diff --git "a/kokoro_24_15s.mlmodelc/model.mil" "b/kokoro_24_15s.mlmodelc/model.mil" new file mode 100644--- /dev/null +++ "b/kokoro_24_15s.mlmodelc/model.mil" @@ -0,0 +1,7602 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3500.14.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.3.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] +{ + func main(tensor attention_mask, tensor input_ids, tensor random_phases, tensor ref_s) { + tensor model_decoder_generator_m_source_l_linear_bias = const()[name = tensor("model_decoder_generator_m_source_l_linear_bias"), val = tensor([-0x1.e28358p-6])]; + tensor model_decoder_generator_m_source_l_linear_weight = const()[name = tensor("model_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 model_decoder_generator_stft_weight_backward_imag = const()[name = tensor("model_decoder_generator_stft_weight_backward_imag"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor model_decoder_generator_stft_weight_backward_real = const()[name = tensor("model_decoder_generator_stft_weight_backward_real"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1024)))]; + tensor model_decoder_generator_stft_weight_forward_imag = const()[name = tensor("model_decoder_generator_stft_weight_forward_imag"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1984)))]; + tensor model_decoder_generator_stft_weight_forward_real = const()[name = tensor("model_decoder_generator_stft_weight_forward_real"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2944)))]; + tensor model_decoder_generator_conv_post_bias = const()[name = tensor("model_decoder_generator_conv_post_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3904)))]; + tensor model_decoder_generator_resblocks_5_alpha2_2 = const()[name = tensor("model_decoder_generator_resblocks_5_alpha2_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4096)))]; + tensor model_decoder_generator_resblocks_5_alpha2_1 = const()[name = tensor("model_decoder_generator_resblocks_5_alpha2_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4672)))]; + tensor model_decoder_generator_resblocks_5_alpha2_0 = const()[name = tensor("model_decoder_generator_resblocks_5_alpha2_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5248)))]; + tensor model_decoder_generator_resblocks_5_alpha1_2 = const()[name = tensor("model_decoder_generator_resblocks_5_alpha1_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5824)))]; + tensor model_decoder_generator_resblocks_5_alpha1_1 = const()[name = tensor("model_decoder_generator_resblocks_5_alpha1_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6400)))]; + tensor model_decoder_generator_resblocks_5_alpha1_0 = const()[name = tensor("model_decoder_generator_resblocks_5_alpha1_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6976)))]; + tensor model_decoder_generator_resblocks_5_adain2_2_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_5_adain2_2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7552)))]; + tensor model_decoder_generator_resblocks_5_adain2_2_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_5_adain2_2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8640)))]; + tensor model_decoder_generator_resblocks_5_adain2_2_norm_bias = const()[name = tensor("model_decoder_generator_resblocks_5_adain2_2_norm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(139776)))]; + tensor model_decoder_generator_resblocks_5_adain2_2_norm_weight = const()[name = tensor("model_decoder_generator_resblocks_5_adain2_2_norm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140352)))]; + tensor model_decoder_generator_resblocks_5_adain2_1_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_5_adain2_1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(140928)))]; + tensor model_decoder_generator_resblocks_5_adain2_1_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_5_adain2_1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(142016)))]; + tensor model_decoder_generator_resblocks_5_adain2_0_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_5_adain2_0_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(273152)))]; + tensor model_decoder_generator_resblocks_5_adain2_0_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_5_adain2_0_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(274240)))]; + tensor model_decoder_generator_resblocks_5_adain1_2_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_5_adain1_2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(405376)))]; + tensor model_decoder_generator_resblocks_5_adain1_2_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_5_adain1_2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(406464)))]; + tensor model_decoder_generator_resblocks_5_adain1_1_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_5_adain1_1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(537600)))]; + tensor model_decoder_generator_resblocks_5_adain1_1_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_5_adain1_1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(538688)))]; + tensor model_decoder_generator_resblocks_5_adain1_0_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_5_adain1_0_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(669824)))]; + tensor model_decoder_generator_resblocks_5_adain1_0_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_5_adain1_0_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(670912)))]; + tensor model_decoder_generator_resblocks_5_convs2_2_bias = const()[name = tensor("model_decoder_generator_resblocks_5_convs2_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(802048)))]; + tensor model_decoder_generator_resblocks_5_convs2_1_bias = const()[name = tensor("model_decoder_generator_resblocks_5_convs2_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(802624)))]; + tensor model_decoder_generator_resblocks_5_convs2_0_bias = const()[name = tensor("model_decoder_generator_resblocks_5_convs2_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(803200)))]; + tensor model_decoder_generator_resblocks_5_convs1_2_bias = const()[name = tensor("model_decoder_generator_resblocks_5_convs1_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(803776)))]; + tensor model_decoder_generator_resblocks_5_convs1_1_bias = const()[name = tensor("model_decoder_generator_resblocks_5_convs1_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(804352)))]; + tensor model_decoder_generator_resblocks_5_convs1_0_bias = const()[name = tensor("model_decoder_generator_resblocks_5_convs1_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(804928)))]; + tensor model_decoder_generator_resblocks_4_alpha2_2 = const()[name = tensor("model_decoder_generator_resblocks_4_alpha2_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(805504)))]; + tensor model_decoder_generator_resblocks_4_alpha2_1 = const()[name = tensor("model_decoder_generator_resblocks_4_alpha2_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(806080)))]; + tensor model_decoder_generator_resblocks_4_alpha2_0 = const()[name = tensor("model_decoder_generator_resblocks_4_alpha2_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(806656)))]; + tensor model_decoder_generator_resblocks_4_alpha1_2 = const()[name = tensor("model_decoder_generator_resblocks_4_alpha1_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(807232)))]; + tensor model_decoder_generator_resblocks_4_alpha1_1 = const()[name = tensor("model_decoder_generator_resblocks_4_alpha1_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(807808)))]; + tensor model_decoder_generator_resblocks_4_alpha1_0 = const()[name = tensor("model_decoder_generator_resblocks_4_alpha1_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(808384)))]; + tensor model_decoder_generator_resblocks_4_adain2_2_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_4_adain2_2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(808960)))]; + tensor model_decoder_generator_resblocks_4_adain2_2_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_4_adain2_2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(810048)))]; + tensor model_decoder_generator_resblocks_4_adain2_1_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_4_adain2_1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(941184)))]; + tensor model_decoder_generator_resblocks_4_adain2_1_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_4_adain2_1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(942272)))]; + tensor model_decoder_generator_resblocks_4_adain2_0_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_4_adain2_0_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1073408)))]; + tensor model_decoder_generator_resblocks_4_adain2_0_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_4_adain2_0_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1074496)))]; + tensor model_decoder_generator_resblocks_4_adain1_2_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_4_adain1_2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1205632)))]; + tensor model_decoder_generator_resblocks_4_adain1_2_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_4_adain1_2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1206720)))]; + tensor model_decoder_generator_resblocks_4_adain1_1_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_4_adain1_1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1337856)))]; + tensor model_decoder_generator_resblocks_4_adain1_1_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_4_adain1_1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1338944)))]; + tensor model_decoder_generator_resblocks_4_adain1_0_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_4_adain1_0_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1470080)))]; + tensor model_decoder_generator_resblocks_4_adain1_0_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_4_adain1_0_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1471168)))]; + tensor model_decoder_generator_resblocks_4_convs2_2_bias = const()[name = tensor("model_decoder_generator_resblocks_4_convs2_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1602304)))]; + tensor model_decoder_generator_resblocks_4_convs2_1_bias = const()[name = tensor("model_decoder_generator_resblocks_4_convs2_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1602880)))]; + tensor model_decoder_generator_resblocks_4_convs2_0_bias = const()[name = tensor("model_decoder_generator_resblocks_4_convs2_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1603456)))]; + tensor model_decoder_generator_resblocks_4_convs1_2_bias = const()[name = tensor("model_decoder_generator_resblocks_4_convs1_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1604032)))]; + tensor model_decoder_generator_resblocks_4_convs1_1_bias = const()[name = tensor("model_decoder_generator_resblocks_4_convs1_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1604608)))]; + tensor model_decoder_generator_resblocks_4_convs1_0_bias = const()[name = tensor("model_decoder_generator_resblocks_4_convs1_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1605184)))]; + tensor model_decoder_generator_resblocks_3_alpha2_2 = const()[name = tensor("model_decoder_generator_resblocks_3_alpha2_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1605760)))]; + tensor model_decoder_generator_resblocks_3_alpha2_1 = const()[name = tensor("model_decoder_generator_resblocks_3_alpha2_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1606336)))]; + tensor model_decoder_generator_resblocks_3_alpha2_0 = const()[name = tensor("model_decoder_generator_resblocks_3_alpha2_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1606912)))]; + tensor model_decoder_generator_resblocks_3_alpha1_2 = const()[name = tensor("model_decoder_generator_resblocks_3_alpha1_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1607488)))]; + tensor model_decoder_generator_resblocks_3_alpha1_1 = const()[name = tensor("model_decoder_generator_resblocks_3_alpha1_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1608064)))]; + tensor model_decoder_generator_resblocks_3_alpha1_0 = const()[name = tensor("model_decoder_generator_resblocks_3_alpha1_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1608640)))]; + tensor model_decoder_generator_resblocks_3_adain2_2_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_3_adain2_2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1609216)))]; + tensor model_decoder_generator_resblocks_3_adain2_2_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_3_adain2_2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1610304)))]; + tensor model_decoder_generator_resblocks_3_adain2_1_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_3_adain2_1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1741440)))]; + tensor model_decoder_generator_resblocks_3_adain2_1_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_3_adain2_1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1742528)))]; + tensor model_decoder_generator_resblocks_3_adain2_0_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_3_adain2_0_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1873664)))]; + tensor model_decoder_generator_resblocks_3_adain2_0_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_3_adain2_0_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1874752)))]; + tensor model_decoder_generator_resblocks_3_adain1_2_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_3_adain1_2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2005888)))]; + tensor model_decoder_generator_resblocks_3_adain1_2_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_3_adain1_2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2006976)))]; + tensor model_decoder_generator_resblocks_3_adain1_1_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_3_adain1_1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2138112)))]; + tensor model_decoder_generator_resblocks_3_adain1_1_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_3_adain1_1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2139200)))]; + tensor model_decoder_generator_resblocks_3_adain1_0_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_3_adain1_0_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2270336)))]; + tensor model_decoder_generator_resblocks_3_adain1_0_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_3_adain1_0_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2271424)))]; + tensor model_decoder_generator_resblocks_3_convs2_2_bias = const()[name = tensor("model_decoder_generator_resblocks_3_convs2_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2402560)))]; + tensor model_decoder_generator_resblocks_3_convs2_1_bias = const()[name = tensor("model_decoder_generator_resblocks_3_convs2_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2403136)))]; + tensor model_decoder_generator_resblocks_3_convs2_0_bias = const()[name = tensor("model_decoder_generator_resblocks_3_convs2_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2403712)))]; + tensor model_decoder_generator_resblocks_3_convs1_2_bias = const()[name = tensor("model_decoder_generator_resblocks_3_convs1_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2404288)))]; + tensor model_decoder_generator_resblocks_3_convs1_1_bias = const()[name = tensor("model_decoder_generator_resblocks_3_convs1_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2404864)))]; + tensor model_decoder_generator_resblocks_3_convs1_0_bias = const()[name = tensor("model_decoder_generator_resblocks_3_convs1_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2405440)))]; + tensor model_decoder_generator_resblocks_2_alpha2_2 = const()[name = tensor("model_decoder_generator_resblocks_2_alpha2_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2406016)))]; + tensor model_decoder_generator_resblocks_2_alpha2_1 = const()[name = tensor("model_decoder_generator_resblocks_2_alpha2_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2407104)))]; + tensor model_decoder_generator_resblocks_2_alpha2_0 = const()[name = tensor("model_decoder_generator_resblocks_2_alpha2_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2408192)))]; + tensor model_decoder_generator_resblocks_2_alpha1_2 = const()[name = tensor("model_decoder_generator_resblocks_2_alpha1_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2409280)))]; + tensor model_decoder_generator_resblocks_2_alpha1_1 = const()[name = tensor("model_decoder_generator_resblocks_2_alpha1_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2410368)))]; + tensor model_decoder_generator_resblocks_2_alpha1_0 = const()[name = tensor("model_decoder_generator_resblocks_2_alpha1_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2411456)))]; + tensor model_decoder_generator_resblocks_2_adain2_2_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_2_adain2_2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2412544)))]; + tensor model_decoder_generator_resblocks_2_adain2_2_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_2_adain2_2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2414656)))]; + tensor model_decoder_generator_resblocks_2_adain2_2_norm_bias = const()[name = tensor("model_decoder_generator_resblocks_2_adain2_2_norm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2676864)))]; + tensor model_decoder_generator_resblocks_2_adain2_2_norm_weight = const()[name = tensor("model_decoder_generator_resblocks_2_adain2_2_norm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2677952)))]; + tensor model_decoder_generator_resblocks_2_adain2_1_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_2_adain2_1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2679040)))]; + tensor model_decoder_generator_resblocks_2_adain2_1_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_2_adain2_1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2681152)))]; + tensor model_decoder_generator_resblocks_2_adain2_0_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_2_adain2_0_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2943360)))]; + tensor model_decoder_generator_resblocks_2_adain2_0_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_2_adain2_0_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2945472)))]; + tensor model_decoder_generator_resblocks_2_adain1_2_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_2_adain1_2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3207680)))]; + tensor model_decoder_generator_resblocks_2_adain1_2_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_2_adain1_2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3209792)))]; + tensor model_decoder_generator_resblocks_2_adain1_1_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_2_adain1_1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3472000)))]; + tensor model_decoder_generator_resblocks_2_adain1_1_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_2_adain1_1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3474112)))]; + tensor model_decoder_generator_resblocks_2_adain1_0_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_2_adain1_0_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3736320)))]; + tensor model_decoder_generator_resblocks_2_adain1_0_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_2_adain1_0_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3738432)))]; + tensor model_decoder_generator_resblocks_2_convs2_2_bias = const()[name = tensor("model_decoder_generator_resblocks_2_convs2_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4000640)))]; + tensor model_decoder_generator_resblocks_2_convs2_1_bias = const()[name = tensor("model_decoder_generator_resblocks_2_convs2_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4001728)))]; + tensor model_decoder_generator_resblocks_2_convs2_0_bias = const()[name = tensor("model_decoder_generator_resblocks_2_convs2_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4002816)))]; + tensor model_decoder_generator_resblocks_2_convs1_2_bias = const()[name = tensor("model_decoder_generator_resblocks_2_convs1_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4003904)))]; + tensor model_decoder_generator_resblocks_2_convs1_1_bias = const()[name = tensor("model_decoder_generator_resblocks_2_convs1_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4004992)))]; + tensor model_decoder_generator_resblocks_2_convs1_0_bias = const()[name = tensor("model_decoder_generator_resblocks_2_convs1_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4006080)))]; + tensor model_decoder_generator_resblocks_1_alpha2_2 = const()[name = tensor("model_decoder_generator_resblocks_1_alpha2_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4007168)))]; + tensor model_decoder_generator_resblocks_1_alpha2_1 = const()[name = tensor("model_decoder_generator_resblocks_1_alpha2_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4008256)))]; + tensor model_decoder_generator_resblocks_1_alpha2_0 = const()[name = tensor("model_decoder_generator_resblocks_1_alpha2_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4009344)))]; + tensor model_decoder_generator_resblocks_1_alpha1_2 = const()[name = tensor("model_decoder_generator_resblocks_1_alpha1_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4010432)))]; + tensor model_decoder_generator_resblocks_1_alpha1_1 = const()[name = tensor("model_decoder_generator_resblocks_1_alpha1_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4011520)))]; + tensor model_decoder_generator_resblocks_1_alpha1_0 = const()[name = tensor("model_decoder_generator_resblocks_1_alpha1_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4012608)))]; + tensor model_decoder_generator_resblocks_1_adain2_2_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_1_adain2_2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4013696)))]; + tensor model_decoder_generator_resblocks_1_adain2_2_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_1_adain2_2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4015808)))]; + tensor model_decoder_generator_resblocks_1_adain2_1_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_1_adain2_1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4278016)))]; + tensor model_decoder_generator_resblocks_1_adain2_1_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_1_adain2_1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4280128)))]; + tensor model_decoder_generator_resblocks_1_adain2_0_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_1_adain2_0_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4542336)))]; + tensor model_decoder_generator_resblocks_1_adain2_0_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_1_adain2_0_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4544448)))]; + tensor model_decoder_generator_resblocks_1_adain1_2_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_1_adain1_2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4806656)))]; + tensor model_decoder_generator_resblocks_1_adain1_2_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_1_adain1_2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4808768)))]; + tensor model_decoder_generator_resblocks_1_adain1_1_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_1_adain1_1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5070976)))]; + tensor model_decoder_generator_resblocks_1_adain1_1_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_1_adain1_1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5073088)))]; + tensor model_decoder_generator_resblocks_1_adain1_0_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_1_adain1_0_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5335296)))]; + tensor model_decoder_generator_resblocks_1_adain1_0_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_1_adain1_0_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5337408)))]; + tensor model_decoder_generator_resblocks_1_convs2_2_bias = const()[name = tensor("model_decoder_generator_resblocks_1_convs2_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5599616)))]; + tensor model_decoder_generator_resblocks_1_convs2_1_bias = const()[name = tensor("model_decoder_generator_resblocks_1_convs2_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5600704)))]; + tensor model_decoder_generator_resblocks_1_convs2_0_bias = const()[name = tensor("model_decoder_generator_resblocks_1_convs2_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5601792)))]; + tensor model_decoder_generator_resblocks_1_convs1_2_bias = const()[name = tensor("model_decoder_generator_resblocks_1_convs1_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5602880)))]; + tensor model_decoder_generator_resblocks_1_convs1_1_bias = const()[name = tensor("model_decoder_generator_resblocks_1_convs1_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5603968)))]; + tensor model_decoder_generator_resblocks_1_convs1_0_bias = const()[name = tensor("model_decoder_generator_resblocks_1_convs1_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5605056)))]; + tensor model_decoder_generator_resblocks_0_alpha2_2 = const()[name = tensor("model_decoder_generator_resblocks_0_alpha2_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5606144)))]; + tensor model_decoder_generator_resblocks_0_alpha2_1 = const()[name = tensor("model_decoder_generator_resblocks_0_alpha2_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5607232)))]; + tensor model_decoder_generator_resblocks_0_alpha2_0 = const()[name = tensor("model_decoder_generator_resblocks_0_alpha2_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5608320)))]; + tensor model_decoder_generator_resblocks_0_alpha1_2 = const()[name = tensor("model_decoder_generator_resblocks_0_alpha1_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5609408)))]; + tensor model_decoder_generator_resblocks_0_alpha1_1 = const()[name = tensor("model_decoder_generator_resblocks_0_alpha1_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5610496)))]; + tensor model_decoder_generator_resblocks_0_alpha1_0 = const()[name = tensor("model_decoder_generator_resblocks_0_alpha1_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5611584)))]; + tensor model_decoder_generator_resblocks_0_adain2_2_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_0_adain2_2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5612672)))]; + tensor model_decoder_generator_resblocks_0_adain2_2_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_0_adain2_2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5614784)))]; + tensor model_decoder_generator_resblocks_0_adain2_1_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_0_adain2_1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5876992)))]; + tensor model_decoder_generator_resblocks_0_adain2_1_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_0_adain2_1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5879104)))]; + tensor model_decoder_generator_resblocks_0_adain2_0_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_0_adain2_0_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6141312)))]; + tensor model_decoder_generator_resblocks_0_adain2_0_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_0_adain2_0_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6143424)))]; + tensor model_decoder_generator_resblocks_0_adain1_2_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_0_adain1_2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6405632)))]; + tensor model_decoder_generator_resblocks_0_adain1_2_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_0_adain1_2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6407744)))]; + tensor model_decoder_generator_resblocks_0_adain1_1_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_0_adain1_1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6669952)))]; + tensor model_decoder_generator_resblocks_0_adain1_1_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_0_adain1_1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6672064)))]; + tensor model_decoder_generator_resblocks_0_adain1_0_fc_bias = const()[name = tensor("model_decoder_generator_resblocks_0_adain1_0_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6934272)))]; + tensor model_decoder_generator_resblocks_0_adain1_0_fc_weight = const()[name = tensor("model_decoder_generator_resblocks_0_adain1_0_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6936384)))]; + tensor model_decoder_generator_resblocks_0_convs2_2_bias = const()[name = tensor("model_decoder_generator_resblocks_0_convs2_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7198592)))]; + tensor model_decoder_generator_resblocks_0_convs2_1_bias = const()[name = tensor("model_decoder_generator_resblocks_0_convs2_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7199680)))]; + tensor model_decoder_generator_resblocks_0_convs2_0_bias = const()[name = tensor("model_decoder_generator_resblocks_0_convs2_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7200768)))]; + tensor model_decoder_generator_resblocks_0_convs1_2_bias = const()[name = tensor("model_decoder_generator_resblocks_0_convs1_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7201856)))]; + tensor model_decoder_generator_resblocks_0_convs1_1_bias = const()[name = tensor("model_decoder_generator_resblocks_0_convs1_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7202944)))]; + tensor model_decoder_generator_resblocks_0_convs1_0_bias = const()[name = tensor("model_decoder_generator_resblocks_0_convs1_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7204032)))]; + tensor model_decoder_generator_ups_1_bias = const()[name = tensor("model_decoder_generator_ups_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7205120)))]; + tensor model_decoder_generator_ups_0_bias = const()[name = tensor("model_decoder_generator_ups_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7205696)))]; + tensor model_decoder_generator_noise_res_1_alpha2_2 = const()[name = tensor("model_decoder_generator_noise_res_1_alpha2_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7206784)))]; + tensor model_decoder_generator_noise_res_1_alpha2_1 = const()[name = tensor("model_decoder_generator_noise_res_1_alpha2_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7207360)))]; + tensor model_decoder_generator_noise_res_1_alpha2_0 = const()[name = tensor("model_decoder_generator_noise_res_1_alpha2_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7207936)))]; + tensor model_decoder_generator_noise_res_1_alpha1_2 = const()[name = tensor("model_decoder_generator_noise_res_1_alpha1_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7208512)))]; + tensor model_decoder_generator_noise_res_1_alpha1_1 = const()[name = tensor("model_decoder_generator_noise_res_1_alpha1_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7209088)))]; + tensor model_decoder_generator_noise_res_1_alpha1_0 = const()[name = tensor("model_decoder_generator_noise_res_1_alpha1_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7209664)))]; + tensor model_decoder_generator_noise_res_1_adain2_2_fc_bias = const()[name = tensor("model_decoder_generator_noise_res_1_adain2_2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7210240)))]; + tensor model_decoder_generator_noise_res_1_adain2_2_fc_weight = const()[name = tensor("model_decoder_generator_noise_res_1_adain2_2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7211328)))]; + tensor model_decoder_generator_noise_res_1_adain2_1_fc_bias = const()[name = tensor("model_decoder_generator_noise_res_1_adain2_1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7342464)))]; + tensor model_decoder_generator_noise_res_1_adain2_1_fc_weight = const()[name = tensor("model_decoder_generator_noise_res_1_adain2_1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7343552)))]; + tensor model_decoder_generator_noise_res_1_adain2_0_fc_bias = const()[name = tensor("model_decoder_generator_noise_res_1_adain2_0_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7474688)))]; + tensor model_decoder_generator_noise_res_1_adain2_0_fc_weight = const()[name = tensor("model_decoder_generator_noise_res_1_adain2_0_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7475776)))]; + tensor model_decoder_generator_noise_res_1_adain1_2_fc_bias = const()[name = tensor("model_decoder_generator_noise_res_1_adain1_2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7606912)))]; + tensor model_decoder_generator_noise_res_1_adain1_2_fc_weight = const()[name = tensor("model_decoder_generator_noise_res_1_adain1_2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7608000)))]; + tensor model_decoder_generator_noise_res_1_adain1_1_fc_bias = const()[name = tensor("model_decoder_generator_noise_res_1_adain1_1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7739136)))]; + tensor model_decoder_generator_noise_res_1_adain1_1_fc_weight = const()[name = tensor("model_decoder_generator_noise_res_1_adain1_1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7740224)))]; + tensor model_decoder_generator_noise_res_1_adain1_0_fc_bias = const()[name = tensor("model_decoder_generator_noise_res_1_adain1_0_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7871360)))]; + tensor model_decoder_generator_noise_res_1_adain1_0_fc_weight = const()[name = tensor("model_decoder_generator_noise_res_1_adain1_0_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7872448)))]; + tensor model_decoder_generator_noise_res_1_convs2_2_bias = const()[name = tensor("model_decoder_generator_noise_res_1_convs2_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8003584)))]; + tensor model_decoder_generator_noise_res_1_convs2_1_bias = const()[name = tensor("model_decoder_generator_noise_res_1_convs2_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8004160)))]; + tensor model_decoder_generator_noise_res_1_convs2_0_bias = const()[name = tensor("model_decoder_generator_noise_res_1_convs2_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8004736)))]; + tensor model_decoder_generator_noise_res_1_convs1_2_bias = const()[name = tensor("model_decoder_generator_noise_res_1_convs1_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8005312)))]; + tensor model_decoder_generator_noise_res_1_convs1_1_bias = const()[name = tensor("model_decoder_generator_noise_res_1_convs1_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8005888)))]; + tensor model_decoder_generator_noise_res_1_convs1_0_bias = const()[name = tensor("model_decoder_generator_noise_res_1_convs1_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8006464)))]; + tensor model_decoder_generator_noise_res_0_alpha2_2 = const()[name = tensor("model_decoder_generator_noise_res_0_alpha2_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8007040)))]; + tensor model_decoder_generator_noise_res_0_alpha2_1 = const()[name = tensor("model_decoder_generator_noise_res_0_alpha2_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8008128)))]; + tensor model_decoder_generator_noise_res_0_alpha2_0 = const()[name = tensor("model_decoder_generator_noise_res_0_alpha2_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8009216)))]; + tensor model_decoder_generator_noise_res_0_alpha1_2 = const()[name = tensor("model_decoder_generator_noise_res_0_alpha1_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8010304)))]; + tensor model_decoder_generator_noise_res_0_alpha1_1 = const()[name = tensor("model_decoder_generator_noise_res_0_alpha1_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8011392)))]; + tensor model_decoder_generator_noise_res_0_alpha1_0 = const()[name = tensor("model_decoder_generator_noise_res_0_alpha1_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8012480)))]; + tensor model_decoder_generator_noise_res_0_adain2_2_fc_bias = const()[name = tensor("model_decoder_generator_noise_res_0_adain2_2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8013568)))]; + tensor model_decoder_generator_noise_res_0_adain2_2_fc_weight = const()[name = tensor("model_decoder_generator_noise_res_0_adain2_2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8015680)))]; + tensor model_decoder_generator_noise_res_0_adain2_1_fc_bias = const()[name = tensor("model_decoder_generator_noise_res_0_adain2_1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8277888)))]; + tensor model_decoder_generator_noise_res_0_adain2_1_fc_weight = const()[name = tensor("model_decoder_generator_noise_res_0_adain2_1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8280000)))]; + tensor model_decoder_generator_noise_res_0_adain2_0_fc_bias = const()[name = tensor("model_decoder_generator_noise_res_0_adain2_0_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8542208)))]; + tensor model_decoder_generator_noise_res_0_adain2_0_fc_weight = const()[name = tensor("model_decoder_generator_noise_res_0_adain2_0_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8544320)))]; + tensor model_decoder_generator_noise_res_0_adain1_2_fc_bias = const()[name = tensor("model_decoder_generator_noise_res_0_adain1_2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8806528)))]; + tensor model_decoder_generator_noise_res_0_adain1_2_fc_weight = const()[name = tensor("model_decoder_generator_noise_res_0_adain1_2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8808640)))]; + tensor model_decoder_generator_noise_res_0_adain1_1_fc_bias = const()[name = tensor("model_decoder_generator_noise_res_0_adain1_1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9070848)))]; + tensor model_decoder_generator_noise_res_0_adain1_1_fc_weight = const()[name = tensor("model_decoder_generator_noise_res_0_adain1_1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9072960)))]; + tensor model_decoder_generator_noise_res_0_adain1_0_fc_bias = const()[name = tensor("model_decoder_generator_noise_res_0_adain1_0_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9335168)))]; + tensor model_decoder_generator_noise_res_0_adain1_0_fc_weight = const()[name = tensor("model_decoder_generator_noise_res_0_adain1_0_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9337280)))]; + tensor model_decoder_generator_noise_res_0_convs2_2_bias = const()[name = tensor("model_decoder_generator_noise_res_0_convs2_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9599488)))]; + tensor model_decoder_generator_noise_res_0_convs2_1_bias = const()[name = tensor("model_decoder_generator_noise_res_0_convs2_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9600576)))]; + tensor model_decoder_generator_noise_res_0_convs2_0_bias = const()[name = tensor("model_decoder_generator_noise_res_0_convs2_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9601664)))]; + tensor model_decoder_generator_noise_res_0_convs1_2_bias = const()[name = tensor("model_decoder_generator_noise_res_0_convs1_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9602752)))]; + tensor model_decoder_generator_noise_res_0_convs1_1_bias = const()[name = tensor("model_decoder_generator_noise_res_0_convs1_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9603840)))]; + tensor model_decoder_generator_noise_res_0_convs1_0_bias = const()[name = tensor("model_decoder_generator_noise_res_0_convs1_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9604928)))]; + tensor model_decoder_generator_noise_convs_1_bias = const()[name = tensor("model_decoder_generator_noise_convs_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9606016)))]; + tensor model_decoder_generator_noise_convs_1_weight = const()[name = tensor("model_decoder_generator_noise_convs_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9606592)))]; + tensor model_decoder_generator_noise_convs_0_bias = const()[name = tensor("model_decoder_generator_noise_convs_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9617920)))]; + tensor model_decoder_generator_noise_convs_0_weight = const()[name = tensor("model_decoder_generator_noise_convs_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9619008)))]; + tensor model_decoder_decode_3_pool_bias = const()[name = tensor("model_decoder_decode_3_pool_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9889408)))]; + tensor model_decoder_decode_3_norm2_fc_bias = const()[name = tensor("model_decoder_decode_3_norm2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9893888)))]; + tensor model_decoder_decode_3_norm2_fc_weight = const()[name = tensor("model_decoder_decode_3_norm2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9898048)))]; + tensor model_decoder_decode_3_norm2_norm_bias = const()[name = tensor("model_decoder_decode_3_norm2_norm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10422400)))]; + tensor model_decoder_decode_3_norm2_norm_weight = const()[name = tensor("model_decoder_decode_3_norm2_norm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10424512)))]; + tensor model_decoder_decode_3_norm1_fc_bias = const()[name = tensor("model_decoder_decode_3_norm1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10426624)))]; + tensor model_decoder_decode_3_norm1_fc_weight = const()[name = tensor("model_decoder_decode_3_norm1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10435456)))]; + tensor model_decoder_decode_3_norm1_norm_bias = const()[name = tensor("model_decoder_decode_3_norm1_norm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11551680)))]; + tensor model_decoder_decode_3_norm1_norm_weight = const()[name = tensor("model_decoder_decode_3_norm1_norm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11556160)))]; + tensor model_decoder_decode_3_conv2_bias = const()[name = tensor("model_decoder_decode_3_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11560640)))]; + tensor model_decoder_decode_3_conv1_bias = const()[name = tensor("model_decoder_decode_3_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11562752)))]; + tensor model_decoder_decode_2_norm2_fc_bias = const()[name = tensor("model_decoder_decode_2_norm2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11564864)))]; + tensor model_decoder_decode_2_norm2_fc_weight = const()[name = tensor("model_decoder_decode_2_norm2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11573120)))]; + tensor model_decoder_decode_2_norm2_norm_bias = const()[name = tensor("model_decoder_decode_2_norm2_norm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12621760)))]; + tensor model_decoder_decode_2_norm2_norm_weight = const()[name = tensor("model_decoder_decode_2_norm2_norm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12625920)))]; + tensor model_decoder_decode_2_norm1_fc_bias = const()[name = tensor("model_decoder_decode_2_norm1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12630080)))]; + tensor model_decoder_decode_2_norm1_fc_weight = const()[name = tensor("model_decoder_decode_2_norm1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12638912)))]; + tensor model_decoder_decode_2_conv2_bias = const()[name = tensor("model_decoder_decode_2_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13755136)))]; + tensor model_decoder_decode_2_conv1_bias = const()[name = tensor("model_decoder_decode_2_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13759296)))]; + tensor model_decoder_decode_1_norm2_fc_bias = const()[name = tensor("model_decoder_decode_1_norm2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13763456)))]; + tensor model_decoder_decode_1_norm2_fc_weight = const()[name = tensor("model_decoder_decode_1_norm2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13771712)))]; + tensor model_decoder_decode_1_norm1_fc_bias = const()[name = tensor("model_decoder_decode_1_norm1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14820352)))]; + tensor model_decoder_decode_1_norm1_fc_weight = const()[name = tensor("model_decoder_decode_1_norm1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14829184)))]; + tensor model_decoder_decode_1_conv2_bias = const()[name = tensor("model_decoder_decode_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15945408)))]; + tensor model_decoder_decode_1_conv1_bias = const()[name = tensor("model_decoder_decode_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15949568)))]; + tensor model_decoder_decode_0_norm2_fc_bias = const()[name = tensor("model_decoder_decode_0_norm2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15953728)))]; + tensor model_decoder_decode_0_norm2_fc_weight = const()[name = tensor("model_decoder_decode_0_norm2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15961984)))]; + tensor model_decoder_decode_0_norm1_fc_bias = const()[name = tensor("model_decoder_decode_0_norm1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17010624)))]; + tensor model_decoder_decode_0_norm1_fc_weight = const()[name = tensor("model_decoder_decode_0_norm1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17019456)))]; + tensor model_decoder_decode_0_conv2_bias = const()[name = tensor("model_decoder_decode_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18135680)))]; + tensor model_decoder_decode_0_conv1_bias = const()[name = tensor("model_decoder_decode_0_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18139840)))]; + tensor model_decoder_asr_res_0_bias = const()[name = tensor("model_decoder_asr_res_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18144000)))]; + tensor model_decoder_encode_norm2_fc_bias = const()[name = tensor("model_decoder_encode_norm2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18144320)))]; + tensor model_decoder_encode_norm2_fc_weight = const()[name = tensor("model_decoder_encode_norm2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18152576)))]; + tensor model_decoder_encode_norm1_fc_bias = const()[name = tensor("model_decoder_encode_norm1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19201216)))]; + tensor model_decoder_encode_norm1_fc_weight = const()[name = tensor("model_decoder_encode_norm1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19205440)))]; + tensor model_decoder_encode_norm1_norm_bias = const()[name = tensor("model_decoder_encode_norm1_norm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19731840)))]; + tensor model_decoder_encode_norm1_norm_weight = const()[name = tensor("model_decoder_encode_norm1_norm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19734016)))]; + tensor model_decoder_encode_conv2_bias = const()[name = tensor("model_decoder_encode_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19736192)))]; + tensor model_decoder_encode_conv1_bias = const()[name = tensor("model_decoder_encode_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19740352)))]; + tensor model_decoder_N_conv_bias = const()[name = tensor("model_decoder_N_conv_bias"), val = tensor([-0x1.e68b08p-2])]; + tensor model_decoder_F0_conv_bias = const()[name = tensor("model_decoder_F0_conv_bias"), val = tensor([-0x1.005f38p-2])]; + tensor model_text_encoder_cnn_2_1_beta = const()[name = tensor("model_text_encoder_cnn_2_1_beta"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19744512)))]; + tensor model_text_encoder_cnn_2_1_gamma = const()[name = tensor("model_text_encoder_cnn_2_1_gamma"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19746624)))]; + tensor model_text_encoder_cnn_2_0_bias = const()[name = tensor("model_text_encoder_cnn_2_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19748736)))]; + tensor model_text_encoder_cnn_1_1_beta = const()[name = tensor("model_text_encoder_cnn_1_1_beta"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19750848)))]; + tensor model_text_encoder_cnn_1_1_gamma = const()[name = tensor("model_text_encoder_cnn_1_1_gamma"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19752960)))]; + tensor model_text_encoder_cnn_1_0_bias = const()[name = tensor("model_text_encoder_cnn_1_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19755072)))]; + tensor model_text_encoder_cnn_0_1_beta = const()[name = tensor("model_text_encoder_cnn_0_1_beta"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19757184)))]; + tensor model_text_encoder_cnn_0_1_gamma = const()[name = tensor("model_text_encoder_cnn_0_1_gamma"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19759296)))]; + tensor model_text_encoder_cnn_0_0_bias = const()[name = tensor("model_text_encoder_cnn_0_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19761408)))]; + tensor model_text_encoder_embedding_weight = const()[name = tensor("model_text_encoder_embedding_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19763520)))]; + tensor model_predictor_N_proj_bias = const()[name = tensor("model_predictor_N_proj_bias"), val = tensor([0x1.13144ap-4])]; + tensor model_predictor_N_proj_weight = const()[name = tensor("model_predictor_N_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20128128)))]; + tensor model_predictor_F0_proj_bias = const()[name = tensor("model_predictor_F0_proj_bias"), val = tensor([0x1.edcbf4p-3])]; + tensor model_predictor_F0_proj_weight = const()[name = tensor("model_predictor_F0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20129216)))]; + tensor model_predictor_N_2_norm2_fc_bias = const()[name = tensor("model_predictor_N_2_norm2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20130304)))]; + tensor model_predictor_N_2_norm2_fc_weight = const()[name = tensor("model_predictor_N_2_norm2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20132416)))]; + tensor model_predictor_N_2_norm1_fc_bias = const()[name = tensor("model_predictor_N_2_norm1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20394624)))]; + tensor model_predictor_N_2_norm1_fc_weight = const()[name = tensor("model_predictor_N_2_norm1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20396736)))]; + tensor model_predictor_N_2_conv2_bias = const()[name = tensor("model_predictor_N_2_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20658944)))]; + tensor model_predictor_N_2_conv1_bias = const()[name = tensor("model_predictor_N_2_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20660032)))]; + tensor model_predictor_N_1_pool_bias = const()[name = tensor("model_predictor_N_1_pool_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20661120)))]; + tensor model_predictor_N_1_norm2_fc_bias = const()[name = tensor("model_predictor_N_1_norm2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20663232)))]; + tensor model_predictor_N_1_norm2_fc_weight = const()[name = tensor("model_predictor_N_1_norm2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20665344)))]; + tensor model_predictor_N_1_norm1_fc_bias = const()[name = tensor("model_predictor_N_1_norm1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20927552)))]; + tensor model_predictor_N_1_norm1_fc_weight = const()[name = tensor("model_predictor_N_1_norm1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20931712)))]; + tensor model_predictor_N_1_conv2_bias = const()[name = tensor("model_predictor_N_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21456064)))]; + tensor model_predictor_N_1_conv1_bias = const()[name = tensor("model_predictor_N_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21457152)))]; + tensor model_predictor_N_0_norm2_fc_bias = const()[name = tensor("model_predictor_N_0_norm2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21458240)))]; + tensor model_predictor_N_0_norm2_fc_weight = const()[name = tensor("model_predictor_N_0_norm2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21462400)))]; + tensor model_predictor_N_0_norm1_fc_bias = const()[name = tensor("model_predictor_N_0_norm1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21986752)))]; + tensor model_predictor_N_0_norm1_fc_weight = const()[name = tensor("model_predictor_N_0_norm1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21990912)))]; + tensor model_predictor_N_0_conv2_bias = const()[name = tensor("model_predictor_N_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22515264)))]; + tensor model_predictor_N_0_conv1_bias = const()[name = tensor("model_predictor_N_0_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22517376)))]; + tensor model_predictor_F0_2_norm2_fc_bias = const()[name = tensor("model_predictor_F0_2_norm2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22519488)))]; + tensor model_predictor_F0_2_norm2_fc_weight = const()[name = tensor("model_predictor_F0_2_norm2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22521600)))]; + tensor model_predictor_F0_2_norm1_fc_bias = const()[name = tensor("model_predictor_F0_2_norm1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22783808)))]; + tensor model_predictor_F0_2_norm1_fc_weight = const()[name = tensor("model_predictor_F0_2_norm1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22785920)))]; + tensor model_predictor_F0_2_conv2_bias = const()[name = tensor("model_predictor_F0_2_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23048128)))]; + tensor model_predictor_F0_2_conv1_bias = const()[name = tensor("model_predictor_F0_2_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23049216)))]; + tensor model_predictor_F0_1_pool_bias = const()[name = tensor("model_predictor_F0_1_pool_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23050304)))]; + tensor model_predictor_F0_1_norm2_fc_bias = const()[name = tensor("model_predictor_F0_1_norm2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23052416)))]; + tensor model_predictor_F0_1_norm2_fc_weight = const()[name = tensor("model_predictor_F0_1_norm2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23054528)))]; + tensor model_predictor_F0_1_norm1_fc_bias = const()[name = tensor("model_predictor_F0_1_norm1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23316736)))]; + tensor model_predictor_F0_1_norm1_fc_weight = const()[name = tensor("model_predictor_F0_1_norm1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23320896)))]; + tensor model_predictor_F0_1_conv2_bias = const()[name = tensor("model_predictor_F0_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23845248)))]; + tensor model_predictor_F0_1_conv1_bias = const()[name = tensor("model_predictor_F0_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23846336)))]; + tensor model_predictor_F0_0_norm2_fc_bias = const()[name = tensor("model_predictor_F0_0_norm2_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23847424)))]; + tensor model_predictor_F0_0_norm2_fc_weight = const()[name = tensor("model_predictor_F0_0_norm2_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23851584)))]; + tensor model_predictor_F0_0_norm1_fc_bias = const()[name = tensor("model_predictor_F0_0_norm1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24375936)))]; + tensor model_predictor_F0_0_norm1_fc_weight = const()[name = tensor("model_predictor_F0_0_norm1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24380096)))]; + tensor model_predictor_F0_0_conv2_bias = const()[name = tensor("model_predictor_F0_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24904448)))]; + tensor model_predictor_F0_0_conv1_bias = const()[name = tensor("model_predictor_F0_0_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24906560)))]; + tensor model_predictor_duration_proj_linear_layer_bias = const()[name = tensor("model_predictor_duration_proj_linear_layer_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24908672)))]; + tensor model_predictor_duration_proj_linear_layer_weight = const()[name = tensor("model_predictor_duration_proj_linear_layer_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24908992)))]; + tensor model_predictor_text_encoder_lstms_5_fc_bias = const()[name = tensor("model_predictor_text_encoder_lstms_5_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25011456)))]; + tensor model_predictor_text_encoder_lstms_5_fc_weight = const()[name = tensor("model_predictor_text_encoder_lstms_5_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25015616)))]; + tensor model_predictor_text_encoder_lstms_3_fc_bias = const()[name = tensor("model_predictor_text_encoder_lstms_3_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25539968)))]; + tensor model_predictor_text_encoder_lstms_3_fc_weight = const()[name = tensor("model_predictor_text_encoder_lstms_3_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25544128)))]; + tensor model_predictor_text_encoder_lstms_1_fc_bias = const()[name = tensor("model_predictor_text_encoder_lstms_1_fc_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26068480)))]; + tensor model_predictor_text_encoder_lstms_1_fc_weight = const()[name = tensor("model_predictor_text_encoder_lstms_1_fc_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26072640)))]; + tensor model_bert_encoder_bias = const()[name = tensor("model_bert_encoder_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26596992)))]; + tensor model_bert_encoder_weight = const()[name = tensor("model_bert_encoder_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26599104)))]; + tensor model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias = const()[name = tensor("model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28172032)))]; + tensor model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight = const()[name = tensor("model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28175168)))]; + tensor model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias = const()[name = tensor("model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34466688)))]; + tensor model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight = const()[name = tensor("model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34474944)))]; + tensor model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias = const()[name = tensor("model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40766464)))]; + tensor model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight = const()[name = tensor("model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40769600)))]; + tensor model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias = const()[name = tensor("model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40772736)))]; + tensor model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight = const()[name = tensor("model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40775872)))]; + tensor model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias = const()[name = tensor("model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43135232)))]; + tensor model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight = const()[name = tensor("model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43138368)))]; + tensor model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias = const()[name = tensor("model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45497728)))]; + tensor model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight = const()[name = tensor("model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45500864)))]; + tensor model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias = const()[name = tensor("model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47860224)))]; + tensor model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight = const()[name = tensor("model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47863360)))]; + tensor model_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias = const()[name = tensor("model_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(50222720)))]; + tensor model_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight = const()[name = tensor("model_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(50225856)))]; + tensor model_bert_encoder_embedding_hidden_mapping_in_bias = const()[name = tensor("model_bert_encoder_embedding_hidden_mapping_in_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50228992)))]; + tensor model_bert_encoder_embedding_hidden_mapping_in_weight = const()[name = tensor("model_bert_encoder_embedding_hidden_mapping_in_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50232128)))]; + tensor model_bert_embeddings_LayerNorm_bias = const()[name = tensor("model_bert_embeddings_LayerNorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50625408)))]; + tensor model_bert_embeddings_LayerNorm_weight = const()[name = tensor("model_bert_embeddings_LayerNorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50625984)))]; + tensor model_bert_embeddings_word_embeddings_weight = const()[name = tensor("model_bert_embeddings_word_embeddings_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50626560)))]; + tensor var_5125 = const()[name = tensor("op_5125"), val = tensor(0)]; + tensor m_1 = equal(x = attention_mask, y = var_5125)[name = tensor("m_1")]; + tensor var_5131 = const()[name = tensor("op_5131"), val = tensor(-0x1.fffffep+127)]; + tensor var_5133 = const()[name = tensor("op_5133"), val = tensor(0x1p+0)]; + tensor var_5140 = const()[name = tensor("op_5140"), 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_validate_indices_0 = const()[name = tensor("inputs_embeds_validate_indices_0"), val = tensor(false)]; + tensor inputs_embeds = gather(axis = inputs_embeds_axis_0, batch_dims = inputs_embeds_batch_dims_0, indices = input_ids, validate_indices = inputs_embeds_validate_indices_0, x = model_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(50717760)))]; + 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(50841728)))]; + 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 = model_bert_embeddings_LayerNorm_bias, epsilon = var_5140, gamma = model_bert_embeddings_LayerNorm_weight, x = input_5)[name = tensor("input_7")]; + tensor var_5171_axes_0 = const()[name = tensor("op_5171_axes_0"), val = tensor([1])]; + tensor var_5171 = expand_dims(axes = var_5171_axes_0, x = attention_mask)[name = tensor("op_5171")]; + tensor var_5172_axes_0 = const()[name = tensor("op_5172_axes_0"), val = tensor([2])]; + tensor var_5172 = expand_dims(axes = var_5172_axes_0, x = var_5171)[name = tensor("op_5172")]; + tensor var_5175_reps_0 = const()[name = tensor("op_5175_reps_0"), val = tensor([1, 1, 242, 1])]; + tensor var_5175 = tile(reps = var_5175_reps_0, x = var_5172)[name = tensor("op_5175")]; + tensor cast_5_dtype_0 = const()[name = tensor("cast_5_dtype_0"), val = tensor("fp32")]; + tensor cast_5 = cast(dtype = cast_5_dtype_0, x = var_5175)[name = tensor("cast_163")]; + tensor inverted_mask = sub(x = var_5133, y = cast_5)[name = tensor("inverted_mask")]; + tensor cast_6_dtype_0 = const()[name = tensor("cast_6_dtype_0"), val = tensor("bool")]; + tensor cast_6 = cast(dtype = cast_6_dtype_0, x = inverted_mask)[name = tensor("cast_162")]; + tensor attention_mask_1 = select(a = var_5131, b = inverted_mask, cond = cast_6)[name = tensor("attention_mask")]; + tensor hidden_states_1 = linear(bias = model_bert_encoder_embedding_hidden_mapping_in_bias, weight = model_bert_encoder_embedding_hidden_mapping_in_weight, x = input_7)[name = tensor("linear_0")]; + tensor x_1 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight, x = hidden_states_1)[name = tensor("linear_1")]; + tensor var_5186 = const()[name = tensor("op_5186"), val = tensor([1, 242, 12, 64])]; + tensor x_3 = reshape(shape = var_5186, x = x_1)[name = tensor("x_3")]; + tensor x_5 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight, x = hidden_states_1)[name = tensor("linear_2")]; + tensor var_5193 = const()[name = tensor("op_5193"), val = tensor([1, 242, 12, 64])]; + tensor x_7 = reshape(shape = var_5193, x = x_5)[name = tensor("x_7")]; + tensor x_9 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight, x = hidden_states_1)[name = tensor("linear_3")]; + tensor var_5200 = const()[name = tensor("op_5200"), val = tensor([1, 242, 12, 64])]; + tensor x_11 = reshape(shape = var_5200, x = x_9)[name = tensor("x_11")]; + tensor var_5202 = const()[name = tensor("op_5202"), val = tensor([0, 2, 1, 3])]; + tensor mul_0_y_0 = const()[name = tensor("mul_0_y_0"), val = tensor(0x1p-3)]; + tensor mul_0 = mul(x = x_3, y = mul_0_y_0)[name = tensor("mul_0")]; + tensor matmul_0_transpose_y_0 = const()[name = tensor("matmul_0_transpose_y_0"), val = tensor(true)]; + tensor matmul_0_transpose_x_0 = const()[name = tensor("matmul_0_transpose_x_0"), val = tensor(false)]; + tensor transpose_77_perm_0 = const()[name = tensor("transpose_77_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_78_perm_0 = const()[name = tensor("transpose_78_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_78 = transpose(perm = transpose_78_perm_0, x = x_7)[name = tensor("transpose_186")]; + tensor transpose_77 = transpose(perm = transpose_77_perm_0, x = mul_0)[name = tensor("transpose_187")]; + tensor matmul_0 = matmul(transpose_x = matmul_0_transpose_x_0, transpose_y = matmul_0_transpose_y_0, x = transpose_77, y = transpose_78)[name = tensor("matmul_0")]; + tensor add_0 = add(x = matmul_0, y = attention_mask_1)[name = tensor("add_0")]; + tensor softmax_0_axis_0 = const()[name = tensor("softmax_0_axis_0"), val = tensor(-1)]; + tensor softmax_0 = softmax(axis = softmax_0_axis_0, x = add_0)[name = tensor("softmax_0")]; + tensor attention_output_1_transpose_x_0 = const()[name = tensor("attention_output_1_transpose_x_0"), val = tensor(false)]; + tensor attention_output_1_transpose_y_0 = const()[name = tensor("attention_output_1_transpose_y_0"), val = tensor(false)]; + tensor value_layer_1 = transpose(perm = var_5202, x = x_11)[name = tensor("transpose_188")]; + tensor attention_output_1 = matmul(transpose_x = attention_output_1_transpose_x_0, transpose_y = attention_output_1_transpose_y_0, x = softmax_0, y = value_layer_1)[name = tensor("attention_output_1")]; + tensor attention_output_3_perm_0 = const()[name = tensor("attention_output_3_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_5206 = const()[name = tensor("op_5206"), val = tensor([1, 242, 768])]; + tensor attention_output_3 = transpose(perm = attention_output_3_perm_0, x = attention_output_1)[name = tensor("transpose_185")]; + tensor input_11 = reshape(shape = var_5206, x = attention_output_3)[name = tensor("input_11")]; + tensor input_13 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight, x = input_11)[name = tensor("linear_4")]; + tensor input_15 = add(x = hidden_states_1, y = input_13)[name = tensor("input_15")]; + tensor input_17_axes_0 = const()[name = tensor("input_17_axes_0"), val = tensor([-1])]; + tensor input_17 = layer_norm(axes = input_17_axes_0, beta = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_5140, gamma = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_15)[name = tensor("input_17")]; + tensor input_19 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight, x = input_17)[name = tensor("linear_5")]; + tensor input_21_mode_0 = const()[name = tensor("input_21_mode_0"), val = tensor("TANH_APPROXIMATION")]; + tensor input_21 = gelu(mode = input_21_mode_0, x = input_19)[name = tensor("input_21")]; + tensor ffn_output_1 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight, x = input_21)[name = tensor("linear_6")]; + tensor input_23 = add(x = ffn_output_1, y = input_17)[name = tensor("input_23")]; + tensor hidden_states_3_axes_0 = const()[name = tensor("hidden_states_3_axes_0"), val = tensor([-1])]; + tensor hidden_states_3 = layer_norm(axes = hidden_states_3_axes_0, beta = model_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias, epsilon = var_5140, gamma = model_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_23)[name = tensor("hidden_states_3")]; + tensor x_13 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight, x = hidden_states_3)[name = tensor("linear_7")]; + tensor var_5235 = const()[name = tensor("op_5235"), val = tensor([1, 242, 12, 64])]; + tensor x_15 = reshape(shape = var_5235, x = x_13)[name = tensor("x_15")]; + tensor x_17 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight, x = hidden_states_3)[name = tensor("linear_8")]; + tensor var_5242 = const()[name = tensor("op_5242"), val = tensor([1, 242, 12, 64])]; + tensor x_19 = reshape(shape = var_5242, x = x_17)[name = tensor("x_19")]; + tensor x_21 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight, x = hidden_states_3)[name = tensor("linear_9")]; + tensor var_5249 = const()[name = tensor("op_5249"), val = tensor([1, 242, 12, 64])]; + tensor x_23 = reshape(shape = var_5249, x = x_21)[name = tensor("x_23")]; + tensor var_5251 = const()[name = tensor("op_5251"), val = tensor([0, 2, 1, 3])]; + tensor mul_1_y_0 = const()[name = tensor("mul_1_y_0"), val = tensor(0x1p-3)]; + tensor mul_1 = mul(x = x_15, y = mul_1_y_0)[name = tensor("mul_1")]; + tensor matmul_1_transpose_y_0 = const()[name = tensor("matmul_1_transpose_y_0"), val = tensor(true)]; + tensor matmul_1_transpose_x_0 = const()[name = tensor("matmul_1_transpose_x_0"), val = tensor(false)]; + tensor transpose_79_perm_0 = const()[name = tensor("transpose_79_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_80_perm_0 = const()[name = tensor("transpose_80_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_80 = transpose(perm = transpose_80_perm_0, x = x_19)[name = tensor("transpose_182")]; + tensor transpose_79 = transpose(perm = transpose_79_perm_0, x = mul_1)[name = tensor("transpose_183")]; + tensor matmul_1 = matmul(transpose_x = matmul_1_transpose_x_0, transpose_y = matmul_1_transpose_y_0, x = transpose_79, y = transpose_80)[name = tensor("matmul_1")]; + tensor add_1 = add(x = matmul_1, y = attention_mask_1)[name = tensor("add_1")]; + tensor softmax_1_axis_0 = const()[name = tensor("softmax_1_axis_0"), val = tensor(-1)]; + tensor softmax_1 = softmax(axis = softmax_1_axis_0, x = add_1)[name = tensor("softmax_1")]; + tensor attention_output_5_transpose_x_0 = const()[name = tensor("attention_output_5_transpose_x_0"), val = tensor(false)]; + tensor attention_output_5_transpose_y_0 = const()[name = tensor("attention_output_5_transpose_y_0"), val = tensor(false)]; + tensor value_layer_3 = transpose(perm = var_5251, x = x_23)[name = tensor("transpose_184")]; + tensor attention_output_5 = matmul(transpose_x = attention_output_5_transpose_x_0, transpose_y = attention_output_5_transpose_y_0, x = softmax_1, y = value_layer_3)[name = tensor("attention_output_5")]; + tensor attention_output_7_perm_0 = const()[name = tensor("attention_output_7_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_5255 = const()[name = tensor("op_5255"), val = tensor([1, 242, 768])]; + tensor attention_output_7 = transpose(perm = attention_output_7_perm_0, x = attention_output_5)[name = tensor("transpose_181")]; + tensor input_25 = reshape(shape = var_5255, x = attention_output_7)[name = tensor("input_25")]; + tensor input_27 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight, x = input_25)[name = tensor("linear_10")]; + tensor input_29 = add(x = hidden_states_3, y = input_27)[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 = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_5140, gamma = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_29)[name = tensor("input_31")]; + tensor input_33 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight, x = input_31)[name = tensor("linear_11")]; + tensor input_35_mode_0 = const()[name = tensor("input_35_mode_0"), val = tensor("TANH_APPROXIMATION")]; + tensor input_35 = gelu(mode = input_35_mode_0, x = input_33)[name = tensor("input_35")]; + tensor ffn_output_3 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight, x = input_35)[name = tensor("linear_12")]; + tensor input_37 = add(x = ffn_output_3, y = input_31)[name = tensor("input_37")]; + tensor hidden_states_5_axes_0 = const()[name = tensor("hidden_states_5_axes_0"), val = tensor([-1])]; + tensor hidden_states_5 = layer_norm(axes = hidden_states_5_axes_0, beta = model_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias, epsilon = var_5140, gamma = model_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_37)[name = tensor("hidden_states_5")]; + tensor x_25 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight, x = hidden_states_5)[name = tensor("linear_13")]; + tensor var_5284 = const()[name = tensor("op_5284"), val = tensor([1, 242, 12, 64])]; + tensor x_27 = reshape(shape = var_5284, x = x_25)[name = tensor("x_27")]; + tensor x_29 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight, x = hidden_states_5)[name = tensor("linear_14")]; + tensor var_5291 = const()[name = tensor("op_5291"), val = tensor([1, 242, 12, 64])]; + tensor x_31 = reshape(shape = var_5291, x = x_29)[name = tensor("x_31")]; + tensor x_33 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight, x = hidden_states_5)[name = tensor("linear_15")]; + tensor var_5298 = const()[name = tensor("op_5298"), val = tensor([1, 242, 12, 64])]; + tensor x_35 = reshape(shape = var_5298, x = x_33)[name = tensor("x_35")]; + tensor var_5300 = const()[name = tensor("op_5300"), val = tensor([0, 2, 1, 3])]; + tensor mul_2_y_0 = const()[name = tensor("mul_2_y_0"), val = tensor(0x1p-3)]; + tensor mul_2 = mul(x = x_27, y = mul_2_y_0)[name = tensor("mul_2")]; + tensor matmul_2_transpose_y_0 = const()[name = tensor("matmul_2_transpose_y_0"), val = tensor(true)]; + tensor matmul_2_transpose_x_0 = const()[name = tensor("matmul_2_transpose_x_0"), val = tensor(false)]; + tensor transpose_81_perm_0 = const()[name = tensor("transpose_81_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_82_perm_0 = const()[name = tensor("transpose_82_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_82 = transpose(perm = transpose_82_perm_0, x = x_31)[name = tensor("transpose_178")]; + tensor transpose_81 = transpose(perm = transpose_81_perm_0, x = mul_2)[name = tensor("transpose_179")]; + tensor matmul_2 = matmul(transpose_x = matmul_2_transpose_x_0, transpose_y = matmul_2_transpose_y_0, x = transpose_81, y = transpose_82)[name = tensor("matmul_2")]; + tensor add_2 = add(x = matmul_2, y = attention_mask_1)[name = tensor("add_2")]; + tensor softmax_2_axis_0 = const()[name = tensor("softmax_2_axis_0"), val = tensor(-1)]; + tensor softmax_2 = softmax(axis = softmax_2_axis_0, x = add_2)[name = tensor("softmax_2")]; + tensor attention_output_9_transpose_x_0 = const()[name = tensor("attention_output_9_transpose_x_0"), val = tensor(false)]; + tensor attention_output_9_transpose_y_0 = const()[name = tensor("attention_output_9_transpose_y_0"), val = tensor(false)]; + tensor value_layer_5 = transpose(perm = var_5300, x = x_35)[name = tensor("transpose_180")]; + tensor attention_output_9 = matmul(transpose_x = attention_output_9_transpose_x_0, transpose_y = attention_output_9_transpose_y_0, x = softmax_2, y = value_layer_5)[name = tensor("attention_output_9")]; + tensor attention_output_11_perm_0 = const()[name = tensor("attention_output_11_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_5304 = const()[name = tensor("op_5304"), val = tensor([1, 242, 768])]; + tensor attention_output_11 = transpose(perm = attention_output_11_perm_0, x = attention_output_9)[name = tensor("transpose_177")]; + tensor input_39 = reshape(shape = var_5304, x = attention_output_11)[name = tensor("input_39")]; + tensor input_41 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight, x = input_39)[name = tensor("linear_16")]; + tensor input_43 = add(x = hidden_states_5, y = input_41)[name = tensor("input_43")]; + tensor input_45_axes_0 = const()[name = tensor("input_45_axes_0"), val = tensor([-1])]; + tensor input_45 = layer_norm(axes = input_45_axes_0, beta = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_5140, gamma = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_43)[name = tensor("input_45")]; + tensor input_47 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight, x = input_45)[name = tensor("linear_17")]; + tensor input_49_mode_0 = const()[name = tensor("input_49_mode_0"), val = tensor("TANH_APPROXIMATION")]; + tensor input_49 = gelu(mode = input_49_mode_0, x = input_47)[name = tensor("input_49")]; + tensor ffn_output_5 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight, x = input_49)[name = tensor("linear_18")]; + tensor input_51 = add(x = ffn_output_5, y = input_45)[name = tensor("input_51")]; + tensor hidden_states_7_axes_0 = const()[name = tensor("hidden_states_7_axes_0"), val = tensor([-1])]; + tensor hidden_states_7 = layer_norm(axes = hidden_states_7_axes_0, beta = model_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias, epsilon = var_5140, gamma = model_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_51)[name = tensor("hidden_states_7")]; + tensor x_37 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight, x = hidden_states_7)[name = tensor("linear_19")]; + tensor var_5333 = const()[name = tensor("op_5333"), val = tensor([1, 242, 12, 64])]; + tensor x_39 = reshape(shape = var_5333, x = x_37)[name = tensor("x_39")]; + tensor x_41 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight, x = hidden_states_7)[name = tensor("linear_20")]; + tensor var_5340 = const()[name = tensor("op_5340"), val = tensor([1, 242, 12, 64])]; + tensor x_43 = reshape(shape = var_5340, x = x_41)[name = tensor("x_43")]; + tensor x_45 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight, x = hidden_states_7)[name = tensor("linear_21")]; + tensor var_5347 = const()[name = tensor("op_5347"), val = tensor([1, 242, 12, 64])]; + tensor x_47 = reshape(shape = var_5347, x = x_45)[name = tensor("x_47")]; + tensor var_5349 = const()[name = tensor("op_5349"), val = tensor([0, 2, 1, 3])]; + tensor mul_3_y_0 = const()[name = tensor("mul_3_y_0"), val = tensor(0x1p-3)]; + tensor mul_3 = mul(x = x_39, y = mul_3_y_0)[name = tensor("mul_3")]; + tensor matmul_3_transpose_y_0 = const()[name = tensor("matmul_3_transpose_y_0"), val = tensor(true)]; + tensor matmul_3_transpose_x_0 = const()[name = tensor("matmul_3_transpose_x_0"), val = tensor(false)]; + tensor transpose_83_perm_0 = const()[name = tensor("transpose_83_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_84_perm_0 = const()[name = tensor("transpose_84_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_84 = transpose(perm = transpose_84_perm_0, x = x_43)[name = tensor("transpose_174")]; + tensor transpose_83 = transpose(perm = transpose_83_perm_0, x = mul_3)[name = tensor("transpose_175")]; + tensor matmul_3 = matmul(transpose_x = matmul_3_transpose_x_0, transpose_y = matmul_3_transpose_y_0, x = transpose_83, y = transpose_84)[name = tensor("matmul_3")]; + tensor add_3 = add(x = matmul_3, y = attention_mask_1)[name = tensor("add_3")]; + tensor softmax_3_axis_0 = const()[name = tensor("softmax_3_axis_0"), val = tensor(-1)]; + tensor softmax_3 = softmax(axis = softmax_3_axis_0, x = add_3)[name = tensor("softmax_3")]; + tensor attention_output_13_transpose_x_0 = const()[name = tensor("attention_output_13_transpose_x_0"), val = tensor(false)]; + tensor attention_output_13_transpose_y_0 = const()[name = tensor("attention_output_13_transpose_y_0"), val = tensor(false)]; + tensor value_layer_7 = transpose(perm = var_5349, x = x_47)[name = tensor("transpose_176")]; + tensor attention_output_13 = matmul(transpose_x = attention_output_13_transpose_x_0, transpose_y = attention_output_13_transpose_y_0, x = softmax_3, y = value_layer_7)[name = tensor("attention_output_13")]; + tensor attention_output_15_perm_0 = const()[name = tensor("attention_output_15_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_5353 = const()[name = tensor("op_5353"), val = tensor([1, 242, 768])]; + tensor attention_output_15 = transpose(perm = attention_output_15_perm_0, x = attention_output_13)[name = tensor("transpose_173")]; + tensor input_53 = reshape(shape = var_5353, x = attention_output_15)[name = tensor("input_53")]; + tensor input_55 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight, x = input_53)[name = tensor("linear_22")]; + tensor input_57 = add(x = hidden_states_7, y = input_55)[name = tensor("input_57")]; + tensor input_59_axes_0 = const()[name = tensor("input_59_axes_0"), val = tensor([-1])]; + tensor input_59 = layer_norm(axes = input_59_axes_0, beta = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_5140, gamma = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_57)[name = tensor("input_59")]; + tensor input_61 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight, x = input_59)[name = tensor("linear_23")]; + tensor input_63_mode_0 = const()[name = tensor("input_63_mode_0"), val = tensor("TANH_APPROXIMATION")]; + tensor input_63 = gelu(mode = input_63_mode_0, x = input_61)[name = tensor("input_63")]; + tensor ffn_output_7 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight, x = input_63)[name = tensor("linear_24")]; + tensor input_65 = add(x = ffn_output_7, y = input_59)[name = tensor("input_65")]; + tensor hidden_states_9_axes_0 = const()[name = tensor("hidden_states_9_axes_0"), val = tensor([-1])]; + tensor hidden_states_9 = layer_norm(axes = hidden_states_9_axes_0, beta = model_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias, epsilon = var_5140, gamma = model_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_65)[name = tensor("hidden_states_9")]; + tensor x_49 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight, x = hidden_states_9)[name = tensor("linear_25")]; + tensor var_5382 = const()[name = tensor("op_5382"), val = tensor([1, 242, 12, 64])]; + tensor x_51 = reshape(shape = var_5382, x = x_49)[name = tensor("x_51")]; + tensor x_53 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight, x = hidden_states_9)[name = tensor("linear_26")]; + tensor var_5389 = const()[name = tensor("op_5389"), val = tensor([1, 242, 12, 64])]; + tensor x_55 = reshape(shape = var_5389, x = x_53)[name = tensor("x_55")]; + tensor x_57 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight, x = hidden_states_9)[name = tensor("linear_27")]; + tensor var_5396 = const()[name = tensor("op_5396"), val = tensor([1, 242, 12, 64])]; + tensor x_59 = reshape(shape = var_5396, x = x_57)[name = tensor("x_59")]; + tensor var_5398 = const()[name = tensor("op_5398"), val = tensor([0, 2, 1, 3])]; + tensor mul_4_y_0 = const()[name = tensor("mul_4_y_0"), val = tensor(0x1p-3)]; + tensor mul_4 = mul(x = x_51, y = mul_4_y_0)[name = tensor("mul_4")]; + tensor matmul_4_transpose_y_0 = const()[name = tensor("matmul_4_transpose_y_0"), val = tensor(true)]; + tensor matmul_4_transpose_x_0 = const()[name = tensor("matmul_4_transpose_x_0"), val = tensor(false)]; + tensor transpose_85_perm_0 = const()[name = tensor("transpose_85_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_86_perm_0 = const()[name = tensor("transpose_86_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_86 = transpose(perm = transpose_86_perm_0, x = x_55)[name = tensor("transpose_170")]; + tensor transpose_85 = transpose(perm = transpose_85_perm_0, x = mul_4)[name = tensor("transpose_171")]; + tensor matmul_4 = matmul(transpose_x = matmul_4_transpose_x_0, transpose_y = matmul_4_transpose_y_0, x = transpose_85, y = transpose_86)[name = tensor("matmul_4")]; + tensor add_4 = add(x = matmul_4, y = attention_mask_1)[name = tensor("add_4")]; + tensor softmax_4_axis_0 = const()[name = tensor("softmax_4_axis_0"), val = tensor(-1)]; + tensor softmax_4 = softmax(axis = softmax_4_axis_0, x = add_4)[name = tensor("softmax_4")]; + tensor attention_output_17_transpose_x_0 = const()[name = tensor("attention_output_17_transpose_x_0"), val = tensor(false)]; + tensor attention_output_17_transpose_y_0 = const()[name = tensor("attention_output_17_transpose_y_0"), val = tensor(false)]; + tensor value_layer_9 = transpose(perm = var_5398, x = x_59)[name = tensor("transpose_172")]; + tensor attention_output_17 = matmul(transpose_x = attention_output_17_transpose_x_0, transpose_y = attention_output_17_transpose_y_0, x = softmax_4, y = value_layer_9)[name = tensor("attention_output_17")]; + tensor attention_output_19_perm_0 = const()[name = tensor("attention_output_19_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_5402 = const()[name = tensor("op_5402"), val = tensor([1, 242, 768])]; + tensor attention_output_19 = transpose(perm = attention_output_19_perm_0, x = attention_output_17)[name = tensor("transpose_169")]; + tensor input_67 = reshape(shape = var_5402, x = attention_output_19)[name = tensor("input_67")]; + tensor input_69 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight, x = input_67)[name = tensor("linear_28")]; + tensor input_71 = add(x = hidden_states_9, y = input_69)[name = tensor("input_71")]; + tensor input_73_axes_0 = const()[name = tensor("input_73_axes_0"), val = tensor([-1])]; + tensor input_73 = layer_norm(axes = input_73_axes_0, beta = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_5140, gamma = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_71)[name = tensor("input_73")]; + tensor input_75 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight, x = input_73)[name = tensor("linear_29")]; + tensor input_77_mode_0 = const()[name = tensor("input_77_mode_0"), val = tensor("TANH_APPROXIMATION")]; + tensor input_77 = gelu(mode = input_77_mode_0, x = input_75)[name = tensor("input_77")]; + tensor ffn_output_9 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight, x = input_77)[name = tensor("linear_30")]; + tensor input_79 = add(x = ffn_output_9, y = input_73)[name = tensor("input_79")]; + tensor hidden_states_11_axes_0 = const()[name = tensor("hidden_states_11_axes_0"), val = tensor([-1])]; + tensor hidden_states_11 = layer_norm(axes = hidden_states_11_axes_0, beta = model_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias, epsilon = var_5140, gamma = model_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_79)[name = tensor("hidden_states_11")]; + tensor x_61 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight, x = hidden_states_11)[name = tensor("linear_31")]; + tensor var_5431 = const()[name = tensor("op_5431"), val = tensor([1, 242, 12, 64])]; + tensor x_63 = reshape(shape = var_5431, x = x_61)[name = tensor("x_63")]; + tensor x_65 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight, x = hidden_states_11)[name = tensor("linear_32")]; + tensor var_5438 = const()[name = tensor("op_5438"), val = tensor([1, 242, 12, 64])]; + tensor x_67 = reshape(shape = var_5438, x = x_65)[name = tensor("x_67")]; + tensor x_69 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight, x = hidden_states_11)[name = tensor("linear_33")]; + tensor var_5445 = const()[name = tensor("op_5445"), val = tensor([1, 242, 12, 64])]; + tensor x_71 = reshape(shape = var_5445, x = x_69)[name = tensor("x_71")]; + tensor var_5447 = const()[name = tensor("op_5447"), val = tensor([0, 2, 1, 3])]; + tensor mul_5_y_0 = const()[name = tensor("mul_5_y_0"), val = tensor(0x1p-3)]; + tensor mul_5 = mul(x = x_63, y = mul_5_y_0)[name = tensor("mul_5")]; + tensor matmul_5_transpose_y_0 = const()[name = tensor("matmul_5_transpose_y_0"), val = tensor(true)]; + tensor matmul_5_transpose_x_0 = const()[name = tensor("matmul_5_transpose_x_0"), val = tensor(false)]; + tensor transpose_87_perm_0 = const()[name = tensor("transpose_87_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_88_perm_0 = const()[name = tensor("transpose_88_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_88 = transpose(perm = transpose_88_perm_0, x = x_67)[name = tensor("transpose_166")]; + tensor transpose_87 = transpose(perm = transpose_87_perm_0, x = mul_5)[name = tensor("transpose_167")]; + tensor matmul_5 = matmul(transpose_x = matmul_5_transpose_x_0, transpose_y = matmul_5_transpose_y_0, x = transpose_87, y = transpose_88)[name = tensor("matmul_5")]; + tensor add_5 = add(x = matmul_5, y = attention_mask_1)[name = tensor("add_5")]; + tensor softmax_5_axis_0 = const()[name = tensor("softmax_5_axis_0"), val = tensor(-1)]; + tensor softmax_5 = softmax(axis = softmax_5_axis_0, x = add_5)[name = tensor("softmax_5")]; + tensor attention_output_21_transpose_x_0 = const()[name = tensor("attention_output_21_transpose_x_0"), val = tensor(false)]; + tensor attention_output_21_transpose_y_0 = const()[name = tensor("attention_output_21_transpose_y_0"), val = tensor(false)]; + tensor value_layer_11 = transpose(perm = var_5447, x = x_71)[name = tensor("transpose_168")]; + tensor attention_output_21 = matmul(transpose_x = attention_output_21_transpose_x_0, transpose_y = attention_output_21_transpose_y_0, x = softmax_5, y = value_layer_11)[name = tensor("attention_output_21")]; + tensor attention_output_23_perm_0 = const()[name = tensor("attention_output_23_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_5451 = const()[name = tensor("op_5451"), val = tensor([1, 242, 768])]; + tensor attention_output_23 = transpose(perm = attention_output_23_perm_0, x = attention_output_21)[name = tensor("transpose_165")]; + tensor input_81 = reshape(shape = var_5451, x = attention_output_23)[name = tensor("input_81")]; + tensor input_83 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight, x = input_81)[name = tensor("linear_34")]; + tensor input_85 = add(x = hidden_states_11, y = input_83)[name = tensor("input_85")]; + tensor input_87_axes_0 = const()[name = tensor("input_87_axes_0"), val = tensor([-1])]; + tensor input_87 = layer_norm(axes = input_87_axes_0, beta = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_5140, gamma = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_85)[name = tensor("input_87")]; + tensor input_89 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight, x = input_87)[name = tensor("linear_35")]; + tensor input_91_mode_0 = const()[name = tensor("input_91_mode_0"), val = tensor("TANH_APPROXIMATION")]; + tensor input_91 = gelu(mode = input_91_mode_0, x = input_89)[name = tensor("input_91")]; + tensor ffn_output_11 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight, x = input_91)[name = tensor("linear_36")]; + tensor input_93 = add(x = ffn_output_11, y = input_87)[name = tensor("input_93")]; + tensor hidden_states_13_axes_0 = const()[name = tensor("hidden_states_13_axes_0"), val = tensor([-1])]; + tensor hidden_states_13 = layer_norm(axes = hidden_states_13_axes_0, beta = model_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias, epsilon = var_5140, gamma = model_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_93)[name = tensor("hidden_states_13")]; + tensor x_73 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight, x = hidden_states_13)[name = tensor("linear_37")]; + tensor var_5480 = const()[name = tensor("op_5480"), val = tensor([1, 242, 12, 64])]; + tensor x_75 = reshape(shape = var_5480, x = x_73)[name = tensor("x_75")]; + tensor x_77 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight, x = hidden_states_13)[name = tensor("linear_38")]; + tensor var_5487 = const()[name = tensor("op_5487"), val = tensor([1, 242, 12, 64])]; + tensor x_79 = reshape(shape = var_5487, x = x_77)[name = tensor("x_79")]; + tensor x_81 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight, x = hidden_states_13)[name = tensor("linear_39")]; + tensor var_5494 = const()[name = tensor("op_5494"), val = tensor([1, 242, 12, 64])]; + tensor x_83 = reshape(shape = var_5494, x = x_81)[name = tensor("x_83")]; + tensor var_5496 = const()[name = tensor("op_5496"), val = tensor([0, 2, 1, 3])]; + tensor mul_6_y_0 = const()[name = tensor("mul_6_y_0"), val = tensor(0x1p-3)]; + tensor mul_6 = mul(x = x_75, y = mul_6_y_0)[name = tensor("mul_6")]; + tensor matmul_6_transpose_y_0 = const()[name = tensor("matmul_6_transpose_y_0"), val = tensor(true)]; + tensor matmul_6_transpose_x_0 = const()[name = tensor("matmul_6_transpose_x_0"), val = tensor(false)]; + tensor transpose_89_perm_0 = const()[name = tensor("transpose_89_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_90_perm_0 = const()[name = tensor("transpose_90_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_90 = transpose(perm = transpose_90_perm_0, x = x_79)[name = tensor("transpose_162")]; + tensor transpose_89 = transpose(perm = transpose_89_perm_0, x = mul_6)[name = tensor("transpose_163")]; + tensor matmul_6 = matmul(transpose_x = matmul_6_transpose_x_0, transpose_y = matmul_6_transpose_y_0, x = transpose_89, y = transpose_90)[name = tensor("matmul_6")]; + tensor add_6 = add(x = matmul_6, y = attention_mask_1)[name = tensor("add_6")]; + tensor softmax_6_axis_0 = const()[name = tensor("softmax_6_axis_0"), val = tensor(-1)]; + tensor softmax_6 = softmax(axis = softmax_6_axis_0, x = add_6)[name = tensor("softmax_6")]; + tensor attention_output_25_transpose_x_0 = const()[name = tensor("attention_output_25_transpose_x_0"), val = tensor(false)]; + tensor attention_output_25_transpose_y_0 = const()[name = tensor("attention_output_25_transpose_y_0"), val = tensor(false)]; + tensor value_layer_13 = transpose(perm = var_5496, x = x_83)[name = tensor("transpose_164")]; + tensor attention_output_25 = matmul(transpose_x = attention_output_25_transpose_x_0, transpose_y = attention_output_25_transpose_y_0, x = softmax_6, y = value_layer_13)[name = tensor("attention_output_25")]; + tensor attention_output_27_perm_0 = const()[name = tensor("attention_output_27_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_5500 = const()[name = tensor("op_5500"), val = tensor([1, 242, 768])]; + tensor attention_output_27 = transpose(perm = attention_output_27_perm_0, x = attention_output_25)[name = tensor("transpose_161")]; + tensor input_95 = reshape(shape = var_5500, x = attention_output_27)[name = tensor("input_95")]; + tensor input_97 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight, x = input_95)[name = tensor("linear_40")]; + tensor input_99 = add(x = hidden_states_13, y = input_97)[name = tensor("input_99")]; + tensor input_101_axes_0 = const()[name = tensor("input_101_axes_0"), val = tensor([-1])]; + tensor input_101 = layer_norm(axes = input_101_axes_0, beta = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_5140, gamma = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_99)[name = tensor("input_101")]; + tensor input_103 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight, x = input_101)[name = tensor("linear_41")]; + tensor input_105_mode_0 = const()[name = tensor("input_105_mode_0"), val = tensor("TANH_APPROXIMATION")]; + tensor input_105 = gelu(mode = input_105_mode_0, x = input_103)[name = tensor("input_105")]; + tensor ffn_output_13 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight, x = input_105)[name = tensor("linear_42")]; + tensor input_107 = add(x = ffn_output_13, y = input_101)[name = tensor("input_107")]; + tensor hidden_states_15_axes_0 = const()[name = tensor("hidden_states_15_axes_0"), val = tensor([-1])]; + tensor hidden_states_15 = layer_norm(axes = hidden_states_15_axes_0, beta = model_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias, epsilon = var_5140, gamma = model_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_107)[name = tensor("hidden_states_15")]; + tensor x_85 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight, x = hidden_states_15)[name = tensor("linear_43")]; + tensor var_5529 = const()[name = tensor("op_5529"), val = tensor([1, 242, 12, 64])]; + tensor x_87 = reshape(shape = var_5529, x = x_85)[name = tensor("x_87")]; + tensor x_89 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight, x = hidden_states_15)[name = tensor("linear_44")]; + tensor var_5536 = const()[name = tensor("op_5536"), val = tensor([1, 242, 12, 64])]; + tensor x_91 = reshape(shape = var_5536, x = x_89)[name = tensor("x_91")]; + tensor x_93 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight, x = hidden_states_15)[name = tensor("linear_45")]; + tensor var_5543 = const()[name = tensor("op_5543"), val = tensor([1, 242, 12, 64])]; + tensor x_95 = reshape(shape = var_5543, x = x_93)[name = tensor("x_95")]; + tensor var_5545 = const()[name = tensor("op_5545"), val = tensor([0, 2, 1, 3])]; + tensor mul_7_y_0 = const()[name = tensor("mul_7_y_0"), val = tensor(0x1p-3)]; + tensor mul_7 = mul(x = x_87, y = mul_7_y_0)[name = tensor("mul_7")]; + tensor matmul_7_transpose_y_0 = const()[name = tensor("matmul_7_transpose_y_0"), val = tensor(true)]; + tensor matmul_7_transpose_x_0 = const()[name = tensor("matmul_7_transpose_x_0"), val = tensor(false)]; + tensor transpose_91_perm_0 = const()[name = tensor("transpose_91_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_92_perm_0 = const()[name = tensor("transpose_92_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_92 = transpose(perm = transpose_92_perm_0, x = x_91)[name = tensor("transpose_158")]; + tensor transpose_91 = transpose(perm = transpose_91_perm_0, x = mul_7)[name = tensor("transpose_159")]; + tensor matmul_7 = matmul(transpose_x = matmul_7_transpose_x_0, transpose_y = matmul_7_transpose_y_0, x = transpose_91, y = transpose_92)[name = tensor("matmul_7")]; + tensor add_7 = add(x = matmul_7, y = attention_mask_1)[name = tensor("add_7")]; + tensor softmax_7_axis_0 = const()[name = tensor("softmax_7_axis_0"), val = tensor(-1)]; + tensor softmax_7 = softmax(axis = softmax_7_axis_0, x = add_7)[name = tensor("softmax_7")]; + tensor attention_output_29_transpose_x_0 = const()[name = tensor("attention_output_29_transpose_x_0"), val = tensor(false)]; + tensor attention_output_29_transpose_y_0 = const()[name = tensor("attention_output_29_transpose_y_0"), val = tensor(false)]; + tensor value_layer_15 = transpose(perm = var_5545, x = x_95)[name = tensor("transpose_160")]; + tensor attention_output_29 = matmul(transpose_x = attention_output_29_transpose_x_0, transpose_y = attention_output_29_transpose_y_0, x = softmax_7, y = value_layer_15)[name = tensor("attention_output_29")]; + tensor attention_output_31_perm_0 = const()[name = tensor("attention_output_31_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_5549 = const()[name = tensor("op_5549"), val = tensor([1, 242, 768])]; + tensor attention_output_31 = transpose(perm = attention_output_31_perm_0, x = attention_output_29)[name = tensor("transpose_157")]; + tensor input_109 = reshape(shape = var_5549, x = attention_output_31)[name = tensor("input_109")]; + tensor input_111 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight, x = input_109)[name = tensor("linear_46")]; + tensor input_113 = add(x = hidden_states_15, y = input_111)[name = tensor("input_113")]; + tensor input_115_axes_0 = const()[name = tensor("input_115_axes_0"), val = tensor([-1])]; + tensor input_115 = layer_norm(axes = input_115_axes_0, beta = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_5140, gamma = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_113)[name = tensor("input_115")]; + tensor input_117 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight, x = input_115)[name = tensor("linear_47")]; + tensor input_119_mode_0 = const()[name = tensor("input_119_mode_0"), val = tensor("TANH_APPROXIMATION")]; + tensor input_119 = gelu(mode = input_119_mode_0, x = input_117)[name = tensor("input_119")]; + tensor ffn_output_15 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight, x = input_119)[name = tensor("linear_48")]; + tensor input_121 = add(x = ffn_output_15, y = input_115)[name = tensor("input_121")]; + tensor hidden_states_17_axes_0 = const()[name = tensor("hidden_states_17_axes_0"), val = tensor([-1])]; + tensor hidden_states_17 = layer_norm(axes = hidden_states_17_axes_0, beta = model_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias, epsilon = var_5140, gamma = model_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_121)[name = tensor("hidden_states_17")]; + tensor x_97 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight, x = hidden_states_17)[name = tensor("linear_49")]; + tensor var_5578 = const()[name = tensor("op_5578"), val = tensor([1, 242, 12, 64])]; + tensor x_99 = reshape(shape = var_5578, x = x_97)[name = tensor("x_99")]; + tensor x_101 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight, x = hidden_states_17)[name = tensor("linear_50")]; + tensor var_5585 = const()[name = tensor("op_5585"), val = tensor([1, 242, 12, 64])]; + tensor x_103 = reshape(shape = var_5585, x = x_101)[name = tensor("x_103")]; + tensor x_105 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight, x = hidden_states_17)[name = tensor("linear_51")]; + tensor var_5592 = const()[name = tensor("op_5592"), val = tensor([1, 242, 12, 64])]; + tensor x_107 = reshape(shape = var_5592, x = x_105)[name = tensor("x_107")]; + tensor var_5594 = const()[name = tensor("op_5594"), val = tensor([0, 2, 1, 3])]; + tensor mul_8_y_0 = const()[name = tensor("mul_8_y_0"), val = tensor(0x1p-3)]; + tensor mul_8 = mul(x = x_99, y = mul_8_y_0)[name = tensor("mul_8")]; + tensor matmul_8_transpose_y_0 = const()[name = tensor("matmul_8_transpose_y_0"), val = tensor(true)]; + tensor matmul_8_transpose_x_0 = const()[name = tensor("matmul_8_transpose_x_0"), val = tensor(false)]; + tensor transpose_93_perm_0 = const()[name = tensor("transpose_93_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_94_perm_0 = const()[name = tensor("transpose_94_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_94 = transpose(perm = transpose_94_perm_0, x = x_103)[name = tensor("transpose_154")]; + tensor transpose_93 = transpose(perm = transpose_93_perm_0, x = mul_8)[name = tensor("transpose_155")]; + tensor matmul_8 = matmul(transpose_x = matmul_8_transpose_x_0, transpose_y = matmul_8_transpose_y_0, x = transpose_93, y = transpose_94)[name = tensor("matmul_8")]; + tensor add_8 = add(x = matmul_8, y = attention_mask_1)[name = tensor("add_8")]; + tensor softmax_8_axis_0 = const()[name = tensor("softmax_8_axis_0"), val = tensor(-1)]; + tensor softmax_8 = softmax(axis = softmax_8_axis_0, x = add_8)[name = tensor("softmax_8")]; + tensor attention_output_33_transpose_x_0 = const()[name = tensor("attention_output_33_transpose_x_0"), val = tensor(false)]; + tensor attention_output_33_transpose_y_0 = const()[name = tensor("attention_output_33_transpose_y_0"), val = tensor(false)]; + tensor value_layer_17 = transpose(perm = var_5594, x = x_107)[name = tensor("transpose_156")]; + tensor attention_output_33 = matmul(transpose_x = attention_output_33_transpose_x_0, transpose_y = attention_output_33_transpose_y_0, x = softmax_8, y = value_layer_17)[name = tensor("attention_output_33")]; + tensor attention_output_35_perm_0 = const()[name = tensor("attention_output_35_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_5598 = const()[name = tensor("op_5598"), val = tensor([1, 242, 768])]; + tensor attention_output_35 = transpose(perm = attention_output_35_perm_0, x = attention_output_33)[name = tensor("transpose_153")]; + tensor input_123 = reshape(shape = var_5598, x = attention_output_35)[name = tensor("input_123")]; + tensor input_125 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight, x = input_123)[name = tensor("linear_52")]; + tensor input_127 = add(x = hidden_states_17, y = input_125)[name = tensor("input_127")]; + tensor input_129_axes_0 = const()[name = tensor("input_129_axes_0"), val = tensor([-1])]; + tensor input_129 = layer_norm(axes = input_129_axes_0, beta = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_5140, gamma = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_127)[name = tensor("input_129")]; + tensor input_131 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight, x = input_129)[name = tensor("linear_53")]; + tensor input_133_mode_0 = const()[name = tensor("input_133_mode_0"), val = tensor("TANH_APPROXIMATION")]; + tensor input_133 = gelu(mode = input_133_mode_0, x = input_131)[name = tensor("input_133")]; + tensor ffn_output_17 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight, x = input_133)[name = tensor("linear_54")]; + tensor input_135 = add(x = ffn_output_17, y = input_129)[name = tensor("input_135")]; + tensor hidden_states_19_axes_0 = const()[name = tensor("hidden_states_19_axes_0"), val = tensor([-1])]; + tensor hidden_states_19 = layer_norm(axes = hidden_states_19_axes_0, beta = model_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias, epsilon = var_5140, gamma = model_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_135)[name = tensor("hidden_states_19")]; + tensor x_109 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight, x = hidden_states_19)[name = tensor("linear_55")]; + tensor var_5627 = const()[name = tensor("op_5627"), val = tensor([1, 242, 12, 64])]; + tensor x_111 = reshape(shape = var_5627, x = x_109)[name = tensor("x_111")]; + tensor x_113 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight, x = hidden_states_19)[name = tensor("linear_56")]; + tensor var_5634 = const()[name = tensor("op_5634"), val = tensor([1, 242, 12, 64])]; + tensor x_115 = reshape(shape = var_5634, x = x_113)[name = tensor("x_115")]; + tensor x_117 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight, x = hidden_states_19)[name = tensor("linear_57")]; + tensor var_5641 = const()[name = tensor("op_5641"), val = tensor([1, 242, 12, 64])]; + tensor x_119 = reshape(shape = var_5641, x = x_117)[name = tensor("x_119")]; + tensor var_5643 = const()[name = tensor("op_5643"), val = tensor([0, 2, 1, 3])]; + tensor mul_9_y_0 = const()[name = tensor("mul_9_y_0"), val = tensor(0x1p-3)]; + tensor mul_9 = mul(x = x_111, y = mul_9_y_0)[name = tensor("mul_9")]; + tensor matmul_9_transpose_y_0 = const()[name = tensor("matmul_9_transpose_y_0"), val = tensor(true)]; + tensor matmul_9_transpose_x_0 = const()[name = tensor("matmul_9_transpose_x_0"), val = tensor(false)]; + tensor transpose_95_perm_0 = const()[name = tensor("transpose_95_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_96_perm_0 = const()[name = tensor("transpose_96_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_96 = transpose(perm = transpose_96_perm_0, x = x_115)[name = tensor("transpose_150")]; + tensor transpose_95 = transpose(perm = transpose_95_perm_0, x = mul_9)[name = tensor("transpose_151")]; + tensor matmul_9 = matmul(transpose_x = matmul_9_transpose_x_0, transpose_y = matmul_9_transpose_y_0, x = transpose_95, y = transpose_96)[name = tensor("matmul_9")]; + tensor add_9 = add(x = matmul_9, y = attention_mask_1)[name = tensor("add_9")]; + tensor softmax_9_axis_0 = const()[name = tensor("softmax_9_axis_0"), val = tensor(-1)]; + tensor softmax_9 = softmax(axis = softmax_9_axis_0, x = add_9)[name = tensor("softmax_9")]; + tensor attention_output_37_transpose_x_0 = const()[name = tensor("attention_output_37_transpose_x_0"), val = tensor(false)]; + tensor attention_output_37_transpose_y_0 = const()[name = tensor("attention_output_37_transpose_y_0"), val = tensor(false)]; + tensor value_layer_19 = transpose(perm = var_5643, x = x_119)[name = tensor("transpose_152")]; + tensor attention_output_37 = matmul(transpose_x = attention_output_37_transpose_x_0, transpose_y = attention_output_37_transpose_y_0, x = softmax_9, y = value_layer_19)[name = tensor("attention_output_37")]; + tensor attention_output_39_perm_0 = const()[name = tensor("attention_output_39_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_5647 = const()[name = tensor("op_5647"), val = tensor([1, 242, 768])]; + tensor attention_output_39 = transpose(perm = attention_output_39_perm_0, x = attention_output_37)[name = tensor("transpose_149")]; + tensor input_137 = reshape(shape = var_5647, x = attention_output_39)[name = tensor("input_137")]; + tensor input_139 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight, x = input_137)[name = tensor("linear_58")]; + tensor input_141 = add(x = hidden_states_19, 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 = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_5140, gamma = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_141)[name = tensor("input_143")]; + tensor input_145 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight, x = input_143)[name = tensor("linear_59")]; + 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_19 