program(1.0)
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.3.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})]
{
    func main<ios17>(tensor<int32, [1, 128]> attention_mask, tensor<int32, [1, 128]> input_ids, tensor<fp32, [1, 9]> random_phases, tensor<fp32, [1, 256]> ref_s, tensor<fp32, [1]> speed) {
            tensor<fp32, [178, 128]> bert_embeddings_word_embeddings_weight = const()[name = tensor<string, []>("bert_embeddings_word_embeddings_weight"), val = tensor<fp32, [178, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
            tensor<fp32, [128]> bert_embeddings_LayerNorm_bias = const()[name = tensor<string, []>("bert_embeddings_LayerNorm_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(91264)))];
            tensor<fp32, [128]> bert_embeddings_LayerNorm_weight = const()[name = tensor<string, []>("bert_embeddings_LayerNorm_weight"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(91840)))];
            tensor<fp32, [768]> bert_encoder_embedding_hidden_mapping_in_bias = const()[name = tensor<string, []>("bert_encoder_embedding_hidden_mapping_in_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(92416)))];
            tensor<fp32, [768, 128]> bert_encoder_embedding_hidden_mapping_in_weight = const()[name = tensor<string, []>("bert_encoder_embedding_hidden_mapping_in_weight"), val = tensor<fp32, [768, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(95552)))];
            tensor<fp32, [768]> bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias = const()[name = tensor<string, []>("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(488832)))];
            tensor<fp32, [768, 768]> bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight = const()[name = tensor<string, []>("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(491968)))];
            tensor<fp32, [768]> bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias = const()[name = tensor<string, []>("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2851328)))];
            tensor<fp32, [768, 768]> bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight = const()[name = tensor<string, []>("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2854464)))];
            tensor<fp32, [768]> bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias = const()[name = tensor<string, []>("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5213824)))];
            tensor<fp32, [768, 768]> bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight = const()[name = tensor<string, []>("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5216960)))];
            tensor<fp32, [768]> bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias = const()[name = tensor<string, []>("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7576320)))];
            tensor<fp32, [768, 768]> bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight = const()[name = tensor<string, []>("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7579456)))];
            tensor<fp32, [768]> bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias = const()[name = tensor<string, []>("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9938816)))];
            tensor<fp32, [768]> bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight = const()[name = tensor<string, []>("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9941952)))];
            tensor<fp32, [2048]> bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias = const()[name = tensor<string, []>("bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9945088)))];
            tensor<fp32, [2048, 768]> bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight = const()[name = tensor<string, []>("bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight"), val = tensor<fp32, [2048, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9953344)))];
            tensor<fp32, [768]> bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias = const()[name = tensor<string, []>("bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16244864)))];
            tensor<fp32, [768, 2048]> bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight = const()[name = tensor<string, []>("bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight"), val = tensor<fp32, [768, 2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16248000)))];
            tensor<fp32, [768]> bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias = const()[name = tensor<string, []>("bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22539520)))];
            tensor<fp32, [768]> bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight = const()[name = tensor<string, []>("bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22542656)))];
            tensor<fp32, [512]> bert_encoder_bias = const()[name = tensor<string, []>("bert_encoder_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22545792)))];
            tensor<fp32, [512, 768]> bert_encoder_weight = const()[name = tensor<string, []>("bert_encoder_weight"), val = tensor<fp32, [512, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22547904)))];
            tensor<fp32, [1024]> de_lstms_1_fc_bias = const()[name = tensor<string, []>("de_lstms_1_fc_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(24120832)))];
            tensor<fp32, [1024, 128]> de_lstms_1_fc_weight = const()[name = tensor<string, []>("de_lstms_1_fc_weight"), val = tensor<fp32, [1024, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(24124992)))];
            tensor<fp32, [1024]> de_lstms_3_fc_bias = const()[name = tensor<string, []>("de_lstms_3_fc_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(24649344)))];
            tensor<fp32, [1024, 128]> de_lstms_3_fc_weight = const()[name = tensor<string, []>("de_lstms_3_fc_weight"), val = tensor<fp32, [1024, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(24653504)))];
            tensor<fp32, [1024]> de_lstms_5_fc_bias = const()[name = tensor<string, []>("de_lstms_5_fc_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25177856)))];
            tensor<fp32, [1024, 128]> de_lstms_5_fc_weight = const()[name = tensor<string, []>("de_lstms_5_fc_weight"), val = tensor<fp32, [1024, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25182016)))];
            tensor<fp32, [50]> pred_duration_proj_linear_layer_bias = const()[name = tensor<string, []>("pred_duration_proj_linear_layer_bias"), val = tensor<fp32, [50]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25706368)))];
            tensor<fp32, [50, 512]> pred_duration_proj_linear_layer_weight = const()[name = tensor<string, []>("pred_duration_proj_linear_layer_weight"), val = tensor<fp32, [50, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25706688)))];
            tensor<fp32, [178, 512]> te_embedding_weight = const()[name = tensor<string, []>("te_embedding_weight"), val = tensor<fp32, [178, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25809152)))];
            tensor<fp32, [512]> te_cnn_0_0_bias = const()[name = tensor<string, []>("te_cnn_0_0_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26173760)))];
            tensor<fp32, [512]> te_cnn_0_1_beta = const()[name = tensor<string, []>("te_cnn_0_1_beta"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26175872)))];
            tensor<fp32, [512]> te_cnn_0_1_gamma = const()[name = tensor<string, []>("te_cnn_0_1_gamma"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26177984)))];
            tensor<fp32, [512]> te_cnn_1_0_bias = const()[name = tensor<string, []>("te_cnn_1_0_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26180096)))];
            tensor<fp32, [512]> te_cnn_1_1_beta = const()[name = tensor<string, []>("te_cnn_1_1_beta"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26182208)))];
            tensor<fp32, [512]> te_cnn_1_1_gamma = const()[name = tensor<string, []>("te_cnn_1_1_gamma"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26184320)))];
            tensor<fp32, [512]> te_cnn_2_0_bias = const()[name = tensor<string, []>("te_cnn_2_0_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26186432)))];
            tensor<fp32, [512]> te_cnn_2_1_beta = const()[name = tensor<string, []>("te_cnn_2_1_beta"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26188544)))];
            tensor<fp32, [512]> te_cnn_2_1_gamma = const()[name = tensor<string, []>("te_cnn_2_1_gamma"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26190656)))];
            tensor<fp32, [1024]> F0_blocks_0_norm1_fc_bias = const()[name = tensor<string, []>("F0_blocks_0_norm1_fc_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26192768)))];
            tensor<fp32, [1024, 128]> F0_blocks_0_norm1_fc_weight = const()[name = tensor<string, []>("F0_blocks_0_norm1_fc_weight"), val = tensor<fp32, [1024, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26196928)))];
            tensor<fp32, [512]> F0_blocks_0_norm1_norm_bias = const()[name = tensor<string, []>("F0_blocks_0_norm1_norm_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26721280)))];
            tensor<fp32, [512]> F0_blocks_0_norm1_norm_weight = const()[name = tensor<string, []>("F0_blocks_0_norm1_norm_weight"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26723392)))];
            tensor<fp32, [512]> F0_blocks_0_conv1_bias = const()[name = tensor<string, []>("F0_blocks_0_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26725504)))];
            tensor<fp32, [1024]> F0_blocks_0_norm2_fc_bias = const()[name = tensor<string, []>("F0_blocks_0_norm2_fc_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26727616)))];
            tensor<fp32, [1024, 128]> F0_blocks_0_norm2_fc_weight = const()[name = tensor<string, []>("F0_blocks_0_norm2_fc_weight"), val = tensor<fp32, [1024, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26731776)))];
            tensor<fp32, [512]> F0_blocks_0_conv2_bias = const()[name = tensor<string, []>("F0_blocks_0_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(27256128)))];
            tensor<fp32, [1024]> F0_blocks_1_norm1_fc_bias = const()[name = tensor<string, []>("F0_blocks_1_norm1_fc_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(27258240)))];
            tensor<fp32, [1024, 128]> F0_blocks_1_norm1_fc_weight = const()[name = tensor<string, []>("F0_blocks_1_norm1_fc_weight"), val = tensor<fp32, [1024, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(27262400)))];
            tensor<fp32, [512]> F0_blocks_1_pool_bias = const()[name = tensor<string, []>("F0_blocks_1_pool_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(27786752)))];
            tensor<fp32, [256]> F0_blocks_1_conv1_bias = const()[name = tensor<string, []>("F0_blocks_1_conv1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(27788864)))];
            tensor<fp32, [512]> F0_blocks_1_norm2_fc_bias = const()[name = tensor<string, []>("F0_blocks_1_norm2_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(27789952)))];
            tensor<fp32, [512, 128]> F0_blocks_1_norm2_fc_weight = const()[name = tensor<string, []>("F0_blocks_1_norm2_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(27792064)))];
            tensor<fp32, [256]> F0_blocks_1_norm2_norm_bias = const()[name = tensor<string, []>("F0_blocks_1_norm2_norm_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28054272)))];
            tensor<fp32, [256]> F0_blocks_1_norm2_norm_weight = const()[name = tensor<string, []>("F0_blocks_1_norm2_norm_weight"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28055360)))];
            tensor<fp32, [256]> F0_blocks_1_conv2_bias = const()[name = tensor<string, []>("F0_blocks_1_conv2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28056448)))];
            tensor<fp32, [512]> F0_blocks_2_norm1_fc_bias = const()[name = tensor<string, []>("F0_blocks_2_norm1_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28057536)))];
            tensor<fp32, [512, 128]> F0_blocks_2_norm1_fc_weight = const()[name = tensor<string, []>("F0_blocks_2_norm1_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28059648)))];
            tensor<fp32, [256]> F0_blocks_2_conv1_bias = const()[name = tensor<string, []>("F0_blocks_2_conv1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28321856)))];
            tensor<fp32, [512]> F0_blocks_2_norm2_fc_bias = const()[name = tensor<string, []>("F0_blocks_2_norm2_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28322944)))];
            tensor<fp32, [512, 128]> F0_blocks_2_norm2_fc_weight = const()[name = tensor<string, []>("F0_blocks_2_norm2_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28325056)))];
            tensor<fp32, [256]> F0_blocks_2_conv2_bias = const()[name = tensor<string, []>("F0_blocks_2_conv2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28587264)))];
            tensor<fp32, [1]> F0_proj_bias = const()[name = tensor<string, []>("F0_proj_bias"), val = tensor<fp32, [1]>([0x1.edcbf4p-3])];
            tensor<fp32, [1, 256, 1]> F0_proj_weight = const()[name = tensor<string, []>("F0_proj_weight"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28588352)))];
            tensor<fp32, [1024]> N_blocks_0_norm1_fc_bias = const()[name = tensor<string, []>("N_blocks_0_norm1_fc_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28589440)))];
            tensor<fp32, [1024, 128]> N_blocks_0_norm1_fc_weight = const()[name = tensor<string, []>("N_blocks_0_norm1_fc_weight"), val = tensor<fp32, [1024, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28593600)))];
            tensor<fp32, [512]> N_blocks_0_conv1_bias = const()[name = tensor<string, []>("N_blocks_0_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29117952)))];
            tensor<fp32, [1024]> N_blocks_0_norm2_fc_bias = const()[name = tensor<string, []>("N_blocks_0_norm2_fc_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29120064)))];
            tensor<fp32, [1024, 128]> N_blocks_0_norm2_fc_weight = const()[name = tensor<string, []>("N_blocks_0_norm2_fc_weight"), val = tensor<fp32, [1024, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29124224)))];
            tensor<fp32, [512]> N_blocks_0_conv2_bias = const()[name = tensor<string, []>("N_blocks_0_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29648576)))];
            tensor<fp32, [1024]> N_blocks_1_norm1_fc_bias = const()[name = tensor<string, []>("N_blocks_1_norm1_fc_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29650688)))];
            tensor<fp32, [1024, 128]> N_blocks_1_norm1_fc_weight = const()[name = tensor<string, []>("N_blocks_1_norm1_fc_weight"), val = tensor<fp32, [1024, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29654848)))];
            tensor<fp32, [512]> N_blocks_1_pool_bias = const()[name = tensor<string, []>("N_blocks_1_pool_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30179200)))];
            tensor<fp32, [256]> N_blocks_1_conv1_bias = const()[name = tensor<string, []>("N_blocks_1_conv1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30181312)))];
            tensor<fp32, [512]> N_blocks_1_norm2_fc_bias = const()[name = tensor<string, []>("N_blocks_1_norm2_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30182400)))];
            tensor<fp32, [512, 128]> N_blocks_1_norm2_fc_weight = const()[name = tensor<string, []>("N_blocks_1_norm2_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30184512)))];
            tensor<fp32, [256]> N_blocks_1_conv2_bias = const()[name = tensor<string, []>("N_blocks_1_conv2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30446720)))];
            tensor<fp32, [512]> N_blocks_2_norm1_fc_bias = const()[name = tensor<string, []>("N_blocks_2_norm1_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30447808)))];
            tensor<fp32, [512, 128]> N_blocks_2_norm1_fc_weight = const()[name = tensor<string, []>("N_blocks_2_norm1_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30449920)))];
            tensor<fp32, [256]> N_blocks_2_conv1_bias = const()[name = tensor<string, []>("N_blocks_2_conv1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30712128)))];
            tensor<fp32, [512]> N_blocks_2_norm2_fc_bias = const()[name = tensor<string, []>("N_blocks_2_norm2_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30713216)))];
            tensor<fp32, [512, 128]> N_blocks_2_norm2_fc_weight = const()[name = tensor<string, []>("N_blocks_2_norm2_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30715328)))];
            tensor<fp32, [256]> N_blocks_2_conv2_bias = const()[name = tensor<string, []>("N_blocks_2_conv2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30977536)))];
            tensor<fp32, [1]> N_proj_bias = const()[name = tensor<string, []>("N_proj_bias"), val = tensor<fp32, [1]>([0x1.13144ap-4])];
            tensor<fp32, [1, 256, 1]> N_proj_weight = const()[name = tensor<string, []>("N_proj_weight"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30978624)))];
            tensor<fp32, [1]> decoder_F0_conv_bias = const()[name = tensor<string, []>("decoder_F0_conv_bias"), val = tensor<fp32, [1]>([-0x1.005f38p-2])];
            tensor<fp32, [1]> decoder_N_conv_bias = const()[name = tensor<string, []>("decoder_N_conv_bias"), val = tensor<fp32, [1]>([-0x1.e68b08p-2])];
            tensor<fp32, [1028]> decoder_encode_norm1_fc_bias = const()[name = tensor<string, []>("decoder_encode_norm1_fc_bias"), val = tensor<fp32, [1028]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30979712)))];
            tensor<fp32, [1028, 128]> decoder_encode_norm1_fc_weight = const()[name = tensor<string, []>("decoder_encode_norm1_fc_weight"), val = tensor<fp32, [1028, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30983936)))];
            tensor<fp32, [514]> decoder_encode_norm1_norm_bias = const()[name = tensor<string, []>("decoder_encode_norm1_norm_bias"), val = tensor<fp32, [514]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(31510336)))];
            tensor<fp32, [514]> decoder_encode_norm1_norm_weight = const()[name = tensor<string, []>("decoder_encode_norm1_norm_weight"), val = tensor<fp32, [514]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(31512512)))];
            tensor<fp32, [1024]> decoder_encode_conv1_bias = const()[name = tensor<string, []>("decoder_encode_conv1_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(31514688)))];
            tensor<fp32, [2048]> decoder_encode_norm2_fc_bias = const()[name = tensor<string, []>("decoder_encode_norm2_fc_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(31518848)))];
            tensor<fp32, [2048, 128]> decoder_encode_norm2_fc_weight = const()[name = tensor<string, []>("decoder_encode_norm2_fc_weight"), val = tensor<fp32, [2048, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(31527104)))];
            tensor<fp32, [1024]> decoder_encode_norm2_norm_bias = const()[name = tensor<string, []>("decoder_encode_norm2_norm_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32575744)))];
            tensor<fp32, [1024]> decoder_encode_norm2_norm_weight = const()[name = tensor<string, []>("decoder_encode_norm2_norm_weight"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32579904)))];
            tensor<fp32, [1024]> decoder_encode_conv2_bias = const()[name = tensor<string, []>("decoder_encode_conv2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32584064)))];
            tensor<fp32, [64]> decoder_asr_res_0_bias = const()[name = tensor<string, []>("decoder_asr_res_0_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32588224)))];
            tensor<fp32, [2180]> decoder_decode_0_norm1_fc_bias = const()[name = tensor<string, []>("decoder_decode_0_norm1_fc_bias"), val = tensor<fp32, [2180]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32588544)))];
            tensor<fp32, [2180, 128]> decoder_decode_0_norm1_fc_weight = const()[name = tensor<string, []>("decoder_decode_0_norm1_fc_weight"), val = tensor<fp32, [2180, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32597376)))];
            tensor<fp32, [1090]> decoder_decode_0_norm1_norm_bias = const()[name = tensor<string, []>("decoder_decode_0_norm1_norm_bias"), val = tensor<fp32, [1090]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33713600)))];
            tensor<fp32, [1090]> decoder_decode_0_norm1_norm_weight = const()[name = tensor<string, []>("decoder_decode_0_norm1_norm_weight"), val = tensor<fp32, [1090]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33718080)))];
            tensor<fp32, [1024]> decoder_decode_0_conv1_bias = const()[name = tensor<string, []>("decoder_decode_0_conv1_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33722560)))];
            tensor<fp32, [2048]> decoder_decode_0_norm2_fc_bias = const()[name = tensor<string, []>("decoder_decode_0_norm2_fc_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33726720)))];
            tensor<fp32, [2048, 128]> decoder_decode_0_norm2_fc_weight = const()[name = tensor<string, []>("decoder_decode_0_norm2_fc_weight"), val = tensor<fp32, [2048, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33734976)))];
            tensor<fp32, [1024]> decoder_decode_0_conv2_bias = const()[name = tensor<string, []>("decoder_decode_0_conv2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(34783616)))];
            tensor<fp32, [2180]> decoder_decode_1_norm1_fc_bias = const()[name = tensor<string, []>("decoder_decode_1_norm1_fc_bias"), val = tensor<fp32, [2180]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(34787776)))];
            tensor<fp32, [2180, 128]> decoder_decode_1_norm1_fc_weight = const()[name = tensor<string, []>("decoder_decode_1_norm1_fc_weight"), val = tensor<fp32, [2180, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(34796608)))];
            tensor<fp32, [1024]> decoder_decode_1_conv1_bias = const()[name = tensor<string, []>("decoder_decode_1_conv1_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(35912832)))];
            tensor<fp32, [2048]> decoder_decode_1_norm2_fc_bias = const()[name = tensor<string, []>("decoder_decode_1_norm2_fc_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(35916992)))];
            tensor<fp32, [2048, 128]> decoder_decode_1_norm2_fc_weight = const()[name = tensor<string, []>("decoder_decode_1_norm2_fc_weight"), val = tensor<fp32, [2048, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(35925248)))];
            tensor<fp32, [1024]> decoder_decode_1_conv2_bias = const()[name = tensor<string, []>("decoder_decode_1_conv2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36973888)))];
            tensor<fp32, [2180]> decoder_decode_2_norm1_fc_bias = const()[name = tensor<string, []>("decoder_decode_2_norm1_fc_bias"), val = tensor<fp32, [2180]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36978048)))];
            tensor<fp32, [2180, 128]> decoder_decode_2_norm1_fc_weight = const()[name = tensor<string, []>("decoder_decode_2_norm1_fc_weight"), val = tensor<fp32, [2180, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36986880)))];
            tensor<fp32, [1024]> decoder_decode_2_conv1_bias = const()[name = tensor<string, []>("decoder_decode_2_conv1_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38103104)))];
            tensor<fp32, [2048]> decoder_decode_2_norm2_fc_bias = const()[name = tensor<string, []>("decoder_decode_2_norm2_fc_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38107264)))];
            tensor<fp32, [2048, 128]> decoder_decode_2_norm2_fc_weight = const()[name = tensor<string, []>("decoder_decode_2_norm2_fc_weight"), val = tensor<fp32, [2048, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38115520)))];
            tensor<fp32, [1024]> decoder_decode_2_conv2_bias = const()[name = tensor<string, []>("decoder_decode_2_conv2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39164160)))];
            tensor<fp32, [2180]> decoder_decode_3_norm1_fc_bias = const()[name = tensor<string, []>("decoder_decode_3_norm1_fc_bias"), val = tensor<fp32, [2180]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39168320)))];
            tensor<fp32, [2180, 128]> decoder_decode_3_norm1_fc_weight = const()[name = tensor<string, []>("decoder_decode_3_norm1_fc_weight"), val = tensor<fp32, [2180, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39177152)))];
            tensor<fp32, [1090]> decoder_decode_3_pool_bias = const()[name = tensor<string, []>("decoder_decode_3_pool_bias"), val = tensor<fp32, [1090]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40293376)))];
            tensor<fp32, [512]> decoder_decode_3_conv1_bias = const()[name = tensor<string, []>("decoder_decode_3_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40297856)))];
            tensor<fp32, [1024]> decoder_decode_3_norm2_fc_bias = const()[name = tensor<string, []>("decoder_decode_3_norm2_fc_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40299968)))];
            tensor<fp32, [1024, 128]> decoder_decode_3_norm2_fc_weight = const()[name = tensor<string, []>("decoder_decode_3_norm2_fc_weight"), val = tensor<fp32, [1024, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40304128)))];
            tensor<fp32, [512]> decoder_decode_3_conv2_bias = const()[name = tensor<string, []>("decoder_decode_3_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40828480)))];
            tensor<fp32, [11, 1, 20]> decoder_generator_stft_weight_backward_imag = const()[name = tensor<string, []>("decoder_generator_stft_weight_backward_imag"), val = tensor<fp32, [11, 1, 20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40830592)))];
            tensor<fp32, [11, 1, 20]> decoder_generator_stft_weight_backward_real = const()[name = tensor<string, []>("decoder_generator_stft_weight_backward_real"), val = tensor<fp32, [11, 1, 20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40831552)))];
            tensor<fp32, [11, 1, 20]> decoder_generator_stft_weight_forward_imag = const()[name = tensor<string, []>("decoder_generator_stft_weight_forward_imag"), val = tensor<fp32, [11, 1, 20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40832512)))];
            tensor<fp32, [11, 1, 20]> decoder_generator_stft_weight_forward_real = const()[name = tensor<string, []>("decoder_generator_stft_weight_forward_real"), val = tensor<fp32, [11, 1, 20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40833472)))];
            tensor<fp32, [1]> decoder_generator_m_source_l_linear_bias = const()[name = tensor<string, []>("decoder_generator_m_source_l_linear_bias"), val = tensor<fp32, [1]>([-0x1.e28358p-6])];
            tensor<fp32, [1, 9]> decoder_generator_m_source_l_linear_weight = const()[name = tensor<string, []>("decoder_generator_m_source_l_linear_weight"), val = tensor<fp32, [1, 9]>([[-0x1.4dfed8p-4, -0x1.7b4864p-3, -0x1.7608cep-3, -0x1.6d4e54p-3, -0x1.946f4ap-4, 0x1.527ebcp-4, 0x1.66277ap-4, -0x1.900fdap-2, -0x1.1871f2p-1]])];
            tensor<fp32, [256]> decoder_generator_noise_convs_0_bias = const()[name = tensor<string, []>("decoder_generator_noise_convs_0_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40834432)))];
            tensor<fp32, [256, 22, 12]> decoder_generator_noise_convs_0_weight = const()[name = tensor<string, []>("decoder_generator_noise_convs_0_weight"), val = tensor<fp32, [256, 22, 12]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40835520)))];
            tensor<fp32, [1, 256, 1]> decoder_generator_noise_res_0_alpha2_2 = const()[name = tensor<string, []>("decoder_generator_noise_res_0_alpha2_2"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41105920)))];
            tensor<fp32, [1, 256, 1]> decoder_generator_noise_res_0_alpha1_2 = const()[name = tensor<string, []>("decoder_generator_noise_res_0_alpha1_2"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41107008)))];
            tensor<fp32, [1, 256, 1]> decoder_generator_noise_res_0_alpha2_1 = const()[name = tensor<string, []>("decoder_generator_noise_res_0_alpha2_1"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41108096)))];
            tensor<fp32, [1, 256, 1]> decoder_generator_noise_res_0_alpha1_1 = const()[name = tensor<string, []>("decoder_generator_noise_res_0_alpha1_1"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41109184)))];
            tensor<fp32, [1, 256, 1]> decoder_generator_noise_res_0_alpha2_0 = const()[name = tensor<string, []>("decoder_generator_noise_res_0_alpha2_0"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41110272)))];
            tensor<fp32, [1, 256, 1]> decoder_generator_noise_res_0_alpha1_0 = const()[name = tensor<string, []>("decoder_generator_noise_res_0_alpha1_0"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41111360)))];
            tensor<fp32, [512]> decoder_generator_noise_res_0_adain1_0_fc_bias = const()[name = tensor<string, []>("decoder_generator_noise_res_0_adain1_0_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41112448)))];
            tensor<fp32, [512, 128]> decoder_generator_noise_res_0_adain1_0_fc_weight = const()[name = tensor<string, []>("decoder_generator_noise_res_0_adain1_0_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41114560)))];
            tensor<fp32, [256]> decoder_generator_noise_res_0_convs1_0_bias = const()[name = tensor<string, []>("decoder_generator_noise_res_0_convs1_0_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41376768)))];
            tensor<fp32, [512]> decoder_generator_noise_res_0_adain2_0_fc_bias = const()[name = tensor<string, []>("decoder_generator_noise_res_0_adain2_0_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41377856)))];
            tensor<fp32, [512, 128]> decoder_generator_noise_res_0_adain2_0_fc_weight = const()[name = tensor<string, []>("decoder_generator_noise_res_0_adain2_0_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41379968)))];
            tensor<fp32, [256]> decoder_generator_noise_res_0_convs2_0_bias = const()[name = tensor<string, []>("decoder_generator_noise_res_0_convs2_0_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41642176)))];
            tensor<fp32, [512]> decoder_generator_noise_res_0_adain1_1_fc_bias = const()[name = tensor<string, []>("decoder_generator_noise_res_0_adain1_1_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41643264)))];
            tensor<fp32, [512, 128]> decoder_generator_noise_res_0_adain1_1_fc_weight = const()[name = tensor<string, []>("decoder_generator_noise_res_0_adain1_1_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41645376)))];
            tensor<fp32, [256]> decoder_generator_noise_res_0_convs1_1_bias = const()[name = tensor<string, []>("decoder_generator_noise_res_0_convs1_1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41907584)))];
            tensor<fp32, [512]> decoder_generator_noise_res_0_adain2_1_fc_bias = const()[name = tensor<string, []>("decoder_generator_noise_res_0_adain2_1_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41908672)))];
            tensor<fp32, [512, 128]> decoder_generator_noise_res_0_adain2_1_fc_weight = const()[name = tensor<string, []>("decoder_generator_noise_res_0_adain2_1_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41910784)))];
            tensor<fp32, [256]> decoder_generator_noise_res_0_convs2_1_bias = const()[name = tensor<string, []>("decoder_generator_noise_res_0_convs2_1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42172992)))];
            tensor<fp32, [512]> decoder_generator_noise_res_0_adain1_2_fc_bias = const()[name = tensor<string, []>("decoder_generator_noise_res_0_adain1_2_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42174080)))];
            tensor<fp32, [512, 128]> decoder_generator_noise_res_0_adain1_2_fc_weight = const()[name = tensor<string, []>("decoder_generator_noise_res_0_adain1_2_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42176192)))];
            tensor<fp32, [256]> decoder_generator_noise_res_0_convs1_2_bias = const()[name = tensor<string, []>("decoder_generator_noise_res_0_convs1_2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42438400)))];
            tensor<fp32, [512]> decoder_generator_noise_res_0_adain2_2_fc_bias = const()[name = tensor<string, []>("decoder_generator_noise_res_0_adain2_2_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42439488)))];
            tensor<fp32, [512, 128]> decoder_generator_noise_res_0_adain2_2_fc_weight = const()[name = tensor<string, []>("decoder_generator_noise_res_0_adain2_2_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42441600)))];
            tensor<fp32, [256]> decoder_generator_noise_res_0_convs2_2_bias = const()[name = tensor<string, []>("decoder_generator_noise_res_0_convs2_2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42703808)))];
            tensor<fp32, [256]> decoder_generator_ups_0_bias = const()[name = tensor<string, []>("decoder_generator_ups_0_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42704896)))];
            tensor<fp32, [1, 256, 1]> decoder_generator_resblocks_0_alpha2_2 = const()[name = tensor<string, []>("decoder_generator_resblocks_0_alpha2_2"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42705984)))];
            tensor<fp32, [1, 256, 1]> decoder_generator_resblocks_0_alpha1_2 = const()[name = tensor<string, []>("decoder_generator_resblocks_0_alpha1_2"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42707072)))];
            tensor<fp32, [1, 256, 1]> decoder_generator_resblocks_0_alpha2_1 = const()[name = tensor<string, []>("decoder_generator_resblocks_0_alpha2_1"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42708160)))];
            tensor<fp32, [1, 256, 1]> decoder_generator_resblocks_0_alpha1_1 = const()[name = tensor<string, []>("decoder_generator_resblocks_0_alpha1_1"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42709248)))];
            tensor<fp32, [1, 256, 1]> decoder_generator_resblocks_0_alpha2_0 = const()[name = tensor<string, []>("decoder_generator_resblocks_0_alpha2_0"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42710336)))];
            tensor<fp32, [1, 256, 1]> decoder_generator_resblocks_0_alpha1_0 = const()[name = tensor<string, []>("decoder_generator_resblocks_0_alpha1_0"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42711424)))];
            tensor<fp32, [512]> decoder_generator_resblocks_0_adain1_0_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_0_adain1_0_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42712512)))];
            tensor<fp32, [512, 128]> decoder_generator_resblocks_0_adain1_0_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_0_adain1_0_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42714624)))];
            tensor<fp32, [256]> decoder_generator_resblocks_0_convs1_0_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_0_convs1_0_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42976832)))];
            tensor<fp32, [512]> decoder_generator_resblocks_0_adain2_0_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_0_adain2_0_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42977920)))];
            tensor<fp32, [512, 128]> decoder_generator_resblocks_0_adain2_0_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_0_adain2_0_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42980032)))];
            tensor<fp32, [256]> decoder_generator_resblocks_0_convs2_0_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_0_convs2_0_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43242240)))];
            tensor<fp32, [512]> decoder_generator_resblocks_0_adain1_1_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_0_adain1_1_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43243328)))];
            tensor<fp32, [512, 128]> decoder_generator_resblocks_0_adain1_1_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_0_adain1_1_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43245440)))];
            tensor<fp32, [256]> decoder_generator_resblocks_0_convs1_1_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_0_convs1_1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43507648)))];
            tensor<fp32, [512]> decoder_generator_resblocks_0_adain2_1_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_0_adain2_1_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43508736)))];
            tensor<fp32, [512, 128]> decoder_generator_resblocks_0_adain2_1_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_0_adain2_1_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43510848)))];
            tensor<fp32, [256]> decoder_generator_resblocks_0_convs2_1_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_0_convs2_1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43773056)))];
            tensor<fp32, [512]> decoder_generator_resblocks_0_adain1_2_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_0_adain1_2_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43774144)))];
            tensor<fp32, [512, 128]> decoder_generator_resblocks_0_adain1_2_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_0_adain1_2_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43776256)))];
            tensor<fp32, [256]> decoder_generator_resblocks_0_convs1_2_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_0_convs1_2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44038464)))];
            tensor<fp32, [512]> decoder_generator_resblocks_0_adain2_2_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_0_adain2_2_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44039552)))];
            tensor<fp32, [512, 128]> decoder_generator_resblocks_0_adain2_2_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_0_adain2_2_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44041664)))];
            tensor<fp32, [256]> decoder_generator_resblocks_0_convs2_2_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_0_convs2_2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44303872)))];
            tensor<fp32, [1, 256, 1]> decoder_generator_resblocks_1_alpha2_2 = const()[name = tensor<string, []>("decoder_generator_resblocks_1_alpha2_2"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44304960)))];
            tensor<fp32, [1, 256, 1]> decoder_generator_resblocks_1_alpha1_2 = const()[name = tensor<string, []>("decoder_generator_resblocks_1_alpha1_2"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44306048)))];
            tensor<fp32, [1, 256, 1]> decoder_generator_resblocks_1_alpha2_1 = const()[name = tensor<string, []>("decoder_generator_resblocks_1_alpha2_1"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44307136)))];
            tensor<fp32, [1, 256, 1]> decoder_generator_resblocks_1_alpha1_1 = const()[name = tensor<string, []>("decoder_generator_resblocks_1_alpha1_1"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44308224)))];
            tensor<fp32, [1, 256, 1]> decoder_generator_resblocks_1_alpha2_0 = const()[name = tensor<string, []>("decoder_generator_resblocks_1_alpha2_0"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44309312)))];
            tensor<fp32, [1, 256, 1]> decoder_generator_resblocks_1_alpha1_0 = const()[name = tensor<string, []>("decoder_generator_resblocks_1_alpha1_0"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44310400)))];
            tensor<fp32, [512]> decoder_generator_resblocks_1_adain1_0_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_1_adain1_0_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44311488)))];
            tensor<fp32, [512, 128]> decoder_generator_resblocks_1_adain1_0_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_1_adain1_0_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44313600)))];
            tensor<fp32, [256]> decoder_generator_resblocks_1_convs1_0_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_1_convs1_0_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44575808)))];
            tensor<fp32, [512]> decoder_generator_resblocks_1_adain2_0_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_1_adain2_0_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44576896)))];
            tensor<fp32, [512, 128]> decoder_generator_resblocks_1_adain2_0_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_1_adain2_0_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44579008)))];
            tensor<fp32, [256]> decoder_generator_resblocks_1_convs2_0_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_1_convs2_0_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44841216)))];
            tensor<fp32, [512]> decoder_generator_resblocks_1_adain1_1_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_1_adain1_1_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44842304)))];
            tensor<fp32, [512, 128]> decoder_generator_resblocks_1_adain1_1_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_1_adain1_1_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44844416)))];
            tensor<fp32, [256]> decoder_generator_resblocks_1_convs1_1_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_1_convs1_1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(45106624)))];
            tensor<fp32, [512]> decoder_generator_resblocks_1_adain2_1_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_1_adain2_1_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(45107712)))];
            tensor<fp32, [512, 128]> decoder_generator_resblocks_1_adain2_1_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_1_adain2_1_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(45109824)))];
            tensor<fp32, [256]> decoder_generator_resblocks_1_convs2_1_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_1_convs2_1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(45372032)))];
            tensor<fp32, [512]> decoder_generator_resblocks_1_adain1_2_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_1_adain1_2_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(45373120)))];
            tensor<fp32, [512, 128]> decoder_generator_resblocks_1_adain1_2_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_1_adain1_2_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(45375232)))];
            tensor<fp32, [256]> decoder_generator_resblocks_1_convs1_2_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_1_convs1_2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(45637440)))];
            tensor<fp32, [512]> decoder_generator_resblocks_1_adain2_2_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_1_adain2_2_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(45638528)))];
            tensor<fp32, [512, 128]> decoder_generator_resblocks_1_adain2_2_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_1_adain2_2_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(45640640)))];
            tensor<fp32, [256]> decoder_generator_resblocks_1_convs2_2_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_1_convs2_2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(45902848)))];
            tensor<fp32, [1, 256, 1]> decoder_generator_resblocks_2_alpha2_2 = const()[name = tensor<string, []>("decoder_generator_resblocks_2_alpha2_2"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(45903936)))];
            tensor<fp32, [1, 256, 1]> decoder_generator_resblocks_2_alpha1_2 = const()[name = tensor<string, []>("decoder_generator_resblocks_2_alpha1_2"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(45905024)))];
            tensor<fp32, [1, 256, 1]> decoder_generator_resblocks_2_alpha2_1 = const()[name = tensor<string, []>("decoder_generator_resblocks_2_alpha2_1"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(45906112)))];
            tensor<fp32, [1, 256, 1]> decoder_generator_resblocks_2_alpha1_1 = const()[name = tensor<string, []>("decoder_generator_resblocks_2_alpha1_1"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(45907200)))];
            tensor<fp32, [1, 256, 1]> decoder_generator_resblocks_2_alpha2_0 = const()[name = tensor<string, []>("decoder_generator_resblocks_2_alpha2_0"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(45908288)))];
            tensor<fp32, [1, 256, 1]> decoder_generator_resblocks_2_alpha1_0 = const()[name = tensor<string, []>("decoder_generator_resblocks_2_alpha1_0"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(45909376)))];
            tensor<fp32, [512]> decoder_generator_resblocks_2_adain1_0_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_2_adain1_0_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(45910464)))];
            tensor<fp32, [512, 128]> decoder_generator_resblocks_2_adain1_0_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_2_adain1_0_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(45912576)))];
            tensor<fp32, [256]> decoder_generator_resblocks_2_convs1_0_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_2_convs1_0_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46174784)))];
            tensor<fp32, [512]> decoder_generator_resblocks_2_adain2_0_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_2_adain2_0_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46175872)))];
            tensor<fp32, [512, 128]> decoder_generator_resblocks_2_adain2_0_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_2_adain2_0_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46177984)))];
            tensor<fp32, [256]> decoder_generator_resblocks_2_convs2_0_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_2_convs2_0_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46440192)))];
            tensor<fp32, [512]> decoder_generator_resblocks_2_adain1_1_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_2_adain1_1_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46441280)))];
            tensor<fp32, [512, 128]> decoder_generator_resblocks_2_adain1_1_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_2_adain1_1_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46443392)))];
            tensor<fp32, [256]> decoder_generator_resblocks_2_convs1_1_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_2_convs1_1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46705600)))];
            tensor<fp32, [512]> decoder_generator_resblocks_2_adain2_1_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_2_adain2_1_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46706688)))];
            tensor<fp32, [512, 128]> decoder_generator_resblocks_2_adain2_1_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_2_adain2_1_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46708800)))];
            tensor<fp32, [256]> decoder_generator_resblocks_2_convs2_1_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_2_convs2_1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46971008)))];
            tensor<fp32, [512]> decoder_generator_resblocks_2_adain1_2_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_2_adain1_2_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46972096)))];
            tensor<fp32, [512, 128]> decoder_generator_resblocks_2_adain1_2_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_2_adain1_2_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46974208)))];
            tensor<fp32, [256]> decoder_generator_resblocks_2_convs1_2_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_2_convs1_2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47236416)))];
            tensor<fp32, [512]> decoder_generator_resblocks_2_adain2_2_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_2_adain2_2_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47237504)))];
            tensor<fp32, [512, 128]> decoder_generator_resblocks_2_adain2_2_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_2_adain2_2_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47239616)))];
            tensor<fp32, [256]> decoder_generator_resblocks_2_convs2_2_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_2_convs2_2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47501824)))];
            tensor<fp32, [128]> decoder_generator_noise_convs_1_bias = const()[name = tensor<string, []>("decoder_generator_noise_convs_1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47502912)))];
            tensor<fp32, [128, 22, 1]> decoder_generator_noise_convs_1_weight = const()[name = tensor<string, []>("decoder_generator_noise_convs_1_weight"), val = tensor<fp32, [128, 22, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47503488)))];
            tensor<fp32, [1, 128, 1]> decoder_generator_noise_res_1_alpha2_2 = const()[name = tensor<string, []>("decoder_generator_noise_res_1_alpha2_2"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47514816)))];
            tensor<fp32, [1, 128, 1]> decoder_generator_noise_res_1_alpha1_2 = const()[name = tensor<string, []>("decoder_generator_noise_res_1_alpha1_2"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47515392)))];
            tensor<fp32, [1, 128, 1]> decoder_generator_noise_res_1_alpha2_1 = const()[name = tensor<string, []>("decoder_generator_noise_res_1_alpha2_1"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47515968)))];
            tensor<fp32, [1, 128, 1]> decoder_generator_noise_res_1_alpha1_1 = const()[name = tensor<string, []>("decoder_generator_noise_res_1_alpha1_1"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47516544)))];
            tensor<fp32, [1, 128, 1]> decoder_generator_noise_res_1_alpha2_0 = const()[name = tensor<string, []>("decoder_generator_noise_res_1_alpha2_0"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47517120)))];
            tensor<fp32, [1, 128, 1]> decoder_generator_noise_res_1_alpha1_0 = const()[name = tensor<string, []>("decoder_generator_noise_res_1_alpha1_0"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47517696)))];
            tensor<fp32, [256]> decoder_generator_noise_res_1_adain1_0_fc_bias = const()[name = tensor<string, []>("decoder_generator_noise_res_1_adain1_0_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47518272)))];
            tensor<fp32, [256, 128]> decoder_generator_noise_res_1_adain1_0_fc_weight = const()[name = tensor<string, []>("decoder_generator_noise_res_1_adain1_0_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47519360)))];
            tensor<fp32, [128]> decoder_generator_noise_res_1_adain1_0_norm_bias = const()[name = tensor<string, []>("decoder_generator_noise_res_1_adain1_0_norm_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47650496)))];
            tensor<fp32, [128]> decoder_generator_noise_res_1_adain1_0_norm_weight = const()[name = tensor<string, []>("decoder_generator_noise_res_1_adain1_0_norm_weight"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47651072)))];
            tensor<fp32, [128]> decoder_generator_noise_res_1_convs1_0_bias = const()[name = tensor<string, []>("decoder_generator_noise_res_1_convs1_0_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47651648)))];
            tensor<fp32, [256]> decoder_generator_noise_res_1_adain2_0_fc_bias = const()[name = tensor<string, []>("decoder_generator_noise_res_1_adain2_0_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47652224)))];
            tensor<fp32, [256, 128]> decoder_generator_noise_res_1_adain2_0_fc_weight = const()[name = tensor<string, []>("decoder_generator_noise_res_1_adain2_0_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47653312)))];
            tensor<fp32, [128]> decoder_generator_noise_res_1_convs2_0_bias = const()[name = tensor<string, []>("decoder_generator_noise_res_1_convs2_0_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47784448)))];
            tensor<fp32, [256]> decoder_generator_noise_res_1_adain1_1_fc_bias = const()[name = tensor<string, []>("decoder_generator_noise_res_1_adain1_1_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47785024)))];
            tensor<fp32, [256, 128]> decoder_generator_noise_res_1_adain1_1_fc_weight = const()[name = tensor<string, []>("decoder_generator_noise_res_1_adain1_1_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47786112)))];
            tensor<fp32, [128]> decoder_generator_noise_res_1_convs1_1_bias = const()[name = tensor<string, []>("decoder_generator_noise_res_1_convs1_1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47917248)))];
            tensor<fp32, [256]> decoder_generator_noise_res_1_adain2_1_fc_bias = const()[name = tensor<string, []>("decoder_generator_noise_res_1_adain2_1_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47917824)))];
            tensor<fp32, [256, 128]> decoder_generator_noise_res_1_adain2_1_fc_weight = const()[name = tensor<string, []>("decoder_generator_noise_res_1_adain2_1_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47918912)))];
            tensor<fp32, [128]> decoder_generator_noise_res_1_convs2_1_bias = const()[name = tensor<string, []>("decoder_generator_noise_res_1_convs2_1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48050048)))];
            tensor<fp32, [256]> decoder_generator_noise_res_1_adain1_2_fc_bias = const()[name = tensor<string, []>("decoder_generator_noise_res_1_adain1_2_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48050624)))];
            tensor<fp32, [256, 128]> decoder_generator_noise_res_1_adain1_2_fc_weight = const()[name = tensor<string, []>("decoder_generator_noise_res_1_adain1_2_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48051712)))];
            tensor<fp32, [128]> decoder_generator_noise_res_1_convs1_2_bias = const()[name = tensor<string, []>("decoder_generator_noise_res_1_convs1_2_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48182848)))];
            tensor<fp32, [256]> decoder_generator_noise_res_1_adain2_2_fc_bias = const()[name = tensor<string, []>("decoder_generator_noise_res_1_adain2_2_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48183424)))];
            tensor<fp32, [256, 128]> decoder_generator_noise_res_1_adain2_2_fc_weight = const()[name = tensor<string, []>("decoder_generator_noise_res_1_adain2_2_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48184512)))];
            tensor<fp32, [128]> decoder_generator_noise_res_1_convs2_2_bias = const()[name = tensor<string, []>("decoder_generator_noise_res_1_convs2_2_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48315648)))];
            tensor<fp32, [128]> decoder_generator_ups_1_bias = const()[name = tensor<string, []>("decoder_generator_ups_1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48316224)))];
            tensor<fp32, [1, 128, 1]> decoder_generator_resblocks_3_alpha2_2 = const()[name = tensor<string, []>("decoder_generator_resblocks_3_alpha2_2"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48316800)))];
            tensor<fp32, [1, 128, 1]> decoder_generator_resblocks_3_alpha1_2 = const()[name = tensor<string, []>("decoder_generator_resblocks_3_alpha1_2"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48317376)))];
            tensor<fp32, [1, 128, 1]> decoder_generator_resblocks_3_alpha2_1 = const()[name = tensor<string, []>("decoder_generator_resblocks_3_alpha2_1"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48317952)))];
            tensor<fp32, [1, 128, 1]> decoder_generator_resblocks_3_alpha1_1 = const()[name = tensor<string, []>("decoder_generator_resblocks_3_alpha1_1"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48318528)))];
            tensor<fp32, [1, 128, 1]> decoder_generator_resblocks_3_alpha2_0 = const()[name = tensor<string, []>("decoder_generator_resblocks_3_alpha2_0"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48319104)))];
            tensor<fp32, [1, 128, 1]> decoder_generator_resblocks_3_alpha1_0 = const()[name = tensor<string, []>("decoder_generator_resblocks_3_alpha1_0"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48319680)))];
            tensor<fp32, [256]> decoder_generator_resblocks_3_adain1_0_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_3_adain1_0_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48320256)))];
            tensor<fp32, [256, 128]> decoder_generator_resblocks_3_adain1_0_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_3_adain1_0_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48321344)))];
            tensor<fp32, [128]> decoder_generator_resblocks_3_convs1_0_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_3_convs1_0_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48452480)))];
            tensor<fp32, [256]> decoder_generator_resblocks_3_adain2_0_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_3_adain2_0_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48453056)))];
            tensor<fp32, [256, 128]> decoder_generator_resblocks_3_adain2_0_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_3_adain2_0_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48454144)))];
            tensor<fp32, [128]> decoder_generator_resblocks_3_convs2_0_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_3_convs2_0_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48585280)))];
            tensor<fp32, [256]> decoder_generator_resblocks_3_adain1_1_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_3_adain1_1_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48585856)))];
            tensor<fp32, [256, 128]> decoder_generator_resblocks_3_adain1_1_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_3_adain1_1_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48586944)))];
            tensor<fp32, [128]> decoder_generator_resblocks_3_convs1_1_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_3_convs1_1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48718080)))];
            tensor<fp32, [256]> decoder_generator_resblocks_3_adain2_1_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_3_adain2_1_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48718656)))];
            tensor<fp32, [256, 128]> decoder_generator_resblocks_3_adain2_1_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_3_adain2_1_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48719744)))];
            tensor<fp32, [128]> decoder_generator_resblocks_3_convs2_1_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_3_convs2_1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48850880)))];
            tensor<fp32, [256]> decoder_generator_resblocks_3_adain1_2_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_3_adain1_2_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48851456)))];
            tensor<fp32, [256, 128]> decoder_generator_resblocks_3_adain1_2_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_3_adain1_2_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48852544)))];
            tensor<fp32, [128]> decoder_generator_resblocks_3_convs1_2_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_3_convs1_2_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48983680)))];
            tensor<fp32, [256]> decoder_generator_resblocks_3_adain2_2_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_3_adain2_2_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48984256)))];
            tensor<fp32, [256, 128]> decoder_generator_resblocks_3_adain2_2_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_3_adain2_2_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48985344)))];
            tensor<fp32, [128]> decoder_generator_resblocks_3_convs2_2_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_3_convs2_2_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49116480)))];
            tensor<fp32, [1, 128, 1]> decoder_generator_resblocks_4_alpha2_2 = const()[name = tensor<string, []>("decoder_generator_resblocks_4_alpha2_2"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49117056)))];
            tensor<fp32, [1, 128, 1]> decoder_generator_resblocks_4_alpha1_2 = const()[name = tensor<string, []>("decoder_generator_resblocks_4_alpha1_2"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49117632)))];
            tensor<fp32, [1, 128, 1]> decoder_generator_resblocks_4_alpha2_1 = const()[name = tensor<string, []>("decoder_generator_resblocks_4_alpha2_1"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49118208)))];
            tensor<fp32, [1, 128, 1]> decoder_generator_resblocks_4_alpha1_1 = const()[name = tensor<string, []>("decoder_generator_resblocks_4_alpha1_1"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49118784)))];
            tensor<fp32, [1, 128, 1]> decoder_generator_resblocks_4_alpha2_0 = const()[name = tensor<string, []>("decoder_generator_resblocks_4_alpha2_0"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49119360)))];
            tensor<fp32, [1, 128, 1]> decoder_generator_resblocks_4_alpha1_0 = const()[name = tensor<string, []>("decoder_generator_resblocks_4_alpha1_0"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49119936)))];
            tensor<fp32, [256]> decoder_generator_resblocks_4_adain1_0_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_4_adain1_0_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49120512)))];
            tensor<fp32, [256, 128]> decoder_generator_resblocks_4_adain1_0_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_4_adain1_0_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49121600)))];
            tensor<fp32, [128]> decoder_generator_resblocks_4_convs1_0_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_4_convs1_0_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49252736)))];
            tensor<fp32, [256]> decoder_generator_resblocks_4_adain2_0_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_4_adain2_0_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49253312)))];
            tensor<fp32, [256, 128]> decoder_generator_resblocks_4_adain2_0_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_4_adain2_0_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49254400)))];
            tensor<fp32, [128]> decoder_generator_resblocks_4_convs2_0_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_4_convs2_0_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49385536)))];
            tensor<fp32, [256]> decoder_generator_resblocks_4_adain1_1_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_4_adain1_1_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49386112)))];
            tensor<fp32, [256, 128]> decoder_generator_resblocks_4_adain1_1_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_4_adain1_1_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49387200)))];
            tensor<fp32, [128]> decoder_generator_resblocks_4_convs1_1_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_4_convs1_1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49518336)))];
            tensor<fp32, [256]> decoder_generator_resblocks_4_adain2_1_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_4_adain2_1_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49518912)))];
            tensor<fp32, [256, 128]> decoder_generator_resblocks_4_adain2_1_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_4_adain2_1_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49520000)))];
            tensor<fp32, [128]> decoder_generator_resblocks_4_convs2_1_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_4_convs2_1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49651136)))];
            tensor<fp32, [256]> decoder_generator_resblocks_4_adain1_2_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_4_adain1_2_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49651712)))];
            tensor<fp32, [256, 128]> decoder_generator_resblocks_4_adain1_2_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_4_adain1_2_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49652800)))];
            tensor<fp32, [128]> decoder_generator_resblocks_4_convs1_2_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_4_convs1_2_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49783936)))];
            tensor<fp32, [256]> decoder_generator_resblocks_4_adain2_2_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_4_adain2_2_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49784512)))];
            tensor<fp32, [256, 128]> decoder_generator_resblocks_4_adain2_2_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_4_adain2_2_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49785600)))];
            tensor<fp32, [128]> decoder_generator_resblocks_4_convs2_2_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_4_convs2_2_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49916736)))];
            tensor<fp32, [1, 128, 1]> decoder_generator_resblocks_5_alpha2_2 = const()[name = tensor<string, []>("decoder_generator_resblocks_5_alpha2_2"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49917312)))];
            tensor<fp32, [1, 128, 1]> decoder_generator_resblocks_5_alpha1_2 = const()[name = tensor<string, []>("decoder_generator_resblocks_5_alpha1_2"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49917888)))];
            tensor<fp32, [1, 128, 1]> decoder_generator_resblocks_5_alpha2_1 = const()[name = tensor<string, []>("decoder_generator_resblocks_5_alpha2_1"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49918464)))];
            tensor<fp32, [1, 128, 1]> decoder_generator_resblocks_5_alpha1_1 = const()[name = tensor<string, []>("decoder_generator_resblocks_5_alpha1_1"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49919040)))];
            tensor<fp32, [1, 128, 1]> decoder_generator_resblocks_5_alpha2_0 = const()[name = tensor<string, []>("decoder_generator_resblocks_5_alpha2_0"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49919616)))];
            tensor<fp32, [1, 128, 1]> decoder_generator_resblocks_5_alpha1_0 = const()[name = tensor<string, []>("decoder_generator_resblocks_5_alpha1_0"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49920192)))];
            tensor<fp32, [256]> decoder_generator_resblocks_5_adain1_0_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_5_adain1_0_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49920768)))];
            tensor<fp32, [256, 128]> decoder_generator_resblocks_5_adain1_0_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_5_adain1_0_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(49921856)))];
            tensor<fp32, [128]> decoder_generator_resblocks_5_convs1_0_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_5_convs1_0_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(50052992)))];
            tensor<fp32, [256]> decoder_generator_resblocks_5_adain2_0_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_5_adain2_0_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(50053568)))];
            tensor<fp32, [256, 128]> decoder_generator_resblocks_5_adain2_0_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_5_adain2_0_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(50054656)))];
            tensor<fp32, [128]> decoder_generator_resblocks_5_convs2_0_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_5_convs2_0_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(50185792)))];
            tensor<fp32, [256]> decoder_generator_resblocks_5_adain1_1_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_5_adain1_1_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(50186368)))];
            tensor<fp32, [256, 128]> decoder_generator_resblocks_5_adain1_1_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_5_adain1_1_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(50187456)))];
            tensor<fp32, [128]> decoder_generator_resblocks_5_convs1_1_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_5_convs1_1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(50318592)))];
            tensor<fp32, [256]> decoder_generator_resblocks_5_adain2_1_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_5_adain2_1_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(50319168)))];
            tensor<fp32, [256, 128]> decoder_generator_resblocks_5_adain2_1_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_5_adain2_1_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(50320256)))];
            tensor<fp32, [128]> decoder_generator_resblocks_5_convs2_1_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_5_convs2_1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(50451392)))];
            tensor<fp32, [256]> decoder_generator_resblocks_5_adain1_2_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_5_adain1_2_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(50451968)))];
            tensor<fp32, [256, 128]> decoder_generator_resblocks_5_adain1_2_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_5_adain1_2_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(50453056)))];
            tensor<fp32, [128]> decoder_generator_resblocks_5_convs1_2_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_5_convs1_2_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(50584192)))];
            tensor<fp32, [256]> decoder_generator_resblocks_5_adain2_2_fc_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_5_adain2_2_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(50584768)))];
            tensor<fp32, [256, 128]> decoder_generator_resblocks_5_adain2_2_fc_weight = const()[name = tensor<string, []>("decoder_generator_resblocks_5_adain2_2_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(50585856)))];
            tensor<fp32, [128]> decoder_generator_resblocks_5_convs2_2_bias = const()[name = tensor<string, []>("decoder_generator_resblocks_5_convs2_2_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(50716992)))];
            tensor<fp32, [22]> decoder_generator_conv_post_bias = const()[name = tensor<string, []>("decoder_generator_conv_post_bias"), val = tensor<fp32, [22]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(50717568)))];
            tensor<int32, [1]> var_55_axes_0 = const()[name = tensor<string, []>("op_55_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<bool, []> var_55_keep_dims_0 = const()[name = tensor<string, []>("op_55_keep_dims_0"), val = tensor<bool, []>(false)];
            tensor<int32, [1]> var_55 = reduce_sum(axes = var_55_axes_0, keep_dims = var_55_keep_dims_0, x = attention_mask)[name = tensor<string, []>("op_55")];
            tensor<int32, [1, 128]> var_67 = const()[name = tensor<string, []>("op_67"), val = tensor<int32, [1, 128]>([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127]])];
            tensor<int32, [1, 128]> var_70 = const()[name = tensor<string, []>("op_70"), val = tensor<int32, [1, 128]>([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128]])];
            tensor<int32, [1]> var_72_axes_0 = const()[name = tensor<string, []>("op_72_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1, 1]> var_72 = expand_dims(axes = var_72_axes_0, x = var_55)[name = tensor<string, []>("op_72")];
            tensor<bool, [1, 128]> m = greater(x = var_70, y = var_72)[name = tensor<string, []>("m")];
            tensor<int32, []> var_80 = const()[name = tensor<string, []>("op_80"), val = tensor<int32, []>(-1)];
            tensor<fp32, []> var_82 = const()[name = tensor<string, []>("op_82"), val = tensor<fp32, []>(0x1.197998p-40)];
            tensor<fp32, []> var_85 = const()[name = tensor<string, []>("op_85"), val = tensor<fp32, []>(0x1p+0)];
            tensor<int32, [1]> var_103_axes_0 = const()[name = tensor<string, []>("op_103_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1, 1, 128]> var_103 = expand_dims(axes = var_103_axes_0, x = attention_mask)[name = tensor<string, []>("op_103")];
            tensor<int32, [1]> extended_attention_mask_axes_0 = const()[name = tensor<string, []>("extended_attention_mask_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1, 1, 1, 128]> extended_attention_mask = expand_dims(axes = extended_attention_mask_axes_0, x = var_103)[name = tensor<string, []>("extended_attention_mask")];
            tensor<string, []> cast_1_dtype_0 = const()[name = tensor<string, []>("cast_1_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<fp32, [1, 1, 1, 128]> cast_1 = cast(dtype = cast_1_dtype_0, x = extended_attention_mask)[name = tensor<string, []>("cast_186")];
            tensor<fp32, [1, 1, 1, 128]> var_106 = sub(x = var_85, y = cast_1)[name = tensor<string, []>("op_106")];
            tensor<fp32, []> var_107 = const()[name = tensor<string, []>("op_107"), val = tensor<fp32, []>(-0x1.fffffep+127)];
            tensor<fp32, [1, 1, 1, 128]> attention_mask_1 = mul(x = var_106, y = var_107)[name = tensor<string, []>("attention_mask")];
            tensor<int32, []> inputs_embeds_axis_0 = const()[name = tensor<string, []>("inputs_embeds_axis_0"), val = tensor<int32, []>(0)];
            tensor<int32, []> inputs_embeds_batch_dims_0 = const()[name = tensor<string, []>("inputs_embeds_batch_dims_0"), val = tensor<int32, []>(0)];
            tensor<bool, []> inputs_embeds_validate_indices_0 = const()[name = tensor<string, []>("inputs_embeds_validate_indices_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 128, 128]> inputs_embeds = gather(axis = inputs_embeds_axis_0, batch_dims = inputs_embeds_batch_dims_0, indices = input_ids, validate_indices = inputs_embeds_validate_indices_0, x = bert_embeddings_word_embeddings_weight)[name = tensor<string, []>("inputs_embeds")];
            tensor<fp32, [1, 128, 128]> token_type_embeddings_1 = const()[name = tensor<string, []>("token_type_embeddings_1"), val = tensor<fp32, [1, 128, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(50717760)))];
            tensor<fp32, [1, 128, 128]> embeddings_1 = add(x = inputs_embeds, y = token_type_embeddings_1)[name = tensor<string, []>("embeddings_1")];
            tensor<fp32, [1, 128, 128]> position_embeddings_1 = const()[name = tensor<string, []>("position_embeddings_1"), val = tensor<fp32, [1, 128, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(50783360)))];
            tensor<fp32, [1, 128, 128]> input_5 = add(x = embeddings_1, y = position_embeddings_1)[name = tensor<string, []>("input_5")];
            tensor<int32, [1]> input_7_axes_0 = const()[name = tensor<string, []>("input_7_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 128, 128]> input_7 = layer_norm(axes = input_7_axes_0, beta = bert_embeddings_LayerNorm_bias, epsilon = var_82, gamma = bert_embeddings_LayerNorm_weight, x = input_5)[name = tensor<string, []>("input_7")];
            tensor<fp32, [1, 128, 768]> input_11 = linear(bias = bert_encoder_embedding_hidden_mapping_in_bias, weight = bert_encoder_embedding_hidden_mapping_in_weight, x = input_7)[name = tensor<string, []>("linear_0")];
            tensor<fp32, [1, 128, 768]> x_1 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight, x = input_11)[name = tensor<string, []>("linear_1")];
            tensor<fp32, [1, 128, 768]> x_5 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight, x = input_11)[name = tensor<string, []>("linear_2")];
            tensor<fp32, [1, 128, 768]> x_9 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight, x = input_11)[name = tensor<string, []>("linear_3")];
            tensor<int32, [4]> var_162 = const()[name = tensor<string, []>("op_162"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_3 = reshape(shape = var_162, x = x_1)[name = tensor<string, []>("x_3")];
            tensor<int32, [4]> var_168 = const()[name = tensor<string, []>("op_168"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_7 = reshape(shape = var_168, x = x_5)[name = tensor<string, []>("x_7")];
            tensor<int32, [4]> var_174 = const()[name = tensor<string, []>("op_174"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_11 = reshape(shape = var_174, x = x_9)[name = tensor<string, []>("x_11")];
            tensor<int32, [4]> var_176 = const()[name = tensor<string, []>("op_176"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<bool, []> attention_scores_1_transpose_x_0 = const()[name = tensor<string, []>("attention_scores_1_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attention_scores_1_transpose_y_0 = const()[name = tensor<string, []>("attention_scores_1_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_65_perm_0 = const()[name = tensor<string, []>("transpose_65_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_66_perm_0 = const()[name = tensor<string, []>("transpose_66_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 12, 64, 128]> transpose_66 = transpose(perm = transpose_66_perm_0, x = x_7)[name = tensor<string, []>("transpose_174")];
            tensor<fp32, [1, 12, 128, 64]> transpose_65 = transpose(perm = transpose_65_perm_0, x = x_3)[name = tensor<string, []>("transpose_175")];
            tensor<fp32, [1, 12, 128, 128]> attention_scores_1 = matmul(transpose_x = attention_scores_1_transpose_x_0, transpose_y = attention_scores_1_transpose_y_0, x = transpose_65, y = transpose_66)[name = tensor<string, []>("attention_scores_1")];
            tensor<fp32, []> _inversed_attention_scores_3_y_0 = const()[name = tensor<string, []>("_inversed_attention_scores_3_y_0"), val = tensor<fp32, []>(0x1p-3)];
            tensor<fp32, [1, 12, 128, 128]> _inversed_attention_scores_3 = mul(x = attention_scores_1, y = _inversed_attention_scores_3_y_0)[name = tensor<string, []>("_inversed_attention_scores_3")];
            tensor<fp32, [1, 12, 128, 128]> input_13 = add(x = _inversed_attention_scores_3, y = attention_mask_1)[name = tensor<string, []>("input_13")];
            tensor<fp32, [1, 12, 128, 128]> input_15 = softmax(axis = var_80, x = input_13)[name = tensor<string, []>("input_15")];
            tensor<bool, []> context_layer_1_transpose_x_0 = const()[name = tensor<string, []>("context_layer_1_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> context_layer_1_transpose_y_0 = const()[name = tensor<string, []>("context_layer_1_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 12, 128, 64]> value_layer_1 = transpose(perm = var_176, x = x_11)[name = tensor<string, []>("transpose_176")];
            tensor<fp32, [1, 12, 128, 64]> context_layer_1 = matmul(transpose_x = context_layer_1_transpose_x_0, transpose_y = context_layer_1_transpose_y_0, x = input_15, y = value_layer_1)[name = tensor<string, []>("context_layer_1")];
            tensor<int32, [4]> var_186_perm_0 = const()[name = tensor<string, []>("op_186_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> concat_1 = const()[name = tensor<string, []>("concat_1"), val = tensor<int32, [3]>([1, 128, 768])];
            tensor<fp32, [1, 128, 12, 64]> var_186 = transpose(perm = var_186_perm_0, x = context_layer_1)[name = tensor<string, []>("transpose_173")];
            tensor<fp32, [1, 128, 768]> input_17 = reshape(shape = concat_1, x = var_186)[name = tensor<string, []>("input_17")];
            tensor<fp32, [1, 128, 768]> input_19 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight, x = input_17)[name = tensor<string, []>("linear_4")];
            tensor<fp32, [1, 128, 768]> input_21 = add(x = input_11, y = input_19)[name = tensor<string, []>("input_21")];
            tensor<int32, [1]> input_23_axes_0 = const()[name = tensor<string, []>("input_23_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 128, 768]> input_23 = layer_norm(axes = input_23_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_82, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_21)[name = tensor<string, []>("input_23")];
            tensor<fp32, [1, 128, 2048]> input_25 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight, x = input_23)[name = tensor<string, []>("linear_5")];
            tensor<string, []> input_27_mode_0 = const()[name = tensor<string, []>("input_27_mode_0"), val = tensor<string, []>("TANH_APPROXIMATION")];
            tensor<fp32, [1, 128, 2048]> input_27 = gelu(mode = input_27_mode_0, x = input_25)[name = tensor<string, []>("input_27")];
            tensor<fp32, [1, 128, 768]> ffn_output_1 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight, x = input_27)[name = tensor<string, []>("linear_6")];
            tensor<fp32, [1, 128, 768]> input_29 = add(x = ffn_output_1, y = input_23)[name = tensor<string, []>("input_29")];
            tensor<int32, [1]> input_31_axes_0 = const()[name = tensor<string, []>("input_31_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 128, 768]> input_31 = layer_norm(axes = input_31_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias, epsilon = var_82, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_29)[name = tensor<string, []>("input_31")];
            tensor<fp32, [1, 128, 768]> x_13 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight, x = input_31)[name = tensor<string, []>("linear_7")];
            tensor<fp32, [1, 128, 768]> x_17 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight, x = input_31)[name = tensor<string, []>("linear_8")];
            tensor<fp32, [1, 128, 768]> x_21 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight, x = input_31)[name = tensor<string, []>("linear_9")];
            tensor<int32, [4]> var_242 = const()[name = tensor<string, []>("op_242"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_15 = reshape(shape = var_242, x = x_13)[name = tensor<string, []>("x_15")];
            tensor<int32, [4]> var_248 = const()[name = tensor<string, []>("op_248"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_19 = reshape(shape = var_248, x = x_17)[name = tensor<string, []>("x_19")];
            tensor<int32, [4]> var_254 = const()[name = tensor<string, []>("op_254"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_23 = reshape(shape = var_254, x = x_21)[name = tensor<string, []>("x_23")];
            tensor<int32, [4]> var_256 = const()[name = tensor<string, []>("op_256"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<bool, []> attention_scores_5_transpose_x_0 = const()[name = tensor<string, []>("attention_scores_5_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attention_scores_5_transpose_y_0 = const()[name = tensor<string, []>("attention_scores_5_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_67_perm_0 = const()[name = tensor<string, []>("transpose_67_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_68_perm_0 = const()[name = tensor<string, []>("transpose_68_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 12, 64, 128]> transpose_68 = transpose(perm = transpose_68_perm_0, x = x_19)[name = tensor<string, []>("transpose_170")];
            tensor<fp32, [1, 12, 128, 64]> transpose_67 = transpose(perm = transpose_67_perm_0, x = x_15)[name = tensor<string, []>("transpose_171")];
            tensor<fp32, [1, 12, 128, 128]> attention_scores_5 = matmul(transpose_x = attention_scores_5_transpose_x_0, transpose_y = attention_scores_5_transpose_y_0, x = transpose_67, y = transpose_68)[name = tensor<string, []>("attention_scores_5")];
            tensor<fp32, []> _inversed_attention_scores_7_y_0 = const()[name = tensor<string, []>("_inversed_attention_scores_7_y_0"), val = tensor<fp32, []>(0x1p-3)];
            tensor<fp32, [1, 12, 128, 128]> _inversed_attention_scores_7 = mul(x = attention_scores_5, y = _inversed_attention_scores_7_y_0)[name = tensor<string, []>("_inversed_attention_scores_7")];
            tensor<fp32, [1, 12, 128, 128]> input_33 = add(x = _inversed_attention_scores_7, y = attention_mask_1)[name = tensor<string, []>("input_33")];
            tensor<fp32, [1, 12, 128, 128]> input_35 = softmax(axis = var_80, x = input_33)[name = tensor<string, []>("input_35")];
            tensor<bool, []> context_layer_3_transpose_x_0 = const()[name = tensor<string, []>("context_layer_3_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> context_layer_3_transpose_y_0 = const()[name = tensor<string, []>("context_layer_3_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 12, 128, 64]> value_layer_3 = transpose(perm = var_256, x = x_23)[name = tensor<string, []>("transpose_172")];
            tensor<fp32, [1, 12, 128, 64]> context_layer_3 = matmul(transpose_x = context_layer_3_transpose_x_0, transpose_y = context_layer_3_transpose_y_0, x = input_35, y = value_layer_3)[name = tensor<string, []>("context_layer_3")];
            tensor<int32, [4]> var_266_perm_0 = const()[name = tensor<string, []>("op_266_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> concat_2 = const()[name = tensor<string, []>("concat_2"), val = tensor<int32, [3]>([1, 128, 768])];
            tensor<fp32, [1, 128, 12, 64]> var_266 = transpose(perm = var_266_perm_0, x = context_layer_3)[name = tensor<string, []>("transpose_169")];
            tensor<fp32, [1, 128, 768]> input_37 = reshape(shape = concat_2, x = var_266)[name = tensor<string, []>("input_37")];
            tensor<fp32, [1, 128, 768]> input_39 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight, x = input_37)[name = tensor<string, []>("linear_10")];
            tensor<fp32, [1, 128, 768]> input_41 = add(x = input_31, y = input_39)[name = tensor<string, []>("input_41")];
            tensor<int32, [1]> input_43_axes_0 = const()[name = tensor<string, []>("input_43_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 128, 768]> input_43 = layer_norm(axes = input_43_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_82, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_41)[name = tensor<string, []>("input_43")];
            tensor<fp32, [1, 128, 2048]> input_45 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight, x = input_43)[name = tensor<string, []>("linear_11")];
            tensor<string, []> input_47_mode_0 = const()[name = tensor<string, []>("input_47_mode_0"), val = tensor<string, []>("TANH_APPROXIMATION")];
            tensor<fp32, [1, 128, 2048]> input_47 = gelu(mode = input_47_mode_0, x = input_45)[name = tensor<string, []>("input_47")];
            tensor<fp32, [1, 128, 768]> ffn_output_3 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight, x = input_47)[name = tensor<string, []>("linear_12")];
            tensor<fp32, [1, 128, 768]> input_49 = add(x = ffn_output_3, y = input_43)[name = tensor<string, []>("input_49")];
            tensor<int32, [1]> input_51_axes_0 = const()[name = tensor<string, []>("input_51_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 128, 768]> input_51 = layer_norm(axes = input_51_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias, epsilon = var_82, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_49)[name = tensor<string, []>("input_51")];
            tensor<fp32, [1, 128, 768]> x_25 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight, x = input_51)[name = tensor<string, []>("linear_13")];
            tensor<fp32, [1, 128, 768]> x_29 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight, x = input_51)[name = tensor<string, []>("linear_14")];
            tensor<fp32, [1, 128, 768]> x_33 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight, x = input_51)[name = tensor<string, []>("linear_15")];
            tensor<int32, [4]> var_322 = const()[name = tensor<string, []>("op_322"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_27 = reshape(shape = var_322, x = x_25)[name = tensor<string, []>("x_27")];
            tensor<int32, [4]> var_328 = const()[name = tensor<string, []>("op_328"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_31 = reshape(shape = var_328, x = x_29)[name = tensor<string, []>("x_31")];
            tensor<int32, [4]> var_334 = const()[name = tensor<string, []>("op_334"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_35 = reshape(shape = var_334, x = x_33)[name = tensor<string, []>("x_35")];
