0 = const()[name = tensor<string, []>("reshape_41_shape_0"), val = tensor<int32, [4]>([2, 640, 24, 40])];
            tensor<fp16, [2, 640, 24, 40]> reshape_41_cast_fp16 = reshape(shape = reshape_41_shape_0, x = real_div_10_cast_fp16)[name = tensor<string, []>("reshape_41_cast_fp16")];
            tensor<fp16, [640]> add_21_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_21_gamma_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(67006400)))];
            tensor<fp16, [640]> add_21_beta_0_to_fp16 = const()[name = tensor<string, []>("add_21_beta_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(67007744)))];
            tensor<fp16, []> add_21_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_21_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 640, 24, 40]> add_21_cast_fp16 = batch_norm(beta = add_21_beta_0_to_fp16, epsilon = add_21_epsilon_0_to_fp16, gamma = add_21_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_41_cast_fp16)[name = tensor<string, []>("add_21_cast_fp16")];
            tensor<fp16, [2, 640, 24, 40]> input_101_cast_fp16 = silu(x = add_21_cast_fp16)[name = tensor<string, []>("input_101_cast_fp16")];
            tensor<int32, [2]> var_948 = const()[name = tensor<string, []>("op_948"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_950 = const()[name = tensor<string, []>("op_950"), 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]>([1, 1, 1, 1])];
            tensor<fp16, [640, 640, 3, 3]> down_blocks_1_resnets_1_conv2_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_resnets_1_conv2_weight_to_fp16"), val = tensor<fp16, [640, 640, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(67009088)))];
            tensor<fp16, [640]> down_blocks_1_resnets_1_conv2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_resnets_1_conv2_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(74381952)))];
            tensor<fp16, [2, 640, 24, 40]> hidden_states_57_cast_fp16 = conv(bias = down_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = var_950, groups = var_649, pad = hidden_states_57_pad_0, pad_type = hidden_states_57_pad_type_0, strides = var_948, weight = down_blocks_1_resnets_1_conv2_weight_to_fp16, x = input_101_cast_fp16)[name = tensor<string, []>("hidden_states_57_cast_fp16")];
            tensor<fp16, [2, 640, 24, 40]> hidden_states_59_cast_fp16 = add(x = input_89_cast_fp16_1, y = hidden_states_57_cast_fp16)[name = tensor<string, []>("hidden_states_59_cast_fp16")];
            tensor<int32, [5]> reshape_44_shape_0 = const()[name = tensor<string, []>("reshape_44_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 24, 40])];
            tensor<fp16, [2, 32, 20, 24, 40]> reshape_44_cast_fp16 = reshape(shape = reshape_44_shape_0, x = hidden_states_59_cast_fp16)[name = tensor<string, []>("reshape_44_cast_fp16")];
            tensor<int32, [3]> reduce_mean_33_axes_0 = const()[name = tensor<string, []>("reduce_mean_33_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_33_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_33_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_33_cast_fp16 = reduce_mean(axes = reduce_mean_33_axes_0, keep_dims = reduce_mean_33_keep_dims_0, x = reshape_44_cast_fp16)[name = tensor<string, []>("reduce_mean_33_cast_fp16")];
            tensor<fp16, [2, 32, 20, 24, 40]> sub_22_cast_fp16 = sub(x = reshape_44_cast_fp16, y = reduce_mean_33_cast_fp16)[name = tensor<string, []>("sub_22_cast_fp16")];
            tensor<fp16, [2, 32, 20, 24, 40]> square_11_cast_fp16 = square(x = sub_22_cast_fp16)[name = tensor<string, []>("square_11_cast_fp16")];
            tensor<int32, [3]> reduce_mean_35_axes_0 = const()[name = tensor<string, []>("reduce_mean_35_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_35_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_35_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_35_cast_fp16 = reduce_mean(axes = reduce_mean_35_axes_0, keep_dims = reduce_mean_35_keep_dims_0, x = square_11_cast_fp16)[name = tensor<string, []>("reduce_mean_35_cast_fp16")];
            tensor<fp16, []> add_22_y_0_to_fp16 = const()[name = tensor<string, []>("add_22_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_22_cast_fp16 = add(x = reduce_mean_35_cast_fp16, y = add_22_y_0_to_fp16)[name = tensor<string, []>("add_22_cast_fp16")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_11_cast_fp16 = sqrt(x = add_22_cast_fp16)[name = tensor<string, []>("sqrt_11_cast_fp16")];
            tensor<fp16, [2, 32, 20, 24, 40]> real_div_11_cast_fp16 = real_div(x = sub_22_cast_fp16, y = sqrt_11_cast_fp16)[name = tensor<string, []>("real_div_11_cast_fp16")];
            tensor<int32, [4]> reshape_45_shape_0 = const()[name = tensor<string, []>("reshape_45_shape_0"), val = tensor<int32, [4]>([2, 640, 24, 40])];
            tensor<fp16, [2, 640, 24, 40]> reshape_45_cast_fp16 = reshape(shape = reshape_45_shape_0, x = real_div_11_cast_fp16)[name = tensor<string, []>("reshape_45_cast_fp16")];
            tensor<fp16, [640]> add_23_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_23_gamma_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(74383296)))];
            tensor<fp16, [640]> add_23_beta_0_to_fp16 = const()[name = tensor<string, []>("add_23_beta_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(74384640)))];
            tensor<fp16, []> add_23_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_23_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 640, 24, 40]> add_23_cast_fp16 = batch_norm(beta = add_23_beta_0_to_fp16, epsilon = add_23_epsilon_0_to_fp16, gamma = add_23_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_45_cast_fp16)[name = tensor<string, []>("add_23_cast_fp16")];
            tensor<int32, [2]> var_970 = const()[name = tensor<string, []>("op_970"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_972 = const()[name = tensor<string, []>("op_972"), 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, [640, 640, 1, 1]> down_blocks_1_attentions_1_proj_in_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_1_proj_in_weight_to_fp16"), val = tensor<fp16, [640, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(74385984)))];
            tensor<fp16, [640]> down_blocks_1_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_1_proj_in_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(75205248)))];
            tensor<fp16, [2, 640, 24, 40]> hidden_states_61_cast_fp16 = conv(bias = down_blocks_1_attentions_1_proj_in_bias_to_fp16, dilations = var_972, groups = var_649, pad = hidden_states_61_pad_0, pad_type = hidden_states_61_pad_type_0, strides = var_970, weight = down_blocks_1_attentions_1_proj_in_weight_to_fp16, x = add_23_cast_fp16)[name = tensor<string, []>("hidden_states_61_cast_fp16")];
            tensor<int32, [4]> var_977 = const()[name = tensor<string, []>("op_977"), val = tensor<int32, [4]>([2, 640, 1, 960])];
            tensor<fp16, [2, 640, 1, 960]> inputs_19_cast_fp16 = reshape(shape = var_977, x = hidden_states_61_cast_fp16)[name = tensor<string, []>("inputs_19_cast_fp16")];
            tensor<int32, [1]> var_987 = const()[name = tensor<string, []>("op_987"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 960]> channels_mean_19_cast_fp16 = reduce_mean(axes = var_987, keep_dims = var_644, x = inputs_19_cast_fp16)[name = tensor<string, []>("channels_mean_19_cast_fp16")];
            tensor<fp16, [2, 640, 1, 960]> 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, [2, 640, 1, 960]> 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_991 = const()[name = tensor<string, []>("op_991"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 960]> var_992_cast_fp16 = reduce_mean(axes = var_991, keep_dims = var_644, x = zero_mean_sq_19_cast_fp16)[name = tensor<string, []>("op_992_cast_fp16")];
            tensor<fp16, []> var_993_to_fp16 = const()[name = tensor<string, []>("op_993_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 960]> var_994_cast_fp16 = add(x = var_992_cast_fp16, y = var_993_to_fp16)[name = tensor<string, []>("op_994_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, [2, 1, 1, 960]> denom_19_cast_fp16 = rsqrt(epsilon = denom_19_epsilon_0_to_fp16, x = var_994_cast_fp16)[name = tensor<string, []>("denom_19_cast_fp16")];
            tensor<fp16, [2, 640, 1, 960]> 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, [1, 640, 1, 1]> var_998_to_fp16 = const()[name = tensor<string, []>("op_998_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(75206592)))];
            tensor<fp16, [2, 640, 1, 960]> var_999_cast_fp16 = add(x = out_19_cast_fp16, y = var_998_to_fp16)[name = tensor<string, []>("op_999_cast_fp16")];
            tensor<fp16, [1, 640, 1, 1]> var_1001_to_fp16 = const()[name = tensor<string, []>("op_1001_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(75207936)))];
            tensor<fp16, [2, 640, 1, 960]> hidden_states_63_cast_fp16 = mul(x = var_999_cast_fp16, y = var_1001_to_fp16)[name = tensor<string, []>("hidden_states_63_cast_fp16")];
            tensor<int32, [2]> var_1008 = const()[name = tensor<string, []>("op_1008"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1010 = const()[name = tensor<string, []>("op_1010"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_13_pad_type_0 = const()[name = tensor<string, []>("q_13_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_13_pad_0 = const()[name = tensor<string, []>("q_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor<fp16, [640, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(75209280)))];
            tensor<fp16, [2, 640, 1, 960]> q_13_cast_fp16 = conv(dilations = var_1010, groups = var_649, pad = q_13_pad_0, pad_type = q_13_pad_type_0, strides = var_1008, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_63_cast_fp16)[name = tensor<string, []>("q_13_cast_fp16")];
            tensor<int32, [2]> var_1014 = const()[name = tensor<string, []>("op_1014"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1016 = const()[name = tensor<string, []>("op_1016"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_13_pad_type_0 = const()[name = tensor<string, []>("k_13_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_13_pad_0 = const()[name = tensor<string, []>("k_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor<fp16, [640, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(76028544)))];
            tensor<fp16, [2, 640, 1, 960]> k_13_cast_fp16 = conv(dilations = var_1016, groups = var_649, pad = k_13_pad_0, pad_type = k_13_pad_type_0, strides = var_1014, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_63_cast_fp16)[name = tensor<string, []>("k_13_cast_fp16")];
            tensor<int32, [2]> var_1020 = const()[name = tensor<string, []>("op_1020"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1022 = const()[name = tensor<string, []>("op_1022"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_13_pad_type_0 = const()[name = tensor<string, []>("v_13_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_13_pad_0 = const()[name = tensor<string, []>("v_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor<fp16, [640, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(76847808)))];
            tensor<fp16, [2, 640, 1, 960]> v_13_cast_fp16 = conv(dilations = var_1022, groups = var_649, pad = v_13_pad_0, pad_type = v_13_pad_type_0, strides = var_1020, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_63_cast_fp16)[name = tensor<string, []>("v_13_cast_fp16")];
            tensor<int32, [4]> var_1026 = const()[name = tensor<string, []>("op_1026"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 960]> var_1027_cast_fp16 = reshape(shape = var_1026, x = q_13_cast_fp16)[name = tensor<string, []>("op_1027_cast_fp16")];
            tensor<int32, [4]> var_1028 = const()[name = tensor<string, []>("op_1028"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 960]> var_1029_cast_fp16 = reshape(shape = var_1028, x = k_13_cast_fp16)[name = tensor<string, []>("op_1029_cast_fp16")];
            tensor<int32, [4]> var_1030 = const()[name = tensor<string, []>("op_1030"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 960]> var_1031_cast_fp16 = reshape(shape = var_1030, x = v_13_cast_fp16)[name = tensor<string, []>("op_1031_cast_fp16")];
            tensor<bool, []> attn_weights_25_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_25_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_25_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_25_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 10, 960, 960]> attn_weights_25_cast_fp16 = matmul(transpose_x = attn_weights_25_transpose_x_0, transpose_y = attn_weights_25_transpose_y_0, x = var_1027_cast_fp16, y = var_1029_cast_fp16)[name = tensor<string, []>("attn_weights_25_cast_fp16")];
            tensor<fp16, [2, 10, 960, 960]> attn_weights_27_cast_fp16 = mul(x = attn_weights_25_cast_fp16, y = var_640_to_fp16)[name = tensor<string, []>("attn_weights_27_cast_fp16")];
            tensor<fp16, [2, 10, 960, 960]> var_1035_cast_fp16 = softmax(axis = var_633, x = attn_weights_27_cast_fp16)[name = tensor<string, []>("op_1035_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, [2, 10, 64, 960]> attn_13_cast_fp16 = matmul(transpose_x = attn_13_transpose_x_0, transpose_y = attn_13_transpose_y_0, x = var_1031_cast_fp16, y = var_1035_cast_fp16)[name = tensor<string, []>("attn_13_cast_fp16")];
            tensor<int32, [4]> var_1039 = const()[name = tensor<string, []>("op_1039"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 960]> input_105_cast_fp16 = reshape(shape = var_1039, x = attn_13_cast_fp16)[name = tensor<string, []>("input_105_cast_fp16")];
            tensor<int32, [2]> var_1044 = const()[name = tensor<string, []>("op_1044"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1046 = const()[name = tensor<string, []>("op_1046"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1048_pad_type_0 = const()[name = tensor<string, []>("op_1048_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1048_pad_0 = const()[name = tensor<string, []>("op_1048_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor<fp16, [640, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(77667072)))];
            tensor<fp16, [640]> down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(78486336)))];
            tensor<fp16, [2, 640, 1, 960]> var_1048_cast_fp16 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_1046, groups = var_649, pad = var_1048_pad_0, pad_type = var_1048_pad_type_0, strides = var_1044, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_105_cast_fp16)[name = tensor<string, []>("op_1048_cast_fp16")];
            tensor<fp16, [2, 640, 1, 960]> inputs_21_cast_fp16 = add(x = var_1048_cast_fp16, y = inputs_19_cast_fp16)[name = tensor<string, []>("inputs_21_cast_fp16")];
            tensor<int32, [1]> var_1052 = const()[name = tensor<string, []>("op_1052"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 960]> channels_mean_21_cast_fp16 = reduce_mean(axes = var_1052, keep_dims = var_644, x = inputs_21_cast_fp16)[name = tensor<string, []>("channels_mean_21_cast_fp16")];
            tensor<fp16, [2, 640, 1, 960]> 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, [2, 640, 1, 960]> 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_1056 = const()[name = tensor<string, []>("op_1056"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 960]> var_1057_cast_fp16 = reduce_mean(axes = var_1056, keep_dims = var_644, x = zero_mean_sq_21_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, [2, 1, 1, 960]> 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_21_epsilon_0_to_fp16 = const()[name = tensor<string, []>("denom_21_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, [2, 1, 1, 960]> denom_21_cast_fp16 = rsqrt(epsilon = denom_21_epsilon_0_to_fp16, x = var_1059_cast_fp16)[name = tensor<string, []>("denom_21_cast_fp16")];
            tensor<fp16, [2, 640, 1, 960]> 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, [1, 640, 1, 1]> var_1063_to_fp16 = const()[name = tensor<string, []>("op_1063_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(78487680)))];
            tensor<fp16, [2, 640, 1, 960]> var_1064_cast_fp16 = add(x = out_21_cast_fp16, y = var_1063_to_fp16)[name = tensor<string, []>("op_1064_cast_fp16")];
            tensor<fp16, [1, 640, 1, 1]> var_1066_to_fp16 = const()[name = tensor<string, []>("op_1066_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(78489024)))];
            tensor<fp16, [2, 640, 1, 960]> hidden_states_65_cast_fp16 = mul(x = var_1064_cast_fp16, y = var_1066_to_fp16)[name = tensor<string, []>("hidden_states_65_cast_fp16")];
            tensor<int32, [2]> var_1073 = const()[name = tensor<string, []>("op_1073"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1075 = const()[name = tensor<string, []>("op_1075"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_15_pad_type_0 = const()[name = tensor<string, []>("q_15_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_15_pad_0 = const()[name = tensor<string, []>("q_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor<fp16, [640, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(78490368)))];
            tensor<fp16, [2, 640, 1, 960]> q_15_cast_fp16 = conv(dilations = var_1075, groups = var_649, pad = q_15_pad_0, pad_type = q_15_pad_type_0, strides = var_1073, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_65_cast_fp16)[name = tensor<string, []>("q_15_cast_fp16")];
            tensor<int32, [2]> var_1079 = const()[name = tensor<string, []>("op_1079"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1081 = const()[name = tensor<string, []>("op_1081"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_15_pad_type_0 = const()[name = tensor<string, []>("k_15_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_15_pad_0 = const()[name = tensor<string, []>("k_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 1024, 1, 1]> down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor<fp16, [640, 1024, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(79309632)))];
            tensor<fp16, [2, 640, 1, 77]> k_15_cast_fp16 = conv(dilations = var_1081, groups = var_649, pad = k_15_pad_0, pad_type = k_15_pad_type_0, strides = var_1079, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor<string, []>("k_15_cast_fp16")];
            tensor<int32, [2]> var_1085 = const()[name = tensor<string, []>("op_1085"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1087 = const()[name = tensor<string, []>("op_1087"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_15_pad_type_0 = const()[name = tensor<string, []>("v_15_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_15_pad_0 = const()[name = tensor<string, []>("v_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 1024, 1, 1]> down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor<fp16, [640, 1024, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(80620416)))];
            tensor<fp16, [2, 640, 1, 77]> v_15_cast_fp16 = conv(dilations = var_1087, groups = var_649, pad = v_15_pad_0, pad_type = v_15_pad_type_0, strides = var_1085, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor<string, []>("v_15_cast_fp16")];
            tensor<int32, [4]> var_1091 = const()[name = tensor<string, []>("op_1091"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 960]> var_1092_cast_fp16 = reshape(shape = var_1091, x = q_15_cast_fp16)[name = tensor<string, []>("op_1092_cast_fp16")];
            tensor<int32, [4]> var_1093 = const()[name = tensor<string, []>("op_1093"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 77]> var_1094_cast_fp16 = reshape(shape = var_1093, x = k_15_cast_fp16)[name = tensor<string, []>("op_1094_cast_fp16")];
            tensor<int32, [4]> var_1095 = const()[name = tensor<string, []>("op_1095"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 77]> var_1096_cast_fp16 = reshape(shape = var_1095, x = v_15_cast_fp16)[name = tensor<string, []>("op_1096_cast_fp16")];
            tensor<bool, []> attn_weights_29_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_29_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_29_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_29_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 10, 960, 77]> attn_weights_29_cast_fp16 = matmul(transpose_x = attn_weights_29_transpose_x_0, transpose_y = attn_weights_29_transpose_y_0, x = var_1092_cast_fp16, y = var_1094_cast_fp16)[name = tensor<string, []>("attn_weights_29_cast_fp16")];
            tensor<fp16, [2, 10, 960, 77]> attn_weights_31_cast_fp16 = mul(x = attn_weights_29_cast_fp16, y = var_640_to_fp16)[name = tensor<string, []>("attn_weights_31_cast_fp16")];
            tensor<fp16, [2, 10, 960, 77]> var_1100_cast_fp16 = softmax(axis = var_633, x = attn_weights_31_cast_fp16)[name = tensor<string, []>("op_1100_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, [2, 10, 64, 960]> attn_15_cast_fp16 = matmul(transpose_x = attn_15_transpose_x_0, transpose_y = attn_15_transpose_y_0, x = var_1096_cast_fp16, y = var_1100_cast_fp16)[name = tensor<string, []>("attn_15_cast_fp16")];
            tensor<int32, [4]> var_1104 = const()[name = tensor<string, []>("op_1104"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 960]> input_107_cast_fp16 = reshape(shape = var_1104, x = attn_15_cast_fp16)[name = tensor<string, []>("input_107_cast_fp16")];
            tensor<int32, [2]> var_1109 = const()[name = tensor<string, []>("op_1109"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1111 = const()[name = tensor<string, []>("op_1111"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1113_pad_type_0 = const()[name = tensor<string, []>("op_1113_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1113_pad_0 = const()[name = tensor<string, []>("op_1113_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor<fp16, [640, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(81931200)))];
            tensor<fp16, [640]> down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(82750464)))];
            tensor<fp16, [2, 640, 1, 960]> var_1113_cast_fp16 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_1111, groups = var_649, pad = var_1113_pad_0, pad_type = var_1113_pad_type_0, strides = var_1109, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_107_cast_fp16)[name = tensor<string, []>("op_1113_cast_fp16")];
            tensor<fp16, [2, 640, 1, 960]> inputs_23_cast_fp16 = add(x = var_1113_cast_fp16, y = inputs_21_cast_fp16)[name = tensor<string, []>("inputs_23_cast_fp16")];
            tensor<int32, [1]> var_1117 = const()[name = tensor<string, []>("op_1117"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 960]> channels_mean_23_cast_fp16 = reduce_mean(axes = var_1117, keep_dims = var_644, x = inputs_23_cast_fp16)[name = tensor<string, []>("channels_mean_23_cast_fp16")];
            tensor<fp16, [2, 640, 1, 960]> 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, [2, 640, 1, 960]> 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_1121 = const()[name = tensor<string, []>("op_1121"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 960]> var_1122_cast_fp16 = reduce_mean(axes = var_1121, keep_dims = var_644, x = zero_mean_sq_23_cast_fp16)[name = tensor<string, []>("op_1122_cast_fp16")];
            tensor<fp16, []> var_1123_to_fp16 = const()[name = tensor<string, []>("op_1123_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 960]> var_1124_cast_fp16 = add(x = var_1122_cast_fp16, y = var_1123_to_fp16)[name = tensor<string, []>("op_1124_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, [2, 1, 1, 960]> denom_23_cast_fp16 = rsqrt(epsilon = denom_23_epsilon_0_to_fp16, x = var_1124_cast_fp16)[name = tensor<string, []>("denom_23_cast_fp16")];
            tensor<fp16, [2, 640, 1, 960]> 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, [1, 640, 1, 1]> var_1128_to_fp16 = const()[name = tensor<string, []>("op_1128_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(82751808)))];
            tensor<fp16, [2, 640, 1, 960]> var_1129_cast_fp16 = add(x = out_23_cast_fp16, y = var_1128_to_fp16)[name = tensor<string, []>("op_1129_cast_fp16")];
            tensor<fp16, [1, 640, 1, 1]> var_1131_to_fp16 = const()[name = tensor<string, []>("op_1131_to_fp16"), val = tensor<fp16, [1, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(82753152)))];
            tensor<fp16, [2, 640, 1, 960]> input_109_cast_fp16 = mul(x = var_1129_cast_fp16, y = var_1131_to_fp16)[name = tensor<string, []>("input_109_cast_fp16")];
            tensor<int32, [2]> var_1139 = const()[name = tensor<string, []>("op_1139"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1141 = const()[name = tensor<string, []>("op_1141"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1143_pad_type_0 = const()[name = tensor<string, []>("op_1143_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1143_pad_0 = const()[name = tensor<string, []>("op_1143_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [5120, 640, 1, 1]> down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor<fp16, [5120, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(82754496)))];
            tensor<fp16, [5120]> down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89308160)))];
            tensor<fp16, [2, 5120, 1, 960]> var_1143_cast_fp16 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_1141, groups = var_649, pad = var_1143_pad_0, pad_type = var_1143_pad_type_0, strides = var_1139, weight = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_109_cast_fp16)[name = tensor<string, []>("op_1143_cast_fp16")];
            tensor<int32, [2]> var_1144_split_sizes_0 = const()[name = tensor<string, []>("op_1144_split_sizes_0"), val = tensor<int32, [2]>([2560, 2560])];
