program(1.0)
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.11.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})]
{
    func main<ios17>(tensor<fp32, [1, 1024]> F0_curve, tensor<fp32, [1, 1024]> N, tensor<fp32, [1, 512, 512]> asr, tensor<fp32, [1, 128]> s) {
            tensor<fp32, [1]> F0_conv_bias = const()[name = tensor<string, []>("F0_conv_bias"), val = tensor<fp32, [1]>([0x1.8443d2p-2])];
            tensor<fp32, [1]> N_conv_bias = const()[name = tensor<string, []>("N_conv_bias"), val = tensor<fp32, [1]>([0x1.44d67ep-1])];
            tensor<fp32, [1028]> encode_norm1_fc_bias = const()[name = tensor<string, []>("encode_norm1_fc_bias"), val = tensor<fp32, [1028]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
            tensor<fp32, [1028, 128]> encode_norm1_fc_weight = const()[name = tensor<string, []>("encode_norm1_fc_weight"), val = tensor<fp32, [1028, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4288)))];
            tensor<fp32, [1024]> encode_conv1_bias = const()[name = tensor<string, []>("encode_conv1_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(530688)))];
            tensor<fp32, [2048]> encode_norm2_fc_bias = const()[name = tensor<string, []>("encode_norm2_fc_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(534848)))];
            tensor<fp32, [2048, 128]> encode_norm2_fc_weight = const()[name = tensor<string, []>("encode_norm2_fc_weight"), val = tensor<fp32, [2048, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(543104)))];
            tensor<fp32, [1024]> encode_conv2_bias = const()[name = tensor<string, []>("encode_conv2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1591744)))];
            tensor<fp32, [64]> asr_res_0_bias = const()[name = tensor<string, []>("asr_res_0_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1595904)))];
            tensor<fp32, [2180]> decode_0_norm1_fc_bias = const()[name = tensor<string, []>("decode_0_norm1_fc_bias"), val = tensor<fp32, [2180]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1596224)))];
            tensor<fp32, [2180, 128]> decode_0_norm1_fc_weight = const()[name = tensor<string, []>("decode_0_norm1_fc_weight"), val = tensor<fp32, [2180, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1605056)))];
            tensor<fp32, [1024]> decode_0_conv1_bias = const()[name = tensor<string, []>("decode_0_conv1_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2721280)))];
            tensor<fp32, [2048]> decode_0_norm2_fc_bias = const()[name = tensor<string, []>("decode_0_norm2_fc_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2725440)))];
            tensor<fp32, [2048, 128]> decode_0_norm2_fc_weight = const()[name = tensor<string, []>("decode_0_norm2_fc_weight"), val = tensor<fp32, [2048, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2733696)))];
            tensor<fp32, [1024]> decode_0_conv2_bias = const()[name = tensor<string, []>("decode_0_conv2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3782336)))];
            tensor<fp32, [2180]> decode_1_norm1_fc_bias = const()[name = tensor<string, []>("decode_1_norm1_fc_bias"), val = tensor<fp32, [2180]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3786496)))];
            tensor<fp32, [2180, 128]> decode_1_norm1_fc_weight = const()[name = tensor<string, []>("decode_1_norm1_fc_weight"), val = tensor<fp32, [2180, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3795328)))];
            tensor<fp32, [1024]> decode_1_conv1_bias = const()[name = tensor<string, []>("decode_1_conv1_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4911552)))];
            tensor<fp32, [2048]> decode_1_norm2_fc_bias = const()[name = tensor<string, []>("decode_1_norm2_fc_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4915712)))];
            tensor<fp32, [2048, 128]> decode_1_norm2_fc_weight = const()[name = tensor<string, []>("decode_1_norm2_fc_weight"), val = tensor<fp32, [2048, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4923968)))];
            tensor<fp32, [1024]> decode_1_conv2_bias = const()[name = tensor<string, []>("decode_1_conv2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5972608)))];
            tensor<fp32, [2180]> decode_2_norm1_fc_bias = const()[name = tensor<string, []>("decode_2_norm1_fc_bias"), val = tensor<fp32, [2180]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5976768)))];
            tensor<fp32, [2180, 128]> decode_2_norm1_fc_weight = const()[name = tensor<string, []>("decode_2_norm1_fc_weight"), val = tensor<fp32, [2180, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5985600)))];
            tensor<fp32, [1024]> decode_2_conv1_bias = const()[name = tensor<string, []>("decode_2_conv1_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7101824)))];
            tensor<fp32, [2048]> decode_2_norm2_fc_bias = const()[name = tensor<string, []>("decode_2_norm2_fc_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7105984)))];
            tensor<fp32, [2048, 128]> decode_2_norm2_fc_weight = const()[name = tensor<string, []>("decode_2_norm2_fc_weight"), val = tensor<fp32, [2048, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7114240)))];
            tensor<fp32, [1024]> decode_2_conv2_bias = const()[name = tensor<string, []>("decode_2_conv2_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8162880)))];
            tensor<fp32, [2180]> decode_3_norm1_fc_bias = const()[name = tensor<string, []>("decode_3_norm1_fc_bias"), val = tensor<fp32, [2180]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8167040)))];
            tensor<fp32, [2180, 128]> decode_3_norm1_fc_weight = const()[name = tensor<string, []>("decode_3_norm1_fc_weight"), val = tensor<fp32, [2180, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8175872)))];
            tensor<fp32, [1090]> decode_3_pool_bias = const()[name = tensor<string, []>("decode_3_pool_bias"), val = tensor<fp32, [1090]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9292096)))];
            tensor<fp32, [512]> decode_3_conv1_bias = const()[name = tensor<string, []>("decode_3_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9296576)))];
            tensor<fp32, [1024]> decode_3_norm2_fc_bias = const()[name = tensor<string, []>("decode_3_norm2_fc_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9298688)))];
            tensor<fp32, [1024, 128]> decode_3_norm2_fc_weight = const()[name = tensor<string, []>("decode_3_norm2_fc_weight"), val = tensor<fp32, [1024, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9302848)))];
            tensor<fp32, [512]> decode_3_conv2_bias = const()[name = tensor<string, []>("decode_3_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9827200)))];
            tensor<fp32, [1, 32, 1]> generator_alphas_4 = const()[name = tensor<string, []>("generator_alphas_4"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9829312)))];
            tensor<fp32, [1, 64, 1]> generator_alphas_3 = const()[name = tensor<string, []>("generator_alphas_3"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9829504)))];
            tensor<fp32, [1, 128, 1]> generator_alphas_2 = const()[name = tensor<string, []>("generator_alphas_2"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9829824)))];
            tensor<fp32, [1, 256, 1]> generator_alphas_1 = const()[name = tensor<string, []>("generator_alphas_1"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9830400)))];
            tensor<fp32, [1, 512, 1]> generator_alphas_0 = const()[name = tensor<string, []>("generator_alphas_0"), val = tensor<fp32, [1, 512, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9831488)))];
            tensor<fp32, [1]> generator_m_source_l_linear_bias = const()[name = tensor<string, []>("generator_m_source_l_linear_bias"), val = tensor<fp32, [1]>([0x1.23e7f2p-6])];
            tensor<fp32, [1, 9]> generator_m_source_l_linear_weight = const()[name = tensor<string, []>("generator_m_source_l_linear_weight"), val = tensor<fp32, [1, 9]>([[-0x1.2aaee4p-12, 0x1.3872d6p-4, -0x1.e6ccbep-7, 0x1.e1debcp-8, -0x1.6ca714p-10, 0x1.8ba75p-10, 0x1.b33eecp-9, -0x1.cd2accp-8, 0x1.07f694p-7]])];
            tensor<fp32, [256]> generator_noise_convs_0_bias = const()[name = tensor<string, []>("generator_noise_convs_0_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9833600)))];
            tensor<fp32, [256, 1, 60]> generator_noise_convs_0_weight = const()[name = tensor<string, []>("generator_noise_convs_0_weight"), val = tensor<fp32, [256, 1, 60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9834688)))];
            tensor<fp32, [1, 256, 1]> generator_noise_res_0_alpha2_2 = const()[name = tensor<string, []>("generator_noise_res_0_alpha2_2"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9896192)))];
            tensor<fp32, [1, 256, 1]> generator_noise_res_0_alpha1_2 = const()[name = tensor<string, []>("generator_noise_res_0_alpha1_2"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9897280)))];
            tensor<fp32, [1, 256, 1]> generator_noise_res_0_alpha2_1 = const()[name = tensor<string, []>("generator_noise_res_0_alpha2_1"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9898368)))];
            tensor<fp32, [1, 256, 1]> generator_noise_res_0_alpha1_1 = const()[name = tensor<string, []>("generator_noise_res_0_alpha1_1"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9899456)))];
            tensor<fp32, [1, 256, 1]> generator_noise_res_0_alpha2_0 = const()[name = tensor<string, []>("generator_noise_res_0_alpha2_0"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9900544)))];
            tensor<fp32, [1, 256, 1]> generator_noise_res_0_alpha1_0 = const()[name = tensor<string, []>("generator_noise_res_0_alpha1_0"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9901632)))];
            tensor<fp32, [512]> generator_noise_res_0_adain1_0_fc_bias = const()[name = tensor<string, []>("generator_noise_res_0_adain1_0_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9902720)))];
            tensor<fp32, [512, 128]> generator_noise_res_0_adain1_0_fc_weight = const()[name = tensor<string, []>("generator_noise_res_0_adain1_0_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9904832)))];
            tensor<fp32, [256]> generator_noise_res_0_convs1_0_bias = const()[name = tensor<string, []>("generator_noise_res_0_convs1_0_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10167040)))];
            tensor<fp32, [512]> generator_noise_res_0_adain2_0_fc_bias = const()[name = tensor<string, []>("generator_noise_res_0_adain2_0_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10168128)))];
            tensor<fp32, [512, 128]> generator_noise_res_0_adain2_0_fc_weight = const()[name = tensor<string, []>("generator_noise_res_0_adain2_0_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10170240)))];
            tensor<fp32, [256]> generator_noise_res_0_convs2_0_bias = const()[name = tensor<string, []>("generator_noise_res_0_convs2_0_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10432448)))];
            tensor<fp32, [512]> generator_noise_res_0_adain1_1_fc_bias = const()[name = tensor<string, []>("generator_noise_res_0_adain1_1_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10433536)))];
            tensor<fp32, [512, 128]> generator_noise_res_0_adain1_1_fc_weight = const()[name = tensor<string, []>("generator_noise_res_0_adain1_1_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10435648)))];
            tensor<fp32, [256]> generator_noise_res_0_convs1_1_bias = const()[name = tensor<string, []>("generator_noise_res_0_convs1_1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10697856)))];
            tensor<fp32, [512]> generator_noise_res_0_adain2_1_fc_bias = const()[name = tensor<string, []>("generator_noise_res_0_adain2_1_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10698944)))];
            tensor<fp32, [512, 128]> generator_noise_res_0_adain2_1_fc_weight = const()[name = tensor<string, []>("generator_noise_res_0_adain2_1_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10701056)))];
            tensor<fp32, [256]> generator_noise_res_0_convs2_1_bias = const()[name = tensor<string, []>("generator_noise_res_0_convs2_1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10963264)))];
            tensor<fp32, [512]> generator_noise_res_0_adain1_2_fc_bias = const()[name = tensor<string, []>("generator_noise_res_0_adain1_2_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10964352)))];
            tensor<fp32, [512, 128]> generator_noise_res_0_adain1_2_fc_weight = const()[name = tensor<string, []>("generator_noise_res_0_adain1_2_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10966464)))];
            tensor<fp32, [256]> generator_noise_res_0_convs1_2_bias = const()[name = tensor<string, []>("generator_noise_res_0_convs1_2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11228672)))];
            tensor<fp32, [512]> generator_noise_res_0_adain2_2_fc_bias = const()[name = tensor<string, []>("generator_noise_res_0_adain2_2_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11229760)))];
            tensor<fp32, [512, 128]> generator_noise_res_0_adain2_2_fc_weight = const()[name = tensor<string, []>("generator_noise_res_0_adain2_2_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11231872)))];
            tensor<fp32, [256]> generator_noise_res_0_convs2_2_bias = const()[name = tensor<string, []>("generator_noise_res_0_convs2_2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11494080)))];
            tensor<fp32, [256]> generator_ups_0_bias = const()[name = tensor<string, []>("generator_ups_0_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11495168)))];
            tensor<fp32, [1, 256, 1]> generator_resblocks_0_alpha2_2 = const()[name = tensor<string, []>("generator_resblocks_0_alpha2_2"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11496256)))];
            tensor<fp32, [1, 256, 1]> generator_resblocks_0_alpha1_2 = const()[name = tensor<string, []>("generator_resblocks_0_alpha1_2"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11497344)))];
            tensor<fp32, [1, 256, 1]> generator_resblocks_0_alpha2_1 = const()[name = tensor<string, []>("generator_resblocks_0_alpha2_1"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11498432)))];
            tensor<fp32, [1, 256, 1]> generator_resblocks_0_alpha1_1 = const()[name = tensor<string, []>("generator_resblocks_0_alpha1_1"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11499520)))];
            tensor<fp32, [1, 256, 1]> generator_resblocks_0_alpha2_0 = const()[name = tensor<string, []>("generator_resblocks_0_alpha2_0"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11500608)))];
            tensor<fp32, [1, 256, 1]> generator_resblocks_0_alpha1_0 = const()[name = tensor<string, []>("generator_resblocks_0_alpha1_0"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11501696)))];
            tensor<fp32, [512]> generator_resblocks_0_adain1_0_fc_bias = const()[name = tensor<string, []>("generator_resblocks_0_adain1_0_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11502784)))];
            tensor<fp32, [512, 128]> generator_resblocks_0_adain1_0_fc_weight = const()[name = tensor<string, []>("generator_resblocks_0_adain1_0_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11504896)))];
            tensor<fp32, [256]> generator_resblocks_0_convs1_0_bias = const()[name = tensor<string, []>("generator_resblocks_0_convs1_0_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11767104)))];
            tensor<fp32, [512]> generator_resblocks_0_adain2_0_fc_bias = const()[name = tensor<string, []>("generator_resblocks_0_adain2_0_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11768192)))];
            tensor<fp32, [512, 128]> generator_resblocks_0_adain2_0_fc_weight = const()[name = tensor<string, []>("generator_resblocks_0_adain2_0_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11770304)))];
            tensor<fp32, [256]> generator_resblocks_0_convs2_0_bias = const()[name = tensor<string, []>("generator_resblocks_0_convs2_0_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12032512)))];
            tensor<fp32, [512]> generator_resblocks_0_adain1_1_fc_bias = const()[name = tensor<string, []>("generator_resblocks_0_adain1_1_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12033600)))];
            tensor<fp32, [512, 128]> generator_resblocks_0_adain1_1_fc_weight = const()[name = tensor<string, []>("generator_resblocks_0_adain1_1_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12035712)))];
            tensor<fp32, [256]> generator_resblocks_0_convs1_1_bias = const()[name = tensor<string, []>("generator_resblocks_0_convs1_1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12297920)))];
            tensor<fp32, [512]> generator_resblocks_0_adain2_1_fc_bias = const()[name = tensor<string, []>("generator_resblocks_0_adain2_1_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12299008)))];
            tensor<fp32, [512, 128]> generator_resblocks_0_adain2_1_fc_weight = const()[name = tensor<string, []>("generator_resblocks_0_adain2_1_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12301120)))];
            tensor<fp32, [256]> generator_resblocks_0_convs2_1_bias = const()[name = tensor<string, []>("generator_resblocks_0_convs2_1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12563328)))];
            tensor<fp32, [512]> generator_resblocks_0_adain1_2_fc_bias = const()[name = tensor<string, []>("generator_resblocks_0_adain1_2_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12564416)))];
            tensor<fp32, [512, 128]> generator_resblocks_0_adain1_2_fc_weight = const()[name = tensor<string, []>("generator_resblocks_0_adain1_2_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12566528)))];
            tensor<fp32, [256]> generator_resblocks_0_convs1_2_bias = const()[name = tensor<string, []>("generator_resblocks_0_convs1_2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12828736)))];
            tensor<fp32, [512]> generator_resblocks_0_adain2_2_fc_bias = const()[name = tensor<string, []>("generator_resblocks_0_adain2_2_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12829824)))];
            tensor<fp32, [512, 128]> generator_resblocks_0_adain2_2_fc_weight = const()[name = tensor<string, []>("generator_resblocks_0_adain2_2_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12831936)))];
            tensor<fp32, [256]> generator_resblocks_0_convs2_2_bias = const()[name = tensor<string, []>("generator_resblocks_0_convs2_2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13094144)))];
            tensor<fp32, [1, 256, 1]> generator_resblocks_1_alpha2_2 = const()[name = tensor<string, []>("generator_resblocks_1_alpha2_2"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13095232)))];
            tensor<fp32, [1, 256, 1]> generator_resblocks_1_alpha1_2 = const()[name = tensor<string, []>("generator_resblocks_1_alpha1_2"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13096320)))];
            tensor<fp32, [1, 256, 1]> generator_resblocks_1_alpha2_1 = const()[name = tensor<string, []>("generator_resblocks_1_alpha2_1"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13097408)))];
            tensor<fp32, [1, 256, 1]> generator_resblocks_1_alpha1_1 = const()[name = tensor<string, []>("generator_resblocks_1_alpha1_1"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13098496)))];
            tensor<fp32, [1, 256, 1]> generator_resblocks_1_alpha2_0 = const()[name = tensor<string, []>("generator_resblocks_1_alpha2_0"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13099584)))];
            tensor<fp32, [1, 256, 1]> generator_resblocks_1_alpha1_0 = const()[name = tensor<string, []>("generator_resblocks_1_alpha1_0"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13100672)))];
            tensor<fp32, [512]> generator_resblocks_1_adain1_0_fc_bias = const()[name = tensor<string, []>("generator_resblocks_1_adain1_0_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13101760)))];
            tensor<fp32, [512, 128]> generator_resblocks_1_adain1_0_fc_weight = const()[name = tensor<string, []>("generator_resblocks_1_adain1_0_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13103872)))];
            tensor<fp32, [256]> generator_resblocks_1_convs1_0_bias = const()[name = tensor<string, []>("generator_resblocks_1_convs1_0_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13366080)))];
            tensor<fp32, [512]> generator_resblocks_1_adain2_0_fc_bias = const()[name = tensor<string, []>("generator_resblocks_1_adain2_0_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13367168)))];
            tensor<fp32, [512, 128]> generator_resblocks_1_adain2_0_fc_weight = const()[name = tensor<string, []>("generator_resblocks_1_adain2_0_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13369280)))];
            tensor<fp32, [256]> generator_resblocks_1_convs2_0_bias = const()[name = tensor<string, []>("generator_resblocks_1_convs2_0_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13631488)))];
            tensor<fp32, [512]> generator_resblocks_1_adain1_1_fc_bias = const()[name = tensor<string, []>("generator_resblocks_1_adain1_1_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13632576)))];
            tensor<fp32, [512, 128]> generator_resblocks_1_adain1_1_fc_weight = const()[name = tensor<string, []>("generator_resblocks_1_adain1_1_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13634688)))];
            tensor<fp32, [256]> generator_resblocks_1_convs1_1_bias = const()[name = tensor<string, []>("generator_resblocks_1_convs1_1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13896896)))];
            tensor<fp32, [512]> generator_resblocks_1_adain2_1_fc_bias = const()[name = tensor<string, []>("generator_resblocks_1_adain2_1_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13897984)))];
            tensor<fp32, [512, 128]> generator_resblocks_1_adain2_1_fc_weight = const()[name = tensor<string, []>("generator_resblocks_1_adain2_1_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13900096)))];
            tensor<fp32, [256]> generator_resblocks_1_convs2_1_bias = const()[name = tensor<string, []>("generator_resblocks_1_convs2_1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14162304)))];
            tensor<fp32, [512]> generator_resblocks_1_adain1_2_fc_bias = const()[name = tensor<string, []>("generator_resblocks_1_adain1_2_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14163392)))];
            tensor<fp32, [512, 128]> generator_resblocks_1_adain1_2_fc_weight = const()[name = tensor<string, []>("generator_resblocks_1_adain1_2_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14165504)))];
            tensor<fp32, [256]> generator_resblocks_1_convs1_2_bias = const()[name = tensor<string, []>("generator_resblocks_1_convs1_2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14427712)))];
            tensor<fp32, [512]> generator_resblocks_1_adain2_2_fc_bias = const()[name = tensor<string, []>("generator_resblocks_1_adain2_2_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14428800)))];
            tensor<fp32, [512, 128]> generator_resblocks_1_adain2_2_fc_weight = const()[name = tensor<string, []>("generator_resblocks_1_adain2_2_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14430912)))];
            tensor<fp32, [256]> generator_resblocks_1_convs2_2_bias = const()[name = tensor<string, []>("generator_resblocks_1_convs2_2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14693120)))];
            tensor<fp32, [1, 256, 1]> generator_resblocks_2_alpha2_2 = const()[name = tensor<string, []>("generator_resblocks_2_alpha2_2"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14694208)))];
            tensor<fp32, [1, 256, 1]> generator_resblocks_2_alpha1_2 = const()[name = tensor<string, []>("generator_resblocks_2_alpha1_2"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14695296)))];
            tensor<fp32, [1, 256, 1]> generator_resblocks_2_alpha2_1 = const()[name = tensor<string, []>("generator_resblocks_2_alpha2_1"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14696384)))];
            tensor<fp32, [1, 256, 1]> generator_resblocks_2_alpha1_1 = const()[name = tensor<string, []>("generator_resblocks_2_alpha1_1"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14697472)))];
            tensor<fp32, [1, 256, 1]> generator_resblocks_2_alpha2_0 = const()[name = tensor<string, []>("generator_resblocks_2_alpha2_0"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14698560)))];
            tensor<fp32, [1, 256, 1]> generator_resblocks_2_alpha1_0 = const()[name = tensor<string, []>("generator_resblocks_2_alpha1_0"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14699648)))];
            tensor<fp32, [512]> generator_resblocks_2_adain1_0_fc_bias = const()[name = tensor<string, []>("generator_resblocks_2_adain1_0_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14700736)))];
            tensor<fp32, [512, 128]> generator_resblocks_2_adain1_0_fc_weight = const()[name = tensor<string, []>("generator_resblocks_2_adain1_0_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14702848)))];
            tensor<fp32, [256]> generator_resblocks_2_convs1_0_bias = const()[name = tensor<string, []>("generator_resblocks_2_convs1_0_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14965056)))];
            tensor<fp32, [512]> generator_resblocks_2_adain2_0_fc_bias = const()[name = tensor<string, []>("generator_resblocks_2_adain2_0_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14966144)))];
            tensor<fp32, [512, 128]> generator_resblocks_2_adain2_0_fc_weight = const()[name = tensor<string, []>("generator_resblocks_2_adain2_0_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14968256)))];
            tensor<fp32, [256]> generator_resblocks_2_convs2_0_bias = const()[name = tensor<string, []>("generator_resblocks_2_convs2_0_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15230464)))];
            tensor<fp32, [512]> generator_resblocks_2_adain1_1_fc_bias = const()[name = tensor<string, []>("generator_resblocks_2_adain1_1_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15231552)))];
            tensor<fp32, [512, 128]> generator_resblocks_2_adain1_1_fc_weight = const()[name = tensor<string, []>("generator_resblocks_2_adain1_1_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15233664)))];
            tensor<fp32, [256]> generator_resblocks_2_convs1_1_bias = const()[name = tensor<string, []>("generator_resblocks_2_convs1_1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15495872)))];
            tensor<fp32, [512]> generator_resblocks_2_adain2_1_fc_bias = const()[name = tensor<string, []>("generator_resblocks_2_adain2_1_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15496960)))];
            tensor<fp32, [512, 128]> generator_resblocks_2_adain2_1_fc_weight = const()[name = tensor<string, []>("generator_resblocks_2_adain2_1_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15499072)))];
            tensor<fp32, [256]> generator_resblocks_2_convs2_1_bias = const()[name = tensor<string, []>("generator_resblocks_2_convs2_1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15761280)))];
            tensor<fp32, [512]> generator_resblocks_2_adain1_2_fc_bias = const()[name = tensor<string, []>("generator_resblocks_2_adain1_2_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15762368)))];
            tensor<fp32, [512, 128]> generator_resblocks_2_adain1_2_fc_weight = const()[name = tensor<string, []>("generator_resblocks_2_adain1_2_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15764480)))];
            tensor<fp32, [256]> generator_resblocks_2_convs1_2_bias = const()[name = tensor<string, []>("generator_resblocks_2_convs1_2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16026688)))];
            tensor<fp32, [512]> generator_resblocks_2_adain2_2_fc_bias = const()[name = tensor<string, []>("generator_resblocks_2_adain2_2_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16027776)))];
            tensor<fp32, [512, 128]> generator_resblocks_2_adain2_2_fc_weight = const()[name = tensor<string, []>("generator_resblocks_2_adain2_2_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16029888)))];
            tensor<fp32, [256]> generator_resblocks_2_convs2_2_bias = const()[name = tensor<string, []>("generator_resblocks_2_convs2_2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16292096)))];
            tensor<fp32, [128]> generator_noise_convs_1_bias = const()[name = tensor<string, []>("generator_noise_convs_1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16293184)))];
            tensor<fp32, [128, 1, 12]> generator_noise_convs_1_weight = const()[name = tensor<string, []>("generator_noise_convs_1_weight"), val = tensor<fp32, [128, 1, 12]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16293760)))];
            tensor<fp32, [1, 128, 1]> generator_noise_res_1_alpha2_2 = const()[name = tensor<string, []>("generator_noise_res_1_alpha2_2"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16299968)))];
            tensor<fp32, [1, 128, 1]> generator_noise_res_1_alpha1_2 = const()[name = tensor<string, []>("generator_noise_res_1_alpha1_2"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16300544)))];
            tensor<fp32, [1, 128, 1]> generator_noise_res_1_alpha2_1 = const()[name = tensor<string, []>("generator_noise_res_1_alpha2_1"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16301120)))];
            tensor<fp32, [1, 128, 1]> generator_noise_res_1_alpha1_1 = const()[name = tensor<string, []>("generator_noise_res_1_alpha1_1"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16301696)))];
            tensor<fp32, [1, 128, 1]> generator_noise_res_1_alpha2_0 = const()[name = tensor<string, []>("generator_noise_res_1_alpha2_0"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16302272)))];
            tensor<fp32, [1, 128, 1]> generator_noise_res_1_alpha1_0 = const()[name = tensor<string, []>("generator_noise_res_1_alpha1_0"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16302848)))];
            tensor<fp32, [256]> generator_noise_res_1_adain1_0_fc_bias = const()[name = tensor<string, []>("generator_noise_res_1_adain1_0_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16303424)))];
            tensor<fp32, [256, 128]> generator_noise_res_1_adain1_0_fc_weight = const()[name = tensor<string, []>("generator_noise_res_1_adain1_0_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16304512)))];
            tensor<fp32, [128]> generator_noise_res_1_convs1_0_bias = const()[name = tensor<string, []>("generator_noise_res_1_convs1_0_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16435648)))];
            tensor<fp32, [256]> generator_noise_res_1_adain2_0_fc_bias = const()[name = tensor<string, []>("generator_noise_res_1_adain2_0_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16436224)))];
            tensor<fp32, [256, 128]> generator_noise_res_1_adain2_0_fc_weight = const()[name = tensor<string, []>("generator_noise_res_1_adain2_0_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16437312)))];
            tensor<fp32, [128]> generator_noise_res_1_convs2_0_bias = const()[name = tensor<string, []>("generator_noise_res_1_convs2_0_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16568448)))];
            tensor<fp32, [256]> generator_noise_res_1_adain1_1_fc_bias = const()[name = tensor<string, []>("generator_noise_res_1_adain1_1_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16569024)))];
            tensor<fp32, [256, 128]> generator_noise_res_1_adain1_1_fc_weight = const()[name = tensor<string, []>("generator_noise_res_1_adain1_1_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16570112)))];
            tensor<fp32, [128]> generator_noise_res_1_convs1_1_bias = const()[name = tensor<string, []>("generator_noise_res_1_convs1_1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16701248)))];
            tensor<fp32, [256]> generator_noise_res_1_adain2_1_fc_bias = const()[name = tensor<string, []>("generator_noise_res_1_adain2_1_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16701824)))];
            tensor<fp32, [256, 128]> generator_noise_res_1_adain2_1_fc_weight = const()[name = tensor<string, []>("generator_noise_res_1_adain2_1_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16702912)))];
            tensor<fp32, [128]> generator_noise_res_1_convs2_1_bias = const()[name = tensor<string, []>("generator_noise_res_1_convs2_1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16834048)))];
            tensor<fp32, [256]> generator_noise_res_1_adain1_2_fc_bias = const()[name = tensor<string, []>("generator_noise_res_1_adain1_2_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16834624)))];
            tensor<fp32, [256, 128]> generator_noise_res_1_adain1_2_fc_weight = const()[name = tensor<string, []>("generator_noise_res_1_adain1_2_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16835712)))];
            tensor<fp32, [128]> generator_noise_res_1_convs1_2_bias = const()[name = tensor<string, []>("generator_noise_res_1_convs1_2_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16966848)))];
            tensor<fp32, [256]> generator_noise_res_1_adain2_2_fc_bias = const()[name = tensor<string, []>("generator_noise_res_1_adain2_2_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16967424)))];
            tensor<fp32, [256, 128]> generator_noise_res_1_adain2_2_fc_weight = const()[name = tensor<string, []>("generator_noise_res_1_adain2_2_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16968512)))];
            tensor<fp32, [128]> generator_noise_res_1_convs2_2_bias = const()[name = tensor<string, []>("generator_noise_res_1_convs2_2_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17099648)))];
            tensor<fp32, [128]> generator_ups_1_bias = const()[name = tensor<string, []>("generator_ups_1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17100224)))];
            tensor<fp32, [1, 128, 1]> generator_resblocks_3_alpha2_2 = const()[name = tensor<string, []>("generator_resblocks_3_alpha2_2"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17100800)))];
            tensor<fp32, [1, 128, 1]> generator_resblocks_3_alpha1_2 = const()[name = tensor<string, []>("generator_resblocks_3_alpha1_2"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17101376)))];
            tensor<fp32, [1, 128, 1]> generator_resblocks_3_alpha2_1 = const()[name = tensor<string, []>("generator_resblocks_3_alpha2_1"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17101952)))];
            tensor<fp32, [1, 128, 1]> generator_resblocks_3_alpha1_1 = const()[name = tensor<string, []>("generator_resblocks_3_alpha1_1"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17102528)))];
            tensor<fp32, [1, 128, 1]> generator_resblocks_3_alpha2_0 = const()[name = tensor<string, []>("generator_resblocks_3_alpha2_0"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17103104)))];
            tensor<fp32, [1, 128, 1]> generator_resblocks_3_alpha1_0 = const()[name = tensor<string, []>("generator_resblocks_3_alpha1_0"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17103680)))];
            tensor<fp32, [256]> generator_resblocks_3_adain1_0_fc_bias = const()[name = tensor<string, []>("generator_resblocks_3_adain1_0_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17104256)))];
            tensor<fp32, [256, 128]> generator_resblocks_3_adain1_0_fc_weight = const()[name = tensor<string, []>("generator_resblocks_3_adain1_0_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17105344)))];
            tensor<fp32, [128]> generator_resblocks_3_convs1_0_bias = const()[name = tensor<string, []>("generator_resblocks_3_convs1_0_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17236480)))];
            tensor<fp32, [256]> generator_resblocks_3_adain2_0_fc_bias = const()[name = tensor<string, []>("generator_resblocks_3_adain2_0_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17237056)))];
            tensor<fp32, [256, 128]> generator_resblocks_3_adain2_0_fc_weight = const()[name = tensor<string, []>("generator_resblocks_3_adain2_0_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17238144)))];
            tensor<fp32, [128]> generator_resblocks_3_convs2_0_bias = const()[name = tensor<string, []>("generator_resblocks_3_convs2_0_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17369280)))];
            tensor<fp32, [256]> generator_resblocks_3_adain1_1_fc_bias = const()[name = tensor<string, []>("generator_resblocks_3_adain1_1_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17369856)))];
            tensor<fp32, [256, 128]> generator_resblocks_3_adain1_1_fc_weight = const()[name = tensor<string, []>("generator_resblocks_3_adain1_1_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17370944)))];
            tensor<fp32, [128]> generator_resblocks_3_convs1_1_bias = const()[name = tensor<string, []>("generator_resblocks_3_convs1_1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17502080)))];
            tensor<fp32, [256]> generator_resblocks_3_adain2_1_fc_bias = const()[name = tensor<string, []>("generator_resblocks_3_adain2_1_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17502656)))];
            tensor<fp32, [256, 128]> generator_resblocks_3_adain2_1_fc_weight = const()[name = tensor<string, []>("generator_resblocks_3_adain2_1_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17503744)))];
            tensor<fp32, [128]> generator_resblocks_3_convs2_1_bias = const()[name = tensor<string, []>("generator_resblocks_3_convs2_1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17634880)))];
            tensor<fp32, [256]> generator_resblocks_3_adain1_2_fc_bias = const()[name = tensor<string, []>("generator_resblocks_3_adain1_2_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17635456)))];
            tensor<fp32, [256, 128]> generator_resblocks_3_adain1_2_fc_weight = const()[name = tensor<string, []>("generator_resblocks_3_adain1_2_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17636544)))];
            tensor<fp32, [128]> generator_resblocks_3_convs1_2_bias = const()[name = tensor<string, []>("generator_resblocks_3_convs1_2_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17767680)))];
            tensor<fp32, [256]> generator_resblocks_3_adain2_2_fc_bias = const()[name = tensor<string, []>("generator_resblocks_3_adain2_2_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17768256)))];
            tensor<fp32, [256, 128]> generator_resblocks_3_adain2_2_fc_weight = const()[name = tensor<string, []>("generator_resblocks_3_adain2_2_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17769344)))];
            tensor<fp32, [128]> generator_resblocks_3_convs2_2_bias = const()[name = tensor<string, []>("generator_resblocks_3_convs2_2_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17900480)))];
            tensor<fp32, [1, 128, 1]> generator_resblocks_4_alpha2_2 = const()[name = tensor<string, []>("generator_resblocks_4_alpha2_2"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17901056)))];
            tensor<fp32, [1, 128, 1]> generator_resblocks_4_alpha1_2 = const()[name = tensor<string, []>("generator_resblocks_4_alpha1_2"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17901632)))];
            tensor<fp32, [1, 128, 1]> generator_resblocks_4_alpha2_1 = const()[name = tensor<string, []>("generator_resblocks_4_alpha2_1"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17902208)))];
            tensor<fp32, [1, 128, 1]> generator_resblocks_4_alpha1_1 = const()[name = tensor<string, []>("generator_resblocks_4_alpha1_1"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17902784)))];