            tensor<int32, [4]> var_336 = const()[name = tensor<string, []>("op_336"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<bool, []> attention_scores_9_transpose_x_0 = const()[name = tensor<string, []>("attention_scores_9_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attention_scores_9_transpose_y_0 = const()[name = tensor<string, []>("attention_scores_9_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_69_perm_0 = const()[name = tensor<string, []>("transpose_69_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_70_perm_0 = const()[name = tensor<string, []>("transpose_70_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 12, 64, 128]> transpose_70 = transpose(perm = transpose_70_perm_0, x = x_31)[name = tensor<string, []>("transpose_166")];
            tensor<fp32, [1, 12, 128, 64]> transpose_69 = transpose(perm = transpose_69_perm_0, x = x_27)[name = tensor<string, []>("transpose_167")];
            tensor<fp32, [1, 12, 128, 128]> attention_scores_9 = matmul(transpose_x = attention_scores_9_transpose_x_0, transpose_y = attention_scores_9_transpose_y_0, x = transpose_69, y = transpose_70)[name = tensor<string, []>("attention_scores_9")];
            tensor<fp32, []> _inversed_attention_scores_11_y_0 = const()[name = tensor<string, []>("_inversed_attention_scores_11_y_0"), val = tensor<fp32, []>(0x1p-3)];
            tensor<fp32, [1, 12, 128, 128]> _inversed_attention_scores_11 = mul(x = attention_scores_9, y = _inversed_attention_scores_11_y_0)[name = tensor<string, []>("_inversed_attention_scores_11")];
            tensor<fp32, [1, 12, 128, 128]> input_53 = add(x = _inversed_attention_scores_11, y = attention_mask_1)[name = tensor<string, []>("input_53")];
            tensor<fp32, [1, 12, 128, 128]> input_55 = softmax(axis = var_80, x = input_53)[name = tensor<string, []>("input_55")];
            tensor<bool, []> context_layer_5_transpose_x_0 = const()[name = tensor<string, []>("context_layer_5_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> context_layer_5_transpose_y_0 = const()[name = tensor<string, []>("context_layer_5_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 12, 128, 64]> value_layer_5 = transpose(perm = var_336, x = x_35)[name = tensor<string, []>("transpose_168")];
            tensor<fp32, [1, 12, 128, 64]> context_layer_5 = matmul(transpose_x = context_layer_5_transpose_x_0, transpose_y = context_layer_5_transpose_y_0, x = input_55, y = value_layer_5)[name = tensor<string, []>("context_layer_5")];
            tensor<int32, [4]> var_346_perm_0 = const()[name = tensor<string, []>("op_346_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> concat_3 = const()[name = tensor<string, []>("concat_3"), val = tensor<int32, [3]>([1, 128, 768])];
            tensor<fp32, [1, 128, 12, 64]> var_346 = transpose(perm = var_346_perm_0, x = context_layer_5)[name = tensor<string, []>("transpose_165")];
            tensor<fp32, [1, 128, 768]> input_57 = reshape(shape = concat_3, x = var_346)[name = tensor<string, []>("input_57")];
            tensor<fp32, [1, 128, 768]> input_59 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight, x = input_57)[name = tensor<string, []>("linear_16")];
            tensor<fp32, [1, 128, 768]> input_61 = add(x = input_51, y = input_59)[name = tensor<string, []>("input_61")];
            tensor<int32, [1]> input_63_axes_0 = const()[name = tensor<string, []>("input_63_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 128, 768]> input_63 = layer_norm(axes = input_63_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_82, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_61)[name = tensor<string, []>("input_63")];
            tensor<fp32, [1, 128, 2048]> input_65 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight, x = input_63)[name = tensor<string, []>("linear_17")];
            tensor<string, []> input_67_mode_0 = const()[name = tensor<string, []>("input_67_mode_0"), val = tensor<string, []>("TANH_APPROXIMATION")];
            tensor<fp32, [1, 128, 2048]> input_67 = gelu(mode = input_67_mode_0, x = input_65)[name = tensor<string, []>("input_67")];
            tensor<fp32, [1, 128, 768]> ffn_output_5 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight, x = input_67)[name = tensor<string, []>("linear_18")];
            tensor<fp32, [1, 128, 768]> input_69 = add(x = ffn_output_5, y = input_63)[name = tensor<string, []>("input_69")];
            tensor<int32, [1]> input_71_axes_0 = const()[name = tensor<string, []>("input_71_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 128, 768]> input_71 = layer_norm(axes = input_71_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias, epsilon = var_82, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_69)[name = tensor<string, []>("input_71")];
            tensor<fp32, [1, 128, 768]> x_37 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight, x = input_71)[name = tensor<string, []>("linear_19")];
            tensor<fp32, [1, 128, 768]> x_41 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight, x = input_71)[name = tensor<string, []>("linear_20")];
            tensor<fp32, [1, 128, 768]> x_45 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight, x = input_71)[name = tensor<string, []>("linear_21")];
            tensor<int32, [4]> var_402 = const()[name = tensor<string, []>("op_402"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_39 = reshape(shape = var_402, x = x_37)[name = tensor<string, []>("x_39")];
            tensor<int32, [4]> var_408 = const()[name = tensor<string, []>("op_408"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_43 = reshape(shape = var_408, x = x_41)[name = tensor<string, []>("x_43")];
            tensor<int32, [4]> var_414 = const()[name = tensor<string, []>("op_414"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_47 = reshape(shape = var_414, x = x_45)[name = tensor<string, []>("x_47")];
            tensor<int32, [4]> var_416 = const()[name = tensor<string, []>("op_416"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<bool, []> attention_scores_13_transpose_x_0 = const()[name = tensor<string, []>("attention_scores_13_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attention_scores_13_transpose_y_0 = const()[name = tensor<string, []>("attention_scores_13_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_71_perm_0 = const()[name = tensor<string, []>("transpose_71_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_72_perm_0 = const()[name = tensor<string, []>("transpose_72_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 12, 64, 128]> transpose_72 = transpose(perm = transpose_72_perm_0, x = x_43)[name = tensor<string, []>("transpose_162")];
            tensor<fp32, [1, 12, 128, 64]> transpose_71 = transpose(perm = transpose_71_perm_0, x = x_39)[name = tensor<string, []>("transpose_163")];
            tensor<fp32, [1, 12, 128, 128]> attention_scores_13 = matmul(transpose_x = attention_scores_13_transpose_x_0, transpose_y = attention_scores_13_transpose_y_0, x = transpose_71, y = transpose_72)[name = tensor<string, []>("attention_scores_13")];
            tensor<fp32, []> _inversed_attention_scores_15_y_0 = const()[name = tensor<string, []>("_inversed_attention_scores_15_y_0"), val = tensor<fp32, []>(0x1p-3)];
            tensor<fp32, [1, 12, 128, 128]> _inversed_attention_scores_15 = mul(x = attention_scores_13, y = _inversed_attention_scores_15_y_0)[name = tensor<string, []>("_inversed_attention_scores_15")];
            tensor<fp32, [1, 12, 128, 128]> input_73 = add(x = _inversed_attention_scores_15, y = attention_mask_1)[name = tensor<string, []>("input_73")];
            tensor<fp32, [1, 12, 128, 128]> input_75 = softmax(axis = var_80, x = input_73)[name = tensor<string, []>("input_75")];
            tensor<bool, []> context_layer_7_transpose_x_0 = const()[name = tensor<string, []>("context_layer_7_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> context_layer_7_transpose_y_0 = const()[name = tensor<string, []>("context_layer_7_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 12, 128, 64]> value_layer_7 = transpose(perm = var_416, x = x_47)[name = tensor<string, []>("transpose_164")];
            tensor<fp32, [1, 12, 128, 64]> context_layer_7 = matmul(transpose_x = context_layer_7_transpose_x_0, transpose_y = context_layer_7_transpose_y_0, x = input_75, y = value_layer_7)[name = tensor<string, []>("context_layer_7")];
            tensor<int32, [4]> var_426_perm_0 = const()[name = tensor<string, []>("op_426_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> concat_4 = const()[name = tensor<string, []>("concat_4"), val = tensor<int32, [3]>([1, 128, 768])];
            tensor<fp32, [1, 128, 12, 64]> var_426 = transpose(perm = var_426_perm_0, x = context_layer_7)[name = tensor<string, []>("transpose_161")];
            tensor<fp32, [1, 128, 768]> input_77 = reshape(shape = concat_4, x = var_426)[name = tensor<string, []>("input_77")];
            tensor<fp32, [1, 128, 768]> input_79 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight, x = input_77)[name = tensor<string, []>("linear_22")];
            tensor<fp32, [1, 128, 768]> input_81 = add(x = input_71, y = input_79)[name = tensor<string, []>("input_81")];
            tensor<int32, [1]> input_83_axes_0 = const()[name = tensor<string, []>("input_83_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 128, 768]> input_83 = layer_norm(axes = input_83_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_82, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_81)[name = tensor<string, []>("input_83")];
            tensor<fp32, [1, 128, 2048]> input_85 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight, x = input_83)[name = tensor<string, []>("linear_23")];
            tensor<string, []> input_87_mode_0 = const()[name = tensor<string, []>("input_87_mode_0"), val = tensor<string, []>("TANH_APPROXIMATION")];
            tensor<fp32, [1, 128, 2048]> input_87 = gelu(mode = input_87_mode_0, x = input_85)[name = tensor<string, []>("input_87")];
            tensor<fp32, [1, 128, 768]> ffn_output_7 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight, x = input_87)[name = tensor<string, []>("linear_24")];
            tensor<fp32, [1, 128, 768]> input_89 = add(x = ffn_output_7, y = input_83)[name = tensor<string, []>("input_89")];
            tensor<int32, [1]> input_91_axes_0 = const()[name = tensor<string, []>("input_91_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 128, 768]> input_91 = layer_norm(axes = input_91_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias, epsilon = var_82, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_89)[name = tensor<string, []>("input_91")];
            tensor<fp32, [1, 128, 768]> x_49 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight, x = input_91)[name = tensor<string, []>("linear_25")];
            tensor<fp32, [1, 128, 768]> x_53 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight, x = input_91)[name = tensor<string, []>("linear_26")];
            tensor<fp32, [1, 128, 768]> x_57 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight, x = input_91)[name = tensor<string, []>("linear_27")];
            tensor<int32, [4]> var_482 = const()[name = tensor<string, []>("op_482"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_51 = reshape(shape = var_482, x = x_49)[name = tensor<string, []>("x_51")];
            tensor<int32, [4]> var_488 = const()[name = tensor<string, []>("op_488"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_55 = reshape(shape = var_488, x = x_53)[name = tensor<string, []>("x_55")];
            tensor<int32, [4]> var_494 = const()[name = tensor<string, []>("op_494"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_59 = reshape(shape = var_494, x = x_57)[name = tensor<string, []>("x_59")];
            tensor<int32, [4]> var_496 = const()[name = tensor<string, []>("op_496"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<bool, []> attention_scores_17_transpose_x_0 = const()[name = tensor<string, []>("attention_scores_17_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attention_scores_17_transpose_y_0 = const()[name = tensor<string, []>("attention_scores_17_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_73_perm_0 = const()[name = tensor<string, []>("transpose_73_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_74_perm_0 = const()[name = tensor<string, []>("transpose_74_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 12, 64, 128]> transpose_74 = transpose(perm = transpose_74_perm_0, x = x_55)[name = tensor<string, []>("transpose_158")];
            tensor<fp32, [1, 12, 128, 64]> transpose_73 = transpose(perm = transpose_73_perm_0, x = x_51)[name = tensor<string, []>("transpose_159")];
            tensor<fp32, [1, 12, 128, 128]> attention_scores_17 = matmul(transpose_x = attention_scores_17_transpose_x_0, transpose_y = attention_scores_17_transpose_y_0, x = transpose_73, y = transpose_74)[name = tensor<string, []>("attention_scores_17")];
            tensor<fp32, []> _inversed_attention_scores_19_y_0 = const()[name = tensor<string, []>("_inversed_attention_scores_19_y_0"), val = tensor<fp32, []>(0x1p-3)];
            tensor<fp32, [1, 12, 128, 128]> _inversed_attention_scores_19 = mul(x = attention_scores_17, y = _inversed_attention_scores_19_y_0)[name = tensor<string, []>("_inversed_attention_scores_19")];
            tensor<fp32, [1, 12, 128, 128]> input_93 = add(x = _inversed_attention_scores_19, y = attention_mask_1)[name = tensor<string, []>("input_93")];
            tensor<fp32, [1, 12, 128, 128]> input_95 = softmax(axis = var_80, x = input_93)[name = tensor<string, []>("input_95")];
            tensor<bool, []> context_layer_9_transpose_x_0 = const()[name = tensor<string, []>("context_layer_9_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> context_layer_9_transpose_y_0 = const()[name = tensor<string, []>("context_layer_9_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 12, 128, 64]> value_layer_9 = transpose(perm = var_496, x = x_59)[name = tensor<string, []>("transpose_160")];
            tensor<fp32, [1, 12, 128, 64]> context_layer_9 = matmul(transpose_x = context_layer_9_transpose_x_0, transpose_y = context_layer_9_transpose_y_0, x = input_95, y = value_layer_9)[name = tensor<string, []>("context_layer_9")];
            tensor<int32, [4]> var_506_perm_0 = const()[name = tensor<string, []>("op_506_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> concat_5 = const()[name = tensor<string, []>("concat_5"), val = tensor<int32, [3]>([1, 128, 768])];
            tensor<fp32, [1, 128, 12, 64]> var_506 = transpose(perm = var_506_perm_0, x = context_layer_9)[name = tensor<string, []>("transpose_157")];
            tensor<fp32, [1, 128, 768]> input_97 = reshape(shape = concat_5, x = var_506)[name = tensor<string, []>("input_97")];
            tensor<fp32, [1, 128, 768]> input_99 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight, x = input_97)[name = tensor<string, []>("linear_28")];
            tensor<fp32, [1, 128, 768]> input_101 = add(x = input_91, y = input_99)[name = tensor<string, []>("input_101")];
            tensor<int32, [1]> input_103_axes_0 = const()[name = tensor<string, []>("input_103_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 128, 768]> input_103 = layer_norm(axes = input_103_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_82, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_101)[name = tensor<string, []>("input_103")];
            tensor<fp32, [1, 128, 2048]> input_105 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight, x = input_103)[name = tensor<string, []>("linear_29")];
            tensor<string, []> input_107_mode_0 = const()[name = tensor<string, []>("input_107_mode_0"), val = tensor<string, []>("TANH_APPROXIMATION")];
            tensor<fp32, [1, 128, 2048]> input_107 = gelu(mode = input_107_mode_0, x = input_105)[name = tensor<string, []>("input_107")];
            tensor<fp32, [1, 128, 768]> ffn_output_9 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight, x = input_107)[name = tensor<string, []>("linear_30")];
            tensor<fp32, [1, 128, 768]> input_109 = add(x = ffn_output_9, y = input_103)[name = tensor<string, []>("input_109")];
            tensor<int32, [1]> input_111_axes_0 = const()[name = tensor<string, []>("input_111_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 128, 768]> input_111 = layer_norm(axes = input_111_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias, epsilon = var_82, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_109)[name = tensor<string, []>("input_111")];
            tensor<fp32, [1, 128, 768]> x_61 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight, x = input_111)[name = tensor<string, []>("linear_31")];
            tensor<fp32, [1, 128, 768]> x_65 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight, x = input_111)[name = tensor<string, []>("linear_32")];
            tensor<fp32, [1, 128, 768]> x_69 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight, x = input_111)[name = tensor<string, []>("linear_33")];
            tensor<int32, [4]> var_562 = const()[name = tensor<string, []>("op_562"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_63 = reshape(shape = var_562, x = x_61)[name = tensor<string, []>("x_63")];
            tensor<int32, [4]> var_568 = const()[name = tensor<string, []>("op_568"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_67 = reshape(shape = var_568, x = x_65)[name = tensor<string, []>("x_67")];
            tensor<int32, [4]> var_574 = const()[name = tensor<string, []>("op_574"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_71 = reshape(shape = var_574, x = x_69)[name = tensor<string, []>("x_71")];
            tensor<int32, [4]> var_576 = const()[name = tensor<string, []>("op_576"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<bool, []> attention_scores_21_transpose_x_0 = const()[name = tensor<string, []>("attention_scores_21_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attention_scores_21_transpose_y_0 = const()[name = tensor<string, []>("attention_scores_21_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_75_perm_0 = const()[name = tensor<string, []>("transpose_75_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_76_perm_0 = const()[name = tensor<string, []>("transpose_76_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 12, 64, 128]> transpose_76 = transpose(perm = transpose_76_perm_0, x = x_67)[name = tensor<string, []>("transpose_154")];
            tensor<fp32, [1, 12, 128, 64]> transpose_75 = transpose(perm = transpose_75_perm_0, x = x_63)[name = tensor<string, []>("transpose_155")];
            tensor<fp32, [1, 12, 128, 128]> attention_scores_21 = matmul(transpose_x = attention_scores_21_transpose_x_0, transpose_y = attention_scores_21_transpose_y_0, x = transpose_75, y = transpose_76)[name = tensor<string, []>("attention_scores_21")];
            tensor<fp32, []> _inversed_attention_scores_23_y_0 = const()[name = tensor<string, []>("_inversed_attention_scores_23_y_0"), val = tensor<fp32, []>(0x1p-3)];
            tensor<fp32, [1, 12, 128, 128]> _inversed_attention_scores_23 = mul(x = attention_scores_21, y = _inversed_attention_scores_23_y_0)[name = tensor<string, []>("_inversed_attention_scores_23")];
            tensor<fp32, [1, 12, 128, 128]> input_113 = add(x = _inversed_attention_scores_23, y = attention_mask_1)[name = tensor<string, []>("input_113")];
            tensor<fp32, [1, 12, 128, 128]> input_115 = softmax(axis = var_80, x = input_113)[name = tensor<string, []>("input_115")];
            tensor<bool, []> context_layer_11_transpose_x_0 = const()[name = tensor<string, []>("context_layer_11_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> context_layer_11_transpose_y_0 = const()[name = tensor<string, []>("context_layer_11_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 12, 128, 64]> value_layer_11 = transpose(perm = var_576, x = x_71)[name = tensor<string, []>("transpose_156")];
            tensor<fp32, [1, 12, 128, 64]> context_layer_11 = matmul(transpose_x = context_layer_11_transpose_x_0, transpose_y = context_layer_11_transpose_y_0, x = input_115, y = value_layer_11)[name = tensor<string, []>("context_layer_11")];
            tensor<int32, [4]> var_586_perm_0 = const()[name = tensor<string, []>("op_586_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> concat_6 = const()[name = tensor<string, []>("concat_6"), val = tensor<int32, [3]>([1, 128, 768])];
            tensor<fp32, [1, 128, 12, 64]> var_586 = transpose(perm = var_586_perm_0, x = context_layer_11)[name = tensor<string, []>("transpose_153")];
            tensor<fp32, [1, 128, 768]> input_117 = reshape(shape = concat_6, x = var_586)[name = tensor<string, []>("input_117")];
            tensor<fp32, [1, 128, 768]> input_119 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight, x = input_117)[name = tensor<string, []>("linear_34")];
            tensor<fp32, [1, 128, 768]> input_121 = add(x = input_111, y = input_119)[name = tensor<string, []>("input_121")];
            tensor<int32, [1]> input_123_axes_0 = const()[name = tensor<string, []>("input_123_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 128, 768]> input_123 = layer_norm(axes = input_123_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_82, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_121)[name = tensor<string, []>("input_123")];
            tensor<fp32, [1, 128, 2048]> input_125 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight, x = input_123)[name = tensor<string, []>("linear_35")];
            tensor<string, []> input_127_mode_0 = const()[name = tensor<string, []>("input_127_mode_0"), val = tensor<string, []>("TANH_APPROXIMATION")];
            tensor<fp32, [1, 128, 2048]> input_127 = gelu(mode = input_127_mode_0, x = input_125)[name = tensor<string, []>("input_127")];
            tensor<fp32, [1, 128, 768]> ffn_output_11 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight, x = input_127)[name = tensor<string, []>("linear_36")];
            tensor<fp32, [1, 128, 768]> input_129 = add(x = ffn_output_11, y = input_123)[name = tensor<string, []>("input_129")];
            tensor<int32, [1]> input_131_axes_0 = const()[name = tensor<string, []>("input_131_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 128, 768]> input_131 = layer_norm(axes = input_131_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias, epsilon = var_82, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_129)[name = tensor<string, []>("input_131")];
            tensor<fp32, [1, 128, 768]> x_73 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight, x = input_131)[name = tensor<string, []>("linear_37")];
            tensor<fp32, [1, 128, 768]> x_77 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight, x = input_131)[name = tensor<string, []>("linear_38")];
            tensor<fp32, [1, 128, 768]> x_81 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight, x = input_131)[name = tensor<string, []>("linear_39")];
            tensor<int32, [4]> var_642 = const()[name = tensor<string, []>("op_642"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_75 = reshape(shape = var_642, x = x_73)[name = tensor<string, []>("x_75")];
            tensor<int32, [4]> var_648 = const()[name = tensor<string, []>("op_648"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_79 = reshape(shape = var_648, x = x_77)[name = tensor<string, []>("x_79")];
            tensor<int32, [4]> var_654 = const()[name = tensor<string, []>("op_654"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_83 = reshape(shape = var_654, x = x_81)[name = tensor<string, []>("x_83")];
            tensor<int32, [4]> var_656 = const()[name = tensor<string, []>("op_656"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<bool, []> attention_scores_25_transpose_x_0 = const()[name = tensor<string, []>("attention_scores_25_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attention_scores_25_transpose_y_0 = const()[name = tensor<string, []>("attention_scores_25_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_77_perm_0 = const()[name = tensor<string, []>("transpose_77_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_78_perm_0 = const()[name = tensor<string, []>("transpose_78_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 12, 64, 128]> transpose_78 = transpose(perm = transpose_78_perm_0, x = x_79)[name = tensor<string, []>("transpose_150")];
            tensor<fp32, [1, 12, 128, 64]> transpose_77 = transpose(perm = transpose_77_perm_0, x = x_75)[name = tensor<string, []>("transpose_151")];
            tensor<fp32, [1, 12, 128, 128]> attention_scores_25 = matmul(transpose_x = attention_scores_25_transpose_x_0, transpose_y = attention_scores_25_transpose_y_0, x = transpose_77, y = transpose_78)[name = tensor<string, []>("attention_scores_25")];
            tensor<fp32, []> _inversed_attention_scores_27_y_0 = const()[name = tensor<string, []>("_inversed_attention_scores_27_y_0"), val = tensor<fp32, []>(0x1p-3)];
            tensor<fp32, [1, 12, 128, 128]> _inversed_attention_scores_27 = mul(x = attention_scores_25, y = _inversed_attention_scores_27_y_0)[name = tensor<string, []>("_inversed_attention_scores_27")];
            tensor<fp32, [1, 12, 128, 128]> input_133 = add(x = _inversed_attention_scores_27, y = attention_mask_1)[name = tensor<string, []>("input_133")];
            tensor<fp32, [1, 12, 128, 128]> input_135 = softmax(axis = var_80, x = input_133)[name = tensor<string, []>("input_135")];
            tensor<bool, []> context_layer_13_transpose_x_0 = const()[name = tensor<string, []>("context_layer_13_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> context_layer_13_transpose_y_0 = const()[name = tensor<string, []>("context_layer_13_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 12, 128, 64]> value_layer_13 = transpose(perm = var_656, x = x_83)[name = tensor<string, []>("transpose_152")];
            tensor<fp32, [1, 12, 128, 64]> context_layer_13 = matmul(transpose_x = context_layer_13_transpose_x_0, transpose_y = context_layer_13_transpose_y_0, x = input_135, y = value_layer_13)[name = tensor<string, []>("context_layer_13")];
            tensor<int32, [4]> var_666_perm_0 = const()[name = tensor<string, []>("op_666_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> concat_7 = const()[name = tensor<string, []>("concat_7"), val = tensor<int32, [3]>([1, 128, 768])];
            tensor<fp32, [1, 128, 12, 64]> var_666 = transpose(perm = var_666_perm_0, x = context_layer_13)[name = tensor<string, []>("transpose_149")];
            tensor<fp32, [1, 128, 768]> input_137 = reshape(shape = concat_7, x = var_666)[name = tensor<string, []>("input_137")];
            tensor<fp32, [1, 128, 768]> input_139 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight, x = input_137)[name = tensor<string, []>("linear_40")];
            tensor<fp32, [1, 128, 768]> input_141 = add(x = input_131, y = input_139)[name = tensor<string, []>("input_141")];
            tensor<int32, [1]> input_143_axes_0 = const()[name = tensor<string, []>("input_143_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 128, 768]> input_143 = layer_norm(axes = input_143_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_82, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_141)[name = tensor<string, []>("input_143")];
            tensor<fp32, [1, 128, 2048]> input_145 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight, x = input_143)[name = tensor<string, []>("linear_41")];
            tensor<string, []> input_147_mode_0 = const()[name = tensor<string, []>("input_147_mode_0"), val = tensor<string, []>("TANH_APPROXIMATION")];
            tensor<fp32, [1, 128, 2048]> input_147 = gelu(mode = input_147_mode_0, x = input_145)[name = tensor<string, []>("input_147")];
            tensor<fp32, [1, 128, 768]> ffn_output_13 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight, x = input_147)[name = tensor<string, []>("linear_42")];
            tensor<fp32, [1, 128, 768]> input_149 = add(x = ffn_output_13, y = input_143)[name = tensor<string, []>("input_149")];
            tensor<int32, [1]> input_151_axes_0 = const()[name = tensor<string, []>("input_151_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 128, 768]> input_151 = layer_norm(axes = input_151_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias, epsilon = var_82, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_149)[name = tensor<string, []>("input_151")];
            tensor<fp32, [1, 128, 768]> x_85 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight, x = input_151)[name = tensor<string, []>("linear_43")];
            tensor<fp32, [1, 128, 768]> x_89 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight, x = input_151)[name = tensor<string, []>("linear_44")];
            tensor<fp32, [1, 128, 768]> x_93 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight, x = input_151)[name = tensor<string, []>("linear_45")];
            tensor<int32, [4]> var_722 = const()[name = tensor<string, []>("op_722"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_87 = reshape(shape = var_722, x = x_85)[name = tensor<string, []>("x_87")];
            tensor<int32, [4]> var_728 = const()[name = tensor<string, []>("op_728"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_91 = reshape(shape = var_728, x = x_89)[name = tensor<string, []>("x_91")];
            tensor<int32, [4]> var_734 = const()[name = tensor<string, []>("op_734"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_95 = reshape(shape = var_734, x = x_93)[name = tensor<string, []>("x_95")];
            tensor<int32, [4]> var_736 = const()[name = tensor<string, []>("op_736"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<bool, []> attention_scores_29_transpose_x_0 = const()[name = tensor<string, []>("attention_scores_29_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attention_scores_29_transpose_y_0 = const()[name = tensor<string, []>("attention_scores_29_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_79_perm_0 = const()[name = tensor<string, []>("transpose_79_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_80_perm_0 = const()[name = tensor<string, []>("transpose_80_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 12, 64, 128]> transpose_80 = transpose(perm = transpose_80_perm_0, x = x_91)[name = tensor<string, []>("transpose_146")];
            tensor<fp32, [1, 12, 128, 64]> transpose_79 = transpose(perm = transpose_79_perm_0, x = x_87)[name = tensor<string, []>("transpose_147")];
            tensor<fp32, [1, 12, 128, 128]> attention_scores_29 = matmul(transpose_x = attention_scores_29_transpose_x_0, transpose_y = attention_scores_29_transpose_y_0, x = transpose_79, y = transpose_80)[name = tensor<string, []>("attention_scores_29")];
            tensor<fp32, []> _inversed_attention_scores_31_y_0 = const()[name = tensor<string, []>("_inversed_attention_scores_31_y_0"), val = tensor<fp32, []>(0x1p-3)];
            tensor<fp32, [1, 12, 128, 128]> _inversed_attention_scores_31 = mul(x = attention_scores_29, y = _inversed_attention_scores_31_y_0)[name = tensor<string, []>("_inversed_attention_scores_31")];
            tensor<fp32, [1, 12, 128, 128]> input_153 = add(x = _inversed_attention_scores_31, y = attention_mask_1)[name = tensor<string, []>("input_153")];
            tensor<fp32, [1, 12, 128, 128]> input_155 = softmax(axis = var_80, x = input_153)[name = tensor<string, []>("input_155")];
            tensor<bool, []> context_layer_15_transpose_x_0 = const()[name = tensor<string, []>("context_layer_15_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> context_layer_15_transpose_y_0 = const()[name = tensor<string, []>("context_layer_15_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 12, 128, 64]> value_layer_15 = transpose(perm = var_736, x = x_95)[name = tensor<string, []>("transpose_148")];
            tensor<fp32, [1, 12, 128, 64]> context_layer_15 = matmul(transpose_x = context_layer_15_transpose_x_0, transpose_y = context_layer_15_transpose_y_0, x = input_155, y = value_layer_15)[name = tensor<string, []>("context_layer_15")];
            tensor<int32, [4]> var_746_perm_0 = const()[name = tensor<string, []>("op_746_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> concat_8 = const()[name = tensor<string, []>("concat_8"), val = tensor<int32, [3]>([1, 128, 768])];
            tensor<fp32, [1, 128, 12, 64]> var_746 = transpose(perm = var_746_perm_0, x = context_layer_15)[name = tensor<string, []>("transpose_145")];
            tensor<fp32, [1, 128, 768]> input_157 = reshape(shape = concat_8, x = var_746)[name = tensor<string, []>("input_157")];
            tensor<fp32, [1, 128, 768]> input_159 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight, x = input_157)[name = tensor<string, []>("linear_46")];
            tensor<fp32, [1, 128, 768]> input_161 = add(x = input_151, y = input_159)[name = tensor<string, []>("input_161")];
            tensor<int32, [1]> input_163_axes_0 = const()[name = tensor<string, []>("input_163_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 128, 768]> input_163 = layer_norm(axes = input_163_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_82, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_161)[name = tensor<string, []>("input_163")];
            tensor<fp32, [1, 128, 2048]> input_165 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight, x = input_163)[name = tensor<string, []>("linear_47")];
            tensor<string, []> input_167_mode_0 = const()[name = tensor<string, []>("input_167_mode_0"), val = tensor<string, []>("TANH_APPROXIMATION")];
            tensor<fp32, [1, 128, 2048]> input_167 = gelu(mode = input_167_mode_0, x = input_165)[name = tensor<string, []>("input_167")];
            tensor<fp32, [1, 128, 768]> ffn_output_15 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight, x = input_167)[name = tensor<string, []>("linear_48")];
            tensor<fp32, [1, 128, 768]> input_169 = add(x = ffn_output_15, y = input_163)[name = tensor<string, []>("input_169")];
            tensor<int32, [1]> input_171_axes_0 = const()[name = tensor<string, []>("input_171_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 128, 768]> input_171 = layer_norm(axes = input_171_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias, epsilon = var_82, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_169)[name = tensor<string, []>("input_171")];
            tensor<fp32, [1, 128, 768]> x_97 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight, x = input_171)[name = tensor<string, []>("linear_49")];
            tensor<fp32, [1, 128, 768]> x_101 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight, x = input_171)[name = tensor<string, []>("linear_50")];
            tensor<fp32, [1, 128, 768]> x_105 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight, x = input_171)[name = tensor<string, []>("linear_51")];
            tensor<int32, [4]> var_802 = const()[name = tensor<string, []>("op_802"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_99 = reshape(shape = var_802, x = x_97)[name = tensor<string, []>("x_99")];
            tensor<int32, [4]> var_808 = const()[name = tensor<string, []>("op_808"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_103 = reshape(shape = var_808, x = x_101)[name = tensor<string, []>("x_103")];
            tensor<int32, [4]> var_814 = const()[name = tensor<string, []>("op_814"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_107 = reshape(shape = var_814, x = x_105)[name = tensor<string, []>("x_107")];
            tensor<int32, [4]> var_816 = const()[name = tensor<string, []>("op_816"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<bool, []> attention_scores_33_transpose_x_0 = const()[name = tensor<string, []>("attention_scores_33_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attention_scores_33_transpose_y_0 = const()[name = tensor<string, []>("attention_scores_33_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_81_perm_0 = const()[name = tensor<string, []>("transpose_81_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_82_perm_0 = const()[name = tensor<string, []>("transpose_82_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 12, 64, 128]> transpose_82 = transpose(perm = transpose_82_perm_0, x = x_103)[name = tensor<string, []>("transpose_142")];
            tensor<fp32, [1, 12, 128, 64]> transpose_81 = transpose(perm = transpose_81_perm_0, x = x_99)[name = tensor<string, []>("transpose_143")];
            tensor<fp32, [1, 12, 128, 128]> attention_scores_33 = matmul(transpose_x = attention_scores_33_transpose_x_0, transpose_y = attention_scores_33_transpose_y_0, x = transpose_81, y = transpose_82)[name = tensor<string, []>("attention_scores_33")];
            tensor<fp32, []> _inversed_attention_scores_35_y_0 = const()[name = tensor<string, []>("_inversed_attention_scores_35_y_0"), val = tensor<fp32, []>(0x1p-3)];
            tensor<fp32, [1, 12, 128, 128]> _inversed_attention_scores_35 = mul(x = attention_scores_33, y = _inversed_attention_scores_35_y_0)[name = tensor<string, []>("_inversed_attention_scores_35")];
            tensor<fp32, [1, 12, 128, 128]> input_173 = add(x = _inversed_attention_scores_35, y = attention_mask_1)[name = tensor<string, []>("input_173")];
            tensor<fp32, [1, 12, 128, 128]> input_175 = softmax(axis = var_80, x = input_173)[name = tensor<string, []>("input_175")];
            tensor<bool, []> context_layer_17_transpose_x_0 = const()[name = tensor<string, []>("context_layer_17_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> context_layer_17_transpose_y_0 = const()[name = tensor<string, []>("context_layer_17_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 12, 128, 64]> value_layer_17 = transpose(perm = var_816, x = x_107)[name = tensor<string, []>("transpose_144")];
            tensor<fp32, [1, 12, 128, 64]> context_layer_17 = matmul(transpose_x = context_layer_17_transpose_x_0, transpose_y = context_layer_17_transpose_y_0, x = input_175, y = value_layer_17)[name = tensor<string, []>("context_layer_17")];
            tensor<int32, [4]> var_826_perm_0 = const()[name = tensor<string, []>("op_826_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> concat_9 = const()[name = tensor<string, []>("concat_9"), val = tensor<int32, [3]>([1, 128, 768])];
            tensor<fp32, [1, 128, 12, 64]> var_826 = transpose(perm = var_826_perm_0, x = context_layer_17)[name = tensor<string, []>("transpose_141")];
            tensor<fp32, [1, 128, 768]> input_177 = reshape(shape = concat_9, x = var_826)[name = tensor<string, []>("input_177")];
            tensor<fp32, [1, 128, 768]> input_179 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight, x = input_177)[name = tensor<string, []>("linear_52")];
            tensor<fp32, [1, 128, 768]> input_181 = add(x = input_171, y = input_179)[name = tensor<string, []>("input_181")];
            tensor<int32, [1]> input_183_axes_0 = const()[name = tensor<string, []>("input_183_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 128, 768]> input_183 = layer_norm(axes = input_183_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_82, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_181)[name = tensor<string, []>("input_183")];
            tensor<fp32, [1, 128, 2048]> input_185 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight, x = input_183)[name = tensor<string, []>("linear_53")];
            tensor<string, []> input_187_mode_0 = const()[name = tensor<string, []>("input_187_mode_0"), val = tensor<string, []>("TANH_APPROXIMATION")];
            tensor<fp32, [1, 128, 2048]> input_187 = gelu(mode = input_187_mode_0, x = input_185)[name = tensor<string, []>("input_187")];
            tensor<fp32, [1, 128, 768]> ffn_output_17 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight, x = input_187)[name = tensor<string, []>("linear_54")];
            tensor<fp32, [1, 128, 768]> input_189 = add(x = ffn_output_17, y = input_183)[name = tensor<string, []>("input_189")];
            tensor<int32, [1]> input_191_axes_0 = const()[name = tensor<string, []>("input_191_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 128, 768]> input_191 = layer_norm(axes = input_191_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias, epsilon = var_82, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_189)[name = tensor<string, []>("input_191")];
            tensor<fp32, [1, 128, 768]> x_109 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight, x = input_191)[name = tensor<string, []>("linear_55")];
            tensor<fp32, [1, 128, 768]> x_113 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight, x = input_191)[name = tensor<string, []>("linear_56")];
            tensor<fp32, [1, 128, 768]> x_117 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight, x = input_191)[name = tensor<string, []>("linear_57")];
            tensor<int32, [4]> var_882 = const()[name = tensor<string, []>("op_882"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_111 = reshape(shape = var_882, x = x_109)[name = tensor<string, []>("x_111")];
            tensor<int32, [4]> var_888 = const()[name = tensor<string, []>("op_888"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_115 = reshape(shape = var_888, x = x_113)[name = tensor<string, []>("x_115")];
            tensor<int32, [4]> var_894 = const()[name = tensor<string, []>("op_894"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_119 = reshape(shape = var_894, x = x_117)[name = tensor<string, []>("x_119")];
            tensor<int32, [4]> var_896 = const()[name = tensor<string, []>("op_896"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<bool, []> attention_scores_37_transpose_x_0 = const()[name = tensor<string, []>("attention_scores_37_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attention_scores_37_transpose_y_0 = const()[name = tensor<string, []>("attention_scores_37_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_83_perm_0 = const()[name = tensor<string, []>("transpose_83_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_84_perm_0 = const()[name = tensor<string, []>("transpose_84_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 12, 64, 128]> transpose_84 = transpose(perm = transpose_84_perm_0, x = x_115)[name = tensor<string, []>("transpose_138")];
            tensor<fp32, [1, 12, 128, 64]> transpose_83 = transpose(perm = transpose_83_perm_0, x = x_111)[name = tensor<string, []>("transpose_139")];
            tensor<fp32, [1, 12, 128, 128]> attention_scores_37 = matmul(transpose_x = attention_scores_37_transpose_x_0, transpose_y = attention_scores_37_transpose_y_0, x = transpose_83, y = transpose_84)[name = tensor<string, []>("attention_scores_37")];
            tensor<fp32, []> _inversed_attention_scores_39_y_0 = const()[name = tensor<string, []>("_inversed_attention_scores_39_y_0"), val = tensor<fp32, []>(0x1p-3)];
            tensor<fp32, [1, 12, 128, 128]> _inversed_attention_scores_39 = mul(x = attention_scores_37, y = _inversed_attention_scores_39_y_0)[name = tensor<string, []>("_inversed_attention_scores_39")];
            tensor<fp32, [1, 12, 128, 128]> input_193 = add(x = _inversed_attention_scores_39, y = attention_mask_1)[name = tensor<string, []>("input_193")];
            tensor<fp32, [1, 12, 128, 128]> input_195 = softmax(axis = var_80, x = input_193)[name = tensor<string, []>("input_195")];
            tensor<bool, []> context_layer_19_transpose_x_0 = const()[name = tensor<string, []>("context_layer_19_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> context_layer_19_transpose_y_0 = const()[name = tensor<string, []>("context_layer_19_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 12, 128, 64]> value_layer_19 = transpose(perm = var_896, x = x_119)[name = tensor<string, []>("transpose_140")];
            tensor<fp32, [1, 12, 128, 64]> context_layer_19 = matmul(transpose_x = context_layer_19_transpose_x_0, transpose_y = context_layer_19_transpose_y_0, x = input_195, y = value_layer_19)[name = tensor<string, []>("context_layer_19")];
            tensor<int32, [4]> var_906_perm_0 = const()[name = tensor<string, []>("op_906_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> concat_10 = const()[name = tensor<string, []>("concat_10"), val = tensor<int32, [3]>([1, 128, 768])];
            tensor<fp32, [1, 128, 12, 64]> var_906 = transpose(perm = var_906_perm_0, x = context_layer_19)[name = tensor<string, []>("transpose_137")];
            tensor<fp32, [1, 128, 768]> input_197 = reshape(shape = concat_10, x = var_906)[name = tensor<string, []>("input_197")];
            tensor<fp32, [1, 128, 768]> input_199 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight, x = input_197)[name = tensor<string, []>("linear_58")];
            tensor<fp32, [1, 128, 768]> input_201 = add(x = input_191, y = input_199)[name = tensor<string, []>("input_201")];
            tensor<int32, [1]> input_203_axes_0 = const()[name = tensor<string, []>("input_203_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 128, 768]> input_203 = layer_norm(axes = input_203_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_82, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_201)[name = tensor<string, []>("input_203")];
            tensor<fp32, [1, 128, 2048]> input_205 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight, x = input_203)[name = tensor<string, []>("linear_59")];
            tensor<string, []> input_207_mode_0 = const()[name = tensor<string, []>("input_207_mode_0"), val = tensor<string, []>("TANH_APPROXIMATION")];
            tensor<fp32, [1, 128, 2048]> input_207 = gelu(mode = input_207_mode_0, x = input_205)[name = tensor<string, []>("input_207")];
            tensor<fp32, [1, 128, 768]> ffn_output_19 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight, x = input_207)[name = tensor<string, []>("linear_60")];
            tensor<fp32, [1, 128, 768]> input_209 = add(x = ffn_output_19, y = input_203)[name = tensor<string, []>("input_209")];
            tensor<int32, [1]> input_211_axes_0 = const()[name = tensor<string, []>("input_211_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 128, 768]> input_211 = layer_norm(axes = input_211_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias, epsilon = var_82, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_209)[name = tensor<string, []>("input_211")];
            tensor<fp32, [1, 128, 768]> x_121 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight, x = input_211)[name = tensor<string, []>("linear_61")];
            tensor<fp32, [1, 128, 768]> x_125 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight, x = input_211)[name = tensor<string, []>("linear_62")];
            tensor<fp32, [1, 128, 768]> x_129 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight, x = input_211)[name = tensor<string, []>("linear_63")];
            tensor<int32, [4]> var_962 = const()[name = tensor<string, []>("op_962"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_123 = reshape(shape = var_962, x = x_121)[name = tensor<string, []>("x_123")];
            tensor<int32, [4]> var_968 = const()[name = tensor<string, []>("op_968"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_127 = reshape(shape = var_968, x = x_125)[name = tensor<string, []>("x_127")];
            tensor<int32, [4]> var_974 = const()[name = tensor<string, []>("op_974"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_131 = reshape(shape = var_974, x = x_129)[name = tensor<string, []>("x_131")];
            tensor<int32, [4]> var_976 = const()[name = tensor<string, []>("op_976"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<bool, []> attention_scores_41_transpose_x_0 = const()[name = tensor<string, []>("attention_scores_41_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attention_scores_41_transpose_y_0 = const()[name = tensor<string, []>("attention_scores_41_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_85_perm_0 = const()[name = tensor<string, []>("transpose_85_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_86_perm_0 = const()[name = tensor<string, []>("transpose_86_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 12, 64, 128]> transpose_86 = transpose(perm = transpose_86_perm_0, x = x_127)[name = tensor<string, []>("transpose_134")];
            tensor<fp32, [1, 12, 128, 64]> transpose_85 = transpose(perm = transpose_85_perm_0, x = x_123)[name = tensor<string, []>("transpose_135")];
            tensor<fp32, [1, 12, 128, 128]> attention_scores_41 = matmul(transpose_x = attention_scores_41_transpose_x_0, transpose_y = attention_scores_41_transpose_y_0, x = transpose_85, y = transpose_86)[name = tensor<string, []>("attention_scores_41")];
            tensor<fp32, []> _inversed_attention_scores_43_y_0 = const()[name = tensor<string, []>("_inversed_attention_scores_43_y_0"), val = tensor<fp32, []>(0x1p-3)];
            tensor<fp32, [1, 12, 128, 128]> _inversed_attention_scores_43 = mul(x = attention_scores_41, y = _inversed_attention_scores_43_y_0)[name = tensor<string, []>("_inversed_attention_scores_43")];
            tensor<fp32, [1, 12, 128, 128]> input_213 = add(x = _inversed_attention_scores_43, y = attention_mask_1)[name = tensor<string, []>("input_213")];
            tensor<fp32, [1, 12, 128, 128]> input_215 = softmax(axis = var_80, x = input_213)[name = tensor<string, []>("input_215")];
            tensor<bool, []> context_layer_21_transpose_x_0 = const()[name = tensor<string, []>("context_layer_21_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> context_layer_21_transpose_y_0 = const()[name = tensor<string, []>("context_layer_21_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 12, 128, 64]> value_layer_21 = transpose(perm = var_976, x = x_131)[name = tensor<string, []>("transpose_136")];
            tensor<fp32, [1, 12, 128, 64]> context_layer_21 = matmul(transpose_x = context_layer_21_transpose_x_0, transpose_y = context_layer_21_transpose_y_0, x = input_215, y = value_layer_21)[name = tensor<string, []>("context_layer_21")];
            tensor<int32, [4]> var_986_perm_0 = const()[name = tensor<string, []>("op_986_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> concat_11 = const()[name = tensor<string, []>("concat_11"), val = tensor<int32, [3]>([1, 128, 768])];
            tensor<fp32, [1, 128, 12, 64]> var_986 = transpose(perm = var_986_perm_0, x = context_layer_21)[name = tensor<string, []>("transpose_133")];
            tensor<fp32, [1, 128, 768]> input_217 = reshape(shape = concat_11, x = var_986)[name = tensor<string, []>("input_217")];
            tensor<fp32, [1, 128, 768]> input_219 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight, x = input_217)[name = tensor<string, []>("linear_64")];
            tensor<fp32, [1, 128, 768]> input_221 = add(x = input_211, y = input_219)[name = tensor<string, []>("input_221")];
            tensor<int32, [1]> input_223_axes_0 = const()[name = tensor<string, []>("input_223_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 128, 768]> input_223 = layer_norm(axes = input_223_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_82, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_221)[name = tensor<string, []>("input_223")];
            tensor<fp32, [1, 128, 2048]> input_225 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight, x = input_223)[name = tensor<string, []>("linear_65")];
            tensor<string, []> input_227_mode_0 = const()[name = tensor<string, []>("input_227_mode_0"), val = tensor<string, []>("TANH_APPROXIMATION")];
            tensor<fp32, [1, 128, 2048]> input_227 = gelu(mode = input_227_mode_0, x = input_225)[name = tensor<string, []>("input_227")];
            tensor<fp32, [1, 128, 768]> ffn_output_21 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight, x = input_227)[name = tensor<string, []>("linear_66")];
            tensor<fp32, [1, 128, 768]> input_229 = add(x = ffn_output_21, y = input_223)[name = tensor<string, []>("input_229")];
            tensor<int32, [1]> input_231_axes_0 = const()[name = tensor<string, []>("input_231_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 128, 768]> input_231 = layer_norm(axes = input_231_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias, epsilon = var_82, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_229)[name = tensor<string, []>("input_231")];
            tensor<fp32, [1, 128, 768]> x_133 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight, x = input_231)[name = tensor<string, []>("linear_67")];
            tensor<fp32, [1, 128, 768]> x_137 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight, x = input_231)[name = tensor<string, []>("linear_68")];
            tensor<fp32, [1, 128, 768]> x_141 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight, x = input_231)[name = tensor<string, []>("linear_69")];
            tensor<int32, [4]> var_1042 = const()[name = tensor<string, []>("op_1042"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_135 = reshape(shape = var_1042, x = x_133)[name = tensor<string, []>("x_135")];
            tensor<int32, [4]> var_1048 = const()[name = tensor<string, []>("op_1048"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_139 = reshape(shape = var_1048, x = x_137)[name = tensor<string, []>("x_139")];
            tensor<int32, [4]> var_1054 = const()[name = tensor<string, []>("op_1054"), val = tensor<int32, [4]>([1, 128, 12, 64])];
            tensor<fp32, [1, 128, 12, 64]> x_143 = reshape(shape = var_1054, x = x_141)[name = tensor<string, []>("x_143")];
            tensor<int32, [4]> var_1056 = const()[name = tensor<string, []>("op_1056"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<bool, []> attention_scores_45_transpose_x_0 = const()[name = tensor<string, []>("attention_scores_45_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> attention_scores_45_transpose_y_0 = const()[name = tensor<string, []>("attention_scores_45_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<int32, [4]> transpose_87_perm_0 = const()[name = tensor<string, []>("transpose_87_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
            tensor<int32, [4]> transpose_88_perm_0 = const()[name = tensor<string, []>("transpose_88_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
            tensor<fp32, [1, 12, 64, 128]> transpose_88 = transpose(perm = transpose_88_perm_0, x = x_139)[name = tensor<string, []>("transpose_130")];
            tensor<fp32, [1, 12, 128, 64]> transpose_87 = transpose(perm = transpose_87_perm_0, x = x_135)[name = tensor<string, []>("transpose_131")];
            tensor<fp32, [1, 12, 128, 128]> attention_scores_45 = matmul(transpose_x = attention_scores_45_transpose_x_0, transpose_y = attention_scores_45_transpose_y_0, x = transpose_87, y = transpose_88)[name = tensor<string, []>("attention_scores_45")];
            tensor<fp32, []> _inversed_attention_scores_y_0 = const()[name = tensor<string, []>("_inversed_attention_scores_y_0"), val = tensor<fp32, []>(0x1p-3)];
            tensor<fp32, [1, 12, 128, 128]> _inversed_attention_scores = mul(x = attention_scores_45, y = _inversed_attention_scores_y_0)[name = tensor<string, []>("_inversed_attention_scores")];
            tensor<fp32, [1, 12, 128, 128]> input_233 = add(x = _inversed_attention_scores, y = attention_mask_1)[name = tensor<string, []>("input_233")];
            tensor<fp32, [1, 12, 128, 128]> input_235 = softmax(axis = var_80, x = input_233)[name = tensor<string, []>("input_235")];
            tensor<bool, []> context_layer_transpose_x_0 = const()[name = tensor<string, []>("context_layer_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> context_layer_transpose_y_0 = const()[name = tensor<string, []>("context_layer_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 12, 128, 64]> value_layer = transpose(perm = var_1056, x = x_143)[name = tensor<string, []>("transpose_132")];
            tensor<fp32, [1, 12, 128, 64]> context_layer = matmul(transpose_x = context_layer_transpose_x_0, transpose_y = context_layer_transpose_y_0, x = input_235, y = value_layer)[name = tensor<string, []>("context_layer")];
            tensor<int32, [4]> var_1066_perm_0 = const()[name = tensor<string, []>("op_1066_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
            tensor<int32, [3]> concat_12 = const()[name = tensor<string, []>("concat_12"), val = tensor<int32, [3]>([1, 128, 768])];
            tensor<fp32, [1, 128, 12, 64]> var_1066 = transpose(perm = var_1066_perm_0, x = context_layer)[name = tensor<string, []>("transpose_129")];
            tensor<fp32, [1, 128, 768]> input_237 = reshape(shape = concat_12, x = var_1066)[name = tensor<string, []>("input_237")];
            tensor<fp32, [1, 128, 768]> input_239 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight, x = input_237)[name = tensor<string, []>("linear_70")];
            tensor<fp32, [1, 128, 768]> input_241 = add(x = input_231, y = input_239)[name = tensor<string, []>("input_241")];
            tensor<int32, [1]> input_243_axes_0 = const()[name = tensor<string, []>("input_243_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 128, 768]> input_243 = layer_norm(axes = input_243_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_82, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_241)[name = tensor<string, []>("input_243")];
            tensor<fp32, [1, 128, 2048]> input_245 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight, x = input_243)[name = tensor<string, []>("linear_71")];
            tensor<string, []> input_247_mode_0 = const()[name = tensor<string, []>("input_247_mode_0"), val = tensor<string, []>("TANH_APPROXIMATION")];
            tensor<fp32, [1, 128, 2048]> input_247 = gelu(mode = input_247_mode_0, x = input_245)[name = tensor<string, []>("input_247")];
            tensor<fp32, [1, 128, 768]> ffn_output_23 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight, x = input_247)[name = tensor<string, []>("linear_72")];
            tensor<fp32, [1, 128, 768]> input_249 = add(x = ffn_output_23, y = input_243)[name = tensor<string, []>("input_249")];
            tensor<int32, [1]> sequence_output_axes_0 = const()[name = tensor<string, []>("sequence_output_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 128, 768]> sequence_output = layer_norm(axes = sequence_output_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias, epsilon = var_82, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_249)[name = tensor<string, []>("sequence_output")];
            tensor<fp32, [1, 128, 512]> var_1102 = linear(bias = bert_encoder_bias, weight = bert_encoder_weight, x = sequence_output)[name = tensor<string, []>("linear_73")];
            tensor<int32, [3]> transpose_19_perm_0 = const()[name = tensor<string, []>("transpose_19_perm_0"), val = tensor<int32, [3]>([-2, 0, -1])];
            tensor<int32, [2]> style_begin_0 = const()[name = tensor<string, []>("style_begin_0"), val = tensor<int32, [2]>([0, 128])];
            tensor<int32, [2]> style_end_0 = const()[name = tensor<string, []>("style_end_0"), val = tensor<int32, [2]>([1, 256])];
            tensor<bool, [2]> style_end_mask_0 = const()[name = tensor<string, []>("style_end_mask_0"), val = tensor<bool, [2]>([true, true])];
            tensor<fp32, [1, 128]> style = slice_by_index(begin = style_begin_0, end = style_end_0, end_mask = style_end_mask_0, x = ref_s)[name = tensor<string, []>("style")];
            tensor<int32, [2]> input_417_begin_0 = const()[name = tensor<string, []>("input_417_begin_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [2]> input_417_end_0 = const()[name = tensor<string, []>("input_417_end_0"), val = tensor<int32, [2]>([1, 128])];
            tensor<bool, [2]> input_417_end_mask_0 = const()[name = tensor<string, []>("input_417_end_mask_0"), val = tensor<bool, [2]>([true, false])];
            tensor<fp32, [1, 128]> input_417 = slice_by_index(begin = input_417_begin_0, end = input_417_end_0, end_mask = input_417_end_mask_0, x = ref_s)[name = tensor<string, []>("input_417")];
            tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = tensor<string, []>("expand_dims_0_axes_0"), val = tensor<int32, [1]>([0])];
            tensor<fp32, [1, 1, 128]> expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = style)[name = tensor<string, []>("expand_dims_0")];
            tensor<int32, [3]> s_reps_0 = const()[name = tensor<string, []>("s_reps_0"), val = tensor<int32, [3]>([128, 1, 1])];
            tensor<fp32, [128, 1, 128]> s = tile(reps = s_reps_0, x = expand_dims_0)[name = tensor<string, []>("s")];
            tensor<int32, []> var_1144 = const()[name = tensor<string, []>("op_1144"), val = tensor<int32, []>(-1)];
            tensor<bool, []> x_149_interleave_0 = const()[name = tensor<string, []>("x_149_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [128, 1, 512]> transpose_19 = transpose(perm = transpose_19_perm_0, x = var_1102)[name = tensor<string, []>("transpose_128")];
            tensor<fp32, [128, 1, 640]> x_149 = concat(axis = var_1144, interleave = x_149_interleave_0, values = (transpose_19, s))[name = tensor<string, []>("x_149")];
            tensor<int32, [1]> var_1147_axes_0 = const()[name = tensor<string, []>("op_1147_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<bool, [1, 128, 1]> var_1147 = expand_dims(axes = var_1147_axes_0, x = m)[name = tensor<string, []>("op_1147")];
            tensor<int32, [3]> var_1150_perm_0 = const()[name = tensor<string, []>("op_1150_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
            tensor<fp32, []> var_1151 = const()[name = tensor<string, []>("op_1151"), val = tensor<fp32, []>(0x0p+0)];
            tensor<bool, [128, 1, 1]> var_1150 = transpose(perm = var_1150_perm_0, x = var_1147)[name = tensor<string, []>("transpose_127")];
            tensor<fp32, [128, 1, 640]> x_151 = select(a = var_1151, b = x_149, cond = var_1150)[name = tensor<string, []>("x_151")];
            tensor<fp32, [1024]> add_0 = const()[name = tensor<string, []>("add_0"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(50848960)))];
            tensor<fp32, [1024]> add_1 = const()[name = tensor<string, []>("add_1"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(50853120)))];
            tensor<fp32, [1024, 640]> concat_18 = const()[name = tensor<string, []>("concat_18"), val = tensor<fp32, [1024, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(50857280)))];
            tensor<fp32, [1024, 256]> concat_19 = const()[name = tensor<string, []>("concat_19"), val = tensor<fp32, [1024, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(53478784)))];
            tensor<fp32, [1024, 640]> concat_20 = const()[name = tensor<string, []>("concat_20"), val = tensor<fp32, [1024, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(54527424)))];
            tensor<fp32, [1024, 256]> concat_21 = const()[name = tensor<string, []>("concat_21"), val = tensor<fp32, [1024, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(57148928)))];
            tensor<fp32, [1, 512]> input_257_batch_first_lstm_h0_reshaped = const()[name = tensor<string, []>("input_257_batch_first_lstm_h0_reshaped"), val = tensor<fp32, [1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(58197568)))];
            tensor<string, []> input_257_batch_first_direction_0 = const()[name = tensor<string, []>("input_257_batch_first_direction_0"), val = tensor<string, []>("bidirectional")];
            tensor<bool, []> input_257_batch_first_output_sequence_0 = const()[name = tensor<string, []>("input_257_batch_first_output_sequence_0"), val = tensor<bool, []>(true)];
            tensor<string, []> input_257_batch_first_recurrent_activation_0 = const()[name = tensor<string, []>("input_257_batch_first_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
            tensor<string, []> input_257_batch_first_cell_activation_0 = const()[name = tensor<string, []>("input_257_batch_first_cell_activation_0"), val = tensor<string, []>("tanh")];
            tensor<string, []> input_257_batch_first_activation_0 = const()[name = tensor<string, []>("input_257_batch_first_activation_0"), val = tensor<string, []>("tanh")];
            tensor<fp32, [128, 1, 512]> input_257_batch_first_0, tensor<fp32, [1, 512]> input_257_batch_first_1, tensor<fp32, [1, 512]> input_257_batch_first_2 = lstm(activation = input_257_batch_first_activation_0, bias = add_0, bias_back = add_1, cell_activation = input_257_batch_first_cell_activation_0, direction = input_257_batch_first_direction_0, initial_c = input_257_batch_first_lstm_h0_reshaped, initial_h = input_257_batch_first_lstm_h0_reshaped, output_sequence = input_257_batch_first_output_sequence_0, recurrent_activation = input_257_batch_first_recurrent_activation_0, weight_hh = concat_19, weight_hh_back = concat_21, weight_ih = concat_18, weight_ih_back = concat_20, x = x_151)[name = tensor<string, []>("input_257_batch_first")];
            tensor<int32, [3]> transpose_32_perm_0 = const()[name = tensor<string, []>("transpose_32_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
            tensor<fp32, []> var_1200 = const()[name = tensor<string, []>("op_1200"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 1024]> h_1 = linear(bias = de_lstms_1_fc_bias, weight = de_lstms_1_fc_weight, x = style)[name = tensor<string, []>("linear_74")];
            tensor<int32, [3]> var_1215 = const()[name = tensor<string, []>("op_1215"), val = tensor<int32, [3]>([1, 1024, 1])];
            tensor<fp32, [1, 1024, 1]> h_3 = reshape(shape = var_1215, x = h_1)[name = tensor<string, []>("h_3")];
            tensor<int32, [2]> var_1217_split_sizes_0 = const()[name = tensor<string, []>("op_1217_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
            tensor<int32, []> var_1217_axis_0 = const()[name = tensor<string, []>("op_1217_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 512, 1]> var_1217_0, tensor<fp32, [1, 512, 1]> var_1217_1 = split(axis = var_1217_axis_0, split_sizes = var_1217_split_sizes_0, x = h_3)[name = tensor<string, []>("op_1217")];
            tensor<int32, [3]> gamma_3_perm_0 = const()[name = tensor<string, []>("gamma_3_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
            tensor<int32, [3]> beta_3_perm_0 = const()[name = tensor<string, []>("beta_3_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
            tensor<int32, [1]> x_163_axes_0 = const()[name = tensor<string, []>("x_163_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 128, 512]> transpose_32 = transpose(perm = transpose_32_perm_0, x = input_257_batch_first_0)[name = tensor<string, []>("transpose_126")];
            tensor<fp32, [1, 128, 512]> x_163 = layer_norm(axes = x_163_axes_0, epsilon = var_1200, x = transpose_32)[name = tensor<string, []>("x_163")];
            tensor<fp32, []> var_1223_promoted = const()[name = tensor<string, []>("op_1223_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 1, 512]> gamma_3 = transpose(perm = gamma_3_perm_0, x = var_1217_0)[name = tensor<string, []>("transpose_125")];
            tensor<fp32, [1, 1, 512]> var_1224 = add(x = gamma_3, y = var_1223_promoted)[name = tensor<string, []>("op_1224")];
            tensor<fp32, [1, 128, 512]> var_1225 = mul(x = var_1224, y = x_163)[name = tensor<string, []>("op_1225")];
            tensor<fp32, [1, 1, 512]> beta_3 = transpose(perm = beta_3_perm_0, x = var_1217_1)[name = tensor<string, []>("transpose_124")];
            tensor<fp32, [1, 128, 512]> x_165 = add(x = var_1225, y = beta_3)[name = tensor<string, []>("x_165")];
            tensor<int32, []> var_1238 = const()[name = tensor<string, []>("op_1238"), val = tensor<int32, []>(1)];
            tensor<bool, []> x_169_interleave_0 = const()[name = tensor<string, []>("x_169_interleave_0"), val = tensor<bool, []>(false)];
            tensor<int32, [3]> transpose_89_perm_0 = const()[name = tensor<string, []>("transpose_89_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<int32, [3]> transpose_90_perm_0 = const()[name = tensor<string, []>("transpose_90_perm_0"), val = tensor<int32, [3]>([1, 2, 0])];
            tensor<fp32, [1, 128, 128]> transpose_90 = transpose(perm = transpose_90_perm_0, x = s)[name = tensor<string, []>("transpose_122")];
            tensor<fp32, [1, 512, 128]> transpose_89 = transpose(perm = transpose_89_perm_0, x = x_165)[name = tensor<string, []>("transpose_123")];
            tensor<fp32, [1, 640, 128]> x_169 = concat(axis = var_1238, interleave = x_169_interleave_0, values = (transpose_89, transpose_90))[name = tensor<string, []>("x_169")];
            tensor<int32, [3]> var_1244_perm_0 = const()[name = tensor<string, []>("op_1244_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<fp32, []> var_1245 = const()[name = tensor<string, []>("op_1245"), val = tensor<fp32, []>(0x0p+0)];
            tensor<bool, [1, 1, 128]> var_1244 = transpose(perm = var_1244_perm_0, x = var_1147)[name = tensor<string, []>("transpose_121")];
            tensor<fp32, [1, 640, 128]> x_171 = select(a = var_1245, b = x_169, cond = var_1244)[name = tensor<string, []>("x_171")];
            tensor<int32, [3]> transpose_23_perm_0 = const()[name = tensor<string, []>("transpose_23_perm_0"), val = tensor<int32, [3]>([-1, 0, -2])];
            tensor<fp32, [1024]> add_2 = const()[name = tensor<string, []>("add_2"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(58199680)))];
            tensor<fp32, [1024]> add_3 = const()[name = tensor<string, []>("add_3"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(58203840)))];
            tensor<fp32, [1024, 640]> concat_28 = const()[name = tensor<string, []>("concat_28"), val = tensor<fp32, [1024, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(58208000)))];
            tensor<fp32, [1024, 256]> concat_29 = const()[name = tensor<string, []>("concat_29"), val = tensor<fp32, [1024, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(60829504)))];
            tensor<fp32, [1024, 640]> concat_30 = const()[name = tensor<string, []>("concat_30"), val = tensor<fp32, [1024, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(61878144)))];
            tensor<fp32, [1024, 256]> concat_31 = const()[name = tensor<string, []>("concat_31"), val = tensor<fp32, [1024, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64499648)))];
            tensor<string, []> input_263_batch_first_direction_0 = const()[name = tensor<string, []>("input_263_batch_first_direction_0"), val = tensor<string, []>("bidirectional")];
            tensor<bool, []> input_263_batch_first_output_sequence_0 = const()[name = tensor<string, []>("input_263_batch_first_output_sequence_0"), val = tensor<bool, []>(true)];
            tensor<string, []> input_263_batch_first_recurrent_activation_0 = const()[name = tensor<string, []>("input_263_batch_first_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
            tensor<string, []> input_263_batch_first_cell_activation_0 = const()[name = tensor<string, []>("input_263_batch_first_cell_activation_0"), val = tensor<string, []>("tanh")];
            tensor<string, []> input_263_batch_first_activation_0 = const()[name = tensor<string, []>("input_263_batch_first_activation_0"), val = tensor<string, []>("tanh")];
            tensor<fp32, [128, 1, 640]> transpose_23 = transpose(perm = transpose_23_perm_0, x = x_171)[name = tensor<string, []>("transpose_120")];
            tensor<fp32, [128, 1, 512]> input_263_batch_first_0, tensor<fp32, [1, 512]> input_263_batch_first_1, tensor<fp32, [1, 512]> input_263_batch_first_2 = lstm(activation = input_263_batch_first_activation_0, bias = add_2, bias_back = add_3, cell_activation = input_263_batch_first_cell_activation_0, direction = input_263_batch_first_direction_0, initial_c = input_257_batch_first_lstm_h0_reshaped, initial_h = input_257_batch_first_lstm_h0_reshaped, output_sequence = input_263_batch_first_output_sequence_0, recurrent_activation = input_263_batch_first_recurrent_activation_0, weight_hh = concat_29, weight_hh_back = concat_31, weight_ih = concat_28, weight_ih_back = concat_30, x = transpose_23)[name = tensor<string, []>("input_263_batch_first")];
            tensor<int32, [3]> transpose_33_perm_0 = const()[name = tensor<string, []>("transpose_33_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
            tensor<fp32, []> var_1288 = const()[name = tensor<string, []>("op_1288"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 1024]> h_5 = linear(bias = de_lstms_3_fc_bias, weight = de_lstms_3_fc_weight, x = style)[name = tensor<string, []>("linear_75")];
            tensor<int32, [3]> var_1303 = const()[name = tensor<string, []>("op_1303"), val = tensor<int32, [3]>([1, 1024, 1])];
            tensor<fp32, [1, 1024, 1]> h_7 = reshape(shape = var_1303, x = h_5)[name = tensor<string, []>("h_7")];
            tensor<int32, [2]> var_1305_split_sizes_0 = const()[name = tensor<string, []>("op_1305_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
            tensor<int32, []> var_1305_axis_0 = const()[name = tensor<string, []>("op_1305_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 512, 1]> var_1305_0, tensor<fp32, [1, 512, 1]> var_1305_1 = split(axis = var_1305_axis_0, split_sizes = var_1305_split_sizes_0, x = h_7)[name = tensor<string, []>("op_1305")];
            tensor<int32, [3]> gamma_7_perm_0 = const()[name = tensor<string, []>("gamma_7_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
            tensor<int32, [3]> beta_7_perm_0 = const()[name = tensor<string, []>("beta_7_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
            tensor<int32, [1]> x_181_axes_0 = const()[name = tensor<string, []>("x_181_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 128, 512]> transpose_33 = transpose(perm = transpose_33_perm_0, x = input_263_batch_first_0)[name = tensor<string, []>("transpose_119")];
            tensor<fp32, [1, 128, 512]> x_181 = layer_norm(axes = x_181_axes_0, epsilon = var_1288, x = transpose_33)[name = tensor<string, []>("x_181")];
            tensor<fp32, []> var_1311_promoted = const()[name = tensor<string, []>("op_1311_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 1, 512]> gamma_7 = transpose(perm = gamma_7_perm_0, x = var_1305_0)[name = tensor<string, []>("transpose_118")];
            tensor<fp32, [1, 1, 512]> var_1312 = add(x = gamma_7, y = var_1311_promoted)[name = tensor<string, []>("op_1312")];
            tensor<fp32, [1, 128, 512]> var_1313 = mul(x = var_1312, y = x_181)[name = tensor<string, []>("op_1313")];
            tensor<fp32, [1, 1, 512]> beta_7 = transpose(perm = beta_7_perm_0, x = var_1305_1)[name = tensor<string, []>("transpose_117")];
            tensor<fp32, [1, 128, 512]> x_183 = add(x = var_1313, y = beta_7)[name = tensor<string, []>("x_183")];
            tensor<int32, []> var_1326 = const()[name = tensor<string, []>("op_1326"), val = tensor<int32, []>(1)];
            tensor<bool, []> x_187_interleave_0 = const()[name = tensor<string, []>("x_187_interleave_0"), val = tensor<bool, []>(false)];
            tensor<int32, [3]> transpose_93_perm_0 = const()[name = tensor<string, []>("transpose_93_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<fp32, [1, 512, 128]> transpose_93 = transpose(perm = transpose_93_perm_0, x = x_183)[name = tensor<string, []>("transpose_116")];
            tensor<fp32, [1, 640, 128]> x_187 = concat(axis = var_1326, interleave = x_187_interleave_0, values = (transpose_93, transpose_90))[name = tensor<string, []>("x_187")];
            tensor<fp32, []> var_1333 = const()[name = tensor<string, []>("op_1333"), val = tensor<fp32, []>(0x0p+0)];
            tensor<fp32, [1, 640, 128]> x_189 = select(a = var_1333, b = x_187, cond = var_1244)[name = tensor<string, []>("x_189")];
            tensor<int32, [3]> transpose_25_perm_0 = const()[name = tensor<string, []>("transpose_25_perm_0"), val = tensor<int32, [3]>([-1, 0, -2])];
            tensor<fp32, [1024]> add_4 = const()[name = tensor<string, []>("add_4"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(65548288)))];
            tensor<fp32, [1024]> add_5 = const()[name = tensor<string, []>("add_5"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(65552448)))];
            tensor<fp32, [1024, 640]> concat_38 = const()[name = tensor<string, []>("concat_38"), val = tensor<fp32, [1024, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(65556608)))];
            tensor<fp32, [1024, 256]> concat_39 = const()[name = tensor<string, []>("concat_39"), val = tensor<fp32, [1024, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(68178112)))];
            tensor<fp32, [1024, 640]> concat_40 = const()[name = tensor<string, []>("concat_40"), val = tensor<fp32, [1024, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(69226752)))];
            tensor<fp32, [1024, 256]> concat_41 = const()[name = tensor<string, []>("concat_41"), val = tensor<fp32, [1024, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(71848256)))];
            tensor<string, []> input_269_batch_first_direction_0 = const()[name = tensor<string, []>("input_269_batch_first_direction_0"), val = tensor<string, []>("bidirectional")];
            tensor<bool, []> input_269_batch_first_output_sequence_0 = const()[name = tensor<string, []>("input_269_batch_first_output_sequence_0"), val = tensor<bool, []>(true)];
            tensor<string, []> input_269_batch_first_recurrent_activation_0 = const()[name = tensor<string, []>("input_269_batch_first_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
            tensor<string, []> input_269_batch_first_cell_activation_0 = const()[name = tensor<string, []>("input_269_batch_first_cell_activation_0"), val = tensor<string, []>("tanh")];
            tensor<string, []> input_269_batch_first_activation_0 = const()[name = tensor<string, []>("input_269_batch_first_activation_0"), val = tensor<string, []>("tanh")];
            tensor<fp32, [128, 1, 640]> transpose_25 = transpose(perm = transpose_25_perm_0, x = x_189)[name = tensor<string, []>("transpose_115")];
            tensor<fp32, [128, 1, 512]> input_269_batch_first_0, tensor<fp32, [1, 512]> input_269_batch_first_1, tensor<fp32, [1, 512]> input_269_batch_first_2 = lstm(activation = input_269_batch_first_activation_0, bias = add_4, bias_back = add_5, cell_activation = input_269_batch_first_cell_activation_0, direction = input_269_batch_first_direction_0, initial_c = input_257_batch_first_lstm_h0_reshaped, initial_h = input_257_batch_first_lstm_h0_reshaped, output_sequence = input_269_batch_first_output_sequence_0, recurrent_activation = input_269_batch_first_recurrent_activation_0, weight_hh = concat_39, weight_hh_back = concat_41, weight_ih = concat_38, weight_ih_back = concat_40, x = transpose_25)[name = tensor<string, []>("input_269_batch_first")];
            tensor<int32, [3]> transpose_34_perm_0 = const()[name = tensor<string, []>("transpose_34_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
            tensor<fp32, []> var_1376 = const()[name = tensor<string, []>("op_1376"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 1024]> h_9 = linear(bias = de_lstms_5_fc_bias, weight = de_lstms_5_fc_weight, x = style)[name = tensor<string, []>("linear_76")];
            tensor<int32, [3]> var_1391 = const()[name = tensor<string, []>("op_1391"), val = tensor<int32, [3]>([1, 1024, 1])];
            tensor<fp32, [1, 1024, 1]> h_11 = reshape(shape = var_1391, x = h_9)[name = tensor<string, []>("h_11")];
            tensor<int32, [2]> var_1393_split_sizes_0 = const()[name = tensor<string, []>("op_1393_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
            tensor<int32, []> var_1393_axis_0 = const()[name = tensor<string, []>("op_1393_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 512, 1]> var_1393_0, tensor<fp32, [1, 512, 1]> var_1393_1 = split(axis = var_1393_axis_0, split_sizes = var_1393_split_sizes_0, x = h_11)[name = tensor<string, []>("op_1393")];
            tensor<int32, [3]> gamma_11_perm_0 = const()[name = tensor<string, []>("gamma_11_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
            tensor<int32, [3]> beta_11_perm_0 = const()[name = tensor<string, []>("beta_11_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
            tensor<int32, [1]> x_199_axes_0 = const()[name = tensor<string, []>("x_199_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 128, 512]> transpose_34 = transpose(perm = transpose_34_perm_0, x = input_269_batch_first_0)[name = tensor<string, []>("transpose_114")];
            tensor<fp32, [1, 128, 512]> x_199 = layer_norm(axes = x_199_axes_0, epsilon = var_1376, x = transpose_34)[name = tensor<string, []>("x_199")];
            tensor<fp32, []> var_1399_promoted = const()[name = tensor<string, []>("op_1399_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 1, 512]> gamma_11 = transpose(perm = gamma_11_perm_0, x = var_1393_0)[name = tensor<string, []>("transpose_113")];
            tensor<fp32, [1, 1, 512]> var_1400 = add(x = gamma_11, y = var_1399_promoted)[name = tensor<string, []>("op_1400")];
            tensor<fp32, [1, 128, 512]> var_1401 = mul(x = var_1400, y = x_199)[name = tensor<string, []>("op_1401")];
            tensor<fp32, [1, 1, 512]> beta_11 = transpose(perm = beta_11_perm_0, x = var_1393_1)[name = tensor<string, []>("transpose_112")];
            tensor<fp32, [1, 128, 512]> x_201 = add(x = var_1401, y = beta_11)[name = tensor<string, []>("x_201")];
            tensor<int32, []> var_1414 = const()[name = tensor<string, []>("op_1414"), val = tensor<int32, []>(1)];
            tensor<bool, []> x_205_interleave_0 = const()[name = tensor<string, []>("x_205_interleave_0"), val = tensor<bool, []>(false)];
            tensor<int32, [3]> transpose_94_perm_0 = const()[name = tensor<string, []>("transpose_94_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<fp32, [1, 512, 128]> transpose_94 = transpose(perm = transpose_94_perm_0, x = x_201)[name = tensor<string, []>("transpose_111")];
            tensor<fp32, [1, 640, 128]> x_205 = concat(axis = var_1414, interleave = x_205_interleave_0, values = (transpose_94, transpose_90))[name = tensor<string, []>("x_205")];
            tensor<fp32, []> var_1421 = const()[name = tensor<string, []>("op_1421"), val = tensor<fp32, []>(0x0p+0)];
            tensor<fp32, [1, 640, 128]> x_207 = select(a = var_1421, b = x_205, cond = var_1244)[name = tensor<string, []>("x_207")];
            tensor<int32, [3]> transpose_27_perm_0 = const()[name = tensor<string, []>("transpose_27_perm_0"), val = tensor<int32, [3]>([-1, 0, -2])];
            tensor<fp32, [1024]> add_6 = const()[name = tensor<string, []>("add_6"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(72896896)))];
            tensor<fp32, [1024]> add_7 = const()[name = tensor<string, []>("add_7"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(72901056)))];
            tensor<fp32, [1024, 640]> concat_48 = const()[name = tensor<string, []>("concat_48"), val = tensor<fp32, [1024, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(72905216)))];
            tensor<fp32, [1024, 256]> concat_49 = const()[name = tensor<string, []>("concat_49"), val = tensor<fp32, [1024, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(75526720)))];
            tensor<fp32, [1024, 640]> concat_50 = const()[name = tensor<string, []>("concat_50"), val = tensor<fp32, [1024, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(76575360)))];
            tensor<fp32, [1024, 256]> concat_51 = const()[name = tensor<string, []>("concat_51"), val = tensor<fp32, [1024, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(79196864)))];
            tensor<string, []> input_275_batch_first_direction_0 = const()[name = tensor<string, []>("input_275_batch_first_direction_0"), val = tensor<string, []>("bidirectional")];
            tensor<bool, []> input_275_batch_first_output_sequence_0 = const()[name = tensor<string, []>("input_275_batch_first_output_sequence_0"), val = tensor<bool, []>(true)];
            tensor<string, []> input_275_batch_first_recurrent_activation_0 = const()[name = tensor<string, []>("input_275_batch_first_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
            tensor<string, []> input_275_batch_first_cell_activation_0 = const()[name = tensor<string, []>("input_275_batch_first_cell_activation_0"), val = tensor<string, []>("tanh")];
            tensor<string, []> input_275_batch_first_activation_0 = const()[name = tensor<string, []>("input_275_batch_first_activation_0"), val = tensor<string, []>("tanh")];
            tensor<fp32, [128, 1, 640]> transpose_27 = transpose(perm = transpose_27_perm_0, x = x_207)[name = tensor<string, []>("transpose_110")];
            tensor<fp32, [128, 1, 512]> input_275_batch_first_0, tensor<fp32, [1, 512]> input_275_batch_first_1, tensor<fp32, [1, 512]> input_275_batch_first_2 = lstm(activation = input_275_batch_first_activation_0, bias = add_6, bias_back = add_7, cell_activation = input_275_batch_first_cell_activation_0, direction = input_275_batch_first_direction_0, initial_c = input_257_batch_first_lstm_h0_reshaped, initial_h = input_257_batch_first_lstm_h0_reshaped, output_sequence = input_275_batch_first_output_sequence_0, recurrent_activation = input_275_batch_first_recurrent_activation_0, weight_hh = concat_49, weight_hh_back = concat_51, weight_ih = concat_48, weight_ih_back = concat_50, x = transpose_27)[name = tensor<string, []>("input_275_batch_first")];
            tensor<int32, [3]> input_275_perm_0 = const()[name = tensor<string, []>("input_275_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
            tensor<fp32, [1, 128, 512]> input_275 = transpose(perm = input_275_perm_0, x = input_275_batch_first_0)[name = tensor<string, []>("transpose_109")];
            tensor<fp32, [1, 128, 50]> duration_1 = linear(bias = pred_duration_proj_linear_layer_bias, weight = pred_duration_proj_linear_layer_weight, x = input_275)[name = tensor<string, []>("linear_77")];
            tensor<fp32, [1, 128, 50]> var_1458 = sigmoid(x = duration_1)[name = tensor<string, []>("op_1458")];
            tensor<int32, [1]> var_1463_axes_0 = const()[name = tensor<string, []>("op_1463_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<bool, []> var_1463_keep_dims_0 = const()[name = tensor<string, []>("op_1463_keep_dims_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 128]> var_1463 = reduce_sum(axes = var_1463_axes_0, keep_dims = var_1463_keep_dims_0, x = var_1458)[name = tensor<string, []>("op_1463")];
            tensor<fp32, [1, 128]> duration = real_div(x = var_1463, y = speed)[name = tensor<string, []>("duration")];
            tensor<fp32, [1, 128]> var_1465 = round(x = duration)[name = tensor<string, []>("op_1465")];
            tensor<fp32, []> const_90 = const()[name = tensor<string, []>("const_90"), val = tensor<fp32, []>(0x1.fffffep+127)];
            tensor<fp32, []> var_1466_promoted = const()[name = tensor<string, []>("op_1466_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128]> clip_0 = clip(alpha = var_1466_promoted, beta = const_90, x = var_1465)[name = tensor<string, []>("clip_0")];
            tensor<bool, [1, 128]> pad_mask = greater_equal(x = var_67, y = var_72)[name = tensor<string, []>("pad_mask")];
            tensor<fp32, []> var_1479 = const()[name = tensor<string, []>("op_1479"), val = tensor<fp32, []>(0x0p+0)];
            tensor<fp32, [1, 128]> pred_dur = select(a = var_1479, b = clip_0, cond = pad_mask)[name = tensor<string, []>("pred_dur")];
            tensor<int32, []> x_209_axis_0 = const()[name = tensor<string, []>("x_209_axis_0"), val = tensor<int32, []>(0)];
