program(1.0)
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.5.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})]
{
    func main<ios17>(tensor<fp32, [1, 1, 160000]> waveform, tensor<fp32, [1, 3, 589]> weights) {
            tensor<fp32, [514, 1, 400]> fbank_dft_weight = const()[name = tensor<string, []>("fbank_dft_weight"), val = tensor<fp32, [514, 1, 400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
            tensor<fp32, [80, 257]> fbank_mel_weight = const()[name = tensor<string, []>("fbank_mel_weight"), val = tensor<fp32, [80, 257]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(822528)))];
            tensor<fp32, [32]> backbone_conv1_bias = const()[name = tensor<string, []>("backbone_conv1_bias"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(904832)))];
            tensor<fp32, [32, 1, 3, 3]> backbone_conv1_weight = const()[name = tensor<string, []>("backbone_conv1_weight"), val = tensor<fp32, [32, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(905024)))];
            tensor<fp32, [32]> backbone_layer1_0_conv1_bias = const()[name = tensor<string, []>("backbone_layer1_0_conv1_bias"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(906240)))];
            tensor<fp32, [32, 32, 3, 3]> backbone_layer1_0_conv1_weight = const()[name = tensor<string, []>("backbone_layer1_0_conv1_weight"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(906432)))];
            tensor<fp32, [32]> backbone_layer1_0_conv2_bias = const()[name = tensor<string, []>("backbone_layer1_0_conv2_bias"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(943360)))];
            tensor<fp32, [32, 32, 3, 3]> backbone_layer1_0_conv2_weight = const()[name = tensor<string, []>("backbone_layer1_0_conv2_weight"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(943552)))];
            tensor<fp32, [32]> backbone_layer1_1_conv1_bias = const()[name = tensor<string, []>("backbone_layer1_1_conv1_bias"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(980480)))];
            tensor<fp32, [32, 32, 3, 3]> backbone_layer1_1_conv1_weight = const()[name = tensor<string, []>("backbone_layer1_1_conv1_weight"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(980672)))];
            tensor<fp32, [32]> backbone_layer1_1_conv2_bias = const()[name = tensor<string, []>("backbone_layer1_1_conv2_bias"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1017600)))];
            tensor<fp32, [32, 32, 3, 3]> backbone_layer1_1_conv2_weight = const()[name = tensor<string, []>("backbone_layer1_1_conv2_weight"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1017792)))];
            tensor<fp32, [32]> backbone_layer1_2_conv1_bias = const()[name = tensor<string, []>("backbone_layer1_2_conv1_bias"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1054720)))];
            tensor<fp32, [32, 32, 3, 3]> backbone_layer1_2_conv1_weight = const()[name = tensor<string, []>("backbone_layer1_2_conv1_weight"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1054912)))];
            tensor<fp32, [32]> backbone_layer1_2_conv2_bias = const()[name = tensor<string, []>("backbone_layer1_2_conv2_bias"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1091840)))];
            tensor<fp32, [32, 32, 3, 3]> backbone_layer1_2_conv2_weight = const()[name = tensor<string, []>("backbone_layer1_2_conv2_weight"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1092032)))];
            tensor<fp32, [64]> backbone_layer2_0_conv1_bias = const()[name = tensor<string, []>("backbone_layer2_0_conv1_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1128960)))];
            tensor<fp32, [64, 32, 3, 3]> backbone_layer2_0_conv1_weight = const()[name = tensor<string, []>("backbone_layer2_0_conv1_weight"), val = tensor<fp32, [64, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1129280)))];
            tensor<fp32, [64]> backbone_layer2_0_conv2_bias = const()[name = tensor<string, []>("backbone_layer2_0_conv2_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1203072)))];
            tensor<fp32, [64, 64, 3, 3]> backbone_layer2_0_conv2_weight = const()[name = tensor<string, []>("backbone_layer2_0_conv2_weight"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1203392)))];
            tensor<fp32, [64]> backbone_layer2_0_shortcut_bias = const()[name = tensor<string, []>("backbone_layer2_0_shortcut_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1350912)))];
            tensor<fp32, [64, 32, 1, 1]> backbone_layer2_0_shortcut_weight = const()[name = tensor<string, []>("backbone_layer2_0_shortcut_weight"), val = tensor<fp32, [64, 32, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1351232)))];
            tensor<fp32, [64]> backbone_layer2_1_conv1_bias = const()[name = tensor<string, []>("backbone_layer2_1_conv1_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1359488)))];
            tensor<fp32, [64, 64, 3, 3]> backbone_layer2_1_conv1_weight = const()[name = tensor<string, []>("backbone_layer2_1_conv1_weight"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1359808)))];
            tensor<fp32, [64]> backbone_layer2_1_conv2_bias = const()[name = tensor<string, []>("backbone_layer2_1_conv2_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1507328)))];
            tensor<fp32, [64, 64, 3, 3]> backbone_layer2_1_conv2_weight = const()[name = tensor<string, []>("backbone_layer2_1_conv2_weight"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1507648)))];
            tensor<fp32, [64]> backbone_layer2_2_conv1_bias = const()[name = tensor<string, []>("backbone_layer2_2_conv1_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1655168)))];
            tensor<fp32, [64, 64, 3, 3]> backbone_layer2_2_conv1_weight = const()[name = tensor<string, []>("backbone_layer2_2_conv1_weight"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1655488)))];
            tensor<fp32, [64]> backbone_layer2_2_conv2_bias = const()[name = tensor<string, []>("backbone_layer2_2_conv2_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1803008)))];
            tensor<fp32, [64, 64, 3, 3]> backbone_layer2_2_conv2_weight = const()[name = tensor<string, []>("backbone_layer2_2_conv2_weight"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1803328)))];
            tensor<fp32, [64]> backbone_layer2_3_conv1_bias = const()[name = tensor<string, []>("backbone_layer2_3_conv1_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1950848)))];
            tensor<fp32, [64, 64, 3, 3]> backbone_layer2_3_conv1_weight = const()[name = tensor<string, []>("backbone_layer2_3_conv1_weight"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1951168)))];
            tensor<fp32, [64]> backbone_layer2_3_conv2_bias = const()[name = tensor<string, []>("backbone_layer2_3_conv2_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2098688)))];
            tensor<fp32, [64, 64, 3, 3]> backbone_layer2_3_conv2_weight = const()[name = tensor<string, []>("backbone_layer2_3_conv2_weight"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2099008)))];
            tensor<fp32, [128]> backbone_layer3_0_conv1_bias = const()[name = tensor<string, []>("backbone_layer3_0_conv1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2246528)))];
            tensor<fp32, [128, 64, 3, 3]> backbone_layer3_0_conv1_weight = const()[name = tensor<string, []>("backbone_layer3_0_conv1_weight"), val = tensor<fp32, [128, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2247104)))];
            tensor<fp32, [128]> backbone_layer3_0_conv2_bias = const()[name = tensor<string, []>("backbone_layer3_0_conv2_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2542080)))];
            tensor<fp32, [128, 128, 3, 3]> backbone_layer3_0_conv2_weight = const()[name = tensor<string, []>("backbone_layer3_0_conv2_weight"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2542656)))];
            tensor<fp32, [128]> backbone_layer3_0_shortcut_bias = const()[name = tensor<string, []>("backbone_layer3_0_shortcut_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3132544)))];