            tensor<fp32, [1, 128, 1]> generator_resblocks_4_alpha2_0 = const()[name = tensor<string, []>("generator_resblocks_4_alpha2_0"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17903360)))];
            tensor<fp32, [1, 128, 1]> generator_resblocks_4_alpha1_0 = const()[name = tensor<string, []>("generator_resblocks_4_alpha1_0"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17903936)))];
            tensor<fp32, [256]> generator_resblocks_4_adain1_0_fc_bias = const()[name = tensor<string, []>("generator_resblocks_4_adain1_0_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17904512)))];
            tensor<fp32, [256, 128]> generator_resblocks_4_adain1_0_fc_weight = const()[name = tensor<string, []>("generator_resblocks_4_adain1_0_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17905600)))];
            tensor<fp32, [128]> generator_resblocks_4_convs1_0_bias = const()[name = tensor<string, []>("generator_resblocks_4_convs1_0_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18036736)))];
            tensor<fp32, [256]> generator_resblocks_4_adain2_0_fc_bias = const()[name = tensor<string, []>("generator_resblocks_4_adain2_0_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18037312)))];
            tensor<fp32, [256, 128]> generator_resblocks_4_adain2_0_fc_weight = const()[name = tensor<string, []>("generator_resblocks_4_adain2_0_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18038400)))];
            tensor<fp32, [128]> generator_resblocks_4_convs2_0_bias = const()[name = tensor<string, []>("generator_resblocks_4_convs2_0_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18169536)))];
            tensor<fp32, [256]> generator_resblocks_4_adain1_1_fc_bias = const()[name = tensor<string, []>("generator_resblocks_4_adain1_1_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18170112)))];
            tensor<fp32, [256, 128]> generator_resblocks_4_adain1_1_fc_weight = const()[name = tensor<string, []>("generator_resblocks_4_adain1_1_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18171200)))];
            tensor<fp32, [128]> generator_resblocks_4_convs1_1_bias = const()[name = tensor<string, []>("generator_resblocks_4_convs1_1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18302336)))];
            tensor<fp32, [256]> generator_resblocks_4_adain2_1_fc_bias = const()[name = tensor<string, []>("generator_resblocks_4_adain2_1_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18302912)))];
            tensor<fp32, [256, 128]> generator_resblocks_4_adain2_1_fc_weight = const()[name = tensor<string, []>("generator_resblocks_4_adain2_1_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18304000)))];
            tensor<fp32, [128]> generator_resblocks_4_convs2_1_bias = const()[name = tensor<string, []>("generator_resblocks_4_convs2_1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18435136)))];
            tensor<fp32, [256]> generator_resblocks_4_adain1_2_fc_bias = const()[name = tensor<string, []>("generator_resblocks_4_adain1_2_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18435712)))];
            tensor<fp32, [256, 128]> generator_resblocks_4_adain1_2_fc_weight = const()[name = tensor<string, []>("generator_resblocks_4_adain1_2_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18436800)))];
            tensor<fp32, [128]> generator_resblocks_4_convs1_2_bias = const()[name = tensor<string, []>("generator_resblocks_4_convs1_2_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18567936)))];
            tensor<fp32, [256]> generator_resblocks_4_adain2_2_fc_bias = const()[name = tensor<string, []>("generator_resblocks_4_adain2_2_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18568512)))];
            tensor<fp32, [256, 128]> generator_resblocks_4_adain2_2_fc_weight = const()[name = tensor<string, []>("generator_resblocks_4_adain2_2_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18569600)))];
            tensor<fp32, [128]> generator_resblocks_4_convs2_2_bias = const()[name = tensor<string, []>("generator_resblocks_4_convs2_2_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18700736)))];
            tensor<fp32, [1, 128, 1]> generator_resblocks_5_alpha2_2 = const()[name = tensor<string, []>("generator_resblocks_5_alpha2_2"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18701312)))];
            tensor<fp32, [1, 128, 1]> generator_resblocks_5_alpha1_2 = const()[name = tensor<string, []>("generator_resblocks_5_alpha1_2"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18701888)))];
            tensor<fp32, [1, 128, 1]> generator_resblocks_5_alpha2_1 = const()[name = tensor<string, []>("generator_resblocks_5_alpha2_1"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18702464)))];
            tensor<fp32, [1, 128, 1]> generator_resblocks_5_alpha1_1 = const()[name = tensor<string, []>("generator_resblocks_5_alpha1_1"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18703040)))];
            tensor<fp32, [1, 128, 1]> generator_resblocks_5_alpha2_0 = const()[name = tensor<string, []>("generator_resblocks_5_alpha2_0"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18703616)))];
            tensor<fp32, [1, 128, 1]> generator_resblocks_5_alpha1_0 = const()[name = tensor<string, []>("generator_resblocks_5_alpha1_0"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18704192)))];
            tensor<fp32, [256]> generator_resblocks_5_adain1_0_fc_bias = const()[name = tensor<string, []>("generator_resblocks_5_adain1_0_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18704768)))];
            tensor<fp32, [256, 128]> generator_resblocks_5_adain1_0_fc_weight = const()[name = tensor<string, []>("generator_resblocks_5_adain1_0_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18705856)))];
            tensor<fp32, [128]> generator_resblocks_5_convs1_0_bias = const()[name = tensor<string, []>("generator_resblocks_5_convs1_0_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18836992)))];
            tensor<fp32, [256]> generator_resblocks_5_adain2_0_fc_bias = const()[name = tensor<string, []>("generator_resblocks_5_adain2_0_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18837568)))];
            tensor<fp32, [256, 128]> generator_resblocks_5_adain2_0_fc_weight = const()[name = tensor<string, []>("generator_resblocks_5_adain2_0_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18838656)))];
            tensor<fp32, [128]> generator_resblocks_5_convs2_0_bias = const()[name = tensor<string, []>("generator_resblocks_5_convs2_0_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18969792)))];
            tensor<fp32, [256]> generator_resblocks_5_adain1_1_fc_bias = const()[name = tensor<string, []>("generator_resblocks_5_adain1_1_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18970368)))];
            tensor<fp32, [256, 128]> generator_resblocks_5_adain1_1_fc_weight = const()[name = tensor<string, []>("generator_resblocks_5_adain1_1_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18971456)))];
            tensor<fp32, [128]> generator_resblocks_5_convs1_1_bias = const()[name = tensor<string, []>("generator_resblocks_5_convs1_1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19102592)))];
            tensor<fp32, [256]> generator_resblocks_5_adain2_1_fc_bias = const()[name = tensor<string, []>("generator_resblocks_5_adain2_1_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19103168)))];
            tensor<fp32, [256, 128]> generator_resblocks_5_adain2_1_fc_weight = const()[name = tensor<string, []>("generator_resblocks_5_adain2_1_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19104256)))];
            tensor<fp32, [128]> generator_resblocks_5_convs2_1_bias = const()[name = tensor<string, []>("generator_resblocks_5_convs2_1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19235392)))];
            tensor<fp32, [256]> generator_resblocks_5_adain1_2_fc_bias = const()[name = tensor<string, []>("generator_resblocks_5_adain1_2_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19235968)))];
            tensor<fp32, [256, 128]> generator_resblocks_5_adain1_2_fc_weight = const()[name = tensor<string, []>("generator_resblocks_5_adain1_2_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19237056)))];
            tensor<fp32, [128]> generator_resblocks_5_convs1_2_bias = const()[name = tensor<string, []>("generator_resblocks_5_convs1_2_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19368192)))];
            tensor<fp32, [256]> generator_resblocks_5_adain2_2_fc_bias = const()[name = tensor<string, []>("generator_resblocks_5_adain2_2_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19368768)))];
            tensor<fp32, [256, 128]> generator_resblocks_5_adain2_2_fc_weight = const()[name = tensor<string, []>("generator_resblocks_5_adain2_2_fc_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19369856)))];
            tensor<fp32, [128]> generator_resblocks_5_convs2_2_bias = const()[name = tensor<string, []>("generator_resblocks_5_convs2_2_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19500992)))];
            tensor<fp32, [64]> generator_noise_convs_2_bias = const()[name = tensor<string, []>("generator_noise_convs_2_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19501568)))];
            tensor<fp32, [64, 1, 4]> generator_noise_convs_2_weight = const()[name = tensor<string, []>("generator_noise_convs_2_weight"), val = tensor<fp32, [64, 1, 4]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19501888)))];
            tensor<fp32, [1, 64, 1]> generator_noise_res_2_alpha2_2 = const()[name = tensor<string, []>("generator_noise_res_2_alpha2_2"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19502976)))];
            tensor<fp32, [1, 64, 1]> generator_noise_res_2_alpha1_2 = const()[name = tensor<string, []>("generator_noise_res_2_alpha1_2"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19503296)))];
            tensor<fp32, [1, 64, 1]> generator_noise_res_2_alpha2_1 = const()[name = tensor<string, []>("generator_noise_res_2_alpha2_1"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19503616)))];
            tensor<fp32, [1, 64, 1]> generator_noise_res_2_alpha1_1 = const()[name = tensor<string, []>("generator_noise_res_2_alpha1_1"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19503936)))];
            tensor<fp32, [1, 64, 1]> generator_noise_res_2_alpha2_0 = const()[name = tensor<string, []>("generator_noise_res_2_alpha2_0"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19504256)))];
            tensor<fp32, [1, 64, 1]> generator_noise_res_2_alpha1_0 = const()[name = tensor<string, []>("generator_noise_res_2_alpha1_0"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19504576)))];
            tensor<fp32, [128]> generator_noise_res_2_adain1_0_fc_bias = const()[name = tensor<string, []>("generator_noise_res_2_adain1_0_fc_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19504896)))];
            tensor<fp32, [128, 128]> generator_noise_res_2_adain1_0_fc_weight = const()[name = tensor<string, []>("generator_noise_res_2_adain1_0_fc_weight"), val = tensor<fp32, [128, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19505472)))];
            tensor<fp32, [64]> generator_noise_res_2_convs1_0_bias = const()[name = tensor<string, []>("generator_noise_res_2_convs1_0_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19571072)))];
            tensor<fp32, [128]> generator_noise_res_2_adain2_0_fc_bias = const()[name = tensor<string, []>("generator_noise_res_2_adain2_0_fc_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19571392)))];
            tensor<fp32, [128, 128]> generator_noise_res_2_adain2_0_fc_weight = const()[name = tensor<string, []>("generator_noise_res_2_adain2_0_fc_weight"), val = tensor<fp32, [128, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19571968)))];
            tensor<fp32, [64]> generator_noise_res_2_convs2_0_bias = const()[name = tensor<string, []>("generator_noise_res_2_convs2_0_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19637568)))];
            tensor<fp32, [128]> generator_noise_res_2_adain1_1_fc_bias = const()[name = tensor<string, []>("generator_noise_res_2_adain1_1_fc_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19637888)))];
            tensor<fp32, [128, 128]> generator_noise_res_2_adain1_1_fc_weight = const()[name = tensor<string, []>("generator_noise_res_2_adain1_1_fc_weight"), val = tensor<fp32, [128, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19638464)))];
            tensor<fp32, [64]> generator_noise_res_2_convs1_1_bias = const()[name = tensor<string, []>("generator_noise_res_2_convs1_1_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19704064)))];
            tensor<fp32, [128]> generator_noise_res_2_adain2_1_fc_bias = const()[name = tensor<string, []>("generator_noise_res_2_adain2_1_fc_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19704384)))];
            tensor<fp32, [128, 128]> generator_noise_res_2_adain2_1_fc_weight = const()[name = tensor<string, []>("generator_noise_res_2_adain2_1_fc_weight"), val = tensor<fp32, [128, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19704960)))];
            tensor<fp32, [64]> generator_noise_res_2_convs2_1_bias = const()[name = tensor<string, []>("generator_noise_res_2_convs2_1_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19770560)))];
            tensor<fp32, [128]> generator_noise_res_2_adain1_2_fc_bias = const()[name = tensor<string, []>("generator_noise_res_2_adain1_2_fc_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19770880)))];
            tensor<fp32, [128, 128]> generator_noise_res_2_adain1_2_fc_weight = const()[name = tensor<string, []>("generator_noise_res_2_adain1_2_fc_weight"), val = tensor<fp32, [128, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19771456)))];
            tensor<fp32, [64]> generator_noise_res_2_convs1_2_bias = const()[name = tensor<string, []>("generator_noise_res_2_convs1_2_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19837056)))];
            tensor<fp32, [128]> generator_noise_res_2_adain2_2_fc_bias = const()[name = tensor<string, []>("generator_noise_res_2_adain2_2_fc_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19837376)))];
            tensor<fp32, [128, 128]> generator_noise_res_2_adain2_2_fc_weight = const()[name = tensor<string, []>("generator_noise_res_2_adain2_2_fc_weight"), val = tensor<fp32, [128, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19837952)))];
            tensor<fp32, [64]> generator_noise_res_2_convs2_2_bias = const()[name = tensor<string, []>("generator_noise_res_2_convs2_2_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19903552)))];
            tensor<fp32, [64]> generator_ups_2_bias = const()[name = tensor<string, []>("generator_ups_2_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19903872)))];
            tensor<fp32, [1, 64, 1]> generator_resblocks_6_alpha2_2 = const()[name = tensor<string, []>("generator_resblocks_6_alpha2_2"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19904192)))];
            tensor<fp32, [1, 64, 1]> generator_resblocks_6_alpha1_2 = const()[name = tensor<string, []>("generator_resblocks_6_alpha1_2"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19904512)))];
            tensor<fp32, [1, 64, 1]> generator_resblocks_6_alpha2_1 = const()[name = tensor<string, []>("generator_resblocks_6_alpha2_1"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19904832)))];
            tensor<fp32, [1, 64, 1]> generator_resblocks_6_alpha1_1 = const()[name = tensor<string, []>("generator_resblocks_6_alpha1_1"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19905152)))];
            tensor<fp32, [1, 64, 1]> generator_resblocks_6_alpha2_0 = const()[name = tensor<string, []>("generator_resblocks_6_alpha2_0"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19905472)))];
            tensor<fp32, [1, 64, 1]> generator_resblocks_6_alpha1_0 = const()[name = tensor<string, []>("generator_resblocks_6_alpha1_0"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19905792)))];
            tensor<fp32, [128]> generator_resblocks_6_adain1_0_fc_bias = const()[name = tensor<string, []>("generator_resblocks_6_adain1_0_fc_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19906112)))];
            tensor<fp32, [128, 128]> generator_resblocks_6_adain1_0_fc_weight = const()[name = tensor<string, []>("generator_resblocks_6_adain1_0_fc_weight"), val = tensor<fp32, [128, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19906688)))];
            tensor<fp32, [64]> generator_resblocks_6_convs1_0_bias = const()[name = tensor<string, []>("generator_resblocks_6_convs1_0_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19972288)))];
            tensor<fp32, [128]> generator_resblocks_6_adain2_0_fc_bias = const()[name = tensor<string, []>("generator_resblocks_6_adain2_0_fc_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19972608)))];
            tensor<fp32, [128, 128]> generator_resblocks_6_adain2_0_fc_weight = const()[name = tensor<string, []>("generator_resblocks_6_adain2_0_fc_weight"), val = tensor<fp32, [128, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19973184)))];
            tensor<fp32, [64]> generator_resblocks_6_convs2_0_bias = const()[name = tensor<string, []>("generator_resblocks_6_convs2_0_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20038784)))];
            tensor<fp32, [128]> generator_resblocks_6_adain1_1_fc_bias = const()[name = tensor<string, []>("generator_resblocks_6_adain1_1_fc_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20039104)))];
            tensor<fp32, [128, 128]> generator_resblocks_6_adain1_1_fc_weight = const()[name = tensor<string, []>("generator_resblocks_6_adain1_1_fc_weight"), val = tensor<fp32, [128, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20039680)))];
            tensor<fp32, [64]> generator_resblocks_6_convs1_1_bias = const()[name = tensor<string, []>("generator_resblocks_6_convs1_1_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20105280)))];
            tensor<fp32, [128]> generator_resblocks_6_adain2_1_fc_bias = const()[name = tensor<string, []>("generator_resblocks_6_adain2_1_fc_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20105600)))];
            tensor<fp32, [128, 128]> generator_resblocks_6_adain2_1_fc_weight = const()[name = tensor<string, []>("generator_resblocks_6_adain2_1_fc_weight"), val = tensor<fp32, [128, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20106176)))];
            tensor<fp32, [64]> generator_resblocks_6_convs2_1_bias = const()[name = tensor<string, []>("generator_resblocks_6_convs2_1_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20171776)))];
            tensor<fp32, [128]> generator_resblocks_6_adain1_2_fc_bias = const()[name = tensor<string, []>("generator_resblocks_6_adain1_2_fc_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20172096)))];
            tensor<fp32, [128, 128]> generator_resblocks_6_adain1_2_fc_weight = const()[name = tensor<string, []>("generator_resblocks_6_adain1_2_fc_weight"), val = tensor<fp32, [128, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20172672)))];
            tensor<fp32, [64]> generator_resblocks_6_convs1_2_bias = const()[name = tensor<string, []>("generator_resblocks_6_convs1_2_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20238272)))];
            tensor<fp32, [128]> generator_resblocks_6_adain2_2_fc_bias = const()[name = tensor<string, []>("generator_resblocks_6_adain2_2_fc_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20238592)))];
            tensor<fp32, [128, 128]> generator_resblocks_6_adain2_2_fc_weight = const()[name = tensor<string, []>("generator_resblocks_6_adain2_2_fc_weight"), val = tensor<fp32, [128, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20239168)))];
            tensor<fp32, [64]> generator_resblocks_6_convs2_2_bias = const()[name = tensor<string, []>("generator_resblocks_6_convs2_2_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20304768)))];
            tensor<fp32, [1, 64, 1]> generator_resblocks_7_alpha2_2 = const()[name = tensor<string, []>("generator_resblocks_7_alpha2_2"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20305088)))];
            tensor<fp32, [1, 64, 1]> generator_resblocks_7_alpha1_2 = const()[name = tensor<string, []>("generator_resblocks_7_alpha1_2"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20305408)))];
            tensor<fp32, [1, 64, 1]> generator_resblocks_7_alpha2_1 = const()[name = tensor<string, []>("generator_resblocks_7_alpha2_1"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20305728)))];
            tensor<fp32, [1, 64, 1]> generator_resblocks_7_alpha1_1 = const()[name = tensor<string, []>("generator_resblocks_7_alpha1_1"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20306048)))];
            tensor<fp32, [1, 64, 1]> generator_resblocks_7_alpha2_0 = const()[name = tensor<string, []>("generator_resblocks_7_alpha2_0"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20306368)))];
            tensor<fp32, [1, 64, 1]> generator_resblocks_7_alpha1_0 = const()[name = tensor<string, []>("generator_resblocks_7_alpha1_0"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20306688)))];
            tensor<fp32, [128]> generator_resblocks_7_adain1_0_fc_bias = const()[name = tensor<string, []>("generator_resblocks_7_adain1_0_fc_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20307008)))];
            tensor<fp32, [128, 128]> generator_resblocks_7_adain1_0_fc_weight = const()[name = tensor<string, []>("generator_resblocks_7_adain1_0_fc_weight"), val = tensor<fp32, [128, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20307584)))];
            tensor<fp32, [64]> generator_resblocks_7_convs1_0_bias = const()[name = tensor<string, []>("generator_resblocks_7_convs1_0_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20373184)))];
            tensor<fp32, [128]> generator_resblocks_7_adain2_0_fc_bias = const()[name = tensor<string, []>("generator_resblocks_7_adain2_0_fc_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20373504)))];
            tensor<fp32, [128, 128]> generator_resblocks_7_adain2_0_fc_weight = const()[name = tensor<string, []>("generator_resblocks_7_adain2_0_fc_weight"), val = tensor<fp32, [128, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20374080)))];
            tensor<fp32, [64]> generator_resblocks_7_convs2_0_bias = const()[name = tensor<string, []>("generator_resblocks_7_convs2_0_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20439680)))];
            tensor<fp32, [128]> generator_resblocks_7_adain1_1_fc_bias = const()[name = tensor<string, []>("generator_resblocks_7_adain1_1_fc_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20440000)))];
            tensor<fp32, [128, 128]> generator_resblocks_7_adain1_1_fc_weight = const()[name = tensor<string, []>("generator_resblocks_7_adain1_1_fc_weight"), val = tensor<fp32, [128, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20440576)))];
            tensor<fp32, [64]> generator_resblocks_7_convs1_1_bias = const()[name = tensor<string, []>("generator_resblocks_7_convs1_1_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20506176)))];
            tensor<fp32, [128]> generator_resblocks_7_adain2_1_fc_bias = const()[name = tensor<string, []>("generator_resblocks_7_adain2_1_fc_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20506496)))];
            tensor<fp32, [128, 128]> generator_resblocks_7_adain2_1_fc_weight = const()[name = tensor<string, []>("generator_resblocks_7_adain2_1_fc_weight"), val = tensor<fp32, [128, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20507072)))];
            tensor<fp32, [64]> generator_resblocks_7_convs2_1_bias = const()[name = tensor<string, []>("generator_resblocks_7_convs2_1_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20572672)))];
            tensor<fp32, [128]> generator_resblocks_7_adain1_2_fc_bias = const()[name = tensor<string, []>("generator_resblocks_7_adain1_2_fc_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20572992)))];
            tensor<fp32, [128, 128]> generator_resblocks_7_adain1_2_fc_weight = const()[name = tensor<string, []>("generator_resblocks_7_adain1_2_fc_weight"), val = tensor<fp32, [128, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20573568)))];
            tensor<fp32, [64]> generator_resblocks_7_convs1_2_bias = const()[name = tensor<string, []>("generator_resblocks_7_convs1_2_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20639168)))];
            tensor<fp32, [128]> generator_resblocks_7_adain2_2_fc_bias = const()[name = tensor<string, []>("generator_resblocks_7_adain2_2_fc_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20639488)))];
            tensor<fp32, [128, 128]> generator_resblocks_7_adain2_2_fc_weight = const()[name = tensor<string, []>("generator_resblocks_7_adain2_2_fc_weight"), val = tensor<fp32, [128, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20640064)))];
            tensor<fp32, [64]> generator_resblocks_7_convs2_2_bias = const()[name = tensor<string, []>("generator_resblocks_7_convs2_2_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20705664)))];
            tensor<fp32, [1, 64, 1]> generator_resblocks_8_alpha2_2 = const()[name = tensor<string, []>("generator_resblocks_8_alpha2_2"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20705984)))];
            tensor<fp32, [1, 64, 1]> generator_resblocks_8_alpha1_2 = const()[name = tensor<string, []>("generator_resblocks_8_alpha1_2"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20706304)))];
            tensor<fp32, [1, 64, 1]> generator_resblocks_8_alpha2_1 = const()[name = tensor<string, []>("generator_resblocks_8_alpha2_1"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20706624)))];
            tensor<fp32, [1, 64, 1]> generator_resblocks_8_alpha1_1 = const()[name = tensor<string, []>("generator_resblocks_8_alpha1_1"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20706944)))];
            tensor<fp32, [1, 64, 1]> generator_resblocks_8_alpha2_0 = const()[name = tensor<string, []>("generator_resblocks_8_alpha2_0"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20707264)))];
            tensor<fp32, [1, 64, 1]> generator_resblocks_8_alpha1_0 = const()[name = tensor<string, []>("generator_resblocks_8_alpha1_0"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20707584)))];
            tensor<fp32, [128]> generator_resblocks_8_adain1_0_fc_bias = const()[name = tensor<string, []>("generator_resblocks_8_adain1_0_fc_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20707904)))];
            tensor<fp32, [128, 128]> generator_resblocks_8_adain1_0_fc_weight = const()[name = tensor<string, []>("generator_resblocks_8_adain1_0_fc_weight"), val = tensor<fp32, [128, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20708480)))];
            tensor<fp32, [64]> generator_resblocks_8_convs1_0_bias = const()[name = tensor<string, []>("generator_resblocks_8_convs1_0_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20774080)))];
            tensor<fp32, [128]> generator_resblocks_8_adain2_0_fc_bias = const()[name = tensor<string, []>("generator_resblocks_8_adain2_0_fc_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20774400)))];
            tensor<fp32, [128, 128]> generator_resblocks_8_adain2_0_fc_weight = const()[name = tensor<string, []>("generator_resblocks_8_adain2_0_fc_weight"), val = tensor<fp32, [128, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20774976)))];
            tensor<fp32, [64]> generator_resblocks_8_convs2_0_bias = const()[name = tensor<string, []>("generator_resblocks_8_convs2_0_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20840576)))];
            tensor<fp32, [128]> generator_resblocks_8_adain1_1_fc_bias = const()[name = tensor<string, []>("generator_resblocks_8_adain1_1_fc_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20840896)))];
            tensor<fp32, [128, 128]> generator_resblocks_8_adain1_1_fc_weight = const()[name = tensor<string, []>("generator_resblocks_8_adain1_1_fc_weight"), val = tensor<fp32, [128, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20841472)))];
            tensor<fp32, [64]> generator_resblocks_8_convs1_1_bias = const()[name = tensor<string, []>("generator_resblocks_8_convs1_1_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20907072)))];
            tensor<fp32, [128]> generator_resblocks_8_adain2_1_fc_bias = const()[name = tensor<string, []>("generator_resblocks_8_adain2_1_fc_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20907392)))];
            tensor<fp32, [128, 128]> generator_resblocks_8_adain2_1_fc_weight = const()[name = tensor<string, []>("generator_resblocks_8_adain2_1_fc_weight"), val = tensor<fp32, [128, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20907968)))];
            tensor<fp32, [64]> generator_resblocks_8_convs2_1_bias = const()[name = tensor<string, []>("generator_resblocks_8_convs2_1_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20973568)))];
            tensor<fp32, [128]> generator_resblocks_8_adain1_2_fc_bias = const()[name = tensor<string, []>("generator_resblocks_8_adain1_2_fc_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20973888)))];
            tensor<fp32, [128, 128]> generator_resblocks_8_adain1_2_fc_weight = const()[name = tensor<string, []>("generator_resblocks_8_adain1_2_fc_weight"), val = tensor<fp32, [128, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20974464)))];
            tensor<fp32, [64]> generator_resblocks_8_convs1_2_bias = const()[name = tensor<string, []>("generator_resblocks_8_convs1_2_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21040064)))];
            tensor<fp32, [128]> generator_resblocks_8_adain2_2_fc_bias = const()[name = tensor<string, []>("generator_resblocks_8_adain2_2_fc_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21040384)))];
            tensor<fp32, [128, 128]> generator_resblocks_8_adain2_2_fc_weight = const()[name = tensor<string, []>("generator_resblocks_8_adain2_2_fc_weight"), val = tensor<fp32, [128, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21040960)))];
            tensor<fp32, [64]> generator_resblocks_8_convs2_2_bias = const()[name = tensor<string, []>("generator_resblocks_8_convs2_2_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21106560)))];
            tensor<fp32, [32]> generator_noise_convs_3_bias = const()[name = tensor<string, []>("generator_noise_convs_3_bias"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21106880)))];
            tensor<fp32, [32, 1, 1]> generator_noise_convs_3_weight = const()[name = tensor<string, []>("generator_noise_convs_3_weight"), val = tensor<fp32, [32, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21107072)))];
            tensor<fp32, [1, 32, 1]> generator_noise_res_3_alpha2_2 = const()[name = tensor<string, []>("generator_noise_res_3_alpha2_2"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21107264)))];
            tensor<fp32, [1, 32, 1]> generator_noise_res_3_alpha1_2 = const()[name = tensor<string, []>("generator_noise_res_3_alpha1_2"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21107456)))];
            tensor<fp32, [1, 32, 1]> generator_noise_res_3_alpha2_1 = const()[name = tensor<string, []>("generator_noise_res_3_alpha2_1"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21107648)))];
            tensor<fp32, [1, 32, 1]> generator_noise_res_3_alpha1_1 = const()[name = tensor<string, []>("generator_noise_res_3_alpha1_1"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21107840)))];
            tensor<fp32, [1, 32, 1]> generator_noise_res_3_alpha2_0 = const()[name = tensor<string, []>("generator_noise_res_3_alpha2_0"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21108032)))];
            tensor<fp32, [1, 32, 1]> generator_noise_res_3_alpha1_0 = const()[name = tensor<string, []>("generator_noise_res_3_alpha1_0"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21108224)))];
            tensor<fp32, [64]> generator_noise_res_3_adain1_0_fc_bias = const()[name = tensor<string, []>("generator_noise_res_3_adain1_0_fc_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21108416)))];
            tensor<fp32, [64, 128]> generator_noise_res_3_adain1_0_fc_weight = const()[name = tensor<string, []>("generator_noise_res_3_adain1_0_fc_weight"), val = tensor<fp32, [64, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21108736)))];
            tensor<fp32, [32]> generator_noise_res_3_convs1_0_bias = const()[name = tensor<string, []>("generator_noise_res_3_convs1_0_bias"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21141568)))];
            tensor<fp32, [64]> generator_noise_res_3_adain2_0_fc_bias = const()[name = tensor<string, []>("generator_noise_res_3_adain2_0_fc_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21141760)))];
            tensor<fp32, [64, 128]> generator_noise_res_3_adain2_0_fc_weight = const()[name = tensor<string, []>("generator_noise_res_3_adain2_0_fc_weight"), val = tensor<fp32, [64, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21142080)))];
            tensor<fp32, [32]> generator_noise_res_3_convs2_0_bias = const()[name = tensor<string, []>("generator_noise_res_3_convs2_0_bias"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21174912)))];
            tensor<fp32, [64]> generator_noise_res_3_adain1_1_fc_bias = const()[name = tensor<string, []>("generator_noise_res_3_adain1_1_fc_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21175104)))];
            tensor<fp32, [64, 128]> generator_noise_res_3_adain1_1_fc_weight = const()[name = tensor<string, []>("generator_noise_res_3_adain1_1_fc_weight"), val = tensor<fp32, [64, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21175424)))];
            tensor<fp32, [32]> generator_noise_res_3_convs1_1_bias = const()[name = tensor<string, []>("generator_noise_res_3_convs1_1_bias"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21208256)))];
            tensor<fp32, [64]> generator_noise_res_3_adain2_1_fc_bias = const()[name = tensor<string, []>("generator_noise_res_3_adain2_1_fc_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21208448)))];
            tensor<fp32, [64, 128]> generator_noise_res_3_adain2_1_fc_weight = const()[name = tensor<string, []>("generator_noise_res_3_adain2_1_fc_weight"), val = tensor<fp32, [64, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21208768)))];
            tensor<fp32, [32]> generator_noise_res_3_convs2_1_bias = const()[name = tensor<string, []>("generator_noise_res_3_convs2_1_bias"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21241600)))];
            tensor<fp32, [64]> generator_noise_res_3_adain1_2_fc_bias = const()[name = tensor<string, []>("generator_noise_res_3_adain1_2_fc_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21241792)))];
            tensor<fp32, [64, 128]> generator_noise_res_3_adain1_2_fc_weight = const()[name = tensor<string, []>("generator_noise_res_3_adain1_2_fc_weight"), val = tensor<fp32, [64, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21242112)))];
            tensor<fp32, [32]> generator_noise_res_3_convs1_2_bias = const()[name = tensor<string, []>("generator_noise_res_3_convs1_2_bias"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21274944)))];
            tensor<fp32, [64]> generator_noise_res_3_adain2_2_fc_bias = const()[name = tensor<string, []>("generator_noise_res_3_adain2_2_fc_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21275136)))];
            tensor<fp32, [64, 128]> generator_noise_res_3_adain2_2_fc_weight = const()[name = tensor<string, []>("generator_noise_res_3_adain2_2_fc_weight"), val = tensor<fp32, [64, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21275456)))];
            tensor<fp32, [32]> generator_noise_res_3_convs2_2_bias = const()[name = tensor<string, []>("generator_noise_res_3_convs2_2_bias"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21308288)))];
            tensor<fp32, [32]> generator_ups_3_bias = const()[name = tensor<string, []>("generator_ups_3_bias"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21308480)))];
            tensor<fp32, [1, 32, 1]> generator_resblocks_9_alpha2_2 = const()[name = tensor<string, []>("generator_resblocks_9_alpha2_2"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21308672)))];
            tensor<fp32, [1, 32, 1]> generator_resblocks_9_alpha1_2 = const()[name = tensor<string, []>("generator_resblocks_9_alpha1_2"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21308864)))];
            tensor<fp32, [1, 32, 1]> generator_resblocks_9_alpha2_1 = const()[name = tensor<string, []>("generator_resblocks_9_alpha2_1"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21309056)))];
            tensor<fp32, [1, 32, 1]> generator_resblocks_9_alpha1_1 = const()[name = tensor<string, []>("generator_resblocks_9_alpha1_1"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21309248)))];
            tensor<fp32, [1, 32, 1]> generator_resblocks_9_alpha2_0 = const()[name = tensor<string, []>("generator_resblocks_9_alpha2_0"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21309440)))];
            tensor<fp32, [1, 32, 1]> generator_resblocks_9_alpha1_0 = const()[name = tensor<string, []>("generator_resblocks_9_alpha1_0"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21309632)))];
            tensor<fp32, [64]> generator_resblocks_9_adain1_0_fc_bias = const()[name = tensor<string, []>("generator_resblocks_9_adain1_0_fc_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21309824)))];
            tensor<fp32, [64, 128]> generator_resblocks_9_adain1_0_fc_weight = const()[name = tensor<string, []>("generator_resblocks_9_adain1_0_fc_weight"), val = tensor<fp32, [64, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21310144)))];
            tensor<fp32, [32]> generator_resblocks_9_convs1_0_bias = const()[name = tensor<string, []>("generator_resblocks_9_convs1_0_bias"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21342976)))];
            tensor<fp32, [64]> generator_resblocks_9_adain2_0_fc_bias = const()[name = tensor<string, []>("generator_resblocks_9_adain2_0_fc_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21343168)))];
            tensor<fp32, [64, 128]> generator_resblocks_9_adain2_0_fc_weight = const()[name = tensor<string, []>("generator_resblocks_9_adain2_0_fc_weight"), val = tensor<fp32, [64, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21343488)))];
            tensor<fp32, [32]> generator_resblocks_9_convs2_0_bias = const()[name = tensor<string, []>("generator_resblocks_9_convs2_0_bias"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21376320)))];
            tensor<fp32, [64]> generator_resblocks_9_adain1_1_fc_bias = const()[name = tensor<string, []>("generator_resblocks_9_adain1_1_fc_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21376512)))];
            tensor<fp32, [64, 128]> generator_resblocks_9_adain1_1_fc_weight = const()[name = tensor<string, []>("generator_resblocks_9_adain1_1_fc_weight"), val = tensor<fp32, [64, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21376832)))];
            tensor<fp32, [32]> generator_resblocks_9_convs1_1_bias = const()[name = tensor<string, []>("generator_resblocks_9_convs1_1_bias"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21409664)))];
            tensor<fp32, [64]> generator_resblocks_9_adain2_1_fc_bias = const()[name = tensor<string, []>("generator_resblocks_9_adain2_1_fc_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21409856)))];
            tensor<fp32, [64, 128]> generator_resblocks_9_adain2_1_fc_weight = const()[name = tensor<string, []>("generator_resblocks_9_adain2_1_fc_weight"), val = tensor<fp32, [64, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21410176)))];
            tensor<fp32, [32]> generator_resblocks_9_convs2_1_bias = const()[name = tensor<string, []>("generator_resblocks_9_convs2_1_bias"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21443008)))];