            tensor<int32, []> x_209_batch_dims_0 = const()[name = tensor<string, []>("x_209_batch_dims_0"), val = tensor<int32, []>(0)];
            tensor<bool, []> x_209_validate_indices_0 = const()[name = tensor<string, []>("x_209_validate_indices_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 128, 512]> x_209 = gather(axis = x_209_axis_0, batch_dims = x_209_batch_dims_0, indices = input_ids, validate_indices = x_209_validate_indices_0, x = te_embedding_weight)[name = tensor<string, []>("x_209")];
            tensor<int32, [3]> x_211_perm_0 = const()[name = tensor<string, []>("x_211_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<int32, [1]> m_exp_axes_0 = const()[name = tensor<string, []>("m_exp_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, [1, 1, 128]> m_exp = expand_dims(axes = m_exp_axes_0, x = m)[name = tensor<string, []>("m_exp")];
            tensor<fp32, []> var_1490 = const()[name = tensor<string, []>("op_1490"), val = tensor<fp32, []>(0x0p+0)];
            tensor<fp32, [1, 512, 128]> x_211 = transpose(perm = x_211_perm_0, x = x_209)[name = tensor<string, []>("transpose_108")];
            tensor<fp32, [1, 512, 128]> input_277 = select(a = var_1490, b = x_211, cond = m_exp)[name = tensor<string, []>("input_277")];
            tensor<fp32, []> var_1492 = const()[name = tensor<string, []>("op_1492"), val = tensor<fp32, []>(0x1.99999ap-3)];
            tensor<fp32, []> var_1495 = const()[name = tensor<string, []>("op_1495"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [512, 512, 5]> weight_15 = const()[name = tensor<string, []>("weight_15"), val = tensor<fp32, [512, 512, 5]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(80245504)))];
            tensor<string, []> x_213_pad_type_0 = const()[name = tensor<string, []>("x_213_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> x_213_pad_0 = const()[name = tensor<string, []>("x_213_pad_0"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, [1]> x_213_strides_0 = const()[name = tensor<string, []>("x_213_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> x_213_dilations_0 = const()[name = tensor<string, []>("x_213_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_213_groups_0 = const()[name = tensor<string, []>("x_213_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 512, 128]> x_213 = conv(bias = te_cnn_0_0_bias, dilations = x_213_dilations_0, groups = x_213_groups_0, pad = x_213_pad_0, pad_type = x_213_pad_type_0, strides = x_213_strides_0, weight = weight_15, x = input_277)[name = tensor<string, []>("x_213")];
            tensor<int32, [3]> input_279_perm_0 = const()[name = tensor<string, []>("input_279_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
            tensor<int32, [1]> x_215_axes_0 = const()[name = tensor<string, []>("x_215_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 128, 512]> input_279 = transpose(perm = input_279_perm_0, x = x_213)[name = tensor<string, []>("transpose_107")];
            tensor<fp32, [1, 128, 512]> x_215 = layer_norm(axes = x_215_axes_0, beta = te_cnn_0_1_beta, epsilon = var_1495, gamma = te_cnn_0_1_gamma, x = input_279)[name = tensor<string, []>("x_215")];
            tensor<int32, [3]> input_281_perm_0 = const()[name = tensor<string, []>("input_281_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
            tensor<fp32, [1, 512, 128]> input_281 = transpose(perm = input_281_perm_0, x = x_215)[name = tensor<string, []>("transpose_106")];
            tensor<fp32, [1, 512, 128]> input_283 = leaky_relu(alpha = var_1492, x = input_281)[name = tensor<string, []>("input_283")];
            tensor<fp32, []> var_1522 = const()[name = tensor<string, []>("op_1522"), val = tensor<fp32, []>(0x0p+0)];
            tensor<fp32, [1, 512, 128]> input_285 = select(a = var_1522, b = input_283, cond = m_exp)[name = tensor<string, []>("input_285")];
            tensor<fp32, []> var_1524 = const()[name = tensor<string, []>("op_1524"), val = tensor<fp32, []>(0x1.99999ap-3)];
            tensor<fp32, []> var_1527 = const()[name = tensor<string, []>("op_1527"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [512, 512, 5]> weight_19 = const()[name = tensor<string, []>("weight_19"), val = tensor<fp32, [512, 512, 5]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(85488448)))];
            tensor<string, []> x_219_pad_type_0 = const()[name = tensor<string, []>("x_219_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> x_219_pad_0 = const()[name = tensor<string, []>("x_219_pad_0"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, [1]> x_219_strides_0 = const()[name = tensor<string, []>("x_219_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> x_219_dilations_0 = const()[name = tensor<string, []>("x_219_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_219_groups_0 = const()[name = tensor<string, []>("x_219_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 512, 128]> x_219 = conv(bias = te_cnn_1_0_bias, dilations = x_219_dilations_0, groups = x_219_groups_0, pad = x_219_pad_0, pad_type = x_219_pad_type_0, strides = x_219_strides_0, weight = weight_19, x = input_285)[name = tensor<string, []>("x_219")];
            tensor<int32, [3]> input_287_perm_0 = const()[name = tensor<string, []>("input_287_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
            tensor<int32, [1]> x_221_axes_0 = const()[name = tensor<string, []>("x_221_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 128, 512]> input_287 = transpose(perm = input_287_perm_0, x = x_219)[name = tensor<string, []>("transpose_105")];
            tensor<fp32, [1, 128, 512]> x_221 = layer_norm(axes = x_221_axes_0, beta = te_cnn_1_1_beta, epsilon = var_1527, gamma = te_cnn_1_1_gamma, x = input_287)[name = tensor<string, []>("x_221")];
            tensor<int32, [3]> input_289_perm_0 = const()[name = tensor<string, []>("input_289_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
            tensor<fp32, [1, 512, 128]> input_289 = transpose(perm = input_289_perm_0, x = x_221)[name = tensor<string, []>("transpose_104")];
            tensor<fp32, [1, 512, 128]> input_291 = leaky_relu(alpha = var_1524, x = input_289)[name = tensor<string, []>("input_291")];
            tensor<fp32, []> var_1554 = const()[name = tensor<string, []>("op_1554"), val = tensor<fp32, []>(0x0p+0)];
            tensor<fp32, [1, 512, 128]> input_293 = select(a = var_1554, b = input_291, cond = m_exp)[name = tensor<string, []>("input_293")];
            tensor<fp32, []> var_1556 = const()[name = tensor<string, []>("op_1556"), val = tensor<fp32, []>(0x1.99999ap-3)];
            tensor<fp32, []> var_1559 = const()[name = tensor<string, []>("op_1559"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [512, 512, 5]> weight_23 = const()[name = tensor<string, []>("weight_23"), val = tensor<fp32, [512, 512, 5]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(90731392)))];
            tensor<string, []> x_225_pad_type_0 = const()[name = tensor<string, []>("x_225_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> x_225_pad_0 = const()[name = tensor<string, []>("x_225_pad_0"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, [1]> x_225_strides_0 = const()[name = tensor<string, []>("x_225_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> x_225_dilations_0 = const()[name = tensor<string, []>("x_225_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_225_groups_0 = const()[name = tensor<string, []>("x_225_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 512, 128]> x_225 = conv(bias = te_cnn_2_0_bias, dilations = x_225_dilations_0, groups = x_225_groups_0, pad = x_225_pad_0, pad_type = x_225_pad_type_0, strides = x_225_strides_0, weight = weight_23, x = input_293)[name = tensor<string, []>("x_225")];
            tensor<int32, [3]> input_295_perm_0 = const()[name = tensor<string, []>("input_295_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
            tensor<int32, [1]> x_227_axes_0 = const()[name = tensor<string, []>("x_227_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp32, [1, 128, 512]> input_295 = transpose(perm = input_295_perm_0, x = x_225)[name = tensor<string, []>("transpose_103")];
            tensor<fp32, [1, 128, 512]> x_227 = layer_norm(axes = x_227_axes_0, beta = te_cnn_2_1_beta, epsilon = var_1559, gamma = te_cnn_2_1_gamma, x = input_295)[name = tensor<string, []>("x_227")];
            tensor<int32, [3]> input_297_perm_0 = const()[name = tensor<string, []>("input_297_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
            tensor<fp32, [1, 512, 128]> input_297 = transpose(perm = input_297_perm_0, x = x_227)[name = tensor<string, []>("transpose_102")];
            tensor<fp32, [1, 512, 128]> input_299 = leaky_relu(alpha = var_1556, x = input_297)[name = tensor<string, []>("input_299")];
            tensor<fp32, []> var_1586 = const()[name = tensor<string, []>("op_1586"), val = tensor<fp32, []>(0x0p+0)];
            tensor<fp32, [1, 512, 128]> x_231 = select(a = var_1586, b = input_299, cond = m_exp)[name = tensor<string, []>("x_231")];
            tensor<int32, [3]> transpose_28_perm_0 = const()[name = tensor<string, []>("transpose_28_perm_0"), val = tensor<int32, [3]>([2, 0, 1])];
            tensor<fp32, [1024]> add_8 = const()[name = tensor<string, []>("add_8"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(95974336)))];
            tensor<fp32, [1024]> add_9 = const()[name = tensor<string, []>("add_9"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(95978496)))];
            tensor<fp32, [1024, 512]> concat_58 = const()[name = tensor<string, []>("concat_58"), val = tensor<fp32, [1024, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(95982656)))];
            tensor<fp32, [1024, 256]> concat_59 = const()[name = tensor<string, []>("concat_59"), val = tensor<fp32, [1024, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(98079872)))];
            tensor<fp32, [1024, 512]> concat_60 = const()[name = tensor<string, []>("concat_60"), val = tensor<fp32, [1024, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(99128512)))];
            tensor<fp32, [1024, 256]> concat_61 = const()[name = tensor<string, []>("concat_61"), val = tensor<fp32, [1024, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(101225728)))];
            tensor<string, []> x_233_batch_first_direction_0 = const()[name = tensor<string, []>("x_233_batch_first_direction_0"), val = tensor<string, []>("bidirectional")];
            tensor<bool, []> x_233_batch_first_output_sequence_0 = const()[name = tensor<string, []>("x_233_batch_first_output_sequence_0"), val = tensor<bool, []>(true)];
            tensor<string, []> x_233_batch_first_recurrent_activation_0 = const()[name = tensor<string, []>("x_233_batch_first_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
            tensor<string, []> x_233_batch_first_cell_activation_0 = const()[name = tensor<string, []>("x_233_batch_first_cell_activation_0"), val = tensor<string, []>("tanh")];
            tensor<string, []> x_233_batch_first_activation_0 = const()[name = tensor<string, []>("x_233_batch_first_activation_0"), val = tensor<string, []>("tanh")];
            tensor<fp32, [128, 1, 512]> transpose_28 = transpose(perm = transpose_28_perm_0, x = x_231)[name = tensor<string, []>("transpose_101")];
            tensor<fp32, [128, 1, 512]> x_233_batch_first_0, tensor<fp32, [1, 512]> x_233_batch_first_1, tensor<fp32, [1, 512]> x_233_batch_first_2 = lstm(activation = x_233_batch_first_activation_0, bias = add_8, bias_back = add_9, cell_activation = x_233_batch_first_cell_activation_0, direction = x_233_batch_first_direction_0, initial_c = input_257_batch_first_lstm_h0_reshaped, initial_h = input_257_batch_first_lstm_h0_reshaped, output_sequence = x_233_batch_first_output_sequence_0, recurrent_activation = x_233_batch_first_recurrent_activation_0, weight_hh = concat_59, weight_hh_back = concat_61, weight_ih = concat_58, weight_ih_back = concat_60, x = transpose_28)[name = tensor<string, []>("x_233_batch_first")];
            tensor<int32, [3]> transpose_29_perm_0 = const()[name = tensor<string, []>("transpose_29_perm_0"), val = tensor<int32, [3]>([1, 2, 0])];
            tensor<fp32, []> var_1622 = const()[name = tensor<string, []>("op_1622"), val = tensor<fp32, []>(0x0p+0)];
            tensor<fp32, [1, 512, 128]> transpose_29 = transpose(perm = transpose_29_perm_0, x = x_233_batch_first_0)[name = tensor<string, []>("transpose_100")];
            tensor<fp32, [1, 512, 128]> t_en = select(a = var_1622, b = transpose_29, cond = m_exp)[name = tensor<string, []>("t_en")];
            tensor<int32, []> var_1627 = const()[name = tensor<string, []>("op_1627"), val = tensor<int32, []>(-1)];
            tensor<bool, []> cumsum_exclusive_0 = const()[name = tensor<string, []>("cumsum_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> cumsum_reverse_0 = const()[name = tensor<string, []>("cumsum_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 128]> cumsum = cumsum(axis = var_1627, exclusive = cumsum_exclusive_0, reverse = cumsum_reverse_0, x = pred_dur)[name = tensor<string, []>("cumsum")];
            tensor<int32, [2]> total_frames_begin_0 = const()[name = tensor<string, []>("total_frames_begin_0"), val = tensor<int32, [2]>([0, -1])];
            tensor<int32, [2]> total_frames_end_0 = const()[name = tensor<string, []>("total_frames_end_0"), val = tensor<int32, [2]>([1, 128])];
            tensor<bool, [2]> total_frames_end_mask_0 = const()[name = tensor<string, []>("total_frames_end_mask_0"), val = tensor<bool, [2]>([true, true])];
            tensor<fp32, [1, 1]> total_frames = slice_by_index(begin = total_frames_begin_0, end = total_frames_end_0, end_mask = total_frames_end_mask_0, x = cumsum)[name = tensor<string, []>("total_frames")];
            tensor<int32, [1]> var_1651_axes_0 = const()[name = tensor<string, []>("op_1651_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1, 1, 128]> var_1651 = expand_dims(axes = var_1651_axes_0, x = cumsum)[name = tensor<string, []>("op_1651")];
            tensor<fp32, [1, 200, 1]> var_1649_promoted = const()[name = tensor<string, []>("op_1649_promoted"), val = tensor<fp32, [1, 200, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(102274368)))];
            tensor<bool, [1, 200, 128]> var_1652 = greater_equal(x = var_1649_promoted, y = var_1651)[name = tensor<string, []>("op_1652")];
            tensor<string, []> cast_83_dtype_0 = const()[name = tensor<string, []>("cast_83_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<int32, [1]> token_counts_axes_0 = const()[name = tensor<string, []>("token_counts_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<bool, []> token_counts_keep_dims_0 = const()[name = tensor<string, []>("token_counts_keep_dims_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 200, 128]> cast_83 = cast(dtype = cast_83_dtype_0, x = var_1652)[name = tensor<string, []>("cast_185")];
            tensor<fp32, [1, 200]> token_counts = reduce_sum(axes = token_counts_axes_0, keep_dims = token_counts_keep_dims_0, x = cast_83)[name = tensor<string, []>("token_counts")];
            tensor<string, []> cast_84_dtype_0 = const()[name = tensor<string, []>("cast_84_dtype_0"), val = tensor<string, []>("int32")];
            tensor<int32, []> var_1671 = const()[name = tensor<string, []>("op_1671"), val = tensor<int32, []>(127)];
            tensor<int32, []> var_1672 = const()[name = tensor<string, []>("op_1672"), val = tensor<int32, []>(0)];
            tensor<int32, [1, 200]> cast_84 = cast(dtype = cast_84_dtype_0, x = token_counts)[name = tensor<string, []>("cast_184")];
            tensor<int32, [1, 200]> minimum_0 = minimum(x = cast_84, y = var_1671)[name = tensor<string, []>("minimum_0")];
            tensor<int32, [1, 200]> maximum_0 = maximum(x = minimum_0, y = var_1672)[name = tensor<string, []>("maximum_0")];
            tensor<fp32, [1, 200]> frame_positions_promoted = const()[name = tensor<string, []>("frame_positions_promoted"), val = tensor<fp32, [1, 200]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(102275264)))];
            tensor<bool, [1, 200]> var_1674 = less(x = frame_positions_promoted, y = total_frames)[name = tensor<string, []>("op_1674")];
            tensor<string, []> cast_85_dtype_0 = const()[name = tensor<string, []>("cast_85_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<int32, [1]> var_1681_axes_0 = const()[name = tensor<string, []>("op_1681_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1, 1, 200]> var_1681 = expand_dims(axes = var_1681_axes_0, x = maximum_0)[name = tensor<string, []>("op_1681")];
            tensor<int32, [3]> idx_d_reps_0 = const()[name = tensor<string, []>("idx_d_reps_0"), val = tensor<int32, [3]>([1, 640, 1])];
            tensor<int32, [1, 640, 200]> idx_d = tile(reps = idx_d_reps_0, x = var_1681)[name = tensor<string, []>("idx_d")];
            tensor<int32, []> var_1688 = const()[name = tensor<string, []>("op_1688"), val = tensor<int32, []>(2)];
            tensor<bool, []> en_1_validate_indices_0 = const()[name = tensor<string, []>("en_1_validate_indices_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 640, 200]> en_1 = gather_along_axis(axis = var_1688, indices = idx_d, validate_indices = en_1_validate_indices_0, x = x_207)[name = tensor<string, []>("en_1")];
            tensor<int32, [1]> var_1692_axes_0 = const()[name = tensor<string, []>("op_1692_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1, 200]> cast_85 = cast(dtype = cast_85_dtype_0, x = var_1674)[name = tensor<string, []>("cast_183")];
            tensor<fp32, [1, 1, 200]> var_1692 = expand_dims(axes = var_1692_axes_0, x = cast_85)[name = tensor<string, []>("op_1692")];
            tensor<fp32, [1, 640, 200]> en = mul(x = en_1, y = var_1692)[name = tensor<string, []>("en")];
            tensor<int32, [3]> idx_t_reps_0 = const()[name = tensor<string, []>("idx_t_reps_0"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<int32, [1, 512, 200]> idx_t = tile(reps = idx_t_reps_0, x = var_1681)[name = tensor<string, []>("idx_t")];
            tensor<int32, []> var_1702 = const()[name = tensor<string, []>("op_1702"), val = tensor<int32, []>(2)];
            tensor<bool, []> asr_1_validate_indices_0 = const()[name = tensor<string, []>("asr_1_validate_indices_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 512, 200]> asr_1 = gather_along_axis(axis = var_1702, indices = idx_t, validate_indices = asr_1_validate_indices_0, x = t_en)[name = tensor<string, []>("asr_1")];
            tensor<fp32, [1, 512, 200]> asr = mul(x = asr_1, y = var_1692)[name = tensor<string, []>("asr")];
            tensor<int32, [3]> transpose_30_perm_0 = const()[name = tensor<string, []>("transpose_30_perm_0"), val = tensor<int32, [3]>([-1, 0, -2])];
            tensor<fp32, [1024]> add_10 = const()[name = tensor<string, []>("add_10"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(102276160)))];
            tensor<fp32, [1024]> add_11 = const()[name = tensor<string, []>("add_11"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(102280320)))];
            tensor<fp32, [1024, 640]> concat_70 = const()[name = tensor<string, []>("concat_70"), val = tensor<fp32, [1024, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(102284480)))];
            tensor<fp32, [1024, 256]> concat_71 = const()[name = tensor<string, []>("concat_71"), val = tensor<fp32, [1024, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(104905984)))];
            tensor<fp32, [1024, 640]> concat_72 = const()[name = tensor<string, []>("concat_72"), val = tensor<fp32, [1024, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(105954624)))];
            tensor<fp32, [1024, 256]> concat_73 = const()[name = tensor<string, []>("concat_73"), val = tensor<fp32, [1024, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(108576128)))];
            tensor<string, []> x_237_batch_first_direction_0 = const()[name = tensor<string, []>("x_237_batch_first_direction_0"), val = tensor<string, []>("bidirectional")];
            tensor<bool, []> x_237_batch_first_output_sequence_0 = const()[name = tensor<string, []>("x_237_batch_first_output_sequence_0"), val = tensor<bool, []>(true)];
            tensor<string, []> x_237_batch_first_recurrent_activation_0 = const()[name = tensor<string, []>("x_237_batch_first_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
            tensor<string, []> x_237_batch_first_cell_activation_0 = const()[name = tensor<string, []>("x_237_batch_first_cell_activation_0"), val = tensor<string, []>("tanh")];
            tensor<string, []> x_237_batch_first_activation_0 = const()[name = tensor<string, []>("x_237_batch_first_activation_0"), val = tensor<string, []>("tanh")];
            tensor<fp32, [200, 1, 640]> transpose_30 = transpose(perm = transpose_30_perm_0, x = en)[name = tensor<string, []>("transpose_99")];
            tensor<fp32, [200, 1, 512]> x_237_batch_first_0, tensor<fp32, [1, 512]> x_237_batch_first_1, tensor<fp32, [1, 512]> x_237_batch_first_2 = lstm(activation = x_237_batch_first_activation_0, bias = add_10, bias_back = add_11, cell_activation = x_237_batch_first_cell_activation_0, direction = x_237_batch_first_direction_0, initial_c = input_257_batch_first_lstm_h0_reshaped, initial_h = input_257_batch_first_lstm_h0_reshaped, output_sequence = x_237_batch_first_output_sequence_0, recurrent_activation = x_237_batch_first_recurrent_activation_0, weight_hh = concat_71, weight_hh_back = concat_73, weight_ih = concat_70, weight_ih_back = concat_72, x = transpose_30)[name = tensor<string, []>("x_237_batch_first")];
            tensor<int32, [3]> x_237_perm_0 = const()[name = tensor<string, []>("x_237_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
            tensor<int32, [3]> input_305_perm_0 = const()[name = tensor<string, []>("input_305_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<fp32, []> var_1746 = const()[name = tensor<string, []>("op_1746"), val = tensor<fp32, []>(0x1.99999ap-3)];
            tensor<fp32, []> var_1749 = const()[name = tensor<string, []>("op_1749"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 1024]> h_13 = linear(bias = F0_blocks_0_norm1_fc_bias, weight = F0_blocks_0_norm1_fc_weight, x = style)[name = tensor<string, []>("linear_78")];
            tensor<int32, [3]> var_1764 = const()[name = tensor<string, []>("op_1764"), val = tensor<int32, [3]>([1, 1024, 1])];
            tensor<fp32, [1, 1024, 1]> h_15 = reshape(shape = var_1764, x = h_13)[name = tensor<string, []>("h_15")];
            tensor<int32, [2]> var_1766_split_sizes_0 = const()[name = tensor<string, []>("op_1766_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
            tensor<int32, []> var_1766_axis_0 = const()[name = tensor<string, []>("op_1766_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 512, 1]> var_1766_0, tensor<fp32, [1, 512, 1]> var_1766_1 = split(axis = var_1766_axis_0, split_sizes = var_1766_split_sizes_0, x = h_15)[name = tensor<string, []>("op_1766")];
            tensor<fp32, []> var_1768_promoted = const()[name = tensor<string, []>("op_1768_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 512, 1]> var_1769 = add(x = var_1766_0, y = var_1768_promoted)[name = tensor<string, []>("op_1769")];
            tensor<fp32, [1, 200, 512]> x_237 = transpose(perm = x_237_perm_0, x = x_237_batch_first_0)[name = tensor<string, []>("transpose_98")];
            tensor<fp32, [1, 512, 200]> input_305 = transpose(perm = input_305_perm_0, x = x_237)[name = tensor<string, []>("transpose_97")];
            tensor<fp32, [1, 512, 200]> var_1772 = instance_norm(beta = F0_blocks_0_norm1_norm_bias, epsilon = var_1749, gamma = F0_blocks_0_norm1_norm_weight, x = input_305)[name = tensor<string, []>("op_1772")];
            tensor<fp32, [1, 512, 200]> var_1773 = mul(x = var_1769, y = var_1772)[name = tensor<string, []>("op_1773")];
            tensor<fp32, [1, 512, 200]> input_307 = add(x = var_1773, y = var_1766_1)[name = tensor<string, []>("input_307")];
            tensor<fp32, [1, 512, 200]> input_309 = leaky_relu(alpha = var_1746, x = input_307)[name = tensor<string, []>("input_309")];
            tensor<fp32, [512, 512, 3]> weight_29 = const()[name = tensor<string, []>("weight_29"), val = tensor<fp32, [512, 512, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(109624768)))];
            tensor<string, []> input_313_pad_type_0 = const()[name = tensor<string, []>("input_313_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_313_pad_0 = const()[name = tensor<string, []>("input_313_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> input_313_strides_0 = const()[name = tensor<string, []>("input_313_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_313_dilations_0 = const()[name = tensor<string, []>("input_313_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_313_groups_0 = const()[name = tensor<string, []>("input_313_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 512, 200]> input_313 = conv(bias = F0_blocks_0_conv1_bias, dilations = input_313_dilations_0, groups = input_313_groups_0, pad = input_313_pad_0, pad_type = input_313_pad_type_0, strides = input_313_strides_0, weight = weight_29, x = input_309)[name = tensor<string, []>("input_313")];
            tensor<fp32, [1, 1024]> h_17 = linear(bias = F0_blocks_0_norm2_fc_bias, weight = F0_blocks_0_norm2_fc_weight, x = style)[name = tensor<string, []>("linear_79")];
            tensor<int32, [3]> var_1795 = const()[name = tensor<string, []>("op_1795"), val = tensor<int32, [3]>([1, 1024, 1])];
            tensor<fp32, [1, 1024, 1]> h_19 = reshape(shape = var_1795, x = h_17)[name = tensor<string, []>("h_19")];
            tensor<int32, [2]> var_1797_split_sizes_0 = const()[name = tensor<string, []>("op_1797_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
            tensor<int32, []> var_1797_axis_0 = const()[name = tensor<string, []>("op_1797_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 512, 1]> var_1797_0, tensor<fp32, [1, 512, 1]> var_1797_1 = split(axis = var_1797_axis_0, split_sizes = var_1797_split_sizes_0, x = h_19)[name = tensor<string, []>("op_1797")];
            tensor<fp32, []> var_1799_promoted = const()[name = tensor<string, []>("op_1799_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 512, 1]> var_1800 = add(x = var_1797_0, y = var_1799_promoted)[name = tensor<string, []>("op_1800")];
            tensor<fp32, [1, 512, 200]> var_1803 = instance_norm(beta = F0_blocks_0_norm1_norm_bias, epsilon = var_1749, gamma = F0_blocks_0_norm1_norm_weight, x = input_313)[name = tensor<string, []>("op_1803")];
            tensor<fp32, [1, 512, 200]> var_1804 = mul(x = var_1800, y = var_1803)[name = tensor<string, []>("op_1804")];
            tensor<fp32, [1, 512, 200]> input_315 = add(x = var_1804, y = var_1797_1)[name = tensor<string, []>("input_315")];
            tensor<fp32, [1, 512, 200]> input_317 = leaky_relu(alpha = var_1746, x = input_315)[name = tensor<string, []>("input_317")];
            tensor<fp32, [512, 512, 3]> weight_33 = const()[name = tensor<string, []>("weight_33"), val = tensor<fp32, [512, 512, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(112770560)))];
            tensor<string, []> out_1_pad_type_0 = const()[name = tensor<string, []>("out_1_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> out_1_pad_0 = const()[name = tensor<string, []>("out_1_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> out_1_strides_0 = const()[name = tensor<string, []>("out_1_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> out_1_dilations_0 = const()[name = tensor<string, []>("out_1_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> out_1_groups_0 = const()[name = tensor<string, []>("out_1_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 512, 200]> out_1 = conv(bias = F0_blocks_0_conv2_bias, dilations = out_1_dilations_0, groups = out_1_groups_0, pad = out_1_pad_0, pad_type = out_1_pad_type_0, strides = out_1_strides_0, weight = weight_33, x = input_317)[name = tensor<string, []>("out_1")];
            tensor<fp32, [1, 512, 200]> var_1819 = add(x = out_1, y = input_305)[name = tensor<string, []>("op_1819")];
            tensor<fp32, []> var_1822 = const()[name = tensor<string, []>("op_1822"), val = tensor<fp32, []>(0x1.6a09e6p-1)];
            tensor<fp32, [1, 512, 200]> input_321 = mul(x = var_1819, y = var_1822)[name = tensor<string, []>("input_321")];
            tensor<fp32, []> var_1830 = const()[name = tensor<string, []>("op_1830"), val = tensor<fp32, []>(0x1.99999ap-3)];
            tensor<fp32, []> var_1834 = const()[name = tensor<string, []>("op_1834"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 1024]> h_21 = linear(bias = F0_blocks_1_norm1_fc_bias, weight = F0_blocks_1_norm1_fc_weight, x = style)[name = tensor<string, []>("linear_80")];
            tensor<int32, [3]> var_1851 = const()[name = tensor<string, []>("op_1851"), val = tensor<int32, [3]>([1, 1024, 1])];
            tensor<fp32, [1, 1024, 1]> h_23 = reshape(shape = var_1851, x = h_21)[name = tensor<string, []>("h_23")];
            tensor<int32, [2]> var_1853_split_sizes_0 = const()[name = tensor<string, []>("op_1853_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
            tensor<int32, []> var_1853_axis_0 = const()[name = tensor<string, []>("op_1853_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 512, 1]> var_1853_0, tensor<fp32, [1, 512, 1]> var_1853_1 = split(axis = var_1853_axis_0, split_sizes = var_1853_split_sizes_0, x = h_23)[name = tensor<string, []>("op_1853")];
            tensor<fp32, []> var_1855_promoted = const()[name = tensor<string, []>("op_1855_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 512, 1]> var_1856 = add(x = var_1853_0, y = var_1855_promoted)[name = tensor<string, []>("op_1856")];
            tensor<fp32, [1, 512, 200]> var_1859 = instance_norm(beta = F0_blocks_0_norm1_norm_bias, epsilon = var_1834, gamma = F0_blocks_0_norm1_norm_weight, x = input_321)[name = tensor<string, []>("op_1859")];
            tensor<fp32, [1, 512, 200]> var_1860 = mul(x = var_1856, y = var_1859)[name = tensor<string, []>("op_1860")];
            tensor<fp32, [1, 512, 200]> input_323 = add(x = var_1860, y = var_1853_1)[name = tensor<string, []>("input_323")];
            tensor<fp32, [1, 512, 200]> input_325 = leaky_relu(alpha = var_1830, x = input_323)[name = tensor<string, []>("input_325")];
            tensor<fp32, [512, 1, 3]> var_1868 = const()[name = tensor<string, []>("op_1868"), val = tensor<fp32, [512, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(115916352)))];
            tensor<string, []> conv_transpose_0_pad_type_0 = const()[name = tensor<string, []>("conv_transpose_0_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> conv_transpose_0_pad_0 = const()[name = tensor<string, []>("conv_transpose_0_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_transpose_0_strides_0 = const()[name = tensor<string, []>("conv_transpose_0_strides_0"), val = tensor<int32, [1]>([2])];
            tensor<int32, []> conv_transpose_0_groups_0 = const()[name = tensor<string, []>("conv_transpose_0_groups_0"), val = tensor<int32, []>(512)];
            tensor<int32, [1]> conv_transpose_0_dilations_0 = const()[name = tensor<string, []>("conv_transpose_0_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [3]> conv_transpose_0_has_output_shape_output_shape_0 = const()[name = tensor<string, []>("conv_transpose_0_has_output_shape_output_shape_0"), val = tensor<int32, [3]>([1, 512, 401])];
            tensor<fp32, [1, 512, 401]> conv_transpose_0_has_output_shape = conv_transpose(bias = F0_blocks_1_pool_bias, dilations = conv_transpose_0_dilations_0, groups = conv_transpose_0_groups_0, output_shape = conv_transpose_0_has_output_shape_output_shape_0, pad = conv_transpose_0_pad_0, pad_type = conv_transpose_0_pad_type_0, strides = conv_transpose_0_strides_0, weight = var_1868, x = input_325)[name = tensor<string, []>("conv_transpose_0_has_output_shape")];
            tensor<int32, [3]> input_327_begin_0 = const()[name = tensor<string, []>("input_327_begin_0"), val = tensor<int32, [3]>([0, 0, 1])];
            tensor<int32, [3]> input_327_end_0 = const()[name = tensor<string, []>("input_327_end_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<bool, [3]> input_327_begin_mask_0 = const()[name = tensor<string, []>("input_327_begin_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<bool, [3]> input_327_end_mask_0 = const()[name = tensor<string, []>("input_327_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 512, 400]> input_327 = slice_by_index(begin = input_327_begin_0, begin_mask = input_327_begin_mask_0, end = input_327_end_0, end_mask = input_327_end_mask_0, x = conv_transpose_0_has_output_shape)[name = tensor<string, []>("input_327")];
            tensor<fp32, [256, 512, 3]> weight_37 = const()[name = tensor<string, []>("weight_37"), val = tensor<fp32, [256, 512, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(115922560)))];
            tensor<string, []> input_331_pad_type_0 = const()[name = tensor<string, []>("input_331_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_331_pad_0 = const()[name = tensor<string, []>("input_331_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> input_331_strides_0 = const()[name = tensor<string, []>("input_331_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_331_dilations_0 = const()[name = tensor<string, []>("input_331_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_331_groups_0 = const()[name = tensor<string, []>("input_331_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 400]> input_331 = conv(bias = F0_blocks_1_conv1_bias, dilations = input_331_dilations_0, groups = input_331_groups_0, pad = input_331_pad_0, pad_type = input_331_pad_type_0, strides = input_331_strides_0, weight = weight_37, x = input_327)[name = tensor<string, []>("input_331")];
            tensor<fp32, [1, 512]> h_25 = linear(bias = F0_blocks_1_norm2_fc_bias, weight = F0_blocks_1_norm2_fc_weight, x = style)[name = tensor<string, []>("linear_81")];
            tensor<int32, [3]> var_1893 = const()[name = tensor<string, []>("op_1893"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_27 = reshape(shape = var_1893, x = h_25)[name = tensor<string, []>("h_27")];
            tensor<int32, [2]> var_1895_split_sizes_0 = const()[name = tensor<string, []>("op_1895_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_1895_axis_0 = const()[name = tensor<string, []>("op_1895_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_1895_0, tensor<fp32, [1, 256, 1]> var_1895_1 = split(axis = var_1895_axis_0, split_sizes = var_1895_split_sizes_0, x = h_27)[name = tensor<string, []>("op_1895")];
            tensor<fp32, []> var_1897_promoted = const()[name = tensor<string, []>("op_1897_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_1898 = add(x = var_1895_0, y = var_1897_promoted)[name = tensor<string, []>("op_1898")];
            tensor<fp32, [1, 256, 400]> var_1901 = instance_norm(beta = F0_blocks_1_norm2_norm_bias, epsilon = var_1834, gamma = F0_blocks_1_norm2_norm_weight, x = input_331)[name = tensor<string, []>("op_1901")];
            tensor<fp32, [1, 256, 400]> var_1902 = mul(x = var_1898, y = var_1901)[name = tensor<string, []>("op_1902")];
            tensor<fp32, [1, 256, 400]> input_333 = add(x = var_1902, y = var_1895_1)[name = tensor<string, []>("input_333")];
            tensor<fp32, [1, 256, 400]> input_335 = leaky_relu(alpha = var_1830, x = input_333)[name = tensor<string, []>("input_335")];
            tensor<fp32, [256, 256, 3]> weight_41 = const()[name = tensor<string, []>("weight_41"), val = tensor<fp32, [256, 256, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117495488)))];
            tensor<string, []> out_3_pad_type_0 = const()[name = tensor<string, []>("out_3_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> out_3_pad_0 = const()[name = tensor<string, []>("out_3_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> out_3_strides_0 = const()[name = tensor<string, []>("out_3_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> out_3_dilations_0 = const()[name = tensor<string, []>("out_3_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> out_3_groups_0 = const()[name = tensor<string, []>("out_3_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 400]> out_3 = conv(bias = F0_blocks_1_conv2_bias, dilations = out_3_dilations_0, groups = out_3_groups_0, pad = out_3_pad_0, pad_type = out_3_pad_type_0, strides = out_3_strides_0, weight = weight_41, x = input_335)[name = tensor<string, []>("out_3")];
            tensor<int32, [1]> expand_dims_1_axes_0 = const()[name = tensor<string, []>("expand_dims_1_axes_0"), val = tensor<int32, [1]>([3])];
            tensor<fp32, [1, 512, 200, 1]> expand_dims_1 = expand_dims(axes = expand_dims_1_axes_0, x = input_321)[name = tensor<string, []>("expand_dims_1")];
            tensor<int32, []> upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = tensor<string, []>("upsample_nearest_neighbor_0_scale_factor_height_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = tensor<string, []>("upsample_nearest_neighbor_0_scale_factor_width_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 512, 400, 1]> upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_1)[name = tensor<string, []>("upsample_nearest_neighbor_0")];
            tensor<int32, [1]> input_339_axes_0 = const()[name = tensor<string, []>("input_339_axes_0"), val = tensor<int32, [1]>([3])];
            tensor<fp32, [1, 512, 400]> input_339 = squeeze(axes = input_339_axes_0, x = upsample_nearest_neighbor_0)[name = tensor<string, []>("input_339")];
            tensor<fp32, [256, 512, 1]> weight_43 = const()[name = tensor<string, []>("weight_43"), val = tensor<fp32, [256, 512, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(118281984)))];
            tensor<string, []> var_1928_pad_type_0 = const()[name = tensor<string, []>("op_1928_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> var_1928_strides_0 = const()[name = tensor<string, []>("op_1928_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> var_1928_pad_0 = const()[name = tensor<string, []>("op_1928_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> var_1928_dilations_0 = const()[name = tensor<string, []>("op_1928_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> var_1928_groups_0 = const()[name = tensor<string, []>("op_1928_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 400]> var_1928 = conv(dilations = var_1928_dilations_0, groups = var_1928_groups_0, pad = var_1928_pad_0, pad_type = var_1928_pad_type_0, strides = var_1928_strides_0, weight = weight_43, x = input_339)[name = tensor<string, []>("op_1928")];
            tensor<fp32, [1, 256, 400]> var_1929 = add(x = out_3, y = var_1928)[name = tensor<string, []>("op_1929")];
            tensor<fp32, []> var_1932 = const()[name = tensor<string, []>("op_1932"), val = tensor<fp32, []>(0x1.6a09e6p-1)];
            tensor<fp32, [1, 256, 400]> input_341 = mul(x = var_1929, y = var_1932)[name = tensor<string, []>("input_341")];
            tensor<fp32, []> var_1938 = const()[name = tensor<string, []>("op_1938"), val = tensor<fp32, []>(0x1.99999ap-3)];
            tensor<fp32, []> var_1941 = const()[name = tensor<string, []>("op_1941"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 512]> h_29 = linear(bias = F0_blocks_2_norm1_fc_bias, weight = F0_blocks_2_norm1_fc_weight, x = style)[name = tensor<string, []>("linear_82")];
            tensor<int32, [3]> var_1956 = const()[name = tensor<string, []>("op_1956"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_31 = reshape(shape = var_1956, x = h_29)[name = tensor<string, []>("h_31")];
            tensor<int32, [2]> var_1958_split_sizes_0 = const()[name = tensor<string, []>("op_1958_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_1958_axis_0 = const()[name = tensor<string, []>("op_1958_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_1958_0, tensor<fp32, [1, 256, 1]> var_1958_1 = split(axis = var_1958_axis_0, split_sizes = var_1958_split_sizes_0, x = h_31)[name = tensor<string, []>("op_1958")];
            tensor<fp32, []> var_1960_promoted = const()[name = tensor<string, []>("op_1960_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_1961 = add(x = var_1958_0, y = var_1960_promoted)[name = tensor<string, []>("op_1961")];
            tensor<fp32, [1, 256, 400]> var_1964 = instance_norm(beta = F0_blocks_1_norm2_norm_bias, epsilon = var_1941, gamma = F0_blocks_1_norm2_norm_weight, x = input_341)[name = tensor<string, []>("op_1964")];
            tensor<fp32, [1, 256, 400]> var_1965 = mul(x = var_1961, y = var_1964)[name = tensor<string, []>("op_1965")];
            tensor<fp32, [1, 256, 400]> input_343 = add(x = var_1965, y = var_1958_1)[name = tensor<string, []>("input_343")];
            tensor<fp32, [1, 256, 400]> input_345 = leaky_relu(alpha = var_1938, x = input_343)[name = tensor<string, []>("input_345")];
            tensor<fp32, [256, 256, 3]> weight_47 = const()[name = tensor<string, []>("weight_47"), val = tensor<fp32, [256, 256, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(118806336)))];
            tensor<string, []> input_349_pad_type_0 = const()[name = tensor<string, []>("input_349_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_349_pad_0 = const()[name = tensor<string, []>("input_349_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> input_349_strides_0 = const()[name = tensor<string, []>("input_349_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_349_dilations_0 = const()[name = tensor<string, []>("input_349_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_349_groups_0 = const()[name = tensor<string, []>("input_349_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 400]> input_349 = conv(bias = F0_blocks_2_conv1_bias, dilations = input_349_dilations_0, groups = input_349_groups_0, pad = input_349_pad_0, pad_type = input_349_pad_type_0, strides = input_349_strides_0, weight = weight_47, x = input_345)[name = tensor<string, []>("input_349")];
            tensor<fp32, [1, 512]> h_33 = linear(bias = F0_blocks_2_norm2_fc_bias, weight = F0_blocks_2_norm2_fc_weight, x = style)[name = tensor<string, []>("linear_83")];
            tensor<int32, [3]> var_1987 = const()[name = tensor<string, []>("op_1987"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_35 = reshape(shape = var_1987, x = h_33)[name = tensor<string, []>("h_35")];
            tensor<int32, [2]> var_1989_split_sizes_0 = const()[name = tensor<string, []>("op_1989_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_1989_axis_0 = const()[name = tensor<string, []>("op_1989_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_1989_0, tensor<fp32, [1, 256, 1]> var_1989_1 = split(axis = var_1989_axis_0, split_sizes = var_1989_split_sizes_0, x = h_35)[name = tensor<string, []>("op_1989")];
            tensor<fp32, []> var_1991_promoted = const()[name = tensor<string, []>("op_1991_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_1992 = add(x = var_1989_0, y = var_1991_promoted)[name = tensor<string, []>("op_1992")];
            tensor<fp32, [1, 256, 400]> var_1995 = instance_norm(beta = F0_blocks_1_norm2_norm_bias, epsilon = var_1941, gamma = F0_blocks_1_norm2_norm_weight, x = input_349)[name = tensor<string, []>("op_1995")];
            tensor<fp32, [1, 256, 400]> var_1996 = mul(x = var_1992, y = var_1995)[name = tensor<string, []>("op_1996")];
            tensor<fp32, [1, 256, 400]> input_351 = add(x = var_1996, y = var_1989_1)[name = tensor<string, []>("input_351")];
            tensor<fp32, [1, 256, 400]> input_353 = leaky_relu(alpha = var_1938, x = input_351)[name = tensor<string, []>("input_353")];
            tensor<fp32, [256, 256, 3]> weight_51 = const()[name = tensor<string, []>("weight_51"), val = tensor<fp32, [256, 256, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(119592832)))];
            tensor<string, []> out_5_pad_type_0 = const()[name = tensor<string, []>("out_5_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> out_5_pad_0 = const()[name = tensor<string, []>("out_5_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> out_5_strides_0 = const()[name = tensor<string, []>("out_5_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> out_5_dilations_0 = const()[name = tensor<string, []>("out_5_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> out_5_groups_0 = const()[name = tensor<string, []>("out_5_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 400]> out_5 = conv(bias = F0_blocks_2_conv2_bias, dilations = out_5_dilations_0, groups = out_5_groups_0, pad = out_5_pad_0, pad_type = out_5_pad_type_0, strides = out_5_strides_0, weight = weight_51, x = input_353)[name = tensor<string, []>("out_5")];
            tensor<fp32, [1, 256, 400]> var_2011 = add(x = out_5, y = input_341)[name = tensor<string, []>("op_2011")];
            tensor<fp32, []> var_2014 = const()[name = tensor<string, []>("op_2014"), val = tensor<fp32, []>(0x1.6a09e6p-1)];
            tensor<fp32, [1, 256, 400]> input_357 = mul(x = var_2011, y = var_2014)[name = tensor<string, []>("input_357")];
            tensor<string, []> F0_1_pad_type_0 = const()[name = tensor<string, []>("F0_1_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> F0_1_strides_0 = const()[name = tensor<string, []>("F0_1_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> F0_1_pad_0 = const()[name = tensor<string, []>("F0_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> F0_1_dilations_0 = const()[name = tensor<string, []>("F0_1_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> F0_1_groups_0 = const()[name = tensor<string, []>("F0_1_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1, 400]> F0_1 = conv(bias = F0_proj_bias, dilations = F0_1_dilations_0, groups = F0_1_groups_0, pad = F0_1_pad_0, pad_type = F0_1_pad_type_0, strides = F0_1_strides_0, weight = F0_proj_weight, x = input_357)[name = tensor<string, []>("F0_1")];
            tensor<fp32, []> var_2034 = const()[name = tensor<string, []>("op_2034"), val = tensor<fp32, []>(0x1.99999ap-3)];
            tensor<fp32, []> var_2037 = const()[name = tensor<string, []>("op_2037"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 1024]> h_37 = linear(bias = N_blocks_0_norm1_fc_bias, weight = N_blocks_0_norm1_fc_weight, x = style)[name = tensor<string, []>("linear_84")];
            tensor<int32, [3]> var_2052 = const()[name = tensor<string, []>("op_2052"), val = tensor<int32, [3]>([1, 1024, 1])];
            tensor<fp32, [1, 1024, 1]> h_39 = reshape(shape = var_2052, x = h_37)[name = tensor<string, []>("h_39")];
            tensor<int32, [2]> var_2054_split_sizes_0 = const()[name = tensor<string, []>("op_2054_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
            tensor<int32, []> var_2054_axis_0 = const()[name = tensor<string, []>("op_2054_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 512, 1]> var_2054_0, tensor<fp32, [1, 512, 1]> var_2054_1 = split(axis = var_2054_axis_0, split_sizes = var_2054_split_sizes_0, x = h_39)[name = tensor<string, []>("op_2054")];
            tensor<fp32, []> var_2056_promoted = const()[name = tensor<string, []>("op_2056_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 512, 1]> var_2057 = add(x = var_2054_0, y = var_2056_promoted)[name = tensor<string, []>("op_2057")];
            tensor<fp32, [1, 512, 200]> var_2061 = mul(x = var_2057, y = var_1772)[name = tensor<string, []>("op_2061")];
            tensor<fp32, [1, 512, 200]> input_361 = add(x = var_2061, y = var_2054_1)[name = tensor<string, []>("input_361")];
            tensor<fp32, [1, 512, 200]> input_363 = leaky_relu(alpha = var_2034, x = input_361)[name = tensor<string, []>("input_363")];
            tensor<fp32, [512, 512, 3]> weight_57 = const()[name = tensor<string, []>("weight_57"), val = tensor<fp32, [512, 512, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(120379328)))];
            tensor<string, []> input_367_pad_type_0 = const()[name = tensor<string, []>("input_367_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_367_pad_0 = const()[name = tensor<string, []>("input_367_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> input_367_strides_0 = const()[name = tensor<string, []>("input_367_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_367_dilations_0 = const()[name = tensor<string, []>("input_367_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_367_groups_0 = const()[name = tensor<string, []>("input_367_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 512, 200]> input_367 = conv(bias = N_blocks_0_conv1_bias, dilations = input_367_dilations_0, groups = input_367_groups_0, pad = input_367_pad_0, pad_type = input_367_pad_type_0, strides = input_367_strides_0, weight = weight_57, x = input_363)[name = tensor<string, []>("input_367")];
            tensor<fp32, [1, 1024]> h_41 = linear(bias = N_blocks_0_norm2_fc_bias, weight = N_blocks_0_norm2_fc_weight, x = style)[name = tensor<string, []>("linear_85")];
            tensor<int32, [3]> var_2083 = const()[name = tensor<string, []>("op_2083"), val = tensor<int32, [3]>([1, 1024, 1])];
            tensor<fp32, [1, 1024, 1]> h_43 = reshape(shape = var_2083, x = h_41)[name = tensor<string, []>("h_43")];
            tensor<int32, [2]> var_2085_split_sizes_0 = const()[name = tensor<string, []>("op_2085_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
            tensor<int32, []> var_2085_axis_0 = const()[name = tensor<string, []>("op_2085_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 512, 1]> var_2085_0, tensor<fp32, [1, 512, 1]> var_2085_1 = split(axis = var_2085_axis_0, split_sizes = var_2085_split_sizes_0, x = h_43)[name = tensor<string, []>("op_2085")];
            tensor<fp32, []> var_2087_promoted = const()[name = tensor<string, []>("op_2087_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 512, 1]> var_2088 = add(x = var_2085_0, y = var_2087_promoted)[name = tensor<string, []>("op_2088")];
            tensor<fp32, [1, 512, 200]> var_2091 = instance_norm(beta = F0_blocks_0_norm1_norm_bias, epsilon = var_2037, gamma = F0_blocks_0_norm1_norm_weight, x = input_367)[name = tensor<string, []>("op_2091")];
            tensor<fp32, [1, 512, 200]> var_2092 = mul(x = var_2088, y = var_2091)[name = tensor<string, []>("op_2092")];
            tensor<fp32, [1, 512, 200]> input_369 = add(x = var_2092, y = var_2085_1)[name = tensor<string, []>("input_369")];
            tensor<fp32, [1, 512, 200]> input_371 = leaky_relu(alpha = var_2034, x = input_369)[name = tensor<string, []>("input_371")];
            tensor<fp32, [512, 512, 3]> weight_61 = const()[name = tensor<string, []>("weight_61"), val = tensor<fp32, [512, 512, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(123525120)))];
            tensor<string, []> out_7_pad_type_0 = const()[name = tensor<string, []>("out_7_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> out_7_pad_0 = const()[name = tensor<string, []>("out_7_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> out_7_strides_0 = const()[name = tensor<string, []>("out_7_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> out_7_dilations_0 = const()[name = tensor<string, []>("out_7_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> out_7_groups_0 = const()[name = tensor<string, []>("out_7_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 512, 200]> out_7 = conv(bias = N_blocks_0_conv2_bias, dilations = out_7_dilations_0, groups = out_7_groups_0, pad = out_7_pad_0, pad_type = out_7_pad_type_0, strides = out_7_strides_0, weight = weight_61, x = input_371)[name = tensor<string, []>("out_7")];
            tensor<fp32, [1, 512, 200]> var_2107 = add(x = out_7, y = input_305)[name = tensor<string, []>("op_2107")];
            tensor<fp32, []> var_2110 = const()[name = tensor<string, []>("op_2110"), val = tensor<fp32, []>(0x1.6a09e6p-1)];
            tensor<fp32, [1, 512, 200]> input_375 = mul(x = var_2107, y = var_2110)[name = tensor<string, []>("input_375")];
            tensor<fp32, []> var_2118 = const()[name = tensor<string, []>("op_2118"), val = tensor<fp32, []>(0x1.99999ap-3)];
            tensor<fp32, []> var_2122 = const()[name = tensor<string, []>("op_2122"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 1024]> h_45 = linear(bias = N_blocks_1_norm1_fc_bias, weight = N_blocks_1_norm1_fc_weight, x = style)[name = tensor<string, []>("linear_86")];
            tensor<int32, [3]> var_2139 = const()[name = tensor<string, []>("op_2139"), val = tensor<int32, [3]>([1, 1024, 1])];
            tensor<fp32, [1, 1024, 1]> h_47 = reshape(shape = var_2139, x = h_45)[name = tensor<string, []>("h_47")];
            tensor<int32, [2]> var_2141_split_sizes_0 = const()[name = tensor<string, []>("op_2141_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
            tensor<int32, []> var_2141_axis_0 = const()[name = tensor<string, []>("op_2141_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 512, 1]> var_2141_0, tensor<fp32, [1, 512, 1]> var_2141_1 = split(axis = var_2141_axis_0, split_sizes = var_2141_split_sizes_0, x = h_47)[name = tensor<string, []>("op_2141")];
            tensor<fp32, []> var_2143_promoted = const()[name = tensor<string, []>("op_2143_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 512, 1]> var_2144 = add(x = var_2141_0, y = var_2143_promoted)[name = tensor<string, []>("op_2144")];
            tensor<fp32, [1, 512, 200]> var_2147 = instance_norm(beta = F0_blocks_0_norm1_norm_bias, epsilon = var_2122, gamma = F0_blocks_0_norm1_norm_weight, x = input_375)[name = tensor<string, []>("op_2147")];
            tensor<fp32, [1, 512, 200]> var_2148 = mul(x = var_2144, y = var_2147)[name = tensor<string, []>("op_2148")];
            tensor<fp32, [1, 512, 200]> input_377 = add(x = var_2148, y = var_2141_1)[name = tensor<string, []>("input_377")];
            tensor<fp32, [1, 512, 200]> input_379 = leaky_relu(alpha = var_2118, x = input_377)[name = tensor<string, []>("input_379")];
            tensor<fp32, [512, 1, 3]> var_2156 = const()[name = tensor<string, []>("op_2156"), val = tensor<fp32, [512, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126670912)))];
            tensor<string, []> conv_transpose_1_pad_type_0 = const()[name = tensor<string, []>("conv_transpose_1_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> conv_transpose_1_pad_0 = const()[name = tensor<string, []>("conv_transpose_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_transpose_1_strides_0 = const()[name = tensor<string, []>("conv_transpose_1_strides_0"), val = tensor<int32, [1]>([2])];
            tensor<int32, []> conv_transpose_1_groups_0 = const()[name = tensor<string, []>("conv_transpose_1_groups_0"), val = tensor<int32, []>(512)];
            tensor<int32, [1]> conv_transpose_1_dilations_0 = const()[name = tensor<string, []>("conv_transpose_1_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [3]> conv_transpose_1_has_output_shape_output_shape_0 = const()[name = tensor<string, []>("conv_transpose_1_has_output_shape_output_shape_0"), val = tensor<int32, [3]>([1, 512, 401])];
            tensor<fp32, [1, 512, 401]> conv_transpose_1_has_output_shape = conv_transpose(bias = N_blocks_1_pool_bias, dilations = conv_transpose_1_dilations_0, groups = conv_transpose_1_groups_0, output_shape = conv_transpose_1_has_output_shape_output_shape_0, pad = conv_transpose_1_pad_0, pad_type = conv_transpose_1_pad_type_0, strides = conv_transpose_1_strides_0, weight = var_2156, x = input_379)[name = tensor<string, []>("conv_transpose_1_has_output_shape")];
            tensor<int32, [3]> input_381_begin_0 = const()[name = tensor<string, []>("input_381_begin_0"), val = tensor<int32, [3]>([0, 0, 1])];
            tensor<int32, [3]> input_381_end_0 = const()[name = tensor<string, []>("input_381_end_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<bool, [3]> input_381_begin_mask_0 = const()[name = tensor<string, []>("input_381_begin_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<bool, [3]> input_381_end_mask_0 = const()[name = tensor<string, []>("input_381_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 512, 400]> input_381 = slice_by_index(begin = input_381_begin_0, begin_mask = input_381_begin_mask_0, end = input_381_end_0, end_mask = input_381_end_mask_0, x = conv_transpose_1_has_output_shape)[name = tensor<string, []>("input_381")];
            tensor<fp32, [256, 512, 3]> weight_65 = const()[name = tensor<string, []>("weight_65"), val = tensor<fp32, [256, 512, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126677120)))];
            tensor<string, []> input_385_pad_type_0 = const()[name = tensor<string, []>("input_385_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_385_pad_0 = const()[name = tensor<string, []>("input_385_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> input_385_strides_0 = const()[name = tensor<string, []>("input_385_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_385_dilations_0 = const()[name = tensor<string, []>("input_385_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_385_groups_0 = const()[name = tensor<string, []>("input_385_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 400]> input_385 = conv(bias = N_blocks_1_conv1_bias, dilations = input_385_dilations_0, groups = input_385_groups_0, pad = input_385_pad_0, pad_type = input_385_pad_type_0, strides = input_385_strides_0, weight = weight_65, x = input_381)[name = tensor<string, []>("input_385")];
            tensor<fp32, [1, 512]> h_49 = linear(bias = N_blocks_1_norm2_fc_bias, weight = N_blocks_1_norm2_fc_weight, x = style)[name = tensor<string, []>("linear_87")];
            tensor<int32, [3]> var_2181 = const()[name = tensor<string, []>("op_2181"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_51 = reshape(shape = var_2181, x = h_49)[name = tensor<string, []>("h_51")];
            tensor<int32, [2]> var_2183_split_sizes_0 = const()[name = tensor<string, []>("op_2183_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_2183_axis_0 = const()[name = tensor<string, []>("op_2183_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_2183_0, tensor<fp32, [1, 256, 1]> var_2183_1 = split(axis = var_2183_axis_0, split_sizes = var_2183_split_sizes_0, x = h_51)[name = tensor<string, []>("op_2183")];
            tensor<fp32, []> var_2185_promoted = const()[name = tensor<string, []>("op_2185_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_2186 = add(x = var_2183_0, y = var_2185_promoted)[name = tensor<string, []>("op_2186")];
            tensor<fp32, [1, 256, 400]> var_2189 = instance_norm(beta = F0_blocks_1_norm2_norm_bias, epsilon = var_2122, gamma = F0_blocks_1_norm2_norm_weight, x = input_385)[name = tensor<string, []>("op_2189")];
            tensor<fp32, [1, 256, 400]> var_2190 = mul(x = var_2186, y = var_2189)[name = tensor<string, []>("op_2190")];
            tensor<fp32, [1, 256, 400]> input_387 = add(x = var_2190, y = var_2183_1)[name = tensor<string, []>("input_387")];
            tensor<fp32, [1, 256, 400]> input_389 = leaky_relu(alpha = var_2118, x = input_387)[name = tensor<string, []>("input_389")];
            tensor<fp32, [256, 256, 3]> weight_69 = const()[name = tensor<string, []>("weight_69"), val = tensor<fp32, [256, 256, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(128250048)))];
            tensor<string, []> out_9_pad_type_0 = const()[name = tensor<string, []>("out_9_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> out_9_pad_0 = const()[name = tensor<string, []>("out_9_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> out_9_strides_0 = const()[name = tensor<string, []>("out_9_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> out_9_dilations_0 = const()[name = tensor<string, []>("out_9_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> out_9_groups_0 = const()[name = tensor<string, []>("out_9_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 400]> out_9 = conv(bias = N_blocks_1_conv2_bias, dilations = out_9_dilations_0, groups = out_9_groups_0, pad = out_9_pad_0, pad_type = out_9_pad_type_0, strides = out_9_strides_0, weight = weight_69, x = input_389)[name = tensor<string, []>("out_9")];
            tensor<int32, [1]> expand_dims_2_axes_0 = const()[name = tensor<string, []>("expand_dims_2_axes_0"), val = tensor<int32, [1]>([3])];
            tensor<fp32, [1, 512, 200, 1]> expand_dims_2 = expand_dims(axes = expand_dims_2_axes_0, x = input_375)[name = tensor<string, []>("expand_dims_2")];
            tensor<int32, []> upsample_nearest_neighbor_1_scale_factor_height_0 = const()[name = tensor<string, []>("upsample_nearest_neighbor_1_scale_factor_height_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> upsample_nearest_neighbor_1_scale_factor_width_0 = const()[name = tensor<string, []>("upsample_nearest_neighbor_1_scale_factor_width_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 512, 400, 1]> upsample_nearest_neighbor_1 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_1_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_1_scale_factor_width_0, x = expand_dims_2)[name = tensor<string, []>("upsample_nearest_neighbor_1")];
            tensor<int32, [1]> input_393_axes_0 = const()[name = tensor<string, []>("input_393_axes_0"), val = tensor<int32, [1]>([3])];
            tensor<fp32, [1, 512, 400]> input_393 = squeeze(axes = input_393_axes_0, x = upsample_nearest_neighbor_1)[name = tensor<string, []>("input_393")];
            tensor<fp32, [256, 512, 1]> weight_71 = const()[name = tensor<string, []>("weight_71"), val = tensor<fp32, [256, 512, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(129036544)))];
            tensor<string, []> var_2216_pad_type_0 = const()[name = tensor<string, []>("op_2216_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> var_2216_strides_0 = const()[name = tensor<string, []>("op_2216_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> var_2216_pad_0 = const()[name = tensor<string, []>("op_2216_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> var_2216_dilations_0 = const()[name = tensor<string, []>("op_2216_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> var_2216_groups_0 = const()[name = tensor<string, []>("op_2216_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 400]> var_2216 = conv(dilations = var_2216_dilations_0, groups = var_2216_groups_0, pad = var_2216_pad_0, pad_type = var_2216_pad_type_0, strides = var_2216_strides_0, weight = weight_71, x = input_393)[name = tensor<string, []>("op_2216")];
            tensor<fp32, [1, 256, 400]> var_2217 = add(x = out_9, y = var_2216)[name = tensor<string, []>("op_2217")];
            tensor<fp32, []> var_2220 = const()[name = tensor<string, []>("op_2220"), val = tensor<fp32, []>(0x1.6a09e6p-1)];
            tensor<fp32, [1, 256, 400]> input_395 = mul(x = var_2217, y = var_2220)[name = tensor<string, []>("input_395")];
            tensor<fp32, []> var_2226 = const()[name = tensor<string, []>("op_2226"), val = tensor<fp32, []>(0x1.99999ap-3)];
            tensor<fp32, []> var_2229 = const()[name = tensor<string, []>("op_2229"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 512]> h_53 = linear(bias = N_blocks_2_norm1_fc_bias, weight = N_blocks_2_norm1_fc_weight, x = style)[name = tensor<string, []>("linear_88")];
            tensor<int32, [3]> var_2244 = const()[name = tensor<string, []>("op_2244"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_55 = reshape(shape = var_2244, x = h_53)[name = tensor<string, []>("h_55")];
            tensor<int32, [2]> var_2246_split_sizes_0 = const()[name = tensor<string, []>("op_2246_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_2246_axis_0 = const()[name = tensor<string, []>("op_2246_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_2246_0, tensor<fp32, [1, 256, 1]> var_2246_1 = split(axis = var_2246_axis_0, split_sizes = var_2246_split_sizes_0, x = h_55)[name = tensor<string, []>("op_2246")];
            tensor<fp32, []> var_2248_promoted = const()[name = tensor<string, []>("op_2248_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_2249 = add(x = var_2246_0, y = var_2248_promoted)[name = tensor<string, []>("op_2249")];
            tensor<fp32, [1, 256, 400]> var_2252 = instance_norm(beta = F0_blocks_1_norm2_norm_bias, epsilon = var_2229, gamma = F0_blocks_1_norm2_norm_weight, x = input_395)[name = tensor<string, []>("op_2252")];
            tensor<fp32, [1, 256, 400]> var_2253 = mul(x = var_2249, y = var_2252)[name = tensor<string, []>("op_2253")];
            tensor<fp32, [1, 256, 400]> input_397 = add(x = var_2253, y = var_2246_1)[name = tensor<string, []>("input_397")];
            tensor<fp32, [1, 256, 400]> input_399 = leaky_relu(alpha = var_2226, x = input_397)[name = tensor<string, []>("input_399")];
            tensor<fp32, [256, 256, 3]> weight_75 = const()[name = tensor<string, []>("weight_75"), val = tensor<fp32, [256, 256, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(129560896)))];
            tensor<string, []> input_403_pad_type_0 = const()[name = tensor<string, []>("input_403_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_403_pad_0 = const()[name = tensor<string, []>("input_403_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> input_403_strides_0 = const()[name = tensor<string, []>("input_403_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_403_dilations_0 = const()[name = tensor<string, []>("input_403_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_403_groups_0 = const()[name = tensor<string, []>("input_403_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 400]> input_403 = conv(bias = N_blocks_2_conv1_bias, dilations = input_403_dilations_0, groups = input_403_groups_0, pad = input_403_pad_0, pad_type = input_403_pad_type_0, strides = input_403_strides_0, weight = weight_75, x = input_399)[name = tensor<string, []>("input_403")];
            tensor<fp32, [1, 512]> h_57 = linear(bias = N_blocks_2_norm2_fc_bias, weight = N_blocks_2_norm2_fc_weight, x = style)[name = tensor<string, []>("linear_89")];
            tensor<int32, [3]> var_2275 = const()[name = tensor<string, []>("op_2275"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_59 = reshape(shape = var_2275, x = h_57)[name = tensor<string, []>("h_59")];
            tensor<int32, [2]> var_2277_split_sizes_0 = const()[name = tensor<string, []>("op_2277_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_2277_axis_0 = const()[name = tensor<string, []>("op_2277_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_2277_0, tensor<fp32, [1, 256, 1]> var_2277_1 = split(axis = var_2277_axis_0, split_sizes = var_2277_split_sizes_0, x = h_59)[name = tensor<string, []>("op_2277")];
            tensor<fp32, []> var_2279_promoted = const()[name = tensor<string, []>("op_2279_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_2280 = add(x = var_2277_0, y = var_2279_promoted)[name = tensor<string, []>("op_2280")];
            tensor<fp32, [1, 256, 400]> var_2283 = instance_norm(beta = F0_blocks_1_norm2_norm_bias, epsilon = var_2229, gamma = F0_blocks_1_norm2_norm_weight, x = input_403)[name = tensor<string, []>("op_2283")];
            tensor<fp32, [1, 256, 400]> var_2284 = mul(x = var_2280, y = var_2283)[name = tensor<string, []>("op_2284")];
            tensor<fp32, [1, 256, 400]> input_405 = add(x = var_2284, y = var_2277_1)[name = tensor<string, []>("input_405")];
            tensor<fp32, [1, 256, 400]> input_407 = leaky_relu(alpha = var_2226, x = input_405)[name = tensor<string, []>("input_407")];
            tensor<fp32, [256, 256, 3]> weight_79 = const()[name = tensor<string, []>("weight_79"), val = tensor<fp32, [256, 256, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(130347392)))];
            tensor<string, []> out_11_pad_type_0 = const()[name = tensor<string, []>("out_11_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> out_11_pad_0 = const()[name = tensor<string, []>("out_11_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> out_11_strides_0 = const()[name = tensor<string, []>("out_11_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> out_11_dilations_0 = const()[name = tensor<string, []>("out_11_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> out_11_groups_0 = const()[name = tensor<string, []>("out_11_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 400]> out_11 = conv(bias = N_blocks_2_conv2_bias, dilations = out_11_dilations_0, groups = out_11_groups_0, pad = out_11_pad_0, pad_type = out_11_pad_type_0, strides = out_11_strides_0, weight = weight_79, x = input_407)[name = tensor<string, []>("out_11")];
            tensor<fp32, [1, 256, 400]> var_2299 = add(x = out_11, y = input_395)[name = tensor<string, []>("op_2299")];
            tensor<fp32, []> var_2302 = const()[name = tensor<string, []>("op_2302"), val = tensor<fp32, []>(0x1.6a09e6p-1)];
            tensor<fp32, [1, 256, 400]> input_411 = mul(x = var_2299, y = var_2302)[name = tensor<string, []>("input_411")];
            tensor<string, []> N_3_pad_type_0 = const()[name = tensor<string, []>("N_3_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> N_3_strides_0 = const()[name = tensor<string, []>("N_3_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> N_3_pad_0 = const()[name = tensor<string, []>("N_3_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> N_3_dilations_0 = const()[name = tensor<string, []>("N_3_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> N_3_groups_0 = const()[name = tensor<string, []>("N_3_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1, 400]> N_3 = conv(bias = N_proj_bias, dilations = N_3_dilations_0, groups = N_3_groups_0, pad = N_3_pad_0, pad_type = N_3_pad_type_0, strides = N_3_strides_0, weight = N_proj_weight, x = input_411)[name = tensor<string, []>("N_3")];
            tensor<int32, [1]> F0_pred_axes_0 = const()[name = tensor<string, []>("F0_pred_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1, 400]> F0_pred = squeeze(axes = F0_pred_axes_0, x = F0_1)[name = tensor<string, []>("F0_pred")];
            tensor<int32, [1]> N_pred_axes_0 = const()[name = tensor<string, []>("N_pred_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1, 400]> N_pred = squeeze(axes = N_pred_axes_0, x = N_3)[name = tensor<string, []>("N_pred")];
            tensor<fp32, []> var_2327_promoted = const()[name = tensor<string, []>("op_2327_promoted"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 1]> var_2328 = mul(x = total_frames, y = var_2327_promoted)[name = tensor<string, []>("op_2328")];
            tensor<fp32, [1, 400]> var_2326_promoted = const()[name = tensor<string, []>("op_2326_promoted"), val = tensor<fp32, [1, 400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(131133888)))];
            tensor<bool, [1, 400]> var_2329 = less(x = var_2326_promoted, y = var_2328)[name = tensor<string, []>("op_2329")];
            tensor<string, []> cast_98_dtype_0 = const()[name = tensor<string, []>("cast_98_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<fp32, [1, 400]> cast_98 = cast(dtype = cast_98_dtype_0, x = var_2329)[name = tensor<string, []>("cast_182")];
            tensor<fp32, [1, 400]> F0_curve = mul(x = F0_pred, y = cast_98)[name = tensor<string, []>("F0_curve")];
            tensor<fp32, [1, 400]> N_5 = mul(x = N_pred, y = cast_98)[name = tensor<string, []>("N_5")];
            tensor<fp32, []> var_2469 = const()[name = tensor<string, []>("op_2469"), val = tensor<fp32, []>(0x1.47ae14p-7)];
            tensor<fp32, []> var_2475 = const()[name = tensor<string, []>("op_2475"), val = tensor<fp32, []>(0x1.99999ap-4)];
            tensor<fp32, []> var_2476 = const()[name = tensor<string, []>("op_2476"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, []> var_2477 = const()[name = tensor<string, []>("op_2477"), val = tensor<fp32, []>(0x1.99999ap-3)];
            tensor<int32, []> var_2486 = const()[name = tensor<string, []>("op_2486"), val = tensor<int32, []>(1)];
            tensor<int32, [1]> input_413_axes_0 = const()[name = tensor<string, []>("input_413_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1, 1, 400]> input_413 = expand_dims(axes = input_413_axes_0, x = F0_curve)[name = tensor<string, []>("input_413")];
            tensor<fp32, [1, 1, 3]> weight_83 = const()[name = tensor<string, []>("weight_83"), val = tensor<fp32, [1, 1, 3]>([[[0x1.a86aaep-5, 0x1.b190bep-5, -0x1.6d8bc6p-6]]])];
            tensor<string, []> F0_pad_type_0 = const()[name = tensor<string, []>("F0_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> F0_pad_0 = const()[name = tensor<string, []>("F0_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> F0_strides_0 = const()[name = tensor<string, []>("F0_strides_0"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1]> F0_dilations_0 = const()[name = tensor<string, []>("F0_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> F0_groups_0 = const()[name = tensor<string, []>("F0_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1, 200]> F0 = conv(bias = decoder_F0_conv_bias, dilations = F0_dilations_0, groups = F0_groups_0, pad = F0_pad_0, pad_type = F0_pad_type_0, strides = F0_strides_0, weight = weight_83, x = input_413)[name = tensor<string, []>("F0")];
            tensor<int32, [1]> input_415_axes_0 = const()[name = tensor<string, []>("input_415_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1, 1, 400]> input_415 = expand_dims(axes = input_415_axes_0, x = N_5)[name = tensor<string, []>("input_415")];
            tensor<fp32, [1, 1, 3]> weight_85 = const()[name = tensor<string, []>("weight_85"), val = tensor<fp32, [1, 1, 3]>([[[0x1.cc2feep-2, 0x1.35c75ep-1, 0x1.b9913ep-2]]])];
            tensor<string, []> N_pad_type_0 = const()[name = tensor<string, []>("N_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> N_pad_0 = const()[name = tensor<string, []>("N_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> N_strides_0 = const()[name = tensor<string, []>("N_strides_0"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1]> N_dilations_0 = const()[name = tensor<string, []>("N_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> N_groups_0 = const()[name = tensor<string, []>("N_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1, 200]> N = conv(bias = decoder_N_conv_bias, dilations = N_dilations_0, groups = N_groups_0, pad = N_pad_0, pad_type = N_pad_type_0, strides = N_strides_0, weight = weight_85, x = input_415)[name = tensor<string, []>("N")];
            tensor<bool, []> input_419_interleave_0 = const()[name = tensor<string, []>("input_419_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 514, 200]> input_419 = concat(axis = var_2486, interleave = input_419_interleave_0, values = (asr, F0, N))[name = tensor<string, []>("input_419")];
            tensor<fp32, [1, 1028]> h_61 = linear(bias = decoder_encode_norm1_fc_bias, weight = decoder_encode_norm1_fc_weight, x = input_417)[name = tensor<string, []>("linear_90")];
            tensor<int32, [3]> var_2538 = const()[name = tensor<string, []>("op_2538"), val = tensor<int32, [3]>([1, 1028, 1])];
            tensor<fp32, [1, 1028, 1]> h_63 = reshape(shape = var_2538, x = h_61)[name = tensor<string, []>("h_63")];
            tensor<int32, [2]> var_2540_split_sizes_0 = const()[name = tensor<string, []>("op_2540_split_sizes_0"), val = tensor<int32, [2]>([514, 514])];
            tensor<int32, []> var_2540_axis_0 = const()[name = tensor<string, []>("op_2540_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 514, 1]> var_2540_0, tensor<fp32, [1, 514, 1]> var_2540_1 = split(axis = var_2540_axis_0, split_sizes = var_2540_split_sizes_0, x = h_63)[name = tensor<string, []>("op_2540")];
            tensor<fp32, []> var_2542_promoted = const()[name = tensor<string, []>("op_2542_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 514, 1]> var_2543 = add(x = var_2540_0, y = var_2542_promoted)[name = tensor<string, []>("op_2543")];
            tensor<fp32, [1, 514, 200]> var_2546 = instance_norm(beta = decoder_encode_norm1_norm_bias, epsilon = var_2476, gamma = decoder_encode_norm1_norm_weight, x = input_419)[name = tensor<string, []>("op_2546")];
            tensor<fp32, [1, 514, 200]> var_2547 = mul(x = var_2543, y = var_2546)[name = tensor<string, []>("op_2547")];
            tensor<fp32, [1, 514, 200]> input_421 = add(x = var_2547, y = var_2540_1)[name = tensor<string, []>("input_421")];
            tensor<fp32, [1, 514, 200]> input_423 = leaky_relu(alpha = var_2477, x = input_421)[name = tensor<string, []>("input_423")];
            tensor<fp32, [1024, 514, 3]> weight_89 = const()[name = tensor<string, []>("weight_89"), val = tensor<fp32, [1024, 514, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(131135552)))];
            tensor<string, []> input_427_pad_type_0 = const()[name = tensor<string, []>("input_427_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_427_pad_0 = const()[name = tensor<string, []>("input_427_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> input_427_strides_0 = const()[name = tensor<string, []>("input_427_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_427_dilations_0 = const()[name = tensor<string, []>("input_427_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_427_groups_0 = const()[name = tensor<string, []>("input_427_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 200]> input_427 = conv(bias = decoder_encode_conv1_bias, dilations = input_427_dilations_0, groups = input_427_groups_0, pad = input_427_pad_0, pad_type = input_427_pad_type_0, strides = input_427_strides_0, weight = weight_89, x = input_423)[name = tensor<string, []>("input_427")];
            tensor<fp32, [1, 2048]> h_65 = linear(bias = decoder_encode_norm2_fc_bias, weight = decoder_encode_norm2_fc_weight, x = input_417)[name = tensor<string, []>("linear_91")];
            tensor<int32, [3]> var_2569 = const()[name = tensor<string, []>("op_2569"), val = tensor<int32, [3]>([1, 2048, 1])];
            tensor<fp32, [1, 2048, 1]> h_67 = reshape(shape = var_2569, x = h_65)[name = tensor<string, []>("h_67")];
            tensor<int32, [2]> var_2571_split_sizes_0 = const()[name = tensor<string, []>("op_2571_split_sizes_0"), val = tensor<int32, [2]>([1024, 1024])];
            tensor<int32, []> var_2571_axis_0 = const()[name = tensor<string, []>("op_2571_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 1]> var_2571_0, tensor<fp32, [1, 1024, 1]> var_2571_1 = split(axis = var_2571_axis_0, split_sizes = var_2571_split_sizes_0, x = h_67)[name = tensor<string, []>("op_2571")];
            tensor<fp32, []> var_2573_promoted = const()[name = tensor<string, []>("op_2573_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 1024, 1]> var_2574 = add(x = var_2571_0, y = var_2573_promoted)[name = tensor<string, []>("op_2574")];
            tensor<fp32, [1, 1024, 200]> var_2577 = instance_norm(beta = decoder_encode_norm2_norm_bias, epsilon = var_2476, gamma = decoder_encode_norm2_norm_weight, x = input_427)[name = tensor<string, []>("op_2577")];
            tensor<fp32, [1, 1024, 200]> var_2578 = mul(x = var_2574, y = var_2577)[name = tensor<string, []>("op_2578")];
            tensor<fp32, [1, 1024, 200]> input_429 = add(x = var_2578, y = var_2571_1)[name = tensor<string, []>("input_429")];
            tensor<fp32, [1, 1024, 200]> input_431 = leaky_relu(alpha = var_2477, x = input_429)[name = tensor<string, []>("input_431")];
            tensor<fp32, [1024, 1024, 3]> weight_93 = const()[name = tensor<string, []>("weight_93"), val = tensor<fp32, [1024, 1024, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(137451648)))];
            tensor<string, []> out_13_pad_type_0 = const()[name = tensor<string, []>("out_13_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> out_13_pad_0 = const()[name = tensor<string, []>("out_13_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> out_13_strides_0 = const()[name = tensor<string, []>("out_13_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> out_13_dilations_0 = const()[name = tensor<string, []>("out_13_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> out_13_groups_0 = const()[name = tensor<string, []>("out_13_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 200]> out_13 = conv(bias = decoder_encode_conv2_bias, dilations = out_13_dilations_0, groups = out_13_groups_0, pad = out_13_pad_0, pad_type = out_13_pad_type_0, strides = out_13_strides_0, weight = weight_93, x = input_431)[name = tensor<string, []>("out_13")];
            tensor<fp32, [1024, 514, 1]> weight_95 = const()[name = tensor<string, []>("weight_95"), val = tensor<fp32, [1024, 514, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(150034624)))];
            tensor<string, []> var_2602_pad_type_0 = const()[name = tensor<string, []>("op_2602_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> var_2602_strides_0 = const()[name = tensor<string, []>("op_2602_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> var_2602_pad_0 = const()[name = tensor<string, []>("op_2602_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> var_2602_dilations_0 = const()[name = tensor<string, []>("op_2602_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> var_2602_groups_0 = const()[name = tensor<string, []>("op_2602_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 200]> var_2602 = conv(dilations = var_2602_dilations_0, groups = var_2602_groups_0, pad = var_2602_pad_0, pad_type = var_2602_pad_type_0, strides = var_2602_strides_0, weight = weight_95, x = input_419)[name = tensor<string, []>("op_2602")];
            tensor<fp32, [1, 1024, 200]> var_2603 = add(x = out_13, y = var_2602)[name = tensor<string, []>("op_2603")];
            tensor<fp32, []> var_2606 = const()[name = tensor<string, []>("op_2606"), val = tensor<fp32, []>(0x1.6a09e6p-1)];
            tensor<fp32, [1, 1024, 200]> x_239 = mul(x = var_2603, y = var_2606)[name = tensor<string, []>("x_239")];
            tensor<fp32, [64, 512, 1]> weight_97 = const()[name = tensor<string, []>("weight_97"), val = tensor<fp32, [64, 512, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(152140032)))];
            tensor<string, []> asr_res_1_pad_type_0 = const()[name = tensor<string, []>("asr_res_1_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> asr_res_1_strides_0 = const()[name = tensor<string, []>("asr_res_1_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> asr_res_1_pad_0 = const()[name = tensor<string, []>("asr_res_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> asr_res_1_dilations_0 = const()[name = tensor<string, []>("asr_res_1_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> asr_res_1_groups_0 = const()[name = tensor<string, []>("asr_res_1_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 200]> asr_res_1 = conv(bias = decoder_asr_res_0_bias, dilations = asr_res_1_dilations_0, groups = asr_res_1_groups_0, pad = asr_res_1_pad_0, pad_type = asr_res_1_pad_type_0, strides = asr_res_1_strides_0, weight = weight_97, x = asr)[name = tensor<string, []>("asr_res_1")];
            tensor<bool, []> input_435_interleave_0 = const()[name = tensor<string, []>("input_435_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1090, 200]> input_435 = concat(axis = var_2486, interleave = input_435_interleave_0, values = (x_239, asr_res_1, F0, N))[name = tensor<string, []>("input_435")];
            tensor<fp32, [1, 2180]> h_69 = linear(bias = decoder_decode_0_norm1_fc_bias, weight = decoder_decode_0_norm1_fc_weight, x = input_417)[name = tensor<string, []>("linear_92")];
            tensor<int32, [3]> var_2634 = const()[name = tensor<string, []>("op_2634"), val = tensor<int32, [3]>([1, 2180, 1])];
            tensor<fp32, [1, 2180, 1]> h_71 = reshape(shape = var_2634, x = h_69)[name = tensor<string, []>("h_71")];
            tensor<int32, [2]> var_2636_split_sizes_0 = const()[name = tensor<string, []>("op_2636_split_sizes_0"), val = tensor<int32, [2]>([1090, 1090])];
            tensor<int32, []> var_2636_axis_0 = const()[name = tensor<string, []>("op_2636_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1090, 1]> var_2636_0, tensor<fp32, [1, 1090, 1]> var_2636_1 = split(axis = var_2636_axis_0, split_sizes = var_2636_split_sizes_0, x = h_71)[name = tensor<string, []>("op_2636")];
            tensor<fp32, []> var_2638_promoted = const()[name = tensor<string, []>("op_2638_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 1090, 1]> var_2639 = add(x = var_2636_0, y = var_2638_promoted)[name = tensor<string, []>("op_2639")];
            tensor<fp32, [1, 1090, 200]> var_2642 = instance_norm(beta = decoder_decode_0_norm1_norm_bias, epsilon = var_2476, gamma = decoder_decode_0_norm1_norm_weight, x = input_435)[name = tensor<string, []>("op_2642")];
            tensor<fp32, [1, 1090, 200]> var_2643 = mul(x = var_2639, y = var_2642)[name = tensor<string, []>("op_2643")];
            tensor<fp32, [1, 1090, 200]> input_437 = add(x = var_2643, y = var_2636_1)[name = tensor<string, []>("input_437")];
            tensor<fp32, [1, 1090, 200]> input_439 = leaky_relu(alpha = var_2477, x = input_437)[name = tensor<string, []>("input_439")];
            tensor<fp32, [1024, 1090, 3]> weight_101 = const()[name = tensor<string, []>("weight_101"), val = tensor<fp32, [1024, 1090, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(152271168)))];
            tensor<string, []> input_443_pad_type_0 = const()[name = tensor<string, []>("input_443_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_443_pad_0 = const()[name = tensor<string, []>("input_443_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> input_443_strides_0 = const()[name = tensor<string, []>("input_443_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_443_dilations_0 = const()[name = tensor<string, []>("input_443_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_443_groups_0 = const()[name = tensor<string, []>("input_443_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 200]> input_443 = conv(bias = decoder_decode_0_conv1_bias, dilations = input_443_dilations_0, groups = input_443_groups_0, pad = input_443_pad_0, pad_type = input_443_pad_type_0, strides = input_443_strides_0, weight = weight_101, x = input_439)[name = tensor<string, []>("input_443")];
            tensor<fp32, [1, 2048]> h_73 = linear(bias = decoder_decode_0_norm2_fc_bias, weight = decoder_decode_0_norm2_fc_weight, x = input_417)[name = tensor<string, []>("linear_93")];
            tensor<int32, [3]> var_2665 = const()[name = tensor<string, []>("op_2665"), val = tensor<int32, [3]>([1, 2048, 1])];
            tensor<fp32, [1, 2048, 1]> h_75 = reshape(shape = var_2665, x = h_73)[name = tensor<string, []>("h_75")];
            tensor<int32, [2]> var_2667_split_sizes_0 = const()[name = tensor<string, []>("op_2667_split_sizes_0"), val = tensor<int32, [2]>([1024, 1024])];
            tensor<int32, []> var_2667_axis_0 = const()[name = tensor<string, []>("op_2667_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 1]> var_2667_0, tensor<fp32, [1, 1024, 1]> var_2667_1 = split(axis = var_2667_axis_0, split_sizes = var_2667_split_sizes_0, x = h_75)[name = tensor<string, []>("op_2667")];
            tensor<fp32, []> var_2669_promoted = const()[name = tensor<string, []>("op_2669_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 1024, 1]> var_2670 = add(x = var_2667_0, y = var_2669_promoted)[name = tensor<string, []>("op_2670")];
            tensor<fp32, [1, 1024, 200]> var_2673 = instance_norm(beta = decoder_encode_norm2_norm_bias, epsilon = var_2476, gamma = decoder_encode_norm2_norm_weight, x = input_443)[name = tensor<string, []>("op_2673")];