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight, x = input_147)[name = tensor("linear_60")]; + tensor input_149 = add(x = ffn_output_19, y = input_143)[name = tensor("input_149")]; + tensor hidden_states_21_axes_0 = const()[name = tensor("hidden_states_21_axes_0"), val = tensor([-1])]; + tensor hidden_states_21 = layer_norm(axes = hidden_states_21_axes_0, beta = model_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias, epsilon = var_5140, gamma = model_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_149)[name = tensor("hidden_states_21")]; + tensor x_121 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight, x = hidden_states_21)[name = tensor("linear_61")]; + tensor var_5676 = const()[name = tensor("op_5676"), val = tensor([1, 242, 12, 64])]; + tensor x_123 = reshape(shape = var_5676, x = x_121)[name = tensor("x_123")]; + tensor x_125 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight, x = hidden_states_21)[name = tensor("linear_62")]; + tensor var_5683 = const()[name = tensor("op_5683"), val = tensor([1, 242, 12, 64])]; + tensor x_127 = reshape(shape = var_5683, x = x_125)[name = tensor("x_127")]; + tensor x_129 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight, x = hidden_states_21)[name = tensor("linear_63")]; + tensor var_5690 = const()[name = tensor("op_5690"), val = tensor([1, 242, 12, 64])]; + tensor x_131 = reshape(shape = var_5690, x = x_129)[name = tensor("x_131")]; + tensor var_5692 = const()[name = tensor("op_5692"), val = tensor([0, 2, 1, 3])]; + tensor mul_10_y_0 = const()[name = tensor("mul_10_y_0"), val = tensor(0x1p-3)]; + tensor mul_10 = mul(x = x_123, y = mul_10_y_0)[name = tensor("mul_10")]; + tensor matmul_10_transpose_y_0 = const()[name = tensor("matmul_10_transpose_y_0"), val = tensor(true)]; + tensor matmul_10_transpose_x_0 = const()[name = tensor("matmul_10_transpose_x_0"), val = tensor(false)]; + tensor transpose_97_perm_0 = const()[name = tensor("transpose_97_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_98_perm_0 = const()[name = tensor("transpose_98_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_98 = transpose(perm = transpose_98_perm_0, x = x_127)[name = tensor("transpose_146")]; + tensor transpose_97 = transpose(perm = transpose_97_perm_0, x = mul_10)[name = tensor("transpose_147")]; + tensor matmul_10 = matmul(transpose_x = matmul_10_transpose_x_0, transpose_y = matmul_10_transpose_y_0, x = transpose_97, y = transpose_98)[name = tensor("matmul_10")]; + tensor add_10 = add(x = matmul_10, y = attention_mask_1)[name = tensor("add_10")]; + tensor softmax_10_axis_0 = const()[name = tensor("softmax_10_axis_0"), val = tensor(-1)]; + tensor softmax_10 = softmax(axis = softmax_10_axis_0, x = add_10)[name = tensor("softmax_10")]; + tensor attention_output_41_transpose_x_0 = const()[name = tensor("attention_output_41_transpose_x_0"), val = tensor(false)]; + tensor attention_output_41_transpose_y_0 = const()[name = tensor("attention_output_41_transpose_y_0"), val = tensor(false)]; + tensor value_layer_21 = transpose(perm = var_5692, x = x_131)[name = tensor("transpose_148")]; + tensor attention_output_41 = matmul(transpose_x = attention_output_41_transpose_x_0, transpose_y = attention_output_41_transpose_y_0, x = softmax_10, y = value_layer_21)[name = tensor("attention_output_41")]; + tensor attention_output_43_perm_0 = const()[name = tensor("attention_output_43_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_5696 = const()[name = tensor("op_5696"), val = tensor([1, 242, 768])]; + tensor attention_output_43 = transpose(perm = attention_output_43_perm_0, x = attention_output_41)[name = tensor("transpose_145")]; + tensor input_151 = reshape(shape = var_5696, x = attention_output_43)[name = tensor("input_151")]; + tensor input_153 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight, x = input_151)[name = tensor("linear_64")]; + tensor input_155 = add(x = hidden_states_21, y = input_153)[name = tensor("input_155")]; + tensor input_157_axes_0 = const()[name = tensor("input_157_axes_0"), val = tensor([-1])]; + tensor input_157 = layer_norm(axes = input_157_axes_0, beta = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_5140, gamma = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_155)[name = tensor("input_157")]; + tensor input_159 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight, x = input_157)[name = tensor("linear_65")]; + tensor input_161_mode_0 = const()[name = tensor("input_161_mode_0"), val = tensor("TANH_APPROXIMATION")]; + tensor input_161 = gelu(mode = input_161_mode_0, x = input_159)[name = tensor("input_161")]; + tensor ffn_output_21 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight, x = input_161)[name = tensor("linear_66")]; + tensor input_163 = add(x = ffn_output_21, y = input_157)[name = tensor("input_163")]; + tensor hidden_states_axes_0 = const()[name = tensor("hidden_states_axes_0"), val = tensor([-1])]; + tensor hidden_states = layer_norm(axes = hidden_states_axes_0, beta = model_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias, epsilon = var_5140, gamma = model_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_163)[name = tensor("hidden_states")]; + tensor x_133 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight, x = hidden_states)[name = tensor("linear_67")]; + tensor var_5725 = const()[name = tensor("op_5725"), val = tensor([1, 242, 12, 64])]; + tensor x_135 = reshape(shape = var_5725, x = x_133)[name = tensor("x_135")]; + tensor x_137 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight, x = hidden_states)[name = tensor("linear_68")]; + tensor var_5732 = const()[name = tensor("op_5732"), val = tensor([1, 242, 12, 64])]; + tensor x_139 = reshape(shape = var_5732, x = x_137)[name = tensor("x_139")]; + tensor x_141 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight, x = hidden_states)[name = tensor("linear_69")]; + tensor var_5739 = const()[name = tensor("op_5739"), val = tensor([1, 242, 12, 64])]; + tensor x_143 = reshape(shape = var_5739, x = x_141)[name = tensor("x_143")]; + tensor var_5741 = const()[name = tensor("op_5741"), val = tensor([0, 2, 1, 3])]; + tensor mul_11_y_0 = const()[name = tensor("mul_11_y_0"), val = tensor(0x1p-3)]; + tensor mul_11 = mul(x = x_135, y = mul_11_y_0)[name = tensor("mul_11")]; + tensor matmul_11_transpose_y_0 = const()[name = tensor("matmul_11_transpose_y_0"), val = tensor(true)]; + tensor matmul_11_transpose_x_0 = const()[name = tensor("matmul_11_transpose_x_0"), val = tensor(false)]; + tensor transpose_99_perm_0 = const()[name = tensor("transpose_99_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_100_perm_0 = const()[name = tensor("transpose_100_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_100 = transpose(perm = transpose_100_perm_0, x = x_139)[name = tensor("transpose_142")]; + tensor transpose_99 = transpose(perm = transpose_99_perm_0, x = mul_11)[name = tensor("transpose_143")]; + tensor matmul_11 = matmul(transpose_x = matmul_11_transpose_x_0, transpose_y = matmul_11_transpose_y_0, x = transpose_99, y = transpose_100)[name = tensor("matmul_11")]; + tensor add_11 = add(x = matmul_11, y = attention_mask_1)[name = tensor("add_11")]; + tensor softmax_11_axis_0 = const()[name = tensor("softmax_11_axis_0"), val = tensor(-1)]; + tensor softmax_11 = softmax(axis = softmax_11_axis_0, x = add_11)[name = tensor("softmax_11")]; + tensor attention_output_45_transpose_x_0 = const()[name = tensor("attention_output_45_transpose_x_0"), val = tensor(false)]; + tensor attention_output_45_transpose_y_0 = const()[name = tensor("attention_output_45_transpose_y_0"), val = tensor(false)]; + tensor value_layer = transpose(perm = var_5741, x = x_143)[name = tensor("transpose_144")]; + tensor attention_output_45 = matmul(transpose_x = attention_output_45_transpose_x_0, transpose_y = attention_output_45_transpose_y_0, x = softmax_11, y = value_layer)[name = tensor("attention_output_45")]; + tensor attention_output_perm_0 = const()[name = tensor("attention_output_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_5745 = const()[name = tensor("op_5745"), val = tensor([1, 242, 768])]; + tensor attention_output = transpose(perm = attention_output_perm_0, x = attention_output_45)[name = tensor("transpose_141")]; + tensor input_165 = reshape(shape = var_5745, x = attention_output)[name = tensor("input_165")]; + tensor input_167 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight, x = input_165)[name = tensor("linear_70")]; + tensor input_169 = add(x = hidden_states, y = input_167)[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 = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_5140, gamma = model_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_169)[name = tensor("input_171")]; + tensor input_173 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight, x = input_171)[name = tensor("linear_71")]; + tensor input_175_mode_0 = const()[name = tensor("input_175_mode_0"), val = tensor("TANH_APPROXIMATION")]; + tensor input_175 = gelu(mode = input_175_mode_0, x = input_173)[name = tensor("input_175")]; + tensor ffn_output_23 = linear(bias = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias, weight = model_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight, x = input_175)[name = tensor("linear_72")]; + tensor input_177 = add(x = ffn_output_23, y = input_171)[name = tensor("input_177")]; + 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 = model_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias, epsilon = var_5140, gamma = model_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_177)[name = tensor("sequence_output")]; + tensor var_5769 = linear(bias = model_bert_encoder_bias, weight = model_bert_encoder_weight, x = sequence_output)[name = tensor("linear_73")]; + tensor transpose_31_perm_0 = const()[name = tensor("transpose_31_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_5784 = const()[name = tensor("op_5784"), val = tensor(0x1.4f8b58p-17)]; + tensor var_5792 = const()[name = tensor("op_5792"), val = tensor(0x0p+0)]; + tensor var_5794 = const()[name = tensor("op_5794"), val = tensor(-1)]; + tensor var_5795 = const()[name = tensor("op_5795"), 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([242, 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_31 = transpose(perm = transpose_31_perm_0, x = var_5769)[name = tensor("transpose_140")]; + tensor x_149 = concat(axis = var_5794, interleave = x_149_interleave_0, values = (transpose_31, s))[name = tensor("x_149")]; + tensor var_5806_axes_0 = const()[name = tensor("op_5806_axes_0"), val = tensor([-1])]; + tensor var_5806 = expand_dims(axes = var_5806_axes_0, x = m_1)[name = tensor("op_5806")]; + tensor var_5807_perm_0 = const()[name = tensor("op_5807_perm_0"), val = tensor([1, 0, 2])]; + tensor var_5807 = transpose(perm = var_5807_perm_0, x = var_5806)[name = tensor("transpose_139")]; + tensor x_151 = select(a = var_5792, b = x_149, cond = var_5807)[name = tensor("x_151")]; + tensor add_12 = const()[name = tensor("add_12"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50965696)))]; + tensor add_13 = const()[name = tensor("add_13"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50969856)))]; + tensor concat_7 = const()[name = tensor("concat_7"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50974016)))]; + tensor concat_8 = const()[name = tensor("concat_8"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(53595520)))]; + tensor concat_9 = const()[name = tensor("concat_9"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54644160)))]; + tensor concat_10 = const()[name = tensor("concat_10"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57265664)))]; + 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(58314304)))]; + 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_12, bias_back = add_13, 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_8, weight_hh_back = concat_10, weight_ih = concat_7, weight_ih_back = concat_9, x = x_151)[name = tensor("x_159_batch_first")]; + tensor transpose_44_perm_0 = const()[name = tensor("transpose_44_perm_0"), val = tensor([1, 0, 2])]; + tensor h_1 = linear(bias = model_predictor_text_encoder_lstms_1_fc_bias, weight = model_predictor_text_encoder_lstms_1_fc_weight, x = style)[name = tensor("linear_74")]; + tensor var_5829 = const()[name = tensor("op_5829"), val = tensor([1, 1024, 1])]; + tensor h_3 = reshape(shape = var_5829, x = h_1)[name = tensor("h_3")]; + tensor var_5831_split_sizes_0 = const()[name = tensor("op_5831_split_sizes_0"), val = tensor([512, 512])]; + tensor var_5831_axis_0 = const()[name = tensor("op_5831_axis_0"), val = tensor(1)]; + tensor var_5831_0, tensor var_5831_1 = split(axis = var_5831_axis_0, split_sizes = var_5831_split_sizes_0, x = h_3)[name = tensor("op_5831")]; + 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_44 = transpose(perm = transpose_44_perm_0, x = x_159_batch_first_0)[name = tensor("transpose_138")]; + tensor x_167 = layer_norm(axes = x_167_axes_0, epsilon = var_5784, x = transpose_44)[name = tensor("x_167")]; + tensor var_5837_promoted = const()[name = tensor("op_5837_promoted"), val = tensor(0x1p+0)]; + tensor gamma_3 = transpose(perm = gamma_3_perm_0, x = var_5831_0)[name = tensor("transpose_137")]; + tensor var_5838 = add(x = gamma_3, y = var_5837_promoted)[name = tensor("op_5838")]; + tensor var_5839 = mul(x = var_5838, y = x_167)[name = tensor("op_5839")]; + tensor beta_3 = transpose(perm = beta_3_perm_0, x = var_5831_1)[name = tensor("transpose_136")]; + tensor x_169 = add(x = var_5839, y = beta_3)[name = tensor("x_169")]; + tensor x_173_interleave_0 = const()[name = tensor("x_173_interleave_0"), val = tensor(false)]; + tensor transpose_101_perm_0 = const()[name = tensor("transpose_101_perm_0"), val = tensor([0, -1, -2])]; + tensor transpose_102_perm_0 = const()[name = tensor("transpose_102_perm_0"), val = tensor([1, 2, 0])]; + tensor transpose_102 = transpose(perm = transpose_102_perm_0, x = s)[name = tensor("transpose_134")]; + tensor transpose_101 = transpose(perm = transpose_101_perm_0, x = x_169)[name = tensor("transpose_135")]; + tensor x_173 = concat(axis = var_5795, interleave = x_173_interleave_0, values = (transpose_101, transpose_102))[name = tensor("x_173")]; + tensor var_5849_perm_0 = const()[name = tensor("op_5849_perm_0"), val = tensor([0, -1, -2])]; + tensor var_5849 = transpose(perm = var_5849_perm_0, x = var_5806)[name = tensor("transpose_133")]; + tensor x_175 = select(a = var_5792, b = x_173, cond = var_5849)[name = tensor("x_175")]; + tensor transpose_35_perm_0 = const()[name = tensor("transpose_35_perm_0"), val = tensor([-1, 0, -2])]; + tensor add_14 = const()[name = tensor("add_14"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58316416)))]; + tensor add_15 = const()[name = tensor("add_15"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58320576)))]; + tensor concat_17 = const()[name = tensor("concat_17"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58324736)))]; + tensor concat_18 = const()[name = tensor("concat_18"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60946240)))]; + tensor concat_19 = const()[name = tensor("concat_19"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61994880)))]; + tensor concat_20 = const()[name = tensor("concat_20"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64616384)))]; + 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_35 = transpose(perm = transpose_35_perm_0, x = x_175)[name = tensor("transpose_132")]; + 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_14, bias_back = add_15, 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_18, weight_hh_back = concat_20, weight_ih = concat_17, weight_ih_back = concat_19, x = transpose_35)[name = tensor("x_179_batch_first")]; + tensor transpose_45_perm_0 = const()[name = tensor("transpose_45_perm_0"), val = tensor([1, 0, 2])]; + tensor h_5 = linear(bias = model_predictor_text_encoder_lstms_3_fc_bias, weight = model_predictor_text_encoder_lstms_3_fc_weight, x = style)[name = tensor("linear_75")]; + tensor var_5869 = const()[name = tensor("op_5869"), val = tensor([1, 1024, 1])]; + tensor h_7 = reshape(shape = var_5869, x = h_5)[name = tensor("h_7")]; + tensor var_5871_split_sizes_0 = const()[name = tensor("op_5871_split_sizes_0"), val = tensor([512, 512])]; + tensor var_5871_axis_0 = const()[name = tensor("op_5871_axis_0"), val = tensor(1)]; + tensor var_5871_0, tensor var_5871_1 = split(axis = var_5871_axis_0, split_sizes = var_5871_split_sizes_0, x = h_7)[name = tensor("op_5871")]; + 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_45 = transpose(perm = transpose_45_perm_0, x = x_179_batch_first_0)[name = tensor("transpose_131")]; + tensor x_187 = layer_norm(axes = x_187_axes_0, epsilon = var_5784, x = transpose_45)[name = tensor("x_187")]; + tensor var_5877_promoted = const()[name = tensor("op_5877_promoted"), val = tensor(0x1p+0)]; + tensor gamma_7 = transpose(perm = gamma_7_perm_0, x = var_5871_0)[name = tensor("transpose_130")]; + tensor var_5878 = add(x = gamma_7, y = var_5877_promoted)[name = tensor("op_5878")]; + tensor var_5879 = mul(x = var_5878, y = x_187)[name = tensor("op_5879")]; + tensor beta_7 = transpose(perm = beta_7_perm_0, x = var_5871_1)[name = tensor("transpose_129")]; + tensor x_189 = add(x = var_5879, y = beta_7)[name = tensor("x_189")]; + tensor x_193_interleave_0 = const()[name = tensor("x_193_interleave_0"), val = tensor(false)]; + tensor transpose_105_perm_0 = const()[name = tensor("transpose_105_perm_0"), val = tensor([0, -1, -2])]; + tensor transpose_105 = transpose(perm = transpose_105_perm_0, x = x_189)[name = tensor("transpose_128")]; + tensor x_193 = concat(axis = var_5795, interleave = x_193_interleave_0, values = (transpose_105, transpose_102))[name = tensor("x_193")]; + tensor x_195 = select(a = var_5792, b = x_193, cond = var_5849)[name = tensor("x_195")]; + tensor transpose_37_perm_0 = const()[name = tensor("transpose_37_perm_0"), val = tensor([-1, 0, -2])]; + tensor add_16 = const()[name = tensor("add_16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65665024)))]; + tensor add_17 = const()[name = tensor("add_17"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65669184)))]; + tensor concat_27 = const()[name = tensor("concat_27"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65673344)))]; + tensor concat_28 = const()[name = tensor("concat_28"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68294848)))]; + tensor concat_29 = const()[name = tensor("concat_29"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69343488)))]; + tensor concat_30 = const()[name = tensor("concat_30"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71964992)))]; + 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_37 = transpose(perm = transpose_37_perm_0, x = x_195)[name = tensor("transpose_127")]; + 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_16, bias_back = add_17, 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_28, weight_hh_back = concat_30, weight_ih = concat_27, weight_ih_back = concat_29, x = transpose_37)[name = tensor("x_199_batch_first")]; + tensor transpose_46_perm_0 = const()[name = tensor("transpose_46_perm_0"), val = tensor([1, 0, 2])]; + tensor h_9 = linear(bias = model_predictor_text_encoder_lstms_5_fc_bias, weight = model_predictor_text_encoder_lstms_5_fc_weight, x = style)[name = tensor("linear_76")]; + tensor var_5909 = const()[name = tensor("op_5909"), val = tensor([1, 1024, 1])]; + tensor h_11 = reshape(shape = var_5909, x = h_9)[name = tensor("h_11")]; + tensor var_5911_split_sizes_0 = const()[name = tensor("op_5911_split_sizes_0"), val = tensor([512, 512])]; + tensor var_5911_axis_0 = const()[name = tensor("op_5911_axis_0"), val = tensor(1)]; + tensor var_5911_0, tensor var_5911_1 = split(axis = var_5911_axis_0, split_sizes = var_5911_split_sizes_0, x = h_11)[name = tensor("op_5911")]; + 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_46 = transpose(perm = transpose_46_perm_0, x = x_199_batch_first_0)[name = tensor("transpose_126")]; + tensor x_207 = layer_norm(axes = x_207_axes_0, epsilon = var_5784, x = transpose_46)[name = tensor("x_207")]; + tensor var_5917_promoted = const()[name = tensor("op_5917_promoted"), val = tensor(0x1p+0)]; + tensor gamma_11 = transpose(perm = gamma_11_perm_0, x = var_5911_0)[name = tensor("transpose_125")]; + tensor var_5918 = add(x = gamma_11, y = var_5917_promoted)[name = tensor("op_5918")]; + tensor var_5919 = mul(x = var_5918, y = x_207)[name = tensor("op_5919")]; + tensor beta_11 = transpose(perm = beta_11_perm_0, x = var_5911_1)[name = tensor("transpose_124")]; + tensor x_209 = add(x = var_5919, y = beta_11)[name = tensor("x_209")]; + tensor x_213_interleave_0 = const()[name = tensor("x_213_interleave_0"), val = tensor(false)]; + tensor transpose_106_perm_0 = const()[name = tensor("transpose_106_perm_0"), val = tensor([0, -1, -2])]; + tensor transpose_106 = transpose(perm = transpose_106_perm_0, x = x_209)[name = tensor("transpose_123")]; + tensor x_213 = concat(axis = var_5795, interleave = x_213_interleave_0, values = (transpose_106, transpose_102))[name = tensor("x_213")]; + tensor x_215 = select(a = var_5792, b = x_213, cond = var_5849)[name = tensor("x_215")]; + tensor transpose_39_perm_0 = const()[name = tensor("transpose_39_perm_0"), val = tensor([-1, 0, -2])]; + tensor add_18 = const()[name = tensor("add_18"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73013632)))]; + tensor add_19 = const()[name = tensor("add_19"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73017792)))]; + tensor concat_37 = const()[name = tensor("concat_37"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73021952)))]; + tensor concat_38 = const()[name = tensor("concat_38"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75643456)))]; + tensor concat_39 = const()[name = tensor("concat_39"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76692096)))]; + tensor concat_40 = const()[name = tensor("concat_40"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79313600)))]; + tensor input_191_batch_first_direction_0 = const()[name = tensor("input_191_batch_first_direction_0"), val = tensor("bidirectional")]; + tensor input_191_batch_first_output_sequence_0 = const()[name = tensor("input_191_batch_first_output_sequence_0"), val = tensor(true)]; + tensor input_191_batch_first_recurrent_activation_0 = const()[name = tensor("input_191_batch_first_recurrent_activation_0"), val = tensor("sigmoid")]; + tensor input_191_batch_first_cell_activation_0 = const()[name = tensor("input_191_batch_first_cell_activation_0"), val = tensor("tanh")]; + tensor input_191_batch_first_activation_0 = const()[name = tensor("input_191_batch_first_activation_0"), val = tensor("tanh")]; + tensor transpose_39 = transpose(perm = transpose_39_perm_0, x = x_215)[name = tensor("transpose_122")]; + tensor input_191_batch_first_0, tensor input_191_batch_first_1, tensor input_191_batch_first_2 = lstm(activation = input_191_batch_first_activation_0, bias = add_18, bias_back = add_19, cell_activation = input_191_batch_first_cell_activation_0, direction = input_191_batch_first_direction_0, initial_c = x_159_batch_first_lstm_h0_reshaped, initial_h = x_159_batch_first_lstm_h0_reshaped, output_sequence = input_191_batch_first_output_sequence_0, recurrent_activation = input_191_batch_first_recurrent_activation_0, weight_hh = concat_38, weight_hh_back = concat_40, weight_ih = concat_37, weight_ih_back = concat_39, x = transpose_39)[name = tensor("input_191_batch_first")]; + tensor input_191_perm_0 = const()[name = tensor("input_191_perm_0"), val = tensor([1, 0, 2])]; + tensor input_191 = transpose(perm = input_191_perm_0, x = input_191_batch_first_0)[name = tensor("transpose_121")]; + tensor duration_1 = linear(bias = model_predictor_duration_proj_linear_layer_bias, weight = model_predictor_duration_proj_linear_layer_weight, x = input_191)[name = tensor("linear_77")]; + tensor var_5958 = sigmoid(x = duration_1)[name = tensor("op_5958")]; + tensor var_5963_axes_0 = const()[name = tensor("op_5963_axes_0"), val = tensor([-1])]; + tensor var_5963_keep_dims_0 = const()[name = tensor("op_5963_keep_dims_0"), val = tensor(false)]; + tensor var_5963 = reduce_sum(axes = var_5963_axes_0, keep_dims = var_5963_keep_dims_0, x = var_5958)[name = tensor("op_5963")]; + tensor var_5966 = round(x = var_5963)[name = tensor("op_5966")]; + tensor const_114 = const()[name = tensor("const_114"), val = tensor(0x1.fffffep+127)]; + tensor var_5967_promoted = const()[name = tensor("op_5967_promoted"), val = tensor(0x1p+0)]; + tensor clip_0 = clip(alpha = var_5967_promoted, beta = const_114, x = var_5966)[name = tensor("clip_0")]; + tensor cast_60_dtype_0 = const()[name = tensor("cast_60_dtype_0"), val = tensor("fp32")]; + tensor cast_60 = cast(dtype = cast_60_dtype_0, x = attention_mask)[name = tensor("cast_161")]; + tensor pred_dur = mul(x = clip_0, y = cast_60)[name = tensor("pred_dur")]; + tensor total_frames_1_axes_0 = const()[name = tensor("total_frames_1_axes_0"), val = tensor([1])]; + tensor total_frames_1_keep_dims_0 = const()[name = tensor("total_frames_1_keep_dims_0"), val = tensor(false)]; + tensor total_frames_1 = reduce_sum(axes = total_frames_1_axes_0, keep_dims = total_frames_1_keep_dims_0, x = pred_dur)[name = tensor("total_frames_1")]; + tensor var_5981_promoted = const()[name = tensor("op_5981_promoted"), val = tensor(0x1.2cp+9)]; + tensor var_5982 = mul(x = total_frames_1, y = var_5981_promoted)[name = tensor("op_5982")]; + tensor cast_61_dtype_0 = const()[name = tensor("cast_61_dtype_0"), val = tensor("int32")]; + tensor var_5992 = const()[name = tensor("op_5992"), val = tensor(1)]; + tensor cumulative_exclusive_0 = const()[name = tensor("cumulative_exclusive_0"), val = tensor(false)]; + tensor cumulative_reverse_0 = const()[name = tensor("cumulative_reverse_0"), val = tensor(false)]; + tensor cumulative = cumsum(axis = var_5992, exclusive = cumulative_exclusive_0, reverse = cumulative_reverse_0, x = pred_dur)[name = tensor("cumulative")]; + tensor var_6011_axes_0 = const()[name = tensor("op_6011_axes_0"), val = tensor([1])]; + tensor var_6011 = expand_dims(axes = var_6011_axes_0, x = cumulative)[name = tensor("op_6011")]; + tensor var_6009_promoted = const()[name = tensor("op_6009_promoted"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80362240)))]; + tensor var_6012 = greater(x = var_6009_promoted, y = var_6011)[name = tensor("op_6012")]; + tensor cast_62_dtype_0 = const()[name = tensor("cast_62_dtype_0"), val = tensor("fp32")]; + tensor token_indices_axes_0 = const()[name = tensor("token_indices_axes_0"), val = tensor([-1])]; + tensor token_indices_keep_dims_0 = const()[name = tensor("token_indices_keep_dims_0"), val = tensor(false)]; + tensor cast_62 = cast(dtype = cast_62_dtype_0, x = var_6012)[name = tensor("cast_159")]; + tensor token_indices = reduce_sum(axes = token_indices_axes_0, keep_dims = token_indices_keep_dims_0, x = cast_62)[name = tensor("token_indices")]; + tensor var_6022_promoted = const()[name = tensor("op_6022_promoted"), val = tensor(0x0p+0)]; + tensor var_6021_promoted = const()[name = tensor("op_6021_promoted"), val = tensor(0x1.e2p+7)]; + tensor clip_1 = clip(alpha = var_6022_promoted, beta = var_6021_promoted, x = token_indices)[name = tensor("clip_1")]; + tensor cast_63_dtype_0 = const()[name = tensor("cast_63_dtype_0"), val = tensor("int32")]; + tensor var_6038_begin_0 = const()[name = tensor("op_6038_begin_0"), val = tensor([0, -1])]; + tensor var_6038_end_0 = const()[name = tensor("op_6038_end_0"), val = tensor([1, 242])]; + tensor var_6038_end_mask_0 = const()[name = tensor("op_6038_end_mask_0"), val = tensor([true, true])]; + tensor var_6038 = slice_by_index(begin = var_6038_begin_0, end = var_6038_end_0, end_mask = var_6038_end_mask_0, x = cumulative)[name = tensor("op_6038")]; + tensor const_117 = const()[name = tensor("const_117"), val = tensor(-0x1.fffffep+127)]; + tensor var_6040_promoted = const()[name = tensor("op_6040_promoted"), val = tensor(0x1.2cp+9)]; + tensor clip_2 = clip(alpha = const_117, beta = var_6040_promoted, x = var_6038)[name = tensor("clip_2")]; + tensor frame_positions_promoted = const()[name = tensor("frame_positions_promoted"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80364736)))]; + tensor var_6042 = less_equal(x = frame_positions_promoted, y = clip_2)[name = tensor("op_6042")]; + tensor cast_64_dtype_0 = const()[name = tensor("cast_64_dtype_0"), val = tensor("fp32")]; + tensor var_6052_axes_0 = const()[name = tensor("op_6052_axes_0"), val = tensor([1])]; + tensor cast_63 = cast(dtype = cast_63_dtype_0, x = clip_1)[name = tensor("cast_158")]; + tensor var_6052 = expand_dims(axes = var_6052_axes_0, x = cast_63)[name = tensor("op_6052")]; + tensor gather_idx_reps_0 = const()[name = tensor("gather_idx_reps_0"), val = tensor([1, 640, 1])]; + tensor gather_idx = tile(reps = gather_idx_reps_0, x = var_6052)[name = tensor("gather_idx")]; + tensor var_6062 = const()[name = tensor("op_6062"), val = tensor(2)]; + tensor var_6064_validate_indices_0 = const()[name = tensor("op_6064_validate_indices_0"), val = tensor(false)]; + tensor var_6064 = gather_along_axis(axis = var_6062, indices = gather_idx, validate_indices = var_6064_validate_indices_0, x = x_215)[name = tensor("op_6064")]; + tensor var_6066_axes_0 = const()[name = tensor("op_6066_axes_0"), val = tensor([1])]; + tensor cast_64 = cast(dtype = cast_64_dtype_0, x = var_6042)[name = tensor("cast_157")]; + tensor var_6066 = expand_dims(axes = var_6066_axes_0, x = cast_64)[name = tensor("op_6066")]; + tensor x_217 = mul(x = var_6064, y = var_6066)[name = tensor("x_217")]; + tensor transpose_40_perm_0 = const()[name = tensor("transpose_40_perm_0"), val = tensor([-1, 0, -2])]; + tensor add_20 = const()[name = tensor("add_20"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80367232)))]; + tensor add_21 = const()[name = tensor("add_21"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80371392)))]; + tensor concat_49 = const()[name = tensor("concat_49"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80375552)))]; + tensor concat_50 = const()[name = tensor("concat_50"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82997056)))]; + tensor concat_51 = const()[name = tensor("concat_51"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84045696)))]; + tensor concat_52 = const()[name = tensor("concat_52"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86667200)))]; + 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_40 = transpose(perm = transpose_40_perm_0, x = x_217)[name = tensor("transpose_120")]; + 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_20, bias_back = add_21, 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_50, weight_hh_back = concat_52, weight_ih = concat_49, weight_ih_back = concat_51, x = transpose_40)[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_195_perm_0 = const()[name = tensor("input_195_perm_0"), val = tensor([0, -1, -2])]; + tensor var_6098 = const()[name = tensor("op_6098"), val = tensor(0x1.99999ap-3)]; + tensor var_6101 = const()[name = tensor("op_6101"), val = tensor(0x1.4f8b58p-17)]; + tensor h_13 = linear(bias = model_predictor_F0_0_norm1_fc_bias, weight = model_predictor_F0_0_norm1_fc_weight, x = style)[name = tensor("linear_78")]; + tensor var_6108 = const()[name = tensor("op_6108"), val = tensor([1, 1024, 1])]; + tensor h_15 = reshape(shape = var_6108, x = h_13)[name = tensor("h_15")]; + tensor var_6110_split_sizes_0 = const()[name = tensor("op_6110_split_sizes_0"), val = tensor([512, 512])]; + tensor var_6110_axis_0 = const()[name = tensor("op_6110_axis_0"), val = tensor(1)]; + tensor var_6110_0, tensor var_6110_1 = split(axis = var_6110_axis_0, split_sizes = var_6110_split_sizes_0, x = h_15)[name = tensor("op_6110")]; + tensor var_6112_promoted = const()[name = tensor("op_6112_promoted"), val = tensor(0x1p+0)]; + tensor var_6113 = add(x = var_6110_0, y = var_6112_promoted)[name = tensor("op_6113")]; + tensor x_219 = transpose(perm = x_219_perm_0, x = x_219_batch_first_0)[name = tensor("transpose_119")]; + tensor input_195 = transpose(perm = input_195_perm_0, x = x_219)[name = tensor("transpose_118")]; + tensor var_6114 = instance_norm(beta = model_decoder_decode_3_norm2_norm_bias, epsilon = var_6101, gamma = model_decoder_decode_3_norm2_norm_weight, x = input_195)[name = tensor("op_6114")]; + tensor var_6115 = mul(x = var_6113, y = var_6114)[name = tensor("op_6115")]; + tensor input_197 = add(x = var_6115, y = var_6110_1)[name = tensor("input_197")]; + tensor input_199 = leaky_relu(alpha = var_6098, x = input_197)[name = tensor("input_199")]; + tensor weight_15 = const()[name = tensor("weight_15"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(87715840)))]; + tensor input_203_pad_type_0 = const()[name = tensor("input_203_pad_type_0"), val = tensor("custom")]; + tensor input_203_pad_0 = const()[name = tensor("input_203_pad_0"), val = tensor([1, 1])]; + tensor input_203_strides_0 = const()[name = tensor("input_203_strides_0"), val = tensor([1])]; + tensor input_203_dilations_0 = const()[name = tensor("input_203_dilations_0"), val = tensor([1])]; + tensor input_203_groups_0 = const()[name = tensor("input_203_groups_0"), val = tensor(1)]; + tensor input_203 = conv(bias = model_predictor_F0_0_conv1_bias, dilations = input_203_dilations_0, groups = input_203_groups_0, pad = input_203_pad_0, pad_type = input_203_pad_type_0, strides = input_203_strides_0, weight = weight_15, x = input_199)[name = tensor("input_203")]; + tensor h_17 = linear(bias = model_predictor_F0_0_norm2_fc_bias, weight = model_predictor_F0_0_norm2_fc_weight, x = style)[name = tensor("linear_79")]; + tensor var_6128 = const()[name = tensor("op_6128"), val = tensor([1, 1024, 1])]; + tensor h_19 = reshape(shape = var_6128, x = h_17)[name = tensor("h_19")]; + tensor var_6130_split_sizes_0 = const()[name = tensor("op_6130_split_sizes_0"), val = tensor([512, 512])]; + tensor var_6130_axis_0 = const()[name = tensor("op_6130_axis_0"), val = tensor(1)]; + tensor var_6130_0, tensor var_6130_1 = split(axis = var_6130_axis_0, split_sizes = var_6130_split_sizes_0, x = h_19)[name = tensor("op_6130")]; + tensor var_6132_promoted = const()[name = tensor("op_6132_promoted"), val = tensor(0x1p+0)]; + tensor var_6133 = add(x = var_6130_0, y = var_6132_promoted)[name = tensor("op_6133")]; + tensor var_6134 = instance_norm(beta = model_decoder_decode_3_norm2_norm_bias, epsilon = var_6101, gamma = model_decoder_decode_3_norm2_norm_weight, x = input_203)[name = tensor("op_6134")]; + tensor var_6135 = mul(x = var_6133, y = var_6134)[name = tensor("op_6135")]; + tensor input_205 = add(x = var_6135, y = var_6130_1)[name = tensor("input_205")]; + tensor input_207 = leaky_relu(alpha = var_6098, x = input_205)[name = tensor("input_207")]; + tensor weight_19 = const()[name = tensor("weight_19"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90861632)))]; + 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 = model_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_207)[name = tensor("out_1")]; + tensor var_6145 = add(x = out_1, y = input_195)[name = tensor("op_6145")]; + tensor var_6148 = const()[name = tensor("op_6148"), val = tensor(0x1.6a09e6p-1)]; + tensor input_211 = mul(x = var_6145, y = var_6148)[name = tensor("input_211")]; + tensor var_6156 = const()[name = tensor("op_6156"), val = tensor(0x1.99999ap-3)]; + tensor var_6160 = const()[name = tensor("op_6160"), val = tensor(0x1.4f8b58p-17)]; + tensor h_21 = linear(bias = model_predictor_F0_1_norm1_fc_bias, weight = model_predictor_F0_1_norm1_fc_weight, x = style)[name = tensor("linear_80")]; + tensor var_6167 = const()[name = tensor("op_6167"), val = tensor([1, 1024, 1])]; + tensor h_23 = reshape(shape = var_6167, x = h_21)[name = tensor("h_23")]; + tensor var_6169_split_sizes_0 = const()[name = tensor("op_6169_split_sizes_0"), val = tensor([512, 512])]; + tensor var_6169_axis_0 = const()[name = tensor("op_6169_axis_0"), val = tensor(1)]; + tensor var_6169_0, tensor var_6169_1 = split(axis = var_6169_axis_0, split_sizes = var_6169_split_sizes_0, x = h_23)[name = tensor("op_6169")]; + tensor var_6171_promoted = const()[name = tensor("op_6171_promoted"), val = tensor(0x1p+0)]; + tensor var_6172 = add(x = var_6169_0, y = var_6171_promoted)[name = tensor("op_6172")]; + tensor var_6173 = instance_norm(beta = model_decoder_decode_3_norm2_norm_bias, epsilon = var_6160, gamma = model_decoder_decode_3_norm2_norm_weight, x = input_211)[name = tensor("op_6173")]; + tensor var_6174 = mul(x = var_6172, y = var_6173)[name = tensor("op_6174")]; + tensor input_213 = add(x = var_6174, y = var_6169_1)[name = tensor("input_213")]; + tensor input_215 = leaky_relu(alpha = var_6156, x = input_213)[name = tensor("input_215")]; + tensor var_6177 = const()[name = tensor("op_6177"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94007424)))]; + 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, 1201])]; + tensor conv_transpose_0_has_output_shape = conv_transpose(bias = model_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_6177, x = input_215)[name = tensor("conv_transpose_0_has_output_shape")]; + tensor input_217_begin_0 = const()[name = tensor("input_217_begin_0"), val = tensor([0, 0, 1])]; + tensor input_217_end_0 = const()[name = tensor("input_217_end_0"), val = tensor([0, 0, 0])]; + tensor input_217_begin_mask_0 = const()[name = tensor("input_217_begin_mask_0"), val = tensor([true, true, false])]; + tensor input_217_end_mask_0 = const()[name = tensor("input_217_end_mask_0"), val = tensor([true, true, true])]; + tensor input_217 = slice_by_index(begin = input_217_begin_0, begin_mask = input_217_begin_mask_0, end = input_217_end_0, end_mask = input_217_end_mask_0, x = conv_transpose_0_has_output_shape)[name = tensor("input_217")]; + tensor weight_23 = const()[name = tensor("weight_23"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94013632)))]; + tensor input_221_pad_type_0 = const()[name = tensor("input_221_pad_type_0"), val = tensor("custom")]; + tensor input_221_pad_0 = const()[name = tensor("input_221_pad_0"), val = tensor([1, 1])]; + tensor input_221_strides_0 = const()[name = tensor("input_221_strides_0"), val = tensor([1])]; + tensor input_221_dilations_0 = const()[name = tensor("input_221_dilations_0"), val = tensor([1])]; + tensor input_221_groups_0 = const()[name = tensor("input_221_groups_0"), val = tensor(1)]; + tensor input_221 = conv(bias = model_predictor_F0_1_conv1_bias, dilations = input_221_dilations_0, groups = input_221_groups_0, pad = input_221_pad_0, pad_type = input_221_pad_type_0, strides = input_221_strides_0, weight = weight_23, x = input_217)[name = tensor("input_221")]; + tensor h_25 = linear(bias = model_predictor_F0_1_norm2_fc_bias, weight = model_predictor_F0_1_norm2_fc_weight, x = style)[name = tensor("linear_81")]; + tensor var_6193 = const()[name = tensor("op_6193"), val = tensor([1, 512, 1])]; + tensor h_27 = reshape(shape = var_6193, x = h_25)[name = tensor("h_27")]; + tensor var_6195_split_sizes_0 = const()[name = tensor("op_6195_split_sizes_0"), val = tensor([256, 256])]; + tensor var_6195_axis_0 = const()[name = tensor("op_6195_axis_0"), val = tensor(1)]; + tensor var_6195_0, tensor var_6195_1 = split(axis = var_6195_axis_0, split_sizes = var_6195_split_sizes_0, x = h_27)[name = tensor("op_6195")]; + tensor var_6197_promoted = const()[name = tensor("op_6197_promoted"), val = tensor(0x1p+0)]; + tensor var_6198 = add(x = var_6195_0, y = var_6197_promoted)[name = tensor("op_6198")]; + tensor var_6199 = instance_norm(beta = model_decoder_generator_resblocks_2_adain2_2_norm_bias, epsilon = var_6160, gamma = model_decoder_generator_resblocks_2_adain2_2_norm_weight, x = input_221)[name = tensor("op_6199")]; + tensor var_6200 = mul(x = var_6198, y = var_6199)[name = tensor("op_6200")]; + tensor input_223 = add(x = var_6200, y = var_6195_1)[name = tensor("input_223")]; + tensor input_225 = leaky_relu(alpha = var_6156, x = input_223)[name = tensor("input_225")]; + tensor weight_27 = const()[name = tensor("weight_27"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95586560)))]; + 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 = model_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_225)[name = tensor("out_3")]; + tensor expand_dims_1_axes_0 = const()[name = tensor("expand_dims_1_axes_0"), val = tensor([3])]; + tensor expand_dims_1 = expand_dims(axes = expand_dims_1_axes_0, x = input_211)[name = tensor("expand_dims_1")]; + 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_1)[name = tensor("upsample_nearest_neighbor_0")]; + tensor input_229_axes_0 = const()[name = tensor("input_229_axes_0"), val = tensor([3])]; + tensor input_229 = squeeze(axes = input_229_axes_0, x = upsample_nearest_neighbor_0)[name = tensor("input_229")]; + tensor weight_29 = const()[name = tensor("weight_29"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96373056)))]; + tensor var_6217_pad_type_0 = const()[name = tensor("op_6217_pad_type_0"), val = tensor("valid")]; + tensor var_6217_strides_0 = const()[name = tensor("op_6217_strides_0"), val = tensor([1])]; + tensor var_6217_pad_0 = const()[name = tensor("op_6217_pad_0"), val = tensor([0, 0])]; + tensor var_6217_dilations_0 = const()[name = tensor("op_6217_dilations_0"), val = tensor([1])]; + tensor var_6217_groups_0 = const()[name = tensor("op_6217_groups_0"), val = tensor(1)]; + tensor var_6217 = conv(dilations = var_6217_dilations_0, groups = var_6217_groups_0, pad = var_6217_pad_0, pad_type = var_6217_pad_type_0, strides = var_6217_strides_0, weight = weight_29, x = input_229)[name = tensor("op_6217")]; + tensor var_6218 = add(x = out_3, y = var_6217)[name = tensor("op_6218")]; + tensor var_6221 = const()[name = tensor("op_6221"), val = tensor(0x1.6a09e6p-1)]; + tensor input_231 = mul(x = var_6218, y = var_6221)[name = tensor("input_231")]; + tensor var_6227 = const()[name = tensor("op_6227"), val = tensor(0x1.99999ap-3)]; + tensor var_6230 = const()[name = tensor("op_6230"), val = tensor(0x1.4f8b58p-17)]; + tensor h_29 = linear(bias = model_predictor_F0_2_norm1_fc_bias, weight = model_predictor_F0_2_norm1_fc_weight, x = style)[name = tensor("linear_82")]; + tensor var_6237 = const()[name = tensor("op_6237"), val = tensor([1, 512, 1])]; + tensor h_31 = reshape(shape = var_6237, x = h_29)[name = tensor("h_31")]; + tensor var_6239_split_sizes_0 = const()[name = tensor("op_6239_split_sizes_0"), val = tensor([256, 256])]; + tensor var_6239_axis_0 = const()[name = tensor("op_6239_axis_0"), val = tensor(1)]; + tensor var_6239_0, tensor var_6239_1 = split(axis = var_6239_axis_0, split_sizes = var_6239_split_sizes_0, x = h_31)[name = tensor("op_6239")]; + tensor var_6241_promoted = const()[name = tensor("op_6241_promoted"), val = tensor(0x1p+0)]; + tensor var_6242 = add(x = var_6239_0, y = var_6241_promoted)[name = tensor("op_6242")]; + tensor var_6243 = instance_norm(beta = model_decoder_generator_resblocks_2_adain2_2_norm_bias, epsilon = var_6230, gamma = model_decoder_generator_resblocks_2_adain2_2_norm_weight, x = input_231)[name = tensor("op_6243")]; + tensor var_6244 = mul(x = var_6242, y = var_6243)[name = tensor("op_6244")]; + tensor input_233 = add(x = var_6244, y = var_6239_1)[name = tensor("input_233")]; + tensor input_235 = leaky_relu(alpha = var_6227, x = input_233)[name = tensor("input_235")]; + tensor weight_33 = const()[name = tensor("weight_33"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96897408)))]; + tensor input_239_pad_type_0 = const()[name = tensor("input_239_pad_type_0"), val = tensor("custom")]; + tensor input_239_pad_0 = const()[name = tensor("input_239_pad_0"), val = tensor([1, 1])]; + tensor input_239_strides_0 = const()[name = tensor("input_239_strides_0"), val = tensor([1])]; + tensor input_239_dilations_0 = const()[name = tensor("input_239_dilations_0"), val = tensor([1])]; + tensor input_239_groups_0 = const()[name = tensor("input_239_groups_0"), val = tensor(1)]; + tensor input_239 = conv(bias = model_predictor_F0_2_conv1_bias, dilations = input_239_dilations_0, groups = input_239_groups_0, pad = input_239_pad_0, pad_type = input_239_pad_type_0, strides = input_239_strides_0, weight = weight_33, x = input_235)[name = tensor("input_239")]; + tensor h_33 = linear(bias = model_predictor_F0_2_norm2_fc_bias, weight = model_predictor_F0_2_norm2_fc_weight, x = style)[name = tensor("linear_83")]; + tensor var_6257 = const()[name = tensor("op_6257"), val = tensor([1, 512, 1])]; + tensor h_35 = reshape(shape = var_6257, x = h_33)[name = tensor("h_35")]; + tensor var_6259_split_sizes_0 = const()[name = tensor("op_6259_split_sizes_0"), val = tensor([256, 256])]; + tensor var_6259_axis_0 = const()[name = tensor("op_6259_axis_0"), val = tensor(1)]; + tensor var_6259_0, tensor var_6259_1 = split(axis = var_6259_axis_0, split_sizes = var_6259_split_sizes_0, x = h_35)[name = tensor("op_6259")]; + tensor var_6261_promoted = const()[name = tensor("op_6261_promoted"), val = tensor(0x1p+0)]; + tensor var_6262 = add(x = var_6259_0, y = var_6261_promoted)[name = tensor("op_6262")]; + tensor var_6263 = instance_norm(beta = model_decoder_generator_resblocks_2_adain2_2_norm_bias, epsilon = var_6230, gamma = model_decoder_generator_resblocks_2_adain2_2_norm_weight, x = input_239)[name = tensor("op_6263")]; + tensor var_6264 = mul(x = var_6262, y = var_6263)[name = tensor("op_6264")]; + tensor input_241 = add(x = var_6264, y = var_6259_1)[name = tensor("input_241")]; + tensor input_243 = leaky_relu(alpha = var_6227, x = input_241)[name = tensor("input_243")]; + tensor weight_37 = const()[name = tensor("weight_37"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97683904)))]; + 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 = model_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_243)[name = tensor("out_5")]; + tensor var_6274 = add(x = out_5, y = input_231)[name = tensor("op_6274")]; + tensor var_6277 = const()[name = tensor("op_6277"), val = tensor(0x1.6a09e6p-1)]; + tensor input_247 = mul(x = var_6274, y = var_6277)[name = tensor("input_247")]; + 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 = model_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 = model_predictor_F0_proj_weight, x = input_247)[name = tensor("F0_1")]; + tensor var_6295 = const()[name = tensor("op_6295"), val = tensor(0x1.99999ap-3)]; + tensor var_6298 = const()[name = tensor("op_6298"), val = tensor(0x1.4f8b58p-17)]; + tensor h_37 = linear(bias = model_predictor_N_0_norm1_fc_bias, weight = model_predictor_N_0_norm1_fc_weight, x = style)[name = tensor("linear_84")]; + tensor var_6305 = const()[name = tensor("op_6305"), val = tensor([1, 1024, 1])]; + tensor h_39 = reshape(shape = var_6305, x = h_37)[name = tensor("h_39")]; + tensor var_6307_split_sizes_0 = const()[name = tensor("op_6307_split_sizes_0"), val = tensor([512, 512])]; + tensor var_6307_axis_0 = const()[name = tensor("op_6307_axis_0"), val = tensor(1)]; + tensor var_6307_0, tensor var_6307_1 = split(axis = var_6307_axis_0, split_sizes = var_6307_split_sizes_0, x = h_39)[name = tensor("op_6307")]; + tensor var_6309_promoted = const()[name = tensor("op_6309_promoted"), val = tensor(0x1p+0)]; + tensor var_6310 = add(x = var_6307_0, y = var_6309_promoted)[name = tensor("op_6310")]; + tensor var_6312 = mul(x = var_6310, y = var_6114)[name = tensor("op_6312")]; + tensor input_251 = add(x = var_6312, y = var_6307_1)[name = tensor("input_251")]; + tensor input_253 = leaky_relu(alpha = var_6295, x = input_251)[name = tensor("input_253")]; + tensor weight_43 = const()[name = tensor("weight_43"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98470400)))]; + tensor input_257_pad_type_0 = const()[name = tensor("input_257_pad_type_0"), val = tensor("custom")]; + tensor input_257_pad_0 = const()[name = tensor("input_257_pad_0"), val = tensor([1, 1])]; + tensor input_257_strides_0 = const()[name = tensor("input_257_strides_0"), val = tensor([1])]; + tensor input_257_dilations_0 = const()[name = tensor("input_257_dilations_0"), val = tensor([1])]; + tensor input_257_groups_0 = const()[name = tensor("input_257_groups_0"), val = tensor(1)]; + tensor input_257 = conv(bias = model_predictor_N_0_conv1_bias, dilations = input_257_dilations_0, groups = input_257_groups_0, pad = input_257_pad_0, pad_type = input_257_pad_type_0, strides = input_257_strides_0, weight = weight_43, x = input_253)[name = tensor("input_257")]; + tensor h_41 = linear(bias = model_predictor_N_0_norm2_fc_bias, weight = model_predictor_N_0_norm2_fc_weight, x = style)[name = tensor("linear_85")]; + tensor var_6325 = const()[name = tensor("op_6325"), val = tensor([1, 1024, 1])]; + tensor h_43 = reshape(shape = var_6325, x = h_41)[name = tensor("h_43")]; + tensor var_6327_split_sizes_0 = const()[name = tensor("op_6327_split_sizes_0"), val = tensor([512, 512])]; + tensor var_6327_axis_0 = const()[name = tensor("op_6327_axis_0"), val = tensor(1)]; + tensor var_6327_0, tensor var_6327_1 = split(axis = var_6327_axis_0, split_sizes = var_6327_split_sizes_0, x = h_43)[name = tensor("op_6327")]; + tensor var_6329_promoted = const()[name = tensor("op_6329_promoted"), val = tensor(0x1p+0)]; + tensor var_6330 = add(x = var_6327_0, y = var_6329_promoted)[name = tensor("op_6330")]; + tensor var_6331 = instance_norm(beta = model_decoder_decode_3_norm2_norm_bias, epsilon = var_6298, gamma = model_decoder_decode_3_norm2_norm_weight, x = input_257)[name = tensor("op_6331")]; + tensor var_6332 = mul(x = var_6330, y = var_6331)[name = tensor("op_6332")]; + tensor input_259 = add(x = var_6332, y = var_6327_1)[name = tensor("input_259")]; + tensor input_261 = leaky_relu(alpha = var_6295, x = input_259)[name = tensor("input_261")]; + tensor weight_47 = const()[name = tensor("weight_47"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(101616192)))]; + 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 = model_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_261)[name = tensor("out_7")]; + tensor var_6342 = add(x = out_7, y = input_195)[name = tensor("op_6342")]; + tensor var_6345 = const()[name = tensor("op_6345"), val = tensor(0x1.6a09e6p-1)]; + tensor input_265 = mul(x = var_6342, y = var_6345)[name = tensor("input_265")]; + tensor var_6353 = const()[name = tensor("op_6353"), val = tensor(0x1.99999ap-3)]; + tensor var_6357 = const()[name = tensor("op_6357"), val = tensor(0x1.4f8b58p-17)]; + tensor h_45 = linear(bias = model_predictor_N_1_norm1_fc_bias, weight = model_predictor_N_1_norm1_fc_weight, x = style)[name = tensor("linear_86")]; + tensor var_6364 = const()[name = tensor("op_6364"), val = tensor([1, 1024, 1])]; + tensor h_47 = reshape(shape = var_6364, x = h_45)[name = tensor("h_47")]; + tensor var_6366_split_sizes_0 = const()[name = tensor("op_6366_split_sizes_0"), val = tensor([512, 512])]; + tensor var_6366_axis_0 = const()[name = tensor("op_6366_axis_0"), val = tensor(1)]; + tensor var_6366_0, tensor var_6366_1 = split(axis = var_6366_axis_0, split_sizes = var_6366_split_sizes_0, x = h_47)[name = tensor("op_6366")]; + tensor var_6368_promoted = const()[name = tensor("op_6368_promoted"), val = tensor(0x1p+0)]; + tensor var_6369 = add(x = var_6366_0, y = var_6368_promoted)[name = tensor("op_6369")]; + tensor var_6370 = instance_norm(beta = model_decoder_decode_3_norm2_norm_bias, epsilon = var_6357, gamma = model_decoder_decode_3_norm2_norm_weight, x = input_265)[name = tensor("op_6370")]; + tensor var_6371 = mul(x = var_6369, y = var_6370)[name = tensor("op_6371")]; + tensor input_267 = add(x = var_6371, y = var_6366_1)[name = tensor("input_267")]; + tensor input_269 = leaky_relu(alpha = var_6353, x = input_267)[name = tensor("input_269")]; + tensor var_6374 = const()[name = tensor("op_6374"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(104761984)))]; + 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, 1201])]; + tensor conv_transpose_1_has_output_shape = conv_transpose(bias = model_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_6374, x = input_269)[name = tensor("conv_transpose_1_has_output_shape")]; + tensor input_271_begin_0 = const()[name = tensor("input_271_begin_0"), val = tensor([0, 0, 1])]; + tensor input_271_end_0 = const()[name = tensor("input_271_end_0"), val = tensor([0, 0, 0])]; + tensor input_271_begin_mask_0 = const()[name = tensor("input_271_begin_mask_0"), val = tensor([true, true, false])]; + tensor input_271_end_mask_0 = const()[name = tensor("input_271_end_mask_0"), val = tensor([true, true, true])]; + tensor input_271 = slice_by_index(begin = input_271_begin_0, begin_mask = input_271_begin_mask_0, end = input_271_end_0, end_mask = input_271_end_mask_0, x = conv_transpose_1_has_output_shape)[name = tensor("input_271")]; + tensor weight_51 = const()[name = tensor("weight_51"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(104768192)))]; + 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 = model_predictor_N_1_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_51, x = input_271)[name = tensor("input_275")]; + tensor h_49 = linear(bias = model_predictor_N_1_norm2_fc_bias, weight = model_predictor_N_1_norm2_fc_weight, x = style)[name = tensor("linear_87")]; + tensor var_6390 = const()[name = tensor("op_6390"), val = tensor([1, 512, 1])]; + tensor h_51 = reshape(shape = var_6390, x = h_49)[name = tensor("h_51")]; + tensor var_6392_split_sizes_0 = const()[name = tensor("op_6392_split_sizes_0"), val = tensor([256, 256])]; + tensor var_6392_axis_0 = const()[name = tensor("op_6392_axis_0"), val = tensor(1)]; + tensor var_6392_0, tensor var_6392_1 = split(axis = var_6392_axis_0, split_sizes = var_6392_split_sizes_0, x = h_51)[name = tensor("op_6392")]; + tensor var_6394_promoted = const()[name = tensor("op_6394_promoted"), val = tensor(0x1p+0)]; + tensor var_6395 = add(x = var_6392_0, y = var_6394_promoted)[name = tensor("op_6395")]; + tensor var_6396 = instance_norm(beta = model_decoder_generator_resblocks_2_adain2_2_norm_bias, epsilon = var_6357, gamma = model_decoder_generator_resblocks_2_adain2_2_norm_weight, x = input_275)[name = tensor("op_6396")]; + tensor var_6397 = mul(x = var_6395, y = var_6396)[name = tensor("op_6397")]; + tensor input_277 = add(x = var_6397, y = var_6392_1)[name = tensor("input_277")]; + tensor input_279 = leaky_relu(alpha = var_6353, x = input_277)[name = tensor("input_279")]; + tensor weight_55 = const()[name = tensor("weight_55"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106341120)))]; + 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 = model_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_279)[name = tensor("out_9")]; + tensor expand_dims_2_axes_0 = const()[name = tensor("expand_dims_2_axes_0"), val = tensor([3])]; + tensor expand_dims_2 = expand_dims(axes = expand_dims_2_axes_0, x = input_265)[name = tensor("expand_dims_2")]; + 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_2)[name = tensor("upsample_nearest_neighbor_1")]; + tensor input_283_axes_0 = const()[name = tensor("input_283_axes_0"), val = tensor([3])]; + tensor input_283 = squeeze(axes = input_283_axes_0, x = upsample_nearest_neighbor_1)[name = tensor("input_283")]; + tensor weight_57 = const()[name = tensor("weight_57"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107127616)))]; + tensor var_6414_pad_type_0 = const()[name = tensor("op_6414_pad_type_0"), val = tensor("valid")]; + tensor var_6414_strides_0 = const()[name = tensor("op_6414_strides_0"), val = tensor([1])]; + tensor var_6414_pad_0 = const()[name = tensor("op_6414_pad_0"), val = tensor([0, 0])]; + tensor var_6414_dilations_0 = const()[name = tensor("op_6414_dilations_0"), val = tensor([1])]; + tensor var_6414_groups_0 = const()[name = tensor("op_6414_groups_0"), val = tensor(1)]; + tensor var_6414 = conv(dilations = var_6414_dilations_0, groups = var_6414_groups_0, pad = var_6414_pad_0, pad_type = var_6414_pad_type_0, strides = var_6414_strides_0, weight = weight_57, x = input_283)[name = tensor("op_6414")]; + tensor var_6415 = add(x = out_9, y = var_6414)[name = tensor("op_6415")]; + tensor var_6418 = const()[name = tensor("op_6418"), val = tensor(0x1.6a09e6p-1)]; + tensor input_285 = mul(x = var_6415, y = var_6418)[name = tensor("input_285")]; + tensor var_6424 = const()[name = tensor("op_6424"), val = tensor(0x1.99999ap-3)]; + tensor var_6427 = const()[name = tensor("op_6427"), val = tensor(0x1.4f8b58p-17)]; + tensor h_53 = linear(bias = model_predictor_N_2_norm1_fc_bias, weight = model_predictor_N_2_norm1_fc_weight, x = style)[name = tensor("linear_88")]; + tensor var_6434 = const()[name = tensor("op_6434"), val = tensor([1, 512, 1])]; + tensor h_55 = reshape(shape = var_6434, x = h_53)[name = tensor("h_55")]; + tensor var_6436_split_sizes_0 = const()[name = tensor("op_6436_split_sizes_0"), val = tensor([256, 256])]; + tensor var_6436_axis_0 = const()[name = tensor("op_6436_axis_0"), val = tensor(1)]; + tensor var_6436_0, tensor var_6436_1 = split(axis = var_6436_axis_0, split_sizes = var_6436_split_sizes_0, x = h_55)[name = tensor("op_6436")]; + tensor var_6438_promoted = const()[name = tensor("op_6438_promoted"), val = tensor(0x1p+0)]; + tensor var_6439 = add(x = var_6436_0, y = var_6438_promoted)[name = tensor("op_6439")]; + tensor var_6440 = instance_norm(beta = model_decoder_generator_resblocks_2_adain2_2_norm_bias, epsilon = var_6427, gamma = model_decoder_generator_resblocks_2_adain2_2_norm_weight, x = input_285)[name = tensor("op_6440")]; + tensor var_6441 = mul(x = var_6439, y = var_6440)[name = tensor("op_6441")]; + tensor input_287 = add(x = var_6441, y = var_6436_1)[name = tensor("input_287")]; + tensor input_289 = leaky_relu(alpha = var_6424, x = input_287)[name = tensor("input_289")]; + tensor weight_61 = const()[name = tensor("weight_61"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107651968)))]; + 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 = model_predictor_N_2_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_61, x = input_289)[name = tensor("input_293")]; + tensor h_57 = linear(bias = model_predictor_N_2_norm2_fc_bias, weight = model_predictor_N_2_norm2_fc_weight, x = style)[name = tensor("linear_89")]; + tensor var_6454 = const()[name = tensor("op_6454"), val = tensor([1, 512, 1])]; + tensor h_59 = reshape(shape = var_6454, x = h_57)[name = tensor("h_59")]; + tensor var_6456_split_sizes_0 = const()[name = tensor("op_6456_split_sizes_0"), val = tensor([256, 256])]; + tensor var_6456_axis_0 = const()[name = tensor("op_6456_axis_0"), val = tensor(1)]; + tensor var_6456_0, tensor var_6456_1 = split(axis = var_6456_axis_0, split_sizes = var_6456_split_sizes_0, x = h_59)[name = tensor("op_6456")]; + tensor var_6458_promoted = const()[name = tensor("op_6458_promoted"), val = tensor(0x1p+0)]; + tensor var_6459 = add(x = var_6456_0, y = var_6458_promoted)[name = tensor("op_6459")]; + tensor var_6460 = instance_norm(beta = model_decoder_generator_resblocks_2_adain2_2_norm_bias, epsilon = var_6427, gamma = model_decoder_generator_resblocks_2_adain2_2_norm_weight, x = input_293)[name = tensor("op_6460")]; + tensor var_6461 = mul(x = var_6459, y = var_6460)[name = tensor("op_6461")]; + tensor input_295 = add(x = var_6461, y = var_6456_1)[name = tensor("input_295")]; + tensor input_297 = leaky_relu(alpha = var_6424, x = input_295)[name = tensor("input_297")]; + tensor weight_65 = const()[name = tensor("weight_65"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108438464)))]; + 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 = model_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_297)[name = tensor("out_11")]; + tensor var_6471 = add(x = out_11, y = input_285)[name = tensor("op_6471")]; + tensor var_6474 = const()[name = tensor("op_6474"), val = tensor(0x1.6a09e6p-1)]; + tensor input_301 = mul(x = var_6471, y = var_6474)[name = tensor("input_301")]; + 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 = model_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 = model_predictor_N_proj_weight, x = input_301)[name = tensor("N_1")]; + tensor F0_pred_axes_0 = const()[name = tensor("F0_pred_axes_0"), val = tensor([1])]; + tensor F0_pred = squeeze(axes = F0_pred_axes_0, x = F0_1)[name = tensor("F0_pred")]; + tensor var_6496 = const()[name = tensor("op_6496"), val = tensor(0x1.4f8b58p-17)]; + tensor var_6498 = const()[name = tensor("op_6498"), val = tensor(0x1.99999ap-3)]; + tensor var_6499 = const()[name = tensor("op_6499"), val = tensor(0x0p+0)]; + 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_validate_indices_0 = const()[name = tensor("x_221_validate_indices_0"), val = tensor(false)]; + tensor x_221 = gather(axis = x_221_axis_0, batch_dims = x_221_batch_dims_0, indices = input_ids, validate_indices = x_221_validate_indices_0, x = model_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 m_axes_0 = const()[name = tensor("m_axes_0"), val = tensor([1])]; + tensor m = expand_dims(axes = m_axes_0, x = m_1)[name = tensor("m")]; + tensor x_223 = transpose(perm = x_223_perm_0, x = x_221)[name = tensor("transpose_117")]; + tensor input_303 = select(a = var_6499, b = x_223, cond = m)[name = tensor("input_303")]; + tensor weight_71 = const()[name = tensor("weight_71"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109224960)))]; + 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_225 = conv(bias = model_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 = input_303)[name = tensor("x_225")]; + tensor input_305_perm_0 = const()[name = tensor("input_305_perm_0"), val = tensor([0, -1, 1])]; + tensor x_227_axes_0 = const()[name = tensor("x_227_axes_0"), val = tensor([-1])]; + tensor input_305 = transpose(perm = input_305_perm_0, x = x_225)[name = tensor("transpose_116")]; + tensor x_227 = layer_norm(axes = x_227_axes_0, beta = model_text_encoder_cnn_0_1_beta, epsilon = var_6496, gamma = model_text_encoder_cnn_0_1_gamma, x = input_305)[name = tensor("x_227")]; + tensor input_307_perm_0 = const()[name = tensor("input_307_perm_0"), val = tensor([0, -1, 1])]; + tensor input_307 = transpose(perm = input_307_perm_0, x = x_227)[name = tensor("transpose_115")]; + tensor input_309 = leaky_relu(alpha = var_6498, x = input_307)[name = tensor("input_309")]; + tensor input_311 = select(a = var_6499, b = input_309, cond = m)[name = tensor("input_311")]; + tensor weight_75 = const()[name = tensor("weight_75"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(114467904)))]; + 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 = model_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_311)[name = tensor("x_231")]; + tensor input_313_perm_0 = const()[name = tensor("input_313_perm_0"), val = tensor([0, -1, 1])]; + tensor x_233_axes_0 = const()[name = tensor("x_233_axes_0"), val = tensor([-1])]; + tensor input_313 = transpose(perm = input_313_perm_0, x = x_231)[name = tensor("transpose_114")]; + tensor x_233 = layer_norm(axes = x_233_axes_0, beta = model_text_encoder_cnn_1_1_beta, epsilon = var_6496, gamma = model_text_encoder_cnn_1_1_gamma, x = input_313)[name = tensor("x_233")]; + tensor input_315_perm_0 = const()[name = tensor("input_315_perm_0"), val = tensor([0, -1, 1])]; + tensor input_315 = transpose(perm = input_315_perm_0, x = x_233)[name = tensor("transpose_113")]; + tensor input_317 = leaky_relu(alpha = var_6498, x = input_315)[name = tensor("input_317")]; + tensor input_319 = select(a = var_6499, b = input_317, cond = m)[name = tensor("input_319")]; + tensor weight_79 = const()[name = tensor("weight_79"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119710848)))]; + 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 = model_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_319)[name = tensor("x_237")]; + tensor input_321_perm_0 = const()[name = tensor("input_321_perm_0"), val = tensor([0, -1, 1])]; + tensor x_239_axes_0 = const()[name = tensor("x_239_axes_0"), val = tensor([-1])]; + tensor input_321 = transpose(perm = input_321_perm_0, x = x_237)[name = tensor("transpose_112")]; + tensor x_239 = layer_norm(axes = x_239_axes_0, beta = model_text_encoder_cnn_2_1_beta, epsilon = var_6496, gamma = model_text_encoder_cnn_2_1_gamma, x = input_321)[name = tensor("x_239")]; + tensor input_323_perm_0 = const()[name = tensor("input_323_perm_0"), val = tensor([0, -1, 1])]; + tensor input_323 = transpose(perm = input_323_perm_0, x = x_239)[name = tensor("transpose_111")]; + tensor input_325 = leaky_relu(alpha = var_6498, x = input_323)[name = tensor("input_325")]; + tensor x_243 = select(a = var_6499, b = input_325, cond = m)[name = tensor("x_243")]; + tensor transpose_41_perm_0 = const()[name = tensor("transpose_41_perm_0"), val = tensor([2, 0, 1])]; + tensor add_22 = const()[name = tensor("add_22"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(124953792)))]; + tensor add_23 = const()[name = tensor("add_23"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(124957952)))]; + tensor concat_59 = const()[name = tensor("concat_59"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(124962112)))]; + tensor concat_60 = const()[name = tensor("concat_60"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127059328)))]; + tensor concat_61 = const()[name = tensor("concat_61"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(128107968)))]; + tensor concat_62 = const()[name = tensor("concat_62"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(130205184)))]; + 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 transpose_41 = transpose(perm = transpose_41_perm_0, x = x_243)[name = tensor("transpose_110")]; + 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_22, bias_back = add_23, 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_60, weight_hh_back = concat_62, weight_ih = concat_59, weight_ih_back = concat_61, x = transpose_41)[name = tensor("x_247_batch_first")]; + tensor transpose_42_perm_0 = const()[name = tensor("transpose_42_perm_0"), val = tensor([1, 2, 0])]; + tensor transpose_42 = transpose(perm = transpose_42_perm_0, x = x_247_batch_first_0)[name = tensor("transpose_109")]; + tensor t_en = select(a = var_6499, b = transpose_42, cond = m)[name = tensor("t_en")]; + tensor gather_idx_text_reps_0 = const()[name = tensor("gather_idx_text_reps_0"), val = tensor([1, 512, 1])]; + tensor gather_idx_text = tile(reps = gather_idx_text_reps_0, x = var_6052)[name = tensor("gather_idx_text")]; + tensor var_6571 = const()[name = tensor("op_6571"), val = tensor(2)]; + tensor var_6573_validate_indices_0 = const()[name = tensor("op_6573_validate_indices_0"), val = tensor(false)]; + tensor var_6573 = gather_along_axis(axis = var_6571, indices = gather_idx_text, validate_indices = var_6573_validate_indices_0, x = t_en)[name = tensor("op_6573")]; + tensor asr = mul(x = var_6573, y = var_6066)[name = tensor("asr")]; + tensor input_331_begin_0 = const()[name = tensor("input_331_begin_0"), val = tensor([0, 0])]; + tensor input_331_end_0 = const()[name = tensor("input_331_end_0"), val = tensor([1, 128])]; + tensor input_331_end_mask_0 = const()[name = tensor("input_331_end_mask_0"), val = tensor([true, false])]; + tensor input_331 = slice_by_index(begin = input_331_begin_0, end = input_331_end_0, end_mask = input_331_end_mask_0, x = ref_s)[name = tensor("input_331")]; + tensor input_327_axes_0 = const()[name = tensor("input_327_axes_0"), val = tensor([1])]; + tensor input_327 = expand_dims(axes = input_327_axes_0, x = F0_pred)[name = tensor("input_327")]; + tensor weight_83 = const()[name = tensor("weight_83"), val = tensor([[[0x1.a86aaep-5, 0x1.b190bep-5, -0x1.6d8bc6p-6]]])]; + tensor F0_processed_pad_type_0 = const()[name = tensor("F0_processed_pad_type_0"), val = tensor("custom")]; + tensor F0_processed_pad_0 = const()[name = tensor("F0_processed_pad_0"), val = tensor([1, 1])]; + tensor F0_processed_strides_0 = const()[name = tensor("F0_processed_strides_0"), val = tensor([2])]; + tensor F0_processed_dilations_0 = const()[name = tensor("F0_processed_dilations_0"), val = tensor([1])]; + tensor F0_processed_groups_0 = const()[name = tensor("F0_processed_groups_0"), val = tensor(1)]; + tensor F0_processed = conv(bias = model_decoder_F0_conv_bias, dilations = F0_processed_dilations_0, groups = F0_processed_groups_0, pad = F0_processed_pad_0, pad_type = F0_processed_pad_type_0, strides = F0_processed_strides_0, weight = weight_83, x = input_327)[name = tensor("F0_processed")]; + tensor weight_85 = const()[name = tensor("weight_85"), val = tensor([[[0x1.cc2feep-2, 0x1.35c75ep-1, 0x1.b9913ep-2]]])]; + tensor N_processed_pad_type_0 = const()[name = tensor("N_processed_pad_type_0"), val = tensor("custom")]; + tensor N_processed_pad_0 = const()[name = tensor("N_processed_pad_0"), val = tensor([1, 1])]; + tensor N_processed_strides_0 = const()[name = tensor("N_processed_strides_0"), val = tensor([2])]; + tensor N_processed_dilations_0 = const()[name = tensor("N_processed_dilations_0"), val = tensor([1])]; + tensor N_processed_groups_0 = const()[name = tensor("N_processed_groups_0"), val = tensor(1)]; + tensor N_processed = conv(bias = model_decoder_N_conv_bias, dilations = N_processed_dilations_0, groups = N_processed_groups_0, pad = N_processed_pad_0, pad_type = N_processed_pad_type_0, strides = N_processed_strides_0, weight = weight_85, x = N_1)[name = tensor("N_processed")]; + tensor var_6616 = const()[name = tensor("op_6616"), val = tensor(1)]; + tensor input_333_interleave_0 = const()[name = tensor("input_333_interleave_0"), val = tensor(false)]; + tensor input_333 = concat(axis = var_6616, interleave = input_333_interleave_0, values = (asr, F0_processed, N_processed))[name = tensor("input_333")]; + tensor var_6623 = const()[name = tensor("op_6623"), val = tensor(0x1.99999ap-3)]; + tensor var_6626 = const()[name = tensor("op_6626"), val = tensor(0x1.4f8b58p-17)]; + tensor h_61 = linear(bias = model_decoder_encode_norm1_fc_bias, weight = model_decoder_encode_norm1_fc_weight, x = input_331)[name = tensor("linear_90")]; + tensor var_6633 = const()[name = tensor("op_6633"), val = tensor([1, 1028, 1])]; + tensor h_63 = reshape(shape = var_6633, x = h_61)[name = tensor("h_63")]; + tensor var_6635_split_sizes_0 = const()[name = tensor("op_6635_split_sizes_0"), val = tensor([514, 514])]; + tensor var_6635_axis_0 = const()[name = tensor("op_6635_axis_0"), val = tensor(1)]; + tensor var_6635_0, tensor var_6635_1 = split(axis = var_6635_axis_0, split_sizes = var_6635_split_sizes_0, x = h_63)[name = tensor("op_6635")]; + tensor var_6637_promoted = const()[name = tensor("op_6637_promoted"), val = tensor(0x1p+0)]; + tensor var_6638 = add(x = var_6635_0, y = var_6637_promoted)[name = tensor("op_6638")]; + tensor var_6639 = instance_norm(beta = model_decoder_encode_norm1_norm_bias, epsilon = var_6626, gamma = model_decoder_encode_norm1_norm_weight, x = input_333)[name = tensor("op_6639")]; + tensor var_6640 = mul(x = var_6638, y = var_6639)[name = tensor("op_6640")]; + tensor input_335 = add(x = var_6640, y = var_6635_1)[name = tensor("input_335")]; + tensor input_337 = leaky_relu(alpha = var_6623, x = input_335)[name = tensor("input_337")]; + tensor weight_89 = const()[name = tensor("weight_89"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131253824)))]; + tensor input_341_pad_type_0 = const()[name = tensor("input_341_pad_type_0"), val = tensor("custom")]; + tensor input_341_pad_0 = const()[name = tensor("input_341_pad_0"), val = tensor([1, 1])]; + tensor input_341_strides_0 = const()[name = tensor("input_341_strides_0"), val = tensor([1])]; + tensor input_341_dilations_0 = const()[name = tensor("input_341_dilations_0"), val = tensor([1])]; + tensor input_341_groups_0 = const()[name = tensor("input_341_groups_0"), val = tensor(1)]; + tensor input_341 = conv(bias = model_decoder_encode_conv1_bias, dilations = input_341_dilations_0, groups = input_341_groups_0, pad = input_341_pad_0, pad_type = input_341_pad_type_0, strides = input_341_strides_0, weight = weight_89, x = input_337)[name = tensor("input_341")]; + tensor h_65 = linear(bias = model_decoder_encode_norm2_fc_bias, weight = model_decoder_encode_norm2_fc_weight, x = input_331)[name = tensor("linear_91")]; + tensor var_6653 = const()[name = tensor("op_6653"), val = tensor([1, 2048, 1])]; + tensor h_67 = reshape(shape = var_6653, x = h_65)[name = tensor("h_67")]; + tensor var_6655_split_sizes_0 = const()[name = tensor("op_6655_split_sizes_0"), val = tensor([1024, 1024])]; + tensor var_6655_axis_0 = const()[name = tensor("op_6655_axis_0"), val = tensor(1)]; + tensor var_6655_0, tensor var_6655_1 = split(axis = var_6655_axis_0, split_sizes = var_6655_split_sizes_0, x = h_67)[name = tensor("op_6655")]; + tensor var_6657_promoted = const()[name = tensor("op_6657_promoted"), val = tensor(0x1p+0)]; + tensor var_6658 = add(x = var_6655_0, y = var_6657_promoted)[name = tensor("op_6658")]; + tensor var_6659 = instance_norm(beta = model_decoder_decode_2_norm2_norm_bias, epsilon = var_6626, gamma = model_decoder_decode_2_norm2_norm_weight, x = input_341)[name = tensor("op_6659")]; + tensor var_6660 = mul(x = var_6658, y = var_6659)[name = tensor("op_6660")]; + tensor input_343 = add(x = var_6660, y = var_6655_1)[name = tensor("input_343")]; + tensor input_345 = leaky_relu(alpha = var_6623, x = input_343)[name = tensor("input_345")]; + tensor weight_93 = const()[name = tensor("weight_93"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(137569920)))]; + 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 = model_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_345)[name = tensor("out_13")]; + tensor weight_95 = const()[name = tensor("weight_95"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(150152896)))]; + tensor var_6675_pad_type_0 = const()[name = tensor("op_6675_pad_type_0"), val = tensor("valid")]; + tensor var_6675_strides_0 = const()[name = tensor("op_6675_strides_0"), val = tensor([1])]; + tensor var_6675_pad_0 = const()[name = tensor("op_6675_pad_0"), val = tensor([0, 0])]; + tensor var_6675_dilations_0 = const()[name = tensor("op_6675_dilations_0"), val = tensor([1])]; + tensor var_6675_groups_0 = const()[name = tensor("op_6675_groups_0"), val = tensor(1)]; + tensor var_6675 = conv(dilations = var_6675_dilations_0, groups = var_6675_groups_0, pad = var_6675_pad_0, pad_type = var_6675_pad_type_0, strides = var_6675_strides_0, weight = weight_95, x = input_333)[name = tensor("op_6675")]; + tensor var_6676 = add(x = out_13, y = var_6675)[name = tensor("op_6676")]; + tensor var_6679 = const()[name = tensor("op_6679"), val = tensor(0x1.6a09e6p-1)]; + tensor x_encoded = mul(x = var_6676, y = var_6679)[name = tensor("x_encoded")]; + tensor weight_97 = const()[name = tensor("weight_97"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152258304)))]; + 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 = model_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 var_6692 = const()[name = tensor("op_6692"), val = tensor(1)]; + tensor input_349_interleave_0 = const()[name = tensor("input_349_interleave_0"), val = tensor(false)]; + tensor input_349 = concat(axis = var_6692, interleave = input_349_interleave_0, values = (x_encoded, asr_res_1, F0_processed, N_processed))[name = tensor("input_349")]; + tensor var_6699 = const()[name = tensor("op_6699"), val = tensor(0x1.99999ap-3)]; + tensor var_6702 = const()[name = tensor("op_6702"), val = tensor(0x1.4f8b58p-17)]; + tensor h_69 = linear(bias = model_decoder_decode_0_norm1_fc_bias, weight = model_decoder_decode_0_norm1_fc_weight, x = input_331)[name = tensor("linear_92")]; + tensor var_6709 = const()[name = tensor("op_6709"), val = tensor([1, 2180, 1])]; + tensor h_71 = reshape(shape = var_6709, x = h_69)[name = tensor("h_71")]; + tensor var_6711_split_sizes_0 = const()[name = tensor("op_6711_split_sizes_0"), val = tensor([1090, 1090])]; + tensor var_6711_axis_0 = const()[name = tensor("op_6711_axis_0"), val = tensor(1)]; + tensor var_6711_0, tensor var_6711_1 = split(axis = var_6711_axis_0, split_sizes = var_6711_split_sizes_0, x = h_71)[name = tensor("op_6711")]; + tensor var_6713_promoted = const()[name = tensor("op_6713_promoted"), val = tensor(0x1p+0)]; + tensor var_6714 = add(x = var_6711_0, y = var_6713_promoted)[name = tensor("op_6714")]; + tensor var_6715 = instance_norm(beta = model_decoder_decode_3_norm1_norm_bias, epsilon = var_6702, gamma = model_decoder_decode_3_norm1_norm_weight, x = input_349)[name = tensor("op_6715")]; + tensor var_6716 = mul(x = var_6714, y = var_6715)[name = tensor("op_6716")]; + tensor input_351 = add(x = var_6716, y = var_6711_1)[name = tensor("input_351")]; + tensor input_353 = leaky_relu(alpha = var_6699, x = input_351)[name = tensor("input_353")]; + tensor weight_101 = const()[name = tensor("weight_101"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152389440)))]; + tensor input_357_pad_type_0 = const()[name = tensor("input_357_pad_type_0"), val = tensor("custom")]; + tensor input_357_pad_0 = const()[name = tensor("input_357_pad_0"), val = tensor([1, 1])]; + tensor input_357_strides_0 = const()[name = tensor("input_357_strides_0"), val = tensor([1])]; + tensor input_357_dilations_0 = const()[name = tensor("input_357_dilations_0"), val = tensor([1])]; + tensor input_357_groups_0 = const()[name = tensor("input_357_groups_0"), val = tensor(1)]; + tensor input_357 = conv(bias = model_decoder_decode_0_conv1_bias, dilations = input_357_dilations_0, groups = input_357_groups_0, pad = input_357_pad_0, pad_type = input_357_pad_type_0, strides = input_357_strides_0, weight = weight_101, x = input_353)[name = tensor("input_357")]; + tensor h_73 = linear(bias = model_decoder_decode_0_norm2_fc_bias, weight = model_decoder_decode_0_norm2_fc_weight, x = input_331)[name = tensor("linear_93")]; + tensor var_6729 = const()[name = tensor("op_6729"), val = tensor([1, 2048, 1])]; + tensor h_75 = reshape(shape = var_6729, x = h_73)[name = tensor("h_75")]; + tensor var_6731_split_sizes_0 = const()[name = tensor("op_6731_split_sizes_0"), val = tensor([1024, 1024])]; + tensor var_6731_axis_0 = const()[name = tensor("op_6731_axis_0"), val = tensor(1)]; + tensor var_6731_0, tensor var_6731_1 = split(axis = var_6731_axis_0, split_sizes = var_6731_split_sizes_0, x = h_75)[name = tensor("op_6731")]; + tensor var_6733_promoted = const()[name = tensor("op_6733_promoted"), val = tensor(0x1p+0)]; + tensor var_6734 = add(x = var_6731_0, y = var_6733_promoted)[name = tensor("op_6734")]; + tensor var_6735 = instance_norm(beta = model_decoder_decode_2_norm2_norm_bias, epsilon = var_6702, gamma = model_decoder_decode_2_norm2_norm_weight, x = input_357)[name = tensor("op_6735")]; + tensor var_6736 = mul(x = var_6734, y = var_6735)[name = tensor("op_6736")]; + tensor input_359 = add(x = var_6736, y = var_6731_1)[name = tensor("input_359")]; + tensor input_361 = leaky_relu(alpha = var_6699, x = input_359)[name = tensor("input_361")]; + tensor weight_105 = const()[name = tensor("weight_105"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165783424)))]; + 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 = model_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_361)[name = tensor("out_15")]; + tensor weight_107 = const()[name = tensor("weight_107"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178366400)))]; + tensor var_6751_pad_type_0 = const()[name = tensor("op_6751_pad_type_0"), val = tensor("valid")]; + tensor var_6751_strides_0 = const()[name = tensor("op_6751_strides_0"), val = tensor([1])]; + tensor var_6751_pad_0 = const()[name = tensor("op_6751_pad_0"), val = tensor([0, 0])]; + tensor var_6751_dilations_0 = const()[name = tensor("op_6751_dilations_0"), val = tensor([1])]; + tensor var_6751_groups_0 = const()[name = tensor("op_6751_groups_0"), val = tensor(1)]; + tensor var_6751 = conv(dilations = var_6751_dilations_0, groups = var_6751_groups_0, pad = var_6751_pad_0, pad_type = var_6751_pad_type_0, strides = var_6751_strides_0, weight = weight_107, x = input_349)[name = tensor("op_6751")]; + tensor var_6752 = add(x = out_15, y = var_6751)[name = tensor("op_6752")]; + tensor var_6755 = const()[name = tensor("op_6755"), val = tensor(0x1.6a09e6p-1)]; + tensor x_current_1 = mul(x = var_6752, y = var_6755)[name = tensor("x_current_1")]; + tensor var_6758 = const()[name = tensor("op_6758"), val = tensor(1)]; + tensor input_365_interleave_0 = const()[name = tensor("input_365_interleave_0"), val = tensor(false)]; + tensor input_365 = concat(axis = var_6758, interleave = input_365_interleave_0, values = (x_current_1, asr_res_1, F0_processed, N_processed))[name = tensor("input_365")]; + tensor var_6765 = const()[name = tensor("op_6765"), val = tensor(0x1.99999ap-3)]; + tensor var_6768 = const()[name = tensor("op_6768"), val = tensor(0x1.4f8b58p-17)]; + tensor h_77 = linear(bias = model_decoder_decode_1_norm1_fc_bias, weight = model_decoder_decode_1_norm1_fc_weight, x = input_331)[name = tensor("linear_94")]; + tensor var_6775 = const()[name = tensor("op_6775"), val = tensor([1, 2180, 1])]; + tensor h_79 = reshape(shape = var_6775, x = h_77)[name = tensor("h_79")]; + tensor var_6777_split_sizes_0 = const()[name = tensor("op_6777_split_sizes_0"), val = tensor([1090, 1090])]; + tensor var_6777_axis_0 = const()[name = tensor("op_6777_axis_0"), val = tensor(1)]; + tensor var_6777_0, tensor var_6777_1 = split(axis = var_6777_axis_0, split_sizes = var_6777_split_sizes_0, x = h_79)[name = tensor("op_6777")]; + tensor var_6779_promoted = const()[name = tensor("op_6779_promoted"), val = tensor(0x1p+0)]; + tensor var_6780 = add(x = var_6777_0, y = var_6779_promoted)[name = tensor("op_6780")]; + tensor var_6781 = instance_norm(beta = model_decoder_decode_3_norm1_norm_bias, epsilon = var_6768, gamma = model_decoder_decode_3_norm1_norm_weight, x = input_365)[name = tensor("op_6781")]; + tensor var_6782 = mul(x = var_6780, y = var_6781)[name = tensor("op_6782")]; + tensor input_367 = add(x = var_6782, y = var_6777_1)[name = tensor("input_367")]; + tensor input_369 = leaky_relu(alpha = var_6765, x = input_367)[name = tensor("input_369")]; + tensor weight_111 = const()[name = tensor("weight_111"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(182831104)))]; + tensor input_373_pad_type_0 = const()[name = tensor("input_373_pad_type_0"), val = tensor("custom")]; + tensor input_373_pad_0 = const()[name = tensor("input_373_pad_0"), val = tensor([1, 1])]; + tensor input_373_strides_0 = const()[name = tensor("input_373_strides_0"), val = tensor([1])]; + tensor input_373_dilations_0 = const()[name = tensor("input_373_dilations_0"), val = tensor([1])]; + tensor input_373_groups_0 = const()[name = tensor("input_373_groups_0"), val = tensor(1)]; + tensor input_373 = conv(bias = model_decoder_decode_1_conv1_bias, dilations = input_373_dilations_0, groups = input_373_groups_0, pad = input_373_pad_0, pad_type = input_373_pad_type_0, strides = input_373_strides_0, weight = weight_111, x = input_369)[name = tensor("input_373")]; + tensor h_81 = linear(bias = model_decoder_decode_1_norm2_fc_bias, weight = model_decoder_decode_1_norm2_fc_weight, x = input_331)[name = tensor("linear_95")]; + tensor var_6795 = const()[name = tensor("op_6795"), val = tensor([1, 2048, 1])]; + tensor h_83 = reshape(shape = var_6795, x = h_81)[name = tensor("h_83")]; + tensor var_6797_split_sizes_0 = const()[name = tensor("op_6797_split_sizes_0"), val = tensor([1024, 1024])]; + tensor var_6797_axis_0 = const()[name = tensor("op_6797_axis_0"), val = tensor(1)]; + tensor var_6797_0, tensor var_6797_1 = split(axis = var_6797_axis_0, split_sizes = var_6797_split_sizes_0, x = h_83)[name = tensor("op_6797")]; + tensor var_6799_promoted = const()[name = tensor("op_6799_promoted"), val = tensor(0x1p+0)]; + tensor var_6800 = add(x = var_6797_0, y = var_6799_promoted)[name = tensor("op_6800")]; + tensor var_6801 = instance_norm(beta = model_decoder_decode_2_norm2_norm_bias, epsilon = var_6768, gamma = model_decoder_decode_2_norm2_norm_weight, x = input_373)[name = tensor("op_6801")]; + tensor var_6802 = mul(x = var_6800, y = var_6801)[name = tensor("op_6802")]; + tensor input_375 = add(x = var_6802, y = var_6797_1)[name = tensor("input_375")]; + tensor input_377 = leaky_relu(alpha = var_6765, x = input_375)[name = tensor("input_377")]; + tensor weight_115 = const()[name = tensor("weight_115"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196225088)))]; + 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 = model_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_377)[name = tensor("out_17")]; + tensor weight_117 = const()[name = tensor("weight_117"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208808064)))]; + tensor var_6817_pad_type_0 = const()[name = tensor("op_6817_pad_type_0"), val = tensor("valid")]; + tensor var_6817_strides_0 = const()[name = tensor("op_6817_strides_0"), val = tensor([1])]; + tensor var_6817_pad_0 = const()[name = tensor("op_6817_pad_0"), val = tensor([0, 0])]; + tensor var_6817_dilations_0 = const()[name = tensor("op_6817_dilations_0"), val = tensor([1])]; + tensor var_6817_groups_0 = const()[name = tensor("op_6817_groups_0"), val = tensor(1)]; + tensor var_6817 = conv(dilations = var_6817_dilations_0, groups = var_6817_groups_0, pad = var_6817_pad_0, pad_type = var_6817_pad_type_0, strides = var_6817_strides_0, weight = weight_117, x = input_365)[name = tensor("op_6817")]; + tensor var_6818 = add(x = out_17, y = var_6817)[name = tensor("op_6818")]; + tensor var_6821 = const()[name = tensor("op_6821"), val = tensor(0x1.6a09e6p-1)]; + tensor x_current_3 = mul(x = var_6818, y = var_6821)[name = tensor("x_current_3")]; + tensor var_6824 = const()[name = tensor("op_6824"), val = tensor(1)]; + tensor input_381_interleave_0 = const()[name = tensor("input_381_interleave_0"), val = tensor(false)]; + tensor input_381 = concat(axis = var_6824, interleave = input_381_interleave_0, values = (x_current_3, asr_res_1, F0_processed, N_processed))[name = tensor("input_381")]; + tensor var_6831 = const()[name = tensor("op_6831"), val = tensor(0x1.99999ap-3)]; + tensor var_6834 = const()[name = tensor("op_6834"), val = tensor(0x1.4f8b58p-17)]; + tensor h_85 = linear(bias = model_decoder_decode_2_norm1_fc_bias, weight = model_decoder_decode_2_norm1_fc_weight, x = input_331)[name = tensor("linear_96")]; + tensor var_6841 = const()[name = tensor("op_6841"), val = tensor([1, 2180, 1])]; + tensor h_87 = reshape(shape = var_6841, x = h_85)[name = tensor("h_87")]; + tensor var_6843_split_sizes_0 = const()[name = tensor("op_6843_split_sizes_0"), val = tensor([1090, 1090])]; + tensor var_6843_axis_0 = const()[name = tensor("op_6843_axis_0"), val = tensor(1)]; + tensor var_6843_0, tensor var_6843_1 = split(axis = var_6843_axis_0, split_sizes = var_6843_split_sizes_0, x = h_87)[name = tensor("op_6843")]; + tensor var_6845_promoted = const()[name = tensor("op_6845_promoted"), val = tensor(0x1p+0)]; + tensor var_6846 = add(x = var_6843_0, y = var_6845_promoted)[name = tensor("op_6846")]; + tensor var_6847 = instance_norm(beta = model_decoder_decode_3_norm1_norm_bias, epsilon = var_6834, gamma = model_decoder_decode_3_norm1_norm_weight, x = input_381)[name = tensor("op_6847")]; + tensor var_6848 = mul(x = var_6846, y = var_6847)[name = tensor("op_6848")]; + tensor input_383 = add(x = var_6848, y = var_6843_1)[name = tensor("input_383")]; + tensor input_385 = leaky_relu(alpha = var_6831, x = input_383)[name = tensor("input_385")]; + tensor weight_121 = const()[name = tensor("weight_121"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213272768)))]; + tensor input_389_pad_type_0 = const()[name = tensor("input_389_pad_type_0"), val = tensor("custom")]; + tensor input_389_pad_0 = const()[name = tensor("input_389_pad_0"), val = tensor([1, 1])]; + tensor input_389_strides_0 = const()[name = tensor("input_389_strides_0"), val = tensor([1])]; + tensor input_389_dilations_0 = const()[name = tensor("input_389_dilations_0"), val = tensor([1])]; + tensor input_389_groups_0 = const()[name = tensor("input_389_groups_0"), val = tensor(1)]; + tensor input_389 = conv(bias = model_decoder_decode_2_conv1_bias, dilations = input_389_dilations_0, groups = input_389_groups_0, pad = input_389_pad_0, pad_type = input_389_pad_type_0, strides = input_389_strides_0, weight = weight_121, x = input_385)[name = tensor("input_389")]; + tensor h_89 = linear(bias = model_decoder_decode_2_norm2_fc_bias, weight = model_decoder_decode_2_norm2_fc_weight, x = input_331)[name = tensor("linear_97")]; + tensor var_6861 = const()[name = tensor("op_6861"), val = tensor([1, 2048, 1])]; + tensor h_91 = reshape(shape = var_6861, x = h_89)[name = tensor("h_91")]; + tensor var_6863_split_sizes_0 = const()[name = tensor("op_6863_split_sizes_0"), val = tensor([1024, 1024])]; + tensor var_6863_axis_0 = const()[name = tensor("op_6863_axis_0"), val = tensor(1)]; + tensor var_6863_0, tensor var_6863_1 = split(axis = var_6863_axis_0, split_sizes = var_6863_split_sizes_0, x = h_91)[name = tensor("op_6863")]; + tensor var_6865_promoted = const()[name = tensor("op_6865_promoted"), val = tensor(0x1p+0)]; + tensor var_6866 = add(x = var_6863_0, y = var_6865_promoted)[name = tensor("op_6866")]; + tensor var_6867 = instance_norm(beta = model_decoder_decode_2_norm2_norm_bias, epsilon = var_6834, gamma = model_decoder_decode_2_norm2_norm_weight, x = input_389)[name = tensor("op_6867")]; + tensor var_6868 = mul(x = var_6866, y = var_6867)[name = tensor("op_6868")]; + tensor input_391 = add(x = var_6868, y = var_6863_1)[name = tensor("input_391")]; + tensor input_393 = leaky_relu(alpha = var_6831, x = input_391)[name = tensor("input_393")]; + tensor weight_125 = const()[name = tensor("weight_125"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(226666752)))]; + 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 = model_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_393)[name = tensor("out_19")]; + tensor weight_127 = const()[name = tensor("weight_127"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(239249728)))]; + tensor var_6883_pad_type_0 = const()[name = tensor("op_6883_pad_type_0"), val = tensor("valid")]; + tensor var_6883_strides_0 = const()[name = tensor("op_6883_strides_0"), val = tensor([1])]; + tensor var_6883_pad_0 = const()[name = tensor("op_6883_pad_0"), val = tensor([0, 0])]; + tensor var_6883_dilations_0 = const()[name = tensor("op_6883_dilations_0"), val = tensor([1])]; + tensor var_6883_groups_0 = const()[name = tensor("op_6883_groups_0"), val = tensor(1)]; + tensor var_6883 = conv(dilations = var_6883_dilations_0, groups = var_6883_groups_0, pad = var_6883_pad_0, pad_type = var_6883_pad_type_0, strides = var_6883_strides_0, weight = weight_127, x = input_381)[name = tensor("op_6883")]; + tensor var_6884 = add(x = out_19, y = var_6883)[name = tensor("op_6884")]; + tensor var_6887 = const()[name = tensor("op_6887"), val = tensor(0x1.6a09e6p-1)]; + tensor x_current = mul(x = var_6884, y = var_6887)[name = tensor("x_current")]; + tensor var_6890 = const()[name = tensor("op_6890"), val = tensor(1)]; + tensor input_397_interleave_0 = const()[name = tensor("input_397_interleave_0"), val = tensor(false)]; + tensor input_397 = concat(axis = var_6890, interleave = input_397_interleave_0, values = (x_current, asr_res_1, F0_processed, N_processed))[name = tensor("input_397")]; + tensor var_6899 = const()[name = tensor("op_6899"), val = tensor(0x1.99999ap-3)]; + tensor var_6903 = const()[name = tensor("op_6903"), val = tensor(0x1.4f8b58p-17)]; + tensor h_93 = linear(bias = model_decoder_decode_3_norm1_fc_bias, weight = model_decoder_decode_3_norm1_fc_weight, x = input_331)[name = tensor("linear_98")]; + tensor var_6910 = const()[name = tensor("op_6910"), val = tensor([1, 2180, 1])]; + tensor h_95 = reshape(shape = var_6910, x = h_93)[name = tensor("h_95")]; + tensor var_6912_split_sizes_0 = const()[name = tensor("op_6912_split_sizes_0"), val = tensor([1090, 1090])]; + tensor var_6912_axis_0 = const()[name = tensor("op_6912_axis_0"), val = tensor(1)]; + tensor var_6912_0, tensor var_6912_1 = split(axis = var_6912_axis_0, split_sizes = var_6912_split_sizes_0, x = h_95)[name = tensor("op_6912")]; + tensor var_6914_promoted = const()[name = tensor("op_6914_promoted"), val = tensor(0x1p+0)]; + tensor var_6915 = add(x = var_6912_0, y = var_6914_promoted)[name = tensor("op_6915")]; + tensor var_6916 = instance_norm(beta = model_decoder_decode_3_norm1_norm_bias, epsilon = var_6903, gamma = model_decoder_decode_3_norm1_norm_weight, x = input_397)[name = tensor("op_6916")]; + tensor var_6917 = mul(x = var_6915, y = var_6916)[name = tensor("op_6917")]; + tensor input_399 = add(x = var_6917, y = var_6912_1)[name = tensor("input_399")]; + tensor input_401 = leaky_relu(alpha = var_6899, x = input_399)[name = tensor("input_401")]; + tensor var_6920 = const()[name = tensor("op_6920"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(243714432)))]; + 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, 1201])]; + tensor conv_transpose_2_has_output_shape = conv_transpose(bias = model_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_6920, x = input_401)[name = tensor("conv_transpose_2_has_output_shape")]; + tensor input_403_begin_0 = const()[name = tensor("input_403_begin_0"), val = tensor([0, 0, 1])]; + tensor input_403_end_0 = const()[name = tensor("input_403_end_0"), val = tensor([0, 0, 0])]; + tensor input_403_begin_mask_0 = const()[name = tensor("input_403_begin_mask_0"), val = tensor([true, true, false])]; + tensor input_403_end_mask_0 = const()[name = tensor("input_403_end_mask_0"), val = tensor([true, true, true])]; + tensor input_403 = slice_by_index(begin = input_403_begin_0, begin_mask = input_403_begin_mask_0, end = input_403_end_0, end_mask = input_403_end_mask_0, x = conv_transpose_2_has_output_shape)[name = tensor("input_403")]; + tensor weight_131 = const()[name = tensor("weight_131"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(243727616)))]; + tensor input_407_pad_type_0 = const()[name = tensor("input_407_pad_type_0"), val = tensor("custom")]; + tensor input_407_pad_0 = const()[name = tensor("input_407_pad_0"), val = tensor([1, 1])]; + tensor input_407_strides_0 = const()[name = tensor("input_407_strides_0"), val = tensor([1])]; + tensor input_407_dilations_0 = const()[name = tensor("input_407_dilations_0"), val = tensor([1])]; + tensor input_407_groups_0 = const()[name = tensor("input_407_groups_0"), val = tensor(1)]; + tensor input_407 = conv(bias = model_decoder_decode_3_conv1_bias, dilations = input_407_dilations_0, groups = input_407_groups_0, pad = input_407_pad_0, pad_type = input_407_pad_type_0, strides = input_407_strides_0, weight = weight_131, x = input_403)[name = tensor("input_407")]; + tensor h_97 = linear(bias = model_decoder_decode_3_norm2_fc_bias, weight = model_decoder_decode_3_norm2_fc_weight, x = input_331)[name = tensor("linear_99")]; + tensor var_6936 = const()[name = tensor("op_6936"), val = tensor([1, 1024, 1])]; + tensor h_99 = reshape(shape = var_6936, x = h_97)[name = tensor("h_99")]; + tensor var_6938_split_sizes_0 = const()[name = tensor("op_6938_split_sizes_0"), val = tensor([512, 512])]; + tensor var_6938_axis_0 = const()[name = tensor("op_6938_axis_0"), val = tensor(1)]; + tensor var_6938_0, tensor var_6938_1 = split(axis = var_6938_axis_0, split_sizes = var_6938_split_sizes_0, x = h_99)[name = tensor("op_6938")]; + tensor var_6940_promoted = const()[name = tensor("op_6940_promoted"), val = tensor(0x1p+0)]; + tensor var_6941 = add(x = var_6938_0, y = var_6940_promoted)[name = tensor("op_6941")]; + tensor var_6942 = instance_norm(beta = model_decoder_decode_3_norm2_norm_bias, epsilon = var_6903, gamma = model_decoder_decode_3_norm2_norm_weight, x = input_407)[name = tensor("op_6942")]; + tensor var_6943 = mul(x = var_6941, y = var_6942)[name = tensor("op_6943")]; + tensor input_409 = add(x = var_6943, y = var_6938_1)[name = tensor("input_409")]; + tensor input_411 = leaky_relu(alpha = var_6899, x = input_409)[name = tensor("input_411")]; + tensor weight_135 = const()[name = tensor("weight_135"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(250424640)))]; + 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 = model_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_411)[name = tensor("out")]; + tensor expand_dims_3_axes_0 = const()[name = tensor("expand_dims_3_axes_0"), val = tensor([3])]; + tensor expand_dims_3 = expand_dims(axes = expand_dims_3_axes_0, x = input_397)[name = tensor("expand_dims_3")]; + 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_3)[name = tensor("upsample_nearest_neighbor_2")]; + tensor input_415_axes_0 = const()[name = tensor("input_415_axes_0"), val = tensor([3])]; + tensor input_415 = squeeze(axes = input_415_axes_0, x = upsample_nearest_neighbor_2)[name = tensor("input_415")]; + tensor weight_137 = const()[name = tensor("weight_137"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(253570432)))]; + tensor var_6960_pad_type_0 = const()[name = tensor("op_6960_pad_type_0"), val = tensor("valid")]; + tensor var_6960_strides_0 = const()[name = tensor("op_6960_strides_0"), val = tensor([1])]; + tensor var_6960_pad_0 = const()[name = tensor("op_6960_pad_0"), val = tensor([0, 0])]; + tensor var_6960_dilations_0 = const()[name = tensor("op_6960_dilations_0"), val = tensor([1])]; + tensor var_6960_groups_0 = const()[name = tensor("op_6960_groups_0"), val = tensor(1)]; + tensor var_6960 = conv(dilations = var_6960_dilations_0, groups = var_6960_groups_0, pad = var_6960_pad_0, pad_type = var_6960_pad_type_0, strides = var_6960_strides_0, weight = weight_137, x = input_415)[name = tensor("op_6960")]; + tensor var_6961 = add(x = out, y = var_6960)[name = tensor("op_6961")]; + tensor var_6964 = const()[name = tensor("op_6964"), val = tensor(0x1.6a09e6p-1)]; + tensor input_425 = mul(x = var_6961, y = var_6964)[name = tensor("input_425")]; + tensor var_6968 = const()[name = tensor("op_6968"), val = tensor(0x1.47ae14p-7)]; + tensor var_6971 = const()[name = tensor("op_6971"), val = tensor(0x1.4f8b58p-17)]; + tensor var_6975 = const()[name = tensor("op_6975"), val = tensor(0x1.99999ap-4)]; + tensor var_7349 = const()[name = tensor("op_7349"), val = tensor(1)]; + tensor expand_dims_4_axes_0 = const()[name = tensor("expand_dims_4_axes_0"), val = tensor([3])]; + tensor expand_dims_4 = expand_dims(axes = expand_dims_4_axes_0, x = input_327)[name = tensor("expand_dims_4")]; + 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_4)[name = tensor("upsample_nearest_neighbor_3")]; + tensor var_7355_axes_0 = const()[name = tensor("op_7355_axes_0"), val = tensor([3])]; + tensor var_7355 = squeeze(axes = var_7355_axes_0, x = upsample_nearest_neighbor_3)[name = tensor("op_7355")]; + tensor f0_1_perm_0 = const()[name = tensor("f0_1_perm_0"), val = tensor([0, 2, 1])]; + tensor var_7359_promoted = const()[name = tensor("op_7359_promoted"), val = tensor(0x1.4p+3)]; + tensor f0_1 = transpose(perm = f0_1_perm_0, x = var_7355)[name = tensor("transpose_108")]; + tensor var_7360 = sub(x = f0_1, y = var_7359_promoted)[name = tensor("op_7360")]; + tensor var_7361 = const()[name = tensor("op_7361"), val = tensor(0x1p-1)]; + tensor var_7362 = mul(x = var_7360, y = var_7361)[name = tensor("op_7362")]; + tensor uv = sigmoid(x = var_7362)[name = tensor("uv")]; + tensor var_7366_promoted = const()[name = tensor("op_7366_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_1, y = var_7366_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 var_7371_begin_0 = const()[name = tensor("op_7371_begin_0"), val = tensor([0, 0, 0])]; + tensor var_7371_end_0 = const()[name = tensor("op_7371_end_0"), val = tensor([1, 1, 9])]; + tensor var_7371_end_mask_0 = const()[name = tensor("op_7371_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7371_squeeze_mask_0 = const()[name = tensor("op_7371_squeeze_mask_0"), val = tensor([false, true, false])]; + tensor var_7371 = slice_by_index(begin = var_7371_begin_0, end = var_7371_end_0, end_mask = var_7371_end_mask_0, squeeze_mask = var_7371_squeeze_mask_0, x = _inversed_rad_values)[name = tensor("op_7371")]; + tensor var_7374 = add(x = var_7371, y = random_phases)[name = tensor("op_7374")]; + tensor concat_66 = const()[name = tensor("concat_66"), val = tensor([0, 0, 0])]; + tensor concat_67 = const()[name = tensor("concat_67"), 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_4 = const()[name = tensor("shape_4"), val = tensor([1, 360000, 9])]; + tensor reduce_prod_0 = const()[name = tensor("reduce_prod_0"), val = tensor(3240000)]; + tensor range_1d_0_start_0 = const()[name = tensor("range_1d_0_start_0"), val = tensor(0)]; + tensor range_1d_0_step_0 = const()[name = tensor("range_1d_0_step_0"), val = tensor(1)]; + tensor range_1d_0 = range_1d(end = reduce_prod_0, start = range_1d_0_start_0, step = range_1d_0_step_0)[name = tensor("range_1d_0")]; + tensor reshape_0 = reshape(shape = shape_4, x = range_1d_0)[name = tensor("reshape_0")]; + tensor slice_by_index_2 = slice_by_index(begin = concat_66, begin_mask = rad_values_internal_tensor_assign_1_begin_mask_0, end = concat_67, 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_0)[name = tensor("slice_by_index_2")]; + tensor reshape_1_shape_0 = const()[name = tensor("reshape_1_shape_0"), val = tensor([-1])]; + tensor reshape_1 = reshape(shape = reshape_1_shape_0, x = slice_by_index_2)[name = tensor("reshape_1")]; + tensor reshape_2_shape_0 = const()[name = tensor("reshape_2_shape_0"), val = tensor([-1])]; + tensor reshape_2 = reshape(shape = reshape_2_shape_0, x = var_7374)[name = tensor("reshape_2")]; + tensor reshape_3_shape_0 = const()[name = tensor("reshape_3_shape_0"), val = tensor([-1])]; + tensor reshape_3 = reshape(shape = reshape_3_shape_0, x = _inversed_rad_values)[name = tensor("reshape_3")]; + 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_validate_indices_0 = const()[name = tensor("scatter_0_validate_indices_0"), val = tensor(false)]; + tensor scatter_0 = scatter(axis = scatter_0_axis_0, data = reshape_3, indices = reshape_1, mode = scatter_0_mode_0, updates = reshape_2, validate_indices = scatter_0_validate_indices_0)[name = tensor("scatter_0")]; + tensor reshape_4 = reshape(shape = shape_4, x = scatter_0)[name = tensor("reshape_4")]; + tensor var_7380_begin_0 = const()[name = tensor("op_7380_begin_0"), val = tensor([0, 0, 0])]; + tensor var_7380_end_0 = const()[name = tensor("op_7380_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7380_end_mask_0 = const()[name = tensor("op_7380_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7380 = slice_by_index(begin = var_7380_begin_0, end = var_7380_end_0, end_mask = var_7380_end_mask_0, x = reshape_4)[name = tensor("op_7380")]; + tensor var_7382_exclusive_0 = const()[name = tensor("op_7382_exclusive_0"), val = tensor(false)]; + tensor var_7382_reverse_0 = const()[name = tensor("op_7382_reverse_0"), val = tensor(false)]; + tensor var_7382 = cumsum(axis = var_7349, exclusive = var_7382_exclusive_0, reverse = var_7382_reverse_0, x = var_7380)[name = tensor("op_7382")]; + tensor var_7384_begin_0 = const()[name = tensor("op_7384_begin_0"), val = tensor([0, 1000, 0])]; + tensor var_7384_end_0 = const()[name = tensor("op_7384_end_0"), val = tensor([1, 2000, 9])]; + tensor var_7384_end_mask_0 = const()[name = tensor("op_7384_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7384 = slice_by_index(begin = var_7384_begin_0, end = var_7384_end_0, end_mask = var_7384_end_mask_0, x = reshape_4)[name = tensor("op_7384")]; + tensor segment_accum_1_exclusive_0 = const()[name = tensor("segment_accum_1_exclusive_0"), val = tensor(false)]; + tensor segment_accum_1_reverse_0 = const()[name = tensor("segment_accum_1_reverse_0"), val = tensor(false)]; + tensor segment_accum_1 = cumsum(axis = var_7349, exclusive = segment_accum_1_exclusive_0, reverse = segment_accum_1_reverse_0, x = var_7384)[name = tensor("segment_accum_1")]; + tensor var_7388_begin_0 = const()[name = tensor("op_7388_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7388_end_0 = const()[name = tensor("op_7388_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7388_end_mask_0 = const()[name = tensor("op_7388_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7388 = slice_by_index(begin = var_7388_begin_0, end = var_7388_end_0, end_mask = var_7388_end_mask_0, x = var_7382)[name = tensor("op_7388")]; + tensor var_7390 = add(x = segment_accum_1, y = var_7388)[name = tensor("op_7390")]; + tensor var_7392_begin_0 = const()[name = tensor("op_7392_begin_0"), val = tensor([0, 2000, 0])]; + tensor var_7392_end_0 = const()[name = tensor("op_7392_end_0"), val = tensor([1, 3000, 9])]; + tensor var_7392_end_mask_0 = const()[name = tensor("op_7392_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7392 = slice_by_index(begin = var_7392_begin_0, end = var_7392_end_0, end_mask = var_7392_end_mask_0, x = reshape_4)[name = tensor("op_7392")]; + tensor segment_accum_3_exclusive_0 = const()[name = tensor("segment_accum_3_exclusive_0"), val = tensor(false)]; + tensor segment_accum_3_reverse_0 = const()[name = tensor("segment_accum_3_reverse_0"), val = tensor(false)]; + tensor segment_accum_3 = cumsum(axis = var_7349, exclusive = segment_accum_3_exclusive_0, reverse = segment_accum_3_reverse_0, x = var_7392)[name = tensor("segment_accum_3")]; + tensor var_7396_begin_0 = const()[name = tensor("op_7396_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7396_end_0 = const()[name = tensor("op_7396_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7396_end_mask_0 = const()[name = tensor("op_7396_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7396 = slice_by_index(begin = var_7396_begin_0, end = var_7396_end_0, end_mask = var_7396_end_mask_0, x = var_7390)[name = tensor("op_7396")]; + tensor var_7398 = add(x = segment_accum_3, y = var_7396)[name = tensor("op_7398")]; + tensor var_7400_begin_0 = const()[name = tensor("op_7400_begin_0"), val = tensor([0, 3000, 0])]; + tensor var_7400_end_0 = const()[name = tensor("op_7400_end_0"), val = tensor([1, 4000, 9])]; + tensor var_7400_end_mask_0 = const()[name = tensor("op_7400_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7400 = slice_by_index(begin = var_7400_begin_0, end = var_7400_end_0, end_mask = var_7400_end_mask_0, x = reshape_4)[name = tensor("op_7400")]; + tensor segment_accum_5_exclusive_0 = const()[name = tensor("segment_accum_5_exclusive_0"), val = tensor(false)]; + tensor segment_accum_5_reverse_0 = const()[name = tensor("segment_accum_5_reverse_0"), val = tensor(false)]; + tensor segment_accum_5 = cumsum(axis = var_7349, exclusive = segment_accum_5_exclusive_0, reverse = segment_accum_5_reverse_0, x = var_7400)[name = tensor("segment_accum_5")]; + tensor var_7404_begin_0 = const()[name = tensor("op_7404_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7404_end_0 = const()[name = tensor("op_7404_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7404_end_mask_0 = const()[name = tensor("op_7404_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7404 = slice_by_index(begin = var_7404_begin_0, end = var_7404_end_0, end_mask = var_7404_end_mask_0, x = var_7398)[name = tensor("op_7404")]; + tensor var_7406 = add(x = segment_accum_5, y = var_7404)[name = tensor("op_7406")]; + tensor var_7408_begin_0 = const()[name = tensor("op_7408_begin_0"), val = tensor([0, 4000, 0])]; + tensor var_7408_end_0 = const()[name = tensor("op_7408_end_0"), val = tensor([1, 5000, 9])]; + tensor var_7408_end_mask_0 = const()[name = tensor("op_7408_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7408 = slice_by_index(begin = var_7408_begin_0, end = var_7408_end_0, end_mask = var_7408_end_mask_0, x = reshape_4)[name = tensor("op_7408")]; + tensor segment_accum_7_exclusive_0 = const()[name = tensor("segment_accum_7_exclusive_0"), val = tensor(false)]; + tensor segment_accum_7_reverse_0 = const()[name = tensor("segment_accum_7_reverse_0"), val = tensor(false)]; + tensor segment_accum_7 = cumsum(axis = var_7349, exclusive = segment_accum_7_exclusive_0, reverse = segment_accum_7_reverse_0, x = var_7408)[name = tensor("segment_accum_7")]; + tensor var_7412_begin_0 = const()[name = tensor("op_7412_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7412_end_0 = const()[name = tensor("op_7412_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7412_end_mask_0 = const()[name = tensor("op_7412_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7412 = slice_by_index(begin = var_7412_begin_0, end = var_7412_end_0, end_mask = var_7412_end_mask_0, x = var_7406)[name = tensor("op_7412")]; + tensor var_7414 = add(x = segment_accum_7, y = var_7412)[name = tensor("op_7414")]; + tensor var_7416_begin_0 = const()[name = tensor("op_7416_begin_0"), val = tensor([0, 5000, 0])]; + tensor var_7416_end_0 = const()[name = tensor("op_7416_end_0"), val = tensor([1, 6000, 9])]; + tensor var_7416_end_mask_0 = const()[name = tensor("op_7416_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7416 = slice_by_index(begin = var_7416_begin_0, end = var_7416_end_0, end_mask = var_7416_end_mask_0, x = reshape_4)[name = tensor("op_7416")]; + tensor segment_accum_9_exclusive_0 = const()[name = tensor("segment_accum_9_exclusive_0"), val = tensor(false)]; + tensor segment_accum_9_reverse_0 = const()[name = tensor("segment_accum_9_reverse_0"), val = tensor(false)]; + tensor segment_accum_9 = cumsum(axis = var_7349, exclusive = segment_accum_9_exclusive_0, reverse = segment_accum_9_reverse_0, x = var_7416)[name = tensor("segment_accum_9")]; + tensor var_7420_begin_0 = const()[name = tensor("op_7420_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7420_end_0 = const()[name = tensor("op_7420_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7420_end_mask_0 = const()[name = tensor("op_7420_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7420 = slice_by_index(begin = var_7420_begin_0, end = var_7420_end_0, end_mask = var_7420_end_mask_0, x = var_7414)[name = tensor("op_7420")]; + tensor var_7422 = add(x = segment_accum_9, y = var_7420)[name = tensor("op_7422")]; + tensor var_7424_begin_0 = const()[name = tensor("op_7424_begin_0"), val = tensor([0, 6000, 0])]; + tensor var_7424_end_0 = const()[name = tensor("op_7424_end_0"), val = tensor([1, 7000, 9])]; + tensor var_7424_end_mask_0 = const()[name = tensor("op_7424_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7424 = slice_by_index(begin = var_7424_begin_0, end = var_7424_end_0, end_mask = var_7424_end_mask_0, x = reshape_4)[name = tensor("op_7424")]; + tensor segment_accum_11_exclusive_0 = const()[name = tensor("segment_accum_11_exclusive_0"), val = tensor(false)]; + tensor segment_accum_11_reverse_0 = const()[name = tensor("segment_accum_11_reverse_0"), val = tensor(false)]; + tensor segment_accum_11 = cumsum(axis = var_7349, exclusive = segment_accum_11_exclusive_0, reverse = segment_accum_11_reverse_0, x = var_7424)[name = tensor("segment_accum_11")]; + tensor var_7428_begin_0 = const()[name = tensor("op_7428_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7428_end_0 = const()[name = tensor("op_7428_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7428_end_mask_0 = const()[name = tensor("op_7428_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7428 = slice_by_index(begin = var_7428_begin_0, end = var_7428_end_0, end_mask = var_7428_end_mask_0, x = var_7422)[name = tensor("op_7428")]; + tensor var_7430 = add(x = segment_accum_11, y = var_7428)[name = tensor("op_7430")]; + tensor var_7432_begin_0 = const()[name = tensor("op_7432_begin_0"), val = tensor([0, 7000, 0])]; + tensor var_7432_end_0 = const()[name = tensor("op_7432_end_0"), val = tensor([1, 8000, 9])]; + tensor var_7432_end_mask_0 = const()[name = tensor("op_7432_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7432 = slice_by_index(begin = var_7432_begin_0, end = var_7432_end_0, end_mask = var_7432_end_mask_0, x = reshape_4)[name = tensor("op_7432")]; + tensor segment_accum_13_exclusive_0 = const()[name = tensor("segment_accum_13_exclusive_0"), val = tensor(false)]; + tensor segment_accum_13_reverse_0 = const()[name = tensor("segment_accum_13_reverse_0"), val = tensor(false)]; + tensor segment_accum_13 = cumsum(axis = var_7349, exclusive = segment_accum_13_exclusive_0, reverse = segment_accum_13_reverse_0, x = var_7432)[name = tensor("segment_accum_13")]; + tensor var_7436_begin_0 = const()[name = tensor("op_7436_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7436_end_0 = const()[name = tensor("op_7436_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7436_end_mask_0 = const()[name = tensor("op_7436_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7436 = slice_by_index(begin = var_7436_begin_0, end = var_7436_end_0, end_mask = var_7436_end_mask_0, x = var_7430)[name = tensor("op_7436")]; + tensor var_7438 = add(x = segment_accum_13, y = var_7436)[name = tensor("op_7438")]; + tensor var_7440_begin_0 = const()[name = tensor("op_7440_begin_0"), val = tensor([0, 8000, 0])]; + tensor var_7440_end_0 = const()[name = tensor("op_7440_end_0"), val = tensor([1, 9000, 9])]; + tensor var_7440_end_mask_0 = const()[name = tensor("op_7440_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7440 = slice_by_index(begin = var_7440_begin_0, end = var_7440_end_0, end_mask = var_7440_end_mask_0, x = reshape_4)[name = tensor("op_7440")]; + tensor segment_accum_15_exclusive_0 = const()[name = tensor("segment_accum_15_exclusive_0"), val = tensor(false)]; + tensor segment_accum_15_reverse_0 = const()[name = tensor("segment_accum_15_reverse_0"), val = tensor(false)]; + tensor segment_accum_15 = cumsum(axis = var_7349, exclusive = segment_accum_15_exclusive_0, reverse = segment_accum_15_reverse_0, x = var_7440)[name = tensor("segment_accum_15")]; + tensor var_7444_begin_0 = const()[name = tensor("op_7444_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7444_end_0 = const()[name = tensor("op_7444_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7444_end_mask_0 = const()[name = tensor("op_7444_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7444 = slice_by_index(begin = var_7444_begin_0, end = var_7444_end_0, end_mask = var_7444_end_mask_0, x = var_7438)[name = tensor("op_7444")]; + tensor var_7446 = add(x = segment_accum_15, y = var_7444)[name = tensor("op_7446")]; + tensor var_7448_begin_0 = const()[name = tensor("op_7448_begin_0"), val = tensor([0, 9000, 0])]; + tensor var_7448_end_0 = const()[name = tensor("op_7448_end_0"), val = tensor([1, 10000, 9])]; + tensor var_7448_end_mask_0 = const()[name = tensor("op_7448_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7448 = slice_by_index(begin = var_7448_begin_0, end = var_7448_end_0, end_mask = var_7448_end_mask_0, x = reshape_4)[name = tensor("op_7448")]; + tensor segment_accum_17_exclusive_0 = const()[name = tensor("segment_accum_17_exclusive_0"), val = tensor(false)]; + tensor segment_accum_17_reverse_0 = const()[name = tensor("segment_accum_17_reverse_0"), val = tensor(false)]; + tensor segment_accum_17 = cumsum(axis = var_7349, exclusive = segment_accum_17_exclusive_0, reverse = segment_accum_17_reverse_0, x = var_7448)[name = tensor("segment_accum_17")]; + tensor var_7452_begin_0 = const()[name = tensor("op_7452_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7452_end_0 = const()[name = tensor("op_7452_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7452_end_mask_0 = const()[name = tensor("op_7452_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7452 = slice_by_index(begin = var_7452_begin_0, end = var_7452_end_0, end_mask = var_7452_end_mask_0, x = var_7446)[name = tensor("op_7452")]; + tensor var_7454 = add(x = segment_accum_17, y = var_7452)[name = tensor("op_7454")]; + tensor var_7456_begin_0 = const()[name = tensor("op_7456_begin_0"), val = tensor([0, 10000, 0])]; + tensor var_7456_end_0 = const()[name = tensor("op_7456_end_0"), val = tensor([1, 11000, 9])]; + 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 = reshape_4)[name = tensor("op_7456")]; + tensor segment_accum_19_exclusive_0 = const()[name = tensor("segment_accum_19_exclusive_0"), val = tensor(false)]; + tensor segment_accum_19_reverse_0 = const()[name = tensor("segment_accum_19_reverse_0"), val = tensor(false)]; + tensor segment_accum_19 = cumsum(axis = var_7349, exclusive = segment_accum_19_exclusive_0, reverse = segment_accum_19_reverse_0, x = var_7456)[name = tensor("segment_accum_19")]; + tensor var_7460_begin_0 = const()[name = tensor("op_7460_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7460_end_0 = const()[name = tensor("op_7460_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7460_end_mask_0 = const()[name = tensor("op_7460_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7460 = slice_by_index(begin = var_7460_begin_0, end = var_7460_end_0, end_mask = var_7460_end_mask_0, x = var_7454)[name = tensor("op_7460")]; + tensor var_7462 = add(x = segment_accum_19, y = var_7460)[name = tensor("op_7462")]; + tensor var_7464_begin_0 = const()[name = tensor("op_7464_begin_0"), val = tensor([0, 11000, 0])]; + tensor var_7464_end_0 = const()[name = tensor("op_7464_end_0"), val = tensor([1, 12000, 9])]; + tensor var_7464_end_mask_0 = const()[name = tensor("op_7464_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7464 = slice_by_index(begin = var_7464_begin_0, end = var_7464_end_0, end_mask = var_7464_end_mask_0, x = reshape_4)[name = tensor("op_7464")]; + tensor segment_accum_21_exclusive_0 = const()[name = tensor("segment_accum_21_exclusive_0"), val = tensor(false)]; + tensor segment_accum_21_reverse_0 = const()[name = tensor("segment_accum_21_reverse_0"), val = tensor(false)]; + tensor segment_accum_21 = cumsum(axis = var_7349, exclusive = segment_accum_21_exclusive_0, reverse = segment_accum_21_reverse_0, x = var_7464)[name = tensor("segment_accum_21")]; + tensor var_7468_begin_0 = const()[name = tensor("op_7468_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7468_end_0 = const()[name = tensor("op_7468_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7468_end_mask_0 = const()[name = tensor("op_7468_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7468 = slice_by_index(begin = var_7468_begin_0, end = var_7468_end_0, end_mask = var_7468_end_mask_0, x = var_7462)[name = tensor("op_7468")]; + tensor var_7470 = add(x = segment_accum_21, y = var_7468)[name = tensor("op_7470")]; + tensor var_7472_begin_0 = const()[name = tensor("op_7472_begin_0"), val = tensor([0, 12000, 0])]; + tensor var_7472_end_0 = const()[name = tensor("op_7472_end_0"), val = tensor([1, 13000, 9])]; + tensor var_7472_end_mask_0 = const()[name = tensor("op_7472_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7472 = slice_by_index(begin = var_7472_begin_0, end = var_7472_end_0, end_mask = var_7472_end_mask_0, x = reshape_4)[name = tensor("op_7472")]; + tensor segment_accum_23_exclusive_0 = const()[name = tensor("segment_accum_23_exclusive_0"), val = tensor(false)]; + tensor segment_accum_23_reverse_0 = const()[name = tensor("segment_accum_23_reverse_0"), val = tensor(false)]; + tensor segment_accum_23 = cumsum(axis = var_7349, exclusive = segment_accum_23_exclusive_0, reverse = segment_accum_23_reverse_0, x = var_7472)[name = tensor("segment_accum_23")]; + tensor var_7476_begin_0 = const()[name = tensor("op_7476_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7476_end_0 = const()[name = tensor("op_7476_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7476_end_mask_0 = const()[name = tensor("op_7476_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7476 = slice_by_index(begin = var_7476_begin_0, end = var_7476_end_0, end_mask = var_7476_end_mask_0, x = var_7470)[name = tensor("op_7476")]; + tensor var_7478 = add(x = segment_accum_23, y = var_7476)[name = tensor("op_7478")]; + tensor var_7480_begin_0 = const()[name = tensor("op_7480_begin_0"), val = tensor([0, 13000, 0])]; + tensor var_7480_end_0 = const()[name = tensor("op_7480_end_0"), val = tensor([1, 14000, 9])]; + tensor var_7480_end_mask_0 = const()[name = tensor("op_7480_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7480 = slice_by_index(begin = var_7480_begin_0, end = var_7480_end_0, end_mask = var_7480_end_mask_0, x = reshape_4)[name = tensor("op_7480")]; + tensor segment_accum_25_exclusive_0 = const()[name = tensor("segment_accum_25_exclusive_0"), val = tensor(false)]; + tensor segment_accum_25_reverse_0 = const()[name = tensor("segment_accum_25_reverse_0"), val = tensor(false)]; + tensor segment_accum_25 = cumsum(axis = var_7349, exclusive = segment_accum_25_exclusive_0, reverse = segment_accum_25_reverse_0, x = var_7480)[name = tensor("segment_accum_25")]; + tensor var_7484_begin_0 = const()[name = tensor("op_7484_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7484_end_0 = const()[name = tensor("op_7484_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7484_end_mask_0 = const()[name = tensor("op_7484_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7484 = slice_by_index(begin = var_7484_begin_0, end = var_7484_end_0, end_mask = var_7484_end_mask_0, x = var_7478)[name = tensor("op_7484")]; + tensor var_7486 = add(x = segment_accum_25, y = var_7484)[name = tensor("op_7486")]; + tensor var_7488_begin_0 = const()[name = tensor("op_7488_begin_0"), val = tensor([0, 14000, 0])]; + tensor var_7488_end_0 = const()[name = tensor("op_7488_end_0"), val = tensor([1, 15000, 9])]; + tensor var_7488_end_mask_0 = const()[name = tensor("op_7488_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7488 = slice_by_index(begin = var_7488_begin_0, end = var_7488_end_0, end_mask = var_7488_end_mask_0, x = reshape_4)[name = tensor("op_7488")]; + tensor segment_accum_27_exclusive_0 = const()[name = tensor("segment_accum_27_exclusive_0"), val = tensor(false)]; + tensor segment_accum_27_reverse_0 = const()[name = tensor("segment_accum_27_reverse_0"), val = tensor(false)]; + tensor segment_accum_27 = cumsum(axis = var_7349, exclusive = segment_accum_27_exclusive_0, reverse = segment_accum_27_reverse_0, x = var_7488)[name = tensor("segment_accum_27")]; + tensor var_7492_begin_0 = const()[name = tensor("op_7492_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7492_end_0 = const()[name = tensor("op_7492_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7492_end_mask_0 = const()[name = tensor("op_7492_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7492 = slice_by_index(begin = var_7492_begin_0, end = var_7492_end_0, end_mask = var_7492_end_mask_0, x = var_7486)[name = tensor("op_7492")]; + tensor var_7494 = add(x = segment_accum_27, y = var_7492)[name = tensor("op_7494")]; + tensor var_7496_begin_0 = const()[name = tensor("op_7496_begin_0"), val = tensor([0, 15000, 0])]; + tensor var_7496_end_0 = const()[name = tensor("op_7496_end_0"), val = tensor([1, 16000, 9])]; + tensor var_7496_end_mask_0 = const()[name = tensor("op_7496_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7496 = slice_by_index(begin = var_7496_begin_0, end = var_7496_end_0, end_mask = var_7496_end_mask_0, x = reshape_4)[name = tensor("op_7496")]; + tensor segment_accum_29_exclusive_0 = const()[name = tensor("segment_accum_29_exclusive_0"), val = tensor(false)]; + tensor segment_accum_29_reverse_0 = const()[name = tensor("segment_accum_29_reverse_0"), val = tensor(false)]; + tensor segment_accum_29 = cumsum(axis = var_7349, exclusive = segment_accum_29_exclusive_0, reverse = segment_accum_29_reverse_0, x = var_7496)[name = tensor("segment_accum_29")]; + tensor var_7500_begin_0 = const()[name = tensor("op_7500_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7500_end_0 = const()[name = tensor("op_7500_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7500_end_mask_0 = const()[name = tensor("op_7500_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7500 = slice_by_index(begin = var_7500_begin_0, end = var_7500_end_0, end_mask = var_7500_end_mask_0, x = var_7494)[name = tensor("op_7500")]; + tensor var_7502 = add(x = segment_accum_29, y = var_7500)[name = tensor("op_7502")]; + tensor var_7504_begin_0 = const()[name = tensor("op_7504_begin_0"), val = tensor([0, 16000, 0])]; + tensor var_7504_end_0 = const()[name = tensor("op_7504_end_0"), val = tensor([1, 17000, 9])]; + tensor var_7504_end_mask_0 = const()[name = tensor("op_7504_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7504 = slice_by_index(begin = var_7504_begin_0, end = var_7504_end_0, end_mask = var_7504_end_mask_0, x = reshape_4)[name = tensor("op_7504")]; + tensor segment_accum_31_exclusive_0 = const()[name = tensor("segment_accum_31_exclusive_0"), val = tensor(false)]; + tensor segment_accum_31_reverse_0 = const()[name = tensor("segment_accum_31_reverse_0"), val = tensor(false)]; + tensor segment_accum_31 = cumsum(axis = var_7349, exclusive = segment_accum_31_exclusive_0, reverse = segment_accum_31_reverse_0, x = var_7504)[name = tensor("segment_accum_31")]; + tensor var_7508_begin_0 = const()[name = tensor("op_7508_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7508_end_0 = const()[name = tensor("op_7508_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7508_end_mask_0 = const()[name = tensor("op_7508_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7508 = slice_by_index(begin = var_7508_begin_0, end = var_7508_end_0, end_mask = var_7508_end_mask_0, x = var_7502)[name = tensor("op_7508")]; + tensor var_7510 = add(x = segment_accum_31, y = var_7508)[name = tensor("op_7510")]; + tensor var_7512_begin_0 = const()[name = tensor("op_7512_begin_0"), val = tensor([0, 17000, 0])]; + tensor var_7512_end_0 = const()[name = tensor("op_7512_end_0"), val = tensor([1, 18000, 9])]; + tensor var_7512_end_mask_0 = const()[name = tensor("op_7512_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7512 = slice_by_index(begin = var_7512_begin_0, end = var_7512_end_0, end_mask = var_7512_end_mask_0, x = reshape_4)[name = tensor("op_7512")]; + tensor segment_accum_33_exclusive_0 = const()[name = tensor("segment_accum_33_exclusive_0"), val = tensor(false)]; + tensor segment_accum_33_reverse_0 = const()[name = tensor("segment_accum_33_reverse_0"), val = tensor(false)]; + tensor segment_accum_33 = cumsum(axis = var_7349, exclusive = segment_accum_33_exclusive_0, reverse = segment_accum_33_reverse_0, x = var_7512)[name = tensor("segment_accum_33")]; + tensor var_7516_begin_0 = const()[name = tensor("op_7516_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7516_end_0 = const()[name = tensor("op_7516_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7516_end_mask_0 = const()[name = tensor("op_7516_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7516 = slice_by_index(begin = var_7516_begin_0, end = var_7516_end_0, end_mask = var_7516_end_mask_0, x = var_7510)[name = tensor("op_7516")]; + tensor var_7518 = add(x = segment_accum_33, y = var_7516)[name = tensor("op_7518")]; + tensor var_7520_begin_0 = const()[name = tensor("op_7520_begin_0"), val = tensor([0, 18000, 0])]; + tensor var_7520_end_0 = const()[name = tensor("op_7520_end_0"), val = tensor([1, 19000, 9])]; + tensor var_7520_end_mask_0 = const()[name = tensor("op_7520_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7520 = slice_by_index(begin = var_7520_begin_0, end = var_7520_end_0, end_mask = var_7520_end_mask_0, x = reshape_4)[name = tensor("op_7520")]; + tensor segment_accum_35_exclusive_0 = const()[name = tensor("segment_accum_35_exclusive_0"), val = tensor(false)]; + tensor segment_accum_35_reverse_0 = const()[name = tensor("segment_accum_35_reverse_0"), val = tensor(false)]; + tensor segment_accum_35 = cumsum(axis = var_7349, exclusive = segment_accum_35_exclusive_0, reverse = segment_accum_35_reverse_0, x = var_7520)[name = tensor("segment_accum_35")]; + tensor var_7524_begin_0 = const()[name = tensor("op_7524_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7524_end_0 = const()[name = tensor("op_7524_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7524_end_mask_0 = const()[name = tensor("op_7524_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7524 = slice_by_index(begin = var_7524_begin_0, end = var_7524_end_0, end_mask = var_7524_end_mask_0, x = var_7518)[name = tensor("op_7524")]; + tensor var_7526 = add(x = segment_accum_35, y = var_7524)[name = tensor("op_7526")]; + tensor var_7528_begin_0 = const()[name = tensor("op_7528_begin_0"), val = tensor([0, 19000, 0])]; + tensor var_7528_end_0 = const()[name = tensor("op_7528_end_0"), val = tensor([1, 20000, 9])]; + tensor var_7528_end_mask_0 = const()[name = tensor("op_7528_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7528 = slice_by_index(begin = var_7528_begin_0, end = var_7528_end_0, end_mask = var_7528_end_mask_0, x = reshape_4)[name = tensor("op_7528")]; + tensor segment_accum_37_exclusive_0 = const()[name = tensor("segment_accum_37_exclusive_0"), val = tensor(false)]; + tensor segment_accum_37_reverse_0 = const()[name = tensor("segment_accum_37_reverse_0"), val = tensor(false)]; + tensor segment_accum_37 = cumsum(axis = var_7349, exclusive = segment_accum_37_exclusive_0, reverse = segment_accum_37_reverse_0, x = var_7528)[name = tensor("segment_accum_37")]; + tensor var_7532_begin_0 = const()[name = tensor("op_7532_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7532_end_0 = const()[name = tensor("op_7532_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7532_end_mask_0 = const()[name = tensor("op_7532_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7532 = slice_by_index(begin = var_7532_begin_0, end = var_7532_end_0, end_mask = var_7532_end_mask_0, x = var_7526)[name = tensor("op_7532")]; + tensor var_7534 = add(x = segment_accum_37, y = var_7532)[name = tensor("op_7534")]; + tensor var_7536_begin_0 = const()[name = tensor("op_7536_begin_0"), val = tensor([0, 20000, 0])]; + tensor var_7536_end_0 = const()[name = tensor("op_7536_end_0"), val = tensor([1, 21000, 9])]; + tensor var_7536_end_mask_0 = const()[name = tensor("op_7536_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7536 = slice_by_index(begin = var_7536_begin_0, end = var_7536_end_0, end_mask = var_7536_end_mask_0, x = reshape_4)[name = tensor("op_7536")]; + tensor segment_accum_39_exclusive_0 = const()[name = tensor("segment_accum_39_exclusive_0"), val = tensor(false)]; + tensor segment_accum_39_reverse_0 = const()[name = tensor("segment_accum_39_reverse_0"), val = tensor(false)]; + tensor segment_accum_39 = cumsum(axis = var_7349, exclusive = segment_accum_39_exclusive_0, reverse = segment_accum_39_reverse_0, x = var_7536)[name = tensor("segment_accum_39")]; + tensor var_7540_begin_0 = const()[name = tensor("op_7540_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7540_end_0 = const()[name = tensor("op_7540_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7540_end_mask_0 = const()[name = tensor("op_7540_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7540 = slice_by_index(begin = var_7540_begin_0, end = var_7540_end_0, end_mask = var_7540_end_mask_0, x = var_7534)[name = tensor("op_7540")]; + tensor var_7542 = add(x = segment_accum_39, y = var_7540)[name = tensor("op_7542")]; + tensor var_7544_begin_0 = const()[name = tensor("op_7544_begin_0"), val = tensor([0, 21000, 0])]; + tensor var_7544_end_0 = const()[name = tensor("op_7544_end_0"), val = tensor([1, 22000, 9])]; + tensor var_7544_end_mask_0 = const()[name = tensor("op_7544_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7544 = slice_by_index(begin = var_7544_begin_0, end = var_7544_end_0, end_mask = var_7544_end_mask_0, x = reshape_4)[name = tensor("op_7544")]; + tensor segment_accum_41_exclusive_0 = const()[name = tensor("segment_accum_41_exclusive_0"), val = tensor(false)]; + tensor segment_accum_41_reverse_0 = const()[name = tensor("segment_accum_41_reverse_0"), val = tensor(false)]; + tensor segment_accum_41 = cumsum(axis = var_7349, exclusive = segment_accum_41_exclusive_0, reverse = segment_accum_41_reverse_0, x = var_7544)[name = tensor("segment_accum_41")]; + tensor var_7548_begin_0 = const()[name = tensor("op_7548_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7548_end_0 = const()[name = tensor("op_7548_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7548_end_mask_0 = const()[name = tensor("op_7548_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7548 = slice_by_index(begin = var_7548_begin_0, end = var_7548_end_0, end_mask = var_7548_end_mask_0, x = var_7542)[name = tensor("op_7548")]; + tensor var_7550 = add(x = segment_accum_41, y = var_7548)[name = tensor("op_7550")]; + tensor var_7552_begin_0 = const()[name = tensor("op_7552_begin_0"), val = tensor([0, 22000, 0])]; + tensor var_7552_end_0 = const()[name = tensor("op_7552_end_0"), val = tensor([1, 23000, 9])]; + tensor var_7552_end_mask_0 = const()[name = tensor("op_7552_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7552 = slice_by_index(begin = var_7552_begin_0, end = var_7552_end_0, end_mask = var_7552_end_mask_0, x = reshape_4)[name = tensor("op_7552")]; + tensor segment_accum_43_exclusive_0 = const()[name = tensor("segment_accum_43_exclusive_0"), val = tensor(false)]; + tensor segment_accum_43_reverse_0 = const()[name = tensor("segment_accum_43_reverse_0"), val = tensor(false)]; + tensor segment_accum_43 = cumsum(axis = var_7349, exclusive = segment_accum_43_exclusive_0, reverse = segment_accum_43_reverse_0, x = var_7552)[name = tensor("segment_accum_43")]; + tensor var_7556_begin_0 = const()[name = tensor("op_7556_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7556_end_0 = const()[name = tensor("op_7556_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7556_end_mask_0 = const()[name = tensor("op_7556_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7556 = slice_by_index(begin = var_7556_begin_0, end = var_7556_end_0, end_mask = var_7556_end_mask_0, x = var_7550)[name = tensor("op_7556")]; + tensor var_7558 = add(x = segment_accum_43, y = var_7556)[name = tensor("op_7558")]; + tensor var_7560_begin_0 = const()[name = tensor("op_7560_begin_0"), val = tensor([0, 23000, 0])]; + tensor var_7560_end_0 = const()[name = tensor("op_7560_end_0"), val = tensor([1, 24000, 9])]; + tensor var_7560_end_mask_0 = const()[name = tensor("op_7560_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7560 = slice_by_index(begin = var_7560_begin_0, end = var_7560_end_0, end_mask = var_7560_end_mask_0, x = reshape_4)[name = tensor("op_7560")]; + tensor segment_accum_45_exclusive_0 = const()[name = tensor("segment_accum_45_exclusive_0"), val = tensor(false)]; + tensor segment_accum_45_reverse_0 = const()[name = tensor("segment_accum_45_reverse_0"), val = tensor(false)]; + tensor segment_accum_45 = cumsum(axis = var_7349, exclusive = segment_accum_45_exclusive_0, reverse = segment_accum_45_reverse_0, x = var_7560)[name = tensor("segment_accum_45")]; + tensor var_7564_begin_0 = const()[name = tensor("op_7564_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7564_end_0 = const()[name = tensor("op_7564_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7564_end_mask_0 = const()[name = tensor("op_7564_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7564 = slice_by_index(begin = var_7564_begin_0, end = var_7564_end_0, end_mask = var_7564_end_mask_0, x = var_7558)[name = tensor("op_7564")]; + tensor var_7566 = add(x = segment_accum_45, y = var_7564)[name = tensor("op_7566")]; + tensor var_7568_begin_0 = const()[name = tensor("op_7568_begin_0"), val = tensor([0, 24000, 0])]; + tensor var_7568_end_0 = const()[name = tensor("op_7568_end_0"), val = tensor([1, 25000, 9])]; + tensor var_7568_end_mask_0 = const()[name = tensor("op_7568_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7568 = slice_by_index(begin = var_7568_begin_0, end = var_7568_end_0, end_mask = var_7568_end_mask_0, x = reshape_4)[name = tensor("op_7568")]; + tensor segment_accum_47_exclusive_0 = const()[name = tensor("segment_accum_47_exclusive_0"), val = tensor(false)]; + tensor segment_accum_47_reverse_0 = const()[name = tensor("segment_accum_47_reverse_0"), val = tensor(false)]; + tensor segment_accum_47 = cumsum(axis = var_7349, exclusive = segment_accum_47_exclusive_0, reverse = segment_accum_47_reverse_0, x = var_7568)[name = tensor("segment_accum_47")]; + tensor var_7572_begin_0 = const()[name = tensor("op_7572_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7572_end_0 = const()[name = tensor("op_7572_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7572_end_mask_0 = const()[name = tensor("op_7572_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7572 = slice_by_index(begin = var_7572_begin_0, end = var_7572_end_0, end_mask = var_7572_end_mask_0, x = var_7566)[name = tensor("op_7572")]; + tensor var_7574 = add(x = segment_accum_47, y = var_7572)[name = tensor("op_7574")]; + tensor var_7576_begin_0 = const()[name = tensor("op_7576_begin_0"), val = tensor([0, 25000, 0])]; + tensor var_7576_end_0 = const()[name = tensor("op_7576_end_0"), val = tensor([1, 26000, 9])]; + tensor var_7576_end_mask_0 = const()[name = tensor("op_7576_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7576 = slice_by_index(begin = var_7576_begin_0, end = var_7576_end_0, end_mask = var_7576_end_mask_0, x = reshape_4)[name = tensor("op_7576")]; + tensor segment_accum_49_exclusive_0 = const()[name = tensor("segment_accum_49_exclusive_0"), val = tensor(false)]; + tensor segment_accum_49_reverse_0 = const()[name = tensor("segment_accum_49_reverse_0"), val = tensor(false)]; + tensor segment_accum_49 = cumsum(axis = var_7349, exclusive = segment_accum_49_exclusive_0, reverse = segment_accum_49_reverse_0, x = var_7576)[name = tensor("segment_accum_49")]; + tensor var_7580_begin_0 = const()[name = tensor("op_7580_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7580_end_0 = const()[name = tensor("op_7580_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7580_end_mask_0 = const()[name = tensor("op_7580_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7580 = slice_by_index(begin = var_7580_begin_0, end = var_7580_end_0, end_mask = var_7580_end_mask_0, x = var_7574)[name = tensor("op_7580")]; + tensor var_7582 = add(x = segment_accum_49, y = var_7580)[name = tensor("op_7582")]; + tensor var_7584_begin_0 = const()[name = tensor("op_7584_begin_0"), val = tensor([0, 26000, 0])]; + tensor var_7584_end_0 = const()[name = tensor("op_7584_end_0"), val = tensor([1, 27000, 9])]; + tensor var_7584_end_mask_0 = const()[name = tensor("op_7584_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7584 = slice_by_index(begin = var_7584_begin_0, end = var_7584_end_0, end_mask = var_7584_end_mask_0, x = reshape_4)[name = tensor("op_7584")]; + tensor segment_accum_51_exclusive_0 = const()[name = tensor("segment_accum_51_exclusive_0"), val = tensor(false)]; + tensor segment_accum_51_reverse_0 = const()[name = tensor("segment_accum_51_reverse_0"), val = tensor(false)]; + tensor segment_accum_51 = cumsum(axis = var_7349, exclusive = segment_accum_51_exclusive_0, reverse = segment_accum_51_reverse_0, x = var_7584)[name = tensor("segment_accum_51")]; + tensor var_7588_begin_0 = const()[name = tensor("op_7588_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7588_end_0 = const()[name = tensor("op_7588_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7588_end_mask_0 = const()[name = tensor("op_7588_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7588 = slice_by_index(begin = var_7588_begin_0, end = var_7588_end_0, end_mask = var_7588_end_mask_0, x = var_7582)[name = tensor("op_7588")]; + tensor var_7590 = add(x = segment_accum_51, y = var_7588)[name = tensor("op_7590")]; + tensor var_7592_begin_0 = const()[name = tensor("op_7592_begin_0"), val = tensor([0, 27000, 0])]; + tensor var_7592_end_0 = const()[name = tensor("op_7592_end_0"), val = tensor([1, 28000, 9])]; + tensor var_7592_end_mask_0 = const()[name = tensor("op_7592_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7592 = slice_by_index(begin = var_7592_begin_0, end = var_7592_end_0, end_mask = var_7592_end_mask_0, x = reshape_4)[name = tensor("op_7592")]; + tensor segment_accum_53_exclusive_0 = const()[name = tensor("segment_accum_53_exclusive_0"), val = tensor(false)]; + tensor segment_accum_53_reverse_0 = const()[name = tensor("segment_accum_53_reverse_0"), val = tensor(false)]; + tensor segment_accum_53 = cumsum(axis = var_7349, exclusive = segment_accum_53_exclusive_0, reverse = segment_accum_53_reverse_0, x = var_7592)[name = tensor("segment_accum_53")]; + tensor var_7596_begin_0 = const()[name = tensor("op_7596_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7596_end_0 = const()[name = tensor("op_7596_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7596_end_mask_0 = const()[name = tensor("op_7596_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7596 = slice_by_index(begin = var_7596_begin_0, end = var_7596_end_0, end_mask = var_7596_end_mask_0, x = var_7590)[name = tensor("op_7596")]; + tensor var_7598 = add(x = segment_accum_53, y = var_7596)[name = tensor("op_7598")]; + tensor var_7600_begin_0 = const()[name = tensor("op_7600_begin_0"), val = tensor([0, 28000, 0])]; + tensor var_7600_end_0 = const()[name = tensor("op_7600_end_0"), val = tensor([1, 29000, 9])]; + tensor var_7600_end_mask_0 = const()[name = tensor("op_7600_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7600 = slice_by_index(begin = var_7600_begin_0, end = var_7600_end_0, end_mask = var_7600_end_mask_0, x = reshape_4)[name = tensor("op_7600")]; + tensor segment_accum_55_exclusive_0 = const()[name = tensor("segment_accum_55_exclusive_0"), val = tensor(false)]; + tensor segment_accum_55_reverse_0 = const()[name = tensor("segment_accum_55_reverse_0"), val = tensor(false)]; + tensor segment_accum_55 = cumsum(axis = var_7349, exclusive = segment_accum_55_exclusive_0, reverse = segment_accum_55_reverse_0, x = var_7600)[name = tensor("segment_accum_55")]; + tensor var_7604_begin_0 = const()[name = tensor("op_7604_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7604_end_0 = const()[name = tensor("op_7604_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7604_end_mask_0 = const()[name = tensor("op_7604_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7604 = slice_by_index(begin = var_7604_begin_0, end = var_7604_end_0, end_mask = var_7604_end_mask_0, x = var_7598)[name = tensor("op_7604")]; + tensor var_7606 = add(x = segment_accum_55, y = var_7604)[name = tensor("op_7606")]; + tensor var_7608_begin_0 = const()[name = tensor("op_7608_begin_0"), val = tensor([0, 29000, 0])]; + tensor var_7608_end_0 = const()[name = tensor("op_7608_end_0"), val = tensor([1, 30000, 9])]; + tensor var_7608_end_mask_0 = const()[name = tensor("op_7608_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7608 = slice_by_index(begin = var_7608_begin_0, end = var_7608_end_0, end_mask = var_7608_end_mask_0, x = reshape_4)[name = tensor("op_7608")]; + tensor segment_accum_57_exclusive_0 = const()[name = tensor("segment_accum_57_exclusive_0"), val = tensor(false)]; + tensor segment_accum_57_reverse_0 = const()[name = tensor("segment_accum_57_reverse_0"), val = tensor(false)]; + tensor segment_accum_57 = cumsum(axis = var_7349, exclusive = segment_accum_57_exclusive_0, reverse = segment_accum_57_reverse_0, x = var_7608)[name = tensor("segment_accum_57")]; + tensor var_7612_begin_0 = const()[name = tensor("op_7612_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7612_end_0 = const()[name = tensor("op_7612_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7612_end_mask_0 = const()[name = tensor("op_7612_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7612 = slice_by_index(begin = var_7612_begin_0, end = var_7612_end_0, end_mask = var_7612_end_mask_0, x = var_7606)[name = tensor("op_7612")]; + tensor var_7614 = add(x = segment_accum_57, y = var_7612)[name = tensor("op_7614")]; + tensor var_7616_begin_0 = const()[name = tensor("op_7616_begin_0"), val = tensor([0, 30000, 0])]; + tensor var_7616_end_0 = const()[name = tensor("op_7616_end_0"), val = tensor([1, 31000, 9])]; + tensor var_7616_end_mask_0 = const()[name = tensor("op_7616_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7616 = slice_by_index(begin = var_7616_begin_0, end = var_7616_end_0, end_mask = var_7616_end_mask_0, x = reshape_4)[name = tensor("op_7616")]; + tensor segment_accum_59_exclusive_0 = const()[name = tensor("segment_accum_59_exclusive_0"), val = tensor(false)]; + tensor segment_accum_59_reverse_0 = const()[name = tensor("segment_accum_59_reverse_0"), val = tensor(false)]; + tensor segment_accum_59 = cumsum(axis = var_7349, exclusive = segment_accum_59_exclusive_0, reverse = segment_accum_59_reverse_0, x = var_7616)[name = tensor("segment_accum_59")]; + tensor var_7620_begin_0 = const()[name = tensor("op_7620_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7620_end_0 = const()[name = tensor("op_7620_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7620_end_mask_0 = const()[name = tensor("op_7620_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7620 = slice_by_index(begin = var_7620_begin_0, end = var_7620_end_0, end_mask = var_7620_end_mask_0, x = var_7614)[name = tensor("op_7620")]; + tensor var_7622 = add(x = segment_accum_59, y = var_7620)[name = tensor("op_7622")]; + tensor var_7624_begin_0 = const()[name = tensor("op_7624_begin_0"), val = tensor([0, 31000, 0])]; + tensor var_7624_end_0 = const()[name = tensor("op_7624_end_0"), val = tensor([1, 32000, 9])]; + tensor var_7624_end_mask_0 = const()[name = tensor("op_7624_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7624 = slice_by_index(begin = var_7624_begin_0, end = var_7624_end_0, end_mask = var_7624_end_mask_0, x = reshape_4)[name = tensor("op_7624")]; + tensor segment_accum_61_exclusive_0 = const()[name = tensor("segment_accum_61_exclusive_0"), val = tensor(false)]; + tensor segment_accum_61_reverse_0 = const()[name = tensor("segment_accum_61_reverse_0"), val = tensor(false)]; + tensor segment_accum_61 = cumsum(axis = var_7349, exclusive = segment_accum_61_exclusive_0, reverse = segment_accum_61_reverse_0, x = var_7624)[name = tensor("segment_accum_61")]; + tensor var_7628_begin_0 = const()[name = tensor("op_7628_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7628_end_0 = const()[name = tensor("op_7628_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7628_end_mask_0 = const()[name = tensor("op_7628_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7628 = slice_by_index(begin = var_7628_begin_0, end = var_7628_end_0, end_mask = var_7628_end_mask_0, x = var_7622)[name = tensor("op_7628")]; + tensor var_7630 = add(x = segment_accum_61, y = var_7628)[name = tensor("op_7630")]; + tensor var_7632_begin_0 = const()[name = tensor("op_7632_begin_0"), val = tensor([0, 32000, 0])]; + tensor var_7632_end_0 = const()[name = tensor("op_7632_end_0"), val = tensor([1, 33000, 9])]; + tensor var_7632_end_mask_0 = const()[name = tensor("op_7632_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7632 = slice_by_index(begin = var_7632_begin_0, end = var_7632_end_0, end_mask = var_7632_end_mask_0, x = reshape_4)[name = tensor("op_7632")]; + tensor segment_accum_63_exclusive_0 = const()[name = tensor("segment_accum_63_exclusive_0"), val = tensor(false)]; + tensor segment_accum_63_reverse_0 = const()[name = tensor("segment_accum_63_reverse_0"), val = tensor(false)]; + tensor segment_accum_63 = cumsum(axis = var_7349, exclusive = segment_accum_63_exclusive_0, reverse = segment_accum_63_reverse_0, x = var_7632)[name = tensor("segment_accum_63")]; + tensor var_7636_begin_0 = const()[name = tensor("op_7636_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7636_end_0 = const()[name = tensor("op_7636_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7636_end_mask_0 = const()[name = tensor("op_7636_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7636 = slice_by_index(begin = var_7636_begin_0, end = var_7636_end_0, end_mask = var_7636_end_mask_0, x = var_7630)[name = tensor("op_7636")]; + tensor var_7638 = add(x = segment_accum_63, y = var_7636)[name = tensor("op_7638")]; + tensor var_7640_begin_0 = const()[name = tensor("op_7640_begin_0"), val = tensor([0, 33000, 0])]; + tensor var_7640_end_0 = const()[name = tensor("op_7640_end_0"), val = tensor([1, 34000, 9])]; + tensor var_7640_end_mask_0 = const()[name = tensor("op_7640_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7640 = slice_by_index(begin = var_7640_begin_0, end = var_7640_end_0, end_mask = var_7640_end_mask_0, x = reshape_4)[name = tensor("op_7640")]; + tensor segment_accum_65_exclusive_0 = const()[name = tensor("segment_accum_65_exclusive_0"), val = tensor(false)]; + tensor segment_accum_65_reverse_0 = const()[name = tensor("segment_accum_65_reverse_0"), val = tensor(false)]; + tensor segment_accum_65 = cumsum(axis = var_7349, exclusive = segment_accum_65_exclusive_0, reverse = segment_accum_65_reverse_0, x = var_7640)[name = tensor("segment_accum_65")]; + tensor var_7644_begin_0 = const()[name = tensor("op_7644_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7644_end_0 = const()[name = tensor("op_7644_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7644_end_mask_0 = const()[name = tensor("op_7644_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7644 = slice_by_index(begin = var_7644_begin_0, end = var_7644_end_0, end_mask = var_7644_end_mask_0, x = var_7638)[name = tensor("op_7644")]; + tensor var_7646 = add(x = segment_accum_65, y = var_7644)[name = tensor("op_7646")]; + tensor var_7648_begin_0 = const()[name = tensor("op_7648_begin_0"), val = tensor([0, 34000, 0])]; + tensor var_7648_end_0 = const()[name = tensor("op_7648_end_0"), val = tensor([1, 35000, 9])]; + tensor var_7648_end_mask_0 = const()[name = tensor("op_7648_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7648 = slice_by_index(begin = var_7648_begin_0, end = var_7648_end_0, end_mask = var_7648_end_mask_0, x = reshape_4)[name = tensor("op_7648")]; + tensor segment_accum_67_exclusive_0 = const()[name = tensor("segment_accum_67_exclusive_0"), val = tensor(false)]; + tensor segment_accum_67_reverse_0 = const()[name = tensor("segment_accum_67_reverse_0"), val = tensor(false)]; + tensor segment_accum_67 = cumsum(axis = var_7349, exclusive = segment_accum_67_exclusive_0, reverse = segment_accum_67_reverse_0, x = var_7648)[name = tensor("segment_accum_67")]; + tensor var_7652_begin_0 = const()[name = tensor("op_7652_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7652_end_0 = const()[name = tensor("op_7652_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7652_end_mask_0 = const()[name = tensor("op_7652_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7652 = slice_by_index(begin = var_7652_begin_0, end = var_7652_end_0, end_mask = var_7652_end_mask_0, x = var_7646)[name = tensor("op_7652")]; + tensor var_7654 = add(x = segment_accum_67, y = var_7652)[name = tensor("op_7654")]; + tensor var_7656_begin_0 = const()[name = tensor("op_7656_begin_0"), val = tensor([0, 35000, 0])]; + tensor var_7656_end_0 = const()[name = tensor("op_7656_end_0"), val = tensor([1, 36000, 9])]; + tensor var_7656_end_mask_0 = const()[name = tensor("op_7656_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7656 = slice_by_index(begin = var_7656_begin_0, end = var_7656_end_0, end_mask = var_7656_end_mask_0, x = reshape_4)[name = tensor("op_7656")]; + tensor segment_accum_69_exclusive_0 = const()[name = tensor("segment_accum_69_exclusive_0"), val = tensor(false)]; + tensor segment_accum_69_reverse_0 = const()[name = tensor("segment_accum_69_reverse_0"), val = tensor(false)]; + tensor segment_accum_69 = cumsum(axis = var_7349, exclusive = segment_accum_69_exclusive_0, reverse = segment_accum_69_reverse_0, x = var_7656)[name = tensor("segment_accum_69")]; + tensor var_7660_begin_0 = const()[name = tensor("op_7660_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7660_end_0 = const()[name = tensor("op_7660_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7660_end_mask_0 = const()[name = tensor("op_7660_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7660 = slice_by_index(begin = var_7660_begin_0, end = var_7660_end_0, end_mask = var_7660_end_mask_0, x = var_7654)[name = tensor("op_7660")]; + tensor var_7662 = add(x = segment_accum_69, y = var_7660)[name = tensor("op_7662")]; + tensor var_7664_begin_0 = const()[name = tensor("op_7664_begin_0"), val = tensor([0, 36000, 0])]; + tensor var_7664_end_0 = const()[name = tensor("op_7664_end_0"), val = tensor([1, 37000, 9])]; + tensor var_7664_end_mask_0 = const()[name = tensor("op_7664_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7664 = slice_by_index(begin = var_7664_begin_0, end = var_7664_end_0, end_mask = var_7664_end_mask_0, x = reshape_4)[name = tensor("op_7664")]; + tensor segment_accum_71_exclusive_0 = const()[name = tensor("segment_accum_71_exclusive_0"), val = tensor(false)]; + tensor segment_accum_71_reverse_0 = const()[name = tensor("segment_accum_71_reverse_0"), val = tensor(false)]; + tensor segment_accum_71 = cumsum(axis = var_7349, exclusive = segment_accum_71_exclusive_0, reverse = segment_accum_71_reverse_0, x = var_7664)[name = tensor("segment_accum_71")]; + tensor var_7668_begin_0 = const()[name = tensor("op_7668_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7668_end_0 = const()[name = tensor("op_7668_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7668_end_mask_0 = const()[name = tensor("op_7668_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7668 = slice_by_index(begin = var_7668_begin_0, end = var_7668_end_0, end_mask = var_7668_end_mask_0, x = var_7662)[name = tensor("op_7668")]; + tensor var_7670 = add(x = segment_accum_71, y = var_7668)[name = tensor("op_7670")]; + tensor var_7672_begin_0 = const()[name = tensor("op_7672_begin_0"), val = tensor([0, 37000, 0])]; + tensor var_7672_end_0 = const()[name = tensor("op_7672_end_0"), val = tensor([1, 38000, 9])]; + tensor var_7672_end_mask_0 = const()[name = tensor("op_7672_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7672 = slice_by_index(begin = var_7672_begin_0, end = var_7672_end_0, end_mask = var_7672_end_mask_0, x = reshape_4)[name = tensor("op_7672")]; + tensor segment_accum_73_exclusive_0 = const()[name = tensor("segment_accum_73_exclusive_0"), val = tensor(false)]; + tensor segment_accum_73_reverse_0 = const()[name = tensor("segment_accum_73_reverse_0"), val = tensor(false)]; + tensor segment_accum_73 = cumsum(axis = var_7349, exclusive = segment_accum_73_exclusive_0, reverse = segment_accum_73_reverse_0, x = var_7672)[name = tensor("segment_accum_73")]; + tensor var_7676_begin_0 = const()[name = tensor("op_7676_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7676_end_0 = const()[name = tensor("op_7676_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7676_end_mask_0 = const()[name = tensor("op_7676_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7676 = slice_by_index(begin = var_7676_begin_0, end = var_7676_end_0, end_mask = var_7676_end_mask_0, x = var_7670)[name = tensor("op_7676")]; + tensor var_7678 = add(x = segment_accum_73, y = var_7676)[name = tensor("op_7678")]; + tensor var_7680_begin_0 = const()[name = tensor("op_7680_begin_0"), val = tensor([0, 38000, 0])]; + tensor var_7680_end_0 = const()[name = tensor("op_7680_end_0"), val = tensor([1, 39000, 9])]; + tensor var_7680_end_mask_0 = const()[name = tensor("op_7680_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7680 = slice_by_index(begin = var_7680_begin_0, end = var_7680_end_0, end_mask = var_7680_end_mask_0, x = reshape_4)[name = tensor("op_7680")]; + tensor segment_accum_75_exclusive_0 = const()[name = tensor("segment_accum_75_exclusive_0"), val = tensor(false)]; + tensor segment_accum_75_reverse_0 = const()[name = tensor("segment_accum_75_reverse_0"), val = tensor(false)]; + tensor segment_accum_75 = cumsum(axis = var_7349, exclusive = segment_accum_75_exclusive_0, reverse = segment_accum_75_reverse_0, x = var_7680)[name = tensor("segment_accum_75")]; + tensor var_7684_begin_0 = const()[name = tensor("op_7684_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7684_end_0 = const()[name = tensor("op_7684_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7684_end_mask_0 = const()[name = tensor("op_7684_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7684 = slice_by_index(begin = var_7684_begin_0, end = var_7684_end_0, end_mask = var_7684_end_mask_0, x = var_7678)[name = tensor("op_7684")]; + tensor var_7686 = add(x = segment_accum_75, y = var_7684)[name = tensor("op_7686")]; + tensor var_7688_begin_0 = const()[name = tensor("op_7688_begin_0"), val = tensor([0, 39000, 0])]; + tensor var_7688_end_0 = const()[name = tensor("op_7688_end_0"), val = tensor([1, 40000, 9])]; + tensor var_7688_end_mask_0 = const()[name = tensor("op_7688_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7688 = slice_by_index(begin = var_7688_begin_0, end = var_7688_end_0, end_mask = var_7688_end_mask_0, x = reshape_4)[name = tensor("op_7688")]; + tensor segment_accum_77_exclusive_0 = const()[name = tensor("segment_accum_77_exclusive_0"), val = tensor(false)]; + tensor segment_accum_77_reverse_0 = const()[name = tensor("segment_accum_77_reverse_0"), val = tensor(false)]; + tensor segment_accum_77 = cumsum(axis = var_7349, exclusive = segment_accum_77_exclusive_0, reverse = segment_accum_77_reverse_0, x = var_7688)[name = tensor("segment_accum_77")]; + tensor var_7692_begin_0 = const()[name = tensor("op_7692_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7692_end_0 = const()[name = tensor("op_7692_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7692_end_mask_0 = const()[name = tensor("op_7692_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7692 = slice_by_index(begin = var_7692_begin_0, end = var_7692_end_0, end_mask = var_7692_end_mask_0, x = var_7686)[name = tensor("op_7692")]; + tensor var_7694 = add(x = segment_accum_77, y = var_7692)[name = tensor("op_7694")]; + tensor var_7696_begin_0 = const()[name = tensor("op_7696_begin_0"), val = tensor([0, 40000, 0])]; + tensor var_7696_end_0 = const()[name = tensor("op_7696_end_0"), val = tensor([1, 41000, 9])]; + tensor var_7696_end_mask_0 = const()[name = tensor("op_7696_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7696 = slice_by_index(begin = var_7696_begin_0, end = var_7696_end_0, end_mask = var_7696_end_mask_0, x = reshape_4)[name = tensor("op_7696")]; + tensor segment_accum_79_exclusive_0 = const()[name = tensor("segment_accum_79_exclusive_0"), val = tensor(false)]; + tensor segment_accum_79_reverse_0 = const()[name = tensor("segment_accum_79_reverse_0"), val = tensor(false)]; + tensor segment_accum_79 = cumsum(axis = var_7349, exclusive = segment_accum_79_exclusive_0, reverse = segment_accum_79_reverse_0, x = var_7696)[name = tensor("segment_accum_79")]; + tensor var_7700_begin_0 = const()[name = tensor("op_7700_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7700_end_0 = const()[name = tensor("op_7700_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7700_end_mask_0 = const()[name = tensor("op_7700_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7700 = slice_by_index(begin = var_7700_begin_0, end = var_7700_end_0, end_mask = var_7700_end_mask_0, x = var_7694)[name = tensor("op_7700")]; + tensor var_7702 = add(x = segment_accum_79, y = var_7700)[name = tensor("op_7702")]; + tensor var_7704_begin_0 = const()[name = tensor("op_7704_begin_0"), val = tensor([0, 41000, 0])]; + tensor var_7704_end_0 = const()[name = tensor("op_7704_end_0"), val = tensor([1, 42000, 9])]; + tensor var_7704_end_mask_0 = const()[name = tensor("op_7704_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7704 = slice_by_index(begin = var_7704_begin_0, end = var_7704_end_0, end_mask = var_7704_end_mask_0, x = reshape_4)[name = tensor("op_7704")]; + tensor segment_accum_81_exclusive_0 = const()[name = tensor("segment_accum_81_exclusive_0"), val = tensor(false)]; + tensor segment_accum_81_reverse_0 = const()[name = tensor("segment_accum_81_reverse_0"), val = tensor(false)]; + tensor segment_accum_81 = cumsum(axis = var_7349, exclusive = segment_accum_81_exclusive_0, reverse = segment_accum_81_reverse_0, x = var_7704)[name = tensor("segment_accum_81")]; + tensor var_7708_begin_0 = const()[name = tensor("op_7708_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7708_end_0 = const()[name = tensor("op_7708_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7708_end_mask_0 = const()[name = tensor("op_7708_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7708 = slice_by_index(begin = var_7708_begin_0, end = var_7708_end_0, end_mask = var_7708_end_mask_0, x = var_7702)[name = tensor("op_7708")]; + tensor var_7710 = add(x = segment_accum_81, y = var_7708)[name = tensor("op_7710")]; + tensor var_7712_begin_0 = const()[name = tensor("op_7712_begin_0"), val = tensor([0, 42000, 0])]; + tensor var_7712_end_0 = const()[name = tensor("op_7712_end_0"), val = tensor([1, 43000, 9])]; + tensor var_7712_end_mask_0 = const()[name = tensor("op_7712_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7712 = slice_by_index(begin = var_7712_begin_0, end = var_7712_end_0, end_mask = var_7712_end_mask_0, x = reshape_4)[name = tensor("op_7712")]; + tensor segment_accum_83_exclusive_0 = const()[name = tensor("segment_accum_83_exclusive_0"), val = tensor(false)]; + tensor segment_accum_83_reverse_0 = const()[name = tensor("segment_accum_83_reverse_0"), val = tensor(false)]; + tensor segment_accum_83 = cumsum(axis = var_7349, exclusive = segment_accum_83_exclusive_0, reverse = segment_accum_83_reverse_0, x = var_7712)[name = tensor("segment_accum_83")]; + tensor var_7716_begin_0 = const()[name = tensor("op_7716_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7716_end_0 = const()[name = tensor("op_7716_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7716_end_mask_0 = const()[name = tensor("op_7716_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7716 = slice_by_index(begin = var_7716_begin_0, end = var_7716_end_0, end_mask = var_7716_end_mask_0, x = var_7710)[name = tensor("op_7716")]; + tensor var_7718 = add(x = segment_accum_83, y = var_7716)[name = tensor("op_7718")]; + tensor var_7720_begin_0 = const()[name = tensor("op_7720_begin_0"), val = tensor([0, 43000, 0])]; + tensor var_7720_end_0 = const()[name = tensor("op_7720_end_0"), val = tensor([1, 44000, 9])]; + tensor var_7720_end_mask_0 = const()[name = tensor("op_7720_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7720 = slice_by_index(begin = var_7720_begin_0, end = var_7720_end_0, end_mask = var_7720_end_mask_0, x = reshape_4)[name = tensor("op_7720")]; + tensor segment_accum_85_exclusive_0 = const()[name = tensor("segment_accum_85_exclusive_0"), val = tensor(false)]; + tensor segment_accum_85_reverse_0 = const()[name = tensor("segment_accum_85_reverse_0"), val = tensor(false)]; + tensor segment_accum_85 = cumsum(axis = var_7349, exclusive = segment_accum_85_exclusive_0, reverse = segment_accum_85_reverse_0, x = var_7720)[name = tensor("segment_accum_85")]; + tensor var_7724_begin_0 = const()[name = tensor("op_7724_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7724_end_0 = const()[name = tensor("op_7724_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7724_end_mask_0 = const()[name = tensor("op_7724_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7724 = slice_by_index(begin = var_7724_begin_0, end = var_7724_end_0, end_mask = var_7724_end_mask_0, x = var_7718)[name = tensor("op_7724")]; + tensor var_7726 = add(x = segment_accum_85, y = var_7724)[name = tensor("op_7726")]; + tensor var_7728_begin_0 = const()[name = tensor("op_7728_begin_0"), val = tensor([0, 44000, 0])]; + tensor var_7728_end_0 = const()[name = tensor("op_7728_end_0"), val = tensor([1, 45000, 9])]; + tensor var_7728_end_mask_0 = const()[name = tensor("op_7728_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7728 = slice_by_index(begin = var_7728_begin_0, end = var_7728_end_0, end_mask = var_7728_end_mask_0, x = reshape_4)[name = tensor("op_7728")]; + tensor segment_accum_87_exclusive_0 = const()[name = tensor("segment_accum_87_exclusive_0"), val = tensor(false)]; + tensor segment_accum_87_reverse_0 = const()[name = tensor("segment_accum_87_reverse_0"), val = tensor(false)]; + tensor segment_accum_87 = cumsum(axis = var_7349, exclusive = segment_accum_87_exclusive_0, reverse = segment_accum_87_reverse_0, x = var_7728)[name = tensor("segment_accum_87")]; + tensor var_7732_begin_0 = const()[name = tensor("op_7732_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7732_end_0 = const()[name = tensor("op_7732_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7732_end_mask_0 = const()[name = tensor("op_7732_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7732 = slice_by_index(begin = var_7732_begin_0, end = var_7732_end_0, end_mask = var_7732_end_mask_0, x = var_7726)[name = tensor("op_7732")]; + tensor var_7734 = add(x = segment_accum_87, y = var_7732)[name = tensor("op_7734")]; + tensor var_7736_begin_0 = const()[name = tensor("op_7736_begin_0"), val = tensor([0, 45000, 0])]; + tensor var_7736_end_0 = const()[name = tensor("op_7736_end_0"), val = tensor([1, 46000, 9])]; + tensor var_7736_end_mask_0 = const()[name = tensor("op_7736_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7736 = slice_by_index(begin = var_7736_begin_0, end = var_7736_end_0, end_mask = var_7736_end_mask_0, x = reshape_4)[name = tensor("op_7736")]; + tensor segment_accum_89_exclusive_0 = const()[name = tensor("segment_accum_89_exclusive_0"), val = tensor(false)]; + tensor segment_accum_89_reverse_0 = const()[name = tensor("segment_accum_89_reverse_0"), val = tensor(false)]; + tensor segment_accum_89 = cumsum(axis = var_7349, exclusive = segment_accum_89_exclusive_0, reverse = segment_accum_89_reverse_0, x = var_7736)[name = tensor("segment_accum_89")]; + tensor var_7740_begin_0 = const()[name = tensor("op_7740_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7740_end_0 = const()[name = tensor("op_7740_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7740_end_mask_0 = const()[name = tensor("op_7740_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7740 = slice_by_index(begin = var_7740_begin_0, end = var_7740_end_0, end_mask = var_7740_end_mask_0, x = var_7734)[name = tensor("op_7740")]; + tensor var_7742 = add(x = segment_accum_89, y = var_7740)[name = tensor("op_7742")]; + tensor var_7744_begin_0 = const()[name = tensor("op_7744_begin_0"), val = tensor([0, 46000, 0])]; + tensor var_7744_end_0 = const()[name = tensor("op_7744_end_0"), val = tensor([1, 47000, 9])]; + tensor var_7744_end_mask_0 = const()[name = tensor("op_7744_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7744 = slice_by_index(begin = var_7744_begin_0, end = var_7744_end_0, end_mask = var_7744_end_mask_0, x = reshape_4)[name = tensor("op_7744")]; + tensor segment_accum_91_exclusive_0 = const()[name = tensor("segment_accum_91_exclusive_0"), val = tensor(false)]; + tensor segment_accum_91_reverse_0 = const()[name = tensor("segment_accum_91_reverse_0"), val = tensor(false)]; + tensor segment_accum_91 = cumsum(axis = var_7349, exclusive = segment_accum_91_exclusive_0, reverse = segment_accum_91_reverse_0, x = var_7744)[name = tensor("segment_accum_91")]; + tensor var_7748_begin_0 = const()[name = tensor("op_7748_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7748_end_0 = const()[name = tensor("op_7748_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7748_end_mask_0 = const()[name = tensor("op_7748_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7748 = slice_by_index(begin = var_7748_begin_0, end = var_7748_end_0, end_mask = var_7748_end_mask_0, x = var_7742)[name = tensor("op_7748")]; + tensor var_7750 = add(x = segment_accum_91, y = var_7748)[name = tensor("op_7750")]; + tensor var_7752_begin_0 = const()[name = tensor("op_7752_begin_0"), val = tensor([0, 47000, 0])]; + tensor var_7752_end_0 = const()[name = tensor("op_7752_end_0"), val = tensor([1, 48000, 9])]; + tensor var_7752_end_mask_0 = const()[name = tensor("op_7752_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7752 = slice_by_index(begin = var_7752_begin_0, end = var_7752_end_0, end_mask = var_7752_end_mask_0, x = reshape_4)[name = tensor("op_7752")]; + tensor segment_accum_93_exclusive_0 = const()[name = tensor("segment_accum_93_exclusive_0"), val = tensor(false)]; + tensor segment_accum_93_reverse_0 = const()[name = tensor("segment_accum_93_reverse_0"), val = tensor(false)]; + tensor segment_accum_93 = cumsum(axis = var_7349, exclusive = segment_accum_93_exclusive_0, reverse = segment_accum_93_reverse_0, x = var_7752)[name = tensor("segment_accum_93")]; + tensor var_7756_begin_0 = const()[name = tensor("op_7756_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7756_end_0 = const()[name = tensor("op_7756_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7756_end_mask_0 = const()[name = tensor("op_7756_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7756 = slice_by_index(begin = var_7756_begin_0, end = var_7756_end_0, end_mask = var_7756_end_mask_0, x = var_7750)[name = tensor("op_7756")]; + tensor var_7758 = add(x = segment_accum_93, y = var_7756)[name = tensor("op_7758")]; + tensor var_7760_begin_0 = const()[name = tensor("op_7760_begin_0"), val = tensor([0, 48000, 0])]; + tensor var_7760_end_0 = const()[name = tensor("op_7760_end_0"), val = tensor([1, 49000, 9])]; + tensor var_7760_end_mask_0 = const()[name = tensor("op_7760_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7760 = slice_by_index(begin = var_7760_begin_0, end = var_7760_end_0, end_mask = var_7760_end_mask_0, x = reshape_4)[name = tensor("op_7760")]; + tensor segment_accum_95_exclusive_0 = const()[name = tensor("segment_accum_95_exclusive_0"), val = tensor(false)]; + tensor segment_accum_95_reverse_0 = const()[name = tensor("segment_accum_95_reverse_0"), val = tensor(false)]; + tensor segment_accum_95 = cumsum(axis = var_7349, exclusive = segment_accum_95_exclusive_0, reverse = segment_accum_95_reverse_0, x = var_7760)[name = tensor("segment_accum_95")]; + tensor var_7764_begin_0 = const()[name = tensor("op_7764_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7764_end_0 = const()[name = tensor("op_7764_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7764_end_mask_0 = const()[name = tensor("op_7764_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7764 = slice_by_index(begin = var_7764_begin_0, end = var_7764_end_0, end_mask = var_7764_end_mask_0, x = var_7758)[name = tensor("op_7764")]; + tensor var_7766 = add(x = segment_accum_95, y = var_7764)[name = tensor("op_7766")]; + tensor var_7768_begin_0 = const()[name = tensor("op_7768_begin_0"), val = tensor([0, 49000, 0])]; + tensor var_7768_end_0 = const()[name = tensor("op_7768_end_0"), val = tensor([1, 50000, 9])]; + tensor var_7768_end_mask_0 = const()[name = tensor("op_7768_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7768 = slice_by_index(begin = var_7768_begin_0, end = var_7768_end_0, end_mask = var_7768_end_mask_0, x = reshape_4)[name = tensor("op_7768")]; + tensor segment_accum_97_exclusive_0 = const()[name = tensor("segment_accum_97_exclusive_0"), val = tensor(false)]; + tensor segment_accum_97_reverse_0 = const()[name = tensor("segment_accum_97_reverse_0"), val = tensor(false)]; + tensor segment_accum_97 = cumsum(axis = var_7349, exclusive = segment_accum_97_exclusive_0, reverse = segment_accum_97_reverse_0, x = var_7768)[name = tensor("segment_accum_97")]; + tensor var_7772_begin_0 = const()[name = tensor("op_7772_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7772_end_0 = const()[name = tensor("op_7772_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7772_end_mask_0 = const()[name = tensor("op_7772_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7772 = slice_by_index(begin = var_7772_begin_0, end = var_7772_end_0, end_mask = var_7772_end_mask_0, x = var_7766)[name = tensor("op_7772")]; + tensor var_7774 = add(x = segment_accum_97, y = var_7772)[name = tensor("op_7774")]; + tensor var_7776_begin_0 = const()[name = tensor("op_7776_begin_0"), val = tensor([0, 50000, 0])]; + tensor var_7776_end_0 = const()[name = tensor("op_7776_end_0"), val = tensor([1, 51000, 9])]; + tensor var_7776_end_mask_0 = const()[name = tensor("op_7776_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7776 = slice_by_index(begin = var_7776_begin_0, end = var_7776_end_0, end_mask = var_7776_end_mask_0, x = reshape_4)[name = tensor("op_7776")]; + tensor segment_accum_99_exclusive_0 = const()[name = tensor("segment_accum_99_exclusive_0"), val = tensor(false)]; + tensor segment_accum_99_reverse_0 = const()[name = tensor("segment_accum_99_reverse_0"), val = tensor(false)]; + tensor segment_accum_99 = cumsum(axis = var_7349, exclusive = segment_accum_99_exclusive_0, reverse = segment_accum_99_reverse_0, x = var_7776)[name = tensor("segment_accum_99")]; + tensor var_7780_begin_0 = const()[name = tensor("op_7780_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7780_end_0 = const()[name = tensor("op_7780_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7780_end_mask_0 = const()[name = tensor("op_7780_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7780 = slice_by_index(begin = var_7780_begin_0, end = var_7780_end_0, end_mask = var_7780_end_mask_0, x = var_7774)[name = tensor("op_7780")]; + tensor var_7782 = add(x = segment_accum_99, y = var_7780)[name = tensor("op_7782")]; + tensor var_7784_begin_0 = const()[name = tensor("op_7784_begin_0"), val = tensor([0, 51000, 0])]; + tensor var_7784_end_0 = const()[name = tensor("op_7784_end_0"), val = tensor([1, 52000, 9])]; + tensor var_7784_end_mask_0 = const()[name = tensor("op_7784_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7784 = slice_by_index(begin = var_7784_begin_0, end = var_7784_end_0, end_mask = var_7784_end_mask_0, x = reshape_4)[name = tensor("op_7784")]; + tensor segment_accum_101_exclusive_0 = const()[name = tensor("segment_accum_101_exclusive_0"), val = tensor(false)]; + tensor segment_accum_101_reverse_0 = const()[name = tensor("segment_accum_101_reverse_0"), val = tensor(false)]; + tensor segment_accum_101 = cumsum(axis = var_7349, exclusive = segment_accum_101_exclusive_0, reverse = segment_accum_101_reverse_0, x = var_7784)[name = tensor("segment_accum_101")]; + tensor var_7788_begin_0 = const()[name = tensor("op_7788_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7788_end_0 = const()[name = tensor("op_7788_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7788_end_mask_0 = const()[name = tensor("op_7788_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7788 = slice_by_index(begin = var_7788_begin_0, end = var_7788_end_0, end_mask = var_7788_end_mask_0, x = var_7782)[name = tensor("op_7788")]; + tensor var_7790 = add(x = segment_accum_101, y = var_7788)[name = tensor("op_7790")]; + tensor var_7792_begin_0 = const()[name = tensor("op_7792_begin_0"), val = tensor([0, 52000, 0])]; + tensor var_7792_end_0 = const()[name = tensor("op_7792_end_0"), val = tensor([1, 53000, 9])]; + tensor var_7792_end_mask_0 = const()[name = tensor("op_7792_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7792 = slice_by_index(begin = var_7792_begin_0, end = var_7792_end_0, end_mask = var_7792_end_mask_0, x = reshape_4)[name = tensor("op_7792")]; + tensor segment_accum_103_exclusive_0 = const()[name = tensor("segment_accum_103_exclusive_0"), val = tensor(false)]; + tensor segment_accum_103_reverse_0 = const()[name = tensor("segment_accum_103_reverse_0"), val = tensor(false)]; + tensor segment_accum_103 = cumsum(axis = var_7349, exclusive = segment_accum_103_exclusive_0, reverse = segment_accum_103_reverse_0, x = var_7792)[name = tensor("segment_accum_103")]; + tensor var_7796_begin_0 = const()[name = tensor("op_7796_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7796_end_0 = const()[name = tensor("op_7796_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7796_end_mask_0 = const()[name = tensor("op_7796_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7796 = slice_by_index(begin = var_7796_begin_0, end = var_7796_end_0, end_mask = var_7796_end_mask_0, x = var_7790)[name = tensor("op_7796")]; + tensor var_7798 = add(x = segment_accum_103, y = var_7796)[name = tensor("op_7798")]; + tensor var_7800_begin_0 = const()[name = tensor("op_7800_begin_0"), val = tensor([0, 53000, 0])]; + tensor var_7800_end_0 = const()[name = tensor("op_7800_end_0"), val = tensor([1, 54000, 9])]; + tensor var_7800_end_mask_0 = const()[name = tensor("op_7800_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7800 = slice_by_index(begin = var_7800_begin_0, end = var_7800_end_0, end_mask = var_7800_end_mask_0, x = reshape_4)[name = tensor("op_7800")]; + tensor segment_accum_105_exclusive_0 = const()[name = tensor("segment_accum_105_exclusive_0"), val = tensor(false)]; + tensor segment_accum_105_reverse_0 = const()[name = tensor("segment_accum_105_reverse_0"), val = tensor(false)]; + tensor segment_accum_105 = cumsum(axis = var_7349, exclusive = segment_accum_105_exclusive_0, reverse = segment_accum_105_reverse_0, x = var_7800)[name = tensor("segment_accum_105")]; + tensor var_7804_begin_0 = const()[name = tensor("op_7804_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7804_end_0 = const()[name = tensor("op_7804_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7804_end_mask_0 = const()[name = tensor("op_7804_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7804 = slice_by_index(begin = var_7804_begin_0, end = var_7804_end_0, end_mask = var_7804_end_mask_0, x = var_7798)[name = tensor("op_7804")]; + tensor var_7806 = add(x = segment_accum_105, y = var_7804)[name = tensor("op_7806")]; + tensor var_7808_begin_0 = const()[name = tensor("op_7808_begin_0"), val = tensor([0, 54000, 0])]; + tensor var_7808_end_0 = const()[name = tensor("op_7808_end_0"), val = tensor([1, 55000, 9])]; + tensor var_7808_end_mask_0 = const()[name = tensor("op_7808_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7808 = slice_by_index(begin = var_7808_begin_0, end = var_7808_end_0, end_mask = var_7808_end_mask_0, x = reshape_4)[name = tensor("op_7808")]; + tensor segment_accum_107_exclusive_0 = const()[name = tensor("segment_accum_107_exclusive_0"), val = tensor(false)]; + tensor segment_accum_107_reverse_0 = const()[name = tensor("segment_accum_107_reverse_0"), val = tensor(false)]; + tensor segment_accum_107 = cumsum(axis = var_7349, exclusive = segment_accum_107_exclusive_0, reverse = segment_accum_107_reverse_0, x = var_7808)[name = tensor("segment_accum_107")]; + tensor var_7812_begin_0 = const()[name = tensor("op_7812_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7812_end_0 = const()[name = tensor("op_7812_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7812_end_mask_0 = const()[name = tensor("op_7812_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7812 = slice_by_index(begin = var_7812_begin_0, end = var_7812_end_0, end_mask = var_7812_end_mask_0, x = var_7806)[name = tensor("op_7812")]; + tensor var_7814 = add(x = segment_accum_107, y = var_7812)[name = tensor("op_7814")]; + tensor var_7816_begin_0 = const()[name = tensor("op_7816_begin_0"), val = tensor([0, 55000, 0])]; + tensor var_7816_end_0 = const()[name = tensor("op_7816_end_0"), val = tensor([1, 56000, 9])]; + tensor var_7816_end_mask_0 = const()[name = tensor("op_7816_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7816 = slice_by_index(begin = var_7816_begin_0, end = var_7816_end_0, end_mask = var_7816_end_mask_0, x = reshape_4)[name = tensor("op_7816")]; + tensor segment_accum_109_exclusive_0 = const()[name = tensor("segment_accum_109_exclusive_0"), val = tensor(false)]; + tensor segment_accum_109_reverse_0 = const()[name = tensor("segment_accum_109_reverse_0"), val = tensor(false)]; + tensor segment_accum_109 = cumsum(axis = var_7349, exclusive = segment_accum_109_exclusive_0, reverse = segment_accum_109_reverse_0, x = var_7816)[name = tensor("segment_accum_109")]; + tensor var_7820_begin_0 = const()[name = tensor("op_7820_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7820_end_0 = const()[name = tensor("op_7820_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7820_end_mask_0 = const()[name = tensor("op_7820_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7820 = slice_by_index(begin = var_7820_begin_0, end = var_7820_end_0, end_mask = var_7820_end_mask_0, x = var_7814)[name = tensor("op_7820")]; + tensor var_7822 = add(x = segment_accum_109, y = var_7820)[name = tensor("op_7822")]; + tensor var_7824_begin_0 = const()[name = tensor("op_7824_begin_0"), val = tensor([0, 56000, 0])]; + tensor var_7824_end_0 = const()[name = tensor("op_7824_end_0"), val = tensor([1, 57000, 9])]; + tensor var_7824_end_mask_0 = const()[name = tensor("op_7824_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7824 = slice_by_index(begin = var_7824_begin_0, end = var_7824_end_0, end_mask = var_7824_end_mask_0, x = reshape_4)[name = tensor("op_7824")]; + tensor segment_accum_111_exclusive_0 = const()[name = tensor("segment_accum_111_exclusive_0"), val = tensor(false)]; + tensor segment_accum_111_reverse_0 = const()[name = tensor("segment_accum_111_reverse_0"), val = tensor(false)]; + tensor segment_accum_111 = cumsum(axis = var_7349, exclusive = segment_accum_111_exclusive_0, reverse = segment_accum_111_reverse_0, x = var_7824)[name = tensor("segment_accum_111")]; + tensor var_7828_begin_0 = const()[name = tensor("op_7828_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7828_end_0 = const()[name = tensor("op_7828_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7828_end_mask_0 = const()[name = tensor("op_7828_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7828 = slice_by_index(begin = var_7828_begin_0, end = var_7828_end_0, end_mask = var_7828_end_mask_0, x = var_7822)[name = tensor("op_7828")]; + tensor var_7830 = add(x = segment_accum_111, y = var_7828)[name = tensor("op_7830")]; + tensor var_7832_begin_0 = const()[name = tensor("op_7832_begin_0"), val = tensor([0, 57000, 0])]; + tensor var_7832_end_0 = const()[name = tensor("op_7832_end_0"), val = tensor([1, 58000, 9])]; + tensor var_7832_end_mask_0 = const()[name = tensor("op_7832_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7832 = slice_by_index(begin = var_7832_begin_0, end = var_7832_end_0, end_mask = var_7832_end_mask_0, x = reshape_4)[name = tensor("op_7832")]; + tensor segment_accum_113_exclusive_0 = const()[name = tensor("segment_accum_113_exclusive_0"), val = tensor(false)]; + tensor segment_accum_113_reverse_0 = const()[name = tensor("segment_accum_113_reverse_0"), val = tensor(false)]; + tensor segment_accum_113 = cumsum(axis = var_7349, exclusive = segment_accum_113_exclusive_0, reverse = segment_accum_113_reverse_0, x = var_7832)[name = tensor("segment_accum_113")]; + tensor var_7836_begin_0 = const()[name = tensor("op_7836_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7836_end_0 = const()[name = tensor("op_7836_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7836_end_mask_0 = const()[name = tensor("op_7836_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7836 = slice_by_index(begin = var_7836_begin_0, end = var_7836_end_0, end_mask = var_7836_end_mask_0, x = var_7830)[name = tensor("op_7836")]; + tensor var_7838 = add(x = segment_accum_113, y = var_7836)[name = tensor("op_7838")]; + tensor var_7840_begin_0 = const()[name = tensor("op_7840_begin_0"), val = tensor([0, 58000, 0])]; + tensor var_7840_end_0 = const()[name = tensor("op_7840_end_0"), val = tensor([1, 59000, 9])]; + tensor var_7840_end_mask_0 = const()[name = tensor("op_7840_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7840 = slice_by_index(begin = var_7840_begin_0, end = var_7840_end_0, end_mask = var_7840_end_mask_0, x = reshape_4)[name = tensor("op_7840")]; + tensor segment_accum_115_exclusive_0 = const()[name = tensor("segment_accum_115_exclusive_0"), val = tensor(false)]; + tensor segment_accum_115_reverse_0 = const()[name = tensor("segment_accum_115_reverse_0"), val = tensor(false)]; + tensor segment_accum_115 = cumsum(axis = var_7349, exclusive = segment_accum_115_exclusive_0, reverse = segment_accum_115_reverse_0, x = var_7840)[name = tensor("segment_accum_115")]; + tensor var_7844_begin_0 = const()[name = tensor("op_7844_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7844_end_0 = const()[name = tensor("op_7844_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7844_end_mask_0 = const()[name = tensor("op_7844_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7844 = slice_by_index(begin = var_7844_begin_0, end = var_7844_end_0, end_mask = var_7844_end_mask_0, x = var_7838)[name = tensor("op_7844")]; + tensor var_7846 = add(x = segment_accum_115, y = var_7844)[name = tensor("op_7846")]; + tensor var_7848_begin_0 = const()[name = tensor("op_7848_begin_0"), val = tensor([0, 59000, 0])]; + tensor var_7848_end_0 = const()[name = tensor("op_7848_end_0"), val = tensor([1, 60000, 9])]; + tensor var_7848_end_mask_0 = const()[name = tensor("op_7848_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7848 = slice_by_index(begin = var_7848_begin_0, end = var_7848_end_0, end_mask = var_7848_end_mask_0, x = reshape_4)[name = tensor("op_7848")]; + tensor segment_accum_117_exclusive_0 = const()[name = tensor("segment_accum_117_exclusive_0"), val = tensor(false)]; + tensor segment_accum_117_reverse_0 = const()[name = tensor("segment_accum_117_reverse_0"), val = tensor(false)]; + tensor segment_accum_117 = cumsum(axis = var_7349, exclusive = segment_accum_117_exclusive_0, reverse = segment_accum_117_reverse_0, x = var_7848)[name = tensor("segment_accum_117")]; + tensor var_7852_begin_0 = const()[name = tensor("op_7852_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7852_end_0 = const()[name = tensor("op_7852_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7852_end_mask_0 = const()[name = tensor("op_7852_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7852 = slice_by_index(begin = var_7852_begin_0, end = var_7852_end_0, end_mask = var_7852_end_mask_0, x = var_7846)[name = tensor("op_7852")]; + tensor var_7854 = add(x = segment_accum_117, y = var_7852)[name = tensor("op_7854")]; + tensor var_7856_begin_0 = const()[name = tensor("op_7856_begin_0"), val = tensor([0, 60000, 0])]; + tensor var_7856_end_0 = const()[name = tensor("op_7856_end_0"), val = tensor([1, 61000, 9])]; + tensor var_7856_end_mask_0 = const()[name = tensor("op_7856_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7856 = slice_by_index(begin = var_7856_begin_0, end = var_7856_end_0, end_mask = var_7856_end_mask_0, x = reshape_4)[name = tensor("op_7856")]; + tensor segment_accum_119_exclusive_0 = const()[name = tensor("segment_accum_119_exclusive_0"), val = tensor(false)]; + tensor segment_accum_119_reverse_0 = const()[name = tensor("segment_accum_119_reverse_0"), val = tensor(false)]; + tensor segment_accum_119 = cumsum(axis = var_7349, exclusive = segment_accum_119_exclusive_0, reverse = segment_accum_119_reverse_0, x = var_7856)[name = tensor("segment_accum_119")]; + tensor var_7860_begin_0 = const()[name = tensor("op_7860_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7860_end_0 = const()[name = tensor("op_7860_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7860_end_mask_0 = const()[name = tensor("op_7860_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7860 = slice_by_index(begin = var_7860_begin_0, end = var_7860_end_0, end_mask = var_7860_end_mask_0, x = var_7854)[name = tensor("op_7860")]; + tensor var_7862 = add(x = segment_accum_119, y = var_7860)[name = tensor("op_7862")]; + tensor var_7864_begin_0 = const()[name = tensor("op_7864_begin_0"), val = tensor([0, 61000, 0])]; + tensor var_7864_end_0 = const()[name = tensor("op_7864_end_0"), val = tensor([1, 62000, 9])]; + tensor var_7864_end_mask_0 = const()[name = tensor("op_7864_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7864 = slice_by_index(begin = var_7864_begin_0, end = var_7864_end_0, end_mask = var_7864_end_mask_0, x = reshape_4)[name = tensor("op_7864")]; + tensor segment_accum_121_exclusive_0 = const()[name = tensor("segment_accum_121_exclusive_0"), val = tensor(false)]; + tensor segment_accum_121_reverse_0 = const()[name = tensor("segment_accum_121_reverse_0"), val = tensor(false)]; + tensor segment_accum_121 = cumsum(axis = var_7349, exclusive = segment_accum_121_exclusive_0, reverse = segment_accum_121_reverse_0, x = var_7864)[name = tensor("segment_accum_121")]; + tensor var_7868_begin_0 = const()[name = tensor("op_7868_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7868_end_0 = const()[name = tensor("op_7868_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7868_end_mask_0 = const()[name = tensor("op_7868_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7868 = slice_by_index(begin = var_7868_begin_0, end = var_7868_end_0, end_mask = var_7868_end_mask_0, x = var_7862)[name = tensor("op_7868")]; + tensor var_7870 = add(x = segment_accum_121, y = var_7868)[name = tensor("op_7870")]; + tensor var_7872_begin_0 = const()[name = tensor("op_7872_begin_0"), val = tensor([0, 62000, 0])]; + tensor var_7872_end_0 = const()[name = tensor("op_7872_end_0"), val = tensor([1, 63000, 9])]; + tensor var_7872_end_mask_0 = const()[name = tensor("op_7872_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7872 = slice_by_index(begin = var_7872_begin_0, end = var_7872_end_0, end_mask = var_7872_end_mask_0, x = reshape_4)[name = tensor("op_7872")]; + tensor segment_accum_123_exclusive_0 = const()[name = tensor("segment_accum_123_exclusive_0"), val = tensor(false)]; + tensor segment_accum_123_reverse_0 = const()[name = tensor("segment_accum_123_reverse_0"), val = tensor(false)]; + tensor segment_accum_123 = cumsum(axis = var_7349, exclusive = segment_accum_123_exclusive_0, reverse = segment_accum_123_reverse_0, x = var_7872)[name = tensor("segment_accum_123")]; + tensor var_7876_begin_0 = const()[name = tensor("op_7876_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7876_end_0 = const()[name = tensor("op_7876_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7876_end_mask_0 = const()[name = tensor("op_7876_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7876 = slice_by_index(begin = var_7876_begin_0, end = var_7876_end_0, end_mask = var_7876_end_mask_0, x = var_7870)[name = tensor("op_7876")]; + tensor var_7878 = add(x = segment_accum_123, y = var_7876)[name = tensor("op_7878")]; + tensor var_7880_begin_0 = const()[name = tensor("op_7880_begin_0"), val = tensor([0, 63000, 0])]; + tensor var_7880_end_0 = const()[name = tensor("op_7880_end_0"), val = tensor([1, 64000, 9])]; + tensor var_7880_end_mask_0 = const()[name = tensor("op_7880_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7880 = slice_by_index(begin = var_7880_begin_0, end = var_7880_end_0, end_mask = var_7880_end_mask_0, x = reshape_4)[name = tensor("op_7880")]; + tensor segment_accum_125_exclusive_0 = const()[name = tensor("segment_accum_125_exclusive_0"), val = tensor(false)]; + tensor segment_accum_125_reverse_0 = const()[name = tensor("segment_accum_125_reverse_0"), val = tensor(false)]; + tensor segment_accum_125 = cumsum(axis = var_7349, exclusive = segment_accum_125_exclusive_0, reverse = segment_accum_125_reverse_0, x = var_7880)[name = tensor("segment_accum_125")]; + tensor var_7884_begin_0 = const()[name = tensor("op_7884_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7884_end_0 = const()[name = tensor("op_7884_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7884_end_mask_0 = const()[name = tensor("op_7884_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7884 = slice_by_index(begin = var_7884_begin_0, end = var_7884_end_0, end_mask = var_7884_end_mask_0, x = var_7878)[name = tensor("op_7884")]; + tensor var_7886 = add(x = segment_accum_125, y = var_7884)[name = tensor("op_7886")]; + tensor var_7888_begin_0 = const()[name = tensor("op_7888_begin_0"), val = tensor([0, 64000, 0])]; + tensor var_7888_end_0 = const()[name = tensor("op_7888_end_0"), val = tensor([1, 65000, 9])]; + tensor var_7888_end_mask_0 = const()[name = tensor("op_7888_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7888 = slice_by_index(begin = var_7888_begin_0, end = var_7888_end_0, end_mask = var_7888_end_mask_0, x = reshape_4)[name = tensor("op_7888")]; + tensor segment_accum_127_exclusive_0 = const()[name = tensor("segment_accum_127_exclusive_0"), val = tensor(false)]; + tensor segment_accum_127_reverse_0 = const()[name = tensor("segment_accum_127_reverse_0"), val = tensor(false)]; + tensor segment_accum_127 = cumsum(axis = var_7349, exclusive = segment_accum_127_exclusive_0, reverse = segment_accum_127_reverse_0, x = var_7888)[name = tensor("segment_accum_127")]; + tensor var_7892_begin_0 = const()[name = tensor("op_7892_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7892_end_0 = const()[name = tensor("op_7892_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7892_end_mask_0 = const()[name = tensor("op_7892_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7892 = slice_by_index(begin = var_7892_begin_0, end = var_7892_end_0, end_mask = var_7892_end_mask_0, x = var_7886)[name = tensor("op_7892")]; + tensor var_7894 = add(x = segment_accum_127, y = var_7892)[name = tensor("op_7894")]; + tensor var_7896_begin_0 = const()[name = tensor("op_7896_begin_0"), val = tensor([0, 65000, 0])]; + tensor var_7896_end_0 = const()[name = tensor("op_7896_end_0"), val = tensor([1, 66000, 9])]; + tensor var_7896_end_mask_0 = const()[name = tensor("op_7896_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7896 = slice_by_index(begin = var_7896_begin_0, end = var_7896_end_0, end_mask = var_7896_end_mask_0, x = reshape_4)[name = tensor("op_7896")]; + tensor segment_accum_129_exclusive_0 = const()[name = tensor("segment_accum_129_exclusive_0"), val = tensor(false)]; + tensor segment_accum_129_reverse_0 = const()[name = tensor("segment_accum_129_reverse_0"), val = tensor(false)]; + tensor segment_accum_129 = cumsum(axis = var_7349, exclusive = segment_accum_129_exclusive_0, reverse = segment_accum_129_reverse_0, x = var_7896)[name = tensor("segment_accum_129")]; + tensor var_7900_begin_0 = const()[name = tensor("op_7900_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7900_end_0 = const()[name = tensor("op_7900_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7900_end_mask_0 = const()[name = tensor("op_7900_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7900 = slice_by_index(begin = var_7900_begin_0, end = var_7900_end_0, end_mask = var_7900_end_mask_0, x = var_7894)[name = tensor("op_7900")]; + tensor var_7902 = add(x = segment_accum_129, y = var_7900)[name = tensor("op_7902")]; + tensor var_7904_begin_0 = const()[name = tensor("op_7904_begin_0"), val = tensor([0, 66000, 0])]; + tensor var_7904_end_0 = const()[name = tensor("op_7904_end_0"), val = tensor([1, 67000, 9])]; + tensor var_7904_end_mask_0 = const()[name = tensor("op_7904_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7904 = slice_by_index(begin = var_7904_begin_0, end = var_7904_end_0, end_mask = var_7904_end_mask_0, x = reshape_4)[name = tensor("op_7904")]; + tensor segment_accum_131_exclusive_0 = const()[name = tensor("segment_accum_131_exclusive_0"), val = tensor(false)]; + tensor segment_accum_131_reverse_0 = const()[name = tensor("segment_accum_131_reverse_0"), val = tensor(false)]; + tensor segment_accum_131 = cumsum(axis = var_7349, exclusive = segment_accum_131_exclusive_0, reverse = segment_accum_131_reverse_0, x = var_7904)[name = tensor("segment_accum_131")]; + tensor var_7908_begin_0 = const()[name = tensor("op_7908_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7908_end_0 = const()[name = tensor("op_7908_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7908_end_mask_0 = const()[name = tensor("op_7908_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7908 = slice_by_index(begin = var_7908_begin_0, end = var_7908_end_0, end_mask = var_7908_end_mask_0, x = var_7902)[name = tensor("op_7908")]; + tensor var_7910 = add(x = segment_accum_131, y = var_7908)[name = tensor("op_7910")]; + tensor var_7912_begin_0 = const()[name = tensor("op_7912_begin_0"), val = tensor([0, 67000, 0])]; + tensor var_7912_end_0 = const()[name = tensor("op_7912_end_0"), val = tensor([1, 68000, 9])]; + tensor var_7912_end_mask_0 = const()[name = tensor("op_7912_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7912 = slice_by_index(begin = var_7912_begin_0, end = var_7912_end_0, end_mask = var_7912_end_mask_0, x = reshape_4)[name = tensor("op_7912")]; + tensor segment_accum_133_exclusive_0 = const()[name = tensor("segment_accum_133_exclusive_0"), val = tensor(false)]; + tensor segment_accum_133_reverse_0 = const()[name = tensor("segment_accum_133_reverse_0"), val = tensor(false)]; + tensor segment_accum_133 = cumsum(axis = var_7349, exclusive = segment_accum_133_exclusive_0, reverse = segment_accum_133_reverse_0, x = var_7912)[name = tensor("segment_accum_133")]; + tensor var_7916_begin_0 = const()[name = tensor("op_7916_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7916_end_0 = const()[name = tensor("op_7916_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7916_end_mask_0 = const()[name = tensor("op_7916_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7916 = slice_by_index(begin = var_7916_begin_0, end = var_7916_end_0, end_mask = var_7916_end_mask_0, x = var_7910)[name = tensor("op_7916")]; + tensor var_7918 = add(x = segment_accum_133, y = var_7916)[name = tensor("op_7918")]; + tensor var_7920_begin_0 = const()[name = tensor("op_7920_begin_0"), val = tensor([0, 68000, 0])]; + tensor var_7920_end_0 = const()[name = tensor("op_7920_end_0"), val = tensor([1, 69000, 9])]; + tensor var_7920_end_mask_0 = const()[name = tensor("op_7920_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7920 = slice_by_index(begin = var_7920_begin_0, end = var_7920_end_0, end_mask = var_7920_end_mask_0, x = reshape_4)[name = tensor("op_7920")]; + tensor segment_accum_135_exclusive_0 = const()[name = tensor("segment_accum_135_exclusive_0"), val = tensor(false)]; + tensor segment_accum_135_reverse_0 = const()[name = tensor("segment_accum_135_reverse_0"), val = tensor(false)]; + tensor segment_accum_135 = cumsum(axis = var_7349, exclusive = segment_accum_135_exclusive_0, reverse = segment_accum_135_reverse_0, x = var_7920)[name = tensor("segment_accum_135")]; + tensor var_7924_begin_0 = const()[name = tensor("op_7924_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7924_end_0 = const()[name = tensor("op_7924_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7924_end_mask_0 = const()[name = tensor("op_7924_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7924 = slice_by_index(begin = var_7924_begin_0, end = var_7924_end_0, end_mask = var_7924_end_mask_0, x = var_7918)[name = tensor("op_7924")]; + tensor var_7926 = add(x = segment_accum_135, y = var_7924)[name = tensor("op_7926")]; + tensor var_7928_begin_0 = const()[name = tensor("op_7928_begin_0"), val = tensor([0, 69000, 0])]; + tensor var_7928_end_0 = const()[name = tensor("op_7928_end_0"), val = tensor([1, 70000, 9])]; + tensor var_7928_end_mask_0 = const()[name = tensor("op_7928_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7928 = slice_by_index(begin = var_7928_begin_0, end = var_7928_end_0, end_mask = var_7928_end_mask_0, x = reshape_4)[name = tensor("op_7928")]; + tensor segment_accum_137_exclusive_0 = const()[name = tensor("segment_accum_137_exclusive_0"), val = tensor(false)]; + tensor segment_accum_137_reverse_0 = const()[name = tensor("segment_accum_137_reverse_0"), val = tensor(false)]; + tensor segment_accum_137 = cumsum(axis = var_7349, exclusive = segment_accum_137_exclusive_0, reverse = segment_accum_137_reverse_0, x = var_7928)[name = tensor("segment_accum_137")]; + tensor var_7932_begin_0 = const()[name = tensor("op_7932_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7932_end_0 = const()[name = tensor("op_7932_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7932_end_mask_0 = const()[name = tensor("op_7932_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7932 = slice_by_index(begin = var_7932_begin_0, end = var_7932_end_0, end_mask = var_7932_end_mask_0, x = var_7926)[name = tensor("op_7932")]; + tensor var_7934 = add(x = segment_accum_137, y = var_7932)[name = tensor("op_7934")]; + tensor var_7936_begin_0 = const()[name = tensor("op_7936_begin_0"), val = tensor([0, 70000, 0])]; + tensor var_7936_end_0 = const()[name = tensor("op_7936_end_0"), val = tensor([1, 71000, 9])]; + tensor var_7936_end_mask_0 = const()[name = tensor("op_7936_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7936 = slice_by_index(begin = var_7936_begin_0, end = var_7936_end_0, end_mask = var_7936_end_mask_0, x = reshape_4)[name = tensor("op_7936")]; + tensor segment_accum_139_exclusive_0 = const()[name = tensor("segment_accum_139_exclusive_0"), val = tensor(false)]; + tensor segment_accum_139_reverse_0 = const()[name = tensor("segment_accum_139_reverse_0"), val = tensor(false)]; + tensor segment_accum_139 = cumsum(axis = var_7349, exclusive = segment_accum_139_exclusive_0, reverse = segment_accum_139_reverse_0, x = var_7936)[name = tensor("segment_accum_139")]; + tensor var_7940_begin_0 = const()[name = tensor("op_7940_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7940_end_0 = const()[name = tensor("op_7940_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7940_end_mask_0 = const()[name = tensor("op_7940_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7940 = slice_by_index(begin = var_7940_begin_0, end = var_7940_end_0, end_mask = var_7940_end_mask_0, x = var_7934)[name = tensor("op_7940")]; + tensor var_7942 = add(x = segment_accum_139, y = var_7940)[name = tensor("op_7942")]; + tensor var_7944_begin_0 = const()[name = tensor("op_7944_begin_0"), val = tensor([0, 71000, 0])]; + tensor var_7944_end_0 = const()[name = tensor("op_7944_end_0"), val = tensor([1, 72000, 9])]; + tensor var_7944_end_mask_0 = const()[name = tensor("op_7944_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7944 = slice_by_index(begin = var_7944_begin_0, end = var_7944_end_0, end_mask = var_7944_end_mask_0, x = reshape_4)[name = tensor("op_7944")]; + tensor segment_accum_141_exclusive_0 = const()[name = tensor("segment_accum_141_exclusive_0"), val = tensor(false)]; + tensor segment_accum_141_reverse_0 = const()[name = tensor("segment_accum_141_reverse_0"), val = tensor(false)]; + tensor segment_accum_141 = cumsum(axis = var_7349, exclusive = segment_accum_141_exclusive_0, reverse = segment_accum_141_reverse_0, x = var_7944)[name = tensor("segment_accum_141")]; + tensor var_7948_begin_0 = const()[name = tensor("op_7948_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7948_end_0 = const()[name = tensor("op_7948_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7948_end_mask_0 = const()[name = tensor("op_7948_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7948 = slice_by_index(begin = var_7948_begin_0, end = var_7948_end_0, end_mask = var_7948_end_mask_0, x = var_7942)[name = tensor("op_7948")]; + tensor var_7950 = add(x = segment_accum_141, y = var_7948)[name = tensor("op_7950")]; + tensor var_7952_begin_0 = const()[name = tensor("op_7952_begin_0"), val = tensor([0, 72000, 0])]; + tensor var_7952_end_0 = const()[name = tensor("op_7952_end_0"), val = tensor([1, 73000, 9])]; + tensor var_7952_end_mask_0 = const()[name = tensor("op_7952_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7952 = slice_by_index(begin = var_7952_begin_0, end = var_7952_end_0, end_mask = var_7952_end_mask_0, x = reshape_4)[name = tensor("op_7952")]; + tensor segment_accum_143_exclusive_0 = const()[name = tensor("segment_accum_143_exclusive_0"), val = tensor(false)]; + tensor segment_accum_143_reverse_0 = const()[name = tensor("segment_accum_143_reverse_0"), val = tensor(false)]; + tensor segment_accum_143 = cumsum(axis = var_7349, exclusive = segment_accum_143_exclusive_0, reverse = segment_accum_143_reverse_0, x = var_7952)[name = tensor("segment_accum_143")]; + tensor var_7956_begin_0 = const()[name = tensor("op_7956_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7956_end_0 = const()[name = tensor("op_7956_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7956_end_mask_0 = const()[name = tensor("op_7956_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7956 = slice_by_index(begin = var_7956_begin_0, end = var_7956_end_0, end_mask = var_7956_end_mask_0, x = var_7950)[name = tensor("op_7956")]; + tensor var_7958 = add(x = segment_accum_143, y = var_7956)[name = tensor("op_7958")]; + tensor var_7960_begin_0 = const()[name = tensor("op_7960_begin_0"), val = tensor([0, 73000, 0])]; + tensor var_7960_end_0 = const()[name = tensor("op_7960_end_0"), val = tensor([1, 74000, 9])]; + tensor var_7960_end_mask_0 = const()[name = tensor("op_7960_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7960 = slice_by_index(begin = var_7960_begin_0, end = var_7960_end_0, end_mask = var_7960_end_mask_0, x = reshape_4)[name = tensor("op_7960")]; + tensor segment_accum_145_exclusive_0 = const()[name = tensor("segment_accum_145_exclusive_0"), val = tensor(false)]; + tensor segment_accum_145_reverse_0 = const()[name = tensor("segment_accum_145_reverse_0"), val = tensor(false)]; + tensor segment_accum_145 = cumsum(axis = var_7349, exclusive = segment_accum_145_exclusive_0, reverse = segment_accum_145_reverse_0, x = var_7960)[name = tensor("segment_accum_145")]; + tensor var_7964_begin_0 = const()[name = tensor("op_7964_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7964_end_0 = const()[name = tensor("op_7964_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7964_end_mask_0 = const()[name = tensor("op_7964_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7964 = slice_by_index(begin = var_7964_begin_0, end = var_7964_end_0, end_mask = var_7964_end_mask_0, x = var_7958)[name = tensor("op_7964")]; + tensor var_7966 = add(x = segment_accum_145, y = var_7964)[name = tensor("op_7966")]; + tensor var_7968_begin_0 = const()[name = tensor("op_7968_begin_0"), val = tensor([0, 74000, 0])]; + tensor var_7968_end_0 = const()[name = tensor("op_7968_end_0"), val = tensor([1, 75000, 9])]; + tensor var_7968_end_mask_0 = const()[name = tensor("op_7968_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7968 = slice_by_index(begin = var_7968_begin_0, end = var_7968_end_0, end_mask = var_7968_end_mask_0, x = reshape_4)[name = tensor("op_7968")]; + tensor segment_accum_147_exclusive_0 = const()[name = tensor("segment_accum_147_exclusive_0"), val = tensor(false)]; + tensor segment_accum_147_reverse_0 = const()[name = tensor("segment_accum_147_reverse_0"), val = tensor(false)]; + tensor segment_accum_147 = cumsum(axis = var_7349, exclusive = segment_accum_147_exclusive_0, reverse = segment_accum_147_reverse_0, x = var_7968)[name = tensor("segment_accum_147")]; + tensor var_7972_begin_0 = const()[name = tensor("op_7972_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7972_end_0 = const()[name = tensor("op_7972_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7972_end_mask_0 = const()[name = tensor("op_7972_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7972 = slice_by_index(begin = var_7972_begin_0, end = var_7972_end_0, end_mask = var_7972_end_mask_0, x = var_7966)[name = tensor("op_7972")]; + tensor var_7974 = add(x = segment_accum_147, y = var_7972)[name = tensor("op_7974")]; + tensor var_7976_begin_0 = const()[name = tensor("op_7976_begin_0"), val = tensor([0, 75000, 0])]; + tensor var_7976_end_0 = const()[name = tensor("op_7976_end_0"), val = tensor([1, 76000, 9])]; + tensor var_7976_end_mask_0 = const()[name = tensor("op_7976_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7976 = slice_by_index(begin = var_7976_begin_0, end = var_7976_end_0, end_mask = var_7976_end_mask_0, x = reshape_4)[name = tensor("op_7976")]; + tensor segment_accum_149_exclusive_0 = const()[name = tensor("segment_accum_149_exclusive_0"), val = tensor(false)]; + tensor segment_accum_149_reverse_0 = const()[name = tensor("segment_accum_149_reverse_0"), val = tensor(false)]; + tensor segment_accum_149 = cumsum(axis = var_7349, exclusive = segment_accum_149_exclusive_0, reverse = segment_accum_149_reverse_0, x = var_7976)[name = tensor("segment_accum_149")]; + tensor var_7980_begin_0 = const()[name = tensor("op_7980_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7980_end_0 = const()[name = tensor("op_7980_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7980_end_mask_0 = const()[name = tensor("op_7980_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7980 = slice_by_index(begin = var_7980_begin_0, end = var_7980_end_0, end_mask = var_7980_end_mask_0, x = var_7974)[name = tensor("op_7980")]; + tensor var_7982 = add(x = segment_accum_149, y = var_7980)[name = tensor("op_7982")]; + tensor var_7984_begin_0 = const()[name = tensor("op_7984_begin_0"), val = tensor([0, 76000, 0])]; + tensor var_7984_end_0 = const()[name = tensor("op_7984_end_0"), val = tensor([1, 77000, 9])]; + tensor var_7984_end_mask_0 = const()[name = tensor("op_7984_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7984 = slice_by_index(begin = var_7984_begin_0, end = var_7984_end_0, end_mask = var_7984_end_mask_0, x = reshape_4)[name = tensor("op_7984")]; + tensor segment_accum_151_exclusive_0 = const()[name = tensor("segment_accum_151_exclusive_0"), val = tensor(false)]; + tensor segment_accum_151_reverse_0 = const()[name = tensor("segment_accum_151_reverse_0"), val = tensor(false)]; + tensor segment_accum_151 = cumsum(axis = var_7349, exclusive = segment_accum_151_exclusive_0, reverse = segment_accum_151_reverse_0, x = var_7984)[name = tensor("segment_accum_151")]; + tensor var_7988_begin_0 = const()[name = tensor("op_7988_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7988_end_0 = const()[name = tensor("op_7988_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7988_end_mask_0 = const()[name = tensor("op_7988_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7988 = slice_by_index(begin = var_7988_begin_0, end = var_7988_end_0, end_mask = var_7988_end_mask_0, x = var_7982)[name = tensor("op_7988")]; + tensor var_7990 = add(x = segment_accum_151, y = var_7988)[name = tensor("op_7990")]; + tensor var_7992_begin_0 = const()[name = tensor("op_7992_begin_0"), val = tensor([0, 77000, 0])]; + tensor var_7992_end_0 = const()[name = tensor("op_7992_end_0"), val = tensor([1, 78000, 9])]; + tensor var_7992_end_mask_0 = const()[name = tensor("op_7992_end_mask_0"), val = tensor([true, false, true])]; + tensor var_7992 = slice_by_index(begin = var_7992_begin_0, end = var_7992_end_0, end_mask = var_7992_end_mask_0, x = reshape_4)[name = tensor("op_7992")]; + tensor segment_accum_153_exclusive_0 = const()[name = tensor("segment_accum_153_exclusive_0"), val = tensor(false)]; + tensor segment_accum_153_reverse_0 = const()[name = tensor("segment_accum_153_reverse_0"), val = tensor(false)]; + tensor segment_accum_153 = cumsum(axis = var_7349, exclusive = segment_accum_153_exclusive_0, reverse = segment_accum_153_reverse_0, x = var_7992)[name = tensor("segment_accum_153")]; + tensor var_7996_begin_0 = const()[name = tensor("op_7996_begin_0"), val = tensor([0, -1, 0])]; + tensor var_7996_end_0 = const()[name = tensor("op_7996_end_0"), val = tensor([1, 1000, 9])]; + tensor var_7996_end_mask_0 = const()[name = tensor("op_7996_end_mask_0"), val = tensor([true, true, true])]; + tensor var_7996 = slice_by_index(begin = var_7996_begin_0, end = var_7996_end_0, end_mask = var_7996_end_mask_0, x = var_7990)[name = tensor("op_7996")]; + tensor var_7998 = add(x = segment_accum_153, y = var_7996)[name = tensor("op_7998")]; + tensor var_8000_begin_0 = const()[name = tensor("op_8000_begin_0"), val = tensor([0, 78000, 0])]; + tensor var_8000_end_0 = const()[name = tensor("op_8000_end_0"), val = tensor([1, 79000, 9])]; + tensor var_8000_end_mask_0 = const()[name = tensor("op_8000_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8000 = slice_by_index(begin = var_8000_begin_0, end = var_8000_end_0, end_mask = var_8000_end_mask_0, x = reshape_4)[name = tensor("op_8000")]; + tensor segment_accum_155_exclusive_0 = const()[name = tensor("segment_accum_155_exclusive_0"), val = tensor(false)]; + tensor segment_accum_155_reverse_0 = const()[name = tensor("segment_accum_155_reverse_0"), val = tensor(false)]; + tensor segment_accum_155 = cumsum(axis = var_7349, exclusive = segment_accum_155_exclusive_0, reverse = segment_accum_155_reverse_0, x = var_8000)[name = tensor("segment_accum_155")]; + tensor var_8004_begin_0 = const()[name = tensor("op_8004_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8004_end_0 = const()[name = tensor("op_8004_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8004_end_mask_0 = const()[name = tensor("op_8004_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8004 = slice_by_index(begin = var_8004_begin_0, end = var_8004_end_0, end_mask = var_8004_end_mask_0, x = var_7998)[name = tensor("op_8004")]; + tensor var_8006 = add(x = segment_accum_155, y = var_8004)[name = tensor("op_8006")]; + tensor var_8008_begin_0 = const()[name = tensor("op_8008_begin_0"), val = tensor([0, 79000, 0])]; + tensor var_8008_end_0 = const()[name = tensor("op_8008_end_0"), val = tensor([1, 80000, 9])]; + tensor var_8008_end_mask_0 = const()[name = tensor("op_8008_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8008 = slice_by_index(begin = var_8008_begin_0, end = var_8008_end_0, end_mask = var_8008_end_mask_0, x = reshape_4)[name = tensor("op_8008")]; + tensor segment_accum_157_exclusive_0 = const()[name = tensor("segment_accum_157_exclusive_0"), val = tensor(false)]; + tensor segment_accum_157_reverse_0 = const()[name = tensor("segment_accum_157_reverse_0"), val = tensor(false)]; + tensor segment_accum_157 = cumsum(axis = var_7349, exclusive = segment_accum_157_exclusive_0, reverse = segment_accum_157_reverse_0, x = var_8008)[name = tensor("segment_accum_157")]; + tensor var_8012_begin_0 = const()[name = tensor("op_8012_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8012_end_0 = const()[name = tensor("op_8012_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8012_end_mask_0 = const()[name = tensor("op_8012_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8012 = slice_by_index(begin = var_8012_begin_0, end = var_8012_end_0, end_mask = var_8012_end_mask_0, x = var_8006)[name = tensor("op_8012")]; + tensor var_8014 = add(x = segment_accum_157, y = var_8012)[name = tensor("op_8014")]; + tensor var_8016_begin_0 = const()[name = tensor("op_8016_begin_0"), val = tensor([0, 80000, 0])]; + tensor var_8016_end_0 = const()[name = tensor("op_8016_end_0"), val = tensor([1, 81000, 9])]; + tensor var_8016_end_mask_0 = const()[name = tensor("op_8016_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8016 = slice_by_index(begin = var_8016_begin_0, end = var_8016_end_0, end_mask = var_8016_end_mask_0, x = reshape_4)[name = tensor("op_8016")]; + tensor segment_accum_159_exclusive_0 = const()[name = tensor("segment_accum_159_exclusive_0"), val = tensor(false)]; + tensor segment_accum_159_reverse_0 = const()[name = tensor("segment_accum_159_reverse_0"), val = tensor(false)]; + tensor segment_accum_159 = cumsum(axis = var_7349, exclusive = segment_accum_159_exclusive_0, reverse = segment_accum_159_reverse_0, x = var_8016)[name = tensor("segment_accum_159")]; + tensor var_8020_begin_0 = const()[name = tensor("op_8020_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8020_end_0 = const()[name = tensor("op_8020_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8020_end_mask_0 = const()[name = tensor("op_8020_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8020 = slice_by_index(begin = var_8020_begin_0, end = var_8020_end_0, end_mask = var_8020_end_mask_0, x = var_8014)[name = tensor("op_8020")]; + tensor var_8022 = add(x = segment_accum_159, y = var_8020)[name = tensor("op_8022")]; + tensor var_8024_begin_0 = const()[name = tensor("op_8024_begin_0"), val = tensor([0, 81000, 0])]; + tensor var_8024_end_0 = const()[name = tensor("op_8024_end_0"), val = tensor([1, 82000, 9])]; + tensor var_8024_end_mask_0 = const()[name = tensor("op_8024_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8024 = slice_by_index(begin = var_8024_begin_0, end = var_8024_end_0, end_mask = var_8024_end_mask_0, x = reshape_4)[name = tensor("op_8024")]; + tensor segment_accum_161_exclusive_0 = const()[name = tensor("segment_accum_161_exclusive_0"), val = tensor(false)]; + tensor segment_accum_161_reverse_0 = const()[name = tensor("segment_accum_161_reverse_0"), val = tensor(false)]; + tensor segment_accum_161 = cumsum(axis = var_7349, exclusive = segment_accum_161_exclusive_0, reverse = segment_accum_161_reverse_0, x = var_8024)[name = tensor("segment_accum_161")]; + tensor var_8028_begin_0 = const()[name = tensor("op_8028_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8028_end_0 = const()[name = tensor("op_8028_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8028_end_mask_0 = const()[name = tensor("op_8028_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8028 = slice_by_index(begin = var_8028_begin_0, end = var_8028_end_0, end_mask = var_8028_end_mask_0, x = var_8022)[name = tensor("op_8028")]; + tensor var_8030 = add(x = segment_accum_161, y = var_8028)[name = tensor("op_8030")]; + tensor var_8032_begin_0 = const()[name = tensor("op_8032_begin_0"), val = tensor([0, 82000, 0])]; + tensor var_8032_end_0 = const()[name = tensor("op_8032_end_0"), val = tensor([1, 83000, 9])]; + tensor var_8032_end_mask_0 = const()[name = tensor("op_8032_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8032 = slice_by_index(begin = var_8032_begin_0, end = var_8032_end_0, end_mask = var_8032_end_mask_0, x = reshape_4)[name = tensor("op_8032")]; + tensor segment_accum_163_exclusive_0 = const()[name = tensor("segment_accum_163_exclusive_0"), val = tensor(false)]; + tensor segment_accum_163_reverse_0 = const()[name = tensor("segment_accum_163_reverse_0"), val = tensor(false)]; + tensor segment_accum_163 = cumsum(axis = var_7349, exclusive = segment_accum_163_exclusive_0, reverse = segment_accum_163_reverse_0, x = var_8032)[name = tensor("segment_accum_163")]; + tensor var_8036_begin_0 = const()[name = tensor("op_8036_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8036_end_0 = const()[name = tensor("op_8036_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8036_end_mask_0 = const()[name = tensor("op_8036_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8036 = slice_by_index(begin = var_8036_begin_0, end = var_8036_end_0, end_mask = var_8036_end_mask_0, x = var_8030)[name = tensor("op_8036")]; + tensor var_8038 = add(x = segment_accum_163, y = var_8036)[name = tensor("op_8038")]; + tensor var_8040_begin_0 = const()[name = tensor("op_8040_begin_0"), val = tensor([0, 83000, 0])]; + tensor var_8040_end_0 = const()[name = tensor("op_8040_end_0"), val = tensor([1, 84000, 9])]; + tensor var_8040_end_mask_0 = const()[name = tensor("op_8040_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8040 = slice_by_index(begin = var_8040_begin_0, end = var_8040_end_0, end_mask = var_8040_end_mask_0, x = reshape_4)[name = tensor("op_8040")]; + tensor segment_accum_165_exclusive_0 = const()[name = tensor("segment_accum_165_exclusive_0"), val = tensor(false)]; + tensor segment_accum_165_reverse_0 = const()[name = tensor("segment_accum_165_reverse_0"), val = tensor(false)]; + tensor segment_accum_165 = cumsum(axis = var_7349, exclusive = segment_accum_165_exclusive_0, reverse = segment_accum_165_reverse_0, x = var_8040)[name = tensor("segment_accum_165")]; + tensor var_8044_begin_0 = const()[name = tensor("op_8044_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8044_end_0 = const()[name = tensor("op_8044_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8044_end_mask_0 = const()[name = tensor("op_8044_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8044 = slice_by_index(begin = var_8044_begin_0, end = var_8044_end_0, end_mask = var_8044_end_mask_0, x = var_8038)[name = tensor("op_8044")]; + tensor var_8046 = add(x = segment_accum_165, y = var_8044)[name = tensor("op_8046")]; + tensor var_8048_begin_0 = const()[name = tensor("op_8048_begin_0"), val = tensor([0, 84000, 0])]; + tensor var_8048_end_0 = const()[name = tensor("op_8048_end_0"), val = tensor([1, 85000, 9])]; + tensor var_8048_end_mask_0 = const()[name = tensor("op_8048_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8048 = slice_by_index(begin = var_8048_begin_0, end = var_8048_end_0, end_mask = var_8048_end_mask_0, x = reshape_4)[name = tensor("op_8048")]; + tensor segment_accum_167_exclusive_0 = const()[name = tensor("segment_accum_167_exclusive_0"), val = tensor(false)]; + tensor segment_accum_167_reverse_0 = const()[name = tensor("segment_accum_167_reverse_0"), val = tensor(false)]; + tensor segment_accum_167 = cumsum(axis = var_7349, exclusive = segment_accum_167_exclusive_0, reverse = segment_accum_167_reverse_0, x = var_8048)[name = tensor("segment_accum_167")]; + tensor var_8052_begin_0 = const()[name = tensor("op_8052_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8052_end_0 = const()[name = tensor("op_8052_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8052_end_mask_0 = const()[name = tensor("op_8052_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8052 = slice_by_index(begin = var_8052_begin_0, end = var_8052_end_0, end_mask = var_8052_end_mask_0, x = var_8046)[name = tensor("op_8052")]; + tensor var_8054 = add(x = segment_accum_167, y = var_8052)[name = tensor("op_8054")]; + tensor var_8056_begin_0 = const()[name = tensor("op_8056_begin_0"), val = tensor([0, 85000, 0])]; + tensor var_8056_end_0 = const()[name = tensor("op_8056_end_0"), val = tensor([1, 86000, 9])]; + tensor var_8056_end_mask_0 = const()[name = tensor("op_8056_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8056 = slice_by_index(begin = var_8056_begin_0, end = var_8056_end_0, end_mask = var_8056_end_mask_0, x = reshape_4)[name = tensor("op_8056")]; + tensor segment_accum_169_exclusive_0 = const()[name = tensor("segment_accum_169_exclusive_0"), val = tensor(false)]; + tensor segment_accum_169_reverse_0 = const()[name = tensor("segment_accum_169_reverse_0"), val = tensor(false)]; + tensor segment_accum_169 = cumsum(axis = var_7349, exclusive = segment_accum_169_exclusive_0, reverse = segment_accum_169_reverse_0, x = var_8056)[name = tensor("segment_accum_169")]; + tensor var_8060_begin_0 = const()[name = tensor("op_8060_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8060_end_0 = const()[name = tensor("op_8060_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8060_end_mask_0 = const()[name = tensor("op_8060_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8060 = slice_by_index(begin = var_8060_begin_0, end = var_8060_end_0, end_mask = var_8060_end_mask_0, x = var_8054)[name = tensor("op_8060")]; + tensor var_8062 = add(x = segment_accum_169, y = var_8060)[name = tensor("op_8062")]; + tensor var_8064_begin_0 = const()[name = tensor("op_8064_begin_0"), val = tensor([0, 86000, 0])]; + tensor var_8064_end_0 = const()[name = tensor("op_8064_end_0"), val = tensor([1, 87000, 9])]; + tensor var_8064_end_mask_0 = const()[name = tensor("op_8064_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8064 = slice_by_index(begin = var_8064_begin_0, end = var_8064_end_0, end_mask = var_8064_end_mask_0, x = reshape_4)[name = tensor("op_8064")]; + tensor segment_accum_171_exclusive_0 = const()[name = tensor("segment_accum_171_exclusive_0"), val = tensor(false)]; + tensor segment_accum_171_reverse_0 = const()[name = tensor("segment_accum_171_reverse_0"), val = tensor(false)]; + tensor segment_accum_171 = cumsum(axis = var_7349, exclusive = segment_accum_171_exclusive_0, reverse = segment_accum_171_reverse_0, x = var_8064)[name = tensor("segment_accum_171")]; + tensor var_8068_begin_0 = const()[name = tensor("op_8068_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8068_end_0 = const()[name = tensor("op_8068_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8068_end_mask_0 = const()[name = tensor("op_8068_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8068 = slice_by_index(begin = var_8068_begin_0, end = var_8068_end_0, end_mask = var_8068_end_mask_0, x = var_8062)[name = tensor("op_8068")]; + tensor var_8070 = add(x = segment_accum_171, y = var_8068)[name = tensor("op_8070")]; + tensor var_8072_begin_0 = const()[name = tensor("op_8072_begin_0"), val = tensor([0, 87000, 0])]; + tensor var_8072_end_0 = const()[name = tensor("op_8072_end_0"), val = tensor([1, 88000, 9])]; + tensor var_8072_end_mask_0 = const()[name = tensor("op_8072_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8072 = slice_by_index(begin = var_8072_begin_0, end = var_8072_end_0, end_mask = var_8072_end_mask_0, x = reshape_4)[name = tensor("op_8072")]; + tensor segment_accum_173_exclusive_0 = const()[name = tensor("segment_accum_173_exclusive_0"), val = tensor(false)]; + tensor segment_accum_173_reverse_0 = const()[name = tensor("segment_accum_173_reverse_0"), val = tensor(false)]; + tensor segment_accum_173 = cumsum(axis = var_7349, exclusive = segment_accum_173_exclusive_0, reverse = segment_accum_173_reverse_0, x = var_8072)[name = tensor("segment_accum_173")]; + tensor var_8076_begin_0 = const()[name = tensor("op_8076_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8076_end_0 = const()[name = tensor("op_8076_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8076_end_mask_0 = const()[name = tensor("op_8076_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8076 = slice_by_index(begin = var_8076_begin_0, end = var_8076_end_0, end_mask = var_8076_end_mask_0, x = var_8070)[name = tensor("op_8076")]; + tensor var_8078 = add(x = segment_accum_173, y = var_8076)[name = tensor("op_8078")]; + tensor var_8080_begin_0 = const()[name = tensor("op_8080_begin_0"), val = tensor([0, 88000, 0])]; + tensor var_8080_end_0 = const()[name = tensor("op_8080_end_0"), val = tensor([1, 89000, 9])]; + tensor var_8080_end_mask_0 = const()[name = tensor("op_8080_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8080 = slice_by_index(begin = var_8080_begin_0, end = var_8080_end_0, end_mask = var_8080_end_mask_0, x = reshape_4)[name = tensor("op_8080")]; + tensor segment_accum_175_exclusive_0 = const()[name = tensor("segment_accum_175_exclusive_0"), val = tensor(false)]; + tensor segment_accum_175_reverse_0 = const()[name = tensor("segment_accum_175_reverse_0"), val = tensor(false)]; + tensor segment_accum_175 = cumsum(axis = var_7349, exclusive = segment_accum_175_exclusive_0, reverse = segment_accum_175_reverse_0, x = var_8080)[name = tensor("segment_accum_175")]; + tensor var_8084_begin_0 = const()[name = tensor("op_8084_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8084_end_0 = const()[name = tensor("op_8084_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8084_end_mask_0 = const()[name = tensor("op_8084_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8084 = slice_by_index(begin = var_8084_begin_0, end = var_8084_end_0, end_mask = var_8084_end_mask_0, x = var_8078)[name = tensor("op_8084")]; + tensor var_8086 = add(x = segment_accum_175, y = var_8084)[name = tensor("op_8086")]; + tensor var_8088_begin_0 = const()[name = tensor("op_8088_begin_0"), val = tensor([0, 89000, 0])]; + tensor var_8088_end_0 = const()[name = tensor("op_8088_end_0"), val = tensor([1, 90000, 9])]; + tensor var_8088_end_mask_0 = const()[name = tensor("op_8088_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8088 = slice_by_index(begin = var_8088_begin_0, end = var_8088_end_0, end_mask = var_8088_end_mask_0, x = reshape_4)[name = tensor("op_8088")]; + tensor segment_accum_177_exclusive_0 = const()[name = tensor("segment_accum_177_exclusive_0"), val = tensor(false)]; + tensor segment_accum_177_reverse_0 = const()[name = tensor("segment_accum_177_reverse_0"), val = tensor(false)]; + tensor segment_accum_177 = cumsum(axis = var_7349, exclusive = segment_accum_177_exclusive_0, reverse = segment_accum_177_reverse_0, x = var_8088)[name = tensor("segment_accum_177")]; + tensor var_8092_begin_0 = const()[name = tensor("op_8092_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8092_end_0 = const()[name = tensor("op_8092_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8092_end_mask_0 = const()[name = tensor("op_8092_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8092 = slice_by_index(begin = var_8092_begin_0, end = var_8092_end_0, end_mask = var_8092_end_mask_0, x = var_8086)[name = tensor("op_8092")]; + tensor var_8094 = add(x = segment_accum_177, y = var_8092)[name = tensor("op_8094")]; + tensor var_8096_begin_0 = const()[name = tensor("op_8096_begin_0"), val = tensor([0, 90000, 0])]; + tensor var_8096_end_0 = const()[name = tensor("op_8096_end_0"), val = tensor([1, 91000, 9])]; + tensor var_8096_end_mask_0 = const()[name = tensor("op_8096_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8096 = slice_by_index(begin = var_8096_begin_0, end = var_8096_end_0, end_mask = var_8096_end_mask_0, x = reshape_4)[name = tensor("op_8096")]; + tensor segment_accum_179_exclusive_0 = const()[name = tensor("segment_accum_179_exclusive_0"), val = tensor(false)]; + tensor segment_accum_179_reverse_0 = const()[name = tensor("segment_accum_179_reverse_0"), val = tensor(false)]; + tensor segment_accum_179 = cumsum(axis = var_7349, exclusive = segment_accum_179_exclusive_0, reverse = segment_accum_179_reverse_0, x = var_8096)[name = tensor("segment_accum_179")]; + tensor var_8100_begin_0 = const()[name = tensor("op_8100_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8100_end_0 = const()[name = tensor("op_8100_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8100_end_mask_0 = const()[name = tensor("op_8100_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8100 = slice_by_index(begin = var_8100_begin_0, end = var_8100_end_0, end_mask = var_8100_end_mask_0, x = var_8094)[name = tensor("op_8100")]; + tensor var_8102 = add(x = segment_accum_179, y = var_8100)[name = tensor("op_8102")]; + tensor var_8104_begin_0 = const()[name = tensor("op_8104_begin_0"), val = tensor([0, 91000, 0])]; + tensor var_8104_end_0 = const()[name = tensor("op_8104_end_0"), val = tensor([1, 92000, 9])]; + tensor var_8104_end_mask_0 = const()[name = tensor("op_8104_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8104 = slice_by_index(begin = var_8104_begin_0, end = var_8104_end_0, end_mask = var_8104_end_mask_0, x = reshape_4)[name = tensor("op_8104")]; + tensor segment_accum_181_exclusive_0 = const()[name = tensor("segment_accum_181_exclusive_0"), val = tensor(false)]; + tensor segment_accum_181_reverse_0 = const()[name = tensor("segment_accum_181_reverse_0"), val = tensor(false)]; + tensor segment_accum_181 = cumsum(axis = var_7349, exclusive = segment_accum_181_exclusive_0, reverse = segment_accum_181_reverse_0, x = var_8104)[name = tensor("segment_accum_181")]; + tensor var_8108_begin_0 = const()[name = tensor("op_8108_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8108_end_0 = const()[name = tensor("op_8108_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8108_end_mask_0 = const()[name = tensor("op_8108_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8108 = slice_by_index(begin = var_8108_begin_0, end = var_8108_end_0, end_mask = var_8108_end_mask_0, x = var_8102)[name = tensor("op_8108")]; + tensor var_8110 = add(x = segment_accum_181, y = var_8108)[name = tensor("op_8110")]; + tensor var_8112_begin_0 = const()[name = tensor("op_8112_begin_0"), val = tensor([0, 92000, 0])]; + tensor var_8112_end_0 = const()[name = tensor("op_8112_end_0"), val = tensor([1, 93000, 9])]; + tensor var_8112_end_mask_0 = const()[name = tensor("op_8112_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8112 = slice_by_index(begin = var_8112_begin_0, end = var_8112_end_0, end_mask = var_8112_end_mask_0, x = reshape_4)[name = tensor("op_8112")]; + tensor segment_accum_183_exclusive_0 = const()[name = tensor("segment_accum_183_exclusive_0"), val = tensor(false)]; + tensor segment_accum_183_reverse_0 = const()[name = tensor("segment_accum_183_reverse_0"), val = tensor(false)]; + tensor segment_accum_183 = cumsum(axis = var_7349, exclusive = segment_accum_183_exclusive_0, reverse = segment_accum_183_reverse_0, x = var_8112)[name = tensor("segment_accum_183")]; + tensor var_8116_begin_0 = const()[name = tensor("op_8116_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8116_end_0 = const()[name = tensor("op_8116_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8116_end_mask_0 = const()[name = tensor("op_8116_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8116 = slice_by_index(begin = var_8116_begin_0, end = var_8116_end_0, end_mask = var_8116_end_mask_0, x = var_8110)[name = tensor("op_8116")]; + tensor var_8118 = add(x = segment_accum_183, y = var_8116)[name = tensor("op_8118")]; + tensor var_8120_begin_0 = const()[name = tensor("op_8120_begin_0"), val = tensor([0, 93000, 0])]; + tensor var_8120_end_0 = const()[name = tensor("op_8120_end_0"), val = tensor([1, 94000, 9])]; + tensor var_8120_end_mask_0 = const()[name = tensor("op_8120_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8120 = slice_by_index(begin = var_8120_begin_0, end = var_8120_end_0, end_mask = var_8120_end_mask_0, x = reshape_4)[name = tensor("op_8120")]; + tensor segment_accum_185_exclusive_0 = const()[name = tensor("segment_accum_185_exclusive_0"), val = tensor(false)]; + tensor segment_accum_185_reverse_0 = const()[name = tensor("segment_accum_185_reverse_0"), val = tensor(false)]; + tensor segment_accum_185 = cumsum(axis = var_7349, exclusive = segment_accum_185_exclusive_0, reverse = segment_accum_185_reverse_0, x = var_8120)[name = tensor("segment_accum_185")]; + tensor var_8124_begin_0 = const()[name = tensor("op_8124_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8124_end_0 = const()[name = tensor("op_8124_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8124_end_mask_0 = const()[name = tensor("op_8124_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8124 = slice_by_index(begin = var_8124_begin_0, end = var_8124_end_0, end_mask = var_8124_end_mask_0, x = var_8118)[name = tensor("op_8124")]; + tensor var_8126 = add(x = segment_accum_185, y = var_8124)[name = tensor("op_8126")]; + tensor var_8128_begin_0 = const()[name = tensor("op_8128_begin_0"), val = tensor([0, 94000, 0])]; + tensor var_8128_end_0 = const()[name = tensor("op_8128_end_0"), val = tensor([1, 95000, 9])]; + tensor var_8128_end_mask_0 = const()[name = tensor("op_8128_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8128 = slice_by_index(begin = var_8128_begin_0, end = var_8128_end_0, end_mask = var_8128_end_mask_0, x = reshape_4)[name = tensor("op_8128")]; + tensor segment_accum_187_exclusive_0 = const()[name = tensor("segment_accum_187_exclusive_0"), val = tensor(false)]; + tensor segment_accum_187_reverse_0 = const()[name = tensor("segment_accum_187_reverse_0"), val = tensor(false)]; + tensor segment_accum_187 = cumsum(axis = var_7349, exclusive = segment_accum_187_exclusive_0, reverse = segment_accum_187_reverse_0, x = var_8128)[name = tensor("segment_accum_187")]; + tensor var_8132_begin_0 = const()[name = tensor("op_8132_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8132_end_0 = const()[name = tensor("op_8132_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8132_end_mask_0 = const()[name = tensor("op_8132_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8132 = slice_by_index(begin = var_8132_begin_0, end = var_8132_end_0, end_mask = var_8132_end_mask_0, x = var_8126)[name = tensor("op_8132")]; + tensor var_8134 = add(x = segment_accum_187, y = var_8132)[name = tensor("op_8134")]; + tensor var_8136_begin_0 = const()[name = tensor("op_8136_begin_0"), val = tensor([0, 95000, 0])]; + tensor var_8136_end_0 = const()[name = tensor("op_8136_end_0"), val = tensor([1, 96000, 9])]; + tensor var_8136_end_mask_0 = const()[name = tensor("op_8136_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8136 = slice_by_index(begin = var_8136_begin_0, end = var_8136_end_0, end_mask = var_8136_end_mask_0, x = reshape_4)[name = tensor("op_8136")]; + tensor segment_accum_189_exclusive_0 = const()[name = tensor("segment_accum_189_exclusive_0"), val = tensor(false)]; + tensor segment_accum_189_reverse_0 = const()[name = tensor("segment_accum_189_reverse_0"), val = tensor(false)]; + tensor segment_accum_189 = cumsum(axis = var_7349, exclusive = segment_accum_189_exclusive_0, reverse = segment_accum_189_reverse_0, x = var_8136)[name = tensor("segment_accum_189")]; + tensor var_8140_begin_0 = const()[name = tensor("op_8140_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8140_end_0 = const()[name = tensor("op_8140_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8140_end_mask_0 = const()[name = tensor("op_8140_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8140 = slice_by_index(begin = var_8140_begin_0, end = var_8140_end_0, end_mask = var_8140_end_mask_0, x = var_8134)[name = tensor("op_8140")]; + tensor var_8142 = add(x = segment_accum_189, y = var_8140)[name = tensor("op_8142")]; + tensor var_8144_begin_0 = const()[name = tensor("op_8144_begin_0"), val = tensor([0, 96000, 0])]; + tensor var_8144_end_0 = const()[name = tensor("op_8144_end_0"), val = tensor([1, 97000, 9])]; + tensor var_8144_end_mask_0 = const()[name = tensor("op_8144_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8144 = slice_by_index(begin = var_8144_begin_0, end = var_8144_end_0, end_mask = var_8144_end_mask_0, x = reshape_4)[name = tensor("op_8144")]; + tensor segment_accum_191_exclusive_0 = const()[name = tensor("segment_accum_191_exclusive_0"), val = tensor(false)]; + tensor segment_accum_191_reverse_0 = const()[name = tensor("segment_accum_191_reverse_0"), val = tensor(false)]; + tensor segment_accum_191 = cumsum(axis = var_7349, exclusive = segment_accum_191_exclusive_0, reverse = segment_accum_191_reverse_0, x = var_8144)[name = tensor("segment_accum_191")]; + tensor var_8148_begin_0 = const()[name = tensor("op_8148_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8148_end_0 = const()[name = tensor("op_8148_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8148_end_mask_0 = const()[name = tensor("op_8148_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8148 = slice_by_index(begin = var_8148_begin_0, end = var_8148_end_0, end_mask = var_8148_end_mask_0, x = var_8142)[name = tensor("op_8148")]; + tensor var_8150 = add(x = segment_accum_191, y = var_8148)[name = tensor("op_8150")]; + tensor var_8152_begin_0 = const()[name = tensor("op_8152_begin_0"), val = tensor([0, 97000, 0])]; + tensor var_8152_end_0 = const()[name = tensor("op_8152_end_0"), val = tensor([1, 98000, 9])]; + tensor var_8152_end_mask_0 = const()[name = tensor("op_8152_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8152 = slice_by_index(begin = var_8152_begin_0, end = var_8152_end_0, end_mask = var_8152_end_mask_0, x = reshape_4)[name = tensor("op_8152")]; + tensor segment_accum_193_exclusive_0 = const()[name = tensor("segment_accum_193_exclusive_0"), val = tensor(false)]; + tensor segment_accum_193_reverse_0 = const()[name = tensor("segment_accum_193_reverse_0"), val = tensor(false)]; + tensor segment_accum_193 = cumsum(axis = var_7349, exclusive = segment_accum_193_exclusive_0, reverse = segment_accum_193_reverse_0, x = var_8152)[name = tensor("segment_accum_193")]; + tensor var_8156_begin_0 = const()[name = tensor("op_8156_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8156_end_0 = const()[name = tensor("op_8156_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8156_end_mask_0 = const()[name = tensor("op_8156_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8156 = slice_by_index(begin = var_8156_begin_0, end = var_8156_end_0, end_mask = var_8156_end_mask_0, x = var_8150)[name = tensor("op_8156")]; + tensor var_8158 = add(x = segment_accum_193, y = var_8156)[name = tensor("op_8158")]; + tensor var_8160_begin_0 = const()[name = tensor("op_8160_begin_0"), val = tensor([0, 98000, 0])]; + tensor var_8160_end_0 = const()[name = tensor("op_8160_end_0"), val = tensor([1, 99000, 9])]; + tensor var_8160_end_mask_0 = const()[name = tensor("op_8160_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8160 = slice_by_index(begin = var_8160_begin_0, end = var_8160_end_0, end_mask = var_8160_end_mask_0, x = reshape_4)[name = tensor("op_8160")]; + tensor segment_accum_195_exclusive_0 = const()[name = tensor("segment_accum_195_exclusive_0"), val = tensor(false)]; + tensor segment_accum_195_reverse_0 = const()[name = tensor("segment_accum_195_reverse_0"), val = tensor(false)]; + tensor segment_accum_195 = cumsum(axis = var_7349, exclusive = segment_accum_195_exclusive_0, reverse = segment_accum_195_reverse_0, x = var_8160)[name = tensor("segment_accum_195")]; + tensor var_8164_begin_0 = const()[name = tensor("op_8164_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8164_end_0 = const()[name = tensor("op_8164_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8164_end_mask_0 = const()[name = tensor("op_8164_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8164 = slice_by_index(begin = var_8164_begin_0, end = var_8164_end_0, end_mask = var_8164_end_mask_0, x = var_8158)[name = tensor("op_8164")]; + tensor var_8166 = add(x = segment_accum_195, y = var_8164)[name = tensor("op_8166")]; + tensor var_8168_begin_0 = const()[name = tensor("op_8168_begin_0"), val = tensor([0, 99000, 0])]; + tensor var_8168_end_0 = const()[name = tensor("op_8168_end_0"), val = tensor([1, 100000, 9])]; + tensor var_8168_end_mask_0 = const()[name = tensor("op_8168_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8168 = slice_by_index(begin = var_8168_begin_0, end = var_8168_end_0, end_mask = var_8168_end_mask_0, x = reshape_4)[name = tensor("op_8168")]; + tensor segment_accum_197_exclusive_0 = const()[name = tensor("segment_accum_197_exclusive_0"), val = tensor(false)]; + tensor segment_accum_197_reverse_0 = const()[name = tensor("segment_accum_197_reverse_0"), val = tensor(false)]; + tensor segment_accum_197 = cumsum(axis = var_7349, exclusive = segment_accum_197_exclusive_0, reverse = segment_accum_197_reverse_0, x = var_8168)[name = tensor("segment_accum_197")]; + tensor var_8172_begin_0 = const()[name = tensor("op_8172_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8172_end_0 = const()[name = tensor("op_8172_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8172_end_mask_0 = const()[name = tensor("op_8172_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8172 = slice_by_index(begin = var_8172_begin_0, end = var_8172_end_0, end_mask = var_8172_end_mask_0, x = var_8166)[name = tensor("op_8172")]; + tensor var_8174 = add(x = segment_accum_197, y = var_8172)[name = tensor("op_8174")]; + tensor var_8176_begin_0 = const()[name = tensor("op_8176_begin_0"), val = tensor([0, 100000, 0])]; + tensor var_8176_end_0 = const()[name = tensor("op_8176_end_0"), val = tensor([1, 101000, 9])]; + tensor var_8176_end_mask_0 = const()[name = tensor("op_8176_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8176 = slice_by_index(begin = var_8176_begin_0, end = var_8176_end_0, end_mask = var_8176_end_mask_0, x = reshape_4)[name = tensor("op_8176")]; + tensor segment_accum_199_exclusive_0 = const()[name = tensor("segment_accum_199_exclusive_0"), val = tensor(false)]; + tensor segment_accum_199_reverse_0 = const()[name = tensor("segment_accum_199_reverse_0"), val = tensor(false)]; + tensor segment_accum_199 = cumsum(axis = var_7349, exclusive = segment_accum_199_exclusive_0, reverse = segment_accum_199_reverse_0, x = var_8176)[name = tensor("segment_accum_199")]; + tensor var_8180_begin_0 = const()[name = tensor("op_8180_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8180_end_0 = const()[name = tensor("op_8180_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8180_end_mask_0 = const()[name = tensor("op_8180_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8180 = slice_by_index(begin = var_8180_begin_0, end = var_8180_end_0, end_mask = var_8180_end_mask_0, x = var_8174)[name = tensor("op_8180")]; + tensor var_8182 = add(x = segment_accum_199, y = var_8180)[name = tensor("op_8182")]; + tensor var_8184_begin_0 = const()[name = tensor("op_8184_begin_0"), val = tensor([0, 101000, 0])]; + tensor var_8184_end_0 = const()[name = tensor("op_8184_end_0"), val = tensor([1, 102000, 9])]; + tensor var_8184_end_mask_0 = const()[name = tensor("op_8184_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8184 = slice_by_index(begin = var_8184_begin_0, end = var_8184_end_0, end_mask = var_8184_end_mask_0, x = reshape_4)[name = tensor("op_8184")]; + tensor segment_accum_201_exclusive_0 = const()[name = tensor("segment_accum_201_exclusive_0"), val = tensor(false)]; + tensor segment_accum_201_reverse_0 = const()[name = tensor("segment_accum_201_reverse_0"), val = tensor(false)]; + tensor segment_accum_201 = cumsum(axis = var_7349, exclusive = segment_accum_201_exclusive_0, reverse = segment_accum_201_reverse_0, x = var_8184)[name = tensor("segment_accum_201")]; + tensor var_8188_begin_0 = const()[name = tensor("op_8188_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8188_end_0 = const()[name = tensor("op_8188_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8188_end_mask_0 = const()[name = tensor("op_8188_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8188 = slice_by_index(begin = var_8188_begin_0, end = var_8188_end_0, end_mask = var_8188_end_mask_0, x = var_8182)[name = tensor("op_8188")]; + tensor var_8190 = add(x = segment_accum_201, y = var_8188)[name = tensor("op_8190")]; + tensor var_8192_begin_0 = const()[name = tensor("op_8192_begin_0"), val = tensor([0, 102000, 0])]; + tensor var_8192_end_0 = const()[name = tensor("op_8192_end_0"), val = tensor([1, 103000, 9])]; + tensor var_8192_end_mask_0 = const()[name = tensor("op_8192_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8192 = slice_by_index(begin = var_8192_begin_0, end = var_8192_end_0, end_mask = var_8192_end_mask_0, x = reshape_4)[name = tensor("op_8192")]; + tensor segment_accum_203_exclusive_0 = const()[name = tensor("segment_accum_203_exclusive_0"), val = tensor(false)]; + tensor segment_accum_203_reverse_0 = const()[name = tensor("segment_accum_203_reverse_0"), val = tensor(false)]; + tensor segment_accum_203 = cumsum(axis = var_7349, exclusive = segment_accum_203_exclusive_0, reverse = segment_accum_203_reverse_0, x = var_8192)[name = tensor("segment_accum_203")]; + tensor var_8196_begin_0 = const()[name = tensor("op_8196_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8196_end_0 = const()[name = tensor("op_8196_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8196_end_mask_0 = const()[name = tensor("op_8196_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8196 = slice_by_index(begin = var_8196_begin_0, end = var_8196_end_0, end_mask = var_8196_end_mask_0, x = var_8190)[name = tensor("op_8196")]; + tensor var_8198 = add(x = segment_accum_203, y = var_8196)[name = tensor("op_8198")]; + tensor var_8200_begin_0 = const()[name = tensor("op_8200_begin_0"), val = tensor([0, 103000, 0])]; + tensor var_8200_end_0 = const()[name = tensor("op_8200_end_0"), val = tensor([1, 104000, 9])]; + tensor var_8200_end_mask_0 = const()[name = tensor("op_8200_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8200 = slice_by_index(begin = var_8200_begin_0, end = var_8200_end_0, end_mask = var_8200_end_mask_0, x = reshape_4)[name = tensor("op_8200")]; + tensor segment_accum_205_exclusive_0 = const()[name = tensor("segment_accum_205_exclusive_0"), val = tensor(false)]; + tensor segment_accum_205_reverse_0 = const()[name = tensor("segment_accum_205_reverse_0"), val = tensor(false)]; + tensor segment_accum_205 = cumsum(axis = var_7349, exclusive = segment_accum_205_exclusive_0, reverse = segment_accum_205_reverse_0, x = var_8200)[name = tensor("segment_accum_205")]; + tensor var_8204_begin_0 = const()[name = tensor("op_8204_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8204_end_0 = const()[name = tensor("op_8204_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8204_end_mask_0 = const()[name = tensor("op_8204_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8204 = slice_by_index(begin = var_8204_begin_0, end = var_8204_end_0, end_mask = var_8204_end_mask_0, x = var_8198)[name = tensor("op_8204")]; + tensor var_8206 = add(x = segment_accum_205, y = var_8204)[name = tensor("op_8206")]; + tensor var_8208_begin_0 = const()[name = tensor("op_8208_begin_0"), val = tensor([0, 104000, 0])]; + tensor var_8208_end_0 = const()[name = tensor("op_8208_end_0"), val = tensor([1, 105000, 9])]; + tensor var_8208_end_mask_0 = const()[name = tensor("op_8208_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8208 = slice_by_index(begin = var_8208_begin_0, end = var_8208_end_0, end_mask = var_8208_end_mask_0, x = reshape_4)[name = tensor("op_8208")]; + tensor segment_accum_207_exclusive_0 = const()[name = tensor("segment_accum_207_exclusive_0"), val = tensor(false)]; + tensor segment_accum_207_reverse_0 = const()[name = tensor("segment_accum_207_reverse_0"), val = tensor(false)]; + tensor segment_accum_207 = cumsum(axis = var_7349, exclusive = segment_accum_207_exclusive_0, reverse = segment_accum_207_reverse_0, x = var_8208)[name = tensor("segment_accum_207")]; + tensor var_8212_begin_0 = const()[name = tensor("op_8212_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8212_end_0 = const()[name = tensor("op_8212_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8212_end_mask_0 = const()[name = tensor("op_8212_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8212 = slice_by_index(begin = var_8212_begin_0, end = var_8212_end_0, end_mask = var_8212_end_mask_0, x = var_8206)[name = tensor("op_8212")]; + tensor var_8214 = add(x = segment_accum_207, y = var_8212)[name = tensor("op_8214")]; + tensor var_8216_begin_0 = const()[name = tensor("op_8216_begin_0"), val = tensor([0, 105000, 0])]; + tensor var_8216_end_0 = const()[name = tensor("op_8216_end_0"), val = tensor([1, 106000, 9])]; + tensor var_8216_end_mask_0 = const()[name = tensor("op_8216_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8216 = slice_by_index(begin = var_8216_begin_0, end = var_8216_end_0, end_mask = var_8216_end_mask_0, x = reshape_4)[name = tensor("op_8216")]; + tensor segment_accum_209_exclusive_0 = const()[name = tensor("segment_accum_209_exclusive_0"), val = tensor(false)]; + tensor segment_accum_209_reverse_0 = const()[name = tensor("segment_accum_209_reverse_0"), val = tensor(false)]; + tensor segment_accum_209 = cumsum(axis = var_7349, exclusive = segment_accum_209_exclusive_0, reverse = segment_accum_209_reverse_0, x = var_8216)[name = tensor("segment_accum_209")]; + tensor var_8220_begin_0 = const()[name = tensor("op_8220_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8220_end_0 = const()[name = tensor("op_8220_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8220_end_mask_0 = const()[name = tensor("op_8220_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8220 = slice_by_index(begin = var_8220_begin_0, end = var_8220_end_0, end_mask = var_8220_end_mask_0, x = var_8214)[name = tensor("op_8220")]; + tensor var_8222 = add(x = segment_accum_209, y = var_8220)[name = tensor("op_8222")]; + tensor var_8224_begin_0 = const()[name = tensor("op_8224_begin_0"), val = tensor([0, 106000, 0])]; + tensor var_8224_end_0 = const()[name = tensor("op_8224_end_0"), val = tensor([1, 107000, 9])]; + tensor var_8224_end_mask_0 = const()[name = tensor("op_8224_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8224 = slice_by_index(begin = var_8224_begin_0, end = var_8224_end_0, end_mask = var_8224_end_mask_0, x = reshape_4)[name = tensor("op_8224")]; + tensor segment_accum_211_exclusive_0 = const()[name = tensor("segment_accum_211_exclusive_0"), val = tensor(false)]; + tensor segment_accum_211_reverse_0 = const()[name = tensor("segment_accum_211_reverse_0"), val = tensor(false)]; + tensor segment_accum_211 = cumsum(axis = var_7349, exclusive = segment_accum_211_exclusive_0, reverse = segment_accum_211_reverse_0, x = var_8224)[name = tensor("segment_accum_211")]; + tensor var_8228_begin_0 = const()[name = tensor("op_8228_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8228_end_0 = const()[name = tensor("op_8228_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8228_end_mask_0 = const()[name = tensor("op_8228_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8228 = slice_by_index(begin = var_8228_begin_0, end = var_8228_end_0, end_mask = var_8228_end_mask_0, x = var_8222)[name = tensor("op_8228")]; + tensor var_8230 = add(x = segment_accum_211, y = var_8228)[name = tensor("op_8230")]; + tensor var_8232_begin_0 = const()[name = tensor("op_8232_begin_0"), val = tensor([0, 107000, 0])]; + tensor var_8232_end_0 = const()[name = tensor("op_8232_end_0"), val = tensor([1, 108000, 9])]; + tensor var_8232_end_mask_0 = const()[name = tensor("op_8232_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8232 = slice_by_index(begin = var_8232_begin_0, end = var_8232_end_0, end_mask = var_8232_end_mask_0, x = reshape_4)[name = tensor("op_8232")]; + tensor segment_accum_213_exclusive_0 = const()[name = tensor("segment_accum_213_exclusive_0"), val = tensor(false)]; + tensor segment_accum_213_reverse_0 = const()[name = tensor("segment_accum_213_reverse_0"), val = tensor(false)]; + tensor segment_accum_213 = cumsum(axis = var_7349, exclusive = segment_accum_213_exclusive_0, reverse = segment_accum_213_reverse_0, x = var_8232)[name = tensor("segment_accum_213")]; + tensor var_8236_begin_0 = const()[name = tensor("op_8236_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8236_end_0 = const()[name = tensor("op_8236_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8236_end_mask_0 = const()[name = tensor("op_8236_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8236 = slice_by_index(begin = var_8236_begin_0, end = var_8236_end_0, end_mask = var_8236_end_mask_0, x = var_8230)[name = tensor("op_8236")]; + tensor var_8238 = add(x = segment_accum_213, y = var_8236)[name = tensor("op_8238")]; + tensor var_8240_begin_0 = const()[name = tensor("op_8240_begin_0"), val = tensor([0, 108000, 0])]; + tensor var_8240_end_0 = const()[name = tensor("op_8240_end_0"), val = tensor([1, 109000, 9])]; + tensor var_8240_end_mask_0 = const()[name = tensor("op_8240_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8240 = slice_by_index(begin = var_8240_begin_0, end = var_8240_end_0, end_mask = var_8240_end_mask_0, x = reshape_4)[name = tensor("op_8240")]; + tensor segment_accum_215_exclusive_0 = const()[name = tensor("segment_accum_215_exclusive_0"), val = tensor(false)]; + tensor segment_accum_215_reverse_0 = const()[name = tensor("segment_accum_215_reverse_0"), val = tensor(false)]; + tensor segment_accum_215 = cumsum(axis = var_7349, exclusive = segment_accum_215_exclusive_0, reverse = segment_accum_215_reverse_0, x = var_8240)[name = tensor("segment_accum_215")]; + tensor var_8244_begin_0 = const()[name = tensor("op_8244_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8244_end_0 = const()[name = tensor("op_8244_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8244_end_mask_0 = const()[name = tensor("op_8244_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8244 = slice_by_index(begin = var_8244_begin_0, end = var_8244_end_0, end_mask = var_8244_end_mask_0, x = var_8238)[name = tensor("op_8244")]; + tensor var_8246 = add(x = segment_accum_215, y = var_8244)[name = tensor("op_8246")]; + tensor var_8248_begin_0 = const()[name = tensor("op_8248_begin_0"), val = tensor([0, 109000, 0])]; + tensor var_8248_end_0 = const()[name = tensor("op_8248_end_0"), val = tensor([1, 110000, 9])]; + tensor var_8248_end_mask_0 = const()[name = tensor("op_8248_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8248 = slice_by_index(begin = var_8248_begin_0, end = var_8248_end_0, end_mask = var_8248_end_mask_0, x = reshape_4)[name = tensor("op_8248")]; + tensor segment_accum_217_exclusive_0 = const()[name = tensor("segment_accum_217_exclusive_0"), val = tensor(false)]; + tensor segment_accum_217_reverse_0 = const()[name = tensor("segment_accum_217_reverse_0"), val = tensor(false)]; + tensor segment_accum_217 = cumsum(axis = var_7349, exclusive = segment_accum_217_exclusive_0, reverse = segment_accum_217_reverse_0, x = var_8248)[name = tensor("segment_accum_217")]; + tensor var_8252_begin_0 = const()[name = tensor("op_8252_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8252_end_0 = const()[name = tensor("op_8252_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8252_end_mask_0 = const()[name = tensor("op_8252_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8252 = slice_by_index(begin = var_8252_begin_0, end = var_8252_end_0, end_mask = var_8252_end_mask_0, x = var_8246)[name = tensor("op_8252")]; + tensor var_8254 = add(x = segment_accum_217, y = var_8252)[name = tensor("op_8254")]; + tensor var_8256_begin_0 = const()[name = tensor("op_8256_begin_0"), val = tensor([0, 110000, 0])]; + tensor var_8256_end_0 = const()[name = tensor("op_8256_end_0"), val = tensor([1, 111000, 9])]; + tensor var_8256_end_mask_0 = const()[name = tensor("op_8256_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8256 = slice_by_index(begin = var_8256_begin_0, end = var_8256_end_0, end_mask = var_8256_end_mask_0, x = reshape_4)[name = tensor("op_8256")]; + tensor segment_accum_219_exclusive_0 = const()[name = tensor("segment_accum_219_exclusive_0"), val = tensor(false)]; + tensor segment_accum_219_reverse_0 = const()[name = tensor("segment_accum_219_reverse_0"), val = tensor(false)]; + tensor segment_accum_219 = cumsum(axis = var_7349, exclusive = segment_accum_219_exclusive_0, reverse = segment_accum_219_reverse_0, x = var_8256)[name = tensor("segment_accum_219")]; + tensor var_8260_begin_0 = const()[name = tensor("op_8260_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8260_end_0 = const()[name = tensor("op_8260_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8260_end_mask_0 = const()[name = tensor("op_8260_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8260 = slice_by_index(begin = var_8260_begin_0, end = var_8260_end_0, end_mask = var_8260_end_mask_0, x = var_8254)[name = tensor("op_8260")]; + tensor var_8262 = add(x = segment_accum_219, y = var_8260)[name = tensor("op_8262")]; + tensor var_8264_begin_0 = const()[name = tensor("op_8264_begin_0"), val = tensor([0, 111000, 0])]; + tensor var_8264_end_0 = const()[name = tensor("op_8264_end_0"), val = tensor([1, 112000, 9])]; + tensor var_8264_end_mask_0 = const()[name = tensor("op_8264_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8264 = slice_by_index(begin = var_8264_begin_0, end = var_8264_end_0, end_mask = var_8264_end_mask_0, x = reshape_4)[name = tensor("op_8264")]; + tensor segment_accum_221_exclusive_0 = const()[name = tensor("segment_accum_221_exclusive_0"), val = tensor(false)]; + tensor segment_accum_221_reverse_0 = const()[name = tensor("segment_accum_221_reverse_0"), val = tensor(false)]; + tensor segment_accum_221 = cumsum(axis = var_7349, exclusive = segment_accum_221_exclusive_0, reverse = segment_accum_221_reverse_0, x = var_8264)[name = tensor("segment_accum_221")]; + tensor var_8268_begin_0 = const()[name = tensor("op_8268_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8268_end_0 = const()[name = tensor("op_8268_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8268_end_mask_0 = const()[name = tensor("op_8268_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8268 = slice_by_index(begin = var_8268_begin_0, end = var_8268_end_0, end_mask = var_8268_end_mask_0, x = var_8262)[name = tensor("op_8268")]; + tensor var_8270 = add(x = segment_accum_221, y = var_8268)[name = tensor("op_8270")]; + tensor var_8272_begin_0 = const()[name = tensor("op_8272_begin_0"), val = tensor([0, 112000, 0])]; + tensor var_8272_end_0 = const()[name = tensor("op_8272_end_0"), val = tensor([1, 113000, 9])]; + tensor var_8272_end_mask_0 = const()[name = tensor("op_8272_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8272 = slice_by_index(begin = var_8272_begin_0, end = var_8272_end_0, end_mask = var_8272_end_mask_0, x = reshape_4)[name = tensor("op_8272")]; + tensor segment_accum_223_exclusive_0 = const()[name = tensor("segment_accum_223_exclusive_0"), val = tensor(false)]; + tensor segment_accum_223_reverse_0 = const()[name = tensor("segment_accum_223_reverse_0"), val = tensor(false)]; + tensor segment_accum_223 = cumsum(axis = var_7349, exclusive = segment_accum_223_exclusive_0, reverse = segment_accum_223_reverse_0, x = var_8272)[name = tensor("segment_accum_223")]; + tensor var_8276_begin_0 = const()[name = tensor("op_8276_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8276_end_0 = const()[name = tensor("op_8276_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8276_end_mask_0 = const()[name = tensor("op_8276_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8276 = slice_by_index(begin = var_8276_begin_0, end = var_8276_end_0, end_mask = var_8276_end_mask_0, x = var_8270)[name = tensor("op_8276")]; + tensor var_8278 = add(x = segment_accum_223, y = var_8276)[name = tensor("op_8278")]; + tensor var_8280_begin_0 = const()[name = tensor("op_8280_begin_0"), val = tensor([0, 113000, 0])]; + tensor var_8280_end_0 = const()[name = tensor("op_8280_end_0"), val = tensor([1, 114000, 9])]; + tensor var_8280_end_mask_0 = const()[name = tensor("op_8280_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8280 = slice_by_index(begin = var_8280_begin_0, end = var_8280_end_0, end_mask = var_8280_end_mask_0, x = reshape_4)[name = tensor("op_8280")]; + tensor segment_accum_225_exclusive_0 = const()[name = tensor("segment_accum_225_exclusive_0"), val = tensor(false)]; + tensor segment_accum_225_reverse_0 = const()[name = tensor("segment_accum_225_reverse_0"), val = tensor(false)]; + tensor segment_accum_225 = cumsum(axis = var_7349, exclusive = segment_accum_225_exclusive_0, reverse = segment_accum_225_reverse_0, x = var_8280)[name = tensor("segment_accum_225")]; + tensor var_8284_begin_0 = const()[name = tensor("op_8284_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8284_end_0 = const()[name = tensor("op_8284_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8284_end_mask_0 = const()[name = tensor("op_8284_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8284 = slice_by_index(begin = var_8284_begin_0, end = var_8284_end_0, end_mask = var_8284_end_mask_0, x = var_8278)[name = tensor("op_8284")]; + tensor var_8286 = add(x = segment_accum_225, y = var_8284)[name = tensor("op_8286")]; + tensor var_8288_begin_0 = const()[name = tensor("op_8288_begin_0"), val = tensor([0, 114000, 0])]; + tensor var_8288_end_0 = const()[name = tensor("op_8288_end_0"), val = tensor([1, 115000, 9])]; + tensor var_8288_end_mask_0 = const()[name = tensor("op_8288_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8288 = slice_by_index(begin = var_8288_begin_0, end = var_8288_end_0, end_mask = var_8288_end_mask_0, x = reshape_4)[name = tensor("op_8288")]; + tensor segment_accum_227_exclusive_0 = const()[name = tensor("segment_accum_227_exclusive_0"), val = tensor(false)]; + tensor segment_accum_227_reverse_0 = const()[name = tensor("segment_accum_227_reverse_0"), val = tensor(false)]; + tensor segment_accum_227 = cumsum(axis = var_7349, exclusive = segment_accum_227_exclusive_0, reverse = segment_accum_227_reverse_0, x = var_8288)[name = tensor("segment_accum_227")]; + tensor var_8292_begin_0 = const()[name = tensor("op_8292_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8292_end_0 = const()[name = tensor("op_8292_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8292_end_mask_0 = const()[name = tensor("op_8292_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8292 = slice_by_index(begin = var_8292_begin_0, end = var_8292_end_0, end_mask = var_8292_end_mask_0, x = var_8286)[name = tensor("op_8292")]; + tensor var_8294 = add(x = segment_accum_227, y = var_8292)[name = tensor("op_8294")]; + tensor var_8296_begin_0 = const()[name = tensor("op_8296_begin_0"), val = tensor([0, 115000, 0])]; + tensor var_8296_end_0 = const()[name = tensor("op_8296_end_0"), val = tensor([1, 116000, 9])]; + tensor var_8296_end_mask_0 = const()[name = tensor("op_8296_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8296 = slice_by_index(begin = var_8296_begin_0, end = var_8296_end_0, end_mask = var_8296_end_mask_0, x = reshape_4)[name = tensor("op_8296")]; + tensor segment_accum_229_exclusive_0 = const()[name = tensor("segment_accum_229_exclusive_0"), val = tensor(false)]; + tensor segment_accum_229_reverse_0 = const()[name = tensor("segment_accum_229_reverse_0"), val = tensor(false)]; + tensor segment_accum_229 = cumsum(axis = var_7349, exclusive = segment_accum_229_exclusive_0, reverse = segment_accum_229_reverse_0, x = var_8296)[name = tensor("segment_accum_229")]; + tensor var_8300_begin_0 = const()[name = tensor("op_8300_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8300_end_0 = const()[name = tensor("op_8300_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8300_end_mask_0 = const()[name = tensor("op_8300_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8300 = slice_by_index(begin = var_8300_begin_0, end = var_8300_end_0, end_mask = var_8300_end_mask_0, x = var_8294)[name = tensor("op_8300")]; + tensor var_8302 = add(x = segment_accum_229, y = var_8300)[name = tensor("op_8302")]; + tensor var_8304_begin_0 = const()[name = tensor("op_8304_begin_0"), val = tensor([0, 116000, 0])]; + tensor var_8304_end_0 = const()[name = tensor("op_8304_end_0"), val = tensor([1, 117000, 9])]; + tensor var_8304_end_mask_0 = const()[name = tensor("op_8304_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8304 = slice_by_index(begin = var_8304_begin_0, end = var_8304_end_0, end_mask = var_8304_end_mask_0, x = reshape_4)[name = tensor("op_8304")]; + tensor segment_accum_231_exclusive_0 = const()[name = tensor("segment_accum_231_exclusive_0"), val = tensor(false)]; + tensor segment_accum_231_reverse_0 = const()[name = tensor("segment_accum_231_reverse_0"), val = tensor(false)]; + tensor segment_accum_231 = cumsum(axis = var_7349, exclusive = segment_accum_231_exclusive_0, reverse = segment_accum_231_reverse_0, x = var_8304)[name = tensor("segment_accum_231")]; + tensor var_8308_begin_0 = const()[name = tensor("op_8308_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8308_end_0 = const()[name = tensor("op_8308_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8308_end_mask_0 = const()[name = tensor("op_8308_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8308 = slice_by_index(begin = var_8308_begin_0, end = var_8308_end_0, end_mask = var_8308_end_mask_0, x = var_8302)[name = tensor("op_8308")]; + tensor var_8310 = add(x = segment_accum_231, y = var_8308)[name = tensor("op_8310")]; + tensor var_8312_begin_0 = const()[name = tensor("op_8312_begin_0"), val = tensor([0, 117000, 0])]; + tensor var_8312_end_0 = const()[name = tensor("op_8312_end_0"), val = tensor([1, 118000, 9])]; + tensor var_8312_end_mask_0 = const()[name = tensor("op_8312_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8312 = slice_by_index(begin = var_8312_begin_0, end = var_8312_end_0, end_mask = var_8312_end_mask_0, x = reshape_4)[name = tensor("op_8312")]; + tensor segment_accum_233_exclusive_0 = const()[name = tensor("segment_accum_233_exclusive_0"), val = tensor(false)]; + tensor segment_accum_233_reverse_0 = const()[name = tensor("segment_accum_233_reverse_0"), val = tensor(false)]; + tensor segment_accum_233 = cumsum(axis = var_7349, exclusive = segment_accum_233_exclusive_0, reverse = segment_accum_233_reverse_0, x = var_8312)[name = tensor("segment_accum_233")]; + tensor var_8316_begin_0 = const()[name = tensor("op_8316_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8316_end_0 = const()[name = tensor("op_8316_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8316_end_mask_0 = const()[name = tensor("op_8316_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8316 = slice_by_index(begin = var_8316_begin_0, end = var_8316_end_0, end_mask = var_8316_end_mask_0, x = var_8310)[name = tensor("op_8316")]; + tensor var_8318 = add(x = segment_accum_233, y = var_8316)[name = tensor("op_8318")]; + tensor var_8320_begin_0 = const()[name = tensor("op_8320_begin_0"), val = tensor([0, 118000, 0])]; + tensor var_8320_end_0 = const()[name = tensor("op_8320_end_0"), val = tensor([1, 119000, 9])]; + tensor var_8320_end_mask_0 = const()[name = tensor("op_8320_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8320 = slice_by_index(begin = var_8320_begin_0, end = var_8320_end_0, end_mask = var_8320_end_mask_0, x = reshape_4)[name = tensor("op_8320")]; + tensor segment_accum_235_exclusive_0 = const()[name = tensor("segment_accum_235_exclusive_0"), val = tensor(false)]; + tensor segment_accum_235_reverse_0 = const()[name = tensor("segment_accum_235_reverse_0"), val = tensor(false)]; + tensor segment_accum_235 = cumsum(axis = var_7349, exclusive = segment_accum_235_exclusive_0, reverse = segment_accum_235_reverse_0, x = var_8320)[name = tensor("segment_accum_235")]; + tensor var_8324_begin_0 = const()[name = tensor("op_8324_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8324_end_0 = const()[name = tensor("op_8324_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8324_end_mask_0 = const()[name = tensor("op_8324_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8324 = slice_by_index(begin = var_8324_begin_0, end = var_8324_end_0, end_mask = var_8324_end_mask_0, x = var_8318)[name = tensor("op_8324")]; + tensor var_8326 = add(x = segment_accum_235, y = var_8324)[name = tensor("op_8326")]; + tensor var_8328_begin_0 = const()[name = tensor("op_8328_begin_0"), val = tensor([0, 119000, 0])]; + tensor var_8328_end_0 = const()[name = tensor("op_8328_end_0"), val = tensor([1, 120000, 9])]; + tensor var_8328_end_mask_0 = const()[name = tensor("op_8328_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8328 = slice_by_index(begin = var_8328_begin_0, end = var_8328_end_0, end_mask = var_8328_end_mask_0, x = reshape_4)[name = tensor("op_8328")]; + tensor segment_accum_237_exclusive_0 = const()[name = tensor("segment_accum_237_exclusive_0"), val = tensor(false)]; + tensor segment_accum_237_reverse_0 = const()[name = tensor("segment_accum_237_reverse_0"), val = tensor(false)]; + tensor segment_accum_237 = cumsum(axis = var_7349, exclusive = segment_accum_237_exclusive_0, reverse = segment_accum_237_reverse_0, x = var_8328)[name = tensor("segment_accum_237")]; + tensor var_8332_begin_0 = const()[name = tensor("op_8332_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8332_end_0 = const()[name = tensor("op_8332_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8332_end_mask_0 = const()[name = tensor("op_8332_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8332 = slice_by_index(begin = var_8332_begin_0, end = var_8332_end_0, end_mask = var_8332_end_mask_0, x = var_8326)[name = tensor("op_8332")]; + tensor var_8334 = add(x = segment_accum_237, y = var_8332)[name = tensor("op_8334")]; + tensor var_8336_begin_0 = const()[name = tensor("op_8336_begin_0"), val = tensor([0, 120000, 0])]; + tensor var_8336_end_0 = const()[name = tensor("op_8336_end_0"), val = tensor([1, 121000, 9])]; + tensor var_8336_end_mask_0 = const()[name = tensor("op_8336_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8336 = slice_by_index(begin = var_8336_begin_0, end = var_8336_end_0, end_mask = var_8336_end_mask_0, x = reshape_4)[name = tensor("op_8336")]; + tensor segment_accum_239_exclusive_0 = const()[name = tensor("segment_accum_239_exclusive_0"), val = tensor(false)]; + tensor segment_accum_239_reverse_0 = const()[name = tensor("segment_accum_239_reverse_0"), val = tensor(false)]; + tensor segment_accum_239 = cumsum(axis = var_7349, exclusive = segment_accum_239_exclusive_0, reverse = segment_accum_239_reverse_0, x = var_8336)[name = tensor("segment_accum_239")]; + tensor var_8340_begin_0 = const()[name = tensor("op_8340_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8340_end_0 = const()[name = tensor("op_8340_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8340_end_mask_0 = const()[name = tensor("op_8340_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8340 = slice_by_index(begin = var_8340_begin_0, end = var_8340_end_0, end_mask = var_8340_end_mask_0, x = var_8334)[name = tensor("op_8340")]; + tensor var_8342 = add(x = segment_accum_239, y = var_8340)[name = tensor("op_8342")]; + tensor var_8344_begin_0 = const()[name = tensor("op_8344_begin_0"), val = tensor([0, 121000, 0])]; + tensor var_8344_end_0 = const()[name = tensor("op_8344_end_0"), val = tensor([1, 122000, 9])]; + tensor var_8344_end_mask_0 = const()[name = tensor("op_8344_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8344 = slice_by_index(begin = var_8344_begin_0, end = var_8344_end_0, end_mask = var_8344_end_mask_0, x = reshape_4)[name = tensor("op_8344")]; + tensor segment_accum_241_exclusive_0 = const()[name = tensor("segment_accum_241_exclusive_0"), val = tensor(false)]; + tensor segment_accum_241_reverse_0 = const()[name = tensor("segment_accum_241_reverse_0"), val = tensor(false)]; + tensor segment_accum_241 = cumsum(axis = var_7349, exclusive = segment_accum_241_exclusive_0, reverse = segment_accum_241_reverse_0, x = var_8344)[name = tensor("segment_accum_241")]; + tensor var_8348_begin_0 = const()[name = tensor("op_8348_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8348_end_0 = const()[name = tensor("op_8348_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8348_end_mask_0 = const()[name = tensor("op_8348_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8348 = slice_by_index(begin = var_8348_begin_0, end = var_8348_end_0, end_mask = var_8348_end_mask_0, x = var_8342)[name = tensor("op_8348")]; + tensor var_8350 = add(x = segment_accum_241, y = var_8348)[name = tensor("op_8350")]; + tensor var_8352_begin_0 = const()[name = tensor("op_8352_begin_0"), val = tensor([0, 122000, 0])]; + tensor var_8352_end_0 = const()[name = tensor("op_8352_end_0"), val = tensor([1, 123000, 9])]; + tensor var_8352_end_mask_0 = const()[name = tensor("op_8352_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8352 = slice_by_index(begin = var_8352_begin_0, end = var_8352_end_0, end_mask = var_8352_end_mask_0, x = reshape_4)[name = tensor("op_8352")]; + tensor segment_accum_243_exclusive_0 = const()[name = tensor("segment_accum_243_exclusive_0"), val = tensor(false)]; + tensor segment_accum_243_reverse_0 = const()[name = tensor("segment_accum_243_reverse_0"), val = tensor(false)]; + tensor segment_accum_243 = cumsum(axis = var_7349, exclusive = segment_accum_243_exclusive_0, reverse = segment_accum_243_reverse_0, x = var_8352)[name = tensor("segment_accum_243")]; + tensor var_8356_begin_0 = const()[name = tensor("op_8356_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8356_end_0 = const()[name = tensor("op_8356_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8356_end_mask_0 = const()[name = tensor("op_8356_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8356 = slice_by_index(begin = var_8356_begin_0, end = var_8356_end_0, end_mask = var_8356_end_mask_0, x = var_8350)[name = tensor("op_8356")]; + tensor var_8358 = add(x = segment_accum_243, y = var_8356)[name = tensor("op_8358")]; + tensor var_8360_begin_0 = const()[name = tensor("op_8360_begin_0"), val = tensor([0, 123000, 0])]; + tensor var_8360_end_0 = const()[name = tensor("op_8360_end_0"), val = tensor([1, 124000, 9])]; + tensor var_8360_end_mask_0 = const()[name = tensor("op_8360_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8360 = slice_by_index(begin = var_8360_begin_0, end = var_8360_end_0, end_mask = var_8360_end_mask_0, x = reshape_4)[name = tensor("op_8360")]; + tensor segment_accum_245_exclusive_0 = const()[name = tensor("segment_accum_245_exclusive_0"), val = tensor(false)]; + tensor segment_accum_245_reverse_0 = const()[name = tensor("segment_accum_245_reverse_0"), val = tensor(false)]; + tensor segment_accum_245 = cumsum(axis = var_7349, exclusive = segment_accum_245_exclusive_0, reverse = segment_accum_245_reverse_0, x = var_8360)[name = tensor("segment_accum_245")]; + tensor var_8364_begin_0 = const()[name = tensor("op_8364_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8364_end_0 = const()[name = tensor("op_8364_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8364_end_mask_0 = const()[name = tensor("op_8364_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8364 = slice_by_index(begin = var_8364_begin_0, end = var_8364_end_0, end_mask = var_8364_end_mask_0, x = var_8358)[name = tensor("op_8364")]; + tensor var_8366 = add(x = segment_accum_245, y = var_8364)[name = tensor("op_8366")]; + tensor var_8368_begin_0 = const()[name = tensor("op_8368_begin_0"), val = tensor([0, 124000, 0])]; + tensor var_8368_end_0 = const()[name = tensor("op_8368_end_0"), val = tensor([1, 125000, 9])]; + tensor var_8368_end_mask_0 = const()[name = tensor("op_8368_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8368 = slice_by_index(begin = var_8368_begin_0, end = var_8368_end_0, end_mask = var_8368_end_mask_0, x = reshape_4)[name = tensor("op_8368")]; + tensor segment_accum_247_exclusive_0 = const()[name = tensor("segment_accum_247_exclusive_0"), val = tensor(false)]; + tensor segment_accum_247_reverse_0 = const()[name = tensor("segment_accum_247_reverse_0"), val = tensor(false)]; + tensor segment_accum_247 = cumsum(axis = var_7349, exclusive = segment_accum_247_exclusive_0, reverse = segment_accum_247_reverse_0, x = var_8368)[name = tensor("segment_accum_247")]; + tensor var_8372_begin_0 = const()[name = tensor("op_8372_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8372_end_0 = const()[name = tensor("op_8372_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8372_end_mask_0 = const()[name = tensor("op_8372_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8372 = slice_by_index(begin = var_8372_begin_0, end = var_8372_end_0, end_mask = var_8372_end_mask_0, x = var_8366)[name = tensor("op_8372")]; + tensor var_8374 = add(x = segment_accum_247, y = var_8372)[name = tensor("op_8374")]; + tensor var_8376_begin_0 = const()[name = tensor("op_8376_begin_0"), val = tensor([0, 125000, 0])]; + tensor var_8376_end_0 = const()[name = tensor("op_8376_end_0"), val = tensor([1, 126000, 9])]; + tensor var_8376_end_mask_0 = const()[name = tensor("op_8376_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8376 = slice_by_index(begin = var_8376_begin_0, end = var_8376_end_0, end_mask = var_8376_end_mask_0, x = reshape_4)[name = tensor("op_8376")]; + tensor segment_accum_249_exclusive_0 = const()[name = tensor("segment_accum_249_exclusive_0"), val = tensor(false)]; + tensor segment_accum_249_reverse_0 = const()[name = tensor("segment_accum_249_reverse_0"), val = tensor(false)]; + tensor segment_accum_249 = cumsum(axis = var_7349, exclusive = segment_accum_249_exclusive_0, reverse = segment_accum_249_reverse_0, x = var_8376)[name = tensor("segment_accum_249")]; + tensor var_8380_begin_0 = const()[name = tensor("op_8380_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8380_end_0 = const()[name = tensor("op_8380_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8380_end_mask_0 = const()[name = tensor("op_8380_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8380 = slice_by_index(begin = var_8380_begin_0, end = var_8380_end_0, end_mask = var_8380_end_mask_0, x = var_8374)[name = tensor("op_8380")]; + tensor var_8382 = add(x = segment_accum_249, y = var_8380)[name = tensor("op_8382")]; + tensor var_8384_begin_0 = const()[name = tensor("op_8384_begin_0"), val = tensor([0, 126000, 0])]; + tensor var_8384_end_0 = const()[name = tensor("op_8384_end_0"), val = tensor([1, 127000, 9])]; + tensor var_8384_end_mask_0 = const()[name = tensor("op_8384_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8384 = slice_by_index(begin = var_8384_begin_0, end = var_8384_end_0, end_mask = var_8384_end_mask_0, x = reshape_4)[name = tensor("op_8384")]; + tensor segment_accum_251_exclusive_0 = const()[name = tensor("segment_accum_251_exclusive_0"), val = tensor(false)]; + tensor segment_accum_251_reverse_0 = const()[name = tensor("segment_accum_251_reverse_0"), val = tensor(false)]; + tensor segment_accum_251 = cumsum(axis = var_7349, exclusive = segment_accum_251_exclusive_0, reverse = segment_accum_251_reverse_0, x = var_8384)[name = tensor("segment_accum_251")]; + tensor var_8388_begin_0 = const()[name = tensor("op_8388_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8388_end_0 = const()[name = tensor("op_8388_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8388_end_mask_0 = const()[name = tensor("op_8388_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8388 = slice_by_index(begin = var_8388_begin_0, end = var_8388_end_0, end_mask = var_8388_end_mask_0, x = var_8382)[name = tensor("op_8388")]; + tensor var_8390 = add(x = segment_accum_251, y = var_8388)[name = tensor("op_8390")]; + tensor var_8392_begin_0 = const()[name = tensor("op_8392_begin_0"), val = tensor([0, 127000, 0])]; + tensor var_8392_end_0 = const()[name = tensor("op_8392_end_0"), val = tensor([1, 128000, 9])]; + tensor var_8392_end_mask_0 = const()[name = tensor("op_8392_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8392 = slice_by_index(begin = var_8392_begin_0, end = var_8392_end_0, end_mask = var_8392_end_mask_0, x = reshape_4)[name = tensor("op_8392")]; + tensor segment_accum_253_exclusive_0 = const()[name = tensor("segment_accum_253_exclusive_0"), val = tensor(false)]; + tensor segment_accum_253_reverse_0 = const()[name = tensor("segment_accum_253_reverse_0"), val = tensor(false)]; + tensor segment_accum_253 = cumsum(axis = var_7349, exclusive = segment_accum_253_exclusive_0, reverse = segment_accum_253_reverse_0, x = var_8392)[name = tensor("segment_accum_253")]; + tensor var_8396_begin_0 = const()[name = tensor("op_8396_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8396_end_0 = const()[name = tensor("op_8396_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8396_end_mask_0 = const()[name = tensor("op_8396_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8396 = slice_by_index(begin = var_8396_begin_0, end = var_8396_end_0, end_mask = var_8396_end_mask_0, x = var_8390)[name = tensor("op_8396")]; + tensor var_8398 = add(x = segment_accum_253, y = var_8396)[name = tensor("op_8398")]; + tensor var_8400_begin_0 = const()[name = tensor("op_8400_begin_0"), val = tensor([0, 128000, 0])]; + tensor var_8400_end_0 = const()[name = tensor("op_8400_end_0"), val = tensor([1, 129000, 9])]; + tensor var_8400_end_mask_0 = const()[name = tensor("op_8400_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8400 = slice_by_index(begin = var_8400_begin_0, end = var_8400_end_0, end_mask = var_8400_end_mask_0, x = reshape_4)[name = tensor("op_8400")]; + tensor segment_accum_255_exclusive_0 = const()[name = tensor("segment_accum_255_exclusive_0"), val = tensor(false)]; + tensor segment_accum_255_reverse_0 = const()[name = tensor("segment_accum_255_reverse_0"), val = tensor(false)]; + tensor segment_accum_255 = cumsum(axis = var_7349, exclusive = segment_accum_255_exclusive_0, reverse = segment_accum_255_reverse_0, x = var_8400)[name = tensor("segment_accum_255")]; + tensor var_8404_begin_0 = const()[name = tensor("op_8404_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8404_end_0 = const()[name = tensor("op_8404_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8404_end_mask_0 = const()[name = tensor("op_8404_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8404 = slice_by_index(begin = var_8404_begin_0, end = var_8404_end_0, end_mask = var_8404_end_mask_0, x = var_8398)[name = tensor("op_8404")]; + tensor var_8406 = add(x = segment_accum_255, y = var_8404)[name = tensor("op_8406")]; + tensor var_8408_begin_0 = const()[name = tensor("op_8408_begin_0"), val = tensor([0, 129000, 0])]; + tensor var_8408_end_0 = const()[name = tensor("op_8408_end_0"), val = tensor([1, 130000, 9])]; + tensor var_8408_end_mask_0 = const()[name = tensor("op_8408_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8408 = slice_by_index(begin = var_8408_begin_0, end = var_8408_end_0, end_mask = var_8408_end_mask_0, x = reshape_4)[name = tensor("op_8408")]; + tensor segment_accum_257_exclusive_0 = const()[name = tensor("segment_accum_257_exclusive_0"), val = tensor(false)]; + tensor segment_accum_257_reverse_0 = const()[name = tensor("segment_accum_257_reverse_0"), val = tensor(false)]; + tensor segment_accum_257 = cumsum(axis = var_7349, exclusive = segment_accum_257_exclusive_0, reverse = segment_accum_257_reverse_0, x = var_8408)[name = tensor("segment_accum_257")]; + tensor var_8412_begin_0 = const()[name = tensor("op_8412_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8412_end_0 = const()[name = tensor("op_8412_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8412_end_mask_0 = const()[name = tensor("op_8412_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8412 = slice_by_index(begin = var_8412_begin_0, end = var_8412_end_0, end_mask = var_8412_end_mask_0, x = var_8406)[name = tensor("op_8412")]; + tensor var_8414 = add(x = segment_accum_257, y = var_8412)[name = tensor("op_8414")]; + tensor var_8416_begin_0 = const()[name = tensor("op_8416_begin_0"), val = tensor([0, 130000, 0])]; + tensor var_8416_end_0 = const()[name = tensor("op_8416_end_0"), val = tensor([1, 131000, 9])]; + tensor var_8416_end_mask_0 = const()[name = tensor("op_8416_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8416 = slice_by_index(begin = var_8416_begin_0, end = var_8416_end_0, end_mask = var_8416_end_mask_0, x = reshape_4)[name = tensor("op_8416")]; + tensor segment_accum_259_exclusive_0 = const()[name = tensor("segment_accum_259_exclusive_0"), val = tensor(false)]; + tensor segment_accum_259_reverse_0 = const()[name = tensor("segment_accum_259_reverse_0"), val = tensor(false)]; + tensor segment_accum_259 = cumsum(axis = var_7349, exclusive = segment_accum_259_exclusive_0, reverse = segment_accum_259_reverse_0, x = var_8416)[name = tensor("segment_accum_259")]; + tensor var_8420_begin_0 = const()[name = tensor("op_8420_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8420_end_0 = const()[name = tensor("op_8420_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8420_end_mask_0 = const()[name = tensor("op_8420_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8420 = slice_by_index(begin = var_8420_begin_0, end = var_8420_end_0, end_mask = var_8420_end_mask_0, x = var_8414)[name = tensor("op_8420")]; + tensor var_8422 = add(x = segment_accum_259, y = var_8420)[name = tensor("op_8422")]; + tensor var_8424_begin_0 = const()[name = tensor("op_8424_begin_0"), val = tensor([0, 131000, 0])]; + tensor var_8424_end_0 = const()[name = tensor("op_8424_end_0"), val = tensor([1, 132000, 9])]; + tensor var_8424_end_mask_0 = const()[name = tensor("op_8424_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8424 = slice_by_index(begin = var_8424_begin_0, end = var_8424_end_0, end_mask = var_8424_end_mask_0, x = reshape_4)[name = tensor("op_8424")]; + tensor segment_accum_261_exclusive_0 = const()[name = tensor("segment_accum_261_exclusive_0"), val = tensor(false)]; + tensor segment_accum_261_reverse_0 = const()[name = tensor("segment_accum_261_reverse_0"), val = tensor(false)]; + tensor segment_accum_261 = cumsum(axis = var_7349, exclusive = segment_accum_261_exclusive_0, reverse = segment_accum_261_reverse_0, x = var_8424)[name = tensor("segment_accum_261")]; + tensor var_8428_begin_0 = const()[name = tensor("op_8428_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8428_end_0 = const()[name = tensor("op_8428_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8428_end_mask_0 = const()[name = tensor("op_8428_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8428 = slice_by_index(begin = var_8428_begin_0, end = var_8428_end_0, end_mask = var_8428_end_mask_0, x = var_8422)[name = tensor("op_8428")]; + tensor var_8430 = add(x = segment_accum_261, y = var_8428)[name = tensor("op_8430")]; + tensor var_8432_begin_0 = const()[name = tensor("op_8432_begin_0"), val = tensor([0, 132000, 0])]; + tensor var_8432_end_0 = const()[name = tensor("op_8432_end_0"), val = tensor([1, 133000, 9])]; + tensor var_8432_end_mask_0 = const()[name = tensor("op_8432_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8432 = slice_by_index(begin = var_8432_begin_0, end = var_8432_end_0, end_mask = var_8432_end_mask_0, x = reshape_4)[name = tensor("op_8432")]; + tensor segment_accum_263_exclusive_0 = const()[name = tensor("segment_accum_263_exclusive_0"), val = tensor(false)]; + tensor segment_accum_263_reverse_0 = const()[name = tensor("segment_accum_263_reverse_0"), val = tensor(false)]; + tensor segment_accum_263 = cumsum(axis = var_7349, exclusive = segment_accum_263_exclusive_0, reverse = segment_accum_263_reverse_0, x = var_8432)[name = tensor("segment_accum_263")]; + tensor var_8436_begin_0 = const()[name = tensor("op_8436_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8436_end_0 = const()[name = tensor("op_8436_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8436_end_mask_0 = const()[name = tensor("op_8436_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8436 = slice_by_index(begin = var_8436_begin_0, end = var_8436_end_0, end_mask = var_8436_end_mask_0, x = var_8430)[name = tensor("op_8436")]; + tensor var_8438 = add(x = segment_accum_263, y = var_8436)[name = tensor("op_8438")]; + tensor var_8440_begin_0 = const()[name = tensor("op_8440_begin_0"), val = tensor([0, 133000, 0])]; + tensor var_8440_end_0 = const()[name = tensor("op_8440_end_0"), val = tensor([1, 134000, 9])]; + tensor var_8440_end_mask_0 = const()[name = tensor("op_8440_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8440 = slice_by_index(begin = var_8440_begin_0, end = var_8440_end_0, end_mask = var_8440_end_mask_0, x = reshape_4)[name = tensor("op_8440")]; + tensor segment_accum_265_exclusive_0 = const()[name = tensor("segment_accum_265_exclusive_0"), val = tensor(false)]; + tensor segment_accum_265_reverse_0 = const()[name = tensor("segment_accum_265_reverse_0"), val = tensor(false)]; + tensor segment_accum_265 = cumsum(axis = var_7349, exclusive = segment_accum_265_exclusive_0, reverse = segment_accum_265_reverse_0, x = var_8440)[name = tensor("segment_accum_265")]; + tensor var_8444_begin_0 = const()[name = tensor("op_8444_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8444_end_0 = const()[name = tensor("op_8444_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8444_end_mask_0 = const()[name = tensor("op_8444_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8444 = slice_by_index(begin = var_8444_begin_0, end = var_8444_end_0, end_mask = var_8444_end_mask_0, x = var_8438)[name = tensor("op_8444")]; + tensor var_8446 = add(x = segment_accum_265, y = var_8444)[name = tensor("op_8446")]; + tensor var_8448_begin_0 = const()[name = tensor("op_8448_begin_0"), val = tensor([0, 134000, 0])]; + tensor var_8448_end_0 = const()[name = tensor("op_8448_end_0"), val = tensor([1, 135000, 9])]; + tensor var_8448_end_mask_0 = const()[name = tensor("op_8448_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8448 = slice_by_index(begin = var_8448_begin_0, end = var_8448_end_0, end_mask = var_8448_end_mask_0, x = reshape_4)[name = tensor("op_8448")]; + tensor segment_accum_267_exclusive_0 = const()[name = tensor("segment_accum_267_exclusive_0"), val = tensor(false)]; + tensor segment_accum_267_reverse_0 = const()[name = tensor("segment_accum_267_reverse_0"), val = tensor(false)]; + tensor segment_accum_267 = cumsum(axis = var_7349, exclusive = segment_accum_267_exclusive_0, reverse = segment_accum_267_reverse_0, x = var_8448)[name = tensor("segment_accum_267")]; + tensor var_8452_begin_0 = const()[name = tensor("op_8452_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8452_end_0 = const()[name = tensor("op_8452_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8452_end_mask_0 = const()[name = tensor("op_8452_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8452 = slice_by_index(begin = var_8452_begin_0, end = var_8452_end_0, end_mask = var_8452_end_mask_0, x = var_8446)[name = tensor("op_8452")]; + tensor var_8454 = add(x = segment_accum_267, y = var_8452)[name = tensor("op_8454")]; + tensor var_8456_begin_0 = const()[name = tensor("op_8456_begin_0"), val = tensor([0, 135000, 0])]; + tensor var_8456_end_0 = const()[name = tensor("op_8456_end_0"), val = tensor([1, 136000, 9])]; + tensor var_8456_end_mask_0 = const()[name = tensor("op_8456_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8456 = slice_by_index(begin = var_8456_begin_0, end = var_8456_end_0, end_mask = var_8456_end_mask_0, x = reshape_4)[name = tensor("op_8456")]; + tensor segment_accum_269_exclusive_0 = const()[name = tensor("segment_accum_269_exclusive_0"), val = tensor(false)]; + tensor segment_accum_269_reverse_0 = const()[name = tensor("segment_accum_269_reverse_0"), val = tensor(false)]; + tensor segment_accum_269 = cumsum(axis = var_7349, exclusive = segment_accum_269_exclusive_0, reverse = segment_accum_269_reverse_0, x = var_8456)[name = tensor("segment_accum_269")]; + tensor var_8460_begin_0 = const()[name = tensor("op_8460_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8460_end_0 = const()[name = tensor("op_8460_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8460_end_mask_0 = const()[name = tensor("op_8460_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8460 = slice_by_index(begin = var_8460_begin_0, end = var_8460_end_0, end_mask = var_8460_end_mask_0, x = var_8454)[name = tensor("op_8460")]; + tensor var_8462 = add(x = segment_accum_269, y = var_8460)[name = tensor("op_8462")]; + tensor var_8464_begin_0 = const()[name = tensor("op_8464_begin_0"), val = tensor([0, 136000, 0])]; + tensor var_8464_end_0 = const()[name = tensor("op_8464_end_0"), val = tensor([1, 137000, 9])]; + tensor var_8464_end_mask_0 = const()[name = tensor("op_8464_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8464 = slice_by_index(begin = var_8464_begin_0, end = var_8464_end_0, end_mask = var_8464_end_mask_0, x = reshape_4)[name = tensor("op_8464")]; + tensor segment_accum_271_exclusive_0 = const()[name = tensor("segment_accum_271_exclusive_0"), val = tensor(false)]; + tensor segment_accum_271_reverse_0 = const()[name = tensor("segment_accum_271_reverse_0"), val = tensor(false)]; + tensor segment_accum_271 = cumsum(axis = var_7349, exclusive = segment_accum_271_exclusive_0, reverse = segment_accum_271_reverse_0, x = var_8464)[name = tensor("segment_accum_271")]; + tensor var_8468_begin_0 = const()[name = tensor("op_8468_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8468_end_0 = const()[name = tensor("op_8468_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8468_end_mask_0 = const()[name = tensor("op_8468_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8468 = slice_by_index(begin = var_8468_begin_0, end = var_8468_end_0, end_mask = var_8468_end_mask_0, x = var_8462)[name = tensor("op_8468")]; + tensor var_8470 = add(x = segment_accum_271, y = var_8468)[name = tensor("op_8470")]; + tensor var_8472_begin_0 = const()[name = tensor("op_8472_begin_0"), val = tensor([0, 137000, 0])]; + tensor var_8472_end_0 = const()[name = tensor("op_8472_end_0"), val = tensor([1, 138000, 9])]; + tensor var_8472_end_mask_0 = const()[name = tensor("op_8472_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8472 = slice_by_index(begin = var_8472_begin_0, end = var_8472_end_0, end_mask = var_8472_end_mask_0, x = reshape_4)[name = tensor("op_8472")]; + tensor segment_accum_273_exclusive_0 = const()[name = tensor("segment_accum_273_exclusive_0"), val = tensor(false)]; + tensor segment_accum_273_reverse_0 = const()[name = tensor("segment_accum_273_reverse_0"), val = tensor(false)]; + tensor segment_accum_273 = cumsum(axis = var_7349, exclusive = segment_accum_273_exclusive_0, reverse = segment_accum_273_reverse_0, x = var_8472)[name = tensor("segment_accum_273")]; + tensor var_8476_begin_0 = const()[name = tensor("op_8476_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8476_end_0 = const()[name = tensor("op_8476_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8476_end_mask_0 = const()[name = tensor("op_8476_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8476 = slice_by_index(begin = var_8476_begin_0, end = var_8476_end_0, end_mask = var_8476_end_mask_0, x = var_8470)[name = tensor("op_8476")]; + tensor var_8478 = add(x = segment_accum_273, y = var_8476)[name = tensor("op_8478")]; + tensor var_8480_begin_0 = const()[name = tensor("op_8480_begin_0"), val = tensor([0, 138000, 0])]; + tensor var_8480_end_0 = const()[name = tensor("op_8480_end_0"), val = tensor([1, 139000, 9])]; + tensor var_8480_end_mask_0 = const()[name = tensor("op_8480_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8480 = slice_by_index(begin = var_8480_begin_0, end = var_8480_end_0, end_mask = var_8480_end_mask_0, x = reshape_4)[name = tensor("op_8480")]; + tensor segment_accum_275_exclusive_0 = const()[name = tensor("segment_accum_275_exclusive_0"), val = tensor(false)]; + tensor segment_accum_275_reverse_0 = const()[name = tensor("segment_accum_275_reverse_0"), val = tensor(false)]; + tensor segment_accum_275 = cumsum(axis = var_7349, exclusive = segment_accum_275_exclusive_0, reverse = segment_accum_275_reverse_0, x = var_8480)[name = tensor("segment_accum_275")]; + tensor var_8484_begin_0 = const()[name = tensor("op_8484_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8484_end_0 = const()[name = tensor("op_8484_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8484_end_mask_0 = const()[name = tensor("op_8484_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8484 = slice_by_index(begin = var_8484_begin_0, end = var_8484_end_0, end_mask = var_8484_end_mask_0, x = var_8478)[name = tensor("op_8484")]; + tensor var_8486 = add(x = segment_accum_275, y = var_8484)[name = tensor("op_8486")]; + tensor var_8488_begin_0 = const()[name = tensor("op_8488_begin_0"), val = tensor([0, 139000, 0])]; + tensor var_8488_end_0 = const()[name = tensor("op_8488_end_0"), val = tensor([1, 140000, 9])]; + tensor var_8488_end_mask_0 = const()[name = tensor("op_8488_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8488 = slice_by_index(begin = var_8488_begin_0, end = var_8488_end_0, end_mask = var_8488_end_mask_0, x = reshape_4)[name = tensor("op_8488")]; + tensor segment_accum_277_exclusive_0 = const()[name = tensor("segment_accum_277_exclusive_0"), val = tensor(false)]; + tensor segment_accum_277_reverse_0 = const()[name = tensor("segment_accum_277_reverse_0"), val = tensor(false)]; + tensor segment_accum_277 = cumsum(axis = var_7349, exclusive = segment_accum_277_exclusive_0, reverse = segment_accum_277_reverse_0, x = var_8488)[name = tensor("segment_accum_277")]; + tensor var_8492_begin_0 = const()[name = tensor("op_8492_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8492_end_0 = const()[name = tensor("op_8492_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8492_end_mask_0 = const()[name = tensor("op_8492_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8492 = slice_by_index(begin = var_8492_begin_0, end = var_8492_end_0, end_mask = var_8492_end_mask_0, x = var_8486)[name = tensor("op_8492")]; + tensor var_8494 = add(x = segment_accum_277, y = var_8492)[name = tensor("op_8494")]; + tensor var_8496_begin_0 = const()[name = tensor("op_8496_begin_0"), val = tensor([0, 140000, 0])]; + tensor var_8496_end_0 = const()[name = tensor("op_8496_end_0"), val = tensor([1, 141000, 9])]; + tensor var_8496_end_mask_0 = const()[name = tensor("op_8496_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8496 = slice_by_index(begin = var_8496_begin_0, end = var_8496_end_0, end_mask = var_8496_end_mask_0, x = reshape_4)[name = tensor("op_8496")]; + tensor segment_accum_279_exclusive_0 = const()[name = tensor("segment_accum_279_exclusive_0"), val = tensor(false)]; + tensor segment_accum_279_reverse_0 = const()[name = tensor("segment_accum_279_reverse_0"), val = tensor(false)]; + tensor segment_accum_279 = cumsum(axis = var_7349, exclusive = segment_accum_279_exclusive_0, reverse = segment_accum_279_reverse_0, x = var_8496)[name = tensor("segment_accum_279")]; + tensor var_8500_begin_0 = const()[name = tensor("op_8500_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8500_end_0 = const()[name = tensor("op_8500_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8500_end_mask_0 = const()[name = tensor("op_8500_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8500 = slice_by_index(begin = var_8500_begin_0, end = var_8500_end_0, end_mask = var_8500_end_mask_0, x = var_8494)[name = tensor("op_8500")]; + tensor var_8502 = add(x = segment_accum_279, y = var_8500)[name = tensor("op_8502")]; + tensor var_8504_begin_0 = const()[name = tensor("op_8504_begin_0"), val = tensor([0, 141000, 0])]; + tensor var_8504_end_0 = const()[name = tensor("op_8504_end_0"), val = tensor([1, 142000, 9])]; + tensor var_8504_end_mask_0 = const()[name = tensor("op_8504_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8504 = slice_by_index(begin = var_8504_begin_0, end = var_8504_end_0, end_mask = var_8504_end_mask_0, x = reshape_4)[name = tensor("op_8504")]; + tensor segment_accum_281_exclusive_0 = const()[name = tensor("segment_accum_281_exclusive_0"), val = tensor(false)]; + tensor segment_accum_281_reverse_0 = const()[name = tensor("segment_accum_281_reverse_0"), val = tensor(false)]; + tensor segment_accum_281 = cumsum(axis = var_7349, exclusive = segment_accum_281_exclusive_0, reverse = segment_accum_281_reverse_0, x = var_8504)[name = tensor("segment_accum_281")]; + tensor var_8508_begin_0 = const()[name = tensor("op_8508_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8508_end_0 = const()[name = tensor("op_8508_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8508_end_mask_0 = const()[name = tensor("op_8508_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8508 = slice_by_index(begin = var_8508_begin_0, end = var_8508_end_0, end_mask = var_8508_end_mask_0, x = var_8502)[name = tensor("op_8508")]; + tensor var_8510 = add(x = segment_accum_281, y = var_8508)[name = tensor("op_8510")]; + tensor var_8512_begin_0 = const()[name = tensor("op_8512_begin_0"), val = tensor([0, 142000, 0])]; + tensor var_8512_end_0 = const()[name = tensor("op_8512_end_0"), val = tensor([1, 143000, 9])]; + tensor var_8512_end_mask_0 = const()[name = tensor("op_8512_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8512 = slice_by_index(begin = var_8512_begin_0, end = var_8512_end_0, end_mask = var_8512_end_mask_0, x = reshape_4)[name = tensor("op_8512")]; + tensor segment_accum_283_exclusive_0 = const()[name = tensor("segment_accum_283_exclusive_0"), val = tensor(false)]; + tensor segment_accum_283_reverse_0 = const()[name = tensor("segment_accum_283_reverse_0"), val = tensor(false)]; + tensor segment_accum_283 = cumsum(axis = var_7349, exclusive = segment_accum_283_exclusive_0, reverse = segment_accum_283_reverse_0, x = var_8512)[name = tensor("segment_accum_283")]; + tensor var_8516_begin_0 = const()[name = tensor("op_8516_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8516_end_0 = const()[name = tensor("op_8516_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8516_end_mask_0 = const()[name = tensor("op_8516_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8516 = slice_by_index(begin = var_8516_begin_0, end = var_8516_end_0, end_mask = var_8516_end_mask_0, x = var_8510)[name = tensor("op_8516")]; + tensor var_8518 = add(x = segment_accum_283, y = var_8516)[name = tensor("op_8518")]; + tensor var_8520_begin_0 = const()[name = tensor("op_8520_begin_0"), val = tensor([0, 143000, 0])]; + tensor var_8520_end_0 = const()[name = tensor("op_8520_end_0"), val = tensor([1, 144000, 9])]; + tensor var_8520_end_mask_0 = const()[name = tensor("op_8520_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8520 = slice_by_index(begin = var_8520_begin_0, end = var_8520_end_0, end_mask = var_8520_end_mask_0, x = reshape_4)[name = tensor("op_8520")]; + tensor segment_accum_285_exclusive_0 = const()[name = tensor("segment_accum_285_exclusive_0"), val = tensor(false)]; + tensor segment_accum_285_reverse_0 = const()[name = tensor("segment_accum_285_reverse_0"), val = tensor(false)]; + tensor segment_accum_285 = cumsum(axis = var_7349, exclusive = segment_accum_285_exclusive_0, reverse = segment_accum_285_reverse_0, x = var_8520)[name = tensor("segment_accum_285")]; + tensor var_8524_begin_0 = const()[name = tensor("op_8524_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8524_end_0 = const()[name = tensor("op_8524_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8524_end_mask_0 = const()[name = tensor("op_8524_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8524 = slice_by_index(begin = var_8524_begin_0, end = var_8524_end_0, end_mask = var_8524_end_mask_0, x = var_8518)[name = tensor("op_8524")]; + tensor var_8526 = add(x = segment_accum_285, y = var_8524)[name = tensor("op_8526")]; + tensor var_8528_begin_0 = const()[name = tensor("op_8528_begin_0"), val = tensor([0, 144000, 0])]; + tensor var_8528_end_0 = const()[name = tensor("op_8528_end_0"), val = tensor([1, 145000, 9])]; + tensor var_8528_end_mask_0 = const()[name = tensor("op_8528_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8528 = slice_by_index(begin = var_8528_begin_0, end = var_8528_end_0, end_mask = var_8528_end_mask_0, x = reshape_4)[name = tensor("op_8528")]; + tensor segment_accum_287_exclusive_0 = const()[name = tensor("segment_accum_287_exclusive_0"), val = tensor(false)]; + tensor segment_accum_287_reverse_0 = const()[name = tensor("segment_accum_287_reverse_0"), val = tensor(false)]; + tensor segment_accum_287 = cumsum(axis = var_7349, exclusive = segment_accum_287_exclusive_0, reverse = segment_accum_287_reverse_0, x = var_8528)[name = tensor("segment_accum_287")]; + tensor var_8532_begin_0 = const()[name = tensor("op_8532_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8532_end_0 = const()[name = tensor("op_8532_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8532_end_mask_0 = const()[name = tensor("op_8532_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8532 = slice_by_index(begin = var_8532_begin_0, end = var_8532_end_0, end_mask = var_8532_end_mask_0, x = var_8526)[name = tensor("op_8532")]; + tensor var_8534 = add(x = segment_accum_287, y = var_8532)[name = tensor("op_8534")]; + tensor var_8536_begin_0 = const()[name = tensor("op_8536_begin_0"), val = tensor([0, 145000, 0])]; + tensor var_8536_end_0 = const()[name = tensor("op_8536_end_0"), val = tensor([1, 146000, 9])]; + tensor var_8536_end_mask_0 = const()[name = tensor("op_8536_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8536 = slice_by_index(begin = var_8536_begin_0, end = var_8536_end_0, end_mask = var_8536_end_mask_0, x = reshape_4)[name = tensor("op_8536")]; + tensor segment_accum_289_exclusive_0 = const()[name = tensor("segment_accum_289_exclusive_0"), val = tensor(false)]; + tensor segment_accum_289_reverse_0 = const()[name = tensor("segment_accum_289_reverse_0"), val = tensor(false)]; + tensor segment_accum_289 = cumsum(axis = var_7349, exclusive = segment_accum_289_exclusive_0, reverse = segment_accum_289_reverse_0, x = var_8536)[name = tensor("segment_accum_289")]; + tensor var_8540_begin_0 = const()[name = tensor("op_8540_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8540_end_0 = const()[name = tensor("op_8540_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8540_end_mask_0 = const()[name = tensor("op_8540_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8540 = slice_by_index(begin = var_8540_begin_0, end = var_8540_end_0, end_mask = var_8540_end_mask_0, x = var_8534)[name = tensor("op_8540")]; + tensor var_8542 = add(x = segment_accum_289, y = var_8540)[name = tensor("op_8542")]; + tensor var_8544_begin_0 = const()[name = tensor("op_8544_begin_0"), val = tensor([0, 146000, 0])]; + tensor var_8544_end_0 = const()[name = tensor("op_8544_end_0"), val = tensor([1, 147000, 9])]; + tensor var_8544_end_mask_0 = const()[name = tensor("op_8544_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8544 = slice_by_index(begin = var_8544_begin_0, end = var_8544_end_0, end_mask = var_8544_end_mask_0, x = reshape_4)[name = tensor("op_8544")]; + tensor segment_accum_291_exclusive_0 = const()[name = tensor("segment_accum_291_exclusive_0"), val = tensor(false)]; + tensor segment_accum_291_reverse_0 = const()[name = tensor("segment_accum_291_reverse_0"), val = tensor(false)]; + tensor segment_accum_291 = cumsum(axis = var_7349, exclusive = segment_accum_291_exclusive_0, reverse = segment_accum_291_reverse_0, x = var_8544)[name = tensor("segment_accum_291")]; + tensor var_8548_begin_0 = const()[name = tensor("op_8548_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8548_end_0 = const()[name = tensor("op_8548_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8548_end_mask_0 = const()[name = tensor("op_8548_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8548 = slice_by_index(begin = var_8548_begin_0, end = var_8548_end_0, end_mask = var_8548_end_mask_0, x = var_8542)[name = tensor("op_8548")]; + tensor var_8550 = add(x = segment_accum_291, y = var_8548)[name = tensor("op_8550")]; + tensor var_8552_begin_0 = const()[name = tensor("op_8552_begin_0"), val = tensor([0, 147000, 0])]; + tensor var_8552_end_0 = const()[name = tensor("op_8552_end_0"), val = tensor([1, 148000, 9])]; + tensor var_8552_end_mask_0 = const()[name = tensor("op_8552_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8552 = slice_by_index(begin = var_8552_begin_0, end = var_8552_end_0, end_mask = var_8552_end_mask_0, x = reshape_4)[name = tensor("op_8552")]; + tensor segment_accum_293_exclusive_0 = const()[name = tensor("segment_accum_293_exclusive_0"), val = tensor(false)]; + tensor segment_accum_293_reverse_0 = const()[name = tensor("segment_accum_293_reverse_0"), val = tensor(false)]; + tensor segment_accum_293 = cumsum(axis = var_7349, exclusive = segment_accum_293_exclusive_0, reverse = segment_accum_293_reverse_0, x = var_8552)[name = tensor("segment_accum_293")]; + tensor var_8556_begin_0 = const()[name = tensor("op_8556_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8556_end_0 = const()[name = tensor("op_8556_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8556_end_mask_0 = const()[name = tensor("op_8556_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8556 = slice_by_index(begin = var_8556_begin_0, end = var_8556_end_0, end_mask = var_8556_end_mask_0, x = var_8550)[name = tensor("op_8556")]; + tensor var_8558 = add(x = segment_accum_293, y = var_8556)[name = tensor("op_8558")]; + tensor var_8560_begin_0 = const()[name = tensor("op_8560_begin_0"), val = tensor([0, 148000, 0])]; + tensor var_8560_end_0 = const()[name = tensor("op_8560_end_0"), val = tensor([1, 149000, 9])]; + tensor var_8560_end_mask_0 = const()[name = tensor("op_8560_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8560 = slice_by_index(begin = var_8560_begin_0, end = var_8560_end_0, end_mask = var_8560_end_mask_0, x = reshape_4)[name = tensor("op_8560")]; + tensor segment_accum_295_exclusive_0 = const()[name = tensor("segment_accum_295_exclusive_0"), val = tensor(false)]; + tensor segment_accum_295_reverse_0 = const()[name = tensor("segment_accum_295_reverse_0"), val = tensor(false)]; + tensor segment_accum_295 = cumsum(axis = var_7349, exclusive = segment_accum_295_exclusive_0, reverse = segment_accum_295_reverse_0, x = var_8560)[name = tensor("segment_accum_295")]; + tensor var_8564_begin_0 = const()[name = tensor("op_8564_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8564_end_0 = const()[name = tensor("op_8564_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8564_end_mask_0 = const()[name = tensor("op_8564_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8564 = slice_by_index(begin = var_8564_begin_0, end = var_8564_end_0, end_mask = var_8564_end_mask_0, x = var_8558)[name = tensor("op_8564")]; + tensor var_8566 = add(x = segment_accum_295, y = var_8564)[name = tensor("op_8566")]; + tensor var_8568_begin_0 = const()[name = tensor("op_8568_begin_0"), val = tensor([0, 149000, 0])]; + tensor var_8568_end_0 = const()[name = tensor("op_8568_end_0"), val = tensor([1, 150000, 9])]; + tensor var_8568_end_mask_0 = const()[name = tensor("op_8568_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8568 = slice_by_index(begin = var_8568_begin_0, end = var_8568_end_0, end_mask = var_8568_end_mask_0, x = reshape_4)[name = tensor("op_8568")]; + tensor segment_accum_297_exclusive_0 = const()[name = tensor("segment_accum_297_exclusive_0"), val = tensor(false)]; + tensor segment_accum_297_reverse_0 = const()[name = tensor("segment_accum_297_reverse_0"), val = tensor(false)]; + tensor segment_accum_297 = cumsum(axis = var_7349, exclusive = segment_accum_297_exclusive_0, reverse = segment_accum_297_reverse_0, x = var_8568)[name = tensor("segment_accum_297")]; + tensor var_8572_begin_0 = const()[name = tensor("op_8572_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8572_end_0 = const()[name = tensor("op_8572_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8572_end_mask_0 = const()[name = tensor("op_8572_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8572 = slice_by_index(begin = var_8572_begin_0, end = var_8572_end_0, end_mask = var_8572_end_mask_0, x = var_8566)[name = tensor("op_8572")]; + tensor var_8574 = add(x = segment_accum_297, y = var_8572)[name = tensor("op_8574")]; + tensor var_8576_begin_0 = const()[name = tensor("op_8576_begin_0"), val = tensor([0, 150000, 0])]; + tensor var_8576_end_0 = const()[name = tensor("op_8576_end_0"), val = tensor([1, 151000, 9])]; + tensor var_8576_end_mask_0 = const()[name = tensor("op_8576_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8576 = slice_by_index(begin = var_8576_begin_0, end = var_8576_end_0, end_mask = var_8576_end_mask_0, x = reshape_4)[name = tensor("op_8576")]; + tensor segment_accum_299_exclusive_0 = const()[name = tensor("segment_accum_299_exclusive_0"), val = tensor(false)]; + tensor segment_accum_299_reverse_0 = const()[name = tensor("segment_accum_299_reverse_0"), val = tensor(false)]; + tensor segment_accum_299 = cumsum(axis = var_7349, exclusive = segment_accum_299_exclusive_0, reverse = segment_accum_299_reverse_0, x = var_8576)[name = tensor("segment_accum_299")]; + tensor var_8580_begin_0 = const()[name = tensor("op_8580_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8580_end_0 = const()[name = tensor("op_8580_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8580_end_mask_0 = const()[name = tensor("op_8580_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8580 = slice_by_index(begin = var_8580_begin_0, end = var_8580_end_0, end_mask = var_8580_end_mask_0, x = var_8574)[name = tensor("op_8580")]; + tensor var_8582 = add(x = segment_accum_299, y = var_8580)[name = tensor("op_8582")]; + tensor var_8584_begin_0 = const()[name = tensor("op_8584_begin_0"), val = tensor([0, 151000, 0])]; + tensor var_8584_end_0 = const()[name = tensor("op_8584_end_0"), val = tensor([1, 152000, 9])]; + tensor var_8584_end_mask_0 = const()[name = tensor("op_8584_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8584 = slice_by_index(begin = var_8584_begin_0, end = var_8584_end_0, end_mask = var_8584_end_mask_0, x = reshape_4)[name = tensor("op_8584")]; + tensor segment_accum_301_exclusive_0 = const()[name = tensor("segment_accum_301_exclusive_0"), val = tensor(false)]; + tensor segment_accum_301_reverse_0 = const()[name = tensor("segment_accum_301_reverse_0"), val = tensor(false)]; + tensor segment_accum_301 = cumsum(axis = var_7349, exclusive = segment_accum_301_exclusive_0, reverse = segment_accum_301_reverse_0, x = var_8584)[name = tensor("segment_accum_301")]; + tensor var_8588_begin_0 = const()[name = tensor("op_8588_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8588_end_0 = const()[name = tensor("op_8588_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8588_end_mask_0 = const()[name = tensor("op_8588_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8588 = slice_by_index(begin = var_8588_begin_0, end = var_8588_end_0, end_mask = var_8588_end_mask_0, x = var_8582)[name = tensor("op_8588")]; + tensor var_8590 = add(x = segment_accum_301, y = var_8588)[name = tensor("op_8590")]; + tensor var_8592_begin_0 = const()[name = tensor("op_8592_begin_0"), val = tensor([0, 152000, 0])]; + tensor var_8592_end_0 = const()[name = tensor("op_8592_end_0"), val = tensor([1, 153000, 9])]; + tensor var_8592_end_mask_0 = const()[name = tensor("op_8592_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8592 = slice_by_index(begin = var_8592_begin_0, end = var_8592_end_0, end_mask = var_8592_end_mask_0, x = reshape_4)[name = tensor("op_8592")]; + tensor segment_accum_303_exclusive_0 = const()[name = tensor("segment_accum_303_exclusive_0"), val = tensor(false)]; + tensor segment_accum_303_reverse_0 = const()[name = tensor("segment_accum_303_reverse_0"), val = tensor(false)]; + tensor segment_accum_303 = cumsum(axis = var_7349, exclusive = segment_accum_303_exclusive_0, reverse = segment_accum_303_reverse_0, x = var_8592)[name = tensor("segment_accum_303")]; + tensor var_8596_begin_0 = const()[name = tensor("op_8596_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8596_end_0 = const()[name = tensor("op_8596_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8596_end_mask_0 = const()[name = tensor("op_8596_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8596 = slice_by_index(begin = var_8596_begin_0, end = var_8596_end_0, end_mask = var_8596_end_mask_0, x = var_8590)[name = tensor("op_8596")]; + tensor var_8598 = add(x = segment_accum_303, y = var_8596)[name = tensor("op_8598")]; + tensor var_8600_begin_0 = const()[name = tensor("op_8600_begin_0"), val = tensor([0, 153000, 0])]; + tensor var_8600_end_0 = const()[name = tensor("op_8600_end_0"), val = tensor([1, 154000, 9])]; + tensor var_8600_end_mask_0 = const()[name = tensor("op_8600_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8600 = slice_by_index(begin = var_8600_begin_0, end = var_8600_end_0, end_mask = var_8600_end_mask_0, x = reshape_4)[name = tensor("op_8600")]; + tensor segment_accum_305_exclusive_0 = const()[name = tensor("segment_accum_305_exclusive_0"), val = tensor(false)]; + tensor segment_accum_305_reverse_0 = const()[name = tensor("segment_accum_305_reverse_0"), val = tensor(false)]; + tensor segment_accum_305 = cumsum(axis = var_7349, exclusive = segment_accum_305_exclusive_0, reverse = segment_accum_305_reverse_0, x = var_8600)[name = tensor("segment_accum_305")]; + tensor var_8604_begin_0 = const()[name = tensor("op_8604_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8604_end_0 = const()[name = tensor("op_8604_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8604_end_mask_0 = const()[name = tensor("op_8604_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8604 = slice_by_index(begin = var_8604_begin_0, end = var_8604_end_0, end_mask = var_8604_end_mask_0, x = var_8598)[name = tensor("op_8604")]; + tensor var_8606 = add(x = segment_accum_305, y = var_8604)[name = tensor("op_8606")]; + tensor var_8608_begin_0 = const()[name = tensor("op_8608_begin_0"), val = tensor([0, 154000, 0])]; + tensor var_8608_end_0 = const()[name = tensor("op_8608_end_0"), val = tensor([1, 155000, 9])]; + tensor var_8608_end_mask_0 = const()[name = tensor("op_8608_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8608 = slice_by_index(begin = var_8608_begin_0, end = var_8608_end_0, end_mask = var_8608_end_mask_0, x = reshape_4)[name = tensor("op_8608")]; + tensor segment_accum_307_exclusive_0 = const()[name = tensor("segment_accum_307_exclusive_0"), val = tensor(false)]; + tensor segment_accum_307_reverse_0 = const()[name = tensor("segment_accum_307_reverse_0"), val = tensor(false)]; + tensor segment_accum_307 = cumsum(axis = var_7349, exclusive = segment_accum_307_exclusive_0, reverse = segment_accum_307_reverse_0, x = var_8608)[name = tensor("segment_accum_307")]; + tensor var_8612_begin_0 = const()[name = tensor("op_8612_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8612_end_0 = const()[name = tensor("op_8612_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8612_end_mask_0 = const()[name = tensor("op_8612_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8612 = slice_by_index(begin = var_8612_begin_0, end = var_8612_end_0, end_mask = var_8612_end_mask_0, x = var_8606)[name = tensor("op_8612")]; + tensor var_8614 = add(x = segment_accum_307, y = var_8612)[name = tensor("op_8614")]; + tensor var_8616_begin_0 = const()[name = tensor("op_8616_begin_0"), val = tensor([0, 155000, 0])]; + tensor var_8616_end_0 = const()[name = tensor("op_8616_end_0"), val = tensor([1, 156000, 9])]; + tensor var_8616_end_mask_0 = const()[name = tensor("op_8616_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8616 = slice_by_index(begin = var_8616_begin_0, end = var_8616_end_0, end_mask = var_8616_end_mask_0, x = reshape_4)[name = tensor("op_8616")]; + tensor segment_accum_309_exclusive_0 = const()[name = tensor("segment_accum_309_exclusive_0"), val = tensor(false)]; + tensor segment_accum_309_reverse_0 = const()[name = tensor("segment_accum_309_reverse_0"), val = tensor(false)]; + tensor segment_accum_309 = cumsum(axis = var_7349, exclusive = segment_accum_309_exclusive_0, reverse = segment_accum_309_reverse_0, x = var_8616)[name = tensor("segment_accum_309")]; + tensor var_8620_begin_0 = const()[name = tensor("op_8620_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8620_end_0 = const()[name = tensor("op_8620_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8620_end_mask_0 = const()[name = tensor("op_8620_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8620 = slice_by_index(begin = var_8620_begin_0, end = var_8620_end_0, end_mask = var_8620_end_mask_0, x = var_8614)[name = tensor("op_8620")]; + tensor var_8622 = add(x = segment_accum_309, y = var_8620)[name = tensor("op_8622")]; + tensor var_8624_begin_0 = const()[name = tensor("op_8624_begin_0"), val = tensor([0, 156000, 0])]; + tensor