program(1.0)
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "4.28.4"}, {"coremlc-version", "1429.0.0"}})]
{
    func main<ios16>(tensor<fp16, [2, 1024, 1, 77]> encoder_hidden_states, tensor<fp16, [2, 4, 64, 64]> 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 = expand_dims(axes = var_42_axes_0, x = timestep);
            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 = mul(x = var_42_cast, y = var_44_to_fp16);
            tensor<fp16, [2, 160]> var_49_cast = sin(x = emb_3_cast);
            tensor<fp16, [2, 160]> var_50_cast = cos(x = emb_3_cast);
            tensor<bool, []> emb_interleave_0 = const()[name = tensor<string, []>("emb_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 320]> emb_cast = concat(axis = var_25, interleave = emb_interleave_0, values = (var_49_cast, var_50_cast));
            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 = slice_by_index(begin = var_54_begin_0, end = var_54_end_0, end_mask = var_54_end_mask_0, x = emb_cast);
            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 = slice_by_index(begin = var_56_begin_0, end = var_56_end_0, end_mask = var_56_end_mask_0, x = emb_cast);
            tensor<bool, []> sample_interleave_0 = const()[name = tensor<string, []>("sample_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 320]> sample_cast = concat(axis = var_25, interleave = sample_interleave_0, values = (var_54_cast, var_56_cast));
            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 = expand_dims(axes = var_66_axes_0, x = sample_cast);
            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 = expand_dims(axes = input_1_axes_0, x = var_66_cast);
            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 = 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);
            tensor<fp16, [2, 1280, 1, 1]> input_5_cast = silu(x = input_3_cast);
            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 = 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);
            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, 64, 64]> input_7_cast_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);
            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, 64, 64])];
            tensor<fp16, [2, 32, 10, 64, 64]> reshape_0_cast = reshape(shape = reshape_0_shape_0, x = input_7_cast_1);
            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 = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = reshape_0_cast);
            tensor<fp16, [2, 32, 10, 64, 64]> sub_0_cast = sub(x = reshape_0_cast, y = reduce_mean_0_cast);
            tensor<fp16, [2, 32, 10, 64, 64]> square_0_cast = square(x = sub_0_cast);
            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 = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = square_0_cast);
            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 = add(x = reduce_mean_2_cast, y = add_0_y_0_to_fp16);
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_0_cast = sqrt(x = add_0_cast);
            tensor<fp16, [2, 32, 10, 64, 64]> real_div_0_cast = real_div(x = sub_0_cast, y = sqrt_0_cast);
            tensor<int32, [4]> reshape_1_shape_0 = const()[name = tensor<string, []>("reshape_1_shape_0"), val = tensor<int32, [4]>([2, 320, 64, 64])];
            tensor<fp16, [2, 320, 64, 64]> reshape_1_cast = reshape(shape = reshape_1_shape_0, x = real_div_0_cast);
            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, 64, 64]> add_1_cast = 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);
            tensor<fp16, [2, 320, 64, 64]> input_11_cast = silu(x = add_1_cast);
            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, 64, 64]> hidden_states_1_cast = 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);
            tensor<fp16, [2, 1280, 1, 1]> input_15_cast_1 = silu(x = input_13_cast);
            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 = 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_1);
            tensor<fp16, [2, 320, 64, 64]> input_17_cast = add(x = hidden_states_1_cast, y = temb_1_cast);
            tensor<int32, [5]> reshape_4_shape_0 = const()[name = tensor<string, []>("reshape_4_shape_0"), val = tensor<int32, [5]>([2, 32, 10, 64, 64])];
            tensor<fp16, [2, 32, 10, 64, 64]> reshape_4_cast = reshape(shape = reshape_4_shape_0, x = input_17_cast);
            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 = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = reshape_4_cast);
            tensor<fp16, [2, 32, 10, 64, 64]> sub_2_cast = sub(x = reshape_4_cast, y = reduce_mean_3_cast);
            tensor<fp16, [2, 32, 10, 64, 64]> square_1_cast = square(x = sub_2_cast);
            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 = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_1_cast);
            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 = add(x = reduce_mean_5_cast, y = add_2_y_0_to_fp16);
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_1_cast = sqrt(x = add_2_cast);
            tensor<fp16, [2, 32, 10, 64, 64]> real_div_1_cast = real_div(x = sub_2_cast, y = sqrt_1_cast);
            tensor<int32, [4]> reshape_5_shape_0 = const()[name = tensor<string, []>("reshape_5_shape_0"), val = tensor<int32, [4]>([2, 320, 64, 64])];
            tensor<fp16, [2, 320, 64, 64]> reshape_5_cast = reshape(shape = reshape_5_shape_0, x = real_div_1_cast);
            tensor<fp16, [320]> add_3_mean_0_to_fp16 = const()[name = tensor<string, []>("add_3_mean_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_variance_0_to_fp16 = const()[name = tensor<string, []>("add_3_variance_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6793088)))];
            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, []>(6793792)))];
            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, []>(6794496)))];
            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, 64, 64]> add_3_cast = 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_3_mean_0_to_fp16, variance = add_3_variance_0_to_fp16, x = reshape_5_cast);
            tensor<fp16, [2, 320, 64, 64]> input_21_cast = silu(x = add_3_cast);
            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, []>(6795200)))];
            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, []>(8638464)))];
            tensor<fp16, [2, 320, 64, 64]> hidden_states_3_cast = 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);
            tensor<fp16, [2, 320, 64, 64]> hidden_states_5_cast = add(x = input_7_cast_1, y = hidden_states_3_cast);
            tensor<int32, [5]> reshape_8_shape_0 = const()[name = tensor<string, []>("reshape_8_shape_0"), val = tensor<int32, [5]>([2, 32, 10, 64, 64])];
            tensor<fp16, [2, 32, 10, 64, 64]> reshape_8_cast = reshape(shape = reshape_8_shape_0, x = hidden_states_5_cast);
            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 = reduce_mean(axes = reduce_mean_6_axes_0, keep_dims = reduce_mean_6_keep_dims_0, x = reshape_8_cast);
            tensor<fp16, [2, 32, 10, 64, 64]> sub_4_cast = sub(x = reshape_8_cast, y = reduce_mean_6_cast);
            tensor<fp16, [2, 32, 10, 64, 64]> square_2_cast = square(x = sub_4_cast);
            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 = reduce_mean(axes = reduce_mean_8_axes_0, keep_dims = reduce_mean_8_keep_dims_0, x = square_2_cast);
            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 = add(x = reduce_mean_8_cast, y = add_4_y_0_to_fp16);
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_2_cast = sqrt(x = add_4_cast);
            tensor<fp16, [2, 32, 10, 64, 64]> real_div_2_cast = real_div(x = sub_4_cast, y = sqrt_2_cast);
            tensor<int32, [4]> reshape_9_shape_0 = const()[name = tensor<string, []>("reshape_9_shape_0"), val = tensor<int32, [4]>([2, 320, 64, 64])];
            tensor<fp16, [2, 320, 64, 64]> reshape_9_cast = reshape(shape = reshape_9_shape_0, x = real_div_2_cast);
            tensor<fp16, [320]> add_5_mean_0_to_fp16 = const()[name = tensor<string, []>("add_5_mean_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8639168)))];
            tensor<fp16, [320]> add_5_variance_0_to_fp16 = const()[name = tensor<string, []>("add_5_variance_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8639872)))];
            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, []>(8640576)))];
            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, []>(8641280)))];
            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, 64, 64]> add_5_cast = 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_5_mean_0_to_fp16, variance = add_5_variance_0_to_fp16, x = reshape_9_cast);
            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, []>(8641984)))];
            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, []>(8846848)))];
            tensor<fp16, [2, 320, 64, 64]> hidden_states_7_cast = 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);
            tensor<int32, [4]> var_182 = const()[name = tensor<string, []>("op_182"), val = tensor<int32, [4]>([2, 320, 1, 4096])];
            tensor<fp16, [2, 320, 1, 4096]> inputs_1_cast = reshape(shape = var_182, x = hidden_states_7_cast);
            tensor<int32, [1]> var_192 = const()[name = tensor<string, []>("op_192"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_1_cast = reduce_mean(axes = var_192, keep_dims = var_106, x = inputs_1_cast);
            tensor<fp16, [2, 320, 1, 4096]> zero_mean_1_cast = sub(x = inputs_1_cast, y = channels_mean_1_cast);
            tensor<fp16, [2, 320, 1, 4096]> zero_mean_sq_1_cast = mul(x = zero_mean_1_cast, y = zero_mean_1_cast);
            tensor<int32, [1]> var_196 = const()[name = tensor<string, []>("op_196"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_197_cast = reduce_mean(axes = var_196, keep_dims = var_106, x = zero_mean_sq_1_cast);
            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, 4096]> var_199_cast = add(x = var_197_cast, y = var_198_to_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, 4096]> denom_1_cast = rsqrt(epsilon = denom_1_epsilon_0_to_fp16, x = var_199_cast);
            tensor<fp16, [2, 320, 1, 4096]> out_1_cast = mul(x = zero_mean_1_cast, y = denom_1_cast);
            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, []>(8847552)))];
            tensor<fp16, [2, 320, 1, 4096]> var_204_cast = add(x = out_1_cast, y = var_203_to_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, []>(8848256)))];
            tensor<fp16, [2, 320, 1, 4096]> hidden_states_9_cast = mul(x = var_204_cast, y = var_206_to_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, []>(8848960)))];
            tensor<fp16, [2, 320, 1, 4096]> q_1_cast = 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);
            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, []>(9053824)))];
            tensor<fp16, [2, 320, 1, 4096]> k_1_cast = 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);
            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, []>(9258688)))];
            tensor<fp16, [2, 320, 1, 4096]> v_1_cast = 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);
            tensor<int32, [4]> var_231 = const()[name = tensor<string, []>("op_231"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 4096]> var_232_cast = reshape(shape = var_231, x = q_1_cast);
            tensor<int32, [4]> var_233 = const()[name = tensor<string, []>("op_233"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 4096]> var_234_cast = reshape(shape = var_233, x = k_1_cast);
            tensor<int32, [4]> var_235 = const()[name = tensor<string, []>("op_235"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 4096]> var_236_cast = reshape(shape = var_235, x = v_1_cast);
            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, 4096, 4096]> attn_weights_1_cast = matmul(transpose_x = attn_weights_1_transpose_x_0, transpose_y = attn_weights_1_transpose_y_0, x = var_232_cast, y = var_234_cast);
            tensor<fp16, []> var_102_to_fp16 = const()[name = tensor<string, []>("op_102_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [2, 5, 4096, 4096]> attn_weights_3_cast = mul(x = attn_weights_1_cast, y = var_102_to_fp16);
            tensor<fp16, [2, 5, 4096, 4096]> var_240_cast = softmax(axis = var_95, x = attn_weights_3_cast);
            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, 4096]> attn_1_cast = matmul(transpose_x = attn_1_transpose_x_0, transpose_y = attn_1_transpose_y_0, x = var_236_cast, y = var_240_cast);
            tensor<int32, [4]> var_244 = const()[name = tensor<string, []>("op_244"), val = tensor<int32, [4]>([2, 320, 1, -1])];
            tensor<fp16, [2, 320, 1, 4096]> input_25_cast = reshape(shape = var_244, x = attn_1_cast);
            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, []>(9463552)))];
            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, []>(9668416)))];
            tensor<fp16, [2, 320, 1, 4096]> var_253_cast = 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);
            tensor<fp16, [2, 320, 1, 4096]> inputs_3_cast = add(x = var_253_cast, y = inputs_1_cast);
            tensor<int32, [1]> var_257 = const()[name = tensor<string, []>("op_257"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_3_cast = reduce_mean(axes = var_257, keep_dims = var_106, x = inputs_3_cast);
            tensor<fp16, [2, 320, 1, 4096]> zero_mean_3_cast = sub(x = inputs_3_cast, y = channels_mean_3_cast);
            tensor<fp16, [2, 320, 1, 4096]> zero_mean_sq_3_cast = mul(x = zero_mean_3_cast, y = zero_mean_3_cast);
            tensor<int32, [1]> var_261 = const()[name = tensor<string, []>("op_261"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_262_cast = reduce_mean(axes = var_261, keep_dims = var_106, x = zero_mean_sq_3_cast);
            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, 4096]> var_264_cast = add(x = var_262_cast, y = var_263_to_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, 4096]> denom_3_cast = rsqrt(epsilon = denom_3_epsilon_0_to_fp16, x = var_264_cast);
            tensor<fp16, [2, 320, 1, 4096]> out_3_cast = mul(x = zero_mean_3_cast, y = denom_3_cast);
            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, []>(9669120)))];
            tensor<fp16, [2, 320, 1, 4096]> var_269_cast = add(x = out_3_cast, y = var_268_to_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, []>(9669824)))];
            tensor<fp16, [2, 320, 1, 4096]> hidden_states_11_cast = mul(x = var_269_cast, y = var_271_to_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, []>(9670528)))];
            tensor<fp16, [2, 320, 1, 4096]> q_3_cast = 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);
            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, []>(9875392)))];
            tensor<fp16, [2, 320, 1, 77]> k_3_cast = 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);
            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, []>(10530816)))];
            tensor<fp16, [2, 320, 1, 77]> v_3_cast = 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);
            tensor<int32, [4]> var_296 = const()[name = tensor<string, []>("op_296"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 4096]> var_297_cast = reshape(shape = var_296, x = q_3_cast);
            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 = reshape(shape = var_298, x = k_3_cast);
            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 = reshape(shape = var_300, x = v_3_cast);
            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, 4096, 77]> attn_weights_5_cast = matmul(transpose_x = attn_weights_5_transpose_x_0, transpose_y = attn_weights_5_transpose_y_0, x = var_297_cast, y = var_299_cast);
            tensor<fp16, [2, 5, 4096, 77]> attn_weights_7_cast = mul(x = attn_weights_5_cast, y = var_102_to_fp16);
            tensor<fp16, [2, 5, 4096, 77]> var_305_cast = softmax(axis = var_95, x = attn_weights_7_cast);
            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, 4096]> attn_3_cast = matmul(transpose_x = attn_3_transpose_x_0, transpose_y = attn_3_transpose_y_0, x = var_301_cast, y = var_305_cast);
            tensor<int32, [4]> var_309 = const()[name = tensor<string, []>("op_309"), val = tensor<int32, [4]>([2, 320, 1, -1])];
            tensor<fp16, [2, 320, 1, 4096]> input_27_cast = reshape(shape = var_309, x = attn_3_cast);
            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, []>(11186240)))];
            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, []>(11391104)))];
            tensor<fp16, [2, 320, 1, 4096]> var_318_cast = 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);
            tensor<fp16, [2, 320, 1, 4096]> inputs_5_cast = add(x = var_318_cast, y = inputs_3_cast);
            tensor<int32, [1]> var_322 = const()[name = tensor<string, []>("op_322"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_5_cast = reduce_mean(axes = var_322, keep_dims = var_106, x = inputs_5_cast);
            tensor<fp16, [2, 320, 1, 4096]> zero_mean_5_cast = sub(x = inputs_5_cast, y = channels_mean_5_cast);
            tensor<fp16, [2, 320, 1, 4096]> zero_mean_sq_5_cast = mul(x = zero_mean_5_cast, y = zero_mean_5_cast);
            tensor<int32, [1]> var_326 = const()[name = tensor<string, []>("op_326"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_327_cast = reduce_mean(axes = var_326, keep_dims = var_106, x = zero_mean_sq_5_cast);
            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, 4096]> var_329_cast = add(x = var_327_cast, y = var_328_to_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, 4096]> denom_5_cast = rsqrt(epsilon = denom_5_epsilon_0_to_fp16, x = var_329_cast);
            tensor<fp16, [2, 320, 1, 4096]> out_5_cast = mul(x = zero_mean_5_cast, y = denom_5_cast);
            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, []>(11391808)))];
            tensor<fp16, [2, 320, 1, 4096]> var_334_cast = add(x = out_5_cast, y = var_333_to_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, []>(11392512)))];
            tensor<fp16, [2, 320, 1, 4096]> input_29_cast = mul(x = var_334_cast, y = var_336_to_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, []>(11393216)))];
            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, []>(13031680)))];
            tensor<fp16, [2, 2560, 1, 4096]> var_348_cast = 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);
            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, 4096]> var_349_cast_0, tensor<fp16, [2, 1280, 1, 4096]> var_349_cast_1 = split(axis = var_349_axis_0, split_sizes = var_349_split_sizes_0, x = var_348_cast);
            tensor<string, []> var_351_mode_0 = const()[name = tensor<string, []>("op_351_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 1280, 1, 4096]> var_351_cast = gelu(mode = var_351_mode_0, x = var_349_cast_1);
            tensor<fp16, [2, 1280, 1, 4096]> input_31_cast = mul(x = var_349_cast_0, y = var_351_cast);
            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, []>(13036864)))];
            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, []>(13856128)))];
            tensor<fp16, [2, 320, 1, 4096]> var_359_cast = 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);
            tensor<fp16, [2, 320, 1, 4096]> hidden_states_15_cast = add(x = var_359_cast, y = inputs_5_cast);
            tensor<int32, [4]> var_361 = const()[name = tensor<string, []>("op_361"), val = tensor<int32, [4]>([2, 320, 64, 64])];
            tensor<fp16, [2, 320, 64, 64]> input_33_cast = reshape(shape = var_361, x = hidden_states_15_cast);
            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, []>(13856832)))];
            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, []>(14061696)))];
            tensor<fp16, [2, 320, 64, 64]> hidden_states_17_cast = 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);
            tensor<fp16, [2, 320, 64, 64]> input_35_cast_1 = add(x = hidden_states_17_cast, y = hidden_states_5_cast);
            tensor<int32, [5]> reshape_12_shape_0 = const()[name = tensor<string, []>("reshape_12_shape_0"), val = tensor<int32, [5]>([2, 32, 10, 64, 64])];
            tensor<fp16, [2, 32, 10, 64, 64]> reshape_12_cast = reshape(shape = reshape_12_shape_0, x = input_35_cast_1);
            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 = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = reshape_12_cast);
            tensor<fp16, [2, 32, 10, 64, 64]> sub_6_cast = sub(x = reshape_12_cast, y = reduce_mean_9_cast);
            tensor<fp16, [2, 32, 10, 64, 64]> square_3_cast = square(x = sub_6_cast);
            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 = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_3_cast);
            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 = add(x = reduce_mean_11_cast, y = add_6_y_0_to_fp16);
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_3_cast = sqrt(x = add_6_cast);