            tensor<fp32, [1, 1024, 200]> var_2674 = mul(x = var_2670, y = var_2673)[name = tensor<string, []>("op_2674")];
            tensor<fp32, [1, 1024, 200]> input_445 = add(x = var_2674, y = var_2667_1)[name = tensor<string, []>("input_445")];
            tensor<fp32, [1, 1024, 200]> input_447 = leaky_relu(alpha = var_2477, x = input_445)[name = tensor<string, []>("input_447")];
            tensor<fp32, [1024, 1024, 3]> weight_105 = const()[name = tensor<string, []>("weight_105"), val = tensor<fp32, [1024, 1024, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165665152)))];
            tensor<string, []> out_15_pad_type_0 = const()[name = tensor<string, []>("out_15_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> out_15_pad_0 = const()[name = tensor<string, []>("out_15_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> out_15_strides_0 = const()[name = tensor<string, []>("out_15_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> out_15_dilations_0 = const()[name = tensor<string, []>("out_15_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> out_15_groups_0 = const()[name = tensor<string, []>("out_15_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 200]> out_15 = conv(bias = decoder_decode_0_conv2_bias, dilations = out_15_dilations_0, groups = out_15_groups_0, pad = out_15_pad_0, pad_type = out_15_pad_type_0, strides = out_15_strides_0, weight = weight_105, x = input_447)[name = tensor<string, []>("out_15")];
            tensor<fp32, [1024, 1090, 1]> weight_107 = const()[name = tensor<string, []>("weight_107"), val = tensor<fp32, [1024, 1090, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(178248128)))];
            tensor<string, []> var_2698_pad_type_0 = const()[name = tensor<string, []>("op_2698_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> var_2698_strides_0 = const()[name = tensor<string, []>("op_2698_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> var_2698_pad_0 = const()[name = tensor<string, []>("op_2698_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> var_2698_dilations_0 = const()[name = tensor<string, []>("op_2698_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> var_2698_groups_0 = const()[name = tensor<string, []>("op_2698_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 200]> var_2698 = conv(dilations = var_2698_dilations_0, groups = var_2698_groups_0, pad = var_2698_pad_0, pad_type = var_2698_pad_type_0, strides = var_2698_strides_0, weight = weight_107, x = input_435)[name = tensor<string, []>("op_2698")];
            tensor<fp32, [1, 1024, 200]> var_2699 = add(x = out_15, y = var_2698)[name = tensor<string, []>("op_2699")];
            tensor<fp32, []> var_2702 = const()[name = tensor<string, []>("op_2702"), val = tensor<fp32, []>(0x1.6a09e6p-1)];
            tensor<fp32, [1, 1024, 200]> x_241 = mul(x = var_2699, y = var_2702)[name = tensor<string, []>("x_241")];
            tensor<bool, []> input_451_interleave_0 = const()[name = tensor<string, []>("input_451_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1090, 200]> input_451 = concat(axis = var_2486, interleave = input_451_interleave_0, values = (x_241, asr_res_1, F0, N))[name = tensor<string, []>("input_451")];
            tensor<fp32, [1, 2180]> h_77 = linear(bias = decoder_decode_1_norm1_fc_bias, weight = decoder_decode_1_norm1_fc_weight, x = input_417)[name = tensor<string, []>("linear_94")];
            tensor<int32, [3]> var_2718 = const()[name = tensor<string, []>("op_2718"), val = tensor<int32, [3]>([1, 2180, 1])];
            tensor<fp32, [1, 2180, 1]> h_79 = reshape(shape = var_2718, x = h_77)[name = tensor<string, []>("h_79")];
            tensor<int32, [2]> var_2720_split_sizes_0 = const()[name = tensor<string, []>("op_2720_split_sizes_0"), val = tensor<int32, [2]>([1090, 1090])];
            tensor<int32, []> var_2720_axis_0 = const()[name = tensor<string, []>("op_2720_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1090, 1]> var_2720_0, tensor<fp32, [1, 1090, 1]> var_2720_1 = split(axis = var_2720_axis_0, split_sizes = var_2720_split_sizes_0, x = h_79)[name = tensor<string, []>("op_2720")];
            tensor<fp32, []> var_2722_promoted = const()[name = tensor<string, []>("op_2722_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 1090, 1]> var_2723 = add(x = var_2720_0, y = var_2722_promoted)[name = tensor<string, []>("op_2723")];
            tensor<fp32, [1, 1090, 200]> var_2726 = instance_norm(beta = decoder_decode_0_norm1_norm_bias, epsilon = var_2476, gamma = decoder_decode_0_norm1_norm_weight, x = input_451)[name = tensor<string, []>("op_2726")];
            tensor<fp32, [1, 1090, 200]> var_2727 = mul(x = var_2723, y = var_2726)[name = tensor<string, []>("op_2727")];
            tensor<fp32, [1, 1090, 200]> input_453 = add(x = var_2727, y = var_2720_1)[name = tensor<string, []>("input_453")];
            tensor<fp32, [1, 1090, 200]> input_455 = leaky_relu(alpha = var_2477, x = input_453)[name = tensor<string, []>("input_455")];
            tensor<fp32, [1024, 1090, 3]> weight_111 = const()[name = tensor<string, []>("weight_111"), val = tensor<fp32, [1024, 1090, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(182712832)))];
            tensor<string, []> input_459_pad_type_0 = const()[name = tensor<string, []>("input_459_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_459_pad_0 = const()[name = tensor<string, []>("input_459_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> input_459_strides_0 = const()[name = tensor<string, []>("input_459_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_459_dilations_0 = const()[name = tensor<string, []>("input_459_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_459_groups_0 = const()[name = tensor<string, []>("input_459_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 200]> input_459 = conv(bias = decoder_decode_1_conv1_bias, dilations = input_459_dilations_0, groups = input_459_groups_0, pad = input_459_pad_0, pad_type = input_459_pad_type_0, strides = input_459_strides_0, weight = weight_111, x = input_455)[name = tensor<string, []>("input_459")];
            tensor<fp32, [1, 2048]> h_81 = linear(bias = decoder_decode_1_norm2_fc_bias, weight = decoder_decode_1_norm2_fc_weight, x = input_417)[name = tensor<string, []>("linear_95")];
            tensor<int32, [3]> var_2749 = const()[name = tensor<string, []>("op_2749"), val = tensor<int32, [3]>([1, 2048, 1])];
            tensor<fp32, [1, 2048, 1]> h_83 = reshape(shape = var_2749, x = h_81)[name = tensor<string, []>("h_83")];
            tensor<int32, [2]> var_2751_split_sizes_0 = const()[name = tensor<string, []>("op_2751_split_sizes_0"), val = tensor<int32, [2]>([1024, 1024])];
            tensor<int32, []> var_2751_axis_0 = const()[name = tensor<string, []>("op_2751_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 1]> var_2751_0, tensor<fp32, [1, 1024, 1]> var_2751_1 = split(axis = var_2751_axis_0, split_sizes = var_2751_split_sizes_0, x = h_83)[name = tensor<string, []>("op_2751")];
            tensor<fp32, []> var_2753_promoted = const()[name = tensor<string, []>("op_2753_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 1024, 1]> var_2754 = add(x = var_2751_0, y = var_2753_promoted)[name = tensor<string, []>("op_2754")];
            tensor<fp32, [1, 1024, 200]> var_2757 = instance_norm(beta = decoder_encode_norm2_norm_bias, epsilon = var_2476, gamma = decoder_encode_norm2_norm_weight, x = input_459)[name = tensor<string, []>("op_2757")];
            tensor<fp32, [1, 1024, 200]> var_2758 = mul(x = var_2754, y = var_2757)[name = tensor<string, []>("op_2758")];
            tensor<fp32, [1, 1024, 200]> input_461 = add(x = var_2758, y = var_2751_1)[name = tensor<string, []>("input_461")];
            tensor<fp32, [1, 1024, 200]> input_463 = leaky_relu(alpha = var_2477, x = input_461)[name = tensor<string, []>("input_463")];
            tensor<fp32, [1024, 1024, 3]> weight_115 = const()[name = tensor<string, []>("weight_115"), val = tensor<fp32, [1024, 1024, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(196106816)))];
            tensor<string, []> out_17_pad_type_0 = const()[name = tensor<string, []>("out_17_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> out_17_pad_0 = const()[name = tensor<string, []>("out_17_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> out_17_strides_0 = const()[name = tensor<string, []>("out_17_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> out_17_dilations_0 = const()[name = tensor<string, []>("out_17_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> out_17_groups_0 = const()[name = tensor<string, []>("out_17_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 200]> out_17 = conv(bias = decoder_decode_1_conv2_bias, dilations = out_17_dilations_0, groups = out_17_groups_0, pad = out_17_pad_0, pad_type = out_17_pad_type_0, strides = out_17_strides_0, weight = weight_115, x = input_463)[name = tensor<string, []>("out_17")];
            tensor<fp32, [1024, 1090, 1]> weight_117 = const()[name = tensor<string, []>("weight_117"), val = tensor<fp32, [1024, 1090, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(208689792)))];
            tensor<string, []> var_2782_pad_type_0 = const()[name = tensor<string, []>("op_2782_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> var_2782_strides_0 = const()[name = tensor<string, []>("op_2782_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> var_2782_pad_0 = const()[name = tensor<string, []>("op_2782_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> var_2782_dilations_0 = const()[name = tensor<string, []>("op_2782_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> var_2782_groups_0 = const()[name = tensor<string, []>("op_2782_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 200]> var_2782 = conv(dilations = var_2782_dilations_0, groups = var_2782_groups_0, pad = var_2782_pad_0, pad_type = var_2782_pad_type_0, strides = var_2782_strides_0, weight = weight_117, x = input_451)[name = tensor<string, []>("op_2782")];
            tensor<fp32, [1, 1024, 200]> var_2783 = add(x = out_17, y = var_2782)[name = tensor<string, []>("op_2783")];
            tensor<fp32, []> var_2786 = const()[name = tensor<string, []>("op_2786"), val = tensor<fp32, []>(0x1.6a09e6p-1)];
            tensor<fp32, [1, 1024, 200]> x_243 = mul(x = var_2783, y = var_2786)[name = tensor<string, []>("x_243")];
            tensor<bool, []> input_467_interleave_0 = const()[name = tensor<string, []>("input_467_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1090, 200]> input_467 = concat(axis = var_2486, interleave = input_467_interleave_0, values = (x_243, asr_res_1, F0, N))[name = tensor<string, []>("input_467")];
            tensor<fp32, [1, 2180]> h_85 = linear(bias = decoder_decode_2_norm1_fc_bias, weight = decoder_decode_2_norm1_fc_weight, x = input_417)[name = tensor<string, []>("linear_96")];
            tensor<int32, [3]> var_2802 = const()[name = tensor<string, []>("op_2802"), val = tensor<int32, [3]>([1, 2180, 1])];
            tensor<fp32, [1, 2180, 1]> h_87 = reshape(shape = var_2802, x = h_85)[name = tensor<string, []>("h_87")];
            tensor<int32, [2]> var_2804_split_sizes_0 = const()[name = tensor<string, []>("op_2804_split_sizes_0"), val = tensor<int32, [2]>([1090, 1090])];
            tensor<int32, []> var_2804_axis_0 = const()[name = tensor<string, []>("op_2804_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1090, 1]> var_2804_0, tensor<fp32, [1, 1090, 1]> var_2804_1 = split(axis = var_2804_axis_0, split_sizes = var_2804_split_sizes_0, x = h_87)[name = tensor<string, []>("op_2804")];
            tensor<fp32, []> var_2806_promoted = const()[name = tensor<string, []>("op_2806_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 1090, 1]> var_2807 = add(x = var_2804_0, y = var_2806_promoted)[name = tensor<string, []>("op_2807")];
            tensor<fp32, [1, 1090, 200]> var_2810 = instance_norm(beta = decoder_decode_0_norm1_norm_bias, epsilon = var_2476, gamma = decoder_decode_0_norm1_norm_weight, x = input_467)[name = tensor<string, []>("op_2810")];
            tensor<fp32, [1, 1090, 200]> var_2811 = mul(x = var_2807, y = var_2810)[name = tensor<string, []>("op_2811")];
            tensor<fp32, [1, 1090, 200]> input_469 = add(x = var_2811, y = var_2804_1)[name = tensor<string, []>("input_469")];
            tensor<fp32, [1, 1090, 200]> input_471 = leaky_relu(alpha = var_2477, x = input_469)[name = tensor<string, []>("input_471")];
            tensor<fp32, [1024, 1090, 3]> weight_121 = const()[name = tensor<string, []>("weight_121"), val = tensor<fp32, [1024, 1090, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(213154496)))];
            tensor<string, []> input_475_pad_type_0 = const()[name = tensor<string, []>("input_475_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_475_pad_0 = const()[name = tensor<string, []>("input_475_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> input_475_strides_0 = const()[name = tensor<string, []>("input_475_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_475_dilations_0 = const()[name = tensor<string, []>("input_475_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_475_groups_0 = const()[name = tensor<string, []>("input_475_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 200]> input_475 = conv(bias = decoder_decode_2_conv1_bias, dilations = input_475_dilations_0, groups = input_475_groups_0, pad = input_475_pad_0, pad_type = input_475_pad_type_0, strides = input_475_strides_0, weight = weight_121, x = input_471)[name = tensor<string, []>("input_475")];
            tensor<fp32, [1, 2048]> h_89 = linear(bias = decoder_decode_2_norm2_fc_bias, weight = decoder_decode_2_norm2_fc_weight, x = input_417)[name = tensor<string, []>("linear_97")];
            tensor<int32, [3]> var_2833 = const()[name = tensor<string, []>("op_2833"), val = tensor<int32, [3]>([1, 2048, 1])];
            tensor<fp32, [1, 2048, 1]> h_91 = reshape(shape = var_2833, x = h_89)[name = tensor<string, []>("h_91")];
            tensor<int32, [2]> var_2835_split_sizes_0 = const()[name = tensor<string, []>("op_2835_split_sizes_0"), val = tensor<int32, [2]>([1024, 1024])];
            tensor<int32, []> var_2835_axis_0 = const()[name = tensor<string, []>("op_2835_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 1]> var_2835_0, tensor<fp32, [1, 1024, 1]> var_2835_1 = split(axis = var_2835_axis_0, split_sizes = var_2835_split_sizes_0, x = h_91)[name = tensor<string, []>("op_2835")];
            tensor<fp32, []> var_2837_promoted = const()[name = tensor<string, []>("op_2837_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 1024, 1]> var_2838 = add(x = var_2835_0, y = var_2837_promoted)[name = tensor<string, []>("op_2838")];
            tensor<fp32, [1, 1024, 200]> var_2841 = instance_norm(beta = decoder_encode_norm2_norm_bias, epsilon = var_2476, gamma = decoder_encode_norm2_norm_weight, x = input_475)[name = tensor<string, []>("op_2841")];
            tensor<fp32, [1, 1024, 200]> var_2842 = mul(x = var_2838, y = var_2841)[name = tensor<string, []>("op_2842")];
            tensor<fp32, [1, 1024, 200]> input_477 = add(x = var_2842, y = var_2835_1)[name = tensor<string, []>("input_477")];
            tensor<fp32, [1, 1024, 200]> input_479 = leaky_relu(alpha = var_2477, x = input_477)[name = tensor<string, []>("input_479")];
            tensor<fp32, [1024, 1024, 3]> weight_125 = const()[name = tensor<string, []>("weight_125"), val = tensor<fp32, [1024, 1024, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(226548480)))];
            tensor<string, []> out_19_pad_type_0 = const()[name = tensor<string, []>("out_19_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> out_19_pad_0 = const()[name = tensor<string, []>("out_19_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> out_19_strides_0 = const()[name = tensor<string, []>("out_19_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> out_19_dilations_0 = const()[name = tensor<string, []>("out_19_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> out_19_groups_0 = const()[name = tensor<string, []>("out_19_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 200]> out_19 = conv(bias = decoder_decode_2_conv2_bias, dilations = out_19_dilations_0, groups = out_19_groups_0, pad = out_19_pad_0, pad_type = out_19_pad_type_0, strides = out_19_strides_0, weight = weight_125, x = input_479)[name = tensor<string, []>("out_19")];
            tensor<fp32, [1024, 1090, 1]> weight_127 = const()[name = tensor<string, []>("weight_127"), val = tensor<fp32, [1024, 1090, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(239131456)))];
            tensor<string, []> var_2866_pad_type_0 = const()[name = tensor<string, []>("op_2866_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> var_2866_strides_0 = const()[name = tensor<string, []>("op_2866_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> var_2866_pad_0 = const()[name = tensor<string, []>("op_2866_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> var_2866_dilations_0 = const()[name = tensor<string, []>("op_2866_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> var_2866_groups_0 = const()[name = tensor<string, []>("op_2866_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 200]> var_2866 = conv(dilations = var_2866_dilations_0, groups = var_2866_groups_0, pad = var_2866_pad_0, pad_type = var_2866_pad_type_0, strides = var_2866_strides_0, weight = weight_127, x = input_467)[name = tensor<string, []>("op_2866")];
            tensor<fp32, [1, 1024, 200]> var_2867 = add(x = out_19, y = var_2866)[name = tensor<string, []>("op_2867")];
            tensor<fp32, []> var_2870 = const()[name = tensor<string, []>("op_2870"), val = tensor<fp32, []>(0x1.6a09e6p-1)];
            tensor<fp32, [1, 1024, 200]> x_245 = mul(x = var_2867, y = var_2870)[name = tensor<string, []>("x_245")];
            tensor<bool, []> input_483_interleave_0 = const()[name = tensor<string, []>("input_483_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1090, 200]> input_483 = concat(axis = var_2486, interleave = input_483_interleave_0, values = (x_245, asr_res_1, F0, N))[name = tensor<string, []>("input_483")];
            tensor<fp32, [1, 2180]> h_93 = linear(bias = decoder_decode_3_norm1_fc_bias, weight = decoder_decode_3_norm1_fc_weight, x = input_417)[name = tensor<string, []>("linear_98")];
            tensor<int32, [3]> var_2887 = const()[name = tensor<string, []>("op_2887"), val = tensor<int32, [3]>([1, 2180, 1])];
            tensor<fp32, [1, 2180, 1]> h_95 = reshape(shape = var_2887, x = h_93)[name = tensor<string, []>("h_95")];
            tensor<int32, [2]> var_2889_split_sizes_0 = const()[name = tensor<string, []>("op_2889_split_sizes_0"), val = tensor<int32, [2]>([1090, 1090])];
            tensor<int32, []> var_2889_axis_0 = const()[name = tensor<string, []>("op_2889_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1090, 1]> var_2889_0, tensor<fp32, [1, 1090, 1]> var_2889_1 = split(axis = var_2889_axis_0, split_sizes = var_2889_split_sizes_0, x = h_95)[name = tensor<string, []>("op_2889")];
            tensor<fp32, []> var_2891_promoted = const()[name = tensor<string, []>("op_2891_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 1090, 1]> var_2892 = add(x = var_2889_0, y = var_2891_promoted)[name = tensor<string, []>("op_2892")];
            tensor<fp32, [1, 1090, 200]> var_2895 = instance_norm(beta = decoder_decode_0_norm1_norm_bias, epsilon = var_2476, gamma = decoder_decode_0_norm1_norm_weight, x = input_483)[name = tensor<string, []>("op_2895")];
            tensor<fp32, [1, 1090, 200]> var_2896 = mul(x = var_2892, y = var_2895)[name = tensor<string, []>("op_2896")];
            tensor<fp32, [1, 1090, 200]> input_485 = add(x = var_2896, y = var_2889_1)[name = tensor<string, []>("input_485")];
            tensor<fp32, [1, 1090, 200]> input_487 = leaky_relu(alpha = var_2477, x = input_485)[name = tensor<string, []>("input_487")];
            tensor<fp32, [1090, 1, 3]> var_2904 = const()[name = tensor<string, []>("op_2904"), val = tensor<fp32, [1090, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(243596160)))];
            tensor<string, []> conv_transpose_2_pad_type_0 = const()[name = tensor<string, []>("conv_transpose_2_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> conv_transpose_2_pad_0 = const()[name = tensor<string, []>("conv_transpose_2_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_transpose_2_strides_0 = const()[name = tensor<string, []>("conv_transpose_2_strides_0"), val = tensor<int32, [1]>([2])];
            tensor<int32, []> conv_transpose_2_groups_0 = const()[name = tensor<string, []>("conv_transpose_2_groups_0"), val = tensor<int32, []>(1090)];
            tensor<int32, [1]> conv_transpose_2_dilations_0 = const()[name = tensor<string, []>("conv_transpose_2_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [3]> conv_transpose_2_has_output_shape_output_shape_0 = const()[name = tensor<string, []>("conv_transpose_2_has_output_shape_output_shape_0"), val = tensor<int32, [3]>([1, 1090, 401])];
            tensor<fp32, [1, 1090, 401]> conv_transpose_2_has_output_shape = conv_transpose(bias = decoder_decode_3_pool_bias, dilations = conv_transpose_2_dilations_0, groups = conv_transpose_2_groups_0, output_shape = conv_transpose_2_has_output_shape_output_shape_0, pad = conv_transpose_2_pad_0, pad_type = conv_transpose_2_pad_type_0, strides = conv_transpose_2_strides_0, weight = var_2904, x = input_487)[name = tensor<string, []>("conv_transpose_2_has_output_shape")];
            tensor<int32, [3]> input_489_begin_0 = const()[name = tensor<string, []>("input_489_begin_0"), val = tensor<int32, [3]>([0, 0, 1])];
            tensor<int32, [3]> input_489_end_0 = const()[name = tensor<string, []>("input_489_end_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<bool, [3]> input_489_begin_mask_0 = const()[name = tensor<string, []>("input_489_begin_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<bool, [3]> input_489_end_mask_0 = const()[name = tensor<string, []>("input_489_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1090, 400]> input_489 = slice_by_index(begin = input_489_begin_0, begin_mask = input_489_begin_mask_0, end = input_489_end_0, end_mask = input_489_end_mask_0, x = conv_transpose_2_has_output_shape)[name = tensor<string, []>("input_489")];
            tensor<fp32, [512, 1090, 3]> weight_131 = const()[name = tensor<string, []>("weight_131"), val = tensor<fp32, [512, 1090, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(243609344)))];
            tensor<string, []> input_493_pad_type_0 = const()[name = tensor<string, []>("input_493_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_493_pad_0 = const()[name = tensor<string, []>("input_493_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> input_493_strides_0 = const()[name = tensor<string, []>("input_493_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_493_dilations_0 = const()[name = tensor<string, []>("input_493_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_493_groups_0 = const()[name = tensor<string, []>("input_493_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 512, 400]> input_493 = conv(bias = decoder_decode_3_conv1_bias, dilations = input_493_dilations_0, groups = input_493_groups_0, pad = input_493_pad_0, pad_type = input_493_pad_type_0, strides = input_493_strides_0, weight = weight_131, x = input_489)[name = tensor<string, []>("input_493")];
            tensor<fp32, [1, 1024]> h_97 = linear(bias = decoder_decode_3_norm2_fc_bias, weight = decoder_decode_3_norm2_fc_weight, x = input_417)[name = tensor<string, []>("linear_99")];
            tensor<int32, [3]> var_2929 = const()[name = tensor<string, []>("op_2929"), val = tensor<int32, [3]>([1, 1024, 1])];
            tensor<fp32, [1, 1024, 1]> h_99 = reshape(shape = var_2929, x = h_97)[name = tensor<string, []>("h_99")];
            tensor<int32, [2]> var_2931_split_sizes_0 = const()[name = tensor<string, []>("op_2931_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
            tensor<int32, []> var_2931_axis_0 = const()[name = tensor<string, []>("op_2931_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 512, 1]> var_2931_0, tensor<fp32, [1, 512, 1]> var_2931_1 = split(axis = var_2931_axis_0, split_sizes = var_2931_split_sizes_0, x = h_99)[name = tensor<string, []>("op_2931")];
            tensor<fp32, []> var_2933_promoted = const()[name = tensor<string, []>("op_2933_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 512, 1]> var_2934 = add(x = var_2931_0, y = var_2933_promoted)[name = tensor<string, []>("op_2934")];
            tensor<fp32, [1, 512, 400]> var_2937 = instance_norm(beta = F0_blocks_0_norm1_norm_bias, epsilon = var_2476, gamma = F0_blocks_0_norm1_norm_weight, x = input_493)[name = tensor<string, []>("op_2937")];
            tensor<fp32, [1, 512, 400]> var_2938 = mul(x = var_2934, y = var_2937)[name = tensor<string, []>("op_2938")];
            tensor<fp32, [1, 512, 400]> input_495 = add(x = var_2938, y = var_2931_1)[name = tensor<string, []>("input_495")];
            tensor<fp32, [1, 512, 400]> input_497 = leaky_relu(alpha = var_2477, x = input_495)[name = tensor<string, []>("input_497")];
            tensor<fp32, [512, 512, 3]> weight_135 = const()[name = tensor<string, []>("weight_135"), val = tensor<fp32, [512, 512, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(250306368)))];
            tensor<string, []> out_pad_type_0 = const()[name = tensor<string, []>("out_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> out_pad_0 = const()[name = tensor<string, []>("out_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> out_strides_0 = const()[name = tensor<string, []>("out_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> out_dilations_0 = const()[name = tensor<string, []>("out_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> out_groups_0 = const()[name = tensor<string, []>("out_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 512, 400]> out = conv(bias = decoder_decode_3_conv2_bias, dilations = out_dilations_0, groups = out_groups_0, pad = out_pad_0, pad_type = out_pad_type_0, strides = out_strides_0, weight = weight_135, x = input_497)[name = tensor<string, []>("out")];
            tensor<int32, [1]> expand_dims_3_axes_0 = const()[name = tensor<string, []>("expand_dims_3_axes_0"), val = tensor<int32, [1]>([3])];
            tensor<fp32, [1, 1090, 200, 1]> expand_dims_3 = expand_dims(axes = expand_dims_3_axes_0, x = input_483)[name = tensor<string, []>("expand_dims_3")];
            tensor<int32, []> upsample_nearest_neighbor_2_scale_factor_height_0 = const()[name = tensor<string, []>("upsample_nearest_neighbor_2_scale_factor_height_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> upsample_nearest_neighbor_2_scale_factor_width_0 = const()[name = tensor<string, []>("upsample_nearest_neighbor_2_scale_factor_width_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1090, 400, 1]> upsample_nearest_neighbor_2 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_2_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_2_scale_factor_width_0, x = expand_dims_3)[name = tensor<string, []>("upsample_nearest_neighbor_2")];
            tensor<int32, [1]> input_501_axes_0 = const()[name = tensor<string, []>("input_501_axes_0"), val = tensor<int32, [1]>([3])];
            tensor<fp32, [1, 1090, 400]> input_501 = squeeze(axes = input_501_axes_0, x = upsample_nearest_neighbor_2)[name = tensor<string, []>("input_501")];
            tensor<fp32, [512, 1090, 1]> weight_137 = const()[name = tensor<string, []>("weight_137"), val = tensor<fp32, [512, 1090, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(253452160)))];
            tensor<string, []> var_2964_pad_type_0 = const()[name = tensor<string, []>("op_2964_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> var_2964_strides_0 = const()[name = tensor<string, []>("op_2964_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> var_2964_pad_0 = const()[name = tensor<string, []>("op_2964_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> var_2964_dilations_0 = const()[name = tensor<string, []>("op_2964_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> var_2964_groups_0 = const()[name = tensor<string, []>("op_2964_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 512, 400]> var_2964 = conv(dilations = var_2964_dilations_0, groups = var_2964_groups_0, pad = var_2964_pad_0, pad_type = var_2964_pad_type_0, strides = var_2964_strides_0, weight = weight_137, x = input_501)[name = tensor<string, []>("op_2964")];
            tensor<fp32, [1, 512, 400]> var_2965 = add(x = out, y = var_2964)[name = tensor<string, []>("op_2965")];
            tensor<fp32, []> var_2968 = const()[name = tensor<string, []>("op_2968"), val = tensor<fp32, []>(0x1.6a09e6p-1)];
            tensor<fp32, [1, 512, 400]> input_511 = mul(x = var_2965, y = var_2968)[name = tensor<string, []>("input_511")];
            tensor<int32, [1]> expand_dims_4_axes_0 = const()[name = tensor<string, []>("expand_dims_4_axes_0"), val = tensor<int32, [1]>([3])];
            tensor<fp32, [1, 1, 400, 1]> expand_dims_4 = expand_dims(axes = expand_dims_4_axes_0, x = input_413)[name = tensor<string, []>("expand_dims_4")];
            tensor<int32, []> upsample_nearest_neighbor_3_scale_factor_height_0 = const()[name = tensor<string, []>("upsample_nearest_neighbor_3_scale_factor_height_0"), val = tensor<int32, []>(300)];
            tensor<int32, []> upsample_nearest_neighbor_3_scale_factor_width_0 = const()[name = tensor<string, []>("upsample_nearest_neighbor_3_scale_factor_width_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1, 120000, 1]> upsample_nearest_neighbor_3 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_3_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_3_scale_factor_width_0, x = expand_dims_4)[name = tensor<string, []>("upsample_nearest_neighbor_3")];
            tensor<int32, [1]> var_3007_axes_0 = const()[name = tensor<string, []>("op_3007_axes_0"), val = tensor<int32, [1]>([3])];
            tensor<fp32, [1, 1, 120000]> var_3007 = squeeze(axes = var_3007_axes_0, x = upsample_nearest_neighbor_3)[name = tensor<string, []>("op_3007")];
            tensor<int32, [3]> f0_perm_0 = const()[name = tensor<string, []>("f0_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [1, 1, 9]> harm_range_promoted = const()[name = tensor<string, []>("harm_range_promoted"), val = tensor<fp32, [1, 1, 9]>([[[0x1p+0, 0x1p+1, 0x1.8p+1, 0x1p+2, 0x1.4p+2, 0x1.8p+2, 0x1.cp+2, 0x1p+3, 0x1.2p+3]]])];
            tensor<fp32, [1, 120000, 1]> f0 = transpose(perm = f0_perm_0, x = var_3007)[name = tensor<string, []>("transpose_96")];
            tensor<fp32, [1, 120000, 9]> f0_values = mul(x = f0, y = harm_range_promoted)[name = tensor<string, []>("f0_values")];
            tensor<fp32, []> _inversed_rad_values_y_0 = const()[name = tensor<string, []>("_inversed_rad_values_y_0"), val = tensor<fp32, []>(0x1.5d867cp-15)];
            tensor<fp32, [1, 120000, 9]> _inversed_rad_values = mul(x = f0_values, y = _inversed_rad_values_y_0)[name = tensor<string, []>("_inversed_rad_values")];
            tensor<int32, [3]> var_3020_begin_0 = const()[name = tensor<string, []>("op_3020_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> var_3020_end_0 = const()[name = tensor<string, []>("op_3020_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3020_end_mask_0 = const()[name = tensor<string, []>("op_3020_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3020 = slice_by_index(begin = var_3020_begin_0, end = var_3020_end_0, end_mask = var_3020_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3020")];
            tensor<bool, []> var_3022_exclusive_0 = const()[name = tensor<string, []>("op_3022_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3022_reverse_0 = const()[name = tensor<string, []>("op_3022_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3022 = cumsum(axis = var_2486, exclusive = var_3022_exclusive_0, reverse = var_3022_reverse_0, x = var_3020)[name = tensor<string, []>("op_3022")];
            tensor<int32, [3]> var_3025_begin_0 = const()[name = tensor<string, []>("op_3025_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3025_end_0 = const()[name = tensor<string, []>("op_3025_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3025_end_mask_0 = const()[name = tensor<string, []>("op_3025_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3025 = slice_by_index(begin = var_3025_begin_0, end = var_3025_end_0, end_mask = var_3025_end_mask_0, x = var_3022)[name = tensor<string, []>("op_3025")];
            tensor<int32, [3]> var_3028_begin_0 = const()[name = tensor<string, []>("op_3028_begin_0"), val = tensor<int32, [3]>([0, 1000, 0])];
            tensor<int32, [3]> var_3028_end_0 = const()[name = tensor<string, []>("op_3028_end_0"), val = tensor<int32, [3]>([1, 2000, 9])];
            tensor<bool, [3]> var_3028_end_mask_0 = const()[name = tensor<string, []>("op_3028_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3028 = slice_by_index(begin = var_3028_begin_0, end = var_3028_end_0, end_mask = var_3028_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3028")];
            tensor<bool, []> var_3030_exclusive_0 = const()[name = tensor<string, []>("op_3030_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3030_reverse_0 = const()[name = tensor<string, []>("op_3030_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3030 = cumsum(axis = var_2486, exclusive = var_3030_exclusive_0, reverse = var_3030_reverse_0, x = var_3028)[name = tensor<string, []>("op_3030")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_3 = add(x = var_3030, y = var_3025)[name = tensor<string, []>("seg_cumsum_3")];
            tensor<int32, [3]> var_3033_begin_0 = const()[name = tensor<string, []>("op_3033_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3033_end_0 = const()[name = tensor<string, []>("op_3033_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3033_end_mask_0 = const()[name = tensor<string, []>("op_3033_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3033 = slice_by_index(begin = var_3033_begin_0, end = var_3033_end_0, end_mask = var_3033_end_mask_0, x = seg_cumsum_3)[name = tensor<string, []>("op_3033")];
            tensor<int32, [3]> var_3036_begin_0 = const()[name = tensor<string, []>("op_3036_begin_0"), val = tensor<int32, [3]>([0, 2000, 0])];
            tensor<int32, [3]> var_3036_end_0 = const()[name = tensor<string, []>("op_3036_end_0"), val = tensor<int32, [3]>([1, 3000, 9])];
            tensor<bool, [3]> var_3036_end_mask_0 = const()[name = tensor<string, []>("op_3036_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3036 = slice_by_index(begin = var_3036_begin_0, end = var_3036_end_0, end_mask = var_3036_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3036")];
            tensor<bool, []> var_3038_exclusive_0 = const()[name = tensor<string, []>("op_3038_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3038_reverse_0 = const()[name = tensor<string, []>("op_3038_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3038 = cumsum(axis = var_2486, exclusive = var_3038_exclusive_0, reverse = var_3038_reverse_0, x = var_3036)[name = tensor<string, []>("op_3038")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_5 = add(x = var_3038, y = var_3033)[name = tensor<string, []>("seg_cumsum_5")];
            tensor<int32, [3]> var_3041_begin_0 = const()[name = tensor<string, []>("op_3041_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3041_end_0 = const()[name = tensor<string, []>("op_3041_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3041_end_mask_0 = const()[name = tensor<string, []>("op_3041_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3041 = slice_by_index(begin = var_3041_begin_0, end = var_3041_end_0, end_mask = var_3041_end_mask_0, x = seg_cumsum_5)[name = tensor<string, []>("op_3041")];
            tensor<int32, [3]> var_3044_begin_0 = const()[name = tensor<string, []>("op_3044_begin_0"), val = tensor<int32, [3]>([0, 3000, 0])];
            tensor<int32, [3]> var_3044_end_0 = const()[name = tensor<string, []>("op_3044_end_0"), val = tensor<int32, [3]>([1, 4000, 9])];
            tensor<bool, [3]> var_3044_end_mask_0 = const()[name = tensor<string, []>("op_3044_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3044 = slice_by_index(begin = var_3044_begin_0, end = var_3044_end_0, end_mask = var_3044_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3044")];
            tensor<bool, []> var_3046_exclusive_0 = const()[name = tensor<string, []>("op_3046_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3046_reverse_0 = const()[name = tensor<string, []>("op_3046_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3046 = cumsum(axis = var_2486, exclusive = var_3046_exclusive_0, reverse = var_3046_reverse_0, x = var_3044)[name = tensor<string, []>("op_3046")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_7 = add(x = var_3046, y = var_3041)[name = tensor<string, []>("seg_cumsum_7")];
            tensor<int32, [3]> var_3049_begin_0 = const()[name = tensor<string, []>("op_3049_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3049_end_0 = const()[name = tensor<string, []>("op_3049_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3049_end_mask_0 = const()[name = tensor<string, []>("op_3049_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3049 = slice_by_index(begin = var_3049_begin_0, end = var_3049_end_0, end_mask = var_3049_end_mask_0, x = seg_cumsum_7)[name = tensor<string, []>("op_3049")];
            tensor<int32, [3]> var_3052_begin_0 = const()[name = tensor<string, []>("op_3052_begin_0"), val = tensor<int32, [3]>([0, 4000, 0])];
            tensor<int32, [3]> var_3052_end_0 = const()[name = tensor<string, []>("op_3052_end_0"), val = tensor<int32, [3]>([1, 5000, 9])];
            tensor<bool, [3]> var_3052_end_mask_0 = const()[name = tensor<string, []>("op_3052_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3052 = slice_by_index(begin = var_3052_begin_0, end = var_3052_end_0, end_mask = var_3052_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3052")];
            tensor<bool, []> var_3054_exclusive_0 = const()[name = tensor<string, []>("op_3054_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3054_reverse_0 = const()[name = tensor<string, []>("op_3054_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3054 = cumsum(axis = var_2486, exclusive = var_3054_exclusive_0, reverse = var_3054_reverse_0, x = var_3052)[name = tensor<string, []>("op_3054")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_9 = add(x = var_3054, y = var_3049)[name = tensor<string, []>("seg_cumsum_9")];
            tensor<int32, [3]> var_3057_begin_0 = const()[name = tensor<string, []>("op_3057_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3057_end_0 = const()[name = tensor<string, []>("op_3057_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3057_end_mask_0 = const()[name = tensor<string, []>("op_3057_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3057 = slice_by_index(begin = var_3057_begin_0, end = var_3057_end_0, end_mask = var_3057_end_mask_0, x = seg_cumsum_9)[name = tensor<string, []>("op_3057")];
            tensor<int32, [3]> var_3060_begin_0 = const()[name = tensor<string, []>("op_3060_begin_0"), val = tensor<int32, [3]>([0, 5000, 0])];
            tensor<int32, [3]> var_3060_end_0 = const()[name = tensor<string, []>("op_3060_end_0"), val = tensor<int32, [3]>([1, 6000, 9])];
            tensor<bool, [3]> var_3060_end_mask_0 = const()[name = tensor<string, []>("op_3060_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3060 = slice_by_index(begin = var_3060_begin_0, end = var_3060_end_0, end_mask = var_3060_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3060")];
            tensor<bool, []> var_3062_exclusive_0 = const()[name = tensor<string, []>("op_3062_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3062_reverse_0 = const()[name = tensor<string, []>("op_3062_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3062 = cumsum(axis = var_2486, exclusive = var_3062_exclusive_0, reverse = var_3062_reverse_0, x = var_3060)[name = tensor<string, []>("op_3062")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_11 = add(x = var_3062, y = var_3057)[name = tensor<string, []>("seg_cumsum_11")];
            tensor<int32, [3]> var_3065_begin_0 = const()[name = tensor<string, []>("op_3065_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3065_end_0 = const()[name = tensor<string, []>("op_3065_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3065_end_mask_0 = const()[name = tensor<string, []>("op_3065_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3065 = slice_by_index(begin = var_3065_begin_0, end = var_3065_end_0, end_mask = var_3065_end_mask_0, x = seg_cumsum_11)[name = tensor<string, []>("op_3065")];
            tensor<int32, [3]> var_3068_begin_0 = const()[name = tensor<string, []>("op_3068_begin_0"), val = tensor<int32, [3]>([0, 6000, 0])];
            tensor<int32, [3]> var_3068_end_0 = const()[name = tensor<string, []>("op_3068_end_0"), val = tensor<int32, [3]>([1, 7000, 9])];
            tensor<bool, [3]> var_3068_end_mask_0 = const()[name = tensor<string, []>("op_3068_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3068 = slice_by_index(begin = var_3068_begin_0, end = var_3068_end_0, end_mask = var_3068_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3068")];
            tensor<bool, []> var_3070_exclusive_0 = const()[name = tensor<string, []>("op_3070_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3070_reverse_0 = const()[name = tensor<string, []>("op_3070_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3070 = cumsum(axis = var_2486, exclusive = var_3070_exclusive_0, reverse = var_3070_reverse_0, x = var_3068)[name = tensor<string, []>("op_3070")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_13 = add(x = var_3070, y = var_3065)[name = tensor<string, []>("seg_cumsum_13")];
            tensor<int32, [3]> var_3073_begin_0 = const()[name = tensor<string, []>("op_3073_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3073_end_0 = const()[name = tensor<string, []>("op_3073_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3073_end_mask_0 = const()[name = tensor<string, []>("op_3073_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3073 = slice_by_index(begin = var_3073_begin_0, end = var_3073_end_0, end_mask = var_3073_end_mask_0, x = seg_cumsum_13)[name = tensor<string, []>("op_3073")];
            tensor<int32, [3]> var_3076_begin_0 = const()[name = tensor<string, []>("op_3076_begin_0"), val = tensor<int32, [3]>([0, 7000, 0])];
            tensor<int32, [3]> var_3076_end_0 = const()[name = tensor<string, []>("op_3076_end_0"), val = tensor<int32, [3]>([1, 8000, 9])];
            tensor<bool, [3]> var_3076_end_mask_0 = const()[name = tensor<string, []>("op_3076_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3076 = slice_by_index(begin = var_3076_begin_0, end = var_3076_end_0, end_mask = var_3076_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3076")];
            tensor<bool, []> var_3078_exclusive_0 = const()[name = tensor<string, []>("op_3078_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3078_reverse_0 = const()[name = tensor<string, []>("op_3078_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3078 = cumsum(axis = var_2486, exclusive = var_3078_exclusive_0, reverse = var_3078_reverse_0, x = var_3076)[name = tensor<string, []>("op_3078")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_15 = add(x = var_3078, y = var_3073)[name = tensor<string, []>("seg_cumsum_15")];
            tensor<int32, [3]> var_3081_begin_0 = const()[name = tensor<string, []>("op_3081_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3081_end_0 = const()[name = tensor<string, []>("op_3081_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3081_end_mask_0 = const()[name = tensor<string, []>("op_3081_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3081 = slice_by_index(begin = var_3081_begin_0, end = var_3081_end_0, end_mask = var_3081_end_mask_0, x = seg_cumsum_15)[name = tensor<string, []>("op_3081")];
            tensor<int32, [3]> var_3084_begin_0 = const()[name = tensor<string, []>("op_3084_begin_0"), val = tensor<int32, [3]>([0, 8000, 0])];
            tensor<int32, [3]> var_3084_end_0 = const()[name = tensor<string, []>("op_3084_end_0"), val = tensor<int32, [3]>([1, 9000, 9])];
            tensor<bool, [3]> var_3084_end_mask_0 = const()[name = tensor<string, []>("op_3084_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3084 = slice_by_index(begin = var_3084_begin_0, end = var_3084_end_0, end_mask = var_3084_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3084")];
            tensor<bool, []> var_3086_exclusive_0 = const()[name = tensor<string, []>("op_3086_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3086_reverse_0 = const()[name = tensor<string, []>("op_3086_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3086 = cumsum(axis = var_2486, exclusive = var_3086_exclusive_0, reverse = var_3086_reverse_0, x = var_3084)[name = tensor<string, []>("op_3086")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_17 = add(x = var_3086, y = var_3081)[name = tensor<string, []>("seg_cumsum_17")];
            tensor<int32, [3]> var_3089_begin_0 = const()[name = tensor<string, []>("op_3089_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3089_end_0 = const()[name = tensor<string, []>("op_3089_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3089_end_mask_0 = const()[name = tensor<string, []>("op_3089_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3089 = slice_by_index(begin = var_3089_begin_0, end = var_3089_end_0, end_mask = var_3089_end_mask_0, x = seg_cumsum_17)[name = tensor<string, []>("op_3089")];
            tensor<int32, [3]> var_3092_begin_0 = const()[name = tensor<string, []>("op_3092_begin_0"), val = tensor<int32, [3]>([0, 9000, 0])];
            tensor<int32, [3]> var_3092_end_0 = const()[name = tensor<string, []>("op_3092_end_0"), val = tensor<int32, [3]>([1, 10000, 9])];
            tensor<bool, [3]> var_3092_end_mask_0 = const()[name = tensor<string, []>("op_3092_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3092 = slice_by_index(begin = var_3092_begin_0, end = var_3092_end_0, end_mask = var_3092_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3092")];
            tensor<bool, []> var_3094_exclusive_0 = const()[name = tensor<string, []>("op_3094_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3094_reverse_0 = const()[name = tensor<string, []>("op_3094_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3094 = cumsum(axis = var_2486, exclusive = var_3094_exclusive_0, reverse = var_3094_reverse_0, x = var_3092)[name = tensor<string, []>("op_3094")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_19 = add(x = var_3094, y = var_3089)[name = tensor<string, []>("seg_cumsum_19")];
            tensor<int32, [3]> var_3097_begin_0 = const()[name = tensor<string, []>("op_3097_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3097_end_0 = const()[name = tensor<string, []>("op_3097_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3097_end_mask_0 = const()[name = tensor<string, []>("op_3097_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3097 = slice_by_index(begin = var_3097_begin_0, end = var_3097_end_0, end_mask = var_3097_end_mask_0, x = seg_cumsum_19)[name = tensor<string, []>("op_3097")];
            tensor<int32, [3]> var_3100_begin_0 = const()[name = tensor<string, []>("op_3100_begin_0"), val = tensor<int32, [3]>([0, 10000, 0])];
            tensor<int32, [3]> var_3100_end_0 = const()[name = tensor<string, []>("op_3100_end_0"), val = tensor<int32, [3]>([1, 11000, 9])];
            tensor<bool, [3]> var_3100_end_mask_0 = const()[name = tensor<string, []>("op_3100_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3100 = slice_by_index(begin = var_3100_begin_0, end = var_3100_end_0, end_mask = var_3100_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3100")];
            tensor<bool, []> var_3102_exclusive_0 = const()[name = tensor<string, []>("op_3102_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3102_reverse_0 = const()[name = tensor<string, []>("op_3102_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3102 = cumsum(axis = var_2486, exclusive = var_3102_exclusive_0, reverse = var_3102_reverse_0, x = var_3100)[name = tensor<string, []>("op_3102")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_21 = add(x = var_3102, y = var_3097)[name = tensor<string, []>("seg_cumsum_21")];
            tensor<int32, [3]> var_3105_begin_0 = const()[name = tensor<string, []>("op_3105_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3105_end_0 = const()[name = tensor<string, []>("op_3105_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3105_end_mask_0 = const()[name = tensor<string, []>("op_3105_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3105 = slice_by_index(begin = var_3105_begin_0, end = var_3105_end_0, end_mask = var_3105_end_mask_0, x = seg_cumsum_21)[name = tensor<string, []>("op_3105")];
            tensor<int32, [3]> var_3108_begin_0 = const()[name = tensor<string, []>("op_3108_begin_0"), val = tensor<int32, [3]>([0, 11000, 0])];
            tensor<int32, [3]> var_3108_end_0 = const()[name = tensor<string, []>("op_3108_end_0"), val = tensor<int32, [3]>([1, 12000, 9])];
            tensor<bool, [3]> var_3108_end_mask_0 = const()[name = tensor<string, []>("op_3108_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3108 = slice_by_index(begin = var_3108_begin_0, end = var_3108_end_0, end_mask = var_3108_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3108")];
            tensor<bool, []> var_3110_exclusive_0 = const()[name = tensor<string, []>("op_3110_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3110_reverse_0 = const()[name = tensor<string, []>("op_3110_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3110 = cumsum(axis = var_2486, exclusive = var_3110_exclusive_0, reverse = var_3110_reverse_0, x = var_3108)[name = tensor<string, []>("op_3110")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_23 = add(x = var_3110, y = var_3105)[name = tensor<string, []>("seg_cumsum_23")];
            tensor<int32, [3]> var_3113_begin_0 = const()[name = tensor<string, []>("op_3113_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3113_end_0 = const()[name = tensor<string, []>("op_3113_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3113_end_mask_0 = const()[name = tensor<string, []>("op_3113_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3113 = slice_by_index(begin = var_3113_begin_0, end = var_3113_end_0, end_mask = var_3113_end_mask_0, x = seg_cumsum_23)[name = tensor<string, []>("op_3113")];
            tensor<int32, [3]> var_3116_begin_0 = const()[name = tensor<string, []>("op_3116_begin_0"), val = tensor<int32, [3]>([0, 12000, 0])];
            tensor<int32, [3]> var_3116_end_0 = const()[name = tensor<string, []>("op_3116_end_0"), val = tensor<int32, [3]>([1, 13000, 9])];
            tensor<bool, [3]> var_3116_end_mask_0 = const()[name = tensor<string, []>("op_3116_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3116 = slice_by_index(begin = var_3116_begin_0, end = var_3116_end_0, end_mask = var_3116_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3116")];
            tensor<bool, []> var_3118_exclusive_0 = const()[name = tensor<string, []>("op_3118_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3118_reverse_0 = const()[name = tensor<string, []>("op_3118_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3118 = cumsum(axis = var_2486, exclusive = var_3118_exclusive_0, reverse = var_3118_reverse_0, x = var_3116)[name = tensor<string, []>("op_3118")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_25 = add(x = var_3118, y = var_3113)[name = tensor<string, []>("seg_cumsum_25")];
            tensor<int32, [3]> var_3121_begin_0 = const()[name = tensor<string, []>("op_3121_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3121_end_0 = const()[name = tensor<string, []>("op_3121_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3121_end_mask_0 = const()[name = tensor<string, []>("op_3121_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3121 = slice_by_index(begin = var_3121_begin_0, end = var_3121_end_0, end_mask = var_3121_end_mask_0, x = seg_cumsum_25)[name = tensor<string, []>("op_3121")];
            tensor<int32, [3]> var_3124_begin_0 = const()[name = tensor<string, []>("op_3124_begin_0"), val = tensor<int32, [3]>([0, 13000, 0])];
            tensor<int32, [3]> var_3124_end_0 = const()[name = tensor<string, []>("op_3124_end_0"), val = tensor<int32, [3]>([1, 14000, 9])];
            tensor<bool, [3]> var_3124_end_mask_0 = const()[name = tensor<string, []>("op_3124_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3124 = slice_by_index(begin = var_3124_begin_0, end = var_3124_end_0, end_mask = var_3124_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3124")];
            tensor<bool, []> var_3126_exclusive_0 = const()[name = tensor<string, []>("op_3126_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3126_reverse_0 = const()[name = tensor<string, []>("op_3126_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3126 = cumsum(axis = var_2486, exclusive = var_3126_exclusive_0, reverse = var_3126_reverse_0, x = var_3124)[name = tensor<string, []>("op_3126")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_27 = add(x = var_3126, y = var_3121)[name = tensor<string, []>("seg_cumsum_27")];
            tensor<int32, [3]> var_3129_begin_0 = const()[name = tensor<string, []>("op_3129_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3129_end_0 = const()[name = tensor<string, []>("op_3129_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3129_end_mask_0 = const()[name = tensor<string, []>("op_3129_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3129 = slice_by_index(begin = var_3129_begin_0, end = var_3129_end_0, end_mask = var_3129_end_mask_0, x = seg_cumsum_27)[name = tensor<string, []>("op_3129")];
            tensor<int32, [3]> var_3132_begin_0 = const()[name = tensor<string, []>("op_3132_begin_0"), val = tensor<int32, [3]>([0, 14000, 0])];
            tensor<int32, [3]> var_3132_end_0 = const()[name = tensor<string, []>("op_3132_end_0"), val = tensor<int32, [3]>([1, 15000, 9])];
            tensor<bool, [3]> var_3132_end_mask_0 = const()[name = tensor<string, []>("op_3132_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3132 = slice_by_index(begin = var_3132_begin_0, end = var_3132_end_0, end_mask = var_3132_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3132")];
            tensor<bool, []> var_3134_exclusive_0 = const()[name = tensor<string, []>("op_3134_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3134_reverse_0 = const()[name = tensor<string, []>("op_3134_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3134 = cumsum(axis = var_2486, exclusive = var_3134_exclusive_0, reverse = var_3134_reverse_0, x = var_3132)[name = tensor<string, []>("op_3134")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_29 = add(x = var_3134, y = var_3129)[name = tensor<string, []>("seg_cumsum_29")];
            tensor<int32, [3]> var_3137_begin_0 = const()[name = tensor<string, []>("op_3137_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3137_end_0 = const()[name = tensor<string, []>("op_3137_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3137_end_mask_0 = const()[name = tensor<string, []>("op_3137_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3137 = slice_by_index(begin = var_3137_begin_0, end = var_3137_end_0, end_mask = var_3137_end_mask_0, x = seg_cumsum_29)[name = tensor<string, []>("op_3137")];
            tensor<int32, [3]> var_3140_begin_0 = const()[name = tensor<string, []>("op_3140_begin_0"), val = tensor<int32, [3]>([0, 15000, 0])];
            tensor<int32, [3]> var_3140_end_0 = const()[name = tensor<string, []>("op_3140_end_0"), val = tensor<int32, [3]>([1, 16000, 9])];
            tensor<bool, [3]> var_3140_end_mask_0 = const()[name = tensor<string, []>("op_3140_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3140 = slice_by_index(begin = var_3140_begin_0, end = var_3140_end_0, end_mask = var_3140_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3140")];
            tensor<bool, []> var_3142_exclusive_0 = const()[name = tensor<string, []>("op_3142_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3142_reverse_0 = const()[name = tensor<string, []>("op_3142_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3142 = cumsum(axis = var_2486, exclusive = var_3142_exclusive_0, reverse = var_3142_reverse_0, x = var_3140)[name = tensor<string, []>("op_3142")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_31 = add(x = var_3142, y = var_3137)[name = tensor<string, []>("seg_cumsum_31")];
            tensor<int32, [3]> var_3145_begin_0 = const()[name = tensor<string, []>("op_3145_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3145_end_0 = const()[name = tensor<string, []>("op_3145_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3145_end_mask_0 = const()[name = tensor<string, []>("op_3145_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3145 = slice_by_index(begin = var_3145_begin_0, end = var_3145_end_0, end_mask = var_3145_end_mask_0, x = seg_cumsum_31)[name = tensor<string, []>("op_3145")];
            tensor<int32, [3]> var_3148_begin_0 = const()[name = tensor<string, []>("op_3148_begin_0"), val = tensor<int32, [3]>([0, 16000, 0])];
            tensor<int32, [3]> var_3148_end_0 = const()[name = tensor<string, []>("op_3148_end_0"), val = tensor<int32, [3]>([1, 17000, 9])];
            tensor<bool, [3]> var_3148_end_mask_0 = const()[name = tensor<string, []>("op_3148_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3148 = slice_by_index(begin = var_3148_begin_0, end = var_3148_end_0, end_mask = var_3148_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3148")];
            tensor<bool, []> var_3150_exclusive_0 = const()[name = tensor<string, []>("op_3150_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3150_reverse_0 = const()[name = tensor<string, []>("op_3150_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3150 = cumsum(axis = var_2486, exclusive = var_3150_exclusive_0, reverse = var_3150_reverse_0, x = var_3148)[name = tensor<string, []>("op_3150")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_33 = add(x = var_3150, y = var_3145)[name = tensor<string, []>("seg_cumsum_33")];
            tensor<int32, [3]> var_3153_begin_0 = const()[name = tensor<string, []>("op_3153_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3153_end_0 = const()[name = tensor<string, []>("op_3153_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3153_end_mask_0 = const()[name = tensor<string, []>("op_3153_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3153 = slice_by_index(begin = var_3153_begin_0, end = var_3153_end_0, end_mask = var_3153_end_mask_0, x = seg_cumsum_33)[name = tensor<string, []>("op_3153")];
            tensor<int32, [3]> var_3156_begin_0 = const()[name = tensor<string, []>("op_3156_begin_0"), val = tensor<int32, [3]>([0, 17000, 0])];
            tensor<int32, [3]> var_3156_end_0 = const()[name = tensor<string, []>("op_3156_end_0"), val = tensor<int32, [3]>([1, 18000, 9])];
            tensor<bool, [3]> var_3156_end_mask_0 = const()[name = tensor<string, []>("op_3156_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3156 = slice_by_index(begin = var_3156_begin_0, end = var_3156_end_0, end_mask = var_3156_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3156")];
            tensor<bool, []> var_3158_exclusive_0 = const()[name = tensor<string, []>("op_3158_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3158_reverse_0 = const()[name = tensor<string, []>("op_3158_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3158 = cumsum(axis = var_2486, exclusive = var_3158_exclusive_0, reverse = var_3158_reverse_0, x = var_3156)[name = tensor<string, []>("op_3158")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_35 = add(x = var_3158, y = var_3153)[name = tensor<string, []>("seg_cumsum_35")];
            tensor<int32, [3]> var_3161_begin_0 = const()[name = tensor<string, []>("op_3161_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3161_end_0 = const()[name = tensor<string, []>("op_3161_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3161_end_mask_0 = const()[name = tensor<string, []>("op_3161_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3161 = slice_by_index(begin = var_3161_begin_0, end = var_3161_end_0, end_mask = var_3161_end_mask_0, x = seg_cumsum_35)[name = tensor<string, []>("op_3161")];
            tensor<int32, [3]> var_3164_begin_0 = const()[name = tensor<string, []>("op_3164_begin_0"), val = tensor<int32, [3]>([0, 18000, 0])];
            tensor<int32, [3]> var_3164_end_0 = const()[name = tensor<string, []>("op_3164_end_0"), val = tensor<int32, [3]>([1, 19000, 9])];
            tensor<bool, [3]> var_3164_end_mask_0 = const()[name = tensor<string, []>("op_3164_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3164 = slice_by_index(begin = var_3164_begin_0, end = var_3164_end_0, end_mask = var_3164_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3164")];
            tensor<bool, []> var_3166_exclusive_0 = const()[name = tensor<string, []>("op_3166_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3166_reverse_0 = const()[name = tensor<string, []>("op_3166_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3166 = cumsum(axis = var_2486, exclusive = var_3166_exclusive_0, reverse = var_3166_reverse_0, x = var_3164)[name = tensor<string, []>("op_3166")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_37 = add(x = var_3166, y = var_3161)[name = tensor<string, []>("seg_cumsum_37")];
            tensor<int32, [3]> var_3169_begin_0 = const()[name = tensor<string, []>("op_3169_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3169_end_0 = const()[name = tensor<string, []>("op_3169_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3169_end_mask_0 = const()[name = tensor<string, []>("op_3169_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3169 = slice_by_index(begin = var_3169_begin_0, end = var_3169_end_0, end_mask = var_3169_end_mask_0, x = seg_cumsum_37)[name = tensor<string, []>("op_3169")];
            tensor<int32, [3]> var_3172_begin_0 = const()[name = tensor<string, []>("op_3172_begin_0"), val = tensor<int32, [3]>([0, 19000, 0])];
            tensor<int32, [3]> var_3172_end_0 = const()[name = tensor<string, []>("op_3172_end_0"), val = tensor<int32, [3]>([1, 20000, 9])];
            tensor<bool, [3]> var_3172_end_mask_0 = const()[name = tensor<string, []>("op_3172_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3172 = slice_by_index(begin = var_3172_begin_0, end = var_3172_end_0, end_mask = var_3172_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3172")];
            tensor<bool, []> var_3174_exclusive_0 = const()[name = tensor<string, []>("op_3174_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3174_reverse_0 = const()[name = tensor<string, []>("op_3174_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3174 = cumsum(axis = var_2486, exclusive = var_3174_exclusive_0, reverse = var_3174_reverse_0, x = var_3172)[name = tensor<string, []>("op_3174")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_39 = add(x = var_3174, y = var_3169)[name = tensor<string, []>("seg_cumsum_39")];
            tensor<int32, [3]> var_3177_begin_0 = const()[name = tensor<string, []>("op_3177_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3177_end_0 = const()[name = tensor<string, []>("op_3177_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3177_end_mask_0 = const()[name = tensor<string, []>("op_3177_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3177 = slice_by_index(begin = var_3177_begin_0, end = var_3177_end_0, end_mask = var_3177_end_mask_0, x = seg_cumsum_39)[name = tensor<string, []>("op_3177")];
            tensor<int32, [3]> var_3180_begin_0 = const()[name = tensor<string, []>("op_3180_begin_0"), val = tensor<int32, [3]>([0, 20000, 0])];
            tensor<int32, [3]> var_3180_end_0 = const()[name = tensor<string, []>("op_3180_end_0"), val = tensor<int32, [3]>([1, 21000, 9])];
            tensor<bool, [3]> var_3180_end_mask_0 = const()[name = tensor<string, []>("op_3180_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3180 = slice_by_index(begin = var_3180_begin_0, end = var_3180_end_0, end_mask = var_3180_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3180")];
            tensor<bool, []> var_3182_exclusive_0 = const()[name = tensor<string, []>("op_3182_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3182_reverse_0 = const()[name = tensor<string, []>("op_3182_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3182 = cumsum(axis = var_2486, exclusive = var_3182_exclusive_0, reverse = var_3182_reverse_0, x = var_3180)[name = tensor<string, []>("op_3182")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_41 = add(x = var_3182, y = var_3177)[name = tensor<string, []>("seg_cumsum_41")];
            tensor<int32, [3]> var_3185_begin_0 = const()[name = tensor<string, []>("op_3185_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3185_end_0 = const()[name = tensor<string, []>("op_3185_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3185_end_mask_0 = const()[name = tensor<string, []>("op_3185_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3185 = slice_by_index(begin = var_3185_begin_0, end = var_3185_end_0, end_mask = var_3185_end_mask_0, x = seg_cumsum_41)[name = tensor<string, []>("op_3185")];
            tensor<int32, [3]> var_3188_begin_0 = const()[name = tensor<string, []>("op_3188_begin_0"), val = tensor<int32, [3]>([0, 21000, 0])];
            tensor<int32, [3]> var_3188_end_0 = const()[name = tensor<string, []>("op_3188_end_0"), val = tensor<int32, [3]>([1, 22000, 9])];
            tensor<bool, [3]> var_3188_end_mask_0 = const()[name = tensor<string, []>("op_3188_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3188 = slice_by_index(begin = var_3188_begin_0, end = var_3188_end_0, end_mask = var_3188_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3188")];
            tensor<bool, []> var_3190_exclusive_0 = const()[name = tensor<string, []>("op_3190_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3190_reverse_0 = const()[name = tensor<string, []>("op_3190_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3190 = cumsum(axis = var_2486, exclusive = var_3190_exclusive_0, reverse = var_3190_reverse_0, x = var_3188)[name = tensor<string, []>("op_3190")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_43 = add(x = var_3190, y = var_3185)[name = tensor<string, []>("seg_cumsum_43")];
            tensor<int32, [3]> var_3193_begin_0 = const()[name = tensor<string, []>("op_3193_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3193_end_0 = const()[name = tensor<string, []>("op_3193_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3193_end_mask_0 = const()[name = tensor<string, []>("op_3193_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3193 = slice_by_index(begin = var_3193_begin_0, end = var_3193_end_0, end_mask = var_3193_end_mask_0, x = seg_cumsum_43)[name = tensor<string, []>("op_3193")];
            tensor<int32, [3]> var_3196_begin_0 = const()[name = tensor<string, []>("op_3196_begin_0"), val = tensor<int32, [3]>([0, 22000, 0])];
            tensor<int32, [3]> var_3196_end_0 = const()[name = tensor<string, []>("op_3196_end_0"), val = tensor<int32, [3]>([1, 23000, 9])];
            tensor<bool, [3]> var_3196_end_mask_0 = const()[name = tensor<string, []>("op_3196_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3196 = slice_by_index(begin = var_3196_begin_0, end = var_3196_end_0, end_mask = var_3196_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3196")];
            tensor<bool, []> var_3198_exclusive_0 = const()[name = tensor<string, []>("op_3198_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3198_reverse_0 = const()[name = tensor<string, []>("op_3198_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3198 = cumsum(axis = var_2486, exclusive = var_3198_exclusive_0, reverse = var_3198_reverse_0, x = var_3196)[name = tensor<string, []>("op_3198")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_45 = add(x = var_3198, y = var_3193)[name = tensor<string, []>("seg_cumsum_45")];
            tensor<int32, [3]> var_3201_begin_0 = const()[name = tensor<string, []>("op_3201_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3201_end_0 = const()[name = tensor<string, []>("op_3201_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3201_end_mask_0 = const()[name = tensor<string, []>("op_3201_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3201 = slice_by_index(begin = var_3201_begin_0, end = var_3201_end_0, end_mask = var_3201_end_mask_0, x = seg_cumsum_45)[name = tensor<string, []>("op_3201")];
            tensor<int32, [3]> var_3204_begin_0 = const()[name = tensor<string, []>("op_3204_begin_0"), val = tensor<int32, [3]>([0, 23000, 0])];
            tensor<int32, [3]> var_3204_end_0 = const()[name = tensor<string, []>("op_3204_end_0"), val = tensor<int32, [3]>([1, 24000, 9])];
            tensor<bool, [3]> var_3204_end_mask_0 = const()[name = tensor<string, []>("op_3204_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3204 = slice_by_index(begin = var_3204_begin_0, end = var_3204_end_0, end_mask = var_3204_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3204")];
            tensor<bool, []> var_3206_exclusive_0 = const()[name = tensor<string, []>("op_3206_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3206_reverse_0 = const()[name = tensor<string, []>("op_3206_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3206 = cumsum(axis = var_2486, exclusive = var_3206_exclusive_0, reverse = var_3206_reverse_0, x = var_3204)[name = tensor<string, []>("op_3206")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_47 = add(x = var_3206, y = var_3201)[name = tensor<string, []>("seg_cumsum_47")];
            tensor<int32, [3]> var_3209_begin_0 = const()[name = tensor<string, []>("op_3209_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3209_end_0 = const()[name = tensor<string, []>("op_3209_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3209_end_mask_0 = const()[name = tensor<string, []>("op_3209_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3209 = slice_by_index(begin = var_3209_begin_0, end = var_3209_end_0, end_mask = var_3209_end_mask_0, x = seg_cumsum_47)[name = tensor<string, []>("op_3209")];
            tensor<int32, [3]> var_3212_begin_0 = const()[name = tensor<string, []>("op_3212_begin_0"), val = tensor<int32, [3]>([0, 24000, 0])];
            tensor<int32, [3]> var_3212_end_0 = const()[name = tensor<string, []>("op_3212_end_0"), val = tensor<int32, [3]>([1, 25000, 9])];
            tensor<bool, [3]> var_3212_end_mask_0 = const()[name = tensor<string, []>("op_3212_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3212 = slice_by_index(begin = var_3212_begin_0, end = var_3212_end_0, end_mask = var_3212_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3212")];
            tensor<bool, []> var_3214_exclusive_0 = const()[name = tensor<string, []>("op_3214_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3214_reverse_0 = const()[name = tensor<string, []>("op_3214_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3214 = cumsum(axis = var_2486, exclusive = var_3214_exclusive_0, reverse = var_3214_reverse_0, x = var_3212)[name = tensor<string, []>("op_3214")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_49 = add(x = var_3214, y = var_3209)[name = tensor<string, []>("seg_cumsum_49")];
            tensor<int32, [3]> var_3217_begin_0 = const()[name = tensor<string, []>("op_3217_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3217_end_0 = const()[name = tensor<string, []>("op_3217_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3217_end_mask_0 = const()[name = tensor<string, []>("op_3217_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3217 = slice_by_index(begin = var_3217_begin_0, end = var_3217_end_0, end_mask = var_3217_end_mask_0, x = seg_cumsum_49)[name = tensor<string, []>("op_3217")];
            tensor<int32, [3]> var_3220_begin_0 = const()[name = tensor<string, []>("op_3220_begin_0"), val = tensor<int32, [3]>([0, 25000, 0])];
            tensor<int32, [3]> var_3220_end_0 = const()[name = tensor<string, []>("op_3220_end_0"), val = tensor<int32, [3]>([1, 26000, 9])];
            tensor<bool, [3]> var_3220_end_mask_0 = const()[name = tensor<string, []>("op_3220_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3220 = slice_by_index(begin = var_3220_begin_0, end = var_3220_end_0, end_mask = var_3220_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3220")];
            tensor<bool, []> var_3222_exclusive_0 = const()[name = tensor<string, []>("op_3222_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3222_reverse_0 = const()[name = tensor<string, []>("op_3222_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3222 = cumsum(axis = var_2486, exclusive = var_3222_exclusive_0, reverse = var_3222_reverse_0, x = var_3220)[name = tensor<string, []>("op_3222")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_51 = add(x = var_3222, y = var_3217)[name = tensor<string, []>("seg_cumsum_51")];
            tensor<int32, [3]> var_3225_begin_0 = const()[name = tensor<string, []>("op_3225_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3225_end_0 = const()[name = tensor<string, []>("op_3225_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3225_end_mask_0 = const()[name = tensor<string, []>("op_3225_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3225 = slice_by_index(begin = var_3225_begin_0, end = var_3225_end_0, end_mask = var_3225_end_mask_0, x = seg_cumsum_51)[name = tensor<string, []>("op_3225")];
            tensor<int32, [3]> var_3228_begin_0 = const()[name = tensor<string, []>("op_3228_begin_0"), val = tensor<int32, [3]>([0, 26000, 0])];
            tensor<int32, [3]> var_3228_end_0 = const()[name = tensor<string, []>("op_3228_end_0"), val = tensor<int32, [3]>([1, 27000, 9])];
            tensor<bool, [3]> var_3228_end_mask_0 = const()[name = tensor<string, []>("op_3228_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3228 = slice_by_index(begin = var_3228_begin_0, end = var_3228_end_0, end_mask = var_3228_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3228")];
            tensor<bool, []> var_3230_exclusive_0 = const()[name = tensor<string, []>("op_3230_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3230_reverse_0 = const()[name = tensor<string, []>("op_3230_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3230 = cumsum(axis = var_2486, exclusive = var_3230_exclusive_0, reverse = var_3230_reverse_0, x = var_3228)[name = tensor<string, []>("op_3230")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_53 = add(x = var_3230, y = var_3225)[name = tensor<string, []>("seg_cumsum_53")];
            tensor<int32, [3]> var_3233_begin_0 = const()[name = tensor<string, []>("op_3233_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3233_end_0 = const()[name = tensor<string, []>("op_3233_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3233_end_mask_0 = const()[name = tensor<string, []>("op_3233_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3233 = slice_by_index(begin = var_3233_begin_0, end = var_3233_end_0, end_mask = var_3233_end_mask_0, x = seg_cumsum_53)[name = tensor<string, []>("op_3233")];
            tensor<int32, [3]> var_3236_begin_0 = const()[name = tensor<string, []>("op_3236_begin_0"), val = tensor<int32, [3]>([0, 27000, 0])];
            tensor<int32, [3]> var_3236_end_0 = const()[name = tensor<string, []>("op_3236_end_0"), val = tensor<int32, [3]>([1, 28000, 9])];
            tensor<bool, [3]> var_3236_end_mask_0 = const()[name = tensor<string, []>("op_3236_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3236 = slice_by_index(begin = var_3236_begin_0, end = var_3236_end_0, end_mask = var_3236_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3236")];
            tensor<bool, []> var_3238_exclusive_0 = const()[name = tensor<string, []>("op_3238_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3238_reverse_0 = const()[name = tensor<string, []>("op_3238_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3238 = cumsum(axis = var_2486, exclusive = var_3238_exclusive_0, reverse = var_3238_reverse_0, x = var_3236)[name = tensor<string, []>("op_3238")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_55 = add(x = var_3238, y = var_3233)[name = tensor<string, []>("seg_cumsum_55")];
            tensor<int32, [3]> var_3241_begin_0 = const()[name = tensor<string, []>("op_3241_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3241_end_0 = const()[name = tensor<string, []>("op_3241_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3241_end_mask_0 = const()[name = tensor<string, []>("op_3241_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3241 = slice_by_index(begin = var_3241_begin_0, end = var_3241_end_0, end_mask = var_3241_end_mask_0, x = seg_cumsum_55)[name = tensor<string, []>("op_3241")];
            tensor<int32, [3]> var_3244_begin_0 = const()[name = tensor<string, []>("op_3244_begin_0"), val = tensor<int32, [3]>([0, 28000, 0])];
            tensor<int32, [3]> var_3244_end_0 = const()[name = tensor<string, []>("op_3244_end_0"), val = tensor<int32, [3]>([1, 29000, 9])];
            tensor<bool, [3]> var_3244_end_mask_0 = const()[name = tensor<string, []>("op_3244_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3244 = slice_by_index(begin = var_3244_begin_0, end = var_3244_end_0, end_mask = var_3244_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3244")];
            tensor<bool, []> var_3246_exclusive_0 = const()[name = tensor<string, []>("op_3246_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3246_reverse_0 = const()[name = tensor<string, []>("op_3246_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3246 = cumsum(axis = var_2486, exclusive = var_3246_exclusive_0, reverse = var_3246_reverse_0, x = var_3244)[name = tensor<string, []>("op_3246")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_57 = add(x = var_3246, y = var_3241)[name = tensor<string, []>("seg_cumsum_57")];
            tensor<int32, [3]> var_3249_begin_0 = const()[name = tensor<string, []>("op_3249_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3249_end_0 = const()[name = tensor<string, []>("op_3249_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3249_end_mask_0 = const()[name = tensor<string, []>("op_3249_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3249 = slice_by_index(begin = var_3249_begin_0, end = var_3249_end_0, end_mask = var_3249_end_mask_0, x = seg_cumsum_57)[name = tensor<string, []>("op_3249")];
            tensor<int32, [3]> var_3252_begin_0 = const()[name = tensor<string, []>("op_3252_begin_0"), val = tensor<int32, [3]>([0, 29000, 0])];
            tensor<int32, [3]> var_3252_end_0 = const()[name = tensor<string, []>("op_3252_end_0"), val = tensor<int32, [3]>([1, 30000, 9])];
            tensor<bool, [3]> var_3252_end_mask_0 = const()[name = tensor<string, []>("op_3252_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3252 = slice_by_index(begin = var_3252_begin_0, end = var_3252_end_0, end_mask = var_3252_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3252")];
            tensor<bool, []> var_3254_exclusive_0 = const()[name = tensor<string, []>("op_3254_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3254_reverse_0 = const()[name = tensor<string, []>("op_3254_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3254 = cumsum(axis = var_2486, exclusive = var_3254_exclusive_0, reverse = var_3254_reverse_0, x = var_3252)[name = tensor<string, []>("op_3254")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_59 = add(x = var_3254, y = var_3249)[name = tensor<string, []>("seg_cumsum_59")];
            tensor<int32, [3]> var_3257_begin_0 = const()[name = tensor<string, []>("op_3257_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3257_end_0 = const()[name = tensor<string, []>("op_3257_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3257_end_mask_0 = const()[name = tensor<string, []>("op_3257_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3257 = slice_by_index(begin = var_3257_begin_0, end = var_3257_end_0, end_mask = var_3257_end_mask_0, x = seg_cumsum_59)[name = tensor<string, []>("op_3257")];
            tensor<int32, [3]> var_3260_begin_0 = const()[name = tensor<string, []>("op_3260_begin_0"), val = tensor<int32, [3]>([0, 30000, 0])];
            tensor<int32, [3]> var_3260_end_0 = const()[name = tensor<string, []>("op_3260_end_0"), val = tensor<int32, [3]>([1, 31000, 9])];
            tensor<bool, [3]> var_3260_end_mask_0 = const()[name = tensor<string, []>("op_3260_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3260 = slice_by_index(begin = var_3260_begin_0, end = var_3260_end_0, end_mask = var_3260_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3260")];
            tensor<bool, []> var_3262_exclusive_0 = const()[name = tensor<string, []>("op_3262_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3262_reverse_0 = const()[name = tensor<string, []>("op_3262_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3262 = cumsum(axis = var_2486, exclusive = var_3262_exclusive_0, reverse = var_3262_reverse_0, x = var_3260)[name = tensor<string, []>("op_3262")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_61 = add(x = var_3262, y = var_3257)[name = tensor<string, []>("seg_cumsum_61")];
            tensor<int32, [3]> var_3265_begin_0 = const()[name = tensor<string, []>("op_3265_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3265_end_0 = const()[name = tensor<string, []>("op_3265_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3265_end_mask_0 = const()[name = tensor<string, []>("op_3265_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3265 = slice_by_index(begin = var_3265_begin_0, end = var_3265_end_0, end_mask = var_3265_end_mask_0, x = seg_cumsum_61)[name = tensor<string, []>("op_3265")];
            tensor<int32, [3]> var_3268_begin_0 = const()[name = tensor<string, []>("op_3268_begin_0"), val = tensor<int32, [3]>([0, 31000, 0])];
            tensor<int32, [3]> var_3268_end_0 = const()[name = tensor<string, []>("op_3268_end_0"), val = tensor<int32, [3]>([1, 32000, 9])];
            tensor<bool, [3]> var_3268_end_mask_0 = const()[name = tensor<string, []>("op_3268_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3268 = slice_by_index(begin = var_3268_begin_0, end = var_3268_end_0, end_mask = var_3268_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3268")];
            tensor<bool, []> var_3270_exclusive_0 = const()[name = tensor<string, []>("op_3270_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3270_reverse_0 = const()[name = tensor<string, []>("op_3270_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3270 = cumsum(axis = var_2486, exclusive = var_3270_exclusive_0, reverse = var_3270_reverse_0, x = var_3268)[name = tensor<string, []>("op_3270")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_63 = add(x = var_3270, y = var_3265)[name = tensor<string, []>("seg_cumsum_63")];
            tensor<int32, [3]> var_3273_begin_0 = const()[name = tensor<string, []>("op_3273_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3273_end_0 = const()[name = tensor<string, []>("op_3273_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3273_end_mask_0 = const()[name = tensor<string, []>("op_3273_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3273 = slice_by_index(begin = var_3273_begin_0, end = var_3273_end_0, end_mask = var_3273_end_mask_0, x = seg_cumsum_63)[name = tensor<string, []>("op_3273")];
            tensor<int32, [3]> var_3276_begin_0 = const()[name = tensor<string, []>("op_3276_begin_0"), val = tensor<int32, [3]>([0, 32000, 0])];
            tensor<int32, [3]> var_3276_end_0 = const()[name = tensor<string, []>("op_3276_end_0"), val = tensor<int32, [3]>([1, 33000, 9])];
            tensor<bool, [3]> var_3276_end_mask_0 = const()[name = tensor<string, []>("op_3276_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3276 = slice_by_index(begin = var_3276_begin_0, end = var_3276_end_0, end_mask = var_3276_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3276")];
            tensor<bool, []> var_3278_exclusive_0 = const()[name = tensor<string, []>("op_3278_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3278_reverse_0 = const()[name = tensor<string, []>("op_3278_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3278 = cumsum(axis = var_2486, exclusive = var_3278_exclusive_0, reverse = var_3278_reverse_0, x = var_3276)[name = tensor<string, []>("op_3278")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_65 = add(x = var_3278, y = var_3273)[name = tensor<string, []>("seg_cumsum_65")];
            tensor<int32, [3]> var_3281_begin_0 = const()[name = tensor<string, []>("op_3281_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3281_end_0 = const()[name = tensor<string, []>("op_3281_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3281_end_mask_0 = const()[name = tensor<string, []>("op_3281_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3281 = slice_by_index(begin = var_3281_begin_0, end = var_3281_end_0, end_mask = var_3281_end_mask_0, x = seg_cumsum_65)[name = tensor<string, []>("op_3281")];
            tensor<int32, [3]> var_3284_begin_0 = const()[name = tensor<string, []>("op_3284_begin_0"), val = tensor<int32, [3]>([0, 33000, 0])];
            tensor<int32, [3]> var_3284_end_0 = const()[name = tensor<string, []>("op_3284_end_0"), val = tensor<int32, [3]>([1, 34000, 9])];
            tensor<bool, [3]> var_3284_end_mask_0 = const()[name = tensor<string, []>("op_3284_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3284 = slice_by_index(begin = var_3284_begin_0, end = var_3284_end_0, end_mask = var_3284_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3284")];
            tensor<bool, []> var_3286_exclusive_0 = const()[name = tensor<string, []>("op_3286_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3286_reverse_0 = const()[name = tensor<string, []>("op_3286_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3286 = cumsum(axis = var_2486, exclusive = var_3286_exclusive_0, reverse = var_3286_reverse_0, x = var_3284)[name = tensor<string, []>("op_3286")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_67 = add(x = var_3286, y = var_3281)[name = tensor<string, []>("seg_cumsum_67")];
            tensor<int32, [3]> var_3289_begin_0 = const()[name = tensor<string, []>("op_3289_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3289_end_0 = const()[name = tensor<string, []>("op_3289_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3289_end_mask_0 = const()[name = tensor<string, []>("op_3289_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3289 = slice_by_index(begin = var_3289_begin_0, end = var_3289_end_0, end_mask = var_3289_end_mask_0, x = seg_cumsum_67)[name = tensor<string, []>("op_3289")];
            tensor<int32, [3]> var_3292_begin_0 = const()[name = tensor<string, []>("op_3292_begin_0"), val = tensor<int32, [3]>([0, 34000, 0])];