            tensor<fp32, [128, 64, 1, 1]> backbone_layer3_0_shortcut_weight = const()[name = tensor<string, []>("backbone_layer3_0_shortcut_weight"), val = tensor<fp32, [128, 64, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3133120)))];
            tensor<fp32, [128]> backbone_layer3_1_conv1_bias = const()[name = tensor<string, []>("backbone_layer3_1_conv1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3165952)))];
            tensor<fp32, [128, 128, 3, 3]> backbone_layer3_1_conv1_weight = const()[name = tensor<string, []>("backbone_layer3_1_conv1_weight"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3166528)))];
            tensor<fp32, [128]> backbone_layer3_1_conv2_bias = const()[name = tensor<string, []>("backbone_layer3_1_conv2_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3756416)))];
            tensor<fp32, [128, 128, 3, 3]> backbone_layer3_1_conv2_weight = const()[name = tensor<string, []>("backbone_layer3_1_conv2_weight"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3756992)))];
            tensor<fp32, [128]> backbone_layer3_2_conv1_bias = const()[name = tensor<string, []>("backbone_layer3_2_conv1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4346880)))];
            tensor<fp32, [128, 128, 3, 3]> backbone_layer3_2_conv1_weight = const()[name = tensor<string, []>("backbone_layer3_2_conv1_weight"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4347456)))];
            tensor<fp32, [128]> backbone_layer3_2_conv2_bias = const()[name = tensor<string, []>("backbone_layer3_2_conv2_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4937344)))];
            tensor<fp32, [128, 128, 3, 3]> backbone_layer3_2_conv2_weight = const()[name = tensor<string, []>("backbone_layer3_2_conv2_weight"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4937920)))];
            tensor<fp32, [128]> backbone_layer3_3_conv1_bias = const()[name = tensor<string, []>("backbone_layer3_3_conv1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5527808)))];
            tensor<fp32, [128, 128, 3, 3]> backbone_layer3_3_conv1_weight = const()[name = tensor<string, []>("backbone_layer3_3_conv1_weight"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5528384)))];
            tensor<fp32, [128]> backbone_layer3_3_conv2_bias = const()[name = tensor<string, []>("backbone_layer3_3_conv2_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6118272)))];
            tensor<fp32, [128, 128, 3, 3]> backbone_layer3_3_conv2_weight = const()[name = tensor<string, []>("backbone_layer3_3_conv2_weight"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6118848)))];
            tensor<fp32, [128]> backbone_layer3_4_conv1_bias = const()[name = tensor<string, []>("backbone_layer3_4_conv1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6708736)))];
            tensor<fp32, [128, 128, 3, 3]> backbone_layer3_4_conv1_weight = const()[name = tensor<string, []>("backbone_layer3_4_conv1_weight"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6709312)))];
            tensor<fp32, [128]> backbone_layer3_4_conv2_bias = const()[name = tensor<string, []>("backbone_layer3_4_conv2_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7299200)))];
            tensor<fp32, [128, 128, 3, 3]> backbone_layer3_4_conv2_weight = const()[name = tensor<string, []>("backbone_layer3_4_conv2_weight"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7299776)))];
            tensor<fp32, [128]> backbone_layer3_5_conv1_bias = const()[name = tensor<string, []>("backbone_layer3_5_conv1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7889664)))];
            tensor<fp32, [128, 128, 3, 3]> backbone_layer3_5_conv1_weight = const()[name = tensor<string, []>("backbone_layer3_5_conv1_weight"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7890240)))];
            tensor<fp32, [128]> backbone_layer3_5_conv2_bias = const()[name = tensor<string, []>("backbone_layer3_5_conv2_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8480128)))];
            tensor<fp32, [128, 128, 3, 3]> backbone_layer3_5_conv2_weight = const()[name = tensor<string, []>("backbone_layer3_5_conv2_weight"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8480704)))];
            tensor<fp32, [256]> backbone_layer4_0_conv1_bias = const()[name = tensor<string, []>("backbone_layer4_0_conv1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9070592)))];
            tensor<fp32, [256, 128, 3, 3]> backbone_layer4_0_conv1_weight = const()[name = tensor<string, []>("backbone_layer4_0_conv1_weight"), val = tensor<fp32, [256, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9071680)))];
            tensor<fp32, [256]> backbone_layer4_0_conv2_bias = const()[name = tensor<string, []>("backbone_layer4_0_conv2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10251392)))];
            tensor<fp32, [256, 256, 3, 3]> backbone_layer4_0_conv2_weight = const()[name = tensor<string, []>("backbone_layer4_0_conv2_weight"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10252480)))];
            tensor<fp32, [256]> backbone_layer4_0_shortcut_bias = const()[name = tensor<string, []>("backbone_layer4_0_shortcut_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12611840)))];
            tensor<fp32, [256, 128, 1, 1]> backbone_layer4_0_shortcut_weight = const()[name = tensor<string, []>("backbone_layer4_0_shortcut_weight"), val = tensor<fp32, [256, 128, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12612928)))];
            tensor<fp32, [256]> backbone_layer4_1_conv1_bias = const()[name = tensor<string, []>("backbone_layer4_1_conv1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12744064)))];
            tensor<fp32, [256, 256, 3, 3]> backbone_layer4_1_conv1_weight = const()[name = tensor<string, []>("backbone_layer4_1_conv1_weight"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12745152)))];
            tensor<fp32, [256]> backbone_layer4_1_conv2_bias = const()[name = tensor<string, []>("backbone_layer4_1_conv2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15104512)))];
            tensor<fp32, [256, 256, 3, 3]> backbone_layer4_1_conv2_weight = const()[name = tensor<string, []>("backbone_layer4_1_conv2_weight"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15105600)))];
            tensor<fp32, [256]> backbone_layer4_2_conv1_bias = const()[name = tensor<string, []>("backbone_layer4_2_conv1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17464960)))];
            tensor<fp32, [256, 256, 3, 3]> backbone_layer4_2_conv1_weight = const()[name = tensor<string, []>("backbone_layer4_2_conv1_weight"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17466048)))];
            tensor<fp32, [256]> backbone_layer4_2_conv2_bias = const()[name = tensor<string, []>("backbone_layer4_2_conv2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19825408)))];
            tensor<fp32, [256, 256, 3, 3]> backbone_layer4_2_conv2_weight = const()[name = tensor<string, []>("backbone_layer4_2_conv2_weight"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19826496)))];
            tensor<fp32, [256]> backbone_embedding_bias = const()[name = tensor<string, []>("backbone_embedding_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22185856)))];
            tensor<fp32, [256, 5120]> backbone_embedding_weight = const()[name = tensor<string, []>("backbone_embedding_weight"), val = tensor<fp32, [256, 5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22186944)))];
            tensor<fp32, []> var_16 = const()[name = tensor<string, []>("op_16"), val = tensor<fp32, []>(0x1p-23)];
            tensor<fp32, []> var_28 = const()[name = tensor<string, []>("op_28"), val = tensor<fp32, []>(0x1p+15)];
            tensor<fp32, [1, 1, 160000]> input_1 = mul(x = waveform, y = var_28)[name = tensor<string, []>("input_1")];
            tensor<string, []> spectrum_pad_type_0 = const()[name = tensor<string, []>("spectrum_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> spectrum_strides_0 = const()[name = tensor<string, []>("spectrum_strides_0"), val = tensor<int32, [1]>([160])];