            tensor<fp16, [2, 32, 10, 64, 64]> real_div_3_cast = real_div(x = sub_6_cast, y = sqrt_3_cast);
            tensor<int32, [4]> reshape_13_shape_0 = const()[name = tensor<string, []>("reshape_13_shape_0"), val = tensor<int32, [4]>([2, 320, 64, 64])];
            tensor<fp16, [2, 320, 64, 64]> reshape_13_cast = reshape(shape = reshape_13_shape_0, x = real_div_3_cast);
            tensor<fp16, [320]> add_7_mean_0_to_fp16 = const()[name = tensor<string, []>("add_7_mean_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14062400)))];
            tensor<fp16, [320]> add_7_variance_0_to_fp16 = const()[name = tensor<string, []>("add_7_variance_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14063104)))];
            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, []>(14063808)))];
            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, []>(14064512)))];
            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, 64, 64]> add_7_cast = 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_7_mean_0_to_fp16, variance = add_7_variance_0_to_fp16, x = reshape_13_cast);
            tensor<fp16, [2, 320, 64, 64]> input_39_cast = silu(x = add_7_cast);
            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, []>(14065216)))];
            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, []>(15908480)))];
            tensor<fp16, [2, 320, 64, 64]> hidden_states_19_cast = 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);
            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, []>(15909184)))];
            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, []>(16728448)))];
            tensor<fp16, [2, 320, 1, 1]> temb_3_cast = 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_1);
            tensor<fp16, [2, 320, 64, 64]> input_43_cast = add(x = hidden_states_19_cast, y = temb_3_cast);
            tensor<int32, [5]> reshape_16_shape_0 = const()[name = tensor<string, []>("reshape_16_shape_0"), val = tensor<int32, [5]>([2, 32, 10, 64, 64])];
            tensor<fp16, [2, 32, 10, 64, 64]> reshape_16_cast = reshape(shape = reshape_16_shape_0, x = input_43_cast);
            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 = reduce_mean(axes = reduce_mean_12_axes_0, keep_dims = reduce_mean_12_keep_dims_0, x = reshape_16_cast);
            tensor<fp16, [2, 32, 10, 64, 64]> sub_8_cast = sub(x = reshape_16_cast, y = reduce_mean_12_cast);
            tensor<fp16, [2, 32, 10, 64, 64]> square_4_cast = square(x = sub_8_cast);
            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 = reduce_mean(axes = reduce_mean_14_axes_0, keep_dims = reduce_mean_14_keep_dims_0, x = square_4_cast);
            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 = add(x = reduce_mean_14_cast, y = add_8_y_0_to_fp16);
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_4_cast = sqrt(x = add_8_cast);
            tensor<fp16, [2, 32, 10, 64, 64]> real_div_4_cast = real_div(x = sub_8_cast, y = sqrt_4_cast);
            tensor<int32, [4]> reshape_17_shape_0 = const()[name = tensor<string, []>("reshape_17_shape_0"), val = tensor<int32, [4]>([2, 320, 64, 64])];
            tensor<fp16, [2, 320, 64, 64]> reshape_17_cast = reshape(shape = reshape_17_shape_0, x = real_div_4_cast);
            tensor<fp16, [320]> add_9_mean_0_to_fp16 = const()[name = tensor<string, []>("add_9_mean_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16729152)))];
            tensor<fp16, [320]> add_9_variance_0_to_fp16 = const()[name = tensor<string, []>("add_9_variance_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16729856)))];
            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, []>(16730560)))];
            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, []>(16731264)))];
            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, 64, 64]> add_9_cast = 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_9_mean_0_to_fp16, variance = add_9_variance_0_to_fp16, x = reshape_17_cast);
            tensor<fp16, [2, 320, 64, 64]> input_47_cast = silu(x = add_9_cast);
            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, []>(16731968)))];
            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, []>(18575232)))];
            tensor<fp16, [2, 320, 64, 64]> hidden_states_21_cast = 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);
            tensor<fp16, [2, 320, 64, 64]> hidden_states_23_cast = add(x = input_35_cast_1, y = hidden_states_21_cast);
            tensor<int32, [5]> reshape_20_shape_0 = const()[name = tensor<string, []>("reshape_20_shape_0"), val = tensor<int32, [5]>([2, 32, 10, 64, 64])];
            tensor<fp16, [2, 32, 10, 64, 64]> reshape_20_cast = reshape(shape = reshape_20_shape_0, x = hidden_states_23_cast);
            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 = reduce_mean(axes = reduce_mean_15_axes_0, keep_dims = reduce_mean_15_keep_dims_0, x = reshape_20_cast);
            tensor<fp16, [2, 32, 10, 64, 64]> sub_10_cast = sub(x = reshape_20_cast, y = reduce_mean_15_cast);
            tensor<fp16, [2, 32, 10, 64, 64]> square_5_cast = square(x = sub_10_cast);
            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 = reduce_mean(axes = reduce_mean_17_axes_0, keep_dims = reduce_mean_17_keep_dims_0, x = square_5_cast);
            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 = add(x = reduce_mean_17_cast, y = add_10_y_0_to_fp16);
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_5_cast = sqrt(x = add_10_cast);
            tensor<fp16, [2, 32, 10, 64, 64]> real_div_5_cast = real_div(x = sub_10_cast, y = sqrt_5_cast);
            tensor<int32, [4]> reshape_21_shape_0 = const()[name = tensor<string, []>("reshape_21_shape_0"), val = tensor<int32, [4]>([2, 320, 64, 64])];
            tensor<fp16, [2, 320, 64, 64]> reshape_21_cast = reshape(shape = reshape_21_shape_0, x = real_div_5_cast);
            tensor<fp16, [320]> add_11_mean_0_to_fp16 = const()[name = tensor<string, []>("add_11_mean_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18575936)))];
            tensor<fp16, [320]> add_11_variance_0_to_fp16 = const()[name = tensor<string, []>("add_11_variance_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18576640)))];
            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, []>(18577344)))];
            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, []>(18578048)))];
            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, 64, 64]> add_11_cast = batch_norm(beta = add_11_beta_0_to_fp16, epsilon = add_11_epsilon_0_to_fp16, gamma = add_11_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_21_cast);
            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, []>(18578752)))];
            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, []>(18783616)))];
            tensor<fp16, [2, 320, 64, 64]> hidden_states_25_cast = 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);
            tensor<int32, [4]> var_431 = const()[name = tensor<string, []>("op_431"), val = tensor<int32, [4]>([2, 320, 1, 4096])];
            tensor<fp16, [2, 320, 1, 4096]> inputs_7_cast = reshape(shape = var_431, x = hidden_states_25_cast);
            tensor<int32, [1]> var_441 = const()[name = tensor<string, []>("op_441"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_7_cast = reduce_mean(axes = var_441, keep_dims = var_106, x = inputs_7_cast);
            tensor<fp16, [2, 320, 1, 4096]> zero_mean_7_cast = sub(x = inputs_7_cast, y = channels_mean_7_cast);
            tensor<fp16, [2, 320, 1, 4096]> zero_mean_sq_7_cast = mul(x = zero_mean_7_cast, y = zero_mean_7_cast);
            tensor<int32, [1]> var_445 = const()[name = tensor<string, []>("op_445"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_446_cast = reduce_mean(axes = var_445, keep_dims = var_106, x = zero_mean_sq_7_cast);
            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, 4096]> var_448_cast = add(x = var_446_cast, y = var_447_to_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, 4096]> denom_7_cast = rsqrt(epsilon = denom_7_epsilon_0_to_fp16, x = var_448_cast);
            tensor<fp16, [2, 320, 1, 4096]> out_7_cast = mul(x = zero_mean_7_cast, y = denom_7_cast);
            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, []>(18784320)))];
            tensor<fp16, [2, 320, 1, 4096]> var_453_cast = add(x = out_7_cast, y = var_452_to_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, []>(18785024)))];
            tensor<fp16, [2, 320, 1, 4096]> hidden_states_27_cast = mul(x = var_453_cast, y = var_455_to_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, []>(18785728)))];
            tensor<fp16, [2, 320, 1, 4096]> q_5_cast = 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);
            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, []>(18990592)))];
            tensor<fp16, [2, 320, 1, 4096]> k_5_cast = 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);
            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, []>(19195456)))];
            tensor<fp16, [2, 320, 1, 4096]> v_5_cast = 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);
            tensor<int32, [4]> var_480 = const()[name = tensor<string, []>("op_480"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 4096]> var_481_cast = reshape(shape = var_480, x = q_5_cast);
            tensor<int32, [4]> var_482 = const()[name = tensor<string, []>("op_482"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 4096]> var_483_cast = reshape(shape = var_482, x = k_5_cast);
            tensor<int32, [4]> var_484 = const()[name = tensor<string, []>("op_484"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 4096]> var_485_cast = reshape(shape = var_484, x = v_5_cast);
            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, 4096, 4096]> attn_weights_9_cast = matmul(transpose_x = attn_weights_9_transpose_x_0, transpose_y = attn_weights_9_transpose_y_0, x = var_481_cast, y = var_483_cast);
            tensor<fp16, [2, 5, 4096, 4096]> attn_weights_11_cast = mul(x = attn_weights_9_cast, y = var_102_to_fp16);
            tensor<fp16, [2, 5, 4096, 4096]> var_489_cast = softmax(axis = var_95, x = attn_weights_11_cast);
            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, 4096]> attn_5_cast = matmul(transpose_x = attn_5_transpose_x_0, transpose_y = attn_5_transpose_y_0, x = var_485_cast, y = var_489_cast);
            tensor<int32, [4]> var_493 = const()[name = tensor<string, []>("op_493"), val = tensor<int32, [4]>([2, 320, 1, -1])];
            tensor<fp16, [2, 320, 1, 4096]> input_51_cast = reshape(shape = var_493, x = attn_5_cast);
            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, []>(19400320)))];
            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, []>(19605184)))];
            tensor<fp16, [2, 320, 1, 4096]> var_502_cast = 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);
            tensor<fp16, [2, 320, 1, 4096]> inputs_9_cast = add(x = var_502_cast, y = inputs_7_cast);
            tensor<int32, [1]> var_506 = const()[name = tensor<string, []>("op_506"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_9_cast = reduce_mean(axes = var_506, keep_dims = var_106, x = inputs_9_cast);
            tensor<fp16, [2, 320, 1, 4096]> zero_mean_9_cast = sub(x = inputs_9_cast, y = channels_mean_9_cast);
            tensor<fp16, [2, 320, 1, 4096]> zero_mean_sq_9_cast = mul(x = zero_mean_9_cast, y = zero_mean_9_cast);
            tensor<int32, [1]> var_510 = const()[name = tensor<string, []>("op_510"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_511_cast = reduce_mean(axes = var_510, keep_dims = var_106, x = zero_mean_sq_9_cast);
            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, 4096]> var_513_cast = add(x = var_511_cast, y = var_512_to_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, 4096]> denom_9_cast = rsqrt(epsilon = denom_9_epsilon_0_to_fp16, x = var_513_cast);
            tensor<fp16, [2, 320, 1, 4096]> out_9_cast = mul(x = zero_mean_9_cast, y = denom_9_cast);
            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, []>(19605888)))];
            tensor<fp16, [2, 320, 1, 4096]> var_518_cast = add(x = out_9_cast, y = var_517_to_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, []>(19606592)))];
            tensor<fp16, [2, 320, 1, 4096]> hidden_states_29_cast = mul(x = var_518_cast, y = var_520_to_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, []>(19607296)))];
            tensor<fp16, [2, 320, 1, 4096]> q_7_cast = 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);
            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, []>(19812160)))];
            tensor<fp16, [2, 320, 1, 77]> k_7_cast = 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);
            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, []>(20467584)))];
            tensor<fp16, [2, 320, 1, 77]> v_7_cast = 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);
            tensor<int32, [4]> var_545 = const()[name = tensor<string, []>("op_545"), val = tensor<int32, [4]>([2, 5, 64, -1])];
            tensor<fp16, [2, 5, 64, 4096]> var_546_cast = reshape(shape = var_545, x = q_7_cast);
            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 = reshape(shape = var_547, x = k_7_cast);
            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 = reshape(shape = var_549, x = v_7_cast);
            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, 4096, 77]> attn_weights_13_cast = matmul(transpose_x = attn_weights_13_transpose_x_0, transpose_y = attn_weights_13_transpose_y_0, x = var_546_cast, y = var_548_cast);
            tensor<fp16, [2, 5, 4096, 77]> attn_weights_15_cast = mul(x = attn_weights_13_cast, y = var_102_to_fp16);
            tensor<fp16, [2, 5, 4096, 77]> var_554_cast = softmax(axis = var_95, x = attn_weights_15_cast);
            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, 4096]> attn_7_cast = matmul(transpose_x = attn_7_transpose_x_0, transpose_y = attn_7_transpose_y_0, x = var_550_cast, y = var_554_cast);
            tensor<int32, [4]> var_558 = const()[name = tensor<string, []>("op_558"), val = tensor<int32, [4]>([2, 320, 1, -1])];
            tensor<fp16, [2, 320, 1, 4096]> input_53_cast = reshape(shape = var_558, x = attn_7_cast);
            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, []>(21123008)))];
            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, []>(21327872)))];
            tensor<fp16, [2, 320, 1, 4096]> var_567_cast = 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);
            tensor<fp16, [2, 320, 1, 4096]> inputs_11_cast = add(x = var_567_cast, y = inputs_9_cast);
            tensor<int32, [1]> var_571 = const()[name = tensor<string, []>("op_571"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> channels_mean_11_cast = reduce_mean(axes = var_571, keep_dims = var_106, x = inputs_11_cast);
            tensor<fp16, [2, 320, 1, 4096]> zero_mean_11_cast = sub(x = inputs_11_cast, y = channels_mean_11_cast);
            tensor<fp16, [2, 320, 1, 4096]> zero_mean_sq_11_cast = mul(x = zero_mean_11_cast, y = zero_mean_11_cast);
            tensor<int32, [1]> var_575 = const()[name = tensor<string, []>("op_575"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 4096]> var_576_cast = reduce_mean(axes = var_575, keep_dims = var_106, x = zero_mean_sq_11_cast);
            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, 4096]> var_578_cast = add(x = var_576_cast, y = var_577_to_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, 4096]> denom_11_cast = rsqrt(epsilon = denom_11_epsilon_0_to_fp16, x = var_578_cast);
            tensor<fp16, [2, 320, 1, 4096]> out_11_cast = mul(x = zero_mean_11_cast, y = denom_11_cast);
            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, []>(21328576)))];
            tensor<fp16, [2, 320, 1, 4096]> var_583_cast = add(x = out_11_cast, y = var_582_to_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, []>(21329280)))];
            tensor<fp16, [2, 320, 1, 4096]> input_55_cast = mul(x = var_583_cast, y = var_585_to_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, []>(21329984)))];
            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, []>(22968448)))];
            tensor<fp16, [2, 2560, 1, 4096]> var_597_cast = 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);
            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, 4096]> var_598_cast_0, tensor<fp16, [2, 1280, 1, 4096]> var_598_cast_1 = split(axis = var_598_axis_0, split_sizes = var_598_split_sizes_0, x = var_597_cast);
            tensor<string, []> var_600_mode_0 = const()[name = tensor<string, []>("op_600_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 1280, 1, 4096]> var_600_cast = gelu(mode = var_600_mode_0, x = var_598_cast_1);
            tensor<fp16, [2, 1280, 1, 4096]> input_57_cast = mul(x = var_598_cast_0, y = var_600_cast);
            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, []>(22973632)))];
            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, []>(23792896)))];
            tensor<fp16, [2, 320, 1, 4096]> var_608_cast = 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);
            tensor<fp16, [2, 320, 1, 4096]> hidden_states_33_cast = add(x = var_608_cast, y = inputs_11_cast);
            tensor<int32, [4]> var_610 = const()[name = tensor<string, []>("op_610"), val = tensor<int32, [4]>([2, 320, 64, 64])];
            tensor<fp16, [2, 320, 64, 64]> input_59_cast = reshape(shape = var_610, x = hidden_states_33_cast);
            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, []>(23793600)))];
            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, []>(23998464)))];
            tensor<fp16, [2, 320, 64, 64]> hidden_states_35_cast = 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);
            tensor<fp16, [2, 320, 64, 64]> input_61_cast_1 = add(x = hidden_states_35_cast, y = hidden_states_23_cast);
            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, []>(23999168)))];
            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, []>(25842432)))];
            tensor<fp16, [2, 320, 32, 32]> input_63_cast_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_1);
            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, 32, 32])];
            tensor<fp16, [2, 32, 10, 32, 32]> reshape_24_cast = reshape(shape = reshape_24_shape_0, x = input_63_cast_1);
            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 = reduce_mean(axes = reduce_mean_18_axes_0, keep_dims = reduce_mean_18_keep_dims_0, x = reshape_24_cast);
            tensor<fp16, [2, 32, 10, 32, 32]> sub_12_cast = sub(x = reshape_24_cast, y = reduce_mean_18_cast);
            tensor<fp16, [2, 32, 10, 32, 32]> square_6_cast = square(x = sub_12_cast);
            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 = reduce_mean(axes = reduce_mean_20_axes_0, keep_dims = reduce_mean_20_keep_dims_0, x = square_6_cast);
            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 = add(x = reduce_mean_20_cast, y = add_12_y_0_to_fp16);
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_6_cast = sqrt(x = add_12_cast);
            tensor<fp16, [2, 32, 10, 32, 32]> real_div_6_cast = real_div(x = sub_12_cast, y = sqrt_6_cast);
            tensor<int32, [4]> reshape_25_shape_0 = const()[name = tensor<string, []>("reshape_25_shape_0"), val = tensor<int32, [4]>([2, 320, 32, 32])];
            tensor<fp16, [2, 320, 32, 32]> reshape_25_cast = reshape(shape = reshape_25_shape_0, x = real_div_6_cast);
            tensor<fp16, [320]> add_13_mean_0_to_fp16 = const()[name = tensor<string, []>("add_13_mean_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25843136)))];
            tensor<fp16, [320]> add_13_variance_0_to_fp16 = const()[name = tensor<string, []>("add_13_variance_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25843840)))];
            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, []>(25844544)))];
            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, []>(25845248)))];
            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, 32, 32]> add_13_cast = 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_13_mean_0_to_fp16, variance = add_13_variance_0_to_fp16, x = reshape_25_cast);
            tensor<fp16, [2, 320, 32, 32]> input_67_cast = silu(x = add_13_cast);
            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, []>(25845952)))];
            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, []>(29532416)))];
            tensor<fp16, [2, 640, 32, 32]> hidden_states_37_cast = 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);
            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, []>(29533760)))];
            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, []>(31172224)))];
            tensor<fp16, [2, 640, 1, 1]> temb_5_cast = 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_1);