            tensor<int32, [3]> var_3292_end_0 = const()[name = tensor<string, []>("op_3292_end_0"), val = tensor<int32, [3]>([1, 35000, 9])];
            tensor<bool, [3]> var_3292_end_mask_0 = const()[name = tensor<string, []>("op_3292_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3292 = slice_by_index(begin = var_3292_begin_0, end = var_3292_end_0, end_mask = var_3292_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3292")];
            tensor<bool, []> var_3294_exclusive_0 = const()[name = tensor<string, []>("op_3294_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3294_reverse_0 = const()[name = tensor<string, []>("op_3294_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3294 = cumsum(axis = var_2486, exclusive = var_3294_exclusive_0, reverse = var_3294_reverse_0, x = var_3292)[name = tensor<string, []>("op_3294")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_69 = add(x = var_3294, y = var_3289)[name = tensor<string, []>("seg_cumsum_69")];
            tensor<int32, [3]> var_3297_begin_0 = const()[name = tensor<string, []>("op_3297_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3297_end_0 = const()[name = tensor<string, []>("op_3297_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3297_end_mask_0 = const()[name = tensor<string, []>("op_3297_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3297 = slice_by_index(begin = var_3297_begin_0, end = var_3297_end_0, end_mask = var_3297_end_mask_0, x = seg_cumsum_69)[name = tensor<string, []>("op_3297")];
            tensor<int32, [3]> var_3300_begin_0 = const()[name = tensor<string, []>("op_3300_begin_0"), val = tensor<int32, [3]>([0, 35000, 0])];
            tensor<int32, [3]> var_3300_end_0 = const()[name = tensor<string, []>("op_3300_end_0"), val = tensor<int32, [3]>([1, 36000, 9])];
            tensor<bool, [3]> var_3300_end_mask_0 = const()[name = tensor<string, []>("op_3300_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3300 = slice_by_index(begin = var_3300_begin_0, end = var_3300_end_0, end_mask = var_3300_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3300")];
            tensor<bool, []> var_3302_exclusive_0 = const()[name = tensor<string, []>("op_3302_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3302_reverse_0 = const()[name = tensor<string, []>("op_3302_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3302 = cumsum(axis = var_2486, exclusive = var_3302_exclusive_0, reverse = var_3302_reverse_0, x = var_3300)[name = tensor<string, []>("op_3302")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_71 = add(x = var_3302, y = var_3297)[name = tensor<string, []>("seg_cumsum_71")];
            tensor<int32, [3]> var_3305_begin_0 = const()[name = tensor<string, []>("op_3305_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3305_end_0 = const()[name = tensor<string, []>("op_3305_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3305_end_mask_0 = const()[name = tensor<string, []>("op_3305_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3305 = slice_by_index(begin = var_3305_begin_0, end = var_3305_end_0, end_mask = var_3305_end_mask_0, x = seg_cumsum_71)[name = tensor<string, []>("op_3305")];
            tensor<int32, [3]> var_3308_begin_0 = const()[name = tensor<string, []>("op_3308_begin_0"), val = tensor<int32, [3]>([0, 36000, 0])];
            tensor<int32, [3]> var_3308_end_0 = const()[name = tensor<string, []>("op_3308_end_0"), val = tensor<int32, [3]>([1, 37000, 9])];
            tensor<bool, [3]> var_3308_end_mask_0 = const()[name = tensor<string, []>("op_3308_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3308 = slice_by_index(begin = var_3308_begin_0, end = var_3308_end_0, end_mask = var_3308_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3308")];
            tensor<bool, []> var_3310_exclusive_0 = const()[name = tensor<string, []>("op_3310_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3310_reverse_0 = const()[name = tensor<string, []>("op_3310_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3310 = cumsum(axis = var_2486, exclusive = var_3310_exclusive_0, reverse = var_3310_reverse_0, x = var_3308)[name = tensor<string, []>("op_3310")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_73 = add(x = var_3310, y = var_3305)[name = tensor<string, []>("seg_cumsum_73")];
            tensor<int32, [3]> var_3313_begin_0 = const()[name = tensor<string, []>("op_3313_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3313_end_0 = const()[name = tensor<string, []>("op_3313_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3313_end_mask_0 = const()[name = tensor<string, []>("op_3313_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3313 = slice_by_index(begin = var_3313_begin_0, end = var_3313_end_0, end_mask = var_3313_end_mask_0, x = seg_cumsum_73)[name = tensor<string, []>("op_3313")];
            tensor<int32, [3]> var_3316_begin_0 = const()[name = tensor<string, []>("op_3316_begin_0"), val = tensor<int32, [3]>([0, 37000, 0])];
            tensor<int32, [3]> var_3316_end_0 = const()[name = tensor<string, []>("op_3316_end_0"), val = tensor<int32, [3]>([1, 38000, 9])];
            tensor<bool, [3]> var_3316_end_mask_0 = const()[name = tensor<string, []>("op_3316_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3316 = slice_by_index(begin = var_3316_begin_0, end = var_3316_end_0, end_mask = var_3316_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3316")];
            tensor<bool, []> var_3318_exclusive_0 = const()[name = tensor<string, []>("op_3318_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3318_reverse_0 = const()[name = tensor<string, []>("op_3318_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3318 = cumsum(axis = var_2486, exclusive = var_3318_exclusive_0, reverse = var_3318_reverse_0, x = var_3316)[name = tensor<string, []>("op_3318")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_75 = add(x = var_3318, y = var_3313)[name = tensor<string, []>("seg_cumsum_75")];
            tensor<int32, [3]> var_3321_begin_0 = const()[name = tensor<string, []>("op_3321_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3321_end_0 = const()[name = tensor<string, []>("op_3321_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3321_end_mask_0 = const()[name = tensor<string, []>("op_3321_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3321 = slice_by_index(begin = var_3321_begin_0, end = var_3321_end_0, end_mask = var_3321_end_mask_0, x = seg_cumsum_75)[name = tensor<string, []>("op_3321")];
            tensor<int32, [3]> var_3324_begin_0 = const()[name = tensor<string, []>("op_3324_begin_0"), val = tensor<int32, [3]>([0, 38000, 0])];
            tensor<int32, [3]> var_3324_end_0 = const()[name = tensor<string, []>("op_3324_end_0"), val = tensor<int32, [3]>([1, 39000, 9])];
            tensor<bool, [3]> var_3324_end_mask_0 = const()[name = tensor<string, []>("op_3324_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3324 = slice_by_index(begin = var_3324_begin_0, end = var_3324_end_0, end_mask = var_3324_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3324")];
            tensor<bool, []> var_3326_exclusive_0 = const()[name = tensor<string, []>("op_3326_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3326_reverse_0 = const()[name = tensor<string, []>("op_3326_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3326 = cumsum(axis = var_2486, exclusive = var_3326_exclusive_0, reverse = var_3326_reverse_0, x = var_3324)[name = tensor<string, []>("op_3326")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_77 = add(x = var_3326, y = var_3321)[name = tensor<string, []>("seg_cumsum_77")];
            tensor<int32, [3]> var_3329_begin_0 = const()[name = tensor<string, []>("op_3329_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3329_end_0 = const()[name = tensor<string, []>("op_3329_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3329_end_mask_0 = const()[name = tensor<string, []>("op_3329_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3329 = slice_by_index(begin = var_3329_begin_0, end = var_3329_end_0, end_mask = var_3329_end_mask_0, x = seg_cumsum_77)[name = tensor<string, []>("op_3329")];
            tensor<int32, [3]> var_3332_begin_0 = const()[name = tensor<string, []>("op_3332_begin_0"), val = tensor<int32, [3]>([0, 39000, 0])];
            tensor<int32, [3]> var_3332_end_0 = const()[name = tensor<string, []>("op_3332_end_0"), val = tensor<int32, [3]>([1, 40000, 9])];
            tensor<bool, [3]> var_3332_end_mask_0 = const()[name = tensor<string, []>("op_3332_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3332 = slice_by_index(begin = var_3332_begin_0, end = var_3332_end_0, end_mask = var_3332_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3332")];
            tensor<bool, []> var_3334_exclusive_0 = const()[name = tensor<string, []>("op_3334_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3334_reverse_0 = const()[name = tensor<string, []>("op_3334_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3334 = cumsum(axis = var_2486, exclusive = var_3334_exclusive_0, reverse = var_3334_reverse_0, x = var_3332)[name = tensor<string, []>("op_3334")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_79 = add(x = var_3334, y = var_3329)[name = tensor<string, []>("seg_cumsum_79")];
            tensor<int32, [3]> var_3337_begin_0 = const()[name = tensor<string, []>("op_3337_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3337_end_0 = const()[name = tensor<string, []>("op_3337_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3337_end_mask_0 = const()[name = tensor<string, []>("op_3337_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3337 = slice_by_index(begin = var_3337_begin_0, end = var_3337_end_0, end_mask = var_3337_end_mask_0, x = seg_cumsum_79)[name = tensor<string, []>("op_3337")];
            tensor<int32, [3]> var_3340_begin_0 = const()[name = tensor<string, []>("op_3340_begin_0"), val = tensor<int32, [3]>([0, 40000, 0])];
            tensor<int32, [3]> var_3340_end_0 = const()[name = tensor<string, []>("op_3340_end_0"), val = tensor<int32, [3]>([1, 41000, 9])];
            tensor<bool, [3]> var_3340_end_mask_0 = const()[name = tensor<string, []>("op_3340_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3340 = slice_by_index(begin = var_3340_begin_0, end = var_3340_end_0, end_mask = var_3340_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3340")];
            tensor<bool, []> var_3342_exclusive_0 = const()[name = tensor<string, []>("op_3342_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3342_reverse_0 = const()[name = tensor<string, []>("op_3342_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3342 = cumsum(axis = var_2486, exclusive = var_3342_exclusive_0, reverse = var_3342_reverse_0, x = var_3340)[name = tensor<string, []>("op_3342")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_81 = add(x = var_3342, y = var_3337)[name = tensor<string, []>("seg_cumsum_81")];
            tensor<int32, [3]> var_3345_begin_0 = const()[name = tensor<string, []>("op_3345_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3345_end_0 = const()[name = tensor<string, []>("op_3345_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3345_end_mask_0 = const()[name = tensor<string, []>("op_3345_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3345 = slice_by_index(begin = var_3345_begin_0, end = var_3345_end_0, end_mask = var_3345_end_mask_0, x = seg_cumsum_81)[name = tensor<string, []>("op_3345")];
            tensor<int32, [3]> var_3348_begin_0 = const()[name = tensor<string, []>("op_3348_begin_0"), val = tensor<int32, [3]>([0, 41000, 0])];
            tensor<int32, [3]> var_3348_end_0 = const()[name = tensor<string, []>("op_3348_end_0"), val = tensor<int32, [3]>([1, 42000, 9])];
            tensor<bool, [3]> var_3348_end_mask_0 = const()[name = tensor<string, []>("op_3348_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3348 = slice_by_index(begin = var_3348_begin_0, end = var_3348_end_0, end_mask = var_3348_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3348")];
            tensor<bool, []> var_3350_exclusive_0 = const()[name = tensor<string, []>("op_3350_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3350_reverse_0 = const()[name = tensor<string, []>("op_3350_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3350 = cumsum(axis = var_2486, exclusive = var_3350_exclusive_0, reverse = var_3350_reverse_0, x = var_3348)[name = tensor<string, []>("op_3350")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_83 = add(x = var_3350, y = var_3345)[name = tensor<string, []>("seg_cumsum_83")];
            tensor<int32, [3]> var_3353_begin_0 = const()[name = tensor<string, []>("op_3353_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3353_end_0 = const()[name = tensor<string, []>("op_3353_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3353_end_mask_0 = const()[name = tensor<string, []>("op_3353_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3353 = slice_by_index(begin = var_3353_begin_0, end = var_3353_end_0, end_mask = var_3353_end_mask_0, x = seg_cumsum_83)[name = tensor<string, []>("op_3353")];
            tensor<int32, [3]> var_3356_begin_0 = const()[name = tensor<string, []>("op_3356_begin_0"), val = tensor<int32, [3]>([0, 42000, 0])];
            tensor<int32, [3]> var_3356_end_0 = const()[name = tensor<string, []>("op_3356_end_0"), val = tensor<int32, [3]>([1, 43000, 9])];
            tensor<bool, [3]> var_3356_end_mask_0 = const()[name = tensor<string, []>("op_3356_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3356 = slice_by_index(begin = var_3356_begin_0, end = var_3356_end_0, end_mask = var_3356_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3356")];
            tensor<bool, []> var_3358_exclusive_0 = const()[name = tensor<string, []>("op_3358_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3358_reverse_0 = const()[name = tensor<string, []>("op_3358_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3358 = cumsum(axis = var_2486, exclusive = var_3358_exclusive_0, reverse = var_3358_reverse_0, x = var_3356)[name = tensor<string, []>("op_3358")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_85 = add(x = var_3358, y = var_3353)[name = tensor<string, []>("seg_cumsum_85")];
            tensor<int32, [3]> var_3361_begin_0 = const()[name = tensor<string, []>("op_3361_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3361_end_0 = const()[name = tensor<string, []>("op_3361_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3361_end_mask_0 = const()[name = tensor<string, []>("op_3361_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3361 = slice_by_index(begin = var_3361_begin_0, end = var_3361_end_0, end_mask = var_3361_end_mask_0, x = seg_cumsum_85)[name = tensor<string, []>("op_3361")];
            tensor<int32, [3]> var_3364_begin_0 = const()[name = tensor<string, []>("op_3364_begin_0"), val = tensor<int32, [3]>([0, 43000, 0])];
            tensor<int32, [3]> var_3364_end_0 = const()[name = tensor<string, []>("op_3364_end_0"), val = tensor<int32, [3]>([1, 44000, 9])];
            tensor<bool, [3]> var_3364_end_mask_0 = const()[name = tensor<string, []>("op_3364_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3364 = slice_by_index(begin = var_3364_begin_0, end = var_3364_end_0, end_mask = var_3364_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3364")];
            tensor<bool, []> var_3366_exclusive_0 = const()[name = tensor<string, []>("op_3366_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3366_reverse_0 = const()[name = tensor<string, []>("op_3366_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3366 = cumsum(axis = var_2486, exclusive = var_3366_exclusive_0, reverse = var_3366_reverse_0, x = var_3364)[name = tensor<string, []>("op_3366")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_87 = add(x = var_3366, y = var_3361)[name = tensor<string, []>("seg_cumsum_87")];
            tensor<int32, [3]> var_3369_begin_0 = const()[name = tensor<string, []>("op_3369_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3369_end_0 = const()[name = tensor<string, []>("op_3369_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3369_end_mask_0 = const()[name = tensor<string, []>("op_3369_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3369 = slice_by_index(begin = var_3369_begin_0, end = var_3369_end_0, end_mask = var_3369_end_mask_0, x = seg_cumsum_87)[name = tensor<string, []>("op_3369")];
            tensor<int32, [3]> var_3372_begin_0 = const()[name = tensor<string, []>("op_3372_begin_0"), val = tensor<int32, [3]>([0, 44000, 0])];
            tensor<int32, [3]> var_3372_end_0 = const()[name = tensor<string, []>("op_3372_end_0"), val = tensor<int32, [3]>([1, 45000, 9])];
            tensor<bool, [3]> var_3372_end_mask_0 = const()[name = tensor<string, []>("op_3372_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3372 = slice_by_index(begin = var_3372_begin_0, end = var_3372_end_0, end_mask = var_3372_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3372")];
            tensor<bool, []> var_3374_exclusive_0 = const()[name = tensor<string, []>("op_3374_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3374_reverse_0 = const()[name = tensor<string, []>("op_3374_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3374 = cumsum(axis = var_2486, exclusive = var_3374_exclusive_0, reverse = var_3374_reverse_0, x = var_3372)[name = tensor<string, []>("op_3374")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_89 = add(x = var_3374, y = var_3369)[name = tensor<string, []>("seg_cumsum_89")];
            tensor<int32, [3]> var_3377_begin_0 = const()[name = tensor<string, []>("op_3377_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3377_end_0 = const()[name = tensor<string, []>("op_3377_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3377_end_mask_0 = const()[name = tensor<string, []>("op_3377_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3377 = slice_by_index(begin = var_3377_begin_0, end = var_3377_end_0, end_mask = var_3377_end_mask_0, x = seg_cumsum_89)[name = tensor<string, []>("op_3377")];
            tensor<int32, [3]> var_3380_begin_0 = const()[name = tensor<string, []>("op_3380_begin_0"), val = tensor<int32, [3]>([0, 45000, 0])];
            tensor<int32, [3]> var_3380_end_0 = const()[name = tensor<string, []>("op_3380_end_0"), val = tensor<int32, [3]>([1, 46000, 9])];
            tensor<bool, [3]> var_3380_end_mask_0 = const()[name = tensor<string, []>("op_3380_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3380 = slice_by_index(begin = var_3380_begin_0, end = var_3380_end_0, end_mask = var_3380_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3380")];
            tensor<bool, []> var_3382_exclusive_0 = const()[name = tensor<string, []>("op_3382_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3382_reverse_0 = const()[name = tensor<string, []>("op_3382_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3382 = cumsum(axis = var_2486, exclusive = var_3382_exclusive_0, reverse = var_3382_reverse_0, x = var_3380)[name = tensor<string, []>("op_3382")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_91 = add(x = var_3382, y = var_3377)[name = tensor<string, []>("seg_cumsum_91")];
            tensor<int32, [3]> var_3385_begin_0 = const()[name = tensor<string, []>("op_3385_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3385_end_0 = const()[name = tensor<string, []>("op_3385_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3385_end_mask_0 = const()[name = tensor<string, []>("op_3385_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3385 = slice_by_index(begin = var_3385_begin_0, end = var_3385_end_0, end_mask = var_3385_end_mask_0, x = seg_cumsum_91)[name = tensor<string, []>("op_3385")];
            tensor<int32, [3]> var_3388_begin_0 = const()[name = tensor<string, []>("op_3388_begin_0"), val = tensor<int32, [3]>([0, 46000, 0])];
            tensor<int32, [3]> var_3388_end_0 = const()[name = tensor<string, []>("op_3388_end_0"), val = tensor<int32, [3]>([1, 47000, 9])];
            tensor<bool, [3]> var_3388_end_mask_0 = const()[name = tensor<string, []>("op_3388_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3388 = slice_by_index(begin = var_3388_begin_0, end = var_3388_end_0, end_mask = var_3388_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3388")];
            tensor<bool, []> var_3390_exclusive_0 = const()[name = tensor<string, []>("op_3390_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3390_reverse_0 = const()[name = tensor<string, []>("op_3390_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3390 = cumsum(axis = var_2486, exclusive = var_3390_exclusive_0, reverse = var_3390_reverse_0, x = var_3388)[name = tensor<string, []>("op_3390")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_93 = add(x = var_3390, y = var_3385)[name = tensor<string, []>("seg_cumsum_93")];
            tensor<int32, [3]> var_3393_begin_0 = const()[name = tensor<string, []>("op_3393_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3393_end_0 = const()[name = tensor<string, []>("op_3393_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3393_end_mask_0 = const()[name = tensor<string, []>("op_3393_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3393 = slice_by_index(begin = var_3393_begin_0, end = var_3393_end_0, end_mask = var_3393_end_mask_0, x = seg_cumsum_93)[name = tensor<string, []>("op_3393")];
            tensor<int32, [3]> var_3396_begin_0 = const()[name = tensor<string, []>("op_3396_begin_0"), val = tensor<int32, [3]>([0, 47000, 0])];
            tensor<int32, [3]> var_3396_end_0 = const()[name = tensor<string, []>("op_3396_end_0"), val = tensor<int32, [3]>([1, 48000, 9])];
            tensor<bool, [3]> var_3396_end_mask_0 = const()[name = tensor<string, []>("op_3396_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3396 = slice_by_index(begin = var_3396_begin_0, end = var_3396_end_0, end_mask = var_3396_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3396")];
            tensor<bool, []> var_3398_exclusive_0 = const()[name = tensor<string, []>("op_3398_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3398_reverse_0 = const()[name = tensor<string, []>("op_3398_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3398 = cumsum(axis = var_2486, exclusive = var_3398_exclusive_0, reverse = var_3398_reverse_0, x = var_3396)[name = tensor<string, []>("op_3398")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_95 = add(x = var_3398, y = var_3393)[name = tensor<string, []>("seg_cumsum_95")];
            tensor<int32, [3]> var_3401_begin_0 = const()[name = tensor<string, []>("op_3401_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3401_end_0 = const()[name = tensor<string, []>("op_3401_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3401_end_mask_0 = const()[name = tensor<string, []>("op_3401_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3401 = slice_by_index(begin = var_3401_begin_0, end = var_3401_end_0, end_mask = var_3401_end_mask_0, x = seg_cumsum_95)[name = tensor<string, []>("op_3401")];
            tensor<int32, [3]> var_3404_begin_0 = const()[name = tensor<string, []>("op_3404_begin_0"), val = tensor<int32, [3]>([0, 48000, 0])];
            tensor<int32, [3]> var_3404_end_0 = const()[name = tensor<string, []>("op_3404_end_0"), val = tensor<int32, [3]>([1, 49000, 9])];
            tensor<bool, [3]> var_3404_end_mask_0 = const()[name = tensor<string, []>("op_3404_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3404 = slice_by_index(begin = var_3404_begin_0, end = var_3404_end_0, end_mask = var_3404_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3404")];
            tensor<bool, []> var_3406_exclusive_0 = const()[name = tensor<string, []>("op_3406_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3406_reverse_0 = const()[name = tensor<string, []>("op_3406_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3406 = cumsum(axis = var_2486, exclusive = var_3406_exclusive_0, reverse = var_3406_reverse_0, x = var_3404)[name = tensor<string, []>("op_3406")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_97 = add(x = var_3406, y = var_3401)[name = tensor<string, []>("seg_cumsum_97")];
            tensor<int32, [3]> var_3409_begin_0 = const()[name = tensor<string, []>("op_3409_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3409_end_0 = const()[name = tensor<string, []>("op_3409_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3409_end_mask_0 = const()[name = tensor<string, []>("op_3409_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3409 = slice_by_index(begin = var_3409_begin_0, end = var_3409_end_0, end_mask = var_3409_end_mask_0, x = seg_cumsum_97)[name = tensor<string, []>("op_3409")];
            tensor<int32, [3]> var_3412_begin_0 = const()[name = tensor<string, []>("op_3412_begin_0"), val = tensor<int32, [3]>([0, 49000, 0])];
            tensor<int32, [3]> var_3412_end_0 = const()[name = tensor<string, []>("op_3412_end_0"), val = tensor<int32, [3]>([1, 50000, 9])];
            tensor<bool, [3]> var_3412_end_mask_0 = const()[name = tensor<string, []>("op_3412_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3412 = slice_by_index(begin = var_3412_begin_0, end = var_3412_end_0, end_mask = var_3412_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3412")];
            tensor<bool, []> var_3414_exclusive_0 = const()[name = tensor<string, []>("op_3414_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3414_reverse_0 = const()[name = tensor<string, []>("op_3414_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3414 = cumsum(axis = var_2486, exclusive = var_3414_exclusive_0, reverse = var_3414_reverse_0, x = var_3412)[name = tensor<string, []>("op_3414")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_99 = add(x = var_3414, y = var_3409)[name = tensor<string, []>("seg_cumsum_99")];
            tensor<int32, [3]> var_3417_begin_0 = const()[name = tensor<string, []>("op_3417_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3417_end_0 = const()[name = tensor<string, []>("op_3417_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3417_end_mask_0 = const()[name = tensor<string, []>("op_3417_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3417 = slice_by_index(begin = var_3417_begin_0, end = var_3417_end_0, end_mask = var_3417_end_mask_0, x = seg_cumsum_99)[name = tensor<string, []>("op_3417")];
            tensor<int32, [3]> var_3420_begin_0 = const()[name = tensor<string, []>("op_3420_begin_0"), val = tensor<int32, [3]>([0, 50000, 0])];
            tensor<int32, [3]> var_3420_end_0 = const()[name = tensor<string, []>("op_3420_end_0"), val = tensor<int32, [3]>([1, 51000, 9])];
            tensor<bool, [3]> var_3420_end_mask_0 = const()[name = tensor<string, []>("op_3420_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3420 = slice_by_index(begin = var_3420_begin_0, end = var_3420_end_0, end_mask = var_3420_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3420")];
            tensor<bool, []> var_3422_exclusive_0 = const()[name = tensor<string, []>("op_3422_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3422_reverse_0 = const()[name = tensor<string, []>("op_3422_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3422 = cumsum(axis = var_2486, exclusive = var_3422_exclusive_0, reverse = var_3422_reverse_0, x = var_3420)[name = tensor<string, []>("op_3422")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_101 = add(x = var_3422, y = var_3417)[name = tensor<string, []>("seg_cumsum_101")];
            tensor<int32, [3]> var_3425_begin_0 = const()[name = tensor<string, []>("op_3425_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3425_end_0 = const()[name = tensor<string, []>("op_3425_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3425_end_mask_0 = const()[name = tensor<string, []>("op_3425_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3425 = slice_by_index(begin = var_3425_begin_0, end = var_3425_end_0, end_mask = var_3425_end_mask_0, x = seg_cumsum_101)[name = tensor<string, []>("op_3425")];
            tensor<int32, [3]> var_3428_begin_0 = const()[name = tensor<string, []>("op_3428_begin_0"), val = tensor<int32, [3]>([0, 51000, 0])];
            tensor<int32, [3]> var_3428_end_0 = const()[name = tensor<string, []>("op_3428_end_0"), val = tensor<int32, [3]>([1, 52000, 9])];
            tensor<bool, [3]> var_3428_end_mask_0 = const()[name = tensor<string, []>("op_3428_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3428 = slice_by_index(begin = var_3428_begin_0, end = var_3428_end_0, end_mask = var_3428_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3428")];
            tensor<bool, []> var_3430_exclusive_0 = const()[name = tensor<string, []>("op_3430_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3430_reverse_0 = const()[name = tensor<string, []>("op_3430_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3430 = cumsum(axis = var_2486, exclusive = var_3430_exclusive_0, reverse = var_3430_reverse_0, x = var_3428)[name = tensor<string, []>("op_3430")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_103 = add(x = var_3430, y = var_3425)[name = tensor<string, []>("seg_cumsum_103")];
            tensor<int32, [3]> var_3433_begin_0 = const()[name = tensor<string, []>("op_3433_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3433_end_0 = const()[name = tensor<string, []>("op_3433_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3433_end_mask_0 = const()[name = tensor<string, []>("op_3433_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3433 = slice_by_index(begin = var_3433_begin_0, end = var_3433_end_0, end_mask = var_3433_end_mask_0, x = seg_cumsum_103)[name = tensor<string, []>("op_3433")];
            tensor<int32, [3]> var_3436_begin_0 = const()[name = tensor<string, []>("op_3436_begin_0"), val = tensor<int32, [3]>([0, 52000, 0])];
            tensor<int32, [3]> var_3436_end_0 = const()[name = tensor<string, []>("op_3436_end_0"), val = tensor<int32, [3]>([1, 53000, 9])];
            tensor<bool, [3]> var_3436_end_mask_0 = const()[name = tensor<string, []>("op_3436_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3436 = slice_by_index(begin = var_3436_begin_0, end = var_3436_end_0, end_mask = var_3436_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3436")];
            tensor<bool, []> var_3438_exclusive_0 = const()[name = tensor<string, []>("op_3438_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3438_reverse_0 = const()[name = tensor<string, []>("op_3438_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3438 = cumsum(axis = var_2486, exclusive = var_3438_exclusive_0, reverse = var_3438_reverse_0, x = var_3436)[name = tensor<string, []>("op_3438")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_105 = add(x = var_3438, y = var_3433)[name = tensor<string, []>("seg_cumsum_105")];
            tensor<int32, [3]> var_3441_begin_0 = const()[name = tensor<string, []>("op_3441_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3441_end_0 = const()[name = tensor<string, []>("op_3441_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3441_end_mask_0 = const()[name = tensor<string, []>("op_3441_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3441 = slice_by_index(begin = var_3441_begin_0, end = var_3441_end_0, end_mask = var_3441_end_mask_0, x = seg_cumsum_105)[name = tensor<string, []>("op_3441")];
            tensor<int32, [3]> var_3444_begin_0 = const()[name = tensor<string, []>("op_3444_begin_0"), val = tensor<int32, [3]>([0, 53000, 0])];
            tensor<int32, [3]> var_3444_end_0 = const()[name = tensor<string, []>("op_3444_end_0"), val = tensor<int32, [3]>([1, 54000, 9])];
            tensor<bool, [3]> var_3444_end_mask_0 = const()[name = tensor<string, []>("op_3444_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3444 = slice_by_index(begin = var_3444_begin_0, end = var_3444_end_0, end_mask = var_3444_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3444")];
            tensor<bool, []> var_3446_exclusive_0 = const()[name = tensor<string, []>("op_3446_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3446_reverse_0 = const()[name = tensor<string, []>("op_3446_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3446 = cumsum(axis = var_2486, exclusive = var_3446_exclusive_0, reverse = var_3446_reverse_0, x = var_3444)[name = tensor<string, []>("op_3446")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_107 = add(x = var_3446, y = var_3441)[name = tensor<string, []>("seg_cumsum_107")];
            tensor<int32, [3]> var_3449_begin_0 = const()[name = tensor<string, []>("op_3449_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3449_end_0 = const()[name = tensor<string, []>("op_3449_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3449_end_mask_0 = const()[name = tensor<string, []>("op_3449_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3449 = slice_by_index(begin = var_3449_begin_0, end = var_3449_end_0, end_mask = var_3449_end_mask_0, x = seg_cumsum_107)[name = tensor<string, []>("op_3449")];
            tensor<int32, [3]> var_3452_begin_0 = const()[name = tensor<string, []>("op_3452_begin_0"), val = tensor<int32, [3]>([0, 54000, 0])];
            tensor<int32, [3]> var_3452_end_0 = const()[name = tensor<string, []>("op_3452_end_0"), val = tensor<int32, [3]>([1, 55000, 9])];
            tensor<bool, [3]> var_3452_end_mask_0 = const()[name = tensor<string, []>("op_3452_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3452 = slice_by_index(begin = var_3452_begin_0, end = var_3452_end_0, end_mask = var_3452_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3452")];
            tensor<bool, []> var_3454_exclusive_0 = const()[name = tensor<string, []>("op_3454_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3454_reverse_0 = const()[name = tensor<string, []>("op_3454_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3454 = cumsum(axis = var_2486, exclusive = var_3454_exclusive_0, reverse = var_3454_reverse_0, x = var_3452)[name = tensor<string, []>("op_3454")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_109 = add(x = var_3454, y = var_3449)[name = tensor<string, []>("seg_cumsum_109")];
            tensor<int32, [3]> var_3457_begin_0 = const()[name = tensor<string, []>("op_3457_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3457_end_0 = const()[name = tensor<string, []>("op_3457_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3457_end_mask_0 = const()[name = tensor<string, []>("op_3457_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3457 = slice_by_index(begin = var_3457_begin_0, end = var_3457_end_0, end_mask = var_3457_end_mask_0, x = seg_cumsum_109)[name = tensor<string, []>("op_3457")];
            tensor<int32, [3]> var_3460_begin_0 = const()[name = tensor<string, []>("op_3460_begin_0"), val = tensor<int32, [3]>([0, 55000, 0])];
            tensor<int32, [3]> var_3460_end_0 = const()[name = tensor<string, []>("op_3460_end_0"), val = tensor<int32, [3]>([1, 56000, 9])];
            tensor<bool, [3]> var_3460_end_mask_0 = const()[name = tensor<string, []>("op_3460_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3460 = slice_by_index(begin = var_3460_begin_0, end = var_3460_end_0, end_mask = var_3460_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3460")];
            tensor<bool, []> var_3462_exclusive_0 = const()[name = tensor<string, []>("op_3462_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3462_reverse_0 = const()[name = tensor<string, []>("op_3462_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3462 = cumsum(axis = var_2486, exclusive = var_3462_exclusive_0, reverse = var_3462_reverse_0, x = var_3460)[name = tensor<string, []>("op_3462")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_111 = add(x = var_3462, y = var_3457)[name = tensor<string, []>("seg_cumsum_111")];
            tensor<int32, [3]> var_3465_begin_0 = const()[name = tensor<string, []>("op_3465_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3465_end_0 = const()[name = tensor<string, []>("op_3465_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3465_end_mask_0 = const()[name = tensor<string, []>("op_3465_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3465 = slice_by_index(begin = var_3465_begin_0, end = var_3465_end_0, end_mask = var_3465_end_mask_0, x = seg_cumsum_111)[name = tensor<string, []>("op_3465")];
            tensor<int32, [3]> var_3468_begin_0 = const()[name = tensor<string, []>("op_3468_begin_0"), val = tensor<int32, [3]>([0, 56000, 0])];
            tensor<int32, [3]> var_3468_end_0 = const()[name = tensor<string, []>("op_3468_end_0"), val = tensor<int32, [3]>([1, 57000, 9])];
            tensor<bool, [3]> var_3468_end_mask_0 = const()[name = tensor<string, []>("op_3468_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3468 = slice_by_index(begin = var_3468_begin_0, end = var_3468_end_0, end_mask = var_3468_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3468")];
            tensor<bool, []> var_3470_exclusive_0 = const()[name = tensor<string, []>("op_3470_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3470_reverse_0 = const()[name = tensor<string, []>("op_3470_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3470 = cumsum(axis = var_2486, exclusive = var_3470_exclusive_0, reverse = var_3470_reverse_0, x = var_3468)[name = tensor<string, []>("op_3470")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_113 = add(x = var_3470, y = var_3465)[name = tensor<string, []>("seg_cumsum_113")];
            tensor<int32, [3]> var_3473_begin_0 = const()[name = tensor<string, []>("op_3473_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3473_end_0 = const()[name = tensor<string, []>("op_3473_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3473_end_mask_0 = const()[name = tensor<string, []>("op_3473_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3473 = slice_by_index(begin = var_3473_begin_0, end = var_3473_end_0, end_mask = var_3473_end_mask_0, x = seg_cumsum_113)[name = tensor<string, []>("op_3473")];
            tensor<int32, [3]> var_3476_begin_0 = const()[name = tensor<string, []>("op_3476_begin_0"), val = tensor<int32, [3]>([0, 57000, 0])];
            tensor<int32, [3]> var_3476_end_0 = const()[name = tensor<string, []>("op_3476_end_0"), val = tensor<int32, [3]>([1, 58000, 9])];
            tensor<bool, [3]> var_3476_end_mask_0 = const()[name = tensor<string, []>("op_3476_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3476 = slice_by_index(begin = var_3476_begin_0, end = var_3476_end_0, end_mask = var_3476_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3476")];
            tensor<bool, []> var_3478_exclusive_0 = const()[name = tensor<string, []>("op_3478_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3478_reverse_0 = const()[name = tensor<string, []>("op_3478_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3478 = cumsum(axis = var_2486, exclusive = var_3478_exclusive_0, reverse = var_3478_reverse_0, x = var_3476)[name = tensor<string, []>("op_3478")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_115 = add(x = var_3478, y = var_3473)[name = tensor<string, []>("seg_cumsum_115")];
            tensor<int32, [3]> var_3481_begin_0 = const()[name = tensor<string, []>("op_3481_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3481_end_0 = const()[name = tensor<string, []>("op_3481_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3481_end_mask_0 = const()[name = tensor<string, []>("op_3481_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3481 = slice_by_index(begin = var_3481_begin_0, end = var_3481_end_0, end_mask = var_3481_end_mask_0, x = seg_cumsum_115)[name = tensor<string, []>("op_3481")];
            tensor<int32, [3]> var_3484_begin_0 = const()[name = tensor<string, []>("op_3484_begin_0"), val = tensor<int32, [3]>([0, 58000, 0])];
            tensor<int32, [3]> var_3484_end_0 = const()[name = tensor<string, []>("op_3484_end_0"), val = tensor<int32, [3]>([1, 59000, 9])];
            tensor<bool, [3]> var_3484_end_mask_0 = const()[name = tensor<string, []>("op_3484_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3484 = slice_by_index(begin = var_3484_begin_0, end = var_3484_end_0, end_mask = var_3484_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3484")];
            tensor<bool, []> var_3486_exclusive_0 = const()[name = tensor<string, []>("op_3486_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3486_reverse_0 = const()[name = tensor<string, []>("op_3486_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3486 = cumsum(axis = var_2486, exclusive = var_3486_exclusive_0, reverse = var_3486_reverse_0, x = var_3484)[name = tensor<string, []>("op_3486")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_117 = add(x = var_3486, y = var_3481)[name = tensor<string, []>("seg_cumsum_117")];
            tensor<int32, [3]> var_3489_begin_0 = const()[name = tensor<string, []>("op_3489_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3489_end_0 = const()[name = tensor<string, []>("op_3489_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3489_end_mask_0 = const()[name = tensor<string, []>("op_3489_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3489 = slice_by_index(begin = var_3489_begin_0, end = var_3489_end_0, end_mask = var_3489_end_mask_0, x = seg_cumsum_117)[name = tensor<string, []>("op_3489")];
            tensor<int32, [3]> var_3492_begin_0 = const()[name = tensor<string, []>("op_3492_begin_0"), val = tensor<int32, [3]>([0, 59000, 0])];
            tensor<int32, [3]> var_3492_end_0 = const()[name = tensor<string, []>("op_3492_end_0"), val = tensor<int32, [3]>([1, 60000, 9])];
            tensor<bool, [3]> var_3492_end_mask_0 = const()[name = tensor<string, []>("op_3492_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3492 = slice_by_index(begin = var_3492_begin_0, end = var_3492_end_0, end_mask = var_3492_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3492")];
            tensor<bool, []> var_3494_exclusive_0 = const()[name = tensor<string, []>("op_3494_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3494_reverse_0 = const()[name = tensor<string, []>("op_3494_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3494 = cumsum(axis = var_2486, exclusive = var_3494_exclusive_0, reverse = var_3494_reverse_0, x = var_3492)[name = tensor<string, []>("op_3494")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_119 = add(x = var_3494, y = var_3489)[name = tensor<string, []>("seg_cumsum_119")];
            tensor<int32, [3]> var_3497_begin_0 = const()[name = tensor<string, []>("op_3497_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3497_end_0 = const()[name = tensor<string, []>("op_3497_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3497_end_mask_0 = const()[name = tensor<string, []>("op_3497_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3497 = slice_by_index(begin = var_3497_begin_0, end = var_3497_end_0, end_mask = var_3497_end_mask_0, x = seg_cumsum_119)[name = tensor<string, []>("op_3497")];
            tensor<int32, [3]> var_3500_begin_0 = const()[name = tensor<string, []>("op_3500_begin_0"), val = tensor<int32, [3]>([0, 60000, 0])];
            tensor<int32, [3]> var_3500_end_0 = const()[name = tensor<string, []>("op_3500_end_0"), val = tensor<int32, [3]>([1, 61000, 9])];
            tensor<bool, [3]> var_3500_end_mask_0 = const()[name = tensor<string, []>("op_3500_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3500 = slice_by_index(begin = var_3500_begin_0, end = var_3500_end_0, end_mask = var_3500_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3500")];
            tensor<bool, []> var_3502_exclusive_0 = const()[name = tensor<string, []>("op_3502_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3502_reverse_0 = const()[name = tensor<string, []>("op_3502_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3502 = cumsum(axis = var_2486, exclusive = var_3502_exclusive_0, reverse = var_3502_reverse_0, x = var_3500)[name = tensor<string, []>("op_3502")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_121 = add(x = var_3502, y = var_3497)[name = tensor<string, []>("seg_cumsum_121")];
            tensor<int32, [3]> var_3505_begin_0 = const()[name = tensor<string, []>("op_3505_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3505_end_0 = const()[name = tensor<string, []>("op_3505_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3505_end_mask_0 = const()[name = tensor<string, []>("op_3505_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3505 = slice_by_index(begin = var_3505_begin_0, end = var_3505_end_0, end_mask = var_3505_end_mask_0, x = seg_cumsum_121)[name = tensor<string, []>("op_3505")];
            tensor<int32, [3]> var_3508_begin_0 = const()[name = tensor<string, []>("op_3508_begin_0"), val = tensor<int32, [3]>([0, 61000, 0])];
            tensor<int32, [3]> var_3508_end_0 = const()[name = tensor<string, []>("op_3508_end_0"), val = tensor<int32, [3]>([1, 62000, 9])];
            tensor<bool, [3]> var_3508_end_mask_0 = const()[name = tensor<string, []>("op_3508_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3508 = slice_by_index(begin = var_3508_begin_0, end = var_3508_end_0, end_mask = var_3508_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3508")];
            tensor<bool, []> var_3510_exclusive_0 = const()[name = tensor<string, []>("op_3510_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3510_reverse_0 = const()[name = tensor<string, []>("op_3510_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3510 = cumsum(axis = var_2486, exclusive = var_3510_exclusive_0, reverse = var_3510_reverse_0, x = var_3508)[name = tensor<string, []>("op_3510")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_123 = add(x = var_3510, y = var_3505)[name = tensor<string, []>("seg_cumsum_123")];
            tensor<int32, [3]> var_3513_begin_0 = const()[name = tensor<string, []>("op_3513_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3513_end_0 = const()[name = tensor<string, []>("op_3513_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3513_end_mask_0 = const()[name = tensor<string, []>("op_3513_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3513 = slice_by_index(begin = var_3513_begin_0, end = var_3513_end_0, end_mask = var_3513_end_mask_0, x = seg_cumsum_123)[name = tensor<string, []>("op_3513")];
            tensor<int32, [3]> var_3516_begin_0 = const()[name = tensor<string, []>("op_3516_begin_0"), val = tensor<int32, [3]>([0, 62000, 0])];
            tensor<int32, [3]> var_3516_end_0 = const()[name = tensor<string, []>("op_3516_end_0"), val = tensor<int32, [3]>([1, 63000, 9])];
            tensor<bool, [3]> var_3516_end_mask_0 = const()[name = tensor<string, []>("op_3516_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3516 = slice_by_index(begin = var_3516_begin_0, end = var_3516_end_0, end_mask = var_3516_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3516")];
            tensor<bool, []> var_3518_exclusive_0 = const()[name = tensor<string, []>("op_3518_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3518_reverse_0 = const()[name = tensor<string, []>("op_3518_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3518 = cumsum(axis = var_2486, exclusive = var_3518_exclusive_0, reverse = var_3518_reverse_0, x = var_3516)[name = tensor<string, []>("op_3518")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_125 = add(x = var_3518, y = var_3513)[name = tensor<string, []>("seg_cumsum_125")];
            tensor<int32, [3]> var_3521_begin_0 = const()[name = tensor<string, []>("op_3521_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3521_end_0 = const()[name = tensor<string, []>("op_3521_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3521_end_mask_0 = const()[name = tensor<string, []>("op_3521_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3521 = slice_by_index(begin = var_3521_begin_0, end = var_3521_end_0, end_mask = var_3521_end_mask_0, x = seg_cumsum_125)[name = tensor<string, []>("op_3521")];
            tensor<int32, [3]> var_3524_begin_0 = const()[name = tensor<string, []>("op_3524_begin_0"), val = tensor<int32, [3]>([0, 63000, 0])];
            tensor<int32, [3]> var_3524_end_0 = const()[name = tensor<string, []>("op_3524_end_0"), val = tensor<int32, [3]>([1, 64000, 9])];
            tensor<bool, [3]> var_3524_end_mask_0 = const()[name = tensor<string, []>("op_3524_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3524 = slice_by_index(begin = var_3524_begin_0, end = var_3524_end_0, end_mask = var_3524_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3524")];
            tensor<bool, []> var_3526_exclusive_0 = const()[name = tensor<string, []>("op_3526_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3526_reverse_0 = const()[name = tensor<string, []>("op_3526_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3526 = cumsum(axis = var_2486, exclusive = var_3526_exclusive_0, reverse = var_3526_reverse_0, x = var_3524)[name = tensor<string, []>("op_3526")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_127 = add(x = var_3526, y = var_3521)[name = tensor<string, []>("seg_cumsum_127")];
            tensor<int32, [3]> var_3529_begin_0 = const()[name = tensor<string, []>("op_3529_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3529_end_0 = const()[name = tensor<string, []>("op_3529_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3529_end_mask_0 = const()[name = tensor<string, []>("op_3529_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3529 = slice_by_index(begin = var_3529_begin_0, end = var_3529_end_0, end_mask = var_3529_end_mask_0, x = seg_cumsum_127)[name = tensor<string, []>("op_3529")];
            tensor<int32, [3]> var_3532_begin_0 = const()[name = tensor<string, []>("op_3532_begin_0"), val = tensor<int32, [3]>([0, 64000, 0])];
            tensor<int32, [3]> var_3532_end_0 = const()[name = tensor<string, []>("op_3532_end_0"), val = tensor<int32, [3]>([1, 65000, 9])];
            tensor<bool, [3]> var_3532_end_mask_0 = const()[name = tensor<string, []>("op_3532_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3532 = slice_by_index(begin = var_3532_begin_0, end = var_3532_end_0, end_mask = var_3532_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3532")];
            tensor<bool, []> var_3534_exclusive_0 = const()[name = tensor<string, []>("op_3534_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3534_reverse_0 = const()[name = tensor<string, []>("op_3534_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3534 = cumsum(axis = var_2486, exclusive = var_3534_exclusive_0, reverse = var_3534_reverse_0, x = var_3532)[name = tensor<string, []>("op_3534")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_129 = add(x = var_3534, y = var_3529)[name = tensor<string, []>("seg_cumsum_129")];
            tensor<int32, [3]> var_3537_begin_0 = const()[name = tensor<string, []>("op_3537_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3537_end_0 = const()[name = tensor<string, []>("op_3537_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3537_end_mask_0 = const()[name = tensor<string, []>("op_3537_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3537 = slice_by_index(begin = var_3537_begin_0, end = var_3537_end_0, end_mask = var_3537_end_mask_0, x = seg_cumsum_129)[name = tensor<string, []>("op_3537")];
            tensor<int32, [3]> var_3540_begin_0 = const()[name = tensor<string, []>("op_3540_begin_0"), val = tensor<int32, [3]>([0, 65000, 0])];
            tensor<int32, [3]> var_3540_end_0 = const()[name = tensor<string, []>("op_3540_end_0"), val = tensor<int32, [3]>([1, 66000, 9])];
            tensor<bool, [3]> var_3540_end_mask_0 = const()[name = tensor<string, []>("op_3540_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3540 = slice_by_index(begin = var_3540_begin_0, end = var_3540_end_0, end_mask = var_3540_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3540")];
            tensor<bool, []> var_3542_exclusive_0 = const()[name = tensor<string, []>("op_3542_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3542_reverse_0 = const()[name = tensor<string, []>("op_3542_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3542 = cumsum(axis = var_2486, exclusive = var_3542_exclusive_0, reverse = var_3542_reverse_0, x = var_3540)[name = tensor<string, []>("op_3542")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_131 = add(x = var_3542, y = var_3537)[name = tensor<string, []>("seg_cumsum_131")];
            tensor<int32, [3]> var_3545_begin_0 = const()[name = tensor<string, []>("op_3545_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3545_end_0 = const()[name = tensor<string, []>("op_3545_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3545_end_mask_0 = const()[name = tensor<string, []>("op_3545_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3545 = slice_by_index(begin = var_3545_begin_0, end = var_3545_end_0, end_mask = var_3545_end_mask_0, x = seg_cumsum_131)[name = tensor<string, []>("op_3545")];
            tensor<int32, [3]> var_3548_begin_0 = const()[name = tensor<string, []>("op_3548_begin_0"), val = tensor<int32, [3]>([0, 66000, 0])];
            tensor<int32, [3]> var_3548_end_0 = const()[name = tensor<string, []>("op_3548_end_0"), val = tensor<int32, [3]>([1, 67000, 9])];
            tensor<bool, [3]> var_3548_end_mask_0 = const()[name = tensor<string, []>("op_3548_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3548 = slice_by_index(begin = var_3548_begin_0, end = var_3548_end_0, end_mask = var_3548_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3548")];
            tensor<bool, []> var_3550_exclusive_0 = const()[name = tensor<string, []>("op_3550_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3550_reverse_0 = const()[name = tensor<string, []>("op_3550_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3550 = cumsum(axis = var_2486, exclusive = var_3550_exclusive_0, reverse = var_3550_reverse_0, x = var_3548)[name = tensor<string, []>("op_3550")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_133 = add(x = var_3550, y = var_3545)[name = tensor<string, []>("seg_cumsum_133")];
            tensor<int32, [3]> var_3553_begin_0 = const()[name = tensor<string, []>("op_3553_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3553_end_0 = const()[name = tensor<string, []>("op_3553_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3553_end_mask_0 = const()[name = tensor<string, []>("op_3553_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3553 = slice_by_index(begin = var_3553_begin_0, end = var_3553_end_0, end_mask = var_3553_end_mask_0, x = seg_cumsum_133)[name = tensor<string, []>("op_3553")];
            tensor<int32, [3]> var_3556_begin_0 = const()[name = tensor<string, []>("op_3556_begin_0"), val = tensor<int32, [3]>([0, 67000, 0])];
            tensor<int32, [3]> var_3556_end_0 = const()[name = tensor<string, []>("op_3556_end_0"), val = tensor<int32, [3]>([1, 68000, 9])];
            tensor<bool, [3]> var_3556_end_mask_0 = const()[name = tensor<string, []>("op_3556_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3556 = slice_by_index(begin = var_3556_begin_0, end = var_3556_end_0, end_mask = var_3556_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3556")];
            tensor<bool, []> var_3558_exclusive_0 = const()[name = tensor<string, []>("op_3558_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3558_reverse_0 = const()[name = tensor<string, []>("op_3558_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3558 = cumsum(axis = var_2486, exclusive = var_3558_exclusive_0, reverse = var_3558_reverse_0, x = var_3556)[name = tensor<string, []>("op_3558")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_135 = add(x = var_3558, y = var_3553)[name = tensor<string, []>("seg_cumsum_135")];
            tensor<int32, [3]> var_3561_begin_0 = const()[name = tensor<string, []>("op_3561_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3561_end_0 = const()[name = tensor<string, []>("op_3561_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3561_end_mask_0 = const()[name = tensor<string, []>("op_3561_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3561 = slice_by_index(begin = var_3561_begin_0, end = var_3561_end_0, end_mask = var_3561_end_mask_0, x = seg_cumsum_135)[name = tensor<string, []>("op_3561")];
            tensor<int32, [3]> var_3564_begin_0 = const()[name = tensor<string, []>("op_3564_begin_0"), val = tensor<int32, [3]>([0, 68000, 0])];
            tensor<int32, [3]> var_3564_end_0 = const()[name = tensor<string, []>("op_3564_end_0"), val = tensor<int32, [3]>([1, 69000, 9])];
            tensor<bool, [3]> var_3564_end_mask_0 = const()[name = tensor<string, []>("op_3564_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3564 = slice_by_index(begin = var_3564_begin_0, end = var_3564_end_0, end_mask = var_3564_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3564")];
            tensor<bool, []> var_3566_exclusive_0 = const()[name = tensor<string, []>("op_3566_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3566_reverse_0 = const()[name = tensor<string, []>("op_3566_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3566 = cumsum(axis = var_2486, exclusive = var_3566_exclusive_0, reverse = var_3566_reverse_0, x = var_3564)[name = tensor<string, []>("op_3566")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_137 = add(x = var_3566, y = var_3561)[name = tensor<string, []>("seg_cumsum_137")];
            tensor<int32, [3]> var_3569_begin_0 = const()[name = tensor<string, []>("op_3569_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3569_end_0 = const()[name = tensor<string, []>("op_3569_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3569_end_mask_0 = const()[name = tensor<string, []>("op_3569_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3569 = slice_by_index(begin = var_3569_begin_0, end = var_3569_end_0, end_mask = var_3569_end_mask_0, x = seg_cumsum_137)[name = tensor<string, []>("op_3569")];
            tensor<int32, [3]> var_3572_begin_0 = const()[name = tensor<string, []>("op_3572_begin_0"), val = tensor<int32, [3]>([0, 69000, 0])];
            tensor<int32, [3]> var_3572_end_0 = const()[name = tensor<string, []>("op_3572_end_0"), val = tensor<int32, [3]>([1, 70000, 9])];
            tensor<bool, [3]> var_3572_end_mask_0 = const()[name = tensor<string, []>("op_3572_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3572 = slice_by_index(begin = var_3572_begin_0, end = var_3572_end_0, end_mask = var_3572_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3572")];
            tensor<bool, []> var_3574_exclusive_0 = const()[name = tensor<string, []>("op_3574_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3574_reverse_0 = const()[name = tensor<string, []>("op_3574_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3574 = cumsum(axis = var_2486, exclusive = var_3574_exclusive_0, reverse = var_3574_reverse_0, x = var_3572)[name = tensor<string, []>("op_3574")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_139 = add(x = var_3574, y = var_3569)[name = tensor<string, []>("seg_cumsum_139")];
            tensor<int32, [3]> var_3577_begin_0 = const()[name = tensor<string, []>("op_3577_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3577_end_0 = const()[name = tensor<string, []>("op_3577_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3577_end_mask_0 = const()[name = tensor<string, []>("op_3577_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3577 = slice_by_index(begin = var_3577_begin_0, end = var_3577_end_0, end_mask = var_3577_end_mask_0, x = seg_cumsum_139)[name = tensor<string, []>("op_3577")];
            tensor<int32, [3]> var_3580_begin_0 = const()[name = tensor<string, []>("op_3580_begin_0"), val = tensor<int32, [3]>([0, 70000, 0])];
            tensor<int32, [3]> var_3580_end_0 = const()[name = tensor<string, []>("op_3580_end_0"), val = tensor<int32, [3]>([1, 71000, 9])];
            tensor<bool, [3]> var_3580_end_mask_0 = const()[name = tensor<string, []>("op_3580_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3580 = slice_by_index(begin = var_3580_begin_0, end = var_3580_end_0, end_mask = var_3580_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3580")];
            tensor<bool, []> var_3582_exclusive_0 = const()[name = tensor<string, []>("op_3582_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3582_reverse_0 = const()[name = tensor<string, []>("op_3582_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3582 = cumsum(axis = var_2486, exclusive = var_3582_exclusive_0, reverse = var_3582_reverse_0, x = var_3580)[name = tensor<string, []>("op_3582")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_141 = add(x = var_3582, y = var_3577)[name = tensor<string, []>("seg_cumsum_141")];
            tensor<int32, [3]> var_3585_begin_0 = const()[name = tensor<string, []>("op_3585_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3585_end_0 = const()[name = tensor<string, []>("op_3585_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3585_end_mask_0 = const()[name = tensor<string, []>("op_3585_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3585 = slice_by_index(begin = var_3585_begin_0, end = var_3585_end_0, end_mask = var_3585_end_mask_0, x = seg_cumsum_141)[name = tensor<string, []>("op_3585")];
            tensor<int32, [3]> var_3588_begin_0 = const()[name = tensor<string, []>("op_3588_begin_0"), val = tensor<int32, [3]>([0, 71000, 0])];
            tensor<int32, [3]> var_3588_end_0 = const()[name = tensor<string, []>("op_3588_end_0"), val = tensor<int32, [3]>([1, 72000, 9])];
            tensor<bool, [3]> var_3588_end_mask_0 = const()[name = tensor<string, []>("op_3588_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3588 = slice_by_index(begin = var_3588_begin_0, end = var_3588_end_0, end_mask = var_3588_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3588")];
            tensor<bool, []> var_3590_exclusive_0 = const()[name = tensor<string, []>("op_3590_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3590_reverse_0 = const()[name = tensor<string, []>("op_3590_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3590 = cumsum(axis = var_2486, exclusive = var_3590_exclusive_0, reverse = var_3590_reverse_0, x = var_3588)[name = tensor<string, []>("op_3590")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_143 = add(x = var_3590, y = var_3585)[name = tensor<string, []>("seg_cumsum_143")];
            tensor<int32, [3]> var_3593_begin_0 = const()[name = tensor<string, []>("op_3593_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3593_end_0 = const()[name = tensor<string, []>("op_3593_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3593_end_mask_0 = const()[name = tensor<string, []>("op_3593_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3593 = slice_by_index(begin = var_3593_begin_0, end = var_3593_end_0, end_mask = var_3593_end_mask_0, x = seg_cumsum_143)[name = tensor<string, []>("op_3593")];
            tensor<int32, [3]> var_3596_begin_0 = const()[name = tensor<string, []>("op_3596_begin_0"), val = tensor<int32, [3]>([0, 72000, 0])];
            tensor<int32, [3]> var_3596_end_0 = const()[name = tensor<string, []>("op_3596_end_0"), val = tensor<int32, [3]>([1, 73000, 9])];
            tensor<bool, [3]> var_3596_end_mask_0 = const()[name = tensor<string, []>("op_3596_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3596 = slice_by_index(begin = var_3596_begin_0, end = var_3596_end_0, end_mask = var_3596_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3596")];
            tensor<bool, []> var_3598_exclusive_0 = const()[name = tensor<string, []>("op_3598_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3598_reverse_0 = const()[name = tensor<string, []>("op_3598_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3598 = cumsum(axis = var_2486, exclusive = var_3598_exclusive_0, reverse = var_3598_reverse_0, x = var_3596)[name = tensor<string, []>("op_3598")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_145 = add(x = var_3598, y = var_3593)[name = tensor<string, []>("seg_cumsum_145")];
            tensor<int32, [3]> var_3601_begin_0 = const()[name = tensor<string, []>("op_3601_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3601_end_0 = const()[name = tensor<string, []>("op_3601_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3601_end_mask_0 = const()[name = tensor<string, []>("op_3601_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3601 = slice_by_index(begin = var_3601_begin_0, end = var_3601_end_0, end_mask = var_3601_end_mask_0, x = seg_cumsum_145)[name = tensor<string, []>("op_3601")];
            tensor<int32, [3]> var_3604_begin_0 = const()[name = tensor<string, []>("op_3604_begin_0"), val = tensor<int32, [3]>([0, 73000, 0])];
            tensor<int32, [3]> var_3604_end_0 = const()[name = tensor<string, []>("op_3604_end_0"), val = tensor<int32, [3]>([1, 74000, 9])];
            tensor<bool, [3]> var_3604_end_mask_0 = const()[name = tensor<string, []>("op_3604_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3604 = slice_by_index(begin = var_3604_begin_0, end = var_3604_end_0, end_mask = var_3604_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3604")];
            tensor<bool, []> var_3606_exclusive_0 = const()[name = tensor<string, []>("op_3606_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3606_reverse_0 = const()[name = tensor<string, []>("op_3606_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3606 = cumsum(axis = var_2486, exclusive = var_3606_exclusive_0, reverse = var_3606_reverse_0, x = var_3604)[name = tensor<string, []>("op_3606")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_147 = add(x = var_3606, y = var_3601)[name = tensor<string, []>("seg_cumsum_147")];
            tensor<int32, [3]> var_3609_begin_0 = const()[name = tensor<string, []>("op_3609_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3609_end_0 = const()[name = tensor<string, []>("op_3609_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3609_end_mask_0 = const()[name = tensor<string, []>("op_3609_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3609 = slice_by_index(begin = var_3609_begin_0, end = var_3609_end_0, end_mask = var_3609_end_mask_0, x = seg_cumsum_147)[name = tensor<string, []>("op_3609")];
            tensor<int32, [3]> var_3612_begin_0 = const()[name = tensor<string, []>("op_3612_begin_0"), val = tensor<int32, [3]>([0, 74000, 0])];
            tensor<int32, [3]> var_3612_end_0 = const()[name = tensor<string, []>("op_3612_end_0"), val = tensor<int32, [3]>([1, 75000, 9])];
            tensor<bool, [3]> var_3612_end_mask_0 = const()[name = tensor<string, []>("op_3612_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3612 = slice_by_index(begin = var_3612_begin_0, end = var_3612_end_0, end_mask = var_3612_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3612")];
            tensor<bool, []> var_3614_exclusive_0 = const()[name = tensor<string, []>("op_3614_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3614_reverse_0 = const()[name = tensor<string, []>("op_3614_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3614 = cumsum(axis = var_2486, exclusive = var_3614_exclusive_0, reverse = var_3614_reverse_0, x = var_3612)[name = tensor<string, []>("op_3614")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_149 = add(x = var_3614, y = var_3609)[name = tensor<string, []>("seg_cumsum_149")];
            tensor<int32, [3]> var_3617_begin_0 = const()[name = tensor<string, []>("op_3617_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3617_end_0 = const()[name = tensor<string, []>("op_3617_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3617_end_mask_0 = const()[name = tensor<string, []>("op_3617_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3617 = slice_by_index(begin = var_3617_begin_0, end = var_3617_end_0, end_mask = var_3617_end_mask_0, x = seg_cumsum_149)[name = tensor<string, []>("op_3617")];
            tensor<int32, [3]> var_3620_begin_0 = const()[name = tensor<string, []>("op_3620_begin_0"), val = tensor<int32, [3]>([0, 75000, 0])];
            tensor<int32, [3]> var_3620_end_0 = const()[name = tensor<string, []>("op_3620_end_0"), val = tensor<int32, [3]>([1, 76000, 9])];
            tensor<bool, [3]> var_3620_end_mask_0 = const()[name = tensor<string, []>("op_3620_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3620 = slice_by_index(begin = var_3620_begin_0, end = var_3620_end_0, end_mask = var_3620_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3620")];
            tensor<bool, []> var_3622_exclusive_0 = const()[name = tensor<string, []>("op_3622_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3622_reverse_0 = const()[name = tensor<string, []>("op_3622_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3622 = cumsum(axis = var_2486, exclusive = var_3622_exclusive_0, reverse = var_3622_reverse_0, x = var_3620)[name = tensor<string, []>("op_3622")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_151 = add(x = var_3622, y = var_3617)[name = tensor<string, []>("seg_cumsum_151")];
            tensor<int32, [3]> var_3625_begin_0 = const()[name = tensor<string, []>("op_3625_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3625_end_0 = const()[name = tensor<string, []>("op_3625_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3625_end_mask_0 = const()[name = tensor<string, []>("op_3625_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3625 = slice_by_index(begin = var_3625_begin_0, end = var_3625_end_0, end_mask = var_3625_end_mask_0, x = seg_cumsum_151)[name = tensor<string, []>("op_3625")];
            tensor<int32, [3]> var_3628_begin_0 = const()[name = tensor<string, []>("op_3628_begin_0"), val = tensor<int32, [3]>([0, 76000, 0])];
            tensor<int32, [3]> var_3628_end_0 = const()[name = tensor<string, []>("op_3628_end_0"), val = tensor<int32, [3]>([1, 77000, 9])];
            tensor<bool, [3]> var_3628_end_mask_0 = const()[name = tensor<string, []>("op_3628_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3628 = slice_by_index(begin = var_3628_begin_0, end = var_3628_end_0, end_mask = var_3628_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3628")];
            tensor<bool, []> var_3630_exclusive_0 = const()[name = tensor<string, []>("op_3630_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3630_reverse_0 = const()[name = tensor<string, []>("op_3630_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3630 = cumsum(axis = var_2486, exclusive = var_3630_exclusive_0, reverse = var_3630_reverse_0, x = var_3628)[name = tensor<string, []>("op_3630")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_153 = add(x = var_3630, y = var_3625)[name = tensor<string, []>("seg_cumsum_153")];
            tensor<int32, [3]> var_3633_begin_0 = const()[name = tensor<string, []>("op_3633_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3633_end_0 = const()[name = tensor<string, []>("op_3633_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3633_end_mask_0 = const()[name = tensor<string, []>("op_3633_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3633 = slice_by_index(begin = var_3633_begin_0, end = var_3633_end_0, end_mask = var_3633_end_mask_0, x = seg_cumsum_153)[name = tensor<string, []>("op_3633")];
            tensor<int32, [3]> var_3636_begin_0 = const()[name = tensor<string, []>("op_3636_begin_0"), val = tensor<int32, [3]>([0, 77000, 0])];
            tensor<int32, [3]> var_3636_end_0 = const()[name = tensor<string, []>("op_3636_end_0"), val = tensor<int32, [3]>([1, 78000, 9])];
            tensor<bool, [3]> var_3636_end_mask_0 = const()[name = tensor<string, []>("op_3636_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3636 = slice_by_index(begin = var_3636_begin_0, end = var_3636_end_0, end_mask = var_3636_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3636")];
            tensor<bool, []> var_3638_exclusive_0 = const()[name = tensor<string, []>("op_3638_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3638_reverse_0 = const()[name = tensor<string, []>("op_3638_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3638 = cumsum(axis = var_2486, exclusive = var_3638_exclusive_0, reverse = var_3638_reverse_0, x = var_3636)[name = tensor<string, []>("op_3638")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_155 = add(x = var_3638, y = var_3633)[name = tensor<string, []>("seg_cumsum_155")];
            tensor<int32, [3]> var_3641_begin_0 = const()[name = tensor<string, []>("op_3641_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3641_end_0 = const()[name = tensor<string, []>("op_3641_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3641_end_mask_0 = const()[name = tensor<string, []>("op_3641_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3641 = slice_by_index(begin = var_3641_begin_0, end = var_3641_end_0, end_mask = var_3641_end_mask_0, x = seg_cumsum_155)[name = tensor<string, []>("op_3641")];
            tensor<int32, [3]> var_3644_begin_0 = const()[name = tensor<string, []>("op_3644_begin_0"), val = tensor<int32, [3]>([0, 78000, 0])];
            tensor<int32, [3]> var_3644_end_0 = const()[name = tensor<string, []>("op_3644_end_0"), val = tensor<int32, [3]>([1, 79000, 9])];
            tensor<bool, [3]> var_3644_end_mask_0 = const()[name = tensor<string, []>("op_3644_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3644 = slice_by_index(begin = var_3644_begin_0, end = var_3644_end_0, end_mask = var_3644_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3644")];
            tensor<bool, []> var_3646_exclusive_0 = const()[name = tensor<string, []>("op_3646_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3646_reverse_0 = const()[name = tensor<string, []>("op_3646_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3646 = cumsum(axis = var_2486, exclusive = var_3646_exclusive_0, reverse = var_3646_reverse_0, x = var_3644)[name = tensor<string, []>("op_3646")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_157 = add(x = var_3646, y = var_3641)[name = tensor<string, []>("seg_cumsum_157")];
            tensor<int32, [3]> var_3649_begin_0 = const()[name = tensor<string, []>("op_3649_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3649_end_0 = const()[name = tensor<string, []>("op_3649_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3649_end_mask_0 = const()[name = tensor<string, []>("op_3649_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3649 = slice_by_index(begin = var_3649_begin_0, end = var_3649_end_0, end_mask = var_3649_end_mask_0, x = seg_cumsum_157)[name = tensor<string, []>("op_3649")];
            tensor<int32, [3]> var_3652_begin_0 = const()[name = tensor<string, []>("op_3652_begin_0"), val = tensor<int32, [3]>([0, 79000, 0])];
            tensor<int32, [3]> var_3652_end_0 = const()[name = tensor<string, []>("op_3652_end_0"), val = tensor<int32, [3]>([1, 80000, 9])];
            tensor<bool, [3]> var_3652_end_mask_0 = const()[name = tensor<string, []>("op_3652_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3652 = slice_by_index(begin = var_3652_begin_0, end = var_3652_end_0, end_mask = var_3652_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3652")];
            tensor<bool, []> var_3654_exclusive_0 = const()[name = tensor<string, []>("op_3654_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3654_reverse_0 = const()[name = tensor<string, []>("op_3654_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3654 = cumsum(axis = var_2486, exclusive = var_3654_exclusive_0, reverse = var_3654_reverse_0, x = var_3652)[name = tensor<string, []>("op_3654")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_159 = add(x = var_3654, y = var_3649)[name = tensor<string, []>("seg_cumsum_159")];
            tensor<int32, [3]> var_3657_begin_0 = const()[name = tensor<string, []>("op_3657_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3657_end_0 = const()[name = tensor<string, []>("op_3657_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3657_end_mask_0 = const()[name = tensor<string, []>("op_3657_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3657 = slice_by_index(begin = var_3657_begin_0, end = var_3657_end_0, end_mask = var_3657_end_mask_0, x = seg_cumsum_159)[name = tensor<string, []>("op_3657")];
            tensor<int32, [3]> var_3660_begin_0 = const()[name = tensor<string, []>("op_3660_begin_0"), val = tensor<int32, [3]>([0, 80000, 0])];
            tensor<int32, [3]> var_3660_end_0 = const()[name = tensor<string, []>("op_3660_end_0"), val = tensor<int32, [3]>([1, 81000, 9])];
            tensor<bool, [3]> var_3660_end_mask_0 = const()[name = tensor<string, []>("op_3660_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3660 = slice_by_index(begin = var_3660_begin_0, end = var_3660_end_0, end_mask = var_3660_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3660")];
            tensor<bool, []> var_3662_exclusive_0 = const()[name = tensor<string, []>("op_3662_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3662_reverse_0 = const()[name = tensor<string, []>("op_3662_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3662 = cumsum(axis = var_2486, exclusive = var_3662_exclusive_0, reverse = var_3662_reverse_0, x = var_3660)[name = tensor<string, []>("op_3662")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_161 = add(x = var_3662, y = var_3657)[name = tensor<string, []>("seg_cumsum_161")];
            tensor<int32, [3]> var_3665_begin_0 = const()[name = tensor<string, []>("op_3665_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3665_end_0 = const()[name = tensor<string, []>("op_3665_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3665_end_mask_0 = const()[name = tensor<string, []>("op_3665_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3665 = slice_by_index(begin = var_3665_begin_0, end = var_3665_end_0, end_mask = var_3665_end_mask_0, x = seg_cumsum_161)[name = tensor<string, []>("op_3665")];
            tensor<int32, [3]> var_3668_begin_0 = const()[name = tensor<string, []>("op_3668_begin_0"), val = tensor<int32, [3]>([0, 81000, 0])];
            tensor<int32, [3]> var_3668_end_0 = const()[name = tensor<string, []>("op_3668_end_0"), val = tensor<int32, [3]>([1, 82000, 9])];
            tensor<bool, [3]> var_3668_end_mask_0 = const()[name = tensor<string, []>("op_3668_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3668 = slice_by_index(begin = var_3668_begin_0, end = var_3668_end_0, end_mask = var_3668_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3668")];
            tensor<bool, []> var_3670_exclusive_0 = const()[name = tensor<string, []>("op_3670_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3670_reverse_0 = const()[name = tensor<string, []>("op_3670_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3670 = cumsum(axis = var_2486, exclusive = var_3670_exclusive_0, reverse = var_3670_reverse_0, x = var_3668)[name = tensor<string, []>("op_3670")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_163 = add(x = var_3670, y = var_3665)[name = tensor<string, []>("seg_cumsum_163")];
            tensor<int32, [3]> var_3673_begin_0 = const()[name = tensor<string, []>("op_3673_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3673_end_0 = const()[name = tensor<string, []>("op_3673_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3673_end_mask_0 = const()[name = tensor<string, []>("op_3673_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3673 = slice_by_index(begin = var_3673_begin_0, end = var_3673_end_0, end_mask = var_3673_end_mask_0, x = seg_cumsum_163)[name = tensor<string, []>("op_3673")];
            tensor<int32, [3]> var_3676_begin_0 = const()[name = tensor<string, []>("op_3676_begin_0"), val = tensor<int32, [3]>([0, 82000, 0])];
            tensor<int32, [3]> var_3676_end_0 = const()[name = tensor<string, []>("op_3676_end_0"), val = tensor<int32, [3]>([1, 83000, 9])];
            tensor<bool, [3]> var_3676_end_mask_0 = const()[name = tensor<string, []>("op_3676_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3676 = slice_by_index(begin = var_3676_begin_0, end = var_3676_end_0, end_mask = var_3676_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3676")];
            tensor<bool, []> var_3678_exclusive_0 = const()[name = tensor<string, []>("op_3678_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3678_reverse_0 = const()[name = tensor<string, []>("op_3678_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3678 = cumsum(axis = var_2486, exclusive = var_3678_exclusive_0, reverse = var_3678_reverse_0, x = var_3676)[name = tensor<string, []>("op_3678")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_165 = add(x = var_3678, y = var_3673)[name = tensor<string, []>("seg_cumsum_165")];
            tensor<int32, [3]> var_3681_begin_0 = const()[name = tensor<string, []>("op_3681_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3681_end_0 = const()[name = tensor<string, []>("op_3681_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3681_end_mask_0 = const()[name = tensor<string, []>("op_3681_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3681 = slice_by_index(begin = var_3681_begin_0, end = var_3681_end_0, end_mask = var_3681_end_mask_0, x = seg_cumsum_165)[name = tensor<string, []>("op_3681")];
            tensor<int32, [3]> var_3684_begin_0 = const()[name = tensor<string, []>("op_3684_begin_0"), val = tensor<int32, [3]>([0, 83000, 0])];
            tensor<int32, [3]> var_3684_end_0 = const()[name = tensor<string, []>("op_3684_end_0"), val = tensor<int32, [3]>([1, 84000, 9])];
            tensor<bool, [3]> var_3684_end_mask_0 = const()[name = tensor<string, []>("op_3684_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3684 = slice_by_index(begin = var_3684_begin_0, end = var_3684_end_0, end_mask = var_3684_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3684")];
            tensor<bool, []> var_3686_exclusive_0 = const()[name = tensor<string, []>("op_3686_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3686_reverse_0 = const()[name = tensor<string, []>("op_3686_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3686 = cumsum(axis = var_2486, exclusive = var_3686_exclusive_0, reverse = var_3686_reverse_0, x = var_3684)[name = tensor<string, []>("op_3686")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_167 = add(x = var_3686, y = var_3681)[name = tensor<string, []>("seg_cumsum_167")];
            tensor<int32, [3]> var_3689_begin_0 = const()[name = tensor<string, []>("op_3689_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3689_end_0 = const()[name = tensor<string, []>("op_3689_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3689_end_mask_0 = const()[name = tensor<string, []>("op_3689_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3689 = slice_by_index(begin = var_3689_begin_0, end = var_3689_end_0, end_mask = var_3689_end_mask_0, x = seg_cumsum_167)[name = tensor<string, []>("op_3689")];
            tensor<int32, [3]> var_3692_begin_0 = const()[name = tensor<string, []>("op_3692_begin_0"), val = tensor<int32, [3]>([0, 84000, 0])];
            tensor<int32, [3]> var_3692_end_0 = const()[name = tensor<string, []>("op_3692_end_0"), val = tensor<int32, [3]>([1, 85000, 9])];
            tensor<bool, [3]> var_3692_end_mask_0 = const()[name = tensor<string, []>("op_3692_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3692 = slice_by_index(begin = var_3692_begin_0, end = var_3692_end_0, end_mask = var_3692_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3692")];
            tensor<bool, []> var_3694_exclusive_0 = const()[name = tensor<string, []>("op_3694_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3694_reverse_0 = const()[name = tensor<string, []>("op_3694_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3694 = cumsum(axis = var_2486, exclusive = var_3694_exclusive_0, reverse = var_3694_reverse_0, x = var_3692)[name = tensor<string, []>("op_3694")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_169 = add(x = var_3694, y = var_3689)[name = tensor<string, []>("seg_cumsum_169")];
            tensor<int32, [3]> var_3697_begin_0 = const()[name = tensor<string, []>("op_3697_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3697_end_0 = const()[name = tensor<string, []>("op_3697_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3697_end_mask_0 = const()[name = tensor<string, []>("op_3697_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3697 = slice_by_index(begin = var_3697_begin_0, end = var_3697_end_0, end_mask = var_3697_end_mask_0, x = seg_cumsum_169)[name = tensor<string, []>("op_3697")];
            tensor<int32, [3]> var_3700_begin_0 = const()[name = tensor<string, []>("op_3700_begin_0"), val = tensor<int32, [3]>([0, 85000, 0])];
            tensor<int32, [3]> var_3700_end_0 = const()[name = tensor<string, []>("op_3700_end_0"), val = tensor<int32, [3]>([1, 86000, 9])];
            tensor<bool, [3]> var_3700_end_mask_0 = const()[name = tensor<string, []>("op_3700_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3700 = slice_by_index(begin = var_3700_begin_0, end = var_3700_end_0, end_mask = var_3700_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3700")];
            tensor<bool, []> var_3702_exclusive_0 = const()[name = tensor<string, []>("op_3702_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3702_reverse_0 = const()[name = tensor<string, []>("op_3702_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3702 = cumsum(axis = var_2486, exclusive = var_3702_exclusive_0, reverse = var_3702_reverse_0, x = var_3700)[name = tensor<string, []>("op_3702")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_171 = add(x = var_3702, y = var_3697)[name = tensor<string, []>("seg_cumsum_171")];
            tensor<int32, [3]> var_3705_begin_0 = const()[name = tensor<string, []>("op_3705_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3705_end_0 = const()[name = tensor<string, []>("op_3705_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3705_end_mask_0 = const()[name = tensor<string, []>("op_3705_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3705 = slice_by_index(begin = var_3705_begin_0, end = var_3705_end_0, end_mask = var_3705_end_mask_0, x = seg_cumsum_171)[name = tensor<string, []>("op_3705")];
            tensor<int32, [3]> var_3708_begin_0 = const()[name = tensor<string, []>("op_3708_begin_0"), val = tensor<int32, [3]>([0, 86000, 0])];
            tensor<int32, [3]> var_3708_end_0 = const()[name = tensor<string, []>("op_3708_end_0"), val = tensor<int32, [3]>([1, 87000, 9])];
            tensor<bool, [3]> var_3708_end_mask_0 = const()[name = tensor<string, []>("op_3708_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3708 = slice_by_index(begin = var_3708_begin_0, end = var_3708_end_0, end_mask = var_3708_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3708")];
            tensor<bool, []> var_3710_exclusive_0 = const()[name = tensor<string, []>("op_3710_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3710_reverse_0 = const()[name = tensor<string, []>("op_3710_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3710 = cumsum(axis = var_2486, exclusive = var_3710_exclusive_0, reverse = var_3710_reverse_0, x = var_3708)[name = tensor<string, []>("op_3710")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_173 = add(x = var_3710, y = var_3705)[name = tensor<string, []>("seg_cumsum_173")];