            tensor<int32, [2]> spectrum_pad_0 = const()[name = tensor<string, []>("spectrum_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> spectrum_dilations_0 = const()[name = tensor<string, []>("spectrum_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> spectrum_groups_0 = const()[name = tensor<string, []>("spectrum_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 514, 998]> spectrum = conv(dilations = spectrum_dilations_0, groups = spectrum_groups_0, pad = spectrum_pad_0, pad_type = spectrum_pad_type_0, strides = spectrum_strides_0, weight = fbank_dft_weight, x = input_1)[name = tensor<string, []>("spectrum")];
            tensor<int32, [3]> var_37_begin_0 = const()[name = tensor<string, []>("op_37_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> var_37_end_0 = const()[name = tensor<string, []>("op_37_end_0"), val = tensor<int32, [3]>([1, 257, 998])];
            tensor<bool, [3]> var_37_end_mask_0 = const()[name = tensor<string, []>("op_37_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
            tensor<fp32, [1, 257, 998]> var_37 = slice_by_index(begin = var_37_begin_0, end = var_37_end_0, end_mask = var_37_end_mask_0, x = spectrum)[name = tensor<string, []>("op_37")];
            tensor<int32, [3]> var_40_begin_0 = const()[name = tensor<string, []>("op_40_begin_0"), val = tensor<int32, [3]>([0, 257, 0])];
            tensor<int32, [3]> var_40_end_0 = const()[name = tensor<string, []>("op_40_end_0"), val = tensor<int32, [3]>([1, 514, 998])];
            tensor<bool, [3]> var_40_end_mask_0 = const()[name = tensor<string, []>("op_40_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 257, 998]> var_40 = slice_by_index(begin = var_40_begin_0, end = var_40_end_0, end_mask = var_40_end_mask_0, x = spectrum)[name = tensor<string, []>("op_40")];
            tensor<fp32, [1, 257, 998]> var_42 = mul(x = var_37, y = var_37)[name = tensor<string, []>("op_42")];
            tensor<fp32, [1, 257, 998]> var_43 = mul(x = var_40, y = var_40)[name = tensor<string, []>("op_43")];
            tensor<fp32, [1, 257, 998]> var_44 = add(x = var_42, y = var_43)[name = tensor<string, []>("op_44")];
            tensor<int32, [3]> input_3_perm_0 = const()[name = tensor<string, []>("input_3_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [80]> linear_0_bias_0 = const()[name = tensor<string, []>("linear_0_bias_0"), val = tensor<fp32, [80]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(27429888)))];
            tensor<fp32, [1, 998, 257]> input_3 = transpose(perm = input_3_perm_0, x = var_44)[name = tensor<string, []>("transpose_1")];
            tensor<fp32, [1, 998, 80]> mel_1 = linear(bias = linear_0_bias_0, weight = fbank_mel_weight, x = input_3)[name = tensor<string, []>("linear_0")];
            tensor<fp32, []> const_0 = const()[name = tensor<string, []>("const_0"), val = tensor<fp32, []>(0x1.fffffep+127)];
            tensor<fp32, [1, 998, 80]> clip_0 = clip(alpha = var_16, beta = const_0, x = mel_1)[name = tensor<string, []>("clip_0")];
            tensor<fp32, []> mel_3_epsilon_0 = const()[name = tensor<string, []>("mel_3_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp32, [1, 998, 80]> mel_3 = log(epsilon = mel_3_epsilon_0, x = clip_0)[name = tensor<string, []>("mel_3")];
            tensor<int32, [1]> var_51_axes_0 = const()[name = tensor<string, []>("op_51_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, []> var_51_keep_dims_0 = const()[name = tensor<string, []>("op_51_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp32, [1, 1, 80]> var_51 = reduce_mean(axes = var_51_axes_0, keep_dims = var_51_keep_dims_0, x = mel_3)[name = tensor<string, []>("op_51")];
            tensor<fp32, [1, 998, 80]> mel_5 = sub(x = mel_3, y = var_51)[name = tensor<string, []>("mel_5")];
            tensor<int32, [1]> var_54_axes_0 = const()[name = tensor<string, []>("op_54_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1, 1, 998, 80]> var_54 = expand_dims(axes = var_54_axes_0, x = mel_5)[name = tensor<string, []>("op_54")];
            tensor<int32, [4]> var_59 = const()[name = tensor<string, []>("op_59"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<string, []> input_7_pad_type_0 = const()[name = tensor<string, []>("input_7_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_7_pad_0 = const()[name = tensor<string, []>("input_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_7_strides_0 = const()[name = tensor<string, []>("input_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_7_dilations_0 = const()[name = tensor<string, []>("input_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> input_7_groups_0 = const()[name = tensor<string, []>("input_7_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1, 80, 998]> input_5 = transpose(perm = var_59, x = var_54)[name = tensor<string, []>("transpose_0")];
            tensor<fp32, [1, 32, 80, 998]> input_7 = conv(bias = backbone_conv1_bias, dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = backbone_conv1_weight, x = input_5)[name = tensor<string, []>("input_7")];
            tensor<fp32, [1, 32, 80, 998]> input_9 = relu(x = input_7)[name = tensor<string, []>("input_9")];
            tensor<string, []> input_11_pad_type_0 = const()[name = tensor<string, []>("input_11_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_11_pad_0 = const()[name = tensor<string, []>("input_11_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_11_strides_0 = const()[name = tensor<string, []>("input_11_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_11_dilations_0 = const()[name = tensor<string, []>("input_11_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> input_11_groups_0 = const()[name = tensor<string, []>("input_11_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 80, 998]> input_11 = conv(bias = backbone_layer1_0_conv1_bias, dilations = input_11_dilations_0, groups = input_11_groups_0, pad = input_11_pad_0, pad_type = input_11_pad_type_0, strides = input_11_strides_0, weight = backbone_layer1_0_conv1_weight, x = input_9)[name = tensor<string, []>("input_11")];
            tensor<fp32, [1, 32, 80, 998]> input_13 = relu(x = input_11)[name = tensor<string, []>("input_13")];
            tensor<string, []> out_1_pad_type_0 = const()[name = tensor<string, []>("out_1_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> out_1_pad_0 = const()[name = tensor<string, []>("out_1_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> out_1_strides_0 = const()[name = tensor<string, []>("out_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> out_1_dilations_0 = const()[name = tensor<string, []>("out_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> out_1_groups_0 = const()[name = tensor<string, []>("out_1_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 80, 998]> out_1 = conv(bias = backbone_layer1_0_conv2_bias, dilations = out_1_dilations_0, groups = out_1_groups_0, pad = out_1_pad_0, pad_type = out_1_pad_type_0, strides = out_1_strides_0, weight = backbone_layer1_0_conv2_weight, x = input_13)[name = tensor<string, []>("out_1")];
            tensor<fp32, [1, 32, 80, 998]> input_15 = add(x = out_1, y = input_9)[name = tensor<string, []>("input_15")];
            tensor<fp32, [1, 32, 80, 998]> input_17 = relu(x = input_15)[name = tensor<string, []>("input_17")];
            tensor<string, []> input_19_pad_type_0 = const()[name = tensor<string, []>("input_19_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_19_pad_0 = const()[name = tensor<string, []>("input_19_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_19_strides_0 = const()[name = tensor<string, []>("input_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_19_dilations_0 = const()[name = tensor<string, []>("input_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> input_19_groups_0 = const()[name = tensor<string, []>("input_19_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 80, 998]> input_19 = conv(bias = backbone_layer1_1_conv1_bias, dilations = input_19_dilations_0, groups = input_19_groups_0, pad = input_19_pad_0, pad_type = input_19_pad_type_0, strides = input_19_strides_0, weight = backbone_layer1_1_conv1_weight, x = input_17)[name = tensor<string, []>("input_19")];