            tensor<fp16, [2, 640, 32, 32]> input_71_cast = add(x = hidden_states_37_cast, y = temb_5_cast);
            tensor<int32, [5]> reshape_28_shape_0 = const()[name = tensor<string, []>("reshape_28_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 32, 32])];
            tensor<fp16, [2, 32, 20, 32, 32]> reshape_28_cast = reshape(shape = reshape_28_shape_0, x = input_71_cast);
            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 = reduce_mean(axes = reduce_mean_21_axes_0, keep_dims = reduce_mean_21_keep_dims_0, x = reshape_28_cast);
            tensor<fp16, [2, 32, 20, 32, 32]> sub_14_cast = sub(x = reshape_28_cast, y = reduce_mean_21_cast);
            tensor<fp16, [2, 32, 20, 32, 32]> square_7_cast = square(x = sub_14_cast);
            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 = reduce_mean(axes = reduce_mean_23_axes_0, keep_dims = reduce_mean_23_keep_dims_0, x = square_7_cast);
            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 = add(x = reduce_mean_23_cast, y = add_14_y_0_to_fp16);
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_7_cast = sqrt(x = add_14_cast);
            tensor<fp16, [2, 32, 20, 32, 32]> real_div_7_cast = real_div(x = sub_14_cast, y = sqrt_7_cast);
            tensor<int32, [4]> reshape_29_shape_0 = const()[name = tensor<string, []>("reshape_29_shape_0"), val = tensor<int32, [4]>([2, 640, 32, 32])];
            tensor<fp16, [2, 640, 32, 32]> reshape_29_cast = reshape(shape = reshape_29_shape_0, x = real_div_7_cast);
            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, []>(31173568)))];
            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, []>(31174912)))];
            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, []>(31176256)))];
            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, []>(31177600)))];
            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, 32, 32]> add_15_cast = 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);
            tensor<fp16, [2, 640, 32, 32]> input_75_cast = silu(x = add_15_cast);
            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, []>(31178944)))];
            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, []>(38551808)))];
            tensor<fp16, [2, 640, 32, 32]> hidden_states_39_cast = 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);
            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, []>(38553152)))];
            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, []>(38962816)))];
            tensor<fp16, [2, 640, 32, 32]> x_1_cast = 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_1);
            tensor<fp16, [2, 640, 32, 32]> hidden_states_41_cast = add(x = x_1_cast, y = hidden_states_39_cast);
            tensor<int32, [5]> reshape_32_shape_0 = const()[name = tensor<string, []>("reshape_32_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 32, 32])];
            tensor<fp16, [2, 32, 20, 32, 32]> reshape_32_cast = reshape(shape = reshape_32_shape_0, x = hidden_states_41_cast);
            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 = reduce_mean(axes = reduce_mean_24_axes_0, keep_dims = reduce_mean_24_keep_dims_0, x = reshape_32_cast);
            tensor<fp16, [2, 32, 20, 32, 32]> sub_16_cast = sub(x = reshape_32_cast, y = reduce_mean_24_cast);
            tensor<fp16, [2, 32, 20, 32, 32]> square_8_cast = square(x = sub_16_cast);
            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 = reduce_mean(axes = reduce_mean_26_axes_0, keep_dims = reduce_mean_26_keep_dims_0, x = square_8_cast);
            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 = add(x = reduce_mean_26_cast, y = add_16_y_0_to_fp16);
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_8_cast = sqrt(x = add_16_cast);
            tensor<fp16, [2, 32, 20, 32, 32]> real_div_8_cast = real_div(x = sub_16_cast, y = sqrt_8_cast);
            tensor<int32, [4]> reshape_33_shape_0 = const()[name = tensor<string, []>("reshape_33_shape_0"), val = tensor<int32, [4]>([2, 640, 32, 32])];
            tensor<fp16, [2, 640, 32, 32]> reshape_33_cast = reshape(shape = reshape_33_shape_0, x = real_div_8_cast);
            tensor<fp16, [640]> add_17_mean_0_to_fp16 = const()[name = tensor<string, []>("add_17_mean_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38964160)))];
            tensor<fp16, [640]> add_17_variance_0_to_fp16 = const()[name = tensor<string, []>("add_17_variance_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38965504)))];
            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, []>(38966848)))];
            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, []>(38968192)))];
            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, 32, 32]> add_17_cast = 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_17_mean_0_to_fp16, variance = add_17_variance_0_to_fp16, x = reshape_33_cast);
            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, []>(38969536)))];
            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, []>(39788800)))];
            tensor<fp16, [2, 640, 32, 32]> hidden_states_43_cast = 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);
            tensor<int32, [4]> var_728 = const()[name = tensor<string, []>("op_728"), val = tensor<int32, [4]>([2, 640, 1, 1024])];
            tensor<fp16, [2, 640, 1, 1024]> inputs_13_cast = reshape(shape = var_728, x = hidden_states_43_cast);
            tensor<int32, [1]> var_738 = const()[name = tensor<string, []>("op_738"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_13_cast = reduce_mean(axes = var_738, keep_dims = var_644, x = inputs_13_cast);
            tensor<fp16, [2, 640, 1, 1024]> zero_mean_13_cast = sub(x = inputs_13_cast, y = channels_mean_13_cast);
            tensor<fp16, [2, 640, 1, 1024]> zero_mean_sq_13_cast = mul(x = zero_mean_13_cast, y = zero_mean_13_cast);
            tensor<int32, [1]> var_742 = const()[name = tensor<string, []>("op_742"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_743_cast = reduce_mean(axes = var_742, keep_dims = var_644, x = zero_mean_sq_13_cast);
            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, 1024]> var_745_cast = add(x = var_743_cast, y = var_744_to_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, 1024]> denom_13_cast = rsqrt(epsilon = denom_13_epsilon_0_to_fp16, x = var_745_cast);
            tensor<fp16, [2, 640, 1, 1024]> out_13_cast = mul(x = zero_mean_13_cast, y = denom_13_cast);
            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, []>(39790144)))];
            tensor<fp16, [2, 640, 1, 1024]> var_750_cast = add(x = out_13_cast, y = var_749_to_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, []>(39791488)))];
            tensor<fp16, [2, 640, 1, 1024]> hidden_states_45_cast = mul(x = var_750_cast, y = var_752_to_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, []>(39792832)))];
            tensor<fp16, [2, 640, 1, 1024]> q_9_cast = 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);
            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, []>(40612096)))];
            tensor<fp16, [2, 640, 1, 1024]> k_9_cast = 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);
            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, []>(41431360)))];
            tensor<fp16, [2, 640, 1, 1024]> v_9_cast = 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);
            tensor<int32, [4]> var_777 = const()[name = tensor<string, []>("op_777"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 1024]> var_778_cast = reshape(shape = var_777, x = q_9_cast);
            tensor<int32, [4]> var_779 = const()[name = tensor<string, []>("op_779"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 1024]> var_780_cast = reshape(shape = var_779, x = k_9_cast);
            tensor<int32, [4]> var_781 = const()[name = tensor<string, []>("op_781"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 1024]> var_782_cast = reshape(shape = var_781, x = v_9_cast);
            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, 1024, 1024]> attn_weights_17_cast = matmul(transpose_x = attn_weights_17_transpose_x_0, transpose_y = attn_weights_17_transpose_y_0, x = var_778_cast, y = var_780_cast);
            tensor<fp16, []> var_640_to_fp16 = const()[name = tensor<string, []>("op_640_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [2, 10, 1024, 1024]> attn_weights_19_cast = mul(x = attn_weights_17_cast, y = var_640_to_fp16);
            tensor<fp16, [2, 10, 1024, 1024]> var_786_cast = softmax(axis = var_633, x = attn_weights_19_cast);
            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, 1024]> attn_9_cast = matmul(transpose_x = attn_9_transpose_x_0, transpose_y = attn_9_transpose_y_0, x = var_782_cast, y = var_786_cast);
            tensor<int32, [4]> var_790 = const()[name = tensor<string, []>("op_790"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 1024]> input_79_cast = reshape(shape = var_790, x = attn_9_cast);
            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, []>(42250624)))];
            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, []>(43069888)))];
            tensor<fp16, [2, 640, 1, 1024]> var_799_cast = 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);
            tensor<fp16, [2, 640, 1, 1024]> inputs_15_cast = add(x = var_799_cast, y = inputs_13_cast);
            tensor<int32, [1]> var_803 = const()[name = tensor<string, []>("op_803"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_15_cast = reduce_mean(axes = var_803, keep_dims = var_644, x = inputs_15_cast);
            tensor<fp16, [2, 640, 1, 1024]> zero_mean_15_cast = sub(x = inputs_15_cast, y = channels_mean_15_cast);
            tensor<fp16, [2, 640, 1, 1024]> zero_mean_sq_15_cast = mul(x = zero_mean_15_cast, y = zero_mean_15_cast);
            tensor<int32, [1]> var_807 = const()[name = tensor<string, []>("op_807"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_808_cast = reduce_mean(axes = var_807, keep_dims = var_644, x = zero_mean_sq_15_cast);
            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, 1024]> var_810_cast = add(x = var_808_cast, y = var_809_to_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, 1024]> denom_15_cast = rsqrt(epsilon = denom_15_epsilon_0_to_fp16, x = var_810_cast);
            tensor<fp16, [2, 640, 1, 1024]> out_15_cast = mul(x = zero_mean_15_cast, y = denom_15_cast);
            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, []>(43071232)))];
            tensor<fp16, [2, 640, 1, 1024]> var_815_cast = add(x = out_15_cast, y = var_814_to_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, []>(43072576)))];
            tensor<fp16, [2, 640, 1, 1024]> hidden_states_47_cast = mul(x = var_815_cast, y = var_817_to_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, []>(43073920)))];
            tensor<fp16, [2, 640, 1, 1024]> q_11_cast = 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);
            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, []>(43893184)))];
            tensor<fp16, [2, 640, 1, 77]> k_11_cast = 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);
            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, []>(45203968)))];
            tensor<fp16, [2, 640, 1, 77]> v_11_cast = 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);
            tensor<int32, [4]> var_842 = const()[name = tensor<string, []>("op_842"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 1024]> var_843_cast = reshape(shape = var_842, x = q_11_cast);
            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 = reshape(shape = var_844, x = k_11_cast);
            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 = reshape(shape = var_846, x = v_11_cast);
            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, 1024, 77]> attn_weights_21_cast = matmul(transpose_x = attn_weights_21_transpose_x_0, transpose_y = attn_weights_21_transpose_y_0, x = var_843_cast, y = var_845_cast);
            tensor<fp16, [2, 10, 1024, 77]> attn_weights_23_cast = mul(x = attn_weights_21_cast, y = var_640_to_fp16);
            tensor<fp16, [2, 10, 1024, 77]> var_851_cast = softmax(axis = var_633, x = attn_weights_23_cast);
            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, 1024]> attn_11_cast = matmul(transpose_x = attn_11_transpose_x_0, transpose_y = attn_11_transpose_y_0, x = var_847_cast, y = var_851_cast);
            tensor<int32, [4]> var_855 = const()[name = tensor<string, []>("op_855"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 1024]> input_81_cast = reshape(shape = var_855, x = attn_11_cast);
            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, []>(46514752)))];
            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, []>(47334016)))];
            tensor<fp16, [2, 640, 1, 1024]> var_864_cast = 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);
            tensor<fp16, [2, 640, 1, 1024]> inputs_17_cast = add(x = var_864_cast, y = inputs_15_cast);
            tensor<int32, [1]> var_868 = const()[name = tensor<string, []>("op_868"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_17_cast = reduce_mean(axes = var_868, keep_dims = var_644, x = inputs_17_cast);
            tensor<fp16, [2, 640, 1, 1024]> zero_mean_17_cast = sub(x = inputs_17_cast, y = channels_mean_17_cast);
            tensor<fp16, [2, 640, 1, 1024]> zero_mean_sq_17_cast = mul(x = zero_mean_17_cast, y = zero_mean_17_cast);
            tensor<int32, [1]> var_872 = const()[name = tensor<string, []>("op_872"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_873_cast = reduce_mean(axes = var_872, keep_dims = var_644, x = zero_mean_sq_17_cast);
            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, 1024]> var_875_cast = add(x = var_873_cast, y = var_874_to_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, 1024]> denom_17_cast = rsqrt(epsilon = denom_17_epsilon_0_to_fp16, x = var_875_cast);
            tensor<fp16, [2, 640, 1, 1024]> out_17_cast = mul(x = zero_mean_17_cast, y = denom_17_cast);
            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, []>(47335360)))];
            tensor<fp16, [2, 640, 1, 1024]> var_880_cast = add(x = out_17_cast, y = var_879_to_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, []>(47336704)))];
            tensor<fp16, [2, 640, 1, 1024]> input_83_cast = mul(x = var_880_cast, y = var_882_to_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, []>(47338048)))];
            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, []>(53891712)))];
            tensor<fp16, [2, 5120, 1, 1024]> var_894_cast = 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);
            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, 1024]> var_895_cast_0, tensor<fp16, [2, 2560, 1, 1024]> var_895_cast_1 = split(axis = var_895_axis_0, split_sizes = var_895_split_sizes_0, x = var_894_cast);
            tensor<string, []> var_897_mode_0 = const()[name = tensor<string, []>("op_897_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 2560, 1, 1024]> var_897_cast = gelu(mode = var_897_mode_0, x = var_895_cast_1);
            tensor<fp16, [2, 2560, 1, 1024]> input_85_cast = mul(x = var_895_cast_0, y = var_897_cast);
            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, []>(53902016)))];
            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, []>(57178880)))];
            tensor<fp16, [2, 640, 1, 1024]> var_905_cast = 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);
            tensor<fp16, [2, 640, 1, 1024]> hidden_states_51_cast = add(x = var_905_cast, y = inputs_17_cast);
            tensor<int32, [4]> var_907 = const()[name = tensor<string, []>("op_907"), val = tensor<int32, [4]>([2, 640, 32, 32])];
            tensor<fp16, [2, 640, 32, 32]> input_87_cast = reshape(shape = var_907, x = hidden_states_51_cast);
            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, []>(57180224)))];
            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, []>(57999488)))];
            tensor<fp16, [2, 640, 32, 32]> hidden_states_53_cast = 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);
            tensor<fp16, [2, 640, 32, 32]> input_89_cast_1 = add(x = hidden_states_53_cast, y = hidden_states_41_cast);
            tensor<int32, [5]> reshape_36_shape_0 = const()[name = tensor<string, []>("reshape_36_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 32, 32])];
            tensor<fp16, [2, 32, 20, 32, 32]> reshape_36_cast = reshape(shape = reshape_36_shape_0, x = input_89_cast_1);
            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 = reduce_mean(axes = reduce_mean_27_axes_0, keep_dims = reduce_mean_27_keep_dims_0, x = reshape_36_cast);
            tensor<fp16, [2, 32, 20, 32, 32]> sub_18_cast = sub(x = reshape_36_cast, y = reduce_mean_27_cast);
            tensor<fp16, [2, 32, 20, 32, 32]> square_9_cast = square(x = sub_18_cast);
            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 = reduce_mean(axes = reduce_mean_29_axes_0, keep_dims = reduce_mean_29_keep_dims_0, x = square_9_cast);
            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 = add(x = reduce_mean_29_cast, y = add_18_y_0_to_fp16);
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_9_cast = sqrt(x = add_18_cast);
            tensor<fp16, [2, 32, 20, 32, 32]> real_div_9_cast = real_div(x = sub_18_cast, y = sqrt_9_cast);
            tensor<int32, [4]> reshape_37_shape_0 = const()[name = tensor<string, []>("reshape_37_shape_0"), val = tensor<int32, [4]>([2, 640, 32, 32])];
            tensor<fp16, [2, 640, 32, 32]> reshape_37_cast = reshape(shape = reshape_37_shape_0, x = real_div_9_cast);
            tensor<fp16, [640]> add_19_mean_0_to_fp16 = const()[name = tensor<string, []>("add_19_mean_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(58000832)))];
            tensor<fp16, [640]> add_19_variance_0_to_fp16 = const()[name = tensor<string, []>("add_19_variance_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(58002176)))];
            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, []>(58003520)))];
            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, []>(58004864)))];
            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, 32, 32]> add_19_cast = batch_norm(beta = add_19_beta_0_to_fp16, epsilon = add_19_epsilon_0_to_fp16, gamma = add_19_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_37_cast);
            tensor<fp16, [2, 640, 32, 32]> input_93_cast = silu(x = add_19_cast);
            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, []>(58006208)))];
            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, []>(65379072)))];
            tensor<fp16, [2, 640, 32, 32]> hidden_states_55_cast = 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);
            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, []>(65380416)))];
            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, []>(67018880)))];
            tensor<fp16, [2, 640, 1, 1]> temb_7_cast = 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_1);
            tensor<fp16, [2, 640, 32, 32]> input_97_cast = add(x = hidden_states_55_cast, y = temb_7_cast);
            tensor<int32, [5]> reshape_40_shape_0 = const()[name = tensor<string, []>("reshape_40_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 32, 32])];
            tensor<fp16, [2, 32, 20, 32, 32]> reshape_40_cast = reshape(shape = reshape_40_shape_0, x = input_97_cast);
            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 = reduce_mean(axes = reduce_mean_30_axes_0, keep_dims = reduce_mean_30_keep_dims_0, x = reshape_40_cast);
            tensor<fp16, [2, 32, 20, 32, 32]> sub_20_cast = sub(x = reshape_40_cast, y = reduce_mean_30_cast);
            tensor<fp16, [2, 32, 20, 32, 32]> square_10_cast = square(x = sub_20_cast);
            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 = reduce_mean(axes = reduce_mean_32_axes_0, keep_dims = reduce_mean_32_keep_dims_0, x = square_10_cast);
            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 = add(x = reduce_mean_32_cast, y = add_20_y_0_to_fp16);
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_10_cast = sqrt(x = add_20_cast);
            tensor<fp16, [2, 32, 20, 32, 32]> real_div_10_cast = real_div(x = sub_20_cast, y = sqrt_10_cast);
            tensor<int32, [4]> reshape_41_shape_0 = const()[name = tensor<string, []>("reshape_41_shape_0"), val = tensor<int32, [4]>([2, 640, 32, 32])];
            tensor<fp16, [2, 640, 32, 32]> reshape_41_cast = reshape(shape = reshape_41_shape_0, x = real_div_10_cast);
            tensor<fp16, [640]> add_21_mean_0_to_fp16 = const()[name = tensor<string, []>("add_21_mean_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(67020224)))];
            tensor<fp16, [640]> add_21_variance_0_to_fp16 = const()[name = tensor<string, []>("add_21_variance_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(67021568)))];
