diff --git "a/kokoro.mlmodelc/model.mil" "b/kokoro.mlmodelc/model.mil" new file mode 100644--- /dev/null +++ "b/kokoro.mlmodelc/model.mil" @@ -0,0 +1,4408 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3405.2.1"}, {"coremlc-version", "3404.23.1"}, {"coremltools-component-torch", "2.1.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] +{ + func main(tensor input_ids, tensor random_phases, tensor ref_s) { + tensor predictor_text_encoder_lstms_5_fc_bias = const()[name = tensor("predictor_text_encoder_lstms_5_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor predictor_text_encoder_lstms_5_fc_weight = const()[name = tensor("predictor_text_encoder_lstms_5_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4224)))]; + tensor predictor_text_encoder_lstms_3_fc_bias = const()[name = tensor("predictor_text_encoder_lstms_3_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(528576)))]; + tensor predictor_text_encoder_lstms_3_fc_weight = const()[name = tensor("predictor_text_encoder_lstms_3_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(532736)))]; + tensor predictor_text_encoder_lstms_1_fc_bias = const()[name = tensor("predictor_text_encoder_lstms_1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1057088)))]; + tensor predictor_text_encoder_lstms_1_fc_weight = const()[name = tensor("predictor_text_encoder_lstms_1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1061248)))]; + tensor bert_embeddings_word_embeddings_weight = const()[name = tensor("bert_embeddings_word_embeddings_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1585600)))]; + tensor bert_embeddings_LayerNorm_bias = const()[name = tensor("bert_embeddings_LayerNorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1676800)))]; + tensor bert_embeddings_LayerNorm_weight = const()[name = tensor("bert_embeddings_LayerNorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1677376)))]; + tensor bert_encoder_embedding_hidden_mapping_in_bias = const()[name = tensor("bert_encoder_embedding_hidden_mapping_in_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1677952)))]; + tensor bert_encoder_embedding_hidden_mapping_in_weight = const()[name = tensor("bert_encoder_embedding_hidden_mapping_in_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1681088)))]; + tensor bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias = const()[name = tensor("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2074368)))]; + tensor bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight = const()[name = tensor("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2077504)))]; + tensor bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias = const()[name = tensor("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4436864)))]; + tensor bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight = const()[name = tensor("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4440000)))]; + tensor bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias = const()[name = tensor("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6799360)))]; + tensor bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight = const()[name = tensor("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6802496)))]; + tensor bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias = const()[name = tensor("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9161856)))]; + tensor bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight = const()[name = tensor("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9164992)))]; + tensor bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias = const()[name = tensor("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11524352)))]; + tensor bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight = const()[name = tensor("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11527488)))]; + tensor bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias = const()[name = tensor("bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11530624)))]; + tensor bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight = const()[name = tensor("bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11538880)))]; + tensor bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias = const()[name = tensor("bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17830400)))]; + tensor bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight = const()[name = tensor("bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17833536)))]; + tensor bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias = const()[name = tensor("bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24125056)))]; + tensor bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight = const()[name = tensor("bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24128192)))]; + tensor bert_encoder_bias = const()[name = tensor("bert_encoder_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24131328)))]; + tensor bert_encoder_weight = const()[name = tensor("bert_encoder_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24133440)))]; + tensor predictor_F0_0_norm1_fc_bias = const()[name = tensor("predictor_F0_0_norm1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25706368)))]; + tensor predictor_F0_0_norm1_fc_weight = const()[name = tensor("predictor_F0_0_norm1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25710528)))]; + tensor predictor_F0_0_norm1_norm_bias = const()[name = tensor("predictor_F0_0_norm1_norm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26234880)))]; + tensor predictor_F0_0_norm1_norm_weight = const()[name = tensor("predictor_F0_0_norm1_norm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26236992)))]; + tensor predictor_F0_0_conv1_bias = const()[name = tensor("predictor_F0_0_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26239104)))]; + tensor predictor_F0_0_norm2_fc_bias = const()[name = tensor("predictor_F0_0_norm2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26241216)))]; + tensor predictor_F0_0_norm2_fc_weight = const()[name = tensor("predictor_F0_0_norm2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26245376)))]; + tensor predictor_F0_0_conv2_bias = const()[name = tensor("predictor_F0_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26769728)))]; + tensor predictor_F0_1_norm1_fc_bias = const()[name = tensor("predictor_F0_1_norm1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26771840)))]; + tensor predictor_F0_1_norm1_fc_weight = const()[name = tensor("predictor_F0_1_norm1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26776000)))]; + tensor predictor_F0_1_pool_bias = const()[name = tensor("predictor_F0_1_pool_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27300352)))]; + tensor predictor_F0_1_conv1_bias = const()[name = tensor("predictor_F0_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27302464)))]; + tensor predictor_F0_1_norm2_fc_bias = const()[name = tensor("predictor_F0_1_norm2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27303552)))]; + tensor predictor_F0_1_norm2_fc_weight = const()[name = tensor("predictor_F0_1_norm2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27305664)))]; + tensor predictor_F0_1_norm2_norm_bias = const()[name = tensor("predictor_F0_1_norm2_norm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27567872)))]; + tensor predictor_F0_1_norm2_norm_weight = const()[name = tensor("predictor_F0_1_norm2_norm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27568960)))]; + tensor predictor_F0_1_conv2_bias = const()[name = tensor("predictor_F0_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27570048)))]; + tensor predictor_F0_2_norm1_fc_bias = const()[name = tensor("predictor_F0_2_norm1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27571136)))]; + tensor predictor_F0_2_norm1_fc_weight = const()[name = tensor("predictor_F0_2_norm1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27573248)))]; + tensor predictor_F0_2_conv1_bias = const()[name = tensor("predictor_F0_2_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27835456)))]; + tensor predictor_F0_2_norm2_fc_bias = const()[name = tensor("predictor_F0_2_norm2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27836544)))]; + tensor predictor_F0_2_norm2_fc_weight = const()[name = tensor("predictor_F0_2_norm2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27838656)))]; + tensor predictor_F0_2_conv2_bias = const()[name = tensor("predictor_F0_2_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28100864)))]; + tensor predictor_F0_proj_bias = const()[name = tensor("predictor_F0_proj_bias"), val = tensor([0x1.edcbf4p-3])]; + tensor predictor_F0_proj_weight = const()[name = tensor("predictor_F0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28101952)))]; + tensor predictor_N_0_norm1_fc_bias = const()[name = tensor("predictor_N_0_norm1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28103040)))]; + tensor predictor_N_0_norm1_fc_weight = const()[name = tensor("predictor_N_0_norm1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28107200)))]; + tensor predictor_N_0_conv1_bias = const()[name = tensor("predictor_N_0_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28631552)))]; + tensor predictor_N_0_norm2_fc_bias = const()[name = tensor("predictor_N_0_norm2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28633664)))]; + tensor predictor_N_0_norm2_fc_weight = const()[name = tensor("predictor_N_0_norm2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28637824)))]; + tensor predictor_N_0_conv2_bias = const()[name = tensor("predictor_N_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29162176)))]; + tensor predictor_N_1_norm1_fc_bias = const()[name = tensor("predictor_N_1_norm1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29164288)))]; + tensor predictor_N_1_norm1_fc_weight = const()[name = tensor("predictor_N_1_norm1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29168448)))]; + tensor predictor_N_1_pool_bias = const()[name = tensor("predictor_N_1_pool_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29692800)))]; + tensor predictor_N_1_conv1_bias = const()[name = tensor("predictor_N_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29694912)))]; + tensor predictor_N_1_norm2_fc_bias = const()[name = tensor("predictor_N_1_norm2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29696000)))]; + tensor predictor_N_1_norm2_fc_weight = const()[name = tensor("predictor_N_1_norm2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29698112)))]; + tensor predictor_N_1_conv2_bias = const()[name = tensor("predictor_N_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29960320)))]; + tensor predictor_N_2_norm1_fc_bias = const()[name = tensor("predictor_N_2_norm1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29961408)))]; + tensor predictor_N_2_norm1_fc_weight = const()[name = tensor("predictor_N_2_norm1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29963520)))]; + tensor predictor_N_2_conv1_bias = const()[name = tensor("predictor_N_2_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30225728)))]; + tensor predictor_N_2_norm2_fc_bias = const()[name = tensor("predictor_N_2_norm2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30226816)))]; + tensor predictor_N_2_norm2_fc_weight = const()[name = tensor("predictor_N_2_norm2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30228928)))]; + tensor predictor_N_2_conv2_bias = const()[name = tensor("predictor_N_2_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30491136)))]; + tensor predictor_N_proj_bias = const()[name = tensor("predictor_N_proj_bias"), val = tensor([0x1.13144ap-4])]; + tensor predictor_N_proj_weight = const()[name = tensor("predictor_N_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30492224)))]; + tensor text_encoder_embedding_weight = const()[name = tensor("text_encoder_embedding_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30493312)))]; + tensor text_encoder_cnn_0_0_bias = const()[name = tensor("text_encoder_cnn_0_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30857920)))]; + tensor text_encoder_cnn_0_1_beta = const()[name = tensor("text_encoder_cnn_0_1_beta"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30860032)))]; + tensor text_encoder_cnn_0_1_gamma = const()[name = tensor("text_encoder_cnn_0_1_gamma"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30862144)))]; + tensor text_encoder_cnn_1_0_bias = const()[name = tensor("text_encoder_cnn_1_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30864256)))]; + tensor text_encoder_cnn_1_1_beta = const()[name = tensor("text_encoder_cnn_1_1_beta"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30866368)))]; + tensor text_encoder_cnn_1_1_gamma = const()[name = tensor("text_encoder_cnn_1_1_gamma"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30868480)))]; + tensor text_encoder_cnn_2_0_bias = const()[name = tensor("text_encoder_cnn_2_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30870592)))]; + tensor text_encoder_cnn_2_1_beta = const()[name = tensor("text_encoder_cnn_2_1_beta"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30872704)))]; + tensor text_encoder_cnn_2_1_gamma = const()[name = tensor("text_encoder_cnn_2_1_gamma"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30874816)))]; + tensor decoder_F0_conv_bias = const()[name = tensor("decoder_F0_conv_bias"), val = tensor([-0x1.005f38p-2])]; + tensor decoder_N_conv_bias = const()[name = tensor("decoder_N_conv_bias"), val = tensor([-0x1.e68b08p-2])]; + tensor decoder_encode_norm1_fc_bias = const()[name = tensor("decoder_encode_norm1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30876928)))]; + tensor decoder_encode_norm1_fc_weight = const()[name = tensor("decoder_encode_norm1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30881152)))]; + tensor decoder_encode_norm1_norm_bias = const()[name = tensor("decoder_encode_norm1_norm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31407552)))]; + tensor decoder_encode_norm1_norm_weight = const()[name = tensor("decoder_encode_norm1_norm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31409728)))]; + tensor decoder_encode_conv1_bias = const()[name = tensor("decoder_encode_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31411904)))]; + tensor decoder_encode_norm2_fc_bias = const()[name = tensor("decoder_encode_norm2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31416064)))]; + tensor decoder_encode_norm2_fc_weight = const()[name = tensor("decoder_encode_norm2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31424320)))]; + tensor decoder_encode_norm2_norm_bias = const()[name = tensor("decoder_encode_norm2_norm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32472960)))]; + tensor decoder_encode_norm2_norm_weight = const()[name = tensor("decoder_encode_norm2_norm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32477120)))]; + tensor decoder_encode_conv2_bias = const()[name = tensor("decoder_encode_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32481280)))]; + tensor decoder_asr_res_0_bias = const()[name = tensor("decoder_asr_res_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32485440)))]; + tensor decoder_decode_0_norm1_fc_bias = const()[name = tensor("decoder_decode_0_norm1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32485760)))]; + tensor decoder_decode_0_norm1_fc_weight = const()[name = tensor("decoder_decode_0_norm1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32494592)))]; + tensor decoder_decode_0_norm1_norm_bias = const()[name = tensor("decoder_decode_0_norm1_norm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33610816)))]; + tensor decoder_decode_0_norm1_norm_weight = const()[name = tensor("decoder_decode_0_norm1_norm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33615296)))]; + tensor decoder_decode_0_conv1_bias = const()[name = tensor("decoder_decode_0_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33619776)))]; + tensor decoder_decode_0_norm2_fc_bias = const()[name = tensor("decoder_decode_0_norm2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33623936)))]; + tensor decoder_decode_0_norm2_fc_weight = const()[name = tensor("decoder_decode_0_norm2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33632192)))]; + tensor decoder_decode_0_conv2_bias = const()[name = tensor("decoder_decode_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34680832)))]; + tensor decoder_decode_1_norm1_fc_bias = const()[name = tensor("decoder_decode_1_norm1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34684992)))]; + tensor decoder_decode_1_norm1_fc_weight = const()[name = tensor("decoder_decode_1_norm1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34693824)))]; + tensor decoder_decode_1_conv1_bias = const()[name = tensor("decoder_decode_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35810048)))]; + tensor decoder_decode_1_norm2_fc_bias = const()[name = tensor("decoder_decode_1_norm2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35814208)))]; + tensor decoder_decode_1_norm2_fc_weight = const()[name = tensor("decoder_decode_1_norm2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35822464)))]; + tensor decoder_decode_1_conv2_bias = const()[name = tensor("decoder_decode_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36871104)))]; + tensor decoder_decode_2_norm1_fc_bias = const()[name = tensor("decoder_decode_2_norm1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36875264)))]; + tensor decoder_decode_2_norm1_fc_weight = const()[name = tensor("decoder_decode_2_norm1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36884096)))]; + tensor decoder_decode_2_conv1_bias = const()[name = tensor("decoder_decode_2_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38000320)))]; + tensor decoder_decode_2_norm2_fc_bias = const()[name = tensor("decoder_decode_2_norm2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38004480)))]; + tensor decoder_decode_2_norm2_fc_weight = const()[name = tensor("decoder_decode_2_norm2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38012736)))]; + tensor decoder_decode_2_conv2_bias = const()[name = tensor("decoder_decode_2_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39061376)))]; + tensor decoder_decode_3_norm1_fc_bias = const()[name = tensor("decoder_decode_3_norm1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39065536)))]; + tensor decoder_decode_3_norm1_fc_weight = const()[name = tensor("decoder_decode_3_norm1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39074368)))]; + tensor decoder_decode_3_pool_bias = const()[name = tensor("decoder_decode_3_pool_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40190592)))]; + tensor decoder_decode_3_conv1_bias = const()[name = tensor("decoder_decode_3_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40195072)))]; + tensor decoder_decode_3_norm2_fc_bias = const()[name = tensor("decoder_decode_3_norm2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40197184)))]; + tensor decoder_decode_3_norm2_fc_weight = const()[name = tensor("decoder_decode_3_norm2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40201344)))]; + tensor decoder_decode_3_conv2_bias = const()[name = tensor("decoder_decode_3_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40725696)))]; + tensor decoder_generator_stft_weight_backward_imag = const()[name = tensor("decoder_generator_stft_weight_backward_imag"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40727808)))]; + tensor decoder_generator_stft_weight_backward_real = const()[name = tensor("decoder_generator_stft_weight_backward_real"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40728768)))]; + tensor decoder_generator_stft_weight_forward_imag = const()[name = tensor("decoder_generator_stft_weight_forward_imag"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40729728)))]; + tensor decoder_generator_stft_weight_forward_real = const()[name = tensor("decoder_generator_stft_weight_forward_real"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40730688)))]; + tensor decoder_generator_m_source_l_linear_bias = const()[name = tensor("decoder_generator_m_source_l_linear_bias"), val = tensor([-0x1.e28358p-6])]; + tensor decoder_generator_m_source_l_linear_weight = const()[name = tensor("decoder_generator_m_source_l_linear_weight"), val = tensor([[-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 decoder_generator_noise_convs_0_bias = const()[name = tensor("decoder_generator_noise_convs_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40731648)))]; + tensor decoder_generator_noise_convs_0_weight = const()[name = tensor("decoder_generator_noise_convs_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40732736)))]; + tensor decoder_generator_noise_res_0_alpha2_2 = const()[name = tensor("decoder_generator_noise_res_0_alpha2_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41003136)))]; + tensor decoder_generator_noise_res_0_alpha1_2 = const()[name = tensor("decoder_generator_noise_res_0_alpha1_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41004224)))]; + tensor decoder_generator_noise_res_0_alpha2_1 = const()[name = tensor("decoder_generator_noise_res_0_alpha2_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41005312)))]; + tensor decoder_generator_noise_res_0_alpha1_1 = const()[name = tensor("decoder_generator_noise_res_0_alpha1_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41006400)))]; + tensor decoder_generator_noise_res_0_alpha2_0 = const()[name = tensor("decoder_generator_noise_res_0_alpha2_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41007488)))]; + tensor decoder_generator_noise_res_0_alpha1_0 = const()[name = tensor("decoder_generator_noise_res_0_alpha1_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41008576)))]; + tensor decoder_generator_noise_res_0_adain1_0_fc_bias = const()[name = tensor("decoder_generator_noise_res_0_adain1_0_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41009664)))]; + tensor decoder_generator_noise_res_0_adain1_0_fc_weight = const()[name = tensor("decoder_generator_noise_res_0_adain1_0_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41011776)))]; + tensor decoder_generator_noise_res_0_convs1_0_bias = const()[name = tensor("decoder_generator_noise_res_0_convs1_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41273984)))]; + tensor decoder_generator_noise_res_0_adain2_0_fc_bias = const()[name = tensor("decoder_generator_noise_res_0_adain2_0_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41275072)))]; + tensor decoder_generator_noise_res_0_adain2_0_fc_weight = const()[name = tensor("decoder_generator_noise_res_0_adain2_0_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41277184)))]; + tensor decoder_generator_noise_res_0_convs2_0_bias = const()[name = tensor("decoder_generator_noise_res_0_convs2_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41539392)))]; + tensor decoder_generator_noise_res_0_adain1_1_fc_bias = const()[name = tensor("decoder_generator_noise_res_0_adain1_1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41540480)))]; + tensor decoder_generator_noise_res_0_adain1_1_fc_weight = const()[name = tensor("decoder_generator_noise_res_0_adain1_1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41542592)))]; + tensor decoder_generator_noise_res_0_convs1_1_bias = const()[name = tensor("decoder_generator_noise_res_0_convs1_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41804800)))]; + tensor decoder_generator_noise_res_0_adain2_1_fc_bias = const()[name = tensor("decoder_generator_noise_res_0_adain2_1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41805888)))]; + tensor decoder_generator_noise_res_0_adain2_1_fc_weight = const()[name = tensor("decoder_generator_noise_res_0_adain2_1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41808000)))]; + tensor decoder_generator_noise_res_0_convs2_1_bias = const()[name = tensor("decoder_generator_noise_res_0_convs2_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42070208)))]; + tensor decoder_generator_noise_res_0_adain1_2_fc_bias = const()[name = tensor("decoder_generator_noise_res_0_adain1_2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42071296)))]; + tensor decoder_generator_noise_res_0_adain1_2_fc_weight = const()[name = tensor("decoder_generator_noise_res_0_adain1_2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42073408)))]; + tensor decoder_generator_noise_res_0_convs1_2_bias = const()[name = tensor("decoder_generator_noise_res_0_convs1_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42335616)))]; + tensor decoder_generator_noise_res_0_adain2_2_fc_bias = const()[name = tensor("decoder_generator_noise_res_0_adain2_2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42336704)))]; + tensor decoder_generator_noise_res_0_adain2_2_fc_weight = const()[name = tensor("decoder_generator_noise_res_0_adain2_2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42338816)))]; + tensor decoder_generator_noise_res_0_convs2_2_bias = const()[name = tensor("decoder_generator_noise_res_0_convs2_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42601024)))]; + tensor decoder_generator_ups_0_bias = const()[name = tensor("decoder_generator_ups_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42602112)))]; + tensor decoder_generator_resblocks_0_alpha2_2 = const()[name = tensor("decoder_generator_resblocks_0_alpha2_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42603200)))]; + tensor decoder_generator_resblocks_0_alpha1_2 = const()[name = tensor("decoder_generator_resblocks_0_alpha1_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42604288)))]; + tensor decoder_generator_resblocks_0_alpha2_1 = const()[name = tensor("decoder_generator_resblocks_0_alpha2_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42605376)))]; + tensor decoder_generator_resblocks_0_alpha1_1 = const()[name = tensor("decoder_generator_resblocks_0_alpha1_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42606464)))]; + tensor decoder_generator_resblocks_0_alpha2_0 = const()[name = tensor("decoder_generator_resblocks_0_alpha2_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42607552)))]; + tensor decoder_generator_resblocks_0_alpha1_0 = const()[name = tensor("decoder_generator_resblocks_0_alpha1_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42608640)))]; + tensor decoder_generator_resblocks_0_adain1_0_fc_bias = const()[name = tensor("decoder_generator_resblocks_0_adain1_0_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42609728)))]; + tensor decoder_generator_resblocks_0_adain1_0_fc_weight = const()[name = tensor("decoder_generator_resblocks_0_adain1_0_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42611840)))]; + tensor decoder_generator_resblocks_0_convs1_0_bias = const()[name = tensor("decoder_generator_resblocks_0_convs1_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42874048)))]; + tensor decoder_generator_resblocks_0_adain2_0_fc_bias = const()[name = tensor("decoder_generator_resblocks_0_adain2_0_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42875136)))]; + tensor decoder_generator_resblocks_0_adain2_0_fc_weight = const()[name = tensor("decoder_generator_resblocks_0_adain2_0_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42877248)))]; + tensor decoder_generator_resblocks_0_convs2_0_bias = const()[name = tensor("decoder_generator_resblocks_0_convs2_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43139456)))]; + tensor decoder_generator_resblocks_0_adain1_1_fc_bias = const()[name = tensor("decoder_generator_resblocks_0_adain1_1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43140544)))]; + tensor decoder_generator_resblocks_0_adain1_1_fc_weight = const()[name = tensor("decoder_generator_resblocks_0_adain1_1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43142656)))]; + tensor decoder_generator_resblocks_0_convs1_1_bias = const()[name = tensor("decoder_generator_resblocks_0_convs1_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43404864)))]; + tensor decoder_generator_resblocks_0_adain2_1_fc_bias = const()[name = tensor("decoder_generator_resblocks_0_adain2_1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43405952)))]; + tensor decoder_generator_resblocks_0_adain2_1_fc_weight = const()[name = tensor("decoder_generator_resblocks_0_adain2_1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43408064)))]; + tensor decoder_generator_resblocks_0_convs2_1_bias = const()[name = tensor("decoder_generator_resblocks_0_convs2_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43670272)))]; + tensor decoder_generator_resblocks_0_adain1_2_fc_bias = const()[name = tensor("decoder_generator_resblocks_0_adain1_2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43671360)))]; + tensor decoder_generator_resblocks_0_adain1_2_fc_weight = const()[name = tensor("decoder_generator_resblocks_0_adain1_2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43673472)))]; + tensor decoder_generator_resblocks_0_convs1_2_bias = const()[name = tensor("decoder_generator_resblocks_0_convs1_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43935680)))]; + tensor decoder_generator_resblocks_0_adain2_2_fc_bias = const()[name = tensor("decoder_generator_resblocks_0_adain2_2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43936768)))]; + tensor decoder_generator_resblocks_0_adain2_2_fc_weight = const()[name = tensor("decoder_generator_resblocks_0_adain2_2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43938880)))]; + tensor decoder_generator_resblocks_0_convs2_2_bias = const()[name = tensor("decoder_generator_resblocks_0_convs2_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44201088)))]; + tensor decoder_generator_resblocks_1_alpha2_2 = const()[name = tensor("decoder_generator_resblocks_1_alpha2_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44202176)))]; + tensor decoder_generator_resblocks_1_alpha1_2 = const()[name = tensor("decoder_generator_resblocks_1_alpha1_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44203264)))]; + tensor decoder_generator_resblocks_1_alpha2_1 = const()[name = tensor("decoder_generator_resblocks_1_alpha2_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44204352)))]; + tensor decoder_generator_resblocks_1_alpha1_1 = const()[name = tensor("decoder_generator_resblocks_1_alpha1_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44205440)))]; + tensor decoder_generator_resblocks_1_alpha2_0 = const()[name = tensor("decoder_generator_resblocks_1_alpha2_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44206528)))]; + tensor decoder_generator_resblocks_1_alpha1_0 = const()[name = tensor("decoder_generator_resblocks_1_alpha1_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44207616)))]; + tensor decoder_generator_resblocks_1_adain1_0_fc_bias = const()[name = tensor("decoder_generator_resblocks_1_adain1_0_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44208704)))]; + tensor decoder_generator_resblocks_1_adain1_0_fc_weight = const()[name = tensor("decoder_generator_resblocks_1_adain1_0_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44210816)))]; + tensor decoder_generator_resblocks_1_convs1_0_bias = const()[name = tensor("decoder_generator_resblocks_1_convs1_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44473024)))]; + tensor decoder_generator_resblocks_1_adain2_0_fc_bias = const()[name = tensor("decoder_generator_resblocks_1_adain2_0_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44474112)))]; + tensor decoder_generator_resblocks_1_adain2_0_fc_weight = const()[name = tensor("decoder_generator_resblocks_1_adain2_0_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44476224)))]; + tensor decoder_generator_resblocks_1_convs2_0_bias = const()[name = tensor("decoder_generator_resblocks_1_convs2_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44738432)))]; + tensor decoder_generator_resblocks_1_adain1_1_fc_bias = const()[name = tensor("decoder_generator_resblocks_1_adain1_1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44739520)))]; + tensor decoder_generator_resblocks_1_adain1_1_fc_weight = const()[name = tensor("decoder_generator_resblocks_1_adain1_1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44741632)))]; + tensor decoder_generator_resblocks_1_convs1_1_bias = const()[name = tensor("decoder_generator_resblocks_1_convs1_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45003840)))]; + tensor decoder_generator_resblocks_1_adain2_1_fc_bias = const()[name = tensor("decoder_generator_resblocks_1_adain2_1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45004928)))]; + tensor decoder_generator_resblocks_1_adain2_1_fc_weight = const()[name = tensor("decoder_generator_resblocks_1_adain2_1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45007040)))]; + tensor decoder_generator_resblocks_1_convs2_1_bias = const()[name = tensor("decoder_generator_resblocks_1_convs2_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45269248)))]; + tensor decoder_generator_resblocks_1_adain1_2_fc_bias = const()[name = tensor("decoder_generator_resblocks_1_adain1_2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45270336)))]; + tensor decoder_generator_resblocks_1_adain1_2_fc_weight = const()[name = tensor("decoder_generator_resblocks_1_adain1_2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45272448)))]; + tensor decoder_generator_resblocks_1_convs1_2_bias = const()[name = tensor("decoder_generator_resblocks_1_convs1_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45534656)))]; + tensor decoder_generator_resblocks_1_adain2_2_fc_bias = const()[name = tensor("decoder_generator_resblocks_1_adain2_2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45535744)))]; + tensor decoder_generator_resblocks_1_adain2_2_fc_weight = const()[name = tensor("decoder_generator_resblocks_1_adain2_2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45537856)))]; + tensor decoder_generator_resblocks_1_convs2_2_bias = const()[name = tensor("decoder_generator_resblocks_1_convs2_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45800064)))]; + tensor decoder_generator_resblocks_2_alpha2_2 = const()[name = tensor("decoder_generator_resblocks_2_alpha2_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45801152)))]; + tensor decoder_generator_resblocks_2_alpha1_2 = const()[name = tensor("decoder_generator_resblocks_2_alpha1_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45802240)))]; + tensor decoder_generator_resblocks_2_alpha2_1 = const()[name = tensor("decoder_generator_resblocks_2_alpha2_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45803328)))]; + tensor decoder_generator_resblocks_2_alpha1_1 = const()[name = tensor("decoder_generator_resblocks_2_alpha1_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45804416)))]; + tensor decoder_generator_resblocks_2_alpha2_0 = const()[name = tensor("decoder_generator_resblocks_2_alpha2_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45805504)))]; + tensor decoder_generator_resblocks_2_alpha1_0 = const()[name = tensor("decoder_generator_resblocks_2_alpha1_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45806592)))]; + tensor decoder_generator_resblocks_2_adain1_0_fc_bias = const()[name = tensor("decoder_generator_resblocks_2_adain1_0_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45807680)))]; + tensor decoder_generator_resblocks_2_adain1_0_fc_weight = const()[name = tensor("decoder_generator_resblocks_2_adain1_0_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45809792)))]; + tensor decoder_generator_resblocks_2_convs1_0_bias = const()[name = tensor("decoder_generator_resblocks_2_convs1_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46072000)))]; + tensor decoder_generator_resblocks_2_adain2_0_fc_bias = const()[name = tensor("decoder_generator_resblocks_2_adain2_0_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46073088)))]; + tensor decoder_generator_resblocks_2_adain2_0_fc_weight = const()[name = tensor("decoder_generator_resblocks_2_adain2_0_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46075200)))]; + tensor decoder_generator_resblocks_2_convs2_0_bias = const()[name = tensor("decoder_generator_resblocks_2_convs2_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46337408)))]; + tensor decoder_generator_resblocks_2_adain1_1_fc_bias = const()[name = tensor("decoder_generator_resblocks_2_adain1_1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46338496)))]; + tensor decoder_generator_resblocks_2_adain1_1_fc_weight = const()[name = tensor("decoder_generator_resblocks_2_adain1_1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46340608)))]; + tensor decoder_generator_resblocks_2_convs1_1_bias = const()[name = tensor("decoder_generator_resblocks_2_convs1_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46602816)))]; + tensor decoder_generator_resblocks_2_adain2_1_fc_bias = const()[name = tensor("decoder_generator_resblocks_2_adain2_1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46603904)))]; + tensor decoder_generator_resblocks_2_adain2_1_fc_weight = const()[name = tensor("decoder_generator_resblocks_2_adain2_1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46606016)))]; + tensor decoder_generator_resblocks_2_convs2_1_bias = const()[name = tensor("decoder_generator_resblocks_2_convs2_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46868224)))]; + tensor decoder_generator_resblocks_2_adain1_2_fc_bias = const()[name = tensor("decoder_generator_resblocks_2_adain1_2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46869312)))]; + tensor decoder_generator_resblocks_2_adain1_2_fc_weight = const()[name = tensor("decoder_generator_resblocks_2_adain1_2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46871424)))]; + tensor decoder_generator_resblocks_2_convs1_2_bias = const()[name = tensor("decoder_generator_resblocks_2_convs1_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47133632)))]; + tensor decoder_generator_resblocks_2_adain2_2_fc_bias = const()[name = tensor("decoder_generator_resblocks_2_adain2_2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47134720)))]; + tensor decoder_generator_resblocks_2_adain2_2_fc_weight = const()[name = tensor("decoder_generator_resblocks_2_adain2_2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47136832)))]; + tensor decoder_generator_resblocks_2_convs2_2_bias = const()[name = tensor("decoder_generator_resblocks_2_convs2_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47399040)))]; + tensor decoder_generator_noise_convs_1_bias = const()[name = tensor("decoder_generator_noise_convs_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47400128)))]; + tensor decoder_generator_noise_convs_1_weight = const()[name = tensor("decoder_generator_noise_convs_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47400704)))]; + tensor decoder_generator_noise_res_1_alpha2_2 = const()[name = tensor("decoder_generator_noise_res_1_alpha2_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47412032)))]; + tensor decoder_generator_noise_res_1_alpha1_2 = const()[name = tensor("decoder_generator_noise_res_1_alpha1_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47412608)))]; + tensor decoder_generator_noise_res_1_alpha2_1 = const()[name = tensor("decoder_generator_noise_res_1_alpha2_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47413184)))]; + tensor decoder_generator_noise_res_1_alpha1_1 = const()[name = tensor("decoder_generator_noise_res_1_alpha1_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47413760)))]; + tensor decoder_generator_noise_res_1_alpha2_0 = const()[name = tensor("decoder_generator_noise_res_1_alpha2_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47414336)))]; + tensor decoder_generator_noise_res_1_alpha1_0 = const()[name = tensor("decoder_generator_noise_res_1_alpha1_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47414912)))]; + tensor decoder_generator_noise_res_1_adain1_0_fc_bias = const()[name = tensor("decoder_generator_noise_res_1_adain1_0_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47415488)))]; + tensor decoder_generator_noise_res_1_adain1_0_fc_weight = const()[name = tensor("decoder_generator_noise_res_1_adain1_0_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47416576)))]; + tensor decoder_generator_noise_res_1_adain1_0_norm_bias = const()[name = tensor("decoder_generator_noise_res_1_adain1_0_norm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47547712)))]; + tensor decoder_generator_noise_res_1_adain1_0_norm_weight = const()[name = tensor("decoder_generator_noise_res_1_adain1_0_norm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47548288)))]; + tensor decoder_generator_noise_res_1_convs1_0_bias = const()[name = tensor("decoder_generator_noise_res_1_convs1_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47548864)))]; + tensor decoder_generator_noise_res_1_adain2_0_fc_bias = const()[name = tensor("decoder_generator_noise_res_1_adain2_0_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47549440)))]; + tensor decoder_generator_noise_res_1_adain2_0_fc_weight = const()[name = tensor("decoder_generator_noise_res_1_adain2_0_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47550528)))]; + tensor decoder_generator_noise_res_1_convs2_0_bias = const()[name = tensor("decoder_generator_noise_res_1_convs2_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47681664)))]; + tensor decoder_generator_noise_res_1_adain1_1_fc_bias = const()[name = tensor("decoder_generator_noise_res_1_adain1_1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47682240)))]; + tensor decoder_generator_noise_res_1_adain1_1_fc_weight = const()[name = tensor("decoder_generator_noise_res_1_adain1_1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47683328)))]; + tensor decoder_generator_noise_res_1_convs1_1_bias = const()[name = tensor("decoder_generator_noise_res_1_convs1_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47814464)))]; + tensor decoder_generator_noise_res_1_adain2_1_fc_bias = const()[name = tensor("decoder_generator_noise_res_1_adain2_1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47815040)))]; + tensor decoder_generator_noise_res_1_adain2_1_fc_weight = const()[name = tensor("decoder_generator_noise_res_1_adain2_1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47816128)))]; + tensor decoder_generator_noise_res_1_convs2_1_bias = const()[name = tensor("decoder_generator_noise_res_1_convs2_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47947264)))]; + tensor decoder_generator_noise_res_1_adain1_2_fc_bias = const()[name = tensor("decoder_generator_noise_res_1_adain1_2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47947840)))]; + tensor decoder_generator_noise_res_1_adain1_2_fc_weight = const()[name = tensor("decoder_generator_noise_res_1_adain1_2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47948928)))]; + tensor decoder_generator_noise_res_1_convs1_2_bias = const()[name = tensor("decoder_generator_noise_res_1_convs1_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48080064)))]; + tensor decoder_generator_noise_res_1_adain2_2_fc_bias = const()[name = tensor("decoder_generator_noise_res_1_adain2_2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48080640)))]; + tensor decoder_generator_noise_res_1_adain2_2_fc_weight = const()[name = tensor("decoder_generator_noise_res_1_adain2_2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48081728)))]; + tensor decoder_generator_noise_res_1_convs2_2_bias = const()[name = tensor("decoder_generator_noise_res_1_convs2_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48212864)))]; + tensor decoder_generator_ups_1_bias = const()[name = tensor("decoder_generator_ups_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48213440)))]; + tensor decoder_generator_resblocks_3_alpha2_2 = const()[name = tensor("decoder_generator_resblocks_3_alpha2_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48214016)))]; + tensor decoder_generator_resblocks_3_alpha1_2 = const()[name = tensor("decoder_generator_resblocks_3_alpha1_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48214592)))]; + tensor decoder_generator_resblocks_3_alpha2_1 = const()[name = tensor("decoder_generator_resblocks_3_alpha2_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48215168)))]; + tensor decoder_generator_resblocks_3_alpha1_1 = const()[name = tensor("decoder_generator_resblocks_3_alpha1_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48215744)))]; + tensor decoder_generator_resblocks_3_alpha2_0 = const()[name = tensor("decoder_generator_resblocks_3_alpha2_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48216320)))]; + tensor decoder_generator_resblocks_3_alpha1_0 = const()[name = tensor("decoder_generator_resblocks_3_alpha1_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48216896)))]; + tensor decoder_generator_resblocks_3_adain1_0_fc_bias = const()[name = tensor("decoder_generator_resblocks_3_adain1_0_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48217472)))]; + tensor decoder_generator_resblocks_3_adain1_0_fc_weight = const()[name = tensor("decoder_generator_resblocks_3_adain1_0_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48218560)))]; + tensor decoder_generator_resblocks_3_convs1_0_bias = const()[name = tensor("decoder_generator_resblocks_3_convs1_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48349696)))]; + tensor decoder_generator_resblocks_3_adain2_0_fc_bias = const()[name = tensor("decoder_generator_resblocks_3_adain2_0_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48350272)))]; + tensor decoder_generator_resblocks_3_adain2_0_fc_weight = const()[name = tensor("decoder_generator_resblocks_3_adain2_0_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48351360)))]; + tensor decoder_generator_resblocks_3_convs2_0_bias = const()[name = tensor("decoder_generator_resblocks_3_convs2_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48482496)))]; + tensor decoder_generator_resblocks_3_adain1_1_fc_bias = const()[name = tensor("decoder_generator_resblocks_3_adain1_1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48483072)))]; + tensor decoder_generator_resblocks_3_adain1_1_fc_weight = const()[name = tensor("decoder_generator_resblocks_3_adain1_1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48484160)))]; + tensor decoder_generator_resblocks_3_convs1_1_bias = const()[name = tensor("decoder_generator_resblocks_3_convs1_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48615296)))]; + tensor decoder_generator_resblocks_3_adain2_1_fc_bias = const()[name = tensor("decoder_generator_resblocks_3_adain2_1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48615872)))]; + tensor decoder_generator_resblocks_3_adain2_1_fc_weight = const()[name = tensor("decoder_generator_resblocks_3_adain2_1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48616960)))]; + tensor decoder_generator_resblocks_3_convs2_1_bias = const()[name = tensor("decoder_generator_resblocks_3_convs2_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48748096)))]; + tensor decoder_generator_resblocks_3_adain1_2_fc_bias = const()[name = tensor("decoder_generator_resblocks_3_adain1_2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48748672)))]; + tensor decoder_generator_resblocks_3_adain1_2_fc_weight = const()[name = tensor("decoder_generator_resblocks_3_adain1_2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48749760)))]; + tensor decoder_generator_resblocks_3_convs1_2_bias = const()[name = tensor("decoder_generator_resblocks_3_convs1_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48880896)))]; + tensor decoder_generator_resblocks_3_adain2_2_fc_bias = const()[name = tensor("decoder_generator_resblocks_3_adain2_2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48881472)))]; + tensor decoder_generator_resblocks_3_adain2_2_fc_weight = const()[name = tensor("decoder_generator_resblocks_3_adain2_2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48882560)))]; + tensor decoder_generator_resblocks_3_convs2_2_bias = const()[name = tensor("decoder_generator_resblocks_3_convs2_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49013696)))]; + tensor decoder_generator_resblocks_4_alpha2_2 = const()[name = tensor("decoder_generator_resblocks_4_alpha2_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49014272)))]; + tensor decoder_generator_resblocks_4_alpha1_2 = const()[name = tensor("decoder_generator_resblocks_4_alpha1_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49014848)))]; + tensor decoder_generator_resblocks_4_alpha2_1 = const()[name = tensor("decoder_generator_resblocks_4_alpha2_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49015424)))]; + tensor decoder_generator_resblocks_4_alpha1_1 = const()[name = tensor("decoder_generator_resblocks_4_alpha1_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49016000)))]; + tensor decoder_generator_resblocks_4_alpha2_0 = const()[name = tensor("decoder_generator_resblocks_4_alpha2_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49016576)))]; + tensor decoder_generator_resblocks_4_alpha1_0 = const()[name = tensor("decoder_generator_resblocks_4_alpha1_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49017152)))]; + tensor decoder_generator_resblocks_4_adain1_0_fc_bias = const()[name = tensor("decoder_generator_resblocks_4_adain1_0_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49017728)))]; + tensor decoder_generator_resblocks_4_adain1_0_fc_weight = const()[name = tensor("decoder_generator_resblocks_4_adain1_0_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49018816)))]; + tensor decoder_generator_resblocks_4_convs1_0_bias = const()[name = tensor("decoder_generator_resblocks_4_convs1_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49149952)))]; + tensor decoder_generator_resblocks_4_adain2_0_fc_bias = const()[name = tensor("decoder_generator_resblocks_4_adain2_0_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49150528)))]; + tensor decoder_generator_resblocks_4_adain2_0_fc_weight = const()[name = tensor("decoder_generator_resblocks_4_adain2_0_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49151616)))]; + tensor decoder_generator_resblocks_4_convs2_0_bias = const()[name = tensor("decoder_generator_resblocks_4_convs2_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49282752)))]; + tensor decoder_generator_resblocks_4_adain1_1_fc_bias = const()[name = tensor("decoder_generator_resblocks_4_adain1_1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49283328)))]; + tensor decoder_generator_resblocks_4_adain1_1_fc_weight = const()[name = tensor("decoder_generator_resblocks_4_adain1_1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49284416)))]; + tensor decoder_generator_resblocks_4_convs1_1_bias = const()[name = tensor("decoder_generator_resblocks_4_convs1_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49415552)))]; + tensor decoder_generator_resblocks_4_adain2_1_fc_bias = const()[name = tensor("decoder_generator_resblocks_4_adain2_1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49416128)))]; + tensor decoder_generator_resblocks_4_adain2_1_fc_weight = const()[name = tensor("decoder_generator_resblocks_4_adain2_1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49417216)))]; + tensor decoder_generator_resblocks_4_convs2_1_bias = const()[name = tensor("decoder_generator_resblocks_4_convs2_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49548352)))]; + tensor decoder_generator_resblocks_4_adain1_2_fc_bias = const()[name = tensor("decoder_generator_resblocks_4_adain1_2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49548928)))]; + tensor decoder_generator_resblocks_4_adain1_2_fc_weight = const()[name = tensor("decoder_generator_resblocks_4_adain1_2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49550016)))]; + tensor decoder_generator_resblocks_4_convs1_2_bias = const()[name = tensor("decoder_generator_resblocks_4_convs1_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49681152)))]; + tensor decoder_generator_resblocks_4_adain2_2_fc_bias = const()[name = tensor("decoder_generator_resblocks_4_adain2_2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49681728)))]; + tensor decoder_generator_resblocks_4_adain2_2_fc_weight = const()[name = tensor("decoder_generator_resblocks_4_adain2_2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49682816)))]; + tensor decoder_generator_resblocks_4_convs2_2_bias = const()[name = tensor("decoder_generator_resblocks_4_convs2_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49813952)))]; + tensor decoder_generator_resblocks_5_alpha2_2 = const()[name = tensor("decoder_generator_resblocks_5_alpha2_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49814528)))]; + tensor decoder_generator_resblocks_5_alpha1_2 = const()[name = tensor("decoder_generator_resblocks_5_alpha1_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49815104)))]; + tensor decoder_generator_resblocks_5_alpha2_1 = const()[name = tensor("decoder_generator_resblocks_5_alpha2_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49815680)))]; + tensor decoder_generator_resblocks_5_alpha1_1 = const()[name = tensor("decoder_generator_resblocks_5_alpha1_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49816256)))]; + tensor decoder_generator_resblocks_5_alpha2_0 = const()[name = tensor("decoder_generator_resblocks_5_alpha2_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49816832)))]; + tensor decoder_generator_resblocks_5_alpha1_0 = const()[name = tensor("decoder_generator_resblocks_5_alpha1_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49817408)))]; + tensor decoder_generator_resblocks_5_adain1_0_fc_bias = const()[name = tensor("decoder_generator_resblocks_5_adain1_0_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49817984)))]; + tensor decoder_generator_resblocks_5_adain1_0_fc_weight = const()[name = tensor("decoder_generator_resblocks_5_adain1_0_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49819072)))]; + tensor decoder_generator_resblocks_5_convs1_0_bias = const()[name = tensor("decoder_generator_resblocks_5_convs1_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49950208)))]; + tensor decoder_generator_resblocks_5_adain2_0_fc_bias = const()[name = tensor("decoder_generator_resblocks_5_adain2_0_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49950784)))]; + tensor decoder_generator_resblocks_5_adain2_0_fc_weight = const()[name = tensor("decoder_generator_resblocks_5_adain2_0_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49951872)))]; + tensor decoder_generator_resblocks_5_convs2_0_bias = const()[name = tensor("decoder_generator_resblocks_5_convs2_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50083008)))]; + tensor decoder_generator_resblocks_5_adain1_1_fc_bias = const()[name = tensor("decoder_generator_resblocks_5_adain1_1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50083584)))]; + tensor decoder_generator_resblocks_5_adain1_1_fc_weight = const()[name = tensor("decoder_generator_resblocks_5_adain1_1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50084672)))]; + tensor decoder_generator_resblocks_5_convs1_1_bias = const()[name = tensor("decoder_generator_resblocks_5_convs1_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50215808)))]; + tensor decoder_generator_resblocks_5_adain2_1_fc_bias = const()[name = tensor("decoder_generator_resblocks_5_adain2_1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50216384)))]; + tensor decoder_generator_resblocks_5_adain2_1_fc_weight = const()[name = tensor("decoder_generator_resblocks_5_adain2_1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50217472)))]; + tensor decoder_generator_resblocks_5_convs2_1_bias = const()[name = tensor("decoder_generator_resblocks_5_convs2_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50348608)))]; + tensor decoder_generator_resblocks_5_adain1_2_fc_bias = const()[name = tensor("decoder_generator_resblocks_5_adain1_2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50349184)))]; + tensor decoder_generator_resblocks_5_adain1_2_fc_weight = const()[name = tensor("decoder_generator_resblocks_5_adain1_2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50350272)))]; + tensor decoder_generator_resblocks_5_convs1_2_bias = const()[name = tensor("decoder_generator_resblocks_5_convs1_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50481408)))]; + tensor decoder_generator_resblocks_5_adain2_2_fc_bias = const()[name = tensor("decoder_generator_resblocks_5_adain2_2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50481984)))]; + tensor decoder_generator_resblocks_5_adain2_2_fc_weight = const()[name = tensor("decoder_generator_resblocks_5_adain2_2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50483072)))]; + tensor decoder_generator_resblocks_5_convs2_2_bias = const()[name = tensor("decoder_generator_resblocks_5_convs2_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50614208)))]; + tensor decoder_generator_conv_post_bias = const()[name = tensor("decoder_generator_conv_post_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50614784)))]; + tensor var_239 = const()[name = tensor("op_239"), val = tensor(-1)]; + tensor var_241 = const()[name = tensor("op_241"), val = tensor(0x1.197998p-40)]; + tensor inputs_embeds_axis_0 = const()[name = tensor("inputs_embeds_axis_0"), val = tensor(0)]; + tensor inputs_embeds_batch_dims_0 = const()[name = tensor("inputs_embeds_batch_dims_0"), val = tensor(0)]; + tensor inputs_embeds = gather(axis = inputs_embeds_axis_0, batch_dims = inputs_embeds_batch_dims_0, indices = input_ids, x = bert_embeddings_word_embeddings_weight)[name = tensor("inputs_embeds")]; + tensor token_type_embeddings_1 = const()[name = tensor("token_type_embeddings_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50614976)))]; + tensor embeddings_1 = add(x = inputs_embeds, y = token_type_embeddings_1)[name = tensor("embeddings_1")]; + tensor position_embeddings_1 = const()[name = tensor("position_embeddings_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50742528)))]; + tensor input_5 = add(x = embeddings_1, y = position_embeddings_1)[name = tensor("input_5")]; + tensor input_7_axes_0 = const()[name = tensor("input_7_axes_0"), val = tensor([-1])]; + tensor input_7 = layer_norm(axes = input_7_axes_0, beta = bert_embeddings_LayerNorm_bias, epsilon = var_241, gamma = bert_embeddings_LayerNorm_weight, x = input_5)[name = tensor("input_7")]; + tensor input_11 = linear(bias = bert_encoder_embedding_hidden_mapping_in_bias, weight = bert_encoder_embedding_hidden_mapping_in_weight, x = input_7)[name = tensor("linear_0")]; + tensor 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("linear_1")]; + tensor 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("linear_2")]; + tensor 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("linear_3")]; + tensor var_317 = const()[name = tensor("op_317"), val = tensor([1, 249, 12, 64])]; + tensor x_3 = reshape(shape = var_317, x = x_1)[name = tensor("x_3")]; + tensor var_323 = const()[name = tensor("op_323"), val = tensor([1, 249, 12, 64])]; + tensor x_7 = reshape(shape = var_323, x = x_5)[name = tensor("x_7")]; + tensor var_329 = const()[name = tensor("op_329"), val = tensor([1, 249, 12, 64])]; + tensor x_11 = reshape(shape = var_329, x = x_9)[name = tensor("x_11")]; + tensor var_331 = const()[name = tensor("op_331"), val = tensor([0, 2, 1, 3])]; + tensor attention_scores_1_transpose_x_0 = const()[name = tensor("attention_scores_1_transpose_x_0"), val = tensor(false)]; + tensor attention_scores_1_transpose_y_0 = const()[name = tensor("attention_scores_1_transpose_y_0"), val = tensor(false)]; + tensor transpose_62_perm_0 = const()[name = tensor("transpose_62_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_63_perm_0 = const()[name = tensor("transpose_63_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_63 = transpose(perm = transpose_63_perm_0, x = x_7)[name = tensor("transpose_166")]; + tensor transpose_62 = transpose(perm = transpose_62_perm_0, x = x_3)[name = tensor("transpose_167")]; + tensor attention_scores_1 = matmul(transpose_x = attention_scores_1_transpose_x_0, transpose_y = attention_scores_1_transpose_y_0, x = transpose_62, y = transpose_63)[name = tensor("attention_scores_1")]; + tensor _inversed_attention_scores_3_y_0 = const()[name = tensor("_inversed_attention_scores_3_y_0"), val = tensor(0x1p-3)]; + tensor _inversed_attention_scores_3 = mul(x = attention_scores_1, y = _inversed_attention_scores_3_y_0)[name = tensor("_inversed_attention_scores_3")]; + tensor input_15 = softmax(axis = var_239, x = _inversed_attention_scores_3)[name = tensor("input_15")]; + tensor context_layer_1_transpose_x_0 = const()[name = tensor("context_layer_1_transpose_x_0"), val = tensor(false)]; + tensor context_layer_1_transpose_y_0 = const()[name = tensor("context_layer_1_transpose_y_0"), val = tensor(false)]; + tensor value_layer_1 = transpose(perm = var_331, x = x_11)[name = tensor("transpose_168")]; + tensor 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("context_layer_1")]; + tensor var_341_perm_0 = const()[name = tensor("op_341_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_2 = const()[name = tensor("concat_2"), val = tensor([1, 249, 768])]; + tensor var_341 = transpose(perm = var_341_perm_0, x = context_layer_1)[name = tensor("transpose_165")]; + tensor input_17 = reshape(shape = concat_2, x = var_341)[name = tensor("input_17")]; + tensor 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("linear_4")]; + tensor input_21 = add(x = input_11, y = input_19)[name = tensor("input_21")]; + tensor input_23_axes_0 = const()[name = tensor("input_23_axes_0"), val = tensor([-1])]; + tensor input_23 = layer_norm(axes = input_23_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_241, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_21)[name = tensor("input_23")]; + tensor 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("linear_5")]; + tensor input_27_mode_0 = const()[name = tensor("input_27_mode_0"), val = tensor("TANH_APPROXIMATION")]; + tensor