            tensor<fp32, [1, 32, 80, 998]> input_21 = relu(x = input_19)[name = tensor<string, []>("input_21")];
            tensor<string, []> out_3_pad_type_0 = const()[name = tensor<string, []>("out_3_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> out_3_pad_0 = const()[name = tensor<string, []>("out_3_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> out_3_strides_0 = const()[name = tensor<string, []>("out_3_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> out_3_dilations_0 = const()[name = tensor<string, []>("out_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> out_3_groups_0 = const()[name = tensor<string, []>("out_3_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 80, 998]> out_3 = conv(bias = backbone_layer1_1_conv2_bias, dilations = out_3_dilations_0, groups = out_3_groups_0, pad = out_3_pad_0, pad_type = out_3_pad_type_0, strides = out_3_strides_0, weight = backbone_layer1_1_conv2_weight, x = input_21)[name = tensor<string, []>("out_3")];
            tensor<fp32, [1, 32, 80, 998]> input_23 = add(x = out_3, y = input_17)[name = tensor<string, []>("input_23")];
            tensor<fp32, [1, 32, 80, 998]> input_25 = relu(x = input_23)[name = tensor<string, []>("input_25")];
            tensor<string, []> input_27_pad_type_0 = const()[name = tensor<string, []>("input_27_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_27_pad_0 = const()[name = tensor<string, []>("input_27_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_27_strides_0 = const()[name = tensor<string, []>("input_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_27_dilations_0 = const()[name = tensor<string, []>("input_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> input_27_groups_0 = const()[name = tensor<string, []>("input_27_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 80, 998]> input_27 = conv(bias = backbone_layer1_2_conv1_bias, dilations = input_27_dilations_0, groups = input_27_groups_0, pad = input_27_pad_0, pad_type = input_27_pad_type_0, strides = input_27_strides_0, weight = backbone_layer1_2_conv1_weight, x = input_25)[name = tensor<string, []>("input_27")];
            tensor<fp32, [1, 32, 80, 998]> input_29 = relu(x = input_27)[name = tensor<string, []>("input_29")];
            tensor<string, []> out_5_pad_type_0 = const()[name = tensor<string, []>("out_5_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> out_5_pad_0 = const()[name = tensor<string, []>("out_5_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> out_5_strides_0 = const()[name = tensor<string, []>("out_5_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> out_5_dilations_0 = const()[name = tensor<string, []>("out_5_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> out_5_groups_0 = const()[name = tensor<string, []>("out_5_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 80, 998]> out_5 = conv(bias = backbone_layer1_2_conv2_bias, dilations = out_5_dilations_0, groups = out_5_groups_0, pad = out_5_pad_0, pad_type = out_5_pad_type_0, strides = out_5_strides_0, weight = backbone_layer1_2_conv2_weight, x = input_29)[name = tensor<string, []>("out_5")];
            tensor<fp32, [1, 32, 80, 998]> input_31 = add(x = out_5, y = input_25)[name = tensor<string, []>("input_31")];
            tensor<fp32, [1, 32, 80, 998]> input_33 = relu(x = input_31)[name = tensor<string, []>("input_33")];
            tensor<string, []> input_35_pad_type_0 = const()[name = tensor<string, []>("input_35_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_35_pad_0 = const()[name = tensor<string, []>("input_35_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_35_strides_0 = const()[name = tensor<string, []>("input_35_strides_0"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, [2]> input_35_dilations_0 = const()[name = tensor<string, []>("input_35_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> input_35_groups_0 = const()[name = tensor<string, []>("input_35_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 40, 499]> input_35 = conv(bias = backbone_layer2_0_conv1_bias, 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 = backbone_layer2_0_conv1_weight, x = input_33)[name = tensor<string, []>("input_35")];
            tensor<fp32, [1, 64, 40, 499]> input_37 = relu(x = input_35)[name = tensor<string, []>("input_37")];
            tensor<string, []> out_7_pad_type_0 = const()[name = tensor<string, []>("out_7_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> out_7_pad_0 = const()[name = tensor<string, []>("out_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> out_7_strides_0 = const()[name = tensor<string, []>("out_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> out_7_dilations_0 = const()[name = tensor<string, []>("out_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> out_7_groups_0 = const()[name = tensor<string, []>("out_7_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 40, 499]> out_7 = conv(bias = backbone_layer2_0_conv2_bias, dilations = out_7_dilations_0, groups = out_7_groups_0, pad = out_7_pad_0, pad_type = out_7_pad_type_0, strides = out_7_strides_0, weight = backbone_layer2_0_conv2_weight, x = input_37)[name = tensor<string, []>("out_7")];
            tensor<string, []> residual_1_pad_type_0 = const()[name = tensor<string, []>("residual_1_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [2]> residual_1_strides_0 = const()[name = tensor<string, []>("residual_1_strides_0"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, [4]> residual_1_pad_0 = const()[name = tensor<string, []>("residual_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> residual_1_dilations_0 = const()[name = tensor<string, []>("residual_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> residual_1_groups_0 = const()[name = tensor<string, []>("residual_1_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 40, 499]> residual_1 = conv(bias = backbone_layer2_0_shortcut_bias, dilations = residual_1_dilations_0, groups = residual_1_groups_0, pad = residual_1_pad_0, pad_type = residual_1_pad_type_0, strides = residual_1_strides_0, weight = backbone_layer2_0_shortcut_weight, x = input_33)[name = tensor<string, []>("residual_1")];
            tensor<fp32, [1, 64, 40, 499]> input_39 = add(x = out_7, y = residual_1)[name = tensor<string, []>("input_39")];
            tensor<fp32, [1, 64, 40, 499]> input_41 = relu(x = input_39)[name = tensor<string, []>("input_41")];
            tensor<string, []> input_43_pad_type_0 = const()[name = tensor<string, []>("input_43_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_43_pad_0 = const()[name = tensor<string, []>("input_43_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_43_strides_0 = const()[name = tensor<string, []>("input_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_43_dilations_0 = const()[name = tensor<string, []>("input_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> input_43_groups_0 = const()[name = tensor<string, []>("input_43_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 40, 499]> input_43 = conv(bias = backbone_layer2_1_conv1_bias, 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 = backbone_layer2_1_conv1_weight, x = input_41)[name = tensor<string, []>("input_43")];
            tensor<fp32, [1, 64, 40, 499]> input_45 = relu(x = input_43)[name = tensor<string, []>("input_45")];
            tensor<string, []> out_9_pad_type_0 = const()[name = tensor<string, []>("out_9_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> out_9_pad_0 = const()[name = tensor<string, []>("out_9_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> out_9_strides_0 = const()[name = tensor<string, []>("out_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> out_9_dilations_0 = const()[name = tensor<string, []>("out_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> out_9_groups_0 = const()[name = tensor<string, []>("out_9_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 40, 499]> out_9 = conv(bias = backbone_layer2_1_conv2_bias, dilations = out_9_dilations_0, groups = out_9_groups_0, pad = out_9_pad_0, pad_type = out_9_pad_type_0, strides = out_9_strides_0, weight = backbone_layer2_1_conv2_weight, x = input_45)[name = tensor<string, []>("out_9")];