            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, []>(67022912)))];
            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, []>(67024256)))];
            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, 32, 32]> add_21_cast = 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_21_mean_0_to_fp16, variance = add_21_variance_0_to_fp16, x = reshape_41_cast);
            tensor<fp16, [2, 640, 32, 32]> input_101_cast = silu(x = add_21_cast);
            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, []>(67025600)))];
            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, []>(74398464)))];
            tensor<fp16, [2, 640, 32, 32]> hidden_states_57_cast = 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);
            tensor<fp16, [2, 640, 32, 32]> hidden_states_59_cast = add(x = input_89_cast_1, y = hidden_states_57_cast);
            tensor<int32, [5]> reshape_44_shape_0 = const()[name = tensor<string, []>("reshape_44_shape_0"), val = tensor<int32, [5]>([2, 32, 20, 32, 32])];
            tensor<fp16, [2, 32, 20, 32, 32]> reshape_44_cast = reshape(shape = reshape_44_shape_0, x = hidden_states_59_cast);
            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 = reduce_mean(axes = reduce_mean_33_axes_0, keep_dims = reduce_mean_33_keep_dims_0, x = reshape_44_cast);
            tensor<fp16, [2, 32, 20, 32, 32]> sub_22_cast = sub(x = reshape_44_cast, y = reduce_mean_33_cast);
            tensor<fp16, [2, 32, 20, 32, 32]> square_11_cast = square(x = sub_22_cast);
            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 = reduce_mean(axes = reduce_mean_35_axes_0, keep_dims = reduce_mean_35_keep_dims_0, x = square_11_cast);
            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 = add(x = reduce_mean_35_cast, y = add_22_y_0_to_fp16);
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_11_cast = sqrt(x = add_22_cast);
            tensor<fp16, [2, 32, 20, 32, 32]> real_div_11_cast = real_div(x = sub_22_cast, y = sqrt_11_cast);
            tensor<int32, [4]> reshape_45_shape_0 = const()[name = tensor<string, []>("reshape_45_shape_0"), val = tensor<int32, [4]>([2, 640, 32, 32])];
            tensor<fp16, [2, 640, 32, 32]> reshape_45_cast = reshape(shape = reshape_45_shape_0, x = real_div_11_cast);
            tensor<fp16, [640]> add_23_mean_0_to_fp16 = const()[name = tensor<string, []>("add_23_mean_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(74399808)))];
            tensor<fp16, [640]> add_23_variance_0_to_fp16 = const()[name = tensor<string, []>("add_23_variance_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(74401152)))];
            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, []>(74402496)))];
            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, []>(74403840)))];
            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, 32, 32]> add_23_cast = 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_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_45_cast);
            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, []>(74405184)))];
            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, []>(75224448)))];
            tensor<fp16, [2, 640, 32, 32]> hidden_states_61_cast = 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);
            tensor<int32, [4]> var_977 = const()[name = tensor<string, []>("op_977"), val = tensor<int32, [4]>([2, 640, 1, 1024])];
            tensor<fp16, [2, 640, 1, 1024]> inputs_19_cast = reshape(shape = var_977, x = hidden_states_61_cast);
            tensor<int32, [1]> var_987 = const()[name = tensor<string, []>("op_987"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_19_cast = reduce_mean(axes = var_987, keep_dims = var_644, x = inputs_19_cast);
            tensor<fp16, [2, 640, 1, 1024]> zero_mean_19_cast = sub(x = inputs_19_cast, y = channels_mean_19_cast);
            tensor<fp16, [2, 640, 1, 1024]> zero_mean_sq_19_cast = mul(x = zero_mean_19_cast, y = zero_mean_19_cast);
            tensor<int32, [1]> var_991 = const()[name = tensor<string, []>("op_991"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_992_cast = reduce_mean(axes = var_991, keep_dims = var_644, x = zero_mean_sq_19_cast);
            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, 1024]> var_994_cast = add(x = var_992_cast, y = var_993_to_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, 1024]> denom_19_cast = rsqrt(epsilon = denom_19_epsilon_0_to_fp16, x = var_994_cast);
            tensor<fp16, [2, 640, 1, 1024]> out_19_cast = mul(x = zero_mean_19_cast, y = denom_19_cast);
            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, []>(75225792)))];
            tensor<fp16, [2, 640, 1, 1024]> var_999_cast = add(x = out_19_cast, y = var_998_to_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, []>(75227136)))];
            tensor<fp16, [2, 640, 1, 1024]> hidden_states_63_cast = mul(x = var_999_cast, y = var_1001_to_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, []>(75228480)))];
            tensor<fp16, [2, 640, 1, 1024]> q_13_cast = 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);
            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, []>(76047744)))];
            tensor<fp16, [2, 640, 1, 1024]> k_13_cast = 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);
            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, []>(76867008)))];
            tensor<fp16, [2, 640, 1, 1024]> v_13_cast = 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);
            tensor<int32, [4]> var_1026 = const()[name = tensor<string, []>("op_1026"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 1024]> var_1027_cast = reshape(shape = var_1026, x = q_13_cast);
            tensor<int32, [4]> var_1028 = const()[name = tensor<string, []>("op_1028"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 1024]> var_1029_cast = reshape(shape = var_1028, x = k_13_cast);
            tensor<int32, [4]> var_1030 = const()[name = tensor<string, []>("op_1030"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 1024]> var_1031_cast = reshape(shape = var_1030, x = v_13_cast);
            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, 1024, 1024]> attn_weights_25_cast = matmul(transpose_x = attn_weights_25_transpose_x_0, transpose_y = attn_weights_25_transpose_y_0, x = var_1027_cast, y = var_1029_cast);
            tensor<fp16, [2, 10, 1024, 1024]> attn_weights_27_cast = mul(x = attn_weights_25_cast, y = var_640_to_fp16);
            tensor<fp16, [2, 10, 1024, 1024]> var_1035_cast = softmax(axis = var_633, x = attn_weights_27_cast);
            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, 1024]> attn_13_cast = matmul(transpose_x = attn_13_transpose_x_0, transpose_y = attn_13_transpose_y_0, x = var_1031_cast, y = var_1035_cast);
            tensor<int32, [4]> var_1039 = const()[name = tensor<string, []>("op_1039"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 1024]> input_105_cast = reshape(shape = var_1039, x = attn_13_cast);
            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, []>(77686272)))];
            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, []>(78505536)))];
            tensor<fp16, [2, 640, 1, 1024]> var_1048_cast = 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);
            tensor<fp16, [2, 640, 1, 1024]> inputs_21_cast = add(x = var_1048_cast, y = inputs_19_cast);
            tensor<int32, [1]> var_1052 = const()[name = tensor<string, []>("op_1052"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_21_cast = reduce_mean(axes = var_1052, keep_dims = var_644, x = inputs_21_cast);
            tensor<fp16, [2, 640, 1, 1024]> zero_mean_21_cast = sub(x = inputs_21_cast, y = channels_mean_21_cast);
            tensor<fp16, [2, 640, 1, 1024]> zero_mean_sq_21_cast = mul(x = zero_mean_21_cast, y = zero_mean_21_cast);
            tensor<int32, [1]> var_1056 = const()[name = tensor<string, []>("op_1056"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_1057_cast = reduce_mean(axes = var_1056, keep_dims = var_644, x = zero_mean_sq_21_cast);
            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, 1024]> var_1059_cast = add(x = var_1057_cast, y = var_1058_to_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, 1024]> denom_21_cast = rsqrt(epsilon = denom_21_epsilon_0_to_fp16, x = var_1059_cast);
            tensor<fp16, [2, 640, 1, 1024]> out_21_cast = mul(x = zero_mean_21_cast, y = denom_21_cast);
            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, []>(78506880)))];
            tensor<fp16, [2, 640, 1, 1024]> var_1064_cast = add(x = out_21_cast, y = var_1063_to_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, []>(78508224)))];
            tensor<fp16, [2, 640, 1, 1024]> hidden_states_65_cast = mul(x = var_1064_cast, y = var_1066_to_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, []>(78509568)))];
            tensor<fp16, [2, 640, 1, 1024]> q_15_cast = 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);
            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, []>(79328832)))];
            tensor<fp16, [2, 640, 1, 77]> k_15_cast = 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);
            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, []>(80639616)))];
            tensor<fp16, [2, 640, 1, 77]> v_15_cast = 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);
            tensor<int32, [4]> var_1091 = const()[name = tensor<string, []>("op_1091"), val = tensor<int32, [4]>([2, 10, 64, -1])];
            tensor<fp16, [2, 10, 64, 1024]> var_1092_cast = reshape(shape = var_1091, x = q_15_cast);
            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 = reshape(shape = var_1093, x = k_15_cast);
            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 = reshape(shape = var_1095, x = v_15_cast);
            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, 1024, 77]> attn_weights_29_cast = matmul(transpose_x = attn_weights_29_transpose_x_0, transpose_y = attn_weights_29_transpose_y_0, x = var_1092_cast, y = var_1094_cast);
            tensor<fp16, [2, 10, 1024, 77]> attn_weights_31_cast = mul(x = attn_weights_29_cast, y = var_640_to_fp16);
            tensor<fp16, [2, 10, 1024, 77]> var_1100_cast = softmax(axis = var_633, x = attn_weights_31_cast);
            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, 1024]> attn_15_cast = matmul(transpose_x = attn_15_transpose_x_0, transpose_y = attn_15_transpose_y_0, x = var_1096_cast, y = var_1100_cast);
            tensor<int32, [4]> var_1104 = const()[name = tensor<string, []>("op_1104"), val = tensor<int32, [4]>([2, 640, 1, -1])];
            tensor<fp16, [2, 640, 1, 1024]> input_107_cast = reshape(shape = var_1104, x = attn_15_cast);
            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, []>(81950400)))];
            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, []>(82769664)))];
            tensor<fp16, [2, 640, 1, 1024]> var_1113_cast = 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);
            tensor<fp16, [2, 640, 1, 1024]> inputs_23_cast = add(x = var_1113_cast, y = inputs_21_cast);
            tensor<int32, [1]> var_1117 = const()[name = tensor<string, []>("op_1117"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> channels_mean_23_cast = reduce_mean(axes = var_1117, keep_dims = var_644, x = inputs_23_cast);
            tensor<fp16, [2, 640, 1, 1024]> zero_mean_23_cast = sub(x = inputs_23_cast, y = channels_mean_23_cast);
            tensor<fp16, [2, 640, 1, 1024]> zero_mean_sq_23_cast = mul(x = zero_mean_23_cast, y = zero_mean_23_cast);
            tensor<int32, [1]> var_1121 = const()[name = tensor<string, []>("op_1121"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 1024]> var_1122_cast = reduce_mean(axes = var_1121, keep_dims = var_644, x = zero_mean_sq_23_cast);
            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, 1024]> var_1124_cast = add(x = var_1122_cast, y = var_1123_to_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, 1024]> denom_23_cast = rsqrt(epsilon = denom_23_epsilon_0_to_fp16, x = var_1124_cast);
            tensor<fp16, [2, 640, 1, 1024]> out_23_cast = mul(x = zero_mean_23_cast, y = denom_23_cast);
            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, []>(82771008)))];
            tensor<fp16, [2, 640, 1, 1024]> var_1129_cast = add(x = out_23_cast, y = var_1128_to_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, []>(82772352)))];
            tensor<fp16, [2, 640, 1, 1024]> input_109_cast = mul(x = var_1129_cast, y = var_1131_to_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, []>(82773696)))];
            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, []>(89327360)))];
            tensor<fp16, [2, 5120, 1, 1024]> var_1143_cast = 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);
            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, 1024]> var_1144_cast_0, tensor<fp16, [2, 2560, 1, 1024]> var_1144_cast_1 = split(axis = var_1144_axis_0, split_sizes = var_1144_split_sizes_0, x = var_1143_cast);
            tensor<string, []> var_1146_mode_0 = const()[name = tensor<string, []>("op_1146_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 2560, 1, 1024]> var_1146_cast = gelu(mode = var_1146_mode_0, x = var_1144_cast_1);
            tensor<fp16, [2, 2560, 1, 1024]> input_111_cast = mul(x = var_1144_cast_0, y = var_1146_cast);
            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, []>(89337664)))];
            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, []>(92614528)))];
            tensor<fp16, [2, 640, 1, 1024]> var_1154_cast = 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);
            tensor<fp16, [2, 640, 1, 1024]> hidden_states_69_cast = add(x = var_1154_cast, y = inputs_23_cast);
            tensor<int32, [4]> var_1156 = const()[name = tensor<string, []>("op_1156"), val = tensor<int32, [4]>([2, 640, 32, 32])];
            tensor<fp16, [2, 640, 32, 32]> input_113_cast = reshape(shape = var_1156, x = hidden_states_69_cast);
            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, []>(92615872)))];
            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, []>(93435136)))];
            tensor<fp16, [2, 640, 32, 32]> hidden_states_71_cast = 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);
            tensor<fp16, [2, 640, 32, 32]> input_115_cast_1 = add(x = hidden_states_71_cast, y = hidden_states_59_cast);
            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, []>(93436480)))];
            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, []>(100809344)))];
            tensor<fp16, [2, 640, 16, 16]> input_117_cast_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_1);
            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, 16, 16])];
            tensor<fp16, [2, 32, 20, 16, 16]> reshape_48_cast = reshape(shape = reshape_48_shape_0, x = input_117_cast_1);
            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 = reduce_mean(axes = reduce_mean_36_axes_0, keep_dims = reduce_mean_36_keep_dims_0, x = reshape_48_cast);
            tensor<fp16, [2, 32, 20, 16, 16]> sub_24_cast = sub(x = reshape_48_cast, y = reduce_mean_36_cast);
            tensor<fp16, [2, 32, 20, 16, 16]> square_12_cast = square(x = sub_24_cast);
            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 = reduce_mean(axes = reduce_mean_38_axes_0, keep_dims = reduce_mean_38_keep_dims_0, x = square_12_cast);
            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 = add(x = reduce_mean_38_cast, y = add_24_y_0_to_fp16);
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_12_cast = sqrt(x = add_24_cast);
            tensor<fp16, [2, 32, 20, 16, 16]> real_div_12_cast = real_div(x = sub_24_cast, y = sqrt_12_cast);
            tensor<int32, [4]> reshape_49_shape_0 = const()[name = tensor<string, []>("reshape_49_shape_0"), val = tensor<int32, [4]>([2, 640, 16, 16])];
            tensor<fp16, [2, 640, 16, 16]> reshape_49_cast = reshape(shape = reshape_49_shape_0, x = real_div_12_cast);
            tensor<fp16, [640]> add_25_mean_0_to_fp16 = const()[name = tensor<string, []>("add_25_mean_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(100810688)))];
            tensor<fp16, [640]> add_25_variance_0_to_fp16 = const()[name = tensor<string, []>("add_25_variance_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(100812032)))];
            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, []>(100813376)))];
            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, []>(100814720)))];
            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, 16, 16]> add_25_cast = 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_25_mean_0_to_fp16, variance = add_25_variance_0_to_fp16, x = reshape_49_cast);
            tensor<fp16, [2, 640, 16, 16]> input_121_cast = silu(x = add_25_cast);
            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, []>(100816064)))];
            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, []>(115561728)))];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_73_cast = 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);
            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, []>(115564352)))];
            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, []>(118841216)))];
            tensor<fp16, [2, 1280, 1, 1]> temb_9_cast = 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_1);
            tensor<fp16, [2, 1280, 16, 16]> input_125_cast = add(x = hidden_states_73_cast, y = temb_9_cast);
            tensor<int32, [5]> reshape_52_shape_0 = const()[name = tensor<string, []>("reshape_52_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 16, 16])];
            tensor<fp16, [2, 32, 40, 16, 16]> reshape_52_cast = reshape(shape = reshape_52_shape_0, x = input_125_cast);
            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 = reduce_mean(axes = reduce_mean_39_axes_0, keep_dims = reduce_mean_39_keep_dims_0, x = reshape_52_cast);
            tensor<fp16, [2, 32, 40, 16, 16]> sub_26_cast = sub(x = reshape_52_cast, y = reduce_mean_39_cast);
            tensor<fp16, [2, 32, 40, 16, 16]> square_13_cast = square(x = sub_26_cast);
            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 = reduce_mean(axes = reduce_mean_41_axes_0, keep_dims = reduce_mean_41_keep_dims_0, x = square_13_cast);
            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 = add(x = reduce_mean_41_cast, y = add_26_y_0_to_fp16);
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_13_cast = sqrt(x = add_26_cast);
            tensor<fp16, [2, 32, 40, 16, 16]> real_div_13_cast = real_div(x = sub_26_cast, y = sqrt_13_cast);
            tensor<int32, [4]> reshape_53_shape_0 = const()[name = tensor<string, []>("reshape_53_shape_0"), val = tensor<int32, [4]>([2, 1280, 16, 16])];
            tensor<fp16, [2, 1280, 16, 16]> reshape_53_cast = reshape(shape = reshape_53_shape_0, x = real_div_13_cast);
            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, []>(118843840)))];
            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, []>(118846464)))];
            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, []>(118849088)))];
            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, []>(118851712)))];
            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, 16, 16]> add_27_cast = 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);
            tensor<fp16, [2, 1280, 16, 16]> input_129_cast = silu(x = add_27_cast);
            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, []>(118854336)))];
            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, []>(148345600)))];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_75_cast = 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);
            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, []>(148348224)))];
            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, []>(149986688)))];
            tensor<fp16, [2, 1280, 16, 16]> x_3_cast = 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_1);
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_77_cast = add(x = x_3_cast, y = hidden_states_75_cast);
            tensor<int32, [5]> reshape_56_shape_0 = const()[name = tensor<string, []>("reshape_56_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 16, 16])];
            tensor<fp16, [2, 32, 40, 16, 16]> reshape_56_cast = reshape(shape = reshape_56_shape_0, x = hidden_states_77_cast);
            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 = reduce_mean(axes = reduce_mean_42_axes_0, keep_dims = reduce_mean_42_keep_dims_0, x = reshape_56_cast);
            tensor<fp16, [2, 32, 40, 16, 16]> sub_28_cast = sub(x = reshape_56_cast, y = reduce_mean_42_cast);