var_8624_end_0 = const()[name = tensor("op_8624_end_0"), val = tensor([1, 157000, 9])]; + tensor var_8624_end_mask_0 = const()[name = tensor("op_8624_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8624 = slice_by_index(begin = var_8624_begin_0, end = var_8624_end_0, end_mask = var_8624_end_mask_0, x = reshape_4)[name = tensor("op_8624")]; + tensor segment_accum_311_exclusive_0 = const()[name = tensor("segment_accum_311_exclusive_0"), val = tensor(false)]; + tensor segment_accum_311_reverse_0 = const()[name = tensor("segment_accum_311_reverse_0"), val = tensor(false)]; + tensor segment_accum_311 = cumsum(axis = var_7349, exclusive = segment_accum_311_exclusive_0, reverse = segment_accum_311_reverse_0, x = var_8624)[name = tensor("segment_accum_311")]; + tensor var_8628_begin_0 = const()[name = tensor("op_8628_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8628_end_0 = const()[name = tensor("op_8628_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8628_end_mask_0 = const()[name = tensor("op_8628_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8628 = slice_by_index(begin = var_8628_begin_0, end = var_8628_end_0, end_mask = var_8628_end_mask_0, x = var_8622)[name = tensor("op_8628")]; + tensor var_8630 = add(x = segment_accum_311, y = var_8628)[name = tensor("op_8630")]; + tensor var_8632_begin_0 = const()[name = tensor("op_8632_begin_0"), val = tensor([0, 157000, 0])]; + tensor var_8632_end_0 = const()[name = tensor("op_8632_end_0"), val = tensor([1, 158000, 9])]; + tensor var_8632_end_mask_0 = const()[name = tensor("op_8632_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8632 = slice_by_index(begin = var_8632_begin_0, end = var_8632_end_0, end_mask = var_8632_end_mask_0, x = reshape_4)[name = tensor("op_8632")]; + tensor segment_accum_313_exclusive_0 = const()[name = tensor("segment_accum_313_exclusive_0"), val = tensor(false)]; + tensor segment_accum_313_reverse_0 = const()[name = tensor("segment_accum_313_reverse_0"), val = tensor(false)]; + tensor segment_accum_313 = cumsum(axis = var_7349, exclusive = segment_accum_313_exclusive_0, reverse = segment_accum_313_reverse_0, x = var_8632)[name = tensor("segment_accum_313")]; + tensor var_8636_begin_0 = const()[name = tensor("op_8636_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8636_end_0 = const()[name = tensor("op_8636_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8636_end_mask_0 = const()[name = tensor("op_8636_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8636 = slice_by_index(begin = var_8636_begin_0, end = var_8636_end_0, end_mask = var_8636_end_mask_0, x = var_8630)[name = tensor("op_8636")]; + tensor var_8638 = add(x = segment_accum_313, y = var_8636)[name = tensor("op_8638")]; + tensor var_8640_begin_0 = const()[name = tensor("op_8640_begin_0"), val = tensor([0, 158000, 0])]; + tensor var_8640_end_0 = const()[name = tensor("op_8640_end_0"), val = tensor([1, 159000, 9])]; + tensor var_8640_end_mask_0 = const()[name = tensor("op_8640_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8640 = slice_by_index(begin = var_8640_begin_0, end = var_8640_end_0, end_mask = var_8640_end_mask_0, x = reshape_4)[name = tensor("op_8640")]; + tensor segment_accum_315_exclusive_0 = const()[name = tensor("segment_accum_315_exclusive_0"), val = tensor(false)]; + tensor segment_accum_315_reverse_0 = const()[name = tensor("segment_accum_315_reverse_0"), val = tensor(false)]; + tensor segment_accum_315 = cumsum(axis = var_7349, exclusive = segment_accum_315_exclusive_0, reverse = segment_accum_315_reverse_0, x = var_8640)[name = tensor("segment_accum_315")]; + tensor var_8644_begin_0 = const()[name = tensor("op_8644_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8644_end_0 = const()[name = tensor("op_8644_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8644_end_mask_0 = const()[name = tensor("op_8644_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8644 = slice_by_index(begin = var_8644_begin_0, end = var_8644_end_0, end_mask = var_8644_end_mask_0, x = var_8638)[name = tensor("op_8644")]; + tensor var_8646 = add(x = segment_accum_315, y = var_8644)[name = tensor("op_8646")]; + tensor var_8648_begin_0 = const()[name = tensor("op_8648_begin_0"), val = tensor([0, 159000, 0])]; + tensor var_8648_end_0 = const()[name = tensor("op_8648_end_0"), val = tensor([1, 160000, 9])]; + tensor var_8648_end_mask_0 = const()[name = tensor("op_8648_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8648 = slice_by_index(begin = var_8648_begin_0, end = var_8648_end_0, end_mask = var_8648_end_mask_0, x = reshape_4)[name = tensor("op_8648")]; + tensor segment_accum_317_exclusive_0 = const()[name = tensor("segment_accum_317_exclusive_0"), val = tensor(false)]; + tensor segment_accum_317_reverse_0 = const()[name = tensor("segment_accum_317_reverse_0"), val = tensor(false)]; + tensor segment_accum_317 = cumsum(axis = var_7349, exclusive = segment_accum_317_exclusive_0, reverse = segment_accum_317_reverse_0, x = var_8648)[name = tensor("segment_accum_317")]; + tensor var_8652_begin_0 = const()[name = tensor("op_8652_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8652_end_0 = const()[name = tensor("op_8652_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8652_end_mask_0 = const()[name = tensor("op_8652_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8652 = slice_by_index(begin = var_8652_begin_0, end = var_8652_end_0, end_mask = var_8652_end_mask_0, x = var_8646)[name = tensor("op_8652")]; + tensor var_8654 = add(x = segment_accum_317, y = var_8652)[name = tensor("op_8654")]; + tensor var_8656_begin_0 = const()[name = tensor("op_8656_begin_0"), val = tensor([0, 160000, 0])]; + tensor var_8656_end_0 = const()[name = tensor("op_8656_end_0"), val = tensor([1, 161000, 9])]; + tensor var_8656_end_mask_0 = const()[name = tensor("op_8656_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8656 = slice_by_index(begin = var_8656_begin_0, end = var_8656_end_0, end_mask = var_8656_end_mask_0, x = reshape_4)[name = tensor("op_8656")]; + tensor segment_accum_319_exclusive_0 = const()[name = tensor("segment_accum_319_exclusive_0"), val = tensor(false)]; + tensor segment_accum_319_reverse_0 = const()[name = tensor("segment_accum_319_reverse_0"), val = tensor(false)]; + tensor segment_accum_319 = cumsum(axis = var_7349, exclusive = segment_accum_319_exclusive_0, reverse = segment_accum_319_reverse_0, x = var_8656)[name = tensor("segment_accum_319")]; + tensor var_8660_begin_0 = const()[name = tensor("op_8660_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8660_end_0 = const()[name = tensor("op_8660_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8660_end_mask_0 = const()[name = tensor("op_8660_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8660 = slice_by_index(begin = var_8660_begin_0, end = var_8660_end_0, end_mask = var_8660_end_mask_0, x = var_8654)[name = tensor("op_8660")]; + tensor var_8662 = add(x = segment_accum_319, y = var_8660)[name = tensor("op_8662")]; + tensor var_8664_begin_0 = const()[name = tensor("op_8664_begin_0"), val = tensor([0, 161000, 0])]; + tensor var_8664_end_0 = const()[name = tensor("op_8664_end_0"), val = tensor([1, 162000, 9])]; + tensor var_8664_end_mask_0 = const()[name = tensor("op_8664_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8664 = slice_by_index(begin = var_8664_begin_0, end = var_8664_end_0, end_mask = var_8664_end_mask_0, x = reshape_4)[name = tensor("op_8664")]; + tensor segment_accum_321_exclusive_0 = const()[name = tensor("segment_accum_321_exclusive_0"), val = tensor(false)]; + tensor segment_accum_321_reverse_0 = const()[name = tensor("segment_accum_321_reverse_0"), val = tensor(false)]; + tensor segment_accum_321 = cumsum(axis = var_7349, exclusive = segment_accum_321_exclusive_0, reverse = segment_accum_321_reverse_0, x = var_8664)[name = tensor("segment_accum_321")]; + tensor var_8668_begin_0 = const()[name = tensor("op_8668_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8668_end_0 = const()[name = tensor("op_8668_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8668_end_mask_0 = const()[name = tensor("op_8668_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8668 = slice_by_index(begin = var_8668_begin_0, end = var_8668_end_0, end_mask = var_8668_end_mask_0, x = var_8662)[name = tensor("op_8668")]; + tensor var_8670 = add(x = segment_accum_321, y = var_8668)[name = tensor("op_8670")]; + tensor var_8672_begin_0 = const()[name = tensor("op_8672_begin_0"), val = tensor([0, 162000, 0])]; + tensor var_8672_end_0 = const()[name = tensor("op_8672_end_0"), val = tensor([1, 163000, 9])]; + tensor var_8672_end_mask_0 = const()[name = tensor("op_8672_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8672 = slice_by_index(begin = var_8672_begin_0, end = var_8672_end_0, end_mask = var_8672_end_mask_0, x = reshape_4)[name = tensor("op_8672")]; + tensor segment_accum_323_exclusive_0 = const()[name = tensor("segment_accum_323_exclusive_0"), val = tensor(false)]; + tensor segment_accum_323_reverse_0 = const()[name = tensor("segment_accum_323_reverse_0"), val = tensor(false)]; + tensor segment_accum_323 = cumsum(axis = var_7349, exclusive = segment_accum_323_exclusive_0, reverse = segment_accum_323_reverse_0, x = var_8672)[name = tensor("segment_accum_323")]; + tensor var_8676_begin_0 = const()[name = tensor("op_8676_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8676_end_0 = const()[name = tensor("op_8676_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8676_end_mask_0 = const()[name = tensor("op_8676_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8676 = slice_by_index(begin = var_8676_begin_0, end = var_8676_end_0, end_mask = var_8676_end_mask_0, x = var_8670)[name = tensor("op_8676")]; + tensor var_8678 = add(x = segment_accum_323, y = var_8676)[name = tensor("op_8678")]; + tensor var_8680_begin_0 = const()[name = tensor("op_8680_begin_0"), val = tensor([0, 163000, 0])]; + tensor var_8680_end_0 = const()[name = tensor("op_8680_end_0"), val = tensor([1, 164000, 9])]; + tensor var_8680_end_mask_0 = const()[name = tensor("op_8680_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8680 = slice_by_index(begin = var_8680_begin_0, end = var_8680_end_0, end_mask = var_8680_end_mask_0, x = reshape_4)[name = tensor("op_8680")]; + tensor segment_accum_325_exclusive_0 = const()[name = tensor("segment_accum_325_exclusive_0"), val = tensor(false)]; + tensor segment_accum_325_reverse_0 = const()[name = tensor("segment_accum_325_reverse_0"), val = tensor(false)]; + tensor segment_accum_325 = cumsum(axis = var_7349, exclusive = segment_accum_325_exclusive_0, reverse = segment_accum_325_reverse_0, x = var_8680)[name = tensor("segment_accum_325")]; + tensor var_8684_begin_0 = const()[name = tensor("op_8684_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8684_end_0 = const()[name = tensor("op_8684_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8684_end_mask_0 = const()[name = tensor("op_8684_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8684 = slice_by_index(begin = var_8684_begin_0, end = var_8684_end_0, end_mask = var_8684_end_mask_0, x = var_8678)[name = tensor("op_8684")]; + tensor var_8686 = add(x = segment_accum_325, y = var_8684)[name = tensor("op_8686")]; + tensor var_8688_begin_0 = const()[name = tensor("op_8688_begin_0"), val = tensor([0, 164000, 0])]; + tensor var_8688_end_0 = const()[name = tensor("op_8688_end_0"), val = tensor([1, 165000, 9])]; + tensor var_8688_end_mask_0 = const()[name = tensor("op_8688_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8688 = slice_by_index(begin = var_8688_begin_0, end = var_8688_end_0, end_mask = var_8688_end_mask_0, x = reshape_4)[name = tensor("op_8688")]; + tensor segment_accum_327_exclusive_0 = const()[name = tensor("segment_accum_327_exclusive_0"), val = tensor(false)]; + tensor segment_accum_327_reverse_0 = const()[name = tensor("segment_accum_327_reverse_0"), val = tensor(false)]; + tensor segment_accum_327 = cumsum(axis = var_7349, exclusive = segment_accum_327_exclusive_0, reverse = segment_accum_327_reverse_0, x = var_8688)[name = tensor("segment_accum_327")]; + tensor var_8692_begin_0 = const()[name = tensor("op_8692_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8692_end_0 = const()[name = tensor("op_8692_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8692_end_mask_0 = const()[name = tensor("op_8692_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8692 = slice_by_index(begin = var_8692_begin_0, end = var_8692_end_0, end_mask = var_8692_end_mask_0, x = var_8686)[name = tensor("op_8692")]; + tensor var_8694 = add(x = segment_accum_327, y = var_8692)[name = tensor("op_8694")]; + tensor var_8696_begin_0 = const()[name = tensor("op_8696_begin_0"), val = tensor([0, 165000, 0])]; + tensor var_8696_end_0 = const()[name = tensor("op_8696_end_0"), val = tensor([1, 166000, 9])]; + tensor var_8696_end_mask_0 = const()[name = tensor("op_8696_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8696 = slice_by_index(begin = var_8696_begin_0, end = var_8696_end_0, end_mask = var_8696_end_mask_0, x = reshape_4)[name = tensor("op_8696")]; + tensor segment_accum_329_exclusive_0 = const()[name = tensor("segment_accum_329_exclusive_0"), val = tensor(false)]; + tensor segment_accum_329_reverse_0 = const()[name = tensor("segment_accum_329_reverse_0"), val = tensor(false)]; + tensor segment_accum_329 = cumsum(axis = var_7349, exclusive = segment_accum_329_exclusive_0, reverse = segment_accum_329_reverse_0, x = var_8696)[name = tensor("segment_accum_329")]; + tensor var_8700_begin_0 = const()[name = tensor("op_8700_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8700_end_0 = const()[name = tensor("op_8700_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8700_end_mask_0 = const()[name = tensor("op_8700_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8700 = slice_by_index(begin = var_8700_begin_0, end = var_8700_end_0, end_mask = var_8700_end_mask_0, x = var_8694)[name = tensor("op_8700")]; + tensor var_8702 = add(x = segment_accum_329, y = var_8700)[name = tensor("op_8702")]; + tensor var_8704_begin_0 = const()[name = tensor("op_8704_begin_0"), val = tensor([0, 166000, 0])]; + tensor var_8704_end_0 = const()[name = tensor("op_8704_end_0"), val = tensor([1, 167000, 9])]; + tensor var_8704_end_mask_0 = const()[name = tensor("op_8704_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8704 = slice_by_index(begin = var_8704_begin_0, end = var_8704_end_0, end_mask = var_8704_end_mask_0, x = reshape_4)[name = tensor("op_8704")]; + tensor segment_accum_331_exclusive_0 = const()[name = tensor("segment_accum_331_exclusive_0"), val = tensor(false)]; + tensor segment_accum_331_reverse_0 = const()[name = tensor("segment_accum_331_reverse_0"), val = tensor(false)]; + tensor segment_accum_331 = cumsum(axis = var_7349, exclusive = segment_accum_331_exclusive_0, reverse = segment_accum_331_reverse_0, x = var_8704)[name = tensor("segment_accum_331")]; + tensor var_8708_begin_0 = const()[name = tensor("op_8708_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8708_end_0 = const()[name = tensor("op_8708_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8708_end_mask_0 = const()[name = tensor("op_8708_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8708 = slice_by_index(begin = var_8708_begin_0, end = var_8708_end_0, end_mask = var_8708_end_mask_0, x = var_8702)[name = tensor("op_8708")]; + tensor var_8710 = add(x = segment_accum_331, y = var_8708)[name = tensor("op_8710")]; + tensor var_8712_begin_0 = const()[name = tensor("op_8712_begin_0"), val = tensor([0, 167000, 0])]; + tensor var_8712_end_0 = const()[name = tensor("op_8712_end_0"), val = tensor([1, 168000, 9])]; + tensor var_8712_end_mask_0 = const()[name = tensor("op_8712_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8712 = slice_by_index(begin = var_8712_begin_0, end = var_8712_end_0, end_mask = var_8712_end_mask_0, x = reshape_4)[name = tensor("op_8712")]; + tensor segment_accum_333_exclusive_0 = const()[name = tensor("segment_accum_333_exclusive_0"), val = tensor(false)]; + tensor segment_accum_333_reverse_0 = const()[name = tensor("segment_accum_333_reverse_0"), val = tensor(false)]; + tensor segment_accum_333 = cumsum(axis = var_7349, exclusive = segment_accum_333_exclusive_0, reverse = segment_accum_333_reverse_0, x = var_8712)[name = tensor("segment_accum_333")]; + tensor var_8716_begin_0 = const()[name = tensor("op_8716_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8716_end_0 = const()[name = tensor("op_8716_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8716_end_mask_0 = const()[name = tensor("op_8716_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8716 = slice_by_index(begin = var_8716_begin_0, end = var_8716_end_0, end_mask = var_8716_end_mask_0, x = var_8710)[name = tensor("op_8716")]; + tensor var_8718 = add(x = segment_accum_333, y = var_8716)[name = tensor("op_8718")]; + tensor var_8720_begin_0 = const()[name = tensor("op_8720_begin_0"), val = tensor([0, 168000, 0])]; + tensor var_8720_end_0 = const()[name = tensor("op_8720_end_0"), val = tensor([1, 169000, 9])]; + tensor var_8720_end_mask_0 = const()[name = tensor("op_8720_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8720 = slice_by_index(begin = var_8720_begin_0, end = var_8720_end_0, end_mask = var_8720_end_mask_0, x = reshape_4)[name = tensor("op_8720")]; + tensor segment_accum_335_exclusive_0 = const()[name = tensor("segment_accum_335_exclusive_0"), val = tensor(false)]; + tensor segment_accum_335_reverse_0 = const()[name = tensor("segment_accum_335_reverse_0"), val = tensor(false)]; + tensor segment_accum_335 = cumsum(axis = var_7349, exclusive = segment_accum_335_exclusive_0, reverse = segment_accum_335_reverse_0, x = var_8720)[name = tensor("segment_accum_335")]; + tensor var_8724_begin_0 = const()[name = tensor("op_8724_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8724_end_0 = const()[name = tensor("op_8724_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8724_end_mask_0 = const()[name = tensor("op_8724_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8724 = slice_by_index(begin = var_8724_begin_0, end = var_8724_end_0, end_mask = var_8724_end_mask_0, x = var_8718)[name = tensor("op_8724")]; + tensor var_8726 = add(x = segment_accum_335, y = var_8724)[name = tensor("op_8726")]; + tensor var_8728_begin_0 = const()[name = tensor("op_8728_begin_0"), val = tensor([0, 169000, 0])]; + tensor var_8728_end_0 = const()[name = tensor("op_8728_end_0"), val = tensor([1, 170000, 9])]; + tensor var_8728_end_mask_0 = const()[name = tensor("op_8728_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8728 = slice_by_index(begin = var_8728_begin_0, end = var_8728_end_0, end_mask = var_8728_end_mask_0, x = reshape_4)[name = tensor("op_8728")]; + tensor segment_accum_337_exclusive_0 = const()[name = tensor("segment_accum_337_exclusive_0"), val = tensor(false)]; + tensor segment_accum_337_reverse_0 = const()[name = tensor("segment_accum_337_reverse_0"), val = tensor(false)]; + tensor segment_accum_337 = cumsum(axis = var_7349, exclusive = segment_accum_337_exclusive_0, reverse = segment_accum_337_reverse_0, x = var_8728)[name = tensor("segment_accum_337")]; + tensor var_8732_begin_0 = const()[name = tensor("op_8732_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8732_end_0 = const()[name = tensor("op_8732_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8732_end_mask_0 = const()[name = tensor("op_8732_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8732 = slice_by_index(begin = var_8732_begin_0, end = var_8732_end_0, end_mask = var_8732_end_mask_0, x = var_8726)[name = tensor("op_8732")]; + tensor var_8734 = add(x = segment_accum_337, y = var_8732)[name = tensor("op_8734")]; + tensor var_8736_begin_0 = const()[name = tensor("op_8736_begin_0"), val = tensor([0, 170000, 0])]; + tensor var_8736_end_0 = const()[name = tensor("op_8736_end_0"), val = tensor([1, 171000, 9])]; + tensor var_8736_end_mask_0 = const()[name = tensor("op_8736_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8736 = slice_by_index(begin = var_8736_begin_0, end = var_8736_end_0, end_mask = var_8736_end_mask_0, x = reshape_4)[name = tensor("op_8736")]; + tensor segment_accum_339_exclusive_0 = const()[name = tensor("segment_accum_339_exclusive_0"), val = tensor(false)]; + tensor segment_accum_339_reverse_0 = const()[name = tensor("segment_accum_339_reverse_0"), val = tensor(false)]; + tensor segment_accum_339 = cumsum(axis = var_7349, exclusive = segment_accum_339_exclusive_0, reverse = segment_accum_339_reverse_0, x = var_8736)[name = tensor("segment_accum_339")]; + tensor var_8740_begin_0 = const()[name = tensor("op_8740_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8740_end_0 = const()[name = tensor("op_8740_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8740_end_mask_0 = const()[name = tensor("op_8740_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8740 = slice_by_index(begin = var_8740_begin_0, end = var_8740_end_0, end_mask = var_8740_end_mask_0, x = var_8734)[name = tensor("op_8740")]; + tensor var_8742 = add(x = segment_accum_339, y = var_8740)[name = tensor("op_8742")]; + tensor var_8744_begin_0 = const()[name = tensor("op_8744_begin_0"), val = tensor([0, 171000, 0])]; + tensor var_8744_end_0 = const()[name = tensor("op_8744_end_0"), val = tensor([1, 172000, 9])]; + tensor var_8744_end_mask_0 = const()[name = tensor("op_8744_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8744 = slice_by_index(begin = var_8744_begin_0, end = var_8744_end_0, end_mask = var_8744_end_mask_0, x = reshape_4)[name = tensor("op_8744")]; + tensor segment_accum_341_exclusive_0 = const()[name = tensor("segment_accum_341_exclusive_0"), val = tensor(false)]; + tensor segment_accum_341_reverse_0 = const()[name = tensor("segment_accum_341_reverse_0"), val = tensor(false)]; + tensor segment_accum_341 = cumsum(axis = var_7349, exclusive = segment_accum_341_exclusive_0, reverse = segment_accum_341_reverse_0, x = var_8744)[name = tensor("segment_accum_341")]; + tensor var_8748_begin_0 = const()[name = tensor("op_8748_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8748_end_0 = const()[name = tensor("op_8748_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8748_end_mask_0 = const()[name = tensor("op_8748_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8748 = slice_by_index(begin = var_8748_begin_0, end = var_8748_end_0, end_mask = var_8748_end_mask_0, x = var_8742)[name = tensor("op_8748")]; + tensor var_8750 = add(x = segment_accum_341, y = var_8748)[name = tensor("op_8750")]; + tensor var_8752_begin_0 = const()[name = tensor("op_8752_begin_0"), val = tensor([0, 172000, 0])]; + tensor var_8752_end_0 = const()[name = tensor("op_8752_end_0"), val = tensor([1, 173000, 9])]; + tensor var_8752_end_mask_0 = const()[name = tensor("op_8752_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8752 = slice_by_index(begin = var_8752_begin_0, end = var_8752_end_0, end_mask = var_8752_end_mask_0, x = reshape_4)[name = tensor("op_8752")]; + tensor segment_accum_343_exclusive_0 = const()[name = tensor("segment_accum_343_exclusive_0"), val = tensor(false)]; + tensor segment_accum_343_reverse_0 = const()[name = tensor("segment_accum_343_reverse_0"), val = tensor(false)]; + tensor segment_accum_343 = cumsum(axis = var_7349, exclusive = segment_accum_343_exclusive_0, reverse = segment_accum_343_reverse_0, x = var_8752)[name = tensor("segment_accum_343")]; + tensor var_8756_begin_0 = const()[name = tensor("op_8756_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8756_end_0 = const()[name = tensor("op_8756_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8756_end_mask_0 = const()[name = tensor("op_8756_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8756 = slice_by_index(begin = var_8756_begin_0, end = var_8756_end_0, end_mask = var_8756_end_mask_0, x = var_8750)[name = tensor("op_8756")]; + tensor var_8758 = add(x = segment_accum_343, y = var_8756)[name = tensor("op_8758")]; + tensor var_8760_begin_0 = const()[name = tensor("op_8760_begin_0"), val = tensor([0, 173000, 0])]; + tensor var_8760_end_0 = const()[name = tensor("op_8760_end_0"), val = tensor([1, 174000, 9])]; + tensor var_8760_end_mask_0 = const()[name = tensor("op_8760_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8760 = slice_by_index(begin = var_8760_begin_0, end = var_8760_end_0, end_mask = var_8760_end_mask_0, x = reshape_4)[name = tensor("op_8760")]; + tensor segment_accum_345_exclusive_0 = const()[name = tensor("segment_accum_345_exclusive_0"), val = tensor(false)]; + tensor segment_accum_345_reverse_0 = const()[name = tensor("segment_accum_345_reverse_0"), val = tensor(false)]; + tensor segment_accum_345 = cumsum(axis = var_7349, exclusive = segment_accum_345_exclusive_0, reverse = segment_accum_345_reverse_0, x = var_8760)[name = tensor("segment_accum_345")]; + tensor var_8764_begin_0 = const()[name = tensor("op_8764_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8764_end_0 = const()[name = tensor("op_8764_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8764_end_mask_0 = const()[name = tensor("op_8764_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8764 = slice_by_index(begin = var_8764_begin_0, end = var_8764_end_0, end_mask = var_8764_end_mask_0, x = var_8758)[name = tensor("op_8764")]; + tensor var_8766 = add(x = segment_accum_345, y = var_8764)[name = tensor("op_8766")]; + tensor var_8768_begin_0 = const()[name = tensor("op_8768_begin_0"), val = tensor([0, 174000, 0])]; + tensor var_8768_end_0 = const()[name = tensor("op_8768_end_0"), val = tensor([1, 175000, 9])]; + tensor var_8768_end_mask_0 = const()[name = tensor("op_8768_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8768 = slice_by_index(begin = var_8768_begin_0, end = var_8768_end_0, end_mask = var_8768_end_mask_0, x = reshape_4)[name = tensor("op_8768")]; + tensor segment_accum_347_exclusive_0 = const()[name = tensor("segment_accum_347_exclusive_0"), val = tensor(false)]; + tensor segment_accum_347_reverse_0 = const()[name = tensor("segment_accum_347_reverse_0"), val = tensor(false)]; + tensor segment_accum_347 = cumsum(axis = var_7349, exclusive = segment_accum_347_exclusive_0, reverse = segment_accum_347_reverse_0, x = var_8768)[name = tensor("segment_accum_347")]; + tensor var_8772_begin_0 = const()[name = tensor("op_8772_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8772_end_0 = const()[name = tensor("op_8772_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8772_end_mask_0 = const()[name = tensor("op_8772_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8772 = slice_by_index(begin = var_8772_begin_0, end = var_8772_end_0, end_mask = var_8772_end_mask_0, x = var_8766)[name = tensor("op_8772")]; + tensor var_8774 = add(x = segment_accum_347, y = var_8772)[name = tensor("op_8774")]; + tensor var_8776_begin_0 = const()[name = tensor("op_8776_begin_0"), val = tensor([0, 175000, 0])]; + tensor var_8776_end_0 = const()[name = tensor("op_8776_end_0"), val = tensor([1, 176000, 9])]; + tensor var_8776_end_mask_0 = const()[name = tensor("op_8776_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8776 = slice_by_index(begin = var_8776_begin_0, end = var_8776_end_0, end_mask = var_8776_end_mask_0, x = reshape_4)[name = tensor("op_8776")]; + tensor segment_accum_349_exclusive_0 = const()[name = tensor("segment_accum_349_exclusive_0"), val = tensor(false)]; + tensor segment_accum_349_reverse_0 = const()[name = tensor("segment_accum_349_reverse_0"), val = tensor(false)]; + tensor segment_accum_349 = cumsum(axis = var_7349, exclusive = segment_accum_349_exclusive_0, reverse = segment_accum_349_reverse_0, x = var_8776)[name = tensor("segment_accum_349")]; + tensor var_8780_begin_0 = const()[name = tensor("op_8780_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8780_end_0 = const()[name = tensor("op_8780_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8780_end_mask_0 = const()[name = tensor("op_8780_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8780 = slice_by_index(begin = var_8780_begin_0, end = var_8780_end_0, end_mask = var_8780_end_mask_0, x = var_8774)[name = tensor("op_8780")]; + tensor var_8782 = add(x = segment_accum_349, y = var_8780)[name = tensor("op_8782")]; + tensor var_8784_begin_0 = const()[name = tensor("op_8784_begin_0"), val = tensor([0, 176000, 0])]; + tensor var_8784_end_0 = const()[name = tensor("op_8784_end_0"), val = tensor([1, 177000, 9])]; + tensor var_8784_end_mask_0 = const()[name = tensor("op_8784_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8784 = slice_by_index(begin = var_8784_begin_0, end = var_8784_end_0, end_mask = var_8784_end_mask_0, x = reshape_4)[name = tensor("op_8784")]; + tensor segment_accum_351_exclusive_0 = const()[name = tensor("segment_accum_351_exclusive_0"), val = tensor(false)]; + tensor segment_accum_351_reverse_0 = const()[name = tensor("segment_accum_351_reverse_0"), val = tensor(false)]; + tensor segment_accum_351 = cumsum(axis = var_7349, exclusive = segment_accum_351_exclusive_0, reverse = segment_accum_351_reverse_0, x = var_8784)[name = tensor("segment_accum_351")]; + tensor var_8788_begin_0 = const()[name = tensor("op_8788_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8788_end_0 = const()[name = tensor("op_8788_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8788_end_mask_0 = const()[name = tensor("op_8788_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8788 = slice_by_index(begin = var_8788_begin_0, end = var_8788_end_0, end_mask = var_8788_end_mask_0, x = var_8782)[name = tensor("op_8788")]; + tensor var_8790 = add(x = segment_accum_351, y = var_8788)[name = tensor("op_8790")]; + tensor var_8792_begin_0 = const()[name = tensor("op_8792_begin_0"), val = tensor([0, 177000, 0])]; + tensor var_8792_end_0 = const()[name = tensor("op_8792_end_0"), val = tensor([1, 178000, 9])]; + tensor var_8792_end_mask_0 = const()[name = tensor("op_8792_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8792 = slice_by_index(begin = var_8792_begin_0, end = var_8792_end_0, end_mask = var_8792_end_mask_0, x = reshape_4)[name = tensor("op_8792")]; + tensor segment_accum_353_exclusive_0 = const()[name = tensor("segment_accum_353_exclusive_0"), val = tensor(false)]; + tensor segment_accum_353_reverse_0 = const()[name = tensor("segment_accum_353_reverse_0"), val = tensor(false)]; + tensor segment_accum_353 = cumsum(axis = var_7349, exclusive = segment_accum_353_exclusive_0, reverse = segment_accum_353_reverse_0, x = var_8792)[name = tensor("segment_accum_353")]; + tensor var_8796_begin_0 = const()[name = tensor("op_8796_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8796_end_0 = const()[name = tensor("op_8796_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8796_end_mask_0 = const()[name = tensor("op_8796_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8796 = slice_by_index(begin = var_8796_begin_0, end = var_8796_end_0, end_mask = var_8796_end_mask_0, x = var_8790)[name = tensor("op_8796")]; + tensor var_8798 = add(x = segment_accum_353, y = var_8796)[name = tensor("op_8798")]; + tensor var_8800_begin_0 = const()[name = tensor("op_8800_begin_0"), val = tensor([0, 178000, 0])]; + tensor var_8800_end_0 = const()[name = tensor("op_8800_end_0"), val = tensor([1, 179000, 9])]; + tensor var_8800_end_mask_0 = const()[name = tensor("op_8800_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8800 = slice_by_index(begin = var_8800_begin_0, end = var_8800_end_0, end_mask = var_8800_end_mask_0, x = reshape_4)[name = tensor("op_8800")]; + tensor segment_accum_355_exclusive_0 = const()[name = tensor("segment_accum_355_exclusive_0"), val = tensor(false)]; + tensor segment_accum_355_reverse_0 = const()[name = tensor("segment_accum_355_reverse_0"), val = tensor(false)]; + tensor segment_accum_355 = cumsum(axis = var_7349, exclusive = segment_accum_355_exclusive_0, reverse = segment_accum_355_reverse_0, x = var_8800)[name = tensor("segment_accum_355")]; + tensor var_8804_begin_0 = const()[name = tensor("op_8804_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8804_end_0 = const()[name = tensor("op_8804_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8804_end_mask_0 = const()[name = tensor("op_8804_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8804 = slice_by_index(begin = var_8804_begin_0, end = var_8804_end_0, end_mask = var_8804_end_mask_0, x = var_8798)[name = tensor("op_8804")]; + tensor var_8806 = add(x = segment_accum_355, y = var_8804)[name = tensor("op_8806")]; + tensor var_8808_begin_0 = const()[name = tensor("op_8808_begin_0"), val = tensor([0, 179000, 0])]; + tensor var_8808_end_0 = const()[name = tensor("op_8808_end_0"), val = tensor([1, 180000, 9])]; + tensor var_8808_end_mask_0 = const()[name = tensor("op_8808_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8808 = slice_by_index(begin = var_8808_begin_0, end = var_8808_end_0, end_mask = var_8808_end_mask_0, x = reshape_4)[name = tensor("op_8808")]; + tensor segment_accum_357_exclusive_0 = const()[name = tensor("segment_accum_357_exclusive_0"), val = tensor(false)]; + tensor segment_accum_357_reverse_0 = const()[name = tensor("segment_accum_357_reverse_0"), val = tensor(false)]; + tensor segment_accum_357 = cumsum(axis = var_7349, exclusive = segment_accum_357_exclusive_0, reverse = segment_accum_357_reverse_0, x = var_8808)[name = tensor("segment_accum_357")]; + tensor var_8812_begin_0 = const()[name = tensor("op_8812_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8812_end_0 = const()[name = tensor("op_8812_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8812_end_mask_0 = const()[name = tensor("op_8812_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8812 = slice_by_index(begin = var_8812_begin_0, end = var_8812_end_0, end_mask = var_8812_end_mask_0, x = var_8806)[name = tensor("op_8812")]; + tensor var_8814 = add(x = segment_accum_357, y = var_8812)[name = tensor("op_8814")]; + tensor var_8816_begin_0 = const()[name = tensor("op_8816_begin_0"), val = tensor([0, 180000, 0])]; + tensor var_8816_end_0 = const()[name = tensor("op_8816_end_0"), val = tensor([1, 181000, 9])]; + tensor var_8816_end_mask_0 = const()[name = tensor("op_8816_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8816 = slice_by_index(begin = var_8816_begin_0, end = var_8816_end_0, end_mask = var_8816_end_mask_0, x = reshape_4)[name = tensor("op_8816")]; + tensor segment_accum_359_exclusive_0 = const()[name = tensor("segment_accum_359_exclusive_0"), val = tensor(false)]; + tensor segment_accum_359_reverse_0 = const()[name = tensor("segment_accum_359_reverse_0"), val = tensor(false)]; + tensor segment_accum_359 = cumsum(axis = var_7349, exclusive = segment_accum_359_exclusive_0, reverse = segment_accum_359_reverse_0, x = var_8816)[name = tensor("segment_accum_359")]; + tensor var_8820_begin_0 = const()[name = tensor("op_8820_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8820_end_0 = const()[name = tensor("op_8820_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8820_end_mask_0 = const()[name = tensor("op_8820_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8820 = slice_by_index(begin = var_8820_begin_0, end = var_8820_end_0, end_mask = var_8820_end_mask_0, x = var_8814)[name = tensor("op_8820")]; + tensor var_8822 = add(x = segment_accum_359, y = var_8820)[name = tensor("op_8822")]; + tensor var_8824_begin_0 = const()[name = tensor("op_8824_begin_0"), val = tensor([0, 181000, 0])]; + tensor var_8824_end_0 = const()[name = tensor("op_8824_end_0"), val = tensor([1, 182000, 9])]; + tensor var_8824_end_mask_0 = const()[name = tensor("op_8824_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8824 = slice_by_index(begin = var_8824_begin_0, end = var_8824_end_0, end_mask = var_8824_end_mask_0, x = reshape_4)[name = tensor("op_8824")]; + tensor segment_accum_361_exclusive_0 = const()[name = tensor("segment_accum_361_exclusive_0"), val = tensor(false)]; + tensor segment_accum_361_reverse_0 = const()[name = tensor("segment_accum_361_reverse_0"), val = tensor(false)]; + tensor segment_accum_361 = cumsum(axis = var_7349, exclusive = segment_accum_361_exclusive_0, reverse = segment_accum_361_reverse_0, x = var_8824)[name = tensor("segment_accum_361")]; + tensor var_8828_begin_0 = const()[name = tensor("op_8828_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8828_end_0 = const()[name = tensor("op_8828_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8828_end_mask_0 = const()[name = tensor("op_8828_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8828 = slice_by_index(begin = var_8828_begin_0, end = var_8828_end_0, end_mask = var_8828_end_mask_0, x = var_8822)[name = tensor("op_8828")]; + tensor var_8830 = add(x = segment_accum_361, y = var_8828)[name = tensor("op_8830")]; + tensor var_8832_begin_0 = const()[name = tensor("op_8832_begin_0"), val = tensor([0, 182000, 0])]; + tensor var_8832_end_0 = const()[name = tensor("op_8832_end_0"), val = tensor([1, 183000, 9])]; + tensor var_8832_end_mask_0 = const()[name = tensor("op_8832_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8832 = slice_by_index(begin = var_8832_begin_0, end = var_8832_end_0, end_mask = var_8832_end_mask_0, x = reshape_4)[name = tensor("op_8832")]; + tensor segment_accum_363_exclusive_0 = const()[name = tensor("segment_accum_363_exclusive_0"), val = tensor(false)]; + tensor segment_accum_363_reverse_0 = const()[name = tensor("segment_accum_363_reverse_0"), val = tensor(false)]; + tensor segment_accum_363 = cumsum(axis = var_7349, exclusive = segment_accum_363_exclusive_0, reverse = segment_accum_363_reverse_0, x = var_8832)[name = tensor("segment_accum_363")]; + tensor var_8836_begin_0 = const()[name = tensor("op_8836_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8836_end_0 = const()[name = tensor("op_8836_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8836_end_mask_0 = const()[name = tensor("op_8836_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8836 = slice_by_index(begin = var_8836_begin_0, end = var_8836_end_0, end_mask = var_8836_end_mask_0, x = var_8830)[name = tensor("op_8836")]; + tensor var_8838 = add(x = segment_accum_363, y = var_8836)[name = tensor("op_8838")]; + tensor var_8840_begin_0 = const()[name = tensor("op_8840_begin_0"), val = tensor([0, 183000, 0])]; + tensor var_8840_end_0 = const()[name = tensor("op_8840_end_0"), val = tensor([1, 184000, 9])]; + tensor var_8840_end_mask_0 = const()[name = tensor("op_8840_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8840 = slice_by_index(begin = var_8840_begin_0, end = var_8840_end_0, end_mask = var_8840_end_mask_0, x = reshape_4)[name = tensor("op_8840")]; + tensor segment_accum_365_exclusive_0 = const()[name = tensor("segment_accum_365_exclusive_0"), val = tensor(false)]; + tensor segment_accum_365_reverse_0 = const()[name = tensor("segment_accum_365_reverse_0"), val = tensor(false)]; + tensor segment_accum_365 = cumsum(axis = var_7349, exclusive = segment_accum_365_exclusive_0, reverse = segment_accum_365_reverse_0, x = var_8840)[name = tensor("segment_accum_365")]; + tensor var_8844_begin_0 = const()[name = tensor("op_8844_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8844_end_0 = const()[name = tensor("op_8844_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8844_end_mask_0 = const()[name = tensor("op_8844_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8844 = slice_by_index(begin = var_8844_begin_0, end = var_8844_end_0, end_mask = var_8844_end_mask_0, x = var_8838)[name = tensor("op_8844")]; + tensor var_8846 = add(x = segment_accum_365, y = var_8844)[name = tensor("op_8846")]; + tensor var_8848_begin_0 = const()[name = tensor("op_8848_begin_0"), val = tensor([0, 184000, 0])]; + tensor var_8848_end_0 = const()[name = tensor("op_8848_end_0"), val = tensor([1, 185000, 9])]; + tensor var_8848_end_mask_0 = const()[name = tensor("op_8848_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8848 = slice_by_index(begin = var_8848_begin_0, end = var_8848_end_0, end_mask = var_8848_end_mask_0, x = reshape_4)[name = tensor("op_8848")]; + tensor segment_accum_367_exclusive_0 = const()[name = tensor("segment_accum_367_exclusive_0"), val = tensor(false)]; + tensor segment_accum_367_reverse_0 = const()[name = tensor("segment_accum_367_reverse_0"), val = tensor(false)]; + tensor segment_accum_367 = cumsum(axis = var_7349, exclusive = segment_accum_367_exclusive_0, reverse = segment_accum_367_reverse_0, x = var_8848)[name = tensor("segment_accum_367")]; + tensor var_8852_begin_0 = const()[name = tensor("op_8852_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8852_end_0 = const()[name = tensor("op_8852_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8852_end_mask_0 = const()[name = tensor("op_8852_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8852 = slice_by_index(begin = var_8852_begin_0, end = var_8852_end_0, end_mask = var_8852_end_mask_0, x = var_8846)[name = tensor("op_8852")]; + tensor var_8854 = add(x = segment_accum_367, y = var_8852)[name = tensor("op_8854")]; + tensor var_8856_begin_0 = const()[name = tensor("op_8856_begin_0"), val = tensor([0, 185000, 0])]; + tensor var_8856_end_0 = const()[name = tensor("op_8856_end_0"), val = tensor([1, 186000, 9])]; + tensor var_8856_end_mask_0 = const()[name = tensor("op_8856_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8856 = slice_by_index(begin = var_8856_begin_0, end = var_8856_end_0, end_mask = var_8856_end_mask_0, x = reshape_4)[name = tensor("op_8856")]; + tensor segment_accum_369_exclusive_0 = const()[name = tensor("segment_accum_369_exclusive_0"), val = tensor(false)]; + tensor segment_accum_369_reverse_0 = const()[name = tensor("segment_accum_369_reverse_0"), val = tensor(false)]; + tensor segment_accum_369 = cumsum(axis = var_7349, exclusive = segment_accum_369_exclusive_0, reverse = segment_accum_369_reverse_0, x = var_8856)[name = tensor("segment_accum_369")]; + tensor var_8860_begin_0 = const()[name = tensor("op_8860_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8860_end_0 = const()[name = tensor("op_8860_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8860_end_mask_0 = const()[name = tensor("op_8860_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8860 = slice_by_index(begin = var_8860_begin_0, end = var_8860_end_0, end_mask = var_8860_end_mask_0, x = var_8854)[name = tensor("op_8860")]; + tensor var_8862 = add(x = segment_accum_369, y = var_8860)[name = tensor("op_8862")]; + tensor var_8864_begin_0 = const()[name = tensor("op_8864_begin_0"), val = tensor([0, 186000, 0])]; + tensor var_8864_end_0 = const()[name = tensor("op_8864_end_0"), val = tensor([1, 187000, 9])]; + tensor var_8864_end_mask_0 = const()[name = tensor("op_8864_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8864 = slice_by_index(begin = var_8864_begin_0, end = var_8864_end_0, end_mask = var_8864_end_mask_0, x = reshape_4)[name = tensor("op_8864")]; + tensor segment_accum_371_exclusive_0 = const()[name = tensor("segment_accum_371_exclusive_0"), val = tensor(false)]; + tensor segment_accum_371_reverse_0 = const()[name = tensor("segment_accum_371_reverse_0"), val = tensor(false)]; + tensor segment_accum_371 = cumsum(axis = var_7349, exclusive = segment_accum_371_exclusive_0, reverse = segment_accum_371_reverse_0, x = var_8864)[name = tensor("segment_accum_371")]; + tensor var_8868_begin_0 = const()[name = tensor("op_8868_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8868_end_0 = const()[name = tensor("op_8868_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8868_end_mask_0 = const()[name = tensor("op_8868_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8868 = slice_by_index(begin = var_8868_begin_0, end = var_8868_end_0, end_mask = var_8868_end_mask_0, x = var_8862)[name = tensor("op_8868")]; + tensor var_8870 = add(x = segment_accum_371, y = var_8868)[name = tensor("op_8870")]; + tensor var_8872_begin_0 = const()[name = tensor("op_8872_begin_0"), val = tensor([0, 187000, 0])]; + tensor var_8872_end_0 = const()[name = tensor("op_8872_end_0"), val = tensor([1, 188000, 9])]; + tensor var_8872_end_mask_0 = const()[name = tensor("op_8872_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8872 = slice_by_index(begin = var_8872_begin_0, end = var_8872_end_0, end_mask = var_8872_end_mask_0, x = reshape_4)[name = tensor("op_8872")]; + tensor segment_accum_373_exclusive_0 = const()[name = tensor("segment_accum_373_exclusive_0"), val = tensor(false)]; + tensor segment_accum_373_reverse_0 = const()[name = tensor("segment_accum_373_reverse_0"), val = tensor(false)]; + tensor segment_accum_373 = cumsum(axis = var_7349, exclusive = segment_accum_373_exclusive_0, reverse = segment_accum_373_reverse_0, x = var_8872)[name = tensor("segment_accum_373")]; + tensor var_8876_begin_0 = const()[name = tensor("op_8876_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8876_end_0 = const()[name = tensor("op_8876_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8876_end_mask_0 = const()[name = tensor("op_8876_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8876 = slice_by_index(begin = var_8876_begin_0, end = var_8876_end_0, end_mask = var_8876_end_mask_0, x = var_8870)[name = tensor("op_8876")]; + tensor var_8878 = add(x = segment_accum_373, y = var_8876)[name = tensor("op_8878")]; + tensor var_8880_begin_0 = const()[name = tensor("op_8880_begin_0"), val = tensor([0, 188000, 0])]; + tensor var_8880_end_0 = const()[name = tensor("op_8880_end_0"), val = tensor([1, 189000, 9])]; + tensor var_8880_end_mask_0 = const()[name = tensor("op_8880_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8880 = slice_by_index(begin = var_8880_begin_0, end = var_8880_end_0, end_mask = var_8880_end_mask_0, x = reshape_4)[name = tensor("op_8880")]; + tensor segment_accum_375_exclusive_0 = const()[name = tensor("segment_accum_375_exclusive_0"), val = tensor(false)]; + tensor segment_accum_375_reverse_0 = const()[name = tensor("segment_accum_375_reverse_0"), val = tensor(false)]; + tensor segment_accum_375 = cumsum(axis = var_7349, exclusive = segment_accum_375_exclusive_0, reverse = segment_accum_375_reverse_0, x = var_8880)[name = tensor("segment_accum_375")]; + tensor var_8884_begin_0 = const()[name = tensor("op_8884_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8884_end_0 = const()[name = tensor("op_8884_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8884_end_mask_0 = const()[name = tensor("op_8884_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8884 = slice_by_index(begin = var_8884_begin_0, end = var_8884_end_0, end_mask = var_8884_end_mask_0, x = var_8878)[name = tensor("op_8884")]; + tensor var_8886 = add(x = segment_accum_375, y = var_8884)[name = tensor("op_8886")]; + tensor var_8888_begin_0 = const()[name = tensor("op_8888_begin_0"), val = tensor([0, 189000, 0])]; + tensor var_8888_end_0 = const()[name = tensor("op_8888_end_0"), val = tensor([1, 190000, 9])]; + tensor var_8888_end_mask_0 = const()[name = tensor("op_8888_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8888 = slice_by_index(begin = var_8888_begin_0, end = var_8888_end_0, end_mask = var_8888_end_mask_0, x = reshape_4)[name = tensor("op_8888")]; + tensor segment_accum_377_exclusive_0 = const()[name = tensor("segment_accum_377_exclusive_0"), val = tensor(false)]; + tensor segment_accum_377_reverse_0 = const()[name = tensor("segment_accum_377_reverse_0"), val = tensor(false)]; + tensor segment_accum_377 = cumsum(axis = var_7349, exclusive = segment_accum_377_exclusive_0, reverse = segment_accum_377_reverse_0, x = var_8888)[name = tensor("segment_accum_377")]; + tensor var_8892_begin_0 = const()[name = tensor("op_8892_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8892_end_0 = const()[name = tensor("op_8892_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8892_end_mask_0 = const()[name = tensor("op_8892_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8892 = slice_by_index(begin = var_8892_begin_0, end = var_8892_end_0, end_mask = var_8892_end_mask_0, x = var_8886)[name = tensor("op_8892")]; + tensor var_8894 = add(x = segment_accum_377, y = var_8892)[name = tensor("op_8894")]; + tensor var_8896_begin_0 = const()[name = tensor("op_8896_begin_0"), val = tensor([0, 190000, 0])]; + tensor var_8896_end_0 = const()[name = tensor("op_8896_end_0"), val = tensor([1, 191000, 9])]; + tensor var_8896_end_mask_0 = const()[name = tensor("op_8896_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8896 = slice_by_index(begin = var_8896_begin_0, end = var_8896_end_0, end_mask = var_8896_end_mask_0, x = reshape_4)[name = tensor("op_8896")]; + tensor segment_accum_379_exclusive_0 = const()[name = tensor("segment_accum_379_exclusive_0"), val = tensor(false)]; + tensor segment_accum_379_reverse_0 = const()[name = tensor("segment_accum_379_reverse_0"), val = tensor(false)]; + tensor segment_accum_379 = cumsum(axis = var_7349, exclusive = segment_accum_379_exclusive_0, reverse = segment_accum_379_reverse_0, x = var_8896)[name = tensor("segment_accum_379")]; + tensor var_8900_begin_0 = const()[name = tensor("op_8900_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8900_end_0 = const()[name = tensor("op_8900_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8900_end_mask_0 = const()[name = tensor("op_8900_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8900 = slice_by_index(begin = var_8900_begin_0, end = var_8900_end_0, end_mask = var_8900_end_mask_0, x = var_8894)[name = tensor("op_8900")]; + tensor var_8902 = add(x = segment_accum_379, y = var_8900)[name = tensor("op_8902")]; + tensor var_8904_begin_0 = const()[name = tensor("op_8904_begin_0"), val = tensor([0, 191000, 0])]; + tensor var_8904_end_0 = const()[name = tensor("op_8904_end_0"), val = tensor([1, 192000, 9])]; + tensor var_8904_end_mask_0 = const()[name = tensor("op_8904_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8904 = slice_by_index(begin = var_8904_begin_0, end = var_8904_end_0, end_mask = var_8904_end_mask_0, x = reshape_4)[name = tensor("op_8904")]; + tensor segment_accum_381_exclusive_0 = const()[name = tensor("segment_accum_381_exclusive_0"), val = tensor(false)]; + tensor segment_accum_381_reverse_0 = const()[name = tensor("segment_accum_381_reverse_0"), val = tensor(false)]; + tensor segment_accum_381 = cumsum(axis = var_7349, exclusive = segment_accum_381_exclusive_0, reverse = segment_accum_381_reverse_0, x = var_8904)[name = tensor("segment_accum_381")]; + tensor var_8908_begin_0 = const()[name = tensor("op_8908_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8908_end_0 = const()[name = tensor("op_8908_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8908_end_mask_0 = const()[name = tensor("op_8908_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8908 = slice_by_index(begin = var_8908_begin_0, end = var_8908_end_0, end_mask = var_8908_end_mask_0, x = var_8902)[name = tensor("op_8908")]; + tensor var_8910 = add(x = segment_accum_381, y = var_8908)[name = tensor("op_8910")]; + tensor var_8912_begin_0 = const()[name = tensor("op_8912_begin_0"), val = tensor([0, 192000, 0])]; + tensor var_8912_end_0 = const()[name = tensor("op_8912_end_0"), val = tensor([1, 193000, 9])]; + tensor var_8912_end_mask_0 = const()[name = tensor("op_8912_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8912 = slice_by_index(begin = var_8912_begin_0, end = var_8912_end_0, end_mask = var_8912_end_mask_0, x = reshape_4)[name = tensor("op_8912")]; + tensor segment_accum_383_exclusive_0 = const()[name = tensor("segment_accum_383_exclusive_0"), val = tensor(false)]; + tensor segment_accum_383_reverse_0 = const()[name = tensor("segment_accum_383_reverse_0"), val = tensor(false)]; + tensor segment_accum_383 = cumsum(axis = var_7349, exclusive = segment_accum_383_exclusive_0, reverse = segment_accum_383_reverse_0, x = var_8912)[name = tensor("segment_accum_383")]; + tensor var_8916_begin_0 = const()[name = tensor("op_8916_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8916_end_0 = const()[name = tensor("op_8916_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8916_end_mask_0 = const()[name = tensor("op_8916_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8916 = slice_by_index(begin = var_8916_begin_0, end = var_8916_end_0, end_mask = var_8916_end_mask_0, x = var_8910)[name = tensor("op_8916")]; + tensor var_8918 = add(x = segment_accum_383, y = var_8916)[name = tensor("op_8918")]; + tensor var_8920_begin_0 = const()[name = tensor("op_8920_begin_0"), val = tensor([0, 193000, 0])]; + tensor var_8920_end_0 = const()[name = tensor("op_8920_end_0"), val = tensor([1, 194000, 9])]; + tensor var_8920_end_mask_0 = const()[name = tensor("op_8920_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8920 = slice_by_index(begin = var_8920_begin_0, end = var_8920_end_0, end_mask = var_8920_end_mask_0, x = reshape_4)[name = tensor("op_8920")]; + tensor segment_accum_385_exclusive_0 = const()[name = tensor("segment_accum_385_exclusive_0"), val = tensor(false)]; + tensor segment_accum_385_reverse_0 = const()[name = tensor("segment_accum_385_reverse_0"), val = tensor(false)]; + tensor segment_accum_385 = cumsum(axis = var_7349, exclusive = segment_accum_385_exclusive_0, reverse = segment_accum_385_reverse_0, x = var_8920)[name = tensor("segment_accum_385")]; + tensor var_8924_begin_0 = const()[name = tensor("op_8924_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8924_end_0 = const()[name = tensor("op_8924_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8924_end_mask_0 = const()[name = tensor("op_8924_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8924 = slice_by_index(begin = var_8924_begin_0, end = var_8924_end_0, end_mask = var_8924_end_mask_0, x = var_8918)[name = tensor("op_8924")]; + tensor var_8926 = add(x = segment_accum_385, y = var_8924)[name = tensor("op_8926")]; + tensor var_8928_begin_0 = const()[name = tensor("op_8928_begin_0"), val = tensor([0, 194000, 0])]; + tensor var_8928_end_0 = const()[name = tensor("op_8928_end_0"), val = tensor([1, 195000, 9])]; + tensor var_8928_end_mask_0 = const()[name = tensor("op_8928_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8928 = slice_by_index(begin = var_8928_begin_0, end = var_8928_end_0, end_mask = var_8928_end_mask_0, x = reshape_4)[name = tensor("op_8928")]; + tensor segment_accum_387_exclusive_0 = const()[name = tensor("segment_accum_387_exclusive_0"), val = tensor(false)]; + tensor segment_accum_387_reverse_0 = const()[name = tensor("segment_accum_387_reverse_0"), val = tensor(false)]; + tensor segment_accum_387 = cumsum(axis = var_7349, exclusive = segment_accum_387_exclusive_0, reverse = segment_accum_387_reverse_0, x = var_8928)[name = tensor("segment_accum_387")]; + tensor var_8932_begin_0 = const()[name = tensor("op_8932_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8932_end_0 = const()[name = tensor("op_8932_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8932_end_mask_0 = const()[name = tensor("op_8932_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8932 = slice_by_index(begin = var_8932_begin_0, end = var_8932_end_0, end_mask = var_8932_end_mask_0, x = var_8926)[name = tensor("op_8932")]; + tensor var_8934 = add(x = segment_accum_387, y = var_8932)[name = tensor("op_8934")]; + tensor var_8936_begin_0 = const()[name = tensor("op_8936_begin_0"), val = tensor([0, 195000, 0])]; + tensor var_8936_end_0 = const()[name = tensor("op_8936_end_0"), val = tensor([1, 196000, 9])]; + tensor var_8936_end_mask_0 = const()[name = tensor("op_8936_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8936 = slice_by_index(begin = var_8936_begin_0, end = var_8936_end_0, end_mask = var_8936_end_mask_0, x = reshape_4)[name = tensor("op_8936")]; + tensor segment_accum_389_exclusive_0 = const()[name = tensor("segment_accum_389_exclusive_0"), val = tensor(false)]; + tensor segment_accum_389_reverse_0 = const()[name = tensor("segment_accum_389_reverse_0"), val = tensor(false)]; + tensor segment_accum_389 = cumsum(axis = var_7349, exclusive = segment_accum_389_exclusive_0, reverse = segment_accum_389_reverse_0, x = var_8936)[name = tensor("segment_accum_389")]; + tensor var_8940_begin_0 = const()[name = tensor("op_8940_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8940_end_0 = const()[name = tensor("op_8940_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8940_end_mask_0 = const()[name = tensor("op_8940_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8940 = slice_by_index(begin = var_8940_begin_0, end = var_8940_end_0, end_mask = var_8940_end_mask_0, x = var_8934)[name = tensor("op_8940")]; + tensor var_8942 = add(x = segment_accum_389, y = var_8940)[name = tensor("op_8942")]; + tensor var_8944_begin_0 = const()[name = tensor("op_8944_begin_0"), val = tensor([0, 196000, 0])]; + tensor var_8944_end_0 = const()[name = tensor("op_8944_end_0"), val = tensor([1, 197000, 9])]; + tensor var_8944_end_mask_0 = const()[name = tensor("op_8944_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8944 = slice_by_index(begin = var_8944_begin_0, end = var_8944_end_0, end_mask = var_8944_end_mask_0, x = reshape_4)[name = tensor("op_8944")]; + tensor segment_accum_391_exclusive_0 = const()[name = tensor("segment_accum_391_exclusive_0"), val = tensor(false)]; + tensor segment_accum_391_reverse_0 = const()[name = tensor("segment_accum_391_reverse_0"), val = tensor(false)]; + tensor segment_accum_391 = cumsum(axis = var_7349, exclusive = segment_accum_391_exclusive_0, reverse = segment_accum_391_reverse_0, x = var_8944)[name = tensor("segment_accum_391")]; + tensor var_8948_begin_0 = const()[name = tensor("op_8948_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8948_end_0 = const()[name = tensor("op_8948_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8948_end_mask_0 = const()[name = tensor("op_8948_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8948 = slice_by_index(begin = var_8948_begin_0, end = var_8948_end_0, end_mask = var_8948_end_mask_0, x = var_8942)[name = tensor("op_8948")]; + tensor var_8950 = add(x = segment_accum_391, y = var_8948)[name = tensor("op_8950")]; + tensor var_8952_begin_0 = const()[name = tensor("op_8952_begin_0"), val = tensor([0, 197000, 0])]; + tensor var_8952_end_0 = const()[name = tensor("op_8952_end_0"), val = tensor([1, 198000, 9])]; + tensor var_8952_end_mask_0 = const()[name = tensor("op_8952_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8952 = slice_by_index(begin = var_8952_begin_0, end = var_8952_end_0, end_mask = var_8952_end_mask_0, x = reshape_4)[name = tensor("op_8952")]; + tensor segment_accum_393_exclusive_0 = const()[name = tensor("segment_accum_393_exclusive_0"), val = tensor(false)]; + tensor segment_accum_393_reverse_0 = const()[name = tensor("segment_accum_393_reverse_0"), val = tensor(false)]; + tensor segment_accum_393 = cumsum(axis = var_7349, exclusive = segment_accum_393_exclusive_0, reverse = segment_accum_393_reverse_0, x = var_8952)[name = tensor("segment_accum_393")]; + tensor var_8956_begin_0 = const()[name = tensor("op_8956_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8956_end_0 = const()[name = tensor("op_8956_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8956_end_mask_0 = const()[name = tensor("op_8956_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8956 = slice_by_index(begin = var_8956_begin_0, end = var_8956_end_0, end_mask = var_8956_end_mask_0, x = var_8950)[name = tensor("op_8956")]; + tensor var_8958 = add(x = segment_accum_393, y = var_8956)[name = tensor("op_8958")]; + tensor var_8960_begin_0 = const()[name = tensor("op_8960_begin_0"), val = tensor([0, 198000, 0])]; + tensor var_8960_end_0 = const()[name = tensor("op_8960_end_0"), val = tensor([1, 199000, 9])]; + tensor var_8960_end_mask_0 = const()[name = tensor("op_8960_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8960 = slice_by_index(begin = var_8960_begin_0, end = var_8960_end_0, end_mask = var_8960_end_mask_0, x = reshape_4)[name = tensor("op_8960")]; + tensor segment_accum_395_exclusive_0 = const()[name = tensor("segment_accum_395_exclusive_0"), val = tensor(false)]; + tensor segment_accum_395_reverse_0 = const()[name = tensor("segment_accum_395_reverse_0"), val = tensor(false)]; + tensor segment_accum_395 = cumsum(axis = var_7349, exclusive = segment_accum_395_exclusive_0, reverse = segment_accum_395_reverse_0, x = var_8960)[name = tensor("segment_accum_395")]; + tensor var_8964_begin_0 = const()[name = tensor("op_8964_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8964_end_0 = const()[name = tensor("op_8964_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8964_end_mask_0 = const()[name = tensor("op_8964_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8964 = slice_by_index(begin = var_8964_begin_0, end = var_8964_end_0, end_mask = var_8964_end_mask_0, x = var_8958)[name = tensor("op_8964")]; + tensor var_8966 = add(x = segment_accum_395, y = var_8964)[name = tensor("op_8966")]; + tensor var_8968_begin_0 = const()[name = tensor("op_8968_begin_0"), val = tensor([0, 199000, 0])]; + tensor var_8968_end_0 = const()[name = tensor("op_8968_end_0"), val = tensor([1, 200000, 9])]; + tensor var_8968_end_mask_0 = const()[name = tensor("op_8968_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8968 = slice_by_index(begin = var_8968_begin_0, end = var_8968_end_0, end_mask = var_8968_end_mask_0, x = reshape_4)[name = tensor("op_8968")]; + tensor segment_accum_397_exclusive_0 = const()[name = tensor("segment_accum_397_exclusive_0"), val = tensor(false)]; + tensor segment_accum_397_reverse_0 = const()[name = tensor("segment_accum_397_reverse_0"), val = tensor(false)]; + tensor segment_accum_397 = cumsum(axis = var_7349, exclusive = segment_accum_397_exclusive_0, reverse = segment_accum_397_reverse_0, x = var_8968)[name = tensor("segment_accum_397")]; + tensor var_8972_begin_0 = const()[name = tensor("op_8972_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8972_end_0 = const()[name = tensor("op_8972_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8972_end_mask_0 = const()[name = tensor("op_8972_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8972 = slice_by_index(begin = var_8972_begin_0, end = var_8972_end_0, end_mask = var_8972_end_mask_0, x = var_8966)[name = tensor("op_8972")]; + tensor var_8974 = add(x = segment_accum_397, y = var_8972)[name = tensor("op_8974")]; + tensor var_8976_begin_0 = const()[name = tensor("op_8976_begin_0"), val = tensor([0, 200000, 0])]; + tensor var_8976_end_0 = const()[name = tensor("op_8976_end_0"), val = tensor([1, 201000, 9])]; + tensor var_8976_end_mask_0 = const()[name = tensor("op_8976_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8976 = slice_by_index(begin = var_8976_begin_0, end = var_8976_end_0, end_mask = var_8976_end_mask_0, x = reshape_4)[name = tensor("op_8976")]; + tensor segment_accum_399_exclusive_0 = const()[name = tensor("segment_accum_399_exclusive_0"), val = tensor(false)]; + tensor segment_accum_399_reverse_0 = const()[name = tensor("segment_accum_399_reverse_0"), val = tensor(false)]; + tensor segment_accum_399 = cumsum(axis = var_7349, exclusive = segment_accum_399_exclusive_0, reverse = segment_accum_399_reverse_0, x = var_8976)[name = tensor("segment_accum_399")]; + tensor var_8980_begin_0 = const()[name = tensor("op_8980_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8980_end_0 = const()[name = tensor("op_8980_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8980_end_mask_0 = const()[name = tensor("op_8980_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8980 = slice_by_index(begin = var_8980_begin_0, end = var_8980_end_0, end_mask = var_8980_end_mask_0, x = var_8974)[name = tensor("op_8980")]; + tensor var_8982 = add(x = segment_accum_399, y = var_8980)[name = tensor("op_8982")]; + tensor var_8984_begin_0 = const()[name = tensor("op_8984_begin_0"), val = tensor([0, 201000, 0])]; + tensor var_8984_end_0 = const()[name = tensor("op_8984_end_0"), val = tensor([1, 202000, 9])]; + tensor var_8984_end_mask_0 = const()[name = tensor("op_8984_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8984 = slice_by_index(begin = var_8984_begin_0, end = var_8984_end_0, end_mask = var_8984_end_mask_0, x = reshape_4)[name = tensor("op_8984")]; + tensor segment_accum_401_exclusive_0 = const()[name = tensor("segment_accum_401_exclusive_0"), val = tensor(false)]; + tensor segment_accum_401_reverse_0 = const()[name = tensor("segment_accum_401_reverse_0"), val = tensor(false)]; + tensor segment_accum_401 = cumsum(axis = var_7349, exclusive = segment_accum_401_exclusive_0, reverse = segment_accum_401_reverse_0, x = var_8984)[name = tensor("segment_accum_401")]; + tensor var_8988_begin_0 = const()[name = tensor("op_8988_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8988_end_0 = const()[name = tensor("op_8988_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8988_end_mask_0 = const()[name = tensor("op_8988_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8988 = slice_by_index(begin = var_8988_begin_0, end = var_8988_end_0, end_mask = var_8988_end_mask_0, x = var_8982)[name = tensor("op_8988")]; + tensor var_8990 = add(x = segment_accum_401, y = var_8988)[name = tensor("op_8990")]; + tensor var_8992_begin_0 = const()[name = tensor("op_8992_begin_0"), val = tensor([0, 202000, 0])]; + tensor var_8992_end_0 = const()[name = tensor("op_8992_end_0"), val = tensor([1, 203000, 9])]; + tensor var_8992_end_mask_0 = const()[name = tensor("op_8992_end_mask_0"), val = tensor([true, false, true])]; + tensor var_8992 = slice_by_index(begin = var_8992_begin_0, end = var_8992_end_0, end_mask = var_8992_end_mask_0, x = reshape_4)[name = tensor("op_8992")]; + tensor segment_accum_403_exclusive_0 = const()[name = tensor("segment_accum_403_exclusive_0"), val = tensor(false)]; + tensor segment_accum_403_reverse_0 = const()[name = tensor("segment_accum_403_reverse_0"), val = tensor(false)]; + tensor segment_accum_403 = cumsum(axis = var_7349, exclusive = segment_accum_403_exclusive_0, reverse = segment_accum_403_reverse_0, x = var_8992)[name = tensor("segment_accum_403")]; + tensor var_8996_begin_0 = const()[name = tensor("op_8996_begin_0"), val = tensor([0, -1, 0])]; + tensor var_8996_end_0 = const()[name = tensor("op_8996_end_0"), val = tensor([1, 1000, 9])]; + tensor var_8996_end_mask_0 = const()[name = tensor("op_8996_end_mask_0"), val = tensor([true, true, true])]; + tensor var_8996 = slice_by_index(begin = var_8996_begin_0, end = var_8996_end_0, end_mask = var_8996_end_mask_0, x = var_8990)[name = tensor("op_8996")]; + tensor var_8998 = add(x = segment_accum_403, y = var_8996)[name = tensor("op_8998")]; + tensor var_9000_begin_0 = const()[name = tensor("op_9000_begin_0"), val = tensor([0, 203000, 0])]; + tensor var_9000_end_0 = const()[name = tensor("op_9000_end_0"), val = tensor([1, 204000, 9])]; + tensor var_9000_end_mask_0 = const()[name = tensor("op_9000_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9000 = slice_by_index(begin = var_9000_begin_0, end = var_9000_end_0, end_mask = var_9000_end_mask_0, x = reshape_4)[name = tensor("op_9000")]; + tensor segment_accum_405_exclusive_0 = const()[name = tensor("segment_accum_405_exclusive_0"), val = tensor(false)]; + tensor segment_accum_405_reverse_0 = const()[name = tensor("segment_accum_405_reverse_0"), val = tensor(false)]; + tensor segment_accum_405 = cumsum(axis = var_7349, exclusive = segment_accum_405_exclusive_0, reverse = segment_accum_405_reverse_0, x = var_9000)[name = tensor("segment_accum_405")]; + tensor var_9004_begin_0 = const()[name = tensor("op_9004_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9004_end_0 = const()[name = tensor("op_9004_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9004_end_mask_0 = const()[name = tensor("op_9004_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9004 = slice_by_index(begin = var_9004_begin_0, end = var_9004_end_0, end_mask = var_9004_end_mask_0, x = var_8998)[name = tensor("op_9004")]; + tensor var_9006 = add(x = segment_accum_405, y = var_9004)[name = tensor("op_9006")]; + tensor var_9008_begin_0 = const()[name = tensor("op_9008_begin_0"), val = tensor([0, 204000, 0])]; + tensor var_9008_end_0 = const()[name = tensor("op_9008_end_0"), val = tensor([1, 205000, 9])]; + tensor var_9008_end_mask_0 = const()[name = tensor("op_9008_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9008 = slice_by_index(begin = var_9008_begin_0, end = var_9008_end_0, end_mask = var_9008_end_mask_0, x = reshape_4)[name = tensor("op_9008")]; + tensor segment_accum_407_exclusive_0 = const()[name = tensor("segment_accum_407_exclusive_0"), val = tensor(false)]; + tensor segment_accum_407_reverse_0 = const()[name = tensor("segment_accum_407_reverse_0"), val = tensor(false)]; + tensor segment_accum_407 = cumsum(axis = var_7349, exclusive = segment_accum_407_exclusive_0, reverse = segment_accum_407_reverse_0, x = var_9008)[name = tensor("segment_accum_407")]; + tensor var_9012_begin_0 = const()[name = tensor("op_9012_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9012_end_0 = const()[name = tensor("op_9012_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9012_end_mask_0 = const()[name = tensor("op_9012_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9012 = slice_by_index(begin = var_9012_begin_0, end = var_9012_end_0, end_mask = var_9012_end_mask_0, x = var_9006)[name = tensor("op_9012")]; + tensor var_9014 = add(x = segment_accum_407, y = var_9012)[name = tensor("op_9014")]; + tensor var_9016_begin_0 = const()[name = tensor("op_9016_begin_0"), val = tensor([0, 205000, 0])]; + tensor var_9016_end_0 = const()[name = tensor("op_9016_end_0"), val = tensor([1, 206000, 9])]; + tensor var_9016_end_mask_0 = const()[name = tensor("op_9016_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9016 = slice_by_index(begin = var_9016_begin_0, end = var_9016_end_0, end_mask = var_9016_end_mask_0, x = reshape_4)[name = tensor("op_9016")]; + tensor segment_accum_409_exclusive_0 = const()[name = tensor("segment_accum_409_exclusive_0"), val = tensor(false)]; + tensor segment_accum_409_reverse_0 = const()[name = tensor("segment_accum_409_reverse_0"), val = tensor(false)]; + tensor segment_accum_409 = cumsum(axis = var_7349, exclusive = segment_accum_409_exclusive_0, reverse = segment_accum_409_reverse_0, x = var_9016)[name = tensor("segment_accum_409")]; + tensor var_9020_begin_0 = const()[name = tensor("op_9020_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9020_end_0 = const()[name = tensor("op_9020_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9020_end_mask_0 = const()[name = tensor("op_9020_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9020 = slice_by_index(begin = var_9020_begin_0, end = var_9020_end_0, end_mask = var_9020_end_mask_0, x = var_9014)[name = tensor("op_9020")]; + tensor var_9022 = add(x = segment_accum_409, y = var_9020)[name = tensor("op_9022")]; + tensor var_9024_begin_0 = const()[name = tensor("op_9024_begin_0"), val = tensor([0, 206000, 0])]; + tensor var_9024_end_0 = const()[name = tensor("op_9024_end_0"), val = tensor([1, 207000, 9])]; + tensor var_9024_end_mask_0 = const()[name = tensor("op_9024_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9024 = slice_by_index(begin = var_9024_begin_0, end = var_9024_end_0, end_mask = var_9024_end_mask_0, x = reshape_4)[name = tensor("op_9024")]; + tensor segment_accum_411_exclusive_0 = const()[name = tensor("segment_accum_411_exclusive_0"), val = tensor(false)]; + tensor segment_accum_411_reverse_0 = const()[name = tensor("segment_accum_411_reverse_0"), val = tensor(false)]; + tensor segment_accum_411 = cumsum(axis = var_7349, exclusive = segment_accum_411_exclusive_0, reverse = segment_accum_411_reverse_0, x = var_9024)[name = tensor("segment_accum_411")]; + tensor var_9028_begin_0 = const()[name = tensor("op_9028_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9028_end_0 = const()[name = tensor("op_9028_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9028_end_mask_0 = const()[name = tensor("op_9028_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9028 = slice_by_index(begin = var_9028_begin_0, end = var_9028_end_0, end_mask = var_9028_end_mask_0, x = var_9022)[name = tensor("op_9028")]; + tensor var_9030 = add(x = segment_accum_411, y = var_9028)[name = tensor("op_9030")]; + tensor var_9032_begin_0 = const()[name = tensor("op_9032_begin_0"), val = tensor([0, 207000, 0])]; + tensor var_9032_end_0 = const()[name = tensor("op_9032_end_0"), val = tensor([1, 208000, 9])]; + tensor var_9032_end_mask_0 = const()[name = tensor("op_9032_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9032 = slice_by_index(begin = var_9032_begin_0, end = var_9032_end_0, end_mask = var_9032_end_mask_0, x = reshape_4)[name = tensor("op_9032")]; + tensor segment_accum_413_exclusive_0 = const()[name = tensor("segment_accum_413_exclusive_0"), val = tensor(false)]; + tensor segment_accum_413_reverse_0 = const()[name = tensor("segment_accum_413_reverse_0"), val = tensor(false)]; + tensor segment_accum_413 = cumsum(axis = var_7349, exclusive = segment_accum_413_exclusive_0, reverse = segment_accum_413_reverse_0, x = var_9032)[name = tensor("segment_accum_413")]; + tensor var_9036_begin_0 = const()[name = tensor("op_9036_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9036_end_0 = const()[name = tensor("op_9036_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9036_end_mask_0 = const()[name = tensor("op_9036_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9036 = slice_by_index(begin = var_9036_begin_0, end = var_9036_end_0, end_mask = var_9036_end_mask_0, x = var_9030)[name = tensor("op_9036")]; + tensor var_9038 = add(x = segment_accum_413, y = var_9036)[name = tensor("op_9038")]; + tensor var_9040_begin_0 = const()[name = tensor("op_9040_begin_0"), val = tensor([0, 208000, 0])]; + tensor var_9040_end_0 = const()[name = tensor("op_9040_end_0"), val = tensor([1, 209000, 9])]; + tensor var_9040_end_mask_0 = const()[name = tensor("op_9040_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9040 = slice_by_index(begin = var_9040_begin_0, end = var_9040_end_0, end_mask = var_9040_end_mask_0, x = reshape_4)[name = tensor("op_9040")]; + tensor segment_accum_415_exclusive_0 = const()[name = tensor("segment_accum_415_exclusive_0"), val = tensor(false)]; + tensor segment_accum_415_reverse_0 = const()[name = tensor("segment_accum_415_reverse_0"), val = tensor(false)]; + tensor segment_accum_415 = cumsum(axis = var_7349, exclusive = segment_accum_415_exclusive_0, reverse = segment_accum_415_reverse_0, x = var_9040)[name = tensor("segment_accum_415")]; + tensor var_9044_begin_0 = const()[name = tensor("op_9044_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9044_end_0 = const()[name = tensor("op_9044_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9044_end_mask_0 = const()[name = tensor("op_9044_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9044 = slice_by_index(begin = var_9044_begin_0, end = var_9044_end_0, end_mask = var_9044_end_mask_0, x = var_9038)[name = tensor("op_9044")]; + tensor var_9046 = add(x = segment_accum_415, y = var_9044)[name = tensor("op_9046")]; + tensor var_9048_begin_0 = const()[name = tensor("op_9048_begin_0"), val = tensor([0, 209000, 0])]; + tensor var_9048_end_0 = const()[name = tensor("op_9048_end_0"), val = tensor([1, 210000, 9])]; + tensor var_9048_end_mask_0 = const()[name = tensor("op_9048_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9048 = slice_by_index(begin = var_9048_begin_0, end = var_9048_end_0, end_mask = var_9048_end_mask_0, x = reshape_4)[name = tensor("op_9048")]; + tensor segment_accum_417_exclusive_0 = const()[name = tensor("segment_accum_417_exclusive_0"), val = tensor(false)]; + tensor segment_accum_417_reverse_0 = const()[name = tensor("segment_accum_417_reverse_0"), val = tensor(false)]; + tensor segment_accum_417 = cumsum(axis = var_7349, exclusive = segment_accum_417_exclusive_0, reverse = segment_accum_417_reverse_0, x = var_9048)[name = tensor("segment_accum_417")]; + tensor var_9052_begin_0 = const()[name = tensor("op_9052_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9052_end_0 = const()[name = tensor("op_9052_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9052_end_mask_0 = const()[name = tensor("op_9052_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9052 = slice_by_index(begin = var_9052_begin_0, end = var_9052_end_0, end_mask = var_9052_end_mask_0, x = var_9046)[name = tensor("op_9052")]; + tensor var_9054 = add(x = segment_accum_417, y = var_9052)[name = tensor("op_9054")]; + tensor var_9056_begin_0 = const()[name = tensor("op_9056_begin_0"), val = tensor([0, 210000, 0])]; + tensor var_9056_end_0 = const()[name = tensor("op_9056_end_0"), val = tensor([1, 211000, 9])]; + tensor var_9056_end_mask_0 = const()[name = tensor("op_9056_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9056 = slice_by_index(begin = var_9056_begin_0, end = var_9056_end_0, end_mask = var_9056_end_mask_0, x = reshape_4)[name = tensor("op_9056")]; + tensor segment_accum_419_exclusive_0 = const()[name = tensor("segment_accum_419_exclusive_0"), val = tensor(false)]; + tensor segment_accum_419_reverse_0 = const()[name = tensor("segment_accum_419_reverse_0"), val = tensor(false)]; + tensor segment_accum_419 = cumsum(axis = var_7349, exclusive = segment_accum_419_exclusive_0, reverse = segment_accum_419_reverse_0, x = var_9056)[name = tensor("segment_accum_419")]; + tensor var_9060_begin_0 = const()[name = tensor("op_9060_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9060_end_0 = const()[name = tensor("op_9060_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9060_end_mask_0 = const()[name = tensor("op_9060_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9060 = slice_by_index(begin = var_9060_begin_0, end = var_9060_end_0, end_mask = var_9060_end_mask_0, x = var_9054)[name = tensor("op_9060")]; + tensor var_9062 = add(x = segment_accum_419, y = var_9060)[name = tensor("op_9062")]; + tensor var_9064_begin_0 = const()[name = tensor("op_9064_begin_0"), val = tensor([0, 211000, 0])]; + tensor var_9064_end_0 = const()[name = tensor("op_9064_end_0"), val = tensor([1, 212000, 9])]; + tensor var_9064_end_mask_0 = const()[name = tensor("op_9064_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9064 = slice_by_index(begin = var_9064_begin_0, end = var_9064_end_0, end_mask = var_9064_end_mask_0, x = reshape_4)[name = tensor("op_9064")]; + tensor segment_accum_421_exclusive_0 = const()[name = tensor("segment_accum_421_exclusive_0"), val = tensor(false)]; + tensor segment_accum_421_reverse_0 = const()[name = tensor("segment_accum_421_reverse_0"), val = tensor(false)]; + tensor segment_accum_421 = cumsum(axis = var_7349, exclusive = segment_accum_421_exclusive_0, reverse = segment_accum_421_reverse_0, x = var_9064)[name = tensor("segment_accum_421")]; + tensor var_9068_begin_0 = const()[name = tensor("op_9068_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9068_end_0 = const()[name = tensor("op_9068_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9068_end_mask_0 = const()[name = tensor("op_9068_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9068 = slice_by_index(begin = var_9068_begin_0, end = var_9068_end_0, end_mask = var_9068_end_mask_0, x = var_9062)[name = tensor("op_9068")]; + tensor var_9070 = add(x = segment_accum_421, y = var_9068)[name = tensor("op_9070")]; + tensor var_9072_begin_0 = const()[name = tensor("op_9072_begin_0"), val = tensor([0, 212000, 0])]; + tensor var_9072_end_0 = const()[name = tensor("op_9072_end_0"), val = tensor([1, 213000, 9])]; + tensor var_9072_end_mask_0 = const()[name = tensor("op_9072_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9072 = slice_by_index(begin = var_9072_begin_0, end = var_9072_end_0, end_mask = var_9072_end_mask_0, x = reshape_4)[name = tensor("op_9072")]; + tensor segment_accum_423_exclusive_0 = const()[name = tensor("segment_accum_423_exclusive_0"), val = tensor(false)]; + tensor segment_accum_423_reverse_0 = const()[name = tensor("segment_accum_423_reverse_0"), val = tensor(false)]; + tensor segment_accum_423 = cumsum(axis = var_7349, exclusive = segment_accum_423_exclusive_0, reverse = segment_accum_423_reverse_0, x = var_9072)[name = tensor("segment_accum_423")]; + tensor var_9076_begin_0 = const()[name = tensor("op_9076_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9076_end_0 = const()[name = tensor("op_9076_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9076_end_mask_0 = const()[name = tensor("op_9076_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9076 = slice_by_index(begin = var_9076_begin_0, end = var_9076_end_0, end_mask = var_9076_end_mask_0, x = var_9070)[name = tensor("op_9076")]; + tensor var_9078 = add(x = segment_accum_423, y = var_9076)[name = tensor("op_9078")]; + tensor var_9080_begin_0 = const()[name = tensor("op_9080_begin_0"), val = tensor([0, 213000, 0])]; + tensor var_9080_end_0 = const()[name = tensor("op_9080_end_0"), val = tensor([1, 214000, 9])]; + tensor var_9080_end_mask_0 = const()[name = tensor("op_9080_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9080 = slice_by_index(begin = var_9080_begin_0, end = var_9080_end_0, end_mask = var_9080_end_mask_0, x = reshape_4)[name = tensor("op_9080")]; + tensor segment_accum_425_exclusive_0 = const()[name = tensor("segment_accum_425_exclusive_0"), val = tensor(false)]; + tensor segment_accum_425_reverse_0 = const()[name = tensor("segment_accum_425_reverse_0"), val = tensor(false)]; + tensor segment_accum_425 = cumsum(axis = var_7349, exclusive = segment_accum_425_exclusive_0, reverse = segment_accum_425_reverse_0, x = var_9080)[name = tensor("segment_accum_425")]; + tensor var_9084_begin_0 = const()[name = tensor("op_9084_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9084_end_0 = const()[name = tensor("op_9084_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9084_end_mask_0 = const()[name = tensor("op_9084_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9084 = slice_by_index(begin = var_9084_begin_0, end = var_9084_end_0, end_mask = var_9084_end_mask_0, x = var_9078)[name = tensor("op_9084")]; + tensor var_9086 = add(x = segment_accum_425, y = var_9084)[name = tensor("op_9086")]; + tensor var_9088_begin_0 = const()[name = tensor("op_9088_begin_0"), val = tensor([0, 214000, 0])]; + tensor var_9088_end_0 = const()[name = tensor("op_9088_end_0"), val = tensor([1, 215000, 9])]; + tensor var_9088_end_mask_0 = const()[name = tensor("op_9088_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9088 = slice_by_index(begin = var_9088_begin_0, end = var_9088_end_0, end_mask = var_9088_end_mask_0, x = reshape_4)[name = tensor("op_9088")]; + tensor segment_accum_427_exclusive_0 = const()[name = tensor("segment_accum_427_exclusive_0"), val = tensor(false)]; + tensor segment_accum_427_reverse_0 = const()[name = tensor("segment_accum_427_reverse_0"), val = tensor(false)]; + tensor segment_accum_427 = cumsum(axis = var_7349, exclusive = segment_accum_427_exclusive_0, reverse = segment_accum_427_reverse_0, x = var_9088)[name = tensor("segment_accum_427")]; + tensor var_9092_begin_0 = const()[name = tensor("op_9092_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9092_end_0 = const()[name = tensor("op_9092_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9092_end_mask_0 = const()[name = tensor("op_9092_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9092 = slice_by_index(begin = var_9092_begin_0, end = var_9092_end_0, end_mask = var_9092_end_mask_0, x = var_9086)[name = tensor("op_9092")]; + tensor var_9094 = add(x = segment_accum_427, y = var_9092)[name = tensor("op_9094")]; + tensor var_9096_begin_0 = const()[name = tensor("op_9096_begin_0"), val = tensor([0, 215000, 0])]; + tensor var_9096_end_0 = const()[name = tensor("op_9096_end_0"), val = tensor([1, 216000, 9])]; + tensor var_9096_end_mask_0 = const()[name = tensor("op_9096_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9096 = slice_by_index(begin = var_9096_begin_0, end = var_9096_end_0, end_mask = var_9096_end_mask_0, x = reshape_4)[name = tensor("op_9096")]; + tensor segment_accum_429_exclusive_0 = const()[name = tensor("segment_accum_429_exclusive_0"), val = tensor(false)]; + tensor segment_accum_429_reverse_0 = const()[name = tensor("segment_accum_429_reverse_0"), val = tensor(false)]; + tensor segment_accum_429 = cumsum(axis = var_7349, exclusive = segment_accum_429_exclusive_0, reverse = segment_accum_429_reverse_0, x = var_9096)[name = tensor("segment_accum_429")]; + tensor var_9100_begin_0 = const()[name = tensor("op_9100_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9100_end_0 = const()[name = tensor("op_9100_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9100_end_mask_0 = const()[name = tensor("op_9100_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9100 = slice_by_index(begin = var_9100_begin_0, end = var_9100_end_0, end_mask = var_9100_end_mask_0, x = var_9094)[name = tensor("op_9100")]; + tensor var_9102 = add(x = segment_accum_429, y = var_9100)[name = tensor("op_9102")]; + tensor var_9104_begin_0 = const()[name = tensor("op_9104_begin_0"), val = tensor([0, 216000, 0])]; + tensor var_9104_end_0 = const()[name = tensor("op_9104_end_0"), val = tensor([1, 217000, 9])]; + tensor var_9104_end_mask_0 = const()[name = tensor("op_9104_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9104 = slice_by_index(begin = var_9104_begin_0, end = var_9104_end_0, end_mask = var_9104_end_mask_0, x = reshape_4)[name = tensor("op_9104")]; + tensor segment_accum_431_exclusive_0 = const()[name = tensor("segment_accum_431_exclusive_0"), val = tensor(false)]; + tensor segment_accum_431_reverse_0 = const()[name = tensor("segment_accum_431_reverse_0"), val = tensor(false)]; + tensor segment_accum_431 = cumsum(axis = var_7349, exclusive = segment_accum_431_exclusive_0, reverse = segment_accum_431_reverse_0, x = var_9104)[name = tensor("segment_accum_431")]; + tensor var_9108_begin_0 = const()[name = tensor("op_9108_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9108_end_0 = const()[name = tensor("op_9108_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9108_end_mask_0 = const()[name = tensor("op_9108_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9108 = slice_by_index(begin = var_9108_begin_0, end = var_9108_end_0, end_mask = var_9108_end_mask_0, x = var_9102)[name = tensor("op_9108")]; + tensor var_9110 = add(x = segment_accum_431, y = var_9108)[name = tensor("op_9110")]; + tensor var_9112_begin_0 = const()[name = tensor("op_9112_begin_0"), val = tensor([0, 217000, 0])]; + tensor var_9112_end_0 = const()[name = tensor("op_9112_end_0"), val = tensor([1, 218000, 9])]; + tensor var_9112_end_mask_0 = const()[name = tensor("op_9112_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9112 = slice_by_index(begin = var_9112_begin_0, end = var_9112_end_0, end_mask = var_9112_end_mask_0, x = reshape_4)[name = tensor("op_9112")]; + tensor segment_accum_433_exclusive_0 = const()[name = tensor("segment_accum_433_exclusive_0"), val = tensor(false)]; + tensor segment_accum_433_reverse_0 = const()[name = tensor("segment_accum_433_reverse_0"), val = tensor(false)]; + tensor segment_accum_433 = cumsum(axis = var_7349, exclusive = segment_accum_433_exclusive_0, reverse = segment_accum_433_reverse_0, x = var_9112)[name = tensor("segment_accum_433")]; + tensor var_9116_begin_0 = const()[name = tensor("op_9116_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9116_end_0 = const()[name = tensor("op_9116_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9116_end_mask_0 = const()[name = tensor("op_9116_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9116 = slice_by_index(begin = var_9116_begin_0, end = var_9116_end_0, end_mask = var_9116_end_mask_0, x = var_9110)[name = tensor("op_9116")]; + tensor var_9118 = add(x = segment_accum_433, y = var_9116)[name = tensor("op_9118")]; + tensor var_9120_begin_0 = const()[name = tensor("op_9120_begin_0"), val = tensor([0, 218000, 0])]; + tensor var_9120_end_0 = const()[name = tensor("op_9120_end_0"), val = tensor([1, 219000, 9])]; + tensor var_9120_end_mask_0 = const()[name = tensor("op_9120_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9120 = slice_by_index(begin = var_9120_begin_0, end = var_9120_end_0, end_mask = var_9120_end_mask_0, x = reshape_4)[name = tensor("op_9120")]; + tensor segment_accum_435_exclusive_0 = const()[name = tensor("segment_accum_435_exclusive_0"), val = tensor(false)]; + tensor segment_accum_435_reverse_0 = const()[name = tensor("segment_accum_435_reverse_0"), val = tensor(false)]; + tensor segment_accum_435 = cumsum(axis = var_7349, exclusive = segment_accum_435_exclusive_0, reverse = segment_accum_435_reverse_0, x = var_9120)[name = tensor("segment_accum_435")]; + tensor var_9124_begin_0 = const()[name = tensor("op_9124_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9124_end_0 = const()[name = tensor("op_9124_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9124_end_mask_0 = const()[name = tensor("op_9124_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9124 = slice_by_index(begin = var_9124_begin_0, end = var_9124_end_0, end_mask = var_9124_end_mask_0, x = var_9118)[name = tensor("op_9124")]; + tensor var_9126 = add(x = segment_accum_435, y = var_9124)[name = tensor("op_9126")]; + tensor var_9128_begin_0 = const()[name = tensor("op_9128_begin_0"), val = tensor([0, 219000, 0])]; + tensor var_9128_end_0 = const()[name = tensor("op_9128_end_0"), val = tensor([1, 220000, 9])]; + tensor var_9128_end_mask_0 = const()[name = tensor("op_9128_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9128 = slice_by_index(begin = var_9128_begin_0, end = var_9128_end_0, end_mask = var_9128_end_mask_0, x = reshape_4)[name = tensor("op_9128")]; + tensor segment_accum_437_exclusive_0 = const()[name = tensor("segment_accum_437_exclusive_0"), val = tensor(false)]; + tensor segment_accum_437_reverse_0 = const()[name = tensor("segment_accum_437_reverse_0"), val = tensor(false)]; + tensor segment_accum_437 = cumsum(axis = var_7349, exclusive = segment_accum_437_exclusive_0, reverse = segment_accum_437_reverse_0, x = var_9128)[name = tensor("segment_accum_437")]; + tensor var_9132_begin_0 = const()[name = tensor("op_9132_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9132_end_0 = const()[name = tensor("op_9132_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9132_end_mask_0 = const()[name = tensor("op_9132_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9132 = slice_by_index(begin = var_9132_begin_0, end = var_9132_end_0, end_mask = var_9132_end_mask_0, x = var_9126)[name = tensor("op_9132")]; + tensor var_9134 = add(x = segment_accum_437, y = var_9132)[name = tensor("op_9134")]; + tensor var_9136_begin_0 = const()[name = tensor("op_9136_begin_0"), val = tensor([0, 220000, 0])]; + tensor var_9136_end_0 = const()[name = tensor("op_9136_end_0"), val = tensor([1, 221000, 9])]; + tensor var_9136_end_mask_0 = const()[name = tensor("op_9136_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9136 = slice_by_index(begin = var_9136_begin_0, end = var_9136_end_0, end_mask = var_9136_end_mask_0, x = reshape_4)[name = tensor("op_9136")]; + tensor segment_accum_439_exclusive_0 = const()[name = tensor("segment_accum_439_exclusive_0"), val = tensor(false)]; + tensor segment_accum_439_reverse_0 = const()[name = tensor("segment_accum_439_reverse_0"), val = tensor(false)]; + tensor segment_accum_439 = cumsum(axis = var_7349, exclusive = segment_accum_439_exclusive_0, reverse = segment_accum_439_reverse_0, x = var_9136)[name = tensor("segment_accum_439")]; + tensor var_9140_begin_0 = const()[name = tensor("op_9140_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9140_end_0 = const()[name = tensor("op_9140_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9140_end_mask_0 = const()[name = tensor("op_9140_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9140 = slice_by_index(begin = var_9140_begin_0, end = var_9140_end_0, end_mask = var_9140_end_mask_0, x = var_9134)[name = tensor("op_9140")]; + tensor var_9142 = add(x = segment_accum_439, y = var_9140)[name = tensor("op_9142")]; + tensor var_9144_begin_0 = const()[name = tensor("op_9144_begin_0"), val = tensor([0, 221000, 0])]; + tensor var_9144_end_0 = const()[name = tensor("op_9144_end_0"), val = tensor([1, 222000, 9])]; + tensor var_9144_end_mask_0 = const()[name = tensor("op_9144_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9144 = slice_by_index(begin = var_9144_begin_0, end = var_9144_end_0, end_mask = var_9144_end_mask_0, x = reshape_4)[name = tensor("op_9144")]; + tensor segment_accum_441_exclusive_0 = const()[name = tensor("segment_accum_441_exclusive_0"), val = tensor(false)]; + tensor segment_accum_441_reverse_0 = const()[name = tensor("segment_accum_441_reverse_0"), val = tensor(false)]; + tensor segment_accum_441 = cumsum(axis = var_7349, exclusive = segment_accum_441_exclusive_0, reverse = segment_accum_441_reverse_0, x = var_9144)[name = tensor("segment_accum_441")]; + tensor var_9148_begin_0 = const()[name = tensor("op_9148_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9148_end_0 = const()[name = tensor("op_9148_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9148_end_mask_0 = const()[name = tensor("op_9148_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9148 = slice_by_index(begin = var_9148_begin_0, end = var_9148_end_0, end_mask = var_9148_end_mask_0, x = var_9142)[name = tensor("op_9148")]; + tensor var_9150 = add(x = segment_accum_441, y = var_9148)[name = tensor("op_9150")]; + tensor var_9152_begin_0 = const()[name = tensor("op_9152_begin_0"), val = tensor([0, 222000, 0])]; + tensor var_9152_end_0 = const()[name = tensor("op_9152_end_0"), val = tensor([1, 223000, 9])]; + tensor var_9152_end_mask_0 = const()[name = tensor("op_9152_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9152 = slice_by_index(begin = var_9152_begin_0, end = var_9152_end_0, end_mask = var_9152_end_mask_0, x = reshape_4)[name = tensor("op_9152")]; + tensor segment_accum_443_exclusive_0 = const()[name = tensor("segment_accum_443_exclusive_0"), val = tensor(false)]; + tensor segment_accum_443_reverse_0 = const()[name = tensor("segment_accum_443_reverse_0"), val = tensor(false)]; + tensor segment_accum_443 = cumsum(axis = var_7349, exclusive = segment_accum_443_exclusive_0, reverse = segment_accum_443_reverse_0, x = var_9152)[name = tensor("segment_accum_443")]; + tensor var_9156_begin_0 = const()[name = tensor("op_9156_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9156_end_0 = const()[name = tensor("op_9156_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9156_end_mask_0 = const()[name = tensor("op_9156_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9156 = slice_by_index(begin = var_9156_begin_0, end = var_9156_end_0, end_mask = var_9156_end_mask_0, x = var_9150)[name = tensor("op_9156")]; + tensor var_9158 = add(x = segment_accum_443, y = var_9156)[name = tensor("op_9158")]; + tensor var_9160_begin_0 = const()[name = tensor("op_9160_begin_0"), val = tensor([0, 223000, 0])]; + tensor var_9160_end_0 = const()[name = tensor("op_9160_end_0"), val = tensor([1, 224000, 9])]; + tensor var_9160_end_mask_0 = const()[name = tensor("op_9160_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9160 = slice_by_index(begin = var_9160_begin_0, end = var_9160_end_0, end_mask = var_9160_end_mask_0, x = reshape_4)[name = tensor("op_9160")]; + tensor segment_accum_445_exclusive_0 = const()[name = tensor("segment_accum_445_exclusive_0"), val = tensor(false)]; + tensor segment_accum_445_reverse_0 = const()[name = tensor("segment_accum_445_reverse_0"), val = tensor(false)]; + tensor segment_accum_445 = cumsum(axis = var_7349, exclusive = segment_accum_445_exclusive_0, reverse = segment_accum_445_reverse_0, x = var_9160)[name = tensor("segment_accum_445")]; + tensor var_9164_begin_0 = const()[name = tensor("op_9164_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9164_end_0 = const()[name = tensor("op_9164_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9164_end_mask_0 = const()[name = tensor("op_9164_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9164 = slice_by_index(begin = var_9164_begin_0, end = var_9164_end_0, end_mask = var_9164_end_mask_0, x = var_9158)[name = tensor("op_9164")]; + tensor var_9166 = add(x = segment_accum_445, y = var_9164)[name = tensor("op_9166")]; + tensor var_9168_begin_0 = const()[name = tensor("op_9168_begin_0"), val = tensor([0, 224000, 0])]; + tensor var_9168_end_0 = const()[name = tensor("op_9168_end_0"), val = tensor([1, 225000, 9])]; + tensor var_9168_end_mask_0 = const()[name = tensor("op_9168_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9168 = slice_by_index(begin = var_9168_begin_0, end = var_9168_end_0, end_mask = var_9168_end_mask_0, x = reshape_4)[name = tensor("op_9168")]; + tensor segment_accum_447_exclusive_0 = const()[name = tensor("segment_accum_447_exclusive_0"), val = tensor(false)]; + tensor segment_accum_447_reverse_0 = const()[name = tensor("segment_accum_447_reverse_0"), val = tensor(false)]; + tensor segment_accum_447 = cumsum(axis = var_7349, exclusive = segment_accum_447_exclusive_0, reverse = segment_accum_447_reverse_0, x = var_9168)[name = tensor("segment_accum_447")]; + tensor var_9172_begin_0 = const()[name = tensor("op_9172_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9172_end_0 = const()[name = tensor("op_9172_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9172_end_mask_0 = const()[name = tensor("op_9172_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9172 = slice_by_index(begin = var_9172_begin_0, end = var_9172_end_0, end_mask = var_9172_end_mask_0, x = var_9166)[name = tensor("op_9172")]; + tensor var_9174 = add(x = segment_accum_447, y = var_9172)[name = tensor("op_9174")]; + tensor var_9176_begin_0 = const()[name = tensor("op_9176_begin_0"), val = tensor([0, 225000, 0])]; + tensor var_9176_end_0 = const()[name = tensor("op_9176_end_0"), val = tensor([1, 226000, 9])]; + tensor var_9176_end_mask_0 = const()[name = tensor("op_9176_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9176 = slice_by_index(begin = var_9176_begin_0, end = var_9176_end_0, end_mask = var_9176_end_mask_0, x = reshape_4)[name = tensor("op_9176")]; + tensor segment_accum_449_exclusive_0 = const()[name = tensor("segment_accum_449_exclusive_0"), val = tensor(false)]; + tensor segment_accum_449_reverse_0 = const()[name = tensor("segment_accum_449_reverse_0"), val = tensor(false)]; + tensor segment_accum_449 = cumsum(axis = var_7349, exclusive = segment_accum_449_exclusive_0, reverse = segment_accum_449_reverse_0, x = var_9176)[name = tensor("segment_accum_449")]; + tensor var_9180_begin_0 = const()[name = tensor("op_9180_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9180_end_0 = const()[name = tensor("op_9180_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9180_end_mask_0 = const()[name = tensor("op_9180_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9180 = slice_by_index(begin = var_9180_begin_0, end = var_9180_end_0, end_mask = var_9180_end_mask_0, x = var_9174)[name = tensor("op_9180")]; + tensor var_9182 = add(x = segment_accum_449, y = var_9180)[name = tensor("op_9182")]; + tensor var_9184_begin_0 = const()[name = tensor("op_9184_begin_0"), val = tensor([0, 226000, 0])]; + tensor var_9184_end_0 = const()[name = tensor("op_9184_end_0"), val = tensor([1, 227000, 9])]; + tensor var_9184_end_mask_0 = const()[name = tensor("op_9184_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9184 = slice_by_index(begin = var_9184_begin_0, end = var_9184_end_0, end_mask = var_9184_end_mask_0, x = reshape_4)[name = tensor("op_9184")]; + tensor segment_accum_451_exclusive_0 = const()[name = tensor("segment_accum_451_exclusive_0"), val = tensor(false)]; + tensor segment_accum_451_reverse_0 = const()[name = tensor("segment_accum_451_reverse_0"), val = tensor(false)]; + tensor segment_accum_451 = cumsum(axis = var_7349, exclusive = segment_accum_451_exclusive_0, reverse = segment_accum_451_reverse_0, x = var_9184)[name = tensor("segment_accum_451")]; + tensor var_9188_begin_0 = const()[name = tensor("op_9188_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9188_end_0 = const()[name = tensor("op_9188_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9188_end_mask_0 = const()[name = tensor("op_9188_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9188 = slice_by_index(begin = var_9188_begin_0, end = var_9188_end_0, end_mask = var_9188_end_mask_0, x = var_9182)[name = tensor("op_9188")]; + tensor var_9190 = add(x = segment_accum_451, y = var_9188)[name = tensor("op_9190")]; + tensor var_9192_begin_0 = const()[name = tensor("op_9192_begin_0"), val = tensor([0, 227000, 0])]; + tensor var_9192_end_0 = const()[name = tensor("op_9192_end_0"), val = tensor([1, 228000, 9])]; + tensor var_9192_end_mask_0 = const()[name = tensor("op_9192_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9192 = slice_by_index(begin = var_9192_begin_0, end = var_9192_end_0, end_mask = var_9192_end_mask_0, x = reshape_4)[name = tensor("op_9192")]; + tensor segment_accum_453_exclusive_0 = const()[name = tensor("segment_accum_453_exclusive_0"), val = tensor(false)]; + tensor segment_accum_453_reverse_0 = const()[name = tensor("segment_accum_453_reverse_0"), val = tensor(false)]; + tensor segment_accum_453 = cumsum(axis = var_7349, exclusive = segment_accum_453_exclusive_0, reverse = segment_accum_453_reverse_0, x = var_9192)[name = tensor("segment_accum_453")]; + tensor var_9196_begin_0 = const()[name = tensor("op_9196_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9196_end_0 = const()[name = tensor("op_9196_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9196_end_mask_0 = const()[name = tensor("op_9196_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9196 = slice_by_index(begin = var_9196_begin_0, end = var_9196_end_0, end_mask = var_9196_end_mask_0, x = var_9190)[name = tensor("op_9196")]; + tensor var_9198 = add(x = segment_accum_453, y = var_9196)[name = tensor("op_9198")]; + tensor var_9200_begin_0 = const()[name = tensor("op_9200_begin_0"), val = tensor([0, 228000, 0])]; + tensor var_9200_end_0 = const()[name = tensor("op_9200_end_0"), val = tensor([1, 229000, 9])]; + tensor var_9200_end_mask_0 = const()[name = tensor("op_9200_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9200 = slice_by_index(begin = var_9200_begin_0, end = var_9200_end_0, end_mask = var_9200_end_mask_0, x = reshape_4)[name = tensor("op_9200")]; + tensor segment_accum_455_exclusive_0 = const()[name = tensor("segment_accum_455_exclusive_0"), val = tensor(false)]; + tensor segment_accum_455_reverse_0 = const()[name = tensor("segment_accum_455_reverse_0"), val = tensor(false)]; + tensor segment_accum_455 = cumsum(axis = var_7349, exclusive = segment_accum_455_exclusive_0, reverse = segment_accum_455_reverse_0, x = var_9200)[name = tensor("segment_accum_455")]; + tensor var_9204_begin_0 = const()[name = tensor("op_9204_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9204_end_0 = const()[name = tensor("op_9204_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9204_end_mask_0 = const()[name = tensor("op_9204_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9204 = slice_by_index(begin = var_9204_begin_0, end = var_9204_end_0, end_mask = var_9204_end_mask_0, x = var_9198)[name = tensor("op_9204")]; + tensor var_9206 = add(x = segment_accum_455, y = var_9204)[name = tensor("op_9206")]; + tensor var_9208_begin_0 = const()[name = tensor("op_9208_begin_0"), val = tensor([0, 229000, 0])]; + tensor var_9208_end_0 = const()[name = tensor("op_9208_end_0"), val = tensor([1, 230000, 9])]; + tensor var_9208_end_mask_0 = const()[name = tensor("op_9208_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9208 = slice_by_index(begin = var_9208_begin_0, end = var_9208_end_0, end_mask = var_9208_end_mask_0, x = reshape_4)[name = tensor("op_9208")]; + tensor segment_accum_457_exclusive_0 = const()[name = tensor("segment_accum_457_exclusive_0"), val = tensor(false)]; + tensor segment_accum_457_reverse_0 = const()[name = tensor("segment_accum_457_reverse_0"), val = tensor(false)]; + tensor segment_accum_457 = cumsum(axis = var_7349, exclusive = segment_accum_457_exclusive_0, reverse = segment_accum_457_reverse_0, x = var_9208)[name = tensor("segment_accum_457")]; + tensor var_9212_begin_0 = const()[name = tensor("op_9212_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9212_end_0 = const()[name = tensor("op_9212_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9212_end_mask_0 = const()[name = tensor("op_9212_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9212 = slice_by_index(begin = var_9212_begin_0, end = var_9212_end_0, end_mask = var_9212_end_mask_0, x = var_9206)[name = tensor("op_9212")]; + tensor var_9214 = add(x = segment_accum_457, y = var_9212)[name = tensor("op_9214")]; + tensor var_9216_begin_0 = const()[name = tensor("op_9216_begin_0"), val = tensor([0, 230000, 0])]; + tensor var_9216_end_0 = const()[name = tensor("op_9216_end_0"), val = tensor([1, 231000, 9])]; + tensor var_9216_end_mask_0 = const()[name = tensor("op_9216_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9216 = slice_by_index(begin = var_9216_begin_0, end = var_9216_end_0, end_mask = var_9216_end_mask_0, x = reshape_4)[name = tensor("op_9216")]; + tensor segment_accum_459_exclusive_0 = const()[name = tensor("segment_accum_459_exclusive_0"), val = tensor(false)]; + tensor segment_accum_459_reverse_0 = const()[name = tensor("segment_accum_459_reverse_0"), val = tensor(false)]; + tensor segment_accum_459 = cumsum(axis = var_7349, exclusive = segment_accum_459_exclusive_0, reverse = segment_accum_459_reverse_0, x = var_9216)[name = tensor("segment_accum_459")]; + tensor var_9220_begin_0 = const()[name = tensor("op_9220_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9220_end_0 = const()[name = tensor("op_9220_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9220_end_mask_0 = const()[name = tensor("op_9220_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9220 = slice_by_index(begin = var_9220_begin_0, end = var_9220_end_0, end_mask = var_9220_end_mask_0, x = var_9214)[name = tensor("op_9220")]; + tensor var_9222 = add(x = segment_accum_459, y = var_9220)[name = tensor("op_9222")]; + tensor var_9224_begin_0 = const()[name = tensor("op_9224_begin_0"), val = tensor([0, 231000, 0])]; + tensor var_9224_end_0 = const()[name = tensor("op_9224_end_0"), val = tensor([1, 232000, 9])]; + tensor var_9224_end_mask_0 = const()[name = tensor("op_9224_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9224 = slice_by_index(begin = var_9224_begin_0, end = var_9224_end_0, end_mask = var_9224_end_mask_0, x = reshape_4)[name = tensor("op_9224")]; + tensor segment_accum_461_exclusive_0 = const()[name = tensor("segment_accum_461_exclusive_0"), val = tensor(false)]; + tensor segment_accum_461_reverse_0 = const()[name = tensor("segment_accum_461_reverse_0"), val = tensor(false)]; + tensor segment_accum_461 = cumsum(axis = var_7349, exclusive = segment_accum_461_exclusive_0, reverse = segment_accum_461_reverse_0, x = var_9224)[name = tensor("segment_accum_461")]; + tensor var_9228_begin_0 = const()[name = tensor("op_9228_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9228_end_0 = const()[name = tensor("op_9228_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9228_end_mask_0 = const()[name = tensor("op_9228_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9228 = slice_by_index(begin = var_9228_begin_0, end = var_9228_end_0, end_mask = var_9228_end_mask_0, x = var_9222)[name = tensor("op_9228")]; + tensor var_9230 = add(x = segment_accum_461, y = var_9228)[name = tensor("op_9230")]; + tensor var_9232_begin_0 = const()[name = tensor("op_9232_begin_0"), val = tensor([0, 232000, 0])]; + tensor var_9232_end_0 = const()[name = tensor("op_9232_end_0"), val = tensor([1, 233000, 9])]; + tensor var_9232_end_mask_0 = const()[name = tensor("op_9232_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9232 = slice_by_index(begin = var_9232_begin_0, end = var_9232_end_0, end_mask = var_9232_end_mask_0, x = reshape_4)[name = tensor("op_9232")]; + tensor segment_accum_463_exclusive_0 = const()[name = tensor("segment_accum_463_exclusive_0"), val = tensor(false)]; + tensor segment_accum_463_reverse_0 = const()[name = tensor("segment_accum_463_reverse_0"), val = tensor(false)]; + tensor segment_accum_463 = cumsum(axis = var_7349, exclusive = segment_accum_463_exclusive_0, reverse = segment_accum_463_reverse_0, x = var_9232)[name = tensor("segment_accum_463")]; + tensor var_9236_begin_0 = const()[name = tensor("op_9236_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9236_end_0 = const()[name = tensor("op_9236_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9236_end_mask_0 = const()[name = tensor("op_9236_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9236 = slice_by_index(begin = var_9236_begin_0, end = var_9236_end_0, end_mask = var_9236_end_mask_0, x = var_9230)[name = tensor("op_9236")]; + tensor var_9238 = add(x = segment_accum_463, y = var_9236)[name = tensor("op_9238")]; + tensor var_9240_begin_0 = const()[name = tensor("op_9240_begin_0"), val = tensor([0, 233000, 0])]; + tensor var_9240_end_0 = const()[name = tensor("op_9240_end_0"), val = tensor([1, 234000, 9])]; + tensor var_9240_end_mask_0 = const()[name = tensor("op_9240_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9240 = slice_by_index(begin = var_9240_begin_0, end = var_9240_end_0, end_mask = var_9240_end_mask_0, x = reshape_4)[name = tensor("op_9240")]; + tensor segment_accum_465_exclusive_0 = const()[name = tensor("segment_accum_465_exclusive_0"), val = tensor(false)]; + tensor segment_accum_465_reverse_0 = const()[name = tensor("segment_accum_465_reverse_0"), val = tensor(false)]; + tensor segment_accum_465 = cumsum(axis = var_7349, exclusive = segment_accum_465_exclusive_0, reverse = segment_accum_465_reverse_0, x = var_9240)[name = tensor("segment_accum_465")]; + tensor var_9244_begin_0 = const()[name = tensor("op_9244_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9244_end_0 = const()[name = tensor("op_9244_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9244_end_mask_0 = const()[name = tensor("op_9244_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9244 = slice_by_index(begin = var_9244_begin_0, end = var_9244_end_0, end_mask = var_9244_end_mask_0, x = var_9238)[name = tensor("op_9244")]; + tensor var_9246 = add(x = segment_accum_465, y = var_9244)[name = tensor("op_9246")]; + tensor var_9248_begin_0 = const()[name = tensor("op_9248_begin_0"), val = tensor([0, 234000, 0])]; + tensor var_9248_end_0 = const()[name = tensor("op_9248_end_0"), val = tensor([1, 235000, 9])]; + tensor var_9248_end_mask_0 = const()[name = tensor("op_9248_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9248 = slice_by_index(begin = var_9248_begin_0, end = var_9248_end_0, end_mask = var_9248_end_mask_0, x = reshape_4)[name = tensor("op_9248")]; + tensor segment_accum_467_exclusive_0 = const()[name = tensor("segment_accum_467_exclusive_0"), val = tensor(false)]; + tensor segment_accum_467_reverse_0 = const()[name = tensor("segment_accum_467_reverse_0"), val = tensor(false)]; + tensor segment_accum_467 = cumsum(axis = var_7349, exclusive = segment_accum_467_exclusive_0, reverse = segment_accum_467_reverse_0, x = var_9248)[name = tensor("segment_accum_467")]; + tensor var_9252_begin_0 = const()[name = tensor("op_9252_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9252_end_0 = const()[name = tensor("op_9252_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9252_end_mask_0 = const()[name = tensor("op_9252_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9252 = slice_by_index(begin = var_9252_begin_0, end = var_9252_end_0, end_mask = var_9252_end_mask_0, x = var_9246)[name = tensor("op_9252")]; + tensor var_9254 = add(x = segment_accum_467, y = var_9252)[name = tensor("op_9254")]; + tensor var_9256_begin_0 = const()[name = tensor("op_9256_begin_0"), val = tensor([0, 235000, 0])]; + tensor var_9256_end_0 = const()[name = tensor("op_9256_end_0"), val = tensor([1, 236000, 9])]; + tensor var_9256_end_mask_0 = const()[name = tensor("op_9256_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9256 = slice_by_index(begin = var_9256_begin_0, end = var_9256_end_0, end_mask = var_9256_end_mask_0, x = reshape_4)[name = tensor("op_9256")]; + tensor segment_accum_469_exclusive_0 = const()[name = tensor("segment_accum_469_exclusive_0"), val = tensor(false)]; + tensor segment_accum_469_reverse_0 = const()[name = tensor("segment_accum_469_reverse_0"), val = tensor(false)]; + tensor segment_accum_469 = cumsum(axis = var_7349, exclusive = segment_accum_469_exclusive_0, reverse = segment_accum_469_reverse_0, x = var_9256)[name = tensor("segment_accum_469")]; + tensor var_9260_begin_0 = const()[name = tensor("op_9260_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9260_end_0 = const()[name = tensor("op_9260_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9260_end_mask_0 = const()[name = tensor("op_9260_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9260 = slice_by_index(begin = var_9260_begin_0, end = var_9260_end_0, end_mask = var_9260_end_mask_0, x = var_9254)[name = tensor("op_9260")]; + tensor var_9262 = add(x = segment_accum_469, y = var_9260)[name = tensor("op_9262")]; + tensor var_9264_begin_0 = const()[name = tensor("op_9264_begin_0"), val = tensor([0, 236000, 0])]; + tensor var_9264_end_0 = const()[name = tensor("op_9264_end_0"), val = tensor([1, 237000, 9])]; + tensor var_9264_end_mask_0 = const()[name = tensor("op_9264_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9264 = slice_by_index(begin = var_9264_begin_0, end = var_9264_end_0, end_mask = var_9264_end_mask_0, x = reshape_4)[name = tensor("op_9264")]; + tensor segment_accum_471_exclusive_0 = const()[name = tensor("segment_accum_471_exclusive_0"), val = tensor(false)]; + tensor segment_accum_471_reverse_0 = const()[name = tensor("segment_accum_471_reverse_0"), val = tensor(false)]; + tensor segment_accum_471 = cumsum(axis = var_7349, exclusive = segment_accum_471_exclusive_0, reverse = segment_accum_471_reverse_0, x = var_9264)[name = tensor("segment_accum_471")]; + tensor var_9268_begin_0 = const()[name = tensor("op_9268_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9268_end_0 = const()[name = tensor("op_9268_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9268_end_mask_0 = const()[name = tensor("op_9268_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9268 = slice_by_index(begin = var_9268_begin_0, end = var_9268_end_0, end_mask = var_9268_end_mask_0, x = var_9262)[name = tensor("op_9268")]; + tensor var_9270 = add(x = segment_accum_471, y = var_9268)[name = tensor("op_9270")]; + tensor var_9272_begin_0 = const()[name = tensor("op_9272_begin_0"), val = tensor([0, 237000, 0])]; + tensor var_9272_end_0 = const()[name = tensor("op_9272_end_0"), val = tensor([1, 238000, 9])]; + tensor var_9272_end_mask_0 = const()[name = tensor("op_9272_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9272 = slice_by_index(begin = var_9272_begin_0, end = var_9272_end_0, end_mask = var_9272_end_mask_0, x = reshape_4)[name = tensor("op_9272")]; + tensor segment_accum_473_exclusive_0 = const()[name = tensor("segment_accum_473_exclusive_0"), val = tensor(false)]; + tensor segment_accum_473_reverse_0 = const()[name = tensor("segment_accum_473_reverse_0"), val = tensor(false)]; + tensor segment_accum_473 = cumsum(axis = var_7349, exclusive = segment_accum_473_exclusive_0, reverse = segment_accum_473_reverse_0, x = var_9272)[name = tensor("segment_accum_473")]; + tensor var_9276_begin_0 = const()[name = tensor("op_9276_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9276_end_0 = const()[name = tensor("op_9276_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9276_end_mask_0 = const()[name = tensor("op_9276_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9276 = slice_by_index(begin = var_9276_begin_0, end = var_9276_end_0, end_mask = var_9276_end_mask_0, x = var_9270)[name = tensor("op_9276")]; + tensor var_9278 = add(x = segment_accum_473, y = var_9276)[name = tensor("op_9278")]; + tensor var_9280_begin_0 = const()[name = tensor("op_9280_begin_0"), val = tensor([0, 238000, 0])]; + tensor var_9280_end_0 = const()[name = tensor("op_9280_end_0"), val = tensor([1, 239000, 9])]; + tensor var_9280_end_mask_0 = const()[name = tensor("op_9280_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9280 = slice_by_index(begin = var_9280_begin_0, end = var_9280_end_0, end_mask = var_9280_end_mask_0, x = reshape_4)[name = tensor("op_9280")]; + tensor segment_accum_475_exclusive_0 = const()[name = tensor("segment_accum_475_exclusive_0"), val = tensor(false)]; + tensor segment_accum_475_reverse_0 = const()[name = tensor("segment_accum_475_reverse_0"), val = tensor(false)]; + tensor segment_accum_475 = cumsum(axis = var_7349, exclusive = segment_accum_475_exclusive_0, reverse = segment_accum_475_reverse_0, x = var_9280)[name = tensor("segment_accum_475")]; + tensor var_9284_begin_0 = const()[name = tensor("op_9284_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9284_end_0 = const()[name = tensor("op_9284_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9284_end_mask_0 = const()[name = tensor("op_9284_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9284 = slice_by_index(begin = var_9284_begin_0, end = var_9284_end_0, end_mask = var_9284_end_mask_0, x = var_9278)[name = tensor("op_9284")]; + tensor var_9286 = add(x = segment_accum_475, y = var_9284)[name = tensor("op_9286")]; + tensor var_9288_begin_0 = const()[name = tensor("op_9288_begin_0"), val = tensor([0, 239000, 0])]; + tensor var_9288_end_0 = const()[name = tensor("op_9288_end_0"), val = tensor([1, 240000, 9])]; + tensor var_9288_end_mask_0 = const()[name = tensor("op_9288_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9288 = slice_by_index(begin = var_9288_begin_0, end = var_9288_end_0, end_mask = var_9288_end_mask_0, x = reshape_4)[name = tensor("op_9288")]; + tensor segment_accum_477_exclusive_0 = const()[name = tensor("segment_accum_477_exclusive_0"), val = tensor(false)]; + tensor segment_accum_477_reverse_0 = const()[name = tensor("segment_accum_477_reverse_0"), val = tensor(false)]; + tensor segment_accum_477 = cumsum(axis = var_7349, exclusive = segment_accum_477_exclusive_0, reverse = segment_accum_477_reverse_0, x = var_9288)[name = tensor("segment_accum_477")]; + tensor var_9292_begin_0 = const()[name = tensor("op_9292_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9292_end_0 = const()[name = tensor("op_9292_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9292_end_mask_0 = const()[name = tensor("op_9292_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9292 = slice_by_index(begin = var_9292_begin_0, end = var_9292_end_0, end_mask = var_9292_end_mask_0, x = var_9286)[name = tensor("op_9292")]; + tensor var_9294 = add(x = segment_accum_477, y = var_9292)[name = tensor("op_9294")]; + tensor var_9296_begin_0 = const()[name = tensor("op_9296_begin_0"), val = tensor([0, 240000, 0])]; + tensor var_9296_end_0 = const()[name = tensor("op_9296_end_0"), val = tensor([1, 241000, 9])]; + tensor var_9296_end_mask_0 = const()[name = tensor("op_9296_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9296 = slice_by_index(begin = var_9296_begin_0, end = var_9296_end_0, end_mask = var_9296_end_mask_0, x = reshape_4)[name = tensor("op_9296")]; + tensor segment_accum_479_exclusive_0 = const()[name = tensor("segment_accum_479_exclusive_0"), val = tensor(false)]; + tensor segment_accum_479_reverse_0 = const()[name = tensor("segment_accum_479_reverse_0"), val = tensor(false)]; + tensor segment_accum_479 = cumsum(axis = var_7349, exclusive = segment_accum_479_exclusive_0, reverse = segment_accum_479_reverse_0, x = var_9296)[name = tensor("segment_accum_479")]; + tensor var_9300_begin_0 = const()[name = tensor("op_9300_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9300_end_0 = const()[name = tensor("op_9300_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9300_end_mask_0 = const()[name = tensor("op_9300_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9300 = slice_by_index(begin = var_9300_begin_0, end = var_9300_end_0, end_mask = var_9300_end_mask_0, x = var_9294)[name = tensor("op_9300")]; + tensor var_9302 = add(x = segment_accum_479, y = var_9300)[name = tensor("op_9302")]; + tensor var_9304_begin_0 = const()[name = tensor("op_9304_begin_0"), val = tensor([0, 241000, 0])]; + tensor var_9304_end_0 = const()[name = tensor("op_9304_end_0"), val = tensor([1, 242000, 9])]; + tensor var_9304_end_mask_0 = const()[name = tensor("op_9304_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9304 = slice_by_index(begin = var_9304_begin_0, end = var_9304_end_0, end_mask = var_9304_end_mask_0, x = reshape_4)[name = tensor("op_9304")]; + tensor segment_accum_481_exclusive_0 = const()[name = tensor("segment_accum_481_exclusive_0"), val = tensor(false)]; + tensor segment_accum_481_reverse_0 = const()[name = tensor("segment_accum_481_reverse_0"), val = tensor(false)]; + tensor segment_accum_481 = cumsum(axis = var_7349, exclusive = segment_accum_481_exclusive_0, reverse = segment_accum_481_reverse_0, x = var_9304)[name = tensor("segment_accum_481")]; + tensor var_9308_begin_0 = const()[name = tensor("op_9308_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9308_end_0 = const()[name = tensor("op_9308_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9308_end_mask_0 = const()[name = tensor("op_9308_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9308 = slice_by_index(begin = var_9308_begin_0, end = var_9308_end_0, end_mask = var_9308_end_mask_0, x = var_9302)[name = tensor("op_9308")]; + tensor var_9310 = add(x = segment_accum_481, y = var_9308)[name = tensor("op_9310")]; + tensor var_9312_begin_0 = const()[name = tensor("op_9312_begin_0"), val = tensor([0, 242000, 0])]; + tensor var_9312_end_0 = const()[name = tensor("op_9312_end_0"), val = tensor([1, 243000, 9])]; + tensor var_9312_end_mask_0 = const()[name = tensor("op_9312_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9312 = slice_by_index(begin = var_9312_begin_0, end = var_9312_end_0, end_mask = var_9312_end_mask_0, x = reshape_4)[name = tensor("op_9312")]; + tensor segment_accum_483_exclusive_0 = const()[name = tensor("segment_accum_483_exclusive_0"), val = tensor(false)]; + tensor segment_accum_483_reverse_0 = const()[name = tensor("segment_accum_483_reverse_0"), val = tensor(false)]; + tensor segment_accum_483 = cumsum(axis = var_7349, exclusive = segment_accum_483_exclusive_0, reverse = segment_accum_483_reverse_0, x = var_9312)[name = tensor("segment_accum_483")]; + tensor var_9316_begin_0 = const()[name = tensor("op_9316_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9316_end_0 = const()[name = tensor("op_9316_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9316_end_mask_0 = const()[name = tensor("op_9316_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9316 = slice_by_index(begin = var_9316_begin_0, end = var_9316_end_0, end_mask = var_9316_end_mask_0, x = var_9310)[name = tensor("op_9316")]; + tensor var_9318 = add(x = segment_accum_483, y = var_9316)[name = tensor("op_9318")]; + tensor var_9320_begin_0 = const()[name = tensor("op_9320_begin_0"), val = tensor([0, 243000, 0])]; + tensor var_9320_end_0 = const()[name = tensor("op_9320_end_0"), val = tensor([1, 244000, 9])]; + tensor var_9320_end_mask_0 = const()[name = tensor("op_9320_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9320 = slice_by_index(begin = var_9320_begin_0, end = var_9320_end_0, end_mask = var_9320_end_mask_0, x = reshape_4)[name = tensor("op_9320")]; + tensor segment_accum_485_exclusive_0 = const()[name = tensor("segment_accum_485_exclusive_0"), val = tensor(false)]; + tensor segment_accum_485_reverse_0 = const()[name = tensor("segment_accum_485_reverse_0"), val = tensor(false)]; + tensor segment_accum_485 = cumsum(axis = var_7349, exclusive = segment_accum_485_exclusive_0, reverse = segment_accum_485_reverse_0, x = var_9320)[name = tensor("segment_accum_485")]; + tensor var_9324_begin_0 = const()[name = tensor("op_9324_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9324_end_0 = const()[name = tensor("op_9324_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9324_end_mask_0 = const()[name = tensor("op_9324_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9324 = slice_by_index(begin = var_9324_begin_0, end = var_9324_end_0, end_mask = var_9324_end_mask_0, x = var_9318)[name = tensor("op_9324")]; + tensor var_9326 = add(x = segment_accum_485, y = var_9324)[name = tensor("op_9326")]; + tensor var_9328_begin_0 = const()[name = tensor("op_9328_begin_0"), val = tensor([0, 244000, 0])]; + tensor var_9328_end_0 = const()[name = tensor("op_9328_end_0"), val = tensor([1, 245000, 9])]; + tensor var_9328_end_mask_0 = const()[name = tensor("op_9328_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9328 = slice_by_index(begin = var_9328_begin_0, end = var_9328_end_0, end_mask = var_9328_end_mask_0, x = reshape_4)[name = tensor("op_9328")]; + tensor segment_accum_487_exclusive_0 = const()[name = tensor("segment_accum_487_exclusive_0"), val = tensor(false)]; + tensor segment_accum_487_reverse_0 = const()[name = tensor("segment_accum_487_reverse_0"), val = tensor(false)]; + tensor segment_accum_487 = cumsum(axis = var_7349, exclusive = segment_accum_487_exclusive_0, reverse = segment_accum_487_reverse_0, x = var_9328)[name = tensor("segment_accum_487")]; + tensor var_9332_begin_0 = const()[name = tensor("op_9332_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9332_end_0 = const()[name = tensor("op_9332_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9332_end_mask_0 = const()[name = tensor("op_9332_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9332 = slice_by_index(begin = var_9332_begin_0, end = var_9332_end_0, end_mask = var_9332_end_mask_0, x = var_9326)[name = tensor("op_9332")]; + tensor var_9334 = add(x = segment_accum_487, y = var_9332)[name = tensor("op_9334")]; + tensor var_9336_begin_0 = const()[name = tensor("op_9336_begin_0"), val = tensor([0, 245000, 0])]; + tensor var_9336_end_0 = const()[name = tensor("op_9336_end_0"), val = tensor([1, 246000, 9])]; + tensor var_9336_end_mask_0 = const()[name = tensor("op_9336_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9336 = slice_by_index(begin = var_9336_begin_0, end = var_9336_end_0, end_mask = var_9336_end_mask_0, x = reshape_4)[name = tensor("op_9336")]; + tensor segment_accum_489_exclusive_0 = const()[name = tensor("segment_accum_489_exclusive_0"), val = tensor(false)]; + tensor segment_accum_489_reverse_0 = const()[name = tensor("segment_accum_489_reverse_0"), val = tensor(false)]; + tensor segment_accum_489 = cumsum(axis = var_7349, exclusive = segment_accum_489_exclusive_0, reverse = segment_accum_489_reverse_0, x = var_9336)[name = tensor("segment_accum_489")]; + tensor var_9340_begin_0 = const()[name = tensor("op_9340_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9340_end_0 = const()[name = tensor("op_9340_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9340_end_mask_0 = const()[name = tensor("op_9340_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9340 = slice_by_index(begin = var_9340_begin_0, end = var_9340_end_0, end_mask = var_9340_end_mask_0, x = var_9334)[name = tensor("op_9340")]; + tensor var_9342 = add(x = segment_accum_489, y = var_9340)[name = tensor("op_9342")]; + tensor var_9344_begin_0 = const()[name = tensor("op_9344_begin_0"), val = tensor([0, 246000, 0])]; + tensor var_9344_end_0 = const()[name = tensor("op_9344_end_0"), val = tensor([1, 247000, 9])]; + tensor var_9344_end_mask_0 = const()[name = tensor("op_9344_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9344 = slice_by_index(begin = var_9344_begin_0, end = var_9344_end_0, end_mask = var_9344_end_mask_0, x = reshape_4)[name = tensor("op_9344")]; + tensor segment_accum_491_exclusive_0 = const()[name = tensor("segment_accum_491_exclusive_0"), val = tensor(false)]; + tensor segment_accum_491_reverse_0 = const()[name = tensor("segment_accum_491_reverse_0"), val = tensor(false)]; + tensor segment_accum_491 = cumsum(axis = var_7349, exclusive = segment_accum_491_exclusive_0, reverse = segment_accum_491_reverse_0, x = var_9344)[name = tensor("segment_accum_491")]; + tensor var_9348_begin_0 = const()[name = tensor("op_9348_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9348_end_0 = const()[name = tensor("op_9348_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9348_end_mask_0 = const()[name = tensor("op_9348_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9348 = slice_by_index(begin = var_9348_begin_0, end = var_9348_end_0, end_mask = var_9348_end_mask_0, x = var_9342)[name = tensor("op_9348")]; + tensor var_9350 = add(x = segment_accum_491, y = var_9348)[name = tensor("op_9350")]; + tensor var_9352_begin_0 = const()[name = tensor("op_9352_begin_0"), val = tensor([0, 247000, 0])]; + tensor var_9352_end_0 = const()[name = tensor("op_9352_end_0"), val = tensor([1, 248000, 9])]; + tensor var_9352_end_mask_0 = const()[name = tensor("op_9352_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9352 = slice_by_index(begin = var_9352_begin_0, end = var_9352_end_0, end_mask = var_9352_end_mask_0, x = reshape_4)[name = tensor("op_9352")]; + tensor segment_accum_493_exclusive_0 = const()[name = tensor("segment_accum_493_exclusive_0"), val = tensor(false)]; + tensor segment_accum_493_reverse_0 = const()[name = tensor("segment_accum_493_reverse_0"), val = tensor(false)]; + tensor segment_accum_493 = cumsum(axis = var_7349, exclusive = segment_accum_493_exclusive_0, reverse = segment_accum_493_reverse_0, x = var_9352)[name = tensor("segment_accum_493")]; + tensor var_9356_begin_0 = const()[name = tensor("op_9356_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9356_end_0 = const()[name = tensor("op_9356_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9356_end_mask_0 = const()[name = tensor("op_9356_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9356 = slice_by_index(begin = var_9356_begin_0, end = var_9356_end_0, end_mask = var_9356_end_mask_0, x = var_9350)[name = tensor("op_9356")]; + tensor var_9358 = add(x = segment_accum_493, y = var_9356)[name = tensor("op_9358")]; + tensor var_9360_begin_0 = const()[name = tensor("op_9360_begin_0"), val = tensor([0, 248000, 0])]; + tensor var_9360_end_0 = const()[name = tensor("op_9360_end_0"), val = tensor([1, 249000, 9])]; + tensor var_9360_end_mask_0 = const()[name = tensor("op_9360_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9360 = slice_by_index(begin = var_9360_begin_0, end = var_9360_end_0, end_mask = var_9360_end_mask_0, x = reshape_4)[name = tensor("op_9360")]; + tensor segment_accum_495_exclusive_0 = const()[name = tensor("segment_accum_495_exclusive_0"), val = tensor(false)]; + tensor segment_accum_495_reverse_0 = const()[name = tensor("segment_accum_495_reverse_0"), val = tensor(false)]; + tensor segment_accum_495 = cumsum(axis = var_7349, exclusive = segment_accum_495_exclusive_0, reverse = segment_accum_495_reverse_0, x = var_9360)[name = tensor("segment_accum_495")]; + tensor var_9364_begin_0 = const()[name = tensor("op_9364_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9364_end_0 = const()[name = tensor("op_9364_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9364_end_mask_0 = const()[name = tensor("op_9364_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9364 = slice_by_index(begin = var_9364_begin_0, end = var_9364_end_0, end_mask = var_9364_end_mask_0, x = var_9358)[name = tensor("op_9364")]; + tensor var_9366 = add(x = segment_accum_495, y = var_9364)[name = tensor("op_9366")]; + tensor var_9368_begin_0 = const()[name = tensor("op_9368_begin_0"), val = tensor([0, 249000, 0])]; + tensor var_9368_end_0 = const()[name = tensor("op_9368_end_0"), val = tensor([1, 250000, 9])]; + tensor var_9368_end_mask_0 = const()[name = tensor("op_9368_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9368 = slice_by_index(begin = var_9368_begin_0, end = var_9368_end_0, end_mask = var_9368_end_mask_0, x = reshape_4)[name = tensor("op_9368")]; + tensor segment_accum_497_exclusive_0 = const()[name = tensor("segment_accum_497_exclusive_0"), val = tensor(false)]; + tensor segment_accum_497_reverse_0 = const()[name = tensor("segment_accum_497_reverse_0"), val = tensor(false)]; + tensor segment_accum_497 = cumsum(axis = var_7349, exclusive = segment_accum_497_exclusive_0, reverse = segment_accum_497_reverse_0, x = var_9368)[name = tensor("segment_accum_497")]; + tensor var_9372_begin_0 = const()[name = tensor("op_9372_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9372_end_0 = const()[name = tensor("op_9372_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9372_end_mask_0 = const()[name = tensor("op_9372_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9372 = slice_by_index(begin = var_9372_begin_0, end = var_9372_end_0, end_mask = var_9372_end_mask_0, x = var_9366)[name = tensor("op_9372")]; + tensor var_9374 = add(x = segment_accum_497, y = var_9372)[name = tensor("op_9374")]; + tensor var_9376_begin_0 = const()[name = tensor("op_9376_begin_0"), val = tensor([0, 250000, 0])]; + tensor var_9376_end_0 = const()[name = tensor("op_9376_end_0"), val = tensor([1, 251000, 9])]; + tensor var_9376_end_mask_0 = const()[name = tensor("op_9376_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9376 = slice_by_index(begin = var_9376_begin_0, end = var_9376_end_0, end_mask = var_9376_end_mask_0, x = reshape_4)[name = tensor("op_9376")]; + tensor segment_accum_499_exclusive_0 = const()[name = tensor("segment_accum_499_exclusive_0"), val = tensor(false)]; + tensor segment_accum_499_reverse_0 = const()[name = tensor("segment_accum_499_reverse_0"), val = tensor(false)]; + tensor segment_accum_499 = cumsum(axis = var_7349, exclusive = segment_accum_499_exclusive_0, reverse = segment_accum_499_reverse_0, x = var_9376)[name = tensor("segment_accum_499")]; + tensor var_9380_begin_0 = const()[name = tensor("op_9380_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9380_end_0 = const()[name = tensor("op_9380_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9380_end_mask_0 = const()[name = tensor("op_9380_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9380 = slice_by_index(begin = var_9380_begin_0, end = var_9380_end_0, end_mask = var_9380_end_mask_0, x = var_9374)[name = tensor("op_9380")]; + tensor var_9382 = add(x = segment_accum_499, y = var_9380)[name = tensor("op_9382")]; + tensor var_9384_begin_0 = const()[name = tensor("op_9384_begin_0"), val = tensor([0, 251000, 0])]; + tensor var_9384_end_0 = const()[name = tensor("op_9384_end_0"), val = tensor([1, 252000, 9])]; + tensor var_9384_end_mask_0 = const()[name = tensor("op_9384_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9384 = slice_by_index(begin = var_9384_begin_0, end = var_9384_end_0, end_mask = var_9384_end_mask_0, x = reshape_4)[name = tensor("op_9384")]; + tensor segment_accum_501_exclusive_0 = const()[name = tensor("segment_accum_501_exclusive_0"), val = tensor(false)]; + tensor segment_accum_501_reverse_0 = const()[name = tensor("segment_accum_501_reverse_0"), val = tensor(false)]; + tensor segment_accum_501 = cumsum(axis = var_7349, exclusive = segment_accum_501_exclusive_0, reverse = segment_accum_501_reverse_0, x = var_9384)[name = tensor("segment_accum_501")]; + tensor var_9388_begin_0 = const()[name = tensor("op_9388_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9388_end_0 = const()[name = tensor("op_9388_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9388_end_mask_0 = const()[name = tensor("op_9388_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9388 = slice_by_index(begin = var_9388_begin_0, end = var_9388_end_0, end_mask = var_9388_end_mask_0, x = var_9382)[name = tensor("op_9388")]; + tensor var_9390 = add(x = segment_accum_501, y = var_9388)[name = tensor("op_9390")]; + tensor var_9392_begin_0 = const()[name = tensor("op_9392_begin_0"), val = tensor([0, 252000, 0])]; + tensor var_9392_end_0 = const()[name = tensor("op_9392_end_0"), val = tensor([1, 253000, 9])]; + tensor var_9392_end_mask_0 = const()[name = tensor("op_9392_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9392 = slice_by_index(begin = var_9392_begin_0, end = var_9392_end_0, end_mask = var_9392_end_mask_0, x = reshape_4)[name = tensor("op_9392")]; + tensor segment_accum_503_exclusive_0 = const()[name = tensor("segment_accum_503_exclusive_0"), val = tensor(false)]; + tensor segment_accum_503_reverse_0 = const()[name = tensor("segment_accum_503_reverse_0"), val = tensor(false)]; + tensor segment_accum_503 = cumsum(axis = var_7349, exclusive = segment_accum_503_exclusive_0, reverse = segment_accum_503_reverse_0, x = var_9392)[name = tensor("segment_accum_503")]; + tensor var_9396_begin_0 = const()[name = tensor("op_9396_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9396_end_0 = const()[name = tensor("op_9396_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9396_end_mask_0 = const()[name = tensor("op_9396_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9396 = slice_by_index(begin = var_9396_begin_0, end = var_9396_end_0, end_mask = var_9396_end_mask_0, x = var_9390)[name = tensor("op_9396")]; + tensor var_9398 = add(x = segment_accum_503, y = var_9396)[name = tensor("op_9398")]; + tensor var_9400_begin_0 = const()[name = tensor("op_9400_begin_0"), val = tensor([0, 253000, 0])]; + tensor var_9400_end_0 = const()[name = tensor("op_9400_end_0"), val = tensor([1, 254000, 9])]; + tensor var_9400_end_mask_0 = const()[name = tensor("op_9400_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9400 = slice_by_index(begin = var_9400_begin_0, end = var_9400_end_0, end_mask = var_9400_end_mask_0, x = reshape_4)[name = tensor("op_9400")]; + tensor segment_accum_505_exclusive_0 = const()[name = tensor("segment_accum_505_exclusive_0"), val = tensor(false)]; + tensor segment_accum_505_reverse_0 = const()[name = tensor("segment_accum_505_reverse_0"), val = tensor(false)]; + tensor segment_accum_505 = cumsum(axis = var_7349, exclusive = segment_accum_505_exclusive_0, reverse = segment_accum_505_reverse_0, x = var_9400)[name = tensor("segment_accum_505")]; + tensor var_9404_begin_0 = const()[name = tensor("op_9404_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9404_end_0 = const()[name = tensor("op_9404_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9404_end_mask_0 = const()[name = tensor("op_9404_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9404 = slice_by_index(begin = var_9404_begin_0, end = var_9404_end_0, end_mask = var_9404_end_mask_0, x = var_9398)[name = tensor("op_9404")]; + tensor var_9406 = add(x = segment_accum_505, y = var_9404)[name = tensor("op_9406")]; + tensor var_9408_begin_0 = const()[name = tensor("op_9408_begin_0"), val = tensor([0, 254000, 0])]; + tensor var_9408_end_0 = const()[name = tensor("op_9408_end_0"), val = tensor([1, 255000, 9])]; + tensor var_9408_end_mask_0 = const()[name = tensor("op_9408_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9408 = slice_by_index(begin = var_9408_begin_0, end = var_9408_end_0, end_mask = var_9408_end_mask_0, x = reshape_4)[name = tensor("op_9408")]; + tensor segment_accum_507_exclusive_0 = const()[name = tensor("segment_accum_507_exclusive_0"), val = tensor(false)]; + tensor segment_accum_507_reverse_0 = const()[name = tensor("segment_accum_507_reverse_0"), val = tensor(false)]; + tensor segment_accum_507 = cumsum(axis = var_7349, exclusive = segment_accum_507_exclusive_0, reverse = segment_accum_507_reverse_0, x = var_9408)[name = tensor("segment_accum_507")]; + tensor var_9412_begin_0 = const()[name = tensor("op_9412_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9412_end_0 = const()[name = tensor("op_9412_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9412_end_mask_0 = const()[name = tensor("op_9412_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9412 = slice_by_index(begin = var_9412_begin_0, end = var_9412_end_0, end_mask = var_9412_end_mask_0, x = var_9406)[name = tensor("op_9412")]; + tensor var_9414 = add(x = segment_accum_507, y = var_9412)[name = tensor("op_9414")]; + tensor var_9416_begin_0 = const()[name = tensor("op_9416_begin_0"), val = tensor([0, 255000, 0])]; + tensor var_9416_end_0 = const()[name = tensor("op_9416_end_0"), val = tensor([1, 256000, 9])]; + tensor var_9416_end_mask_0 = const()[name = tensor("op_9416_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9416 = slice_by_index(begin = var_9416_begin_0, end = var_9416_end_0, end_mask = var_9416_end_mask_0, x = reshape_4)[name = tensor("op_9416")]; + tensor segment_accum_509_exclusive_0 = const()[name = tensor("segment_accum_509_exclusive_0"), val = tensor(false)]; + tensor segment_accum_509_reverse_0 = const()[name = tensor("segment_accum_509_reverse_0"), val = tensor(false)]; + tensor segment_accum_509 = cumsum(axis = var_7349, exclusive = segment_accum_509_exclusive_0, reverse = segment_accum_509_reverse_0, x = var_9416)[name = tensor("segment_accum_509")]; + tensor var_9420_begin_0 = const()[name = tensor("op_9420_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9420_end_0 = const()[name = tensor("op_9420_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9420_end_mask_0 = const()[name = tensor("op_9420_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9420 = slice_by_index(begin = var_9420_begin_0, end = var_9420_end_0, end_mask = var_9420_end_mask_0, x = var_9414)[name = tensor("op_9420")]; + tensor var_9422 = add(x = segment_accum_509, y = var_9420)[name = tensor("op_9422")]; + tensor var_9424_begin_0 = const()[name = tensor("op_9424_begin_0"), val = tensor([0, 256000, 0])]; + tensor var_9424_end_0 = const()[name = tensor("op_9424_end_0"), val = tensor([1, 257000, 9])]; + tensor var_9424_end_mask_0 = const()[name = tensor("op_9424_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9424 = slice_by_index(begin = var_9424_begin_0, end = var_9424_end_0, end_mask = var_9424_end_mask_0, x = reshape_4)[name = tensor("op_9424")]; + tensor segment_accum_511_exclusive_0 = const()[name = tensor("segment_accum_511_exclusive_0"), val = tensor(false)]; + tensor segment_accum_511_reverse_0 = const()[name = tensor("segment_accum_511_reverse_0"), val = tensor(false)]; + tensor segment_accum_511 = cumsum(axis = var_7349, exclusive = segment_accum_511_exclusive_0, reverse = segment_accum_511_reverse_0, x = var_9424)[name = tensor("segment_accum_511")]; + tensor var_9428_begin_0 = const()[name = tensor("op_9428_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9428_end_0 = const()[name = tensor("op_9428_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9428_end_mask_0 = const()[name = tensor("op_9428_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9428 = slice_by_index(begin = var_9428_begin_0, end = var_9428_end_0, end_mask = var_9428_end_mask_0, x = var_9422)[name = tensor("op_9428")]; + tensor var_9430 = add(x = segment_accum_511, y = var_9428)[name = tensor("op_9430")]; + tensor var_9432_begin_0 = const()[name = tensor("op_9432_begin_0"), val = tensor([0, 257000, 0])]; + tensor var_9432_end_0 = const()[name = tensor("op_9432_end_0"), val = tensor([1, 258000, 9])]; + tensor var_9432_end_mask_0 = const()[name = tensor("op_9432_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9432 = slice_by_index(begin = var_9432_begin_0, end = var_9432_end_0, end_mask = var_9432_end_mask_0, x = reshape_4)[name = tensor("op_9432")]; + tensor segment_accum_513_exclusive_0 = const()[name = tensor("segment_accum_513_exclusive_0"), val = tensor(false)]; + tensor segment_accum_513_reverse_0 = const()[name = tensor("segment_accum_513_reverse_0"), val = tensor(false)]; + tensor segment_accum_513 = cumsum(axis = var_7349, exclusive = segment_accum_513_exclusive_0, reverse = segment_accum_513_reverse_0, x = var_9432)[name = tensor("segment_accum_513")]; + tensor var_9436_begin_0 = const()[name = tensor("op_9436_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9436_end_0 = const()[name = tensor("op_9436_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9436_end_mask_0 = const()[name = tensor("op_9436_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9436 = slice_by_index(begin = var_9436_begin_0, end = var_9436_end_0, end_mask = var_9436_end_mask_0, x = var_9430)[name = tensor("op_9436")]; + tensor var_9438 = add(x = segment_accum_513, y = var_9436)[name = tensor("op_9438")]; + tensor var_9440_begin_0 = const()[name = tensor("op_9440_begin_0"), val = tensor([0, 258000, 0])]; + tensor var_9440_end_0 = const()[name = tensor("op_9440_end_0"), val = tensor([1, 259000, 9])]; + tensor var_9440_end_mask_0 = const()[name = tensor("op_9440_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9440 = slice_by_index(begin = var_9440_begin_0, end = var_9440_end_0, end_mask = var_9440_end_mask_0, x = reshape_4)[name = tensor("op_9440")]; + tensor segment_accum_515_exclusive_0 = const()[name = tensor("segment_accum_515_exclusive_0"), val = tensor(false)]; + tensor segment_accum_515_reverse_0 = const()[name = tensor("segment_accum_515_reverse_0"), val = tensor(false)]; + tensor segment_accum_515 = cumsum(axis = var_7349, exclusive = segment_accum_515_exclusive_0, reverse = segment_accum_515_reverse_0, x = var_9440)[name = tensor("segment_accum_515")]; + tensor var_9444_begin_0 = const()[name = tensor("op_9444_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9444_end_0 = const()[name = tensor("op_9444_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9444_end_mask_0 = const()[name = tensor("op_9444_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9444 = slice_by_index(begin = var_9444_begin_0, end = var_9444_end_0, end_mask = var_9444_end_mask_0, x = var_9438)[name = tensor("op_9444")]; + tensor var_9446 = add(x = segment_accum_515, y = var_9444)[name = tensor("op_9446")]; + tensor var_9448_begin_0 = const()[name = tensor("op_9448_begin_0"), val = tensor([0, 259000, 0])]; + tensor var_9448_end_0 = const()[name = tensor("op_9448_end_0"), val = tensor([1, 260000, 9])]; + tensor var_9448_end_mask_0 = const()[name = tensor("op_9448_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9448 = slice_by_index(begin = var_9448_begin_0, end = var_9448_end_0, end_mask = var_9448_end_mask_0, x = reshape_4)[name = tensor("op_9448")]; + tensor segment_accum_517_exclusive_0 = const()[name = tensor("segment_accum_517_exclusive_0"), val = tensor(false)]; + tensor segment_accum_517_reverse_0 = const()[name = tensor("segment_accum_517_reverse_0"), val = tensor(false)]; + tensor segment_accum_517 = cumsum(axis = var_7349, exclusive = segment_accum_517_exclusive_0, reverse = segment_accum_517_reverse_0, x = var_9448)[name = tensor("segment_accum_517")]; + tensor var_9452_begin_0 = const()[name = tensor("op_9452_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9452_end_0 = const()[name = tensor("op_9452_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9452_end_mask_0 = const()[name = tensor("op_9452_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9452 = slice_by_index(begin = var_9452_begin_0, end = var_9452_end_0, end_mask = var_9452_end_mask_0, x = var_9446)[name = tensor("op_9452")]; + tensor var_9454 = add(x = segment_accum_517, y = var_9452)[name = tensor("op_9454")]; + tensor var_9456_begin_0 = const()[name = tensor("op_9456_begin_0"), val = tensor([0, 260000, 0])]; + tensor var_9456_end_0 = const()[name = tensor("op_9456_end_0"), val = tensor([1, 261000, 9])]; + tensor var_9456_end_mask_0 = const()[name = tensor("op_9456_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9456 = slice_by_index(begin = var_9456_begin_0, end = var_9456_end_0, end_mask = var_9456_end_mask_0, x = reshape_4)[name = tensor("op_9456")]; + tensor segment_accum_519_exclusive_0 = const()[name = tensor("segment_accum_519_exclusive_0"), val = tensor(false)]; + tensor segment_accum_519_reverse_0 = const()[name = tensor("segment_accum_519_reverse_0"), val = tensor(false)]; + tensor segment_accum_519 = cumsum(axis = var_7349, exclusive = segment_accum_519_exclusive_0, reverse = segment_accum_519_reverse_0, x = var_9456)[name = tensor("segment_accum_519")]; + tensor var_9460_begin_0 = const()[name = tensor("op_9460_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9460_end_0 = const()[name = tensor("op_9460_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9460_end_mask_0 = const()[name = tensor("op_9460_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9460 = slice_by_index(begin = var_9460_begin_0, end = var_9460_end_0, end_mask = var_9460_end_mask_0, x = var_9454)[name = tensor("op_9460")]; + tensor var_9462 = add(x = segment_accum_519, y = var_9460)[name = tensor("op_9462")]; + tensor var_9464_begin_0 = const()[name = tensor("op_9464_begin_0"), val = tensor([0, 261000, 0])]; + tensor var_9464_end_0 = const()[name = tensor("op_9464_end_0"), val = tensor([1, 262000, 9])]; + tensor var_9464_end_mask_0 = const()[name = tensor("op_9464_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9464 = slice_by_index(begin = var_9464_begin_0, end = var_9464_end_0, end_mask = var_9464_end_mask_0, x = reshape_4)[name = tensor("op_9464")]; + tensor segment_accum_521_exclusive_0 = const()[name = tensor("segment_accum_521_exclusive_0"), val = tensor(false)]; + tensor segment_accum_521_reverse_0 = const()[name = tensor("segment_accum_521_reverse_0"), val = tensor(false)]; + tensor segment_accum_521 = cumsum(axis = var_7349, exclusive = segment_accum_521_exclusive_0, reverse = segment_accum_521_reverse_0, x = var_9464)[name = tensor("segment_accum_521")]; + tensor var_9468_begin_0 = const()[name = tensor("op_9468_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9468_end_0 = const()[name = tensor("op_9468_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9468_end_mask_0 = const()[name = tensor("op_9468_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9468 = slice_by_index(begin = var_9468_begin_0, end = var_9468_end_0, end_mask = var_9468_end_mask_0, x = var_9462)[name = tensor("op_9468")]; + tensor var_9470 = add(x = segment_accum_521, y = var_9468)[name = tensor("op_9470")]; + tensor var_9472_begin_0 = const()[name = tensor("op_9472_begin_0"), val = tensor([0, 262000, 0])]; + tensor var_9472_end_0 = const()[name = tensor("op_9472_end_0"), val = tensor([1, 263000, 9])]; + tensor var_9472_end_mask_0 = const()[name = tensor("op_9472_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9472 = slice_by_index(begin = var_9472_begin_0, end = var_9472_end_0, end_mask = var_9472_end_mask_0, x = reshape_4)[name = tensor("op_9472")]; + tensor segment_accum_523_exclusive_0 = const()[name = tensor("segment_accum_523_exclusive_0"), val = tensor(false)]; + tensor segment_accum_523_reverse_0 = const()[name = tensor("segment_accum_523_reverse_0"), val = tensor(false)]; + tensor segment_accum_523 = cumsum(axis = var_7349, exclusive = segment_accum_523_exclusive_0, reverse = segment_accum_523_reverse_0, x = var_9472)[name = tensor("segment_accum_523")]; + tensor var_9476_begin_0 = const()[name = tensor("op_9476_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9476_end_0 = const()[name = tensor("op_9476_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9476_end_mask_0 = const()[name = tensor("op_9476_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9476 = slice_by_index(begin = var_9476_begin_0, end = var_9476_end_0, end_mask = var_9476_end_mask_0, x = var_9470)[name = tensor("op_9476")]; + tensor var_9478 = add(x = segment_accum_523, y = var_9476)[name = tensor("op_9478")]; + tensor var_9480_begin_0 = const()[name = tensor("op_9480_begin_0"), val = tensor([0, 263000, 0])]; + tensor var_9480_end_0 = const()[name = tensor("op_9480_end_0"), val = tensor([1, 264000, 9])]; + tensor var_9480_end_mask_0 = const()[name = tensor("op_9480_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9480 = slice_by_index(begin = var_9480_begin_0, end = var_9480_end_0, end_mask = var_9480_end_mask_0, x = reshape_4)[name = tensor("op_9480")]; + tensor segment_accum_525_exclusive_0 = const()[name = tensor("segment_accum_525_exclusive_0"), val = tensor(false)]; + tensor segment_accum_525_reverse_0 = const()[name = tensor("segment_accum_525_reverse_0"), val = tensor(false)]; + tensor segment_accum_525 = cumsum(axis = var_7349, exclusive = segment_accum_525_exclusive_0, reverse = segment_accum_525_reverse_0, x = var_9480)[name = tensor("segment_accum_525")]; + tensor var_9484_begin_0 = const()[name = tensor("op_9484_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9484_end_0 = const()[name = tensor("op_9484_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9484_end_mask_0 = const()[name = tensor("op_9484_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9484 = slice_by_index(begin = var_9484_begin_0, end = var_9484_end_0, end_mask = var_9484_end_mask_0, x = var_9478)[name = tensor("op_9484")]; + tensor var_9486 = add(x = segment_accum_525, y = var_9484)[name = tensor("op_9486")]; + tensor var_9488_begin_0 = const()[name = tensor("op_9488_begin_0"), val = tensor([0, 264000, 0])]; + tensor var_9488_end_0 = const()[name = tensor("op_9488_end_0"), val = tensor([1, 265000, 9])]; + tensor var_9488_end_mask_0 = const()[name = tensor("op_9488_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9488 = slice_by_index(begin = var_9488_begin_0, end = var_9488_end_0, end_mask = var_9488_end_mask_0, x = reshape_4)[name = tensor("op_9488")]; + tensor segment_accum_527_exclusive_0 = const()[name = tensor("segment_accum_527_exclusive_0"), val = tensor(false)]; + tensor segment_accum_527_reverse_0 = const()[name = tensor("segment_accum_527_reverse_0"), val = tensor(false)]; + tensor segment_accum_527 = cumsum(axis = var_7349, exclusive = segment_accum_527_exclusive_0, reverse = segment_accum_527_reverse_0, x = var_9488)[name = tensor("segment_accum_527")]; + tensor var_9492_begin_0 = const()[name = tensor("op_9492_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9492_end_0 = const()[name = tensor("op_9492_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9492_end_mask_0 = const()[name = tensor("op_9492_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9492 = slice_by_index(begin = var_9492_begin_0, end = var_9492_end_0, end_mask = var_9492_end_mask_0, x = var_9486)[name = tensor("op_9492")]; + tensor var_9494 = add(x = segment_accum_527, y = var_9492)[name = tensor("op_9494")]; + tensor var_9496_begin_0 = const()[name = tensor("op_9496_begin_0"), val = tensor([0, 265000, 0])]; + tensor var_9496_end_0 = const()[name = tensor("op_9496_end_0"), val = tensor([1, 266000, 9])]; + tensor var_9496_end_mask_0 = const()[name = tensor("op_9496_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9496 = slice_by_index(begin = var_9496_begin_0, end = var_9496_end_0, end_mask = var_9496_end_mask_0, x = reshape_4)[name = tensor("op_9496")]; + tensor segment_accum_529_exclusive_0 = const()[name = tensor("segment_accum_529_exclusive_0"), val = tensor(false)]; + tensor segment_accum_529_reverse_0 = const()[name = tensor("segment_accum_529_reverse_0"), val = tensor(false)]; + tensor segment_accum_529 = cumsum(axis = var_7349, exclusive = segment_accum_529_exclusive_0, reverse = segment_accum_529_reverse_0, x = var_9496)[name = tensor("segment_accum_529")]; + tensor var_9500_begin_0 = const()[name = tensor("op_9500_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9500_end_0 = const()[name = tensor("op_9500_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9500_end_mask_0 = const()[name = tensor("op_9500_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9500 = slice_by_index(begin = var_9500_begin_0, end = var_9500_end_0, end_mask = var_9500_end_mask_0, x = var_9494)[name = tensor("op_9500")]; + tensor var_9502 = add(x = segment_accum_529, y = var_9500)[name = tensor("op_9502")]; + tensor var_9504_begin_0 = const()[name = tensor("op_9504_begin_0"), val = tensor([0, 266000, 0])]; + tensor var_9504_end_0 = const()[name = tensor("op_9504_end_0"), val = tensor([1, 267000, 9])]; + tensor var_9504_end_mask_0 = const()[name = tensor("op_9504_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9504 = slice_by_index(begin = var_9504_begin_0, end = var_9504_end_0, end_mask = var_9504_end_mask_0, x = reshape_4)[name = tensor("op_9504")]; + tensor segment_accum_531_exclusive_0 = const()[name = tensor("segment_accum_531_exclusive_0"), val = tensor(false)]; + tensor segment_accum_531_reverse_0 = const()[name = tensor("segment_accum_531_reverse_0"), val = tensor(false)]; + tensor segment_accum_531 = cumsum(axis = var_7349, exclusive = segment_accum_531_exclusive_0, reverse = segment_accum_531_reverse_0, x = var_9504)[name = tensor("segment_accum_531")]; + tensor var_9508_begin_0 = const()[name = tensor("op_9508_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9508_end_0 = const()[name = tensor("op_9508_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9508_end_mask_0 = const()[name = tensor("op_9508_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9508 = slice_by_index(begin = var_9508_begin_0, end = var_9508_end_0, end_mask = var_9508_end_mask_0, x = var_9502)[name = tensor("op_9508")]; + tensor var_9510 = add(x = segment_accum_531, y = var_9508)[name = tensor("op_9510")]; + tensor var_9512_begin_0 = const()[name = tensor("op_9512_begin_0"), val = tensor([0, 267000, 0])]; + tensor var_9512_end_0 = const()[name = tensor("op_9512_end_0"), val = tensor([1, 268000, 9])]; + tensor var_9512_end_mask_0 = const()[name = tensor("op_9512_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9512 = slice_by_index(begin = var_9512_begin_0, end = var_9512_end_0, end_mask = var_9512_end_mask_0, x = reshape_4)[name = tensor("op_9512")]; + tensor segment_accum_533_exclusive_0 = const()[name = tensor("segment_accum_533_exclusive_0"), val = tensor(false)]; + tensor segment_accum_533_reverse_0 = const()[name = tensor("segment_accum_533_reverse_0"), val = tensor(false)]; + tensor segment_accum_533 = cumsum(axis = var_7349, exclusive = segment_accum_533_exclusive_0, reverse = segment_accum_533_reverse_0, x = var_9512)[name = tensor("segment_accum_533")]; + tensor var_9516_begin_0 = const()[name = tensor("op_9516_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9516_end_0 = const()[name = tensor("op_9516_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9516_end_mask_0 = const()[name = tensor("op_9516_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9516 = slice_by_index(begin = var_9516_begin_0, end = var_9516_end_0, end_mask = var_9516_end_mask_0, x = var_9510)[name = tensor("op_9516")]; + tensor var_9518 = add(x = segment_accum_533, y = var_9516)[name = tensor("op_9518")]; + tensor var_9520_begin_0 = const()[name = tensor("op_9520_begin_0"), val = tensor([0, 268000, 0])]; + tensor var_9520_end_0 = const()[name = tensor("op_9520_end_0"), val = tensor([1, 269000, 9])]; + tensor var_9520_end_mask_0 = const()[name = tensor("op_9520_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9520 = slice_by_index(begin = var_9520_begin_0, end = var_9520_end_0, end_mask = var_9520_end_mask_0, x = reshape_4)[name = tensor("op_9520")]; + tensor segment_accum_535_exclusive_0 = const()[name = tensor("segment_accum_535_exclusive_0"), val = tensor(false)]; + tensor segment_accum_535_reverse_0 = const()[name = tensor("segment_accum_535_reverse_0"), val = tensor(false)]; + tensor segment_accum_535 = cumsum(axis = var_7349, exclusive = segment_accum_535_exclusive_0, reverse = segment_accum_535_reverse_0, x = var_9520)[name = tensor("segment_accum_535")]; + tensor var_9524_begin_0 = const()[name = tensor("op_9524_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9524_end_0 = const()[name = tensor("op_9524_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9524_end_mask_0 = const()[name = tensor("op_9524_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9524 = slice_by_index(begin = var_9524_begin_0, end = var_9524_end_0, end_mask = var_9524_end_mask_0, x = var_9518)[name = tensor("op_9524")]; + tensor var_9526 = add(x = segment_accum_535, y = var_9524)[name = tensor("op_9526")]; + tensor var_9528_begin_0 = const()[name = tensor("op_9528_begin_0"), val = tensor([0, 269000, 0])]; + tensor var_9528_end_0 = const()[name = tensor("op_9528_end_0"), val = tensor([1, 270000, 9])]; + tensor var_9528_end_mask_0 = const()[name = tensor("op_9528_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9528 = slice_by_index(begin = var_9528_begin_0, end = var_9528_end_0, end_mask = var_9528_end_mask_0, x = reshape_4)[name = tensor("op_9528")]; + tensor segment_accum_537_exclusive_0 = const()[name = tensor("segment_accum_537_exclusive_0"), val = tensor(false)]; + tensor segment_accum_537_reverse_0 = const()[name = tensor("segment_accum_537_reverse_0"), val = tensor(false)]; + tensor segment_accum_537 = cumsum(axis = var_7349, exclusive = segment_accum_537_exclusive_0, reverse = segment_accum_537_reverse_0, x = var_9528)[name = tensor("segment_accum_537")]; + tensor var_9532_begin_0 = const()[name = tensor("op_9532_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9532_end_0 = const()[name = tensor("op_9532_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9532_end_mask_0 = const()[name = tensor("op_9532_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9532 = slice_by_index(begin = var_9532_begin_0, end = var_9532_end_0, end_mask = var_9532_end_mask_0, x = var_9526)[name = tensor("op_9532")]; + tensor var_9534 = add(x = segment_accum_537, y = var_9532)[name = tensor("op_9534")]; + tensor var_9536_begin_0 = const()[name = tensor("op_9536_begin_0"), val = tensor([0, 270000, 0])]; + tensor var_9536_end_0 = const()[name = tensor("op_9536_end_0"), val = tensor([1, 271000, 9])]; + tensor var_9536_end_mask_0 = const()[name = tensor("op_9536_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9536 = slice_by_index(begin = var_9536_begin_0, end = var_9536_end_0, end_mask = var_9536_end_mask_0, x = reshape_4)[name = tensor("op_9536")]; + tensor segment_accum_539_exclusive_0 = const()[name = tensor("segment_accum_539_exclusive_0"), val = tensor(false)]; + tensor segment_accum_539_reverse_0 = const()[name = tensor("segment_accum_539_reverse_0"), val = tensor(false)]; + tensor segment_accum_539 = cumsum(axis = var_7349, exclusive = segment_accum_539_exclusive_0, reverse = segment_accum_539_reverse_0, x = var_9536)[name = tensor("segment_accum_539")]; + tensor var_9540_begin_0 = const()[name = tensor("op_9540_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9540_end_0 = const()[name = tensor("op_9540_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9540_end_mask_0 = const()[name = tensor("op_9540_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9540 = slice_by_index(begin = var_9540_begin_0, end = var_9540_end_0, end_mask = var_9540_end_mask_0, x = var_9534)[name = tensor("op_9540")]; + tensor var_9542 = add(x = segment_accum_539, y = var_9540)[name = tensor("op_9542")]; + tensor var_9544_begin_0 = const()[name = tensor("op_9544_begin_0"), val = tensor([0, 271000, 0])]; + tensor var_9544_end_0 = const()[name = tensor("op_9544_end_0"), val = tensor([1, 272000, 9])]; + tensor var_9544_end_mask_0 = const()[name = tensor("op_9544_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9544 = slice_by_index(begin = var_9544_begin_0, end = var_9544_end_0, end_mask = var_9544_end_mask_0, x = reshape_4)[name = tensor("op_9544")]; + tensor segment_accum_541_exclusive_0 = const()[name = tensor("segment_accum_541_exclusive_0"), val = tensor(false)]; + tensor segment_accum_541_reverse_0 = const()[name = tensor("segment_accum_541_reverse_0"), val = tensor(false)]; + tensor segment_accum_541 = cumsum(axis = var_7349, exclusive = segment_accum_541_exclusive_0, reverse = segment_accum_541_reverse_0, x = var_9544)[name = tensor("segment_accum_541")]; + tensor var_9548_begin_0 = const()[name = tensor("op_9548_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9548_end_0 = const()[name = tensor("op_9548_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9548_end_mask_0 = const()[name = tensor("op_9548_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9548 = slice_by_index(begin = var_9548_begin_0, end = var_9548_end_0, end_mask = var_9548_end_mask_0, x = var_9542)[name = tensor("op_9548")]; + tensor var_9550 = add(x = segment_accum_541, y = var_9548)[name = tensor("op_9550")]; + tensor var_9552_begin_0 = const()[name = tensor("op_9552_begin_0"), val = tensor([0, 272000, 0])]; + tensor var_9552_end_0 = const()[name = tensor("op_9552_end_0"), val = tensor([1, 273000, 9])]; + tensor var_9552_end_mask_0 = const()[name = tensor("op_9552_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9552 = slice_by_index(begin = var_9552_begin_0, end = var_9552_end_0, end_mask = var_9552_end_mask_0, x = reshape_4)[name = tensor("op_9552")]; + tensor segment_accum_543_exclusive_0 = const()[name = tensor("segment_accum_543_exclusive_0"), val = tensor(false)]; + tensor segment_accum_543_reverse_0 = const()[name = tensor("segment_accum_543_reverse_0"), val = tensor(false)]; + tensor segment_accum_543 = cumsum(axis = var_7349, exclusive = segment_accum_543_exclusive_0, reverse = segment_accum_543_reverse_0, x = var_9552)[name = tensor("segment_accum_543")]; + tensor var_9556_begin_0 = const()[name = tensor("op_9556_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9556_end_0 = const()[name = tensor("op_9556_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9556_end_mask_0 = const()[name = tensor("op_9556_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9556 = slice_by_index(begin = var_9556_begin_0, end = var_9556_end_0, end_mask = var_9556_end_mask_0, x = var_9550)[name = tensor("op_9556")]; + tensor var_9558 = add(x = segment_accum_543, y = var_9556)[name = tensor("op_9558")]; + tensor var_9560_begin_0 = const()[name = tensor("op_9560_begin_0"), val = tensor([0, 273000, 0])]; + tensor var_9560_end_0 = const()[name = tensor("op_9560_end_0"), val = tensor([1, 274000, 9])]; + tensor var_9560_end_mask_0 = const()[name = tensor("op_9560_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9560 = slice_by_index(begin = var_9560_begin_0, end = var_9560_end_0, end_mask = var_9560_end_mask_0, x = reshape_4)[name = tensor("op_9560")]; + tensor segment_accum_545_exclusive_0 = const()[name = tensor("segment_accum_545_exclusive_0"), val = tensor(false)]; + tensor segment_accum_545_reverse_0 = const()[name = tensor("segment_accum_545_reverse_0"), val = tensor(false)]; + tensor segment_accum_545 = cumsum(axis = var_7349, exclusive = segment_accum_545_exclusive_0, reverse = segment_accum_545_reverse_0, x = var_9560)[name = tensor("segment_accum_545")]; + tensor var_9564_begin_0 = const()[name = tensor("op_9564_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9564_end_0 = const()[name = tensor("op_9564_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9564_end_mask_0 = const()[name = tensor("op_9564_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9564 = slice_by_index(begin = var_9564_begin_0, end = var_9564_end_0, end_mask = var_9564_end_mask_0, x = var_9558)[name = tensor("op_9564")]; + tensor var_9566 = add(x = segment_accum_545, y = var_9564)[name = tensor("op_9566")]; + tensor var_9568_begin_0 = const()[name = tensor("op_9568_begin_0"), val = tensor([0, 274000, 0])]; + tensor var_9568_end_0 = const()[name = tensor("op_9568_end_0"), val = tensor([1, 275000, 9])]; + tensor var_9568_end_mask_0 = const()[name = tensor("op_9568_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9568 = slice_by_index(begin = var_9568_begin_0, end = var_9568_end_0, end_mask = var_9568_end_mask_0, x = reshape_4)[name = tensor("op_9568")]; + tensor segment_accum_547_exclusive_0 = const()[name = tensor("segment_accum_547_exclusive_0"), val = tensor(false)]; + tensor segment_accum_547_reverse_0 = const()[name = tensor("segment_accum_547_reverse_0"), val = tensor(false)]; + tensor segment_accum_547 = cumsum(axis = var_7349, exclusive = segment_accum_547_exclusive_0, reverse = segment_accum_547_reverse_0, x = var_9568)[name = tensor("segment_accum_547")]; + tensor var_9572_begin_0 = const()[name = tensor("op_9572_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9572_end_0 = const()[name = tensor("op_9572_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9572_end_mask_0 = const()[name = tensor("op_9572_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9572 = slice_by_index(begin = var_9572_begin_0, end = var_9572_end_0, end_mask = var_9572_end_mask_0, x = var_9566)[name = tensor("op_9572")]; + tensor var_9574 = add(x = segment_accum_547, y = var_9572)[name = tensor("op_9574")]; + tensor var_9576_begin_0 = const()[name = tensor("op_9576_begin_0"), val = tensor([0, 275000, 0])]; + tensor var_9576_end_0 = const()[name = tensor("op_9576_end_0"), val = tensor([1, 276000, 9])]; + tensor var_9576_end_mask_0 = const()[name = tensor("op_9576_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9576 = slice_by_index(begin = var_9576_begin_0, end = var_9576_end_0, end_mask = var_9576_end_mask_0, x = reshape_4)[name = tensor("op_9576")]; + tensor segment_accum_549_exclusive_0 = const()[name = tensor("segment_accum_549_exclusive_0"), val = tensor(false)]; + tensor segment_accum_549_reverse_0 = const()[name = tensor("segment_accum_549_reverse_0"), val = tensor(false)]; + tensor segment_accum_549 = cumsum(axis = var_7349, exclusive = segment_accum_549_exclusive_0, reverse = segment_accum_549_reverse_0, x = var_9576)[name = tensor("segment_accum_549")]; + tensor var_9580_begin_0 = const()[name = tensor("op_9580_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9580_end_0 = const()[name = tensor("op_9580_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9580_end_mask_0 = const()[name = tensor("op_9580_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9580 = slice_by_index(begin = var_9580_begin_0, end = var_9580_end_0, end_mask = var_9580_end_mask_0, x = var_9574)[name = tensor("op_9580")]; + tensor var_9582 = add(x = segment_accum_549, y = var_9580)[name = tensor("op_9582")]; + tensor var_9584_begin_0 = const()[name = tensor("op_9584_begin_0"), val = tensor([0, 276000, 0])]; + tensor var_9584_end_0 = const()[name = tensor("op_9584_end_0"), val = tensor([1, 277000, 9])]; + tensor var_9584_end_mask_0 = const()[name = tensor("op_9584_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9584 = slice_by_index(begin = var_9584_begin_0, end = var_9584_end_0, end_mask = var_9584_end_mask_0, x = reshape_4)[name = tensor("op_9584")]; + tensor segment_accum_551_exclusive_0 = const()[name = tensor("segment_accum_551_exclusive_0"), val = tensor(false)]; + tensor segment_accum_551_reverse_0 = const()[name = tensor("segment_accum_551_reverse_0"), val = tensor(false)]; + tensor segment_accum_551 = cumsum(axis = var_7349, exclusive = segment_accum_551_exclusive_0, reverse = segment_accum_551_reverse_0, x = var_9584)[name = tensor("segment_accum_551")]; + tensor var_9588_begin_0 = const()[name = tensor("op_9588_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9588_end_0 = const()[name = tensor("op_9588_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9588_end_mask_0 = const()[name = tensor("op_9588_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9588 = slice_by_index(begin = var_9588_begin_0, end = var_9588_end_0, end_mask = var_9588_end_mask_0, x = var_9582)[name = tensor("op_9588")]; + tensor var_9590 = add(x = segment_accum_551, y = var_9588)[name = tensor("op_9590")]; + tensor var_9592_begin_0 = const()[name = tensor("op_9592_begin_0"), val = tensor([0, 277000, 0])]; + tensor var_9592_end_0 = const()[name = tensor("op_9592_end_0"), val = tensor([1, 278000, 9])]; + tensor var_9592_end_mask_0 = const()[name = tensor("op_9592_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9592 = slice_by_index(begin = var_9592_begin_0, end = var_9592_end_0, end_mask = var_9592_end_mask_0, x = reshape_4)[name = tensor("op_9592")]; + tensor segment_accum_553_exclusive_0 = const()[name = tensor("segment_accum_553_exclusive_0"), val = tensor(false)]; + tensor segment_accum_553_reverse_0 = const()[name = tensor("segment_accum_553_reverse_0"), val = tensor(false)]; + tensor segment_accum_553 = cumsum(axis = var_7349, exclusive = segment_accum_553_exclusive_0, reverse = segment_accum_553_reverse_0, x = var_9592)[name = tensor("segment_accum_553")]; + tensor var_9596_begin_0 = const()[name = tensor("op_9596_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9596_end_0 = const()[name = tensor("op_9596_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9596_end_mask_0 = const()[name = tensor("op_9596_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9596 = slice_by_index(begin = var_9596_begin_0, end = var_9596_end_0, end_mask = var_9596_end_mask_0, x = var_9590)[name = tensor("op_9596")]; + tensor var_9598 = add(x = segment_accum_553, y = var_9596)[name = tensor("op_9598")]; + tensor var_9600_begin_0 = const()[name = tensor("op_9600_begin_0"), val = tensor([0, 278000, 0])]; + tensor var_9600_end_0 = const()[name = tensor("op_9600_end_0"), val = tensor([1, 279000, 9])]; + tensor var_9600_end_mask_0 = const()[name = tensor("op_9600_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9600 = slice_by_index(begin = var_9600_begin_0, end = var_9600_end_0, end_mask = var_9600_end_mask_0, x = reshape_4)[name = tensor("op_9600")]; + tensor segment_accum_555_exclusive_0 = const()[name = tensor("segment_accum_555_exclusive_0"), val = tensor(false)]; + tensor segment_accum_555_reverse_0 = const()[name = tensor("segment_accum_555_reverse_0"), val = tensor(false)]; + tensor segment_accum_555 = cumsum(axis = var_7349, exclusive = segment_accum_555_exclusive_0, reverse = segment_accum_555_reverse_0, x = var_9600)[name = tensor("segment_accum_555")]; + tensor var_9604_begin_0 = const()[name = tensor("op_9604_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9604_end_0 = const()[name = tensor("op_9604_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9604_end_mask_0 = const()[name = tensor("op_9604_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9604 = slice_by_index(begin = var_9604_begin_0, end = var_9604_end_0, end_mask = var_9604_end_mask_0, x = var_9598)[name = tensor("op_9604")]; + tensor var_9606 = add(x = segment_accum_555, y = var_9604)[name = tensor("op_9606")]; + tensor var_9608_begin_0 = const()[name = tensor("op_9608_begin_0"), val = tensor([0, 279000, 0])]; + tensor var_9608_end_0 = const()[name = tensor("op_9608_end_0"), val = tensor([1, 280000, 9])]; + tensor var_9608_end_mask_0 = const()[name = tensor("op_9608_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9608 = slice_by_index(begin = var_9608_begin_0, end = var_9608_end_0, end_mask = var_9608_end_mask_0, x = reshape_4)[name = tensor("op_9608")]; + tensor segment_accum_557_exclusive_0 = const()[name = tensor("segment_accum_557_exclusive_0"), val = tensor(false)]; + tensor segment_accum_557_reverse_0 = const()[name = tensor("segment_accum_557_reverse_0"), val = tensor(false)]; + tensor segment_accum_557 = cumsum(axis = var_7349, exclusive = segment_accum_557_exclusive_0, reverse = segment_accum_557_reverse_0, x = var_9608)[name = tensor("segment_accum_557")]; + tensor var_9612_begin_0 = const()[name = tensor("op_9612_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9612_end_0 = const()[name = tensor("op_9612_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9612_end_mask_0 = const()[name = tensor("op_9612_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9612 = slice_by_index(begin = var_9612_begin_0, end = var_9612_end_0, end_mask = var_9612_end_mask_0, x = var_9606)[name = tensor("op_9612")]; + tensor var_9614 = add(x = segment_accum_557, y = var_9612)[name = tensor("op_9614")]; + tensor var_9616_begin_0 = const()[name = tensor("op_9616_begin_0"), val = tensor([0, 280000, 0])]; + tensor var_9616_end_0 = const()[name = tensor("op_9616_end_0"), val = tensor([1, 281000, 9])]; + tensor var_9616_end_mask_0 = const()[name = tensor("op_9616_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9616 = slice_by_index(begin = var_9616_begin_0, end = var_9616_end_0, end_mask = var_9616_end_mask_0, x = reshape_4)[name = tensor("op_9616")]; + tensor segment_accum_559_exclusive_0 = const()[name = tensor("segment_accum_559_exclusive_0"), val = tensor(false)]; + tensor segment_accum_559_reverse_0 = const()[name = tensor("segment_accum_559_reverse_0"), val = tensor(false)]; + tensor segment_accum_559 = cumsum(axis = var_7349, exclusive = segment_accum_559_exclusive_0, reverse = segment_accum_559_reverse_0, x = var_9616)[name = tensor("segment_accum_559")]; + tensor var_9620_begin_0 = const()[name = tensor("op_9620_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9620_end_0 = const()[name = tensor("op_9620_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9620_end_mask_0 = const()[name = tensor("op_9620_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9620 = slice_by_index(begin = var_9620_begin_0, end = var_9620_end_0, end_mask = var_9620_end_mask_0, x = var_9614)[name = tensor("op_9620")]; + tensor var_9622 = add(x = segment_accum_559, y = var_9620)[name = tensor("op_9622")]; + tensor var_9624_begin_0 = const()[name = tensor("op_9624_begin_0"), val = tensor([0, 281000, 0])]; + tensor var_9624_end_0 = const()[name = tensor("op_9624_end_0"), val = tensor([1, 282000, 9])]; + tensor var_9624_end_mask_0 = const()[name = tensor("op_9624_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9624 = slice_by_index(begin = var_9624_begin_0, end = var_9624_end_0, end_mask = var_9624_end_mask_0, x = reshape_4)[name = tensor("op_9624")]; + tensor segment_accum_561_exclusive_0 = const()[name = tensor("segment_accum_561_exclusive_0"), val = tensor(false)]; + tensor segment_accum_561_reverse_0 = const()[name = tensor("segment_accum_561_reverse_0"), val = tensor(false)]; + tensor segment_accum_561 = cumsum(axis = var_7349, exclusive = segment_accum_561_exclusive_0, reverse = segment_accum_561_reverse_0, x = var_9624)[name = tensor("segment_accum_561")]; + tensor var_9628_begin_0 = const()[name = tensor("op_9628_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9628_end_0 = const()[name = tensor("op_9628_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9628_end_mask_0 = const()[name = tensor("op_9628_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9628 = slice_by_index(begin = var_9628_begin_0, end = var_9628_end_0, end_mask = var_9628_end_mask_0, x = var_9622)[name = tensor("op_9628")]; + tensor var_9630 = add(x = segment_accum_561, y = var_9628)[name = tensor("op_9630")]; + tensor var_9632_begin_0 = const()[name = tensor("op_9632_begin_0"), val = tensor([0, 282000, 0])]; + tensor var_9632_end_0 = const()[name = tensor("op_9632_end_0"), val = tensor([1, 283000, 9])]; + tensor var_9632_end_mask_0 = const()[name = tensor("op_9632_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9632 = slice_by_index(begin = var_9632_begin_0, end = var_9632_end_0, end_mask = var_9632_end_mask_0, x = reshape_4)[name = tensor("op_9632")]; + tensor segment_accum_563_exclusive_0 = const()[name = tensor("segment_accum_563_exclusive_0"), val = tensor(false)]; + tensor segment_accum_563_reverse_0 = const()[name = tensor("segment_accum_563_reverse_0"), val = tensor(false)]; + tensor segment_accum_563 = cumsum(axis = var_7349, exclusive = segment_accum_563_exclusive_0, reverse = segment_accum_563_reverse_0, x = var_9632)[name = tensor("segment_accum_563")]; + tensor var_9636_begin_0 = const()[name = tensor("op_9636_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9636_end_0 = const()[name = tensor("op_9636_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9636_end_mask_0 = const()[name = tensor("op_9636_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9636 = slice_by_index(begin = var_9636_begin_0, end = var_9636_end_0, end_mask = var_9636_end_mask_0, x = var_9630)[name = tensor("op_9636")]; + tensor var_9638 = add(x = segment_accum_563, y = var_9636)[name = tensor("op_9638")]; + tensor var_9640_begin_0 = const()[name = tensor("op_9640_begin_0"), val = tensor([0, 283000, 0])]; + tensor var_9640_end_0 = const()[name = tensor("op_9640_end_0"), val = tensor([1, 284000, 9])]; + tensor var_9640_end_mask_0 = const()[name = tensor("op_9640_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9640 = slice_by_index(begin = var_9640_begin_0, end = var_9640_end_0, end_mask = var_9640_end_mask_0, x = reshape_4)[name = tensor("op_9640")]; + tensor segment_accum_565_exclusive_0 = const()[name = tensor("segment_accum_565_exclusive_0"), val = tensor(false)]; + tensor segment_accum_565_reverse_0 = const()[name = tensor("segment_accum_565_reverse_0"), val = tensor(false)]; + tensor segment_accum_565 = cumsum(axis = var_7349, exclusive = segment_accum_565_exclusive_0, reverse = segment_accum_565_reverse_0, x = var_9640)[name = tensor("segment_accum_565")]; + tensor var_9644_begin_0 = const()[name = tensor("op_9644_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9644_end_0 = const()[name = tensor("op_9644_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9644_end_mask_0 = const()[name = tensor("op_9644_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9644 = slice_by_index(begin = var_9644_begin_0, end = var_9644_end_0, end_mask = var_9644_end_mask_0, x = var_9638)[name = tensor("op_9644")]; + tensor var_9646 = add(x = segment_accum_565, y = var_9644)[name = tensor("op_9646")]; + tensor var_9648_begin_0 = const()[name = tensor("op_9648_begin_0"), val = tensor([0, 284000, 0])]; + tensor var_9648_end_0 = const()[name = tensor("op_9648_end_0"), val = tensor([1, 285000, 9])]; + tensor var_9648_end_mask_0 = const()[name = tensor("op_9648_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9648 = slice_by_index(begin = var_9648_begin_0, end = var_9648_end_0, end_mask = var_9648_end_mask_0, x = reshape_4)[name = tensor("op_9648")]; + tensor segment_accum_567_exclusive_0 = const()[name = tensor("segment_accum_567_exclusive_0"), val = tensor(false)]; + tensor segment_accum_567_reverse_0 = const()[name = tensor("segment_accum_567_reverse_0"), val = tensor(false)]; + tensor segment_accum_567 = cumsum(axis = var_7349, exclusive = segment_accum_567_exclusive_0, reverse = segment_accum_567_reverse_0, x = var_9648)[name = tensor("segment_accum_567")]; + tensor var_9652_begin_0 = const()[name = tensor("op_9652_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9652_end_0 = const()[name = tensor("op_9652_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9652_end_mask_0 = const()[name = tensor("op_9652_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9652 = slice_by_index(begin = var_9652_begin_0, end = var_9652_end_0, end_mask = var_9652_end_mask_0, x = var_9646)[name = tensor("op_9652")]; + tensor var_9654 = add(x = segment_accum_567, y = var_9652)[name = tensor("op_9654")]; + tensor var_9656_begin_0 = const()[name = tensor("op_9656_begin_0"), val = tensor([0, 285000, 0])]; + tensor var_9656_end_0 = const()[name = tensor("op_9656_end_0"), val = tensor([1, 286000, 9])]; + tensor var_9656_end_mask_0 = const()[name = tensor("op_9656_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9656 = slice_by_index(begin = var_9656_begin_0, end = var_9656_end_0, end_mask = var_9656_end_mask_0, x = reshape_4)[name = tensor("op_9656")]; + tensor segment_accum_569_exclusive_0 = const()[name = tensor("segment_accum_569_exclusive_0"), val = tensor(false)]; + tensor segment_accum_569_reverse_0 = const()[name = tensor("segment_accum_569_reverse_0"), val = tensor(false)]; + tensor segment_accum_569 = cumsum(axis = var_7349, exclusive = segment_accum_569_exclusive_0, reverse = segment_accum_569_reverse_0, x = var_9656)[name = tensor("segment_accum_569")]; + tensor var_9660_begin_0 = const()[name = tensor("op_9660_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9660_end_0 = const()[name = tensor("op_9660_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9660_end_mask_0 = const()[name = tensor("op_9660_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9660 = slice_by_index(begin = var_9660_begin_0, end = var_9660_end_0, end_mask = var_9660_end_mask_0, x = var_9654)[name = tensor("op_9660")]; + tensor var_9662 = add(x = segment_accum_569, y = var_9660)[name = tensor("op_9662")]; + tensor var_9664_begin_0 = const()[name = tensor("op_9664_begin_0"), val = tensor([0, 286000, 0])]; + tensor var_9664_end_0 = const()[name = tensor("op_9664_end_0"), val = tensor([1, 287000, 9])]; + tensor var_9664_end_mask_0 = const()[name = tensor("op_9664_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9664 = slice_by_index(begin = var_9664_begin_0, end = var_9664_end_0, end_mask = var_9664_end_mask_0, x = reshape_4)[name = tensor("op_9664")]; + tensor segment_accum_571_exclusive_0 = const()[name = tensor("segment_accum_571_exclusive_0"), val = tensor(false)]; + tensor segment_accum_571_reverse_0 = const()[name = tensor("segment_accum_571_reverse_0"), val = tensor(false)]; + tensor segment_accum_571 = cumsum(axis = var_7349, exclusive = segment_accum_571_exclusive_0, reverse = segment_accum_571_reverse_0, x = var_9664)[name = tensor("segment_accum_571")]; + tensor var_9668_begin_0 = const()[name = tensor("op_9668_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9668_end_0 = const()[name = tensor("op_9668_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9668_end_mask_0 = const()[name = tensor("op_9668_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9668 = slice_by_index(begin = var_9668_begin_0, end = var_9668_end_0, end_mask = var_9668_end_mask_0, x = var_9662)[name = tensor("op_9668")]; + tensor var_9670 = add(x = segment_accum_571, y = var_9668)[name = tensor("op_9670")]; + tensor var_9672_begin_0 = const()[name = tensor("op_9672_begin_0"), val = tensor([0, 287000, 0])]; + tensor var_9672_end_0 = const()[name = tensor("op_9672_end_0"), val = tensor([1, 288000, 9])]; + tensor var_9672_end_mask_0 = const()[name = tensor("op_9672_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9672 = slice_by_index(begin = var_9672_begin_0, end = var_9672_end_0, end_mask = var_9672_end_mask_0, x = reshape_4)[name = tensor("op_9672")]; + tensor segment_accum_573_exclusive_0 = const()[name = tensor("segment_accum_573_exclusive_0"), val = tensor(false)]; + tensor segment_accum_573_reverse_0 = const()[name = tensor("segment_accum_573_reverse_0"), val = tensor(false)]; + tensor segment_accum_573 = cumsum(axis = var_7349, exclusive = segment_accum_573_exclusive_0, reverse = segment_accum_573_reverse_0, x = var_9672)[name = tensor("segment_accum_573")]; + tensor var_9676_begin_0 = const()[name = tensor("op_9676_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9676_end_0 = const()[name = tensor("op_9676_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9676_end_mask_0 = const()[name = tensor("op_9676_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9676 = slice_by_index(begin = var_9676_begin_0, end = var_9676_end_0, end_mask = var_9676_end_mask_0, x = var_9670)[name = tensor("op_9676")]; + tensor var_9678 = add(x = segment_accum_573, y = var_9676)[name = tensor("op_9678")]; + tensor var_9680_begin_0 = const()[name = tensor("op_9680_begin_0"), val = tensor([0, 288000, 0])]; + tensor var_9680_end_0 = const()[name = tensor("op_9680_end_0"), val = tensor([1, 289000, 9])]; + tensor var_9680_end_mask_0 = const()[name = tensor("op_9680_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9680 = slice_by_index(begin = var_9680_begin_0, end = var_9680_end_0, end_mask = var_9680_end_mask_0, x = reshape_4)[name = tensor("op_9680")]; + tensor segment_accum_575_exclusive_0 = const()[name = tensor("segment_accum_575_exclusive_0"), val = tensor(false)]; + tensor segment_accum_575_reverse_0 = const()[name = tensor("segment_accum_575_reverse_0"), val = tensor(false)]; + tensor segment_accum_575 = cumsum(axis = var_7349, exclusive = segment_accum_575_exclusive_0, reverse = segment_accum_575_reverse_0, x = var_9680)[name = tensor("segment_accum_575")]; + tensor var_9684_begin_0 = const()[name = tensor("op_9684_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9684_end_0 = const()[name = tensor("op_9684_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9684_end_mask_0 = const()[name = tensor("op_9684_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9684 = slice_by_index(begin = var_9684_begin_0, end = var_9684_end_0, end_mask = var_9684_end_mask_0, x = var_9678)[name = tensor("op_9684")]; + tensor var_9686 = add(x = segment_accum_575, y = var_9684)[name = tensor("op_9686")]; + tensor var_9688_begin_0 = const()[name = tensor("op_9688_begin_0"), val = tensor([0, 289000, 0])]; + tensor var_9688_end_0 = const()[name = tensor("op_9688_end_0"), val = tensor([1, 290000, 9])]; + tensor var_9688_end_mask_0 = const()[name = tensor("op_9688_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9688 = slice_by_index(begin = var_9688_begin_0, end = var_9688_end_0, end_mask = var_9688_end_mask_0, x = reshape_4)[name = tensor("op_9688")]; + tensor segment_accum_577_exclusive_0 = const()[name = tensor("segment_accum_577_exclusive_0"), val = tensor(false)]; + tensor segment_accum_577_reverse_0 = const()[name = tensor("segment_accum_577_reverse_0"), val = tensor(false)]; + tensor segment_accum_577 = cumsum(axis = var_7349, exclusive = segment_accum_577_exclusive_0, reverse = segment_accum_577_reverse_0, x = var_9688)[name = tensor("segment_accum_577")]; + tensor var_9692_begin_0 = const()[name = tensor("op_9692_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9692_end_0 = const()[name = tensor("op_9692_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9692_end_mask_0 = const()[name = tensor("op_9692_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9692 = slice_by_index(begin = var_9692_begin_0, end = var_9692_end_0, end_mask = var_9692_end_mask_0, x = var_9686)[name = tensor("op_9692")]; + tensor var_9694 = add(x = segment_accum_577, y = var_9692)[name = tensor("op_9694")]; + tensor var_9696_begin_0 = const()[name = tensor("op_9696_begin_0"), val = tensor([0, 290000, 0])]; + tensor var_9696_end_0 = const()[name = tensor("op_9696_end_0"), val = tensor([1, 291000, 9])]; + tensor var_9696_end_mask_0 = const()[name = tensor("op_9696_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9696 = slice_by_index(begin = var_9696_begin_0, end = var_9696_end_0, end_mask = var_9696_end_mask_0, x = reshape_4)[name = tensor("op_9696")]; + tensor segment_accum_579_exclusive_0 = const()[name = tensor("segment_accum_579_exclusive_0"), val = tensor(false)]; + tensor segment_accum_579_reverse_0 = const()[name = tensor("segment_accum_579_reverse_0"), val = tensor(false)]; + tensor segment_accum_579 = cumsum(axis = var_7349, exclusive = segment_accum_579_exclusive_0, reverse = segment_accum_579_reverse_0, x = var_9696)[name = tensor("segment_accum_579")]; + tensor var_9700_begin_0 = const()[name = tensor("op_9700_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9700_end_0 = const()[name = tensor("op_9700_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9700_end_mask_0 = const()[name = tensor("op_9700_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9700 = slice_by_index(begin = var_9700_begin_0, end = var_9700_end_0, end_mask = var_9700_end_mask_0, x = var_9694)[name = tensor("op_9700")]; + tensor var_9702 = add(x = segment_accum_579, y = var_9700)[name = tensor("op_9702")]; + tensor var_9704_begin_0 = const()[name = tensor("op_9704_begin_0"), val = tensor([0, 291000, 0])]; + tensor var_9704_end_0 = const()[name = tensor("op_9704_end_0"), val = tensor([1, 292000, 9])]; + tensor var_9704_end_mask_0 = const()[name = tensor("op_9704_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9704 = slice_by_index(begin = var_9704_begin_0, end = var_9704_end_0, end_mask = var_9704_end_mask_0, x = reshape_4)[name = tensor("op_9704")]; + tensor segment_accum_581_exclusive_0 = const()[name = tensor("segment_accum_581_exclusive_0"), val = tensor(false)]; + tensor segment_accum_581_reverse_0 = const()[name = tensor("segment_accum_581_reverse_0"), val = tensor(false)]; + tensor segment_accum_581 = cumsum(axis = var_7349, exclusive = segment_accum_581_exclusive_0, reverse = segment_accum_581_reverse_0, x = var_9704)[name = tensor("segment_accum_581")]; + tensor var_9708_begin_0 = const()[name = tensor("op_9708_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9708_end_0 = const()[name = tensor("op_9708_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9708_end_mask_0 = const()[name = tensor("op_9708_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9708 = slice_by_index(begin = var_9708_begin_0, end = var_9708_end_0, end_mask = var_9708_end_mask_0, x = var_9702)[name = tensor("op_9708")]; + tensor var_9710 = add(x = segment_accum_581, y = var_9708)[name = tensor("op_9710")]; + tensor var_9712_begin_0 = const()[name = tensor("op_9712_begin_0"), val = tensor([0, 292000, 0])]; + tensor var_9712_end_0 = const()[name = tensor("op_9712_end_0"), val = tensor([1, 293000, 9])]; + tensor var_9712_end_mask_0 = const()[name = tensor("op_9712_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9712 = slice_by_index(begin = var_9712_begin_0, end = var_9712_end_0, end_mask = var_9712_end_mask_0, x = reshape_4)[name = tensor("op_9712")]; + tensor segment_accum_583_exclusive_0 = const()[name = tensor("segment_accum_583_exclusive_0"), val = tensor(false)]; + tensor segment_accum_583_reverse_0 = const()[name = tensor("segment_accum_583_reverse_0"), val = tensor(false)]; + tensor segment_accum_583 = cumsum(axis = var_7349, exclusive = segment_accum_583_exclusive_0, reverse = segment_accum_583_reverse_0, x = var_9712)[name = tensor("segment_accum_583")]; + tensor var_9716_begin_0 = const()[name = tensor("op_9716_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9716_end_0 = const()[name = tensor("op_9716_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9716_end_mask_0 = const()[name = tensor("op_9716_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9716 = slice_by_index(begin = var_9716_begin_0, end = var_9716_end_0, end_mask = var_9716_end_mask_0, x = var_9710)[name = tensor("op_9716")]; + tensor var_9718 = add(x = segment_accum_583, y = var_9716)[name = tensor("op_9718")]; + tensor var_9720_begin_0 = const()[name = tensor("op_9720_begin_0"), val = tensor([0, 293000, 0])]; + tensor var_9720_end_0 = const()[name = tensor("op_9720_end_0"), val = tensor([1, 294000, 9])]; + tensor var_9720_end_mask_0 = const()[name = tensor("op_9720_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9720 = slice_by_index(begin = var_9720_begin_0, end = var_9720_end_0, end_mask = var_9720_end_mask_0, x = reshape_4)[name = tensor("op_9720")]; + tensor segment_accum_585_exclusive_0 = const()[name = tensor("segment_accum_585_exclusive_0"), val = tensor(false)]; + tensor segment_accum_585_reverse_0 = const()[name = tensor("segment_accum_585_reverse_0"), val = tensor(false)]; + tensor segment_accum_585 = cumsum(axis = var_7349, exclusive = segment_accum_585_exclusive_0, reverse = segment_accum_585_reverse_0, x = var_9720)[name = tensor("segment_accum_585")]; + tensor var_9724_begin_0 = const()[name = tensor("op_9724_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9724_end_0 = const()[name = tensor("op_9724_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9724_end_mask_0 = const()[name = tensor("op_9724_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9724 = slice_by_index(begin = var_9724_begin_0, end = var_9724_end_0, end_mask = var_9724_end_mask_0, x = var_9718)[name = tensor("op_9724")]; + tensor var_9726 = add(x = segment_accum_585, y = var_9724)[name = tensor("op_9726")]; + tensor var_9728_begin_0 = const()[name = tensor("op_9728_begin_0"), val = tensor([0, 294000, 0])]; + tensor var_9728_end_0 = const()[name = tensor("op_9728_end_0"), val = tensor([1, 295000, 9])]; + tensor var_9728_end_mask_0 = const()[name = tensor("op_9728_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9728 = slice_by_index(begin = var_9728_begin_0, end = var_9728_end_0, end_mask = var_9728_end_mask_0, x = reshape_4)[name = tensor("op_9728")]; + tensor segment_accum_587_exclusive_0 = const()[name = tensor("segment_accum_587_exclusive_0"), val = tensor(false)]; + tensor segment_accum_587_reverse_0 = const()[name = tensor("segment_accum_587_reverse_0"), val = tensor(false)]; + tensor segment_accum_587 = cumsum(axis = var_7349, exclusive = segment_accum_587_exclusive_0, reverse = segment_accum_587_reverse_0, x = var_9728)[name = tensor("segment_accum_587")]; + tensor var_9732_begin_0 = const()[name = tensor("op_9732_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9732_end_0 = const()[name = tensor("op_9732_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9732_end_mask_0 = const()[name = tensor("op_9732_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9732 = slice_by_index(begin = var_9732_begin_0, end = var_9732_end_0, end_mask = var_9732_end_mask_0, x = var_9726)[name = tensor("op_9732")]; + tensor var_9734 = add(x = segment_accum_587, y = var_9732)[name = tensor("op_9734")]; + tensor var_9736_begin_0 = const()[name = tensor("op_9736_begin_0"), val = tensor([0, 295000, 0])]; + tensor var_9736_end_0 = const()[name = tensor("op_9736_end_0"), val = tensor([1, 296000, 9])]; + tensor var_9736_end_mask_0 = const()[name = tensor("op_9736_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9736 = slice_by_index(begin = var_9736_begin_0, end = var_9736_end_0, end_mask = var_9736_end_mask_0, x = reshape_4)[name = tensor("op_9736")]; + tensor segment_accum_589_exclusive_0 = const()[name = tensor("segment_accum_589_exclusive_0"), val = tensor(false)]; + tensor segment_accum_589_reverse_0 = const()[name = tensor("segment_accum_589_reverse_0"), val = tensor(false)]; + tensor segment_accum_589 = cumsum(axis = var_7349, exclusive = segment_accum_589_exclusive_0, reverse = segment_accum_589_reverse_0, x = var_9736)[name = tensor("segment_accum_589")]; + tensor var_9740_begin_0 = const()[name = tensor("op_9740_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9740_end_0 = const()[name = tensor("op_9740_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9740_end_mask_0 = const()[name = tensor("op_9740_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9740 = slice_by_index(begin = var_9740_begin_0, end = var_9740_end_0, end_mask = var_9740_end_mask_0, x = var_9734)[name = tensor("op_9740")]; + tensor var_9742 = add(x = segment_accum_589, y = var_9740)[name = tensor("op_9742")]; + tensor var_9744_begin_0 = const()[name = tensor("op_9744_begin_0"), val = tensor([0, 296000, 0])]; + tensor var_9744_end_0 = const()[name = tensor("op_9744_end_0"), val = tensor([1, 297000, 9])]; + tensor var_9744_end_mask_0 = const()[name = tensor("op_9744_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9744 = slice_by_index(begin = var_9744_begin_0, end = var_9744_end_0, end_mask = var_9744_end_mask_0, x = reshape_4)[name = tensor("op_9744")]; + tensor segment_accum_591_exclusive_0 = const()[name = tensor("segment_accum_591_exclusive_0"), val = tensor(false)]; + tensor segment_accum_591_reverse_0 = const()[name = tensor("segment_accum_591_reverse_0"), val = tensor(false)]; + tensor segment_accum_591 = cumsum(axis = var_7349, exclusive = segment_accum_591_exclusive_0, reverse = segment_accum_591_reverse_0, x = var_9744)[name = tensor("segment_accum_591")]; + tensor var_9748_begin_0 = const()[name = tensor("op_9748_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9748_end_0 = const()[name = tensor("op_9748_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9748_end_mask_0 = const()[name = tensor("op_9748_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9748 = slice_by_index(begin = var_9748_begin_0, end = var_9748_end_0, end_mask = var_9748_end_mask_0, x = var_9742)[name = tensor("op_9748")]; + tensor var_9750 = add(x = segment_accum_591, y = var_9748)[name = tensor("op_9750")]; + tensor var_9752_begin_0 = const()[name = tensor("op_9752_begin_0"), val = tensor([0, 297000, 0])]; + tensor var_9752_end_0 = const()[name = tensor("op_9752_end_0"), val = tensor([1, 298000, 9])]; + tensor var_9752_end_mask_0 = const()[name = tensor("op_9752_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9752 = slice_by_index(begin = var_9752_begin_0, end = var_9752_end_0, end_mask = var_9752_end_mask_0, x = reshape_4)[name = tensor("op_9752")]; + tensor segment_accum_593_exclusive_0 = const()[name = tensor("segment_accum_593_exclusive_0"), val = tensor(false)]; + tensor segment_accum_593_reverse_0 = const()[name = tensor("segment_accum_593_reverse_0"), val = tensor(false)]; + tensor segment_accum_593 = cumsum(axis = var_7349, exclusive = segment_accum_593_exclusive_0, reverse = segment_accum_593_reverse_0, x = var_9752)[name = tensor("segment_accum_593")]; + tensor var_9756_begin_0 = const()[name = tensor("op_9756_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9756_end_0 = const()[name = tensor("op_9756_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9756_end_mask_0 = const()[name = tensor("op_9756_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9756 = slice_by_index(begin = var_9756_begin_0, end = var_9756_end_0, end_mask = var_9756_end_mask_0, x = var_9750)[name = tensor("op_9756")]; + tensor var_9758 = add(x = segment_accum_593, y = var_9756)[name = tensor("op_9758")]; + tensor var_9760_begin_0 = const()[name = tensor("op_9760_begin_0"), val = tensor([0, 298000, 0])]; + tensor var_9760_end_0 = const()[name = tensor("op_9760_end_0"), val = tensor([1, 299000, 9])]; + tensor var_9760_end_mask_0 = const()[name = tensor("op_9760_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9760 = slice_by_index(begin = var_9760_begin_0, end = var_9760_end_0, end_mask = var_9760_end_mask_0, x = reshape_4)[name = tensor("op_9760")]; + tensor segment_accum_595_exclusive_0 = const()[name = tensor("segment_accum_595_exclusive_0"), val = tensor(false)]; + tensor segment_accum_595_reverse_0 = const()[name = tensor("segment_accum_595_reverse_0"), val = tensor(false)]; + tensor segment_accum_595 = cumsum(axis = var_7349, exclusive = segment_accum_595_exclusive_0, reverse = segment_accum_595_reverse_0, x = var_9760)[name = tensor("segment_accum_595")]; + tensor var_9764_begin_0 = const()[name = tensor("op_9764_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9764_end_0 = const()[name = tensor("op_9764_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9764_end_mask_0 = const()[name = tensor("op_9764_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9764 = slice_by_index(begin = var_9764_begin_0, end = var_9764_end_0, end_mask = var_9764_end_mask_0, x = var_9758)[name = tensor("op_9764")]; + tensor var_9766 = add(x = segment_accum_595, y = var_9764)[name = tensor("op_9766")]; + tensor var_9768_begin_0 = const()[name = tensor("op_9768_begin_0"), val = tensor([0, 299000, 0])]; + tensor var_9768_end_0 = const()[name = tensor("op_9768_end_0"), val = tensor([1, 300000, 9])]; + tensor var_9768_end_mask_0 = const()[name = tensor("op_9768_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9768 = slice_by_index(begin = var_9768_begin_0, end = var_9768_end_0, end_mask = var_9768_end_mask_0, x = reshape_4)[name = tensor("op_9768")]; + tensor segment_accum_597_exclusive_0 = const()[name = tensor("segment_accum_597_exclusive_0"), val = tensor(false)]; + tensor segment_accum_597_reverse_0 = const()[name = tensor("segment_accum_597_reverse_0"), val = tensor(false)]; + tensor segment_accum_597 = cumsum(axis = var_7349, exclusive = segment_accum_597_exclusive_0, reverse = segment_accum_597_reverse_0, x = var_9768)[name = tensor("segment_accum_597")]; + tensor var_9772_begin_0 = const()[name = tensor("op_9772_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9772_end_0 = const()[name = tensor("op_9772_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9772_end_mask_0 = const()[name = tensor("op_9772_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9772 = slice_by_index(begin = var_9772_begin_0, end = var_9772_end_0, end_mask = var_9772_end_mask_0, x = var_9766)[name = tensor("op_9772")]; + tensor var_9774 = add(x = segment_accum_597, y = var_9772)[name = tensor("op_9774")]; + tensor var_9776_begin_0 = const()[name = tensor("op_9776_begin_0"), val = tensor([0, 300000, 0])]; + tensor var_9776_end_0 = const()[name = tensor("op_9776_end_0"), val = tensor([1, 301000, 9])]; + tensor var_9776_end_mask_0 = const()[name = tensor("op_9776_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9776 = slice_by_index(begin = var_9776_begin_0, end = var_9776_end_0, end_mask = var_9776_end_mask_0, x = reshape_4)[name = tensor("op_9776")]; + tensor segment_accum_599_exclusive_0 = const()[name = tensor("segment_accum_599_exclusive_0"), val = tensor(false)]; + tensor segment_accum_599_reverse_0 = const()[name = tensor("segment_accum_599_reverse_0"), val = tensor(false)]; + tensor segment_accum_599 = cumsum(axis = var_7349, exclusive = segment_accum_599_exclusive_0, reverse = segment_accum_599_reverse_0, x = var_9776)[name = tensor("segment_accum_599")]; + tensor var_9780_begin_0 = const()[name = tensor("op_9780_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9780_end_0 = const()[name = tensor("op_9780_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9780_end_mask_0 = const()[name = tensor("op_9780_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9780 = slice_by_index(begin = var_9780_begin_0, end = var_9780_end_0, end_mask = var_9780_end_mask_0, x = var_9774)[name = tensor("op_9780")]; + tensor var_9782 = add(x = segment_accum_599, y = var_9780)[name = tensor("op_9782")]; + tensor var_9784_begin_0 = const()[name = tensor("op_9784_begin_0"), val = tensor([0, 301000, 0])]; + tensor var_9784_end_0 = const()[name = tensor("op_9784_end_0"), val = tensor([1, 302000, 9])]; + tensor var_9784_end_mask_0 = const()[name = tensor("op_9784_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9784 = slice_by_index(begin = var_9784_begin_0, end = var_9784_end_0, end_mask = var_9784_end_mask_0, x = reshape_4)[name = tensor("op_9784")]; + tensor segment_accum_601_exclusive_0 = const()[name = tensor("segment_accum_601_exclusive_0"), val = tensor(false)]; + tensor segment_accum_601_reverse_0 = const()[name = tensor("segment_accum_601_reverse_0"), val = tensor(false)]; + tensor segment_accum_601 = cumsum(axis = var_7349, exclusive = segment_accum_601_exclusive_0, reverse = segment_accum_601_reverse_0, x = var_9784)[name = tensor("segment_accum_601")]; + tensor var_9788_begin_0 = const()[name = tensor("op_9788_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9788_end_0 = const()[name = tensor("op_9788_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9788_end_mask_0 = const()[name = tensor("op_9788_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9788 = slice_by_index(begin = var_9788_begin_0, end = var_9788_end_0, end_mask = var_9788_end_mask_0, x = var_9782)[name = tensor("op_9788")]; + tensor var_9790 = add(x = segment_accum_601, y = var_9788)[name = tensor("op_9790")]; + tensor var_9792_begin_0 = const()[name = tensor("op_9792_begin_0"), val = tensor([0, 302000, 0])]; + tensor var_9792_end_0 = const()[name = tensor("op_9792_end_0"), val = tensor([1, 303000, 9])]; + tensor var_9792_end_mask_0 = const()[name = tensor("op_9792_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9792 = slice_by_index(begin = var_9792_begin_0, end = var_9792_end_0, end_mask = var_9792_end_mask_0, x = reshape_4)[name = tensor("op_9792")]; + tensor segment_accum_603_exclusive_0 = const()[name = tensor("segment_accum_603_exclusive_0"), val = tensor(false)]; + tensor segment_accum_603_reverse_0 = const()[name = tensor("segment_accum_603_reverse_0"), val = tensor(false)]; + tensor segment_accum_603 = cumsum(axis = var_7349, exclusive = segment_accum_603_exclusive_0, reverse = segment_accum_603_reverse_0, x = var_9792)[name = tensor("segment_accum_603")]; + tensor var_9796_begin_0 = const()[name = tensor("op_9796_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9796_end_0 = const()[name = tensor("op_9796_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9796_end_mask_0 = const()[name = tensor("op_9796_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9796 = slice_by_index(begin = var_9796_begin_0, end = var_9796_end_0, end_mask = var_9796_end_mask_0, x = var_9790)[name = tensor("op_9796")]; + tensor var_9798 = add(x = segment_accum_603, y = var_9796)[name = tensor("op_9798")]; + tensor var_9800_begin_0 = const()[name = tensor("op_9800_begin_0"), val = tensor([0, 303000, 0])]; + tensor var_9800_end_0 = const()[name = tensor("op_9800_end_0"), val = tensor([1, 304000, 9])]; + tensor var_9800_end_mask_0 = const()[name = tensor("op_9800_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9800 = slice_by_index(begin = var_9800_begin_0, end = var_9800_end_0, end_mask = var_9800_end_mask_0, x = reshape_4)[name = tensor("op_9800")]; + tensor segment_accum_605_exclusive_0 = const()[name = tensor("segment_accum_605_exclusive_0"), val = tensor(false)]; + tensor segment_accum_605_reverse_0 = const()[name = tensor("segment_accum_605_reverse_0"), val = tensor(false)]; + tensor segment_accum_605 = cumsum(axis = var_7349, exclusive = segment_accum_605_exclusive_0, reverse = segment_accum_605_reverse_0, x = var_9800)[name = tensor("segment_accum_605")]; + tensor var_9804_begin_0 = const()[name = tensor("op_9804_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9804_end_0 = const()[name = tensor("op_9804_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9804_end_mask_0 = const()[name = tensor("op_9804_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9804 = slice_by_index(begin = var_9804_begin_0, end = var_9804_end_0, end_mask = var_9804_end_mask_0, x = var_9798)[name = tensor("op_9804")]; + tensor var_9806 = add(x = segment_accum_605, y = var_9804)[name = tensor("op_9806")]; + tensor var_9808_begin_0 = const()[name = tensor("op_9808_begin_0"), val = tensor([0, 304000, 0])]; + tensor var_9808_end_0 = const()[name = tensor("op_9808_end_0"), val = tensor([1, 305000, 9])]; + tensor var_9808_end_mask_0 = const()[name = tensor("op_9808_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9808 = slice_by_index(begin = var_9808_begin_0, end = var_9808_end_0, end_mask = var_9808_end_mask_0, x = reshape_4)[name = tensor("op_9808")]; + tensor segment_accum_607_exclusive_0 = const()[name = tensor("segment_accum_607_exclusive_0"), val = tensor(false)]; + tensor segment_accum_607_reverse_0 = const()[name = tensor("segment_accum_607_reverse_0"), val = tensor(false)]; + tensor segment_accum_607 = cumsum(axis = var_7349, exclusive = segment_accum_607_exclusive_0, reverse = segment_accum_607_reverse_0, x = var_9808)[name = tensor("segment_accum_607")]; + tensor var_9812_begin_0 = const()[name = tensor("op_9812_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9812_end_0 = const()[name = tensor("op_9812_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9812_end_mask_0 = const()[name = tensor("op_9812_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9812 = slice_by_index(begin = var_9812_begin_0, end = var_9812_end_0, end_mask = var_9812_end_mask_0, x = var_9806)[name = tensor("op_9812")]; + tensor var_9814 = add(x = segment_accum_607, y = var_9812)[name = tensor("op_9814")]; + tensor var_9816_begin_0 = const()[name = tensor("op_9816_begin_0"), val = tensor([0, 305000, 0])]; + tensor var_9816_end_0 = const()[name = tensor("op_9816_end_0"), val = tensor([1, 306000, 9])]; + tensor var_9816_end_mask_0 = const()[name = tensor("op_9816_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9816 = slice_by_index(begin = var_9816_begin_0, end = var_9816_end_0, end_mask = var_9816_end_mask_0, x = reshape_4)[name = tensor("op_9816")]; + tensor segment_accum_609_exclusive_0 = const()[name = tensor("segment_accum_609_exclusive_0"), val = tensor(false)]; + tensor segment_accum_609_reverse_0 = const()[name = tensor("segment_accum_609_reverse_0"), val = tensor(false)]; + tensor segment_accum_609 = cumsum(axis = var_7349, exclusive = segment_accum_609_exclusive_0, reverse = segment_accum_609_reverse_0, x = var_9816)[name = tensor("segment_accum_609")]; + tensor var_9820_begin_0 = const()[name = tensor("op_9820_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9820_end_0 = const()[name = tensor("op_9820_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9820_end_mask_0 = const()[name = tensor("op_9820_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9820 = slice_by_index(begin = var_9820_begin_0, end = var_9820_end_0, end_mask = var_9820_end_mask_0, x = var_9814)[name = tensor("op_9820")]; + tensor var_9822 = add(x = segment_accum_609, y = var_9820)[name = tensor("op_9822")]; + tensor var_9824_begin_0 = const()[name = tensor("op_9824_begin_0"), val = tensor([0, 306000, 0])]; + tensor var_9824_end_0 = const()[name = tensor("op_9824_end_0"), val = tensor([1, 307000, 9])]; + tensor var_9824_end_mask_0 = const()[name = tensor("op_9824_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9824 = slice_by_index(begin = var_9824_begin_0, end = var_9824_end_0, end_mask = var_9824_end_mask_0, x = reshape_4)[name = tensor("op_9824")]; + tensor segment_accum_611_exclusive_0 = const()[name = tensor("segment_accum_611_exclusive_0"), val = tensor(false)]; + tensor segment_accum_611_reverse_0 = const()[name = tensor("segment_accum_611_reverse_0"), val = tensor(false)]; + tensor segment_accum_611 = cumsum(axis = var_7349, exclusive = segment_accum_611_exclusive_0, reverse = segment_accum_611_reverse_0, x = var_9824)[name = tensor("segment_accum_611")]; + tensor var_9828_begin_0 = const()[name = tensor("op_9828_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9828_end_0 = const()[name = tensor("op_9828_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9828_end_mask_0 = const()[name = tensor("op_9828_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9828 = slice_by_index(begin = var_9828_begin_0, end = var_9828_end_0, end_mask = var_9828_end_mask_0, x = var_9822)[name = tensor("op_9828")]; + tensor var_9830 = add(x = segment_accum_611, y = var_9828)[name = tensor("op_9830")]; + tensor var_9832_begin_0 = const()[name = tensor("op_9832_begin_0"), val = tensor([0, 307000, 0])]; + tensor var_9832_end_0 = const()[name = tensor("op_9832_end_0"), val = tensor([1, 308000, 9])]; + tensor var_9832_end_mask_0 = const()[name = tensor("op_9832_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9832 = slice_by_index(begin = var_9832_begin_0, end = var_9832_end_0, end_mask = var_9832_end_mask_0, x = reshape_4)[name = tensor("op_9832")]; + tensor segment_accum_613_exclusive_0 = const()[name = tensor("segment_accum_613_exclusive_0"), val = tensor(false)]; + tensor