            tensor<int32, [3]> var_3713_begin_0 = const()[name = tensor<string, []>("op_3713_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3713_end_0 = const()[name = tensor<string, []>("op_3713_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3713_end_mask_0 = const()[name = tensor<string, []>("op_3713_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3713 = slice_by_index(begin = var_3713_begin_0, end = var_3713_end_0, end_mask = var_3713_end_mask_0, x = seg_cumsum_173)[name = tensor<string, []>("op_3713")];
            tensor<int32, [3]> var_3716_begin_0 = const()[name = tensor<string, []>("op_3716_begin_0"), val = tensor<int32, [3]>([0, 87000, 0])];
            tensor<int32, [3]> var_3716_end_0 = const()[name = tensor<string, []>("op_3716_end_0"), val = tensor<int32, [3]>([1, 88000, 9])];
            tensor<bool, [3]> var_3716_end_mask_0 = const()[name = tensor<string, []>("op_3716_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3716 = slice_by_index(begin = var_3716_begin_0, end = var_3716_end_0, end_mask = var_3716_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3716")];
            tensor<bool, []> var_3718_exclusive_0 = const()[name = tensor<string, []>("op_3718_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3718_reverse_0 = const()[name = tensor<string, []>("op_3718_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3718 = cumsum(axis = var_2486, exclusive = var_3718_exclusive_0, reverse = var_3718_reverse_0, x = var_3716)[name = tensor<string, []>("op_3718")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_175 = add(x = var_3718, y = var_3713)[name = tensor<string, []>("seg_cumsum_175")];
            tensor<int32, [3]> var_3721_begin_0 = const()[name = tensor<string, []>("op_3721_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3721_end_0 = const()[name = tensor<string, []>("op_3721_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3721_end_mask_0 = const()[name = tensor<string, []>("op_3721_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3721 = slice_by_index(begin = var_3721_begin_0, end = var_3721_end_0, end_mask = var_3721_end_mask_0, x = seg_cumsum_175)[name = tensor<string, []>("op_3721")];
            tensor<int32, [3]> var_3724_begin_0 = const()[name = tensor<string, []>("op_3724_begin_0"), val = tensor<int32, [3]>([0, 88000, 0])];
            tensor<int32, [3]> var_3724_end_0 = const()[name = tensor<string, []>("op_3724_end_0"), val = tensor<int32, [3]>([1, 89000, 9])];
            tensor<bool, [3]> var_3724_end_mask_0 = const()[name = tensor<string, []>("op_3724_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3724 = slice_by_index(begin = var_3724_begin_0, end = var_3724_end_0, end_mask = var_3724_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3724")];
            tensor<bool, []> var_3726_exclusive_0 = const()[name = tensor<string, []>("op_3726_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3726_reverse_0 = const()[name = tensor<string, []>("op_3726_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3726 = cumsum(axis = var_2486, exclusive = var_3726_exclusive_0, reverse = var_3726_reverse_0, x = var_3724)[name = tensor<string, []>("op_3726")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_177 = add(x = var_3726, y = var_3721)[name = tensor<string, []>("seg_cumsum_177")];
            tensor<int32, [3]> var_3729_begin_0 = const()[name = tensor<string, []>("op_3729_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3729_end_0 = const()[name = tensor<string, []>("op_3729_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3729_end_mask_0 = const()[name = tensor<string, []>("op_3729_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3729 = slice_by_index(begin = var_3729_begin_0, end = var_3729_end_0, end_mask = var_3729_end_mask_0, x = seg_cumsum_177)[name = tensor<string, []>("op_3729")];
            tensor<int32, [3]> var_3732_begin_0 = const()[name = tensor<string, []>("op_3732_begin_0"), val = tensor<int32, [3]>([0, 89000, 0])];
            tensor<int32, [3]> var_3732_end_0 = const()[name = tensor<string, []>("op_3732_end_0"), val = tensor<int32, [3]>([1, 90000, 9])];
            tensor<bool, [3]> var_3732_end_mask_0 = const()[name = tensor<string, []>("op_3732_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3732 = slice_by_index(begin = var_3732_begin_0, end = var_3732_end_0, end_mask = var_3732_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3732")];
            tensor<bool, []> var_3734_exclusive_0 = const()[name = tensor<string, []>("op_3734_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3734_reverse_0 = const()[name = tensor<string, []>("op_3734_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3734 = cumsum(axis = var_2486, exclusive = var_3734_exclusive_0, reverse = var_3734_reverse_0, x = var_3732)[name = tensor<string, []>("op_3734")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_179 = add(x = var_3734, y = var_3729)[name = tensor<string, []>("seg_cumsum_179")];
            tensor<int32, [3]> var_3737_begin_0 = const()[name = tensor<string, []>("op_3737_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3737_end_0 = const()[name = tensor<string, []>("op_3737_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3737_end_mask_0 = const()[name = tensor<string, []>("op_3737_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3737 = slice_by_index(begin = var_3737_begin_0, end = var_3737_end_0, end_mask = var_3737_end_mask_0, x = seg_cumsum_179)[name = tensor<string, []>("op_3737")];
            tensor<int32, [3]> var_3740_begin_0 = const()[name = tensor<string, []>("op_3740_begin_0"), val = tensor<int32, [3]>([0, 90000, 0])];
            tensor<int32, [3]> var_3740_end_0 = const()[name = tensor<string, []>("op_3740_end_0"), val = tensor<int32, [3]>([1, 91000, 9])];
            tensor<bool, [3]> var_3740_end_mask_0 = const()[name = tensor<string, []>("op_3740_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3740 = slice_by_index(begin = var_3740_begin_0, end = var_3740_end_0, end_mask = var_3740_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3740")];
            tensor<bool, []> var_3742_exclusive_0 = const()[name = tensor<string, []>("op_3742_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3742_reverse_0 = const()[name = tensor<string, []>("op_3742_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3742 = cumsum(axis = var_2486, exclusive = var_3742_exclusive_0, reverse = var_3742_reverse_0, x = var_3740)[name = tensor<string, []>("op_3742")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_181 = add(x = var_3742, y = var_3737)[name = tensor<string, []>("seg_cumsum_181")];
            tensor<int32, [3]> var_3745_begin_0 = const()[name = tensor<string, []>("op_3745_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3745_end_0 = const()[name = tensor<string, []>("op_3745_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3745_end_mask_0 = const()[name = tensor<string, []>("op_3745_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3745 = slice_by_index(begin = var_3745_begin_0, end = var_3745_end_0, end_mask = var_3745_end_mask_0, x = seg_cumsum_181)[name = tensor<string, []>("op_3745")];
            tensor<int32, [3]> var_3748_begin_0 = const()[name = tensor<string, []>("op_3748_begin_0"), val = tensor<int32, [3]>([0, 91000, 0])];
            tensor<int32, [3]> var_3748_end_0 = const()[name = tensor<string, []>("op_3748_end_0"), val = tensor<int32, [3]>([1, 92000, 9])];
            tensor<bool, [3]> var_3748_end_mask_0 = const()[name = tensor<string, []>("op_3748_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3748 = slice_by_index(begin = var_3748_begin_0, end = var_3748_end_0, end_mask = var_3748_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3748")];
            tensor<bool, []> var_3750_exclusive_0 = const()[name = tensor<string, []>("op_3750_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3750_reverse_0 = const()[name = tensor<string, []>("op_3750_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3750 = cumsum(axis = var_2486, exclusive = var_3750_exclusive_0, reverse = var_3750_reverse_0, x = var_3748)[name = tensor<string, []>("op_3750")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_183 = add(x = var_3750, y = var_3745)[name = tensor<string, []>("seg_cumsum_183")];
            tensor<int32, [3]> var_3753_begin_0 = const()[name = tensor<string, []>("op_3753_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3753_end_0 = const()[name = tensor<string, []>("op_3753_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3753_end_mask_0 = const()[name = tensor<string, []>("op_3753_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3753 = slice_by_index(begin = var_3753_begin_0, end = var_3753_end_0, end_mask = var_3753_end_mask_0, x = seg_cumsum_183)[name = tensor<string, []>("op_3753")];
            tensor<int32, [3]> var_3756_begin_0 = const()[name = tensor<string, []>("op_3756_begin_0"), val = tensor<int32, [3]>([0, 92000, 0])];
            tensor<int32, [3]> var_3756_end_0 = const()[name = tensor<string, []>("op_3756_end_0"), val = tensor<int32, [3]>([1, 93000, 9])];
            tensor<bool, [3]> var_3756_end_mask_0 = const()[name = tensor<string, []>("op_3756_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3756 = slice_by_index(begin = var_3756_begin_0, end = var_3756_end_0, end_mask = var_3756_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3756")];
            tensor<bool, []> var_3758_exclusive_0 = const()[name = tensor<string, []>("op_3758_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3758_reverse_0 = const()[name = tensor<string, []>("op_3758_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3758 = cumsum(axis = var_2486, exclusive = var_3758_exclusive_0, reverse = var_3758_reverse_0, x = var_3756)[name = tensor<string, []>("op_3758")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_185 = add(x = var_3758, y = var_3753)[name = tensor<string, []>("seg_cumsum_185")];
            tensor<int32, [3]> var_3761_begin_0 = const()[name = tensor<string, []>("op_3761_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3761_end_0 = const()[name = tensor<string, []>("op_3761_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3761_end_mask_0 = const()[name = tensor<string, []>("op_3761_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3761 = slice_by_index(begin = var_3761_begin_0, end = var_3761_end_0, end_mask = var_3761_end_mask_0, x = seg_cumsum_185)[name = tensor<string, []>("op_3761")];
            tensor<int32, [3]> var_3764_begin_0 = const()[name = tensor<string, []>("op_3764_begin_0"), val = tensor<int32, [3]>([0, 93000, 0])];
            tensor<int32, [3]> var_3764_end_0 = const()[name = tensor<string, []>("op_3764_end_0"), val = tensor<int32, [3]>([1, 94000, 9])];
            tensor<bool, [3]> var_3764_end_mask_0 = const()[name = tensor<string, []>("op_3764_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3764 = slice_by_index(begin = var_3764_begin_0, end = var_3764_end_0, end_mask = var_3764_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3764")];
            tensor<bool, []> var_3766_exclusive_0 = const()[name = tensor<string, []>("op_3766_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3766_reverse_0 = const()[name = tensor<string, []>("op_3766_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3766 = cumsum(axis = var_2486, exclusive = var_3766_exclusive_0, reverse = var_3766_reverse_0, x = var_3764)[name = tensor<string, []>("op_3766")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_187 = add(x = var_3766, y = var_3761)[name = tensor<string, []>("seg_cumsum_187")];
            tensor<int32, [3]> var_3769_begin_0 = const()[name = tensor<string, []>("op_3769_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3769_end_0 = const()[name = tensor<string, []>("op_3769_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3769_end_mask_0 = const()[name = tensor<string, []>("op_3769_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3769 = slice_by_index(begin = var_3769_begin_0, end = var_3769_end_0, end_mask = var_3769_end_mask_0, x = seg_cumsum_187)[name = tensor<string, []>("op_3769")];
            tensor<int32, [3]> var_3772_begin_0 = const()[name = tensor<string, []>("op_3772_begin_0"), val = tensor<int32, [3]>([0, 94000, 0])];
            tensor<int32, [3]> var_3772_end_0 = const()[name = tensor<string, []>("op_3772_end_0"), val = tensor<int32, [3]>([1, 95000, 9])];
            tensor<bool, [3]> var_3772_end_mask_0 = const()[name = tensor<string, []>("op_3772_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3772 = slice_by_index(begin = var_3772_begin_0, end = var_3772_end_0, end_mask = var_3772_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3772")];
            tensor<bool, []> var_3774_exclusive_0 = const()[name = tensor<string, []>("op_3774_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3774_reverse_0 = const()[name = tensor<string, []>("op_3774_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3774 = cumsum(axis = var_2486, exclusive = var_3774_exclusive_0, reverse = var_3774_reverse_0, x = var_3772)[name = tensor<string, []>("op_3774")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_189 = add(x = var_3774, y = var_3769)[name = tensor<string, []>("seg_cumsum_189")];
            tensor<int32, [3]> var_3777_begin_0 = const()[name = tensor<string, []>("op_3777_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3777_end_0 = const()[name = tensor<string, []>("op_3777_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3777_end_mask_0 = const()[name = tensor<string, []>("op_3777_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3777 = slice_by_index(begin = var_3777_begin_0, end = var_3777_end_0, end_mask = var_3777_end_mask_0, x = seg_cumsum_189)[name = tensor<string, []>("op_3777")];
            tensor<int32, [3]> var_3780_begin_0 = const()[name = tensor<string, []>("op_3780_begin_0"), val = tensor<int32, [3]>([0, 95000, 0])];
            tensor<int32, [3]> var_3780_end_0 = const()[name = tensor<string, []>("op_3780_end_0"), val = tensor<int32, [3]>([1, 96000, 9])];
            tensor<bool, [3]> var_3780_end_mask_0 = const()[name = tensor<string, []>("op_3780_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3780 = slice_by_index(begin = var_3780_begin_0, end = var_3780_end_0, end_mask = var_3780_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3780")];
            tensor<bool, []> var_3782_exclusive_0 = const()[name = tensor<string, []>("op_3782_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3782_reverse_0 = const()[name = tensor<string, []>("op_3782_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3782 = cumsum(axis = var_2486, exclusive = var_3782_exclusive_0, reverse = var_3782_reverse_0, x = var_3780)[name = tensor<string, []>("op_3782")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_191 = add(x = var_3782, y = var_3777)[name = tensor<string, []>("seg_cumsum_191")];
            tensor<int32, [3]> var_3785_begin_0 = const()[name = tensor<string, []>("op_3785_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3785_end_0 = const()[name = tensor<string, []>("op_3785_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3785_end_mask_0 = const()[name = tensor<string, []>("op_3785_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3785 = slice_by_index(begin = var_3785_begin_0, end = var_3785_end_0, end_mask = var_3785_end_mask_0, x = seg_cumsum_191)[name = tensor<string, []>("op_3785")];
            tensor<int32, [3]> var_3788_begin_0 = const()[name = tensor<string, []>("op_3788_begin_0"), val = tensor<int32, [3]>([0, 96000, 0])];
            tensor<int32, [3]> var_3788_end_0 = const()[name = tensor<string, []>("op_3788_end_0"), val = tensor<int32, [3]>([1, 97000, 9])];
            tensor<bool, [3]> var_3788_end_mask_0 = const()[name = tensor<string, []>("op_3788_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3788 = slice_by_index(begin = var_3788_begin_0, end = var_3788_end_0, end_mask = var_3788_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3788")];
            tensor<bool, []> var_3790_exclusive_0 = const()[name = tensor<string, []>("op_3790_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3790_reverse_0 = const()[name = tensor<string, []>("op_3790_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3790 = cumsum(axis = var_2486, exclusive = var_3790_exclusive_0, reverse = var_3790_reverse_0, x = var_3788)[name = tensor<string, []>("op_3790")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_193 = add(x = var_3790, y = var_3785)[name = tensor<string, []>("seg_cumsum_193")];
            tensor<int32, [3]> var_3793_begin_0 = const()[name = tensor<string, []>("op_3793_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3793_end_0 = const()[name = tensor<string, []>("op_3793_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3793_end_mask_0 = const()[name = tensor<string, []>("op_3793_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3793 = slice_by_index(begin = var_3793_begin_0, end = var_3793_end_0, end_mask = var_3793_end_mask_0, x = seg_cumsum_193)[name = tensor<string, []>("op_3793")];
            tensor<int32, [3]> var_3796_begin_0 = const()[name = tensor<string, []>("op_3796_begin_0"), val = tensor<int32, [3]>([0, 97000, 0])];
            tensor<int32, [3]> var_3796_end_0 = const()[name = tensor<string, []>("op_3796_end_0"), val = tensor<int32, [3]>([1, 98000, 9])];
            tensor<bool, [3]> var_3796_end_mask_0 = const()[name = tensor<string, []>("op_3796_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3796 = slice_by_index(begin = var_3796_begin_0, end = var_3796_end_0, end_mask = var_3796_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3796")];
            tensor<bool, []> var_3798_exclusive_0 = const()[name = tensor<string, []>("op_3798_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3798_reverse_0 = const()[name = tensor<string, []>("op_3798_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3798 = cumsum(axis = var_2486, exclusive = var_3798_exclusive_0, reverse = var_3798_reverse_0, x = var_3796)[name = tensor<string, []>("op_3798")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_195 = add(x = var_3798, y = var_3793)[name = tensor<string, []>("seg_cumsum_195")];
            tensor<int32, [3]> var_3801_begin_0 = const()[name = tensor<string, []>("op_3801_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3801_end_0 = const()[name = tensor<string, []>("op_3801_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3801_end_mask_0 = const()[name = tensor<string, []>("op_3801_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3801 = slice_by_index(begin = var_3801_begin_0, end = var_3801_end_0, end_mask = var_3801_end_mask_0, x = seg_cumsum_195)[name = tensor<string, []>("op_3801")];
            tensor<int32, [3]> var_3804_begin_0 = const()[name = tensor<string, []>("op_3804_begin_0"), val = tensor<int32, [3]>([0, 98000, 0])];
            tensor<int32, [3]> var_3804_end_0 = const()[name = tensor<string, []>("op_3804_end_0"), val = tensor<int32, [3]>([1, 99000, 9])];
            tensor<bool, [3]> var_3804_end_mask_0 = const()[name = tensor<string, []>("op_3804_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3804 = slice_by_index(begin = var_3804_begin_0, end = var_3804_end_0, end_mask = var_3804_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3804")];
            tensor<bool, []> var_3806_exclusive_0 = const()[name = tensor<string, []>("op_3806_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3806_reverse_0 = const()[name = tensor<string, []>("op_3806_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3806 = cumsum(axis = var_2486, exclusive = var_3806_exclusive_0, reverse = var_3806_reverse_0, x = var_3804)[name = tensor<string, []>("op_3806")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_197 = add(x = var_3806, y = var_3801)[name = tensor<string, []>("seg_cumsum_197")];
            tensor<int32, [3]> var_3809_begin_0 = const()[name = tensor<string, []>("op_3809_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3809_end_0 = const()[name = tensor<string, []>("op_3809_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3809_end_mask_0 = const()[name = tensor<string, []>("op_3809_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3809 = slice_by_index(begin = var_3809_begin_0, end = var_3809_end_0, end_mask = var_3809_end_mask_0, x = seg_cumsum_197)[name = tensor<string, []>("op_3809")];
            tensor<int32, [3]> var_3812_begin_0 = const()[name = tensor<string, []>("op_3812_begin_0"), val = tensor<int32, [3]>([0, 99000, 0])];
            tensor<int32, [3]> var_3812_end_0 = const()[name = tensor<string, []>("op_3812_end_0"), val = tensor<int32, [3]>([1, 100000, 9])];
            tensor<bool, [3]> var_3812_end_mask_0 = const()[name = tensor<string, []>("op_3812_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3812 = slice_by_index(begin = var_3812_begin_0, end = var_3812_end_0, end_mask = var_3812_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3812")];
            tensor<bool, []> var_3814_exclusive_0 = const()[name = tensor<string, []>("op_3814_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3814_reverse_0 = const()[name = tensor<string, []>("op_3814_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3814 = cumsum(axis = var_2486, exclusive = var_3814_exclusive_0, reverse = var_3814_reverse_0, x = var_3812)[name = tensor<string, []>("op_3814")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_199 = add(x = var_3814, y = var_3809)[name = tensor<string, []>("seg_cumsum_199")];
            tensor<int32, [3]> var_3817_begin_0 = const()[name = tensor<string, []>("op_3817_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3817_end_0 = const()[name = tensor<string, []>("op_3817_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3817_end_mask_0 = const()[name = tensor<string, []>("op_3817_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3817 = slice_by_index(begin = var_3817_begin_0, end = var_3817_end_0, end_mask = var_3817_end_mask_0, x = seg_cumsum_199)[name = tensor<string, []>("op_3817")];
            tensor<int32, [3]> var_3820_begin_0 = const()[name = tensor<string, []>("op_3820_begin_0"), val = tensor<int32, [3]>([0, 100000, 0])];
            tensor<int32, [3]> var_3820_end_0 = const()[name = tensor<string, []>("op_3820_end_0"), val = tensor<int32, [3]>([1, 101000, 9])];
            tensor<bool, [3]> var_3820_end_mask_0 = const()[name = tensor<string, []>("op_3820_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3820 = slice_by_index(begin = var_3820_begin_0, end = var_3820_end_0, end_mask = var_3820_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3820")];
            tensor<bool, []> var_3822_exclusive_0 = const()[name = tensor<string, []>("op_3822_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3822_reverse_0 = const()[name = tensor<string, []>("op_3822_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3822 = cumsum(axis = var_2486, exclusive = var_3822_exclusive_0, reverse = var_3822_reverse_0, x = var_3820)[name = tensor<string, []>("op_3822")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_201 = add(x = var_3822, y = var_3817)[name = tensor<string, []>("seg_cumsum_201")];
            tensor<int32, [3]> var_3825_begin_0 = const()[name = tensor<string, []>("op_3825_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3825_end_0 = const()[name = tensor<string, []>("op_3825_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3825_end_mask_0 = const()[name = tensor<string, []>("op_3825_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3825 = slice_by_index(begin = var_3825_begin_0, end = var_3825_end_0, end_mask = var_3825_end_mask_0, x = seg_cumsum_201)[name = tensor<string, []>("op_3825")];
            tensor<int32, [3]> var_3828_begin_0 = const()[name = tensor<string, []>("op_3828_begin_0"), val = tensor<int32, [3]>([0, 101000, 0])];
            tensor<int32, [3]> var_3828_end_0 = const()[name = tensor<string, []>("op_3828_end_0"), val = tensor<int32, [3]>([1, 102000, 9])];
            tensor<bool, [3]> var_3828_end_mask_0 = const()[name = tensor<string, []>("op_3828_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3828 = slice_by_index(begin = var_3828_begin_0, end = var_3828_end_0, end_mask = var_3828_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3828")];
            tensor<bool, []> var_3830_exclusive_0 = const()[name = tensor<string, []>("op_3830_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3830_reverse_0 = const()[name = tensor<string, []>("op_3830_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3830 = cumsum(axis = var_2486, exclusive = var_3830_exclusive_0, reverse = var_3830_reverse_0, x = var_3828)[name = tensor<string, []>("op_3830")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_203 = add(x = var_3830, y = var_3825)[name = tensor<string, []>("seg_cumsum_203")];
            tensor<int32, [3]> var_3833_begin_0 = const()[name = tensor<string, []>("op_3833_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3833_end_0 = const()[name = tensor<string, []>("op_3833_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3833_end_mask_0 = const()[name = tensor<string, []>("op_3833_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3833 = slice_by_index(begin = var_3833_begin_0, end = var_3833_end_0, end_mask = var_3833_end_mask_0, x = seg_cumsum_203)[name = tensor<string, []>("op_3833")];
            tensor<int32, [3]> var_3836_begin_0 = const()[name = tensor<string, []>("op_3836_begin_0"), val = tensor<int32, [3]>([0, 102000, 0])];
            tensor<int32, [3]> var_3836_end_0 = const()[name = tensor<string, []>("op_3836_end_0"), val = tensor<int32, [3]>([1, 103000, 9])];
            tensor<bool, [3]> var_3836_end_mask_0 = const()[name = tensor<string, []>("op_3836_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3836 = slice_by_index(begin = var_3836_begin_0, end = var_3836_end_0, end_mask = var_3836_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3836")];
            tensor<bool, []> var_3838_exclusive_0 = const()[name = tensor<string, []>("op_3838_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3838_reverse_0 = const()[name = tensor<string, []>("op_3838_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3838 = cumsum(axis = var_2486, exclusive = var_3838_exclusive_0, reverse = var_3838_reverse_0, x = var_3836)[name = tensor<string, []>("op_3838")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_205 = add(x = var_3838, y = var_3833)[name = tensor<string, []>("seg_cumsum_205")];
            tensor<int32, [3]> var_3841_begin_0 = const()[name = tensor<string, []>("op_3841_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3841_end_0 = const()[name = tensor<string, []>("op_3841_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3841_end_mask_0 = const()[name = tensor<string, []>("op_3841_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3841 = slice_by_index(begin = var_3841_begin_0, end = var_3841_end_0, end_mask = var_3841_end_mask_0, x = seg_cumsum_205)[name = tensor<string, []>("op_3841")];
            tensor<int32, [3]> var_3844_begin_0 = const()[name = tensor<string, []>("op_3844_begin_0"), val = tensor<int32, [3]>([0, 103000, 0])];
            tensor<int32, [3]> var_3844_end_0 = const()[name = tensor<string, []>("op_3844_end_0"), val = tensor<int32, [3]>([1, 104000, 9])];
            tensor<bool, [3]> var_3844_end_mask_0 = const()[name = tensor<string, []>("op_3844_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3844 = slice_by_index(begin = var_3844_begin_0, end = var_3844_end_0, end_mask = var_3844_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3844")];
            tensor<bool, []> var_3846_exclusive_0 = const()[name = tensor<string, []>("op_3846_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3846_reverse_0 = const()[name = tensor<string, []>("op_3846_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3846 = cumsum(axis = var_2486, exclusive = var_3846_exclusive_0, reverse = var_3846_reverse_0, x = var_3844)[name = tensor<string, []>("op_3846")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_207 = add(x = var_3846, y = var_3841)[name = tensor<string, []>("seg_cumsum_207")];
            tensor<int32, [3]> var_3849_begin_0 = const()[name = tensor<string, []>("op_3849_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3849_end_0 = const()[name = tensor<string, []>("op_3849_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3849_end_mask_0 = const()[name = tensor<string, []>("op_3849_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3849 = slice_by_index(begin = var_3849_begin_0, end = var_3849_end_0, end_mask = var_3849_end_mask_0, x = seg_cumsum_207)[name = tensor<string, []>("op_3849")];
            tensor<int32, [3]> var_3852_begin_0 = const()[name = tensor<string, []>("op_3852_begin_0"), val = tensor<int32, [3]>([0, 104000, 0])];
            tensor<int32, [3]> var_3852_end_0 = const()[name = tensor<string, []>("op_3852_end_0"), val = tensor<int32, [3]>([1, 105000, 9])];
            tensor<bool, [3]> var_3852_end_mask_0 = const()[name = tensor<string, []>("op_3852_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3852 = slice_by_index(begin = var_3852_begin_0, end = var_3852_end_0, end_mask = var_3852_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3852")];
            tensor<bool, []> var_3854_exclusive_0 = const()[name = tensor<string, []>("op_3854_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3854_reverse_0 = const()[name = tensor<string, []>("op_3854_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3854 = cumsum(axis = var_2486, exclusive = var_3854_exclusive_0, reverse = var_3854_reverse_0, x = var_3852)[name = tensor<string, []>("op_3854")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_209 = add(x = var_3854, y = var_3849)[name = tensor<string, []>("seg_cumsum_209")];
            tensor<int32, [3]> var_3857_begin_0 = const()[name = tensor<string, []>("op_3857_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3857_end_0 = const()[name = tensor<string, []>("op_3857_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3857_end_mask_0 = const()[name = tensor<string, []>("op_3857_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3857 = slice_by_index(begin = var_3857_begin_0, end = var_3857_end_0, end_mask = var_3857_end_mask_0, x = seg_cumsum_209)[name = tensor<string, []>("op_3857")];
            tensor<int32, [3]> var_3860_begin_0 = const()[name = tensor<string, []>("op_3860_begin_0"), val = tensor<int32, [3]>([0, 105000, 0])];
            tensor<int32, [3]> var_3860_end_0 = const()[name = tensor<string, []>("op_3860_end_0"), val = tensor<int32, [3]>([1, 106000, 9])];
            tensor<bool, [3]> var_3860_end_mask_0 = const()[name = tensor<string, []>("op_3860_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3860 = slice_by_index(begin = var_3860_begin_0, end = var_3860_end_0, end_mask = var_3860_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3860")];
            tensor<bool, []> var_3862_exclusive_0 = const()[name = tensor<string, []>("op_3862_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3862_reverse_0 = const()[name = tensor<string, []>("op_3862_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3862 = cumsum(axis = var_2486, exclusive = var_3862_exclusive_0, reverse = var_3862_reverse_0, x = var_3860)[name = tensor<string, []>("op_3862")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_211 = add(x = var_3862, y = var_3857)[name = tensor<string, []>("seg_cumsum_211")];
            tensor<int32, [3]> var_3865_begin_0 = const()[name = tensor<string, []>("op_3865_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3865_end_0 = const()[name = tensor<string, []>("op_3865_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3865_end_mask_0 = const()[name = tensor<string, []>("op_3865_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3865 = slice_by_index(begin = var_3865_begin_0, end = var_3865_end_0, end_mask = var_3865_end_mask_0, x = seg_cumsum_211)[name = tensor<string, []>("op_3865")];
            tensor<int32, [3]> var_3868_begin_0 = const()[name = tensor<string, []>("op_3868_begin_0"), val = tensor<int32, [3]>([0, 106000, 0])];
            tensor<int32, [3]> var_3868_end_0 = const()[name = tensor<string, []>("op_3868_end_0"), val = tensor<int32, [3]>([1, 107000, 9])];
            tensor<bool, [3]> var_3868_end_mask_0 = const()[name = tensor<string, []>("op_3868_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3868 = slice_by_index(begin = var_3868_begin_0, end = var_3868_end_0, end_mask = var_3868_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3868")];
            tensor<bool, []> var_3870_exclusive_0 = const()[name = tensor<string, []>("op_3870_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3870_reverse_0 = const()[name = tensor<string, []>("op_3870_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3870 = cumsum(axis = var_2486, exclusive = var_3870_exclusive_0, reverse = var_3870_reverse_0, x = var_3868)[name = tensor<string, []>("op_3870")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_213 = add(x = var_3870, y = var_3865)[name = tensor<string, []>("seg_cumsum_213")];
            tensor<int32, [3]> var_3873_begin_0 = const()[name = tensor<string, []>("op_3873_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3873_end_0 = const()[name = tensor<string, []>("op_3873_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3873_end_mask_0 = const()[name = tensor<string, []>("op_3873_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3873 = slice_by_index(begin = var_3873_begin_0, end = var_3873_end_0, end_mask = var_3873_end_mask_0, x = seg_cumsum_213)[name = tensor<string, []>("op_3873")];
            tensor<int32, [3]> var_3876_begin_0 = const()[name = tensor<string, []>("op_3876_begin_0"), val = tensor<int32, [3]>([0, 107000, 0])];
            tensor<int32, [3]> var_3876_end_0 = const()[name = tensor<string, []>("op_3876_end_0"), val = tensor<int32, [3]>([1, 108000, 9])];
            tensor<bool, [3]> var_3876_end_mask_0 = const()[name = tensor<string, []>("op_3876_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3876 = slice_by_index(begin = var_3876_begin_0, end = var_3876_end_0, end_mask = var_3876_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3876")];
            tensor<bool, []> var_3878_exclusive_0 = const()[name = tensor<string, []>("op_3878_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3878_reverse_0 = const()[name = tensor<string, []>("op_3878_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3878 = cumsum(axis = var_2486, exclusive = var_3878_exclusive_0, reverse = var_3878_reverse_0, x = var_3876)[name = tensor<string, []>("op_3878")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_215 = add(x = var_3878, y = var_3873)[name = tensor<string, []>("seg_cumsum_215")];
            tensor<int32, [3]> var_3881_begin_0 = const()[name = tensor<string, []>("op_3881_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3881_end_0 = const()[name = tensor<string, []>("op_3881_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3881_end_mask_0 = const()[name = tensor<string, []>("op_3881_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3881 = slice_by_index(begin = var_3881_begin_0, end = var_3881_end_0, end_mask = var_3881_end_mask_0, x = seg_cumsum_215)[name = tensor<string, []>("op_3881")];
            tensor<int32, [3]> var_3884_begin_0 = const()[name = tensor<string, []>("op_3884_begin_0"), val = tensor<int32, [3]>([0, 108000, 0])];
            tensor<int32, [3]> var_3884_end_0 = const()[name = tensor<string, []>("op_3884_end_0"), val = tensor<int32, [3]>([1, 109000, 9])];
            tensor<bool, [3]> var_3884_end_mask_0 = const()[name = tensor<string, []>("op_3884_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3884 = slice_by_index(begin = var_3884_begin_0, end = var_3884_end_0, end_mask = var_3884_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3884")];
            tensor<bool, []> var_3886_exclusive_0 = const()[name = tensor<string, []>("op_3886_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3886_reverse_0 = const()[name = tensor<string, []>("op_3886_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3886 = cumsum(axis = var_2486, exclusive = var_3886_exclusive_0, reverse = var_3886_reverse_0, x = var_3884)[name = tensor<string, []>("op_3886")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_217 = add(x = var_3886, y = var_3881)[name = tensor<string, []>("seg_cumsum_217")];
            tensor<int32, [3]> var_3889_begin_0 = const()[name = tensor<string, []>("op_3889_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3889_end_0 = const()[name = tensor<string, []>("op_3889_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3889_end_mask_0 = const()[name = tensor<string, []>("op_3889_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3889 = slice_by_index(begin = var_3889_begin_0, end = var_3889_end_0, end_mask = var_3889_end_mask_0, x = seg_cumsum_217)[name = tensor<string, []>("op_3889")];
            tensor<int32, [3]> var_3892_begin_0 = const()[name = tensor<string, []>("op_3892_begin_0"), val = tensor<int32, [3]>([0, 109000, 0])];
            tensor<int32, [3]> var_3892_end_0 = const()[name = tensor<string, []>("op_3892_end_0"), val = tensor<int32, [3]>([1, 110000, 9])];
            tensor<bool, [3]> var_3892_end_mask_0 = const()[name = tensor<string, []>("op_3892_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3892 = slice_by_index(begin = var_3892_begin_0, end = var_3892_end_0, end_mask = var_3892_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3892")];
            tensor<bool, []> var_3894_exclusive_0 = const()[name = tensor<string, []>("op_3894_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3894_reverse_0 = const()[name = tensor<string, []>("op_3894_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3894 = cumsum(axis = var_2486, exclusive = var_3894_exclusive_0, reverse = var_3894_reverse_0, x = var_3892)[name = tensor<string, []>("op_3894")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_219 = add(x = var_3894, y = var_3889)[name = tensor<string, []>("seg_cumsum_219")];
            tensor<int32, [3]> var_3897_begin_0 = const()[name = tensor<string, []>("op_3897_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3897_end_0 = const()[name = tensor<string, []>("op_3897_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3897_end_mask_0 = const()[name = tensor<string, []>("op_3897_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3897 = slice_by_index(begin = var_3897_begin_0, end = var_3897_end_0, end_mask = var_3897_end_mask_0, x = seg_cumsum_219)[name = tensor<string, []>("op_3897")];
            tensor<int32, [3]> var_3900_begin_0 = const()[name = tensor<string, []>("op_3900_begin_0"), val = tensor<int32, [3]>([0, 110000, 0])];
            tensor<int32, [3]> var_3900_end_0 = const()[name = tensor<string, []>("op_3900_end_0"), val = tensor<int32, [3]>([1, 111000, 9])];
            tensor<bool, [3]> var_3900_end_mask_0 = const()[name = tensor<string, []>("op_3900_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3900 = slice_by_index(begin = var_3900_begin_0, end = var_3900_end_0, end_mask = var_3900_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3900")];
            tensor<bool, []> var_3902_exclusive_0 = const()[name = tensor<string, []>("op_3902_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3902_reverse_0 = const()[name = tensor<string, []>("op_3902_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3902 = cumsum(axis = var_2486, exclusive = var_3902_exclusive_0, reverse = var_3902_reverse_0, x = var_3900)[name = tensor<string, []>("op_3902")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_221 = add(x = var_3902, y = var_3897)[name = tensor<string, []>("seg_cumsum_221")];
            tensor<int32, [3]> var_3905_begin_0 = const()[name = tensor<string, []>("op_3905_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3905_end_0 = const()[name = tensor<string, []>("op_3905_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3905_end_mask_0 = const()[name = tensor<string, []>("op_3905_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3905 = slice_by_index(begin = var_3905_begin_0, end = var_3905_end_0, end_mask = var_3905_end_mask_0, x = seg_cumsum_221)[name = tensor<string, []>("op_3905")];
            tensor<int32, [3]> var_3908_begin_0 = const()[name = tensor<string, []>("op_3908_begin_0"), val = tensor<int32, [3]>([0, 111000, 0])];
            tensor<int32, [3]> var_3908_end_0 = const()[name = tensor<string, []>("op_3908_end_0"), val = tensor<int32, [3]>([1, 112000, 9])];
            tensor<bool, [3]> var_3908_end_mask_0 = const()[name = tensor<string, []>("op_3908_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3908 = slice_by_index(begin = var_3908_begin_0, end = var_3908_end_0, end_mask = var_3908_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3908")];
            tensor<bool, []> var_3910_exclusive_0 = const()[name = tensor<string, []>("op_3910_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3910_reverse_0 = const()[name = tensor<string, []>("op_3910_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3910 = cumsum(axis = var_2486, exclusive = var_3910_exclusive_0, reverse = var_3910_reverse_0, x = var_3908)[name = tensor<string, []>("op_3910")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_223 = add(x = var_3910, y = var_3905)[name = tensor<string, []>("seg_cumsum_223")];
            tensor<int32, [3]> var_3913_begin_0 = const()[name = tensor<string, []>("op_3913_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3913_end_0 = const()[name = tensor<string, []>("op_3913_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3913_end_mask_0 = const()[name = tensor<string, []>("op_3913_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3913 = slice_by_index(begin = var_3913_begin_0, end = var_3913_end_0, end_mask = var_3913_end_mask_0, x = seg_cumsum_223)[name = tensor<string, []>("op_3913")];
            tensor<int32, [3]> var_3916_begin_0 = const()[name = tensor<string, []>("op_3916_begin_0"), val = tensor<int32, [3]>([0, 112000, 0])];
            tensor<int32, [3]> var_3916_end_0 = const()[name = tensor<string, []>("op_3916_end_0"), val = tensor<int32, [3]>([1, 113000, 9])];
            tensor<bool, [3]> var_3916_end_mask_0 = const()[name = tensor<string, []>("op_3916_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3916 = slice_by_index(begin = var_3916_begin_0, end = var_3916_end_0, end_mask = var_3916_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3916")];
            tensor<bool, []> var_3918_exclusive_0 = const()[name = tensor<string, []>("op_3918_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3918_reverse_0 = const()[name = tensor<string, []>("op_3918_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3918 = cumsum(axis = var_2486, exclusive = var_3918_exclusive_0, reverse = var_3918_reverse_0, x = var_3916)[name = tensor<string, []>("op_3918")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_225 = add(x = var_3918, y = var_3913)[name = tensor<string, []>("seg_cumsum_225")];
            tensor<int32, [3]> var_3921_begin_0 = const()[name = tensor<string, []>("op_3921_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3921_end_0 = const()[name = tensor<string, []>("op_3921_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3921_end_mask_0 = const()[name = tensor<string, []>("op_3921_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3921 = slice_by_index(begin = var_3921_begin_0, end = var_3921_end_0, end_mask = var_3921_end_mask_0, x = seg_cumsum_225)[name = tensor<string, []>("op_3921")];
            tensor<int32, [3]> var_3924_begin_0 = const()[name = tensor<string, []>("op_3924_begin_0"), val = tensor<int32, [3]>([0, 113000, 0])];
            tensor<int32, [3]> var_3924_end_0 = const()[name = tensor<string, []>("op_3924_end_0"), val = tensor<int32, [3]>([1, 114000, 9])];
            tensor<bool, [3]> var_3924_end_mask_0 = const()[name = tensor<string, []>("op_3924_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3924 = slice_by_index(begin = var_3924_begin_0, end = var_3924_end_0, end_mask = var_3924_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3924")];
            tensor<bool, []> var_3926_exclusive_0 = const()[name = tensor<string, []>("op_3926_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3926_reverse_0 = const()[name = tensor<string, []>("op_3926_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3926 = cumsum(axis = var_2486, exclusive = var_3926_exclusive_0, reverse = var_3926_reverse_0, x = var_3924)[name = tensor<string, []>("op_3926")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_227 = add(x = var_3926, y = var_3921)[name = tensor<string, []>("seg_cumsum_227")];
            tensor<int32, [3]> var_3929_begin_0 = const()[name = tensor<string, []>("op_3929_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3929_end_0 = const()[name = tensor<string, []>("op_3929_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3929_end_mask_0 = const()[name = tensor<string, []>("op_3929_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3929 = slice_by_index(begin = var_3929_begin_0, end = var_3929_end_0, end_mask = var_3929_end_mask_0, x = seg_cumsum_227)[name = tensor<string, []>("op_3929")];
            tensor<int32, [3]> var_3932_begin_0 = const()[name = tensor<string, []>("op_3932_begin_0"), val = tensor<int32, [3]>([0, 114000, 0])];
            tensor<int32, [3]> var_3932_end_0 = const()[name = tensor<string, []>("op_3932_end_0"), val = tensor<int32, [3]>([1, 115000, 9])];
            tensor<bool, [3]> var_3932_end_mask_0 = const()[name = tensor<string, []>("op_3932_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3932 = slice_by_index(begin = var_3932_begin_0, end = var_3932_end_0, end_mask = var_3932_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3932")];
            tensor<bool, []> var_3934_exclusive_0 = const()[name = tensor<string, []>("op_3934_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3934_reverse_0 = const()[name = tensor<string, []>("op_3934_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3934 = cumsum(axis = var_2486, exclusive = var_3934_exclusive_0, reverse = var_3934_reverse_0, x = var_3932)[name = tensor<string, []>("op_3934")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_229 = add(x = var_3934, y = var_3929)[name = tensor<string, []>("seg_cumsum_229")];
            tensor<int32, [3]> var_3937_begin_0 = const()[name = tensor<string, []>("op_3937_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3937_end_0 = const()[name = tensor<string, []>("op_3937_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3937_end_mask_0 = const()[name = tensor<string, []>("op_3937_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3937 = slice_by_index(begin = var_3937_begin_0, end = var_3937_end_0, end_mask = var_3937_end_mask_0, x = seg_cumsum_229)[name = tensor<string, []>("op_3937")];
            tensor<int32, [3]> var_3940_begin_0 = const()[name = tensor<string, []>("op_3940_begin_0"), val = tensor<int32, [3]>([0, 115000, 0])];
            tensor<int32, [3]> var_3940_end_0 = const()[name = tensor<string, []>("op_3940_end_0"), val = tensor<int32, [3]>([1, 116000, 9])];
            tensor<bool, [3]> var_3940_end_mask_0 = const()[name = tensor<string, []>("op_3940_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3940 = slice_by_index(begin = var_3940_begin_0, end = var_3940_end_0, end_mask = var_3940_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3940")];
            tensor<bool, []> var_3942_exclusive_0 = const()[name = tensor<string, []>("op_3942_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3942_reverse_0 = const()[name = tensor<string, []>("op_3942_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3942 = cumsum(axis = var_2486, exclusive = var_3942_exclusive_0, reverse = var_3942_reverse_0, x = var_3940)[name = tensor<string, []>("op_3942")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_231 = add(x = var_3942, y = var_3937)[name = tensor<string, []>("seg_cumsum_231")];
            tensor<int32, [3]> var_3945_begin_0 = const()[name = tensor<string, []>("op_3945_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3945_end_0 = const()[name = tensor<string, []>("op_3945_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3945_end_mask_0 = const()[name = tensor<string, []>("op_3945_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3945 = slice_by_index(begin = var_3945_begin_0, end = var_3945_end_0, end_mask = var_3945_end_mask_0, x = seg_cumsum_231)[name = tensor<string, []>("op_3945")];
            tensor<int32, [3]> var_3948_begin_0 = const()[name = tensor<string, []>("op_3948_begin_0"), val = tensor<int32, [3]>([0, 116000, 0])];
            tensor<int32, [3]> var_3948_end_0 = const()[name = tensor<string, []>("op_3948_end_0"), val = tensor<int32, [3]>([1, 117000, 9])];
            tensor<bool, [3]> var_3948_end_mask_0 = const()[name = tensor<string, []>("op_3948_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3948 = slice_by_index(begin = var_3948_begin_0, end = var_3948_end_0, end_mask = var_3948_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3948")];
            tensor<bool, []> var_3950_exclusive_0 = const()[name = tensor<string, []>("op_3950_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3950_reverse_0 = const()[name = tensor<string, []>("op_3950_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3950 = cumsum(axis = var_2486, exclusive = var_3950_exclusive_0, reverse = var_3950_reverse_0, x = var_3948)[name = tensor<string, []>("op_3950")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_233 = add(x = var_3950, y = var_3945)[name = tensor<string, []>("seg_cumsum_233")];
            tensor<int32, [3]> var_3953_begin_0 = const()[name = tensor<string, []>("op_3953_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3953_end_0 = const()[name = tensor<string, []>("op_3953_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3953_end_mask_0 = const()[name = tensor<string, []>("op_3953_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3953 = slice_by_index(begin = var_3953_begin_0, end = var_3953_end_0, end_mask = var_3953_end_mask_0, x = seg_cumsum_233)[name = tensor<string, []>("op_3953")];
            tensor<int32, [3]> var_3956_begin_0 = const()[name = tensor<string, []>("op_3956_begin_0"), val = tensor<int32, [3]>([0, 117000, 0])];
            tensor<int32, [3]> var_3956_end_0 = const()[name = tensor<string, []>("op_3956_end_0"), val = tensor<int32, [3]>([1, 118000, 9])];
            tensor<bool, [3]> var_3956_end_mask_0 = const()[name = tensor<string, []>("op_3956_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3956 = slice_by_index(begin = var_3956_begin_0, end = var_3956_end_0, end_mask = var_3956_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3956")];
            tensor<bool, []> var_3958_exclusive_0 = const()[name = tensor<string, []>("op_3958_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3958_reverse_0 = const()[name = tensor<string, []>("op_3958_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3958 = cumsum(axis = var_2486, exclusive = var_3958_exclusive_0, reverse = var_3958_reverse_0, x = var_3956)[name = tensor<string, []>("op_3958")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_235 = add(x = var_3958, y = var_3953)[name = tensor<string, []>("seg_cumsum_235")];
            tensor<int32, [3]> var_3961_begin_0 = const()[name = tensor<string, []>("op_3961_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3961_end_0 = const()[name = tensor<string, []>("op_3961_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3961_end_mask_0 = const()[name = tensor<string, []>("op_3961_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3961 = slice_by_index(begin = var_3961_begin_0, end = var_3961_end_0, end_mask = var_3961_end_mask_0, x = seg_cumsum_235)[name = tensor<string, []>("op_3961")];
            tensor<int32, [3]> var_3964_begin_0 = const()[name = tensor<string, []>("op_3964_begin_0"), val = tensor<int32, [3]>([0, 118000, 0])];
            tensor<int32, [3]> var_3964_end_0 = const()[name = tensor<string, []>("op_3964_end_0"), val = tensor<int32, [3]>([1, 119000, 9])];
            tensor<bool, [3]> var_3964_end_mask_0 = const()[name = tensor<string, []>("op_3964_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 1000, 9]> var_3964 = slice_by_index(begin = var_3964_begin_0, end = var_3964_end_0, end_mask = var_3964_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3964")];
            tensor<bool, []> var_3966_exclusive_0 = const()[name = tensor<string, []>("op_3966_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3966_reverse_0 = const()[name = tensor<string, []>("op_3966_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3966 = cumsum(axis = var_2486, exclusive = var_3966_exclusive_0, reverse = var_3966_reverse_0, x = var_3964)[name = tensor<string, []>("op_3966")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum_237 = add(x = var_3966, y = var_3961)[name = tensor<string, []>("seg_cumsum_237")];
            tensor<int32, [3]> var_3969_begin_0 = const()[name = tensor<string, []>("op_3969_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_3969_end_0 = const()[name = tensor<string, []>("op_3969_end_0"), val = tensor<int32, [3]>([1, 1000, 9])];
            tensor<bool, [3]> var_3969_end_mask_0 = const()[name = tensor<string, []>("op_3969_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_3969 = slice_by_index(begin = var_3969_begin_0, end = var_3969_end_0, end_mask = var_3969_end_mask_0, x = seg_cumsum_237)[name = tensor<string, []>("op_3969")];
            tensor<int32, [3]> var_3972_begin_0 = const()[name = tensor<string, []>("op_3972_begin_0"), val = tensor<int32, [3]>([0, 119000, 0])];
            tensor<int32, [3]> var_3972_end_0 = const()[name = tensor<string, []>("op_3972_end_0"), val = tensor<int32, [3]>([1, 1, 9])];
            tensor<bool, [3]> var_3972_end_mask_0 = const()[name = tensor<string, []>("op_3972_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1000, 9]> var_3972 = slice_by_index(begin = var_3972_begin_0, end = var_3972_end_0, end_mask = var_3972_end_mask_0, x = _inversed_rad_values)[name = tensor<string, []>("op_3972")];
            tensor<bool, []> var_3974_exclusive_0 = const()[name = tensor<string, []>("op_3974_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_3974_reverse_0 = const()[name = tensor<string, []>("op_3974_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1000, 9]> var_3974 = cumsum(axis = var_2486, exclusive = var_3974_exclusive_0, reverse = var_3974_reverse_0, x = var_3972)[name = tensor<string, []>("op_3974")];
            tensor<fp32, [1, 1000, 9]> seg_cumsum = add(x = var_3974, y = var_3969)[name = tensor<string, []>("seg_cumsum")];
            tensor<bool, []> phase_accum_interleave_0 = const()[name = tensor<string, []>("phase_accum_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 120000, 9]> phase_accum = concat(axis = var_2486, interleave = phase_accum_interleave_0, values = (var_3022, seg_cumsum_3, seg_cumsum_5, seg_cumsum_7, seg_cumsum_9, seg_cumsum_11, seg_cumsum_13, seg_cumsum_15, seg_cumsum_17, seg_cumsum_19, seg_cumsum_21, seg_cumsum_23, seg_cumsum_25, seg_cumsum_27, seg_cumsum_29, seg_cumsum_31, seg_cumsum_33, seg_cumsum_35, seg_cumsum_37, seg_cumsum_39, seg_cumsum_41, seg_cumsum_43, seg_cumsum_45, seg_cumsum_47, seg_cumsum_49, seg_cumsum_51, seg_cumsum_53, seg_cumsum_55, seg_cumsum_57, seg_cumsum_59, seg_cumsum_61, seg_cumsum_63, seg_cumsum_65, seg_cumsum_67, seg_cumsum_69, seg_cumsum_71, seg_cumsum_73, seg_cumsum_75, seg_cumsum_77, seg_cumsum_79, seg_cumsum_81, seg_cumsum_83, seg_cumsum_85, seg_cumsum_87, seg_cumsum_89, seg_cumsum_91, seg_cumsum_93, seg_cumsum_95, seg_cumsum_97, seg_cumsum_99, seg_cumsum_101, seg_cumsum_103, seg_cumsum_105, seg_cumsum_107, seg_cumsum_109, seg_cumsum_111, seg_cumsum_113, seg_cumsum_115, seg_cumsum_117, seg_cumsum_119, seg_cumsum_121, seg_cumsum_123, seg_cumsum_125, seg_cumsum_127, seg_cumsum_129, seg_cumsum_131, seg_cumsum_133, seg_cumsum_135, seg_cumsum_137, seg_cumsum_139, seg_cumsum_141, seg_cumsum_143, seg_cumsum_145, seg_cumsum_147, seg_cumsum_149, seg_cumsum_151, seg_cumsum_153, seg_cumsum_155, seg_cumsum_157, seg_cumsum_159, seg_cumsum_161, seg_cumsum_163, seg_cumsum_165, seg_cumsum_167, seg_cumsum_169, seg_cumsum_171, seg_cumsum_173, seg_cumsum_175, seg_cumsum_177, seg_cumsum_179, seg_cumsum_181, seg_cumsum_183, seg_cumsum_185, seg_cumsum_187, seg_cumsum_189, seg_cumsum_191, seg_cumsum_193, seg_cumsum_195, seg_cumsum_197, seg_cumsum_199, seg_cumsum_201, seg_cumsum_203, seg_cumsum_205, seg_cumsum_207, seg_cumsum_209, seg_cumsum_211, seg_cumsum_213, seg_cumsum_215, seg_cumsum_217, seg_cumsum_219, seg_cumsum_221, seg_cumsum_223, seg_cumsum_225, seg_cumsum_227, seg_cumsum_229, seg_cumsum_231, seg_cumsum_233, seg_cumsum_235, seg_cumsum_237, seg_cumsum))[name = tensor<string, []>("phase_accum")];
            tensor<fp32, []> var_3978 = const()[name = tensor<string, []>("op_3978"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 120000, 9]> var_3979 = mul(x = phase_accum, y = var_3978)[name = tensor<string, []>("op_3979")];
            tensor<fp32, []> var_3980 = const()[name = tensor<string, []>("op_3980"), val = tensor<fp32, []>(0x1.921fb6p+1)];
            tensor<fp32, [1, 120000, 9]> phase_1 = mul(x = var_3979, y = var_3980)[name = tensor<string, []>("phase_1")];
            tensor<fp32, [1, 120000, 9]> var_3982 = sin(x = phase_1)[name = tensor<string, []>("op_3982")];
            tensor<fp32, []> var_3983 = const()[name = tensor<string, []>("op_3983"), val = tensor<fp32, []>(0x1.99999ap-4)];
            tensor<fp32, [1, 120000, 9]> sine_waves = mul(x = var_3982, y = var_3983)[name = tensor<string, []>("sine_waves")];
            tensor<fp32, []> var_2339_promoted = const()[name = tensor<string, []>("op_2339_promoted"), val = tensor<fp32, []>(0x1.4p+3)];
            tensor<bool, [1, 120000, 1]> var_3985 = greater(x = f0, y = var_2339_promoted)[name = tensor<string, []>("op_3985")];
            tensor<string, []> cast_111_dtype_0 = const()[name = tensor<string, []>("cast_111_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<fp32, [1, 120000, 1]> cast_111 = cast(dtype = cast_111_dtype_0, x = var_3985)[name = tensor<string, []>("cast_181")];
            tensor<fp32, [1, 120000, 9]> var_3988 = mul(x = sine_waves, y = cast_111)[name = tensor<string, []>("op_3988")];
            tensor<fp32, [1, 120000, 1]> input_507 = linear(bias = decoder_generator_m_source_l_linear_bias, weight = decoder_generator_m_source_l_linear_weight, x = var_3988)[name = tensor<string, []>("linear_100")];
            tensor<fp32, [1, 120000, 1]> har_source = tanh(x = input_507)[name = tensor<string, []>("har_source")];
            tensor<int32, [3]> var_3997_perm_0 = const()[name = tensor<string, []>("op_3997_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<int32, [1]> input_509_axes_0 = const()[name = tensor<string, []>("input_509_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1, 1, 120000]> var_3997 = transpose(perm = var_3997_perm_0, x = har_source)[name = tensor<string, []>("transpose_95")];
            tensor<fp32, [1, 120000]> input_509 = squeeze(axes = input_509_axes_0, x = var_3997)[name = tensor<string, []>("input_509")];
            tensor<fp32, []> const_156 = const()[name = tensor<string, []>("const_156"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [4]> waveform_1_pad_0 = const()[name = tensor<string, []>("waveform_1_pad_0"), val = tensor<int32, [4]>([0, 0, 10, 10])];
            tensor<string, []> waveform_1_mode_0 = const()[name = tensor<string, []>("waveform_1_mode_0"), val = tensor<string, []>("replicate")];
            tensor<fp32, [1, 120020]> waveform_1 = pad(constant_val = const_156, mode = waveform_1_mode_0, pad = waveform_1_pad_0, x = input_509)[name = tensor<string, []>("waveform_1")];
            tensor<int32, [1]> x_247_axes_0 = const()[name = tensor<string, []>("x_247_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1, 1, 120020]> x_247 = expand_dims(axes = x_247_axes_0, x = waveform_1)[name = tensor<string, []>("x_247")];
            tensor<string, []> real_out_pad_type_0 = const()[name = tensor<string, []>("real_out_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> real_out_strides_0 = const()[name = tensor<string, []>("real_out_strides_0"), val = tensor<int32, [1]>([5])];
            tensor<int32, [2]> real_out_pad_0 = const()[name = tensor<string, []>("real_out_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> real_out_dilations_0 = const()[name = tensor<string, []>("real_out_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> real_out_groups_0 = const()[name = tensor<string, []>("real_out_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 11, 24001]> real_out = conv(dilations = real_out_dilations_0, groups = real_out_groups_0, pad = real_out_pad_0, pad_type = real_out_pad_type_0, strides = real_out_strides_0, weight = decoder_generator_stft_weight_forward_real, x = x_247)[name = tensor<string, []>("real_out")];
            tensor<string, []> imag_out_pad_type_0 = const()[name = tensor<string, []>("imag_out_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> imag_out_strides_0 = const()[name = tensor<string, []>("imag_out_strides_0"), val = tensor<int32, [1]>([5])];
            tensor<int32, [2]> imag_out_pad_0 = const()[name = tensor<string, []>("imag_out_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> imag_out_dilations_0 = const()[name = tensor<string, []>("imag_out_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> imag_out_groups_0 = const()[name = tensor<string, []>("imag_out_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 11, 24001]> imag_out = conv(dilations = imag_out_dilations_0, groups = imag_out_groups_0, pad = imag_out_pad_0, pad_type = imag_out_pad_type_0, strides = imag_out_strides_0, weight = decoder_generator_stft_weight_forward_imag, x = x_247)[name = tensor<string, []>("imag_out")];
            tensor<fp32, []> var_2483_promoted = const()[name = tensor<string, []>("op_2483_promoted"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 11, 24001]> var_4012 = pow(x = real_out, y = var_2483_promoted)[name = tensor<string, []>("op_4012")];
            tensor<fp32, []> var_2483_promoted_1 = const()[name = tensor<string, []>("op_2483_promoted_1"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 11, 24001]> var_4013 = pow(x = imag_out, y = var_2483_promoted_1)[name = tensor<string, []>("op_4013")];
            tensor<fp32, [1, 11, 24001]> var_4014 = add(x = var_4012, y = var_4013)[name = tensor<string, []>("op_4014")];
            tensor<fp32, []> var_4015 = const()[name = tensor<string, []>("op_4015"), val = tensor<fp32, []>(0x1.6849b8p-47)];
            tensor<fp32, [1, 11, 24001]> var_4016 = add(x = var_4014, y = var_4015)[name = tensor<string, []>("op_4016")];
            tensor<fp32, [1, 11, 24001]> har_spec = sqrt(x = var_4016)[name = tensor<string, []>("har_spec")];
            tensor<fp32, []> less_0_y_0 = const()[name = tensor<string, []>("less_0_y_0"), val = tensor<fp32, []>(0x0p+0)];
            tensor<bool, [1, 11, 24001]> less_0 = less(x = imag_out, y = less_0_y_0)[name = tensor<string, []>("less_0")];
            tensor<fp32, []> greater_0_y_0 = const()[name = tensor<string, []>("greater_0_y_0"), val = tensor<fp32, []>(0x0p+0)];
            tensor<bool, [1, 11, 24001]> greater_0 = greater(x = imag_out, y = greater_0_y_0)[name = tensor<string, []>("greater_0")];
            tensor<fp32, []> less_1_y_0 = const()[name = tensor<string, []>("less_1_y_0"), val = tensor<fp32, []>(0x0p+0)];
            tensor<bool, [1, 11, 24001]> less_1 = less(x = real_out, y = less_1_y_0)[name = tensor<string, []>("less_1")];
            tensor<fp32, []> equal_0_y_0 = const()[name = tensor<string, []>("equal_0_y_0"), val = tensor<fp32, []>(0x0p+0)];
            tensor<bool, [1, 11, 24001]> equal_0 = equal(x = real_out, y = equal_0_y_0)[name = tensor<string, []>("equal_0")];
            tensor<bool, [1, 11, 24001]> logical_and_0 = logical_and(x = greater_0, y = less_1)[name = tensor<string, []>("logical_and_0")];
            tensor<bool, [1, 11, 24001]> logical_and_1 = logical_and(x = less_0, y = less_1)[name = tensor<string, []>("logical_and_1")];
            tensor<bool, [1, 11, 24001]> logical_and_2 = logical_and(x = greater_0, y = equal_0)[name = tensor<string, []>("logical_and_2")];
            tensor<bool, [1, 11, 24001]> logical_and_3 = logical_and(x = less_0, y = equal_0)[name = tensor<string, []>("logical_and_3")];
            tensor<string, []> cast_112_dtype_0 = const()[name = tensor<string, []>("cast_112_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<string, []> cast_113_dtype_0 = const()[name = tensor<string, []>("cast_113_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<string, []> cast_114_dtype_0 = const()[name = tensor<string, []>("cast_114_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<string, []> cast_115_dtype_0 = const()[name = tensor<string, []>("cast_115_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<fp32, []> mul_0_y_0 = const()[name = tensor<string, []>("mul_0_y_0"), val = tensor<fp32, []>(0x1.921fb6p+1)];
            tensor<fp32, [1, 11, 24001]> cast_112 = cast(dtype = cast_112_dtype_0, x = logical_and_0)[name = tensor<string, []>("cast_180")];
            tensor<fp32, [1, 11, 24001]> mul_0 = mul(x = cast_112, y = mul_0_y_0)[name = tensor<string, []>("mul_0")];
            tensor<fp32, []> mul_1_y_0 = const()[name = tensor<string, []>("mul_1_y_0"), val = tensor<fp32, []>(0x1.921fb6p+1)];
            tensor<fp32, [1, 11, 24001]> cast_113 = cast(dtype = cast_113_dtype_0, x = logical_and_1)[name = tensor<string, []>("cast_179")];
            tensor<fp32, [1, 11, 24001]> mul_1 = mul(x = cast_113, y = mul_1_y_0)[name = tensor<string, []>("mul_1")];
            tensor<fp32, []> sub_0_x_0 = const()[name = tensor<string, []>("sub_0_x_0"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 11, 24001]> cast_114 = cast(dtype = cast_114_dtype_0, x = logical_and_2)[name = tensor<string, []>("cast_178")];
            tensor<fp32, [1, 11, 24001]> sub_0 = sub(x = sub_0_x_0, y = cast_114)[name = tensor<string, []>("sub_0")];
            tensor<fp32, []> mul_2_y_0 = const()[name = tensor<string, []>("mul_2_y_0"), val = tensor<fp32, []>(0x1.921fb6p+0)];
            tensor<fp32, [1, 11, 24001]> mul_2 = mul(x = cast_114, y = mul_2_y_0)[name = tensor<string, []>("mul_2")];
            tensor<fp32, []> sub_1_x_0 = const()[name = tensor<string, []>("sub_1_x_0"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 11, 24001]> cast_115 = cast(dtype = cast_115_dtype_0, x = logical_and_3)[name = tensor<string, []>("cast_177")];
            tensor<fp32, [1, 11, 24001]> sub_1 = sub(x = sub_1_x_0, y = cast_115)[name = tensor<string, []>("sub_1")];
            tensor<fp32, []> mul_3_y_0 = const()[name = tensor<string, []>("mul_3_y_0"), val = tensor<fp32, []>(-0x1.921fb6p+0)];
            tensor<fp32, [1, 11, 24001]> mul_3 = mul(x = cast_115, y = mul_3_y_0)[name = tensor<string, []>("mul_3")];
            tensor<fp32, []> greater_1_y_0 = const()[name = tensor<string, []>("greater_1_y_0"), val = tensor<fp32, []>(-0x1.5798eep-27)];
            tensor<bool, [1, 11, 24001]> greater_1 = greater(x = real_out, y = greater_1_y_0)[name = tensor<string, []>("greater_1")];
            tensor<fp32, []> less_2_y_0 = const()[name = tensor<string, []>("less_2_y_0"), val = tensor<fp32, []>(0x1.5798eep-27)];
            tensor<bool, [1, 11, 24001]> less_2 = less(x = real_out, y = less_2_y_0)[name = tensor<string, []>("less_2")];
            tensor<bool, [1, 11, 24001]> logical_and_4 = logical_and(x = greater_1, y = less_2)[name = tensor<string, []>("logical_and_4")];
            tensor<string, []> cast_116_dtype_0 = const()[name = tensor<string, []>("cast_116_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<fp32, []> mul_4_y_0 = const()[name = tensor<string, []>("mul_4_y_0"), val = tensor<fp32, []>(0x1.5798eep-26)];
            tensor<fp32, [1, 11, 24001]> cast_116 = cast(dtype = cast_116_dtype_0, x = logical_and_4)[name = tensor<string, []>("cast_176")];
            tensor<fp32, [1, 11, 24001]> mul_4 = mul(x = cast_116, y = mul_4_y_0)[name = tensor<string, []>("mul_4")];
            tensor<fp32, [1, 11, 24001]> add_12 = add(x = real_out, y = mul_4)[name = tensor<string, []>("add_12")];
            tensor<fp32, [1, 11, 24001]> real_div_38 = real_div(x = imag_out, y = add_12)[name = tensor<string, []>("real_div_38")];
            tensor<fp32, [1, 11, 24001]> atan_0 = atan(x = real_div_38)[name = tensor<string, []>("atan_0")];
            tensor<fp32, [1, 11, 24001]> add_13 = add(x = atan_0, y = mul_0)[name = tensor<string, []>("add_13")];
            tensor<fp32, [1, 11, 24001]> sub_2 = sub(x = add_13, y = mul_1)[name = tensor<string, []>("sub_2")];
            tensor<fp32, [1, 11, 24001]> mul_5 = mul(x = sub_2, y = sub_0)[name = tensor<string, []>("mul_5")];
            tensor<fp32, [1, 11, 24001]> add_14 = add(x = mul_5, y = mul_2)[name = tensor<string, []>("add_14")];
            tensor<fp32, [1, 11, 24001]> mul_6 = mul(x = add_14, y = sub_1)[name = tensor<string, []>("mul_6")];
            tensor<fp32, [1, 11, 24001]> phase_3 = add(x = mul_6, y = mul_3)[name = tensor<string, []>("phase_3")];
            tensor<fp32, []> var_2482_promoted = const()[name = tensor<string, []>("op_2482_promoted"), val = tensor<fp32, []>(0x0p+0)];
            tensor<bool, [1, 11, 24001]> var_4019 = equal(x = imag_out, y = var_2482_promoted)[name = tensor<string, []>("op_4019")];
            tensor<bool, [1, 11, 24001]> correction_mask = logical_and(x = var_4019, y = less_1)[name = tensor<string, []>("correction_mask")];
            tensor<string, []> cast_119_dtype_0 = const()[name = tensor<string, []>("cast_119_dtype_0"), val = tensor<string, []>("int32")];
            tensor<int32, [1, 11, 24001]> cast_119 = cast(dtype = cast_119_dtype_0, x = correction_mask)[name = tensor<string, []>("cast_175")];
            tensor<int32, [?, 3]> non_zero_0 = non_zero(x = cast_119)[name = tensor<string, []>("non_zero_0")];
            tensor<int32, [2]> shape_13 = shape(x = non_zero_0)[name = tensor<string, []>("shape_13")];
            tensor<int32, [1]> slice_by_index_24_begin_0 = const()[name = tensor<string, []>("slice_by_index_24_begin_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> slice_by_index_24_end_0 = const()[name = tensor<string, []>("slice_by_index_24_end_0"), val = tensor<int32, [1]>([0])];
            tensor<bool, [1]> slice_by_index_24_squeeze_mask_0 = const()[name = tensor<string, []>("slice_by_index_24_squeeze_mask_0"), val = tensor<bool, [1]>([true])];
            tensor<int32, []> slice_by_index_24 = slice_by_index(begin = slice_by_index_24_begin_0, end = slice_by_index_24_end_0, squeeze_mask = slice_by_index_24_squeeze_mask_0, x = shape_13)[name = tensor<string, []>("slice_by_index_24")];
            tensor<int32, []> concat_76_axis_0 = const()[name = tensor<string, []>("concat_76_axis_0"), val = tensor<int32, []>(0)];
            tensor<bool, []> concat_76_interleave_0 = const()[name = tensor<string, []>("concat_76_interleave_0"), val = tensor<bool, []>(false)];
            tensor<int32, [1]> concat_76 = concat(axis = concat_76_axis_0, interleave = concat_76_interleave_0, values = slice_by_index_24)[name = tensor<string, []>("concat_76")];
            tensor<fp32, [1]> expand_dims_5 = const()[name = tensor<string, []>("expand_dims_5"), val = tensor<fp32, [1]>([0x1.921fb6p+1])];
            tensor<fp32, [?]> var_2463_broadcasted = tile(reps = concat_76, x = expand_dims_5)[name = tensor<string, []>("op_2463_broadcasted")];
            tensor<int32, []> greater_equal_0_y_0 = const()[name = tensor<string, []>("greater_equal_0_y_0"), val = tensor<int32, []>(0)];
            tensor<bool, [?, 3]> greater_equal_0 = greater_equal(x = non_zero_0, y = greater_equal_0_y_0)[name = tensor<string, []>("greater_equal_0")];
            tensor<int32, [3]> shape_15 = const()[name = tensor<string, []>("shape_15"), val = tensor<int32, [3]>([1, 11, 24001])];
            tensor<int32, [?, 3]> add_15 = add(x = non_zero_0, y = shape_15)[name = tensor<string, []>("add_15")];
            tensor<int32, [?, 3]> select_0 = select(a = non_zero_0, b = add_15, cond = greater_equal_0)[name = tensor<string, []>("select_0")];
            tensor<string, []> har_phase_mode_0 = const()[name = tensor<string, []>("har_phase_mode_0"), val = tensor<string, []>("update")];
            tensor<bool, []> har_phase_validate_indices_0 = const()[name = tensor<string, []>("har_phase_validate_indices_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 11, 24001]> har_phase = scatter_nd(data = phase_3, indices = select_0, mode = har_phase_mode_0, updates = var_2463_broadcasted, validate_indices = har_phase_validate_indices_0)[name = tensor<string, []>("har_phase")];
            tensor<bool, []> input_513_interleave_0 = const()[name = tensor<string, []>("input_513_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 22, 24001]> input_513 = concat(axis = var_2486, interleave = input_513_interleave_0, values = (har_spec, har_phase))[name = tensor<string, []>("input_513")];
            tensor<fp32, [1, 512, 400]> input_539 = leaky_relu(alpha = var_2475, x = input_511)[name = tensor<string, []>("input_539")];
            tensor<string, []> input_515_pad_type_0 = const()[name = tensor<string, []>("input_515_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_515_pad_0 = const()[name = tensor<string, []>("input_515_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> input_515_strides_0 = const()[name = tensor<string, []>("input_515_strides_0"), val = tensor<int32, [1]>([6])];
            tensor<int32, [1]> input_515_dilations_0 = const()[name = tensor<string, []>("input_515_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_515_groups_0 = const()[name = tensor<string, []>("input_515_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 4000]> input_515 = conv(bias = decoder_generator_noise_convs_0_bias, dilations = input_515_dilations_0, groups = input_515_groups_0, pad = input_515_pad_0, pad_type = input_515_pad_type_0, strides = input_515_strides_0, weight = decoder_generator_noise_convs_0_weight, x = input_513)[name = tensor<string, []>("input_515")];
            tensor<fp32, [1, 512]> h_101 = linear(bias = decoder_generator_noise_res_0_adain1_0_fc_bias, weight = decoder_generator_noise_res_0_adain1_0_fc_weight, x = input_417)[name = tensor<string, []>("linear_101")];
            tensor<int32, [3]> var_4077 = const()[name = tensor<string, []>("op_4077"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_103 = reshape(shape = var_4077, x = h_101)[name = tensor<string, []>("h_103")];
            tensor<int32, [2]> var_4079_split_sizes_0 = const()[name = tensor<string, []>("op_4079_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_4079_axis_0 = const()[name = tensor<string, []>("op_4079_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_4079_0, tensor<fp32, [1, 256, 1]> var_4079_1 = split(axis = var_4079_axis_0, split_sizes = var_4079_split_sizes_0, x = h_103)[name = tensor<string, []>("op_4079")];
            tensor<fp32, []> var_4081_promoted = const()[name = tensor<string, []>("op_4081_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_4082 = add(x = var_4079_0, y = var_4081_promoted)[name = tensor<string, []>("op_4082")];
            tensor<fp32, [1, 256, 4000]> var_4085 = instance_norm(beta = F0_blocks_1_norm2_norm_bias, epsilon = var_2476, gamma = F0_blocks_1_norm2_norm_weight, x = input_515)[name = tensor<string, []>("op_4085")];
            tensor<fp32, [1, 256, 4000]> var_4086 = mul(x = var_4082, y = var_4085)[name = tensor<string, []>("op_4086")];
            tensor<fp32, [1, 256, 4000]> xt_1 = add(x = var_4086, y = var_4079_1)[name = tensor<string, []>("xt_1")];
            tensor<fp32, [1, 256, 1]> var_4088 = const()[name = tensor<string, []>("op_4088"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(255684544)))];
            tensor<fp32, [1, 256, 4000]> var_4091 = mul(x = decoder_generator_noise_res_0_alpha1_0, y = xt_1)[name = tensor<string, []>("op_4091")];
            tensor<fp32, [1, 256, 4000]> var_4092 = sin(x = var_4091)[name = tensor<string, []>("op_4092")];
            tensor<fp32, []> var_2483_promoted_2 = const()[name = tensor<string, []>("op_2483_promoted_2"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 4000]> var_4093 = pow(x = var_4092, y = var_2483_promoted_2)[name = tensor<string, []>("op_4093")];
            tensor<fp32, [1, 256, 4000]> var_4094 = mul(x = var_4088, y = var_4093)[name = tensor<string, []>("op_4094")];
            tensor<fp32, [1, 256, 4000]> input_517 = add(x = xt_1, y = var_4094)[name = tensor<string, []>("input_517")];
            tensor<fp32, [256, 256, 7]> weight_143 = const()[name = tensor<string, []>("weight_143"), val = tensor<fp32, [256, 256, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(255685632)))];
            tensor<string, []> input_519_pad_type_0 = const()[name = tensor<string, []>("input_519_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_519_pad_0 = const()[name = tensor<string, []>("input_519_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> input_519_strides_0 = const()[name = tensor<string, []>("input_519_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_519_dilations_0 = const()[name = tensor<string, []>("input_519_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_519_groups_0 = const()[name = tensor<string, []>("input_519_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 4000]> input_519 = conv(bias = decoder_generator_noise_res_0_convs1_0_bias, dilations = input_519_dilations_0, groups = input_519_groups_0, pad = input_519_pad_0, pad_type = input_519_pad_type_0, strides = input_519_strides_0, weight = weight_143, x = input_517)[name = tensor<string, []>("input_519")];
            tensor<fp32, [1, 512]> h_105 = linear(bias = decoder_generator_noise_res_0_adain2_0_fc_bias, weight = decoder_generator_noise_res_0_adain2_0_fc_weight, x = input_417)[name = tensor<string, []>("linear_102")];
            tensor<int32, [3]> var_4114 = const()[name = tensor<string, []>("op_4114"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_107 = reshape(shape = var_4114, x = h_105)[name = tensor<string, []>("h_107")];
            tensor<int32, [2]> var_4116_split_sizes_0 = const()[name = tensor<string, []>("op_4116_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_4116_axis_0 = const()[name = tensor<string, []>("op_4116_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_4116_0, tensor<fp32, [1, 256, 1]> var_4116_1 = split(axis = var_4116_axis_0, split_sizes = var_4116_split_sizes_0, x = h_107)[name = tensor<string, []>("op_4116")];
            tensor<fp32, []> var_4118_promoted = const()[name = tensor<string, []>("op_4118_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_4119 = add(x = var_4116_0, y = var_4118_promoted)[name = tensor<string, []>("op_4119")];
            tensor<fp32, [1, 256, 4000]> var_4122 = instance_norm(beta = F0_blocks_1_norm2_norm_bias, epsilon = var_2476, gamma = F0_blocks_1_norm2_norm_weight, x = input_519)[name = tensor<string, []>("op_4122")];
            tensor<fp32, [1, 256, 4000]> var_4123 = mul(x = var_4119, y = var_4122)[name = tensor<string, []>("op_4123")];
            tensor<fp32, [1, 256, 4000]> xt_3 = add(x = var_4123, y = var_4116_1)[name = tensor<string, []>("xt_3")];
            tensor<fp32, [1, 256, 1]> var_4125 = const()[name = tensor<string, []>("op_4125"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(257520704)))];
            tensor<fp32, [1, 256, 4000]> var_4128 = mul(x = decoder_generator_noise_res_0_alpha2_0, y = xt_3)[name = tensor<string, []>("op_4128")];
            tensor<fp32, [1, 256, 4000]> var_4129 = sin(x = var_4128)[name = tensor<string, []>("op_4129")];
            tensor<fp32, []> var_2483_promoted_3 = const()[name = tensor<string, []>("op_2483_promoted_3"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 4000]> var_4130 = pow(x = var_4129, y = var_2483_promoted_3)[name = tensor<string, []>("op_4130")];
            tensor<fp32, [1, 256, 4000]> var_4131 = mul(x = var_4125, y = var_4130)[name = tensor<string, []>("op_4131")];
            tensor<fp32, [1, 256, 4000]> input_521 = add(x = xt_3, y = var_4131)[name = tensor<string, []>("input_521")];
            tensor<fp32, [256, 256, 7]> weight_147 = const()[name = tensor<string, []>("weight_147"), val = tensor<fp32, [256, 256, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(257521792)))];
            tensor<string, []> xt_5_pad_type_0 = const()[name = tensor<string, []>("xt_5_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_5_pad_0 = const()[name = tensor<string, []>("xt_5_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> xt_5_strides_0 = const()[name = tensor<string, []>("xt_5_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_5_dilations_0 = const()[name = tensor<string, []>("xt_5_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_5_groups_0 = const()[name = tensor<string, []>("xt_5_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 4000]> xt_5 = conv(bias = decoder_generator_noise_res_0_convs2_0_bias, dilations = xt_5_dilations_0, groups = xt_5_groups_0, pad = xt_5_pad_0, pad_type = xt_5_pad_type_0, strides = xt_5_strides_0, weight = weight_147, x = input_521)[name = tensor<string, []>("xt_5")];
            tensor<fp32, [1, 256, 4000]> input_523 = add(x = xt_5, y = input_515)[name = tensor<string, []>("input_523")];
            tensor<fp32, [1, 512]> h_109 = linear(bias = decoder_generator_noise_res_0_adain1_1_fc_bias, weight = decoder_generator_noise_res_0_adain1_1_fc_weight, x = input_417)[name = tensor<string, []>("linear_103")];
            tensor<int32, [3]> var_4152 = const()[name = tensor<string, []>("op_4152"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_111 = reshape(shape = var_4152, x = h_109)[name = tensor<string, []>("h_111")];
            tensor<int32, [2]> var_4154_split_sizes_0 = const()[name = tensor<string, []>("op_4154_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_4154_axis_0 = const()[name = tensor<string, []>("op_4154_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_4154_0, tensor<fp32, [1, 256, 1]> var_4154_1 = split(axis = var_4154_axis_0, split_sizes = var_4154_split_sizes_0, x = h_111)[name = tensor<string, []>("op_4154")];
            tensor<fp32, []> var_4156_promoted = const()[name = tensor<string, []>("op_4156_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_4157 = add(x = var_4154_0, y = var_4156_promoted)[name = tensor<string, []>("op_4157")];
            tensor<fp32, [1, 256, 4000]> var_4160 = instance_norm(beta = F0_blocks_1_norm2_norm_bias, epsilon = var_2476, gamma = F0_blocks_1_norm2_norm_weight, x = input_523)[name = tensor<string, []>("op_4160")];
            tensor<fp32, [1, 256, 4000]> var_4161 = mul(x = var_4157, y = var_4160)[name = tensor<string, []>("op_4161")];
            tensor<fp32, [1, 256, 4000]> xt_7 = add(x = var_4161, y = var_4154_1)[name = tensor<string, []>("xt_7")];
            tensor<fp32, [1, 256, 1]> var_4163 = const()[name = tensor<string, []>("op_4163"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(259356864)))];
            tensor<fp32, [1, 256, 4000]> var_4166 = mul(x = decoder_generator_noise_res_0_alpha1_1, y = xt_7)[name = tensor<string, []>("op_4166")];
            tensor<fp32, [1, 256, 4000]> var_4167 = sin(x = var_4166)[name = tensor<string, []>("op_4167")];
            tensor<fp32, []> var_2483_promoted_4 = const()[name = tensor<string, []>("op_2483_promoted_4"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 4000]> var_4168 = pow(x = var_4167, y = var_2483_promoted_4)[name = tensor<string, []>("op_4168")];
            tensor<fp32, [1, 256, 4000]> var_4169 = mul(x = var_4163, y = var_4168)[name = tensor<string, []>("op_4169")];
            tensor<fp32, [1, 256, 4000]> input_525 = add(x = xt_7, y = var_4169)[name = tensor<string, []>("input_525")];
            tensor<fp32, [256, 256, 7]> weight_151 = const()[name = tensor<string, []>("weight_151"), val = tensor<fp32, [256, 256, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(259357952)))];
            tensor<string, []> input_527_pad_type_0 = const()[name = tensor<string, []>("input_527_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_527_pad_0 = const()[name = tensor<string, []>("input_527_pad_0"), val = tensor<int32, [2]>([9, 9])];
            tensor<int32, [1]> input_527_dilations_0 = const()[name = tensor<string, []>("input_527_dilations_0"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> input_527_strides_0 = const()[name = tensor<string, []>("input_527_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_527_groups_0 = const()[name = tensor<string, []>("input_527_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 4000]> input_527 = conv(bias = decoder_generator_noise_res_0_convs1_1_bias, dilations = input_527_dilations_0, groups = input_527_groups_0, pad = input_527_pad_0, pad_type = input_527_pad_type_0, strides = input_527_strides_0, weight = weight_151, x = input_525)[name = tensor<string, []>("input_527")];
            tensor<fp32, [1, 512]> h_113 = linear(bias = decoder_generator_noise_res_0_adain2_1_fc_bias, weight = decoder_generator_noise_res_0_adain2_1_fc_weight, x = input_417)[name = tensor<string, []>("linear_104")];
            tensor<int32, [3]> var_4189 = const()[name = tensor<string, []>("op_4189"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_115 = reshape(shape = var_4189, x = h_113)[name = tensor<string, []>("h_115")];
            tensor<int32, [2]> var_4191_split_sizes_0 = const()[name = tensor<string, []>("op_4191_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_4191_axis_0 = const()[name = tensor<string, []>("op_4191_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_4191_0, tensor<fp32, [1, 256, 1]> var_4191_1 = split(axis = var_4191_axis_0, split_sizes = var_4191_split_sizes_0, x = h_115)[name = tensor<string, []>("op_4191")];
            tensor<fp32, []> var_4193_promoted = const()[name = tensor<string, []>("op_4193_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_4194 = add(x = var_4191_0, y = var_4193_promoted)[name = tensor<string, []>("op_4194")];
            tensor<fp32, [1, 256, 4000]> var_4197 = instance_norm(beta = F0_blocks_1_norm2_norm_bias, epsilon = var_2476, gamma = F0_blocks_1_norm2_norm_weight, x = input_527)[name = tensor<string, []>("op_4197")];
            tensor<fp32, [1, 256, 4000]> var_4198 = mul(x = var_4194, y = var_4197)[name = tensor<string, []>("op_4198")];
            tensor<fp32, [1, 256, 4000]> xt_9 = add(x = var_4198, y = var_4191_1)[name = tensor<string, []>("xt_9")];
            tensor<fp32, [1, 256, 1]> var_4200 = const()[name = tensor<string, []>("op_4200"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(261193024)))];
            tensor<fp32, [1, 256, 4000]> var_4203 = mul(x = decoder_generator_noise_res_0_alpha2_1, y = xt_9)[name = tensor<string, []>("op_4203")];
            tensor<fp32, [1, 256, 4000]> var_4204 = sin(x = var_4203)[name = tensor<string, []>("op_4204")];