            tensor<fp16, [2, 32, 40, 16, 16]> square_14_cast = square(x = sub_28_cast);
            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 = reduce_mean(axes = reduce_mean_44_axes_0, keep_dims = reduce_mean_44_keep_dims_0, x = square_14_cast);
            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 = add(x = reduce_mean_44_cast, y = add_28_y_0_to_fp16);
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_14_cast = sqrt(x = add_28_cast);
            tensor<fp16, [2, 32, 40, 16, 16]> real_div_14_cast = real_div(x = sub_28_cast, y = sqrt_14_cast);
            tensor<int32, [4]> reshape_57_shape_0 = const()[name = tensor<string, []>("reshape_57_shape_0"), val = tensor<int32, [4]>([2, 1280, 16, 16])];
            tensor<fp16, [2, 1280, 16, 16]> reshape_57_cast = reshape(shape = reshape_57_shape_0, x = real_div_14_cast);
            tensor<fp16, [1280]> add_29_mean_0_to_fp16 = const()[name = tensor<string, []>("add_29_mean_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149989312)))];
            tensor<fp16, [1280]> add_29_variance_0_to_fp16 = const()[name = tensor<string, []>("add_29_variance_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149991936)))];
            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, []>(149994560)))];
            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, []>(149997184)))];
            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, 16, 16]> add_29_cast = 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_29_mean_0_to_fp16, variance = add_29_variance_0_to_fp16, x = reshape_57_cast);
            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, []>(149999808)))];
            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, []>(153276672)))];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_79_cast = 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);
            tensor<int32, [4]> var_1274 = const()[name = tensor<string, []>("op_1274"), val = tensor<int32, [4]>([2, 1280, 1, 256])];
            tensor<fp16, [2, 1280, 1, 256]> inputs_25_cast = reshape(shape = var_1274, x = hidden_states_79_cast);
            tensor<int32, [1]> var_1284 = const()[name = tensor<string, []>("op_1284"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> channels_mean_25_cast = reduce_mean(axes = var_1284, keep_dims = var_1190, x = inputs_25_cast);
            tensor<fp16, [2, 1280, 1, 256]> zero_mean_25_cast = sub(x = inputs_25_cast, y = channels_mean_25_cast);
            tensor<fp16, [2, 1280, 1, 256]> zero_mean_sq_25_cast = mul(x = zero_mean_25_cast, y = zero_mean_25_cast);
            tensor<int32, [1]> var_1288 = const()[name = tensor<string, []>("op_1288"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> var_1289_cast = reduce_mean(axes = var_1288, keep_dims = var_1190, x = zero_mean_sq_25_cast);
            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, 256]> var_1291_cast = add(x = var_1289_cast, y = var_1290_to_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, 256]> denom_25_cast = rsqrt(epsilon = denom_25_epsilon_0_to_fp16, x = var_1291_cast);
            tensor<fp16, [2, 1280, 1, 256]> out_25_cast = mul(x = zero_mean_25_cast, y = denom_25_cast);
            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, []>(153279296)))];
            tensor<fp16, [2, 1280, 1, 256]> var_1296_cast = add(x = out_25_cast, y = var_1295_to_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, []>(153281920)))];
            tensor<fp16, [2, 1280, 1, 256]> hidden_states_81_cast = mul(x = var_1296_cast, y = var_1298_to_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, []>(153284544)))];
            tensor<fp16, [2, 1280, 1, 256]> q_17_cast = 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);
            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, []>(156561408)))];
            tensor<fp16, [2, 1280, 1, 256]> k_17_cast = 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);
            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, []>(159838272)))];
            tensor<fp16, [2, 1280, 1, 256]> v_17_cast = 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);
            tensor<int32, [4]> var_1323 = const()[name = tensor<string, []>("op_1323"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 256]> var_1324_cast = reshape(shape = var_1323, x = q_17_cast);
            tensor<int32, [4]> var_1325 = const()[name = tensor<string, []>("op_1325"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 256]> var_1326_cast = reshape(shape = var_1325, x = k_17_cast);
            tensor<int32, [4]> var_1327 = const()[name = tensor<string, []>("op_1327"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 256]> var_1328_cast = reshape(shape = var_1327, x = v_17_cast);
            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, 256, 256]> attn_weights_33_cast = matmul(transpose_x = attn_weights_33_transpose_x_0, transpose_y = attn_weights_33_transpose_y_0, x = var_1324_cast, y = var_1326_cast);
            tensor<fp16, []> var_1186_to_fp16 = const()[name = tensor<string, []>("op_1186_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [2, 20, 256, 256]> attn_weights_35_cast = mul(x = attn_weights_33_cast, y = var_1186_to_fp16);
            tensor<fp16, [2, 20, 256, 256]> var_1332_cast = softmax(axis = var_1179, x = attn_weights_35_cast);
            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, 256]> attn_17_cast = matmul(transpose_x = attn_17_transpose_x_0, transpose_y = attn_17_transpose_y_0, x = var_1328_cast, y = var_1332_cast);
            tensor<int32, [4]> var_1336 = const()[name = tensor<string, []>("op_1336"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 256]> input_133_cast = reshape(shape = var_1336, x = attn_17_cast);
            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, []>(163115136)))];
            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, []>(166392000)))];
            tensor<fp16, [2, 1280, 1, 256]> var_1345_cast = 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);
            tensor<fp16, [2, 1280, 1, 256]> inputs_27_cast = add(x = var_1345_cast, y = inputs_25_cast);
            tensor<int32, [1]> var_1349 = const()[name = tensor<string, []>("op_1349"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> channels_mean_27_cast = reduce_mean(axes = var_1349, keep_dims = var_1190, x = inputs_27_cast);
            tensor<fp16, [2, 1280, 1, 256]> zero_mean_27_cast = sub(x = inputs_27_cast, y = channels_mean_27_cast);
            tensor<fp16, [2, 1280, 1, 256]> zero_mean_sq_27_cast = mul(x = zero_mean_27_cast, y = zero_mean_27_cast);
            tensor<int32, [1]> var_1353 = const()[name = tensor<string, []>("op_1353"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> var_1354_cast = reduce_mean(axes = var_1353, keep_dims = var_1190, x = zero_mean_sq_27_cast);
            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, 256]> var_1356_cast = add(x = var_1354_cast, y = var_1355_to_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, 256]> denom_27_cast = rsqrt(epsilon = denom_27_epsilon_0_to_fp16, x = var_1356_cast);
            tensor<fp16, [2, 1280, 1, 256]> out_27_cast = mul(x = zero_mean_27_cast, y = denom_27_cast);
            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, []>(166394624)))];
            tensor<fp16, [2, 1280, 1, 256]> var_1361_cast = add(x = out_27_cast, y = var_1360_to_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, []>(166397248)))];
            tensor<fp16, [2, 1280, 1, 256]> hidden_states_83_cast = mul(x = var_1361_cast, y = var_1363_to_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, []>(166399872)))];
            tensor<fp16, [2, 1280, 1, 256]> q_19_cast = 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);
            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, []>(169676736)))];
            tensor<fp16, [2, 1280, 1, 77]> k_19_cast = 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);
            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, []>(172298240)))];
            tensor<fp16, [2, 1280, 1, 77]> v_19_cast = 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);
            tensor<int32, [4]> var_1388 = const()[name = tensor<string, []>("op_1388"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 256]> var_1389_cast = reshape(shape = var_1388, x = q_19_cast);
            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 = reshape(shape = var_1390, x = k_19_cast);
            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 = reshape(shape = var_1392, x = v_19_cast);
            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, 256, 77]> attn_weights_37_cast = matmul(transpose_x = attn_weights_37_transpose_x_0, transpose_y = attn_weights_37_transpose_y_0, x = var_1389_cast, y = var_1391_cast);
            tensor<fp16, [2, 20, 256, 77]> attn_weights_39_cast = mul(x = attn_weights_37_cast, y = var_1186_to_fp16);
            tensor<fp16, [2, 20, 256, 77]> var_1397_cast = softmax(axis = var_1179, x = attn_weights_39_cast);
            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, 256]> attn_19_cast = matmul(transpose_x = attn_19_transpose_x_0, transpose_y = attn_19_transpose_y_0, x = var_1393_cast, y = var_1397_cast);
            tensor<int32, [4]> var_1401 = const()[name = tensor<string, []>("op_1401"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 256]> input_135_cast = reshape(shape = var_1401, x = attn_19_cast);
            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, []>(174919744)))];
            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, []>(178196608)))];
            tensor<fp16, [2, 1280, 1, 256]> var_1410_cast = 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);
            tensor<fp16, [2, 1280, 1, 256]> inputs_29_cast = add(x = var_1410_cast, y = inputs_27_cast);
            tensor<int32, [1]> var_1414 = const()[name = tensor<string, []>("op_1414"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> channels_mean_29_cast = reduce_mean(axes = var_1414, keep_dims = var_1190, x = inputs_29_cast);
            tensor<fp16, [2, 1280, 1, 256]> zero_mean_29_cast = sub(x = inputs_29_cast, y = channels_mean_29_cast);
            tensor<fp16, [2, 1280, 1, 256]> zero_mean_sq_29_cast = mul(x = zero_mean_29_cast, y = zero_mean_29_cast);
            tensor<int32, [1]> var_1418 = const()[name = tensor<string, []>("op_1418"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> var_1419_cast = reduce_mean(axes = var_1418, keep_dims = var_1190, x = zero_mean_sq_29_cast);
            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, 256]> var_1421_cast = add(x = var_1419_cast, y = var_1420_to_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, 256]> denom_29_cast = rsqrt(epsilon = denom_29_epsilon_0_to_fp16, x = var_1421_cast);
            tensor<fp16, [2, 1280, 1, 256]> out_29_cast = mul(x = zero_mean_29_cast, y = denom_29_cast);
            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, []>(178199232)))];
            tensor<fp16, [2, 1280, 1, 256]> var_1426_cast = add(x = out_29_cast, y = var_1425_to_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, []>(178201856)))];
            tensor<fp16, [2, 1280, 1, 256]> input_137_cast = mul(x = var_1426_cast, y = var_1428_to_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, []>(178204480)))];
            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, []>(204418944)))];
            tensor<fp16, [2, 10240, 1, 256]> var_1440_cast = 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);
            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, 256]> var_1441_cast_0, tensor<fp16, [2, 5120, 1, 256]> var_1441_cast_1 = split(axis = var_1441_axis_0, split_sizes = var_1441_split_sizes_0, x = var_1440_cast);
            tensor<string, []> var_1443_mode_0 = const()[name = tensor<string, []>("op_1443_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 256]> var_1443_cast = gelu(mode = var_1443_mode_0, x = var_1441_cast_1);
            tensor<fp16, [2, 5120, 1, 256]> input_139_cast = mul(x = var_1441_cast_0, y = var_1443_cast);
            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, []>(204439488)))];
            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, []>(217546752)))];
            tensor<fp16, [2, 1280, 1, 256]> var_1451_cast = 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);
            tensor<fp16, [2, 1280, 1, 256]> hidden_states_87_cast = add(x = var_1451_cast, y = inputs_29_cast);
            tensor<int32, [4]> var_1453 = const()[name = tensor<string, []>("op_1453"), val = tensor<int32, [4]>([2, 1280, 16, 16])];
            tensor<fp16, [2, 1280, 16, 16]> input_141_cast = reshape(shape = var_1453, x = hidden_states_87_cast);
            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, []>(217549376)))];
            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, []>(220826240)))];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_89_cast = 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);
            tensor<fp16, [2, 1280, 16, 16]> input_143_cast_1 = add(x = hidden_states_89_cast, y = hidden_states_77_cast);
            tensor<int32, [5]> reshape_60_shape_0 = const()[name = tensor<string, []>("reshape_60_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 16, 16])];
            tensor<fp16, [2, 32, 40, 16, 16]> reshape_60_cast = reshape(shape = reshape_60_shape_0, x = input_143_cast_1);
            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 = reduce_mean(axes = reduce_mean_45_axes_0, keep_dims = reduce_mean_45_keep_dims_0, x = reshape_60_cast);
            tensor<fp16, [2, 32, 40, 16, 16]> sub_30_cast = sub(x = reshape_60_cast, y = reduce_mean_45_cast);
            tensor<fp16, [2, 32, 40, 16, 16]> square_15_cast = square(x = sub_30_cast);
            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 = reduce_mean(axes = reduce_mean_47_axes_0, keep_dims = reduce_mean_47_keep_dims_0, x = square_15_cast);
            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 = add(x = reduce_mean_47_cast, y = add_30_y_0_to_fp16);
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_15_cast = sqrt(x = add_30_cast);
            tensor<fp16, [2, 32, 40, 16, 16]> real_div_15_cast = real_div(x = sub_30_cast, y = sqrt_15_cast);
            tensor<int32, [4]> reshape_61_shape_0 = const()[name = tensor<string, []>("reshape_61_shape_0"), val = tensor<int32, [4]>([2, 1280, 16, 16])];
            tensor<fp16, [2, 1280, 16, 16]> reshape_61_cast = reshape(shape = reshape_61_shape_0, x = real_div_15_cast);
            tensor<fp16, [1280]> add_31_mean_0_to_fp16 = const()[name = tensor<string, []>("add_31_mean_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(220828864)))];
            tensor<fp16, [1280]> add_31_variance_0_to_fp16 = const()[name = tensor<string, []>("add_31_variance_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(220831488)))];
            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, []>(220834112)))];
            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, []>(220836736)))];
            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, 16, 16]> add_31_cast = 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_31_mean_0_to_fp16, variance = add_31_variance_0_to_fp16, x = reshape_61_cast);
            tensor<fp16, [2, 1280, 16, 16]> input_147_cast = silu(x = add_31_cast);
            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, []>(220839360)))];
            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, []>(250330624)))];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_91_cast = 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);
            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, []>(250333248)))];
            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, []>(253610112)))];
            tensor<fp16, [2, 1280, 1, 1]> temb_11_cast = 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_1);
            tensor<fp16, [2, 1280, 16, 16]> input_151_cast = add(x = hidden_states_91_cast, y = temb_11_cast);
            tensor<int32, [5]> reshape_64_shape_0 = const()[name = tensor<string, []>("reshape_64_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 16, 16])];
            tensor<fp16, [2, 32, 40, 16, 16]> reshape_64_cast = reshape(shape = reshape_64_shape_0, x = input_151_cast);
            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 = reduce_mean(axes = reduce_mean_48_axes_0, keep_dims = reduce_mean_48_keep_dims_0, x = reshape_64_cast);
            tensor<fp16, [2, 32, 40, 16, 16]> sub_32_cast = sub(x = reshape_64_cast, y = reduce_mean_48_cast);
            tensor<fp16, [2, 32, 40, 16, 16]> square_16_cast = square(x = sub_32_cast);
            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 = reduce_mean(axes = reduce_mean_50_axes_0, keep_dims = reduce_mean_50_keep_dims_0, x = square_16_cast);
            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 = add(x = reduce_mean_50_cast, y = add_32_y_0_to_fp16);
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_16_cast = sqrt(x = add_32_cast);
            tensor<fp16, [2, 32, 40, 16, 16]> real_div_16_cast = real_div(x = sub_32_cast, y = sqrt_16_cast);
            tensor<int32, [4]> reshape_65_shape_0 = const()[name = tensor<string, []>("reshape_65_shape_0"), val = tensor<int32, [4]>([2, 1280, 16, 16])];
            tensor<fp16, [2, 1280, 16, 16]> reshape_65_cast = reshape(shape = reshape_65_shape_0, x = real_div_16_cast);
            tensor<fp16, [1280]> add_33_mean_0_to_fp16 = const()[name = tensor<string, []>("add_33_mean_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(253612736)))];
            tensor<fp16, [1280]> add_33_variance_0_to_fp16 = const()[name = tensor<string, []>("add_33_variance_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(253615360)))];
            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, []>(253617984)))];
            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, []>(253620608)))];
            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, 16, 16]> add_33_cast = 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_33_mean_0_to_fp16, variance = add_33_variance_0_to_fp16, x = reshape_65_cast);
            tensor<fp16, [2, 1280, 16, 16]> input_155_cast = silu(x = add_33_cast);
            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, []>(253623232)))];
            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, []>(283114496)))];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_93_cast = 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);
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_95_cast = add(x = input_143_cast_1, y = hidden_states_93_cast);
            tensor<int32, [5]> reshape_68_shape_0 = const()[name = tensor<string, []>("reshape_68_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 16, 16])];
            tensor<fp16, [2, 32, 40, 16, 16]> reshape_68_cast = reshape(shape = reshape_68_shape_0, x = hidden_states_95_cast);
            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 = reduce_mean(axes = reduce_mean_51_axes_0, keep_dims = reduce_mean_51_keep_dims_0, x = reshape_68_cast);
            tensor<fp16, [2, 32, 40, 16, 16]> sub_34_cast = sub(x = reshape_68_cast, y = reduce_mean_51_cast);
            tensor<fp16, [2, 32, 40, 16, 16]> square_17_cast = square(x = sub_34_cast);
            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 = reduce_mean(axes = reduce_mean_53_axes_0, keep_dims = reduce_mean_53_keep_dims_0, x = square_17_cast);
            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 = add(x = reduce_mean_53_cast, y = add_34_y_0_to_fp16);
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_17_cast = sqrt(x = add_34_cast);
            tensor<fp16, [2, 32, 40, 16, 16]> real_div_17_cast = real_div(x = sub_34_cast, y = sqrt_17_cast);
            tensor<int32, [4]> reshape_69_shape_0 = const()[name = tensor<string, []>("reshape_69_shape_0"), val = tensor<int32, [4]>([2, 1280, 16, 16])];
            tensor<fp16, [2, 1280, 16, 16]> reshape_69_cast = reshape(shape = reshape_69_shape_0, x = real_div_17_cast);
            tensor<fp16, [1280]> add_35_mean_0_to_fp16 = const()[name = tensor<string, []>("add_35_mean_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(283117120)))];
            tensor<fp16, [1280]> add_35_variance_0_to_fp16 = const()[name = tensor<string, []>("add_35_variance_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(283119744)))];
            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, []>(283122368)))];
            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, []>(283124992)))];
            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, 16, 16]> add_35_cast = 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_35_mean_0_to_fp16, variance = add_35_variance_0_to_fp16, x = reshape_69_cast);
            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, []>(283127616)))];