segment_accum_613_reverse_0 = const()[name = tensor("segment_accum_613_reverse_0"), val = tensor(false)]; + tensor segment_accum_613 = cumsum(axis = var_7349, exclusive = segment_accum_613_exclusive_0, reverse = segment_accum_613_reverse_0, x = var_9832)[name = tensor("segment_accum_613")]; + tensor var_9836_begin_0 = const()[name = tensor("op_9836_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9836_end_0 = const()[name = tensor("op_9836_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9836_end_mask_0 = const()[name = tensor("op_9836_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9836 = slice_by_index(begin = var_9836_begin_0, end = var_9836_end_0, end_mask = var_9836_end_mask_0, x = var_9830)[name = tensor("op_9836")]; + tensor var_9838 = add(x = segment_accum_613, y = var_9836)[name = tensor("op_9838")]; + tensor var_9840_begin_0 = const()[name = tensor("op_9840_begin_0"), val = tensor([0, 308000, 0])]; + tensor var_9840_end_0 = const()[name = tensor("op_9840_end_0"), val = tensor([1, 309000, 9])]; + tensor var_9840_end_mask_0 = const()[name = tensor("op_9840_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9840 = slice_by_index(begin = var_9840_begin_0, end = var_9840_end_0, end_mask = var_9840_end_mask_0, x = reshape_4)[name = tensor("op_9840")]; + tensor segment_accum_615_exclusive_0 = const()[name = tensor("segment_accum_615_exclusive_0"), val = tensor(false)]; + tensor segment_accum_615_reverse_0 = const()[name = tensor("segment_accum_615_reverse_0"), val = tensor(false)]; + tensor segment_accum_615 = cumsum(axis = var_7349, exclusive = segment_accum_615_exclusive_0, reverse = segment_accum_615_reverse_0, x = var_9840)[name = tensor("segment_accum_615")]; + tensor var_9844_begin_0 = const()[name = tensor("op_9844_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9844_end_0 = const()[name = tensor("op_9844_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9844_end_mask_0 = const()[name = tensor("op_9844_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9844 = slice_by_index(begin = var_9844_begin_0, end = var_9844_end_0, end_mask = var_9844_end_mask_0, x = var_9838)[name = tensor("op_9844")]; + tensor var_9846 = add(x = segment_accum_615, y = var_9844)[name = tensor("op_9846")]; + tensor var_9848_begin_0 = const()[name = tensor("op_9848_begin_0"), val = tensor([0, 309000, 0])]; + tensor var_9848_end_0 = const()[name = tensor("op_9848_end_0"), val = tensor([1, 310000, 9])]; + tensor var_9848_end_mask_0 = const()[name = tensor("op_9848_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9848 = slice_by_index(begin = var_9848_begin_0, end = var_9848_end_0, end_mask = var_9848_end_mask_0, x = reshape_4)[name = tensor("op_9848")]; + tensor segment_accum_617_exclusive_0 = const()[name = tensor("segment_accum_617_exclusive_0"), val = tensor(false)]; + tensor segment_accum_617_reverse_0 = const()[name = tensor("segment_accum_617_reverse_0"), val = tensor(false)]; + tensor segment_accum_617 = cumsum(axis = var_7349, exclusive = segment_accum_617_exclusive_0, reverse = segment_accum_617_reverse_0, x = var_9848)[name = tensor("segment_accum_617")]; + tensor var_9852_begin_0 = const()[name = tensor("op_9852_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9852_end_0 = const()[name = tensor("op_9852_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9852_end_mask_0 = const()[name = tensor("op_9852_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9852 = slice_by_index(begin = var_9852_begin_0, end = var_9852_end_0, end_mask = var_9852_end_mask_0, x = var_9846)[name = tensor("op_9852")]; + tensor var_9854 = add(x = segment_accum_617, y = var_9852)[name = tensor("op_9854")]; + tensor var_9856_begin_0 = const()[name = tensor("op_9856_begin_0"), val = tensor([0, 310000, 0])]; + tensor var_9856_end_0 = const()[name = tensor("op_9856_end_0"), val = tensor([1, 311000, 9])]; + tensor var_9856_end_mask_0 = const()[name = tensor("op_9856_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9856 = slice_by_index(begin = var_9856_begin_0, end = var_9856_end_0, end_mask = var_9856_end_mask_0, x = reshape_4)[name = tensor("op_9856")]; + tensor segment_accum_619_exclusive_0 = const()[name = tensor("segment_accum_619_exclusive_0"), val = tensor(false)]; + tensor segment_accum_619_reverse_0 = const()[name = tensor("segment_accum_619_reverse_0"), val = tensor(false)]; + tensor segment_accum_619 = cumsum(axis = var_7349, exclusive = segment_accum_619_exclusive_0, reverse = segment_accum_619_reverse_0, x = var_9856)[name = tensor("segment_accum_619")]; + tensor var_9860_begin_0 = const()[name = tensor("op_9860_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9860_end_0 = const()[name = tensor("op_9860_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9860_end_mask_0 = const()[name = tensor("op_9860_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9860 = slice_by_index(begin = var_9860_begin_0, end = var_9860_end_0, end_mask = var_9860_end_mask_0, x = var_9854)[name = tensor("op_9860")]; + tensor var_9862 = add(x = segment_accum_619, y = var_9860)[name = tensor("op_9862")]; + tensor var_9864_begin_0 = const()[name = tensor("op_9864_begin_0"), val = tensor([0, 311000, 0])]; + tensor var_9864_end_0 = const()[name = tensor("op_9864_end_0"), val = tensor([1, 312000, 9])]; + tensor var_9864_end_mask_0 = const()[name = tensor("op_9864_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9864 = slice_by_index(begin = var_9864_begin_0, end = var_9864_end_0, end_mask = var_9864_end_mask_0, x = reshape_4)[name = tensor("op_9864")]; + tensor segment_accum_621_exclusive_0 = const()[name = tensor("segment_accum_621_exclusive_0"), val = tensor(false)]; + tensor segment_accum_621_reverse_0 = const()[name = tensor("segment_accum_621_reverse_0"), val = tensor(false)]; + tensor segment_accum_621 = cumsum(axis = var_7349, exclusive = segment_accum_621_exclusive_0, reverse = segment_accum_621_reverse_0, x = var_9864)[name = tensor("segment_accum_621")]; + tensor var_9868_begin_0 = const()[name = tensor("op_9868_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9868_end_0 = const()[name = tensor("op_9868_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9868_end_mask_0 = const()[name = tensor("op_9868_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9868 = slice_by_index(begin = var_9868_begin_0, end = var_9868_end_0, end_mask = var_9868_end_mask_0, x = var_9862)[name = tensor("op_9868")]; + tensor var_9870 = add(x = segment_accum_621, y = var_9868)[name = tensor("op_9870")]; + tensor var_9872_begin_0 = const()[name = tensor("op_9872_begin_0"), val = tensor([0, 312000, 0])]; + tensor var_9872_end_0 = const()[name = tensor("op_9872_end_0"), val = tensor([1, 313000, 9])]; + tensor var_9872_end_mask_0 = const()[name = tensor("op_9872_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9872 = slice_by_index(begin = var_9872_begin_0, end = var_9872_end_0, end_mask = var_9872_end_mask_0, x = reshape_4)[name = tensor("op_9872")]; + tensor segment_accum_623_exclusive_0 = const()[name = tensor("segment_accum_623_exclusive_0"), val = tensor(false)]; + tensor segment_accum_623_reverse_0 = const()[name = tensor("segment_accum_623_reverse_0"), val = tensor(false)]; + tensor segment_accum_623 = cumsum(axis = var_7349, exclusive = segment_accum_623_exclusive_0, reverse = segment_accum_623_reverse_0, x = var_9872)[name = tensor("segment_accum_623")]; + tensor var_9876_begin_0 = const()[name = tensor("op_9876_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9876_end_0 = const()[name = tensor("op_9876_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9876_end_mask_0 = const()[name = tensor("op_9876_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9876 = slice_by_index(begin = var_9876_begin_0, end = var_9876_end_0, end_mask = var_9876_end_mask_0, x = var_9870)[name = tensor("op_9876")]; + tensor var_9878 = add(x = segment_accum_623, y = var_9876)[name = tensor("op_9878")]; + tensor var_9880_begin_0 = const()[name = tensor("op_9880_begin_0"), val = tensor([0, 313000, 0])]; + tensor var_9880_end_0 = const()[name = tensor("op_9880_end_0"), val = tensor([1, 314000, 9])]; + tensor var_9880_end_mask_0 = const()[name = tensor("op_9880_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9880 = slice_by_index(begin = var_9880_begin_0, end = var_9880_end_0, end_mask = var_9880_end_mask_0, x = reshape_4)[name = tensor("op_9880")]; + tensor segment_accum_625_exclusive_0 = const()[name = tensor("segment_accum_625_exclusive_0"), val = tensor(false)]; + tensor segment_accum_625_reverse_0 = const()[name = tensor("segment_accum_625_reverse_0"), val = tensor(false)]; + tensor segment_accum_625 = cumsum(axis = var_7349, exclusive = segment_accum_625_exclusive_0, reverse = segment_accum_625_reverse_0, x = var_9880)[name = tensor("segment_accum_625")]; + tensor var_9884_begin_0 = const()[name = tensor("op_9884_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9884_end_0 = const()[name = tensor("op_9884_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9884_end_mask_0 = const()[name = tensor("op_9884_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9884 = slice_by_index(begin = var_9884_begin_0, end = var_9884_end_0, end_mask = var_9884_end_mask_0, x = var_9878)[name = tensor("op_9884")]; + tensor var_9886 = add(x = segment_accum_625, y = var_9884)[name = tensor("op_9886")]; + tensor var_9888_begin_0 = const()[name = tensor("op_9888_begin_0"), val = tensor([0, 314000, 0])]; + tensor var_9888_end_0 = const()[name = tensor("op_9888_end_0"), val = tensor([1, 315000, 9])]; + tensor var_9888_end_mask_0 = const()[name = tensor("op_9888_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9888 = slice_by_index(begin = var_9888_begin_0, end = var_9888_end_0, end_mask = var_9888_end_mask_0, x = reshape_4)[name = tensor("op_9888")]; + tensor segment_accum_627_exclusive_0 = const()[name = tensor("segment_accum_627_exclusive_0"), val = tensor(false)]; + tensor segment_accum_627_reverse_0 = const()[name = tensor("segment_accum_627_reverse_0"), val = tensor(false)]; + tensor segment_accum_627 = cumsum(axis = var_7349, exclusive = segment_accum_627_exclusive_0, reverse = segment_accum_627_reverse_0, x = var_9888)[name = tensor("segment_accum_627")]; + tensor var_9892_begin_0 = const()[name = tensor("op_9892_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9892_end_0 = const()[name = tensor("op_9892_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9892_end_mask_0 = const()[name = tensor("op_9892_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9892 = slice_by_index(begin = var_9892_begin_0, end = var_9892_end_0, end_mask = var_9892_end_mask_0, x = var_9886)[name = tensor("op_9892")]; + tensor var_9894 = add(x = segment_accum_627, y = var_9892)[name = tensor("op_9894")]; + tensor var_9896_begin_0 = const()[name = tensor("op_9896_begin_0"), val = tensor([0, 315000, 0])]; + tensor var_9896_end_0 = const()[name = tensor("op_9896_end_0"), val = tensor([1, 316000, 9])]; + tensor var_9896_end_mask_0 = const()[name = tensor("op_9896_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9896 = slice_by_index(begin = var_9896_begin_0, end = var_9896_end_0, end_mask = var_9896_end_mask_0, x = reshape_4)[name = tensor("op_9896")]; + tensor segment_accum_629_exclusive_0 = const()[name = tensor("segment_accum_629_exclusive_0"), val = tensor(false)]; + tensor segment_accum_629_reverse_0 = const()[name = tensor("segment_accum_629_reverse_0"), val = tensor(false)]; + tensor segment_accum_629 = cumsum(axis = var_7349, exclusive = segment_accum_629_exclusive_0, reverse = segment_accum_629_reverse_0, x = var_9896)[name = tensor("segment_accum_629")]; + tensor var_9900_begin_0 = const()[name = tensor("op_9900_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9900_end_0 = const()[name = tensor("op_9900_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9900_end_mask_0 = const()[name = tensor("op_9900_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9900 = slice_by_index(begin = var_9900_begin_0, end = var_9900_end_0, end_mask = var_9900_end_mask_0, x = var_9894)[name = tensor("op_9900")]; + tensor var_9902 = add(x = segment_accum_629, y = var_9900)[name = tensor("op_9902")]; + tensor var_9904_begin_0 = const()[name = tensor("op_9904_begin_0"), val = tensor([0, 316000, 0])]; + tensor var_9904_end_0 = const()[name = tensor("op_9904_end_0"), val = tensor([1, 317000, 9])]; + tensor var_9904_end_mask_0 = const()[name = tensor("op_9904_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9904 = slice_by_index(begin = var_9904_begin_0, end = var_9904_end_0, end_mask = var_9904_end_mask_0, x = reshape_4)[name = tensor("op_9904")]; + tensor segment_accum_631_exclusive_0 = const()[name = tensor("segment_accum_631_exclusive_0"), val = tensor(false)]; + tensor segment_accum_631_reverse_0 = const()[name = tensor("segment_accum_631_reverse_0"), val = tensor(false)]; + tensor segment_accum_631 = cumsum(axis = var_7349, exclusive = segment_accum_631_exclusive_0, reverse = segment_accum_631_reverse_0, x = var_9904)[name = tensor("segment_accum_631")]; + tensor var_9908_begin_0 = const()[name = tensor("op_9908_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9908_end_0 = const()[name = tensor("op_9908_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9908_end_mask_0 = const()[name = tensor("op_9908_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9908 = slice_by_index(begin = var_9908_begin_0, end = var_9908_end_0, end_mask = var_9908_end_mask_0, x = var_9902)[name = tensor("op_9908")]; + tensor var_9910 = add(x = segment_accum_631, y = var_9908)[name = tensor("op_9910")]; + tensor var_9912_begin_0 = const()[name = tensor("op_9912_begin_0"), val = tensor([0, 317000, 0])]; + tensor var_9912_end_0 = const()[name = tensor("op_9912_end_0"), val = tensor([1, 318000, 9])]; + tensor var_9912_end_mask_0 = const()[name = tensor("op_9912_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9912 = slice_by_index(begin = var_9912_begin_0, end = var_9912_end_0, end_mask = var_9912_end_mask_0, x = reshape_4)[name = tensor("op_9912")]; + tensor segment_accum_633_exclusive_0 = const()[name = tensor("segment_accum_633_exclusive_0"), val = tensor(false)]; + tensor segment_accum_633_reverse_0 = const()[name = tensor("segment_accum_633_reverse_0"), val = tensor(false)]; + tensor segment_accum_633 = cumsum(axis = var_7349, exclusive = segment_accum_633_exclusive_0, reverse = segment_accum_633_reverse_0, x = var_9912)[name = tensor("segment_accum_633")]; + tensor var_9916_begin_0 = const()[name = tensor("op_9916_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9916_end_0 = const()[name = tensor("op_9916_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9916_end_mask_0 = const()[name = tensor("op_9916_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9916 = slice_by_index(begin = var_9916_begin_0, end = var_9916_end_0, end_mask = var_9916_end_mask_0, x = var_9910)[name = tensor("op_9916")]; + tensor var_9918 = add(x = segment_accum_633, y = var_9916)[name = tensor("op_9918")]; + tensor var_9920_begin_0 = const()[name = tensor("op_9920_begin_0"), val = tensor([0, 318000, 0])]; + tensor var_9920_end_0 = const()[name = tensor("op_9920_end_0"), val = tensor([1, 319000, 9])]; + tensor var_9920_end_mask_0 = const()[name = tensor("op_9920_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9920 = slice_by_index(begin = var_9920_begin_0, end = var_9920_end_0, end_mask = var_9920_end_mask_0, x = reshape_4)[name = tensor("op_9920")]; + tensor segment_accum_635_exclusive_0 = const()[name = tensor("segment_accum_635_exclusive_0"), val = tensor(false)]; + tensor segment_accum_635_reverse_0 = const()[name = tensor("segment_accum_635_reverse_0"), val = tensor(false)]; + tensor segment_accum_635 = cumsum(axis = var_7349, exclusive = segment_accum_635_exclusive_0, reverse = segment_accum_635_reverse_0, x = var_9920)[name = tensor("segment_accum_635")]; + tensor var_9924_begin_0 = const()[name = tensor("op_9924_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9924_end_0 = const()[name = tensor("op_9924_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9924_end_mask_0 = const()[name = tensor("op_9924_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9924 = slice_by_index(begin = var_9924_begin_0, end = var_9924_end_0, end_mask = var_9924_end_mask_0, x = var_9918)[name = tensor("op_9924")]; + tensor var_9926 = add(x = segment_accum_635, y = var_9924)[name = tensor("op_9926")]; + tensor var_9928_begin_0 = const()[name = tensor("op_9928_begin_0"), val = tensor([0, 319000, 0])]; + tensor var_9928_end_0 = const()[name = tensor("op_9928_end_0"), val = tensor([1, 320000, 9])]; + tensor var_9928_end_mask_0 = const()[name = tensor("op_9928_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9928 = slice_by_index(begin = var_9928_begin_0, end = var_9928_end_0, end_mask = var_9928_end_mask_0, x = reshape_4)[name = tensor("op_9928")]; + tensor segment_accum_637_exclusive_0 = const()[name = tensor("segment_accum_637_exclusive_0"), val = tensor(false)]; + tensor segment_accum_637_reverse_0 = const()[name = tensor("segment_accum_637_reverse_0"), val = tensor(false)]; + tensor segment_accum_637 = cumsum(axis = var_7349, exclusive = segment_accum_637_exclusive_0, reverse = segment_accum_637_reverse_0, x = var_9928)[name = tensor("segment_accum_637")]; + tensor var_9932_begin_0 = const()[name = tensor("op_9932_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9932_end_0 = const()[name = tensor("op_9932_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9932_end_mask_0 = const()[name = tensor("op_9932_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9932 = slice_by_index(begin = var_9932_begin_0, end = var_9932_end_0, end_mask = var_9932_end_mask_0, x = var_9926)[name = tensor("op_9932")]; + tensor var_9934 = add(x = segment_accum_637, y = var_9932)[name = tensor("op_9934")]; + tensor var_9936_begin_0 = const()[name = tensor("op_9936_begin_0"), val = tensor([0, 320000, 0])]; + tensor var_9936_end_0 = const()[name = tensor("op_9936_end_0"), val = tensor([1, 321000, 9])]; + tensor var_9936_end_mask_0 = const()[name = tensor("op_9936_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9936 = slice_by_index(begin = var_9936_begin_0, end = var_9936_end_0, end_mask = var_9936_end_mask_0, x = reshape_4)[name = tensor("op_9936")]; + tensor segment_accum_639_exclusive_0 = const()[name = tensor("segment_accum_639_exclusive_0"), val = tensor(false)]; + tensor segment_accum_639_reverse_0 = const()[name = tensor("segment_accum_639_reverse_0"), val = tensor(false)]; + tensor segment_accum_639 = cumsum(axis = var_7349, exclusive = segment_accum_639_exclusive_0, reverse = segment_accum_639_reverse_0, x = var_9936)[name = tensor("segment_accum_639")]; + tensor var_9940_begin_0 = const()[name = tensor("op_9940_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9940_end_0 = const()[name = tensor("op_9940_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9940_end_mask_0 = const()[name = tensor("op_9940_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9940 = slice_by_index(begin = var_9940_begin_0, end = var_9940_end_0, end_mask = var_9940_end_mask_0, x = var_9934)[name = tensor("op_9940")]; + tensor var_9942 = add(x = segment_accum_639, y = var_9940)[name = tensor("op_9942")]; + tensor var_9944_begin_0 = const()[name = tensor("op_9944_begin_0"), val = tensor([0, 321000, 0])]; + tensor var_9944_end_0 = const()[name = tensor("op_9944_end_0"), val = tensor([1, 322000, 9])]; + tensor var_9944_end_mask_0 = const()[name = tensor("op_9944_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9944 = slice_by_index(begin = var_9944_begin_0, end = var_9944_end_0, end_mask = var_9944_end_mask_0, x = reshape_4)[name = tensor("op_9944")]; + tensor segment_accum_641_exclusive_0 = const()[name = tensor("segment_accum_641_exclusive_0"), val = tensor(false)]; + tensor segment_accum_641_reverse_0 = const()[name = tensor("segment_accum_641_reverse_0"), val = tensor(false)]; + tensor segment_accum_641 = cumsum(axis = var_7349, exclusive = segment_accum_641_exclusive_0, reverse = segment_accum_641_reverse_0, x = var_9944)[name = tensor("segment_accum_641")]; + tensor var_9948_begin_0 = const()[name = tensor("op_9948_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9948_end_0 = const()[name = tensor("op_9948_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9948_end_mask_0 = const()[name = tensor("op_9948_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9948 = slice_by_index(begin = var_9948_begin_0, end = var_9948_end_0, end_mask = var_9948_end_mask_0, x = var_9942)[name = tensor("op_9948")]; + tensor var_9950 = add(x = segment_accum_641, y = var_9948)[name = tensor("op_9950")]; + tensor var_9952_begin_0 = const()[name = tensor("op_9952_begin_0"), val = tensor([0, 322000, 0])]; + tensor var_9952_end_0 = const()[name = tensor("op_9952_end_0"), val = tensor([1, 323000, 9])]; + tensor var_9952_end_mask_0 = const()[name = tensor("op_9952_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9952 = slice_by_index(begin = var_9952_begin_0, end = var_9952_end_0, end_mask = var_9952_end_mask_0, x = reshape_4)[name = tensor("op_9952")]; + tensor segment_accum_643_exclusive_0 = const()[name = tensor("segment_accum_643_exclusive_0"), val = tensor(false)]; + tensor segment_accum_643_reverse_0 = const()[name = tensor("segment_accum_643_reverse_0"), val = tensor(false)]; + tensor segment_accum_643 = cumsum(axis = var_7349, exclusive = segment_accum_643_exclusive_0, reverse = segment_accum_643_reverse_0, x = var_9952)[name = tensor("segment_accum_643")]; + tensor var_9956_begin_0 = const()[name = tensor("op_9956_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9956_end_0 = const()[name = tensor("op_9956_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9956_end_mask_0 = const()[name = tensor("op_9956_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9956 = slice_by_index(begin = var_9956_begin_0, end = var_9956_end_0, end_mask = var_9956_end_mask_0, x = var_9950)[name = tensor("op_9956")]; + tensor var_9958 = add(x = segment_accum_643, y = var_9956)[name = tensor("op_9958")]; + tensor var_9960_begin_0 = const()[name = tensor("op_9960_begin_0"), val = tensor([0, 323000, 0])]; + tensor var_9960_end_0 = const()[name = tensor("op_9960_end_0"), val = tensor([1, 324000, 9])]; + tensor var_9960_end_mask_0 = const()[name = tensor("op_9960_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9960 = slice_by_index(begin = var_9960_begin_0, end = var_9960_end_0, end_mask = var_9960_end_mask_0, x = reshape_4)[name = tensor("op_9960")]; + tensor segment_accum_645_exclusive_0 = const()[name = tensor("segment_accum_645_exclusive_0"), val = tensor(false)]; + tensor segment_accum_645_reverse_0 = const()[name = tensor("segment_accum_645_reverse_0"), val = tensor(false)]; + tensor segment_accum_645 = cumsum(axis = var_7349, exclusive = segment_accum_645_exclusive_0, reverse = segment_accum_645_reverse_0, x = var_9960)[name = tensor("segment_accum_645")]; + tensor var_9964_begin_0 = const()[name = tensor("op_9964_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9964_end_0 = const()[name = tensor("op_9964_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9964_end_mask_0 = const()[name = tensor("op_9964_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9964 = slice_by_index(begin = var_9964_begin_0, end = var_9964_end_0, end_mask = var_9964_end_mask_0, x = var_9958)[name = tensor("op_9964")]; + tensor var_9966 = add(x = segment_accum_645, y = var_9964)[name = tensor("op_9966")]; + tensor var_9968_begin_0 = const()[name = tensor("op_9968_begin_0"), val = tensor([0, 324000, 0])]; + tensor var_9968_end_0 = const()[name = tensor("op_9968_end_0"), val = tensor([1, 325000, 9])]; + tensor var_9968_end_mask_0 = const()[name = tensor("op_9968_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9968 = slice_by_index(begin = var_9968_begin_0, end = var_9968_end_0, end_mask = var_9968_end_mask_0, x = reshape_4)[name = tensor("op_9968")]; + tensor segment_accum_647_exclusive_0 = const()[name = tensor("segment_accum_647_exclusive_0"), val = tensor(false)]; + tensor segment_accum_647_reverse_0 = const()[name = tensor("segment_accum_647_reverse_0"), val = tensor(false)]; + tensor segment_accum_647 = cumsum(axis = var_7349, exclusive = segment_accum_647_exclusive_0, reverse = segment_accum_647_reverse_0, x = var_9968)[name = tensor("segment_accum_647")]; + tensor var_9972_begin_0 = const()[name = tensor("op_9972_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9972_end_0 = const()[name = tensor("op_9972_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9972_end_mask_0 = const()[name = tensor("op_9972_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9972 = slice_by_index(begin = var_9972_begin_0, end = var_9972_end_0, end_mask = var_9972_end_mask_0, x = var_9966)[name = tensor("op_9972")]; + tensor var_9974 = add(x = segment_accum_647, y = var_9972)[name = tensor("op_9974")]; + tensor var_9976_begin_0 = const()[name = tensor("op_9976_begin_0"), val = tensor([0, 325000, 0])]; + tensor var_9976_end_0 = const()[name = tensor("op_9976_end_0"), val = tensor([1, 326000, 9])]; + tensor var_9976_end_mask_0 = const()[name = tensor("op_9976_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9976 = slice_by_index(begin = var_9976_begin_0, end = var_9976_end_0, end_mask = var_9976_end_mask_0, x = reshape_4)[name = tensor("op_9976")]; + tensor segment_accum_649_exclusive_0 = const()[name = tensor("segment_accum_649_exclusive_0"), val = tensor(false)]; + tensor segment_accum_649_reverse_0 = const()[name = tensor("segment_accum_649_reverse_0"), val = tensor(false)]; + tensor segment_accum_649 = cumsum(axis = var_7349, exclusive = segment_accum_649_exclusive_0, reverse = segment_accum_649_reverse_0, x = var_9976)[name = tensor("segment_accum_649")]; + tensor var_9980_begin_0 = const()[name = tensor("op_9980_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9980_end_0 = const()[name = tensor("op_9980_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9980_end_mask_0 = const()[name = tensor("op_9980_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9980 = slice_by_index(begin = var_9980_begin_0, end = var_9980_end_0, end_mask = var_9980_end_mask_0, x = var_9974)[name = tensor("op_9980")]; + tensor var_9982 = add(x = segment_accum_649, y = var_9980)[name = tensor("op_9982")]; + tensor var_9984_begin_0 = const()[name = tensor("op_9984_begin_0"), val = tensor([0, 326000, 0])]; + tensor var_9984_end_0 = const()[name = tensor("op_9984_end_0"), val = tensor([1, 327000, 9])]; + tensor var_9984_end_mask_0 = const()[name = tensor("op_9984_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9984 = slice_by_index(begin = var_9984_begin_0, end = var_9984_end_0, end_mask = var_9984_end_mask_0, x = reshape_4)[name = tensor("op_9984")]; + tensor segment_accum_651_exclusive_0 = const()[name = tensor("segment_accum_651_exclusive_0"), val = tensor(false)]; + tensor segment_accum_651_reverse_0 = const()[name = tensor("segment_accum_651_reverse_0"), val = tensor(false)]; + tensor segment_accum_651 = cumsum(axis = var_7349, exclusive = segment_accum_651_exclusive_0, reverse = segment_accum_651_reverse_0, x = var_9984)[name = tensor("segment_accum_651")]; + tensor var_9988_begin_0 = const()[name = tensor("op_9988_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9988_end_0 = const()[name = tensor("op_9988_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9988_end_mask_0 = const()[name = tensor("op_9988_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9988 = slice_by_index(begin = var_9988_begin_0, end = var_9988_end_0, end_mask = var_9988_end_mask_0, x = var_9982)[name = tensor("op_9988")]; + tensor var_9990 = add(x = segment_accum_651, y = var_9988)[name = tensor("op_9990")]; + tensor var_9992_begin_0 = const()[name = tensor("op_9992_begin_0"), val = tensor([0, 327000, 0])]; + tensor var_9992_end_0 = const()[name = tensor("op_9992_end_0"), val = tensor([1, 328000, 9])]; + tensor var_9992_end_mask_0 = const()[name = tensor("op_9992_end_mask_0"), val = tensor([true, false, true])]; + tensor var_9992 = slice_by_index(begin = var_9992_begin_0, end = var_9992_end_0, end_mask = var_9992_end_mask_0, x = reshape_4)[name = tensor("op_9992")]; + tensor segment_accum_653_exclusive_0 = const()[name = tensor("segment_accum_653_exclusive_0"), val = tensor(false)]; + tensor segment_accum_653_reverse_0 = const()[name = tensor("segment_accum_653_reverse_0"), val = tensor(false)]; + tensor segment_accum_653 = cumsum(axis = var_7349, exclusive = segment_accum_653_exclusive_0, reverse = segment_accum_653_reverse_0, x = var_9992)[name = tensor("segment_accum_653")]; + tensor var_9996_begin_0 = const()[name = tensor("op_9996_begin_0"), val = tensor([0, -1, 0])]; + tensor var_9996_end_0 = const()[name = tensor("op_9996_end_0"), val = tensor([1, 1000, 9])]; + tensor var_9996_end_mask_0 = const()[name = tensor("op_9996_end_mask_0"), val = tensor([true, true, true])]; + tensor var_9996 = slice_by_index(begin = var_9996_begin_0, end = var_9996_end_0, end_mask = var_9996_end_mask_0, x = var_9990)[name = tensor("op_9996")]; + tensor var_9998 = add(x = segment_accum_653, y = var_9996)[name = tensor("op_9998")]; + tensor var_10000_begin_0 = const()[name = tensor("op_10000_begin_0"), val = tensor([0, 328000, 0])]; + tensor var_10000_end_0 = const()[name = tensor("op_10000_end_0"), val = tensor([1, 329000, 9])]; + tensor var_10000_end_mask_0 = const()[name = tensor("op_10000_end_mask_0"), val = tensor([true, false, true])]; + tensor var_10000 = slice_by_index(begin = var_10000_begin_0, end = var_10000_end_0, end_mask = var_10000_end_mask_0, x = reshape_4)[name = tensor("op_10000")]; + tensor segment_accum_655_exclusive_0 = const()[name = tensor("segment_accum_655_exclusive_0"), val = tensor(false)]; + tensor segment_accum_655_reverse_0 = const()[name = tensor("segment_accum_655_reverse_0"), val = tensor(false)]; + tensor segment_accum_655 = cumsum(axis = var_7349, exclusive = segment_accum_655_exclusive_0, reverse = segment_accum_655_reverse_0, x = var_10000)[name = tensor("segment_accum_655")]; + tensor var_10004_begin_0 = const()[name = tensor("op_10004_begin_0"), val = tensor([0, -1, 0])]; + tensor var_10004_end_0 = const()[name = tensor("op_10004_end_0"), val = tensor([1, 1000, 9])]; + tensor var_10004_end_mask_0 = const()[name = tensor("op_10004_end_mask_0"), val = tensor([true, true, true])]; + tensor var_10004 = slice_by_index(begin = var_10004_begin_0, end = var_10004_end_0, end_mask = var_10004_end_mask_0, x = var_9998)[name = tensor("op_10004")]; + tensor var_10006 = add(x = segment_accum_655, y = var_10004)[name = tensor("op_10006")]; + tensor var_10008_begin_0 = const()[name = tensor("op_10008_begin_0"), val = tensor([0, 329000, 0])]; + tensor var_10008_end_0 = const()[name = tensor("op_10008_end_0"), val = tensor([1, 330000, 9])]; + tensor var_10008_end_mask_0 = const()[name = tensor("op_10008_end_mask_0"), val = tensor([true, false, true])]; + tensor var_10008 = slice_by_index(begin = var_10008_begin_0, end = var_10008_end_0, end_mask = var_10008_end_mask_0, x = reshape_4)[name = tensor("op_10008")]; + tensor segment_accum_657_exclusive_0 = const()[name = tensor("segment_accum_657_exclusive_0"), val = tensor(false)]; + tensor segment_accum_657_reverse_0 = const()[name = tensor("segment_accum_657_reverse_0"), val = tensor(false)]; + tensor segment_accum_657 = cumsum(axis = var_7349, exclusive = segment_accum_657_exclusive_0, reverse = segment_accum_657_reverse_0, x = var_10008)[name = tensor("segment_accum_657")]; + tensor var_10012_begin_0 = const()[name = tensor("op_10012_begin_0"), val = tensor([0, -1, 0])]; + tensor var_10012_end_0 = const()[name = tensor("op_10012_end_0"), val = tensor([1, 1000, 9])]; + tensor var_10012_end_mask_0 = const()[name = tensor("op_10012_end_mask_0"), val = tensor([true, true, true])]; + tensor var_10012 = slice_by_index(begin = var_10012_begin_0, end = var_10012_end_0, end_mask = var_10012_end_mask_0, x = var_10006)[name = tensor("op_10012")]; + tensor var_10014 = add(x = segment_accum_657, y = var_10012)[name = tensor("op_10014")]; + tensor var_10016_begin_0 = const()[name = tensor("op_10016_begin_0"), val = tensor([0, 330000, 0])]; + tensor var_10016_end_0 = const()[name = tensor("op_10016_end_0"), val = tensor([1, 331000, 9])]; + tensor var_10016_end_mask_0 = const()[name = tensor("op_10016_end_mask_0"), val = tensor([true, false, true])]; + tensor var_10016 = slice_by_index(begin = var_10016_begin_0, end = var_10016_end_0, end_mask = var_10016_end_mask_0, x = reshape_4)[name = tensor("op_10016")]; + tensor segment_accum_659_exclusive_0 = const()[name = tensor("segment_accum_659_exclusive_0"), val = tensor(false)]; + tensor segment_accum_659_reverse_0 = const()[name = tensor("segment_accum_659_reverse_0"), val = tensor(false)]; + tensor segment_accum_659 = cumsum(axis = var_7349, exclusive = segment_accum_659_exclusive_0, reverse = segment_accum_659_reverse_0, x = var_10016)[name = tensor("segment_accum_659")]; + tensor var_10020_begin_0 = const()[name = tensor("op_10020_begin_0"), val = tensor([0, -1, 0])]; + tensor var_10020_end_0 = const()[name = tensor("op_10020_end_0"), val = tensor([1, 1000, 9])]; + tensor var_10020_end_mask_0 = const()[name = tensor("op_10020_end_mask_0"), val = tensor([true, true, true])]; + tensor var_10020 = slice_by_index(begin = var_10020_begin_0, end = var_10020_end_0, end_mask = var_10020_end_mask_0, x = var_10014)[name = tensor("op_10020")]; + tensor var_10022 = add(x = segment_accum_659, y = var_10020)[name = tensor("op_10022")]; + tensor var_10024_begin_0 = const()[name = tensor("op_10024_begin_0"), val = tensor([0, 331000, 0])]; + tensor var_10024_end_0 = const()[name = tensor("op_10024_end_0"), val = tensor([1, 332000, 9])]; + tensor var_10024_end_mask_0 = const()[name = tensor("op_10024_end_mask_0"), val = tensor([true, false, true])]; + tensor var_10024 = slice_by_index(begin = var_10024_begin_0, end = var_10024_end_0, end_mask = var_10024_end_mask_0, x = reshape_4)[name = tensor("op_10024")]; + tensor segment_accum_661_exclusive_0 = const()[name = tensor("segment_accum_661_exclusive_0"), val = tensor(false)]; + tensor segment_accum_661_reverse_0 = const()[name = tensor("segment_accum_661_reverse_0"), val = tensor(false)]; + tensor segment_accum_661 = cumsum(axis = var_7349, exclusive = segment_accum_661_exclusive_0, reverse = segment_accum_661_reverse_0, x = var_10024)[name = tensor("segment_accum_661")]; + tensor var_10028_begin_0 = const()[name = tensor("op_10028_begin_0"), val = tensor([0, -1, 0])]; + tensor var_10028_end_0 = const()[name = tensor("op_10028_end_0"), val = tensor([1, 1000, 9])]; + tensor var_10028_end_mask_0 = const()[name = tensor("op_10028_end_mask_0"), val = tensor([true, true, true])]; + tensor var_10028 = slice_by_index(begin = var_10028_begin_0, end = var_10028_end_0, end_mask = var_10028_end_mask_0, x = var_10022)[name = tensor("op_10028")]; + tensor var_10030 = add(x = segment_accum_661, y = var_10028)[name = tensor("op_10030")]; + tensor var_10032_begin_0 = const()[name = tensor("op_10032_begin_0"), val = tensor([0, 332000, 0])]; + tensor var_10032_end_0 = const()[name = tensor("op_10032_end_0"), val = tensor([1, 333000, 9])]; + tensor var_10032_end_mask_0 = const()[name = tensor("op_10032_end_mask_0"), val = tensor([true, false, true])]; + tensor var_10032 = slice_by_index(begin = var_10032_begin_0, end = var_10032_end_0, end_mask = var_10032_end_mask_0, x = reshape_4)[name = tensor("op_10032")]; + tensor segment_accum_663_exclusive_0 = const()[name = tensor("segment_accum_663_exclusive_0"), val = tensor(false)]; + tensor segment_accum_663_reverse_0 = const()[name = tensor("segment_accum_663_reverse_0"), val = tensor(false)]; + tensor segment_accum_663 = cumsum(axis = var_7349, exclusive = segment_accum_663_exclusive_0, reverse = segment_accum_663_reverse_0, x = var_10032)[name = tensor("segment_accum_663")]; + tensor var_10036_begin_0 = const()[name = tensor("op_10036_begin_0"), val = tensor([0, -1, 0])]; + tensor var_10036_end_0 = const()[name = tensor("op_10036_end_0"), val = tensor([1, 1000, 9])]; + tensor var_10036_end_mask_0 = const()[name = tensor("op_10036_end_mask_0"), val = tensor([true, true, true])]; + tensor var_10036 = slice_by_index(begin = var_10036_begin_0, end = var_10036_end_0, end_mask = var_10036_end_mask_0, x = var_10030)[name = tensor("op_10036")]; + tensor var_10038 = add(x = segment_accum_663, y = var_10036)[name = tensor("op_10038")]; + tensor var_10040_begin_0 = const()[name = tensor("op_10040_begin_0"), val = tensor([0, 333000, 0])]; + tensor var_10040_end_0 = const()[name = tensor("op_10040_end_0"), val = tensor([1, 334000, 9])]; + tensor var_10040_end_mask_0 = const()[name = tensor("op_10040_end_mask_0"), val = tensor([true, false, true])]; + tensor var_10040 = slice_by_index(begin = var_10040_begin_0, end = var_10040_end_0, end_mask = var_10040_end_mask_0, x = reshape_4)[name = tensor("op_10040")]; + tensor segment_accum_665_exclusive_0 = const()[name = tensor("segment_accum_665_exclusive_0"), val = tensor(false)]; + tensor segment_accum_665_reverse_0 = const()[name = tensor("segment_accum_665_reverse_0"), val = tensor(false)]; + tensor segment_accum_665 = cumsum(axis = var_7349, exclusive = segment_accum_665_exclusive_0, reverse = segment_accum_665_reverse_0, x = var_10040)[name = tensor("segment_accum_665")]; + tensor var_10044_begin_0 = const()[name = tensor("op_10044_begin_0"), val = tensor([0, -1, 0])]; + tensor var_10044_end_0 = const()[name = tensor("op_10044_end_0"), val = tensor([1, 1000, 9])]; + tensor var_10044_end_mask_0 = const()[name = tensor("op_10044_end_mask_0"), val = tensor([true, true, true])]; + tensor var_10044 = slice_by_index(begin = var_10044_begin_0, end = var_10044_end_0, end_mask = var_10044_end_mask_0, x = var_10038)[name = tensor("op_10044")]; + tensor var_10046 = add(x = segment_accum_665, y = var_10044)[name = tensor("op_10046")]; + tensor var_10048_begin_0 = const()[name = tensor("op_10048_begin_0"), val = tensor([0, 334000, 0])]; + tensor var_10048_end_0 = const()[name = tensor("op_10048_end_0"), val = tensor([1, 335000, 9])]; + tensor var_10048_end_mask_0 = const()[name = tensor("op_10048_end_mask_0"), val = tensor([true, false, true])]; + tensor var_10048 = slice_by_index(begin = var_10048_begin_0, end = var_10048_end_0, end_mask = var_10048_end_mask_0, x = reshape_4)[name = tensor("op_10048")]; + tensor segment_accum_667_exclusive_0 = const()[name = tensor("segment_accum_667_exclusive_0"), val = tensor(false)]; + tensor segment_accum_667_reverse_0 = const()[name = tensor("segment_accum_667_reverse_0"), val = tensor(false)]; + tensor segment_accum_667 = cumsum(axis = var_7349, exclusive = segment_accum_667_exclusive_0, reverse = segment_accum_667_reverse_0, x = var_10048)[name = tensor("segment_accum_667")]; + tensor var_10052_begin_0 = const()[name = tensor("op_10052_begin_0"), val = tensor([0, -1, 0])]; + tensor var_10052_end_0 = const()[name = tensor("op_10052_end_0"), val = tensor([1, 1000, 9])]; + tensor var_10052_end_mask_0 = const()[name = tensor("op_10052_end_mask_0"), val = tensor([true, true, true])]; + tensor var_10052 = slice_by_index(begin = var_10052_begin_0, end = var_10052_end_0, end_mask = var_10052_end_mask_0, x = var_10046)[name = tensor("op_10052")]; + tensor var_10054 = add(x = segment_accum_667, y = var_10052)[name = tensor("op_10054")]; + tensor var_10056_begin_0 = const()[name = tensor("op_10056_begin_0"), val = tensor([0, 335000, 0])]; + tensor var_10056_end_0 = const()[name = tensor("op_10056_end_0"), val = tensor([1, 336000, 9])]; + tensor var_10056_end_mask_0 = const()[name = tensor("op_10056_end_mask_0"), val = tensor([true, false, true])]; + tensor var_10056 = slice_by_index(begin = var_10056_begin_0, end = var_10056_end_0, end_mask = var_10056_end_mask_0, x = reshape_4)[name = tensor("op_10056")]; + tensor segment_accum_669_exclusive_0 = const()[name = tensor("segment_accum_669_exclusive_0"), val = tensor(false)]; + tensor segment_accum_669_reverse_0 = const()[name = tensor("segment_accum_669_reverse_0"), val = tensor(false)]; + tensor segment_accum_669 = cumsum(axis = var_7349, exclusive = segment_accum_669_exclusive_0, reverse = segment_accum_669_reverse_0, x = var_10056)[name = tensor("segment_accum_669")]; + tensor var_10060_begin_0 = const()[name = tensor("op_10060_begin_0"), val = tensor([0, -1, 0])]; + tensor var_10060_end_0 = const()[name = tensor("op_10060_end_0"), val = tensor([1, 1000, 9])]; + tensor var_10060_end_mask_0 = const()[name = tensor("op_10060_end_mask_0"), val = tensor([true, true, true])]; + tensor var_10060 = slice_by_index(begin = var_10060_begin_0, end = var_10060_end_0, end_mask = var_10060_end_mask_0, x = var_10054)[name = tensor("op_10060")]; + tensor var_10062 = add(x = segment_accum_669, y = var_10060)[name = tensor("op_10062")]; + tensor var_10064_begin_0 = const()[name = tensor("op_10064_begin_0"), val = tensor([0, 336000, 0])]; + tensor var_10064_end_0 = const()[name = tensor("op_10064_end_0"), val = tensor([1, 337000, 9])]; + tensor var_10064_end_mask_0 = const()[name = tensor("op_10064_end_mask_0"), val = tensor([true, false, true])]; + tensor var_10064 = slice_by_index(begin = var_10064_begin_0, end = var_10064_end_0, end_mask = var_10064_end_mask_0, x = reshape_4)[name = tensor("op_10064")]; + tensor segment_accum_671_exclusive_0 = const()[name = tensor("segment_accum_671_exclusive_0"), val = tensor(false)]; + tensor segment_accum_671_reverse_0 = const()[name = tensor("segment_accum_671_reverse_0"), val = tensor(false)]; + tensor segment_accum_671 = cumsum(axis = var_7349, exclusive = segment_accum_671_exclusive_0, reverse = segment_accum_671_reverse_0, x = var_10064)[name = tensor("segment_accum_671")]; + tensor var_10068_begin_0 = const()[name = tensor("op_10068_begin_0"), val = tensor([0, -1, 0])]; + tensor var_10068_end_0 = const()[name = tensor("op_10068_end_0"), val = tensor([1, 1000, 9])]; + tensor var_10068_end_mask_0 = const()[name = tensor("op_10068_end_mask_0"), val = tensor([true, true, true])]; + tensor var_10068 = slice_by_index(begin = var_10068_begin_0, end = var_10068_end_0, end_mask = var_10068_end_mask_0, x = var_10062)[name = tensor("op_10068")]; + tensor var_10070 = add(x = segment_accum_671, y = var_10068)[name = tensor("op_10070")]; + tensor var_10072_begin_0 = const()[name = tensor("op_10072_begin_0"), val = tensor([0, 337000, 0])]; + tensor var_10072_end_0 = const()[name = tensor("op_10072_end_0"), val = tensor([1, 338000, 9])]; + tensor var_10072_end_mask_0 = const()[name = tensor("op_10072_end_mask_0"), val = tensor([true, false, true])]; + tensor var_10072 = slice_by_index(begin = var_10072_begin_0, end = var_10072_end_0, end_mask = var_10072_end_mask_0, x = reshape_4)[name = tensor("op_10072")]; + tensor segment_accum_673_exclusive_0 = const()[name = tensor("segment_accum_673_exclusive_0"), val = tensor(false)]; + tensor segment_accum_673_reverse_0 = const()[name = tensor("segment_accum_673_reverse_0"), val = tensor(false)]; + tensor segment_accum_673 = cumsum(axis = var_7349, exclusive = segment_accum_673_exclusive_0, reverse = segment_accum_673_reverse_0, x = var_10072)[name = tensor("segment_accum_673")]; + tensor var_10076_begin_0 = const()[name = tensor("op_10076_begin_0"), val = tensor([0, -1, 0])]; + tensor var_10076_end_0 = const()[name = tensor("op_10076_end_0"), val = tensor([1, 1000, 9])]; + tensor var_10076_end_mask_0 = const()[name = tensor("op_10076_end_mask_0"), val = tensor([true, true, true])]; + tensor var_10076 = slice_by_index(begin = var_10076_begin_0, end = var_10076_end_0, end_mask = var_10076_end_mask_0, x = var_10070)[name = tensor("op_10076")]; + tensor var_10078 = add(x = segment_accum_673, y = var_10076)[name = tensor("op_10078")]; + tensor var_10080_begin_0 = const()[name = tensor("op_10080_begin_0"), val = tensor([0, 338000, 0])]; + tensor var_10080_end_0 = const()[name = tensor("op_10080_end_0"), val = tensor([1, 339000, 9])]; + tensor var_10080_end_mask_0 = const()[name = tensor("op_10080_end_mask_0"), val = tensor([true, false, true])]; + tensor var_10080 = slice_by_index(begin = var_10080_begin_0, end = var_10080_end_0, end_mask = var_10080_end_mask_0, x = reshape_4)[name = tensor("op_10080")]; + tensor segment_accum_675_exclusive_0 = const()[name = tensor("segment_accum_675_exclusive_0"), val = tensor(false)]; + tensor segment_accum_675_reverse_0 = const()[name = tensor("segment_accum_675_reverse_0"), val = tensor(false)]; + tensor segment_accum_675 = cumsum(axis = var_7349, exclusive = segment_accum_675_exclusive_0, reverse = segment_accum_675_reverse_0, x = var_10080)[name = tensor("segment_accum_675")]; + tensor var_10084_begin_0 = const()[name = tensor("op_10084_begin_0"), val = tensor([0, -1, 0])]; + tensor var_10084_end_0 = const()[name = tensor("op_10084_end_0"), val = tensor([1, 1000, 9])]; + tensor var_10084_end_mask_0 = const()[name = tensor("op_10084_end_mask_0"), val = tensor([true, true, true])]; + tensor var_10084 = slice_by_index(begin = var_10084_begin_0, end = var_10084_end_0, end_mask = var_10084_end_mask_0, x = var_10078)[name = tensor("op_10084")]; + tensor var_10086 = add(x = segment_accum_675, y = var_10084)[name = tensor("op_10086")]; + tensor var_10088_begin_0 = const()[name = tensor("op_10088_begin_0"), val = tensor([0, 339000, 0])]; + tensor var_10088_end_0 = const()[name = tensor("op_10088_end_0"), val = tensor([1, 340000, 9])]; + tensor var_10088_end_mask_0 = const()[name = tensor("op_10088_end_mask_0"), val = tensor([true, false, true])]; + tensor var_10088 = slice_by_index(begin = var_10088_begin_0, end = var_10088_end_0, end_mask = var_10088_end_mask_0, x = reshape_4)[name = tensor("op_10088")]; + tensor segment_accum_677_exclusive_0 = const()[name = tensor("segment_accum_677_exclusive_0"), val = tensor(false)]; + tensor segment_accum_677_reverse_0 = const()[name = tensor("segment_accum_677_reverse_0"), val = tensor(false)]; + tensor segment_accum_677 = cumsum(axis = var_7349, exclusive = segment_accum_677_exclusive_0, reverse = segment_accum_677_reverse_0, x = var_10088)[name = tensor("segment_accum_677")]; + tensor var_10092_begin_0 = const()[name = tensor("op_10092_begin_0"), val = tensor([0, -1, 0])]; + tensor var_10092_end_0 = const()[name = tensor("op_10092_end_0"), val = tensor([1, 1000, 9])]; + tensor var_10092_end_mask_0 = const()[name = tensor("op_10092_end_mask_0"), val = tensor([true, true, true])]; + tensor var_10092 = slice_by_index(begin = var_10092_begin_0, end = var_10092_end_0, end_mask = var_10092_end_mask_0, x = var_10086)[name = tensor("op_10092")]; + tensor var_10094 = add(x = segment_accum_677, y = var_10092)[name = tensor("op_10094")]; + tensor var_10096_begin_0 = const()[name = tensor("op_10096_begin_0"), val = tensor([0, 340000, 0])]; + tensor var_10096_end_0 = const()[name = tensor("op_10096_end_0"), val = tensor([1, 341000, 9])]; + tensor var_10096_end_mask_0 = const()[name = tensor("op_10096_end_mask_0"), val = tensor([true, false, true])]; + tensor var_10096 = slice_by_index(begin = var_10096_begin_0, end = var_10096_end_0, end_mask = var_10096_end_mask_0, x = reshape_4)[name = tensor("op_10096")]; + tensor segment_accum_679_exclusive_0 = const()[name = tensor("segment_accum_679_exclusive_0"), val = tensor(false)]; + tensor segment_accum_679_reverse_0 = const()[name = tensor("segment_accum_679_reverse_0"), val = tensor(false)]; + tensor segment_accum_679 = cumsum(axis = var_7349, exclusive = segment_accum_679_exclusive_0, reverse = segment_accum_679_reverse_0, x = var_10096)[name = tensor("segment_accum_679")]; + tensor var_10100_begin_0 = const()[name = tensor("op_10100_begin_0"), val = tensor([0, -1, 0])]; + tensor var_10100_end_0 = const()[name = tensor("op_10100_end_0"), val = tensor([1, 1000, 9])]; + tensor var_10100_end_mask_0 = const()[name = tensor("op_10100_end_mask_0"), val = tensor([true, true, true])]; + tensor var_10100 = slice_by_index(begin = var_10100_begin_0, end = var_10100_end_0, end_mask = var_10100_end_mask_0, x = var_10094)[name = tensor("op_10100")]; + tensor var_10102 = add(x = segment_accum_679, y = var_10100)[name = tensor("op_10102")]; + tensor var_10104_begin_0 = const()[name = tensor("op_10104_begin_0"), val = tensor([0, 341000, 0])]; + tensor var_10104_end_0 = const()[name = tensor("op_10104_end_0"), val = tensor([1, 342000, 9])]; + tensor var_10104_end_mask_0 = const()[name = tensor("op_10104_end_mask_0"), val = tensor([true, false, true])]; + tensor var_10104 = slice_by_index(begin = var_10104_begin_0, end = var_10104_end_0, end_mask = var_10104_end_mask_0, x = reshape_4)[name = tensor("op_10104")]; + tensor segment_accum_681_exclusive_0 = const()[name = tensor("segment_accum_681_exclusive_0"), val = tensor(false)]; + tensor segment_accum_681_reverse_0 = const()[name = tensor("segment_accum_681_reverse_0"), val = tensor(false)]; + tensor segment_accum_681 = cumsum(axis = var_7349, exclusive = segment_accum_681_exclusive_0, reverse = segment_accum_681_reverse_0, x = var_10104)[name = tensor("segment_accum_681")]; + tensor var_10108_begin_0 = const()[name = tensor("op_10108_begin_0"), val = tensor([0, -1, 0])]; + tensor var_10108_end_0 = const()[name = tensor("op_10108_end_0"), val = tensor([1, 1000, 9])]; + tensor var_10108_end_mask_0 = const()[name = tensor("op_10108_end_mask_0"), val = tensor([true, true, true])]; + tensor var_10108 = slice_by_index(begin = var_10108_begin_0, end = var_10108_end_0, end_mask = var_10108_end_mask_0, x = var_10102)[name = tensor("op_10108")]; + tensor var_10110 = add(x = segment_accum_681, y = var_10108)[name = tensor("op_10110")]; + tensor var_10112_begin_0 = const()[name = tensor("op_10112_begin_0"), val = tensor([0, 342000, 0])]; + tensor var_10112_end_0 = const()[name = tensor("op_10112_end_0"), val = tensor([1, 343000, 9])]; + tensor var_10112_end_mask_0 = const()[name = tensor("op_10112_end_mask_0"), val = tensor([true, false, true])]; + tensor var_10112 = slice_by_index(begin = var_10112_begin_0, end = var_10112_end_0, end_mask = var_10112_end_mask_0, x = reshape_4)[name = tensor("op_10112")]; + tensor segment_accum_683_exclusive_0 = const()[name = tensor("segment_accum_683_exclusive_0"), val = tensor(false)]; + tensor segment_accum_683_reverse_0 = const()[name = tensor("segment_accum_683_reverse_0"), val = tensor(false)]; + tensor segment_accum_683 = cumsum(axis = var_7349, exclusive = segment_accum_683_exclusive_0, reverse = segment_accum_683_reverse_0, x = var_10112)[name = tensor("segment_accum_683")]; + tensor var_10116_begin_0 = const()[name = tensor("op_10116_begin_0"), val = tensor([0, -1, 0])]; + tensor var_10116_end_0 = const()[name = tensor("op_10116_end_0"), val = tensor([1, 1000, 9])]; + tensor var_10116_end_mask_0 = const()[name = tensor("op_10116_end_mask_0"), val = tensor([true, true, true])]; + tensor var_10116 = slice_by_index(begin = var_10116_begin_0, end = var_10116_end_0, end_mask = var_10116_end_mask_0, x = var_10110)[name = tensor("op_10116")]; + tensor var_10118 = add(x = segment_accum_683, y = var_10116)[name = tensor("op_10118")]; + tensor var_10120_begin_0 = const()[name = tensor("op_10120_begin_0"), val = tensor([0, 343000, 0])]; + tensor var_10120_end_0 = const()[name = tensor("op_10120_end_0"), val = tensor([1, 344000, 9])]; + tensor var_10120_end_mask_0 = const()[name = tensor("op_10120_end_mask_0"), val = tensor([true, false, true])]; + tensor var_10120 = slice_by_index(begin = var_10120_begin_0, end = var_10120_end_0, end_mask = var_10120_end_mask_0, x = reshape_4)[name = tensor("op_10120")]; + tensor segment_accum_685_exclusive_0 = const()[name = tensor("segment_accum_685_exclusive_0"), val = tensor(false)]; + tensor segment_accum_685_reverse_0 = const()[name = tensor("segment_accum_685_reverse_0"), val = tensor(false)]; + tensor segment_accum_685 = cumsum(axis = var_7349, exclusive = segment_accum_685_exclusive_0, reverse = segment_accum_685_reverse_0, x = var_10120)[name = tensor("segment_accum_685")]; + tensor var_10124_begin_0 = const()[name = tensor("op_10124_begin_0"), val = tensor([0, -1, 0])]; + tensor var_10124_end_0 = const()[name = tensor("op_10124_end_0"), val = tensor([1, 1000, 9])]; + tensor var_10124_end_mask_0 = const()[name = tensor("op_10124_end_mask_0"), val = tensor([true, true, true])]; + tensor var_10124 = slice_by_index(begin = var_10124_begin_0, end = var_10124_end_0, end_mask = var_10124_end_mask_0, x = var_10118)[name = tensor("op_10124")]; + tensor var_10126 = add(x = segment_accum_685, y = var_10124)[name = tensor("op_10126")]; + tensor var_10128_begin_0 = const()[name = tensor("op_10128_begin_0"), val = tensor([0, 344000, 0])]; + tensor var_10128_end_0 = const()[name = tensor("op_10128_end_0"), val = tensor([1, 345000, 9])]; + tensor var_10128_end_mask_0 = const()[name = tensor("op_10128_end_mask_0"), val = tensor([true, false, true])]; + tensor var_10128 = slice_by_index(begin = var_10128_begin_0, end = var_10128_end_0, end_mask = var_10128_end_mask_0, x = reshape_4)[name = tensor("op_10128")]; + tensor segment_accum_687_exclusive_0 = const()[name = tensor("segment_accum_687_exclusive_0"), val = tensor(false)]; + tensor segment_accum_687_reverse_0 = const()[name = tensor("segment_accum_687_reverse_0"), val = tensor(false)]; + tensor segment_accum_687 = cumsum(axis = var_7349, exclusive = segment_accum_687_exclusive_0, reverse = segment_accum_687_reverse_0, x = var_10128)[name = tensor("segment_accum_687")]; + tensor var_10132_begin_0 = const()[name = tensor("op_10132_begin_0"), val = tensor([0, -1, 0])]; + tensor var_10132_end_0 = const()[name = tensor("op_10132_end_0"), val = tensor([1, 1000, 9])]; + tensor var_10132_end_mask_0 = const()[name = tensor("op_10132_end_mask_0"), val = tensor([true, true, true])]; + tensor var_10132 = slice_by_index(begin = var_10132_begin_0, end = var_10132_end_0, end_mask = var_10132_end_mask_0, x = var_10126)[name = tensor("op_10132")]; + tensor var_10134 = add(x = segment_accum_687, y = var_10132)[name = tensor("op_10134")]; + tensor var_10136_begin_0 = const()[name = tensor("op_10136_begin_0"), val = tensor([0, 345000, 0])]; + tensor var_10136_end_0 = const()[name = tensor("op_10136_end_0"), val = tensor([1, 346000, 9])]; + tensor var_10136_end_mask_0 = const()[name = tensor("op_10136_end_mask_0"), val = tensor([true, false, true])]; + tensor var_10136 = slice_by_index(begin = var_10136_begin_0, end = var_10136_end_0, end_mask = var_10136_end_mask_0, x = reshape_4)[name = tensor("op_10136")]; + tensor segment_accum_689_exclusive_0 = const()[name = tensor("segment_accum_689_exclusive_0"), val = tensor(false)]; + tensor segment_accum_689_reverse_0 = const()[name = tensor("segment_accum_689_reverse_0"), val = tensor(false)]; + tensor segment_accum_689 = cumsum(axis = var_7349, exclusive = segment_accum_689_exclusive_0, reverse = segment_accum_689_reverse_0, x = var_10136)[name = tensor("segment_accum_689")]; + tensor var_10140_begin_0 = const()[name = tensor("op_10140_begin_0"), val = tensor([0, -1, 0])]; + tensor var_10140_end_0 = const()[name = tensor("op_10140_end_0"), val = tensor([1, 1000, 9])]; + tensor var_10140_end_mask_0 = const()[name = tensor("op_10140_end_mask_0"), val = tensor([true, true, true])]; + tensor var_10140 = slice_by_index(begin = var_10140_begin_0, end = var_10140_end_0, end_mask = var_10140_end_mask_0, x = var_10134)[name = tensor("op_10140")]; + tensor var_10142 = add(x = segment_accum_689, y = var_10140)[name = tensor("op_10142")]; + tensor var_10144_begin_0 = const()[name = tensor("op_10144_begin_0"), val = tensor([0, 346000, 0])]; + tensor var_10144_end_0 = const()[name = tensor("op_10144_end_0"), val = tensor([1, 347000, 9])]; + tensor var_10144_end_mask_0 = const()[name = tensor("op_10144_end_mask_0"), val = tensor([true, false, true])]; + tensor var_10144 = slice_by_index(begin = var_10144_begin_0, end = var_10144_end_0, end_mask = var_10144_end_mask_0, x = reshape_4)[name = tensor("op_10144")]; + tensor segment_accum_691_exclusive_0 = const()[name = tensor("segment_accum_691_exclusive_0"), val = tensor(false)]; + tensor segment_accum_691_reverse_0 = const()[name = tensor("segment_accum_691_reverse_0"), val = tensor(false)]; + tensor segment_accum_691 = cumsum(axis = var_7349, exclusive = segment_accum_691_exclusive_0, reverse = segment_accum_691_reverse_0, x = var_10144)[name = tensor("segment_accum_691")]; + tensor var_10148_begin_0 = const()[name = tensor("op_10148_begin_0"), val = tensor([0, -1, 0])]; + tensor var_10148_end_0 = const()[name = tensor("op_10148_end_0"), val = tensor([1, 1000, 9])]; + tensor var_10148_end_mask_0 = const()[name = tensor("op_10148_end_mask_0"), val = tensor([true, true, true])]; + tensor var_10148 = slice_by_index(begin = var_10148_begin_0, end = var_10148_end_0, end_mask = var_10148_end_mask_0, x = var_10142)[name = tensor("op_10148")]; + tensor var_10150 = add(x = segment_accum_691, y = var_10148)[name = tensor("op_10150")]; + tensor var_10152_begin_0 = const()[name = tensor("op_10152_begin_0"), val = tensor([0, 347000, 0])]; + tensor var_10152_end_0 = const()[name = tensor("op_10152_end_0"), val = tensor([1, 348000, 9])]; + tensor var_10152_end_mask_0 = const()[name = tensor("op_10152_end_mask_0"), val = tensor([true, false, true])]; + tensor var_10152 = slice_by_index(begin = var_10152_begin_0, end = var_10152_end_0, end_mask = var_10152_end_mask_0, x = reshape_4)[name = tensor("op_10152")]; + tensor segment_accum_693_exclusive_0 = const()[name = tensor("segment_accum_693_exclusive_0"), val = tensor(false)]; + tensor segment_accum_693_reverse_0 = const()[name = tensor("segment_accum_693_reverse_0"), val = tensor(false)]; + tensor segment_accum_693 = cumsum(axis = var_7349, exclusive = segment_accum_693_exclusive_0, reverse = segment_accum_693_reverse_0, x = var_10152)[name = tensor("segment_accum_693")]; + tensor var_10156_begin_0 = const()[name = tensor("op_10156_begin_0"), val = tensor([0, -1, 0])]; + tensor var_10156_end_0 = const()[name = tensor("op_10156_end_0"), val = tensor([1, 1000, 9])]; + tensor var_10156_end_mask_0 = const()[name = tensor("op_10156_end_mask_0"), val = tensor([true, true, true])]; + tensor var_10156 = slice_by_index(begin = var_10156_begin_0, end = var_10156_end_0, end_mask = var_10156_end_mask_0, x = var_10150)[name = tensor("op_10156")]; + tensor var_10158 = add(x = segment_accum_693, y = var_10156)[name = tensor("op_10158")]; + tensor var_10160_begin_0 = const()[name = tensor("op_10160_begin_0"), val = tensor([0, 348000, 0])]; + tensor var_10160_end_0 = const()[name = tensor("op_10160_end_0"), val = tensor([1, 349000, 9])]; + tensor var_10160_end_mask_0 = const()[name = tensor("op_10160_end_mask_0"), val = tensor([true, false, true])]; + tensor var_10160 = slice_by_index(begin = var_10160_begin_0, end = var_10160_end_0, end_mask = var_10160_end_mask_0, x = reshape_4)[name = tensor("op_10160")]; + tensor segment_accum_695_exclusive_0 = const()[name = tensor("segment_accum_695_exclusive_0"), val = tensor(false)]; + tensor segment_accum_695_reverse_0 = const()[name = tensor("segment_accum_695_reverse_0"), val = tensor(false)]; + tensor segment_accum_695 = cumsum(axis = var_7349, exclusive = segment_accum_695_exclusive_0, reverse = segment_accum_695_reverse_0, x = var_10160)[name = tensor("segment_accum_695")]; + tensor var_10164_begin_0 = const()[name = tensor("op_10164_begin_0"), val = tensor([0, -1, 0])]; + tensor var_10164_end_0 = const()[name = tensor("op_10164_end_0"), val = tensor([1, 1000, 9])]; + tensor var_10164_end_mask_0 = const()[name = tensor("op_10164_end_mask_0"), val = tensor([true, true, true])]; + tensor var_10164 = slice_by_index(begin = var_10164_begin_0, end = var_10164_end_0, end_mask = var_10164_end_mask_0, x = var_10158)[name = tensor("op_10164")]; + tensor var_10166 = add(x = segment_accum_695, y = var_10164)[name = tensor("op_10166")]; + tensor var_10168_begin_0 = const()[name = tensor("op_10168_begin_0"), val = tensor([0, 349000, 0])]; + tensor var_10168_end_0 = const()[name = tensor("op_10168_end_0"), val = tensor([1, 350000, 9])]; + tensor var_10168_end_mask_0 = const()[name = tensor("op_10168_end_mask_0"), val = tensor([true, false, true])]; + tensor var_10168 = slice_by_index(begin = var_10168_begin_0, end = var_10168_end_0, end_mask = var_10168_end_mask_0, x = reshape_4)[name = tensor("op_10168")]; + tensor segment_accum_697_exclusive_0 = const()[name = tensor("segment_accum_697_exclusive_0"), val = tensor(false)]; + tensor segment_accum_697_reverse_0 = const()[name = tensor("segment_accum_697_reverse_0"), val = tensor(false)]; + tensor segment_accum_697 = cumsum(axis = var_7349, exclusive = segment_accum_697_exclusive_0, reverse = segment_accum_697_reverse_0, x = var_10168)[name = tensor("segment_accum_697")]; + tensor var_10172_begin_0 = const()[name = tensor("op_10172_begin_0"), val = tensor([0, -1, 0])]; + tensor var_10172_end_0 = const()[name = tensor("op_10172_end_0"), val = tensor([1, 1000, 9])]; + tensor var_10172_end_mask_0 = const()[name = tensor("op_10172_end_mask_0"), val = tensor([true, true, true])]; + tensor var_10172 = slice_by_index(begin = var_10172_begin_0, end = var_10172_end_0, end_mask = var_10172_end_mask_0, x = var_10166)[name = tensor("op_10172")]; + tensor var_10174 = add(x = segment_accum_697, y = var_10172)[name = tensor("op_10174")]; + tensor var_10176_begin_0 = const()[name = tensor("op_10176_begin_0"), val = tensor([0, 350000, 0])]; + tensor var_10176_end_0 = const()[name = tensor("op_10176_end_0"), val = tensor([1, 351000, 9])]; + tensor var_10176_end_mask_0 = const()[name = tensor("op_10176_end_mask_0"), val = tensor([true, false, true])]; + tensor var_10176 = slice_by_index(begin = var_10176_begin_0, end = var_10176_end_0, end_mask = var_10176_end_mask_0, x = reshape_4)[name = tensor("op_10176")]; + tensor segment_accum_699_exclusive_0 = const()[name = tensor("segment_accum_699_exclusive_0"), val = tensor(false)]; + tensor segment_accum_699_reverse_0 = const()[name = tensor("segment_accum_699_reverse_0"), val = tensor(false)]; + tensor segment_accum_699 = cumsum(axis = var_7349, exclusive = segment_accum_699_exclusive_0, reverse = segment_accum_699_reverse_0, x = var_10176)[name = tensor("segment_accum_699")]; + tensor var_10180_begin_0 = const()[name = tensor("op_10180_begin_0"), val = tensor([0, -1, 0])]; + tensor var_10180_end_0 = const()[name = tensor("op_10180_end_0"), val = tensor([1, 1000, 9])]; + tensor var_10180_end_mask_0 = const()[name = tensor("op_10180_end_mask_0"), val = tensor([true, true, true])]; + tensor var_10180 = slice_by_index(begin = var_10180_begin_0, end = var_10180_end_0, end_mask = var_10180_end_mask_0, x = var_10174)[name = tensor("op_10180")]; + tensor var_10182 = add(x = segment_accum_699, y = var_10180)[name = tensor("op_10182")]; + tensor var_10184_begin_0 = const()[name = tensor("op_10184_begin_0"), val = tensor([0, 351000, 0])]; + tensor var_10184_end_0 = const()[name = tensor("op_10184_end_0"), val = tensor([1, 352000, 9])]; + tensor var_10184_end_mask_0 = const()[name = tensor("op_10184_end_mask_0"), val = tensor([true, false, true])]; + tensor var_10184 = slice_by_index(begin = var_10184_begin_0, end = var_10184_end_0, end_mask = var_10184_end_mask_0, x = reshape_4)[name = tensor("op_10184")]; + tensor segment_accum_701_exclusive_0 = const()[name = tensor("segment_accum_701_exclusive_0"), val = tensor(false)]; + tensor segment_accum_701_reverse_0 = const()[name = tensor("segment_accum_701_reverse_0"), val = tensor(false)]; + tensor segment_accum_701 = cumsum(axis = var_7349, exclusive = segment_accum_701_exclusive_0, reverse = segment_accum_701_reverse_0, x = var_10184)[name = tensor("segment_accum_701")]; + tensor var_10188_begin_0 = const()[name = tensor("op_10188_begin_0"), val = tensor([0, -1, 0])]; + tensor var_10188_end_0 = const()[name = tensor("op_10188_end_0"), val = tensor([1, 1000, 9])]; + tensor var_10188_end_mask_0 = const()[name = tensor("op_10188_end_mask_0"), val = tensor([true, true, true])]; + tensor var_10188 = slice_by_index(begin = var_10188_begin_0, end = var_10188_end_0, end_mask = var_10188_end_mask_0, x = var_10182)[name = tensor("op_10188")]; + tensor var_10190 = add(x = segment_accum_701, y = var_10188)[name = tensor("op_10190")]; + tensor var_10192_begin_0 = const()[name = tensor("op_10192_begin_0"), val = tensor([0, 352000, 0])]; + tensor var_10192_end_0 = const()[name = tensor("op_10192_end_0"), val = tensor([1, 353000, 9])]; + tensor var_10192_end_mask_0 = const()[name = tensor("op_10192_end_mask_0"), val = tensor([true, false, true])]; + tensor var_10192 = slice_by_index(begin = var_10192_begin_0, end = var_10192_end_0, end_mask = var_10192_end_mask_0, x = reshape_4)[name = tensor("op_10192")]; + tensor segment_accum_703_exclusive_0 = const()[name = tensor("segment_accum_703_exclusive_0"), val = tensor(false)]; + tensor segment_accum_703_reverse_0 = const()[name = tensor("segment_accum_703_reverse_0"), val = tensor(false)]; + tensor segment_accum_703 = cumsum(axis = var_7349, exclusive = segment_accum_703_exclusive_0, reverse = segment_accum_703_reverse_0, x = var_10192)[name = tensor("segment_accum_703")]; + tensor var_10196_begin_0 = const()[name = tensor("op_10196_begin_0"), val = tensor([0, -1, 0])]; + tensor var_10196_end_0 = const()[name = tensor("op_10196_end_0"), val = tensor([1, 1000, 9])]; + tensor var_10196_end_mask_0 = const()[name = tensor("op_10196_end_mask_0"), val = tensor([true, true, true])]; + tensor var_10196 = slice_by_index(begin = var_10196_begin_0, end = var_10196_end_0, end_mask = var_10196_end_mask_0, x = var_10190)[name = tensor("op_10196")]; + tensor var_10198 = add(x = segment_accum_703, y = var_10196)[name = tensor("op_10198")]; + tensor var_10200_begin_0 = const()[name = tensor("op_10200_begin_0"), val = tensor([0, 353000, 0])]; + tensor var_10200_end_0 = const()[name = tensor("op_10200_end_0"), val = tensor([1, 354000, 9])]; + tensor var_10200_end_mask_0 = const()[name = tensor("op_10200_end_mask_0"), val = tensor([true, false, true])]; + tensor var_10200 = slice_by_index(begin = var_10200_begin_0, end = var_10200_end_0, end_mask = var_10200_end_mask_0, x = reshape_4)[name = tensor("op_10200")]; + tensor segment_accum_705_exclusive_0 = const()[name = tensor("segment_accum_705_exclusive_0"), val = tensor(false)]; + tensor segment_accum_705_reverse_0 = const()[name = tensor("segment_accum_705_reverse_0"), val = tensor(false)]; + tensor segment_accum_705 = cumsum(axis = var_7349, exclusive = segment_accum_705_exclusive_0, reverse = segment_accum_705_reverse_0, x = var_10200)[name = tensor("segment_accum_705")]; + tensor var_10204_begin_0 = const()[name = tensor("op_10204_begin_0"), val = tensor([0, -1, 0])]; + tensor var_10204_end_0 = const()[name = tensor("op_10204_end_0"), val = tensor([1, 1000, 9])]; + tensor var_10204_end_mask_0 = const()[name = tensor("op_10204_end_mask_0"), val = tensor([true, true, true])]; + tensor var_10204 = slice_by_index(begin = var_10204_begin_0, end = var_10204_end_0, end_mask = var_10204_end_mask_0, x = var_10198)[name = tensor("op_10204")]; + tensor var_10206 = add(x = segment_accum_705, y = var_10204)[name = tensor("op_10206")]; + tensor var_10208_begin_0 = const()[name = tensor("op_10208_begin_0"), val = tensor([0, 354000, 0])]; + tensor var_10208_end_0 = const()[name = tensor("op_10208_end_0"), val = tensor([1, 355000, 9])]; + tensor var_10208_end_mask_0 = const()[name = tensor("op_10208_end_mask_0"), val = tensor([true, false, true])]; + tensor var_10208 = slice_by_index(begin = var_10208_begin_0, end = var_10208_end_0, end_mask = var_10208_end_mask_0, x = reshape_4)[name = tensor("op_10208")]; + tensor segment_accum_707_exclusive_0 = const()[name = tensor("segment_accum_707_exclusive_0"), val = tensor(false)]; + tensor segment_accum_707_reverse_0 = const()[name = tensor("segment_accum_707_reverse_0"), val = tensor(false)]; + tensor segment_accum_707 = cumsum(axis = var_7349, exclusive = segment_accum_707_exclusive_0, reverse = segment_accum_707_reverse_0, x = var_10208)[name = tensor("segment_accum_707")]; + tensor var_10212_begin_0 = const()[name = tensor("op_10212_begin_0"), val = tensor([0, -1, 0])]; + tensor var_10212_end_0 = const()[name = tensor("op_10212_end_0"), val = tensor([1, 1000, 9])]; + tensor var_10212_end_mask_0 = const()[name = tensor("op_10212_end_mask_0"), val = tensor([true, true, true])]; + tensor var_10212 = slice_by_index(begin = var_10212_begin_0, end = var_10212_end_0, end_mask = var_10212_end_mask_0, x = var_10206)[name = tensor("op_10212")]; + tensor var_10214 = add(x = segment_accum_707, y = var_10212)[name = tensor("op_10214")]; + tensor var_10216_begin_0 = const()[name = tensor("op_10216_begin_0"), val = tensor([0, 355000, 0])]; + tensor var_10216_end_0 = const()[name = tensor("op_10216_end_0"), val = tensor([1, 356000, 9])]; + tensor var_10216_end_mask_0 = const()[name = tensor("op_10216_end_mask_0"), val = tensor([true, false, true])]; + tensor var_10216 = slice_by_index(begin = var_10216_begin_0, end = var_10216_end_0, end_mask = var_10216_end_mask_0, x = reshape_4)[name = tensor("op_10216")]; + tensor segment_accum_709_exclusive_0 = const()[name = tensor("segment_accum_709_exclusive_0"), val = tensor(false)]; + tensor segment_accum_709_reverse_0 = const()[name = tensor("segment_accum_709_reverse_0"), val = tensor(false)]; + tensor segment_accum_709 = cumsum(axis = var_7349, exclusive = segment_accum_709_exclusive_0, reverse = segment_accum_709_reverse_0, x = var_10216)[name = tensor("segment_accum_709")]; + tensor var_10220_begin_0 = const()[name = tensor("op_10220_begin_0"), val = tensor([0, -1, 0])]; + tensor var_10220_end_0 = const()[name = tensor("op_10220_end_0"), val = tensor([1, 1000, 9])]; + tensor var_10220_end_mask_0 = const()[name = tensor("op_10220_end_mask_0"), val = tensor([true, true, true])]; + tensor var_10220 = slice_by_index(begin = var_10220_begin_0, end = var_10220_end_0, end_mask = var_10220_end_mask_0, x = var_10214)[name = tensor("op_10220")]; + tensor var_10222 = add(x = segment_accum_709, y = var_10220)[name = tensor("op_10222")]; + tensor var_10224_begin_0 = const()[name = tensor("op_10224_begin_0"), val = tensor([0, 356000, 0])]; + tensor var_10224_end_0 = const()[name = tensor("op_10224_end_0"), val = tensor([1, 357000, 9])]; + tensor var_10224_end_mask_0 = const()[name = tensor("op_10224_end_mask_0"), val = tensor([true, false, true])]; + tensor var_10224 = slice_by_index(begin = var_10224_begin_0, end = var_10224_end_0, end_mask = var_10224_end_mask_0, x = reshape_4)[name = tensor("op_10224")]; + tensor segment_accum_711_exclusive_0 = const()[name = tensor("segment_accum_711_exclusive_0"), val = tensor(false)]; + tensor segment_accum_711_reverse_0 = const()[name = tensor("segment_accum_711_reverse_0"), val = tensor(false)]; + tensor segment_accum_711 = cumsum(axis = var_7349, exclusive = segment_accum_711_exclusive_0, reverse = segment_accum_711_reverse_0, x = var_10224)[name = tensor("segment_accum_711")]; + tensor var_10228_begin_0 = const()[name = tensor("op_10228_begin_0"), val = tensor([0, -1, 0])]; + tensor var_10228_end_0 = const()[name = tensor("op_10228_end_0"), val = tensor([1, 1000, 9])]; + tensor var_10228_end_mask_0 = const()[name = tensor("op_10228_end_mask_0"), val = tensor([true, true, true])]; + tensor var_10228 = slice_by_index(begin = var_10228_begin_0, end = var_10228_end_0, end_mask = var_10228_end_mask_0, x = var_10222)[name = tensor("op_10228")]; + tensor var_10230 = add(x = segment_accum_711, y = var_10228)[name = tensor("op_10230")]; + tensor var_10232_begin_0 = const()[name = tensor("op_10232_begin_0"), val = tensor([0, 357000, 0])]; + tensor var_10232_end_0 = const()[name = tensor("op_10232_end_0"), val = tensor([1, 358000, 9])]; + tensor var_10232_end_mask_0 = const()[name = tensor("op_10232_end_mask_0"), val = tensor([true, false, true])]; + tensor var_10232 = slice_by_index(begin = var_10232_begin_0, end = var_10232_end_0, end_mask = var_10232_end_mask_0, x = reshape_4)[name = tensor("op_10232")]; + tensor segment_accum_713_exclusive_0 = const()[name = tensor("segment_accum_713_exclusive_0"), val = tensor(false)]; + tensor segment_accum_713_reverse_0 = const()[name = tensor("segment_accum_713_reverse_0"), val = tensor(false)]; + tensor segment_accum_713 = cumsum(axis = var_7349, exclusive = segment_accum_713_exclusive_0, reverse = segment_accum_713_reverse_0, x = var_10232)[name = tensor("segment_accum_713")]; + tensor var_10236_begin_0 = const()[name = tensor("op_10236_begin_0"), val = tensor([0, -1, 0])]; + tensor var_10236_end_0 = const()[name = tensor("op_10236_end_0"), val = tensor([1, 1000, 9])]; + tensor var_10236_end_mask_0 = const()[name = tensor("op_10236_end_mask_0"), val = tensor([true, true, true])]; + tensor var_10236 = slice_by_index(begin = var_10236_begin_0, end = var_10236_end_0, end_mask = var_10236_end_mask_0, x = var_10230)[name = tensor("op_10236")]; + tensor var_10238 = add(x = segment_accum_713, y = var_10236)[name = tensor("op_10238")]; + tensor var_10240_begin_0 = const()[name = tensor("op_10240_begin_0"), val = tensor([0, 358000, 0])]; + tensor var_10240_end_0 = const()[name = tensor("op_10240_end_0"), val = tensor([1, 359000, 9])]; + tensor var_10240_end_mask_0 = const()[name = tensor("op_10240_end_mask_0"), val = tensor([true, false, true])]; + tensor var_10240 = slice_by_index(begin = var_10240_begin_0, end = var_10240_end_0, end_mask = var_10240_end_mask_0, x = reshape_4)[name = tensor("op_10240")]; + tensor segment_accum_715_exclusive_0 = const()[name = tensor("segment_accum_715_exclusive_0"), val = tensor(false)]; + tensor segment_accum_715_reverse_0 = const()[name = tensor("segment_accum_715_reverse_0"), val = tensor(false)]; + tensor segment_accum_715 = cumsum(axis = var_7349, exclusive = segment_accum_715_exclusive_0, reverse = segment_accum_715_reverse_0, x = var_10240)[name = tensor("segment_accum_715")]; + tensor var_10244_begin_0 = const()[name = tensor("op_10244_begin_0"), val = tensor([0, -1, 0])]; + tensor var_10244_end_0 = const()[name = tensor("op_10244_end_0"), val = tensor([1, 1000, 9])]; + tensor var_10244_end_mask_0 = const()[name = tensor("op_10244_end_mask_0"), val = tensor([true, true, true])]; + tensor var_10244 = slice_by_index(begin = var_10244_begin_0, end = var_10244_end_0, end_mask = var_10244_end_mask_0, x = var_10238)[name = tensor("op_10244")]; + tensor var_10246 = add(x = segment_accum_715, y = var_10244)[name = tensor("op_10246")]; + tensor var_10248_begin_0 = const()[name = tensor("op_10248_begin_0"), val = tensor([0, 359000, 0])]; + tensor var_10248_end_0 = const()[name = tensor("op_10248_end_0"), val = tensor([1, 1, 9])]; + tensor var_10248_end_mask_0 = const()[name = tensor("op_10248_end_mask_0"), val = tensor([true, true, true])]; + tensor var_10248 = slice_by_index(begin = var_10248_begin_0, end = var_10248_end_0, end_mask = var_10248_end_mask_0, x = reshape_4)[name = tensor("op_10248")]; + tensor segment_accum_717_exclusive_0 = const()[name = tensor("segment_accum_717_exclusive_0"), val = tensor(false)]; + tensor segment_accum_717_reverse_0 = const()[name = tensor("segment_accum_717_reverse_0"), val = tensor(false)]; + tensor segment_accum_717 = cumsum(axis = var_7349, exclusive = segment_accum_717_exclusive_0, reverse = segment_accum_717_reverse_0, x = var_10248)[name = tensor("segment_accum_717")]; + tensor var_10252_begin_0 = const()[name = tensor("op_10252_begin_0"), val = tensor([0, -1, 0])]; + tensor var_10252_end_0 = const()[name = tensor("op_10252_end_0"), val = tensor([1, 1000, 9])]; + tensor var_10252_end_mask_0 = const()[name = tensor("op_10252_end_mask_0"), val = tensor([true, true, true])]; + tensor var_10252 = slice_by_index(begin = var_10252_begin_0, end = var_10252_end_0, end_mask = var_10252_end_mask_0, x = var_10246)[name = tensor("op_10252")]; + tensor segment_accum = add(x = segment_accum_717, y = var_10252)[name = tensor("segment_accum")]; + tensor phase_accum_1_interleave_0 = const()[name = tensor("phase_accum_1_interleave_0"), val = tensor(false)]; + tensor phase_accum_1 = concat(axis = var_7349, interleave = phase_accum_1_interleave_0, values = (var_7382, var_7390, var_7398, var_7406, var_7414, var_7422, var_7430, var_7438, var_7446, var_7454, var_7462, var_7470, var_7478, var_7486, var_7494, var_7502, var_7510, var_7518, var_7526, var_7534, var_7542, var_7550, var_7558, var_7566, var_7574, var_7582, var_7590, var_7598, var_7606, var_7614, var_7622, var_7630, var_7638, var_7646, var_7654, var_7662, var_7670, var_7678, var_7686, var_7694, var_7702, var_7710, var_7718, var_7726, var_7734, var_7742, var_7750, var_7758, var_7766, var_7774, var_7782, var_7790, var_7798, var_7806, var_7814, var_7822, var_7830, var_7838, var_7846, var_7854, var_7862, var_7870, var_7878, var_7886, var_7894, var_7902, var_7910, var_7918, var_7926, var_7934, var_7942, var_7950, var_7958, var_7966, var_7974, var_7982, var_7990, var_7998, var_8006, var_8014, var_8022, var_8030, var_8038, var_8046, var_8054, var_8062, var_8070, var_8078, var_8086, var_8094, var_8102, var_8110, var_8118, var_8126, var_8134, var_8142, var_8150, var_8158, var_8166, var_8174, var_8182, var_8190, var_8198, var_8206, var_8214, var_8222, var_8230, var_8238, var_8246, var_8254, var_8262, var_8270, var_8278, var_8286, var_8294, var_8302, var_8310, var_8318, var_8326, var_8334, var_8342, var_8350, var_8358, var_8366, var_8374, var_8382, var_8390, var_8398, var_8406, var_8414, var_8422, var_8430, var_8438, var_8446, var_8454, var_8462, var_8470, var_8478, var_8486, var_8494, var_8502, var_8510, var_8518, var_8526, var_8534, var_8542, var_8550, var_8558, var_8566, var_8574, var_8582, var_8590, var_8598, var_8606, var_8614, var_8622, var_8630, var_8638, var_8646, var_8654, var_8662, var_8670, var_8678, var_8686, var_8694, var_8702, var_8710, var_8718, var_8726, var_8734, var_8742, var_8750, var_8758, var_8766, var_8774, var_8782, var_8790, var_8798, var_8806, var_8814, var_8822, var_8830, var_8838, var_8846, var_8854, var_8862, var_8870, var_8878, var_8886, var_8894, var_8902, var_8910, var_8918, var_8926, var_8934, var_8942, var_8950, var_8958, var_8966, var_8974, var_8982, var_8990, var_8998, var_9006, var_9014, var_9022, var_9030, var_9038, var_9046, var_9054, var_9062, var_9070, var_9078, var_9086, var_9094, var_9102, var_9110, var_9118, var_9126, var_9134, var_9142, var_9150, var_9158, var_9166, var_9174, var_9182, var_9190, var_9198, var_9206, var_9214, var_9222, var_9230, var_9238, var_9246, var_9254, var_9262, var_9270, var_9278, var_9286, var_9294, var_9302, var_9310, var_9318, var_9326, var_9334, var_9342, var_9350, var_9358, var_9366, var_9374, var_9382, var_9390, var_9398, var_9406, var_9414, var_9422, var_9430, var_9438, var_9446, var_9454, var_9462, var_9470, var_9478, var_9486, var_9494, var_9502, var_9510, var_9518, var_9526, var_9534, var_9542, var_9550, var_9558, var_9566, var_9574, var_9582, var_9590, var_9598, var_9606, var_9614, var_9622, var_9630, var_9638, var_9646, var_9654, var_9662, var_9670, var_9678, var_9686, var_9694, var_9702, var_9710, var_9718, var_9726, var_9734, var_9742, var_9750, var_9758, var_9766, var_9774, var_9782, var_9790, var_9798, var_9806, var_9814, var_9822, var_9830, var_9838, var_9846, var_9854, var_9862, var_9870, var_9878, var_9886, var_9894, var_9902, var_9910, var_9918, var_9926, var_9934, var_9942, var_9950, var_9958, var_9966, var_9974, var_9982, var_9990, var_9998, var_10006, var_10014, var_10022, var_10030, var_10038, var_10046, var_10054, var_10062, var_10070, var_10078, var_10086, var_10094, var_10102, var_10110, var_10118, var_10126, var_10134, var_10142, var_10150, var_10158, var_10166, var_10174, var_10182, var_10190, var_10198, var_10206, var_10214, var_10222, var_10230, var_10238, var_10246, segment_accum))[name = tensor("phase_accum_1")]; + tensor var_10257 = floor(x = phase_accum_1)[name = tensor("op_10257")]; + tensor var_10258 = sub(x = phase_accum_1, y = var_10257)[name = tensor("op_10258")]; + tensor var_10259_promoted = const()[name = tensor("op_10259_promoted"), val = tensor(0x1p+1)]; + tensor var_10260 = mul(x = var_10258, y = var_10259_promoted)[name = tensor("op_10260")]; + tensor var_10261 = const()[name = tensor("op_10261"), val = tensor(0x1.921fb6p+1)]; + tensor phase_accum = mul(x = var_10260, y = var_10261)[name = tensor("phase_accum")]; + tensor var_10263 = sin(x = phase_accum)[name = tensor("op_10263")]; + tensor var_10264 = const()[name = tensor("op_10264"), val = tensor(0x1.99999ap-4)]; + tensor var_10265 = mul(x = var_10263, y = var_10264)[name = tensor("op_10265")]; + tensor sine_waves = mul(x = var_10265, y = uv)[name = tensor("sine_waves")]; + tensor input_421 = linear(bias = model_decoder_generator_m_source_l_linear_bias, weight = model_decoder_generator_m_source_l_linear_weight, x = sine_waves)[name = tensor("linear_100")]; + tensor har_source = tanh(x = input_421)[name = tensor("har_source")]; + tensor var_10270_perm_0 = const()[name = tensor("op_10270_perm_0"), val = tensor([0, 2, 1])]; + tensor input_423_axes_0 = const()[name = tensor("input_423_axes_0"), val = tensor([1])]; + tensor var_10270 = transpose(perm = var_10270_perm_0, x = har_source)[name = tensor("transpose_107")]; + tensor input_423 = squeeze(axes = input_423_axes_0, x = var_10270)[name = tensor("input_423")]; + tensor const_178 = const()[name = tensor("const_178"), val = tensor(0x0p+0)]; + tensor waveform_1_pad_0 = const()[name = tensor("waveform_1_pad_0"), val = tensor([0, 0, 10, 10])]; + tensor waveform_1_mode_0 = const()[name = tensor("waveform_1_mode_0"), val = tensor("replicate")]; + tensor waveform_1 = pad(constant_val = const_178, mode = waveform_1_mode_0, pad = waveform_1_pad_0, x = input_423)[name = tensor("waveform_1")]; + tensor x_251_axes_0 = const()[name = tensor("x_251_axes_0"), val = tensor([1])]; + tensor x_251 = expand_dims(axes = x_251_axes_0, x = waveform_1)[name = tensor("x_251")]; + 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 = model_decoder_generator_stft_weight_forward_real, x = x_251)[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 = model_decoder_generator_stft_weight_forward_imag, x = x_251)[name = tensor("imag_out")]; + tensor var_7346_promoted = const()[name = tensor("op_7346_promoted"), val = tensor(0x1p+1)]; + tensor var_10285 = pow(x = real_out, y = var_7346_promoted)[name = tensor("op_10285")]; + tensor var_7346_promoted_1 = const()[name = tensor("op_7346_promoted_1"), val = tensor(0x1p+1)]; + tensor var_10286 = pow(x = imag_out, y = var_7346_promoted_1)[name = tensor("op_10286")]; + tensor var_10287 = add(x = var_10285, y = var_10286)[name = tensor("op_10287")]; + tensor var_10288 = const()[name = tensor("op_10288"), val = tensor(0x1.6849b8p-47)]; + tensor var_10289 = add(x = var_10287, y = var_10288)[name = tensor("op_10289")]; + tensor har_spec = sqrt(x = var_10289)[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_94_dtype_0 = const()[name = tensor("cast_94_dtype_0"), val = tensor("fp32")]; + tensor cast_95_dtype_0 = const()[name = tensor("cast_95_dtype_0"), val = tensor("fp32")]; + tensor cast_96_dtype_0 = const()[name = tensor("cast_96_dtype_0"), val = tensor("fp32")]; + tensor cast_97_dtype_0 = const()[name = tensor("cast_97_dtype_0"), val = tensor("fp32")]; + tensor mul_12_y_0 = const()[name = tensor("mul_12_y_0"), val = tensor(0x1.921fb6p+1)]; + tensor cast_94 = cast(dtype = cast_94_dtype_0, x = logical_and_0)[name = tensor("cast_156")]; + tensor mul_12 = mul(x = cast_94, y = mul_12_y_0)[name = tensor("mul_12")]; + tensor mul_13_y_0 = const()[name = tensor("mul_13_y_0"), val = tensor(0x1.921fb6p+1)]; + tensor cast_95 = cast(dtype = cast_95_dtype_0, x = logical_and_1)[name = tensor("cast_155")]; + tensor mul_13 = mul(x = cast_95, y = mul_13_y_0)[name = tensor("mul_13")]; + tensor sub_0_x_0 = const()[name = tensor("sub_0_x_0"), val = tensor(0x1p+0)]; + tensor cast_96 = cast(dtype = cast_96_dtype_0, x = logical_and_2)[name = tensor("cast_154")]; + tensor sub_0 = sub(x = sub_0_x_0, y = cast_96)[name = tensor("sub_0")]; + tensor mul_14_y_0 = const()[name = tensor("mul_14_y_0"), val = tensor(0x1.921fb6p+0)]; + tensor mul_14 = mul(x = cast_96, y = mul_14_y_0)[name = tensor("mul_14")]; + tensor sub_1_x_0 = const()[name = tensor("sub_1_x_0"), val = tensor(0x1p+0)]; + tensor cast_97 = cast(dtype = cast_97_dtype_0, x = logical_and_3)[name = tensor("cast_153")]; + tensor sub_1 = sub(x = sub_1_x_0, y = cast_97)[name = tensor("sub_1")]; + tensor mul_15_y_0 = const()[name = tensor("mul_15_y_0"), val = tensor(-0x1.921fb6p+0)]; + tensor mul_15 = mul(x = cast_97, y = mul_15_y_0)[name = tensor("mul_15")]; + 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_98_dtype_0 = const()[name = tensor("cast_98_dtype_0"), val = tensor("fp32")]; + tensor mul_16_y_0 = const()[name = tensor("mul_16_y_0"), val = tensor(0x1.5798eep-26)]; + tensor cast_98 = cast(dtype = cast_98_dtype_0, x = logical_and_4)[name = tensor("cast_152")]; + tensor mul_16 = mul(x = cast_98, y = mul_16_y_0)[name = tensor("mul_16")]; + tensor add_24 = add(x = real_out, y = mul_16)[name = tensor("add_24")]; + tensor real_div_38 = real_div(x = imag_out, y = add_24)[name = tensor("real_div_38")]; + tensor atan_0 = atan(x = real_div_38)[name = tensor("atan_0")]; + tensor add_25 = add(x = atan_0, y = mul_12)[name = tensor("add_25")]; + tensor sub_2 = sub(x = add_25, y = mul_13)[name = tensor("sub_2")]; + tensor mul_17 = mul(x = sub_2, y = sub_0)[name = tensor("mul_17")]; + tensor add_26 = add(x = mul_17, y = mul_14)[name = tensor("add_26")]; + tensor mul_18 = mul(x = add_26, y = sub_1)[name = tensor("mul_18")]; + tensor phase_1 = add(x = mul_18, y = mul_15)[name = tensor("phase_1")]; + tensor var_7351_promoted = const()[name = tensor("op_7351_promoted"), val = tensor(0x0p+0)]; + tensor var_10292 = equal(x = imag_out, y = var_7351_promoted)[name = tensor("op_10292")]; + tensor correction_mask = logical_and(x = var_10292, y = less_1)[name = tensor("correction_mask")]; + tensor cast_101_dtype_0 = const()[name = tensor("cast_101_dtype_0"), val = tensor("int32")]; + tensor cast_101 = cast(dtype = cast_101_dtype_0, x = correction_mask)[name = tensor("cast_151")]; + tensor non_zero_0 = non_zero(x = cast_101)[name = tensor("non_zero_0")]; + tensor shape_0 = shape(x = non_zero_0)[name = tensor("shape_0")]; + tensor slice_by_index_0_begin_0 = const()[name = tensor("slice_by_index_0_begin_0"), val = tensor([0])]; + tensor slice_by_index_0_end_0 = const()[name = tensor("slice_by_index_0_end_0"), val = tensor([0])]; + tensor slice_by_index_0_squeeze_mask_0 = const()[name = tensor("slice_by_index_0_squeeze_mask_0"), val = tensor([true])]; + tensor slice_by_index_0 = slice_by_index(begin = slice_by_index_0_begin_0, end = slice_by_index_0_end_0, squeeze_mask = slice_by_index_0_squeeze_mask_0, x = shape_0)[name = tensor("slice_by_index_0")]; + tensor concat_68_axis_0 = const()[name = tensor("concat_68_axis_0"), val = tensor(0)]; + tensor concat_68_interleave_0 = const()[name = tensor("concat_68_interleave_0"), val = tensor(false)]; + tensor concat_68 = concat(axis = concat_68_axis_0, interleave = concat_68_interleave_0, values = slice_by_index_0)[name = tensor("concat_68")]; + tensor expand_dims_7 = const()[name = tensor("expand_dims_7"), val = tensor([0x1.921fb6p+1])]; + tensor var_6976_broadcasted = tile(reps = concat_68, x = expand_dims_7)[name = tensor("op_6976_broadcasted")]; + tensor greater_equal_0_y_0 = const()[name = tensor("greater_equal_0_y_0"), val = tensor(0)]; + tensor greater_equal_0 = greater_equal(x = non_zero_0, y = greater_equal_0_y_0)[name = tensor("greater_equal_0")]; + tensor shape_2 = const()[name = tensor("shape_2"), val = tensor([1, 11, 72001])]; + tensor add_27 = add(x = non_zero_0, y = shape_2)[name = tensor("add_27")]; + tensor select_0 = select(a = non_zero_0, b = add_27, cond = greater_equal_0)[name = tensor("select_0")]; + tensor har_phase_mode_0 = const()[name = tensor("har_phase_mode_0"), val = tensor("update")]; + tensor har_phase_validate_indices_0 = const()[name = tensor("har_phase_validate_indices_0"), val = tensor(false)]; + tensor har_phase = scatter_nd(data = phase_1, indices = select_0, mode = har_phase_mode_0, updates = var_6976_broadcasted, validate_indices = har_phase_validate_indices_0)[name = tensor("har_phase")]; + tensor input_427_interleave_0 = const()[name = tensor("input_427_interleave_0"), val = tensor(false)]; + tensor input_427 = concat(axis = var_7349, interleave = input_427_interleave_0, values = (har_spec, har_phase))[name = tensor("input_427")]; + tensor input_453 = leaky_relu(alpha = var_6975, x = input_425)[name = tensor("input_453")]; + 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([3, 3])]; + tensor input_429_strides_0 = const()[name = tensor("input_429_strides_0"), val = tensor([6])]; + 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 = model_decoder_generator_noise_convs_0_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 = model_decoder_generator_noise_convs_0_weight, x = input_427)[name = tensor("input_429")]; + tensor h_101 = linear(bias = model_decoder_generator_noise_res_0_adain1_0_fc_bias, weight = model_decoder_generator_noise_res_0_adain1_0_fc_weight, x = input_331)[name = tensor("linear_101")]; + tensor var_10308 = const()[name = tensor("op_10308"), val = tensor([1, 512, 1])]; + tensor h_103 = reshape(shape = var_10308, x = h_101)[name = tensor("h_103")]; + tensor var_10310_split_sizes_0 = const()[name = tensor("op_10310_split_sizes_0"), val = tensor([256, 256])]; + tensor var_10310_axis_0 = const()[name = tensor("op_10310_axis_0"), val = tensor(1)]; + tensor var_10310_0, tensor var_10310_1 = split(axis = var_10310_axis_0, split_sizes = var_10310_split_sizes_0, x = h_103)[name = tensor("op_10310")]; + tensor var_10312_promoted = const()[name = tensor("op_10312_promoted"), val = tensor(0x1p+0)]; + tensor var_10313 = add(x = var_10310_0, y = var_10312_promoted)[name = tensor("op_10313")]; + tensor var_10314 = instance_norm(beta = model_decoder_generator_resblocks_2_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_2_adain2_2_norm_weight, x = input_429)[name = tensor("op_10314")]; + tensor var_10315 = mul(x = var_10313, y = var_10314)[name = tensor("op_10315")]; + tensor xt_1 = add(x = var_10315, y = var_10310_1)[name = tensor("xt_1")]; + tensor var_10317 = const()[name = tensor("op_10317"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(255802816)))]; + tensor var_10320 = mul(x = model_decoder_generator_noise_res_0_alpha1_0, y = xt_1)[name = tensor("op_10320")]; + tensor var_10321 = sin(x = var_10320)[name = tensor("op_10321")]; + tensor var_7346_promoted_2 = const()[name = tensor("op_7346_promoted_2"), val = tensor(0x1p+1)]; + tensor var_10322 = pow(x = var_10321, y = var_7346_promoted_2)[name = tensor("op_10322")]; + tensor var_10323 = mul(x = var_10317, y = var_10322)[name = tensor("op_10323")]; + tensor input_431 = add(x = xt_1, y = var_10323)[name = tensor("input_431")]; + tensor weight_143 = const()[name = tensor("weight_143"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(255803904)))]; + tensor input_433_pad_type_0 = const()[name = tensor("input_433_pad_type_0"), val = tensor("custom")]; + tensor input_433_pad_0 = const()[name = tensor("input_433_pad_0"), val = tensor([3, 3])]; + tensor input_433_strides_0 = const()[name = tensor("input_433_strides_0"), val = tensor([1])]; + tensor input_433_dilations_0 = const()[name = tensor("input_433_dilations_0"), val = tensor([1])]; + tensor input_433_groups_0 = const()[name = tensor("input_433_groups_0"), val = tensor(1)]; + tensor input_433 = conv(bias = model_decoder_generator_noise_res_0_convs1_0_bias, dilations = input_433_dilations_0, groups = input_433_groups_0, pad = input_433_pad_0, pad_type = input_433_pad_type_0, strides = input_433_strides_0, weight = weight_143, x = input_431)[name = tensor("input_433")]; + tensor h_105 = linear(bias = model_decoder_generator_noise_res_0_adain2_0_fc_bias, weight = model_decoder_generator_noise_res_0_adain2_0_fc_weight, x = input_331)[name = tensor("linear_102")]; + tensor var_10334 = const()[name = tensor("op_10334"), val = tensor([1, 512, 1])]; + tensor h_107 = reshape(shape = var_10334, x = h_105)[name = tensor("h_107")]; + tensor var_10336_split_sizes_0 = const()[name = tensor("op_10336_split_sizes_0"), val = tensor([256, 256])]; + tensor var_10336_axis_0 = const()[name = tensor("op_10336_axis_0"), val = tensor(1)]; + tensor var_10336_0, tensor var_10336_1 = split(axis = var_10336_axis_0, split_sizes = var_10336_split_sizes_0, x = h_107)[name = tensor("op_10336")]; + tensor var_10338_promoted = const()[name = tensor("op_10338_promoted"), val = tensor(0x1p+0)]; + tensor var_10339 = add(x = var_10336_0, y = var_10338_promoted)[name = tensor("op_10339")]; + tensor var_10340 = instance_norm(beta = model_decoder_generator_resblocks_2_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_2_adain2_2_norm_weight, x = input_433)[name = tensor("op_10340")]; + tensor var_10341 = mul(x = var_10339, y = var_10340)[name = tensor("op_10341")]; + tensor xt_3 = add(x = var_10341, y = var_10336_1)[name = tensor("xt_3")]; + tensor var_10343 = const()[name = tensor("op_10343"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(257638976)))]; + tensor var_10346 = mul(x = model_decoder_generator_noise_res_0_alpha2_0, y = xt_3)[name = tensor("op_10346")]; + tensor var_10347 = sin(x = var_10346)[name = tensor("op_10347")]; + tensor var_7346_promoted_3 = const()[name = tensor("op_7346_promoted_3"), val = tensor(0x1p+1)]; + tensor var_10348 = pow(x = var_10347, y = var_7346_promoted_3)[name = tensor("op_10348")]; + tensor var_10349 = mul(x = var_10343, y = var_10348)[name = tensor("op_10349")]; + tensor input_435 = add(x = xt_3, y = var_10349)[name = tensor("input_435")]; + tensor weight_147 = const()[name = tensor("weight_147"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(257640064)))]; + 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 = model_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_435)[name = tensor("xt_5")]; + tensor input_437 = add(x = xt_5, y = input_429)[name = tensor("input_437")]; + tensor h_109 = linear(bias = model_decoder_generator_noise_res_0_adain1_1_fc_bias, weight = model_decoder_generator_noise_res_0_adain1_1_fc_weight, x = input_331)[name = tensor("linear_103")]; + tensor var_10361 = const()[name = tensor("op_10361"), val = tensor([1, 512, 1])]; + tensor h_111 = reshape(shape = var_10361, x = h_109)[name = tensor("h_111")]; + tensor var_10363_split_sizes_0 = const()[name = tensor("op_10363_split_sizes_0"), val = tensor([256, 256])]; + tensor var_10363_axis_0 = const()[name = tensor("op_10363_axis_0"), val = tensor(1)]; + tensor var_10363_0, tensor var_10363_1 = split(axis = var_10363_axis_0, split_sizes = var_10363_split_sizes_0, x = h_111)[name = tensor("op_10363")]; + tensor var_10365_promoted = const()[name = tensor("op_10365_promoted"), val = tensor(0x1p+0)]; + tensor var_10366 = add(x = var_10363_0, y = var_10365_promoted)[name = tensor("op_10366")]; + tensor var_10367 = instance_norm(beta = model_decoder_generator_resblocks_2_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_2_adain2_2_norm_weight, x = input_437)[name = tensor("op_10367")]; + tensor var_10368 = mul(x = var_10366, y = var_10367)[name = tensor("op_10368")]; + tensor xt_7 = add(x = var_10368, y = var_10363_1)[name = tensor("xt_7")]; + tensor var_10370 = const()[name = tensor("op_10370"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(259475136)))]; + tensor var_10373 = mul(x = model_decoder_generator_noise_res_0_alpha1_1, y = xt_7)[name = tensor("op_10373")]; + tensor var_10374 = sin(x = var_10373)[name = tensor("op_10374")]; + tensor var_7346_promoted_4 = const()[name = tensor("op_7346_promoted_4"), val = tensor(0x1p+1)]; + tensor var_10375 = pow(x = var_10374, y = var_7346_promoted_4)[name = tensor("op_10375")]; + tensor var_10376 = mul(x = var_10370, y = var_10375)[name = tensor("op_10376")]; + tensor input_439 = add(x = xt_7, y = var_10376)[name = tensor("input_439")]; + tensor weight_151 = const()[name = tensor("weight_151"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(259476224)))]; + tensor input_441_pad_type_0 = const()[name = tensor("input_441_pad_type_0"), val = tensor("custom")]; + tensor input_441_pad_0 = const()[name = tensor("input_441_pad_0"), val = tensor([9, 9])]; + tensor input_441_dilations_0 = const()[name = tensor("input_441_dilations_0"), val = tensor([3])]; + tensor input_441_strides_0 = const()[name = tensor("input_441_strides_0"), val = tensor([1])]; + tensor input_441_groups_0 = const()[name = tensor("input_441_groups_0"), val = tensor(1)]; + tensor input_441 = conv(bias = model_decoder_generator_noise_res_0_convs1_1_bias, dilations = input_441_dilations_0, groups = input_441_groups_0, pad = input_441_pad_0, pad_type = input_441_pad_type_0, strides = input_441_strides_0, weight = weight_151, x = input_439)[name = tensor("input_441")]; + tensor h_113 = linear(bias = model_decoder_generator_noise_res_0_adain2_1_fc_bias, weight = model_decoder_generator_noise_res_0_adain2_1_fc_weight, x = input_331)[name = tensor("linear_104")]; + tensor var_10387 = const()[name = tensor("op_10387"), val = tensor([1, 512, 1])]; + tensor h_115 = reshape(shape = var_10387, x = h_113)[name = tensor("h_115")]; + tensor var_10389_split_sizes_0 = const()[name = tensor("op_10389_split_sizes_0"), val = tensor([256, 256])]; + tensor var_10389_axis_0 = const()[name = tensor("op_10389_axis_0"), val = tensor(1)]; + tensor var_10389_0, tensor var_10389_1 = split(axis = var_10389_axis_0, split_sizes = var_10389_split_sizes_0, x = h_115)[name = tensor("op_10389")]; + tensor var_10391_promoted = const()[name = tensor("op_10391_promoted"), val = tensor(0x1p+0)]; + tensor var_10392 = add(x = var_10389_0, y = var_10391_promoted)[name = tensor("op_10392")]; + tensor var_10393 = instance_norm(beta = model_decoder_generator_resblocks_2_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_2_adain2_2_norm_weight, x = input_441)[name = tensor("op_10393")]; + tensor var_10394 = mul(x = var_10392, y = var_10393)[name = tensor("op_10394")]; + tensor xt_9 = add(x = var_10394, y = var_10389_1)[name = tensor("xt_9")]; + tensor var_10396 = const()[name = tensor("op_10396"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261311296)))]; + tensor var_10399 = mul(x = model_decoder_generator_noise_res_0_alpha2_1, y = xt_9)[name = tensor("op_10399")]; + tensor var_10400 = sin(x = var_10399)[name = tensor("op_10400")]; + tensor var_7346_promoted_5 = const()[name = tensor("op_7346_promoted_5"), val = tensor(0x1p+1)]; + tensor var_10401 = pow(x = var_10400, y = var_7346_promoted_5)[name = tensor("op_10401")]; + tensor var_10402 = mul(x = var_10396, y = var_10401)[name = tensor("op_10402")]; + tensor input_443 = add(x = xt_9, y = var_10402)[name = tensor("input_443")]; + tensor weight_155 = const()[name = tensor("weight_155"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261312384)))]; + 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 = model_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_443)[name = tensor("xt_11")]; + tensor input_445 = add(x = xt_11, y = input_437)[name = tensor("input_445")]; + tensor h_117 = linear(bias = model_decoder_generator_noise_res_0_adain1_2_fc_bias, weight = model_decoder_generator_noise_res_0_adain1_2_fc_weight, x = input_331)[name = tensor("linear_105")]; + tensor var_10414 = const()[name = tensor("op_10414"), val = tensor([1, 512, 1])]; + tensor h_119 = reshape(shape = var_10414, x = h_117)[name = tensor("h_119")]; + tensor var_10416_split_sizes_0 = const()[name = tensor("op_10416_split_sizes_0"), val = tensor([256, 256])]; + tensor var_10416_axis_0 = const()[name = tensor("op_10416_axis_0"), val = tensor(1)]; + tensor var_10416_0, tensor var_10416_1 = split(axis = var_10416_axis_0, split_sizes = var_10416_split_sizes_0, x = h_119)[name = tensor("op_10416")]; + tensor var_10418_promoted = const()[name = tensor("op_10418_promoted"), val = tensor(0x1p+0)]; + tensor var_10419 = add(x = var_10416_0, y = var_10418_promoted)[name = tensor("op_10419")]; + tensor var_10420 = instance_norm(beta = model_decoder_generator_resblocks_2_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_2_adain2_2_norm_weight, x = input_445)[name = tensor("op_10420")]; + tensor var_10421 = mul(x = var_10419, y = var_10420)[name = tensor("op_10421")]; + tensor xt_13 = add(x = var_10421, y = var_10416_1)[name = tensor("xt_13")]; + tensor var_10423 = const()[name = tensor("op_10423"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(263147456)))]; + tensor var_10426 = mul(x = model_decoder_generator_noise_res_0_alpha1_2, y = xt_13)[name = tensor("op_10426")]; + tensor var_10427 = sin(x = var_10426)[name = tensor("op_10427")]; + tensor var_7346_promoted_6 = const()[name = tensor("op_7346_promoted_6"), val = tensor(0x1p+1)]; + tensor var_10428 = pow(x = var_10427, y = var_7346_promoted_6)[name = tensor("op_10428")]; + tensor var_10429 = mul(x = var_10423, y = var_10428)[name = tensor("op_10429")]; + tensor input_447 = add(x = xt_13, y = var_10429)[name = tensor("input_447")]; + tensor weight_159 = const()[name = tensor("weight_159"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(263148544)))]; + tensor input_449_pad_type_0 = const()[name = tensor("input_449_pad_type_0"), val = tensor("custom")]; + tensor input_449_pad_0 = const()[name = tensor("input_449_pad_0"), val = tensor([15, 15])]; + tensor input_449_dilations_0 = const()[name = tensor("input_449_dilations_0"), val = tensor([5])]; + tensor input_449_strides_0 = const()[name = tensor("input_449_strides_0"), val = tensor([1])]; + tensor input_449_groups_0 = const()[name = tensor("input_449_groups_0"), val = tensor(1)]; + tensor input_449 = conv(bias = model_decoder_generator_noise_res_0_convs1_2_bias, dilations = input_449_dilations_0, groups = input_449_groups_0, pad = input_449_pad_0, pad_type = input_449_pad_type_0, strides = input_449_strides_0, weight = weight_159, x = input_447)[name = tensor("input_449")]; + tensor h_121 = linear(bias = model_decoder_generator_noise_res_0_adain2_2_fc_bias, weight = model_decoder_generator_noise_res_0_adain2_2_fc_weight, x = input_331)[name = tensor("linear_106")]; + tensor var_10440 = const()[name = tensor("op_10440"), val = tensor([1, 512, 1])]; + tensor h_123 = reshape(shape = var_10440, x = h_121)[name = tensor("h_123")]; + tensor var_10442_split_sizes_0 = const()[name = tensor("op_10442_split_sizes_0"), val = tensor([256, 256])]; + tensor var_10442_axis_0 = const()[name = tensor("op_10442_axis_0"), val = tensor(1)]; + tensor var_10442_0, tensor var_10442_1 = split(axis = var_10442_axis_0, split_sizes = var_10442_split_sizes_0, x = h_123)[name = tensor("op_10442")]; + tensor var_10444_promoted = const()[name = tensor("op_10444_promoted"), val = tensor(0x1p+0)]; + tensor var_10445 = add(x = var_10442_0, y = var_10444_promoted)[name = tensor("op_10445")]; + tensor var_10446 = instance_norm(beta = model_decoder_generator_resblocks_2_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_2_adain2_2_norm_weight, x = input_449)[name = tensor("op_10446")]; + tensor var_10447 = mul(x = var_10445, y = var_10446)[name = tensor("op_10447")]; + tensor xt_15 = add(x = var_10447, y = var_10442_1)[name = tensor("xt_15")]; + tensor var_10449 = const()[name = tensor("op_10449"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(264983616)))]; + tensor var_10452 = mul(x = model_decoder_generator_noise_res_0_alpha2_2, y = xt_15)[name = tensor("op_10452")]; + tensor var_10453 = sin(x = var_10452)[name = tensor("op_10453")]; + tensor var_7346_promoted_7 = const()[name = tensor("op_7346_promoted_7"), val = tensor(0x1p+1)]; + tensor var_10454 = pow(x = var_10453, y = var_7346_promoted_7)[name = tensor("op_10454")]; + tensor var_10455 = mul(x = var_10449, y = var_10454)[name = tensor("op_10455")]; + tensor input_451 = add(x = xt_15, y = var_10455)[name = tensor("input_451")]; + tensor weight_163 = const()[name = tensor("weight_163"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(264984704)))]; + 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 = model_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_451)[name = tensor("xt_17")]; + tensor x_source_1 = add(x = xt_17, y = input_445)[name = tensor("x_source_1")]; + tensor var_10464 = const()[name = tensor("op_10464"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(266819776)))]; + tensor x_253_pad_type_0 = const()[name = tensor("x_253_pad_type_0"), val = tensor("custom")]; + tensor x_253_pad_0 = const()[name = tensor("x_253_pad_0"), val = tensor([5, 5])]; + tensor x_253_strides_0 = const()[name = tensor("x_253_strides_0"), val = tensor([10])]; + tensor x_253_dilations_0 = const()[name = tensor("x_253_dilations_0"), val = tensor([1])]; + tensor x_253_groups_0 = const()[name = tensor("x_253_groups_0"), val = tensor(1)]; + tensor x_253_has_output_shape_output_shape_0 = const()[name = tensor("x_253_has_output_shape_output_shape_0"), val = tensor([1, 256, 12000])]; + tensor x_253_has_output_shape = conv_transpose(bias = model_decoder_generator_ups_0_bias, dilations = x_253_dilations_0, groups = x_253_groups_0, output_shape = x_253_has_output_shape_output_shape_0, pad = x_253_pad_0, pad_type = x_253_pad_type_0, strides = x_253_strides_0, weight = var_10464, x = input_453)[name = tensor("x_253_has_output_shape")]; + tensor input_455 = add(x = x_253_has_output_shape, y = x_source_1)[name = tensor("input_455")]; + tensor h_125 = linear(bias = model_decoder_generator_resblocks_0_adain1_0_fc_bias, weight = model_decoder_generator_resblocks_0_adain1_0_fc_weight, x = input_331)[name = tensor("linear_107")]; + tensor var_10474 = const()[name = tensor("op_10474"), val = tensor([1, 512, 1])]; + tensor h_127 = reshape(shape = var_10474, x = h_125)[name = tensor("h_127")]; + tensor var_10476_split_sizes_0 = const()[name = tensor("op_10476_split_sizes_0"), val = tensor([256, 256])]; + tensor var_10476_axis_0 = const()[name = tensor("op_10476_axis_0"), val = tensor(1)]; + tensor var_10476_0, tensor var_10476_1 = split(axis = var_10476_axis_0, split_sizes = var_10476_split_sizes_0, x = h_127)[name = tensor("op_10476")]; + tensor var_10478_promoted = const()[name = tensor("op_10478_promoted"), val = tensor(0x1p+0)]; + tensor var_10479 = add(x = var_10476_0, y = var_10478_promoted)[name = tensor("op_10479")]; + tensor var_10480 = instance_norm(beta = model_decoder_generator_resblocks_2_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_2_adain2_2_norm_weight, x = input_455)[name = tensor("op_10480")]; + tensor var_10481 = mul(x = var_10479, y = var_10480)[name = tensor("op_10481")]; + tensor xt_19 = add(x = var_10481, y = var_10476_1)[name = tensor("xt_19")]; + tensor var_10483 = const()[name = tensor("op_10483"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(277305600)))]; + tensor var_10486 = mul(x = model_decoder_generator_resblocks_0_alpha1_0, y = xt_19)[name = tensor("op_10486")]; + tensor var_10487 = sin(x = var_10486)[name = tensor("op_10487")]; + tensor var_7346_promoted_8 = const()[name = tensor("op_7346_promoted_8"), val = tensor(0x1p+1)]; + tensor var_10488 = pow(x = var_10487, y = var_7346_promoted_8)[name = tensor("op_10488")]; + tensor var_10489 = mul(x = var_10483, y = var_10488)[name = tensor("op_10489")]; + tensor