            tensor<fp32, [1, 64, 40, 499]> input_47 = add(x = out_9, y = input_41)[name = tensor<string, []>("input_47")];
            tensor<fp32, [1, 64, 40, 499]> input_49 = relu(x = input_47)[name = tensor<string, []>("input_49")];
            tensor<string, []> input_51_pad_type_0 = const()[name = tensor<string, []>("input_51_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_51_pad_0 = const()[name = tensor<string, []>("input_51_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_51_strides_0 = const()[name = tensor<string, []>("input_51_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_51_dilations_0 = const()[name = tensor<string, []>("input_51_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> input_51_groups_0 = const()[name = tensor<string, []>("input_51_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 40, 499]> input_51 = conv(bias = backbone_layer2_2_conv1_bias, dilations = input_51_dilations_0, groups = input_51_groups_0, pad = input_51_pad_0, pad_type = input_51_pad_type_0, strides = input_51_strides_0, weight = backbone_layer2_2_conv1_weight, x = input_49)[name = tensor<string, []>("input_51")];
            tensor<fp32, [1, 64, 40, 499]> input_53 = relu(x = input_51)[name = tensor<string, []>("input_53")];
            tensor<string, []> out_11_pad_type_0 = const()[name = tensor<string, []>("out_11_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> out_11_pad_0 = const()[name = tensor<string, []>("out_11_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> out_11_strides_0 = const()[name = tensor<string, []>("out_11_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> out_11_dilations_0 = const()[name = tensor<string, []>("out_11_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> out_11_groups_0 = const()[name = tensor<string, []>("out_11_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 40, 499]> out_11 = conv(bias = backbone_layer2_2_conv2_bias, dilations = out_11_dilations_0, groups = out_11_groups_0, pad = out_11_pad_0, pad_type = out_11_pad_type_0, strides = out_11_strides_0, weight = backbone_layer2_2_conv2_weight, x = input_53)[name = tensor<string, []>("out_11")];
            tensor<fp32, [1, 64, 40, 499]> input_55 = add(x = out_11, y = input_49)[name = tensor<string, []>("input_55")];
            tensor<fp32, [1, 64, 40, 499]> input_57 = relu(x = input_55)[name = tensor<string, []>("input_57")];
            tensor<string, []> input_59_pad_type_0 = const()[name = tensor<string, []>("input_59_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_59_pad_0 = const()[name = tensor<string, []>("input_59_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_59_strides_0 = const()[name = tensor<string, []>("input_59_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_59_dilations_0 = const()[name = tensor<string, []>("input_59_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> input_59_groups_0 = const()[name = tensor<string, []>("input_59_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 40, 499]> input_59 = conv(bias = backbone_layer2_3_conv1_bias, dilations = input_59_dilations_0, groups = input_59_groups_0, pad = input_59_pad_0, pad_type = input_59_pad_type_0, strides = input_59_strides_0, weight = backbone_layer2_3_conv1_weight, x = input_57)[name = tensor<string, []>("input_59")];
            tensor<fp32, [1, 64, 40, 499]> input_61 = relu(x = input_59)[name = tensor<string, []>("input_61")];
            tensor<string, []> out_13_pad_type_0 = const()[name = tensor<string, []>("out_13_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> out_13_pad_0 = const()[name = tensor<string, []>("out_13_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> out_13_strides_0 = const()[name = tensor<string, []>("out_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> out_13_dilations_0 = const()[name = tensor<string, []>("out_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> out_13_groups_0 = const()[name = tensor<string, []>("out_13_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 40, 499]> out_13 = conv(bias = backbone_layer2_3_conv2_bias, dilations = out_13_dilations_0, groups = out_13_groups_0, pad = out_13_pad_0, pad_type = out_13_pad_type_0, strides = out_13_strides_0, weight = backbone_layer2_3_conv2_weight, x = input_61)[name = tensor<string, []>("out_13")];
            tensor<fp32, [1, 64, 40, 499]> input_63 = add(x = out_13, y = input_57)[name = tensor<string, []>("input_63")];
            tensor<fp32, [1, 64, 40, 499]> input_65 = relu(x = input_63)[name = tensor<string, []>("input_65")];
            tensor<string, []> input_67_pad_type_0 = const()[name = tensor<string, []>("input_67_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_67_pad_0 = const()[name = tensor<string, []>("input_67_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_67_strides_0 = const()[name = tensor<string, []>("input_67_strides_0"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, [2]> input_67_dilations_0 = const()[name = tensor<string, []>("input_67_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> input_67_groups_0 = const()[name = tensor<string, []>("input_67_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 20, 250]> input_67 = conv(bias = backbone_layer3_0_conv1_bias, 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 = backbone_layer3_0_conv1_weight, x = input_65)[name = tensor<string, []>("input_67")];
            tensor<fp32, [1, 128, 20, 250]> input_69 = relu(x = input_67)[name = tensor<string, []>("input_69")];
            tensor<string, []> out_15_pad_type_0 = const()[name = tensor<string, []>("out_15_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> out_15_pad_0 = const()[name = tensor<string, []>("out_15_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> out_15_strides_0 = const()[name = tensor<string, []>("out_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> out_15_dilations_0 = const()[name = tensor<string, []>("out_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> out_15_groups_0 = const()[name = tensor<string, []>("out_15_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 20, 250]> out_15 = conv(bias = backbone_layer3_0_conv2_bias, dilations = out_15_dilations_0, groups = out_15_groups_0, pad = out_15_pad_0, pad_type = out_15_pad_type_0, strides = out_15_strides_0, weight = backbone_layer3_0_conv2_weight, x = input_69)[name = tensor<string, []>("out_15")];
            tensor<string, []> residual_3_pad_type_0 = const()[name = tensor<string, []>("residual_3_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [2]> residual_3_strides_0 = const()[name = tensor<string, []>("residual_3_strides_0"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, [4]> residual_3_pad_0 = const()[name = tensor<string, []>("residual_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> residual_3_dilations_0 = const()[name = tensor<string, []>("residual_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> residual_3_groups_0 = const()[name = tensor<string, []>("residual_3_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 20, 250]> residual_3 = conv(bias = backbone_layer3_0_shortcut_bias, dilations = residual_3_dilations_0, groups = residual_3_groups_0, pad = residual_3_pad_0, pad_type = residual_3_pad_type_0, strides = residual_3_strides_0, weight = backbone_layer3_0_shortcut_weight, x = input_65)[name = tensor<string, []>("residual_3")];
            tensor<fp32, [1, 128, 20, 250]> input_71 = add(x = out_15, y = residual_3)[name = tensor<string, []>("input_71")];
            tensor<fp32, [1, 128, 20, 250]> input_73 = relu(x = input_71)[name = tensor<string, []>("input_73")];