            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, []>(286404480)))];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_97_cast = 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);
            tensor<int32, [4]> var_1523 = const()[name = tensor<string, []>("op_1523"), val = tensor<int32, [4]>([2, 1280, 1, 256])];
            tensor<fp16, [2, 1280, 1, 256]> inputs_31_cast = reshape(shape = var_1523, x = hidden_states_97_cast);
            tensor<int32, [1]> var_1533 = const()[name = tensor<string, []>("op_1533"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> channels_mean_31_cast = reduce_mean(axes = var_1533, keep_dims = var_1190, x = inputs_31_cast);
            tensor<fp16, [2, 1280, 1, 256]> zero_mean_31_cast = sub(x = inputs_31_cast, y = channels_mean_31_cast);
            tensor<fp16, [2, 1280, 1, 256]> zero_mean_sq_31_cast = mul(x = zero_mean_31_cast, y = zero_mean_31_cast);
            tensor<int32, [1]> var_1537 = const()[name = tensor<string, []>("op_1537"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> var_1538_cast = reduce_mean(axes = var_1537, keep_dims = var_1190, x = zero_mean_sq_31_cast);
            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, 256]> var_1540_cast = add(x = var_1538_cast, y = var_1539_to_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, 256]> denom_31_cast = rsqrt(epsilon = denom_31_epsilon_0_to_fp16, x = var_1540_cast);
            tensor<fp16, [2, 1280, 1, 256]> out_31_cast = mul(x = zero_mean_31_cast, y = denom_31_cast);
            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, []>(286407104)))];
            tensor<fp16, [2, 1280, 1, 256]> var_1545_cast = add(x = out_31_cast, y = var_1544_to_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, []>(286409728)))];
            tensor<fp16, [2, 1280, 1, 256]> hidden_states_99_cast = mul(x = var_1545_cast, y = var_1547_to_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, []>(286412352)))];
            tensor<fp16, [2, 1280, 1, 256]> q_21_cast = 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);
            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, []>(289689216)))];
            tensor<fp16, [2, 1280, 1, 256]> k_21_cast = 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);
            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, []>(292966080)))];
            tensor<fp16, [2, 1280, 1, 256]> v_21_cast = 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);
            tensor<int32, [4]> var_1572 = const()[name = tensor<string, []>("op_1572"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 256]> var_1573_cast = reshape(shape = var_1572, x = q_21_cast);
            tensor<int32, [4]> var_1574 = const()[name = tensor<string, []>("op_1574"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 256]> var_1575_cast = reshape(shape = var_1574, x = k_21_cast);
            tensor<int32, [4]> var_1576 = const()[name = tensor<string, []>("op_1576"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 256]> var_1577_cast = reshape(shape = var_1576, x = v_21_cast);
            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, 256, 256]> attn_weights_41_cast = matmul(transpose_x = attn_weights_41_transpose_x_0, transpose_y = attn_weights_41_transpose_y_0, x = var_1573_cast, y = var_1575_cast);
            tensor<fp16, [2, 20, 256, 256]> attn_weights_43_cast = mul(x = attn_weights_41_cast, y = var_1186_to_fp16);
            tensor<fp16, [2, 20, 256, 256]> var_1581_cast = softmax(axis = var_1179, x = attn_weights_43_cast);
            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, 256]> attn_21_cast = matmul(transpose_x = attn_21_transpose_x_0, transpose_y = attn_21_transpose_y_0, x = var_1577_cast, y = var_1581_cast);
            tensor<int32, [4]> var_1585 = const()[name = tensor<string, []>("op_1585"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 256]> input_159_cast = reshape(shape = var_1585, x = attn_21_cast);
            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, []>(296242944)))];
            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, []>(299519808)))];
            tensor<fp16, [2, 1280, 1, 256]> var_1594_cast = 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);
            tensor<fp16, [2, 1280, 1, 256]> inputs_33_cast = add(x = var_1594_cast, y = inputs_31_cast);
            tensor<int32, [1]> var_1598 = const()[name = tensor<string, []>("op_1598"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> channels_mean_33_cast = reduce_mean(axes = var_1598, keep_dims = var_1190, x = inputs_33_cast);
            tensor<fp16, [2, 1280, 1, 256]> zero_mean_33_cast = sub(x = inputs_33_cast, y = channels_mean_33_cast);
            tensor<fp16, [2, 1280, 1, 256]> zero_mean_sq_33_cast = mul(x = zero_mean_33_cast, y = zero_mean_33_cast);
            tensor<int32, [1]> var_1602 = const()[name = tensor<string, []>("op_1602"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> var_1603_cast = reduce_mean(axes = var_1602, keep_dims = var_1190, x = zero_mean_sq_33_cast);
            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, 256]> var_1605_cast = add(x = var_1603_cast, y = var_1604_to_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, 256]> denom_33_cast = rsqrt(epsilon = denom_33_epsilon_0_to_fp16, x = var_1605_cast);
            tensor<fp16, [2, 1280, 1, 256]> out_33_cast = mul(x = zero_mean_33_cast, y = denom_33_cast);
            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, []>(299522432)))];
            tensor<fp16, [2, 1280, 1, 256]> var_1610_cast = add(x = out_33_cast, y = var_1609_to_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, []>(299525056)))];
            tensor<fp16, [2, 1280, 1, 256]> hidden_states_101_cast = mul(x = var_1610_cast, y = var_1612_to_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, []>(299527680)))];
            tensor<fp16, [2, 1280, 1, 256]> q_23_cast = 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);
            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, []>(302804544)))];
            tensor<fp16, [2, 1280, 1, 77]> k_23_cast = 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);
            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, []>(305426048)))];
            tensor<fp16, [2, 1280, 1, 77]> v_23_cast = 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);
            tensor<int32, [4]> var_1637 = const()[name = tensor<string, []>("op_1637"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 256]> var_1638_cast = reshape(shape = var_1637, x = q_23_cast);
            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 = reshape(shape = var_1639, x = k_23_cast);
            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 = reshape(shape = var_1641, x = v_23_cast);
            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, 256, 77]> attn_weights_45_cast = matmul(transpose_x = attn_weights_45_transpose_x_0, transpose_y = attn_weights_45_transpose_y_0, x = var_1638_cast, y = var_1640_cast);
            tensor<fp16, [2, 20, 256, 77]> attn_weights_47_cast = mul(x = attn_weights_45_cast, y = var_1186_to_fp16);
            tensor<fp16, [2, 20, 256, 77]> var_1646_cast = softmax(axis = var_1179, x = attn_weights_47_cast);
            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, 256]> attn_23_cast = matmul(transpose_x = attn_23_transpose_x_0, transpose_y = attn_23_transpose_y_0, x = var_1642_cast, y = var_1646_cast);
            tensor<int32, [4]> var_1650 = const()[name = tensor<string, []>("op_1650"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 256]> input_161_cast = reshape(shape = var_1650, x = attn_23_cast);
            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, []>(308047552)))];
            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, []>(311324416)))];
            tensor<fp16, [2, 1280, 1, 256]> var_1659_cast = 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);
            tensor<fp16, [2, 1280, 1, 256]> inputs_35_cast = add(x = var_1659_cast, y = inputs_33_cast);
            tensor<int32, [1]> var_1663 = const()[name = tensor<string, []>("op_1663"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> channels_mean_35_cast = reduce_mean(axes = var_1663, keep_dims = var_1190, x = inputs_35_cast);
            tensor<fp16, [2, 1280, 1, 256]> zero_mean_35_cast = sub(x = inputs_35_cast, y = channels_mean_35_cast);
            tensor<fp16, [2, 1280, 1, 256]> zero_mean_sq_35_cast = mul(x = zero_mean_35_cast, y = zero_mean_35_cast);
            tensor<int32, [1]> var_1667 = const()[name = tensor<string, []>("op_1667"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 256]> var_1668_cast = reduce_mean(axes = var_1667, keep_dims = var_1190, x = zero_mean_sq_35_cast);
            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, 256]> var_1670_cast = add(x = var_1668_cast, y = var_1669_to_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, 256]> denom_35_cast = rsqrt(epsilon = denom_35_epsilon_0_to_fp16, x = var_1670_cast);
            tensor<fp16, [2, 1280, 1, 256]> out_35_cast = mul(x = zero_mean_35_cast, y = denom_35_cast);
            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, []>(311327040)))];
            tensor<fp16, [2, 1280, 1, 256]> var_1675_cast = add(x = out_35_cast, y = var_1674_to_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, []>(311329664)))];
            tensor<fp16, [2, 1280, 1, 256]> input_163_cast = mul(x = var_1675_cast, y = var_1677_to_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, []>(311332288)))];
            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, []>(337546752)))];
            tensor<fp16, [2, 10240, 1, 256]> var_1689_cast = 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);
            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, 256]> var_1690_cast_0, tensor<fp16, [2, 5120, 1, 256]> var_1690_cast_1 = split(axis = var_1690_axis_0, split_sizes = var_1690_split_sizes_0, x = var_1689_cast);
            tensor<string, []> var_1692_mode_0 = const()[name = tensor<string, []>("op_1692_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 256]> var_1692_cast = gelu(mode = var_1692_mode_0, x = var_1690_cast_1);
            tensor<fp16, [2, 5120, 1, 256]> input_165_cast = mul(x = var_1690_cast_0, y = var_1692_cast);
            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, []>(337567296)))];
            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, []>(350674560)))];
            tensor<fp16, [2, 1280, 1, 256]> var_1700_cast = 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);
            tensor<fp16, [2, 1280, 1, 256]> hidden_states_105_cast = add(x = var_1700_cast, y = inputs_35_cast);
            tensor<int32, [4]> var_1702 = const()[name = tensor<string, []>("op_1702"), val = tensor<int32, [4]>([2, 1280, 16, 16])];
            tensor<fp16, [2, 1280, 16, 16]> input_167_cast = reshape(shape = var_1702, x = hidden_states_105_cast);
            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, []>(350677184)))];
            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, []>(353954048)))];
            tensor<fp16, [2, 1280, 16, 16]> hidden_states_107_cast = 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);
            tensor<fp16, [2, 1280, 16, 16]> input_169_cast_1 = add(x = hidden_states_107_cast, y = hidden_states_95_cast);
            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, []>(353956672)))];
            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, []>(383447936)))];
            tensor<fp16, [2, 1280, 8, 8]> input_171_cast_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_1);
            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, 8, 8])];
            tensor<fp16, [2, 32, 40, 8, 8]> reshape_72_cast = reshape(shape = reshape_72_shape_0, x = input_171_cast_1);
            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 = reduce_mean(axes = reduce_mean_54_axes_0, keep_dims = reduce_mean_54_keep_dims_0, x = reshape_72_cast);
            tensor<fp16, [2, 32, 40, 8, 8]> sub_36_cast = sub(x = reshape_72_cast, y = reduce_mean_54_cast);
            tensor<fp16, [2, 32, 40, 8, 8]> square_18_cast = square(x = sub_36_cast);
            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 = reduce_mean(axes = reduce_mean_56_axes_0, keep_dims = reduce_mean_56_keep_dims_0, x = square_18_cast);
            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 = add(x = reduce_mean_56_cast, y = add_36_y_0_to_fp16);
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_18_cast = sqrt(x = add_36_cast);
            tensor<fp16, [2, 32, 40, 8, 8]> real_div_18_cast = real_div(x = sub_36_cast, y = sqrt_18_cast);
            tensor<int32, [4]> reshape_73_shape_0 = const()[name = tensor<string, []>("reshape_73_shape_0"), val = tensor<int32, [4]>([2, 1280, 8, 8])];
            tensor<fp16, [2, 1280, 8, 8]> reshape_73_cast = reshape(shape = reshape_73_shape_0, x = real_div_18_cast);
            tensor<fp16, [1280]> add_37_mean_0_to_fp16 = const()[name = tensor<string, []>("add_37_mean_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(383450560)))];
            tensor<fp16, [1280]> add_37_variance_0_to_fp16 = const()[name = tensor<string, []>("add_37_variance_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(383453184)))];
            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, []>(383455808)))];
            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, []>(383458432)))];
            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, 8, 8]> add_37_cast = batch_norm(beta = add_37_beta_0_to_fp16, epsilon = add_37_epsilon_0_to_fp16, gamma = add_37_gamma_0_to_fp16, mean = add_37_mean_0_to_fp16, variance = add_37_variance_0_to_fp16, x = reshape_73_cast);
            tensor<fp16, [2, 1280, 8, 8]> input_175_cast = silu(x = add_37_cast);
            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, []>(383461056)))];
            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, []>(412952320)))];
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_109_cast = 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);
            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, []>(412954944)))];
            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, []>(416231808)))];
            tensor<fp16, [2, 1280, 1, 1]> temb_13_cast = 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_1);
            tensor<fp16, [2, 1280, 8, 8]> input_179_cast = add(x = hidden_states_109_cast, y = temb_13_cast);
            tensor<int32, [5]> reshape_76_shape_0 = const()[name = tensor<string, []>("reshape_76_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 8, 8])];
            tensor<fp16, [2, 32, 40, 8, 8]> reshape_76_cast = reshape(shape = reshape_76_shape_0, x = input_179_cast);
            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 = reduce_mean(axes = reduce_mean_57_axes_0, keep_dims = reduce_mean_57_keep_dims_0, x = reshape_76_cast);
            tensor<fp16, [2, 32, 40, 8, 8]> sub_38_cast = sub(x = reshape_76_cast, y = reduce_mean_57_cast);
            tensor<fp16, [2, 32, 40, 8, 8]> square_19_cast = square(x = sub_38_cast);
            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 = reduce_mean(axes = reduce_mean_59_axes_0, keep_dims = reduce_mean_59_keep_dims_0, x = square_19_cast);
            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 = add(x = reduce_mean_59_cast, y = add_38_y_0_to_fp16);
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_19_cast = sqrt(x = add_38_cast);
            tensor<fp16, [2, 32, 40, 8, 8]> real_div_19_cast = real_div(x = sub_38_cast, y = sqrt_19_cast);
            tensor<int32, [4]> reshape_77_shape_0 = const()[name = tensor<string, []>("reshape_77_shape_0"), val = tensor<int32, [4]>([2, 1280, 8, 8])];
            tensor<fp16, [2, 1280, 8, 8]> reshape_77_cast = reshape(shape = reshape_77_shape_0, x = real_div_19_cast);
            tensor<fp16, [1280]> add_39_mean_0_to_fp16 = const()[name = tensor<string, []>("add_39_mean_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(416234432)))];
            tensor<fp16, [1280]> add_39_variance_0_to_fp16 = const()[name = tensor<string, []>("add_39_variance_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(416237056)))];
            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, []>(416239680)))];
            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, []>(416242304)))];
            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, 8, 8]> add_39_cast = 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_39_mean_0_to_fp16, variance = add_39_variance_0_to_fp16, x = reshape_77_cast);
            tensor<fp16, [2, 1280, 8, 8]> input_183_cast = silu(x = add_39_cast);
            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, []>(416244928)))];
            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, []>(445736192)))];
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_111_cast = 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);
            tensor<fp16, [2, 1280, 8, 8]> input_185_cast = add(x = input_171_cast_1, y = hidden_states_111_cast);
            tensor<int32, [5]> reshape_80_shape_0 = const()[name = tensor<string, []>("reshape_80_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 8, 8])];
            tensor<fp16, [2, 32, 40, 8, 8]> reshape_80_cast = reshape(shape = reshape_80_shape_0, x = input_185_cast);
            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 = reduce_mean(axes = reduce_mean_60_axes_0, keep_dims = reduce_mean_60_keep_dims_0, x = reshape_80_cast);
            tensor<fp16, [2, 32, 40, 8, 8]> sub_40_cast = sub(x = reshape_80_cast, y = reduce_mean_60_cast);
            tensor<fp16, [2, 32, 40, 8, 8]> square_20_cast = square(x = sub_40_cast);
            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 = reduce_mean(axes = reduce_mean_62_axes_0, keep_dims = reduce_mean_62_keep_dims_0, x = square_20_cast);
            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 = add(x = reduce_mean_62_cast, y = add_40_y_0_to_fp16);
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_20_cast = sqrt(x = add_40_cast);
            tensor<fp16, [2, 32, 40, 8, 8]> real_div_20_cast = real_div(x = sub_40_cast, y = sqrt_20_cast);
            tensor<int32, [4]> reshape_81_shape_0 = const()[name = tensor<string, []>("reshape_81_shape_0"), val = tensor<int32, [4]>([2, 1280, 8, 8])];
            tensor<fp16, [2, 1280, 8, 8]> reshape_81_cast = reshape(shape = reshape_81_shape_0, x = real_div_20_cast);
            tensor<fp16, [1280]> add_41_mean_0_to_fp16 = const()[name = tensor<string, []>("add_41_mean_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(445738816)))];
            tensor<fp16, [1280]> add_41_variance_0_to_fp16 = const()[name = tensor<string, []>("add_41_variance_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(445741440)))];
            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, []>(445744064)))];
            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, []>(445746688)))];
            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, 8, 8]> add_41_cast = 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_41_mean_0_to_fp16, variance = add_41_variance_0_to_fp16, x = reshape_81_cast);
            tensor<fp16, [2, 1280, 8, 8]> input_189_cast = silu(x = add_41_cast);
            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, []>(445749312)))];
            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, []>(475240576)))];
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_113_cast = 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);
            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, []>(475243200)))];
            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, []>(478520064)))];
            tensor<fp16, [2, 1280, 1, 1]> temb_15_cast = 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_1);
            tensor<fp16, [2, 1280, 8, 8]> input_193_cast = add(x = hidden_states_113_cast, y = temb_15_cast);
            tensor<int32, [5]> reshape_84_shape_0 = const()[name = tensor<string, []>("reshape_84_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 8, 8])];
            tensor<fp16, [2, 32, 40, 8, 8]> reshape_84_cast = reshape(shape = reshape_84_shape_0, x = input_193_cast);
            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 = reduce_mean(axes = reduce_mean_63_axes_0, keep_dims = reduce_mean_63_keep_dims_0, x = reshape_84_cast);
            tensor<fp16, [2, 32, 40, 8, 8]> sub_42_cast = sub(x = reshape_84_cast, y = reduce_mean_63_cast);
            tensor<fp16, [2, 32, 40, 8, 8]> square_21_cast = square(x = sub_42_cast);
            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 = reduce_mean(axes = reduce_mean_65_axes_0, keep_dims = reduce_mean_65_keep_dims_0, x = square_21_cast);