            tensor<int32, []> var_1144_axis_0 = const()[name = tensor<string, []>("op_1144_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 2560, 1, 960]> var_1144_cast_fp16_0, tensor<fp16, [2, 2560, 1, 960]> var_1144_cast_fp16_1 = split(axis = var_1144_axis_0, split_sizes = var_1144_split_sizes_0, x = var_1143_cast_fp16)[name = tensor<string, []>("op_1144_cast_fp16")];
            tensor<string, []> var_1146_mode_0 = const()[name = tensor<string, []>("op_1146_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 2560, 1, 960]> var_1146_cast_fp16 = gelu(mode = var_1146_mode_0, x = var_1144_cast_fp16_1)[name = tensor<string, []>("op_1146_cast_fp16")];
            tensor<fp16, [2, 2560, 1, 960]> input_111_cast_fp16 = mul(x = var_1144_cast_fp16_0, y = var_1146_cast_fp16)[name = tensor<string, []>("input_111_cast_fp16")];
            tensor<int32, [2]> var_1150 = const()[name = tensor<string, []>("op_1150"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1152 = const()[name = tensor<string, []>("op_1152"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1154_pad_type_0 = const()[name = tensor<string, []>("op_1154_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1154_pad_0 = const()[name = tensor<string, []>("op_1154_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 2560, 1, 1]> down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor<fp16, [640, 2560, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89318464)))];
            tensor<fp16, [640]> down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(92595328)))];
            tensor<fp16, [2, 640, 1, 960]> var_1154_cast_fp16 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_1152, groups = var_649, pad = var_1154_pad_0, pad_type = var_1154_pad_type_0, strides = var_1150, weight = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_111_cast_fp16)[name = tensor<string, []>("op_1154_cast_fp16")];
            tensor<fp16, [2, 640, 1, 960]> hidden_states_69_cast_fp16 = add(x = var_1154_cast_fp16, y = inputs_23_cast_fp16)[name = tensor<string, []>("hidden_states_69_cast_fp16")];
            tensor<int32, [4]> var_1156 = const()[name = tensor<string, []>("op_1156"), val = tensor<int32, [4]>([2, 640, 24, 40])];
            tensor<fp16, [2, 640, 24, 40]> input_113_cast_fp16 = reshape(shape = var_1156, x = hidden_states_69_cast_fp16)[name = tensor<string, []>("input_113_cast_fp16")];
            tensor<int32, [2]> var_1160 = const()[name = tensor<string, []>("op_1160"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1162 = const()[name = tensor<string, []>("op_1162"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_71_pad_type_0 = const()[name = tensor<string, []>("hidden_states_71_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_71_pad_0 = const()[name = tensor<string, []>("hidden_states_71_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [640, 640, 1, 1]> down_blocks_1_attentions_1_proj_out_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_1_proj_out_weight_to_fp16"), val = tensor<fp16, [640, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(92596672)))];
            tensor<fp16, [640]> down_blocks_1_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_attentions_1_proj_out_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(93415936)))];
            tensor<fp16, [2, 640, 24, 40]> hidden_states_71_cast_fp16 = conv(bias = down_blocks_1_attentions_1_proj_out_bias_to_fp16, dilations = var_1162, groups = var_649, pad = hidden_states_71_pad_0, pad_type = hidden_states_71_pad_type_0, strides = var_1160, weight = down_blocks_1_attentions_1_proj_out_weight_to_fp16, x = input_113_cast_fp16)[name = tensor<string, []>("hidden_states_71_cast_fp16")];
            tensor<fp16, [2, 640, 24, 40]> input_115_cast_fp16_1 = add(x = hidden_states_71_cast_fp16, y = hidden_states_59_cast_fp16)[name = tensor<string, []>("input_115_cast_fp16")];
            tensor<int32, [2]> var_1169 = const()[name = tensor<string, []>("op_1169"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, [2]> var_1171 = const()[name = tensor<string, []>("op_1171"), 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]>([1, 1, 1, 1])];
            tensor<fp16, [640, 640, 3, 3]> down_blocks_1_downsamplers_0_conv_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_downsamplers_0_conv_weight_to_fp16"), val = tensor<fp16, [640, 640, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(93417280)))];
            tensor<fp16, [640]> down_blocks_1_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_1_downsamplers_0_conv_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(100790144)))];
            tensor<fp16, [2, 640, 12, 20]> input_117_cast_fp16_1 = conv(bias = down_blocks_1_downsamplers_0_conv_bias_to_fp16, dilations = var_1171, groups = var_649, pad = input_117_pad_0, pad_type = input_117_pad_type_0, strides = var_1169, weight = down_blocks_1_downsamplers_0_conv_weight_to_fp16, x = input_115_cast_fp16_1)[name = tensor<string, []>("input_117_cast_fp16")];
            tensor<int32, []> var_1179 = const()[name = tensor<string, []>("op_1179"), val = tensor<int32, []>(3)];
            tensor<bool, []> var_1190 = const()[name = tensor<string, []>("op_1190"), val = tensor<bool, []>(true)];
            tensor<int32, []> var_1195 = const()[name = tensor<string, []>("op_1195"), val = tensor<int32, []>(1)];
            tensor<int32, [5]> reshape_48_shape_0 = const()[name = tensor<string, []>("reshape_48_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 12, 20])];
            tensor<fp16, [2, 32, 20, 12, 20]> reshape_48_cast_fp16 = reshape(shape = reshape_48_shape_0, x = input_117_cast_fp16_1)[name = tensor<string, []>("reshape_48_cast_fp16")];
            tensor<int32, [3]> reduce_mean_36_axes_0 = const()[name = tensor<string, []>("reduce_mean_36_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_36_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_36_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_36_cast_fp16 = reduce_mean(axes = reduce_mean_36_axes_0, keep_dims = reduce_mean_36_keep_dims_0, x = reshape_48_cast_fp16)[name = tensor<string, []>("reduce_mean_36_cast_fp16")];
            tensor<fp16, [2, 32, 20, 12, 20]> sub_24_cast_fp16 = sub(x = reshape_48_cast_fp16, y = reduce_mean_36_cast_fp16)[name = tensor<string, []>("sub_24_cast_fp16")];
            tensor<fp16, [2, 32, 20, 12, 20]> square_12_cast_fp16 = square(x = sub_24_cast_fp16)[name = tensor<string, []>("square_12_cast_fp16")];
            tensor<int32, [3]> reduce_mean_38_axes_0 = const()[name = tensor<string, []>("reduce_mean_38_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_38_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_38_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_38_cast_fp16 = reduce_mean(axes = reduce_mean_38_axes_0, keep_dims = reduce_mean_38_keep_dims_0, x = square_12_cast_fp16)[name = tensor<string, []>("reduce_mean_38_cast_fp16")];
            tensor<fp16, []> add_24_y_0_to_fp16 = const()[name = tensor<string, []>("add_24_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_24_cast_fp16 = add(x = reduce_mean_38_cast_fp16, y = add_24_y_0_to_fp16)[name = tensor<string, []>("add_24_cast_fp16")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_12_cast_fp16 = sqrt(x = add_24_cast_fp16)[name = tensor<string, []>("sqrt_12_cast_fp16")];
            tensor<fp16, [2, 32, 20, 12, 20]> real_div_12_cast_fp16 = real_div(x = sub_24_cast_fp16, y = sqrt_12_cast_fp16)[name = tensor<string, []>("real_div_12_cast_fp16")];
            tensor<int32, [4]> reshape_49_shape_0 = const()[name = tensor<string, []>("reshape_49_shape_0"), val = tensor<int32, [4]>([2, 640, 12, 20])];
            tensor<fp16, [2, 640, 12, 20]> reshape_49_cast_fp16 = reshape(shape = reshape_49_shape_0, x = real_div_12_cast_fp16)[name = tensor<string, []>("reshape_49_cast_fp16")];
            tensor<fp16, [640]> add_25_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_25_gamma_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(100791488)))];
            tensor<fp16, [640]> add_25_beta_0_to_fp16 = const()[name = tensor<string, []>("add_25_beta_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(100792832)))];
            tensor<fp16, []> add_25_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_25_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 640, 12, 20]> add_25_cast_fp16 = batch_norm(beta = add_25_beta_0_to_fp16, epsilon = add_25_epsilon_0_to_fp16, gamma = add_25_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_49_cast_fp16)[name = tensor<string, []>("add_25_cast_fp16")];
            tensor<fp16, [2, 640, 12, 20]> input_121_cast_fp16 = silu(x = add_25_cast_fp16)[name = tensor<string, []>("input_121_cast_fp16")];
            tensor<int32, [2]> var_1218 = const()[name = tensor<string, []>("op_1218"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1220 = const()[name = tensor<string, []>("op_1220"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_73_pad_type_0 = const()[name = tensor<string, []>("hidden_states_73_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_73_pad_0 = const()[name = tensor<string, []>("hidden_states_73_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 640, 3, 3]> down_blocks_2_resnets_0_conv1_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_resnets_0_conv1_weight_to_fp16"), val = tensor<fp16, [1280, 640, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(100794176)))];
            tensor<fp16, [1280]> down_blocks_2_resnets_0_conv1_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_resnets_0_conv1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(115539840)))];
            tensor<fp16, [2, 1280, 12, 20]> hidden_states_73_cast_fp16 = conv(bias = down_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = var_1220, groups = var_1195, pad = hidden_states_73_pad_0, pad_type = hidden_states_73_pad_type_0, strides = var_1218, weight = down_blocks_2_resnets_0_conv1_weight_to_fp16, x = input_121_cast_fp16)[name = tensor<string, []>("hidden_states_73_cast_fp16")];
            tensor<int32, [2]> var_1226 = const()[name = tensor<string, []>("op_1226"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1228 = const()[name = tensor<string, []>("op_1228"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> temb_9_pad_type_0 = const()[name = tensor<string, []>("temb_9_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> temb_9_pad_0 = const()[name = tensor<string, []>("temb_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(115542464)))];
            tensor<fp16, [1280]> down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(118819328)))];
            tensor<fp16, [2, 1280, 1, 1]> temb_9_cast_fp16 = conv(bias = down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_1228, groups = var_1195, pad = temb_9_pad_0, pad_type = temb_9_pad_type_0, strides = var_1226, weight = down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16_1)[name = tensor<string, []>("temb_9_cast_fp16")];
            tensor<fp16, [2, 1280, 12, 20]> input_125_cast_fp16 = add(x = hidden_states_73_cast_fp16, y = temb_9_cast_fp16)[name = tensor<string, []>("input_125_cast_fp16")];
            tensor<int32, [5]> reshape_52_shape_0 = const()[name = tensor<string, []>("reshape_52_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 12, 20])];
            tensor<fp16, [2, 32, 40, 12, 20]> reshape_52_cast_fp16 = reshape(shape = reshape_52_shape_0, x = input_125_cast_fp16)[name = tensor<string, []>("reshape_52_cast_fp16")];
            tensor<int32, [3]> reduce_mean_39_axes_0 = const()[name = tensor<string, []>("reduce_mean_39_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_39_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_39_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_39_cast_fp16 = reduce_mean(axes = reduce_mean_39_axes_0, keep_dims = reduce_mean_39_keep_dims_0, x = reshape_52_cast_fp16)[name = tensor<string, []>("reduce_mean_39_cast_fp16")];
            tensor<fp16, [2, 32, 40, 12, 20]> sub_26_cast_fp16 = sub(x = reshape_52_cast_fp16, y = reduce_mean_39_cast_fp16)[name = tensor<string, []>("sub_26_cast_fp16")];
            tensor<fp16, [2, 32, 40, 12, 20]> square_13_cast_fp16 = square(x = sub_26_cast_fp16)[name = tensor<string, []>("square_13_cast_fp16")];
            tensor<int32, [3]> reduce_mean_41_axes_0 = const()[name = tensor<string, []>("reduce_mean_41_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_41_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_41_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_41_cast_fp16 = reduce_mean(axes = reduce_mean_41_axes_0, keep_dims = reduce_mean_41_keep_dims_0, x = square_13_cast_fp16)[name = tensor<string, []>("reduce_mean_41_cast_fp16")];
            tensor<fp16, []> add_26_y_0_to_fp16 = const()[name = tensor<string, []>("add_26_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_26_cast_fp16 = add(x = reduce_mean_41_cast_fp16, y = add_26_y_0_to_fp16)[name = tensor<string, []>("add_26_cast_fp16")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_13_cast_fp16 = sqrt(x = add_26_cast_fp16)[name = tensor<string, []>("sqrt_13_cast_fp16")];
            tensor<fp16, [2, 32, 40, 12, 20]> real_div_13_cast_fp16 = real_div(x = sub_26_cast_fp16, y = sqrt_13_cast_fp16)[name = tensor<string, []>("real_div_13_cast_fp16")];
            tensor<int32, [4]> reshape_53_shape_0 = const()[name = tensor<string, []>("reshape_53_shape_0"), val = tensor<int32, [4]>([2, 1280, 12, 20])];
            tensor<fp16, [2, 1280, 12, 20]> reshape_53_cast_fp16 = reshape(shape = reshape_53_shape_0, x = real_div_13_cast_fp16)[name = tensor<string, []>("reshape_53_cast_fp16")];
            tensor<fp16, [1280]> add_27_mean_0_to_fp16 = const()[name = tensor<string, []>("add_27_mean_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(118821952)))];
            tensor<fp16, [1280]> add_27_variance_0_to_fp16 = const()[name = tensor<string, []>("add_27_variance_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(118824576)))];
            tensor<fp16, [1280]> add_27_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_27_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(118827200)))];
            tensor<fp16, [1280]> add_27_beta_0_to_fp16 = const()[name = tensor<string, []>("add_27_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(118829824)))];
            tensor<fp16, []> add_27_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_27_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1280, 12, 20]> add_27_cast_fp16 = batch_norm(beta = add_27_beta_0_to_fp16, epsilon = add_27_epsilon_0_to_fp16, gamma = add_27_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_53_cast_fp16)[name = tensor<string, []>("add_27_cast_fp16")];
            tensor<fp16, [2, 1280, 12, 20]> input_129_cast_fp16 = silu(x = add_27_cast_fp16)[name = tensor<string, []>("input_129_cast_fp16")];
            tensor<int32, [2]> var_1238 = const()[name = tensor<string, []>("op_1238"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1240 = const()[name = tensor<string, []>("op_1240"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_75_pad_type_0 = const()[name = tensor<string, []>("hidden_states_75_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_75_pad_0 = const()[name = tensor<string, []>("hidden_states_75_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 1280, 3, 3]> down_blocks_2_resnets_0_conv2_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_resnets_0_conv2_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(118832448)))];
            tensor<fp16, [1280]> down_blocks_2_resnets_0_conv2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_resnets_0_conv2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(148323712)))];
            tensor<fp16, [2, 1280, 12, 20]> hidden_states_75_cast_fp16 = conv(bias = down_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = var_1240, groups = var_1195, pad = hidden_states_75_pad_0, pad_type = hidden_states_75_pad_type_0, strides = var_1238, weight = down_blocks_2_resnets_0_conv2_weight_to_fp16, x = input_129_cast_fp16)[name = tensor<string, []>("hidden_states_75_cast_fp16")];
            tensor<int32, [2]> var_1245 = const()[name = tensor<string, []>("op_1245"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1247 = const()[name = tensor<string, []>("op_1247"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> x_3_pad_type_0 = const()[name = tensor<string, []>("x_3_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> x_3_pad_0 = const()[name = tensor<string, []>("x_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 640, 1, 1]> down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor<fp16, [1280, 640, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(148326336)))];
            tensor<fp16, [1280]> down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149964800)))];
            tensor<fp16, [2, 1280, 12, 20]> x_3_cast_fp16 = conv(bias = down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_1247, groups = var_1195, pad = x_3_pad_0, pad_type = x_3_pad_type_0, strides = var_1245, weight = down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16, x = input_117_cast_fp16_1)[name = tensor<string, []>("x_3_cast_fp16")];
            tensor<fp16, [2, 1280, 12, 20]> hidden_states_77_cast_fp16 = add(x = x_3_cast_fp16, y = hidden_states_75_cast_fp16)[name = tensor<string, []>("hidden_states_77_cast_fp16")];
            tensor<int32, [5]> reshape_56_shape_0 = const()[name = tensor<string, []>("reshape_56_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 12, 20])];
            tensor<fp16, [2, 32, 40, 12, 20]> reshape_56_cast_fp16 = reshape(shape = reshape_56_shape_0, x = hidden_states_77_cast_fp16)[name = tensor<string, []>("reshape_56_cast_fp16")];
            tensor<int32, [3]> reduce_mean_42_axes_0 = const()[name = tensor<string, []>("reduce_mean_42_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_42_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_42_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_42_cast_fp16 = reduce_mean(axes = reduce_mean_42_axes_0, keep_dims = reduce_mean_42_keep_dims_0, x = reshape_56_cast_fp16)[name = tensor<string, []>("reduce_mean_42_cast_fp16")];
            tensor<fp16, [2, 32, 40, 12, 20]> sub_28_cast_fp16 = sub(x = reshape_56_cast_fp16, y = reduce_mean_42_cast_fp16)[name = tensor<string, []>("sub_28_cast_fp16")];
            tensor<fp16, [2, 32, 40, 12, 20]> square_14_cast_fp16 = square(x = sub_28_cast_fp16)[name = tensor<string, []>("square_14_cast_fp16")];
            tensor<int32, [3]> reduce_mean_44_axes_0 = const()[name = tensor<string, []>("reduce_mean_44_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_44_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_44_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_44_cast_fp16 = reduce_mean(axes = reduce_mean_44_axes_0, keep_dims = reduce_mean_44_keep_dims_0, x = square_14_cast_fp16)[name = tensor<string, []>("reduce_mean_44_cast_fp16")];
            tensor<fp16, []> add_28_y_0_to_fp16 = const()[name = tensor<string, []>("add_28_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_28_cast_fp16 = add(x = reduce_mean_44_cast_fp16, y = add_28_y_0_to_fp16)[name = tensor<string, []>("add_28_cast_fp16")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_14_cast_fp16 = sqrt(x = add_28_cast_fp16)[name = tensor<string, []>("sqrt_14_cast_fp16")];
            tensor<fp16, [2, 32, 40, 12, 20]> real_div_14_cast_fp16 = real_div(x = sub_28_cast_fp16, y = sqrt_14_cast_fp16)[name = tensor<string, []>("real_div_14_cast_fp16")];
            tensor<int32, [4]> reshape_57_shape_0 = const()[name = tensor<string, []>("reshape_57_shape_0"), val = tensor<int32, [4]>([2, 1280, 12, 20])];
            tensor<fp16, [2, 1280, 12, 20]> reshape_57_cast_fp16 = reshape(shape = reshape_57_shape_0, x = real_div_14_cast_fp16)[name = tensor<string, []>("reshape_57_cast_fp16")];
            tensor<fp16, [1280]> add_29_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_29_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149967424)))];
            tensor<fp16, [1280]> add_29_beta_0_to_fp16 = const()[name = tensor<string, []>("add_29_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149970048)))];
            tensor<fp16, []> add_29_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_29_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1280, 12, 20]> add_29_cast_fp16 = batch_norm(beta = add_29_beta_0_to_fp16, epsilon = add_29_epsilon_0_to_fp16, gamma = add_29_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_57_cast_fp16)[name = tensor<string, []>("add_29_cast_fp16")];
            tensor<int32, [2]> var_1267 = const()[name = tensor<string, []>("op_1267"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1269 = const()[name = tensor<string, []>("op_1269"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_79_pad_type_0 = const()[name = tensor<string, []>("hidden_states_79_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_79_pad_0 = const()[name = tensor<string, []>("hidden_states_79_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_proj_in_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149972672)))];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_proj_in_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(153249536)))];
            tensor<fp16, [2, 1280, 12, 20]> hidden_states_79_cast_fp16 = conv(bias = down_blocks_2_attentions_0_proj_in_bias_to_fp16, dilations = var_1269, groups = var_1195, pad = hidden_states_79_pad_0, pad_type = hidden_states_79_pad_type_0, strides = var_1267, weight = down_blocks_2_attentions_0_proj_in_weight_to_fp16, x = add_29_cast_fp16)[name = tensor<string, []>("hidden_states_79_cast_fp16")];
            tensor<int32, [4]> var_1274 = const()[name = tensor<string, []>("op_1274"), val = tensor<int32, [4]>([2, 1280, 1, 240])];
            tensor<fp16, [2, 1280, 1, 240]> inputs_25_cast_fp16 = reshape(shape = var_1274, x = hidden_states_79_cast_fp16)[name = tensor<string, []>("inputs_25_cast_fp16")];
            tensor<int32, [1]> var_1284 = const()[name = tensor<string, []>("op_1284"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 240]> channels_mean_25_cast_fp16 = reduce_mean(axes = var_1284, keep_dims = var_1190, x = inputs_25_cast_fp16)[name = tensor<string, []>("channels_mean_25_cast_fp16")];
            tensor<fp16, [2, 1280, 1, 240]> 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, [2, 1280, 1, 240]> 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_1288 = const()[name = tensor<string, []>("op_1288"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 240]> var_1289_cast_fp16 = reduce_mean(axes = var_1288, keep_dims = var_1190, x = zero_mean_sq_25_cast_fp16)[name = tensor<string, []>("op_1289_cast_fp16")];
            tensor<fp16, []> var_1290_to_fp16 = const()[name = tensor<string, []>("op_1290_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 240]> var_1291_cast_fp16 = add(x = var_1289_cast_fp16, y = var_1290_to_fp16)[name = tensor<string, []>("op_1291_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, [2, 1, 1, 240]> denom_25_cast_fp16 = rsqrt(epsilon = denom_25_epsilon_0_to_fp16, x = var_1291_cast_fp16)[name = tensor<string, []>("denom_25_cast_fp16")];
            tensor<fp16, [2, 1280, 1, 240]> 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, [1, 1280, 1, 1]> var_1295_to_fp16 = const()[name = tensor<string, []>("op_1295_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(153252160)))];
            tensor<fp16, [2, 1280, 1, 240]> var_1296_cast_fp16 = add(x = out_25_cast_fp16, y = var_1295_to_fp16)[name = tensor<string, []>("op_1296_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1]> var_1298_to_fp16 = const()[name = tensor<string, []>("op_1298_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(153254784)))];
            tensor<fp16, [2, 1280, 1, 240]> hidden_states_81_cast_fp16 = mul(x = var_1296_cast_fp16, y = var_1298_to_fp16)[name = tensor<string, []>("hidden_states_81_cast_fp16")];
            tensor<int32, [2]> var_1305 = const()[name = tensor<string, []>("op_1305"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1307 = const()[name = tensor<string, []>("op_1307"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_17_pad_type_0 = const()[name = tensor<string, []>("q_17_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_17_pad_0 = const()[name = tensor<string, []>("q_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(153257408)))];
            tensor<fp16, [2, 1280, 1, 240]> q_17_cast_fp16 = conv(dilations = var_1307, groups = var_1195, pad = q_17_pad_0, pad_type = q_17_pad_type_0, strides = var_1305, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_81_cast_fp16)[name = tensor<string, []>("q_17_cast_fp16")];
            tensor<int32, [2]> var_1311 = const()[name = tensor<string, []>("op_1311"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1313 = const()[name = tensor<string, []>("op_1313"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_17_pad_type_0 = const()[name = tensor<string, []>("k_17_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_17_pad_0 = const()[name = tensor<string, []>("k_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(156534272)))];
            tensor<fp16, [2, 1280, 1, 240]> k_17_cast_fp16 = conv(dilations = var_1313, groups = var_1195, pad = k_17_pad_0, pad_type = k_17_pad_type_0, strides = var_1311, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_81_cast_fp16)[name = tensor<string, []>("k_17_cast_fp16")];
            tensor<int32, [2]> var_1317 = const()[name = tensor<string, []>("op_1317"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1319 = const()[name = tensor<string, []>("op_1319"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_17_pad_type_0 = const()[name = tensor<string, []>("v_17_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_17_pad_0 = const()[name = tensor<string, []>("v_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(159811136)))];
