diff --git "a/CamPlusPlus_v2.mlmodelc/model.mil" "b/CamPlusPlus_v2.mlmodelc/model.mil" new file mode 100644--- /dev/null +++ "b/CamPlusPlus_v2.mlmodelc/model.mil" @@ -0,0 +1,4693 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.1"}, {"coremltools-component-torch", "2.5.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] +{ + func main(tensor feats) [FlexibleShapeInformation = tuple, dict, tensor>>, tuple, dict, list, ?>>>>((("DefaultShapes", {{"feats", [1, 400, 80]}}), ("RangeDims", {{"feats", [[1, 1], [32, 5998], [80, 80]]}})))] { + tensor model_xvector_block1_tdnnd1_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block1_tdnnd1_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor model_xvector_block1_tdnnd1_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block1_tdnnd1_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(640)))]; + tensor model_xvector_block1_tdnnd1_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block1_tdnnd1_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1216)))]; + tensor model_xvector_block1_tdnnd1_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block1_tdnnd1_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1792)))]; + tensor model_xvector_block1_tdnnd1_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block1_tdnnd1_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2368)))]; + tensor model_xvector_block1_tdnnd1_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block1_tdnnd1_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51584)))]; + tensor model_xvector_block1_tdnnd1_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block1_tdnnd1_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51904)))]; + tensor model_xvector_block1_tdnnd1_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block1_tdnnd1_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84736)))]; + tensor model_xvector_block1_tdnnd1_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block1_tdnnd1_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84928)))]; + tensor model_xvector_block1_tdnnd2_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block1_tdnnd2_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93184)))]; + tensor model_xvector_block1_tdnnd2_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block1_tdnnd2_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93888)))]; + tensor model_xvector_block1_tdnnd2_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block1_tdnnd2_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94592)))]; + tensor model_xvector_block1_tdnnd2_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block1_tdnnd2_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95296)))]; + tensor model_xvector_block1_tdnnd2_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block1_tdnnd2_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96000)))]; + tensor model_xvector_block1_tdnnd2_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block1_tdnnd2_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(145216)))]; + tensor model_xvector_block1_tdnnd2_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block1_tdnnd2_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(145536)))]; + tensor model_xvector_block1_tdnnd2_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block1_tdnnd2_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178368)))]; + tensor model_xvector_block1_tdnnd2_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block1_tdnnd2_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178560)))]; + tensor model_xvector_block1_tdnnd3_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block1_tdnnd3_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(186816)))]; + tensor model_xvector_block1_tdnnd3_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block1_tdnnd3_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(187648)))]; + tensor model_xvector_block1_tdnnd3_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block1_tdnnd3_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(188480)))]; + tensor model_xvector_block1_tdnnd3_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block1_tdnnd3_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(189312)))]; + tensor model_xvector_block1_tdnnd3_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block1_tdnnd3_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190144)))]; + tensor model_xvector_block1_tdnnd3_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block1_tdnnd3_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(239360)))]; + tensor model_xvector_block1_tdnnd3_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block1_tdnnd3_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(239680)))]; + tensor model_xvector_block1_tdnnd3_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block1_tdnnd3_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(272512)))]; + tensor model_xvector_block1_tdnnd3_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block1_tdnnd3_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(272704)))]; + tensor model_xvector_block1_tdnnd4_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block1_tdnnd4_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(280960)))]; + tensor model_xvector_block1_tdnnd4_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block1_tdnnd4_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(281920)))]; + tensor model_xvector_block1_tdnnd4_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block1_tdnnd4_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(282880)))]; + tensor model_xvector_block1_tdnnd4_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block1_tdnnd4_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283840)))]; + tensor model_xvector_block1_tdnnd4_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block1_tdnnd4_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(284800)))]; + tensor model_xvector_block1_tdnnd4_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block1_tdnnd4_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(334016)))]; + tensor model_xvector_block1_tdnnd4_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block1_tdnnd4_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(334336)))]; + tensor model_xvector_block1_tdnnd4_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block1_tdnnd4_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(367168)))]; + tensor model_xvector_block1_tdnnd4_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block1_tdnnd4_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(367360)))]; + tensor model_xvector_block1_tdnnd5_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block1_tdnnd5_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(375616)))]; + tensor model_xvector_block1_tdnnd5_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block1_tdnnd5_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(376704)))]; + tensor model_xvector_block1_tdnnd5_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block1_tdnnd5_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(377792)))]; + tensor model_xvector_block1_tdnnd5_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block1_tdnnd5_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(378880)))]; + tensor model_xvector_block1_tdnnd5_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block1_tdnnd5_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(379968)))]; + tensor model_xvector_block1_tdnnd5_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block1_tdnnd5_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(429184)))]; + tensor model_xvector_block1_tdnnd5_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block1_tdnnd5_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(429504)))]; + tensor model_xvector_block1_tdnnd5_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block1_tdnnd5_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(462336)))]; + tensor model_xvector_block1_tdnnd5_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block1_tdnnd5_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(462528)))]; + tensor model_xvector_block1_tdnnd6_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block1_tdnnd6_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(470784)))]; + tensor model_xvector_block1_tdnnd6_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block1_tdnnd6_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(472000)))]; + tensor model_xvector_block1_tdnnd6_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block1_tdnnd6_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(473216)))]; + tensor model_xvector_block1_tdnnd6_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block1_tdnnd6_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(474432)))]; + tensor model_xvector_block1_tdnnd6_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block1_tdnnd6_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(475648)))]; + tensor model_xvector_block1_tdnnd6_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block1_tdnnd6_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(524864)))]; + tensor model_xvector_block1_tdnnd6_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block1_tdnnd6_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(525184)))]; + tensor model_xvector_block1_tdnnd6_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block1_tdnnd6_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(558016)))]; + tensor model_xvector_block1_tdnnd6_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block1_tdnnd6_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(558208)))]; + tensor model_xvector_block1_tdnnd7_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block1_tdnnd7_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(566464)))]; + tensor model_xvector_block1_tdnnd7_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block1_tdnnd7_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(567808)))]; + tensor model_xvector_block1_tdnnd7_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block1_tdnnd7_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(569152)))]; + tensor model_xvector_block1_tdnnd7_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block1_tdnnd7_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(570496)))]; + tensor model_xvector_block1_tdnnd7_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block1_tdnnd7_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(571840)))]; + tensor model_xvector_block1_tdnnd7_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block1_tdnnd7_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621056)))]; + tensor model_xvector_block1_tdnnd7_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block1_tdnnd7_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621376)))]; + tensor model_xvector_block1_tdnnd7_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block1_tdnnd7_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(654208)))]; + tensor model_xvector_block1_tdnnd7_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block1_tdnnd7_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(654400)))]; + tensor model_xvector_block1_tdnnd8_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block1_tdnnd8_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(662656)))]; + tensor model_xvector_block1_tdnnd8_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block1_tdnnd8_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(664128)))]; + tensor model_xvector_block1_tdnnd8_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block1_tdnnd8_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(665600)))]; + tensor model_xvector_block1_tdnnd8_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block1_tdnnd8_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(667072)))]; + tensor model_xvector_block1_tdnnd8_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block1_tdnnd8_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(668544)))]; + tensor model_xvector_block1_tdnnd8_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block1_tdnnd8_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(717760)))]; + tensor model_xvector_block1_tdnnd8_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block1_tdnnd8_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(718080)))]; + tensor model_xvector_block1_tdnnd8_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block1_tdnnd8_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(750912)))]; + tensor model_xvector_block1_tdnnd8_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block1_tdnnd8_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(751104)))]; + tensor model_xvector_block1_tdnnd9_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block1_tdnnd9_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(759360)))]; + tensor model_xvector_block1_tdnnd9_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block1_tdnnd9_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(760960)))]; + tensor model_xvector_block1_tdnnd9_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block1_tdnnd9_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(762560)))]; + tensor model_xvector_block1_tdnnd9_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block1_tdnnd9_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(764160)))]; + tensor model_xvector_block1_tdnnd9_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block1_tdnnd9_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(765760)))]; + tensor model_xvector_block1_tdnnd9_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block1_tdnnd9_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(814976)))]; + tensor model_xvector_block1_tdnnd9_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block1_tdnnd9_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(815296)))]; + tensor model_xvector_block1_tdnnd9_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block1_tdnnd9_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(848128)))]; + tensor model_xvector_block1_tdnnd9_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block1_tdnnd9_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(848320)))]; + tensor model_xvector_block1_tdnnd10_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block1_tdnnd10_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(856576)))]; + tensor model_xvector_block1_tdnnd10_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block1_tdnnd10_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(858304)))]; + tensor model_xvector_block1_tdnnd10_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block1_tdnnd10_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(860032)))]; + tensor model_xvector_block1_tdnnd10_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block1_tdnnd10_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(861760)))]; + tensor model_xvector_block1_tdnnd10_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block1_tdnnd10_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(863488)))]; + tensor model_xvector_block1_tdnnd10_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block1_tdnnd10_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(912704)))]; + tensor model_xvector_block1_tdnnd10_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block1_tdnnd10_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(913024)))]; + tensor model_xvector_block1_tdnnd10_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block1_tdnnd10_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(945856)))]; + tensor model_xvector_block1_tdnnd10_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block1_tdnnd10_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(946048)))]; + tensor model_xvector_block1_tdnnd11_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block1_tdnnd11_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(954304)))]; + tensor model_xvector_block1_tdnnd11_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block1_tdnnd11_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(956160)))]; + tensor model_xvector_block1_tdnnd11_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block1_tdnnd11_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(958016)))]; + tensor model_xvector_block1_tdnnd11_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block1_tdnnd11_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(959872)))]; + tensor model_xvector_block1_tdnnd11_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block1_tdnnd11_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(961728)))]; + tensor model_xvector_block1_tdnnd11_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block1_tdnnd11_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1010944)))]; + tensor model_xvector_block1_tdnnd11_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block1_tdnnd11_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1011264)))]; + tensor model_xvector_block1_tdnnd11_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block1_tdnnd11_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1044096)))]; + tensor model_xvector_block1_tdnnd11_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block1_tdnnd11_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1044288)))]; + tensor model_xvector_block1_tdnnd12_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block1_tdnnd12_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1052544)))]; + tensor model_xvector_block1_tdnnd12_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block1_tdnnd12_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1054528)))]; + tensor model_xvector_block1_tdnnd12_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block1_tdnnd12_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1056512)))]; + tensor model_xvector_block1_tdnnd12_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block1_tdnnd12_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1058496)))]; + tensor model_xvector_block1_tdnnd12_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block1_tdnnd12_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1060480)))]; + tensor model_xvector_block1_tdnnd12_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block1_tdnnd12_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1109696)))]; + tensor model_xvector_block1_tdnnd12_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block1_tdnnd12_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1110016)))]; + tensor model_xvector_block1_tdnnd12_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block1_tdnnd12_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1142848)))]; + tensor model_xvector_block1_tdnnd12_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block1_tdnnd12_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1143040)))]; + tensor model_xvector_transit1_nonlinear_batchnorm_running_var = const()[name = tensor("model_xvector_transit1_nonlinear_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1151296)))]; + tensor model_xvector_transit1_nonlinear_batchnorm_running_mean = const()[name = tensor("model_xvector_transit1_nonlinear_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1153408)))]; + tensor model_xvector_transit1_nonlinear_batchnorm_bias = const()[name = tensor("model_xvector_transit1_nonlinear_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1155520)))]; + tensor model_xvector_transit1_nonlinear_batchnorm_weight = const()[name = tensor("model_xvector_transit1_nonlinear_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1157632)))]; + tensor model_xvector_transit1_linear_weight = const()[name = tensor("model_xvector_transit1_linear_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1159744)))]; + tensor model_xvector_block2_tdnnd1_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block2_tdnnd1_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1684096)))]; + tensor model_xvector_block2_tdnnd1_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block2_tdnnd1_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1685184)))]; + tensor model_xvector_block2_tdnnd1_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block2_tdnnd1_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1686272)))]; + tensor model_xvector_block2_tdnnd1_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block2_tdnnd1_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1687360)))]; + tensor model_xvector_block2_tdnnd1_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block2_tdnnd1_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1688448)))]; + tensor model_xvector_block2_tdnnd1_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block2_tdnnd1_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1737664)))]; + tensor model_xvector_block2_tdnnd1_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block2_tdnnd1_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1737984)))]; + tensor model_xvector_block2_tdnnd1_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block2_tdnnd1_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1770816)))]; + tensor model_xvector_block2_tdnnd1_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block2_tdnnd1_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1771008)))]; + tensor model_xvector_block2_tdnnd2_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block2_tdnnd2_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1779264)))]; + tensor model_xvector_block2_tdnnd2_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block2_tdnnd2_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1780480)))]; + tensor model_xvector_block2_tdnnd2_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block2_tdnnd2_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1781696)))]; + tensor model_xvector_block2_tdnnd2_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block2_tdnnd2_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1782912)))]; + tensor model_xvector_block2_tdnnd2_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block2_tdnnd2_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1784128)))]; + tensor model_xvector_block2_tdnnd2_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block2_tdnnd2_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1833344)))]; + tensor model_xvector_block2_tdnnd2_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block2_tdnnd2_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1833664)))]; + tensor model_xvector_block2_tdnnd2_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block2_tdnnd2_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1866496)))]; + tensor model_xvector_block2_tdnnd2_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block2_tdnnd2_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1866688)))]; + tensor model_xvector_block2_tdnnd3_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block2_tdnnd3_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1874944)))]; + tensor model_xvector_block2_tdnnd3_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block2_tdnnd3_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1876288)))]; + tensor model_xvector_block2_tdnnd3_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block2_tdnnd3_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1877632)))]; + tensor model_xvector_block2_tdnnd3_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block2_tdnnd3_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1878976)))]; + tensor model_xvector_block2_tdnnd3_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block2_tdnnd3_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1880320)))]; + tensor model_xvector_block2_tdnnd3_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block2_tdnnd3_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1929536)))]; + tensor model_xvector_block2_tdnnd3_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block2_tdnnd3_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1929856)))]; + tensor model_xvector_block2_tdnnd3_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block2_tdnnd3_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1962688)))]; + tensor model_xvector_block2_tdnnd3_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block2_tdnnd3_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1962880)))]; + tensor model_xvector_block2_tdnnd4_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block2_tdnnd4_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1971136)))]; + tensor model_xvector_block2_tdnnd4_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block2_tdnnd4_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1972608)))]; + tensor model_xvector_block2_tdnnd4_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block2_tdnnd4_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1974080)))]; + tensor model_xvector_block2_tdnnd4_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block2_tdnnd4_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1975552)))]; + tensor model_xvector_block2_tdnnd4_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block2_tdnnd4_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1977024)))]; + tensor model_xvector_block2_tdnnd4_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block2_tdnnd4_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2026240)))]; + tensor model_xvector_block2_tdnnd4_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block2_tdnnd4_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2026560)))]; + tensor model_xvector_block2_tdnnd4_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block2_tdnnd4_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2059392)))]; + tensor model_xvector_block2_tdnnd4_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block2_tdnnd4_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2059584)))]; + tensor model_xvector_block2_tdnnd5_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block2_tdnnd5_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2067840)))]; + tensor model_xvector_block2_tdnnd5_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block2_tdnnd5_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2069440)))]; + tensor model_xvector_block2_tdnnd5_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block2_tdnnd5_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2071040)))]; + tensor model_xvector_block2_tdnnd5_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block2_tdnnd5_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2072640)))]; + tensor model_xvector_block2_tdnnd5_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block2_tdnnd5_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2074240)))]; + tensor model_xvector_block2_tdnnd5_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block2_tdnnd5_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2123456)))]; + tensor model_xvector_block2_tdnnd5_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block2_tdnnd5_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2123776)))]; + tensor model_xvector_block2_tdnnd5_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block2_tdnnd5_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2156608)))]; + tensor model_xvector_block2_tdnnd5_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block2_tdnnd5_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2156800)))]; + tensor model_xvector_block2_tdnnd6_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block2_tdnnd6_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2165056)))]; + tensor model_xvector_block2_tdnnd6_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block2_tdnnd6_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2166784)))]; + tensor model_xvector_block2_tdnnd6_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block2_tdnnd6_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2168512)))]; + tensor model_xvector_block2_tdnnd6_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block2_tdnnd6_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2170240)))]; + tensor model_xvector_block2_tdnnd6_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block2_tdnnd6_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2171968)))]; + tensor model_xvector_block2_tdnnd6_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block2_tdnnd6_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2221184)))]; + tensor model_xvector_block2_tdnnd6_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block2_tdnnd6_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2221504)))]; + tensor model_xvector_block2_tdnnd6_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block2_tdnnd6_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2254336)))]; + tensor model_xvector_block2_tdnnd6_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block2_tdnnd6_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2254528)))]; + tensor model_xvector_block2_tdnnd7_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block2_tdnnd7_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2262784)))]; + tensor model_xvector_block2_tdnnd7_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block2_tdnnd7_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2264640)))]; + tensor model_xvector_block2_tdnnd7_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block2_tdnnd7_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2266496)))]; + tensor model_xvector_block2_tdnnd7_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block2_tdnnd7_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2268352)))]; + tensor model_xvector_block2_tdnnd7_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block2_tdnnd7_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2270208)))]; + tensor model_xvector_block2_tdnnd7_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block2_tdnnd7_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2319424)))]; + tensor model_xvector_block2_tdnnd7_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block2_tdnnd7_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2319744)))]; + tensor model_xvector_block2_tdnnd7_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block2_tdnnd7_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2352576)))]; + tensor model_xvector_block2_tdnnd7_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block2_tdnnd7_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2352768)))]; + tensor model_xvector_block2_tdnnd8_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block2_tdnnd8_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2361024)))]; + tensor model_xvector_block2_tdnnd8_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block2_tdnnd8_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2363008)))]; + tensor model_xvector_block2_tdnnd8_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block2_tdnnd8_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2364992)))]; + tensor model_xvector_block2_tdnnd8_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block2_tdnnd8_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2366976)))]; + tensor model_xvector_block2_tdnnd8_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block2_tdnnd8_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2368960)))]; + tensor model_xvector_block2_tdnnd8_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block2_tdnnd8_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2418176)))]; + tensor model_xvector_block2_tdnnd8_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block2_tdnnd8_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2418496)))]; + tensor model_xvector_block2_tdnnd8_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block2_tdnnd8_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2451328)))]; + tensor model_xvector_block2_tdnnd8_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block2_tdnnd8_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2451520)))]; + tensor model_xvector_block2_tdnnd9_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block2_tdnnd9_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2459776)))]; + tensor model_xvector_block2_tdnnd9_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block2_tdnnd9_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2461888)))]; + tensor model_xvector_block2_tdnnd9_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block2_tdnnd9_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2464000)))]; + tensor model_xvector_block2_tdnnd9_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block2_tdnnd9_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2466112)))]; + tensor model_xvector_block2_tdnnd9_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block2_tdnnd9_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2468224)))]; + tensor model_xvector_block2_tdnnd9_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block2_tdnnd9_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2517440)))]; + tensor model_xvector_block2_tdnnd9_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block2_tdnnd9_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2517760)))]; + tensor model_xvector_block2_tdnnd9_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block2_tdnnd9_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2550592)))]; + tensor model_xvector_block2_tdnnd9_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block2_tdnnd9_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2550784)))]; + tensor model_xvector_block2_tdnnd10_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block2_tdnnd10_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2559040)))]; + tensor model_xvector_block2_tdnnd10_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block2_tdnnd10_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2561280)))]; + tensor model_xvector_block2_tdnnd10_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block2_tdnnd10_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2563520)))]; + tensor model_xvector_block2_tdnnd10_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block2_tdnnd10_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2565760)))]; + tensor model_xvector_block2_tdnnd10_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block2_tdnnd10_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2568000)))]; + tensor model_xvector_block2_tdnnd10_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block2_tdnnd10_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2617216)))]; + tensor model_xvector_block2_tdnnd10_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block2_tdnnd10_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2617536)))]; + tensor model_xvector_block2_tdnnd10_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block2_tdnnd10_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2650368)))]; + tensor model_xvector_block2_tdnnd10_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block2_tdnnd10_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2650560)))]; + tensor model_xvector_block2_tdnnd11_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block2_tdnnd11_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2658816)))]; + tensor model_xvector_block2_tdnnd11_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block2_tdnnd11_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2661184)))]; + tensor model_xvector_block2_tdnnd11_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block2_tdnnd11_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2663552)))]; + tensor model_xvector_block2_tdnnd11_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block2_tdnnd11_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2665920)))]; + tensor model_xvector_block2_tdnnd11_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block2_tdnnd11_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2668288)))]; + tensor model_xvector_block2_tdnnd11_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block2_tdnnd11_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2717504)))]; + tensor model_xvector_block2_tdnnd11_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block2_tdnnd11_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2717824)))]; + tensor model_xvector_block2_tdnnd11_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block2_tdnnd11_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2750656)))]; + tensor model_xvector_block2_tdnnd11_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block2_tdnnd11_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2750848)))]; + tensor model_xvector_block2_tdnnd12_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block2_tdnnd12_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2759104)))]; + tensor model_xvector_block2_tdnnd12_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block2_tdnnd12_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2761600)))]; + tensor model_xvector_block2_tdnnd12_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block2_tdnnd12_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2764096)))]; + tensor model_xvector_block2_tdnnd12_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block2_tdnnd12_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2766592)))]; + tensor model_xvector_block2_tdnnd12_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block2_tdnnd12_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2769088)))]; + tensor model_xvector_block2_tdnnd12_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block2_tdnnd12_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2818304)))]; + tensor model_xvector_block2_tdnnd12_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block2_tdnnd12_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2818624)))]; + tensor model_xvector_block2_tdnnd12_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block2_tdnnd12_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2851456)))]; + tensor model_xvector_block2_tdnnd12_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block2_tdnnd12_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2851648)))]; + tensor model_xvector_block2_tdnnd13_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block2_tdnnd13_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2859904)))]; + tensor model_xvector_block2_tdnnd13_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block2_tdnnd13_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2862528)))]; + tensor model_xvector_block2_tdnnd13_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block2_tdnnd13_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2865152)))]; + tensor model_xvector_block2_tdnnd13_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block2_tdnnd13_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2867776)))]; + tensor model_xvector_block2_tdnnd13_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block2_tdnnd13_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2870400)))]; + tensor model_xvector_block2_tdnnd13_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block2_tdnnd13_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2919616)))]; + tensor model_xvector_block2_tdnnd13_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block2_tdnnd13_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2919936)))]; + tensor model_xvector_block2_tdnnd13_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block2_tdnnd13_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2952768)))]; + tensor model_xvector_block2_tdnnd13_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block2_tdnnd13_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2952960)))]; + tensor model_xvector_block2_tdnnd14_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block2_tdnnd14_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2961216)))]; + tensor model_xvector_block2_tdnnd14_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block2_tdnnd14_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2963968)))]; + tensor model_xvector_block2_tdnnd14_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block2_tdnnd14_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2966720)))]; + tensor model_xvector_block2_tdnnd14_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block2_tdnnd14_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2969472)))]; + tensor model_xvector_block2_tdnnd14_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block2_tdnnd14_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2972224)))]; + tensor model_xvector_block2_tdnnd14_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block2_tdnnd14_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3021440)))]; + tensor model_xvector_block2_tdnnd14_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block2_tdnnd14_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3021760)))]; + tensor model_xvector_block2_tdnnd14_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block2_tdnnd14_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3054592)))]; + tensor model_xvector_block2_tdnnd14_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block2_tdnnd14_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3054784)))]; + tensor model_xvector_block2_tdnnd15_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block2_tdnnd15_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3063040)))]; + tensor model_xvector_block2_tdnnd15_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block2_tdnnd15_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3065920)))]; + tensor model_xvector_block2_tdnnd15_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block2_tdnnd15_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3068800)))]; + tensor model_xvector_block2_tdnnd15_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block2_tdnnd15_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3071680)))]; + tensor model_xvector_block2_tdnnd15_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block2_tdnnd15_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3074560)))]; + tensor model_xvector_block2_tdnnd15_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block2_tdnnd15_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3123776)))]; + tensor model_xvector_block2_tdnnd15_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block2_tdnnd15_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3124096)))]; + tensor model_xvector_block2_tdnnd15_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block2_tdnnd15_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3156928)))]; + tensor model_xvector_block2_tdnnd15_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block2_tdnnd15_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3157120)))]; + tensor model_xvector_block2_tdnnd16_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block2_tdnnd16_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3165376)))]; + tensor model_xvector_block2_tdnnd16_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block2_tdnnd16_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3168384)))]; + tensor model_xvector_block2_tdnnd16_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block2_tdnnd16_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3171392)))]; + tensor model_xvector_block2_tdnnd16_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block2_tdnnd16_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3174400)))]; + tensor model_xvector_block2_tdnnd16_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block2_tdnnd16_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3177408)))]; + tensor model_xvector_block2_tdnnd16_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block2_tdnnd16_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3226624)))]; + tensor model_xvector_block2_tdnnd16_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block2_tdnnd16_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3226944)))]; + tensor model_xvector_block2_tdnnd16_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block2_tdnnd16_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3259776)))]; + tensor model_xvector_block2_tdnnd16_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block2_tdnnd16_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3259968)))]; + tensor model_xvector_block2_tdnnd17_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block2_tdnnd17_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3268224)))]; + tensor model_xvector_block2_tdnnd17_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block2_tdnnd17_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3271360)))]; + tensor model_xvector_block2_tdnnd17_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block2_tdnnd17_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3274496)))]; + tensor model_xvector_block2_tdnnd17_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block2_tdnnd17_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3277632)))]; + tensor model_xvector_block2_tdnnd17_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block2_tdnnd17_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3280768)))]; + tensor model_xvector_block2_tdnnd17_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block2_tdnnd17_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3329984)))]; + tensor model_xvector_block2_tdnnd17_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block2_tdnnd17_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3330304)))]; + tensor model_xvector_block2_tdnnd17_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block2_tdnnd17_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3363136)))]; + tensor model_xvector_block2_tdnnd17_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block2_tdnnd17_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3363328)))]; + tensor model_xvector_block2_tdnnd18_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block2_tdnnd18_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3371584)))]; + tensor model_xvector_block2_tdnnd18_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block2_tdnnd18_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3374848)))]; + tensor model_xvector_block2_tdnnd18_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block2_tdnnd18_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3378112)))]; + tensor model_xvector_block2_tdnnd18_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block2_tdnnd18_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3381376)))]; + tensor model_xvector_block2_tdnnd18_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block2_tdnnd18_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3384640)))]; + tensor model_xvector_block2_tdnnd18_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block2_tdnnd18_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3433856)))]; + tensor model_xvector_block2_tdnnd18_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block2_tdnnd18_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3434176)))]; + tensor model_xvector_block2_tdnnd18_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block2_tdnnd18_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3467008)))]; + tensor model_xvector_block2_tdnnd18_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block2_tdnnd18_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3467200)))]; + tensor model_xvector_block2_tdnnd19_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block2_tdnnd19_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3475456)))]; + tensor model_xvector_block2_tdnnd19_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block2_tdnnd19_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3478848)))]; + tensor model_xvector_block2_tdnnd19_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block2_tdnnd19_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3482240)))]; + tensor model_xvector_block2_tdnnd19_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block2_tdnnd19_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3485632)))]; + tensor model_xvector_block2_tdnnd19_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block2_tdnnd19_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3489024)))]; + tensor model_xvector_block2_tdnnd19_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block2_tdnnd19_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3538240)))]; + tensor model_xvector_block2_tdnnd19_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block2_tdnnd19_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3538560)))]; + tensor model_xvector_block2_tdnnd19_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block2_tdnnd19_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3571392)))]; + tensor model_xvector_block2_tdnnd19_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block2_tdnnd19_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3571584)))]; + tensor model_xvector_block2_tdnnd20_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block2_tdnnd20_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3579840)))]; + tensor model_xvector_block2_tdnnd20_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block2_tdnnd20_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3583360)))]; + tensor model_xvector_block2_tdnnd20_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block2_tdnnd20_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3586880)))]; + tensor model_xvector_block2_tdnnd20_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block2_tdnnd20_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3590400)))]; + tensor model_xvector_block2_tdnnd20_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block2_tdnnd20_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3593920)))]; + tensor model_xvector_block2_tdnnd20_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block2_tdnnd20_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3643136)))]; + tensor model_xvector_block2_tdnnd20_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block2_tdnnd20_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3643456)))]; + tensor model_xvector_block2_tdnnd20_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block2_tdnnd20_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3676288)))]; + tensor model_xvector_block2_tdnnd20_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block2_tdnnd20_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3676480)))]; + tensor model_xvector_block2_tdnnd21_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block2_tdnnd21_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3684736)))]; + tensor model_xvector_block2_tdnnd21_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block2_tdnnd21_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3688384)))]; + tensor model_xvector_block2_tdnnd21_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block2_tdnnd21_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3692032)))]; + tensor model_xvector_block2_tdnnd21_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block2_tdnnd21_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3695680)))]; + tensor model_xvector_block2_tdnnd21_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block2_tdnnd21_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3699328)))]; + tensor model_xvector_block2_tdnnd21_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block2_tdnnd21_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3748544)))]; + tensor model_xvector_block2_tdnnd21_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block2_tdnnd21_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3748864)))]; + tensor model_xvector_block2_tdnnd21_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block2_tdnnd21_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3781696)))]; + tensor model_xvector_block2_tdnnd21_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block2_tdnnd21_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3781888)))]; + tensor model_xvector_block2_tdnnd22_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block2_tdnnd22_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3790144)))]; + tensor model_xvector_block2_tdnnd22_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block2_tdnnd22_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3793920)))]; + tensor model_xvector_block2_tdnnd22_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block2_tdnnd22_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3797696)))]; + tensor model_xvector_block2_tdnnd22_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block2_tdnnd22_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3801472)))]; + tensor model_xvector_block2_tdnnd22_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block2_tdnnd22_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3805248)))]; + tensor model_xvector_block2_tdnnd22_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block2_tdnnd22_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3854464)))]; + tensor model_xvector_block2_tdnnd22_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block2_tdnnd22_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3854784)))]; + tensor model_xvector_block2_tdnnd22_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block2_tdnnd22_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3887616)))]; + tensor model_xvector_block2_tdnnd22_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block2_tdnnd22_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3887808)))]; + tensor model_xvector_block2_tdnnd23_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block2_tdnnd23_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3896064)))]; + tensor model_xvector_block2_tdnnd23_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block2_tdnnd23_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3899968)))]; + tensor model_xvector_block2_tdnnd23_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block2_tdnnd23_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3903872)))]; + tensor model_xvector_block2_tdnnd23_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block2_tdnnd23_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3907776)))]; + tensor model_xvector_block2_tdnnd23_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block2_tdnnd23_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3911680)))]; + tensor model_xvector_block2_tdnnd23_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block2_tdnnd23_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3960896)))]; + tensor model_xvector_block2_tdnnd23_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block2_tdnnd23_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3961216)))]; + tensor model_xvector_block2_tdnnd23_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block2_tdnnd23_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3994048)))]; + tensor model_xvector_block2_tdnnd23_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block2_tdnnd23_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3994240)))]; + tensor model_xvector_block2_tdnnd24_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block2_tdnnd24_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4002496)))]; + tensor model_xvector_block2_tdnnd24_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block2_tdnnd24_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4006528)))]; + tensor model_xvector_block2_tdnnd24_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block2_tdnnd24_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4010560)))]; + tensor model_xvector_block2_tdnnd24_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block2_tdnnd24_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4014592)))]; + tensor model_xvector_block2_tdnnd24_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block2_tdnnd24_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4018624)))]; + tensor model_xvector_block2_tdnnd24_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block2_tdnnd24_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4067840)))]; + tensor model_xvector_block2_tdnnd24_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block2_tdnnd24_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4068160)))]; + tensor model_xvector_block2_tdnnd24_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block2_tdnnd24_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4100992)))]; + tensor model_xvector_block2_tdnnd24_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block2_tdnnd24_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4101184)))]; + tensor model_xvector_transit2_nonlinear_batchnorm_running_var = const()[name = tensor("model_xvector_transit2_nonlinear_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4109440)))]; + tensor model_xvector_transit2_nonlinear_batchnorm_running_mean = const()[name = tensor("model_xvector_transit2_nonlinear_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4113600)))]; + tensor model_xvector_transit2_nonlinear_batchnorm_bias = const()[name = tensor("model_xvector_transit2_nonlinear_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4117760)))]; + tensor model_xvector_transit2_nonlinear_batchnorm_weight = const()[name = tensor("model_xvector_transit2_nonlinear_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4121920)))]; + tensor model_xvector_transit2_linear_weight = const()[name = tensor("model_xvector_transit2_linear_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4126080)))]; + tensor model_xvector_block3_tdnnd1_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block3_tdnnd1_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6223296)))]; + tensor model_xvector_block3_tdnnd1_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block3_tdnnd1_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6225408)))]; + tensor model_xvector_block3_tdnnd1_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block3_tdnnd1_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6227520)))]; + tensor model_xvector_block3_tdnnd1_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block3_tdnnd1_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6229632)))]; + tensor model_xvector_block3_tdnnd1_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block3_tdnnd1_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6231744)))]; + tensor model_xvector_block3_tdnnd1_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block3_tdnnd1_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6280960)))]; + tensor model_xvector_block3_tdnnd1_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block3_tdnnd1_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6281280)))]; + tensor model_xvector_block3_tdnnd1_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block3_tdnnd1_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6314112)))]; + tensor model_xvector_block3_tdnnd1_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block3_tdnnd1_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6314304)))]; + tensor model_xvector_block3_tdnnd2_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block3_tdnnd2_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6322560)))]; + tensor model_xvector_block3_tdnnd2_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block3_tdnnd2_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6324800)))]; + tensor model_xvector_block3_tdnnd2_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block3_tdnnd2_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6327040)))]; + tensor model_xvector_block3_tdnnd2_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block3_tdnnd2_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6329280)))]; + tensor model_xvector_block3_tdnnd2_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block3_tdnnd2_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6331520)))]; + tensor model_xvector_block3_tdnnd2_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block3_tdnnd2_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6380736)))]; + tensor model_xvector_block3_tdnnd2_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block3_tdnnd2_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6381056)))]; + tensor model_xvector_block3_tdnnd2_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block3_tdnnd2_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6413888)))]; + tensor model_xvector_block3_tdnnd2_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block3_tdnnd2_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6414080)))]; + tensor model_xvector_block3_tdnnd3_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block3_tdnnd3_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6422336)))]; + tensor model_xvector_block3_tdnnd3_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block3_tdnnd3_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6424704)))]; + tensor model_xvector_block3_tdnnd3_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block3_tdnnd3_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6427072)))]; + tensor model_xvector_block3_tdnnd3_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block3_tdnnd3_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6429440)))]; + tensor model_xvector_block3_tdnnd3_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block3_tdnnd3_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6431808)))]; + tensor model_xvector_block3_tdnnd3_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block3_tdnnd3_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6481024)))]; + tensor model_xvector_block3_tdnnd3_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block3_tdnnd3_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6481344)))]; + tensor model_xvector_block3_tdnnd3_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block3_tdnnd3_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6514176)))]; + tensor model_xvector_block3_tdnnd3_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block3_tdnnd3_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6514368)))]; + tensor model_xvector_block3_tdnnd4_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block3_tdnnd4_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6522624)))]; + tensor model_xvector_block3_tdnnd4_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block3_tdnnd4_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6525120)))]; + tensor model_xvector_block3_tdnnd4_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block3_tdnnd4_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6527616)))]; + tensor model_xvector_block3_tdnnd4_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block3_tdnnd4_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6530112)))]; + tensor model_xvector_block3_tdnnd4_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block3_tdnnd4_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6532608)))]; + tensor model_xvector_block3_tdnnd4_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block3_tdnnd4_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6581824)))]; + tensor model_xvector_block3_tdnnd4_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block3_tdnnd4_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6582144)))]; + tensor model_xvector_block3_tdnnd4_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block3_tdnnd4_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6614976)))]; + tensor model_xvector_block3_tdnnd4_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block3_tdnnd4_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6615168)))]; + tensor model_xvector_block3_tdnnd5_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block3_tdnnd5_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6623424)))]; + tensor model_xvector_block3_tdnnd5_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block3_tdnnd5_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6626048)))]; + tensor model_xvector_block3_tdnnd5_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block3_tdnnd5_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6628672)))]; + tensor model_xvector_block3_tdnnd5_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block3_tdnnd5_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6631296)))]; + tensor model_xvector_block3_tdnnd5_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block3_tdnnd5_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6633920)))]; + tensor model_xvector_block3_tdnnd5_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block3_tdnnd5_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6683136)))]; + tensor model_xvector_block3_tdnnd5_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block3_tdnnd5_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6683456)))]; + tensor model_xvector_block3_tdnnd5_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block3_tdnnd5_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6716288)))]; + tensor model_xvector_block3_tdnnd5_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block3_tdnnd5_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6716480)))]; + tensor model_xvector_block3_tdnnd6_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block3_tdnnd6_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6724736)))]; + tensor model_xvector_block3_tdnnd6_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block3_tdnnd6_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6727488)))]; + tensor model_xvector_block3_tdnnd6_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block3_tdnnd6_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6730240)))]; + tensor model_xvector_block3_tdnnd6_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block3_tdnnd6_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6732992)))]; + tensor model_xvector_block3_tdnnd6_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block3_tdnnd6_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6735744)))]; + tensor model_xvector_block3_tdnnd6_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block3_tdnnd6_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6784960)))]; + tensor model_xvector_block3_tdnnd6_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block3_tdnnd6_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6785280)))]; + tensor model_xvector_block3_tdnnd6_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block3_tdnnd6_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6818112)))]; + tensor model_xvector_block3_tdnnd6_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block3_tdnnd6_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6818304)))]; + tensor model_xvector_block3_tdnnd7_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block3_tdnnd7_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6826560)))]; + tensor model_xvector_block3_tdnnd7_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block3_tdnnd7_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6829440)))]; + tensor model_xvector_block3_tdnnd7_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block3_tdnnd7_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6832320)))]; + tensor model_xvector_block3_tdnnd7_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block3_tdnnd7_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6835200)))]; + tensor model_xvector_block3_tdnnd7_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block3_tdnnd7_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6838080)))]; + tensor model_xvector_block3_tdnnd7_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block3_tdnnd7_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6887296)))]; + tensor model_xvector_block3_tdnnd7_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block3_tdnnd7_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6887616)))]; + tensor model_xvector_block3_tdnnd7_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block3_tdnnd7_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6920448)))]; + tensor model_xvector_block3_tdnnd7_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block3_tdnnd7_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6920640)))]; + tensor model_xvector_block3_tdnnd8_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block3_tdnnd8_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6928896)))]; + tensor model_xvector_block3_tdnnd8_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block3_tdnnd8_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6931904)))]; + tensor model_xvector_block3_tdnnd8_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block3_tdnnd8_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6934912)))]; + tensor model_xvector_block3_tdnnd8_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block3_tdnnd8_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6937920)))]; + tensor model_xvector_block3_tdnnd8_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block3_tdnnd8_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6940928)))]; + tensor model_xvector_block3_tdnnd8_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block3_tdnnd8_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6990144)))]; + tensor model_xvector_block3_tdnnd8_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block3_tdnnd8_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6990464)))]; + tensor model_xvector_block3_tdnnd8_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block3_tdnnd8_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7023296)))]; + tensor model_xvector_block3_tdnnd8_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block3_tdnnd8_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7023488)))]; + tensor model_xvector_block3_tdnnd9_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block3_tdnnd9_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7031744)))]; + tensor model_xvector_block3_tdnnd9_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block3_tdnnd9_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7034880)))]; + tensor model_xvector_block3_tdnnd9_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block3_tdnnd9_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7038016)))]; + tensor model_xvector_block3_tdnnd9_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block3_tdnnd9_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7041152)))]; + tensor model_xvector_block3_tdnnd9_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block3_tdnnd9_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7044288)))]; + tensor model_xvector_block3_tdnnd9_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block3_tdnnd9_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7093504)))]; + tensor