            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 = add(x = reduce_mean_65_cast, y = add_42_y_0_to_fp16);
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_21_cast = sqrt(x = add_42_cast);
            tensor<fp16, [2, 32, 40, 8, 8]> real_div_21_cast = real_div(x = sub_42_cast, y = sqrt_21_cast);
            tensor<int32, [4]> reshape_85_shape_0 = const()[name = tensor<string, []>("reshape_85_shape_0"), val = tensor<int32, [4]>([2, 1280, 8, 8])];
            tensor<fp16, [2, 1280, 8, 8]> reshape_85_cast = reshape(shape = reshape_85_shape_0, x = real_div_21_cast);
            tensor<fp16, [1280]> add_43_mean_0_to_fp16 = const()[name = tensor<string, []>("add_43_mean_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(478522688)))];
            tensor<fp16, [1280]> add_43_variance_0_to_fp16 = const()[name = tensor<string, []>("add_43_variance_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(478525312)))];
            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, []>(478527936)))];
            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, []>(478530560)))];
            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, 8, 8]> add_43_cast = 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_43_mean_0_to_fp16, variance = add_43_variance_0_to_fp16, x = reshape_85_cast);
            tensor<fp16, [2, 1280, 8, 8]> input_197_cast = silu(x = add_43_cast);
            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, []>(478533184)))];
            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, []>(508024448)))];
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_115_cast = 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);
            tensor<fp16, [2, 1280, 8, 8]> input_199_cast = add(x = input_185_cast, y = hidden_states_115_cast);
            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, 8, 8])];
            tensor<fp16, [2, 32, 40, 8, 8]> reshape_88_cast = reshape(shape = reshape_88_shape_0, x = input_199_cast);
            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 = reduce_mean(axes = reduce_mean_66_axes_0, keep_dims = reduce_mean_66_keep_dims_0, x = reshape_88_cast);
            tensor<fp16, [2, 32, 40, 8, 8]> sub_44_cast = sub(x = reshape_88_cast, y = reduce_mean_66_cast);
            tensor<fp16, [2, 32, 40, 8, 8]> square_22_cast = square(x = sub_44_cast);
            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 = reduce_mean(axes = reduce_mean_68_axes_0, keep_dims = reduce_mean_68_keep_dims_0, x = square_22_cast);
            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 = add(x = reduce_mean_68_cast, y = add_44_y_0_to_fp16);
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_22_cast = sqrt(x = add_44_cast);
            tensor<fp16, [2, 32, 40, 8, 8]> real_div_22_cast = real_div(x = sub_44_cast, y = sqrt_22_cast);
            tensor<int32, [4]> reshape_89_shape_0 = const()[name = tensor<string, []>("reshape_89_shape_0"), val = tensor<int32, [4]>([2, 1280, 8, 8])];
            tensor<fp16, [2, 1280, 8, 8]> reshape_89_cast = reshape(shape = reshape_89_shape_0, x = real_div_22_cast);
            tensor<fp16, [1280]> add_45_mean_0_to_fp16 = const()[name = tensor<string, []>("add_45_mean_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(508027072)))];
            tensor<fp16, [1280]> add_45_variance_0_to_fp16 = const()[name = tensor<string, []>("add_45_variance_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(508029696)))];
            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, []>(508032320)))];
            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, []>(508034944)))];
            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, 8, 8]> add_45_cast = 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_45_mean_0_to_fp16, variance = add_45_variance_0_to_fp16, x = reshape_89_cast);
            tensor<fp16, [2, 1280, 8, 8]> input_203_cast = silu(x = add_45_cast);
            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, []>(508037568)))];
            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, []>(537528832)))];
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_117_cast = 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);
            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, []>(537531456)))];
            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, []>(540808320)))];
            tensor<fp16, [2, 1280, 1, 1]> temb_17_cast = 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_1);
            tensor<fp16, [2, 1280, 8, 8]> input_207_cast = add(x = hidden_states_117_cast, y = temb_17_cast);
            tensor<int32, [5]> reshape_92_shape_0 = const()[name = tensor<string, []>("reshape_92_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 8, 8])];
            tensor<fp16, [2, 32, 40, 8, 8]> reshape_92_cast = reshape(shape = reshape_92_shape_0, x = input_207_cast);
            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 = reduce_mean(axes = reduce_mean_69_axes_0, keep_dims = reduce_mean_69_keep_dims_0, x = reshape_92_cast);
            tensor<fp16, [2, 32, 40, 8, 8]> sub_46_cast = sub(x = reshape_92_cast, y = reduce_mean_69_cast);
            tensor<fp16, [2, 32, 40, 8, 8]> square_23_cast = square(x = sub_46_cast);
            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 = reduce_mean(axes = reduce_mean_71_axes_0, keep_dims = reduce_mean_71_keep_dims_0, x = square_23_cast);
            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 = add(x = reduce_mean_71_cast, y = add_46_y_0_to_fp16);
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_23_cast = sqrt(x = add_46_cast);
            tensor<fp16, [2, 32, 40, 8, 8]> real_div_23_cast = real_div(x = sub_46_cast, y = sqrt_23_cast);
            tensor<int32, [4]> reshape_93_shape_0 = const()[name = tensor<string, []>("reshape_93_shape_0"), val = tensor<int32, [4]>([2, 1280, 8, 8])];
            tensor<fp16, [2, 1280, 8, 8]> reshape_93_cast = reshape(shape = reshape_93_shape_0, x = real_div_23_cast);
            tensor<fp16, [1280]> add_47_mean_0_to_fp16 = const()[name = tensor<string, []>("add_47_mean_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(540810944)))];
            tensor<fp16, [1280]> add_47_variance_0_to_fp16 = const()[name = tensor<string, []>("add_47_variance_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(540813568)))];
            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, []>(540816192)))];
            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, []>(540818816)))];
            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, 8, 8]> add_47_cast = 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_47_mean_0_to_fp16, variance = add_47_variance_0_to_fp16, x = reshape_93_cast);
            tensor<fp16, [2, 1280, 8, 8]> input_211_cast = silu(x = add_47_cast);
            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, []>(540821440)))];
            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, []>(570312704)))];
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_119_cast = 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);
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_121_cast = add(x = input_199_cast, y = hidden_states_119_cast);
            tensor<int32, [5]> reshape_96_shape_0 = const()[name = tensor<string, []>("reshape_96_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 8, 8])];
            tensor<fp16, [2, 32, 40, 8, 8]> reshape_96_cast = reshape(shape = reshape_96_shape_0, x = hidden_states_121_cast);
            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 = reduce_mean(axes = reduce_mean_72_axes_0, keep_dims = reduce_mean_72_keep_dims_0, x = reshape_96_cast);
            tensor<fp16, [2, 32, 40, 8, 8]> sub_48_cast = sub(x = reshape_96_cast, y = reduce_mean_72_cast);
            tensor<fp16, [2, 32, 40, 8, 8]> square_24_cast = square(x = sub_48_cast);
            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 = reduce_mean(axes = reduce_mean_74_axes_0, keep_dims = reduce_mean_74_keep_dims_0, x = square_24_cast);
            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 = add(x = reduce_mean_74_cast, y = add_48_y_0_to_fp16);
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_24_cast = sqrt(x = add_48_cast);
            tensor<fp16, [2, 32, 40, 8, 8]> real_div_24_cast = real_div(x = sub_48_cast, y = sqrt_24_cast);
            tensor<int32, [4]> reshape_97_shape_0 = const()[name = tensor<string, []>("reshape_97_shape_0"), val = tensor<int32, [4]>([2, 1280, 8, 8])];
            tensor<fp16, [2, 1280, 8, 8]> reshape_97_cast = reshape(shape = reshape_97_shape_0, x = real_div_24_cast);
            tensor<fp16, [1280]> add_49_mean_0_to_fp16 = const()[name = tensor<string, []>("add_49_mean_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(570315328)))];
            tensor<fp16, [1280]> add_49_variance_0_to_fp16 = const()[name = tensor<string, []>("add_49_variance_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(570317952)))];
            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, []>(570320576)))];
            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, []>(570323200)))];
            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, 8, 8]> add_49_cast = batch_norm(beta = add_49_beta_0_to_fp16, epsilon = add_49_epsilon_0_to_fp16, gamma = add_49_gamma_0_to_fp16, mean = add_49_mean_0_to_fp16, variance = add_49_variance_0_to_fp16, x = reshape_97_cast);
            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, []>(570325824)))];
            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, []>(573602688)))];
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_123_cast = 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);
            tensor<int32, [4]> var_1895 = const()[name = tensor<string, []>("op_1895"), val = tensor<int32, [4]>([2, 1280, 1, 64])];
            tensor<fp16, [2, 1280, 1, 64]> inputs_37_cast = reshape(shape = var_1895, x = hidden_states_123_cast);
            tensor<int32, [1]> var_1905 = const()[name = tensor<string, []>("op_1905"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 64]> channels_mean_37_cast = reduce_mean(axes = var_1905, keep_dims = var_1823, x = inputs_37_cast);
            tensor<fp16, [2, 1280, 1, 64]> zero_mean_37_cast = sub(x = inputs_37_cast, y = channels_mean_37_cast);
            tensor<fp16, [2, 1280, 1, 64]> zero_mean_sq_37_cast = mul(x = zero_mean_37_cast, y = zero_mean_37_cast);
            tensor<int32, [1]> var_1909 = const()[name = tensor<string, []>("op_1909"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 64]> var_1910_cast = reduce_mean(axes = var_1909, keep_dims = var_1823, x = zero_mean_sq_37_cast);
            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, 64]> var_1912_cast = add(x = var_1910_cast, y = var_1911_to_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, 64]> denom_37_cast = rsqrt(epsilon = denom_37_epsilon_0_to_fp16, x = var_1912_cast);
            tensor<fp16, [2, 1280, 1, 64]> out_37_cast = mul(x = zero_mean_37_cast, y = denom_37_cast);
            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, []>(573605312)))];
            tensor<fp16, [2, 1280, 1, 64]> var_1917_cast = add(x = out_37_cast, y = var_1916_to_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, []>(573607936)))];
            tensor<fp16, [2, 1280, 1, 64]> hidden_states_125_cast = mul(x = var_1917_cast, y = var_1919_to_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, []>(573610560)))];
            tensor<fp16, [2, 1280, 1, 64]> q_25_cast = 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);
            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, []>(576887424)))];
            tensor<fp16, [2, 1280, 1, 64]> k_25_cast = 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);
            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, []>(580164288)))];
            tensor<fp16, [2, 1280, 1, 64]> v_25_cast = 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);
            tensor<int32, [4]> var_1944 = const()[name = tensor<string, []>("op_1944"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 64]> var_1945_cast = reshape(shape = var_1944, x = q_25_cast);
            tensor<int32, [4]> var_1946 = const()[name = tensor<string, []>("op_1946"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 64]> var_1947_cast = reshape(shape = var_1946, x = k_25_cast);
            tensor<int32, [4]> var_1948 = const()[name = tensor<string, []>("op_1948"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 64]> var_1949_cast = reshape(shape = var_1948, x = v_25_cast);
            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, 64, 64]> attn_weights_49_cast = matmul(transpose_x = attn_weights_49_transpose_x_0, transpose_y = attn_weights_49_transpose_y_0, x = var_1945_cast, y = var_1947_cast);
            tensor<fp16, []> var_1819_to_fp16 = const()[name = tensor<string, []>("op_1819_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
            tensor<fp16, [2, 20, 64, 64]> attn_weights_51_cast = mul(x = attn_weights_49_cast, y = var_1819_to_fp16);
            tensor<fp16, [2, 20, 64, 64]> var_1953_cast = softmax(axis = var_1812, x = attn_weights_51_cast);
            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, 64]> attn_25_cast = matmul(transpose_x = attn_25_transpose_x_0, transpose_y = attn_25_transpose_y_0, x = var_1949_cast, y = var_1953_cast);
            tensor<int32, [4]> var_1957 = const()[name = tensor<string, []>("op_1957"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 64]> input_215_cast = reshape(shape = var_1957, x = attn_25_cast);
            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, []>(583441152)))];
            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, []>(586718016)))];
            tensor<fp16, [2, 1280, 1, 64]> var_1966_cast = 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);
            tensor<fp16, [2, 1280, 1, 64]> inputs_39_cast = add(x = var_1966_cast, y = inputs_37_cast);
            tensor<int32, [1]> var_1970 = const()[name = tensor<string, []>("op_1970"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 64]> channels_mean_39_cast = reduce_mean(axes = var_1970, keep_dims = var_1823, x = inputs_39_cast);
            tensor<fp16, [2, 1280, 1, 64]> zero_mean_39_cast = sub(x = inputs_39_cast, y = channels_mean_39_cast);
            tensor<fp16, [2, 1280, 1, 64]> zero_mean_sq_39_cast = mul(x = zero_mean_39_cast, y = zero_mean_39_cast);
            tensor<int32, [1]> var_1974 = const()[name = tensor<string, []>("op_1974"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 64]> var_1975_cast = reduce_mean(axes = var_1974, keep_dims = var_1823, x = zero_mean_sq_39_cast);
            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, 64]> var_1977_cast = add(x = var_1975_cast, y = var_1976_to_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, 64]> denom_39_cast = rsqrt(epsilon = denom_39_epsilon_0_to_fp16, x = var_1977_cast);
            tensor<fp16, [2, 1280, 1, 64]> out_39_cast = mul(x = zero_mean_39_cast, y = denom_39_cast);
            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, []>(586720640)))];
            tensor<fp16, [2, 1280, 1, 64]> var_1982_cast = add(x = out_39_cast, y = var_1981_to_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, []>(586723264)))];
            tensor<fp16, [2, 1280, 1, 64]> hidden_states_127_cast = mul(x = var_1982_cast, y = var_1984_to_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, []>(586725888)))];
            tensor<fp16, [2, 1280, 1, 64]> q_27_cast = 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);
            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, []>(590002752)))];
            tensor<fp16, [2, 1280, 1, 77]> k_27_cast = 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);
            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, []>(592624256)))];
            tensor<fp16, [2, 1280, 1, 77]> v_27_cast = 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);
            tensor<int32, [4]> var_2009 = const()[name = tensor<string, []>("op_2009"), val = tensor<int32, [4]>([2, 20, 64, -1])];
            tensor<fp16, [2, 20, 64, 64]> var_2010_cast = reshape(shape = var_2009, x = q_27_cast);
            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 = reshape(shape = var_2011, x = k_27_cast);
            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 = reshape(shape = var_2013, x = v_27_cast);
            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, 64, 77]> attn_weights_53_cast = matmul(transpose_x = attn_weights_53_transpose_x_0, transpose_y = attn_weights_53_transpose_y_0, x = var_2010_cast, y = var_2012_cast);
            tensor<fp16, [2, 20, 64, 77]> attn_weights_55_cast = mul(x = attn_weights_53_cast, y = var_1819_to_fp16);
            tensor<fp16, [2, 20, 64, 77]> var_2018_cast = softmax(axis = var_1812, x = attn_weights_55_cast);
            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, 64]> attn_27_cast = matmul(transpose_x = attn_27_transpose_x_0, transpose_y = attn_27_transpose_y_0, x = var_2014_cast, y = var_2018_cast);
            tensor<int32, [4]> var_2022 = const()[name = tensor<string, []>("op_2022"), val = tensor<int32, [4]>([2, 1280, 1, -1])];
            tensor<fp16, [2, 1280, 1, 64]> input_217_cast = reshape(shape = var_2022, x = attn_27_cast);
            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, []>(595245760)))];
            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, []>(598522624)))];
            tensor<fp16, [2, 1280, 1, 64]> var_2031_cast = 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);
            tensor<fp16, [2, 1280, 1, 64]> inputs_41_cast = add(x = var_2031_cast, y = inputs_39_cast);
            tensor<int32, [1]> var_2035 = const()[name = tensor<string, []>("op_2035"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 64]> channels_mean_41_cast = reduce_mean(axes = var_2035, keep_dims = var_1823, x = inputs_41_cast);
            tensor<fp16, [2, 1280, 1, 64]> zero_mean_41_cast = sub(x = inputs_41_cast, y = channels_mean_41_cast);
            tensor<fp16, [2, 1280, 1, 64]> zero_mean_sq_41_cast = mul(x = zero_mean_41_cast, y = zero_mean_41_cast);
            tensor<int32, [1]> var_2039 = const()[name = tensor<string, []>("op_2039"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2, 1, 1, 64]> var_2040_cast = reduce_mean(axes = var_2039, keep_dims = var_1823, x = zero_mean_sq_41_cast);
            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, 64]> var_2042_cast = add(x = var_2040_cast, y = var_2041_to_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, 64]> denom_41_cast = rsqrt(epsilon = denom_41_epsilon_0_to_fp16, x = var_2042_cast);
            tensor<fp16, [2, 1280, 1, 64]> out_41_cast = mul(x = zero_mean_41_cast, y = denom_41_cast);
            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, []>(598525248)))];
            tensor<fp16, [2, 1280, 1, 64]> var_2047_cast = add(x = out_41_cast, y = var_2046_to_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, []>(598527872)))];
            tensor<fp16, [2, 1280, 1, 64]> input_219_cast = mul(x = var_2047_cast, y = var_2049_to_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, []>(598530496)))];
            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, []>(624744960)))];
            tensor<fp16, [2, 10240, 1, 64]> var_2061_cast = 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);
            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, 64]> var_2062_cast_0, tensor<fp16, [2, 5120, 1, 64]> var_2062_cast_1 = split(axis = var_2062_axis_0, split_sizes = var_2062_split_sizes_0, x = var_2061_cast);
            tensor<string, []> var_2064_mode_0 = const()[name = tensor<string, []>("op_2064_mode_0"), val = tensor<string, []>("EXACT")];
            tensor<fp16, [2, 5120, 1, 64]> var_2064_cast = gelu(mode = var_2064_mode_0, x = var_2062_cast_1);
            tensor<fp16, [2, 5120, 1, 64]> input_221_cast = mul(x = var_2062_cast_0, y = var_2064_cast);
            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, []>(624765504)))];
            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, []>(637872768)))];
            tensor<fp16, [2, 1280, 1, 64]> var_2072_cast = 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);
            tensor<fp16, [2, 1280, 1, 64]> hidden_states_131_cast = add(x = var_2072_cast, y = inputs_41_cast);
            tensor<int32, [4]> var_2074 = const()[name = tensor<string, []>("op_2074"), val = tensor<int32, [4]>([2, 1280, 8, 8])];
            tensor<fp16, [2, 1280, 8, 8]> input_223_cast = reshape(shape = var_2074, x = hidden_states_131_cast);
            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, []>(637875392)))];
            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, []>(641152256)))];