            tensor<fp32, []> var_2483_promoted_5 = const()[name = tensor<string, []>("op_2483_promoted_5"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 4000]> var_4205 = pow(x = var_4204, y = var_2483_promoted_5)[name = tensor<string, []>("op_4205")];
            tensor<fp32, [1, 256, 4000]> var_4206 = mul(x = var_4200, y = var_4205)[name = tensor<string, []>("op_4206")];
            tensor<fp32, [1, 256, 4000]> input_529 = add(x = xt_9, y = var_4206)[name = tensor<string, []>("input_529")];
            tensor<fp32, [256, 256, 7]> weight_155 = const()[name = tensor<string, []>("weight_155"), val = tensor<fp32, [256, 256, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(261194112)))];
            tensor<string, []> xt_11_pad_type_0 = const()[name = tensor<string, []>("xt_11_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_11_pad_0 = const()[name = tensor<string, []>("xt_11_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> xt_11_strides_0 = const()[name = tensor<string, []>("xt_11_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_11_dilations_0 = const()[name = tensor<string, []>("xt_11_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_11_groups_0 = const()[name = tensor<string, []>("xt_11_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 4000]> xt_11 = conv(bias = decoder_generator_noise_res_0_convs2_1_bias, dilations = xt_11_dilations_0, groups = xt_11_groups_0, pad = xt_11_pad_0, pad_type = xt_11_pad_type_0, strides = xt_11_strides_0, weight = weight_155, x = input_529)[name = tensor<string, []>("xt_11")];
            tensor<fp32, [1, 256, 4000]> input_531 = add(x = xt_11, y = input_523)[name = tensor<string, []>("input_531")];
            tensor<fp32, [1, 512]> h_117 = linear(bias = decoder_generator_noise_res_0_adain1_2_fc_bias, weight = decoder_generator_noise_res_0_adain1_2_fc_weight, x = input_417)[name = tensor<string, []>("linear_105")];
            tensor<int32, [3]> var_4227 = const()[name = tensor<string, []>("op_4227"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_119 = reshape(shape = var_4227, x = h_117)[name = tensor<string, []>("h_119")];
            tensor<int32, [2]> var_4229_split_sizes_0 = const()[name = tensor<string, []>("op_4229_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_4229_axis_0 = const()[name = tensor<string, []>("op_4229_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_4229_0, tensor<fp32, [1, 256, 1]> var_4229_1 = split(axis = var_4229_axis_0, split_sizes = var_4229_split_sizes_0, x = h_119)[name = tensor<string, []>("op_4229")];
            tensor<fp32, []> var_4231_promoted = const()[name = tensor<string, []>("op_4231_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_4232 = add(x = var_4229_0, y = var_4231_promoted)[name = tensor<string, []>("op_4232")];
            tensor<fp32, [1, 256, 4000]> var_4235 = instance_norm(beta = F0_blocks_1_norm2_norm_bias, epsilon = var_2476, gamma = F0_blocks_1_norm2_norm_weight, x = input_531)[name = tensor<string, []>("op_4235")];
            tensor<fp32, [1, 256, 4000]> var_4236 = mul(x = var_4232, y = var_4235)[name = tensor<string, []>("op_4236")];
            tensor<fp32, [1, 256, 4000]> xt_13 = add(x = var_4236, y = var_4229_1)[name = tensor<string, []>("xt_13")];
            tensor<fp32, [1, 256, 1]> var_4238 = const()[name = tensor<string, []>("op_4238"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(263029184)))];
            tensor<fp32, [1, 256, 4000]> var_4241 = mul(x = decoder_generator_noise_res_0_alpha1_2, y = xt_13)[name = tensor<string, []>("op_4241")];
            tensor<fp32, [1, 256, 4000]> var_4242 = sin(x = var_4241)[name = tensor<string, []>("op_4242")];
            tensor<fp32, []> var_2483_promoted_6 = const()[name = tensor<string, []>("op_2483_promoted_6"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 4000]> var_4243 = pow(x = var_4242, y = var_2483_promoted_6)[name = tensor<string, []>("op_4243")];
            tensor<fp32, [1, 256, 4000]> var_4244 = mul(x = var_4238, y = var_4243)[name = tensor<string, []>("op_4244")];
            tensor<fp32, [1, 256, 4000]> input_533 = add(x = xt_13, y = var_4244)[name = tensor<string, []>("input_533")];
            tensor<fp32, [256, 256, 7]> weight_159 = const()[name = tensor<string, []>("weight_159"), val = tensor<fp32, [256, 256, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(263030272)))];
            tensor<string, []> input_535_pad_type_0 = const()[name = tensor<string, []>("input_535_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_535_pad_0 = const()[name = tensor<string, []>("input_535_pad_0"), val = tensor<int32, [2]>([15, 15])];
            tensor<int32, [1]> input_535_dilations_0 = const()[name = tensor<string, []>("input_535_dilations_0"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> input_535_strides_0 = const()[name = tensor<string, []>("input_535_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_535_groups_0 = const()[name = tensor<string, []>("input_535_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 4000]> input_535 = conv(bias = decoder_generator_noise_res_0_convs1_2_bias, dilations = input_535_dilations_0, groups = input_535_groups_0, pad = input_535_pad_0, pad_type = input_535_pad_type_0, strides = input_535_strides_0, weight = weight_159, x = input_533)[name = tensor<string, []>("input_535")];
            tensor<fp32, [1, 512]> h_121 = linear(bias = decoder_generator_noise_res_0_adain2_2_fc_bias, weight = decoder_generator_noise_res_0_adain2_2_fc_weight, x = input_417)[name = tensor<string, []>("linear_106")];
            tensor<int32, [3]> var_4264 = const()[name = tensor<string, []>("op_4264"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_123 = reshape(shape = var_4264, x = h_121)[name = tensor<string, []>("h_123")];
            tensor<int32, [2]> var_4266_split_sizes_0 = const()[name = tensor<string, []>("op_4266_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_4266_axis_0 = const()[name = tensor<string, []>("op_4266_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_4266_0, tensor<fp32, [1, 256, 1]> var_4266_1 = split(axis = var_4266_axis_0, split_sizes = var_4266_split_sizes_0, x = h_123)[name = tensor<string, []>("op_4266")];
            tensor<fp32, []> var_4268_promoted = const()[name = tensor<string, []>("op_4268_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_4269 = add(x = var_4266_0, y = var_4268_promoted)[name = tensor<string, []>("op_4269")];
            tensor<fp32, [1, 256, 4000]> var_4272 = instance_norm(beta = F0_blocks_1_norm2_norm_bias, epsilon = var_2476, gamma = F0_blocks_1_norm2_norm_weight, x = input_535)[name = tensor<string, []>("op_4272")];
            tensor<fp32, [1, 256, 4000]> var_4273 = mul(x = var_4269, y = var_4272)[name = tensor<string, []>("op_4273")];
            tensor<fp32, [1, 256, 4000]> xt_15 = add(x = var_4273, y = var_4266_1)[name = tensor<string, []>("xt_15")];
            tensor<fp32, [1, 256, 1]> var_4275 = const()[name = tensor<string, []>("op_4275"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(264865344)))];
            tensor<fp32, [1, 256, 4000]> var_4278 = mul(x = decoder_generator_noise_res_0_alpha2_2, y = xt_15)[name = tensor<string, []>("op_4278")];
            tensor<fp32, [1, 256, 4000]> var_4279 = sin(x = var_4278)[name = tensor<string, []>("op_4279")];
            tensor<fp32, []> var_2483_promoted_7 = const()[name = tensor<string, []>("op_2483_promoted_7"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 4000]> var_4280 = pow(x = var_4279, y = var_2483_promoted_7)[name = tensor<string, []>("op_4280")];
            tensor<fp32, [1, 256, 4000]> var_4281 = mul(x = var_4275, y = var_4280)[name = tensor<string, []>("op_4281")];
            tensor<fp32, [1, 256, 4000]> input_537 = add(x = xt_15, y = var_4281)[name = tensor<string, []>("input_537")];
            tensor<fp32, [256, 256, 7]> weight_163 = const()[name = tensor<string, []>("weight_163"), val = tensor<fp32, [256, 256, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(264866432)))];
            tensor<string, []> xt_17_pad_type_0 = const()[name = tensor<string, []>("xt_17_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_17_pad_0 = const()[name = tensor<string, []>("xt_17_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> xt_17_strides_0 = const()[name = tensor<string, []>("xt_17_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_17_dilations_0 = const()[name = tensor<string, []>("xt_17_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_17_groups_0 = const()[name = tensor<string, []>("xt_17_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 4000]> xt_17 = conv(bias = decoder_generator_noise_res_0_convs2_2_bias, dilations = xt_17_dilations_0, groups = xt_17_groups_0, pad = xt_17_pad_0, pad_type = xt_17_pad_type_0, strides = xt_17_strides_0, weight = weight_163, x = input_537)[name = tensor<string, []>("xt_17")];
            tensor<fp32, [1, 256, 4000]> x_source_1 = add(x = xt_17, y = input_531)[name = tensor<string, []>("x_source_1")];
            tensor<fp32, [512, 256, 20]> var_4300 = const()[name = tensor<string, []>("op_4300"), val = tensor<fp32, [512, 256, 20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(266701504)))];
            tensor<string, []> x_249_pad_type_0 = const()[name = tensor<string, []>("x_249_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> x_249_pad_0 = const()[name = tensor<string, []>("x_249_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> x_249_strides_0 = const()[name = tensor<string, []>("x_249_strides_0"), val = tensor<int32, [1]>([10])];
            tensor<int32, [1]> x_249_dilations_0 = const()[name = tensor<string, []>("x_249_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_249_groups_0 = const()[name = tensor<string, []>("x_249_groups_0"), val = tensor<int32, []>(1)];
            tensor<int32, [3]> x_249_has_output_shape_output_shape_0 = const()[name = tensor<string, []>("x_249_has_output_shape_output_shape_0"), val = tensor<int32, [3]>([1, 256, 4000])];
            tensor<fp32, [1, 256, 4000]> x_249_has_output_shape = conv_transpose(bias = decoder_generator_ups_0_bias, dilations = x_249_dilations_0, groups = x_249_groups_0, output_shape = x_249_has_output_shape_output_shape_0, pad = x_249_pad_0, pad_type = x_249_pad_type_0, strides = x_249_strides_0, weight = var_4300, x = input_539)[name = tensor<string, []>("x_249_has_output_shape")];
            tensor<fp32, [1, 256, 4000]> input_541 = add(x = x_249_has_output_shape, y = x_source_1)[name = tensor<string, []>("input_541")];
            tensor<fp32, [1, 512]> h_125 = linear(bias = decoder_generator_resblocks_0_adain1_0_fc_bias, weight = decoder_generator_resblocks_0_adain1_0_fc_weight, x = input_417)[name = tensor<string, []>("linear_107")];
            tensor<int32, [3]> var_4350 = const()[name = tensor<string, []>("op_4350"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_127 = reshape(shape = var_4350, x = h_125)[name = tensor<string, []>("h_127")];
            tensor<int32, [2]> var_4352_split_sizes_0 = const()[name = tensor<string, []>("op_4352_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_4352_axis_0 = const()[name = tensor<string, []>("op_4352_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_4352_0, tensor<fp32, [1, 256, 1]> var_4352_1 = split(axis = var_4352_axis_0, split_sizes = var_4352_split_sizes_0, x = h_127)[name = tensor<string, []>("op_4352")];
            tensor<fp32, []> var_4354_promoted = const()[name = tensor<string, []>("op_4354_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_4355 = add(x = var_4352_0, y = var_4354_promoted)[name = tensor<string, []>("op_4355")];
            tensor<fp32, [1, 256, 4000]> var_4358 = instance_norm(beta = F0_blocks_1_norm2_norm_bias, epsilon = var_2476, gamma = F0_blocks_1_norm2_norm_weight, x = input_541)[name = tensor<string, []>("op_4358")];
            tensor<fp32, [1, 256, 4000]> var_4359 = mul(x = var_4355, y = var_4358)[name = tensor<string, []>("op_4359")];
            tensor<fp32, [1, 256, 4000]> xt_19 = add(x = var_4359, y = var_4352_1)[name = tensor<string, []>("xt_19")];
            tensor<fp32, [1, 256, 1]> var_4361 = const()[name = tensor<string, []>("op_4361"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(277187328)))];
            tensor<fp32, [1, 256, 4000]> var_4364 = mul(x = decoder_generator_resblocks_0_alpha1_0, y = xt_19)[name = tensor<string, []>("op_4364")];
            tensor<fp32, [1, 256, 4000]> var_4365 = sin(x = var_4364)[name = tensor<string, []>("op_4365")];
            tensor<fp32, []> var_2483_promoted_8 = const()[name = tensor<string, []>("op_2483_promoted_8"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 4000]> var_4366 = pow(x = var_4365, y = var_2483_promoted_8)[name = tensor<string, []>("op_4366")];
            tensor<fp32, [1, 256, 4000]> var_4367 = mul(x = var_4361, y = var_4366)[name = tensor<string, []>("op_4367")];
            tensor<fp32, [1, 256, 4000]> input_543 = add(x = xt_19, y = var_4367)[name = tensor<string, []>("input_543")];
            tensor<fp32, [256, 256, 3]> weight_167 = const()[name = tensor<string, []>("weight_167"), val = tensor<fp32, [256, 256, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(277188416)))];
            tensor<string, []> input_545_pad_type_0 = const()[name = tensor<string, []>("input_545_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_545_pad_0 = const()[name = tensor<string, []>("input_545_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> input_545_strides_0 = const()[name = tensor<string, []>("input_545_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_545_dilations_0 = const()[name = tensor<string, []>("input_545_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_545_groups_0 = const()[name = tensor<string, []>("input_545_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 4000]> input_545 = conv(bias = decoder_generator_resblocks_0_convs1_0_bias, dilations = input_545_dilations_0, groups = input_545_groups_0, pad = input_545_pad_0, pad_type = input_545_pad_type_0, strides = input_545_strides_0, weight = weight_167, x = input_543)[name = tensor<string, []>("input_545")];
            tensor<fp32, [1, 512]> h_129 = linear(bias = decoder_generator_resblocks_0_adain2_0_fc_bias, weight = decoder_generator_resblocks_0_adain2_0_fc_weight, x = input_417)[name = tensor<string, []>("linear_108")];
            tensor<int32, [3]> var_4387 = const()[name = tensor<string, []>("op_4387"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_131 = reshape(shape = var_4387, x = h_129)[name = tensor<string, []>("h_131")];
            tensor<int32, [2]> var_4389_split_sizes_0 = const()[name = tensor<string, []>("op_4389_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_4389_axis_0 = const()[name = tensor<string, []>("op_4389_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_4389_0, tensor<fp32, [1, 256, 1]> var_4389_1 = split(axis = var_4389_axis_0, split_sizes = var_4389_split_sizes_0, x = h_131)[name = tensor<string, []>("op_4389")];
            tensor<fp32, []> var_4391_promoted = const()[name = tensor<string, []>("op_4391_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_4392 = add(x = var_4389_0, y = var_4391_promoted)[name = tensor<string, []>("op_4392")];
            tensor<fp32, [1, 256, 4000]> var_4395 = instance_norm(beta = F0_blocks_1_norm2_norm_bias, epsilon = var_2476, gamma = F0_blocks_1_norm2_norm_weight, x = input_545)[name = tensor<string, []>("op_4395")];
            tensor<fp32, [1, 256, 4000]> var_4396 = mul(x = var_4392, y = var_4395)[name = tensor<string, []>("op_4396")];
            tensor<fp32, [1, 256, 4000]> xt_21 = add(x = var_4396, y = var_4389_1)[name = tensor<string, []>("xt_21")];
            tensor<fp32, [1, 256, 1]> var_4398 = const()[name = tensor<string, []>("op_4398"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(277974912)))];
            tensor<fp32, [1, 256, 4000]> var_4401 = mul(x = decoder_generator_resblocks_0_alpha2_0, y = xt_21)[name = tensor<string, []>("op_4401")];
            tensor<fp32, [1, 256, 4000]> var_4402 = sin(x = var_4401)[name = tensor<string, []>("op_4402")];
            tensor<fp32, []> var_2483_promoted_9 = const()[name = tensor<string, []>("op_2483_promoted_9"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 4000]> var_4403 = pow(x = var_4402, y = var_2483_promoted_9)[name = tensor<string, []>("op_4403")];
            tensor<fp32, [1, 256, 4000]> var_4404 = mul(x = var_4398, y = var_4403)[name = tensor<string, []>("op_4404")];
            tensor<fp32, [1, 256, 4000]> input_547 = add(x = xt_21, y = var_4404)[name = tensor<string, []>("input_547")];
            tensor<fp32, [256, 256, 3]> weight_171 = const()[name = tensor<string, []>("weight_171"), val = tensor<fp32, [256, 256, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(277976000)))];
            tensor<string, []> xt_23_pad_type_0 = const()[name = tensor<string, []>("xt_23_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_23_pad_0 = const()[name = tensor<string, []>("xt_23_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> xt_23_strides_0 = const()[name = tensor<string, []>("xt_23_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_23_dilations_0 = const()[name = tensor<string, []>("xt_23_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_23_groups_0 = const()[name = tensor<string, []>("xt_23_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 4000]> xt_23 = conv(bias = decoder_generator_resblocks_0_convs2_0_bias, dilations = xt_23_dilations_0, groups = xt_23_groups_0, pad = xt_23_pad_0, pad_type = xt_23_pad_type_0, strides = xt_23_strides_0, weight = weight_171, x = input_547)[name = tensor<string, []>("xt_23")];
            tensor<fp32, [1, 256, 4000]> input_549 = add(x = xt_23, y = input_541)[name = tensor<string, []>("input_549")];
            tensor<fp32, [1, 512]> h_133 = linear(bias = decoder_generator_resblocks_0_adain1_1_fc_bias, weight = decoder_generator_resblocks_0_adain1_1_fc_weight, x = input_417)[name = tensor<string, []>("linear_109")];
            tensor<int32, [3]> var_4425 = const()[name = tensor<string, []>("op_4425"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_135 = reshape(shape = var_4425, x = h_133)[name = tensor<string, []>("h_135")];
            tensor<int32, [2]> var_4427_split_sizes_0 = const()[name = tensor<string, []>("op_4427_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_4427_axis_0 = const()[name = tensor<string, []>("op_4427_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_4427_0, tensor<fp32, [1, 256, 1]> var_4427_1 = split(axis = var_4427_axis_0, split_sizes = var_4427_split_sizes_0, x = h_135)[name = tensor<string, []>("op_4427")];
            tensor<fp32, []> var_4429_promoted = const()[name = tensor<string, []>("op_4429_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_4430 = add(x = var_4427_0, y = var_4429_promoted)[name = tensor<string, []>("op_4430")];
            tensor<fp32, [1, 256, 4000]> var_4433 = instance_norm(beta = F0_blocks_1_norm2_norm_bias, epsilon = var_2476, gamma = F0_blocks_1_norm2_norm_weight, x = input_549)[name = tensor<string, []>("op_4433")];
            tensor<fp32, [1, 256, 4000]> var_4434 = mul(x = var_4430, y = var_4433)[name = tensor<string, []>("op_4434")];
            tensor<fp32, [1, 256, 4000]> xt_25 = add(x = var_4434, y = var_4427_1)[name = tensor<string, []>("xt_25")];
            tensor<fp32, [1, 256, 1]> var_4436 = const()[name = tensor<string, []>("op_4436"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(278762496)))];
            tensor<fp32, [1, 256, 4000]> var_4439 = mul(x = decoder_generator_resblocks_0_alpha1_1, y = xt_25)[name = tensor<string, []>("op_4439")];
            tensor<fp32, [1, 256, 4000]> var_4440 = sin(x = var_4439)[name = tensor<string, []>("op_4440")];
            tensor<fp32, []> var_2483_promoted_10 = const()[name = tensor<string, []>("op_2483_promoted_10"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 4000]> var_4441 = pow(x = var_4440, y = var_2483_promoted_10)[name = tensor<string, []>("op_4441")];
            tensor<fp32, [1, 256, 4000]> var_4442 = mul(x = var_4436, y = var_4441)[name = tensor<string, []>("op_4442")];
            tensor<fp32, [1, 256, 4000]> input_551 = add(x = xt_25, y = var_4442)[name = tensor<string, []>("input_551")];
            tensor<fp32, [256, 256, 3]> weight_175 = const()[name = tensor<string, []>("weight_175"), val = tensor<fp32, [256, 256, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(278763584)))];
            tensor<string, []> input_553_pad_type_0 = const()[name = tensor<string, []>("input_553_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_553_pad_0 = const()[name = tensor<string, []>("input_553_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> input_553_dilations_0 = const()[name = tensor<string, []>("input_553_dilations_0"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> input_553_strides_0 = const()[name = tensor<string, []>("input_553_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_553_groups_0 = const()[name = tensor<string, []>("input_553_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 4000]> input_553 = conv(bias = decoder_generator_resblocks_0_convs1_1_bias, dilations = input_553_dilations_0, groups = input_553_groups_0, pad = input_553_pad_0, pad_type = input_553_pad_type_0, strides = input_553_strides_0, weight = weight_175, x = input_551)[name = tensor<string, []>("input_553")];
            tensor<fp32, [1, 512]> h_137 = linear(bias = decoder_generator_resblocks_0_adain2_1_fc_bias, weight = decoder_generator_resblocks_0_adain2_1_fc_weight, x = input_417)[name = tensor<string, []>("linear_110")];
            tensor<int32, [3]> var_4462 = const()[name = tensor<string, []>("op_4462"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_139 = reshape(shape = var_4462, x = h_137)[name = tensor<string, []>("h_139")];
            tensor<int32, [2]> var_4464_split_sizes_0 = const()[name = tensor<string, []>("op_4464_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_4464_axis_0 = const()[name = tensor<string, []>("op_4464_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_4464_0, tensor<fp32, [1, 256, 1]> var_4464_1 = split(axis = var_4464_axis_0, split_sizes = var_4464_split_sizes_0, x = h_139)[name = tensor<string, []>("op_4464")];
            tensor<fp32, []> var_4466_promoted = const()[name = tensor<string, []>("op_4466_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_4467 = add(x = var_4464_0, y = var_4466_promoted)[name = tensor<string, []>("op_4467")];
            tensor<fp32, [1, 256, 4000]> var_4470 = instance_norm(beta = F0_blocks_1_norm2_norm_bias, epsilon = var_2476, gamma = F0_blocks_1_norm2_norm_weight, x = input_553)[name = tensor<string, []>("op_4470")];
            tensor<fp32, [1, 256, 4000]> var_4471 = mul(x = var_4467, y = var_4470)[name = tensor<string, []>("op_4471")];
            tensor<fp32, [1, 256, 4000]> xt_27 = add(x = var_4471, y = var_4464_1)[name = tensor<string, []>("xt_27")];
            tensor<fp32, [1, 256, 1]> var_4473 = const()[name = tensor<string, []>("op_4473"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(279550080)))];
            tensor<fp32, [1, 256, 4000]> var_4476 = mul(x = decoder_generator_resblocks_0_alpha2_1, y = xt_27)[name = tensor<string, []>("op_4476")];
            tensor<fp32, [1, 256, 4000]> var_4477 = sin(x = var_4476)[name = tensor<string, []>("op_4477")];
            tensor<fp32, []> var_2483_promoted_11 = const()[name = tensor<string, []>("op_2483_promoted_11"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 4000]> var_4478 = pow(x = var_4477, y = var_2483_promoted_11)[name = tensor<string, []>("op_4478")];
            tensor<fp32, [1, 256, 4000]> var_4479 = mul(x = var_4473, y = var_4478)[name = tensor<string, []>("op_4479")];
            tensor<fp32, [1, 256, 4000]> input_555 = add(x = xt_27, y = var_4479)[name = tensor<string, []>("input_555")];
            tensor<fp32, [256, 256, 3]> weight_179 = const()[name = tensor<string, []>("weight_179"), val = tensor<fp32, [256, 256, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(279551168)))];
            tensor<string, []> xt_29_pad_type_0 = const()[name = tensor<string, []>("xt_29_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_29_pad_0 = const()[name = tensor<string, []>("xt_29_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> xt_29_strides_0 = const()[name = tensor<string, []>("xt_29_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_29_dilations_0 = const()[name = tensor<string, []>("xt_29_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_29_groups_0 = const()[name = tensor<string, []>("xt_29_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 4000]> xt_29 = conv(bias = decoder_generator_resblocks_0_convs2_1_bias, dilations = xt_29_dilations_0, groups = xt_29_groups_0, pad = xt_29_pad_0, pad_type = xt_29_pad_type_0, strides = xt_29_strides_0, weight = weight_179, x = input_555)[name = tensor<string, []>("xt_29")];
            tensor<fp32, [1, 256, 4000]> input_557 = add(x = xt_29, y = input_549)[name = tensor<string, []>("input_557")];
            tensor<fp32, [1, 512]> h_141 = linear(bias = decoder_generator_resblocks_0_adain1_2_fc_bias, weight = decoder_generator_resblocks_0_adain1_2_fc_weight, x = input_417)[name = tensor<string, []>("linear_111")];
            tensor<int32, [3]> var_4500 = const()[name = tensor<string, []>("op_4500"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_143 = reshape(shape = var_4500, x = h_141)[name = tensor<string, []>("h_143")];
            tensor<int32, [2]> var_4502_split_sizes_0 = const()[name = tensor<string, []>("op_4502_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_4502_axis_0 = const()[name = tensor<string, []>("op_4502_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_4502_0, tensor<fp32, [1, 256, 1]> var_4502_1 = split(axis = var_4502_axis_0, split_sizes = var_4502_split_sizes_0, x = h_143)[name = tensor<string, []>("op_4502")];
            tensor<fp32, []> var_4504_promoted = const()[name = tensor<string, []>("op_4504_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_4505 = add(x = var_4502_0, y = var_4504_promoted)[name = tensor<string, []>("op_4505")];
            tensor<fp32, [1, 256, 4000]> var_4508 = instance_norm(beta = F0_blocks_1_norm2_norm_bias, epsilon = var_2476, gamma = F0_blocks_1_norm2_norm_weight, x = input_557)[name = tensor<string, []>("op_4508")];
            tensor<fp32, [1, 256, 4000]> var_4509 = mul(x = var_4505, y = var_4508)[name = tensor<string, []>("op_4509")];
            tensor<fp32, [1, 256, 4000]> xt_31 = add(x = var_4509, y = var_4502_1)[name = tensor<string, []>("xt_31")];
            tensor<fp32, [1, 256, 1]> var_4511 = const()[name = tensor<string, []>("op_4511"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(280337664)))];
            tensor<fp32, [1, 256, 4000]> var_4514 = mul(x = decoder_generator_resblocks_0_alpha1_2, y = xt_31)[name = tensor<string, []>("op_4514")];
            tensor<fp32, [1, 256, 4000]> var_4515 = sin(x = var_4514)[name = tensor<string, []>("op_4515")];
            tensor<fp32, []> var_2483_promoted_12 = const()[name = tensor<string, []>("op_2483_promoted_12"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 4000]> var_4516 = pow(x = var_4515, y = var_2483_promoted_12)[name = tensor<string, []>("op_4516")];
            tensor<fp32, [1, 256, 4000]> var_4517 = mul(x = var_4511, y = var_4516)[name = tensor<string, []>("op_4517")];
            tensor<fp32, [1, 256, 4000]> input_559 = add(x = xt_31, y = var_4517)[name = tensor<string, []>("input_559")];
            tensor<fp32, [256, 256, 3]> weight_183 = const()[name = tensor<string, []>("weight_183"), val = tensor<fp32, [256, 256, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(280338752)))];
            tensor<string, []> input_561_pad_type_0 = const()[name = tensor<string, []>("input_561_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_561_pad_0 = const()[name = tensor<string, []>("input_561_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> input_561_dilations_0 = const()[name = tensor<string, []>("input_561_dilations_0"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> input_561_strides_0 = const()[name = tensor<string, []>("input_561_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_561_groups_0 = const()[name = tensor<string, []>("input_561_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 4000]> input_561 = conv(bias = decoder_generator_resblocks_0_convs1_2_bias, dilations = input_561_dilations_0, groups = input_561_groups_0, pad = input_561_pad_0, pad_type = input_561_pad_type_0, strides = input_561_strides_0, weight = weight_183, x = input_559)[name = tensor<string, []>("input_561")];
            tensor<fp32, [1, 512]> h_145 = linear(bias = decoder_generator_resblocks_0_adain2_2_fc_bias, weight = decoder_generator_resblocks_0_adain2_2_fc_weight, x = input_417)[name = tensor<string, []>("linear_112")];
            tensor<int32, [3]> var_4537 = const()[name = tensor<string, []>("op_4537"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_147 = reshape(shape = var_4537, x = h_145)[name = tensor<string, []>("h_147")];
            tensor<int32, [2]> var_4539_split_sizes_0 = const()[name = tensor<string, []>("op_4539_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_4539_axis_0 = const()[name = tensor<string, []>("op_4539_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_4539_0, tensor<fp32, [1, 256, 1]> var_4539_1 = split(axis = var_4539_axis_0, split_sizes = var_4539_split_sizes_0, x = h_147)[name = tensor<string, []>("op_4539")];
            tensor<fp32, []> var_4541_promoted = const()[name = tensor<string, []>("op_4541_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_4542 = add(x = var_4539_0, y = var_4541_promoted)[name = tensor<string, []>("op_4542")];
            tensor<fp32, [1, 256, 4000]> var_4545 = instance_norm(beta = F0_blocks_1_norm2_norm_bias, epsilon = var_2476, gamma = F0_blocks_1_norm2_norm_weight, x = input_561)[name = tensor<string, []>("op_4545")];
            tensor<fp32, [1, 256, 4000]> var_4546 = mul(x = var_4542, y = var_4545)[name = tensor<string, []>("op_4546")];
            tensor<fp32, [1, 256, 4000]> xt_33 = add(x = var_4546, y = var_4539_1)[name = tensor<string, []>("xt_33")];
            tensor<fp32, [1, 256, 1]> var_4548 = const()[name = tensor<string, []>("op_4548"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(281125248)))];
            tensor<fp32, [1, 256, 4000]> var_4551 = mul(x = decoder_generator_resblocks_0_alpha2_2, y = xt_33)[name = tensor<string, []>("op_4551")];
            tensor<fp32, [1, 256, 4000]> var_4552 = sin(x = var_4551)[name = tensor<string, []>("op_4552")];
            tensor<fp32, []> var_2483_promoted_13 = const()[name = tensor<string, []>("op_2483_promoted_13"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 4000]> var_4553 = pow(x = var_4552, y = var_2483_promoted_13)[name = tensor<string, []>("op_4553")];
            tensor<fp32, [1, 256, 4000]> var_4554 = mul(x = var_4548, y = var_4553)[name = tensor<string, []>("op_4554")];
            tensor<fp32, [1, 256, 4000]> input_563 = add(x = xt_33, y = var_4554)[name = tensor<string, []>("input_563")];
            tensor<fp32, [256, 256, 3]> weight_187 = const()[name = tensor<string, []>("weight_187"), val = tensor<fp32, [256, 256, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(281126336)))];
            tensor<string, []> xt_35_pad_type_0 = const()[name = tensor<string, []>("xt_35_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_35_pad_0 = const()[name = tensor<string, []>("xt_35_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> xt_35_strides_0 = const()[name = tensor<string, []>("xt_35_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_35_dilations_0 = const()[name = tensor<string, []>("xt_35_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_35_groups_0 = const()[name = tensor<string, []>("xt_35_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 4000]> xt_35 = conv(bias = decoder_generator_resblocks_0_convs2_2_bias, dilations = xt_35_dilations_0, groups = xt_35_groups_0, pad = xt_35_pad_0, pad_type = xt_35_pad_type_0, strides = xt_35_strides_0, weight = weight_187, x = input_563)[name = tensor<string, []>("xt_35")];
            tensor<fp32, [1, 256, 4000]> xs_1 = add(x = xt_35, y = input_557)[name = tensor<string, []>("xs_1")];
            tensor<fp32, [1, 512]> h_149 = linear(bias = decoder_generator_resblocks_1_adain1_0_fc_bias, weight = decoder_generator_resblocks_1_adain1_0_fc_weight, x = input_417)[name = tensor<string, []>("linear_113")];
            tensor<int32, [3]> var_4611 = const()[name = tensor<string, []>("op_4611"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_151 = reshape(shape = var_4611, x = h_149)[name = tensor<string, []>("h_151")];
            tensor<int32, [2]> var_4613_split_sizes_0 = const()[name = tensor<string, []>("op_4613_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_4613_axis_0 = const()[name = tensor<string, []>("op_4613_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_4613_0, tensor<fp32, [1, 256, 1]> var_4613_1 = split(axis = var_4613_axis_0, split_sizes = var_4613_split_sizes_0, x = h_151)[name = tensor<string, []>("op_4613")];
            tensor<fp32, []> var_4615_promoted = const()[name = tensor<string, []>("op_4615_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_4616 = add(x = var_4613_0, y = var_4615_promoted)[name = tensor<string, []>("op_4616")];
            tensor<fp32, [1, 256, 4000]> var_4620 = mul(x = var_4616, y = var_4358)[name = tensor<string, []>("op_4620")];
            tensor<fp32, [1, 256, 4000]> xt_37 = add(x = var_4620, y = var_4613_1)[name = tensor<string, []>("xt_37")];
            tensor<fp32, [1, 256, 1]> var_4622 = const()[name = tensor<string, []>("op_4622"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(281912832)))];
            tensor<fp32, [1, 256, 4000]> var_4625 = mul(x = decoder_generator_resblocks_1_alpha1_0, y = xt_37)[name = tensor<string, []>("op_4625")];
            tensor<fp32, [1, 256, 4000]> var_4626 = sin(x = var_4625)[name = tensor<string, []>("op_4626")];
            tensor<fp32, []> var_2483_promoted_14 = const()[name = tensor<string, []>("op_2483_promoted_14"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 4000]> var_4627 = pow(x = var_4626, y = var_2483_promoted_14)[name = tensor<string, []>("op_4627")];
            tensor<fp32, [1, 256, 4000]> var_4628 = mul(x = var_4622, y = var_4627)[name = tensor<string, []>("op_4628")];
            tensor<fp32, [1, 256, 4000]> input_565 = add(x = xt_37, y = var_4628)[name = tensor<string, []>("input_565")];
            tensor<fp32, [256, 256, 7]> weight_191 = const()[name = tensor<string, []>("weight_191"), val = tensor<fp32, [256, 256, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(281913920)))];
            tensor<string, []> input_567_pad_type_0 = const()[name = tensor<string, []>("input_567_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_567_pad_0 = const()[name = tensor<string, []>("input_567_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> input_567_strides_0 = const()[name = tensor<string, []>("input_567_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_567_dilations_0 = const()[name = tensor<string, []>("input_567_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_567_groups_0 = const()[name = tensor<string, []>("input_567_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 4000]> input_567 = conv(bias = decoder_generator_resblocks_1_convs1_0_bias, dilations = input_567_dilations_0, groups = input_567_groups_0, pad = input_567_pad_0, pad_type = input_567_pad_type_0, strides = input_567_strides_0, weight = weight_191, x = input_565)[name = tensor<string, []>("input_567")];
            tensor<fp32, [1, 512]> h_153 = linear(bias = decoder_generator_resblocks_1_adain2_0_fc_bias, weight = decoder_generator_resblocks_1_adain2_0_fc_weight, x = input_417)[name = tensor<string, []>("linear_114")];
            tensor<int32, [3]> var_4648 = const()[name = tensor<string, []>("op_4648"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_155 = reshape(shape = var_4648, x = h_153)[name = tensor<string, []>("h_155")];
            tensor<int32, [2]> var_4650_split_sizes_0 = const()[name = tensor<string, []>("op_4650_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_4650_axis_0 = const()[name = tensor<string, []>("op_4650_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_4650_0, tensor<fp32, [1, 256, 1]> var_4650_1 = split(axis = var_4650_axis_0, split_sizes = var_4650_split_sizes_0, x = h_155)[name = tensor<string, []>("op_4650")];
            tensor<fp32, []> var_4652_promoted = const()[name = tensor<string, []>("op_4652_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_4653 = add(x = var_4650_0, y = var_4652_promoted)[name = tensor<string, []>("op_4653")];
            tensor<fp32, [1, 256, 4000]> var_4656 = instance_norm(beta = F0_blocks_1_norm2_norm_bias, epsilon = var_2476, gamma = F0_blocks_1_norm2_norm_weight, x = input_567)[name = tensor<string, []>("op_4656")];
            tensor<fp32, [1, 256, 4000]> var_4657 = mul(x = var_4653, y = var_4656)[name = tensor<string, []>("op_4657")];
            tensor<fp32, [1, 256, 4000]> xt_39 = add(x = var_4657, y = var_4650_1)[name = tensor<string, []>("xt_39")];
            tensor<fp32, [1, 256, 1]> var_4659 = const()[name = tensor<string, []>("op_4659"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(283748992)))];
            tensor<fp32, [1, 256, 4000]> var_4662 = mul(x = decoder_generator_resblocks_1_alpha2_0, y = xt_39)[name = tensor<string, []>("op_4662")];
            tensor<fp32, [1, 256, 4000]> var_4663 = sin(x = var_4662)[name = tensor<string, []>("op_4663")];
            tensor<fp32, []> var_2483_promoted_15 = const()[name = tensor<string, []>("op_2483_promoted_15"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 4000]> var_4664 = pow(x = var_4663, y = var_2483_promoted_15)[name = tensor<string, []>("op_4664")];
            tensor<fp32, [1, 256, 4000]> var_4665 = mul(x = var_4659, y = var_4664)[name = tensor<string, []>("op_4665")];
            tensor<fp32, [1, 256, 4000]> input_569 = add(x = xt_39, y = var_4665)[name = tensor<string, []>("input_569")];
            tensor<fp32, [256, 256, 7]> weight_195 = const()[name = tensor<string, []>("weight_195"), val = tensor<fp32, [256, 256, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(283750080)))];
            tensor<string, []> xt_41_pad_type_0 = const()[name = tensor<string, []>("xt_41_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_41_pad_0 = const()[name = tensor<string, []>("xt_41_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> xt_41_strides_0 = const()[name = tensor<string, []>("xt_41_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_41_dilations_0 = const()[name = tensor<string, []>("xt_41_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_41_groups_0 = const()[name = tensor<string, []>("xt_41_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 4000]> xt_41 = conv(bias = decoder_generator_resblocks_1_convs2_0_bias, dilations = xt_41_dilations_0, groups = xt_41_groups_0, pad = xt_41_pad_0, pad_type = xt_41_pad_type_0, strides = xt_41_strides_0, weight = weight_195, x = input_569)[name = tensor<string, []>("xt_41")];
            tensor<fp32, [1, 256, 4000]> input_571 = add(x = xt_41, y = input_541)[name = tensor<string, []>("input_571")];
            tensor<fp32, [1, 512]> h_157 = linear(bias = decoder_generator_resblocks_1_adain1_1_fc_bias, weight = decoder_generator_resblocks_1_adain1_1_fc_weight, x = input_417)[name = tensor<string, []>("linear_115")];
            tensor<int32, [3]> var_4686 = const()[name = tensor<string, []>("op_4686"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_159 = reshape(shape = var_4686, x = h_157)[name = tensor<string, []>("h_159")];
            tensor<int32, [2]> var_4688_split_sizes_0 = const()[name = tensor<string, []>("op_4688_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_4688_axis_0 = const()[name = tensor<string, []>("op_4688_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_4688_0, tensor<fp32, [1, 256, 1]> var_4688_1 = split(axis = var_4688_axis_0, split_sizes = var_4688_split_sizes_0, x = h_159)[name = tensor<string, []>("op_4688")];
            tensor<fp32, []> var_4690_promoted = const()[name = tensor<string, []>("op_4690_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_4691 = add(x = var_4688_0, y = var_4690_promoted)[name = tensor<string, []>("op_4691")];
            tensor<fp32, [1, 256, 4000]> var_4694 = instance_norm(beta = F0_blocks_1_norm2_norm_bias, epsilon = var_2476, gamma = F0_blocks_1_norm2_norm_weight, x = input_571)[name = tensor<string, []>("op_4694")];
            tensor<fp32, [1, 256, 4000]> var_4695 = mul(x = var_4691, y = var_4694)[name = tensor<string, []>("op_4695")];
            tensor<fp32, [1, 256, 4000]> xt_43 = add(x = var_4695, y = var_4688_1)[name = tensor<string, []>("xt_43")];
            tensor<fp32, [1, 256, 1]> var_4697 = const()[name = tensor<string, []>("op_4697"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(285585152)))];
            tensor<fp32, [1, 256, 4000]> var_4700 = mul(x = decoder_generator_resblocks_1_alpha1_1, y = xt_43)[name = tensor<string, []>("op_4700")];
            tensor<fp32, [1, 256, 4000]> var_4701 = sin(x = var_4700)[name = tensor<string, []>("op_4701")];
            tensor<fp32, []> var_2483_promoted_16 = const()[name = tensor<string, []>("op_2483_promoted_16"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 4000]> var_4702 = pow(x = var_4701, y = var_2483_promoted_16)[name = tensor<string, []>("op_4702")];
            tensor<fp32, [1, 256, 4000]> var_4703 = mul(x = var_4697, y = var_4702)[name = tensor<string, []>("op_4703")];
            tensor<fp32, [1, 256, 4000]> input_573 = add(x = xt_43, y = var_4703)[name = tensor<string, []>("input_573")];
            tensor<fp32, [256, 256, 7]> weight_199 = const()[name = tensor<string, []>("weight_199"), val = tensor<fp32, [256, 256, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(285586240)))];
            tensor<string, []> input_575_pad_type_0 = const()[name = tensor<string, []>("input_575_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_575_pad_0 = const()[name = tensor<string, []>("input_575_pad_0"), val = tensor<int32, [2]>([9, 9])];
            tensor<int32, [1]> input_575_dilations_0 = const()[name = tensor<string, []>("input_575_dilations_0"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> input_575_strides_0 = const()[name = tensor<string, []>("input_575_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_575_groups_0 = const()[name = tensor<string, []>("input_575_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 4000]> input_575 = conv(bias = decoder_generator_resblocks_1_convs1_1_bias, dilations = input_575_dilations_0, groups = input_575_groups_0, pad = input_575_pad_0, pad_type = input_575_pad_type_0, strides = input_575_strides_0, weight = weight_199, x = input_573)[name = tensor<string, []>("input_575")];
            tensor<fp32, [1, 512]> h_161 = linear(bias = decoder_generator_resblocks_1_adain2_1_fc_bias, weight = decoder_generator_resblocks_1_adain2_1_fc_weight, x = input_417)[name = tensor<string, []>("linear_116")];
            tensor<int32, [3]> var_4723 = const()[name = tensor<string, []>("op_4723"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_163 = reshape(shape = var_4723, x = h_161)[name = tensor<string, []>("h_163")];
            tensor<int32, [2]> var_4725_split_sizes_0 = const()[name = tensor<string, []>("op_4725_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_4725_axis_0 = const()[name = tensor<string, []>("op_4725_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_4725_0, tensor<fp32, [1, 256, 1]> var_4725_1 = split(axis = var_4725_axis_0, split_sizes = var_4725_split_sizes_0, x = h_163)[name = tensor<string, []>("op_4725")];
            tensor<fp32, []> var_4727_promoted = const()[name = tensor<string, []>("op_4727_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_4728 = add(x = var_4725_0, y = var_4727_promoted)[name = tensor<string, []>("op_4728")];
            tensor<fp32, [1, 256, 4000]> var_4731 = instance_norm(beta = F0_blocks_1_norm2_norm_bias, epsilon = var_2476, gamma = F0_blocks_1_norm2_norm_weight, x = input_575)[name = tensor<string, []>("op_4731")];
            tensor<fp32, [1, 256, 4000]> var_4732 = mul(x = var_4728, y = var_4731)[name = tensor<string, []>("op_4732")];
            tensor<fp32, [1, 256, 4000]> xt_45 = add(x = var_4732, y = var_4725_1)[name = tensor<string, []>("xt_45")];
            tensor<fp32, [1, 256, 1]> var_4734 = const()[name = tensor<string, []>("op_4734"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(287421312)))];
            tensor<fp32, [1, 256, 4000]> var_4737 = mul(x = decoder_generator_resblocks_1_alpha2_1, y = xt_45)[name = tensor<string, []>("op_4737")];
            tensor<fp32, [1, 256, 4000]> var_4738 = sin(x = var_4737)[name = tensor<string, []>("op_4738")];
            tensor<fp32, []> var_2483_promoted_17 = const()[name = tensor<string, []>("op_2483_promoted_17"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 4000]> var_4739 = pow(x = var_4738, y = var_2483_promoted_17)[name = tensor<string, []>("op_4739")];
            tensor<fp32, [1, 256, 4000]> var_4740 = mul(x = var_4734, y = var_4739)[name = tensor<string, []>("op_4740")];
            tensor<fp32, [1, 256, 4000]> input_577 = add(x = xt_45, y = var_4740)[name = tensor<string, []>("input_577")];
            tensor<fp32, [256, 256, 7]> weight_203 = const()[name = tensor<string, []>("weight_203"), val = tensor<fp32, [256, 256, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(287422400)))];
            tensor<string, []> xt_47_pad_type_0 = const()[name = tensor<string, []>("xt_47_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_47_pad_0 = const()[name = tensor<string, []>("xt_47_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> xt_47_strides_0 = const()[name = tensor<string, []>("xt_47_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_47_dilations_0 = const()[name = tensor<string, []>("xt_47_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_47_groups_0 = const()[name = tensor<string, []>("xt_47_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 4000]> xt_47 = conv(bias = decoder_generator_resblocks_1_convs2_1_bias, dilations = xt_47_dilations_0, groups = xt_47_groups_0, pad = xt_47_pad_0, pad_type = xt_47_pad_type_0, strides = xt_47_strides_0, weight = weight_203, x = input_577)[name = tensor<string, []>("xt_47")];
            tensor<fp32, [1, 256, 4000]> input_579 = add(x = xt_47, y = input_571)[name = tensor<string, []>("input_579")];
            tensor<fp32, [1, 512]> h_165 = linear(bias = decoder_generator_resblocks_1_adain1_2_fc_bias, weight = decoder_generator_resblocks_1_adain1_2_fc_weight, x = input_417)[name = tensor<string, []>("linear_117")];
            tensor<int32, [3]> var_4761 = const()[name = tensor<string, []>("op_4761"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_167 = reshape(shape = var_4761, x = h_165)[name = tensor<string, []>("h_167")];
            tensor<int32, [2]> var_4763_split_sizes_0 = const()[name = tensor<string, []>("op_4763_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_4763_axis_0 = const()[name = tensor<string, []>("op_4763_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_4763_0, tensor<fp32, [1, 256, 1]> var_4763_1 = split(axis = var_4763_axis_0, split_sizes = var_4763_split_sizes_0, x = h_167)[name = tensor<string, []>("op_4763")];
            tensor<fp32, []> var_4765_promoted = const()[name = tensor<string, []>("op_4765_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_4766 = add(x = var_4763_0, y = var_4765_promoted)[name = tensor<string, []>("op_4766")];
            tensor<fp32, [1, 256, 4000]> var_4769 = instance_norm(beta = F0_blocks_1_norm2_norm_bias, epsilon = var_2476, gamma = F0_blocks_1_norm2_norm_weight, x = input_579)[name = tensor<string, []>("op_4769")];
            tensor<fp32, [1, 256, 4000]> var_4770 = mul(x = var_4766, y = var_4769)[name = tensor<string, []>("op_4770")];
            tensor<fp32, [1, 256, 4000]> xt_49 = add(x = var_4770, y = var_4763_1)[name = tensor<string, []>("xt_49")];
            tensor<fp32, [1, 256, 1]> var_4772 = const()[name = tensor<string, []>("op_4772"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(289257472)))];
            tensor<fp32, [1, 256, 4000]> var_4775 = mul(x = decoder_generator_resblocks_1_alpha1_2, y = xt_49)[name = tensor<string, []>("op_4775")];
            tensor<fp32, [1, 256, 4000]> var_4776 = sin(x = var_4775)[name = tensor<string, []>("op_4776")];
            tensor<fp32, []> var_2483_promoted_18 = const()[name = tensor<string, []>("op_2483_promoted_18"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 4000]> var_4777 = pow(x = var_4776, y = var_2483_promoted_18)[name = tensor<string, []>("op_4777")];
            tensor<fp32, [1, 256, 4000]> var_4778 = mul(x = var_4772, y = var_4777)[name = tensor<string, []>("op_4778")];
            tensor<fp32, [1, 256, 4000]> input_581 = add(x = xt_49, y = var_4778)[name = tensor<string, []>("input_581")];
            tensor<fp32, [256, 256, 7]> weight_207 = const()[name = tensor<string, []>("weight_207"), val = tensor<fp32, [256, 256, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(289258560)))];
            tensor<string, []> input_583_pad_type_0 = const()[name = tensor<string, []>("input_583_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_583_pad_0 = const()[name = tensor<string, []>("input_583_pad_0"), val = tensor<int32, [2]>([15, 15])];
            tensor<int32, [1]> input_583_dilations_0 = const()[name = tensor<string, []>("input_583_dilations_0"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> input_583_strides_0 = const()[name = tensor<string, []>("input_583_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_583_groups_0 = const()[name = tensor<string, []>("input_583_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 4000]> input_583 = conv(bias = decoder_generator_resblocks_1_convs1_2_bias, dilations = input_583_dilations_0, groups = input_583_groups_0, pad = input_583_pad_0, pad_type = input_583_pad_type_0, strides = input_583_strides_0, weight = weight_207, x = input_581)[name = tensor<string, []>("input_583")];
            tensor<fp32, [1, 512]> h_169 = linear(bias = decoder_generator_resblocks_1_adain2_2_fc_bias, weight = decoder_generator_resblocks_1_adain2_2_fc_weight, x = input_417)[name = tensor<string, []>("linear_118")];
            tensor<int32, [3]> var_4798 = const()[name = tensor<string, []>("op_4798"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_171 = reshape(shape = var_4798, x = h_169)[name = tensor<string, []>("h_171")];
            tensor<int32, [2]> var_4800_split_sizes_0 = const()[name = tensor<string, []>("op_4800_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_4800_axis_0 = const()[name = tensor<string, []>("op_4800_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_4800_0, tensor<fp32, [1, 256, 1]> var_4800_1 = split(axis = var_4800_axis_0, split_sizes = var_4800_split_sizes_0, x = h_171)[name = tensor<string, []>("op_4800")];
            tensor<fp32, []> var_4802_promoted = const()[name = tensor<string, []>("op_4802_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_4803 = add(x = var_4800_0, y = var_4802_promoted)[name = tensor<string, []>("op_4803")];
            tensor<fp32, [1, 256, 4000]> var_4806 = instance_norm(beta = F0_blocks_1_norm2_norm_bias, epsilon = var_2476, gamma = F0_blocks_1_norm2_norm_weight, x = input_583)[name = tensor<string, []>("op_4806")];
            tensor<fp32, [1, 256, 4000]> var_4807 = mul(x = var_4803, y = var_4806)[name = tensor<string, []>("op_4807")];
            tensor<fp32, [1, 256, 4000]> xt_51 = add(x = var_4807, y = var_4800_1)[name = tensor<string, []>("xt_51")];
            tensor<fp32, [1, 256, 1]> var_4809 = const()[name = tensor<string, []>("op_4809"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(291093632)))];
            tensor<fp32, [1, 256, 4000]> var_4812 = mul(x = decoder_generator_resblocks_1_alpha2_2, y = xt_51)[name = tensor<string, []>("op_4812")];
            tensor<fp32, [1, 256, 4000]> var_4813 = sin(x = var_4812)[name = tensor<string, []>("op_4813")];
            tensor<fp32, []> var_2483_promoted_19 = const()[name = tensor<string, []>("op_2483_promoted_19"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 4000]> var_4814 = pow(x = var_4813, y = var_2483_promoted_19)[name = tensor<string, []>("op_4814")];
            tensor<fp32, [1, 256, 4000]> var_4815 = mul(x = var_4809, y = var_4814)[name = tensor<string, []>("op_4815")];
            tensor<fp32, [1, 256, 4000]> input_585 = add(x = xt_51, y = var_4815)[name = tensor<string, []>("input_585")];
            tensor<fp32, [256, 256, 7]> weight_211 = const()[name = tensor<string, []>("weight_211"), val = tensor<fp32, [256, 256, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(291094720)))];
            tensor<string, []> xt_53_pad_type_0 = const()[name = tensor<string, []>("xt_53_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_53_pad_0 = const()[name = tensor<string, []>("xt_53_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> xt_53_strides_0 = const()[name = tensor<string, []>("xt_53_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_53_dilations_0 = const()[name = tensor<string, []>("xt_53_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_53_groups_0 = const()[name = tensor<string, []>("xt_53_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 4000]> xt_53 = conv(bias = decoder_generator_resblocks_1_convs2_2_bias, dilations = xt_53_dilations_0, groups = xt_53_groups_0, pad = xt_53_pad_0, pad_type = xt_53_pad_type_0, strides = xt_53_strides_0, weight = weight_211, x = input_585)[name = tensor<string, []>("xt_53")];
            tensor<fp32, [1, 256, 4000]> var_4828 = add(x = xt_53, y = input_579)[name = tensor<string, []>("op_4828")];
            tensor<fp32, [1, 256, 4000]> xs_3 = add(x = xs_1, y = var_4828)[name = tensor<string, []>("xs_3")];
            tensor<fp32, [1, 512]> h_173 = linear(bias = decoder_generator_resblocks_2_adain1_0_fc_bias, weight = decoder_generator_resblocks_2_adain1_0_fc_weight, x = input_417)[name = tensor<string, []>("linear_119")];
            tensor<int32, [3]> var_4873 = const()[name = tensor<string, []>("op_4873"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_175 = reshape(shape = var_4873, x = h_173)[name = tensor<string, []>("h_175")];
            tensor<int32, [2]> var_4875_split_sizes_0 = const()[name = tensor<string, []>("op_4875_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_4875_axis_0 = const()[name = tensor<string, []>("op_4875_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_4875_0, tensor<fp32, [1, 256, 1]> var_4875_1 = split(axis = var_4875_axis_0, split_sizes = var_4875_split_sizes_0, x = h_175)[name = tensor<string, []>("op_4875")];
            tensor<fp32, []> var_4877_promoted = const()[name = tensor<string, []>("op_4877_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_4878 = add(x = var_4875_0, y = var_4877_promoted)[name = tensor<string, []>("op_4878")];
            tensor<fp32, [1, 256, 4000]> var_4882 = mul(x = var_4878, y = var_4358)[name = tensor<string, []>("op_4882")];
            tensor<fp32, [1, 256, 4000]> xt_55 = add(x = var_4882, y = var_4875_1)[name = tensor<string, []>("xt_55")];
            tensor<fp32, [1, 256, 1]> var_4884 = const()[name = tensor<string, []>("op_4884"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(292929792)))];
            tensor<fp32, [1, 256, 4000]> var_4887 = mul(x = decoder_generator_resblocks_2_alpha1_0, y = xt_55)[name = tensor<string, []>("op_4887")];
            tensor<fp32, [1, 256, 4000]> var_4888 = sin(x = var_4887)[name = tensor<string, []>("op_4888")];
            tensor<fp32, []> var_2483_promoted_20 = const()[name = tensor<string, []>("op_2483_promoted_20"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 4000]> var_4889 = pow(x = var_4888, y = var_2483_promoted_20)[name = tensor<string, []>("op_4889")];
            tensor<fp32, [1, 256, 4000]> var_4890 = mul(x = var_4884, y = var_4889)[name = tensor<string, []>("op_4890")];
            tensor<fp32, [1, 256, 4000]> input_587 = add(x = xt_55, y = var_4890)[name = tensor<string, []>("input_587")];
            tensor<fp32, [256, 256, 11]> weight_215 = const()[name = tensor<string, []>("weight_215"), val = tensor<fp32, [256, 256, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(292930880)))];
            tensor<string, []> input_589_pad_type_0 = const()[name = tensor<string, []>("input_589_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_589_pad_0 = const()[name = tensor<string, []>("input_589_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> input_589_strides_0 = const()[name = tensor<string, []>("input_589_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_589_dilations_0 = const()[name = tensor<string, []>("input_589_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_589_groups_0 = const()[name = tensor<string, []>("input_589_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 4000]> input_589 = conv(bias = decoder_generator_resblocks_2_convs1_0_bias, dilations = input_589_dilations_0, groups = input_589_groups_0, pad = input_589_pad_0, pad_type = input_589_pad_type_0, strides = input_589_strides_0, weight = weight_215, x = input_587)[name = tensor<string, []>("input_589")];
            tensor<fp32, [1, 512]> h_177 = linear(bias = decoder_generator_resblocks_2_adain2_0_fc_bias, weight = decoder_generator_resblocks_2_adain2_0_fc_weight, x = input_417)[name = tensor<string, []>("linear_120")];
            tensor<int32, [3]> var_4910 = const()[name = tensor<string, []>("op_4910"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_179 = reshape(shape = var_4910, x = h_177)[name = tensor<string, []>("h_179")];
            tensor<int32, [2]> var_4912_split_sizes_0 = const()[name = tensor<string, []>("op_4912_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_4912_axis_0 = const()[name = tensor<string, []>("op_4912_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_4912_0, tensor<fp32, [1, 256, 1]> var_4912_1 = split(axis = var_4912_axis_0, split_sizes = var_4912_split_sizes_0, x = h_179)[name = tensor<string, []>("op_4912")];
            tensor<fp32, []> var_4914_promoted = const()[name = tensor<string, []>("op_4914_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_4915 = add(x = var_4912_0, y = var_4914_promoted)[name = tensor<string, []>("op_4915")];
            tensor<fp32, [1, 256, 4000]> var_4918 = instance_norm(beta = F0_blocks_1_norm2_norm_bias, epsilon = var_2476, gamma = F0_blocks_1_norm2_norm_weight, x = input_589)[name = tensor<string, []>("op_4918")];
            tensor<fp32, [1, 256, 4000]> var_4919 = mul(x = var_4915, y = var_4918)[name = tensor<string, []>("op_4919")];
            tensor<fp32, [1, 256, 4000]> xt_57 = add(x = var_4919, y = var_4912_1)[name = tensor<string, []>("xt_57")];
            tensor<fp32, [1, 256, 1]> var_4921 = const()[name = tensor<string, []>("op_4921"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(295814528)))];
            tensor<fp32, [1, 256, 4000]> var_4924 = mul(x = decoder_generator_resblocks_2_alpha2_0, y = xt_57)[name = tensor<string, []>("op_4924")];
            tensor<fp32, [1, 256, 4000]> var_4925 = sin(x = var_4924)[name = tensor<string, []>("op_4925")];
            tensor<fp32, []> var_2483_promoted_21 = const()[name = tensor<string, []>("op_2483_promoted_21"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 4000]> var_4926 = pow(x = var_4925, y = var_2483_promoted_21)[name = tensor<string, []>("op_4926")];
            tensor<fp32, [1, 256, 4000]> var_4927 = mul(x = var_4921, y = var_4926)[name = tensor<string, []>("op_4927")];
            tensor<fp32, [1, 256, 4000]> input_591 = add(x = xt_57, y = var_4927)[name = tensor<string, []>("input_591")];
            tensor<fp32, [256, 256, 11]> weight_219 = const()[name = tensor<string, []>("weight_219"), val = tensor<fp32, [256, 256, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(295815616)))];
            tensor<string, []> xt_59_pad_type_0 = const()[name = tensor<string, []>("xt_59_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_59_pad_0 = const()[name = tensor<string, []>("xt_59_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> xt_59_strides_0 = const()[name = tensor<string, []>("xt_59_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_59_dilations_0 = const()[name = tensor<string, []>("xt_59_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_59_groups_0 = const()[name = tensor<string, []>("xt_59_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 4000]> xt_59 = conv(bias = decoder_generator_resblocks_2_convs2_0_bias, dilations = xt_59_dilations_0, groups = xt_59_groups_0, pad = xt_59_pad_0, pad_type = xt_59_pad_type_0, strides = xt_59_strides_0, weight = weight_219, x = input_591)[name = tensor<string, []>("xt_59")];
            tensor<fp32, [1, 256, 4000]> input_593 = add(x = xt_59, y = input_541)[name = tensor<string, []>("input_593")];
            tensor<fp32, [1, 512]> h_181 = linear(bias = decoder_generator_resblocks_2_adain1_1_fc_bias, weight = decoder_generator_resblocks_2_adain1_1_fc_weight, x = input_417)[name = tensor<string, []>("linear_121")];
            tensor<int32, [3]> var_4948 = const()[name = tensor<string, []>("op_4948"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_183 = reshape(shape = var_4948, x = h_181)[name = tensor<string, []>("h_183")];
            tensor<int32, [2]> var_4950_split_sizes_0 = const()[name = tensor<string, []>("op_4950_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_4950_axis_0 = const()[name = tensor<string, []>("op_4950_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_4950_0, tensor<fp32, [1, 256, 1]> var_4950_1 = split(axis = var_4950_axis_0, split_sizes = var_4950_split_sizes_0, x = h_183)[name = tensor<string, []>("op_4950")];
            tensor<fp32, []> var_4952_promoted = const()[name = tensor<string, []>("op_4952_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_4953 = add(x = var_4950_0, y = var_4952_promoted)[name = tensor<string, []>("op_4953")];
            tensor<fp32, [1, 256, 4000]> var_4956 = instance_norm(beta = F0_blocks_1_norm2_norm_bias, epsilon = var_2476, gamma = F0_blocks_1_norm2_norm_weight, x = input_593)[name = tensor<string, []>("op_4956")];
            tensor<fp32, [1, 256, 4000]> var_4957 = mul(x = var_4953, y = var_4956)[name = tensor<string, []>("op_4957")];
            tensor<fp32, [1, 256, 4000]> xt_61 = add(x = var_4957, y = var_4950_1)[name = tensor<string, []>("xt_61")];
            tensor<fp32, [1, 256, 1]> var_4959 = const()[name = tensor<string, []>("op_4959"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(298699264)))];
            tensor<fp32, [1, 256, 4000]> var_4962 = mul(x = decoder_generator_resblocks_2_alpha1_1, y = xt_61)[name = tensor<string, []>("op_4962")];
            tensor<fp32, [1, 256, 4000]> var_4963 = sin(x = var_4962)[name = tensor<string, []>("op_4963")];
            tensor<fp32, []> var_2483_promoted_22 = const()[name = tensor<string, []>("op_2483_promoted_22"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 4000]> var_4964 = pow(x = var_4963, y = var_2483_promoted_22)[name = tensor<string, []>("op_4964")];
            tensor<fp32, [1, 256, 4000]> var_4965 = mul(x = var_4959, y = var_4964)[name = tensor<string, []>("op_4965")];
            tensor<fp32, [1, 256, 4000]> input_595 = add(x = xt_61, y = var_4965)[name = tensor<string, []>("input_595")];
            tensor<fp32, [256, 256, 11]> weight_223 = const()[name = tensor<string, []>("weight_223"), val = tensor<fp32, [256, 256, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(298700352)))];
            tensor<string, []> input_597_pad_type_0 = const()[name = tensor<string, []>("input_597_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_597_pad_0 = const()[name = tensor<string, []>("input_597_pad_0"), val = tensor<int32, [2]>([15, 15])];
            tensor<int32, [1]> input_597_dilations_0 = const()[name = tensor<string, []>("input_597_dilations_0"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> input_597_strides_0 = const()[name = tensor<string, []>("input_597_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_597_groups_0 = const()[name = tensor<string, []>("input_597_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 4000]> input_597 = conv(bias = decoder_generator_resblocks_2_convs1_1_bias, dilations = input_597_dilations_0, groups = input_597_groups_0, pad = input_597_pad_0, pad_type = input_597_pad_type_0, strides = input_597_strides_0, weight = weight_223, x = input_595)[name = tensor<string, []>("input_597")];
            tensor<fp32, [1, 512]> h_185 = linear(bias = decoder_generator_resblocks_2_adain2_1_fc_bias, weight = decoder_generator_resblocks_2_adain2_1_fc_weight, x = input_417)[name = tensor<string, []>("linear_122")];
            tensor<int32, [3]> var_4985 = const()[name = tensor<string, []>("op_4985"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_187 = reshape(shape = var_4985, x = h_185)[name = tensor<string, []>("h_187")];
            tensor<int32, [2]> var_4987_split_sizes_0 = const()[name = tensor<string, []>("op_4987_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_4987_axis_0 = const()[name = tensor<string, []>("op_4987_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_4987_0, tensor<fp32, [1, 256, 1]> var_4987_1 = split(axis = var_4987_axis_0, split_sizes = var_4987_split_sizes_0, x = h_187)[name = tensor<string, []>("op_4987")];
            tensor<fp32, []> var_4989_promoted = const()[name = tensor<string, []>("op_4989_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_4990 = add(x = var_4987_0, y = var_4989_promoted)[name = tensor<string, []>("op_4990")];
            tensor<fp32, [1, 256, 4000]> var_4993 = instance_norm(beta = F0_blocks_1_norm2_norm_bias, epsilon = var_2476, gamma = F0_blocks_1_norm2_norm_weight, x = input_597)[name = tensor<string, []>("op_4993")];
            tensor<fp32, [1, 256, 4000]> var_4994 = mul(x = var_4990, y = var_4993)[name = tensor<string, []>("op_4994")];
            tensor<fp32, [1, 256, 4000]> xt_63 = add(x = var_4994, y = var_4987_1)[name = tensor<string, []>("xt_63")];
            tensor<fp32, [1, 256, 1]> var_4996 = const()[name = tensor<string, []>("op_4996"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(301584000)))];
            tensor<fp32, [1, 256, 4000]> var_4999 = mul(x = decoder_generator_resblocks_2_alpha2_1, y = xt_63)[name = tensor<string, []>("op_4999")];
            tensor<fp32, [1, 256, 4000]> var_5000 = sin(x = var_4999)[name = tensor<string, []>("op_5000")];
            tensor<fp32, []> var_2483_promoted_23 = const()[name = tensor<string, []>("op_2483_promoted_23"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 4000]> var_5001 = pow(x = var_5000, y = var_2483_promoted_23)[name = tensor<string, []>("op_5001")];
            tensor<fp32, [1, 256, 4000]> var_5002 = mul(x = var_4996, y = var_5001)[name = tensor<string, []>("op_5002")];
            tensor<fp32, [1, 256, 4000]> input_599 = add(x = xt_63, y = var_5002)[name = tensor<string, []>("input_599")];
            tensor<fp32, [256, 256, 11]> weight_227 = const()[name = tensor<string, []>("weight_227"), val = tensor<fp32, [256, 256, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(301585088)))];
            tensor<string, []> xt_65_pad_type_0 = const()[name = tensor<string, []>("xt_65_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_65_pad_0 = const()[name = tensor<string, []>("xt_65_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> xt_65_strides_0 = const()[name = tensor<string, []>("xt_65_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_65_dilations_0 = const()[name = tensor<string, []>("xt_65_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_65_groups_0 = const()[name = tensor<string, []>("xt_65_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 4000]> xt_65 = conv(bias = decoder_generator_resblocks_2_convs2_1_bias, dilations = xt_65_dilations_0, groups = xt_65_groups_0, pad = xt_65_pad_0, pad_type = xt_65_pad_type_0, strides = xt_65_strides_0, weight = weight_227, x = input_599)[name = tensor<string, []>("xt_65")];
            tensor<fp32, [1, 256, 4000]> input_601 = add(x = xt_65, y = input_593)[name = tensor<string, []>("input_601")];
            tensor<fp32, [1, 512]> h_189 = linear(bias = decoder_generator_resblocks_2_adain1_2_fc_bias, weight = decoder_generator_resblocks_2_adain1_2_fc_weight, x = input_417)[name = tensor<string, []>("linear_123")];
            tensor<int32, [3]> var_5023 = const()[name = tensor<string, []>("op_5023"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_191 = reshape(shape = var_5023, x = h_189)[name = tensor<string, []>("h_191")];
            tensor<int32, [2]> var_5025_split_sizes_0 = const()[name = tensor<string, []>("op_5025_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_5025_axis_0 = const()[name = tensor<string, []>("op_5025_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_5025_0, tensor<fp32, [1, 256, 1]> var_5025_1 = split(axis = var_5025_axis_0, split_sizes = var_5025_split_sizes_0, x = h_191)[name = tensor<string, []>("op_5025")];
            tensor<fp32, []> var_5027_promoted = const()[name = tensor<string, []>("op_5027_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_5028 = add(x = var_5025_0, y = var_5027_promoted)[name = tensor<string, []>("op_5028")];
            tensor<fp32, [1, 256, 4000]> var_5031 = instance_norm(beta = F0_blocks_1_norm2_norm_bias, epsilon = var_2476, gamma = F0_blocks_1_norm2_norm_weight, x = input_601)[name = tensor<string, []>("op_5031")];
            tensor<fp32, [1, 256, 4000]> var_5032 = mul(x = var_5028, y = var_5031)[name = tensor<string, []>("op_5032")];
            tensor<fp32, [1, 256, 4000]> xt_67 = add(x = var_5032, y = var_5025_1)[name = tensor<string, []>("xt_67")];
            tensor<fp32, [1, 256, 1]> var_5034 = const()[name = tensor<string, []>("op_5034"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(304468736)))];
            tensor<fp32, [1, 256, 4000]> var_5037 = mul(x = decoder_generator_resblocks_2_alpha1_2, y = xt_67)[name = tensor<string, []>("op_5037")];
            tensor<fp32, [1, 256, 4000]> var_5038 = sin(x = var_5037)[name = tensor<string, []>("op_5038")];
            tensor<fp32, []> var_2483_promoted_24 = const()[name = tensor<string, []>("op_2483_promoted_24"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 4000]> var_5039 = pow(x = var_5038, y = var_2483_promoted_24)[name = tensor<string, []>("op_5039")];
            tensor<fp32, [1, 256, 4000]> var_5040 = mul(x = var_5034, y = var_5039)[name = tensor<string, []>("op_5040")];
            tensor<fp32, [1, 256, 4000]> input_603 = add(x = xt_67, y = var_5040)[name = tensor<string, []>("input_603")];
            tensor<fp32, [256, 256, 11]> weight_231 = const()[name = tensor<string, []>("weight_231"), val = tensor<fp32, [256, 256, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(304469824)))];
            tensor<string, []> input_605_pad_type_0 = const()[name = tensor<string, []>("input_605_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_605_pad_0 = const()[name = tensor<string, []>("input_605_pad_0"), val = tensor<int32, [2]>([25, 25])];
            tensor<int32, [1]> input_605_dilations_0 = const()[name = tensor<string, []>("input_605_dilations_0"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> input_605_strides_0 = const()[name = tensor<string, []>("input_605_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_605_groups_0 = const()[name = tensor<string, []>("input_605_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 4000]> input_605 = conv(bias = decoder_generator_resblocks_2_convs1_2_bias, dilations = input_605_dilations_0, groups = input_605_groups_0, pad = input_605_pad_0, pad_type = input_605_pad_type_0, strides = input_605_strides_0, weight = weight_231, x = input_603)[name = tensor<string, []>("input_605")];
            tensor<fp32, [1, 512]> h_193 = linear(bias = decoder_generator_resblocks_2_adain2_2_fc_bias, weight = decoder_generator_resblocks_2_adain2_2_fc_weight, x = input_417)[name = tensor<string, []>("linear_124")];
            tensor<int32, [3]> var_5060 = const()[name = tensor<string, []>("op_5060"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_195 = reshape(shape = var_5060, x = h_193)[name = tensor<string, []>("h_195")];
            tensor<int32, [2]> var_5062_split_sizes_0 = const()[name = tensor<string, []>("op_5062_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_5062_axis_0 = const()[name = tensor<string, []>("op_5062_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_5062_0, tensor<fp32, [1, 256, 1]> var_5062_1 = split(axis = var_5062_axis_0, split_sizes = var_5062_split_sizes_0, x = h_195)[name = tensor<string, []>("op_5062")];
            tensor<fp32, []> var_5064_promoted = const()[name = tensor<string, []>("op_5064_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_5065 = add(x = var_5062_0, y = var_5064_promoted)[name = tensor<string, []>("op_5065")];
            tensor<fp32, [1, 256, 4000]> var_5068 = instance_norm(beta = F0_blocks_1_norm2_norm_bias, epsilon = var_2476, gamma = F0_blocks_1_norm2_norm_weight, x = input_605)[name = tensor<string, []>("op_5068")];
            tensor<fp32, [1, 256, 4000]> var_5069 = mul(x = var_5065, y = var_5068)[name = tensor<string, []>("op_5069")];
            tensor<fp32, [1, 256, 4000]> xt_69 = add(x = var_5069, y = var_5062_1)[name = tensor<string, []>("xt_69")];
            tensor<fp32, [1, 256, 1]> var_5071 = const()[name = tensor<string, []>("op_5071"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(307353472)))];
            tensor<fp32, [1, 256, 4000]> var_5074 = mul(x = decoder_generator_resblocks_2_alpha2_2, y = xt_69)[name = tensor<string, []>("op_5074")];
            tensor<fp32, [1, 256, 4000]> var_5075 = sin(x = var_5074)[name = tensor<string, []>("op_5075")];
            tensor<fp32, []> var_2483_promoted_25 = const()[name = tensor<string, []>("op_2483_promoted_25"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 4000]> var_5076 = pow(x = var_5075, y = var_2483_promoted_25)[name = tensor<string, []>("op_5076")];
            tensor<fp32, [1, 256, 4000]> var_5077 = mul(x = var_5071, y = var_5076)[name = tensor<string, []>("op_5077")];
            tensor<fp32, [1, 256, 4000]> input_607 = add(x = xt_69, y = var_5077)[name = tensor<string, []>("input_607")];
            tensor<fp32, [256, 256, 11]> weight_235 = const()[name = tensor<string, []>("weight_235"), val = tensor<fp32, [256, 256, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(307354560)))];
            tensor<string, []> xt_71_pad_type_0 = const()[name = tensor<string, []>("xt_71_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_71_pad_0 = const()[name = tensor<string, []>("xt_71_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> xt_71_strides_0 = const()[name = tensor<string, []>("xt_71_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_71_dilations_0 = const()[name = tensor<string, []>("xt_71_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_71_groups_0 = const()[name = tensor<string, []>("xt_71_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 4000]> xt_71 = conv(bias = decoder_generator_resblocks_2_convs2_2_bias, dilations = xt_71_dilations_0, groups = xt_71_groups_0, pad = xt_71_pad_0, pad_type = xt_71_pad_type_0, strides = xt_71_strides_0, weight = weight_235, x = input_607)[name = tensor<string, []>("xt_71")];
            tensor<fp32, [1, 256, 4000]> var_5090 = add(x = xt_71, y = input_601)[name = tensor<string, []>("op_5090")];
            tensor<fp32, [1, 256, 4000]> xs_5 = add(x = xs_3, y = var_5090)[name = tensor<string, []>("xs_5")];
            tensor<fp32, []> _inversed_input_609_y_0 = const()[name = tensor<string, []>("_inversed_input_609_y_0"), val = tensor<fp32, []>(0x1.555556p-2)];
            tensor<fp32, [1, 256, 4000]> _inversed_input_609 = mul(x = xs_5, y = _inversed_input_609_y_0)[name = tensor<string, []>("_inversed_input_609")];
            tensor<fp32, [1, 256, 4000]> input_635 = leaky_relu(alpha = var_2475, x = _inversed_input_609)[name = tensor<string, []>("input_635")];
            tensor<string, []> input_611_pad_type_0 = const()[name = tensor<string, []>("input_611_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_611_strides_0 = const()[name = tensor<string, []>("input_611_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_611_pad_0 = const()[name = tensor<string, []>("input_611_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_611_dilations_0 = const()[name = tensor<string, []>("input_611_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_611_groups_0 = const()[name = tensor<string, []>("input_611_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 24001]> input_611 = conv(bias = decoder_generator_noise_convs_1_bias, dilations = input_611_dilations_0, groups = input_611_groups_0, pad = input_611_pad_0, pad_type = input_611_pad_type_0, strides = input_611_strides_0, weight = decoder_generator_noise_convs_1_weight, x = input_513)[name = tensor<string, []>("input_611")];
            tensor<fp32, [1, 256]> h_197 = linear(bias = decoder_generator_noise_res_1_adain1_0_fc_bias, weight = decoder_generator_noise_res_1_adain1_0_fc_weight, x = input_417)[name = tensor<string, []>("linear_125")];
            tensor<int32, [3]> var_5145 = const()[name = tensor<string, []>("op_5145"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_199 = reshape(shape = var_5145, x = h_197)[name = tensor<string, []>("h_199")];
            tensor<int32, [2]> var_5147_split_sizes_0 = const()[name = tensor<string, []>("op_5147_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_5147_axis_0 = const()[name = tensor<string, []>("op_5147_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_5147_0, tensor<fp32, [1, 128, 1]> var_5147_1 = split(axis = var_5147_axis_0, split_sizes = var_5147_split_sizes_0, x = h_199)[name = tensor<string, []>("op_5147")];
            tensor<fp32, []> var_5149_promoted = const()[name = tensor<string, []>("op_5149_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_5150 = add(x = var_5147_0, y = var_5149_promoted)[name = tensor<string, []>("op_5150")];
            tensor<fp32, [1, 128, 24001]> var_5153 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_2476, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_611)[name = tensor<string, []>("op_5153")];
            tensor<fp32, [1, 128, 24001]> var_5154 = mul(x = var_5150, y = var_5153)[name = tensor<string, []>("op_5154")];
            tensor<fp32, [1, 128, 24001]> xt_73 = add(x = var_5154, y = var_5147_1)[name = tensor<string, []>("xt_73")];
            tensor<fp32, [1, 128, 1]> var_5156 = const()[name = tensor<string, []>("op_5156"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310238208)))];
            tensor<fp32, [1, 128, 24001]> var_5159 = mul(x = decoder_generator_noise_res_1_alpha1_0, y = xt_73)[name = tensor<string, []>("op_5159")];
            tensor<fp32, [1, 128, 24001]> var_5160 = sin(x = var_5159)[name = tensor<string, []>("op_5160")];
            tensor<fp32, []> var_2483_promoted_26 = const()[name = tensor<string, []>("op_2483_promoted_26"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 24001]> var_5161 = pow(x = var_5160, y = var_2483_promoted_26)[name = tensor<string, []>("op_5161")];
            tensor<fp32, [1, 128, 24001]> var_5162 = mul(x = var_5156, y = var_5161)[name = tensor<string, []>("op_5162")];
            tensor<fp32, [1, 128, 24001]> input_613 = add(x = xt_73, y = var_5162)[name = tensor<string, []>("input_613")];
            tensor<fp32, [128, 128, 11]> weight_241 = const()[name = tensor<string, []>("weight_241"), val = tensor<fp32, [128, 128, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310238784)))];
            tensor<string, []> input_615_pad_type_0 = const()[name = tensor<string, []>("input_615_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_615_pad_0 = const()[name = tensor<string, []>("input_615_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> input_615_strides_0 = const()[name = tensor<string, []>("input_615_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_615_dilations_0 = const()[name = tensor<string, []>("input_615_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_615_groups_0 = const()[name = tensor<string, []>("input_615_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 24001]> input_615 = conv(bias = decoder_generator_noise_res_1_convs1_0_bias, dilations = input_615_dilations_0, groups = input_615_groups_0, pad = input_615_pad_0, pad_type = input_615_pad_type_0, strides = input_615_strides_0, weight = weight_241, x = input_613)[name = tensor<string, []>("input_615")];
            tensor<fp32, [1, 256]> h_201 = linear(bias = decoder_generator_noise_res_1_adain2_0_fc_bias, weight = decoder_generator_noise_res_1_adain2_0_fc_weight, x = input_417)[name = tensor<string, []>("linear_126")];
            tensor<int32, [3]> var_5182 = const()[name = tensor<string, []>("op_5182"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_203 = reshape(shape = var_5182, x = h_201)[name = tensor<string, []>("h_203")];
            tensor<int32, [2]> var_5184_split_sizes_0 = const()[name = tensor<string, []>("op_5184_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_5184_axis_0 = const()[name = tensor<string, []>("op_5184_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_5184_0, tensor<fp32, [1, 128, 1]> var_5184_1 = split(axis = var_5184_axis_0, split_sizes = var_5184_split_sizes_0, x = h_203)[name = tensor<string, []>("op_5184")];
            tensor<fp32, []> var_5186_promoted = const()[name = tensor<string, []>("op_5186_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_5187 = add(x = var_5184_0, y = var_5186_promoted)[name = tensor<string, []>("op_5187")];
            tensor<fp32, [1, 128, 24001]> var_5190 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_2476, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_615)[name = tensor<string, []>("op_5190")];
            tensor<fp32, [1, 128, 24001]> var_5191 = mul(x = var_5187, y = var_5190)[name = tensor<string, []>("op_5191")];
            tensor<fp32, [1, 128, 24001]> xt_75 = add(x = var_5191, y = var_5184_1)[name = tensor<string, []>("xt_75")];
            tensor<fp32, [1, 128, 1]> var_5193 = const()[name = tensor<string, []>("op_5193"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310959744)))];
            tensor<fp32, [1, 128, 24001]> var_5196 = mul(x = decoder_generator_noise_res_1_alpha2_0, y = xt_75)[name = tensor<string, []>("op_5196")];
            tensor<fp32, [1, 128, 24001]> var_5197 = sin(x = var_5196)[name = tensor<string, []>("op_5197")];
            tensor<fp32, []> var_2483_promoted_27 = const()[name = tensor<string, []>("op_2483_promoted_27"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 24001]> var_5198 = pow(x = var_5197, y = var_2483_promoted_27)[name = tensor<string, []>("op_5198")];
            tensor<fp32, [1, 128, 24001]> var_5199 = mul(x = var_5193, y = var_5198)[name = tensor<string, []>("op_5199")];
            tensor<fp32, [1, 128, 24001]> input_617 = add(x = xt_75, y = var_5199)[name = tensor<string, []>("input_617")];
            tensor<fp32, [128, 128, 11]> weight_245 = const()[name = tensor<string, []>("weight_245"), val = tensor<fp32, [128, 128, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(310960320)))];
            tensor<string, []> xt_77_pad_type_0 = const()[name = tensor<string, []>("xt_77_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_77_pad_0 = const()[name = tensor<string, []>("xt_77_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> xt_77_strides_0 = const()[name = tensor<string, []>("xt_77_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_77_dilations_0 = const()[name = tensor<string, []>("xt_77_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_77_groups_0 = const()[name = tensor<string, []>("xt_77_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 24001]> xt_77 = conv(bias = decoder_generator_noise_res_1_convs2_0_bias, dilations = xt_77_dilations_0, groups = xt_77_groups_0, pad = xt_77_pad_0, pad_type = xt_77_pad_type_0, strides = xt_77_strides_0, weight = weight_245, x = input_617)[name = tensor<string, []>("xt_77")];
            tensor<fp32, [1, 128, 24001]> input_619 = add(x = xt_77, y = input_611)[name = tensor<string, []>("input_619")];
            tensor<fp32, [1, 256]> h_205 = linear(bias = decoder_generator_noise_res_1_adain1_1_fc_bias, weight = decoder_generator_noise_res_1_adain1_1_fc_weight, x = input_417)[name = tensor<string, []>("linear_127")];
            tensor<int32, [3]> var_5220 = const()[name = tensor<string, []>("op_5220"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_207 = reshape(shape = var_5220, x = h_205)[name = tensor<string, []>("h_207")];
            tensor<int32, [2]> var_5222_split_sizes_0 = const()[name = tensor<string, []>("op_5222_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_5222_axis_0 = const()[name = tensor<string, []>("op_5222_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_5222_0, tensor<fp32, [1, 128, 1]> var_5222_1 = split(axis = var_5222_axis_0, split_sizes = var_5222_split_sizes_0, x = h_207)[name = tensor<string, []>("op_5222")];
            tensor<fp32, []> var_5224_promoted = const()[name = tensor<string, []>("op_5224_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_5225 = add(x = var_5222_0, y = var_5224_promoted)[name = tensor<string, []>("op_5225")];
            tensor<fp32, [1, 128, 24001]> var_5228 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_2476, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_619)[name = tensor<string, []>("op_5228")];
            tensor<fp32, [1, 128, 24001]> var_5229 = mul(x = var_5225, y = var_5228)[name = tensor<string, []>("op_5229")];
            tensor<fp32, [1, 128, 24001]> xt_79 = add(x = var_5229, y = var_5222_1)[name = tensor<string, []>("xt_79")];
            tensor<fp32, [1, 128, 1]> var_5231 = const()[name = tensor<string, []>("op_5231"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(311681280)))];
            tensor<fp32, [1, 128, 24001]> var_5234 = mul(x = decoder_generator_noise_res_1_alpha1_1, y = xt_79)[name = tensor<string, []>("op_5234")];
            tensor<fp32, [1, 128, 24001]> var_5235 = sin(x = var_5234)[name = tensor<string, []>("op_5235")];
            tensor<fp32, []> var_2483_promoted_28 = const()[name = tensor<string, []>("op_2483_promoted_28"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 24001]> var_5236 = pow(x = var_5235, y = var_2483_promoted_28)[name = tensor<string, []>("op_5236")];
            tensor<fp32, [1, 128, 24001]> var_5237 = mul(x = var_5231, y = var_5236)[name = tensor<string, []>("op_5237")];
            tensor<fp32, [1, 128, 24001]> input_621 = add(x = xt_79, y = var_5237)[name = tensor<string, []>("input_621")];
            tensor<fp32, [128, 128, 11]> weight_249 = const()[name = tensor<string, []>("weight_249"), val = tensor<fp32, [128, 128, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(311681856)))];