            tensor<string, []> input_75_pad_type_0 = const()[name = tensor<string, []>("input_75_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_75_pad_0 = const()[name = tensor<string, []>("input_75_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_75_strides_0 = const()[name = tensor<string, []>("input_75_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_75_dilations_0 = const()[name = tensor<string, []>("input_75_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> input_75_groups_0 = const()[name = tensor<string, []>("input_75_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 20, 250]> input_75 = conv(bias = backbone_layer3_1_conv1_bias, dilations = input_75_dilations_0, groups = input_75_groups_0, pad = input_75_pad_0, pad_type = input_75_pad_type_0, strides = input_75_strides_0, weight = backbone_layer3_1_conv1_weight, x = input_73)[name = tensor<string, []>("input_75")];
            tensor<fp32, [1, 128, 20, 250]> input_77 = relu(x = input_75)[name = tensor<string, []>("input_77")];
            tensor<string, []> out_17_pad_type_0 = const()[name = tensor<string, []>("out_17_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> out_17_pad_0 = const()[name = tensor<string, []>("out_17_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> out_17_strides_0 = const()[name = tensor<string, []>("out_17_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> out_17_dilations_0 = const()[name = tensor<string, []>("out_17_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> out_17_groups_0 = const()[name = tensor<string, []>("out_17_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 20, 250]> out_17 = conv(bias = backbone_layer3_1_conv2_bias, dilations = out_17_dilations_0, groups = out_17_groups_0, pad = out_17_pad_0, pad_type = out_17_pad_type_0, strides = out_17_strides_0, weight = backbone_layer3_1_conv2_weight, x = input_77)[name = tensor<string, []>("out_17")];
            tensor<fp32, [1, 128, 20, 250]> input_79 = add(x = out_17, y = input_73)[name = tensor<string, []>("input_79")];
            tensor<fp32, [1, 128, 20, 250]> input_81 = relu(x = input_79)[name = tensor<string, []>("input_81")];
            tensor<string, []> input_83_pad_type_0 = const()[name = tensor<string, []>("input_83_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_83_pad_0 = const()[name = tensor<string, []>("input_83_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_83_strides_0 = const()[name = tensor<string, []>("input_83_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_83_dilations_0 = const()[name = tensor<string, []>("input_83_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> input_83_groups_0 = const()[name = tensor<string, []>("input_83_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 20, 250]> input_83 = conv(bias = backbone_layer3_2_conv1_bias, dilations = input_83_dilations_0, groups = input_83_groups_0, pad = input_83_pad_0, pad_type = input_83_pad_type_0, strides = input_83_strides_0, weight = backbone_layer3_2_conv1_weight, x = input_81)[name = tensor<string, []>("input_83")];
            tensor<fp32, [1, 128, 20, 250]> input_85 = relu(x = input_83)[name = tensor<string, []>("input_85")];
            tensor<string, []> out_19_pad_type_0 = const()[name = tensor<string, []>("out_19_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> out_19_pad_0 = const()[name = tensor<string, []>("out_19_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> out_19_strides_0 = const()[name = tensor<string, []>("out_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> out_19_dilations_0 = const()[name = tensor<string, []>("out_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> out_19_groups_0 = const()[name = tensor<string, []>("out_19_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 20, 250]> out_19 = conv(bias = backbone_layer3_2_conv2_bias, dilations = out_19_dilations_0, groups = out_19_groups_0, pad = out_19_pad_0, pad_type = out_19_pad_type_0, strides = out_19_strides_0, weight = backbone_layer3_2_conv2_weight, x = input_85)[name = tensor<string, []>("out_19")];
            tensor<fp32, [1, 128, 20, 250]> input_87 = add(x = out_19, y = input_81)[name = tensor<string, []>("input_87")];
            tensor<fp32, [1, 128, 20, 250]> input_89 = relu(x = input_87)[name = tensor<string, []>("input_89")];
            tensor<string, []> input_91_pad_type_0 = const()[name = tensor<string, []>("input_91_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_91_pad_0 = const()[name = tensor<string, []>("input_91_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_91_strides_0 = const()[name = tensor<string, []>("input_91_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_91_dilations_0 = const()[name = tensor<string, []>("input_91_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> input_91_groups_0 = const()[name = tensor<string, []>("input_91_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 20, 250]> input_91 = conv(bias = backbone_layer3_3_conv1_bias, dilations = input_91_dilations_0, groups = input_91_groups_0, pad = input_91_pad_0, pad_type = input_91_pad_type_0, strides = input_91_strides_0, weight = backbone_layer3_3_conv1_weight, x = input_89)[name = tensor<string, []>("input_91")];
            tensor<fp32, [1, 128, 20, 250]> input_93 = relu(x = input_91)[name = tensor<string, []>("input_93")];
            tensor<string, []> out_21_pad_type_0 = const()[name = tensor<string, []>("out_21_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> out_21_pad_0 = const()[name = tensor<string, []>("out_21_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> out_21_strides_0 = const()[name = tensor<string, []>("out_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> out_21_dilations_0 = const()[name = tensor<string, []>("out_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> out_21_groups_0 = const()[name = tensor<string, []>("out_21_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 20, 250]> out_21 = conv(bias = backbone_layer3_3_conv2_bias, dilations = out_21_dilations_0, groups = out_21_groups_0, pad = out_21_pad_0, pad_type = out_21_pad_type_0, strides = out_21_strides_0, weight = backbone_layer3_3_conv2_weight, x = input_93)[name = tensor<string, []>("out_21")];
            tensor<fp32, [1, 128, 20, 250]> input_95 = add(x = out_21, y = input_89)[name = tensor<string, []>("input_95")];
            tensor<fp32, [1, 128, 20, 250]> input_97 = relu(x = input_95)[name = tensor<string, []>("input_97")];
            tensor<string, []> input_99_pad_type_0 = const()[name = tensor<string, []>("input_99_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_99_pad_0 = const()[name = tensor<string, []>("input_99_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_99_strides_0 = const()[name = tensor<string, []>("input_99_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_99_dilations_0 = const()[name = tensor<string, []>("input_99_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> input_99_groups_0 = const()[name = tensor<string, []>("input_99_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 20, 250]> input_99 = conv(bias = backbone_layer3_4_conv1_bias, dilations = input_99_dilations_0, groups = input_99_groups_0, pad = input_99_pad_0, pad_type = input_99_pad_type_0, strides = input_99_strides_0, weight = backbone_layer3_4_conv1_weight, x = input_97)[name = tensor<string, []>("input_99")];
            tensor<fp32, [1, 128, 20, 250]> input_101 = relu(x = input_99)[name = tensor<string, []>("input_101")];
            tensor<string, []> out_23_pad_type_0 = const()[name = tensor<string, []>("out_23_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> out_23_pad_0 = const()[name = tensor<string, []>("out_23_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> out_23_strides_0 = const()[name = tensor<string, []>("out_23_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> out_23_dilations_0 = const()[name = tensor<string, []>("out_23_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> out_23_groups_0 = const()[name = tensor<string, []>("out_23_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 20, 250]> out_23 = conv(bias = backbone_layer3_4_conv2_bias, dilations = out_23_dilations_0, groups = out_23_groups_0, pad = out_23_pad_0, pad_type = out_23_pad_type_0, strides = out_23_strides_0, weight = backbone_layer3_4_conv2_weight, x = input_101)[name = tensor<string, []>("out_23")];