            tensor<fp16, [2, 1280, 1, 240]> v_17_cast_fp16 = conv(dilations = var_1319, groups = var_1195, pad = v_17_pad_0, pad_type = v_17_pad_type_0, strides = var_1317, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_81_cast_fp16)[name = tensor<string, []>("v_17_cast_fp16")];
            tensor<int32, [4]> var_1323 = const()[name = tensor<string, []>("op_1323"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 240]> var_1324_cast_fp16 = reshape(shape = var_1323, x = q_17_cast_fp16)[name = tensor<string, []>("op_1324_cast_fp16")];
            tensor<int32, [4]> var_1325 = const()[name = tensor<string, []>("op_1325"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 240]> var_1326_cast_fp16 = reshape(shape = var_1325, x = k_17_cast_fp16)[name = tensor<string, []>("op_1326_cast_fp16")];
            tensor<int32, [4]> var_1327 = const()[name = tensor<string, []>("op_1327"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 240]> var_1328_cast_fp16 = reshape(shape = var_1327, x = v_17_cast_fp16)[name = tensor<string, []>("op_1328_cast_fp16")];
            tensor<bool, []> attn_weights_33_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_33_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_33_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_33_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 240, 240]> attn_weights_33_cast_fp16 = matmul(transpose_x = attn_weights_33_transpose_x_0, transpose_y = attn_weights_33_transpose_y_0, x = var_1324_cast_fp16, y = var_1326_cast_fp16)[name = tensor<string, []>("attn_weights_33_cast_fp16")];
            tensor<fp16, []> var_1186_to_fp16 = const()[name = tensor<string, []>("op_1186_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [2, 20, 240, 240]> attn_weights_35_cast_fp16 = mul(x = attn_weights_33_cast_fp16, y = var_1186_to_fp16)[name = tensor<string, []>("attn_weights_35_cast_fp16")];
            tensor<fp16, [2, 20, 240, 240]> var_1332_cast_fp16 = softmax(axis = var_1179, x = attn_weights_35_cast_fp16)[name = tensor<string, []>("op_1332_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, [2, 20, 64, 240]> attn_17_cast_fp16 = matmul(transpose_x = attn_17_transpose_x_0, transpose_y = attn_17_transpose_y_0, x = var_1328_cast_fp16, y = var_1332_cast_fp16)[name = tensor<string, []>("attn_17_cast_fp16")];
            tensor<int32, [4]> var_1336 = const()[name = tensor<string, []>("op_1336"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 240]> input_133_cast_fp16 = reshape(shape = var_1336, x = attn_17_cast_fp16)[name = tensor<string, []>("input_133_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, []> var_1345_pad_type_0 = const()[name = tensor<string, []>("op_1345_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1345_pad_0 = const()[name = tensor<string, []>("op_1345_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(163088000)))];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(166364864)))];
            tensor<fp16, [2, 1280, 1, 240]> var_1345_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_1343, groups = var_1195, pad = var_1345_pad_0, pad_type = var_1345_pad_type_0, strides = var_1341, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_133_cast_fp16)[name = tensor<string, []>("op_1345_cast_fp16")];
            tensor<fp16, [2, 1280, 1, 240]> inputs_27_cast_fp16 = add(x = var_1345_cast_fp16, y = inputs_25_cast_fp16)[name = tensor<string, []>("inputs_27_cast_fp16")];
            tensor<int32, [1]> var_1349 = const()[name = tensor<string, []>("op_1349"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 240]> channels_mean_27_cast_fp16 = reduce_mean(axes = var_1349, keep_dims = var_1190, x = inputs_27_cast_fp16)[name = tensor<string, []>("channels_mean_27_cast_fp16")];
            tensor<fp16, [2, 1280, 1, 240]> 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, [2, 1280, 1, 240]> 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_1353 = const()[name = tensor<string, []>("op_1353"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 240]> var_1354_cast_fp16 = reduce_mean(axes = var_1353, keep_dims = var_1190, x = zero_mean_sq_27_cast_fp16)[name = tensor<string, []>("op_1354_cast_fp16")];
            tensor<fp16, []> var_1355_to_fp16 = const()[name = tensor<string, []>("op_1355_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 240]> var_1356_cast_fp16 = add(x = var_1354_cast_fp16, y = var_1355_to_fp16)[name = tensor<string, []>("op_1356_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, [2, 1, 1, 240]> denom_27_cast_fp16 = rsqrt(epsilon = denom_27_epsilon_0_to_fp16, x = var_1356_cast_fp16)[name = tensor<string, []>("denom_27_cast_fp16")];
            tensor<fp16, [2, 1280, 1, 240]> 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, [1, 1280, 1, 1]> var_1360_to_fp16 = const()[name = tensor<string, []>("op_1360_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(166367488)))];
            tensor<fp16, [2, 1280, 1, 240]> var_1361_cast_fp16 = add(x = out_27_cast_fp16, y = var_1360_to_fp16)[name = tensor<string, []>("op_1361_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1]> var_1363_to_fp16 = const()[name = tensor<string, []>("op_1363_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(166370112)))];
            tensor<fp16, [2, 1280, 1, 240]> hidden_states_83_cast_fp16 = mul(x = var_1361_cast_fp16, y = var_1363_to_fp16)[name = tensor<string, []>("hidden_states_83_cast_fp16")];
            tensor<int32, [2]> var_1370 = const()[name = tensor<string, []>("op_1370"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1372 = const()[name = tensor<string, []>("op_1372"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_19_pad_type_0 = const()[name = tensor<string, []>("q_19_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_19_pad_0 = const()[name = tensor<string, []>("q_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(166372736)))];
            tensor<fp16, [2, 1280, 1, 240]> q_19_cast_fp16 = conv(dilations = var_1372, groups = var_1195, pad = q_19_pad_0, pad_type = q_19_pad_type_0, strides = var_1370, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_83_cast_fp16)[name = tensor<string, []>("q_19_cast_fp16")];
            tensor<int32, [2]> var_1376 = const()[name = tensor<string, []>("op_1376"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1378 = const()[name = tensor<string, []>("op_1378"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_19_pad_type_0 = const()[name = tensor<string, []>("k_19_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_19_pad_0 = const()[name = tensor<string, []>("k_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1024, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor<fp16, [1280, 1024, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(169649600)))];
            tensor<fp16, [2, 1280, 1, 77]> k_19_cast_fp16 = conv(dilations = var_1378, groups = var_1195, pad = k_19_pad_0, pad_type = k_19_pad_type_0, strides = var_1376, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor<string, []>("k_19_cast_fp16")];
            tensor<int32, [2]> var_1382 = const()[name = tensor<string, []>("op_1382"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1384 = const()[name = tensor<string, []>("op_1384"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_19_pad_type_0 = const()[name = tensor<string, []>("v_19_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_19_pad_0 = const()[name = tensor<string, []>("v_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1024, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor<fp16, [1280, 1024, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(172271104)))];
            tensor<fp16, [2, 1280, 1, 77]> v_19_cast_fp16 = conv(dilations = var_1384, groups = var_1195, pad = v_19_pad_0, pad_type = v_19_pad_type_0, strides = var_1382, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor<string, []>("v_19_cast_fp16")];
            tensor<int32, [4]> var_1388 = const()[name = tensor<string, []>("op_1388"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 240]> var_1389_cast_fp16 = reshape(shape = var_1388, x = q_19_cast_fp16)[name = tensor<string, []>("op_1389_cast_fp16")];
            tensor<int32, [4]> var_1390 = const()[name = tensor<string, []>("op_1390"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_1391_cast_fp16 = reshape(shape = var_1390, x = k_19_cast_fp16)[name = tensor<string, []>("op_1391_cast_fp16")];
            tensor<int32, [4]> var_1392 = const()[name = tensor<string, []>("op_1392"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_1393_cast_fp16 = reshape(shape = var_1392, x = v_19_cast_fp16)[name = tensor<string, []>("op_1393_cast_fp16")];
            tensor<bool, []> attn_weights_37_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_37_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_37_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_37_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 240, 77]> attn_weights_37_cast_fp16 = matmul(transpose_x = attn_weights_37_transpose_x_0, transpose_y = attn_weights_37_transpose_y_0, x = var_1389_cast_fp16, y = var_1391_cast_fp16)[name = tensor<string, []>("attn_weights_37_cast_fp16")];
            tensor<fp16, [2, 20, 240, 77]> attn_weights_39_cast_fp16 = mul(x = attn_weights_37_cast_fp16, y = var_1186_to_fp16)[name = tensor<string, []>("attn_weights_39_cast_fp16")];
            tensor<fp16, [2, 20, 240, 77]> var_1397_cast_fp16 = softmax(axis = var_1179, x = attn_weights_39_cast_fp16)[name = tensor<string, []>("op_1397_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, [2, 20, 64, 240]> attn_19_cast_fp16 = matmul(transpose_x = attn_19_transpose_x_0, transpose_y = attn_19_transpose_y_0, x = var_1393_cast_fp16, y = var_1397_cast_fp16)[name = tensor<string, []>("attn_19_cast_fp16")];
            tensor<int32, [4]> var_1401 = const()[name = tensor<string, []>("op_1401"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 240]> input_135_cast_fp16 = reshape(shape = var_1401, x = attn_19_cast_fp16)[name = tensor<string, []>("input_135_cast_fp16")];
            tensor<int32, [2]> var_1406 = const()[name = tensor<string, []>("op_1406"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1408 = const()[name = tensor<string, []>("op_1408"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1410_pad_type_0 = const()[name = tensor<string, []>("op_1410_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1410_pad_0 = const()[name = tensor<string, []>("op_1410_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(174892608)))];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(178169472)))];
            tensor<fp16, [2, 1280, 1, 240]> var_1410_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_1408, groups = var_1195, pad = var_1410_pad_0, pad_type = var_1410_pad_type_0, strides = var_1406, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_135_cast_fp16)[name = tensor<string, []>("op_1410_cast_fp16")];
            tensor<fp16, [2, 1280, 1, 240]> inputs_29_cast_fp16 = add(x = var_1410_cast_fp16, y = inputs_27_cast_fp16)[name = tensor<string, []>("inputs_29_cast_fp16")];
            tensor<int32, [1]> var_1414 = const()[name = tensor<string, []>("op_1414"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 240]> channels_mean_29_cast_fp16 = reduce_mean(axes = var_1414, keep_dims = var_1190, x = inputs_29_cast_fp16)[name = tensor<string, []>("channels_mean_29_cast_fp16")];
            tensor<fp16, [2, 1280, 1, 240]> 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, [2, 1280, 1, 240]> 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_1418 = const()[name = tensor<string, []>("op_1418"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 240]> var_1419_cast_fp16 = reduce_mean(axes = var_1418, keep_dims = var_1190, x = zero_mean_sq_29_cast_fp16)[name = tensor<string, []>("op_1419_cast_fp16")];
            tensor<fp16, []> var_1420_to_fp16 = const()[name = tensor<string, []>("op_1420_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 240]> var_1421_cast_fp16 = add(x = var_1419_cast_fp16, y = var_1420_to_fp16)[name = tensor<string, []>("op_1421_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, [2, 1, 1, 240]> denom_29_cast_fp16 = rsqrt(epsilon = denom_29_epsilon_0_to_fp16, x = var_1421_cast_fp16)[name = tensor<string, []>("denom_29_cast_fp16")];
            tensor<fp16, [2, 1280, 1, 240]> 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, [1, 1280, 1, 1]> var_1425_to_fp16 = const()[name = tensor<string, []>("op_1425_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(178172096)))];
            tensor<fp16, [2, 1280, 1, 240]> var_1426_cast_fp16 = add(x = out_29_cast_fp16, y = var_1425_to_fp16)[name = tensor<string, []>("op_1426_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1]> var_1428_to_fp16 = const()[name = tensor<string, []>("op_1428_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(178174720)))];
            tensor<fp16, [2, 1280, 1, 240]> input_137_cast_fp16 = mul(x = var_1426_cast_fp16, y = var_1428_to_fp16)[name = tensor<string, []>("input_137_cast_fp16")];
            tensor<int32, [2]> var_1436 = const()[name = tensor<string, []>("op_1436"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1438 = const()[name = tensor<string, []>("op_1438"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1440_pad_type_0 = const()[name = tensor<string, []>("op_1440_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1440_pad_0 = const()[name = tensor<string, []>("op_1440_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor<fp16, [10240, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(178177344)))];
            tensor<fp16, [10240]> down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(204391808)))];
            tensor<fp16, [2, 10240, 1, 240]> var_1440_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_1438, groups = var_1195, pad = var_1440_pad_0, pad_type = var_1440_pad_type_0, strides = var_1436, weight = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_137_cast_fp16)[name = tensor<string, []>("op_1440_cast_fp16")];
            tensor<int32, [2]> var_1441_split_sizes_0 = const()[name = tensor<string, []>("op_1441_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_1441_axis_0 = const()[name = tensor<string, []>("op_1441_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 240]> var_1441_cast_fp16_0, tensor<fp16, [2, 5120, 1, 240]> var_1441_cast_fp16_1 = split(axis = var_1441_axis_0, split_sizes = var_1441_split_sizes_0, x = var_1440_cast_fp16)[name = tensor<string, []>("op_1441_cast_fp16")];
            tensor<string, []> var_1443_mode_0 = const()[name = tensor<string, []>("op_1443_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 240]> var_1443_cast_fp16 = gelu(mode = var_1443_mode_0, x = var_1441_cast_fp16_1)[name = tensor<string, []>("op_1443_cast_fp16")];
            tensor<fp16, [2, 5120, 1, 240]> input_139_cast_fp16 = mul(x = var_1441_cast_fp16_0, y = var_1443_cast_fp16)[name = tensor<string, []>("input_139_cast_fp16")];
            tensor<int32, [2]> var_1447 = const()[name = tensor<string, []>("op_1447"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1449 = const()[name = tensor<string, []>("op_1449"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1451_pad_type_0 = const()[name = tensor<string, []>("op_1451_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1451_pad_0 = const()[name = tensor<string, []>("op_1451_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(204412352)))];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(217519616)))];
            tensor<fp16, [2, 1280, 1, 240]> var_1451_cast_fp16 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_1449, groups = var_1195, pad = var_1451_pad_0, pad_type = var_1451_pad_type_0, strides = var_1447, weight = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_139_cast_fp16)[name = tensor<string, []>("op_1451_cast_fp16")];
            tensor<fp16, [2, 1280, 1, 240]> hidden_states_87_cast_fp16 = add(x = var_1451_cast_fp16, y = inputs_29_cast_fp16)[name = tensor<string, []>("hidden_states_87_cast_fp16")];
            tensor<int32, [4]> var_1453 = const()[name = tensor<string, []>("op_1453"), val = tensor<int32, [4]>([2, 1280, 12, 20])];
            tensor<fp16, [2, 1280, 12, 20]> input_141_cast_fp16 = reshape(shape = var_1453, x = hidden_states_87_cast_fp16)[name = tensor<string, []>("input_141_cast_fp16")];
            tensor<int32, [2]> var_1457 = const()[name = tensor<string, []>("op_1457"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1459 = const()[name = tensor<string, []>("op_1459"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_89_pad_type_0 = const()[name = tensor<string, []>("hidden_states_89_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_89_pad_0 = const()[name = tensor<string, []>("hidden_states_89_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_proj_out_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(217522240)))];
            tensor<fp16, [1280]> down_blocks_2_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_0_proj_out_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(220799104)))];
            tensor<fp16, [2, 1280, 12, 20]> hidden_states_89_cast_fp16 = conv(bias = down_blocks_2_attentions_0_proj_out_bias_to_fp16, dilations = var_1459, groups = var_1195, pad = hidden_states_89_pad_0, pad_type = hidden_states_89_pad_type_0, strides = var_1457, weight = down_blocks_2_attentions_0_proj_out_weight_to_fp16, x = input_141_cast_fp16)[name = tensor<string, []>("hidden_states_89_cast_fp16")];
            tensor<fp16, [2, 1280, 12, 20]> input_143_cast_fp16_1 = add(x = hidden_states_89_cast_fp16, y = hidden_states_77_cast_fp16)[name = tensor<string, []>("input_143_cast_fp16")];
            tensor<int32, [5]> reshape_60_shape_0 = const()[name = tensor<string, []>("reshape_60_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 12, 20])];
            tensor<fp16, [2, 32, 40, 12, 20]> reshape_60_cast_fp16 = reshape(shape = reshape_60_shape_0, x = input_143_cast_fp16_1)[name = tensor<string, []>("reshape_60_cast_fp16")];
            tensor<int32, [3]> reduce_mean_45_axes_0 = const()[name = tensor<string, []>("reduce_mean_45_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_45_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_45_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_45_cast_fp16 = reduce_mean(axes = reduce_mean_45_axes_0, keep_dims = reduce_mean_45_keep_dims_0, x = reshape_60_cast_fp16)[name = tensor<string, []>("reduce_mean_45_cast_fp16")];
            tensor<fp16, [2, 32, 40, 12, 20]> sub_30_cast_fp16 = sub(x = reshape_60_cast_fp16, y = reduce_mean_45_cast_fp16)[name = tensor<string, []>("sub_30_cast_fp16")];
            tensor<fp16, [2, 32, 40, 12, 20]> square_15_cast_fp16 = square(x = sub_30_cast_fp16)[name = tensor<string, []>("square_15_cast_fp16")];
            tensor<int32, [3]> reduce_mean_47_axes_0 = const()[name = tensor<string, []>("reduce_mean_47_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_47_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_47_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_47_cast_fp16 = reduce_mean(axes = reduce_mean_47_axes_0, keep_dims = reduce_mean_47_keep_dims_0, x = square_15_cast_fp16)[name = tensor<string, []>("reduce_mean_47_cast_fp16")];
            tensor<fp16, []> add_30_y_0_to_fp16 = const()[name = tensor<string, []>("add_30_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_30_cast_fp16 = add(x = reduce_mean_47_cast_fp16, y = add_30_y_0_to_fp16)[name = tensor<string, []>("add_30_cast_fp16")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_15_cast_fp16 = sqrt(x = add_30_cast_fp16)[name = tensor<string, []>("sqrt_15_cast_fp16")];
            tensor<fp16, [2, 32, 40, 12, 20]> real_div_15_cast_fp16 = real_div(x = sub_30_cast_fp16, y = sqrt_15_cast_fp16)[name = tensor<string, []>("real_div_15_cast_fp16")];
            tensor<int32, [4]> reshape_61_shape_0 = const()[name = tensor<string, []>("reshape_61_shape_0"), val = tensor<int32, [4]>([2, 1280, 12, 20])];
            tensor<fp16, [2, 1280, 12, 20]> reshape_61_cast_fp16 = reshape(shape = reshape_61_shape_0, x = real_div_15_cast_fp16)[name = tensor<string, []>("reshape_61_cast_fp16")];
            tensor<fp16, [1280]> add_31_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_31_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(220801728)))];
            tensor<fp16, [1280]> add_31_beta_0_to_fp16 = const()[name = tensor<string, []>("add_31_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(220804352)))];
            tensor<fp16, []> add_31_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_31_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1280, 12, 20]> add_31_cast_fp16 = batch_norm(beta = add_31_beta_0_to_fp16, epsilon = add_31_epsilon_0_to_fp16, gamma = add_31_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_61_cast_fp16)[name = tensor<string, []>("add_31_cast_fp16")];
            tensor<fp16, [2, 1280, 12, 20]> input_147_cast_fp16 = silu(x = add_31_cast_fp16)[name = tensor<string, []>("input_147_cast_fp16")];
            tensor<int32, [2]> var_1474 = const()[name = tensor<string, []>("op_1474"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1476 = const()[name = tensor<string, []>("op_1476"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_91_pad_type_0 = const()[name = tensor<string, []>("hidden_states_91_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_91_pad_0 = const()[name = tensor<string, []>("hidden_states_91_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 1280, 3, 3]> down_blocks_2_resnets_1_conv1_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_resnets_1_conv1_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(220806976)))];
            tensor<fp16, [1280]> down_blocks_2_resnets_1_conv1_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_resnets_1_conv1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(250298240)))];
            tensor<fp16, [2, 1280, 12, 20]> hidden_states_91_cast_fp16 = conv(bias = down_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = var_1476, groups = var_1195, pad = hidden_states_91_pad_0, pad_type = hidden_states_91_pad_type_0, strides = var_1474, weight = down_blocks_2_resnets_1_conv1_weight_to_fp16, x = input_147_cast_fp16)[name = tensor<string, []>("hidden_states_91_cast_fp16")];
            tensor<int32, [2]> var_1482 = const()[name = tensor<string, []>("op_1482"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1484 = const()[name = tensor<string, []>("op_1484"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> temb_11_pad_type_0 = const()[name = tensor<string, []>("temb_11_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> temb_11_pad_0 = const()[name = tensor<string, []>("temb_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(250300864)))];
            tensor<fp16, [1280]> down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(253577728)))];
            tensor<fp16, [2, 1280, 1, 1]> temb_11_cast_fp16 = conv(bias = down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_1484, groups = var_1195, pad = temb_11_pad_0, pad_type = temb_11_pad_type_0, strides = var_1482, weight = down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16_1)[name = tensor<string, []>("temb_11_cast_fp16")];
            tensor<fp16, [2, 1280, 12, 20]> input_151_cast_fp16 = add(x = hidden_states_91_cast_fp16, y = temb_11_cast_fp16)[name = tensor<string, []>("input_151_cast_fp16")];
            tensor<int32, [5]> reshape_64_shape_0 = const()[name = tensor<string, []>("reshape_64_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 12, 20])];
            tensor<fp16, [2, 32, 40, 12, 20]> reshape_64_cast_fp16 = reshape(shape = reshape_64_shape_0, x = input_151_cast_fp16)[name = tensor<string, []>("reshape_64_cast_fp16")];
            tensor<int32, [3]> reduce_mean_48_axes_0 = const()[name = tensor<string, []>("reduce_mean_48_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_48_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_48_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_48_cast_fp16 = reduce_mean(axes = reduce_mean_48_axes_0, keep_dims = reduce_mean_48_keep_dims_0, x = reshape_64_cast_fp16)[name = tensor<string, []>("reduce_mean_48_cast_fp16")];
            tensor<fp16, [2, 32, 40, 12, 20]> sub_32_cast_fp16 = sub(x = reshape_64_cast_fp16, y = reduce_mean_48_cast_fp16)[name = tensor<string, []>("sub_32_cast_fp16")];
            tensor<fp16, [2, 32, 40, 12, 20]> square_16_cast_fp16 = square(x = sub_32_cast_fp16)[name = tensor<string, []>("square_16_cast_fp16")];
            tensor<int32, [3]> reduce_mean_50_axes_0 = const()[name = tensor<string, []>("reduce_mean_50_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_50_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_50_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_50_cast_fp16 = reduce_mean(axes = reduce_mean_50_axes_0, keep_dims = reduce_mean_50_keep_dims_0, x = square_16_cast_fp16)[name = tensor<string, []>("reduce_mean_50_cast_fp16")];
            tensor<fp16, []> add_32_y_0_to_fp16 = const()[name = tensor<string, []>("add_32_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_32_cast_fp16 = add(x = reduce_mean_50_cast_fp16, y = add_32_y_0_to_fp16)[name = tensor<string, []>("add_32_cast_fp16")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_16_cast_fp16 = sqrt(x = add_32_cast_fp16)[name = tensor<string, []>("sqrt_16_cast_fp16")];
            tensor<fp16, [2, 32, 40, 12, 20]> real_div_16_cast_fp16 = real_div(x = sub_32_cast_fp16, y = sqrt_16_cast_fp16)[name = tensor<string, []>("real_div_16_cast_fp16")];
            tensor<int32, [4]> reshape_65_shape_0 = const()[name = tensor<string, []>("reshape_65_shape_0"), val = tensor<int32, [4]>([2, 1280, 12, 20])];