            tensor<fp32, [64]> generator_resblocks_9_adain1_2_fc_bias = const()[name = tensor<string, []>("generator_resblocks_9_adain1_2_fc_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21443200)))];
            tensor<fp32, [64, 128]> generator_resblocks_9_adain1_2_fc_weight = const()[name = tensor<string, []>("generator_resblocks_9_adain1_2_fc_weight"), val = tensor<fp32, [64, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21443520)))];
            tensor<fp32, [32]> generator_resblocks_9_convs1_2_bias = const()[name = tensor<string, []>("generator_resblocks_9_convs1_2_bias"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21476352)))];
            tensor<fp32, [64]> generator_resblocks_9_adain2_2_fc_bias = const()[name = tensor<string, []>("generator_resblocks_9_adain2_2_fc_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21476544)))];
            tensor<fp32, [64, 128]> generator_resblocks_9_adain2_2_fc_weight = const()[name = tensor<string, []>("generator_resblocks_9_adain2_2_fc_weight"), val = tensor<fp32, [64, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21476864)))];
            tensor<fp32, [32]> generator_resblocks_9_convs2_2_bias = const()[name = tensor<string, []>("generator_resblocks_9_convs2_2_bias"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21509696)))];
            tensor<fp32, [1, 32, 1]> generator_resblocks_10_alpha2_2 = const()[name = tensor<string, []>("generator_resblocks_10_alpha2_2"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21509888)))];
            tensor<fp32, [1, 32, 1]> generator_resblocks_10_alpha1_2 = const()[name = tensor<string, []>("generator_resblocks_10_alpha1_2"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21510080)))];
            tensor<fp32, [1, 32, 1]> generator_resblocks_10_alpha2_1 = const()[name = tensor<string, []>("generator_resblocks_10_alpha2_1"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21510272)))];
            tensor<fp32, [1, 32, 1]> generator_resblocks_10_alpha1_1 = const()[name = tensor<string, []>("generator_resblocks_10_alpha1_1"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21510464)))];
            tensor<fp32, [1, 32, 1]> generator_resblocks_10_alpha2_0 = const()[name = tensor<string, []>("generator_resblocks_10_alpha2_0"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21510656)))];
            tensor<fp32, [1, 32, 1]> generator_resblocks_10_alpha1_0 = const()[name = tensor<string, []>("generator_resblocks_10_alpha1_0"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21510848)))];
            tensor<fp32, [64]> generator_resblocks_10_adain1_0_fc_bias = const()[name = tensor<string, []>("generator_resblocks_10_adain1_0_fc_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21511040)))];
            tensor<fp32, [64, 128]> generator_resblocks_10_adain1_0_fc_weight = const()[name = tensor<string, []>("generator_resblocks_10_adain1_0_fc_weight"), val = tensor<fp32, [64, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21511360)))];
            tensor<fp32, [32]> generator_resblocks_10_convs1_0_bias = const()[name = tensor<string, []>("generator_resblocks_10_convs1_0_bias"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21544192)))];
            tensor<fp32, [64]> generator_resblocks_10_adain2_0_fc_bias = const()[name = tensor<string, []>("generator_resblocks_10_adain2_0_fc_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21544384)))];
            tensor<fp32, [64, 128]> generator_resblocks_10_adain2_0_fc_weight = const()[name = tensor<string, []>("generator_resblocks_10_adain2_0_fc_weight"), val = tensor<fp32, [64, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21544704)))];
            tensor<fp32, [32]> generator_resblocks_10_convs2_0_bias = const()[name = tensor<string, []>("generator_resblocks_10_convs2_0_bias"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21577536)))];
            tensor<fp32, [64]> generator_resblocks_10_adain1_1_fc_bias = const()[name = tensor<string, []>("generator_resblocks_10_adain1_1_fc_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21577728)))];
            tensor<fp32, [64, 128]> generator_resblocks_10_adain1_1_fc_weight = const()[name = tensor<string, []>("generator_resblocks_10_adain1_1_fc_weight"), val = tensor<fp32, [64, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21578048)))];
            tensor<fp32, [32]> generator_resblocks_10_convs1_1_bias = const()[name = tensor<string, []>("generator_resblocks_10_convs1_1_bias"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21610880)))];
            tensor<fp32, [64]> generator_resblocks_10_adain2_1_fc_bias = const()[name = tensor<string, []>("generator_resblocks_10_adain2_1_fc_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21611072)))];
            tensor<fp32, [64, 128]> generator_resblocks_10_adain2_1_fc_weight = const()[name = tensor<string, []>("generator_resblocks_10_adain2_1_fc_weight"), val = tensor<fp32, [64, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21611392)))];
            tensor<fp32, [32]> generator_resblocks_10_convs2_1_bias = const()[name = tensor<string, []>("generator_resblocks_10_convs2_1_bias"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21644224)))];
            tensor<fp32, [64]> generator_resblocks_10_adain1_2_fc_bias = const()[name = tensor<string, []>("generator_resblocks_10_adain1_2_fc_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21644416)))];
            tensor<fp32, [64, 128]> generator_resblocks_10_adain1_2_fc_weight = const()[name = tensor<string, []>("generator_resblocks_10_adain1_2_fc_weight"), val = tensor<fp32, [64, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21644736)))];
            tensor<fp32, [32]> generator_resblocks_10_convs1_2_bias = const()[name = tensor<string, []>("generator_resblocks_10_convs1_2_bias"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21677568)))];
            tensor<fp32, [64]> generator_resblocks_10_adain2_2_fc_bias = const()[name = tensor<string, []>("generator_resblocks_10_adain2_2_fc_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21677760)))];
            tensor<fp32, [64, 128]> generator_resblocks_10_adain2_2_fc_weight = const()[name = tensor<string, []>("generator_resblocks_10_adain2_2_fc_weight"), val = tensor<fp32, [64, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21678080)))];
            tensor<fp32, [32]> generator_resblocks_10_convs2_2_bias = const()[name = tensor<string, []>("generator_resblocks_10_convs2_2_bias"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21710912)))];
            tensor<fp32, [1, 32, 1]> generator_resblocks_11_alpha2_2 = const()[name = tensor<string, []>("generator_resblocks_11_alpha2_2"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21711104)))];
            tensor<fp32, [1, 32, 1]> generator_resblocks_11_alpha1_2 = const()[name = tensor<string, []>("generator_resblocks_11_alpha1_2"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21711296)))];
            tensor<fp32, [1, 32, 1]> generator_resblocks_11_alpha2_1 = const()[name = tensor<string, []>("generator_resblocks_11_alpha2_1"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21711488)))];
            tensor<fp32, [1, 32, 1]> generator_resblocks_11_alpha1_1 = const()[name = tensor<string, []>("generator_resblocks_11_alpha1_1"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21711680)))];
            tensor<fp32, [1, 32, 1]> generator_resblocks_11_alpha2_0 = const()[name = tensor<string, []>("generator_resblocks_11_alpha2_0"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21711872)))];
            tensor<fp32, [1, 32, 1]> generator_resblocks_11_alpha1_0 = const()[name = tensor<string, []>("generator_resblocks_11_alpha1_0"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21712064)))];
            tensor<fp32, [64]> generator_resblocks_11_adain1_0_fc_bias = const()[name = tensor<string, []>("generator_resblocks_11_adain1_0_fc_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21712256)))];
            tensor<fp32, [64, 128]> generator_resblocks_11_adain1_0_fc_weight = const()[name = tensor<string, []>("generator_resblocks_11_adain1_0_fc_weight"), val = tensor<fp32, [64, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21712576)))];
            tensor<fp32, [32]> generator_resblocks_11_convs1_0_bias = const()[name = tensor<string, []>("generator_resblocks_11_convs1_0_bias"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21745408)))];
            tensor<fp32, [64]> generator_resblocks_11_adain2_0_fc_bias = const()[name = tensor<string, []>("generator_resblocks_11_adain2_0_fc_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21745600)))];
            tensor<fp32, [64, 128]> generator_resblocks_11_adain2_0_fc_weight = const()[name = tensor<string, []>("generator_resblocks_11_adain2_0_fc_weight"), val = tensor<fp32, [64, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21745920)))];
            tensor<fp32, [32]> generator_resblocks_11_convs2_0_bias = const()[name = tensor<string, []>("generator_resblocks_11_convs2_0_bias"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21778752)))];
            tensor<fp32, [64]> generator_resblocks_11_adain1_1_fc_bias = const()[name = tensor<string, []>("generator_resblocks_11_adain1_1_fc_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21778944)))];
            tensor<fp32, [64, 128]> generator_resblocks_11_adain1_1_fc_weight = const()[name = tensor<string, []>("generator_resblocks_11_adain1_1_fc_weight"), val = tensor<fp32, [64, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21779264)))];
            tensor<fp32, [32]> generator_resblocks_11_convs1_1_bias = const()[name = tensor<string, []>("generator_resblocks_11_convs1_1_bias"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21812096)))];
            tensor<fp32, [64]> generator_resblocks_11_adain2_1_fc_bias = const()[name = tensor<string, []>("generator_resblocks_11_adain2_1_fc_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21812288)))];
            tensor<fp32, [64, 128]> generator_resblocks_11_adain2_1_fc_weight = const()[name = tensor<string, []>("generator_resblocks_11_adain2_1_fc_weight"), val = tensor<fp32, [64, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21812608)))];
            tensor<fp32, [32]> generator_resblocks_11_convs2_1_bias = const()[name = tensor<string, []>("generator_resblocks_11_convs2_1_bias"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21845440)))];
            tensor<fp32, [64]> generator_resblocks_11_adain1_2_fc_bias = const()[name = tensor<string, []>("generator_resblocks_11_adain1_2_fc_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21845632)))];
            tensor<fp32, [64, 128]> generator_resblocks_11_adain1_2_fc_weight = const()[name = tensor<string, []>("generator_resblocks_11_adain1_2_fc_weight"), val = tensor<fp32, [64, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21845952)))];
            tensor<fp32, [32]> generator_resblocks_11_convs1_2_bias = const()[name = tensor<string, []>("generator_resblocks_11_convs1_2_bias"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21878784)))];
            tensor<fp32, [64]> generator_resblocks_11_adain2_2_fc_bias = const()[name = tensor<string, []>("generator_resblocks_11_adain2_2_fc_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21878976)))];
            tensor<fp32, [64, 128]> generator_resblocks_11_adain2_2_fc_weight = const()[name = tensor<string, []>("generator_resblocks_11_adain2_2_fc_weight"), val = tensor<fp32, [64, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21879296)))];
            tensor<fp32, [32]> generator_resblocks_11_convs2_2_bias = const()[name = tensor<string, []>("generator_resblocks_11_convs2_2_bias"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21912128)))];
            tensor<fp32, [1]> generator_conv_post_bias = const()[name = tensor<string, []>("generator_conv_post_bias"), val = tensor<fp32, [1]>([0x1.25d976p-16])];
            tensor<int32, [1]> input_1_axes_0 = const()[name = tensor<string, []>("input_1_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1, 1, 1024]> input_1 = expand_dims(axes = input_1_axes_0, x = F0_curve)[name = tensor<string, []>("input_1")];
            tensor<fp32, [1, 1, 3]> weight_1 = const()[name = tensor<string, []>("weight_1"), val = tensor<fp32, [1, 1, 3]>([[[0x1.5663dap-5, -0x1.864e36p-5, -0x1.5048p-4]]])];
            tensor<string, []> F0_pad_type_0 = const()[name = tensor<string, []>("F0_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> F0_pad_0 = const()[name = tensor<string, []>("F0_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> F0_strides_0 = const()[name = tensor<string, []>("F0_strides_0"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1]> F0_dilations_0 = const()[name = tensor<string, []>("F0_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> F0_groups_0 = const()[name = tensor<string, []>("F0_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1, 512]> F0 = conv(bias = F0_conv_bias, dilations = F0_dilations_0, groups = F0_groups_0, pad = F0_pad_0, pad_type = F0_pad_type_0, strides = F0_strides_0, weight = weight_1, x = input_1)[name = tensor<string, []>("F0")];
            tensor<int32, [1]> input_3_axes_0 = const()[name = tensor<string, []>("input_3_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp32, [1, 1, 1024]> input_3 = expand_dims(axes = input_3_axes_0, x = N)[name = tensor<string, []>("input_3")];
            tensor<fp32, [1, 1, 3]> weight_3 = const()[name = tensor<string, []>("weight_3"), val = tensor<fp32, [1, 1, 3]>([[[0x1.1d454p-5, -0x1.710ff4p-1, -0x1.89a072p-1]]])];
            tensor<string, []> Nc_pad_type_0 = const()[name = tensor<string, []>("Nc_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> Nc_pad_0 = const()[name = tensor<string, []>("Nc_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> Nc_strides_0 = const()[name = tensor<string, []>("Nc_strides_0"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1]> Nc_dilations_0 = const()[name = tensor<string, []>("Nc_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> Nc_groups_0 = const()[name = tensor<string, []>("Nc_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1, 512]> Nc = conv(bias = N_conv_bias, dilations = Nc_dilations_0, groups = Nc_groups_0, pad = Nc_pad_0, pad_type = Nc_pad_type_0, strides = Nc_strides_0, weight = weight_3, x = input_3)[name = tensor<string, []>("Nc")];
            tensor<int32, []> var_57 = const()[name = tensor<string, []>("op_57"), val = tensor<int32, []>(1)];
            tensor<bool, []> input_5_interleave_0 = const()[name = tensor<string, []>("input_5_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 514, 512]> input_5 = concat(axis = var_57, interleave = input_5_interleave_0, values = (asr, F0, Nc))[name = tensor<string, []>("input_5")];
            tensor<fp32, []> var_61 = const()[name = tensor<string, []>("op_61"), val = tensor<fp32, []>(0x1.99999ap-3)];
            tensor<fp32, []> var_64 = const()[name = tensor<string, []>("op_64"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 1028]> h_1 = linear(bias = encode_norm1_fc_bias, weight = encode_norm1_fc_weight, x = s)[name = tensor<string, []>("linear_0")];
            tensor<int32, [3]> var_90 = const()[name = tensor<string, []>("op_90"), val = tensor<int32, [3]>([1, 1028, 1])];
            tensor<fp32, [1, 1028, 1]> h_3 = reshape(shape = var_90, x = h_1)[name = tensor<string, []>("h_3")];
            tensor<int32, [2]> var_92_split_sizes_0 = const()[name = tensor<string, []>("op_92_split_sizes_0"), val = tensor<int32, [2]>([514, 514])];
            tensor<int32, []> var_92_axis_0 = const()[name = tensor<string, []>("op_92_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 514, 1]> var_92_0, tensor<fp32, [1, 514, 1]> var_92_1 = split(axis = var_92_axis_0, split_sizes = var_92_split_sizes_0, x = h_3)[name = tensor<string, []>("op_92")];
            tensor<fp32, []> var_94_promoted = const()[name = tensor<string, []>("op_94_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 514, 1]> var_95 = add(x = var_92_0, y = var_94_promoted)[name = tensor<string, []>("op_95")];
            tensor<fp32, [1, 514, 512]> var_96 = instance_norm(epsilon = var_64, x = input_5)[name = tensor<string, []>("op_96")];
            tensor<fp32, [1, 514, 512]> var_97 = mul(x = var_95, y = var_96)[name = tensor<string, []>("op_97")];
            tensor<fp32, [1, 514, 512]> input_7 = add(x = var_97, y = var_92_1)[name = tensor<string, []>("input_7")];
            tensor<fp32, [1, 514, 512]> input_9 = leaky_relu(alpha = var_61, x = input_7)[name = tensor<string, []>("input_9")];
            tensor<fp32, [1024, 514, 3]> weight_5 = const()[name = tensor<string, []>("weight_5"), val = tensor<fp32, [1024, 514, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21912320)))];
            tensor<string, []> input_13_pad_type_0 = const()[name = tensor<string, []>("input_13_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_13_pad_0 = const()[name = tensor<string, []>("input_13_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> input_13_strides_0 = const()[name = tensor<string, []>("input_13_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_13_dilations_0 = const()[name = tensor<string, []>("input_13_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_13_groups_0 = const()[name = tensor<string, []>("input_13_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 512]> input_13 = conv(bias = encode_conv1_bias, dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = weight_5, x = input_9)[name = tensor<string, []>("input_13")];
            tensor<fp32, [1, 2048]> h_5 = linear(bias = encode_norm2_fc_bias, weight = encode_norm2_fc_weight, x = s)[name = tensor<string, []>("linear_1")];
            tensor<int32, [3]> var_114 = const()[name = tensor<string, []>("op_114"), val = tensor<int32, [3]>([1, 2048, 1])];
            tensor<fp32, [1, 2048, 1]> h_7 = reshape(shape = var_114, x = h_5)[name = tensor<string, []>("h_7")];
            tensor<int32, [2]> var_116_split_sizes_0 = const()[name = tensor<string, []>("op_116_split_sizes_0"), val = tensor<int32, [2]>([1024, 1024])];
            tensor<int32, []> var_116_axis_0 = const()[name = tensor<string, []>("op_116_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 1]> var_116_0, tensor<fp32, [1, 1024, 1]> var_116_1 = split(axis = var_116_axis_0, split_sizes = var_116_split_sizes_0, x = h_7)[name = tensor<string, []>("op_116")];
            tensor<fp32, []> var_118_promoted = const()[name = tensor<string, []>("op_118_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 1024, 1]> var_119 = add(x = var_116_0, y = var_118_promoted)[name = tensor<string, []>("op_119")];
            tensor<fp32, [1, 1024, 512]> var_120 = instance_norm(epsilon = var_64, x = input_13)[name = tensor<string, []>("op_120")];
            tensor<fp32, [1, 1024, 512]> var_121 = mul(x = var_119, y = var_120)[name = tensor<string, []>("op_121")];
            tensor<fp32, [1, 1024, 512]> input_15 = add(x = var_121, y = var_116_1)[name = tensor<string, []>("input_15")];
            tensor<fp32, [1, 1024, 512]> input_17 = leaky_relu(alpha = var_61, x = input_15)[name = tensor<string, []>("input_17")];
            tensor<fp32, [1024, 1024, 3]> weight_7 = const()[name = tensor<string, []>("weight_7"), val = tensor<fp32, [1024, 1024, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28228416)))];
            tensor<string, []> out_1_pad_type_0 = const()[name = tensor<string, []>("out_1_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> out_1_pad_0 = const()[name = tensor<string, []>("out_1_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> out_1_strides_0 = const()[name = tensor<string, []>("out_1_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> out_1_dilations_0 = const()[name = tensor<string, []>("out_1_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> out_1_groups_0 = const()[name = tensor<string, []>("out_1_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 512]> out_1 = conv(bias = encode_conv2_bias, dilations = out_1_dilations_0, groups = out_1_groups_0, pad = out_1_pad_0, pad_type = out_1_pad_type_0, strides = out_1_strides_0, weight = weight_7, x = input_17)[name = tensor<string, []>("out_1")];
            tensor<fp32, [1024, 514, 1]> weight_9 = const()[name = tensor<string, []>("weight_9"), val = tensor<fp32, [1024, 514, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40811392)))];
            tensor<string, []> var_137_pad_type_0 = const()[name = tensor<string, []>("op_137_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> var_137_strides_0 = const()[name = tensor<string, []>("op_137_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> var_137_pad_0 = const()[name = tensor<string, []>("op_137_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> var_137_dilations_0 = const()[name = tensor<string, []>("op_137_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> var_137_groups_0 = const()[name = tensor<string, []>("op_137_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 512]> var_137 = conv(dilations = var_137_dilations_0, groups = var_137_groups_0, pad = var_137_pad_0, pad_type = var_137_pad_type_0, strides = var_137_strides_0, weight = weight_9, x = input_5)[name = tensor<string, []>("op_137")];
            tensor<fp32, [1, 1024, 512]> var_138 = add(x = out_1, y = var_137)[name = tensor<string, []>("op_138")];
            tensor<fp32, []> _inversed_x_1_y_0 = const()[name = tensor<string, []>("_inversed_x_1_y_0"), val = tensor<fp32, []>(0x1.6a09e6p-1)];
            tensor<fp32, [1, 1024, 512]> _inversed_x_1 = mul(x = var_138, y = _inversed_x_1_y_0)[name = tensor<string, []>("_inversed_x_1")];
            tensor<fp32, [64, 512, 1]> weight_11 = const()[name = tensor<string, []>("weight_11"), val = tensor<fp32, [64, 512, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42916800)))];
            tensor<string, []> asr_res_1_pad_type_0 = const()[name = tensor<string, []>("asr_res_1_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> asr_res_1_strides_0 = const()[name = tensor<string, []>("asr_res_1_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> asr_res_1_pad_0 = const()[name = tensor<string, []>("asr_res_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> asr_res_1_dilations_0 = const()[name = tensor<string, []>("asr_res_1_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> asr_res_1_groups_0 = const()[name = tensor<string, []>("asr_res_1_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 512]> asr_res_1 = conv(bias = asr_res_0_bias, dilations = asr_res_1_dilations_0, groups = asr_res_1_groups_0, pad = asr_res_1_pad_0, pad_type = asr_res_1_pad_type_0, strides = asr_res_1_strides_0, weight = weight_11, x = asr)[name = tensor<string, []>("asr_res_1")];
            tensor<int32, []> var_158 = const()[name = tensor<string, []>("op_158"), val = tensor<int32, []>(1)];
            tensor<bool, []> input_21_interleave_0 = const()[name = tensor<string, []>("input_21_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1090, 512]> input_21 = concat(axis = var_158, interleave = input_21_interleave_0, values = (_inversed_x_1, asr_res_1, F0, Nc))[name = tensor<string, []>("input_21")];
            tensor<fp32, []> var_162 = const()[name = tensor<string, []>("op_162"), val = tensor<fp32, []>(0x1.99999ap-3)];
            tensor<fp32, []> var_165 = const()[name = tensor<string, []>("op_165"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 2180]> h_9 = linear(bias = decode_0_norm1_fc_bias, weight = decode_0_norm1_fc_weight, x = s)[name = tensor<string, []>("linear_2")];
            tensor<int32, [3]> var_191 = const()[name = tensor<string, []>("op_191"), val = tensor<int32, [3]>([1, 2180, 1])];
            tensor<fp32, [1, 2180, 1]> h_11 = reshape(shape = var_191, x = h_9)[name = tensor<string, []>("h_11")];
            tensor<int32, [2]> var_193_split_sizes_0 = const()[name = tensor<string, []>("op_193_split_sizes_0"), val = tensor<int32, [2]>([1090, 1090])];
            tensor<int32, []> var_193_axis_0 = const()[name = tensor<string, []>("op_193_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1090, 1]> var_193_0, tensor<fp32, [1, 1090, 1]> var_193_1 = split(axis = var_193_axis_0, split_sizes = var_193_split_sizes_0, x = h_11)[name = tensor<string, []>("op_193")];
            tensor<fp32, []> var_195_promoted = const()[name = tensor<string, []>("op_195_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 1090, 1]> var_196 = add(x = var_193_0, y = var_195_promoted)[name = tensor<string, []>("op_196")];
            tensor<fp32, [1, 1090, 512]> var_197 = instance_norm(epsilon = var_165, x = input_21)[name = tensor<string, []>("op_197")];
            tensor<fp32, [1, 1090, 512]> var_198 = mul(x = var_196, y = var_197)[name = tensor<string, []>("op_198")];
            tensor<fp32, [1, 1090, 512]> input_23 = add(x = var_198, y = var_193_1)[name = tensor<string, []>("input_23")];
            tensor<fp32, [1, 1090, 512]> input_25 = leaky_relu(alpha = var_162, x = input_23)[name = tensor<string, []>("input_25")];
            tensor<fp32, [1024, 1090, 3]> weight_13 = const()[name = tensor<string, []>("weight_13"), val = tensor<fp32, [1024, 1090, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43047936)))];
            tensor<string, []> input_29_pad_type_0 = const()[name = tensor<string, []>("input_29_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_29_pad_0 = const()[name = tensor<string, []>("input_29_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> input_29_strides_0 = const()[name = tensor<string, []>("input_29_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_29_dilations_0 = const()[name = tensor<string, []>("input_29_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_29_groups_0 = const()[name = tensor<string, []>("input_29_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 512]> input_29 = conv(bias = decode_0_conv1_bias, 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 = weight_13, x = input_25)[name = tensor<string, []>("input_29")];
            tensor<fp32, [1, 2048]> h_13 = linear(bias = decode_0_norm2_fc_bias, weight = decode_0_norm2_fc_weight, x = s)[name = tensor<string, []>("linear_3")];
            tensor<int32, [3]> var_215 = const()[name = tensor<string, []>("op_215"), val = tensor<int32, [3]>([1, 2048, 1])];
            tensor<fp32, [1, 2048, 1]> h_15 = reshape(shape = var_215, x = h_13)[name = tensor<string, []>("h_15")];
            tensor<int32, [2]> var_217_split_sizes_0 = const()[name = tensor<string, []>("op_217_split_sizes_0"), val = tensor<int32, [2]>([1024, 1024])];
            tensor<int32, []> var_217_axis_0 = const()[name = tensor<string, []>("op_217_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 1]> var_217_0, tensor<fp32, [1, 1024, 1]> var_217_1 = split(axis = var_217_axis_0, split_sizes = var_217_split_sizes_0, x = h_15)[name = tensor<string, []>("op_217")];
            tensor<fp32, []> var_219_promoted = const()[name = tensor<string, []>("op_219_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 1024, 1]> var_220 = add(x = var_217_0, y = var_219_promoted)[name = tensor<string, []>("op_220")];
            tensor<fp32, [1, 1024, 512]> var_221 = instance_norm(epsilon = var_165, x = input_29)[name = tensor<string, []>("op_221")];
            tensor<fp32, [1, 1024, 512]> var_222 = mul(x = var_220, y = var_221)[name = tensor<string, []>("op_222")];
            tensor<fp32, [1, 1024, 512]> input_31 = add(x = var_222, y = var_217_1)[name = tensor<string, []>("input_31")];
            tensor<fp32, [1, 1024, 512]> input_33 = leaky_relu(alpha = var_162, x = input_31)[name = tensor<string, []>("input_33")];
            tensor<fp32, [1024, 1024, 3]> weight_15 = const()[name = tensor<string, []>("weight_15"), val = tensor<fp32, [1024, 1024, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(56441920)))];
            tensor<string, []> out_3_pad_type_0 = const()[name = tensor<string, []>("out_3_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> out_3_pad_0 = const()[name = tensor<string, []>("out_3_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> out_3_strides_0 = const()[name = tensor<string, []>("out_3_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> out_3_dilations_0 = const()[name = tensor<string, []>("out_3_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> out_3_groups_0 = const()[name = tensor<string, []>("out_3_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 512]> out_3 = conv(bias = decode_0_conv2_bias, dilations = out_3_dilations_0, groups = out_3_groups_0, pad = out_3_pad_0, pad_type = out_3_pad_type_0, strides = out_3_strides_0, weight = weight_15, x = input_33)[name = tensor<string, []>("out_3")];
            tensor<fp32, [1024, 1090, 1]> weight_17 = const()[name = tensor<string, []>("weight_17"), val = tensor<fp32, [1024, 1090, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(69024896)))];
            tensor<string, []> var_238_pad_type_0 = const()[name = tensor<string, []>("op_238_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> var_238_strides_0 = const()[name = tensor<string, []>("op_238_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> var_238_pad_0 = const()[name = tensor<string, []>("op_238_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> var_238_dilations_0 = const()[name = tensor<string, []>("op_238_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> var_238_groups_0 = const()[name = tensor<string, []>("op_238_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 512]> var_238 = conv(dilations = var_238_dilations_0, groups = var_238_groups_0, pad = var_238_pad_0, pad_type = var_238_pad_type_0, strides = var_238_strides_0, weight = weight_17, x = input_21)[name = tensor<string, []>("op_238")];
            tensor<fp32, [1, 1024, 512]> var_239 = add(x = out_3, y = var_238)[name = tensor<string, []>("op_239")];
            tensor<fp32, []> _inversed_x_3_y_0 = const()[name = tensor<string, []>("_inversed_x_3_y_0"), val = tensor<fp32, []>(0x1.6a09e6p-1)];
            tensor<fp32, [1, 1024, 512]> _inversed_x_3 = mul(x = var_239, y = _inversed_x_3_y_0)[name = tensor<string, []>("_inversed_x_3")];
            tensor<int32, []> var_243 = const()[name = tensor<string, []>("op_243"), val = tensor<int32, []>(1)];
            tensor<bool, []> input_37_interleave_0 = const()[name = tensor<string, []>("input_37_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1090, 512]> input_37 = concat(axis = var_243, interleave = input_37_interleave_0, values = (_inversed_x_3, asr_res_1, F0, Nc))[name = tensor<string, []>("input_37")];
            tensor<fp32, []> var_247 = const()[name = tensor<string, []>("op_247"), val = tensor<fp32, []>(0x1.99999ap-3)];
            tensor<fp32, []> var_250 = const()[name = tensor<string, []>("op_250"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 2180]> h_17 = linear(bias = decode_1_norm1_fc_bias, weight = decode_1_norm1_fc_weight, x = s)[name = tensor<string, []>("linear_4")];
            tensor<int32, [3]> var_276 = const()[name = tensor<string, []>("op_276"), val = tensor<int32, [3]>([1, 2180, 1])];
            tensor<fp32, [1, 2180, 1]> h_19 = reshape(shape = var_276, x = h_17)[name = tensor<string, []>("h_19")];
            tensor<int32, [2]> var_278_split_sizes_0 = const()[name = tensor<string, []>("op_278_split_sizes_0"), val = tensor<int32, [2]>([1090, 1090])];
            tensor<int32, []> var_278_axis_0 = const()[name = tensor<string, []>("op_278_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1090, 1]> var_278_0, tensor<fp32, [1, 1090, 1]> var_278_1 = split(axis = var_278_axis_0, split_sizes = var_278_split_sizes_0, x = h_19)[name = tensor<string, []>("op_278")];
            tensor<fp32, []> var_280_promoted = const()[name = tensor<string, []>("op_280_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 1090, 1]> var_281 = add(x = var_278_0, y = var_280_promoted)[name = tensor<string, []>("op_281")];
            tensor<fp32, [1, 1090, 512]> var_282 = instance_norm(epsilon = var_250, x = input_37)[name = tensor<string, []>("op_282")];
            tensor<fp32, [1, 1090, 512]> var_283 = mul(x = var_281, y = var_282)[name = tensor<string, []>("op_283")];
            tensor<fp32, [1, 1090, 512]> input_39 = add(x = var_283, y = var_278_1)[name = tensor<string, []>("input_39")];
            tensor<fp32, [1, 1090, 512]> input_41 = leaky_relu(alpha = var_247, x = input_39)[name = tensor<string, []>("input_41")];
            tensor<fp32, [1024, 1090, 3]> weight_19 = const()[name = tensor<string, []>("weight_19"), val = tensor<fp32, [1024, 1090, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(73489600)))];
            tensor<string, []> input_45_pad_type_0 = const()[name = tensor<string, []>("input_45_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_45_pad_0 = const()[name = tensor<string, []>("input_45_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> input_45_strides_0 = const()[name = tensor<string, []>("input_45_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_45_dilations_0 = const()[name = tensor<string, []>("input_45_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_45_groups_0 = const()[name = tensor<string, []>("input_45_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 512]> input_45 = conv(bias = decode_1_conv1_bias, dilations = input_45_dilations_0, groups = input_45_groups_0, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = input_45_strides_0, weight = weight_19, x = input_41)[name = tensor<string, []>("input_45")];
            tensor<fp32, [1, 2048]> h_21 = linear(bias = decode_1_norm2_fc_bias, weight = decode_1_norm2_fc_weight, x = s)[name = tensor<string, []>("linear_5")];
            tensor<int32, [3]> var_300 = const()[name = tensor<string, []>("op_300"), val = tensor<int32, [3]>([1, 2048, 1])];
            tensor<fp32, [1, 2048, 1]> h_23 = reshape(shape = var_300, x = h_21)[name = tensor<string, []>("h_23")];
            tensor<int32, [2]> var_302_split_sizes_0 = const()[name = tensor<string, []>("op_302_split_sizes_0"), val = tensor<int32, [2]>([1024, 1024])];
            tensor<int32, []> var_302_axis_0 = const()[name = tensor<string, []>("op_302_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 1]> var_302_0, tensor<fp32, [1, 1024, 1]> var_302_1 = split(axis = var_302_axis_0, split_sizes = var_302_split_sizes_0, x = h_23)[name = tensor<string, []>("op_302")];
            tensor<fp32, []> var_304_promoted = const()[name = tensor<string, []>("op_304_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 1024, 1]> var_305 = add(x = var_302_0, y = var_304_promoted)[name = tensor<string, []>("op_305")];
            tensor<fp32, [1, 1024, 512]> var_306 = instance_norm(epsilon = var_250, x = input_45)[name = tensor<string, []>("op_306")];
            tensor<fp32, [1, 1024, 512]> var_307 = mul(x = var_305, y = var_306)[name = tensor<string, []>("op_307")];
            tensor<fp32, [1, 1024, 512]> input_47 = add(x = var_307, y = var_302_1)[name = tensor<string, []>("input_47")];
            tensor<fp32, [1, 1024, 512]> input_49 = leaky_relu(alpha = var_247, x = input_47)[name = tensor<string, []>("input_49")];
            tensor<fp32, [1024, 1024, 3]> weight_21 = const()[name = tensor<string, []>("weight_21"), val = tensor<fp32, [1024, 1024, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(86883584)))];
            tensor<string, []> out_5_pad_type_0 = const()[name = tensor<string, []>("out_5_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> out_5_pad_0 = const()[name = tensor<string, []>("out_5_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> out_5_strides_0 = const()[name = tensor<string, []>("out_5_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> out_5_dilations_0 = const()[name = tensor<string, []>("out_5_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> out_5_groups_0 = const()[name = tensor<string, []>("out_5_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 512]> out_5 = conv(bias = decode_1_conv2_bias, dilations = out_5_dilations_0, groups = out_5_groups_0, pad = out_5_pad_0, pad_type = out_5_pad_type_0, strides = out_5_strides_0, weight = weight_21, x = input_49)[name = tensor<string, []>("out_5")];
            tensor<fp32, [1024, 1090, 1]> weight_23 = const()[name = tensor<string, []>("weight_23"), val = tensor<fp32, [1024, 1090, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(99466560)))];
            tensor<string, []> var_323_pad_type_0 = const()[name = tensor<string, []>("op_323_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> var_323_strides_0 = const()[name = tensor<string, []>("op_323_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> var_323_pad_0 = const()[name = tensor<string, []>("op_323_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> var_323_dilations_0 = const()[name = tensor<string, []>("op_323_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> var_323_groups_0 = const()[name = tensor<string, []>("op_323_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 512]> var_323 = conv(dilations = var_323_dilations_0, groups = var_323_groups_0, pad = var_323_pad_0, pad_type = var_323_pad_type_0, strides = var_323_strides_0, weight = weight_23, x = input_37)[name = tensor<string, []>("op_323")];
            tensor<fp32, [1, 1024, 512]> var_324 = add(x = out_5, y = var_323)[name = tensor<string, []>("op_324")];
            tensor<fp32, []> _inversed_x_5_y_0 = const()[name = tensor<string, []>("_inversed_x_5_y_0"), val = tensor<fp32, []>(0x1.6a09e6p-1)];
            tensor<fp32, [1, 1024, 512]> _inversed_x_5 = mul(x = var_324, y = _inversed_x_5_y_0)[name = tensor<string, []>("_inversed_x_5")];
            tensor<int32, []> var_328 = const()[name = tensor<string, []>("op_328"), val = tensor<int32, []>(1)];
            tensor<bool, []> input_53_interleave_0 = const()[name = tensor<string, []>("input_53_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1090, 512]> input_53 = concat(axis = var_328, interleave = input_53_interleave_0, values = (_inversed_x_5, asr_res_1, F0, Nc))[name = tensor<string, []>("input_53")];
            tensor<fp32, []> var_332 = const()[name = tensor<string, []>("op_332"), val = tensor<fp32, []>(0x1.99999ap-3)];
            tensor<fp32, []> var_335 = const()[name = tensor<string, []>("op_335"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 2180]> h_25 = linear(bias = decode_2_norm1_fc_bias, weight = decode_2_norm1_fc_weight, x = s)[name = tensor<string, []>("linear_6")];
            tensor<int32, [3]> var_361 = const()[name = tensor<string, []>("op_361"), val = tensor<int32, [3]>([1, 2180, 1])];
            tensor<fp32, [1, 2180, 1]> h_27 = reshape(shape = var_361, x = h_25)[name = tensor<string, []>("h_27")];
            tensor<int32, [2]> var_363_split_sizes_0 = const()[name = tensor<string, []>("op_363_split_sizes_0"), val = tensor<int32, [2]>([1090, 1090])];
            tensor<int32, []> var_363_axis_0 = const()[name = tensor<string, []>("op_363_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1090, 1]> var_363_0, tensor<fp32, [1, 1090, 1]> var_363_1 = split(axis = var_363_axis_0, split_sizes = var_363_split_sizes_0, x = h_27)[name = tensor<string, []>("op_363")];
            tensor<fp32, []> var_365_promoted = const()[name = tensor<string, []>("op_365_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 1090, 1]> var_366 = add(x = var_363_0, y = var_365_promoted)[name = tensor<string, []>("op_366")];
            tensor<fp32, [1, 1090, 512]> var_367 = instance_norm(epsilon = var_335, x = input_53)[name = tensor<string, []>("op_367")];
            tensor<fp32, [1, 1090, 512]> var_368 = mul(x = var_366, y = var_367)[name = tensor<string, []>("op_368")];
            tensor<fp32, [1, 1090, 512]> input_55 = add(x = var_368, y = var_363_1)[name = tensor<string, []>("input_55")];