model_xvector_block3_tdnnd9_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block3_tdnnd9_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7093824)))]; + tensor model_xvector_block3_tdnnd9_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block3_tdnnd9_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7126656)))]; + tensor model_xvector_block3_tdnnd9_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block3_tdnnd9_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7126848)))]; + tensor model_xvector_block3_tdnnd10_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block3_tdnnd10_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7135104)))]; + tensor model_xvector_block3_tdnnd10_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block3_tdnnd10_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7138368)))]; + tensor model_xvector_block3_tdnnd10_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block3_tdnnd10_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7141632)))]; + tensor model_xvector_block3_tdnnd10_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block3_tdnnd10_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7144896)))]; + tensor model_xvector_block3_tdnnd10_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block3_tdnnd10_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7148160)))]; + tensor model_xvector_block3_tdnnd10_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block3_tdnnd10_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7197376)))]; + tensor model_xvector_block3_tdnnd10_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block3_tdnnd10_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7197696)))]; + tensor model_xvector_block3_tdnnd10_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block3_tdnnd10_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7230528)))]; + tensor model_xvector_block3_tdnnd10_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block3_tdnnd10_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7230720)))]; + tensor model_xvector_block3_tdnnd11_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block3_tdnnd11_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7238976)))]; + tensor model_xvector_block3_tdnnd11_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block3_tdnnd11_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7242368)))]; + tensor model_xvector_block3_tdnnd11_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block3_tdnnd11_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7245760)))]; + tensor model_xvector_block3_tdnnd11_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block3_tdnnd11_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7249152)))]; + tensor model_xvector_block3_tdnnd11_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block3_tdnnd11_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7252544)))]; + tensor model_xvector_block3_tdnnd11_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block3_tdnnd11_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7301760)))]; + tensor model_xvector_block3_tdnnd11_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block3_tdnnd11_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7302080)))]; + tensor model_xvector_block3_tdnnd11_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block3_tdnnd11_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7334912)))]; + tensor model_xvector_block3_tdnnd11_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block3_tdnnd11_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7335104)))]; + tensor model_xvector_block3_tdnnd12_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block3_tdnnd12_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7343360)))]; + tensor model_xvector_block3_tdnnd12_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block3_tdnnd12_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7346880)))]; + tensor model_xvector_block3_tdnnd12_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block3_tdnnd12_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7350400)))]; + tensor model_xvector_block3_tdnnd12_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block3_tdnnd12_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7353920)))]; + tensor model_xvector_block3_tdnnd12_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block3_tdnnd12_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7357440)))]; + tensor model_xvector_block3_tdnnd12_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block3_tdnnd12_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7406656)))]; + tensor model_xvector_block3_tdnnd12_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block3_tdnnd12_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7406976)))]; + tensor model_xvector_block3_tdnnd12_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block3_tdnnd12_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7439808)))]; + tensor model_xvector_block3_tdnnd12_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block3_tdnnd12_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7440000)))]; + tensor model_xvector_block3_tdnnd13_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block3_tdnnd13_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7448256)))]; + tensor model_xvector_block3_tdnnd13_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block3_tdnnd13_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7451904)))]; + tensor model_xvector_block3_tdnnd13_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block3_tdnnd13_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7455552)))]; + tensor model_xvector_block3_tdnnd13_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block3_tdnnd13_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7459200)))]; + tensor model_xvector_block3_tdnnd13_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block3_tdnnd13_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7462848)))]; + tensor model_xvector_block3_tdnnd13_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block3_tdnnd13_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7512064)))]; + tensor model_xvector_block3_tdnnd13_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block3_tdnnd13_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7512384)))]; + tensor model_xvector_block3_tdnnd13_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block3_tdnnd13_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7545216)))]; + tensor model_xvector_block3_tdnnd13_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block3_tdnnd13_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7545408)))]; + tensor model_xvector_block3_tdnnd14_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block3_tdnnd14_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7553664)))]; + tensor model_xvector_block3_tdnnd14_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block3_tdnnd14_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7557440)))]; + tensor model_xvector_block3_tdnnd14_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block3_tdnnd14_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7561216)))]; + tensor model_xvector_block3_tdnnd14_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block3_tdnnd14_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7564992)))]; + tensor model_xvector_block3_tdnnd14_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block3_tdnnd14_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7568768)))]; + tensor model_xvector_block3_tdnnd14_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block3_tdnnd14_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7617984)))]; + tensor model_xvector_block3_tdnnd14_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block3_tdnnd14_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7618304)))]; + tensor model_xvector_block3_tdnnd14_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block3_tdnnd14_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7651136)))]; + tensor model_xvector_block3_tdnnd14_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block3_tdnnd14_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7651328)))]; + tensor model_xvector_block3_tdnnd15_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block3_tdnnd15_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7659584)))]; + tensor model_xvector_block3_tdnnd15_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block3_tdnnd15_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7663488)))]; + tensor model_xvector_block3_tdnnd15_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block3_tdnnd15_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7667392)))]; + tensor model_xvector_block3_tdnnd15_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block3_tdnnd15_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7671296)))]; + tensor model_xvector_block3_tdnnd15_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block3_tdnnd15_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7675200)))]; + tensor model_xvector_block3_tdnnd15_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block3_tdnnd15_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7724416)))]; + tensor model_xvector_block3_tdnnd15_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block3_tdnnd15_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7724736)))]; + tensor model_xvector_block3_tdnnd15_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block3_tdnnd15_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7757568)))]; + tensor model_xvector_block3_tdnnd15_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block3_tdnnd15_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7757760)))]; + tensor model_xvector_block3_tdnnd16_nonlinear1_batchnorm_running_var = const()[name = tensor("model_xvector_block3_tdnnd16_nonlinear1_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7766016)))]; + tensor model_xvector_block3_tdnnd16_nonlinear1_batchnorm_running_mean = const()[name = tensor("model_xvector_block3_tdnnd16_nonlinear1_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7770048)))]; + tensor model_xvector_block3_tdnnd16_nonlinear1_batchnorm_bias = const()[name = tensor("model_xvector_block3_tdnnd16_nonlinear1_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7774080)))]; + tensor model_xvector_block3_tdnnd16_nonlinear1_batchnorm_weight = const()[name = tensor("model_xvector_block3_tdnnd16_nonlinear1_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7778112)))]; + tensor model_xvector_block3_tdnnd16_cam_layer_linear_local_weight = const()[name = tensor("model_xvector_block3_tdnnd16_cam_layer_linear_local_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7782144)))]; + tensor model_xvector_block3_tdnnd16_cam_layer_linear1_bias = const()[name = tensor("model_xvector_block3_tdnnd16_cam_layer_linear1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7831360)))]; + tensor model_xvector_block3_tdnnd16_cam_layer_linear1_weight = const()[name = tensor("model_xvector_block3_tdnnd16_cam_layer_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7831680)))]; + tensor model_xvector_block3_tdnnd16_cam_layer_linear2_bias = const()[name = tensor("model_xvector_block3_tdnnd16_cam_layer_linear2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7864512)))]; + tensor model_xvector_block3_tdnnd16_cam_layer_linear2_weight = const()[name = tensor("model_xvector_block3_tdnnd16_cam_layer_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7864704)))]; + tensor model_xvector_transit3_nonlinear_batchnorm_running_var = const()[name = tensor("model_xvector_transit3_nonlinear_batchnorm_running_var"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7872960)))]; + tensor model_xvector_transit3_nonlinear_batchnorm_running_mean = const()[name = tensor("model_xvector_transit3_nonlinear_batchnorm_running_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7877120)))]; + tensor model_xvector_transit3_nonlinear_batchnorm_bias = const()[name = tensor("model_xvector_transit3_nonlinear_batchnorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7881280)))]; + tensor model_xvector_transit3_nonlinear_batchnorm_weight = const()[name = tensor("model_xvector_transit3_nonlinear_batchnorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7885440)))]; + tensor model_xvector_dense_linear_weight = const()[name = tensor("model_xvector_dense_linear_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7889600)))]; + tensor var_3 = const()[name = tensor("op_3"), val = tensor(100)]; + tensor var_4 = const()[name = tensor("op_4"), val = tensor(-1)]; + tensor var_8 = const()[name = tensor("op_8"), val = tensor(0x1.4f8b58p-17)]; + tensor var_11 = const()[name = tensor("op_11"), val = tensor(1)]; + tensor var_16 = const()[name = tensor("op_16"), val = tensor([0, 2, 1])]; + tensor input_1_axes_0 = const()[name = tensor("input_1_axes_0"), val = tensor([1])]; + tensor x_1 = transpose(perm = var_16, x = feats)[name = tensor("transpose_0")]; + tensor input_1 = expand_dims(axes = input_1_axes_0, x = x_1)[name = tensor("input_1")]; + tensor input_3_pad_type_0 = const()[name = tensor("input_3_pad_type_0"), val = tensor("custom")]; + tensor input_3_pad_0 = const()[name = tensor("input_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_3_strides_0 = const()[name = tensor("input_3_strides_0"), val = tensor([1, 1])]; + tensor input_3_dilations_0 = const()[name = tensor("input_3_dilations_0"), val = tensor([1, 1])]; + tensor input_3_groups_0 = const()[name = tensor("input_3_groups_0"), val = tensor(1)]; + tensor const_0 = const()[name = tensor("const_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8676096)))]; + tensor const_1 = const()[name = tensor("const_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8677312)))]; + tensor input_5 = conv(bias = const_1, dilations = input_3_dilations_0, groups = input_3_groups_0, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = input_3_strides_0, weight = const_0, x = input_1)[name = tensor("input_5")]; + tensor input_7 = relu(x = input_5)[name = tensor("input_7")]; + tensor input_9_pad_type_0 = const()[name = tensor("input_9_pad_type_0"), val = tensor("custom")]; + tensor input_9_pad_0 = const()[name = tensor("input_9_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_9_strides_0 = const()[name = tensor("input_9_strides_0"), val = tensor([2, 1])]; + tensor input_9_dilations_0 = const()[name = tensor("input_9_dilations_0"), val = tensor([1, 1])]; + tensor input_9_groups_0 = const()[name = tensor("input_9_groups_0"), val = tensor(1)]; + tensor const_2 = const()[name = tensor("const_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8677504)))]; + tensor const_3 = const()[name = tensor("const_3"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8714432)))]; + tensor input_11 = conv(bias = const_3, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = const_2, x = input_7)[name = tensor("input_11")]; + tensor input_13 = relu(x = input_11)[name = tensor("input_13")]; + tensor input_15_pad_type_0 = const()[name = tensor("input_15_pad_type_0"), val = tensor("custom")]; + tensor input_15_pad_0 = const()[name = tensor("input_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_15_strides_0 = const()[name = tensor("input_15_strides_0"), val = tensor([1, 1])]; + tensor input_15_dilations_0 = const()[name = tensor("input_15_dilations_0"), val = tensor([1, 1])]; + tensor input_15_groups_0 = const()[name = tensor("input_15_groups_0"), val = tensor(1)]; + tensor const_4 = const()[name = tensor("const_4"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8714624)))]; + tensor const_5 = const()[name = tensor("const_5"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8751552)))]; + tensor out_1 = conv(bias = const_5, dilations = input_15_dilations_0, groups = input_15_groups_0, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = input_15_strides_0, weight = const_4, x = input_13)[name = tensor("out_1")]; + tensor input_17_pad_type_0 = const()[name = tensor("input_17_pad_type_0"), val = tensor("valid")]; + tensor input_17_strides_0 = const()[name = tensor("input_17_strides_0"), val = tensor([2, 1])]; + tensor input_17_pad_0 = const()[name = tensor("input_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_17_dilations_0 = const()[name = tensor("input_17_dilations_0"), val = tensor([1, 1])]; + tensor input_17_groups_0 = const()[name = tensor("input_17_groups_0"), val = tensor(1)]; + tensor const_6 = const()[name = tensor("const_6"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8751744)))]; + tensor const_7 = const()[name = tensor("const_7"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8755904)))]; + tensor var_79 = conv(bias = const_7, dilations = input_17_dilations_0, groups = input_17_groups_0, pad = input_17_pad_0, pad_type = input_17_pad_type_0, strides = input_17_strides_0, weight = const_6, x = input_7)[name = tensor("op_79")]; + tensor input_19 = add(x = out_1, y = var_79)[name = tensor("input_19")]; + tensor input_21 = relu(x = input_19)[name = tensor("input_21")]; + tensor input_23_pad_type_0 = const()[name = tensor("input_23_pad_type_0"), val = tensor("custom")]; + tensor input_23_pad_0 = const()[name = tensor("input_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_23_strides_0 = const()[name = tensor("input_23_strides_0"), val = tensor([1, 1])]; + tensor input_23_dilations_0 = const()[name = tensor("input_23_dilations_0"), val = tensor([1, 1])]; + tensor input_23_groups_0 = const()[name = tensor("input_23_groups_0"), val = tensor(1)]; + tensor const_8 = const()[name = tensor("const_8"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8756096)))]; + tensor const_9 = const()[name = tensor("const_9"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8793024)))]; + tensor input_25 = conv(bias = const_9, dilations = input_23_dilations_0, groups = input_23_groups_0, pad = input_23_pad_0, pad_type = input_23_pad_type_0, strides = input_23_strides_0, weight = const_8, x = input_21)[name = tensor("input_25")]; + tensor input_27 = relu(x = input_25)[name = tensor("input_27")]; + tensor input_29_pad_type_0 = const()[name = tensor("input_29_pad_type_0"), val = tensor("custom")]; + tensor input_29_pad_0 = const()[name = tensor("input_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_29_strides_0 = const()[name = tensor("input_29_strides_0"), val = tensor([1, 1])]; + tensor input_29_dilations_0 = const()[name = tensor("input_29_dilations_0"), val = tensor([1, 1])]; + tensor input_29_groups_0 = const()[name = tensor("input_29_groups_0"), val = tensor(1)]; + tensor const_10 = const()[name = tensor("const_10"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8793216)))]; + tensor const_11 = const()[name = tensor("const_11"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8830144)))]; + tensor out_3 = conv(bias = const_11, dilations = input_29_dilations_0, groups = input_29_groups_0, pad = input_29_pad_0, pad_type = input_29_pad_type_0, strides = input_29_strides_0, weight = const_10, x = input_27)[name = tensor("out_3")]; + tensor input_31 = add(x = out_3, y = input_21)[name = tensor("input_31")]; + tensor input_33 = relu(x = input_31)[name = tensor("input_33")]; + tensor input_35_pad_type_0 = const()[name = tensor("input_35_pad_type_0"), val = tensor("custom")]; + tensor input_35_pad_0 = const()[name = tensor("input_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_35_strides_0 = const()[name = tensor("input_35_strides_0"), val = tensor([2, 1])]; + tensor input_35_dilations_0 = const()[name = tensor("input_35_dilations_0"), val = tensor([1, 1])]; + tensor input_35_groups_0 = const()[name = tensor("input_35_groups_0"), val = tensor(1)]; + tensor const_12 = const()[name = tensor("const_12"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8830336)))]; + tensor const_13 = const()[name = tensor("const_13"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8867264)))]; + tensor input_37 = conv(bias = const_13, dilations = input_35_dilations_0, groups = input_35_groups_0, pad = input_35_pad_0, pad_type = input_35_pad_type_0, strides = input_35_strides_0, weight = const_12, x = input_33)[name = tensor("input_37")]; + tensor input_39 = relu(x = input_37)[name = tensor("input_39")]; + tensor input_41_pad_type_0 = const()[name = tensor("input_41_pad_type_0"), val = tensor("custom")]; + tensor input_41_pad_0 = const()[name = tensor("input_41_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_41_strides_0 = const()[name = tensor("input_41_strides_0"), val = tensor([1, 1])]; + tensor input_41_dilations_0 = const()[name = tensor("input_41_dilations_0"), val = tensor([1, 1])]; + tensor input_41_groups_0 = const()[name = tensor("input_41_groups_0"), val = tensor(1)]; + tensor const_14 = const()[name = tensor("const_14"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8867456)))]; + tensor const_15 = const()[name = tensor("const_15"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8904384)))]; + tensor out_5 = conv(bias = const_15, dilations = input_41_dilations_0, groups = input_41_groups_0, pad = input_41_pad_0, pad_type = input_41_pad_type_0, strides = input_41_strides_0, weight = const_14, x = input_39)[name = tensor("out_5")]; + tensor input_43_pad_type_0 = const()[name = tensor("input_43_pad_type_0"), val = tensor("valid")]; + tensor input_43_strides_0 = const()[name = tensor("input_43_strides_0"), val = tensor([2, 1])]; + tensor input_43_pad_0 = const()[name = tensor("input_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_43_dilations_0 = const()[name = tensor("input_43_dilations_0"), val = tensor([1, 1])]; + tensor input_43_groups_0 = const()[name = tensor("input_43_groups_0"), val = tensor(1)]; + tensor const_16 = const()[name = tensor("const_16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8904576)))]; + tensor const_17 = const()[name = tensor("const_17"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8908736)))]; + tensor var_153 = conv(bias = const_17, dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = const_16, x = input_33)[name = tensor("op_153")]; + tensor input_45 = add(x = out_5, y = var_153)[name = tensor("input_45")]; + tensor input_47 = relu(x = input_45)[name = tensor("input_47")]; + tensor input_49_pad_type_0 = const()[name = tensor("input_49_pad_type_0"), val = tensor("custom")]; + tensor input_49_pad_0 = const()[name = tensor("input_49_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_49_strides_0 = const()[name = tensor("input_49_strides_0"), val = tensor([1, 1])]; + tensor input_49_dilations_0 = const()[name = tensor("input_49_dilations_0"), val = tensor([1, 1])]; + tensor input_49_groups_0 = const()[name = tensor("input_49_groups_0"), val = tensor(1)]; + tensor const_18 = const()[name = tensor("const_18"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8908928)))]; + tensor const_19 = const()[name = tensor("const_19"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8945856)))]; + tensor input_51 = conv(bias = const_19, dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = const_18, x = input_47)[name = tensor("input_51")]; + tensor input_53 = relu(x = input_51)[name = tensor("input_53")]; + tensor input_55_pad_type_0 = const()[name = tensor("input_55_pad_type_0"), val = tensor("custom")]; + tensor input_55_pad_0 = const()[name = tensor("input_55_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_55_strides_0 = const()[name = tensor("input_55_strides_0"), val = tensor([1, 1])]; + tensor input_55_dilations_0 = const()[name = tensor("input_55_dilations_0"), val = tensor([1, 1])]; + tensor input_55_groups_0 = const()[name = tensor("input_55_groups_0"), val = tensor(1)]; + tensor const_20 = const()[name = tensor("const_20"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8946048)))]; + tensor const_21 = const()[name = tensor("const_21"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8982976)))]; + tensor out_7 = conv(bias = const_21, dilations = input_55_dilations_0, groups = input_55_groups_0, pad = input_55_pad_0, pad_type = input_55_pad_type_0, strides = input_55_strides_0, weight = const_20, x = input_53)[name = tensor("out_7")]; + tensor input_57 = add(x = out_7, y = input_47)[name = tensor("input_57")]; + tensor input_59 = relu(x = input_57)[name = tensor("input_59")]; + tensor input_61_pad_type_0 = const()[name = tensor("input_61_pad_type_0"), val = tensor("custom")]; + tensor input_61_pad_0 = const()[name = tensor("input_61_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_61_strides_0 = const()[name = tensor("input_61_strides_0"), val = tensor([2, 1])]; + tensor input_61_dilations_0 = const()[name = tensor("input_61_dilations_0"), val = tensor([1, 1])]; + tensor input_61_groups_0 = const()[name = tensor("input_61_groups_0"), val = tensor(1)]; + tensor const_22 = const()[name = tensor("const_22"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8983168)))]; + tensor const_23 = const()[name = tensor("const_23"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9020096)))]; + tensor input_63 = conv(bias = const_23, dilations = input_61_dilations_0, groups = input_61_groups_0, pad = input_61_pad_0, pad_type = input_61_pad_type_0, strides = input_61_strides_0, weight = const_22, x = input_59)[name = tensor("input_63")]; + tensor out = relu(x = input_63)[name = tensor("out")]; + tensor concat_0x = const()[name = tensor("concat_0x"), val = tensor([1, 320, -1])]; + tensor input_65 = reshape(shape = concat_0x, x = out)[name = tensor("input_65")]; + tensor input_67_pad_type_0 = const()[name = tensor("input_67_pad_type_0"), val = tensor("custom")]; + tensor input_67_pad_0 = const()[name = tensor("input_67_pad_0"), val = tensor([2, 2])]; + tensor input_67_strides_0 = const()[name = tensor("input_67_strides_0"), val = tensor([2])]; + tensor input_67_dilations_0 = const()[name = tensor("input_67_dilations_0"), val = tensor([1])]; + tensor input_67_groups_0 = const()[name = tensor("input_67_groups_0"), val = tensor(1)]; + tensor const_24 = const()[name = tensor("const_24"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9020288)))]; + tensor const_25 = const()[name = tensor("const_25"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9839552)))]; + tensor input_69 = conv(bias = const_25, dilations = input_67_dilations_0, groups = input_67_groups_0, pad = input_67_pad_0, pad_type = input_67_pad_type_0, strides = input_67_strides_0, weight = const_24, x = input_65)[name = tensor("input_69")]; + tensor input_71 = relu(x = input_69)[name = tensor("input_71")]; + tensor input_73 = batch_norm(beta = model_xvector_block1_tdnnd1_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block1_tdnnd1_nonlinear1_batchnorm_weight, mean = model_xvector_block1_tdnnd1_nonlinear1_batchnorm_running_mean, variance = model_xvector_block1_tdnnd1_nonlinear1_batchnorm_running_var, x = input_71)[name = tensor("input_73")]; + tensor input_75 = relu(x = input_73)[name = tensor("input_75")]; + tensor input_77_pad_type_0 = const()[name = tensor("input_77_pad_type_0"), val = tensor("valid")]; + tensor input_77_strides_0 = const()[name = tensor("input_77_strides_0"), val = tensor([1])]; + tensor input_77_pad_0 = const()[name = tensor("input_77_pad_0"), val = tensor([0, 0])]; + tensor input_77_dilations_0 = const()[name = tensor("input_77_dilations_0"), val = tensor([1])]; + tensor input_77_groups_0 = const()[name = tensor("input_77_groups_0"), val = tensor(1)]; + tensor const_26 = const()[name = tensor("const_26"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9840128)))]; + tensor const_27 = const()[name = tensor("const_27"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9905728)))]; + tensor input_79 = conv(bias = const_27, dilations = input_77_dilations_0, groups = input_77_groups_0, pad = input_77_pad_0, pad_type = input_77_pad_type_0, strides = input_77_strides_0, weight = const_26, x = input_75)[name = tensor("input_79")]; + tensor input_81 = relu(x = input_79)[name = tensor("input_81")]; + tensor y_1_pad_type_0 = const()[name = tensor("y_1_pad_type_0"), val = tensor("custom")]; + tensor y_1_pad_0 = const()[name = tensor("y_1_pad_0"), val = tensor([1, 1])]; + tensor y_1_strides_0 = const()[name = tensor("y_1_strides_0"), val = tensor([1])]; + tensor y_1_dilations_0 = const()[name = tensor("y_1_dilations_0"), val = tensor([1])]; + tensor y_1_groups_0 = const()[name = tensor("y_1_groups_0"), val = tensor(1)]; + tensor y_1 = conv(dilations = y_1_dilations_0, groups = y_1_groups_0, pad = y_1_pad_0, pad_type = y_1_pad_type_0, strides = y_1_strides_0, weight = model_xvector_block1_tdnnd1_cam_layer_linear_local_weight, x = input_81)[name = tensor("y_1")]; + tensor var_277_axes_0 = const()[name = tensor("op_277_axes_0"), val = tensor([-1])]; + tensor var_277_keep_dims_0 = const()[name = tensor("op_277_keep_dims_0"), val = tensor(true)]; + tensor var_277 = reduce_mean(axes = var_277_axes_0, keep_dims = var_277_keep_dims_0, x = input_81)[name = tensor("op_277")]; + tensor var_278 = const()[name = tensor("op_278"), val = tensor([100])]; + tensor var_279 = const()[name = tensor("op_279"), val = tensor([100])]; + tensor seg_1_pad_type_0 = const()[name = tensor("seg_1_pad_type_0"), val = tensor("custom")]; + tensor seg_1_pad_0 = const()[name = tensor("seg_1_pad_0"), val = tensor([0, 0])]; + tensor seg_1_exclude_padding_from_average_0 = const()[name = tensor("seg_1_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_1_ceil_mode_0 = const()[name = tensor("seg_1_ceil_mode_0"), val = tensor(true)]; + tensor seg_1 = avg_pool(ceil_mode = seg_1_ceil_mode_0, exclude_padding_from_average = seg_1_exclude_padding_from_average_0, kernel_sizes = var_278, pad = seg_1_pad_0, pad_type = seg_1_pad_type_0, strides = var_279, x = input_81)[name = tensor("seg_1")]; + tensor var_282_shape = shape(x = seg_1)[name = tensor("op_282_shape")]; + tensor gather_4 = const()[name = tensor("gather_4"), val = tensor(1)]; + tensor gather_5 = const()[name = tensor("gather_5"), val = tensor(128)]; + tensor select_6 = const()[name = tensor("select_6"), val = tensor(2)]; + tensor gather_6_axis_0 = const()[name = tensor("gather_6_axis_0"), val = tensor(0)]; + tensor gather_6_batch_dims_0 = const()[name = tensor("gather_6_batch_dims_0"), val = tensor(0)]; + tensor gather_6_validate_indices_0 = const()[name = tensor("gather_6_validate_indices_0"), val = tensor(false)]; + tensor gather_6 = gather(axis = gather_6_axis_0, batch_dims = gather_6_batch_dims_0, indices = select_6, validate_indices = gather_6_validate_indices_0, x = var_282_shape)[name = tensor("gather_6")]; + tensor var_285_axes_0 = const()[name = tensor("op_285_axes_0"), val = tensor([-1])]; + tensor var_285 = expand_dims(axes = var_285_axes_0, x = seg_1)[name = tensor("op_285")]; + tensor concat_1_axis_0 = const()[name = tensor("concat_1_axis_0"), val = tensor(0)]; + tensor concat_1_interleave_0 = const()[name = tensor("concat_1_interleave_0"), val = tensor(false)]; + tensor concat_1 = concat(axis = concat_1_axis_0, interleave = concat_1_interleave_0, values = (gather_4, gather_5, gather_6, var_3))[name = tensor("concat_1")]; + tensor shape_7 = shape(x = var_285)[name = tensor("shape_7")]; + tensor real_div_0 = real_div(x = concat_1, y = shape_7)[name = tensor("real_div_0")]; + tensor var_287 = tile(reps = real_div_0, x = var_285)[name = tensor("op_287")]; + tensor var_288 = const()[name = tensor("op_288"), val = tensor([1, 128, -1])]; + tensor seg_3 = reshape(shape = var_288, x = var_287)[name = tensor("seg_3")]; + tensor var_290_shape = shape(x = input_81)[name = tensor("op_290_shape")]; + tensor select_7 = const()[name = tensor("select_7"), val = tensor(2)]; + tensor gather_7_axis_0 = const()[name = tensor("gather_7_axis_0"), val = tensor(0)]; + tensor gather_7_batch_dims_0 = const()[name = tensor("gather_7_batch_dims_0"), val = tensor(0)]; + tensor gather_7_validate_indices_0 = const()[name = tensor("gather_7_validate_indices_0"), val = tensor(false)]; + tensor gather_7 = gather(axis = gather_7_axis_0, batch_dims = gather_7_batch_dims_0, indices = select_7, validate_indices = gather_7_validate_indices_0, x = var_290_shape)[name = tensor("gather_7")]; + tensor concat_2_values0_0 = const()[name = tensor("concat_2_values0_0"), val = tensor(1)]; + tensor concat_2_values1_0 = const()[name = tensor("concat_2_values1_0"), val = tensor(128)]; + tensor concat_2_axis_0 = const()[name = tensor("concat_2_axis_0"), val = tensor(0)]; + tensor concat_2_interleave_0 = const()[name = tensor("concat_2_interleave_0"), val = tensor(false)]; + tensor concat_2 = concat(axis = concat_2_axis_0, interleave = concat_2_interleave_0, values = (concat_2_values0_0, concat_2_values1_0, gather_7))[name = tensor("concat_2")]; + tensor var_291_begin_0 = const()[name = tensor("op_291_begin_0"), val = tensor([0, 0, 0])]; + tensor var_291_end_mask_0 = const()[name = tensor("op_291_end_mask_0"), val = tensor([true, true, false])]; + tensor var_291 = slice_by_index(begin = var_291_begin_0, end = concat_2, end_mask = var_291_end_mask_0, x = seg_3)[name = tensor("op_291")]; + tensor input_83 = add(x = var_277, y = var_291)[name = tensor("input_83")]; + tensor input_85_pad_type_0 = const()[name = tensor("input_85_pad_type_0"), val = tensor("valid")]; + tensor input_85_strides_0 = const()[name = tensor("input_85_strides_0"), val = tensor([1])]; + tensor input_85_pad_0 = const()[name = tensor("input_85_pad_0"), val = tensor([0, 0])]; + tensor input_85_dilations_0 = const()[name = tensor("input_85_dilations_0"), val = tensor([1])]; + tensor input_85_groups_0 = const()[name = tensor("input_85_groups_0"), val = tensor(1)]; + tensor input_85 = conv(bias = model_xvector_block1_tdnnd1_cam_layer_linear1_bias, dilations = input_85_dilations_0, groups = input_85_groups_0, pad = input_85_pad_0, pad_type = input_85_pad_type_0, strides = input_85_strides_0, weight = model_xvector_block1_tdnnd1_cam_layer_linear1_weight, x = input_83)[name = tensor("input_85")]; + tensor input_87 = relu(x = input_85)[name = tensor("input_87")]; + tensor input_89_pad_type_0 = const()[name = tensor("input_89_pad_type_0"), val = tensor("valid")]; + tensor input_89_strides_0 = const()[name = tensor("input_89_strides_0"), val = tensor([1])]; + tensor input_89_pad_0 = const()[name = tensor("input_89_pad_0"), val = tensor([0, 0])]; + tensor input_89_dilations_0 = const()[name = tensor("input_89_dilations_0"), val = tensor([1])]; + tensor input_89_groups_0 = const()[name = tensor("input_89_groups_0"), val = tensor(1)]; + tensor input_89 = conv(bias = model_xvector_block1_tdnnd1_cam_layer_linear2_bias, dilations = input_89_dilations_0, groups = input_89_groups_0, pad = input_89_pad_0, pad_type = input_89_pad_type_0, strides = input_89_strides_0, weight = model_xvector_block1_tdnnd1_cam_layer_linear2_weight, x = input_87)[name = tensor("input_89")]; + tensor m_1 = sigmoid(x = input_89)[name = tensor("m_1")]; + tensor var_309 = mul(x = y_1, y = m_1)[name = tensor("op_309")]; + tensor input_91_interleave_0 = const()[name = tensor("input_91_interleave_0"), val = tensor(false)]; + tensor input_91 = concat(axis = var_11, interleave = input_91_interleave_0, values = (input_71, var_309))[name = tensor("input_91")]; + tensor input_93 = batch_norm(beta = model_xvector_block1_tdnnd2_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block1_tdnnd2_nonlinear1_batchnorm_weight, mean = model_xvector_block1_tdnnd2_nonlinear1_batchnorm_running_mean, variance = model_xvector_block1_tdnnd2_nonlinear1_batchnorm_running_var, x = input_91)[name = tensor("input_93")]; + tensor input_95 = relu(x = input_93)[name = tensor("input_95")]; + tensor input_97_pad_type_0 = const()[name = tensor("input_97_pad_type_0"), val = tensor("valid")]; + tensor input_97_strides_0 = const()[name = tensor("input_97_strides_0"), val = tensor([1])]; + tensor input_97_pad_0 = const()[name = tensor("input_97_pad_0"), val = tensor([0, 0])]; + tensor input_97_dilations_0 = const()[name = tensor("input_97_dilations_0"), val = tensor([1])]; + tensor input_97_groups_0 = const()[name = tensor("input_97_groups_0"), val = tensor(1)]; + tensor const_28 = const()[name = tensor("const_28"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9906304)))]; + tensor const_29 = const()[name = tensor("const_29"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9988288)))]; + tensor input_99 = conv(bias = const_29, dilations = input_97_dilations_0, groups = input_97_groups_0, pad = input_97_pad_0, pad_type = input_97_pad_type_0, strides = input_97_strides_0, weight = const_28, x = input_95)[name = tensor("input_99")]; + tensor input_101 = relu(x = input_99)[name = tensor("input_101")]; + tensor y_3_pad_type_0 = const()[name = tensor("y_3_pad_type_0"), val = tensor("custom")]; + tensor y_3_pad_0 = const()[name = tensor("y_3_pad_0"), val = tensor([1, 1])]; + tensor y_3_strides_0 = const()[name = tensor("y_3_strides_0"), val = tensor([1])]; + tensor y_3_dilations_0 = const()[name = tensor("y_3_dilations_0"), val = tensor([1])]; + tensor y_3_groups_0 = const()[name = tensor("y_3_groups_0"), val = tensor(1)]; + tensor y_3 = conv(dilations = y_3_dilations_0, groups = y_3_groups_0, pad = y_3_pad_0, pad_type = y_3_pad_type_0, strides = y_3_strides_0, weight = model_xvector_block1_tdnnd2_cam_layer_linear_local_weight, x = input_101)[name = tensor("y_3")]; + tensor var_346_axes_0 = const()[name = tensor("op_346_axes_0"), val = tensor([-1])]; + tensor var_346_keep_dims_0 = const()[name = tensor("op_346_keep_dims_0"), val = tensor(true)]; + tensor var_346 = reduce_mean(axes = var_346_axes_0, keep_dims = var_346_keep_dims_0, x = input_101)[name = tensor("op_346")]; + tensor var_347 = const()[name = tensor("op_347"), val = tensor([100])]; + tensor var_348 = const()[name = tensor("op_348"), val = tensor([100])]; + tensor seg_5_pad_type_0 = const()[name = tensor("seg_5_pad_type_0"), val = tensor("custom")]; + tensor seg_5_pad_0 = const()[name = tensor("seg_5_pad_0"), val = tensor([0, 0])]; + tensor seg_5_exclude_padding_from_average_0 = const()[name = tensor("seg_5_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_5_ceil_mode_0 = const()[name = tensor("seg_5_ceil_mode_0"), val = tensor(true)]; + tensor seg_5 = avg_pool(ceil_mode = seg_5_ceil_mode_0, exclude_padding_from_average = seg_5_exclude_padding_from_average_0, kernel_sizes = var_347, pad = seg_5_pad_0, pad_type = seg_5_pad_type_0, strides = var_348, x = input_101)[name = tensor("seg_5")]; + tensor var_351_shape = shape(x = seg_5)[name = tensor("op_351_shape")]; + tensor gather_8 = const()[name = tensor("gather_8"), val = tensor(1)]; + tensor gather_9 = const()[name = tensor("gather_9"), val = tensor(128)]; + tensor select_10 = const()[name = tensor("select_10"), val = tensor(2)]; + tensor gather_10_axis_0 = const()[name = tensor("gather_10_axis_0"), val = tensor(0)]; + tensor gather_10_batch_dims_0 = const()[name = tensor("gather_10_batch_dims_0"), val = tensor(0)]; + tensor gather_10_validate_indices_0 = const()[name = tensor("gather_10_validate_indices_0"), val = tensor(false)]; + tensor gather_10 = gather(axis = gather_10_axis_0, batch_dims = gather_10_batch_dims_0, indices = select_10, validate_indices = gather_10_validate_indices_0, x = var_351_shape)[name = tensor("gather_10")]; + tensor var_354_axes_0 = const()[name = tensor("op_354_axes_0"), val = tensor([-1])]; + tensor var_354 = expand_dims(axes = var_354_axes_0, x = seg_5)[name = tensor("op_354")]; + tensor concat_3_axis_0 = const()[name = tensor("concat_3_axis_0"), val = tensor(0)]; + tensor concat_3_interleave_0 = const()[name = tensor("concat_3_interleave_0"), val = tensor(false)]; + tensor concat_3 = concat(axis = concat_3_axis_0, interleave = concat_3_interleave_0, values = (gather_8, gather_9, gather_10, var_3))[name = tensor("concat_3")]; + tensor shape_12 = shape(x = var_354)[name = tensor("shape_12")]; + tensor real_div_1 = real_div(x = concat_3, y = shape_12)[name = tensor("real_div_1")]; + tensor var_356 = tile(reps = real_div_1, x = var_354)[name = tensor("op_356")]; + tensor var_357 = const()[name = tensor("op_357"), val = tensor([1, 128, -1])]; + tensor seg_7 = reshape(shape = var_357, x = var_356)[name = tensor("seg_7")]; + tensor var_359_shape = shape(x = input_101)[name = tensor("op_359_shape")]; + tensor select_11 = const()[name = tensor("select_11"), val = tensor(2)]; + tensor gather_11_axis_0 = const()[name = tensor("gather_11_axis_0"), val = tensor(0)]; + tensor gather_11_batch_dims_0 = const()[name = tensor("gather_11_batch_dims_0"), val = tensor(0)]; + tensor gather_11_validate_indices_0 = const()[name = tensor("gather_11_validate_indices_0"), val = tensor(false)]; + tensor gather_11 = gather(axis = gather_11_axis_0, batch_dims = gather_11_batch_dims_0, indices = select_11, validate_indices = gather_11_validate_indices_0, x = var_359_shape)[name = tensor("gather_11")]; + tensor concat_4_values0_0 = const()[name = tensor("concat_4_values0_0"), val = tensor(1)]; + tensor concat_4_values1_0 = const()[name = tensor("concat_4_values1_0"), val = tensor(128)]; + tensor concat_4_axis_0 = const()[name = tensor("concat_4_axis_0"), val = tensor(0)]; + tensor concat_4_interleave_0 = const()[name = tensor("concat_4_interleave_0"), val = tensor(false)]; + tensor concat_4 = concat(axis = concat_4_axis_0, interleave = concat_4_interleave_0, values = (concat_4_values0_0, concat_4_values1_0, gather_11))[name = tensor("concat_4")]; + tensor var_360_begin_0 = const()[name = tensor("op_360_begin_0"), val = tensor([0, 0, 0])]; + tensor var_360_end_mask_0 = const()[name = tensor("op_360_end_mask_0"), val = tensor([true, true, false])]; + tensor var_360 = slice_by_index(begin = var_360_begin_0, end = concat_4, end_mask = var_360_end_mask_0, x = seg_7)[name = tensor("op_360")]; + tensor input_103 = add(x = var_346, y = var_360)[name = tensor("input_103")]; + tensor input_105_pad_type_0 = const()[name = tensor("input_105_pad_type_0"), val = tensor("valid")]; + tensor input_105_strides_0 = const()[name = tensor("input_105_strides_0"), val = tensor([1])]; + tensor input_105_pad_0 = const()[name = tensor("input_105_pad_0"), val = tensor([0, 0])]; + tensor input_105_dilations_0 = const()[name = tensor("input_105_dilations_0"), val = tensor([1])]; + tensor input_105_groups_0 = const()[name = tensor("input_105_groups_0"), val = tensor(1)]; + tensor input_105 = conv(bias = model_xvector_block1_tdnnd2_cam_layer_linear1_bias, dilations = input_105_dilations_0, groups = input_105_groups_0, pad = input_105_pad_0, pad_type = input_105_pad_type_0, strides = input_105_strides_0, weight = model_xvector_block1_tdnnd2_cam_layer_linear1_weight, x = input_103)[name = tensor("input_105")]; + tensor input_107 = relu(x = input_105)[name = tensor("input_107")]; + tensor input_109_pad_type_0 = const()[name = tensor("input_109_pad_type_0"), val = tensor("valid")]; + tensor input_109_strides_0 = const()[name = tensor("input_109_strides_0"), val = tensor([1])]; + tensor input_109_pad_0 = const()[name = tensor("input_109_pad_0"), val = tensor([0, 0])]; + tensor input_109_dilations_0 = const()[name = tensor("input_109_dilations_0"), val = tensor([1])]; + tensor input_109_groups_0 = const()[name = tensor("input_109_groups_0"), val = tensor(1)]; + tensor input_109 = conv(bias = model_xvector_block1_tdnnd2_cam_layer_linear2_bias, dilations = input_109_dilations_0, groups = input_109_groups_0, pad = input_109_pad_0, pad_type = input_109_pad_type_0, strides = input_109_strides_0, weight = model_xvector_block1_tdnnd2_cam_layer_linear2_weight, x = input_107)[name = tensor("input_109")]; + tensor m_3 = sigmoid(x = input_109)[name = tensor("m_3")]; + tensor var_378 = mul(x = y_3, y = m_3)[name = tensor("op_378")]; + tensor input_111_interleave_0 = const()[name = tensor("input_111_interleave_0"), val = tensor(false)]; + tensor input_111 = concat(axis = var_11, interleave = input_111_interleave_0, values = (input_91, var_378))[name = tensor("input_111")]; + tensor input_113 = batch_norm(beta = model_xvector_block1_tdnnd3_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block1_tdnnd3_nonlinear1_batchnorm_weight, mean = model_xvector_block1_tdnnd3_nonlinear1_batchnorm_running_mean, variance = model_xvector_block1_tdnnd3_nonlinear1_batchnorm_running_var, x = input_111)[name = tensor("input_113")]; + tensor input_115 = relu(x = input_113)[name = tensor("input_115")]; + tensor input_117_pad_type_0 = const()[name = tensor("input_117_pad_type_0"), val = tensor("valid")]; + tensor input_117_strides_0 = const()[name = tensor("input_117_strides_0"), val = tensor([1])]; + tensor input_117_pad_0 = const()[name = tensor("input_117_pad_0"), val = tensor([0, 0])]; + tensor input_117_dilations_0 = const()[name = tensor("input_117_dilations_0"), val = tensor([1])]; + tensor input_117_groups_0 = const()[name = tensor("input_117_groups_0"), val = tensor(1)]; + tensor const_30 = const()[name = tensor("const_30"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9988864)))]; + tensor const_31 = const()[name = tensor("const_31"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10087232)))]; + tensor input_119 = conv(bias = const_31, dilations = input_117_dilations_0, groups = input_117_groups_0, pad = input_117_pad_0, pad_type = input_117_pad_type_0, strides = input_117_strides_0, weight = const_30, x = input_115)[name = tensor("input_119")]; + tensor input_121 = relu(x = input_119)[name = tensor("input_121")]; + tensor y_5_pad_type_0 = const()[name = tensor("y_5_pad_type_0"), val = tensor("custom")]; + tensor y_5_pad_0 = const()[name = tensor("y_5_pad_0"), val = tensor([1, 1])]; + tensor y_5_strides_0 = const()[name = tensor("y_5_strides_0"), val = tensor([1])]; + tensor y_5_dilations_0 = const()[name = tensor("y_5_dilations_0"), val = tensor([1])]; + tensor y_5_groups_0 = const()[name = tensor("y_5_groups_0"), val = tensor(1)]; + tensor y_5 = conv(dilations = y_5_dilations_0, groups = y_5_groups_0, pad = y_5_pad_0, pad_type = y_5_pad_type_0, strides = y_5_strides_0, weight = model_xvector_block1_tdnnd3_cam_layer_linear_local_weight, x = input_121)[name = tensor("y_5")]; + tensor var_415_axes_0 = const()[name = tensor("op_415_axes_0"), val = tensor([-1])]; + tensor var_415_keep_dims_0 = const()[name = tensor("op_415_keep_dims_0"), val = tensor(true)]; + tensor var_415 = reduce_mean(axes = var_415_axes_0, keep_dims = var_415_keep_dims_0, x = input_121)[name = tensor("op_415")]; + tensor var_416 = const()[name = tensor("op_416"), val = tensor([100])]; + tensor var_417 = const()[name = tensor("op_417"), val = tensor([100])]; + tensor seg_9_pad_type_0 = const()[name = tensor("seg_9_pad_type_0"), val = tensor("custom")]; + tensor seg_9_pad_0 = const()[name = tensor("seg_9_pad_0"), val = tensor([0, 0])]; + tensor seg_9_exclude_padding_from_average_0 = const()[name = tensor("seg_9_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_9_ceil_mode_0 = const()[name = tensor("seg_9_ceil_mode_0"), val = tensor(true)]; + tensor seg_9 = avg_pool(ceil_mode = seg_9_ceil_mode_0, exclude_padding_from_average = seg_9_exclude_padding_from_average_0, kernel_sizes = var_416, pad = seg_9_pad_0, pad_type = seg_9_pad_type_0, strides = var_417, x = input_121)[name = tensor("seg_9")]; + tensor var_420_shape = shape(x = seg_9)[name = tensor("op_420_shape")]; + tensor gather_12 = const()[name = tensor("gather_12"), val = tensor(1)]; + tensor gather_13 = const()[name = tensor("gather_13"), val = tensor(128)]; + tensor select_14 = const()[name = tensor("select_14"), val = tensor(2)]; + tensor gather_14_axis_0 = const()[name = tensor("gather_14_axis_0"), val = tensor(0)]; + tensor gather_14_batch_dims_0 = const()[name = tensor("gather_14_batch_dims_0"), val = tensor(0)]; + tensor gather_14_validate_indices_0 = const()[name = tensor("gather_14_validate_indices_0"), val = tensor(false)]; + tensor gather_14 = gather(axis = gather_14_axis_0, batch_dims = gather_14_batch_dims_0, indices = select_14, validate_indices = gather_14_validate_indices_0, x = var_420_shape)[name = tensor("gather_14")]; + tensor var_423_axes_0 = const()[name = tensor("op_423_axes_0"), val = tensor([-1])]; + tensor var_423 = expand_dims(axes = var_423_axes_0, x = seg_9)[name = tensor("op_423")]; + tensor concat_5_axis_0 = const()[name = tensor("concat_5_axis_0"), val = tensor(0)]; + tensor concat_5_interleave_0 = const()[name = tensor("concat_5_interleave_0"), val = tensor(false)]; + tensor concat_5 = concat(axis = concat_5_axis_0, interleave = concat_5_interleave_0, values = (gather_12, gather_13, gather_14, var_3))[name = tensor("concat_5")]; + tensor shape_17 = shape(x = var_423)[name = tensor("shape_17")]; + tensor real_div_2 = real_div(x = concat_5, y = shape_17)[name = tensor("real_div_2")]; + tensor var_425 = tile(reps = real_div_2, x = var_423)[name = tensor("op_425")]; + tensor var_426 = const()[name = tensor("op_426"), val = tensor([1, 128, -1])]; + tensor seg_11 = reshape(shape = var_426, x = var_425)[name = tensor("seg_11")]; + tensor var_428_shape = shape(x = input_121)[name = tensor("op_428_shape")]; + tensor select_15 = const()[name = tensor("select_15"), val = tensor(2)]; + tensor gather_15_axis_0 = const()[name = tensor("gather_15_axis_0"), val = tensor(0)]; + tensor gather_15_batch_dims_0 = const()[name = tensor("gather_15_batch_dims_0"), val = tensor(0)]; + tensor gather_15_validate_indices_0 = const()[name = tensor("gather_15_validate_indices_0"), val = tensor(false)]; + tensor gather_15 = gather(axis = gather_15_axis_0, batch_dims = gather_15_batch_dims_0, indices = select_15, validate_indices = gather_15_validate_indices_0, x = var_428_shape)[name = tensor("gather_15")]; + tensor concat_6_values0_0 = const()[name = tensor("concat_6_values0_0"), val = tensor(1)]; + tensor concat_6_values1_0 = const()[name = tensor("concat_6_values1_0"), val = tensor(128)]; + tensor concat_6_axis_0 = const()[name = tensor("concat_6_axis_0"), val = tensor(0)]; + tensor concat_6_interleave_0 = const()[name = tensor("concat_6_interleave_0"), val = tensor(false)]; + tensor concat_6 = concat(axis = concat_6_axis_0, interleave = concat_6_interleave_0, values = (concat_6_values0_0, concat_6_values1_0, gather_15))[name = tensor("concat_6")]; + tensor var_429_begin_0 = const()[name = tensor("op_429_begin_0"), val = tensor([0, 0, 0])]; + tensor var_429_end_mask_0 = const()[name = tensor("op_429_end_mask_0"), val = tensor([true, true, false])]; + tensor var_429 = slice_by_index(begin = var_429_begin_0, end = concat_6, end_mask = var_429_end_mask_0, x = seg_11)[name = tensor("op_429")]; + tensor input_123 = add(x = var_415, y = var_429)[name = tensor("input_123")]; + tensor input_125_pad_type_0 = const()[name = tensor("input_125_pad_type_0"), val = tensor("valid")]; + tensor input_125_strides_0 = const()[name = tensor("input_125_strides_0"), val = tensor([1])]; + tensor input_125_pad_0 = const()[name = tensor("input_125_pad_0"), val = tensor([0, 0])]; + tensor input_125_dilations_0 = const()[name = tensor("input_125_dilations_0"), val = tensor([1])]; + tensor input_125_groups_0 = const()[name = tensor("input_125_groups_0"), val = tensor(1)]; + tensor input_125 = conv(bias = model_xvector_block1_tdnnd3_cam_layer_linear1_bias, dilations = input_125_dilations_0, groups = input_125_groups_0, pad = input_125_pad_0, pad_type = input_125_pad_type_0, strides = input_125_strides_0, weight = model_xvector_block1_tdnnd3_cam_layer_linear1_weight, x = input_123)[name = tensor("input_125")]; + tensor input_127 = relu(x = input_125)[name = tensor("input_127")]; + tensor input_129_pad_type_0 = const()[name = tensor("input_129_pad_type_0"), val = tensor("valid")]; + tensor input_129_strides_0 = const()[name = tensor("input_129_strides_0"), val = tensor([1])]; + tensor input_129_pad_0 = const()[name = tensor("input_129_pad_0"), val = tensor([0, 0])]; + tensor input_129_dilations_0 = const()[name = tensor("input_129_dilations_0"), val = tensor([1])]; + tensor input_129_groups_0 = const()[name = tensor("input_129_groups_0"), val = tensor(1)]; + tensor input_129 = conv(bias = model_xvector_block1_tdnnd3_cam_layer_linear2_bias, dilations = input_129_dilations_0, groups = input_129_groups_0, pad = input_129_pad_0, pad_type = input_129_pad_type_0, strides = input_129_strides_0, weight = model_xvector_block1_tdnnd3_cam_layer_linear2_weight, x = input_127)[name = tensor("input_129")]; + tensor m_5 = sigmoid(x = input_129)[name = tensor("m_5")]; + tensor var_447 = mul(x = y_5, y = m_5)[name = tensor("op_447")]; + tensor input_131_interleave_0 = const()[name = tensor("input_131_interleave_0"), val = tensor(false)]; + tensor input_131 = concat(axis = var_11, interleave = input_131_interleave_0, values = (input_111, var_447))[name = tensor("input_131")]; + tensor input_133 = batch_norm(beta = model_xvector_block1_tdnnd4_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block1_tdnnd4_nonlinear1_batchnorm_weight, mean = model_xvector_block1_tdnnd4_nonlinear1_batchnorm_running_mean, variance = model_xvector_block1_tdnnd4_nonlinear1_batchnorm_running_var, x = input_131)[name = tensor("input_133")]; + tensor input_135 = relu(x = input_133)[name = tensor("input_135")]; + tensor input_137_pad_type_0 = const()[name = tensor("input_137_pad_type_0"), val = tensor("valid")]; + tensor input_137_strides_0 = const()[name = tensor("input_137_strides_0"), val = tensor([1])]; + tensor input_137_pad_0 = const()[name = tensor("input_137_pad_0"), val = tensor([0, 0])]; + tensor input_137_dilations_0 = const()[name = tensor("input_137_dilations_0"), val = tensor([1])]; + tensor input_137_groups_0 = const()[name = tensor("input_137_groups_0"), val = tensor(1)]; + tensor const_32 = const()[name = tensor("const_32"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10087808)))]; + tensor const_33 = const()[name = tensor("const_33"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10202560)))]; + tensor input_139 = conv(bias = const_33, dilations = input_137_dilations_0, groups = input_137_groups_0, pad = input_137_pad_0, pad_type = input_137_pad_type_0, strides = input_137_strides_0, weight = const_32, x = input_135)[name = tensor("input_139")]; + tensor input_141 = relu(x = input_139)[name = tensor("input_141")]; + tensor y_7_pad_type_0 = const()[name = tensor("y_7_pad_type_0"), val = tensor("custom")]; + tensor y_7_pad_0 = const()[name = tensor("y_7_pad_0"), val = tensor([1, 1])]; + tensor y_7_strides_0 = const()[name = tensor("y_7_strides_0"), val = tensor([1])]; + tensor y_7_dilations_0 = const()[name = tensor("y_7_dilations_0"), val = tensor([1])]; + tensor y_7_groups_0 = const()[name = tensor("y_7_groups_0"), val = tensor(1)]; + tensor y_7 = conv(dilations = y_7_dilations_0, groups = y_7_groups_0, pad = y_7_pad_0, pad_type = y_7_pad_type_0, strides = y_7_strides_0, weight = model_xvector_block1_tdnnd4_cam_layer_linear_local_weight, x = input_141)[name = tensor("y_7")]; + tensor var_484_axes_0 = const()[name = tensor("op_484_axes_0"), val = tensor([-1])]; + tensor var_484_keep_dims_0 = const()[name = tensor("op_484_keep_dims_0"), val = tensor(true)]; + tensor var_484 = reduce_mean(axes = var_484_axes_0, keep_dims = var_484_keep_dims_0, x = input_141)[name = tensor("op_484")]; + tensor var_485 = const()[name = tensor("op_485"), val = tensor([100])]; + tensor var_486 = const()[name = tensor("op_486"), val = tensor([100])]; + tensor seg_13_pad_type_0 = const()[name = tensor("seg_13_pad_type_0"), val = tensor("custom")]; + tensor seg_13_pad_0 = const()[name = tensor("seg_13_pad_0"), val = tensor([0, 0])]; + tensor seg_13_exclude_padding_from_average_0 = const()[name = tensor("seg_13_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_13_ceil_mode_0 = const()[name = tensor("seg_13_ceil_mode_0"), val = tensor(true)]; + tensor seg_13 = avg_pool(ceil_mode = seg_13_ceil_mode_0, exclude_padding_from_average = seg_13_exclude_padding_from_average_0, kernel_sizes = var_485, pad = seg_13_pad_0, pad_type = seg_13_pad_type_0, strides = var_486, x = input_141)[name = tensor("seg_13")]; + tensor var_489_shape = shape(x = seg_13)[name = tensor("op_489_shape")]; + tensor gather_16 = const()[name = tensor("gather_16"), val = tensor(1)]; + tensor gather_17 = const()[name = tensor("gather_17"), val = tensor(128)]; + tensor select_18 = const()[name = tensor("select_18"), val = tensor(2)]; + tensor gather_18_axis_0 = const()[name = tensor("gather_18_axis_0"), val = tensor(0)]; + tensor gather_18_batch_dims_0 = const()[name = tensor("gather_18_batch_dims_0"), val = tensor(0)]; + tensor gather_18_validate_indices_0 = const()[name = tensor("gather_18_validate_indices_0"), val = tensor(false)]; + tensor gather_18 = gather(axis = gather_18_axis_0, batch_dims = gather_18_batch_dims_0, indices = select_18, validate_indices = gather_18_validate_indices_0, x = var_489_shape)[name = tensor("gather_18")]; + tensor var_492_axes_0 = const()[name = tensor("op_492_axes_0"), val = tensor([-1])]; + tensor var_492 = expand_dims(axes = var_492_axes_0, x = seg_13)[name = tensor("op_492")]; + tensor concat_7_axis_0 = const()[name = tensor("concat_7_axis_0"), val = tensor(0)]; + tensor concat_7_interleave_0 = const()[name = tensor("concat_7_interleave_0"), val = tensor(false)]; + tensor concat_7 = concat(axis = concat_7_axis_0, interleave = concat_7_interleave_0, values = (gather_16, gather_17, gather_18, var_3))[name = tensor("concat_7")]; + tensor shape_22 = shape(x = var_492)[name = tensor("shape_22")]; + tensor real_div_3 = real_div(x = concat_7, y = shape_22)[name = tensor("real_div_3")]; + tensor var_494 = tile(reps = real_div_3, x = var_492)[name = tensor("op_494")]; + tensor var_495 = const()[name = tensor("op_495"), val = tensor([1, 128, -1])]; + tensor seg_15 = reshape(shape = var_495, x = var_494)[name = tensor("seg_15")]; + tensor var_497_shape = shape(x = input_141)[name = tensor("op_497_shape")]; + tensor select_19 = const()[name = tensor("select_19"), val = tensor(2)]; + tensor gather_19_axis_0 = const()[name = tensor("gather_19_axis_0"), val = tensor(0)]; + tensor gather_19_batch_dims_0 = const()[name = tensor("gather_19_batch_dims_0"), val = tensor(0)]; + tensor gather_19_validate_indices_0 = const()[name = tensor("gather_19_validate_indices_0"), val = tensor(false)]; + tensor gather_19 = gather(axis = gather_19_axis_0, batch_dims = gather_19_batch_dims_0, indices = select_19, validate_indices = gather_19_validate_indices_0, x = var_497_shape)[name = tensor("gather_19")]; + tensor concat_8_values0_0 = const()[name = tensor("concat_8_values0_0"), val = tensor(1)]; + tensor concat_8_values1_0 = const()[name = tensor("concat_8_values1_0"), val = tensor(128)]; + tensor concat_8_axis_0 = const()[name = tensor("concat_8_axis_0"), val = tensor(0)]; + tensor concat_8_interleave_0 = const()[name = tensor("concat_8_interleave_0"), val = tensor(false)]; + tensor concat_8 = concat(axis = concat_8_axis_0, interleave = concat_8_interleave_0, values = (concat_8_values0_0, concat_8_values1_0, gather_19))[name = tensor("concat_8")]; + tensor var_498_begin_0 = const()[name = tensor("op_498_begin_0"), val = tensor([0, 0, 0])]; + tensor var_498_end_mask_0 = const()[name = tensor("op_498_end_mask_0"), val = tensor([true, true, false])]; + tensor var_498 = slice_by_index(begin = var_498_begin_0, end = concat_8, end_mask = var_498_end_mask_0, x = seg_15)[name = tensor("op_498")]; + tensor input_143 = add(x = var_484, y = var_498)[name = tensor("input_143")]; + tensor input_145_pad_type_0 = const()[name = tensor("input_145_pad_type_0"), val = tensor("valid")]; + tensor input_145_strides_0 = const()[name = tensor("input_145_strides_0"), val = tensor([1])]; + tensor input_145_pad_0 = const()[name = tensor("input_145_pad_0"), val = tensor([0, 0])]; + tensor input_145_dilations_0 = const()[name = tensor("input_145_dilations_0"), val = tensor([1])]; + tensor input_145_groups_0 = const()[name = tensor("input_145_groups_0"), val = tensor(1)]; + tensor input_145 = conv(bias = model_xvector_block1_tdnnd4_cam_layer_linear1_bias, dilations = input_145_dilations_0, groups = input_145_groups_0, pad = input_145_pad_0, pad_type = input_145_pad_type_0, strides = input_145_strides_0, weight = model_xvector_block1_tdnnd4_cam_layer_linear1_weight, x = input_143)[name = tensor("input_145")]; + tensor input_147 = relu(x = input_145)[name = tensor("input_147")]; + tensor input_149_pad_type_0 = const()[name = tensor("input_149_pad_type_0"), val = tensor("valid")]; + tensor input_149_strides_0 = const()[name = tensor("input_149_strides_0"), val = tensor([1])]; + tensor input_149_pad_0 = const()[name = tensor("input_149_pad_0"), val = tensor([0, 0])]; + tensor input_149_dilations_0 = const()[name = tensor("input_149_dilations_0"), val = tensor([1])]; + tensor input_149_groups_0 = const()[name = tensor("input_149_groups_0"), val = tensor(1)]; + tensor input_149 = conv(bias = model_xvector_block1_tdnnd4_cam_layer_linear2_bias, dilations = input_149_dilations_0, groups = input_149_groups_0, pad = input_149_pad_0, pad_type = input_149_pad_type_0, strides = input_149_strides_0, weight = model_xvector_block1_tdnnd4_cam_layer_linear2_weight, x = input_147)[name = tensor("input_149")]; + tensor m_7 = sigmoid(x = input_149)[name = tensor("m_7")]; + tensor var_516 = mul(x = y_7, y = m_7)[name = tensor("op_516")]; + tensor input_151_interleave_0 = const()[name = tensor("input_151_interleave_0"), val = tensor(false)]; + tensor input_151 = concat(axis = var_11, interleave = input_151_interleave_0, values = (input_131, var_516))[name = tensor("input_151")]; + tensor input_153 = batch_norm(beta = model_xvector_block1_tdnnd5_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block1_tdnnd5_nonlinear1_batchnorm_weight, mean = model_xvector_block1_tdnnd5_nonlinear1_batchnorm_running_mean, variance = model_xvector_block1_tdnnd5_nonlinear1_batchnorm_running_var, x = input_151)[name = tensor("input_153")]; + tensor input_155 = relu(x = input_153)[name = tensor("input_155")]; + tensor input_157_pad_type_0 = const()[name = tensor("input_157_pad_type_0"), val = tensor("valid")]; + tensor input_157_strides_0 = const()[name = tensor("input_157_strides_0"), val = tensor([1])]; + tensor input_157_pad_0 = const()[name = tensor("input_157_pad_0"), val = tensor([0, 0])]; + tensor input_157_dilations_0 = const()[name = tensor("input_157_dilations_0"), val = tensor([1])]; + tensor input_157_groups_0 = const()[name = tensor("input_157_groups_0"), val = tensor(1)]; + tensor const_34 = const()[name = tensor("const_34"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10203136)))]; + tensor const_35 = const()[name = tensor("const_35"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10334272)))]; + tensor input_159 = conv(bias = const_35, dilations = input_157_dilations_0, groups = input_157_groups_0, pad = input_157_pad_0, pad_type = input_157_pad_type_0, strides = input_157_strides_0, weight = const_34, x = input_155)[name = tensor("input_159")]; + tensor input_161 = relu(x = input_159)[name = tensor("input_161")]; + tensor y_9_pad_type_0 = const()[name = tensor("y_9_pad_type_0"), val = tensor("custom")]; + tensor y_9_pad_0 = const()[name = tensor("y_9_pad_0"), val = tensor([1, 1])]; + tensor y_9_strides_0 = const()[name = tensor("y_9_strides_0"), val = tensor([1])]; + tensor y_9_dilations_0 = const()[name = tensor("y_9_dilations_0"), val = tensor([1])]; + tensor y_9_groups_0 = const()[name = tensor("y_9_groups_0"), val = tensor(1)]; + tensor y_9 = conv(dilations = y_9_dilations_0, groups = y_9_groups_0, pad = y_9_pad_0, pad_type = y_9_pad_type_0, strides = y_9_strides_0, weight = model_xvector_block1_tdnnd5_cam_layer_linear_local_weight, x = input_161)[name = tensor("y_9")]; + tensor var_553_axes_0 = const()[name = tensor("op_553_axes_0"), val = tensor([-1])]; + tensor var_553_keep_dims_0 = const()[name = tensor("op_553_keep_dims_0"), val = tensor(true)]; + tensor var_553 = reduce_mean(axes = var_553_axes_0, keep_dims = var_553_keep_dims_0, x = input_161)[name = tensor("op_553")]; + tensor var_554 = const()[name = tensor("op_554"), val = tensor([100])]; + tensor var_555 = const()[name = tensor("op_555"), val = tensor([100])]; + tensor seg_17_pad_type_0 = const()[name = tensor("seg_17_pad_type_0"), val = tensor("custom")]; + tensor seg_17_pad_0 = const()[name = tensor("seg_17_pad_0"), val = tensor([0, 0])]; + tensor seg_17_exclude_padding_from_average_0 = const()[name = tensor("seg_17_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_17_ceil_mode_0 = const()[name = tensor("seg_17_ceil_mode_0"), val = tensor(true)]; + tensor seg_17 = avg_pool(ceil_mode = seg_17_ceil_mode_0, exclude_padding_from_average = seg_17_exclude_padding_from_average_0, kernel_sizes = var_554, pad = seg_17_pad_0, pad_type = seg_17_pad_type_0, strides = var_555, x = input_161)[name = tensor("seg_17")]; + tensor var_558_shape = shape(x = seg_17)[name = tensor("op_558_shape")]; + tensor gather_20 = const()[name = tensor("gather_20"), val = tensor(1)]; + tensor gather_21 = const()[name = tensor("gather_21"), val = tensor(128)]; + tensor select_22 = const()[name = tensor("select_22"), val = tensor(2)]; + tensor gather_22_axis_0 = const()[name = tensor("gather_22_axis_0"), val = tensor(0)]; + tensor gather_22_batch_dims_0 = const()[name = tensor("gather_22_batch_dims_0"), val = tensor(0)]; + tensor gather_22_validate_indices_0 = const()[name = tensor("gather_22_validate_indices_0"), val = tensor(false)]; + tensor gather_22 = gather(axis = gather_22_axis_0, batch_dims = gather_22_batch_dims_0, indices = select_22, validate_indices = gather_22_validate_indices_0, x = var_558_shape)[name = tensor("gather_22")]; + tensor var_561_axes_0 = const()[name = tensor("op_561_axes_0"), val = tensor([-1])]; + tensor var_561 = expand_dims(axes = var_561_axes_0, x = seg_17)[name = tensor("op_561")]; + tensor concat_9_axis_0 = const()[name = tensor("concat_9_axis_0"), val = tensor(0)]; + tensor concat_9_interleave_0 = const()[name = tensor("concat_9_interleave_0"), val = tensor(false)]; + tensor concat_9 = concat(axis = concat_9_axis_0, interleave = concat_9_interleave_0, values = (gather_20, gather_21, gather_22, var_3))[name = tensor("concat_9")]; + tensor shape_27 = shape(x = var_561)[name = tensor("shape_27")]; + tensor real_div_4 = real_div(x = concat_9, y = shape_27)[name = tensor("real_div_4")]; + tensor var_563 = tile(reps = real_div_4, x = var_561)[name = tensor("op_563")]; + tensor var_564 = const()[name = tensor("op_564"), val = tensor([1, 128, -1])]; + tensor seg_19 = reshape(shape = var_564, x = var_563)[name = tensor("seg_19")]; + tensor var_566_shape = shape(x = input_161)[name = tensor("op_566_shape")]; + tensor select_23 = const()[name = tensor("select_23"), val = tensor(2)]; + tensor gather_23_axis_0 = const()[name = tensor("gather_23_axis_0"), val = tensor(0)]; + tensor gather_23_batch_dims_0 = const()[name = tensor("gather_23_batch_dims_0"), val = tensor(0)]; + tensor gather_23_validate_indices_0 = const()[name = tensor("gather_23_validate_indices_0"), val = tensor(false)]; + tensor gather_23 = gather(axis = gather_23_axis_0, batch_dims = gather_23_batch_dims_0, indices = select_23, validate_indices = gather_23_validate_indices_0, x = var_566_shape)[name = tensor("gather_23")]; + tensor concat_10_values0_0 = const()[name = tensor("concat_10_values0_0"), val = tensor(1)]; + tensor concat_10_values1_0 = const()[name = tensor("concat_10_values1_0"), val = tensor(128)]; + tensor concat_10_axis_0 = const()[name = tensor("concat_10_axis_0"), val = tensor(0)]; + tensor concat_10_interleave_0 = const()[name = tensor("concat_10_interleave_0"), val = tensor(false)]; + tensor concat_10 = concat(axis = concat_10_axis_0, interleave = concat_10_interleave_0, values = (concat_10_values0_0, concat_10_values1_0, gather_23))[name = tensor("concat_10")]; + tensor var_567_begin_0 = const()[name = tensor("op_567_begin_0"), val = tensor([0, 0, 0])]; + tensor var_567_end_mask_0 = const()[name = tensor("op_567_end_mask_0"), val = tensor([true, true, false])]; + tensor var_567 = slice_by_index(begin = var_567_begin_0, end = concat_10, end_mask = var_567_end_mask_0, x = seg_19)[name = tensor("op_567")]; + tensor input_163 = add(x = var_553, y = var_567)[name = tensor("input_163")]; + tensor input_165_pad_type_0 = const()[name = tensor("input_165_pad_type_0"), val = tensor("valid")]; + tensor input_165_strides_0 = const()[name = tensor("input_165_strides_0"), val = tensor([1])]; + tensor input_165_pad_0 = const()[name = tensor("input_165_pad_0"), val = tensor([0, 0])]; + tensor input_165_dilations_0 = const()[name = tensor("input_165_dilations_0"), val = tensor([1])]; + tensor input_165_groups_0 = const()[name = tensor("input_165_groups_0"), val = tensor(1)]; + tensor input_165 = conv(bias = model_xvector_block1_tdnnd5_cam_layer_linear1_bias, dilations = input_165_dilations_0, groups = input_165_groups_0, pad = input_165_pad_0, pad_type = input_165_pad_type_0, strides = input_165_strides_0, weight = model_xvector_block1_tdnnd5_cam_layer_linear1_weight, x = input_163)[name = tensor("input_165")]; + tensor input_167 = relu(x = input_165)[name = tensor("input_167")]; + tensor input_169_pad_type_0 = const()[name = tensor("input_169_pad_type_0"), val = tensor("valid")]; + tensor input_169_strides_0 = const()[name = tensor("input_169_strides_0"), val = tensor([1])]; + tensor input_169_pad_0 = const()[name = tensor("input_169_pad_0"), val = tensor([0, 0])]; + tensor input_169_dilations_0 = const()[name = tensor("input_169_dilations_0"), val = tensor([1])]; + tensor input_169_groups_0 = const()[name = tensor("input_169_groups_0"), val = tensor(1)]; + tensor input_169 = conv(bias = model_xvector_block1_tdnnd5_cam_layer_linear2_bias, dilations = input_169_dilations_0, groups = input_169_groups_0, pad = input_169_pad_0, pad_type = input_169_pad_type_0, strides = input_169_strides_0, weight = model_xvector_block1_tdnnd5_cam_layer_linear2_weight, x = input_167)[name = tensor("input_169")]; + tensor m_9 = sigmoid(x = input_169)[name = tensor("m_9")]; + tensor var_585 = mul(x = y_9, y = m_9)[name = tensor("op_585")]; + tensor input_171_interleave_0 = const()[name = tensor("input_171_interleave_0"), val = tensor(false)]; + tensor input_171 = concat(axis = var_11, interleave = input_171_interleave_0, values = (input_151, var_585))[name = tensor("input_171")]; + tensor input_173 = batch_norm(beta = model_xvector_block1_tdnnd6_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block1_tdnnd6_nonlinear1_batchnorm_weight, mean = model_xvector_block1_tdnnd6_nonlinear1_batchnorm_running_mean, variance = model_xvector_block1_tdnnd6_nonlinear1_batchnorm_running_var, x = input_171)[name = tensor("input_173")]; + tensor input_175 = relu(x = input_173)[name = tensor("input_175")]; + tensor input_177_pad_type_0 = const()[name = tensor("input_177_pad_type_0"), val = tensor("valid")]; + tensor input_177_strides_0 = const()[name = tensor("input_177_strides_0"), val = tensor([1])]; + tensor input_177_pad_0 = const()[name = tensor("input_177_pad_0"), val = tensor([0, 0])]; + tensor input_177_dilations_0 = const()[name = tensor("input_177_dilations_0"), val = tensor([1])]; + tensor input_177_groups_0 = const()[name = tensor("input_177_groups_0"), val = tensor(1)]; + tensor const_36 = const()[name = tensor("const_36"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10334848)))]; + tensor const_37 = const()[name = tensor("const_37"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10482368)))]; + tensor input_179 = conv(bias = const_37, dilations = input_177_dilations_0, groups = input_177_groups_0, pad = input_177_pad_0, pad_type = input_177_pad_type_0, strides = input_177_strides_0, weight = const_36, x = input_175)[name = tensor("input_179")]; + tensor input_181 = relu(x = input_179)[name = tensor("input_181")]; + tensor y_11_pad_type_0 = const()[name = tensor("y_11_pad_type_0"), val = tensor("custom")]; + tensor y_11_pad_0 = const()[name = tensor("y_11_pad_0"), val = tensor([1, 1])]; + tensor y_11_strides_0 = const()[name = tensor("y_11_strides_0"), val = tensor([1])]; + tensor y_11_dilations_0 = const()[name = tensor("y_11_dilations_0"), val = tensor([1])]; + tensor y_11_groups_0 = const()[name = tensor("y_11_groups_0"), val = tensor(1)]; + tensor y_11 = conv(dilations = y_11_dilations_0, groups = y_11_groups_0, pad = y_11_pad_0, pad_type = y_11_pad_type_0, strides = y_11_strides_0, weight = model_xvector_block1_tdnnd6_cam_layer_linear_local_weight, x = input_181)[name = tensor("y_11")]; + tensor var_622_axes_0 = const()[name = tensor("op_622_axes_0"), val = tensor([-1])]; + tensor var_622_keep_dims_0 = const()[name = tensor("op_622_keep_dims_0"), val = tensor(true)]; + tensor var_622 = reduce_mean(axes = var_622_axes_0, keep_dims = var_622_keep_dims_0, x = input_181)[name = tensor("op_622")]; + tensor var_623 = const()[name = tensor("op_623"), val = tensor([100])]; + tensor var_624 = const()[name = tensor("op_624"), val = tensor([100])]; + tensor seg_21_pad_type_0 = const()[name = tensor("seg_21_pad_type_0"), val = tensor("custom")]; + tensor seg_21_pad_0 = const()[name = tensor("seg_21_pad_0"), val = tensor([0, 0])]; + tensor seg_21_exclude_padding_from_average_0 = const()[name = tensor("seg_21_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_21_ceil_mode_0 = const()[name = tensor("seg_21_ceil_mode_0"), val = tensor(true)]; + tensor seg_21 = avg_pool(ceil_mode = seg_21_ceil_mode_0, exclude_padding_from_average = seg_21_exclude_padding_from_average_0, kernel_sizes = var_623, pad = seg_21_pad_0, pad_type = seg_21_pad_type_0, strides = var_624, x = input_181)[name = tensor("seg_21")]; + tensor var_627_shape = shape(x = seg_21)[name = tensor("op_627_shape")]; + tensor gather_24 = const()[name = tensor("gather_24"), val = tensor(1)]; + tensor gather_25 = const()[name = tensor("gather_25"), val = tensor(128)]; + tensor select_26 = const()[name = tensor("select_26"), val = tensor(2)]; + tensor gather_26_axis_0 = const()[name = tensor("gather_26_axis_0"), val = tensor(0)]; + tensor gather_26_batch_dims_0 = const()[name = tensor("gather_26_batch_dims_0"), val = tensor(0)]; + tensor gather_26_validate_indices_0 = const()[name = tensor("gather_26_validate_indices_0"), val = tensor(false)]; + tensor gather_26 = gather(axis = gather_26_axis_0, batch_dims = gather_26_batch_dims_0, indices = select_26, validate_indices = gather_26_validate_indices_0, x = var_627_shape)[name = tensor("gather_26")]; + tensor var_630_axes_0 = const()[name = tensor("op_630_axes_0"), val = tensor([-1])]; + tensor var_630 = expand_dims(axes = var_630_axes_0, x = seg_21)[name = tensor("op_630")]; + tensor concat_11_axis_0 = const()[name = tensor("concat_11_axis_0"), val = tensor(0)]; + tensor concat_11_interleave_0 = const()[name = tensor("concat_11_interleave_0"), val = tensor(false)]; + tensor concat_11 = concat(axis = concat_11_axis_0, interleave = concat_11_interleave_0, values = (gather_24, gather_25, gather_26, var_3))[name = tensor("concat_11")]; + tensor shape_32 = shape(x = var_630)[name = tensor("shape_32")]; + tensor real_div_5 = real_div(x = concat_11, y = shape_32)[name = tensor("real_div_5")]; + tensor var_632 = tile(reps = real_div_5, x = var_630)[name = tensor("op_632")]; + tensor var_633 = const()[name = tensor("op_633"), val = tensor([1, 128, -1])]; + tensor seg_23 = reshape(shape = var_633, x = var_632)[name = tensor("seg_23")]; + tensor var_635_shape = shape(x = input_181)[name = tensor("op_635_shape")]; + tensor select_27 = const()[name = tensor("select_27"), val = tensor(2)]; + tensor gather_27_axis_0 = const()[name = tensor("gather_27_axis_0"), val = tensor(0)]; + tensor gather_27_batch_dims_0 = const()[name = tensor("gather_27_batch_dims_0"), val = tensor(0)]; + tensor gather_27_validate_indices_0 = const()[name = tensor("gather_27_validate_indices_0"), val = tensor(false)]; + tensor gather_27 = gather(axis = gather_27_axis_0, batch_dims = gather_27_batch_dims_0, indices = select_27, validate_indices = gather_27_validate_indices_0, x = var_635_shape)[name = tensor("gather_27")]; + tensor concat_12_values0_0 = const()[name = tensor("concat_12_values0_0"), val = tensor(1)]; + tensor concat_12_values1_0 = const()[name = tensor("concat_12_values1_0"), val = tensor(128)]; + tensor concat_12_axis_0 = const()[name = tensor("concat_12_axis_0"), val = tensor(0)]; + tensor concat_12_interleave_0 = const()[name = tensor("concat_12_interleave_0"), val = tensor(false)]; + tensor concat_12 = concat(axis = concat_12_axis_0, interleave = concat_12_interleave_0, values = (concat_12_values0_0, concat_12_values1_0, gather_27))[name = tensor("concat_12")]; + tensor var_636_begin_0 = const()[name = tensor("op_636_begin_0"), val = tensor([0, 0, 0])]; + tensor var_636_end_mask_0 = const()[name = tensor("op_636_end_mask_0"), val = tensor([true, true, false])]; + tensor var_636 = slice_by_index(begin = var_636_begin_0, end = concat_12, end_mask = var_636_end_mask_0, x = seg_23)[name = tensor("op_636")]; + tensor input_183 = add(x = var_622, y = var_636)[name = tensor("input_183")]; + tensor input_185_pad_type_0 = const()[name = tensor("input_185_pad_type_0"), val = tensor("valid")]; + tensor input_185_strides_0 = const()[name = tensor("input_185_strides_0"), val = tensor([1])]; + tensor input_185_pad_0 = const()[name = tensor("input_185_pad_0"), val = tensor([0, 0])]; + tensor input_185_dilations_0 = const()[name = tensor("input_185_dilations_0"), val = tensor([1])]; + tensor input_185_groups_0 = const()[name = tensor("input_185_groups_0"), val = tensor(1)]; + tensor input_185 = conv(bias = model_xvector_block1_tdnnd6_cam_layer_linear1_bias, dilations = input_185_dilations_0, groups = input_185_groups_0, pad = input_185_pad_0, pad_type = input_185_pad_type_0, strides = input_185_strides_0, weight = model_xvector_block1_tdnnd6_cam_layer_linear1_weight, x = input_183)[name = tensor("input_185")]; + tensor input_187 = relu(x = input_185)[name = tensor("input_187")]; + tensor input_189_pad_type_0 = const()[name = tensor("input_189_pad_type_0"), val = tensor("valid")]; + tensor input_189_strides_0 = const()[name = tensor("input_189_strides_0"), val = tensor([1])]; + tensor input_189_pad_0 = const()[name = tensor("input_189_pad_0"), val = tensor([0, 0])]; + tensor input_189_dilations_0 = const()[name = tensor("input_189_dilations_0"), val = tensor([1])]; + tensor input_189_groups_0 = const()[name = tensor("input_189_groups_0"), val = tensor(1)]; + tensor input_189 = conv(bias = model_xvector_block1_tdnnd6_cam_layer_linear2_bias, dilations = input_189_dilations_0, groups = input_189_groups_0, pad = input_189_pad_0, pad_type = input_189_pad_type_0, strides = input_189_strides_0, weight = model_xvector_block1_tdnnd6_cam_layer_linear2_weight, x = input_187)[name = tensor("input_189")]; + tensor m_11 = sigmoid(x = input_189)[name = tensor("m_11")]; + tensor var_654 = mul(x = y_11, y = m_11)[name = tensor("op_654")]; + tensor input_191_interleave_0 = const()[name = tensor("input_191_interleave_0"), val = tensor(false)]; + tensor input_191 = concat(axis = var_11, interleave = input_191_interleave_0, values = (input_171, var_654))[name = tensor("input_191")]; + tensor input_193 = batch_norm(beta = model_xvector_block1_tdnnd7_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block1_tdnnd7_nonlinear1_batchnorm_weight, mean = model_xvector_block1_tdnnd7_nonlinear1_batchnorm_running_mean, variance = model_xvector_block1_tdnnd7_nonlinear1_batchnorm_running_var, x = input_191)[name = tensor("input_193")]; + tensor input_195 = relu(x = input_193)[name = tensor("input_195")]; + tensor input_197_pad_type_0 = const()[name = tensor("input_197_pad_type_0"), val = tensor("valid")]; + tensor input_197_strides_0 = const()[name = tensor("input_197_strides_0"), val = tensor([1])]; + tensor input_197_pad_0 = const()[name = tensor("input_197_pad_0"), val = tensor([0, 0])]; + tensor input_197_dilations_0 = const()[name = tensor("input_197_dilations_0"), val = tensor([1])]; + tensor input_197_groups_0 = const()[name = tensor("input_197_groups_0"), val = tensor(1)]; + tensor const_38 = const()[name = tensor("const_38"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10482944)))]; + tensor const_39 = const()[name = tensor("const_39"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10646848)))]; + tensor input_199 = conv(bias = const_39, dilations = input_197_dilations_0, groups = input_197_groups_0, pad = input_197_pad_0, pad_type = input_197_pad_type_0, strides = input_197_strides_0, weight = const_38, x = input_195)[name = tensor("input_199")]; + tensor input_201 = relu(x = input_199)[name = tensor("input_201")]; + tensor y_13_pad_type_0 = const()[name = tensor("y_13_pad_type_0"), val = tensor("custom")]; + tensor y_13_pad_0 = const()[name = tensor("y_13_pad_0"), val = tensor([1, 1])]; + tensor y_13_strides_0 = const()[name = tensor("y_13_strides_0"), val = tensor([1])]; + tensor y_13_dilations_0 = const()[name = tensor("y_13_dilations_0"), val = tensor([1])]; + tensor y_13_groups_0 = const()[name = tensor("y_13_groups_0"), val = tensor(1)]; + tensor y_13 = conv(dilations = y_13_dilations_0, groups = y_13_groups_0, pad = y_13_pad_0, pad_type = y_13_pad_type_0, strides = y_13_strides_0, weight = model_xvector_block1_tdnnd7_cam_layer_linear_local_weight, x = input_201)[name = tensor("y_13")]; + tensor var_691_axes_0 = const()[name = tensor("op_691_axes_0"), val = tensor([-1])]; + tensor var_691_keep_dims_0 = const()[name = tensor("op_691_keep_dims_0"), val = tensor(true)]; + tensor var_691 = reduce_mean(axes = var_691_axes_0, keep_dims = var_691_keep_dims_0, x = input_201)[name = tensor("op_691")]; + tensor var_692 = const()[name = tensor("op_692"), val = tensor([100])]; + tensor var_693 = const()[name = tensor("op_693"), val = tensor([100])]; + tensor seg_25_pad_type_0 = const()[name = tensor("seg_25_pad_type_0"), val = tensor("custom")]; + tensor seg_25_pad_0 = const()[name = tensor("seg_25_pad_0"), val = tensor([0, 0])]; + tensor seg_25_exclude_padding_from_average_0 = const()[name = tensor("seg_25_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_25_ceil_mode_0 = const()[name = tensor("seg_25_ceil_mode_0"), val = tensor(true)]; + tensor seg_25 = avg_pool(ceil_mode = seg_25_ceil_mode_0, exclude_padding_from_average = seg_25_exclude_padding_from_average_0, kernel_sizes = var_692, pad = seg_25_pad_0, pad_type = seg_25_pad_type_0, strides = var_693, x = input_201)[name = tensor("seg_25")]; + tensor var_696_shape = shape(x = seg_25)[name = tensor("op_696_shape")]; + tensor gather_28 = const()[name = tensor("gather_28"), val = tensor(1)]; + tensor gather_29 = const()[name = tensor("gather_29"), val = tensor(128)]; + tensor select_30 = const()[name = tensor("select_30"), val = tensor(2)]; + tensor gather_30_axis_0 = const()[name = tensor("gather_30_axis_0"), val = tensor(0)]; + tensor gather_30_batch_dims_0 = const()[name = tensor("gather_30_batch_dims_0"), val = tensor(0)]; + tensor gather_30_validate_indices_0 = const()[name = tensor("gather_30_validate_indices_0"), val = tensor(false)]; + tensor gather_30 = gather(axis = gather_30_axis_0, batch_dims = gather_30_batch_dims_0, indices = select_30, validate_indices = gather_30_validate_indices_0, x = var_696_shape)[name = tensor("gather_30")]; + tensor var_699_axes_0 = const()[name = tensor("op_699_axes_0"), val = tensor([-1])]; + tensor var_699 = expand_dims(axes = var_699_axes_0, x = seg_25)[name = tensor("op_699")]; + tensor concat_13_axis_0 = const()[name = tensor("concat_13_axis_0"), val = tensor(0)]; + tensor concat_13_interleave_0 = const()[name = tensor("concat_13_interleave_0"), val = tensor(false)]; + tensor concat_13 = concat(axis = concat_13_axis_0, interleave = concat_13_interleave_0, values = (gather_28, gather_29, gather_30, var_3))[name = tensor("concat_13")]; + tensor shape_37 = shape(x = var_699)[name = tensor("shape_37")]; + tensor real_div_6 = real_div(x = concat_13, y = shape_37)[name = tensor("real_div_6")]; + tensor var_701 = tile(reps = real_div_6, x = var_699)[name = tensor("op_701")]; + tensor var_702 = const()[name = tensor("op_702"), val = tensor([1, 128, -1])]; + tensor seg_27 = reshape(shape = var_702, x = var_701)[name = tensor("seg_27")]; + tensor var_704_shape = shape(x = input_201)[name = tensor("op_704_shape")]; + tensor select_31 = const()[name = tensor("select_31"), val = tensor(2)]; + tensor gather_31_axis_0 = const()[name = tensor("gather_31_axis_0"), val = tensor(0)]; + tensor gather_31_batch_dims_0 = const()[name = tensor("gather_31_batch_dims_0"), val = tensor(0)]; + tensor gather_31_validate_indices_0 = const()[name = tensor("gather_31_validate_indices_0"), val = tensor(false)]; + tensor gather_31 = gather(axis = gather_31_axis_0, batch_dims = gather_31_batch_dims_0, indices = select_31, validate_indices = gather_31_validate_indices_0, x = var_704_shape)[name = tensor("gather_31")]; + tensor concat_14_values0_0 = const()[name = tensor("concat_14_values0_0"), val = tensor(1)]; + tensor concat_14_values1_0 = const()[name = tensor("concat_14_values1_0"), val = tensor(128)]; + tensor concat_14_axis_0 = const()[name = tensor("concat_14_axis_0"), val = tensor(0)]; + tensor concat_14_interleave_0 = const()[name = tensor("concat_14_interleave_0"), val = tensor(false)]; + tensor concat_14 = concat(axis = concat_14_axis_0, interleave = concat_14_interleave_0, values = (concat_14_values0_0, concat_14_values1_0, gather_31))[name = tensor("concat_14")]; + tensor var_705_begin_0 = const()[name = tensor("op_705_begin_0"), val = tensor([0, 0, 0])]; + tensor var_705_end_mask_0 = const()[name = tensor("op_705_end_mask_0"), val = tensor([true, true, false])]; + tensor var_705 = slice_by_index(begin = var_705_begin_0, end = concat_14, end_mask = var_705_end_mask_0, x = seg_27)[name = tensor("op_705")]; + tensor input_203 = add(x = var_691, y = var_705)[name = tensor("input_203")]; + tensor input_205_pad_type_0 = const()[name = tensor("input_205_pad_type_0"), val = tensor("valid")]; + tensor input_205_strides_0 = const()[name = tensor("input_205_strides_0"), val = tensor([1])]; + tensor input_205_pad_0 = const()[name = tensor("input_205_pad_0"), val = tensor([0, 0])]; + tensor input_205_dilations_0 = const()[name = tensor("input_205_dilations_0"), val = tensor([1])]; + tensor input_205_groups_0 = const()[name = tensor("input_205_groups_0"), val = tensor(1)]; + tensor input_205 = conv(bias = model_xvector_block1_tdnnd7_cam_layer_linear1_bias, dilations = input_205_dilations_0, groups = input_205_groups_0, pad = input_205_pad_0, pad_type = input_205_pad_type_0, strides = input_205_strides_0, weight = model_xvector_block1_tdnnd7_cam_layer_linear1_weight, x = input_203)[name = tensor("input_205")]; + tensor input_207 = relu(x = input_205)[name = tensor("input_207")]; + tensor input_209_pad_type_0 = const()[name = tensor("input_209_pad_type_0"), val = tensor("valid")]; + tensor input_209_strides_0 = const()[name = tensor("input_209_strides_0"), val = tensor([1])]; + tensor input_209_pad_0 = const()[name = tensor("input_209_pad_0"), val = tensor([0, 0])]; + tensor input_209_dilations_0 = const()[name = tensor("input_209_dilations_0"), val = tensor([1])]; + tensor input_209_groups_0 = const()[name = tensor("input_209_groups_0"), val = tensor(1)]; + tensor input_209 = conv(bias = model_xvector_block1_tdnnd7_cam_layer_linear2_bias, dilations = input_209_dilations_0, groups = input_209_groups_0, pad = input_209_pad_0, pad_type = input_209_pad_type_0, strides = input_209_strides_0, weight = model_xvector_block1_tdnnd7_cam_layer_linear2_weight, x = input_207)[name = tensor("input_209")]; + tensor m_13 = sigmoid(x = input_209)[name = tensor("m_13")]; + tensor var_723 = mul(x = y_13, y = m_13)[name = tensor("op_723")]; + tensor input_211_interleave_0 = const()[name = tensor("input_211_interleave_0"), val = tensor(false)]; + tensor input_211 = concat(axis = var_11, interleave = input_211_interleave_0, values = (input_191, var_723))[name = tensor("input_211")]; + tensor input_213 = batch_norm(beta = model_xvector_block1_tdnnd8_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block1_tdnnd8_nonlinear1_batchnorm_weight, mean = model_xvector_block1_tdnnd8_nonlinear1_batchnorm_running_mean, variance = model_xvector_block1_tdnnd8_nonlinear1_batchnorm_running_var, x = input_211)[name = tensor("input_213")]; + tensor input_215 = relu(x = input_213)[name = tensor("input_215")]; + tensor input_217_pad_type_0 = const()[name = tensor("input_217_pad_type_0"), val = tensor("valid")]; + tensor input_217_strides_0 = const()[name = tensor("input_217_strides_0"), val = tensor([1])]; + tensor input_217_pad_0 = const()[name = tensor("input_217_pad_0"), val = tensor([0, 0])]; + tensor input_217_dilations_0 = const()[name = tensor("input_217_dilations_0"), val = tensor([1])]; + tensor input_217_groups_0 = const()[name = tensor("input_217_groups_0"), val = tensor(1)]; + tensor const_40 = const()[name = tensor("const_40"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10647424)))]; + tensor const_41 = const()[name = tensor("const_41"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10827712)))]; + tensor input_219 = conv(bias = const_41, dilations = input_217_dilations_0, groups = input_217_groups_0, pad = input_217_pad_0, pad_type = input_217_pad_type_0, strides = input_217_strides_0, weight = const_40, x = input_215)[name = tensor("input_219")]; + tensor input_221 = relu(x = input_219)[name = tensor("input_221")]; + tensor y_15_pad_type_0 = const()[name = tensor("y_15_pad_type_0"), val = tensor("custom")]; + tensor y_15_pad_0 = const()[name = tensor("y_15_pad_0"), val = tensor([1, 1])]; + tensor y_15_strides_0 = const()[name = tensor("y_15_strides_0"), val = tensor([1])]; + tensor y_15_dilations_0 = const()[name = tensor("y_15_dilations_0"), val = tensor([1])]; + tensor y_15_groups_0 = const()[name = tensor("y_15_groups_0"), val = tensor(1)]; + tensor y_15 = conv(dilations = y_15_dilations_0, groups = y_15_groups_0, pad = y_15_pad_0, pad_type = y_15_pad_type_0, strides = y_15_strides_0, weight = model_xvector_block1_tdnnd8_cam_layer_linear_local_weight, x = input_221)[name = tensor("y_15")]; + tensor var_760_axes_0 = const()[name = tensor("op_760_axes_0"), val = tensor([-1])]; + tensor var_760_keep_dims_0 = const()[name = tensor("op_760_keep_dims_0"), val = tensor(true)]; + tensor var_760 = reduce_mean(axes = var_760_axes_0, keep_dims = var_760_keep_dims_0, x = input_221)[name = tensor("op_760")]; + tensor var_761 = const()[name = tensor("op_761"), val = tensor([100])]; + tensor var_762 = const()[name = tensor("op_762"), val = tensor([100])]; + tensor seg_29_pad_type_0 = const()[name = tensor("seg_29_pad_type_0"), val = tensor("custom")]; + tensor seg_29_pad_0 = const()[name = tensor("seg_29_pad_0"), val = tensor([0, 0])]; + tensor seg_29_exclude_padding_from_average_0 = const()[name = tensor("seg_29_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_29_ceil_mode_0 = const()[name = tensor("seg_29_ceil_mode_0"), val = tensor(true)]; + tensor seg_29 = avg_pool(ceil_mode = seg_29_ceil_mode_0, exclude_padding_from_average = seg_29_exclude_padding_from_average_0, kernel_sizes = var_761, pad = seg_29_pad_0, pad_type = seg_29_pad_type_0, strides = var_762, x = input_221)[name = tensor("seg_29")]; + tensor var_765_shape = shape(x = seg_29)[name = tensor("op_765_shape")]; + tensor gather_32 = const()[name = tensor("gather_32"), val = tensor(1)]; + tensor gather_33 = const()[name = tensor("gather_33"), val = tensor(128)]; + tensor select_34 = const()[name = tensor("select_34"), val = tensor(2)]; + tensor gather_34_axis_0 = const()[name = tensor("gather_34_axis_0"), val = tensor(0)]; + tensor gather_34_batch_dims_0 = const()[name = tensor("gather_34_batch_dims_0"), val = tensor(0)]; + tensor gather_34_validate_indices_0 = const()[name = tensor("gather_34_validate_indices_0"), val = tensor(false)]; + tensor gather_34 = gather(axis = gather_34_axis_0, batch_dims = gather_34_batch_dims_0, indices = select_34, validate_indices = gather_34_validate_indices_0, x = var_765_shape)[name = tensor("gather_34")]; + tensor var_768_axes_0 = const()[name = tensor("op_768_axes_0"), val = tensor([-1])]; + tensor var_768 = expand_dims(axes = var_768_axes_0, x = seg_29)[name = tensor("op_768")]; + tensor concat_15_axis_0 = const()[name = tensor("concat_15_axis_0"), val = tensor(0)]; + tensor concat_15_interleave_0 = const()[name = tensor("concat_15_interleave_0"), val = tensor(false)]; + tensor concat_15 = concat(axis = concat_15_axis_0, interleave = concat_15_interleave_0, values = (gather_32, gather_33, gather_34, var_3))[name = tensor("concat_15")]; + tensor shape_42 = shape(x = var_768)[name = tensor("shape_42")]; + tensor real_div_7 = real_div(x = concat_15, y = shape_42)[name = tensor("real_div_7")]; + tensor var_770 = tile(reps = real_div_7, x = var_768)[name = tensor("op_770")]; + tensor var_771 = const()[name = tensor("op_771"), val = tensor([1, 128, -1])]; + tensor seg_31 = reshape(shape = var_771, x = var_770)[name = tensor("seg_31")]; + tensor var_773_shape = shape(x = input_221)[name = tensor("op_773_shape")]; + tensor select_35 = const()[name = tensor("select_35"), val = tensor(2)]; + tensor gather_35_axis_0 = const()[name = tensor("gather_35_axis_0"), val = tensor(0)]; + tensor gather_35_batch_dims_0 = const()[name = tensor("gather_35_batch_dims_0"), val = tensor(0)]; + tensor gather_35_validate_indices_0 = const()[name = tensor("gather_35_validate_indices_0"), val = tensor(false)]; + tensor gather_35 = gather(axis = gather_35_axis_0, batch_dims = gather_35_batch_dims_0, indices = select_35, validate_indices = gather_35_validate_indices_0, x = var_773_shape)[name = tensor("gather_35")]; + tensor concat_16_values0_0 = const()[name = tensor("concat_16_values0_0"), val = tensor(1)]; + tensor concat_16_values1_0 = const()[name = tensor("concat_16_values1_0"), val = tensor(128)]; + tensor concat_16_axis_0 = const()[name = tensor("concat_16_axis_0"), val = tensor(0)]; + tensor concat_16_interleave_0 = const()[name = tensor("concat_16_interleave_0"), val = tensor(false)]; + tensor concat_16 = concat(axis = concat_16_axis_0, interleave = concat_16_interleave_0, values = (concat_16_values0_0, concat_16_values1_0, gather_35))[name = tensor("concat_16")]; + tensor var_774_begin_0 = const()[name = tensor("op_774_begin_0"), val = tensor([0, 0, 0])]; + tensor var_774_end_mask_0 = const()[name = tensor("op_774_end_mask_0"), val = tensor([true, true, false])]; + tensor var_774 = slice_by_index(begin = var_774_begin_0, end = concat_16, end_mask = var_774_end_mask_0, x = seg_31)[name = tensor("op_774")]; + tensor input_223 = add(x = var_760, y = var_774)[name = tensor("input_223")]; + tensor input_225_pad_type_0 = const()[name = tensor("input_225_pad_type_0"), val = tensor("valid")]; + tensor input_225_strides_0 = const()[name = tensor("input_225_strides_0"), val = tensor([1])]; + tensor input_225_pad_0 = const()[name = tensor("input_225_pad_0"), val = tensor([0, 0])]; + tensor input_225_dilations_0 = const()[name = tensor("input_225_dilations_0"), val = tensor([1])]; + tensor input_225_groups_0 = const()[name = tensor("input_225_groups_0"), val = tensor(1)]; + tensor input_225 = conv(bias = model_xvector_block1_tdnnd8_cam_layer_linear1_bias, dilations = input_225_dilations_0, groups = input_225_groups_0, pad = input_225_pad_0, pad_type = input_225_pad_type_0, strides = input_225_strides_0, weight = model_xvector_block1_tdnnd8_cam_layer_linear1_weight, x = input_223)[name = tensor("input_225")]; + tensor input_227 = relu(x = input_225)[name = tensor("input_227")]; + tensor input_229_pad_type_0 = const()[name = tensor("input_229_pad_type_0"), val = tensor("valid")]; + tensor input_229_strides_0 = const()[name = tensor("input_229_strides_0"), val = tensor([1])]; + tensor input_229_pad_0 = const()[name = tensor("input_229_pad_0"), val = tensor([0, 0])]; + tensor input_229_dilations_0 = const()[name = tensor("input_229_dilations_0"), val = tensor([1])]; + tensor input_229_groups_0 = const()[name = tensor("input_229_groups_0"), val = tensor(1)]; + tensor input_229 = conv(bias = model_xvector_block1_tdnnd8_cam_layer_linear2_bias, dilations = input_229_dilations_0, groups = input_229_groups_0, pad = input_229_pad_0, pad_type = input_229_pad_type_0, strides = input_229_strides_0, weight = model_xvector_block1_tdnnd8_cam_layer_linear2_weight, x = input_227)[name = tensor("input_229")]; + tensor m_15 = sigmoid(x = input_229)[name = tensor("m_15")]; + tensor var_792 = mul(x = y_15, y = m_15)[name = tensor("op_792")]; + tensor input_231_interleave_0 = const()[name = tensor("input_231_interleave_0"), val = tensor(false)]; + tensor input_231 = concat(axis = var_11, interleave = input_231_interleave_0, values = (input_211, var_792))[name = tensor("input_231")]; + tensor input_233 = batch_norm(beta = model_xvector_block1_tdnnd9_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block1_tdnnd9_nonlinear1_batchnorm_weight, mean = model_xvector_block1_tdnnd9_nonlinear1_batchnorm_running_mean, variance = model_xvector_block1_tdnnd9_nonlinear1_batchnorm_running_var, x = input_231)[name = tensor("input_233")]; + tensor input_235 = relu(x = input_233)[name = tensor("input_235")]; + tensor input_237_pad_type_0 = const()[name = tensor("input_237_pad_type_0"), val = tensor("valid")]; + tensor input_237_strides_0 = const()[name = tensor("input_237_strides_0"), val = tensor([1])]; + tensor input_237_pad_0 = const()[name = tensor("input_237_pad_0"), val = tensor([0, 0])]; + tensor input_237_dilations_0 = const()[name = tensor("input_237_dilations_0"), val = tensor([1])]; + tensor input_237_groups_0 = const()[name = tensor("input_237_groups_0"), val = tensor(1)]; + tensor const_42 = const()[name = tensor("const_42"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10828288)))]; + tensor const_43 = const()[name = tensor("const_43"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11024960)))]; + tensor input_239 = conv(bias = const_43, dilations = input_237_dilations_0, groups = input_237_groups_0, pad = input_237_pad_0, pad_type = input_237_pad_type_0, strides = input_237_strides_0, weight = const_42, x = input_235)[name = tensor("input_239")]; + tensor input_241 = relu(x = input_239)[name = tensor("input_241")]; + tensor y_17_pad_type_0 = const()[name = tensor("y_17_pad_type_0"), val = tensor("custom")]; + tensor y_17_pad_0 = const()[name = tensor("y_17_pad_0"), val = tensor([1, 1])]; + tensor y_17_strides_0 = const()[name = tensor("y_17_strides_0"), val = tensor([1])]; + tensor y_17_dilations_0 = const()[name = tensor("y_17_dilations_0"), val = tensor([1])]; + tensor y_17_groups_0 = const()[name = tensor("y_17_groups_0"), val = tensor(1)]; + tensor y_17 = conv(dilations = y_17_dilations_0, groups = y_17_groups_0, pad = y_17_pad_0, pad_type = y_17_pad_type_0, strides = y_17_strides_0, weight = model_xvector_block1_tdnnd9_cam_layer_linear_local_weight, x = input_241)[name = tensor("y_17")]; + tensor var_829_axes_0 = const()[name = tensor("op_829_axes_0"), val = tensor([-1])]; + tensor var_829_keep_dims_0 = const()[name = tensor("op_829_keep_dims_0"), val = tensor(true)]; + tensor var_829 = reduce_mean(axes = var_829_axes_0, keep_dims = var_829_keep_dims_0, x = input_241)[name = tensor("op_829")]; + tensor var_830 = const()[name = tensor("op_830"), val = tensor([100])]; + tensor var_831 = const()[name = tensor("op_831"), val = tensor([100])]; + tensor seg_33_pad_type_0 = const()[name = tensor("seg_33_pad_type_0"), val = tensor("custom")]; + tensor seg_33_pad_0 = const()[name = tensor("seg_33_pad_0"), val = tensor([0, 0])]; + tensor seg_33_exclude_padding_from_average_0 = const()[name = tensor("seg_33_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_33_ceil_mode_0 = const()[name = tensor("seg_33_ceil_mode_0"), val = tensor(true)]; + tensor seg_33 = avg_pool(ceil_mode = seg_33_ceil_mode_0, exclude_padding_from_average = seg_33_exclude_padding_from_average_0, kernel_sizes = var_830, pad = seg_33_pad_0, pad_type = seg_33_pad_type_0, strides = var_831, x = input_241)[name = tensor("seg_33")]; + tensor var_834_shape = shape(x = seg_33)[name = tensor("op_834_shape")]; + tensor gather_36 = const()[name = tensor("gather_36"), val = tensor(1)]; + tensor gather_37 = const()[name = tensor("gather_37"), val = tensor(128)]; + tensor select_38 = const()[name = tensor("select_38"), val = tensor(2)]; + tensor gather_38_axis_0 = const()[name = tensor("gather_38_axis_0"), val = tensor(0)]; + tensor gather_38_batch_dims_0 = const()[name = tensor("gather_38_batch_dims_0"), val = tensor(0)]; + tensor gather_38_validate_indices_0 = const()[name = tensor("gather_38_validate_indices_0"), val = tensor(false)]; + tensor gather_38 = gather(axis = gather_38_axis_0, batch_dims = gather_38_batch_dims_0, indices = select_38, validate_indices = gather_38_validate_indices_0, x = var_834_shape)[name = tensor("gather_38")]; + tensor var_837_axes_0 = const()[name = tensor("op_837_axes_0"), val = tensor([-1])]; + tensor var_837 = expand_dims(axes = var_837_axes_0, x = seg_33)[name = tensor("op_837")]; + tensor concat_17_axis_0 = const()[name = tensor("concat_17_axis_0"), val = tensor(0)]; + tensor concat_17_interleave_0 = const()[name = tensor("concat_17_interleave_0"), val = tensor(false)]; + tensor concat_17 = concat(axis = concat_17_axis_0, interleave = concat_17_interleave_0, values = (gather_36, gather_37, gather_38, var_3))[name = tensor("concat_17")]; + tensor shape_47 = shape(x = var_837)[name = tensor("shape_47")]; + tensor real_div_8 = real_div(x = concat_17, y = shape_47)[name = tensor("real_div_8")]; + tensor var_839 = tile(reps = real_div_8, x = var_837)[name = tensor("op_839")]; + tensor var_840 = const()[name = tensor("op_840"), val = tensor([1, 128, -1])]; + tensor seg_35 = reshape(shape = var_840, x = var_839)[name = tensor("seg_35")]; + tensor var_842_shape = shape(x = input_241)[name = tensor("op_842_shape")]; + tensor select_39 = const()[name = tensor("select_39"), val = tensor(2)]; + tensor gather_39_axis_0 = const()[name = tensor("gather_39_axis_0"), val = tensor(0)]; + tensor gather_39_batch_dims_0 = const()[name = tensor("gather_39_batch_dims_0"), val = tensor(0)]; + tensor gather_39_validate_indices_0 = const()[name = tensor("gather_39_validate_indices_0"), val = tensor(false)]; + tensor gather_39 = gather(axis = gather_39_axis_0, batch_dims = gather_39_batch_dims_0, indices = select_39, validate_indices = gather_39_validate_indices_0, x = var_842_shape)[name = tensor("gather_39")]; + tensor concat_18_values0_0 = const()[name = tensor("concat_18_values0_0"), val = tensor(1)]; + tensor concat_18_values1_0 = const()[name = tensor("concat_18_values1_0"), val = tensor(128)]; + tensor concat_18_axis_0 = const()[name = tensor("concat_18_axis_0"), val = tensor(0)]; + tensor concat_18_interleave_0 = const()[name = tensor("concat_18_interleave_0"), val = tensor(false)]; + tensor concat_18 = concat(axis = concat_18_axis_0, interleave = concat_18_interleave_0, values = (concat_18_values0_0, concat_18_values1_0, gather_39))[name = tensor("concat_18")]; + tensor var_843_begin_0 = const()[name = tensor("op_843_begin_0"), val = tensor([0, 0, 0])]; + tensor var_843_end_mask_0 = const()[name = tensor("op_843_end_mask_0"), val = tensor([true, true, false])]; + tensor var_843 = slice_by_index(begin = var_843_begin_0, end = concat_18, end_mask = var_843_end_mask_0, x = seg_35)[name = tensor("op_843")]; + tensor input_243 = add(x = var_829, y = var_843)[name = tensor("input_243")]; + tensor input_245_pad_type_0 = const()[name = tensor("input_245_pad_type_0"), val = tensor("valid")]; + tensor input_245_strides_0 = const()[name = tensor("input_245_strides_0"), val = tensor([1])]; + tensor input_245_pad_0 = const()[name = tensor("input_245_pad_0"), val = tensor([0, 0])]; + tensor input_245_dilations_0 = const()[name = tensor("input_245_dilations_0"), val = tensor([1])]; + tensor input_245_groups_0 = const()[name = tensor("input_245_groups_0"), val = tensor(1)]; + tensor input_245 = conv(bias = model_xvector_block1_tdnnd9_cam_layer_linear1_bias, dilations = input_245_dilations_0, groups = input_245_groups_0, pad = input_245_pad_0, pad_type = input_245_pad_type_0, strides = input_245_strides_0, weight = model_xvector_block1_tdnnd9_cam_layer_linear1_weight, x = input_243)[name = tensor("input_245")]; + tensor input_247 = relu(x = input_245)[name = tensor("input_247")]; + tensor input_249_pad_type_0 = const()[name = tensor("input_249_pad_type_0"), val = tensor("valid")]; + tensor input_249_strides_0 = const()[name = tensor("input_249_strides_0"), val = tensor([1])]; + tensor input_249_pad_0 = const()[name = tensor("input_249_pad_0"), val = tensor([0, 0])]; + tensor input_249_dilations_0 = const()[name = tensor("input_249_dilations_0"), val = tensor([1])]; + tensor input_249_groups_0 = const()[name = tensor("input_249_groups_0"), val = tensor(1)]; + tensor input_249 = conv(bias = model_xvector_block1_tdnnd9_cam_layer_linear2_bias, dilations = input_249_dilations_0, groups = input_249_groups_0, pad = input_249_pad_0, pad_type = input_249_pad_type_0, strides = input_249_strides_0, weight = model_xvector_block1_tdnnd9_cam_layer_linear2_weight, x = input_247)[name = tensor("input_249")]; + tensor m_17 = sigmoid(x = input_249)[name = tensor("m_17")]; + tensor var_861 = mul(x = y_17, y = m_17)[name = tensor("op_861")]; + tensor input_251_interleave_0 = const()[name = tensor("input_251_interleave_0"), val = tensor(false)]; + tensor input_251 = concat(axis = var_11, interleave = input_251_interleave_0, values = (input_231, var_861))[name = tensor("input_251")]; + tensor input_253 = batch_norm(beta = model_xvector_block1_tdnnd10_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block1_tdnnd10_nonlinear1_batchnorm_weight, mean = model_xvector_block1_tdnnd10_nonlinear1_batchnorm_running_mean, variance = model_xvector_block1_tdnnd10_nonlinear1_batchnorm_running_var, x = input_251)[name = tensor("input_253")]; + tensor input_255 = relu(x = input_253)[name = tensor("input_255")]; + tensor input_257_pad_type_0 = const()[name = tensor("input_257_pad_type_0"), val = tensor("valid")]; + tensor input_257_strides_0 = const()[name = tensor("input_257_strides_0"), val = tensor([1])]; + tensor input_257_pad_0 = const()[name = tensor("input_257_pad_0"), val = tensor([0, 0])]; + 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 const_44 = const()[name = tensor("const_44"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11025536)))]; + tensor const_45 = const()[name = tensor("const_45"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11238592)))]; + tensor input_259 = conv(bias = const_45, 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 = const_44, x = input_255)[name = tensor("input_259")]; + tensor input_261 = relu(x = input_259)[name = tensor("input_261")]; + tensor y_19_pad_type_0 = const()[name = tensor("y_19_pad_type_0"), val = tensor("custom")]; + tensor y_19_pad_0 = const()[name = tensor("y_19_pad_0"), val = tensor([1, 1])]; + tensor y_19_strides_0 = const()[name = tensor("y_19_strides_0"), val = tensor([1])]; + tensor y_19_dilations_0 = const()[name = tensor("y_19_dilations_0"), val = tensor([1])]; + tensor y_19_groups_0 = const()[name = tensor("y_19_groups_0"), val = tensor(1)]; + tensor y_19 = conv(dilations = y_19_dilations_0, groups = y_19_groups_0, pad = y_19_pad_0, pad_type = y_19_pad_type_0, strides = y_19_strides_0, weight = model_xvector_block1_tdnnd10_cam_layer_linear_local_weight, x = input_261)[name = tensor("y_19")]; + tensor var_898_axes_0 = const()[name = tensor("op_898_axes_0"), val = tensor([-1])]; + tensor var_898_keep_dims_0 = const()[name = tensor("op_898_keep_dims_0"), val = tensor(true)]; + tensor var_898 = reduce_mean(axes = var_898_axes_0, keep_dims = var_898_keep_dims_0, x = input_261)[name = tensor("op_898")]; + tensor var_899 = const()[name = tensor("op_899"), val = tensor([100])]; + tensor var_900 = const()[name = tensor("op_900"), val = tensor([100])]; + tensor seg_37_pad_type_0 = const()[name = tensor("seg_37_pad_type_0"), val = tensor("custom")]; + tensor seg_37_pad_0 = const()[name = tensor("seg_37_pad_0"), val = tensor([0, 0])]; + tensor seg_37_exclude_padding_from_average_0 = const()[name = tensor("seg_37_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_37_ceil_mode_0 = const()[name = tensor("seg_37_ceil_mode_0"), val = tensor(true)]; + tensor seg_37 = avg_pool(ceil_mode = seg_37_ceil_mode_0, exclude_padding_from_average = seg_37_exclude_padding_from_average_0, kernel_sizes = var_899, pad = seg_37_pad_0, pad_type = seg_37_pad_type_0, strides = var_900, x = input_261)[name = tensor("seg_37")]; + tensor var_903_shape = shape(x = seg_37)[name = tensor("op_903_shape")]; + tensor gather_40 = const()[name = tensor("gather_40"), val = tensor(1)]; + tensor gather_41 = const()[name = tensor("gather_41"), val = tensor(128)]; + tensor select_42 = const()[name = tensor("select_42"), val = tensor(2)]; + tensor gather_42_axis_0 = const()[name = tensor("gather_42_axis_0"), val = tensor(0)]; + tensor gather_42_batch_dims_0 = const()[name = tensor("gather_42_batch_dims_0"), val = tensor(0)]; + tensor gather_42_validate_indices_0 = const()[name = tensor("gather_42_validate_indices_0"), val = tensor(false)]; + tensor gather_42 = gather(axis = gather_42_axis_0, batch_dims = gather_42_batch_dims_0, indices = select_42, validate_indices = gather_42_validate_indices_0, x = var_903_shape)[name = tensor("gather_42")]; + tensor var_906_axes_0 = const()[name = tensor("op_906_axes_0"), val = tensor([-1])]; + tensor var_906 = expand_dims(axes = var_906_axes_0, x = seg_37)[name = tensor("op_906")]; + tensor concat_19_axis_0 = const()[name = tensor("concat_19_axis_0"), val = tensor(0)]; + tensor concat_19_interleave_0 = const()[name = tensor("concat_19_interleave_0"), val = tensor(false)]; + tensor concat_19 = concat(axis = concat_19_axis_0, interleave = concat_19_interleave_0, values = (gather_40, gather_41, gather_42, var_3))[name = tensor("concat_19")]; + tensor shape_52 = shape(x = var_906)[name = tensor("shape_52")]; + tensor real_div_9 = real_div(x = concat_19, y = shape_52)[name = tensor("real_div_9")]; + tensor var_908 = tile(reps = real_div_9, x = var_906)[name = tensor("op_908")]; + tensor var_909 = const()[name = tensor("op_909"), val = tensor([1, 128, -1])]; + tensor seg_39 = reshape(shape = var_909, x = var_908)[name = tensor("seg_39")]; + tensor var_911_shape = shape(x = input_261)[name = tensor("op_911_shape")]; + tensor select_43 = const()[name = tensor("select_43"), val = tensor(2)]; + tensor gather_43_axis_0 = const()[name = tensor("gather_43_axis_0"), val = tensor(0)]; + tensor gather_43_batch_dims_0 = const()[name = tensor("gather_43_batch_dims_0"), val = tensor(0)]; + tensor gather_43_validate_indices_0 = const()[name = tensor("gather_43_validate_indices_0"), val = tensor(false)]; + tensor gather_43 = gather(axis = gather_43_axis_0, batch_dims = gather_43_batch_dims_0, indices = select_43, validate_indices = gather_43_validate_indices_0, x = var_911_shape)[name = tensor("gather_43")]; + tensor concat_20_values0_0 = const()[name = tensor("concat_20_values0_0"), val = tensor(1)]; + tensor concat_20_values1_0 = const()[name = tensor("concat_20_values1_0"), val = tensor(128)]; + tensor concat_20_axis_0 = const()[name = tensor("concat_20_axis_0"), val = tensor(0)]; + tensor concat_20_interleave_0 = const()[name = tensor("concat_20_interleave_0"), val = tensor(false)]; + tensor concat_20 = concat(axis = concat_20_axis_0, interleave = concat_20_interleave_0, values = (concat_20_values0_0, concat_20_values1_0, gather_43))[name = tensor("concat_20")]; + tensor var_912_begin_0 = const()[name = tensor("op_912_begin_0"), val = tensor([0, 0, 0])]; + tensor var_912_end_mask_0 = const()[name = tensor("op_912_end_mask_0"), val = tensor([true, true, false])]; + tensor var_912 = slice_by_index(begin = var_912_begin_0, end = concat_20, end_mask = var_912_end_mask_0, x = seg_39)[name = tensor("op_912")]; + tensor input_263 = add(x = var_898, y = var_912)[name = tensor("input_263")]; + tensor input_265_pad_type_0 = const()[name = tensor("input_265_pad_type_0"), val = tensor("valid")]; + tensor input_265_strides_0 = const()[name = tensor("input_265_strides_0"), val = tensor([1])]; + tensor input_265_pad_0 = const()[name = tensor("input_265_pad_0"), val = tensor([0, 0])]; + tensor input_265_dilations_0 = const()[name = tensor("input_265_dilations_0"), val = tensor([1])]; + tensor input_265_groups_0 = const()[name = tensor("input_265_groups_0"), val = tensor(1)]; + tensor input_265 = conv(bias = model_xvector_block1_tdnnd10_cam_layer_linear1_bias, dilations = input_265_dilations_0, groups = input_265_groups_0, pad = input_265_pad_0, pad_type = input_265_pad_type_0, strides = input_265_strides_0, weight = model_xvector_block1_tdnnd10_cam_layer_linear1_weight, x = input_263)[name = tensor("input_265")]; + tensor input_267 = relu(x = input_265)[name = tensor("input_267")]; + tensor input_269_pad_type_0 = const()[name = tensor("input_269_pad_type_0"), val = tensor("valid")]; + tensor input_269_strides_0 = const()[name = tensor("input_269_strides_0"), val = tensor([1])]; + tensor input_269_pad_0 = const()[name = tensor("input_269_pad_0"), val = tensor([0, 0])]; + tensor input_269_dilations_0 = const()[name = tensor("input_269_dilations_0"), val = tensor([1])]; + tensor input_269_groups_0 = const()[name = tensor("input_269_groups_0"), val = tensor(1)]; + tensor input_269 = conv(bias = model_xvector_block1_tdnnd10_cam_layer_linear2_bias, dilations = input_269_dilations_0, groups = input_269_groups_0, pad = input_269_pad_0, pad_type = input_269_pad_type_0, strides = input_269_strides_0, weight = model_xvector_block1_tdnnd10_cam_layer_linear2_weight, x = input_267)[name = tensor("input_269")]; + tensor m_19 = sigmoid(x = input_269)[name = tensor("m_19")]; + tensor var_930 = mul(x = y_19, y = m_19)[name = tensor("op_930")]; + tensor input_271_interleave_0 = const()[name = tensor("input_271_interleave_0"), val = tensor(false)]; + tensor input_271 = concat(axis = var_11, interleave = input_271_interleave_0, values = (input_251, var_930))[name = tensor("input_271")]; + tensor input_273 = batch_norm(beta = model_xvector_block1_tdnnd11_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block1_tdnnd11_nonlinear1_batchnorm_weight, mean = model_xvector_block1_tdnnd11_nonlinear1_batchnorm_running_mean, variance = model_xvector_block1_tdnnd11_nonlinear1_batchnorm_running_var, x = input_271)[name = tensor("input_273")]; + tensor input_275 = relu(x = input_273)[name = tensor("input_275")]; + tensor input_277_pad_type_0 = const()[name = tensor("input_277_pad_type_0"), val = tensor("valid")]; + tensor input_277_strides_0 = const()[name = tensor("input_277_strides_0"), val = tensor([1])]; + tensor input_277_pad_0 = const()[name = tensor("input_277_pad_0"), val = tensor([0, 0])]; + tensor input_277_dilations_0 = const()[name = tensor("input_277_dilations_0"), val = tensor([1])]; + tensor input_277_groups_0 = const()[name = tensor("input_277_groups_0"), val = tensor(1)]; + tensor const_46 = const()[name = tensor("const_46"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11239168)))]; + tensor const_47 = const()[name = tensor("const_47"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11468608)))]; + tensor input_279 = conv(bias = const_47, dilations = input_277_dilations_0, groups = input_277_groups_0, pad = input_277_pad_0, pad_type = input_277_pad_type_0, strides = input_277_strides_0, weight = const_46, x = input_275)[name = tensor("input_279")]; + tensor input_281 = relu(x = input_279)[name = tensor("input_281")]; + tensor y_21_pad_type_0 = const()[name = tensor("y_21_pad_type_0"), val = tensor("custom")]; + tensor y_21_pad_0 = const()[name = tensor("y_21_pad_0"), val = tensor([1, 1])]; + tensor y_21_strides_0 = const()[name = tensor("y_21_strides_0"), val = tensor([1])]; + tensor y_21_dilations_0 = const()[name = tensor("y_21_dilations_0"), val = tensor([1])]; + tensor y_21_groups_0 = const()[name = tensor("y_21_groups_0"), val = tensor(1)]; + tensor y_21 = conv(dilations = y_21_dilations_0, groups = y_21_groups_0, pad = y_21_pad_0, pad_type = y_21_pad_type_0, strides = y_21_strides_0, weight = model_xvector_block1_tdnnd11_cam_layer_linear_local_weight, x = input_281)[name = tensor("y_21")]; + tensor var_967_axes_0 = const()[name = tensor("op_967_axes_0"), val = tensor([-1])]; + tensor var_967_keep_dims_0 = const()[name = tensor("op_967_keep_dims_0"), val = tensor(true)]; + tensor var_967 = reduce_mean(axes = var_967_axes_0, keep_dims = var_967_keep_dims_0, x = input_281)[name = tensor("op_967")]; + tensor var_968 = const()[name = tensor("op_968"), val = tensor([100])]; + tensor var_969 = const()[name = tensor("op_969"), val = tensor([100])]; + tensor seg_41_pad_type_0 = const()[name = tensor("seg_41_pad_type_0"), val = tensor("custom")]; + tensor seg_41_pad_0 = const()[name = tensor("seg_41_pad_0"), val = tensor([0, 0])]; + tensor seg_41_exclude_padding_from_average_0 = const()[name = tensor("seg_41_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_41_ceil_mode_0 = const()[name = tensor("seg_41_ceil_mode_0"), val = tensor(true)]; + tensor seg_41 = avg_pool(ceil_mode = seg_41_ceil_mode_0, exclude_padding_from_average = seg_41_exclude_padding_from_average_0, kernel_sizes = var_968, pad = seg_41_pad_0, pad_type = seg_41_pad_type_0, strides = var_969, x = input_281)[name = tensor("seg_41")]; + tensor var_972_shape = shape(x = seg_41)[name = tensor("op_972_shape")]; + tensor gather_44 = const()[name = tensor("gather_44"), val = tensor(1)]; + tensor gather_45 = const()[name = tensor("gather_45"), val = tensor(128)]; + tensor select_46 = const()[name = tensor("select_46"), val = tensor(2)]; + tensor gather_46_axis_0 = const()[name = tensor("gather_46_axis_0"), val = tensor(0)]; + tensor gather_46_batch_dims_0 = const()[name = tensor("gather_46_batch_dims_0"), val = tensor(0)]; + tensor gather_46_validate_indices_0 = const()[name = tensor("gather_46_validate_indices_0"), val = tensor(false)]; + tensor gather_46 = gather(axis = gather_46_axis_0, batch_dims = gather_46_batch_dims_0, indices = select_46, validate_indices = gather_46_validate_indices_0, x = var_972_shape)[name = tensor("gather_46")]; + tensor var_975_axes_0 = const()[name = tensor("op_975_axes_0"), val = tensor([-1])]; + tensor var_975 = expand_dims(axes = var_975_axes_0, x = seg_41)[name = tensor("op_975")]; + tensor concat_21_axis_0 = const()[name = tensor("concat_21_axis_0"), val = tensor(0)]; + tensor concat_21_interleave_0 = const()[name = tensor("concat_21_interleave_0"), val = tensor(false)]; + tensor concat_21 = concat(axis = concat_21_axis_0, interleave = concat_21_interleave_0, values = (gather_44, gather_45, gather_46, var_3))[name = tensor("concat_21")]; + tensor shape_57 = shape(x = var_975)[name = tensor("shape_57")]; + tensor real_div_10 = real_div(x = concat_21, y = shape_57)[name = tensor("real_div_10")]; + tensor var_977 = tile(reps = real_div_10, x = var_975)[name = tensor("op_977")]; + tensor var_978 = const()[name = tensor("op_978"), val = tensor([1, 128, -1])]; + tensor seg_43 = reshape(shape = var_978, x = var_977)[name = tensor("seg_43")]; + tensor var_980_shape = shape(x = input_281)[name = tensor("op_980_shape")]; + tensor select_47 = const()[name = tensor("select_47"), val = tensor(2)]; + tensor gather_47_axis_0 = const()[name = tensor("gather_47_axis_0"), val = tensor(0)]; + tensor gather_47_batch_dims_0 = const()[name = tensor("gather_47_batch_dims_0"), val = tensor(0)]; + tensor gather_47_validate_indices_0 = const()[name = tensor("gather_47_validate_indices_0"), val = tensor(false)]; + tensor gather_47 = gather(axis = gather_47_axis_0, batch_dims = gather_47_batch_dims_0, indices = select_47, validate_indices = gather_47_validate_indices_0, x = var_980_shape)[name = tensor("gather_47")]; + tensor concat_22_values0_0 = const()[name = tensor("concat_22_values0_0"), val = tensor(1)]; + tensor concat_22_values1_0 = const()[name = tensor("concat_22_values1_0"), val = tensor(128)]; + tensor concat_22_axis_0 = const()[name = tensor("concat_22_axis_0"), val = tensor(0)]; + tensor concat_22_interleave_0 = const()[name = tensor("concat_22_interleave_0"), val = tensor(false)]; + tensor concat_22 = concat(axis = concat_22_axis_0, interleave = concat_22_interleave_0, values = (concat_22_values0_0, concat_22_values1_0, gather_47))[name = tensor("concat_22")]; + tensor var_981_begin_0 = const()[name = tensor("op_981_begin_0"), val = tensor([0, 0, 0])]; + tensor var_981_end_mask_0 = const()[name = tensor("op_981_end_mask_0"), val = tensor([true, true, false])]; + tensor var_981 = slice_by_index(begin = var_981_begin_0, end = concat_22, end_mask = var_981_end_mask_0, x = seg_43)[name = tensor("op_981")]; + tensor input_283 = add(x = var_967, y = var_981)[name = tensor("input_283")]; + tensor input_285_pad_type_0 = const()[name = tensor("input_285_pad_type_0"), val = tensor("valid")]; + tensor input_285_strides_0 = const()[name = tensor("input_285_strides_0"), val = tensor([1])]; + tensor input_285_pad_0 = const()[name = tensor("input_285_pad_0"), val = tensor([0, 0])]; + tensor input_285_dilations_0 = const()[name = tensor("input_285_dilations_0"), val = tensor([1])]; + tensor input_285_groups_0 = const()[name = tensor("input_285_groups_0"), val = tensor(1)]; + tensor input_285 = conv(bias = model_xvector_block1_tdnnd11_cam_layer_linear1_bias, dilations = input_285_dilations_0, groups = input_285_groups_0, pad = input_285_pad_0, pad_type = input_285_pad_type_0, strides = input_285_strides_0, weight = model_xvector_block1_tdnnd11_cam_layer_linear1_weight, x = input_283)[name = tensor("input_285")]; + tensor input_287 = relu(x = input_285)[name = tensor("input_287")]; + tensor input_289_pad_type_0 = const()[name = tensor("input_289_pad_type_0"), val = tensor("valid")]; + tensor input_289_strides_0 = const()[name = tensor("input_289_strides_0"), val = tensor([1])]; + tensor input_289_pad_0 = const()[name = tensor("input_289_pad_0"), val = tensor([0, 0])]; + tensor input_289_dilations_0 = const()[name = tensor("input_289_dilations_0"), val = tensor([1])]; + tensor input_289_groups_0 = const()[name = tensor("input_289_groups_0"), val = tensor(1)]; + tensor input_289 = conv(bias = model_xvector_block1_tdnnd11_cam_layer_linear2_bias, dilations = input_289_dilations_0, groups = input_289_groups_0, pad = input_289_pad_0, pad_type = input_289_pad_type_0, strides = input_289_strides_0, weight = model_xvector_block1_tdnnd11_cam_layer_linear2_weight, x = input_287)[name = tensor("input_289")]; + tensor m_21 = sigmoid(x = input_289)[name = tensor("m_21")]; + tensor var_999 = mul(x = y_21, y = m_21)[name = tensor("op_999")]; + tensor input_291_interleave_0 = const()[name = tensor("input_291_interleave_0"), val = tensor(false)]; + tensor input_291 = concat(axis = var_11, interleave = input_291_interleave_0, values = (input_271, var_999))[name = tensor("input_291")]; + tensor input_293 = batch_norm(beta = model_xvector_block1_tdnnd12_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block1_tdnnd12_nonlinear1_batchnorm_weight, mean = model_xvector_block1_tdnnd12_nonlinear1_batchnorm_running_mean, variance = model_xvector_block1_tdnnd12_nonlinear1_batchnorm_running_var, x = input_291)[name = tensor("input_293")]; + tensor input_295 = relu(x = input_293)[name = tensor("input_295")]; + tensor input_297_pad_type_0 = const()[name = tensor("input_297_pad_type_0"), val = tensor("valid")]; + tensor input_297_strides_0 = const()[name = tensor("input_297_strides_0"), val = tensor([1])]; + tensor input_297_pad_0 = const()[name = tensor("input_297_pad_0"), val = tensor([0, 0])]; + tensor input_297_dilations_0 = const()[name = tensor("input_297_dilations_0"), val = tensor([1])]; + tensor input_297_groups_0 = const()[name = tensor("input_297_groups_0"), val = tensor(1)]; + tensor const_48 = const()[name = tensor("const_48"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11469184)))]; + tensor const_49 = const()[name = tensor("const_49"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11715008)))]; + tensor input_299 = conv(bias = const_49, dilations = input_297_dilations_0, groups = input_297_groups_0, pad = input_297_pad_0, pad_type = input_297_pad_type_0, strides = input_297_strides_0, weight = const_48, x = input_295)[name = tensor("input_299")]; + tensor input_301 = relu(x = input_299)[name = tensor("input_301")]; + tensor y_23_pad_type_0 = const()[name = tensor("y_23_pad_type_0"), val = tensor("custom")]; + tensor y_23_pad_0 = const()[name = tensor("y_23_pad_0"), val = tensor([1, 1])]; + tensor y_23_strides_0 = const()[name = tensor("y_23_strides_0"), val = tensor([1])]; + tensor y_23_dilations_0 = const()[name = tensor("y_23_dilations_0"), val = tensor([1])]; + tensor y_23_groups_0 = const()[name = tensor("y_23_groups_0"), val = tensor(1)]; + tensor y_23 = conv(dilations = y_23_dilations_0, groups = y_23_groups_0, pad = y_23_pad_0, pad_type = y_23_pad_type_0, strides = y_23_strides_0, weight = model_xvector_block1_tdnnd12_cam_layer_linear_local_weight, x = input_301)[name = tensor("y_23")]; + tensor var_1036_axes_0 = const()[name = tensor("op_1036_axes_0"), val = tensor([-1])]; + tensor var_1036_keep_dims_0 = const()[name = tensor("op_1036_keep_dims_0"), val = tensor(true)]; + tensor var_1036 = reduce_mean(axes = var_1036_axes_0, keep_dims = var_1036_keep_dims_0, x = input_301)[name = tensor("op_1036")]; + tensor var_1037 = const()[name = tensor("op_1037"), val = tensor([100])]; + tensor var_1038 = const()[name = tensor("op_1038"), val = tensor([100])]; + tensor seg_45_pad_type_0 = const()[name = tensor("seg_45_pad_type_0"), val = tensor("custom")]; + tensor seg_45_pad_0 = const()[name = tensor("seg_45_pad_0"), val = tensor([0, 0])]; + tensor seg_45_exclude_padding_from_average_0 = const()[name = tensor("seg_45_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_45_ceil_mode_0 = const()[name = tensor("seg_45_ceil_mode_0"), val = tensor(true)]; + tensor seg_45 = avg_pool(ceil_mode = seg_45_ceil_mode_0, exclude_padding_from_average = seg_45_exclude_padding_from_average_0, kernel_sizes = var_1037, pad = seg_45_pad_0, pad_type = seg_45_pad_type_0, strides = var_1038, x = input_301)[name = tensor("seg_45")]; + tensor var_1041_shape = shape(x = seg_45)[name = tensor("op_1041_shape")]; + tensor gather_48 = const()[name = tensor("gather_48"), val = tensor(1)]; + tensor gather_49 = const()[name = tensor("gather_49"), val = tensor(128)]; + tensor select_50 = const()[name = tensor("select_50"), val = tensor(2)]; + tensor gather_50_axis_0 = const()[name = tensor("gather_50_axis_0"), val = tensor(0)]; + tensor gather_50_batch_dims_0 = const()[name = tensor("gather_50_batch_dims_0"), val = tensor(0)]; + tensor gather_50_validate_indices_0 = const()[name = tensor("gather_50_validate_indices_0"), val = tensor(false)]; + tensor gather_50 = gather(axis = gather_50_axis_0, batch_dims = gather_50_batch_dims_0, indices = select_50, validate_indices = gather_50_validate_indices_0, x = var_1041_shape)[name = tensor("gather_50")]; + tensor var_1044_axes_0 = const()[name = tensor("op_1044_axes_0"), val = tensor([-1])]; + tensor var_1044 = expand_dims(axes = var_1044_axes_0, x = seg_45)[name = tensor("op_1044")]; + tensor concat_23_axis_0 = const()[name = tensor("concat_23_axis_0"), val = tensor(0)]; + tensor concat_23_interleave_0 = const()[name = tensor("concat_23_interleave_0"), val = tensor(false)]; + tensor concat_23 = concat(axis = concat_23_axis_0, interleave = concat_23_interleave_0, values = (gather_48, gather_49, gather_50, var_3))[name = tensor("concat_23")]; + tensor shape_62 = shape(x = var_1044)[name = tensor("shape_62")]; + tensor real_div_11 = real_div(x = concat_23, y = shape_62)[name = tensor("real_div_11")]; + tensor var_1046 = tile(reps = real_div_11, x = var_1044)[name = tensor("op_1046")]; + tensor var_1047 = const()[name = tensor("op_1047"), val = tensor([1, 128, -1])]; + tensor seg_47 = reshape(shape = var_1047, x = var_1046)[name = tensor("seg_47")]; + tensor var_1049_shape = shape(x = input_301)[name = tensor("op_1049_shape")]; + tensor select_51 = const()[name = tensor("select_51"), val = tensor(2)]; + tensor gather_51_axis_0 = const()[name = tensor("gather_51_axis_0"), val = tensor(0)]; + tensor gather_51_batch_dims_0 = const()[name = tensor("gather_51_batch_dims_0"), val = tensor(0)]; + tensor gather_51_validate_indices_0 = const()[name = tensor("gather_51_validate_indices_0"), val = tensor(false)]; + tensor gather_51 = gather(axis = gather_51_axis_0, batch_dims = gather_51_batch_dims_0, indices = select_51, validate_indices = gather_51_validate_indices_0, x = var_1049_shape)[name = tensor("gather_51")]; + tensor concat_24_values0_0 = const()[name = tensor("concat_24_values0_0"), val = tensor(1)]; + tensor concat_24_values1_0 = const()[name = tensor("concat_24_values1_0"), val = tensor(128)]; + tensor concat_24_axis_0 = const()[name = tensor("concat_24_axis_0"), val = tensor(0)]; + tensor concat_24_interleave_0 = const()[name = tensor("concat_24_interleave_0"), val = tensor(false)]; + tensor concat_24 = concat(axis = concat_24_axis_0, interleave = concat_24_interleave_0, values = (concat_24_values0_0, concat_24_values1_0, gather_51))[name = tensor("concat_24")]; + tensor var_1050_begin_0 = const()[name = tensor("op_1050_begin_0"), val = tensor([0, 0, 0])]; + tensor var_1050_end_mask_0 = const()[name = tensor("op_1050_end_mask_0"), val = tensor([true, true, false])]; + tensor var_1050 = slice_by_index(begin = var_1050_begin_0, end = concat_24, end_mask = var_1050_end_mask_0, x = seg_47)[name = tensor("op_1050")]; + tensor input_303 = add(x = var_1036, y = var_1050)[name = tensor("input_303")]; + tensor input_305_pad_type_0 = const()[name = tensor("input_305_pad_type_0"), val = tensor("valid")]; + tensor input_305_strides_0 = const()[name = tensor("input_305_strides_0"), val = tensor([1])]; + tensor input_305_pad_0 = const()[name = tensor("input_305_pad_0"), val = tensor([0, 0])]; + tensor input_305_dilations_0 = const()[name = tensor("input_305_dilations_0"), val = tensor([1])]; + tensor input_305_groups_0 = const()[name = tensor("input_305_groups_0"), val = tensor(1)]; + tensor input_305 = conv(bias = model_xvector_block1_tdnnd12_cam_layer_linear1_bias, dilations = input_305_dilations_0, groups = input_305_groups_0, pad = input_305_pad_0, pad_type = input_305_pad_type_0, strides = input_305_strides_0, weight = model_xvector_block1_tdnnd12_cam_layer_linear1_weight, x = input_303)[name = tensor("input_305")]; + tensor input_307 = relu(x = input_305)[name = tensor("input_307")]; + tensor input_309_pad_type_0 = const()[name = tensor("input_309_pad_type_0"), val = tensor("valid")]; + tensor input_309_strides_0 = const()[name = tensor("input_309_strides_0"), val = tensor([1])]; + tensor input_309_pad_0 = const()[name = tensor("input_309_pad_0"), val = tensor([0, 0])]; + tensor input_309_dilations_0 = const()[name = tensor("input_309_dilations_0"), val = tensor([1])]; + tensor input_309_groups_0 = const()[name = tensor("input_309_groups_0"), val = tensor(1)]; + tensor input_309 = conv(bias = model_xvector_block1_tdnnd12_cam_layer_linear2_bias, dilations = input_309_dilations_0, groups = input_309_groups_0, pad = input_309_pad_0, pad_type = input_309_pad_type_0, strides = input_309_strides_0, weight = model_xvector_block1_tdnnd12_cam_layer_linear2_weight, x = input_307)[name = tensor("input_309")]; + tensor m_23 = sigmoid(x = input_309)[name = tensor("m_23")]; + tensor var_1068 = mul(x = y_23, y = m_23)[name = tensor("op_1068")]; + tensor input_311_interleave_0 = const()[name = tensor("input_311_interleave_0"), val = tensor(false)]; + tensor input_311 = concat(axis = var_11, interleave = input_311_interleave_0, values = (input_291, var_1068))[name = tensor("input_311")]; + tensor input_313 = batch_norm(beta = model_xvector_transit1_nonlinear_batchnorm_bias, epsilon = var_8, gamma = model_xvector_transit1_nonlinear_batchnorm_weight, mean = model_xvector_transit1_nonlinear_batchnorm_running_mean, variance = model_xvector_transit1_nonlinear_batchnorm_running_var, x = input_311)[name = tensor("input_313")]; + tensor input_315 = relu(x = input_313)[name = tensor("input_315")]; + tensor input_317_pad_type_0 = const()[name = tensor("input_317_pad_type_0"), val = tensor("valid")]; + tensor input_317_strides_0 = const()[name = tensor("input_317_strides_0"), val = tensor([1])]; + tensor input_317_pad_0 = const()[name = tensor("input_317_pad_0"), val = tensor([0, 0])]; + tensor input_317_dilations_0 = const()[name = tensor("input_317_dilations_0"), val = tensor([1])]; + tensor input_317_groups_0 = const()[name = tensor("input_317_groups_0"), val = tensor(1)]; + tensor input_317 = conv(dilations = input_317_dilations_0, groups = input_317_groups_0, pad = input_317_pad_0, pad_type = input_317_pad_type_0, strides = input_317_strides_0, weight = model_xvector_transit1_linear_weight, x = input_315)[name = tensor("input_317")]; + tensor input_319 = batch_norm(beta = model_xvector_block2_tdnnd1_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block2_tdnnd1_nonlinear1_batchnorm_weight, mean = model_xvector_block2_tdnnd1_nonlinear1_batchnorm_running_mean, variance = model_xvector_block2_tdnnd1_nonlinear1_batchnorm_running_var, x = input_317)[name = tensor("input_319")]; + tensor input_321 = relu(x = input_319)[name = tensor("input_321")]; + tensor input_323_pad_type_0 = const()[name = tensor("input_323_pad_type_0"), val = tensor("valid")]; + tensor input_323_strides_0 = const()[name = tensor("input_323_strides_0"), val = tensor([1])]; + tensor input_323_pad_0 = const()[name = tensor("input_323_pad_0"), val = tensor([0, 0])]; + tensor input_323_dilations_0 = const()[name = tensor("input_323_dilations_0"), val = tensor([1])]; + tensor input_323_groups_0 = const()[name = tensor("input_323_groups_0"), val = tensor(1)]; + tensor const_50 = const()[name = tensor("const_50"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11715584)))]; + tensor const_51 = const()[name = tensor("const_51"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11846720)))]; + tensor input_325 = conv(bias = const_51, dilations = input_323_dilations_0, groups = input_323_groups_0, pad = input_323_pad_0, pad_type = input_323_pad_type_0, strides = input_323_strides_0, weight = const_50, x = input_321)[name = tensor("input_325")]; + tensor input_327 = relu(x = input_325)[name = tensor("input_327")]; + tensor y_25_pad_type_0 = const()[name = tensor("y_25_pad_type_0"), val = tensor("custom")]; + tensor y_25_pad_0 = const()[name = tensor("y_25_pad_0"), val = tensor([2, 2])]; + tensor y_25_dilations_0 = const()[name = tensor("y_25_dilations_0"), val = tensor([2])]; + tensor y_25_strides_0 = const()[name = tensor("y_25_strides_0"), val = tensor([1])]; + tensor y_25_groups_0 = const()[name = tensor("y_25_groups_0"), val = tensor(1)]; + tensor y_25 = conv(dilations = y_25_dilations_0, groups = y_25_groups_0, pad = y_25_pad_0, pad_type = y_25_pad_type_0, strides = y_25_strides_0, weight = model_xvector_block2_tdnnd1_cam_layer_linear_local_weight, x = input_327)[name = tensor("y_25")]; + tensor var_1144_axes_0 = const()[name = tensor("op_1144_axes_0"), val = tensor([-1])]; + tensor var_1144_keep_dims_0 = const()[name = tensor("op_1144_keep_dims_0"), val = tensor(true)]; + tensor var_1144 = reduce_mean(axes = var_1144_axes_0, keep_dims = var_1144_keep_dims_0, x = input_327)[name = tensor("op_1144")]; + tensor var_1145 = const()[name = tensor("op_1145"), val = tensor([100])]; + tensor var_1146 = const()[name = tensor("op_1146"), val = tensor([100])]; + tensor seg_49_pad_type_0 = const()[name = tensor("seg_49_pad_type_0"), val = tensor("custom")]; + tensor seg_49_pad_0 = const()[name = tensor("seg_49_pad_0"), val = tensor([0, 0])]; + tensor seg_49_exclude_padding_from_average_0 = const()[name = tensor("seg_49_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_49_ceil_mode_0 = const()[name = tensor("seg_49_ceil_mode_0"), val = tensor(true)]; + tensor seg_49 = avg_pool(ceil_mode = seg_49_ceil_mode_0, exclude_padding_from_average = seg_49_exclude_padding_from_average_0, kernel_sizes = var_1145, pad = seg_49_pad_0, pad_type = seg_49_pad_type_0, strides = var_1146, x = input_327)[name = tensor("seg_49")]; + tensor var_1149_shape = shape(x = seg_49)[name = tensor("op_1149_shape")]; + tensor gather_52 = const()[name = tensor("gather_52"), val = tensor(1)]; + tensor gather_53 = const()[name = tensor("gather_53"), val = tensor(128)]; + tensor select_54 = const()[name = tensor("select_54"), val = tensor(2)]; + tensor gather_54_axis_0 = const()[name = tensor("gather_54_axis_0"), val = tensor(0)]; + tensor gather_54_batch_dims_0 = const()[name = tensor("gather_54_batch_dims_0"), val = tensor(0)]; + tensor gather_54_validate_indices_0 = const()[name = tensor("gather_54_validate_indices_0"), val = tensor(false)]; + tensor gather_54 = gather(axis = gather_54_axis_0, batch_dims = gather_54_batch_dims_0, indices = select_54, validate_indices = gather_54_validate_indices_0, x = var_1149_shape)[name = tensor("gather_54")]; + tensor var_1152_axes_0 = const()[name = tensor("op_1152_axes_0"), val = tensor([-1])]; + tensor var_1152 = expand_dims(axes = var_1152_axes_0, x = seg_49)[name = tensor("op_1152")]; + tensor concat_25_axis_0 = const()[name = tensor("concat_25_axis_0"), val = tensor(0)]; + tensor concat_25_interleave_0 = const()[name = tensor("concat_25_interleave_0"), val = tensor(false)]; + tensor concat_25 = concat(axis = concat_25_axis_0, interleave = concat_25_interleave_0, values = (gather_52, gather_53, gather_54, var_3))[name = tensor("concat_25")]; + tensor shape_67 = shape(x = var_1152)[name = tensor("shape_67")]; + tensor real_div_12 = real_div(x = concat_25, y = shape_67)[name = tensor("real_div_12")]; + tensor var_1154 = tile(reps = real_div_12, x = var_1152)[name = tensor("op_1154")]; + tensor var_1155 = const()[name = tensor("op_1155"), val = tensor([1, 128, -1])]; + tensor seg_51 = reshape(shape = var_1155, x = var_1154)[name = tensor("seg_51")]; + tensor var_1157_shape = shape(x = input_327)[name = tensor("op_1157_shape")]; + tensor select_55 = const()[name = tensor("select_55"), val = tensor(2)]; + tensor gather_55_axis_0 = const()[name = tensor("gather_55_axis_0"), val = tensor(0)]; + tensor gather_55_batch_dims_0 = const()[name = tensor("gather_55_batch_dims_0"), val = tensor(0)]; + tensor gather_55_validate_indices_0 = const()[name = tensor("gather_55_validate_indices_0"), val = tensor(false)]; + tensor gather_55 = gather(axis = gather_55_axis_0, batch_dims = gather_55_batch_dims_0, indices = select_55, validate_indices = gather_55_validate_indices_0, x = var_1157_shape)[name = tensor("gather_55")]; + tensor concat_26_values0_0 = const()[name = tensor("concat_26_values0_0"), val = tensor(1)]; + tensor concat_26_values1_0 = const()[name = tensor("concat_26_values1_0"), val = tensor(128)]; + tensor concat_26_axis_0 = const()[name = tensor("concat_26_axis_0"), val = tensor(0)]; + tensor concat_26_interleave_0 = const()[name = tensor("concat_26_interleave_0"), val = tensor(false)]; + tensor concat_26 = concat(axis = concat_26_axis_0, interleave = concat_26_interleave_0, values = (concat_26_values0_0, concat_26_values1_0, gather_55))[name = tensor("concat_26")]; + tensor var_1158_begin_0 = const()[name = tensor("op_1158_begin_0"), val = tensor([0, 0, 0])]; + tensor var_1158_end_mask_0 = const()[name = tensor("op_1158_end_mask_0"), val = tensor([true, true, false])]; + tensor var_1158 = slice_by_index(begin = var_1158_begin_0, end = concat_26, end_mask = var_1158_end_mask_0, x = seg_51)[name = tensor("op_1158")]; + tensor input_329 = add(x = var_1144, y = var_1158)[name = tensor("input_329")]; + tensor input_331_pad_type_0 = const()[name = tensor("input_331_pad_type_0"), val = tensor("valid")]; + tensor input_331_strides_0 = const()[name = tensor("input_331_strides_0"), val = tensor([1])]; + tensor input_331_pad_0 = const()[name = tensor("input_331_pad_0"), val = tensor([0, 0])]; + tensor input_331_dilations_0 = const()[name = tensor("input_331_dilations_0"), val = tensor([1])]; + tensor input_331_groups_0 = const()[name = tensor("input_331_groups_0"), val = tensor(1)]; + tensor input_331 = conv(bias = model_xvector_block2_tdnnd1_cam_layer_linear1_bias, dilations = input_331_dilations_0, groups = input_331_groups_0, pad = input_331_pad_0, pad_type = input_331_pad_type_0, strides = input_331_strides_0, weight = model_xvector_block2_tdnnd1_cam_layer_linear1_weight, x = input_329)[name = tensor("input_331")]; + tensor input_333 = relu(x = input_331)[name = tensor("input_333")]; + tensor input_335_pad_type_0 = const()[name = tensor("input_335_pad_type_0"), val = tensor("valid")]; + tensor input_335_strides_0 = const()[name = tensor("input_335_strides_0"), val = tensor([1])]; + tensor input_335_pad_0 = const()[name = tensor("input_335_pad_0"), val = tensor([0, 0])]; + tensor input_335_dilations_0 = const()[name = tensor("input_335_dilations_0"), val = tensor([1])]; + tensor input_335_groups_0 = const()[name = tensor("input_335_groups_0"), val = tensor(1)]; + tensor input_335 = conv(bias = model_xvector_block2_tdnnd1_cam_layer_linear2_bias, dilations = input_335_dilations_0, groups = input_335_groups_0, pad = input_335_pad_0, pad_type = input_335_pad_type_0, strides = input_335_strides_0, weight = model_xvector_block2_tdnnd1_cam_layer_linear2_weight, x = input_333)[name = tensor("input_335")]; + tensor m_25 = sigmoid(x = input_335)[name = tensor("m_25")]; + tensor var_1176 = mul(x = y_25, y = m_25)[name = tensor("op_1176")]; + tensor input_337_interleave_0 = const()[name = tensor("input_337_interleave_0"), val = tensor(false)]; + tensor input_337 = concat(axis = var_11, interleave = input_337_interleave_0, values = (input_317, var_1176))[name = tensor("input_337")]; + tensor input_339 = batch_norm(beta = model_xvector_block2_tdnnd2_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block2_tdnnd2_nonlinear1_batchnorm_weight, mean = model_xvector_block2_tdnnd2_nonlinear1_batchnorm_running_mean, variance = model_xvector_block2_tdnnd2_nonlinear1_batchnorm_running_var, x = input_337)[name = tensor("input_339")]; + tensor input_341 = relu(x = input_339)[name = tensor("input_341")]; + tensor input_343_pad_type_0 = const()[name = tensor("input_343_pad_type_0"), val = tensor("valid")]; + tensor input_343_strides_0 = const()[name = tensor("input_343_strides_0"), val = tensor([1])]; + tensor input_343_pad_0 = const()[name = tensor("input_343_pad_0"), val = tensor([0, 0])]; + tensor input_343_dilations_0 = const()[name = tensor("input_343_dilations_0"), val = tensor([1])]; + tensor input_343_groups_0 = const()[name = tensor("input_343_groups_0"), val = tensor(1)]; + tensor const_52 = const()[name = tensor("const_52"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11847296)))]; + tensor const_53 = const()[name = tensor("const_53"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11994816)))]; + tensor input_345 = conv(bias = const_53, dilations = input_343_dilations_0, groups = input_343_groups_0, pad = input_343_pad_0, pad_type = input_343_pad_type_0, strides = input_343_strides_0, weight = const_52, x = input_341)[name = tensor("input_345")]; + tensor input_347 = relu(x = input_345)[name = tensor("input_347")]; + tensor y_27_pad_type_0 = const()[name = tensor("y_27_pad_type_0"), val = tensor("custom")]; + tensor y_27_pad_0 = const()[name = tensor("y_27_pad_0"), val = tensor([2, 2])]; + tensor y_27_dilations_0 = const()[name = tensor("y_27_dilations_0"), val = tensor([2])]; + tensor y_27_strides_0 = const()[name = tensor("y_27_strides_0"), val = tensor([1])]; + tensor y_27_groups_0 = const()[name = tensor("y_27_groups_0"), val = tensor(1)]; + tensor y_27 = conv(dilations = y_27_dilations_0, groups = y_27_groups_0, pad = y_27_pad_0, pad_type = y_27_pad_type_0, strides = y_27_strides_0, weight = model_xvector_block2_tdnnd2_cam_layer_linear_local_weight, x = input_347)[name = tensor("y_27")]; + tensor var_1213_axes_0 = const()[name = tensor("op_1213_axes_0"), val = tensor([-1])]; + tensor var_1213_keep_dims_0 = const()[name = tensor("op_1213_keep_dims_0"), val = tensor(true)]; + tensor var_1213 = reduce_mean(axes = var_1213_axes_0, keep_dims = var_1213_keep_dims_0, x = input_347)[name = tensor("op_1213")]; + tensor var_1214 = const()[name = tensor("op_1214"), val = tensor([100])]; + tensor var_1215 = const()[name = tensor("op_1215"), val = tensor([100])]; + tensor seg_53_pad_type_0 = const()[name = tensor("seg_53_pad_type_0"), val = tensor("custom")]; + tensor seg_53_pad_0 = const()[name = tensor("seg_53_pad_0"), val = tensor([0, 0])]; + tensor seg_53_exclude_padding_from_average_0 = const()[name = tensor("seg_53_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_53_ceil_mode_0 = const()[name = tensor("seg_53_ceil_mode_0"), val = tensor(true)]; + tensor seg_53 = avg_pool(ceil_mode = seg_53_ceil_mode_0, exclude_padding_from_average = seg_53_exclude_padding_from_average_0, kernel_sizes = var_1214, pad = seg_53_pad_0, pad_type = seg_53_pad_type_0, strides = var_1215, x = input_347)[name = tensor("seg_53")]; + tensor var_1218_shape = shape(x = seg_53)[name = tensor("op_1218_shape")]; + tensor gather_56 = const()[name = tensor("gather_56"), val = tensor(1)]; + tensor gather_57 = const()[name = tensor("gather_57"), val = tensor(128)]; + tensor select_58 = const()[name = tensor("select_58"), val = tensor(2)]; + tensor gather_58_axis_0 = const()[name = tensor("gather_58_axis_0"), val = tensor(0)]; + tensor gather_58_batch_dims_0 = const()[name = tensor("gather_58_batch_dims_0"), val = tensor(0)]; + tensor gather_58_validate_indices_0 = const()[name = tensor("gather_58_validate_indices_0"), val = tensor(false)]; + tensor gather_58 = gather(axis = gather_58_axis_0, batch_dims = gather_58_batch_dims_0, indices = select_58, validate_indices = gather_58_validate_indices_0, x = var_1218_shape)[name = tensor("gather_58")]; + tensor var_1221_axes_0 = const()[name = tensor("op_1221_axes_0"), val = tensor([-1])]; + tensor var_1221 = expand_dims(axes = var_1221_axes_0, x = seg_53)[name = tensor("op_1221")]; + tensor concat_27_axis_0 = const()[name = tensor("concat_27_axis_0"), val = tensor(0)]; + tensor concat_27_interleave_0 = const()[name = tensor("concat_27_interleave_0"), val = tensor(false)]; + tensor concat_27 = concat(axis = concat_27_axis_0, interleave = concat_27_interleave_0, values = (gather_56, gather_57, gather_58, var_3))[name = tensor("concat_27")]; + tensor shape_72 = shape(x = var_1221)[name = tensor("shape_72")]; + tensor real_div_13 = real_div(x = concat_27, y = shape_72)[name = tensor("real_div_13")]; + tensor var_1223 = tile(reps = real_div_13, x = var_1221)[name = tensor("op_1223")]; + tensor var_1224 = const()[name = tensor("op_1224"), val = tensor([1, 128, -1])]; + tensor seg_55 = reshape(shape = var_1224, x = var_1223)[name = tensor("seg_55")]; + tensor var_1226_shape = shape(x = input_347)[name = tensor("op_1226_shape")]; + tensor select_59 = const()[name = tensor("select_59"), val = tensor(2)]; + tensor gather_59_axis_0 = const()[name = tensor("gather_59_axis_0"), val = tensor(0)]; + tensor gather_59_batch_dims_0 = const()[name = tensor("gather_59_batch_dims_0"), val = tensor(0)]; + tensor gather_59_validate_indices_0 = const()[name = tensor("gather_59_validate_indices_0"), val = tensor(false)]; + tensor gather_59 = gather(axis = gather_59_axis_0, batch_dims = gather_59_batch_dims_0, indices = select_59, validate_indices = gather_59_validate_indices_0, x = var_1226_shape)[name = tensor("gather_59")]; + tensor concat_28_values0_0 = const()[name = tensor("concat_28_values0_0"), val = tensor(1)]; + tensor concat_28_values1_0 = const()[name = tensor("concat_28_values1_0"), val = tensor(128)]; + tensor concat_28_axis_0 = const()[name = tensor("concat_28_axis_0"), val = tensor(0)]; + tensor concat_28_interleave_0 = const()[name = tensor("concat_28_interleave_0"), val = tensor(false)]; + tensor concat_28 = concat(axis = concat_28_axis_0, interleave = concat_28_interleave_0, values = (concat_28_values0_0, concat_28_values1_0, gather_59))[name = tensor("concat_28")]; + tensor var_1227_begin_0 = const()[name = tensor("op_1227_begin_0"), val = tensor([0, 0, 0])]; + tensor var_1227_end_mask_0 = const()[name = tensor("op_1227_end_mask_0"), val = tensor([true, true, false])]; + tensor var_1227 = slice_by_index(begin = var_1227_begin_0, end = concat_28, end_mask = var_1227_end_mask_0, x = seg_55)[name = tensor("op_1227")]; + tensor input_349 = add(x = var_1213, y = var_1227)[name = tensor("input_349")]; + tensor input_351_pad_type_0 = const()[name = tensor("input_351_pad_type_0"), val = tensor("valid")]; + tensor input_351_strides_0 = const()[name = tensor("input_351_strides_0"), val = tensor([1])]; + tensor input_351_pad_0 = const()[name = tensor("input_351_pad_0"), val = tensor([0, 0])]; + tensor input_351_dilations_0 = const()[name = tensor("input_351_dilations_0"), val = tensor([1])]; + tensor input_351_groups_0 = const()[name = tensor("input_351_groups_0"), val = tensor(1)]; + tensor input_351 = conv(bias = model_xvector_block2_tdnnd2_cam_layer_linear1_bias, dilations = input_351_dilations_0, groups = input_351_groups_0, pad = input_351_pad_0, pad_type = input_351_pad_type_0, strides = input_351_strides_0, weight = model_xvector_block2_tdnnd2_cam_layer_linear1_weight, x = input_349)[name = tensor("input_351")]; + tensor input_353 = relu(x = input_351)[name = tensor("input_353")]; + tensor input_355_pad_type_0 = const()[name = tensor("input_355_pad_type_0"), val = tensor("valid")]; + tensor input_355_strides_0 = const()[name = tensor("input_355_strides_0"), val = tensor([1])]; + tensor input_355_pad_0 = const()[name = tensor("input_355_pad_0"), val = tensor([0, 0])]; + tensor input_355_dilations_0 = const()[name = tensor("input_355_dilations_0"), val = tensor([1])]; + tensor input_355_groups_0 = const()[name = tensor("input_355_groups_0"), val = tensor(1)]; + tensor input_355 = conv(bias = model_xvector_block2_tdnnd2_cam_layer_linear2_bias, dilations = input_355_dilations_0, groups = input_355_groups_0, pad = input_355_pad_0, pad_type = input_355_pad_type_0, strides = input_355_strides_0, weight = model_xvector_block2_tdnnd2_cam_layer_linear2_weight, x = input_353)[name = tensor("input_355")]; + tensor m_27 = sigmoid(x = input_355)[name = tensor("m_27")]; + tensor var_1245 = mul(x = y_27, y = m_27)[name = tensor("op_1245")]; + tensor input_357_interleave_0 = const()[name = tensor("input_357_interleave_0"), val = tensor(false)]; + tensor input_357 = concat(axis = var_11, interleave = input_357_interleave_0, values = (input_337, var_1245))[name = tensor("input_357")]; + tensor input_359 = batch_norm(beta = model_xvector_block2_tdnnd3_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block2_tdnnd3_nonlinear1_batchnorm_weight, mean = model_xvector_block2_tdnnd3_nonlinear1_batchnorm_running_mean, variance = model_xvector_block2_tdnnd3_nonlinear1_batchnorm_running_var, x = input_357)[name = tensor("input_359")]; + tensor input_361 = relu(x = input_359)[name = tensor("input_361")]; + tensor input_363_pad_type_0 = const()[name = tensor("input_363_pad_type_0"), val = tensor("valid")]; + tensor input_363_strides_0 = const()[name = tensor("input_363_strides_0"), val = tensor([1])]; + tensor input_363_pad_0 = const()[name = tensor("input_363_pad_0"), val = tensor([0, 0])]; + tensor input_363_dilations_0 = const()[name = tensor("input_363_dilations_0"), val = tensor([1])]; + tensor input_363_groups_0 = const()[name = tensor("input_363_groups_0"), val = tensor(1)]; + tensor const_54 = const()[name = tensor("const_54"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11995392)))]; + tensor const_55 = const()[name = tensor("const_55"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12159296)))]; + tensor input_365 = conv(bias = const_55, dilations = input_363_dilations_0, groups = input_363_groups_0, pad = input_363_pad_0, pad_type = input_363_pad_type_0, strides = input_363_strides_0, weight = const_54, x = input_361)[name = tensor("input_365")]; + tensor input_367 = relu(x = input_365)[name = tensor("input_367")]; + tensor y_29_pad_type_0 = const()[name = tensor("y_29_pad_type_0"), val = tensor("custom")]; + tensor y_29_pad_0 = const()[name = tensor("y_29_pad_0"), val = tensor([2, 2])]; + tensor y_29_dilations_0 = const()[name = tensor("y_29_dilations_0"), val = tensor([2])]; + tensor y_29_strides_0 = const()[name = tensor("y_29_strides_0"), val = tensor([1])]; + tensor y_29_groups_0 = const()[name = tensor("y_29_groups_0"), val = tensor(1)]; + tensor y_29 = conv(dilations = y_29_dilations_0, groups = y_29_groups_0, pad = y_29_pad_0, pad_type = y_29_pad_type_0, strides = y_29_strides_0, weight = model_xvector_block2_tdnnd3_cam_layer_linear_local_weight, x = input_367)[name = tensor("y_29")]; + tensor var_1282_axes_0 = const()[name = tensor("op_1282_axes_0"), val = tensor([-1])]; + tensor var_1282_keep_dims_0 = const()[name = tensor("op_1282_keep_dims_0"), val = tensor(true)]; + tensor var_1282 = reduce_mean(axes = var_1282_axes_0, keep_dims = var_1282_keep_dims_0, x = input_367)[name = tensor("op_1282")]; + tensor var_1283 = const()[name = tensor("op_1283"), val = tensor([100])]; + tensor var_1284 = const()[name = tensor("op_1284"), val = tensor([100])]; + tensor seg_57_pad_type_0 = const()[name = tensor("seg_57_pad_type_0"), val = tensor("custom")]; + tensor seg_57_pad_0 = const()[name = tensor("seg_57_pad_0"), val = tensor([0, 0])]; + tensor seg_57_exclude_padding_from_average_0 = const()[name = tensor("seg_57_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_57_ceil_mode_0 = const()[name = tensor("seg_57_ceil_mode_0"), val = tensor(true)]; + tensor seg_57 = avg_pool(ceil_mode = seg_57_ceil_mode_0, exclude_padding_from_average = seg_57_exclude_padding_from_average_0, kernel_sizes = var_1283, pad = seg_57_pad_0, pad_type = seg_57_pad_type_0, strides = var_1284, x = input_367)[name = tensor("seg_57")]; + tensor var_1287_shape = shape(x = seg_57)[name = tensor("op_1287_shape")]; + tensor gather_60 = const()[name = tensor("gather_60"), val = tensor(1)]; + tensor gather_61 = const()[name = tensor("gather_61"), val = tensor(128)]; + tensor select_62 = const()[name = tensor("select_62"), val = tensor(2)]; + tensor gather_62_axis_0 = const()[name = tensor("gather_62_axis_0"), val = tensor(0)]; + tensor gather_62_batch_dims_0 = const()[name = tensor("gather_62_batch_dims_0"), val = tensor(0)]; + tensor gather_62_validate_indices_0 = const()[name = tensor("gather_62_validate_indices_0"), val = tensor(false)]; + tensor gather_62 = gather(axis = gather_62_axis_0, batch_dims = gather_62_batch_dims_0, indices = select_62, validate_indices = gather_62_validate_indices_0, x = var_1287_shape)[name = tensor("gather_62")]; + tensor var_1290_axes_0 = const()[name = tensor("op_1290_axes_0"), val = tensor([-1])]; + tensor var_1290 = expand_dims(axes = var_1290_axes_0, x = seg_57)[name = tensor("op_1290")]; + tensor concat_29_axis_0 = const()[name = tensor("concat_29_axis_0"), val = tensor(0)]; + tensor concat_29_interleave_0 = const()[name = tensor("concat_29_interleave_0"), val = tensor(false)]; + tensor concat_29 = concat(axis = concat_29_axis_0, interleave = concat_29_interleave_0, values = (gather_60, gather_61, gather_62, var_3))[name = tensor("concat_29")]; + tensor shape_77 = shape(x = var_1290)[name = tensor("shape_77")]; + tensor real_div_14 = real_div(x = concat_29, y = shape_77)[name = tensor("real_div_14")]; + tensor var_1292 = tile(reps = real_div_14, x = var_1290)[name = tensor("op_1292")]; + tensor var_1293 = const()[name = tensor("op_1293"), val = tensor([1, 128, -1])]; + tensor seg_59 = reshape(shape = var_1293, x = var_1292)[name = tensor("seg_59")]; + tensor var_1295_shape = shape(x = input_367)[name = tensor("op_1295_shape")]; + tensor select_63 = const()[name = tensor("select_63"), val = tensor(2)]; + tensor gather_63_axis_0 = const()[name = tensor("gather_63_axis_0"), val = tensor(0)]; + tensor gather_63_batch_dims_0 = const()[name = tensor("gather_63_batch_dims_0"), val = tensor(0)]; + tensor gather_63_validate_indices_0 = const()[name = tensor("gather_63_validate_indices_0"), val = tensor(false)]; + tensor gather_63 = gather(axis = gather_63_axis_0, batch_dims = gather_63_batch_dims_0, indices = select_63, validate_indices = gather_63_validate_indices_0, x = var_1295_shape)[name = tensor("gather_63")]; + tensor concat_30_values0_0 = const()[name = tensor("concat_30_values0_0"), val = tensor(1)]; + tensor concat_30_values1_0 = const()[name = tensor("concat_30_values1_0"), val = tensor(128)]; + tensor concat_30_axis_0 = const()[name = tensor("concat_30_axis_0"), val = tensor(0)]; + tensor concat_30_interleave_0 = const()[name = tensor("concat_30_interleave_0"), val = tensor(false)]; + tensor concat_30 = concat(axis = concat_30_axis_0, interleave = concat_30_interleave_0, values = (concat_30_values0_0, concat_30_values1_0, gather_63))[name = tensor("concat_30")]; + tensor var_1296_begin_0 = const()[name = tensor("op_1296_begin_0"), val = tensor([0, 0, 0])]; + tensor var_1296_end_mask_0 = const()[name = tensor("op_1296_end_mask_0"), val = tensor([true, true, false])]; + tensor var_1296 = slice_by_index(begin = var_1296_begin_0, end = concat_30, end_mask = var_1296_end_mask_0, x = seg_59)[name = tensor("op_1296")]; + tensor input_369 = add(x = var_1282, y = var_1296)[name = tensor("input_369")]; + tensor input_371_pad_type_0 = const()[name = tensor("input_371_pad_type_0"), val = tensor("valid")]; + tensor input_371_strides_0 = const()[name = tensor("input_371_strides_0"), val = tensor([1])]; + tensor input_371_pad_0 = const()[name = tensor("input_371_pad_0"), val = tensor([0, 0])]; + tensor input_371_dilations_0 = const()[name = tensor("input_371_dilations_0"), val = tensor([1])]; + tensor input_371_groups_0 = const()[name = tensor("input_371_groups_0"), val = tensor(1)]; + tensor input_371 = conv(bias = model_xvector_block2_tdnnd3_cam_layer_linear1_bias, dilations = input_371_dilations_0, groups = input_371_groups_0, pad = input_371_pad_0, pad_type = input_371_pad_type_0, strides = input_371_strides_0, weight = model_xvector_block2_tdnnd3_cam_layer_linear1_weight, x = input_369)[name = tensor("input_371")]; + tensor input_373 = relu(x = input_371)[name = tensor("input_373")]; + tensor input_375_pad_type_0 = const()[name = tensor("input_375_pad_type_0"), val = tensor("valid")]; + tensor input_375_strides_0 = const()[name = tensor("input_375_strides_0"), val = tensor([1])]; + tensor input_375_pad_0 = const()[name = tensor("input_375_pad_0"), val = tensor([0, 0])]; + tensor input_375_dilations_0 = const()[name = tensor("input_375_dilations_0"), val = tensor([1])]; + tensor input_375_groups_0 = const()[name = tensor("input_375_groups_0"), val = tensor(1)]; + tensor input_375 = conv(bias = model_xvector_block2_tdnnd3_cam_layer_linear2_bias, dilations = input_375_dilations_0, groups = input_375_groups_0, pad = input_375_pad_0, pad_type = input_375_pad_type_0, strides = input_375_strides_0, weight = model_xvector_block2_tdnnd3_cam_layer_linear2_weight, x = input_373)[name = tensor("input_375")]; + tensor m_29 = sigmoid(x = input_375)[name = tensor("m_29")]; + tensor var_1314 = mul(x = y_29, y = m_29)[name = tensor("op_1314")]; + tensor input_377_interleave_0 = const()[name = tensor("input_377_interleave_0"), val = tensor(false)]; + tensor input_377 = concat(axis = var_11, interleave = input_377_interleave_0, values = (input_357, var_1314))[name = tensor("input_377")]; + tensor input_379 = batch_norm(beta = model_xvector_block2_tdnnd4_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block2_tdnnd4_nonlinear1_batchnorm_weight, mean = model_xvector_block2_tdnnd4_nonlinear1_batchnorm_running_mean, variance = model_xvector_block2_tdnnd4_nonlinear1_batchnorm_running_var, x = input_377)[name = tensor("input_379")]; + tensor input_381 = relu(x = input_379)[name = tensor("input_381")]; + tensor input_383_pad_type_0 = const()[name = tensor("input_383_pad_type_0"), val = tensor("valid")]; + tensor input_383_strides_0 = const()[name = tensor("input_383_strides_0"), val = tensor([1])]; + tensor input_383_pad_0 = const()[name = tensor("input_383_pad_0"), val = tensor([0, 0])]; + tensor input_383_dilations_0 = const()[name = tensor("input_383_dilations_0"), val = tensor([1])]; + tensor input_383_groups_0 = const()[name = tensor("input_383_groups_0"), val = tensor(1)]; + tensor const_56 = const()[name = tensor("const_56"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12159872)))]; + tensor const_57 = const()[name = tensor("const_57"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12340160)))]; + tensor input_385 = conv(bias = const_57, dilations = input_383_dilations_0, groups = input_383_groups_0, pad = input_383_pad_0, pad_type = input_383_pad_type_0, strides = input_383_strides_0, weight = const_56, x = input_381)[name = tensor("input_385")]; + tensor input_387 = relu(x = input_385)[name = tensor("input_387")]; + tensor y_31_pad_type_0 = const()[name = tensor("y_31_pad_type_0"), val = tensor("custom")]; + tensor y_31_pad_0 = const()[name = tensor("y_31_pad_0"), val = tensor([2, 2])]; + tensor y_31_dilations_0 = const()[name = tensor("y_31_dilations_0"), val = tensor([2])]; + tensor y_31_strides_0 = const()[name = tensor("y_31_strides_0"), val = tensor([1])]; + tensor y_31_groups_0 = const()[name = tensor("y_31_groups_0"), val = tensor(1)]; + tensor y_31 = conv(dilations = y_31_dilations_0, groups = y_31_groups_0, pad = y_31_pad_0, pad_type = y_31_pad_type_0, strides = y_31_strides_0, weight = model_xvector_block2_tdnnd4_cam_layer_linear_local_weight, x = input_387)[name = tensor("y_31")]; + tensor var_1351_axes_0 = const()[name = tensor("op_1351_axes_0"), val = tensor([-1])]; + tensor var_1351_keep_dims_0 = const()[name = tensor("op_1351_keep_dims_0"), val = tensor(true)]; + tensor var_1351 = reduce_mean(axes = var_1351_axes_0, keep_dims = var_1351_keep_dims_0, x = input_387)[name = tensor("op_1351")]; + tensor var_1352 = const()[name = tensor("op_1352"), val = tensor([100])]; + tensor var_1353 = const()[name = tensor("op_1353"), val = tensor([100])]; + tensor seg_61_pad_type_0 = const()[name = tensor("seg_61_pad_type_0"), val = tensor("custom")]; + tensor seg_61_pad_0 = const()[name = tensor("seg_61_pad_0"), val = tensor([0, 0])]; + tensor seg_61_exclude_padding_from_average_0 = const()[name = tensor("seg_61_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_61_ceil_mode_0 = const()[name = tensor("seg_61_ceil_mode_0"), val = tensor(true)]; + tensor seg_61 = avg_pool(ceil_mode = seg_61_ceil_mode_0, exclude_padding_from_average = seg_61_exclude_padding_from_average_0, kernel_sizes = var_1352, pad = seg_61_pad_0, pad_type = seg_61_pad_type_0, strides = var_1353, x = input_387)[name = tensor("seg_61")]; + tensor var_1356_shape = shape(x = seg_61)[name = tensor("op_1356_shape")]; + tensor gather_64 = const()[name = tensor("gather_64"), val = tensor(1)]; + tensor gather_65 = const()[name = tensor("gather_65"), val = tensor(128)]; + tensor select_66 = const()[name = tensor("select_66"), val = tensor(2)]; + tensor gather_66_axis_0 = const()[name = tensor("gather_66_axis_0"), val = tensor(0)]; + tensor gather_66_batch_dims_0 = const()[name = tensor("gather_66_batch_dims_0"), val = tensor(0)]; + tensor gather_66_validate_indices_0 = const()[name = tensor("gather_66_validate_indices_0"), val = tensor(false)]; + tensor gather_66 = gather(axis = gather_66_axis_0, batch_dims = gather_66_batch_dims_0, indices = select_66, validate_indices = gather_66_validate_indices_0, x = var_1356_shape)[name = tensor("gather_66")]; + tensor var_1359_axes_0 = const()[name = tensor("op_1359_axes_0"), val = tensor([-1])]; + tensor var_1359 = expand_dims(axes = var_1359_axes_0, x = seg_61)[name = tensor("op_1359")]; + tensor concat_31_axis_0 = const()[name = tensor("concat_31_axis_0"), val = tensor(0)]; + tensor concat_31_interleave_0 = const()[name = tensor("concat_31_interleave_0"), val = tensor(false)]; + tensor concat_31 = concat(axis = concat_31_axis_0, interleave = concat_31_interleave_0, values = (gather_64, gather_65, gather_66, var_3))[name = tensor("concat_31")]; + tensor shape_82 = shape(x = var_1359)[name = tensor("shape_82")]; + tensor real_div_15 = real_div(x = concat_31, y = shape_82)[name = tensor("real_div_15")]; + tensor var_1361 = tile(reps = real_div_15, x = var_1359)[name = tensor("op_1361")]; + tensor var_1362 = const()[name = tensor("op_1362"), val = tensor([1, 128, -1])]; + tensor seg_63 = reshape(shape = var_1362, x = var_1361)[name = tensor("seg_63")]; + tensor var_1364_shape = shape(x = input_387)[name = tensor("op_1364_shape")]; + tensor select_67 = const()[name = tensor("select_67"), val = tensor(2)]; + tensor gather_67_axis_0 = const()[name = tensor("gather_67_axis_0"), val = tensor(0)]; + tensor gather_67_batch_dims_0 = const()[name = tensor("gather_67_batch_dims_0"), val = tensor(0)]; + tensor gather_67_validate_indices_0 = const()[name = tensor("gather_67_validate_indices_0"), val = tensor(false)]; + tensor gather_67 = gather(axis = gather_67_axis_0, batch_dims = gather_67_batch_dims_0, indices = select_67, validate_indices = gather_67_validate_indices_0, x = var_1364_shape)[name = tensor("gather_67")]; + tensor concat_32_values0_0 = const()[name = tensor("concat_32_values0_0"), val = tensor(1)]; + tensor concat_32_values1_0 = const()[name = tensor("concat_32_values1_0"), val = tensor(128)]; + tensor concat_32_axis_0 = const()[name = tensor("concat_32_axis_0"), val = tensor(0)]; + tensor concat_32_interleave_0 = const()[name = tensor("concat_32_interleave_0"), val = tensor(false)]; + tensor concat_32 = concat(axis = concat_32_axis_0, interleave = concat_32_interleave_0, values = (concat_32_values0_0, concat_32_values1_0, gather_67))[name = tensor("concat_32")]; + tensor var_1365_begin_0 = const()[name = tensor("op_1365_begin_0"), val = tensor([0, 0, 0])]; + tensor var_1365_end_mask_0 = const()[name = tensor("op_1365_end_mask_0"), val = tensor([true, true, false])]; + tensor var_1365 = slice_by_index(begin = var_1365_begin_0, end = concat_32, end_mask = var_1365_end_mask_0, x = seg_63)[name = tensor("op_1365")]; + tensor input_389 = add(x = var_1351, y = var_1365)[name = tensor("input_389")]; + tensor input_391_pad_type_0 = const()[name = tensor("input_391_pad_type_0"), val = tensor("valid")]; + tensor input_391_strides_0 = const()[name = tensor("input_391_strides_0"), val = tensor([1])]; + tensor input_391_pad_0 = const()[name = tensor("input_391_pad_0"), val = tensor([0, 0])]; + tensor input_391_dilations_0 = const()[name = tensor("input_391_dilations_0"), val = tensor([1])]; + tensor input_391_groups_0 = const()[name = tensor("input_391_groups_0"), val = tensor(1)]; + tensor input_391 = conv(bias = model_xvector_block2_tdnnd4_cam_layer_linear1_bias, dilations = input_391_dilations_0, groups = input_391_groups_0, pad = input_391_pad_0, pad_type = input_391_pad_type_0, strides = input_391_strides_0, weight = model_xvector_block2_tdnnd4_cam_layer_linear1_weight, x = input_389)[name = tensor("input_391")]; + tensor input_393 = relu(x = input_391)[name = tensor("input_393")]; + tensor input_395_pad_type_0 = const()[name = tensor("input_395_pad_type_0"), val = tensor("valid")]; + tensor input_395_strides_0 = const()[name = tensor("input_395_strides_0"), val = tensor([1])]; + tensor input_395_pad_0 = const()[name = tensor("input_395_pad_0"), val = tensor([0, 0])]; + tensor input_395_dilations_0 = const()[name = tensor("input_395_dilations_0"), val = tensor([1])]; + tensor input_395_groups_0 = const()[name = tensor("input_395_groups_0"), val = tensor(1)]; + tensor input_395 = conv(bias = model_xvector_block2_tdnnd4_cam_layer_linear2_bias, dilations = input_395_dilations_0, groups = input_395_groups_0, pad = input_395_pad_0, pad_type = input_395_pad_type_0, strides = input_395_strides_0, weight = model_xvector_block2_tdnnd4_cam_layer_linear2_weight, x = input_393)[name = tensor("input_395")]; + tensor m_31 = sigmoid(x = input_395)[name = tensor("m_31")]; + tensor var_1383 = mul(x = y_31, y = m_31)[name = tensor("op_1383")]; + tensor input_397_interleave_0 = const()[name = tensor("input_397_interleave_0"), val = tensor(false)]; + tensor input_397 = concat(axis = var_11, interleave = input_397_interleave_0, values = (input_377, var_1383))[name = tensor("input_397")]; + tensor input_399 = batch_norm(beta = model_xvector_block2_tdnnd5_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block2_tdnnd5_nonlinear1_batchnorm_weight, mean = model_xvector_block2_tdnnd5_nonlinear1_batchnorm_running_mean, variance = model_xvector_block2_tdnnd5_nonlinear1_batchnorm_running_var, x = input_397)[name = tensor("input_399")]; + tensor input_401 = relu(x = input_399)[name = tensor("input_401")]; + tensor input_403_pad_type_0 = const()[name = tensor("input_403_pad_type_0"), val = tensor("valid")]; + tensor input_403_strides_0 = const()[name = tensor("input_403_strides_0"), val = tensor([1])]; + tensor input_403_pad_0 = const()[name = tensor("input_403_pad_0"), val = tensor([0, 0])]; + tensor input_403_dilations_0 = const()[name = tensor("input_403_dilations_0"), val = tensor([1])]; + tensor input_403_groups_0 = const()[name = tensor("input_403_groups_0"), val = tensor(1)]; + tensor const_58 = const()[name = tensor("const_58"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12340736)))]; + tensor const_59 = const()[name = tensor("const_59"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12537408)))]; + tensor input_405 = conv(bias = const_59, dilations = input_403_dilations_0, groups = input_403_groups_0, pad = input_403_pad_0, pad_type = input_403_pad_type_0, strides = input_403_strides_0, weight = const_58, x = input_401)[name = tensor("input_405")]; + tensor input_407 = relu(x = input_405)[name = tensor("input_407")]; + tensor y_33_pad_type_0 = const()[name = tensor("y_33_pad_type_0"), val = tensor("custom")]; + tensor y_33_pad_0 = const()[name = tensor("y_33_pad_0"), val = tensor([2, 2])]; + tensor y_33_dilations_0 = const()[name = tensor("y_33_dilations_0"), val = tensor([2])]; + tensor y_33_strides_0 = const()[name = tensor("y_33_strides_0"), val = tensor([1])]; + tensor y_33_groups_0 = const()[name = tensor("y_33_groups_0"), val = tensor(1)]; + tensor y_33 = conv(dilations = y_33_dilations_0, groups = y_33_groups_0, pad = y_33_pad_0, pad_type = y_33_pad_type_0, strides = y_33_strides_0, weight = model_xvector_block2_tdnnd5_cam_layer_linear_local_weight, x = input_407)[name = tensor("y_33")]; + tensor var_1420_axes_0 = const()[name = tensor("op_1420_axes_0"), val = tensor([-1])]; + tensor var_1420_keep_dims_0 = const()[name = tensor("op_1420_keep_dims_0"), val = tensor(true)]; + tensor var_1420 = reduce_mean(axes = var_1420_axes_0, keep_dims = var_1420_keep_dims_0, x = input_407)[name = tensor("op_1420")]; + tensor var_1421 = const()[name = tensor("op_1421"), val = tensor([100])]; + tensor var_1422 = const()[name = tensor("op_1422"), val = tensor([100])]; + tensor seg_65_pad_type_0 = const()[name = tensor("seg_65_pad_type_0"), val = tensor("custom")]; + tensor seg_65_pad_0 = const()[name = tensor("seg_65_pad_0"), val = tensor([0, 0])]; + tensor seg_65_exclude_padding_from_average_0 = const()[name = tensor("seg_65_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_65_ceil_mode_0 = const()[name = tensor("seg_65_ceil_mode_0"), val = tensor(true)]; + tensor seg_65 = avg_pool(ceil_mode = seg_65_ceil_mode_0, exclude_padding_from_average = seg_65_exclude_padding_from_average_0, kernel_sizes = var_1421, pad = seg_65_pad_0, pad_type = seg_65_pad_type_0, strides = var_1422, x = input_407)[name = tensor("seg_65")]; + tensor var_1425_shape = shape(x = seg_65)[name = tensor("op_1425_shape")]; + tensor gather_68 = const()[name = tensor("gather_68"), val = tensor(1)]; + tensor gather_69 = const()[name = tensor("gather_69"), val = tensor(128)]; + tensor select_70 = const()[name = tensor("select_70"), val = tensor(2)]; + tensor gather_70_axis_0 = const()[name = tensor("gather_70_axis_0"), val = tensor(0)]; + tensor gather_70_batch_dims_0 = const()[name = tensor("gather_70_batch_dims_0"), val = tensor(0)]; + tensor gather_70_validate_indices_0 = const()[name = tensor("gather_70_validate_indices_0"), val = tensor(false)]; + tensor gather_70 = gather(axis = gather_70_axis_0, batch_dims = gather_70_batch_dims_0, indices = select_70, validate_indices = gather_70_validate_indices_0, x = var_1425_shape)[name = tensor("gather_70")]; + tensor var_1428_axes_0 = const()[name = tensor("op_1428_axes_0"), val = tensor([-1])]; + tensor var_1428 = expand_dims(axes = var_1428_axes_0, x = seg_65)[name = tensor("op_1428")]; + tensor concat_33_axis_0 = const()[name = tensor("concat_33_axis_0"), val = tensor(0)]; + tensor concat_33_interleave_0 = const()[name = tensor("concat_33_interleave_0"), val = tensor(false)]; + tensor concat_33 = concat(axis = concat_33_axis_0, interleave = concat_33_interleave_0, values = (gather_68, gather_69, gather_70, var_3))[name = tensor("concat_33")]; + tensor shape_87 = shape(x = var_1428)[name = tensor("shape_87")]; + tensor real_div_16 = real_div(x = concat_33, y = shape_87)[name = tensor("real_div_16")]; + tensor var_1430 = tile(reps = real_div_16, x = var_1428)[name = tensor("op_1430")]; + tensor var_1431 = const()[name = tensor("op_1431"), val = tensor([1, 128, -1])]; + tensor seg_67 = reshape(shape = var_1431, x = var_1430)[name = tensor("seg_67")]; + tensor var_1433_shape = shape(x = input_407)[name = tensor("op_1433_shape")]; + tensor select_71 = const()[name = tensor("select_71"), val = tensor(2)]; + tensor gather_71_axis_0 = const()[name = tensor("gather_71_axis_0"), val = tensor(0)]; + tensor gather_71_batch_dims_0 = const()[name = tensor("gather_71_batch_dims_0"), val = tensor(0)]; + tensor gather_71_validate_indices_0 = const()[name = tensor("gather_71_validate_indices_0"), val = tensor(false)]; + tensor gather_71 = gather(axis = gather_71_axis_0, batch_dims = gather_71_batch_dims_0, indices = select_71, validate_indices = gather_71_validate_indices_0, x = var_1433_shape)[name = tensor("gather_71")]; + tensor concat_34_values0_0 = const()[name = tensor("concat_34_values0_0"), val = tensor(1)]; + tensor concat_34_values1_0 = const()[name = tensor("concat_34_values1_0"), val = tensor(128)]; + tensor concat_34_axis_0 = const()[name = tensor("concat_34_axis_0"), val = tensor(0)]; + tensor concat_34_interleave_0 = const()[name = tensor("concat_34_interleave_0"), val = tensor(false)]; + tensor concat_34 = concat(axis = concat_34_axis_0, interleave = concat_34_interleave_0, values = (concat_34_values0_0, concat_34_values1_0, gather_71))[name = tensor("concat_34")]; + tensor var_1434_begin_0 = const()[name = tensor("op_1434_begin_0"), val = tensor([0, 0, 0])]; + tensor var_1434_end_mask_0 = const()[name = tensor("op_1434_end_mask_0"), val = tensor([true, true, false])]; + tensor var_1434 = slice_by_index(begin = var_1434_begin_0, end = concat_34, end_mask = var_1434_end_mask_0, x = seg_67)[name = tensor("op_1434")]; + tensor input_409 = add(x = var_1420, y = var_1434)[name = tensor("input_409")]; + tensor input_411_pad_type_0 = const()[name = tensor("input_411_pad_type_0"), val = tensor("valid")]; + tensor input_411_strides_0 = const()[name = tensor("input_411_strides_0"), val = tensor([1])]; + tensor input_411_pad_0 = const()[name = tensor("input_411_pad_0"), val = tensor([0, 0])]; + tensor input_411_dilations_0 = const()[name = tensor("input_411_dilations_0"), val = tensor([1])]; + tensor input_411_groups_0 = const()[name = tensor("input_411_groups_0"), val = tensor(1)]; + tensor input_411 = conv(bias = model_xvector_block2_tdnnd5_cam_layer_linear1_bias, dilations = input_411_dilations_0, groups = input_411_groups_0, pad = input_411_pad_0, pad_type = input_411_pad_type_0, strides = input_411_strides_0, weight = model_xvector_block2_tdnnd5_cam_layer_linear1_weight, x = input_409)[name = tensor("input_411")]; + tensor input_413 = relu(x = input_411)[name = tensor("input_413")]; + tensor input_415_pad_type_0 = const()[name = tensor("input_415_pad_type_0"), val = tensor("valid")]; + tensor input_415_strides_0 = const()[name = tensor("input_415_strides_0"), val = tensor([1])]; + tensor input_415_pad_0 = const()[name = tensor("input_415_pad_0"), val = tensor([0, 0])]; + tensor input_415_dilations_0 = const()[name = tensor("input_415_dilations_0"), val = tensor([1])]; + tensor input_415_groups_0 = const()[name = tensor("input_415_groups_0"), val = tensor(1)]; + tensor input_415 = conv(bias = model_xvector_block2_tdnnd5_cam_layer_linear2_bias, dilations = input_415_dilations_0, groups = input_415_groups_0, pad = input_415_pad_0, pad_type = input_415_pad_type_0, strides = input_415_strides_0, weight = model_xvector_block2_tdnnd5_cam_layer_linear2_weight, x = input_413)[name = tensor("input_415")]; + tensor m_33 = sigmoid(x = input_415)[name = tensor("m_33")]; + tensor var_1452 = mul(x = y_33, y = m_33)[name = tensor("op_1452")]; + tensor input_417_interleave_0 = const()[name = tensor("input_417_interleave_0"), val = tensor(false)]; + tensor input_417 = concat(axis = var_11, interleave = input_417_interleave_0, values = (input_397, var_1452))[name = tensor("input_417")]; + tensor input_419 = batch_norm(beta = model_xvector_block2_tdnnd6_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block2_tdnnd6_nonlinear1_batchnorm_weight, mean = model_xvector_block2_tdnnd6_nonlinear1_batchnorm_running_mean, variance = model_xvector_block2_tdnnd6_nonlinear1_batchnorm_running_var, x = input_417)[name = tensor("input_419")]; + tensor input_421 = relu(x = input_419)[name = tensor("input_421")]; + tensor input_423_pad_type_0 = const()[name = tensor("input_423_pad_type_0"), val = tensor("valid")]; + tensor input_423_strides_0 = const()[name = tensor("input_423_strides_0"), val = tensor([1])]; + tensor input_423_pad_0 = const()[name = tensor("input_423_pad_0"), val = tensor([0, 0])]; + tensor input_423_dilations_0 = const()[name = tensor("input_423_dilations_0"), val = tensor([1])]; + tensor input_423_groups_0 = const()[name = tensor("input_423_groups_0"), val = tensor(1)]; + tensor const_60 = const()[name = tensor("const_60"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12537984)))]; + tensor const_61 = const()[name = tensor("const_61"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12751040)))]; + tensor input_425 = conv(bias = const_61, dilations = input_423_dilations_0, groups = input_423_groups_0, pad = input_423_pad_0, pad_type = input_423_pad_type_0, strides = input_423_strides_0, weight = const_60, x = input_421)[name = tensor("input_425")]; + tensor input_427 = relu(x = input_425)[name = tensor("input_427")]; + tensor y_35_pad_type_0 = const()[name = tensor("y_35_pad_type_0"), val = tensor("custom")]; + tensor y_35_pad_0 = const()[name = tensor("y_35_pad_0"), val = tensor([2, 2])]; + tensor y_35_dilations_0 = const()[name = tensor("y_35_dilations_0"), val = tensor([2])]; + tensor y_35_strides_0 = const()[name = tensor("y_35_strides_0"), val = tensor([1])]; + tensor y_35_groups_0 = const()[name = tensor("y_35_groups_0"), val = tensor(1)]; + tensor y_35 = conv(dilations = y_35_dilations_0, groups = y_35_groups_0, pad = y_35_pad_0, pad_type = y_35_pad_type_0, strides = y_35_strides_0, weight = model_xvector_block2_tdnnd6_cam_layer_linear_local_weight, x = input_427)[name = tensor("y_35")]; + tensor var_1489_axes_0 = const()[name = tensor("op_1489_axes_0"), val = tensor([-1])]; + tensor var_1489_keep_dims_0 = const()[name = tensor("op_1489_keep_dims_0"), val = tensor(true)]; + tensor var_1489 = reduce_mean(axes = var_1489_axes_0, keep_dims = var_1489_keep_dims_0, x = input_427)[name = tensor("op_1489")]; + tensor var_1490 = const()[name = tensor("op_1490"), val = tensor([100])]; + tensor var_1491 = const()[name = tensor("op_1491"), val = tensor([100])]; + tensor seg_69_pad_type_0 = const()[name = tensor("seg_69_pad_type_0"), val = tensor("custom")]; + tensor seg_69_pad_0 = const()[name = tensor("seg_69_pad_0"), val = tensor([0, 0])]; + tensor seg_69_exclude_padding_from_average_0 = const()[name = tensor("seg_69_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_69_ceil_mode_0 = const()[name = tensor("seg_69_ceil_mode_0"), val = tensor(true)]; + tensor seg_69 = avg_pool(ceil_mode = seg_69_ceil_mode_0, exclude_padding_from_average = seg_69_exclude_padding_from_average_0, kernel_sizes = var_1490, pad = seg_69_pad_0, pad_type = seg_69_pad_type_0, strides = var_1491, x = input_427)[name = tensor("seg_69")]; + tensor var_1494_shape = shape(x = seg_69)[name = tensor("op_1494_shape")]; + tensor gather_72 = const()[name = tensor("gather_72"), val = tensor(1)]; + tensor gather_73 = const()[name = tensor("gather_73"), val = tensor(128)]; + tensor select_74 = const()[name = tensor("select_74"), val = tensor(2)]; + tensor gather_74_axis_0 = const()[name = tensor("gather_74_axis_0"), val = tensor(0)]; + tensor gather_74_batch_dims_0 = const()[name = tensor("gather_74_batch_dims_0"), val = tensor(0)]; + tensor gather_74_validate_indices_0 = const()[name = tensor("gather_74_validate_indices_0"), val = tensor(false)]; + tensor gather_74 = gather(axis = gather_74_axis_0, batch_dims = gather_74_batch_dims_0, indices = select_74, validate_indices = gather_74_validate_indices_0, x = var_1494_shape)[name = tensor("gather_74")]; + tensor var_1497_axes_0 = const()[name = tensor("op_1497_axes_0"), val = tensor([-1])]; + tensor var_1497 = expand_dims(axes = var_1497_axes_0, x = seg_69)[name = tensor("op_1497")]; + tensor concat_35_axis_0 = const()[name = tensor("concat_35_axis_0"), val = tensor(0)]; + tensor concat_35_interleave_0 = const()[name = tensor("concat_35_interleave_0"), val = tensor(false)]; + tensor concat_35 = concat(axis = concat_35_axis_0, interleave = concat_35_interleave_0, values = (gather_72, gather_73, gather_74, var_3))[name = tensor("concat_35")]; + tensor shape_92 = shape(x = var_1497)[name = tensor("shape_92")]; + tensor real_div_17 = real_div(x = concat_35, y = shape_92)[name = tensor("real_div_17")]; + tensor var_1499 = tile(reps = real_div_17, x = var_1497)[name = tensor("op_1499")]; + tensor var_1500 = const()[name = tensor("op_1500"), val = tensor([1, 128, -1])]; + tensor seg_71 = reshape(shape = var_1500, x = var_1499)[name = tensor("seg_71")]; + tensor var_1502_shape = shape(x = input_427)[name = tensor("op_1502_shape")]; + tensor select_75 = const()[name = tensor("select_75"), val = tensor(2)]; + tensor gather_75_axis_0 = const()[name = tensor("gather_75_axis_0"), val = tensor(0)]; + tensor gather_75_batch_dims_0 = const()[name = tensor("gather_75_batch_dims_0"), val = tensor(0)]; + tensor gather_75_validate_indices_0 = const()[name = tensor("gather_75_validate_indices_0"), val = tensor(false)]; + tensor gather_75 = gather(axis = gather_75_axis_0, batch_dims = gather_75_batch_dims_0, indices = select_75, validate_indices = gather_75_validate_indices_0, x = var_1502_shape)[name = tensor("gather_75")]; + tensor concat_36_values0_0 = const()[name = tensor("concat_36_values0_0"), val = tensor(1)]; + tensor concat_36_values1_0 = const()[name = tensor("concat_36_values1_0"), val = tensor(128)]; + tensor concat_36_axis_0 = const()[name = tensor("concat_36_axis_0"), val = tensor(0)]; + tensor concat_36_interleave_0 = const()[name = tensor("concat_36_interleave_0"), val = tensor(false)]; + tensor concat_36 = concat(axis = concat_36_axis_0, interleave = concat_36_interleave_0, values = (concat_36_values0_0, concat_36_values1_0, gather_75))[name = tensor("concat_36")]; + tensor var_1503_begin_0 = const()[name = tensor("op_1503_begin_0"), val = tensor([0, 0, 0])]; + tensor var_1503_end_mask_0 = const()[name = tensor("op_1503_end_mask_0"), val = tensor([true, true, false])]; + tensor var_1503 = slice_by_index(begin = var_1503_begin_0, end = concat_36, end_mask = var_1503_end_mask_0, x = seg_71)[name = tensor("op_1503")]; + tensor input_429 = add(x = var_1489, y = var_1503)[name = tensor("input_429")]; + tensor input_431_pad_type_0 = const()[name = tensor("input_431_pad_type_0"), val = tensor("valid")]; + tensor input_431_strides_0 = const()[name = tensor("input_431_strides_0"), val = tensor([1])]; + tensor input_431_pad_0 = const()[name = tensor("input_431_pad_0"), val = tensor([0, 0])]; + tensor input_431_dilations_0 = const()[name = tensor("input_431_dilations_0"), val = tensor([1])]; + tensor input_431_groups_0 = const()[name = tensor("input_431_groups_0"), val = tensor(1)]; + tensor input_431 = conv(bias = model_xvector_block2_tdnnd6_cam_layer_linear1_bias, dilations = input_431_dilations_0, groups = input_431_groups_0, pad = input_431_pad_0, pad_type = input_431_pad_type_0, strides = input_431_strides_0, weight = model_xvector_block2_tdnnd6_cam_layer_linear1_weight, x = input_429)[name = tensor("input_431")]; + tensor input_433 = relu(x = input_431)[name = tensor("input_433")]; + tensor input_435_pad_type_0 = const()[name = tensor("input_435_pad_type_0"), val = tensor("valid")]; + tensor input_435_strides_0 = const()[name = tensor("input_435_strides_0"), val = tensor([1])]; + tensor input_435_pad_0 = const()[name = tensor("input_435_pad_0"), val = tensor([0, 0])]; + tensor input_435_dilations_0 = const()[name = tensor("input_435_dilations_0"), val = tensor([1])]; + tensor input_435_groups_0 = const()[name = tensor("input_435_groups_0"), val = tensor(1)]; + tensor input_435 = conv(bias = model_xvector_block2_tdnnd6_cam_layer_linear2_bias, dilations = input_435_dilations_0, groups = input_435_groups_0, pad = input_435_pad_0, pad_type = input_435_pad_type_0, strides = input_435_strides_0, weight = model_xvector_block2_tdnnd6_cam_layer_linear2_weight, x = input_433)[name = tensor("input_435")]; + tensor m_35 = sigmoid(x = input_435)[name = tensor("m_35")]; + tensor var_1521 = mul(x = y_35, y = m_35)[name = tensor("op_1521")]; + tensor input_437_interleave_0 = const()[name = tensor("input_437_interleave_0"), val = tensor(false)]; + tensor input_437 = concat(axis = var_11, interleave = input_437_interleave_0, values = (input_417, var_1521))[name = tensor("input_437")]; + tensor input_439 = batch_norm(beta = model_xvector_block2_tdnnd7_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block2_tdnnd7_nonlinear1_batchnorm_weight, mean = model_xvector_block2_tdnnd7_nonlinear1_batchnorm_running_mean, variance = model_xvector_block2_tdnnd7_nonlinear1_batchnorm_running_var, x = input_437)[name = tensor("input_439")]; + tensor input_441 = relu(x = input_439)[name = tensor("input_441")]; + tensor input_443_pad_type_0 = const()[name = tensor("input_443_pad_type_0"), val = tensor("valid")]; + tensor input_443_strides_0 = const()[name = tensor("input_443_strides_0"), val = tensor([1])]; + tensor input_443_pad_0 = const()[name = tensor("input_443_pad_0"), val = tensor([0, 0])]; + tensor input_443_dilations_0 = const()[name = tensor("input_443_dilations_0"), val = tensor([1])]; + tensor input_443_groups_0 = const()[name = tensor("input_443_groups_0"), val = tensor(1)]; + tensor const_62 = const()[name = tensor("const_62"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12751616)))]; + tensor const_63 = const()[name = tensor("const_63"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12981056)))]; + tensor input_445 = conv(bias = const_63, dilations = input_443_dilations_0, groups = input_443_groups_0, pad = input_443_pad_0, pad_type = input_443_pad_type_0, strides = input_443_strides_0, weight = const_62, x = input_441)[name = tensor("input_445")]; + tensor input_447 = relu(x = input_445)[name = tensor("input_447")]; + tensor y_37_pad_type_0 = const()[name = tensor("y_37_pad_type_0"), val = tensor("custom")]; + tensor y_37_pad_0 = const()[name = tensor("y_37_pad_0"), val = tensor([2, 2])]; + tensor y_37_dilations_0 = const()[name = tensor("y_37_dilations_0"), val = tensor([2])]; + tensor y_37_strides_0 = const()[name = tensor("y_37_strides_0"), val = tensor([1])]; + tensor y_37_groups_0 = const()[name = tensor("y_37_groups_0"), val = tensor(1)]; + tensor y_37 = conv(dilations = y_37_dilations_0, groups = y_37_groups_0, pad = y_37_pad_0, pad_type = y_37_pad_type_0, strides = y_37_strides_0, weight = model_xvector_block2_tdnnd7_cam_layer_linear_local_weight, x = input_447)[name = tensor("y_37")]; + tensor var_1558_axes_0 = const()[name = tensor("op_1558_axes_0"), val = tensor([-1])]; + tensor var_1558_keep_dims_0 = const()[name = tensor("op_1558_keep_dims_0"), val = tensor(true)]; + tensor var_1558 = reduce_mean(axes = var_1558_axes_0, keep_dims = var_1558_keep_dims_0, x = input_447)[name = tensor("op_1558")]; + tensor var_1559 = const()[name = tensor("op_1559"), val = tensor([100])]; + tensor var_1560 = const()[name = tensor("op_1560"), val = tensor([100])]; + tensor seg_73_pad_type_0 = const()[name = tensor("seg_73_pad_type_0"), val = tensor("custom")]; + tensor seg_73_pad_0 = const()[name = tensor("seg_73_pad_0"), val = tensor([0, 0])]; + tensor seg_73_exclude_padding_from_average_0 = const()[name = tensor("seg_73_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_73_ceil_mode_0 = const()[name = tensor("seg_73_ceil_mode_0"), val = tensor(true)]; + tensor seg_73 = avg_pool(ceil_mode = seg_73_ceil_mode_0, exclude_padding_from_average = seg_73_exclude_padding_from_average_0, kernel_sizes = var_1559, pad = seg_73_pad_0, pad_type = seg_73_pad_type_0, strides = var_1560, x = input_447)[name = tensor("seg_73")]; + tensor var_1563_shape = shape(x = seg_73)[name = tensor("op_1563_shape")]; + tensor gather_76 = const()[name = tensor("gather_76"), val = tensor(1)]; + tensor gather_77 = const()[name = tensor("gather_77"), val = tensor(128)]; + tensor select_78 = const()[name = tensor("select_78"), val = tensor(2)]; + tensor gather_78_axis_0 = const()[name = tensor("gather_78_axis_0"), val = tensor(0)]; + tensor gather_78_batch_dims_0 = const()[name = tensor("gather_78_batch_dims_0"), val = tensor(0)]; + tensor gather_78_validate_indices_0 = const()[name = tensor("gather_78_validate_indices_0"), val = tensor(false)]; + tensor gather_78 = gather(axis = gather_78_axis_0, batch_dims = gather_78_batch_dims_0, indices = select_78, validate_indices = gather_78_validate_indices_0, x = var_1563_shape)[name = tensor("gather_78")]; + tensor var_1566_axes_0 = const()[name = tensor("op_1566_axes_0"), val = tensor([-1])]; + tensor var_1566 = expand_dims(axes = var_1566_axes_0, x = seg_73)[name = tensor("op_1566")]; + tensor concat_37_axis_0 = const()[name = tensor("concat_37_axis_0"), val = tensor(0)]; + tensor concat_37_interleave_0 = const()[name = tensor("concat_37_interleave_0"), val = tensor(false)]; + tensor concat_37 = concat(axis = concat_37_axis_0, interleave = concat_37_interleave_0, values = (gather_76, gather_77, gather_78, var_3))[name = tensor("concat_37")]; + tensor shape_97 = shape(x = var_1566)[name = tensor("shape_97")]; + tensor real_div_18 = real_div(x = concat_37, y = shape_97)[name = tensor("real_div_18")]; + tensor var_1568 = tile(reps = real_div_18, x = var_1566)[name = tensor("op_1568")]; + tensor var_1569 = const()[name = tensor("op_1569"), val = tensor([1, 128, -1])]; + tensor seg_75 = reshape(shape = var_1569, x = var_1568)[name = tensor("seg_75")]; + tensor var_1571_shape = shape(x = input_447)[name = tensor("op_1571_shape")]; + tensor select_79 = const()[name = tensor("select_79"), val = tensor(2)]; + tensor gather_79_axis_0 = const()[name = tensor("gather_79_axis_0"), val = tensor(0)]; + tensor gather_79_batch_dims_0 = const()[name = tensor("gather_79_batch_dims_0"), val = tensor(0)]; + tensor gather_79_validate_indices_0 = const()[name = tensor("gather_79_validate_indices_0"), val = tensor(false)]; + tensor gather_79 = gather(axis = gather_79_axis_0, batch_dims = gather_79_batch_dims_0, indices = select_79, validate_indices = gather_79_validate_indices_0, x = var_1571_shape)[name = tensor("gather_79")]; + tensor concat_38_values0_0 = const()[name = tensor("concat_38_values0_0"), val = tensor(1)]; + tensor concat_38_values1_0 = const()[name = tensor("concat_38_values1_0"), val = tensor(128)]; + tensor concat_38_axis_0 = const()[name = tensor("concat_38_axis_0"), val = tensor(0)]; + tensor concat_38_interleave_0 = const()[name = tensor("concat_38_interleave_0"), val = tensor(false)]; + tensor concat_38 = concat(axis = concat_38_axis_0, interleave = concat_38_interleave_0, values = (concat_38_values0_0, concat_38_values1_0, gather_79))[name = tensor("concat_38")]; + tensor var_1572_begin_0 = const()[name = tensor("op_1572_begin_0"), val = tensor([0, 0, 0])]; + tensor var_1572_end_mask_0 = const()[name = tensor("op_1572_end_mask_0"), val = tensor([true, true, false])]; + tensor var_1572 = slice_by_index(begin = var_1572_begin_0, end = concat_38, end_mask = var_1572_end_mask_0, x = seg_75)[name = tensor("op_1572")]; + tensor input_449 = add(x = var_1558, y = var_1572)[name = tensor("input_449")]; + tensor input_451_pad_type_0 = const()[name = tensor("input_451_pad_type_0"), val = tensor("valid")]; + tensor input_451_strides_0 = const()[name = tensor("input_451_strides_0"), val = tensor([1])]; + tensor input_451_pad_0 = const()[name = tensor("input_451_pad_0"), val = tensor([0, 0])]; + tensor input_451_dilations_0 = const()[name = tensor("input_451_dilations_0"), val = tensor([1])]; + tensor input_451_groups_0 = const()[name = tensor("input_451_groups_0"), val = tensor(1)]; + tensor input_451 = conv(bias = model_xvector_block2_tdnnd7_cam_layer_linear1_bias, dilations = input_451_dilations_0, groups = input_451_groups_0, pad = input_451_pad_0, pad_type = input_451_pad_type_0, strides = input_451_strides_0, weight = model_xvector_block2_tdnnd7_cam_layer_linear1_weight, x = input_449)[name = tensor("input_451")]; + tensor input_453 = relu(x = input_451)[name = tensor("input_453")]; + tensor input_455_pad_type_0 = const()[name = tensor("input_455_pad_type_0"), val = tensor("valid")]; + tensor input_455_strides_0 = const()[name = tensor("input_455_strides_0"), val = tensor([1])]; + tensor input_455_pad_0 = const()[name = tensor("input_455_pad_0"), val = tensor([0, 0])]; + tensor input_455_dilations_0 = const()[name = tensor("input_455_dilations_0"), val = tensor([1])]; + tensor input_455_groups_0 = const()[name = tensor("input_455_groups_0"), val = tensor(1)]; + tensor input_455 = conv(bias = model_xvector_block2_tdnnd7_cam_layer_linear2_bias, dilations = input_455_dilations_0, groups = input_455_groups_0, pad = input_455_pad_0, pad_type = input_455_pad_type_0, strides = input_455_strides_0, weight = model_xvector_block2_tdnnd7_cam_layer_linear2_weight, x = input_453)[name = tensor("input_455")]; + tensor m_37 = sigmoid(x = input_455)[name = tensor("m_37")]; + tensor var_1590 = mul(x = y_37, y = m_37)[name = tensor("op_1590")]; + tensor input_457_interleave_0 = const()[name = tensor("input_457_interleave_0"), val = tensor(false)]; + tensor input_457 = concat(axis = var_11, interleave = input_457_interleave_0, values = (input_437, var_1590))[name = tensor("input_457")]; + tensor input_459 = batch_norm(beta = model_xvector_block2_tdnnd8_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block2_tdnnd8_nonlinear1_batchnorm_weight, mean = model_xvector_block2_tdnnd8_nonlinear1_batchnorm_running_mean, variance = model_xvector_block2_tdnnd8_nonlinear1_batchnorm_running_var, x = input_457)[name = tensor("input_459")]; + tensor input_461 = relu(x = input_459)[name = tensor("input_461")]; + tensor input_463_pad_type_0 = const()[name = tensor("input_463_pad_type_0"), val = tensor("valid")]; + tensor input_463_strides_0 = const()[name = tensor("input_463_strides_0"), val = tensor([1])]; + tensor input_463_pad_0 = const()[name = tensor("input_463_pad_0"), val = tensor([0, 0])]; + tensor input_463_dilations_0 = const()[name = tensor("input_463_dilations_0"), val = tensor([1])]; + tensor input_463_groups_0 = const()[name = tensor("input_463_groups_0"), val = tensor(1)]; + tensor const_64 = const()[name = tensor("const_64"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12981632)))]; + tensor const_65 = const()[name = tensor("const_65"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13227456)))]; + tensor input_465 = conv(bias = const_65, dilations = input_463_dilations_0, groups = input_463_groups_0, pad = input_463_pad_0, pad_type = input_463_pad_type_0, strides = input_463_strides_0, weight = const_64, x = input_461)[name = tensor("input_465")]; + tensor input_467 = relu(x = input_465)[name = tensor("input_467")]; + tensor y_39_pad_type_0 = const()[name = tensor("y_39_pad_type_0"), val = tensor("custom")]; + tensor y_39_pad_0 = const()[name = tensor("y_39_pad_0"), val = tensor([2, 2])]; + tensor y_39_dilations_0 = const()[name = tensor("y_39_dilations_0"), val = tensor([2])]; + tensor y_39_strides_0 = const()[name = tensor("y_39_strides_0"), val = tensor([1])]; + tensor y_39_groups_0 = const()[name = tensor("y_39_groups_0"), val = tensor(1)]; + tensor y_39 = conv(dilations = y_39_dilations_0, groups = y_39_groups_0, pad = y_39_pad_0, pad_type = y_39_pad_type_0, strides = y_39_strides_0, weight = model_xvector_block2_tdnnd8_cam_layer_linear_local_weight, x = input_467)[name = tensor("y_39")]; + tensor var_1627_axes_0 = const()[name = tensor("op_1627_axes_0"), val = tensor([-1])]; + tensor var_1627_keep_dims_0 = const()[name = tensor("op_1627_keep_dims_0"), val = tensor(true)]; + tensor var_1627 = reduce_mean(axes = var_1627_axes_0, keep_dims = var_1627_keep_dims_0, x = input_467)[name = tensor("op_1627")]; + tensor var_1628 = const()[name = tensor("op_1628"), val = tensor([100])]; + tensor var_1629 = const()[name = tensor("op_1629"), val = tensor([100])]; + tensor seg_77_pad_type_0 = const()[name = tensor("seg_77_pad_type_0"), val = tensor("custom")]; + tensor seg_77_pad_0 = const()[name = tensor("seg_77_pad_0"), val = tensor([0, 0])]; + tensor seg_77_exclude_padding_from_average_0 = const()[name = tensor("seg_77_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_77_ceil_mode_0 = const()[name = tensor("seg_77_ceil_mode_0"), val = tensor(true)]; + tensor seg_77 = avg_pool(ceil_mode = seg_77_ceil_mode_0, exclude_padding_from_average = seg_77_exclude_padding_from_average_0, kernel_sizes = var_1628, pad = seg_77_pad_0, pad_type = seg_77_pad_type_0, strides = var_1629, x = input_467)[name = tensor("seg_77")]; + tensor var_1632_shape = shape(x = seg_77)[name = tensor("op_1632_shape")]; + tensor gather_80 = const()[name = tensor("gather_80"), val = tensor(1)]; + tensor gather_81 = const()[name = tensor("gather_81"), val = tensor(128)]; + tensor select_82 = const()[name = tensor("select_82"), val = tensor(2)]; + tensor gather_82_axis_0 = const()[name = tensor("gather_82_axis_0"), val = tensor(0)]; + tensor gather_82_batch_dims_0 = const()[name = tensor("gather_82_batch_dims_0"), val = tensor(0)]; + tensor gather_82_validate_indices_0 = const()[name = tensor("gather_82_validate_indices_0"), val = tensor(false)]; + tensor gather_82 = gather(axis = gather_82_axis_0, batch_dims = gather_82_batch_dims_0, indices = select_82, validate_indices = gather_82_validate_indices_0, x = var_1632_shape)[name = tensor("gather_82")]; + tensor var_1635_axes_0 = const()[name = tensor("op_1635_axes_0"), val = tensor([-1])]; + tensor var_1635 = expand_dims(axes = var_1635_axes_0, x = seg_77)[name = tensor("op_1635")]; + tensor concat_39_axis_0 = const()[name = tensor("concat_39_axis_0"), val = tensor(0)]; + tensor concat_39_interleave_0 = const()[name = tensor("concat_39_interleave_0"), val = tensor(false)]; + tensor concat_39 = concat(axis = concat_39_axis_0, interleave = concat_39_interleave_0, values = (gather_80, gather_81, gather_82, var_3))[name = tensor("concat_39")]; + tensor shape_102 = shape(x = var_1635)[name = tensor("shape_102")]; + tensor real_div_19 = real_div(x = concat_39, y = shape_102)[name = tensor("real_div_19")]; + tensor var_1637 = tile(reps = real_div_19, x = var_1635)[name = tensor("op_1637")]; + tensor var_1638 = const()[name = tensor("op_1638"), val = tensor([1, 128, -1])]; + tensor seg_79 = reshape(shape = var_1638, x = var_1637)[name = tensor("seg_79")]; + tensor var_1640_shape = shape(x = input_467)[name = tensor("op_1640_shape")]; + tensor select_83 = const()[name = tensor("select_83"), val = tensor(2)]; + tensor gather_83_axis_0 = const()[name = tensor("gather_83_axis_0"), val = tensor(0)]; + tensor gather_83_batch_dims_0 = const()[name = tensor("gather_83_batch_dims_0"), val = tensor(0)]; + tensor gather_83_validate_indices_0 = const()[name = tensor("gather_83_validate_indices_0"), val = tensor(false)]; + tensor gather_83 = gather(axis = gather_83_axis_0, batch_dims = gather_83_batch_dims_0, indices = select_83, validate_indices = gather_83_validate_indices_0, x = var_1640_shape)[name = tensor("gather_83")]; + tensor concat_40_values0_0 = const()[name = tensor("concat_40_values0_0"), val = tensor(1)]; + tensor concat_40_values1_0 = const()[name = tensor("concat_40_values1_0"), val = tensor(128)]; + tensor concat_40_axis_0 = const()[name = tensor("concat_40_axis_0"), val = tensor(0)]; + tensor concat_40_interleave_0 = const()[name = tensor("concat_40_interleave_0"), val = tensor(false)]; + tensor concat_40 = concat(axis = concat_40_axis_0, interleave = concat_40_interleave_0, values = (concat_40_values0_0, concat_40_values1_0, gather_83))[name = tensor("concat_40")]; + tensor var_1641_begin_0 = const()[name = tensor("op_1641_begin_0"), val = tensor([0, 0, 0])]; + tensor var_1641_end_mask_0 = const()[name = tensor("op_1641_end_mask_0"), val = tensor([true, true, false])]; + tensor var_1641 = slice_by_index(begin = var_1641_begin_0, end = concat_40, end_mask = var_1641_end_mask_0, x = seg_79)[name = tensor("op_1641")]; + tensor input_469 = add(x = var_1627, y = var_1641)[name = tensor("input_469")]; + tensor input_471_pad_type_0 = const()[name = tensor("input_471_pad_type_0"), val = tensor("valid")]; + tensor input_471_strides_0 = const()[name = tensor("input_471_strides_0"), val = tensor([1])]; + tensor input_471_pad_0 = const()[name = tensor("input_471_pad_0"), val = tensor([0, 0])]; + tensor input_471_dilations_0 = const()[name = tensor("input_471_dilations_0"), val = tensor([1])]; + tensor input_471_groups_0 = const()[name = tensor("input_471_groups_0"), val = tensor(1)]; + tensor input_471 = conv(bias = model_xvector_block2_tdnnd8_cam_layer_linear1_bias, dilations = input_471_dilations_0, groups = input_471_groups_0, pad = input_471_pad_0, pad_type = input_471_pad_type_0, strides = input_471_strides_0, weight = model_xvector_block2_tdnnd8_cam_layer_linear1_weight, x = input_469)[name = tensor("input_471")]; + tensor input_473 = relu(x = input_471)[name = tensor("input_473")]; + tensor input_475_pad_type_0 = const()[name = tensor("input_475_pad_type_0"), val = tensor("valid")]; + tensor input_475_strides_0 = const()[name = tensor("input_475_strides_0"), val = tensor([1])]; + tensor input_475_pad_0 = const()[name = tensor("input_475_pad_0"), val = tensor([0, 0])]; + tensor input_475_dilations_0 = const()[name = tensor("input_475_dilations_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_xvector_block2_tdnnd8_cam_layer_linear2_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 = model_xvector_block2_tdnnd8_cam_layer_linear2_weight, x = input_473)[name = tensor("input_475")]; + tensor m_39 = sigmoid(x = input_475)[name = tensor("m_39")]; + tensor var_1659 = mul(x = y_39, y = m_39)[name = tensor("op_1659")]; + tensor input_477_interleave_0 = const()[name = tensor("input_477_interleave_0"), val = tensor(false)]; + tensor input_477 = concat(axis = var_11, interleave = input_477_interleave_0, values = (input_457, var_1659))[name = tensor("input_477")]; + tensor input_479 = batch_norm(beta = model_xvector_block2_tdnnd9_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block2_tdnnd9_nonlinear1_batchnorm_weight, mean = model_xvector_block2_tdnnd9_nonlinear1_batchnorm_running_mean, variance = model_xvector_block2_tdnnd9_nonlinear1_batchnorm_running_var, x = input_477)[name = tensor("input_479")]; + tensor input_481 = relu(x = input_479)[name = tensor("input_481")]; + tensor input_483_pad_type_0 = const()[name = tensor("input_483_pad_type_0"), val = tensor("valid")]; + tensor input_483_strides_0 = const()[name = tensor("input_483_strides_0"), val = tensor([1])]; + tensor input_483_pad_0 = const()[name = tensor("input_483_pad_0"), val = tensor([0, 0])]; + tensor input_483_dilations_0 = const()[name = tensor("input_483_dilations_0"), val = tensor([1])]; + tensor input_483_groups_0 = const()[name = tensor("input_483_groups_0"), val = tensor(1)]; + tensor const_66 = const()[name = tensor("const_66"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13228032)))]; + tensor const_67 = const()[name = tensor("const_67"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13490240)))]; + tensor input_485 = conv(bias = const_67, dilations = input_483_dilations_0, groups = input_483_groups_0, pad = input_483_pad_0, pad_type = input_483_pad_type_0, strides = input_483_strides_0, weight = const_66, x = input_481)[name = tensor("input_485")]; + tensor input_487 = relu(x = input_485)[name = tensor("input_487")]; + tensor y_41_pad_type_0 = const()[name = tensor("y_41_pad_type_0"), val = tensor("custom")]; + tensor y_41_pad_0 = const()[name = tensor("y_41_pad_0"), val = tensor([2, 2])]; + tensor y_41_dilations_0 = const()[name = tensor("y_41_dilations_0"), val = tensor([2])]; + tensor y_41_strides_0 = const()[name = tensor("y_41_strides_0"), val = tensor([1])]; + tensor y_41_groups_0 = const()[name = tensor("y_41_groups_0"), val = tensor(1)]; + tensor y_41 = conv(dilations = y_41_dilations_0, groups = y_41_groups_0, pad = y_41_pad_0, pad_type = y_41_pad_type_0, strides = y_41_strides_0, weight = model_xvector_block2_tdnnd9_cam_layer_linear_local_weight, x = input_487)[name = tensor("y_41")]; + tensor var_1696_axes_0 = const()[name = tensor("op_1696_axes_0"), val = tensor([-1])]; + tensor var_1696_keep_dims_0 = const()[name = tensor("op_1696_keep_dims_0"), val = tensor(true)]; + tensor var_1696 = reduce_mean(axes = var_1696_axes_0, keep_dims = var_1696_keep_dims_0, x = input_487)[name = tensor("op_1696")]; + tensor var_1697 = const()[name = tensor("op_1697"), val = tensor([100])]; + tensor var_1698 = const()[name = tensor("op_1698"), val = tensor([100])]; + tensor seg_81_pad_type_0 = const()[name = tensor("seg_81_pad_type_0"), val = tensor("custom")]; + tensor seg_81_pad_0 = const()[name = tensor("seg_81_pad_0"), val = tensor([0, 0])]; + tensor seg_81_exclude_padding_from_average_0 = const()[name = tensor("seg_81_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_81_ceil_mode_0 = const()[name = tensor("seg_81_ceil_mode_0"), val = tensor(true)]; + tensor seg_81 = avg_pool(ceil_mode = seg_81_ceil_mode_0, exclude_padding_from_average = seg_81_exclude_padding_from_average_0, kernel_sizes = var_1697, pad = seg_81_pad_0, pad_type = seg_81_pad_type_0, strides = var_1698, x = input_487)[name = tensor("seg_81")]; + tensor var_1701_shape = shape(x = seg_81)[name = tensor("op_1701_shape")]; + tensor gather_84 = const()[name = tensor("gather_84"), val = tensor(1)]; + tensor gather_85 = const()[name = tensor("gather_85"), val = tensor(128)]; + tensor select_86 = const()[name = tensor("select_86"), val = tensor(2)]; + tensor gather_86_axis_0 = const()[name = tensor("gather_86_axis_0"), val = tensor(0)]; + tensor gather_86_batch_dims_0 = const()[name = tensor("gather_86_batch_dims_0"), val = tensor(0)]; + tensor gather_86_validate_indices_0 = const()[name = tensor("gather_86_validate_indices_0"), val = tensor(false)]; + tensor gather_86 = gather(axis = gather_86_axis_0, batch_dims = gather_86_batch_dims_0, indices = select_86, validate_indices = gather_86_validate_indices_0, x = var_1701_shape)[name = tensor("gather_86")]; + tensor var_1704_axes_0 = const()[name = tensor("op_1704_axes_0"), val = tensor([-1])]; + tensor var_1704 = expand_dims(axes = var_1704_axes_0, x = seg_81)[name = tensor("op_1704")]; + tensor concat_41_axis_0 = const()[name = tensor("concat_41_axis_0"), val = tensor(0)]; + tensor concat_41_interleave_0 = const()[name = tensor("concat_41_interleave_0"), val = tensor(false)]; + tensor concat_41 = concat(axis = concat_41_axis_0, interleave = concat_41_interleave_0, values = (gather_84, gather_85, gather_86, var_3))[name = tensor("concat_41")]; + tensor shape_107 = shape(x = var_1704)[name = tensor("shape_107")]; + tensor real_div_20 = real_div(x = concat_41, y = shape_107)[name = tensor("real_div_20")]; + tensor var_1706 = tile(reps = real_div_20, x = var_1704)[name = tensor("op_1706")]; + tensor var_1707 = const()[name = tensor("op_1707"), val = tensor([1, 128, -1])]; + tensor seg_83 = reshape(shape = var_1707, x = var_1706)[name = tensor("seg_83")]; + tensor var_1709_shape = shape(x = input_487)[name = tensor("op_1709_shape")]; + tensor select_87 = const()[name = tensor("select_87"), val = tensor(2)]; + tensor gather_87_axis_0 = const()[name = tensor("gather_87_axis_0"), val = tensor(0)]; + tensor gather_87_batch_dims_0 = const()[name = tensor("gather_87_batch_dims_0"), val = tensor(0)]; + tensor gather_87_validate_indices_0 = const()[name = tensor("gather_87_validate_indices_0"), val = tensor(false)]; + tensor gather_87 = gather(axis = gather_87_axis_0, batch_dims = gather_87_batch_dims_0, indices = select_87, validate_indices = gather_87_validate_indices_0, x = var_1709_shape)[name = tensor("gather_87")]; + tensor concat_42_values0_0 = const()[name = tensor("concat_42_values0_0"), val = tensor(1)]; + tensor concat_42_values1_0 = const()[name = tensor("concat_42_values1_0"), val = tensor(128)]; + tensor concat_42_axis_0 = const()[name = tensor("concat_42_axis_0"), val = tensor(0)]; + tensor concat_42_interleave_0 = const()[name = tensor("concat_42_interleave_0"), val = tensor(false)]; + tensor concat_42 = concat(axis = concat_42_axis_0, interleave = concat_42_interleave_0, values = (concat_42_values0_0, concat_42_values1_0, gather_87))[name = tensor("concat_42")]; + tensor var_1710_begin_0 = const()[name = tensor("op_1710_begin_0"), val = tensor([0, 0, 0])]; + tensor var_1710_end_mask_0 = const()[name = tensor("op_1710_end_mask_0"), val = tensor([true, true, false])]; + tensor var_1710 = slice_by_index(begin = var_1710_begin_0, end = concat_42, end_mask = var_1710_end_mask_0, x = seg_83)[name = tensor("op_1710")]; + tensor input_489 = add(x = var_1696, y = var_1710)[name = tensor("input_489")]; + tensor input_491_pad_type_0 = const()[name = tensor("input_491_pad_type_0"), val = tensor("valid")]; + tensor input_491_strides_0 = const()[name = tensor("input_491_strides_0"), val = tensor([1])]; + tensor input_491_pad_0 = const()[name = tensor("input_491_pad_0"), val = tensor([0, 0])]; + tensor input_491_dilations_0 = const()[name = tensor("input_491_dilations_0"), val = tensor([1])]; + tensor input_491_groups_0 = const()[name = tensor("input_491_groups_0"), val = tensor(1)]; + tensor input_491 = conv(bias = model_xvector_block2_tdnnd9_cam_layer_linear1_bias, dilations = input_491_dilations_0, groups = input_491_groups_0, pad = input_491_pad_0, pad_type = input_491_pad_type_0, strides = input_491_strides_0, weight = model_xvector_block2_tdnnd9_cam_layer_linear1_weight, x = input_489)[name = tensor("input_491")]; + tensor input_493 = relu(x = input_491)[name = tensor("input_493")]; + tensor input_495_pad_type_0 = const()[name = tensor("input_495_pad_type_0"), val = tensor("valid")]; + tensor input_495_strides_0 = const()[name = tensor("input_495_strides_0"), val = tensor([1])]; + tensor input_495_pad_0 = const()[name = tensor("input_495_pad_0"), val = tensor([0, 0])]; + tensor input_495_dilations_0 = const()[name = tensor("input_495_dilations_0"), val = tensor([1])]; + tensor input_495_groups_0 = const()[name = tensor("input_495_groups_0"), val = tensor(1)]; + tensor input_495 = conv(bias = model_xvector_block2_tdnnd9_cam_layer_linear2_bias, dilations = input_495_dilations_0, groups = input_495_groups_0, pad = input_495_pad_0, pad_type = input_495_pad_type_0, strides = input_495_strides_0, weight = model_xvector_block2_tdnnd9_cam_layer_linear2_weight, x = input_493)[name = tensor("input_495")]; + tensor m_41 = sigmoid(x = input_495)[name = tensor("m_41")]; + tensor var_1728 = mul(x = y_41, y = m_41)[name = tensor("op_1728")]; + tensor input_497_interleave_0 = const()[name = tensor("input_497_interleave_0"), val = tensor(false)]; + tensor input_497 = concat(axis = var_11, interleave = input_497_interleave_0, values = (input_477, var_1728))[name = tensor("input_497")]; + tensor input_499 = batch_norm(beta = model_xvector_block2_tdnnd10_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block2_tdnnd10_nonlinear1_batchnorm_weight, mean = model_xvector_block2_tdnnd10_nonlinear1_batchnorm_running_mean, variance = model_xvector_block2_tdnnd10_nonlinear1_batchnorm_running_var, x = input_497)[name = tensor("input_499")]; + tensor input_501 = relu(x = input_499)[name = tensor("input_501")]; + tensor input_503_pad_type_0 = const()[name = tensor("input_503_pad_type_0"), val = tensor("valid")]; + tensor input_503_strides_0 = const()[name = tensor("input_503_strides_0"), val = tensor([1])]; + tensor input_503_pad_0 = const()[name = tensor("input_503_pad_0"), val = tensor([0, 0])]; + 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 const_68 = const()[name = tensor("const_68"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13490816)))]; + tensor const_69 = const()[name = tensor("const_69"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13769408)))]; + tensor input_505 = conv(bias = const_69, 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 = const_68, x = input_501)[name = tensor("input_505")]; + tensor input_507 = relu(x = input_505)[name = tensor("input_507")]; + tensor y_43_pad_type_0 = const()[name = tensor("y_43_pad_type_0"), val = tensor("custom")]; + tensor y_43_pad_0 = const()[name = tensor("y_43_pad_0"), val = tensor([2, 2])]; + tensor y_43_dilations_0 = const()[name = tensor("y_43_dilations_0"), val = tensor([2])]; + tensor y_43_strides_0 = const()[name = tensor("y_43_strides_0"), val = tensor([1])]; + tensor y_43_groups_0 = const()[name = tensor("y_43_groups_0"), val = tensor(1)]; + tensor y_43 = conv(dilations = y_43_dilations_0, groups = y_43_groups_0, pad = y_43_pad_0, pad_type = y_43_pad_type_0, strides = y_43_strides_0, weight = model_xvector_block2_tdnnd10_cam_layer_linear_local_weight, x = input_507)[name = tensor("y_43")]; + tensor var_1765_axes_0 = const()[name = tensor("op_1765_axes_0"), val = tensor([-1])]; + tensor var_1765_keep_dims_0 = const()[name = tensor("op_1765_keep_dims_0"), val = tensor(true)]; + tensor var_1765 = reduce_mean(axes = var_1765_axes_0, keep_dims = var_1765_keep_dims_0, x = input_507)[name = tensor("op_1765")]; + tensor var_1766 = const()[name = tensor("op_1766"), val = tensor([100])]; + tensor var_1767 = const()[name = tensor("op_1767"), val = tensor([100])]; + tensor seg_85_pad_type_0 = const()[name = tensor("seg_85_pad_type_0"), val = tensor("custom")]; + tensor seg_85_pad_0 = const()[name = tensor("seg_85_pad_0"), val = tensor([0, 0])]; + tensor seg_85_exclude_padding_from_average_0 = const()[name = tensor("seg_85_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_85_ceil_mode_0 = const()[name = tensor("seg_85_ceil_mode_0"), val = tensor(true)]; + tensor seg_85 = avg_pool(ceil_mode = seg_85_ceil_mode_0, exclude_padding_from_average = seg_85_exclude_padding_from_average_0, kernel_sizes = var_1766, pad = seg_85_pad_0, pad_type = seg_85_pad_type_0, strides = var_1767, x = input_507)[name = tensor("seg_85")]; + tensor var_1770_shape = shape(x = seg_85)[name = tensor("op_1770_shape")]; + tensor gather_88 = const()[name = tensor("gather_88"), val = tensor(1)]; + tensor gather_89 = const()[name = tensor("gather_89"), val = tensor(128)]; + tensor select_90 = const()[name = tensor("select_90"), val = tensor(2)]; + tensor gather_90_axis_0 = const()[name = tensor("gather_90_axis_0"), val = tensor(0)]; + tensor gather_90_batch_dims_0 = const()[name = tensor("gather_90_batch_dims_0"), val = tensor(0)]; + tensor gather_90_validate_indices_0 = const()[name = tensor("gather_90_validate_indices_0"), val = tensor(false)]; + tensor gather_90 = gather(axis = gather_90_axis_0, batch_dims = gather_90_batch_dims_0, indices = select_90, validate_indices = gather_90_validate_indices_0, x = var_1770_shape)[name = tensor("gather_90")]; + tensor var_1773_axes_0 = const()[name = tensor("op_1773_axes_0"), val = tensor([-1])]; + tensor var_1773 = expand_dims(axes = var_1773_axes_0, x = seg_85)[name = tensor("op_1773")]; + tensor concat_43_axis_0 = const()[name = tensor("concat_43_axis_0"), val = tensor(0)]; + tensor concat_43_interleave_0 = const()[name = tensor("concat_43_interleave_0"), val = tensor(false)]; + tensor concat_43 = concat(axis = concat_43_axis_0, interleave = concat_43_interleave_0, values = (gather_88, gather_89, gather_90, var_3))[name = tensor("concat_43")]; + tensor shape_112 = shape(x = var_1773)[name = tensor("shape_112")]; + tensor real_div_21 = real_div(x = concat_43, y = shape_112)[name = tensor("real_div_21")]; + tensor var_1775 = tile(reps = real_div_21, x = var_1773)[name = tensor("op_1775")]; + tensor var_1776 = const()[name = tensor("op_1776"), val = tensor([1, 128, -1])]; + tensor seg_87 = reshape(shape = var_1776, x = var_1775)[name = tensor("seg_87")]; + tensor var_1778_shape = shape(x = input_507)[name = tensor("op_1778_shape")]; + tensor select_91 = const()[name = tensor("select_91"), val = tensor(2)]; + tensor gather_91_axis_0 = const()[name = tensor("gather_91_axis_0"), val = tensor(0)]; + tensor gather_91_batch_dims_0 = const()[name = tensor("gather_91_batch_dims_0"), val = tensor(0)]; + tensor gather_91_validate_indices_0 = const()[name = tensor("gather_91_validate_indices_0"), val = tensor(false)]; + tensor gather_91 = gather(axis = gather_91_axis_0, batch_dims = gather_91_batch_dims_0, indices = select_91, validate_indices = gather_91_validate_indices_0, x = var_1778_shape)[name = tensor("gather_91")]; + tensor concat_44_values0_0 = const()[name = tensor("concat_44_values0_0"), val = tensor(1)]; + tensor concat_44_values1_0 = const()[name = tensor("concat_44_values1_0"), val = tensor(128)]; + tensor concat_44_axis_0 = const()[name = tensor("concat_44_axis_0"), val = tensor(0)]; + tensor concat_44_interleave_0 = const()[name = tensor("concat_44_interleave_0"), val = tensor(false)]; + tensor concat_44 = concat(axis = concat_44_axis_0, interleave = concat_44_interleave_0, values = (concat_44_values0_0, concat_44_values1_0, gather_91))[name = tensor("concat_44")]; + tensor var_1779_begin_0 = const()[name = tensor("op_1779_begin_0"), val = tensor([0, 0, 0])]; + tensor var_1779_end_mask_0 = const()[name = tensor("op_1779_end_mask_0"), val = tensor([true, true, false])]; + tensor var_1779 = slice_by_index(begin = var_1779_begin_0, end = concat_44, end_mask = var_1779_end_mask_0, x = seg_87)[name = tensor("op_1779")]; + tensor input_509 = add(x = var_1765, y = var_1779)[name = tensor("input_509")]; + tensor input_511_pad_type_0 = const()[name = tensor("input_511_pad_type_0"), val = tensor("valid")]; + tensor input_511_strides_0 = const()[name = tensor("input_511_strides_0"), val = tensor([1])]; + tensor input_511_pad_0 = const()[name = tensor("input_511_pad_0"), val = tensor([0, 0])]; + tensor input_511_dilations_0 = const()[name = tensor("input_511_dilations_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_xvector_block2_tdnnd10_cam_layer_linear1_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 = model_xvector_block2_tdnnd10_cam_layer_linear1_weight, x = input_509)[name = tensor("input_511")]; + tensor input_513 = relu(x = input_511)[name = tensor("input_513")]; + tensor input_515_pad_type_0 = const()[name = tensor("input_515_pad_type_0"), val = tensor("valid")]; + tensor input_515_strides_0 = const()[name = tensor("input_515_strides_0"), val = tensor([1])]; + tensor input_515_pad_0 = const()[name = tensor("input_515_pad_0"), val = tensor([0, 0])]; + tensor input_515_dilations_0 = const()[name = tensor("input_515_dilations_0"), val = tensor([1])]; + tensor input_515_groups_0 = const()[name = tensor("input_515_groups_0"), val = tensor(1)]; + tensor input_515 = conv(bias = model_xvector_block2_tdnnd10_cam_layer_linear2_bias, dilations = input_515_dilations_0, groups = input_515_groups_0, pad = input_515_pad_0, pad_type = input_515_pad_type_0, strides = input_515_strides_0, weight = model_xvector_block2_tdnnd10_cam_layer_linear2_weight, x = input_513)[name = tensor("input_515")]; + tensor m_43 = sigmoid(x = input_515)[name = tensor("m_43")]; + tensor var_1797 = mul(x = y_43, y = m_43)[name = tensor("op_1797")]; + tensor input_517_interleave_0 = const()[name = tensor("input_517_interleave_0"), val = tensor(false)]; + tensor input_517 = concat(axis = var_11, interleave = input_517_interleave_0, values = (input_497, var_1797))[name = tensor("input_517")]; + tensor input_519 = batch_norm(beta = model_xvector_block2_tdnnd11_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block2_tdnnd11_nonlinear1_batchnorm_weight, mean = model_xvector_block2_tdnnd11_nonlinear1_batchnorm_running_mean, variance = model_xvector_block2_tdnnd11_nonlinear1_batchnorm_running_var, x = input_517)[name = tensor("input_519")]; + tensor input_521 = relu(x = input_519)[name = tensor("input_521")]; + tensor input_523_pad_type_0 = const()[name = tensor("input_523_pad_type_0"), val = tensor("valid")]; + tensor input_523_strides_0 = const()[name = tensor("input_523_strides_0"), val = tensor([1])]; + tensor input_523_pad_0 = const()[name = tensor("input_523_pad_0"), val = tensor([0, 0])]; + tensor input_523_dilations_0 = const()[name = tensor("input_523_dilations_0"), val = tensor([1])]; + tensor input_523_groups_0 = const()[name = tensor("input_523_groups_0"), val = tensor(1)]; + tensor const_70 = const()[name = tensor("const_70"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13769984)))]; + tensor const_71 = const()[name = tensor("const_71"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14064960)))]; + tensor input_525 = conv(bias = const_71, dilations = input_523_dilations_0, groups = input_523_groups_0, pad = input_523_pad_0, pad_type = input_523_pad_type_0, strides = input_523_strides_0, weight = const_70, x = input_521)[name = tensor("input_525")]; + tensor input_527 = relu(x = input_525)[name = tensor("input_527")]; + tensor y_45_pad_type_0 = const()[name = tensor("y_45_pad_type_0"), val = tensor("custom")]; + tensor y_45_pad_0 = const()[name = tensor("y_45_pad_0"), val = tensor([2, 2])]; + tensor y_45_dilations_0 = const()[name = tensor("y_45_dilations_0"), val = tensor([2])]; + tensor y_45_strides_0 = const()[name = tensor("y_45_strides_0"), val = tensor([1])]; + tensor y_45_groups_0 = const()[name = tensor("y_45_groups_0"), val = tensor(1)]; + tensor y_45 = conv(dilations = y_45_dilations_0, groups = y_45_groups_0, pad = y_45_pad_0, pad_type = y_45_pad_type_0, strides = y_45_strides_0, weight = model_xvector_block2_tdnnd11_cam_layer_linear_local_weight, x = input_527)[name = tensor("y_45")]; + tensor var_1834_axes_0 = const()[name = tensor("op_1834_axes_0"), val = tensor([-1])]; + tensor var_1834_keep_dims_0 = const()[name = tensor("op_1834_keep_dims_0"), val = tensor(true)]; + tensor var_1834 = reduce_mean(axes = var_1834_axes_0, keep_dims = var_1834_keep_dims_0, x = input_527)[name = tensor("op_1834")]; + tensor var_1835 = const()[name = tensor("op_1835"), val = tensor([100])]; + tensor var_1836 = const()[name = tensor("op_1836"), val = tensor([100])]; + tensor seg_89_pad_type_0 = const()[name = tensor("seg_89_pad_type_0"), val = tensor("custom")]; + tensor seg_89_pad_0 = const()[name = tensor("seg_89_pad_0"), val = tensor([0, 0])]; + tensor seg_89_exclude_padding_from_average_0 = const()[name = tensor("seg_89_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_89_ceil_mode_0 = const()[name = tensor("seg_89_ceil_mode_0"), val = tensor(true)]; + tensor seg_89 = avg_pool(ceil_mode = seg_89_ceil_mode_0, exclude_padding_from_average = seg_89_exclude_padding_from_average_0, kernel_sizes = var_1835, pad = seg_89_pad_0, pad_type = seg_89_pad_type_0, strides = var_1836, x = input_527)[name = tensor("seg_89")]; + tensor var_1839_shape = shape(x = seg_89)[name = tensor("op_1839_shape")]; + tensor gather_92 = const()[name = tensor("gather_92"), val = tensor(1)]; + tensor gather_93 = const()[name = tensor("gather_93"), val = tensor(128)]; + tensor select_94 = const()[name = tensor("select_94"), val = tensor(2)]; + tensor gather_94_axis_0 = const()[name = tensor("gather_94_axis_0"), val = tensor(0)]; + tensor gather_94_batch_dims_0 = const()[name = tensor("gather_94_batch_dims_0"), val = tensor(0)]; + tensor gather_94_validate_indices_0 = const()[name = tensor("gather_94_validate_indices_0"), val = tensor(false)]; + tensor gather_94 = gather(axis = gather_94_axis_0, batch_dims = gather_94_batch_dims_0, indices = select_94, validate_indices = gather_94_validate_indices_0, x = var_1839_shape)[name = tensor("gather_94")]; + tensor var_1842_axes_0 = const()[name = tensor("op_1842_axes_0"), val = tensor([-1])]; + tensor var_1842 = expand_dims(axes = var_1842_axes_0, x = seg_89)[name = tensor("op_1842")]; + tensor concat_45_axis_0 = const()[name = tensor("concat_45_axis_0"), val = tensor(0)]; + tensor concat_45_interleave_0 = const()[name = tensor("concat_45_interleave_0"), val = tensor(false)]; + tensor concat_45 = concat(axis = concat_45_axis_0, interleave = concat_45_interleave_0, values = (gather_92, gather_93, gather_94, var_3))[name = tensor("concat_45")]; + tensor shape_117 = shape(x = var_1842)[name = tensor("shape_117")]; + tensor real_div_22 = real_div(x = concat_45, y = shape_117)[name = tensor("real_div_22")]; + tensor var_1844 = tile(reps = real_div_22, x = var_1842)[name = tensor("op_1844")]; + tensor var_1845 = const()[name = tensor("op_1845"), val = tensor([1, 128, -1])]; + tensor seg_91 = reshape(shape = var_1845, x = var_1844)[name = tensor("seg_91")]; + tensor var_1847_shape = shape(x = input_527)[name = tensor("op_1847_shape")]; + tensor select_95 = const()[name = tensor("select_95"), val = tensor(2)]; + tensor gather_95_axis_0 = const()[name = tensor("gather_95_axis_0"), val = tensor(0)]; + tensor gather_95_batch_dims_0 = const()[name = tensor("gather_95_batch_dims_0"), val = tensor(0)]; + tensor gather_95_validate_indices_0 = const()[name = tensor("gather_95_validate_indices_0"), val = tensor(false)]; + tensor gather_95 = gather(axis = gather_95_axis_0, batch_dims = gather_95_batch_dims_0, indices = select_95, validate_indices = gather_95_validate_indices_0, x = var_1847_shape)[name = tensor("gather_95")]; + tensor concat_46_values0_0 = const()[name = tensor("concat_46_values0_0"), val = tensor(1)]; + tensor concat_46_values1_0 = const()[name = tensor("concat_46_values1_0"), val = tensor(128)]; + tensor concat_46_axis_0 = const()[name = tensor("concat_46_axis_0"), val = tensor(0)]; + tensor concat_46_interleave_0 = const()[name = tensor("concat_46_interleave_0"), val = tensor(false)]; + tensor concat_46 = concat(axis = concat_46_axis_0, interleave = concat_46_interleave_0, values = (concat_46_values0_0, concat_46_values1_0, gather_95))[name = tensor("concat_46")]; + tensor var_1848_begin_0 = const()[name = tensor("op_1848_begin_0"), val = tensor([0, 0, 0])]; + tensor var_1848_end_mask_0 = const()[name = tensor("op_1848_end_mask_0"), val = tensor([true, true, false])]; + tensor var_1848 = slice_by_index(begin = var_1848_begin_0, end = concat_46, end_mask = var_1848_end_mask_0, x = seg_91)[name = tensor("op_1848")]; + tensor input_529 = add(x = var_1834, y = var_1848)[name = tensor("input_529")]; + tensor input_531_pad_type_0 = const()[name = tensor("input_531_pad_type_0"), val = tensor("valid")]; + tensor input_531_strides_0 = const()[name = tensor("input_531_strides_0"), val = tensor([1])]; + tensor input_531_pad_0 = const()[name = tensor("input_531_pad_0"), val = tensor([0, 0])]; + tensor input_531_dilations_0 = const()[name = tensor("input_531_dilations_0"), val = tensor([1])]; + tensor input_531_groups_0 = const()[name = tensor("input_531_groups_0"), val = tensor(1)]; + tensor input_531 = conv(bias = model_xvector_block2_tdnnd11_cam_layer_linear1_bias, dilations = input_531_dilations_0, groups = input_531_groups_0, pad = input_531_pad_0, pad_type = input_531_pad_type_0, strides = input_531_strides_0, weight = model_xvector_block2_tdnnd11_cam_layer_linear1_weight, x = input_529)[name = tensor("input_531")]; + tensor input_533 = relu(x = input_531)[name = tensor("input_533")]; + tensor input_535_pad_type_0 = const()[name = tensor("input_535_pad_type_0"), val = tensor("valid")]; + tensor input_535_strides_0 = const()[name = tensor("input_535_strides_0"), val = tensor([1])]; + tensor input_535_pad_0 = const()[name = tensor("input_535_pad_0"), val = tensor([0, 0])]; + tensor input_535_dilations_0 = const()[name = tensor("input_535_dilations_0"), val = tensor([1])]; + tensor input_535_groups_0 = const()[name = tensor("input_535_groups_0"), val = tensor(1)]; + tensor input_535 = conv(bias = model_xvector_block2_tdnnd11_cam_layer_linear2_bias, dilations = input_535_dilations_0, groups = input_535_groups_0, pad = input_535_pad_0, pad_type = input_535_pad_type_0, strides = input_535_strides_0, weight = model_xvector_block2_tdnnd11_cam_layer_linear2_weight, x = input_533)[name = tensor("input_535")]; + tensor m_45 = sigmoid(x = input_535)[name = tensor("m_45")]; + tensor var_1866 = mul(x = y_45, y = m_45)[name = tensor("op_1866")]; + tensor input_537_interleave_0 = const()[name = tensor("input_537_interleave_0"), val = tensor(false)]; + tensor input_537 = concat(axis = var_11, interleave = input_537_interleave_0, values = (input_517, var_1866))[name = tensor("input_537")]; + tensor input_539 = batch_norm(beta = model_xvector_block2_tdnnd12_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block2_tdnnd12_nonlinear1_batchnorm_weight, mean = model_xvector_block2_tdnnd12_nonlinear1_batchnorm_running_mean, variance = model_xvector_block2_tdnnd12_nonlinear1_batchnorm_running_var, x = input_537)[name = tensor("input_539")]; + tensor input_541 = relu(x = input_539)[name = tensor("input_541")]; + tensor input_543_pad_type_0 = const()[name = tensor("input_543_pad_type_0"), val = tensor("valid")]; + tensor input_543_strides_0 = const()[name = tensor("input_543_strides_0"), val = tensor([1])]; + tensor input_543_pad_0 = const()[name = tensor("input_543_pad_0"), val = tensor([0, 0])]; + tensor input_543_dilations_0 = const()[name = tensor("input_543_dilations_0"), val = tensor([1])]; + tensor input_543_groups_0 = const()[name = tensor("input_543_groups_0"), val = tensor(1)]; + tensor const_72 = const()[name = tensor("const_72"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14065536)))]; + tensor const_73 = const()[name = tensor("const_73"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14376896)))]; + tensor input_545 = conv(bias = const_73, dilations = input_543_dilations_0, groups = input_543_groups_0, pad = input_543_pad_0, pad_type = input_543_pad_type_0, strides = input_543_strides_0, weight = const_72, x = input_541)[name = tensor("input_545")]; + tensor input_547 = relu(x = input_545)[name = tensor("input_547")]; + tensor y_47_pad_type_0 = const()[name = tensor("y_47_pad_type_0"), val = tensor("custom")]; + tensor y_47_pad_0 = const()[name = tensor("y_47_pad_0"), val = tensor([2, 2])]; + tensor y_47_dilations_0 = const()[name = tensor("y_47_dilations_0"), val = tensor([2])]; + tensor y_47_strides_0 = const()[name = tensor("y_47_strides_0"), val = tensor([1])]; + tensor y_47_groups_0 = const()[name = tensor("y_47_groups_0"), val = tensor(1)]; + tensor y_47 = conv(dilations = y_47_dilations_0, groups = y_47_groups_0, pad = y_47_pad_0, pad_type = y_47_pad_type_0, strides = y_47_strides_0, weight = model_xvector_block2_tdnnd12_cam_layer_linear_local_weight, x = input_547)[name = tensor("y_47")]; + tensor var_1903_axes_0 = const()[name = tensor("op_1903_axes_0"), val = tensor([-1])]; + tensor var_1903_keep_dims_0 = const()[name = tensor("op_1903_keep_dims_0"), val = tensor(true)]; + tensor var_1903 = reduce_mean(axes = var_1903_axes_0, keep_dims = var_1903_keep_dims_0, x = input_547)[name = tensor("op_1903")]; + tensor var_1904 = const()[name = tensor("op_1904"), val = tensor([100])]; + tensor var_1905 = const()[name = tensor("op_1905"), val = tensor([100])]; + tensor seg_93_pad_type_0 = const()[name = tensor("seg_93_pad_type_0"), val = tensor("custom")]; + tensor seg_93_pad_0 = const()[name = tensor("seg_93_pad_0"), val = tensor([0, 0])]; + tensor seg_93_exclude_padding_from_average_0 = const()[name = tensor("seg_93_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_93_ceil_mode_0 = const()[name = tensor("seg_93_ceil_mode_0"), val = tensor(true)]; + tensor seg_93 = avg_pool(ceil_mode = seg_93_ceil_mode_0, exclude_padding_from_average = seg_93_exclude_padding_from_average_0, kernel_sizes = var_1904, pad = seg_93_pad_0, pad_type = seg_93_pad_type_0, strides = var_1905, x = input_547)[name = tensor("seg_93")]; + tensor var_1908_shape = shape(x = seg_93)[name = tensor("op_1908_shape")]; + tensor gather_96 = const()[name = tensor("gather_96"), val = tensor(1)]; + tensor gather_97 = const()[name = tensor("gather_97"), val = tensor(128)]; + tensor select_98 = const()[name = tensor("select_98"), val = tensor(2)]; + tensor gather_98_axis_0 = const()[name = tensor("gather_98_axis_0"), val = tensor(0)]; + tensor gather_98_batch_dims_0 = const()[name = tensor("gather_98_batch_dims_0"), val = tensor(0)]; + tensor gather_98_validate_indices_0 = const()[name = tensor("gather_98_validate_indices_0"), val = tensor(false)]; + tensor gather_98 = gather(axis = gather_98_axis_0, batch_dims = gather_98_batch_dims_0, indices = select_98, validate_indices = gather_98_validate_indices_0, x = var_1908_shape)[name = tensor("gather_98")]; + tensor var_1911_axes_0 = const()[name = tensor("op_1911_axes_0"), val = tensor([-1])]; + tensor var_1911 = expand_dims(axes = var_1911_axes_0, x = seg_93)[name = tensor("op_1911")]; + tensor concat_47_axis_0 = const()[name = tensor("concat_47_axis_0"), val = tensor(0)]; + tensor concat_47_interleave_0 = const()[name = tensor("concat_47_interleave_0"), val = tensor(false)]; + tensor concat_47 = concat(axis = concat_47_axis_0, interleave = concat_47_interleave_0, values = (gather_96, gather_97, gather_98, var_3))[name = tensor("concat_47")]; + tensor shape_122 = shape(x = var_1911)[name = tensor("shape_122")]; + tensor real_div_23 = real_div(x = concat_47, y = shape_122)[name = tensor("real_div_23")]; + tensor var_1913 = tile(reps = real_div_23, x = var_1911)[name = tensor("op_1913")]; + tensor var_1914 = const()[name = tensor("op_1914"), val = tensor([1, 128, -1])]; + tensor seg_95 = reshape(shape = var_1914, x = var_1913)[name = tensor("seg_95")]; + tensor var_1916_shape = shape(x = input_547)[name = tensor("op_1916_shape")]; + tensor select_99 = const()[name = tensor("select_99"), val = tensor(2)]; + tensor gather_99_axis_0 = const()[name = tensor("gather_99_axis_0"), val = tensor(0)]; + tensor gather_99_batch_dims_0 = const()[name = tensor("gather_99_batch_dims_0"), val = tensor(0)]; + tensor gather_99_validate_indices_0 = const()[name = tensor("gather_99_validate_indices_0"), val = tensor(false)]; + tensor gather_99 = gather(axis = gather_99_axis_0, batch_dims = gather_99_batch_dims_0, indices = select_99, validate_indices = gather_99_validate_indices_0, x = var_1916_shape)[name = tensor("gather_99")]; + tensor concat_48_values0_0 = const()[name = tensor("concat_48_values0_0"), val = tensor(1)]; + tensor concat_48_values1_0 = const()[name = tensor("concat_48_values1_0"), val = tensor(128)]; + tensor concat_48_axis_0 = const()[name = tensor("concat_48_axis_0"), val = tensor(0)]; + tensor concat_48_interleave_0 = const()[name = tensor("concat_48_interleave_0"), val = tensor(false)]; + tensor concat_48 = concat(axis = concat_48_axis_0, interleave = concat_48_interleave_0, values = (concat_48_values0_0, concat_48_values1_0, gather_99))[name = tensor("concat_48")]; + tensor var_1917_begin_0 = const()[name = tensor("op_1917_begin_0"), val = tensor([0, 0, 0])]; + tensor var_1917_end_mask_0 = const()[name = tensor("op_1917_end_mask_0"), val = tensor([true, true, false])]; + tensor var_1917 = slice_by_index(begin = var_1917_begin_0, end = concat_48, end_mask = var_1917_end_mask_0, x = seg_95)[name = tensor("op_1917")]; + tensor input_549 = add(x = var_1903, y = var_1917)[name = tensor("input_549")]; + tensor input_551_pad_type_0 = const()[name = tensor("input_551_pad_type_0"), val = tensor("valid")]; + tensor input_551_strides_0 = const()[name = tensor("input_551_strides_0"), val = tensor([1])]; + tensor input_551_pad_0 = const()[name = tensor("input_551_pad_0"), val = tensor([0, 0])]; + tensor input_551_dilations_0 = const()[name = tensor("input_551_dilations_0"), val = tensor([1])]; + tensor input_551_groups_0 = const()[name = tensor("input_551_groups_0"), val = tensor(1)]; + tensor input_551 = conv(bias = model_xvector_block2_tdnnd12_cam_layer_linear1_bias, dilations = input_551_dilations_0, groups = input_551_groups_0, pad = input_551_pad_0, pad_type = input_551_pad_type_0, strides = input_551_strides_0, weight = model_xvector_block2_tdnnd12_cam_layer_linear1_weight, x = input_549)[name = tensor("input_551")]; + tensor input_553 = relu(x = input_551)[name = tensor("input_553")]; + tensor input_555_pad_type_0 = const()[name = tensor("input_555_pad_type_0"), val = tensor("valid")]; + tensor input_555_strides_0 = const()[name = tensor("input_555_strides_0"), val = tensor([1])]; + tensor input_555_pad_0 = const()[name = tensor("input_555_pad_0"), val = tensor([0, 0])]; + tensor input_555_dilations_0 = const()[name = tensor("input_555_dilations_0"), val = tensor([1])]; + tensor input_555_groups_0 = const()[name = tensor("input_555_groups_0"), val = tensor(1)]; + tensor input_555 = conv(bias = model_xvector_block2_tdnnd12_cam_layer_linear2_bias, dilations = input_555_dilations_0, groups = input_555_groups_0, pad = input_555_pad_0, pad_type = input_555_pad_type_0, strides = input_555_strides_0, weight = model_xvector_block2_tdnnd12_cam_layer_linear2_weight, x = input_553)[name = tensor("input_555")]; + tensor m_47 = sigmoid(x = input_555)[name = tensor("m_47")]; + tensor var_1935 = mul(x = y_47, y = m_47)[name = tensor("op_1935")]; + tensor input_557_interleave_0 = const()[name = tensor("input_557_interleave_0"), val = tensor(false)]; + tensor input_557 = concat(axis = var_11, interleave = input_557_interleave_0, values = (input_537, var_1935))[name = tensor("input_557")]; + tensor input_559 = batch_norm(beta = model_xvector_block2_tdnnd13_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block2_tdnnd13_nonlinear1_batchnorm_weight, mean = model_xvector_block2_tdnnd13_nonlinear1_batchnorm_running_mean, variance = model_xvector_block2_tdnnd13_nonlinear1_batchnorm_running_var, x = input_557)[name = tensor("input_559")]; + tensor input_561 = relu(x = input_559)[name = tensor("input_561")]; + tensor input_563_pad_type_0 = const()[name = tensor("input_563_pad_type_0"), val = tensor("valid")]; + tensor input_563_strides_0 = const()[name = tensor("input_563_strides_0"), val = tensor([1])]; + tensor input_563_pad_0 = const()[name = tensor("input_563_pad_0"), val = tensor([0, 0])]; + tensor input_563_dilations_0 = const()[name = tensor("input_563_dilations_0"), val = tensor([1])]; + tensor input_563_groups_0 = const()[name = tensor("input_563_groups_0"), val = tensor(1)]; + tensor const_74 = const()[name = tensor("const_74"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14377472)))]; + tensor const_75 = const()[name = tensor("const_75"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14705216)))]; + tensor input_565 = conv(bias = const_75, dilations = input_563_dilations_0, groups = input_563_groups_0, pad = input_563_pad_0, pad_type = input_563_pad_type_0, strides = input_563_strides_0, weight = const_74, x = input_561)[name = tensor("input_565")]; + tensor input_567 = relu(x = input_565)[name = tensor("input_567")]; + tensor y_49_pad_type_0 = const()[name = tensor("y_49_pad_type_0"), val = tensor("custom")]; + tensor y_49_pad_0 = const()[name = tensor("y_49_pad_0"), val = tensor([2, 2])]; + tensor y_49_dilations_0 = const()[name = tensor("y_49_dilations_0"), val = tensor([2])]; + tensor y_49_strides_0 = const()[name = tensor("y_49_strides_0"), val = tensor([1])]; + tensor y_49_groups_0 = const()[name = tensor("y_49_groups_0"), val = tensor(1)]; + tensor y_49 = conv(dilations = y_49_dilations_0, groups = y_49_groups_0, pad = y_49_pad_0, pad_type = y_49_pad_type_0, strides = y_49_strides_0, weight = model_xvector_block2_tdnnd13_cam_layer_linear_local_weight, x = input_567)[name = tensor("y_49")]; + tensor var_1972_axes_0 = const()[name = tensor("op_1972_axes_0"), val = tensor([-1])]; + tensor var_1972_keep_dims_0 = const()[name = tensor("op_1972_keep_dims_0"), val = tensor(true)]; + tensor var_1972 = reduce_mean(axes = var_1972_axes_0, keep_dims = var_1972_keep_dims_0, x = input_567)[name = tensor("op_1972")]; + tensor var_1973 = const()[name = tensor("op_1973"), val = tensor([100])]; + tensor var_1974 = const()[name = tensor("op_1974"), val = tensor([100])]; + tensor seg_97_pad_type_0 = const()[name = tensor("seg_97_pad_type_0"), val = tensor("custom")]; + tensor seg_97_pad_0 = const()[name = tensor("seg_97_pad_0"), val = tensor([0, 0])]; + tensor seg_97_exclude_padding_from_average_0 = const()[name = tensor("seg_97_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_97_ceil_mode_0 = const()[name = tensor("seg_97_ceil_mode_0"), val = tensor(true)]; + tensor seg_97 = avg_pool(ceil_mode = seg_97_ceil_mode_0, exclude_padding_from_average = seg_97_exclude_padding_from_average_0, kernel_sizes = var_1973, pad = seg_97_pad_0, pad_type = seg_97_pad_type_0, strides = var_1974, x = input_567)[name = tensor("seg_97")]; + tensor var_1977_shape = shape(x = seg_97)[name = tensor("op_1977_shape")]; + tensor gather_100 = const()[name = tensor("gather_100"), val = tensor(1)]; + tensor gather_101 = const()[name = tensor("gather_101"), val = tensor(128)]; + tensor select_102 = const()[name = tensor("select_102"), val = tensor(2)]; + tensor gather_102_axis_0 = const()[name = tensor("gather_102_axis_0"), val = tensor(0)]; + tensor gather_102_batch_dims_0 = const()[name = tensor("gather_102_batch_dims_0"), val = tensor(0)]; + tensor gather_102_validate_indices_0 = const()[name = tensor("gather_102_validate_indices_0"), val = tensor(false)]; + tensor gather_102 = gather(axis = gather_102_axis_0, batch_dims = gather_102_batch_dims_0, indices = select_102, validate_indices = gather_102_validate_indices_0, x = var_1977_shape)[name = tensor("gather_102")]; + tensor var_1980_axes_0 = const()[name = tensor("op_1980_axes_0"), val = tensor([-1])]; + tensor var_1980 = expand_dims(axes = var_1980_axes_0, x = seg_97)[name = tensor("op_1980")]; + tensor concat_49_axis_0 = const()[name = tensor("concat_49_axis_0"), val = tensor(0)]; + tensor concat_49_interleave_0 = const()[name = tensor("concat_49_interleave_0"), val = tensor(false)]; + tensor concat_49 = concat(axis = concat_49_axis_0, interleave = concat_49_interleave_0, values = (gather_100, gather_101, gather_102, var_3))[name = tensor("concat_49")]; + tensor shape_127 = shape(x = var_1980)[name = tensor("shape_127")]; + tensor real_div_24 = real_div(x = concat_49, y = shape_127)[name = tensor("real_div_24")]; + tensor var_1982 = tile(reps = real_div_24, x = var_1980)[name = tensor("op_1982")]; + tensor var_1983 = const()[name = tensor("op_1983"), val = tensor([1, 128, -1])]; + tensor seg_99 = reshape(shape = var_1983, x = var_1982)[name = tensor("seg_99")]; + tensor var_1985_shape = shape(x = input_567)[name = tensor("op_1985_shape")]; + tensor select_103 = const()[name = tensor("select_103"), val = tensor(2)]; + tensor gather_103_axis_0 = const()[name = tensor("gather_103_axis_0"), val = tensor(0)]; + tensor gather_103_batch_dims_0 = const()[name = tensor("gather_103_batch_dims_0"), val = tensor(0)]; + tensor gather_103_validate_indices_0 = const()[name = tensor("gather_103_validate_indices_0"), val = tensor(false)]; + tensor gather_103 = gather(axis = gather_103_axis_0, batch_dims = gather_103_batch_dims_0, indices = select_103, validate_indices = gather_103_validate_indices_0, x = var_1985_shape)[name = tensor("gather_103")]; + tensor concat_50_values0_0 = const()[name = tensor("concat_50_values0_0"), val = tensor(1)]; + tensor concat_50_values1_0 = const()[name = tensor("concat_50_values1_0"), val = tensor(128)]; + tensor concat_50_axis_0 = const()[name = tensor("concat_50_axis_0"), val = tensor(0)]; + tensor concat_50_interleave_0 = const()[name = tensor("concat_50_interleave_0"), val = tensor(false)]; + tensor concat_50 = concat(axis = concat_50_axis_0, interleave = concat_50_interleave_0, values = (concat_50_values0_0, concat_50_values1_0, gather_103))[name = tensor("concat_50")]; + tensor var_1986_begin_0 = const()[name = tensor("op_1986_begin_0"), val = tensor([0, 0, 0])]; + tensor var_1986_end_mask_0 = const()[name = tensor("op_1986_end_mask_0"), val = tensor([true, true, false])]; + tensor var_1986 = slice_by_index(begin = var_1986_begin_0, end = concat_50, end_mask = var_1986_end_mask_0, x = seg_99)[name = tensor("op_1986")]; + tensor input_569 = add(x = var_1972, y = var_1986)[name = tensor("input_569")]; + tensor input_571_pad_type_0 = const()[name = tensor("input_571_pad_type_0"), val = tensor("valid")]; + tensor input_571_strides_0 = const()[name = tensor("input_571_strides_0"), val = tensor([1])]; + tensor input_571_pad_0 = const()[name = tensor("input_571_pad_0"), val = tensor([0, 0])]; + tensor input_571_dilations_0 = const()[name = tensor("input_571_dilations_0"), val = tensor([1])]; + tensor input_571_groups_0 = const()[name = tensor("input_571_groups_0"), val = tensor(1)]; + tensor input_571 = conv(bias = model_xvector_block2_tdnnd13_cam_layer_linear1_bias, dilations = input_571_dilations_0, groups = input_571_groups_0, pad = input_571_pad_0, pad_type = input_571_pad_type_0, strides = input_571_strides_0, weight = model_xvector_block2_tdnnd13_cam_layer_linear1_weight, x = input_569)[name = tensor("input_571")]; + tensor input_573 = relu(x = input_571)[name = tensor("input_573")]; + tensor input_575_pad_type_0 = const()[name = tensor("input_575_pad_type_0"), val = tensor("valid")]; + tensor input_575_strides_0 = const()[name = tensor("input_575_strides_0"), val = tensor([1])]; + tensor input_575_pad_0 = const()[name = tensor("input_575_pad_0"), val = tensor([0, 0])]; + tensor input_575_dilations_0 = const()[name = tensor("input_575_dilations_0"), val = tensor([1])]; + tensor input_575_groups_0 = const()[name = tensor("input_575_groups_0"), val = tensor(1)]; + tensor input_575 = conv(bias = model_xvector_block2_tdnnd13_cam_layer_linear2_bias, dilations = input_575_dilations_0, groups = input_575_groups_0, pad = input_575_pad_0, pad_type = input_575_pad_type_0, strides = input_575_strides_0, weight = model_xvector_block2_tdnnd13_cam_layer_linear2_weight, x = input_573)[name = tensor("input_575")]; + tensor m_49 = sigmoid(x = input_575)[name = tensor("m_49")]; + tensor var_2004 = mul(x = y_49, y = m_49)[name = tensor("op_2004")]; + tensor input_577_interleave_0 = const()[name = tensor("input_577_interleave_0"), val = tensor(false)]; + tensor input_577 = concat(axis = var_11, interleave = input_577_interleave_0, values = (input_557, var_2004))[name = tensor("input_577")]; + tensor input_579 = batch_norm(beta = model_xvector_block2_tdnnd14_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block2_tdnnd14_nonlinear1_batchnorm_weight, mean = model_xvector_block2_tdnnd14_nonlinear1_batchnorm_running_mean, variance = model_xvector_block2_tdnnd14_nonlinear1_batchnorm_running_var, x = input_577)[name = tensor("input_579")]; + tensor input_581 = relu(x = input_579)[name = tensor("input_581")]; + tensor input_583_pad_type_0 = const()[name = tensor("input_583_pad_type_0"), val = tensor("valid")]; + tensor input_583_strides_0 = const()[name = tensor("input_583_strides_0"), val = tensor([1])]; + tensor input_583_pad_0 = const()[name = tensor("input_583_pad_0"), val = tensor([0, 0])]; + tensor input_583_dilations_0 = const()[name = tensor("input_583_dilations_0"), val = tensor([1])]; + tensor input_583_groups_0 = const()[name = tensor("input_583_groups_0"), val = tensor(1)]; + tensor const_76 = const()[name = tensor("const_76"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14705792)))]; + tensor const_77 = const()[name = tensor("const_77"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15049920)))]; + tensor input_585 = conv(bias = const_77, dilations = input_583_dilations_0, groups = input_583_groups_0, pad = input_583_pad_0, pad_type = input_583_pad_type_0, strides = input_583_strides_0, weight = const_76, x = input_581)[name = tensor("input_585")]; + tensor input_587 = relu(x = input_585)[name = tensor("input_587")]; + tensor y_51_pad_type_0 = const()[name = tensor("y_51_pad_type_0"), val = tensor("custom")]; + tensor y_51_pad_0 = const()[name = tensor("y_51_pad_0"), val = tensor([2, 2])]; + tensor y_51_dilations_0 = const()[name = tensor("y_51_dilations_0"), val = tensor([2])]; + tensor y_51_strides_0 = const()[name = tensor("y_51_strides_0"), val = tensor([1])]; + tensor y_51_groups_0 = const()[name = tensor("y_51_groups_0"), val = tensor(1)]; + tensor y_51 = conv(dilations = y_51_dilations_0, groups = y_51_groups_0, pad = y_51_pad_0, pad_type = y_51_pad_type_0, strides = y_51_strides_0, weight = model_xvector_block2_tdnnd14_cam_layer_linear_local_weight, x = input_587)[name = tensor("y_51")]; + tensor var_2041_axes_0 = const()[name = tensor("op_2041_axes_0"), val = tensor([-1])]; + tensor var_2041_keep_dims_0 = const()[name = tensor("op_2041_keep_dims_0"), val = tensor(true)]; + tensor var_2041 = reduce_mean(axes = var_2041_axes_0, keep_dims = var_2041_keep_dims_0, x = input_587)[name = tensor("op_2041")]; + tensor var_2042 = const()[name = tensor("op_2042"), val = tensor([100])]; + tensor var_2043 = const()[name = tensor("op_2043"), val = tensor([100])]; + tensor seg_101_pad_type_0 = const()[name = tensor("seg_101_pad_type_0"), val = tensor("custom")]; + tensor seg_101_pad_0 = const()[name = tensor("seg_101_pad_0"), val = tensor([0, 0])]; + tensor seg_101_exclude_padding_from_average_0 = const()[name = tensor("seg_101_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_101_ceil_mode_0 = const()[name = tensor("seg_101_ceil_mode_0"), val = tensor(true)]; + tensor seg_101 = avg_pool(ceil_mode = seg_101_ceil_mode_0, exclude_padding_from_average = seg_101_exclude_padding_from_average_0, kernel_sizes = var_2042, pad = seg_101_pad_0, pad_type = seg_101_pad_type_0, strides = var_2043, x = input_587)[name = tensor("seg_101")]; + tensor var_2046_shape = shape(x = seg_101)[name = tensor("op_2046_shape")]; + tensor gather_104 = const()[name = tensor("gather_104"), val = tensor(1)]; + tensor gather_105 = const()[name = tensor("gather_105"), val = tensor(128)]; + tensor select_106 = const()[name = tensor("select_106"), val = tensor(2)]; + tensor gather_106_axis_0 = const()[name = tensor("gather_106_axis_0"), val = tensor(0)]; + tensor gather_106_batch_dims_0 = const()[name = tensor("gather_106_batch_dims_0"), val = tensor(0)]; + tensor gather_106_validate_indices_0 = const()[name = tensor("gather_106_validate_indices_0"), val = tensor(false)]; + tensor gather_106 = gather(axis = gather_106_axis_0, batch_dims = gather_106_batch_dims_0, indices = select_106, validate_indices = gather_106_validate_indices_0, x = var_2046_shape)[name = tensor("gather_106")]; + tensor var_2049_axes_0 = const()[name = tensor("op_2049_axes_0"), val = tensor([-1])]; + tensor var_2049 = expand_dims(axes = var_2049_axes_0, x = seg_101)[name = tensor("op_2049")]; + tensor concat_51_axis_0 = const()[name = tensor("concat_51_axis_0"), val = tensor(0)]; + tensor concat_51_interleave_0 = const()[name = tensor("concat_51_interleave_0"), val = tensor(false)]; + tensor concat_51 = concat(axis = concat_51_axis_0, interleave = concat_51_interleave_0, values = (gather_104, gather_105, gather_106, var_3))[name = tensor("concat_51")]; + tensor shape_132 = shape(x = var_2049)[name = tensor("shape_132")]; + tensor real_div_25 = real_div(x = concat_51, y = shape_132)[name = tensor("real_div_25")]; + tensor var_2051 = tile(reps = real_div_25, x = var_2049)[name = tensor("op_2051")]; + tensor var_2052 = const()[name = tensor("op_2052"), val = tensor([1, 128, -1])]; + tensor seg_103 = reshape(shape = var_2052, x = var_2051)[name = tensor("seg_103")]; + tensor var_2054_shape = shape(x = input_587)[name = tensor("op_2054_shape")]; + tensor select_107 = const()[name = tensor("select_107"), val = tensor(2)]; + tensor gather_107_axis_0 = const()[name = tensor("gather_107_axis_0"), val = tensor(0)]; + tensor gather_107_batch_dims_0 = const()[name = tensor("gather_107_batch_dims_0"), val = tensor(0)]; + tensor gather_107_validate_indices_0 = const()[name = tensor("gather_107_validate_indices_0"), val = tensor(false)]; + tensor gather_107 = gather(axis = gather_107_axis_0, batch_dims = gather_107_batch_dims_0, indices = select_107, validate_indices = gather_107_validate_indices_0, x = var_2054_shape)[name = tensor("gather_107")]; + tensor concat_52_values0_0 = const()[name = tensor("concat_52_values0_0"), val = tensor(1)]; + tensor concat_52_values1_0 = const()[name = tensor("concat_52_values1_0"), val = tensor(128)]; + tensor concat_52_axis_0 = const()[name = tensor("concat_52_axis_0"), val = tensor(0)]; + tensor concat_52_interleave_0 = const()[name = tensor("concat_52_interleave_0"), val = tensor(false)]; + tensor concat_52 = concat(axis = concat_52_axis_0, interleave = concat_52_interleave_0, values = (concat_52_values0_0, concat_52_values1_0, gather_107))[name = tensor("concat_52")]; + tensor var_2055_begin_0 = const()[name = tensor("op_2055_begin_0"), val = tensor([0, 0, 0])]; + tensor var_2055_end_mask_0 = const()[name = tensor("op_2055_end_mask_0"), val = tensor([true, true, false])]; + tensor var_2055 = slice_by_index(begin = var_2055_begin_0, end = concat_52, end_mask = var_2055_end_mask_0, x = seg_103)[name = tensor("op_2055")]; + tensor input_589 = add(x = var_2041, y = var_2055)[name = tensor("input_589")]; + tensor input_591_pad_type_0 = const()[name = tensor("input_591_pad_type_0"), val = tensor("valid")]; + tensor input_591_strides_0 = const()[name = tensor("input_591_strides_0"), val = tensor([1])]; + tensor input_591_pad_0 = const()[name = tensor("input_591_pad_0"), val = tensor([0, 0])]; + tensor input_591_dilations_0 = const()[name = tensor("input_591_dilations_0"), val = tensor([1])]; + tensor input_591_groups_0 = const()[name = tensor("input_591_groups_0"), val = tensor(1)]; + tensor input_591 = conv(bias = model_xvector_block2_tdnnd14_cam_layer_linear1_bias, dilations = input_591_dilations_0, groups = input_591_groups_0, pad = input_591_pad_0, pad_type = input_591_pad_type_0, strides = input_591_strides_0, weight = model_xvector_block2_tdnnd14_cam_layer_linear1_weight, x = input_589)[name = tensor("input_591")]; + tensor input_593 = relu(x = input_591)[name = tensor("input_593")]; + tensor input_595_pad_type_0 = const()[name = tensor("input_595_pad_type_0"), val = tensor("valid")]; + tensor input_595_strides_0 = const()[name = tensor("input_595_strides_0"), val = tensor([1])]; + tensor input_595_pad_0 = const()[name = tensor("input_595_pad_0"), val = tensor([0, 0])]; + tensor input_595_dilations_0 = const()[name = tensor("input_595_dilations_0"), val = tensor([1])]; + tensor input_595_groups_0 = const()[name = tensor("input_595_groups_0"), val = tensor(1)]; + tensor input_595 = conv(bias = model_xvector_block2_tdnnd14_cam_layer_linear2_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 = model_xvector_block2_tdnnd14_cam_layer_linear2_weight, x = input_593)[name = tensor("input_595")]; + tensor m_51 = sigmoid(x = input_595)[name = tensor("m_51")]; + tensor var_2073 = mul(x = y_51, y = m_51)[name = tensor("op_2073")]; + tensor input_597_interleave_0 = const()[name = tensor("input_597_interleave_0"), val = tensor(false)]; + tensor input_597 = concat(axis = var_11, interleave = input_597_interleave_0, values = (input_577, var_2073))[name = tensor("input_597")]; + tensor input_599 = batch_norm(beta = model_xvector_block2_tdnnd15_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block2_tdnnd15_nonlinear1_batchnorm_weight, mean = model_xvector_block2_tdnnd15_nonlinear1_batchnorm_running_mean, variance = model_xvector_block2_tdnnd15_nonlinear1_batchnorm_running_var, x = input_597)[name = tensor("input_599")]; + tensor input_601 = relu(x = input_599)[name = tensor("input_601")]; + tensor input_603_pad_type_0 = const()[name = tensor("input_603_pad_type_0"), val = tensor("valid")]; + tensor input_603_strides_0 = const()[name = tensor("input_603_strides_0"), val = tensor([1])]; + tensor input_603_pad_0 = const()[name = tensor("input_603_pad_0"), val = tensor([0, 0])]; + tensor input_603_dilations_0 = const()[name = tensor("input_603_dilations_0"), val = tensor([1])]; + tensor input_603_groups_0 = const()[name = tensor("input_603_groups_0"), val = tensor(1)]; + tensor const_78 = const()[name = tensor("const_78"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15050496)))]; + tensor const_79 = const()[name = tensor("const_79"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15411008)))]; + tensor input_605 = conv(bias = const_79, dilations = input_603_dilations_0, groups = input_603_groups_0, pad = input_603_pad_0, pad_type = input_603_pad_type_0, strides = input_603_strides_0, weight = const_78, x = input_601)[name = tensor("input_605")]; + tensor input_607 = relu(x = input_605)[name = tensor("input_607")]; + tensor y_53_pad_type_0 = const()[name = tensor("y_53_pad_type_0"), val = tensor("custom")]; + tensor y_53_pad_0 = const()[name = tensor("y_53_pad_0"), val = tensor([2, 2])]; + tensor y_53_dilations_0 = const()[name = tensor("y_53_dilations_0"), val = tensor([2])]; + tensor y_53_strides_0 = const()[name = tensor("y_53_strides_0"), val = tensor([1])]; + tensor y_53_groups_0 = const()[name = tensor("y_53_groups_0"), val = tensor(1)]; + tensor y_53 = conv(dilations = y_53_dilations_0, groups = y_53_groups_0, pad = y_53_pad_0, pad_type = y_53_pad_type_0, strides = y_53_strides_0, weight = model_xvector_block2_tdnnd15_cam_layer_linear_local_weight, x = input_607)[name = tensor("y_53")]; + tensor var_2110_axes_0 = const()[name = tensor("op_2110_axes_0"), val = tensor([-1])]; + tensor var_2110_keep_dims_0 = const()[name = tensor("op_2110_keep_dims_0"), val = tensor(true)]; + tensor var_2110 = reduce_mean(axes = var_2110_axes_0, keep_dims = var_2110_keep_dims_0, x = input_607)[name = tensor("op_2110")]; + tensor var_2111 = const()[name = tensor("op_2111"), val = tensor([100])]; + tensor var_2112 = const()[name = tensor("op_2112"), val = tensor([100])]; + tensor seg_105_pad_type_0 = const()[name = tensor("seg_105_pad_type_0"), val = tensor("custom")]; + tensor seg_105_pad_0 = const()[name = tensor("seg_105_pad_0"), val = tensor([0, 0])]; + tensor seg_105_exclude_padding_from_average_0 = const()[name = tensor("seg_105_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_105_ceil_mode_0 = const()[name = tensor("seg_105_ceil_mode_0"), val = tensor(true)]; + tensor seg_105 = avg_pool(ceil_mode = seg_105_ceil_mode_0, exclude_padding_from_average = seg_105_exclude_padding_from_average_0, kernel_sizes = var_2111, pad = seg_105_pad_0, pad_type = seg_105_pad_type_0, strides = var_2112, x = input_607)[name = tensor("seg_105")]; + tensor var_2115_shape = shape(x = seg_105)[name = tensor("op_2115_shape")]; + tensor gather_108 = const()[name = tensor("gather_108"), val = tensor(1)]; + tensor gather_109 = const()[name = tensor("gather_109"), val = tensor(128)]; + tensor select_110 = const()[name = tensor("select_110"), val = tensor(2)]; + tensor gather_110_axis_0 = const()[name = tensor("gather_110_axis_0"), val = tensor(0)]; + tensor gather_110_batch_dims_0 = const()[name = tensor("gather_110_batch_dims_0"), val = tensor(0)]; + tensor gather_110_validate_indices_0 = const()[name = tensor("gather_110_validate_indices_0"), val = tensor(false)]; + tensor gather_110 = gather(axis = gather_110_axis_0, batch_dims = gather_110_batch_dims_0, indices = select_110, validate_indices = gather_110_validate_indices_0, x = var_2115_shape)[name = tensor("gather_110")]; + tensor var_2118_axes_0 = const()[name = tensor("op_2118_axes_0"), val = tensor([-1])]; + tensor var_2118 = expand_dims(axes = var_2118_axes_0, x = seg_105)[name = tensor("op_2118")]; + tensor concat_53_axis_0 = const()[name = tensor("concat_53_axis_0"), val = tensor(0)]; + tensor concat_53_interleave_0 = const()[name = tensor("concat_53_interleave_0"), val = tensor(false)]; + tensor concat_53 = concat(axis = concat_53_axis_0, interleave = concat_53_interleave_0, values = (gather_108, gather_109, gather_110, var_3))[name = tensor("concat_53")]; + tensor shape_137 = shape(x = var_2118)[name = tensor("shape_137")]; + tensor real_div_26 = real_div(x = concat_53, y = shape_137)[name = tensor("real_div_26")]; + tensor var_2120 = tile(reps = real_div_26, x = var_2118)[name = tensor("op_2120")]; + tensor var_2121 = const()[name = tensor("op_2121"), val = tensor([1, 128, -1])]; + tensor seg_107 = reshape(shape = var_2121, x = var_2120)[name = tensor("seg_107")]; + tensor var_2123_shape = shape(x = input_607)[name = tensor("op_2123_shape")]; + tensor select_111 = const()[name = tensor("select_111"), val = tensor(2)]; + tensor gather_111_axis_0 = const()[name = tensor("gather_111_axis_0"), val = tensor(0)]; + tensor gather_111_batch_dims_0 = const()[name = tensor("gather_111_batch_dims_0"), val = tensor(0)]; + tensor gather_111_validate_indices_0 = const()[name = tensor("gather_111_validate_indices_0"), val = tensor(false)]; + tensor gather_111 = gather(axis = gather_111_axis_0, batch_dims = gather_111_batch_dims_0, indices = select_111, validate_indices = gather_111_validate_indices_0, x = var_2123_shape)[name = tensor("gather_111")]; + tensor concat_54_values0_0 = const()[name = tensor("concat_54_values0_0"), val = tensor(1)]; + tensor concat_54_values1_0 = const()[name = tensor("concat_54_values1_0"), val = tensor(128)]; + tensor concat_54_axis_0 = const()[name = tensor("concat_54_axis_0"), val = tensor(0)]; + tensor concat_54_interleave_0 = const()[name = tensor("concat_54_interleave_0"), val = tensor(false)]; + tensor concat_54 = concat(axis = concat_54_axis_0, interleave = concat_54_interleave_0, values = (concat_54_values0_0, concat_54_values1_0, gather_111))[name = tensor("concat_54")]; + tensor var_2124_begin_0 = const()[name = tensor("op_2124_begin_0"), val = tensor([0, 0, 0])]; + tensor var_2124_end_mask_0 = const()[name = tensor("op_2124_end_mask_0"), val = tensor([true, true, false])]; + tensor var_2124 = slice_by_index(begin = var_2124_begin_0, end = concat_54, end_mask = var_2124_end_mask_0, x = seg_107)[name = tensor("op_2124")]; + tensor input_609 = add(x = var_2110, y = var_2124)[name = tensor("input_609")]; + tensor input_611_pad_type_0 = const()[name = tensor("input_611_pad_type_0"), val = tensor("valid")]; + tensor input_611_strides_0 = const()[name = tensor("input_611_strides_0"), val = tensor([1])]; + tensor input_611_pad_0 = const()[name = tensor("input_611_pad_0"), val = tensor([0, 0])]; + tensor input_611_dilations_0 = const()[name = tensor("input_611_dilations_0"), val = tensor([1])]; + tensor input_611_groups_0 = const()[name = tensor("input_611_groups_0"), val = tensor(1)]; + tensor input_611 = conv(bias = model_xvector_block2_tdnnd15_cam_layer_linear1_bias, dilations = input_611_dilations_0, groups = input_611_groups_0, pad = input_611_pad_0, pad_type = input_611_pad_type_0, strides = input_611_strides_0, weight = model_xvector_block2_tdnnd15_cam_layer_linear1_weight, x = input_609)[name = tensor("input_611")]; + tensor input_613 = relu(x = input_611)[name = tensor("input_613")]; + tensor input_615_pad_type_0 = const()[name = tensor("input_615_pad_type_0"), val = tensor("valid")]; + tensor input_615_strides_0 = const()[name = tensor("input_615_strides_0"), val = tensor([1])]; + tensor input_615_pad_0 = const()[name = tensor("input_615_pad_0"), val = tensor([0, 0])]; + tensor input_615_dilations_0 = const()[name = tensor("input_615_dilations_0"), val = tensor([1])]; + tensor input_615_groups_0 = const()[name = tensor("input_615_groups_0"), val = tensor(1)]; + tensor input_615 = conv(bias = model_xvector_block2_tdnnd15_cam_layer_linear2_bias, dilations = input_615_dilations_0, groups = input_615_groups_0, pad = input_615_pad_0, pad_type = input_615_pad_type_0, strides = input_615_strides_0, weight = model_xvector_block2_tdnnd15_cam_layer_linear2_weight, x = input_613)[name = tensor("input_615")]; + tensor m_53 = sigmoid(x = input_615)[name = tensor("m_53")]; + tensor var_2142 = mul(x = y_53, y = m_53)[name = tensor("op_2142")]; + tensor input_617_interleave_0 = const()[name = tensor("input_617_interleave_0"), val = tensor(false)]; + tensor input_617 = concat(axis = var_11, interleave = input_617_interleave_0, values = (input_597, var_2142))[name = tensor("input_617")]; + tensor input_619 = batch_norm(beta = model_xvector_block2_tdnnd16_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block2_tdnnd16_nonlinear1_batchnorm_weight, mean = model_xvector_block2_tdnnd16_nonlinear1_batchnorm_running_mean, variance = model_xvector_block2_tdnnd16_nonlinear1_batchnorm_running_var, x = input_617)[name = tensor("input_619")]; + tensor input_621 = relu(x = input_619)[name = tensor("input_621")]; + tensor input_623_pad_type_0 = const()[name = tensor("input_623_pad_type_0"), val = tensor("valid")]; + tensor input_623_strides_0 = const()[name = tensor("input_623_strides_0"), val = tensor([1])]; + tensor input_623_pad_0 = const()[name = tensor("input_623_pad_0"), val = tensor([0, 0])]; + tensor input_623_dilations_0 = const()[name = tensor("input_623_dilations_0"), val = tensor([1])]; + tensor input_623_groups_0 = const()[name = tensor("input_623_groups_0"), val = tensor(1)]; + tensor const_80 = const()[name = tensor("const_80"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15411584)))]; + tensor const_81 = const()[name = tensor("const_81"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15788480)))]; + tensor input_625 = conv(bias = const_81, dilations = input_623_dilations_0, groups = input_623_groups_0, pad = input_623_pad_0, pad_type = input_623_pad_type_0, strides = input_623_strides_0, weight = const_80, x = input_621)[name = tensor("input_625")]; + tensor input_627 = relu(x = input_625)[name = tensor("input_627")]; + tensor y_55_pad_type_0 = const()[name = tensor("y_55_pad_type_0"), val = tensor("custom")]; + tensor y_55_pad_0 = const()[name = tensor("y_55_pad_0"), val = tensor([2, 2])]; + tensor y_55_dilations_0 = const()[name = tensor("y_55_dilations_0"), val = tensor([2])]; + tensor y_55_strides_0 = const()[name = tensor("y_55_strides_0"), val = tensor([1])]; + tensor y_55_groups_0 = const()[name = tensor("y_55_groups_0"), val = tensor(1)]; + tensor y_55 = conv(dilations = y_55_dilations_0, groups = y_55_groups_0, pad = y_55_pad_0, pad_type = y_55_pad_type_0, strides = y_55_strides_0, weight = model_xvector_block2_tdnnd16_cam_layer_linear_local_weight, x = input_627)[name = tensor("y_55")]; + tensor var_2179_axes_0 = const()[name = tensor("op_2179_axes_0"), val = tensor([-1])]; + tensor var_2179_keep_dims_0 = const()[name = tensor("op_2179_keep_dims_0"), val = tensor(true)]; + tensor var_2179 = reduce_mean(axes = var_2179_axes_0, keep_dims = var_2179_keep_dims_0, x = input_627)[name = tensor("op_2179")]; + tensor var_2180 = const()[name = tensor("op_2180"), val = tensor([100])]; + tensor var_2181 = const()[name = tensor("op_2181"), val = tensor([100])]; + tensor seg_109_pad_type_0 = const()[name = tensor("seg_109_pad_type_0"), val = tensor("custom")]; + tensor seg_109_pad_0 = const()[name = tensor("seg_109_pad_0"), val = tensor([0, 0])]; + tensor seg_109_exclude_padding_from_average_0 = const()[name = tensor("seg_109_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_109_ceil_mode_0 = const()[name = tensor("seg_109_ceil_mode_0"), val = tensor(true)]; + tensor seg_109 = avg_pool(ceil_mode = seg_109_ceil_mode_0, exclude_padding_from_average = seg_109_exclude_padding_from_average_0, kernel_sizes = var_2180, pad = seg_109_pad_0, pad_type = seg_109_pad_type_0, strides = var_2181, x = input_627)[name = tensor("seg_109")]; + tensor var_2184_shape = shape(x = seg_109)[name = tensor("op_2184_shape")]; + tensor gather_112 = const()[name = tensor("gather_112"), val = tensor(1)]; + tensor gather_113 = const()[name = tensor("gather_113"), val = tensor(128)]; + tensor select_114 = const()[name = tensor("select_114"), val = tensor(2)]; + tensor gather_114_axis_0 = const()[name = tensor("gather_114_axis_0"), val = tensor(0)]; + tensor gather_114_batch_dims_0 = const()[name = tensor("gather_114_batch_dims_0"), val = tensor(0)]; + tensor gather_114_validate_indices_0 = const()[name = tensor("gather_114_validate_indices_0"), val = tensor(false)]; + tensor gather_114 = gather(axis = gather_114_axis_0, batch_dims = gather_114_batch_dims_0, indices = select_114, validate_indices = gather_114_validate_indices_0, x = var_2184_shape)[name = tensor("gather_114")]; + tensor var_2187_axes_0 = const()[name = tensor("op_2187_axes_0"), val = tensor([-1])]; + tensor var_2187 = expand_dims(axes = var_2187_axes_0, x = seg_109)[name = tensor("op_2187")]; + tensor concat_55_axis_0 = const()[name = tensor("concat_55_axis_0"), val = tensor(0)]; + tensor concat_55_interleave_0 = const()[name = tensor("concat_55_interleave_0"), val = tensor(false)]; + tensor concat_55 = concat(axis = concat_55_axis_0, interleave = concat_55_interleave_0, values = (gather_112, gather_113, gather_114, var_3))[name = tensor("concat_55")]; + tensor shape_142 = shape(x = var_2187)[name = tensor("shape_142")]; + tensor real_div_27 = real_div(x = concat_55, y = shape_142)[name = tensor("real_div_27")]; + tensor var_2189 = tile(reps = real_div_27, x = var_2187)[name = tensor("op_2189")]; + tensor var_2190 = const()[name = tensor("op_2190"), val = tensor([1, 128, -1])]; + tensor seg_111 = reshape(shape = var_2190, x = var_2189)[name = tensor("seg_111")]; + tensor var_2192_shape = shape(x = input_627)[name = tensor("op_2192_shape")]; + tensor select_115 = const()[name = tensor("select_115"), val = tensor(2)]; + tensor gather_115_axis_0 = const()[name = tensor("gather_115_axis_0"), val = tensor(0)]; + tensor gather_115_batch_dims_0 = const()[name = tensor("gather_115_batch_dims_0"), val = tensor(0)]; + tensor gather_115_validate_indices_0 = const()[name = tensor("gather_115_validate_indices_0"), val = tensor(false)]; + tensor gather_115 = gather(axis = gather_115_axis_0, batch_dims = gather_115_batch_dims_0, indices = select_115, validate_indices = gather_115_validate_indices_0, x = var_2192_shape)[name = tensor("gather_115")]; + tensor concat_56_values0_0 = const()[name = tensor("concat_56_values0_0"), val = tensor(1)]; + tensor concat_56_values1_0 = const()[name = tensor("concat_56_values1_0"), val = tensor(128)]; + tensor concat_56_axis_0 = const()[name = tensor("concat_56_axis_0"), val = tensor(0)]; + tensor concat_56_interleave_0 = const()[name = tensor("concat_56_interleave_0"), val = tensor(false)]; + tensor concat_56 = concat(axis = concat_56_axis_0, interleave = concat_56_interleave_0, values = (concat_56_values0_0, concat_56_values1_0, gather_115))[name = tensor("concat_56")]; + tensor var_2193_begin_0 = const()[name = tensor("op_2193_begin_0"), val = tensor([0, 0, 0])]; + tensor var_2193_end_mask_0 = const()[name = tensor("op_2193_end_mask_0"), val = tensor([true, true, false])]; + tensor var_2193 = slice_by_index(begin = var_2193_begin_0, end = concat_56, end_mask = var_2193_end_mask_0, x = seg_111)[name = tensor("op_2193")]; + tensor input_629 = add(x = var_2179, y = var_2193)[name = tensor("input_629")]; + tensor input_631_pad_type_0 = const()[name = tensor("input_631_pad_type_0"), val = tensor("valid")]; + tensor input_631_strides_0 = const()[name = tensor("input_631_strides_0"), val = tensor([1])]; + tensor input_631_pad_0 = const()[name = tensor("input_631_pad_0"), val = tensor([0, 0])]; + tensor input_631_dilations_0 = const()[name = tensor("input_631_dilations_0"), val = tensor([1])]; + tensor input_631_groups_0 = const()[name = tensor("input_631_groups_0"), val = tensor(1)]; + tensor input_631 = conv(bias = model_xvector_block2_tdnnd16_cam_layer_linear1_bias, dilations = input_631_dilations_0, groups = input_631_groups_0, pad = input_631_pad_0, pad_type = input_631_pad_type_0, strides = input_631_strides_0, weight = model_xvector_block2_tdnnd16_cam_layer_linear1_weight, x = input_629)[name = tensor("input_631")]; + tensor input_633 = relu(x = input_631)[name = tensor("input_633")]; + tensor input_635_pad_type_0 = const()[name = tensor("input_635_pad_type_0"), val = tensor("valid")]; + tensor input_635_strides_0 = const()[name = tensor("input_635_strides_0"), val = tensor([1])]; + tensor input_635_pad_0 = const()[name = tensor("input_635_pad_0"), val = tensor([0, 0])]; + tensor input_635_dilations_0 = const()[name = tensor("input_635_dilations_0"), val = tensor([1])]; + tensor input_635_groups_0 = const()[name = tensor("input_635_groups_0"), val = tensor(1)]; + tensor input_635 = conv(bias = model_xvector_block2_tdnnd16_cam_layer_linear2_bias, dilations = input_635_dilations_0, groups = input_635_groups_0, pad = input_635_pad_0, pad_type = input_635_pad_type_0, strides = input_635_strides_0, weight = model_xvector_block2_tdnnd16_cam_layer_linear2_weight, x = input_633)[name = tensor("input_635")]; + tensor m_55 = sigmoid(x = input_635)[name = tensor("m_55")]; + tensor var_2211 = mul(x = y_55, y = m_55)[name = tensor("op_2211")]; + tensor input_637_interleave_0 = const()[name = tensor("input_637_interleave_0"), val = tensor(false)]; + tensor input_637 = concat(axis = var_11, interleave = input_637_interleave_0, values = (input_617, var_2211))[name = tensor("input_637")]; + tensor input_639 = batch_norm(beta = model_xvector_block2_tdnnd17_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block2_tdnnd17_nonlinear1_batchnorm_weight, mean = model_xvector_block2_tdnnd17_nonlinear1_batchnorm_running_mean, variance = model_xvector_block2_tdnnd17_nonlinear1_batchnorm_running_var, x = input_637)[name = tensor("input_639")]; + tensor input_641 = relu(x = input_639)[name = tensor("input_641")]; + tensor input_643_pad_type_0 = const()[name = tensor("input_643_pad_type_0"), val = tensor("valid")]; + tensor input_643_strides_0 = const()[name = tensor("input_643_strides_0"), val = tensor([1])]; + tensor input_643_pad_0 = const()[name = tensor("input_643_pad_0"), val = tensor([0, 0])]; + tensor input_643_dilations_0 = const()[name = tensor("input_643_dilations_0"), val = tensor([1])]; + tensor input_643_groups_0 = const()[name = tensor("input_643_groups_0"), val = tensor(1)]; + tensor const_82 = const()[name = tensor("const_82"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15789056)))]; + tensor const_83 = const()[name = tensor("const_83"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16182336)))]; + tensor input_645 = conv(bias = const_83, dilations = input_643_dilations_0, groups = input_643_groups_0, pad = input_643_pad_0, pad_type = input_643_pad_type_0, strides = input_643_strides_0, weight = const_82, x = input_641)[name = tensor("input_645")]; + tensor input_647 = relu(x = input_645)[name = tensor("input_647")]; + tensor y_57_pad_type_0 = const()[name = tensor("y_57_pad_type_0"), val = tensor("custom")]; + tensor y_57_pad_0 = const()[name = tensor("y_57_pad_0"), val = tensor([2, 2])]; + tensor y_57_dilations_0 = const()[name = tensor("y_57_dilations_0"), val = tensor([2])]; + tensor y_57_strides_0 = const()[name = tensor("y_57_strides_0"), val = tensor([1])]; + tensor y_57_groups_0 = const()[name = tensor("y_57_groups_0"), val = tensor(1)]; + tensor y_57 = conv(dilations = y_57_dilations_0, groups = y_57_groups_0, pad = y_57_pad_0, pad_type = y_57_pad_type_0, strides = y_57_strides_0, weight = model_xvector_block2_tdnnd17_cam_layer_linear_local_weight, x = input_647)[name = tensor("y_57")]; + tensor var_2248_axes_0 = const()[name = tensor("op_2248_axes_0"), val = tensor([-1])]; + tensor var_2248_keep_dims_0 = const()[name = tensor("op_2248_keep_dims_0"), val = tensor(true)]; + tensor var_2248 = reduce_mean(axes = var_2248_axes_0, keep_dims = var_2248_keep_dims_0, x = input_647)[name = tensor("op_2248")]; + tensor var_2249 = const()[name = tensor("op_2249"), val = tensor([100])]; + tensor var_2250 = const()[name = tensor("op_2250"), val = tensor([100])]; + tensor seg_113_pad_type_0 = const()[name = tensor("seg_113_pad_type_0"), val = tensor("custom")]; + tensor seg_113_pad_0 = const()[name = tensor("seg_113_pad_0"), val = tensor([0, 0])]; + tensor seg_113_exclude_padding_from_average_0 = const()[name = tensor("seg_113_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_113_ceil_mode_0 = const()[name = tensor("seg_113_ceil_mode_0"), val = tensor(true)]; + tensor seg_113 = avg_pool(ceil_mode = seg_113_ceil_mode_0, exclude_padding_from_average = seg_113_exclude_padding_from_average_0, kernel_sizes = var_2249, pad = seg_113_pad_0, pad_type = seg_113_pad_type_0, strides = var_2250, x = input_647)[name = tensor("seg_113")]; + tensor var_2253_shape = shape(x = seg_113)[name = tensor("op_2253_shape")]; + tensor gather_116 = const()[name = tensor("gather_116"), val = tensor(1)]; + tensor gather_117 = const()[name = tensor("gather_117"), val = tensor(128)]; + tensor select_118 = const()[name = tensor("select_118"), val = tensor(2)]; + tensor gather_118_axis_0 = const()[name = tensor("gather_118_axis_0"), val = tensor(0)]; + tensor gather_118_batch_dims_0 = const()[name = tensor("gather_118_batch_dims_0"), val = tensor(0)]; + tensor gather_118_validate_indices_0 = const()[name = tensor("gather_118_validate_indices_0"), val = tensor(false)]; + tensor gather_118 = gather(axis = gather_118_axis_0, batch_dims = gather_118_batch_dims_0, indices = select_118, validate_indices = gather_118_validate_indices_0, x = var_2253_shape)[name = tensor("gather_118")]; + tensor var_2256_axes_0 = const()[name = tensor("op_2256_axes_0"), val = tensor([-1])]; + tensor var_2256 = expand_dims(axes = var_2256_axes_0, x = seg_113)[name = tensor("op_2256")]; + tensor concat_57_axis_0 = const()[name = tensor("concat_57_axis_0"), val = tensor(0)]; + tensor concat_57_interleave_0 = const()[name = tensor("concat_57_interleave_0"), val = tensor(false)]; + tensor concat_57 = concat(axis = concat_57_axis_0, interleave = concat_57_interleave_0, values = (gather_116, gather_117, gather_118, var_3))[name = tensor("concat_57")]; + tensor shape_147 = shape(x = var_2256)[name = tensor("shape_147")]; + tensor real_div_28 = real_div(x = concat_57, y = shape_147)[name = tensor("real_div_28")]; + tensor var_2258 = tile(reps = real_div_28, x = var_2256)[name = tensor("op_2258")]; + tensor var_2259 = const()[name = tensor("op_2259"), val = tensor([1, 128, -1])]; + tensor seg_115 = reshape(shape = var_2259, x = var_2258)[name = tensor("seg_115")]; + tensor var_2261_shape = shape(x = input_647)[name = tensor("op_2261_shape")]; + tensor select_119 = const()[name = tensor("select_119"), val = tensor(2)]; + tensor gather_119_axis_0 = const()[name = tensor("gather_119_axis_0"), val = tensor(0)]; + tensor gather_119_batch_dims_0 = const()[name = tensor("gather_119_batch_dims_0"), val = tensor(0)]; + tensor gather_119_validate_indices_0 = const()[name = tensor("gather_119_validate_indices_0"), val = tensor(false)]; + tensor gather_119 = gather(axis = gather_119_axis_0, batch_dims = gather_119_batch_dims_0, indices = select_119, validate_indices = gather_119_validate_indices_0, x = var_2261_shape)[name = tensor("gather_119")]; + tensor concat_58_values0_0 = const()[name = tensor("concat_58_values0_0"), val = tensor(1)]; + tensor concat_58_values1_0 = const()[name = tensor("concat_58_values1_0"), val = tensor(128)]; + tensor concat_58_axis_0 = const()[name = tensor("concat_58_axis_0"), val = tensor(0)]; + tensor concat_58_interleave_0 = const()[name = tensor("concat_58_interleave_0"), val = tensor(false)]; + tensor concat_58 = concat(axis = concat_58_axis_0, interleave = concat_58_interleave_0, values = (concat_58_values0_0, concat_58_values1_0, gather_119))[name = tensor("concat_58")]; + tensor var_2262_begin_0 = const()[name = tensor("op_2262_begin_0"), val = tensor([0, 0, 0])]; + tensor var_2262_end_mask_0 = const()[name = tensor("op_2262_end_mask_0"), val = tensor([true, true, false])]; + tensor var_2262 = slice_by_index(begin = var_2262_begin_0, end = concat_58, end_mask = var_2262_end_mask_0, x = seg_115)[name = tensor("op_2262")]; + tensor input_649 = add(x = var_2248, y = var_2262)[name = tensor("input_649")]; + tensor input_651_pad_type_0 = const()[name = tensor("input_651_pad_type_0"), val = tensor("valid")]; + tensor input_651_strides_0 = const()[name = tensor("input_651_strides_0"), val = tensor([1])]; + tensor input_651_pad_0 = const()[name = tensor("input_651_pad_0"), val = tensor([0, 0])]; + tensor input_651_dilations_0 = const()[name = tensor("input_651_dilations_0"), val = tensor([1])]; + tensor input_651_groups_0 = const()[name = tensor("input_651_groups_0"), val = tensor(1)]; + tensor input_651 = conv(bias = model_xvector_block2_tdnnd17_cam_layer_linear1_bias, dilations = input_651_dilations_0, groups = input_651_groups_0, pad = input_651_pad_0, pad_type = input_651_pad_type_0, strides = input_651_strides_0, weight = model_xvector_block2_tdnnd17_cam_layer_linear1_weight, x = input_649)[name = tensor("input_651")]; + tensor input_653 = relu(x = input_651)[name = tensor("input_653")]; + tensor input_655_pad_type_0 = const()[name = tensor("input_655_pad_type_0"), val = tensor("valid")]; + tensor input_655_strides_0 = const()[name = tensor("input_655_strides_0"), val = tensor([1])]; + tensor input_655_pad_0 = const()[name = tensor("input_655_pad_0"), val = tensor([0, 0])]; + tensor input_655_dilations_0 = const()[name = tensor("input_655_dilations_0"), val = tensor([1])]; + tensor input_655_groups_0 = const()[name = tensor("input_655_groups_0"), val = tensor(1)]; + tensor input_655 = conv(bias = model_xvector_block2_tdnnd17_cam_layer_linear2_bias, dilations = input_655_dilations_0, groups = input_655_groups_0, pad = input_655_pad_0, pad_type = input_655_pad_type_0, strides = input_655_strides_0, weight = model_xvector_block2_tdnnd17_cam_layer_linear2_weight, x = input_653)[name = tensor("input_655")]; + tensor m_57 = sigmoid(x = input_655)[name = tensor("m_57")]; + tensor var_2280 = mul(x = y_57, y = m_57)[name = tensor("op_2280")]; + tensor input_657_interleave_0 = const()[name = tensor("input_657_interleave_0"), val = tensor(false)]; + tensor input_657 = concat(axis = var_11, interleave = input_657_interleave_0, values = (input_637, var_2280))[name = tensor("input_657")]; + tensor input_659 = batch_norm(beta = model_xvector_block2_tdnnd18_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block2_tdnnd18_nonlinear1_batchnorm_weight, mean = model_xvector_block2_tdnnd18_nonlinear1_batchnorm_running_mean, variance = model_xvector_block2_tdnnd18_nonlinear1_batchnorm_running_var, x = input_657)[name = tensor("input_659")]; + tensor input_661 = relu(x = input_659)[name = tensor("input_661")]; + tensor input_663_pad_type_0 = const()[name = tensor("input_663_pad_type_0"), val = tensor("valid")]; + tensor input_663_strides_0 = const()[name = tensor("input_663_strides_0"), val = tensor([1])]; + tensor input_663_pad_0 = const()[name = tensor("input_663_pad_0"), val = tensor([0, 0])]; + tensor input_663_dilations_0 = const()[name = tensor("input_663_dilations_0"), val = tensor([1])]; + tensor input_663_groups_0 = const()[name = tensor("input_663_groups_0"), val = tensor(1)]; + tensor const_84 = const()[name = tensor("const_84"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16182912)))]; + tensor const_85 = const()[name = tensor("const_85"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16592576)))]; + tensor input_665 = conv(bias = const_85, dilations = input_663_dilations_0, groups = input_663_groups_0, pad = input_663_pad_0, pad_type = input_663_pad_type_0, strides = input_663_strides_0, weight = const_84, x = input_661)[name = tensor("input_665")]; + tensor input_667 = relu(x = input_665)[name = tensor("input_667")]; + tensor y_59_pad_type_0 = const()[name = tensor("y_59_pad_type_0"), val = tensor("custom")]; + tensor y_59_pad_0 = const()[name = tensor("y_59_pad_0"), val = tensor([2, 2])]; + tensor y_59_dilations_0 = const()[name = tensor("y_59_dilations_0"), val = tensor([2])]; + tensor y_59_strides_0 = const()[name = tensor("y_59_strides_0"), val = tensor([1])]; + tensor y_59_groups_0 = const()[name = tensor("y_59_groups_0"), val = tensor(1)]; + tensor y_59 = conv(dilations = y_59_dilations_0, groups = y_59_groups_0, pad = y_59_pad_0, pad_type = y_59_pad_type_0, strides = y_59_strides_0, weight = model_xvector_block2_tdnnd18_cam_layer_linear_local_weight, x = input_667)[name = tensor("y_59")]; + tensor var_2317_axes_0 = const()[name = tensor("op_2317_axes_0"), val = tensor([-1])]; + tensor var_2317_keep_dims_0 = const()[name = tensor("op_2317_keep_dims_0"), val = tensor(true)]; + tensor var_2317 = reduce_mean(axes = var_2317_axes_0, keep_dims = var_2317_keep_dims_0, x = input_667)[name = tensor("op_2317")]; + tensor var_2318 = const()[name = tensor("op_2318"), val = tensor([100])]; + tensor var_2319 = const()[name = tensor("op_2319"), val = tensor([100])]; + tensor seg_117_pad_type_0 = const()[name = tensor("seg_117_pad_type_0"), val = tensor("custom")]; + tensor seg_117_pad_0 = const()[name = tensor("seg_117_pad_0"), val = tensor([0, 0])]; + tensor seg_117_exclude_padding_from_average_0 = const()[name = tensor("seg_117_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_117_ceil_mode_0 = const()[name = tensor("seg_117_ceil_mode_0"), val = tensor(true)]; + tensor seg_117 = avg_pool(ceil_mode = seg_117_ceil_mode_0, exclude_padding_from_average = seg_117_exclude_padding_from_average_0, kernel_sizes = var_2318, pad = seg_117_pad_0, pad_type = seg_117_pad_type_0, strides = var_2319, x = input_667)[name = tensor("seg_117")]; + tensor var_2322_shape = shape(x = seg_117)[name = tensor("op_2322_shape")]; + tensor gather_120 = const()[name = tensor("gather_120"), val = tensor(1)]; + tensor gather_121 = const()[name = tensor("gather_121"), val = tensor(128)]; + tensor select_122 = const()[name = tensor("select_122"), val = tensor(2)]; + tensor gather_122_axis_0 = const()[name = tensor("gather_122_axis_0"), val = tensor(0)]; + tensor gather_122_batch_dims_0 = const()[name = tensor("gather_122_batch_dims_0"), val = tensor(0)]; + tensor gather_122_validate_indices_0 = const()[name = tensor("gather_122_validate_indices_0"), val = tensor(false)]; + tensor gather_122 = gather(axis = gather_122_axis_0, batch_dims = gather_122_batch_dims_0, indices = select_122, validate_indices = gather_122_validate_indices_0, x = var_2322_shape)[name = tensor("gather_122")]; + tensor var_2325_axes_0 = const()[name = tensor("op_2325_axes_0"), val = tensor([-1])]; + tensor var_2325 = expand_dims(axes = var_2325_axes_0, x = seg_117)[name = tensor("op_2325")]; + tensor concat_59_axis_0 = const()[name = tensor("concat_59_axis_0"), val = tensor(0)]; + tensor concat_59_interleave_0 = const()[name = tensor("concat_59_interleave_0"), val = tensor(false)]; + tensor concat_59 = concat(axis = concat_59_axis_0, interleave = concat_59_interleave_0, values = (gather_120, gather_121, gather_122, var_3))[name = tensor("concat_59")]; + tensor shape_152 = shape(x = var_2325)[name = tensor("shape_152")]; + tensor real_div_29 = real_div(x = concat_59, y = shape_152)[name = tensor("real_div_29")]; + tensor var_2327 = tile(reps = real_div_29, x = var_2325)[name = tensor("op_2327")]; + tensor var_2328 = const()[name = tensor("op_2328"), val = tensor([1, 128, -1])]; + tensor seg_119 = reshape(shape = var_2328, x = var_2327)[name = tensor("seg_119")]; + tensor var_2330_shape = shape(x = input_667)[name = tensor("op_2330_shape")]; + tensor select_123 = const()[name = tensor("select_123"), val = tensor(2)]; + tensor gather_123_axis_0 = const()[name = tensor("gather_123_axis_0"), val = tensor(0)]; + tensor gather_123_batch_dims_0 = const()[name = tensor("gather_123_batch_dims_0"), val = tensor(0)]; + tensor gather_123_validate_indices_0 = const()[name = tensor("gather_123_validate_indices_0"), val = tensor(false)]; + tensor gather_123 = gather(axis = gather_123_axis_0, batch_dims = gather_123_batch_dims_0, indices = select_123, validate_indices = gather_123_validate_indices_0, x = var_2330_shape)[name = tensor("gather_123")]; + tensor concat_60_values0_0 = const()[name = tensor("concat_60_values0_0"), val = tensor(1)]; + tensor concat_60_values1_0 = const()[name = tensor("concat_60_values1_0"), val = tensor(128)]; + tensor concat_60_axis_0 = const()[name = tensor("concat_60_axis_0"), val = tensor(0)]; + tensor concat_60_interleave_0 = const()[name = tensor("concat_60_interleave_0"), val = tensor(false)]; + tensor concat_60 = concat(axis = concat_60_axis_0, interleave = concat_60_interleave_0, values = (concat_60_values0_0, concat_60_values1_0, gather_123))[name = tensor("concat_60")]; + tensor var_2331_begin_0 = const()[name = tensor("op_2331_begin_0"), val = tensor([0, 0, 0])]; + tensor var_2331_end_mask_0 = const()[name = tensor("op_2331_end_mask_0"), val = tensor([true, true, false])]; + tensor var_2331 = slice_by_index(begin = var_2331_begin_0, end = concat_60, end_mask = var_2331_end_mask_0, x = seg_119)[name = tensor("op_2331")]; + tensor input_669 = add(x = var_2317, y = var_2331)[name = tensor("input_669")]; + tensor input_671_pad_type_0 = const()[name = tensor("input_671_pad_type_0"), val = tensor("valid")]; + tensor input_671_strides_0 = const()[name = tensor("input_671_strides_0"), val = tensor([1])]; + tensor input_671_pad_0 = const()[name = tensor("input_671_pad_0"), val = tensor([0, 0])]; + tensor input_671_dilations_0 = const()[name = tensor("input_671_dilations_0"), val = tensor([1])]; + tensor input_671_groups_0 = const()[name = tensor("input_671_groups_0"), val = tensor(1)]; + tensor input_671 = conv(bias = model_xvector_block2_tdnnd18_cam_layer_linear1_bias, dilations = input_671_dilations_0, groups = input_671_groups_0, pad = input_671_pad_0, pad_type = input_671_pad_type_0, strides = input_671_strides_0, weight = model_xvector_block2_tdnnd18_cam_layer_linear1_weight, x = input_669)[name = tensor("input_671")]; + tensor input_673 = relu(x = input_671)[name = tensor("input_673")]; + tensor input_675_pad_type_0 = const()[name = tensor("input_675_pad_type_0"), val = tensor("valid")]; + tensor input_675_strides_0 = const()[name = tensor("input_675_strides_0"), val = tensor([1])]; + tensor input_675_pad_0 = const()[name = tensor("input_675_pad_0"), val = tensor([0, 0])]; + tensor input_675_dilations_0 = const()[name = tensor("input_675_dilations_0"), val = tensor([1])]; + tensor input_675_groups_0 = const()[name = tensor("input_675_groups_0"), val = tensor(1)]; + tensor input_675 = conv(bias = model_xvector_block2_tdnnd18_cam_layer_linear2_bias, dilations = input_675_dilations_0, groups = input_675_groups_0, pad = input_675_pad_0, pad_type = input_675_pad_type_0, strides = input_675_strides_0, weight = model_xvector_block2_tdnnd18_cam_layer_linear2_weight, x = input_673)[name = tensor("input_675")]; + tensor m_59 = sigmoid(x = input_675)[name = tensor("m_59")]; + tensor var_2349 = mul(x = y_59, y = m_59)[name = tensor("op_2349")]; + tensor input_677_interleave_0 = const()[name = tensor("input_677_interleave_0"), val = tensor(false)]; + tensor input_677 = concat(axis = var_11, interleave = input_677_interleave_0, values = (input_657, var_2349))[name = tensor("input_677")]; + tensor input_679 = batch_norm(beta = model_xvector_block2_tdnnd19_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block2_tdnnd19_nonlinear1_batchnorm_weight, mean = model_xvector_block2_tdnnd19_nonlinear1_batchnorm_running_mean, variance = model_xvector_block2_tdnnd19_nonlinear1_batchnorm_running_var, x = input_677)[name = tensor("input_679")]; + tensor input_681 = relu(x = input_679)[name = tensor("input_681")]; + tensor input_683_pad_type_0 = const()[name = tensor("input_683_pad_type_0"), val = tensor("valid")]; + tensor input_683_strides_0 = const()[name = tensor("input_683_strides_0"), val = tensor([1])]; + tensor input_683_pad_0 = const()[name = tensor("input_683_pad_0"), val = tensor([0, 0])]; + tensor input_683_dilations_0 = const()[name = tensor("input_683_dilations_0"), val = tensor([1])]; + tensor input_683_groups_0 = const()[name = tensor("input_683_groups_0"), val = tensor(1)]; + tensor const_86 = const()[name = tensor("const_86"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16593152)))]; + tensor const_87 = const()[name = tensor("const_87"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17019200)))]; + tensor input_685 = conv(bias = const_87, dilations = input_683_dilations_0, groups = input_683_groups_0, pad = input_683_pad_0, pad_type = input_683_pad_type_0, strides = input_683_strides_0, weight = const_86, x = input_681)[name = tensor("input_685")]; + tensor input_687 = relu(x = input_685)[name = tensor("input_687")]; + tensor y_61_pad_type_0 = const()[name = tensor("y_61_pad_type_0"), val = tensor("custom")]; + tensor y_61_pad_0 = const()[name = tensor("y_61_pad_0"), val = tensor([2, 2])]; + tensor y_61_dilations_0 = const()[name = tensor("y_61_dilations_0"), val = tensor([2])]; + tensor y_61_strides_0 = const()[name = tensor("y_61_strides_0"), val = tensor([1])]; + tensor y_61_groups_0 = const()[name = tensor("y_61_groups_0"), val = tensor(1)]; + tensor y_61 = conv(dilations = y_61_dilations_0, groups = y_61_groups_0, pad = y_61_pad_0, pad_type = y_61_pad_type_0, strides = y_61_strides_0, weight = model_xvector_block2_tdnnd19_cam_layer_linear_local_weight, x = input_687)[name = tensor("y_61")]; + tensor var_2386_axes_0 = const()[name = tensor("op_2386_axes_0"), val = tensor([-1])]; + tensor var_2386_keep_dims_0 = const()[name = tensor("op_2386_keep_dims_0"), val = tensor(true)]; + tensor var_2386 = reduce_mean(axes = var_2386_axes_0, keep_dims = var_2386_keep_dims_0, x = input_687)[name = tensor("op_2386")]; + tensor var_2387 = const()[name = tensor("op_2387"), val = tensor([100])]; + tensor var_2388 = const()[name = tensor("op_2388"), val = tensor([100])]; + tensor seg_121_pad_type_0 = const()[name = tensor("seg_121_pad_type_0"), val = tensor("custom")]; + tensor seg_121_pad_0 = const()[name = tensor("seg_121_pad_0"), val = tensor([0, 0])]; + tensor seg_121_exclude_padding_from_average_0 = const()[name = tensor("seg_121_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_121_ceil_mode_0 = const()[name = tensor("seg_121_ceil_mode_0"), val = tensor(true)]; + tensor seg_121 = avg_pool(ceil_mode = seg_121_ceil_mode_0, exclude_padding_from_average = seg_121_exclude_padding_from_average_0, kernel_sizes = var_2387, pad = seg_121_pad_0, pad_type = seg_121_pad_type_0, strides = var_2388, x = input_687)[name = tensor("seg_121")]; + tensor var_2391_shape = shape(x = seg_121)[name = tensor("op_2391_shape")]; + tensor gather_124 = const()[name = tensor("gather_124"), val = tensor(1)]; + tensor gather_125 = const()[name = tensor("gather_125"), val = tensor(128)]; + tensor select_126 = const()[name = tensor("select_126"), val = tensor(2)]; + tensor gather_126_axis_0 = const()[name = tensor("gather_126_axis_0"), val = tensor(0)]; + tensor gather_126_batch_dims_0 = const()[name = tensor("gather_126_batch_dims_0"), val = tensor(0)]; + tensor gather_126_validate_indices_0 = const()[name = tensor("gather_126_validate_indices_0"), val = tensor(false)]; + tensor gather_126 = gather(axis = gather_126_axis_0, batch_dims = gather_126_batch_dims_0, indices = select_126, validate_indices = gather_126_validate_indices_0, x = var_2391_shape)[name = tensor("gather_126")]; + tensor var_2394_axes_0 = const()[name = tensor("op_2394_axes_0"), val = tensor([-1])]; + tensor var_2394 = expand_dims(axes = var_2394_axes_0, x = seg_121)[name = tensor("op_2394")]; + tensor concat_61_axis_0 = const()[name = tensor("concat_61_axis_0"), val = tensor(0)]; + tensor concat_61_interleave_0 = const()[name = tensor("concat_61_interleave_0"), val = tensor(false)]; + tensor concat_61 = concat(axis = concat_61_axis_0, interleave = concat_61_interleave_0, values = (gather_124, gather_125, gather_126, var_3))[name = tensor("concat_61")]; + tensor shape_157 = shape(x = var_2394)[name = tensor("shape_157")]; + tensor real_div_30 = real_div(x = concat_61, y = shape_157)[name = tensor("real_div_30")]; + tensor var_2396 = tile(reps = real_div_30, x = var_2394)[name = tensor("op_2396")]; + tensor var_2397 = const()[name = tensor("op_2397"), val = tensor([1, 128, -1])]; + tensor seg_123 = reshape(shape = var_2397, x = var_2396)[name = tensor("seg_123")]; + tensor var_2399_shape = shape(x = input_687)[name = tensor("op_2399_shape")]; + tensor select_127 = const()[name = tensor("select_127"), val = tensor(2)]; + tensor gather_127_axis_0 = const()[name = tensor("gather_127_axis_0"), val = tensor(0)]; + tensor gather_127_batch_dims_0 = const()[name = tensor("gather_127_batch_dims_0"), val = tensor(0)]; + tensor gather_127_validate_indices_0 = const()[name = tensor("gather_127_validate_indices_0"), val = tensor(false)]; + tensor gather_127 = gather(axis = gather_127_axis_0, batch_dims = gather_127_batch_dims_0, indices = select_127, validate_indices = gather_127_validate_indices_0, x = var_2399_shape)[name = tensor("gather_127")]; + tensor concat_62_values0_0 = const()[name = tensor("concat_62_values0_0"), val = tensor(1)]; + tensor concat_62_values1_0 = const()[name = tensor("concat_62_values1_0"), val = tensor(128)]; + tensor concat_62_axis_0 = const()[name = tensor("concat_62_axis_0"), val = tensor(0)]; + tensor concat_62_interleave_0 = const()[name = tensor("concat_62_interleave_0"), val = tensor(false)]; + tensor concat_62 = concat(axis = concat_62_axis_0, interleave = concat_62_interleave_0, values = (concat_62_values0_0, concat_62_values1_0, gather_127))[name = tensor("concat_62")]; + tensor var_2400_begin_0 = const()[name = tensor("op_2400_begin_0"), val = tensor([0, 0, 0])]; + tensor var_2400_end_mask_0 = const()[name = tensor("op_2400_end_mask_0"), val = tensor([true, true, false])]; + tensor var_2400 = slice_by_index(begin = var_2400_begin_0, end = concat_62, end_mask = var_2400_end_mask_0, x = seg_123)[name = tensor("op_2400")]; + tensor input_689 = add(x = var_2386, y = var_2400)[name = tensor("input_689")]; + tensor input_691_pad_type_0 = const()[name = tensor("input_691_pad_type_0"), val = tensor("valid")]; + tensor input_691_strides_0 = const()[name = tensor("input_691_strides_0"), val = tensor([1])]; + tensor input_691_pad_0 = const()[name = tensor("input_691_pad_0"), val = tensor([0, 0])]; + tensor input_691_dilations_0 = const()[name = tensor("input_691_dilations_0"), val = tensor([1])]; + tensor input_691_groups_0 = const()[name = tensor("input_691_groups_0"), val = tensor(1)]; + tensor input_691 = conv(bias = model_xvector_block2_tdnnd19_cam_layer_linear1_bias, dilations = input_691_dilations_0, groups = input_691_groups_0, pad = input_691_pad_0, pad_type = input_691_pad_type_0, strides = input_691_strides_0, weight = model_xvector_block2_tdnnd19_cam_layer_linear1_weight, x = input_689)[name = tensor("input_691")]; + tensor input_693 = relu(x = input_691)[name = tensor("input_693")]; + tensor input_695_pad_type_0 = const()[name = tensor("input_695_pad_type_0"), val = tensor("valid")]; + tensor input_695_strides_0 = const()[name = tensor("input_695_strides_0"), val = tensor([1])]; + tensor input_695_pad_0 = const()[name = tensor("input_695_pad_0"), val = tensor([0, 0])]; + tensor input_695_dilations_0 = const()[name = tensor("input_695_dilations_0"), val = tensor([1])]; + tensor input_695_groups_0 = const()[name = tensor("input_695_groups_0"), val = tensor(1)]; + tensor input_695 = conv(bias = model_xvector_block2_tdnnd19_cam_layer_linear2_bias, dilations = input_695_dilations_0, groups = input_695_groups_0, pad = input_695_pad_0, pad_type = input_695_pad_type_0, strides = input_695_strides_0, weight = model_xvector_block2_tdnnd19_cam_layer_linear2_weight, x = input_693)[name = tensor("input_695")]; + tensor m_61 = sigmoid(x = input_695)[name = tensor("m_61")]; + tensor var_2418 = mul(x = y_61, y = m_61)[name = tensor("op_2418")]; + tensor input_697_interleave_0 = const()[name = tensor("input_697_interleave_0"), val = tensor(false)]; + tensor input_697 = concat(axis = var_11, interleave = input_697_interleave_0, values = (input_677, var_2418))[name = tensor("input_697")]; + tensor input_699 = batch_norm(beta = model_xvector_block2_tdnnd20_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block2_tdnnd20_nonlinear1_batchnorm_weight, mean = model_xvector_block2_tdnnd20_nonlinear1_batchnorm_running_mean, variance = model_xvector_block2_tdnnd20_nonlinear1_batchnorm_running_var, x = input_697)[name = tensor("input_699")]; + tensor input_701 = relu(x = input_699)[name = tensor("input_701")]; + tensor input_703_pad_type_0 = const()[name = tensor("input_703_pad_type_0"), val = tensor("valid")]; + tensor input_703_strides_0 = const()[name = tensor("input_703_strides_0"), val = tensor([1])]; + tensor input_703_pad_0 = const()[name = tensor("input_703_pad_0"), val = tensor([0, 0])]; + tensor input_703_dilations_0 = const()[name = tensor("input_703_dilations_0"), val = tensor([1])]; + tensor input_703_groups_0 = const()[name = tensor("input_703_groups_0"), val = tensor(1)]; + tensor const_88 = const()[name = tensor("const_88"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17019776)))]; + tensor const_89 = const()[name = tensor("const_89"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17462208)))]; + tensor input_705 = conv(bias = const_89, dilations = input_703_dilations_0, groups = input_703_groups_0, pad = input_703_pad_0, pad_type = input_703_pad_type_0, strides = input_703_strides_0, weight = const_88, x = input_701)[name = tensor("input_705")]; + tensor input_707 = relu(x = input_705)[name = tensor("input_707")]; + tensor y_63_pad_type_0 = const()[name = tensor("y_63_pad_type_0"), val = tensor("custom")]; + tensor y_63_pad_0 = const()[name = tensor("y_63_pad_0"), val = tensor([2, 2])]; + tensor y_63_dilations_0 = const()[name = tensor("y_63_dilations_0"), val = tensor([2])]; + tensor y_63_strides_0 = const()[name = tensor("y_63_strides_0"), val = tensor([1])]; + tensor y_63_groups_0 = const()[name = tensor("y_63_groups_0"), val = tensor(1)]; + tensor y_63 = conv(dilations = y_63_dilations_0, groups = y_63_groups_0, pad = y_63_pad_0, pad_type = y_63_pad_type_0, strides = y_63_strides_0, weight = model_xvector_block2_tdnnd20_cam_layer_linear_local_weight, x = input_707)[name = tensor("y_63")]; + tensor var_2455_axes_0 = const()[name = tensor("op_2455_axes_0"), val = tensor([-1])]; + tensor var_2455_keep_dims_0 = const()[name = tensor("op_2455_keep_dims_0"), val = tensor(true)]; + tensor var_2455 = reduce_mean(axes = var_2455_axes_0, keep_dims = var_2455_keep_dims_0, x = input_707)[name = tensor("op_2455")]; + tensor var_2456 = const()[name = tensor("op_2456"), val = tensor([100])]; + tensor var_2457 = const()[name = tensor("op_2457"), val = tensor([100])]; + tensor seg_125_pad_type_0 = const()[name = tensor("seg_125_pad_type_0"), val = tensor("custom")]; + tensor seg_125_pad_0 = const()[name = tensor("seg_125_pad_0"), val = tensor([0, 0])]; + tensor seg_125_exclude_padding_from_average_0 = const()[name = tensor("seg_125_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_125_ceil_mode_0 = const()[name = tensor("seg_125_ceil_mode_0"), val = tensor(true)]; + tensor seg_125 = avg_pool(ceil_mode = seg_125_ceil_mode_0, exclude_padding_from_average = seg_125_exclude_padding_from_average_0, kernel_sizes = var_2456, pad = seg_125_pad_0, pad_type = seg_125_pad_type_0, strides = var_2457, x = input_707)[name = tensor("seg_125")]; + tensor var_2460_shape = shape(x = seg_125)[name = tensor("op_2460_shape")]; + tensor gather_128 = const()[name = tensor("gather_128"), val = tensor(1)]; + tensor gather_129 = const()[name = tensor("gather_129"), val = tensor(128)]; + tensor select_130 = const()[name = tensor("select_130"), val = tensor(2)]; + tensor gather_130_axis_0 = const()[name = tensor("gather_130_axis_0"), val = tensor(0)]; + tensor gather_130_batch_dims_0 = const()[name = tensor("gather_130_batch_dims_0"), val = tensor(0)]; + tensor gather_130_validate_indices_0 = const()[name = tensor("gather_130_validate_indices_0"), val = tensor(false)]; + tensor gather_130 = gather(axis = gather_130_axis_0, batch_dims = gather_130_batch_dims_0, indices = select_130, validate_indices = gather_130_validate_indices_0, x = var_2460_shape)[name = tensor("gather_130")]; + tensor var_2463_axes_0 = const()[name = tensor("op_2463_axes_0"), val = tensor([-1])]; + tensor var_2463 = expand_dims(axes = var_2463_axes_0, x = seg_125)[name = tensor("op_2463")]; + tensor concat_63_axis_0 = const()[name = tensor("concat_63_axis_0"), val = tensor(0)]; + tensor concat_63_interleave_0 = const()[name = tensor("concat_63_interleave_0"), val = tensor(false)]; + tensor concat_63 = concat(axis = concat_63_axis_0, interleave = concat_63_interleave_0, values = (gather_128, gather_129, gather_130, var_3))[name = tensor("concat_63")]; + tensor shape_162 = shape(x = var_2463)[name = tensor("shape_162")]; + tensor real_div_31 = real_div(x = concat_63, y = shape_162)[name = tensor("real_div_31")]; + tensor var_2465 = tile(reps = real_div_31, x = var_2463)[name = tensor("op_2465")]; + tensor var_2466 = const()[name = tensor("op_2466"), val = tensor([1, 128, -1])]; + tensor seg_127 = reshape(shape = var_2466, x = var_2465)[name = tensor("seg_127")]; + tensor var_2468_shape = shape(x = input_707)[name = tensor("op_2468_shape")]; + tensor select_131 = const()[name = tensor("select_131"), val = tensor(2)]; + tensor gather_131_axis_0 = const()[name = tensor("gather_131_axis_0"), val = tensor(0)]; + tensor gather_131_batch_dims_0 = const()[name = tensor("gather_131_batch_dims_0"), val = tensor(0)]; + tensor gather_131_validate_indices_0 = const()[name = tensor("gather_131_validate_indices_0"), val = tensor(false)]; + tensor gather_131 = gather(axis = gather_131_axis_0, batch_dims = gather_131_batch_dims_0, indices = select_131, validate_indices = gather_131_validate_indices_0, x = var_2468_shape)[name = tensor("gather_131")]; + tensor concat_64_values0_0 = const()[name = tensor("concat_64_values0_0"), val = tensor(1)]; + tensor concat_64_values1_0 = const()[name = tensor("concat_64_values1_0"), val = tensor(128)]; + tensor concat_64_axis_0 = const()[name = tensor("concat_64_axis_0"), val = tensor(0)]; + tensor concat_64_interleave_0 = const()[name = tensor("concat_64_interleave_0"), val = tensor(false)]; + tensor concat_64 = concat(axis = concat_64_axis_0, interleave = concat_64_interleave_0, values = (concat_64_values0_0, concat_64_values1_0, gather_131))[name = tensor("concat_64")]; + tensor var_2469_begin_0 = const()[name = tensor("op_2469_begin_0"), val = tensor([0, 0, 0])]; + tensor var_2469_end_mask_0 = const()[name = tensor("op_2469_end_mask_0"), val = tensor([true, true, false])]; + tensor var_2469 = slice_by_index(begin = var_2469_begin_0, end = concat_64, end_mask = var_2469_end_mask_0, x = seg_127)[name = tensor("op_2469")]; + tensor input_709 = add(x = var_2455, y = var_2469)[name = tensor("input_709")]; + tensor input_711_pad_type_0 = const()[name = tensor("input_711_pad_type_0"), val = tensor("valid")]; + tensor input_711_strides_0 = const()[name = tensor("input_711_strides_0"), val = tensor([1])]; + tensor input_711_pad_0 = const()[name = tensor("input_711_pad_0"), val = tensor([0, 0])]; + tensor input_711_dilations_0 = const()[name = tensor("input_711_dilations_0"), val = tensor([1])]; + tensor input_711_groups_0 = const()[name = tensor("input_711_groups_0"), val = tensor(1)]; + tensor input_711 = conv(bias = model_xvector_block2_tdnnd20_cam_layer_linear1_bias, dilations = input_711_dilations_0, groups = input_711_groups_0, pad = input_711_pad_0, pad_type = input_711_pad_type_0, strides = input_711_strides_0, weight = model_xvector_block2_tdnnd20_cam_layer_linear1_weight, x = input_709)[name = tensor("input_711")]; + tensor input_713 = relu(x = input_711)[name = tensor("input_713")]; + tensor input_715_pad_type_0 = const()[name = tensor("input_715_pad_type_0"), val = tensor("valid")]; + tensor input_715_strides_0 = const()[name = tensor("input_715_strides_0"), val = tensor([1])]; + tensor input_715_pad_0 = const()[name = tensor("input_715_pad_0"), val = tensor([0, 0])]; + tensor input_715_dilations_0 = const()[name = tensor("input_715_dilations_0"), val = tensor([1])]; + tensor input_715_groups_0 = const()[name = tensor("input_715_groups_0"), val = tensor(1)]; + tensor input_715 = conv(bias = model_xvector_block2_tdnnd20_cam_layer_linear2_bias, dilations = input_715_dilations_0, groups = input_715_groups_0, pad = input_715_pad_0, pad_type = input_715_pad_type_0, strides = input_715_strides_0, weight = model_xvector_block2_tdnnd20_cam_layer_linear2_weight, x = input_713)[name = tensor("input_715")]; + tensor m_63 = sigmoid(x = input_715)[name = tensor("m_63")]; + tensor var_2487 = mul(x = y_63, y = m_63)[name = tensor("op_2487")]; + tensor input_717_interleave_0 = const()[name = tensor("input_717_interleave_0"), val = tensor(false)]; + tensor input_717 = concat(axis = var_11, interleave = input_717_interleave_0, values = (input_697, var_2487))[name = tensor("input_717")]; + tensor input_719 = batch_norm(beta = model_xvector_block2_tdnnd21_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block2_tdnnd21_nonlinear1_batchnorm_weight, mean = model_xvector_block2_tdnnd21_nonlinear1_batchnorm_running_mean, variance = model_xvector_block2_tdnnd21_nonlinear1_batchnorm_running_var, x = input_717)[name = tensor("input_719")]; + tensor input_721 = relu(x = input_719)[name = tensor("input_721")]; + tensor input_723_pad_type_0 = const()[name = tensor("input_723_pad_type_0"), val = tensor("valid")]; + tensor input_723_strides_0 = const()[name = tensor("input_723_strides_0"), val = tensor([1])]; + tensor input_723_pad_0 = const()[name = tensor("input_723_pad_0"), val = tensor([0, 0])]; + tensor input_723_dilations_0 = const()[name = tensor("input_723_dilations_0"), val = tensor([1])]; + tensor input_723_groups_0 = const()[name = tensor("input_723_groups_0"), val = tensor(1)]; + tensor const_90 = const()[name = tensor("const_90"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17462784)))]; + tensor const_91 = const()[name = tensor("const_91"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17921600)))]; + tensor input_725 = conv(bias = const_91, dilations = input_723_dilations_0, groups = input_723_groups_0, pad = input_723_pad_0, pad_type = input_723_pad_type_0, strides = input_723_strides_0, weight = const_90, x = input_721)[name = tensor("input_725")]; + tensor input_727 = relu(x = input_725)[name = tensor("input_727")]; + tensor y_65_pad_type_0 = const()[name = tensor("y_65_pad_type_0"), val = tensor("custom")]; + tensor y_65_pad_0 = const()[name = tensor("y_65_pad_0"), val = tensor([2, 2])]; + tensor y_65_dilations_0 = const()[name = tensor("y_65_dilations_0"), val = tensor([2])]; + tensor y_65_strides_0 = const()[name = tensor("y_65_strides_0"), val = tensor([1])]; + tensor y_65_groups_0 = const()[name = tensor("y_65_groups_0"), val = tensor(1)]; + tensor y_65 = conv(dilations = y_65_dilations_0, groups = y_65_groups_0, pad = y_65_pad_0, pad_type = y_65_pad_type_0, strides = y_65_strides_0, weight = model_xvector_block2_tdnnd21_cam_layer_linear_local_weight, x = input_727)[name = tensor("y_65")]; + tensor var_2524_axes_0 = const()[name = tensor("op_2524_axes_0"), val = tensor([-1])]; + tensor var_2524_keep_dims_0 = const()[name = tensor("op_2524_keep_dims_0"), val = tensor(true)]; + tensor var_2524 = reduce_mean(axes = var_2524_axes_0, keep_dims = var_2524_keep_dims_0, x = input_727)[name = tensor("op_2524")]; + tensor var_2525 = const()[name = tensor("op_2525"), val = tensor([100])]; + tensor var_2526 = const()[name = tensor("op_2526"), val = tensor([100])]; + tensor seg_129_pad_type_0 = const()[name = tensor("seg_129_pad_type_0"), val = tensor("custom")]; + tensor seg_129_pad_0 = const()[name = tensor("seg_129_pad_0"), val = tensor([0, 0])]; + tensor seg_129_exclude_padding_from_average_0 = const()[name = tensor("seg_129_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_129_ceil_mode_0 = const()[name = tensor("seg_129_ceil_mode_0"), val = tensor(true)]; + tensor seg_129 = avg_pool(ceil_mode = seg_129_ceil_mode_0, exclude_padding_from_average = seg_129_exclude_padding_from_average_0, kernel_sizes = var_2525, pad = seg_129_pad_0, pad_type = seg_129_pad_type_0, strides = var_2526, x = input_727)[name = tensor("seg_129")]; + tensor var_2529_shape = shape(x = seg_129)[name = tensor("op_2529_shape")]; + tensor gather_132 = const()[name = tensor("gather_132"), val = tensor(1)]; + tensor gather_133 = const()[name = tensor("gather_133"), val = tensor(128)]; + tensor select_134 = const()[name = tensor("select_134"), val = tensor(2)]; + tensor gather_134_axis_0 = const()[name = tensor("gather_134_axis_0"), val = tensor(0)]; + tensor gather_134_batch_dims_0 = const()[name = tensor("gather_134_batch_dims_0"), val = tensor(0)]; + tensor gather_134_validate_indices_0 = const()[name = tensor("gather_134_validate_indices_0"), val = tensor(false)]; + tensor gather_134 = gather(axis = gather_134_axis_0, batch_dims = gather_134_batch_dims_0, indices = select_134, validate_indices = gather_134_validate_indices_0, x = var_2529_shape)[name = tensor("gather_134")]; + tensor var_2532_axes_0 = const()[name = tensor("op_2532_axes_0"), val = tensor([-1])]; + tensor var_2532 = expand_dims(axes = var_2532_axes_0, x = seg_129)[name = tensor("op_2532")]; + tensor concat_65_axis_0 = const()[name = tensor("concat_65_axis_0"), val = tensor(0)]; + tensor concat_65_interleave_0 = const()[name = tensor("concat_65_interleave_0"), val = tensor(false)]; + tensor concat_65 = concat(axis = concat_65_axis_0, interleave = concat_65_interleave_0, values = (gather_132, gather_133, gather_134, var_3))[name = tensor("concat_65")]; + tensor shape_167 = shape(x = var_2532)[name = tensor("shape_167")]; + tensor real_div_32 = real_div(x = concat_65, y = shape_167)[name = tensor("real_div_32")]; + tensor var_2534 = tile(reps = real_div_32, x = var_2532)[name = tensor("op_2534")]; + tensor var_2535 = const()[name = tensor("op_2535"), val = tensor([1, 128, -1])]; + tensor seg_131 = reshape(shape = var_2535, x = var_2534)[name = tensor("seg_131")]; + tensor var_2537_shape = shape(x = input_727)[name = tensor("op_2537_shape")]; + tensor select_135 = const()[name = tensor("select_135"), val = tensor(2)]; + tensor gather_135_axis_0 = const()[name = tensor("gather_135_axis_0"), val = tensor(0)]; + tensor gather_135_batch_dims_0 = const()[name = tensor("gather_135_batch_dims_0"), val = tensor(0)]; + tensor gather_135_validate_indices_0 = const()[name = tensor("gather_135_validate_indices_0"), val = tensor(false)]; + tensor gather_135 = gather(axis = gather_135_axis_0, batch_dims = gather_135_batch_dims_0, indices = select_135, validate_indices = gather_135_validate_indices_0, x = var_2537_shape)[name = tensor("gather_135")]; + tensor concat_66_values0_0 = const()[name = tensor("concat_66_values0_0"), val = tensor(1)]; + tensor concat_66_values1_0 = const()[name = tensor("concat_66_values1_0"), val = tensor(128)]; + tensor concat_66_axis_0 = const()[name = tensor("concat_66_axis_0"), val = tensor(0)]; + tensor concat_66_interleave_0 = const()[name = tensor("concat_66_interleave_0"), val = tensor(false)]; + tensor concat_66 = concat(axis = concat_66_axis_0, interleave = concat_66_interleave_0, values = (concat_66_values0_0, concat_66_values1_0, gather_135))[name = tensor("concat_66")]; + tensor var_2538_begin_0 = const()[name = tensor("op_2538_begin_0"), val = tensor([0, 0, 0])]; + tensor var_2538_end_mask_0 = const()[name = tensor("op_2538_end_mask_0"), val = tensor([true, true, false])]; + tensor var_2538 = slice_by_index(begin = var_2538_begin_0, end = concat_66, end_mask = var_2538_end_mask_0, x = seg_131)[name = tensor("op_2538")]; + tensor input_729 = add(x = var_2524, y = var_2538)[name = tensor("input_729")]; + tensor input_731_pad_type_0 = const()[name = tensor("input_731_pad_type_0"), val = tensor("valid")]; + tensor input_731_strides_0 = const()[name = tensor("input_731_strides_0"), val = tensor([1])]; + tensor input_731_pad_0 = const()[name = tensor("input_731_pad_0"), val = tensor([0, 0])]; + tensor input_731_dilations_0 = const()[name = tensor("input_731_dilations_0"), val = tensor([1])]; + tensor input_731_groups_0 = const()[name = tensor("input_731_groups_0"), val = tensor(1)]; + tensor input_731 = conv(bias = model_xvector_block2_tdnnd21_cam_layer_linear1_bias, dilations = input_731_dilations_0, groups = input_731_groups_0, pad = input_731_pad_0, pad_type = input_731_pad_type_0, strides = input_731_strides_0, weight = model_xvector_block2_tdnnd21_cam_layer_linear1_weight, x = input_729)[name = tensor("input_731")]; + tensor input_733 = relu(x = input_731)[name = tensor("input_733")]; + tensor input_735_pad_type_0 = const()[name = tensor("input_735_pad_type_0"), val = tensor("valid")]; + tensor input_735_strides_0 = const()[name = tensor("input_735_strides_0"), val = tensor([1])]; + tensor input_735_pad_0 = const()[name = tensor("input_735_pad_0"), val = tensor([0, 0])]; + tensor input_735_dilations_0 = const()[name = tensor("input_735_dilations_0"), val = tensor([1])]; + tensor input_735_groups_0 = const()[name = tensor("input_735_groups_0"), val = tensor(1)]; + tensor input_735 = conv(bias = model_xvector_block2_tdnnd21_cam_layer_linear2_bias, dilations = input_735_dilations_0, groups = input_735_groups_0, pad = input_735_pad_0, pad_type = input_735_pad_type_0, strides = input_735_strides_0, weight = model_xvector_block2_tdnnd21_cam_layer_linear2_weight, x = input_733)[name = tensor("input_735")]; + tensor m_65 = sigmoid(x = input_735)[name = tensor("m_65")]; + tensor var_2556 = mul(x = y_65, y = m_65)[name = tensor("op_2556")]; + tensor input_737_interleave_0 = const()[name = tensor("input_737_interleave_0"), val = tensor(false)]; + tensor input_737 = concat(axis = var_11, interleave = input_737_interleave_0, values = (input_717, var_2556))[name = tensor("input_737")]; + tensor input_739 = batch_norm(beta = model_xvector_block2_tdnnd22_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block2_tdnnd22_nonlinear1_batchnorm_weight, mean = model_xvector_block2_tdnnd22_nonlinear1_batchnorm_running_mean, variance = model_xvector_block2_tdnnd22_nonlinear1_batchnorm_running_var, x = input_737)[name = tensor("input_739")]; + tensor input_741 = relu(x = input_739)[name = tensor("input_741")]; + tensor input_743_pad_type_0 = const()[name = tensor("input_743_pad_type_0"), val = tensor("valid")]; + tensor input_743_strides_0 = const()[name = tensor("input_743_strides_0"), val = tensor([1])]; + tensor input_743_pad_0 = const()[name = tensor("input_743_pad_0"), val = tensor([0, 0])]; + tensor input_743_dilations_0 = const()[name = tensor("input_743_dilations_0"), val = tensor([1])]; + tensor input_743_groups_0 = const()[name = tensor("input_743_groups_0"), val = tensor(1)]; + tensor const_92 = const()[name = tensor("const_92"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17922176)))]; + tensor const_93 = const()[name = tensor("const_93"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18397376)))]; + tensor input_745 = conv(bias = const_93, dilations = input_743_dilations_0, groups = input_743_groups_0, pad = input_743_pad_0, pad_type = input_743_pad_type_0, strides = input_743_strides_0, weight = const_92, x = input_741)[name = tensor("input_745")]; + tensor input_747 = relu(x = input_745)[name = tensor("input_747")]; + tensor y_67_pad_type_0 = const()[name = tensor("y_67_pad_type_0"), val = tensor("custom")]; + tensor y_67_pad_0 = const()[name = tensor("y_67_pad_0"), val = tensor([2, 2])]; + tensor y_67_dilations_0 = const()[name = tensor("y_67_dilations_0"), val = tensor([2])]; + tensor y_67_strides_0 = const()[name = tensor("y_67_strides_0"), val = tensor([1])]; + tensor y_67_groups_0 = const()[name = tensor("y_67_groups_0"), val = tensor(1)]; + tensor y_67 = conv(dilations = y_67_dilations_0, groups = y_67_groups_0, pad = y_67_pad_0, pad_type = y_67_pad_type_0, strides = y_67_strides_0, weight = model_xvector_block2_tdnnd22_cam_layer_linear_local_weight, x = input_747)[name = tensor("y_67")]; + tensor var_2593_axes_0 = const()[name = tensor("op_2593_axes_0"), val = tensor([-1])]; + tensor var_2593_keep_dims_0 = const()[name = tensor("op_2593_keep_dims_0"), val = tensor(true)]; + tensor var_2593 = reduce_mean(axes = var_2593_axes_0, keep_dims = var_2593_keep_dims_0, x = input_747)[name = tensor("op_2593")]; + tensor var_2594 = const()[name = tensor("op_2594"), val = tensor([100])]; + tensor var_2595 = const()[name = tensor("op_2595"), val = tensor([100])]; + tensor seg_133_pad_type_0 = const()[name = tensor("seg_133_pad_type_0"), val = tensor("custom")]; + tensor seg_133_pad_0 = const()[name = tensor("seg_133_pad_0"), val = tensor([0, 0])]; + tensor seg_133_exclude_padding_from_average_0 = const()[name = tensor("seg_133_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_133_ceil_mode_0 = const()[name = tensor("seg_133_ceil_mode_0"), val = tensor(true)]; + tensor seg_133 = avg_pool(ceil_mode = seg_133_ceil_mode_0, exclude_padding_from_average = seg_133_exclude_padding_from_average_0, kernel_sizes = var_2594, pad = seg_133_pad_0, pad_type = seg_133_pad_type_0, strides = var_2595, x = input_747)[name = tensor("seg_133")]; + tensor var_2598_shape = shape(x = seg_133)[name = tensor("op_2598_shape")]; + tensor gather_136 = const()[name = tensor("gather_136"), val = tensor(1)]; + tensor gather_137 = const()[name = tensor("gather_137"), val = tensor(128)]; + tensor select_138 = const()[name = tensor("select_138"), val = tensor(2)]; + tensor gather_138_axis_0 = const()[name = tensor("gather_138_axis_0"), val = tensor(0)]; + tensor gather_138_batch_dims_0 = const()[name = tensor("gather_138_batch_dims_0"), val = tensor(0)]; + tensor gather_138_validate_indices_0 = const()[name = tensor("gather_138_validate_indices_0"), val = tensor(false)]; + tensor gather_138 = gather(axis = gather_138_axis_0, batch_dims = gather_138_batch_dims_0, indices = select_138, validate_indices = gather_138_validate_indices_0, x = var_2598_shape)[name = tensor("gather_138")]; + tensor var_2601_axes_0 = const()[name = tensor("op_2601_axes_0"), val = tensor([-1])]; + tensor var_2601 = expand_dims(axes = var_2601_axes_0, x = seg_133)[name = tensor("op_2601")]; + tensor concat_67_axis_0 = const()[name = tensor("concat_67_axis_0"), val = tensor(0)]; + tensor concat_67_interleave_0 = const()[name = tensor("concat_67_interleave_0"), val = tensor(false)]; + tensor concat_67 = concat(axis = concat_67_axis_0, interleave = concat_67_interleave_0, values = (gather_136, gather_137, gather_138, var_3))[name = tensor("concat_67")]; + tensor shape_172 = shape(x = var_2601)[name = tensor("shape_172")]; + tensor real_div_33 = real_div(x = concat_67, y = shape_172)[name = tensor("real_div_33")]; + tensor var_2603 = tile(reps = real_div_33, x = var_2601)[name = tensor("op_2603")]; + tensor var_2604 = const()[name = tensor("op_2604"), val = tensor([1, 128, -1])]; + tensor seg_135 = reshape(shape = var_2604, x = var_2603)[name = tensor("seg_135")]; + tensor var_2606_shape = shape(x = input_747)[name = tensor("op_2606_shape")]; + tensor select_139 = const()[name = tensor("select_139"), val = tensor(2)]; + tensor gather_139_axis_0 = const()[name = tensor("gather_139_axis_0"), val = tensor(0)]; + tensor gather_139_batch_dims_0 = const()[name = tensor("gather_139_batch_dims_0"), val = tensor(0)]; + tensor gather_139_validate_indices_0 = const()[name = tensor("gather_139_validate_indices_0"), val = tensor(false)]; + tensor gather_139 = gather(axis = gather_139_axis_0, batch_dims = gather_139_batch_dims_0, indices = select_139, validate_indices = gather_139_validate_indices_0, x = var_2606_shape)[name = tensor("gather_139")]; + tensor concat_68_values0_0 = const()[name = tensor("concat_68_values0_0"), val = tensor(1)]; + tensor concat_68_values1_0 = const()[name = tensor("concat_68_values1_0"), val = tensor(128)]; + 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 = (concat_68_values0_0, concat_68_values1_0, gather_139))[name = tensor("concat_68")]; + tensor var_2607_begin_0 = const()[name = tensor("op_2607_begin_0"), val = tensor([0, 0, 0])]; + tensor var_2607_end_mask_0 = const()[name = tensor("op_2607_end_mask_0"), val = tensor([true, true, false])]; + tensor var_2607 = slice_by_index(begin = var_2607_begin_0, end = concat_68, end_mask = var_2607_end_mask_0, x = seg_135)[name = tensor("op_2607")]; + tensor input_749 = add(x = var_2593, y = var_2607)[name = tensor("input_749")]; + tensor input_751_pad_type_0 = const()[name = tensor("input_751_pad_type_0"), val = tensor("valid")]; + tensor input_751_strides_0 = const()[name = tensor("input_751_strides_0"), val = tensor([1])]; + tensor input_751_pad_0 = const()[name = tensor("input_751_pad_0"), val = tensor([0, 0])]; + tensor input_751_dilations_0 = const()[name = tensor("input_751_dilations_0"), val = tensor([1])]; + tensor input_751_groups_0 = const()[name = tensor("input_751_groups_0"), val = tensor(1)]; + tensor input_751 = conv(bias = model_xvector_block2_tdnnd22_cam_layer_linear1_bias, dilations = input_751_dilations_0, groups = input_751_groups_0, pad = input_751_pad_0, pad_type = input_751_pad_type_0, strides = input_751_strides_0, weight = model_xvector_block2_tdnnd22_cam_layer_linear1_weight, x = input_749)[name = tensor("input_751")]; + tensor input_753 = relu(x = input_751)[name = tensor("input_753")]; + tensor input_755_pad_type_0 = const()[name = tensor("input_755_pad_type_0"), val = tensor("valid")]; + tensor input_755_strides_0 = const()[name = tensor("input_755_strides_0"), val = tensor([1])]; + tensor input_755_pad_0 = const()[name = tensor("input_755_pad_0"), val = tensor([0, 0])]; + tensor input_755_dilations_0 = const()[name = tensor("input_755_dilations_0"), val = tensor([1])]; + tensor input_755_groups_0 = const()[name = tensor("input_755_groups_0"), val = tensor(1)]; + tensor input_755 = conv(bias = model_xvector_block2_tdnnd22_cam_layer_linear2_bias, dilations = input_755_dilations_0, groups = input_755_groups_0, pad = input_755_pad_0, pad_type = input_755_pad_type_0, strides = input_755_strides_0, weight = model_xvector_block2_tdnnd22_cam_layer_linear2_weight, x = input_753)[name = tensor("input_755")]; + tensor m_67 = sigmoid(x = input_755)[name = tensor("m_67")]; + tensor var_2625 = mul(x = y_67, y = m_67)[name = tensor("op_2625")]; + tensor input_757_interleave_0 = const()[name = tensor("input_757_interleave_0"), val = tensor(false)]; + tensor input_757 = concat(axis = var_11, interleave = input_757_interleave_0, values = (input_737, var_2625))[name = tensor("input_757")]; + tensor input_759 = batch_norm(beta = model_xvector_block2_tdnnd23_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block2_tdnnd23_nonlinear1_batchnorm_weight, mean = model_xvector_block2_tdnnd23_nonlinear1_batchnorm_running_mean, variance = model_xvector_block2_tdnnd23_nonlinear1_batchnorm_running_var, x = input_757)[name = tensor("input_759")]; + tensor input_761 = relu(x = input_759)[name = tensor("input_761")]; + tensor input_763_pad_type_0 = const()[name = tensor("input_763_pad_type_0"), val = tensor("valid")]; + tensor input_763_strides_0 = const()[name = tensor("input_763_strides_0"), val = tensor([1])]; + tensor input_763_pad_0 = const()[name = tensor("input_763_pad_0"), val = tensor([0, 0])]; + tensor input_763_dilations_0 = const()[name = tensor("input_763_dilations_0"), val = tensor([1])]; + tensor input_763_groups_0 = const()[name = tensor("input_763_groups_0"), val = tensor(1)]; + tensor const_94 = const()[name = tensor("const_94"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18397952)))]; + tensor const_95 = const()[name = tensor("const_95"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18889536)))]; + tensor input_765 = conv(bias = const_95, dilations = input_763_dilations_0, groups = input_763_groups_0, pad = input_763_pad_0, pad_type = input_763_pad_type_0, strides = input_763_strides_0, weight = const_94, x = input_761)[name = tensor("input_765")]; + tensor input_767 = relu(x = input_765)[name = tensor("input_767")]; + tensor y_69_pad_type_0 = const()[name = tensor("y_69_pad_type_0"), val = tensor("custom")]; + tensor y_69_pad_0 = const()[name = tensor("y_69_pad_0"), val = tensor([2, 2])]; + tensor y_69_dilations_0 = const()[name = tensor("y_69_dilations_0"), val = tensor([2])]; + tensor y_69_strides_0 = const()[name = tensor("y_69_strides_0"), val = tensor([1])]; + tensor y_69_groups_0 = const()[name = tensor("y_69_groups_0"), val = tensor(1)]; + tensor y_69 = conv(dilations = y_69_dilations_0, groups = y_69_groups_0, pad = y_69_pad_0, pad_type = y_69_pad_type_0, strides = y_69_strides_0, weight = model_xvector_block2_tdnnd23_cam_layer_linear_local_weight, x = input_767)[name = tensor("y_69")]; + tensor var_2662_axes_0 = const()[name = tensor("op_2662_axes_0"), val = tensor([-1])]; + tensor var_2662_keep_dims_0 = const()[name = tensor("op_2662_keep_dims_0"), val = tensor(true)]; + tensor var_2662 = reduce_mean(axes = var_2662_axes_0, keep_dims = var_2662_keep_dims_0, x = input_767)[name = tensor("op_2662")]; + tensor var_2663 = const()[name = tensor("op_2663"), val = tensor([100])]; + tensor var_2664 = const()[name = tensor("op_2664"), val = tensor([100])]; + tensor seg_137_pad_type_0 = const()[name = tensor("seg_137_pad_type_0"), val = tensor("custom")]; + tensor seg_137_pad_0 = const()[name = tensor("seg_137_pad_0"), val = tensor([0, 0])]; + tensor seg_137_exclude_padding_from_average_0 = const()[name = tensor("seg_137_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_137_ceil_mode_0 = const()[name = tensor("seg_137_ceil_mode_0"), val = tensor(true)]; + tensor seg_137 = avg_pool(ceil_mode = seg_137_ceil_mode_0, exclude_padding_from_average = seg_137_exclude_padding_from_average_0, kernel_sizes = var_2663, pad = seg_137_pad_0, pad_type = seg_137_pad_type_0, strides = var_2664, x = input_767)[name = tensor("seg_137")]; + tensor var_2667_shape = shape(x = seg_137)[name = tensor("op_2667_shape")]; + tensor gather_140 = const()[name = tensor("gather_140"), val = tensor(1)]; + tensor gather_141 = const()[name = tensor("gather_141"), val = tensor(128)]; + tensor select_142 = const()[name = tensor("select_142"), val = tensor(2)]; + tensor gather_142_axis_0 = const()[name = tensor("gather_142_axis_0"), val = tensor(0)]; + tensor gather_142_batch_dims_0 = const()[name = tensor("gather_142_batch_dims_0"), val = tensor(0)]; + tensor gather_142_validate_indices_0 = const()[name = tensor("gather_142_validate_indices_0"), val = tensor(false)]; + tensor gather_142 = gather(axis = gather_142_axis_0, batch_dims = gather_142_batch_dims_0, indices = select_142, validate_indices = gather_142_validate_indices_0, x = var_2667_shape)[name = tensor("gather_142")]; + tensor var_2670_axes_0 = const()[name = tensor("op_2670_axes_0"), val = tensor([-1])]; + tensor var_2670 = expand_dims(axes = var_2670_axes_0, x = seg_137)[name = tensor("op_2670")]; + tensor concat_69_axis_0 = const()[name = tensor("concat_69_axis_0"), val = tensor(0)]; + tensor concat_69_interleave_0 = const()[name = tensor("concat_69_interleave_0"), val = tensor(false)]; + tensor concat_69 = concat(axis = concat_69_axis_0, interleave = concat_69_interleave_0, values = (gather_140, gather_141, gather_142, var_3))[name = tensor("concat_69")]; + tensor shape_177 = shape(x = var_2670)[name = tensor("shape_177")]; + tensor real_div_34 = real_div(x = concat_69, y = shape_177)[name = tensor("real_div_34")]; + tensor var_2672 = tile(reps = real_div_34, x = var_2670)[name = tensor("op_2672")]; + tensor var_2673 = const()[name = tensor("op_2673"), val = tensor([1, 128, -1])]; + tensor seg_139 = reshape(shape = var_2673, x = var_2672)[name = tensor("seg_139")]; + tensor var_2675_shape = shape(x = input_767)[name = tensor("op_2675_shape")]; + tensor select_143 = const()[name = tensor("select_143"), val = tensor(2)]; + tensor gather_143_axis_0 = const()[name = tensor("gather_143_axis_0"), val = tensor(0)]; + tensor gather_143_batch_dims_0 = const()[name = tensor("gather_143_batch_dims_0"), val = tensor(0)]; + tensor gather_143_validate_indices_0 = const()[name = tensor("gather_143_validate_indices_0"), val = tensor(false)]; + tensor gather_143 = gather(axis = gather_143_axis_0, batch_dims = gather_143_batch_dims_0, indices = select_143, validate_indices = gather_143_validate_indices_0, x = var_2675_shape)[name = tensor("gather_143")]; + tensor concat_70_values0_0 = const()[name = tensor("concat_70_values0_0"), val = tensor(1)]; + tensor concat_70_values1_0 = const()[name = tensor("concat_70_values1_0"), val = tensor(128)]; + tensor concat_70_axis_0 = const()[name = tensor("concat_70_axis_0"), val = tensor(0)]; + tensor concat_70_interleave_0 = const()[name = tensor("concat_70_interleave_0"), val = tensor(false)]; + tensor concat_70 = concat(axis = concat_70_axis_0, interleave = concat_70_interleave_0, values = (concat_70_values0_0, concat_70_values1_0, gather_143))[name = tensor("concat_70")]; + tensor var_2676_begin_0 = const()[name = tensor("op_2676_begin_0"), val = tensor([0, 0, 0])]; + tensor var_2676_end_mask_0 = const()[name = tensor("op_2676_end_mask_0"), val = tensor([true, true, false])]; + tensor var_2676 = slice_by_index(begin = var_2676_begin_0, end = concat_70, end_mask = var_2676_end_mask_0, x = seg_139)[name = tensor("op_2676")]; + tensor input_769 = add(x = var_2662, y = var_2676)[name = tensor("input_769")]; + tensor input_771_pad_type_0 = const()[name = tensor("input_771_pad_type_0"), val = tensor("valid")]; + tensor input_771_strides_0 = const()[name = tensor("input_771_strides_0"), val = tensor([1])]; + tensor input_771_pad_0 = const()[name = tensor("input_771_pad_0"), val = tensor([0, 0])]; + tensor input_771_dilations_0 = const()[name = tensor("input_771_dilations_0"), val = tensor([1])]; + tensor input_771_groups_0 = const()[name = tensor("input_771_groups_0"), val = tensor(1)]; + tensor input_771 = conv(bias = model_xvector_block2_tdnnd23_cam_layer_linear1_bias, dilations = input_771_dilations_0, groups = input_771_groups_0, pad = input_771_pad_0, pad_type = input_771_pad_type_0, strides = input_771_strides_0, weight = model_xvector_block2_tdnnd23_cam_layer_linear1_weight, x = input_769)[name = tensor("input_771")]; + tensor input_773 = relu(x = input_771)[name = tensor("input_773")]; + tensor input_775_pad_type_0 = const()[name = tensor("input_775_pad_type_0"), val = tensor("valid")]; + tensor input_775_strides_0 = const()[name = tensor("input_775_strides_0"), val = tensor([1])]; + tensor input_775_pad_0 = const()[name = tensor("input_775_pad_0"), val = tensor([0, 0])]; + tensor input_775_dilations_0 = const()[name = tensor("input_775_dilations_0"), val = tensor([1])]; + tensor input_775_groups_0 = const()[name = tensor("input_775_groups_0"), val = tensor(1)]; + tensor input_775 = conv(bias = model_xvector_block2_tdnnd23_cam_layer_linear2_bias, dilations = input_775_dilations_0, groups = input_775_groups_0, pad = input_775_pad_0, pad_type = input_775_pad_type_0, strides = input_775_strides_0, weight = model_xvector_block2_tdnnd23_cam_layer_linear2_weight, x = input_773)[name = tensor("input_775")]; + tensor m_69 = sigmoid(x = input_775)[name = tensor("m_69")]; + tensor var_2694 = mul(x = y_69, y = m_69)[name = tensor("op_2694")]; + tensor input_777_interleave_0 = const()[name = tensor("input_777_interleave_0"), val = tensor(false)]; + tensor input_777 = concat(axis = var_11, interleave = input_777_interleave_0, values = (input_757, var_2694))[name = tensor("input_777")]; + tensor input_779 = batch_norm(beta = model_xvector_block2_tdnnd24_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block2_tdnnd24_nonlinear1_batchnorm_weight, mean = model_xvector_block2_tdnnd24_nonlinear1_batchnorm_running_mean, variance = model_xvector_block2_tdnnd24_nonlinear1_batchnorm_running_var, x = input_777)[name = tensor("input_779")]; + tensor input_781 = relu(x = input_779)[name = tensor("input_781")]; + tensor input_783_pad_type_0 = const()[name = tensor("input_783_pad_type_0"), val = tensor("valid")]; + tensor input_783_strides_0 = const()[name = tensor("input_783_strides_0"), val = tensor([1])]; + tensor input_783_pad_0 = const()[name = tensor("input_783_pad_0"), val = tensor([0, 0])]; + tensor input_783_dilations_0 = const()[name = tensor("input_783_dilations_0"), val = tensor([1])]; + tensor input_783_groups_0 = const()[name = tensor("input_783_groups_0"), val = tensor(1)]; + tensor const_96 = const()[name = tensor("const_96"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18890112)))]; + tensor const_97 = const()[name = tensor("const_97"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19398080)))]; + tensor input_785 = conv(bias = const_97, dilations = input_783_dilations_0, groups = input_783_groups_0, pad = input_783_pad_0, pad_type = input_783_pad_type_0, strides = input_783_strides_0, weight = const_96, x = input_781)[name = tensor("input_785")]; + tensor input_787 = relu(x = input_785)[name = tensor("input_787")]; + tensor y_71_pad_type_0 = const()[name = tensor("y_71_pad_type_0"), val = tensor("custom")]; + tensor y_71_pad_0 = const()[name = tensor("y_71_pad_0"), val = tensor([2, 2])]; + tensor y_71_dilations_0 = const()[name = tensor("y_71_dilations_0"), val = tensor([2])]; + tensor y_71_strides_0 = const()[name = tensor("y_71_strides_0"), val = tensor([1])]; + tensor y_71_groups_0 = const()[name = tensor("y_71_groups_0"), val = tensor(1)]; + tensor y_71 = conv(dilations = y_71_dilations_0, groups = y_71_groups_0, pad = y_71_pad_0, pad_type = y_71_pad_type_0, strides = y_71_strides_0, weight = model_xvector_block2_tdnnd24_cam_layer_linear_local_weight, x = input_787)[name = tensor("y_71")]; + tensor var_2731_axes_0 = const()[name = tensor("op_2731_axes_0"), val = tensor([-1])]; + tensor var_2731_keep_dims_0 = const()[name = tensor("op_2731_keep_dims_0"), val = tensor(true)]; + tensor var_2731 = reduce_mean(axes = var_2731_axes_0, keep_dims = var_2731_keep_dims_0, x = input_787)[name = tensor("op_2731")]; + tensor var_2732 = const()[name = tensor("op_2732"), val = tensor([100])]; + tensor var_2733 = const()[name = tensor("op_2733"), val = tensor([100])]; + tensor seg_141_pad_type_0 = const()[name = tensor("seg_141_pad_type_0"), val = tensor("custom")]; + tensor seg_141_pad_0 = const()[name = tensor("seg_141_pad_0"), val = tensor([0, 0])]; + tensor seg_141_exclude_padding_from_average_0 = const()[name = tensor("seg_141_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_141_ceil_mode_0 = const()[name = tensor("seg_141_ceil_mode_0"), val = tensor(true)]; + tensor seg_141 = avg_pool(ceil_mode = seg_141_ceil_mode_0, exclude_padding_from_average = seg_141_exclude_padding_from_average_0, kernel_sizes = var_2732, pad = seg_141_pad_0, pad_type = seg_141_pad_type_0, strides = var_2733, x = input_787)[name = tensor("seg_141")]; + tensor var_2736_shape = shape(x = seg_141)[name = tensor("op_2736_shape")]; + tensor gather_144 = const()[name = tensor("gather_144"), val = tensor(1)]; + tensor gather_145 = const()[name = tensor("gather_145"), val = tensor(128)]; + tensor select_146 = const()[name = tensor("select_146"), val = tensor(2)]; + tensor gather_146_axis_0 = const()[name = tensor("gather_146_axis_0"), val = tensor(0)]; + tensor gather_146_batch_dims_0 = const()[name = tensor("gather_146_batch_dims_0"), val = tensor(0)]; + tensor gather_146_validate_indices_0 = const()[name = tensor("gather_146_validate_indices_0"), val = tensor(false)]; + tensor gather_146 = gather(axis = gather_146_axis_0, batch_dims = gather_146_batch_dims_0, indices = select_146, validate_indices = gather_146_validate_indices_0, x = var_2736_shape)[name = tensor("gather_146")]; + tensor var_2739_axes_0 = const()[name = tensor("op_2739_axes_0"), val = tensor([-1])]; + tensor var_2739 = expand_dims(axes = var_2739_axes_0, x = seg_141)[name = tensor("op_2739")]; + tensor concat_71_axis_0 = const()[name = tensor("concat_71_axis_0"), val = tensor(0)]; + tensor concat_71_interleave_0 = const()[name = tensor("concat_71_interleave_0"), val = tensor(false)]; + tensor concat_71 = concat(axis = concat_71_axis_0, interleave = concat_71_interleave_0, values = (gather_144, gather_145, gather_146, var_3))[name = tensor("concat_71")]; + tensor shape_182 = shape(x = var_2739)[name = tensor("shape_182")]; + tensor real_div_35 = real_div(x = concat_71, y = shape_182)[name = tensor("real_div_35")]; + tensor var_2741 = tile(reps = real_div_35, x = var_2739)[name = tensor("op_2741")]; + tensor var_2742 = const()[name = tensor("op_2742"), val = tensor([1, 128, -1])]; + tensor seg_143 = reshape(shape = var_2742, x = var_2741)[name = tensor("seg_143")]; + tensor var_2744_shape = shape(x = input_787)[name = tensor("op_2744_shape")]; + tensor select_147 = const()[name = tensor("select_147"), val = tensor(2)]; + tensor gather_147_axis_0 = const()[name = tensor("gather_147_axis_0"), val = tensor(0)]; + tensor gather_147_batch_dims_0 = const()[name = tensor("gather_147_batch_dims_0"), val = tensor(0)]; + tensor gather_147_validate_indices_0 = const()[name = tensor("gather_147_validate_indices_0"), val = tensor(false)]; + tensor gather_147 = gather(axis = gather_147_axis_0, batch_dims = gather_147_batch_dims_0, indices = select_147, validate_indices = gather_147_validate_indices_0, x = var_2744_shape)[name = tensor("gather_147")]; + tensor concat_72_values0_0 = const()[name = tensor("concat_72_values0_0"), val = tensor(1)]; + tensor concat_72_values1_0 = const()[name = tensor("concat_72_values1_0"), val = tensor(128)]; + tensor concat_72_axis_0 = const()[name = tensor("concat_72_axis_0"), val = tensor(0)]; + tensor concat_72_interleave_0 = const()[name = tensor("concat_72_interleave_0"), val = tensor(false)]; + tensor concat_72 = concat(axis = concat_72_axis_0, interleave = concat_72_interleave_0, values = (concat_72_values0_0, concat_72_values1_0, gather_147))[name = tensor("concat_72")]; + tensor var_2745_begin_0 = const()[name = tensor("op_2745_begin_0"), val = tensor([0, 0, 0])]; + tensor var_2745_end_mask_0 = const()[name = tensor("op_2745_end_mask_0"), val = tensor([true, true, false])]; + tensor var_2745 = slice_by_index(begin = var_2745_begin_0, end = concat_72, end_mask = var_2745_end_mask_0, x = seg_143)[name = tensor("op_2745")]; + tensor input_789 = add(x = var_2731, y = var_2745)[name = tensor("input_789")]; + tensor input_791_pad_type_0 = const()[name = tensor("input_791_pad_type_0"), val = tensor("valid")]; + tensor input_791_strides_0 = const()[name = tensor("input_791_strides_0"), val = tensor([1])]; + tensor input_791_pad_0 = const()[name = tensor("input_791_pad_0"), val = tensor([0, 0])]; + tensor input_791_dilations_0 = const()[name = tensor("input_791_dilations_0"), val = tensor([1])]; + tensor input_791_groups_0 = const()[name = tensor("input_791_groups_0"), val = tensor(1)]; + tensor input_791 = conv(bias = model_xvector_block2_tdnnd24_cam_layer_linear1_bias, dilations = input_791_dilations_0, groups = input_791_groups_0, pad = input_791_pad_0, pad_type = input_791_pad_type_0, strides = input_791_strides_0, weight = model_xvector_block2_tdnnd24_cam_layer_linear1_weight, x = input_789)[name = tensor("input_791")]; + tensor input_793 = relu(x = input_791)[name = tensor("input_793")]; + tensor input_795_pad_type_0 = const()[name = tensor("input_795_pad_type_0"), val = tensor("valid")]; + tensor input_795_strides_0 = const()[name = tensor("input_795_strides_0"), val = tensor([1])]; + tensor input_795_pad_0 = const()[name = tensor("input_795_pad_0"), val = tensor([0, 0])]; + tensor input_795_dilations_0 = const()[name = tensor("input_795_dilations_0"), val = tensor([1])]; + tensor input_795_groups_0 = const()[name = tensor("input_795_groups_0"), val = tensor(1)]; + tensor input_795 = conv(bias = model_xvector_block2_tdnnd24_cam_layer_linear2_bias, dilations = input_795_dilations_0, groups = input_795_groups_0, pad = input_795_pad_0, pad_type = input_795_pad_type_0, strides = input_795_strides_0, weight = model_xvector_block2_tdnnd24_cam_layer_linear2_weight, x = input_793)[name = tensor("input_795")]; + tensor m_71 = sigmoid(x = input_795)[name = tensor("m_71")]; + tensor var_2763 = mul(x = y_71, y = m_71)[name = tensor("op_2763")]; + tensor input_797_interleave_0 = const()[name = tensor("input_797_interleave_0"), val = tensor(false)]; + tensor input_797 = concat(axis = var_11, interleave = input_797_interleave_0, values = (input_777, var_2763))[name = tensor("input_797")]; + tensor input_799 = batch_norm(beta = model_xvector_transit2_nonlinear_batchnorm_bias, epsilon = var_8, gamma = model_xvector_transit2_nonlinear_batchnorm_weight, mean = model_xvector_transit2_nonlinear_batchnorm_running_mean, variance = model_xvector_transit2_nonlinear_batchnorm_running_var, x = input_797)[name = tensor("input_799")]; + tensor input_801 = relu(x = input_799)[name = tensor("input_801")]; + tensor input_803_pad_type_0 = const()[name = tensor("input_803_pad_type_0"), val = tensor("valid")]; + tensor input_803_strides_0 = const()[name = tensor("input_803_strides_0"), val = tensor([1])]; + tensor input_803_pad_0 = const()[name = tensor("input_803_pad_0"), val = tensor([0, 0])]; + tensor input_803_dilations_0 = const()[name = tensor("input_803_dilations_0"), val = tensor([1])]; + tensor input_803_groups_0 = const()[name = tensor("input_803_groups_0"), val = tensor(1)]; + tensor input_803 = conv(dilations = input_803_dilations_0, groups = input_803_groups_0, pad = input_803_pad_0, pad_type = input_803_pad_type_0, strides = input_803_strides_0, weight = model_xvector_transit2_linear_weight, x = input_801)[name = tensor("input_803")]; + tensor input_805 = batch_norm(beta = model_xvector_block3_tdnnd1_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block3_tdnnd1_nonlinear1_batchnorm_weight, mean = model_xvector_block3_tdnnd1_nonlinear1_batchnorm_running_mean, variance = model_xvector_block3_tdnnd1_nonlinear1_batchnorm_running_var, x = input_803)[name = tensor("input_805")]; + tensor input_807 = relu(x = input_805)[name = tensor("input_807")]; + tensor input_809_pad_type_0 = const()[name = tensor("input_809_pad_type_0"), val = tensor("valid")]; + tensor input_809_strides_0 = const()[name = tensor("input_809_strides_0"), val = tensor([1])]; + tensor input_809_pad_0 = const()[name = tensor("input_809_pad_0"), val = tensor([0, 0])]; + tensor input_809_dilations_0 = const()[name = tensor("input_809_dilations_0"), val = tensor([1])]; + tensor input_809_groups_0 = const()[name = tensor("input_809_groups_0"), val = tensor(1)]; + tensor const_98 = const()[name = tensor("const_98"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19398656)))]; + tensor const_99 = const()[name = tensor("const_99"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19660864)))]; + tensor input_811 = conv(bias = const_99, dilations = input_809_dilations_0, groups = input_809_groups_0, pad = input_809_pad_0, pad_type = input_809_pad_type_0, strides = input_809_strides_0, weight = const_98, x = input_807)[name = tensor("input_811")]; + tensor input_813 = relu(x = input_811)[name = tensor("input_813")]; + tensor y_73_pad_type_0 = const()[name = tensor("y_73_pad_type_0"), val = tensor("custom")]; + tensor y_73_pad_0 = const()[name = tensor("y_73_pad_0"), val = tensor([2, 2])]; + tensor y_73_dilations_0 = const()[name = tensor("y_73_dilations_0"), val = tensor([2])]; + tensor y_73_strides_0 = const()[name = tensor("y_73_strides_0"), val = tensor([1])]; + tensor y_73_groups_0 = const()[name = tensor("y_73_groups_0"), val = tensor(1)]; + tensor y_73 = conv(dilations = y_73_dilations_0, groups = y_73_groups_0, pad = y_73_pad_0, pad_type = y_73_pad_type_0, strides = y_73_strides_0, weight = model_xvector_block3_tdnnd1_cam_layer_linear_local_weight, x = input_813)[name = tensor("y_73")]; + tensor var_2831_axes_0 = const()[name = tensor("op_2831_axes_0"), val = tensor([-1])]; + tensor var_2831_keep_dims_0 = const()[name = tensor("op_2831_keep_dims_0"), val = tensor(true)]; + tensor var_2831 = reduce_mean(axes = var_2831_axes_0, keep_dims = var_2831_keep_dims_0, x = input_813)[name = tensor("op_2831")]; + tensor var_2832 = const()[name = tensor("op_2832"), val = tensor([100])]; + tensor var_2833 = const()[name = tensor("op_2833"), val = tensor([100])]; + tensor seg_145_pad_type_0 = const()[name = tensor("seg_145_pad_type_0"), val = tensor("custom")]; + tensor seg_145_pad_0 = const()[name = tensor("seg_145_pad_0"), val = tensor([0, 0])]; + tensor seg_145_exclude_padding_from_average_0 = const()[name = tensor("seg_145_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_145_ceil_mode_0 = const()[name = tensor("seg_145_ceil_mode_0"), val = tensor(true)]; + tensor seg_145 = avg_pool(ceil_mode = seg_145_ceil_mode_0, exclude_padding_from_average = seg_145_exclude_padding_from_average_0, kernel_sizes = var_2832, pad = seg_145_pad_0, pad_type = seg_145_pad_type_0, strides = var_2833, x = input_813)[name = tensor("seg_145")]; + tensor var_2836_shape = shape(x = seg_145)[name = tensor("op_2836_shape")]; + tensor gather_148 = const()[name = tensor("gather_148"), val = tensor(1)]; + tensor gather_149 = const()[name = tensor("gather_149"), val = tensor(128)]; + tensor select_150 = const()[name = tensor("select_150"), val = tensor(2)]; + tensor gather_150_axis_0 = const()[name = tensor("gather_150_axis_0"), val = tensor(0)]; + tensor gather_150_batch_dims_0 = const()[name = tensor("gather_150_batch_dims_0"), val = tensor(0)]; + tensor gather_150_validate_indices_0 = const()[name = tensor("gather_150_validate_indices_0"), val = tensor(false)]; + tensor gather_150 = gather(axis = gather_150_axis_0, batch_dims = gather_150_batch_dims_0, indices = select_150, validate_indices = gather_150_validate_indices_0, x = var_2836_shape)[name = tensor("gather_150")]; + tensor var_2839_axes_0 = const()[name = tensor("op_2839_axes_0"), val = tensor([-1])]; + tensor var_2839 = expand_dims(axes = var_2839_axes_0, x = seg_145)[name = tensor("op_2839")]; + tensor concat_73_axis_0 = const()[name = tensor("concat_73_axis_0"), val = tensor(0)]; + tensor concat_73_interleave_0 = const()[name = tensor("concat_73_interleave_0"), val = tensor(false)]; + tensor concat_73 = concat(axis = concat_73_axis_0, interleave = concat_73_interleave_0, values = (gather_148, gather_149, gather_150, var_3))[name = tensor("concat_73")]; + tensor shape_187 = shape(x = var_2839)[name = tensor("shape_187")]; + tensor real_div_36 = real_div(x = concat_73, y = shape_187)[name = tensor("real_div_36")]; + tensor var_2841 = tile(reps = real_div_36, x = var_2839)[name = tensor("op_2841")]; + tensor var_2842 = const()[name = tensor("op_2842"), val = tensor([1, 128, -1])]; + tensor seg_147 = reshape(shape = var_2842, x = var_2841)[name = tensor("seg_147")]; + tensor var_2844_shape = shape(x = input_813)[name = tensor("op_2844_shape")]; + tensor select_151 = const()[name = tensor("select_151"), val = tensor(2)]; + tensor gather_151_axis_0 = const()[name = tensor("gather_151_axis_0"), val = tensor(0)]; + tensor gather_151_batch_dims_0 = const()[name = tensor("gather_151_batch_dims_0"), val = tensor(0)]; + tensor gather_151_validate_indices_0 = const()[name = tensor("gather_151_validate_indices_0"), val = tensor(false)]; + tensor gather_151 = gather(axis = gather_151_axis_0, batch_dims = gather_151_batch_dims_0, indices = select_151, validate_indices = gather_151_validate_indices_0, x = var_2844_shape)[name = tensor("gather_151")]; + tensor concat_74_values0_0 = const()[name = tensor("concat_74_values0_0"), val = tensor(1)]; + tensor concat_74_values1_0 = const()[name = tensor("concat_74_values1_0"), val = tensor(128)]; + tensor concat_74_axis_0 = const()[name = tensor("concat_74_axis_0"), val = tensor(0)]; + tensor concat_74_interleave_0 = const()[name = tensor("concat_74_interleave_0"), val = tensor(false)]; + tensor concat_74 = concat(axis = concat_74_axis_0, interleave = concat_74_interleave_0, values = (concat_74_values0_0, concat_74_values1_0, gather_151))[name = tensor("concat_74")]; + tensor var_2845_begin_0 = const()[name = tensor("op_2845_begin_0"), val = tensor([0, 0, 0])]; + tensor var_2845_end_mask_0 = const()[name = tensor("op_2845_end_mask_0"), val = tensor([true, true, false])]; + tensor var_2845 = slice_by_index(begin = var_2845_begin_0, end = concat_74, end_mask = var_2845_end_mask_0, x = seg_147)[name = tensor("op_2845")]; + tensor input_815 = add(x = var_2831, y = var_2845)[name = tensor("input_815")]; + tensor input_817_pad_type_0 = const()[name = tensor("input_817_pad_type_0"), val = tensor("valid")]; + tensor input_817_strides_0 = const()[name = tensor("input_817_strides_0"), val = tensor([1])]; + tensor input_817_pad_0 = const()[name = tensor("input_817_pad_0"), val = tensor([0, 0])]; + tensor input_817_dilations_0 = const()[name = tensor("input_817_dilations_0"), val = tensor([1])]; + tensor input_817_groups_0 = const()[name = tensor("input_817_groups_0"), val = tensor(1)]; + tensor input_817 = conv(bias = model_xvector_block3_tdnnd1_cam_layer_linear1_bias, dilations = input_817_dilations_0, groups = input_817_groups_0, pad = input_817_pad_0, pad_type = input_817_pad_type_0, strides = input_817_strides_0, weight = model_xvector_block3_tdnnd1_cam_layer_linear1_weight, x = input_815)[name = tensor("input_817")]; + tensor input_819 = relu(x = input_817)[name = tensor("input_819")]; + tensor input_821_pad_type_0 = const()[name = tensor("input_821_pad_type_0"), val = tensor("valid")]; + tensor input_821_strides_0 = const()[name = tensor("input_821_strides_0"), val = tensor([1])]; + tensor input_821_pad_0 = const()[name = tensor("input_821_pad_0"), val = tensor([0, 0])]; + tensor input_821_dilations_0 = const()[name = tensor("input_821_dilations_0"), val = tensor([1])]; + tensor input_821_groups_0 = const()[name = tensor("input_821_groups_0"), val = tensor(1)]; + tensor input_821 = conv(bias = model_xvector_block3_tdnnd1_cam_layer_linear2_bias, dilations = input_821_dilations_0, groups = input_821_groups_0, pad = input_821_pad_0, pad_type = input_821_pad_type_0, strides = input_821_strides_0, weight = model_xvector_block3_tdnnd1_cam_layer_linear2_weight, x = input_819)[name = tensor("input_821")]; + tensor m_73 = sigmoid(x = input_821)[name = tensor("m_73")]; + tensor var_2863 = mul(x = y_73, y = m_73)[name = tensor("op_2863")]; + tensor input_823_interleave_0 = const()[name = tensor("input_823_interleave_0"), val = tensor(false)]; + tensor input_823 = concat(axis = var_11, interleave = input_823_interleave_0, values = (input_803, var_2863))[name = tensor("input_823")]; + tensor input_825 = batch_norm(beta = model_xvector_block3_tdnnd2_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block3_tdnnd2_nonlinear1_batchnorm_weight, mean = model_xvector_block3_tdnnd2_nonlinear1_batchnorm_running_mean, variance = model_xvector_block3_tdnnd2_nonlinear1_batchnorm_running_var, x = input_823)[name = tensor("input_825")]; + tensor input_827 = relu(x = input_825)[name = tensor("input_827")]; + tensor input_829_pad_type_0 = const()[name = tensor("input_829_pad_type_0"), val = tensor("valid")]; + tensor input_829_strides_0 = const()[name = tensor("input_829_strides_0"), val = tensor([1])]; + tensor input_829_pad_0 = const()[name = tensor("input_829_pad_0"), val = tensor([0, 0])]; + tensor input_829_dilations_0 = const()[name = tensor("input_829_dilations_0"), val = tensor([1])]; + tensor input_829_groups_0 = const()[name = tensor("input_829_groups_0"), val = tensor(1)]; + tensor const_100 = const()[name = tensor("const_100"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19661440)))]; + tensor const_101 = const()[name = tensor("const_101"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19940032)))]; + tensor input_831 = conv(bias = const_101, dilations = input_829_dilations_0, groups = input_829_groups_0, pad = input_829_pad_0, pad_type = input_829_pad_type_0, strides = input_829_strides_0, weight = const_100, x = input_827)[name = tensor("input_831")]; + tensor input_833 = relu(x = input_831)[name = tensor("input_833")]; + tensor y_75_pad_type_0 = const()[name = tensor("y_75_pad_type_0"), val = tensor("custom")]; + tensor y_75_pad_0 = const()[name = tensor("y_75_pad_0"), val = tensor([2, 2])]; + tensor y_75_dilations_0 = const()[name = tensor("y_75_dilations_0"), val = tensor([2])]; + tensor y_75_strides_0 = const()[name = tensor("y_75_strides_0"), val = tensor([1])]; + tensor y_75_groups_0 = const()[name = tensor("y_75_groups_0"), val = tensor(1)]; + tensor y_75 = conv(dilations = y_75_dilations_0, groups = y_75_groups_0, pad = y_75_pad_0, pad_type = y_75_pad_type_0, strides = y_75_strides_0, weight = model_xvector_block3_tdnnd2_cam_layer_linear_local_weight, x = input_833)[name = tensor("y_75")]; + tensor var_2900_axes_0 = const()[name = tensor("op_2900_axes_0"), val = tensor([-1])]; + tensor var_2900_keep_dims_0 = const()[name = tensor("op_2900_keep_dims_0"), val = tensor(true)]; + tensor var_2900 = reduce_mean(axes = var_2900_axes_0, keep_dims = var_2900_keep_dims_0, x = input_833)[name = tensor("op_2900")]; + tensor var_2901 = const()[name = tensor("op_2901"), val = tensor([100])]; + tensor var_2902 = const()[name = tensor("op_2902"), val = tensor([100])]; + tensor seg_149_pad_type_0 = const()[name = tensor("seg_149_pad_type_0"), val = tensor("custom")]; + tensor seg_149_pad_0 = const()[name = tensor("seg_149_pad_0"), val = tensor([0, 0])]; + tensor seg_149_exclude_padding_from_average_0 = const()[name = tensor("seg_149_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_149_ceil_mode_0 = const()[name = tensor("seg_149_ceil_mode_0"), val = tensor(true)]; + tensor seg_149 = avg_pool(ceil_mode = seg_149_ceil_mode_0, exclude_padding_from_average = seg_149_exclude_padding_from_average_0, kernel_sizes = var_2901, pad = seg_149_pad_0, pad_type = seg_149_pad_type_0, strides = var_2902, x = input_833)[name = tensor("seg_149")]; + tensor var_2905_shape = shape(x = seg_149)[name = tensor("op_2905_shape")]; + tensor gather_152 = const()[name = tensor("gather_152"), val = tensor(1)]; + tensor gather_153 = const()[name = tensor("gather_153"), val = tensor(128)]; + tensor select_154 = const()[name = tensor("select_154"), val = tensor(2)]; + tensor gather_154_axis_0 = const()[name = tensor("gather_154_axis_0"), val = tensor(0)]; + tensor gather_154_batch_dims_0 = const()[name = tensor("gather_154_batch_dims_0"), val = tensor(0)]; + tensor gather_154_validate_indices_0 = const()[name = tensor("gather_154_validate_indices_0"), val = tensor(false)]; + tensor gather_154 = gather(axis = gather_154_axis_0, batch_dims = gather_154_batch_dims_0, indices = select_154, validate_indices = gather_154_validate_indices_0, x = var_2905_shape)[name = tensor("gather_154")]; + tensor var_2908_axes_0 = const()[name = tensor("op_2908_axes_0"), val = tensor([-1])]; + tensor var_2908 = expand_dims(axes = var_2908_axes_0, x = seg_149)[name = tensor("op_2908")]; + tensor concat_75_axis_0 = const()[name = tensor("concat_75_axis_0"), val = tensor(0)]; + tensor concat_75_interleave_0 = const()[name = tensor("concat_75_interleave_0"), val = tensor(false)]; + tensor concat_75 = concat(axis = concat_75_axis_0, interleave = concat_75_interleave_0, values = (gather_152, gather_153, gather_154, var_3))[name = tensor("concat_75")]; + tensor shape_192 = shape(x = var_2908)[name = tensor("shape_192")]; + tensor real_div_37 = real_div(x = concat_75, y = shape_192)[name = tensor("real_div_37")]; + tensor var_2910 = tile(reps = real_div_37, x = var_2908)[name = tensor("op_2910")]; + tensor var_2911 = const()[name = tensor("op_2911"), val = tensor([1, 128, -1])]; + tensor seg_151 = reshape(shape = var_2911, x = var_2910)[name = tensor("seg_151")]; + tensor var_2913_shape = shape(x = input_833)[name = tensor("op_2913_shape")]; + tensor select_155 = const()[name = tensor("select_155"), val = tensor(2)]; + tensor gather_155_axis_0 = const()[name = tensor("gather_155_axis_0"), val = tensor(0)]; + tensor gather_155_batch_dims_0 = const()[name = tensor("gather_155_batch_dims_0"), val = tensor(0)]; + tensor gather_155_validate_indices_0 = const()[name = tensor("gather_155_validate_indices_0"), val = tensor(false)]; + tensor gather_155 = gather(axis = gather_155_axis_0, batch_dims = gather_155_batch_dims_0, indices = select_155, validate_indices = gather_155_validate_indices_0, x = var_2913_shape)[name = tensor("gather_155")]; + tensor concat_76_values0_0 = const()[name = tensor("concat_76_values0_0"), val = tensor(1)]; + tensor concat_76_values1_0 = const()[name = tensor("concat_76_values1_0"), val = tensor(128)]; + tensor concat_76_axis_0 = const()[name = tensor("concat_76_axis_0"), val = tensor(0)]; + tensor concat_76_interleave_0 = const()[name = tensor("concat_76_interleave_0"), val = tensor(false)]; + tensor concat_76 = concat(axis = concat_76_axis_0, interleave = concat_76_interleave_0, values = (concat_76_values0_0, concat_76_values1_0, gather_155))[name = tensor("concat_76")]; + tensor var_2914_begin_0 = const()[name = tensor("op_2914_begin_0"), val = tensor([0, 0, 0])]; + tensor var_2914_end_mask_0 = const()[name = tensor("op_2914_end_mask_0"), val = tensor([true, true, false])]; + tensor var_2914 = slice_by_index(begin = var_2914_begin_0, end = concat_76, end_mask = var_2914_end_mask_0, x = seg_151)[name = tensor("op_2914")]; + tensor input_835 = add(x = var_2900, y = var_2914)[name = tensor("input_835")]; + tensor input_837_pad_type_0 = const()[name = tensor("input_837_pad_type_0"), val = tensor("valid")]; + tensor input_837_strides_0 = const()[name = tensor("input_837_strides_0"), val = tensor([1])]; + tensor input_837_pad_0 = const()[name = tensor("input_837_pad_0"), val = tensor([0, 0])]; + tensor input_837_dilations_0 = const()[name = tensor("input_837_dilations_0"), val = tensor([1])]; + tensor input_837_groups_0 = const()[name = tensor("input_837_groups_0"), val = tensor(1)]; + tensor input_837 = conv(bias = model_xvector_block3_tdnnd2_cam_layer_linear1_bias, dilations = input_837_dilations_0, groups = input_837_groups_0, pad = input_837_pad_0, pad_type = input_837_pad_type_0, strides = input_837_strides_0, weight = model_xvector_block3_tdnnd2_cam_layer_linear1_weight, x = input_835)[name = tensor("input_837")]; + tensor input_839 = relu(x = input_837)[name = tensor("input_839")]; + tensor input_841_pad_type_0 = const()[name = tensor("input_841_pad_type_0"), val = tensor("valid")]; + tensor input_841_strides_0 = const()[name = tensor("input_841_strides_0"), val = tensor([1])]; + tensor input_841_pad_0 = const()[name = tensor("input_841_pad_0"), val = tensor([0, 0])]; + tensor input_841_dilations_0 = const()[name = tensor("input_841_dilations_0"), val = tensor([1])]; + tensor input_841_groups_0 = const()[name = tensor("input_841_groups_0"), val = tensor(1)]; + tensor input_841 = conv(bias = model_xvector_block3_tdnnd2_cam_layer_linear2_bias, dilations = input_841_dilations_0, groups = input_841_groups_0, pad = input_841_pad_0, pad_type = input_841_pad_type_0, strides = input_841_strides_0, weight = model_xvector_block3_tdnnd2_cam_layer_linear2_weight, x = input_839)[name = tensor("input_841")]; + tensor m_75 = sigmoid(x = input_841)[name = tensor("m_75")]; + tensor var_2932 = mul(x = y_75, y = m_75)[name = tensor("op_2932")]; + tensor input_843_interleave_0 = const()[name = tensor("input_843_interleave_0"), val = tensor(false)]; + tensor input_843 = concat(axis = var_11, interleave = input_843_interleave_0, values = (input_823, var_2932))[name = tensor("input_843")]; + tensor input_845 = batch_norm(beta = model_xvector_block3_tdnnd3_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block3_tdnnd3_nonlinear1_batchnorm_weight, mean = model_xvector_block3_tdnnd3_nonlinear1_batchnorm_running_mean, variance = model_xvector_block3_tdnnd3_nonlinear1_batchnorm_running_var, x = input_843)[name = tensor("input_845")]; + tensor input_847 = relu(x = input_845)[name = tensor("input_847")]; + tensor input_849_pad_type_0 = const()[name = tensor("input_849_pad_type_0"), val = tensor("valid")]; + tensor input_849_strides_0 = const()[name = tensor("input_849_strides_0"), val = tensor([1])]; + tensor input_849_pad_0 = const()[name = tensor("input_849_pad_0"), val = tensor([0, 0])]; + tensor input_849_dilations_0 = const()[name = tensor("input_849_dilations_0"), val = tensor([1])]; + tensor input_849_groups_0 = const()[name = tensor("input_849_groups_0"), val = tensor(1)]; + tensor const_102 = const()[name = tensor("const_102"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19940608)))]; + tensor const_103 = const()[name = tensor("const_103"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20235584)))]; + tensor input_851 = conv(bias = const_103, dilations = input_849_dilations_0, groups = input_849_groups_0, pad = input_849_pad_0, pad_type = input_849_pad_type_0, strides = input_849_strides_0, weight = const_102, x = input_847)[name = tensor("input_851")]; + tensor input_853 = relu(x = input_851)[name = tensor("input_853")]; + tensor y_77_pad_type_0 = const()[name = tensor("y_77_pad_type_0"), val = tensor("custom")]; + tensor y_77_pad_0 = const()[name = tensor("y_77_pad_0"), val = tensor([2, 2])]; + tensor y_77_dilations_0 = const()[name = tensor("y_77_dilations_0"), val = tensor([2])]; + tensor y_77_strides_0 = const()[name = tensor("y_77_strides_0"), val = tensor([1])]; + tensor y_77_groups_0 = const()[name = tensor("y_77_groups_0"), val = tensor(1)]; + tensor y_77 = conv(dilations = y_77_dilations_0, groups = y_77_groups_0, pad = y_77_pad_0, pad_type = y_77_pad_type_0, strides = y_77_strides_0, weight = model_xvector_block3_tdnnd3_cam_layer_linear_local_weight, x = input_853)[name = tensor("y_77")]; + tensor var_2969_axes_0 = const()[name = tensor("op_2969_axes_0"), val = tensor([-1])]; + tensor var_2969_keep_dims_0 = const()[name = tensor("op_2969_keep_dims_0"), val = tensor(true)]; + tensor var_2969 = reduce_mean(axes = var_2969_axes_0, keep_dims = var_2969_keep_dims_0, x = input_853)[name = tensor("op_2969")]; + tensor var_2970 = const()[name = tensor("op_2970"), val = tensor([100])]; + tensor var_2971 = const()[name = tensor("op_2971"), val = tensor([100])]; + tensor seg_153_pad_type_0 = const()[name = tensor("seg_153_pad_type_0"), val = tensor("custom")]; + tensor seg_153_pad_0 = const()[name = tensor("seg_153_pad_0"), val = tensor([0, 0])]; + tensor seg_153_exclude_padding_from_average_0 = const()[name = tensor("seg_153_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_153_ceil_mode_0 = const()[name = tensor("seg_153_ceil_mode_0"), val = tensor(true)]; + tensor seg_153 = avg_pool(ceil_mode = seg_153_ceil_mode_0, exclude_padding_from_average = seg_153_exclude_padding_from_average_0, kernel_sizes = var_2970, pad = seg_153_pad_0, pad_type = seg_153_pad_type_0, strides = var_2971, x = input_853)[name = tensor("seg_153")]; + tensor var_2974_shape = shape(x = seg_153)[name = tensor("op_2974_shape")]; + tensor gather_156 = const()[name = tensor("gather_156"), val = tensor(1)]; + tensor gather_157 = const()[name = tensor("gather_157"), val = tensor(128)]; + tensor select_158 = const()[name = tensor("select_158"), val = tensor(2)]; + tensor gather_158_axis_0 = const()[name = tensor("gather_158_axis_0"), val = tensor(0)]; + tensor gather_158_batch_dims_0 = const()[name = tensor("gather_158_batch_dims_0"), val = tensor(0)]; + tensor gather_158_validate_indices_0 = const()[name = tensor("gather_158_validate_indices_0"), val = tensor(false)]; + tensor gather_158 = gather(axis = gather_158_axis_0, batch_dims = gather_158_batch_dims_0, indices = select_158, validate_indices = gather_158_validate_indices_0, x = var_2974_shape)[name = tensor("gather_158")]; + tensor var_2977_axes_0 = const()[name = tensor("op_2977_axes_0"), val = tensor([-1])]; + tensor var_2977 = expand_dims(axes = var_2977_axes_0, x = seg_153)[name = tensor("op_2977")]; + tensor concat_77_axis_0 = const()[name = tensor("concat_77_axis_0"), val = tensor(0)]; + tensor concat_77_interleave_0 = const()[name = tensor("concat_77_interleave_0"), val = tensor(false)]; + tensor concat_77 = concat(axis = concat_77_axis_0, interleave = concat_77_interleave_0, values = (gather_156, gather_157, gather_158, var_3))[name = tensor("concat_77")]; + tensor shape_197 = shape(x = var_2977)[name = tensor("shape_197")]; + tensor real_div_38 = real_div(x = concat_77, y = shape_197)[name = tensor("real_div_38")]; + tensor var_2979 = tile(reps = real_div_38, x = var_2977)[name = tensor("op_2979")]; + tensor var_2980 = const()[name = tensor("op_2980"), val = tensor([1, 128, -1])]; + tensor seg_155 = reshape(shape = var_2980, x = var_2979)[name = tensor("seg_155")]; + tensor var_2982_shape = shape(x = input_853)[name = tensor("op_2982_shape")]; + tensor select_159 = const()[name = tensor("select_159"), val = tensor(2)]; + tensor gather_159_axis_0 = const()[name = tensor("gather_159_axis_0"), val = tensor(0)]; + tensor gather_159_batch_dims_0 = const()[name = tensor("gather_159_batch_dims_0"), val = tensor(0)]; + tensor gather_159_validate_indices_0 = const()[name = tensor("gather_159_validate_indices_0"), val = tensor(false)]; + tensor gather_159 = gather(axis = gather_159_axis_0, batch_dims = gather_159_batch_dims_0, indices = select_159, validate_indices = gather_159_validate_indices_0, x = var_2982_shape)[name = tensor("gather_159")]; + tensor concat_78_values0_0 = const()[name = tensor("concat_78_values0_0"), val = tensor(1)]; + tensor concat_78_values1_0 = const()[name = tensor("concat_78_values1_0"), val = tensor(128)]; + tensor concat_78_axis_0 = const()[name = tensor("concat_78_axis_0"), val = tensor(0)]; + tensor concat_78_interleave_0 = const()[name = tensor("concat_78_interleave_0"), val = tensor(false)]; + tensor concat_78 = concat(axis = concat_78_axis_0, interleave = concat_78_interleave_0, values = (concat_78_values0_0, concat_78_values1_0, gather_159))[name = tensor("concat_78")]; + tensor var_2983_begin_0 = const()[name = tensor("op_2983_begin_0"), val = tensor([0, 0, 0])]; + tensor var_2983_end_mask_0 = const()[name = tensor("op_2983_end_mask_0"), val = tensor([true, true, false])]; + tensor var_2983 = slice_by_index(begin = var_2983_begin_0, end = concat_78, end_mask = var_2983_end_mask_0, x = seg_155)[name = tensor("op_2983")]; + tensor input_855 = add(x = var_2969, y = var_2983)[name = tensor("input_855")]; + tensor input_857_pad_type_0 = const()[name = tensor("input_857_pad_type_0"), val = tensor("valid")]; + tensor input_857_strides_0 = const()[name = tensor("input_857_strides_0"), val = tensor([1])]; + tensor input_857_pad_0 = const()[name = tensor("input_857_pad_0"), val = tensor([0, 0])]; + tensor input_857_dilations_0 = const()[name = tensor("input_857_dilations_0"), val = tensor([1])]; + tensor input_857_groups_0 = const()[name = tensor("input_857_groups_0"), val = tensor(1)]; + tensor input_857 = conv(bias = model_xvector_block3_tdnnd3_cam_layer_linear1_bias, dilations = input_857_dilations_0, groups = input_857_groups_0, pad = input_857_pad_0, pad_type = input_857_pad_type_0, strides = input_857_strides_0, weight = model_xvector_block3_tdnnd3_cam_layer_linear1_weight, x = input_855)[name = tensor("input_857")]; + tensor input_859 = relu(x = input_857)[name = tensor("input_859")]; + tensor input_861_pad_type_0 = const()[name = tensor("input_861_pad_type_0"), val = tensor("valid")]; + tensor input_861_strides_0 = const()[name = tensor("input_861_strides_0"), val = tensor([1])]; + tensor input_861_pad_0 = const()[name = tensor("input_861_pad_0"), val = tensor([0, 0])]; + tensor input_861_dilations_0 = const()[name = tensor("input_861_dilations_0"), val = tensor([1])]; + tensor input_861_groups_0 = const()[name = tensor("input_861_groups_0"), val = tensor(1)]; + tensor input_861 = conv(bias = model_xvector_block3_tdnnd3_cam_layer_linear2_bias, dilations = input_861_dilations_0, groups = input_861_groups_0, pad = input_861_pad_0, pad_type = input_861_pad_type_0, strides = input_861_strides_0, weight = model_xvector_block3_tdnnd3_cam_layer_linear2_weight, x = input_859)[name = tensor("input_861")]; + tensor m_77 = sigmoid(x = input_861)[name = tensor("m_77")]; + tensor var_3001 = mul(x = y_77, y = m_77)[name = tensor("op_3001")]; + tensor input_863_interleave_0 = const()[name = tensor("input_863_interleave_0"), val = tensor(false)]; + tensor input_863 = concat(axis = var_11, interleave = input_863_interleave_0, values = (input_843, var_3001))[name = tensor("input_863")]; + tensor input_865 = batch_norm(beta = model_xvector_block3_tdnnd4_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block3_tdnnd4_nonlinear1_batchnorm_weight, mean = model_xvector_block3_tdnnd4_nonlinear1_batchnorm_running_mean, variance = model_xvector_block3_tdnnd4_nonlinear1_batchnorm_running_var, x = input_863)[name = tensor("input_865")]; + tensor input_867 = relu(x = input_865)[name = tensor("input_867")]; + tensor input_869_pad_type_0 = const()[name = tensor("input_869_pad_type_0"), val = tensor("valid")]; + tensor input_869_strides_0 = const()[name = tensor("input_869_strides_0"), val = tensor([1])]; + tensor input_869_pad_0 = const()[name = tensor("input_869_pad_0"), val = tensor([0, 0])]; + tensor input_869_dilations_0 = const()[name = tensor("input_869_dilations_0"), val = tensor([1])]; + tensor input_869_groups_0 = const()[name = tensor("input_869_groups_0"), val = tensor(1)]; + tensor const_104 = const()[name = tensor("const_104"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20236160)))]; + tensor const_105 = const()[name = tensor("const_105"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20547520)))]; + tensor input_871 = conv(bias = const_105, dilations = input_869_dilations_0, groups = input_869_groups_0, pad = input_869_pad_0, pad_type = input_869_pad_type_0, strides = input_869_strides_0, weight = const_104, x = input_867)[name = tensor("input_871")]; + tensor input_873 = relu(x = input_871)[name = tensor("input_873")]; + tensor y_79_pad_type_0 = const()[name = tensor("y_79_pad_type_0"), val = tensor("custom")]; + tensor y_79_pad_0 = const()[name = tensor("y_79_pad_0"), val = tensor([2, 2])]; + tensor y_79_dilations_0 = const()[name = tensor("y_79_dilations_0"), val = tensor([2])]; + tensor y_79_strides_0 = const()[name = tensor("y_79_strides_0"), val = tensor([1])]; + tensor y_79_groups_0 = const()[name = tensor("y_79_groups_0"), val = tensor(1)]; + tensor y_79 = conv(dilations = y_79_dilations_0, groups = y_79_groups_0, pad = y_79_pad_0, pad_type = y_79_pad_type_0, strides = y_79_strides_0, weight = model_xvector_block3_tdnnd4_cam_layer_linear_local_weight, x = input_873)[name = tensor("y_79")]; + tensor var_3038_axes_0 = const()[name = tensor("op_3038_axes_0"), val = tensor([-1])]; + tensor var_3038_keep_dims_0 = const()[name = tensor("op_3038_keep_dims_0"), val = tensor(true)]; + tensor var_3038 = reduce_mean(axes = var_3038_axes_0, keep_dims = var_3038_keep_dims_0, x = input_873)[name = tensor("op_3038")]; + tensor var_3039 = const()[name = tensor("op_3039"), val = tensor([100])]; + tensor var_3040 = const()[name = tensor("op_3040"), val = tensor([100])]; + tensor seg_157_pad_type_0 = const()[name = tensor("seg_157_pad_type_0"), val = tensor("custom")]; + tensor seg_157_pad_0 = const()[name = tensor("seg_157_pad_0"), val = tensor([0, 0])]; + tensor seg_157_exclude_padding_from_average_0 = const()[name = tensor("seg_157_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_157_ceil_mode_0 = const()[name = tensor("seg_157_ceil_mode_0"), val = tensor(true)]; + tensor seg_157 = avg_pool(ceil_mode = seg_157_ceil_mode_0, exclude_padding_from_average = seg_157_exclude_padding_from_average_0, kernel_sizes = var_3039, pad = seg_157_pad_0, pad_type = seg_157_pad_type_0, strides = var_3040, x = input_873)[name = tensor("seg_157")]; + tensor var_3043_shape = shape(x = seg_157)[name = tensor("op_3043_shape")]; + tensor gather_160 = const()[name = tensor("gather_160"), val = tensor(1)]; + tensor gather_161 = const()[name = tensor("gather_161"), val = tensor(128)]; + tensor select_162 = const()[name = tensor("select_162"), val = tensor(2)]; + tensor gather_162_axis_0 = const()[name = tensor("gather_162_axis_0"), val = tensor(0)]; + tensor gather_162_batch_dims_0 = const()[name = tensor("gather_162_batch_dims_0"), val = tensor(0)]; + tensor gather_162_validate_indices_0 = const()[name = tensor("gather_162_validate_indices_0"), val = tensor(false)]; + tensor gather_162 = gather(axis = gather_162_axis_0, batch_dims = gather_162_batch_dims_0, indices = select_162, validate_indices = gather_162_validate_indices_0, x = var_3043_shape)[name = tensor("gather_162")]; + tensor var_3046_axes_0 = const()[name = tensor("op_3046_axes_0"), val = tensor([-1])]; + tensor var_3046 = expand_dims(axes = var_3046_axes_0, x = seg_157)[name = tensor("op_3046")]; + tensor concat_79_axis_0 = const()[name = tensor("concat_79_axis_0"), val = tensor(0)]; + tensor concat_79_interleave_0 = const()[name = tensor("concat_79_interleave_0"), val = tensor(false)]; + tensor concat_79 = concat(axis = concat_79_axis_0, interleave = concat_79_interleave_0, values = (gather_160, gather_161, gather_162, var_3))[name = tensor("concat_79")]; + tensor shape_202 = shape(x = var_3046)[name = tensor("shape_202")]; + tensor real_div_39 = real_div(x = concat_79, y = shape_202)[name = tensor("real_div_39")]; + tensor var_3048 = tile(reps = real_div_39, x = var_3046)[name = tensor("op_3048")]; + tensor var_3049 = const()[name = tensor("op_3049"), val = tensor([1, 128, -1])]; + tensor seg_159 = reshape(shape = var_3049, x = var_3048)[name = tensor("seg_159")]; + tensor var_3051_shape = shape(x = input_873)[name = tensor("op_3051_shape")]; + tensor select_163 = const()[name = tensor("select_163"), val = tensor(2)]; + tensor gather_163_axis_0 = const()[name = tensor("gather_163_axis_0"), val = tensor(0)]; + tensor gather_163_batch_dims_0 = const()[name = tensor("gather_163_batch_dims_0"), val = tensor(0)]; + tensor gather_163_validate_indices_0 = const()[name = tensor("gather_163_validate_indices_0"), val = tensor(false)]; + tensor gather_163 = gather(axis = gather_163_axis_0, batch_dims = gather_163_batch_dims_0, indices = select_163, validate_indices = gather_163_validate_indices_0, x = var_3051_shape)[name = tensor("gather_163")]; + tensor concat_80_values0_0 = const()[name = tensor("concat_80_values0_0"), val = tensor(1)]; + tensor concat_80_values1_0 = const()[name = tensor("concat_80_values1_0"), val = tensor(128)]; + tensor concat_80_axis_0 = const()[name = tensor("concat_80_axis_0"), val = tensor(0)]; + tensor concat_80_interleave_0 = const()[name = tensor("concat_80_interleave_0"), val = tensor(false)]; + tensor concat_80 = concat(axis = concat_80_axis_0, interleave = concat_80_interleave_0, values = (concat_80_values0_0, concat_80_values1_0, gather_163))[name = tensor("concat_80")]; + tensor var_3052_begin_0 = const()[name = tensor("op_3052_begin_0"), val = tensor([0, 0, 0])]; + tensor var_3052_end_mask_0 = const()[name = tensor("op_3052_end_mask_0"), val = tensor([true, true, false])]; + tensor var_3052 = slice_by_index(begin = var_3052_begin_0, end = concat_80, end_mask = var_3052_end_mask_0, x = seg_159)[name = tensor("op_3052")]; + tensor input_875 = add(x = var_3038, y = var_3052)[name = tensor("input_875")]; + tensor input_877_pad_type_0 = const()[name = tensor("input_877_pad_type_0"), val = tensor("valid")]; + tensor input_877_strides_0 = const()[name = tensor("input_877_strides_0"), val = tensor([1])]; + tensor input_877_pad_0 = const()[name = tensor("input_877_pad_0"), val = tensor([0, 0])]; + tensor input_877_dilations_0 = const()[name = tensor("input_877_dilations_0"), val = tensor([1])]; + tensor input_877_groups_0 = const()[name = tensor("input_877_groups_0"), val = tensor(1)]; + tensor input_877 = conv(bias = model_xvector_block3_tdnnd4_cam_layer_linear1_bias, dilations = input_877_dilations_0, groups = input_877_groups_0, pad = input_877_pad_0, pad_type = input_877_pad_type_0, strides = input_877_strides_0, weight = model_xvector_block3_tdnnd4_cam_layer_linear1_weight, x = input_875)[name = tensor("input_877")]; + tensor input_879 = relu(x = input_877)[name = tensor("input_879")]; + tensor input_881_pad_type_0 = const()[name = tensor("input_881_pad_type_0"), val = tensor("valid")]; + tensor input_881_strides_0 = const()[name = tensor("input_881_strides_0"), val = tensor([1])]; + tensor input_881_pad_0 = const()[name = tensor("input_881_pad_0"), val = tensor([0, 0])]; + tensor input_881_dilations_0 = const()[name = tensor("input_881_dilations_0"), val = tensor([1])]; + tensor input_881_groups_0 = const()[name = tensor("input_881_groups_0"), val = tensor(1)]; + tensor input_881 = conv(bias = model_xvector_block3_tdnnd4_cam_layer_linear2_bias, dilations = input_881_dilations_0, groups = input_881_groups_0, pad = input_881_pad_0, pad_type = input_881_pad_type_0, strides = input_881_strides_0, weight = model_xvector_block3_tdnnd4_cam_layer_linear2_weight, x = input_879)[name = tensor("input_881")]; + tensor m_79 = sigmoid(x = input_881)[name = tensor("m_79")]; + tensor var_3070 = mul(x = y_79, y = m_79)[name = tensor("op_3070")]; + tensor input_883_interleave_0 = const()[name = tensor("input_883_interleave_0"), val = tensor(false)]; + tensor input_883 = concat(axis = var_11, interleave = input_883_interleave_0, values = (input_863, var_3070))[name = tensor("input_883")]; + tensor input_885 = batch_norm(beta = model_xvector_block3_tdnnd5_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block3_tdnnd5_nonlinear1_batchnorm_weight, mean = model_xvector_block3_tdnnd5_nonlinear1_batchnorm_running_mean, variance = model_xvector_block3_tdnnd5_nonlinear1_batchnorm_running_var, x = input_883)[name = tensor("input_885")]; + tensor input_887 = relu(x = input_885)[name = tensor("input_887")]; + tensor input_889_pad_type_0 = const()[name = tensor("input_889_pad_type_0"), val = tensor("valid")]; + tensor input_889_strides_0 = const()[name = tensor("input_889_strides_0"), val = tensor([1])]; + tensor input_889_pad_0 = const()[name = tensor("input_889_pad_0"), val = tensor([0, 0])]; + tensor input_889_dilations_0 = const()[name = tensor("input_889_dilations_0"), val = tensor([1])]; + tensor input_889_groups_0 = const()[name = tensor("input_889_groups_0"), val = tensor(1)]; + tensor const_106 = const()[name = tensor("const_106"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20548096)))]; + tensor const_107 = const()[name = tensor("const_107"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20875840)))]; + tensor input_891 = conv(bias = const_107, dilations = input_889_dilations_0, groups = input_889_groups_0, pad = input_889_pad_0, pad_type = input_889_pad_type_0, strides = input_889_strides_0, weight = const_106, x = input_887)[name = tensor("input_891")]; + tensor input_893 = relu(x = input_891)[name = tensor("input_893")]; + tensor y_81_pad_type_0 = const()[name = tensor("y_81_pad_type_0"), val = tensor("custom")]; + tensor y_81_pad_0 = const()[name = tensor("y_81_pad_0"), val = tensor([2, 2])]; + tensor y_81_dilations_0 = const()[name = tensor("y_81_dilations_0"), val = tensor([2])]; + tensor y_81_strides_0 = const()[name = tensor("y_81_strides_0"), val = tensor([1])]; + tensor y_81_groups_0 = const()[name = tensor("y_81_groups_0"), val = tensor(1)]; + tensor y_81 = conv(dilations = y_81_dilations_0, groups = y_81_groups_0, pad = y_81_pad_0, pad_type = y_81_pad_type_0, strides = y_81_strides_0, weight = model_xvector_block3_tdnnd5_cam_layer_linear_local_weight, x = input_893)[name = tensor("y_81")]; + tensor var_3107_axes_0 = const()[name = tensor("op_3107_axes_0"), val = tensor([-1])]; + tensor var_3107_keep_dims_0 = const()[name = tensor("op_3107_keep_dims_0"), val = tensor(true)]; + tensor var_3107 = reduce_mean(axes = var_3107_axes_0, keep_dims = var_3107_keep_dims_0, x = input_893)[name = tensor("op_3107")]; + tensor var_3108 = const()[name = tensor("op_3108"), val = tensor([100])]; + tensor var_3109 = const()[name = tensor("op_3109"), val = tensor([100])]; + tensor seg_161_pad_type_0 = const()[name = tensor("seg_161_pad_type_0"), val = tensor("custom")]; + tensor seg_161_pad_0 = const()[name = tensor("seg_161_pad_0"), val = tensor([0, 0])]; + tensor seg_161_exclude_padding_from_average_0 = const()[name = tensor("seg_161_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_161_ceil_mode_0 = const()[name = tensor("seg_161_ceil_mode_0"), val = tensor(true)]; + tensor seg_161 = avg_pool(ceil_mode = seg_161_ceil_mode_0, exclude_padding_from_average = seg_161_exclude_padding_from_average_0, kernel_sizes = var_3108, pad = seg_161_pad_0, pad_type = seg_161_pad_type_0, strides = var_3109, x = input_893)[name = tensor("seg_161")]; + tensor var_3112_shape = shape(x = seg_161)[name = tensor("op_3112_shape")]; + tensor gather_164 = const()[name = tensor("gather_164"), val = tensor(1)]; + tensor gather_165 = const()[name = tensor("gather_165"), val = tensor(128)]; + tensor select_166 = const()[name = tensor("select_166"), val = tensor(2)]; + tensor gather_166_axis_0 = const()[name = tensor("gather_166_axis_0"), val = tensor(0)]; + tensor gather_166_batch_dims_0 = const()[name = tensor("gather_166_batch_dims_0"), val = tensor(0)]; + tensor gather_166_validate_indices_0 = const()[name = tensor("gather_166_validate_indices_0"), val = tensor(false)]; + tensor gather_166 = gather(axis = gather_166_axis_0, batch_dims = gather_166_batch_dims_0, indices = select_166, validate_indices = gather_166_validate_indices_0, x = var_3112_shape)[name = tensor("gather_166")]; + tensor var_3115_axes_0 = const()[name = tensor("op_3115_axes_0"), val = tensor([-1])]; + tensor var_3115 = expand_dims(axes = var_3115_axes_0, x = seg_161)[name = tensor("op_3115")]; + tensor concat_81_axis_0 = const()[name = tensor("concat_81_axis_0"), val = tensor(0)]; + tensor concat_81_interleave_0 = const()[name = tensor("concat_81_interleave_0"), val = tensor(false)]; + tensor concat_81 = concat(axis = concat_81_axis_0, interleave = concat_81_interleave_0, values = (gather_164, gather_165, gather_166, var_3))[name = tensor("concat_81")]; + tensor shape_207 = shape(x = var_3115)[name = tensor("shape_207")]; + tensor real_div_40 = real_div(x = concat_81, y = shape_207)[name = tensor("real_div_40")]; + tensor var_3117 = tile(reps = real_div_40, x = var_3115)[name = tensor("op_3117")]; + tensor var_3118 = const()[name = tensor("op_3118"), val = tensor([1, 128, -1])]; + tensor seg_163 = reshape(shape = var_3118, x = var_3117)[name = tensor("seg_163")]; + tensor var_3120_shape = shape(x = input_893)[name = tensor("op_3120_shape")]; + tensor select_167 = const()[name = tensor("select_167"), val = tensor(2)]; + tensor gather_167_axis_0 = const()[name = tensor("gather_167_axis_0"), val = tensor(0)]; + tensor gather_167_batch_dims_0 = const()[name = tensor("gather_167_batch_dims_0"), val = tensor(0)]; + tensor gather_167_validate_indices_0 = const()[name = tensor("gather_167_validate_indices_0"), val = tensor(false)]; + tensor gather_167 = gather(axis = gather_167_axis_0, batch_dims = gather_167_batch_dims_0, indices = select_167, validate_indices = gather_167_validate_indices_0, x = var_3120_shape)[name = tensor("gather_167")]; + tensor concat_82_values0_0 = const()[name = tensor("concat_82_values0_0"), val = tensor(1)]; + tensor concat_82_values1_0 = const()[name = tensor("concat_82_values1_0"), val = tensor(128)]; + tensor concat_82_axis_0 = const()[name = tensor("concat_82_axis_0"), val = tensor(0)]; + tensor concat_82_interleave_0 = const()[name = tensor("concat_82_interleave_0"), val = tensor(false)]; + tensor concat_82 = concat(axis = concat_82_axis_0, interleave = concat_82_interleave_0, values = (concat_82_values0_0, concat_82_values1_0, gather_167))[name = tensor("concat_82")]; + tensor var_3121_begin_0 = const()[name = tensor("op_3121_begin_0"), val = tensor([0, 0, 0])]; + tensor var_3121_end_mask_0 = const()[name = tensor("op_3121_end_mask_0"), val = tensor([true, true, false])]; + tensor var_3121 = slice_by_index(begin = var_3121_begin_0, end = concat_82, end_mask = var_3121_end_mask_0, x = seg_163)[name = tensor("op_3121")]; + tensor input_895 = add(x = var_3107, y = var_3121)[name = tensor("input_895")]; + tensor input_897_pad_type_0 = const()[name = tensor("input_897_pad_type_0"), val = tensor("valid")]; + tensor input_897_strides_0 = const()[name = tensor("input_897_strides_0"), val = tensor([1])]; + tensor input_897_pad_0 = const()[name = tensor("input_897_pad_0"), val = tensor([0, 0])]; + tensor input_897_dilations_0 = const()[name = tensor("input_897_dilations_0"), val = tensor([1])]; + tensor input_897_groups_0 = const()[name = tensor("input_897_groups_0"), val = tensor(1)]; + tensor input_897 = conv(bias = model_xvector_block3_tdnnd5_cam_layer_linear1_bias, dilations = input_897_dilations_0, groups = input_897_groups_0, pad = input_897_pad_0, pad_type = input_897_pad_type_0, strides = input_897_strides_0, weight = model_xvector_block3_tdnnd5_cam_layer_linear1_weight, x = input_895)[name = tensor("input_897")]; + tensor input_899 = relu(x = input_897)[name = tensor("input_899")]; + tensor input_901_pad_type_0 = const()[name = tensor("input_901_pad_type_0"), val = tensor("valid")]; + tensor input_901_strides_0 = const()[name = tensor("input_901_strides_0"), val = tensor([1])]; + tensor input_901_pad_0 = const()[name = tensor("input_901_pad_0"), val = tensor([0, 0])]; + tensor input_901_dilations_0 = const()[name = tensor("input_901_dilations_0"), val = tensor([1])]; + tensor input_901_groups_0 = const()[name = tensor("input_901_groups_0"), val = tensor(1)]; + tensor input_901 = conv(bias = model_xvector_block3_tdnnd5_cam_layer_linear2_bias, dilations = input_901_dilations_0, groups = input_901_groups_0, pad = input_901_pad_0, pad_type = input_901_pad_type_0, strides = input_901_strides_0, weight = model_xvector_block3_tdnnd5_cam_layer_linear2_weight, x = input_899)[name = tensor("input_901")]; + tensor m_81 = sigmoid(x = input_901)[name = tensor("m_81")]; + tensor var_3139 = mul(x = y_81, y = m_81)[name = tensor("op_3139")]; + tensor input_903_interleave_0 = const()[name = tensor("input_903_interleave_0"), val = tensor(false)]; + tensor input_903 = concat(axis = var_11, interleave = input_903_interleave_0, values = (input_883, var_3139))[name = tensor("input_903")]; + tensor input_905 = batch_norm(beta = model_xvector_block3_tdnnd6_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block3_tdnnd6_nonlinear1_batchnorm_weight, mean = model_xvector_block3_tdnnd6_nonlinear1_batchnorm_running_mean, variance = model_xvector_block3_tdnnd6_nonlinear1_batchnorm_running_var, x = input_903)[name = tensor("input_905")]; + tensor input_907 = relu(x = input_905)[name = tensor("input_907")]; + tensor input_909_pad_type_0 = const()[name = tensor("input_909_pad_type_0"), val = tensor("valid")]; + tensor input_909_strides_0 = const()[name = tensor("input_909_strides_0"), val = tensor([1])]; + tensor input_909_pad_0 = const()[name = tensor("input_909_pad_0"), val = tensor([0, 0])]; + tensor input_909_dilations_0 = const()[name = tensor("input_909_dilations_0"), val = tensor([1])]; + tensor input_909_groups_0 = const()[name = tensor("input_909_groups_0"), val = tensor(1)]; + tensor const_108 = const()[name = tensor("const_108"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20876416)))]; + tensor const_109 = const()[name = tensor("const_109"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21220544)))]; + tensor input_911 = conv(bias = const_109, dilations = input_909_dilations_0, groups = input_909_groups_0, pad = input_909_pad_0, pad_type = input_909_pad_type_0, strides = input_909_strides_0, weight = const_108, x = input_907)[name = tensor("input_911")]; + tensor input_913 = relu(x = input_911)[name = tensor("input_913")]; + tensor y_83_pad_type_0 = const()[name = tensor("y_83_pad_type_0"), val = tensor("custom")]; + tensor y_83_pad_0 = const()[name = tensor("y_83_pad_0"), val = tensor([2, 2])]; + tensor y_83_dilations_0 = const()[name = tensor("y_83_dilations_0"), val = tensor([2])]; + tensor y_83_strides_0 = const()[name = tensor("y_83_strides_0"), val = tensor([1])]; + tensor y_83_groups_0 = const()[name = tensor("y_83_groups_0"), val = tensor(1)]; + tensor y_83 = conv(dilations = y_83_dilations_0, groups = y_83_groups_0, pad = y_83_pad_0, pad_type = y_83_pad_type_0, strides = y_83_strides_0, weight = model_xvector_block3_tdnnd6_cam_layer_linear_local_weight, x = input_913)[name = tensor("y_83")]; + tensor var_3176_axes_0 = const()[name = tensor("op_3176_axes_0"), val = tensor([-1])]; + tensor var_3176_keep_dims_0 = const()[name = tensor("op_3176_keep_dims_0"), val = tensor(true)]; + tensor var_3176 = reduce_mean(axes = var_3176_axes_0, keep_dims = var_3176_keep_dims_0, x = input_913)[name = tensor("op_3176")]; + tensor var_3177 = const()[name = tensor("op_3177"), val = tensor([100])]; + tensor var_3178 = const()[name = tensor("op_3178"), val = tensor([100])]; + tensor seg_165_pad_type_0 = const()[name = tensor("seg_165_pad_type_0"), val = tensor("custom")]; + tensor seg_165_pad_0 = const()[name = tensor("seg_165_pad_0"), val = tensor([0, 0])]; + tensor seg_165_exclude_padding_from_average_0 = const()[name = tensor("seg_165_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_165_ceil_mode_0 = const()[name = tensor("seg_165_ceil_mode_0"), val = tensor(true)]; + tensor seg_165 = avg_pool(ceil_mode = seg_165_ceil_mode_0, exclude_padding_from_average = seg_165_exclude_padding_from_average_0, kernel_sizes = var_3177, pad = seg_165_pad_0, pad_type = seg_165_pad_type_0, strides = var_3178, x = input_913)[name = tensor("seg_165")]; + tensor var_3181_shape = shape(x = seg_165)[name = tensor("op_3181_shape")]; + tensor gather_168 = const()[name = tensor("gather_168"), val = tensor(1)]; + tensor gather_169 = const()[name = tensor("gather_169"), val = tensor(128)]; + tensor select_170 = const()[name = tensor("select_170"), val = tensor(2)]; + tensor gather_170_axis_0 = const()[name = tensor("gather_170_axis_0"), val = tensor(0)]; + tensor gather_170_batch_dims_0 = const()[name = tensor("gather_170_batch_dims_0"), val = tensor(0)]; + tensor gather_170_validate_indices_0 = const()[name = tensor("gather_170_validate_indices_0"), val = tensor(false)]; + tensor gather_170 = gather(axis = gather_170_axis_0, batch_dims = gather_170_batch_dims_0, indices = select_170, validate_indices = gather_170_validate_indices_0, x = var_3181_shape)[name = tensor("gather_170")]; + tensor var_3184_axes_0 = const()[name = tensor("op_3184_axes_0"), val = tensor([-1])]; + tensor var_3184 = expand_dims(axes = var_3184_axes_0, x = seg_165)[name = tensor("op_3184")]; + tensor concat_83_axis_0 = const()[name = tensor("concat_83_axis_0"), val = tensor(0)]; + tensor concat_83_interleave_0 = const()[name = tensor("concat_83_interleave_0"), val = tensor(false)]; + tensor concat_83 = concat(axis = concat_83_axis_0, interleave = concat_83_interleave_0, values = (gather_168, gather_169, gather_170, var_3))[name = tensor("concat_83")]; + tensor shape_212 = shape(x = var_3184)[name = tensor("shape_212")]; + tensor real_div_41 = real_div(x = concat_83, y = shape_212)[name = tensor("real_div_41")]; + tensor var_3186 = tile(reps = real_div_41, x = var_3184)[name = tensor("op_3186")]; + tensor var_3187 = const()[name = tensor("op_3187"), val = tensor([1, 128, -1])]; + tensor seg_167 = reshape(shape = var_3187, x = var_3186)[name = tensor("seg_167")]; + tensor var_3189_shape = shape(x = input_913)[name = tensor("op_3189_shape")]; + tensor select_171 = const()[name = tensor("select_171"), val = tensor(2)]; + tensor gather_171_axis_0 = const()[name = tensor("gather_171_axis_0"), val = tensor(0)]; + tensor gather_171_batch_dims_0 = const()[name = tensor("gather_171_batch_dims_0"), val = tensor(0)]; + tensor gather_171_validate_indices_0 = const()[name = tensor("gather_171_validate_indices_0"), val = tensor(false)]; + tensor gather_171 = gather(axis = gather_171_axis_0, batch_dims = gather_171_batch_dims_0, indices = select_171, validate_indices = gather_171_validate_indices_0, x = var_3189_shape)[name = tensor("gather_171")]; + tensor concat_84_values0_0 = const()[name = tensor("concat_84_values0_0"), val = tensor(1)]; + tensor concat_84_values1_0 = const()[name = tensor("concat_84_values1_0"), val = tensor(128)]; + tensor concat_84_axis_0 = const()[name = tensor("concat_84_axis_0"), val = tensor(0)]; + tensor concat_84_interleave_0 = const()[name = tensor("concat_84_interleave_0"), val = tensor(false)]; + tensor concat_84 = concat(axis = concat_84_axis_0, interleave = concat_84_interleave_0, values = (concat_84_values0_0, concat_84_values1_0, gather_171))[name = tensor("concat_84")]; + tensor var_3190_begin_0 = const()[name = tensor("op_3190_begin_0"), val = tensor([0, 0, 0])]; + tensor var_3190_end_mask_0 = const()[name = tensor("op_3190_end_mask_0"), val = tensor([true, true, false])]; + tensor var_3190 = slice_by_index(begin = var_3190_begin_0, end = concat_84, end_mask = var_3190_end_mask_0, x = seg_167)[name = tensor("op_3190")]; + tensor input_915 = add(x = var_3176, y = var_3190)[name = tensor("input_915")]; + tensor input_917_pad_type_0 = const()[name = tensor("input_917_pad_type_0"), val = tensor("valid")]; + tensor input_917_strides_0 = const()[name = tensor("input_917_strides_0"), val = tensor([1])]; + tensor input_917_pad_0 = const()[name = tensor("input_917_pad_0"), val = tensor([0, 0])]; + tensor input_917_dilations_0 = const()[name = tensor("input_917_dilations_0"), val = tensor([1])]; + tensor input_917_groups_0 = const()[name = tensor("input_917_groups_0"), val = tensor(1)]; + tensor input_917 = conv(bias = model_xvector_block3_tdnnd6_cam_layer_linear1_bias, dilations = input_917_dilations_0, groups = input_917_groups_0, pad = input_917_pad_0, pad_type = input_917_pad_type_0, strides = input_917_strides_0, weight = model_xvector_block3_tdnnd6_cam_layer_linear1_weight, x = input_915)[name = tensor("input_917")]; + tensor input_919 = relu(x = input_917)[name = tensor("input_919")]; + tensor input_921_pad_type_0 = const()[name = tensor("input_921_pad_type_0"), val = tensor("valid")]; + tensor input_921_strides_0 = const()[name = tensor("input_921_strides_0"), val = tensor([1])]; + tensor input_921_pad_0 = const()[name = tensor("input_921_pad_0"), val = tensor([0, 0])]; + tensor input_921_dilations_0 = const()[name = tensor("input_921_dilations_0"), val = tensor([1])]; + tensor input_921_groups_0 = const()[name = tensor("input_921_groups_0"), val = tensor(1)]; + tensor input_921 = conv(bias = model_xvector_block3_tdnnd6_cam_layer_linear2_bias, dilations = input_921_dilations_0, groups = input_921_groups_0, pad = input_921_pad_0, pad_type = input_921_pad_type_0, strides = input_921_strides_0, weight = model_xvector_block3_tdnnd6_cam_layer_linear2_weight, x = input_919)[name = tensor("input_921")]; + tensor m_83 = sigmoid(x = input_921)[name = tensor("m_83")]; + tensor var_3208 = mul(x = y_83, y = m_83)[name = tensor("op_3208")]; + tensor input_923_interleave_0 = const()[name = tensor("input_923_interleave_0"), val = tensor(false)]; + tensor input_923 = concat(axis = var_11, interleave = input_923_interleave_0, values = (input_903, var_3208))[name = tensor("input_923")]; + tensor input_925 = batch_norm(beta = model_xvector_block3_tdnnd7_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block3_tdnnd7_nonlinear1_batchnorm_weight, mean = model_xvector_block3_tdnnd7_nonlinear1_batchnorm_running_mean, variance = model_xvector_block3_tdnnd7_nonlinear1_batchnorm_running_var, x = input_923)[name = tensor("input_925")]; + tensor input_927 = relu(x = input_925)[name = tensor("input_927")]; + tensor input_929_pad_type_0 = const()[name = tensor("input_929_pad_type_0"), val = tensor("valid")]; + tensor input_929_strides_0 = const()[name = tensor("input_929_strides_0"), val = tensor([1])]; + tensor input_929_pad_0 = const()[name = tensor("input_929_pad_0"), val = tensor([0, 0])]; + tensor input_929_dilations_0 = const()[name = tensor("input_929_dilations_0"), val = tensor([1])]; + tensor input_929_groups_0 = const()[name = tensor("input_929_groups_0"), val = tensor(1)]; + tensor const_110 = const()[name = tensor("const_110"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21221120)))]; + tensor const_111 = const()[name = tensor("const_111"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21581632)))]; + tensor input_931 = conv(bias = const_111, dilations = input_929_dilations_0, groups = input_929_groups_0, pad = input_929_pad_0, pad_type = input_929_pad_type_0, strides = input_929_strides_0, weight = const_110, x = input_927)[name = tensor("input_931")]; + tensor input_933 = relu(x = input_931)[name = tensor("input_933")]; + tensor y_85_pad_type_0 = const()[name = tensor("y_85_pad_type_0"), val = tensor("custom")]; + tensor y_85_pad_0 = const()[name = tensor("y_85_pad_0"), val = tensor([2, 2])]; + tensor y_85_dilations_0 = const()[name = tensor("y_85_dilations_0"), val = tensor([2])]; + tensor y_85_strides_0 = const()[name = tensor("y_85_strides_0"), val = tensor([1])]; + tensor y_85_groups_0 = const()[name = tensor("y_85_groups_0"), val = tensor(1)]; + tensor y_85 = conv(dilations = y_85_dilations_0, groups = y_85_groups_0, pad = y_85_pad_0, pad_type = y_85_pad_type_0, strides = y_85_strides_0, weight = model_xvector_block3_tdnnd7_cam_layer_linear_local_weight, x = input_933)[name = tensor("y_85")]; + tensor var_3245_axes_0 = const()[name = tensor("op_3245_axes_0"), val = tensor([-1])]; + tensor var_3245_keep_dims_0 = const()[name = tensor("op_3245_keep_dims_0"), val = tensor(true)]; + tensor var_3245 = reduce_mean(axes = var_3245_axes_0, keep_dims = var_3245_keep_dims_0, x = input_933)[name = tensor("op_3245")]; + tensor var_3246 = const()[name = tensor("op_3246"), val = tensor([100])]; + tensor var_3247 = const()[name = tensor("op_3247"), val = tensor([100])]; + tensor seg_169_pad_type_0 = const()[name = tensor("seg_169_pad_type_0"), val = tensor("custom")]; + tensor seg_169_pad_0 = const()[name = tensor("seg_169_pad_0"), val = tensor([0, 0])]; + tensor seg_169_exclude_padding_from_average_0 = const()[name = tensor("seg_169_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_169_ceil_mode_0 = const()[name = tensor("seg_169_ceil_mode_0"), val = tensor(true)]; + tensor seg_169 = avg_pool(ceil_mode = seg_169_ceil_mode_0, exclude_padding_from_average = seg_169_exclude_padding_from_average_0, kernel_sizes = var_3246, pad = seg_169_pad_0, pad_type = seg_169_pad_type_0, strides = var_3247, x = input_933)[name = tensor("seg_169")]; + tensor var_3250_shape = shape(x = seg_169)[name = tensor("op_3250_shape")]; + tensor gather_172 = const()[name = tensor("gather_172"), val = tensor(1)]; + tensor gather_173 = const()[name = tensor("gather_173"), val = tensor(128)]; + tensor select_174 = const()[name = tensor("select_174"), val = tensor(2)]; + tensor gather_174_axis_0 = const()[name = tensor("gather_174_axis_0"), val = tensor(0)]; + tensor gather_174_batch_dims_0 = const()[name = tensor("gather_174_batch_dims_0"), val = tensor(0)]; + tensor gather_174_validate_indices_0 = const()[name = tensor("gather_174_validate_indices_0"), val = tensor(false)]; + tensor gather_174 = gather(axis = gather_174_axis_0, batch_dims = gather_174_batch_dims_0, indices = select_174, validate_indices = gather_174_validate_indices_0, x = var_3250_shape)[name = tensor("gather_174")]; + tensor var_3253_axes_0 = const()[name = tensor("op_3253_axes_0"), val = tensor([-1])]; + tensor var_3253 = expand_dims(axes = var_3253_axes_0, x = seg_169)[name = tensor("op_3253")]; + tensor concat_85_axis_0 = const()[name = tensor("concat_85_axis_0"), val = tensor(0)]; + tensor concat_85_interleave_0 = const()[name = tensor("concat_85_interleave_0"), val = tensor(false)]; + tensor concat_85 = concat(axis = concat_85_axis_0, interleave = concat_85_interleave_0, values = (gather_172, gather_173, gather_174, var_3))[name = tensor("concat_85")]; + tensor shape_217 = shape(x = var_3253)[name = tensor("shape_217")]; + tensor real_div_42 = real_div(x = concat_85, y = shape_217)[name = tensor("real_div_42")]; + tensor var_3255 = tile(reps = real_div_42, x = var_3253)[name = tensor("op_3255")]; + tensor var_3256 = const()[name = tensor("op_3256"), val = tensor([1, 128, -1])]; + tensor seg_171 = reshape(shape = var_3256, x = var_3255)[name = tensor("seg_171")]; + tensor var_3258_shape = shape(x = input_933)[name = tensor("op_3258_shape")]; + tensor select_175 = const()[name = tensor("select_175"), val = tensor(2)]; + tensor gather_175_axis_0 = const()[name = tensor("gather_175_axis_0"), val = tensor(0)]; + tensor gather_175_batch_dims_0 = const()[name = tensor("gather_175_batch_dims_0"), val = tensor(0)]; + tensor gather_175_validate_indices_0 = const()[name = tensor("gather_175_validate_indices_0"), val = tensor(false)]; + tensor gather_175 = gather(axis = gather_175_axis_0, batch_dims = gather_175_batch_dims_0, indices = select_175, validate_indices = gather_175_validate_indices_0, x = var_3258_shape)[name = tensor("gather_175")]; + tensor concat_86_values0_0 = const()[name = tensor("concat_86_values0_0"), val = tensor(1)]; + tensor concat_86_values1_0 = const()[name = tensor("concat_86_values1_0"), val = tensor(128)]; + tensor concat_86_axis_0 = const()[name = tensor("concat_86_axis_0"), val = tensor(0)]; + tensor concat_86_interleave_0 = const()[name = tensor("concat_86_interleave_0"), val = tensor(false)]; + tensor concat_86 = concat(axis = concat_86_axis_0, interleave = concat_86_interleave_0, values = (concat_86_values0_0, concat_86_values1_0, gather_175))[name = tensor("concat_86")]; + tensor var_3259_begin_0 = const()[name = tensor("op_3259_begin_0"), val = tensor([0, 0, 0])]; + tensor var_3259_end_mask_0 = const()[name = tensor("op_3259_end_mask_0"), val = tensor([true, true, false])]; + tensor var_3259 = slice_by_index(begin = var_3259_begin_0, end = concat_86, end_mask = var_3259_end_mask_0, x = seg_171)[name = tensor("op_3259")]; + tensor input_935 = add(x = var_3245, y = var_3259)[name = tensor("input_935")]; + tensor input_937_pad_type_0 = const()[name = tensor("input_937_pad_type_0"), val = tensor("valid")]; + tensor input_937_strides_0 = const()[name = tensor("input_937_strides_0"), val = tensor([1])]; + tensor input_937_pad_0 = const()[name = tensor("input_937_pad_0"), val = tensor([0, 0])]; + tensor input_937_dilations_0 = const()[name = tensor("input_937_dilations_0"), val = tensor([1])]; + tensor input_937_groups_0 = const()[name = tensor("input_937_groups_0"), val = tensor(1)]; + tensor input_937 = conv(bias = model_xvector_block3_tdnnd7_cam_layer_linear1_bias, dilations = input_937_dilations_0, groups = input_937_groups_0, pad = input_937_pad_0, pad_type = input_937_pad_type_0, strides = input_937_strides_0, weight = model_xvector_block3_tdnnd7_cam_layer_linear1_weight, x = input_935)[name = tensor("input_937")]; + tensor input_939 = relu(x = input_937)[name = tensor("input_939")]; + tensor input_941_pad_type_0 = const()[name = tensor("input_941_pad_type_0"), val = tensor("valid")]; + tensor input_941_strides_0 = const()[name = tensor("input_941_strides_0"), val = tensor([1])]; + tensor input_941_pad_0 = const()[name = tensor("input_941_pad_0"), val = tensor([0, 0])]; + tensor input_941_dilations_0 = const()[name = tensor("input_941_dilations_0"), val = tensor([1])]; + tensor input_941_groups_0 = const()[name = tensor("input_941_groups_0"), val = tensor(1)]; + tensor input_941 = conv(bias = model_xvector_block3_tdnnd7_cam_layer_linear2_bias, dilations = input_941_dilations_0, groups = input_941_groups_0, pad = input_941_pad_0, pad_type = input_941_pad_type_0, strides = input_941_strides_0, weight = model_xvector_block3_tdnnd7_cam_layer_linear2_weight, x = input_939)[name = tensor("input_941")]; + tensor m_85 = sigmoid(x = input_941)[name = tensor("m_85")]; + tensor var_3277 = mul(x = y_85, y = m_85)[name = tensor("op_3277")]; + tensor input_943_interleave_0 = const()[name = tensor("input_943_interleave_0"), val = tensor(false)]; + tensor input_943 = concat(axis = var_11, interleave = input_943_interleave_0, values = (input_923, var_3277))[name = tensor("input_943")]; + tensor input_945 = batch_norm(beta = model_xvector_block3_tdnnd8_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block3_tdnnd8_nonlinear1_batchnorm_weight, mean = model_xvector_block3_tdnnd8_nonlinear1_batchnorm_running_mean, variance = model_xvector_block3_tdnnd8_nonlinear1_batchnorm_running_var, x = input_943)[name = tensor("input_945")]; + tensor input_947 = relu(x = input_945)[name = tensor("input_947")]; + tensor input_949_pad_type_0 = const()[name = tensor("input_949_pad_type_0"), val = tensor("valid")]; + tensor input_949_strides_0 = const()[name = tensor("input_949_strides_0"), val = tensor([1])]; + tensor input_949_pad_0 = const()[name = tensor("input_949_pad_0"), val = tensor([0, 0])]; + tensor input_949_dilations_0 = const()[name = tensor("input_949_dilations_0"), val = tensor([1])]; + tensor input_949_groups_0 = const()[name = tensor("input_949_groups_0"), val = tensor(1)]; + tensor const_112 = const()[name = tensor("const_112"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21582208)))]; + tensor const_113 = const()[name = tensor("const_113"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21959104)))]; + tensor input_951 = conv(bias = const_113, dilations = input_949_dilations_0, groups = input_949_groups_0, pad = input_949_pad_0, pad_type = input_949_pad_type_0, strides = input_949_strides_0, weight = const_112, x = input_947)[name = tensor("input_951")]; + tensor input_953 = relu(x = input_951)[name = tensor("input_953")]; + tensor y_87_pad_type_0 = const()[name = tensor("y_87_pad_type_0"), val = tensor("custom")]; + tensor y_87_pad_0 = const()[name = tensor("y_87_pad_0"), val = tensor([2, 2])]; + tensor y_87_dilations_0 = const()[name = tensor("y_87_dilations_0"), val = tensor([2])]; + tensor y_87_strides_0 = const()[name = tensor("y_87_strides_0"), val = tensor([1])]; + tensor y_87_groups_0 = const()[name = tensor("y_87_groups_0"), val = tensor(1)]; + tensor y_87 = conv(dilations = y_87_dilations_0, groups = y_87_groups_0, pad = y_87_pad_0, pad_type = y_87_pad_type_0, strides = y_87_strides_0, weight = model_xvector_block3_tdnnd8_cam_layer_linear_local_weight, x = input_953)[name = tensor("y_87")]; + tensor var_3314_axes_0 = const()[name = tensor("op_3314_axes_0"), val = tensor([-1])]; + tensor var_3314_keep_dims_0 = const()[name = tensor("op_3314_keep_dims_0"), val = tensor(true)]; + tensor var_3314 = reduce_mean(axes = var_3314_axes_0, keep_dims = var_3314_keep_dims_0, x = input_953)[name = tensor("op_3314")]; + tensor var_3315 = const()[name = tensor("op_3315"), val = tensor([100])]; + tensor var_3316 = const()[name = tensor("op_3316"), val = tensor([100])]; + tensor seg_173_pad_type_0 = const()[name = tensor("seg_173_pad_type_0"), val = tensor("custom")]; + tensor seg_173_pad_0 = const()[name = tensor("seg_173_pad_0"), val = tensor([0, 0])]; + tensor seg_173_exclude_padding_from_average_0 = const()[name = tensor("seg_173_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_173_ceil_mode_0 = const()[name = tensor("seg_173_ceil_mode_0"), val = tensor(true)]; + tensor seg_173 = avg_pool(ceil_mode = seg_173_ceil_mode_0, exclude_padding_from_average = seg_173_exclude_padding_from_average_0, kernel_sizes = var_3315, pad = seg_173_pad_0, pad_type = seg_173_pad_type_0, strides = var_3316, x = input_953)[name = tensor("seg_173")]; + tensor var_3319_shape = shape(x = seg_173)[name = tensor("op_3319_shape")]; + tensor gather_176 = const()[name = tensor("gather_176"), val = tensor(1)]; + tensor gather_177 = const()[name = tensor("gather_177"), val = tensor(128)]; + tensor select_178 = const()[name = tensor("select_178"), val = tensor(2)]; + tensor gather_178_axis_0 = const()[name = tensor("gather_178_axis_0"), val = tensor(0)]; + tensor gather_178_batch_dims_0 = const()[name = tensor("gather_178_batch_dims_0"), val = tensor(0)]; + tensor gather_178_validate_indices_0 = const()[name = tensor("gather_178_validate_indices_0"), val = tensor(false)]; + tensor gather_178 = gather(axis = gather_178_axis_0, batch_dims = gather_178_batch_dims_0, indices = select_178, validate_indices = gather_178_validate_indices_0, x = var_3319_shape)[name = tensor("gather_178")]; + tensor var_3322_axes_0 = const()[name = tensor("op_3322_axes_0"), val = tensor([-1])]; + tensor var_3322 = expand_dims(axes = var_3322_axes_0, x = seg_173)[name = tensor("op_3322")]; + tensor concat_87_axis_0 = const()[name = tensor("concat_87_axis_0"), val = tensor(0)]; + tensor concat_87_interleave_0 = const()[name = tensor("concat_87_interleave_0"), val = tensor(false)]; + tensor concat_87 = concat(axis = concat_87_axis_0, interleave = concat_87_interleave_0, values = (gather_176, gather_177, gather_178, var_3))[name = tensor("concat_87")]; + tensor shape_222 = shape(x = var_3322)[name = tensor("shape_222")]; + tensor real_div_43 = real_div(x = concat_87, y = shape_222)[name = tensor("real_div_43")]; + tensor var_3324 = tile(reps = real_div_43, x = var_3322)[name = tensor("op_3324")]; + tensor var_3325 = const()[name = tensor("op_3325"), val = tensor([1, 128, -1])]; + tensor seg_175 = reshape(shape = var_3325, x = var_3324)[name = tensor("seg_175")]; + tensor var_3327_shape = shape(x = input_953)[name = tensor("op_3327_shape")]; + tensor select_179 = const()[name = tensor("select_179"), val = tensor(2)]; + tensor gather_179_axis_0 = const()[name = tensor("gather_179_axis_0"), val = tensor(0)]; + tensor gather_179_batch_dims_0 = const()[name = tensor("gather_179_batch_dims_0"), val = tensor(0)]; + tensor gather_179_validate_indices_0 = const()[name = tensor("gather_179_validate_indices_0"), val = tensor(false)]; + tensor gather_179 = gather(axis = gather_179_axis_0, batch_dims = gather_179_batch_dims_0, indices = select_179, validate_indices = gather_179_validate_indices_0, x = var_3327_shape)[name = tensor("gather_179")]; + tensor concat_88_values0_0 = const()[name = tensor("concat_88_values0_0"), val = tensor(1)]; + tensor concat_88_values1_0 = const()[name = tensor("concat_88_values1_0"), val = tensor(128)]; + tensor concat_88_axis_0 = const()[name = tensor("concat_88_axis_0"), val = tensor(0)]; + tensor concat_88_interleave_0 = const()[name = tensor("concat_88_interleave_0"), val = tensor(false)]; + tensor concat_88 = concat(axis = concat_88_axis_0, interleave = concat_88_interleave_0, values = (concat_88_values0_0, concat_88_values1_0, gather_179))[name = tensor("concat_88")]; + tensor var_3328_begin_0 = const()[name = tensor("op_3328_begin_0"), val = tensor([0, 0, 0])]; + tensor var_3328_end_mask_0 = const()[name = tensor("op_3328_end_mask_0"), val = tensor([true, true, false])]; + tensor var_3328 = slice_by_index(begin = var_3328_begin_0, end = concat_88, end_mask = var_3328_end_mask_0, x = seg_175)[name = tensor("op_3328")]; + tensor input_955 = add(x = var_3314, y = var_3328)[name = tensor("input_955")]; + tensor input_957_pad_type_0 = const()[name = tensor("input_957_pad_type_0"), val = tensor("valid")]; + tensor input_957_strides_0 = const()[name = tensor("input_957_strides_0"), val = tensor([1])]; + tensor input_957_pad_0 = const()[name = tensor("input_957_pad_0"), val = tensor([0, 0])]; + tensor input_957_dilations_0 = const()[name = tensor("input_957_dilations_0"), val = tensor([1])]; + tensor input_957_groups_0 = const()[name = tensor("input_957_groups_0"), val = tensor(1)]; + tensor input_957 = conv(bias = model_xvector_block3_tdnnd8_cam_layer_linear1_bias, dilations = input_957_dilations_0, groups = input_957_groups_0, pad = input_957_pad_0, pad_type = input_957_pad_type_0, strides = input_957_strides_0, weight = model_xvector_block3_tdnnd8_cam_layer_linear1_weight, x = input_955)[name = tensor("input_957")]; + tensor input_959 = relu(x = input_957)[name = tensor("input_959")]; + tensor input_961_pad_type_0 = const()[name = tensor("input_961_pad_type_0"), val = tensor("valid")]; + tensor input_961_strides_0 = const()[name = tensor("input_961_strides_0"), val = tensor([1])]; + tensor input_961_pad_0 = const()[name = tensor("input_961_pad_0"), val = tensor([0, 0])]; + tensor input_961_dilations_0 = const()[name = tensor("input_961_dilations_0"), val = tensor([1])]; + tensor input_961_groups_0 = const()[name = tensor("input_961_groups_0"), val = tensor(1)]; + tensor input_961 = conv(bias = model_xvector_block3_tdnnd8_cam_layer_linear2_bias, dilations = input_961_dilations_0, groups = input_961_groups_0, pad = input_961_pad_0, pad_type = input_961_pad_type_0, strides = input_961_strides_0, weight = model_xvector_block3_tdnnd8_cam_layer_linear2_weight, x = input_959)[name = tensor("input_961")]; + tensor m_87 = sigmoid(x = input_961)[name = tensor("m_87")]; + tensor var_3346 = mul(x = y_87, y = m_87)[name = tensor("op_3346")]; + tensor input_963_interleave_0 = const()[name = tensor("input_963_interleave_0"), val = tensor(false)]; + tensor input_963 = concat(axis = var_11, interleave = input_963_interleave_0, values = (input_943, var_3346))[name = tensor("input_963")]; + tensor input_965 = batch_norm(beta = model_xvector_block3_tdnnd9_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block3_tdnnd9_nonlinear1_batchnorm_weight, mean = model_xvector_block3_tdnnd9_nonlinear1_batchnorm_running_mean, variance = model_xvector_block3_tdnnd9_nonlinear1_batchnorm_running_var, x = input_963)[name = tensor("input_965")]; + tensor input_967 = relu(x = input_965)[name = tensor("input_967")]; + tensor input_969_pad_type_0 = const()[name = tensor("input_969_pad_type_0"), val = tensor("valid")]; + tensor input_969_strides_0 = const()[name = tensor("input_969_strides_0"), val = tensor([1])]; + tensor input_969_pad_0 = const()[name = tensor("input_969_pad_0"), val = tensor([0, 0])]; + tensor input_969_dilations_0 = const()[name = tensor("input_969_dilations_0"), val = tensor([1])]; + tensor input_969_groups_0 = const()[name = tensor("input_969_groups_0"), val = tensor(1)]; + tensor const_114 = const()[name = tensor("const_114"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21959680)))]; + tensor const_115 = const()[name = tensor("const_115"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22352960)))]; + tensor input_971 = conv(bias = const_115, dilations = input_969_dilations_0, groups = input_969_groups_0, pad = input_969_pad_0, pad_type = input_969_pad_type_0, strides = input_969_strides_0, weight = const_114, x = input_967)[name = tensor("input_971")]; + tensor input_973 = relu(x = input_971)[name = tensor("input_973")]; + tensor y_89_pad_type_0 = const()[name = tensor("y_89_pad_type_0"), val = tensor("custom")]; + tensor y_89_pad_0 = const()[name = tensor("y_89_pad_0"), val = tensor([2, 2])]; + tensor y_89_dilations_0 = const()[name = tensor("y_89_dilations_0"), val = tensor([2])]; + tensor y_89_strides_0 = const()[name = tensor("y_89_strides_0"), val = tensor([1])]; + tensor y_89_groups_0 = const()[name = tensor("y_89_groups_0"), val = tensor(1)]; + tensor y_89 = conv(dilations = y_89_dilations_0, groups = y_89_groups_0, pad = y_89_pad_0, pad_type = y_89_pad_type_0, strides = y_89_strides_0, weight = model_xvector_block3_tdnnd9_cam_layer_linear_local_weight, x = input_973)[name = tensor("y_89")]; + tensor var_3383_axes_0 = const()[name = tensor("op_3383_axes_0"), val = tensor([-1])]; + tensor var_3383_keep_dims_0 = const()[name = tensor("op_3383_keep_dims_0"), val = tensor(true)]; + tensor var_3383 = reduce_mean(axes = var_3383_axes_0, keep_dims = var_3383_keep_dims_0, x = input_973)[name = tensor("op_3383")]; + tensor var_3384 = const()[name = tensor("op_3384"), val = tensor([100])]; + tensor var_3385 = const()[name = tensor("op_3385"), val = tensor([100])]; + tensor seg_177_pad_type_0 = const()[name = tensor("seg_177_pad_type_0"), val = tensor("custom")]; + tensor seg_177_pad_0 = const()[name = tensor("seg_177_pad_0"), val = tensor([0, 0])]; + tensor seg_177_exclude_padding_from_average_0 = const()[name = tensor("seg_177_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_177_ceil_mode_0 = const()[name = tensor("seg_177_ceil_mode_0"), val = tensor(true)]; + tensor seg_177 = avg_pool(ceil_mode = seg_177_ceil_mode_0, exclude_padding_from_average = seg_177_exclude_padding_from_average_0, kernel_sizes = var_3384, pad = seg_177_pad_0, pad_type = seg_177_pad_type_0, strides = var_3385, x = input_973)[name = tensor("seg_177")]; + tensor var_3388_shape = shape(x = seg_177)[name = tensor("op_3388_shape")]; + tensor gather_180 = const()[name = tensor("gather_180"), val = tensor(1)]; + tensor gather_181 = const()[name = tensor("gather_181"), val = tensor(128)]; + tensor select_182 = const()[name = tensor("select_182"), val = tensor(2)]; + tensor gather_182_axis_0 = const()[name = tensor("gather_182_axis_0"), val = tensor(0)]; + tensor gather_182_batch_dims_0 = const()[name = tensor("gather_182_batch_dims_0"), val = tensor(0)]; + tensor gather_182_validate_indices_0 = const()[name = tensor("gather_182_validate_indices_0"), val = tensor(false)]; + tensor gather_182 = gather(axis = gather_182_axis_0, batch_dims = gather_182_batch_dims_0, indices = select_182, validate_indices = gather_182_validate_indices_0, x = var_3388_shape)[name = tensor("gather_182")]; + tensor var_3391_axes_0 = const()[name = tensor("op_3391_axes_0"), val = tensor([-1])]; + tensor var_3391 = expand_dims(axes = var_3391_axes_0, x = seg_177)[name = tensor("op_3391")]; + tensor concat_89_axis_0 = const()[name = tensor("concat_89_axis_0"), val = tensor(0)]; + tensor concat_89_interleave_0 = const()[name = tensor("concat_89_interleave_0"), val = tensor(false)]; + tensor concat_89 = concat(axis = concat_89_axis_0, interleave = concat_89_interleave_0, values = (gather_180, gather_181, gather_182, var_3))[name = tensor("concat_89")]; + tensor shape_227 = shape(x = var_3391)[name = tensor("shape_227")]; + tensor real_div_44 = real_div(x = concat_89, y = shape_227)[name = tensor("real_div_44")]; + tensor var_3393 = tile(reps = real_div_44, x = var_3391)[name = tensor("op_3393")]; + tensor var_3394 = const()[name = tensor("op_3394"), val = tensor([1, 128, -1])]; + tensor seg_179 = reshape(shape = var_3394, x = var_3393)[name = tensor("seg_179")]; + tensor var_3396_shape = shape(x = input_973)[name = tensor("op_3396_shape")]; + tensor select_183 = const()[name = tensor("select_183"), val = tensor(2)]; + tensor gather_183_axis_0 = const()[name = tensor("gather_183_axis_0"), val = tensor(0)]; + tensor gather_183_batch_dims_0 = const()[name = tensor("gather_183_batch_dims_0"), val = tensor(0)]; + tensor gather_183_validate_indices_0 = const()[name = tensor("gather_183_validate_indices_0"), val = tensor(false)]; + tensor gather_183 = gather(axis = gather_183_axis_0, batch_dims = gather_183_batch_dims_0, indices = select_183, validate_indices = gather_183_validate_indices_0, x = var_3396_shape)[name = tensor("gather_183")]; + tensor concat_90_values0_0 = const()[name = tensor("concat_90_values0_0"), val = tensor(1)]; + tensor concat_90_values1_0 = const()[name = tensor("concat_90_values1_0"), val = tensor(128)]; + tensor concat_90_axis_0 = const()[name = tensor("concat_90_axis_0"), val = tensor(0)]; + tensor concat_90_interleave_0 = const()[name = tensor("concat_90_interleave_0"), val = tensor(false)]; + tensor concat_90 = concat(axis = concat_90_axis_0, interleave = concat_90_interleave_0, values = (concat_90_values0_0, concat_90_values1_0, gather_183))[name = tensor("concat_90")]; + tensor var_3397_begin_0 = const()[name = tensor("op_3397_begin_0"), val = tensor([0, 0, 0])]; + tensor var_3397_end_mask_0 = const()[name = tensor("op_3397_end_mask_0"), val = tensor([true, true, false])]; + tensor var_3397 = slice_by_index(begin = var_3397_begin_0, end = concat_90, end_mask = var_3397_end_mask_0, x = seg_179)[name = tensor("op_3397")]; + tensor input_975 = add(x = var_3383, y = var_3397)[name = tensor("input_975")]; + tensor input_977_pad_type_0 = const()[name = tensor("input_977_pad_type_0"), val = tensor("valid")]; + tensor input_977_strides_0 = const()[name = tensor("input_977_strides_0"), val = tensor([1])]; + tensor input_977_pad_0 = const()[name = tensor("input_977_pad_0"), val = tensor([0, 0])]; + tensor input_977_dilations_0 = const()[name = tensor("input_977_dilations_0"), val = tensor([1])]; + tensor input_977_groups_0 = const()[name = tensor("input_977_groups_0"), val = tensor(1)]; + tensor input_977 = conv(bias = model_xvector_block3_tdnnd9_cam_layer_linear1_bias, dilations = input_977_dilations_0, groups = input_977_groups_0, pad = input_977_pad_0, pad_type = input_977_pad_type_0, strides = input_977_strides_0, weight = model_xvector_block3_tdnnd9_cam_layer_linear1_weight, x = input_975)[name = tensor("input_977")]; + tensor input_979 = relu(x = input_977)[name = tensor("input_979")]; + tensor input_981_pad_type_0 = const()[name = tensor("input_981_pad_type_0"), val = tensor("valid")]; + tensor input_981_strides_0 = const()[name = tensor("input_981_strides_0"), val = tensor([1])]; + tensor input_981_pad_0 = const()[name = tensor("input_981_pad_0"), val = tensor([0, 0])]; + tensor input_981_dilations_0 = const()[name = tensor("input_981_dilations_0"), val = tensor([1])]; + tensor input_981_groups_0 = const()[name = tensor("input_981_groups_0"), val = tensor(1)]; + tensor input_981 = conv(bias = model_xvector_block3_tdnnd9_cam_layer_linear2_bias, dilations = input_981_dilations_0, groups = input_981_groups_0, pad = input_981_pad_0, pad_type = input_981_pad_type_0, strides = input_981_strides_0, weight = model_xvector_block3_tdnnd9_cam_layer_linear2_weight, x = input_979)[name = tensor("input_981")]; + tensor m_89 = sigmoid(x = input_981)[name = tensor("m_89")]; + tensor var_3415 = mul(x = y_89, y = m_89)[name = tensor("op_3415")]; + tensor input_983_interleave_0 = const()[name = tensor("input_983_interleave_0"), val = tensor(false)]; + tensor input_983 = concat(axis = var_11, interleave = input_983_interleave_0, values = (input_963, var_3415))[name = tensor("input_983")]; + tensor input_985 = batch_norm(beta = model_xvector_block3_tdnnd10_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block3_tdnnd10_nonlinear1_batchnorm_weight, mean = model_xvector_block3_tdnnd10_nonlinear1_batchnorm_running_mean, variance = model_xvector_block3_tdnnd10_nonlinear1_batchnorm_running_var, x = input_983)[name = tensor("input_985")]; + tensor input_987 = relu(x = input_985)[name = tensor("input_987")]; + tensor input_989_pad_type_0 = const()[name = tensor("input_989_pad_type_0"), val = tensor("valid")]; + tensor input_989_strides_0 = const()[name = tensor("input_989_strides_0"), val = tensor([1])]; + tensor input_989_pad_0 = const()[name = tensor("input_989_pad_0"), val = tensor([0, 0])]; + tensor input_989_dilations_0 = const()[name = tensor("input_989_dilations_0"), val = tensor([1])]; + tensor input_989_groups_0 = const()[name = tensor("input_989_groups_0"), val = tensor(1)]; + tensor const_116 = const()[name = tensor("const_116"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22353536)))]; + tensor const_117 = const()[name = tensor("const_117"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22763200)))]; + tensor input_991 = conv(bias = const_117, dilations = input_989_dilations_0, groups = input_989_groups_0, pad = input_989_pad_0, pad_type = input_989_pad_type_0, strides = input_989_strides_0, weight = const_116, x = input_987)[name = tensor("input_991")]; + tensor input_993 = relu(x = input_991)[name = tensor("input_993")]; + tensor y_91_pad_type_0 = const()[name = tensor("y_91_pad_type_0"), val = tensor("custom")]; + tensor y_91_pad_0 = const()[name = tensor("y_91_pad_0"), val = tensor([2, 2])]; + tensor y_91_dilations_0 = const()[name = tensor("y_91_dilations_0"), val = tensor([2])]; + tensor y_91_strides_0 = const()[name = tensor("y_91_strides_0"), val = tensor([1])]; + tensor y_91_groups_0 = const()[name = tensor("y_91_groups_0"), val = tensor(1)]; + tensor y_91 = conv(dilations = y_91_dilations_0, groups = y_91_groups_0, pad = y_91_pad_0, pad_type = y_91_pad_type_0, strides = y_91_strides_0, weight = model_xvector_block3_tdnnd10_cam_layer_linear_local_weight, x = input_993)[name = tensor("y_91")]; + tensor var_3452_axes_0 = const()[name = tensor("op_3452_axes_0"), val = tensor([-1])]; + tensor var_3452_keep_dims_0 = const()[name = tensor("op_3452_keep_dims_0"), val = tensor(true)]; + tensor var_3452 = reduce_mean(axes = var_3452_axes_0, keep_dims = var_3452_keep_dims_0, x = input_993)[name = tensor("op_3452")]; + tensor var_3453 = const()[name = tensor("op_3453"), val = tensor([100])]; + tensor var_3454 = const()[name = tensor("op_3454"), val = tensor([100])]; + tensor seg_181_pad_type_0 = const()[name = tensor("seg_181_pad_type_0"), val = tensor("custom")]; + tensor seg_181_pad_0 = const()[name = tensor("seg_181_pad_0"), val = tensor([0, 0])]; + tensor seg_181_exclude_padding_from_average_0 = const()[name = tensor("seg_181_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_181_ceil_mode_0 = const()[name = tensor("seg_181_ceil_mode_0"), val = tensor(true)]; + tensor seg_181 = avg_pool(ceil_mode = seg_181_ceil_mode_0, exclude_padding_from_average = seg_181_exclude_padding_from_average_0, kernel_sizes = var_3453, pad = seg_181_pad_0, pad_type = seg_181_pad_type_0, strides = var_3454, x = input_993)[name = tensor("seg_181")]; + tensor var_3457_shape = shape(x = seg_181)[name = tensor("op_3457_shape")]; + tensor gather_184 = const()[name = tensor("gather_184"), val = tensor(1)]; + tensor gather_185 = const()[name = tensor("gather_185"), val = tensor(128)]; + tensor select_186 = const()[name = tensor("select_186"), val = tensor(2)]; + tensor gather_186_axis_0 = const()[name = tensor("gather_186_axis_0"), val = tensor(0)]; + tensor gather_186_batch_dims_0 = const()[name = tensor("gather_186_batch_dims_0"), val = tensor(0)]; + tensor gather_186_validate_indices_0 = const()[name = tensor("gather_186_validate_indices_0"), val = tensor(false)]; + tensor gather_186 = gather(axis = gather_186_axis_0, batch_dims = gather_186_batch_dims_0, indices = select_186, validate_indices = gather_186_validate_indices_0, x = var_3457_shape)[name = tensor("gather_186")]; + tensor var_3460_axes_0 = const()[name = tensor("op_3460_axes_0"), val = tensor([-1])]; + tensor var_3460 = expand_dims(axes = var_3460_axes_0, x = seg_181)[name = tensor("op_3460")]; + tensor concat_91_axis_0 = const()[name = tensor("concat_91_axis_0"), val = tensor(0)]; + tensor concat_91_interleave_0 = const()[name = tensor("concat_91_interleave_0"), val = tensor(false)]; + tensor concat_91 = concat(axis = concat_91_axis_0, interleave = concat_91_interleave_0, values = (gather_184, gather_185, gather_186, var_3))[name = tensor("concat_91")]; + tensor shape_232 = shape(x = var_3460)[name = tensor("shape_232")]; + tensor real_div_45 = real_div(x = concat_91, y = shape_232)[name = tensor("real_div_45")]; + tensor var_3462 = tile(reps = real_div_45, x = var_3460)[name = tensor("op_3462")]; + tensor var_3463 = const()[name = tensor("op_3463"), val = tensor([1, 128, -1])]; + tensor seg_183 = reshape(shape = var_3463, x = var_3462)[name = tensor("seg_183")]; + tensor var_3465_shape = shape(x = input_993)[name = tensor("op_3465_shape")]; + tensor select_187 = const()[name = tensor("select_187"), val = tensor(2)]; + tensor gather_187_axis_0 = const()[name = tensor("gather_187_axis_0"), val = tensor(0)]; + tensor gather_187_batch_dims_0 = const()[name = tensor("gather_187_batch_dims_0"), val = tensor(0)]; + tensor gather_187_validate_indices_0 = const()[name = tensor("gather_187_validate_indices_0"), val = tensor(false)]; + tensor gather_187 = gather(axis = gather_187_axis_0, batch_dims = gather_187_batch_dims_0, indices = select_187, validate_indices = gather_187_validate_indices_0, x = var_3465_shape)[name = tensor("gather_187")]; + tensor concat_92_values0_0 = const()[name = tensor("concat_92_values0_0"), val = tensor(1)]; + tensor concat_92_values1_0 = const()[name = tensor("concat_92_values1_0"), val = tensor(128)]; + tensor concat_92_axis_0 = const()[name = tensor("concat_92_axis_0"), val = tensor(0)]; + tensor concat_92_interleave_0 = const()[name = tensor("concat_92_interleave_0"), val = tensor(false)]; + tensor concat_92 = concat(axis = concat_92_axis_0, interleave = concat_92_interleave_0, values = (concat_92_values0_0, concat_92_values1_0, gather_187))[name = tensor("concat_92")]; + tensor var_3466_begin_0 = const()[name = tensor("op_3466_begin_0"), val = tensor([0, 0, 0])]; + tensor var_3466_end_mask_0 = const()[name = tensor("op_3466_end_mask_0"), val = tensor([true, true, false])]; + tensor var_3466 = slice_by_index(begin = var_3466_begin_0, end = concat_92, end_mask = var_3466_end_mask_0, x = seg_183)[name = tensor("op_3466")]; + tensor input_995 = add(x = var_3452, y = var_3466)[name = tensor("input_995")]; + tensor input_997_pad_type_0 = const()[name = tensor("input_997_pad_type_0"), val = tensor("valid")]; + tensor input_997_strides_0 = const()[name = tensor("input_997_strides_0"), val = tensor([1])]; + tensor input_997_pad_0 = const()[name = tensor("input_997_pad_0"), val = tensor([0, 0])]; + tensor input_997_dilations_0 = const()[name = tensor("input_997_dilations_0"), val = tensor([1])]; + tensor input_997_groups_0 = const()[name = tensor("input_997_groups_0"), val = tensor(1)]; + tensor input_997 = conv(bias = model_xvector_block3_tdnnd10_cam_layer_linear1_bias, dilations = input_997_dilations_0, groups = input_997_groups_0, pad = input_997_pad_0, pad_type = input_997_pad_type_0, strides = input_997_strides_0, weight = model_xvector_block3_tdnnd10_cam_layer_linear1_weight, x = input_995)[name = tensor("input_997")]; + tensor input_999 = relu(x = input_997)[name = tensor("input_999")]; + tensor input_1001_pad_type_0 = const()[name = tensor("input_1001_pad_type_0"), val = tensor("valid")]; + tensor input_1001_strides_0 = const()[name = tensor("input_1001_strides_0"), val = tensor([1])]; + tensor input_1001_pad_0 = const()[name = tensor("input_1001_pad_0"), val = tensor([0, 0])]; + tensor input_1001_dilations_0 = const()[name = tensor("input_1001_dilations_0"), val = tensor([1])]; + tensor input_1001_groups_0 = const()[name = tensor("input_1001_groups_0"), val = tensor(1)]; + tensor input_1001 = conv(bias = model_xvector_block3_tdnnd10_cam_layer_linear2_bias, dilations = input_1001_dilations_0, groups = input_1001_groups_0, pad = input_1001_pad_0, pad_type = input_1001_pad_type_0, strides = input_1001_strides_0, weight = model_xvector_block3_tdnnd10_cam_layer_linear2_weight, x = input_999)[name = tensor("input_1001")]; + tensor m_91 = sigmoid(x = input_1001)[name = tensor("m_91")]; + tensor var_3484 = mul(x = y_91, y = m_91)[name = tensor("op_3484")]; + tensor input_1003_interleave_0 = const()[name = tensor("input_1003_interleave_0"), val = tensor(false)]; + tensor input_1003 = concat(axis = var_11, interleave = input_1003_interleave_0, values = (input_983, var_3484))[name = tensor("input_1003")]; + tensor input_1005 = batch_norm(beta = model_xvector_block3_tdnnd11_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block3_tdnnd11_nonlinear1_batchnorm_weight, mean = model_xvector_block3_tdnnd11_nonlinear1_batchnorm_running_mean, variance = model_xvector_block3_tdnnd11_nonlinear1_batchnorm_running_var, x = input_1003)[name = tensor("input_1005")]; + tensor input_1007 = relu(x = input_1005)[name = tensor("input_1007")]; + tensor input_1009_pad_type_0 = const()[name = tensor("input_1009_pad_type_0"), val = tensor("valid")]; + tensor input_1009_strides_0 = const()[name = tensor("input_1009_strides_0"), val = tensor([1])]; + tensor input_1009_pad_0 = const()[name = tensor("input_1009_pad_0"), val = tensor([0, 0])]; + tensor input_1009_dilations_0 = const()[name = tensor("input_1009_dilations_0"), val = tensor([1])]; + tensor input_1009_groups_0 = const()[name = tensor("input_1009_groups_0"), val = tensor(1)]; + tensor const_118 = const()[name = tensor("const_118"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22763776)))]; + tensor const_119 = const()[name = tensor("const_119"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23189824)))]; + tensor input_1011 = conv(bias = const_119, dilations = input_1009_dilations_0, groups = input_1009_groups_0, pad = input_1009_pad_0, pad_type = input_1009_pad_type_0, strides = input_1009_strides_0, weight = const_118, x = input_1007)[name = tensor("input_1011")]; + tensor input_1013 = relu(x = input_1011)[name = tensor("input_1013")]; + tensor y_93_pad_type_0 = const()[name = tensor("y_93_pad_type_0"), val = tensor("custom")]; + tensor y_93_pad_0 = const()[name = tensor("y_93_pad_0"), val = tensor([2, 2])]; + tensor y_93_dilations_0 = const()[name = tensor("y_93_dilations_0"), val = tensor([2])]; + tensor y_93_strides_0 = const()[name = tensor("y_93_strides_0"), val = tensor([1])]; + tensor y_93_groups_0 = const()[name = tensor("y_93_groups_0"), val = tensor(1)]; + tensor y_93 = conv(dilations = y_93_dilations_0, groups = y_93_groups_0, pad = y_93_pad_0, pad_type = y_93_pad_type_0, strides = y_93_strides_0, weight = model_xvector_block3_tdnnd11_cam_layer_linear_local_weight, x = input_1013)[name = tensor("y_93")]; + tensor var_3521_axes_0 = const()[name = tensor("op_3521_axes_0"), val = tensor([-1])]; + tensor var_3521_keep_dims_0 = const()[name = tensor("op_3521_keep_dims_0"), val = tensor(true)]; + tensor var_3521 = reduce_mean(axes = var_3521_axes_0, keep_dims = var_3521_keep_dims_0, x = input_1013)[name = tensor("op_3521")]; + tensor var_3522 = const()[name = tensor("op_3522"), val = tensor([100])]; + tensor var_3523 = const()[name = tensor("op_3523"), val = tensor([100])]; + tensor seg_185_pad_type_0 = const()[name = tensor("seg_185_pad_type_0"), val = tensor("custom")]; + tensor seg_185_pad_0 = const()[name = tensor("seg_185_pad_0"), val = tensor([0, 0])]; + tensor seg_185_exclude_padding_from_average_0 = const()[name = tensor("seg_185_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_185_ceil_mode_0 = const()[name = tensor("seg_185_ceil_mode_0"), val = tensor(true)]; + tensor seg_185 = avg_pool(ceil_mode = seg_185_ceil_mode_0, exclude_padding_from_average = seg_185_exclude_padding_from_average_0, kernel_sizes = var_3522, pad = seg_185_pad_0, pad_type = seg_185_pad_type_0, strides = var_3523, x = input_1013)[name = tensor("seg_185")]; + tensor var_3526_shape = shape(x = seg_185)[name = tensor("op_3526_shape")]; + tensor gather_188 = const()[name = tensor("gather_188"), val = tensor(1)]; + tensor gather_189 = const()[name = tensor("gather_189"), val = tensor(128)]; + tensor select_190 = const()[name = tensor("select_190"), val = tensor(2)]; + tensor gather_190_axis_0 = const()[name = tensor("gather_190_axis_0"), val = tensor(0)]; + tensor gather_190_batch_dims_0 = const()[name = tensor("gather_190_batch_dims_0"), val = tensor(0)]; + tensor gather_190_validate_indices_0 = const()[name = tensor("gather_190_validate_indices_0"), val = tensor(false)]; + tensor gather_190 = gather(axis = gather_190_axis_0, batch_dims = gather_190_batch_dims_0, indices = select_190, validate_indices = gather_190_validate_indices_0, x = var_3526_shape)[name = tensor("gather_190")]; + tensor var_3529_axes_0 = const()[name = tensor("op_3529_axes_0"), val = tensor([-1])]; + tensor var_3529 = expand_dims(axes = var_3529_axes_0, x = seg_185)[name = tensor("op_3529")]; + tensor concat_93_axis_0 = const()[name = tensor("concat_93_axis_0"), val = tensor(0)]; + tensor concat_93_interleave_0 = const()[name = tensor("concat_93_interleave_0"), val = tensor(false)]; + tensor concat_93 = concat(axis = concat_93_axis_0, interleave = concat_93_interleave_0, values = (gather_188, gather_189, gather_190, var_3))[name = tensor("concat_93")]; + tensor shape_237 = shape(x = var_3529)[name = tensor("shape_237")]; + tensor real_div_46 = real_div(x = concat_93, y = shape_237)[name = tensor("real_div_46")]; + tensor var_3531 = tile(reps = real_div_46, x = var_3529)[name = tensor("op_3531")]; + tensor var_3532 = const()[name = tensor("op_3532"), val = tensor([1, 128, -1])]; + tensor seg_187 = reshape(shape = var_3532, x = var_3531)[name = tensor("seg_187")]; + tensor var_3534_shape = shape(x = input_1013)[name = tensor("op_3534_shape")]; + tensor select_191 = const()[name = tensor("select_191"), val = tensor(2)]; + tensor gather_191_axis_0 = const()[name = tensor("gather_191_axis_0"), val = tensor(0)]; + tensor gather_191_batch_dims_0 = const()[name = tensor("gather_191_batch_dims_0"), val = tensor(0)]; + tensor gather_191_validate_indices_0 = const()[name = tensor("gather_191_validate_indices_0"), val = tensor(false)]; + tensor gather_191 = gather(axis = gather_191_axis_0, batch_dims = gather_191_batch_dims_0, indices = select_191, validate_indices = gather_191_validate_indices_0, x = var_3534_shape)[name = tensor("gather_191")]; + tensor concat_94_values0_0 = const()[name = tensor("concat_94_values0_0"), val = tensor(1)]; + tensor concat_94_values1_0 = const()[name = tensor("concat_94_values1_0"), val = tensor(128)]; + tensor concat_94_axis_0 = const()[name = tensor("concat_94_axis_0"), val = tensor(0)]; + tensor concat_94_interleave_0 = const()[name = tensor("concat_94_interleave_0"), val = tensor(false)]; + tensor concat_94 = concat(axis = concat_94_axis_0, interleave = concat_94_interleave_0, values = (concat_94_values0_0, concat_94_values1_0, gather_191))[name = tensor("concat_94")]; + tensor var_3535_begin_0 = const()[name = tensor("op_3535_begin_0"), val = tensor([0, 0, 0])]; + tensor var_3535_end_mask_0 = const()[name = tensor("op_3535_end_mask_0"), val = tensor([true, true, false])]; + tensor var_3535 = slice_by_index(begin = var_3535_begin_0, end = concat_94, end_mask = var_3535_end_mask_0, x = seg_187)[name = tensor("op_3535")]; + tensor input_1015 = add(x = var_3521, y = var_3535)[name = tensor("input_1015")]; + tensor input_1017_pad_type_0 = const()[name = tensor("input_1017_pad_type_0"), val = tensor("valid")]; + tensor input_1017_strides_0 = const()[name = tensor("input_1017_strides_0"), val = tensor([1])]; + tensor input_1017_pad_0 = const()[name = tensor("input_1017_pad_0"), val = tensor([0, 0])]; + tensor input_1017_dilations_0 = const()[name = tensor("input_1017_dilations_0"), val = tensor([1])]; + tensor input_1017_groups_0 = const()[name = tensor("input_1017_groups_0"), val = tensor(1)]; + tensor input_1017 = conv(bias = model_xvector_block3_tdnnd11_cam_layer_linear1_bias, dilations = input_1017_dilations_0, groups = input_1017_groups_0, pad = input_1017_pad_0, pad_type = input_1017_pad_type_0, strides = input_1017_strides_0, weight = model_xvector_block3_tdnnd11_cam_layer_linear1_weight, x = input_1015)[name = tensor("input_1017")]; + tensor input_1019 = relu(x = input_1017)[name = tensor("input_1019")]; + tensor input_1021_pad_type_0 = const()[name = tensor("input_1021_pad_type_0"), val = tensor("valid")]; + tensor input_1021_strides_0 = const()[name = tensor("input_1021_strides_0"), val = tensor([1])]; + tensor input_1021_pad_0 = const()[name = tensor("input_1021_pad_0"), val = tensor([0, 0])]; + tensor input_1021_dilations_0 = const()[name = tensor("input_1021_dilations_0"), val = tensor([1])]; + tensor input_1021_groups_0 = const()[name = tensor("input_1021_groups_0"), val = tensor(1)]; + tensor input_1021 = conv(bias = model_xvector_block3_tdnnd11_cam_layer_linear2_bias, dilations = input_1021_dilations_0, groups = input_1021_groups_0, pad = input_1021_pad_0, pad_type = input_1021_pad_type_0, strides = input_1021_strides_0, weight = model_xvector_block3_tdnnd11_cam_layer_linear2_weight, x = input_1019)[name = tensor("input_1021")]; + tensor m_93 = sigmoid(x = input_1021)[name = tensor("m_93")]; + tensor var_3553 = mul(x = y_93, y = m_93)[name = tensor("op_3553")]; + tensor input_1023_interleave_0 = const()[name = tensor("input_1023_interleave_0"), val = tensor(false)]; + tensor input_1023 = concat(axis = var_11, interleave = input_1023_interleave_0, values = (input_1003, var_3553))[name = tensor("input_1023")]; + tensor input_1025 = batch_norm(beta = model_xvector_block3_tdnnd12_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block3_tdnnd12_nonlinear1_batchnorm_weight, mean = model_xvector_block3_tdnnd12_nonlinear1_batchnorm_running_mean, variance = model_xvector_block3_tdnnd12_nonlinear1_batchnorm_running_var, x = input_1023)[name = tensor("input_1025")]; + tensor input_1027 = relu(x = input_1025)[name = tensor("input_1027")]; + tensor input_1029_pad_type_0 = const()[name = tensor("input_1029_pad_type_0"), val = tensor("valid")]; + tensor input_1029_strides_0 = const()[name = tensor("input_1029_strides_0"), val = tensor([1])]; + tensor input_1029_pad_0 = const()[name = tensor("input_1029_pad_0"), val = tensor([0, 0])]; + tensor input_1029_dilations_0 = const()[name = tensor("input_1029_dilations_0"), val = tensor([1])]; + tensor input_1029_groups_0 = const()[name = tensor("input_1029_groups_0"), val = tensor(1)]; + tensor const_120 = const()[name = tensor("const_120"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23190400)))]; + tensor const_121 = const()[name = tensor("const_121"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23632832)))]; + tensor input_1031 = conv(bias = const_121, dilations = input_1029_dilations_0, groups = input_1029_groups_0, pad = input_1029_pad_0, pad_type = input_1029_pad_type_0, strides = input_1029_strides_0, weight = const_120, x = input_1027)[name = tensor("input_1031")]; + tensor input_1033 = relu(x = input_1031)[name = tensor("input_1033")]; + tensor y_95_pad_type_0 = const()[name = tensor("y_95_pad_type_0"), val = tensor("custom")]; + tensor y_95_pad_0 = const()[name = tensor("y_95_pad_0"), val = tensor([2, 2])]; + tensor y_95_dilations_0 = const()[name = tensor("y_95_dilations_0"), val = tensor([2])]; + tensor y_95_strides_0 = const()[name = tensor("y_95_strides_0"), val = tensor([1])]; + tensor y_95_groups_0 = const()[name = tensor("y_95_groups_0"), val = tensor(1)]; + tensor y_95 = conv(dilations = y_95_dilations_0, groups = y_95_groups_0, pad = y_95_pad_0, pad_type = y_95_pad_type_0, strides = y_95_strides_0, weight = model_xvector_block3_tdnnd12_cam_layer_linear_local_weight, x = input_1033)[name = tensor("y_95")]; + tensor var_3590_axes_0 = const()[name = tensor("op_3590_axes_0"), val = tensor([-1])]; + tensor var_3590_keep_dims_0 = const()[name = tensor("op_3590_keep_dims_0"), val = tensor(true)]; + tensor var_3590 = reduce_mean(axes = var_3590_axes_0, keep_dims = var_3590_keep_dims_0, x = input_1033)[name = tensor("op_3590")]; + tensor var_3591 = const()[name = tensor("op_3591"), val = tensor([100])]; + tensor var_3592 = const()[name = tensor("op_3592"), val = tensor([100])]; + tensor seg_189_pad_type_0 = const()[name = tensor("seg_189_pad_type_0"), val = tensor("custom")]; + tensor seg_189_pad_0 = const()[name = tensor("seg_189_pad_0"), val = tensor([0, 0])]; + tensor seg_189_exclude_padding_from_average_0 = const()[name = tensor("seg_189_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_189_ceil_mode_0 = const()[name = tensor("seg_189_ceil_mode_0"), val = tensor(true)]; + tensor seg_189 = avg_pool(ceil_mode = seg_189_ceil_mode_0, exclude_padding_from_average = seg_189_exclude_padding_from_average_0, kernel_sizes = var_3591, pad = seg_189_pad_0, pad_type = seg_189_pad_type_0, strides = var_3592, x = input_1033)[name = tensor("seg_189")]; + tensor var_3595_shape = shape(x = seg_189)[name = tensor("op_3595_shape")]; + tensor gather_192 = const()[name = tensor("gather_192"), val = tensor(1)]; + tensor gather_193 = const()[name = tensor("gather_193"), val = tensor(128)]; + tensor select_194 = const()[name = tensor("select_194"), val = tensor(2)]; + tensor gather_194_axis_0 = const()[name = tensor("gather_194_axis_0"), val = tensor(0)]; + tensor gather_194_batch_dims_0 = const()[name = tensor("gather_194_batch_dims_0"), val = tensor(0)]; + tensor gather_194_validate_indices_0 = const()[name = tensor("gather_194_validate_indices_0"), val = tensor(false)]; + tensor gather_194 = gather(axis = gather_194_axis_0, batch_dims = gather_194_batch_dims_0, indices = select_194, validate_indices = gather_194_validate_indices_0, x = var_3595_shape)[name = tensor("gather_194")]; + tensor var_3598_axes_0 = const()[name = tensor("op_3598_axes_0"), val = tensor([-1])]; + tensor var_3598 = expand_dims(axes = var_3598_axes_0, x = seg_189)[name = tensor("op_3598")]; + tensor concat_95_axis_0 = const()[name = tensor("concat_95_axis_0"), val = tensor(0)]; + tensor concat_95_interleave_0 = const()[name = tensor("concat_95_interleave_0"), val = tensor(false)]; + tensor concat_95 = concat(axis = concat_95_axis_0, interleave = concat_95_interleave_0, values = (gather_192, gather_193, gather_194, var_3))[name = tensor("concat_95")]; + tensor shape_242 = shape(x = var_3598)[name = tensor("shape_242")]; + tensor real_div_47 = real_div(x = concat_95, y = shape_242)[name = tensor("real_div_47")]; + tensor var_3600 = tile(reps = real_div_47, x = var_3598)[name = tensor("op_3600")]; + tensor var_3601 = const()[name = tensor("op_3601"), val = tensor([1, 128, -1])]; + tensor seg_191 = reshape(shape = var_3601, x = var_3600)[name = tensor("seg_191")]; + tensor var_3603_shape = shape(x = input_1033)[name = tensor("op_3603_shape")]; + tensor select_195 = const()[name = tensor("select_195"), val = tensor(2)]; + tensor gather_195_axis_0 = const()[name = tensor("gather_195_axis_0"), val = tensor(0)]; + tensor gather_195_batch_dims_0 = const()[name = tensor("gather_195_batch_dims_0"), val = tensor(0)]; + tensor gather_195_validate_indices_0 = const()[name = tensor("gather_195_validate_indices_0"), val = tensor(false)]; + tensor gather_195 = gather(axis = gather_195_axis_0, batch_dims = gather_195_batch_dims_0, indices = select_195, validate_indices = gather_195_validate_indices_0, x = var_3603_shape)[name = tensor("gather_195")]; + tensor concat_96_values0_0 = const()[name = tensor("concat_96_values0_0"), val = tensor(1)]; + tensor concat_96_values1_0 = const()[name = tensor("concat_96_values1_0"), val = tensor(128)]; + tensor concat_96_axis_0 = const()[name = tensor("concat_96_axis_0"), val = tensor(0)]; + tensor concat_96_interleave_0 = const()[name = tensor("concat_96_interleave_0"), val = tensor(false)]; + tensor concat_96 = concat(axis = concat_96_axis_0, interleave = concat_96_interleave_0, values = (concat_96_values0_0, concat_96_values1_0, gather_195))[name = tensor("concat_96")]; + tensor var_3604_begin_0 = const()[name = tensor("op_3604_begin_0"), val = tensor([0, 0, 0])]; + tensor var_3604_end_mask_0 = const()[name = tensor("op_3604_end_mask_0"), val = tensor([true, true, false])]; + tensor var_3604 = slice_by_index(begin = var_3604_begin_0, end = concat_96, end_mask = var_3604_end_mask_0, x = seg_191)[name = tensor("op_3604")]; + tensor input_1035 = add(x = var_3590, y = var_3604)[name = tensor("input_1035")]; + tensor input_1037_pad_type_0 = const()[name = tensor("input_1037_pad_type_0"), val = tensor("valid")]; + tensor input_1037_strides_0 = const()[name = tensor("input_1037_strides_0"), val = tensor([1])]; + tensor input_1037_pad_0 = const()[name = tensor("input_1037_pad_0"), val = tensor([0, 0])]; + tensor input_1037_dilations_0 = const()[name = tensor("input_1037_dilations_0"), val = tensor([1])]; + tensor input_1037_groups_0 = const()[name = tensor("input_1037_groups_0"), val = tensor(1)]; + tensor input_1037 = conv(bias = model_xvector_block3_tdnnd12_cam_layer_linear1_bias, dilations = input_1037_dilations_0, groups = input_1037_groups_0, pad = input_1037_pad_0, pad_type = input_1037_pad_type_0, strides = input_1037_strides_0, weight = model_xvector_block3_tdnnd12_cam_layer_linear1_weight, x = input_1035)[name = tensor("input_1037")]; + tensor input_1039 = relu(x = input_1037)[name = tensor("input_1039")]; + tensor input_1041_pad_type_0 = const()[name = tensor("input_1041_pad_type_0"), val = tensor("valid")]; + tensor input_1041_strides_0 = const()[name = tensor("input_1041_strides_0"), val = tensor([1])]; + tensor input_1041_pad_0 = const()[name = tensor("input_1041_pad_0"), val = tensor([0, 0])]; + tensor input_1041_dilations_0 = const()[name = tensor("input_1041_dilations_0"), val = tensor([1])]; + tensor input_1041_groups_0 = const()[name = tensor("input_1041_groups_0"), val = tensor(1)]; + tensor input_1041 = conv(bias = model_xvector_block3_tdnnd12_cam_layer_linear2_bias, dilations = input_1041_dilations_0, groups = input_1041_groups_0, pad = input_1041_pad_0, pad_type = input_1041_pad_type_0, strides = input_1041_strides_0, weight = model_xvector_block3_tdnnd12_cam_layer_linear2_weight, x = input_1039)[name = tensor("input_1041")]; + tensor m_95 = sigmoid(x = input_1041)[name = tensor("m_95")]; + tensor var_3622 = mul(x = y_95, y = m_95)[name = tensor("op_3622")]; + tensor input_1043_interleave_0 = const()[name = tensor("input_1043_interleave_0"), val = tensor(false)]; + tensor input_1043 = concat(axis = var_11, interleave = input_1043_interleave_0, values = (input_1023, var_3622))[name = tensor("input_1043")]; + tensor input_1045 = batch_norm(beta = model_xvector_block3_tdnnd13_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block3_tdnnd13_nonlinear1_batchnorm_weight, mean = model_xvector_block3_tdnnd13_nonlinear1_batchnorm_running_mean, variance = model_xvector_block3_tdnnd13_nonlinear1_batchnorm_running_var, x = input_1043)[name = tensor("input_1045")]; + tensor input_1047 = relu(x = input_1045)[name = tensor("input_1047")]; + tensor input_1049_pad_type_0 = const()[name = tensor("input_1049_pad_type_0"), val = tensor("valid")]; + tensor input_1049_strides_0 = const()[name = tensor("input_1049_strides_0"), val = tensor([1])]; + tensor input_1049_pad_0 = const()[name = tensor("input_1049_pad_0"), val = tensor([0, 0])]; + tensor input_1049_dilations_0 = const()[name = tensor("input_1049_dilations_0"), val = tensor([1])]; + tensor input_1049_groups_0 = const()[name = tensor("input_1049_groups_0"), val = tensor(1)]; + tensor const_122 = const()[name = tensor("const_122"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23633408)))]; + tensor const_123 = const()[name = tensor("const_123"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24092224)))]; + tensor input_1051 = conv(bias = const_123, dilations = input_1049_dilations_0, groups = input_1049_groups_0, pad = input_1049_pad_0, pad_type = input_1049_pad_type_0, strides = input_1049_strides_0, weight = const_122, x = input_1047)[name = tensor("input_1051")]; + tensor input_1053 = relu(x = input_1051)[name = tensor("input_1053")]; + tensor y_97_pad_type_0 = const()[name = tensor("y_97_pad_type_0"), val = tensor("custom")]; + tensor y_97_pad_0 = const()[name = tensor("y_97_pad_0"), val = tensor([2, 2])]; + tensor y_97_dilations_0 = const()[name = tensor("y_97_dilations_0"), val = tensor([2])]; + tensor y_97_strides_0 = const()[name = tensor("y_97_strides_0"), val = tensor([1])]; + tensor y_97_groups_0 = const()[name = tensor("y_97_groups_0"), val = tensor(1)]; + tensor y_97 = conv(dilations = y_97_dilations_0, groups = y_97_groups_0, pad = y_97_pad_0, pad_type = y_97_pad_type_0, strides = y_97_strides_0, weight = model_xvector_block3_tdnnd13_cam_layer_linear_local_weight, x = input_1053)[name = tensor("y_97")]; + tensor var_3659_axes_0 = const()[name = tensor("op_3659_axes_0"), val = tensor([-1])]; + tensor var_3659_keep_dims_0 = const()[name = tensor("op_3659_keep_dims_0"), val = tensor(true)]; + tensor var_3659 = reduce_mean(axes = var_3659_axes_0, keep_dims = var_3659_keep_dims_0, x = input_1053)[name = tensor("op_3659")]; + tensor var_3660 = const()[name = tensor("op_3660"), val = tensor([100])]; + tensor var_3661 = const()[name = tensor("op_3661"), val = tensor([100])]; + tensor seg_193_pad_type_0 = const()[name = tensor("seg_193_pad_type_0"), val = tensor("custom")]; + tensor seg_193_pad_0 = const()[name = tensor("seg_193_pad_0"), val = tensor([0, 0])]; + tensor seg_193_exclude_padding_from_average_0 = const()[name = tensor("seg_193_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_193_ceil_mode_0 = const()[name = tensor("seg_193_ceil_mode_0"), val = tensor(true)]; + tensor seg_193 = avg_pool(ceil_mode = seg_193_ceil_mode_0, exclude_padding_from_average = seg_193_exclude_padding_from_average_0, kernel_sizes = var_3660, pad = seg_193_pad_0, pad_type = seg_193_pad_type_0, strides = var_3661, x = input_1053)[name = tensor("seg_193")]; + tensor var_3664_shape = shape(x = seg_193)[name = tensor("op_3664_shape")]; + tensor gather_196 = const()[name = tensor("gather_196"), val = tensor(1)]; + tensor gather_197 = const()[name = tensor("gather_197"), val = tensor(128)]; + tensor select_198 = const()[name = tensor("select_198"), val = tensor(2)]; + tensor gather_198_axis_0 = const()[name = tensor("gather_198_axis_0"), val = tensor(0)]; + tensor gather_198_batch_dims_0 = const()[name = tensor("gather_198_batch_dims_0"), val = tensor(0)]; + tensor gather_198_validate_indices_0 = const()[name = tensor("gather_198_validate_indices_0"), val = tensor(false)]; + tensor gather_198 = gather(axis = gather_198_axis_0, batch_dims = gather_198_batch_dims_0, indices = select_198, validate_indices = gather_198_validate_indices_0, x = var_3664_shape)[name = tensor("gather_198")]; + tensor var_3667_axes_0 = const()[name = tensor("op_3667_axes_0"), val = tensor([-1])]; + tensor var_3667 = expand_dims(axes = var_3667_axes_0, x = seg_193)[name = tensor("op_3667")]; + tensor concat_97_axis_0 = const()[name = tensor("concat_97_axis_0"), val = tensor(0)]; + tensor concat_97_interleave_0 = const()[name = tensor("concat_97_interleave_0"), val = tensor(false)]; + tensor concat_97 = concat(axis = concat_97_axis_0, interleave = concat_97_interleave_0, values = (gather_196, gather_197, gather_198, var_3))[name = tensor("concat_97")]; + tensor shape_247 = shape(x = var_3667)[name = tensor("shape_247")]; + tensor real_div_48 = real_div(x = concat_97, y = shape_247)[name = tensor("real_div_48")]; + tensor var_3669 = tile(reps = real_div_48, x = var_3667)[name = tensor("op_3669")]; + tensor var_3670 = const()[name = tensor("op_3670"), val = tensor([1, 128, -1])]; + tensor seg_195 = reshape(shape = var_3670, x = var_3669)[name = tensor("seg_195")]; + tensor var_3672_shape = shape(x = input_1053)[name = tensor("op_3672_shape")]; + tensor select_199 = const()[name = tensor("select_199"), val = tensor(2)]; + tensor gather_199_axis_0 = const()[name = tensor("gather_199_axis_0"), val = tensor(0)]; + tensor gather_199_batch_dims_0 = const()[name = tensor("gather_199_batch_dims_0"), val = tensor(0)]; + tensor gather_199_validate_indices_0 = const()[name = tensor("gather_199_validate_indices_0"), val = tensor(false)]; + tensor gather_199 = gather(axis = gather_199_axis_0, batch_dims = gather_199_batch_dims_0, indices = select_199, validate_indices = gather_199_validate_indices_0, x = var_3672_shape)[name = tensor("gather_199")]; + tensor concat_98_values0_0 = const()[name = tensor("concat_98_values0_0"), val = tensor(1)]; + tensor concat_98_values1_0 = const()[name = tensor("concat_98_values1_0"), val = tensor(128)]; + tensor concat_98_axis_0 = const()[name = tensor("concat_98_axis_0"), val = tensor(0)]; + tensor concat_98_interleave_0 = const()[name = tensor("concat_98_interleave_0"), val = tensor(false)]; + tensor concat_98 = concat(axis = concat_98_axis_0, interleave = concat_98_interleave_0, values = (concat_98_values0_0, concat_98_values1_0, gather_199))[name = tensor("concat_98")]; + tensor var_3673_begin_0 = const()[name = tensor("op_3673_begin_0"), val = tensor([0, 0, 0])]; + tensor var_3673_end_mask_0 = const()[name = tensor("op_3673_end_mask_0"), val = tensor([true, true, false])]; + tensor var_3673 = slice_by_index(begin = var_3673_begin_0, end = concat_98, end_mask = var_3673_end_mask_0, x = seg_195)[name = tensor("op_3673")]; + tensor input_1055 = add(x = var_3659, y = var_3673)[name = tensor("input_1055")]; + tensor input_1057_pad_type_0 = const()[name = tensor("input_1057_pad_type_0"), val = tensor("valid")]; + tensor input_1057_strides_0 = const()[name = tensor("input_1057_strides_0"), val = tensor([1])]; + tensor input_1057_pad_0 = const()[name = tensor("input_1057_pad_0"), val = tensor([0, 0])]; + tensor input_1057_dilations_0 = const()[name = tensor("input_1057_dilations_0"), val = tensor([1])]; + tensor input_1057_groups_0 = const()[name = tensor("input_1057_groups_0"), val = tensor(1)]; + tensor input_1057 = conv(bias = model_xvector_block3_tdnnd13_cam_layer_linear1_bias, dilations = input_1057_dilations_0, groups = input_1057_groups_0, pad = input_1057_pad_0, pad_type = input_1057_pad_type_0, strides = input_1057_strides_0, weight = model_xvector_block3_tdnnd13_cam_layer_linear1_weight, x = input_1055)[name = tensor("input_1057")]; + tensor input_1059 = relu(x = input_1057)[name = tensor("input_1059")]; + tensor input_1061_pad_type_0 = const()[name = tensor("input_1061_pad_type_0"), val = tensor("valid")]; + tensor input_1061_strides_0 = const()[name = tensor("input_1061_strides_0"), val = tensor([1])]; + tensor input_1061_pad_0 = const()[name = tensor("input_1061_pad_0"), val = tensor([0, 0])]; + tensor input_1061_dilations_0 = const()[name = tensor("input_1061_dilations_0"), val = tensor([1])]; + tensor input_1061_groups_0 = const()[name = tensor("input_1061_groups_0"), val = tensor(1)]; + tensor input_1061 = conv(bias = model_xvector_block3_tdnnd13_cam_layer_linear2_bias, dilations = input_1061_dilations_0, groups = input_1061_groups_0, pad = input_1061_pad_0, pad_type = input_1061_pad_type_0, strides = input_1061_strides_0, weight = model_xvector_block3_tdnnd13_cam_layer_linear2_weight, x = input_1059)[name = tensor("input_1061")]; + tensor m_97 = sigmoid(x = input_1061)[name = tensor("m_97")]; + tensor var_3691 = mul(x = y_97, y = m_97)[name = tensor("op_3691")]; + tensor input_1063_interleave_0 = const()[name = tensor("input_1063_interleave_0"), val = tensor(false)]; + tensor input_1063 = concat(axis = var_11, interleave = input_1063_interleave_0, values = (input_1043, var_3691))[name = tensor("input_1063")]; + tensor input_1065 = batch_norm(beta = model_xvector_block3_tdnnd14_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block3_tdnnd14_nonlinear1_batchnorm_weight, mean = model_xvector_block3_tdnnd14_nonlinear1_batchnorm_running_mean, variance = model_xvector_block3_tdnnd14_nonlinear1_batchnorm_running_var, x = input_1063)[name = tensor("input_1065")]; + tensor input_1067 = relu(x = input_1065)[name = tensor("input_1067")]; + tensor input_1069_pad_type_0 = const()[name = tensor("input_1069_pad_type_0"), val = tensor("valid")]; + tensor input_1069_strides_0 = const()[name = tensor("input_1069_strides_0"), val = tensor([1])]; + tensor input_1069_pad_0 = const()[name = tensor("input_1069_pad_0"), val = tensor([0, 0])]; + tensor input_1069_dilations_0 = const()[name = tensor("input_1069_dilations_0"), val = tensor([1])]; + tensor input_1069_groups_0 = const()[name = tensor("input_1069_groups_0"), val = tensor(1)]; + tensor const_124 = const()[name = tensor("const_124"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24092800)))]; + tensor const_125 = const()[name = tensor("const_125"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24568000)))]; + tensor input_1071 = conv(bias = const_125, dilations = input_1069_dilations_0, groups = input_1069_groups_0, pad = input_1069_pad_0, pad_type = input_1069_pad_type_0, strides = input_1069_strides_0, weight = const_124, x = input_1067)[name = tensor("input_1071")]; + tensor input_1073 = relu(x = input_1071)[name = tensor("input_1073")]; + tensor y_99_pad_type_0 = const()[name = tensor("y_99_pad_type_0"), val = tensor("custom")]; + tensor y_99_pad_0 = const()[name = tensor("y_99_pad_0"), val = tensor([2, 2])]; + tensor y_99_dilations_0 = const()[name = tensor("y_99_dilations_0"), val = tensor([2])]; + tensor y_99_strides_0 = const()[name = tensor("y_99_strides_0"), val = tensor([1])]; + tensor y_99_groups_0 = const()[name = tensor("y_99_groups_0"), val = tensor(1)]; + tensor y_99 = conv(dilations = y_99_dilations_0, groups = y_99_groups_0, pad = y_99_pad_0, pad_type = y_99_pad_type_0, strides = y_99_strides_0, weight = model_xvector_block3_tdnnd14_cam_layer_linear_local_weight, x = input_1073)[name = tensor("y_99")]; + tensor var_3728_axes_0 = const()[name = tensor("op_3728_axes_0"), val = tensor([-1])]; + tensor var_3728_keep_dims_0 = const()[name = tensor("op_3728_keep_dims_0"), val = tensor(true)]; + tensor var_3728 = reduce_mean(axes = var_3728_axes_0, keep_dims = var_3728_keep_dims_0, x = input_1073)[name = tensor("op_3728")]; + tensor var_3729 = const()[name = tensor("op_3729"), val = tensor([100])]; + tensor var_3730 = const()[name = tensor("op_3730"), val = tensor([100])]; + tensor seg_197_pad_type_0 = const()[name = tensor("seg_197_pad_type_0"), val = tensor("custom")]; + tensor seg_197_pad_0 = const()[name = tensor("seg_197_pad_0"), val = tensor([0, 0])]; + tensor seg_197_exclude_padding_from_average_0 = const()[name = tensor("seg_197_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_197_ceil_mode_0 = const()[name = tensor("seg_197_ceil_mode_0"), val = tensor(true)]; + tensor seg_197 = avg_pool(ceil_mode = seg_197_ceil_mode_0, exclude_padding_from_average = seg_197_exclude_padding_from_average_0, kernel_sizes = var_3729, pad = seg_197_pad_0, pad_type = seg_197_pad_type_0, strides = var_3730, x = input_1073)[name = tensor("seg_197")]; + tensor var_3733_shape = shape(x = seg_197)[name = tensor("op_3733_shape")]; + tensor gather_200 = const()[name = tensor("gather_200"), val = tensor(1)]; + tensor gather_201 = const()[name = tensor("gather_201"), val = tensor(128)]; + tensor select_202 = const()[name = tensor("select_202"), val = tensor(2)]; + tensor gather_202_axis_0 = const()[name = tensor("gather_202_axis_0"), val = tensor(0)]; + tensor gather_202_batch_dims_0 = const()[name = tensor("gather_202_batch_dims_0"), val = tensor(0)]; + tensor gather_202_validate_indices_0 = const()[name = tensor("gather_202_validate_indices_0"), val = tensor(false)]; + tensor gather_202 = gather(axis = gather_202_axis_0, batch_dims = gather_202_batch_dims_0, indices = select_202, validate_indices = gather_202_validate_indices_0, x = var_3733_shape)[name = tensor("gather_202")]; + tensor var_3736_axes_0 = const()[name = tensor("op_3736_axes_0"), val = tensor([-1])]; + tensor var_3736 = expand_dims(axes = var_3736_axes_0, x = seg_197)[name = tensor("op_3736")]; + tensor concat_99_axis_0 = const()[name = tensor("concat_99_axis_0"), val = tensor(0)]; + tensor concat_99_interleave_0 = const()[name = tensor("concat_99_interleave_0"), val = tensor(false)]; + tensor concat_99 = concat(axis = concat_99_axis_0, interleave = concat_99_interleave_0, values = (gather_200, gather_201, gather_202, var_3))[name = tensor("concat_99")]; + tensor shape_252 = shape(x = var_3736)[name = tensor("shape_252")]; + tensor real_div_49 = real_div(x = concat_99, y = shape_252)[name = tensor("real_div_49")]; + tensor var_3738 = tile(reps = real_div_49, x = var_3736)[name = tensor("op_3738")]; + tensor var_3739 = const()[name = tensor("op_3739"), val = tensor([1, 128, -1])]; + tensor seg_199 = reshape(shape = var_3739, x = var_3738)[name = tensor("seg_199")]; + tensor var_3741_shape = shape(x = input_1073)[name = tensor("op_3741_shape")]; + tensor select_203 = const()[name = tensor("select_203"), val = tensor(2)]; + tensor gather_203_axis_0 = const()[name = tensor("gather_203_axis_0"), val = tensor(0)]; + tensor gather_203_batch_dims_0 = const()[name = tensor("gather_203_batch_dims_0"), val = tensor(0)]; + tensor gather_203_validate_indices_0 = const()[name = tensor("gather_203_validate_indices_0"), val = tensor(false)]; + tensor gather_203 = gather(axis = gather_203_axis_0, batch_dims = gather_203_batch_dims_0, indices = select_203, validate_indices = gather_203_validate_indices_0, x = var_3741_shape)[name = tensor("gather_203")]; + tensor concat_100_values0_0 = const()[name = tensor("concat_100_values0_0"), val = tensor(1)]; + tensor concat_100_values1_0 = const()[name = tensor("concat_100_values1_0"), val = tensor(128)]; + tensor concat_100_axis_0 = const()[name = tensor("concat_100_axis_0"), val = tensor(0)]; + tensor concat_100_interleave_0 = const()[name = tensor("concat_100_interleave_0"), val = tensor(false)]; + tensor concat_100 = concat(axis = concat_100_axis_0, interleave = concat_100_interleave_0, values = (concat_100_values0_0, concat_100_values1_0, gather_203))[name = tensor("concat_100")]; + tensor var_3742_begin_0 = const()[name = tensor("op_3742_begin_0"), val = tensor([0, 0, 0])]; + tensor var_3742_end_mask_0 = const()[name = tensor("op_3742_end_mask_0"), val = tensor([true, true, false])]; + tensor var_3742 = slice_by_index(begin = var_3742_begin_0, end = concat_100, end_mask = var_3742_end_mask_0, x = seg_199)[name = tensor("op_3742")]; + tensor input_1075 = add(x = var_3728, y = var_3742)[name = tensor("input_1075")]; + tensor input_1077_pad_type_0 = const()[name = tensor("input_1077_pad_type_0"), val = tensor("valid")]; + tensor input_1077_strides_0 = const()[name = tensor("input_1077_strides_0"), val = tensor([1])]; + tensor input_1077_pad_0 = const()[name = tensor("input_1077_pad_0"), val = tensor([0, 0])]; + tensor input_1077_dilations_0 = const()[name = tensor("input_1077_dilations_0"), val = tensor([1])]; + tensor input_1077_groups_0 = const()[name = tensor("input_1077_groups_0"), val = tensor(1)]; + tensor input_1077 = conv(bias = model_xvector_block3_tdnnd14_cam_layer_linear1_bias, dilations = input_1077_dilations_0, groups = input_1077_groups_0, pad = input_1077_pad_0, pad_type = input_1077_pad_type_0, strides = input_1077_strides_0, weight = model_xvector_block3_tdnnd14_cam_layer_linear1_weight, x = input_1075)[name = tensor("input_1077")]; + tensor input_1079 = relu(x = input_1077)[name = tensor("input_1079")]; + tensor input_1081_pad_type_0 = const()[name = tensor("input_1081_pad_type_0"), val = tensor("valid")]; + tensor input_1081_strides_0 = const()[name = tensor("input_1081_strides_0"), val = tensor([1])]; + tensor input_1081_pad_0 = const()[name = tensor("input_1081_pad_0"), val = tensor([0, 0])]; + tensor input_1081_dilations_0 = const()[name = tensor("input_1081_dilations_0"), val = tensor([1])]; + tensor input_1081_groups_0 = const()[name = tensor("input_1081_groups_0"), val = tensor(1)]; + tensor input_1081 = conv(bias = model_xvector_block3_tdnnd14_cam_layer_linear2_bias, dilations = input_1081_dilations_0, groups = input_1081_groups_0, pad = input_1081_pad_0, pad_type = input_1081_pad_type_0, strides = input_1081_strides_0, weight = model_xvector_block3_tdnnd14_cam_layer_linear2_weight, x = input_1079)[name = tensor("input_1081")]; + tensor m_99 = sigmoid(x = input_1081)[name = tensor("m_99")]; + tensor var_3760 = mul(x = y_99, y = m_99)[name = tensor("op_3760")]; + tensor input_1083_interleave_0 = const()[name = tensor("input_1083_interleave_0"), val = tensor(false)]; + tensor input_1083 = concat(axis = var_11, interleave = input_1083_interleave_0, values = (input_1063, var_3760))[name = tensor("input_1083")]; + tensor input_1085 = batch_norm(beta = model_xvector_block3_tdnnd15_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block3_tdnnd15_nonlinear1_batchnorm_weight, mean = model_xvector_block3_tdnnd15_nonlinear1_batchnorm_running_mean, variance = model_xvector_block3_tdnnd15_nonlinear1_batchnorm_running_var, x = input_1083)[name = tensor("input_1085")]; + tensor input_1087 = relu(x = input_1085)[name = tensor("input_1087")]; + tensor input_1089_pad_type_0 = const()[name = tensor("input_1089_pad_type_0"), val = tensor("valid")]; + tensor input_1089_strides_0 = const()[name = tensor("input_1089_strides_0"), val = tensor([1])]; + tensor input_1089_pad_0 = const()[name = tensor("input_1089_pad_0"), val = tensor([0, 0])]; + tensor input_1089_dilations_0 = const()[name = tensor("input_1089_dilations_0"), val = tensor([1])]; + tensor input_1089_groups_0 = const()[name = tensor("input_1089_groups_0"), val = tensor(1)]; + tensor const_126 = const()[name = tensor("const_126"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24568576)))]; + tensor const_127 = const()[name = tensor("const_127"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25060160)))]; + tensor input_1091 = conv(bias = const_127, dilations = input_1089_dilations_0, groups = input_1089_groups_0, pad = input_1089_pad_0, pad_type = input_1089_pad_type_0, strides = input_1089_strides_0, weight = const_126, x = input_1087)[name = tensor("input_1091")]; + tensor input_1093 = relu(x = input_1091)[name = tensor("input_1093")]; + tensor y_101_pad_type_0 = const()[name = tensor("y_101_pad_type_0"), val = tensor("custom")]; + tensor y_101_pad_0 = const()[name = tensor("y_101_pad_0"), val = tensor([2, 2])]; + tensor y_101_dilations_0 = const()[name = tensor("y_101_dilations_0"), val = tensor([2])]; + tensor y_101_strides_0 = const()[name = tensor("y_101_strides_0"), val = tensor([1])]; + tensor y_101_groups_0 = const()[name = tensor("y_101_groups_0"), val = tensor(1)]; + tensor y_101 = conv(dilations = y_101_dilations_0, groups = y_101_groups_0, pad = y_101_pad_0, pad_type = y_101_pad_type_0, strides = y_101_strides_0, weight = model_xvector_block3_tdnnd15_cam_layer_linear_local_weight, x = input_1093)[name = tensor("y_101")]; + tensor var_3797_axes_0 = const()[name = tensor("op_3797_axes_0"), val = tensor([-1])]; + tensor var_3797_keep_dims_0 = const()[name = tensor("op_3797_keep_dims_0"), val = tensor(true)]; + tensor var_3797 = reduce_mean(axes = var_3797_axes_0, keep_dims = var_3797_keep_dims_0, x = input_1093)[name = tensor("op_3797")]; + tensor var_3798 = const()[name = tensor("op_3798"), val = tensor([100])]; + tensor var_3799 = const()[name = tensor("op_3799"), val = tensor([100])]; + tensor seg_201_pad_type_0 = const()[name = tensor("seg_201_pad_type_0"), val = tensor("custom")]; + tensor seg_201_pad_0 = const()[name = tensor("seg_201_pad_0"), val = tensor([0, 0])]; + tensor seg_201_exclude_padding_from_average_0 = const()[name = tensor("seg_201_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_201_ceil_mode_0 = const()[name = tensor("seg_201_ceil_mode_0"), val = tensor(true)]; + tensor seg_201 = avg_pool(ceil_mode = seg_201_ceil_mode_0, exclude_padding_from_average = seg_201_exclude_padding_from_average_0, kernel_sizes = var_3798, pad = seg_201_pad_0, pad_type = seg_201_pad_type_0, strides = var_3799, x = input_1093)[name = tensor("seg_201")]; + tensor var_3802_shape = shape(x = seg_201)[name = tensor("op_3802_shape")]; + tensor gather_204 = const()[name = tensor("gather_204"), val = tensor(1)]; + tensor gather_205 = const()[name = tensor("gather_205"), val = tensor(128)]; + tensor select_206 = const()[name = tensor("select_206"), val = tensor(2)]; + tensor gather_206_axis_0 = const()[name = tensor("gather_206_axis_0"), val = tensor(0)]; + tensor gather_206_batch_dims_0 = const()[name = tensor("gather_206_batch_dims_0"), val = tensor(0)]; + tensor gather_206_validate_indices_0 = const()[name = tensor("gather_206_validate_indices_0"), val = tensor(false)]; + tensor gather_206 = gather(axis = gather_206_axis_0, batch_dims = gather_206_batch_dims_0, indices = select_206, validate_indices = gather_206_validate_indices_0, x = var_3802_shape)[name = tensor("gather_206")]; + tensor var_3805_axes_0 = const()[name = tensor("op_3805_axes_0"), val = tensor([-1])]; + tensor var_3805 = expand_dims(axes = var_3805_axes_0, x = seg_201)[name = tensor("op_3805")]; + tensor concat_101_axis_0 = const()[name = tensor("concat_101_axis_0"), val = tensor(0)]; + tensor concat_101_interleave_0 = const()[name = tensor("concat_101_interleave_0"), val = tensor(false)]; + tensor concat_101 = concat(axis = concat_101_axis_0, interleave = concat_101_interleave_0, values = (gather_204, gather_205, gather_206, var_3))[name = tensor("concat_101")]; + tensor shape_257 = shape(x = var_3805)[name = tensor("shape_257")]; + tensor real_div_50 = real_div(x = concat_101, y = shape_257)[name = tensor("real_div_50")]; + tensor var_3807 = tile(reps = real_div_50, x = var_3805)[name = tensor("op_3807")]; + tensor var_3808 = const()[name = tensor("op_3808"), val = tensor([1, 128, -1])]; + tensor seg_203 = reshape(shape = var_3808, x = var_3807)[name = tensor("seg_203")]; + tensor var_3810_shape = shape(x = input_1093)[name = tensor("op_3810_shape")]; + tensor select_207 = const()[name = tensor("select_207"), val = tensor(2)]; + tensor gather_207_axis_0 = const()[name = tensor("gather_207_axis_0"), val = tensor(0)]; + tensor gather_207_batch_dims_0 = const()[name = tensor("gather_207_batch_dims_0"), val = tensor(0)]; + tensor gather_207_validate_indices_0 = const()[name = tensor("gather_207_validate_indices_0"), val = tensor(false)]; + tensor gather_207 = gather(axis = gather_207_axis_0, batch_dims = gather_207_batch_dims_0, indices = select_207, validate_indices = gather_207_validate_indices_0, x = var_3810_shape)[name = tensor("gather_207")]; + tensor concat_102_values0_0 = const()[name = tensor("concat_102_values0_0"), val = tensor(1)]; + tensor concat_102_values1_0 = const()[name = tensor("concat_102_values1_0"), val = tensor(128)]; + tensor concat_102_axis_0 = const()[name = tensor("concat_102_axis_0"), val = tensor(0)]; + tensor concat_102_interleave_0 = const()[name = tensor("concat_102_interleave_0"), val = tensor(false)]; + tensor concat_102 = concat(axis = concat_102_axis_0, interleave = concat_102_interleave_0, values = (concat_102_values0_0, concat_102_values1_0, gather_207))[name = tensor("concat_102")]; + tensor var_3811_begin_0 = const()[name = tensor("op_3811_begin_0"), val = tensor([0, 0, 0])]; + tensor var_3811_end_mask_0 = const()[name = tensor("op_3811_end_mask_0"), val = tensor([true, true, false])]; + tensor var_3811 = slice_by_index(begin = var_3811_begin_0, end = concat_102, end_mask = var_3811_end_mask_0, x = seg_203)[name = tensor("op_3811")]; + tensor input_1095 = add(x = var_3797, y = var_3811)[name = tensor("input_1095")]; + tensor input_1097_pad_type_0 = const()[name = tensor("input_1097_pad_type_0"), val = tensor("valid")]; + tensor input_1097_strides_0 = const()[name = tensor("input_1097_strides_0"), val = tensor([1])]; + tensor input_1097_pad_0 = const()[name = tensor("input_1097_pad_0"), val = tensor([0, 0])]; + tensor input_1097_dilations_0 = const()[name = tensor("input_1097_dilations_0"), val = tensor([1])]; + tensor input_1097_groups_0 = const()[name = tensor("input_1097_groups_0"), val = tensor(1)]; + tensor input_1097 = conv(bias = model_xvector_block3_tdnnd15_cam_layer_linear1_bias, dilations = input_1097_dilations_0, groups = input_1097_groups_0, pad = input_1097_pad_0, pad_type = input_1097_pad_type_0, strides = input_1097_strides_0, weight = model_xvector_block3_tdnnd15_cam_layer_linear1_weight, x = input_1095)[name = tensor("input_1097")]; + tensor input_1099 = relu(x = input_1097)[name = tensor("input_1099")]; + tensor input_1101_pad_type_0 = const()[name = tensor("input_1101_pad_type_0"), val = tensor("valid")]; + tensor input_1101_strides_0 = const()[name = tensor("input_1101_strides_0"), val = tensor([1])]; + tensor input_1101_pad_0 = const()[name = tensor("input_1101_pad_0"), val = tensor([0, 0])]; + tensor input_1101_dilations_0 = const()[name = tensor("input_1101_dilations_0"), val = tensor([1])]; + tensor input_1101_groups_0 = const()[name = tensor("input_1101_groups_0"), val = tensor(1)]; + tensor input_1101 = conv(bias = model_xvector_block3_tdnnd15_cam_layer_linear2_bias, dilations = input_1101_dilations_0, groups = input_1101_groups_0, pad = input_1101_pad_0, pad_type = input_1101_pad_type_0, strides = input_1101_strides_0, weight = model_xvector_block3_tdnnd15_cam_layer_linear2_weight, x = input_1099)[name = tensor("input_1101")]; + tensor m_101 = sigmoid(x = input_1101)[name = tensor("m_101")]; + tensor var_3829 = mul(x = y_101, y = m_101)[name = tensor("op_3829")]; + tensor input_1103_interleave_0 = const()[name = tensor("input_1103_interleave_0"), val = tensor(false)]; + tensor input_1103 = concat(axis = var_11, interleave = input_1103_interleave_0, values = (input_1083, var_3829))[name = tensor("input_1103")]; + tensor input_1105 = batch_norm(beta = model_xvector_block3_tdnnd16_nonlinear1_batchnorm_bias, epsilon = var_8, gamma = model_xvector_block3_tdnnd16_nonlinear1_batchnorm_weight, mean = model_xvector_block3_tdnnd16_nonlinear1_batchnorm_running_mean, variance = model_xvector_block3_tdnnd16_nonlinear1_batchnorm_running_var, x = input_1103)[name = tensor("input_1105")]; + tensor input_1107 = relu(x = input_1105)[name = tensor("input_1107")]; + tensor input_1109_pad_type_0 = const()[name = tensor("input_1109_pad_type_0"), val = tensor("valid")]; + tensor input_1109_strides_0 = const()[name = tensor("input_1109_strides_0"), val = tensor([1])]; + tensor input_1109_pad_0 = const()[name = tensor("input_1109_pad_0"), val = tensor([0, 0])]; + tensor input_1109_dilations_0 = const()[name = tensor("input_1109_dilations_0"), val = tensor([1])]; + tensor input_1109_groups_0 = const()[name = tensor("input_1109_groups_0"), val = tensor(1)]; + tensor const_128 = const()[name = tensor("const_128"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25060736)))]; + tensor const_129 = const()[name = tensor("const_129"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25568704)))]; + tensor input_1111 = conv(bias = const_129, dilations = input_1109_dilations_0, groups = input_1109_groups_0, pad = input_1109_pad_0, pad_type = input_1109_pad_type_0, strides = input_1109_strides_0, weight = const_128, x = input_1107)[name = tensor("input_1111")]; + tensor input_1113 = relu(x = input_1111)[name = tensor("input_1113")]; + tensor y_pad_type_0 = const()[name = tensor("y_pad_type_0"), val = tensor("custom")]; + tensor y_pad_0 = const()[name = tensor("y_pad_0"), val = tensor([2, 2])]; + tensor y_dilations_0 = const()[name = tensor("y_dilations_0"), val = tensor([2])]; + tensor y_strides_0 = const()[name = tensor("y_strides_0"), val = tensor([1])]; + tensor y_groups_0 = const()[name = tensor("y_groups_0"), val = tensor(1)]; + tensor y = conv(dilations = y_dilations_0, groups = y_groups_0, pad = y_pad_0, pad_type = y_pad_type_0, strides = y_strides_0, weight = model_xvector_block3_tdnnd16_cam_layer_linear_local_weight, x = input_1113)[name = tensor("y")]; + tensor var_3866_axes_0 = const()[name = tensor("op_3866_axes_0"), val = tensor([-1])]; + tensor var_3866_keep_dims_0 = const()[name = tensor("op_3866_keep_dims_0"), val = tensor(true)]; + tensor var_3866 = reduce_mean(axes = var_3866_axes_0, keep_dims = var_3866_keep_dims_0, x = input_1113)[name = tensor("op_3866")]; + tensor var_3867 = const()[name = tensor("op_3867"), val = tensor([100])]; + tensor var_3868 = const()[name = tensor("op_3868"), val = tensor([100])]; + tensor seg_205_pad_type_0 = const()[name = tensor("seg_205_pad_type_0"), val = tensor("custom")]; + tensor seg_205_pad_0 = const()[name = tensor("seg_205_pad_0"), val = tensor([0, 0])]; + tensor seg_205_exclude_padding_from_average_0 = const()[name = tensor("seg_205_exclude_padding_from_average_0"), val = tensor(true)]; + tensor seg_205_ceil_mode_0 = const()[name = tensor("seg_205_ceil_mode_0"), val = tensor(true)]; + tensor seg_205 = avg_pool(ceil_mode = seg_205_ceil_mode_0, exclude_padding_from_average = seg_205_exclude_padding_from_average_0, kernel_sizes = var_3867, pad = seg_205_pad_0, pad_type = seg_205_pad_type_0, strides = var_3868, x = input_1113)[name = tensor("seg_205")]; + tensor var_3871_shape = shape(x = seg_205)[name = tensor("op_3871_shape")]; + tensor gather_208 = const()[name = tensor("gather_208"), val = tensor(1)]; + tensor gather_209 = const()[name = tensor("gather_209"), val = tensor(128)]; + tensor select_210 = const()[name = tensor("select_210"), val = tensor(2)]; + tensor gather_210_axis_0 = const()[name = tensor("gather_210_axis_0"), val = tensor(0)]; + tensor gather_210_batch_dims_0 = const()[name = tensor("gather_210_batch_dims_0"), val = tensor(0)]; + tensor gather_210_validate_indices_0 = const()[name = tensor("gather_210_validate_indices_0"), val = tensor(false)]; + tensor gather_210 = gather(axis = gather_210_axis_0, batch_dims = gather_210_batch_dims_0, indices = select_210, validate_indices = gather_210_validate_indices_0, x = var_3871_shape)[name = tensor("gather_210")]; + tensor var_3874_axes_0 = const()[name = tensor("op_3874_axes_0"), val = tensor([-1])]; + tensor var_3874 = expand_dims(axes = var_3874_axes_0, x = seg_205)[name = tensor("op_3874")]; + tensor concat_103_axis_0 = const()[name = tensor("concat_103_axis_0"), val = tensor(0)]; + tensor concat_103_interleave_0 = const()[name = tensor("concat_103_interleave_0"), val = tensor(false)]; + tensor concat_103 = concat(axis = concat_103_axis_0, interleave = concat_103_interleave_0, values = (gather_208, gather_209, gather_210, var_3))[name = tensor("concat_103")]; + tensor shape_262 = shape(x = var_3874)[name = tensor("shape_262")]; + tensor real_div_51 = real_div(x = concat_103, y = shape_262)[name = tensor("real_div_51")]; + tensor var_3876 = tile(reps = real_div_51, x = var_3874)[name = tensor("op_3876")]; + tensor var_3877 = const()[name = tensor("op_3877"), val = tensor([1, 128, -1])]; + tensor seg = reshape(shape = var_3877, x = var_3876)[name = tensor("seg")]; + tensor var_3879_shape = shape(x = input_1113)[name = tensor("op_3879_shape")]; + tensor select_211 = const()[name = tensor("select_211"), val = tensor(2)]; + tensor gather_211_axis_0 = const()[name = tensor("gather_211_axis_0"), val = tensor(0)]; + tensor gather_211_batch_dims_0 = const()[name = tensor("gather_211_batch_dims_0"), val = tensor(0)]; + tensor gather_211_validate_indices_0 = const()[name = tensor("gather_211_validate_indices_0"), val = tensor(false)]; + tensor gather_211 = gather(axis = gather_211_axis_0, batch_dims = gather_211_batch_dims_0, indices = select_211, validate_indices = gather_211_validate_indices_0, x = var_3879_shape)[name = tensor("gather_211")]; + tensor concat_104_values0_0 = const()[name = tensor("concat_104_values0_0"), val = tensor(1)]; + tensor concat_104_values1_0 = const()[name = tensor("concat_104_values1_0"), val = tensor(128)]; + tensor concat_104_axis_0 = const()[name = tensor("concat_104_axis_0"), val = tensor(0)]; + tensor concat_104_interleave_0 = const()[name = tensor("concat_104_interleave_0"), val = tensor(false)]; + tensor concat_104 = concat(axis = concat_104_axis_0, interleave = concat_104_interleave_0, values = (concat_104_values0_0, concat_104_values1_0, gather_211))[name = tensor("concat_104")]; + tensor var_3880_begin_0 = const()[name = tensor("op_3880_begin_0"), val = tensor([0, 0, 0])]; + tensor var_3880_end_mask_0 = const()[name = tensor("op_3880_end_mask_0"), val = tensor([true, true, false])]; + tensor var_3880 = slice_by_index(begin = var_3880_begin_0, end = concat_104, end_mask = var_3880_end_mask_0, x = seg)[name = tensor("op_3880")]; + tensor input_1115 = add(x = var_3866, y = var_3880)[name = tensor("input_1115")]; + tensor input_1117_pad_type_0 = const()[name = tensor("input_1117_pad_type_0"), val = tensor("valid")]; + tensor input_1117_strides_0 = const()[name = tensor("input_1117_strides_0"), val = tensor([1])]; + tensor input_1117_pad_0 = const()[name = tensor("input_1117_pad_0"), val = tensor([0, 0])]; + tensor input_1117_dilations_0 = const()[name = tensor("input_1117_dilations_0"), val = tensor([1])]; + tensor input_1117_groups_0 = const()[name = tensor("input_1117_groups_0"), val = tensor(1)]; + tensor input_1117 = conv(bias = model_xvector_block3_tdnnd16_cam_layer_linear1_bias, dilations = input_1117_dilations_0, groups = input_1117_groups_0, pad = input_1117_pad_0, pad_type = input_1117_pad_type_0, strides = input_1117_strides_0, weight = model_xvector_block3_tdnnd16_cam_layer_linear1_weight, x = input_1115)[name = tensor("input_1117")]; + tensor input_1119 = relu(x = input_1117)[name = tensor("input_1119")]; + tensor input_1121_pad_type_0 = const()[name = tensor("input_1121_pad_type_0"), val = tensor("valid")]; + tensor input_1121_strides_0 = const()[name = tensor("input_1121_strides_0"), val = tensor([1])]; + tensor input_1121_pad_0 = const()[name = tensor("input_1121_pad_0"), val = tensor([0, 0])]; + tensor input_1121_dilations_0 = const()[name = tensor("input_1121_dilations_0"), val = tensor([1])]; + tensor input_1121_groups_0 = const()[name = tensor("input_1121_groups_0"), val = tensor(1)]; + tensor input_1121 = conv(bias = model_xvector_block3_tdnnd16_cam_layer_linear2_bias, dilations = input_1121_dilations_0, groups = input_1121_groups_0, pad = input_1121_pad_0, pad_type = input_1121_pad_type_0, strides = input_1121_strides_0, weight = model_xvector_block3_tdnnd16_cam_layer_linear2_weight, x = input_1119)[name = tensor("input_1121")]; + tensor m = sigmoid(x = input_1121)[name = tensor("m")]; + tensor var_3898 = mul(x = y, y = m)[name = tensor("op_3898")]; + tensor input_1123_interleave_0 = const()[name = tensor("input_1123_interleave_0"), val = tensor(false)]; + tensor input_1123 = concat(axis = var_11, interleave = input_1123_interleave_0, values = (input_1103, var_3898))[name = tensor("input_1123")]; + tensor input_1125 = batch_norm(beta = model_xvector_transit3_nonlinear_batchnorm_bias, epsilon = var_8, gamma = model_xvector_transit3_nonlinear_batchnorm_weight, mean = model_xvector_transit3_nonlinear_batchnorm_running_mean, variance = model_xvector_transit3_nonlinear_batchnorm_running_var, x = input_1123)[name = tensor("input_1125")]; + tensor input_1127 = relu(x = input_1125)[name = tensor("input_1127")]; + tensor input_1129_pad_type_0 = const()[name = tensor("input_1129_pad_type_0"), val = tensor("valid")]; + tensor input_1129_strides_0 = const()[name = tensor("input_1129_strides_0"), val = tensor([1])]; + tensor input_1129_pad_0 = const()[name = tensor("input_1129_pad_0"), val = tensor([0, 0])]; + tensor input_1129_dilations_0 = const()[name = tensor("input_1129_dilations_0"), val = tensor([1])]; + tensor input_1129_groups_0 = const()[name = tensor("input_1129_groups_0"), val = tensor(1)]; + tensor const_130 = const()[name = tensor("const_130"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25569280)))]; + tensor const_131 = const()[name = tensor("const_131"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27666496)))]; + tensor input_1131 = conv(bias = const_131, dilations = input_1129_dilations_0, groups = input_1129_groups_0, pad = input_1129_pad_0, pad_type = input_1129_pad_type_0, strides = input_1129_strides_0, weight = const_130, x = input_1127)[name = tensor("input_1131")]; + tensor x_3 = relu(x = input_1131)[name = tensor("x_3")]; + tensor mean_axes_0 = const()[name = tensor("mean_axes_0"), val = tensor([-1])]; + tensor mean_keep_dims_0 = const()[name = tensor("mean_keep_dims_0"), val = tensor(false)]; + tensor mean = reduce_mean(axes = mean_axes_0, keep_dims = mean_keep_dims_0, x = x_3)[name = tensor("mean")]; + tensor reduce_mean_0_axes_0 = const()[name = tensor("reduce_mean_0_axes_0"), val = tensor([-1])]; + tensor reduce_mean_0_keep_dims_0 = const()[name = tensor("reduce_mean_0_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_0 = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = x_3)[name = tensor("reduce_mean_0")]; + tensor sub_0 = sub(x = x_3, y = reduce_mean_0)[name = tensor("sub_0")]; + tensor square_0 = square(x = sub_0)[name = tensor("square_0")]; + tensor reduce_mean_1_axes_0 = const()[name = tensor("reduce_mean_1_axes_0"), val = tensor([-1])]; + tensor reduce_mean_1_keep_dims_0 = const()[name = tensor("reduce_mean_1_keep_dims_0"), val = tensor(false)]; + tensor reduce_mean_1 = reduce_mean(axes = reduce_mean_1_axes_0, keep_dims = reduce_mean_1_keep_dims_0, x = square_0)[name = tensor("reduce_mean_1")]; + tensor shape_264 = shape(x = x_3)[name = tensor("shape_264")]; + tensor slice_by_index_212_begin_0 = const()[name = tensor("slice_by_index_212_begin_0"), val = tensor([-1])]; + tensor slice_by_index_212_end_0 = const()[name = tensor("slice_by_index_212_end_0"), val = tensor([0])]; + tensor slice_by_index_212_squeeze_mask_0 = const()[name = tensor("slice_by_index_212_squeeze_mask_0"), val = tensor([true])]; + tensor slice_by_index_212 = slice_by_index(begin = slice_by_index_212_begin_0, end = slice_by_index_212_end_0, squeeze_mask = slice_by_index_212_squeeze_mask_0, x = shape_264)[name = tensor("slice_by_index_212")]; + tensor concat_105_axis_0 = const()[name = tensor("concat_105_axis_0"), val = tensor(0)]; + tensor concat_105_interleave_0 = const()[name = tensor("concat_105_interleave_0"), val = tensor(false)]; + tensor concat_105 = concat(axis = concat_105_axis_0, interleave = concat_105_interleave_0, values = slice_by_index_212)[name = tensor("concat_105")]; + tensor reduce_prod_0_keep_dims_0 = const()[name = tensor("reduce_prod_0_keep_dims_0"), val = tensor(false)]; + tensor reduce_prod_0 = reduce_prod(keep_dims = reduce_prod_0_keep_dims_0, x = concat_105)[name = tensor("reduce_prod_0")]; + tensor cast_105_dtype_0 = const()[name = tensor("cast_105_dtype_0"), val = tensor("fp32")]; + tensor sub_1_y_0 = const()[name = tensor("sub_1_y_0"), val = tensor(0x1p+0)]; + tensor cast_105 = cast(dtype = cast_105_dtype_0, x = reduce_prod_0)[name = tensor("cast_106")]; + tensor sub_1 = sub(x = cast_105, y = sub_1_y_0)[name = tensor("sub_1")]; + tensor real_div_52 = real_div(x = cast_105, y = sub_1)[name = tensor("real_div_52")]; + tensor mul_0 = mul(x = reduce_mean_1, y = real_div_52)[name = tensor("mul_0")]; + tensor sqrt_0 = sqrt(x = mul_0)[name = tensor("sqrt_0")]; + tensor x_interleave_0 = const()[name = tensor("x_interleave_0"), val = tensor(false)]; + tensor x = concat(axis = var_4, interleave = x_interleave_0, values = (mean, sqrt_0))[name = tensor("x")]; + tensor input_1133_axes_0 = const()[name = tensor("input_1133_axes_0"), val = tensor([-1])]; + tensor input_1133 = expand_dims(axes = input_1133_axes_0, x = x)[name = tensor("input_1133")]; + tensor var_3937_pad_type_0 = const()[name = tensor("op_3937_pad_type_0"), val = tensor("valid")]; + tensor var_3937_strides_0 = const()[name = tensor("op_3937_strides_0"), val = tensor([1])]; + tensor var_3937_pad_0 = const()[name = tensor("op_3937_pad_0"), val = tensor([0, 0])]; + tensor var_3937_dilations_0 = const()[name = tensor("op_3937_dilations_0"), val = tensor([1])]; + tensor var_3937_groups_0 = const()[name = tensor("op_3937_groups_0"), val = tensor(1)]; + tensor var_3937 = conv(dilations = var_3937_dilations_0, groups = var_3937_groups_0, pad = var_3937_pad_0, pad_type = var_3937_pad_type_0, strides = var_3937_strides_0, weight = model_xvector_dense_linear_weight, x = input_1133)[name = tensor("op_3937")]; + tensor input_axes_0 = const()[name = tensor("input_axes_0"), val = tensor([-1])]; + tensor input = squeeze(axes = input_axes_0, x = var_3937)[name = tensor("input")]; + tensor reshape_0 = const()[name = tensor("reshape_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27668608)))]; + tensor sub_2 = sub(x = input, y = reshape_0)[name = tensor("sub_2")]; + tensor _inversed_3942_y_0 = const()[name = tensor("_inversed_3942_y_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27669440)))]; + tensor embedding = mul(x = sub_2, y = _inversed_3942_y_0)[name = tensor("_inversed_3942")]; + } -> (embedding); +} \ No newline at end of file