            tensor<fp16, [2, 1280, 12, 20]> reshape_65_cast_fp16 = reshape(shape = reshape_65_shape_0, x = real_div_16_cast_fp16)[name = tensor<string, []>("reshape_65_cast_fp16")];
            tensor<fp16, [1280]> add_33_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_33_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(253580352)))];
            tensor<fp16, [1280]> add_33_beta_0_to_fp16 = const()[name = tensor<string, []>("add_33_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(253582976)))];
            tensor<fp16, []> add_33_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_33_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1280, 12, 20]> add_33_cast_fp16 = batch_norm(beta = add_33_beta_0_to_fp16, epsilon = add_33_epsilon_0_to_fp16, gamma = add_33_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_65_cast_fp16)[name = tensor<string, []>("add_33_cast_fp16")];
            tensor<fp16, [2, 1280, 12, 20]> input_155_cast_fp16 = silu(x = add_33_cast_fp16)[name = tensor<string, []>("input_155_cast_fp16")];
            tensor<int32, [2]> var_1494 = const()[name = tensor<string, []>("op_1494"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1496 = const()[name = tensor<string, []>("op_1496"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_93_pad_type_0 = const()[name = tensor<string, []>("hidden_states_93_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_93_pad_0 = const()[name = tensor<string, []>("hidden_states_93_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 1280, 3, 3]> down_blocks_2_resnets_1_conv2_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_resnets_1_conv2_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(253585600)))];
            tensor<fp16, [1280]> down_blocks_2_resnets_1_conv2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_resnets_1_conv2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(283076864)))];
            tensor<fp16, [2, 1280, 12, 20]> hidden_states_93_cast_fp16 = conv(bias = down_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = var_1496, groups = var_1195, pad = hidden_states_93_pad_0, pad_type = hidden_states_93_pad_type_0, strides = var_1494, weight = down_blocks_2_resnets_1_conv2_weight_to_fp16, x = input_155_cast_fp16)[name = tensor<string, []>("hidden_states_93_cast_fp16")];
            tensor<fp16, [2, 1280, 12, 20]> hidden_states_95_cast_fp16 = add(x = input_143_cast_fp16_1, y = hidden_states_93_cast_fp16)[name = tensor<string, []>("hidden_states_95_cast_fp16")];
            tensor<int32, [5]> reshape_68_shape_0 = const()[name = tensor<string, []>("reshape_68_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 12, 20])];
            tensor<fp16, [2, 32, 40, 12, 20]> reshape_68_cast_fp16 = reshape(shape = reshape_68_shape_0, x = hidden_states_95_cast_fp16)[name = tensor<string, []>("reshape_68_cast_fp16")];
            tensor<int32, [3]> reduce_mean_51_axes_0 = const()[name = tensor<string, []>("reduce_mean_51_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_51_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_51_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_51_cast_fp16 = reduce_mean(axes = reduce_mean_51_axes_0, keep_dims = reduce_mean_51_keep_dims_0, x = reshape_68_cast_fp16)[name = tensor<string, []>("reduce_mean_51_cast_fp16")];
            tensor<fp16, [2, 32, 40, 12, 20]> sub_34_cast_fp16 = sub(x = reshape_68_cast_fp16, y = reduce_mean_51_cast_fp16)[name = tensor<string, []>("sub_34_cast_fp16")];
            tensor<fp16, [2, 32, 40, 12, 20]> square_17_cast_fp16 = square(x = sub_34_cast_fp16)[name = tensor<string, []>("square_17_cast_fp16")];
            tensor<int32, [3]> reduce_mean_53_axes_0 = const()[name = tensor<string, []>("reduce_mean_53_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_53_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_53_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_53_cast_fp16 = reduce_mean(axes = reduce_mean_53_axes_0, keep_dims = reduce_mean_53_keep_dims_0, x = square_17_cast_fp16)[name = tensor<string, []>("reduce_mean_53_cast_fp16")];
            tensor<fp16, []> add_34_y_0_to_fp16 = const()[name = tensor<string, []>("add_34_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_34_cast_fp16 = add(x = reduce_mean_53_cast_fp16, y = add_34_y_0_to_fp16)[name = tensor<string, []>("add_34_cast_fp16")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_17_cast_fp16 = sqrt(x = add_34_cast_fp16)[name = tensor<string, []>("sqrt_17_cast_fp16")];
            tensor<fp16, [2, 32, 40, 12, 20]> real_div_17_cast_fp16 = real_div(x = sub_34_cast_fp16, y = sqrt_17_cast_fp16)[name = tensor<string, []>("real_div_17_cast_fp16")];
            tensor<int32, [4]> reshape_69_shape_0 = const()[name = tensor<string, []>("reshape_69_shape_0"), val = tensor<int32, [4]>([2, 1280, 12, 20])];
            tensor<fp16, [2, 1280, 12, 20]> reshape_69_cast_fp16 = reshape(shape = reshape_69_shape_0, x = real_div_17_cast_fp16)[name = tensor<string, []>("reshape_69_cast_fp16")];
            tensor<fp16, [1280]> add_35_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_35_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(283079488)))];
            tensor<fp16, [1280]> add_35_beta_0_to_fp16 = const()[name = tensor<string, []>("add_35_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(283082112)))];
            tensor<fp16, []> add_35_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_35_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1280, 12, 20]> add_35_cast_fp16 = batch_norm(beta = add_35_beta_0_to_fp16, epsilon = add_35_epsilon_0_to_fp16, gamma = add_35_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_69_cast_fp16)[name = tensor<string, []>("add_35_cast_fp16")];
            tensor<int32, [2]> var_1516 = const()[name = tensor<string, []>("op_1516"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1518 = const()[name = tensor<string, []>("op_1518"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_97_pad_type_0 = const()[name = tensor<string, []>("hidden_states_97_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_97_pad_0 = const()[name = tensor<string, []>("hidden_states_97_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_proj_in_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_proj_in_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(283084736)))];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_proj_in_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(286361600)))];
            tensor<fp16, [2, 1280, 12, 20]> hidden_states_97_cast_fp16 = conv(bias = down_blocks_2_attentions_1_proj_in_bias_to_fp16, dilations = var_1518, groups = var_1195, pad = hidden_states_97_pad_0, pad_type = hidden_states_97_pad_type_0, strides = var_1516, weight = down_blocks_2_attentions_1_proj_in_weight_to_fp16, x = add_35_cast_fp16)[name = tensor<string, []>("hidden_states_97_cast_fp16")];
            tensor<int32, [4]> var_1523 = const()[name = tensor<string, []>("op_1523"), val = tensor<int32, [4]>([2, 1280, 1, 240])];
            tensor<fp16, [2, 1280, 1, 240]> inputs_31_cast_fp16 = reshape(shape = var_1523, x = hidden_states_97_cast_fp16)[name = tensor<string, []>("inputs_31_cast_fp16")];
            tensor<int32, [1]> var_1533 = const()[name = tensor<string, []>("op_1533"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 240]> channels_mean_31_cast_fp16 = reduce_mean(axes = var_1533, keep_dims = var_1190, x = inputs_31_cast_fp16)[name = tensor<string, []>("channels_mean_31_cast_fp16")];
            tensor<fp16, [2, 1280, 1, 240]> 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, [2, 1280, 1, 240]> 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_1537 = const()[name = tensor<string, []>("op_1537"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 240]> var_1538_cast_fp16 = reduce_mean(axes = var_1537, keep_dims = var_1190, x = zero_mean_sq_31_cast_fp16)[name = tensor<string, []>("op_1538_cast_fp16")];
            tensor<fp16, []> var_1539_to_fp16 = const()[name = tensor<string, []>("op_1539_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 240]> var_1540_cast_fp16 = add(x = var_1538_cast_fp16, y = var_1539_to_fp16)[name = tensor<string, []>("op_1540_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, [2, 1, 1, 240]> denom_31_cast_fp16 = rsqrt(epsilon = denom_31_epsilon_0_to_fp16, x = var_1540_cast_fp16)[name = tensor<string, []>("denom_31_cast_fp16")];
            tensor<fp16, [2, 1280, 1, 240]> 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, [1, 1280, 1, 1]> var_1544_to_fp16 = const()[name = tensor<string, []>("op_1544_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(286364224)))];
            tensor<fp16, [2, 1280, 1, 240]> var_1545_cast_fp16 = add(x = out_31_cast_fp16, y = var_1544_to_fp16)[name = tensor<string, []>("op_1545_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1]> var_1547_to_fp16 = const()[name = tensor<string, []>("op_1547_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(286366848)))];
            tensor<fp16, [2, 1280, 1, 240]> hidden_states_99_cast_fp16 = mul(x = var_1545_cast_fp16, y = var_1547_to_fp16)[name = tensor<string, []>("hidden_states_99_cast_fp16")];
            tensor<int32, [2]> var_1554 = const()[name = tensor<string, []>("op_1554"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1556 = const()[name = tensor<string, []>("op_1556"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_21_pad_type_0 = const()[name = tensor<string, []>("q_21_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_21_pad_0 = const()[name = tensor<string, []>("q_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(286369472)))];
            tensor<fp16, [2, 1280, 1, 240]> q_21_cast_fp16 = conv(dilations = var_1556, groups = var_1195, pad = q_21_pad_0, pad_type = q_21_pad_type_0, strides = var_1554, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_99_cast_fp16)[name = tensor<string, []>("q_21_cast_fp16")];
            tensor<int32, [2]> var_1560 = const()[name = tensor<string, []>("op_1560"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1562 = const()[name = tensor<string, []>("op_1562"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_21_pad_type_0 = const()[name = tensor<string, []>("k_21_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_21_pad_0 = const()[name = tensor<string, []>("k_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(289646336)))];
            tensor<fp16, [2, 1280, 1, 240]> k_21_cast_fp16 = conv(dilations = var_1562, groups = var_1195, pad = k_21_pad_0, pad_type = k_21_pad_type_0, strides = var_1560, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_99_cast_fp16)[name = tensor<string, []>("k_21_cast_fp16")];
            tensor<int32, [2]> var_1566 = const()[name = tensor<string, []>("op_1566"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1568 = const()[name = tensor<string, []>("op_1568"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_21_pad_type_0 = const()[name = tensor<string, []>("v_21_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_21_pad_0 = const()[name = tensor<string, []>("v_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(292923200)))];
            tensor<fp16, [2, 1280, 1, 240]> v_21_cast_fp16 = conv(dilations = var_1568, groups = var_1195, pad = v_21_pad_0, pad_type = v_21_pad_type_0, strides = var_1566, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_99_cast_fp16)[name = tensor<string, []>("v_21_cast_fp16")];
            tensor<int32, [4]> var_1572 = const()[name = tensor<string, []>("op_1572"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 240]> var_1573_cast_fp16 = reshape(shape = var_1572, x = q_21_cast_fp16)[name = tensor<string, []>("op_1573_cast_fp16")];
            tensor<int32, [4]> var_1574 = const()[name = tensor<string, []>("op_1574"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 240]> var_1575_cast_fp16 = reshape(shape = var_1574, x = k_21_cast_fp16)[name = tensor<string, []>("op_1575_cast_fp16")];
            tensor<int32, [4]> var_1576 = const()[name = tensor<string, []>("op_1576"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 240]> var_1577_cast_fp16 = reshape(shape = var_1576, x = v_21_cast_fp16)[name = tensor<string, []>("op_1577_cast_fp16")];
            tensor<bool, []> attn_weights_41_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_41_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_41_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_41_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 240, 240]> attn_weights_41_cast_fp16 = matmul(transpose_x = attn_weights_41_transpose_x_0, transpose_y = attn_weights_41_transpose_y_0, x = var_1573_cast_fp16, y = var_1575_cast_fp16)[name = tensor<string, []>("attn_weights_41_cast_fp16")];
            tensor<fp16, [2, 20, 240, 240]> attn_weights_43_cast_fp16 = mul(x = attn_weights_41_cast_fp16, y = var_1186_to_fp16)[name = tensor<string, []>("attn_weights_43_cast_fp16")];
            tensor<fp16, [2, 20, 240, 240]> var_1581_cast_fp16 = softmax(axis = var_1179, x = attn_weights_43_cast_fp16)[name = tensor<string, []>("op_1581_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, [2, 20, 64, 240]> attn_21_cast_fp16 = matmul(transpose_x = attn_21_transpose_x_0, transpose_y = attn_21_transpose_y_0, x = var_1577_cast_fp16, y = var_1581_cast_fp16)[name = tensor<string, []>("attn_21_cast_fp16")];
            tensor<int32, [4]> var_1585 = const()[name = tensor<string, []>("op_1585"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 240]> input_159_cast_fp16 = reshape(shape = var_1585, x = attn_21_cast_fp16)[name = tensor<string, []>("input_159_cast_fp16")];
            tensor<int32, [2]> var_1590 = const()[name = tensor<string, []>("op_1590"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1592 = const()[name = tensor<string, []>("op_1592"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1594_pad_type_0 = const()[name = tensor<string, []>("op_1594_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1594_pad_0 = const()[name = tensor<string, []>("op_1594_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(296200064)))];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(299476928)))];
            tensor<fp16, [2, 1280, 1, 240]> var_1594_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_1592, groups = var_1195, pad = var_1594_pad_0, pad_type = var_1594_pad_type_0, strides = var_1590, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_159_cast_fp16)[name = tensor<string, []>("op_1594_cast_fp16")];
            tensor<fp16, [2, 1280, 1, 240]> inputs_33_cast_fp16 = add(x = var_1594_cast_fp16, y = inputs_31_cast_fp16)[name = tensor<string, []>("inputs_33_cast_fp16")];
            tensor<int32, [1]> var_1598 = const()[name = tensor<string, []>("op_1598"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 240]> channels_mean_33_cast_fp16 = reduce_mean(axes = var_1598, keep_dims = var_1190, x = inputs_33_cast_fp16)[name = tensor<string, []>("channels_mean_33_cast_fp16")];
            tensor<fp16, [2, 1280, 1, 240]> 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, [2, 1280, 1, 240]> 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_1602 = const()[name = tensor<string, []>("op_1602"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 240]> var_1603_cast_fp16 = reduce_mean(axes = var_1602, keep_dims = var_1190, x = zero_mean_sq_33_cast_fp16)[name = tensor<string, []>("op_1603_cast_fp16")];
            tensor<fp16, []> var_1604_to_fp16 = const()[name = tensor<string, []>("op_1604_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 240]> var_1605_cast_fp16 = add(x = var_1603_cast_fp16, y = var_1604_to_fp16)[name = tensor<string, []>("op_1605_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, [2, 1, 1, 240]> denom_33_cast_fp16 = rsqrt(epsilon = denom_33_epsilon_0_to_fp16, x = var_1605_cast_fp16)[name = tensor<string, []>("denom_33_cast_fp16")];
            tensor<fp16, [2, 1280, 1, 240]> 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, [1, 1280, 1, 1]> var_1609_to_fp16 = const()[name = tensor<string, []>("op_1609_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(299479552)))];
            tensor<fp16, [2, 1280, 1, 240]> var_1610_cast_fp16 = add(x = out_33_cast_fp16, y = var_1609_to_fp16)[name = tensor<string, []>("op_1610_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1]> var_1612_to_fp16 = const()[name = tensor<string, []>("op_1612_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(299482176)))];
            tensor<fp16, [2, 1280, 1, 240]> hidden_states_101_cast_fp16 = mul(x = var_1610_cast_fp16, y = var_1612_to_fp16)[name = tensor<string, []>("hidden_states_101_cast_fp16")];
            tensor<int32, [2]> var_1619 = const()[name = tensor<string, []>("op_1619"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1621 = const()[name = tensor<string, []>("op_1621"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_23_pad_type_0 = const()[name = tensor<string, []>("q_23_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_23_pad_0 = const()[name = tensor<string, []>("q_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(299484800)))];
            tensor<fp16, [2, 1280, 1, 240]> q_23_cast_fp16 = conv(dilations = var_1621, groups = var_1195, pad = q_23_pad_0, pad_type = q_23_pad_type_0, strides = var_1619, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_101_cast_fp16)[name = tensor<string, []>("q_23_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, []> k_23_pad_type_0 = const()[name = tensor<string, []>("k_23_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_23_pad_0 = const()[name = tensor<string, []>("k_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1024, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor<fp16, [1280, 1024, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(302761664)))];
            tensor<fp16, [2, 1280, 1, 77]> k_23_cast_fp16 = conv(dilations = var_1627, groups = var_1195, pad = k_23_pad_0, pad_type = k_23_pad_type_0, strides = var_1625, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor<string, []>("k_23_cast_fp16")];
            tensor<int32, [2]> var_1631 = const()[name = tensor<string, []>("op_1631"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1633 = const()[name = tensor<string, []>("op_1633"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_23_pad_type_0 = const()[name = tensor<string, []>("v_23_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_23_pad_0 = const()[name = tensor<string, []>("v_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1024, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor<fp16, [1280, 1024, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(305383168)))];
            tensor<fp16, [2, 1280, 1, 77]> v_23_cast_fp16 = conv(dilations = var_1633, groups = var_1195, pad = v_23_pad_0, pad_type = v_23_pad_type_0, strides = var_1631, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor<string, []>("v_23_cast_fp16")];
            tensor<int32, [4]> var_1637 = const()[name = tensor<string, []>("op_1637"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 240]> var_1638_cast_fp16 = reshape(shape = var_1637, x = q_23_cast_fp16)[name = tensor<string, []>("op_1638_cast_fp16")];
            tensor<int32, [4]> var_1639 = const()[name = tensor<string, []>("op_1639"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_1640_cast_fp16 = reshape(shape = var_1639, x = k_23_cast_fp16)[name = tensor<string, []>("op_1640_cast_fp16")];
            tensor<int32, [4]> var_1641 = const()[name = tensor<string, []>("op_1641"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_1642_cast_fp16 = reshape(shape = var_1641, x = v_23_cast_fp16)[name = tensor<string, []>("op_1642_cast_fp16")];
            tensor<bool, []> attn_weights_45_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_45_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_45_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_45_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 240, 77]> attn_weights_45_cast_fp16 = matmul(transpose_x = attn_weights_45_transpose_x_0, transpose_y = attn_weights_45_transpose_y_0, x = var_1638_cast_fp16, y = var_1640_cast_fp16)[name = tensor<string, []>("attn_weights_45_cast_fp16")];
            tensor<fp16, [2, 20, 240, 77]> attn_weights_47_cast_fp16 = mul(x = attn_weights_45_cast_fp16, y = var_1186_to_fp16)[name = tensor<string, []>("attn_weights_47_cast_fp16")];
            tensor<fp16, [2, 20, 240, 77]> var_1646_cast_fp16 = softmax(axis = var_1179, x = attn_weights_47_cast_fp16)[name = tensor<string, []>("op_1646_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, [2, 20, 64, 240]> attn_23_cast_fp16 = matmul(transpose_x = attn_23_transpose_x_0, transpose_y = attn_23_transpose_y_0, x = var_1642_cast_fp16, y = var_1646_cast_fp16)[name = tensor<string, []>("attn_23_cast_fp16")];
            tensor<int32, [4]> var_1650 = const()[name = tensor<string, []>("op_1650"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 240]> input_161_cast_fp16 = reshape(shape = var_1650, x = attn_23_cast_fp16)[name = tensor<string, []>("input_161_cast_fp16")];
            tensor<int32, [2]> var_1655 = const()[name = tensor<string, []>("op_1655"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1657 = const()[name = tensor<string, []>("op_1657"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1659_pad_type_0 = const()[name = tensor<string, []>("op_1659_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1659_pad_0 = const()[name = tensor<string, []>("op_1659_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(308004672)))];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(311281536)))];
            tensor<fp16, [2, 1280, 1, 240]> var_1659_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_1657, groups = var_1195, pad = var_1659_pad_0, pad_type = var_1659_pad_type_0, strides = var_1655, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_161_cast_fp16)[name = tensor<string, []>("op_1659_cast_fp16")];
            tensor<fp16, [2, 1280, 1, 240]> inputs_35_cast_fp16 = add(x = var_1659_cast_fp16, y = inputs_33_cast_fp16)[name = tensor<string, []>("inputs_35_cast_fp16")];
            tensor<int32, [1]> var_1663 = const()[name = tensor<string, []>("op_1663"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 240]> channels_mean_35_cast_fp16 = reduce_mean(axes = var_1663, keep_dims = var_1190, x = inputs_35_cast_fp16)[name = tensor<string, []>("channels_mean_35_cast_fp16")];
            tensor<fp16, [2, 1280, 1, 240]> 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, [2, 1280, 1, 240]> 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_1667 = const()[name = tensor<string, []>("op_1667"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 240]> var_1668_cast_fp16 = reduce_mean(axes = var_1667, keep_dims = var_1190, x = zero_mean_sq_35_cast_fp16)[name = tensor<string, []>("op_1668_cast_fp16")];
            tensor<fp16, []> var_1669_to_fp16 = const()[name = tensor<string, []>("op_1669_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 240]> var_1670_cast_fp16 = add(x = var_1668_cast_fp16, y = var_1669_to_fp16)[name = tensor<string, []>("op_1670_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, [2, 1, 1, 240]> denom_35_cast_fp16 = rsqrt(epsilon = denom_35_epsilon_0_to_fp16, x = var_1670_cast_fp16)[name = tensor<string, []>("denom_35_cast_fp16")];
            tensor<fp16, [2, 1280, 1, 240]> 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, [1, 1280, 1, 1]> var_1674_to_fp16 = const()[name = tensor<string, []>("op_1674_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(311284160)))];
            tensor<fp16, [2, 1280, 1, 240]> var_1675_cast_fp16 = add(x = out_35_cast_fp16, y = var_1674_to_fp16)[name = tensor<string, []>("op_1675_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1]> var_1677_to_fp16 = const()[name = tensor<string, []>("op_1677_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(311286784)))];
            tensor<fp16, [2, 1280, 1, 240]> input_163_cast_fp16 = mul(x = var_1675_cast_fp16, y = var_1677_to_fp16)[name = tensor<string, []>("input_163_cast_fp16")];
            tensor<int32, [2]> var_1685 = const()[name = tensor<string, []>("op_1685"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1687 = const()[name = tensor<string, []>("op_1687"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1689_pad_type_0 = const()[name = tensor<string, []>("op_1689_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1689_pad_0 = const()[name = tensor<string, []>("op_1689_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor<fp16, [10240, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(311289408)))];
            tensor<fp16, [10240]> down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(337503872)))];
            tensor<fp16, [2, 10240, 1, 240]> var_1689_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_1687, groups = var_1195, pad = var_1689_pad_0, pad_type = var_1689_pad_type_0, strides = var_1685, weight = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_163_cast_fp16)[name = tensor<string, []>("op_1689_cast_fp16")];
            tensor<int32, [2]> var_1690_split_sizes_0 = const()[name = tensor<string, []>("op_1690_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_1690_axis_0 = const()[name = tensor<string, []>("op_1690_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 240]> var_1690_cast_fp16_0, tensor<fp16, [2, 5120, 1, 240]> var_1690_cast_fp16_1 = split(axis = var_1690_axis_0, split_sizes = var_1690_split_sizes_0, x = var_1689_cast_fp16)[name = tensor<string, []>("op_1690_cast_fp16")];
            tensor<string, []> var_1692_mode_0 = const()[name = tensor<string, []>("op_1692_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 240]> var_1692_cast_fp16 = gelu(mode = var_1692_mode_0, x = var_1690_cast_fp16_1)[name = tensor<string, []>("op_1692_cast_fp16")];