            tensor<fp32, [1, 1090, 512]> input_57 = leaky_relu(alpha = var_332, x = input_55)[name = tensor<string, []>("input_57")];
            tensor<fp32, [1024, 1090, 3]> weight_25 = const()[name = tensor<string, []>("weight_25"), val = tensor<fp32, [1024, 1090, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(103931264)))];
            tensor<string, []> input_61_pad_type_0 = const()[name = tensor<string, []>("input_61_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_61_pad_0 = const()[name = tensor<string, []>("input_61_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> input_61_strides_0 = const()[name = tensor<string, []>("input_61_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_61_dilations_0 = const()[name = tensor<string, []>("input_61_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_61_groups_0 = const()[name = tensor<string, []>("input_61_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 512]> input_61 = conv(bias = decode_2_conv1_bias, 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 = weight_25, x = input_57)[name = tensor<string, []>("input_61")];
            tensor<fp32, [1, 2048]> h_29 = linear(bias = decode_2_norm2_fc_bias, weight = decode_2_norm2_fc_weight, x = s)[name = tensor<string, []>("linear_7")];
            tensor<int32, [3]> var_385 = const()[name = tensor<string, []>("op_385"), val = tensor<int32, [3]>([1, 2048, 1])];
            tensor<fp32, [1, 2048, 1]> h_31 = reshape(shape = var_385, x = h_29)[name = tensor<string, []>("h_31")];
            tensor<int32, [2]> var_387_split_sizes_0 = const()[name = tensor<string, []>("op_387_split_sizes_0"), val = tensor<int32, [2]>([1024, 1024])];
            tensor<int32, []> var_387_axis_0 = const()[name = tensor<string, []>("op_387_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 1]> var_387_0, tensor<fp32, [1, 1024, 1]> var_387_1 = split(axis = var_387_axis_0, split_sizes = var_387_split_sizes_0, x = h_31)[name = tensor<string, []>("op_387")];
            tensor<fp32, []> var_389_promoted = const()[name = tensor<string, []>("op_389_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 1024, 1]> var_390 = add(x = var_387_0, y = var_389_promoted)[name = tensor<string, []>("op_390")];
            tensor<fp32, [1, 1024, 512]> var_391 = instance_norm(epsilon = var_335, x = input_61)[name = tensor<string, []>("op_391")];
            tensor<fp32, [1, 1024, 512]> var_392 = mul(x = var_390, y = var_391)[name = tensor<string, []>("op_392")];
            tensor<fp32, [1, 1024, 512]> input_63 = add(x = var_392, y = var_387_1)[name = tensor<string, []>("input_63")];
            tensor<fp32, [1, 1024, 512]> input_65 = leaky_relu(alpha = var_332, x = input_63)[name = tensor<string, []>("input_65")];
            tensor<fp32, [1024, 1024, 3]> weight_27 = const()[name = tensor<string, []>("weight_27"), val = tensor<fp32, [1024, 1024, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(117325248)))];
            tensor<string, []> out_7_pad_type_0 = const()[name = tensor<string, []>("out_7_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> out_7_pad_0 = const()[name = tensor<string, []>("out_7_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> out_7_strides_0 = const()[name = tensor<string, []>("out_7_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> out_7_dilations_0 = const()[name = tensor<string, []>("out_7_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> out_7_groups_0 = const()[name = tensor<string, []>("out_7_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 512]> out_7 = conv(bias = decode_2_conv2_bias, dilations = out_7_dilations_0, groups = out_7_groups_0, pad = out_7_pad_0, pad_type = out_7_pad_type_0, strides = out_7_strides_0, weight = weight_27, x = input_65)[name = tensor<string, []>("out_7")];
            tensor<fp32, [1024, 1090, 1]> weight_29 = const()[name = tensor<string, []>("weight_29"), val = tensor<fp32, [1024, 1090, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(129908224)))];
            tensor<string, []> var_408_pad_type_0 = const()[name = tensor<string, []>("op_408_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> var_408_strides_0 = const()[name = tensor<string, []>("op_408_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> var_408_pad_0 = const()[name = tensor<string, []>("op_408_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> var_408_dilations_0 = const()[name = tensor<string, []>("op_408_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> var_408_groups_0 = const()[name = tensor<string, []>("op_408_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1024, 512]> var_408 = conv(dilations = var_408_dilations_0, groups = var_408_groups_0, pad = var_408_pad_0, pad_type = var_408_pad_type_0, strides = var_408_strides_0, weight = weight_29, x = input_53)[name = tensor<string, []>("op_408")];
            tensor<fp32, [1, 1024, 512]> var_409 = add(x = out_7, y = var_408)[name = tensor<string, []>("op_409")];
            tensor<fp32, []> _inversed_x_7_y_0 = const()[name = tensor<string, []>("_inversed_x_7_y_0"), val = tensor<fp32, []>(0x1.6a09e6p-1)];
            tensor<fp32, [1, 1024, 512]> _inversed_x_7 = mul(x = var_409, y = _inversed_x_7_y_0)[name = tensor<string, []>("_inversed_x_7")];
            tensor<int32, []> var_413 = const()[name = tensor<string, []>("op_413"), val = tensor<int32, []>(1)];
            tensor<bool, []> input_69_interleave_0 = const()[name = tensor<string, []>("input_69_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1090, 512]> input_69 = concat(axis = var_413, interleave = input_69_interleave_0, values = (_inversed_x_7, asr_res_1, F0, Nc))[name = tensor<string, []>("input_69")];
            tensor<fp32, []> var_419 = const()[name = tensor<string, []>("op_419"), val = tensor<fp32, []>(0x1.99999ap-3)];
            tensor<fp32, []> var_423 = const()[name = tensor<string, []>("op_423"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<fp32, [1, 2180]> h_33 = linear(bias = decode_3_norm1_fc_bias, weight = decode_3_norm1_fc_weight, x = s)[name = tensor<string, []>("linear_8")];
            tensor<int32, [3]> var_454 = const()[name = tensor<string, []>("op_454"), val = tensor<int32, [3]>([1, 2180, 1])];
            tensor<fp32, [1, 2180, 1]> h_35 = reshape(shape = var_454, x = h_33)[name = tensor<string, []>("h_35")];
            tensor<int32, [2]> var_456_split_sizes_0 = const()[name = tensor<string, []>("op_456_split_sizes_0"), val = tensor<int32, [2]>([1090, 1090])];
            tensor<int32, []> var_456_axis_0 = const()[name = tensor<string, []>("op_456_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1090, 1]> var_456_0, tensor<fp32, [1, 1090, 1]> var_456_1 = split(axis = var_456_axis_0, split_sizes = var_456_split_sizes_0, x = h_35)[name = tensor<string, []>("op_456")];
            tensor<fp32, []> var_458_promoted = const()[name = tensor<string, []>("op_458_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 1090, 1]> var_459 = add(x = var_456_0, y = var_458_promoted)[name = tensor<string, []>("op_459")];
            tensor<fp32, [1, 1090, 512]> var_460 = instance_norm(epsilon = var_423, x = input_69)[name = tensor<string, []>("op_460")];
            tensor<fp32, [1, 1090, 512]> var_461 = mul(x = var_459, y = var_460)[name = tensor<string, []>("op_461")];
            tensor<fp32, [1, 1090, 512]> input_71 = add(x = var_461, y = var_456_1)[name = tensor<string, []>("input_71")];
            tensor<fp32, [1, 1090, 512]> input_73 = leaky_relu(alpha = var_419, x = input_71)[name = tensor<string, []>("input_73")];
            tensor<fp32, [1090, 1, 3]> var_464 = const()[name = tensor<string, []>("op_464"), val = tensor<fp32, [1090, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(134372928)))];
            tensor<string, []> conv_transpose_0_pad_type_0 = const()[name = tensor<string, []>("conv_transpose_0_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> conv_transpose_0_pad_0 = const()[name = tensor<string, []>("conv_transpose_0_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_transpose_0_strides_0 = const()[name = tensor<string, []>("conv_transpose_0_strides_0"), val = tensor<int32, [1]>([2])];
            tensor<int32, []> conv_transpose_0_groups_0 = const()[name = tensor<string, []>("conv_transpose_0_groups_0"), val = tensor<int32, []>(1090)];
            tensor<int32, [1]> conv_transpose_0_dilations_0 = const()[name = tensor<string, []>("conv_transpose_0_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [3]> conv_transpose_0_has_output_shape_output_shape_0 = const()[name = tensor<string, []>("conv_transpose_0_has_output_shape_output_shape_0"), val = tensor<int32, [3]>([1, 1090, 1025])];
            tensor<fp32, [1, 1090, 1025]> conv_transpose_0_has_output_shape = conv_transpose(bias = decode_3_pool_bias, dilations = conv_transpose_0_dilations_0, groups = conv_transpose_0_groups_0, output_shape = conv_transpose_0_has_output_shape_output_shape_0, pad = conv_transpose_0_pad_0, pad_type = conv_transpose_0_pad_type_0, strides = conv_transpose_0_strides_0, weight = var_464, x = input_73)[name = tensor<string, []>("conv_transpose_0_has_output_shape")];
            tensor<int32, [3]> input_75_begin_0 = const()[name = tensor<string, []>("input_75_begin_0"), val = tensor<int32, [3]>([0, 0, 1])];
            tensor<int32, [3]> input_75_end_0 = const()[name = tensor<string, []>("input_75_end_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<bool, [3]> input_75_begin_mask_0 = const()[name = tensor<string, []>("input_75_begin_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<bool, [3]> input_75_end_mask_0 = const()[name = tensor<string, []>("input_75_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1090, 1024]> input_75 = slice_by_index(begin = input_75_begin_0, begin_mask = input_75_begin_mask_0, end = input_75_end_0, end_mask = input_75_end_mask_0, x = conv_transpose_0_has_output_shape)[name = tensor<string, []>("input_75")];
            tensor<fp32, [512, 1090, 3]> weight_31 = const()[name = tensor<string, []>("weight_31"), val = tensor<fp32, [512, 1090, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(134386112)))];
            tensor<string, []> input_79_pad_type_0 = const()[name = tensor<string, []>("input_79_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_79_pad_0 = const()[name = tensor<string, []>("input_79_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> input_79_strides_0 = const()[name = tensor<string, []>("input_79_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_79_dilations_0 = const()[name = tensor<string, []>("input_79_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_79_groups_0 = const()[name = tensor<string, []>("input_79_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 512, 1024]> input_79 = conv(bias = decode_3_conv1_bias, dilations = input_79_dilations_0, groups = input_79_groups_0, pad = input_79_pad_0, pad_type = input_79_pad_type_0, strides = input_79_strides_0, weight = weight_31, x = input_75)[name = tensor<string, []>("input_79")];
            tensor<fp32, [1, 1024]> h_37 = linear(bias = decode_3_norm2_fc_bias, weight = decode_3_norm2_fc_weight, x = s)[name = tensor<string, []>("linear_9")];
            tensor<int32, [3]> var_485 = const()[name = tensor<string, []>("op_485"), val = tensor<int32, [3]>([1, 1024, 1])];
            tensor<fp32, [1, 1024, 1]> h_39 = reshape(shape = var_485, x = h_37)[name = tensor<string, []>("h_39")];
            tensor<int32, [2]> var_487_split_sizes_0 = const()[name = tensor<string, []>("op_487_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
            tensor<int32, []> var_487_axis_0 = const()[name = tensor<string, []>("op_487_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 512, 1]> var_487_0, tensor<fp32, [1, 512, 1]> var_487_1 = split(axis = var_487_axis_0, split_sizes = var_487_split_sizes_0, x = h_39)[name = tensor<string, []>("op_487")];
            tensor<fp32, []> var_489_promoted = const()[name = tensor<string, []>("op_489_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 512, 1]> var_490 = add(x = var_487_0, y = var_489_promoted)[name = tensor<string, []>("op_490")];
            tensor<fp32, [1, 512, 1024]> var_491 = instance_norm(epsilon = var_423, x = input_79)[name = tensor<string, []>("op_491")];
            tensor<fp32, [1, 512, 1024]> var_492 = mul(x = var_490, y = var_491)[name = tensor<string, []>("op_492")];
            tensor<fp32, [1, 512, 1024]> input_81 = add(x = var_492, y = var_487_1)[name = tensor<string, []>("input_81")];
            tensor<fp32, [1, 512, 1024]> input_83 = leaky_relu(alpha = var_419, x = input_81)[name = tensor<string, []>("input_83")];
            tensor<fp32, [512, 512, 3]> weight_33 = const()[name = tensor<string, []>("weight_33"), val = tensor<fp32, [512, 512, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(141083136)))];
            tensor<string, []> out_pad_type_0 = const()[name = tensor<string, []>("out_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> out_pad_0 = const()[name = tensor<string, []>("out_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> out_strides_0 = const()[name = tensor<string, []>("out_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> out_dilations_0 = const()[name = tensor<string, []>("out_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> out_groups_0 = const()[name = tensor<string, []>("out_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 512, 1024]> out = conv(bias = decode_3_conv2_bias, dilations = out_dilations_0, groups = out_groups_0, pad = out_pad_0, pad_type = out_pad_type_0, strides = out_strides_0, weight = weight_33, x = input_83)[name = tensor<string, []>("out")];
            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, 1090, 512, 1]> expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = input_69)[name = tensor<string, []>("expand_dims_0")];
            tensor<int32, []> upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = tensor<string, []>("upsample_nearest_neighbor_0_scale_factor_height_0"), val = tensor<int32, []>(2)];
            tensor<int32, []> upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = tensor<string, []>("upsample_nearest_neighbor_0_scale_factor_width_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1090, 1024, 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]> input_87_axes_0 = const()[name = tensor<string, []>("input_87_axes_0"), val = tensor<int32, [1]>([3])];
            tensor<fp32, [1, 1090, 1024]> input_87 = squeeze(axes = input_87_axes_0, x = upsample_nearest_neighbor_0)[name = tensor<string, []>("input_87")];
            tensor<fp32, [512, 1090, 1]> weight_35 = const()[name = tensor<string, []>("weight_35"), val = tensor<fp32, [512, 1090, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(144228928)))];
            tensor<string, []> var_510_pad_type_0 = const()[name = tensor<string, []>("op_510_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> var_510_strides_0 = const()[name = tensor<string, []>("op_510_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> var_510_pad_0 = const()[name = tensor<string, []>("op_510_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> var_510_dilations_0 = const()[name = tensor<string, []>("op_510_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> var_510_groups_0 = const()[name = tensor<string, []>("op_510_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 512, 1024]> var_510 = conv(dilations = var_510_dilations_0, groups = var_510_groups_0, pad = var_510_pad_0, pad_type = var_510_pad_type_0, strides = var_510_strides_0, weight = weight_35, x = input_87)[name = tensor<string, []>("op_510")];
            tensor<fp32, [1, 512, 1024]> var_511 = add(x = out, y = var_510)[name = tensor<string, []>("op_511")];
            tensor<fp32, []> _inversed_x_9_y_0 = const()[name = tensor<string, []>("_inversed_x_9_y_0"), val = tensor<fp32, []>(0x1.6a09e6p-1)];
            tensor<fp32, [1, 512, 1024]> _inversed_x_9 = mul(x = var_511, y = _inversed_x_9_y_0)[name = tensor<string, []>("_inversed_x_9")];
            tensor<fp32, []> var_516 = const()[name = tensor<string, []>("op_516"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
            tensor<int32, []> var_535 = const()[name = tensor<string, []>("op_535"), val = tensor<int32, []>(1)];
            tensor<int32, [1]> expand_dims_1_axes_0 = const()[name = tensor<string, []>("expand_dims_1_axes_0"), val = tensor<int32, [1]>([3])];
            tensor<fp32, [1, 1, 1024, 1]> expand_dims_1 = expand_dims(axes = expand_dims_1_axes_0, x = input_1)[name = tensor<string, []>("expand_dims_1")];
            tensor<int32, []> upsample_nearest_neighbor_1_scale_factor_height_0 = const()[name = tensor<string, []>("upsample_nearest_neighbor_1_scale_factor_height_0"), val = tensor<int32, []>(300)];
            tensor<int32, []> upsample_nearest_neighbor_1_scale_factor_width_0 = const()[name = tensor<string, []>("upsample_nearest_neighbor_1_scale_factor_width_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1, 307200, 1]> upsample_nearest_neighbor_1 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_1_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_1_scale_factor_width_0, x = expand_dims_1)[name = tensor<string, []>("upsample_nearest_neighbor_1")];
            tensor<int32, [1]> var_629_axes_0 = const()[name = tensor<string, []>("op_629_axes_0"), val = tensor<int32, [1]>([3])];
            tensor<fp32, [1, 1, 307200]> var_629 = squeeze(axes = var_629_axes_0, x = upsample_nearest_neighbor_1)[name = tensor<string, []>("op_629")];
            tensor<int32, [3]> f0_perm_0 = const()[name = tensor<string, []>("f0_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [1, 1, 9]> const_21 = const()[name = tensor<string, []>("const_21"), val = tensor<fp32, [1, 1, 9]>([[[0x1p+0, 0x1p+1, 0x1.8p+1, 0x1p+2, 0x1.4p+2, 0x1.8p+2, 0x1.cp+2, 0x1p+3, 0x1.2p+3]]])];
            tensor<fp32, [1, 307200, 1]> f0 = transpose(perm = f0_perm_0, x = var_629)[name = tensor<string, []>("transpose_11")];
            tensor<fp32, [1, 307200, 9]> f0_values = mul(x = f0, y = const_21)[name = tensor<string, []>("f0_values")];
            tensor<fp32, []> _inversed_rad_div_y_0 = const()[name = tensor<string, []>("_inversed_rad_div_y_0"), val = tensor<fp32, []>(0x1.5d867cp-15)];
            tensor<fp32, [1, 307200, 9]> _inversed_rad_div = mul(x = f0_values, y = _inversed_rad_div_y_0)[name = tensor<string, []>("_inversed_rad_div")];
            tensor<fp32, [1, 307200, 9]> var_637 = floor(x = _inversed_rad_div)[name = tensor<string, []>("op_637")];
            tensor<fp32, [1, 307200, 9]> rad_mod = sub(x = _inversed_rad_div, y = var_637)[name = tensor<string, []>("rad_mod")];
            tensor<int32, [3]> var_640_begin_0 = const()[name = tensor<string, []>("op_640_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> var_640_end_0 = const()[name = tensor<string, []>("op_640_end_0"), val = tensor<int32, [3]>([1, 307200, 9])];
            tensor<bool, [3]> var_640_end_mask_0 = const()[name = tensor<string, []>("op_640_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<int32, [3]> var_640_stride_0 = const()[name = tensor<string, []>("op_640_stride_0"), val = tensor<int32, [3]>([1, 300, 1])];
            tensor<fp32, [1, 1024, 9]> var_640 = slice_by_index(begin = var_640_begin_0, end = var_640_end_0, end_mask = var_640_end_mask_0, stride = var_640_stride_0, x = rad_mod)[name = tensor<string, []>("op_640")];
            tensor<bool, []> var_642_exclusive_0 = const()[name = tensor<string, []>("op_642_exclusive_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> var_642_reverse_0 = const()[name = tensor<string, []>("op_642_reverse_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1024, 9]> var_642 = cumsum(axis = var_535, exclusive = var_642_exclusive_0, reverse = var_642_reverse_0, x = var_640)[name = tensor<string, []>("op_642")];
            tensor<fp32, []> var_643_promoted = const()[name = tensor<string, []>("op_643_promoted"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 1024, 9]> var_644 = mul(x = var_642, y = var_643_promoted)[name = tensor<string, []>("op_644")];
            tensor<fp32, []> var_645 = const()[name = tensor<string, []>("op_645"), val = tensor<fp32, []>(0x1.921fb6p+1)];
            tensor<fp32, [1, 1024, 9]> phase = mul(x = var_644, y = var_645)[name = tensor<string, []>("phase")];
            tensor<fp32, []> var_647_promoted = const()[name = tensor<string, []>("op_647_promoted"), val = tensor<fp32, []>(0x1.2cp+8)];
            tensor<fp32, [1, 1024, 9]> phase_scaled = mul(x = phase, y = var_647_promoted)[name = tensor<string, []>("phase_scaled")];
            tensor<int32, [3]> transpose_0_perm_0 = const()[name = tensor<string, []>("transpose_0_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
            tensor<int32, [3]> tile_0_reps_0 = const()[name = tensor<string, []>("tile_0_reps_0"), val = tensor<int32, [3]>([300, 1, 1])];
            tensor<fp32, [1024, 1, 9]> transpose_0 = transpose(perm = transpose_0_perm_0, x = phase_scaled)[name = tensor<string, []>("transpose_10")];
            tensor<fp32, [307200, 1, 9]> tile_0 = tile(reps = tile_0_reps_0, x = transpose_0)[name = tensor<string, []>("tile_0")];
            tensor<int32, [4]> concat_0 = const()[name = tensor<string, []>("concat_0"), val = tensor<int32, [4]>([300, 1024, 1, 9])];
            tensor<fp32, [300, 1024, 1, 9]> reshape_0 = reshape(shape = concat_0, x = tile_0)[name = tensor<string, []>("reshape_0")];
            tensor<int32, [4]> transpose_1_perm_0 = const()[name = tensor<string, []>("transpose_1_perm_0"), val = tensor<int32, [4]>([1, 0, 2, 3])];
            tensor<int32, [3]> concat_1 = const()[name = tensor<string, []>("concat_1"), val = tensor<int32, [3]>([-1, 1, 9])];
            tensor<fp32, [1024, 300, 1, 9]> transpose_1 = transpose(perm = transpose_1_perm_0, x = reshape_0)[name = tensor<string, []>("transpose_9")];
            tensor<fp32, [307200, 1, 9]> reshape_1 = reshape(shape = concat_1, x = transpose_1)[name = tensor<string, []>("reshape_1")];
            tensor<int32, [3]> left_perm_0 = const()[name = tensor<string, []>("left_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
            tensor<int32, [3]> var_651_begin_0 = const()[name = tensor<string, []>("op_651_begin_0"), val = tensor<int32, [3]>([0, -1, 0])];
            tensor<int32, [3]> var_651_end_0 = const()[name = tensor<string, []>("op_651_end_0"), val = tensor<int32, [3]>([1, 1024, 9])];
            tensor<bool, [3]> var_651_end_mask_0 = const()[name = tensor<string, []>("op_651_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1, 9]> var_651 = slice_by_index(begin = var_651_begin_0, end = var_651_end_0, end_mask = var_651_end_mask_0, x = phase_scaled)[name = tensor<string, []>("op_651")];
            tensor<int32, [3]> var_654_begin_0 = const()[name = tensor<string, []>("op_654_begin_0"), val = tensor<int32, [3]>([0, 1, 0])];
            tensor<int32, [3]> var_654_end_0 = const()[name = tensor<string, []>("op_654_end_0"), val = tensor<int32, [3]>([1, 1024, 9])];
            tensor<bool, [3]> var_654_end_mask_0 = const()[name = tensor<string, []>("op_654_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp32, [1, 1023, 9]> var_654 = slice_by_index(begin = var_654_begin_0, end = var_654_end_0, end_mask = var_654_end_mask_0, x = phase_scaled)[name = tensor<string, []>("op_654")];
            tensor<bool, []> shifted_interleave_0 = const()[name = tensor<string, []>("shifted_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp32, [1, 1024, 9]> shifted = concat(axis = var_535, interleave = shifted_interleave_0, values = (var_654, var_651))[name = tensor<string, []>("shifted")];
            tensor<int32, [3]> transpose_2_perm_0 = const()[name = tensor<string, []>("transpose_2_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
            tensor<int32, [3]> tile_1_reps_0 = const()[name = tensor<string, []>("tile_1_reps_0"), val = tensor<int32, [3]>([300, 1, 1])];
            tensor<fp32, [1024, 1, 9]> transpose_2 = transpose(perm = transpose_2_perm_0, x = shifted)[name = tensor<string, []>("transpose_7")];
            tensor<fp32, [307200, 1, 9]> tile_1 = tile(reps = tile_1_reps_0, x = transpose_2)[name = tensor<string, []>("tile_1")];
            tensor<int32, [4]> concat_2 = const()[name = tensor<string, []>("concat_2"), val = tensor<int32, [4]>([300, 1024, 1, 9])];
            tensor<fp32, [300, 1024, 1, 9]> reshape_2 = reshape(shape = concat_2, x = tile_1)[name = tensor<string, []>("reshape_2")];
            tensor<int32, [4]> transpose_3_perm_0 = const()[name = tensor<string, []>("transpose_3_perm_0"), val = tensor<int32, [4]>([1, 0, 2, 3])];
            tensor<int32, [3]> concat_3 = const()[name = tensor<string, []>("concat_3"), val = tensor<int32, [3]>([-1, 1, 9])];
            tensor<fp32, [1024, 300, 1, 9]> transpose_3 = transpose(perm = transpose_3_perm_0, x = reshape_2)[name = tensor<string, []>("transpose_6")];
            tensor<fp32, [307200, 1, 9]> reshape_3 = reshape(shape = concat_3, x = transpose_3)[name = tensor<string, []>("reshape_3")];
            tensor<int32, [3]> right_perm_0 = const()[name = tensor<string, []>("right_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
            tensor<fp32, [1, 307200, 1]> const_22 = const()[name = tensor<string, []>("const_22"), val = tensor<fp32, [1, 307200, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(146461312)))];
            tensor<fp32, [1, 307200, 1]> var_660 = const()[name = tensor<string, []>("op_660"), val = tensor<fp32, [1, 307200, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(147690176)))];
            tensor<fp32, [1, 307200, 9]> left = transpose(perm = left_perm_0, x = reshape_1)[name = tensor<string, []>("transpose_8")];
            tensor<fp32, [1, 307200, 9]> var_661 = mul(x = left, y = var_660)[name = tensor<string, []>("op_661")];
            tensor<fp32, [1, 307200, 9]> right = transpose(perm = right_perm_0, x = reshape_3)[name = tensor<string, []>("transpose_5")];
            tensor<fp32, [1, 307200, 9]> var_662 = mul(x = right, y = const_22)[name = tensor<string, []>("op_662")];
            tensor<fp32, [1, 307200, 9]> phase_up = add(x = var_661, y = var_662)[name = tensor<string, []>("phase_up")];
            tensor<fp32, [1, 307200, 9]> sines = sin(x = phase_up)[name = tensor<string, []>("sines")];
            tensor<fp32, []> var_665 = const()[name = tensor<string, []>("op_665"), val = tensor<fp32, []>(0x1.99999ap-4)];
            tensor<fp32, [1, 307200, 9]> sine_waves = mul(x = sines, y = var_665)[name = tensor<string, []>("sine_waves")];
            tensor<fp32, []> var_523_promoted = const()[name = tensor<string, []>("op_523_promoted"), val = tensor<fp32, []>(0x1.4p+3)];
            tensor<bool, [1, 307200, 1]> var_667 = greater(x = f0, y = var_523_promoted)[name = tensor<string, []>("op_667")];
            tensor<string, []> cast_22_dtype_0 = const()[name = tensor<string, []>("cast_22_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<fp32, []> var_669 = const()[name = tensor<string, []>("op_669"), val = tensor<fp32, []>(0x1.89374cp-9)];
            tensor<fp32, [1, 307200, 1]> cast_22 = cast(dtype = cast_22_dtype_0, x = var_667)[name = tensor<string, []>("cast_129")];
            tensor<fp32, [1, 307200, 1]> var_670 = mul(x = cast_22, y = var_669)[name = tensor<string, []>("op_670")];
            tensor<fp32, []> var_535_promoted = const()[name = tensor<string, []>("op_535_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 307200, 1]> var_671 = sub(x = var_535_promoted, y = cast_22)[name = tensor<string, []>("op_671")];
            tensor<fp32, []> var_672 = const()[name = tensor<string, []>("op_672"), val = tensor<fp32, []>(0x1.99999ap-4)];
            tensor<fp32, [1, 307200, 1]> var_673 = mul(x = var_671, y = var_672)[name = tensor<string, []>("op_673")];
            tensor<fp32, []> _inversed_675_y_0 = const()[name = tensor<string, []>("_inversed_675_y_0"), val = tensor<fp32, []>(0x1.555556p-2)];
            tensor<fp32, [1, 307200, 1]> _inversed_675 = mul(x = var_673, y = _inversed_675_y_0)[name = tensor<string, []>("_inversed_675")];
            tensor<fp32, [1, 307200, 1]> noise_amp = add(x = var_670, y = _inversed_675)[name = tensor<string, []>("noise_amp")];
            tensor<fp32, []> var_677 = const()[name = tensor<string, []>("op_677"), val = tensor<fp32, []>(0x1.9p+6)];
            tensor<fp32, [1, 307200, 9]> var_678 = mul(x = f0_values, y = var_677)[name = tensor<string, []>("op_678")];
            tensor<fp32, [1, 307200, 9]> var_679 = sin(x = var_678)[name = tensor<string, []>("op_679")];
            tensor<fp32, [1, 307200, 9]> noise = mul(x = noise_amp, y = var_679)[name = tensor<string, []>("noise")];
            tensor<fp32, [1, 307200, 9]> var_681 = mul(x = sine_waves, y = cast_22)[name = tensor<string, []>("op_681")];
            tensor<fp32, [1, 307200, 9]> input_91 = add(x = var_681, y = noise)[name = tensor<string, []>("input_91")];
            tensor<fp32, [1, 307200, 1]> input_93 = linear(bias = generator_m_source_l_linear_bias, weight = generator_m_source_l_linear_weight, x = input_91)[name = tensor<string, []>("linear_10")];
            tensor<fp32, [1, 307200, 1]> har_source = tanh(x = input_93)[name = tensor<string, []>("har_source")];
            tensor<int32, [3]> input_95_perm_0 = const()[name = tensor<string, []>("input_95_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [1, 512, 1]> var_691 = const()[name = tensor<string, []>("op_691"), val = tensor<fp32, [1, 512, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(148919040)))];
            tensor<fp32, [1, 512, 1024]> var_694 = mul(x = generator_alphas_0, y = _inversed_x_9)[name = tensor<string, []>("op_694")];
            tensor<fp32, [1, 512, 1024]> var_695 = sin(x = var_694)[name = tensor<string, []>("op_695")];
            tensor<fp32, []> var_532_promoted = const()[name = tensor<string, []>("op_532_promoted"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 512, 1024]> var_696 = pow(x = var_695, y = var_532_promoted)[name = tensor<string, []>("op_696")];
            tensor<fp32, [1, 512, 1024]> var_697 = mul(x = var_691, y = var_696)[name = tensor<string, []>("op_697")];
            tensor<fp32, [1, 512, 1024]> input_121 = add(x = _inversed_x_9, y = var_697)[name = tensor<string, []>("input_121")];
            tensor<string, []> input_97_pad_type_0 = const()[name = tensor<string, []>("input_97_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_97_pad_0 = const()[name = tensor<string, []>("input_97_pad_0"), val = tensor<int32, [2]>([15, 15])];
            tensor<int32, [1]> input_97_strides_0 = const()[name = tensor<string, []>("input_97_strides_0"), val = tensor<int32, [1]>([30])];
            tensor<int32, [1]> input_97_dilations_0 = const()[name = tensor<string, []>("input_97_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_97_groups_0 = const()[name = tensor<string, []>("input_97_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1, 307200]> input_95 = transpose(perm = input_95_perm_0, x = har_source)[name = tensor<string, []>("transpose_4")];
            tensor<fp32, [1, 256, 10240]> input_97 = conv(bias = generator_noise_convs_0_bias, 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 = generator_noise_convs_0_weight, x = input_95)[name = tensor<string, []>("input_97")];
            tensor<fp32, [1, 512]> h_41 = linear(bias = generator_noise_res_0_adain1_0_fc_bias, weight = generator_noise_res_0_adain1_0_fc_weight, x = s)[name = tensor<string, []>("linear_11")];
            tensor<int32, [3]> var_784 = const()[name = tensor<string, []>("op_784"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_43 = reshape(shape = var_784, x = h_41)[name = tensor<string, []>("h_43")];
            tensor<int32, [2]> var_786_split_sizes_0 = const()[name = tensor<string, []>("op_786_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_786_axis_0 = const()[name = tensor<string, []>("op_786_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_786_0, tensor<fp32, [1, 256, 1]> var_786_1 = split(axis = var_786_axis_0, split_sizes = var_786_split_sizes_0, x = h_43)[name = tensor<string, []>("op_786")];
            tensor<fp32, []> var_788_promoted = const()[name = tensor<string, []>("op_788_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_789 = add(x = var_786_0, y = var_788_promoted)[name = tensor<string, []>("op_789")];
            tensor<fp32, [1, 256, 10240]> var_790 = instance_norm(epsilon = var_516, x = input_97)[name = tensor<string, []>("op_790")];
            tensor<fp32, [1, 256, 10240]> var_791 = mul(x = var_789, y = var_790)[name = tensor<string, []>("op_791")];
            tensor<fp32, [1, 256, 10240]> xt_1 = add(x = var_791, y = var_786_1)[name = tensor<string, []>("xt_1")];
            tensor<fp32, [1, 256, 1]> var_793 = const()[name = tensor<string, []>("op_793"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(148921152)))];
            tensor<fp32, [1, 256, 10240]> var_796 = mul(x = generator_noise_res_0_alpha1_0, y = xt_1)[name = tensor<string, []>("op_796")];
            tensor<fp32, [1, 256, 10240]> var_797 = sin(x = var_796)[name = tensor<string, []>("op_797")];
            tensor<fp32, []> var_532_promoted_1 = const()[name = tensor<string, []>("op_532_promoted_1"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 10240]> var_798 = pow(x = var_797, y = var_532_promoted_1)[name = tensor<string, []>("op_798")];
            tensor<fp32, [1, 256, 10240]> var_799 = mul(x = var_793, y = var_798)[name = tensor<string, []>("op_799")];
            tensor<fp32, [1, 256, 10240]> input_99 = add(x = xt_1, y = var_799)[name = tensor<string, []>("input_99")];
            tensor<fp32, [256, 256, 7]> weight_39 = const()[name = tensor<string, []>("weight_39"), val = tensor<fp32, [256, 256, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(148922240)))];
            tensor<string, []> input_101_pad_type_0 = const()[name = tensor<string, []>("input_101_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_101_pad_0 = const()[name = tensor<string, []>("input_101_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> input_101_strides_0 = const()[name = tensor<string, []>("input_101_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_101_dilations_0 = const()[name = tensor<string, []>("input_101_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_101_groups_0 = const()[name = tensor<string, []>("input_101_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 10240]> input_101 = conv(bias = generator_noise_res_0_convs1_0_bias, dilations = input_101_dilations_0, groups = input_101_groups_0, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = input_101_strides_0, weight = weight_39, x = input_99)[name = tensor<string, []>("input_101")];
            tensor<fp32, [1, 512]> h_45 = linear(bias = generator_noise_res_0_adain2_0_fc_bias, weight = generator_noise_res_0_adain2_0_fc_weight, x = s)[name = tensor<string, []>("linear_12")];
            tensor<int32, [3]> var_814 = const()[name = tensor<string, []>("op_814"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_47 = reshape(shape = var_814, x = h_45)[name = tensor<string, []>("h_47")];
            tensor<int32, [2]> var_816_split_sizes_0 = const()[name = tensor<string, []>("op_816_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_816_axis_0 = const()[name = tensor<string, []>("op_816_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_816_0, tensor<fp32, [1, 256, 1]> var_816_1 = split(axis = var_816_axis_0, split_sizes = var_816_split_sizes_0, x = h_47)[name = tensor<string, []>("op_816")];
            tensor<fp32, []> var_818_promoted = const()[name = tensor<string, []>("op_818_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_819 = add(x = var_816_0, y = var_818_promoted)[name = tensor<string, []>("op_819")];
            tensor<fp32, [1, 256, 10240]> var_820 = instance_norm(epsilon = var_516, x = input_101)[name = tensor<string, []>("op_820")];
            tensor<fp32, [1, 256, 10240]> var_821 = mul(x = var_819, y = var_820)[name = tensor<string, []>("op_821")];
            tensor<fp32, [1, 256, 10240]> xt_3 = add(x = var_821, y = var_816_1)[name = tensor<string, []>("xt_3")];
            tensor<fp32, [1, 256, 1]> var_823 = const()[name = tensor<string, []>("op_823"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(150757312)))];
            tensor<fp32, [1, 256, 10240]> var_826 = mul(x = generator_noise_res_0_alpha2_0, y = xt_3)[name = tensor<string, []>("op_826")];
            tensor<fp32, [1, 256, 10240]> var_827 = sin(x = var_826)[name = tensor<string, []>("op_827")];
            tensor<fp32, []> var_532_promoted_2 = const()[name = tensor<string, []>("op_532_promoted_2"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 10240]> var_828 = pow(x = var_827, y = var_532_promoted_2)[name = tensor<string, []>("op_828")];
            tensor<fp32, [1, 256, 10240]> var_829 = mul(x = var_823, y = var_828)[name = tensor<string, []>("op_829")];
            tensor<fp32, [1, 256, 10240]> input_103 = add(x = xt_3, y = var_829)[name = tensor<string, []>("input_103")];
            tensor<fp32, [256, 256, 7]> weight_41 = const()[name = tensor<string, []>("weight_41"), val = tensor<fp32, [256, 256, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(150758400)))];
            tensor<string, []> xt_5_pad_type_0 = const()[name = tensor<string, []>("xt_5_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_5_pad_0 = const()[name = tensor<string, []>("xt_5_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> xt_5_strides_0 = const()[name = tensor<string, []>("xt_5_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_5_dilations_0 = const()[name = tensor<string, []>("xt_5_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_5_groups_0 = const()[name = tensor<string, []>("xt_5_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 10240]> xt_5 = conv(bias = generator_noise_res_0_convs2_0_bias, dilations = xt_5_dilations_0, groups = xt_5_groups_0, pad = xt_5_pad_0, pad_type = xt_5_pad_type_0, strides = xt_5_strides_0, weight = weight_41, x = input_103)[name = tensor<string, []>("xt_5")];
            tensor<fp32, [1, 256, 10240]> input_105 = add(x = xt_5, y = input_97)[name = tensor<string, []>("input_105")];
            tensor<fp32, [1, 512]> h_49 = linear(bias = generator_noise_res_0_adain1_1_fc_bias, weight = generator_noise_res_0_adain1_1_fc_weight, x = s)[name = tensor<string, []>("linear_13")];
            tensor<int32, [3]> var_845 = const()[name = tensor<string, []>("op_845"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_51 = reshape(shape = var_845, x = h_49)[name = tensor<string, []>("h_51")];
            tensor<int32, [2]> var_847_split_sizes_0 = const()[name = tensor<string, []>("op_847_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_847_axis_0 = const()[name = tensor<string, []>("op_847_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_847_0, tensor<fp32, [1, 256, 1]> var_847_1 = split(axis = var_847_axis_0, split_sizes = var_847_split_sizes_0, x = h_51)[name = tensor<string, []>("op_847")];
            tensor<fp32, []> var_849_promoted = const()[name = tensor<string, []>("op_849_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_850 = add(x = var_847_0, y = var_849_promoted)[name = tensor<string, []>("op_850")];
            tensor<fp32, [1, 256, 10240]> var_851 = instance_norm(epsilon = var_516, x = input_105)[name = tensor<string, []>("op_851")];
            tensor<fp32, [1, 256, 10240]> var_852 = mul(x = var_850, y = var_851)[name = tensor<string, []>("op_852")];
            tensor<fp32, [1, 256, 10240]> xt_7 = add(x = var_852, y = var_847_1)[name = tensor<string, []>("xt_7")];
            tensor<fp32, [1, 256, 1]> var_854 = const()[name = tensor<string, []>("op_854"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(152593472)))];