            tensor<fp32, [1, 128, 20, 250]> input_103 = add(x = out_23, y = input_97)[name = tensor<string, []>("input_103")];
            tensor<fp32, [1, 128, 20, 250]> input_105 = relu(x = input_103)[name = tensor<string, []>("input_105")];
            tensor<string, []> input_107_pad_type_0 = const()[name = tensor<string, []>("input_107_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_107_pad_0 = const()[name = tensor<string, []>("input_107_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_107_strides_0 = const()[name = tensor<string, []>("input_107_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_107_dilations_0 = const()[name = tensor<string, []>("input_107_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> input_107_groups_0 = const()[name = tensor<string, []>("input_107_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 20, 250]> input_107 = conv(bias = backbone_layer3_5_conv1_bias, dilations = input_107_dilations_0, groups = input_107_groups_0, pad = input_107_pad_0, pad_type = input_107_pad_type_0, strides = input_107_strides_0, weight = backbone_layer3_5_conv1_weight, x = input_105)[name = tensor<string, []>("input_107")];
            tensor<fp32, [1, 128, 20, 250]> input_109 = relu(x = input_107)[name = tensor<string, []>("input_109")];
            tensor<string, []> out_25_pad_type_0 = const()[name = tensor<string, []>("out_25_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> out_25_pad_0 = const()[name = tensor<string, []>("out_25_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> out_25_strides_0 = const()[name = tensor<string, []>("out_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> out_25_dilations_0 = const()[name = tensor<string, []>("out_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> out_25_groups_0 = const()[name = tensor<string, []>("out_25_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 20, 250]> out_25 = conv(bias = backbone_layer3_5_conv2_bias, dilations = out_25_dilations_0, groups = out_25_groups_0, pad = out_25_pad_0, pad_type = out_25_pad_type_0, strides = out_25_strides_0, weight = backbone_layer3_5_conv2_weight, x = input_109)[name = tensor<string, []>("out_25")];
            tensor<fp32, [1, 128, 20, 250]> input_111 = add(x = out_25, y = input_105)[name = tensor<string, []>("input_111")];
            tensor<fp32, [1, 128, 20, 250]> input_113 = relu(x = input_111)[name = tensor<string, []>("input_113")];
            tensor<string, []> input_115_pad_type_0 = const()[name = tensor<string, []>("input_115_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_115_pad_0 = const()[name = tensor<string, []>("input_115_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_115_strides_0 = const()[name = tensor<string, []>("input_115_strides_0"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, [2]> input_115_dilations_0 = const()[name = tensor<string, []>("input_115_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> input_115_groups_0 = const()[name = tensor<string, []>("input_115_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 10, 125]> input_115 = conv(bias = backbone_layer4_0_conv1_bias, dilations = input_115_dilations_0, groups = input_115_groups_0, pad = input_115_pad_0, pad_type = input_115_pad_type_0, strides = input_115_strides_0, weight = backbone_layer4_0_conv1_weight, x = input_113)[name = tensor<string, []>("input_115")];
            tensor<fp32, [1, 256, 10, 125]> input_117 = relu(x = input_115)[name = tensor<string, []>("input_117")];
            tensor<string, []> out_27_pad_type_0 = const()[name = tensor<string, []>("out_27_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> out_27_pad_0 = const()[name = tensor<string, []>("out_27_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> out_27_strides_0 = const()[name = tensor<string, []>("out_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> out_27_dilations_0 = const()[name = tensor<string, []>("out_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> out_27_groups_0 = const()[name = tensor<string, []>("out_27_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 10, 125]> out_27 = conv(bias = backbone_layer4_0_conv2_bias, dilations = out_27_dilations_0, groups = out_27_groups_0, pad = out_27_pad_0, pad_type = out_27_pad_type_0, strides = out_27_strides_0, weight = backbone_layer4_0_conv2_weight, x = input_117)[name = tensor<string, []>("out_27")];
            tensor<string, []> residual_pad_type_0 = const()[name = tensor<string, []>("residual_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [2]> residual_strides_0 = const()[name = tensor<string, []>("residual_strides_0"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, [4]> residual_pad_0 = const()[name = tensor<string, []>("residual_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> residual_dilations_0 = const()[name = tensor<string, []>("residual_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> residual_groups_0 = const()[name = tensor<string, []>("residual_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 10, 125]> residual = conv(bias = backbone_layer4_0_shortcut_bias, dilations = residual_dilations_0, groups = residual_groups_0, pad = residual_pad_0, pad_type = residual_pad_type_0, strides = residual_strides_0, weight = backbone_layer4_0_shortcut_weight, x = input_113)[name = tensor<string, []>("residual")];
            tensor<fp32, [1, 256, 10, 125]> input_119 = add(x = out_27, y = residual)[name = tensor<string, []>("input_119")];
            tensor<fp32, [1, 256, 10, 125]> input_121 = relu(x = input_119)[name = tensor<string, []>("input_121")];
            tensor<string, []> input_123_pad_type_0 = const()[name = tensor<string, []>("input_123_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_123_pad_0 = const()[name = tensor<string, []>("input_123_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_123_strides_0 = const()[name = tensor<string, []>("input_123_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_123_dilations_0 = const()[name = tensor<string, []>("input_123_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> input_123_groups_0 = const()[name = tensor<string, []>("input_123_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 10, 125]> input_123 = conv(bias = backbone_layer4_1_conv1_bias, dilations = input_123_dilations_0, groups = input_123_groups_0, pad = input_123_pad_0, pad_type = input_123_pad_type_0, strides = input_123_strides_0, weight = backbone_layer4_1_conv1_weight, x = input_121)[name = tensor<string, []>("input_123")];
            tensor<fp32, [1, 256, 10, 125]> input_125 = relu(x = input_123)[name = tensor<string, []>("input_125")];
            tensor<string, []> out_29_pad_type_0 = const()[name = tensor<string, []>("out_29_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> out_29_pad_0 = const()[name = tensor<string, []>("out_29_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> out_29_strides_0 = const()[name = tensor<string, []>("out_29_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> out_29_dilations_0 = const()[name = tensor<string, []>("out_29_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> out_29_groups_0 = const()[name = tensor<string, []>("out_29_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 10, 125]> out_29 = conv(bias = backbone_layer4_1_conv2_bias, dilations = out_29_dilations_0, groups = out_29_groups_0, pad = out_29_pad_0, pad_type = out_29_pad_type_0, strides = out_29_strides_0, weight = backbone_layer4_1_conv2_weight, x = input_125)[name = tensor<string, []>("out_29")];
            tensor<fp32, [1, 256, 10, 125]> input_127 = add(x = out_29, y = input_121)[name = tensor<string, []>("input_127")];
            tensor<fp32, [1, 256, 10, 125]> input_129 = relu(x = input_127)[name = tensor<string, []>("input_129")];