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_133_cast = 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);
            tensor<fp16, [2, 1280, 8, 8]> input_225_cast = add(x = hidden_states_133_cast, y = hidden_states_121_cast);
            tensor<int32, [5]> reshape_100_shape_0 = const()[name = tensor<string, []>("reshape_100_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 8, 8])];
            tensor<fp16, [2, 32, 40, 8, 8]> reshape_100_cast = reshape(shape = reshape_100_shape_0, x = input_225_cast);
            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 = reduce_mean(axes = reduce_mean_75_axes_0, keep_dims = reduce_mean_75_keep_dims_0, x = reshape_100_cast);
            tensor<fp16, [2, 32, 40, 8, 8]> sub_50_cast = sub(x = reshape_100_cast, y = reduce_mean_75_cast);
            tensor<fp16, [2, 32, 40, 8, 8]> square_25_cast = square(x = sub_50_cast);
            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 = reduce_mean(axes = reduce_mean_77_axes_0, keep_dims = reduce_mean_77_keep_dims_0, x = square_25_cast);
            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 = add(x = reduce_mean_77_cast, y = add_50_y_0_to_fp16);
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_25_cast = sqrt(x = add_50_cast);
            tensor<fp16, [2, 32, 40, 8, 8]> real_div_25_cast = real_div(x = sub_50_cast, y = sqrt_25_cast);
            tensor<int32, [4]> reshape_101_shape_0 = const()[name = tensor<string, []>("reshape_101_shape_0"), val = tensor<int32, [4]>([2, 1280, 8, 8])];
            tensor<fp16, [2, 1280, 8, 8]> reshape_101_cast = reshape(shape = reshape_101_shape_0, x = real_div_25_cast);
            tensor<fp16, [1280]> add_51_mean_0_to_fp16 = const()[name = tensor<string, []>("add_51_mean_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(641154880)))];
            tensor<fp16, [1280]> add_51_variance_0_to_fp16 = const()[name = tensor<string, []>("add_51_variance_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(641157504)))];
            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, []>(641160128)))];
            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, []>(641162752)))];
            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, 8, 8]> add_51_cast = 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_51_mean_0_to_fp16, variance = add_51_variance_0_to_fp16, x = reshape_101_cast);
            tensor<fp16, [2, 1280, 8, 8]> input_229_cast = silu(x = add_51_cast);
            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, []>(641165376)))];
            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, []>(670656640)))];
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_135_cast = 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);
            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, []>(670659264)))];
            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, []>(673936128)))];
            tensor<fp16, [2, 1280, 1, 1]> temb_19_cast = 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_1);
            tensor<fp16, [2, 1280, 8, 8]> input_233_cast = add(x = hidden_states_135_cast, y = temb_19_cast);
            tensor<int32, [5]> reshape_104_shape_0 = const()[name = tensor<string, []>("reshape_104_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 8, 8])];
            tensor<fp16, [2, 32, 40, 8, 8]> reshape_104_cast = reshape(shape = reshape_104_shape_0, x = input_233_cast);
            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 = reduce_mean(axes = reduce_mean_78_axes_0, keep_dims = reduce_mean_78_keep_dims_0, x = reshape_104_cast);
            tensor<fp16, [2, 32, 40, 8, 8]> sub_52_cast = sub(x = reshape_104_cast, y = reduce_mean_78_cast);
            tensor<fp16, [2, 32, 40, 8, 8]> square_26_cast = square(x = sub_52_cast);
            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 = reduce_mean(axes = reduce_mean_80_axes_0, keep_dims = reduce_mean_80_keep_dims_0, x = square_26_cast);
            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 = add(x = reduce_mean_80_cast, y = add_52_y_0_to_fp16);
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_26_cast = sqrt(x = add_52_cast);
            tensor<fp16, [2, 32, 40, 8, 8]> real_div_26_cast = real_div(x = sub_52_cast, y = sqrt_26_cast);
            tensor<int32, [4]> reshape_105_shape_0 = const()[name = tensor<string, []>("reshape_105_shape_0"), val = tensor<int32, [4]>([2, 1280, 8, 8])];
            tensor<fp16, [2, 1280, 8, 8]> reshape_105_cast = reshape(shape = reshape_105_shape_0, x = real_div_26_cast);
            tensor<fp16, [1280]> add_53_mean_0_to_fp16 = const()[name = tensor<string, []>("add_53_mean_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(673938752)))];
            tensor<fp16, [1280]> add_53_variance_0_to_fp16 = const()[name = tensor<string, []>("add_53_variance_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(673941376)))];
            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, []>(673944000)))];
            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, []>(673946624)))];
            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, 8, 8]> add_53_cast = 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_53_mean_0_to_fp16, variance = add_53_variance_0_to_fp16, x = reshape_105_cast);
            tensor<fp16, [2, 1280, 8, 8]> input_237_cast = silu(x = add_53_cast);
            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, []>(673949248)))];
            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, []>(703440512)))];
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_137_cast = 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);
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_139_cast = add(x = input_225_cast, y = hidden_states_137_cast);
            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, 8, 8]> input_239_cast = concat(axis = var_2128, interleave = input_239_interleave_0, values = (hidden_states_139_cast, input_199_cast));
            tensor<int32, [5]> reshape_108_shape_0 = const()[name = tensor<string, []>("reshape_108_shape_0"), val = tensor<int32, [5]>([2, 32, 80, 8, 8])];
            tensor<fp16, [2, 32, 80, 8, 8]> reshape_108_cast = reshape(shape = reshape_108_shape_0, x = input_239_cast);
            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 = reduce_mean(axes = reduce_mean_81_axes_0, keep_dims = reduce_mean_81_keep_dims_0, x = reshape_108_cast);
            tensor<fp16, [2, 32, 80, 8, 8]> sub_54_cast = sub(x = reshape_108_cast, y = reduce_mean_81_cast);
            tensor<fp16, [2, 32, 80, 8, 8]> square_27_cast = square(x = sub_54_cast);
            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 = reduce_mean(axes = reduce_mean_83_axes_0, keep_dims = reduce_mean_83_keep_dims_0, x = square_27_cast);
            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 = add(x = reduce_mean_83_cast, y = add_54_y_0_to_fp16);
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_27_cast = sqrt(x = add_54_cast);
            tensor<fp16, [2, 32, 80, 8, 8]> real_div_27_cast = real_div(x = sub_54_cast, y = sqrt_27_cast);
            tensor<int32, [4]> reshape_109_shape_0 = const()[name = tensor<string, []>("reshape_109_shape_0"), val = tensor<int32, [4]>([2, 2560, 8, 8])];
            tensor<fp16, [2, 2560, 8, 8]> reshape_109_cast = reshape(shape = reshape_109_shape_0, x = real_div_27_cast);
            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, []>(703443136)))];
            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, []>(703448320)))];
            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, []>(703453504)))];
            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, []>(703458688)))];
            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, 8, 8]> add_55_cast = 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);
            tensor<fp16, [2, 2560, 8, 8]> input_243_cast = silu(x = add_55_cast);
            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, []>(703463872)))];
            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, []>(762446336)))];
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_141_cast = 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);
            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, []>(762448960)))];
            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, []>(765725824)))];
            tensor<fp16, [2, 1280, 1, 1]> temb_21_cast = 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_1);
            tensor<fp16, [2, 1280, 8, 8]> input_247_cast = add(x = hidden_states_141_cast, y = temb_21_cast);
            tensor<int32, [5]> reshape_112_shape_0 = const()[name = tensor<string, []>("reshape_112_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 8, 8])];
            tensor<fp16, [2, 32, 40, 8, 8]> reshape_112_cast = reshape(shape = reshape_112_shape_0, x = input_247_cast);
            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 = reduce_mean(axes = reduce_mean_84_axes_0, keep_dims = reduce_mean_84_keep_dims_0, x = reshape_112_cast);
            tensor<fp16, [2, 32, 40, 8, 8]> sub_56_cast = sub(x = reshape_112_cast, y = reduce_mean_84_cast);
            tensor<fp16, [2, 32, 40, 8, 8]> square_28_cast = square(x = sub_56_cast);
            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 = reduce_mean(axes = reduce_mean_86_axes_0, keep_dims = reduce_mean_86_keep_dims_0, x = square_28_cast);
            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 = add(x = reduce_mean_86_cast, y = add_56_y_0_to_fp16);
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_28_cast = sqrt(x = add_56_cast);
            tensor<fp16, [2, 32, 40, 8, 8]> real_div_28_cast = real_div(x = sub_56_cast, y = sqrt_28_cast);
            tensor<int32, [4]> reshape_113_shape_0 = const()[name = tensor<string, []>("reshape_113_shape_0"), val = tensor<int32, [4]>([2, 1280, 8, 8])];
            tensor<fp16, [2, 1280, 8, 8]> reshape_113_cast = reshape(shape = reshape_113_shape_0, x = real_div_28_cast);
            tensor<fp16, [1280]> add_57_mean_0_to_fp16 = const()[name = tensor<string, []>("add_57_mean_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(765728448)))];
            tensor<fp16, [1280]> add_57_variance_0_to_fp16 = const()[name = tensor<string, []>("add_57_variance_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(765731072)))];
            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, []>(765733696)))];
            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, []>(765736320)))];
            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, 8, 8]> add_57_cast = 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_57_mean_0_to_fp16, variance = add_57_variance_0_to_fp16, x = reshape_113_cast);
            tensor<fp16, [2, 1280, 8, 8]> input_251_cast = silu(x = add_57_cast);
            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, []>(765738944)))];
            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, []>(795230208)))];
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_143_cast = 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);
            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, []>(795232832)))];
            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, []>(801786496)))];
            tensor<fp16, [2, 1280, 8, 8]> x_5_cast = 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);
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_145_cast = add(x = x_5_cast, y = hidden_states_143_cast);
            tensor<bool, []> input_253_interleave_0 = const()[name = tensor<string, []>("input_253_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [2, 2560, 8, 8]> input_253_cast_1 = concat(axis = var_2128, interleave = input_253_interleave_0, values = (hidden_states_145_cast, input_185_cast));
            tensor<int32, [5]> reshape_116_shape_0 = const()[name = tensor<string, []>("reshape_116_shape_0"), val = tensor<int32, [5]>([2, 32, 80, 8, 8])];
            tensor<fp16, [2, 32, 80, 8, 8]> reshape_116_cast = reshape(shape = reshape_116_shape_0, x = input_253_cast_1);
            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 = reduce_mean(axes = reduce_mean_87_axes_0, keep_dims = reduce_mean_87_keep_dims_0, x = reshape_116_cast);
            tensor<fp16, [2, 32, 80, 8, 8]> sub_58_cast = sub(x = reshape_116_cast, y = reduce_mean_87_cast);
            tensor<fp16, [2, 32, 80, 8, 8]> square_29_cast = square(x = sub_58_cast);
            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 = reduce_mean(axes = reduce_mean_89_axes_0, keep_dims = reduce_mean_89_keep_dims_0, x = square_29_cast);
            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 = add(x = reduce_mean_89_cast, y = add_58_y_0_to_fp16);
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_29_cast = sqrt(x = add_58_cast);
            tensor<fp16, [2, 32, 80, 8, 8]> real_div_29_cast = real_div(x = sub_58_cast, y = sqrt_29_cast);
            tensor<int32, [4]> reshape_117_shape_0 = const()[name = tensor<string, []>("reshape_117_shape_0"), val = tensor<int32, [4]>([2, 2560, 8, 8])];
            tensor<fp16, [2, 2560, 8, 8]> reshape_117_cast = reshape(shape = reshape_117_shape_0, x = real_div_29_cast);
            tensor<fp16, [2560]> add_59_mean_0_to_fp16 = const()[name = tensor<string, []>("add_59_mean_0_to_fp16"), val = tensor<fp16, [2560]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(801789120)))];
            tensor<fp16, [2560]> add_59_variance_0_to_fp16 = const()[name = tensor<string, []>("add_59_variance_0_to_fp16"), val = tensor<fp16, [2560]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(801794304)))];
            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, []>(801799488)))];
            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, []>(801804672)))];
            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, 8, 8]> add_59_cast = 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_59_mean_0_to_fp16, variance = add_59_variance_0_to_fp16, x = reshape_117_cast);
            tensor<fp16, [2, 2560, 8, 8]> input_257_cast = silu(x = add_59_cast);
            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, []>(801809856)))];
            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, []>(860792320)))];
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_147_cast = 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);
            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, []>(860794944)))];
            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, []>(864071808)))];
            tensor<fp16, [2, 1280, 1, 1]> temb_23_cast = 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_1);
            tensor<fp16, [2, 1280, 8, 8]> input_261_cast = add(x = hidden_states_147_cast, y = temb_23_cast);
            tensor<int32, [5]> reshape_120_shape_0 = const()[name = tensor<string, []>("reshape_120_shape_0"), val = tensor<int32, [5]>([2, 32, 40, 8, 8])];
            tensor<fp16, [2, 32, 40, 8, 8]> reshape_120_cast = reshape(shape = reshape_120_shape_0, x = input_261_cast);
            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 = reduce_mean(axes = reduce_mean_90_axes_0, keep_dims = reduce_mean_90_keep_dims_0, x = reshape_120_cast);
            tensor<fp16, [2, 32, 40, 8, 8]> sub_60_cast = sub(x = reshape_120_cast, y = reduce_mean_90_cast);
            tensor<fp16, [2, 32, 40, 8, 8]> square_30_cast = square(x = sub_60_cast);
            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 = reduce_mean(axes = reduce_mean_92_axes_0, keep_dims = reduce_mean_92_keep_dims_0, x = square_30_cast);
            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 = add(x = reduce_mean_92_cast, y = add_60_y_0_to_fp16);
            tensor<fp16, [2, 32, 1, 1, 1]> sqrt_30_cast = sqrt(x = add_60_cast);
            tensor<fp16, [2, 32, 40, 8, 8]> real_div_30_cast = real_div(x = sub_60_cast, y = sqrt_30_cast);
            tensor<int32, [4]> reshape_121_shape_0 = const()[name = tensor<string, []>("reshape_121_shape_0"), val = tensor<int32, [4]>([2, 1280, 8, 8])];
            tensor<fp16, [2, 1280, 8, 8]> reshape_121_cast = reshape(shape = reshape_121_shape_0, x = real_div_30_cast);
            tensor<fp16, [1280]> add_61_mean_0_to_fp16 = const()[name = tensor<string, []>("add_61_mean_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(864074432)))];
            tensor<fp16, [1280]> add_61_variance_0_to_fp16 = const()[name = tensor<string, []>("add_61_variance_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(864077056)))];
            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, []>(864079680)))];
            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, []>(864082304)))];
            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, 8, 8]> add_61_cast = 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_61_mean_0_to_fp16, variance = add_61_variance_0_to_fp16, x = reshape_121_cast);
            tensor<fp16, [2, 1280, 8, 8]> input_265_cast = silu(x = add_61_cast);
            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, []>(864084928)))];
            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, []>(893576192)))];
            tensor<fp16, [2, 1280, 8, 8]> hidden_states_149_cast_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);
            tensor<string, []> input_61_cast_dtype_0 = const()[name = tensor<string, []>("input_61_cast_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<string, []> input_143_cast_dtype_0 = const()[name = tensor<string, []>("input_143_cast_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<string, []> input_171_cast_dtype_0 = const()[name = tensor<string, []>("input_171_cast_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<string, []> hidden_states_149_cast_dtype_0 = const()[name = tensor<string, []>("hidden_states_149_cast_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<string, []> input_7_cast_dtype_0 = const()[name = tensor<string, []>("input_7_cast_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<string, []> input_89_cast_dtype_0 = const()[name = tensor<string, []>("input_89_cast_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<string, []> input_117_cast_dtype_0 = const()[name = tensor<string, []>("input_117_cast_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<string, []> input_169_cast_dtype_0 = const()[name = tensor<string, []>("input_169_cast_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<string, []> input_253_cast_dtype_0 = const()[name = tensor<string, []>("input_253_cast_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<string, []> input_35_cast_dtype_0 = const()[name = tensor<string, []>("input_35_cast_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<string, []> input_63_cast_dtype_0 = const()[name = tensor<string, []>("input_63_cast_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<string, []> input_15_cast_dtype_0 = const()[name = tensor<string, []>("input_15_cast_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<string, []> input_115_cast_dtype_0 = const()[name = tensor<string, []>("input_115_cast_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<fp32, [2, 640, 32, 32]> input_115_cast = cast(dtype = input_115_cast_dtype_0, x = input_115_cast_1);
            tensor<fp32, [2, 1280, 1, 1]> input_15_cast = cast(dtype = input_15_cast_dtype_0, x = input_15_cast_1);
            tensor<fp32, [2, 320, 32, 32]> input_63_cast = cast(dtype = input_63_cast_dtype_0, x = input_63_cast_1);
            tensor<fp32, [2, 320, 64, 64]> input_35_cast = cast(dtype = input_35_cast_dtype_0, x = input_35_cast_1);
            tensor<fp32, [2, 2560, 8, 8]> input_253_cast = cast(dtype = input_253_cast_dtype_0, x = input_253_cast_1);
            tensor<fp32, [2, 1280, 16, 16]> input_169_cast = cast(dtype = input_169_cast_dtype_0, x = input_169_cast_1);
            tensor<fp32, [2, 640, 16, 16]> input_117_cast = cast(dtype = input_117_cast_dtype_0, x = input_117_cast_1);
            tensor<fp32, [2, 640, 32, 32]> input_89_cast = cast(dtype = input_89_cast_dtype_0, x = input_89_cast_1);
            tensor<fp32, [2, 320, 64, 64]> input_7_cast = cast(dtype = input_7_cast_dtype_0, x = input_7_cast_1);
            tensor<fp32, [2, 1280, 8, 8]> hidden_states_149_cast = cast(dtype = hidden_states_149_cast_dtype_0, x = hidden_states_149_cast_1);
            tensor<fp32, [2, 1280, 8, 8]> input_171_cast = cast(dtype = input_171_cast_dtype_0, x = input_171_cast_1);
            tensor<fp32, [2, 1280, 16, 16]> input_143_cast = cast(dtype = input_143_cast_dtype_0, x = input_143_cast_1);
            tensor<fp32, [2, 320, 64, 64]> input_61_cast = cast(dtype = input_61_cast_dtype_0, x = input_61_cast_1);
        } -> (input_61_cast, input_143_cast, input_171_cast, hidden_states_149_cast, input_7_cast, input_89_cast, input_117_cast, input_169_cast, input_253_cast, input_35_cast, input_63_cast, input_15_cast, input_115_cast);
}