            tensor<fp32, [1, 256, 10240]> var_857 = mul(x = generator_noise_res_0_alpha1_1, y = xt_7)[name = tensor<string, []>("op_857")];
            tensor<fp32, [1, 256, 10240]> var_858 = sin(x = var_857)[name = tensor<string, []>("op_858")];
            tensor<fp32, []> var_532_promoted_3 = const()[name = tensor<string, []>("op_532_promoted_3"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 10240]> var_859 = pow(x = var_858, y = var_532_promoted_3)[name = tensor<string, []>("op_859")];
            tensor<fp32, [1, 256, 10240]> var_860 = mul(x = var_854, y = var_859)[name = tensor<string, []>("op_860")];
            tensor<fp32, [1, 256, 10240]> input_107 = add(x = xt_7, y = var_860)[name = tensor<string, []>("input_107")];
            tensor<fp32, [256, 256, 7]> weight_43 = const()[name = tensor<string, []>("weight_43"), val = tensor<fp32, [256, 256, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(152594560)))];
            tensor<string, []> input_109_pad_type_0 = const()[name = tensor<string, []>("input_109_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_109_pad_0 = const()[name = tensor<string, []>("input_109_pad_0"), val = tensor<int32, [2]>([9, 9])];
            tensor<int32, [1]> input_109_dilations_0 = const()[name = tensor<string, []>("input_109_dilations_0"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> input_109_strides_0 = const()[name = tensor<string, []>("input_109_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_109_groups_0 = const()[name = tensor<string, []>("input_109_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 10240]> input_109 = conv(bias = generator_noise_res_0_convs1_1_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 = weight_43, x = input_107)[name = tensor<string, []>("input_109")];
            tensor<fp32, [1, 512]> h_53 = linear(bias = generator_noise_res_0_adain2_1_fc_bias, weight = generator_noise_res_0_adain2_1_fc_weight, x = s)[name = tensor<string, []>("linear_14")];
            tensor<int32, [3]> var_875 = const()[name = tensor<string, []>("op_875"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_55 = reshape(shape = var_875, x = h_53)[name = tensor<string, []>("h_55")];
            tensor<int32, [2]> var_877_split_sizes_0 = const()[name = tensor<string, []>("op_877_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_877_axis_0 = const()[name = tensor<string, []>("op_877_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_877_0, tensor<fp32, [1, 256, 1]> var_877_1 = split(axis = var_877_axis_0, split_sizes = var_877_split_sizes_0, x = h_55)[name = tensor<string, []>("op_877")];
            tensor<fp32, []> var_879_promoted = const()[name = tensor<string, []>("op_879_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_880 = add(x = var_877_0, y = var_879_promoted)[name = tensor<string, []>("op_880")];
            tensor<fp32, [1, 256, 10240]> var_881 = instance_norm(epsilon = var_516, x = input_109)[name = tensor<string, []>("op_881")];
            tensor<fp32, [1, 256, 10240]> var_882 = mul(x = var_880, y = var_881)[name = tensor<string, []>("op_882")];
            tensor<fp32, [1, 256, 10240]> xt_9 = add(x = var_882, y = var_877_1)[name = tensor<string, []>("xt_9")];
            tensor<fp32, [1, 256, 1]> var_884 = const()[name = tensor<string, []>("op_884"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154429632)))];
            tensor<fp32, [1, 256, 10240]> var_887 = mul(x = generator_noise_res_0_alpha2_1, y = xt_9)[name = tensor<string, []>("op_887")];
            tensor<fp32, [1, 256, 10240]> var_888 = sin(x = var_887)[name = tensor<string, []>("op_888")];
            tensor<fp32, []> var_532_promoted_4 = const()[name = tensor<string, []>("op_532_promoted_4"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 10240]> var_889 = pow(x = var_888, y = var_532_promoted_4)[name = tensor<string, []>("op_889")];
            tensor<fp32, [1, 256, 10240]> var_890 = mul(x = var_884, y = var_889)[name = tensor<string, []>("op_890")];
            tensor<fp32, [1, 256, 10240]> input_111 = add(x = xt_9, y = var_890)[name = tensor<string, []>("input_111")];
            tensor<fp32, [256, 256, 7]> weight_45 = const()[name = tensor<string, []>("weight_45"), val = tensor<fp32, [256, 256, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154430720)))];
            tensor<string, []> xt_11_pad_type_0 = const()[name = tensor<string, []>("xt_11_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_11_pad_0 = const()[name = tensor<string, []>("xt_11_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> xt_11_strides_0 = const()[name = tensor<string, []>("xt_11_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_11_dilations_0 = const()[name = tensor<string, []>("xt_11_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_11_groups_0 = const()[name = tensor<string, []>("xt_11_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 10240]> xt_11 = conv(bias = generator_noise_res_0_convs2_1_bias, dilations = xt_11_dilations_0, groups = xt_11_groups_0, pad = xt_11_pad_0, pad_type = xt_11_pad_type_0, strides = xt_11_strides_0, weight = weight_45, x = input_111)[name = tensor<string, []>("xt_11")];
            tensor<fp32, [1, 256, 10240]> input_113 = add(x = xt_11, y = input_105)[name = tensor<string, []>("input_113")];
            tensor<fp32, [1, 512]> h_57 = linear(bias = generator_noise_res_0_adain1_2_fc_bias, weight = generator_noise_res_0_adain1_2_fc_weight, x = s)[name = tensor<string, []>("linear_15")];
            tensor<int32, [3]> var_906 = const()[name = tensor<string, []>("op_906"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_59 = reshape(shape = var_906, x = h_57)[name = tensor<string, []>("h_59")];
            tensor<int32, [2]> var_908_split_sizes_0 = const()[name = tensor<string, []>("op_908_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_908_axis_0 = const()[name = tensor<string, []>("op_908_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_908_0, tensor<fp32, [1, 256, 1]> var_908_1 = split(axis = var_908_axis_0, split_sizes = var_908_split_sizes_0, x = h_59)[name = tensor<string, []>("op_908")];
            tensor<fp32, []> var_910_promoted = const()[name = tensor<string, []>("op_910_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_911 = add(x = var_908_0, y = var_910_promoted)[name = tensor<string, []>("op_911")];
            tensor<fp32, [1, 256, 10240]> var_912 = instance_norm(epsilon = var_516, x = input_113)[name = tensor<string, []>("op_912")];
            tensor<fp32, [1, 256, 10240]> var_913 = mul(x = var_911, y = var_912)[name = tensor<string, []>("op_913")];
            tensor<fp32, [1, 256, 10240]> xt_13 = add(x = var_913, y = var_908_1)[name = tensor<string, []>("xt_13")];
            tensor<fp32, [1, 256, 1]> var_915 = const()[name = tensor<string, []>("op_915"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(156265792)))];
            tensor<fp32, [1, 256, 10240]> var_918 = mul(x = generator_noise_res_0_alpha1_2, y = xt_13)[name = tensor<string, []>("op_918")];
            tensor<fp32, [1, 256, 10240]> var_919 = sin(x = var_918)[name = tensor<string, []>("op_919")];
            tensor<fp32, []> var_532_promoted_5 = const()[name = tensor<string, []>("op_532_promoted_5"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 10240]> var_920 = pow(x = var_919, y = var_532_promoted_5)[name = tensor<string, []>("op_920")];
            tensor<fp32, [1, 256, 10240]> var_921 = mul(x = var_915, y = var_920)[name = tensor<string, []>("op_921")];
            tensor<fp32, [1, 256, 10240]> input_115 = add(x = xt_13, y = var_921)[name = tensor<string, []>("input_115")];
            tensor<fp32, [256, 256, 7]> weight_47 = const()[name = tensor<string, []>("weight_47"), val = tensor<fp32, [256, 256, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(156266880)))];
            tensor<string, []> input_117_pad_type_0 = const()[name = tensor<string, []>("input_117_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_117_pad_0 = const()[name = tensor<string, []>("input_117_pad_0"), val = tensor<int32, [2]>([15, 15])];
            tensor<int32, [1]> input_117_dilations_0 = const()[name = tensor<string, []>("input_117_dilations_0"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> input_117_strides_0 = const()[name = tensor<string, []>("input_117_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_117_groups_0 = const()[name = tensor<string, []>("input_117_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 10240]> input_117 = conv(bias = generator_noise_res_0_convs1_2_bias, 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 = weight_47, x = input_115)[name = tensor<string, []>("input_117")];
            tensor<fp32, [1, 512]> h_61 = linear(bias = generator_noise_res_0_adain2_2_fc_bias, weight = generator_noise_res_0_adain2_2_fc_weight, x = s)[name = tensor<string, []>("linear_16")];
            tensor<int32, [3]> var_936 = const()[name = tensor<string, []>("op_936"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_63 = reshape(shape = var_936, x = h_61)[name = tensor<string, []>("h_63")];
            tensor<int32, [2]> var_938_split_sizes_0 = const()[name = tensor<string, []>("op_938_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_938_axis_0 = const()[name = tensor<string, []>("op_938_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_938_0, tensor<fp32, [1, 256, 1]> var_938_1 = split(axis = var_938_axis_0, split_sizes = var_938_split_sizes_0, x = h_63)[name = tensor<string, []>("op_938")];
            tensor<fp32, []> var_940_promoted = const()[name = tensor<string, []>("op_940_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_941 = add(x = var_938_0, y = var_940_promoted)[name = tensor<string, []>("op_941")];
            tensor<fp32, [1, 256, 10240]> var_942 = instance_norm(epsilon = var_516, x = input_117)[name = tensor<string, []>("op_942")];
            tensor<fp32, [1, 256, 10240]> var_943 = mul(x = var_941, y = var_942)[name = tensor<string, []>("op_943")];
            tensor<fp32, [1, 256, 10240]> xt_15 = add(x = var_943, y = var_938_1)[name = tensor<string, []>("xt_15")];
            tensor<fp32, [1, 256, 1]> var_945 = const()[name = tensor<string, []>("op_945"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(158101952)))];
            tensor<fp32, [1, 256, 10240]> var_948 = mul(x = generator_noise_res_0_alpha2_2, y = xt_15)[name = tensor<string, []>("op_948")];
            tensor<fp32, [1, 256, 10240]> var_949 = sin(x = var_948)[name = tensor<string, []>("op_949")];
            tensor<fp32, []> var_532_promoted_6 = const()[name = tensor<string, []>("op_532_promoted_6"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 10240]> var_950 = pow(x = var_949, y = var_532_promoted_6)[name = tensor<string, []>("op_950")];
            tensor<fp32, [1, 256, 10240]> var_951 = mul(x = var_945, y = var_950)[name = tensor<string, []>("op_951")];
            tensor<fp32, [1, 256, 10240]> input_119 = add(x = xt_15, y = var_951)[name = tensor<string, []>("input_119")];
            tensor<fp32, [256, 256, 7]> weight_49 = const()[name = tensor<string, []>("weight_49"), val = tensor<fp32, [256, 256, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(158103040)))];
            tensor<string, []> xt_17_pad_type_0 = const()[name = tensor<string, []>("xt_17_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_17_pad_0 = const()[name = tensor<string, []>("xt_17_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> xt_17_strides_0 = const()[name = tensor<string, []>("xt_17_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_17_dilations_0 = const()[name = tensor<string, []>("xt_17_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_17_groups_0 = const()[name = tensor<string, []>("xt_17_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 10240]> xt_17 = conv(bias = generator_noise_res_0_convs2_2_bias, dilations = xt_17_dilations_0, groups = xt_17_groups_0, pad = xt_17_pad_0, pad_type = xt_17_pad_type_0, strides = xt_17_strides_0, weight = weight_49, x = input_119)[name = tensor<string, []>("xt_17")];
            tensor<fp32, [1, 256, 10240]> x_source_1 = add(x = xt_17, y = input_113)[name = tensor<string, []>("x_source_1")];
            tensor<fp32, [512, 256, 20]> var_961 = const()[name = tensor<string, []>("op_961"), val = tensor<fp32, [512, 256, 20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(159938112)))];
            tensor<string, []> x_11_pad_type_0 = const()[name = tensor<string, []>("x_11_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> x_11_pad_0 = const()[name = tensor<string, []>("x_11_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> x_11_strides_0 = const()[name = tensor<string, []>("x_11_strides_0"), val = tensor<int32, [1]>([10])];
            tensor<int32, [1]> x_11_dilations_0 = const()[name = tensor<string, []>("x_11_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_11_groups_0 = const()[name = tensor<string, []>("x_11_groups_0"), val = tensor<int32, []>(1)];
            tensor<int32, [3]> x_11_has_output_shape_output_shape_0 = const()[name = tensor<string, []>("x_11_has_output_shape_output_shape_0"), val = tensor<int32, [3]>([1, 256, 10240])];
            tensor<fp32, [1, 256, 10240]> x_11_has_output_shape = conv_transpose(bias = generator_ups_0_bias, dilations = x_11_dilations_0, groups = x_11_groups_0, output_shape = x_11_has_output_shape_output_shape_0, pad = x_11_pad_0, pad_type = x_11_pad_type_0, strides = x_11_strides_0, weight = var_961, x = input_121)[name = tensor<string, []>("x_11_has_output_shape")];
            tensor<fp32, [1, 256, 10240]> input_123 = add(x = x_11_has_output_shape, y = x_source_1)[name = tensor<string, []>("input_123")];
            tensor<fp32, [1, 512]> h_65 = linear(bias = generator_resblocks_0_adain1_0_fc_bias, weight = generator_resblocks_0_adain1_0_fc_weight, x = s)[name = tensor<string, []>("linear_17")];
            tensor<int32, [3]> var_1047 = const()[name = tensor<string, []>("op_1047"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_67 = reshape(shape = var_1047, x = h_65)[name = tensor<string, []>("h_67")];
            tensor<int32, [2]> var_1049_split_sizes_0 = const()[name = tensor<string, []>("op_1049_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_1049_axis_0 = const()[name = tensor<string, []>("op_1049_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_1049_0, tensor<fp32, [1, 256, 1]> var_1049_1 = split(axis = var_1049_axis_0, split_sizes = var_1049_split_sizes_0, x = h_67)[name = tensor<string, []>("op_1049")];
            tensor<fp32, []> var_1051_promoted = const()[name = tensor<string, []>("op_1051_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_1052 = add(x = var_1049_0, y = var_1051_promoted)[name = tensor<string, []>("op_1052")];
            tensor<fp32, [1, 256, 10240]> var_1053 = instance_norm(epsilon = var_516, x = input_123)[name = tensor<string, []>("op_1053")];
            tensor<fp32, [1, 256, 10240]> var_1054 = mul(x = var_1052, y = var_1053)[name = tensor<string, []>("op_1054")];
            tensor<fp32, [1, 256, 10240]> xt_19 = add(x = var_1054, y = var_1049_1)[name = tensor<string, []>("xt_19")];
            tensor<fp32, [1, 256, 1]> var_1056 = const()[name = tensor<string, []>("op_1056"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(170423936)))];
            tensor<fp32, [1, 256, 10240]> var_1059 = mul(x = generator_resblocks_0_alpha1_0, y = xt_19)[name = tensor<string, []>("op_1059")];
            tensor<fp32, [1, 256, 10240]> var_1060 = sin(x = var_1059)[name = tensor<string, []>("op_1060")];
            tensor<fp32, []> var_532_promoted_7 = const()[name = tensor<string, []>("op_532_promoted_7"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 10240]> var_1061 = pow(x = var_1060, y = var_532_promoted_7)[name = tensor<string, []>("op_1061")];
            tensor<fp32, [1, 256, 10240]> var_1062 = mul(x = var_1056, y = var_1061)[name = tensor<string, []>("op_1062")];
            tensor<fp32, [1, 256, 10240]> input_125 = add(x = xt_19, y = var_1062)[name = tensor<string, []>("input_125")];
            tensor<fp32, [256, 256, 3]> weight_51 = const()[name = tensor<string, []>("weight_51"), val = tensor<fp32, [256, 256, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(170425024)))];
            tensor<string, []> input_127_pad_type_0 = const()[name = tensor<string, []>("input_127_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_127_pad_0 = const()[name = tensor<string, []>("input_127_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> input_127_strides_0 = const()[name = tensor<string, []>("input_127_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_127_dilations_0 = const()[name = tensor<string, []>("input_127_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_127_groups_0 = const()[name = tensor<string, []>("input_127_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 10240]> input_127 = conv(bias = generator_resblocks_0_convs1_0_bias, dilations = input_127_dilations_0, groups = input_127_groups_0, pad = input_127_pad_0, pad_type = input_127_pad_type_0, strides = input_127_strides_0, weight = weight_51, x = input_125)[name = tensor<string, []>("input_127")];
            tensor<fp32, [1, 512]> h_69 = linear(bias = generator_resblocks_0_adain2_0_fc_bias, weight = generator_resblocks_0_adain2_0_fc_weight, x = s)[name = tensor<string, []>("linear_18")];
            tensor<int32, [3]> var_1077 = const()[name = tensor<string, []>("op_1077"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_71 = reshape(shape = var_1077, x = h_69)[name = tensor<string, []>("h_71")];
            tensor<int32, [2]> var_1079_split_sizes_0 = const()[name = tensor<string, []>("op_1079_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_1079_axis_0 = const()[name = tensor<string, []>("op_1079_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_1079_0, tensor<fp32, [1, 256, 1]> var_1079_1 = split(axis = var_1079_axis_0, split_sizes = var_1079_split_sizes_0, x = h_71)[name = tensor<string, []>("op_1079")];
            tensor<fp32, []> var_1081_promoted = const()[name = tensor<string, []>("op_1081_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_1082 = add(x = var_1079_0, y = var_1081_promoted)[name = tensor<string, []>("op_1082")];
            tensor<fp32, [1, 256, 10240]> var_1083 = instance_norm(epsilon = var_516, x = input_127)[name = tensor<string, []>("op_1083")];
            tensor<fp32, [1, 256, 10240]> var_1084 = mul(x = var_1082, y = var_1083)[name = tensor<string, []>("op_1084")];
            tensor<fp32, [1, 256, 10240]> xt_21 = add(x = var_1084, y = var_1079_1)[name = tensor<string, []>("xt_21")];
            tensor<fp32, [1, 256, 1]> var_1086 = const()[name = tensor<string, []>("op_1086"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(171211520)))];
            tensor<fp32, [1, 256, 10240]> var_1089 = mul(x = generator_resblocks_0_alpha2_0, y = xt_21)[name = tensor<string, []>("op_1089")];
            tensor<fp32, [1, 256, 10240]> var_1090 = sin(x = var_1089)[name = tensor<string, []>("op_1090")];
            tensor<fp32, []> var_532_promoted_8 = const()[name = tensor<string, []>("op_532_promoted_8"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 10240]> var_1091 = pow(x = var_1090, y = var_532_promoted_8)[name = tensor<string, []>("op_1091")];
            tensor<fp32, [1, 256, 10240]> var_1092 = mul(x = var_1086, y = var_1091)[name = tensor<string, []>("op_1092")];
            tensor<fp32, [1, 256, 10240]> input_129 = add(x = xt_21, y = var_1092)[name = tensor<string, []>("input_129")];
            tensor<fp32, [256, 256, 3]> weight_53 = const()[name = tensor<string, []>("weight_53"), val = tensor<fp32, [256, 256, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(171212608)))];
            tensor<string, []> xt_23_pad_type_0 = const()[name = tensor<string, []>("xt_23_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_23_pad_0 = const()[name = tensor<string, []>("xt_23_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> xt_23_strides_0 = const()[name = tensor<string, []>("xt_23_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_23_dilations_0 = const()[name = tensor<string, []>("xt_23_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_23_groups_0 = const()[name = tensor<string, []>("xt_23_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 10240]> xt_23 = conv(bias = generator_resblocks_0_convs2_0_bias, dilations = xt_23_dilations_0, groups = xt_23_groups_0, pad = xt_23_pad_0, pad_type = xt_23_pad_type_0, strides = xt_23_strides_0, weight = weight_53, x = input_129)[name = tensor<string, []>("xt_23")];
            tensor<fp32, [1, 256, 10240]> input_131 = add(x = xt_23, y = input_123)[name = tensor<string, []>("input_131")];
            tensor<fp32, [1, 512]> h_73 = linear(bias = generator_resblocks_0_adain1_1_fc_bias, weight = generator_resblocks_0_adain1_1_fc_weight, x = s)[name = tensor<string, []>("linear_19")];
            tensor<int32, [3]> var_1108 = const()[name = tensor<string, []>("op_1108"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_75 = reshape(shape = var_1108, x = h_73)[name = tensor<string, []>("h_75")];
            tensor<int32, [2]> var_1110_split_sizes_0 = const()[name = tensor<string, []>("op_1110_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_1110_axis_0 = const()[name = tensor<string, []>("op_1110_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_1110_0, tensor<fp32, [1, 256, 1]> var_1110_1 = split(axis = var_1110_axis_0, split_sizes = var_1110_split_sizes_0, x = h_75)[name = tensor<string, []>("op_1110")];
            tensor<fp32, []> var_1112_promoted = const()[name = tensor<string, []>("op_1112_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_1113 = add(x = var_1110_0, y = var_1112_promoted)[name = tensor<string, []>("op_1113")];
            tensor<fp32, [1, 256, 10240]> var_1114 = instance_norm(epsilon = var_516, x = input_131)[name = tensor<string, []>("op_1114")];
            tensor<fp32, [1, 256, 10240]> var_1115 = mul(x = var_1113, y = var_1114)[name = tensor<string, []>("op_1115")];
            tensor<fp32, [1, 256, 10240]> xt_25 = add(x = var_1115, y = var_1110_1)[name = tensor<string, []>("xt_25")];
            tensor<fp32, [1, 256, 1]> var_1117 = const()[name = tensor<string, []>("op_1117"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(171999104)))];
            tensor<fp32, [1, 256, 10240]> var_1120 = mul(x = generator_resblocks_0_alpha1_1, y = xt_25)[name = tensor<string, []>("op_1120")];
            tensor<fp32, [1, 256, 10240]> var_1121 = sin(x = var_1120)[name = tensor<string, []>("op_1121")];
            tensor<fp32, []> var_532_promoted_9 = const()[name = tensor<string, []>("op_532_promoted_9"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 10240]> var_1122 = pow(x = var_1121, y = var_532_promoted_9)[name = tensor<string, []>("op_1122")];
            tensor<fp32, [1, 256, 10240]> var_1123 = mul(x = var_1117, y = var_1122)[name = tensor<string, []>("op_1123")];
            tensor<fp32, [1, 256, 10240]> input_133 = add(x = xt_25, y = var_1123)[name = tensor<string, []>("input_133")];
            tensor<fp32, [256, 256, 3]> weight_55 = const()[name = tensor<string, []>("weight_55"), val = tensor<fp32, [256, 256, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(172000192)))];
            tensor<string, []> input_135_pad_type_0 = const()[name = tensor<string, []>("input_135_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_135_pad_0 = const()[name = tensor<string, []>("input_135_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> input_135_dilations_0 = const()[name = tensor<string, []>("input_135_dilations_0"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> input_135_strides_0 = const()[name = tensor<string, []>("input_135_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_135_groups_0 = const()[name = tensor<string, []>("input_135_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 10240]> input_135 = conv(bias = generator_resblocks_0_convs1_1_bias, dilations = input_135_dilations_0, groups = input_135_groups_0, pad = input_135_pad_0, pad_type = input_135_pad_type_0, strides = input_135_strides_0, weight = weight_55, x = input_133)[name = tensor<string, []>("input_135")];
            tensor<fp32, [1, 512]> h_77 = linear(bias = generator_resblocks_0_adain2_1_fc_bias, weight = generator_resblocks_0_adain2_1_fc_weight, x = s)[name = tensor<string, []>("linear_20")];
            tensor<int32, [3]> var_1138 = const()[name = tensor<string, []>("op_1138"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_79 = reshape(shape = var_1138, x = h_77)[name = tensor<string, []>("h_79")];
            tensor<int32, [2]> var_1140_split_sizes_0 = const()[name = tensor<string, []>("op_1140_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_1140_axis_0 = const()[name = tensor<string, []>("op_1140_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_1140_0, tensor<fp32, [1, 256, 1]> var_1140_1 = split(axis = var_1140_axis_0, split_sizes = var_1140_split_sizes_0, x = h_79)[name = tensor<string, []>("op_1140")];
            tensor<fp32, []> var_1142_promoted = const()[name = tensor<string, []>("op_1142_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_1143 = add(x = var_1140_0, y = var_1142_promoted)[name = tensor<string, []>("op_1143")];
            tensor<fp32, [1, 256, 10240]> var_1144 = instance_norm(epsilon = var_516, x = input_135)[name = tensor<string, []>("op_1144")];
            tensor<fp32, [1, 256, 10240]> var_1145 = mul(x = var_1143, y = var_1144)[name = tensor<string, []>("op_1145")];
            tensor<fp32, [1, 256, 10240]> xt_27 = add(x = var_1145, y = var_1140_1)[name = tensor<string, []>("xt_27")];
            tensor<fp32, [1, 256, 1]> var_1147 = const()[name = tensor<string, []>("op_1147"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(172786688)))];
            tensor<fp32, [1, 256, 10240]> var_1150 = mul(x = generator_resblocks_0_alpha2_1, y = xt_27)[name = tensor<string, []>("op_1150")];
            tensor<fp32, [1, 256, 10240]> var_1151 = sin(x = var_1150)[name = tensor<string, []>("op_1151")];
            tensor<fp32, []> var_532_promoted_10 = const()[name = tensor<string, []>("op_532_promoted_10"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 10240]> var_1152 = pow(x = var_1151, y = var_532_promoted_10)[name = tensor<string, []>("op_1152")];
            tensor<fp32, [1, 256, 10240]> var_1153 = mul(x = var_1147, y = var_1152)[name = tensor<string, []>("op_1153")];
            tensor<fp32, [1, 256, 10240]> input_137 = add(x = xt_27, y = var_1153)[name = tensor<string, []>("input_137")];
            tensor<fp32, [256, 256, 3]> weight_57 = const()[name = tensor<string, []>("weight_57"), val = tensor<fp32, [256, 256, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(172787776)))];
            tensor<string, []> xt_29_pad_type_0 = const()[name = tensor<string, []>("xt_29_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_29_pad_0 = const()[name = tensor<string, []>("xt_29_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> xt_29_strides_0 = const()[name = tensor<string, []>("xt_29_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_29_dilations_0 = const()[name = tensor<string, []>("xt_29_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_29_groups_0 = const()[name = tensor<string, []>("xt_29_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 10240]> xt_29 = conv(bias = generator_resblocks_0_convs2_1_bias, dilations = xt_29_dilations_0, groups = xt_29_groups_0, pad = xt_29_pad_0, pad_type = xt_29_pad_type_0, strides = xt_29_strides_0, weight = weight_57, x = input_137)[name = tensor<string, []>("xt_29")];
            tensor<fp32, [1, 256, 10240]> input_139 = add(x = xt_29, y = input_131)[name = tensor<string, []>("input_139")];
            tensor<fp32, [1, 512]> h_81 = linear(bias = generator_resblocks_0_adain1_2_fc_bias, weight = generator_resblocks_0_adain1_2_fc_weight, x = s)[name = tensor<string, []>("linear_21")];
            tensor<int32, [3]> var_1169 = const()[name = tensor<string, []>("op_1169"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_83 = reshape(shape = var_1169, x = h_81)[name = tensor<string, []>("h_83")];
            tensor<int32, [2]> var_1171_split_sizes_0 = const()[name = tensor<string, []>("op_1171_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_1171_axis_0 = const()[name = tensor<string, []>("op_1171_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_1171_0, tensor<fp32, [1, 256, 1]> var_1171_1 = split(axis = var_1171_axis_0, split_sizes = var_1171_split_sizes_0, x = h_83)[name = tensor<string, []>("op_1171")];
            tensor<fp32, []> var_1173_promoted = const()[name = tensor<string, []>("op_1173_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_1174 = add(x = var_1171_0, y = var_1173_promoted)[name = tensor<string, []>("op_1174")];
            tensor<fp32, [1, 256, 10240]> var_1175 = instance_norm(epsilon = var_516, x = input_139)[name = tensor<string, []>("op_1175")];
            tensor<fp32, [1, 256, 10240]> var_1176 = mul(x = var_1174, y = var_1175)[name = tensor<string, []>("op_1176")];
            tensor<fp32, [1, 256, 10240]> xt_31 = add(x = var_1176, y = var_1171_1)[name = tensor<string, []>("xt_31")];
            tensor<fp32, [1, 256, 1]> var_1178 = const()[name = tensor<string, []>("op_1178"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(173574272)))];
            tensor<fp32, [1, 256, 10240]> var_1181 = mul(x = generator_resblocks_0_alpha1_2, y = xt_31)[name = tensor<string, []>("op_1181")];
            tensor<fp32, [1, 256, 10240]> var_1182 = sin(x = var_1181)[name = tensor<string, []>("op_1182")];
            tensor<fp32, []> var_532_promoted_11 = const()[name = tensor<string, []>("op_532_promoted_11"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 10240]> var_1183 = pow(x = var_1182, y = var_532_promoted_11)[name = tensor<string, []>("op_1183")];
            tensor<fp32, [1, 256, 10240]> var_1184 = mul(x = var_1178, y = var_1183)[name = tensor<string, []>("op_1184")];
            tensor<fp32, [1, 256, 10240]> input_141 = add(x = xt_31, y = var_1184)[name = tensor<string, []>("input_141")];
            tensor<fp32, [256, 256, 3]> weight_59 = const()[name = tensor<string, []>("weight_59"), val = tensor<fp32, [256, 256, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(173575360)))];
            tensor<string, []> input_143_pad_type_0 = const()[name = tensor<string, []>("input_143_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_143_pad_0 = const()[name = tensor<string, []>("input_143_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> input_143_dilations_0 = const()[name = tensor<string, []>("input_143_dilations_0"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> input_143_strides_0 = const()[name = tensor<string, []>("input_143_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_143_groups_0 = const()[name = tensor<string, []>("input_143_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 10240]> input_143 = conv(bias = generator_resblocks_0_convs1_2_bias, dilations = input_143_dilations_0, groups = input_143_groups_0, pad = input_143_pad_0, pad_type = input_143_pad_type_0, strides = input_143_strides_0, weight = weight_59, x = input_141)[name = tensor<string, []>("input_143")];
            tensor<fp32, [1, 512]> h_85 = linear(bias = generator_resblocks_0_adain2_2_fc_bias, weight = generator_resblocks_0_adain2_2_fc_weight, x = s)[name = tensor<string, []>("linear_22")];
            tensor<int32, [3]> var_1199 = const()[name = tensor<string, []>("op_1199"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_87 = reshape(shape = var_1199, x = h_85)[name = tensor<string, []>("h_87")];
            tensor<int32, [2]> var_1201_split_sizes_0 = const()[name = tensor<string, []>("op_1201_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_1201_axis_0 = const()[name = tensor<string, []>("op_1201_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_1201_0, tensor<fp32, [1, 256, 1]> var_1201_1 = split(axis = var_1201_axis_0, split_sizes = var_1201_split_sizes_0, x = h_87)[name = tensor<string, []>("op_1201")];
            tensor<fp32, []> var_1203_promoted = const()[name = tensor<string, []>("op_1203_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_1204 = add(x = var_1201_0, y = var_1203_promoted)[name = tensor<string, []>("op_1204")];
            tensor<fp32, [1, 256, 10240]> var_1205 = instance_norm(epsilon = var_516, x = input_143)[name = tensor<string, []>("op_1205")];
            tensor<fp32, [1, 256, 10240]> var_1206 = mul(x = var_1204, y = var_1205)[name = tensor<string, []>("op_1206")];
            tensor<fp32, [1, 256, 10240]> xt_33 = add(x = var_1206, y = var_1201_1)[name = tensor<string, []>("xt_33")];
            tensor<fp32, [1, 256, 1]> var_1208 = const()[name = tensor<string, []>("op_1208"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(174361856)))];
            tensor<fp32, [1, 256, 10240]> var_1211 = mul(x = generator_resblocks_0_alpha2_2, y = xt_33)[name = tensor<string, []>("op_1211")];
            tensor<fp32, [1, 256, 10240]> var_1212 = sin(x = var_1211)[name = tensor<string, []>("op_1212")];
            tensor<fp32, []> var_532_promoted_12 = const()[name = tensor<string, []>("op_532_promoted_12"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 10240]> var_1213 = pow(x = var_1212, y = var_532_promoted_12)[name = tensor<string, []>("op_1213")];
            tensor<fp32, [1, 256, 10240]> var_1214 = mul(x = var_1208, y = var_1213)[name = tensor<string, []>("op_1214")];
            tensor<fp32, [1, 256, 10240]> input_145 = add(x = xt_33, y = var_1214)[name = tensor<string, []>("input_145")];
            tensor<fp32, [256, 256, 3]> weight_61 = const()[name = tensor<string, []>("weight_61"), val = tensor<fp32, [256, 256, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(174362944)))];
            tensor<string, []> xt_35_pad_type_0 = const()[name = tensor<string, []>("xt_35_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_35_pad_0 = const()[name = tensor<string, []>("xt_35_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> xt_35_strides_0 = const()[name = tensor<string, []>("xt_35_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_35_dilations_0 = const()[name = tensor<string, []>("xt_35_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_35_groups_0 = const()[name = tensor<string, []>("xt_35_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 10240]> xt_35 = conv(bias = generator_resblocks_0_convs2_2_bias, dilations = xt_35_dilations_0, groups = xt_35_groups_0, pad = xt_35_pad_0, pad_type = xt_35_pad_type_0, strides = xt_35_strides_0, weight = weight_61, x = input_145)[name = tensor<string, []>("xt_35")];
            tensor<fp32, [1, 256, 10240]> xs_1 = add(x = xt_35, y = input_139)[name = tensor<string, []>("xs_1")];
            tensor<fp32, [1, 512]> h_89 = linear(bias = generator_resblocks_1_adain1_0_fc_bias, weight = generator_resblocks_1_adain1_0_fc_weight, x = s)[name = tensor<string, []>("linear_23")];
            tensor<int32, [3]> var_1302 = const()[name = tensor<string, []>("op_1302"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_91 = reshape(shape = var_1302, x = h_89)[name = tensor<string, []>("h_91")];
            tensor<int32, [2]> var_1304_split_sizes_0 = const()[name = tensor<string, []>("op_1304_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_1304_axis_0 = const()[name = tensor<string, []>("op_1304_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_1304_0, tensor<fp32, [1, 256, 1]> var_1304_1 = split(axis = var_1304_axis_0, split_sizes = var_1304_split_sizes_0, x = h_91)[name = tensor<string, []>("op_1304")];
            tensor<fp32, []> var_1306_promoted = const()[name = tensor<string, []>("op_1306_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_1307 = add(x = var_1304_0, y = var_1306_promoted)[name = tensor<string, []>("op_1307")];
            tensor<fp32, [1, 256, 10240]> var_1309 = mul(x = var_1307, y = var_1053)[name = tensor<string, []>("op_1309")];
            tensor<fp32, [1, 256, 10240]> xt_37 = add(x = var_1309, y = var_1304_1)[name = tensor<string, []>("xt_37")];
            tensor<fp32, [1, 256, 1]> var_1311 = const()[name = tensor<string, []>("op_1311"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(175149440)))];
            tensor<fp32, [1, 256, 10240]> var_1314 = mul(x = generator_resblocks_1_alpha1_0, y = xt_37)[name = tensor<string, []>("op_1314")];
            tensor<fp32, [1, 256, 10240]> var_1315 = sin(x = var_1314)[name = tensor<string, []>("op_1315")];
            tensor<fp32, []> var_532_promoted_13 = const()[name = tensor<string, []>("op_532_promoted_13"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 10240]> var_1316 = pow(x = var_1315, y = var_532_promoted_13)[name = tensor<string, []>("op_1316")];
            tensor<fp32, [1, 256, 10240]> var_1317 = mul(x = var_1311, y = var_1316)[name = tensor<string, []>("op_1317")];
            tensor<fp32, [1, 256, 10240]> input_147 = add(x = xt_37, y = var_1317)[name = tensor<string, []>("input_147")];
            tensor<fp32, [256, 256, 7]> weight_63 = const()[name = tensor<string, []>("weight_63"), val = tensor<fp32, [256, 256, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(175150528)))];
            tensor<string, []> input_149_pad_type_0 = const()[name = tensor<string, []>("input_149_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_149_pad_0 = const()[name = tensor<string, []>("input_149_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> input_149_strides_0 = const()[name = tensor<string, []>("input_149_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_149_dilations_0 = const()[name = tensor<string, []>("input_149_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_149_groups_0 = const()[name = tensor<string, []>("input_149_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 10240]> input_149 = conv(bias = generator_resblocks_1_convs1_0_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 = weight_63, x = input_147)[name = tensor<string, []>("input_149")];
            tensor<fp32, [1, 512]> h_93 = linear(bias = generator_resblocks_1_adain2_0_fc_bias, weight = generator_resblocks_1_adain2_0_fc_weight, x = s)[name = tensor<string, []>("linear_24")];
            tensor<int32, [3]> var_1332 = const()[name = tensor<string, []>("op_1332"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_95 = reshape(shape = var_1332, x = h_93)[name = tensor<string, []>("h_95")];
            tensor<int32, [2]> var_1334_split_sizes_0 = const()[name = tensor<string, []>("op_1334_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_1334_axis_0 = const()[name = tensor<string, []>("op_1334_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_1334_0, tensor<fp32, [1, 256, 1]> var_1334_1 = split(axis = var_1334_axis_0, split_sizes = var_1334_split_sizes_0, x = h_95)[name = tensor<string, []>("op_1334")];
            tensor<fp32, []> var_1336_promoted = const()[name = tensor<string, []>("op_1336_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_1337 = add(x = var_1334_0, y = var_1336_promoted)[name = tensor<string, []>("op_1337")];
            tensor<fp32, [1, 256, 10240]> var_1338 = instance_norm(epsilon = var_516, x = input_149)[name = tensor<string, []>("op_1338")];
            tensor<fp32, [1, 256, 10240]> var_1339 = mul(x = var_1337, y = var_1338)[name = tensor<string, []>("op_1339")];
            tensor<fp32, [1, 256, 10240]> xt_39 = add(x = var_1339, y = var_1334_1)[name = tensor<string, []>("xt_39")];
            tensor<fp32, [1, 256, 1]> var_1341 = const()[name = tensor<string, []>("op_1341"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(176985600)))];
            tensor<fp32, [1, 256, 10240]> var_1344 = mul(x = generator_resblocks_1_alpha2_0, y = xt_39)[name = tensor<string, []>("op_1344")];
            tensor<fp32, [1, 256, 10240]> var_1345 = sin(x = var_1344)[name = tensor<string, []>("op_1345")];