input_27 = gelu(mode = input_27_mode_0, x = input_25)[name = tensor("input_27")]; + tensor 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("linear_6")]; + tensor input_29 = add(x = ffn_output_1, y = input_23)[name = tensor("input_29")]; + tensor input_31_axes_0 = const()[name = tensor("input_31_axes_0"), val = tensor([-1])]; + tensor 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_241, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_29)[name = tensor("input_31")]; + tensor 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("linear_7")]; + tensor 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("linear_8")]; + tensor 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("linear_9")]; + tensor var_397 = const()[name = tensor("op_397"), val = tensor([1, 249, 12, 64])]; + tensor x_15 = reshape(shape = var_397, x = x_13)[name = tensor("x_15")]; + tensor var_403 = const()[name = tensor("op_403"), val = tensor([1, 249, 12, 64])]; + tensor x_19 = reshape(shape = var_403, x = x_17)[name = tensor("x_19")]; + tensor var_409 = const()[name = tensor("op_409"), val = tensor([1, 249, 12, 64])]; + tensor x_23 = reshape(shape = var_409, x = x_21)[name = tensor("x_23")]; + tensor var_411 = const()[name = tensor("op_411"), val = tensor([0, 2, 1, 3])]; + tensor attention_scores_5_transpose_x_0 = const()[name = tensor("attention_scores_5_transpose_x_0"), val = tensor(false)]; + tensor attention_scores_5_transpose_y_0 = const()[name = tensor("attention_scores_5_transpose_y_0"), val = tensor(false)]; + tensor transpose_64_perm_0 = const()[name = tensor("transpose_64_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_65_perm_0 = const()[name = tensor("transpose_65_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_65 = transpose(perm = transpose_65_perm_0, x = x_19)[name = tensor("transpose_162")]; + tensor transpose_64 = transpose(perm = transpose_64_perm_0, x = x_15)[name = tensor("transpose_163")]; + tensor attention_scores_5 = matmul(transpose_x = attention_scores_5_transpose_x_0, transpose_y = attention_scores_5_transpose_y_0, x = transpose_64, y = transpose_65)[name = tensor("attention_scores_5")]; + tensor _inversed_attention_scores_7_y_0 = const()[name = tensor("_inversed_attention_scores_7_y_0"), val = tensor(0x1p-3)]; + tensor _inversed_attention_scores_7 = mul(x = attention_scores_5, y = _inversed_attention_scores_7_y_0)[name = tensor("_inversed_attention_scores_7")]; + tensor input_35 = softmax(axis = var_239, x = _inversed_attention_scores_7)[name = tensor("input_35")]; + tensor context_layer_3_transpose_x_0 = const()[name = tensor("context_layer_3_transpose_x_0"), val = tensor(false)]; + tensor context_layer_3_transpose_y_0 = const()[name = tensor("context_layer_3_transpose_y_0"), val = tensor(false)]; + tensor value_layer_3 = transpose(perm = var_411, x = x_23)[name = tensor("transpose_164")]; + tensor 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("context_layer_3")]; + tensor var_421_perm_0 = const()[name = tensor("op_421_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_3 = const()[name = tensor("concat_3"), val = tensor([1, 249, 768])]; + tensor var_421 = transpose(perm = var_421_perm_0, x = context_layer_3)[name = tensor("transpose_161")]; + tensor input_37 = reshape(shape = concat_3, x = var_421)[name = tensor("input_37")]; + tensor 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("linear_10")]; + tensor input_41 = add(x = input_31, y = input_39)[name = tensor("input_41")]; + tensor input_43_axes_0 = const()[name = tensor("input_43_axes_0"), val = tensor([-1])]; + tensor input_43 = layer_norm(axes = input_43_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_241, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_41)[name = tensor("input_43")]; + tensor 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("linear_11")]; + tensor input_47_mode_0 = const()[name = tensor("input_47_mode_0"), val = tensor("TANH_APPROXIMATION")]; + tensor input_47 = gelu(mode = input_47_mode_0, x = input_45)[name = tensor("input_47")]; + tensor 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("linear_12")]; + tensor input_49 = add(x = ffn_output_3, y = input_43)[name = tensor("input_49")]; + tensor input_51_axes_0 = const()[name = tensor("input_51_axes_0"), val = tensor([-1])]; + tensor 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_241, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_49)[name = tensor("input_51")]; + tensor 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("linear_13")]; + tensor 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("linear_14")]; + tensor 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("linear_15")]; + tensor var_477 = const()[name = tensor("op_477"), val = tensor([1, 249, 12, 64])]; + tensor x_27 = reshape(shape = var_477, x = x_25)[name = tensor("x_27")]; + tensor var_483 = const()[name = tensor("op_483"), val = tensor([1, 249, 12, 64])]; + tensor x_31 = reshape(shape = var_483, x = x_29)[name = tensor("x_31")]; + tensor var_489 = const()[name = tensor("op_489"), val = tensor([1, 249, 12, 64])]; + tensor x_35 = reshape(shape = var_489, x = x_33)[name = tensor("x_35")]; + tensor var_491 = const()[name = tensor("op_491"), val = tensor([0, 2, 1, 3])]; + tensor attention_scores_9_transpose_x_0 = const()[name = tensor("attention_scores_9_transpose_x_0"), val = tensor(false)]; + tensor attention_scores_9_transpose_y_0 = const()[name = tensor("attention_scores_9_transpose_y_0"), val = tensor(false)]; + tensor transpose_66_perm_0 = const()[name = tensor("transpose_66_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_67_perm_0 = const()[name = tensor("transpose_67_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_67 = transpose(perm = transpose_67_perm_0, x = x_31)[name = tensor("transpose_158")]; + tensor transpose_66 = transpose(perm = transpose_66_perm_0, x = x_27)[name = tensor("transpose_159")]; + tensor attention_scores_9 = matmul(transpose_x = attention_scores_9_transpose_x_0, transpose_y = attention_scores_9_transpose_y_0, x = transpose_66, y = transpose_67)[name = tensor("attention_scores_9")]; + tensor _inversed_attention_scores_11_y_0 = const()[name = tensor("_inversed_attention_scores_11_y_0"), val = tensor(0x1p-3)]; + tensor _inversed_attention_scores_11 = mul(x = attention_scores_9, y = _inversed_attention_scores_11_y_0)[name = tensor("_inversed_attention_scores_11")]; + tensor input_55 = softmax(axis = var_239, x = _inversed_attention_scores_11)[name = tensor("input_55")]; + tensor context_layer_5_transpose_x_0 = const()[name = tensor("context_layer_5_transpose_x_0"), val = tensor(false)]; + tensor context_layer_5_transpose_y_0 = const()[name = tensor("context_layer_5_transpose_y_0"), val = tensor(false)]; + tensor value_layer_5 = transpose(perm = var_491, x = x_35)[name = tensor("transpose_160")]; + tensor 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("context_layer_5")]; + tensor var_501_perm_0 = const()[name = tensor("op_501_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_4 = const()[name = tensor("concat_4"), val = tensor([1, 249, 768])]; + tensor var_501 = transpose(perm = var_501_perm_0, x = context_layer_5)[name = tensor("transpose_157")]; + tensor input_57 = reshape(shape = concat_4, x = var_501)[name = tensor("input_57")]; + tensor 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("linear_16")]; + tensor input_61 = add(x = input_51, y = input_59)[name = tensor("input_61")]; + tensor input_63_axes_0 = const()[name = tensor("input_63_axes_0"), val = tensor([-1])]; + tensor input_63 = layer_norm(axes = input_63_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_241, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_61)[name = tensor("input_63")]; + tensor 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("linear_17")]; + tensor input_67_mode_0 = const()[name = tensor("input_67_mode_0"), val = tensor("TANH_APPROXIMATION")]; + tensor input_67 = gelu(mode = input_67_mode_0, x = input_65)[name = tensor("input_67")]; + tensor 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("linear_18")]; + tensor input_69 = add(x = ffn_output_5, y = input_63)[name = tensor("input_69")]; + tensor input_71_axes_0 = const()[name = tensor("input_71_axes_0"), val = tensor([-1])]; + tensor 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_241, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_69)[name = tensor("input_71")]; + tensor 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("linear_19")]; + tensor 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("linear_20")]; + tensor 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("linear_21")]; + tensor var_557 = const()[name = tensor("op_557"), val = tensor([1, 249, 12, 64])]; + tensor x_39 = reshape(shape = var_557, x = x_37)[name = tensor("x_39")]; + tensor var_563 = const()[name = tensor("op_563"), val = tensor([1, 249, 12, 64])]; + tensor x_43 = reshape(shape = var_563, x = x_41)[name = tensor("x_43")]; + tensor var_569 = const()[name = tensor("op_569"), val = tensor([1, 249, 12, 64])]; + tensor x_47 = reshape(shape = var_569, x = x_45)[name = tensor("x_47")]; + tensor var_571 = const()[name = tensor("op_571"), val = tensor([0, 2, 1, 3])]; + tensor attention_scores_13_transpose_x_0 = const()[name = tensor("attention_scores_13_transpose_x_0"), val = tensor(false)]; + tensor attention_scores_13_transpose_y_0 = const()[name = tensor("attention_scores_13_transpose_y_0"), val = tensor(false)]; + tensor transpose_68_perm_0 = const()[name = tensor("transpose_68_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_69_perm_0 = const()[name = tensor("transpose_69_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_69 = transpose(perm = transpose_69_perm_0, x = x_43)[name = tensor("transpose_154")]; + tensor transpose_68 = transpose(perm = transpose_68_perm_0, x = x_39)[name = tensor("transpose_155")]; + tensor attention_scores_13 = matmul(transpose_x = attention_scores_13_transpose_x_0, transpose_y = attention_scores_13_transpose_y_0, x = transpose_68, y = transpose_69)[name = tensor("attention_scores_13")]; + tensor _inversed_attention_scores_15_y_0 = const()[name = tensor("_inversed_attention_scores_15_y_0"), val = tensor(0x1p-3)]; + tensor _inversed_attention_scores_15 = mul(x = attention_scores_13, y = _inversed_attention_scores_15_y_0)[name = tensor("_inversed_attention_scores_15")]; + tensor input_75 = softmax(axis = var_239, x = _inversed_attention_scores_15)[name = tensor("input_75")]; + tensor context_layer_7_transpose_x_0 = const()[name = tensor("context_layer_7_transpose_x_0"), val = tensor(false)]; + tensor context_layer_7_transpose_y_0 = const()[name = tensor("context_layer_7_transpose_y_0"), val = tensor(false)]; + tensor value_layer_7 = transpose(perm = var_571, x = x_47)[name = tensor("transpose_156")]; + tensor 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("context_layer_7")]; + tensor var_581_perm_0 = const()[name = tensor("op_581_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_5 = const()[name = tensor("concat_5"), val = tensor([1, 249, 768])]; + tensor var_581 = transpose(perm = var_581_perm_0, x = context_layer_7)[name = tensor("transpose_153")]; + tensor input_77 = reshape(shape = concat_5, x = var_581)[name = tensor("input_77")]; + tensor 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("linear_22")]; + tensor input_81 = add(x = input_71, y = input_79)[name = tensor("input_81")]; + tensor input_83_axes_0 = const()[name = tensor("input_83_axes_0"), val = tensor([-1])]; + tensor input_83 = layer_norm(axes = input_83_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_241, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_81)[name = tensor("input_83")]; + tensor 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("linear_23")]; + tensor input_87_mode_0 = const()[name = tensor("input_87_mode_0"), val = tensor("TANH_APPROXIMATION")]; + tensor input_87 = gelu(mode = input_87_mode_0, x = input_85)[name = tensor("input_87")]; + tensor 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("linear_24")]; + tensor input_89 = add(x = ffn_output_7, y = input_83)[name = tensor("input_89")]; + tensor input_91_axes_0 = const()[name = tensor("input_91_axes_0"), val = tensor([-1])]; + tensor 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_241, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_89)[name = tensor("input_91")]; + tensor 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("linear_25")]; + tensor 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("linear_26")]; + tensor 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("linear_27")]; + tensor var_637 = const()[name = tensor("op_637"), val = tensor([1, 249, 12, 64])]; + tensor x_51 = reshape(shape = var_637, x = x_49)[name = tensor("x_51")]; + tensor var_643 = const()[name = tensor("op_643"), val = tensor([1, 249, 12, 64])]; + tensor x_55 = reshape(shape = var_643, x = x_53)[name = tensor("x_55")]; + tensor var_649 = const()[name = tensor("op_649"), val = tensor([1, 249, 12, 64])]; + tensor x_59 = reshape(shape = var_649, x = x_57)[name = tensor("x_59")]; + tensor var_651 = const()[name = tensor("op_651"), val = tensor([0, 2, 1, 3])]; + tensor attention_scores_17_transpose_x_0 = const()[name = tensor("attention_scores_17_transpose_x_0"), val = tensor(false)]; + tensor attention_scores_17_transpose_y_0 = const()[name = tensor("attention_scores_17_transpose_y_0"), val = tensor(false)]; + tensor transpose_70_perm_0 = const()[name = tensor("transpose_70_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_71_perm_0 = const()[name = tensor("transpose_71_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_71 = transpose(perm = transpose_71_perm_0, x = x_55)[name = tensor("transpose_150")]; + tensor transpose_70 = transpose(perm = transpose_70_perm_0, x = x_51)[name = tensor("transpose_151")]; + tensor attention_scores_17 = matmul(transpose_x = attention_scores_17_transpose_x_0, transpose_y = attention_scores_17_transpose_y_0, x = transpose_70, y = transpose_71)[name = tensor("attention_scores_17")]; + tensor _inversed_attention_scores_19_y_0 = const()[name = tensor("_inversed_attention_scores_19_y_0"), val = tensor(0x1p-3)]; + tensor _inversed_attention_scores_19 = mul(x = attention_scores_17, y = _inversed_attention_scores_19_y_0)[name = tensor("_inversed_attention_scores_19")]; + tensor input_95 = softmax(axis = var_239, x = _inversed_attention_scores_19)[name = tensor("input_95")]; + tensor context_layer_9_transpose_x_0 = const()[name = tensor("context_layer_9_transpose_x_0"), val = tensor(false)]; + tensor context_layer_9_transpose_y_0 = const()[name = tensor("context_layer_9_transpose_y_0"), val = tensor(false)]; + tensor value_layer_9 = transpose(perm = var_651, x = x_59)[name = tensor("transpose_152")]; + tensor 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("context_layer_9")]; + tensor var_661_perm_0 = const()[name = tensor("op_661_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_6 = const()[name = tensor("concat_6"), val = tensor([1, 249, 768])]; + tensor var_661 = transpose(perm = var_661_perm_0, x = context_layer_9)[name = tensor("transpose_149")]; + tensor input_97 = reshape(shape = concat_6, x = var_661)[name = tensor("input_97")]; + tensor 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("linear_28")]; + tensor input_101 = add(x = input_91, y = input_99)[name = tensor("input_101")]; + tensor input_103_axes_0 = const()[name = tensor("input_103_axes_0"), val = tensor([-1])]; + tensor input_103 = layer_norm(axes = input_103_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_241, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_101)[name = tensor("input_103")]; + tensor 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("linear_29")]; + tensor input_107_mode_0 = const()[name = tensor("input_107_mode_0"), val = tensor("TANH_APPROXIMATION")]; + tensor input_107 = gelu(mode = input_107_mode_0, x = input_105)[name = tensor("input_107")]; + tensor 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("linear_30")]; + tensor input_109 = add(x = ffn_output_9, y = input_103)[name = tensor("input_109")]; + tensor input_111_axes_0 = const()[name = tensor("input_111_axes_0"), val = tensor([-1])]; + tensor 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_241, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_109)[name = tensor("input_111")]; + tensor 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("linear_31")]; + tensor 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("linear_32")]; + tensor 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("linear_33")]; + tensor var_717 = const()[name = tensor("op_717"), val = tensor([1, 249, 12, 64])]; + tensor x_63 = reshape(shape = var_717, x = x_61)[name = tensor("x_63")]; + tensor var_723 = const()[name = tensor("op_723"), val = tensor([1, 249, 12, 64])]; + tensor x_67 = reshape(shape = var_723, x = x_65)[name = tensor("x_67")]; + tensor var_729 = const()[name = tensor("op_729"), val = tensor([1, 249, 12, 64])]; + tensor x_71 = reshape(shape = var_729, x = x_69)[name = tensor("x_71")]; + tensor var_731 = const()[name = tensor("op_731"), val = tensor([0, 2, 1, 3])]; + tensor attention_scores_21_transpose_x_0 = const()[name = tensor("attention_scores_21_transpose_x_0"), val = tensor(false)]; + tensor attention_scores_21_transpose_y_0 = const()[name = tensor("attention_scores_21_transpose_y_0"), val = tensor(false)]; + tensor transpose_72_perm_0 = const()[name = tensor("transpose_72_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_73_perm_0 = const()[name = tensor("transpose_73_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_73 = transpose(perm = transpose_73_perm_0, x = x_67)[name = tensor("transpose_146")]; + tensor transpose_72 = transpose(perm = transpose_72_perm_0, x = x_63)[name = tensor("transpose_147")]; + tensor attention_scores_21 = matmul(transpose_x = attention_scores_21_transpose_x_0, transpose_y = attention_scores_21_transpose_y_0, x = transpose_72, y = transpose_73)[name = tensor("attention_scores_21")]; + tensor _inversed_attention_scores_23_y_0 = const()[name = tensor("_inversed_attention_scores_23_y_0"), val = tensor(0x1p-3)]; + tensor _inversed_attention_scores_23 = mul(x = attention_scores_21, y = _inversed_attention_scores_23_y_0)[name = tensor("_inversed_attention_scores_23")]; + tensor input_115 = softmax(axis = var_239, x = _inversed_attention_scores_23)[name = tensor("input_115")]; + tensor context_layer_11_transpose_x_0 = const()[name = tensor("context_layer_11_transpose_x_0"), val = tensor(false)]; + tensor context_layer_11_transpose_y_0 = const()[name = tensor("context_layer_11_transpose_y_0"), val = tensor(false)]; + tensor value_layer_11 = transpose(perm = var_731, x = x_71)[name = tensor("transpose_148")]; + tensor 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("context_layer_11")]; + tensor var_741_perm_0 = const()[name = tensor("op_741_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_7 = const()[name = tensor("concat_7"), val = tensor([1, 249, 768])]; + tensor var_741 = transpose(perm = var_741_perm_0, x = context_layer_11)[name = tensor("transpose_145")]; + tensor input_117 = reshape(shape = concat_7, x = var_741)[name = tensor("input_117")]; + tensor 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("linear_34")]; + tensor input_121 = add(x = input_111, y = input_119)[name = tensor("input_121")]; + tensor input_123_axes_0 = const()[name = tensor("input_123_axes_0"), val = tensor([-1])]; + tensor input_123 = layer_norm(axes = input_123_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_241, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_121)[name = tensor("input_123")]; + tensor 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("linear_35")]; + tensor input_127_mode_0 = const()[name = tensor("input_127_mode_0"), val = tensor("TANH_APPROXIMATION")]; + tensor input_127 = gelu(mode = input_127_mode_0, x = input_125)[name = tensor("input_127")]; + tensor 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("linear_36")]; + tensor input_129 = add(x = ffn_output_11, y = input_123)[name = tensor("input_129")]; + tensor input_131_axes_0 = const()[name = tensor("input_131_axes_0"), val = tensor([-1])]; + tensor 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_241, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_129)[name = tensor("input_131")]; + tensor 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("linear_37")]; + tensor 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("linear_38")]; + tensor 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("linear_39")]; + tensor var_797 = const()[name = tensor("op_797"), val = tensor([1, 249, 12, 64])]; + tensor x_75 = reshape(shape = var_797, x = x_73)[name = tensor("x_75")]; + tensor var_803 = const()[name = tensor("op_803"), val = tensor([1, 249, 12, 64])]; + tensor x_79 = reshape(shape = var_803, x = x_77)[name = tensor("x_79")]; + tensor var_809 = const()[name = tensor("op_809"), val = tensor([1, 249, 12, 64])]; + tensor x_83 = reshape(shape = var_809, x = x_81)[name = tensor("x_83")]; + tensor var_811 = const()[name = tensor("op_811"), val = tensor([0, 2, 1, 3])]; + tensor attention_scores_25_transpose_x_0 = const()[name = tensor("attention_scores_25_transpose_x_0"), val = tensor(false)]; + tensor attention_scores_25_transpose_y_0 = const()[name = tensor("attention_scores_25_transpose_y_0"), val = tensor(false)]; + tensor transpose_74_perm_0 = const()[name = tensor("transpose_74_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_75_perm_0 = const()[name = tensor("transpose_75_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_75 = transpose(perm = transpose_75_perm_0, x = x_79)[name = tensor("transpose_142")]; + tensor transpose_74 = transpose(perm = transpose_74_perm_0, x = x_75)[name = tensor("transpose_143")]; + tensor attention_scores_25 = matmul(transpose_x = attention_scores_25_transpose_x_0, transpose_y = attention_scores_25_transpose_y_0, x = transpose_74, y = transpose_75)[name = tensor("attention_scores_25")]; + tensor _inversed_attention_scores_27_y_0 = const()[name = tensor("_inversed_attention_scores_27_y_0"), val = tensor(0x1p-3)]; + tensor _inversed_attention_scores_27 = mul(x = attention_scores_25, y = _inversed_attention_scores_27_y_0)[name = tensor("_inversed_attention_scores_27")]; + tensor input_135 = softmax(axis = var_239, x = _inversed_attention_scores_27)[name = tensor("input_135")]; + tensor context_layer_13_transpose_x_0 = const()[name = tensor("context_layer_13_transpose_x_0"), val = tensor(false)]; + tensor context_layer_13_transpose_y_0 = const()[name = tensor("context_layer_13_transpose_y_0"), val = tensor(false)]; + tensor value_layer_13 = transpose(perm = var_811, x = x_83)[name = tensor("transpose_144")]; + tensor 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("context_layer_13")]; + tensor var_821_perm_0 = const()[name = tensor("op_821_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_8 = const()[name = tensor("concat_8"), val = tensor([1, 249, 768])]; + tensor var_821 = transpose(perm = var_821_perm_0, x = context_layer_13)[name = tensor("transpose_141")]; + tensor input_137 = reshape(shape = concat_8, x = var_821)[name = tensor("input_137")]; + tensor 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("linear_40")]; + tensor input_141 = add(x = input_131, y = input_139)[name = tensor("input_141")]; + tensor input_143_axes_0 = const()[name = tensor("input_143_axes_0"), val = tensor([-1])]; + tensor input_143 = layer_norm(axes = input_143_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_241, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_141)[name = tensor("input_143")]; + tensor 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("linear_41")]; + tensor input_147_mode_0 = const()[name = tensor("input_147_mode_0"), val = tensor("TANH_APPROXIMATION")]; + tensor input_147 = gelu(mode = input_147_mode_0, x = input_145)[name = tensor("input_147")]; + tensor 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("linear_42")]; + tensor input_149 = add(x = ffn_output_13, y = input_143)[name = tensor("input_149")]; + tensor input_151_axes_0 = const()[name = tensor("input_151_axes_0"), val = tensor([-1])]; + tensor 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_241, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_149)[name = tensor("input_151")]; + tensor 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("linear_43")]; + tensor 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("linear_44")]; + tensor 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("linear_45")]; + tensor var_877 = const()[name = tensor("op_877"), val = tensor([1, 249, 12, 64])]; + tensor x_87 = reshape(shape = var_877, x = x_85)[name = tensor("x_87")]; + tensor var_883 = const()[name = tensor("op_883"), val = tensor([1, 249, 12, 64])]; + tensor x_91 = reshape(shape = var_883, x = x_89)[name = tensor("x_91")]; + tensor var_889 = const()[name = tensor("op_889"), val = tensor([1, 249, 12, 64])]; + tensor x_95 = reshape(shape = var_889, x = x_93)[name = tensor("x_95")]; + tensor var_891 = const()[name = tensor("op_891"), val = tensor([0, 2, 1, 3])]; + tensor attention_scores_29_transpose_x_0 = const()[name = tensor("attention_scores_29_transpose_x_0"), val = tensor(false)]; + tensor attention_scores_29_transpose_y_0 = const()[name = tensor("attention_scores_29_transpose_y_0"), val = tensor(false)]; + tensor transpose_76_perm_0 = const()[name = tensor("transpose_76_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_77_perm_0 = const()[name = tensor("transpose_77_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_77 = transpose(perm = transpose_77_perm_0, x = x_91)[name = tensor("transpose_138")]; + tensor transpose_76 = transpose(perm = transpose_76_perm_0, x = x_87)[name = tensor("transpose_139")]; + tensor attention_scores_29 = matmul(transpose_x = attention_scores_29_transpose_x_0, transpose_y = attention_scores_29_transpose_y_0, x = transpose_76, y = transpose_77)[name = tensor("attention_scores_29")]; + tensor _inversed_attention_scores_31_y_0 = const()[name = tensor("_inversed_attention_scores_31_y_0"), val = tensor(0x1p-3)]; + tensor _inversed_attention_scores_31 = mul(x = attention_scores_29, y = _inversed_attention_scores_31_y_0)[name = tensor("_inversed_attention_scores_31")]; + tensor input_155 = softmax(axis = var_239, x = _inversed_attention_scores_31)[name = tensor("input_155")]; + tensor context_layer_15_transpose_x_0 = const()[name = tensor("context_layer_15_transpose_x_0"), val = tensor(false)]; + tensor context_layer_15_transpose_y_0 = const()[name = tensor("context_layer_15_transpose_y_0"), val = tensor(false)]; + tensor value_layer_15 = transpose(perm = var_891, x = x_95)[name = tensor("transpose_140")]; + tensor 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("context_layer_15")]; + tensor var_901_perm_0 = const()[name = tensor("op_901_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_9 = const()[name = tensor("concat_9"), val = tensor([1, 249, 768])]; + tensor var_901 = transpose(perm = var_901_perm_0, x = context_layer_15)[name = tensor("transpose_137")]; + tensor input_157 = reshape(shape = concat_9, x = var_901)[name = tensor("input_157")]; + tensor 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("linear_46")]; + tensor input_161 = add(x = input_151, y = input_159)[name = tensor("input_161")]; + tensor input_163_axes_0 = const()[name = tensor("input_163_axes_0"), val = tensor([-1])]; + tensor input_163 = layer_norm(axes = input_163_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_241, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_161)[name = tensor("input_163")]; + tensor 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("linear_47")]; + tensor input_167_mode_0 = const()[name = tensor("input_167_mode_0"), val = tensor("TANH_APPROXIMATION")]; + tensor input_167 = gelu(mode = input_167_mode_0, x = input_165)[name = tensor("input_167")]; + tensor 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("linear_48")]; + tensor input_169 = add(x = ffn_output_15, y = input_163)[name = tensor("input_169")]; + tensor input_171_axes_0 = const()[name = tensor("input_171_axes_0"), val = tensor([-1])]; + tensor 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_241, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_169)[name = tensor("input_171")]; + tensor 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("linear_49")]; + tensor 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("linear_50")]; + tensor 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("linear_51")]; + tensor var_957 = const()[name = tensor("op_957"), val = tensor([1, 249, 12, 64])]; + tensor x_99 = reshape(shape = var_957, x = x_97)[name = tensor("x_99")]; + tensor var_963 = const()[name = tensor("op_963"), val = tensor([1, 249, 12, 64])]; + tensor x_103 = reshape(shape = var_963, x = x_101)[name = tensor("x_103")]; + tensor var_969 = const()[name = tensor("op_969"), val = tensor([1, 249, 12, 64])]; + tensor x_107 = reshape(shape = var_969, x = x_105)[name = tensor("x_107")]; + tensor var_971 = const()[name = tensor("op_971"), val = tensor([0, 2, 1, 3])]; + tensor attention_scores_33_transpose_x_0 = const()[name = tensor("attention_scores_33_transpose_x_0"), val = tensor(false)]; + tensor attention_scores_33_transpose_y_0 = const()[name = tensor("attention_scores_33_transpose_y_0"), val = tensor(false)]; + tensor transpose_78_perm_0 = const()[name = tensor("transpose_78_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_79_perm_0 = const()[name = tensor("transpose_79_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_79 = transpose(perm = transpose_79_perm_0, x = x_103)[name = tensor("transpose_134")]; + tensor transpose_78 = transpose(perm = transpose_78_perm_0, x = x_99)[name = tensor("transpose_135")]; + tensor attention_scores_33 = matmul(transpose_x = attention_scores_33_transpose_x_0, transpose_y = attention_scores_33_transpose_y_0, x = transpose_78, y = transpose_79)[name = tensor("attention_scores_33")]; + tensor _inversed_attention_scores_35_y_0 = const()[name = tensor("_inversed_attention_scores_35_y_0"), val = tensor(0x1p-3)]; + tensor _inversed_attention_scores_35 = mul(x = attention_scores_33, y = _inversed_attention_scores_35_y_0)[name = tensor("_inversed_attention_scores_35")]; + tensor input_175 = softmax(axis = var_239, x = _inversed_attention_scores_35)[name = tensor("input_175")]; + tensor context_layer_17_transpose_x_0 = const()[name = tensor("context_layer_17_transpose_x_0"), val = tensor(false)]; + tensor context_layer_17_transpose_y_0 = const()[name = tensor("context_layer_17_transpose_y_0"), val = tensor(false)]; + tensor value_layer_17 = transpose(perm = var_971, x = x_107)[name = tensor("transpose_136")]; + tensor 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("context_layer_17")]; + tensor var_981_perm_0 = const()[name = tensor("op_981_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_10 = const()[name = tensor("concat_10"), val = tensor([1, 249, 768])]; + tensor var_981 = transpose(perm = var_981_perm_0, x = context_layer_17)[name = tensor("transpose_133")]; + tensor input_177 = reshape(shape = concat_10, x = var_981)[name = tensor("input_177")]; + tensor 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("linear_52")]; + tensor input_181 = add(x = input_171, y = input_179)[name = tensor("input_181")]; + tensor input_183_axes_0 = const()[name = tensor("input_183_axes_0"), val = tensor([-1])]; + tensor input_183 = layer_norm(axes = input_183_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_241, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_181)[name = tensor("input_183")]; + tensor 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("linear_53")]; + tensor input_187_mode_0 = const()[name = tensor("input_187_mode_0"), val = tensor("TANH_APPROXIMATION")]; + tensor input_187 = gelu(mode = input_187_mode_0, x = input_185)[name = tensor("input_187")]; + tensor 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("linear_54")]; + tensor input_189 = add(x = ffn_output_17, y = input_183)[name = tensor("input_189")]; + tensor input_191_axes_0 = const()[name = tensor("input_191_axes_0"), val = tensor([-1])]; + tensor 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_241, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_189)[name = tensor("input_191")]; + tensor 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("linear_55")]; + tensor 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("linear_56")]; + tensor 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("linear_57")]; + tensor var_1037 = const()[name = tensor("op_1037"), val = tensor([1, 249, 12, 64])]; + tensor x_111 = reshape(shape = var_1037, x = x_109)[name = tensor("x_111")]; + tensor var_1043 = const()[name = tensor("op_1043"), val = tensor([1, 249, 12, 64])]; + tensor x_115 = reshape(shape = var_1043, x = x_113)[name = tensor("x_115")]; + tensor var_1049 = const()[name = tensor("op_1049"), val = tensor([1, 249, 12, 64])]; + tensor x_119 = reshape(shape = var_1049, x = x_117)[name = tensor("x_119")]; + tensor var_1051 = const()[name = tensor("op_1051"), val = tensor([0, 2, 1, 3])]; + tensor attention_scores_37_transpose_x_0 = const()[name = tensor("attention_scores_37_transpose_x_0"), val = tensor(false)]; + tensor attention_scores_37_transpose_y_0 = const()[name = tensor("attention_scores_37_transpose_y_0"), val = tensor(false)]; + tensor transpose_80_perm_0 = const()[name = tensor("transpose_80_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_81_perm_0 = const()[name = tensor("transpose_81_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_81 = transpose(perm = transpose_81_perm_0, x = x_115)[name = tensor("transpose_130")]; + tensor transpose_80 = transpose(perm = transpose_80_perm_0, x = x_111)[name = tensor("transpose_131")]; + tensor attention_scores_37 = matmul(transpose_x = attention_scores_37_transpose_x_0, transpose_y = attention_scores_37_transpose_y_0, x = transpose_80, y = transpose_81)[name = tensor("attention_scores_37")]; + tensor _inversed_attention_scores_39_y_0 = const()[name = tensor("_inversed_attention_scores_39_y_0"), val = tensor(0x1p-3)]; + tensor _inversed_attention_scores_39 = mul(x = attention_scores_37, y = _inversed_attention_scores_39_y_0)[name = tensor("_inversed_attention_scores_39")]; + tensor input_195 = softmax(axis = var_239, x = _inversed_attention_scores_39)[name = tensor("input_195")]; + tensor context_layer_19_transpose_x_0 = const()[name = tensor("context_layer_19_transpose_x_0"), val = tensor(false)]; + tensor context_layer_19_transpose_y_0 = const()[name = tensor("context_layer_19_transpose_y_0"), val = tensor(false)]; + tensor value_layer_19 = transpose(perm = var_1051, x = x_119)[name = tensor("transpose_132")]; + tensor 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("context_layer_19")]; + tensor var_1061_perm_0 = const()[name = tensor("op_1061_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_11 = const()[name = tensor("concat_11"), val = tensor([1, 249, 768])]; + tensor var_1061 = transpose(perm = var_1061_perm_0, x = context_layer_19)[name = tensor("transpose_129")]; + tensor input_197 = reshape(shape = concat_11, x = var_1061)[name = tensor("input_197")]; + tensor 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("linear_58")]; + tensor input_201 = add(x = input_191, y = input_199)[name = tensor("input_201")]; + tensor input_203_axes_0 = const()[name = tensor("input_203_axes_0"), val = tensor([-1])]; + tensor input_203 = layer_norm(axes = input_203_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_241, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_201)[name = tensor("input_203")]; + tensor 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("linear_59")]; + tensor input_207_mode_0 = const()[name = tensor("input_207_mode_0"), val = tensor("TANH_APPROXIMATION")]; + tensor input_207 = gelu(mode = input_207_mode_0, x = input_205)[name = tensor("input_207")]; + tensor 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("linear_60")]; + tensor input_209 = add(x = ffn_output_19, y = input_203)[name = tensor("input_209")]; + tensor input_211_axes_0 = const()[name = tensor("input_211_axes_0"), val = tensor([-1])]; + tensor 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_241, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_209)[name = tensor("input_211")]; + tensor 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("linear_61")]; + tensor 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("linear_62")]; + tensor 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("linear_63")]; + tensor var_1117 = const()[name = tensor("op_1117"), val = tensor([1, 249, 12, 64])]; + tensor x_123 = reshape(shape = var_1117, x = x_121)[name = tensor("x_123")]; + tensor var_1123 = const()[name = tensor("op_1123"), val = tensor([1, 249, 12, 64])]; + tensor x_127 = reshape(shape = var_1123, x = x_125)[name = tensor("x_127")]; + tensor var_1129 = const()[name = tensor("op_1129"), val = tensor([1, 249, 12, 64])]; + tensor x_131 = reshape(shape = var_1129, x = x_129)[name = tensor("x_131")]; + tensor var_1131 = const()[name = tensor("op_1131"), val = tensor([0, 2, 1, 3])]; + tensor attention_scores_41_transpose_x_0 = const()[name = tensor("attention_scores_41_transpose_x_0"), val = tensor(false)]; + tensor attention_scores_41_transpose_y_0 = const()[name = tensor("attention_scores_41_transpose_y_0"), val = tensor(false)]; + tensor transpose_82_perm_0 = const()[name = tensor("transpose_82_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_83_perm_0 = const()[name = tensor("transpose_83_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_83 = transpose(perm = transpose_83_perm_0, x = x_127)[name = tensor("transpose_126")]; + tensor transpose_82 = transpose(perm = transpose_82_perm_0, x = x_123)[name = tensor("transpose_127")]; + tensor attention_scores_41 = matmul(transpose_x = attention_scores_41_transpose_x_0, transpose_y = attention_scores_41_transpose_y_0, x = transpose_82, y = transpose_83)[name = tensor("attention_scores_41")]; + tensor _inversed_attention_scores_43_y_0 = const()[name = tensor("_inversed_attention_scores_43_y_0"), val = tensor(0x1p-3)]; + tensor _inversed_attention_scores_43 = mul(x = attention_scores_41, y = _inversed_attention_scores_43_y_0)[name = tensor("_inversed_attention_scores_43")]; + tensor input_215 = softmax(axis = var_239, x = _inversed_attention_scores_43)[name = tensor("input_215")]; + tensor context_layer_21_transpose_x_0 = const()[name = tensor("context_layer_21_transpose_x_0"), val = tensor(false)]; + tensor context_layer_21_transpose_y_0 = const()[name = tensor("context_layer_21_transpose_y_0"), val = tensor(false)]; + tensor value_layer_21 = transpose(perm = var_1131, x = x_131)[name = tensor("transpose_128")]; + tensor 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("context_layer_21")]; + tensor var_1141_perm_0 = const()[name = tensor("op_1141_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_12 = const()[name = tensor("concat_12"), val = tensor([1, 249, 768])]; + tensor var_1141 = transpose(perm = var_1141_perm_0, x = context_layer_21)[name = tensor("transpose_125")]; + tensor input_217 = reshape(shape = concat_12, x = var_1141)[name = tensor("input_217")]; + tensor 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("linear_64")]; + tensor input_221 = add(x = input_211, y = input_219)[name = tensor("input_221")]; + tensor input_223_axes_0 = const()[name = tensor("input_223_axes_0"), val = tensor([-1])]; + tensor input_223 = layer_norm(axes = input_223_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_241, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_221)[name = tensor("input_223")]; + tensor 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("linear_65")]; + tensor input_227_mode_0 = const()[name = tensor("input_227_mode_0"), val = tensor("TANH_APPROXIMATION")]; + tensor input_227 = gelu(mode = input_227_mode_0, x = input_225)[name = tensor("input_227")]; + tensor 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("linear_66")]; + tensor input_229 = add(x = ffn_output_21, y = input_223)[name = tensor("input_229")]; + tensor input_231_axes_0 = const()[name = tensor("input_231_axes_0"), val = tensor([-1])]; + tensor 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_241, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_229)[name = tensor("input_231")]; + tensor 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("linear_67")]; + tensor 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("linear_68")]; + tensor 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("linear_69")]; + tensor var_1197 = const()[name = tensor("op_1197"), val = tensor([1, 249, 12, 64])]; + tensor x_135 = reshape(shape = var_1197, x = x_133)[name = tensor("x_135")]; + tensor var_1203 = const()[name = tensor("op_1203"), val = tensor([1, 249, 12, 64])]; + tensor x_139 = reshape(shape = var_1203, x = x_137)[name = tensor("x_139")]; + tensor var_1209 = const()[name = tensor("op_1209"), val = tensor([1, 249, 12, 64])]; + tensor x_143 = reshape(shape = var_1209, x = x_141)[name = tensor("x_143")]; + tensor var_1211 = const()[name = tensor("op_1211"), val = tensor([0, 2, 1, 3])]; + tensor attention_scores_45_transpose_x_0 = const()[name = tensor("attention_scores_45_transpose_x_0"), val = tensor(false)]; + tensor attention_scores_45_transpose_y_0 = const()[name = tensor("attention_scores_45_transpose_y_0"), val = tensor(false)]; + tensor transpose_84_perm_0 = const()[name = tensor("transpose_84_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_85_perm_0 = const()[name = tensor("transpose_85_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_85 = transpose(perm = transpose_85_perm_0, x = x_139)[name = tensor("transpose_122")]; + tensor transpose_84 = transpose(perm = transpose_84_perm_0, x = x_135)[name = tensor("transpose_123")]; + tensor attention_scores_45 = matmul(transpose_x = attention_scores_45_transpose_x_0, transpose_y = attention_scores_45_transpose_y_0, x = transpose_84, y = transpose_85)[name = tensor("attention_scores_45")]; + tensor _inversed_attention_scores_y_0 = const()[name = tensor("_inversed_attention_scores_y_0"), val = tensor(0x1p-3)]; + tensor _inversed_attention_scores = mul(x = attention_scores_45, y = _inversed_attention_scores_y_0)[name = tensor("_inversed_attention_scores")]; + tensor input_235 = softmax(axis = var_239, x = _inversed_attention_scores)[name = tensor("input_235")]; + tensor context_layer_transpose_x_0 = const()[name = tensor("context_layer_transpose_x_0"), val = tensor(false)]; + tensor context_layer_transpose_y_0 = const()[name = tensor("context_layer_transpose_y_0"), val = tensor(false)]; + tensor value_layer = transpose(perm = var_1211, x = x_143)[name = tensor("transpose_124")]; + tensor 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("context_layer")]; + tensor var_1221_perm_0 = const()[name = tensor("op_1221_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_13 = const()[name = tensor("concat_13"), val = tensor([1, 249, 768])]; + tensor var_1221 = transpose(perm = var_1221_perm_0, x = context_layer)[name = tensor("transpose_121")]; + tensor input_237 = reshape(shape = concat_13, x = var_1221)[name = tensor("input_237")]; + tensor 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("linear_70")]; + tensor input_241 = add(x = input_231, y = input_239)[name = tensor("input_241")]; + tensor input_243_axes_0 = const()[name = tensor("input_243_axes_0"), val = tensor([-1])]; + tensor input_243 = layer_norm(axes = input_243_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_241, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_241)[name = tensor("input_243")]; + tensor 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("linear_71")]; + tensor input_247_mode_0 = const()[name = tensor("input_247_mode_0"), val = tensor("TANH_APPROXIMATION")]; + tensor input_247 = gelu(mode = input_247_mode_0, x = input_245)[name = tensor("input_247")]; + tensor 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("linear_72")]; + tensor input_249 = add(x = ffn_output_23, y = input_243)[name = tensor("input_249")]; + tensor sequence_output_axes_0 = const()[name = tensor("sequence_output_axes_0"), val = tensor([-1])]; + tensor 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_241, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_249)[name = tensor("sequence_output")]; + tensor var_1257 = linear(bias = bert_encoder_bias, weight = bert_encoder_weight, x = sequence_output)[name = tensor("linear_73")]; + tensor transpose_18_perm_0 = const()[name = tensor("transpose_18_perm_0"), val = tensor([-2, 0, -1])]; + tensor style_begin_0 = const()[name = tensor("style_begin_0"), val = tensor([0, 128])]; + tensor style_end_0 = const()[name = tensor("style_end_0"), val = tensor([1, 256])]; + tensor style_end_mask_0 = const()[name = tensor("style_end_mask_0"), val = tensor([true, true])]; + tensor style = slice_by_index(begin = style_begin_0, end = style_end_0, end_mask = style_end_mask_0, x = ref_s)[name = tensor("style")]; + tensor var_1272 = const()[name = tensor("op_1272"), val = tensor(0x1.4f8b58p-17)]; + tensor var_1281 = const()[name = tensor("op_1281"), val = tensor(-1)]; + tensor var_1282 = const()[name = tensor("op_1282"), val = tensor(1)]; + tensor expand_dims_0_axes_0 = const()[name = tensor("expand_dims_0_axes_0"), val = tensor([0])]; + tensor expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = style)[name = tensor("expand_dims_0")]; + tensor s_reps_0 = const()[name = tensor("s_reps_0"), val = tensor([249, 1, 1])]; + tensor s = tile(reps = s_reps_0, x = expand_dims_0)[name = tensor("s")]; + tensor x_149_interleave_0 = const()[name = tensor("x_149_interleave_0"), val = tensor(false)]; + tensor transpose_18 = transpose(perm = transpose_18_perm_0, x = var_1257)[name = tensor("transpose_120")]; + tensor x_149 = concat(axis = var_1281, interleave = x_149_interleave_0, values = (transpose_18, s))[name = tensor("x_149")]; + tensor add_0 = const()[name = tensor("add_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50870080)))]; + tensor add_1 = const()[name = tensor("add_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50874240)))]; + tensor concat_19 = const()[name = tensor("concat_19"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50878400)))]; + tensor concat_20 = const()[name = tensor("concat_20"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(53499904)))]; + tensor concat_21 = const()[name = tensor("concat_21"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54548544)))]; + tensor concat_22 = const()[name = tensor("concat_22"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57170048)))]; + tensor x_159_batch_first_lstm_h0_reshaped = const()[name = tensor("x_159_batch_first_lstm_h0_reshaped"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58218688)))]; + tensor x_159_batch_first_direction_0 = const()[name = tensor("x_159_batch_first_direction_0"), val = tensor("bidirectional")]; + tensor x_159_batch_first_output_sequence_0 = const()[name = tensor("x_159_batch_first_output_sequence_0"), val = tensor(true)]; + tensor x_159_batch_first_recurrent_activation_0 = const()[name = tensor("x_159_batch_first_recurrent_activation_0"), val = tensor("sigmoid")]; + tensor x_159_batch_first_cell_activation_0 = const()[name = tensor("x_159_batch_first_cell_activation_0"), val = tensor("tanh")]; + tensor x_159_batch_first_activation_0 = const()[name = tensor("x_159_batch_first_activation_0"), val = tensor("tanh")]; + tensor x_159_batch_first_0, tensor x_159_batch_first_1, tensor x_159_batch_first_2 = lstm(activation = x_159_batch_first_activation_0, bias = add_0, bias_back = add_1, cell_activation = x_159_batch_first_cell_activation_0, direction = x_159_batch_first_direction_0, initial_c = x_159_batch_first_lstm_h0_reshaped, initial_h = x_159_batch_first_lstm_h0_reshaped, output_sequence = x_159_batch_first_output_sequence_0, recurrent_activation = x_159_batch_first_recurrent_activation_0, weight_hh = concat_20, weight_hh_back = concat_22, weight_ih = concat_19, weight_ih_back = concat_21, x = x_149)[name = tensor("x_159_batch_first")]; + tensor transpose_29_perm_0 = const()[name = tensor("transpose_29_perm_0"), val = tensor([1, 0, 2])]; + tensor h_1 = linear(bias = predictor_text_encoder_lstms_1_fc_bias, weight = predictor_text_encoder_lstms_1_fc_weight, x = style)[name = tensor("linear_74")]; + tensor var_1316 = const()[name = tensor("op_1316"), val = tensor([1, 1024, 1])]; + tensor h_3 = reshape(shape = var_1316, x = h_1)[name = tensor("h_3")]; + tensor var_1318_split_sizes_0 = const()[name = tensor("op_1318_split_sizes_0"), val = tensor([512, 512])]; + tensor var_1318_axis_0 = const()[name = tensor("op_1318_axis_0"), val = tensor(1)]; + tensor var_1318_0, tensor var_1318_1 = split(axis = var_1318_axis_0, split_sizes = var_1318_split_sizes_0, x = h_3)[name = tensor("op_1318")]; + tensor gamma_3_perm_0 = const()[name = tensor("gamma_3_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_3_perm_0 = const()[name = tensor("beta_3_perm_0"), val = tensor([0, -1, 1])]; + tensor x_167_axes_0 = const()[name = tensor("x_167_axes_0"), val = tensor([-1])]; + tensor transpose_29 = transpose(perm = transpose_29_perm_0, x = x_159_batch_first_0)[name = tensor("transpose_119")]; + tensor x_167 = layer_norm(axes = x_167_axes_0, epsilon = var_1272, x = transpose_29)[name = tensor("x_167")]; + tensor var_1324_promoted = const()[name = tensor("op_1324_promoted"), val = tensor(0x1p+0)]; + tensor gamma_3 = transpose(perm = gamma_3_perm_0, x = var_1318_0)[name = tensor("transpose_118")]; + tensor var_1325 = add(x = gamma_3, y = var_1324_promoted)[name = tensor("op_1325")]; + tensor var_1326 = mul(x = var_1325, y = x_167)[name = tensor("op_1326")]; + tensor beta_3 = transpose(perm = beta_3_perm_0, x = var_1318_1)[name = tensor("transpose_117")]; + tensor x_169 = add(x = var_1326, y = beta_3)[name = tensor("x_169")]; + tensor x_173_interleave_0 = const()[name = tensor("x_173_interleave_0"), val = tensor(false)]; + tensor transpose_86_perm_0 = const()[name = tensor("transpose_86_perm_0"), val = tensor([0, -1, -2])]; + tensor transpose_87_perm_0 = const()[name = tensor("transpose_87_perm_0"), val = tensor([1, 2, 0])]; + tensor transpose_87 = transpose(perm = transpose_87_perm_0, x = s)[name = tensor("transpose_115")]; + tensor transpose_86 = transpose(perm = transpose_86_perm_0, x = x_169)[name = tensor("transpose_116")]; + tensor x_173 = concat(axis = var_1282, interleave = x_173_interleave_0, values = (transpose_86, transpose_87))[name = tensor("x_173")]; + tensor transpose_22_perm_0 = const()[name = tensor("transpose_22_perm_0"), val = tensor([-1, 0, -2])]; + tensor add_2 = const()[name = tensor("add_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58220800)))]; + tensor add_3 = const()[name = tensor("add_3"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58224960)))]; + tensor concat_29 = const()[name = tensor("concat_29"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58229120)))]; + tensor concat_30 = const()[name = tensor("concat_30"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60850624)))]; + tensor concat_31 = const()[name = tensor("concat_31"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61899264)))]; + tensor concat_32 = const()[name = tensor("concat_32"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64520768)))]; + tensor x_179_batch_first_direction_0 = const()[name = tensor("x_179_batch_first_direction_0"), val = tensor("bidirectional")]; + tensor x_179_batch_first_output_sequence_0 = const()[name = tensor("x_179_batch_first_output_sequence_0"), val = tensor(true)]; + tensor x_179_batch_first_recurrent_activation_0 = const()[name = tensor("x_179_batch_first_recurrent_activation_0"), val = tensor("sigmoid")]; + tensor x_179_batch_first_cell_activation_0 = const()[name = tensor("x_179_batch_first_cell_activation_0"), val = tensor("tanh")]; + tensor x_179_batch_first_activation_0 = const()[name = tensor("x_179_batch_first_activation_0"), val = tensor("tanh")]; + tensor transpose_22 = transpose(perm = transpose_22_perm_0, x = x_173)[name = tensor("transpose_114")]; + tensor x_179_batch_first_0, tensor x_179_batch_first_1, tensor x_179_batch_first_2 = lstm(activation = x_179_batch_first_activation_0, bias = add_2, bias_back = add_3, cell_activation = x_179_batch_first_cell_activation_0, direction = x_179_batch_first_direction_0, initial_c = x_159_batch_first_lstm_h0_reshaped, initial_h = x_159_batch_first_lstm_h0_reshaped, output_sequence = x_179_batch_first_output_sequence_0, recurrent_activation = x_179_batch_first_recurrent_activation_0, weight_hh = concat_30, weight_hh_back = concat_32, weight_ih = concat_29, weight_ih_back = concat_31, x = transpose_22)[name = tensor("x_179_batch_first")]; + tensor transpose_30_perm_0 = const()[name = tensor("transpose_30_perm_0"), val = tensor([1, 0, 2])]; + tensor h_5 = linear(bias = predictor_text_encoder_lstms_3_fc_bias, weight = predictor_text_encoder_lstms_3_fc_weight, x = style)[name = tensor("linear_75")]; + tensor var_1356 = const()[name = tensor("op_1356"), val = tensor([1, 1024, 1])]; + tensor h_7 = reshape(shape = var_1356, x = h_5)[name = tensor("h_7")]; + tensor var_1358_split_sizes_0 = const()[name = tensor("op_1358_split_sizes_0"), val = tensor([512, 512])]; + tensor var_1358_axis_0 = const()[name = tensor("op_1358_axis_0"), val = tensor(1)]; + tensor var_1358_0, tensor var_1358_1 = split(axis = var_1358_axis_0, split_sizes = var_1358_split_sizes_0, x = h_7)[name = tensor("op_1358")]; + tensor gamma_7_perm_0 = const()[name = tensor("gamma_7_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_7_perm_0 = const()[name = tensor("beta_7_perm_0"), val = tensor([0, -1, 1])]; + tensor x_187_axes_0 = const()[name = tensor("x_187_axes_0"), val = tensor([-1])]; + tensor transpose_30 = transpose(perm = transpose_30_perm_0, x = x_179_batch_first_0)[name = tensor("transpose_113")]; + tensor x_187 = layer_norm(axes = x_187_axes_0, epsilon = var_1272, x = transpose_30)[name = tensor("x_187")]; + tensor var_1364_promoted = const()[name = tensor("op_1364_promoted"), val = tensor(0x1p+0)]; + tensor gamma_7 = transpose(perm = gamma_7_perm_0, x = var_1358_0)[name = tensor("transpose_112")]; + tensor var_1365 = add(x = gamma_7, y = var_1364_promoted)[name = tensor("op_1365")]; + tensor var_1366 = mul(x = var_1365, y = x_187)[name = tensor("op_1366")]; + tensor beta_7 = transpose(perm = beta_7_perm_0, x = var_1358_1)[name = tensor("transpose_111")]; + tensor x_189 = add(x = var_1366, y = beta_7)[name = tensor("x_189")]; + tensor x_193_interleave_0 = const()[name = tensor("x_193_interleave_0"), val = tensor(false)]; + tensor transpose_90_perm_0 = const()[name = tensor("transpose_90_perm_0"), val = tensor([0, -1, -2])]; + tensor transpose_90 = transpose(perm = transpose_90_perm_0, x = x_189)[name = tensor("transpose_110")]; + tensor x_193 = concat(axis = var_1282, interleave = x_193_interleave_0, values = (transpose_90, transpose_87))[name = tensor("x_193")]; + tensor transpose_24_perm_0 = const()[name = tensor("transpose_24_perm_0"), val = tensor([-1, 0, -2])]; + tensor add_4 = const()[name = tensor("add_4"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65569408)))]; + tensor add_5 = const()[name = tensor("add_5"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65573568)))]; + tensor concat_39 = const()[name = tensor("concat_39"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65577728)))]; + tensor concat_40 = const()[name = tensor("concat_40"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68199232)))]; + tensor concat_41 = const()[name = tensor("concat_41"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69247872)))]; + tensor concat_42 = const()[name = tensor("concat_42"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71869376)))]; + tensor x_199_batch_first_direction_0 = const()[name = tensor("x_199_batch_first_direction_0"), val = tensor("bidirectional")]; + tensor x_199_batch_first_output_sequence_0 = const()[name = tensor("x_199_batch_first_output_sequence_0"), val = tensor(true)]; + tensor x_199_batch_first_recurrent_activation_0 = const()[name = tensor("x_199_batch_first_recurrent_activation_0"), val = tensor("sigmoid")]; + tensor x_199_batch_first_cell_activation_0 = const()[name = tensor("x_199_batch_first_cell_activation_0"), val = tensor("tanh")]; + tensor x_199_batch_first_activation_0 = const()[name = tensor("x_199_batch_first_activation_0"), val = tensor("tanh")]; + tensor transpose_24 = transpose(perm = transpose_24_perm_0, x = x_193)[name = tensor("transpose_109")]; + tensor x_199_batch_first_0, tensor x_199_batch_first_1, tensor x_199_batch_first_2 = lstm(activation = x_199_batch_first_activation_0, bias = add_4, bias_back = add_5, cell_activation = x_199_batch_first_cell_activation_0, direction = x_199_batch_first_direction_0, initial_c = x_159_batch_first_lstm_h0_reshaped, initial_h = x_159_batch_first_lstm_h0_reshaped, output_sequence = x_199_batch_first_output_sequence_0, recurrent_activation = x_199_batch_first_recurrent_activation_0, weight_hh = concat_40, weight_hh_back = concat_42, weight_ih = concat_39, weight_ih_back = concat_41, x = transpose_24)[name = tensor("x_199_batch_first")]; + tensor transpose_31_perm_0 = const()[name = tensor("transpose_31_perm_0"), val = tensor([1, 0, 2])]; + tensor h_9 = linear(bias = predictor_text_encoder_lstms_5_fc_bias, weight = predictor_text_encoder_lstms_5_fc_weight, x = style)[name = tensor("linear_76")]; + tensor var_1396 = const()[name = tensor("op_1396"), val = tensor([1, 1024, 1])]; + tensor h_11 = reshape(shape = var_1396, x = h_9)[name = tensor("h_11")]; + tensor var_1398_split_sizes_0 = const()[name = tensor("op_1398_split_sizes_0"), val = tensor([512, 512])]; + tensor var_1398_axis_0 = const()[name = tensor("op_1398_axis_0"), val = tensor(1)]; + tensor var_1398_0, tensor var_1398_1 = split(axis = var_1398_axis_0, split_sizes = var_1398_split_sizes_0, x = h_11)[name = tensor("op_1398")]; + tensor gamma_11_perm_0 = const()[name = tensor("gamma_11_perm_0"), val = tensor([0, -1, 1])]; + tensor beta_11_perm_0 = const()[name = tensor("beta_11_perm_0"), val = tensor([0, -1, 1])]; + tensor x_207_axes_0 = const()[name = tensor("x_207_axes_0"), val = tensor([-1])]; + tensor transpose_31 = transpose(perm = transpose_31_perm_0, x = x_199_batch_first_0)[name = tensor("transpose_108")]; + tensor x_207 = layer_norm(axes = x_207_axes_0, epsilon = var_1272, x = transpose_31)[name = tensor("x_207")]; + tensor var_1404_promoted = const()[name = tensor("op_1404_promoted"), val = tensor(0x1p+0)]; + tensor gamma_11 = transpose(perm = gamma_11_perm_0, x = var_1398_0)[name = tensor("transpose_107")]; + tensor var_1405 = add(x = gamma_11, y = var_1404_promoted)[name = tensor("op_1405")]; + tensor var_1406 = mul(x = var_1405, y = x_207)[name = tensor("op_1406")]; + tensor beta_11 = transpose(perm = beta_11_perm_0, x = var_1398_1)[name = tensor("transpose_106")]; + tensor x_209 = add(x = var_1406, y = beta_11)[name = tensor("x_209")]; + tensor x_213_interleave_0 = const()[name = tensor("x_213_interleave_0"), val = tensor(false)]; + tensor transpose_91_perm_0 = const()[name = tensor("transpose_91_perm_0"), val = tensor([0, -1, -2])]; + tensor transpose_91 = transpose(perm = transpose_91_perm_0, x = x_209)[name = tensor("transpose_105")]; + tensor x_213 = concat(axis = var_1282, interleave = x_213_interleave_0, values = (transpose_91, transpose_87))[name = tensor("x_213")]; + tensor const_92 = const()[name = tensor("const_92"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72918016)))]; + tensor slice_by_index_25 = const()[name = tensor("slice_by_index_25"), val = tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])]; + tensor reshape_3 = const()[name = tensor("reshape_3"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72918144)))]; + tensor scatter_0_mode_0 = const()[name = tensor("scatter_0_mode_0"), val = tensor("update")]; + tensor scatter_0_axis_0 = const()[name = tensor("scatter_0_axis_0"), val = tensor(0)]; + tensor scatter_0 = scatter(axis = scatter_0_axis_0, data = reshape_3, indices = slice_by_index_25, mode = scatter_0_mode_0, updates = const_92)[name = tensor("scatter_0")]; + tensor const_93 = const()[name = tensor("const_93"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_26 = const()[name = tensor("slice_by_index_26"), val = tensor([634, 635])]; + tensor scatter_1_mode_0 = const()[name = tensor("scatter_1_mode_0"), val = tensor("update")]; + tensor scatter_1_axis_0 = const()[name = tensor("scatter_1_axis_0"), val = tensor(0)]; + tensor scatter_1 = scatter(axis = scatter_1_axis_0, data = scatter_0, indices = slice_by_index_26, mode = scatter_1_mode_0, updates = const_93)[name = tensor("scatter_1")]; + tensor const_94 = const()[name = tensor("const_94"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_27 = const()[name = tensor("slice_by_index_27"), val = tensor([1257, 1258])]; + tensor scatter_2_mode_0 = const()[name = tensor("scatter_2_mode_0"), val = tensor("update")]; + tensor scatter_2_axis_0 = const()[name = tensor("scatter_2_axis_0"), val = tensor(0)]; + tensor scatter_2 = scatter(axis = scatter_2_axis_0, data = scatter_1, indices = slice_by_index_27, mode = scatter_2_mode_0, updates = const_94)[name = tensor("scatter_2")]; + tensor const_95 = const()[name = tensor("const_95"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_28 = const()[name = tensor("slice_by_index_28"), val = tensor([1880, 1881, 1882])]; + tensor scatter_3_mode_0 = const()[name = tensor("scatter_3_mode_0"), val = tensor("update")]; + tensor scatter_3_axis_0 = const()[name = tensor("scatter_3_axis_0"), val = tensor(0)]; + tensor scatter_3 = scatter(axis = scatter_3_axis_0, data = scatter_2, indices = slice_by_index_28, mode = scatter_3_mode_0, updates = const_95)[name = tensor("scatter_3")]; + tensor const_96 = const()[name = tensor("const_96"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_29 = const()[name = tensor("slice_by_index_29"), val = tensor([2504, 2505])]; + tensor scatter_4_mode_0 = const()[name = tensor("scatter_4_mode_0"), val = tensor("update")]; + tensor scatter_4_axis_0 = const()[name = tensor("scatter_4_axis_0"), val = tensor(0)]; + tensor scatter_4 = scatter(axis = scatter_4_axis_0, data = scatter_3, indices = slice_by_index_29, mode = scatter_4_mode_0, updates = const_96)[name = tensor("scatter_4")]; + tensor const_97 = const()[name = tensor("const_97"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_30 = const()[name = tensor("slice_by_index_30"), val = tensor([3127, 3128])]; + tensor scatter_5_mode_0 = const()[name = tensor("scatter_5_mode_0"), val = tensor("update")]; + tensor scatter_5_axis_0 = const()[name = tensor("scatter_5_axis_0"), val = tensor(0)]; + tensor scatter_5 = scatter(axis = scatter_5_axis_0, data = scatter_4, indices = slice_by_index_30, mode = scatter_5_mode_0, updates = const_97)[name = tensor("scatter_5")]; + tensor const_98 = const()[name = tensor("const_98"), val = tensor([0x1p+0])]; + tensor slice_by_index_31 = const()[name = tensor("slice_by_index_31"), val = tensor([3750])]; + tensor scatter_6_mode_0 = const()[name = tensor("scatter_6_mode_0"), val = tensor("update")]; + tensor scatter_6_axis_0 = const()[name = tensor("scatter_6_axis_0"), val = tensor(0)]; + tensor scatter_6 = scatter(axis = scatter_6_axis_0, data = scatter_5, indices = slice_by_index_31, mode = scatter_6_mode_0, updates = const_98)[name = tensor("scatter_6")]; + tensor const_99 = const()[name = tensor("const_99"), val = tensor([0x1p+0])]; + tensor slice_by_index_32 = const()[name = tensor("slice_by_index_32"), val = tensor([4372])]; + tensor scatter_7_mode_0 = const()[name = tensor("scatter_7_mode_0"), val = tensor("update")]; + tensor scatter_7_axis_0 = const()[name = tensor("scatter_7_axis_0"), val = tensor(0)]; + tensor scatter_7 = scatter(axis = scatter_7_axis_0, data = scatter_6, indices = slice_by_index_32, mode = scatter_7_mode_0, updates = const_99)[name = tensor("scatter_7")]; + tensor const_100 = const()[name = tensor("const_100"), val = tensor([0x1p+0])]; + tensor slice_by_index_33 = const()[name = tensor("slice_by_index_33"), val = tensor([4994])]; + tensor scatter_8_mode_0 = const()[name = tensor("scatter_8_mode_0"), val = tensor("update")]; + tensor scatter_8_axis_0 = const()[name = tensor("scatter_8_axis_0"), val = tensor(0)]; + tensor scatter_8 = scatter(axis = scatter_8_axis_0, data = scatter_7, indices = slice_by_index_33, mode = scatter_8_mode_0, updates = const_100)[name = tensor("scatter_8")]; + tensor const_101 = const()[name = tensor("const_101"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_34 = const()[name = tensor("slice_by_index_34"), val = tensor([5616, 5617])]; + tensor scatter_9_mode_0 = const()[name = tensor("scatter_9_mode_0"), val = tensor("update")]; + tensor scatter_9_axis_0 = const()[name = tensor("scatter_9_axis_0"), val = tensor(0)]; + tensor scatter_9 = scatter(axis = scatter_9_axis_0, data = scatter_8, indices = slice_by_index_34, mode = scatter_9_mode_0, updates = const_101)[name = tensor("scatter_9")]; + tensor const_102 = const()[name = tensor("const_102"), val = tensor([0x1p+0])]; + tensor slice_by_index_35 = const()[name = tensor("slice_by_index_35"), val = tensor([6239])]; + tensor scatter_10_mode_0 = const()[name = tensor("scatter_10_mode_0"), val = tensor("update")]; + tensor scatter_10_axis_0 = const()[name = tensor("scatter_10_axis_0"), val = tensor(0)]; + tensor scatter_10 = scatter(axis = scatter_10_axis_0, data = scatter_9, indices = slice_by_index_35, mode = scatter_10_mode_0, updates = const_102)[name = tensor("scatter_10")]; + tensor const_103 = const()[name = tensor("const_103"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_36 = const()[name = tensor("slice_by_index_36"), val = tensor([6861, 6862, 6863])]; + tensor scatter_11_mode_0 = const()[name = tensor("scatter_11_mode_0"), val = tensor("update")]; + tensor scatter_11_axis_0 = const()[name = tensor("scatter_11_axis_0"), val = tensor(0)]; + tensor scatter_11 = scatter(axis = scatter_11_axis_0, data = scatter_10, indices = slice_by_index_36, mode = scatter_11_mode_0, updates = const_103)[name = tensor("scatter_11")]; + tensor const_104 = const()[name = tensor("const_104"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_37 = const()[name = tensor("slice_by_index_37"), val = tensor([7485, 7486])]; + tensor scatter_12_mode_0 = const()[name = tensor("scatter_12_mode_0"), val = tensor("update")]; + tensor scatter_12_axis_0 = const()[name = tensor("scatter_12_axis_0"), val = tensor(0)]; + tensor scatter_12 = scatter(axis = scatter_12_axis_0, data = scatter_11, indices = slice_by_index_37, mode = scatter_12_mode_0, updates = const_104)[name = tensor("scatter_12")]; + tensor const_105 = const()[name = tensor("const_105"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_38 = const()[name = tensor("slice_by_index_38"), val = tensor([8108, 8109, 8110, 8111])]; + tensor scatter_13_mode_0 = const()[name = tensor("scatter_13_mode_0"), val = tensor("update")]; + tensor scatter_13_axis_0 = const()[name = tensor("scatter_13_axis_0"), val = tensor(0)]; + tensor scatter_13 = scatter(axis = scatter_13_axis_0, data = scatter_12, indices = slice_by_index_38, mode = scatter_13_mode_0, updates = const_105)[name = tensor("scatter_13")]; + tensor const_106 = const()[name = tensor("const_106"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_39 = const()[name = tensor("slice_by_index_39"), val = tensor([8733, 8734, 8735, 8736])]; + tensor scatter_14_mode_0 = const()[name = tensor("scatter_14_mode_0"), val = tensor("update")]; + tensor scatter_14_axis_0 = const()[name = tensor("scatter_14_axis_0"), val = tensor(0)]; + tensor scatter_14 = scatter(axis = scatter_14_axis_0, data = scatter_13, indices = slice_by_index_39, mode = scatter_14_mode_0, updates = const_106)[name = tensor("scatter_14")]; + tensor const_107 = const()[name = tensor("const_107"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_40 = const()[name = tensor("slice_by_index_40"), val = tensor([9358, 9359])]; + tensor scatter_15_mode_0 = const()[name = tensor("scatter_15_mode_0"), val = tensor("update")]; + tensor scatter_15_axis_0 = const()[name = tensor("scatter_15_axis_0"), val = tensor(0)]; + tensor scatter_15 = scatter(axis = scatter_15_axis_0, data = scatter_14, indices = slice_by_index_40, mode = scatter_15_mode_0, updates = const_107)[name = tensor("scatter_15")]; + tensor const_108 = const()[name = tensor("const_108"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_41 = const()[name = tensor("slice_by_index_41"), val = tensor([9981, 9982])]; + tensor scatter_16_mode_0 = const()[name = tensor("scatter_16_mode_0"), val = tensor("update")]; + tensor scatter_16_axis_0 = const()[name = tensor("scatter_16_axis_0"), val = tensor(0)]; + tensor scatter_16 = scatter(axis = scatter_16_axis_0, data = scatter_15, indices = slice_by_index_41, mode = scatter_16_mode_0, updates = const_108)[name = tensor("scatter_16")]; + tensor const_109 = const()[name = tensor("const_109"), val = tensor([0x1p+0])]; + tensor slice_by_index_42 = const()[name = tensor("slice_by_index_42"), val = tensor([10604])]; + tensor scatter_17_mode_0 = const()[name = tensor("scatter_17_mode_0"), val = tensor("update")]; + tensor scatter_17_axis_0 = const()[name = tensor("scatter_17_axis_0"), val = tensor(0)]; + tensor scatter_17 = scatter(axis = scatter_17_axis_0, data = scatter_16, indices = slice_by_index_42, mode = scatter_17_mode_0, updates = const_109)[name = tensor("scatter_17")]; + tensor const_110 = const()[name = tensor("const_110"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_43 = const()[name = tensor("slice_by_index_43"), val = tensor([11226, 11227])]; + tensor scatter_18_mode_0 = const()[name = tensor("scatter_18_mode_0"), val = tensor("update")]; + tensor scatter_18_axis_0 = const()[name = tensor("scatter_18_axis_0"), val = tensor(0)]; + tensor scatter_18 = scatter(axis = scatter_18_axis_0, data = scatter_17, indices = slice_by_index_43, mode = scatter_18_mode_0, updates = const_110)[name = tensor("scatter_18")]; + tensor const_111 = const()[name = tensor("const_111"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_44 = const()[name = tensor("slice_by_index_44"), val = tensor([11849, 11850, 11851, 11852])]; + tensor scatter_19_mode_0 = const()[name = tensor("scatter_19_mode_0"), val = tensor("update")]; + tensor scatter_19_axis_0 = const()[name = tensor("scatter_19_axis_0"), val = tensor(0)]; + tensor scatter_19 = scatter(axis = scatter_19_axis_0, data = scatter_18, indices = slice_by_index_44, mode = scatter_19_mode_0, updates = const_111)[name = tensor("scatter_19")]; + tensor const_112 = const()[name = tensor("const_112"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_45 = const()[name = tensor("slice_by_index_45"), val = tensor([12474, 12475])]; + tensor scatter_20_mode_0 = const()[name = tensor("scatter_20_mode_0"), val = tensor("update")]; + tensor scatter_20_axis_0 = const()[name = tensor("scatter_20_axis_0"), val = tensor(0)]; + tensor scatter_20 = scatter(axis = scatter_20_axis_0, data = scatter_19, indices = slice_by_index_45, mode = scatter_20_mode_0, updates = const_112)[name = tensor("scatter_20")]; + tensor const_113 = const()[name = tensor("const_113"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_46 = const()[name = tensor("slice_by_index_46"), val = tensor([13097, 13098, 13099])]; + tensor scatter_21_mode_0 = const()[name = tensor("scatter_21_mode_0"), val = tensor("update")]; + tensor scatter_21_axis_0 = const()[name = tensor("scatter_21_axis_0"), val = tensor(0)]; + tensor scatter_21 = scatter(axis = scatter_21_axis_0, data = scatter_20, indices = slice_by_index_46, mode = scatter_21_mode_0, updates = const_113)[name = tensor("scatter_21")]; + tensor const_114 = const()[name = tensor("const_114"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_47 = const()[name = tensor("slice_by_index_47"), val = tensor([13721, 13722])]; + tensor scatter_22_mode_0 = const()[name = tensor("scatter_22_mode_0"), val = tensor("update")]; + tensor scatter_22_axis_0 = const()[name = tensor("scatter_22_axis_0"), val = tensor(0)]; + tensor scatter_22 = scatter(axis = scatter_22_axis_0, data = scatter_21, indices = slice_by_index_47, mode = scatter_22_mode_0, updates = const_114)[name = tensor("scatter_22")]; + tensor const_115 = const()[name = tensor("const_115"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_48 = const()[name = tensor("slice_by_index_48"), val = tensor([14344, 14345])]; + tensor scatter_23_mode_0 = const()[name = tensor("scatter_23_mode_0"), val = tensor("update")]; + tensor scatter_23_axis_0 = const()[name = tensor("scatter_23_axis_0"), val = tensor(0)]; + tensor scatter_23 = scatter(axis = scatter_23_axis_0, data = scatter_22, indices = slice_by_index_48, mode = scatter_23_mode_0, updates = const_115)[name = tensor("scatter_23")]; + tensor const_116 = const()[name = tensor("const_116"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_49 = const()[name = tensor("slice_by_index_49"), val = tensor([14967, 14968])]; + tensor scatter_24_mode_0 = const()[name = tensor("scatter_24_mode_0"), val = tensor("update")]; + tensor scatter_24_axis_0 = const()[name = tensor("scatter_24_axis_0"), val = tensor(0)]; + tensor scatter_24 = scatter(axis = scatter_24_axis_0, data = scatter_23, indices = slice_by_index_49, mode = scatter_24_mode_0, updates = const_116)[name = tensor("scatter_24")]; + tensor const_117 = const()[name = tensor("const_117"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_50 = const()[name = tensor("slice_by_index_50"), val = tensor([15590, 15591])]; + tensor scatter_25_mode_0 = const()[name = tensor("scatter_25_mode_0"), val = tensor("update")]; + tensor scatter_25_axis_0 = const()[name = tensor("scatter_25_axis_0"), val = tensor(0)]; + tensor scatter_25 = scatter(axis = scatter_25_axis_0, data = scatter_24, indices = slice_by_index_50, mode = scatter_25_mode_0, updates = const_117)[name = tensor("scatter_25")]; + tensor const_118 = const()[name = tensor("const_118"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_51 = const()[name = tensor("slice_by_index_51"), val = tensor([16213, 16214])]; + tensor scatter_26_mode_0 = const()[name = tensor("scatter_26_mode_0"), val = tensor("update")]; + tensor scatter_26_axis_0 = const()[name = tensor("scatter_26_axis_0"), val = tensor(0)]; + tensor scatter_26 = scatter(axis = scatter_26_axis_0, data = scatter_25, indices = slice_by_index_51, mode = scatter_26_mode_0, updates = const_118)[name = tensor("scatter_26")]; + tensor const_119 = const()[name = tensor("const_119"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_52 = const()[name = tensor("slice_by_index_52"), val = tensor([16836, 16837])]; + tensor scatter_27_mode_0 = const()[name = tensor("scatter_27_mode_0"), val = tensor("update")]; + tensor scatter_27_axis_0 = const()[name = tensor("scatter_27_axis_0"), val = tensor(0)]; + tensor scatter_27 = scatter(axis = scatter_27_axis_0, data = scatter_26, indices = slice_by_index_52, mode = scatter_27_mode_0, updates = const_119)[name = tensor("scatter_27")]; + tensor const_120 = const()[name = tensor("const_120"), val = tensor([0x1p+0])]; + tensor slice_by_index_53 = const()[name = tensor("slice_by_index_53"), val = tensor([17459])]; + tensor scatter_28_mode_0 = const()[name = tensor("scatter_28_mode_0"), val = tensor("update")]; + tensor scatter_28_axis_0 = const()[name = tensor("scatter_28_axis_0"), val = tensor(0)]; + tensor scatter_28 = scatter(axis = scatter_28_axis_0, data = scatter_27, indices = slice_by_index_53, mode = scatter_28_mode_0, updates = const_120)[name = tensor("scatter_28")]; + tensor const_121 = const()[name = tensor("const_121"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_54 = const()[name = tensor("slice_by_index_54"), val = tensor([18081, 18082, 18083])]; + tensor scatter_29_mode_0 = const()[name = tensor("scatter_29_mode_0"), val = tensor("update")]; + tensor scatter_29_axis_0 = const()[name = tensor("scatter_29_axis_0"), val = tensor(0)]; + tensor scatter_29 = scatter(axis = scatter_29_axis_0, data = scatter_28, indices = slice_by_index_54, mode = scatter_29_mode_0, updates = const_121)[name = tensor("scatter_29")]; + tensor const_122 = const()[name = tensor("const_122"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_55 = const()[name = tensor("slice_by_index_55"), val = tensor([18705, 18706])]; + tensor scatter_30_mode_0 = const()[name = tensor("scatter_30_mode_0"), val = tensor("update")]; + tensor scatter_30_axis_0 = const()[name = tensor("scatter_30_axis_0"), val = tensor(0)]; + tensor scatter_30 = scatter(axis = scatter_30_axis_0, data = scatter_29, indices = slice_by_index_55, mode = scatter_30_mode_0, updates = const_122)[name = tensor("scatter_30")]; + tensor const_123 = const()[name = tensor("const_123"), val = tensor([0x1p+0])]; + tensor slice_by_index_56 = const()[name = tensor("slice_by_index_56"), val = tensor([19328])]; + tensor scatter_31_mode_0 = const()[name = tensor("scatter_31_mode_0"), val = tensor("update")]; + tensor scatter_31_axis_0 = const()[name = tensor("scatter_31_axis_0"), val = tensor(0)]; + tensor scatter_31 = scatter(axis = scatter_31_axis_0, data = scatter_30, indices = slice_by_index_56, mode = scatter_31_mode_0, updates = const_123)[name = tensor("scatter_31")]; + tensor const_124 = const()[name = tensor("const_124"), val = tensor([0x1p+0])]; + tensor slice_by_index_57 = const()[name = tensor("slice_by_index_57"), val = tensor([19950])]; + tensor scatter_32_mode_0 = const()[name = tensor("scatter_32_mode_0"), val = tensor("update")]; + tensor scatter_32_axis_0 = const()[name = tensor("scatter_32_axis_0"), val = tensor(0)]; + tensor scatter_32 = scatter(axis = scatter_32_axis_0, data = scatter_31, indices = slice_by_index_57, mode = scatter_32_mode_0, updates = const_124)[name = tensor("scatter_32")]; + tensor const_125 = const()[name = tensor("const_125"), val = tensor([0x1p+0])]; + tensor slice_by_index_58 = const()[name = tensor("slice_by_index_58"), val = tensor([20572])]; + tensor scatter_33_mode_0 = const()[name = tensor("scatter_33_mode_0"), val = tensor("update")]; + tensor scatter_33_axis_0 = const()[name = tensor("scatter_33_axis_0"), val = tensor(0)]; + tensor scatter_33 = scatter(axis = scatter_33_axis_0, data = scatter_32, indices = slice_by_index_58, mode = scatter_33_mode_0, updates = const_125)[name = tensor("scatter_33")]; + tensor const_126 = const()[name = tensor("const_126"), val = tensor([0x1p+0])]; + tensor slice_by_index_59 = const()[name = tensor("slice_by_index_59"), val = tensor([21194])]; + tensor scatter_34_mode_0 = const()[name = tensor("scatter_34_mode_0"), val = tensor("update")]; + tensor scatter_34_axis_0 = const()[name = tensor("scatter_34_axis_0"), val = tensor(0)]; + tensor scatter_34 = scatter(axis = scatter_34_axis_0, data = scatter_33, indices = slice_by_index_59, mode = scatter_34_mode_0, updates = const_126)[name = tensor("scatter_34")]; + tensor const_127 = const()[name = tensor("const_127"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_60 = const()[name = tensor("slice_by_index_60"), val = tensor([21816, 21817])]; + tensor scatter_35_mode_0 = const()[name = tensor("scatter_35_mode_0"), val = tensor("update")]; + tensor scatter_35_axis_0 = const()[name = tensor("scatter_35_axis_0"), val = tensor(0)]; + tensor scatter_35 = scatter(axis = scatter_35_axis_0, data = scatter_34, indices = slice_by_index_60, mode = scatter_35_mode_0, updates = const_127)[name = tensor("scatter_35")]; + tensor const_128 = const()[name = tensor("const_128"), val = tensor([0x1p+0])]; + tensor slice_by_index_61 = const()[name = tensor("slice_by_index_61"), val = tensor([22439])]; + tensor scatter_36_mode_0 = const()[name = tensor("scatter_36_mode_0"), val = tensor("update")]; + tensor scatter_36_axis_0 = const()[name = tensor("scatter_36_axis_0"), val = tensor(0)]; + tensor scatter_36 = scatter(axis = scatter_36_axis_0, data = scatter_35, indices = slice_by_index_61, mode = scatter_36_mode_0, updates = const_128)[name = tensor("scatter_36")]; + tensor const_129 = const()[name = tensor("const_129"), val = tensor([0x1p+0])]; + tensor slice_by_index_62 = const()[name = tensor("slice_by_index_62"), val = tensor([23061])]; + tensor scatter_37_mode_0 = const()[name = tensor("scatter_37_mode_0"), val = tensor("update")]; + tensor scatter_37_axis_0 = const()[name = tensor("scatter_37_axis_0"), val = tensor(0)]; + tensor scatter_37 = scatter(axis = scatter_37_axis_0, data = scatter_36, indices = slice_by_index_62, mode = scatter_37_mode_0, updates = const_129)[name = tensor("scatter_37")]; + tensor const_130 = const()[name = tensor("const_130"), val = tensor([0x1p+0])]; + tensor slice_by_index_63 = const()[name = tensor("slice_by_index_63"), val = tensor([23683])]; + tensor scatter_38_mode_0 = const()[name = tensor("scatter_38_mode_0"), val = tensor("update")]; + tensor scatter_38_axis_0 = const()[name = tensor("scatter_38_axis_0"), val = tensor(0)]; + tensor scatter_38 = scatter(axis = scatter_38_axis_0, data = scatter_37, indices = slice_by_index_63, mode = scatter_38_mode_0, updates = const_130)[name = tensor("scatter_38")]; + tensor const_131 = const()[name = tensor("const_131"), val = tensor([0x1p+0])]; + tensor slice_by_index_64 = const()[name = tensor("slice_by_index_64"), val = tensor([24305])]; + tensor scatter_39_mode_0 = const()[name = tensor("scatter_39_mode_0"), val = tensor("update")]; + tensor scatter_39_axis_0 = const()[name = tensor("scatter_39_axis_0"), val = tensor(0)]; + tensor scatter_39 = scatter(axis = scatter_39_axis_0, data = scatter_38, indices = slice_by_index_64, mode = scatter_39_mode_0, updates = const_131)[name = tensor("scatter_39")]; + tensor const_132 = const()[name = tensor("const_132"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_65 = const()[name = tensor("slice_by_index_65"), val = tensor([24927, 24928])]; + tensor scatter_40_mode_0 = const()[name = tensor("scatter_40_mode_0"), val = tensor("update")]; + tensor scatter_40_axis_0 = const()[name = tensor("scatter_40_axis_0"), val = tensor(0)]; + tensor scatter_40 = scatter(axis = scatter_40_axis_0, data = scatter_39, indices = slice_by_index_65, mode = scatter_40_mode_0, updates = const_132)[name = tensor("scatter_40")]; + tensor const_133 = const()[name = tensor("const_133"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_66 = const()[name = tensor("slice_by_index_66"), val = tensor([25550, 25551, 25552])]; + tensor scatter_41_mode_0 = const()[name = tensor("scatter_41_mode_0"), val = tensor("update")]; + tensor scatter_41_axis_0 = const()[name = tensor("scatter_41_axis_0"), val = tensor(0)]; + tensor scatter_41 = scatter(axis = scatter_41_axis_0, data = scatter_40, indices = slice_by_index_66, mode = scatter_41_mode_0, updates = const_133)[name = tensor("scatter_41")]; + tensor const_134 = const()[name = tensor("const_134"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_67 = const()[name = tensor("slice_by_index_67"), val = tensor([26174, 26175])]; + tensor scatter_42_mode_0 = const()[name = tensor("scatter_42_mode_0"), val = tensor("update")]; + tensor scatter_42_axis_0 = const()[name = tensor("scatter_42_axis_0"), val = tensor(0)]; + tensor scatter_42 = scatter(axis = scatter_42_axis_0, data = scatter_41, indices = slice_by_index_67, mode = scatter_42_mode_0, updates = const_134)[name = tensor("scatter_42")]; + tensor const_135 = const()[name = tensor("const_135"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_68 = const()[name = tensor("slice_by_index_68"), val = tensor([26797, 26798])]; + tensor scatter_43_mode_0 = const()[name = tensor("scatter_43_mode_0"), val = tensor("update")]; + tensor scatter_43_axis_0 = const()[name = tensor("scatter_43_axis_0"), val = tensor(0)]; + tensor scatter_43 = scatter(axis = scatter_43_axis_0, data = scatter_42, indices = slice_by_index_68, mode = scatter_43_mode_0, updates = const_135)[name = tensor("scatter_43")]; + tensor const_136 = const()[name = tensor("const_136"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_69 = const()[name = tensor("slice_by_index_69"), val = tensor([27420, 27421])]; + tensor scatter_44_mode_0 = const()[name = tensor("scatter_44_mode_0"), val = tensor("update")]; + tensor scatter_44_axis_0 = const()[name = tensor("scatter_44_axis_0"), val = tensor(0)]; + tensor scatter_44 = scatter(axis = scatter_44_axis_0, data = scatter_43, indices = slice_by_index_69, mode = scatter_44_mode_0, updates = const_136)[name = tensor("scatter_44")]; + tensor const_137 = const()[name = tensor("const_137"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_70 = const()[name = tensor("slice_by_index_70"), val = tensor([28043, 28044, 28045])]; + tensor scatter_45_mode_0 = const()[name = tensor("scatter_45_mode_0"), val = tensor("update")]; + tensor scatter_45_axis_0 = const()[name = tensor("scatter_45_axis_0"), val = tensor(0)]; + tensor scatter_45 = scatter(axis = scatter_45_axis_0, data = scatter_44, indices = slice_by_index_70, mode = scatter_45_mode_0, updates = const_137)[name = tensor("scatter_45")]; + tensor const_138 = const()[name = tensor("const_138"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_71 = const()[name = tensor("slice_by_index_71"), val = tensor([28667, 28668, 28669])]; + tensor scatter_46_mode_0 = const()[name = tensor("scatter_46_mode_0"), val = tensor("update")]; + tensor scatter_46_axis_0 = const()[name = tensor("scatter_46_axis_0"), val = tensor(0)]; + tensor scatter_46 = scatter(axis = scatter_46_axis_0, data = scatter_45, indices = slice_by_index_71, mode = scatter_46_mode_0, updates = const_138)[name = tensor("scatter_46")]; + tensor const_139 = const()[name = tensor("const_139"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_72 = const()[name = tensor("slice_by_index_72"), val = tensor([29291, 29292, 29293, 29294, 29295, 29296])]; + tensor scatter_47_mode_0 = const()[name = tensor("scatter_47_mode_0"), val = tensor("update")]; + tensor scatter_47_axis_0 = const()[name = tensor("scatter_47_axis_0"), val = tensor(0)]; + tensor scatter_47 = scatter(axis = scatter_47_axis_0, data = scatter_46, indices = slice_by_index_72, mode = scatter_47_mode_0, updates = const_139)[name = tensor("scatter_47")]; + tensor const_140 = const()[name = tensor("const_140"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_73 = const()[name = tensor("slice_by_index_73"), val = tensor([29918, 29919, 29920])]; + tensor scatter_48_mode_0 = const()[name = tensor("scatter_48_mode_0"), val = tensor("update")]; + tensor scatter_48_axis_0 = const()[name = tensor("scatter_48_axis_0"), val = tensor(0)]; + tensor scatter_48 = scatter(axis = scatter_48_axis_0, data = scatter_47, indices = slice_by_index_73, mode = scatter_48_mode_0, updates = const_140)[name = tensor("scatter_48")]; + tensor const_141 = const()[name = tensor("const_141"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_74 = const()[name = tensor("slice_by_index_74"), val = tensor([30542, 30543])]; + tensor scatter_49_mode_0 = const()[name = tensor("scatter_49_mode_0"), val = tensor("update")]; + tensor scatter_49_axis_0 = const()[name = tensor("scatter_49_axis_0"), val = tensor(0)]; + tensor scatter_49 = scatter(axis = scatter_49_axis_0, data = scatter_48, indices = slice_by_index_74, mode = scatter_49_mode_0, updates = const_141)[name = tensor("scatter_49")]; + tensor const_142 = const()[name = tensor("const_142"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_75 = const()[name = tensor("slice_by_index_75"), val = tensor([31165, 31166])]; + tensor scatter_50_mode_0 = const()[name = tensor("scatter_50_mode_0"), val = tensor("update")]; + tensor scatter_50_axis_0 = const()[name = tensor("scatter_50_axis_0"), val = tensor(0)]; + tensor scatter_50 = scatter(axis = scatter_50_axis_0, data = scatter_49, indices = slice_by_index_75, mode = scatter_50_mode_0, updates = const_142)[name = tensor("scatter_50")]; + tensor const_143 = const()[name = tensor("const_143"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_76 = const()[name = tensor("slice_by_index_76"), val = tensor([31788, 31789])]; + tensor scatter_51_mode_0 = const()[name = tensor("scatter_51_mode_0"), val = tensor("update")]; + tensor scatter_51_axis_0 = const()[name = tensor("scatter_51_axis_0"), val = tensor(0)]; + tensor scatter_51 = scatter(axis = scatter_51_axis_0, data = scatter_50, indices = slice_by_index_76, mode = scatter_51_mode_0, updates = const_143)[name = tensor("scatter_51")]; + tensor const_144 = const()[name = tensor("const_144"), val = tensor([0x1p+0])]; + tensor slice_by_index_77 = const()[name = tensor("slice_by_index_77"), val = tensor([32411])]; + tensor scatter_52_mode_0 = const()[name = tensor("scatter_52_mode_0"), val = tensor("update")]; + tensor scatter_52_axis_0 = const()[name = tensor("scatter_52_axis_0"), val = tensor(0)]; + tensor scatter_52 = scatter(axis = scatter_52_axis_0, data = scatter_51, indices = slice_by_index_77, mode = scatter_52_mode_0, updates = const_144)[name = tensor("scatter_52")]; + tensor const_145 = const()[name = tensor("const_145"), val = tensor([0x1p+0])]; + tensor slice_by_index_78 = const()[name = tensor("slice_by_index_78"), val = tensor([33033])]; + tensor scatter_53_mode_0 = const()[name = tensor("scatter_53_mode_0"), val = tensor("update")]; + tensor scatter_53_axis_0 = const()[name = tensor("scatter_53_axis_0"), val = tensor(0)]; + tensor scatter_53 = scatter(axis = scatter_53_axis_0, data = scatter_52, indices = slice_by_index_78, mode = scatter_53_mode_0, updates = const_145)[name = tensor("scatter_53")]; + tensor const_146 = const()[name = tensor("const_146"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_79 = const()[name = tensor("slice_by_index_79"), val = tensor([33655, 33656])]; + tensor scatter_54_mode_0 = const()[name = tensor("scatter_54_mode_0"), val = tensor("update")]; + tensor scatter_54_axis_0 = const()[name = tensor("scatter_54_axis_0"), val = tensor(0)]; + tensor scatter_54 = scatter(axis = scatter_54_axis_0, data = scatter_53, indices = slice_by_index_79, mode = scatter_54_mode_0, updates = const_146)[name = tensor("scatter_54")]; + tensor const_147 = const()[name = tensor("const_147"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_80 = const()[name = tensor("slice_by_index_80"), val = tensor([34278, 34279])]; + tensor scatter_55_mode_0 = const()[name = tensor("scatter_55_mode_0"), val = tensor("update")]; + tensor scatter_55_axis_0 = const()[name = tensor("scatter_55_axis_0"), val = tensor(0)]; + tensor scatter_55 = scatter(axis = scatter_55_axis_0, data = scatter_54, indices = slice_by_index_80, mode = scatter_55_mode_0, updates = const_147)[name = tensor("scatter_55")]; + tensor const_148 = const()[name = tensor("const_148"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_81 = const()[name = tensor("slice_by_index_81"), val = tensor([34901, 34902])]; + tensor scatter_56_mode_0 = const()[name = tensor("scatter_56_mode_0"), val = tensor("update")]; + tensor scatter_56_axis_0 = const()[name = tensor("scatter_56_axis_0"), val = tensor(0)]; + tensor scatter_56 = scatter(axis = scatter_56_axis_0, data = scatter_55, indices = slice_by_index_81, mode = scatter_56_mode_0, updates = const_148)[name = tensor("scatter_56")]; + tensor const_149 = const()[name = tensor("const_149"), val = tensor([0x1p+0])]; + tensor slice_by_index_82 = const()[name = tensor("slice_by_index_82"), val = tensor([35524])]; + tensor scatter_57_mode_0 = const()[name = tensor("scatter_57_mode_0"), val = tensor("update")]; + tensor scatter_57_axis_0 = const()[name = tensor("scatter_57_axis_0"), val = tensor(0)]; + tensor scatter_57 = scatter(axis = scatter_57_axis_0, data = scatter_56, indices = slice_by_index_82, mode = scatter_57_mode_0, updates = const_149)[name = tensor("scatter_57")]; + tensor const_150 = const()[name = tensor("const_150"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_83 = const()[name = tensor("slice_by_index_83"), val = tensor([36146, 36147, 36148])]; + tensor scatter_58_mode_0 = const()[name = tensor("scatter_58_mode_0"), val = tensor("update")]; + tensor scatter_58_axis_0 = const()[name = tensor("scatter_58_axis_0"), val = tensor(0)]; + tensor scatter_58 = scatter(axis = scatter_58_axis_0, data = scatter_57, indices = slice_by_index_83, mode = scatter_58_mode_0, updates = const_150)[name = tensor("scatter_58")]; + tensor const_151 = const()[name = tensor("const_151"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_84 = const()[name = tensor("slice_by_index_84"), val = tensor([36770, 36771, 36772])]; + tensor scatter_59_mode_0 = const()[name = tensor("scatter_59_mode_0"), val = tensor("update")]; + tensor scatter_59_axis_0 = const()[name = tensor("scatter_59_axis_0"), val = tensor(0)]; + tensor scatter_59 = scatter(axis = scatter_59_axis_0, data = scatter_58, indices = slice_by_index_84, mode = scatter_59_mode_0, updates = const_151)[name = tensor("scatter_59")]; + tensor const_152 = const()[name = tensor("const_152"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_85 = const()[name = tensor("slice_by_index_85"), val = tensor([37394, 37395, 37396])]; + tensor scatter_60_mode_0 = const()[name = tensor("scatter_60_mode_0"), val = tensor("update")]; + tensor scatter_60_axis_0 = const()[name = tensor("scatter_60_axis_0"), val = tensor(0)]; + tensor scatter_60 = scatter(axis = scatter_60_axis_0, data = scatter_59, indices = slice_by_index_85, mode = scatter_60_mode_0, updates = const_152)[name = tensor("scatter_60")]; + tensor const_153 = const()[name = tensor("const_153"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_86 = const()[name = tensor("slice_by_index_86"), val = tensor([38018, 38019])]; + tensor scatter_61_mode_0 = const()[name = tensor("scatter_61_mode_0"), val = tensor("update")]; + tensor scatter_61_axis_0 = const()[name = tensor("scatter_61_axis_0"), val = tensor(0)]; + tensor scatter_61 = scatter(axis = scatter_61_axis_0, data = scatter_60, indices = slice_by_index_86, mode = scatter_61_mode_0, updates = const_153)[name = tensor("scatter_61")]; + tensor const_154 = const()[name = tensor("const_154"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_87 = const()[name = tensor("slice_by_index_87"), val = tensor([38641, 38642])]; + tensor scatter_62_mode_0 = const()[name = tensor("scatter_62_mode_0"), val = tensor("update")]; + tensor scatter_62_axis_0 = const()[name = tensor("scatter_62_axis_0"), val = tensor(0)]; + tensor scatter_62 = scatter(axis = scatter_62_axis_0, data = scatter_61, indices = slice_by_index_87, mode = scatter_62_mode_0, updates = const_154)[name = tensor("scatter_62")]; + tensor const_155 = const()[name = tensor("const_155"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_88 = const()[name = tensor("slice_by_index_88"), val = tensor([39264, 39265])]; + tensor scatter_63_mode_0 = const()[name = tensor("scatter_63_mode_0"), val = tensor("update")]; + tensor scatter_63_axis_0 = const()[name = tensor("scatter_63_axis_0"), val = tensor(0)]; + tensor scatter_63 = scatter(axis = scatter_63_axis_0, data = scatter_62, indices = slice_by_index_88, mode = scatter_63_mode_0, updates = const_155)[name = tensor("scatter_63")]; + tensor const_156 = const()[name = tensor("const_156"), val = tensor([0x1p+0])]; + tensor slice_by_index_89 = const()[name = tensor("slice_by_index_89"), val = tensor([39887])]; + tensor scatter_64_mode_0 = const()[name = tensor("scatter_64_mode_0"), val = tensor("update")]; + tensor scatter_64_axis_0 = const()[name = tensor("scatter_64_axis_0"), val = tensor(0)]; + tensor scatter_64 = scatter(axis = scatter_64_axis_0, data = scatter_63, indices = slice_by_index_89, mode = scatter_64_mode_0, updates = const_156)[name = tensor("scatter_64")]; + tensor const_157 = const()[name = tensor("const_157"), val = tensor([0x1p+0])]; + tensor slice_by_index_90 = const()[name = tensor("slice_by_index_90"), val = tensor([40509])]; + tensor scatter_65_mode_0 = const()[name = tensor("scatter_65_mode_0"), val = tensor("update")]; + tensor scatter_65_axis_0 = const()[name = tensor("scatter_65_axis_0"), val = tensor(0)]; + tensor scatter_65 = scatter(axis = scatter_65_axis_0, data = scatter_64, indices = slice_by_index_90, mode = scatter_65_mode_0, updates = const_157)[name = tensor("scatter_65")]; + tensor const_158 = const()[name = tensor("const_158"), val = tensor([0x1p+0])]; + tensor slice_by_index_91 = const()[name = tensor("slice_by_index_91"), val = tensor([41131])]; + tensor scatter_66_mode_0 = const()[name = tensor("scatter_66_mode_0"), val = tensor("update")]; + tensor scatter_66_axis_0 = const()[name = tensor("scatter_66_axis_0"), val = tensor(0)]; + tensor scatter_66 = scatter(axis = scatter_66_axis_0, data = scatter_65, indices = slice_by_index_91, mode = scatter_66_mode_0, updates = const_158)[name = tensor("scatter_66")]; + tensor const_159 = const()[name = tensor("const_159"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_92 = const()[name = tensor("slice_by_index_92"), val = tensor([41753, 41754, 41755])]; + tensor scatter_67_mode_0 = const()[name = tensor("scatter_67_mode_0"), val = tensor("update")]; + tensor scatter_67_axis_0 = const()[name = tensor("scatter_67_axis_0"), val = tensor(0)]; + tensor scatter_67 = scatter(axis = scatter_67_axis_0, data = scatter_66, indices = slice_by_index_92, mode = scatter_67_mode_0, updates = const_159)[name = tensor("scatter_67")]; + tensor const_160 = const()[name = tensor("const_160"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_93 = const()[name = tensor("slice_by_index_93"), val = tensor([42377, 42378, 42379])]; + tensor scatter_68_mode_0 = const()[name = tensor("scatter_68_mode_0"), val = tensor("update")]; + tensor scatter_68_axis_0 = const()[name = tensor("scatter_68_axis_0"), val = tensor(0)]; + tensor scatter_68 = scatter(axis = scatter_68_axis_0, data = scatter_67, indices = slice_by_index_93, mode = scatter_68_mode_0, updates = const_160)[name = tensor("scatter_68")]; + tensor const_161 = const()[name = tensor("const_161"), val = tensor([0x1p+0])]; + tensor slice_by_index_94 = const()[name = tensor("slice_by_index_94"), val = tensor([43001])]; + tensor scatter_69_mode_0 = const()[name = tensor("scatter_69_mode_0"), val = tensor("update")]; + tensor scatter_69_axis_0 = const()[name = tensor("scatter_69_axis_0"), val = tensor(0)]; + tensor scatter_69 = scatter(axis = scatter_69_axis_0, data = scatter_68, indices = slice_by_index_94, mode = scatter_69_mode_0, updates = const_161)[name = tensor("scatter_69")]; + tensor const_162 = const()[name = tensor("const_162"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_95 = const()[name = tensor("slice_by_index_95"), val = tensor([43623, 43624, 43625])]; + tensor scatter_70_mode_0 = const()[name = tensor("scatter_70_mode_0"), val = tensor("update")]; + tensor scatter_70_axis_0 = const()[name = tensor("scatter_70_axis_0"), val = tensor(0)]; + tensor scatter_70 = scatter(axis = scatter_70_axis_0, data = scatter_69, indices = slice_by_index_95, mode = scatter_70_mode_0, updates = const_162)[name = tensor("scatter_70")]; + tensor const_163 = const()[name = tensor("const_163"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_96 = const()[name = tensor("slice_by_index_96"), val = tensor([44247, 44248, 44249])]; + tensor scatter_71_mode_0 = const()[name = tensor("scatter_71_mode_0"), val = tensor("update")]; + tensor scatter_71_axis_0 = const()[name = tensor("scatter_71_axis_0"), val = tensor(0)]; + tensor scatter_71 = scatter(axis = scatter_71_axis_0, data = scatter_70, indices = slice_by_index_96, mode = scatter_71_mode_0, updates = const_163)[name = tensor("scatter_71")]; + tensor const_164 = const()[name = tensor("const_164"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_97 = const()[name = tensor("slice_by_index_97"), val = tensor([44871, 44872, 44873])]; + tensor scatter_72_mode_0 = const()[name = tensor("scatter_72_mode_0"), val = tensor("update")]; + tensor scatter_72_axis_0 = const()[name = tensor("scatter_72_axis_0"), val = tensor(0)]; + tensor scatter_72 = scatter(axis = scatter_72_axis_0, data = scatter_71, indices = slice_by_index_97, mode = scatter_72_mode_0, updates = const_164)[name = tensor("scatter_72")]; + tensor const_165 = const()[name = tensor("const_165"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_98 = const()[name = tensor("slice_by_index_98"), val = tensor([45495, 45496])]; + tensor scatter_73_mode_0 = const()[name = tensor("scatter_73_mode_0"), val = tensor("update")]; + tensor scatter_73_axis_0 = const()[name = tensor("scatter_73_axis_0"), val = tensor(0)]; + tensor scatter_73 = scatter(axis = scatter_73_axis_0, data = scatter_72, indices = slice_by_index_98, mode = scatter_73_mode_0, updates = const_165)[name = tensor("scatter_73")]; + tensor const_166 = const()[name = tensor("const_166"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_99 = const()[name = tensor("slice_by_index_99"), val = tensor([46118, 46119])]; + tensor scatter_74_mode_0 = const()[name = tensor("scatter_74_mode_0"), val = tensor("update")]; + tensor scatter_74_axis_0 = const()[name = tensor("scatter_74_axis_0"), val = tensor(0)]; + tensor scatter_74 = scatter(axis = scatter_74_axis_0, data = scatter_73, indices = slice_by_index_99, mode = scatter_74_mode_0, updates = const_166)[name = tensor("scatter_74")]; + tensor const_167 = const()[name = tensor("const_167"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_100 = const()[name = tensor("slice_by_index_100"), val = tensor([46741, 46742])]; + tensor scatter_75_mode_0 = const()[name = tensor("scatter_75_mode_0"), val = tensor("update")]; + tensor scatter_75_axis_0 = const()[name = tensor("scatter_75_axis_0"), val = tensor(0)]; + tensor scatter_75 = scatter(axis = scatter_75_axis_0, data = scatter_74, indices = slice_by_index_100, mode = scatter_75_mode_0, updates = const_167)[name = tensor("scatter_75")]; + tensor const_168 = const()[name = tensor("const_168"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_101 = const()[name = tensor("slice_by_index_101"), val = tensor([47364, 47365, 47366])]; + tensor scatter_76_mode_0 = const()[name = tensor("scatter_76_mode_0"), val = tensor("update")]; + tensor scatter_76_axis_0 = const()[name = tensor("scatter_76_axis_0"), val = tensor(0)]; + tensor scatter_76 = scatter(axis = scatter_76_axis_0, data = scatter_75, indices = slice_by_index_101, mode = scatter_76_mode_0, updates = const_168)[name = tensor("scatter_76")]; + tensor const_169 = const()[name = tensor("const_169"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_102 = const()[name = tensor("slice_by_index_102"), val = tensor([47988, 47989])]; + tensor scatter_77_mode_0 = const()[name = tensor("scatter_77_mode_0"), val = tensor("update")]; + tensor scatter_77_axis_0 = const()[name = tensor("scatter_77_axis_0"), val = tensor(0)]; + tensor scatter_77 = scatter(axis = scatter_77_axis_0, data = scatter_76, indices = slice_by_index_102, mode = scatter_77_mode_0, updates = const_169)[name = tensor("scatter_77")]; + tensor const_170 = const()[name = tensor("const_170"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_103 = const()[name = tensor("slice_by_index_103"), val = tensor([48611, 48612, 48613, 48614])]; + tensor scatter_78_mode_0 = const()[name = tensor("scatter_78_mode_0"), val = tensor("update")]; + tensor scatter_78_axis_0 = const()[name = tensor("scatter_78_axis_0"), val = tensor(0)]; + tensor scatter_78 = scatter(axis = scatter_78_axis_0, data = scatter_77, indices = slice_by_index_103, mode = scatter_78_mode_0, updates = const_170)[name = tensor("scatter_78")]; + tensor const_171 = const()[name = tensor("const_171"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_104 = const()[name = tensor("slice_by_index_104"), val = tensor([49236, 49237])]; + tensor scatter_79_mode_0 = const()[name = tensor("scatter_79_mode_0"), val = tensor("update")]; + tensor scatter_79_axis_0 = const()[name = tensor("scatter_79_axis_0"), val = tensor(0)]; + tensor scatter_79 = scatter(axis = scatter_79_axis_0, data = scatter_78, indices = slice_by_index_104, mode = scatter_79_mode_0, updates = const_171)[name = tensor("scatter_79")]; + tensor const_172 = const()[name = tensor("const_172"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_105 = const()[name = tensor("slice_by_index_105"), val = tensor([49859, 49860])]; + tensor scatter_80_mode_0 = const()[name = tensor("scatter_80_mode_0"), val = tensor("update")]; + tensor scatter_80_axis_0 = const()[name = tensor("scatter_80_axis_0"), val = tensor(0)]; + tensor scatter_80 = scatter(axis = scatter_80_axis_0, data = scatter_79, indices = slice_by_index_105, mode = scatter_80_mode_0, updates = const_172)[name = tensor("scatter_80")]; + tensor const_173 = const()[name = tensor("const_173"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_106 = const()[name = tensor("slice_by_index_106"), val = tensor([50482, 50483])]; + tensor scatter_81_mode_0 = const()[name = tensor("scatter_81_mode_0"), val = tensor("update")]; + tensor scatter_81_axis_0 = const()[name = tensor("scatter_81_axis_0"), val = tensor(0)]; + tensor scatter_81 = scatter(axis = scatter_81_axis_0, data = scatter_80, indices = slice_by_index_106, mode = scatter_81_mode_0, updates = const_173)[name = tensor("scatter_81")]; + tensor const_174 = const()[name = tensor("const_174"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_107 = const()[name = tensor("slice_by_index_107"), val = tensor([51105, 51106])]; + tensor scatter_82_mode_0 = const()[name = tensor("scatter_82_mode_0"), val = tensor("update")]; + tensor scatter_82_axis_0 = const()[name = tensor("scatter_82_axis_0"), val = tensor(0)]; + tensor scatter_82 = scatter(axis = scatter_82_axis_0, data = scatter_81, indices = slice_by_index_107, mode = scatter_82_mode_0, updates = const_174)[name = tensor("scatter_82")]; + tensor const_175 = const()[name = tensor("const_175"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_108 = const()[name = tensor("slice_by_index_108"), val = tensor([51728, 51729])]; + tensor scatter_83_mode_0 = const()[name = tensor("scatter_83_mode_0"), val = tensor("update")]; + tensor scatter_83_axis_0 = const()[name = tensor("scatter_83_axis_0"), val = tensor(0)]; + tensor scatter_83 = scatter(axis = scatter_83_axis_0, data = scatter_82, indices = slice_by_index_108, mode = scatter_83_mode_0, updates = const_175)[name = tensor("scatter_83")]; + tensor const_176 = const()[name = tensor("const_176"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_109 = const()[name = tensor("slice_by_index_109"), val = tensor([52351, 52352, 52353])]; + tensor scatter_84_mode_0 = const()[name = tensor("scatter_84_mode_0"), val = tensor("update")]; + tensor scatter_84_axis_0 = const()[name = tensor("scatter_84_axis_0"), val = tensor(0)]; + tensor scatter_84 = scatter(axis = scatter_84_axis_0, data = scatter_83, indices = slice_by_index_109, mode = scatter_84_mode_0, updates = const_176)[name = tensor("scatter_84")]; + tensor const_177 = const()[name = tensor("const_177"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_110 = const()[name = tensor("slice_by_index_110"), val = tensor([52975, 52976, 52977, 52978])]; + tensor scatter_85_mode_0 = const()[name = tensor("scatter_85_mode_0"), val = tensor("update")]; + tensor scatter_85_axis_0 = const()[name = tensor("scatter_85_axis_0"), val = tensor(0)]; + tensor scatter_85 = scatter(axis = scatter_85_axis_0, data = scatter_84, indices = slice_by_index_110, mode = scatter_85_mode_0, updates = const_177)[name = tensor("scatter_85")]; + tensor const_178 = const()[name = tensor("const_178"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_111 = const()[name = tensor("slice_by_index_111"), val = tensor([53600, 53601, 53602])]; + tensor scatter_86_mode_0 = const()[name = tensor("scatter_86_mode_0"), val = tensor("update")]; + tensor scatter_86_axis_0 = const()[name = tensor("scatter_86_axis_0"), val = tensor(0)]; + tensor scatter_86 = scatter(axis = scatter_86_axis_0, data = scatter_85, indices = slice_by_index_111, mode = scatter_86_mode_0, updates = const_178)[name = tensor("scatter_86")]; + tensor const_179 = const()[name = tensor("const_179"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_112 = const()[name = tensor("slice_by_index_112"), val = tensor([54224, 54225])]; + tensor scatter_87_mode_0 = const()[name = tensor("scatter_87_mode_0"), val = tensor("update")]; + tensor scatter_87_axis_0 = const()[name = tensor("scatter_87_axis_0"), val = tensor(0)]; + tensor scatter_87 = scatter(axis = scatter_87_axis_0, data = scatter_86, indices = slice_by_index_112, mode = scatter_87_mode_0, updates = const_179)[name = tensor("scatter_87")]; + tensor const_180 = const()[name = tensor("const_180"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_113 = const()[name = tensor("slice_by_index_113"), val = tensor([54847, 54848, 54849])]; + tensor scatter_88_mode_0 = const()[name = tensor("scatter_88_mode_0"), val = tensor("update")]; + tensor scatter_88_axis_0 = const()[name = tensor("scatter_88_axis_0"), val = tensor(0)]; + tensor scatter_88 = scatter(axis = scatter_88_axis_0, data = scatter_87, indices = slice_by_index_113, mode = scatter_88_mode_0, updates = const_180)[name = tensor("scatter_88")]; + tensor const_181 = const()[name = tensor("const_181"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_114 = const()[name = tensor("slice_by_index_114"), val = tensor([55471, 55472, 55473, 55474])]; + tensor scatter_89_mode_0 = const()[name = tensor("scatter_89_mode_0"), val = tensor("update")]; + tensor scatter_89_axis_0 = const()[name = tensor("scatter_89_axis_0"), val = tensor(0)]; + tensor scatter_89 = scatter(axis = scatter_89_axis_0, data = scatter_88, indices = slice_by_index_114, mode = scatter_89_mode_0, updates = const_181)[name = tensor("scatter_89")]; + tensor const_182 = const()[name = tensor("const_182"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_115 = const()[name = tensor("slice_by_index_115"), val = tensor([56096, 56097, 56098])]; + tensor scatter_90_mode_0 = const()[name = tensor("scatter_90_mode_0"), val = tensor("update")]; + tensor scatter_90_axis_0 = const()[name = tensor("scatter_90_axis_0"), val = tensor(0)]; + tensor scatter_90 = scatter(axis = scatter_90_axis_0, data = scatter_89, indices = slice_by_index_115, mode = scatter_90_mode_0, updates = const_182)[name = tensor("scatter_90")]; + tensor const_183 = const()[name = tensor("const_183"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_116 = const()[name = tensor("slice_by_index_116"), val = tensor([56720, 56721])]; + tensor scatter_91_mode_0 = const()[name = tensor("scatter_91_mode_0"), val = tensor("update")]; + tensor scatter_91_axis_0 = const()[name = tensor("scatter_91_axis_0"), val = tensor(0)]; + tensor scatter_91 = scatter(axis = scatter_91_axis_0, data = scatter_90, indices = slice_by_index_116, mode = scatter_91_mode_0, updates = const_183)[name = tensor("scatter_91")]; + tensor const_184 = const()[name = tensor("const_184"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_117 = const()[name = tensor("slice_by_index_117"), val = tensor([57343, 57344])]; + tensor scatter_92_mode_0 = const()[name = tensor("scatter_92_mode_0"), val = tensor("update")]; + tensor scatter_92_axis_0 = const()[name = tensor("scatter_92_axis_0"), val = tensor(0)]; + tensor scatter_92 = scatter(axis = scatter_92_axis_0, data = scatter_91, indices = slice_by_index_117, mode = scatter_92_mode_0, updates = const_184)[name = tensor("scatter_92")]; + tensor const_185 = const()[name = tensor("const_185"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_118 = const()[name = tensor("slice_by_index_118"), val = tensor([57966, 57967, 57968])]; + tensor scatter_93_mode_0 = const()[name = tensor("scatter_93_mode_0"), val = tensor("update")]; + tensor scatter_93_axis_0 = const()[name = tensor("scatter_93_axis_0"), val = tensor(0)]; + tensor scatter_93 = scatter(axis = scatter_93_axis_0, data = scatter_92, indices = slice_by_index_118, mode = scatter_93_mode_0, updates = const_185)[name = tensor("scatter_93")]; + tensor const_186 = const()[name = tensor("const_186"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_119 = const()[name = tensor("slice_by_index_119"), val = tensor([58590, 58591, 58592, 58593])]; + tensor scatter_94_mode_0 = const()[name = tensor("scatter_94_mode_0"), val = tensor("update")]; + tensor scatter_94_axis_0 = const()[name = tensor("scatter_94_axis_0"), val = tensor(0)]; + tensor scatter_94 = scatter(axis = scatter_94_axis_0, data = scatter_93, indices = slice_by_index_119, mode = scatter_94_mode_0, updates = const_186)[name = tensor("scatter_94")]; + tensor const_187 = const()[name = tensor("const_187"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_120 = const()[name = tensor("slice_by_index_120"), val = tensor([59215, 59216])]; + tensor scatter_95_mode_0 = const()[name = tensor("scatter_95_mode_0"), val = tensor("update")]; + tensor scatter_95_axis_0 = const()[name = tensor("scatter_95_axis_0"), val = tensor(0)]; + tensor scatter_95 = scatter(axis = scatter_95_axis_0, data = scatter_94, indices = slice_by_index_120, mode = scatter_95_mode_0, updates = const_187)[name = tensor("scatter_95")]; + tensor const_188 = const()[name = tensor("const_188"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_121 = const()[name = tensor("slice_by_index_121"), val = tensor([59838, 59839, 59840])]; + tensor scatter_96_mode_0 = const()[name = tensor("scatter_96_mode_0"), val = tensor("update")]; + tensor scatter_96_axis_0 = const()[name = tensor("scatter_96_axis_0"), val = tensor(0)]; + tensor scatter_96 = scatter(axis = scatter_96_axis_0, data = scatter_95, indices = slice_by_index_121, mode = scatter_96_mode_0, updates = const_188)[name = tensor("scatter_96")]; + tensor const_189 = const()[name = tensor("const_189"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_122 = const()[name = tensor("slice_by_index_122"), val = tensor([60462, 60463, 60464, 60465])]; + tensor scatter_97_mode_0 = const()[name = tensor("scatter_97_mode_0"), val = tensor("update")]; + tensor scatter_97_axis_0 = const()[name = tensor("scatter_97_axis_0"), val = tensor(0)]; + tensor scatter_97 = scatter(axis = scatter_97_axis_0, data = scatter_96, indices = slice_by_index_122, mode = scatter_97_mode_0, updates = const_189)[name = tensor("scatter_97")]; + tensor const_190 = const()[name = tensor("const_190"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_123 = const()[name = tensor("slice_by_index_123"), val = tensor([61087, 61088, 61089])]; + tensor scatter_98_mode_0 = const()[name = tensor("scatter_98_mode_0"), val = tensor("update")]; + tensor scatter_98_axis_0 = const()[name = tensor("scatter_98_axis_0"), val = tensor(0)]; + tensor scatter_98 = scatter(axis = scatter_98_axis_0, data = scatter_97, indices = slice_by_index_123, mode = scatter_98_mode_0, updates = const_190)[name = tensor("scatter_98")]; + tensor const_191 = const()[name = tensor("const_191"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_124 = const()[name = tensor("slice_by_index_124"), val = tensor([61711, 61712])]; + tensor scatter_99_mode_0 = const()[name = tensor("scatter_99_mode_0"), val = tensor("update")]; + tensor scatter_99_axis_0 = const()[name = tensor("scatter_99_axis_0"), val = tensor(0)]; + tensor scatter_99 = scatter(axis = scatter_99_axis_0, data = scatter_98, indices = slice_by_index_124, mode = scatter_99_mode_0, updates = const_191)[name = tensor("scatter_99")]; + tensor const_192 = const()[name = tensor("const_192"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_125 = const()[name = tensor("slice_by_index_125"), val = tensor([62334, 62335, 62336])]; + tensor scatter_100_mode_0 = const()[name = tensor("scatter_100_mode_0"), val = tensor("update")]; + tensor scatter_100_axis_0 = const()[name = tensor("scatter_100_axis_0"), val = tensor(0)]; + tensor scatter_100 = scatter(axis = scatter_100_axis_0, data = scatter_99, indices = slice_by_index_125, mode = scatter_100_mode_0, updates = const_192)[name = tensor("scatter_100")]; + tensor const_193 = const()[name = tensor("const_193"), val = tensor([0x1p+0])]; + tensor slice_by_index_126 = const()[name = tensor("slice_by_index_126"), val = tensor([62958])]; + tensor scatter_101_mode_0 = const()[name = tensor("scatter_101_mode_0"), val = tensor("update")]; + tensor scatter_101_axis_0 = const()[name = tensor("scatter_101_axis_0"), val = tensor(0)]; + tensor scatter_101 = scatter(axis = scatter_101_axis_0, data = scatter_100, indices = slice_by_index_126, mode = scatter_101_mode_0, updates = const_193)[name = tensor("scatter_101")]; + tensor const_194 = const()[name = tensor("const_194"), val = tensor([0x1p+0])]; + tensor slice_by_index_127 = const()[name = tensor("slice_by_index_127"), val = tensor([63580])]; + tensor scatter_102_mode_0 = const()[name = tensor("scatter_102_mode_0"), val = tensor("update")]; + tensor scatter_102_axis_0 = const()[name = tensor("scatter_102_axis_0"), val = tensor(0)]; + tensor scatter_102 = scatter(axis = scatter_102_axis_0, data = scatter_101, indices = slice_by_index_127, mode = scatter_102_mode_0, updates = const_194)[name = tensor("scatter_102")]; + tensor const_195 = const()[name = tensor("const_195"), val = tensor([0x1p+0])]; + tensor slice_by_index_128 = const()[name = tensor("slice_by_index_128"), val = tensor([64202])]; + tensor scatter_103_mode_0 = const()[name = tensor("scatter_103_mode_0"), val = tensor("update")]; + tensor scatter_103_axis_0 = const()[name = tensor("scatter_103_axis_0"), val = tensor(0)]; + tensor scatter_103 = scatter(axis = scatter_103_axis_0, data = scatter_102, indices = slice_by_index_128, mode = scatter_103_mode_0, updates = const_195)[name = tensor("scatter_103")]; + tensor const_196 = const()[name = tensor("const_196"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_129 = const()[name = tensor("slice_by_index_129"), val = tensor([64824, 64825])]; + tensor scatter_104_mode_0 = const()[name = tensor("scatter_104_mode_0"), val = tensor("update")]; + tensor scatter_104_axis_0 = const()[name = tensor("scatter_104_axis_0"), val = tensor(0)]; + tensor scatter_104 = scatter(axis = scatter_104_axis_0, data = scatter_103, indices = slice_by_index_129, mode = scatter_104_mode_0, updates = const_196)[name = tensor("scatter_104")]; + tensor const_197 = const()[name = tensor("const_197"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_130 = const()[name = tensor("slice_by_index_130"), val = tensor([65447, 65448])]; + tensor scatter_105_mode_0 = const()[name = tensor("scatter_105_mode_0"), val = tensor("update")]; + tensor scatter_105_axis_0 = const()[name = tensor("scatter_105_axis_0"), val = tensor(0)]; + tensor scatter_105 = scatter(axis = scatter_105_axis_0, data = scatter_104, indices = slice_by_index_130, mode = scatter_105_mode_0, updates = const_197)[name = tensor("scatter_105")]; + tensor const_198 = const()[name = tensor("const_198"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_131 = const()[name = tensor("slice_by_index_131"), val = tensor([66070, 66071, 66072, 66073, 66074])]; + tensor scatter_106_mode_0 = const()[name = tensor("scatter_106_mode_0"), val = tensor("update")]; + tensor scatter_106_axis_0 = const()[name = tensor("scatter_106_axis_0"), val = tensor(0)]; + tensor scatter_106 = scatter(axis = scatter_106_axis_0, data = scatter_105, indices = slice_by_index_131, mode = scatter_106_mode_0, updates = const_198)[name = tensor("scatter_106")]; + tensor const_199 = const()[name = tensor("const_199"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_132 = const()[name = tensor("slice_by_index_132"), val = tensor([66696, 66697, 66698, 66699, 66700])]; + tensor scatter_107_mode_0 = const()[name = tensor("scatter_107_mode_0"), val = tensor("update")]; + tensor scatter_107_axis_0 = const()[name = tensor("scatter_107_axis_0"), val = tensor(0)]; + tensor scatter_107 = scatter(axis = scatter_107_axis_0, data = scatter_106, indices = slice_by_index_132, mode = scatter_107_mode_0, updates = const_199)[name = tensor("scatter_107")]; + tensor const_200 = const()[name = tensor("const_200"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73536768)))]; + tensor slice_by_index_133 = const()[name = tensor("slice_by_index_133"), val = tensor([67322, 67323, 67324, 67325, 67326, 67327, 67328, 67329, 67330, 67331, 67332, 67333, 67334, 67335, 67336, 67337, 67338, 67339, 67340])]; + tensor scatter_108_mode_0 = const()[name = tensor("scatter_108_mode_0"), val = tensor("update")]; + tensor scatter_108_axis_0 = const()[name = tensor("scatter_108_axis_0"), val = tensor(0)]; + tensor scatter_108 = scatter(axis = scatter_108_axis_0, data = scatter_107, indices = slice_by_index_133, mode = scatter_108_mode_0, updates = const_200)[name = tensor("scatter_108")]; + tensor const_201 = const()[name = tensor("const_201"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_134 = const()[name = tensor("slice_by_index_134"), val = tensor([67962, 67963, 67964])]; + tensor scatter_109_mode_0 = const()[name = tensor("scatter_109_mode_0"), val = tensor("update")]; + tensor scatter_109_axis_0 = const()[name = tensor("scatter_109_axis_0"), val = tensor(0)]; + tensor scatter_109 = scatter(axis = scatter_109_axis_0, data = scatter_108, indices = slice_by_index_134, mode = scatter_109_mode_0, updates = const_201)[name = tensor("scatter_109")]; + tensor const_202 = const()[name = tensor("const_202"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_135 = const()[name = tensor("slice_by_index_135"), val = tensor([68586, 68587])]; + tensor scatter_110_mode_0 = const()[name = tensor("scatter_110_mode_0"), val = tensor("update")]; + tensor scatter_110_axis_0 = const()[name = tensor("scatter_110_axis_0"), val = tensor(0)]; + tensor scatter_110 = scatter(axis = scatter_110_axis_0, data = scatter_109, indices = slice_by_index_135, mode = scatter_110_mode_0, updates = const_202)[name = tensor("scatter_110")]; + tensor const_203 = const()[name = tensor("const_203"), val = tensor([0x1p+0])]; + tensor slice_by_index_136 = const()[name = tensor("slice_by_index_136"), val = tensor([69209])]; + tensor scatter_111_mode_0 = const()[name = tensor("scatter_111_mode_0"), val = tensor("update")]; + tensor scatter_111_axis_0 = const()[name = tensor("scatter_111_axis_0"), val = tensor(0)]; + tensor scatter_111 = scatter(axis = scatter_111_axis_0, data = scatter_110, indices = slice_by_index_136, mode = scatter_111_mode_0, updates = const_203)[name = tensor("scatter_111")]; + tensor const_204 = const()[name = tensor("const_204"), val = tensor([0x1p+0])]; + tensor slice_by_index_137 = const()[name = tensor("slice_by_index_137"), val = tensor([69831])]; + tensor scatter_112_mode_0 = const()[name = tensor("scatter_112_mode_0"), val = tensor("update")]; + tensor scatter_112_axis_0 = const()[name = tensor("scatter_112_axis_0"), val = tensor(0)]; + tensor scatter_112 = scatter(axis = scatter_112_axis_0, data = scatter_111, indices = slice_by_index_137, mode = scatter_112_mode_0, updates = const_204)[name = tensor("scatter_112")]; + tensor const_205 = const()[name = tensor("const_205"), val = tensor([0x1p+0])]; + tensor slice_by_index_138 = const()[name = tensor("slice_by_index_138"), val = tensor([70453])]; + tensor scatter_113_mode_0 = const()[name = tensor("scatter_113_mode_0"), val = tensor("update")]; + tensor scatter_113_axis_0 = const()[name = tensor("scatter_113_axis_0"), val = tensor(0)]; + tensor scatter_113 = scatter(axis = scatter_113_axis_0, data = scatter_112, indices = slice_by_index_138, mode = scatter_113_mode_0, updates = const_205)[name = tensor("scatter_113")]; + tensor const_206 = const()[name = tensor("const_206"), val = tensor([0x1p+0])]; + tensor slice_by_index_139 = const()[name = tensor("slice_by_index_139"), val = tensor([71075])]; + tensor scatter_114_mode_0 = const()[name = tensor("scatter_114_mode_0"), val = tensor("update")]; + tensor scatter_114_axis_0 = const()[name = tensor("scatter_114_axis_0"), val = tensor(0)]; + tensor scatter_114 = scatter(axis = scatter_114_axis_0, data = scatter_113, indices = slice_by_index_139, mode = scatter_114_mode_0, updates = const_206)[name = tensor("scatter_114")]; + tensor const_207 = const()[name = tensor("const_207"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_140 = const()[name = tensor("slice_by_index_140"), val = tensor([71697, 71698])]; + tensor scatter_115_mode_0 = const()[name = tensor("scatter_115_mode_0"), val = tensor("update")]; + tensor scatter_115_axis_0 = const()[name = tensor("scatter_115_axis_0"), val = tensor(0)]; + tensor scatter_115 = scatter(axis = scatter_115_axis_0, data = scatter_114, indices = slice_by_index_140, mode = scatter_115_mode_0, updates = const_207)[name = tensor("scatter_115")]; + tensor const_208 = const()[name = tensor("const_208"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_141 = const()[name = tensor("slice_by_index_141"), val = tensor([72320, 72321])]; + tensor scatter_116_mode_0 = const()[name = tensor("scatter_116_mode_0"), val = tensor("update")]; + tensor scatter_116_axis_0 = const()[name = tensor("scatter_116_axis_0"), val = tensor(0)]; + tensor scatter_116 = scatter(axis = scatter_116_axis_0, data = scatter_115, indices = slice_by_index_141, mode = scatter_116_mode_0, updates = const_208)[name = tensor("scatter_116")]; + tensor const_209 = const()[name = tensor("const_209"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_142 = const()[name = tensor("slice_by_index_142"), val = tensor([72943, 72944, 72945, 72946, 72947, 72948, 72949])]; + tensor scatter_117_mode_0 = const()[name = tensor("scatter_117_mode_0"), val = tensor("update")]; + tensor scatter_117_axis_0 = const()[name = tensor("scatter_117_axis_0"), val = tensor(0)]; + tensor scatter_117 = scatter(axis = scatter_117_axis_0, data = scatter_116, indices = slice_by_index_142, mode = scatter_117_mode_0, updates = const_209)[name = tensor("scatter_117")]; + tensor const_210 = const()[name = tensor("const_210"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73536960)))]; + tensor slice_by_index_143 = const()[name = tensor("slice_by_index_143"), val = tensor([73571, 73572, 73573, 73574, 73575, 73576, 73577, 73578, 73579, 73580, 73581, 73582, 73583, 73584, 73585])]; + tensor scatter_118_mode_0 = const()[name = tensor("scatter_118_mode_0"), val = tensor("update")]; + tensor scatter_118_axis_0 = const()[name = tensor("scatter_118_axis_0"), val = tensor(0)]; + tensor scatter_118 = scatter(axis = scatter_118_axis_0, data = scatter_117, indices = slice_by_index_143, mode = scatter_118_mode_0, updates = const_210)[name = tensor("scatter_118")]; + tensor const_211 = const()[name = tensor("const_211"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_144 = const()[name = tensor("slice_by_index_144"), val = tensor([74207, 74208, 74209, 74210])]; + tensor scatter_119_mode_0 = const()[name = tensor("scatter_119_mode_0"), val = tensor("update")]; + tensor scatter_119_axis_0 = const()[name = tensor("scatter_119_axis_0"), val = tensor(0)]; + tensor scatter_119 = scatter(axis = scatter_119_axis_0, data = scatter_118, indices = slice_by_index_144, mode = scatter_119_mode_0, updates = const_211)[name = tensor("scatter_119")]; + tensor const_212 = const()[name = tensor("const_212"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_145 = const()[name = tensor("slice_by_index_145"), val = tensor([74832, 74833, 74834])]; + tensor scatter_120_mode_0 = const()[name = tensor("scatter_120_mode_0"), val = tensor("update")]; + tensor scatter_120_axis_0 = const()[name = tensor("scatter_120_axis_0"), val = tensor(0)]; + tensor scatter_120 = scatter(axis = scatter_120_axis_0, data = scatter_119, indices = slice_by_index_145, mode = scatter_120_mode_0, updates = const_212)[name = tensor("scatter_120")]; + tensor const_213 = const()[name = tensor("const_213"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_146 = const()[name = tensor("slice_by_index_146"), val = tensor([75456, 75457])]; + tensor scatter_121_mode_0 = const()[name = tensor("scatter_121_mode_0"), val = tensor("update")]; + tensor scatter_121_axis_0 = const()[name = tensor("scatter_121_axis_0"), val = tensor(0)]; + tensor scatter_121 = scatter(axis = scatter_121_axis_0, data = scatter_120, indices = slice_by_index_146, mode = scatter_121_mode_0, updates = const_213)[name = tensor("scatter_121")]; + tensor const_214 = const()[name = tensor("const_214"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_147 = const()[name = tensor("slice_by_index_147"), val = tensor([76079, 76080, 76081])]; + tensor scatter_122_mode_0 = const()[name = tensor("scatter_122_mode_0"), val = tensor("update")]; + tensor scatter_122_axis_0 = const()[name = tensor("scatter_122_axis_0"), val = tensor(0)]; + tensor scatter_122 = scatter(axis = scatter_122_axis_0, data = scatter_121, indices = slice_by_index_147, mode = scatter_122_mode_0, updates = const_214)[name = tensor("scatter_122")]; + tensor const_215 = const()[name = tensor("const_215"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_148 = const()[name = tensor("slice_by_index_148"), val = tensor([76703, 76704, 76705])]; + tensor scatter_123_mode_0 = const()[name = tensor("scatter_123_mode_0"), val = tensor("update")]; + tensor scatter_123_axis_0 = const()[name = tensor("scatter_123_axis_0"), val = tensor(0)]; + tensor scatter_123 = scatter(axis = scatter_123_axis_0, data = scatter_122, indices = slice_by_index_148, mode = scatter_123_mode_0, updates = const_215)[name = tensor("scatter_123")]; + tensor const_216 = const()[name = tensor("const_216"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_149 = const()[name = tensor("slice_by_index_149"), val = tensor([77327, 77328, 77329])]; + tensor scatter_124_mode_0 = const()[name = tensor("scatter_124_mode_0"), val = tensor("update")]; + tensor scatter_124_axis_0 = const()[name = tensor("scatter_124_axis_0"), val = tensor(0)]; + tensor scatter_124 = scatter(axis = scatter_124_axis_0, data = scatter_123, indices = slice_by_index_149, mode = scatter_124_mode_0, updates = const_216)[name = tensor("scatter_124")]; + tensor const_217 = const()[name = tensor("const_217"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_150 = const()[name = tensor("slice_by_index_150"), val = tensor([77951, 77952])]; + tensor scatter_125_mode_0 = const()[name = tensor("scatter_125_mode_0"), val = tensor("update")]; + tensor scatter_125_axis_0 = const()[name = tensor("scatter_125_axis_0"), val = tensor(0)]; + tensor scatter_125 = scatter(axis = scatter_125_axis_0, data = scatter_124, indices = slice_by_index_150, mode = scatter_125_mode_0, updates = const_217)[name = tensor("scatter_125")]; + tensor const_218 = const()[name = tensor("const_218"), val = tensor([0x1p+0])]; + tensor slice_by_index_151 = const()[name = tensor("slice_by_index_151"), val = tensor([78574])]; + tensor scatter_126_mode_0 = const()[name = tensor("scatter_126_mode_0"), val = tensor("update")]; + tensor scatter_126_axis_0 = const()[name = tensor("scatter_126_axis_0"), val = tensor(0)]; + tensor scatter_126 = scatter(axis = scatter_126_axis_0, data = scatter_125, indices = slice_by_index_151, mode = scatter_126_mode_0, updates = const_218)[name = tensor("scatter_126")]; + tensor const_219 = const()[name = tensor("const_219"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_152 = const()[name = tensor("slice_by_index_152"), val = tensor([79196, 79197, 79198])]; + tensor scatter_127_mode_0 = const()[name = tensor("scatter_127_mode_0"), val = tensor("update")]; + tensor scatter_127_axis_0 = const()[name = tensor("scatter_127_axis_0"), val = tensor(0)]; + tensor scatter_127 = scatter(axis = scatter_127_axis_0, data = scatter_126, indices = slice_by_index_152, mode = scatter_127_mode_0, updates = const_219)[name = tensor("scatter_127")]; + tensor const_220 = const()[name = tensor("const_220"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_153 = const()[name = tensor("slice_by_index_153"), val = tensor([79820, 79821, 79822])]; + tensor scatter_128_mode_0 = const()[name = tensor("scatter_128_mode_0"), val = tensor("update")]; + tensor scatter_128_axis_0 = const()[name = tensor("scatter_128_axis_0"), val = tensor(0)]; + tensor scatter_128 = scatter(axis = scatter_128_axis_0, data = scatter_127, indices = slice_by_index_153, mode = scatter_128_mode_0, updates = const_220)[name = tensor("scatter_128")]; + tensor const_221 = const()[name = tensor("const_221"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_154 = const()[name = tensor("slice_by_index_154"), val = tensor([80444, 80445])]; + tensor scatter_129_mode_0 = const()[name = tensor("scatter_129_mode_0"), val = tensor("update")]; + tensor scatter_129_axis_0 = const()[name = tensor("scatter_129_axis_0"), val = tensor(0)]; + tensor scatter_129 = scatter(axis = scatter_129_axis_0, data = scatter_128, indices = slice_by_index_154, mode = scatter_129_mode_0, updates = const_221)[name = tensor("scatter_129")]; + tensor const_222 = const()[name = tensor("const_222"), val = tensor([0x1p+0])]; + tensor slice_by_index_155 = const()[name = tensor("slice_by_index_155"), val = tensor([81067])]; + tensor scatter_130_mode_0 = const()[name = tensor("scatter_130_mode_0"), val = tensor("update")]; + tensor scatter_130_axis_0 = const()[name = tensor("scatter_130_axis_0"), val = tensor(0)]; + tensor scatter_130 = scatter(axis = scatter_130_axis_0, data = scatter_129, indices = slice_by_index_155, mode = scatter_130_mode_0, updates = const_222)[name = tensor("scatter_130")]; + tensor const_223 = const()[name = tensor("const_223"), val = tensor([0x1p+0])]; + tensor slice_by_index_156 = const()[name = tensor("slice_by_index_156"), val = tensor([81689])]; + tensor scatter_131_mode_0 = const()[name = tensor("scatter_131_mode_0"), val = tensor("update")]; + tensor scatter_131_axis_0 = const()[name = tensor("scatter_131_axis_0"), val = tensor(0)]; + tensor scatter_131 = scatter(axis = scatter_131_axis_0, data = scatter_130, indices = slice_by_index_156, mode = scatter_131_mode_0, updates = const_223)[name = tensor("scatter_131")]; + tensor const_224 = const()[name = tensor("const_224"), val = tensor([0x1p+0])]; + tensor slice_by_index_157 = const()[name = tensor("slice_by_index_157"), val = tensor([82311])]; + tensor scatter_132_mode_0 = const()[name = tensor("scatter_132_mode_0"), val = tensor("update")]; + tensor scatter_132_axis_0 = const()[name = tensor("scatter_132_axis_0"), val = tensor(0)]; + tensor scatter_132 = scatter(axis = scatter_132_axis_0, data = scatter_131, indices = slice_by_index_157, mode = scatter_132_mode_0, updates = const_224)[name = tensor("scatter_132")]; + tensor const_225 = const()[name = tensor("const_225"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_158 = const()[name = tensor("slice_by_index_158"), val = tensor([82933, 82934])]; + tensor scatter_133_mode_0 = const()[name = tensor("scatter_133_mode_0"), val = tensor("update")]; + tensor scatter_133_axis_0 = const()[name = tensor("scatter_133_axis_0"), val = tensor(0)]; + tensor scatter_133 = scatter(axis = scatter_133_axis_0, data = scatter_132, indices = slice_by_index_158, mode = scatter_133_mode_0, updates = const_225)[name = tensor("scatter_133")]; + tensor const_226 = const()[name = tensor("const_226"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_159 = const()[name = tensor("slice_by_index_159"), val = tensor([83556, 83557, 83558])]; + tensor scatter_134_mode_0 = const()[name = tensor("scatter_134_mode_0"), val = tensor("update")]; + tensor scatter_134_axis_0 = const()[name = tensor("scatter_134_axis_0"), val = tensor(0)]; + tensor scatter_134 = scatter(axis = scatter_134_axis_0, data = scatter_133, indices = slice_by_index_159, mode = scatter_134_mode_0, updates = const_226)[name = tensor("scatter_134")]; + tensor const_227 = const()[name = tensor("const_227"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_160 = const()[name = tensor("slice_by_index_160"), val = tensor([84180, 84181, 84182])]; + tensor scatter_135_mode_0 = const()[name = tensor("scatter_135_mode_0"), val = tensor("update")]; + tensor scatter_135_axis_0 = const()[name = tensor("scatter_135_axis_0"), val = tensor(0)]; + tensor scatter_135 = scatter(axis = scatter_135_axis_0, data = scatter_134, indices = slice_by_index_160, mode = scatter_135_mode_0, updates = const_227)[name = tensor("scatter_135")]; + tensor const_228 = const()[name = tensor("const_228"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_161 = const()[name = tensor("slice_by_index_161"), val = tensor([84804, 84805])]; + tensor scatter_136_mode_0 = const()[name = tensor("scatter_136_mode_0"), val = tensor("update")]; + tensor scatter_136_axis_0 = const()[name = tensor("scatter_136_axis_0"), val = tensor(0)]; + tensor scatter_136 = scatter(axis = scatter_136_axis_0, data = scatter_135, indices = slice_by_index_161, mode = scatter_136_mode_0, updates = const_228)[name = tensor("scatter_136")]; + tensor const_229 = const()[name = tensor("const_229"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_162 = const()[name = tensor("slice_by_index_162"), val = tensor([85427, 85428])]; + tensor scatter_137_mode_0 = const()[name = tensor("scatter_137_mode_0"), val = tensor("update")]; + tensor scatter_137_axis_0 = const()[name = tensor("scatter_137_axis_0"), val = tensor(0)]; + tensor scatter_137 = scatter(axis = scatter_137_axis_0, data = scatter_136, indices = slice_by_index_162, mode = scatter_137_mode_0, updates = const_229)[name = tensor("scatter_137")]; + tensor const_230 = const()[name = tensor("const_230"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_163 = const()[name = tensor("slice_by_index_163"), val = tensor([86050, 86051])]; + tensor scatter_138_mode_0 = const()[name = tensor("scatter_138_mode_0"), val = tensor("update")]; + tensor scatter_138_axis_0 = const()[name = tensor("scatter_138_axis_0"), val = tensor(0)]; + tensor scatter_138 = scatter(axis = scatter_138_axis_0, data = scatter_137, indices = slice_by_index_163, mode = scatter_138_mode_0, updates = const_230)[name = tensor("scatter_138")]; + tensor const_231 = const()[name = tensor("const_231"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_164 = const()[name = tensor("slice_by_index_164"), val = tensor([86673, 86674])]; + tensor scatter_139_mode_0 = const()[name = tensor("scatter_139_mode_0"), val = tensor("update")]; + tensor scatter_139_axis_0 = const()[name = tensor("scatter_139_axis_0"), val = tensor(0)]; + tensor scatter_139 = scatter(axis = scatter_139_axis_0, data = scatter_138, indices = slice_by_index_164, mode = scatter_139_mode_0, updates = const_231)[name = tensor("scatter_139")]; + tensor const_232 = const()[name = tensor("const_232"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_165 = const()[name = tensor("slice_by_index_165"), val = tensor([87296, 87297])]; + tensor scatter_140_mode_0 = const()[name = tensor("scatter_140_mode_0"), val = tensor("update")]; + tensor scatter_140_axis_0 = const()[name = tensor("scatter_140_axis_0"), val = tensor(0)]; + tensor scatter_140 = scatter(axis = scatter_140_axis_0, data = scatter_139, indices = slice_by_index_165, mode = scatter_140_mode_0, updates = const_232)[name = tensor("scatter_140")]; + tensor const_233 = const()[name = tensor("const_233"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_166 = const()[name = tensor("slice_by_index_166"), val = tensor([87919, 87920, 87921])]; + tensor scatter_141_mode_0 = const()[name = tensor("scatter_141_mode_0"), val = tensor("update")]; + tensor scatter_141_axis_0 = const()[name = tensor("scatter_141_axis_0"), val = tensor(0)]; + tensor scatter_141 = scatter(axis = scatter_141_axis_0, data = scatter_140, indices = slice_by_index_166, mode = scatter_141_mode_0, updates = const_233)[name = tensor("scatter_141")]; + tensor const_234 = const()[name = tensor("const_234"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_167 = const()[name = tensor("slice_by_index_167"), val = tensor([88543, 88544])]; + tensor scatter_142_mode_0 = const()[name = tensor("scatter_142_mode_0"), val = tensor("update")]; + tensor scatter_142_axis_0 = const()[name = tensor("scatter_142_axis_0"), val = tensor(0)]; + tensor scatter_142 = scatter(axis = scatter_142_axis_0, data = scatter_141, indices = slice_by_index_167, mode = scatter_142_mode_0, updates = const_234)[name = tensor("scatter_142")]; + tensor const_235 = const()[name = tensor("const_235"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_168 = const()[name = tensor("slice_by_index_168"), val = tensor([89166, 89167])]; + tensor scatter_143_mode_0 = const()[name = tensor("scatter_143_mode_0"), val = tensor("update")]; + tensor scatter_143_axis_0 = const()[name = tensor("scatter_143_axis_0"), val = tensor(0)]; + tensor scatter_143 = scatter(axis = scatter_143_axis_0, data = scatter_142, indices = slice_by_index_168, mode = scatter_143_mode_0, updates = const_235)[name = tensor("scatter_143")]; + tensor const_236 = const()[name = tensor("const_236"), val = tensor([0x1p+0])]; + tensor slice_by_index_169 = const()[name = tensor("slice_by_index_169"), val = tensor([89789])]; + tensor scatter_144_mode_0 = const()[name = tensor("scatter_144_mode_0"), val = tensor("update")]; + tensor scatter_144_axis_0 = const()[name = tensor("scatter_144_axis_0"), val = tensor(0)]; + tensor scatter_144 = scatter(axis = scatter_144_axis_0, data = scatter_143, indices = slice_by_index_169, mode = scatter_144_mode_0, updates = const_236)[name = tensor("scatter_144")]; + tensor const_237 = const()[name = tensor("const_237"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_170 = const()[name = tensor("slice_by_index_170"), val = tensor([90411, 90412])]; + tensor scatter_145_mode_0 = const()[name = tensor("scatter_145_mode_0"), val = tensor("update")]; + tensor scatter_145_axis_0 = const()[name = tensor("scatter_145_axis_0"), val = tensor(0)]; + tensor scatter_145 = scatter(axis = scatter_145_axis_0, data = scatter_144, indices = slice_by_index_170, mode = scatter_145_mode_0, updates = const_237)[name = tensor("scatter_145")]; + tensor const_238 = const()[name = tensor("const_238"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_171 = const()[name = tensor("slice_by_index_171"), val = tensor([91034, 91035])]; + tensor scatter_146_mode_0 = const()[name = tensor("scatter_146_mode_0"), val = tensor("update")]; + tensor scatter_146_axis_0 = const()[name = tensor("scatter_146_axis_0"), val = tensor(0)]; + tensor scatter_146 = scatter(axis = scatter_146_axis_0, data = scatter_145, indices = slice_by_index_171, mode = scatter_146_mode_0, updates = const_238)[name = tensor("scatter_146")]; + tensor const_239 = const()[name = tensor("const_239"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_172 = const()[name = tensor("slice_by_index_172"), val = tensor([91657, 91658])]; + tensor scatter_147_mode_0 = const()[name = tensor("scatter_147_mode_0"), val = tensor("update")]; + tensor scatter_147_axis_0 = const()[name = tensor("scatter_147_axis_0"), val = tensor(0)]; + tensor scatter_147 = scatter(axis = scatter_147_axis_0, data = scatter_146, indices = slice_by_index_172, mode = scatter_147_mode_0, updates = const_239)[name = tensor("scatter_147")]; + tensor const_240 = const()[name = tensor("const_240"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_173 = const()[name = tensor("slice_by_index_173"), val = tensor([92280, 92281])]; + tensor scatter_148_mode_0 = const()[name = tensor("scatter_148_mode_0"), val = tensor("update")]; + tensor scatter_148_axis_0 = const()[name = tensor("scatter_148_axis_0"), val = tensor(0)]; + tensor scatter_148 = scatter(axis = scatter_148_axis_0, data = scatter_147, indices = slice_by_index_173, mode = scatter_148_mode_0, updates = const_240)[name = tensor("scatter_148")]; + tensor const_241 = const()[name = tensor("const_241"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_174 = const()[name = tensor("slice_by_index_174"), val = tensor([92903, 92904])]; + tensor scatter_149_mode_0 = const()[name = tensor("scatter_149_mode_0"), val = tensor("update")]; + tensor scatter_149_axis_0 = const()[name = tensor("scatter_149_axis_0"), val = tensor(0)]; + tensor scatter_149 = scatter(axis = scatter_149_axis_0, data = scatter_148, indices = slice_by_index_174, mode = scatter_149_mode_0, updates = const_241)[name = tensor("scatter_149")]; + tensor const_242 = const()[name = tensor("const_242"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_175 = const()[name = tensor("slice_by_index_175"), val = tensor([93526, 93527])]; + tensor scatter_150_mode_0 = const()[name = tensor("scatter_150_mode_0"), val = tensor("update")]; + tensor scatter_150_axis_0 = const()[name = tensor("scatter_150_axis_0"), val = tensor(0)]; + tensor scatter_150 = scatter(axis = scatter_150_axis_0, data = scatter_149, indices = slice_by_index_175, mode = scatter_150_mode_0, updates = const_242)[name = tensor("scatter_150")]; + tensor const_243 = const()[name = tensor("const_243"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_176 = const()[name = tensor("slice_by_index_176"), val = tensor([94149, 94150])]; + tensor scatter_151_mode_0 = const()[name = tensor("scatter_151_mode_0"), val = tensor("update")]; + tensor scatter_151_axis_0 = const()[name = tensor("scatter_151_axis_0"), val = tensor(0)]; + tensor scatter_151 = scatter(axis = scatter_151_axis_0, data = scatter_150, indices = slice_by_index_176, mode = scatter_151_mode_0, updates = const_243)[name = tensor("scatter_151")]; + tensor const_244 = const()[name = tensor("const_244"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_177 = const()[name = tensor("slice_by_index_177"), val = tensor([94772, 94773])]; + tensor scatter_152_mode_0 = const()[name = tensor("scatter_152_mode_0"), val = tensor("update")]; + tensor scatter_152_axis_0 = const()[name = tensor("scatter_152_axis_0"), val = tensor(0)]; + tensor scatter_152 = scatter(axis = scatter_152_axis_0, data = scatter_151, indices = slice_by_index_177, mode = scatter_152_mode_0, updates = const_244)[name = tensor("scatter_152")]; + tensor const_245 = const()[name = tensor("const_245"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_178 = const()[name = tensor("slice_by_index_178"), val = tensor([95395, 95396, 95397])]; + tensor scatter_153_mode_0 = const()[name = tensor("scatter_153_mode_0"), val = tensor("update")]; + tensor scatter_153_axis_0 = const()[name = tensor("scatter_153_axis_0"), val = tensor(0)]; + tensor scatter_153 = scatter(axis = scatter_153_axis_0, data = scatter_152, indices = slice_by_index_178, mode = scatter_153_mode_0, updates = const_245)[name = tensor("scatter_153")]; + tensor const_246 = const()[name = tensor("const_246"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_179 = const()[name = tensor("slice_by_index_179"), val = tensor([96019, 96020])]; + tensor scatter_154_mode_0 = const()[name = tensor("scatter_154_mode_0"), val = tensor("update")]; + tensor scatter_154_axis_0 = const()[name = tensor("scatter_154_axis_0"), val = tensor(0)]; + tensor scatter_154 = scatter(axis = scatter_154_axis_0, data = scatter_153, indices = slice_by_index_179, mode = scatter_154_mode_0, updates = const_246)[name = tensor("scatter_154")]; + tensor const_247 = const()[name = tensor("const_247"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_180 = const()[name = tensor("slice_by_index_180"), val = tensor([96642, 96643, 96644])]; + tensor scatter_155_mode_0 = const()[name = tensor("scatter_155_mode_0"), val = tensor("update")]; + tensor scatter_155_axis_0 = const()[name = tensor("scatter_155_axis_0"), val = tensor(0)]; + tensor scatter_155 = scatter(axis = scatter_155_axis_0, data = scatter_154, indices = slice_by_index_180, mode = scatter_155_mode_0, updates = const_247)[name = tensor("scatter_155")]; + tensor const_248 = const()[name = tensor("const_248"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_181 = const()[name = tensor("slice_by_index_181"), val = tensor([97266, 97267, 97268])]; + tensor scatter_156_mode_0 = const()[name = tensor("scatter_156_mode_0"), val = tensor("update")]; + tensor scatter_156_axis_0 = const()[name = tensor("scatter_156_axis_0"), val = tensor(0)]; + tensor scatter_156 = scatter(axis = scatter_156_axis_0, data = scatter_155, indices = slice_by_index_181, mode = scatter_156_mode_0, updates = const_248)[name = tensor("scatter_156")]; + tensor const_249 = const()[name = tensor("const_249"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_182 = const()[name = tensor("slice_by_index_182"), val = tensor([97890, 97891, 97892])]; + tensor scatter_157_mode_0 = const()[name = tensor("scatter_157_mode_0"), val = tensor("update")]; + tensor scatter_157_axis_0 = const()[name = tensor("scatter_157_axis_0"), val = tensor(0)]; + tensor scatter_157 = scatter(axis = scatter_157_axis_0, data = scatter_156, indices = slice_by_index_182, mode = scatter_157_mode_0, updates = const_249)[name = tensor("scatter_157")]; + tensor const_250 = const()[name = tensor("const_250"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_183 = const()[name = tensor("slice_by_index_183"), val = tensor([98514, 98515, 98516])]; + tensor scatter_158_mode_0 = const()[name = tensor("scatter_158_mode_0"), val = tensor("update")]; + tensor scatter_158_axis_0 = const()[name = tensor("scatter_158_axis_0"), val = tensor(0)]; + tensor scatter_158 = scatter(axis = scatter_158_axis_0, data = scatter_157, indices = slice_by_index_183, mode = scatter_158_mode_0, updates = const_250)[name = tensor("scatter_158")]; + tensor const_251 = const()[name = tensor("const_251"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_184 = const()[name = tensor("slice_by_index_184"), val = tensor([99138, 99139, 99140, 99141])]; + tensor scatter_159_mode_0 = const()[name = tensor("scatter_159_mode_0"), val = tensor("update")]; + tensor scatter_159_axis_0 = const()[name = tensor("scatter_159_axis_0"), val = tensor(0)]; + tensor scatter_159 = scatter(axis = scatter_159_axis_0, data = scatter_158, indices = slice_by_index_184, mode = scatter_159_mode_0, updates = const_251)[name = tensor("scatter_159")]; + tensor const_252 = const()[name = tensor("const_252"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_185 = const()[name = tensor("slice_by_index_185"), val = tensor([99763, 99764])]; + tensor scatter_160_mode_0 = const()[name = tensor("scatter_160_mode_0"), val = tensor("update")]; + tensor scatter_160_axis_0 = const()[name = tensor("scatter_160_axis_0"), val = tensor(0)]; + tensor scatter_160 = scatter(axis = scatter_160_axis_0, data = scatter_159, indices = slice_by_index_185, mode = scatter_160_mode_0, updates = const_252)[name = tensor("scatter_160")]; + tensor const_253 = const()[name = tensor("const_253"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_186 = const()[name = tensor("slice_by_index_186"), val = tensor([100386, 100387])]; + tensor scatter_161_mode_0 = const()[name = tensor("scatter_161_mode_0"), val = tensor("update")]; + tensor scatter_161_axis_0 = const()[name = tensor("scatter_161_axis_0"), val = tensor(0)]; + tensor scatter_161 = scatter(axis = scatter_161_axis_0, data = scatter_160, indices = slice_by_index_186, mode = scatter_161_mode_0, updates = const_253)[name = tensor("scatter_161")]; + tensor const_254 = const()[name = tensor("const_254"), val = tensor([0x1p+0])]; + tensor slice_by_index_187 = const()[name = tensor("slice_by_index_187"), val = tensor([101009])]; + tensor scatter_162_mode_0 = const()[name = tensor("scatter_162_mode_0"), val = tensor("update")]; + tensor scatter_162_axis_0 = const()[name = tensor("scatter_162_axis_0"), val = tensor(0)]; + tensor scatter_162 = scatter(axis = scatter_162_axis_0, data = scatter_161, indices = slice_by_index_187, mode = scatter_162_mode_0, updates = const_254)[name = tensor("scatter_162")]; + tensor const_255 = const()[name = tensor("const_255"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_188 = const()[name = tensor("slice_by_index_188"), val = tensor([101631, 101632])]; + tensor scatter_163_mode_0 = const()[name = tensor("scatter_163_mode_0"), val = tensor("update")]; + tensor scatter_163_axis_0 = const()[name = tensor("scatter_163_axis_0"), val = tensor(0)]; + tensor scatter_163 = scatter(axis = scatter_163_axis_0, data = scatter_162, indices = slice_by_index_188, mode = scatter_163_mode_0, updates = const_255)[name = tensor("scatter_163")]; + tensor const_256 = const()[name = tensor("const_256"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_189 = const()[name = tensor("slice_by_index_189"), val = tensor([102254, 102255, 102256])]; + tensor scatter_164_mode_0 = const()[name = tensor("scatter_164_mode_0"), val = tensor("update")]; + tensor scatter_164_axis_0 = const()[name = tensor("scatter_164_axis_0"), val = tensor(0)]; + tensor scatter_164 = scatter(axis = scatter_164_axis_0, data = scatter_163, indices = slice_by_index_189, mode = scatter_164_mode_0, updates = const_256)[name = tensor("scatter_164")]; + tensor const_257 = const()[name = tensor("const_257"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_190 = const()[name = tensor("slice_by_index_190"), val = tensor([102878, 102879, 102880])]; + tensor scatter_165_mode_0 = const()[name = tensor("scatter_165_mode_0"), val = tensor("update")]; + tensor scatter_165_axis_0 = const()[name = tensor("scatter_165_axis_0"), val = tensor(0)]; + tensor scatter_165 = scatter(axis = scatter_165_axis_0, data = scatter_164, indices = slice_by_index_190, mode = scatter_165_mode_0, updates = const_257)[name = tensor("scatter_165")]; + tensor const_258 = const()[name = tensor("const_258"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_191 = const()[name = tensor("slice_by_index_191"), val = tensor([103502, 103503])]; + tensor scatter_166_mode_0 = const()[name = tensor("scatter_166_mode_0"), val = tensor("update")]; + tensor scatter_166_axis_0 = const()[name = tensor("scatter_166_axis_0"), val = tensor(0)]; + tensor scatter_166 = scatter(axis = scatter_166_axis_0, data = scatter_165, indices = slice_by_index_191, mode = scatter_166_mode_0, updates = const_258)[name = tensor("scatter_166")]; + tensor const_259 = const()[name = tensor("const_259"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_192 = const()[name = tensor("slice_by_index_192"), val = tensor([104125, 104126])]; + tensor scatter_167_mode_0 = const()[name = tensor("scatter_167_mode_0"), val = tensor("update")]; + tensor scatter_167_axis_0 = const()[name = tensor("scatter_167_axis_0"), val = tensor(0)]; + tensor scatter_167 = scatter(axis = scatter_167_axis_0, data = scatter_166, indices = slice_by_index_192, mode = scatter_167_mode_0, updates = const_259)[name = tensor("scatter_167")]; + tensor const_260 = const()[name = tensor("const_260"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_193 = const()[name = tensor("slice_by_index_193"), val = tensor([104748, 104749])]; + tensor scatter_168_mode_0 = const()[name = tensor("scatter_168_mode_0"), val = tensor("update")]; + tensor scatter_168_axis_0 = const()[name = tensor("scatter_168_axis_0"), val = tensor(0)]; + tensor scatter_168 = scatter(axis = scatter_168_axis_0, data = scatter_167, indices = slice_by_index_193, mode = scatter_168_mode_0, updates = const_260)[name = tensor("scatter_168")]; + tensor const_261 = const()[name = tensor("const_261"), val = tensor([0x1p+0])]; + tensor slice_by_index_194 = const()[name = tensor("slice_by_index_194"), val = tensor([105371])]; + tensor scatter_169_mode_0 = const()[name = tensor("scatter_169_mode_0"), val = tensor("update")]; + tensor scatter_169_axis_0 = const()[name = tensor("scatter_169_axis_0"), val = tensor(0)]; + tensor scatter_169 = scatter(axis = scatter_169_axis_0, data = scatter_168, indices = slice_by_index_194, mode = scatter_169_mode_0, updates = const_261)[name = tensor("scatter_169")]; + tensor const_262 = const()[name = tensor("const_262"), val = tensor([0x1p+0])]; + tensor slice_by_index_195 = const()[name = tensor("slice_by_index_195"), val = tensor([105993])]; + tensor scatter_170_mode_0 = const()[name = tensor("scatter_170_mode_0"), val = tensor("update")]; + tensor scatter_170_axis_0 = const()[name = tensor("scatter_170_axis_0"), val = tensor(0)]; + tensor scatter_170 = scatter(axis = scatter_170_axis_0, data = scatter_169, indices = slice_by_index_195, mode = scatter_170_mode_0, updates = const_262)[name = tensor("scatter_170")]; + tensor const_263 = const()[name = tensor("const_263"), val = tensor([0x1p+0])]; + tensor slice_by_index_196 = const()[name = tensor("slice_by_index_196"), val = tensor([106615])]; + tensor scatter_171_mode_0 = const()[name = tensor("scatter_171_mode_0"), val = tensor("update")]; + tensor scatter_171_axis_0 = const()[name = tensor("scatter_171_axis_0"), val = tensor(0)]; + tensor scatter_171 = scatter(axis = scatter_171_axis_0, data = scatter_170, indices = slice_by_index_196, mode = scatter_171_mode_0, updates = const_263)[name = tensor("scatter_171")]; + tensor const_264 = const()[name = tensor("const_264"), val = tensor([0x1p+0])]; + tensor slice_by_index_197 = const()[name = tensor("slice_by_index_197"), val = tensor([107237])]; + tensor scatter_172_mode_0 = const()[name = tensor("scatter_172_mode_0"), val = tensor("update")]; + tensor scatter_172_axis_0 = const()[name = tensor("scatter_172_axis_0"), val = tensor(0)]; + tensor scatter_172 = scatter(axis = scatter_172_axis_0, data = scatter_171, indices = slice_by_index_197, mode = scatter_172_mode_0, updates = const_264)[name = tensor("scatter_172")]; + tensor const_265 = const()[name = tensor("const_265"), val = tensor([0x1p+0])]; + tensor slice_by_index_198 = const()[name = tensor("slice_by_index_198"), val = tensor([107859])]; + tensor scatter_173_mode_0 = const()[name = tensor("scatter_173_mode_0"), val = tensor("update")]; + tensor scatter_173_axis_0 = const()[name = tensor("scatter_173_axis_0"), val = tensor(0)]; + tensor scatter_173 = scatter(axis = scatter_173_axis_0, data = scatter_172, indices = slice_by_index_198, mode = scatter_173_mode_0, updates = const_265)[name = tensor("scatter_173")]; + tensor const_266 = const()[name = tensor("const_266"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_199 = const()[name = tensor("slice_by_index_199"), val = tensor([108481, 108482])]; + tensor scatter_174_mode_0 = const()[name = tensor("scatter_174_mode_0"), val = tensor("update")]; + tensor scatter_174_axis_0 = const()[name = tensor("scatter_174_axis_0"), val = tensor(0)]; + tensor scatter_174 = scatter(axis = scatter_174_axis_0, data = scatter_173, indices = slice_by_index_199, mode = scatter_174_mode_0, updates = const_266)[name = tensor("scatter_174")]; + tensor const_267 = const()[name = tensor("const_267"), val = tensor([0x1p+0])]; + tensor slice_by_index_200 = const()[name = tensor("slice_by_index_200"), val = tensor([109104])]; + tensor scatter_175_mode_0 = const()[name = tensor("scatter_175_mode_0"), val = tensor("update")]; + tensor scatter_175_axis_0 = const()[name = tensor("scatter_175_axis_0"), val = tensor(0)]; + tensor scatter_175 = scatter(axis = scatter_175_axis_0, data = scatter_174, indices = slice_by_index_200, mode = scatter_175_mode_0, updates = const_267)[name = tensor("scatter_175")]; + tensor const_268 = const()[name = tensor("const_268"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_201 = const()[name = tensor("slice_by_index_201"), val = tensor([109726, 109727, 109728])]; + tensor scatter_176_mode_0 = const()[name = tensor("scatter_176_mode_0"), val = tensor("update")]; + tensor scatter_176_axis_0 = const()[name = tensor("scatter_176_axis_0"), val = tensor(0)]; + tensor scatter_176 = scatter(axis = scatter_176_axis_0, data = scatter_175, indices = slice_by_index_201, mode = scatter_176_mode_0, updates = const_268)[name = tensor("scatter_176")]; + tensor const_269 = const()[name = tensor("const_269"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_202 = const()[name = tensor("slice_by_index_202"), val = tensor([110350, 110351])]; + tensor scatter_177_mode_0 = const()[name = tensor("scatter_177_mode_0"), val = tensor("update")]; + tensor scatter_177_axis_0 = const()[name = tensor("scatter_177_axis_0"), val = tensor(0)]; + tensor scatter_177 = scatter(axis = scatter_177_axis_0, data = scatter_176, indices = slice_by_index_202, mode = scatter_177_mode_0, updates = const_269)[name = tensor("scatter_177")]; + tensor const_270 = const()[name = tensor("const_270"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_203 = const()[name = tensor("slice_by_index_203"), val = tensor([110973, 110974, 110975, 110976])]; + tensor scatter_178_mode_0 = const()[name = tensor("scatter_178_mode_0"), val = tensor("update")]; + tensor scatter_178_axis_0 = const()[name = tensor("scatter_178_axis_0"), val = tensor(0)]; + tensor scatter_178 = scatter(axis = scatter_178_axis_0, data = scatter_177, indices = slice_by_index_203, mode = scatter_178_mode_0, updates = const_270)[name = tensor("scatter_178")]; + tensor const_271 = const()[name = tensor("const_271"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_204 = const()[name = tensor("slice_by_index_204"), val = tensor([111598, 111599, 111600, 111601])]; + tensor scatter_179_mode_0 = const()[name = tensor("scatter_179_mode_0"), val = tensor("update")]; + tensor scatter_179_axis_0 = const()[name = tensor("scatter_179_axis_0"), val = tensor(0)]; + tensor scatter_179 = scatter(axis = scatter_179_axis_0, data = scatter_178, indices = slice_by_index_204, mode = scatter_179_mode_0, updates = const_271)[name = tensor("scatter_179")]; + tensor const_272 = const()[name = tensor("const_272"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_205 = const()[name = tensor("slice_by_index_205"), val = tensor([112223, 112224, 112225])]; + tensor scatter_180_mode_0 = const()[name = tensor("scatter_180_mode_0"), val = tensor("update")]; + tensor scatter_180_axis_0 = const()[name = tensor("scatter_180_axis_0"), val = tensor(0)]; + tensor scatter_180 = scatter(axis = scatter_180_axis_0, data = scatter_179, indices = slice_by_index_205, mode = scatter_180_mode_0, updates = const_272)[name = tensor("scatter_180")]; + tensor const_273 = const()[name = tensor("const_273"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_206 = const()[name = tensor("slice_by_index_206"), val = tensor([112847, 112848, 112849])]; + tensor scatter_181_mode_0 = const()[name = tensor("scatter_181_mode_0"), val = tensor("update")]; + tensor scatter_181_axis_0 = const()[name = tensor("scatter_181_axis_0"), val = tensor(0)]; + tensor scatter_181 = scatter(axis = scatter_181_axis_0, data = scatter_180, indices = slice_by_index_206, mode = scatter_181_mode_0, updates = const_273)[name = tensor("scatter_181")]; + tensor const_274 = const()[name = tensor("const_274"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73537088)))]; + tensor slice_by_index_207 = const()[name = tensor("slice_by_index_207"), val = tensor([113471, 113472, 113473, 113474, 113475, 113476, 113477, 113478, 113479, 113480, 113481, 113482, 113483, 113484, 113485])]; + tensor scatter_182_mode_0 = const()[name = tensor("scatter_182_mode_0"), val = tensor("update")]; + tensor scatter_182_axis_0 = const()[name = tensor("scatter_182_axis_0"), val = tensor(0)]; + tensor scatter_182 = scatter(axis = scatter_182_axis_0, data = scatter_181, indices = slice_by_index_207, mode = scatter_182_mode_0, updates = const_274)[name = tensor("scatter_182")]; + tensor const_275 = const()[name = tensor("const_275"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_208 = const()[name = tensor("slice_by_index_208"), val = tensor([114107, 114108, 114109, 114110])]; + tensor scatter_183_mode_0 = const()[name = tensor("scatter_183_mode_0"), val = tensor("update")]; + tensor scatter_183_axis_0 = const()[name = tensor("scatter_183_axis_0"), val = tensor(0)]; + tensor scatter_183 = scatter(axis = scatter_183_axis_0, data = scatter_182, indices = slice_by_index_208, mode = scatter_183_mode_0, updates = const_275)[name = tensor("scatter_183")]; + tensor const_276 = const()[name = tensor("const_276"), val = tensor([0x1p+0])]; + tensor slice_by_index_209 = const()[name = tensor("slice_by_index_209"), val = tensor([114732])]; + tensor scatter_184_mode_0 = const()[name = tensor("scatter_184_mode_0"), val = tensor("update")]; + tensor scatter_184_axis_0 = const()[name = tensor("scatter_184_axis_0"), val = tensor(0)]; + tensor scatter_184 = scatter(axis = scatter_184_axis_0, data = scatter_183, indices = slice_by_index_209, mode = scatter_184_mode_0, updates = const_276)[name = tensor("scatter_184")]; + tensor const_277 = const()[name = tensor("const_277"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_210 = const()[name = tensor("slice_by_index_210"), val = tensor([115354, 115355])]; + tensor scatter_185_mode_0 = const()[name = tensor("scatter_185_mode_0"), val = tensor("update")]; + tensor scatter_185_axis_0 = const()[name = tensor("scatter_185_axis_0"), val = tensor(0)]; + tensor scatter_185 = scatter(axis = scatter_185_axis_0, data = scatter_184, indices = slice_by_index_210, mode = scatter_185_mode_0, updates = const_277)[name = tensor("scatter_185")]; + tensor const_278 = const()[name = tensor("const_278"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_211 = const()[name = tensor("slice_by_index_211"), val = tensor([115977, 115978])]; + tensor scatter_186_mode_0 = const()[name = tensor("scatter_186_mode_0"), val = tensor("update")]; + tensor scatter_186_axis_0 = const()[name = tensor("scatter_186_axis_0"), val = tensor(0)]; + tensor scatter_186 = scatter(axis = scatter_186_axis_0, data = scatter_185, indices = slice_by_index_211, mode = scatter_186_mode_0, updates = const_278)[name = tensor("scatter_186")]; + tensor const_279 = const()[name = tensor("const_279"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_212 = const()[name = tensor("slice_by_index_212"), val = tensor([116600, 116601])]; + tensor scatter_187_mode_0 = const()[name = tensor("scatter_187_mode_0"), val = tensor("update")]; + tensor scatter_187_axis_0 = const()[name = tensor("scatter_187_axis_0"), val = tensor(0)]; + tensor scatter_187 = scatter(axis = scatter_187_axis_0, data = scatter_186, indices = slice_by_index_212, mode = scatter_187_mode_0, updates = const_279)[name = tensor("scatter_187")]; + tensor const_280 = const()[name = tensor("const_280"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_213 = const()[name = tensor("slice_by_index_213"), val = tensor([117223, 117224, 117225])]; + tensor scatter_188_mode_0 = const()[name = tensor("scatter_188_mode_0"), val = tensor("update")]; + tensor scatter_188_axis_0 = const()[name = tensor("scatter_188_axis_0"), val = tensor(0)]; + tensor scatter_188 = scatter(axis = scatter_188_axis_0, data = scatter_187, indices = slice_by_index_213, mode = scatter_188_mode_0, updates = const_280)[name = tensor("scatter_188")]; + tensor const_281 = const()[name = tensor("const_281"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_214 = const()[name = tensor("slice_by_index_214"), val = tensor([117847, 117848, 117849])]; + tensor scatter_189_mode_0 = const()[name = tensor("scatter_189_mode_0"), val = tensor("update")]; + tensor scatter_189_axis_0 = const()[name = tensor("scatter_189_axis_0"), val = tensor(0)]; + tensor scatter_189 = scatter(axis = scatter_189_axis_0, data = scatter_188, indices = slice_by_index_214, mode = scatter_189_mode_0, updates = const_281)[name = tensor("scatter_189")]; + tensor const_282 = const()[name = tensor("const_282"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_215 = const()[name = tensor("slice_by_index_215"), val = tensor([118471, 118472])]; + tensor scatter_190_mode_0 = const()[name = tensor("scatter_190_mode_0"), val = tensor("update")]; + tensor scatter_190_axis_0 = const()[name = tensor("scatter_190_axis_0"), val = tensor(0)]; + tensor scatter_190 = scatter(axis = scatter_190_axis_0, data = scatter_189, indices = slice_by_index_215, mode = scatter_190_mode_0, updates = const_282)[name = tensor("scatter_190")]; + tensor const_283 = const()[name = tensor("const_283"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_216 = const()[name = tensor("slice_by_index_216"), val = tensor([119094, 119095])]; + tensor scatter_191_mode_0 = const()[name = tensor("scatter_191_mode_0"), val = tensor("update")]; + tensor scatter_191_axis_0 = const()[name = tensor("scatter_191_axis_0"), val = tensor(0)]; + tensor scatter_191 = scatter(axis = scatter_191_axis_0, data = scatter_190, indices = slice_by_index_216, mode = scatter_191_mode_0, updates = const_283)[name = tensor("scatter_191")]; + tensor const_284 = const()[name = tensor("const_284"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_217 = const()[name = tensor("slice_by_index_217"), val = tensor([119717, 119718])]; + tensor scatter_192_mode_0 = const()[name = tensor("scatter_192_mode_0"), val = tensor("update")]; + tensor scatter_192_axis_0 = const()[name = tensor("scatter_192_axis_0"), val = tensor(0)]; + tensor scatter_192 = scatter(axis = scatter_192_axis_0, data = scatter_191, indices = slice_by_index_217, mode = scatter_192_mode_0, updates = const_284)[name = tensor("scatter_192")]; + tensor const_285 = const()[name = tensor("const_285"), val = tensor([0x1p+0])]; + tensor slice_by_index_218 = const()[name = tensor("slice_by_index_218"), val = tensor([120340])]; + tensor scatter_193_mode_0 = const()[name = tensor("scatter_193_mode_0"), val = tensor("update")]; + tensor scatter_193_axis_0 = const()[name = tensor("scatter_193_axis_0"), val = tensor(0)]; + tensor scatter_193 = scatter(axis = scatter_193_axis_0, data = scatter_192, indices = slice_by_index_218, mode = scatter_193_mode_0, updates = const_285)[name = tensor("scatter_193")]; + tensor const_286 = const()[name = tensor("const_286"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_219 = const()[name = tensor("slice_by_index_219"), val = tensor([120962, 120963])]; + tensor scatter_194_mode_0 = const()[name = tensor("scatter_194_mode_0"), val = tensor("update")]; + tensor scatter_194_axis_0 = const()[name = tensor("scatter_194_axis_0"), val = tensor(0)]; + tensor scatter_194 = scatter(axis = scatter_194_axis_0, data = scatter_193, indices = slice_by_index_219, mode = scatter_194_mode_0, updates = const_286)[name = tensor("scatter_194")]; + tensor const_287 = const()[name = tensor("const_287"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_220 = const()[name = tensor("slice_by_index_220"), val = tensor([121585, 121586, 121587])]; + tensor scatter_195_mode_0 = const()[name = tensor("scatter_195_mode_0"), val = tensor("update")]; + tensor scatter_195_axis_0 = const()[name = tensor("scatter_195_axis_0"), val = tensor(0)]; + tensor scatter_195 = scatter(axis = scatter_195_axis_0, data = scatter_194, indices = slice_by_index_220, mode = scatter_195_mode_0, updates = const_287)[name = tensor("scatter_195")]; + tensor const_288 = const()[name = tensor("const_288"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_221 = const()[name = tensor("slice_by_index_221"), val = tensor([122209, 122210, 122211, 122212, 122213])]; + tensor scatter_196_mode_0 = const()[name = tensor("scatter_196_mode_0"), val = tensor("update")]; + tensor scatter_196_axis_0 = const()[name = tensor("scatter_196_axis_0"), val = tensor(0)]; + tensor scatter_196 = scatter(axis = scatter_196_axis_0, data = scatter_195, indices = slice_by_index_221, mode = scatter_196_mode_0, updates = const_288)[name = tensor("scatter_196")]; + tensor const_289 = const()[name = tensor("const_289"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_222 = const()[name = tensor("slice_by_index_222"), val = tensor([122835, 122836])]; + tensor scatter_197_mode_0 = const()[name = tensor("scatter_197_mode_0"), val = tensor("update")]; + tensor scatter_197_axis_0 = const()[name = tensor("scatter_197_axis_0"), val = tensor(0)]; + tensor scatter_197 = scatter(axis = scatter_197_axis_0, data = scatter_196, indices = slice_by_index_222, mode = scatter_197_mode_0, updates = const_289)[name = tensor("scatter_197")]; + tensor const_290 = const()[name = tensor("const_290"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_223 = const()[name = tensor("slice_by_index_223"), val = tensor([123458, 123459])]; + tensor scatter_198_mode_0 = const()[name = tensor("scatter_198_mode_0"), val = tensor("update")]; + tensor scatter_198_axis_0 = const()[name = tensor("scatter_198_axis_0"), val = tensor(0)]; + tensor scatter_198 = scatter(axis = scatter_198_axis_0, data = scatter_197, indices = slice_by_index_223, mode = scatter_198_mode_0, updates = const_290)[name = tensor("scatter_198")]; + tensor const_291 = const()[name = tensor("const_291"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_224 = const()[name = tensor("slice_by_index_224"), val = tensor([124081, 124082])]; + tensor scatter_199_mode_0 = const()[name = tensor("scatter_199_mode_0"), val = tensor("update")]; + tensor scatter_199_axis_0 = const()[name = tensor("scatter_199_axis_0"), val = tensor(0)]; + tensor scatter_199 = scatter(axis = scatter_199_axis_0, data = scatter_198, indices = slice_by_index_224, mode = scatter_199_mode_0, updates = const_291)[name = tensor("scatter_199")]; + tensor const_292 = const()[name = tensor("const_292"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_225 = const()[name = tensor("slice_by_index_225"), val = tensor([124704, 124705])]; + tensor scatter_200_mode_0 = const()[name = tensor("scatter_200_mode_0"), val = tensor("update")]; + tensor scatter_200_axis_0 = const()[name = tensor("scatter_200_axis_0"), val = tensor(0)]; + tensor scatter_200 = scatter(axis = scatter_200_axis_0, data = scatter_199, indices = slice_by_index_225, mode = scatter_200_mode_0, updates = const_292)[name = tensor("scatter_200")]; + tensor const_293 = const()[name = tensor("const_293"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_226 = const()[name = tensor("slice_by_index_226"), val = tensor([125327, 125328])]; + tensor scatter_201_mode_0 = const()[name = tensor("scatter_201_mode_0"), val = tensor("update")]; + tensor scatter_201_axis_0 = const()[name = tensor("scatter_201_axis_0"), val = tensor(0)]; + tensor scatter_201 = scatter(axis = scatter_201_axis_0, data = scatter_200, indices = slice_by_index_226, mode = scatter_201_mode_0, updates = const_293)[name = tensor("scatter_201")]; + tensor const_294 = const()[name = tensor("const_294"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_227 = const()[name = tensor("slice_by_index_227"), val = tensor([125950, 125951])]; + tensor scatter_202_mode_0 = const()[name = tensor("scatter_202_mode_0"), val = tensor("update")]; + tensor scatter_202_axis_0 = const()[name = tensor("scatter_202_axis_0"), val = tensor(0)]; + tensor scatter_202 = scatter(axis = scatter_202_axis_0, data = scatter_201, indices = slice_by_index_227, mode = scatter_202_mode_0, updates = const_294)[name = tensor("scatter_202")]; + tensor const_295 = const()[name = tensor("const_295"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_228 = const()[name = tensor("slice_by_index_228"), val = tensor([126573, 126574])]; + tensor scatter_203_mode_0 = const()[name = tensor("scatter_203_mode_0"), val = tensor("update")]; + tensor scatter_203_axis_0 = const()[name = tensor("scatter_203_axis_0"), val = tensor(0)]; + tensor scatter_203 = scatter(axis = scatter_203_axis_0, data = scatter_202, indices = slice_by_index_228, mode = scatter_203_mode_0, updates = const_295)[name = tensor("scatter_203")]; + tensor const_296 = const()[name = tensor("const_296"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_229 = const()[name = tensor("slice_by_index_229"), val = tensor([127196, 127197])]; + tensor scatter_204_mode_0 = const()[name = tensor("scatter_204_mode_0"), val = tensor("update")]; + tensor scatter_204_axis_0 = const()[name = tensor("scatter_204_axis_0"), val = tensor(0)]; + tensor scatter_204 = scatter(axis = scatter_204_axis_0, data = scatter_203, indices = slice_by_index_229, mode = scatter_204_mode_0, updates = const_296)[name = tensor("scatter_204")]; + tensor const_297 = const()[name = tensor("const_297"), val = tensor([0x1p+0])]; + tensor slice_by_index_230 = const()[name = tensor("slice_by_index_230"), val = tensor([127819])]; + tensor scatter_205_mode_0 = const()[name = tensor("scatter_205_mode_0"), val = tensor("update")]; + tensor scatter_205_axis_0 = const()[name = tensor("scatter_205_axis_0"), val = tensor(0)]; + tensor scatter_205 = scatter(axis = scatter_205_axis_0, data = scatter_204, indices = slice_by_index_230, mode = scatter_205_mode_0, updates = const_297)[name = tensor("scatter_205")]; + tensor const_298 = const()[name = tensor("const_298"), val = tensor([0x1p+0])]; + tensor slice_by_index_231 = const()[name = tensor("slice_by_index_231"), val = tensor([128441])]; + tensor scatter_206_mode_0 = const()[name = tensor("scatter_206_mode_0"), val = tensor("update")]; + tensor scatter_206_axis_0 = const()[name = tensor("scatter_206_axis_0"), val = tensor(0)]; + tensor scatter_206 = scatter(axis = scatter_206_axis_0, data = scatter_205, indices = slice_by_index_231, mode = scatter_206_mode_0, updates = const_298)[name = tensor("scatter_206")]; + tensor const_299 = const()[name = tensor("const_299"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_232 = const()[name = tensor("slice_by_index_232"), val = tensor([129063, 129064])]; + tensor scatter_207_mode_0 = const()[name = tensor("scatter_207_mode_0"), val = tensor("update")]; + tensor scatter_207_axis_0 = const()[name = tensor("scatter_207_axis_0"), val = tensor(0)]; + tensor scatter_207 = scatter(axis = scatter_207_axis_0, data = scatter_206, indices = slice_by_index_232, mode = scatter_207_mode_0, updates = const_299)[name = tensor("scatter_207")]; + tensor const_300 = const()[name = tensor("const_300"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_233 = const()[name = tensor("slice_by_index_233"), val = tensor([129686, 129687])]; + tensor scatter_208_mode_0 = const()[name = tensor("scatter_208_mode_0"), val = tensor("update")]; + tensor scatter_208_axis_0 = const()[name = tensor("scatter_208_axis_0"), val = tensor(0)]; + tensor scatter_208 = scatter(axis = scatter_208_axis_0, data = scatter_207, indices = slice_by_index_233, mode = scatter_208_mode_0, updates = const_300)[name = tensor("scatter_208")]; + tensor const_301 = const()[name = tensor("const_301"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_234 = const()[name = tensor("slice_by_index_234"), val = tensor([130309, 130310])]; + tensor scatter_209_mode_0 = const()[name = tensor("scatter_209_mode_0"), val = tensor("update")]; + tensor scatter_209_axis_0 = const()[name = tensor("scatter_209_axis_0"), val = tensor(0)]; + tensor scatter_209 = scatter(axis = scatter_209_axis_0, data = scatter_208, indices = slice_by_index_234, mode = scatter_209_mode_0, updates = const_301)[name = tensor("scatter_209")]; + tensor const_302 = const()[name = tensor("const_302"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_235 = const()[name = tensor("slice_by_index_235"), val = tensor([130932, 130933])]; + tensor scatter_210_mode_0 = const()[name = tensor("scatter_210_mode_0"), val = tensor("update")]; + tensor scatter_210_axis_0 = const()[name = tensor("scatter_210_axis_0"), val = tensor(0)]; + tensor scatter_210 = scatter(axis = scatter_210_axis_0, data = scatter_209, indices = slice_by_index_235, mode = scatter_210_mode_0, updates = const_302)[name = tensor("scatter_210")]; + tensor const_303 = const()[name = tensor("const_303"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_236 = const()[name = tensor("slice_by_index_236"), val = tensor([131555, 131556])]; + tensor scatter_211_mode_0 = const()[name = tensor("scatter_211_mode_0"), val = tensor("update")]; + tensor scatter_211_axis_0 = const()[name = tensor("scatter_211_axis_0"), val = tensor(0)]; + tensor scatter_211 = scatter(axis = scatter_211_axis_0, data = scatter_210, indices = slice_by_index_236, mode = scatter_211_mode_0, updates = const_303)[name = tensor("scatter_211")]; + tensor const_304 = const()[name = tensor("const_304"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_237 = const()[name = tensor("slice_by_index_237"), val = tensor([132178, 132179])]; + tensor scatter_212_mode_0 = const()[name = tensor("scatter_212_mode_0"), val = tensor("update")]; + tensor scatter_212_axis_0 = const()[name = tensor("scatter_212_axis_0"), val = tensor(0)]; + tensor scatter_212 = scatter(axis = scatter_212_axis_0, data = scatter_211, indices = slice_by_index_237, mode = scatter_212_mode_0, updates = const_304)[name = tensor("scatter_212")]; + tensor const_305 = const()[name = tensor("const_305"), val = tensor([0x1p+0])]; + tensor slice_by_index_238 = const()[name = tensor("slice_by_index_238"), val = tensor([132801])]; + tensor scatter_213_mode_0 = const()[name = tensor("scatter_213_mode_0"), val = tensor("update")]; + tensor scatter_213_axis_0 = const()[name = tensor("scatter_213_axis_0"), val = tensor(0)]; + tensor scatter_213 = scatter(axis = scatter_213_axis_0, data = scatter_212, indices = slice_by_index_238, mode = scatter_213_mode_0, updates = const_305)[name = tensor("scatter_213")]; + tensor const_306 = const()[name = tensor("const_306"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_239 = const()[name = tensor("slice_by_index_239"), val = tensor([133423, 133424])]; + tensor scatter_214_mode_0 = const()[name = tensor("scatter_214_mode_0"), val = tensor("update")]; + tensor scatter_214_axis_0 = const()[name = tensor("scatter_214_axis_0"), val = tensor(0)]; + tensor scatter_214 = scatter(axis = scatter_214_axis_0, data = scatter_213, indices = slice_by_index_239, mode = scatter_214_mode_0, updates = const_306)[name = tensor("scatter_214")]; + tensor const_307 = const()[name = tensor("const_307"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_240 = const()[name = tensor("slice_by_index_240"), val = tensor([134046, 134047])]; + tensor scatter_215_mode_0 = const()[name = tensor("scatter_215_mode_0"), val = tensor("update")]; + tensor scatter_215_axis_0 = const()[name = tensor("scatter_215_axis_0"), val = tensor(0)]; + tensor scatter_215 = scatter(axis = scatter_215_axis_0, data = scatter_214, indices = slice_by_index_240, mode = scatter_215_mode_0, updates = const_307)[name = tensor("scatter_215")]; + tensor const_308 = const()[name = tensor("const_308"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_241 = const()[name = tensor("slice_by_index_241"), val = tensor([134669, 134670])]; + tensor scatter_216_mode_0 = const()[name = tensor("scatter_216_mode_0"), val = tensor("update")]; + tensor scatter_216_axis_0 = const()[name = tensor("scatter_216_axis_0"), val = tensor(0)]; + tensor scatter_216 = scatter(axis = scatter_216_axis_0, data = scatter_215, indices = slice_by_index_241, mode = scatter_216_mode_0, updates = const_308)[name = tensor("scatter_216")]; + tensor const_309 = const()[name = tensor("const_309"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_242 = const()[name = tensor("slice_by_index_242"), val = tensor([135292, 135293, 135294])]; + tensor scatter_217_mode_0 = const()[name = tensor("scatter_217_mode_0"), val = tensor("update")]; + tensor scatter_217_axis_0 = const()[name = tensor("scatter_217_axis_0"), val = tensor(0)]; + tensor scatter_217 = scatter(axis = scatter_217_axis_0, data = scatter_216, indices = slice_by_index_242, mode = scatter_217_mode_0, updates = const_309)[name = tensor("scatter_217")]; + tensor const_310 = const()[name = tensor("const_310"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_243 = const()[name = tensor("slice_by_index_243"), val = tensor([135916, 135917, 135918])]; + tensor scatter_218_mode_0 = const()[name = tensor("scatter_218_mode_0"), val = tensor("update")]; + tensor scatter_218_axis_0 = const()[name = tensor("scatter_218_axis_0"), val = tensor(0)]; + tensor scatter_218 = scatter(axis = scatter_218_axis_0, data = scatter_217, indices = slice_by_index_243, mode = scatter_218_mode_0, updates = const_310)[name = tensor("scatter_218")]; + tensor const_311 = const()[name = tensor("const_311"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_244 = const()[name = tensor("slice_by_index_244"), val = tensor([136540, 136541, 136542])]; + tensor scatter_219_mode_0 = const()[name = tensor("scatter_219_mode_0"), val = tensor("update")]; + tensor scatter_219_axis_0 = const()[name = tensor("scatter_219_axis_0"), val = tensor(0)]; + tensor scatter_219 = scatter(axis = scatter_219_axis_0, data = scatter_218, indices = slice_by_index_244, mode = scatter_219_mode_0, updates = const_311)[name = tensor("scatter_219")]; + tensor const_312 = const()[name = tensor("const_312"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_245 = const()[name = tensor("slice_by_index_245"), val = tensor([137164, 137165])]; + tensor scatter_220_mode_0 = const()[name = tensor("scatter_220_mode_0"), val = tensor("update")]; + tensor scatter_220_axis_0 = const()[name = tensor("scatter_220_axis_0"), val = tensor(0)]; + tensor scatter_220 = scatter(axis = scatter_220_axis_0, data = scatter_219, indices = slice_by_index_245, mode = scatter_220_mode_0, updates = const_312)[name = tensor("scatter_220")]; + tensor const_313 = const()[name = tensor("const_313"), val = tensor([0x1p+0])]; + tensor slice_by_index_246 = const()[name = tensor("slice_by_index_246"), val = tensor([137787])]; + tensor scatter_221_mode_0 = const()[name = tensor("scatter_221_mode_0"), val = tensor("update")]; + tensor scatter_221_axis_0 = const()[name = tensor("scatter_221_axis_0"), val = tensor(0)]; + tensor scatter_221 = scatter(axis = scatter_221_axis_0, data = scatter_220, indices = slice_by_index_246, mode = scatter_221_mode_0, updates = const_313)[name = tensor("scatter_221")]; + tensor const_314 = const()[name = tensor("const_314"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_247 = const()[name = tensor("slice_by_index_247"), val = tensor([138409, 138410])]; + tensor scatter_222_mode_0 = const()[name = tensor("scatter_222_mode_0"), val = tensor("update")]; + tensor scatter_222_axis_0 = const()[name = tensor("scatter_222_axis_0"), val = tensor(0)]; + tensor