            tensor<fp16, [2, 5120, 1, 240]> input_165_cast_fp16 = mul(x = var_1690_cast_fp16_0, y = var_1692_cast_fp16)[name = tensor<string, []>("input_165_cast_fp16")];
            tensor<int32, [2]> var_1696 = const()[name = tensor<string, []>("op_1696"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1698 = const()[name = tensor<string, []>("op_1698"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1700_pad_type_0 = const()[name = tensor<string, []>("op_1700_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1700_pad_0 = const()[name = tensor<string, []>("op_1700_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(337524416)))];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(350631680)))];
            tensor<fp16, [2, 1280, 1, 240]> var_1700_cast_fp16 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_1698, groups = var_1195, pad = var_1700_pad_0, pad_type = var_1700_pad_type_0, strides = var_1696, weight = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_165_cast_fp16)[name = tensor<string, []>("op_1700_cast_fp16")];
            tensor<fp16, [2, 1280, 1, 240]> hidden_states_105_cast_fp16 = add(x = var_1700_cast_fp16, y = inputs_35_cast_fp16)[name = tensor<string, []>("hidden_states_105_cast_fp16")];
            tensor<int32, [4]> var_1702 = const()[name = tensor<string, []>("op_1702"), val = tensor<int32, [4]>([2, 1280, 12, 20])];
            tensor<fp16, [2, 1280, 12, 20]> input_167_cast_fp16 = reshape(shape = var_1702, x = hidden_states_105_cast_fp16)[name = tensor<string, []>("input_167_cast_fp16")];
            tensor<int32, [2]> var_1706 = const()[name = tensor<string, []>("op_1706"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1708 = const()[name = tensor<string, []>("op_1708"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_107_pad_type_0 = const()[name = tensor<string, []>("hidden_states_107_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_107_pad_0 = const()[name = tensor<string, []>("hidden_states_107_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_2_attentions_1_proj_out_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_proj_out_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(350634304)))];
            tensor<fp16, [1280]> down_blocks_2_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_attentions_1_proj_out_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(353911168)))];
            tensor<fp16, [2, 1280, 12, 20]> hidden_states_107_cast_fp16 = conv(bias = down_blocks_2_attentions_1_proj_out_bias_to_fp16, dilations = var_1708, groups = var_1195, pad = hidden_states_107_pad_0, pad_type = hidden_states_107_pad_type_0, strides = var_1706, weight = down_blocks_2_attentions_1_proj_out_weight_to_fp16, x = input_167_cast_fp16)[name = tensor<string, []>("hidden_states_107_cast_fp16")];
            tensor<fp16, [2, 1280, 12, 20]> input_169_cast_fp16_1 = add(x = hidden_states_107_cast_fp16, y = hidden_states_95_cast_fp16)[name = tensor<string, []>("input_169_cast_fp16")];
            tensor<int32, [2]> var_1715 = const()[name = tensor<string, []>("op_1715"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, [2]> var_1717 = const()[name = tensor<string, []>("op_1717"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> input_171_pad_type_0 = const()[name = tensor<string, []>("input_171_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_171_pad_0 = const()[name = tensor<string, []>("input_171_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 1280, 3, 3]> down_blocks_2_downsamplers_0_conv_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_downsamplers_0_conv_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(353913792)))];
            tensor<fp16, [1280]> down_blocks_2_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_2_downsamplers_0_conv_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(383405056)))];
            tensor<fp16, [2, 1280, 6, 10]> input_171_cast_fp16_1 = conv(bias = down_blocks_2_downsamplers_0_conv_bias_to_fp16, dilations = var_1717, groups = var_1195, pad = input_171_pad_0, pad_type = input_171_pad_type_0, strides = var_1715, weight = down_blocks_2_downsamplers_0_conv_weight_to_fp16, x = input_169_cast_fp16_1)[name = tensor<string, []>("input_171_cast_fp16")];
            tensor<int32, []> var_1729 = const()[name = tensor<string, []>("op_1729"), val = tensor<int32, []>(1)];
            tensor<int32, [5]> reshape_72_shape_0 = const()[name = tensor<string, []>("reshape_72_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 6, 10])];
            tensor<fp16, [2, 32, 40, 6, 10]> reshape_72_cast_fp16 = reshape(shape = reshape_72_shape_0, x = input_171_cast_fp16_1)[name = tensor<string, []>("reshape_72_cast_fp16")];
            tensor<int32, [3]> reduce_mean_54_axes_0 = const()[name = tensor<string, []>("reduce_mean_54_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_54_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_54_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_54_cast_fp16 = reduce_mean(axes = reduce_mean_54_axes_0, keep_dims = reduce_mean_54_keep_dims_0, x = reshape_72_cast_fp16)[name = tensor<string, []>("reduce_mean_54_cast_fp16")];
            tensor<fp16, [2, 32, 40, 6, 10]> sub_36_cast_fp16 = sub(x = reshape_72_cast_fp16, y = reduce_mean_54_cast_fp16)[name = tensor<string, []>("sub_36_cast_fp16")];
            tensor<fp16, [2, 32, 40, 6, 10]> square_18_cast_fp16 = square(x = sub_36_cast_fp16)[name = tensor<string, []>("square_18_cast_fp16")];
            tensor<int32, [3]> reduce_mean_56_axes_0 = const()[name = tensor<string, []>("reduce_mean_56_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_56_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_56_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_56_cast_fp16 = reduce_mean(axes = reduce_mean_56_axes_0, keep_dims = reduce_mean_56_keep_dims_0, x = square_18_cast_fp16)[name = tensor<string, []>("reduce_mean_56_cast_fp16")];
            tensor<fp16, []> add_36_y_0_to_fp16 = const()[name = tensor<string, []>("add_36_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_36_cast_fp16 = add(x = reduce_mean_56_cast_fp16, y = add_36_y_0_to_fp16)[name = tensor<string, []>("add_36_cast_fp16")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_18_cast_fp16 = sqrt(x = add_36_cast_fp16)[name = tensor<string, []>("sqrt_18_cast_fp16")];
            tensor<fp16, [2, 32, 40, 6, 10]> real_div_18_cast_fp16 = real_div(x = sub_36_cast_fp16, y = sqrt_18_cast_fp16)[name = tensor<string, []>("real_div_18_cast_fp16")];
            tensor<int32, [4]> reshape_73_shape_0 = const()[name = tensor<string, []>("reshape_73_shape_0"), val = tensor<int32, [4]>([2, 1280, 6, 10])];
            tensor<fp16, [2, 1280, 6, 10]> reshape_73_cast_fp16 = reshape(shape = reshape_73_shape_0, x = real_div_18_cast_fp16)[name = tensor<string, []>("reshape_73_cast_fp16")];
            tensor<fp16, [1280]> add_37_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_37_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(383407680)))];
            tensor<fp16, [1280]> add_37_beta_0_to_fp16 = const()[name = tensor<string, []>("add_37_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(383410304)))];
            tensor<fp16, []> add_37_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_37_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1280, 6, 10]> add_37_cast_fp16 = batch_norm(beta = add_37_beta_0_to_fp16, epsilon = add_37_epsilon_0_to_fp16, gamma = add_37_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_73_cast_fp16)[name = tensor<string, []>("add_37_cast_fp16")];
            tensor<fp16, [2, 1280, 6, 10]> input_175_cast_fp16 = silu(x = add_37_cast_fp16)[name = tensor<string, []>("input_175_cast_fp16")];
            tensor<int32, [2]> var_1745 = const()[name = tensor<string, []>("op_1745"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1747 = const()[name = tensor<string, []>("op_1747"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_109_pad_type_0 = const()[name = tensor<string, []>("hidden_states_109_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_109_pad_0 = const()[name = tensor<string, []>("hidden_states_109_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 1280, 3, 3]> down_blocks_3_resnets_0_conv1_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_3_resnets_0_conv1_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(383412928)))];
            tensor<fp16, [1280]> down_blocks_3_resnets_0_conv1_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_3_resnets_0_conv1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(412904192)))];
            tensor<fp16, [2, 1280, 6, 10]> hidden_states_109_cast_fp16 = conv(bias = down_blocks_3_resnets_0_conv1_bias_to_fp16, dilations = var_1747, groups = var_1729, pad = hidden_states_109_pad_0, pad_type = hidden_states_109_pad_type_0, strides = var_1745, weight = down_blocks_3_resnets_0_conv1_weight_to_fp16, x = input_175_cast_fp16)[name = tensor<string, []>("hidden_states_109_cast_fp16")];
            tensor<int32, [2]> var_1753 = const()[name = tensor<string, []>("op_1753"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1755 = const()[name = tensor<string, []>("op_1755"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> temb_13_pad_type_0 = const()[name = tensor<string, []>("temb_13_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> temb_13_pad_0 = const()[name = tensor<string, []>("temb_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_3_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_3_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(412906816)))];
            tensor<fp16, [1280]> down_blocks_3_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_3_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(416183680)))];
            tensor<fp16, [2, 1280, 1, 1]> temb_13_cast_fp16 = conv(bias = down_blocks_3_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_1755, groups = var_1729, pad = temb_13_pad_0, pad_type = temb_13_pad_type_0, strides = var_1753, weight = down_blocks_3_resnets_0_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16_1)[name = tensor<string, []>("temb_13_cast_fp16")];
            tensor<fp16, [2, 1280, 6, 10]> input_179_cast_fp16 = add(x = hidden_states_109_cast_fp16, y = temb_13_cast_fp16)[name = tensor<string, []>("input_179_cast_fp16")];
            tensor<int32, [5]> reshape_76_shape_0 = const()[name = tensor<string, []>("reshape_76_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 6, 10])];
            tensor<fp16, [2, 32, 40, 6, 10]> reshape_76_cast_fp16 = reshape(shape = reshape_76_shape_0, x = input_179_cast_fp16)[name = tensor<string, []>("reshape_76_cast_fp16")];
            tensor<int32, [3]> reduce_mean_57_axes_0 = const()[name = tensor<string, []>("reduce_mean_57_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_57_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_57_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_57_cast_fp16 = reduce_mean(axes = reduce_mean_57_axes_0, keep_dims = reduce_mean_57_keep_dims_0, x = reshape_76_cast_fp16)[name = tensor<string, []>("reduce_mean_57_cast_fp16")];
            tensor<fp16, [2, 32, 40, 6, 10]> sub_38_cast_fp16 = sub(x = reshape_76_cast_fp16, y = reduce_mean_57_cast_fp16)[name = tensor<string, []>("sub_38_cast_fp16")];
            tensor<fp16, [2, 32, 40, 6, 10]> square_19_cast_fp16 = square(x = sub_38_cast_fp16)[name = tensor<string, []>("square_19_cast_fp16")];
            tensor<int32, [3]> reduce_mean_59_axes_0 = const()[name = tensor<string, []>("reduce_mean_59_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_59_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_59_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_59_cast_fp16 = reduce_mean(axes = reduce_mean_59_axes_0, keep_dims = reduce_mean_59_keep_dims_0, x = square_19_cast_fp16)[name = tensor<string, []>("reduce_mean_59_cast_fp16")];
            tensor<fp16, []> add_38_y_0_to_fp16 = const()[name = tensor<string, []>("add_38_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_38_cast_fp16 = add(x = reduce_mean_59_cast_fp16, y = add_38_y_0_to_fp16)[name = tensor<string, []>("add_38_cast_fp16")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_19_cast_fp16 = sqrt(x = add_38_cast_fp16)[name = tensor<string, []>("sqrt_19_cast_fp16")];
            tensor<fp16, [2, 32, 40, 6, 10]> real_div_19_cast_fp16 = real_div(x = sub_38_cast_fp16, y = sqrt_19_cast_fp16)[name = tensor<string, []>("real_div_19_cast_fp16")];
            tensor<int32, [4]> reshape_77_shape_0 = const()[name = tensor<string, []>("reshape_77_shape_0"), val = tensor<int32, [4]>([2, 1280, 6, 10])];
            tensor<fp16, [2, 1280, 6, 10]> reshape_77_cast_fp16 = reshape(shape = reshape_77_shape_0, x = real_div_19_cast_fp16)[name = tensor<string, []>("reshape_77_cast_fp16")];
            tensor<fp16, [1280]> add_39_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_39_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(416186304)))];
            tensor<fp16, [1280]> add_39_beta_0_to_fp16 = const()[name = tensor<string, []>("add_39_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(416188928)))];
            tensor<fp16, []> add_39_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_39_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1280, 6, 10]> add_39_cast_fp16 = batch_norm(beta = add_39_beta_0_to_fp16, epsilon = add_39_epsilon_0_to_fp16, gamma = add_39_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_77_cast_fp16)[name = tensor<string, []>("add_39_cast_fp16")];
            tensor<fp16, [2, 1280, 6, 10]> input_183_cast_fp16 = silu(x = add_39_cast_fp16)[name = tensor<string, []>("input_183_cast_fp16")];
            tensor<int32, [2]> var_1765 = const()[name = tensor<string, []>("op_1765"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1767 = const()[name = tensor<string, []>("op_1767"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_111_pad_type_0 = const()[name = tensor<string, []>("hidden_states_111_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_111_pad_0 = const()[name = tensor<string, []>("hidden_states_111_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 1280, 3, 3]> down_blocks_3_resnets_0_conv2_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_3_resnets_0_conv2_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(416191552)))];
            tensor<fp16, [1280]> down_blocks_3_resnets_0_conv2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_3_resnets_0_conv2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(445682816)))];
            tensor<fp16, [2, 1280, 6, 10]> hidden_states_111_cast_fp16 = conv(bias = down_blocks_3_resnets_0_conv2_bias_to_fp16, dilations = var_1767, groups = var_1729, pad = hidden_states_111_pad_0, pad_type = hidden_states_111_pad_type_0, strides = var_1765, weight = down_blocks_3_resnets_0_conv2_weight_to_fp16, x = input_183_cast_fp16)[name = tensor<string, []>("hidden_states_111_cast_fp16")];
            tensor<fp16, [2, 1280, 6, 10]> input_185_cast_fp16 = add(x = input_171_cast_fp16_1, y = hidden_states_111_cast_fp16)[name = tensor<string, []>("input_185_cast_fp16")];
            tensor<int32, [5]> reshape_80_shape_0 = const()[name = tensor<string, []>("reshape_80_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 6, 10])];
            tensor<fp16, [2, 32, 40, 6, 10]> reshape_80_cast_fp16 = reshape(shape = reshape_80_shape_0, x = input_185_cast_fp16)[name = tensor<string, []>("reshape_80_cast_fp16")];
            tensor<int32, [3]> reduce_mean_60_axes_0 = const()[name = tensor<string, []>("reduce_mean_60_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_60_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_60_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_60_cast_fp16 = reduce_mean(axes = reduce_mean_60_axes_0, keep_dims = reduce_mean_60_keep_dims_0, x = reshape_80_cast_fp16)[name = tensor<string, []>("reduce_mean_60_cast_fp16")];
            tensor<fp16, [2, 32, 40, 6, 10]> sub_40_cast_fp16 = sub(x = reshape_80_cast_fp16, y = reduce_mean_60_cast_fp16)[name = tensor<string, []>("sub_40_cast_fp16")];
            tensor<fp16, [2, 32, 40, 6, 10]> square_20_cast_fp16 = square(x = sub_40_cast_fp16)[name = tensor<string, []>("square_20_cast_fp16")];
            tensor<int32, [3]> reduce_mean_62_axes_0 = const()[name = tensor<string, []>("reduce_mean_62_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_62_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_62_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_62_cast_fp16 = reduce_mean(axes = reduce_mean_62_axes_0, keep_dims = reduce_mean_62_keep_dims_0, x = square_20_cast_fp16)[name = tensor<string, []>("reduce_mean_62_cast_fp16")];
            tensor<fp16, []> add_40_y_0_to_fp16 = const()[name = tensor<string, []>("add_40_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_40_cast_fp16 = add(x = reduce_mean_62_cast_fp16, y = add_40_y_0_to_fp16)[name = tensor<string, []>("add_40_cast_fp16")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_20_cast_fp16 = sqrt(x = add_40_cast_fp16)[name = tensor<string, []>("sqrt_20_cast_fp16")];
            tensor<fp16, [2, 32, 40, 6, 10]> real_div_20_cast_fp16 = real_div(x = sub_40_cast_fp16, y = sqrt_20_cast_fp16)[name = tensor<string, []>("real_div_20_cast_fp16")];
            tensor<int32, [4]> reshape_81_shape_0 = const()[name = tensor<string, []>("reshape_81_shape_0"), val = tensor<int32, [4]>([2, 1280, 6, 10])];
            tensor<fp16, [2, 1280, 6, 10]> reshape_81_cast_fp16 = reshape(shape = reshape_81_shape_0, x = real_div_20_cast_fp16)[name = tensor<string, []>("reshape_81_cast_fp16")];
            tensor<fp16, [1280]> add_41_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_41_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(445685440)))];
            tensor<fp16, [1280]> add_41_beta_0_to_fp16 = const()[name = tensor<string, []>("add_41_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(445688064)))];
            tensor<fp16, []> add_41_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_41_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1280, 6, 10]> add_41_cast_fp16 = batch_norm(beta = add_41_beta_0_to_fp16, epsilon = add_41_epsilon_0_to_fp16, gamma = add_41_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_81_cast_fp16)[name = tensor<string, []>("add_41_cast_fp16")];
            tensor<fp16, [2, 1280, 6, 10]> input_189_cast_fp16 = silu(x = add_41_cast_fp16)[name = tensor<string, []>("input_189_cast_fp16")];
            tensor<int32, [2]> var_1782 = const()[name = tensor<string, []>("op_1782"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1784 = const()[name = tensor<string, []>("op_1784"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_113_pad_type_0 = const()[name = tensor<string, []>("hidden_states_113_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_113_pad_0 = const()[name = tensor<string, []>("hidden_states_113_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 1280, 3, 3]> down_blocks_3_resnets_1_conv1_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_3_resnets_1_conv1_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(445690688)))];
            tensor<fp16, [1280]> down_blocks_3_resnets_1_conv1_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_3_resnets_1_conv1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(475181952)))];
            tensor<fp16, [2, 1280, 6, 10]> hidden_states_113_cast_fp16 = conv(bias = down_blocks_3_resnets_1_conv1_bias_to_fp16, dilations = var_1784, groups = var_1729, pad = hidden_states_113_pad_0, pad_type = hidden_states_113_pad_type_0, strides = var_1782, weight = down_blocks_3_resnets_1_conv1_weight_to_fp16, x = input_189_cast_fp16)[name = tensor<string, []>("hidden_states_113_cast_fp16")];
            tensor<int32, [2]> var_1790 = const()[name = tensor<string, []>("op_1790"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1792 = const()[name = tensor<string, []>("op_1792"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> temb_15_pad_type_0 = const()[name = tensor<string, []>("temb_15_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> temb_15_pad_0 = const()[name = tensor<string, []>("temb_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> down_blocks_3_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_3_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(475184576)))];
            tensor<fp16, [1280]> down_blocks_3_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_3_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(478461440)))];
            tensor<fp16, [2, 1280, 1, 1]> temb_15_cast_fp16 = conv(bias = down_blocks_3_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_1792, groups = var_1729, pad = temb_15_pad_0, pad_type = temb_15_pad_type_0, strides = var_1790, weight = down_blocks_3_resnets_1_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16_1)[name = tensor<string, []>("temb_15_cast_fp16")];
            tensor<fp16, [2, 1280, 6, 10]> input_193_cast_fp16 = add(x = hidden_states_113_cast_fp16, y = temb_15_cast_fp16)[name = tensor<string, []>("input_193_cast_fp16")];
            tensor<int32, [5]> reshape_84_shape_0 = const()[name = tensor<string, []>("reshape_84_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 6, 10])];
            tensor<fp16, [2, 32, 40, 6, 10]> reshape_84_cast_fp16 = reshape(shape = reshape_84_shape_0, x = input_193_cast_fp16)[name = tensor<string, []>("reshape_84_cast_fp16")];
            tensor<int32, [3]> reduce_mean_63_axes_0 = const()[name = tensor<string, []>("reduce_mean_63_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_63_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_63_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_63_cast_fp16 = reduce_mean(axes = reduce_mean_63_axes_0, keep_dims = reduce_mean_63_keep_dims_0, x = reshape_84_cast_fp16)[name = tensor<string, []>("reduce_mean_63_cast_fp16")];
            tensor<fp16, [2, 32, 40, 6, 10]> sub_42_cast_fp16 = sub(x = reshape_84_cast_fp16, y = reduce_mean_63_cast_fp16)[name = tensor<string, []>("sub_42_cast_fp16")];
            tensor<fp16, [2, 32, 40, 6, 10]> square_21_cast_fp16 = square(x = sub_42_cast_fp16)[name = tensor<string, []>("square_21_cast_fp16")];
            tensor<int32, [3]> reduce_mean_65_axes_0 = const()[name = tensor<string, []>("reduce_mean_65_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_65_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_65_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_65_cast_fp16 = reduce_mean(axes = reduce_mean_65_axes_0, keep_dims = reduce_mean_65_keep_dims_0, x = square_21_cast_fp16)[name = tensor<string, []>("reduce_mean_65_cast_fp16")];
            tensor<fp16, []> add_42_y_0_to_fp16 = const()[name = tensor<string, []>("add_42_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_42_cast_fp16 = add(x = reduce_mean_65_cast_fp16, y = add_42_y_0_to_fp16)[name = tensor<string, []>("add_42_cast_fp16")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_21_cast_fp16 = sqrt(x = add_42_cast_fp16)[name = tensor<string, []>("sqrt_21_cast_fp16")];
            tensor<fp16, [2, 32, 40, 6, 10]> real_div_21_cast_fp16 = real_div(x = sub_42_cast_fp16, y = sqrt_21_cast_fp16)[name = tensor<string, []>("real_div_21_cast_fp16")];
            tensor<int32, [4]> reshape_85_shape_0 = const()[name = tensor<string, []>("reshape_85_shape_0"), val = tensor<int32, [4]>([2, 1280, 6, 10])];
            tensor<fp16, [2, 1280, 6, 10]> reshape_85_cast_fp16 = reshape(shape = reshape_85_shape_0, x = real_div_21_cast_fp16)[name = tensor<string, []>("reshape_85_cast_fp16")];
            tensor<fp16, [1280]> add_43_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_43_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(478464064)))];
            tensor<fp16, [1280]> add_43_beta_0_to_fp16 = const()[name = tensor<string, []>("add_43_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(478466688)))];
            tensor<fp16, []> add_43_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_43_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1280, 6, 10]> add_43_cast_fp16 = batch_norm(beta = add_43_beta_0_to_fp16, epsilon = add_43_epsilon_0_to_fp16, gamma = add_43_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_85_cast_fp16)[name = tensor<string, []>("add_43_cast_fp16")];
            tensor<fp16, [2, 1280, 6, 10]> input_197_cast_fp16 = silu(x = add_43_cast_fp16)[name = tensor<string, []>("input_197_cast_fp16")];
            tensor<int32, [2]> var_1802 = const()[name = tensor<string, []>("op_1802"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1804 = const()[name = tensor<string, []>("op_1804"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_115_pad_type_0 = const()[name = tensor<string, []>("hidden_states_115_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_115_pad_0 = const()[name = tensor<string, []>("hidden_states_115_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 1280, 3, 3]> down_blocks_3_resnets_1_conv2_weight_to_fp16 = const()[name = tensor<string, []>("down_blocks_3_resnets_1_conv2_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(478469312)))];
            tensor<fp16, [1280]> down_blocks_3_resnets_1_conv2_bias_to_fp16 = const()[name = tensor<string, []>("down_blocks_3_resnets_1_conv2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(507960576)))];
            tensor<fp16, [2, 1280, 6, 10]> hidden_states_115_cast_fp16 = conv(bias = down_blocks_3_resnets_1_conv2_bias_to_fp16, dilations = var_1804, groups = var_1729, pad = hidden_states_115_pad_0, pad_type = hidden_states_115_pad_type_0, strides = var_1802, weight = down_blocks_3_resnets_1_conv2_weight_to_fp16, x = input_197_cast_fp16)[name = tensor<string, []>("hidden_states_115_cast_fp16")];