input_457 = add(x = xt_19, y = var_10489)[name = tensor("input_457")]; + tensor weight_167 = const()[name = tensor("weight_167"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(277306688)))]; + tensor input_459_pad_type_0 = const()[name = tensor("input_459_pad_type_0"), val = tensor("custom")]; + tensor input_459_pad_0 = const()[name = tensor("input_459_pad_0"), val = tensor([1, 1])]; + tensor input_459_strides_0 = const()[name = tensor("input_459_strides_0"), val = tensor([1])]; + tensor input_459_dilations_0 = const()[name = tensor("input_459_dilations_0"), val = tensor([1])]; + tensor input_459_groups_0 = const()[name = tensor("input_459_groups_0"), val = tensor(1)]; + tensor input_459 = conv(bias = model_decoder_generator_resblocks_0_convs1_0_bias, dilations = input_459_dilations_0, groups = input_459_groups_0, pad = input_459_pad_0, pad_type = input_459_pad_type_0, strides = input_459_strides_0, weight = weight_167, x = input_457)[name = tensor("input_459")]; + tensor h_129 = linear(bias = model_decoder_generator_resblocks_0_adain2_0_fc_bias, weight = model_decoder_generator_resblocks_0_adain2_0_fc_weight, x = input_331)[name = tensor("linear_108")]; + tensor var_10500 = const()[name = tensor("op_10500"), val = tensor([1, 512, 1])]; + tensor h_131 = reshape(shape = var_10500, x = h_129)[name = tensor("h_131")]; + tensor var_10502_split_sizes_0 = const()[name = tensor("op_10502_split_sizes_0"), val = tensor([256, 256])]; + tensor var_10502_axis_0 = const()[name = tensor("op_10502_axis_0"), val = tensor(1)]; + tensor var_10502_0, tensor var_10502_1 = split(axis = var_10502_axis_0, split_sizes = var_10502_split_sizes_0, x = h_131)[name = tensor("op_10502")]; + tensor var_10504_promoted = const()[name = tensor("op_10504_promoted"), val = tensor(0x1p+0)]; + tensor var_10505 = add(x = var_10502_0, y = var_10504_promoted)[name = tensor("op_10505")]; + tensor var_10506 = instance_norm(beta = model_decoder_generator_resblocks_2_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_2_adain2_2_norm_weight, x = input_459)[name = tensor("op_10506")]; + tensor var_10507 = mul(x = var_10505, y = var_10506)[name = tensor("op_10507")]; + tensor xt_21 = add(x = var_10507, y = var_10502_1)[name = tensor("xt_21")]; + tensor var_10509 = const()[name = tensor("op_10509"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(278093184)))]; + tensor var_10512 = mul(x = model_decoder_generator_resblocks_0_alpha2_0, y = xt_21)[name = tensor("op_10512")]; + tensor var_10513 = sin(x = var_10512)[name = tensor("op_10513")]; + tensor var_7346_promoted_9 = const()[name = tensor("op_7346_promoted_9"), val = tensor(0x1p+1)]; + tensor var_10514 = pow(x = var_10513, y = var_7346_promoted_9)[name = tensor("op_10514")]; + tensor var_10515 = mul(x = var_10509, y = var_10514)[name = tensor("op_10515")]; + tensor input_461 = add(x = xt_21, y = var_10515)[name = tensor("input_461")]; + tensor weight_171 = const()[name = tensor("weight_171"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(278094272)))]; + 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 = model_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_461)[name = tensor("xt_23")]; + tensor input_463 = add(x = xt_23, y = input_455)[name = tensor("input_463")]; + tensor h_133 = linear(bias = model_decoder_generator_resblocks_0_adain1_1_fc_bias, weight = model_decoder_generator_resblocks_0_adain1_1_fc_weight, x = input_331)[name = tensor("linear_109")]; + tensor var_10527 = const()[name = tensor("op_10527"), val = tensor([1, 512, 1])]; + tensor h_135 = reshape(shape = var_10527, x = h_133)[name = tensor("h_135")]; + tensor var_10529_split_sizes_0 = const()[name = tensor("op_10529_split_sizes_0"), val = tensor([256, 256])]; + tensor var_10529_axis_0 = const()[name = tensor("op_10529_axis_0"), val = tensor(1)]; + tensor var_10529_0, tensor var_10529_1 = split(axis = var_10529_axis_0, split_sizes = var_10529_split_sizes_0, x = h_135)[name = tensor("op_10529")]; + tensor var_10531_promoted = const()[name = tensor("op_10531_promoted"), val = tensor(0x1p+0)]; + tensor var_10532 = add(x = var_10529_0, y = var_10531_promoted)[name = tensor("op_10532")]; + tensor var_10533 = instance_norm(beta = model_decoder_generator_resblocks_2_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_2_adain2_2_norm_weight, x = input_463)[name = tensor("op_10533")]; + tensor var_10534 = mul(x = var_10532, y = var_10533)[name = tensor("op_10534")]; + tensor xt_25 = add(x = var_10534, y = var_10529_1)[name = tensor("xt_25")]; + tensor var_10536 = const()[name = tensor("op_10536"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(278880768)))]; + tensor var_10539 = mul(x = model_decoder_generator_resblocks_0_alpha1_1, y = xt_25)[name = tensor("op_10539")]; + tensor var_10540 = sin(x = var_10539)[name = tensor("op_10540")]; + tensor var_7346_promoted_10 = const()[name = tensor("op_7346_promoted_10"), val = tensor(0x1p+1)]; + tensor var_10541 = pow(x = var_10540, y = var_7346_promoted_10)[name = tensor("op_10541")]; + tensor var_10542 = mul(x = var_10536, y = var_10541)[name = tensor("op_10542")]; + tensor input_465 = add(x = xt_25, y = var_10542)[name = tensor("input_465")]; + tensor weight_175 = const()[name = tensor("weight_175"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(278881856)))]; + tensor input_467_pad_type_0 = const()[name = tensor("input_467_pad_type_0"), val = tensor("custom")]; + tensor input_467_pad_0 = const()[name = tensor("input_467_pad_0"), val = tensor([3, 3])]; + tensor input_467_dilations_0 = const()[name = tensor("input_467_dilations_0"), val = tensor([3])]; + tensor input_467_strides_0 = const()[name = tensor("input_467_strides_0"), val = tensor([1])]; + tensor input_467_groups_0 = const()[name = tensor("input_467_groups_0"), val = tensor(1)]; + tensor input_467 = conv(bias = model_decoder_generator_resblocks_0_convs1_1_bias, dilations = input_467_dilations_0, groups = input_467_groups_0, pad = input_467_pad_0, pad_type = input_467_pad_type_0, strides = input_467_strides_0, weight = weight_175, x = input_465)[name = tensor("input_467")]; + tensor h_137 = linear(bias = model_decoder_generator_resblocks_0_adain2_1_fc_bias, weight = model_decoder_generator_resblocks_0_adain2_1_fc_weight, x = input_331)[name = tensor("linear_110")]; + tensor var_10553 = const()[name = tensor("op_10553"), val = tensor([1, 512, 1])]; + tensor h_139 = reshape(shape = var_10553, x = h_137)[name = tensor("h_139")]; + tensor var_10555_split_sizes_0 = const()[name = tensor("op_10555_split_sizes_0"), val = tensor([256, 256])]; + tensor var_10555_axis_0 = const()[name = tensor("op_10555_axis_0"), val = tensor(1)]; + tensor var_10555_0, tensor var_10555_1 = split(axis = var_10555_axis_0, split_sizes = var_10555_split_sizes_0, x = h_139)[name = tensor("op_10555")]; + tensor var_10557_promoted = const()[name = tensor("op_10557_promoted"), val = tensor(0x1p+0)]; + tensor var_10558 = add(x = var_10555_0, y = var_10557_promoted)[name = tensor("op_10558")]; + tensor var_10559 = instance_norm(beta = model_decoder_generator_resblocks_2_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_2_adain2_2_norm_weight, x = input_467)[name = tensor("op_10559")]; + tensor var_10560 = mul(x = var_10558, y = var_10559)[name = tensor("op_10560")]; + tensor xt_27 = add(x = var_10560, y = var_10555_1)[name = tensor("xt_27")]; + tensor var_10562 = const()[name = tensor("op_10562"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(279668352)))]; + tensor var_10565 = mul(x = model_decoder_generator_resblocks_0_alpha2_1, y = xt_27)[name = tensor("op_10565")]; + tensor var_10566 = sin(x = var_10565)[name = tensor("op_10566")]; + tensor var_7346_promoted_11 = const()[name = tensor("op_7346_promoted_11"), val = tensor(0x1p+1)]; + tensor var_10567 = pow(x = var_10566, y = var_7346_promoted_11)[name = tensor("op_10567")]; + tensor var_10568 = mul(x = var_10562, y = var_10567)[name = tensor("op_10568")]; + tensor input_469 = add(x = xt_27, y = var_10568)[name = tensor("input_469")]; + tensor weight_179 = const()[name = tensor("weight_179"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(279669440)))]; + 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 = model_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_469)[name = tensor("xt_29")]; + tensor input_471 = add(x = xt_29, y = input_463)[name = tensor("input_471")]; + tensor h_141 = linear(bias = model_decoder_generator_resblocks_0_adain1_2_fc_bias, weight = model_decoder_generator_resblocks_0_adain1_2_fc_weight, x = input_331)[name = tensor("linear_111")]; + tensor var_10580 = const()[name = tensor("op_10580"), val = tensor([1, 512, 1])]; + tensor h_143 = reshape(shape = var_10580, x = h_141)[name = tensor("h_143")]; + tensor var_10582_split_sizes_0 = const()[name = tensor("op_10582_split_sizes_0"), val = tensor([256, 256])]; + tensor var_10582_axis_0 = const()[name = tensor("op_10582_axis_0"), val = tensor(1)]; + tensor var_10582_0, tensor var_10582_1 = split(axis = var_10582_axis_0, split_sizes = var_10582_split_sizes_0, x = h_143)[name = tensor("op_10582")]; + tensor var_10584_promoted = const()[name = tensor("op_10584_promoted"), val = tensor(0x1p+0)]; + tensor var_10585 = add(x = var_10582_0, y = var_10584_promoted)[name = tensor("op_10585")]; + tensor var_10586 = instance_norm(beta = model_decoder_generator_resblocks_2_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_2_adain2_2_norm_weight, x = input_471)[name = tensor("op_10586")]; + tensor var_10587 = mul(x = var_10585, y = var_10586)[name = tensor("op_10587")]; + tensor xt_31 = add(x = var_10587, y = var_10582_1)[name = tensor("xt_31")]; + tensor var_10589 = const()[name = tensor("op_10589"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(280455936)))]; + tensor var_10592 = mul(x = model_decoder_generator_resblocks_0_alpha1_2, y = xt_31)[name = tensor("op_10592")]; + tensor var_10593 = sin(x = var_10592)[name = tensor("op_10593")]; + tensor var_7346_promoted_12 = const()[name = tensor("op_7346_promoted_12"), val = tensor(0x1p+1)]; + tensor var_10594 = pow(x = var_10593, y = var_7346_promoted_12)[name = tensor("op_10594")]; + tensor var_10595 = mul(x = var_10589, y = var_10594)[name = tensor("op_10595")]; + tensor input_473 = add(x = xt_31, y = var_10595)[name = tensor("input_473")]; + tensor weight_183 = const()[name = tensor("weight_183"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(280457024)))]; + tensor input_475_pad_type_0 = const()[name = tensor("input_475_pad_type_0"), val = tensor("custom")]; + tensor input_475_pad_0 = const()[name = tensor("input_475_pad_0"), val = tensor([5, 5])]; + tensor input_475_dilations_0 = const()[name = tensor("input_475_dilations_0"), val = tensor([5])]; + tensor input_475_strides_0 = const()[name = tensor("input_475_strides_0"), val = tensor([1])]; + tensor input_475_groups_0 = const()[name = tensor("input_475_groups_0"), val = tensor(1)]; + tensor input_475 = conv(bias = model_decoder_generator_resblocks_0_convs1_2_bias, dilations = input_475_dilations_0, groups = input_475_groups_0, pad = input_475_pad_0, pad_type = input_475_pad_type_0, strides = input_475_strides_0, weight = weight_183, x = input_473)[name = tensor("input_475")]; + tensor h_145 = linear(bias = model_decoder_generator_resblocks_0_adain2_2_fc_bias, weight = model_decoder_generator_resblocks_0_adain2_2_fc_weight, x = input_331)[name = tensor("linear_112")]; + tensor var_10606 = const()[name = tensor("op_10606"), val = tensor([1, 512, 1])]; + tensor h_147 = reshape(shape = var_10606, x = h_145)[name = tensor("h_147")]; + tensor var_10608_split_sizes_0 = const()[name = tensor("op_10608_split_sizes_0"), val = tensor([256, 256])]; + tensor var_10608_axis_0 = const()[name = tensor("op_10608_axis_0"), val = tensor(1)]; + tensor var_10608_0, tensor var_10608_1 = split(axis = var_10608_axis_0, split_sizes = var_10608_split_sizes_0, x = h_147)[name = tensor("op_10608")]; + tensor var_10610_promoted = const()[name = tensor("op_10610_promoted"), val = tensor(0x1p+0)]; + tensor var_10611 = add(x = var_10608_0, y = var_10610_promoted)[name = tensor("op_10611")]; + tensor var_10612 = instance_norm(beta = model_decoder_generator_resblocks_2_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_2_adain2_2_norm_weight, x = input_475)[name = tensor("op_10612")]; + tensor var_10613 = mul(x = var_10611, y = var_10612)[name = tensor("op_10613")]; + tensor xt_33 = add(x = var_10613, y = var_10608_1)[name = tensor("xt_33")]; + tensor var_10615 = const()[name = tensor("op_10615"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(281243520)))]; + tensor var_10618 = mul(x = model_decoder_generator_resblocks_0_alpha2_2, y = xt_33)[name = tensor("op_10618")]; + tensor var_10619 = sin(x = var_10618)[name = tensor("op_10619")]; + tensor var_7346_promoted_13 = const()[name = tensor("op_7346_promoted_13"), val = tensor(0x1p+1)]; + tensor var_10620 = pow(x = var_10619, y = var_7346_promoted_13)[name = tensor("op_10620")]; + tensor var_10621 = mul(x = var_10615, y = var_10620)[name = tensor("op_10621")]; + tensor input_477 = add(x = xt_33, y = var_10621)[name = tensor("input_477")]; + tensor weight_187 = const()[name = tensor("weight_187"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(281244608)))]; + 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 = model_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_477)[name = tensor("xt_35")]; + tensor var_10629 = add(x = xt_35, y = input_471)[name = tensor("op_10629")]; + tensor h_149 = linear(bias = model_decoder_generator_resblocks_1_adain1_0_fc_bias, weight = model_decoder_generator_resblocks_1_adain1_0_fc_weight, x = input_331)[name = tensor("linear_113")]; + tensor var_10633 = const()[name = tensor("op_10633"), val = tensor([1, 512, 1])]; + tensor h_151 = reshape(shape = var_10633, x = h_149)[name = tensor("h_151")]; + tensor var_10635_split_sizes_0 = const()[name = tensor("op_10635_split_sizes_0"), val = tensor([256, 256])]; + tensor var_10635_axis_0 = const()[name = tensor("op_10635_axis_0"), val = tensor(1)]; + tensor var_10635_0, tensor var_10635_1 = split(axis = var_10635_axis_0, split_sizes = var_10635_split_sizes_0, x = h_151)[name = tensor("op_10635")]; + tensor var_10637_promoted = const()[name = tensor("op_10637_promoted"), val = tensor(0x1p+0)]; + tensor var_10638 = add(x = var_10635_0, y = var_10637_promoted)[name = tensor("op_10638")]; + tensor var_10640 = mul(x = var_10638, y = var_10480)[name = tensor("op_10640")]; + tensor xt_37 = add(x = var_10640, y = var_10635_1)[name = tensor("xt_37")]; + tensor var_10642 = const()[name = tensor("op_10642"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(282031104)))]; + tensor var_10645 = mul(x = model_decoder_generator_resblocks_1_alpha1_0, y = xt_37)[name = tensor("op_10645")]; + tensor var_10646 = sin(x = var_10645)[name = tensor("op_10646")]; + tensor var_7346_promoted_14 = const()[name = tensor("op_7346_promoted_14"), val = tensor(0x1p+1)]; + tensor var_10647 = pow(x = var_10646, y = var_7346_promoted_14)[name = tensor("op_10647")]; + tensor var_10648 = mul(x = var_10642, y = var_10647)[name = tensor("op_10648")]; + tensor input_479 = add(x = xt_37, y = var_10648)[name = tensor("input_479")]; + tensor weight_191 = const()[name = tensor("weight_191"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(282032192)))]; + tensor input_481_pad_type_0 = const()[name = tensor("input_481_pad_type_0"), val = tensor("custom")]; + tensor input_481_pad_0 = const()[name = tensor("input_481_pad_0"), val = tensor([3, 3])]; + tensor input_481_strides_0 = const()[name = tensor("input_481_strides_0"), val = tensor([1])]; + tensor input_481_dilations_0 = const()[name = tensor("input_481_dilations_0"), val = tensor([1])]; + tensor input_481_groups_0 = const()[name = tensor("input_481_groups_0"), val = tensor(1)]; + tensor input_481 = conv(bias = model_decoder_generator_resblocks_1_convs1_0_bias, dilations = input_481_dilations_0, groups = input_481_groups_0, pad = input_481_pad_0, pad_type = input_481_pad_type_0, strides = input_481_strides_0, weight = weight_191, x = input_479)[name = tensor("input_481")]; + tensor h_153 = linear(bias = model_decoder_generator_resblocks_1_adain2_0_fc_bias, weight = model_decoder_generator_resblocks_1_adain2_0_fc_weight, x = input_331)[name = tensor("linear_114")]; + tensor var_10659 = const()[name = tensor("op_10659"), val = tensor([1, 512, 1])]; + tensor h_155 = reshape(shape = var_10659, x = h_153)[name = tensor("h_155")]; + tensor var_10661_split_sizes_0 = const()[name = tensor("op_10661_split_sizes_0"), val = tensor([256, 256])]; + tensor var_10661_axis_0 = const()[name = tensor("op_10661_axis_0"), val = tensor(1)]; + tensor var_10661_0, tensor var_10661_1 = split(axis = var_10661_axis_0, split_sizes = var_10661_split_sizes_0, x = h_155)[name = tensor("op_10661")]; + tensor var_10663_promoted = const()[name = tensor("op_10663_promoted"), val = tensor(0x1p+0)]; + tensor var_10664 = add(x = var_10661_0, y = var_10663_promoted)[name = tensor("op_10664")]; + tensor var_10665 = instance_norm(beta = model_decoder_generator_resblocks_2_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_2_adain2_2_norm_weight, x = input_481)[name = tensor("op_10665")]; + tensor var_10666 = mul(x = var_10664, y = var_10665)[name = tensor("op_10666")]; + tensor xt_39 = add(x = var_10666, y = var_10661_1)[name = tensor("xt_39")]; + tensor var_10668 = const()[name = tensor("op_10668"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283867264)))]; + tensor var_10671 = mul(x = model_decoder_generator_resblocks_1_alpha2_0, y = xt_39)[name = tensor("op_10671")]; + tensor var_10672 = sin(x = var_10671)[name = tensor("op_10672")]; + tensor var_7346_promoted_15 = const()[name = tensor("op_7346_promoted_15"), val = tensor(0x1p+1)]; + tensor var_10673 = pow(x = var_10672, y = var_7346_promoted_15)[name = tensor("op_10673")]; + tensor var_10674 = mul(x = var_10668, y = var_10673)[name = tensor("op_10674")]; + tensor input_483 = add(x = xt_39, y = var_10674)[name = tensor("input_483")]; + tensor weight_195 = const()[name = tensor("weight_195"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283868352)))]; + 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 = model_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_483)[name = tensor("xt_41")]; + tensor input_485 = add(x = xt_41, y = input_455)[name = tensor("input_485")]; + tensor h_157 = linear(bias = model_decoder_generator_resblocks_1_adain1_1_fc_bias, weight = model_decoder_generator_resblocks_1_adain1_1_fc_weight, x = input_331)[name = tensor("linear_115")]; + tensor var_10686 = const()[name = tensor("op_10686"), val = tensor([1, 512, 1])]; + tensor h_159 = reshape(shape = var_10686, x = h_157)[name = tensor("h_159")]; + tensor var_10688_split_sizes_0 = const()[name = tensor("op_10688_split_sizes_0"), val = tensor([256, 256])]; + tensor var_10688_axis_0 = const()[name = tensor("op_10688_axis_0"), val = tensor(1)]; + tensor var_10688_0, tensor var_10688_1 = split(axis = var_10688_axis_0, split_sizes = var_10688_split_sizes_0, x = h_159)[name = tensor("op_10688")]; + tensor var_10690_promoted = const()[name = tensor("op_10690_promoted"), val = tensor(0x1p+0)]; + tensor var_10691 = add(x = var_10688_0, y = var_10690_promoted)[name = tensor("op_10691")]; + tensor var_10692 = instance_norm(beta = model_decoder_generator_resblocks_2_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_2_adain2_2_norm_weight, x = input_485)[name = tensor("op_10692")]; + tensor var_10693 = mul(x = var_10691, y = var_10692)[name = tensor("op_10693")]; + tensor xt_43 = add(x = var_10693, y = var_10688_1)[name = tensor("xt_43")]; + tensor var_10695 = const()[name = tensor("op_10695"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(285703424)))]; + tensor var_10698 = mul(x = model_decoder_generator_resblocks_1_alpha1_1, y = xt_43)[name = tensor("op_10698")]; + tensor var_10699 = sin(x = var_10698)[name = tensor("op_10699")]; + tensor var_7346_promoted_16 = const()[name = tensor("op_7346_promoted_16"), val = tensor(0x1p+1)]; + tensor var_10700 = pow(x = var_10699, y = var_7346_promoted_16)[name = tensor("op_10700")]; + tensor var_10701 = mul(x = var_10695, y = var_10700)[name = tensor("op_10701")]; + tensor input_487 = add(x = xt_43, y = var_10701)[name = tensor("input_487")]; + tensor weight_199 = const()[name = tensor("weight_199"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(285704512)))]; + tensor input_489_pad_type_0 = const()[name = tensor("input_489_pad_type_0"), val = tensor("custom")]; + tensor input_489_pad_0 = const()[name = tensor("input_489_pad_0"), val = tensor([9, 9])]; + tensor input_489_dilations_0 = const()[name = tensor("input_489_dilations_0"), val = tensor([3])]; + tensor input_489_strides_0 = const()[name = tensor("input_489_strides_0"), val = tensor([1])]; + tensor input_489_groups_0 = const()[name = tensor("input_489_groups_0"), val = tensor(1)]; + tensor input_489 = conv(bias = model_decoder_generator_resblocks_1_convs1_1_bias, dilations = input_489_dilations_0, groups = input_489_groups_0, pad = input_489_pad_0, pad_type = input_489_pad_type_0, strides = input_489_strides_0, weight = weight_199, x = input_487)[name = tensor("input_489")]; + tensor h_161 = linear(bias = model_decoder_generator_resblocks_1_adain2_1_fc_bias, weight = model_decoder_generator_resblocks_1_adain2_1_fc_weight, x = input_331)[name = tensor("linear_116")]; + tensor var_10712 = const()[name = tensor("op_10712"), val = tensor([1, 512, 1])]; + tensor h_163 = reshape(shape = var_10712, x = h_161)[name = tensor("h_163")]; + tensor var_10714_split_sizes_0 = const()[name = tensor("op_10714_split_sizes_0"), val = tensor([256, 256])]; + tensor var_10714_axis_0 = const()[name = tensor("op_10714_axis_0"), val = tensor(1)]; + tensor var_10714_0, tensor var_10714_1 = split(axis = var_10714_axis_0, split_sizes = var_10714_split_sizes_0, x = h_163)[name = tensor("op_10714")]; + tensor var_10716_promoted = const()[name = tensor("op_10716_promoted"), val = tensor(0x1p+0)]; + tensor var_10717 = add(x = var_10714_0, y = var_10716_promoted)[name = tensor("op_10717")]; + tensor var_10718 = instance_norm(beta = model_decoder_generator_resblocks_2_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_2_adain2_2_norm_weight, x = input_489)[name = tensor("op_10718")]; + tensor var_10719 = mul(x = var_10717, y = var_10718)[name = tensor("op_10719")]; + tensor xt_45 = add(x = var_10719, y = var_10714_1)[name = tensor("xt_45")]; + tensor var_10721 = const()[name = tensor("op_10721"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(287539584)))]; + tensor var_10724 = mul(x = model_decoder_generator_resblocks_1_alpha2_1, y = xt_45)[name = tensor("op_10724")]; + tensor var_10725 = sin(x = var_10724)[name = tensor("op_10725")]; + tensor var_7346_promoted_17 = const()[name = tensor("op_7346_promoted_17"), val = tensor(0x1p+1)]; + tensor var_10726 = pow(x = var_10725, y = var_7346_promoted_17)[name = tensor("op_10726")]; + tensor var_10727 = mul(x = var_10721, y = var_10726)[name = tensor("op_10727")]; + tensor input_491 = add(x = xt_45, y = var_10727)[name = tensor("input_491")]; + tensor weight_203 = const()[name = tensor("weight_203"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(287540672)))]; + 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 = model_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_491)[name = tensor("xt_47")]; + tensor input_493 = add(x = xt_47, y = input_485)[name = tensor("input_493")]; + tensor h_165 = linear(bias = model_decoder_generator_resblocks_1_adain1_2_fc_bias, weight = model_decoder_generator_resblocks_1_adain1_2_fc_weight, x = input_331)[name = tensor("linear_117")]; + tensor var_10739 = const()[name = tensor("op_10739"), val = tensor([1, 512, 1])]; + tensor h_167 = reshape(shape = var_10739, x = h_165)[name = tensor("h_167")]; + tensor var_10741_split_sizes_0 = const()[name = tensor("op_10741_split_sizes_0"), val = tensor([256, 256])]; + tensor var_10741_axis_0 = const()[name = tensor("op_10741_axis_0"), val = tensor(1)]; + tensor var_10741_0, tensor var_10741_1 = split(axis = var_10741_axis_0, split_sizes = var_10741_split_sizes_0, x = h_167)[name = tensor("op_10741")]; + tensor var_10743_promoted = const()[name = tensor("op_10743_promoted"), val = tensor(0x1p+0)]; + tensor var_10744 = add(x = var_10741_0, y = var_10743_promoted)[name = tensor("op_10744")]; + tensor var_10745 = instance_norm(beta = model_decoder_generator_resblocks_2_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_2_adain2_2_norm_weight, x = input_493)[name = tensor("op_10745")]; + tensor var_10746 = mul(x = var_10744, y = var_10745)[name = tensor("op_10746")]; + tensor xt_49 = add(x = var_10746, y = var_10741_1)[name = tensor("xt_49")]; + tensor var_10748 = const()[name = tensor("op_10748"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(289375744)))]; + tensor var_10751 = mul(x = model_decoder_generator_resblocks_1_alpha1_2, y = xt_49)[name = tensor("op_10751")]; + tensor var_10752 = sin(x = var_10751)[name = tensor("op_10752")]; + tensor var_7346_promoted_18 = const()[name = tensor("op_7346_promoted_18"), val = tensor(0x1p+1)]; + tensor var_10753 = pow(x = var_10752, y = var_7346_promoted_18)[name = tensor("op_10753")]; + tensor var_10754 = mul(x = var_10748, y = var_10753)[name = tensor("op_10754")]; + tensor input_495 = add(x = xt_49, y = var_10754)[name = tensor("input_495")]; + tensor weight_207 = const()[name = tensor("weight_207"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(289376832)))]; + tensor input_497_pad_type_0 = const()[name = tensor("input_497_pad_type_0"), val = tensor("custom")]; + tensor input_497_pad_0 = const()[name = tensor("input_497_pad_0"), val = tensor([15, 15])]; + tensor input_497_dilations_0 = const()[name = tensor("input_497_dilations_0"), val = tensor([5])]; + tensor input_497_strides_0 = const()[name = tensor("input_497_strides_0"), val = tensor([1])]; + tensor input_497_groups_0 = const()[name = tensor("input_497_groups_0"), val = tensor(1)]; + tensor input_497 = conv(bias = model_decoder_generator_resblocks_1_convs1_2_bias, dilations = input_497_dilations_0, groups = input_497_groups_0, pad = input_497_pad_0, pad_type = input_497_pad_type_0, strides = input_497_strides_0, weight = weight_207, x = input_495)[name = tensor("input_497")]; + tensor h_169 = linear(bias = model_decoder_generator_resblocks_1_adain2_2_fc_bias, weight = model_decoder_generator_resblocks_1_adain2_2_fc_weight, x = input_331)[name = tensor("linear_118")]; + tensor var_10765 = const()[name = tensor("op_10765"), val = tensor([1, 512, 1])]; + tensor h_171 = reshape(shape = var_10765, x = h_169)[name = tensor("h_171")]; + tensor var_10767_split_sizes_0 = const()[name = tensor("op_10767_split_sizes_0"), val = tensor([256, 256])]; + tensor var_10767_axis_0 = const()[name = tensor("op_10767_axis_0"), val = tensor(1)]; + tensor var_10767_0, tensor var_10767_1 = split(axis = var_10767_axis_0, split_sizes = var_10767_split_sizes_0, x = h_171)[name = tensor("op_10767")]; + tensor var_10769_promoted = const()[name = tensor("op_10769_promoted"), val = tensor(0x1p+0)]; + tensor var_10770 = add(x = var_10767_0, y = var_10769_promoted)[name = tensor("op_10770")]; + tensor var_10771 = instance_norm(beta = model_decoder_generator_resblocks_2_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_2_adain2_2_norm_weight, x = input_497)[name = tensor("op_10771")]; + tensor var_10772 = mul(x = var_10770, y = var_10771)[name = tensor("op_10772")]; + tensor xt_51 = add(x = var_10772, y = var_10767_1)[name = tensor("xt_51")]; + tensor var_10774 = const()[name = tensor("op_10774"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(291211904)))]; + tensor var_10777 = mul(x = model_decoder_generator_resblocks_1_alpha2_2, y = xt_51)[name = tensor("op_10777")]; + tensor var_10778 = sin(x = var_10777)[name = tensor("op_10778")]; + tensor var_7346_promoted_19 = const()[name = tensor("op_7346_promoted_19"), val = tensor(0x1p+1)]; + tensor var_10779 = pow(x = var_10778, y = var_7346_promoted_19)[name = tensor("op_10779")]; + tensor var_10780 = mul(x = var_10774, y = var_10779)[name = tensor("op_10780")]; + tensor input_499 = add(x = xt_51, y = var_10780)[name = tensor("input_499")]; + tensor weight_211 = const()[name = tensor("weight_211"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(291212992)))]; + 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 = model_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_499)[name = tensor("xt_53")]; + tensor var_10788 = add(x = xt_53, y = input_493)[name = tensor("op_10788")]; + tensor h_173 = linear(bias = model_decoder_generator_resblocks_2_adain1_0_fc_bias, weight = model_decoder_generator_resblocks_2_adain1_0_fc_weight, x = input_331)[name = tensor("linear_119")]; + tensor var_10792 = const()[name = tensor("op_10792"), val = tensor([1, 512, 1])]; + tensor h_175 = reshape(shape = var_10792, x = h_173)[name = tensor("h_175")]; + tensor var_10794_split_sizes_0 = const()[name = tensor("op_10794_split_sizes_0"), val = tensor([256, 256])]; + tensor var_10794_axis_0 = const()[name = tensor("op_10794_axis_0"), val = tensor(1)]; + tensor var_10794_0, tensor var_10794_1 = split(axis = var_10794_axis_0, split_sizes = var_10794_split_sizes_0, x = h_175)[name = tensor("op_10794")]; + tensor var_10796_promoted = const()[name = tensor("op_10796_promoted"), val = tensor(0x1p+0)]; + tensor var_10797 = add(x = var_10794_0, y = var_10796_promoted)[name = tensor("op_10797")]; + tensor var_10799 = mul(x = var_10797, y = var_10480)[name = tensor("op_10799")]; + tensor xt_55 = add(x = var_10799, y = var_10794_1)[name = tensor("xt_55")]; + tensor var_10801 = const()[name = tensor("op_10801"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(293048064)))]; + tensor var_10804 = mul(x = model_decoder_generator_resblocks_2_alpha1_0, y = xt_55)[name = tensor("op_10804")]; + tensor var_10805 = sin(x = var_10804)[name = tensor("op_10805")]; + tensor var_7346_promoted_20 = const()[name = tensor("op_7346_promoted_20"), val = tensor(0x1p+1)]; + tensor var_10806 = pow(x = var_10805, y = var_7346_promoted_20)[name = tensor("op_10806")]; + tensor var_10807 = mul(x = var_10801, y = var_10806)[name = tensor("op_10807")]; + tensor input_501 = add(x = xt_55, y = var_10807)[name = tensor("input_501")]; + tensor weight_215 = const()[name = tensor("weight_215"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(293049152)))]; + 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([5, 5])]; + 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 = model_decoder_generator_resblocks_2_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_215, x = input_501)[name = tensor("input_503")]; + tensor h_177 = linear(bias = model_decoder_generator_resblocks_2_adain2_0_fc_bias, weight = model_decoder_generator_resblocks_2_adain2_0_fc_weight, x = input_331)[name = tensor("linear_120")]; + tensor var_10818 = const()[name = tensor("op_10818"), val = tensor([1, 512, 1])]; + tensor h_179 = reshape(shape = var_10818, x = h_177)[name = tensor("h_179")]; + tensor var_10820_split_sizes_0 = const()[name = tensor("op_10820_split_sizes_0"), val = tensor([256, 256])]; + tensor var_10820_axis_0 = const()[name = tensor("op_10820_axis_0"), val = tensor(1)]; + tensor var_10820_0, tensor var_10820_1 = split(axis = var_10820_axis_0, split_sizes = var_10820_split_sizes_0, x = h_179)[name = tensor("op_10820")]; + tensor var_10822_promoted = const()[name = tensor("op_10822_promoted"), val = tensor(0x1p+0)]; + tensor var_10823 = add(x = var_10820_0, y = var_10822_promoted)[name = tensor("op_10823")]; + tensor var_10824 = instance_norm(beta = model_decoder_generator_resblocks_2_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_2_adain2_2_norm_weight, x = input_503)[name = tensor("op_10824")]; + tensor var_10825 = mul(x = var_10823, y = var_10824)[name = tensor("op_10825")]; + tensor xt_57 = add(x = var_10825, y = var_10820_1)[name = tensor("xt_57")]; + tensor var_10827 = const()[name = tensor("op_10827"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(295932800)))]; + tensor var_10830 = mul(x = model_decoder_generator_resblocks_2_alpha2_0, y = xt_57)[name = tensor("op_10830")]; + tensor var_10831 = sin(x = var_10830)[name = tensor("op_10831")]; + tensor var_7346_promoted_21 = const()[name = tensor("op_7346_promoted_21"), val = tensor(0x1p+1)]; + tensor var_10832 = pow(x = var_10831, y = var_7346_promoted_21)[name = tensor("op_10832")]; + tensor var_10833 = mul(x = var_10827, y = var_10832)[name = tensor("op_10833")]; + tensor input_505 = add(x = xt_57, y = var_10833)[name = tensor("input_505")]; + tensor weight_219 = const()[name = tensor("weight_219"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(295933888)))]; + 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 = model_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_505)[name = tensor("xt_59")]; + tensor input_507 = add(x = xt_59, y = input_455)[name = tensor("input_507")]; + tensor h_181 = linear(bias = model_decoder_generator_resblocks_2_adain1_1_fc_bias, weight = model_decoder_generator_resblocks_2_adain1_1_fc_weight, x = input_331)[name = tensor("linear_121")]; + tensor var_10845 = const()[name = tensor("op_10845"), val = tensor([1, 512, 1])]; + tensor h_183 = reshape(shape = var_10845, x = h_181)[name = tensor("h_183")]; + tensor var_10847_split_sizes_0 = const()[name = tensor("op_10847_split_sizes_0"), val = tensor([256, 256])]; + tensor var_10847_axis_0 = const()[name = tensor("op_10847_axis_0"), val = tensor(1)]; + tensor var_10847_0, tensor var_10847_1 = split(axis = var_10847_axis_0, split_sizes = var_10847_split_sizes_0, x = h_183)[name = tensor("op_10847")]; + tensor var_10849_promoted = const()[name = tensor("op_10849_promoted"), val = tensor(0x1p+0)]; + tensor var_10850 = add(x = var_10847_0, y = var_10849_promoted)[name = tensor("op_10850")]; + tensor var_10851 = instance_norm(beta = model_decoder_generator_resblocks_2_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_2_adain2_2_norm_weight, x = input_507)[name = tensor("op_10851")]; + tensor var_10852 = mul(x = var_10850, y = var_10851)[name = tensor("op_10852")]; + tensor xt_61 = add(x = var_10852, y = var_10847_1)[name = tensor("xt_61")]; + tensor var_10854 = const()[name = tensor("op_10854"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(298817536)))]; + tensor var_10857 = mul(x = model_decoder_generator_resblocks_2_alpha1_1, y = xt_61)[name = tensor("op_10857")]; + tensor var_10858 = sin(x = var_10857)[name = tensor("op_10858")]; + tensor var_7346_promoted_22 = const()[name = tensor("op_7346_promoted_22"), val = tensor(0x1p+1)]; + tensor var_10859 = pow(x = var_10858, y = var_7346_promoted_22)[name = tensor("op_10859")]; + tensor var_10860 = mul(x = var_10854, y = var_10859)[name = tensor("op_10860")]; + tensor input_509 = add(x = xt_61, y = var_10860)[name = tensor("input_509")]; + tensor weight_223 = const()[name = tensor("weight_223"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(298818624)))]; + 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([15, 15])]; + 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 = model_decoder_generator_resblocks_2_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_223, x = input_509)[name = tensor("input_511")]; + tensor h_185 = linear(bias = model_decoder_generator_resblocks_2_adain2_1_fc_bias, weight = model_decoder_generator_resblocks_2_adain2_1_fc_weight, x = input_331)[name = tensor("linear_122")]; + tensor var_10871 = const()[name = tensor("op_10871"), val = tensor([1, 512, 1])]; + tensor h_187 = reshape(shape = var_10871, x = h_185)[name = tensor("h_187")]; + tensor var_10873_split_sizes_0 = const()[name = tensor("op_10873_split_sizes_0"), val = tensor([256, 256])]; + tensor var_10873_axis_0 = const()[name = tensor("op_10873_axis_0"), val = tensor(1)]; + tensor var_10873_0, tensor var_10873_1 = split(axis = var_10873_axis_0, split_sizes = var_10873_split_sizes_0, x = h_187)[name = tensor("op_10873")]; + tensor var_10875_promoted = const()[name = tensor("op_10875_promoted"), val = tensor(0x1p+0)]; + tensor var_10876 = add(x = var_10873_0, y = var_10875_promoted)[name = tensor("op_10876")]; + tensor var_10877 = instance_norm(beta = model_decoder_generator_resblocks_2_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_2_adain2_2_norm_weight, x = input_511)[name = tensor("op_10877")]; + tensor var_10878 = mul(x = var_10876, y = var_10877)[name = tensor("op_10878")]; + tensor xt_63 = add(x = var_10878, y = var_10873_1)[name = tensor("xt_63")]; + tensor var_10880 = const()[name = tensor("op_10880"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(301702272)))]; + tensor var_10883 = mul(x = model_decoder_generator_resblocks_2_alpha2_1, y = xt_63)[name = tensor("op_10883")]; + tensor var_10884 = sin(x = var_10883)[name = tensor("op_10884")]; + tensor var_7346_promoted_23 = const()[name = tensor("op_7346_promoted_23"), val = tensor(0x1p+1)]; + tensor var_10885 = pow(x = var_10884, y = var_7346_promoted_23)[name = tensor("op_10885")]; + tensor var_10886 = mul(x = var_10880, y = var_10885)[name = tensor("op_10886")]; + tensor input_513 = add(x = xt_63, y = var_10886)[name = tensor("input_513")]; + tensor weight_227 = const()[name = tensor("weight_227"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(301703360)))]; + 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 = model_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_513)[name = tensor("xt_65")]; + tensor input_515 = add(x = xt_65, y = input_507)[name = tensor("input_515")]; + tensor h_189 = linear(bias = model_decoder_generator_resblocks_2_adain1_2_fc_bias, weight = model_decoder_generator_resblocks_2_adain1_2_fc_weight, x = input_331)[name = tensor("linear_123")]; + tensor var_10898 = const()[name = tensor("op_10898"), val = tensor([1, 512, 1])]; + tensor h_191 = reshape(shape = var_10898, x = h_189)[name = tensor("h_191")]; + tensor var_10900_split_sizes_0 = const()[name = tensor("op_10900_split_sizes_0"), val = tensor([256, 256])]; + tensor var_10900_axis_0 = const()[name = tensor("op_10900_axis_0"), val = tensor(1)]; + tensor var_10900_0, tensor var_10900_1 = split(axis = var_10900_axis_0, split_sizes = var_10900_split_sizes_0, x = h_191)[name = tensor("op_10900")]; + tensor var_10902_promoted = const()[name = tensor("op_10902_promoted"), val = tensor(0x1p+0)]; + tensor var_10903 = add(x = var_10900_0, y = var_10902_promoted)[name = tensor("op_10903")]; + tensor var_10904 = instance_norm(beta = model_decoder_generator_resblocks_2_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_2_adain2_2_norm_weight, x = input_515)[name = tensor("op_10904")]; + tensor var_10905 = mul(x = var_10903, y = var_10904)[name = tensor("op_10905")]; + tensor xt_67 = add(x = var_10905, y = var_10900_1)[name = tensor("xt_67")]; + tensor var_10907 = const()[name = tensor("op_10907"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304587008)))]; + tensor var_10910 = mul(x = model_decoder_generator_resblocks_2_alpha1_2, y = xt_67)[name = tensor("op_10910")]; + tensor var_10911 = sin(x = var_10910)[name = tensor("op_10911")]; + tensor var_7346_promoted_24 = const()[name = tensor("op_7346_promoted_24"), val = tensor(0x1p+1)]; + tensor var_10912 = pow(x = var_10911, y = var_7346_promoted_24)[name = tensor("op_10912")]; + tensor var_10913 = mul(x = var_10907, y = var_10912)[name = tensor("op_10913")]; + tensor input_517 = add(x = xt_67, y = var_10913)[name = tensor("input_517")]; + tensor weight_231 = const()[name = tensor("weight_231"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304588096)))]; + 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([25, 25])]; + 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 = model_decoder_generator_resblocks_2_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_231, x = input_517)[name = tensor("input_519")]; + tensor h_193 = linear(bias = model_decoder_generator_resblocks_2_adain2_2_fc_bias, weight = model_decoder_generator_resblocks_2_adain2_2_fc_weight, x = input_331)[name = tensor("linear_124")]; + tensor var_10924 = const()[name = tensor("op_10924"), val = tensor([1, 512, 1])]; + tensor h_195 = reshape(shape = var_10924, x = h_193)[name = tensor("h_195")]; + tensor var_10926_split_sizes_0 = const()[name = tensor("op_10926_split_sizes_0"), val = tensor([256, 256])]; + tensor var_10926_axis_0 = const()[name = tensor("op_10926_axis_0"), val = tensor(1)]; + tensor var_10926_0, tensor var_10926_1 = split(axis = var_10926_axis_0, split_sizes = var_10926_split_sizes_0, x = h_195)[name = tensor("op_10926")]; + tensor var_10928_promoted = const()[name = tensor("op_10928_promoted"), val = tensor(0x1p+0)]; + tensor var_10929 = add(x = var_10926_0, y = var_10928_promoted)[name = tensor("op_10929")]; + tensor var_10930 = instance_norm(beta = model_decoder_generator_resblocks_2_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_2_adain2_2_norm_weight, x = input_519)[name = tensor("op_10930")]; + tensor var_10931 = mul(x = var_10929, y = var_10930)[name = tensor("op_10931")]; + tensor xt_69 = add(x = var_10931, y = var_10926_1)[name = tensor("xt_69")]; + tensor var_10933 = const()[name = tensor("op_10933"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(307471744)))]; + tensor var_10936 = mul(x = model_decoder_generator_resblocks_2_alpha2_2, y = xt_69)[name = tensor("op_10936")]; + tensor var_10937 = sin(x = var_10936)[name = tensor("op_10937")]; + tensor var_7346_promoted_25 = const()[name = tensor("op_7346_promoted_25"), val = tensor(0x1p+1)]; + tensor var_10938 = pow(x = var_10937, y = var_7346_promoted_25)[name = tensor("op_10938")]; + tensor var_10939 = mul(x = var_10933, y = var_10938)[name = tensor("op_10939")]; + tensor input_521 = add(x = xt_69, y = var_10939)[name = tensor("input_521")]; + tensor weight_235 = const()[name = tensor("weight_235"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(307472832)))]; + 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 = model_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_521)[name = tensor("xt_71")]; + tensor var_10947 = add(x = xt_71, y = input_515)[name = tensor("op_10947")]; + tensor var_10949_axis_0 = const()[name = tensor("op_10949_axis_0"), val = tensor(0)]; + tensor var_10949 = stack(axis = var_10949_axis_0, values = (var_10629, var_10788, var_10947))[name = tensor("op_10949")]; + tensor input_523_axes_0 = const()[name = tensor("input_523_axes_0"), val = tensor([0])]; + tensor input_523_keep_dims_0 = const()[name = tensor("input_523_keep_dims_0"), val = tensor(false)]; + tensor input_523 = reduce_mean(axes = input_523_axes_0, keep_dims = input_523_keep_dims_0, x = var_10949)[name = tensor("input_523")]; + tensor input_549 = leaky_relu(alpha = var_6975, x = input_523)[name = tensor("input_549")]; + tensor input_525_pad_type_0 = const()[name = tensor("input_525_pad_type_0"), val = tensor("valid")]; + tensor input_525_strides_0 = const()[name = tensor("input_525_strides_0"), val = tensor([1])]; + tensor input_525_pad_0 = const()[name = tensor("input_525_pad_0"), val = tensor([0, 0])]; + tensor input_525_dilations_0 = const()[name = tensor("input_525_dilations_0"), val = tensor([1])]; + tensor input_525_groups_0 = const()[name = tensor("input_525_groups_0"), val = tensor(1)]; + tensor input_525 = conv(bias = model_decoder_generator_noise_convs_1_bias, dilations = input_525_dilations_0, groups = input_525_groups_0, pad = input_525_pad_0, pad_type = input_525_pad_type_0, strides = input_525_strides_0, weight = model_decoder_generator_noise_convs_1_weight, x = input_427)[name = tensor("input_525")]; + tensor h_197 = linear(bias = model_decoder_generator_noise_res_1_adain1_0_fc_bias, weight = model_decoder_generator_noise_res_1_adain1_0_fc_weight, x = input_331)[name = tensor("linear_125")]; + tensor var_10961 = const()[name = tensor("op_10961"), val = tensor([1, 256, 1])]; + tensor h_199 = reshape(shape = var_10961, x = h_197)[name = tensor("h_199")]; + tensor var_10963_split_sizes_0 = const()[name = tensor("op_10963_split_sizes_0"), val = tensor([128, 128])]; + tensor var_10963_axis_0 = const()[name = tensor("op_10963_axis_0"), val = tensor(1)]; + tensor var_10963_0, tensor var_10963_1 = split(axis = var_10963_axis_0, split_sizes = var_10963_split_sizes_0, x = h_199)[name = tensor("op_10963")]; + tensor var_10965_promoted = const()[name = tensor("op_10965_promoted"), val = tensor(0x1p+0)]; + tensor var_10966 = add(x = var_10963_0, y = var_10965_promoted)[name = tensor("op_10966")]; + tensor var_10967 = instance_norm(beta = model_decoder_generator_resblocks_5_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_5_adain2_2_norm_weight, x = input_525)[name = tensor("op_10967")]; + tensor var_10968 = mul(x = var_10966, y = var_10967)[name = tensor("op_10968")]; + tensor xt_73 = add(x = var_10968, y = var_10963_1)[name = tensor("xt_73")]; + tensor var_10970 = const()[name = tensor("op_10970"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(310356480)))]; + tensor var_10973 = mul(x = model_decoder_generator_noise_res_1_alpha1_0, y = xt_73)[name = tensor("op_10973")]; + tensor var_10974 = sin(x = var_10973)[name = tensor("op_10974")]; + tensor var_7346_promoted_26 = const()[name = tensor("op_7346_promoted_26"), val = tensor(0x1p+1)]; + tensor var_10975 = pow(x = var_10974, y = var_7346_promoted_26)[name = tensor("op_10975")]; + tensor var_10976 = mul(x = var_10970, y = var_10975)[name = tensor("op_10976")]; + tensor input_527 = add(x = xt_73, y = var_10976)[name = tensor("input_527")]; + tensor weight_241 = const()[name = tensor("weight_241"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(310357056)))]; + 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([5, 5])]; + 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 = model_decoder_generator_noise_res_1_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_241, x = input_527)[name = tensor("input_529")]; + tensor h_201 = linear(bias = model_decoder_generator_noise_res_1_adain2_0_fc_bias, weight = model_decoder_generator_noise_res_1_adain2_0_fc_weight, x = input_331)[name = tensor("linear_126")]; + tensor var_10987 = const()[name = tensor("op_10987"), val = tensor([1, 256, 1])]; + tensor h_203 = reshape(shape = var_10987, x = h_201)[name = tensor("h_203")]; + tensor var_10989_split_sizes_0 = const()[name = tensor("op_10989_split_sizes_0"), val = tensor([128, 128])]; + tensor var_10989_axis_0 = const()[name = tensor("op_10989_axis_0"), val = tensor(1)]; + tensor var_10989_0, tensor var_10989_1 = split(axis = var_10989_axis_0, split_sizes = var_10989_split_sizes_0, x = h_203)[name = tensor("op_10989")]; + tensor var_10991_promoted = const()[name = tensor("op_10991_promoted"), val = tensor(0x1p+0)]; + tensor var_10992 = add(x = var_10989_0, y = var_10991_promoted)[name = tensor("op_10992")]; + tensor var_10993 = instance_norm(beta = model_decoder_generator_resblocks_5_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_5_adain2_2_norm_weight, x = input_529)[name = tensor("op_10993")]; + tensor var_10994 = mul(x = var_10992, y = var_10993)[name = tensor("op_10994")]; + tensor xt_75 = add(x = var_10994, y = var_10989_1)[name = tensor("xt_75")]; + tensor var_10996 = const()[name = tensor("op_10996"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(311078016)))]; + tensor var_10999 = mul(x = model_decoder_generator_noise_res_1_alpha2_0, y = xt_75)[name = tensor("op_10999")]; + tensor var_11000 = sin(x = var_10999)[name = tensor("op_11000")]; + tensor var_7346_promoted_27 = const()[name = tensor("op_7346_promoted_27"), val = tensor(0x1p+1)]; + tensor var_11001 = pow(x = var_11000, y = var_7346_promoted_27)[name = tensor("op_11001")]; + tensor var_11002 = mul(x = var_10996, y = var_11001)[name = tensor("op_11002")]; + tensor input_531 = add(x = xt_75, y = var_11002)[name = tensor("input_531")]; + tensor weight_245 = const()[name = tensor("weight_245"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(311078592)))]; + 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 = model_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_531)[name = tensor("xt_77")]; + tensor input_533 = add(x = xt_77, y = input_525)[name = tensor("input_533")]; + tensor h_205 = linear(bias = model_decoder_generator_noise_res_1_adain1_1_fc_bias, weight = model_decoder_generator_noise_res_1_adain1_1_fc_weight, x = input_331)[name = tensor("linear_127")]; + tensor var_11014 = const()[name = tensor("op_11014"), val = tensor([1, 256, 1])]; + tensor h_207 = reshape(shape = var_11014, x = h_205)[name = tensor("h_207")]; + tensor var_11016_split_sizes_0 = const()[name = tensor("op_11016_split_sizes_0"), val = tensor([128, 128])]; + tensor var_11016_axis_0 = const()[name = tensor("op_11016_axis_0"), val = tensor(1)]; + tensor var_11016_0, tensor var_11016_1 = split(axis = var_11016_axis_0, split_sizes = var_11016_split_sizes_0, x = h_207)[name = tensor("op_11016")]; + tensor var_11018_promoted = const()[name = tensor("op_11018_promoted"), val = tensor(0x1p+0)]; + tensor var_11019 = add(x = var_11016_0, y = var_11018_promoted)[name = tensor("op_11019")]; + tensor var_11020 = instance_norm(beta = model_decoder_generator_resblocks_5_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_5_adain2_2_norm_weight, x = input_533)[name = tensor("op_11020")]; + tensor var_11021 = mul(x = var_11019, y = var_11020)[name = tensor("op_11021")]; + tensor xt_79 = add(x = var_11021, y = var_11016_1)[name = tensor("xt_79")]; + tensor var_11023 = const()[name = tensor("op_11023"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(311799552)))]; + tensor var_11026 = mul(x = model_decoder_generator_noise_res_1_alpha1_1, y = xt_79)[name = tensor("op_11026")]; + tensor var_11027 = sin(x = var_11026)[name = tensor("op_11027")]; + tensor var_7346_promoted_28 = const()[name = tensor("op_7346_promoted_28"), val = tensor(0x1p+1)]; + tensor var_11028 = pow(x = var_11027, y = var_7346_promoted_28)[name = tensor("op_11028")]; + tensor var_11029 = mul(x = var_11023, y = var_11028)[name = tensor("op_11029")]; + tensor input_535 = add(x = xt_79, y = var_11029)[name = tensor("input_535")]; + tensor weight_249 = const()[name = tensor("weight_249"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(311800128)))]; + 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([15, 15])]; + 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 = model_decoder_generator_noise_res_1_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_249, x = input_535)[name = tensor("input_537")]; + tensor h_209 = linear(bias = model_decoder_generator_noise_res_1_adain2_1_fc_bias, weight = model_decoder_generator_noise_res_1_adain2_1_fc_weight, x = input_331)[name = tensor("linear_128")]; + tensor var_11040 = const()[name = tensor("op_11040"), val = tensor([1, 256, 1])]; + tensor h_211 = reshape(shape = var_11040, x = h_209)[name = tensor("h_211")]; + tensor var_11042_split_sizes_0 = const()[name = tensor("op_11042_split_sizes_0"), val = tensor([128, 128])]; + tensor var_11042_axis_0 = const()[name = tensor("op_11042_axis_0"), val = tensor(1)]; + tensor var_11042_0, tensor var_11042_1 = split(axis = var_11042_axis_0, split_sizes = var_11042_split_sizes_0, x = h_211)[name = tensor("op_11042")]; + tensor var_11044_promoted = const()[name = tensor("op_11044_promoted"), val = tensor(0x1p+0)]; + tensor var_11045 = add(x = var_11042_0, y = var_11044_promoted)[name = tensor("op_11045")]; + tensor var_11046 = instance_norm(beta = model_decoder_generator_resblocks_5_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_5_adain2_2_norm_weight, x = input_537)[name = tensor("op_11046")]; + tensor var_11047 = mul(x = var_11045, y = var_11046)[name = tensor("op_11047")]; + tensor xt_81 = add(x = var_11047, y = var_11042_1)[name = tensor("xt_81")]; + tensor var_11049 = const()[name = tensor("op_11049"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(312521088)))]; + tensor var_11052 = mul(x = model_decoder_generator_noise_res_1_alpha2_1, y = xt_81)[name = tensor("op_11052")]; + tensor var_11053 = sin(x = var_11052)[name = tensor("op_11053")]; + tensor var_7346_promoted_29 = const()[name = tensor("op_7346_promoted_29"), val = tensor(0x1p+1)]; + tensor var_11054 = pow(x = var_11053, y = var_7346_promoted_29)[name = tensor("op_11054")]; + tensor var_11055 = mul(x = var_11049, y = var_11054)[name = tensor("op_11055")]; + tensor input_539 = add(x = xt_81, y = var_11055)[name = tensor("input_539")]; + tensor weight_253 = const()[name = tensor("weight_253"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(312521664)))]; + 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 = model_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_539)[name = tensor("xt_83")]; + tensor input_541 = add(x = xt_83, y = input_533)[name = tensor("input_541")]; + tensor h_213 = linear(bias = model_decoder_generator_noise_res_1_adain1_2_fc_bias, weight = model_decoder_generator_noise_res_1_adain1_2_fc_weight, x = input_331)[name = tensor("linear_129")]; + tensor var_11067 = const()[name = tensor("op_11067"), val = tensor([1, 256, 1])]; + tensor h_215 = reshape(shape = var_11067, x = h_213)[name = tensor("h_215")]; + tensor var_11069_split_sizes_0 = const()[name = tensor("op_11069_split_sizes_0"), val = tensor([128, 128])]; + tensor var_11069_axis_0 = const()[name = tensor("op_11069_axis_0"), val = tensor(1)]; + tensor var_11069_0, tensor var_11069_1 = split(axis = var_11069_axis_0, split_sizes = var_11069_split_sizes_0, x = h_215)[name = tensor("op_11069")]; + tensor var_11071_promoted = const()[name = tensor("op_11071_promoted"), val = tensor(0x1p+0)]; + tensor var_11072 = add(x = var_11069_0, y = var_11071_promoted)[name = tensor("op_11072")]; + tensor var_11073 = instance_norm(beta = model_decoder_generator_resblocks_5_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_5_adain2_2_norm_weight, x = input_541)[name = tensor("op_11073")]; + tensor var_11074 = mul(x = var_11072, y = var_11073)[name = tensor("op_11074")]; + tensor xt_85 = add(x = var_11074, y = var_11069_1)[name = tensor("xt_85")]; + tensor var_11076 = const()[name = tensor("op_11076"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(313242624)))]; + tensor var_11079 = mul(x = model_decoder_generator_noise_res_1_alpha1_2, y = xt_85)[name = tensor("op_11079")]; + tensor var_11080 = sin(x = var_11079)[name = tensor("op_11080")]; + tensor var_7346_promoted_30 = const()[name = tensor("op_7346_promoted_30"), val = tensor(0x1p+1)]; + tensor var_11081 = pow(x = var_11080, y = var_7346_promoted_30)[name = tensor("op_11081")]; + tensor var_11082 = mul(x = var_11076, y = var_11081)[name = tensor("op_11082")]; + tensor input_543 = add(x = xt_85, y = var_11082)[name = tensor("input_543")]; + tensor weight_257 = const()[name = tensor("weight_257"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(313243200)))]; + 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([25, 25])]; + 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 = model_decoder_generator_noise_res_1_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_257, x = input_543)[name = tensor("input_545")]; + tensor h_217 = linear(bias = model_decoder_generator_noise_res_1_adain2_2_fc_bias, weight = model_decoder_generator_noise_res_1_adain2_2_fc_weight, x = input_331)[name = tensor("linear_130")]; + tensor var_11093 = const()[name = tensor("op_11093"), val = tensor([1, 256, 1])]; + tensor h_219 = reshape(shape = var_11093, x = h_217)[name = tensor("h_219")]; + tensor var_11095_split_sizes_0 = const()[name = tensor("op_11095_split_sizes_0"), val = tensor([128, 128])]; + tensor var_11095_axis_0 = const()[name = tensor("op_11095_axis_0"), val = tensor(1)]; + tensor var_11095_0, tensor var_11095_1 = split(axis = var_11095_axis_0, split_sizes = var_11095_split_sizes_0, x = h_219)[name = tensor("op_11095")]; + tensor var_11097_promoted = const()[name = tensor("op_11097_promoted"), val = tensor(0x1p+0)]; + tensor var_11098 = add(x = var_11095_0, y = var_11097_promoted)[name = tensor("op_11098")]; + tensor var_11099 = instance_norm(beta = model_decoder_generator_resblocks_5_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_5_adain2_2_norm_weight, x = input_545)[name = tensor("op_11099")]; + tensor var_11100 = mul(x = var_11098, y = var_11099)[name = tensor("op_11100")]; + tensor xt_87 = add(x = var_11100, y = var_11095_1)[name = tensor("xt_87")]; + tensor var_11102 = const()[name = tensor("op_11102"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(313964160)))]; + tensor var_11105 = mul(x = model_decoder_generator_noise_res_1_alpha2_2, y = xt_87)[name = tensor("op_11105")]; + tensor var_11106 = sin(x = var_11105)[name = tensor("op_11106")]; + tensor var_7346_promoted_31 = const()[name = tensor("op_7346_promoted_31"), val = tensor(0x1p+1)]; + tensor var_11107 = pow(x = var_11106, y = var_7346_promoted_31)[name = tensor("op_11107")]; + tensor var_11108 = mul(x = var_11102, y = var_11107)[name = tensor("op_11108")]; + tensor input_547 = add(x = xt_87, y = var_11108)[name = tensor("input_547")]; + tensor weight_261 = const()[name = tensor("weight_261"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(313964736)))]; + 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 = model_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_547)[name = tensor("xt_89")]; + tensor x_source = add(x = xt_89, y = input_541)[name = tensor("x_source")]; + tensor var_11117 = const()[name = tensor("op_11117"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(314685696)))]; + 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([6])]; + 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_has_output_shape_output_shape_0 = const()[name = tensor("input_551_has_output_shape_output_shape_0"), val = tensor([1, 128, 72000])]; + tensor input_551_has_output_shape = conv_transpose(bias = model_decoder_generator_ups_1_bias, dilations = input_551_dilations_0, groups = input_551_groups_0, output_shape = input_551_has_output_shape_output_shape_0, pad = input_551_pad_0, pad_type = input_551_pad_type_0, strides = input_551_strides_0, weight = var_11117, x = input_549)[name = tensor("input_551_has_output_shape")]; + tensor const_239 = const()[name = tensor("const_239"), val = tensor(0x0p+0)]; + tensor x_255_pad_0 = const()[name = tensor("x_255_pad_0"), val = tensor([0, 0, 0, 0, 1, 0])]; + tensor x_255_mode_0 = const()[name = tensor("x_255_mode_0"), val = tensor("reflect")]; + tensor x_255 = pad(constant_val = const_239, mode = x_255_mode_0, pad = x_255_pad_0, x = input_551_has_output_shape)[name = tensor("x_255")]; + tensor input_553 = add(x = x_255, y = x_source)[name = tensor("input_553")]; + tensor h_221 = linear(bias = model_decoder_generator_resblocks_3_adain1_0_fc_bias, weight = model_decoder_generator_resblocks_3_adain1_0_fc_weight, x = input_331)[name = tensor("linear_131")]; + tensor var_11129 = const()[name = tensor("op_11129"), val = tensor([1, 256, 1])]; + tensor h_223 = reshape(shape = var_11129, x = h_221)[name = tensor("h_223")]; + tensor var_11131_split_sizes_0 = const()[name = tensor("op_11131_split_sizes_0"), val = tensor([128, 128])]; + tensor var_11131_axis_0 = const()[name = tensor("op_11131_axis_0"), val = tensor(1)]; + tensor var_11131_0, tensor var_11131_1 = split(axis = var_11131_axis_0, split_sizes = var_11131_split_sizes_0, x = h_223)[name = tensor("op_11131")]; + tensor var_11133_promoted = const()[name = tensor("op_11133_promoted"), val = tensor(0x1p+0)]; + tensor var_11134 = add(x = var_11131_0, y = var_11133_promoted)[name = tensor("op_11134")]; + tensor var_11135 = instance_norm(beta = model_decoder_generator_resblocks_5_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_5_adain2_2_norm_weight, x = input_553)[name = tensor("op_11135")]; + tensor var_11136 = mul(x = var_11134, y = var_11135)[name = tensor("op_11136")]; + tensor xt_91 = add(x = var_11136, y = var_11131_1)[name = tensor("xt_91")]; + tensor var_11138 = const()[name = tensor("op_11138"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(316258624)))]; + tensor var_11141 = mul(x = model_decoder_generator_resblocks_3_alpha1_0, y = xt_91)[name = tensor("op_11141")]; + tensor var_11142 = sin(x = var_11141)[name = tensor("op_11142")]; + tensor var_7346_promoted_32 = const()[name = tensor("op_7346_promoted_32"), val = tensor(0x1p+1)]; + tensor var_11143 = pow(x = var_11142, y = var_7346_promoted_32)[name = tensor("op_11143")]; + tensor var_11144 = mul(x = var_11138, y = var_11143)[name = tensor("op_11144")]; + tensor input_555 = add(x = xt_91, y = var_11144)[name = tensor("input_555")]; + tensor weight_265 = const()[name = tensor("weight_265"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(316259200)))]; + tensor input_557_pad_type_0 = const()[name = tensor("input_557_pad_type_0"), val = tensor("custom")]; + tensor input_557_pad_0 = const()[name = tensor("input_557_pad_0"), val = tensor([1, 1])]; + tensor input_557_strides_0 = const()[name = tensor("input_557_strides_0"), val = tensor([1])]; + tensor input_557_dilations_0 = const()[name = tensor("input_557_dilations_0"), val = tensor([1])]; + tensor input_557_groups_0 = const()[name = tensor("input_557_groups_0"), val = tensor(1)]; + tensor input_557 = conv(bias = model_decoder_generator_resblocks_3_convs1_0_bias, dilations = input_557_dilations_0, groups = input_557_groups_0, pad = input_557_pad_0, pad_type = input_557_pad_type_0, strides = input_557_strides_0, weight = weight_265, x = input_555)[name = tensor("input_557")]; + tensor h_225 = linear(bias = model_decoder_generator_resblocks_3_adain2_0_fc_bias, weight = model_decoder_generator_resblocks_3_adain2_0_fc_weight, x = input_331)[name = tensor("linear_132")]; + tensor var_11155 = const()[name = tensor("op_11155"), val = tensor([1, 256, 1])]; + tensor h_227 = reshape(shape = var_11155, x = h_225)[name = tensor("h_227")]; + tensor var_11157_split_sizes_0 = const()[name = tensor("op_11157_split_sizes_0"), val = tensor([128, 128])]; + tensor var_11157_axis_0 = const()[name = tensor("op_11157_axis_0"), val = tensor(1)]; + tensor var_11157_0, tensor var_11157_1 = split(axis = var_11157_axis_0, split_sizes = var_11157_split_sizes_0, x = h_227)[name = tensor("op_11157")]; + tensor var_11159_promoted = const()[name = tensor("op_11159_promoted"), val = tensor(0x1p+0)]; + tensor var_11160 = add(x = var_11157_0, y = var_11159_promoted)[name = tensor("op_11160")]; + tensor var_11161 = instance_norm(beta = model_decoder_generator_resblocks_5_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_5_adain2_2_norm_weight, x = input_557)[name = tensor("op_11161")]; + tensor var_11162 = mul(x = var_11160, y = var_11161)[name = tensor("op_11162")]; + tensor xt_93 = add(x = var_11162, y = var_11157_1)[name = tensor("xt_93")]; + tensor var_11164 = const()[name = tensor("op_11164"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(316455872)))]; + tensor var_11167 = mul(x = model_decoder_generator_resblocks_3_alpha2_0, y = xt_93)[name = tensor("op_11167")]; + tensor var_11168 = sin(x = var_11167)[name = tensor("op_11168")]; + tensor var_7346_promoted_33 = const()[name = tensor("op_7346_promoted_33"), val = tensor(0x1p+1)]; + tensor var_11169 = pow(x = var_11168, y = var_7346_promoted_33)[name = tensor("op_11169")]; + tensor var_11170 = mul(x = var_11164, y = var_11169)[name = tensor("op_11170")]; + tensor input_559 = add(x = xt_93, y = var_11170)[name = tensor("input_559")]; + tensor weight_269 = const()[name = tensor("weight_269"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(316456448)))]; + 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 = model_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_559)[name = tensor("xt_95")]; + tensor input_561 = add(x = xt_95, y = input_553)[name = tensor("input_561")]; + tensor h_229 = linear(bias = model_decoder_generator_resblocks_3_adain1_1_fc_bias, weight = model_decoder_generator_resblocks_3_adain1_1_fc_weight, x = input_331)[name = tensor("linear_133")]; + tensor var_11182 = const()[name = tensor("op_11182"), val = tensor([1, 256, 1])]; + tensor h_231 = reshape(shape = var_11182, x = h_229)[name = tensor("h_231")]; + tensor var_11184_split_sizes_0 = const()[name = tensor("op_11184_split_sizes_0"), val = tensor([128, 128])]; + tensor var_11184_axis_0 = const()[name = tensor("op_11184_axis_0"), val = tensor(1)]; + tensor var_11184_0, tensor var_11184_1 = split(axis = var_11184_axis_0, split_sizes = var_11184_split_sizes_0, x = h_231)[name = tensor("op_11184")]; + tensor var_11186_promoted = const()[name = tensor("op_11186_promoted"), val = tensor(0x1p+0)]; + tensor var_11187 = add(x = var_11184_0, y = var_11186_promoted)[name = tensor("op_11187")]; + tensor var_11188 = instance_norm(beta = model_decoder_generator_resblocks_5_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_5_adain2_2_norm_weight, x = input_561)[name = tensor("op_11188")]; + tensor var_11189 = mul(x = var_11187, y = var_11188)[name = tensor("op_11189")]; + tensor xt_97 = add(x = var_11189, y = var_11184_1)[name = tensor("xt_97")]; + tensor var_11191 = const()[name = tensor("op_11191"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(316653120)))]; + tensor var_11194 = mul(x = model_decoder_generator_resblocks_3_alpha1_1, y = xt_97)[name = tensor("op_11194")]; + tensor var_11195 = sin(x = var_11194)[name = tensor("op_11195")]; + tensor var_7346_promoted_34 = const()[name = tensor("op_7346_promoted_34"), val = tensor(0x1p+1)]; + tensor var_11196 = pow(x = var_11195, y = var_7346_promoted_34)[name = tensor("op_11196")]; + tensor var_11197 = mul(x = var_11191, y = var_11196)[name = tensor("op_11197")]; + tensor input_563 = add(x = xt_97, y = var_11197)[name = tensor("input_563")]; + tensor weight_273 = const()[name = tensor("weight_273"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(316653696)))]; + tensor input_565_pad_type_0 = const()[name = tensor("input_565_pad_type_0"), val = tensor("custom")]; + tensor input_565_pad_0 = const()[name = tensor("input_565_pad_0"), val = tensor([3, 3])]; + tensor input_565_dilations_0 = const()[name = tensor("input_565_dilations_0"), val = tensor([3])]; + tensor input_565_strides_0 = const()[name = tensor("input_565_strides_0"), val = tensor([1])]; + tensor input_565_groups_0 = const()[name = tensor("input_565_groups_0"), val = tensor(1)]; + tensor input_565 = conv(bias = model_decoder_generator_resblocks_3_convs1_1_bias, dilations = input_565_dilations_0, groups = input_565_groups_0, pad = input_565_pad_0, pad_type = input_565_pad_type_0, strides = input_565_strides_0, weight = weight_273, x = input_563)[name = tensor("input_565")]; + tensor h_233 = linear(bias = model_decoder_generator_resblocks_3_adain2_1_fc_bias, weight = model_decoder_generator_resblocks_3_adain2_1_fc_weight, x = input_331)[name = tensor("linear_134")]; + tensor var_11208 = const()[name = tensor("op_11208"), val = tensor([1, 256, 1])]; + tensor h_235 = reshape(shape = var_11208, x = h_233)[name = tensor("h_235")]; + tensor var_11210_split_sizes_0 = const()[name = tensor("op_11210_split_sizes_0"), val = tensor([128, 128])]; + tensor var_11210_axis_0 = const()[name = tensor("op_11210_axis_0"), val = tensor(1)]; + tensor var_11210_0, tensor var_11210_1 = split(axis = var_11210_axis_0, split_sizes = var_11210_split_sizes_0, x = h_235)[name = tensor("op_11210")]; + tensor var_11212_promoted = const()[name = tensor("op_11212_promoted"), val = tensor(0x1p+0)]; + tensor var_11213 = add(x = var_11210_0, y = var_11212_promoted)[name = tensor("op_11213")]; + tensor var_11214 = instance_norm(beta = model_decoder_generator_resblocks_5_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_5_adain2_2_norm_weight, x = input_565)[name = tensor("op_11214")]; + tensor var_11215 = mul(x = var_11213, y = var_11214)[name = tensor("op_11215")]; + tensor xt_99 = add(x = var_11215, y = var_11210_1)[name = tensor("xt_99")]; + tensor var_11217 = const()[name = tensor("op_11217"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(316850368)))]; + tensor var_11220 = mul(x = model_decoder_generator_resblocks_3_alpha2_1, y = xt_99)[name = tensor("op_11220")]; + tensor var_11221 = sin(x = var_11220)[name = tensor("op_11221")]; + tensor var_7346_promoted_35 = const()[name = tensor("op_7346_promoted_35"), val = tensor(0x1p+1)]; + tensor var_11222 = pow(x = var_11221, y = var_7346_promoted_35)[name = tensor("op_11222")]; + tensor var_11223 = mul(x = var_11217, y = var_11222)[name = tensor("op_11223")]; + tensor input_567 = add(x = xt_99, y = var_11223)[name = tensor("input_567")]; + tensor weight_277 = const()[name = tensor("weight_277"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(316850944)))]; + 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 = model_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_567)[name = tensor("xt_101")]; + tensor input_569 = add(x = xt_101, y = input_561)[name = tensor("input_569")]; + tensor h_237 = linear(bias = model_decoder_generator_resblocks_3_adain1_2_fc_bias, weight = model_decoder_generator_resblocks_3_adain1_2_fc_weight, x = input_331)[name = tensor("linear_135")]; + tensor var_11235 = const()[name = tensor("op_11235"), val = tensor([1, 256, 1])]; + tensor h_239 = reshape(shape = var_11235, x = h_237)[name = tensor("h_239")]; + tensor var_11237_split_sizes_0 = const()[name = tensor("op_11237_split_sizes_0"), val = tensor([128, 128])]; + tensor var_11237_axis_0 = const()[name = tensor("op_11237_axis_0"), val = tensor(1)]; + tensor var_11237_0, tensor var_11237_1 = split(axis = var_11237_axis_0, split_sizes = var_11237_split_sizes_0, x = h_239)[name = tensor("op_11237")]; + tensor var_11239_promoted = const()[name = tensor("op_11239_promoted"), val = tensor(0x1p+0)]; + tensor var_11240 = add(x = var_11237_0, y = var_11239_promoted)[name = tensor("op_11240")]; + tensor var_11241 = instance_norm(beta = model_decoder_generator_resblocks_5_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_5_adain2_2_norm_weight, x = input_569)[name = tensor("op_11241")]; + tensor var_11242 = mul(x = var_11240, y = var_11241)[name = tensor("op_11242")]; + tensor xt_103 = add(x = var_11242, y = var_11237_1)[name = tensor("xt_103")]; + tensor var_11244 = const()[name = tensor("op_11244"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(317047616)))]; + tensor var_11247 = mul(x = model_decoder_generator_resblocks_3_alpha1_2, y = xt_103)[name = tensor("op_11247")]; + tensor var_11248 = sin(x = var_11247)[name = tensor("op_11248")]; + tensor var_7346_promoted_36 = const()[name = tensor("op_7346_promoted_36"), val = tensor(0x1p+1)]; + tensor var_11249 = pow(x = var_11248, y = var_7346_promoted_36)[name = tensor("op_11249")]; + tensor var_11250 = mul(x = var_11244, y = var_11249)[name = tensor("op_11250")]; + tensor input_571 = add(x = xt_103, y = var_11250)[name = tensor("input_571")]; + tensor weight_281 = const()[name = tensor("weight_281"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(317048192)))]; + 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_dilations_0 = const()[name = tensor("input_573_dilations_0"), val = tensor([5])]; + tensor input_573_strides_0 = const()[name = tensor("input_573_strides_0"), val = tensor([1])]; + tensor input_573_groups_0 = const()[name = tensor("input_573_groups_0"), val = tensor(1)]; + tensor input_573 = conv(bias = model_decoder_generator_resblocks_3_convs1_2_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_281, x = input_571)[name = tensor("input_573")]; + tensor h_241 = linear(bias = model_decoder_generator_resblocks_3_adain2_2_fc_bias, weight = model_decoder_generator_resblocks_3_adain2_2_fc_weight, x = input_331)[name = tensor("linear_136")]; + tensor var_11261 = const()[name = tensor("op_11261"), val = tensor([1, 256, 1])]; + tensor h_243 = reshape(shape = var_11261, x = h_241)[name = tensor("h_243")]; + tensor var_11263_split_sizes_0 = const()[name = tensor("op_11263_split_sizes_0"), val = tensor([128, 128])]; + tensor var_11263_axis_0 = const()[name = tensor("op_11263_axis_0"), val = tensor(1)]; + tensor var_11263_0, tensor var_11263_1 = split(axis = var_11263_axis_0, split_sizes = var_11263_split_sizes_0, x = h_243)[name = tensor("op_11263")]; + tensor var_11265_promoted = const()[name = tensor("op_11265_promoted"), val = tensor(0x1p+0)]; + tensor var_11266 = add(x = var_11263_0, y = var_11265_promoted)[name = tensor("op_11266")]; + tensor var_11267 = instance_norm(beta = model_decoder_generator_resblocks_5_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_5_adain2_2_norm_weight, x = input_573)[name = tensor("op_11267")]; + tensor var_11268 = mul(x = var_11266, y = var_11267)[name = tensor("op_11268")]; + tensor xt_105 = add(x = var_11268, y = var_11263_1)[name = tensor("xt_105")]; + tensor var_11270 = const()[name = tensor("op_11270"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(317244864)))]; + tensor var_11273 = mul(x = model_decoder_generator_resblocks_3_alpha2_2, y = xt_105)[name = tensor("op_11273")]; + tensor var_11274 = sin(x = var_11273)[name = tensor("op_11274")]; + tensor var_7346_promoted_37 = const()[name = tensor("op_7346_promoted_37"), val = tensor(0x1p+1)]; + tensor var_11275 = pow(x = var_11274, y = var_7346_promoted_37)[name = tensor("op_11275")]; + tensor var_11276 = mul(x = var_11270, y = var_11275)[name = tensor("op_11276")]; + tensor input_575 = add(x = xt_105, y = var_11276)[name = tensor("input_575")]; + tensor weight_285 = const()[name = tensor("weight_285"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(317245440)))]; + 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 = model_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_575)[name = tensor("xt_107")]; + tensor var_11284 = add(x = xt_107, y = input_569)[name = tensor("op_11284")]; + tensor h_245 = linear(bias = model_decoder_generator_resblocks_4_adain1_0_fc_bias, weight = model_decoder_generator_resblocks_4_adain1_0_fc_weight, x = input_331)[name = tensor("linear_137")]; + tensor var_11288 = const()[name = tensor("op_11288"), val = tensor([1, 256, 1])]; + tensor h_247 = reshape(shape = var_11288, x = h_245)[name = tensor("h_247")]; + tensor var_11290_split_sizes_0 = const()[name = tensor("op_11290_split_sizes_0"), val = tensor([128, 128])]; + tensor var_11290_axis_0 = const()[name = tensor("op_11290_axis_0"), val = tensor(1)]; + tensor var_11290_0, tensor var_11290_1 = split(axis = var_11290_axis_0, split_sizes = var_11290_split_sizes_0, x = h_247)[name = tensor("op_11290")]; + tensor var_11292_promoted = const()[name = tensor("op_11292_promoted"), val = tensor(0x1p+0)]; + tensor var_11293 = add(x = var_11290_0, y = var_11292_promoted)[name = tensor("op_11293")]; + tensor var_11295 = mul(x = var_11293, y = var_11135)[name = tensor("op_11295")]; + tensor xt_109 = add(x = var_11295, y = var_11290_1)[name = tensor("xt_109")]; + tensor var_11297 = const()[name = tensor("op_11297"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(317442112)))]; + tensor var_11300 = mul(x = model_decoder_generator_resblocks_4_alpha1_0, y = xt_109)[name = tensor("op_11300")]; + tensor var_11301 = sin(x = var_11300)[name = tensor("op_11301")]; + tensor var_7346_promoted_38 = const()[name = tensor("op_7346_promoted_38"), val = tensor(0x1p+1)]; + tensor var_11302 = pow(x = var_11301, y = var_7346_promoted_38)[name = tensor("op_11302")]; + tensor var_11303 = mul(x = var_11297, y = var_11302)[name = tensor("op_11303")]; + tensor input_577 = add(x = xt_109, y = var_11303)[name = tensor("input_577")]; + tensor weight_289 = const()[name = tensor("weight_289"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(317442688)))]; + tensor input_579_pad_type_0 = const()[name = tensor("input_579_pad_type_0"), val = tensor("custom")]; + tensor input_579_pad_0 = const()[name = tensor("input_579_pad_0"), val = tensor([3, 3])]; + tensor input_579_strides_0 = const()[name = tensor("input_579_strides_0"), val = tensor([1])]; + tensor input_579_dilations_0 = const()[name = tensor("input_579_dilations_0"), val = tensor([1])]; + tensor input_579_groups_0 = const()[name = tensor("input_579_groups_0"), val = tensor(1)]; + tensor input_579 = conv(bias = model_decoder_generator_resblocks_4_convs1_0_bias, dilations = input_579_dilations_0, groups = input_579_groups_0, pad = input_579_pad_0, pad_type = input_579_pad_type_0, strides = input_579_strides_0, weight = weight_289, x = input_577)[name = tensor("input_579")]; + tensor h_249 = linear(bias = model_decoder_generator_resblocks_4_adain2_0_fc_bias, weight = model_decoder_generator_resblocks_4_adain2_0_fc_weight, x = input_331)[name = tensor("linear_138")]; + tensor var_11314 = const()[name = tensor("op_11314"), val = tensor([1, 256, 1])]; + tensor h_251 = reshape(shape = var_11314, x = h_249)[name = tensor("h_251")]; + tensor var_11316_split_sizes_0 = const()[name = tensor("op_11316_split_sizes_0"), val = tensor([128, 128])]; + tensor var_11316_axis_0 = const()[name = tensor("op_11316_axis_0"), val = tensor(1)]; + tensor var_11316_0, tensor var_11316_1 = split(axis = var_11316_axis_0, split_sizes = var_11316_split_sizes_0, x = h_251)[name = tensor("op_11316")]; + tensor var_11318_promoted = const()[name = tensor("op_11318_promoted"), val = tensor(0x1p+0)]; + tensor var_11319 = add(x = var_11316_0, y = var_11318_promoted)[name = tensor("op_11319")]; + tensor var_11320 = instance_norm(beta = model_decoder_generator_resblocks_5_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_5_adain2_2_norm_weight, x = input_579)[name = tensor("op_11320")]; + tensor var_11321 = mul(x = var_11319, y = var_11320)[name = tensor("op_11321")]; + tensor xt_111 = add(x = var_11321, y = var_11316_1)[name = tensor("xt_111")]; + tensor var_11323 = const()[name = tensor("op_11323"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(317901504)))]; + tensor var_11326 = mul(x = model_decoder_generator_resblocks_4_alpha2_0, y = xt_111)[name = tensor("op_11326")]; + tensor var_11327 = sin(x = var_11326)[name = tensor("op_11327")]; + tensor var_7346_promoted_39 = const()[name = tensor("op_7346_promoted_39"), val = tensor(0x1p+1)]; + tensor var_11328 = pow(x = var_11327, y = var_7346_promoted_39)[name = tensor("op_11328")]; + tensor var_11329 = mul(x = var_11323, y = var_11328)[name = tensor("op_11329")]; + tensor input_581 = add(x = xt_111, y = var_11329)[name = tensor("input_581")]; + tensor weight_293 = const()[name = tensor("weight_293"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(317902080)))]; + 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 = model_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_581)[name = tensor("xt_113")]; + tensor input_583 = add(x = xt_113, y = input_553)[name = tensor("input_583")]; + tensor h_253 = linear(bias = model_decoder_generator_resblocks_4_adain1_1_fc_bias, weight = model_decoder_generator_resblocks_4_adain1_1_fc_weight, x = input_331)[name = tensor("linear_139")]; + tensor var_11341 = const()[name = tensor("op_11341"), val = tensor([1, 256, 1])]; + tensor h_255 = reshape(shape = var_11341, x = h_253)[name = tensor("h_255")]; + tensor var_11343_split_sizes_0 = const()[name = tensor("op_11343_split_sizes_0"), val = tensor([128, 128])]; + tensor var_11343_axis_0 = const()[name = tensor("op_11343_axis_0"), val = tensor(1)]; + tensor var_11343_0, tensor var_11343_1 = split(axis = var_11343_axis_0, split_sizes = var_11343_split_sizes_0, x = h_255)[name = tensor("op_11343")]; + tensor var_11345_promoted = const()[name = tensor("op_11345_promoted"), val = tensor(0x1p+0)]; + tensor var_11346 = add(x = var_11343_0, y = var_11345_promoted)[name = tensor("op_11346")]; + tensor var_11347 = instance_norm(beta = model_decoder_generator_resblocks_5_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_5_adain2_2_norm_weight, x = input_583)[name = tensor("op_11347")]; + tensor var_11348 = mul(x = var_11346, y = var_11347)[name = tensor("op_11348")]; + tensor xt_115 = add(x = var_11348, y = var_11343_1)[name = tensor("xt_115")]; + tensor var_11350 = const()[name = tensor("op_11350"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(318360896)))]; + tensor var_11353 = mul(x = model_decoder_generator_resblocks_4_alpha1_1, y = xt_115)[name = tensor("op_11353")]; + tensor var_11354 = sin(x = var_11353)[name = tensor("op_11354")]; + tensor var_7346_promoted_40 = const()[name = tensor("op_7346_promoted_40"), val = tensor(0x1p+1)]; + tensor var_11355 = pow(x = var_11354, y = var_7346_promoted_40)[name = tensor("op_11355")]; + tensor var_11356 = mul(x = var_11350, y = var_11355)[name = tensor("op_11356")]; + tensor input_585 = add(x = xt_115, y = var_11356)[name = tensor("input_585")]; + tensor weight_297 = const()[name = tensor("weight_297"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(318361472)))]; + tensor input_587_pad_type_0 = const()[name = tensor("input_587_pad_type_0"), val = tensor("custom")]; + tensor input_587_pad_0 = const()[name = tensor("input_587_pad_0"), val = tensor([9, 9])]; + tensor input_587_dilations_0 = const()[name = tensor("input_587_dilations_0"), val = tensor([3])]; + tensor input_587_strides_0 = const()[name = tensor("input_587_strides_0"), val = tensor([1])]; + tensor input_587_groups_0 = const()[name = tensor("input_587_groups_0"), val = tensor(1)]; + tensor input_587 = conv(bias = model_decoder_generator_resblocks_4_convs1_1_bias, dilations = input_587_dilations_0, groups = input_587_groups_0, pad = input_587_pad_0, pad_type = input_587_pad_type_0, strides = input_587_strides_0, weight = weight_297, x = input_585)[name = tensor("input_587")]; + tensor h_257 = linear(bias = model_decoder_generator_resblocks_4_adain2_1_fc_bias, weight = model_decoder_generator_resblocks_4_adain2_1_fc_weight, x = input_331)[name = tensor("linear_140")]; + tensor var_11367 = const()[name = tensor("op_11367"), val = tensor([1, 256, 1])]; + tensor h_259 = reshape(shape = var_11367, x = h_257)[name = tensor("h_259")]; + tensor var_11369_split_sizes_0 = const()[name = tensor("op_11369_split_sizes_0"), val = tensor([128, 128])]; + tensor var_11369_axis_0 = const()[name = tensor("op_11369_axis_0"), val = tensor(1)]; + tensor var_11369_0, tensor var_11369_1 = split(axis = var_11369_axis_0, split_sizes = var_11369_split_sizes_0, x = h_259)[name = tensor("op_11369")]; + tensor var_11371_promoted = const()[name = tensor("op_11371_promoted"), val = tensor(0x1p+0)]; + tensor var_11372 = add(x = var_11369_0, y = var_11371_promoted)[name = tensor("op_11372")]; + tensor var_11373 = instance_norm(beta = model_decoder_generator_resblocks_5_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_5_adain2_2_norm_weight, x = input_587)[name = tensor("op_11373")]; + tensor var_11374 = mul(x = var_11372, y = var_11373)[name = tensor("op_11374")]; + tensor xt_117 = add(x = var_11374, y = var_11369_1)[name = tensor("xt_117")]; + tensor var_11376 = const()[name = tensor("op_11376"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(318820288)))]; + tensor var_11379 = mul(x = model_decoder_generator_resblocks_4_alpha2_1, y = xt_117)[name = tensor("op_11379")]; + tensor var_11380 = sin(x = var_11379)[name = tensor("op_11380")]; + tensor var_7346_promoted_41 = const()[name = tensor("op_7346_promoted_41"), val = tensor(0x1p+1)]; + tensor var_11381 = pow(x = var_11380, y = var_7346_promoted_41)[name = tensor("op_11381")]; + tensor var_11382 = mul(x = var_11376, y = var_11381)[name = tensor("op_11382")]; + tensor input_589 = add(x = xt_117, y = var_11382)[name = tensor("input_589")]; + tensor weight_301 = const()[name = tensor("weight_301"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(318820864)))]; + 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 = model_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_589)[name = tensor("xt_119")]; + tensor input_591 = add(x = xt_119, y = input_583)[name = tensor("input_591")]; + tensor h_261 = linear(bias = model_decoder_generator_resblocks_4_adain1_2_fc_bias, weight = model_decoder_generator_resblocks_4_adain1_2_fc_weight, x = input_331)[name = tensor("linear_141")]; + tensor var_11394 = const()[name = tensor("op_11394"), val = tensor([1, 256, 1])]; + tensor h_263 = reshape(shape = var_11394, x = h_261)[name = tensor("h_263")]; + tensor var_11396_split_sizes_0 = const()[name = tensor("op_11396_split_sizes_0"), val = tensor([128, 128])]; + tensor var_11396_axis_0 = const()[name = tensor("op_11396_axis_0"), val = tensor(1)]; + tensor var_11396_0, tensor var_11396_1 = split(axis = var_11396_axis_0, split_sizes = var_11396_split_sizes_0, x = h_263)[name = tensor("op_11396")]; + tensor var_11398_promoted = const()[name = tensor("op_11398_promoted"), val = tensor(0x1p+0)]; + tensor var_11399 = add(x = var_11396_0, y = var_11398_promoted)[name = tensor("op_11399")]; + tensor var_11400 = instance_norm(beta = model_decoder_generator_resblocks_5_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_5_adain2_2_norm_weight, x = input_591)[name = tensor("op_11400")]; + tensor var_11401 = mul(x = var_11399, y = var_11400)[name = tensor("op_11401")]; + tensor xt_121 = add(x = var_11401, y = var_11396_1)[name = tensor("xt_121")]; + tensor var_11403 = const()[name = tensor("op_11403"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(319279680)))]; + tensor var_11406 = mul(x = model_decoder_generator_resblocks_4_alpha1_2, y = xt_121)[name = tensor("op_11406")]; + tensor var_11407 = sin(x = var_11406)[name = tensor("op_11407")]; + tensor var_7346_promoted_42 = const()[name = tensor("op_7346_promoted_42"), val = tensor(0x1p+1)]; + tensor var_11408 = pow(x = var_11407, y = var_7346_promoted_42)[name = tensor("op_11408")]; + tensor var_11409 = mul(x = var_11403, y = var_11408)[name = tensor("op_11409")]; + tensor input_593 = add(x = xt_121, y = var_11409)[name = tensor("input_593")]; + tensor weight_305 = const()[name = tensor("weight_305"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(319280256)))]; + tensor input_595_pad_type_0 = const()[name = tensor("input_595_pad_type_0"), val = tensor("custom")]; + tensor input_595_pad_0 = const()[name = tensor("input_595_pad_0"), val = tensor([15, 15])]; + tensor input_595_dilations_0 = const()[name = tensor("input_595_dilations_0"), val = tensor([5])]; + tensor input_595_strides_0 = const()[name = tensor("input_595_strides_0"), val = tensor([1])]; + tensor input_595_groups_0 = const()[name = tensor("input_595_groups_0"), val = tensor(1)]; + tensor input_595 = conv(bias = model_decoder_generator_resblocks_4_convs1_2_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 = weight_305, x = input_593)[name = tensor("input_595")]; + tensor h_265 = linear(bias = model_decoder_generator_resblocks_4_adain2_2_fc_bias, weight = model_decoder_generator_resblocks_4_adain2_2_fc_weight, x = input_331)[name = tensor("linear_142")]; + tensor var_11420 = const()[name = tensor("op_11420"), val = tensor([1, 256, 1])]; + tensor h_267 = reshape(shape = var_11420, x = h_265)[name = tensor("h_267")]; + tensor var_11422_split_sizes_0 = const()[name = tensor("op_11422_split_sizes_0"), val = tensor([128, 128])]; + tensor var_11422_axis_0 = const()[name = tensor("op_11422_axis_0"), val = tensor(1)]; + tensor var_11422_0, tensor var_11422_1 = split(axis = var_11422_axis_0, split_sizes = var_11422_split_sizes_0, x = h_267)[name = tensor("op_11422")]; + tensor var_11424_promoted = const()[name = tensor("op_11424_promoted"), val = tensor(0x1p+0)]; + tensor var_11425 = add(x = var_11422_0, y = var_11424_promoted)[name = tensor("op_11425")]; + tensor var_11426 = instance_norm(beta = model_decoder_generator_resblocks_5_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_5_adain2_2_norm_weight, x = input_595)[name = tensor("op_11426")]; + tensor var_11427 = mul(x = var_11425, y = var_11426)[name = tensor("op_11427")]; + tensor xt_123 = add(x = var_11427, y = var_11422_1)[name = tensor("xt_123")]; + tensor var_11429 = const()[name = tensor("op_11429"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(319739072)))]; + tensor var_11432 = mul(x = model_decoder_generator_resblocks_4_alpha2_2, y = xt_123)[name = tensor("op_11432")]; + tensor var_11433 = sin(x = var_11432)[name = tensor("op_11433")]; + tensor var_7346_promoted_43 = const()[name = tensor("op_7346_promoted_43"), val = tensor(0x1p+1)]; + tensor var_11434 = pow(x = var_11433, y = var_7346_promoted_43)[name = tensor("op_11434")]; + tensor var_11435 = mul(x = var_11429, y = var_11434)[name = tensor("op_11435")]; + tensor input_597 = add(x = xt_123, y = var_11435)[name = tensor("input_597")]; + tensor weight_309 = const()[name = tensor("weight_309"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(319739648)))]; + 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 = model_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_597)[name = tensor("xt_125")]; + tensor var_11443 = add(x = xt_125, y = input_591)[name = tensor("op_11443")]; + tensor h_269 = linear(bias = model_decoder_generator_resblocks_5_adain1_0_fc_bias, weight = model_decoder_generator_resblocks_5_adain1_0_fc_weight, x = input_331)[name = tensor("linear_143")]; + tensor var_11447 = const()[name = tensor("op_11447"), val = tensor([1, 256, 1])]; + tensor h_271 = reshape(shape = var_11447, x = h_269)[name = tensor("h_271")]; + tensor var_11449_split_sizes_0 = const()[name = tensor("op_11449_split_sizes_0"), val = tensor([128, 128])]; + tensor var_11449_axis_0 = const()[name = tensor("op_11449_axis_0"), val = tensor(1)]; + tensor var_11449_0, tensor var_11449_1 = split(axis = var_11449_axis_0, split_sizes = var_11449_split_sizes_0, x = h_271)[name = tensor("op_11449")]; + tensor var_11451_promoted = const()[name = tensor("op_11451_promoted"), val = tensor(0x1p+0)]; + tensor var_11452 = add(x = var_11449_0, y = var_11451_promoted)[name = tensor("op_11452")]; + tensor var_11454 = mul(x = var_11452, y = var_11135)[name = tensor("op_11454")]; + tensor xt_127 = add(x = var_11454, y = var_11449_1)[name = tensor("xt_127")]; + tensor var_11456 = const()[name = tensor("op_11456"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(320198464)))]; + tensor var_11459 = mul(x = model_decoder_generator_resblocks_5_alpha1_0, y = xt_127)[name = tensor("op_11459")]; + tensor var_11460 = sin(x = var_11459)[name = tensor("op_11460")]; + tensor var_7346_promoted_44 = const()[name = tensor("op_7346_promoted_44"), val = tensor(0x1p+1)]; + tensor var_11461 = pow(x = var_11460, y = var_7346_promoted_44)[name = tensor("op_11461")]; + tensor var_11462 = mul(x = var_11456, y = var_11461)[name = tensor("op_11462")]; + tensor input_599 = add(x = xt_127, y = var_11462)[name = tensor("input_599")]; + tensor weight_313 = const()[name = tensor("weight_313"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(320199040)))]; + tensor input_601_pad_type_0 = const()[name = tensor("input_601_pad_type_0"), val = tensor("custom")]; + tensor input_601_pad_0 = const()[name = tensor("input_601_pad_0"), val = tensor([5, 5])]; + tensor input_601_strides_0 = const()[name = tensor("input_601_strides_0"), val = tensor([1])]; + tensor input_601_dilations_0 = const()[name = tensor("input_601_dilations_0"), val = tensor([1])]; + tensor input_601_groups_0 = const()[name = tensor("input_601_groups_0"), val = tensor(1)]; + tensor input_601 = conv(bias = model_decoder_generator_resblocks_5_convs1_0_bias, dilations = input_601_dilations_0, groups = input_601_groups_0, pad = input_601_pad_0, pad_type = input_601_pad_type_0, strides = input_601_strides_0, weight = weight_313, x = input_599)[name = tensor("input_601")]; + tensor h_273 = linear(bias = model_decoder_generator_resblocks_5_adain2_0_fc_bias, weight = model_decoder_generator_resblocks_5_adain2_0_fc_weight, x = input_331)[name = tensor("linear_144")]; + tensor var_11473 = const()[name = tensor("op_11473"), val = tensor([1, 256, 1])]; + tensor h_275 = reshape(shape = var_11473, x = h_273)[name = tensor("h_275")]; + tensor var_11475_split_sizes_0 = const()[name = tensor("op_11475_split_sizes_0"), val = tensor([128, 128])]; + tensor var_11475_axis_0 = const()[name = tensor("op_11475_axis_0"), val = tensor(1)]; + tensor var_11475_0, tensor var_11475_1 = split(axis = var_11475_axis_0, split_sizes = var_11475_split_sizes_0, x = h_275)[name = tensor("op_11475")]; + tensor var_11477_promoted = const()[name = tensor("op_11477_promoted"), val = tensor(0x1p+0)]; + tensor var_11478 = add(x = var_11475_0, y = var_11477_promoted)[name = tensor("op_11478")]; + tensor var_11479 = instance_norm(beta = model_decoder_generator_resblocks_5_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_5_adain2_2_norm_weight, x = input_601)[name = tensor("op_11479")]; + tensor var_11480 = mul(x = var_11478, y = var_11479)[name = tensor("op_11480")]; + tensor xt_129 = add(x = var_11480, y = var_11475_1)[name = tensor("xt_129")]; + tensor var_11482 = const()[name = tensor("op_11482"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(320920000)))]; + tensor var_11485 = mul(x = model_decoder_generator_resblocks_5_alpha2_0, y = xt_129)[name = tensor("op_11485")]; + tensor var_11486 = sin(x = var_11485)[name = tensor("op_11486")]; + tensor var_7346_promoted_45 = const()[name = tensor("op_7346_promoted_45"), val = tensor(0x1p+1)]; + tensor var_11487 = pow(x = var_11486, y = var_7346_promoted_45)[name = tensor("op_11487")]; + tensor var_11488 = mul(x = var_11482, y = var_11487)[name = tensor("op_11488")]; + tensor input_603 = add(x = xt_129, y = var_11488)[name = tensor("input_603")]; + tensor weight_317 = const()[name = tensor("weight_317"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(320920576)))]; + 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 = model_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_603)[name = tensor("xt_131")]; + tensor input_605 = add(x = xt_131, y = input_553)[name = tensor("input_605")]; + tensor h_277 = linear(bias = model_decoder_generator_resblocks_5_adain1_1_fc_bias, weight = model_decoder_generator_resblocks_5_adain1_1_fc_weight, x = input_331)[name = tensor("linear_145")]; + tensor var_11500 = const()[name = tensor("op_11500"), val = tensor([1, 256, 1])]; + tensor h_279 = reshape(shape = var_11500, x = h_277)[name = tensor("h_279")]; + tensor var_11502_split_sizes_0 = const()[name = tensor("op_11502_split_sizes_0"), val = tensor([128, 128])]; + tensor var_11502_axis_0 = const()[name = tensor("op_11502_axis_0"), val = tensor(1)]; + tensor var_11502_0, tensor var_11502_1 = split(axis = var_11502_axis_0, split_sizes = var_11502_split_sizes_0, x = h_279)[name = tensor("op_11502")]; + tensor var_11504_promoted = const()[name = tensor("op_11504_promoted"), val = tensor(0x1p+0)]; + tensor var_11505 = add(x = var_11502_0, y = var_11504_promoted)[name = tensor("op_11505")]; + tensor var_11506 = instance_norm(beta = model_decoder_generator_resblocks_5_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_5_adain2_2_norm_weight, x = input_605)[name = tensor("op_11506")]; + tensor var_11507 = mul(x = var_11505, y = var_11506)[name = tensor("op_11507")]; + tensor xt_133 = add(x = var_11507, y = var_11502_1)[name = tensor("xt_133")]; + tensor var_11509 = const()[name = tensor("op_11509"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(321641536)))]; + tensor var_11512 = mul(x = model_decoder_generator_resblocks_5_alpha1_1, y = xt_133)[name = tensor("op_11512")]; + tensor var_11513 = sin(x = var_11512)[name = tensor("op_11513")]; + tensor var_7346_promoted_46 = const()[name = tensor("op_7346_promoted_46"), val = tensor(0x1p+1)]; + tensor var_11514 = pow(x = var_11513, y = var_7346_promoted_46)[name = tensor("op_11514")]; + tensor var_11515 = mul(x = var_11509, y = var_11514)[name = tensor("op_11515")]; + tensor input_607 = add(x = xt_133, y = var_11515)[name = tensor("input_607")]; + tensor weight_321 = const()[name = tensor("weight_321"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(321642112)))]; + tensor input_609_pad_type_0 = const()[name = tensor("input_609_pad_type_0"), val = tensor("custom")]; + tensor input_609_pad_0 = const()[name = tensor("input_609_pad_0"), val = tensor([15, 15])]; + tensor input_609_dilations_0 = const()[name = tensor("input_609_dilations_0"), val = tensor([3])]; + tensor input_609_strides_0 = const()[name = tensor("input_609_strides_0"), val = tensor([1])]; + tensor input_609_groups_0 = const()[name = tensor("input_609_groups_0"), val = tensor(1)]; + tensor input_609 = conv(bias = model_decoder_generator_resblocks_5_convs1_1_bias, dilations = input_609_dilations_0, groups = input_609_groups_0, pad = input_609_pad_0, pad_type = input_609_pad_type_0, strides = input_609_strides_0, weight = weight_321, x = input_607)[name = tensor("input_609")]; + tensor h_281 = linear(bias = model_decoder_generator_resblocks_5_adain2_1_fc_bias, weight = model_decoder_generator_resblocks_5_adain2_1_fc_weight, x = input_331)[name = tensor("linear_146")]; + tensor var_11526 = const()[name = tensor("op_11526"), val = tensor([1, 256, 1])]; + tensor h_283 = reshape(shape = var_11526, x = h_281)[name = tensor("h_283")]; + tensor var_11528_split_sizes_0 = const()[name = tensor("op_11528_split_sizes_0"), val = tensor([128, 128])]; + tensor var_11528_axis_0 = const()[name = tensor("op_11528_axis_0"), val = tensor(1)]; + tensor var_11528_0, tensor var_11528_1 = split(axis = var_11528_axis_0, split_sizes = var_11528_split_sizes_0, x = h_283)[name = tensor("op_11528")]; + tensor var_11530_promoted = const()[name = tensor("op_11530_promoted"), val = tensor(0x1p+0)]; + tensor var_11531 = add(x = var_11528_0, y = var_11530_promoted)[name = tensor("op_11531")]; + tensor var_11532 = instance_norm(beta = model_decoder_generator_resblocks_5_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_5_adain2_2_norm_weight, x = input_609)[name = tensor("op_11532")]; + tensor var_11533 = mul(x = var_11531, y = var_11532)[name = tensor("op_11533")]; + tensor xt_135 = add(x = var_11533, y = var_11528_1)[name = tensor("xt_135")]; + tensor var_11535 = const()[name = tensor("op_11535"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(322363072)))]; + tensor var_11538 = mul(x = model_decoder_generator_resblocks_5_alpha2_1, y = xt_135)[name = tensor("op_11538")]; + tensor var_11539 = sin(x = var_11538)[name = tensor("op_11539")]; + tensor var_7346_promoted_47 = const()[name = tensor("op_7346_promoted_47"), val = tensor(0x1p+1)]; + tensor var_11540 = pow(x = var_11539, y = var_7346_promoted_47)[name = tensor("op_11540")]; + tensor var_11541 = mul(x = var_11535, y = var_11540)[name = tensor("op_11541")]; + tensor input_611 = add(x = xt_135, y = var_11541)[name = tensor("input_611")]; + tensor weight_325 = const()[name = tensor("weight_325"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(322363648)))]; + 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 = model_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_611)[name = tensor("xt_137")]; + tensor input_613 = add(x = xt_137, y = input_605)[name = tensor("input_613")]; + tensor h_285 = linear(bias = model_decoder_generator_resblocks_5_adain1_2_fc_bias, weight = model_decoder_generator_resblocks_5_adain1_2_fc_weight, x = input_331)[name = tensor("linear_147")]; + tensor var_11553 = const()[name = tensor("op_11553"), val = tensor([1, 256, 1])]; + tensor h_287 = reshape(shape = var_11553, x = h_285)[name = tensor("h_287")]; + tensor var_11555_split_sizes_0 = const()[name = tensor("op_11555_split_sizes_0"), val = tensor([128, 128])]; + tensor var_11555_axis_0 = const()[name = tensor("op_11555_axis_0"), val = tensor(1)]; + tensor var_11555_0, tensor var_11555_1 = split(axis = var_11555_axis_0, split_sizes = var_11555_split_sizes_0, x = h_287)[name = tensor("op_11555")]; + tensor var_11557_promoted = const()[name = tensor("op_11557_promoted"), val = tensor(0x1p+0)]; + tensor var_11558 = add(x = var_11555_0, y = var_11557_promoted)[name = tensor("op_11558")]; + tensor var_11559 = instance_norm(beta = model_decoder_generator_resblocks_5_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_5_adain2_2_norm_weight, x = input_613)[name = tensor("op_11559")]; + tensor var_11560 = mul(x = var_11558, y = var_11559)[name = tensor("op_11560")]; + tensor xt_139 = add(x = var_11560, y = var_11555_1)[name = tensor("xt_139")]; + tensor var_11562 = const()[name = tensor("op_11562"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(323084608)))]; + tensor var_11565 = mul(x = model_decoder_generator_resblocks_5_alpha1_2, y = xt_139)[name = tensor("op_11565")]; + tensor var_11566 = sin(x = var_11565)[name = tensor("op_11566")]; + tensor var_7346_promoted_48 = const()[name = tensor("op_7346_promoted_48"), val = tensor(0x1p+1)]; + tensor var_11567 = pow(x = var_11566, y = var_7346_promoted_48)[name = tensor("op_11567")]; + tensor var_11568 = mul(x = var_11562, y = var_11567)[name = tensor("op_11568")]; + tensor input_615 = add(x = xt_139, y = var_11568)[name = tensor("input_615")]; + tensor weight_329 = const()[name = tensor("weight_329"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(323085184)))]; + tensor input_617_pad_type_0 = const()[name = tensor("input_617_pad_type_0"), val = tensor("custom")]; + tensor input_617_pad_0 = const()[name = tensor("input_617_pad_0"), val = tensor([25, 25])]; + tensor input_617_dilations_0 = const()[name = tensor("input_617_dilations_0"), val = tensor([5])]; + tensor input_617_strides_0 = const()[name = tensor("input_617_strides_0"), val = tensor([1])]; + tensor input_617_groups_0 = const()[name = tensor("input_617_groups_0"), val = tensor(1)]; + tensor input_617 = conv(bias = model_decoder_generator_resblocks_5_convs1_2_bias, dilations = input_617_dilations_0, groups = input_617_groups_0, pad = input_617_pad_0, pad_type = input_617_pad_type_0, strides = input_617_strides_0, weight = weight_329, x = input_615)[name = tensor("input_617")]; + tensor h_289 = linear(bias = model_decoder_generator_resblocks_5_adain2_2_fc_bias, weight = model_decoder_generator_resblocks_5_adain2_2_fc_weight, x = input_331)[name = tensor("linear_148")]; + tensor var_11579 = const()[name = tensor("op_11579"), val = tensor([1, 256, 1])]; + tensor h = reshape(shape = var_11579, x = h_289)[name = tensor("h")]; + tensor var_11581_split_sizes_0 = const()[name = tensor("op_11581_split_sizes_0"), val = tensor([128, 128])]; + tensor var_11581_axis_0 = const()[name = tensor("op_11581_axis_0"), val = tensor(1)]; + tensor var_11581_0, tensor var_11581_1 = split(axis = var_11581_axis_0, split_sizes = var_11581_split_sizes_0, x = h)[name = tensor("op_11581")]; + tensor var_11583_promoted = const()[name = tensor("op_11583_promoted"), val = tensor(0x1p+0)]; + tensor var_11584 = add(x = var_11581_0, y = var_11583_promoted)[name = tensor("op_11584")]; + tensor var_11585 = instance_norm(beta = model_decoder_generator_resblocks_5_adain2_2_norm_bias, epsilon = var_6971, gamma = model_decoder_generator_resblocks_5_adain2_2_norm_weight, x = input_617)[name = tensor("op_11585")]; + tensor var_11586 = mul(x = var_11584, y = var_11585)[name = tensor("op_11586")]; + tensor xt_141 = add(x = var_11586, y = var_11581_1)[name = tensor("xt_141")]; + tensor var_11588 = const()[name = tensor("op_11588"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(323806144)))]; + tensor var_11591 = mul(x = model_decoder_generator_resblocks_5_alpha2_2, y = xt_141)[name = tensor("op_11591")]; + tensor var_11592 = sin(x = var_11591)[name = tensor("op_11592")]; + tensor var_7346_promoted_49 = const()[name = tensor("op_7346_promoted_49"), val = tensor(0x1p+1)]; + tensor var_11593 = pow(x = var_11592, y = var_7346_promoted_49)[name = tensor("op_11593")]; + tensor var_11594 = mul(x = var_11588, y = var_11593)[name = tensor("op_11594")]; + tensor input_619 = add(x = xt_141, y = var_11594)[name = tensor("input_619")]; + tensor weight_333 = const()[name = tensor("weight_333"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(323806720)))]; + 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 = model_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_619)[name = tensor("xt")]; + tensor var_11602 = add(x = xt, y = input_613)[name = tensor("op_11602")]; + tensor var_11604_axis_0 = const()[name = tensor("op_11604_axis_0"), val = tensor(0)]; + tensor var_11604 = stack(axis = var_11604_axis_0, values = (var_11284, var_11443, var_11602))[name = tensor("op_11604")]; + tensor input_621_axes_0 = const()[name = tensor("input_621_axes_0"), val = tensor([0])]; + tensor input_621_keep_dims_0 = const()[name = tensor("input_621_keep_dims_0"), val = tensor(false)]; + tensor input_621 = reduce_mean(axes = input_621_axes_0, keep_dims = input_621_keep_dims_0, x = var_11604)[name = tensor("input_621")]; + tensor input = leaky_relu(alpha = var_6968, x = input_621)[name = tensor("input")]; + tensor weight_335 = const()[name = tensor("weight_335"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(324527680)))]; + 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 = model_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_11615_begin_0 = const()[name = tensor("op_11615_begin_0"), val = tensor([0, 0, 0])]; + tensor var_11615_end_0 = const()[name = tensor("op_11615_end_0"), val = tensor([1, 11, 72001])]; + tensor var_11615_end_mask_0 = const()[name = tensor("op_11615_end_mask_0"), val = tensor([true, false, true])]; + tensor var_11615 = slice_by_index(begin = var_11615_begin_0, end = var_11615_end_0, end_mask = var_11615_end_mask_0, x = x)[name = tensor("op_11615")]; + tensor var_6966_promoted = const()[name = tensor("op_6966_promoted"), val = tensor(-0x1.4p+3)]; + tensor var_7343_promoted = const()[name = tensor("op_7343_promoted"), val = tensor(0x1.4p+3)]; + tensor clip_3 = clip(alpha = var_6966_promoted, beta = var_7343_promoted, x = var_11615)[name = tensor("clip_3")]; + tensor magnitude = exp(x = clip_3)[name = tensor("magnitude")]; + tensor var_11620_begin_0 = const()[name = tensor("op_11620_begin_0"), val = tensor([0, 11, 0])]; + tensor var_11620_end_0 = const()[name = tensor("op_11620_end_0"), val = tensor([1, 22, 72001])]; + tensor var_11620_end_mask_0 = const()[name = tensor("op_11620_end_mask_0"), val = tensor([true, true, true])]; + tensor var_11620 = slice_by_index(begin = var_11620_begin_0, end = var_11620_end_0, end_mask = var_11620_end_mask_0, x = x)[name = tensor("op_11620")]; + tensor phase = sin(x = var_11620)[name = tensor("phase")]; + tensor var_11623 = cos(x = phase)[name = tensor("op_11623")]; + tensor real_part = mul(x = magnitude, y = var_11623)[name = tensor("real_part")]; + tensor var_11625 = sin(x = phase)[name = tensor("op_11625")]; + tensor imag_part = mul(x = magnitude, y = var_11625)[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, 360020])]; + 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 = model_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, 360020])]; + 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 = model_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 var_11638_begin_0 = const()[name = tensor("op_11638_begin_0"), val = tensor([0, 0, 10])]; + tensor var_11638_end_0 = const()[name = tensor("op_11638_end_0"), val = tensor([1, 1, 360010])]; + tensor var_11638_end_mask_0 = const()[name = tensor("op_11638_end_mask_0"), val = tensor([true, true, false])]; + tensor audio = slice_by_index(begin = var_11638_begin_0, end = var_11638_end_0, end_mask = var_11638_end_mask_0, x = waveform)[name = tensor("op_11638")]; + tensor audio_length_samples = cast(dtype = cast_61_dtype_0, x = var_5982)[name = tensor("cast_160")]; + } -> (audio, audio_length_samples, pred_dur); +} \ No newline at end of file