scatter_222 = scatter(axis = scatter_222_axis_0, data = scatter_221, indices = slice_by_index_247, mode = scatter_222_mode_0, updates = const_314)[name = tensor("scatter_222")]; + tensor const_315 = const()[name = tensor("const_315"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_248 = const()[name = tensor("slice_by_index_248"), val = tensor([139032, 139033, 139034])]; + tensor scatter_223_mode_0 = const()[name = tensor("scatter_223_mode_0"), val = tensor("update")]; + tensor scatter_223_axis_0 = const()[name = tensor("scatter_223_axis_0"), val = tensor(0)]; + tensor scatter_223 = scatter(axis = scatter_223_axis_0, data = scatter_222, indices = slice_by_index_248, mode = scatter_223_mode_0, updates = const_315)[name = tensor("scatter_223")]; + tensor const_316 = const()[name = tensor("const_316"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_249 = const()[name = tensor("slice_by_index_249"), val = tensor([139656, 139657, 139658])]; + tensor scatter_224_mode_0 = const()[name = tensor("scatter_224_mode_0"), val = tensor("update")]; + tensor scatter_224_axis_0 = const()[name = tensor("scatter_224_axis_0"), val = tensor(0)]; + tensor scatter_224 = scatter(axis = scatter_224_axis_0, data = scatter_223, indices = slice_by_index_249, mode = scatter_224_mode_0, updates = const_316)[name = tensor("scatter_224")]; + tensor const_317 = const()[name = tensor("const_317"), val = tensor([0x1p+0])]; + tensor slice_by_index_250 = const()[name = tensor("slice_by_index_250"), val = tensor([140280])]; + tensor scatter_225_mode_0 = const()[name = tensor("scatter_225_mode_0"), val = tensor("update")]; + tensor scatter_225_axis_0 = const()[name = tensor("scatter_225_axis_0"), val = tensor(0)]; + tensor scatter_225 = scatter(axis = scatter_225_axis_0, data = scatter_224, indices = slice_by_index_250, mode = scatter_225_mode_0, updates = const_317)[name = tensor("scatter_225")]; + tensor const_318 = const()[name = tensor("const_318"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_251 = const()[name = tensor("slice_by_index_251"), val = tensor([140902, 140903])]; + tensor scatter_226_mode_0 = const()[name = tensor("scatter_226_mode_0"), val = tensor("update")]; + tensor scatter_226_axis_0 = const()[name = tensor("scatter_226_axis_0"), val = tensor(0)]; + tensor scatter_226 = scatter(axis = scatter_226_axis_0, data = scatter_225, indices = slice_by_index_251, mode = scatter_226_mode_0, updates = const_318)[name = tensor("scatter_226")]; + tensor const_319 = const()[name = tensor("const_319"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_252 = const()[name = tensor("slice_by_index_252"), val = tensor([141525, 141526])]; + tensor scatter_227_mode_0 = const()[name = tensor("scatter_227_mode_0"), val = tensor("update")]; + tensor scatter_227_axis_0 = const()[name = tensor("scatter_227_axis_0"), val = tensor(0)]; + tensor scatter_227 = scatter(axis = scatter_227_axis_0, data = scatter_226, indices = slice_by_index_252, mode = scatter_227_mode_0, updates = const_319)[name = tensor("scatter_227")]; + tensor const_320 = const()[name = tensor("const_320"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_253 = const()[name = tensor("slice_by_index_253"), val = tensor([142148, 142149])]; + tensor scatter_228_mode_0 = const()[name = tensor("scatter_228_mode_0"), val = tensor("update")]; + tensor scatter_228_axis_0 = const()[name = tensor("scatter_228_axis_0"), val = tensor(0)]; + tensor scatter_228 = scatter(axis = scatter_228_axis_0, data = scatter_227, indices = slice_by_index_253, mode = scatter_228_mode_0, updates = const_320)[name = tensor("scatter_228")]; + tensor const_321 = const()[name = tensor("const_321"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_254 = const()[name = tensor("slice_by_index_254"), val = tensor([142771, 142772])]; + tensor scatter_229_mode_0 = const()[name = tensor("scatter_229_mode_0"), val = tensor("update")]; + tensor scatter_229_axis_0 = const()[name = tensor("scatter_229_axis_0"), val = tensor(0)]; + tensor scatter_229 = scatter(axis = scatter_229_axis_0, data = scatter_228, indices = slice_by_index_254, mode = scatter_229_mode_0, updates = const_321)[name = tensor("scatter_229")]; + tensor const_322 = const()[name = tensor("const_322"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_255 = const()[name = tensor("slice_by_index_255"), val = tensor([143394, 143395, 143396, 143397])]; + tensor scatter_230_mode_0 = const()[name = tensor("scatter_230_mode_0"), val = tensor("update")]; + tensor scatter_230_axis_0 = const()[name = tensor("scatter_230_axis_0"), val = tensor(0)]; + tensor scatter_230 = scatter(axis = scatter_230_axis_0, data = scatter_229, indices = slice_by_index_255, mode = scatter_230_mode_0, updates = const_322)[name = tensor("scatter_230")]; + tensor const_323 = const()[name = tensor("const_323"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_256 = const()[name = tensor("slice_by_index_256"), val = tensor([144019, 144020])]; + tensor scatter_231_mode_0 = const()[name = tensor("scatter_231_mode_0"), val = tensor("update")]; + tensor scatter_231_axis_0 = const()[name = tensor("scatter_231_axis_0"), val = tensor(0)]; + tensor scatter_231 = scatter(axis = scatter_231_axis_0, data = scatter_230, indices = slice_by_index_256, mode = scatter_231_mode_0, updates = const_323)[name = tensor("scatter_231")]; + tensor const_324 = const()[name = tensor("const_324"), val = tensor([0x1p+0])]; + tensor slice_by_index_257 = const()[name = tensor("slice_by_index_257"), val = tensor([144642])]; + tensor scatter_232_mode_0 = const()[name = tensor("scatter_232_mode_0"), val = tensor("update")]; + tensor scatter_232_axis_0 = const()[name = tensor("scatter_232_axis_0"), val = tensor(0)]; + tensor scatter_232 = scatter(axis = scatter_232_axis_0, data = scatter_231, indices = slice_by_index_257, mode = scatter_232_mode_0, updates = const_324)[name = tensor("scatter_232")]; + tensor const_325 = const()[name = tensor("const_325"), val = tensor([0x1p+0])]; + tensor slice_by_index_258 = const()[name = tensor("slice_by_index_258"), val = tensor([145264])]; + tensor scatter_233_mode_0 = const()[name = tensor("scatter_233_mode_0"), val = tensor("update")]; + tensor scatter_233_axis_0 = const()[name = tensor("scatter_233_axis_0"), val = tensor(0)]; + tensor scatter_233 = scatter(axis = scatter_233_axis_0, data = scatter_232, indices = slice_by_index_258, mode = scatter_233_mode_0, updates = const_325)[name = tensor("scatter_233")]; + tensor const_326 = const()[name = tensor("const_326"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_259 = const()[name = tensor("slice_by_index_259"), val = tensor([145886, 145887])]; + tensor scatter_234_mode_0 = const()[name = tensor("scatter_234_mode_0"), val = tensor("update")]; + tensor scatter_234_axis_0 = const()[name = tensor("scatter_234_axis_0"), val = tensor(0)]; + tensor scatter_234 = scatter(axis = scatter_234_axis_0, data = scatter_233, indices = slice_by_index_259, mode = scatter_234_mode_0, updates = const_326)[name = tensor("scatter_234")]; + tensor const_327 = const()[name = tensor("const_327"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_260 = const()[name = tensor("slice_by_index_260"), val = tensor([146509, 146510, 146511])]; + tensor scatter_235_mode_0 = const()[name = tensor("scatter_235_mode_0"), val = tensor("update")]; + tensor scatter_235_axis_0 = const()[name = tensor("scatter_235_axis_0"), val = tensor(0)]; + tensor scatter_235 = scatter(axis = scatter_235_axis_0, data = scatter_234, indices = slice_by_index_260, mode = scatter_235_mode_0, updates = const_327)[name = tensor("scatter_235")]; + tensor const_328 = const()[name = tensor("const_328"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_261 = const()[name = tensor("slice_by_index_261"), val = tensor([147133, 147134])]; + tensor scatter_236_mode_0 = const()[name = tensor("scatter_236_mode_0"), val = tensor("update")]; + tensor scatter_236_axis_0 = const()[name = tensor("scatter_236_axis_0"), val = tensor(0)]; + tensor scatter_236 = scatter(axis = scatter_236_axis_0, data = scatter_235, indices = slice_by_index_261, mode = scatter_236_mode_0, updates = const_328)[name = tensor("scatter_236")]; + tensor const_329 = const()[name = tensor("const_329"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_262 = const()[name = tensor("slice_by_index_262"), val = tensor([147756, 147757])]; + tensor scatter_237_mode_0 = const()[name = tensor("scatter_237_mode_0"), val = tensor("update")]; + tensor scatter_237_axis_0 = const()[name = tensor("scatter_237_axis_0"), val = tensor(0)]; + tensor scatter_237 = scatter(axis = scatter_237_axis_0, data = scatter_236, indices = slice_by_index_262, mode = scatter_237_mode_0, updates = const_329)[name = tensor("scatter_237")]; + tensor const_330 = const()[name = tensor("const_330"), val = tensor([0x1p+0])]; + tensor slice_by_index_263 = const()[name = tensor("slice_by_index_263"), val = tensor([148379])]; + tensor scatter_238_mode_0 = const()[name = tensor("scatter_238_mode_0"), val = tensor("update")]; + tensor scatter_238_axis_0 = const()[name = tensor("scatter_238_axis_0"), val = tensor(0)]; + tensor scatter_238 = scatter(axis = scatter_238_axis_0, data = scatter_237, indices = slice_by_index_263, mode = scatter_238_mode_0, updates = const_330)[name = tensor("scatter_238")]; + tensor const_331 = const()[name = tensor("const_331"), val = tensor([0x1p+0])]; + tensor slice_by_index_264 = const()[name = tensor("slice_by_index_264"), val = tensor([149001])]; + tensor scatter_239_mode_0 = const()[name = tensor("scatter_239_mode_0"), val = tensor("update")]; + tensor scatter_239_axis_0 = const()[name = tensor("scatter_239_axis_0"), val = tensor(0)]; + tensor scatter_239 = scatter(axis = scatter_239_axis_0, data = scatter_238, indices = slice_by_index_264, mode = scatter_239_mode_0, updates = const_331)[name = tensor("scatter_239")]; + tensor const_332 = const()[name = tensor("const_332"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_265 = const()[name = tensor("slice_by_index_265"), val = tensor([149623, 149624, 149625])]; + tensor scatter_240_mode_0 = const()[name = tensor("scatter_240_mode_0"), val = tensor("update")]; + tensor scatter_240_axis_0 = const()[name = tensor("scatter_240_axis_0"), val = tensor(0)]; + tensor scatter_240 = scatter(axis = scatter_240_axis_0, data = scatter_239, indices = slice_by_index_265, mode = scatter_240_mode_0, updates = const_332)[name = tensor("scatter_240")]; + tensor const_333 = const()[name = tensor("const_333"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_266 = const()[name = tensor("slice_by_index_266"), val = tensor([150247, 150248])]; + tensor scatter_241_mode_0 = const()[name = tensor("scatter_241_mode_0"), val = tensor("update")]; + tensor scatter_241_axis_0 = const()[name = tensor("scatter_241_axis_0"), val = tensor(0)]; + tensor scatter_241 = scatter(axis = scatter_241_axis_0, data = scatter_240, indices = slice_by_index_266, mode = scatter_241_mode_0, updates = const_333)[name = tensor("scatter_241")]; + tensor const_334 = const()[name = tensor("const_334"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_267 = const()[name = tensor("slice_by_index_267"), val = tensor([150870, 150871])]; + tensor scatter_242_mode_0 = const()[name = tensor("scatter_242_mode_0"), val = tensor("update")]; + tensor scatter_242_axis_0 = const()[name = tensor("scatter_242_axis_0"), val = tensor(0)]; + tensor scatter_242 = scatter(axis = scatter_242_axis_0, data = scatter_241, indices = slice_by_index_267, mode = scatter_242_mode_0, updates = const_334)[name = tensor("scatter_242")]; + tensor const_335 = const()[name = tensor("const_335"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_268 = const()[name = tensor("slice_by_index_268"), val = tensor([151493, 151494])]; + tensor scatter_243_mode_0 = const()[name = tensor("scatter_243_mode_0"), val = tensor("update")]; + tensor scatter_243_axis_0 = const()[name = tensor("scatter_243_axis_0"), val = tensor(0)]; + tensor scatter_243 = scatter(axis = scatter_243_axis_0, data = scatter_242, indices = slice_by_index_268, mode = scatter_243_mode_0, updates = const_335)[name = tensor("scatter_243")]; + tensor const_336 = const()[name = tensor("const_336"), val = tensor([0x1p+0, 0x1p+0])]; + tensor slice_by_index_269 = const()[name = tensor("slice_by_index_269"), val = tensor([152116, 152117])]; + tensor scatter_244_mode_0 = const()[name = tensor("scatter_244_mode_0"), val = tensor("update")]; + tensor scatter_244_axis_0 = const()[name = tensor("scatter_244_axis_0"), val = tensor(0)]; + tensor scatter_244 = scatter(axis = scatter_244_axis_0, data = scatter_243, indices = slice_by_index_269, mode = scatter_244_mode_0, updates = const_336)[name = tensor("scatter_244")]; + tensor const_337 = const()[name = tensor("const_337"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_270 = const()[name = tensor("slice_by_index_270"), val = tensor([152739, 152740, 152741])]; + tensor scatter_245_mode_0 = const()[name = tensor("scatter_245_mode_0"), val = tensor("update")]; + tensor scatter_245_axis_0 = const()[name = tensor("scatter_245_axis_0"), val = tensor(0)]; + tensor scatter_245 = scatter(axis = scatter_245_axis_0, data = scatter_244, indices = slice_by_index_270, mode = scatter_245_mode_0, updates = const_337)[name = tensor("scatter_245")]; + tensor const_338 = const()[name = tensor("const_338"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73537216)))]; + tensor slice_by_index_271 = const()[name = tensor("slice_by_index_271"), val = tensor([153363, 153364, 153365, 153366, 153367, 153368, 153369, 153370, 153371, 153372, 153373, 153374, 153375, 153376, 153377, 153378, 153379])]; + tensor scatter_246_mode_0 = const()[name = tensor("scatter_246_mode_0"), val = tensor("update")]; + tensor scatter_246_axis_0 = const()[name = tensor("scatter_246_axis_0"), val = tensor(0)]; + tensor scatter_246 = scatter(axis = scatter_246_axis_0, data = scatter_245, indices = slice_by_index_271, mode = scatter_246_mode_0, updates = const_338)[name = tensor("scatter_246")]; + tensor const_339 = const()[name = tensor("const_339"), val = tensor([0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0])]; + tensor slice_by_index_272 = const()[name = tensor("slice_by_index_272"), val = tensor([154001, 154002, 154003, 154004, 154005, 154006])]; + tensor scatter_247_mode_0 = const()[name = tensor("scatter_247_mode_0"), val = tensor("update")]; + tensor scatter_247_axis_0 = const()[name = tensor("scatter_247_axis_0"), val = tensor(0)]; + tensor scatter_247 = scatter(axis = scatter_247_axis_0, data = scatter_246, indices = slice_by_index_272, mode = scatter_247_mode_0, updates = const_339)[name = tensor("scatter_247")]; + tensor const_340 = const()[name = tensor("const_340"), val = tensor([0x1p+0])]; + tensor shape_262 = const()[name = tensor("shape_262"), val = tensor([249, 621])]; + tensor slice_by_index_273 = const()[name = tensor("slice_by_index_273"), val = tensor([154628])]; + tensor scatter_248_mode_0 = const()[name = tensor("scatter_248_mode_0"), val = tensor("update")]; + tensor scatter_248_axis_0 = const()[name = tensor("scatter_248_axis_0"), val = tensor(0)]; + tensor scatter_248 = scatter(axis = scatter_248_axis_0, data = scatter_247, indices = slice_by_index_273, mode = scatter_248_mode_0, updates = const_340)[name = tensor("scatter_248")]; + tensor reshape_1244 = reshape(shape = shape_262, x = scatter_248)[name = tensor("reshape_1244")]; + tensor pred_aln_trg_axes_0 = const()[name = tensor("pred_aln_trg_axes_0"), val = tensor([0])]; + tensor pred_aln_trg = expand_dims(axes = pred_aln_trg_axes_0, x = reshape_1244)[name = tensor("pred_aln_trg")]; + tensor x_217_transpose_x_0 = const()[name = tensor("x_217_transpose_x_0"), val = tensor(false)]; + tensor x_217_transpose_y_0 = const()[name = tensor("x_217_transpose_y_0"), val = tensor(false)]; + tensor x_217 = matmul(transpose_x = x_217_transpose_x_0, transpose_y = x_217_transpose_y_0, x = x_213, y = pred_aln_trg)[name = tensor("x_217")]; + tensor transpose_26_perm_0 = const()[name = tensor("transpose_26_perm_0"), val = tensor([-1, 0, -2])]; + tensor add_6 = const()[name = tensor("add_6"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73537408)))]; + tensor add_7 = const()[name = tensor("add_7"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73541568)))]; + tensor concat_547 = const()[name = tensor("concat_547"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73545728)))]; + tensor concat_548 = const()[name = tensor("concat_548"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76167232)))]; + tensor concat_549 = const()[name = tensor("concat_549"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77215872)))]; + tensor concat_550 = const()[name = tensor("concat_550"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79837376)))]; + tensor x_219_batch_first_direction_0 = const()[name = tensor("x_219_batch_first_direction_0"), val = tensor("bidirectional")]; + tensor x_219_batch_first_output_sequence_0 = const()[name = tensor("x_219_batch_first_output_sequence_0"), val = tensor(true)]; + tensor x_219_batch_first_recurrent_activation_0 = const()[name = tensor("x_219_batch_first_recurrent_activation_0"), val = tensor("sigmoid")]; + tensor x_219_batch_first_cell_activation_0 = const()[name = tensor("x_219_batch_first_cell_activation_0"), val = tensor("tanh")]; + tensor x_219_batch_first_activation_0 = const()[name = tensor("x_219_batch_first_activation_0"), val = tensor("tanh")]; + tensor transpose_26 = transpose(perm = transpose_26_perm_0, x = x_217)[name = tensor("transpose_104")]; + tensor x_219_batch_first_0, tensor x_219_batch_first_1, tensor x_219_batch_first_2 = lstm(activation = x_219_batch_first_activation_0, bias = add_6, bias_back = add_7, cell_activation = x_219_batch_first_cell_activation_0, direction = x_219_batch_first_direction_0, initial_c = x_159_batch_first_lstm_h0_reshaped, initial_h = x_159_batch_first_lstm_h0_reshaped, output_sequence = x_219_batch_first_output_sequence_0, recurrent_activation = x_219_batch_first_recurrent_activation_0, weight_hh = concat_548, weight_hh_back = concat_550, weight_ih = concat_547, weight_ih_back = concat_549, x = transpose_26)[name = tensor("x_219_batch_first")]; + tensor x_219_perm_0 = const()[name = tensor("x_219_perm_0"), val = tensor([1, 0, 2])]; + tensor input_267_perm_0 = const()[name = tensor("input_267_perm_0"), val = tensor([0, -1, -2])]; + tensor var_3960 = const()[name = tensor("op_3960"), val = tensor(0x1.99999ap-3)]; + tensor var_3963 = const()[name = tensor("op_3963"), val = tensor(0x1.4f8b58p-17)]; + tensor h_13 = linear(bias = predictor_F0_0_norm1_fc_bias, weight = predictor_F0_0_norm1_fc_weight, x = style)[name = tensor("linear_77")]; + tensor var_3978 = const()[name = tensor("op_3978"), val = tensor([1, 1024, 1])]; + tensor h_15 = reshape(shape = var_3978, x = h_13)[name = tensor("h_15")]; + tensor var_3980_split_sizes_0 = const()[name = tensor("op_3980_split_sizes_0"), val = tensor([512, 512])]; + tensor var_3980_axis_0 = const()[name = tensor("op_3980_axis_0"), val = tensor(1)]; + tensor var_3980_0, tensor var_3980_1 = split(axis = var_3980_axis_0, split_sizes = var_3980_split_sizes_0, x = h_15)[name = tensor("op_3980")]; + tensor var_3982_promoted = const()[name = tensor("op_3982_promoted"), val = tensor(0x1p+0)]; + tensor var_3983 = add(x = var_3980_0, y = var_3982_promoted)[name = tensor("op_3983")]; + tensor x_219 = transpose(perm = x_219_perm_0, x = x_219_batch_first_0)[name = tensor("transpose_103")]; + tensor input_267 = transpose(perm = input_267_perm_0, x = x_219)[name = tensor("transpose_102")]; + tensor var_3986 = instance_norm(beta = predictor_F0_0_norm1_norm_bias, epsilon = var_3963, gamma = predictor_F0_0_norm1_norm_weight, x = input_267)[name = tensor("op_3986")]; + tensor var_3987 = mul(x = var_3983, y = var_3986)[name = tensor("op_3987")]; + tensor input_269 = add(x = var_3987, y = var_3980_1)[name = tensor("input_269")]; + tensor input_271 = leaky_relu(alpha = var_3960, x = input_269)[name = tensor("input_271")]; + tensor weight_15 = const()[name = tensor("weight_15"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80886016)))]; + tensor input_275_pad_type_0 = const()[name = tensor("input_275_pad_type_0"), val = tensor("custom")]; + tensor input_275_pad_0 = const()[name = tensor("input_275_pad_0"), val = tensor([1, 1])]; + tensor input_275_strides_0 = const()[name = tensor("input_275_strides_0"), val = tensor([1])]; + tensor input_275_dilations_0 = const()[name = tensor("input_275_dilations_0"), val = tensor([1])]; + tensor input_275_groups_0 = const()[name = tensor("input_275_groups_0"), val = tensor(1)]; + tensor input_275 = conv(bias = predictor_F0_0_conv1_bias, dilations = input_275_dilations_0, groups = input_275_groups_0, pad = input_275_pad_0, pad_type = input_275_pad_type_0, strides = input_275_strides_0, weight = weight_15, x = input_271)[name = tensor("input_275")]; + tensor h_17 = linear(bias = predictor_F0_0_norm2_fc_bias, weight = predictor_F0_0_norm2_fc_weight, x = style)[name = tensor("linear_78")]; + tensor var_4009 = const()[name = tensor("op_4009"), val = tensor([1, 1024, 1])]; + tensor h_19 = reshape(shape = var_4009, x = h_17)[name = tensor("h_19")]; + tensor var_4011_split_sizes_0 = const()[name = tensor("op_4011_split_sizes_0"), val = tensor([512, 512])]; + tensor var_4011_axis_0 = const()[name = tensor("op_4011_axis_0"), val = tensor(1)]; + tensor var_4011_0, tensor var_4011_1 = split(axis = var_4011_axis_0, split_sizes = var_4011_split_sizes_0, x = h_19)[name = tensor("op_4011")]; + tensor var_4013_promoted = const()[name = tensor("op_4013_promoted"), val = tensor(0x1p+0)]; + tensor var_4014 = add(x = var_4011_0, y = var_4013_promoted)[name = tensor("op_4014")]; + tensor var_4017 = instance_norm(beta = predictor_F0_0_norm1_norm_bias, epsilon = var_3963, gamma = predictor_F0_0_norm1_norm_weight, x = input_275)[name = tensor("op_4017")]; + tensor var_4018 = mul(x = var_4014, y = var_4017)[name = tensor("op_4018")]; + tensor input_277 = add(x = var_4018, y = var_4011_1)[name = tensor("input_277")]; + tensor input_279 = leaky_relu(alpha = var_3960, x = input_277)[name = tensor("input_279")]; + tensor weight_19 = const()[name = tensor("weight_19"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84031808)))]; + tensor out_1_pad_type_0 = const()[name = tensor("out_1_pad_type_0"), val = tensor("custom")]; + tensor out_1_pad_0 = const()[name = tensor("out_1_pad_0"), val = tensor([1, 1])]; + tensor out_1_strides_0 = const()[name = tensor("out_1_strides_0"), val = tensor([1])]; + tensor out_1_dilations_0 = const()[name = tensor("out_1_dilations_0"), val = tensor([1])]; + tensor out_1_groups_0 = const()[name = tensor("out_1_groups_0"), val = tensor(1)]; + tensor out_1 = conv(bias = predictor_F0_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_19, x = input_279)[name = tensor("out_1")]; + tensor var_4033 = add(x = out_1, y = input_267)[name = tensor("op_4033")]; + tensor var_4036 = const()[name = tensor("op_4036"), val = tensor(0x1.6a09e6p-1)]; + tensor input_283 = mul(x = var_4033, y = var_4036)[name = tensor("input_283")]; + tensor var_4044 = const()[name = tensor("op_4044"), val = tensor(0x1.99999ap-3)]; + tensor var_4048 = const()[name = tensor("op_4048"), val = tensor(0x1.4f8b58p-17)]; + tensor h_21 = linear(bias = predictor_F0_1_norm1_fc_bias, weight = predictor_F0_1_norm1_fc_weight, x = style)[name = tensor("linear_79")]; + tensor var_4065 = const()[name = tensor("op_4065"), val = tensor([1, 1024, 1])]; + tensor h_23 = reshape(shape = var_4065, x = h_21)[name = tensor("h_23")]; + tensor var_4067_split_sizes_0 = const()[name = tensor("op_4067_split_sizes_0"), val = tensor([512, 512])]; + tensor var_4067_axis_0 = const()[name = tensor("op_4067_axis_0"), val = tensor(1)]; + tensor var_4067_0, tensor var_4067_1 = split(axis = var_4067_axis_0, split_sizes = var_4067_split_sizes_0, x = h_23)[name = tensor("op_4067")]; + tensor var_4069_promoted = const()[name = tensor("op_4069_promoted"), val = tensor(0x1p+0)]; + tensor var_4070 = add(x = var_4067_0, y = var_4069_promoted)[name = tensor("op_4070")]; + tensor var_4073 = instance_norm(beta = predictor_F0_0_norm1_norm_bias, epsilon = var_4048, gamma = predictor_F0_0_norm1_norm_weight, x = input_283)[name = tensor("op_4073")]; + tensor var_4074 = mul(x = var_4070, y = var_4073)[name = tensor("op_4074")]; + tensor input_285 = add(x = var_4074, y = var_4067_1)[name = tensor("input_285")]; + tensor input_287 = leaky_relu(alpha = var_4044, x = input_285)[name = tensor("input_287")]; + tensor var_4082 = const()[name = tensor("op_4082"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(87177600)))]; + tensor conv_transpose_0_pad_type_0 = const()[name = tensor("conv_transpose_0_pad_type_0"), val = tensor("custom")]; + tensor conv_transpose_0_pad_0 = const()[name = tensor("conv_transpose_0_pad_0"), val = tensor([0, 0])]; + tensor conv_transpose_0_strides_0 = const()[name = tensor("conv_transpose_0_strides_0"), val = tensor([2])]; + tensor conv_transpose_0_groups_0 = const()[name = tensor("conv_transpose_0_groups_0"), val = tensor(512)]; + tensor conv_transpose_0_dilations_0 = const()[name = tensor("conv_transpose_0_dilations_0"), val = tensor([1])]; + tensor conv_transpose_0_has_output_shape_output_shape_0 = const()[name = tensor("conv_transpose_0_has_output_shape_output_shape_0"), val = tensor([1, 512, 1243])]; + tensor conv_transpose_0_has_output_shape = conv_transpose(bias = predictor_F0_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_4082, x = input_287)[name = tensor("conv_transpose_0_has_output_shape")]; + tensor input_289_begin_0 = const()[name = tensor("input_289_begin_0"), val = tensor([0, 0, 1])]; + tensor input_289_end_0 = const()[name = tensor("input_289_end_0"), val = tensor([0, 0, 0])]; + tensor input_289_begin_mask_0 = const()[name = tensor("input_289_begin_mask_0"), val = tensor([true, true, false])]; + tensor input_289_end_mask_0 = const()[name = tensor("input_289_end_mask_0"), val = tensor([true, true, true])]; + tensor input_289 = slice_by_index(begin = input_289_begin_0, begin_mask = input_289_begin_mask_0, end = input_289_end_0, end_mask = input_289_end_mask_0, x = conv_transpose_0_has_output_shape)[name = tensor("input_289")]; + tensor weight_23 = const()[name = tensor("weight_23"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(87183808)))]; + tensor input_293_pad_type_0 = const()[name = tensor("input_293_pad_type_0"), val = tensor("custom")]; + tensor input_293_pad_0 = const()[name = tensor("input_293_pad_0"), val = tensor([1, 1])]; + tensor input_293_strides_0 = const()[name = tensor("input_293_strides_0"), val = tensor([1])]; + tensor input_293_dilations_0 = const()[name = tensor("input_293_dilations_0"), val = tensor([1])]; + tensor input_293_groups_0 = const()[name = tensor("input_293_groups_0"), val = tensor(1)]; + tensor input_293 = conv(bias = predictor_F0_1_conv1_bias, dilations = input_293_dilations_0, groups = input_293_groups_0, pad = input_293_pad_0, pad_type = input_293_pad_type_0, strides = input_293_strides_0, weight = weight_23, x = input_289)[name = tensor("input_293")]; + tensor h_25 = linear(bias = predictor_F0_1_norm2_fc_bias, weight = predictor_F0_1_norm2_fc_weight, x = style)[name = tensor("linear_80")]; + tensor var_4107 = const()[name = tensor("op_4107"), val = tensor([1, 512, 1])]; + tensor h_27 = reshape(shape = var_4107, x = h_25)[name = tensor("h_27")]; + tensor var_4109_split_sizes_0 = const()[name = tensor("op_4109_split_sizes_0"), val = tensor([256, 256])]; + tensor var_4109_axis_0 = const()[name = tensor("op_4109_axis_0"), val = tensor(1)]; + tensor var_4109_0, tensor var_4109_1 = split(axis = var_4109_axis_0, split_sizes = var_4109_split_sizes_0, x = h_27)[name = tensor("op_4109")]; + tensor var_4111_promoted = const()[name = tensor("op_4111_promoted"), val = tensor(0x1p+0)]; + tensor var_4112 = add(x = var_4109_0, y = var_4111_promoted)[name = tensor("op_4112")]; + tensor var_4115 = instance_norm(beta = predictor_F0_1_norm2_norm_bias, epsilon = var_4048, gamma = predictor_F0_1_norm2_norm_weight, x = input_293)[name = tensor("op_4115")]; + tensor var_4116 = mul(x = var_4112, y = var_4115)[name = tensor("op_4116")]; + tensor input_295 = add(x = var_4116, y = var_4109_1)[name = tensor("input_295")]; + tensor input_297 = leaky_relu(alpha = var_4044, x = input_295)[name = tensor("input_297")]; + tensor weight_27 = const()[name = tensor("weight_27"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88756736)))]; + tensor out_3_pad_type_0 = const()[name = tensor("out_3_pad_type_0"), val = tensor("custom")]; + tensor out_3_pad_0 = const()[name = tensor("out_3_pad_0"), val = tensor([1, 1])]; + tensor out_3_strides_0 = const()[name = tensor("out_3_strides_0"), val = tensor([1])]; + tensor out_3_dilations_0 = const()[name = tensor("out_3_dilations_0"), val = tensor([1])]; + tensor out_3_groups_0 = const()[name = tensor("out_3_groups_0"), val = tensor(1)]; + tensor out_3 = conv(bias = predictor_F0_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_27, x = input_297)[name = tensor("out_3")]; + tensor expand_dims_499_axes_0 = const()[name = tensor("expand_dims_499_axes_0"), val = tensor([3])]; + tensor expand_dims_499 = expand_dims(axes = expand_dims_499_axes_0, x = input_283)[name = tensor("expand_dims_499")]; + tensor upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = tensor("upsample_nearest_neighbor_0_scale_factor_height_0"), val = tensor(2)]; + tensor upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = tensor("upsample_nearest_neighbor_0_scale_factor_width_0"), val = tensor(1)]; + tensor 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_499)[name = tensor("upsample_nearest_neighbor_0")]; + tensor input_301_axes_0 = const()[name = tensor("input_301_axes_0"), val = tensor([3])]; + tensor input_301 = squeeze(axes = input_301_axes_0, x = upsample_nearest_neighbor_0)[name = tensor("input_301")]; + tensor weight_29 = const()[name = tensor("weight_29"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89543232)))]; + tensor var_4142_pad_type_0 = const()[name = tensor("op_4142_pad_type_0"), val = tensor("valid")]; + tensor var_4142_strides_0 = const()[name = tensor("op_4142_strides_0"), val = tensor([1])]; + tensor var_4142_pad_0 = const()[name = tensor("op_4142_pad_0"), val = tensor([0, 0])]; + tensor var_4142_dilations_0 = const()[name = tensor("op_4142_dilations_0"), val = tensor([1])]; + tensor var_4142_groups_0 = const()[name = tensor("op_4142_groups_0"), val = tensor(1)]; + tensor var_4142 = conv(dilations = var_4142_dilations_0, groups = var_4142_groups_0, pad = var_4142_pad_0, pad_type = var_4142_pad_type_0, strides = var_4142_strides_0, weight = weight_29, x = input_301)[name = tensor("op_4142")]; + tensor var_4143 = add(x = out_3, y = var_4142)[name = tensor("op_4143")]; + tensor var_4146 = const()[name = tensor("op_4146"), val = tensor(0x1.6a09e6p-1)]; + tensor input_303 = mul(x = var_4143, y = var_4146)[name = tensor("input_303")]; + tensor var_4152 = const()[name = tensor("op_4152"), val = tensor(0x1.99999ap-3)]; + tensor var_4155 = const()[name = tensor("op_4155"), val = tensor(0x1.4f8b58p-17)]; + tensor h_29 = linear(bias = predictor_F0_2_norm1_fc_bias, weight = predictor_F0_2_norm1_fc_weight, x = style)[name = tensor("linear_81")]; + tensor var_4170 = const()[name = tensor("op_4170"), val = tensor([1, 512, 1])]; + tensor h_31 = reshape(shape = var_4170, x = h_29)[name = tensor("h_31")]; + tensor var_4172_split_sizes_0 = const()[name = tensor("op_4172_split_sizes_0"), val = tensor([256, 256])]; + tensor var_4172_axis_0 = const()[name = tensor("op_4172_axis_0"), val = tensor(1)]; + tensor var_4172_0, tensor var_4172_1 = split(axis = var_4172_axis_0, split_sizes = var_4172_split_sizes_0, x = h_31)[name = tensor("op_4172")]; + tensor var_4174_promoted = const()[name = tensor("op_4174_promoted"), val = tensor(0x1p+0)]; + tensor var_4175 = add(x = var_4172_0, y = var_4174_promoted)[name = tensor("op_4175")]; + tensor var_4178 = instance_norm(beta = predictor_F0_1_norm2_norm_bias, epsilon = var_4155, gamma = predictor_F0_1_norm2_norm_weight, x = input_303)[name = tensor("op_4178")]; + tensor var_4179 = mul(x = var_4175, y = var_4178)[name = tensor("op_4179")]; + tensor input_305 = add(x = var_4179, y = var_4172_1)[name = tensor("input_305")]; + tensor input_307 = leaky_relu(alpha = var_4152, x = input_305)[name = tensor("input_307")]; + tensor weight_33 = const()[name = tensor("weight_33"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90067584)))]; + tensor input_311_pad_type_0 = const()[name = tensor("input_311_pad_type_0"), val = tensor("custom")]; + tensor input_311_pad_0 = const()[name = tensor("input_311_pad_0"), val = tensor([1, 1])]; + tensor input_311_strides_0 = const()[name = tensor("input_311_strides_0"), val = tensor([1])]; + tensor input_311_dilations_0 = const()[name = tensor("input_311_dilations_0"), val = tensor([1])]; + tensor input_311_groups_0 = const()[name = tensor("input_311_groups_0"), val = tensor(1)]; + tensor input_311 = conv(bias = predictor_F0_2_conv1_bias, dilations = input_311_dilations_0, groups = input_311_groups_0, pad = input_311_pad_0, pad_type = input_311_pad_type_0, strides = input_311_strides_0, weight = weight_33, x = input_307)[name = tensor("input_311")]; + tensor h_33 = linear(bias = predictor_F0_2_norm2_fc_bias, weight = predictor_F0_2_norm2_fc_weight, x = style)[name = tensor("linear_82")]; + tensor var_4201 = const()[name = tensor("op_4201"), val = tensor([1, 512, 1])]; + tensor h_35 = reshape(shape = var_4201, x = h_33)[name = tensor("h_35")]; + tensor var_4203_split_sizes_0 = const()[name = tensor("op_4203_split_sizes_0"), val = tensor([256, 256])]; + tensor var_4203_axis_0 = const()[name = tensor("op_4203_axis_0"), val = tensor(1)]; + tensor var_4203_0, tensor var_4203_1 = split(axis = var_4203_axis_0, split_sizes = var_4203_split_sizes_0, x = h_35)[name = tensor("op_4203")]; + tensor var_4205_promoted = const()[name = tensor("op_4205_promoted"), val = tensor(0x1p+0)]; + tensor var_4206 = add(x = var_4203_0, y = var_4205_promoted)[name = tensor("op_4206")]; + tensor var_4209 = instance_norm(beta = predictor_F0_1_norm2_norm_bias, epsilon = var_4155, gamma = predictor_F0_1_norm2_norm_weight, x = input_311)[name = tensor("op_4209")]; + tensor var_4210 = mul(x = var_4206, y = var_4209)[name = tensor("op_4210")]; + tensor input_313 = add(x = var_4210, y = var_4203_1)[name = tensor("input_313")]; + tensor input_315 = leaky_relu(alpha = var_4152, x = input_313)[name = tensor("input_315")]; + tensor weight_37 = const()[name = tensor("weight_37"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90854080)))]; + tensor out_5_pad_type_0 = const()[name = tensor("out_5_pad_type_0"), val = tensor("custom")]; + tensor out_5_pad_0 = const()[name = tensor("out_5_pad_0"), val = tensor([1, 1])]; + tensor out_5_strides_0 = const()[name = tensor("out_5_strides_0"), val = tensor([1])]; + tensor out_5_dilations_0 = const()[name = tensor("out_5_dilations_0"), val = tensor([1])]; + tensor out_5_groups_0 = const()[name = tensor("out_5_groups_0"), val = tensor(1)]; + tensor out_5 = conv(bias = predictor_F0_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_37, x = input_315)[name = tensor("out_5")]; + tensor var_4225 = add(x = out_5, y = input_303)[name = tensor("op_4225")]; + tensor var_4228 = const()[name = tensor("op_4228"), val = tensor(0x1.6a09e6p-1)]; + tensor input_319 = mul(x = var_4225, y = var_4228)[name = tensor("input_319")]; + tensor F0_1_pad_type_0 = const()[name = tensor("F0_1_pad_type_0"), val = tensor("valid")]; + tensor F0_1_strides_0 = const()[name = tensor("F0_1_strides_0"), val = tensor([1])]; + tensor F0_1_pad_0 = const()[name = tensor("F0_1_pad_0"), val = tensor([0, 0])]; + tensor F0_1_dilations_0 = const()[name = tensor("F0_1_dilations_0"), val = tensor([1])]; + tensor F0_1_groups_0 = const()[name = tensor("F0_1_groups_0"), val = tensor(1)]; + tensor F0_1 = conv(bias = predictor_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 = predictor_F0_proj_weight, x = input_319)[name = tensor("F0_1")]; + tensor var_4248 = const()[name = tensor("op_4248"), val = tensor(0x1.99999ap-3)]; + tensor var_4251 = const()[name = tensor("op_4251"), val = tensor(0x1.4f8b58p-17)]; + tensor h_37 = linear(bias = predictor_N_0_norm1_fc_bias, weight = predictor_N_0_norm1_fc_weight, x = style)[name = tensor("linear_83")]; + tensor var_4266 = const()[name = tensor("op_4266"), val = tensor([1, 1024, 1])]; + tensor h_39 = reshape(shape = var_4266, x = h_37)[name = tensor("h_39")]; + tensor var_4268_split_sizes_0 = const()[name = tensor("op_4268_split_sizes_0"), val = tensor([512, 512])]; + tensor var_4268_axis_0 = const()[name = tensor("op_4268_axis_0"), val = tensor(1)]; + tensor var_4268_0, tensor var_4268_1 = split(axis = var_4268_axis_0, split_sizes = var_4268_split_sizes_0, x = h_39)[name = tensor("op_4268")]; + tensor var_4270_promoted = const()[name = tensor("op_4270_promoted"), val = tensor(0x1p+0)]; + tensor var_4271 = add(x = var_4268_0, y = var_4270_promoted)[name = tensor("op_4271")]; + tensor var_4275 = mul(x = var_4271, y = var_3986)[name = tensor("op_4275")]; + tensor input_323 = add(x = var_4275, y = var_4268_1)[name = tensor("input_323")]; + tensor input_325 = leaky_relu(alpha = var_4248, x = input_323)[name = tensor("input_325")]; + tensor weight_43 = const()[name = tensor("weight_43"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(91640576)))]; + tensor input_329_pad_type_0 = const()[name = tensor("input_329_pad_type_0"), val = tensor("custom")]; + tensor input_329_pad_0 = const()[name = tensor("input_329_pad_0"), val = tensor([1, 1])]; + tensor input_329_strides_0 = const()[name = tensor("input_329_strides_0"), val = tensor([1])]; + tensor input_329_dilations_0 = const()[name = tensor("input_329_dilations_0"), val = tensor([1])]; + tensor input_329_groups_0 = const()[name = tensor("input_329_groups_0"), val = tensor(1)]; + tensor input_329 = conv(bias = predictor_N_0_conv1_bias, dilations = input_329_dilations_0, groups = input_329_groups_0, pad = input_329_pad_0, pad_type = input_329_pad_type_0, strides = input_329_strides_0, weight = weight_43, x = input_325)[name = tensor("input_329")]; + tensor h_41 = linear(bias = predictor_N_0_norm2_fc_bias, weight = predictor_N_0_norm2_fc_weight, x = style)[name = tensor("linear_84")]; + tensor var_4297 = const()[name = tensor("op_4297"), val = tensor([1, 1024, 1])]; + tensor h_43 = reshape(shape = var_4297, x = h_41)[name = tensor("h_43")]; + tensor var_4299_split_sizes_0 = const()[name = tensor("op_4299_split_sizes_0"), val = tensor([512, 512])]; + tensor var_4299_axis_0 = const()[name = tensor("op_4299_axis_0"), val = tensor(1)]; + tensor var_4299_0, tensor var_4299_1 = split(axis = var_4299_axis_0, split_sizes = var_4299_split_sizes_0, x = h_43)[name = tensor("op_4299")]; + tensor var_4301_promoted = const()[name = tensor("op_4301_promoted"), val = tensor(0x1p+0)]; + tensor var_4302 = add(x = var_4299_0, y = var_4301_promoted)[name = tensor("op_4302")]; + tensor var_4305 = instance_norm(beta = predictor_F0_0_norm1_norm_bias, epsilon = var_4251, gamma = predictor_F0_0_norm1_norm_weight, x = input_329)[name = tensor("op_4305")]; + tensor var_4306 = mul(x = var_4302, y = var_4305)[name = tensor("op_4306")]; + tensor input_331 = add(x = var_4306, y = var_4299_1)[name = tensor("input_331")]; + tensor input_333 = leaky_relu(alpha = var_4248, x = input_331)[name = tensor("input_333")]; + tensor weight_47 = const()[name = tensor("weight_47"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94786368)))]; + tensor out_7_pad_type_0 = const()[name = tensor("out_7_pad_type_0"), val = tensor("custom")]; + tensor out_7_pad_0 = const()[name = tensor("out_7_pad_0"), val = tensor([1, 1])]; + tensor out_7_strides_0 = const()[name = tensor("out_7_strides_0"), val = tensor([1])]; + tensor out_7_dilations_0 = const()[name = tensor("out_7_dilations_0"), val = tensor([1])]; + tensor out_7_groups_0 = const()[name = tensor("out_7_groups_0"), val = tensor(1)]; + tensor out_7 = conv(bias = predictor_N_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_47, x = input_333)[name = tensor("out_7")]; + tensor var_4321 = add(x = out_7, y = input_267)[name = tensor("op_4321")]; + tensor var_4324 = const()[name = tensor("op_4324"), val = tensor(0x1.6a09e6p-1)]; + tensor input_337 = mul(x = var_4321, y = var_4324)[name = tensor("input_337")]; + tensor var_4332 = const()[name = tensor("op_4332"), val = tensor(0x1.99999ap-3)]; + tensor var_4336 = const()[name = tensor("op_4336"), val = tensor(0x1.4f8b58p-17)]; + tensor h_45 = linear(bias = predictor_N_1_norm1_fc_bias, weight = predictor_N_1_norm1_fc_weight, x = style)[name = tensor("linear_85")]; + tensor var_4353 = const()[name = tensor("op_4353"), val = tensor([1, 1024, 1])]; + tensor h_47 = reshape(shape = var_4353, x = h_45)[name = tensor("h_47")]; + tensor var_4355_split_sizes_0 = const()[name = tensor("op_4355_split_sizes_0"), val = tensor([512, 512])]; + tensor var_4355_axis_0 = const()[name = tensor("op_4355_axis_0"), val = tensor(1)]; + tensor var_4355_0, tensor var_4355_1 = split(axis = var_4355_axis_0, split_sizes = var_4355_split_sizes_0, x = h_47)[name = tensor("op_4355")]; + tensor var_4357_promoted = const()[name = tensor("op_4357_promoted"), val = tensor(0x1p+0)]; + tensor var_4358 = add(x = var_4355_0, y = var_4357_promoted)[name = tensor("op_4358")]; + tensor var_4361 = instance_norm(beta = predictor_F0_0_norm1_norm_bias, epsilon = var_4336, gamma = predictor_F0_0_norm1_norm_weight, x = input_337)[name = tensor("op_4361")]; + tensor var_4362 = mul(x = var_4358, y = var_4361)[name = tensor("op_4362")]; + tensor input_339 = add(x = var_4362, y = var_4355_1)[name = tensor("input_339")]; + tensor input_341 = leaky_relu(alpha = var_4332, x = input_339)[name = tensor("input_341")]; + tensor var_4370 = const()[name = tensor("op_4370"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97932160)))]; + tensor conv_transpose_1_pad_type_0 = const()[name = tensor("conv_transpose_1_pad_type_0"), val = tensor("custom")]; + tensor conv_transpose_1_pad_0 = const()[name = tensor("conv_transpose_1_pad_0"), val = tensor([0, 0])]; + tensor conv_transpose_1_strides_0 = const()[name = tensor("conv_transpose_1_strides_0"), val = tensor([2])]; + tensor conv_transpose_1_groups_0 = const()[name = tensor("conv_transpose_1_groups_0"), val = tensor(512)]; + tensor conv_transpose_1_dilations_0 = const()[name = tensor("conv_transpose_1_dilations_0"), val = tensor([1])]; + tensor conv_transpose_1_has_output_shape_output_shape_0 = const()[name = tensor("conv_transpose_1_has_output_shape_output_shape_0"), val = tensor([1, 512, 1243])]; + tensor conv_transpose_1_has_output_shape = conv_transpose(bias = predictor_N_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_4370, x = input_341)[name = tensor("conv_transpose_1_has_output_shape")]; + tensor input_343_begin_0 = const()[name = tensor("input_343_begin_0"), val = tensor([0, 0, 1])]; + tensor input_343_end_0 = const()[name = tensor("input_343_end_0"), val = tensor([0, 0, 0])]; + tensor input_343_begin_mask_0 = const()[name = tensor("input_343_begin_mask_0"), val = tensor([true, true, false])]; + tensor input_343_end_mask_0 = const()[name = tensor("input_343_end_mask_0"), val = tensor([true, true, true])]; + tensor input_343 = slice_by_index(begin = input_343_begin_0, begin_mask = input_343_begin_mask_0, end = input_343_end_0, end_mask = input_343_end_mask_0, x = conv_transpose_1_has_output_shape)[name = tensor("input_343")]; + tensor weight_51 = const()[name = tensor("weight_51"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97938368)))]; + tensor input_347_pad_type_0 = const()[name = tensor("input_347_pad_type_0"), val = tensor("custom")]; + tensor input_347_pad_0 = const()[name = tensor("input_347_pad_0"), val = tensor([1, 1])]; + tensor input_347_strides_0 = const()[name = tensor("input_347_strides_0"), val = tensor([1])]; + tensor input_347_dilations_0 = const()[name = tensor("input_347_dilations_0"), val = tensor([1])]; + tensor input_347_groups_0 = const()[name = tensor("input_347_groups_0"), val = tensor(1)]; + tensor input_347 = conv(bias = predictor_N_1_conv1_bias, dilations = input_347_dilations_0, groups = input_347_groups_0, pad = input_347_pad_0, pad_type = input_347_pad_type_0, strides = input_347_strides_0, weight = weight_51, x = input_343)[name = tensor("input_347")]; + tensor h_49 = linear(bias = predictor_N_1_norm2_fc_bias, weight = predictor_N_1_norm2_fc_weight, x = style)[name = tensor("linear_86")]; + tensor var_4395 = const()[name = tensor("op_4395"), val = tensor([1, 512, 1])]; + tensor h_51 = reshape(shape = var_4395, x = h_49)[name = tensor("h_51")]; + tensor var_4397_split_sizes_0 = const()[name = tensor("op_4397_split_sizes_0"), val = tensor([256, 256])]; + tensor var_4397_axis_0 = const()[name = tensor("op_4397_axis_0"), val = tensor(1)]; + tensor var_4397_0, tensor var_4397_1 = split(axis = var_4397_axis_0, split_sizes = var_4397_split_sizes_0, x = h_51)[name = tensor("op_4397")]; + tensor var_4399_promoted = const()[name = tensor("op_4399_promoted"), val = tensor(0x1p+0)]; + tensor var_4400 = add(x = var_4397_0, y = var_4399_promoted)[name = tensor("op_4400")]; + tensor var_4403 = instance_norm(beta = predictor_F0_1_norm2_norm_bias, epsilon = var_4336, gamma = predictor_F0_1_norm2_norm_weight, x = input_347)[name = tensor("op_4403")]; + tensor var_4404 = mul(x = var_4400, y = var_4403)[name = tensor("op_4404")]; + tensor input_349 = add(x = var_4404, y = var_4397_1)[name = tensor("input_349")]; + tensor input_351 = leaky_relu(alpha = var_4332, x = input_349)[name = tensor("input_351")]; + tensor weight_55 = const()[name = tensor("weight_55"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(99511296)))]; + tensor out_9_pad_type_0 = const()[name = tensor("out_9_pad_type_0"), val = tensor("custom")]; + tensor out_9_pad_0 = const()[name = tensor("out_9_pad_0"), val = tensor([1, 1])]; + tensor out_9_strides_0 = const()[name = tensor("out_9_strides_0"), val = tensor([1])]; + tensor out_9_dilations_0 = const()[name = tensor("out_9_dilations_0"), val = tensor([1])]; + tensor out_9_groups_0 = const()[name = tensor("out_9_groups_0"), val = tensor(1)]; + tensor out_9 = conv(bias = predictor_N_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_55, x = input_351)[name = tensor("out_9")]; + tensor expand_dims_500_axes_0 = const()[name = tensor("expand_dims_500_axes_0"), val = tensor([3])]; + tensor expand_dims_500 = expand_dims(axes = expand_dims_500_axes_0, x = input_337)[name = tensor("expand_dims_500")]; + tensor upsample_nearest_neighbor_1_scale_factor_height_0 = const()[name = tensor("upsample_nearest_neighbor_1_scale_factor_height_0"), val = tensor(2)]; + tensor upsample_nearest_neighbor_1_scale_factor_width_0 = const()[name = tensor("upsample_nearest_neighbor_1_scale_factor_width_0"), val = tensor(1)]; + tensor 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_500)[name = tensor("upsample_nearest_neighbor_1")]; + tensor input_355_axes_0 = const()[name = tensor("input_355_axes_0"), val = tensor([3])]; + tensor input_355 = squeeze(axes = input_355_axes_0, x = upsample_nearest_neighbor_1)[name = tensor("input_355")]; + tensor weight_57 = const()[name = tensor("weight_57"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100297792)))]; + tensor var_4430_pad_type_0 = const()[name = tensor("op_4430_pad_type_0"), val = tensor("valid")]; + tensor var_4430_strides_0 = const()[name = tensor("op_4430_strides_0"), val = tensor([1])]; + tensor var_4430_pad_0 = const()[name = tensor("op_4430_pad_0"), val = tensor([0, 0])]; + tensor var_4430_dilations_0 = const()[name = tensor("op_4430_dilations_0"), val = tensor([1])]; + tensor var_4430_groups_0 = const()[name = tensor("op_4430_groups_0"), val = tensor(1)]; + tensor var_4430 = conv(dilations = var_4430_dilations_0, groups = var_4430_groups_0, pad = var_4430_pad_0, pad_type = var_4430_pad_type_0, strides = var_4430_strides_0, weight = weight_57, x = input_355)[name = tensor("op_4430")]; + tensor var_4431 = add(x = out_9, y = var_4430)[name = tensor("op_4431")]; + tensor var_4434 = const()[name = tensor("op_4434"), val = tensor(0x1.6a09e6p-1)]; + tensor input_357 = mul(x = var_4431, y = var_4434)[name = tensor("input_357")]; + tensor var_4440 = const()[name = tensor("op_4440"), val = tensor(0x1.99999ap-3)]; + tensor var_4443 = const()[name = tensor("op_4443"), val = tensor(0x1.4f8b58p-17)]; + tensor h_53 = linear(bias = predictor_N_2_norm1_fc_bias, weight = predictor_N_2_norm1_fc_weight, x = style)[name = tensor("linear_87")]; + tensor var_4458 = const()[name = tensor("op_4458"), val = tensor([1, 512, 1])]; + tensor h_55 = reshape(shape = var_4458, x = h_53)[name = tensor("h_55")]; + tensor var_4460_split_sizes_0 = const()[name = tensor("op_4460_split_sizes_0"), val = tensor([256, 256])]; + tensor var_4460_axis_0 = const()[name = tensor("op_4460_axis_0"), val = tensor(1)]; + tensor var_4460_0, tensor var_4460_1 = split(axis = var_4460_axis_0, split_sizes = var_4460_split_sizes_0, x = h_55)[name = tensor("op_4460")]; + tensor var_4462_promoted = const()[name = tensor("op_4462_promoted"), val = tensor(0x1p+0)]; + tensor var_4463 = add(x = var_4460_0, y = var_4462_promoted)[name = tensor("op_4463")]; + tensor var_4466 = instance_norm(beta = predictor_F0_1_norm2_norm_bias, epsilon = var_4443, gamma = predictor_F0_1_norm2_norm_weight, x = input_357)[name = tensor("op_4466")]; + tensor var_4467 = mul(x = var_4463, y = var_4466)[name = tensor("op_4467")]; + tensor input_359 = add(x = var_4467, y = var_4460_1)[name = tensor("input_359")]; + tensor input_361 = leaky_relu(alpha = var_4440, x = input_359)[name = tensor("input_361")]; + tensor weight_61 = const()[name = tensor("weight_61"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100822144)))]; + tensor input_365_pad_type_0 = const()[name = tensor("input_365_pad_type_0"), val = tensor("custom")]; + tensor input_365_pad_0 = const()[name = tensor("input_365_pad_0"), val = tensor([1, 1])]; + tensor input_365_strides_0 = const()[name = tensor("input_365_strides_0"), val = tensor([1])]; + tensor input_365_dilations_0 = const()[name = tensor("input_365_dilations_0"), val = tensor([1])]; + tensor input_365_groups_0 = const()[name = tensor("input_365_groups_0"), val = tensor(1)]; + tensor input_365 = conv(bias = predictor_N_2_conv1_bias, dilations = input_365_dilations_0, groups = input_365_groups_0, pad = input_365_pad_0, pad_type = input_365_pad_type_0, strides = input_365_strides_0, weight = weight_61, x = input_361)[name = tensor("input_365")]; + tensor h_57 = linear(bias = predictor_N_2_norm2_fc_bias, weight = predictor_N_2_norm2_fc_weight, x = style)[name = tensor("linear_88")]; + tensor var_4489 = const()[name = tensor("op_4489"), val = tensor([1, 512, 1])]; + tensor h_59 = reshape(shape = var_4489, x = h_57)[name = tensor("h_59")]; + tensor var_4491_split_sizes_0 = const()[name = tensor("op_4491_split_sizes_0"), val = tensor([256, 256])]; + tensor var_4491_axis_0 = const()[name = tensor("op_4491_axis_0"), val = tensor(1)]; + tensor var_4491_0, tensor var_4491_1 = split(axis = var_4491_axis_0, split_sizes = var_4491_split_sizes_0, x = h_59)[name = tensor("op_4491")]; + tensor var_4493_promoted = const()[name = tensor("op_4493_promoted"), val = tensor(0x1p+0)]; + tensor var_4494 = add(x = var_4491_0, y = var_4493_promoted)[name = tensor("op_4494")]; + tensor var_4497 = instance_norm(beta = predictor_F0_1_norm2_norm_bias, epsilon = var_4443, gamma = predictor_F0_1_norm2_norm_weight, x = input_365)[name = tensor("op_4497")]; + tensor var_4498 = mul(x = var_4494, y = var_4497)[name = tensor("op_4498")]; + tensor input_367 = add(x = var_4498, y = var_4491_1)[name = tensor("input_367")]; + tensor input_369 = leaky_relu(alpha = var_4440, x = input_367)[name = tensor("input_369")]; + tensor weight_65 = const()[name = tensor("weight_65"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(101608640)))]; + tensor out_11_pad_type_0 = const()[name = tensor("out_11_pad_type_0"), val = tensor("custom")]; + tensor out_11_pad_0 = const()[name = tensor("out_11_pad_0"), val = tensor([1, 1])]; + tensor out_11_strides_0 = const()[name = tensor("out_11_strides_0"), val = tensor([1])]; + tensor out_11_dilations_0 = const()[name = tensor("out_11_dilations_0"), val = tensor([1])]; + tensor out_11_groups_0 = const()[name = tensor("out_11_groups_0"), val = tensor(1)]; + tensor out_11 = conv(bias = predictor_N_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_65, x = input_369)[name = tensor("out_11")]; + tensor var_4513 = add(x = out_11, y = input_357)[name = tensor("op_4513")]; + tensor var_4516 = const()[name = tensor("op_4516"), val = tensor(0x1.6a09e6p-1)]; + tensor input_373 = mul(x = var_4513, y = var_4516)[name = tensor("input_373")]; + tensor N_1_pad_type_0 = const()[name = tensor("N_1_pad_type_0"), val = tensor("valid")]; + tensor N_1_strides_0 = const()[name = tensor("N_1_strides_0"), val = tensor([1])]; + tensor N_1_pad_0 = const()[name = tensor("N_1_pad_0"), val = tensor([0, 0])]; + tensor N_1_dilations_0 = const()[name = tensor("N_1_dilations_0"), val = tensor([1])]; + tensor N_1_groups_0 = const()[name = tensor("N_1_groups_0"), val = tensor(1)]; + tensor N_1 = conv(bias = predictor_N_proj_bias, dilations = N_1_dilations_0, groups = N_1_groups_0, pad = N_1_pad_0, pad_type = N_1_pad_type_0, strides = N_1_strides_0, weight = predictor_N_proj_weight, x = input_373)[name = tensor("N_1")]; + tensor F0_pred_1_axes_0 = const()[name = tensor("F0_pred_1_axes_0"), val = tensor([1])]; + tensor F0_pred_1 = squeeze(axes = F0_pred_1_axes_0, x = F0_1)[name = tensor("F0_pred_1")]; + tensor var_4533 = const()[name = tensor("op_4533"), val = tensor(0x1.9p+5)]; + tensor const_372 = const()[name = tensor("const_372"), val = tensor(0x1.fffffep+127)]; + tensor clip_0 = clip(alpha = var_4533, beta = const_372, x = F0_pred_1)[name = tensor("clip_0")]; + tensor var_4558 = const()[name = tensor("op_4558"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102395136)))]; + tensor F0_pred = add(x = clip_0, y = var_4558)[name = tensor("F0_pred")]; + tensor var_4568 = const()[name = tensor("op_4568"), val = tensor(0x1.4f8b58p-17)]; + tensor var_4570 = const()[name = tensor("op_4570"), val = tensor(0x1.99999ap-3)]; + tensor x_221_axis_0 = const()[name = tensor("x_221_axis_0"), val = tensor(0)]; + tensor x_221_batch_dims_0 = const()[name = tensor("x_221_batch_dims_0"), val = tensor(0)]; + tensor x_221 = gather(axis = x_221_axis_0, batch_dims = x_221_batch_dims_0, indices = input_ids, x = text_encoder_embedding_weight)[name = tensor("x_221")]; + tensor x_223_perm_0 = const()[name = tensor("x_223_perm_0"), val = tensor([0, 2, 1])]; + tensor weight_71 = const()[name = tensor("weight_71"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102400192)))]; + tensor x_225_pad_type_0 = const()[name = tensor("x_225_pad_type_0"), val = tensor("custom")]; + tensor x_225_pad_0 = const()[name = tensor("x_225_pad_0"), val = tensor([2, 2])]; + tensor x_225_strides_0 = const()[name = tensor("x_225_strides_0"), val = tensor([1])]; + tensor x_225_dilations_0 = const()[name = tensor("x_225_dilations_0"), val = tensor([1])]; + tensor x_225_groups_0 = const()[name = tensor("x_225_groups_0"), val = tensor(1)]; + tensor x_223 = transpose(perm = x_223_perm_0, x = x_221)[name = tensor("transpose_101")]; + tensor x_225 = conv(bias = text_encoder_cnn_0_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_71, x = x_223)[name = tensor("x_225")]; + tensor input_377_perm_0 = const()[name = tensor("input_377_perm_0"), val = tensor([0, -1, 1])]; + tensor x_227_axes_0 = const()[name = tensor("x_227_axes_0"), val = tensor([-1])]; + tensor input_377 = transpose(perm = input_377_perm_0, x = x_225)[name = tensor("transpose_100")]; + tensor x_227 = layer_norm(axes = x_227_axes_0, beta = text_encoder_cnn_0_1_beta, epsilon = var_4568, gamma = text_encoder_cnn_0_1_gamma, x = input_377)[name = tensor("x_227")]; + tensor input_379_perm_0 = const()[name = tensor("input_379_perm_0"), val = tensor([0, -1, 1])]; + tensor input_379 = transpose(perm = input_379_perm_0, x = x_227)[name = tensor("transpose_99")]; + tensor input_381 = leaky_relu(alpha = var_4570, x = input_379)[name = tensor("input_381")]; + tensor weight_75 = const()[name = tensor("weight_75"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107643136)))]; + tensor x_231_pad_type_0 = const()[name = tensor("x_231_pad_type_0"), val = tensor("custom")]; + tensor x_231_pad_0 = const()[name = tensor("x_231_pad_0"), val = tensor([2, 2])]; + tensor x_231_strides_0 = const()[name = tensor("x_231_strides_0"), val = tensor([1])]; + tensor x_231_dilations_0 = const()[name = tensor("x_231_dilations_0"), val = tensor([1])]; + tensor x_231_groups_0 = const()[name = tensor("x_231_groups_0"), val = tensor(1)]; + tensor x_231 = conv(bias = text_encoder_cnn_1_0_bias, dilations = x_231_dilations_0, groups = x_231_groups_0, pad = x_231_pad_0, pad_type = x_231_pad_type_0, strides = x_231_strides_0, weight = weight_75, x = input_381)[name = tensor("x_231")]; + tensor input_385_perm_0 = const()[name = tensor("input_385_perm_0"), val = tensor([0, -1, 1])]; + tensor x_233_axes_0 = const()[name = tensor("x_233_axes_0"), val = tensor([-1])]; + tensor input_385 = transpose(perm = input_385_perm_0, x = x_231)[name = tensor("transpose_98")]; + tensor x_233 = layer_norm(axes = x_233_axes_0, beta = text_encoder_cnn_1_1_beta, epsilon = var_4568, gamma = text_encoder_cnn_1_1_gamma, x = input_385)[name = tensor("x_233")]; + tensor input_387_perm_0 = const()[name = tensor("input_387_perm_0"), val = tensor([0, -1, 1])]; + tensor input_387 = transpose(perm = input_387_perm_0, x = x_233)[name = tensor("transpose_97")]; + tensor input_389 = leaky_relu(alpha = var_4570, x = input_387)[name = tensor("input_389")]; + tensor weight_79 = const()[name = tensor("weight_79"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(112886080)))]; + tensor x_237_pad_type_0 = const()[name = tensor("x_237_pad_type_0"), val = tensor("custom")]; + tensor x_237_pad_0 = const()[name = tensor("x_237_pad_0"), val = tensor([2, 2])]; + tensor x_237_strides_0 = const()[name = tensor("x_237_strides_0"), val = tensor([1])]; + tensor x_237_dilations_0 = const()[name = tensor("x_237_dilations_0"), val = tensor([1])]; + tensor x_237_groups_0 = const()[name = tensor("x_237_groups_0"), val = tensor(1)]; + tensor x_237 = conv(bias = text_encoder_cnn_2_0_bias, dilations = x_237_dilations_0, groups = x_237_groups_0, pad = x_237_pad_0, pad_type = x_237_pad_type_0, strides = x_237_strides_0, weight = weight_79, x = input_389)[name = tensor("x_237")]; + tensor input_393_perm_0 = const()[name = tensor("input_393_perm_0"), val = tensor([0, -1, 1])]; + tensor x_239_axes_0 = const()[name = tensor("x_239_axes_0"), val = tensor([-1])]; + tensor input_393 = transpose(perm = input_393_perm_0, x = x_237)[name = tensor("transpose_96")]; + tensor x_239 = layer_norm(axes = x_239_axes_0, beta = text_encoder_cnn_2_1_beta, epsilon = var_4568, gamma = text_encoder_cnn_2_1_gamma, x = input_393)[name = tensor("x_239")]; + tensor input_397 = leaky_relu(alpha = var_4570, x = x_239)[name = tensor("input_397")]; + tensor x_245_batch_first_transpose_perm_0 = const()[name = tensor("x_245_batch_first_transpose_perm_0"), val = tensor([1, 0, 2])]; + tensor add_8 = const()[name = tensor("add_8"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(118129024)))]; + tensor add_9 = const()[name = tensor("add_9"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(118133184)))]; + tensor concat_557 = const()[name = tensor("concat_557"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(118137344)))]; + tensor concat_558 = const()[name = tensor("concat_558"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120234560)))]; + tensor concat_559 = const()[name = tensor("concat_559"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121283200)))]; + tensor concat_560 = const()[name = tensor("concat_560"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123380416)))]; + tensor x_247_batch_first_direction_0 = const()[name = tensor("x_247_batch_first_direction_0"), val = tensor("bidirectional")]; + tensor x_247_batch_first_output_sequence_0 = const()[name = tensor("x_247_batch_first_output_sequence_0"), val = tensor(true)]; + tensor x_247_batch_first_recurrent_activation_0 = const()[name = tensor("x_247_batch_first_recurrent_activation_0"), val = tensor("sigmoid")]; + tensor x_247_batch_first_cell_activation_0 = const()[name = tensor("x_247_batch_first_cell_activation_0"), val = tensor("tanh")]; + tensor x_247_batch_first_activation_0 = const()[name = tensor("x_247_batch_first_activation_0"), val = tensor("tanh")]; + tensor x_245_batch_first_transpose = transpose(perm = x_245_batch_first_transpose_perm_0, x = input_397)[name = tensor("transpose_95")]; + tensor x_247_batch_first_0, tensor x_247_batch_first_1, tensor x_247_batch_first_2 = lstm(activation = x_247_batch_first_activation_0, bias = add_8, bias_back = add_9, cell_activation = x_247_batch_first_cell_activation_0, direction = x_247_batch_first_direction_0, initial_c = x_159_batch_first_lstm_h0_reshaped, initial_h = x_159_batch_first_lstm_h0_reshaped, output_sequence = x_247_batch_first_output_sequence_0, recurrent_activation = x_247_batch_first_recurrent_activation_0, weight_hh = concat_558, weight_hh_back = concat_560, weight_ih = concat_557, weight_ih_back = concat_559, x = x_245_batch_first_transpose)[name = tensor("x_247_batch_first")]; + tensor transpose_27_perm_0 = const()[name = tensor("transpose_27_perm_0"), val = tensor([1, 2, 0])]; + tensor asr_transpose_x_0 = const()[name = tensor("asr_transpose_x_0"), val = tensor(false)]; + tensor asr_transpose_y_0 = const()[name = tensor("asr_transpose_y_0"), val = tensor(false)]; + tensor transpose_27 = transpose(perm = transpose_27_perm_0, x = x_247_batch_first_0)[name = tensor("transpose_94")]; + tensor asr = matmul(transpose_x = asr_transpose_x_0, transpose_y = asr_transpose_y_0, x = transpose_27, y = pred_aln_trg)[name = tensor("asr")]; + tensor input_403_begin_0 = const()[name = tensor("input_403_begin_0"), val = tensor([0, 0])]; + tensor input_403_end_0 = const()[name = tensor("input_403_end_0"), val = tensor([1, 128])]; + tensor input_403_end_mask_0 = const()[name = tensor("input_403_end_mask_0"), val = tensor([true, false])]; + tensor input_403 = slice_by_index(begin = input_403_begin_0, end = input_403_end_0, end_mask = input_403_end_mask_0, x = ref_s)[name = tensor("input_403")]; + tensor var_4699 = const()[name = tensor("op_4699"), val = tensor(0x1.47ae14p-7)]; + tensor var_4705 = const()[name = tensor("op_4705"), val = tensor(0x1.99999ap-4)]; + tensor var_4706 = const()[name = tensor("op_4706"), val = tensor(0x1.4f8b58p-17)]; + tensor var_4707 = const()[name = tensor("op_4707"), val = tensor(0x1.99999ap-3)]; + tensor var_4716 = const()[name = tensor("op_4716"), val = tensor(1)]; + tensor input_399_axes_0 = const()[name = tensor("input_399_axes_0"), val = tensor([1])]; + tensor input_399 = expand_dims(axes = input_399_axes_0, x = F0_pred)[name = tensor("input_399")]; + tensor weight_83 = const()[name = tensor("weight_83"), val = tensor([[[0x1.a86aaep-5, 0x1.b190bep-5, -0x1.6d8bc6p-6]]])]; + tensor F0_3_pad_type_0 = const()[name = tensor("F0_3_pad_type_0"), val = tensor("custom")]; + tensor F0_3_pad_0 = const()[name = tensor("F0_3_pad_0"), val = tensor([1, 1])]; + tensor F0_3_strides_0 = const()[name = tensor("F0_3_strides_0"), val = tensor([2])]; + tensor F0_3_dilations_0 = const()[name = tensor("F0_3_dilations_0"), val = tensor([1])]; + tensor F0_3_groups_0 = const()[name = tensor("F0_3_groups_0"), val = tensor(1)]; + tensor F0_3 = conv(bias = decoder_F0_conv_bias, dilations = F0_3_dilations_0, groups = F0_3_groups_0, pad = F0_3_pad_0, pad_type = F0_3_pad_type_0, strides = F0_3_strides_0, weight = weight_83, x = input_399)[name = tensor("F0_3")]; + tensor weight_85 = const()[name = tensor("weight_85"), val = tensor([[[0x1.cc2feep-2, 0x1.35c75ep-1, 0x1.b9913ep-2]]])]; + tensor N_3_pad_type_0 = const()[name = tensor("N_3_pad_type_0"), val = tensor("custom")]; + tensor N_3_pad_0 = const()[name = tensor("N_3_pad_0"), val = tensor([1, 1])]; + tensor N_3_strides_0 = const()[name = tensor("N_3_strides_0"), val = tensor([2])]; + tensor N_3_dilations_0 = const()[name = tensor("N_3_dilations_0"), val = tensor([1])]; + tensor N_3_groups_0 = const()[name = tensor("N_3_groups_0"), val = tensor(1)]; + tensor N_3 = conv(bias = decoder_N_conv_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 = weight_85, x = N_1)[name = tensor("N_3")]; + tensor input_405_interleave_0 = const()[name = tensor("input_405_interleave_0"), val = tensor(false)]; + tensor input_405 = concat(axis = var_4716, interleave = input_405_interleave_0, values = (asr, F0_3, N_3))[name = tensor("input_405")]; + tensor h_61 = linear(bias = decoder_encode_norm1_fc_bias, weight = decoder_encode_norm1_fc_weight, x = input_403)[name = tensor("linear_89")]; + tensor var_4768 = const()[name = tensor("op_4768"), val = tensor([1, 1028, 1])]; + tensor h_63 = reshape(shape = var_4768, x = h_61)[name = tensor("h_63")]; + tensor var_4770_split_sizes_0 = const()[name = tensor("op_4770_split_sizes_0"), val = tensor([514, 514])]; + tensor var_4770_axis_0 = const()[name = tensor("op_4770_axis_0"), val = tensor(1)]; + tensor var_4770_0, tensor var_4770_1 = split(axis = var_4770_axis_0, split_sizes = var_4770_split_sizes_0, x = h_63)[name = tensor("op_4770")]; + tensor var_4772_promoted = const()[name = tensor("op_4772_promoted"), val = tensor(0x1p+0)]; + tensor var_4773 = add(x = var_4770_0, y = var_4772_promoted)[name = tensor("op_4773")]; + tensor var_4776 = instance_norm(beta = decoder_encode_norm1_norm_bias, epsilon = var_4706, gamma = decoder_encode_norm1_norm_weight, x = input_405)[name = tensor("op_4776")]; + tensor var_4777 = mul(x = var_4773, y = var_4776)[name = tensor("op_4777")]; + tensor input_407 = add(x = var_4777, y = var_4770_1)[name = tensor("input_407")]; + tensor input_409 = leaky_relu(alpha = var_4707, x = input_407)[name = tensor("input_409")]; + tensor weight_89 = const()[name = tensor("weight_89"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(124429056)))]; + tensor input_413_pad_type_0 = const()[name = tensor("input_413_pad_type_0"), val = tensor("custom")]; + tensor input_413_pad_0 = const()[name = tensor("input_413_pad_0"), val = tensor([1, 1])]; + tensor input_413_strides_0 = const()[name = tensor("input_413_strides_0"), val = tensor([1])]; + tensor input_413_dilations_0 = const()[name = tensor("input_413_dilations_0"), val = tensor([1])]; + tensor input_413_groups_0 = const()[name = tensor("input_413_groups_0"), val = tensor(1)]; + tensor input_413 = conv(bias = decoder_encode_conv1_bias, dilations = input_413_dilations_0, groups = input_413_groups_0, pad = input_413_pad_0, pad_type = input_413_pad_type_0, strides = input_413_strides_0, weight = weight_89, x = input_409)[name = tensor("input_413")]; + tensor h_65 = linear(bias = decoder_encode_norm2_fc_bias, weight = decoder_encode_norm2_fc_weight, x = input_403)[name = tensor("linear_90")]; + tensor var_4799 = const()[name = tensor("op_4799"), val = tensor([1, 2048, 1])]; + tensor h_67 = reshape(shape = var_4799, x = h_65)[name = tensor("h_67")]; + tensor var_4801_split_sizes_0 = const()[name = tensor("op_4801_split_sizes_0"), val = tensor([1024, 1024])]; + tensor var_4801_axis_0 = const()[name = tensor("op_4801_axis_0"), val = tensor(1)]; + tensor var_4801_0, tensor var_4801_1 = split(axis = var_4801_axis_0, split_sizes = var_4801_split_sizes_0, x = h_67)[name = tensor("op_4801")]; + tensor var_4803_promoted = const()[name = tensor("op_4803_promoted"), val = tensor(0x1p+0)]; + tensor var_4804 = add(x = var_4801_0, y = var_4803_promoted)[name = tensor("op_4804")]; + tensor var_4807 = instance_norm(beta = decoder_encode_norm2_norm_bias, epsilon = var_4706, gamma = decoder_encode_norm2_norm_weight, x = input_413)[name = tensor("op_4807")]; + tensor var_4808 = mul(x = var_4804, y = var_4807)[name = tensor("op_4808")]; + tensor input_415 = add(x = var_4808, y = var_4801_1)[name = tensor("input_415")]; + tensor input_417 = leaky_relu(alpha = var_4707, x = input_415)[name = tensor("input_417")]; + tensor weight_93 = const()[name = tensor("weight_93"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(130745152)))]; + tensor out_13_pad_type_0 = const()[name = tensor("out_13_pad_type_0"), val = tensor("custom")]; + tensor out_13_pad_0 = const()[name = tensor("out_13_pad_0"), val = tensor([1, 1])]; + tensor out_13_strides_0 = const()[name = tensor("out_13_strides_0"), val = tensor([1])]; + tensor out_13_dilations_0 = const()[name = tensor("out_13_dilations_0"), val = tensor([1])]; + tensor out_13_groups_0 = const()[name = tensor("out_13_groups_0"), val = tensor(1)]; + tensor 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_417)[name = tensor("out_13")]; + tensor weight_95 = const()[name = tensor("weight_95"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(143328128)))]; + tensor var_4832_pad_type_0 = const()[name = tensor("op_4832_pad_type_0"), val = tensor("valid")]; + tensor var_4832_strides_0 = const()[name = tensor("op_4832_strides_0"), val = tensor([1])]; + tensor var_4832_pad_0 = const()[name = tensor("op_4832_pad_0"), val = tensor([0, 0])]; + tensor var_4832_dilations_0 = const()[name = tensor("op_4832_dilations_0"), val = tensor([1])]; + tensor var_4832_groups_0 = const()[name = tensor("op_4832_groups_0"), val = tensor(1)]; + tensor var_4832 = conv(dilations = var_4832_dilations_0, groups = var_4832_groups_0, pad = var_4832_pad_0, pad_type = var_4832_pad_type_0, strides = var_4832_strides_0, weight = weight_95, x = input_405)[name = tensor("op_4832")]; + tensor var_4833 = add(x = out_13, y = var_4832)[name = tensor("op_4833")]; + tensor var_4836 = const()[name = tensor("op_4836"), val = tensor(0x1.6a09e6p-1)]; + tensor x_251 = mul(x = var_4833, y = var_4836)[name = tensor("x_251")]; + tensor weight_97 = const()[name = tensor("weight_97"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(145433536)))]; + tensor asr_res_1_pad_type_0 = const()[name = tensor("asr_res_1_pad_type_0"), val = tensor("valid")]; + tensor asr_res_1_strides_0 = const()[name = tensor("asr_res_1_strides_0"), val = tensor([1])]; + tensor asr_res_1_pad_0 = const()[name = tensor("asr_res_1_pad_0"), val = tensor([0, 0])]; + tensor asr_res_1_dilations_0 = const()[name = tensor("asr_res_1_dilations_0"), val = tensor([1])]; + tensor asr_res_1_groups_0 = const()[name = tensor("asr_res_1_groups_0"), val = tensor(1)]; + tensor 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("asr_res_1")]; + tensor input_421_interleave_0 = const()[name = tensor("input_421_interleave_0"), val = tensor(false)]; + tensor input_421 = concat(axis = var_4716, interleave = input_421_interleave_0, values = (x_251, asr_res_1, F0_3, N_3))[name = tensor("input_421")]; + tensor h_69 = linear(bias = decoder_decode_0_norm1_fc_bias, weight = decoder_decode_0_norm1_fc_weight, x = input_403)[name = tensor("linear_91")]; + tensor var_4864 = const()[name = tensor("op_4864"), val = tensor([1, 2180, 1])]; + tensor h_71 = reshape(shape = var_4864, x = h_69)[name = tensor("h_71")]; + tensor var_4866_split_sizes_0 = const()[name = tensor("op_4866_split_sizes_0"), val = tensor([1090, 1090])]; + tensor var_4866_axis_0 = const()[name = tensor("op_4866_axis_0"), val = tensor(1)]; + tensor var_4866_0, tensor var_4866_1 = split(axis = var_4866_axis_0, split_sizes = var_4866_split_sizes_0, x = h_71)[name = tensor("op_4866")]; + tensor var_4868_promoted = const()[name = tensor("op_4868_promoted"), val = tensor(0x1p+0)]; + tensor var_4869 = add(x = var_4866_0, y = var_4868_promoted)[name = tensor("op_4869")]; + tensor var_4872 = instance_norm(beta = decoder_decode_0_norm1_norm_bias, epsilon = var_4706, gamma = decoder_decode_0_norm1_norm_weight, x = input_421)[name = tensor("op_4872")]; + tensor var_4873 = mul(x = var_4869, y = var_4872)[name = tensor("op_4873")]; + tensor input_423 = add(x = var_4873, y = var_4866_1)[name = tensor("input_423")]; + tensor input_425 = leaky_relu(alpha = var_4707, x = input_423)[name = tensor("input_425")]; + tensor weight_101 = const()[name = tensor("weight_101"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(145564672)))]; + tensor input_429_pad_type_0 = const()[name = tensor("input_429_pad_type_0"), val = tensor("custom")]; + tensor input_429_pad_0 = const()[name = tensor("input_429_pad_0"), val = tensor([1, 1])]; + tensor input_429_strides_0 = const()[name = tensor("input_429_strides_0"), val = tensor([1])]; + tensor input_429_dilations_0 = const()[name = tensor("input_429_dilations_0"), val = tensor([1])]; + tensor input_429_groups_0 = const()[name = tensor("input_429_groups_0"), val = tensor(1)]; + tensor input_429 = conv(bias = decoder_decode_0_conv1_bias, dilations = input_429_dilations_0, groups = input_429_groups_0, pad = input_429_pad_0, pad_type = input_429_pad_type_0, strides = input_429_strides_0, weight = weight_101, x = input_425)[name = tensor("input_429")]; + tensor h_73 = linear(bias = decoder_decode_0_norm2_fc_bias, weight = decoder_decode_0_norm2_fc_weight, x = input_403)[name = tensor("linear_92")]; + tensor var_4895 = const()[name = tensor("op_4895"), val = tensor([1, 2048, 1])]; + tensor h_75 = reshape(shape = var_4895, x = h_73)[name = tensor("h_75")]; + tensor var_4897_split_sizes_0 = const()[name = tensor("op_4897_split_sizes_0"), val = tensor([1024, 1024])]; + tensor var_4897_axis_0 = const()[name = tensor("op_4897_axis_0"), val = tensor(1)]; + tensor var_4897_0, tensor var_4897_1 = split(axis = var_4897_axis_0, split_sizes = var_4897_split_sizes_0, x = h_75)[name = tensor("op_4897")]; + tensor var_4899_promoted = const()[name = tensor("op_4899_promoted"), val = tensor(0x1p+0)]; + tensor var_4900 = add(x = var_4897_0, y = var_4899_promoted)[name = tensor("op_4900")]; + tensor var_4903 = instance_norm(beta = decoder_encode_norm2_norm_bias, epsilon = var_4706, gamma = decoder_encode_norm2_norm_weight, x = input_429)[name = tensor("op_4903")]; + tensor var_4904 = mul(x = var_4900, y = var_4903)[name = tensor("op_4904")]; + tensor input_431 = add(x = var_4904, y = var_4897_1)[name = tensor("input_431")]; + tensor input_433 = leaky_relu(alpha = var_4707, x = input_431)[name = tensor("input_433")]; + tensor weight_105 = const()[name = tensor("weight_105"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(158958656)))]; + tensor out_15_pad_type_0 = const()[name = tensor("out_15_pad_type_0"), val = tensor("custom")]; + tensor out_15_pad_0 = const()[name = tensor("out_15_pad_0"), val = tensor([1, 1])]; + tensor out_15_strides_0 = const()[name = tensor("out_15_strides_0"), val = tensor([1])]; + tensor out_15_dilations_0 = const()[name = tensor("out_15_dilations_0"), val = tensor([1])]; + tensor out_15_groups_0 = const()[name = tensor("out_15_groups_0"), val = tensor(1)]; + tensor 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_433)[name = tensor("out_15")]; + tensor weight_107 = const()[name = tensor("weight_107"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(171541632)))]; + tensor var_4928_pad_type_0 = const()[name = tensor("op_4928_pad_type_0"), val = tensor("valid")]; + tensor var_4928_strides_0 = const()[name = tensor("op_4928_strides_0"), val = tensor([1])]; + tensor var_4928_pad_0 = const()[name = tensor("op_4928_pad_0"), val = tensor([0, 0])]; + tensor var_4928_dilations_0 = const()[name = tensor("op_4928_dilations_0"), val = tensor([1])]; + tensor var_4928_groups_0 = const()[name = tensor("op_4928_groups_0"), val = tensor(1)]; + tensor var_4928 = conv(dilations = var_4928_dilations_0, groups = var_4928_groups_0, pad = var_4928_pad_0, pad_type = var_4928_pad_type_0, strides = var_4928_strides_0, weight = weight_107, x = input_421)[name = tensor("op_4928")]; + tensor var_4929 = add(x = out_15, y = var_4928)[name = tensor("op_4929")]; + tensor var_4932 = const()[name = tensor("op_4932"), val = tensor(0x1.6a09e6p-1)]; + tensor x_253 = mul(x = var_4929, y = var_4932)[name = tensor("x_253")]; + tensor input_437_interleave_0 = const()[name = tensor("input_437_interleave_0"), val = tensor(false)]; + tensor input_437 = concat(axis = var_4716, interleave = input_437_interleave_0, values = (x_253, asr_res_1, F0_3, N_3))[name = tensor("input_437")]; + tensor h_77 = linear(bias = decoder_decode_1_norm1_fc_bias, weight = decoder_decode_1_norm1_fc_weight, x = input_403)[name = tensor("linear_93")]; + tensor var_4948 = const()[name = tensor("op_4948"), val = tensor([1, 2180, 1])]; + tensor h_79 = reshape(shape = var_4948, x = h_77)[name = tensor("h_79")]; + tensor var_4950_split_sizes_0 = const()[name = tensor("op_4950_split_sizes_0"), val = tensor([1090, 1090])]; + tensor var_4950_axis_0 = const()[name = tensor("op_4950_axis_0"), val = tensor(1)]; + tensor var_4950_0, tensor var_4950_1 = split(axis = var_4950_axis_0, split_sizes = var_4950_split_sizes_0, x = h_79)[name = tensor("op_4950")]; + tensor var_4952_promoted = const()[name = tensor("op_4952_promoted"), val = tensor(0x1p+0)]; + tensor var_4953 = add(x = var_4950_0, y = var_4952_promoted)[name = tensor("op_4953")]; + tensor var_4956 = instance_norm(beta = decoder_decode_0_norm1_norm_bias, epsilon = var_4706, gamma = decoder_decode_0_norm1_norm_weight, x = input_437)[name = tensor("op_4956")]; + tensor var_4957 = mul(x = var_4953, y = var_4956)[name = tensor("op_4957")]; + tensor input_439 = add(x = var_4957, y = var_4950_1)[name = tensor("input_439")]; + tensor input_441 = leaky_relu(alpha = var_4707, x = input_439)[name = tensor("input_441")]; + tensor weight_111 = const()[name = tensor("weight_111"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(176006336)))]; + tensor input_445_pad_type_0 = const()[name = tensor("input_445_pad_type_0"), val = tensor("custom")]; + tensor input_445_pad_0 = const()[name = tensor("input_445_pad_0"), val = tensor([1, 1])]; + tensor input_445_strides_0 = const()[name = tensor("input_445_strides_0"), val = tensor([1])]; + tensor input_445_dilations_0 = const()[name = tensor("input_445_dilations_0"), val = tensor([1])]; + tensor input_445_groups_0 = const()[name = tensor("input_445_groups_0"), val = tensor(1)]; + tensor input_445 = conv(bias = decoder_decode_1_conv1_bias, dilations = input_445_dilations_0, groups = input_445_groups_0, pad = input_445_pad_0, pad_type = input_445_pad_type_0, strides = input_445_strides_0, weight = weight_111, x = input_441)[name = tensor("input_445")]; + tensor h_81 = linear(bias = decoder_decode_1_norm2_fc_bias, weight = decoder_decode_1_norm2_fc_weight, x = input_403)[name = tensor("linear_94")]; + tensor var_4979 = const()[name = tensor("op_4979"), val = tensor([1, 2048, 1])]; + tensor h_83 = reshape(shape = var_4979, x = h_81)[name = tensor("h_83")]; + tensor var_4981_split_sizes_0 = const()[name = tensor("op_4981_split_sizes_0"), val = tensor([1024, 1024])]; + tensor var_4981_axis_0 = const()[name = tensor("op_4981_axis_0"), val = tensor(1)]; + tensor var_4981_0, tensor var_4981_1 = split(axis = var_4981_axis_0, split_sizes = var_4981_split_sizes_0, x = h_83)[name = tensor("op_4981")]; + tensor var_4983_promoted = const()[name = tensor("op_4983_promoted"), val = tensor(0x1p+0)]; + tensor var_4984 = add(x = var_4981_0, y = var_4983_promoted)[name = tensor("op_4984")]; + tensor var_4987 = instance_norm(beta = decoder_encode_norm2_norm_bias, epsilon = var_4706, gamma = decoder_encode_norm2_norm_weight, x = input_445)[name = tensor("op_4987")]; + tensor var_4988 = mul(x = var_4984, y = var_4987)[name = tensor("op_4988")]; + tensor input_447 = add(x = var_4988, y = var_4981_1)[name = tensor("input_447")]; + tensor input_449 = leaky_relu(alpha = var_4707, x = input_447)[name = tensor("input_449")]; + tensor weight_115 = const()[name = tensor("weight_115"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(189400320)))]; + tensor out_17_pad_type_0 = const()[name = tensor("out_17_pad_type_0"), val = tensor("custom")]; + tensor out_17_pad_0 = const()[name = tensor("out_17_pad_0"), val = tensor([1, 1])]; + tensor out_17_strides_0 = const()[name = tensor("out_17_strides_0"), val = tensor([1])]; + tensor out_17_dilations_0 = const()[name = tensor("out_17_dilations_0"), val = tensor([1])]; + tensor out_17_groups_0 = const()[name = tensor("out_17_groups_0"), val = tensor(1)]; + tensor 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_449)[name = tensor("out_17")]; + tensor weight_117 = const()[name = tensor("weight_117"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(201983296)))]; + tensor var_5012_pad_type_0 = const()[name = tensor("op_5012_pad_type_0"), val = tensor("valid")]; + tensor var_5012_strides_0 = const()[name = tensor("op_5012_strides_0"), val = tensor([1])]; + tensor var_5012_pad_0 = const()[name = tensor("op_5012_pad_0"), val = tensor([0, 0])]; + tensor var_5012_dilations_0 = const()[name = tensor("op_5012_dilations_0"), val = tensor([1])]; + tensor var_5012_groups_0 = const()[name = tensor("op_5012_groups_0"), val = tensor(1)]; + tensor var_5012 = conv(dilations = var_5012_dilations_0, groups = var_5012_groups_0, pad = var_5012_pad_0, pad_type = var_5012_pad_type_0, strides = var_5012_strides_0, weight = weight_117, x = input_437)[name = tensor("op_5012")]; + tensor var_5013 = add(x = out_17, y = var_5012)[name = tensor("op_5013")]; + tensor var_5016 = const()[name = tensor("op_5016"), val = tensor(0x1.6a09e6p-1)]; + tensor x_255 = mul(x = var_5013, y = var_5016)[name = tensor("x_255")]; + tensor input_453_interleave_0 = const()[name = tensor("input_453_interleave_0"), val = tensor(false)]; + tensor input_453 = concat(axis = var_4716, interleave = input_453_interleave_0, values = (x_255, asr_res_1, F0_3, N_3))[name = tensor("input_453")]; + tensor h_85 = linear(bias = decoder_decode_2_norm1_fc_bias, weight = decoder_decode_2_norm1_fc_weight, x = input_403)[name = tensor("linear_95")]; + tensor var_5032 = const()[name = tensor("op_5032"), val = tensor([1, 2180, 1])]; + tensor h_87 = reshape(shape = var_5032, x = h_85)[name = tensor("h_87")]; + tensor var_5034_split_sizes_0 = const()[name = tensor("op_5034_split_sizes_0"), val = tensor([1090, 1090])]; + tensor var_5034_axis_0 = const()[name = tensor("op_5034_axis_0"), val = tensor(1)]; + tensor var_5034_0, tensor var_5034_1 = split(axis = var_5034_axis_0, split_sizes = var_5034_split_sizes_0, x = h_87)[name = tensor("op_5034")]; + tensor var_5036_promoted = const()[name = tensor("op_5036_promoted"), val = tensor(0x1p+0)]; + tensor var_5037 = add(x = var_5034_0, y = var_5036_promoted)[name = tensor("op_5037")]; + tensor var_5040 = instance_norm(beta = decoder_decode_0_norm1_norm_bias, epsilon = var_4706, gamma = decoder_decode_0_norm1_norm_weight, x = input_453)[name = tensor("op_5040")]; + tensor var_5041 = mul(x = var_5037, y = var_5040)[name = tensor("op_5041")]; + tensor input_455 = add(x = var_5041, y = var_5034_1)[name = tensor("input_455")]; + tensor input_457 = leaky_relu(alpha = var_4707, x = input_455)[name = tensor("input_457")]; + tensor weight_121 = const()[name = tensor("weight_121"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206448000)))]; + tensor input_461_pad_type_0 = const()[name = tensor("input_461_pad_type_0"), val = tensor("custom")]; + tensor input_461_pad_0 = const()[name = tensor("input_461_pad_0"), val = tensor([1, 1])]; + tensor input_461_strides_0 = const()[name = tensor("input_461_strides_0"), val = tensor([1])]; + tensor input_461_dilations_0 = const()[name = tensor("input_461_dilations_0"), val = tensor([1])]; + tensor input_461_groups_0 = const()[name = tensor("input_461_groups_0"), val = tensor(1)]; + tensor input_461 = conv(bias = decoder_decode_2_conv1_bias, dilations = input_461_dilations_0, groups = input_461_groups_0, pad = input_461_pad_0, pad_type = input_461_pad_type_0, strides = input_461_strides_0, weight = weight_121, x = input_457)[name = tensor("input_461")]; + tensor h_89 = linear(bias = decoder_decode_2_norm2_fc_bias, weight = decoder_decode_2_norm2_fc_weight, x = input_403)[name = tensor("linear_96")]; + tensor var_5063 = const()[name = tensor("op_5063"), val = tensor([1, 2048, 1])]; + tensor h_91 = reshape(shape = var_5063, x = h_89)[name = tensor("h_91")]; + tensor var_5065_split_sizes_0 = const()[name = tensor("op_5065_split_sizes_0"), val = tensor([1024, 1024])]; + tensor var_5065_axis_0 = const()[name = tensor("op_5065_axis_0"), val = tensor(1)]; + tensor var_5065_0, tensor var_5065_1 = split(axis = var_5065_axis_0, split_sizes = var_5065_split_sizes_0, x = h_91)[name = tensor("op_5065")]; + tensor var_5067_promoted = const()[name = tensor("op_5067_promoted"), val = tensor(0x1p+0)]; + tensor var_5068 = add(x = var_5065_0, y = var_5067_promoted)[name = tensor("op_5068")]; + tensor var_5071 = instance_norm(beta = decoder_encode_norm2_norm_bias, epsilon = var_4706, gamma = decoder_encode_norm2_norm_weight, x = input_461)[name = tensor("op_5071")]; + tensor var_5072 = mul(x = var_5068, y = var_5071)[name = tensor("op_5072")]; + tensor input_463 = add(x = var_5072, y = var_5065_1)[name = tensor("input_463")]; + tensor input_465 = leaky_relu(alpha = var_4707, x = input_463)[name = tensor("input_465")]; + tensor weight_125 = const()[name = tensor("weight_125"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(219841984)))]; + tensor out_19_pad_type_0 = const()[name = tensor("out_19_pad_type_0"), val = tensor("custom")]; + tensor out_19_pad_0 = const()[name = tensor("out_19_pad_0"), val = tensor([1, 1])]; + tensor out_19_strides_0 = const()[name = tensor("out_19_strides_0"), val = tensor([1])]; + tensor out_19_dilations_0 = const()[name = tensor("out_19_dilations_0"), val = tensor([1])]; + tensor out_19_groups_0 = const()[name = tensor("out_19_groups_0"), val = tensor(1)]; + tensor 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_465)[name = tensor("out_19")]; + tensor weight_127 = const()[name = tensor("weight_127"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(232424960)))]; + tensor var_5096_pad_type_0 = const()[name = tensor("op_5096_pad_type_0"), val = tensor("valid")]; + tensor var_5096_strides_0 = const()[name = tensor("op_5096_strides_0"), val = tensor([1])]; + tensor var_5096_pad_0 = const()[name = tensor("op_5096_pad_0"), val = tensor([0, 0])]; + tensor var_5096_dilations_0 = const()[name = tensor("op_5096_dilations_0"), val = tensor([1])]; + tensor var_5096_groups_0 = const()[name = tensor("op_5096_groups_0"), val = tensor(1)]; + tensor var_5096 = conv(dilations = var_5096_dilations_0, groups = var_5096_groups_0, pad = var_5096_pad_0, pad_type = var_5096_pad_type_0, strides = var_5096_strides_0, weight = weight_127, x = input_453)[name = tensor("op_5096")]; + tensor var_5097 = add(x = out_19, y = var_5096)[name = tensor("op_5097")]; + tensor var_5100 = const()[name = tensor("op_5100"), val = tensor(0x1.6a09e6p-1)]; + tensor x_257 = mul(x = var_5097, y = var_5100)[name = tensor("x_257")]; + tensor input_469_interleave_0 = const()[name = tensor("input_469_interleave_0"), val = tensor(false)]; + tensor input_469 = concat(axis = var_4716, interleave = input_469_interleave_0, values = (x_257, asr_res_1, F0_3, N_3))[name = tensor("input_469")]; + tensor h_93 = linear(bias = decoder_decode_3_norm1_fc_bias, weight = decoder_decode_3_norm1_fc_weight, x = input_403)[name = tensor("linear_97")]; + tensor var_5117 = const()[name = tensor("op_5117"), val = tensor([1, 2180, 1])]; + tensor h_95 = reshape(shape = var_5117, x = h_93)[name = tensor("h_95")]; + tensor var_5119_split_sizes_0 = const()[name = tensor("op_5119_split_sizes_0"), val = tensor([1090, 1090])]; + tensor var_5119_axis_0 = const()[name = tensor("op_5119_axis_0"), val = tensor(1)]; + tensor var_5119_0, tensor var_5119_1 = split(axis = var_5119_axis_0, split_sizes = var_5119_split_sizes_0, x = h_95)[name = tensor("op_5119")]; + tensor var_5121_promoted = const()[name = tensor("op_5121_promoted"), val = tensor(0x1p+0)]; + tensor var_5122 = add(x = var_5119_0, y = var_5121_promoted)[name = tensor("op_5122")]; + tensor var_5125 = instance_norm(beta = decoder_decode_0_norm1_norm_bias, epsilon = var_4706, gamma = decoder_decode_0_norm1_norm_weight, x = input_469)[name = tensor("op_5125")]; + tensor var_5126 = mul(x = var_5122, y = var_5125)[name = tensor("op_5126")]; + tensor input_471 = add(x = var_5126, y = var_5119_1)[name = tensor("input_471")]; + tensor input_473 = leaky_relu(alpha = var_4707, x = input_471)[name = tensor("input_473")]; + tensor var_5134 = const()[name = tensor("op_5134"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(236889664)))]; + tensor conv_transpose_2_pad_type_0 = const()[name = tensor("conv_transpose_2_pad_type_0"), val = tensor("custom")]; + tensor conv_transpose_2_pad_0 = const()[name = tensor("conv_transpose_2_pad_0"), val = tensor([0, 0])]; + tensor conv_transpose_2_strides_0 = const()[name = tensor("conv_transpose_2_strides_0"), val = tensor([2])]; + tensor conv_transpose_2_groups_0 = const()[name = tensor("conv_transpose_2_groups_0"), val = tensor(1090)]; + tensor conv_transpose_2_dilations_0 = const()[name = tensor("conv_transpose_2_dilations_0"), val = tensor([1])]; + tensor conv_transpose_2_has_output_shape_output_shape_0 = const()[name = tensor("conv_transpose_2_has_output_shape_output_shape_0"), val = tensor([1, 1090, 1243])]; + tensor 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_5134, x = input_473)[name = tensor("conv_transpose_2_has_output_shape")]; + tensor input_475_begin_0 = const()[name = tensor("input_475_begin_0"), val = tensor([0, 0, 1])]; + tensor input_475_end_0 = const()[name = tensor("input_475_end_0"), val = tensor([0, 0, 0])]; + tensor input_475_begin_mask_0 = const()[name = tensor("input_475_begin_mask_0"), val = tensor([true, true, false])]; + tensor input_475_end_mask_0 = const()[name = tensor("input_475_end_mask_0"), val = tensor([true, true, true])]; + tensor input_475 = slice_by_index(begin = input_475_begin_0, begin_mask = input_475_begin_mask_0, end = input_475_end_0, end_mask = input_475_end_mask_0, x = conv_transpose_2_has_output_shape)[name = tensor("input_475")]; + tensor weight_131 = const()[name = tensor("weight_131"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(236902848)))]; + tensor input_479_pad_type_0 = const()[name = tensor("input_479_pad_type_0"), val = tensor("custom")]; + tensor input_479_pad_0 = const()[name = tensor("input_479_pad_0"), val = tensor([1, 1])]; + tensor input_479_strides_0 = const()[name = tensor("input_479_strides_0"), val = tensor([1])]; + tensor input_479_dilations_0 = const()[name = tensor("input_479_dilations_0"), val = tensor([1])]; + tensor input_479_groups_0 = const()[name = tensor("input_479_groups_0"), val = tensor(1)]; + tensor input_479 = conv(bias = decoder_decode_3_conv1_bias, dilations = input_479_dilations_0, groups = input_479_groups_0, pad = input_479_pad_0, pad_type = input_479_pad_type_0, strides = input_479_strides_0, weight = weight_131, x = input_475)[name = tensor("input_479")]; + tensor h_97 = linear(bias = decoder_decode_3_norm2_fc_bias, weight = decoder_decode_3_norm2_fc_weight, x = input_403)[name = tensor("linear_98")]; + tensor var_5159 = const()[name = tensor("op_5159"), val = tensor([1, 1024, 1])]; + tensor h_99 = reshape(shape = var_5159, x = h_97)[name = tensor("h_99")]; + tensor var_5161_split_sizes_0 = const()[name = tensor("op_5161_split_sizes_0"), val = tensor([512, 512])]; + tensor var_5161_axis_0 = const()[name = tensor("op_5161_axis_0"), val = tensor(1)]; + tensor var_5161_0, tensor var_5161_1 = split(axis = var_5161_axis_0, split_sizes = var_5161_split_sizes_0, x = h_99)[name = tensor("op_5161")]; + tensor var_5163_promoted = const()[name = tensor("op_5163_promoted"), val = tensor(0x1p+0)]; + tensor var_5164 = add(x = var_5161_0, y = var_5163_promoted)[name = tensor("op_5164")]; + tensor var_5167 = instance_norm(beta = predictor_F0_0_norm1_norm_bias, epsilon = var_4706, gamma = predictor_F0_0_norm1_norm_weight, x = input_479)[name = tensor("op_5167")]; + tensor var_5168 = mul(x = var_5164, y = var_5167)[name = tensor("op_5168")]; + tensor input_481 = add(x = var_5168, y = var_5161_1)[name = tensor("input_481")]; + tensor input_483 = leaky_relu(alpha = var_4707, x = input_481)[name = tensor("input_483")]; + tensor weight_135 = const()[name = tensor("weight_135"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(243599872)))]; + tensor out_pad_type_0 = const()[name = tensor("out_pad_type_0"), val = tensor("custom")]; + tensor out_pad_0 = const()[name = tensor("out_pad_0"), val = tensor([1, 1])]; + tensor out_strides_0 = const()[name = tensor("out_strides_0"), val = tensor([1])]; + tensor out_dilations_0 = const()[name = tensor("out_dilations_0"), val = tensor([1])]; + tensor out_groups_0 = const()[name = tensor("out_groups_0"), val = tensor(1)]; + tensor 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_483)[name = tensor("out")]; + tensor expand_dims_501_axes_0 = const()[name = tensor("expand_dims_501_axes_0"), val = tensor([3])]; + tensor expand_dims_501 = expand_dims(axes = expand_dims_501_axes_0, x = input_469)[name = tensor("expand_dims_501")]; + tensor upsample_nearest_neighbor_2_scale_factor_height_0 = const()[name = tensor("upsample_nearest_neighbor_2_scale_factor_height_0"), val = tensor(2)]; + tensor upsample_nearest_neighbor_2_scale_factor_width_0 = const()[name = tensor("upsample_nearest_neighbor_2_scale_factor_width_0"), val = tensor(1)]; + tensor 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_501)[name = tensor("upsample_nearest_neighbor_2")]; + tensor input_487_axes_0 = const()[name = tensor("input_487_axes_0"), val = tensor([3])]; + tensor input_487 = squeeze(axes = input_487_axes_0, x = upsample_nearest_neighbor_2)[name = tensor("input_487")]; + tensor weight_137 = const()[name = tensor("weight_137"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(246745664)))]; + tensor var_5194_pad_type_0 = const()[name = tensor("op_5194_pad_type_0"), val = tensor("valid")]; + tensor var_5194_strides_0 = const()[name = tensor("op_5194_strides_0"), val = tensor([1])]; + tensor var_5194_pad_0 = const()[name = tensor("op_5194_pad_0"), val = tensor([0, 0])]; + tensor var_5194_dilations_0 = const()[name = tensor("op_5194_dilations_0"), val = tensor([1])]; + tensor var_5194_groups_0 = const()[name = tensor("op_5194_groups_0"), val = tensor(1)]; + tensor var_5194 = conv(dilations = var_5194_dilations_0, groups = var_5194_groups_0, pad = var_5194_pad_0, pad_type = var_5194_pad_type_0, strides = var_5194_strides_0, weight = weight_137, x = input_487)[name = tensor("op_5194")]; + tensor var_5195 = add(x = out, y = var_5194)[name = tensor("op_5195")]; + tensor var_5198 = const()[name = tensor("op_5198"), val = tensor(0x1.6a09e6p-1)]; + tensor input_495 = mul(x = var_5195, y = var_5198)[name = tensor("input_495")]; + tensor expand_dims_502_axes_0 = const()[name = tensor("expand_dims_502_axes_0"), val = tensor([3])]; + tensor expand_dims_502 = expand_dims(axes = expand_dims_502_axes_0, x = input_399)[name = tensor("expand_dims_502")]; + tensor upsample_nearest_neighbor_3_scale_factor_height_0 = const()[name = tensor("upsample_nearest_neighbor_3_scale_factor_height_0"), val = tensor(300)]; + tensor upsample_nearest_neighbor_3_scale_factor_width_0 = const()[name = tensor("upsample_nearest_neighbor_3_scale_factor_width_0"), val = tensor(1)]; + tensor 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_502)[name = tensor("upsample_nearest_neighbor_3")]; + tensor var_5237_axes_0 = const()[name = tensor("op_5237_axes_0"), val = tensor([3])]; + tensor var_5237 = squeeze(axes = var_5237_axes_0, x = upsample_nearest_neighbor_3)[name = tensor("op_5237")]; + tensor f0_perm_0 = const()[name = tensor("f0_perm_0"), val = tensor([0, 2, 1])]; + tensor var_5240 = const()[name = tensor("op_5240"), val = tensor(0x1.4p+2)]; + tensor f0 = transpose(perm = f0_perm_0, x = var_5237)[name = tensor("transpose_93")]; + tensor var_5241 = sub(x = f0, y = var_5240)[name = tensor("op_5241")]; + tensor var_5242 = const()[name = tensor("op_5242"), val = tensor(0x1p-1)]; + tensor var_5243 = mul(x = var_5241, y = var_5242)[name = tensor("op_5243")]; + tensor uv = sigmoid(x = var_5243)[name = tensor("uv")]; + tensor var_5247_promoted = const()[name = tensor("op_5247_promoted"), val = tensor([[[0x1p+0, 0x1p+1, 0x1.8p+1, 0x1p+2, 0x1.4p+2, 0x1.8p+2, 0x1.cp+2, 0x1p+3, 0x1.2p+3]]])]; + tensor fn = mul(x = f0, y = var_5247_promoted)[name = tensor("fn")]; + tensor _inversed_rad_values_y_0 = const()[name = tensor("_inversed_rad_values_y_0"), val = tensor(0x1.5d867cp-15)]; + tensor _inversed_rad_values = mul(x = fn, y = _inversed_rad_values_y_0)[name = tensor("_inversed_rad_values")]; + tensor random_phases_axes_0 = const()[name = tensor("random_phases_axes_0"), val = tensor([0])]; + tensor random_phases_1 = expand_dims(axes = random_phases_axes_0, x = random_phases)[name = tensor("random_phases")]; + tensor var_5253_begin_0 = const()[name = tensor("op_5253_begin_0"), val = tensor([0, 0, 0])]; + tensor var_5253_end_0 = const()[name = tensor("op_5253_end_0"), val = tensor([1, 1, 9])]; + tensor var_5253_end_mask_0 = const()[name = tensor("op_5253_end_mask_0"), val = tensor([true, false, true])]; + tensor var_5253_squeeze_mask_0 = const()[name = tensor("op_5253_squeeze_mask_0"), val = tensor([false, true, false])]; + tensor var_5253 = slice_by_index(begin = var_5253_begin_0, end = var_5253_end_0, end_mask = var_5253_end_mask_0, squeeze_mask = var_5253_squeeze_mask_0, x = _inversed_rad_values)[name = tensor("op_5253")]; + tensor var_5255_axes_0 = const()[name = tensor("op_5255_axes_0"), val = tensor([1])]; + tensor var_5255 = squeeze(axes = var_5255_axes_0, x = random_phases_1)[name = tensor("op_5255")]; + tensor var_5256 = add(x = var_5253, y = var_5255)[name = tensor("op_5256")]; + tensor concat_563 = const()[name = tensor("concat_563"), val = tensor([0, 0, 0])]; + tensor concat_564 = const()[name = tensor("concat_564"), val = tensor([0, 0, 0])]; + tensor rad_values_internal_tensor_assign_1_stride_0 = const()[name = tensor("rad_values_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1])]; + tensor rad_values_internal_tensor_assign_1_begin_mask_0 = const()[name = tensor("rad_values_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, false, false])]; + tensor rad_values_internal_tensor_assign_1_end_mask_0 = const()[name = tensor("rad_values_internal_tensor_assign_1_end_mask_0"), val = tensor([true, false, true])]; + tensor rad_values_internal_tensor_assign_1_squeeze_mask_0 = const()[name = tensor("rad_values_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([false, true, false])]; + tensor shape_263 = const()[name = tensor("shape_263"), val = tensor([1, 372600, 9])]; + tensor reduce_prod_249 = const()[name = tensor("reduce_prod_249"), val = tensor(3353400)]; + tensor range_1d_249_start_0 = const()[name = tensor("range_1d_249_start_0"), val = tensor(0)]; + tensor range_1d_249_step_0 = const()[name = tensor("range_1d_249_step_0"), val = tensor(1)]; + tensor range_1d_249 = range_1d(end = reduce_prod_249, start = range_1d_249_start_0, step = range_1d_249_step_0)[name = tensor("range_1d_249")]; + tensor reshape_1245 = reshape(shape = shape_263, x = range_1d_249)[name = tensor("reshape_1245")]; + tensor slice_by_index_274 = slice_by_index(begin = concat_563, begin_mask = rad_values_internal_tensor_assign_1_begin_mask_0, end = concat_564, end_mask = rad_values_internal_tensor_assign_1_end_mask_0, squeeze_mask = rad_values_internal_tensor_assign_1_squeeze_mask_0, stride = rad_values_internal_tensor_assign_1_stride_0, x = reshape_1245)[name = tensor("slice_by_index_274")]; + tensor reshape_1246_shape_0 = const()[name = tensor("reshape_1246_shape_0"), val = tensor([-1])]; + tensor reshape_1246 = reshape(shape = reshape_1246_shape_0, x = slice_by_index_274)[name = tensor("reshape_1246")]; + tensor reshape_1247_shape_0 = const()[name = tensor("reshape_1247_shape_0"), val = tensor([-1])]; + tensor reshape_1247 = reshape(shape = reshape_1247_shape_0, x = var_5256)[name = tensor("reshape_1247")]; + tensor reshape_1248_shape_0 = const()[name = tensor("reshape_1248_shape_0"), val = tensor([-1])]; + tensor reshape_1248 = reshape(shape = reshape_1248_shape_0, x = _inversed_rad_values)[name = tensor("reshape_1248")]; + tensor scatter_249_mode_0 = const()[name = tensor("scatter_249_mode_0"), val = tensor("update")]; + tensor scatter_249_axis_0 = const()[name = tensor("scatter_249_axis_0"), val = tensor(0)]; + tensor scatter_249 = scatter(axis = scatter_249_axis_0, data = reshape_1248, indices = reshape_1246, mode = scatter_249_mode_0, updates = reshape_1247)[name = tensor("scatter_249")]; + tensor reshape_1249 = reshape(shape = shape_263, x = scatter_249)[name = tensor("reshape_1249")]; + tensor phase_accum_exclusive_0 = const()[name = tensor("phase_accum_exclusive_0"), val = tensor(false)]; + tensor phase_accum_reverse_0 = const()[name = tensor("phase_accum_reverse_0"), val = tensor(false)]; + tensor phase_accum = cumsum(axis = var_4716, exclusive = phase_accum_exclusive_0, reverse = phase_accum_reverse_0, x = reshape_1249)[name = tensor("phase_accum")]; + tensor var_5262 = floor(x = phase_accum)[name = tensor("op_5262")]; + tensor var_5263 = sub(x = phase_accum, y = var_5262)[name = tensor("op_5263")]; + tensor var_5264_promoted = const()[name = tensor("op_5264_promoted"), val = tensor(0x1p+1)]; + tensor var_5265 = mul(x = var_5263, y = var_5264_promoted)[name = tensor("op_5265")]; + tensor var_5266 = const()[name = tensor("op_5266"), val = tensor(0x1.921fb6p+1)]; + tensor phase_wrapped = mul(x = var_5265, y = var_5266)[name = tensor("phase_wrapped")]; + tensor var_5268 = sin(x = phase_wrapped)[name = tensor("op_5268")]; + tensor var_5269 = const()[name = tensor("op_5269"), val = tensor(0x1.99999ap-4)]; + tensor var_5270 = mul(x = var_5268, y = var_5269)[name = tensor("op_5270")]; + tensor input_491 = mul(x = var_5270, y = uv)[name = tensor("input_491")]; + tensor input_493 = linear(bias = decoder_generator_m_source_l_linear_bias, weight = decoder_generator_m_source_l_linear_weight, x = input_491)[name = tensor("linear_99")]; + tensor har_source = tanh(x = input_493)[name = tensor("har_source")]; + tensor var_5276_perm_0 = const()[name = tensor("op_5276_perm_0"), val = tensor([0, 2, 1])]; + tensor waveform_1_axes_0 = const()[name = tensor("waveform_1_axes_0"), val = tensor([1])]; + tensor var_5276 = transpose(perm = var_5276_perm_0, x = har_source)[name = tensor("transpose_92")]; + tensor waveform_1 = squeeze(axes = waveform_1_axes_0, x = var_5276)[name = tensor("waveform_1")]; + tensor const_403 = const()[name = tensor("const_403"), val = tensor(0x0p+0)]; + tensor waveform_3_pad_0 = const()[name = tensor("waveform_3_pad_0"), val = tensor([0, 0, 10, 10])]; + tensor waveform_3_mode_0 = const()[name = tensor("waveform_3_mode_0"), val = tensor("replicate")]; + tensor waveform_3 = pad(constant_val = const_403, mode = waveform_3_mode_0, pad = waveform_3_pad_0, x = waveform_1)[name = tensor("waveform_3")]; + tensor x_259_axes_0 = const()[name = tensor("x_259_axes_0"), val = tensor([1])]; + tensor x_259 = expand_dims(axes = x_259_axes_0, x = waveform_3)[name = tensor("x_259")]; + tensor real_out_pad_type_0 = const()[name = tensor("real_out_pad_type_0"), val = tensor("valid")]; + tensor real_out_strides_0 = const()[name = tensor("real_out_strides_0"), val = tensor([5])]; + tensor real_out_pad_0 = const()[name = tensor("real_out_pad_0"), val = tensor([0, 0])]; + tensor real_out_dilations_0 = const()[name = tensor("real_out_dilations_0"), val = tensor([1])]; + tensor real_out_groups_0 = const()[name = tensor("real_out_groups_0"), val = tensor(1)]; + tensor 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_259)[name = tensor("real_out")]; + tensor imag_out_pad_type_0 = const()[name = tensor("imag_out_pad_type_0"), val = tensor("valid")]; + tensor imag_out_strides_0 = const()[name = tensor("imag_out_strides_0"), val = tensor([5])]; + tensor imag_out_pad_0 = const()[name = tensor("imag_out_pad_0"), val = tensor([0, 0])]; + tensor imag_out_dilations_0 = const()[name = tensor("imag_out_dilations_0"), val = tensor([1])]; + tensor imag_out_groups_0 = const()[name = tensor("imag_out_groups_0"), val = tensor(1)]; + tensor 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_259)[name = tensor("imag_out")]; + tensor var_4713_promoted = const()[name = tensor("op_4713_promoted"), val = tensor(0x1p+1)]; + tensor var_5291 = pow(x = real_out, y = var_4713_promoted)[name = tensor("op_5291")]; + tensor var_4713_promoted_1 = const()[name = tensor("op_4713_promoted_1"), val = tensor(0x1p+1)]; + tensor var_5292 = pow(x = imag_out, y = var_4713_promoted_1)[name = tensor("op_5292")]; + tensor var_5293 = add(x = var_5291, y = var_5292)[name = tensor("op_5293")]; + tensor var_5294 = const()[name = tensor("op_5294"), val = tensor(0x1.6849b8p-47)]; + tensor var_5295 = add(x = var_5293, y = var_5294)[name = tensor("op_5295")]; + tensor har_spec = sqrt(x = var_5295)[name = tensor("har_spec")]; + tensor less_0_y_0 = const()[name = tensor("less_0_y_0"), val = tensor(0x0p+0)]; + tensor less_0 = less(x = imag_out, y = less_0_y_0)[name = tensor("less_0")]; + tensor greater_0_y_0 = const()[name = tensor("greater_0_y_0"), val = tensor(0x0p+0)]; + tensor greater_0 = greater(x = imag_out, y = greater_0_y_0)[name = tensor("greater_0")]; + tensor less_1_y_0 = const()[name = tensor("less_1_y_0"), val = tensor(0x0p+0)]; + tensor less_1 = less(x = real_out, y = less_1_y_0)[name = tensor("less_1")]; + tensor equal_0_y_0 = const()[name = tensor("equal_0_y_0"), val = tensor(0x0p+0)]; + tensor equal_0 = equal(x = real_out, y = equal_0_y_0)[name = tensor("equal_0")]; + tensor logical_and_0 = logical_and(x = greater_0, y = less_1)[name = tensor("logical_and_0")]; + tensor logical_and_1 = logical_and(x = less_0, y = less_1)[name = tensor("logical_and_1")]; + tensor logical_and_2 = logical_and(x = greater_0, y = equal_0)[name = tensor("logical_and_2")]; + tensor logical_and_3 = logical_and(x = less_0, y = equal_0)[name = tensor("logical_and_3")]; + tensor cast_104_dtype_0 = const()[name = tensor("cast_104_dtype_0"), val = tensor("fp32")]; + tensor cast_105_dtype_0 = const()[name = tensor("cast_105_dtype_0"), val = tensor("fp32")]; + tensor cast_106_dtype_0 = const()[name = tensor("cast_106_dtype_0"), val = tensor("fp32")]; + tensor cast_107_dtype_0 = const()[name = tensor("cast_107_dtype_0"), val = tensor("fp32")]; + tensor mul_0_y_0 = const()[name = tensor("mul_0_y_0"), val = tensor(0x1.921fb6p+1)]; + tensor cast_104 = cast(dtype = cast_104_dtype_0, x = logical_and_0)[name = tensor("cast_173")]; + tensor mul_0 = mul(x = cast_104, y = mul_0_y_0)[name = tensor("mul_0")]; + tensor mul_1_y_0 = const()[name = tensor("mul_1_y_0"), val = tensor(0x1.921fb6p+1)]; + tensor cast_105 = cast(dtype = cast_105_dtype_0, x = logical_and_1)[name = tensor("cast_172")]; + tensor mul_1 = mul(x = cast_105, y = mul_1_y_0)[name = tensor("mul_1")]; + tensor sub_0_x_0 = const()[name = tensor("sub_0_x_0"), val = tensor(0x1p+0)]; + tensor cast_106 = cast(dtype = cast_106_dtype_0, x = logical_and_2)[name = tensor("cast_171")]; + tensor sub_0 = sub(x = sub_0_x_0, y = cast_106)[name = tensor("sub_0")]; + tensor mul_2_y_0 = const()[name = tensor("mul_2_y_0"), val = tensor(0x1.921fb6p+0)]; + tensor mul_2 = mul(x = cast_106, y = mul_2_y_0)[name = tensor("mul_2")]; + tensor sub_1_x_0 = const()[name = tensor("sub_1_x_0"), val = tensor(0x1p+0)]; + tensor cast_107 = cast(dtype = cast_107_dtype_0, x = logical_and_3)[name = tensor("cast_170")]; + tensor sub_1 = sub(x = sub_1_x_0, y = cast_107)[name = tensor("sub_1")]; + tensor mul_3_y_0 = const()[name = tensor("mul_3_y_0"), val = tensor(-0x1.921fb6p+0)]; + tensor mul_3 = mul(x = cast_107, y = mul_3_y_0)[name = tensor("mul_3")]; + tensor greater_1_y_0 = const()[name = tensor("greater_1_y_0"), val = tensor(-0x1.5798eep-27)]; + tensor greater_1 = greater(x = real_out, y = greater_1_y_0)[name = tensor("greater_1")]; + tensor less_2_y_0 = const()[name = tensor("less_2_y_0"), val = tensor(0x1.5798eep-27)]; + tensor less_2 = less(x = real_out, y = less_2_y_0)[name = tensor("less_2")]; + tensor logical_and_4 = logical_and(x = greater_1, y = less_2)[name = tensor("logical_and_4")]; + tensor cast_108_dtype_0 = const()[name = tensor("cast_108_dtype_0"), val = tensor("fp32")]; + tensor mul_4_y_0 = const()[name = tensor("mul_4_y_0"), val = tensor(0x1.5798eep-26)]; + tensor cast_108 = cast(dtype = cast_108_dtype_0, x = logical_and_4)[name = tensor("cast_169")]; + tensor mul_4 = mul(x = cast_108, y = mul_4_y_0)[name = tensor("mul_4")]; + tensor add_10 = add(x = real_out, y = mul_4)[name = tensor("add_10")]; + tensor real_div_38 = real_div(x = imag_out, y = add_10)[name = tensor("real_div_38")]; + tensor atan_0 = atan(x = real_div_38)[name = tensor("atan_0")]; + tensor add_11 = add(x = atan_0, y = mul_0)[name = tensor("add_11")]; + tensor sub_2 = sub(x = add_11, y = mul_1)[name = tensor("sub_2")]; + tensor mul_5 = mul(x = sub_2, y = sub_0)[name = tensor("mul_5")]; + tensor add_12 = add(x = mul_5, y = mul_2)[name = tensor("add_12")]; + tensor mul_6 = mul(x = add_12, y = sub_1)[name = tensor("mul_6")]; + tensor phase_1 = add(x = mul_6, y = mul_3)[name = tensor("phase_1")]; + tensor var_4712_promoted = const()[name = tensor("op_4712_promoted"), val = tensor(0x0p+0)]; + tensor var_5298 = equal(x = imag_out, y = var_4712_promoted)[name = tensor("op_5298")]; + tensor correction_mask = logical_and(x = var_5298, y = less_1)[name = tensor("correction_mask")]; + tensor cast_111_dtype_0 = const()[name = tensor("cast_111_dtype_0"), val = tensor("int32")]; + tensor cast_111 = cast(dtype = cast_111_dtype_0, x = correction_mask)[name = tensor("cast_168")]; + tensor non_zero_0 = non_zero(x = cast_111)[name = tensor("non_zero_0")]; + tensor shape_12 = shape(x = non_zero_0)[name = tensor("shape_12")]; + tensor slice_by_index_24_begin_0 = const()[name = tensor("slice_by_index_24_begin_0"), val = tensor([0])]; + tensor slice_by_index_24_end_0 = const()[name = tensor("slice_by_index_24_end_0"), val = tensor([0])]; + tensor slice_by_index_24_squeeze_mask_0 = const()[name = tensor("slice_by_index_24_squeeze_mask_0"), val = tensor([true])]; + tensor 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_12)[name = tensor("slice_by_index_24")]; + tensor concat_565_axis_0 = const()[name = tensor("concat_565_axis_0"), val = tensor(0)]; + tensor concat_565_interleave_0 = const()[name = tensor("concat_565_interleave_0"), val = tensor(false)]; + tensor concat_565 = concat(axis = concat_565_axis_0, interleave = concat_565_interleave_0, values = slice_by_index_24)[name = tensor("concat_565")]; + tensor expand_dims_505 = const()[name = tensor("expand_dims_505"), val = tensor([0x1.921fb6p+1])]; + tensor var_4693_broadcasted = tile(reps = concat_565, x = expand_dims_505)[name = tensor("op_4693_broadcasted")]; + tensor har_phase_mode_0 = const()[name = tensor("har_phase_mode_0"), val = tensor("update")]; + tensor har_phase = scatter_nd(data = phase_1, indices = non_zero_0, mode = har_phase_mode_0, updates = var_4693_broadcasted)[name = tensor("har_phase")]; + tensor input_497_interleave_0 = const()[name = tensor("input_497_interleave_0"), val = tensor(false)]; + tensor input_497 = concat(axis = var_4716, interleave = input_497_interleave_0, values = (har_spec, har_phase))[name = tensor("input_497")]; + tensor input_523 = leaky_relu(alpha = var_4705, x = input_495)[name = tensor("input_523")]; + tensor input_499_pad_type_0 = const()[name = tensor("input_499_pad_type_0"), val = tensor("custom")]; + tensor input_499_pad_0 = const()[name = tensor("input_499_pad_0"), val = tensor([3, 3])]; + tensor input_499_strides_0 = const()[name = tensor("input_499_strides_0"), val = tensor([6])]; + tensor input_499_dilations_0 = const()[name = tensor("input_499_dilations_0"), val = tensor([1])]; + tensor input_499_groups_0 = const()[name = tensor("input_499_groups_0"), val = tensor(1)]; + tensor input_499 = conv(bias = decoder_generator_noise_convs_0_bias, dilations = input_499_dilations_0, groups = input_499_groups_0, pad = input_499_pad_0, pad_type = input_499_pad_type_0, strides = input_499_strides_0, weight = decoder_generator_noise_convs_0_weight, x = input_497)[name = tensor("input_499")]; + tensor 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_403)[name = tensor("linear_100")]; + tensor var_5356 = const()[name = tensor("op_5356"), val = tensor([1, 512, 1])]; + tensor h_103 = reshape(shape = var_5356, x = h_101)[name = tensor("h_103")]; + tensor var_5358_split_sizes_0 = const()[name = tensor("op_5358_split_sizes_0"), val = tensor([256, 256])]; + tensor var_5358_axis_0 = const()[name = tensor("op_5358_axis_0"), val = tensor(1)]; + tensor var_5358_0, tensor var_5358_1 = split(axis = var_5358_axis_0, split_sizes = var_5358_split_sizes_0, x = h_103)[name = tensor("op_5358")]; + tensor var_5360_promoted = const()[name = tensor("op_5360_promoted"), val = tensor(0x1p+0)]; + tensor var_5361 = add(x = var_5358_0, y = var_5360_promoted)[name = tensor("op_5361")]; + tensor var_5364 = instance_norm(beta = predictor_F0_1_norm2_norm_bias, epsilon = var_4706, gamma = predictor_F0_1_norm2_norm_weight, x = input_499)[name = tensor("op_5364")]; + tensor var_5365 = mul(x = var_5361, y = var_5364)[name = tensor("op_5365")]; + tensor xt_1 = add(x = var_5365, y = var_5358_1)[name = tensor("xt_1")]; + tensor var_5367 = const()[name = tensor("op_5367"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(248978048)))]; + tensor var_5370 = mul(x = decoder_generator_noise_res_0_alpha1_0, y = xt_1)[name = tensor("op_5370")]; + tensor var_5371 = sin(x = var_5370)[name = tensor("op_5371")]; + tensor var_4713_promoted_2 = const()[name = tensor("op_4713_promoted_2"), val = tensor(0x1p+1)]; + tensor var_5372 = pow(x = var_5371, y = var_4713_promoted_2)[name = tensor("op_5372")]; + tensor var_5373 = mul(x = var_5367, y = var_5372)[name = tensor("op_5373")]; + tensor input_501 = add(x = xt_1, y = var_5373)[name = tensor("input_501")]; + tensor weight_143 = const()[name = tensor("weight_143"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(248979136)))]; + tensor input_503_pad_type_0 = const()[name = tensor("input_503_pad_type_0"), val = tensor("custom")]; + tensor input_503_pad_0 = const()[name = tensor("input_503_pad_0"), val = tensor([3, 3])]; + tensor input_503_strides_0 = const()[name = tensor("input_503_strides_0"), val = tensor([1])]; + tensor input_503_dilations_0 = const()[name = tensor("input_503_dilations_0"), val = tensor([1])]; + tensor input_503_groups_0 = const()[name = tensor("input_503_groups_0"), val = tensor(1)]; + tensor input_503 = conv(bias = decoder_generator_noise_res_0_convs1_0_bias, dilations = input_503_dilations_0, groups = input_503_groups_0, pad = input_503_pad_0, pad_type = input_503_pad_type_0, strides = input_503_strides_0, weight = weight_143, x = input_501)[name = tensor("input_503")]; + tensor 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_403)[name = tensor("linear_101")]; + tensor var_5393 = const()[name = tensor("op_5393"), val = tensor([1, 512, 1])]; + tensor h_107 = reshape(shape = var_5393, x = h_105)[name = tensor("h_107")]; + tensor var_5395_split_sizes_0 = const()[name = tensor("op_5395_split_sizes_0"), val = tensor([256, 256])]; + tensor var_5395_axis_0 = const()[name = tensor("op_5395_axis_0"), val = tensor(1)]; + tensor var_5395_0, tensor var_5395_1 = split(axis = var_5395_axis_0, split_sizes = var_5395_split_sizes_0, x = h_107)[name = tensor("op_5395")]; + tensor var_5397_promoted = const()[name = tensor("op_5397_promoted"), val = tensor(0x1p+0)]; + tensor var_5398 = add(x = var_5395_0, y = var_5397_promoted)[name = tensor("op_5398")]; + tensor var_5401 = instance_norm(beta = predictor_F0_1_norm2_norm_bias, epsilon = var_4706, gamma = predictor_F0_1_norm2_norm_weight, x = input_503)[name = tensor("op_5401")]; + tensor var_5402 = mul(x = var_5398, y = var_5401)[name = tensor("op_5402")]; + tensor xt_3 = add(x = var_5402, y = var_5395_1)[name = tensor("xt_3")]; + tensor var_5404 = const()[name = tensor("op_5404"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(250814208)))]; + tensor var_5407 = mul(x = decoder_generator_noise_res_0_alpha2_0, y = xt_3)[name = tensor("op_5407")]; + tensor var_5408 = sin(x = var_5407)[name = tensor("op_5408")]; + tensor var_4713_promoted_3 = const()[name = tensor("op_4713_promoted_3"), val = tensor(0x1p+1)]; + tensor var_5409 = pow(x = var_5408, y = var_4713_promoted_3)[name = tensor("op_5409")]; + tensor var_5410 = mul(x = var_5404, y = var_5409)[name = tensor("op_5410")]; + tensor input_505 = add(x = xt_3, y = var_5410)[name = tensor("input_505")]; + tensor weight_147 = const()[name = tensor("weight_147"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(250815296)))]; + tensor xt_5_pad_type_0 = const()[name = tensor("xt_5_pad_type_0"), val = tensor("custom")]; + tensor xt_5_pad_0 = const()[name = tensor("xt_5_pad_0"), val = tensor([3, 3])]; + tensor xt_5_strides_0 = const()[name = tensor("xt_5_strides_0"), val = tensor([1])]; + tensor xt_5_dilations_0 = const()[name = tensor("xt_5_dilations_0"), val = tensor([1])]; + tensor xt_5_groups_0 = const()[name = tensor("xt_5_groups_0"), val = tensor(1)]; + tensor 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_505)[name = tensor("xt_5")]; + tensor input_507 = add(x = xt_5, y = input_499)[name = tensor("input_507")]; + tensor 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_403)[name = tensor("linear_102")]; + tensor var_5431 = const()[name = tensor("op_5431"), val = tensor([1, 512, 1])]; + tensor h_111 = reshape(shape = var_5431, x = h_109)[name = tensor("h_111")]; + tensor var_5433_split_sizes_0 = const()[name = tensor("op_5433_split_sizes_0"), val = tensor([256, 256])]; + tensor var_5433_axis_0 = const()[name = tensor("op_5433_axis_0"), val = tensor(1)]; + tensor var_5433_0, tensor var_5433_1 = split(axis = var_5433_axis_0, split_sizes = var_5433_split_sizes_0, x = h_111)[name = tensor("op_5433")]; + tensor var_5435_promoted = const()[name = tensor("op_5435_promoted"), val = tensor(0x1p+0)]; + tensor var_5436 = add(x = var_5433_0, y = var_5435_promoted)[name = tensor("op_5436")]; + tensor var_5439 = instance_norm(beta = predictor_F0_1_norm2_norm_bias, epsilon = var_4706, gamma = predictor_F0_1_norm2_norm_weight, x = input_507)[name = tensor("op_5439")]; + tensor var_5440 = mul(x = var_5436, y = var_5439)[name = tensor("op_5440")]; + tensor xt_7 = add(x = var_5440, y = var_5433_1)[name = tensor("xt_7")]; + tensor var_5442 = const()[name = tensor("op_5442"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(252650368)))]; + tensor var_5445 = mul(x = decoder_generator_noise_res_0_alpha1_1, y = xt_7)[name = tensor("op_5445")]; + tensor var_5446 = sin(x = var_5445)[name = tensor("op_5446")]; + tensor var_4713_promoted_4 = const()[name = tensor("op_4713_promoted_4"), val = tensor(0x1p+1)]; + tensor var_5447 = pow(x = var_5446, y = var_4713_promoted_4)[name = tensor("op_5447")]; + tensor var_5448 = mul(x = var_5442, y = var_5447)[name = tensor("op_5448")]; + tensor input_509 = add(x = xt_7, y = var_5448)[name = tensor("input_509")]; + tensor weight_151 = const()[name = tensor("weight_151"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(252651456)))]; + tensor input_511_pad_type_0 = const()[name = tensor("input_511_pad_type_0"), val = tensor("custom")]; + tensor input_511_pad_0 = const()[name = tensor("input_511_pad_0"), val = tensor([9, 9])]; + tensor input_511_dilations_0 = const()[name = tensor("input_511_dilations_0"), val = tensor([3])]; + tensor input_511_strides_0 = const()[name = tensor("input_511_strides_0"), val = tensor([1])]; + tensor input_511_groups_0 = const()[name = tensor("input_511_groups_0"), val = tensor(1)]; + tensor input_511 = conv(bias = decoder_generator_noise_res_0_convs1_1_bias, dilations = input_511_dilations_0, groups = input_511_groups_0, pad = input_511_pad_0, pad_type = input_511_pad_type_0, strides = input_511_strides_0, weight = weight_151, x = input_509)[name = tensor("input_511")]; + tensor 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_403)[name = tensor("linear_103")]; + tensor var_5468 = const()[name = tensor("op_5468"), val = tensor([1, 512, 1])]; + tensor h_115 = reshape(shape = var_5468, x = h_113)[name = tensor("h_115")]; + tensor var_5470_split_sizes_0 = const()[name = tensor("op_5470_split_sizes_0"), val = tensor([256, 256])]; + tensor var_5470_axis_0 = const()[name = tensor("op_5470_axis_0"), val = tensor(1)]; + tensor var_5470_0, tensor var_5470_1 = split(axis = var_5470_axis_0, split_sizes = var_5470_split_sizes_0, x = h_115)[name = tensor("op_5470")]; + tensor var_5472_promoted = const()[name = tensor("op_5472_promoted"), val = tensor(0x1p+0)]; + tensor var_5473 = add(x = var_5470_0, y = var_5472_promoted)[name = tensor("op_5473")]; + tensor var_5476 = instance_norm(beta = predictor_F0_1_norm2_norm_bias, epsilon = var_4706, gamma = predictor_F0_1_norm2_norm_weight, x = input_511)[name = tensor("op_5476")]; + tensor var_5477 = mul(x = var_5473, y = var_5476)[name = tensor("op_5477")]; + tensor xt_9 = add(x = var_5477, y = var_5470_1)[name = tensor("xt_9")]; + tensor var_5479 = const()[name = tensor("op_5479"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(254486528)))]; + tensor var_5482 = mul(x = decoder_generator_noise_res_0_alpha2_1, y = xt_9)[name = tensor("op_5482")]; + tensor var_5483 = sin(x = var_5482)[name = tensor("op_5483")]; + tensor var_4713_promoted_5 = const()[name = tensor("op_4713_promoted_5"), val = tensor(0x1p+1)]; + tensor var_5484 = pow(x = var_5483, y = var_4713_promoted_5)[name = tensor("op_5484")]; + tensor var_5485 = mul(x = var_5479, y = var_5484)[name = tensor("op_5485")]; + tensor input_513 = add(x = xt_9, y = var_5485)[name = tensor("input_513")]; + tensor weight_155 = const()[name = tensor("weight_155"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(254487616)))]; + tensor xt_11_pad_type_0 = const()[name = tensor("xt_11_pad_type_0"), val = tensor("custom")]; + tensor xt_11_pad_0 = const()[name = tensor("xt_11_pad_0"), val = tensor([3, 3])]; + tensor xt_11_strides_0 = const()[name = tensor("xt_11_strides_0"), val = tensor([1])]; + tensor xt_11_dilations_0 = const()[name = tensor("xt_11_dilations_0"), val = tensor([1])]; + tensor xt_11_groups_0 = const()[name = tensor("xt_11_groups_0"), val = tensor(1)]; + tensor 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_513)[name = tensor("xt_11")]; + tensor input_515 = add(x = xt_11, y = input_507)[name = tensor("input_515")]; + tensor 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_403)[name = tensor("linear_104")]; + tensor var_5506 = const()[name = tensor("op_5506"), val = tensor([1, 512, 1])]; + tensor h_119 = reshape(shape = var_5506, x = h_117)[name = tensor("h_119")]; + tensor var_5508_split_sizes_0 = const()[name = tensor("op_5508_split_sizes_0"), val = tensor([256, 256])]; + tensor var_5508_axis_0 = const()[name = tensor("op_5508_axis_0"), val = tensor(1)]; + tensor var_5508_0, tensor var_5508_1 = split(axis = var_5508_axis_0, split_sizes = var_5508_split_sizes_0, x = h_119)[name = tensor("op_5508")]; + tensor var_5510_promoted = const()[name = tensor("op_5510_promoted"), val = tensor(0x1p+0)]; + tensor var_5511 = add(x = var_5508_0, y = var_5510_promoted)[name = tensor("op_5511")]; + tensor var_5514 = instance_norm(beta = predictor_F0_1_norm2_norm_bias, epsilon = var_4706, gamma = predictor_F0_1_norm2_norm_weight, x = input_515)[name = tensor("op_5514")]; + tensor var_5515 = mul(x = var_5511, y = var_5514)[name = tensor("op_5515")]; + tensor xt_13 = add(x = var_5515, y = var_5508_1)[name = tensor("xt_13")]; + tensor var_5517 = const()[name = tensor("op_5517"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(256322688)))]; + tensor var_5520 = mul(x = decoder_generator_noise_res_0_alpha1_2, y = xt_13)[name = tensor("op_5520")]; + tensor var_5521 = sin(x = var_5520)[name = tensor("op_5521")]; + tensor