            tensor<fp16, [2, 1280, 6, 10]> input_199_cast_fp16 = add(x = input_185_cast_fp16, y = hidden_states_115_cast_fp16)[name = tensor<string, []>("input_199_cast_fp16")];
            tensor<int32, []> var_1812 = const()[name = tensor<string, []>("op_1812"), val = tensor<int32, []>(3)];
            tensor<bool, []> var_1823 = const()[name = tensor<string, []>("op_1823"), val = tensor<bool, []>(true)];
            tensor<int32, []> var_1828 = const()[name = tensor<string, []>("op_1828"), val = tensor<int32, []>(1)];
            tensor<int32, [5]> reshape_88_shape_0 = const()[name = tensor<string, []>("reshape_88_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 6, 10])];
            tensor<fp16, [2, 32, 40, 6, 10]> reshape_88_cast_fp16 = reshape(shape = reshape_88_shape_0, x = input_199_cast_fp16)[name = tensor<string, []>("reshape_88_cast_fp16")];
            tensor<int32, [3]> reduce_mean_66_axes_0 = const()[name = tensor<string, []>("reduce_mean_66_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_66_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_66_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_66_cast_fp16 = reduce_mean(axes = reduce_mean_66_axes_0, keep_dims = reduce_mean_66_keep_dims_0, x = reshape_88_cast_fp16)[name = tensor<string, []>("reduce_mean_66_cast_fp16")];
            tensor<fp16, [2, 32, 40, 6, 10]> sub_44_cast_fp16 = sub(x = reshape_88_cast_fp16, y = reduce_mean_66_cast_fp16)[name = tensor<string, []>("sub_44_cast_fp16")];
            tensor<fp16, [2, 32, 40, 6, 10]> square_22_cast_fp16 = square(x = sub_44_cast_fp16)[name = tensor<string, []>("square_22_cast_fp16")];
            tensor<int32, [3]> reduce_mean_68_axes_0 = const()[name = tensor<string, []>("reduce_mean_68_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_68_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_68_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_68_cast_fp16 = reduce_mean(axes = reduce_mean_68_axes_0, keep_dims = reduce_mean_68_keep_dims_0, x = square_22_cast_fp16)[name = tensor<string, []>("reduce_mean_68_cast_fp16")];
            tensor<fp16, []> add_44_y_0_to_fp16 = const()[name = tensor<string, []>("add_44_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_44_cast_fp16 = add(x = reduce_mean_68_cast_fp16, y = add_44_y_0_to_fp16)[name = tensor<string, []>("add_44_cast_fp16")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_22_cast_fp16 = sqrt(x = add_44_cast_fp16)[name = tensor<string, []>("sqrt_22_cast_fp16")];
            tensor<fp16, [2, 32, 40, 6, 10]> real_div_22_cast_fp16 = real_div(x = sub_44_cast_fp16, y = sqrt_22_cast_fp16)[name = tensor<string, []>("real_div_22_cast_fp16")];
            tensor<int32, [4]> reshape_89_shape_0 = const()[name = tensor<string, []>("reshape_89_shape_0"), val = tensor<int32, [4]>([2, 1280, 6, 10])];
            tensor<fp16, [2, 1280, 6, 10]> reshape_89_cast_fp16 = reshape(shape = reshape_89_shape_0, x = real_div_22_cast_fp16)[name = tensor<string, []>("reshape_89_cast_fp16")];
            tensor<fp16, [1280]> add_45_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_45_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(507963200)))];
            tensor<fp16, [1280]> add_45_beta_0_to_fp16 = const()[name = tensor<string, []>("add_45_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(507965824)))];
            tensor<fp16, []> add_45_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_45_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1280, 6, 10]> add_45_cast_fp16 = batch_norm(beta = add_45_beta_0_to_fp16, epsilon = add_45_epsilon_0_to_fp16, gamma = add_45_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_89_cast_fp16)[name = tensor<string, []>("add_45_cast_fp16")];
            tensor<fp16, [2, 1280, 6, 10]> input_203_cast_fp16 = silu(x = add_45_cast_fp16)[name = tensor<string, []>("input_203_cast_fp16")];
            tensor<int32, [2]> var_1846 = const()[name = tensor<string, []>("op_1846"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1848 = const()[name = tensor<string, []>("op_1848"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_117_pad_type_0 = const()[name = tensor<string, []>("hidden_states_117_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_117_pad_0 = const()[name = tensor<string, []>("hidden_states_117_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 1280, 3, 3]> mid_block_resnets_0_conv1_weight_to_fp16 = const()[name = tensor<string, []>("mid_block_resnets_0_conv1_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(507968448)))];
            tensor<fp16, [1280]> mid_block_resnets_0_conv1_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_resnets_0_conv1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(537459712)))];
            tensor<fp16, [2, 1280, 6, 10]> hidden_states_117_cast_fp16 = conv(bias = mid_block_resnets_0_conv1_bias_to_fp16, dilations = var_1848, groups = var_1828, pad = hidden_states_117_pad_0, pad_type = hidden_states_117_pad_type_0, strides = var_1846, weight = mid_block_resnets_0_conv1_weight_to_fp16, x = input_203_cast_fp16)[name = tensor<string, []>("hidden_states_117_cast_fp16")];
            tensor<int32, [2]> var_1854 = const()[name = tensor<string, []>("op_1854"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1856 = const()[name = tensor<string, []>("op_1856"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> temb_17_pad_type_0 = const()[name = tensor<string, []>("temb_17_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> temb_17_pad_0 = const()[name = tensor<string, []>("temb_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor<string, []>("mid_block_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(537462336)))];
            tensor<fp16, [1280]> mid_block_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(540739200)))];
            tensor<fp16, [2, 1280, 1, 1]> temb_17_cast_fp16 = conv(bias = mid_block_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_1856, groups = var_1828, pad = temb_17_pad_0, pad_type = temb_17_pad_type_0, strides = var_1854, weight = mid_block_resnets_0_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16_1)[name = tensor<string, []>("temb_17_cast_fp16")];
            tensor<fp16, [2, 1280, 6, 10]> input_207_cast_fp16 = add(x = hidden_states_117_cast_fp16, y = temb_17_cast_fp16)[name = tensor<string, []>("input_207_cast_fp16")];
            tensor<int32, [5]> reshape_92_shape_0 = const()[name = tensor<string, []>("reshape_92_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 6, 10])];
            tensor<fp16, [2, 32, 40, 6, 10]> reshape_92_cast_fp16 = reshape(shape = reshape_92_shape_0, x = input_207_cast_fp16)[name = tensor<string, []>("reshape_92_cast_fp16")];
            tensor<int32, [3]> reduce_mean_69_axes_0 = const()[name = tensor<string, []>("reduce_mean_69_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_69_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_69_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_69_cast_fp16 = reduce_mean(axes = reduce_mean_69_axes_0, keep_dims = reduce_mean_69_keep_dims_0, x = reshape_92_cast_fp16)[name = tensor<string, []>("reduce_mean_69_cast_fp16")];
            tensor<fp16, [2, 32, 40, 6, 10]> sub_46_cast_fp16 = sub(x = reshape_92_cast_fp16, y = reduce_mean_69_cast_fp16)[name = tensor<string, []>("sub_46_cast_fp16")];
            tensor<fp16, [2, 32, 40, 6, 10]> square_23_cast_fp16 = square(x = sub_46_cast_fp16)[name = tensor<string, []>("square_23_cast_fp16")];
            tensor<int32, [3]> reduce_mean_71_axes_0 = const()[name = tensor<string, []>("reduce_mean_71_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_71_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_71_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_71_cast_fp16 = reduce_mean(axes = reduce_mean_71_axes_0, keep_dims = reduce_mean_71_keep_dims_0, x = square_23_cast_fp16)[name = tensor<string, []>("reduce_mean_71_cast_fp16")];
            tensor<fp16, []> add_46_y_0_to_fp16 = const()[name = tensor<string, []>("add_46_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_46_cast_fp16 = add(x = reduce_mean_71_cast_fp16, y = add_46_y_0_to_fp16)[name = tensor<string, []>("add_46_cast_fp16")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_23_cast_fp16 = sqrt(x = add_46_cast_fp16)[name = tensor<string, []>("sqrt_23_cast_fp16")];
            tensor<fp16, [2, 32, 40, 6, 10]> real_div_23_cast_fp16 = real_div(x = sub_46_cast_fp16, y = sqrt_23_cast_fp16)[name = tensor<string, []>("real_div_23_cast_fp16")];
            tensor<int32, [4]> reshape_93_shape_0 = const()[name = tensor<string, []>("reshape_93_shape_0"), val = tensor<int32, [4]>([2, 1280, 6, 10])];
            tensor<fp16, [2, 1280, 6, 10]> reshape_93_cast_fp16 = reshape(shape = reshape_93_shape_0, x = real_div_23_cast_fp16)[name = tensor<string, []>("reshape_93_cast_fp16")];
            tensor<fp16, [1280]> add_47_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_47_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(540741824)))];
            tensor<fp16, [1280]> add_47_beta_0_to_fp16 = const()[name = tensor<string, []>("add_47_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(540744448)))];
            tensor<fp16, []> add_47_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_47_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1280, 6, 10]> add_47_cast_fp16 = batch_norm(beta = add_47_beta_0_to_fp16, epsilon = add_47_epsilon_0_to_fp16, gamma = add_47_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_93_cast_fp16)[name = tensor<string, []>("add_47_cast_fp16")];
            tensor<fp16, [2, 1280, 6, 10]> input_211_cast_fp16 = silu(x = add_47_cast_fp16)[name = tensor<string, []>("input_211_cast_fp16")];
            tensor<int32, [2]> var_1866 = const()[name = tensor<string, []>("op_1866"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1868 = const()[name = tensor<string, []>("op_1868"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_119_pad_type_0 = const()[name = tensor<string, []>("hidden_states_119_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_119_pad_0 = const()[name = tensor<string, []>("hidden_states_119_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 1280, 3, 3]> mid_block_resnets_0_conv2_weight_to_fp16 = const()[name = tensor<string, []>("mid_block_resnets_0_conv2_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(540747072)))];
            tensor<fp16, [1280]> mid_block_resnets_0_conv2_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_resnets_0_conv2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(570238336)))];
            tensor<fp16, [2, 1280, 6, 10]> hidden_states_119_cast_fp16 = conv(bias = mid_block_resnets_0_conv2_bias_to_fp16, dilations = var_1868, groups = var_1828, pad = hidden_states_119_pad_0, pad_type = hidden_states_119_pad_type_0, strides = var_1866, weight = mid_block_resnets_0_conv2_weight_to_fp16, x = input_211_cast_fp16)[name = tensor<string, []>("hidden_states_119_cast_fp16")];
            tensor<fp16, [2, 1280, 6, 10]> hidden_states_121_cast_fp16 = add(x = input_199_cast_fp16, y = hidden_states_119_cast_fp16)[name = tensor<string, []>("hidden_states_121_cast_fp16")];
            tensor<int32, [5]> reshape_96_shape_0 = const()[name = tensor<string, []>("reshape_96_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 6, 10])];
            tensor<fp16, [2, 32, 40, 6, 10]> reshape_96_cast_fp16 = reshape(shape = reshape_96_shape_0, x = hidden_states_121_cast_fp16)[name = tensor<string, []>("reshape_96_cast_fp16")];
            tensor<int32, [3]> reduce_mean_72_axes_0 = const()[name = tensor<string, []>("reduce_mean_72_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_72_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_72_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_72_cast_fp16 = reduce_mean(axes = reduce_mean_72_axes_0, keep_dims = reduce_mean_72_keep_dims_0, x = reshape_96_cast_fp16)[name = tensor<string, []>("reduce_mean_72_cast_fp16")];
            tensor<fp16, [2, 32, 40, 6, 10]> sub_48_cast_fp16 = sub(x = reshape_96_cast_fp16, y = reduce_mean_72_cast_fp16)[name = tensor<string, []>("sub_48_cast_fp16")];
            tensor<fp16, [2, 32, 40, 6, 10]> square_24_cast_fp16 = square(x = sub_48_cast_fp16)[name = tensor<string, []>("square_24_cast_fp16")];
            tensor<int32, [3]> reduce_mean_74_axes_0 = const()[name = tensor<string, []>("reduce_mean_74_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_74_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_74_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_74_cast_fp16 = reduce_mean(axes = reduce_mean_74_axes_0, keep_dims = reduce_mean_74_keep_dims_0, x = square_24_cast_fp16)[name = tensor<string, []>("reduce_mean_74_cast_fp16")];
            tensor<fp16, []> add_48_y_0_to_fp16 = const()[name = tensor<string, []>("add_48_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_48_cast_fp16 = add(x = reduce_mean_74_cast_fp16, y = add_48_y_0_to_fp16)[name = tensor<string, []>("add_48_cast_fp16")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_24_cast_fp16 = sqrt(x = add_48_cast_fp16)[name = tensor<string, []>("sqrt_24_cast_fp16")];
            tensor<fp16, [2, 32, 40, 6, 10]> real_div_24_cast_fp16 = real_div(x = sub_48_cast_fp16, y = sqrt_24_cast_fp16)[name = tensor<string, []>("real_div_24_cast_fp16")];
            tensor<int32, [4]> reshape_97_shape_0 = const()[name = tensor<string, []>("reshape_97_shape_0"), val = tensor<int32, [4]>([2, 1280, 6, 10])];
            tensor<fp16, [2, 1280, 6, 10]> reshape_97_cast_fp16 = reshape(shape = reshape_97_shape_0, x = real_div_24_cast_fp16)[name = tensor<string, []>("reshape_97_cast_fp16")];
            tensor<fp16, [1280]> add_49_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_49_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(570240960)))];
            tensor<fp16, [1280]> add_49_beta_0_to_fp16 = const()[name = tensor<string, []>("add_49_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(570243584)))];
            tensor<fp16, []> add_49_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_49_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1280, 6, 10]> add_49_cast_fp16 = batch_norm(beta = add_49_beta_0_to_fp16, epsilon = add_49_epsilon_0_to_fp16, gamma = add_49_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_97_cast_fp16)[name = tensor<string, []>("add_49_cast_fp16")];
            tensor<int32, [2]> var_1888 = const()[name = tensor<string, []>("op_1888"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1890 = const()[name = tensor<string, []>("op_1890"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_123_pad_type_0 = const()[name = tensor<string, []>("hidden_states_123_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_123_pad_0 = const()[name = tensor<string, []>("hidden_states_123_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_proj_in_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(570246208)))];
            tensor<fp16, [1280]> mid_block_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_proj_in_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(573523072)))];
            tensor<fp16, [2, 1280, 6, 10]> hidden_states_123_cast_fp16 = conv(bias = mid_block_attentions_0_proj_in_bias_to_fp16, dilations = var_1890, groups = var_1828, pad = hidden_states_123_pad_0, pad_type = hidden_states_123_pad_type_0, strides = var_1888, weight = mid_block_attentions_0_proj_in_weight_to_fp16, x = add_49_cast_fp16)[name = tensor<string, []>("hidden_states_123_cast_fp16")];
            tensor<int32, [4]> var_1895 = const()[name = tensor<string, []>("op_1895"), val = tensor<int32, [4]>([2, 1280, 1, 60])];
            tensor<fp16, [2, 1280, 1, 60]> inputs_37_cast_fp16 = reshape(shape = var_1895, x = hidden_states_123_cast_fp16)[name = tensor<string, []>("inputs_37_cast_fp16")];
            tensor<int32, [1]> var_1905 = const()[name = tensor<string, []>("op_1905"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 60]> channels_mean_37_cast_fp16 = reduce_mean(axes = var_1905, keep_dims = var_1823, x = inputs_37_cast_fp16)[name = tensor<string, []>("channels_mean_37_cast_fp16")];
            tensor<fp16, [2, 1280, 1, 60]> 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, [2, 1280, 1, 60]> 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_1909 = const()[name = tensor<string, []>("op_1909"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 60]> var_1910_cast_fp16 = reduce_mean(axes = var_1909, keep_dims = var_1823, x = zero_mean_sq_37_cast_fp16)[name = tensor<string, []>("op_1910_cast_fp16")];
            tensor<fp16, []> var_1911_to_fp16 = const()[name = tensor<string, []>("op_1911_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 60]> var_1912_cast_fp16 = add(x = var_1910_cast_fp16, y = var_1911_to_fp16)[name = tensor<string, []>("op_1912_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, [2, 1, 1, 60]> denom_37_cast_fp16 = rsqrt(epsilon = denom_37_epsilon_0_to_fp16, x = var_1912_cast_fp16)[name = tensor<string, []>("denom_37_cast_fp16")];
            tensor<fp16, [2, 1280, 1, 60]> 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, [1, 1280, 1, 1]> var_1916_to_fp16 = const()[name = tensor<string, []>("op_1916_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(573525696)))];
            tensor<fp16, [2, 1280, 1, 60]> var_1917_cast_fp16 = add(x = out_37_cast_fp16, y = var_1916_to_fp16)[name = tensor<string, []>("op_1917_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1]> var_1919_to_fp16 = const()[name = tensor<string, []>("op_1919_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(573528320)))];
            tensor<fp16, [2, 1280, 1, 60]> hidden_states_125_cast_fp16 = mul(x = var_1917_cast_fp16, y = var_1919_to_fp16)[name = tensor<string, []>("hidden_states_125_cast_fp16")];
            tensor<int32, [2]> var_1926 = const()[name = tensor<string, []>("op_1926"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1928 = const()[name = tensor<string, []>("op_1928"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_25_pad_type_0 = const()[name = tensor<string, []>("q_25_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_25_pad_0 = const()[name = tensor<string, []>("q_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(573530944)))];
            tensor<fp16, [2, 1280, 1, 60]> q_25_cast_fp16 = conv(dilations = var_1928, groups = var_1828, pad = q_25_pad_0, pad_type = q_25_pad_type_0, strides = var_1926, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_125_cast_fp16)[name = tensor<string, []>("q_25_cast_fp16")];
            tensor<int32, [2]> var_1932 = const()[name = tensor<string, []>("op_1932"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1934 = const()[name = tensor<string, []>("op_1934"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_25_pad_type_0 = const()[name = tensor<string, []>("k_25_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_25_pad_0 = const()[name = tensor<string, []>("k_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(576807808)))];
            tensor<fp16, [2, 1280, 1, 60]> k_25_cast_fp16 = conv(dilations = var_1934, groups = var_1828, pad = k_25_pad_0, pad_type = k_25_pad_type_0, strides = var_1932, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_125_cast_fp16)[name = tensor<string, []>("k_25_cast_fp16")];
            tensor<int32, [2]> var_1938 = const()[name = tensor<string, []>("op_1938"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1940 = const()[name = tensor<string, []>("op_1940"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_25_pad_type_0 = const()[name = tensor<string, []>("v_25_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_25_pad_0 = const()[name = tensor<string, []>("v_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(580084672)))];
            tensor<fp16, [2, 1280, 1, 60]> v_25_cast_fp16 = conv(dilations = var_1940, groups = var_1828, pad = v_25_pad_0, pad_type = v_25_pad_type_0, strides = var_1938, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_125_cast_fp16)[name = tensor<string, []>("v_25_cast_fp16")];
            tensor<int32, [4]> var_1944 = const()[name = tensor<string, []>("op_1944"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 60]> var_1945_cast_fp16 = reshape(shape = var_1944, x = q_25_cast_fp16)[name = tensor<string, []>("op_1945_cast_fp16")];
            tensor<int32, [4]> var_1946 = const()[name = tensor<string, []>("op_1946"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 60]> var_1947_cast_fp16 = reshape(shape = var_1946, x = k_25_cast_fp16)[name = tensor<string, []>("op_1947_cast_fp16")];
            tensor<int32, [4]> var_1948 = const()[name = tensor<string, []>("op_1948"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 60]> var_1949_cast_fp16 = reshape(shape = var_1948, x = v_25_cast_fp16)[name = tensor<string, []>("op_1949_cast_fp16")];
            tensor<bool, []> attn_weights_49_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_49_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_49_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_49_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 60, 60]> attn_weights_49_cast_fp16 = matmul(transpose_x = attn_weights_49_transpose_x_0, transpose_y = attn_weights_49_transpose_y_0, x = var_1945_cast_fp16, y = var_1947_cast_fp16)[name = tensor<string, []>("attn_weights_49_cast_fp16")];
            tensor<fp16, []> var_1819_to_fp16 = const()[name = tensor<string, []>("op_1819_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [2, 20, 60, 60]> attn_weights_51_cast_fp16 = mul(x = attn_weights_49_cast_fp16, y = var_1819_to_fp16)[name = tensor<string, []>("attn_weights_51_cast_fp16")];
            tensor<fp16, [2, 20, 60, 60]> var_1953_cast_fp16 = softmax(axis = var_1812, x = attn_weights_51_cast_fp16)[name = tensor<string, []>("op_1953_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, [2, 20, 64, 60]> attn_25_cast_fp16 = matmul(transpose_x = attn_25_transpose_x_0, transpose_y = attn_25_transpose_y_0, x = var_1949_cast_fp16, y = var_1953_cast_fp16)[name = tensor<string, []>("attn_25_cast_fp16")];
            tensor<int32, [4]> var_1957 = const()[name = tensor<string, []>("op_1957"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 60]> input_215_cast_fp16 = reshape(shape = var_1957, x = attn_25_cast_fp16)[name = tensor<string, []>("input_215_cast_fp16")];
            tensor<int32, [2]> var_1962 = const()[name = tensor<string, []>("op_1962"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1964 = const()[name = tensor<string, []>("op_1964"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_1966_pad_type_0 = const()[name = tensor<string, []>("op_1966_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_1966_pad_0 = const()[name = tensor<string, []>("op_1966_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(583361536)))];
            tensor<fp16, [1280]> mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(586638400)))];
            tensor<fp16, [2, 1280, 1, 60]> var_1966_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_1964, groups = var_1828, pad = var_1966_pad_0, pad_type = var_1966_pad_type_0, strides = var_1962, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_215_cast_fp16)[name = tensor<string, []>("op_1966_cast_fp16")];
            tensor<fp16, [2, 1280, 1, 60]> inputs_39_cast_fp16 = add(x = var_1966_cast_fp16, y = inputs_37_cast_fp16)[name = tensor<string, []>("inputs_39_cast_fp16")];
            tensor<int32, [1]> var_1970 = const()[name = tensor<string, []>("op_1970"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 60]> channels_mean_39_cast_fp16 = reduce_mean(axes = var_1970, keep_dims = var_1823, x = inputs_39_cast_fp16)[name = tensor<string, []>("channels_mean_39_cast_fp16")];
            tensor<fp16, [2, 1280, 1, 60]> 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, [2, 1280, 1, 60]> 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_1974 = const()[name = tensor<string, []>("op_1974"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 60]> var_1975_cast_fp16 = reduce_mean(axes = var_1974, keep_dims = var_1823, x = zero_mean_sq_39_cast_fp16)[name = tensor<string, []>("op_1975_cast_fp16")];
            tensor<fp16, []> var_1976_to_fp16 = const()[name = tensor<string, []>("op_1976_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 60]> var_1977_cast_fp16 = add(x = var_1975_cast_fp16, y = var_1976_to_fp16)[name = tensor<string, []>("op_1977_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, [2, 1, 1, 60]> denom_39_cast_fp16 = rsqrt(epsilon = denom_39_epsilon_0_to_fp16, x = var_1977_cast_fp16)[name = tensor<string, []>("denom_39_cast_fp16")];
            tensor<fp16, [2, 1280, 1, 60]> 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, [1, 1280, 1, 1]> var_1981_to_fp16 = const()[name = tensor<string, []>("op_1981_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(586641024)))];
            tensor<fp16, [2, 1280, 1, 60]> var_1982_cast_fp16 = add(x = out_39_cast_fp16, y = var_1981_to_fp16)[name = tensor<string, []>("op_1982_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1]> var_1984_to_fp16 = const()[name = tensor<string, []>("op_1984_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(586643648)))];
            tensor<fp16, [2, 1280, 1, 60]> hidden_states_127_cast_fp16 = mul(x = var_1982_cast_fp16, y = var_1984_to_fp16)[name = tensor<string, []>("hidden_states_127_cast_fp16")];
            tensor<int32, [2]> var_1991 = const()[name = tensor<string, []>("op_1991"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1993 = const()[name = tensor<string, []>("op_1993"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> q_27_pad_type_0 = const()[name = tensor<string, []>("q_27_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> q_27_pad_0 = const()[name = tensor<string, []>("q_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(586646272)))];