            tensor<string, []> input_623_pad_type_0 = const()[name = tensor<string, []>("input_623_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_623_pad_0 = const()[name = tensor<string, []>("input_623_pad_0"), val = tensor<int32, [2]>([15, 15])];
            tensor<int32, [1]> input_623_dilations_0 = const()[name = tensor<string, []>("input_623_dilations_0"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> input_623_strides_0 = const()[name = tensor<string, []>("input_623_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_623_groups_0 = const()[name = tensor<string, []>("input_623_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 24001]> input_623 = conv(bias = decoder_generator_noise_res_1_convs1_1_bias, dilations = input_623_dilations_0, groups = input_623_groups_0, pad = input_623_pad_0, pad_type = input_623_pad_type_0, strides = input_623_strides_0, weight = weight_249, x = input_621)[name = tensor<string, []>("input_623")];
            tensor<fp32, [1, 256]> h_209 = linear(bias = decoder_generator_noise_res_1_adain2_1_fc_bias, weight = decoder_generator_noise_res_1_adain2_1_fc_weight, x = input_417)[name = tensor<string, []>("linear_128")];
            tensor<int32, [3]> var_5257 = const()[name = tensor<string, []>("op_5257"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_211 = reshape(shape = var_5257, x = h_209)[name = tensor<string, []>("h_211")];
            tensor<int32, [2]> var_5259_split_sizes_0 = const()[name = tensor<string, []>("op_5259_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_5259_axis_0 = const()[name = tensor<string, []>("op_5259_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_5259_0, tensor<fp32, [1, 128, 1]> var_5259_1 = split(axis = var_5259_axis_0, split_sizes = var_5259_split_sizes_0, x = h_211)[name = tensor<string, []>("op_5259")];
            tensor<fp32, []> var_5261_promoted = const()[name = tensor<string, []>("op_5261_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_5262 = add(x = var_5259_0, y = var_5261_promoted)[name = tensor<string, []>("op_5262")];
            tensor<fp32, [1, 128, 24001]> var_5265 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_2476, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_623)[name = tensor<string, []>("op_5265")];
            tensor<fp32, [1, 128, 24001]> var_5266 = mul(x = var_5262, y = var_5265)[name = tensor<string, []>("op_5266")];
            tensor<fp32, [1, 128, 24001]> xt_81 = add(x = var_5266, y = var_5259_1)[name = tensor<string, []>("xt_81")];
            tensor<fp32, [1, 128, 1]> var_5268 = const()[name = tensor<string, []>("op_5268"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(312402816)))];
            tensor<fp32, [1, 128, 24001]> var_5271 = mul(x = decoder_generator_noise_res_1_alpha2_1, y = xt_81)[name = tensor<string, []>("op_5271")];
            tensor<fp32, [1, 128, 24001]> var_5272 = sin(x = var_5271)[name = tensor<string, []>("op_5272")];
            tensor<fp32, []> var_2483_promoted_29 = const()[name = tensor<string, []>("op_2483_promoted_29"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 24001]> var_5273 = pow(x = var_5272, y = var_2483_promoted_29)[name = tensor<string, []>("op_5273")];
            tensor<fp32, [1, 128, 24001]> var_5274 = mul(x = var_5268, y = var_5273)[name = tensor<string, []>("op_5274")];
            tensor<fp32, [1, 128, 24001]> input_625 = add(x = xt_81, y = var_5274)[name = tensor<string, []>("input_625")];
            tensor<fp32, [128, 128, 11]> weight_253 = const()[name = tensor<string, []>("weight_253"), val = tensor<fp32, [128, 128, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(312403392)))];
            tensor<string, []> xt_83_pad_type_0 = const()[name = tensor<string, []>("xt_83_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_83_pad_0 = const()[name = tensor<string, []>("xt_83_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> xt_83_strides_0 = const()[name = tensor<string, []>("xt_83_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_83_dilations_0 = const()[name = tensor<string, []>("xt_83_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_83_groups_0 = const()[name = tensor<string, []>("xt_83_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 24001]> xt_83 = conv(bias = decoder_generator_noise_res_1_convs2_1_bias, dilations = xt_83_dilations_0, groups = xt_83_groups_0, pad = xt_83_pad_0, pad_type = xt_83_pad_type_0, strides = xt_83_strides_0, weight = weight_253, x = input_625)[name = tensor<string, []>("xt_83")];
            tensor<fp32, [1, 128, 24001]> input_627 = add(x = xt_83, y = input_619)[name = tensor<string, []>("input_627")];
            tensor<fp32, [1, 256]> h_213 = linear(bias = decoder_generator_noise_res_1_adain1_2_fc_bias, weight = decoder_generator_noise_res_1_adain1_2_fc_weight, x = input_417)[name = tensor<string, []>("linear_129")];
            tensor<int32, [3]> var_5295 = const()[name = tensor<string, []>("op_5295"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_215 = reshape(shape = var_5295, x = h_213)[name = tensor<string, []>("h_215")];
            tensor<int32, [2]> var_5297_split_sizes_0 = const()[name = tensor<string, []>("op_5297_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_5297_axis_0 = const()[name = tensor<string, []>("op_5297_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_5297_0, tensor<fp32, [1, 128, 1]> var_5297_1 = split(axis = var_5297_axis_0, split_sizes = var_5297_split_sizes_0, x = h_215)[name = tensor<string, []>("op_5297")];
            tensor<fp32, []> var_5299_promoted = const()[name = tensor<string, []>("op_5299_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_5300 = add(x = var_5297_0, y = var_5299_promoted)[name = tensor<string, []>("op_5300")];
            tensor<fp32, [1, 128, 24001]> var_5303 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_2476, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_627)[name = tensor<string, []>("op_5303")];
            tensor<fp32, [1, 128, 24001]> var_5304 = mul(x = var_5300, y = var_5303)[name = tensor<string, []>("op_5304")];
            tensor<fp32, [1, 128, 24001]> xt_85 = add(x = var_5304, y = var_5297_1)[name = tensor<string, []>("xt_85")];
            tensor<fp32, [1, 128, 1]> var_5306 = const()[name = tensor<string, []>("op_5306"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(313124352)))];
            tensor<fp32, [1, 128, 24001]> var_5309 = mul(x = decoder_generator_noise_res_1_alpha1_2, y = xt_85)[name = tensor<string, []>("op_5309")];
            tensor<fp32, [1, 128, 24001]> var_5310 = sin(x = var_5309)[name = tensor<string, []>("op_5310")];
            tensor<fp32, []> var_2483_promoted_30 = const()[name = tensor<string, []>("op_2483_promoted_30"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 24001]> var_5311 = pow(x = var_5310, y = var_2483_promoted_30)[name = tensor<string, []>("op_5311")];
            tensor<fp32, [1, 128, 24001]> var_5312 = mul(x = var_5306, y = var_5311)[name = tensor<string, []>("op_5312")];
            tensor<fp32, [1, 128, 24001]> input_629 = add(x = xt_85, y = var_5312)[name = tensor<string, []>("input_629")];
            tensor<fp32, [128, 128, 11]> weight_257 = const()[name = tensor<string, []>("weight_257"), val = tensor<fp32, [128, 128, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(313124928)))];
            tensor<string, []> input_631_pad_type_0 = const()[name = tensor<string, []>("input_631_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_631_pad_0 = const()[name = tensor<string, []>("input_631_pad_0"), val = tensor<int32, [2]>([25, 25])];
            tensor<int32, [1]> input_631_dilations_0 = const()[name = tensor<string, []>("input_631_dilations_0"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> input_631_strides_0 = const()[name = tensor<string, []>("input_631_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_631_groups_0 = const()[name = tensor<string, []>("input_631_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 24001]> input_631 = conv(bias = decoder_generator_noise_res_1_convs1_2_bias, dilations = input_631_dilations_0, groups = input_631_groups_0, pad = input_631_pad_0, pad_type = input_631_pad_type_0, strides = input_631_strides_0, weight = weight_257, x = input_629)[name = tensor<string, []>("input_631")];
            tensor<fp32, [1, 256]> h_217 = linear(bias = decoder_generator_noise_res_1_adain2_2_fc_bias, weight = decoder_generator_noise_res_1_adain2_2_fc_weight, x = input_417)[name = tensor<string, []>("linear_130")];
            tensor<int32, [3]> var_5332 = const()[name = tensor<string, []>("op_5332"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_219 = reshape(shape = var_5332, x = h_217)[name = tensor<string, []>("h_219")];
            tensor<int32, [2]> var_5334_split_sizes_0 = const()[name = tensor<string, []>("op_5334_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_5334_axis_0 = const()[name = tensor<string, []>("op_5334_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_5334_0, tensor<fp32, [1, 128, 1]> var_5334_1 = split(axis = var_5334_axis_0, split_sizes = var_5334_split_sizes_0, x = h_219)[name = tensor<string, []>("op_5334")];
            tensor<fp32, []> var_5336_promoted = const()[name = tensor<string, []>("op_5336_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_5337 = add(x = var_5334_0, y = var_5336_promoted)[name = tensor<string, []>("op_5337")];
            tensor<fp32, [1, 128, 24001]> var_5340 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_2476, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_631)[name = tensor<string, []>("op_5340")];
            tensor<fp32, [1, 128, 24001]> var_5341 = mul(x = var_5337, y = var_5340)[name = tensor<string, []>("op_5341")];
            tensor<fp32, [1, 128, 24001]> xt_87 = add(x = var_5341, y = var_5334_1)[name = tensor<string, []>("xt_87")];
            tensor<fp32, [1, 128, 1]> var_5343 = const()[name = tensor<string, []>("op_5343"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(313845888)))];
            tensor<fp32, [1, 128, 24001]> var_5346 = mul(x = decoder_generator_noise_res_1_alpha2_2, y = xt_87)[name = tensor<string, []>("op_5346")];
            tensor<fp32, [1, 128, 24001]> var_5347 = sin(x = var_5346)[name = tensor<string, []>("op_5347")];
            tensor<fp32, []> var_2483_promoted_31 = const()[name = tensor<string, []>("op_2483_promoted_31"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 24001]> var_5348 = pow(x = var_5347, y = var_2483_promoted_31)[name = tensor<string, []>("op_5348")];
            tensor<fp32, [1, 128, 24001]> var_5349 = mul(x = var_5343, y = var_5348)[name = tensor<string, []>("op_5349")];
            tensor<fp32, [1, 128, 24001]> input_633 = add(x = xt_87, y = var_5349)[name = tensor<string, []>("input_633")];
            tensor<fp32, [128, 128, 11]> weight_261 = const()[name = tensor<string, []>("weight_261"), val = tensor<fp32, [128, 128, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(313846464)))];
            tensor<string, []> xt_89_pad_type_0 = const()[name = tensor<string, []>("xt_89_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_89_pad_0 = const()[name = tensor<string, []>("xt_89_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> xt_89_strides_0 = const()[name = tensor<string, []>("xt_89_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_89_dilations_0 = const()[name = tensor<string, []>("xt_89_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_89_groups_0 = const()[name = tensor<string, []>("xt_89_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 24001]> xt_89 = conv(bias = decoder_generator_noise_res_1_convs2_2_bias, dilations = xt_89_dilations_0, groups = xt_89_groups_0, pad = xt_89_pad_0, pad_type = xt_89_pad_type_0, strides = xt_89_strides_0, weight = weight_261, x = input_633)[name = tensor<string, []>("xt_89")];
            tensor<fp32, [1, 128, 24001]> x_source = add(x = xt_89, y = input_627)[name = tensor<string, []>("x_source")];
            tensor<fp32, [256, 128, 12]> var_5368 = const()[name = tensor<string, []>("op_5368"), val = tensor<fp32, [256, 128, 12]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(314567424)))];
            tensor<string, []> input_637_pad_type_0 = const()[name = tensor<string, []>("input_637_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_637_pad_0 = const()[name = tensor<string, []>("input_637_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> input_637_strides_0 = const()[name = tensor<string, []>("input_637_strides_0"), val = tensor<int32, [1]>([6])];
            tensor<int32, [1]> input_637_dilations_0 = const()[name = tensor<string, []>("input_637_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_637_groups_0 = const()[name = tensor<string, []>("input_637_groups_0"), val = tensor<int32, []>(1)];
            tensor<int32, [3]> input_637_has_output_shape_output_shape_0 = const()[name = tensor<string, []>("input_637_has_output_shape_output_shape_0"), val = tensor<int32, [3]>([1, 128, 24000])];
            tensor<fp32, [1, 128, 24000]> input_637_has_output_shape = conv_transpose(bias = decoder_generator_ups_1_bias, dilations = input_637_dilations_0, groups = input_637_groups_0, output_shape = input_637_has_output_shape_output_shape_0, pad = input_637_pad_0, pad_type = input_637_pad_type_0, strides = input_637_strides_0, weight = var_5368, x = input_635)[name = tensor<string, []>("input_637_has_output_shape")];
            tensor<fp32, []> const_217 = const()[name = tensor<string, []>("const_217"), val = tensor<fp32, []>(0x0p+0)];
            tensor<int32, [6]> x_251_pad_0 = const()[name = tensor<string, []>("x_251_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 1, 0])];
            tensor<string, []> x_251_mode_0 = const()[name = tensor<string, []>("x_251_mode_0"), val = tensor<string, []>("reflect")];
            tensor<fp32, [1, 128, 24001]> x_251 = pad(constant_val = const_217, mode = x_251_mode_0, pad = x_251_pad_0, x = input_637_has_output_shape)[name = tensor<string, []>("x_251")];
            tensor<fp32, [1, 128, 24001]> input_639 = add(x = x_251, y = x_source)[name = tensor<string, []>("input_639")];
            tensor<fp32, [1, 256]> h_221 = linear(bias = decoder_generator_resblocks_3_adain1_0_fc_bias, weight = decoder_generator_resblocks_3_adain1_0_fc_weight, x = input_417)[name = tensor<string, []>("linear_131")];
            tensor<int32, [3]> var_5420 = const()[name = tensor<string, []>("op_5420"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_223 = reshape(shape = var_5420, x = h_221)[name = tensor<string, []>("h_223")];
            tensor<int32, [2]> var_5422_split_sizes_0 = const()[name = tensor<string, []>("op_5422_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_5422_axis_0 = const()[name = tensor<string, []>("op_5422_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_5422_0, tensor<fp32, [1, 128, 1]> var_5422_1 = split(axis = var_5422_axis_0, split_sizes = var_5422_split_sizes_0, x = h_223)[name = tensor<string, []>("op_5422")];
            tensor<fp32, []> var_5424_promoted = const()[name = tensor<string, []>("op_5424_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_5425 = add(x = var_5422_0, y = var_5424_promoted)[name = tensor<string, []>("op_5425")];
            tensor<fp32, [1, 128, 24001]> var_5428 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_2476, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_639)[name = tensor<string, []>("op_5428")];
            tensor<fp32, [1, 128, 24001]> var_5429 = mul(x = var_5425, y = var_5428)[name = tensor<string, []>("op_5429")];
            tensor<fp32, [1, 128, 24001]> xt_91 = add(x = var_5429, y = var_5422_1)[name = tensor<string, []>("xt_91")];
            tensor<fp32, [1, 128, 1]> var_5431 = const()[name = tensor<string, []>("op_5431"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(316140352)))];
            tensor<fp32, [1, 128, 24001]> var_5434 = mul(x = decoder_generator_resblocks_3_alpha1_0, y = xt_91)[name = tensor<string, []>("op_5434")];
            tensor<fp32, [1, 128, 24001]> var_5435 = sin(x = var_5434)[name = tensor<string, []>("op_5435")];
            tensor<fp32, []> var_2483_promoted_32 = const()[name = tensor<string, []>("op_2483_promoted_32"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 24001]> var_5436 = pow(x = var_5435, y = var_2483_promoted_32)[name = tensor<string, []>("op_5436")];
            tensor<fp32, [1, 128, 24001]> var_5437 = mul(x = var_5431, y = var_5436)[name = tensor<string, []>("op_5437")];
            tensor<fp32, [1, 128, 24001]> input_641 = add(x = xt_91, y = var_5437)[name = tensor<string, []>("input_641")];
            tensor<fp32, [128, 128, 3]> weight_265 = const()[name = tensor<string, []>("weight_265"), val = tensor<fp32, [128, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(316140928)))];
            tensor<string, []> input_643_pad_type_0 = const()[name = tensor<string, []>("input_643_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_643_pad_0 = const()[name = tensor<string, []>("input_643_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> input_643_strides_0 = const()[name = tensor<string, []>("input_643_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_643_dilations_0 = const()[name = tensor<string, []>("input_643_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_643_groups_0 = const()[name = tensor<string, []>("input_643_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 24001]> input_643 = conv(bias = decoder_generator_resblocks_3_convs1_0_bias, dilations = input_643_dilations_0, groups = input_643_groups_0, pad = input_643_pad_0, pad_type = input_643_pad_type_0, strides = input_643_strides_0, weight = weight_265, x = input_641)[name = tensor<string, []>("input_643")];
            tensor<fp32, [1, 256]> h_225 = linear(bias = decoder_generator_resblocks_3_adain2_0_fc_bias, weight = decoder_generator_resblocks_3_adain2_0_fc_weight, x = input_417)[name = tensor<string, []>("linear_132")];
            tensor<int32, [3]> var_5457 = const()[name = tensor<string, []>("op_5457"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_227 = reshape(shape = var_5457, x = h_225)[name = tensor<string, []>("h_227")];
            tensor<int32, [2]> var_5459_split_sizes_0 = const()[name = tensor<string, []>("op_5459_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_5459_axis_0 = const()[name = tensor<string, []>("op_5459_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_5459_0, tensor<fp32, [1, 128, 1]> var_5459_1 = split(axis = var_5459_axis_0, split_sizes = var_5459_split_sizes_0, x = h_227)[name = tensor<string, []>("op_5459")];
            tensor<fp32, []> var_5461_promoted = const()[name = tensor<string, []>("op_5461_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_5462 = add(x = var_5459_0, y = var_5461_promoted)[name = tensor<string, []>("op_5462")];
            tensor<fp32, [1, 128, 24001]> var_5465 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_2476, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_643)[name = tensor<string, []>("op_5465")];
            tensor<fp32, [1, 128, 24001]> var_5466 = mul(x = var_5462, y = var_5465)[name = tensor<string, []>("op_5466")];
            tensor<fp32, [1, 128, 24001]> xt_93 = add(x = var_5466, y = var_5459_1)[name = tensor<string, []>("xt_93")];
            tensor<fp32, [1, 128, 1]> var_5468 = const()[name = tensor<string, []>("op_5468"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(316337600)))];
            tensor<fp32, [1, 128, 24001]> var_5471 = mul(x = decoder_generator_resblocks_3_alpha2_0, y = xt_93)[name = tensor<string, []>("op_5471")];
            tensor<fp32, [1, 128, 24001]> var_5472 = sin(x = var_5471)[name = tensor<string, []>("op_5472")];
            tensor<fp32, []> var_2483_promoted_33 = const()[name = tensor<string, []>("op_2483_promoted_33"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 24001]> var_5473 = pow(x = var_5472, y = var_2483_promoted_33)[name = tensor<string, []>("op_5473")];
            tensor<fp32, [1, 128, 24001]> var_5474 = mul(x = var_5468, y = var_5473)[name = tensor<string, []>("op_5474")];
            tensor<fp32, [1, 128, 24001]> input_645 = add(x = xt_93, y = var_5474)[name = tensor<string, []>("input_645")];
            tensor<fp32, [128, 128, 3]> weight_269 = const()[name = tensor<string, []>("weight_269"), val = tensor<fp32, [128, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(316338176)))];
            tensor<string, []> xt_95_pad_type_0 = const()[name = tensor<string, []>("xt_95_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_95_pad_0 = const()[name = tensor<string, []>("xt_95_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> xt_95_strides_0 = const()[name = tensor<string, []>("xt_95_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_95_dilations_0 = const()[name = tensor<string, []>("xt_95_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_95_groups_0 = const()[name = tensor<string, []>("xt_95_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 24001]> xt_95 = conv(bias = decoder_generator_resblocks_3_convs2_0_bias, dilations = xt_95_dilations_0, groups = xt_95_groups_0, pad = xt_95_pad_0, pad_type = xt_95_pad_type_0, strides = xt_95_strides_0, weight = weight_269, x = input_645)[name = tensor<string, []>("xt_95")];
            tensor<fp32, [1, 128, 24001]> input_647 = add(x = xt_95, y = input_639)[name = tensor<string, []>("input_647")];
            tensor<fp32, [1, 256]> h_229 = linear(bias = decoder_generator_resblocks_3_adain1_1_fc_bias, weight = decoder_generator_resblocks_3_adain1_1_fc_weight, x = input_417)[name = tensor<string, []>("linear_133")];
            tensor<int32, [3]> var_5495 = const()[name = tensor<string, []>("op_5495"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_231 = reshape(shape = var_5495, x = h_229)[name = tensor<string, []>("h_231")];
            tensor<int32, [2]> var_5497_split_sizes_0 = const()[name = tensor<string, []>("op_5497_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_5497_axis_0 = const()[name = tensor<string, []>("op_5497_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_5497_0, tensor<fp32, [1, 128, 1]> var_5497_1 = split(axis = var_5497_axis_0, split_sizes = var_5497_split_sizes_0, x = h_231)[name = tensor<string, []>("op_5497")];
            tensor<fp32, []> var_5499_promoted = const()[name = tensor<string, []>("op_5499_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_5500 = add(x = var_5497_0, y = var_5499_promoted)[name = tensor<string, []>("op_5500")];
            tensor<fp32, [1, 128, 24001]> var_5503 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_2476, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_647)[name = tensor<string, []>("op_5503")];
            tensor<fp32, [1, 128, 24001]> var_5504 = mul(x = var_5500, y = var_5503)[name = tensor<string, []>("op_5504")];
            tensor<fp32, [1, 128, 24001]> xt_97 = add(x = var_5504, y = var_5497_1)[name = tensor<string, []>("xt_97")];
            tensor<fp32, [1, 128, 1]> var_5506 = const()[name = tensor<string, []>("op_5506"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(316534848)))];
            tensor<fp32, [1, 128, 24001]> var_5509 = mul(x = decoder_generator_resblocks_3_alpha1_1, y = xt_97)[name = tensor<string, []>("op_5509")];
            tensor<fp32, [1, 128, 24001]> var_5510 = sin(x = var_5509)[name = tensor<string, []>("op_5510")];
            tensor<fp32, []> var_2483_promoted_34 = const()[name = tensor<string, []>("op_2483_promoted_34"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 24001]> var_5511 = pow(x = var_5510, y = var_2483_promoted_34)[name = tensor<string, []>("op_5511")];
            tensor<fp32, [1, 128, 24001]> var_5512 = mul(x = var_5506, y = var_5511)[name = tensor<string, []>("op_5512")];
            tensor<fp32, [1, 128, 24001]> input_649 = add(x = xt_97, y = var_5512)[name = tensor<string, []>("input_649")];
            tensor<fp32, [128, 128, 3]> weight_273 = const()[name = tensor<string, []>("weight_273"), val = tensor<fp32, [128, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(316535424)))];
            tensor<string, []> input_651_pad_type_0 = const()[name = tensor<string, []>("input_651_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_651_pad_0 = const()[name = tensor<string, []>("input_651_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> input_651_dilations_0 = const()[name = tensor<string, []>("input_651_dilations_0"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> input_651_strides_0 = const()[name = tensor<string, []>("input_651_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_651_groups_0 = const()[name = tensor<string, []>("input_651_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 24001]> input_651 = conv(bias = decoder_generator_resblocks_3_convs1_1_bias, dilations = input_651_dilations_0, groups = input_651_groups_0, pad = input_651_pad_0, pad_type = input_651_pad_type_0, strides = input_651_strides_0, weight = weight_273, x = input_649)[name = tensor<string, []>("input_651")];
            tensor<fp32, [1, 256]> h_233 = linear(bias = decoder_generator_resblocks_3_adain2_1_fc_bias, weight = decoder_generator_resblocks_3_adain2_1_fc_weight, x = input_417)[name = tensor<string, []>("linear_134")];
            tensor<int32, [3]> var_5532 = const()[name = tensor<string, []>("op_5532"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_235 = reshape(shape = var_5532, x = h_233)[name = tensor<string, []>("h_235")];
            tensor<int32, [2]> var_5534_split_sizes_0 = const()[name = tensor<string, []>("op_5534_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_5534_axis_0 = const()[name = tensor<string, []>("op_5534_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_5534_0, tensor<fp32, [1, 128, 1]> var_5534_1 = split(axis = var_5534_axis_0, split_sizes = var_5534_split_sizes_0, x = h_235)[name = tensor<string, []>("op_5534")];
            tensor<fp32, []> var_5536_promoted = const()[name = tensor<string, []>("op_5536_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_5537 = add(x = var_5534_0, y = var_5536_promoted)[name = tensor<string, []>("op_5537")];
            tensor<fp32, [1, 128, 24001]> var_5540 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_2476, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_651)[name = tensor<string, []>("op_5540")];
            tensor<fp32, [1, 128, 24001]> var_5541 = mul(x = var_5537, y = var_5540)[name = tensor<string, []>("op_5541")];
            tensor<fp32, [1, 128, 24001]> xt_99 = add(x = var_5541, y = var_5534_1)[name = tensor<string, []>("xt_99")];
            tensor<fp32, [1, 128, 1]> var_5543 = const()[name = tensor<string, []>("op_5543"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(316732096)))];
            tensor<fp32, [1, 128, 24001]> var_5546 = mul(x = decoder_generator_resblocks_3_alpha2_1, y = xt_99)[name = tensor<string, []>("op_5546")];
            tensor<fp32, [1, 128, 24001]> var_5547 = sin(x = var_5546)[name = tensor<string, []>("op_5547")];
            tensor<fp32, []> var_2483_promoted_35 = const()[name = tensor<string, []>("op_2483_promoted_35"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 24001]> var_5548 = pow(x = var_5547, y = var_2483_promoted_35)[name = tensor<string, []>("op_5548")];
            tensor<fp32, [1, 128, 24001]> var_5549 = mul(x = var_5543, y = var_5548)[name = tensor<string, []>("op_5549")];
            tensor<fp32, [1, 128, 24001]> input_653 = add(x = xt_99, y = var_5549)[name = tensor<string, []>("input_653")];
            tensor<fp32, [128, 128, 3]> weight_277 = const()[name = tensor<string, []>("weight_277"), val = tensor<fp32, [128, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(316732672)))];
            tensor<string, []> xt_101_pad_type_0 = const()[name = tensor<string, []>("xt_101_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_101_pad_0 = const()[name = tensor<string, []>("xt_101_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> xt_101_strides_0 = const()[name = tensor<string, []>("xt_101_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_101_dilations_0 = const()[name = tensor<string, []>("xt_101_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_101_groups_0 = const()[name = tensor<string, []>("xt_101_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 24001]> xt_101 = conv(bias = decoder_generator_resblocks_3_convs2_1_bias, dilations = xt_101_dilations_0, groups = xt_101_groups_0, pad = xt_101_pad_0, pad_type = xt_101_pad_type_0, strides = xt_101_strides_0, weight = weight_277, x = input_653)[name = tensor<string, []>("xt_101")];
            tensor<fp32, [1, 128, 24001]> input_655 = add(x = xt_101, y = input_647)[name = tensor<string, []>("input_655")];
            tensor<fp32, [1, 256]> h_237 = linear(bias = decoder_generator_resblocks_3_adain1_2_fc_bias, weight = decoder_generator_resblocks_3_adain1_2_fc_weight, x = input_417)[name = tensor<string, []>("linear_135")];
            tensor<int32, [3]> var_5570 = const()[name = tensor<string, []>("op_5570"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_239 = reshape(shape = var_5570, x = h_237)[name = tensor<string, []>("h_239")];
            tensor<int32, [2]> var_5572_split_sizes_0 = const()[name = tensor<string, []>("op_5572_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_5572_axis_0 = const()[name = tensor<string, []>("op_5572_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_5572_0, tensor<fp32, [1, 128, 1]> var_5572_1 = split(axis = var_5572_axis_0, split_sizes = var_5572_split_sizes_0, x = h_239)[name = tensor<string, []>("op_5572")];
            tensor<fp32, []> var_5574_promoted = const()[name = tensor<string, []>("op_5574_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_5575 = add(x = var_5572_0, y = var_5574_promoted)[name = tensor<string, []>("op_5575")];
            tensor<fp32, [1, 128, 24001]> var_5578 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_2476, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_655)[name = tensor<string, []>("op_5578")];
            tensor<fp32, [1, 128, 24001]> var_5579 = mul(x = var_5575, y = var_5578)[name = tensor<string, []>("op_5579")];
            tensor<fp32, [1, 128, 24001]> xt_103 = add(x = var_5579, y = var_5572_1)[name = tensor<string, []>("xt_103")];
            tensor<fp32, [1, 128, 1]> var_5581 = const()[name = tensor<string, []>("op_5581"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(316929344)))];
            tensor<fp32, [1, 128, 24001]> var_5584 = mul(x = decoder_generator_resblocks_3_alpha1_2, y = xt_103)[name = tensor<string, []>("op_5584")];
            tensor<fp32, [1, 128, 24001]> var_5585 = sin(x = var_5584)[name = tensor<string, []>("op_5585")];
            tensor<fp32, []> var_2483_promoted_36 = const()[name = tensor<string, []>("op_2483_promoted_36"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 24001]> var_5586 = pow(x = var_5585, y = var_2483_promoted_36)[name = tensor<string, []>("op_5586")];
            tensor<fp32, [1, 128, 24001]> var_5587 = mul(x = var_5581, y = var_5586)[name = tensor<string, []>("op_5587")];
            tensor<fp32, [1, 128, 24001]> input_657 = add(x = xt_103, y = var_5587)[name = tensor<string, []>("input_657")];
            tensor<fp32, [128, 128, 3]> weight_281 = const()[name = tensor<string, []>("weight_281"), val = tensor<fp32, [128, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(316929920)))];
            tensor<string, []> input_659_pad_type_0 = const()[name = tensor<string, []>("input_659_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_659_pad_0 = const()[name = tensor<string, []>("input_659_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> input_659_dilations_0 = const()[name = tensor<string, []>("input_659_dilations_0"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> input_659_strides_0 = const()[name = tensor<string, []>("input_659_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_659_groups_0 = const()[name = tensor<string, []>("input_659_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 24001]> input_659 = conv(bias = decoder_generator_resblocks_3_convs1_2_bias, dilations = input_659_dilations_0, groups = input_659_groups_0, pad = input_659_pad_0, pad_type = input_659_pad_type_0, strides = input_659_strides_0, weight = weight_281, x = input_657)[name = tensor<string, []>("input_659")];
            tensor<fp32, [1, 256]> h_241 = linear(bias = decoder_generator_resblocks_3_adain2_2_fc_bias, weight = decoder_generator_resblocks_3_adain2_2_fc_weight, x = input_417)[name = tensor<string, []>("linear_136")];
            tensor<int32, [3]> var_5607 = const()[name = tensor<string, []>("op_5607"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_243 = reshape(shape = var_5607, x = h_241)[name = tensor<string, []>("h_243")];
            tensor<int32, [2]> var_5609_split_sizes_0 = const()[name = tensor<string, []>("op_5609_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_5609_axis_0 = const()[name = tensor<string, []>("op_5609_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_5609_0, tensor<fp32, [1, 128, 1]> var_5609_1 = split(axis = var_5609_axis_0, split_sizes = var_5609_split_sizes_0, x = h_243)[name = tensor<string, []>("op_5609")];
            tensor<fp32, []> var_5611_promoted = const()[name = tensor<string, []>("op_5611_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_5612 = add(x = var_5609_0, y = var_5611_promoted)[name = tensor<string, []>("op_5612")];
            tensor<fp32, [1, 128, 24001]> var_5615 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_2476, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_659)[name = tensor<string, []>("op_5615")];
            tensor<fp32, [1, 128, 24001]> var_5616 = mul(x = var_5612, y = var_5615)[name = tensor<string, []>("op_5616")];
            tensor<fp32, [1, 128, 24001]> xt_105 = add(x = var_5616, y = var_5609_1)[name = tensor<string, []>("xt_105")];
            tensor<fp32, [1, 128, 1]> var_5618 = const()[name = tensor<string, []>("op_5618"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(317126592)))];
            tensor<fp32, [1, 128, 24001]> var_5621 = mul(x = decoder_generator_resblocks_3_alpha2_2, y = xt_105)[name = tensor<string, []>("op_5621")];
            tensor<fp32, [1, 128, 24001]> var_5622 = sin(x = var_5621)[name = tensor<string, []>("op_5622")];
            tensor<fp32, []> var_2483_promoted_37 = const()[name = tensor<string, []>("op_2483_promoted_37"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 24001]> var_5623 = pow(x = var_5622, y = var_2483_promoted_37)[name = tensor<string, []>("op_5623")];
            tensor<fp32, [1, 128, 24001]> var_5624 = mul(x = var_5618, y = var_5623)[name = tensor<string, []>("op_5624")];
            tensor<fp32, [1, 128, 24001]> input_661 = add(x = xt_105, y = var_5624)[name = tensor<string, []>("input_661")];
            tensor<fp32, [128, 128, 3]> weight_285 = const()[name = tensor<string, []>("weight_285"), val = tensor<fp32, [128, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(317127168)))];
            tensor<string, []> xt_107_pad_type_0 = const()[name = tensor<string, []>("xt_107_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_107_pad_0 = const()[name = tensor<string, []>("xt_107_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> xt_107_strides_0 = const()[name = tensor<string, []>("xt_107_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_107_dilations_0 = const()[name = tensor<string, []>("xt_107_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_107_groups_0 = const()[name = tensor<string, []>("xt_107_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 24001]> xt_107 = conv(bias = decoder_generator_resblocks_3_convs2_2_bias, dilations = xt_107_dilations_0, groups = xt_107_groups_0, pad = xt_107_pad_0, pad_type = xt_107_pad_type_0, strides = xt_107_strides_0, weight = weight_285, x = input_661)[name = tensor<string, []>("xt_107")];
            tensor<fp32, [1, 128, 24001]> xs_7 = add(x = xt_107, y = input_655)[name = tensor<string, []>("xs_7")];
            tensor<fp32, [1, 256]> h_245 = linear(bias = decoder_generator_resblocks_4_adain1_0_fc_bias, weight = decoder_generator_resblocks_4_adain1_0_fc_weight, x = input_417)[name = tensor<string, []>("linear_137")];
            tensor<int32, [3]> var_5681 = const()[name = tensor<string, []>("op_5681"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_247 = reshape(shape = var_5681, x = h_245)[name = tensor<string, []>("h_247")];
            tensor<int32, [2]> var_5683_split_sizes_0 = const()[name = tensor<string, []>("op_5683_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_5683_axis_0 = const()[name = tensor<string, []>("op_5683_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_5683_0, tensor<fp32, [1, 128, 1]> var_5683_1 = split(axis = var_5683_axis_0, split_sizes = var_5683_split_sizes_0, x = h_247)[name = tensor<string, []>("op_5683")];
            tensor<fp32, []> var_5685_promoted = const()[name = tensor<string, []>("op_5685_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_5686 = add(x = var_5683_0, y = var_5685_promoted)[name = tensor<string, []>("op_5686")];
            tensor<fp32, [1, 128, 24001]> var_5690 = mul(x = var_5686, y = var_5428)[name = tensor<string, []>("op_5690")];
            tensor<fp32, [1, 128, 24001]> xt_109 = add(x = var_5690, y = var_5683_1)[name = tensor<string, []>("xt_109")];
            tensor<fp32, [1, 128, 1]> var_5692 = const()[name = tensor<string, []>("op_5692"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(317323840)))];
            tensor<fp32, [1, 128, 24001]> var_5695 = mul(x = decoder_generator_resblocks_4_alpha1_0, y = xt_109)[name = tensor<string, []>("op_5695")];
            tensor<fp32, [1, 128, 24001]> var_5696 = sin(x = var_5695)[name = tensor<string, []>("op_5696")];
            tensor<fp32, []> var_2483_promoted_38 = const()[name = tensor<string, []>("op_2483_promoted_38"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 24001]> var_5697 = pow(x = var_5696, y = var_2483_promoted_38)[name = tensor<string, []>("op_5697")];
            tensor<fp32, [1, 128, 24001]> var_5698 = mul(x = var_5692, y = var_5697)[name = tensor<string, []>("op_5698")];
            tensor<fp32, [1, 128, 24001]> input_663 = add(x = xt_109, y = var_5698)[name = tensor<string, []>("input_663")];
            tensor<fp32, [128, 128, 7]> weight_289 = const()[name = tensor<string, []>("weight_289"), val = tensor<fp32, [128, 128, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(317324416)))];
            tensor<string, []> input_665_pad_type_0 = const()[name = tensor<string, []>("input_665_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_665_pad_0 = const()[name = tensor<string, []>("input_665_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> input_665_strides_0 = const()[name = tensor<string, []>("input_665_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_665_dilations_0 = const()[name = tensor<string, []>("input_665_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_665_groups_0 = const()[name = tensor<string, []>("input_665_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 24001]> input_665 = conv(bias = decoder_generator_resblocks_4_convs1_0_bias, dilations = input_665_dilations_0, groups = input_665_groups_0, pad = input_665_pad_0, pad_type = input_665_pad_type_0, strides = input_665_strides_0, weight = weight_289, x = input_663)[name = tensor<string, []>("input_665")];
            tensor<fp32, [1, 256]> h_249 = linear(bias = decoder_generator_resblocks_4_adain2_0_fc_bias, weight = decoder_generator_resblocks_4_adain2_0_fc_weight, x = input_417)[name = tensor<string, []>("linear_138")];
            tensor<int32, [3]> var_5718 = const()[name = tensor<string, []>("op_5718"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_251 = reshape(shape = var_5718, x = h_249)[name = tensor<string, []>("h_251")];
            tensor<int32, [2]> var_5720_split_sizes_0 = const()[name = tensor<string, []>("op_5720_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_5720_axis_0 = const()[name = tensor<string, []>("op_5720_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_5720_0, tensor<fp32, [1, 128, 1]> var_5720_1 = split(axis = var_5720_axis_0, split_sizes = var_5720_split_sizes_0, x = h_251)[name = tensor<string, []>("op_5720")];
            tensor<fp32, []> var_5722_promoted = const()[name = tensor<string, []>("op_5722_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_5723 = add(x = var_5720_0, y = var_5722_promoted)[name = tensor<string, []>("op_5723")];
            tensor<fp32, [1, 128, 24001]> var_5726 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_2476, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_665)[name = tensor<string, []>("op_5726")];
            tensor<fp32, [1, 128, 24001]> var_5727 = mul(x = var_5723, y = var_5726)[name = tensor<string, []>("op_5727")];
            tensor<fp32, [1, 128, 24001]> xt_111 = add(x = var_5727, y = var_5720_1)[name = tensor<string, []>("xt_111")];
            tensor<fp32, [1, 128, 1]> var_5729 = const()[name = tensor<string, []>("op_5729"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(317783232)))];
            tensor<fp32, [1, 128, 24001]> var_5732 = mul(x = decoder_generator_resblocks_4_alpha2_0, y = xt_111)[name = tensor<string, []>("op_5732")];
            tensor<fp32, [1, 128, 24001]> var_5733 = sin(x = var_5732)[name = tensor<string, []>("op_5733")];
            tensor<fp32, []> var_2483_promoted_39 = const()[name = tensor<string, []>("op_2483_promoted_39"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 24001]> var_5734 = pow(x = var_5733, y = var_2483_promoted_39)[name = tensor<string, []>("op_5734")];
            tensor<fp32, [1, 128, 24001]> var_5735 = mul(x = var_5729, y = var_5734)[name = tensor<string, []>("op_5735")];
            tensor<fp32, [1, 128, 24001]> input_667 = add(x = xt_111, y = var_5735)[name = tensor<string, []>("input_667")];
            tensor<fp32, [128, 128, 7]> weight_293 = const()[name = tensor<string, []>("weight_293"), val = tensor<fp32, [128, 128, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(317783808)))];
            tensor<string, []> xt_113_pad_type_0 = const()[name = tensor<string, []>("xt_113_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_113_pad_0 = const()[name = tensor<string, []>("xt_113_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> xt_113_strides_0 = const()[name = tensor<string, []>("xt_113_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_113_dilations_0 = const()[name = tensor<string, []>("xt_113_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_113_groups_0 = const()[name = tensor<string, []>("xt_113_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 24001]> xt_113 = conv(bias = decoder_generator_resblocks_4_convs2_0_bias, dilations = xt_113_dilations_0, groups = xt_113_groups_0, pad = xt_113_pad_0, pad_type = xt_113_pad_type_0, strides = xt_113_strides_0, weight = weight_293, x = input_667)[name = tensor<string, []>("xt_113")];
            tensor<fp32, [1, 128, 24001]> input_669 = add(x = xt_113, y = input_639)[name = tensor<string, []>("input_669")];
            tensor<fp32, [1, 256]> h_253 = linear(bias = decoder_generator_resblocks_4_adain1_1_fc_bias, weight = decoder_generator_resblocks_4_adain1_1_fc_weight, x = input_417)[name = tensor<string, []>("linear_139")];
            tensor<int32, [3]> var_5756 = const()[name = tensor<string, []>("op_5756"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_255 = reshape(shape = var_5756, x = h_253)[name = tensor<string, []>("h_255")];
            tensor<int32, [2]> var_5758_split_sizes_0 = const()[name = tensor<string, []>("op_5758_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_5758_axis_0 = const()[name = tensor<string, []>("op_5758_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_5758_0, tensor<fp32, [1, 128, 1]> var_5758_1 = split(axis = var_5758_axis_0, split_sizes = var_5758_split_sizes_0, x = h_255)[name = tensor<string, []>("op_5758")];
            tensor<fp32, []> var_5760_promoted = const()[name = tensor<string, []>("op_5760_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_5761 = add(x = var_5758_0, y = var_5760_promoted)[name = tensor<string, []>("op_5761")];
            tensor<fp32, [1, 128, 24001]> var_5764 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_2476, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_669)[name = tensor<string, []>("op_5764")];
            tensor<fp32, [1, 128, 24001]> var_5765 = mul(x = var_5761, y = var_5764)[name = tensor<string, []>("op_5765")];
            tensor<fp32, [1, 128, 24001]> xt_115 = add(x = var_5765, y = var_5758_1)[name = tensor<string, []>("xt_115")];
            tensor<fp32, [1, 128, 1]> var_5767 = const()[name = tensor<string, []>("op_5767"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(318242624)))];
            tensor<fp32, [1, 128, 24001]> var_5770 = mul(x = decoder_generator_resblocks_4_alpha1_1, y = xt_115)[name = tensor<string, []>("op_5770")];
            tensor<fp32, [1, 128, 24001]> var_5771 = sin(x = var_5770)[name = tensor<string, []>("op_5771")];
            tensor<fp32, []> var_2483_promoted_40 = const()[name = tensor<string, []>("op_2483_promoted_40"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 24001]> var_5772 = pow(x = var_5771, y = var_2483_promoted_40)[name = tensor<string, []>("op_5772")];
            tensor<fp32, [1, 128, 24001]> var_5773 = mul(x = var_5767, y = var_5772)[name = tensor<string, []>("op_5773")];
            tensor<fp32, [1, 128, 24001]> input_671 = add(x = xt_115, y = var_5773)[name = tensor<string, []>("input_671")];
            tensor<fp32, [128, 128, 7]> weight_297 = const()[name = tensor<string, []>("weight_297"), val = tensor<fp32, [128, 128, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(318243200)))];
            tensor<string, []> input_673_pad_type_0 = const()[name = tensor<string, []>("input_673_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_673_pad_0 = const()[name = tensor<string, []>("input_673_pad_0"), val = tensor<int32, [2]>([9, 9])];
            tensor<int32, [1]> input_673_dilations_0 = const()[name = tensor<string, []>("input_673_dilations_0"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> input_673_strides_0 = const()[name = tensor<string, []>("input_673_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_673_groups_0 = const()[name = tensor<string, []>("input_673_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 24001]> input_673 = conv(bias = decoder_generator_resblocks_4_convs1_1_bias, dilations = input_673_dilations_0, groups = input_673_groups_0, pad = input_673_pad_0, pad_type = input_673_pad_type_0, strides = input_673_strides_0, weight = weight_297, x = input_671)[name = tensor<string, []>("input_673")];
            tensor<fp32, [1, 256]> h_257 = linear(bias = decoder_generator_resblocks_4_adain2_1_fc_bias, weight = decoder_generator_resblocks_4_adain2_1_fc_weight, x = input_417)[name = tensor<string, []>("linear_140")];
            tensor<int32, [3]> var_5793 = const()[name = tensor<string, []>("op_5793"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_259 = reshape(shape = var_5793, x = h_257)[name = tensor<string, []>("h_259")];
            tensor<int32, [2]> var_5795_split_sizes_0 = const()[name = tensor<string, []>("op_5795_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_5795_axis_0 = const()[name = tensor<string, []>("op_5795_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_5795_0, tensor<fp32, [1, 128, 1]> var_5795_1 = split(axis = var_5795_axis_0, split_sizes = var_5795_split_sizes_0, x = h_259)[name = tensor<string, []>("op_5795")];
            tensor<fp32, []> var_5797_promoted = const()[name = tensor<string, []>("op_5797_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_5798 = add(x = var_5795_0, y = var_5797_promoted)[name = tensor<string, []>("op_5798")];
            tensor<fp32, [1, 128, 24001]> var_5801 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_2476, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_673)[name = tensor<string, []>("op_5801")];
            tensor<fp32, [1, 128, 24001]> var_5802 = mul(x = var_5798, y = var_5801)[name = tensor<string, []>("op_5802")];
            tensor<fp32, [1, 128, 24001]> xt_117 = add(x = var_5802, y = var_5795_1)[name = tensor<string, []>("xt_117")];
            tensor<fp32, [1, 128, 1]> var_5804 = const()[name = tensor<string, []>("op_5804"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(318702016)))];
            tensor<fp32, [1, 128, 24001]> var_5807 = mul(x = decoder_generator_resblocks_4_alpha2_1, y = xt_117)[name = tensor<string, []>("op_5807")];
            tensor<fp32, [1, 128, 24001]> var_5808 = sin(x = var_5807)[name = tensor<string, []>("op_5808")];
            tensor<fp32, []> var_2483_promoted_41 = const()[name = tensor<string, []>("op_2483_promoted_41"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 24001]> var_5809 = pow(x = var_5808, y = var_2483_promoted_41)[name = tensor<string, []>("op_5809")];
            tensor<fp32, [1, 128, 24001]> var_5810 = mul(x = var_5804, y = var_5809)[name = tensor<string, []>("op_5810")];
            tensor<fp32, [1, 128, 24001]> input_675 = add(x = xt_117, y = var_5810)[name = tensor<string, []>("input_675")];
            tensor<fp32, [128, 128, 7]> weight_301 = const()[name = tensor<string, []>("weight_301"), val = tensor<fp32, [128, 128, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(318702592)))];
            tensor<string, []> xt_119_pad_type_0 = const()[name = tensor<string, []>("xt_119_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_119_pad_0 = const()[name = tensor<string, []>("xt_119_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> xt_119_strides_0 = const()[name = tensor<string, []>("xt_119_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_119_dilations_0 = const()[name = tensor<string, []>("xt_119_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_119_groups_0 = const()[name = tensor<string, []>("xt_119_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 24001]> xt_119 = conv(bias = decoder_generator_resblocks_4_convs2_1_bias, dilations = xt_119_dilations_0, groups = xt_119_groups_0, pad = xt_119_pad_0, pad_type = xt_119_pad_type_0, strides = xt_119_strides_0, weight = weight_301, x = input_675)[name = tensor<string, []>("xt_119")];
            tensor<fp32, [1, 128, 24001]> input_677 = add(x = xt_119, y = input_669)[name = tensor<string, []>("input_677")];
            tensor<fp32, [1, 256]> h_261 = linear(bias = decoder_generator_resblocks_4_adain1_2_fc_bias, weight = decoder_generator_resblocks_4_adain1_2_fc_weight, x = input_417)[name = tensor<string, []>("linear_141")];
            tensor<int32, [3]> var_5831 = const()[name = tensor<string, []>("op_5831"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_263 = reshape(shape = var_5831, x = h_261)[name = tensor<string, []>("h_263")];
            tensor<int32, [2]> var_5833_split_sizes_0 = const()[name = tensor<string, []>("op_5833_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_5833_axis_0 = const()[name = tensor<string, []>("op_5833_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_5833_0, tensor<fp32, [1, 128, 1]> var_5833_1 = split(axis = var_5833_axis_0, split_sizes = var_5833_split_sizes_0, x = h_263)[name = tensor<string, []>("op_5833")];
            tensor<fp32, []> var_5835_promoted = const()[name = tensor<string, []>("op_5835_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_5836 = add(x = var_5833_0, y = var_5835_promoted)[name = tensor<string, []>("op_5836")];
            tensor<fp32, [1, 128, 24001]> var_5839 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_2476, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_677)[name = tensor<string, []>("op_5839")];
            tensor<fp32, [1, 128, 24001]> var_5840 = mul(x = var_5836, y = var_5839)[name = tensor<string, []>("op_5840")];
            tensor<fp32, [1, 128, 24001]> xt_121 = add(x = var_5840, y = var_5833_1)[name = tensor<string, []>("xt_121")];
            tensor<fp32, [1, 128, 1]> var_5842 = const()[name = tensor<string, []>("op_5842"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(319161408)))];
            tensor<fp32, [1, 128, 24001]> var_5845 = mul(x = decoder_generator_resblocks_4_alpha1_2, y = xt_121)[name = tensor<string, []>("op_5845")];
            tensor<fp32, [1, 128, 24001]> var_5846 = sin(x = var_5845)[name = tensor<string, []>("op_5846")];
            tensor<fp32, []> var_2483_promoted_42 = const()[name = tensor<string, []>("op_2483_promoted_42"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 24001]> var_5847 = pow(x = var_5846, y = var_2483_promoted_42)[name = tensor<string, []>("op_5847")];
            tensor<fp32, [1, 128, 24001]> var_5848 = mul(x = var_5842, y = var_5847)[name = tensor<string, []>("op_5848")];
            tensor<fp32, [1, 128, 24001]> input_679 = add(x = xt_121, y = var_5848)[name = tensor<string, []>("input_679")];
            tensor<fp32, [128, 128, 7]> weight_305 = const()[name = tensor<string, []>("weight_305"), val = tensor<fp32, [128, 128, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(319161984)))];
            tensor<string, []> input_681_pad_type_0 = const()[name = tensor<string, []>("input_681_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_681_pad_0 = const()[name = tensor<string, []>("input_681_pad_0"), val = tensor<int32, [2]>([15, 15])];
            tensor<int32, [1]> input_681_dilations_0 = const()[name = tensor<string, []>("input_681_dilations_0"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> input_681_strides_0 = const()[name = tensor<string, []>("input_681_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_681_groups_0 = const()[name = tensor<string, []>("input_681_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 24001]> input_681 = conv(bias = decoder_generator_resblocks_4_convs1_2_bias, dilations = input_681_dilations_0, groups = input_681_groups_0, pad = input_681_pad_0, pad_type = input_681_pad_type_0, strides = input_681_strides_0, weight = weight_305, x = input_679)[name = tensor<string, []>("input_681")];
            tensor<fp32, [1, 256]> h_265 = linear(bias = decoder_generator_resblocks_4_adain2_2_fc_bias, weight = decoder_generator_resblocks_4_adain2_2_fc_weight, x = input_417)[name = tensor<string, []>("linear_142")];
            tensor<int32, [3]> var_5868 = const()[name = tensor<string, []>("op_5868"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_267 = reshape(shape = var_5868, x = h_265)[name = tensor<string, []>("h_267")];
            tensor<int32, [2]> var_5870_split_sizes_0 = const()[name = tensor<string, []>("op_5870_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_5870_axis_0 = const()[name = tensor<string, []>("op_5870_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_5870_0, tensor<fp32, [1, 128, 1]> var_5870_1 = split(axis = var_5870_axis_0, split_sizes = var_5870_split_sizes_0, x = h_267)[name = tensor<string, []>("op_5870")];
            tensor<fp32, []> var_5872_promoted = const()[name = tensor<string, []>("op_5872_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_5873 = add(x = var_5870_0, y = var_5872_promoted)[name = tensor<string, []>("op_5873")];
            tensor<fp32, [1, 128, 24001]> var_5876 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_2476, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_681)[name = tensor<string, []>("op_5876")];
            tensor<fp32, [1, 128, 24001]> var_5877 = mul(x = var_5873, y = var_5876)[name = tensor<string, []>("op_5877")];
            tensor<fp32, [1, 128, 24001]> xt_123 = add(x = var_5877, y = var_5870_1)[name = tensor<string, []>("xt_123")];
            tensor<fp32, [1, 128, 1]> var_5879 = const()[name = tensor<string, []>("op_5879"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(319620800)))];
            tensor<fp32, [1, 128, 24001]> var_5882 = mul(x = decoder_generator_resblocks_4_alpha2_2, y = xt_123)[name = tensor<string, []>("op_5882")];
            tensor<fp32, [1, 128, 24001]> var_5883 = sin(x = var_5882)[name = tensor<string, []>("op_5883")];
            tensor<fp32, []> var_2483_promoted_43 = const()[name = tensor<string, []>("op_2483_promoted_43"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 24001]> var_5884 = pow(x = var_5883, y = var_2483_promoted_43)[name = tensor<string, []>("op_5884")];
            tensor<fp32, [1, 128, 24001]> var_5885 = mul(x = var_5879, y = var_5884)[name = tensor<string, []>("op_5885")];
            tensor<fp32, [1, 128, 24001]> input_683 = add(x = xt_123, y = var_5885)[name = tensor<string, []>("input_683")];
            tensor<fp32, [128, 128, 7]> weight_309 = const()[name = tensor<string, []>("weight_309"), val = tensor<fp32, [128, 128, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(319621376)))];
            tensor<string, []> xt_125_pad_type_0 = const()[name = tensor<string, []>("xt_125_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_125_pad_0 = const()[name = tensor<string, []>("xt_125_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> xt_125_strides_0 = const()[name = tensor<string, []>("xt_125_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_125_dilations_0 = const()[name = tensor<string, []>("xt_125_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_125_groups_0 = const()[name = tensor<string, []>("xt_125_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 24001]> xt_125 = conv(bias = decoder_generator_resblocks_4_convs2_2_bias, dilations = xt_125_dilations_0, groups = xt_125_groups_0, pad = xt_125_pad_0, pad_type = xt_125_pad_type_0, strides = xt_125_strides_0, weight = weight_309, x = input_683)[name = tensor<string, []>("xt_125")];
            tensor<fp32, [1, 128, 24001]> var_5898 = add(x = xt_125, y = input_677)[name = tensor<string, []>("op_5898")];
            tensor<fp32, [1, 128, 24001]> xs_9 = add(x = xs_7, y = var_5898)[name = tensor<string, []>("xs_9")];
            tensor<fp32, [1, 256]> h_269 = linear(bias = decoder_generator_resblocks_5_adain1_0_fc_bias, weight = decoder_generator_resblocks_5_adain1_0_fc_weight, x = input_417)[name = tensor<string, []>("linear_143")];
            tensor<int32, [3]> var_5943 = const()[name = tensor<string, []>("op_5943"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_271 = reshape(shape = var_5943, x = h_269)[name = tensor<string, []>("h_271")];
            tensor<int32, [2]> var_5945_split_sizes_0 = const()[name = tensor<string, []>("op_5945_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_5945_axis_0 = const()[name = tensor<string, []>("op_5945_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_5945_0, tensor<fp32, [1, 128, 1]> var_5945_1 = split(axis = var_5945_axis_0, split_sizes = var_5945_split_sizes_0, x = h_271)[name = tensor<string, []>("op_5945")];
            tensor<fp32, []> var_5947_promoted = const()[name = tensor<string, []>("op_5947_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_5948 = add(x = var_5945_0, y = var_5947_promoted)[name = tensor<string, []>("op_5948")];
            tensor<fp32, [1, 128, 24001]> var_5952 = mul(x = var_5948, y = var_5428)[name = tensor<string, []>("op_5952")];
            tensor<fp32, [1, 128, 24001]> xt_127 = add(x = var_5952, y = var_5945_1)[name = tensor<string, []>("xt_127")];
            tensor<fp32, [1, 128, 1]> var_5954 = const()[name = tensor<string, []>("op_5954"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(320080192)))];
            tensor<fp32, [1, 128, 24001]> var_5957 = mul(x = decoder_generator_resblocks_5_alpha1_0, y = xt_127)[name = tensor<string, []>("op_5957")];
            tensor<fp32, [1, 128, 24001]> var_5958 = sin(x = var_5957)[name = tensor<string, []>("op_5958")];
            tensor<fp32, []> var_2483_promoted_44 = const()[name = tensor<string, []>("op_2483_promoted_44"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 24001]> var_5959 = pow(x = var_5958, y = var_2483_promoted_44)[name = tensor<string, []>("op_5959")];
            tensor<fp32, [1, 128, 24001]> var_5960 = mul(x = var_5954, y = var_5959)[name = tensor<string, []>("op_5960")];
            tensor<fp32, [1, 128, 24001]> input_685 = add(x = xt_127, y = var_5960)[name = tensor<string, []>("input_685")];
            tensor<fp32, [128, 128, 11]> weight_313 = const()[name = tensor<string, []>("weight_313"), val = tensor<fp32, [128, 128, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(320080768)))];
            tensor<string, []> input_687_pad_type_0 = const()[name = tensor<string, []>("input_687_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_687_pad_0 = const()[name = tensor<string, []>("input_687_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> input_687_strides_0 = const()[name = tensor<string, []>("input_687_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_687_dilations_0 = const()[name = tensor<string, []>("input_687_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_687_groups_0 = const()[name = tensor<string, []>("input_687_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 24001]> input_687 = conv(bias = decoder_generator_resblocks_5_convs1_0_bias, dilations = input_687_dilations_0, groups = input_687_groups_0, pad = input_687_pad_0, pad_type = input_687_pad_type_0, strides = input_687_strides_0, weight = weight_313, x = input_685)[name = tensor<string, []>("input_687")];
            tensor<fp32, [1, 256]> h_273 = linear(bias = decoder_generator_resblocks_5_adain2_0_fc_bias, weight = decoder_generator_resblocks_5_adain2_0_fc_weight, x = input_417)[name = tensor<string, []>("linear_144")];
            tensor<int32, [3]> var_5980 = const()[name = tensor<string, []>("op_5980"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_275 = reshape(shape = var_5980, x = h_273)[name = tensor<string, []>("h_275")];
            tensor<int32, [2]> var_5982_split_sizes_0 = const()[name = tensor<string, []>("op_5982_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_5982_axis_0 = const()[name = tensor<string, []>("op_5982_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_5982_0, tensor<fp32, [1, 128, 1]> var_5982_1 = split(axis = var_5982_axis_0, split_sizes = var_5982_split_sizes_0, x = h_275)[name = tensor<string, []>("op_5982")];
            tensor<fp32, []> var_5984_promoted = const()[name = tensor<string, []>("op_5984_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_5985 = add(x = var_5982_0, y = var_5984_promoted)[name = tensor<string, []>("op_5985")];
            tensor<fp32, [1, 128, 24001]> var_5988 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_2476, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_687)[name = tensor<string, []>("op_5988")];
            tensor<fp32, [1, 128, 24001]> var_5989 = mul(x = var_5985, y = var_5988)[name = tensor<string, []>("op_5989")];
            tensor<fp32, [1, 128, 24001]> xt_129 = add(x = var_5989, y = var_5982_1)[name = tensor<string, []>("xt_129")];
            tensor<fp32, [1, 128, 1]> var_5991 = const()[name = tensor<string, []>("op_5991"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(320801728)))];
            tensor<fp32, [1, 128, 24001]> var_5994 = mul(x = decoder_generator_resblocks_5_alpha2_0, y = xt_129)[name = tensor<string, []>("op_5994")];
            tensor<fp32, [1, 128, 24001]> var_5995 = sin(x = var_5994)[name = tensor<string, []>("op_5995")];
            tensor<fp32, []> var_2483_promoted_45 = const()[name = tensor<string, []>("op_2483_promoted_45"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 24001]> var_5996 = pow(x = var_5995, y = var_2483_promoted_45)[name = tensor<string, []>("op_5996")];
            tensor<fp32, [1, 128, 24001]> var_5997 = mul(x = var_5991, y = var_5996)[name = tensor<string, []>("op_5997")];
            tensor<fp32, [1, 128, 24001]> input_689 = add(x = xt_129, y = var_5997)[name = tensor<string, []>("input_689")];
            tensor<fp32, [128, 128, 11]> weight_317 = const()[name = tensor<string, []>("weight_317"), val = tensor<fp32, [128, 128, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(320802304)))];
            tensor<string, []> xt_131_pad_type_0 = const()[name = tensor<string, []>("xt_131_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_131_pad_0 = const()[name = tensor<string, []>("xt_131_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> xt_131_strides_0 = const()[name = tensor<string, []>("xt_131_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_131_dilations_0 = const()[name = tensor<string, []>("xt_131_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_131_groups_0 = const()[name = tensor<string, []>("xt_131_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 24001]> xt_131 = conv(bias = decoder_generator_resblocks_5_convs2_0_bias, dilations = xt_131_dilations_0, groups = xt_131_groups_0, pad = xt_131_pad_0, pad_type = xt_131_pad_type_0, strides = xt_131_strides_0, weight = weight_317, x = input_689)[name = tensor<string, []>("xt_131")];
            tensor<fp32, [1, 128, 24001]> input_691 = add(x = xt_131, y = input_639)[name = tensor<string, []>("input_691")];
            tensor<fp32, [1, 256]> h_277 = linear(bias = decoder_generator_resblocks_5_adain1_1_fc_bias, weight = decoder_generator_resblocks_5_adain1_1_fc_weight, x = input_417)[name = tensor<string, []>("linear_145")];
            tensor<int32, [3]> var_6018 = const()[name = tensor<string, []>("op_6018"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_279 = reshape(shape = var_6018, x = h_277)[name = tensor<string, []>("h_279")];
            tensor<int32, [2]> var_6020_split_sizes_0 = const()[name = tensor<string, []>("op_6020_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_6020_axis_0 = const()[name = tensor<string, []>("op_6020_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_6020_0, tensor<fp32, [1, 128, 1]> var_6020_1 = split(axis = var_6020_axis_0, split_sizes = var_6020_split_sizes_0, x = h_279)[name = tensor<string, []>("op_6020")];
            tensor<fp32, []> var_6022_promoted = const()[name = tensor<string, []>("op_6022_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_6023 = add(x = var_6020_0, y = var_6022_promoted)[name = tensor<string, []>("op_6023")];
            tensor<fp32, [1, 128, 24001]> var_6026 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_2476, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_691)[name = tensor<string, []>("op_6026")];
            tensor<fp32, [1, 128, 24001]> var_6027 = mul(x = var_6023, y = var_6026)[name = tensor<string, []>("op_6027")];
            tensor<fp32, [1, 128, 24001]> xt_133 = add(x = var_6027, y = var_6020_1)[name = tensor<string, []>("xt_133")];
            tensor<fp32, [1, 128, 1]> var_6029 = const()[name = tensor<string, []>("op_6029"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(321523264)))];
            tensor<fp32, [1, 128, 24001]> var_6032 = mul(x = decoder_generator_resblocks_5_alpha1_1, y = xt_133)[name = tensor<string, []>("op_6032")];
            tensor<fp32, [1, 128, 24001]> var_6033 = sin(x = var_6032)[name = tensor<string, []>("op_6033")];
            tensor<fp32, []> var_2483_promoted_46 = const()[name = tensor<string, []>("op_2483_promoted_46"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 24001]> var_6034 = pow(x = var_6033, y = var_2483_promoted_46)[name = tensor<string, []>("op_6034")];
            tensor<fp32, [1, 128, 24001]> var_6035 = mul(x = var_6029, y = var_6034)[name = tensor<string, []>("op_6035")];
            tensor<fp32, [1, 128, 24001]> input_693 = add(x = xt_133, y = var_6035)[name = tensor<string, []>("input_693")];
            tensor<fp32, [128, 128, 11]> weight_321 = const()[name = tensor<string, []>("weight_321"), val = tensor<fp32, [128, 128, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(321523840)))];
            tensor<string, []> input_695_pad_type_0 = const()[name = tensor<string, []>("input_695_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_695_pad_0 = const()[name = tensor<string, []>("input_695_pad_0"), val = tensor<int32, [2]>([15, 15])];
            tensor<int32, [1]> input_695_dilations_0 = const()[name = tensor<string, []>("input_695_dilations_0"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> input_695_strides_0 = const()[name = tensor<string, []>("input_695_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_695_groups_0 = const()[name = tensor<string, []>("input_695_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 24001]> input_695 = conv(bias = decoder_generator_resblocks_5_convs1_1_bias, dilations = input_695_dilations_0, groups = input_695_groups_0, pad = input_695_pad_0, pad_type = input_695_pad_type_0, strides = input_695_strides_0, weight = weight_321, x = input_693)[name = tensor<string, []>("input_695")];
            tensor<fp32, [1, 256]> h_281 = linear(bias = decoder_generator_resblocks_5_adain2_1_fc_bias, weight = decoder_generator_resblocks_5_adain2_1_fc_weight, x = input_417)[name = tensor<string, []>("linear_146")];
            tensor<int32, [3]> var_6055 = const()[name = tensor<string, []>("op_6055"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_283 = reshape(shape = var_6055, x = h_281)[name = tensor<string, []>("h_283")];
            tensor<int32, [2]> var_6057_split_sizes_0 = const()[name = tensor<string, []>("op_6057_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_6057_axis_0 = const()[name = tensor<string, []>("op_6057_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_6057_0, tensor<fp32, [1, 128, 1]> var_6057_1 = split(axis = var_6057_axis_0, split_sizes = var_6057_split_sizes_0, x = h_283)[name = tensor<string, []>("op_6057")];
            tensor<fp32, []> var_6059_promoted = const()[name = tensor<string, []>("op_6059_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_6060 = add(x = var_6057_0, y = var_6059_promoted)[name = tensor<string, []>("op_6060")];
            tensor<fp32, [1, 128, 24001]> var_6063 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_2476, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_695)[name = tensor<string, []>("op_6063")];
            tensor<fp32, [1, 128, 24001]> var_6064 = mul(x = var_6060, y = var_6063)[name = tensor<string, []>("op_6064")];
            tensor<fp32, [1, 128, 24001]> xt_135 = add(x = var_6064, y = var_6057_1)[name = tensor<string, []>("xt_135")];
            tensor<fp32, [1, 128, 1]> var_6066 = const()[name = tensor<string, []>("op_6066"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(322244800)))];
            tensor<fp32, [1, 128, 24001]> var_6069 = mul(x = decoder_generator_resblocks_5_alpha2_1, y = xt_135)[name = tensor<string, []>("op_6069")];
            tensor<fp32, [1, 128, 24001]> var_6070 = sin(x = var_6069)[name = tensor<string, []>("op_6070")];
            tensor<fp32, []> var_2483_promoted_47 = const()[name = tensor<string, []>("op_2483_promoted_47"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 24001]> var_6071 = pow(x = var_6070, y = var_2483_promoted_47)[name = tensor<string, []>("op_6071")];
            tensor<fp32, [1, 128, 24001]> var_6072 = mul(x = var_6066, y = var_6071)[name = tensor<string, []>("op_6072")];
            tensor<fp32, [1, 128, 24001]> input_697 = add(x = xt_135, y = var_6072)[name = tensor<string, []>("input_697")];
            tensor<fp32, [128, 128, 11]> weight_325 = const()[name = tensor<string, []>("weight_325"), val = tensor<fp32, [128, 128, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(322245376)))];
            tensor<string, []> xt_137_pad_type_0 = const()[name = tensor<string, []>("xt_137_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_137_pad_0 = const()[name = tensor<string, []>("xt_137_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> xt_137_strides_0 = const()[name = tensor<string, []>("xt_137_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_137_dilations_0 = const()[name = tensor<string, []>("xt_137_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_137_groups_0 = const()[name = tensor<string, []>("xt_137_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 24001]> xt_137 = conv(bias = decoder_generator_resblocks_5_convs2_1_bias, dilations = xt_137_dilations_0, groups = xt_137_groups_0, pad = xt_137_pad_0, pad_type = xt_137_pad_type_0, strides = xt_137_strides_0, weight = weight_325, x = input_697)[name = tensor<string, []>("xt_137")];
            tensor<fp32, [1, 128, 24001]> input_699 = add(x = xt_137, y = input_691)[name = tensor<string, []>("input_699")];
            tensor<fp32, [1, 256]> h_285 = linear(bias = decoder_generator_resblocks_5_adain1_2_fc_bias, weight = decoder_generator_resblocks_5_adain1_2_fc_weight, x = input_417)[name = tensor<string, []>("linear_147")];
            tensor<int32, [3]> var_6093 = const()[name = tensor<string, []>("op_6093"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_287 = reshape(shape = var_6093, x = h_285)[name = tensor<string, []>("h_287")];
            tensor<int32, [2]> var_6095_split_sizes_0 = const()[name = tensor<string, []>("op_6095_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_6095_axis_0 = const()[name = tensor<string, []>("op_6095_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_6095_0, tensor<fp32, [1, 128, 1]> var_6095_1 = split(axis = var_6095_axis_0, split_sizes = var_6095_split_sizes_0, x = h_287)[name = tensor<string, []>("op_6095")];
            tensor<fp32, []> var_6097_promoted = const()[name = tensor<string, []>("op_6097_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_6098 = add(x = var_6095_0, y = var_6097_promoted)[name = tensor<string, []>("op_6098")];
            tensor<fp32, [1, 128, 24001]> var_6101 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_2476, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_699)[name = tensor<string, []>("op_6101")];
            tensor<fp32, [1, 128, 24001]> var_6102 = mul(x = var_6098, y = var_6101)[name = tensor<string, []>("op_6102")];
            tensor<fp32, [1, 128, 24001]> xt_139 = add(x = var_6102, y = var_6095_1)[name = tensor<string, []>("xt_139")];
            tensor<fp32, [1, 128, 1]> var_6104 = const()[name = tensor<string, []>("op_6104"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(322966336)))];
            tensor<fp32, [1, 128, 24001]> var_6107 = mul(x = decoder_generator_resblocks_5_alpha1_2, y = xt_139)[name = tensor<string, []>("op_6107")];
            tensor<fp32, [1, 128, 24001]> var_6108 = sin(x = var_6107)[name = tensor<string, []>("op_6108")];
            tensor<fp32, []> var_2483_promoted_48 = const()[name = tensor<string, []>("op_2483_promoted_48"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 24001]> var_6109 = pow(x = var_6108, y = var_2483_promoted_48)[name = tensor<string, []>("op_6109")];
            tensor<fp32, [1, 128, 24001]> var_6110 = mul(x = var_6104, y = var_6109)[name = tensor<string, []>("op_6110")];
            tensor<fp32, [1, 128, 24001]> input_701 = add(x = xt_139, y = var_6110)[name = tensor<string, []>("input_701")];
            tensor<fp32, [128, 128, 11]> weight_329 = const()[name = tensor<string, []>("weight_329"), val = tensor<fp32, [128, 128, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(322966912)))];
            tensor<string, []> input_703_pad_type_0 = const()[name = tensor<string, []>("input_703_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_703_pad_0 = const()[name = tensor<string, []>("input_703_pad_0"), val = tensor<int32, [2]>([25, 25])];
            tensor<int32, [1]> input_703_dilations_0 = const()[name = tensor<string, []>("input_703_dilations_0"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> input_703_strides_0 = const()[name = tensor<string, []>("input_703_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_703_groups_0 = const()[name = tensor<string, []>("input_703_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 24001]> input_703 = conv(bias = decoder_generator_resblocks_5_convs1_2_bias, dilations = input_703_dilations_0, groups = input_703_groups_0, pad = input_703_pad_0, pad_type = input_703_pad_type_0, strides = input_703_strides_0, weight = weight_329, x = input_701)[name = tensor<string, []>("input_703")];
            tensor<fp32, [1, 256]> h_289 = linear(bias = decoder_generator_resblocks_5_adain2_2_fc_bias, weight = decoder_generator_resblocks_5_adain2_2_fc_weight, x = input_417)[name = tensor<string, []>("linear_148")];
            tensor<int32, [3]> var_6130 = const()[name = tensor<string, []>("op_6130"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h = reshape(shape = var_6130, x = h_289)[name = tensor<string, []>("h")];
            tensor<int32, [2]> var_6132_split_sizes_0 = const()[name = tensor<string, []>("op_6132_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_6132_axis_0 = const()[name = tensor<string, []>("op_6132_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_6132_0, tensor<fp32, [1, 128, 1]> var_6132_1 = split(axis = var_6132_axis_0, split_sizes = var_6132_split_sizes_0, x = h)[name = tensor<string, []>("op_6132")];
            tensor<fp32, []> var_6134_promoted = const()[name = tensor<string, []>("op_6134_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_6135 = add(x = var_6132_0, y = var_6134_promoted)[name = tensor<string, []>("op_6135")];
            tensor<fp32, [1, 128, 24001]> var_6138 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_2476, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_703)[name = tensor<string, []>("op_6138")];
            tensor<fp32, [1, 128, 24001]> var_6139 = mul(x = var_6135, y = var_6138)[name = tensor<string, []>("op_6139")];
            tensor<fp32, [1, 128, 24001]> xt_141 = add(x = var_6139, y = var_6132_1)[name = tensor<string, []>("xt_141")];
            tensor<fp32, [1, 128, 1]> var_6141 = const()[name = tensor<string, []>("op_6141"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(323687872)))];
            tensor<fp32, [1, 128, 24001]> var_6144 = mul(x = decoder_generator_resblocks_5_alpha2_2, y = xt_141)[name = tensor<string, []>("op_6144")];
            tensor<fp32, [1, 128, 24001]> var_6145 = sin(x = var_6144)[name = tensor<string, []>("op_6145")];
            tensor<fp32, []> var_2483_promoted_49 = const()[name = tensor<string, []>("op_2483_promoted_49"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 24001]> var_6146 = pow(x = var_6145, y = var_2483_promoted_49)[name = tensor<string, []>("op_6146")];
            tensor<fp32, [1, 128, 24001]> var_6147 = mul(x = var_6141, y = var_6146)[name = tensor<string, []>("op_6147")];
            tensor<fp32, [1, 128, 24001]> input_705 = add(x = xt_141, y = var_6147)[name = tensor<string, []>("input_705")];
            tensor<fp32, [128, 128, 11]> weight_333 = const()[name = tensor<string, []>("weight_333"), val = tensor<fp32, [128, 128, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(323688448)))];
            tensor<string, []> xt_pad_type_0 = const()[name = tensor<string, []>("xt_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_pad_0 = const()[name = tensor<string, []>("xt_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> xt_strides_0 = const()[name = tensor<string, []>("xt_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_dilations_0 = const()[name = tensor<string, []>("xt_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_groups_0 = const()[name = tensor<string, []>("xt_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 24001]> xt = conv(bias = decoder_generator_resblocks_5_convs2_2_bias, dilations = xt_dilations_0, groups = xt_groups_0, pad = xt_pad_0, pad_type = xt_pad_type_0, strides = xt_strides_0, weight = weight_333, x = input_705)[name = tensor<string, []>("xt")];
            tensor<fp32, [1, 128, 24001]> var_6160 = add(x = xt, y = input_699)[name = tensor<string, []>("op_6160")];
            tensor<fp32, [1, 128, 24001]> xs = add(x = xs_9, y = var_6160)[name = tensor<string, []>("xs")];
            tensor<fp32, []> _inversed_input_707_y_0 = const()[name = tensor<string, []>("_inversed_input_707_y_0"), val = tensor<fp32, []>(0x1.555556p-2)];
            tensor<fp32, [1, 128, 24001]> _inversed_input_707 = mul(x = xs, y = _inversed_input_707_y_0)[name = tensor<string, []>("_inversed_input_707")];
            tensor<fp32, [1, 128, 24001]> input = leaky_relu(alpha = var_2469, x = _inversed_input_707)[name = tensor<string, []>("input")];
            tensor<fp32, [22, 128, 7]> weight_335 = const()[name = tensor<string, []>("weight_335"), val = tensor<fp32, [22, 128, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(324409408)))];
            tensor<string, []> x_pad_type_0 = const()[name = tensor<string, []>("x_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> x_pad_0 = const()[name = tensor<string, []>("x_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> x_strides_0 = const()[name = tensor<string, []>("x_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> x_dilations_0 = const()[name = tensor<string, []>("x_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_groups_0 = const()[name = tensor<string, []>("x_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 22, 24001]> x = conv(bias = decoder_generator_conv_post_bias, dilations = x_dilations_0, groups = x_groups_0, pad = x_pad_0, pad_type = x_pad_type_0, strides = x_strides_0, weight = weight_335, x = input)[name = tensor<string, []>("x")];
            tensor<int32, [3]> var_6177_begin_0 = const()[name = tensor<string, []>("op_6177_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> var_6177_end_0 = const()[name = tensor<string, []>("op_6177_end_0"), val = tensor<int32, [3]>([1, 11, 24001])];
            tensor<bool, [3]> var_6177_end_mask_0 = const()[name = tensor<string, []>("op_6177_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 11, 24001]> var_6177 = slice_by_index(begin = var_6177_begin_0, end = var_6177_end_0, end_mask = var_6177_end_mask_0, x = x)[name = tensor<string, []>("op_6177")];
            tensor<fp32, [1, 11, 24001]> magnitude = exp(x = var_6177)[name = tensor<string, []>("magnitude")];
            tensor<int32, [3]> var_6181_begin_0 = const()[name = tensor<string, []>("op_6181_begin_0"), val = tensor<int32, [3]>([0, 11, 0])];
            tensor<int32, [3]> var_6181_end_0 = const()[name = tensor<string, []>("op_6181_end_0"), val = tensor<int32, [3]>([1, 22, 24001])];
            tensor<bool, [3]> var_6181_end_mask_0 = const()[name = tensor<string, []>("op_6181_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 11, 24001]> var_6181 = slice_by_index(begin = var_6181_begin_0, end = var_6181_end_0, end_mask = var_6181_end_mask_0, x = x)[name = tensor<string, []>("op_6181")];
            tensor<fp32, [1, 11, 24001]> phase = sin(x = var_6181)[name = tensor<string, []>("phase")];
            tensor<fp32, [1, 11, 24001]> var_6184 = cos(x = phase)[name = tensor<string, []>("op_6184")];
            tensor<fp32, [1, 11, 24001]> real_part = mul(x = magnitude, y = var_6184)[name = tensor<string, []>("real_part")];
            tensor<fp32, [1, 11, 24001]> var_6186 = sin(x = phase)[name = tensor<string, []>("op_6186")];
            tensor<fp32, [1, 11, 24001]> imag_part = mul(x = magnitude, y = var_6186)[name = tensor<string, []>("imag_part")];
            tensor<string, []> real_rec_pad_type_0 = const()[name = tensor<string, []>("real_rec_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> real_rec_strides_0 = const()[name = tensor<string, []>("real_rec_strides_0"), val = tensor<int32, [1]>([5])];
            tensor<int32, [2]> real_rec_pad_0 = const()[name = tensor<string, []>("real_rec_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> real_rec_dilations_0 = const()[name = tensor<string, []>("real_rec_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> real_rec_groups_0 = const()[name = tensor<string, []>("real_rec_groups_0"), val = tensor<int32, []>(1)];
            tensor<int32, [3]> real_rec_has_output_shape_output_shape_0 = const()[name = tensor<string, []>("real_rec_has_output_shape_output_shape_0"), val = tensor<int32, [3]>([1, 1, 120020])];
            tensor<fp32, [1, 1, 120020]> real_rec_has_output_shape = conv_transpose(dilations = real_rec_dilations_0, groups = real_rec_groups_0, output_shape = real_rec_has_output_shape_output_shape_0, pad = real_rec_pad_0, pad_type = real_rec_pad_type_0, strides = real_rec_strides_0, weight = decoder_generator_stft_weight_backward_real, x = real_part)[name = tensor<string, []>("real_rec_has_output_shape")];
            tensor<string, []> imag_rec_pad_type_0 = const()[name = tensor<string, []>("imag_rec_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> imag_rec_strides_0 = const()[name = tensor<string, []>("imag_rec_strides_0"), val = tensor<int32, [1]>([5])];
            tensor<int32, [2]> imag_rec_pad_0 = const()[name = tensor<string, []>("imag_rec_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> imag_rec_dilations_0 = const()[name = tensor<string, []>("imag_rec_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> imag_rec_groups_0 = const()[name = tensor<string, []>("imag_rec_groups_0"), val = tensor<int32, []>(1)];
            tensor<int32, [3]> imag_rec_has_output_shape_output_shape_0 = const()[name = tensor<string, []>("imag_rec_has_output_shape_output_shape_0"), val = tensor<int32, [3]>([1, 1, 120020])];
            tensor<fp32, [1, 1, 120020]> imag_rec_has_output_shape = conv_transpose(dilations = imag_rec_dilations_0, groups = imag_rec_groups_0, output_shape = imag_rec_has_output_shape_output_shape_0, pad = imag_rec_pad_0, pad_type = imag_rec_pad_type_0, strides = imag_rec_strides_0, weight = decoder_generator_stft_weight_backward_imag, x = imag_part)[name = tensor<string, []>("imag_rec_has_output_shape")];
            tensor<fp32, [1, 1, 120020]> waveform = sub(x = real_rec_has_output_shape, y = imag_rec_has_output_shape)[name = tensor<string, []>("waveform")];
            tensor<int32, [3]> var_6199_begin_0 = const()[name = tensor<string, []>("op_6199_begin_0"), val = tensor<int32, [3]>([0, 0, 10])];
            tensor<int32, [3]> var_6199_end_0 = const()[name = tensor<string, []>("op_6199_end_0"), val = tensor<int32, [3]>([1, 1, 120010])];
            tensor<bool, [3]> var_6199_end_mask_0 = const()[name = tensor<string, []>("op_6199_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp32, [1, 1, 120000]> audio = slice_by_index(begin = var_6199_begin_0, end = var_6199_end_0, end_mask = var_6199_end_mask_0, x = waveform)[name = tensor<string, []>("op_6199")];
            tensor<fp32, []> var_6200_promoted = const()[name = tensor<string, []>("op_6200_promoted"), val = tensor<fp32, []>(0x1.2cp+9)];
            tensor<fp32, [1, 1]> var_6201 = mul(x = total_frames, y = var_6200_promoted)[name = tensor<string, []>("op_6201")];
            tensor<string, []> cast_173_dtype_0 = const()[name = tensor<string, []>("cast_173_dtype_0"), val = tensor<string, []>("int32")];
            tensor<int32, [1]> var_6208_axes_0 = const()[name = tensor<string, []>("op_6208_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<int32, [1, 1]> cast_173 = cast(dtype = cast_173_dtype_0, x = var_6201)[name = tensor<string, []>("cast_174")];
            tensor<int32, [1]> audio_length_samples = squeeze(axes = var_6208_axes_0, x = cast_173)[name = tensor<string, []>("op_6208")];
            tensor<fp32, [1, 9]> random_phases_tmp = identity(x = random_phases)[name = tensor<string, []>("random_phases_tmp")];
        } -> (audio, audio_length_samples, pred_dur);
}