var_4713_promoted_6 = const()[name = tensor("op_4713_promoted_6"), val = tensor(0x1p+1)]; + tensor var_5522 = pow(x = var_5521, y = var_4713_promoted_6)[name = tensor("op_5522")]; + tensor var_5523 = mul(x = var_5517, y = var_5522)[name = tensor("op_5523")]; + tensor input_517 = add(x = xt_13, y = var_5523)[name = tensor("input_517")]; + tensor weight_159 = const()[name = tensor("weight_159"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(256323776)))]; + tensor input_519_pad_type_0 = const()[name = tensor("input_519_pad_type_0"), val = tensor("custom")]; + tensor input_519_pad_0 = const()[name = tensor("input_519_pad_0"), val = tensor([15, 15])]; + tensor input_519_dilations_0 = const()[name = tensor("input_519_dilations_0"), val = tensor([5])]; + tensor input_519_strides_0 = const()[name = tensor("input_519_strides_0"), val = tensor([1])]; + tensor input_519_groups_0 = const()[name = tensor("input_519_groups_0"), val = tensor(1)]; + tensor input_519 = conv(bias = decoder_generator_noise_res_0_convs1_2_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_159, x = input_517)[name = tensor("input_519")]; + tensor 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_403)[name = tensor("linear_105")]; + tensor var_5543 = const()[name = tensor("op_5543"), val = tensor([1, 512, 1])]; + tensor h_123 = reshape(shape = var_5543, x = h_121)[name = tensor("h_123")]; + tensor var_5545_split_sizes_0 = const()[name = tensor("op_5545_split_sizes_0"), val = tensor([256, 256])]; + tensor var_5545_axis_0 = const()[name = tensor("op_5545_axis_0"), val = tensor(1)]; + tensor var_5545_0, tensor var_5545_1 = split(axis = var_5545_axis_0, split_sizes = var_5545_split_sizes_0, x = h_123)[name = tensor("op_5545")]; + tensor var_5547_promoted = const()[name = tensor("op_5547_promoted"), val = tensor(0x1p+0)]; + tensor var_5548 = add(x = var_5545_0, y = var_5547_promoted)[name = tensor("op_5548")]; + tensor var_5551 = instance_norm(beta = predictor_F0_1_norm2_norm_bias, epsilon = var_4706, gamma = predictor_F0_1_norm2_norm_weight, x = input_519)[name = tensor("op_5551")]; + tensor var_5552 = mul(x = var_5548, y = var_5551)[name = tensor("op_5552")]; + tensor xt_15 = add(x = var_5552, y = var_5545_1)[name = tensor("xt_15")]; + tensor var_5554 = const()[name = tensor("op_5554"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(258158848)))]; + tensor var_5557 = mul(x = decoder_generator_noise_res_0_alpha2_2, y = xt_15)[name = tensor("op_5557")]; + tensor var_5558 = sin(x = var_5557)[name = tensor("op_5558")]; + tensor var_4713_promoted_7 = const()[name = tensor("op_4713_promoted_7"), val = tensor(0x1p+1)]; + tensor var_5559 = pow(x = var_5558, y = var_4713_promoted_7)[name = tensor("op_5559")]; + tensor var_5560 = mul(x = var_5554, y = var_5559)[name = tensor("op_5560")]; + tensor input_521 = add(x = xt_15, y = var_5560)[name = tensor("input_521")]; + tensor weight_163 = const()[name = tensor("weight_163"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(258159936)))]; + tensor xt_17_pad_type_0 = const()[name = tensor("xt_17_pad_type_0"), val = tensor("custom")]; + tensor xt_17_pad_0 = const()[name = tensor("xt_17_pad_0"), val = tensor([3, 3])]; + tensor xt_17_strides_0 = const()[name = tensor("xt_17_strides_0"), val = tensor([1])]; + tensor xt_17_dilations_0 = const()[name = tensor("xt_17_dilations_0"), val = tensor([1])]; + tensor xt_17_groups_0 = const()[name = tensor("xt_17_groups_0"), val = tensor(1)]; + tensor 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_521)[name = tensor("xt_17")]; + tensor x_source_1 = add(x = xt_17, y = input_515)[name = tensor("x_source_1")]; + tensor var_5579 = const()[name = tensor("op_5579"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(259995008)))]; + tensor x_261_pad_type_0 = const()[name = tensor("x_261_pad_type_0"), val = tensor("custom")]; + tensor x_261_pad_0 = const()[name = tensor("x_261_pad_0"), val = tensor([5, 5])]; + tensor x_261_strides_0 = const()[name = tensor("x_261_strides_0"), val = tensor([10])]; + tensor x_261_dilations_0 = const()[name = tensor("x_261_dilations_0"), val = tensor([1])]; + tensor x_261_groups_0 = const()[name = tensor("x_261_groups_0"), val = tensor(1)]; + tensor x_261_has_output_shape_output_shape_0 = const()[name = tensor("x_261_has_output_shape_output_shape_0"), val = tensor([1, 256, 12420])]; + tensor x_261_has_output_shape = conv_transpose(bias = decoder_generator_ups_0_bias, dilations = x_261_dilations_0, groups = x_261_groups_0, output_shape = x_261_has_output_shape_output_shape_0, pad = x_261_pad_0, pad_type = x_261_pad_type_0, strides = x_261_strides_0, weight = var_5579, x = input_523)[name = tensor("x_261_has_output_shape")]; + tensor input_525 = add(x = x_261_has_output_shape, y = x_source_1)[name = tensor("input_525")]; + tensor h_125 = linear(bias = decoder_generator_resblocks_0_adain1_0_fc_bias, weight = decoder_generator_resblocks_0_adain1_0_fc_weight, x = input_403)[name = tensor("linear_106")]; + tensor var_5629 = const()[name = tensor("op_5629"), val = tensor([1, 512, 1])]; + tensor h_127 = reshape(shape = var_5629, x = h_125)[name = tensor("h_127")]; + tensor var_5631_split_sizes_0 = const()[name = tensor("op_5631_split_sizes_0"), val = tensor([256, 256])]; + tensor var_5631_axis_0 = const()[name = tensor("op_5631_axis_0"), val = tensor(1)]; + tensor var_5631_0, tensor var_5631_1 = split(axis = var_5631_axis_0, split_sizes = var_5631_split_sizes_0, x = h_127)[name = tensor("op_5631")]; + tensor var_5633_promoted = const()[name = tensor("op_5633_promoted"), val = tensor(0x1p+0)]; + tensor var_5634 = add(x = var_5631_0, y = var_5633_promoted)[name = tensor("op_5634")]; + tensor var_5637 = instance_norm(beta = predictor_F0_1_norm2_norm_bias, epsilon = var_4706, gamma = predictor_F0_1_norm2_norm_weight, x = input_525)[name = tensor("op_5637")]; + tensor var_5638 = mul(x = var_5634, y = var_5637)[name = tensor("op_5638")]; + tensor xt_19 = add(x = var_5638, y = var_5631_1)[name = tensor("xt_19")]; + tensor var_5640 = const()[name = tensor("op_5640"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(270480832)))]; + tensor var_5643 = mul(x = decoder_generator_resblocks_0_alpha1_0, y = xt_19)[name = tensor("op_5643")]; + tensor var_5644 = sin(x = var_5643)[name = tensor("op_5644")]; + tensor var_4713_promoted_8 = const()[name = tensor("op_4713_promoted_8"), val = tensor(0x1p+1)]; + tensor var_5645 = pow(x = var_5644, y = var_4713_promoted_8)[name = tensor("op_5645")]; + tensor var_5646 = mul(x = var_5640, y = var_5645)[name = tensor("op_5646")]; + tensor input_527 = add(x = xt_19, y = var_5646)[name = tensor("input_527")]; + tensor weight_167 = const()[name = tensor("weight_167"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(270481920)))]; + tensor input_529_pad_type_0 = const()[name = tensor("input_529_pad_type_0"), val = tensor("custom")]; + tensor input_529_pad_0 = const()[name = tensor("input_529_pad_0"), val = tensor([1, 1])]; + tensor input_529_strides_0 = const()[name = tensor("input_529_strides_0"), val = tensor([1])]; + tensor input_529_dilations_0 = const()[name = tensor("input_529_dilations_0"), val = tensor([1])]; + tensor input_529_groups_0 = const()[name = tensor("input_529_groups_0"), val = tensor(1)]; + tensor input_529 = conv(bias = decoder_generator_resblocks_0_convs1_0_bias, dilations = input_529_dilations_0, groups = input_529_groups_0, pad = input_529_pad_0, pad_type = input_529_pad_type_0, strides = input_529_strides_0, weight = weight_167, x = input_527)[name = tensor("input_529")]; + tensor h_129 = linear(bias = decoder_generator_resblocks_0_adain2_0_fc_bias, weight = decoder_generator_resblocks_0_adain2_0_fc_weight, x = input_403)[name = tensor("linear_107")]; + tensor var_5666 = const()[name = tensor("op_5666"), val = tensor([1, 512, 1])]; + tensor h_131 = reshape(shape = var_5666, x = h_129)[name = tensor("h_131")]; + tensor var_5668_split_sizes_0 = const()[name = tensor("op_5668_split_sizes_0"), val = tensor([256, 256])]; + tensor var_5668_axis_0 = const()[name = tensor("op_5668_axis_0"), val = tensor(1)]; + tensor var_5668_0, tensor var_5668_1 = split(axis = var_5668_axis_0, split_sizes = var_5668_split_sizes_0, x = h_131)[name = tensor("op_5668")]; + tensor var_5670_promoted = const()[name = tensor("op_5670_promoted"), val = tensor(0x1p+0)]; + tensor var_5671 = add(x = var_5668_0, y = var_5670_promoted)[name = tensor("op_5671")]; + tensor var_5674 = instance_norm(beta = predictor_F0_1_norm2_norm_bias, epsilon = var_4706, gamma = predictor_F0_1_norm2_norm_weight, x = input_529)[name = tensor("op_5674")]; + tensor var_5675 = mul(x = var_5671, y = var_5674)[name = tensor("op_5675")]; + tensor xt_21 = add(x = var_5675, y = var_5668_1)[name = tensor("xt_21")]; + tensor var_5677 = const()[name = tensor("op_5677"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(271268416)))]; + tensor var_5680 = mul(x = decoder_generator_resblocks_0_alpha2_0, y = xt_21)[name = tensor("op_5680")]; + tensor var_5681 = sin(x = var_5680)[name = tensor("op_5681")]; + tensor var_4713_promoted_9 = const()[name = tensor("op_4713_promoted_9"), val = tensor(0x1p+1)]; + tensor var_5682 = pow(x = var_5681, y = var_4713_promoted_9)[name = tensor("op_5682")]; + tensor var_5683 = mul(x = var_5677, y = var_5682)[name = tensor("op_5683")]; + tensor input_531 = add(x = xt_21, y = var_5683)[name = tensor("input_531")]; + tensor weight_171 = const()[name = tensor("weight_171"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(271269504)))]; + tensor xt_23_pad_type_0 = const()[name = tensor("xt_23_pad_type_0"), val = tensor("custom")]; + tensor xt_23_pad_0 = const()[name = tensor("xt_23_pad_0"), val = tensor([1, 1])]; + tensor xt_23_strides_0 = const()[name = tensor("xt_23_strides_0"), val = tensor([1])]; + tensor xt_23_dilations_0 = const()[name = tensor("xt_23_dilations_0"), val = tensor([1])]; + tensor xt_23_groups_0 = const()[name = tensor("xt_23_groups_0"), val = tensor(1)]; + tensor 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_531)[name = tensor("xt_23")]; + tensor input_533 = add(x = xt_23, y = input_525)[name = tensor("input_533")]; + tensor h_133 = linear(bias = decoder_generator_resblocks_0_adain1_1_fc_bias, weight = decoder_generator_resblocks_0_adain1_1_fc_weight, x = input_403)[name = tensor("linear_108")]; + tensor var_5704 = const()[name = tensor("op_5704"), val = tensor([1, 512, 1])]; + tensor h_135 = reshape(shape = var_5704, x = h_133)[name = tensor("h_135")]; + tensor var_5706_split_sizes_0 = const()[name = tensor("op_5706_split_sizes_0"), val = tensor([256, 256])]; + tensor var_5706_axis_0 = const()[name = tensor("op_5706_axis_0"), val = tensor(1)]; + tensor var_5706_0, tensor var_5706_1 = split(axis = var_5706_axis_0, split_sizes = var_5706_split_sizes_0, x = h_135)[name = tensor("op_5706")]; + tensor var_5708_promoted = const()[name = tensor("op_5708_promoted"), val = tensor(0x1p+0)]; + tensor var_5709 = add(x = var_5706_0, y = var_5708_promoted)[name = tensor("op_5709")]; + tensor var_5712 = instance_norm(beta = predictor_F0_1_norm2_norm_bias, epsilon = var_4706, gamma = predictor_F0_1_norm2_norm_weight, x = input_533)[name = tensor("op_5712")]; + tensor var_5713 = mul(x = var_5709, y = var_5712)[name = tensor("op_5713")]; + tensor xt_25 = add(x = var_5713, y = var_5706_1)[name = tensor("xt_25")]; + tensor var_5715 = const()[name = tensor("op_5715"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(272056000)))]; + tensor var_5718 = mul(x = decoder_generator_resblocks_0_alpha1_1, y = xt_25)[name = tensor("op_5718")]; + tensor var_5719 = sin(x = var_5718)[name = tensor("op_5719")]; + tensor var_4713_promoted_10 = const()[name = tensor("op_4713_promoted_10"), val = tensor(0x1p+1)]; + tensor var_5720 = pow(x = var_5719, y = var_4713_promoted_10)[name = tensor("op_5720")]; + tensor var_5721 = mul(x = var_5715, y = var_5720)[name = tensor("op_5721")]; + tensor input_535 = add(x = xt_25, y = var_5721)[name = tensor("input_535")]; + tensor weight_175 = const()[name = tensor("weight_175"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(272057088)))]; + tensor input_537_pad_type_0 = const()[name = tensor("input_537_pad_type_0"), val = tensor("custom")]; + tensor input_537_pad_0 = const()[name = tensor("input_537_pad_0"), val = tensor([3, 3])]; + tensor input_537_dilations_0 = const()[name = tensor("input_537_dilations_0"), val = tensor([3])]; + tensor input_537_strides_0 = const()[name = tensor("input_537_strides_0"), val = tensor([1])]; + tensor input_537_groups_0 = const()[name = tensor("input_537_groups_0"), val = tensor(1)]; + tensor input_537 = conv(bias = decoder_generator_resblocks_0_convs1_1_bias, dilations = input_537_dilations_0, groups = input_537_groups_0, pad = input_537_pad_0, pad_type = input_537_pad_type_0, strides = input_537_strides_0, weight = weight_175, x = input_535)[name = tensor("input_537")]; + tensor h_137 = linear(bias = decoder_generator_resblocks_0_adain2_1_fc_bias, weight = decoder_generator_resblocks_0_adain2_1_fc_weight, x = input_403)[name = tensor("linear_109")]; + tensor var_5741 = const()[name = tensor("op_5741"), val = tensor([1, 512, 1])]; + tensor h_139 = reshape(shape = var_5741, x = h_137)[name = tensor("h_139")]; + tensor var_5743_split_sizes_0 = const()[name = tensor("op_5743_split_sizes_0"), val = tensor([256, 256])]; + tensor var_5743_axis_0 = const()[name = tensor("op_5743_axis_0"), val = tensor(1)]; + tensor var_5743_0, tensor var_5743_1 = split(axis = var_5743_axis_0, split_sizes = var_5743_split_sizes_0, x = h_139)[name = tensor("op_5743")]; + tensor var_5745_promoted = const()[name = tensor("op_5745_promoted"), val = tensor(0x1p+0)]; + tensor var_5746 = add(x = var_5743_0, y = var_5745_promoted)[name = tensor("op_5746")]; + tensor var_5749 = instance_norm(beta = predictor_F0_1_norm2_norm_bias, epsilon = var_4706, gamma = predictor_F0_1_norm2_norm_weight, x = input_537)[name = tensor("op_5749")]; + tensor var_5750 = mul(x = var_5746, y = var_5749)[name = tensor("op_5750")]; + tensor xt_27 = add(x = var_5750, y = var_5743_1)[name = tensor("xt_27")]; + tensor var_5752 = const()[name = tensor("op_5752"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(272843584)))]; + tensor var_5755 = mul(x = decoder_generator_resblocks_0_alpha2_1, y = xt_27)[name = tensor("op_5755")]; + tensor var_5756 = sin(x = var_5755)[name = tensor("op_5756")]; + tensor var_4713_promoted_11 = const()[name = tensor("op_4713_promoted_11"), val = tensor(0x1p+1)]; + tensor var_5757 = pow(x = var_5756, y = var_4713_promoted_11)[name = tensor("op_5757")]; + tensor var_5758 = mul(x = var_5752, y = var_5757)[name = tensor("op_5758")]; + tensor input_539 = add(x = xt_27, y = var_5758)[name = tensor("input_539")]; + tensor weight_179 = const()[name = tensor("weight_179"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(272844672)))]; + tensor xt_29_pad_type_0 = const()[name = tensor("xt_29_pad_type_0"), val = tensor("custom")]; + tensor xt_29_pad_0 = const()[name = tensor("xt_29_pad_0"), val = tensor([1, 1])]; + tensor xt_29_strides_0 = const()[name = tensor("xt_29_strides_0"), val = tensor([1])]; + tensor xt_29_dilations_0 = const()[name = tensor("xt_29_dilations_0"), val = tensor([1])]; + tensor xt_29_groups_0 = const()[name = tensor("xt_29_groups_0"), val = tensor(1)]; + tensor 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_539)[name = tensor("xt_29")]; + tensor input_541 = add(x = xt_29, y = input_533)[name = tensor("input_541")]; + tensor h_141 = linear(bias = decoder_generator_resblocks_0_adain1_2_fc_bias, weight = decoder_generator_resblocks_0_adain1_2_fc_weight, x = input_403)[name = tensor("linear_110")]; + tensor var_5779 = const()[name = tensor("op_5779"), val = tensor([1, 512, 1])]; + tensor h_143 = reshape(shape = var_5779, x = h_141)[name = tensor("h_143")]; + tensor var_5781_split_sizes_0 = const()[name = tensor("op_5781_split_sizes_0"), val = tensor([256, 256])]; + tensor var_5781_axis_0 = const()[name = tensor("op_5781_axis_0"), val = tensor(1)]; + tensor var_5781_0, tensor var_5781_1 = split(axis = var_5781_axis_0, split_sizes = var_5781_split_sizes_0, x = h_143)[name = tensor("op_5781")]; + tensor var_5783_promoted = const()[name = tensor("op_5783_promoted"), val = tensor(0x1p+0)]; + tensor var_5784 = add(x = var_5781_0, y = var_5783_promoted)[name = tensor("op_5784")]; + tensor var_5787 = instance_norm(beta = predictor_F0_1_norm2_norm_bias, epsilon = var_4706, gamma = predictor_F0_1_norm2_norm_weight, x = input_541)[name = tensor("op_5787")]; + tensor var_5788 = mul(x = var_5784, y = var_5787)[name = tensor("op_5788")]; + tensor xt_31 = add(x = var_5788, y = var_5781_1)[name = tensor("xt_31")]; + tensor var_5790 = const()[name = tensor("op_5790"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(273631168)))]; + tensor var_5793 = mul(x = decoder_generator_resblocks_0_alpha1_2, y = xt_31)[name = tensor("op_5793")]; + tensor var_5794 = sin(x = var_5793)[name = tensor("op_5794")]; + tensor var_4713_promoted_12 = const()[name = tensor("op_4713_promoted_12"), val = tensor(0x1p+1)]; + tensor var_5795 = pow(x = var_5794, y = var_4713_promoted_12)[name = tensor("op_5795")]; + tensor var_5796 = mul(x = var_5790, y = var_5795)[name = tensor("op_5796")]; + tensor input_543 = add(x = xt_31, y = var_5796)[name = tensor("input_543")]; + tensor weight_183 = const()[name = tensor("weight_183"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(273632256)))]; + tensor input_545_pad_type_0 = const()[name = tensor("input_545_pad_type_0"), val = tensor("custom")]; + tensor input_545_pad_0 = const()[name = tensor("input_545_pad_0"), val = tensor([5, 5])]; + tensor input_545_dilations_0 = const()[name = tensor("input_545_dilations_0"), val = tensor([5])]; + tensor input_545_strides_0 = const()[name = tensor("input_545_strides_0"), val = tensor([1])]; + tensor input_545_groups_0 = const()[name = tensor("input_545_groups_0"), val = tensor(1)]; + tensor input_545 = conv(bias = decoder_generator_resblocks_0_convs1_2_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_183, x = input_543)[name = tensor("input_545")]; + tensor h_145 = linear(bias = decoder_generator_resblocks_0_adain2_2_fc_bias, weight = decoder_generator_resblocks_0_adain2_2_fc_weight, x = input_403)[name = tensor("linear_111")]; + tensor var_5816 = const()[name = tensor("op_5816"), val = tensor([1, 512, 1])]; + tensor h_147 = reshape(shape = var_5816, x = h_145)[name = tensor("h_147")]; + tensor var_5818_split_sizes_0 = const()[name = tensor("op_5818_split_sizes_0"), val = tensor([256, 256])]; + tensor var_5818_axis_0 = const()[name = tensor("op_5818_axis_0"), val = tensor(1)]; + tensor var_5818_0, tensor var_5818_1 = split(axis = var_5818_axis_0, split_sizes = var_5818_split_sizes_0, x = h_147)[name = tensor("op_5818")]; + tensor var_5820_promoted = const()[name = tensor("op_5820_promoted"), val = tensor(0x1p+0)]; + tensor var_5821 = add(x = var_5818_0, y = var_5820_promoted)[name = tensor("op_5821")]; + tensor var_5824 = instance_norm(beta = predictor_F0_1_norm2_norm_bias, epsilon = var_4706, gamma = predictor_F0_1_norm2_norm_weight, x = input_545)[name = tensor("op_5824")]; + tensor var_5825 = mul(x = var_5821, y = var_5824)[name = tensor("op_5825")]; + tensor xt_33 = add(x = var_5825, y = var_5818_1)[name = tensor("xt_33")]; + tensor var_5827 = const()[name = tensor("op_5827"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(274418752)))]; + tensor var_5830 = mul(x = decoder_generator_resblocks_0_alpha2_2, y = xt_33)[name = tensor("op_5830")]; + tensor var_5831 = sin(x = var_5830)[name = tensor("op_5831")]; + tensor var_4713_promoted_13 = const()[name = tensor("op_4713_promoted_13"), val = tensor(0x1p+1)]; + tensor var_5832 = pow(x = var_5831, y = var_4713_promoted_13)[name = tensor("op_5832")]; + tensor var_5833 = mul(x = var_5827, y = var_5832)[name = tensor("op_5833")]; + tensor input_547 = add(x = xt_33, y = var_5833)[name = tensor("input_547")]; + tensor weight_187 = const()[name = tensor("weight_187"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(274419840)))]; + tensor xt_35_pad_type_0 = const()[name = tensor("xt_35_pad_type_0"), val = tensor("custom")]; + tensor xt_35_pad_0 = const()[name = tensor("xt_35_pad_0"), val = tensor([1, 1])]; + tensor xt_35_strides_0 = const()[name = tensor("xt_35_strides_0"), val = tensor([1])]; + tensor xt_35_dilations_0 = const()[name = tensor("xt_35_dilations_0"), val = tensor([1])]; + tensor xt_35_groups_0 = const()[name = tensor("xt_35_groups_0"), val = tensor(1)]; + tensor 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_547)[name = tensor("xt_35")]; + tensor xs_1 = add(x = xt_35, y = input_541)[name = tensor("xs_1")]; + tensor h_149 = linear(bias = decoder_generator_resblocks_1_adain1_0_fc_bias, weight = decoder_generator_resblocks_1_adain1_0_fc_weight, x = input_403)[name = tensor("linear_112")]; + tensor var_5890 = const()[name = tensor("op_5890"), val = tensor([1, 512, 1])]; + tensor h_151 = reshape(shape = var_5890, x = h_149)[name = tensor("h_151")]; + tensor var_5892_split_sizes_0 = const()[name = tensor("op_5892_split_sizes_0"), val = tensor([256, 256])]; + tensor var_5892_axis_0 = const()[name = tensor("op_5892_axis_0"), val = tensor(1)]; + tensor var_5892_0, tensor var_5892_1 = split(axis = var_5892_axis_0, split_sizes = var_5892_split_sizes_0, x = h_151)[name = tensor("op_5892")]; + tensor var_5894_promoted = const()[name = tensor("op_5894_promoted"), val = tensor(0x1p+0)]; + tensor var_5895 = add(x = var_5892_0, y = var_5894_promoted)[name = tensor("op_5895")]; + tensor var_5899 = mul(x = var_5895, y = var_5637)[name = tensor("op_5899")]; + tensor xt_37 = add(x = var_5899, y = var_5892_1)[name = tensor("xt_37")]; + tensor var_5901 = const()[name = tensor("op_5901"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(275206336)))]; + tensor var_5904 = mul(x = decoder_generator_resblocks_1_alpha1_0, y = xt_37)[name = tensor("op_5904")]; + tensor var_5905 = sin(x = var_5904)[name = tensor("op_5905")]; + tensor var_4713_promoted_14 = const()[name = tensor("op_4713_promoted_14"), val = tensor(0x1p+1)]; + tensor var_5906 = pow(x = var_5905, y = var_4713_promoted_14)[name = tensor("op_5906")]; + tensor var_5907 = mul(x = var_5901, y = var_5906)[name = tensor("op_5907")]; + tensor input_549 = add(x = xt_37, y = var_5907)[name = tensor("input_549")]; + tensor weight_191 = const()[name = tensor("weight_191"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(275207424)))]; + tensor input_551_pad_type_0 = const()[name = tensor("input_551_pad_type_0"), val = tensor("custom")]; + tensor input_551_pad_0 = const()[name = tensor("input_551_pad_0"), val = tensor([3, 3])]; + tensor input_551_strides_0 = const()[name = tensor("input_551_strides_0"), val = tensor([1])]; + tensor input_551_dilations_0 = const()[name = tensor("input_551_dilations_0"), val = tensor([1])]; + tensor input_551_groups_0 = const()[name = tensor("input_551_groups_0"), val = tensor(1)]; + tensor input_551 = conv(bias = decoder_generator_resblocks_1_convs1_0_bias, dilations = input_551_dilations_0, groups = input_551_groups_0, pad = input_551_pad_0, pad_type = input_551_pad_type_0, strides = input_551_strides_0, weight = weight_191, x = input_549)[name = tensor("input_551")]; + tensor h_153 = linear(bias = decoder_generator_resblocks_1_adain2_0_fc_bias, weight = decoder_generator_resblocks_1_adain2_0_fc_weight, x = input_403)[name = tensor("linear_113")]; + tensor var_5927 = const()[name = tensor("op_5927"), val = tensor([1, 512, 1])]; + tensor h_155 = reshape(shape = var_5927, x = h_153)[name = tensor("h_155")]; + tensor var_5929_split_sizes_0 = const()[name = tensor("op_5929_split_sizes_0"), val = tensor([256, 256])]; + tensor var_5929_axis_0 = const()[name = tensor("op_5929_axis_0"), val = tensor(1)]; + tensor var_5929_0, tensor var_5929_1 = split(axis = var_5929_axis_0, split_sizes = var_5929_split_sizes_0, x = h_155)[name = tensor("op_5929")]; + tensor var_5931_promoted = const()[name = tensor("op_5931_promoted"), val = tensor(0x1p+0)]; + tensor var_5932 = add(x = var_5929_0, y = var_5931_promoted)[name = tensor("op_5932")]; + tensor var_5935 = instance_norm(beta = predictor_F0_1_norm2_norm_bias, epsilon = var_4706, gamma = predictor_F0_1_norm2_norm_weight, x = input_551)[name = tensor("op_5935")]; + tensor var_5936 = mul(x = var_5932, y = var_5935)[name = tensor("op_5936")]; + tensor xt_39 = add(x = var_5936, y = var_5929_1)[name = tensor("xt_39")]; + tensor var_5938 = const()[name = tensor("op_5938"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(277042496)))]; + tensor var_5941 = mul(x = decoder_generator_resblocks_1_alpha2_0, y = xt_39)[name = tensor("op_5941")]; + tensor var_5942 = sin(x = var_5941)[name = tensor("op_5942")]; + tensor var_4713_promoted_15 = const()[name = tensor("op_4713_promoted_15"), val = tensor(0x1p+1)]; + tensor var_5943 = pow(x = var_5942, y = var_4713_promoted_15)[name = tensor("op_5943")]; + tensor var_5944 = mul(x = var_5938, y = var_5943)[name = tensor("op_5944")]; + tensor input_553 = add(x = xt_39, y = var_5944)[name = tensor("input_553")]; + tensor weight_195 = const()[name = tensor("weight_195"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(277043584)))]; + tensor xt_41_pad_type_0 = const()[name = tensor("xt_41_pad_type_0"), val = tensor("custom")]; + tensor xt_41_pad_0 = const()[name = tensor("xt_41_pad_0"), val = tensor([3, 3])]; + tensor xt_41_strides_0 = const()[name = tensor("xt_41_strides_0"), val = tensor([1])]; + tensor xt_41_dilations_0 = const()[name = tensor("xt_41_dilations_0"), val = tensor([1])]; + tensor xt_41_groups_0 = const()[name = tensor("xt_41_groups_0"), val = tensor(1)]; + tensor 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_553)[name = tensor("xt_41")]; + tensor input_555 = add(x = xt_41, y = input_525)[name = tensor("input_555")]; + tensor h_157 = linear(bias = decoder_generator_resblocks_1_adain1_1_fc_bias, weight = decoder_generator_resblocks_1_adain1_1_fc_weight, x = input_403)[name = tensor("linear_114")]; + tensor var_5965 = const()[name = tensor("op_5965"), val = tensor([1, 512, 1])]; + tensor h_159 = reshape(shape = var_5965, x = h_157)[name = tensor("h_159")]; + tensor var_5967_split_sizes_0 = const()[name = tensor("op_5967_split_sizes_0"), val = tensor([256, 256])]; + tensor var_5967_axis_0 = const()[name = tensor("op_5967_axis_0"), val = tensor(1)]; + tensor var_5967_0, tensor var_5967_1 = split(axis = var_5967_axis_0, split_sizes = var_5967_split_sizes_0, x = h_159)[name = tensor("op_5967")]; + tensor var_5969_promoted = const()[name = tensor("op_5969_promoted"), val = tensor(0x1p+0)]; + tensor var_5970 = add(x = var_5967_0, y = var_5969_promoted)[name = tensor("op_5970")]; + tensor var_5973 = instance_norm(beta = predictor_F0_1_norm2_norm_bias, epsilon = var_4706, gamma = predictor_F0_1_norm2_norm_weight, x = input_555)[name = tensor("op_5973")]; + tensor var_5974 = mul(x = var_5970, y = var_5973)[name = tensor("op_5974")]; + tensor xt_43 = add(x = var_5974, y = var_5967_1)[name = tensor("xt_43")]; + tensor var_5976 = const()[name = tensor("op_5976"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(278878656)))]; + tensor var_5979 = mul(x = decoder_generator_resblocks_1_alpha1_1, y = xt_43)[name = tensor("op_5979")]; + tensor var_5980 = sin(x = var_5979)[name = tensor("op_5980")]; + tensor var_4713_promoted_16 = const()[name = tensor("op_4713_promoted_16"), val = tensor(0x1p+1)]; + tensor var_5981 = pow(x = var_5980, y = var_4713_promoted_16)[name = tensor("op_5981")]; + tensor var_5982 = mul(x = var_5976, y = var_5981)[name = tensor("op_5982")]; + tensor input_557 = add(x = xt_43, y = var_5982)[name = tensor("input_557")]; + tensor weight_199 = const()[name = tensor("weight_199"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(278879744)))]; + tensor input_559_pad_type_0 = const()[name = tensor("input_559_pad_type_0"), val = tensor("custom")]; + tensor input_559_pad_0 = const()[name = tensor("input_559_pad_0"), val = tensor([9, 9])]; + tensor input_559_dilations_0 = const()[name = tensor("input_559_dilations_0"), val = tensor([3])]; + tensor input_559_strides_0 = const()[name = tensor("input_559_strides_0"), val = tensor([1])]; + tensor input_559_groups_0 = const()[name = tensor("input_559_groups_0"), val = tensor(1)]; + tensor input_559 = conv(bias = decoder_generator_resblocks_1_convs1_1_bias, dilations = input_559_dilations_0, groups = input_559_groups_0, pad = input_559_pad_0, pad_type = input_559_pad_type_0, strides = input_559_strides_0, weight = weight_199, x = input_557)[name = tensor("input_559")]; + tensor h_161 = linear(bias = decoder_generator_resblocks_1_adain2_1_fc_bias, weight = decoder_generator_resblocks_1_adain2_1_fc_weight, x = input_403)[name = tensor("linear_115")]; + tensor var_6002 = const()[name = tensor("op_6002"), val = tensor([1, 512, 1])]; + tensor h_163 = reshape(shape = var_6002, x = h_161)[name = tensor("h_163")]; + tensor var_6004_split_sizes_0 = const()[name = tensor("op_6004_split_sizes_0"), val = tensor([256, 256])]; + tensor var_6004_axis_0 = const()[name = tensor("op_6004_axis_0"), val = tensor(1)]; + tensor var_6004_0, tensor var_6004_1 = split(axis = var_6004_axis_0, split_sizes = var_6004_split_sizes_0, x = h_163)[name = tensor("op_6004")]; + tensor var_6006_promoted = const()[name = tensor("op_6006_promoted"), val = tensor(0x1p+0)]; + tensor var_6007 = add(x = var_6004_0, y = var_6006_promoted)[name = tensor("op_6007")]; + tensor var_6010 = instance_norm(beta = predictor_F0_1_norm2_norm_bias, epsilon = var_4706, gamma = predictor_F0_1_norm2_norm_weight, x = input_559)[name = tensor("op_6010")]; + tensor var_6011 = mul(x = var_6007, y = var_6010)[name = tensor("op_6011")]; + tensor xt_45 = add(x = var_6011, y = var_6004_1)[name = tensor("xt_45")]; + tensor var_6013 = const()[name = tensor("op_6013"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(280714816)))]; + tensor var_6016 = mul(x = decoder_generator_resblocks_1_alpha2_1, y = xt_45)[name = tensor("op_6016")]; + tensor var_6017 = sin(x = var_6016)[name = tensor("op_6017")]; + tensor var_4713_promoted_17 = const()[name = tensor("op_4713_promoted_17"), val = tensor(0x1p+1)]; + tensor var_6018 = pow(x = var_6017, y = var_4713_promoted_17)[name = tensor("op_6018")]; + tensor var_6019 = mul(x = var_6013, y = var_6018)[name = tensor("op_6019")]; + tensor input_561 = add(x = xt_45, y = var_6019)[name = tensor("input_561")]; + tensor weight_203 = const()[name = tensor("weight_203"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(280715904)))]; + tensor xt_47_pad_type_0 = const()[name = tensor("xt_47_pad_type_0"), val = tensor("custom")]; + tensor xt_47_pad_0 = const()[name = tensor("xt_47_pad_0"), val = tensor([3, 3])]; + tensor xt_47_strides_0 = const()[name = tensor("xt_47_strides_0"), val = tensor([1])]; + tensor xt_47_dilations_0 = const()[name = tensor("xt_47_dilations_0"), val = tensor([1])]; + tensor xt_47_groups_0 = const()[name = tensor("xt_47_groups_0"), val = tensor(1)]; + tensor 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_561)[name = tensor("xt_47")]; + tensor input_563 = add(x = xt_47, y = input_555)[name = tensor("input_563")]; + tensor h_165 = linear(bias = decoder_generator_resblocks_1_adain1_2_fc_bias, weight = decoder_generator_resblocks_1_adain1_2_fc_weight, x = input_403)[name = tensor("linear_116")]; + tensor var_6040 = const()[name = tensor("op_6040"), val = tensor([1, 512, 1])]; + tensor h_167 = reshape(shape = var_6040, x = h_165)[name = tensor("h_167")]; + tensor var_6042_split_sizes_0 = const()[name = tensor("op_6042_split_sizes_0"), val = tensor([256, 256])]; + tensor var_6042_axis_0 = const()[name = tensor("op_6042_axis_0"), val = tensor(1)]; + tensor var_6042_0, tensor var_6042_1 = split(axis = var_6042_axis_0, split_sizes = var_6042_split_sizes_0, x = h_167)[name = tensor("op_6042")]; + tensor var_6044_promoted = const()[name = tensor("op_6044_promoted"), val = tensor(0x1p+0)]; + tensor var_6045 = add(x = var_6042_0, y = var_6044_promoted)[name = tensor("op_6045")]; + tensor var_6048 = instance_norm(beta = predictor_F0_1_norm2_norm_bias, epsilon = var_4706, gamma = predictor_F0_1_norm2_norm_weight, x = input_563)[name = tensor("op_6048")]; + tensor var_6049 = mul(x = var_6045, y = var_6048)[name = tensor("op_6049")]; + tensor xt_49 = add(x = var_6049, y = var_6042_1)[name = tensor("xt_49")]; + tensor var_6051 = const()[name = tensor("op_6051"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(282550976)))]; + tensor var_6054 = mul(x = decoder_generator_resblocks_1_alpha1_2, y = xt_49)[name = tensor("op_6054")]; + tensor var_6055 = sin(x = var_6054)[name = tensor("op_6055")]; + tensor var_4713_promoted_18 = const()[name = tensor("op_4713_promoted_18"), val = tensor(0x1p+1)]; + tensor var_6056 = pow(x = var_6055, y = var_4713_promoted_18)[name = tensor("op_6056")]; + tensor var_6057 = mul(x = var_6051, y = var_6056)[name = tensor("op_6057")]; + tensor input_565 = add(x = xt_49, y = var_6057)[name = tensor("input_565")]; + tensor weight_207 = const()[name = tensor("weight_207"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(282552064)))]; + tensor input_567_pad_type_0 = const()[name = tensor("input_567_pad_type_0"), val = tensor("custom")]; + tensor input_567_pad_0 = const()[name = tensor("input_567_pad_0"), val = tensor([15, 15])]; + tensor input_567_dilations_0 = const()[name = tensor("input_567_dilations_0"), val = tensor([5])]; + tensor input_567_strides_0 = const()[name = tensor("input_567_strides_0"), val = tensor([1])]; + tensor input_567_groups_0 = const()[name = tensor("input_567_groups_0"), val = tensor(1)]; + tensor input_567 = conv(bias = decoder_generator_resblocks_1_convs1_2_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_207, x = input_565)[name = tensor("input_567")]; + tensor h_169 = linear(bias = decoder_generator_resblocks_1_adain2_2_fc_bias, weight = decoder_generator_resblocks_1_adain2_2_fc_weight, x = input_403)[name = tensor("linear_117")]; + tensor var_6077 = const()[name = tensor("op_6077"), val = tensor([1, 512, 1])]; + tensor h_171 = reshape(shape = var_6077, x = h_169)[name = tensor("h_171")]; + tensor var_6079_split_sizes_0 = const()[name = tensor("op_6079_split_sizes_0"), val = tensor([256, 256])]; + tensor var_6079_axis_0 = const()[name = tensor("op_6079_axis_0"), val = tensor(1)]; + tensor var_6079_0, tensor var_6079_1 = split(axis = var_6079_axis_0, split_sizes = var_6079_split_sizes_0, x = h_171)[name = tensor("op_6079")]; + tensor var_6081_promoted = const()[name = tensor("op_6081_promoted"), val = tensor(0x1p+0)]; + tensor var_6082 = add(x = var_6079_0, y = var_6081_promoted)[name = tensor("op_6082")]; + tensor var_6085 = instance_norm(beta = predictor_F0_1_norm2_norm_bias, epsilon = var_4706, gamma = predictor_F0_1_norm2_norm_weight, x = input_567)[name = tensor("op_6085")]; + tensor var_6086 = mul(x = var_6082, y = var_6085)[name = tensor("op_6086")]; + tensor xt_51 = add(x = var_6086, y = var_6079_1)[name = tensor("xt_51")]; + tensor var_6088 = const()[name = tensor("op_6088"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(284387136)))]; + tensor var_6091 = mul(x = decoder_generator_resblocks_1_alpha2_2, y = xt_51)[name = tensor("op_6091")]; + tensor var_6092 = sin(x = var_6091)[name = tensor("op_6092")]; + tensor var_4713_promoted_19 = const()[name = tensor("op_4713_promoted_19"), val = tensor(0x1p+1)]; + tensor var_6093 = pow(x = var_6092, y = var_4713_promoted_19)[name = tensor("op_6093")]; + tensor var_6094 = mul(x = var_6088, y = var_6093)[name = tensor("op_6094")]; + tensor input_569 = add(x = xt_51, y = var_6094)[name = tensor("input_569")]; + tensor weight_211 = const()[name = tensor("weight_211"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(284388224)))]; + tensor xt_53_pad_type_0 = const()[name = tensor("xt_53_pad_type_0"), val = tensor("custom")]; + tensor xt_53_pad_0 = const()[name = tensor("xt_53_pad_0"), val = tensor([3, 3])]; + tensor xt_53_strides_0 = const()[name = tensor("xt_53_strides_0"), val = tensor([1])]; + tensor xt_53_dilations_0 = const()[name = tensor("xt_53_dilations_0"), val = tensor([1])]; + tensor xt_53_groups_0 = const()[name = tensor("xt_53_groups_0"), val = tensor(1)]; + tensor 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_569)[name = tensor("xt_53")]; + tensor var_6107 = add(x = xt_53, y = input_563)[name = tensor("op_6107")]; + tensor xs_3 = add(x = xs_1, y = var_6107)[name = tensor("xs_3")]; + tensor h_173 = linear(bias = decoder_generator_resblocks_2_adain1_0_fc_bias, weight = decoder_generator_resblocks_2_adain1_0_fc_weight, x = input_403)[name = tensor("linear_118")]; + tensor var_6152 = const()[name = tensor("op_6152"), val = tensor([1, 512, 1])]; + tensor h_175 = reshape(shape = var_6152, x = h_173)[name = tensor("h_175")]; + tensor var_6154_split_sizes_0 = const()[name = tensor("op_6154_split_sizes_0"), val = tensor([256, 256])]; + tensor var_6154_axis_0 = const()[name = tensor("op_6154_axis_0"), val = tensor(1)]; + tensor var_6154_0, tensor var_6154_1 = split(axis = var_6154_axis_0, split_sizes = var_6154_split_sizes_0, x = h_175)[name = tensor("op_6154")]; + tensor var_6156_promoted = const()[name = tensor("op_6156_promoted"), val = tensor(0x1p+0)]; + tensor var_6157 = add(x = var_6154_0, y = var_6156_promoted)[name = tensor("op_6157")]; + tensor var_6161 = mul(x = var_6157, y = var_5637)[name = tensor("op_6161")]; + tensor xt_55 = add(x = var_6161, y = var_6154_1)[name = tensor("xt_55")]; + tensor var_6163 = const()[name = tensor("op_6163"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(286223296)))]; + tensor var_6166 = mul(x = decoder_generator_resblocks_2_alpha1_0, y = xt_55)[name = tensor("op_6166")]; + tensor var_6167 = sin(x = var_6166)[name = tensor("op_6167")]; + tensor var_4713_promoted_20 = const()[name = tensor("op_4713_promoted_20"), val = tensor(0x1p+1)]; + tensor var_6168 = pow(x = var_6167, y = var_4713_promoted_20)[name = tensor("op_6168")]; + tensor var_6169 = mul(x = var_6163, y = var_6168)[name = tensor("op_6169")]; + tensor input_571 = add(x = xt_55, y = var_6169)[name = tensor("input_571")]; + tensor weight_215 = const()[name = tensor("weight_215"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(286224384)))]; + tensor input_573_pad_type_0 = const()[name = tensor("input_573_pad_type_0"), val = tensor("custom")]; + tensor input_573_pad_0 = const()[name = tensor("input_573_pad_0"), val = tensor([5, 5])]; + tensor input_573_strides_0 = const()[name = tensor("input_573_strides_0"), val = tensor([1])]; + tensor input_573_dilations_0 = const()[name = tensor("input_573_dilations_0"), val = tensor([1])]; + tensor input_573_groups_0 = const()[name = tensor("input_573_groups_0"), val = tensor(1)]; + tensor input_573 = conv(bias = decoder_generator_resblocks_2_convs1_0_bias, dilations = input_573_dilations_0, groups = input_573_groups_0, pad = input_573_pad_0, pad_type = input_573_pad_type_0, strides = input_573_strides_0, weight = weight_215, x = input_571)[name = tensor("input_573")]; + tensor h_177 = linear(bias = decoder_generator_resblocks_2_adain2_0_fc_bias, weight = decoder_generator_resblocks_2_adain2_0_fc_weight, x = input_403)[name = tensor("linear_119")]; + tensor var_6189 = const()[name = tensor("op_6189"), val = tensor([1, 512, 1])]; + tensor h_179 = reshape(shape = var_6189, x = h_177)[name = tensor("h_179")]; + tensor var_6191_split_sizes_0 = const()[name = tensor("op_6191_split_sizes_0"), val = tensor([256, 256])]; + tensor var_6191_axis_0 = const()[name = tensor("op_6191_axis_0"), val = tensor(1)]; + tensor var_6191_0, tensor var_6191_1 = split(axis = var_6191_axis_0, split_sizes = var_6191_split_sizes_0, x = h_179)[name = tensor("op_6191")]; + tensor var_6193_promoted = const()[name = tensor("op_6193_promoted"), val = tensor(0x1p+0)]; + tensor var_6194 = add(x = var_6191_0, y = var_6193_promoted)[name = tensor("op_6194")]; + tensor var_6197 = instance_norm(beta = predictor_F0_1_norm2_norm_bias, epsilon = var_4706, gamma = predictor_F0_1_norm2_norm_weight, x = input_573)[name = tensor("op_6197")]; + tensor var_6198 = mul(x = var_6194, y = var_6197)[name = tensor("op_6198")]; + tensor xt_57 = add(x = var_6198, y = var_6191_1)[name = tensor("xt_57")]; + tensor var_6200 = const()[name = tensor("op_6200"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(289108032)))]; + tensor var_6203 = mul(x = decoder_generator_resblocks_2_alpha2_0, y = xt_57)[name = tensor("op_6203")]; + tensor var_6204 = sin(x = var_6203)[name = tensor("op_6204")]; + tensor var_4713_promoted_21 = const()[name = tensor("op_4713_promoted_21"), val = tensor(0x1p+1)]; + tensor var_6205 = pow(x = var_6204, y = var_4713_promoted_21)[name = tensor("op_6205")]; + tensor var_6206 = mul(x = var_6200, y = var_6205)[name = tensor("op_6206")]; + tensor input_575 = add(x = xt_57, y = var_6206)[name = tensor("input_575")]; + tensor weight_219 = const()[name = tensor("weight_219"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(289109120)))]; + tensor xt_59_pad_type_0 = const()[name = tensor("xt_59_pad_type_0"), val = tensor("custom")]; + tensor xt_59_pad_0 = const()[name = tensor("xt_59_pad_0"), val = tensor([5, 5])]; + tensor xt_59_strides_0 = const()[name = tensor("xt_59_strides_0"), val = tensor([1])]; + tensor xt_59_dilations_0 = const()[name = tensor("xt_59_dilations_0"), val = tensor([1])]; + tensor xt_59_groups_0 = const()[name = tensor("xt_59_groups_0"), val = tensor(1)]; + tensor 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_575)[name = tensor("xt_59")]; + tensor input_577 = add(x = xt_59, y = input_525)[name = tensor("input_577")]; + tensor h_181 = linear(bias = decoder_generator_resblocks_2_adain1_1_fc_bias, weight = decoder_generator_resblocks_2_adain1_1_fc_weight, x = input_403)[name = tensor("linear_120")]; + tensor var_6227 = const()[name = tensor("op_6227"), val = tensor([1, 512, 1])]; + tensor h_183 = reshape(shape = var_6227, x = h_181)[name = tensor("h_183")]; + tensor var_6229_split_sizes_0 = const()[name = tensor("op_6229_split_sizes_0"), val = tensor([256, 256])]; + tensor var_6229_axis_0 = const()[name = tensor("op_6229_axis_0"), val = tensor(1)]; + tensor var_6229_0, tensor var_6229_1 = split(axis = var_6229_axis_0, split_sizes = var_6229_split_sizes_0, x = h_183)[name = tensor("op_6229")]; + tensor var_6231_promoted = const()[name = tensor("op_6231_promoted"), val = tensor(0x1p+0)]; + tensor var_6232 = add(x = var_6229_0, y = var_6231_promoted)[name = tensor("op_6232")]; + tensor var_6235 = instance_norm(beta = predictor_F0_1_norm2_norm_bias, epsilon = var_4706, gamma = predictor_F0_1_norm2_norm_weight, x = input_577)[name = tensor("op_6235")]; + tensor var_6236 = mul(x = var_6232, y = var_6235)[name = tensor("op_6236")]; + tensor xt_61 = add(x = var_6236, y = var_6229_1)[name = tensor("xt_61")]; + tensor var_6238 = const()[name = tensor("op_6238"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(291992768)))]; + tensor var_6241 = mul(x = decoder_generator_resblocks_2_alpha1_1, y = xt_61)[name = tensor("op_6241")]; + tensor var_6242 = sin(x = var_6241)[name = tensor("op_6242")]; + tensor var_4713_promoted_22 = const()[name = tensor("op_4713_promoted_22"), val = tensor(0x1p+1)]; + tensor var_6243 = pow(x = var_6242, y = var_4713_promoted_22)[name = tensor("op_6243")]; + tensor var_6244 = mul(x = var_6238, y = var_6243)[name = tensor("op_6244")]; + tensor input_579 = add(x = xt_61, y = var_6244)[name = tensor("input_579")]; + tensor weight_223 = const()[name = tensor("weight_223"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(291993856)))]; + tensor input_581_pad_type_0 = const()[name = tensor("input_581_pad_type_0"), val = tensor("custom")]; + tensor input_581_pad_0 = const()[name = tensor("input_581_pad_0"), val = tensor([15, 15])]; + tensor input_581_dilations_0 = const()[name = tensor("input_581_dilations_0"), val = tensor([3])]; + tensor input_581_strides_0 = const()[name = tensor("input_581_strides_0"), val = tensor([1])]; + tensor input_581_groups_0 = const()[name = tensor("input_581_groups_0"), val = tensor(1)]; + tensor input_581 = conv(bias = decoder_generator_resblocks_2_convs1_1_bias, dilations = input_581_dilations_0, groups = input_581_groups_0, pad = input_581_pad_0, pad_type = input_581_pad_type_0, strides = input_581_strides_0, weight = weight_223, x = input_579)[name = tensor("input_581")]; + tensor h_185 = linear(bias = decoder_generator_resblocks_2_adain2_1_fc_bias, weight = decoder_generator_resblocks_2_adain2_1_fc_weight, x = input_403)[name = tensor("linear_121")]; + tensor var_6264 = const()[name = tensor("op_6264"), val = tensor([1, 512, 1])]; + tensor h_187 = reshape(shape = var_6264, x = h_185)[name = tensor("h_187")]; + tensor var_6266_split_sizes_0 = const()[name = tensor("op_6266_split_sizes_0"), val = tensor([256, 256])]; + tensor var_6266_axis_0 = const()[name = tensor("op_6266_axis_0"), val = tensor(1)]; + tensor var_6266_0, tensor var_6266_1 = split(axis = var_6266_axis_0, split_sizes = var_6266_split_sizes_0, x = h_187)[name = tensor("op_6266")]; + tensor var_6268_promoted = const()[name = tensor("op_6268_promoted"), val = tensor(0x1p+0)]; + tensor var_6269 = add(x = var_6266_0, y = var_6268_promoted)[name = tensor("op_6269")]; + tensor var_6272 = instance_norm(beta = predictor_F0_1_norm2_norm_bias, epsilon = var_4706, gamma = predictor_F0_1_norm2_norm_weight, x = input_581)[name = tensor("op_6272")]; + tensor var_6273 = mul(x = var_6269, y = var_6272)[name = tensor("op_6273")]; + tensor xt_63 = add(x = var_6273, y = var_6266_1)[name = tensor("xt_63")]; + tensor var_6275 = const()[name = tensor("op_6275"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(294877504)))]; + tensor var_6278 = mul(x = decoder_generator_resblocks_2_alpha2_1, y = xt_63)[name = tensor("op_6278")]; + tensor var_6279 = sin(x = var_6278)[name = tensor("op_6279")]; + tensor var_4713_promoted_23 = const()[name = tensor("op_4713_promoted_23"), val = tensor(0x1p+1)]; + tensor var_6280 = pow(x = var_6279, y = var_4713_promoted_23)[name = tensor("op_6280")]; + tensor var_6281 = mul(x = var_6275, y = var_6280)[name = tensor("op_6281")]; + tensor input_583 = add(x = xt_63, y = var_6281)[name = tensor("input_583")]; + tensor weight_227 = const()[name = tensor("weight_227"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(294878592)))]; + tensor xt_65_pad_type_0 = const()[name = tensor("xt_65_pad_type_0"), val = tensor("custom")]; + tensor xt_65_pad_0 = const()[name = tensor("xt_65_pad_0"), val = tensor([5, 5])]; + tensor xt_65_strides_0 = const()[name = tensor("xt_65_strides_0"), val = tensor([1])]; + tensor xt_65_dilations_0 = const()[name = tensor("xt_65_dilations_0"), val = tensor([1])]; + tensor xt_65_groups_0 = const()[name = tensor("xt_65_groups_0"), val = tensor(1)]; + tensor 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_583)[name = tensor("xt_65")]; + tensor input_585 = add(x = xt_65, y = input_577)[name = tensor("input_585")]; + tensor h_189 = linear(bias = decoder_generator_resblocks_2_adain1_2_fc_bias, weight = decoder_generator_resblocks_2_adain1_2_fc_weight, x = input_403)[name = tensor("linear_122")]; + tensor var_6302 = const()[name = tensor("op_6302"), val = tensor([1, 512, 1])]; + tensor h_191 = reshape(shape = var_6302, x = h_189)[name = tensor("h_191")]; + tensor var_6304_split_sizes_0 = const()[name = tensor("op_6304_split_sizes_0"), val = tensor([256, 256])]; + tensor var_6304_axis_0 = const()[name = tensor("op_6304_axis_0"), val = tensor(1)]; + tensor var_6304_0, tensor var_6304_1 = split(axis = var_6304_axis_0, split_sizes = var_6304_split_sizes_0, x = h_191)[name = tensor("op_6304")]; + tensor var_6306_promoted = const()[name = tensor("op_6306_promoted"), val = tensor(0x1p+0)]; + tensor var_6307 = add(x = var_6304_0, y = var_6306_promoted)[name = tensor("op_6307")]; + tensor var_6310 = instance_norm(beta = predictor_F0_1_norm2_norm_bias, epsilon = var_4706, gamma = predictor_F0_1_norm2_norm_weight, x = input_585)[name = tensor("op_6310")]; + tensor var_6311 = mul(x = var_6307, y = var_6310)[name = tensor("op_6311")]; + tensor xt_67 = add(x = var_6311, y = var_6304_1)[name = tensor("xt_67")]; + tensor var_6313 = const()[name = tensor("op_6313"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(297762240)))]; + tensor var_6316 = mul(x = decoder_generator_resblocks_2_alpha1_2, y = xt_67)[name = tensor("op_6316")]; + tensor var_6317 = sin(x = var_6316)[name = tensor("op_6317")]; + tensor var_4713_promoted_24 = const()[name = tensor("op_4713_promoted_24"), val = tensor(0x1p+1)]; + tensor var_6318 = pow(x = var_6317, y = var_4713_promoted_24)[name = tensor("op_6318")]; + tensor var_6319 = mul(x = var_6313, y = var_6318)[name = tensor("op_6319")]; + tensor input_587 = add(x = xt_67, y = var_6319)[name = tensor("input_587")]; + tensor weight_231 = const()[name = tensor("weight_231"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(297763328)))]; + tensor input_589_pad_type_0 = const()[name = tensor("input_589_pad_type_0"), val = tensor("custom")]; + tensor input_589_pad_0 = const()[name = tensor("input_589_pad_0"), val = tensor([25, 25])]; + tensor input_589_dilations_0 = const()[name = tensor("input_589_dilations_0"), val = tensor([5])]; + tensor input_589_strides_0 = const()[name = tensor("input_589_strides_0"), val = tensor([1])]; + tensor input_589_groups_0 = const()[name = tensor("input_589_groups_0"), val = tensor(1)]; + tensor input_589 = conv(bias = decoder_generator_resblocks_2_convs1_2_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_231, x = input_587)[name = tensor("input_589")]; + tensor h_193 = linear(bias = decoder_generator_resblocks_2_adain2_2_fc_bias, weight = decoder_generator_resblocks_2_adain2_2_fc_weight, x = input_403)[name = tensor("linear_123")]; + tensor var_6339 = const()[name = tensor("op_6339"), val = tensor([1, 512, 1])]; + tensor h_195 = reshape(shape = var_6339, x = h_193)[name = tensor("h_195")]; + tensor var_6341_split_sizes_0 = const()[name = tensor("op_6341_split_sizes_0"), val = tensor([256, 256])]; + tensor var_6341_axis_0 = const()[name = tensor("op_6341_axis_0"), val = tensor(1)]; + tensor var_6341_0, tensor var_6341_1 = split(axis = var_6341_axis_0, split_sizes = var_6341_split_sizes_0, x = h_195)[name = tensor("op_6341")]; + tensor var_6343_promoted = const()[name = tensor("op_6343_promoted"), val = tensor(0x1p+0)]; + tensor var_6344 = add(x = var_6341_0, y = var_6343_promoted)[name = tensor("op_6344")]; + tensor var_6347 = instance_norm(beta = predictor_F0_1_norm2_norm_bias, epsilon = var_4706, gamma = predictor_F0_1_norm2_norm_weight, x = input_589)[name = tensor("op_6347")]; + tensor var_6348 = mul(x = var_6344, y = var_6347)[name = tensor("op_6348")]; + tensor xt_69 = add(x = var_6348, y = var_6341_1)[name = tensor("xt_69")]; + tensor var_6350 = const()[name = tensor("op_6350"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(300646976)))]; + tensor var_6353 = mul(x = decoder_generator_resblocks_2_alpha2_2, y = xt_69)[name = tensor("op_6353")]; + tensor var_6354 = sin(x = var_6353)[name = tensor("op_6354")]; + tensor var_4713_promoted_25 = const()[name = tensor("op_4713_promoted_25"), val = tensor(0x1p+1)]; + tensor var_6355 = pow(x = var_6354, y = var_4713_promoted_25)[name = tensor("op_6355")]; + tensor var_6356 = mul(x = var_6350, y = var_6355)[name = tensor("op_6356")]; + tensor input_591 = add(x = xt_69, y = var_6356)[name = tensor("input_591")]; + tensor weight_235 = const()[name = tensor("weight_235"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(300648064)))]; + tensor xt_71_pad_type_0 = const()[name = tensor("xt_71_pad_type_0"), val = tensor("custom")]; + tensor xt_71_pad_0 = const()[name = tensor("xt_71_pad_0"), val = tensor([5, 5])]; + tensor xt_71_strides_0 = const()[name = tensor("xt_71_strides_0"), val = tensor([1])]; + tensor xt_71_dilations_0 = const()[name = tensor("xt_71_dilations_0"), val = tensor([1])]; + tensor xt_71_groups_0 = const()[name = tensor("xt_71_groups_0"), val = tensor(1)]; + tensor 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_591)[name = tensor("xt_71")]; + tensor var_6369 = add(x = xt_71, y = input_585)[name = tensor("op_6369")]; + tensor xs_5 = add(x = xs_3, y = var_6369)[name = tensor("xs_5")]; + tensor _inversed_input_593_y_0 = const()[name = tensor("_inversed_input_593_y_0"), val = tensor(0x1.555556p-2)]; + tensor _inversed_input_593 = mul(x = xs_5, y = _inversed_input_593_y_0)[name = tensor("_inversed_input_593")]; + tensor input_619 = leaky_relu(alpha = var_4705, x = _inversed_input_593)[name = tensor("input_619")]; + tensor input_595_pad_type_0 = const()[name = tensor("input_595_pad_type_0"), val = tensor("valid")]; + tensor input_595_strides_0 = const()[name = tensor("input_595_strides_0"), val = tensor([1])]; + tensor input_595_pad_0 = const()[name = tensor("input_595_pad_0"), val = tensor([0, 0])]; + tensor input_595_dilations_0 = const()[name = tensor("input_595_dilations_0"), val = tensor([1])]; + tensor input_595_groups_0 = const()[name = tensor("input_595_groups_0"), val = tensor(1)]; + tensor input_595 = conv(bias = decoder_generator_noise_convs_1_bias, dilations = input_595_dilations_0, groups = input_595_groups_0, pad = input_595_pad_0, pad_type = input_595_pad_type_0, strides = input_595_strides_0, weight = decoder_generator_noise_convs_1_weight, x = input_497)[name = tensor("input_595")]; + tensor 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_403)[name = tensor("linear_124")]; + tensor var_6424 = const()[name = tensor("op_6424"), val = tensor([1, 256, 1])]; + tensor h_199 = reshape(shape = var_6424, x = h_197)[name = tensor("h_199")]; + tensor var_6426_split_sizes_0 = const()[name = tensor("op_6426_split_sizes_0"), val = tensor([128, 128])]; + tensor var_6426_axis_0 = const()[name = tensor("op_6426_axis_0"), val = tensor(1)]; + tensor var_6426_0, tensor var_6426_1 = split(axis = var_6426_axis_0, split_sizes = var_6426_split_sizes_0, x = h_199)[name = tensor("op_6426")]; + tensor var_6428_promoted = const()[name = tensor("op_6428_promoted"), val = tensor(0x1p+0)]; + tensor var_6429 = add(x = var_6426_0, y = var_6428_promoted)[name = tensor("op_6429")]; + tensor var_6432 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_4706, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_595)[name = tensor("op_6432")]; + tensor var_6433 = mul(x = var_6429, y = var_6432)[name = tensor("op_6433")]; + tensor xt_73 = add(x = var_6433, y = var_6426_1)[name = tensor("xt_73")]; + tensor var_6435 = const()[name = tensor("op_6435"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(303531712)))]; + tensor var_6438 = mul(x = decoder_generator_noise_res_1_alpha1_0, y = xt_73)[name = tensor("op_6438")]; + tensor var_6439 = sin(x = var_6438)[name = tensor("op_6439")]; + tensor var_4713_promoted_26 = const()[name = tensor("op_4713_promoted_26"), val = tensor(0x1p+1)]; + tensor var_6440 = pow(x = var_6439, y = var_4713_promoted_26)[name = tensor("op_6440")]; + tensor var_6441 = mul(x = var_6435, y = var_6440)[name = tensor("op_6441")]; + tensor input_597 = add(x = xt_73, y = var_6441)[name = tensor("input_597")]; + tensor weight_241 = const()[name = tensor("weight_241"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(303532288)))]; + tensor input_599_pad_type_0 = const()[name = tensor("input_599_pad_type_0"), val = tensor("custom")]; + tensor input_599_pad_0 = const()[name = tensor("input_599_pad_0"), val = tensor([5, 5])]; + tensor input_599_strides_0 = const()[name = tensor("input_599_strides_0"), val = tensor([1])]; + tensor input_599_dilations_0 = const()[name = tensor("input_599_dilations_0"), val = tensor([1])]; + tensor input_599_groups_0 = const()[name = tensor("input_599_groups_0"), val = tensor(1)]; + tensor input_599 = conv(bias = decoder_generator_noise_res_1_convs1_0_bias, dilations = input_599_dilations_0, groups = input_599_groups_0, pad = input_599_pad_0, pad_type = input_599_pad_type_0, strides = input_599_strides_0, weight = weight_241, x = input_597)[name = tensor("input_599")]; + tensor 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_403)[name = tensor("linear_125")]; + tensor var_6461 = const()[name = tensor("op_6461"), val = tensor([1, 256, 1])]; + tensor h_203 = reshape(shape = var_6461, x = h_201)[name = tensor("h_203")]; + tensor var_6463_split_sizes_0 = const()[name = tensor("op_6463_split_sizes_0"), val = tensor([128, 128])]; + tensor var_6463_axis_0 = const()[name = tensor("op_6463_axis_0"), val = tensor(1)]; + tensor var_6463_0, tensor var_6463_1 = split(axis = var_6463_axis_0, split_sizes = var_6463_split_sizes_0, x = h_203)[name = tensor("op_6463")]; + tensor var_6465_promoted = const()[name = tensor("op_6465_promoted"), val = tensor(0x1p+0)]; + tensor var_6466 = add(x = var_6463_0, y = var_6465_promoted)[name = tensor("op_6466")]; + tensor var_6469 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_4706, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_599)[name = tensor("op_6469")]; + tensor var_6470 = mul(x = var_6466, y = var_6469)[name = tensor("op_6470")]; + tensor xt_75 = add(x = var_6470, y = var_6463_1)[name = tensor("xt_75")]; + tensor var_6472 = const()[name = tensor("op_6472"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304253248)))]; + tensor var_6475 = mul(x = decoder_generator_noise_res_1_alpha2_0, y = xt_75)[name = tensor("op_6475")]; + tensor var_6476 = sin(x = var_6475)[name = tensor("op_6476")]; + tensor var_4713_promoted_27 = const()[name = tensor("op_4713_promoted_27"), val = tensor(0x1p+1)]; + tensor var_6477 = pow(x = var_6476, y = var_4713_promoted_27)[name = tensor("op_6477")]; + tensor var_6478 = mul(x = var_6472, y = var_6477)[name = tensor("op_6478")]; + tensor input_601 = add(x = xt_75, y = var_6478)[name = tensor("input_601")]; + tensor weight_245 = const()[name = tensor("weight_245"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304253824)))]; + tensor xt_77_pad_type_0 = const()[name = tensor("xt_77_pad_type_0"), val = tensor("custom")]; + tensor xt_77_pad_0 = const()[name = tensor("xt_77_pad_0"), val = tensor([5, 5])]; + tensor xt_77_strides_0 = const()[name = tensor("xt_77_strides_0"), val = tensor([1])]; + tensor xt_77_dilations_0 = const()[name = tensor("xt_77_dilations_0"), val = tensor([1])]; + tensor xt_77_groups_0 = const()[name = tensor("xt_77_groups_0"), val = tensor(1)]; + tensor 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_601)[name = tensor("xt_77")]; + tensor input_603 = add(x = xt_77, y = input_595)[name = tensor("input_603")]; + tensor 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_403)[name = tensor("linear_126")]; + tensor var_6499 = const()[name = tensor("op_6499"), val = tensor([1, 256, 1])]; + tensor h_207 = reshape(shape = var_6499, x = h_205)[name = tensor("h_207")]; + tensor var_6501_split_sizes_0 = const()[name = tensor("op_6501_split_sizes_0"), val = tensor([128, 128])]; + tensor var_6501_axis_0 = const()[name = tensor("op_6501_axis_0"), val = tensor(1)]; + tensor var_6501_0, tensor var_6501_1 = split(axis = var_6501_axis_0, split_sizes = var_6501_split_sizes_0, x = h_207)[name = tensor("op_6501")]; + tensor var_6503_promoted = const()[name = tensor("op_6503_promoted"), val = tensor(0x1p+0)]; + tensor var_6504 = add(x = var_6501_0, y = var_6503_promoted)[name = tensor("op_6504")]; + tensor var_6507 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_4706, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_603)[name = tensor("op_6507")]; + tensor var_6508 = mul(x = var_6504, y = var_6507)[name = tensor("op_6508")]; + tensor xt_79 = add(x = var_6508, y = var_6501_1)[name = tensor("xt_79")]; + tensor var_6510 = const()[name = tensor("op_6510"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304974784)))]; + tensor var_6513 = mul(x = decoder_generator_noise_res_1_alpha1_1, y = xt_79)[name = tensor("op_6513")]; + tensor var_6514 = sin(x = var_6513)[name = tensor("op_6514")]; + tensor var_4713_promoted_28 = const()[name = tensor("op_4713_promoted_28"), val = tensor(0x1p+1)]; + tensor var_6515 = pow(x = var_6514, y = var_4713_promoted_28)[name = tensor("op_6515")]; + tensor var_6516 = mul(x = var_6510, y = var_6515)[name = tensor("op_6516")]; + tensor input_605 = add(x = xt_79, y = var_6516)[name = tensor("input_605")]; + tensor weight_249 = const()[name = tensor("weight_249"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304975360)))]; + tensor input_607_pad_type_0 = const()[name = tensor("input_607_pad_type_0"), val = tensor("custom")]; + tensor input_607_pad_0 = const()[name = tensor("input_607_pad_0"), val = tensor([15, 15])]; + tensor input_607_dilations_0 = const()[name = tensor("input_607_dilations_0"), val = tensor([3])]; + tensor input_607_strides_0 = const()[name = tensor("input_607_strides_0"), val = tensor([1])]; + tensor input_607_groups_0 = const()[name = tensor("input_607_groups_0"), val = tensor(1)]; + tensor input_607 = conv(bias = decoder_generator_noise_res_1_convs1_1_bias, dilations = input_607_dilations_0, groups = input_607_groups_0, pad = input_607_pad_0, pad_type = input_607_pad_type_0, strides = input_607_strides_0, weight = weight_249, x = input_605)[name = tensor("input_607")]; + tensor 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_403)[name = tensor("linear_127")]; + tensor var_6536 = const()[name = tensor("op_6536"), val = tensor([1, 256, 1])]; + tensor h_211 = reshape(shape = var_6536, x = h_209)[name = tensor("h_211")]; + tensor var_6538_split_sizes_0 = const()[name = tensor("op_6538_split_sizes_0"), val = tensor([128, 128])]; + tensor var_6538_axis_0 = const()[name = tensor("op_6538_axis_0"), val = tensor(1)]; + tensor var_6538_0, tensor var_6538_1 = split(axis = var_6538_axis_0, split_sizes = var_6538_split_sizes_0, x = h_211)[name = tensor("op_6538")]; + tensor var_6540_promoted = const()[name = tensor("op_6540_promoted"), val = tensor(0x1p+0)]; + tensor var_6541 = add(x = var_6538_0, y = var_6540_promoted)[name = tensor("op_6541")]; + tensor var_6544 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_4706, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_607)[name = tensor("op_6544")]; + tensor var_6545 = mul(x = var_6541, y = var_6544)[name = tensor("op_6545")]; + tensor xt_81 = add(x = var_6545, y = var_6538_1)[name = tensor("xt_81")]; + tensor var_6547 = const()[name = tensor("op_6547"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(305696320)))]; + tensor var_6550 = mul(x = decoder_generator_noise_res_1_alpha2_1, y = xt_81)[name = tensor("op_6550")]; + tensor var_6551 = sin(x = var_6550)[name = tensor("op_6551")]; + tensor var_4713_promoted_29 = const()[name = tensor("op_4713_promoted_29"), val = tensor(0x1p+1)]; + tensor var_6552 = pow(x = var_6551, y = var_4713_promoted_29)[name = tensor("op_6552")]; + tensor var_6553 = mul(x = var_6547, y = var_6552)[name = tensor("op_6553")]; + tensor input_609 = add(x = xt_81, y = var_6553)[name = tensor("input_609")]; + tensor weight_253 = const()[name = tensor("weight_253"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(305696896)))]; + tensor xt_83_pad_type_0 = const()[name = tensor("xt_83_pad_type_0"), val = tensor("custom")]; + tensor xt_83_pad_0 = const()[name = tensor("xt_83_pad_0"), val = tensor([5, 5])]; + tensor xt_83_strides_0 = const()[name = tensor("xt_83_strides_0"), val = tensor([1])]; + tensor xt_83_dilations_0 = const()[name = tensor("xt_83_dilations_0"), val = tensor([1])]; + tensor xt_83_groups_0 = const()[name = tensor("xt_83_groups_0"), val = tensor(1)]; + tensor 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_609)[name = tensor("xt_83")]; + tensor input_611 = add(x = xt_83, y = input_603)[name = tensor("input_611")]; + tensor 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_403)[name = tensor("linear_128")]; + tensor var_6574 = const()[name = tensor("op_6574"), val = tensor([1, 256, 1])]; + tensor h_215 = reshape(shape = var_6574, x = h_213)[name = tensor("h_215")]; + tensor var_6576_split_sizes_0 = const()[name = tensor("op_6576_split_sizes_0"), val = tensor([128, 128])]; + tensor var_6576_axis_0 = const()[name = tensor("op_6576_axis_0"), val = tensor(1)]; + tensor var_6576_0, tensor var_6576_1 = split(axis = var_6576_axis_0, split_sizes = var_6576_split_sizes_0, x = h_215)[name = tensor("op_6576")]; + tensor var_6578_promoted = const()[name = tensor("op_6578_promoted"), val = tensor(0x1p+0)]; + tensor var_6579 = add(x = var_6576_0, y = var_6578_promoted)[name = tensor("op_6579")]; + tensor var_6582 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_4706, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_611)[name = tensor("op_6582")]; + tensor var_6583 = mul(x = var_6579, y = var_6582)[name = tensor("op_6583")]; + tensor xt_85 = add(x = var_6583, y = var_6576_1)[name = tensor("xt_85")]; + tensor var_6585 = const()[name = tensor("op_6585"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(306417856)))]; + tensor var_6588 = mul(x = decoder_generator_noise_res_1_alpha1_2, y = xt_85)[name = tensor("op_6588")]; + tensor var_6589 = sin(x = var_6588)[name = tensor("op_6589")]; + tensor var_4713_promoted_30 = const()[name = tensor("op_4713_promoted_30"), val = tensor(0x1p+1)]; + tensor var_6590 = pow(x = var_6589, y = var_4713_promoted_30)[name = tensor("op_6590")]; + tensor var_6591 = mul(x = var_6585, y = var_6590)[name = tensor("op_6591")]; + tensor input_613 = add(x = xt_85, y = var_6591)[name = tensor("input_613")]; + tensor weight_257 = const()[name = tensor("weight_257"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(306418432)))]; + tensor input_615_pad_type_0 = const()[name = tensor("input_615_pad_type_0"), val = tensor("custom")]; + tensor input_615_pad_0 = const()[name = tensor("input_615_pad_0"), val = tensor([25, 25])]; + tensor input_615_dilations_0 = const()[name = tensor("input_615_dilations_0"), val = tensor([5])]; + tensor input_615_strides_0 = const()[name = tensor("input_615_strides_0"), val = tensor([1])]; + tensor input_615_groups_0 = const()[name = tensor("input_615_groups_0"), val = tensor(1)]; + tensor input_615 = conv(bias = decoder_generator_noise_res_1_convs1_2_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_257, x = input_613)[name = tensor("input_615")]; + tensor 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_403)[name = tensor("linear_129")]; + tensor var_6611 = const()[name = tensor("op_6611"), val = tensor([1, 256, 1])]; + tensor h_219 = reshape(shape = var_6611, x = h_217)[name = tensor("h_219")]; + tensor var_6613_split_sizes_0 = const()[name = tensor("op_6613_split_sizes_0"), val = tensor([128, 128])]; + tensor var_6613_axis_0 = const()[name = tensor("op_6613_axis_0"), val = tensor(1)]; + tensor var_6613_0, tensor var_6613_1 = split(axis = var_6613_axis_0, split_sizes = var_6613_split_sizes_0, x = h_219)[name = tensor("op_6613")]; + tensor var_6615_promoted = const()[name = tensor("op_6615_promoted"), val = tensor(0x1p+0)]; + tensor var_6616 = add(x = var_6613_0, y = var_6615_promoted)[name = tensor("op_6616")]; + tensor var_6619 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_4706, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_615)[name = tensor("op_6619")]; + tensor var_6620 = mul(x = var_6616, y = var_6619)[name = tensor("op_6620")]; + tensor xt_87 = add(x = var_6620, y = var_6613_1)[name = tensor("xt_87")]; + tensor var_6622 = const()[name = tensor("op_6622"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(307139392)))]; + tensor var_6625 = mul(x = decoder_generator_noise_res_1_alpha2_2, y = xt_87)[name = tensor("op_6625")]; + tensor var_6626 = sin(x = var_6625)[name = tensor("op_6626")]; + tensor var_4713_promoted_31 = const()[name = tensor("op_4713_promoted_31"), val = tensor(0x1p+1)]; + tensor var_6627 = pow(x = var_6626, y = var_4713_promoted_31)[name = tensor("op_6627")]; + tensor var_6628 = mul(x = var_6622, y = var_6627)[name = tensor("op_6628")]; + tensor input_617 = add(x = xt_87, y = var_6628)[name = tensor("input_617")]; + tensor weight_261 = const()[name = tensor("weight_261"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(307139968)))]; + tensor xt_89_pad_type_0 = const()[name = tensor("xt_89_pad_type_0"), val = tensor("custom")]; + tensor xt_89_pad_0 = const()[name = tensor("xt_89_pad_0"), val = tensor([5, 5])]; + tensor xt_89_strides_0 = const()[name = tensor("xt_89_strides_0"), val = tensor([1])]; + tensor xt_89_dilations_0 = const()[name = tensor("xt_89_dilations_0"), val = tensor([1])]; + tensor xt_89_groups_0 = const()[name = tensor("xt_89_groups_0"), val = tensor(1)]; + tensor 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_617)[name = tensor("xt_89")]; + tensor x_source = add(x = xt_89, y = input_611)[name = tensor("x_source")]; + tensor var_6647 = const()[name = tensor("op_6647"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(307860928)))]; + tensor input_621_pad_type_0 = const()[name = tensor("input_621_pad_type_0"), val = tensor("custom")]; + tensor input_621_pad_0 = const()[name = tensor("input_621_pad_0"), val = tensor([3, 3])]; + tensor input_621_strides_0 = const()[name = tensor("input_621_strides_0"), val = tensor([6])]; + tensor input_621_dilations_0 = const()[name = tensor("input_621_dilations_0"), val = tensor([1])]; + tensor input_621_groups_0 = const()[name = tensor("input_621_groups_0"), val = tensor(1)]; + tensor input_621_has_output_shape_output_shape_0 = const()[name = tensor("input_621_has_output_shape_output_shape_0"), val = tensor([1, 128, 74520])]; + tensor input_621_has_output_shape = conv_transpose(bias = decoder_generator_ups_1_bias, dilations = input_621_dilations_0, groups = input_621_groups_0, output_shape = input_621_has_output_shape_output_shape_0, pad = input_621_pad_0, pad_type = input_621_pad_type_0, strides = input_621_strides_0, weight = var_6647, x = input_619)[name = tensor("input_621_has_output_shape")]; + tensor const_464 = const()[name = tensor("const_464"), val = tensor(0x0p+0)]; + tensor x_263_pad_0 = const()[name = tensor("x_263_pad_0"), val = tensor([0, 0, 0, 0, 1, 0])]; + tensor x_263_mode_0 = const()[name = tensor("x_263_mode_0"), val = tensor("reflect")]; + tensor x_263 = pad(constant_val = const_464, mode = x_263_mode_0, pad = x_263_pad_0, x = input_621_has_output_shape)[name = tensor("x_263")]; + tensor input_623 = add(x = x_263, y = x_source)[name = tensor("input_623")]; + tensor h_221 = linear(bias = decoder_generator_resblocks_3_adain1_0_fc_bias, weight = decoder_generator_resblocks_3_adain1_0_fc_weight, x = input_403)[name = tensor("linear_130")]; + tensor var_6699 = const()[name = tensor("op_6699"), val = tensor([1, 256, 1])]; + tensor h_223 = reshape(shape = var_6699, x = h_221)[name = tensor("h_223")]; + tensor var_6701_split_sizes_0 = const()[name = tensor("op_6701_split_sizes_0"), val = tensor([128, 128])]; + tensor var_6701_axis_0 = const()[name = tensor("op_6701_axis_0"), val = tensor(1)]; + tensor var_6701_0, tensor var_6701_1 = split(axis = var_6701_axis_0, split_sizes = var_6701_split_sizes_0, x = h_223)[name = tensor("op_6701")]; + tensor var_6703_promoted = const()[name = tensor("op_6703_promoted"), val = tensor(0x1p+0)]; + tensor var_6704 = add(x = var_6701_0, y = var_6703_promoted)[name = tensor("op_6704")]; + tensor var_6707 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_4706, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_623)[name = tensor("op_6707")]; + tensor var_6708 = mul(x = var_6704, y = var_6707)[name = tensor("op_6708")]; + tensor xt_91 = add(x = var_6708, y = var_6701_1)[name = tensor("xt_91")]; + tensor var_6710 = const()[name = tensor("op_6710"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309433856)))]; + tensor var_6713 = mul(x = decoder_generator_resblocks_3_alpha1_0, y = xt_91)[name = tensor("op_6713")]; + tensor var_6714 = sin(x = var_6713)[name = tensor("op_6714")]; + tensor var_4713_promoted_32 = const()[name = tensor("op_4713_promoted_32"), val = tensor(0x1p+1)]; + tensor var_6715 = pow(x = var_6714, y = var_4713_promoted_32)[name = tensor("op_6715")]; + tensor var_6716 = mul(x = var_6710, y = var_6715)[name = tensor("op_6716")]; + tensor input_625 = add(x = xt_91, y = var_6716)[name = tensor("input_625")]; + tensor weight_265 = const()[name = tensor("weight_265"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309434432)))]; + tensor input_627_pad_type_0 = const()[name = tensor("input_627_pad_type_0"), val = tensor("custom")]; + tensor input_627_pad_0 = const()[name = tensor("input_627_pad_0"), val = tensor([1, 1])]; + tensor input_627_strides_0 = const()[name = tensor("input_627_strides_0"), val = tensor([1])]; + tensor input_627_dilations_0 = const()[name = tensor("input_627_dilations_0"), val = tensor([1])]; + tensor input_627_groups_0 = const()[name = tensor("input_627_groups_0"), val = tensor(1)]; + tensor input_627 = conv(bias = decoder_generator_resblocks_3_convs1_0_bias, dilations = input_627_dilations_0, groups = input_627_groups_0, pad = input_627_pad_0, pad_type = input_627_pad_type_0, strides = input_627_strides_0, weight = weight_265, x = input_625)[name = tensor("input_627")]; + tensor h_225 = linear(bias = decoder_generator_resblocks_3_adain2_0_fc_bias, weight = decoder_generator_resblocks_3_adain2_0_fc_weight, x = input_403)[name = tensor("linear_131")]; + tensor var_6736 = const()[name = tensor("op_6736"), val = tensor([1, 256, 1])]; + tensor h_227 = reshape(shape = var_6736, x = h_225)[name = tensor("h_227")]; + tensor var_6738_split_sizes_0 = const()[name = tensor("op_6738_split_sizes_0"), val = tensor([128, 128])]; + tensor var_6738_axis_0 = const()[name = tensor("op_6738_axis_0"), val = tensor(1)]; + tensor var_6738_0, tensor var_6738_1 = split(axis = var_6738_axis_0, split_sizes = var_6738_split_sizes_0, x = h_227)[name = tensor("op_6738")]; + tensor var_6740_promoted = const()[name = tensor("op_6740_promoted"), val = tensor(0x1p+0)]; + tensor var_6741 = add(x = var_6738_0, y = var_6740_promoted)[name = tensor("op_6741")]; + tensor var_6744 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_4706, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_627)[name = tensor("op_6744")]; + tensor var_6745 = mul(x = var_6741, y = var_6744)[name = tensor("op_6745")]; + tensor xt_93 = add(x = var_6745, y = var_6738_1)[name = tensor("xt_93")]; + tensor var_6747 = const()[name = tensor("op_6747"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309631104)))]; + tensor var_6750 = mul(x = decoder_generator_resblocks_3_alpha2_0, y = xt_93)[name = tensor("op_6750")]; + tensor var_6751 = sin(x = var_6750)[name = tensor("op_6751")]; + tensor var_4713_promoted_33 = const()[name = tensor("op_4713_promoted_33"), val = tensor(0x1p+1)]; + tensor var_6752 = pow(x = var_6751, y = var_4713_promoted_33)[name = tensor("op_6752")]; + tensor var_6753 = mul(x = var_6747, y = var_6752)[name = tensor("op_6753")]; + tensor input_629 = add(x = xt_93, y = var_6753)[name = tensor("input_629")]; + tensor weight_269 = const()[name = tensor("weight_269"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309631680)))]; + tensor xt_95_pad_type_0 = const()[name = tensor("xt_95_pad_type_0"), val = tensor("custom")]; + tensor xt_95_pad_0 = const()[name = tensor("xt_95_pad_0"), val = tensor([1, 1])]; + tensor xt_95_strides_0 = const()[name = tensor("xt_95_strides_0"), val = tensor([1])]; + tensor xt_95_dilations_0 = const()[name = tensor("xt_95_dilations_0"), val = tensor([1])]; + tensor xt_95_groups_0 = const()[name = tensor("xt_95_groups_0"), val = tensor(1)]; + tensor 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_629)[name = tensor("xt_95")]; + tensor input_631 = add(x = xt_95, y = input_623)[name = tensor("input_631")]; + tensor h_229 = linear(bias = decoder_generator_resblocks_3_adain1_1_fc_bias, weight = decoder_generator_resblocks_3_adain1_1_fc_weight, x = input_403)[name = tensor("linear_132")]; + tensor var_6774 = const()[name = tensor("op_6774"), val = tensor([1, 256, 1])]; + tensor h_231 = reshape(shape = var_6774, x = h_229)[name = tensor("h_231")]; + tensor var_6776_split_sizes_0 = const()[name = tensor("op_6776_split_sizes_0"), val = tensor([128, 128])]; + tensor var_6776_axis_0 = const()[name = tensor("op_6776_axis_0"), val = tensor(1)]; + tensor var_6776_0, tensor var_6776_1 = split(axis = var_6776_axis_0, split_sizes = var_6776_split_sizes_0, x = h_231)[name = tensor("op_6776")]; + tensor var_6778_promoted = const()[name = tensor("op_6778_promoted"), val = tensor(0x1p+0)]; + tensor var_6779 = add(x = var_6776_0, y = var_6778_promoted)[name = tensor("op_6779")]; + tensor var_6782 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_4706, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_631)[name = tensor("op_6782")]; + tensor var_6783 = mul(x = var_6779, y = var_6782)[name = tensor("op_6783")]; + tensor xt_97 = add(x = var_6783, y = var_6776_1)[name = tensor("xt_97")]; + tensor var_6785 = const()[name = tensor("op_6785"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309828352)))]; + tensor var_6788 = mul(x = decoder_generator_resblocks_3_alpha1_1, y = xt_97)[name = tensor("op_6788")]; + tensor var_6789 = sin(x = var_6788)[name = tensor("op_6789")]; + tensor var_4713_promoted_34 = const()[name = tensor("op_4713_promoted_34"), val = tensor(0x1p+1)]; + tensor var_6790 = pow(x = var_6789, y = var_4713_promoted_34)[name = tensor("op_6790")]; + tensor var_6791 = mul(x = var_6785, y = var_6790)[name = tensor("op_6791")]; + tensor input_633 = add(x = xt_97, y = var_6791)[name = tensor("input_633")]; + tensor weight_273 = const()[name = tensor("weight_273"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309828928)))]; + tensor input_635_pad_type_0 = const()[name = tensor("input_635_pad_type_0"), val = tensor("custom")]; + tensor input_635_pad_0 = const()[name = tensor("input_635_pad_0"), val = tensor([3, 3])]; + tensor input_635_dilations_0 = const()[name = tensor("input_635_dilations_0"), val = tensor([3])]; + tensor input_635_strides_0 = const()[name = tensor("input_635_strides_0"), val = tensor([1])]; + tensor input_635_groups_0 = const()[name = tensor("input_635_groups_0"), val = tensor(1)]; + tensor input_635 = conv(bias = decoder_generator_resblocks_3_convs1_1_bias, dilations = input_635_dilations_0, groups = input_635_groups_0, pad = input_635_pad_0, pad_type = input_635_pad_type_0, strides = input_635_strides_0, weight = weight_273, x = input_633)[name = tensor("input_635")]; + tensor h_233 = linear(bias = decoder_generator_resblocks_3_adain2_1_fc_bias, weight = decoder_generator_resblocks_3_adain2_1_fc_weight, x = input_403)[name = tensor("linear_133")]; + tensor var_6811 = const()[name = tensor("op_6811"), val = tensor([1, 256, 1])]; + tensor h_235 = reshape(shape = var_6811, x = h_233)[name = tensor("h_235")]; + tensor var_6813_split_sizes_0 = const()[name = tensor("op_6813_split_sizes_0"), val = tensor([128, 128])]; + tensor var_6813_axis_0 = const()[name = tensor("op_6813_axis_0"), val = tensor(1)]; + tensor var_6813_0, tensor var_6813_1 = split(axis = var_6813_axis_0, split_sizes = var_6813_split_sizes_0, x = h_235)[name = tensor("op_6813")]; + tensor var_6815_promoted = const()[name = tensor("op_6815_promoted"), val = tensor(0x1p+0)]; + tensor var_6816 = add(x = var_6813_0, y = var_6815_promoted)[name = tensor("op_6816")]; + tensor var_6819 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_4706, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_635)[name = tensor("op_6819")]; + tensor var_6820 = mul(x = var_6816, y = var_6819)[name = tensor("op_6820")]; + tensor xt_99 = add(x = var_6820, y = var_6813_1)[name = tensor("xt_99")]; + tensor var_6822 = const()[name = tensor("op_6822"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(310025600)))]; + tensor var_6825 = mul(x = decoder_generator_resblocks_3_alpha2_1, y = xt_99)[name = tensor("op_6825")]; + tensor var_6826 = sin(x = var_6825)[name = tensor("op_6826")]; + tensor var_4713_promoted_35 = const()[name = tensor("op_4713_promoted_35"), val = tensor(0x1p+1)]; + tensor var_6827 = pow(x = var_6826, y = var_4713_promoted_35)[name = tensor("op_6827")]; + tensor var_6828 = mul(x = var_6822, y = var_6827)[name = tensor("op_6828")]; + tensor input_637 = add(x = xt_99, y = var_6828)[name = tensor("input_637")]; + tensor weight_277 = const()[name = tensor("weight_277"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(310026176)))]; + tensor xt_101_pad_type_0 = const()[name = tensor("xt_101_pad_type_0"), val = tensor("custom")]; + tensor xt_101_pad_0 = const()[name = tensor("xt_101_pad_0"), val = tensor([1, 1])]; + tensor xt_101_strides_0 = const()[name = tensor("xt_101_strides_0"), val = tensor([1])]; + tensor xt_101_dilations_0 = const()[name = tensor("xt_101_dilations_0"), val = tensor([1])]; + tensor xt_101_groups_0 = const()[name = tensor("xt_101_groups_0"), val = tensor(1)]; + tensor 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_637)[name = tensor("xt_101")]; + tensor input_639 = add(x = xt_101, y = input_631)[name = tensor("input_639")]; + tensor h_237 = linear(bias = decoder_generator_resblocks_3_adain1_2_fc_bias, weight = decoder_generator_resblocks_3_adain1_2_fc_weight, x = input_403)[name = tensor("linear_134")]; + tensor var_6849 = const()[name = tensor("op_6849"), val = tensor([1, 256, 1])]; + tensor h_239 = reshape(shape = var_6849, x = h_237)[name = tensor("h_239")]; + tensor var_6851_split_sizes_0 = const()[name = tensor("op_6851_split_sizes_0"), val = tensor([128, 128])]; + tensor var_6851_axis_0 = const()[name = tensor("op_6851_axis_0"), val = tensor(1)]; + tensor var_6851_0, tensor var_6851_1 = split(axis = var_6851_axis_0, split_sizes = var_6851_split_sizes_0, x = h_239)[name = tensor("op_6851")]; + tensor var_6853_promoted = const()[name = tensor("op_6853_promoted"), val = tensor(0x1p+0)]; + tensor var_6854 = add(x = var_6851_0, y = var_6853_promoted)[name = tensor("op_6854")]; + tensor var_6857 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_4706, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_639)[name = tensor("op_6857")]; + tensor var_6858 = mul(x = var_6854, y = var_6857)[name = tensor("op_6858")]; + tensor xt_103 = add(x = var_6858, y = var_6851_1)[name = tensor("xt_103")]; + tensor var_6860 = const()[name = tensor("op_6860"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(310222848)))]; + tensor var_6863 = mul(x = decoder_generator_resblocks_3_alpha1_2, y = xt_103)[name = tensor("op_6863")]; + tensor var_6864 = sin(x = var_6863)[name = tensor("op_6864")]; + tensor var_4713_promoted_36 = const()[name = tensor("op_4713_promoted_36"), val = tensor(0x1p+1)]; + tensor var_6865 = pow(x = var_6864, y = var_4713_promoted_36)[name = tensor("op_6865")]; + tensor var_6866 = mul(x = var_6860, y = var_6865)[name = tensor("op_6866")]; + tensor input_641 = add(x = xt_103, y = var_6866)[name = tensor("input_641")]; + tensor weight_281 = const()[name = tensor("weight_281"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(310223424)))]; + tensor input_643_pad_type_0 = const()[name = tensor("input_643_pad_type_0"), val = tensor("custom")]; + tensor input_643_pad_0 = const()[name = tensor("input_643_pad_0"), val = tensor([5, 5])]; + tensor input_643_dilations_0 = const()[name = tensor("input_643_dilations_0"), val = tensor([5])]; + tensor input_643_strides_0 = const()[name = tensor("input_643_strides_0"), val = tensor([1])]; + tensor input_643_groups_0 = const()[name = tensor("input_643_groups_0"), val = tensor(1)]; + tensor input_643 = conv(bias = decoder_generator_resblocks_3_convs1_2_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_281, x = input_641)[name = tensor("input_643")]; + tensor h_241 = linear(bias = decoder_generator_resblocks_3_adain2_2_fc_bias, weight = decoder_generator_resblocks_3_adain2_2_fc_weight, x = input_403)[name = tensor("linear_135")]; + tensor var_6886 = const()[name = tensor("op_6886"), val = tensor([1, 256, 1])]; + tensor h_243 = reshape(shape = var_6886, x = h_241)[name = tensor("h_243")]; + tensor var_6888_split_sizes_0 = const()[name = tensor("op_6888_split_sizes_0"), val = tensor([128, 128])]; + tensor var_6888_axis_0 = const()[name = tensor("op_6888_axis_0"), val = tensor(1)]; + tensor var_6888_0, tensor var_6888_1 = split(axis = var_6888_axis_0, split_sizes = var_6888_split_sizes_0, x = h_243)[name = tensor("op_6888")]; + tensor var_6890_promoted = const()[name = tensor("op_6890_promoted"), val = tensor(0x1p+0)]; + tensor var_6891 = add(x = var_6888_0, y = var_6890_promoted)[name = tensor("op_6891")]; + tensor var_6894 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_4706, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_643)[name = tensor("op_6894")]; + tensor var_6895 = mul(x = var_6891, y = var_6894)[name = tensor("op_6895")]; + tensor xt_105 = add(x = var_6895, y = var_6888_1)[name = tensor("xt_105")]; + tensor var_6897 = const()[name = tensor("op_6897"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(310420096)))]; + tensor var_6900 = mul(x = decoder_generator_resblocks_3_alpha2_2, y = xt_105)[name = tensor("op_6900")]; + tensor var_6901 = sin(x = var_6900)[name = tensor("op_6901")]; + tensor var_4713_promoted_37 = const()[name = tensor("op_4713_promoted_37"), val = tensor(0x1p+1)]; + tensor var_6902 = pow(x = var_6901, y = var_4713_promoted_37)[name = tensor("op_6902")]; + tensor var_6903 = mul(x = var_6897, y = var_6902)[name = tensor("op_6903")]; + tensor input_645 = add(x = xt_105, y = var_6903)[name = tensor("input_645")]; + tensor weight_285 = const()[name = tensor("weight_285"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(310420672)))]; + tensor xt_107_pad_type_0 = const()[name = tensor("xt_107_pad_type_0"), val = tensor("custom")]; + tensor xt_107_pad_0 = const()[name = tensor("xt_107_pad_0"), val = tensor([1, 1])]; + tensor xt_107_strides_0 = const()[name = tensor("xt_107_strides_0"), val = tensor([1])]; + tensor xt_107_dilations_0 = const()[name = tensor("xt_107_dilations_0"), val = tensor([1])]; + tensor xt_107_groups_0 = const()[name = tensor("xt_107_groups_0"), val = tensor(1)]; + tensor 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_645)[name = tensor("xt_107")]; + tensor xs_7 = add(x = xt_107, y = input_639)[name = tensor("xs_7")]; + tensor h_245 = linear(bias = decoder_generator_resblocks_4_adain1_0_fc_bias, weight = decoder_generator_resblocks_4_adain1_0_fc_weight, x = input_403)[name = tensor("linear_136")]; + tensor var_6960 = const()[name = tensor("op_6960"), val = tensor([1, 256, 1])]; + tensor h_247 = reshape(shape = var_6960, x = h_245)[name = tensor("h_247")]; + tensor var_6962_split_sizes_0 = const()[name = tensor("op_6962_split_sizes_0"), val = tensor([128, 128])]; + tensor var_6962_axis_0 = const()[name = tensor("op_6962_axis_0"), val = tensor(1)]; + tensor var_6962_0, tensor var_6962_1 = split(axis = var_6962_axis_0, split_sizes = var_6962_split_sizes_0, x = h_247)[name = tensor("op_6962")]; + tensor var_6964_promoted = const()[name = tensor("op_6964_promoted"), val = tensor(0x1p+0)]; + tensor var_6965 = add(x = var_6962_0, y = var_6964_promoted)[name = tensor("op_6965")]; + tensor var_6969 = mul(x = var_6965, y = var_6707)[name = tensor("op_6969")]; + tensor xt_109 = add(x = var_6969, y = var_6962_1)[name = tensor("xt_109")]; + tensor var_6971 = const()[name = tensor("op_6971"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(310617344)))]; + tensor var_6974 = mul(x = decoder_generator_resblocks_4_alpha1_0, y = xt_109)[name = tensor("op_6974")]; + tensor var_6975 = sin(x = var_6974)[name = tensor("op_6975")]; + tensor var_4713_promoted_38 = const()[name = tensor("op_4713_promoted_38"), val = tensor(0x1p+1)]; + tensor var_6976 = pow(x = var_6975, y = var_4713_promoted_38)[name = tensor("op_6976")]; + tensor var_6977 = mul(x = var_6971, y = var_6976)[name = tensor("op_6977")]; + tensor input_647 = add(x = xt_109, y = var_6977)[name = tensor("input_647")]; + tensor weight_289 = const()[name = tensor("weight_289"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(310617920)))]; + tensor input_649_pad_type_0 = const()[name = tensor("input_649_pad_type_0"), val = tensor("custom")]; + tensor input_649_pad_0 = const()[name = tensor("input_649_pad_0"), val = tensor([3, 3])]; + tensor input_649_strides_0 = const()[name = tensor("input_649_strides_0"), val = tensor([1])]; + tensor input_649_dilations_0 = const()[name = tensor("input_649_dilations_0"), val = tensor([1])]; + tensor input_649_groups_0 = const()[name = tensor("input_649_groups_0"), val = tensor(1)]; + tensor input_649 = conv(bias = decoder_generator_resblocks_4_convs1_0_bias, dilations = input_649_dilations_0, groups = input_649_groups_0, pad = input_649_pad_0, pad_type = input_649_pad_type_0, strides = input_649_strides_0, weight = weight_289, x = input_647)[name = tensor("input_649")]; + tensor h_249 = linear(bias = decoder_generator_resblocks_4_adain2_0_fc_bias, weight = decoder_generator_resblocks_4_adain2_0_fc_weight, x = input_403)[name = tensor("linear_137")]; + tensor var_6997 = const()[name = tensor("op_6997"), val = tensor([1, 256, 1])]; + tensor h_251 = reshape(shape = var_6997, x = h_249)[name = tensor("h_251")]; + tensor var_6999_split_sizes_0 = const()[name = tensor("op_6999_split_sizes_0"), val = tensor([128, 128])]; + tensor var_6999_axis_0 = const()[name = tensor("op_6999_axis_0"), val = tensor(1)]; + tensor var_6999_0, tensor var_6999_1 = split(axis = var_6999_axis_0, split_sizes = var_6999_split_sizes_0, x = h_251)[name = tensor("op_6999")]; + tensor var_7001_promoted = const()[name = tensor("op_7001_promoted"), val = tensor(0x1p+0)]; + tensor var_7002 = add(x = var_6999_0, y = var_7001_promoted)[name = tensor("op_7002")]; + tensor var_7005 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_4706, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_649)[name = tensor("op_7005")]; + tensor var_7006 = mul(x = var_7002, y = var_7005)[name = tensor("op_7006")]; + tensor xt_111 = add(x = var_7006, y = var_6999_1)[name = tensor("xt_111")]; + tensor var_7008 = const()[name = tensor("op_7008"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(311076736)))]; + tensor var_7011 = mul(x = decoder_generator_resblocks_4_alpha2_0, y = xt_111)[name = tensor("op_7011")]; + tensor var_7012 = sin(x = var_7011)[name = tensor("op_7012")]; + tensor var_4713_promoted_39 = const()[name = tensor("op_4713_promoted_39"), val = tensor(0x1p+1)]; + tensor var_7013 = pow(x = var_7012, y = var_4713_promoted_39)[name = tensor("op_7013")]; + tensor var_7014 = mul(x = var_7008, y = var_7013)[name = tensor("op_7014")]; + tensor input_651 = add(x = xt_111, y = var_7014)[name = tensor("input_651")]; + tensor weight_293 = const()[name = tensor("weight_293"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(311077312)))]; + tensor xt_113_pad_type_0 = const()[name = tensor("xt_113_pad_type_0"), val = tensor("custom")]; + tensor xt_113_pad_0 = const()[name = tensor("xt_113_pad_0"), val = tensor([3, 3])]; + tensor xt_113_strides_0 = const()[name = tensor("xt_113_strides_0"), val = tensor([1])]; + tensor xt_113_dilations_0 = const()[name = tensor("xt_113_dilations_0"), val = tensor([1])]; + tensor xt_113_groups_0 = const()[name = tensor("xt_113_groups_0"), val = tensor(1)]; + tensor 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_651)[name = tensor("xt_113")]; + tensor input_653 = add(x = xt_113, y = input_623)[name = tensor("input_653")]; + tensor h_253 = linear(bias = decoder_generator_resblocks_4_adain1_1_fc_bias, weight = decoder_generator_resblocks_4_adain1_1_fc_weight, x = input_403)[name = tensor("linear_138")]; + tensor var_7035 = const()[name = tensor("op_7035"), val = tensor([1, 256, 1])]; + tensor h_255 = reshape(shape = var_7035, x = h_253)[name = tensor("h_255")]; + tensor var_7037_split_sizes_0 = const()[name = tensor("op_7037_split_sizes_0"), val = tensor([128, 128])]; + tensor var_7037_axis_0 = const()[name = tensor("op_7037_axis_0"), val = tensor(1)]; + tensor var_7037_0, tensor var_7037_1 = split(axis = var_7037_axis_0, split_sizes = var_7037_split_sizes_0, x = h_255)[name = tensor("op_7037")]; + tensor var_7039_promoted = const()[name = tensor("op_7039_promoted"), val = tensor(0x1p+0)]; + tensor var_7040 = add(x = var_7037_0, y = var_7039_promoted)[name = tensor("op_7040")]; + tensor var_7043 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_4706, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_653)[name = tensor("op_7043")]; + tensor var_7044 = mul(x = var_7040, y = var_7043)[name = tensor("op_7044")]; + tensor xt_115 = add(x = var_7044, y = var_7037_1)[name = tensor("xt_115")]; + tensor var_7046 = const()[name = tensor("op_7046"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(311536128)))]; + tensor var_7049 = mul(x = decoder_generator_resblocks_4_alpha1_1, y = xt_115)[name = tensor("op_7049")]; + tensor var_7050 = sin(x = var_7049)[name = tensor("op_7050")]; + tensor var_4713_promoted_40 = const()[name = tensor("op_4713_promoted_40"), val = tensor(0x1p+1)]; + tensor var_7051 = pow(x = var_7050, y = var_4713_promoted_40)[name = tensor("op_7051")]; + tensor var_7052 = mul(x = var_7046, y = var_7051)[name = tensor("op_7052")]; + tensor input_655 = add(x = xt_115, y = var_7052)[name = tensor("input_655")]; + tensor weight_297 = const()[name = tensor("weight_297"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(311536704)))]; + tensor input_657_pad_type_0 = const()[name = tensor("input_657_pad_type_0"), val = tensor("custom")]; + tensor input_657_pad_0 = const()[name = tensor("input_657_pad_0"), val = tensor([9, 9])]; + tensor input_657_dilations_0 = const()[name = tensor("input_657_dilations_0"), val = tensor([3])]; + tensor input_657_strides_0 = const()[name = tensor("input_657_strides_0"), val = tensor([1])]; + tensor input_657_groups_0 = const()[name = tensor("input_657_groups_0"), val = tensor(1)]; + tensor input_657 = conv(bias = decoder_generator_resblocks_4_convs1_1_bias, dilations = input_657_dilations_0, groups = input_657_groups_0, pad = input_657_pad_0, pad_type = input_657_pad_type_0, strides = input_657_strides_0, weight = weight_297, x = input_655)[name = tensor("input_657")]; + tensor h_257 = linear(bias = decoder_generator_resblocks_4_adain2_1_fc_bias, weight = decoder_generator_resblocks_4_adain2_1_fc_weight, x = input_403)[name = tensor("linear_139")]; + tensor var_7072 = const()[name = tensor("op_7072"), val = tensor([1, 256, 1])]; + tensor h_259 = reshape(shape = var_7072, x = h_257)[name = tensor("h_259")]; + tensor var_7074_split_sizes_0 = const()[name = tensor("op_7074_split_sizes_0"), val = tensor([128, 128])]; + tensor var_7074_axis_0 = const()[name = tensor("op_7074_axis_0"), val = tensor(1)]; + tensor var_7074_0, tensor var_7074_1 = split(axis = var_7074_axis_0, split_sizes = var_7074_split_sizes_0, x = h_259)[name = tensor("op_7074")]; + tensor var_7076_promoted = const()[name = tensor("op_7076_promoted"), val = tensor(0x1p+0)]; + tensor var_7077 = add(x = var_7074_0, y = var_7076_promoted)[name = tensor("op_7077")]; + tensor var_7080 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_4706, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_657)[name = tensor("op_7080")]; + tensor var_7081 = mul(x = var_7077, y = var_7080)[name = tensor("op_7081")]; + tensor xt_117 = add(x = var_7081, y = var_7074_1)[name = tensor("xt_117")]; + tensor var_7083 = const()[name = tensor("op_7083"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(311995520)))]; + tensor var_7086 = mul(x = decoder_generator_resblocks_4_alpha2_1, y = xt_117)[name = tensor("op_7086")]; + tensor var_7087 = sin(x = var_7086)[name = tensor("op_7087")]; + tensor var_4713_promoted_41 = const()[name = tensor("op_4713_promoted_41"), val = tensor(0x1p+1)]; + tensor var_7088 = pow(x = var_7087, y = var_4713_promoted_41)[name = tensor("op_7088")]; + tensor var_7089 = mul(x = var_7083, y = var_7088)[name = tensor("op_7089")]; + tensor input_659 = add(x = xt_117, y = var_7089)[name = tensor("input_659")]; + tensor weight_301 = const()[name = tensor("weight_301"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(311996096)))]; + tensor xt_119_pad_type_0 = const()[name = tensor("xt_119_pad_type_0"), val = tensor("custom")]; + tensor xt_119_pad_0 = const()[name = tensor("xt_119_pad_0"), val = tensor([3, 3])]; + tensor xt_119_strides_0 = const()[name = tensor("xt_119_strides_0"), val = tensor([1])]; + tensor xt_119_dilations_0 = const()[name = tensor("xt_119_dilations_0"), val = tensor([1])]; + tensor xt_119_groups_0 = const()[name = tensor("xt_119_groups_0"), val = tensor(1)]; + tensor 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_659)[name = tensor("xt_119")]; + tensor input_661 = add(x = xt_119, y = input_653)[name = tensor("input_661")]; + tensor h_261 = linear(bias = decoder_generator_resblocks_4_adain1_2_fc_bias, weight = decoder_generator_resblocks_4_adain1_2_fc_weight, x = input_403)[name = tensor("linear_140")]; + tensor var_7110 = const()[name = tensor("op_7110"), val = tensor([1, 256, 1])]; + tensor h_263 = reshape(shape = var_7110, x = h_261)[name = tensor("h_263")]; + tensor var_7112_split_sizes_0 = const()[name = tensor("op_7112_split_sizes_0"), val = tensor([128, 128])]; + tensor var_7112_axis_0 = const()[name = tensor("op_7112_axis_0"), val = tensor(1)]; + tensor var_7112_0, tensor var_7112_1 = split(axis = var_7112_axis_0, split_sizes = var_7112_split_sizes_0, x = h_263)[name = tensor("op_7112")]; + tensor var_7114_promoted = const()[name = tensor("op_7114_promoted"), val = tensor(0x1p+0)]; + tensor var_7115 = add(x = var_7112_0, y = var_7114_promoted)[name = tensor("op_7115")]; + tensor var_7118 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_4706, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_661)[name = tensor("op_7118")]; + tensor var_7119 = mul(x = var_7115, y = var_7118)[name = tensor("op_7119")]; + tensor xt_121 = add(x = var_7119, y = var_7112_1)[name = tensor("xt_121")]; + tensor var_7121 = const()[name = tensor("op_7121"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(312454912)))]; + tensor var_7124 = mul(x = decoder_generator_resblocks_4_alpha1_2, y = xt_121)[name = tensor("op_7124")]; + tensor var_7125 = sin(x = var_7124)[name = tensor("op_7125")]; + tensor var_4713_promoted_42 = const()[name = tensor("op_4713_promoted_42"), val = tensor(0x1p+1)]; + tensor var_7126 = pow(x = var_7125, y = var_4713_promoted_42)[name = tensor("op_7126")]; + tensor var_7127 = mul(x = var_7121, y = var_7126)[name = tensor("op_7127")]; + tensor input_663 = add(x = xt_121, y = var_7127)[name = tensor("input_663")]; + tensor weight_305 = const()[name = tensor("weight_305"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(312455488)))]; + tensor input_665_pad_type_0 = const()[name = tensor("input_665_pad_type_0"), val = tensor("custom")]; + tensor input_665_pad_0 = const()[name = tensor("input_665_pad_0"), val = tensor([15, 15])]; + tensor input_665_dilations_0 = const()[name = tensor("input_665_dilations_0"), val = tensor([5])]; + tensor input_665_strides_0 = const()[name = tensor("input_665_strides_0"), val = tensor([1])]; + tensor input_665_groups_0 = const()[name = tensor("input_665_groups_0"), val = tensor(1)]; + tensor input_665 = conv(bias = decoder_generator_resblocks_4_convs1_2_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_305, x = input_663)[name = tensor("input_665")]; + tensor h_265 = linear(bias = decoder_generator_resblocks_4_adain2_2_fc_bias, weight = decoder_generator_resblocks_4_adain2_2_fc_weight, x = input_403)[name = tensor("linear_141")]; + tensor var_7147 = const()[name = tensor("op_7147"), val = tensor([1, 256, 1])]; + tensor h_267 = reshape(shape = var_7147, x = h_265)[name = tensor("h_267")]; + tensor var_7149_split_sizes_0 = const()[name = tensor("op_7149_split_sizes_0"), val = tensor([128, 128])]; + tensor var_7149_axis_0 = const()[name = tensor("op_7149_axis_0"), val = tensor(1)]; + tensor var_7149_0, tensor var_7149_1 = split(axis = var_7149_axis_0, split_sizes = var_7149_split_sizes_0, x = h_267)[name = tensor("op_7149")]; + tensor var_7151_promoted = const()[name = tensor("op_7151_promoted"), val = tensor(0x1p+0)]; + tensor var_7152 = add(x = var_7149_0, y = var_7151_promoted)[name = tensor("op_7152")]; + tensor var_7155 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_4706, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_665)[name = tensor("op_7155")]; + tensor var_7156 = mul(x = var_7152, y = var_7155)[name = tensor("op_7156")]; + tensor xt_123 = add(x = var_7156, y = var_7149_1)[name = tensor("xt_123")]; + tensor var_7158 = const()[name = tensor("op_7158"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(312914304)))]; + tensor var_7161 = mul(x = decoder_generator_resblocks_4_alpha2_2, y = xt_123)[name = tensor("op_7161")]; + tensor var_7162 = sin(x = var_7161)[name = tensor("op_7162")]; + tensor var_4713_promoted_43 = const()[name = tensor("op_4713_promoted_43"), val = tensor(0x1p+1)]; + tensor var_7163 = pow(x = var_7162, y = var_4713_promoted_43)[name = tensor("op_7163")]; + tensor var_7164 = mul(x = var_7158, y = var_7163)[name = tensor("op_7164")]; + tensor input_667 = add(x = xt_123, y = var_7164)[name = tensor("input_667")]; + tensor weight_309 = const()[name = tensor("weight_309"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(312914880)))]; + tensor xt_125_pad_type_0 = const()[name = tensor("xt_125_pad_type_0"), val = tensor("custom")]; + tensor xt_125_pad_0 = const()[name = tensor("xt_125_pad_0"), val = tensor([3, 3])]; + tensor xt_125_strides_0 = const()[name = tensor("xt_125_strides_0"), val = tensor([1])]; + tensor xt_125_dilations_0 = const()[name = tensor("xt_125_dilations_0"), val = tensor([1])]; + tensor xt_125_groups_0 = const()[name = tensor("xt_125_groups_0"), val = tensor(1)]; + tensor 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_667)[name = tensor("xt_125")]; + tensor var_7177 = add(x = xt_125, y = input_661)[name = tensor("op_7177")]; + tensor xs_9 = add(x = xs_7, y = var_7177)[name = tensor("xs_9")]; + tensor h_269 = linear(bias = decoder_generator_resblocks_5_adain1_0_fc_bias, weight = decoder_generator_resblocks_5_adain1_0_fc_weight, x = input_403)[name = tensor("linear_142")]; + tensor var_7222 = const()[name = tensor("op_7222"), val = tensor([1, 256, 1])]; + tensor h_271 = reshape(shape = var_7222, x = h_269)[name = tensor("h_271")]; + tensor var_7224_split_sizes_0 = const()[name = tensor("op_7224_split_sizes_0"), val = tensor([128, 128])]; + tensor var_7224_axis_0 = const()[name = tensor("op_7224_axis_0"), val = tensor(1)]; + tensor var_7224_0, tensor var_7224_1 = split(axis = var_7224_axis_0, split_sizes = var_7224_split_sizes_0, x = h_271)[name = tensor("op_7224")]; + tensor var_7226_promoted = const()[name = tensor("op_7226_promoted"), val = tensor(0x1p+0)]; + tensor var_7227 = add(x = var_7224_0, y = var_7226_promoted)[name = tensor("op_7227")]; + tensor var_7231 = mul(x = var_7227, y = var_6707)[name = tensor("op_7231")]; + tensor xt_127 = add(x = var_7231, y = var_7224_1)[name = tensor("xt_127")]; + tensor var_7233 = const()[name = tensor("op_7233"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(313373696)))]; + tensor var_7236 = mul(x = decoder_generator_resblocks_5_alpha1_0, y = xt_127)[name = tensor("op_7236")]; + tensor var_7237 = sin(x = var_7236)[name = tensor("op_7237")]; + tensor var_4713_promoted_44 = const()[name = tensor("op_4713_promoted_44"), val = tensor(0x1p+1)]; + tensor var_7238 = pow(x = var_7237, y = var_4713_promoted_44)[name = tensor("op_7238")]; + tensor var_7239 = mul(x = var_7233, y = var_7238)[name = tensor("op_7239")]; + tensor input_669 = add(x = xt_127, y = var_7239)[name = tensor("input_669")]; + tensor weight_313 = const()[name = tensor("weight_313"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(313374272)))]; + tensor input_671_pad_type_0 = const()[name = tensor("input_671_pad_type_0"), val = tensor("custom")]; + tensor input_671_pad_0 = const()[name = tensor("input_671_pad_0"), val = tensor([5, 5])]; + tensor input_671_strides_0 = const()[name = tensor("input_671_strides_0"), val = tensor([1])]; + tensor input_671_dilations_0 = const()[name = tensor("input_671_dilations_0"), val = tensor([1])]; + tensor input_671_groups_0 = const()[name = tensor("input_671_groups_0"), val = tensor(1)]; + tensor input_671 = conv(bias = decoder_generator_resblocks_5_convs1_0_bias, dilations = input_671_dilations_0, groups = input_671_groups_0, pad = input_671_pad_0, pad_type = input_671_pad_type_0, strides = input_671_strides_0, weight = weight_313, x = input_669)[name = tensor("input_671")]; + tensor h_273 = linear(bias = decoder_generator_resblocks_5_adain2_0_fc_bias, weight = decoder_generator_resblocks_5_adain2_0_fc_weight, x = input_403)[name = tensor("linear_143")]; + tensor var_7259 = const()[name = tensor("op_7259"), val = tensor([1, 256, 1])]; + tensor h_275 = reshape(shape = var_7259, x = h_273)[name = tensor("h_275")]; + tensor var_7261_split_sizes_0 = const()[name = tensor("op_7261_split_sizes_0"), val = tensor([128, 128])]; + tensor var_7261_axis_0 = const()[name = tensor("op_7261_axis_0"), val = tensor(1)]; + tensor var_7261_0, tensor var_7261_1 = split(axis = var_7261_axis_0, split_sizes = var_7261_split_sizes_0, x = h_275)[name = tensor("op_7261")]; + tensor var_7263_promoted = const()[name = tensor("op_7263_promoted"), val = tensor(0x1p+0)]; + tensor var_7264 = add(x = var_7261_0, y = var_7263_promoted)[name = tensor("op_7264")]; + tensor var_7267 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_4706, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_671)[name = tensor("op_7267")]; + tensor var_7268 = mul(x = var_7264, y = var_7267)[name = tensor("op_7268")]; + tensor xt_129 = add(x = var_7268, y = var_7261_1)[name = tensor("xt_129")]; + tensor var_7270 = const()[name = tensor("op_7270"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(314095232)))]; + tensor var_7273 = mul(x = decoder_generator_resblocks_5_alpha2_0, y = xt_129)[name = tensor("op_7273")]; + tensor var_7274 = sin(x = var_7273)[name = tensor("op_7274")]; + tensor var_4713_promoted_45 = const()[name = tensor("op_4713_promoted_45"), val = tensor(0x1p+1)]; + tensor var_7275 = pow(x = var_7274, y = var_4713_promoted_45)[name = tensor("op_7275")]; + tensor var_7276 = mul(x = var_7270, y = var_7275)[name = tensor("op_7276")]; + tensor input_673 = add(x = xt_129, y = var_7276)[name = tensor("input_673")]; + tensor weight_317 = const()[name = tensor("weight_317"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(314095808)))]; + tensor xt_131_pad_type_0 = const()[name = tensor("xt_131_pad_type_0"), val = tensor("custom")]; + tensor xt_131_pad_0 = const()[name = tensor("xt_131_pad_0"), val = tensor([5, 5])]; + tensor xt_131_strides_0 = const()[name = tensor("xt_131_strides_0"), val = tensor([1])]; + tensor xt_131_dilations_0 = const()[name = tensor("xt_131_dilations_0"), val = tensor([1])]; + tensor xt_131_groups_0 = const()[name = tensor("xt_131_groups_0"), val = tensor(1)]; + tensor 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_673)[name = tensor("xt_131")]; + tensor input_675 = add(x = xt_131, y = input_623)[name = tensor("input_675")]; + tensor h_277 = linear(bias = decoder_generator_resblocks_5_adain1_1_fc_bias, weight = decoder_generator_resblocks_5_adain1_1_fc_weight, x = input_403)[name = tensor("linear_144")]; + tensor var_7297 = const()[name = tensor("op_7297"), val = tensor([1, 256, 1])]; + tensor h_279 = reshape(shape = var_7297, x = h_277)[name = tensor("h_279")]; + tensor var_7299_split_sizes_0 = const()[name = tensor("op_7299_split_sizes_0"), val = tensor([128, 128])]; + tensor var_7299_axis_0 = const()[name = tensor("op_7299_axis_0"), val = tensor(1)]; + tensor var_7299_0, tensor var_7299_1 = split(axis = var_7299_axis_0, split_sizes = var_7299_split_sizes_0, x = h_279)[name = tensor("op_7299")]; + tensor var_7301_promoted = const()[name = tensor("op_7301_promoted"), val = tensor(0x1p+0)]; + tensor var_7302 = add(x = var_7299_0, y = var_7301_promoted)[name = tensor("op_7302")]; + tensor var_7305 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_4706, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_675)[name = tensor("op_7305")]; + tensor var_7306 = mul(x = var_7302, y = var_7305)[name = tensor("op_7306")]; + tensor xt_133 = add(x = var_7306, y = var_7299_1)[name = tensor("xt_133")]; + tensor var_7308 = const()[name = tensor("op_7308"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(314816768)))]; + tensor var_7311 = mul(x = decoder_generator_resblocks_5_alpha1_1, y = xt_133)[name = tensor("op_7311")]; + tensor var_7312 = sin(x = var_7311)[name = tensor("op_7312")]; + tensor var_4713_promoted_46 = const()[name = tensor("op_4713_promoted_46"), val = tensor(0x1p+1)]; + tensor var_7313 = pow(x = var_7312, y = var_4713_promoted_46)[name = tensor("op_7313")]; + tensor var_7314 = mul(x = var_7308, y = var_7313)[name = tensor("op_7314")]; + tensor input_677 = add(x = xt_133, y = var_7314)[name = tensor("input_677")]; + tensor weight_321 = const()[name = tensor("weight_321"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(314817344)))]; + tensor input_679_pad_type_0 = const()[name = tensor("input_679_pad_type_0"), val = tensor("custom")]; + tensor input_679_pad_0 = const()[name = tensor("input_679_pad_0"), val = tensor([15, 15])]; + tensor input_679_dilations_0 = const()[name = tensor("input_679_dilations_0"), val = tensor([3])]; + tensor input_679_strides_0 = const()[name = tensor("input_679_strides_0"), val = tensor([1])]; + tensor input_679_groups_0 = const()[name = tensor("input_679_groups_0"), val = tensor(1)]; + tensor input_679 = conv(bias = decoder_generator_resblocks_5_convs1_1_bias, dilations = input_679_dilations_0, groups = input_679_groups_0, pad = input_679_pad_0, pad_type = input_679_pad_type_0, strides = input_679_strides_0, weight = weight_321, x = input_677)[name = tensor("input_679")]; + tensor h_281 = linear(bias = decoder_generator_resblocks_5_adain2_1_fc_bias, weight = decoder_generator_resblocks_5_adain2_1_fc_weight, x = input_403)[name = tensor("linear_145")]; + tensor var_7334 = const()[name = tensor("op_7334"), val = tensor([1, 256, 1])]; + tensor h_283 = reshape(shape = var_7334, x = h_281)[name = tensor("h_283")]; + tensor var_7336_split_sizes_0 = const()[name = tensor("op_7336_split_sizes_0"), val = tensor([128, 128])]; + tensor var_7336_axis_0 = const()[name = tensor("op_7336_axis_0"), val = tensor(1)]; + tensor var_7336_0, tensor var_7336_1 = split(axis = var_7336_axis_0, split_sizes = var_7336_split_sizes_0, x = h_283)[name = tensor("op_7336")]; + tensor var_7338_promoted = const()[name = tensor("op_7338_promoted"), val = tensor(0x1p+0)]; + tensor var_7339 = add(x = var_7336_0, y = var_7338_promoted)[name = tensor("op_7339")]; + tensor var_7342 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_4706, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_679)[name = tensor("op_7342")]; + tensor var_7343 = mul(x = var_7339, y = var_7342)[name = tensor("op_7343")]; + tensor xt_135 = add(x = var_7343, y = var_7336_1)[name = tensor("xt_135")]; + tensor var_7345 = const()[name = tensor("op_7345"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(315538304)))]; + tensor var_7348 = mul(x = decoder_generator_resblocks_5_alpha2_1, y = xt_135)[name = tensor("op_7348")]; + tensor var_7349 = sin(x = var_7348)[name = tensor("op_7349")]; + tensor var_4713_promoted_47 = const()[name = tensor("op_4713_promoted_47"), val = tensor(0x1p+1)]; + tensor var_7350 = pow(x = var_7349, y = var_4713_promoted_47)[name = tensor("op_7350")]; + tensor var_7351 = mul(x = var_7345, y = var_7350)[name = tensor("op_7351")]; + tensor input_681 = add(x = xt_135, y = var_7351)[name = tensor("input_681")]; + tensor weight_325 = const()[name = tensor("weight_325"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(315538880)))]; + tensor xt_137_pad_type_0 = const()[name = tensor("xt_137_pad_type_0"), val = tensor("custom")]; + tensor xt_137_pad_0 = const()[name = tensor("xt_137_pad_0"), val = tensor([5, 5])]; + tensor xt_137_strides_0 = const()[name = tensor("xt_137_strides_0"), val = tensor([1])]; + tensor xt_137_dilations_0 = const()[name = tensor("xt_137_dilations_0"), val = tensor([1])]; + tensor xt_137_groups_0 = const()[name = tensor("xt_137_groups_0"), val = tensor(1)]; + tensor 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_681)[name = tensor("xt_137")]; + tensor input_683 = add(x = xt_137, y = input_675)[name = tensor("input_683")]; + tensor h_285 = linear(bias = decoder_generator_resblocks_5_adain1_2_fc_bias, weight = decoder_generator_resblocks_5_adain1_2_fc_weight, x = input_403)[name = tensor("linear_146")]; + tensor var_7372 = const()[name = tensor("op_7372"), val = tensor([1, 256, 1])]; + tensor h_287 = reshape(shape = var_7372, x = h_285)[name = tensor("h_287")]; + tensor var_7374_split_sizes_0 = const()[name = tensor("op_7374_split_sizes_0"), val = tensor([128, 128])]; + tensor var_7374_axis_0 = const()[name = tensor("op_7374_axis_0"), val = tensor(1)]; + tensor var_7374_0, tensor var_7374_1 = split(axis = var_7374_axis_0, split_sizes = var_7374_split_sizes_0, x = h_287)[name = tensor("op_7374")]; + tensor var_7376_promoted = const()[name = tensor("op_7376_promoted"), val = tensor(0x1p+0)]; + tensor var_7377 = add(x = var_7374_0, y = var_7376_promoted)[name = tensor("op_7377")]; + tensor var_7380 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_4706, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_683)[name = tensor("op_7380")]; + tensor var_7381 = mul(x = var_7377, y = var_7380)[name = tensor("op_7381")]; + tensor xt_139 = add(x = var_7381, y = var_7374_1)[name = tensor("xt_139")]; + tensor var_7383 = const()[name = tensor("op_7383"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(316259840)))]; + tensor var_7386 = mul(x = decoder_generator_resblocks_5_alpha1_2, y = xt_139)[name = tensor("op_7386")]; + tensor var_7387 = sin(x = var_7386)[name = tensor("op_7387")]; + tensor var_4713_promoted_48 = const()[name = tensor("op_4713_promoted_48"), val = tensor(0x1p+1)]; + tensor var_7388 = pow(x = var_7387, y = var_4713_promoted_48)[name = tensor("op_7388")]; + tensor var_7389 = mul(x = var_7383, y = var_7388)[name = tensor("op_7389")]; + tensor input_685 = add(x = xt_139, y = var_7389)[name = tensor("input_685")]; + tensor weight_329 = const()[name = tensor("weight_329"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(316260416)))]; + tensor input_687_pad_type_0 = const()[name = tensor("input_687_pad_type_0"), val = tensor("custom")]; + tensor input_687_pad_0 = const()[name = tensor("input_687_pad_0"), val = tensor([25, 25])]; + tensor input_687_dilations_0 = const()[name = tensor("input_687_dilations_0"), val = tensor([5])]; + tensor input_687_strides_0 = const()[name = tensor("input_687_strides_0"), val = tensor([1])]; + tensor input_687_groups_0 = const()[name = tensor("input_687_groups_0"), val = tensor(1)]; + tensor input_687 = conv(bias = decoder_generator_resblocks_5_convs1_2_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_329, x = input_685)[name = tensor("input_687")]; + tensor h_289 = linear(bias = decoder_generator_resblocks_5_adain2_2_fc_bias, weight = decoder_generator_resblocks_5_adain2_2_fc_weight, x = input_403)[name = tensor("linear_147")]; + tensor var_7409 = const()[name = tensor("op_7409"), val = tensor([1, 256, 1])]; + tensor h = reshape(shape = var_7409, x = h_289)[name = tensor("h")]; + tensor var_7411_split_sizes_0 = const()[name = tensor("op_7411_split_sizes_0"), val = tensor([128, 128])]; + tensor var_7411_axis_0 = const()[name = tensor("op_7411_axis_0"), val = tensor(1)]; + tensor var_7411_0, tensor var_7411_1 = split(axis = var_7411_axis_0, split_sizes = var_7411_split_sizes_0, x = h)[name = tensor("op_7411")]; + tensor var_7413_promoted = const()[name = tensor("op_7413_promoted"), val = tensor(0x1p+0)]; + tensor var_7414 = add(x = var_7411_0, y = var_7413_promoted)[name = tensor("op_7414")]; + tensor var_7417 = instance_norm(beta = decoder_generator_noise_res_1_adain1_0_norm_bias, epsilon = var_4706, gamma = decoder_generator_noise_res_1_adain1_0_norm_weight, x = input_687)[name = tensor("op_7417")]; + tensor var_7418 = mul(x = var_7414, y = var_7417)[name = tensor("op_7418")]; + tensor xt_141 = add(x = var_7418, y = var_7411_1)[name = tensor("xt_141")]; + tensor var_7420 = const()[name = tensor("op_7420"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(316981376)))]; + tensor var_7423 = mul(x = decoder_generator_resblocks_5_alpha2_2, y = xt_141)[name = tensor("op_7423")]; + tensor var_7424 = sin(x = var_7423)[name = tensor("op_7424")]; + tensor var_4713_promoted_49 = const()[name = tensor("op_4713_promoted_49"), val = tensor(0x1p+1)]; + tensor var_7425 = pow(x = var_7424, y = var_4713_promoted_49)[name = tensor("op_7425")]; + tensor var_7426 = mul(x = var_7420, y = var_7425)[name = tensor("op_7426")]; + tensor input_689 = add(x = xt_141, y = var_7426)[name = tensor("input_689")]; + tensor weight_333 = const()[name = tensor("weight_333"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(316981952)))]; + tensor xt_pad_type_0 = const()[name = tensor("xt_pad_type_0"), val = tensor("custom")]; + tensor xt_pad_0 = const()[name = tensor("xt_pad_0"), val = tensor([5, 5])]; + tensor xt_strides_0 = const()[name = tensor("xt_strides_0"), val = tensor([1])]; + tensor xt_dilations_0 = const()[name = tensor("xt_dilations_0"), val = tensor([1])]; + tensor xt_groups_0 = const()[name = tensor("xt_groups_0"), val = tensor(1)]; + tensor 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_689)[name = tensor("xt")]; + tensor var_7439 = add(x = xt, y = input_683)[name = tensor("op_7439")]; + tensor xs = add(x = xs_9, y = var_7439)[name = tensor("xs")]; + tensor _inversed_input_691_y_0 = const()[name = tensor("_inversed_input_691_y_0"), val = tensor(0x1.555556p-2)]; + tensor _inversed_input_691 = mul(x = xs, y = _inversed_input_691_y_0)[name = tensor("_inversed_input_691")]; + tensor input = leaky_relu(alpha = var_4699, x = _inversed_input_691)[name = tensor("input")]; + tensor weight_335 = const()[name = tensor("weight_335"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(317702912)))]; + tensor x_pad_type_0 = const()[name = tensor("x_pad_type_0"), val = tensor("custom")]; + tensor x_pad_0 = const()[name = tensor("x_pad_0"), val = tensor([3, 3])]; + tensor x_strides_0 = const()[name = tensor("x_strides_0"), val = tensor([1])]; + tensor x_dilations_0 = const()[name = tensor("x_dilations_0"), val = tensor([1])]; + tensor x_groups_0 = const()[name = tensor("x_groups_0"), val = tensor(1)]; + tensor 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("x")]; + tensor var_7456_begin_0 = const()[name = tensor("op_7456_begin_0"), val = tensor([0, 0, 0])]; + tensor var_7456_end_0 = const()[name = tensor("op_7456_end_0"), val = tensor([1, 11, 74521])]; + tensor var_7456_end_mask_0 = const()[name = tensor("op_7456_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7456 = slice_by_index(begin = var_7456_begin_0, end = var_7456_end_0, end_mask = var_7456_end_mask_0, x = x)[name = tensor("op_7456")]; + tensor var_4701_promoted = const()[name = tensor("op_4701_promoted"), val = tensor(-0x1.4p+3)]; + tensor var_4689_promoted = const()[name = tensor("op_4689_promoted"), val = tensor(0x1.4p+3)]; + tensor clip_1 = clip(alpha = var_4701_promoted, beta = var_4689_promoted, x = var_7456)[name = tensor("clip_1")]; + tensor magnitude = exp(x = clip_1)[name = tensor("magnitude")]; + tensor var_7461_begin_0 = const()[name = tensor("op_7461_begin_0"), val = tensor([0, 11, 0])]; + tensor var_7461_end_0 = const()[name = tensor("op_7461_end_0"), val = tensor([1, 22, 74521])]; + tensor var_7461_end_mask_0 = const()[name = tensor("op_7461_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7461 = slice_by_index(begin = var_7461_begin_0, end = var_7461_end_0, end_mask = var_7461_end_mask_0, x = x)[name = tensor("op_7461")]; + tensor phase = sin(x = var_7461)[name = tensor("phase")]; + tensor var_7464 = cos(x = phase)[name = tensor("op_7464")]; + tensor real_part = mul(x = magnitude, y = var_7464)[name = tensor("real_part")]; + tensor var_7466 = sin(x = phase)[name = tensor("op_7466")]; + tensor imag_part = mul(x = magnitude, y = var_7466)[name = tensor("imag_part")]; + tensor real_rec_pad_type_0 = const()[name = tensor("real_rec_pad_type_0"), val = tensor("valid")]; + tensor real_rec_strides_0 = const()[name = tensor("real_rec_strides_0"), val = tensor([5])]; + tensor real_rec_pad_0 = const()[name = tensor("real_rec_pad_0"), val = tensor([0, 0])]; + tensor real_rec_dilations_0 = const()[name = tensor("real_rec_dilations_0"), val = tensor([1])]; + tensor real_rec_groups_0 = const()[name = tensor("real_rec_groups_0"), val = tensor(1)]; + tensor real_rec_has_output_shape_output_shape_0 = const()[name = tensor("real_rec_has_output_shape_output_shape_0"), val = tensor([1, 1, 372620])]; + tensor 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("real_rec_has_output_shape")]; + tensor imag_rec_pad_type_0 = const()[name = tensor("imag_rec_pad_type_0"), val = tensor("valid")]; + tensor imag_rec_strides_0 = const()[name = tensor("imag_rec_strides_0"), val = tensor([5])]; + tensor imag_rec_pad_0 = const()[name = tensor("imag_rec_pad_0"), val = tensor([0, 0])]; + tensor imag_rec_dilations_0 = const()[name = tensor("imag_rec_dilations_0"), val = tensor([1])]; + tensor imag_rec_groups_0 = const()[name = tensor("imag_rec_groups_0"), val = tensor(1)]; + tensor imag_rec_has_output_shape_output_shape_0 = const()[name = tensor("imag_rec_has_output_shape_output_shape_0"), val = tensor([1, 1, 372620])]; + tensor 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("imag_rec_has_output_shape")]; + tensor waveform = sub(x = real_rec_has_output_shape, y = imag_rec_has_output_shape)[name = tensor("waveform")]; + tensor audio_begin_0 = const()[name = tensor("audio_begin_0"), val = tensor([0, 0, 10])]; + tensor audio_end_0 = const()[name = tensor("audio_end_0"), val = tensor([1, 1, 372610])]; + tensor audio_end_mask_0 = const()[name = tensor("audio_end_mask_0"), val = tensor([true, true, false])]; + tensor audio = slice_by_index(begin = audio_begin_0, end = audio_end_0, end_mask = audio_end_mask_0, x = waveform)[name = tensor("audio")]; + tensor const_501 = const()[name = tensor("const_501"), val = tensor(372600)]; + tensor const_502 = const()[name = tensor("const_502"), val = tensor(0)]; + tensor const_503 = const()[name = tensor("const_503"), val = tensor(1)]; + tensor t = range_1d(end = const_501, start = const_502, step = const_503)[name = tensor("t")]; + tensor var_7482 = const()[name = tensor("op_7482"), val = tensor(2)]; + tensor var_7483 = mul(x = t, y = var_7482)[name = tensor("op_7483")]; + tensor var_7484 = const()[name = tensor("op_7484"), val = tensor(0x1.921fb6p+1)]; + tensor var_7483_promoted_dtype_0 = const()[name = tensor("op_7483_promoted_dtype_0"), val = tensor("fp32")]; + tensor var_7483_promoted = cast(dtype = var_7483_promoted_dtype_0, x = var_7483)[name = tensor("cast_167")]; + tensor var_7485 = mul(x = var_7483_promoted, y = var_7484)[name = tensor("op_7485")]; + tensor var_7486_promoted = const()[name = tensor("op_7486_promoted"), val = tensor(0x1.9p+5)]; + tensor var_7487 = mul(x = var_7485, y = var_7486_promoted)[name = tensor("op_7487")]; + tensor _inversed_7489_y_0 = const()[name = tensor("_inversed_7489_y_0"), val = tensor(0x1.5d867cp-15)]; + tensor _inversed_7489 = mul(x = var_7487, y = _inversed_7489_y_0)[name = tensor("_inversed_7489")]; + tensor var_7490 = sin(x = _inversed_7489)[name = tensor("op_7490")]; + tensor var_7491 = const()[name = tensor("op_7491"), val = tensor(0x1.a36e2ep-14)]; + tensor tiny_signal = mul(x = var_7490, y = var_7491)[name = tensor("tiny_signal")]; + tensor var_7493_axes_0 = const()[name = tensor("op_7493_axes_0"), val = tensor([0])]; + tensor var_7493 = expand_dims(axes = var_7493_axes_0, x = tiny_signal)[name = tensor("op_7493")]; + tensor var_7494_axes_0 = const()[name = tensor("op_7494_axes_0"), val = tensor([0])]; + tensor var_7494 = expand_dims(axes = var_7494_axes_0, x = var_7493)[name = tensor("op_7494")]; + tensor var_7495 = add(x = audio, y = var_7494)[name = tensor("op_7495")]; + } -> (var_7495); +} \ No newline at end of file