            tensor<fp16, [2, 1280, 1, 60]> q_27_cast_fp16 = conv(dilations = var_1993, groups = var_1828, pad = q_27_pad_0, pad_type = q_27_pad_type_0, strides = var_1991, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_127_cast_fp16)[name = tensor<string, []>("q_27_cast_fp16")];
            tensor<int32, [2]> var_1997 = const()[name = tensor<string, []>("op_1997"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_1999 = const()[name = tensor<string, []>("op_1999"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> k_27_pad_type_0 = const()[name = tensor<string, []>("k_27_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> k_27_pad_0 = const()[name = tensor<string, []>("k_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1024, 1, 1]> mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor<fp16, [1280, 1024, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(589923136)))];
            tensor<fp16, [2, 1280, 1, 77]> k_27_cast_fp16 = conv(dilations = var_1999, groups = var_1828, pad = k_27_pad_0, pad_type = k_27_pad_type_0, strides = var_1997, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor<string, []>("k_27_cast_fp16")];
            tensor<int32, [2]> var_2003 = const()[name = tensor<string, []>("op_2003"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2005 = const()[name = tensor<string, []>("op_2005"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> v_27_pad_type_0 = const()[name = tensor<string, []>("v_27_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> v_27_pad_0 = const()[name = tensor<string, []>("v_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1024, 1, 1]> mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor<fp16, [1280, 1024, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(592544640)))];
            tensor<fp16, [2, 1280, 1, 77]> v_27_cast_fp16 = conv(dilations = var_2005, groups = var_1828, pad = v_27_pad_0, pad_type = v_27_pad_type_0, strides = var_2003, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor<string, []>("v_27_cast_fp16")];
            tensor<int32, [4]> var_2009 = const()[name = tensor<string, []>("op_2009"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 60]> var_2010_cast_fp16 = reshape(shape = var_2009, x = q_27_cast_fp16)[name = tensor<string, []>("op_2010_cast_fp16")];
            tensor<int32, [4]> var_2011 = const()[name = tensor<string, []>("op_2011"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_2012_cast_fp16 = reshape(shape = var_2011, x = k_27_cast_fp16)[name = tensor<string, []>("op_2012_cast_fp16")];
            tensor<int32, [4]> var_2013 = const()[name = tensor<string, []>("op_2013"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 77]> var_2014_cast_fp16 = reshape(shape = var_2013, x = v_27_cast_fp16)[name = tensor<string, []>("op_2014_cast_fp16")];
            tensor<bool, []> attn_weights_53_transpose_x_0 = const()[name = tensor<string, []>("attn_weights_53_transpose_x_0"), val = tensor<bool, []>(true)];
            tensor<bool, []> attn_weights_53_transpose_y_0 = const()[name = tensor<string, []>("attn_weights_53_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 20, 60, 77]> attn_weights_53_cast_fp16 = matmul(transpose_x = attn_weights_53_transpose_x_0, transpose_y = attn_weights_53_transpose_y_0, x = var_2010_cast_fp16, y = var_2012_cast_fp16)[name = tensor<string, []>("attn_weights_53_cast_fp16")];
            tensor<fp16, [2, 20, 60, 77]> attn_weights_55_cast_fp16 = mul(x = attn_weights_53_cast_fp16, y = var_1819_to_fp16)[name = tensor<string, []>("attn_weights_55_cast_fp16")];
            tensor<fp16, [2, 20, 60, 77]> var_2018_cast_fp16 = softmax(axis = var_1812, x = attn_weights_55_cast_fp16)[name = tensor<string, []>("op_2018_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, [2, 20, 64, 60]> attn_27_cast_fp16 = matmul(transpose_x = attn_27_transpose_x_0, transpose_y = attn_27_transpose_y_0, x = var_2014_cast_fp16, y = var_2018_cast_fp16)[name = tensor<string, []>("attn_27_cast_fp16")];
            tensor<int32, [4]> var_2022 = const()[name = tensor<string, []>("op_2022"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 60]> input_217_cast_fp16 = reshape(shape = var_2022, x = attn_27_cast_fp16)[name = tensor<string, []>("input_217_cast_fp16")];
            tensor<int32, [2]> var_2027 = const()[name = tensor<string, []>("op_2027"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2029 = const()[name = tensor<string, []>("op_2029"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_2031_pad_type_0 = const()[name = tensor<string, []>("op_2031_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2031_pad_0 = const()[name = tensor<string, []>("op_2031_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(595166144)))];
            tensor<fp16, [1280]> mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(598443008)))];
            tensor<fp16, [2, 1280, 1, 60]> var_2031_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_2029, groups = var_1828, pad = var_2031_pad_0, pad_type = var_2031_pad_type_0, strides = var_2027, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_217_cast_fp16)[name = tensor<string, []>("op_2031_cast_fp16")];
            tensor<fp16, [2, 1280, 1, 60]> inputs_41_cast_fp16 = add(x = var_2031_cast_fp16, y = inputs_39_cast_fp16)[name = tensor<string, []>("inputs_41_cast_fp16")];
            tensor<int32, [1]> var_2035 = const()[name = tensor<string, []>("op_2035"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 60]> channels_mean_41_cast_fp16 = reduce_mean(axes = var_2035, keep_dims = var_1823, x = inputs_41_cast_fp16)[name = tensor<string, []>("channels_mean_41_cast_fp16")];
            tensor<fp16, [2, 1280, 1, 60]> 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, [2, 1280, 1, 60]> 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_2039 = const()[name = tensor<string, []>("op_2039"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 60]> var_2040_cast_fp16 = reduce_mean(axes = var_2039, keep_dims = var_1823, x = zero_mean_sq_41_cast_fp16)[name = tensor<string, []>("op_2040_cast_fp16")];
            tensor<fp16, []> var_2041_to_fp16 = const()[name = tensor<string, []>("op_2041_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1, 1, 60]> var_2042_cast_fp16 = add(x = var_2040_cast_fp16, y = var_2041_to_fp16)[name = tensor<string, []>("op_2042_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, [2, 1, 1, 60]> denom_41_cast_fp16 = rsqrt(epsilon = denom_41_epsilon_0_to_fp16, x = var_2042_cast_fp16)[name = tensor<string, []>("denom_41_cast_fp16")];
            tensor<fp16, [2, 1280, 1, 60]> 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, [1, 1280, 1, 1]> var_2046_to_fp16 = const()[name = tensor<string, []>("op_2046_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(598445632)))];
            tensor<fp16, [2, 1280, 1, 60]> var_2047_cast_fp16 = add(x = out_41_cast_fp16, y = var_2046_to_fp16)[name = tensor<string, []>("op_2047_cast_fp16")];
            tensor<fp16, [1, 1280, 1, 1]> var_2049_to_fp16 = const()[name = tensor<string, []>("op_2049_to_fp16"), val = tensor<fp16, [1, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(598448256)))];
            tensor<fp16, [2, 1280, 1, 60]> input_219_cast_fp16 = mul(x = var_2047_cast_fp16, y = var_2049_to_fp16)[name = tensor<string, []>("input_219_cast_fp16")];
            tensor<int32, [2]> var_2057 = const()[name = tensor<string, []>("op_2057"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2059 = const()[name = tensor<string, []>("op_2059"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_2061_pad_type_0 = const()[name = tensor<string, []>("op_2061_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2061_pad_0 = const()[name = tensor<string, []>("op_2061_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [10240, 1280, 1, 1]> mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor<fp16, [10240, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(598450880)))];
            tensor<fp16, [10240]> mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor<fp16, [10240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(624665344)))];
            tensor<fp16, [2, 10240, 1, 60]> var_2061_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_2059, groups = var_1828, pad = var_2061_pad_0, pad_type = var_2061_pad_type_0, strides = var_2057, weight = mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_219_cast_fp16)[name = tensor<string, []>("op_2061_cast_fp16")];
            tensor<int32, [2]> var_2062_split_sizes_0 = const()[name = tensor<string, []>("op_2062_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
            tensor<int32, []> var_2062_axis_0 = const()[name = tensor<string, []>("op_2062_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [2, 5120, 1, 60]> var_2062_cast_fp16_0, tensor<fp16, [2, 5120, 1, 60]> var_2062_cast_fp16_1 = split(axis = var_2062_axis_0, split_sizes = var_2062_split_sizes_0, x = var_2061_cast_fp16)[name = tensor<string, []>("op_2062_cast_fp16")];
            tensor<string, []> var_2064_mode_0 = const()[name = tensor<string, []>("op_2064_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 60]> var_2064_cast_fp16 = gelu(mode = var_2064_mode_0, x = var_2062_cast_fp16_1)[name = tensor<string, []>("op_2064_cast_fp16")];
            tensor<fp16, [2, 5120, 1, 60]> input_221_cast_fp16 = mul(x = var_2062_cast_fp16_0, y = var_2064_cast_fp16)[name = tensor<string, []>("input_221_cast_fp16")];
            tensor<int32, [2]> var_2068 = const()[name = tensor<string, []>("op_2068"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2070 = const()[name = tensor<string, []>("op_2070"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> var_2072_pad_type_0 = const()[name = tensor<string, []>("op_2072_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> var_2072_pad_0 = const()[name = tensor<string, []>("op_2072_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 5120, 1, 1]> mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(624685888)))];
            tensor<fp16, [1280]> mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(637793152)))];
            tensor<fp16, [2, 1280, 1, 60]> var_2072_cast_fp16 = conv(bias = mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_2070, groups = var_1828, pad = var_2072_pad_0, pad_type = var_2072_pad_type_0, strides = var_2068, weight = mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_221_cast_fp16)[name = tensor<string, []>("op_2072_cast_fp16")];
            tensor<fp16, [2, 1280, 1, 60]> hidden_states_131_cast_fp16 = add(x = var_2072_cast_fp16, y = inputs_41_cast_fp16)[name = tensor<string, []>("hidden_states_131_cast_fp16")];
            tensor<int32, [4]> var_2074 = const()[name = tensor<string, []>("op_2074"), val = tensor<int32, [4]>([2, 1280, 6, 10])];
            tensor<fp16, [2, 1280, 6, 10]> input_223_cast_fp16 = reshape(shape = var_2074, x = hidden_states_131_cast_fp16)[name = tensor<string, []>("input_223_cast_fp16")];
            tensor<int32, [2]> var_2078 = const()[name = tensor<string, []>("op_2078"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2080 = const()[name = tensor<string, []>("op_2080"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_133_pad_type_0 = const()[name = tensor<string, []>("hidden_states_133_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_133_pad_0 = const()[name = tensor<string, []>("hidden_states_133_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_proj_out_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(637795776)))];
            tensor<fp16, [1280]> mid_block_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_attentions_0_proj_out_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(641072640)))];
            tensor<fp16, [2, 1280, 6, 10]> hidden_states_133_cast_fp16 = conv(bias = mid_block_attentions_0_proj_out_bias_to_fp16, dilations = var_2080, groups = var_1828, pad = hidden_states_133_pad_0, pad_type = hidden_states_133_pad_type_0, strides = var_2078, weight = mid_block_attentions_0_proj_out_weight_to_fp16, x = input_223_cast_fp16)[name = tensor<string, []>("hidden_states_133_cast_fp16")];
            tensor<fp16, [2, 1280, 6, 10]> input_225_cast_fp16 = add(x = hidden_states_133_cast_fp16, y = hidden_states_121_cast_fp16)[name = tensor<string, []>("input_225_cast_fp16")];
            tensor<int32, [5]> reshape_100_shape_0 = const()[name = tensor<string, []>("reshape_100_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 6, 10])];
            tensor<fp16, [2, 32, 40, 6, 10]> reshape_100_cast_fp16 = reshape(shape = reshape_100_shape_0, x = input_225_cast_fp16)[name = tensor<string, []>("reshape_100_cast_fp16")];
            tensor<int32, [3]> reduce_mean_75_axes_0 = const()[name = tensor<string, []>("reduce_mean_75_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_75_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_75_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_75_cast_fp16 = reduce_mean(axes = reduce_mean_75_axes_0, keep_dims = reduce_mean_75_keep_dims_0, x = reshape_100_cast_fp16)[name = tensor<string, []>("reduce_mean_75_cast_fp16")];
            tensor<fp16, [2, 32, 40, 6, 10]> sub_50_cast_fp16 = sub(x = reshape_100_cast_fp16, y = reduce_mean_75_cast_fp16)[name = tensor<string, []>("sub_50_cast_fp16")];
            tensor<fp16, [2, 32, 40, 6, 10]> square_25_cast_fp16 = square(x = sub_50_cast_fp16)[name = tensor<string, []>("square_25_cast_fp16")];
            tensor<int32, [3]> reduce_mean_77_axes_0 = const()[name = tensor<string, []>("reduce_mean_77_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_77_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_77_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_77_cast_fp16 = reduce_mean(axes = reduce_mean_77_axes_0, keep_dims = reduce_mean_77_keep_dims_0, x = square_25_cast_fp16)[name = tensor<string, []>("reduce_mean_77_cast_fp16")];
            tensor<fp16, []> add_50_y_0_to_fp16 = const()[name = tensor<string, []>("add_50_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_50_cast_fp16 = add(x = reduce_mean_77_cast_fp16, y = add_50_y_0_to_fp16)[name = tensor<string, []>("add_50_cast_fp16")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_25_cast_fp16 = sqrt(x = add_50_cast_fp16)[name = tensor<string, []>("sqrt_25_cast_fp16")];
            tensor<fp16, [2, 32, 40, 6, 10]> real_div_25_cast_fp16 = real_div(x = sub_50_cast_fp16, y = sqrt_25_cast_fp16)[name = tensor<string, []>("real_div_25_cast_fp16")];
            tensor<int32, [4]> reshape_101_shape_0 = const()[name = tensor<string, []>("reshape_101_shape_0"), val = tensor<int32, [4]>([2, 1280, 6, 10])];
            tensor<fp16, [2, 1280, 6, 10]> reshape_101_cast_fp16 = reshape(shape = reshape_101_shape_0, x = real_div_25_cast_fp16)[name = tensor<string, []>("reshape_101_cast_fp16")];
            tensor<fp16, [1280]> add_51_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_51_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(641075264)))];
            tensor<fp16, [1280]> add_51_beta_0_to_fp16 = const()[name = tensor<string, []>("add_51_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(641077888)))];
            tensor<fp16, []> add_51_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_51_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1280, 6, 10]> add_51_cast_fp16 = batch_norm(beta = add_51_beta_0_to_fp16, epsilon = add_51_epsilon_0_to_fp16, gamma = add_51_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_101_cast_fp16)[name = tensor<string, []>("add_51_cast_fp16")];
            tensor<fp16, [2, 1280, 6, 10]> input_229_cast_fp16 = silu(x = add_51_cast_fp16)[name = tensor<string, []>("input_229_cast_fp16")];
            tensor<int32, [2]> var_2095 = const()[name = tensor<string, []>("op_2095"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2097 = const()[name = tensor<string, []>("op_2097"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_135_pad_type_0 = const()[name = tensor<string, []>("hidden_states_135_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_135_pad_0 = const()[name = tensor<string, []>("hidden_states_135_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 1280, 3, 3]> mid_block_resnets_1_conv1_weight_to_fp16 = const()[name = tensor<string, []>("mid_block_resnets_1_conv1_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(641080512)))];
            tensor<fp16, [1280]> mid_block_resnets_1_conv1_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_resnets_1_conv1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(670571776)))];
            tensor<fp16, [2, 1280, 6, 10]> hidden_states_135_cast_fp16 = conv(bias = mid_block_resnets_1_conv1_bias_to_fp16, dilations = var_2097, groups = var_1828, pad = hidden_states_135_pad_0, pad_type = hidden_states_135_pad_type_0, strides = var_2095, weight = mid_block_resnets_1_conv1_weight_to_fp16, x = input_229_cast_fp16)[name = tensor<string, []>("hidden_states_135_cast_fp16")];
            tensor<int32, [2]> var_2103 = const()[name = tensor<string, []>("op_2103"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2105 = const()[name = tensor<string, []>("op_2105"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> temb_19_pad_type_0 = const()[name = tensor<string, []>("temb_19_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> temb_19_pad_0 = const()[name = tensor<string, []>("temb_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> mid_block_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor<string, []>("mid_block_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(670574400)))];
            tensor<fp16, [1280]> mid_block_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(673851264)))];
            tensor<fp16, [2, 1280, 1, 1]> temb_19_cast_fp16 = conv(bias = mid_block_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_2105, groups = var_1828, pad = temb_19_pad_0, pad_type = temb_19_pad_type_0, strides = var_2103, weight = mid_block_resnets_1_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16_1)[name = tensor<string, []>("temb_19_cast_fp16")];
            tensor<fp16, [2, 1280, 6, 10]> input_233_cast_fp16 = add(x = hidden_states_135_cast_fp16, y = temb_19_cast_fp16)[name = tensor<string, []>("input_233_cast_fp16")];
            tensor<int32, [5]> reshape_104_shape_0 = const()[name = tensor<string, []>("reshape_104_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 6, 10])];
            tensor<fp16, [2, 32, 40, 6, 10]> reshape_104_cast_fp16 = reshape(shape = reshape_104_shape_0, x = input_233_cast_fp16)[name = tensor<string, []>("reshape_104_cast_fp16")];
            tensor<int32, [3]> reduce_mean_78_axes_0 = const()[name = tensor<string, []>("reduce_mean_78_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_78_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_78_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_78_cast_fp16 = reduce_mean(axes = reduce_mean_78_axes_0, keep_dims = reduce_mean_78_keep_dims_0, x = reshape_104_cast_fp16)[name = tensor<string, []>("reduce_mean_78_cast_fp16")];
            tensor<fp16, [2, 32, 40, 6, 10]> sub_52_cast_fp16 = sub(x = reshape_104_cast_fp16, y = reduce_mean_78_cast_fp16)[name = tensor<string, []>("sub_52_cast_fp16")];
            tensor<fp16, [2, 32, 40, 6, 10]> square_26_cast_fp16 = square(x = sub_52_cast_fp16)[name = tensor<string, []>("square_26_cast_fp16")];
            tensor<int32, [3]> reduce_mean_80_axes_0 = const()[name = tensor<string, []>("reduce_mean_80_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_80_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_80_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_80_cast_fp16 = reduce_mean(axes = reduce_mean_80_axes_0, keep_dims = reduce_mean_80_keep_dims_0, x = square_26_cast_fp16)[name = tensor<string, []>("reduce_mean_80_cast_fp16")];
            tensor<fp16, []> add_52_y_0_to_fp16 = const()[name = tensor<string, []>("add_52_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_52_cast_fp16 = add(x = reduce_mean_80_cast_fp16, y = add_52_y_0_to_fp16)[name = tensor<string, []>("add_52_cast_fp16")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_26_cast_fp16 = sqrt(x = add_52_cast_fp16)[name = tensor<string, []>("sqrt_26_cast_fp16")];
            tensor<fp16, [2, 32, 40, 6, 10]> real_div_26_cast_fp16 = real_div(x = sub_52_cast_fp16, y = sqrt_26_cast_fp16)[name = tensor<string, []>("real_div_26_cast_fp16")];
            tensor<int32, [4]> reshape_105_shape_0 = const()[name = tensor<string, []>("reshape_105_shape_0"), val = tensor<int32, [4]>([2, 1280, 6, 10])];
            tensor<fp16, [2, 1280, 6, 10]> reshape_105_cast_fp16 = reshape(shape = reshape_105_shape_0, x = real_div_26_cast_fp16)[name = tensor<string, []>("reshape_105_cast_fp16")];
            tensor<fp16, [1280]> add_53_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_53_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(673853888)))];
            tensor<fp16, [1280]> add_53_beta_0_to_fp16 = const()[name = tensor<string, []>("add_53_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(673856512)))];
            tensor<fp16, []> add_53_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_53_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1280, 6, 10]> add_53_cast_fp16 = batch_norm(beta = add_53_beta_0_to_fp16, epsilon = add_53_epsilon_0_to_fp16, gamma = add_53_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_105_cast_fp16)[name = tensor<string, []>("add_53_cast_fp16")];
            tensor<fp16, [2, 1280, 6, 10]> input_237_cast_fp16 = silu(x = add_53_cast_fp16)[name = tensor<string, []>("input_237_cast_fp16")];
            tensor<int32, [2]> var_2115 = const()[name = tensor<string, []>("op_2115"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2117 = const()[name = tensor<string, []>("op_2117"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_137_pad_type_0 = const()[name = tensor<string, []>("hidden_states_137_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_137_pad_0 = const()[name = tensor<string, []>("hidden_states_137_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 1280, 3, 3]> mid_block_resnets_1_conv2_weight_to_fp16 = const()[name = tensor<string, []>("mid_block_resnets_1_conv2_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(673859136)))];
            tensor<fp16, [1280]> mid_block_resnets_1_conv2_bias_to_fp16 = const()[name = tensor<string, []>("mid_block_resnets_1_conv2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(703350400)))];
            tensor<fp16, [2, 1280, 6, 10]> hidden_states_137_cast_fp16 = conv(bias = mid_block_resnets_1_conv2_bias_to_fp16, dilations = var_2117, groups = var_1828, pad = hidden_states_137_pad_0, pad_type = hidden_states_137_pad_type_0, strides = var_2115, weight = mid_block_resnets_1_conv2_weight_to_fp16, x = input_237_cast_fp16)[name = tensor<string, []>("hidden_states_137_cast_fp16")];
            tensor<fp16, [2, 1280, 6, 10]> hidden_states_139_cast_fp16 = add(x = input_225_cast_fp16, y = hidden_states_137_cast_fp16)[name = tensor<string, []>("hidden_states_139_cast_fp16")];
            tensor<int32, []> var_2128 = const()[name = tensor<string, []>("op_2128"), val = tensor<int32, []>(1)];
            tensor<bool, []> input_239_interleave_0 = const()[name = tensor<string, []>("input_239_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 2560, 6, 10]> input_239_cast_fp16 = concat(axis = var_2128, interleave = input_239_interleave_0, values = (hidden_states_139_cast_fp16, input_199_cast_fp16))[name = tensor<string, []>("input_239_cast_fp16")];
            tensor<int32, [5]> reshape_108_shape_0 = const()[name = tensor<string, []>("reshape_108_shape_0"), val = tensor<int32, [5]>([2, 32, 80, 6, 10])];
            tensor<fp16, [2, 32, 80, 6, 10]> reshape_108_cast_fp16 = reshape(shape = reshape_108_shape_0, x = input_239_cast_fp16)[name = tensor<string, []>("reshape_108_cast_fp16")];
            tensor<int32, [3]> reduce_mean_81_axes_0 = const()[name = tensor<string, []>("reduce_mean_81_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_81_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_81_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_81_cast_fp16 = reduce_mean(axes = reduce_mean_81_axes_0, keep_dims = reduce_mean_81_keep_dims_0, x = reshape_108_cast_fp16)[name = tensor<string, []>("reduce_mean_81_cast_fp16")];
            tensor<fp16, [2, 32, 80, 6, 10]> sub_54_cast_fp16 = sub(x = reshape_108_cast_fp16, y = reduce_mean_81_cast_fp16)[name = tensor<string, []>("sub_54_cast_fp16")];
            tensor<fp16, [2, 32, 80, 6, 10]> square_27_cast_fp16 = square(x = sub_54_cast_fp16)[name = tensor<string, []>("square_27_cast_fp16")];
            tensor<int32, [3]> reduce_mean_83_axes_0 = const()[name = tensor<string, []>("reduce_mean_83_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_83_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_83_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_83_cast_fp16 = reduce_mean(axes = reduce_mean_83_axes_0, keep_dims = reduce_mean_83_keep_dims_0, x = square_27_cast_fp16)[name = tensor<string, []>("reduce_mean_83_cast_fp16")];
            tensor<fp16, []> add_54_y_0_to_fp16 = const()[name = tensor<string, []>("add_54_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_54_cast_fp16 = add(x = reduce_mean_83_cast_fp16, y = add_54_y_0_to_fp16)[name = tensor<string, []>("add_54_cast_fp16")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_27_cast_fp16 = sqrt(x = add_54_cast_fp16)[name = tensor<string, []>("sqrt_27_cast_fp16")];
            tensor<fp16, [2, 32, 80, 6, 10]> real_div_27_cast_fp16 = real_div(x = sub_54_cast_fp16, y = sqrt_27_cast_fp16)[name = tensor<string, []>("real_div_27_cast_fp16")];