            tensor<fp32, []> var_532_promoted_14 = const()[name = tensor<string, []>("op_532_promoted_14"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 10240]> var_1346 = pow(x = var_1345, y = var_532_promoted_14)[name = tensor<string, []>("op_1346")];
            tensor<fp32, [1, 256, 10240]> var_1347 = mul(x = var_1341, y = var_1346)[name = tensor<string, []>("op_1347")];
            tensor<fp32, [1, 256, 10240]> input_151 = add(x = xt_39, y = var_1347)[name = tensor<string, []>("input_151")];
            tensor<fp32, [256, 256, 7]> weight_65 = const()[name = tensor<string, []>("weight_65"), val = tensor<fp32, [256, 256, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(176986688)))];
            tensor<string, []> xt_41_pad_type_0 = const()[name = tensor<string, []>("xt_41_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_41_pad_0 = const()[name = tensor<string, []>("xt_41_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> xt_41_strides_0 = const()[name = tensor<string, []>("xt_41_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_41_dilations_0 = const()[name = tensor<string, []>("xt_41_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_41_groups_0 = const()[name = tensor<string, []>("xt_41_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 10240]> xt_41 = conv(bias = generator_resblocks_1_convs2_0_bias, dilations = xt_41_dilations_0, groups = xt_41_groups_0, pad = xt_41_pad_0, pad_type = xt_41_pad_type_0, strides = xt_41_strides_0, weight = weight_65, x = input_151)[name = tensor<string, []>("xt_41")];
            tensor<fp32, [1, 256, 10240]> input_153 = add(x = xt_41, y = input_123)[name = tensor<string, []>("input_153")];
            tensor<fp32, [1, 512]> h_97 = linear(bias = generator_resblocks_1_adain1_1_fc_bias, weight = generator_resblocks_1_adain1_1_fc_weight, x = s)[name = tensor<string, []>("linear_25")];
            tensor<int32, [3]> var_1363 = const()[name = tensor<string, []>("op_1363"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_99 = reshape(shape = var_1363, x = h_97)[name = tensor<string, []>("h_99")];
            tensor<int32, [2]> var_1365_split_sizes_0 = const()[name = tensor<string, []>("op_1365_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_1365_axis_0 = const()[name = tensor<string, []>("op_1365_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_1365_0, tensor<fp32, [1, 256, 1]> var_1365_1 = split(axis = var_1365_axis_0, split_sizes = var_1365_split_sizes_0, x = h_99)[name = tensor<string, []>("op_1365")];
            tensor<fp32, []> var_1367_promoted = const()[name = tensor<string, []>("op_1367_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_1368 = add(x = var_1365_0, y = var_1367_promoted)[name = tensor<string, []>("op_1368")];
            tensor<fp32, [1, 256, 10240]> var_1369 = instance_norm(epsilon = var_516, x = input_153)[name = tensor<string, []>("op_1369")];
            tensor<fp32, [1, 256, 10240]> var_1370 = mul(x = var_1368, y = var_1369)[name = tensor<string, []>("op_1370")];
            tensor<fp32, [1, 256, 10240]> xt_43 = add(x = var_1370, y = var_1365_1)[name = tensor<string, []>("xt_43")];
            tensor<fp32, [1, 256, 1]> var_1372 = const()[name = tensor<string, []>("op_1372"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(178821760)))];
            tensor<fp32, [1, 256, 10240]> var_1375 = mul(x = generator_resblocks_1_alpha1_1, y = xt_43)[name = tensor<string, []>("op_1375")];
            tensor<fp32, [1, 256, 10240]> var_1376 = sin(x = var_1375)[name = tensor<string, []>("op_1376")];
            tensor<fp32, []> var_532_promoted_15 = const()[name = tensor<string, []>("op_532_promoted_15"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 10240]> var_1377 = pow(x = var_1376, y = var_532_promoted_15)[name = tensor<string, []>("op_1377")];
            tensor<fp32, [1, 256, 10240]> var_1378 = mul(x = var_1372, y = var_1377)[name = tensor<string, []>("op_1378")];
            tensor<fp32, [1, 256, 10240]> input_155 = add(x = xt_43, y = var_1378)[name = tensor<string, []>("input_155")];
            tensor<fp32, [256, 256, 7]> weight_67 = const()[name = tensor<string, []>("weight_67"), val = tensor<fp32, [256, 256, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(178822848)))];
            tensor<string, []> input_157_pad_type_0 = const()[name = tensor<string, []>("input_157_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_157_pad_0 = const()[name = tensor<string, []>("input_157_pad_0"), val = tensor<int32, [2]>([9, 9])];
            tensor<int32, [1]> input_157_dilations_0 = const()[name = tensor<string, []>("input_157_dilations_0"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> input_157_strides_0 = const()[name = tensor<string, []>("input_157_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_157_groups_0 = const()[name = tensor<string, []>("input_157_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 10240]> input_157 = conv(bias = generator_resblocks_1_convs1_1_bias, 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 = weight_67, x = input_155)[name = tensor<string, []>("input_157")];
            tensor<fp32, [1, 512]> h_101 = linear(bias = generator_resblocks_1_adain2_1_fc_bias, weight = generator_resblocks_1_adain2_1_fc_weight, x = s)[name = tensor<string, []>("linear_26")];
            tensor<int32, [3]> var_1393 = const()[name = tensor<string, []>("op_1393"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_103 = reshape(shape = var_1393, x = h_101)[name = tensor<string, []>("h_103")];
            tensor<int32, [2]> var_1395_split_sizes_0 = const()[name = tensor<string, []>("op_1395_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_1395_axis_0 = const()[name = tensor<string, []>("op_1395_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_1395_0, tensor<fp32, [1, 256, 1]> var_1395_1 = split(axis = var_1395_axis_0, split_sizes = var_1395_split_sizes_0, x = h_103)[name = tensor<string, []>("op_1395")];
            tensor<fp32, []> var_1397_promoted = const()[name = tensor<string, []>("op_1397_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_1398 = add(x = var_1395_0, y = var_1397_promoted)[name = tensor<string, []>("op_1398")];
            tensor<fp32, [1, 256, 10240]> var_1399 = instance_norm(epsilon = var_516, x = input_157)[name = tensor<string, []>("op_1399")];
            tensor<fp32, [1, 256, 10240]> var_1400 = mul(x = var_1398, y = var_1399)[name = tensor<string, []>("op_1400")];
            tensor<fp32, [1, 256, 10240]> xt_45 = add(x = var_1400, y = var_1395_1)[name = tensor<string, []>("xt_45")];
            tensor<fp32, [1, 256, 1]> var_1402 = const()[name = tensor<string, []>("op_1402"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(180657920)))];
            tensor<fp32, [1, 256, 10240]> var_1405 = mul(x = generator_resblocks_1_alpha2_1, y = xt_45)[name = tensor<string, []>("op_1405")];
            tensor<fp32, [1, 256, 10240]> var_1406 = sin(x = var_1405)[name = tensor<string, []>("op_1406")];
            tensor<fp32, []> var_532_promoted_16 = const()[name = tensor<string, []>("op_532_promoted_16"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 10240]> var_1407 = pow(x = var_1406, y = var_532_promoted_16)[name = tensor<string, []>("op_1407")];
            tensor<fp32, [1, 256, 10240]> var_1408 = mul(x = var_1402, y = var_1407)[name = tensor<string, []>("op_1408")];
            tensor<fp32, [1, 256, 10240]> input_159 = add(x = xt_45, y = var_1408)[name = tensor<string, []>("input_159")];
            tensor<fp32, [256, 256, 7]> weight_69 = const()[name = tensor<string, []>("weight_69"), val = tensor<fp32, [256, 256, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(180659008)))];
            tensor<string, []> xt_47_pad_type_0 = const()[name = tensor<string, []>("xt_47_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_47_pad_0 = const()[name = tensor<string, []>("xt_47_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> xt_47_strides_0 = const()[name = tensor<string, []>("xt_47_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_47_dilations_0 = const()[name = tensor<string, []>("xt_47_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_47_groups_0 = const()[name = tensor<string, []>("xt_47_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 10240]> xt_47 = conv(bias = generator_resblocks_1_convs2_1_bias, dilations = xt_47_dilations_0, groups = xt_47_groups_0, pad = xt_47_pad_0, pad_type = xt_47_pad_type_0, strides = xt_47_strides_0, weight = weight_69, x = input_159)[name = tensor<string, []>("xt_47")];
            tensor<fp32, [1, 256, 10240]> input_161 = add(x = xt_47, y = input_153)[name = tensor<string, []>("input_161")];
            tensor<fp32, [1, 512]> h_105 = linear(bias = generator_resblocks_1_adain1_2_fc_bias, weight = generator_resblocks_1_adain1_2_fc_weight, x = s)[name = tensor<string, []>("linear_27")];
            tensor<int32, [3]> var_1424 = const()[name = tensor<string, []>("op_1424"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_107 = reshape(shape = var_1424, x = h_105)[name = tensor<string, []>("h_107")];
            tensor<int32, [2]> var_1426_split_sizes_0 = const()[name = tensor<string, []>("op_1426_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_1426_axis_0 = const()[name = tensor<string, []>("op_1426_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_1426_0, tensor<fp32, [1, 256, 1]> var_1426_1 = split(axis = var_1426_axis_0, split_sizes = var_1426_split_sizes_0, x = h_107)[name = tensor<string, []>("op_1426")];
            tensor<fp32, []> var_1428_promoted = const()[name = tensor<string, []>("op_1428_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_1429 = add(x = var_1426_0, y = var_1428_promoted)[name = tensor<string, []>("op_1429")];
            tensor<fp32, [1, 256, 10240]> var_1430 = instance_norm(epsilon = var_516, x = input_161)[name = tensor<string, []>("op_1430")];
            tensor<fp32, [1, 256, 10240]> var_1431 = mul(x = var_1429, y = var_1430)[name = tensor<string, []>("op_1431")];
            tensor<fp32, [1, 256, 10240]> xt_49 = add(x = var_1431, y = var_1426_1)[name = tensor<string, []>("xt_49")];
            tensor<fp32, [1, 256, 1]> var_1433 = const()[name = tensor<string, []>("op_1433"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(182494080)))];
            tensor<fp32, [1, 256, 10240]> var_1436 = mul(x = generator_resblocks_1_alpha1_2, y = xt_49)[name = tensor<string, []>("op_1436")];
            tensor<fp32, [1, 256, 10240]> var_1437 = sin(x = var_1436)[name = tensor<string, []>("op_1437")];
            tensor<fp32, []> var_532_promoted_17 = const()[name = tensor<string, []>("op_532_promoted_17"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 10240]> var_1438 = pow(x = var_1437, y = var_532_promoted_17)[name = tensor<string, []>("op_1438")];
            tensor<fp32, [1, 256, 10240]> var_1439 = mul(x = var_1433, y = var_1438)[name = tensor<string, []>("op_1439")];
            tensor<fp32, [1, 256, 10240]> input_163 = add(x = xt_49, y = var_1439)[name = tensor<string, []>("input_163")];
            tensor<fp32, [256, 256, 7]> weight_71 = const()[name = tensor<string, []>("weight_71"), val = tensor<fp32, [256, 256, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(182495168)))];
            tensor<string, []> input_165_pad_type_0 = const()[name = tensor<string, []>("input_165_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_165_pad_0 = const()[name = tensor<string, []>("input_165_pad_0"), val = tensor<int32, [2]>([15, 15])];
            tensor<int32, [1]> input_165_dilations_0 = const()[name = tensor<string, []>("input_165_dilations_0"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> input_165_strides_0 = const()[name = tensor<string, []>("input_165_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_165_groups_0 = const()[name = tensor<string, []>("input_165_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 10240]> input_165 = conv(bias = generator_resblocks_1_convs1_2_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 = weight_71, x = input_163)[name = tensor<string, []>("input_165")];
            tensor<fp32, [1, 512]> h_109 = linear(bias = generator_resblocks_1_adain2_2_fc_bias, weight = generator_resblocks_1_adain2_2_fc_weight, x = s)[name = tensor<string, []>("linear_28")];
            tensor<int32, [3]> var_1454 = const()[name = tensor<string, []>("op_1454"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_111 = reshape(shape = var_1454, x = h_109)[name = tensor<string, []>("h_111")];
            tensor<int32, [2]> var_1456_split_sizes_0 = const()[name = tensor<string, []>("op_1456_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_1456_axis_0 = const()[name = tensor<string, []>("op_1456_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_1456_0, tensor<fp32, [1, 256, 1]> var_1456_1 = split(axis = var_1456_axis_0, split_sizes = var_1456_split_sizes_0, x = h_111)[name = tensor<string, []>("op_1456")];
            tensor<fp32, []> var_1458_promoted = const()[name = tensor<string, []>("op_1458_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_1459 = add(x = var_1456_0, y = var_1458_promoted)[name = tensor<string, []>("op_1459")];
            tensor<fp32, [1, 256, 10240]> var_1460 = instance_norm(epsilon = var_516, x = input_165)[name = tensor<string, []>("op_1460")];
            tensor<fp32, [1, 256, 10240]> var_1461 = mul(x = var_1459, y = var_1460)[name = tensor<string, []>("op_1461")];
            tensor<fp32, [1, 256, 10240]> xt_51 = add(x = var_1461, y = var_1456_1)[name = tensor<string, []>("xt_51")];
            tensor<fp32, [1, 256, 1]> var_1463 = const()[name = tensor<string, []>("op_1463"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(184330240)))];
            tensor<fp32, [1, 256, 10240]> var_1466 = mul(x = generator_resblocks_1_alpha2_2, y = xt_51)[name = tensor<string, []>("op_1466")];
            tensor<fp32, [1, 256, 10240]> var_1467 = sin(x = var_1466)[name = tensor<string, []>("op_1467")];
            tensor<fp32, []> var_532_promoted_18 = const()[name = tensor<string, []>("op_532_promoted_18"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 10240]> var_1468 = pow(x = var_1467, y = var_532_promoted_18)[name = tensor<string, []>("op_1468")];
            tensor<fp32, [1, 256, 10240]> var_1469 = mul(x = var_1463, y = var_1468)[name = tensor<string, []>("op_1469")];
            tensor<fp32, [1, 256, 10240]> input_167 = add(x = xt_51, y = var_1469)[name = tensor<string, []>("input_167")];
            tensor<fp32, [256, 256, 7]> weight_73 = const()[name = tensor<string, []>("weight_73"), val = tensor<fp32, [256, 256, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(184331328)))];
            tensor<string, []> xt_53_pad_type_0 = const()[name = tensor<string, []>("xt_53_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_53_pad_0 = const()[name = tensor<string, []>("xt_53_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> xt_53_strides_0 = const()[name = tensor<string, []>("xt_53_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_53_dilations_0 = const()[name = tensor<string, []>("xt_53_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_53_groups_0 = const()[name = tensor<string, []>("xt_53_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 10240]> xt_53 = conv(bias = generator_resblocks_1_convs2_2_bias, dilations = xt_53_dilations_0, groups = xt_53_groups_0, pad = xt_53_pad_0, pad_type = xt_53_pad_type_0, strides = xt_53_strides_0, weight = weight_73, x = input_167)[name = tensor<string, []>("xt_53")];
            tensor<fp32, [1, 256, 10240]> var_1478 = add(x = xt_53, y = input_161)[name = tensor<string, []>("op_1478")];
            tensor<fp32, [1, 256, 10240]> xs_3 = add(x = xs_1, y = var_1478)[name = tensor<string, []>("xs_3")];
            tensor<fp32, [1, 512]> h_113 = linear(bias = generator_resblocks_2_adain1_0_fc_bias, weight = generator_resblocks_2_adain1_0_fc_weight, x = s)[name = tensor<string, []>("linear_29")];
            tensor<int32, [3]> var_1558 = const()[name = tensor<string, []>("op_1558"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_115 = reshape(shape = var_1558, x = h_113)[name = tensor<string, []>("h_115")];
            tensor<int32, [2]> var_1560_split_sizes_0 = const()[name = tensor<string, []>("op_1560_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_1560_axis_0 = const()[name = tensor<string, []>("op_1560_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_1560_0, tensor<fp32, [1, 256, 1]> var_1560_1 = split(axis = var_1560_axis_0, split_sizes = var_1560_split_sizes_0, x = h_115)[name = tensor<string, []>("op_1560")];
            tensor<fp32, []> var_1562_promoted = const()[name = tensor<string, []>("op_1562_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_1563 = add(x = var_1560_0, y = var_1562_promoted)[name = tensor<string, []>("op_1563")];
            tensor<fp32, [1, 256, 10240]> var_1565 = mul(x = var_1563, y = var_1053)[name = tensor<string, []>("op_1565")];
            tensor<fp32, [1, 256, 10240]> xt_55 = add(x = var_1565, y = var_1560_1)[name = tensor<string, []>("xt_55")];
            tensor<fp32, [1, 256, 1]> var_1567 = const()[name = tensor<string, []>("op_1567"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(186166400)))];
            tensor<fp32, [1, 256, 10240]> var_1570 = mul(x = generator_resblocks_2_alpha1_0, y = xt_55)[name = tensor<string, []>("op_1570")];
            tensor<fp32, [1, 256, 10240]> var_1571 = sin(x = var_1570)[name = tensor<string, []>("op_1571")];
            tensor<fp32, []> var_532_promoted_19 = const()[name = tensor<string, []>("op_532_promoted_19"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 10240]> var_1572 = pow(x = var_1571, y = var_532_promoted_19)[name = tensor<string, []>("op_1572")];
            tensor<fp32, [1, 256, 10240]> var_1573 = mul(x = var_1567, y = var_1572)[name = tensor<string, []>("op_1573")];
            tensor<fp32, [1, 256, 10240]> input_169 = add(x = xt_55, y = var_1573)[name = tensor<string, []>("input_169")];
            tensor<fp32, [256, 256, 11]> weight_75 = const()[name = tensor<string, []>("weight_75"), val = tensor<fp32, [256, 256, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(186167488)))];
            tensor<string, []> input_171_pad_type_0 = const()[name = tensor<string, []>("input_171_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_171_pad_0 = const()[name = tensor<string, []>("input_171_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> input_171_strides_0 = const()[name = tensor<string, []>("input_171_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_171_dilations_0 = const()[name = tensor<string, []>("input_171_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_171_groups_0 = const()[name = tensor<string, []>("input_171_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 10240]> input_171 = conv(bias = generator_resblocks_2_convs1_0_bias, dilations = input_171_dilations_0, groups = input_171_groups_0, pad = input_171_pad_0, pad_type = input_171_pad_type_0, strides = input_171_strides_0, weight = weight_75, x = input_169)[name = tensor<string, []>("input_171")];
            tensor<fp32, [1, 512]> h_117 = linear(bias = generator_resblocks_2_adain2_0_fc_bias, weight = generator_resblocks_2_adain2_0_fc_weight, x = s)[name = tensor<string, []>("linear_30")];
            tensor<int32, [3]> var_1588 = const()[name = tensor<string, []>("op_1588"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_119 = reshape(shape = var_1588, x = h_117)[name = tensor<string, []>("h_119")];
            tensor<int32, [2]> var_1590_split_sizes_0 = const()[name = tensor<string, []>("op_1590_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_1590_axis_0 = const()[name = tensor<string, []>("op_1590_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_1590_0, tensor<fp32, [1, 256, 1]> var_1590_1 = split(axis = var_1590_axis_0, split_sizes = var_1590_split_sizes_0, x = h_119)[name = tensor<string, []>("op_1590")];
            tensor<fp32, []> var_1592_promoted = const()[name = tensor<string, []>("op_1592_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_1593 = add(x = var_1590_0, y = var_1592_promoted)[name = tensor<string, []>("op_1593")];
            tensor<fp32, [1, 256, 10240]> var_1594 = instance_norm(epsilon = var_516, x = input_171)[name = tensor<string, []>("op_1594")];
            tensor<fp32, [1, 256, 10240]> var_1595 = mul(x = var_1593, y = var_1594)[name = tensor<string, []>("op_1595")];
            tensor<fp32, [1, 256, 10240]> xt_57 = add(x = var_1595, y = var_1590_1)[name = tensor<string, []>("xt_57")];
            tensor<fp32, [1, 256, 1]> var_1597 = const()[name = tensor<string, []>("op_1597"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(189051136)))];
            tensor<fp32, [1, 256, 10240]> var_1600 = mul(x = generator_resblocks_2_alpha2_0, y = xt_57)[name = tensor<string, []>("op_1600")];
            tensor<fp32, [1, 256, 10240]> var_1601 = sin(x = var_1600)[name = tensor<string, []>("op_1601")];
            tensor<fp32, []> var_532_promoted_20 = const()[name = tensor<string, []>("op_532_promoted_20"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 10240]> var_1602 = pow(x = var_1601, y = var_532_promoted_20)[name = tensor<string, []>("op_1602")];
            tensor<fp32, [1, 256, 10240]> var_1603 = mul(x = var_1597, y = var_1602)[name = tensor<string, []>("op_1603")];
            tensor<fp32, [1, 256, 10240]> input_173 = add(x = xt_57, y = var_1603)[name = tensor<string, []>("input_173")];
            tensor<fp32, [256, 256, 11]> weight_77 = const()[name = tensor<string, []>("weight_77"), val = tensor<fp32, [256, 256, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(189052224)))];
            tensor<string, []> xt_59_pad_type_0 = const()[name = tensor<string, []>("xt_59_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_59_pad_0 = const()[name = tensor<string, []>("xt_59_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> xt_59_strides_0 = const()[name = tensor<string, []>("xt_59_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_59_dilations_0 = const()[name = tensor<string, []>("xt_59_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_59_groups_0 = const()[name = tensor<string, []>("xt_59_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 10240]> xt_59 = conv(bias = generator_resblocks_2_convs2_0_bias, dilations = xt_59_dilations_0, groups = xt_59_groups_0, pad = xt_59_pad_0, pad_type = xt_59_pad_type_0, strides = xt_59_strides_0, weight = weight_77, x = input_173)[name = tensor<string, []>("xt_59")];
            tensor<fp32, [1, 256, 10240]> input_175 = add(x = xt_59, y = input_123)[name = tensor<string, []>("input_175")];
            tensor<fp32, [1, 512]> h_121 = linear(bias = generator_resblocks_2_adain1_1_fc_bias, weight = generator_resblocks_2_adain1_1_fc_weight, x = s)[name = tensor<string, []>("linear_31")];
            tensor<int32, [3]> var_1619 = const()[name = tensor<string, []>("op_1619"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_123 = reshape(shape = var_1619, x = h_121)[name = tensor<string, []>("h_123")];
            tensor<int32, [2]> var_1621_split_sizes_0 = const()[name = tensor<string, []>("op_1621_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_1621_axis_0 = const()[name = tensor<string, []>("op_1621_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_1621_0, tensor<fp32, [1, 256, 1]> var_1621_1 = split(axis = var_1621_axis_0, split_sizes = var_1621_split_sizes_0, x = h_123)[name = tensor<string, []>("op_1621")];
            tensor<fp32, []> var_1623_promoted = const()[name = tensor<string, []>("op_1623_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_1624 = add(x = var_1621_0, y = var_1623_promoted)[name = tensor<string, []>("op_1624")];
            tensor<fp32, [1, 256, 10240]> var_1625 = instance_norm(epsilon = var_516, x = input_175)[name = tensor<string, []>("op_1625")];
            tensor<fp32, [1, 256, 10240]> var_1626 = mul(x = var_1624, y = var_1625)[name = tensor<string, []>("op_1626")];
            tensor<fp32, [1, 256, 10240]> xt_61 = add(x = var_1626, y = var_1621_1)[name = tensor<string, []>("xt_61")];
            tensor<fp32, [1, 256, 1]> var_1628 = const()[name = tensor<string, []>("op_1628"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(191935872)))];
            tensor<fp32, [1, 256, 10240]> var_1631 = mul(x = generator_resblocks_2_alpha1_1, y = xt_61)[name = tensor<string, []>("op_1631")];
            tensor<fp32, [1, 256, 10240]> var_1632 = sin(x = var_1631)[name = tensor<string, []>("op_1632")];
            tensor<fp32, []> var_532_promoted_21 = const()[name = tensor<string, []>("op_532_promoted_21"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 10240]> var_1633 = pow(x = var_1632, y = var_532_promoted_21)[name = tensor<string, []>("op_1633")];
            tensor<fp32, [1, 256, 10240]> var_1634 = mul(x = var_1628, y = var_1633)[name = tensor<string, []>("op_1634")];
            tensor<fp32, [1, 256, 10240]> input_177 = add(x = xt_61, y = var_1634)[name = tensor<string, []>("input_177")];
            tensor<fp32, [256, 256, 11]> weight_79 = const()[name = tensor<string, []>("weight_79"), val = tensor<fp32, [256, 256, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(191936960)))];
            tensor<string, []> input_179_pad_type_0 = const()[name = tensor<string, []>("input_179_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_179_pad_0 = const()[name = tensor<string, []>("input_179_pad_0"), val = tensor<int32, [2]>([15, 15])];
            tensor<int32, [1]> input_179_dilations_0 = const()[name = tensor<string, []>("input_179_dilations_0"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> input_179_strides_0 = const()[name = tensor<string, []>("input_179_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_179_groups_0 = const()[name = tensor<string, []>("input_179_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 10240]> input_179 = conv(bias = generator_resblocks_2_convs1_1_bias, dilations = input_179_dilations_0, groups = input_179_groups_0, pad = input_179_pad_0, pad_type = input_179_pad_type_0, strides = input_179_strides_0, weight = weight_79, x = input_177)[name = tensor<string, []>("input_179")];
            tensor<fp32, [1, 512]> h_125 = linear(bias = generator_resblocks_2_adain2_1_fc_bias, weight = generator_resblocks_2_adain2_1_fc_weight, x = s)[name = tensor<string, []>("linear_32")];
            tensor<int32, [3]> var_1649 = const()[name = tensor<string, []>("op_1649"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_127 = reshape(shape = var_1649, x = h_125)[name = tensor<string, []>("h_127")];
            tensor<int32, [2]> var_1651_split_sizes_0 = const()[name = tensor<string, []>("op_1651_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_1651_axis_0 = const()[name = tensor<string, []>("op_1651_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_1651_0, tensor<fp32, [1, 256, 1]> var_1651_1 = split(axis = var_1651_axis_0, split_sizes = var_1651_split_sizes_0, x = h_127)[name = tensor<string, []>("op_1651")];
            tensor<fp32, []> var_1653_promoted = const()[name = tensor<string, []>("op_1653_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_1654 = add(x = var_1651_0, y = var_1653_promoted)[name = tensor<string, []>("op_1654")];
            tensor<fp32, [1, 256, 10240]> var_1655 = instance_norm(epsilon = var_516, x = input_179)[name = tensor<string, []>("op_1655")];
            tensor<fp32, [1, 256, 10240]> var_1656 = mul(x = var_1654, y = var_1655)[name = tensor<string, []>("op_1656")];
            tensor<fp32, [1, 256, 10240]> xt_63 = add(x = var_1656, y = var_1651_1)[name = tensor<string, []>("xt_63")];
            tensor<fp32, [1, 256, 1]> var_1658 = const()[name = tensor<string, []>("op_1658"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(194820608)))];
            tensor<fp32, [1, 256, 10240]> var_1661 = mul(x = generator_resblocks_2_alpha2_1, y = xt_63)[name = tensor<string, []>("op_1661")];
            tensor<fp32, [1, 256, 10240]> var_1662 = sin(x = var_1661)[name = tensor<string, []>("op_1662")];
            tensor<fp32, []> var_532_promoted_22 = const()[name = tensor<string, []>("op_532_promoted_22"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 10240]> var_1663 = pow(x = var_1662, y = var_532_promoted_22)[name = tensor<string, []>("op_1663")];
            tensor<fp32, [1, 256, 10240]> var_1664 = mul(x = var_1658, y = var_1663)[name = tensor<string, []>("op_1664")];
            tensor<fp32, [1, 256, 10240]> input_181 = add(x = xt_63, y = var_1664)[name = tensor<string, []>("input_181")];
            tensor<fp32, [256, 256, 11]> weight_81 = const()[name = tensor<string, []>("weight_81"), val = tensor<fp32, [256, 256, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(194821696)))];
            tensor<string, []> xt_65_pad_type_0 = const()[name = tensor<string, []>("xt_65_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_65_pad_0 = const()[name = tensor<string, []>("xt_65_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> xt_65_strides_0 = const()[name = tensor<string, []>("xt_65_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_65_dilations_0 = const()[name = tensor<string, []>("xt_65_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_65_groups_0 = const()[name = tensor<string, []>("xt_65_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 10240]> xt_65 = conv(bias = generator_resblocks_2_convs2_1_bias, dilations = xt_65_dilations_0, groups = xt_65_groups_0, pad = xt_65_pad_0, pad_type = xt_65_pad_type_0, strides = xt_65_strides_0, weight = weight_81, x = input_181)[name = tensor<string, []>("xt_65")];
            tensor<fp32, [1, 256, 10240]> input_183 = add(x = xt_65, y = input_175)[name = tensor<string, []>("input_183")];
            tensor<fp32, [1, 512]> h_129 = linear(bias = generator_resblocks_2_adain1_2_fc_bias, weight = generator_resblocks_2_adain1_2_fc_weight, x = s)[name = tensor<string, []>("linear_33")];
            tensor<int32, [3]> var_1680 = const()[name = tensor<string, []>("op_1680"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_131 = reshape(shape = var_1680, x = h_129)[name = tensor<string, []>("h_131")];
            tensor<int32, [2]> var_1682_split_sizes_0 = const()[name = tensor<string, []>("op_1682_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_1682_axis_0 = const()[name = tensor<string, []>("op_1682_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_1682_0, tensor<fp32, [1, 256, 1]> var_1682_1 = split(axis = var_1682_axis_0, split_sizes = var_1682_split_sizes_0, x = h_131)[name = tensor<string, []>("op_1682")];
            tensor<fp32, []> var_1684_promoted = const()[name = tensor<string, []>("op_1684_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_1685 = add(x = var_1682_0, y = var_1684_promoted)[name = tensor<string, []>("op_1685")];
            tensor<fp32, [1, 256, 10240]> var_1686 = instance_norm(epsilon = var_516, x = input_183)[name = tensor<string, []>("op_1686")];
            tensor<fp32, [1, 256, 10240]> var_1687 = mul(x = var_1685, y = var_1686)[name = tensor<string, []>("op_1687")];
            tensor<fp32, [1, 256, 10240]> xt_67 = add(x = var_1687, y = var_1682_1)[name = tensor<string, []>("xt_67")];
            tensor<fp32, [1, 256, 1]> var_1689 = const()[name = tensor<string, []>("op_1689"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197705344)))];
            tensor<fp32, [1, 256, 10240]> var_1692 = mul(x = generator_resblocks_2_alpha1_2, y = xt_67)[name = tensor<string, []>("op_1692")];
            tensor<fp32, [1, 256, 10240]> var_1693 = sin(x = var_1692)[name = tensor<string, []>("op_1693")];
            tensor<fp32, []> var_532_promoted_23 = const()[name = tensor<string, []>("op_532_promoted_23"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 10240]> var_1694 = pow(x = var_1693, y = var_532_promoted_23)[name = tensor<string, []>("op_1694")];
            tensor<fp32, [1, 256, 10240]> var_1695 = mul(x = var_1689, y = var_1694)[name = tensor<string, []>("op_1695")];
            tensor<fp32, [1, 256, 10240]> input_185 = add(x = xt_67, y = var_1695)[name = tensor<string, []>("input_185")];
            tensor<fp32, [256, 256, 11]> weight_83 = const()[name = tensor<string, []>("weight_83"), val = tensor<fp32, [256, 256, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197706432)))];
            tensor<string, []> input_187_pad_type_0 = const()[name = tensor<string, []>("input_187_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_187_pad_0 = const()[name = tensor<string, []>("input_187_pad_0"), val = tensor<int32, [2]>([25, 25])];
            tensor<int32, [1]> input_187_dilations_0 = const()[name = tensor<string, []>("input_187_dilations_0"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> input_187_strides_0 = const()[name = tensor<string, []>("input_187_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_187_groups_0 = const()[name = tensor<string, []>("input_187_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 10240]> input_187 = conv(bias = generator_resblocks_2_convs1_2_bias, dilations = input_187_dilations_0, groups = input_187_groups_0, pad = input_187_pad_0, pad_type = input_187_pad_type_0, strides = input_187_strides_0, weight = weight_83, x = input_185)[name = tensor<string, []>("input_187")];
            tensor<fp32, [1, 512]> h_133 = linear(bias = generator_resblocks_2_adain2_2_fc_bias, weight = generator_resblocks_2_adain2_2_fc_weight, x = s)[name = tensor<string, []>("linear_34")];
            tensor<int32, [3]> var_1710 = const()[name = tensor<string, []>("op_1710"), val = tensor<int32, [3]>([1, 512, 1])];
            tensor<fp32, [1, 512, 1]> h_135 = reshape(shape = var_1710, x = h_133)[name = tensor<string, []>("h_135")];
            tensor<int32, [2]> var_1712_split_sizes_0 = const()[name = tensor<string, []>("op_1712_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            tensor<int32, []> var_1712_axis_0 = const()[name = tensor<string, []>("op_1712_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 1]> var_1712_0, tensor<fp32, [1, 256, 1]> var_1712_1 = split(axis = var_1712_axis_0, split_sizes = var_1712_split_sizes_0, x = h_135)[name = tensor<string, []>("op_1712")];
            tensor<fp32, []> var_1714_promoted = const()[name = tensor<string, []>("op_1714_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 256, 1]> var_1715 = add(x = var_1712_0, y = var_1714_promoted)[name = tensor<string, []>("op_1715")];
            tensor<fp32, [1, 256, 10240]> var_1716 = instance_norm(epsilon = var_516, x = input_187)[name = tensor<string, []>("op_1716")];
            tensor<fp32, [1, 256, 10240]> var_1717 = mul(x = var_1715, y = var_1716)[name = tensor<string, []>("op_1717")];
            tensor<fp32, [1, 256, 10240]> xt_69 = add(x = var_1717, y = var_1712_1)[name = tensor<string, []>("xt_69")];
            tensor<fp32, [1, 256, 1]> var_1719 = const()[name = tensor<string, []>("op_1719"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(200590080)))];
            tensor<fp32, [1, 256, 10240]> var_1722 = mul(x = generator_resblocks_2_alpha2_2, y = xt_69)[name = tensor<string, []>("op_1722")];
            tensor<fp32, [1, 256, 10240]> var_1723 = sin(x = var_1722)[name = tensor<string, []>("op_1723")];
            tensor<fp32, []> var_532_promoted_24 = const()[name = tensor<string, []>("op_532_promoted_24"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 10240]> var_1724 = pow(x = var_1723, y = var_532_promoted_24)[name = tensor<string, []>("op_1724")];
            tensor<fp32, [1, 256, 10240]> var_1725 = mul(x = var_1719, y = var_1724)[name = tensor<string, []>("op_1725")];
            tensor<fp32, [1, 256, 10240]> input_189 = add(x = xt_69, y = var_1725)[name = tensor<string, []>("input_189")];
            tensor<fp32, [256, 256, 11]> weight_85 = const()[name = tensor<string, []>("weight_85"), val = tensor<fp32, [256, 256, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(200591168)))];
            tensor<string, []> xt_71_pad_type_0 = const()[name = tensor<string, []>("xt_71_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_71_pad_0 = const()[name = tensor<string, []>("xt_71_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> xt_71_strides_0 = const()[name = tensor<string, []>("xt_71_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_71_dilations_0 = const()[name = tensor<string, []>("xt_71_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_71_groups_0 = const()[name = tensor<string, []>("xt_71_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 256, 10240]> xt_71 = conv(bias = generator_resblocks_2_convs2_2_bias, dilations = xt_71_dilations_0, groups = xt_71_groups_0, pad = xt_71_pad_0, pad_type = xt_71_pad_type_0, strides = xt_71_strides_0, weight = weight_85, x = input_189)[name = tensor<string, []>("xt_71")];
            tensor<fp32, [1, 256, 10240]> var_1734 = add(x = xt_71, y = input_183)[name = tensor<string, []>("op_1734")];
            tensor<fp32, [1, 256, 10240]> xs_5 = add(x = xs_3, y = var_1734)[name = tensor<string, []>("xs_5")];
            tensor<fp32, []> _inversed_x_13_y_0 = const()[name = tensor<string, []>("_inversed_x_13_y_0"), val = tensor<fp32, []>(0x1.555556p-2)];
            tensor<fp32, [1, 256, 10240]> _inversed_x_13 = mul(x = xs_5, y = _inversed_x_13_y_0)[name = tensor<string, []>("_inversed_x_13")];
            tensor<fp32, [1, 256, 1]> var_1738 = const()[name = tensor<string, []>("op_1738"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(203474816)))];
            tensor<fp32, [1, 256, 10240]> var_1741 = mul(x = generator_alphas_1, y = _inversed_x_13)[name = tensor<string, []>("op_1741")];
            tensor<fp32, [1, 256, 10240]> var_1742 = sin(x = var_1741)[name = tensor<string, []>("op_1742")];
            tensor<fp32, []> var_532_promoted_25 = const()[name = tensor<string, []>("op_532_promoted_25"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 256, 10240]> var_1743 = pow(x = var_1742, y = var_532_promoted_25)[name = tensor<string, []>("op_1743")];
            tensor<fp32, [1, 256, 10240]> var_1744 = mul(x = var_1738, y = var_1743)[name = tensor<string, []>("op_1744")];
            tensor<fp32, [1, 256, 10240]> input_215 = add(x = _inversed_x_13, y = var_1744)[name = tensor<string, []>("input_215")];
            tensor<string, []> input_191_pad_type_0 = const()[name = tensor<string, []>("input_191_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_191_pad_0 = const()[name = tensor<string, []>("input_191_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> input_191_strides_0 = const()[name = tensor<string, []>("input_191_strides_0"), val = tensor<int32, [1]>([6])];
            tensor<int32, [1]> input_191_dilations_0 = const()[name = tensor<string, []>("input_191_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_191_groups_0 = const()[name = tensor<string, []>("input_191_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 51200]> input_191 = conv(bias = generator_noise_convs_1_bias, dilations = input_191_dilations_0, groups = input_191_groups_0, pad = input_191_pad_0, pad_type = input_191_pad_type_0, strides = input_191_strides_0, weight = generator_noise_convs_1_weight, x = input_95)[name = tensor<string, []>("input_191")];
            tensor<fp32, [1, 256]> h_137 = linear(bias = generator_noise_res_1_adain1_0_fc_bias, weight = generator_noise_res_1_adain1_0_fc_weight, x = s)[name = tensor<string, []>("linear_35")];
            tensor<int32, [3]> var_1831 = const()[name = tensor<string, []>("op_1831"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_139 = reshape(shape = var_1831, x = h_137)[name = tensor<string, []>("h_139")];
            tensor<int32, [2]> var_1833_split_sizes_0 = const()[name = tensor<string, []>("op_1833_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_1833_axis_0 = const()[name = tensor<string, []>("op_1833_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_1833_0, tensor<fp32, [1, 128, 1]> var_1833_1 = split(axis = var_1833_axis_0, split_sizes = var_1833_split_sizes_0, x = h_139)[name = tensor<string, []>("op_1833")];