            tensor<string, []> input_131_pad_type_0 = const()[name = tensor<string, []>("input_131_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> input_131_pad_0 = const()[name = tensor<string, []>("input_131_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_131_strides_0 = const()[name = tensor<string, []>("input_131_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_131_dilations_0 = const()[name = tensor<string, []>("input_131_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> input_131_groups_0 = const()[name = tensor<string, []>("input_131_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 10, 125]> input_131 = conv(bias = backbone_layer4_2_conv1_bias, dilations = input_131_dilations_0, groups = input_131_groups_0, pad = input_131_pad_0, pad_type = input_131_pad_type_0, strides = input_131_strides_0, weight = backbone_layer4_2_conv1_weight, x = input_129)[name = tensor<string, []>("input_131")];
            tensor<fp32, [1, 256, 10, 125]> input_133 = relu(x = input_131)[name = tensor<string, []>("input_133")];
            tensor<string, []> out_pad_type_0 = const()[name = tensor<string, []>("out_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [4]> out_pad_0 = const()[name = tensor<string, []>("out_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> out_strides_0 = const()[name = tensor<string, []>("out_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> out_dilations_0 = const()[name = tensor<string, []>("out_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, []> out_groups_0 = const()[name = tensor<string, []>("out_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 10, 125]> out = conv(bias = backbone_layer4_2_conv2_bias, dilations = out_dilations_0, groups = out_groups_0, pad = out_pad_0, pad_type = out_pad_type_0, strides = out_strides_0, weight = backbone_layer4_2_conv2_weight, x = input_133)[name = tensor<string, []>("out")];
            tensor<fp32, [1, 256, 10, 125]> input_135 = add(x = out, y = input_129)[name = tensor<string, []>("input_135")];
            tensor<fp32, [1, 256, 10, 125]> x = relu(x = input_135)[name = tensor<string, []>("x")];
            tensor<int32, [3]> var_442 = const()[name = tensor<string, []>("op_442"), val = tensor<int32, [3]>([1, 2560, -1])];
            tensor<fp32, [1, 2560, 125]> sequences_1 = reshape(shape = var_442, x = x)[name = tensor<string, []>("sequences_1")];
            tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = tensor<string, []>("expand_dims_0_axes_0"), val = tensor<int32, [1]>([3])];
            tensor<fp32, [1, 3, 589, 1]> expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = weights)[name = tensor<string, []>("expand_dims_0")];
            tensor<fp32, []> upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = tensor<string, []>("upsample_nearest_neighbor_0_scale_factor_height_0"), val = tensor<fp32, []>(0x1.b2a2a4p-3)];
            tensor<fp32, []> upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = tensor<string, []>("upsample_nearest_neighbor_0_scale_factor_width_0"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 3, 125, 1]> upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = tensor<string, []>("upsample_nearest_neighbor_0")];
            tensor<int32, [1]> weights_3_axes_0 = const()[name = tensor<string, []>("weights_3_axes_0"), val = tensor<int32, [1]>([3])];
            tensor<fp32, [1, 3, 125]> weights_3 = squeeze(axes = weights_3_axes_0, x = upsample_nearest_neighbor_0)[name = tensor<string, []>("weights_3")];
            tensor<int32, [1]> sequences_axes_0 = const()[name = tensor<string, []>("sequences_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1, 1, 2560, 125]> sequences = expand_dims(axes = sequences_axes_0, x = sequences_1)[name = tensor<string, []>("sequences")];
            tensor<int32, [1]> weights_axes_0 = const()[name = tensor<string, []>("weights_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<fp32, [1, 3, 1, 125]> weights_1 = expand_dims(axes = weights_axes_0, x = weights_3)[name = tensor<string, []>("weights")];
            tensor<int32, [1]> var_459_axes_0 = const()[name = tensor<string, []>("op_459_axes_0"), val = tensor<int32, [1]>([3])];
            tensor<bool, []> var_459_keep_dims_0 = const()[name = tensor<string, []>("op_459_keep_dims_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 3, 1]> var_459 = reduce_sum(axes = var_459_axes_0, keep_dims = var_459_keep_dims_0, x = weights_1)[name = tensor<string, []>("op_459")];
            tensor<fp32, []> var_461 = const()[name = tensor<string, []>("op_461"), val = tensor<fp32, []>(0x1.5798eep-27)];
            tensor<fp32, [1, 3, 1]> value_sum = add(x = var_459, y = var_461)[name = tensor<string, []>("value_sum")];
            tensor<fp32, [1, 3, 2560, 125]> var_463 = mul(x = sequences, y = weights_1)[name = tensor<string, []>("op_463")];
            tensor<int32, [1]> var_468_axes_0 = const()[name = tensor<string, []>("op_468_axes_0"), val = tensor<int32, [1]>([3])];
            tensor<bool, []> var_468_keep_dims_0 = const()[name = tensor<string, []>("op_468_keep_dims_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 3, 2560]> var_468 = reduce_sum(axes = var_468_axes_0, keep_dims = var_468_keep_dims_0, x = var_463)[name = tensor<string, []>("op_468")];
            tensor<fp32, [1, 3, 2560]> mean = real_div(x = var_468, y = value_sum)[name = tensor<string, []>("mean")];
            tensor<int32, [1]> var_471_axes_0 = const()[name = tensor<string, []>("op_471_axes_0"), val = tensor<int32, [1]>([3])];
            tensor<fp32, [1, 3, 2560, 1]> var_471 = expand_dims(axes = var_471_axes_0, x = mean)[name = tensor<string, []>("op_471")];
            tensor<fp32, [1, 3, 2560, 125]> var_473 = sub(x = sequences, y = var_471)[name = tensor<string, []>("op_473")];
            tensor<fp32, [1, 3, 2560, 125]> squared_deviation = mul(x = var_473, y = var_473)[name = tensor<string, []>("squared_deviation")];
            tensor<fp32, [1, 3, 1, 125]> var_475 = mul(x = weights_1, y = weights_1)[name = tensor<string, []>("op_475")];
            tensor<int32, [1]> squared_weight_sum_axes_0 = const()[name = tensor<string, []>("squared_weight_sum_axes_0"), val = tensor<int32, [1]>([3])];
            tensor<bool, []> squared_weight_sum_keep_dims_0 = const()[name = tensor<string, []>("squared_weight_sum_keep_dims_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 3, 1]> squared_weight_sum = reduce_sum(axes = squared_weight_sum_axes_0, keep_dims = squared_weight_sum_keep_dims_0, x = var_475)[name = tensor<string, []>("squared_weight_sum")];
            tensor<fp32, [1, 3, 1]> var_481 = real_div(x = squared_weight_sum, y = value_sum)[name = tensor<string, []>("op_481")];
            tensor<fp32, [1, 3, 1]> var_483 = sub(x = value_sum, y = var_481)[name = tensor<string, []>("op_483")];
            tensor<fp32, []> var_485 = const()[name = tensor<string, []>("op_485"), val = tensor<fp32, []>(0x1.5798eep-27)];
            tensor<fp32, [1, 3, 1]> denominator = add(x = var_483, y = var_485)[name = tensor<string, []>("denominator")];
            tensor<fp32, [1, 3, 2560, 125]> var_487 = mul(x = squared_deviation, y = weights_1)[name = tensor<string, []>("op_487")];
            tensor<int32, [1]> var_492_axes_0 = const()[name = tensor<string, []>("op_492_axes_0"), val = tensor<int32, [1]>([3])];
            tensor<bool, []> var_492_keep_dims_0 = const()[name = tensor<string, []>("op_492_keep_dims_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 3, 2560]> var_492 = reduce_sum(axes = var_492_axes_0, keep_dims = var_492_keep_dims_0, x = var_487)[name = tensor<string, []>("op_492")];
            tensor<fp32, [1, 3, 2560]> variance = real_div(x = var_492, y = denominator)[name = tensor<string, []>("variance")];
            tensor<fp32, [1, 3, 2560]> var_494 = sqrt(x = variance)[name = tensor<string, []>("op_494")];
            tensor<int32, []> var_496 = const()[name = tensor<string, []>("op_496"), val = tensor<int32, []>(2)];
            tensor<bool, []> input_interleave_0 = const()[name = tensor<string, []>("input_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 3, 5120]> input = concat(axis = var_496, interleave = input_interleave_0, values = (mean, var_494))[name = tensor<string, []>("input")];
            tensor<fp32, [1, 3, 256]> embedding = linear(bias = backbone_embedding_bias, weight = backbone_embedding_weight, x = input)[name = tensor<string, []>("linear_1")];
        } -> (embedding);
}