            tensor<int32, [4]> reshape_109_shape_0 = const()[name = tensor<string, []>("reshape_109_shape_0"), val = tensor<int32, [4]>([2, 2560, 6, 10])];
            tensor<fp16, [2, 2560, 6, 10]> reshape_109_cast_fp16 = reshape(shape = reshape_109_shape_0, x = real_div_27_cast_fp16)[name = tensor<string, []>("reshape_109_cast_fp16")];
            tensor<fp16, [2560]> add_55_mean_0_to_fp16 = const()[name = tensor<string, []>("add_55_mean_0_to_fp16"), val = tensor<fp16, [2560]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(703353024)))];
            tensor<fp16, [2560]> add_55_variance_0_to_fp16 = const()[name = tensor<string, []>("add_55_variance_0_to_fp16"), val = tensor<fp16, [2560]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(703358208)))];
            tensor<fp16, [2560]> add_55_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_55_gamma_0_to_fp16"), val = tensor<fp16, [2560]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(703363392)))];
            tensor<fp16, [2560]> add_55_beta_0_to_fp16 = const()[name = tensor<string, []>("add_55_beta_0_to_fp16"), val = tensor<fp16, [2560]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(703368576)))];
            tensor<fp16, []> add_55_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_55_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 2560, 6, 10]> add_55_cast_fp16 = batch_norm(beta = add_55_beta_0_to_fp16, epsilon = add_55_epsilon_0_to_fp16, gamma = add_55_gamma_0_to_fp16, mean = add_55_mean_0_to_fp16, variance = add_55_variance_0_to_fp16, x = reshape_109_cast_fp16)[name = tensor<string, []>("add_55_cast_fp16")];
            tensor<fp16, [2, 2560, 6, 10]> input_243_cast_fp16 = silu(x = add_55_cast_fp16)[name = tensor<string, []>("input_243_cast_fp16")];
            tensor<int32, [2]> var_2151 = const()[name = tensor<string, []>("op_2151"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2153 = const()[name = tensor<string, []>("op_2153"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_141_pad_type_0 = const()[name = tensor<string, []>("hidden_states_141_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_141_pad_0 = const()[name = tensor<string, []>("hidden_states_141_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 2560, 3, 3]> up_blocks_0_resnets_0_conv1_weight_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_resnets_0_conv1_weight_to_fp16"), val = tensor<fp16, [1280, 2560, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(703373760)))];
            tensor<fp16, [1280]> up_blocks_0_resnets_0_conv1_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_resnets_0_conv1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(762356224)))];
            tensor<fp16, [2, 1280, 6, 10]> hidden_states_141_cast_fp16 = conv(bias = up_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = var_2153, groups = var_2128, pad = hidden_states_141_pad_0, pad_type = hidden_states_141_pad_type_0, strides = var_2151, weight = up_blocks_0_resnets_0_conv1_weight_to_fp16, x = input_243_cast_fp16)[name = tensor<string, []>("hidden_states_141_cast_fp16")];
            tensor<int32, [2]> var_2159 = const()[name = tensor<string, []>("op_2159"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2161 = const()[name = tensor<string, []>("op_2161"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> temb_21_pad_type_0 = const()[name = tensor<string, []>("temb_21_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> temb_21_pad_0 = const()[name = tensor<string, []>("temb_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(762358848)))];
            tensor<fp16, [1280]> up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(765635712)))];
            tensor<fp16, [2, 1280, 1, 1]> temb_21_cast_fp16 = conv(bias = up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_2161, groups = var_2128, pad = temb_21_pad_0, pad_type = temb_21_pad_type_0, strides = var_2159, weight = up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16_1)[name = tensor<string, []>("temb_21_cast_fp16")];
            tensor<fp16, [2, 1280, 6, 10]> input_247_cast_fp16 = add(x = hidden_states_141_cast_fp16, y = temb_21_cast_fp16)[name = tensor<string, []>("input_247_cast_fp16")];
            tensor<int32, [5]> reshape_112_shape_0 = const()[name = tensor<string, []>("reshape_112_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 6, 10])];
            tensor<fp16, [2, 32, 40, 6, 10]> reshape_112_cast_fp16 = reshape(shape = reshape_112_shape_0, x = input_247_cast_fp16)[name = tensor<string, []>("reshape_112_cast_fp16")];
            tensor<int32, [3]> reduce_mean_84_axes_0 = const()[name = tensor<string, []>("reduce_mean_84_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_84_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_84_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_84_cast_fp16 = reduce_mean(axes = reduce_mean_84_axes_0, keep_dims = reduce_mean_84_keep_dims_0, x = reshape_112_cast_fp16)[name = tensor<string, []>("reduce_mean_84_cast_fp16")];
            tensor<fp16, [2, 32, 40, 6, 10]> sub_56_cast_fp16 = sub(x = reshape_112_cast_fp16, y = reduce_mean_84_cast_fp16)[name = tensor<string, []>("sub_56_cast_fp16")];
            tensor<fp16, [2, 32, 40, 6, 10]> square_28_cast_fp16 = square(x = sub_56_cast_fp16)[name = tensor<string, []>("square_28_cast_fp16")];
            tensor<int32, [3]> reduce_mean_86_axes_0 = const()[name = tensor<string, []>("reduce_mean_86_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_86_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_86_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_86_cast_fp16 = reduce_mean(axes = reduce_mean_86_axes_0, keep_dims = reduce_mean_86_keep_dims_0, x = square_28_cast_fp16)[name = tensor<string, []>("reduce_mean_86_cast_fp16")];
            tensor<fp16, []> add_56_y_0_to_fp16 = const()[name = tensor<string, []>("add_56_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_56_cast_fp16 = add(x = reduce_mean_86_cast_fp16, y = add_56_y_0_to_fp16)[name = tensor<string, []>("add_56_cast_fp16")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_28_cast_fp16 = sqrt(x = add_56_cast_fp16)[name = tensor<string, []>("sqrt_28_cast_fp16")];
            tensor<fp16, [2, 32, 40, 6, 10]> real_div_28_cast_fp16 = real_div(x = sub_56_cast_fp16, y = sqrt_28_cast_fp16)[name = tensor<string, []>("real_div_28_cast_fp16")];
            tensor<int32, [4]> reshape_113_shape_0 = const()[name = tensor<string, []>("reshape_113_shape_0"), val = tensor<int32, [4]>([2, 1280, 6, 10])];
            tensor<fp16, [2, 1280, 6, 10]> reshape_113_cast_fp16 = reshape(shape = reshape_113_shape_0, x = real_div_28_cast_fp16)[name = tensor<string, []>("reshape_113_cast_fp16")];
            tensor<fp16, [1280]> add_57_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_57_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(765638336)))];
            tensor<fp16, [1280]> add_57_beta_0_to_fp16 = const()[name = tensor<string, []>("add_57_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(765640960)))];
            tensor<fp16, []> add_57_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_57_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1280, 6, 10]> add_57_cast_fp16 = batch_norm(beta = add_57_beta_0_to_fp16, epsilon = add_57_epsilon_0_to_fp16, gamma = add_57_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_113_cast_fp16)[name = tensor<string, []>("add_57_cast_fp16")];
            tensor<fp16, [2, 1280, 6, 10]> input_251_cast_fp16 = silu(x = add_57_cast_fp16)[name = tensor<string, []>("input_251_cast_fp16")];
            tensor<int32, [2]> var_2171 = const()[name = tensor<string, []>("op_2171"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2173 = const()[name = tensor<string, []>("op_2173"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_143_pad_type_0 = const()[name = tensor<string, []>("hidden_states_143_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_143_pad_0 = const()[name = tensor<string, []>("hidden_states_143_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 1280, 3, 3]> up_blocks_0_resnets_0_conv2_weight_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_resnets_0_conv2_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(765643584)))];
            tensor<fp16, [1280]> up_blocks_0_resnets_0_conv2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_resnets_0_conv2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(795134848)))];
            tensor<fp16, [2, 1280, 6, 10]> hidden_states_143_cast_fp16 = conv(bias = up_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = var_2173, groups = var_2128, pad = hidden_states_143_pad_0, pad_type = hidden_states_143_pad_type_0, strides = var_2171, weight = up_blocks_0_resnets_0_conv2_weight_to_fp16, x = input_251_cast_fp16)[name = tensor<string, []>("hidden_states_143_cast_fp16")];
            tensor<int32, [2]> var_2178 = const()[name = tensor<string, []>("op_2178"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2180 = const()[name = tensor<string, []>("op_2180"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> x_5_pad_type_0 = const()[name = tensor<string, []>("x_5_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> x_5_pad_0 = const()[name = tensor<string, []>("x_5_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 2560, 1, 1]> up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor<fp16, [1280, 2560, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(795137472)))];
            tensor<fp16, [1280]> up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(801691136)))];
            tensor<fp16, [2, 1280, 6, 10]> x_5_cast_fp16 = conv(bias = up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_2180, groups = var_2128, pad = x_5_pad_0, pad_type = x_5_pad_type_0, strides = var_2178, weight = up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16, x = input_239_cast_fp16)[name = tensor<string, []>("x_5_cast_fp16")];
            tensor<fp16, [2, 1280, 6, 10]> hidden_states_145_cast_fp16 = add(x = x_5_cast_fp16, y = hidden_states_143_cast_fp16)[name = tensor<string, []>("hidden_states_145_cast_fp16")];
            tensor<bool, []> input_253_interleave_0 = const()[name = tensor<string, []>("input_253_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 2560, 6, 10]> input_253_cast_fp16_1 = concat(axis = var_2128, interleave = input_253_interleave_0, values = (hidden_states_145_cast_fp16, input_185_cast_fp16))[name = tensor<string, []>("input_253_cast_fp16")];
            tensor<int32, [5]> reshape_116_shape_0 = const()[name = tensor<string, []>("reshape_116_shape_0"), val = tensor<int32, [5]>([2, 32, 80, 6, 10])];
            tensor<fp16, [2, 32, 80, 6, 10]> reshape_116_cast_fp16 = reshape(shape = reshape_116_shape_0, x = input_253_cast_fp16_1)[name = tensor<string, []>("reshape_116_cast_fp16")];
            tensor<int32, [3]> reduce_mean_87_axes_0 = const()[name = tensor<string, []>("reduce_mean_87_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_87_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_87_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_87_cast_fp16 = reduce_mean(axes = reduce_mean_87_axes_0, keep_dims = reduce_mean_87_keep_dims_0, x = reshape_116_cast_fp16)[name = tensor<string, []>("reduce_mean_87_cast_fp16")];
            tensor<fp16, [2, 32, 80, 6, 10]> sub_58_cast_fp16 = sub(x = reshape_116_cast_fp16, y = reduce_mean_87_cast_fp16)[name = tensor<string, []>("sub_58_cast_fp16")];
            tensor<fp16, [2, 32, 80, 6, 10]> square_29_cast_fp16 = square(x = sub_58_cast_fp16)[name = tensor<string, []>("square_29_cast_fp16")];
            tensor<int32, [3]> reduce_mean_89_axes_0 = const()[name = tensor<string, []>("reduce_mean_89_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_89_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_89_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_89_cast_fp16 = reduce_mean(axes = reduce_mean_89_axes_0, keep_dims = reduce_mean_89_keep_dims_0, x = square_29_cast_fp16)[name = tensor<string, []>("reduce_mean_89_cast_fp16")];
            tensor<fp16, []> add_58_y_0_to_fp16 = const()[name = tensor<string, []>("add_58_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_58_cast_fp16 = add(x = reduce_mean_89_cast_fp16, y = add_58_y_0_to_fp16)[name = tensor<string, []>("add_58_cast_fp16")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_29_cast_fp16 = sqrt(x = add_58_cast_fp16)[name = tensor<string, []>("sqrt_29_cast_fp16")];
            tensor<fp16, [2, 32, 80, 6, 10]> real_div_29_cast_fp16 = real_div(x = sub_58_cast_fp16, y = sqrt_29_cast_fp16)[name = tensor<string, []>("real_div_29_cast_fp16")];
            tensor<int32, [4]> reshape_117_shape_0 = const()[name = tensor<string, []>("reshape_117_shape_0"), val = tensor<int32, [4]>([2, 2560, 6, 10])];
            tensor<fp16, [2, 2560, 6, 10]> reshape_117_cast_fp16 = reshape(shape = reshape_117_shape_0, x = real_div_29_cast_fp16)[name = tensor<string, []>("reshape_117_cast_fp16")];
            tensor<fp16, [2560]> add_59_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_59_gamma_0_to_fp16"), val = tensor<fp16, [2560]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(801693760)))];
            tensor<fp16, [2560]> add_59_beta_0_to_fp16 = const()[name = tensor<string, []>("add_59_beta_0_to_fp16"), val = tensor<fp16, [2560]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(801698944)))];
            tensor<fp16, []> add_59_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_59_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 2560, 6, 10]> add_59_cast_fp16 = batch_norm(beta = add_59_beta_0_to_fp16, epsilon = add_59_epsilon_0_to_fp16, gamma = add_59_gamma_0_to_fp16, mean = add_55_mean_0_to_fp16, variance = add_55_variance_0_to_fp16, x = reshape_117_cast_fp16)[name = tensor<string, []>("add_59_cast_fp16")];
            tensor<fp16, [2, 2560, 6, 10]> input_257_cast_fp16 = silu(x = add_59_cast_fp16)[name = tensor<string, []>("input_257_cast_fp16")];
            tensor<int32, [2]> var_2198 = const()[name = tensor<string, []>("op_2198"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2200 = const()[name = tensor<string, []>("op_2200"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_147_pad_type_0 = const()[name = tensor<string, []>("hidden_states_147_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_147_pad_0 = const()[name = tensor<string, []>("hidden_states_147_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 2560, 3, 3]> up_blocks_0_resnets_1_conv1_weight_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_resnets_1_conv1_weight_to_fp16"), val = tensor<fp16, [1280, 2560, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(801704128)))];
            tensor<fp16, [1280]> up_blocks_0_resnets_1_conv1_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_resnets_1_conv1_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(860686592)))];
            tensor<fp16, [2, 1280, 6, 10]> hidden_states_147_cast_fp16 = conv(bias = up_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = var_2200, groups = var_2128, pad = hidden_states_147_pad_0, pad_type = hidden_states_147_pad_type_0, strides = var_2198, weight = up_blocks_0_resnets_1_conv1_weight_to_fp16, x = input_257_cast_fp16)[name = tensor<string, []>("hidden_states_147_cast_fp16")];
            tensor<int32, [2]> var_2206 = const()[name = tensor<string, []>("op_2206"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2208 = const()[name = tensor<string, []>("op_2208"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> temb_23_pad_type_0 = const()[name = tensor<string, []>("temb_23_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> temb_23_pad_0 = const()[name = tensor<string, []>("temb_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(860689216)))];
            tensor<fp16, [1280]> up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(863966080)))];
            tensor<fp16, [2, 1280, 1, 1]> temb_23_cast_fp16 = conv(bias = up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_2208, groups = var_2128, pad = temb_23_pad_0, pad_type = temb_23_pad_type_0, strides = var_2206, weight = up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16, x = input_15_cast_fp16_1)[name = tensor<string, []>("temb_23_cast_fp16")];
            tensor<fp16, [2, 1280, 6, 10]> input_261_cast_fp16 = add(x = hidden_states_147_cast_fp16, y = temb_23_cast_fp16)[name = tensor<string, []>("input_261_cast_fp16")];
            tensor<int32, [5]> reshape_120_shape_0 = const()[name = tensor<string, []>("reshape_120_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 6, 10])];
            tensor<fp16, [2, 32, 40, 6, 10]> reshape_120_cast_fp16 = reshape(shape = reshape_120_shape_0, x = input_261_cast_fp16)[name = tensor<string, []>("reshape_120_cast_fp16")];
            tensor<int32, [3]> reduce_mean_90_axes_0 = const()[name = tensor<string, []>("reduce_mean_90_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_90_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_90_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_90_cast_fp16 = reduce_mean(axes = reduce_mean_90_axes_0, keep_dims = reduce_mean_90_keep_dims_0, x = reshape_120_cast_fp16)[name = tensor<string, []>("reduce_mean_90_cast_fp16")];
            tensor<fp16, [2, 32, 40, 6, 10]> sub_60_cast_fp16 = sub(x = reshape_120_cast_fp16, y = reduce_mean_90_cast_fp16)[name = tensor<string, []>("sub_60_cast_fp16")];
            tensor<fp16, [2, 32, 40, 6, 10]> square_30_cast_fp16 = square(x = sub_60_cast_fp16)[name = tensor<string, []>("square_30_cast_fp16")];
            tensor<int32, [3]> reduce_mean_92_axes_0 = const()[name = tensor<string, []>("reduce_mean_92_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
            tensor<bool, []> reduce_mean_92_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_92_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [2, 32, 1, 1, 1]> reduce_mean_92_cast_fp16 = reduce_mean(axes = reduce_mean_92_axes_0, keep_dims = reduce_mean_92_keep_dims_0, x = square_30_cast_fp16)[name = tensor<string, []>("reduce_mean_92_cast_fp16")];
            tensor<fp16, []> add_60_y_0_to_fp16 = const()[name = tensor<string, []>("add_60_y_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 32, 1, 1, 1]> add_60_cast_fp16 = add(x = reduce_mean_92_cast_fp16, y = add_60_y_0_to_fp16)[name = tensor<string, []>("add_60_cast_fp16")];
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_30_cast_fp16 = sqrt(x = add_60_cast_fp16)[name = tensor<string, []>("sqrt_30_cast_fp16")];
            tensor<fp16, [2, 32, 40, 6, 10]> real_div_30_cast_fp16 = real_div(x = sub_60_cast_fp16, y = sqrt_30_cast_fp16)[name = tensor<string, []>("real_div_30_cast_fp16")];
            tensor<int32, [4]> reshape_121_shape_0 = const()[name = tensor<string, []>("reshape_121_shape_0"), val = tensor<int32, [4]>([2, 1280, 6, 10])];
            tensor<fp16, [2, 1280, 6, 10]> reshape_121_cast_fp16 = reshape(shape = reshape_121_shape_0, x = real_div_30_cast_fp16)[name = tensor<string, []>("reshape_121_cast_fp16")];
            tensor<fp16, [1280]> add_61_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_61_gamma_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(863968704)))];
            tensor<fp16, [1280]> add_61_beta_0_to_fp16 = const()[name = tensor<string, []>("add_61_beta_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(863971328)))];
            tensor<fp16, []> add_61_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_61_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [2, 1280, 6, 10]> add_61_cast_fp16 = batch_norm(beta = add_61_beta_0_to_fp16, epsilon = add_61_epsilon_0_to_fp16, gamma = add_61_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_121_cast_fp16)[name = tensor<string, []>("add_61_cast_fp16")];
            tensor<fp16, [2, 1280, 6, 10]> input_265_cast_fp16 = silu(x = add_61_cast_fp16)[name = tensor<string, []>("input_265_cast_fp16")];
            tensor<int32, [2]> var_2218 = const()[name = tensor<string, []>("op_2218"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_2220 = const()[name = tensor<string, []>("op_2220"), val = tensor<int32, [2]>([1, 1])];
            tensor<string, []> hidden_states_149_pad_type_0 = const()[name = tensor<string, []>("hidden_states_149_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> hidden_states_149_pad_0 = const()[name = tensor<string, []>("hidden_states_149_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<fp16, [1280, 1280, 3, 3]> up_blocks_0_resnets_1_conv2_weight_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_resnets_1_conv2_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(863973952)))];
            tensor<fp16, [1280]> up_blocks_0_resnets_1_conv2_bias_to_fp16 = const()[name = tensor<string, []>("up_blocks_0_resnets_1_conv2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(893465216)))];
            tensor<fp16, [2, 1280, 6, 10]> hidden_states_149_cast_fp16_1 = conv(bias = up_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = var_2220, groups = var_2128, pad = hidden_states_149_pad_0, pad_type = hidden_states_149_pad_type_0, strides = var_2218, weight = up_blocks_0_resnets_1_conv2_weight_to_fp16, x = input_265_cast_fp16)[name = tensor<string, []>("hidden_states_149_cast_fp16")];
            tensor<string, []> input_61_cast_fp16_dtype_0 = const()[name = tensor<string, []>("input_61_cast_fp16_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<string, []> hidden_states_149_cast_fp16_dtype_0 = const()[name = tensor<string, []>("hidden_states_149_cast_fp16_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<string, []> input_15_cast_fp16_dtype_0 = const()[name = tensor<string, []>("input_15_cast_fp16_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<string, []> input_117_cast_fp16_dtype_0 = const()[name = tensor<string, []>("input_117_cast_fp16_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<string, []> input_115_cast_fp16_dtype_0 = const()[name = tensor<string, []>("input_115_cast_fp16_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<string, []> input_7_cast_fp16_dtype_0 = const()[name = tensor<string, []>("input_7_cast_fp16_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<string, []> input_171_cast_fp16_dtype_0 = const()[name = tensor<string, []>("input_171_cast_fp16_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<string, []> input_169_cast_fp16_dtype_0 = const()[name = tensor<string, []>("input_169_cast_fp16_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<string, []> input_253_cast_fp16_dtype_0 = const()[name = tensor<string, []>("input_253_cast_fp16_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<string, []> input_35_cast_fp16_dtype_0 = const()[name = tensor<string, []>("input_35_cast_fp16_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<string, []> input_89_cast_fp16_dtype_0 = const()[name = tensor<string, []>("input_89_cast_fp16_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<string, []> input_143_cast_fp16_dtype_0 = const()[name = tensor<string, []>("input_143_cast_fp16_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<string, []> input_63_cast_fp16_dtype_0 = const()[name = tensor<string, []>("input_63_cast_fp16_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<fp32, [2, 320, 24, 40]> input_63_cast_fp16 = cast(dtype = input_63_cast_fp16_dtype_0, x = input_63_cast_fp16_1)[name = tensor<string, []>("cast_13")];
            tensor<fp32, [2, 1280, 12, 20]> input_143_cast_fp16 = cast(dtype = input_143_cast_fp16_dtype_0, x = input_143_cast_fp16_1)[name = tensor<string, []>("cast_14")];
            tensor<fp32, [2, 640, 24, 40]> input_89_cast_fp16 = cast(dtype = input_89_cast_fp16_dtype_0, x = input_89_cast_fp16_1)[name = tensor<string, []>("cast_15")];
            tensor<fp32, [2, 320, 48, 80]> input_35_cast_fp16 = cast(dtype = input_35_cast_fp16_dtype_0, x = input_35_cast_fp16_1)[name = tensor<string, []>("cast_16")];
            tensor<fp32, [2, 2560, 6, 10]> input_253_cast_fp16 = cast(dtype = input_253_cast_fp16_dtype_0, x = input_253_cast_fp16_1)[name = tensor<string, []>("cast_17")];
            tensor<fp32, [2, 1280, 12, 20]> input_169_cast_fp16 = cast(dtype = input_169_cast_fp16_dtype_0, x = input_169_cast_fp16_1)[name = tensor<string, []>("cast_18")];
            tensor<fp32, [2, 1280, 6, 10]> input_171_cast_fp16 = cast(dtype = input_171_cast_fp16_dtype_0, x = input_171_cast_fp16_1)[name = tensor<string, []>("cast_19")];
            tensor<fp32, [2, 320, 48, 80]> input_7_cast_fp16 = cast(dtype = input_7_cast_fp16_dtype_0, x = input_7_cast_fp16_1)[name = tensor<string, []>("cast_20")];
            tensor<fp32, [2, 640, 24, 40]> input_115_cast_fp16 = cast(dtype = input_115_cast_fp16_dtype_0, x = input_115_cast_fp16_1)[name = tensor<string, []>("cast_21")];
            tensor<fp32, [2, 640, 12, 20]> input_117_cast_fp16 = cast(dtype = input_117_cast_fp16_dtype_0, x = input_117_cast_fp16_1)[name = tensor<string, []>("cast_22")];
            tensor<fp32, [2, 1280, 1, 1]> input_15_cast_fp16 = cast(dtype = input_15_cast_fp16_dtype_0, x = input_15_cast_fp16_1)[name = tensor<string, []>("cast_23")];
            tensor<fp32, [2, 1280, 6, 10]> hidden_states_149_cast_fp16 = cast(dtype = hidden_states_149_cast_fp16_dtype_0, x = hidden_states_149_cast_fp16_1)[name = tensor<string, []>("cast_24")];
            tensor<fp32, [2, 320, 48, 80]> input_61_cast_fp16 = cast(dtype = input_61_cast_fp16_dtype_0, x = input_61_cast_fp16_1)[name = tensor<string, []>("cast_25")];
        } -> (input_61_cast_fp16, hidden_states_149_cast_fp16, input_15_cast_fp16, input_117_cast_fp16, input_115_cast_fp16, input_7_cast_fp16, input_171_cast_fp16, input_169_cast_fp16, input_253_cast_fp16, input_35_cast_fp16, input_89_cast_fp16, input_143_cast_fp16, input_63_cast_fp16);
}