            tensor<fp32, []> var_1835_promoted = const()[name = tensor<string, []>("op_1835_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_1836 = add(x = var_1833_0, y = var_1835_promoted)[name = tensor<string, []>("op_1836")];
            tensor<fp32, [1, 128, 51200]> var_1837 = instance_norm(epsilon = var_516, x = input_191)[name = tensor<string, []>("op_1837")];
            tensor<fp32, [1, 128, 51200]> var_1838 = mul(x = var_1836, y = var_1837)[name = tensor<string, []>("op_1838")];
            tensor<fp32, [1, 128, 51200]> xt_73 = add(x = var_1838, y = var_1833_1)[name = tensor<string, []>("xt_73")];
            tensor<fp32, [1, 128, 1]> var_1840 = const()[name = tensor<string, []>("op_1840"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(203475904)))];
            tensor<fp32, [1, 128, 51200]> var_1843 = mul(x = generator_noise_res_1_alpha1_0, y = xt_73)[name = tensor<string, []>("op_1843")];
            tensor<fp32, [1, 128, 51200]> var_1844 = sin(x = var_1843)[name = tensor<string, []>("op_1844")];
            tensor<fp32, []> var_532_promoted_26 = const()[name = tensor<string, []>("op_532_promoted_26"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 51200]> var_1845 = pow(x = var_1844, y = var_532_promoted_26)[name = tensor<string, []>("op_1845")];
            tensor<fp32, [1, 128, 51200]> var_1846 = mul(x = var_1840, y = var_1845)[name = tensor<string, []>("op_1846")];
            tensor<fp32, [1, 128, 51200]> input_193 = add(x = xt_73, y = var_1846)[name = tensor<string, []>("input_193")];
            tensor<fp32, [128, 128, 7]> weight_89 = const()[name = tensor<string, []>("weight_89"), val = tensor<fp32, [128, 128, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(203476480)))];
            tensor<string, []> input_195_pad_type_0 = const()[name = tensor<string, []>("input_195_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_195_pad_0 = const()[name = tensor<string, []>("input_195_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> input_195_strides_0 = const()[name = tensor<string, []>("input_195_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_195_dilations_0 = const()[name = tensor<string, []>("input_195_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_195_groups_0 = const()[name = tensor<string, []>("input_195_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 51200]> input_195 = conv(bias = generator_noise_res_1_convs1_0_bias, dilations = input_195_dilations_0, groups = input_195_groups_0, pad = input_195_pad_0, pad_type = input_195_pad_type_0, strides = input_195_strides_0, weight = weight_89, x = input_193)[name = tensor<string, []>("input_195")];
            tensor<fp32, [1, 256]> h_141 = linear(bias = generator_noise_res_1_adain2_0_fc_bias, weight = generator_noise_res_1_adain2_0_fc_weight, x = s)[name = tensor<string, []>("linear_36")];
            tensor<int32, [3]> var_1861 = const()[name = tensor<string, []>("op_1861"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_143 = reshape(shape = var_1861, x = h_141)[name = tensor<string, []>("h_143")];
            tensor<int32, [2]> var_1863_split_sizes_0 = const()[name = tensor<string, []>("op_1863_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_1863_axis_0 = const()[name = tensor<string, []>("op_1863_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_1863_0, tensor<fp32, [1, 128, 1]> var_1863_1 = split(axis = var_1863_axis_0, split_sizes = var_1863_split_sizes_0, x = h_143)[name = tensor<string, []>("op_1863")];
            tensor<fp32, []> var_1865_promoted = const()[name = tensor<string, []>("op_1865_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_1866 = add(x = var_1863_0, y = var_1865_promoted)[name = tensor<string, []>("op_1866")];
            tensor<fp32, [1, 128, 51200]> var_1867 = instance_norm(epsilon = var_516, x = input_195)[name = tensor<string, []>("op_1867")];
            tensor<fp32, [1, 128, 51200]> var_1868 = mul(x = var_1866, y = var_1867)[name = tensor<string, []>("op_1868")];
            tensor<fp32, [1, 128, 51200]> xt_75 = add(x = var_1868, y = var_1863_1)[name = tensor<string, []>("xt_75")];
            tensor<fp32, [1, 128, 1]> var_1870 = const()[name = tensor<string, []>("op_1870"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(203935296)))];
            tensor<fp32, [1, 128, 51200]> var_1873 = mul(x = generator_noise_res_1_alpha2_0, y = xt_75)[name = tensor<string, []>("op_1873")];
            tensor<fp32, [1, 128, 51200]> var_1874 = sin(x = var_1873)[name = tensor<string, []>("op_1874")];
            tensor<fp32, []> var_532_promoted_27 = const()[name = tensor<string, []>("op_532_promoted_27"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 51200]> var_1875 = pow(x = var_1874, y = var_532_promoted_27)[name = tensor<string, []>("op_1875")];
            tensor<fp32, [1, 128, 51200]> var_1876 = mul(x = var_1870, y = var_1875)[name = tensor<string, []>("op_1876")];
            tensor<fp32, [1, 128, 51200]> input_197 = add(x = xt_75, y = var_1876)[name = tensor<string, []>("input_197")];
            tensor<fp32, [128, 128, 7]> weight_91 = const()[name = tensor<string, []>("weight_91"), val = tensor<fp32, [128, 128, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(203935872)))];
            tensor<string, []> xt_77_pad_type_0 = const()[name = tensor<string, []>("xt_77_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_77_pad_0 = const()[name = tensor<string, []>("xt_77_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> xt_77_strides_0 = const()[name = tensor<string, []>("xt_77_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_77_dilations_0 = const()[name = tensor<string, []>("xt_77_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_77_groups_0 = const()[name = tensor<string, []>("xt_77_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 51200]> xt_77 = conv(bias = generator_noise_res_1_convs2_0_bias, dilations = xt_77_dilations_0, groups = xt_77_groups_0, pad = xt_77_pad_0, pad_type = xt_77_pad_type_0, strides = xt_77_strides_0, weight = weight_91, x = input_197)[name = tensor<string, []>("xt_77")];
            tensor<fp32, [1, 128, 51200]> input_199 = add(x = xt_77, y = input_191)[name = tensor<string, []>("input_199")];
            tensor<fp32, [1, 256]> h_145 = linear(bias = generator_noise_res_1_adain1_1_fc_bias, weight = generator_noise_res_1_adain1_1_fc_weight, x = s)[name = tensor<string, []>("linear_37")];
            tensor<int32, [3]> var_1892 = const()[name = tensor<string, []>("op_1892"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_147 = reshape(shape = var_1892, x = h_145)[name = tensor<string, []>("h_147")];
            tensor<int32, [2]> var_1894_split_sizes_0 = const()[name = tensor<string, []>("op_1894_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_1894_axis_0 = const()[name = tensor<string, []>("op_1894_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_1894_0, tensor<fp32, [1, 128, 1]> var_1894_1 = split(axis = var_1894_axis_0, split_sizes = var_1894_split_sizes_0, x = h_147)[name = tensor<string, []>("op_1894")];
            tensor<fp32, []> var_1896_promoted = const()[name = tensor<string, []>("op_1896_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_1897 = add(x = var_1894_0, y = var_1896_promoted)[name = tensor<string, []>("op_1897")];
            tensor<fp32, [1, 128, 51200]> var_1898 = instance_norm(epsilon = var_516, x = input_199)[name = tensor<string, []>("op_1898")];
            tensor<fp32, [1, 128, 51200]> var_1899 = mul(x = var_1897, y = var_1898)[name = tensor<string, []>("op_1899")];
            tensor<fp32, [1, 128, 51200]> xt_79 = add(x = var_1899, y = var_1894_1)[name = tensor<string, []>("xt_79")];
            tensor<fp32, [1, 128, 1]> var_1901 = const()[name = tensor<string, []>("op_1901"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(204394688)))];
            tensor<fp32, [1, 128, 51200]> var_1904 = mul(x = generator_noise_res_1_alpha1_1, y = xt_79)[name = tensor<string, []>("op_1904")];
            tensor<fp32, [1, 128, 51200]> var_1905 = sin(x = var_1904)[name = tensor<string, []>("op_1905")];
            tensor<fp32, []> var_532_promoted_28 = const()[name = tensor<string, []>("op_532_promoted_28"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 51200]> var_1906 = pow(x = var_1905, y = var_532_promoted_28)[name = tensor<string, []>("op_1906")];
            tensor<fp32, [1, 128, 51200]> var_1907 = mul(x = var_1901, y = var_1906)[name = tensor<string, []>("op_1907")];
            tensor<fp32, [1, 128, 51200]> input_201 = add(x = xt_79, y = var_1907)[name = tensor<string, []>("input_201")];
            tensor<fp32, [128, 128, 7]> weight_93 = const()[name = tensor<string, []>("weight_93"), val = tensor<fp32, [128, 128, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(204395264)))];
            tensor<string, []> input_203_pad_type_0 = const()[name = tensor<string, []>("input_203_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_203_pad_0 = const()[name = tensor<string, []>("input_203_pad_0"), val = tensor<int32, [2]>([9, 9])];
            tensor<int32, [1]> input_203_dilations_0 = const()[name = tensor<string, []>("input_203_dilations_0"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> input_203_strides_0 = const()[name = tensor<string, []>("input_203_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_203_groups_0 = const()[name = tensor<string, []>("input_203_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 51200]> input_203 = conv(bias = generator_noise_res_1_convs1_1_bias, dilations = input_203_dilations_0, groups = input_203_groups_0, pad = input_203_pad_0, pad_type = input_203_pad_type_0, strides = input_203_strides_0, weight = weight_93, x = input_201)[name = tensor<string, []>("input_203")];
            tensor<fp32, [1, 256]> h_149 = linear(bias = generator_noise_res_1_adain2_1_fc_bias, weight = generator_noise_res_1_adain2_1_fc_weight, x = s)[name = tensor<string, []>("linear_38")];
            tensor<int32, [3]> var_1922 = const()[name = tensor<string, []>("op_1922"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_151 = reshape(shape = var_1922, x = h_149)[name = tensor<string, []>("h_151")];
            tensor<int32, [2]> var_1924_split_sizes_0 = const()[name = tensor<string, []>("op_1924_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_1924_axis_0 = const()[name = tensor<string, []>("op_1924_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_1924_0, tensor<fp32, [1, 128, 1]> var_1924_1 = split(axis = var_1924_axis_0, split_sizes = var_1924_split_sizes_0, x = h_151)[name = tensor<string, []>("op_1924")];
            tensor<fp32, []> var_1926_promoted = const()[name = tensor<string, []>("op_1926_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_1927 = add(x = var_1924_0, y = var_1926_promoted)[name = tensor<string, []>("op_1927")];
            tensor<fp32, [1, 128, 51200]> var_1928 = instance_norm(epsilon = var_516, x = input_203)[name = tensor<string, []>("op_1928")];
            tensor<fp32, [1, 128, 51200]> var_1929 = mul(x = var_1927, y = var_1928)[name = tensor<string, []>("op_1929")];
            tensor<fp32, [1, 128, 51200]> xt_81 = add(x = var_1929, y = var_1924_1)[name = tensor<string, []>("xt_81")];
            tensor<fp32, [1, 128, 1]> var_1931 = const()[name = tensor<string, []>("op_1931"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(204854080)))];
            tensor<fp32, [1, 128, 51200]> var_1934 = mul(x = generator_noise_res_1_alpha2_1, y = xt_81)[name = tensor<string, []>("op_1934")];
            tensor<fp32, [1, 128, 51200]> var_1935 = sin(x = var_1934)[name = tensor<string, []>("op_1935")];
            tensor<fp32, []> var_532_promoted_29 = const()[name = tensor<string, []>("op_532_promoted_29"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 51200]> var_1936 = pow(x = var_1935, y = var_532_promoted_29)[name = tensor<string, []>("op_1936")];
            tensor<fp32, [1, 128, 51200]> var_1937 = mul(x = var_1931, y = var_1936)[name = tensor<string, []>("op_1937")];
            tensor<fp32, [1, 128, 51200]> input_205 = add(x = xt_81, y = var_1937)[name = tensor<string, []>("input_205")];
            tensor<fp32, [128, 128, 7]> weight_95 = const()[name = tensor<string, []>("weight_95"), val = tensor<fp32, [128, 128, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(204854656)))];
            tensor<string, []> xt_83_pad_type_0 = const()[name = tensor<string, []>("xt_83_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_83_pad_0 = const()[name = tensor<string, []>("xt_83_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> xt_83_strides_0 = const()[name = tensor<string, []>("xt_83_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_83_dilations_0 = const()[name = tensor<string, []>("xt_83_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_83_groups_0 = const()[name = tensor<string, []>("xt_83_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 51200]> xt_83 = conv(bias = generator_noise_res_1_convs2_1_bias, dilations = xt_83_dilations_0, groups = xt_83_groups_0, pad = xt_83_pad_0, pad_type = xt_83_pad_type_0, strides = xt_83_strides_0, weight = weight_95, x = input_205)[name = tensor<string, []>("xt_83")];
            tensor<fp32, [1, 128, 51200]> input_207 = add(x = xt_83, y = input_199)[name = tensor<string, []>("input_207")];
            tensor<fp32, [1, 256]> h_153 = linear(bias = generator_noise_res_1_adain1_2_fc_bias, weight = generator_noise_res_1_adain1_2_fc_weight, x = s)[name = tensor<string, []>("linear_39")];
            tensor<int32, [3]> var_1953 = const()[name = tensor<string, []>("op_1953"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_155 = reshape(shape = var_1953, x = h_153)[name = tensor<string, []>("h_155")];
            tensor<int32, [2]> var_1955_split_sizes_0 = const()[name = tensor<string, []>("op_1955_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_1955_axis_0 = const()[name = tensor<string, []>("op_1955_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_1955_0, tensor<fp32, [1, 128, 1]> var_1955_1 = split(axis = var_1955_axis_0, split_sizes = var_1955_split_sizes_0, x = h_155)[name = tensor<string, []>("op_1955")];
            tensor<fp32, []> var_1957_promoted = const()[name = tensor<string, []>("op_1957_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_1958 = add(x = var_1955_0, y = var_1957_promoted)[name = tensor<string, []>("op_1958")];
            tensor<fp32, [1, 128, 51200]> var_1959 = instance_norm(epsilon = var_516, x = input_207)[name = tensor<string, []>("op_1959")];
            tensor<fp32, [1, 128, 51200]> var_1960 = mul(x = var_1958, y = var_1959)[name = tensor<string, []>("op_1960")];
            tensor<fp32, [1, 128, 51200]> xt_85 = add(x = var_1960, y = var_1955_1)[name = tensor<string, []>("xt_85")];
            tensor<fp32, [1, 128, 1]> var_1962 = const()[name = tensor<string, []>("op_1962"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(205313472)))];
            tensor<fp32, [1, 128, 51200]> var_1965 = mul(x = generator_noise_res_1_alpha1_2, y = xt_85)[name = tensor<string, []>("op_1965")];
            tensor<fp32, [1, 128, 51200]> var_1966 = sin(x = var_1965)[name = tensor<string, []>("op_1966")];
            tensor<fp32, []> var_532_promoted_30 = const()[name = tensor<string, []>("op_532_promoted_30"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 51200]> var_1967 = pow(x = var_1966, y = var_532_promoted_30)[name = tensor<string, []>("op_1967")];
            tensor<fp32, [1, 128, 51200]> var_1968 = mul(x = var_1962, y = var_1967)[name = tensor<string, []>("op_1968")];
            tensor<fp32, [1, 128, 51200]> input_209 = add(x = xt_85, y = var_1968)[name = tensor<string, []>("input_209")];
            tensor<fp32, [128, 128, 7]> weight_97 = const()[name = tensor<string, []>("weight_97"), val = tensor<fp32, [128, 128, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(205314048)))];
            tensor<string, []> input_211_pad_type_0 = const()[name = tensor<string, []>("input_211_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_211_pad_0 = const()[name = tensor<string, []>("input_211_pad_0"), val = tensor<int32, [2]>([15, 15])];
            tensor<int32, [1]> input_211_dilations_0 = const()[name = tensor<string, []>("input_211_dilations_0"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> input_211_strides_0 = const()[name = tensor<string, []>("input_211_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_211_groups_0 = const()[name = tensor<string, []>("input_211_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 51200]> input_211 = conv(bias = generator_noise_res_1_convs1_2_bias, dilations = input_211_dilations_0, groups = input_211_groups_0, pad = input_211_pad_0, pad_type = input_211_pad_type_0, strides = input_211_strides_0, weight = weight_97, x = input_209)[name = tensor<string, []>("input_211")];
            tensor<fp32, [1, 256]> h_157 = linear(bias = generator_noise_res_1_adain2_2_fc_bias, weight = generator_noise_res_1_adain2_2_fc_weight, x = s)[name = tensor<string, []>("linear_40")];
            tensor<int32, [3]> var_1983 = const()[name = tensor<string, []>("op_1983"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_159 = reshape(shape = var_1983, x = h_157)[name = tensor<string, []>("h_159")];
            tensor<int32, [2]> var_1985_split_sizes_0 = const()[name = tensor<string, []>("op_1985_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_1985_axis_0 = const()[name = tensor<string, []>("op_1985_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_1985_0, tensor<fp32, [1, 128, 1]> var_1985_1 = split(axis = var_1985_axis_0, split_sizes = var_1985_split_sizes_0, x = h_159)[name = tensor<string, []>("op_1985")];
            tensor<fp32, []> var_1987_promoted = const()[name = tensor<string, []>("op_1987_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_1988 = add(x = var_1985_0, y = var_1987_promoted)[name = tensor<string, []>("op_1988")];
            tensor<fp32, [1, 128, 51200]> var_1989 = instance_norm(epsilon = var_516, x = input_211)[name = tensor<string, []>("op_1989")];
            tensor<fp32, [1, 128, 51200]> var_1990 = mul(x = var_1988, y = var_1989)[name = tensor<string, []>("op_1990")];
            tensor<fp32, [1, 128, 51200]> xt_87 = add(x = var_1990, y = var_1985_1)[name = tensor<string, []>("xt_87")];
            tensor<fp32, [1, 128, 1]> var_1992 = const()[name = tensor<string, []>("op_1992"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(205772864)))];
            tensor<fp32, [1, 128, 51200]> var_1995 = mul(x = generator_noise_res_1_alpha2_2, y = xt_87)[name = tensor<string, []>("op_1995")];
            tensor<fp32, [1, 128, 51200]> var_1996 = sin(x = var_1995)[name = tensor<string, []>("op_1996")];
            tensor<fp32, []> var_532_promoted_31 = const()[name = tensor<string, []>("op_532_promoted_31"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 51200]> var_1997 = pow(x = var_1996, y = var_532_promoted_31)[name = tensor<string, []>("op_1997")];
            tensor<fp32, [1, 128, 51200]> var_1998 = mul(x = var_1992, y = var_1997)[name = tensor<string, []>("op_1998")];
            tensor<fp32, [1, 128, 51200]> input_213 = add(x = xt_87, y = var_1998)[name = tensor<string, []>("input_213")];
            tensor<fp32, [128, 128, 7]> weight_99 = const()[name = tensor<string, []>("weight_99"), val = tensor<fp32, [128, 128, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(205773440)))];
            tensor<string, []> xt_89_pad_type_0 = const()[name = tensor<string, []>("xt_89_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_89_pad_0 = const()[name = tensor<string, []>("xt_89_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> xt_89_strides_0 = const()[name = tensor<string, []>("xt_89_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_89_dilations_0 = const()[name = tensor<string, []>("xt_89_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_89_groups_0 = const()[name = tensor<string, []>("xt_89_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 51200]> xt_89 = conv(bias = generator_noise_res_1_convs2_2_bias, dilations = xt_89_dilations_0, groups = xt_89_groups_0, pad = xt_89_pad_0, pad_type = xt_89_pad_type_0, strides = xt_89_strides_0, weight = weight_99, x = input_213)[name = tensor<string, []>("xt_89")];
            tensor<fp32, [1, 128, 51200]> x_source_3 = add(x = xt_89, y = input_207)[name = tensor<string, []>("x_source_3")];
            tensor<fp32, [256, 128, 10]> var_2008 = const()[name = tensor<string, []>("op_2008"), val = tensor<fp32, [256, 128, 10]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(206232256)))];
            tensor<string, []> conv_transpose_1_pad_type_0 = const()[name = tensor<string, []>("conv_transpose_1_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> conv_transpose_1_pad_0 = const()[name = tensor<string, []>("conv_transpose_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_transpose_1_strides_0 = const()[name = tensor<string, []>("conv_transpose_1_strides_0"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> conv_transpose_1_dilations_0 = const()[name = tensor<string, []>("conv_transpose_1_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv_transpose_1_groups_0 = const()[name = tensor<string, []>("conv_transpose_1_groups_0"), val = tensor<int32, []>(1)];
            tensor<int32, [3]> conv_transpose_1_has_output_shape_output_shape_0 = const()[name = tensor<string, []>("conv_transpose_1_has_output_shape_output_shape_0"), val = tensor<int32, [3]>([1, 128, 51205])];
            tensor<fp32, [1, 128, 51205]> conv_transpose_1_has_output_shape = conv_transpose(bias = generator_ups_1_bias, dilations = conv_transpose_1_dilations_0, groups = conv_transpose_1_groups_0, output_shape = conv_transpose_1_has_output_shape_output_shape_0, pad = conv_transpose_1_pad_0, pad_type = conv_transpose_1_pad_type_0, strides = conv_transpose_1_strides_0, weight = var_2008, x = input_215)[name = tensor<string, []>("conv_transpose_1_has_output_shape")];
            tensor<int32, [3]> x_15_begin_0 = const()[name = tensor<string, []>("x_15_begin_0"), val = tensor<int32, [3]>([0, 0, 3])];
            tensor<int32, [3]> x_15_end_0 = const()[name = tensor<string, []>("x_15_end_0"), val = tensor<int32, [3]>([0, 0, 51203])];
            tensor<bool, [3]> x_15_begin_mask_0 = const()[name = tensor<string, []>("x_15_begin_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<bool, [3]> x_15_end_mask_0 = const()[name = tensor<string, []>("x_15_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp32, [1, 128, 51200]> x_15 = slice_by_index(begin = x_15_begin_0, begin_mask = x_15_begin_mask_0, end = x_15_end_0, end_mask = x_15_end_mask_0, x = conv_transpose_1_has_output_shape)[name = tensor<string, []>("x_15")];
            tensor<fp32, [1, 128, 51200]> input_217 = add(x = x_15, y = x_source_3)[name = tensor<string, []>("input_217")];
            tensor<fp32, [1, 256]> h_161 = linear(bias = generator_resblocks_3_adain1_0_fc_bias, weight = generator_resblocks_3_adain1_0_fc_weight, x = s)[name = tensor<string, []>("linear_41")];
            tensor<int32, [3]> var_2094 = const()[name = tensor<string, []>("op_2094"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_163 = reshape(shape = var_2094, x = h_161)[name = tensor<string, []>("h_163")];
            tensor<int32, [2]> var_2096_split_sizes_0 = const()[name = tensor<string, []>("op_2096_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_2096_axis_0 = const()[name = tensor<string, []>("op_2096_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_2096_0, tensor<fp32, [1, 128, 1]> var_2096_1 = split(axis = var_2096_axis_0, split_sizes = var_2096_split_sizes_0, x = h_163)[name = tensor<string, []>("op_2096")];
            tensor<fp32, []> var_2098_promoted = const()[name = tensor<string, []>("op_2098_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_2099 = add(x = var_2096_0, y = var_2098_promoted)[name = tensor<string, []>("op_2099")];
            tensor<fp32, [1, 128, 51200]> var_2100 = instance_norm(epsilon = var_516, x = input_217)[name = tensor<string, []>("op_2100")];
            tensor<fp32, [1, 128, 51200]> var_2101 = mul(x = var_2099, y = var_2100)[name = tensor<string, []>("op_2101")];
            tensor<fp32, [1, 128, 51200]> xt_91 = add(x = var_2101, y = var_2096_1)[name = tensor<string, []>("xt_91")];
            tensor<fp32, [1, 128, 1]> var_2103 = const()[name = tensor<string, []>("op_2103"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(207543040)))];
            tensor<fp32, [1, 128, 51200]> var_2106 = mul(x = generator_resblocks_3_alpha1_0, y = xt_91)[name = tensor<string, []>("op_2106")];
            tensor<fp32, [1, 128, 51200]> var_2107 = sin(x = var_2106)[name = tensor<string, []>("op_2107")];
            tensor<fp32, []> var_532_promoted_32 = const()[name = tensor<string, []>("op_532_promoted_32"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 51200]> var_2108 = pow(x = var_2107, y = var_532_promoted_32)[name = tensor<string, []>("op_2108")];
            tensor<fp32, [1, 128, 51200]> var_2109 = mul(x = var_2103, y = var_2108)[name = tensor<string, []>("op_2109")];
            tensor<fp32, [1, 128, 51200]> input_219 = add(x = xt_91, y = var_2109)[name = tensor<string, []>("input_219")];
            tensor<fp32, [128, 128, 3]> weight_101 = const()[name = tensor<string, []>("weight_101"), val = tensor<fp32, [128, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(207543616)))];
            tensor<string, []> input_221_pad_type_0 = const()[name = tensor<string, []>("input_221_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_221_pad_0 = const()[name = tensor<string, []>("input_221_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> input_221_strides_0 = const()[name = tensor<string, []>("input_221_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_221_dilations_0 = const()[name = tensor<string, []>("input_221_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_221_groups_0 = const()[name = tensor<string, []>("input_221_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 51200]> input_221 = conv(bias = generator_resblocks_3_convs1_0_bias, dilations = input_221_dilations_0, groups = input_221_groups_0, pad = input_221_pad_0, pad_type = input_221_pad_type_0, strides = input_221_strides_0, weight = weight_101, x = input_219)[name = tensor<string, []>("input_221")];
            tensor<fp32, [1, 256]> h_165 = linear(bias = generator_resblocks_3_adain2_0_fc_bias, weight = generator_resblocks_3_adain2_0_fc_weight, x = s)[name = tensor<string, []>("linear_42")];
            tensor<int32, [3]> var_2124 = const()[name = tensor<string, []>("op_2124"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_167 = reshape(shape = var_2124, x = h_165)[name = tensor<string, []>("h_167")];
            tensor<int32, [2]> var_2126_split_sizes_0 = const()[name = tensor<string, []>("op_2126_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_2126_axis_0 = const()[name = tensor<string, []>("op_2126_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_2126_0, tensor<fp32, [1, 128, 1]> var_2126_1 = split(axis = var_2126_axis_0, split_sizes = var_2126_split_sizes_0, x = h_167)[name = tensor<string, []>("op_2126")];
            tensor<fp32, []> var_2128_promoted = const()[name = tensor<string, []>("op_2128_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_2129 = add(x = var_2126_0, y = var_2128_promoted)[name = tensor<string, []>("op_2129")];
            tensor<fp32, [1, 128, 51200]> var_2130 = instance_norm(epsilon = var_516, x = input_221)[name = tensor<string, []>("op_2130")];
            tensor<fp32, [1, 128, 51200]> var_2131 = mul(x = var_2129, y = var_2130)[name = tensor<string, []>("op_2131")];
            tensor<fp32, [1, 128, 51200]> xt_93 = add(x = var_2131, y = var_2126_1)[name = tensor<string, []>("xt_93")];
            tensor<fp32, [1, 128, 1]> var_2133 = const()[name = tensor<string, []>("op_2133"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(207740288)))];
            tensor<fp32, [1, 128, 51200]> var_2136 = mul(x = generator_resblocks_3_alpha2_0, y = xt_93)[name = tensor<string, []>("op_2136")];
            tensor<fp32, [1, 128, 51200]> var_2137 = sin(x = var_2136)[name = tensor<string, []>("op_2137")];
            tensor<fp32, []> var_532_promoted_33 = const()[name = tensor<string, []>("op_532_promoted_33"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 51200]> var_2138 = pow(x = var_2137, y = var_532_promoted_33)[name = tensor<string, []>("op_2138")];
            tensor<fp32, [1, 128, 51200]> var_2139 = mul(x = var_2133, y = var_2138)[name = tensor<string, []>("op_2139")];
            tensor<fp32, [1, 128, 51200]> input_223 = add(x = xt_93, y = var_2139)[name = tensor<string, []>("input_223")];
            tensor<fp32, [128, 128, 3]> weight_103 = const()[name = tensor<string, []>("weight_103"), val = tensor<fp32, [128, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(207740864)))];
            tensor<string, []> xt_95_pad_type_0 = const()[name = tensor<string, []>("xt_95_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_95_pad_0 = const()[name = tensor<string, []>("xt_95_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> xt_95_strides_0 = const()[name = tensor<string, []>("xt_95_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_95_dilations_0 = const()[name = tensor<string, []>("xt_95_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_95_groups_0 = const()[name = tensor<string, []>("xt_95_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 51200]> xt_95 = conv(bias = generator_resblocks_3_convs2_0_bias, dilations = xt_95_dilations_0, groups = xt_95_groups_0, pad = xt_95_pad_0, pad_type = xt_95_pad_type_0, strides = xt_95_strides_0, weight = weight_103, x = input_223)[name = tensor<string, []>("xt_95")];
            tensor<fp32, [1, 128, 51200]> input_225 = add(x = xt_95, y = input_217)[name = tensor<string, []>("input_225")];
            tensor<fp32, [1, 256]> h_169 = linear(bias = generator_resblocks_3_adain1_1_fc_bias, weight = generator_resblocks_3_adain1_1_fc_weight, x = s)[name = tensor<string, []>("linear_43")];
            tensor<int32, [3]> var_2155 = const()[name = tensor<string, []>("op_2155"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_171 = reshape(shape = var_2155, x = h_169)[name = tensor<string, []>("h_171")];
            tensor<int32, [2]> var_2157_split_sizes_0 = const()[name = tensor<string, []>("op_2157_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_2157_axis_0 = const()[name = tensor<string, []>("op_2157_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_2157_0, tensor<fp32, [1, 128, 1]> var_2157_1 = split(axis = var_2157_axis_0, split_sizes = var_2157_split_sizes_0, x = h_171)[name = tensor<string, []>("op_2157")];
            tensor<fp32, []> var_2159_promoted = const()[name = tensor<string, []>("op_2159_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_2160 = add(x = var_2157_0, y = var_2159_promoted)[name = tensor<string, []>("op_2160")];
            tensor<fp32, [1, 128, 51200]> var_2161 = instance_norm(epsilon = var_516, x = input_225)[name = tensor<string, []>("op_2161")];
            tensor<fp32, [1, 128, 51200]> var_2162 = mul(x = var_2160, y = var_2161)[name = tensor<string, []>("op_2162")];
            tensor<fp32, [1, 128, 51200]> xt_97 = add(x = var_2162, y = var_2157_1)[name = tensor<string, []>("xt_97")];
            tensor<fp32, [1, 128, 1]> var_2164 = const()[name = tensor<string, []>("op_2164"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(207937536)))];
            tensor<fp32, [1, 128, 51200]> var_2167 = mul(x = generator_resblocks_3_alpha1_1, y = xt_97)[name = tensor<string, []>("op_2167")];
            tensor<fp32, [1, 128, 51200]> var_2168 = sin(x = var_2167)[name = tensor<string, []>("op_2168")];
            tensor<fp32, []> var_532_promoted_34 = const()[name = tensor<string, []>("op_532_promoted_34"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 51200]> var_2169 = pow(x = var_2168, y = var_532_promoted_34)[name = tensor<string, []>("op_2169")];
            tensor<fp32, [1, 128, 51200]> var_2170 = mul(x = var_2164, y = var_2169)[name = tensor<string, []>("op_2170")];
            tensor<fp32, [1, 128, 51200]> input_227 = add(x = xt_97, y = var_2170)[name = tensor<string, []>("input_227")];
            tensor<fp32, [128, 128, 3]> weight_105 = const()[name = tensor<string, []>("weight_105"), val = tensor<fp32, [128, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(207938112)))];
            tensor<string, []> input_229_pad_type_0 = const()[name = tensor<string, []>("input_229_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_229_pad_0 = const()[name = tensor<string, []>("input_229_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> input_229_dilations_0 = const()[name = tensor<string, []>("input_229_dilations_0"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> input_229_strides_0 = const()[name = tensor<string, []>("input_229_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_229_groups_0 = const()[name = tensor<string, []>("input_229_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 51200]> input_229 = conv(bias = generator_resblocks_3_convs1_1_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 = weight_105, x = input_227)[name = tensor<string, []>("input_229")];
            tensor<fp32, [1, 256]> h_173 = linear(bias = generator_resblocks_3_adain2_1_fc_bias, weight = generator_resblocks_3_adain2_1_fc_weight, x = s)[name = tensor<string, []>("linear_44")];
            tensor<int32, [3]> var_2185 = const()[name = tensor<string, []>("op_2185"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_175 = reshape(shape = var_2185, x = h_173)[name = tensor<string, []>("h_175")];
            tensor<int32, [2]> var_2187_split_sizes_0 = const()[name = tensor<string, []>("op_2187_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_2187_axis_0 = const()[name = tensor<string, []>("op_2187_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_2187_0, tensor<fp32, [1, 128, 1]> var_2187_1 = split(axis = var_2187_axis_0, split_sizes = var_2187_split_sizes_0, x = h_175)[name = tensor<string, []>("op_2187")];
            tensor<fp32, []> var_2189_promoted = const()[name = tensor<string, []>("op_2189_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_2190 = add(x = var_2187_0, y = var_2189_promoted)[name = tensor<string, []>("op_2190")];
            tensor<fp32, [1, 128, 51200]> var_2191 = instance_norm(epsilon = var_516, x = input_229)[name = tensor<string, []>("op_2191")];
            tensor<fp32, [1, 128, 51200]> var_2192 = mul(x = var_2190, y = var_2191)[name = tensor<string, []>("op_2192")];
            tensor<fp32, [1, 128, 51200]> xt_99 = add(x = var_2192, y = var_2187_1)[name = tensor<string, []>("xt_99")];
            tensor<fp32, [1, 128, 1]> var_2194 = const()[name = tensor<string, []>("op_2194"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(208134784)))];
            tensor<fp32, [1, 128, 51200]> var_2197 = mul(x = generator_resblocks_3_alpha2_1, y = xt_99)[name = tensor<string, []>("op_2197")];
            tensor<fp32, [1, 128, 51200]> var_2198 = sin(x = var_2197)[name = tensor<string, []>("op_2198")];
            tensor<fp32, []> var_532_promoted_35 = const()[name = tensor<string, []>("op_532_promoted_35"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 51200]> var_2199 = pow(x = var_2198, y = var_532_promoted_35)[name = tensor<string, []>("op_2199")];
            tensor<fp32, [1, 128, 51200]> var_2200 = mul(x = var_2194, y = var_2199)[name = tensor<string, []>("op_2200")];
            tensor<fp32, [1, 128, 51200]> input_231 = add(x = xt_99, y = var_2200)[name = tensor<string, []>("input_231")];
            tensor<fp32, [128, 128, 3]> weight_107 = const()[name = tensor<string, []>("weight_107"), val = tensor<fp32, [128, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(208135360)))];
            tensor<string, []> xt_101_pad_type_0 = const()[name = tensor<string, []>("xt_101_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_101_pad_0 = const()[name = tensor<string, []>("xt_101_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> xt_101_strides_0 = const()[name = tensor<string, []>("xt_101_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_101_dilations_0 = const()[name = tensor<string, []>("xt_101_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_101_groups_0 = const()[name = tensor<string, []>("xt_101_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 51200]> xt_101 = conv(bias = generator_resblocks_3_convs2_1_bias, dilations = xt_101_dilations_0, groups = xt_101_groups_0, pad = xt_101_pad_0, pad_type = xt_101_pad_type_0, strides = xt_101_strides_0, weight = weight_107, x = input_231)[name = tensor<string, []>("xt_101")];
            tensor<fp32, [1, 128, 51200]> input_233 = add(x = xt_101, y = input_225)[name = tensor<string, []>("input_233")];
            tensor<fp32, [1, 256]> h_177 = linear(bias = generator_resblocks_3_adain1_2_fc_bias, weight = generator_resblocks_3_adain1_2_fc_weight, x = s)[name = tensor<string, []>("linear_45")];
            tensor<int32, [3]> var_2216 = const()[name = tensor<string, []>("op_2216"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_179 = reshape(shape = var_2216, x = h_177)[name = tensor<string, []>("h_179")];
            tensor<int32, [2]> var_2218_split_sizes_0 = const()[name = tensor<string, []>("op_2218_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_2218_axis_0 = const()[name = tensor<string, []>("op_2218_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_2218_0, tensor<fp32, [1, 128, 1]> var_2218_1 = split(axis = var_2218_axis_0, split_sizes = var_2218_split_sizes_0, x = h_179)[name = tensor<string, []>("op_2218")];
            tensor<fp32, []> var_2220_promoted = const()[name = tensor<string, []>("op_2220_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_2221 = add(x = var_2218_0, y = var_2220_promoted)[name = tensor<string, []>("op_2221")];
            tensor<fp32, [1, 128, 51200]> var_2222 = instance_norm(epsilon = var_516, x = input_233)[name = tensor<string, []>("op_2222")];
            tensor<fp32, [1, 128, 51200]> var_2223 = mul(x = var_2221, y = var_2222)[name = tensor<string, []>("op_2223")];
            tensor<fp32, [1, 128, 51200]> xt_103 = add(x = var_2223, y = var_2218_1)[name = tensor<string, []>("xt_103")];
            tensor<fp32, [1, 128, 1]> var_2225 = const()[name = tensor<string, []>("op_2225"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(208332032)))];
            tensor<fp32, [1, 128, 51200]> var_2228 = mul(x = generator_resblocks_3_alpha1_2, y = xt_103)[name = tensor<string, []>("op_2228")];
            tensor<fp32, [1, 128, 51200]> var_2229 = sin(x = var_2228)[name = tensor<string, []>("op_2229")];
            tensor<fp32, []> var_532_promoted_36 = const()[name = tensor<string, []>("op_532_promoted_36"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 51200]> var_2230 = pow(x = var_2229, y = var_532_promoted_36)[name = tensor<string, []>("op_2230")];
            tensor<fp32, [1, 128, 51200]> var_2231 = mul(x = var_2225, y = var_2230)[name = tensor<string, []>("op_2231")];
            tensor<fp32, [1, 128, 51200]> input_235 = add(x = xt_103, y = var_2231)[name = tensor<string, []>("input_235")];
            tensor<fp32, [128, 128, 3]> weight_109 = const()[name = tensor<string, []>("weight_109"), val = tensor<fp32, [128, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(208332608)))];
            tensor<string, []> input_237_pad_type_0 = const()[name = tensor<string, []>("input_237_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_237_pad_0 = const()[name = tensor<string, []>("input_237_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> input_237_dilations_0 = const()[name = tensor<string, []>("input_237_dilations_0"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> input_237_strides_0 = const()[name = tensor<string, []>("input_237_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_237_groups_0 = const()[name = tensor<string, []>("input_237_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 51200]> input_237 = conv(bias = generator_resblocks_3_convs1_2_bias, 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 = weight_109, x = input_235)[name = tensor<string, []>("input_237")];
            tensor<fp32, [1, 256]> h_181 = linear(bias = generator_resblocks_3_adain2_2_fc_bias, weight = generator_resblocks_3_adain2_2_fc_weight, x = s)[name = tensor<string, []>("linear_46")];
            tensor<int32, [3]> var_2246 = const()[name = tensor<string, []>("op_2246"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_183 = reshape(shape = var_2246, x = h_181)[name = tensor<string, []>("h_183")];
            tensor<int32, [2]> var_2248_split_sizes_0 = const()[name = tensor<string, []>("op_2248_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_2248_axis_0 = const()[name = tensor<string, []>("op_2248_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_2248_0, tensor<fp32, [1, 128, 1]> var_2248_1 = split(axis = var_2248_axis_0, split_sizes = var_2248_split_sizes_0, x = h_183)[name = tensor<string, []>("op_2248")];
            tensor<fp32, []> var_2250_promoted = const()[name = tensor<string, []>("op_2250_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_2251 = add(x = var_2248_0, y = var_2250_promoted)[name = tensor<string, []>("op_2251")];
            tensor<fp32, [1, 128, 51200]> var_2252 = instance_norm(epsilon = var_516, x = input_237)[name = tensor<string, []>("op_2252")];
            tensor<fp32, [1, 128, 51200]> var_2253 = mul(x = var_2251, y = var_2252)[name = tensor<string, []>("op_2253")];
            tensor<fp32, [1, 128, 51200]> xt_105 = add(x = var_2253, y = var_2248_1)[name = tensor<string, []>("xt_105")];
            tensor<fp32, [1, 128, 1]> var_2255 = const()[name = tensor<string, []>("op_2255"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(208529280)))];
            tensor<fp32, [1, 128, 51200]> var_2258 = mul(x = generator_resblocks_3_alpha2_2, y = xt_105)[name = tensor<string, []>("op_2258")];
            tensor<fp32, [1, 128, 51200]> var_2259 = sin(x = var_2258)[name = tensor<string, []>("op_2259")];
            tensor<fp32, []> var_532_promoted_37 = const()[name = tensor<string, []>("op_532_promoted_37"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 51200]> var_2260 = pow(x = var_2259, y = var_532_promoted_37)[name = tensor<string, []>("op_2260")];
            tensor<fp32, [1, 128, 51200]> var_2261 = mul(x = var_2255, y = var_2260)[name = tensor<string, []>("op_2261")];
            tensor<fp32, [1, 128, 51200]> input_239 = add(x = xt_105, y = var_2261)[name = tensor<string, []>("input_239")];
            tensor<fp32, [128, 128, 3]> weight_111 = const()[name = tensor<string, []>("weight_111"), val = tensor<fp32, [128, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(208529856)))];
            tensor<string, []> xt_107_pad_type_0 = const()[name = tensor<string, []>("xt_107_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_107_pad_0 = const()[name = tensor<string, []>("xt_107_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> xt_107_strides_0 = const()[name = tensor<string, []>("xt_107_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_107_dilations_0 = const()[name = tensor<string, []>("xt_107_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_107_groups_0 = const()[name = tensor<string, []>("xt_107_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 51200]> xt_107 = conv(bias = generator_resblocks_3_convs2_2_bias, dilations = xt_107_dilations_0, groups = xt_107_groups_0, pad = xt_107_pad_0, pad_type = xt_107_pad_type_0, strides = xt_107_strides_0, weight = weight_111, x = input_239)[name = tensor<string, []>("xt_107")];
            tensor<fp32, [1, 128, 51200]> xs_7 = add(x = xt_107, y = input_233)[name = tensor<string, []>("xs_7")];
            tensor<fp32, [1, 256]> h_185 = linear(bias = generator_resblocks_4_adain1_0_fc_bias, weight = generator_resblocks_4_adain1_0_fc_weight, x = s)[name = tensor<string, []>("linear_47")];
            tensor<int32, [3]> var_2349 = const()[name = tensor<string, []>("op_2349"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_187 = reshape(shape = var_2349, x = h_185)[name = tensor<string, []>("h_187")];
            tensor<int32, [2]> var_2351_split_sizes_0 = const()[name = tensor<string, []>("op_2351_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_2351_axis_0 = const()[name = tensor<string, []>("op_2351_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_2351_0, tensor<fp32, [1, 128, 1]> var_2351_1 = split(axis = var_2351_axis_0, split_sizes = var_2351_split_sizes_0, x = h_187)[name = tensor<string, []>("op_2351")];
            tensor<fp32, []> var_2353_promoted = const()[name = tensor<string, []>("op_2353_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_2354 = add(x = var_2351_0, y = var_2353_promoted)[name = tensor<string, []>("op_2354")];
            tensor<fp32, [1, 128, 51200]> var_2356 = mul(x = var_2354, y = var_2100)[name = tensor<string, []>("op_2356")];
            tensor<fp32, [1, 128, 51200]> xt_109 = add(x = var_2356, y = var_2351_1)[name = tensor<string, []>("xt_109")];
            tensor<fp32, [1, 128, 1]> var_2358 = const()[name = tensor<string, []>("op_2358"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(208726528)))];
            tensor<fp32, [1, 128, 51200]> var_2361 = mul(x = generator_resblocks_4_alpha1_0, y = xt_109)[name = tensor<string, []>("op_2361")];
            tensor<fp32, [1, 128, 51200]> var_2362 = sin(x = var_2361)[name = tensor<string, []>("op_2362")];
            tensor<fp32, []> var_532_promoted_38 = const()[name = tensor<string, []>("op_532_promoted_38"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 51200]> var_2363 = pow(x = var_2362, y = var_532_promoted_38)[name = tensor<string, []>("op_2363")];
            tensor<fp32, [1, 128, 51200]> var_2364 = mul(x = var_2358, y = var_2363)[name = tensor<string, []>("op_2364")];
            tensor<fp32, [1, 128, 51200]> input_241 = add(x = xt_109, y = var_2364)[name = tensor<string, []>("input_241")];
            tensor<fp32, [128, 128, 7]> weight_113 = const()[name = tensor<string, []>("weight_113"), val = tensor<fp32, [128, 128, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(208727104)))];
            tensor<string, []> input_243_pad_type_0 = const()[name = tensor<string, []>("input_243_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_243_pad_0 = const()[name = tensor<string, []>("input_243_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> input_243_strides_0 = const()[name = tensor<string, []>("input_243_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_243_dilations_0 = const()[name = tensor<string, []>("input_243_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_243_groups_0 = const()[name = tensor<string, []>("input_243_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 51200]> input_243 = conv(bias = generator_resblocks_4_convs1_0_bias, dilations = input_243_dilations_0, groups = input_243_groups_0, pad = input_243_pad_0, pad_type = input_243_pad_type_0, strides = input_243_strides_0, weight = weight_113, x = input_241)[name = tensor<string, []>("input_243")];
            tensor<fp32, [1, 256]> h_189 = linear(bias = generator_resblocks_4_adain2_0_fc_bias, weight = generator_resblocks_4_adain2_0_fc_weight, x = s)[name = tensor<string, []>("linear_48")];
            tensor<int32, [3]> var_2379 = const()[name = tensor<string, []>("op_2379"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_191 = reshape(shape = var_2379, x = h_189)[name = tensor<string, []>("h_191")];
            tensor<int32, [2]> var_2381_split_sizes_0 = const()[name = tensor<string, []>("op_2381_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_2381_axis_0 = const()[name = tensor<string, []>("op_2381_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_2381_0, tensor<fp32, [1, 128, 1]> var_2381_1 = split(axis = var_2381_axis_0, split_sizes = var_2381_split_sizes_0, x = h_191)[name = tensor<string, []>("op_2381")];
            tensor<fp32, []> var_2383_promoted = const()[name = tensor<string, []>("op_2383_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_2384 = add(x = var_2381_0, y = var_2383_promoted)[name = tensor<string, []>("op_2384")];
            tensor<fp32, [1, 128, 51200]> var_2385 = instance_norm(epsilon = var_516, x = input_243)[name = tensor<string, []>("op_2385")];
            tensor<fp32, [1, 128, 51200]> var_2386 = mul(x = var_2384, y = var_2385)[name = tensor<string, []>("op_2386")];
            tensor<fp32, [1, 128, 51200]> xt_111 = add(x = var_2386, y = var_2381_1)[name = tensor<string, []>("xt_111")];
            tensor<fp32, [1, 128, 1]> var_2388 = const()[name = tensor<string, []>("op_2388"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(209185920)))];
            tensor<fp32, [1, 128, 51200]> var_2391 = mul(x = generator_resblocks_4_alpha2_0, y = xt_111)[name = tensor<string, []>("op_2391")];
            tensor<fp32, [1, 128, 51200]> var_2392 = sin(x = var_2391)[name = tensor<string, []>("op_2392")];
            tensor<fp32, []> var_532_promoted_39 = const()[name = tensor<string, []>("op_532_promoted_39"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 51200]> var_2393 = pow(x = var_2392, y = var_532_promoted_39)[name = tensor<string, []>("op_2393")];
            tensor<fp32, [1, 128, 51200]> var_2394 = mul(x = var_2388, y = var_2393)[name = tensor<string, []>("op_2394")];
            tensor<fp32, [1, 128, 51200]> input_245 = add(x = xt_111, y = var_2394)[name = tensor<string, []>("input_245")];
            tensor<fp32, [128, 128, 7]> weight_115 = const()[name = tensor<string, []>("weight_115"), val = tensor<fp32, [128, 128, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(209186496)))];
            tensor<string, []> xt_113_pad_type_0 = const()[name = tensor<string, []>("xt_113_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_113_pad_0 = const()[name = tensor<string, []>("xt_113_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> xt_113_strides_0 = const()[name = tensor<string, []>("xt_113_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_113_dilations_0 = const()[name = tensor<string, []>("xt_113_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_113_groups_0 = const()[name = tensor<string, []>("xt_113_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 51200]> xt_113 = conv(bias = generator_resblocks_4_convs2_0_bias, dilations = xt_113_dilations_0, groups = xt_113_groups_0, pad = xt_113_pad_0, pad_type = xt_113_pad_type_0, strides = xt_113_strides_0, weight = weight_115, x = input_245)[name = tensor<string, []>("xt_113")];
            tensor<fp32, [1, 128, 51200]> input_247 = add(x = xt_113, y = input_217)[name = tensor<string, []>("input_247")];
            tensor<fp32, [1, 256]> h_193 = linear(bias = generator_resblocks_4_adain1_1_fc_bias, weight = generator_resblocks_4_adain1_1_fc_weight, x = s)[name = tensor<string, []>("linear_49")];
            tensor<int32, [3]> var_2410 = const()[name = tensor<string, []>("op_2410"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_195 = reshape(shape = var_2410, x = h_193)[name = tensor<string, []>("h_195")];
            tensor<int32, [2]> var_2412_split_sizes_0 = const()[name = tensor<string, []>("op_2412_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_2412_axis_0 = const()[name = tensor<string, []>("op_2412_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_2412_0, tensor<fp32, [1, 128, 1]> var_2412_1 = split(axis = var_2412_axis_0, split_sizes = var_2412_split_sizes_0, x = h_195)[name = tensor<string, []>("op_2412")];
            tensor<fp32, []> var_2414_promoted = const()[name = tensor<string, []>("op_2414_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_2415 = add(x = var_2412_0, y = var_2414_promoted)[name = tensor<string, []>("op_2415")];
            tensor<fp32, [1, 128, 51200]> var_2416 = instance_norm(epsilon = var_516, x = input_247)[name = tensor<string, []>("op_2416")];
            tensor<fp32, [1, 128, 51200]> var_2417 = mul(x = var_2415, y = var_2416)[name = tensor<string, []>("op_2417")];
            tensor<fp32, [1, 128, 51200]> xt_115 = add(x = var_2417, y = var_2412_1)[name = tensor<string, []>("xt_115")];
            tensor<fp32, [1, 128, 1]> var_2419 = const()[name = tensor<string, []>("op_2419"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(209645312)))];
            tensor<fp32, [1, 128, 51200]> var_2422 = mul(x = generator_resblocks_4_alpha1_1, y = xt_115)[name = tensor<string, []>("op_2422")];
            tensor<fp32, [1, 128, 51200]> var_2423 = sin(x = var_2422)[name = tensor<string, []>("op_2423")];
            tensor<fp32, []> var_532_promoted_40 = const()[name = tensor<string, []>("op_532_promoted_40"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 51200]> var_2424 = pow(x = var_2423, y = var_532_promoted_40)[name = tensor<string, []>("op_2424")];
            tensor<fp32, [1, 128, 51200]> var_2425 = mul(x = var_2419, y = var_2424)[name = tensor<string, []>("op_2425")];
            tensor<fp32, [1, 128, 51200]> input_249 = add(x = xt_115, y = var_2425)[name = tensor<string, []>("input_249")];
            tensor<fp32, [128, 128, 7]> weight_117 = const()[name = tensor<string, []>("weight_117"), val = tensor<fp32, [128, 128, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(209645888)))];
            tensor<string, []> input_251_pad_type_0 = const()[name = tensor<string, []>("input_251_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_251_pad_0 = const()[name = tensor<string, []>("input_251_pad_0"), val = tensor<int32, [2]>([9, 9])];
            tensor<int32, [1]> input_251_dilations_0 = const()[name = tensor<string, []>("input_251_dilations_0"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> input_251_strides_0 = const()[name = tensor<string, []>("input_251_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_251_groups_0 = const()[name = tensor<string, []>("input_251_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 51200]> input_251 = conv(bias = generator_resblocks_4_convs1_1_bias, dilations = input_251_dilations_0, groups = input_251_groups_0, pad = input_251_pad_0, pad_type = input_251_pad_type_0, strides = input_251_strides_0, weight = weight_117, x = input_249)[name = tensor<string, []>("input_251")];
            tensor<fp32, [1, 256]> h_197 = linear(bias = generator_resblocks_4_adain2_1_fc_bias, weight = generator_resblocks_4_adain2_1_fc_weight, x = s)[name = tensor<string, []>("linear_50")];
            tensor<int32, [3]> var_2440 = const()[name = tensor<string, []>("op_2440"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_199 = reshape(shape = var_2440, x = h_197)[name = tensor<string, []>("h_199")];
            tensor<int32, [2]> var_2442_split_sizes_0 = const()[name = tensor<string, []>("op_2442_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_2442_axis_0 = const()[name = tensor<string, []>("op_2442_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_2442_0, tensor<fp32, [1, 128, 1]> var_2442_1 = split(axis = var_2442_axis_0, split_sizes = var_2442_split_sizes_0, x = h_199)[name = tensor<string, []>("op_2442")];
            tensor<fp32, []> var_2444_promoted = const()[name = tensor<string, []>("op_2444_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_2445 = add(x = var_2442_0, y = var_2444_promoted)[name = tensor<string, []>("op_2445")];
            tensor<fp32, [1, 128, 51200]> var_2446 = instance_norm(epsilon = var_516, x = input_251)[name = tensor<string, []>("op_2446")];
            tensor<fp32, [1, 128, 51200]> var_2447 = mul(x = var_2445, y = var_2446)[name = tensor<string, []>("op_2447")];
            tensor<fp32, [1, 128, 51200]> xt_117 = add(x = var_2447, y = var_2442_1)[name = tensor<string, []>("xt_117")];
            tensor<fp32, [1, 128, 1]> var_2449 = const()[name = tensor<string, []>("op_2449"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(210104704)))];
            tensor<fp32, [1, 128, 51200]> var_2452 = mul(x = generator_resblocks_4_alpha2_1, y = xt_117)[name = tensor<string, []>("op_2452")];
            tensor<fp32, [1, 128, 51200]> var_2453 = sin(x = var_2452)[name = tensor<string, []>("op_2453")];
            tensor<fp32, []> var_532_promoted_41 = const()[name = tensor<string, []>("op_532_promoted_41"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 51200]> var_2454 = pow(x = var_2453, y = var_532_promoted_41)[name = tensor<string, []>("op_2454")];
            tensor<fp32, [1, 128, 51200]> var_2455 = mul(x = var_2449, y = var_2454)[name = tensor<string, []>("op_2455")];
            tensor<fp32, [1, 128, 51200]> input_253 = add(x = xt_117, y = var_2455)[name = tensor<string, []>("input_253")];
            tensor<fp32, [128, 128, 7]> weight_119 = const()[name = tensor<string, []>("weight_119"), val = tensor<fp32, [128, 128, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(210105280)))];
            tensor<string, []> xt_119_pad_type_0 = const()[name = tensor<string, []>("xt_119_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_119_pad_0 = const()[name = tensor<string, []>("xt_119_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> xt_119_strides_0 = const()[name = tensor<string, []>("xt_119_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_119_dilations_0 = const()[name = tensor<string, []>("xt_119_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_119_groups_0 = const()[name = tensor<string, []>("xt_119_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 51200]> xt_119 = conv(bias = generator_resblocks_4_convs2_1_bias, dilations = xt_119_dilations_0, groups = xt_119_groups_0, pad = xt_119_pad_0, pad_type = xt_119_pad_type_0, strides = xt_119_strides_0, weight = weight_119, x = input_253)[name = tensor<string, []>("xt_119")];
            tensor<fp32, [1, 128, 51200]> input_255 = add(x = xt_119, y = input_247)[name = tensor<string, []>("input_255")];
            tensor<fp32, [1, 256]> h_201 = linear(bias = generator_resblocks_4_adain1_2_fc_bias, weight = generator_resblocks_4_adain1_2_fc_weight, x = s)[name = tensor<string, []>("linear_51")];
            tensor<int32, [3]> var_2471 = const()[name = tensor<string, []>("op_2471"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_203 = reshape(shape = var_2471, x = h_201)[name = tensor<string, []>("h_203")];
            tensor<int32, [2]> var_2473_split_sizes_0 = const()[name = tensor<string, []>("op_2473_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_2473_axis_0 = const()[name = tensor<string, []>("op_2473_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_2473_0, tensor<fp32, [1, 128, 1]> var_2473_1 = split(axis = var_2473_axis_0, split_sizes = var_2473_split_sizes_0, x = h_203)[name = tensor<string, []>("op_2473")];
            tensor<fp32, []> var_2475_promoted = const()[name = tensor<string, []>("op_2475_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_2476 = add(x = var_2473_0, y = var_2475_promoted)[name = tensor<string, []>("op_2476")];
            tensor<fp32, [1, 128, 51200]> var_2477 = instance_norm(epsilon = var_516, x = input_255)[name = tensor<string, []>("op_2477")];
            tensor<fp32, [1, 128, 51200]> var_2478 = mul(x = var_2476, y = var_2477)[name = tensor<string, []>("op_2478")];
            tensor<fp32, [1, 128, 51200]> xt_121 = add(x = var_2478, y = var_2473_1)[name = tensor<string, []>("xt_121")];
            tensor<fp32, [1, 128, 1]> var_2480 = const()[name = tensor<string, []>("op_2480"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(210564096)))];
            tensor<fp32, [1, 128, 51200]> var_2483 = mul(x = generator_resblocks_4_alpha1_2, y = xt_121)[name = tensor<string, []>("op_2483")];
            tensor<fp32, [1, 128, 51200]> var_2484 = sin(x = var_2483)[name = tensor<string, []>("op_2484")];
            tensor<fp32, []> var_532_promoted_42 = const()[name = tensor<string, []>("op_532_promoted_42"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 51200]> var_2485 = pow(x = var_2484, y = var_532_promoted_42)[name = tensor<string, []>("op_2485")];
            tensor<fp32, [1, 128, 51200]> var_2486 = mul(x = var_2480, y = var_2485)[name = tensor<string, []>("op_2486")];
            tensor<fp32, [1, 128, 51200]> input_257 = add(x = xt_121, y = var_2486)[name = tensor<string, []>("input_257")];
            tensor<fp32, [128, 128, 7]> weight_121 = const()[name = tensor<string, []>("weight_121"), val = tensor<fp32, [128, 128, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(210564672)))];
            tensor<string, []> input_259_pad_type_0 = const()[name = tensor<string, []>("input_259_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_259_pad_0 = const()[name = tensor<string, []>("input_259_pad_0"), val = tensor<int32, [2]>([15, 15])];
            tensor<int32, [1]> input_259_dilations_0 = const()[name = tensor<string, []>("input_259_dilations_0"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> input_259_strides_0 = const()[name = tensor<string, []>("input_259_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_259_groups_0 = const()[name = tensor<string, []>("input_259_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 51200]> input_259 = conv(bias = generator_resblocks_4_convs1_2_bias, dilations = input_259_dilations_0, groups = input_259_groups_0, pad = input_259_pad_0, pad_type = input_259_pad_type_0, strides = input_259_strides_0, weight = weight_121, x = input_257)[name = tensor<string, []>("input_259")];
            tensor<fp32, [1, 256]> h_205 = linear(bias = generator_resblocks_4_adain2_2_fc_bias, weight = generator_resblocks_4_adain2_2_fc_weight, x = s)[name = tensor<string, []>("linear_52")];
            tensor<int32, [3]> var_2501 = const()[name = tensor<string, []>("op_2501"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_207 = reshape(shape = var_2501, x = h_205)[name = tensor<string, []>("h_207")];
            tensor<int32, [2]> var_2503_split_sizes_0 = const()[name = tensor<string, []>("op_2503_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_2503_axis_0 = const()[name = tensor<string, []>("op_2503_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_2503_0, tensor<fp32, [1, 128, 1]> var_2503_1 = split(axis = var_2503_axis_0, split_sizes = var_2503_split_sizes_0, x = h_207)[name = tensor<string, []>("op_2503")];
            tensor<fp32, []> var_2505_promoted = const()[name = tensor<string, []>("op_2505_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_2506 = add(x = var_2503_0, y = var_2505_promoted)[name = tensor<string, []>("op_2506")];
            tensor<fp32, [1, 128, 51200]> var_2507 = instance_norm(epsilon = var_516, x = input_259)[name = tensor<string, []>("op_2507")];
            tensor<fp32, [1, 128, 51200]> var_2508 = mul(x = var_2506, y = var_2507)[name = tensor<string, []>("op_2508")];
            tensor<fp32, [1, 128, 51200]> xt_123 = add(x = var_2508, y = var_2503_1)[name = tensor<string, []>("xt_123")];
            tensor<fp32, [1, 128, 1]> var_2510 = const()[name = tensor<string, []>("op_2510"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211023488)))];
            tensor<fp32, [1, 128, 51200]> var_2513 = mul(x = generator_resblocks_4_alpha2_2, y = xt_123)[name = tensor<string, []>("op_2513")];
            tensor<fp32, [1, 128, 51200]> var_2514 = sin(x = var_2513)[name = tensor<string, []>("op_2514")];
            tensor<fp32, []> var_532_promoted_43 = const()[name = tensor<string, []>("op_532_promoted_43"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 51200]> var_2515 = pow(x = var_2514, y = var_532_promoted_43)[name = tensor<string, []>("op_2515")];
            tensor<fp32, [1, 128, 51200]> var_2516 = mul(x = var_2510, y = var_2515)[name = tensor<string, []>("op_2516")];
            tensor<fp32, [1, 128, 51200]> input_261 = add(x = xt_123, y = var_2516)[name = tensor<string, []>("input_261")];
            tensor<fp32, [128, 128, 7]> weight_123 = const()[name = tensor<string, []>("weight_123"), val = tensor<fp32, [128, 128, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211024064)))];
            tensor<string, []> xt_125_pad_type_0 = const()[name = tensor<string, []>("xt_125_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_125_pad_0 = const()[name = tensor<string, []>("xt_125_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> xt_125_strides_0 = const()[name = tensor<string, []>("xt_125_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_125_dilations_0 = const()[name = tensor<string, []>("xt_125_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_125_groups_0 = const()[name = tensor<string, []>("xt_125_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 51200]> xt_125 = conv(bias = generator_resblocks_4_convs2_2_bias, dilations = xt_125_dilations_0, groups = xt_125_groups_0, pad = xt_125_pad_0, pad_type = xt_125_pad_type_0, strides = xt_125_strides_0, weight = weight_123, x = input_261)[name = tensor<string, []>("xt_125")];
            tensor<fp32, [1, 128, 51200]> var_2525 = add(x = xt_125, y = input_255)[name = tensor<string, []>("op_2525")];
            tensor<fp32, [1, 128, 51200]> xs_9 = add(x = xs_7, y = var_2525)[name = tensor<string, []>("xs_9")];
            tensor<fp32, [1, 256]> h_209 = linear(bias = generator_resblocks_5_adain1_0_fc_bias, weight = generator_resblocks_5_adain1_0_fc_weight, x = s)[name = tensor<string, []>("linear_53")];
            tensor<int32, [3]> var_2605 = const()[name = tensor<string, []>("op_2605"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_211 = reshape(shape = var_2605, x = h_209)[name = tensor<string, []>("h_211")];
            tensor<int32, [2]> var_2607_split_sizes_0 = const()[name = tensor<string, []>("op_2607_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_2607_axis_0 = const()[name = tensor<string, []>("op_2607_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_2607_0, tensor<fp32, [1, 128, 1]> var_2607_1 = split(axis = var_2607_axis_0, split_sizes = var_2607_split_sizes_0, x = h_211)[name = tensor<string, []>("op_2607")];
            tensor<fp32, []> var_2609_promoted = const()[name = tensor<string, []>("op_2609_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_2610 = add(x = var_2607_0, y = var_2609_promoted)[name = tensor<string, []>("op_2610")];
            tensor<fp32, [1, 128, 51200]> var_2612 = mul(x = var_2610, y = var_2100)[name = tensor<string, []>("op_2612")];
            tensor<fp32, [1, 128, 51200]> xt_127 = add(x = var_2612, y = var_2607_1)[name = tensor<string, []>("xt_127")];
            tensor<fp32, [1, 128, 1]> var_2614 = const()[name = tensor<string, []>("op_2614"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211482880)))];
            tensor<fp32, [1, 128, 51200]> var_2617 = mul(x = generator_resblocks_5_alpha1_0, y = xt_127)[name = tensor<string, []>("op_2617")];
            tensor<fp32, [1, 128, 51200]> var_2618 = sin(x = var_2617)[name = tensor<string, []>("op_2618")];
            tensor<fp32, []> var_532_promoted_44 = const()[name = tensor<string, []>("op_532_promoted_44"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 51200]> var_2619 = pow(x = var_2618, y = var_532_promoted_44)[name = tensor<string, []>("op_2619")];
            tensor<fp32, [1, 128, 51200]> var_2620 = mul(x = var_2614, y = var_2619)[name = tensor<string, []>("op_2620")];
            tensor<fp32, [1, 128, 51200]> input_263 = add(x = xt_127, y = var_2620)[name = tensor<string, []>("input_263")];
            tensor<fp32, [128, 128, 11]> weight_125 = const()[name = tensor<string, []>("weight_125"), val = tensor<fp32, [128, 128, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(211483456)))];
            tensor<string, []> input_265_pad_type_0 = const()[name = tensor<string, []>("input_265_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_265_pad_0 = const()[name = tensor<string, []>("input_265_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> input_265_strides_0 = const()[name = tensor<string, []>("input_265_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_265_dilations_0 = const()[name = tensor<string, []>("input_265_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_265_groups_0 = const()[name = tensor<string, []>("input_265_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 51200]> input_265 = conv(bias = generator_resblocks_5_convs1_0_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 = weight_125, x = input_263)[name = tensor<string, []>("input_265")];
            tensor<fp32, [1, 256]> h_213 = linear(bias = generator_resblocks_5_adain2_0_fc_bias, weight = generator_resblocks_5_adain2_0_fc_weight, x = s)[name = tensor<string, []>("linear_54")];
            tensor<int32, [3]> var_2635 = const()[name = tensor<string, []>("op_2635"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_215 = reshape(shape = var_2635, x = h_213)[name = tensor<string, []>("h_215")];
            tensor<int32, [2]> var_2637_split_sizes_0 = const()[name = tensor<string, []>("op_2637_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_2637_axis_0 = const()[name = tensor<string, []>("op_2637_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_2637_0, tensor<fp32, [1, 128, 1]> var_2637_1 = split(axis = var_2637_axis_0, split_sizes = var_2637_split_sizes_0, x = h_215)[name = tensor<string, []>("op_2637")];
            tensor<fp32, []> var_2639_promoted = const()[name = tensor<string, []>("op_2639_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_2640 = add(x = var_2637_0, y = var_2639_promoted)[name = tensor<string, []>("op_2640")];
            tensor<fp32, [1, 128, 51200]> var_2641 = instance_norm(epsilon = var_516, x = input_265)[name = tensor<string, []>("op_2641")];
            tensor<fp32, [1, 128, 51200]> var_2642 = mul(x = var_2640, y = var_2641)[name = tensor<string, []>("op_2642")];
            tensor<fp32, [1, 128, 51200]> xt_129 = add(x = var_2642, y = var_2637_1)[name = tensor<string, []>("xt_129")];
            tensor<fp32, [1, 128, 1]> var_2644 = const()[name = tensor<string, []>("op_2644"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(212204416)))];
            tensor<fp32, [1, 128, 51200]> var_2647 = mul(x = generator_resblocks_5_alpha2_0, y = xt_129)[name = tensor<string, []>("op_2647")];
            tensor<fp32, [1, 128, 51200]> var_2648 = sin(x = var_2647)[name = tensor<string, []>("op_2648")];
            tensor<fp32, []> var_532_promoted_45 = const()[name = tensor<string, []>("op_532_promoted_45"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 51200]> var_2649 = pow(x = var_2648, y = var_532_promoted_45)[name = tensor<string, []>("op_2649")];
            tensor<fp32, [1, 128, 51200]> var_2650 = mul(x = var_2644, y = var_2649)[name = tensor<string, []>("op_2650")];
            tensor<fp32, [1, 128, 51200]> input_267 = add(x = xt_129, y = var_2650)[name = tensor<string, []>("input_267")];
            tensor<fp32, [128, 128, 11]> weight_127 = const()[name = tensor<string, []>("weight_127"), val = tensor<fp32, [128, 128, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(212204992)))];
            tensor<string, []> xt_131_pad_type_0 = const()[name = tensor<string, []>("xt_131_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_131_pad_0 = const()[name = tensor<string, []>("xt_131_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> xt_131_strides_0 = const()[name = tensor<string, []>("xt_131_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_131_dilations_0 = const()[name = tensor<string, []>("xt_131_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_131_groups_0 = const()[name = tensor<string, []>("xt_131_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 51200]> xt_131 = conv(bias = generator_resblocks_5_convs2_0_bias, dilations = xt_131_dilations_0, groups = xt_131_groups_0, pad = xt_131_pad_0, pad_type = xt_131_pad_type_0, strides = xt_131_strides_0, weight = weight_127, x = input_267)[name = tensor<string, []>("xt_131")];
            tensor<fp32, [1, 128, 51200]> input_269 = add(x = xt_131, y = input_217)[name = tensor<string, []>("input_269")];
            tensor<fp32, [1, 256]> h_217 = linear(bias = generator_resblocks_5_adain1_1_fc_bias, weight = generator_resblocks_5_adain1_1_fc_weight, x = s)[name = tensor<string, []>("linear_55")];
            tensor<int32, [3]> var_2666 = const()[name = tensor<string, []>("op_2666"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_219 = reshape(shape = var_2666, x = h_217)[name = tensor<string, []>("h_219")];
            tensor<int32, [2]> var_2668_split_sizes_0 = const()[name = tensor<string, []>("op_2668_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_2668_axis_0 = const()[name = tensor<string, []>("op_2668_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_2668_0, tensor<fp32, [1, 128, 1]> var_2668_1 = split(axis = var_2668_axis_0, split_sizes = var_2668_split_sizes_0, x = h_219)[name = tensor<string, []>("op_2668")];
            tensor<fp32, []> var_2670_promoted = const()[name = tensor<string, []>("op_2670_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_2671 = add(x = var_2668_0, y = var_2670_promoted)[name = tensor<string, []>("op_2671")];
            tensor<fp32, [1, 128, 51200]> var_2672 = instance_norm(epsilon = var_516, x = input_269)[name = tensor<string, []>("op_2672")];
            tensor<fp32, [1, 128, 51200]> var_2673 = mul(x = var_2671, y = var_2672)[name = tensor<string, []>("op_2673")];
            tensor<fp32, [1, 128, 51200]> xt_133 = add(x = var_2673, y = var_2668_1)[name = tensor<string, []>("xt_133")];
            tensor<fp32, [1, 128, 1]> var_2675 = const()[name = tensor<string, []>("op_2675"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(212925952)))];
            tensor<fp32, [1, 128, 51200]> var_2678 = mul(x = generator_resblocks_5_alpha1_1, y = xt_133)[name = tensor<string, []>("op_2678")];
            tensor<fp32, [1, 128, 51200]> var_2679 = sin(x = var_2678)[name = tensor<string, []>("op_2679")];
            tensor<fp32, []> var_532_promoted_46 = const()[name = tensor<string, []>("op_532_promoted_46"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 51200]> var_2680 = pow(x = var_2679, y = var_532_promoted_46)[name = tensor<string, []>("op_2680")];
            tensor<fp32, [1, 128, 51200]> var_2681 = mul(x = var_2675, y = var_2680)[name = tensor<string, []>("op_2681")];
            tensor<fp32, [1, 128, 51200]> input_271 = add(x = xt_133, y = var_2681)[name = tensor<string, []>("input_271")];
            tensor<fp32, [128, 128, 11]> weight_129 = const()[name = tensor<string, []>("weight_129"), val = tensor<fp32, [128, 128, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(212926528)))];
            tensor<string, []> input_273_pad_type_0 = const()[name = tensor<string, []>("input_273_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_273_pad_0 = const()[name = tensor<string, []>("input_273_pad_0"), val = tensor<int32, [2]>([15, 15])];
            tensor<int32, [1]> input_273_dilations_0 = const()[name = tensor<string, []>("input_273_dilations_0"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> input_273_strides_0 = const()[name = tensor<string, []>("input_273_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_273_groups_0 = const()[name = tensor<string, []>("input_273_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 51200]> input_273 = conv(bias = generator_resblocks_5_convs1_1_bias, dilations = input_273_dilations_0, groups = input_273_groups_0, pad = input_273_pad_0, pad_type = input_273_pad_type_0, strides = input_273_strides_0, weight = weight_129, x = input_271)[name = tensor<string, []>("input_273")];
            tensor<fp32, [1, 256]> h_221 = linear(bias = generator_resblocks_5_adain2_1_fc_bias, weight = generator_resblocks_5_adain2_1_fc_weight, x = s)[name = tensor<string, []>("linear_56")];
            tensor<int32, [3]> var_2696 = const()[name = tensor<string, []>("op_2696"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_223 = reshape(shape = var_2696, x = h_221)[name = tensor<string, []>("h_223")];
            tensor<int32, [2]> var_2698_split_sizes_0 = const()[name = tensor<string, []>("op_2698_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_2698_axis_0 = const()[name = tensor<string, []>("op_2698_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_2698_0, tensor<fp32, [1, 128, 1]> var_2698_1 = split(axis = var_2698_axis_0, split_sizes = var_2698_split_sizes_0, x = h_223)[name = tensor<string, []>("op_2698")];
            tensor<fp32, []> var_2700_promoted = const()[name = tensor<string, []>("op_2700_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_2701 = add(x = var_2698_0, y = var_2700_promoted)[name = tensor<string, []>("op_2701")];
            tensor<fp32, [1, 128, 51200]> var_2702 = instance_norm(epsilon = var_516, x = input_273)[name = tensor<string, []>("op_2702")];
            tensor<fp32, [1, 128, 51200]> var_2703 = mul(x = var_2701, y = var_2702)[name = tensor<string, []>("op_2703")];
            tensor<fp32, [1, 128, 51200]> xt_135 = add(x = var_2703, y = var_2698_1)[name = tensor<string, []>("xt_135")];
            tensor<fp32, [1, 128, 1]> var_2705 = const()[name = tensor<string, []>("op_2705"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(213647488)))];
            tensor<fp32, [1, 128, 51200]> var_2708 = mul(x = generator_resblocks_5_alpha2_1, y = xt_135)[name = tensor<string, []>("op_2708")];
            tensor<fp32, [1, 128, 51200]> var_2709 = sin(x = var_2708)[name = tensor<string, []>("op_2709")];
            tensor<fp32, []> var_532_promoted_47 = const()[name = tensor<string, []>("op_532_promoted_47"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 51200]> var_2710 = pow(x = var_2709, y = var_532_promoted_47)[name = tensor<string, []>("op_2710")];
            tensor<fp32, [1, 128, 51200]> var_2711 = mul(x = var_2705, y = var_2710)[name = tensor<string, []>("op_2711")];
            tensor<fp32, [1, 128, 51200]> input_275 = add(x = xt_135, y = var_2711)[name = tensor<string, []>("input_275")];
            tensor<fp32, [128, 128, 11]> weight_131 = const()[name = tensor<string, []>("weight_131"), val = tensor<fp32, [128, 128, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(213648064)))];
            tensor<string, []> xt_137_pad_type_0 = const()[name = tensor<string, []>("xt_137_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_137_pad_0 = const()[name = tensor<string, []>("xt_137_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> xt_137_strides_0 = const()[name = tensor<string, []>("xt_137_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_137_dilations_0 = const()[name = tensor<string, []>("xt_137_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_137_groups_0 = const()[name = tensor<string, []>("xt_137_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 51200]> xt_137 = conv(bias = generator_resblocks_5_convs2_1_bias, dilations = xt_137_dilations_0, groups = xt_137_groups_0, pad = xt_137_pad_0, pad_type = xt_137_pad_type_0, strides = xt_137_strides_0, weight = weight_131, x = input_275)[name = tensor<string, []>("xt_137")];
            tensor<fp32, [1, 128, 51200]> input_277 = add(x = xt_137, y = input_269)[name = tensor<string, []>("input_277")];
            tensor<fp32, [1, 256]> h_225 = linear(bias = generator_resblocks_5_adain1_2_fc_bias, weight = generator_resblocks_5_adain1_2_fc_weight, x = s)[name = tensor<string, []>("linear_57")];
            tensor<int32, [3]> var_2727 = const()[name = tensor<string, []>("op_2727"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_227 = reshape(shape = var_2727, x = h_225)[name = tensor<string, []>("h_227")];
            tensor<int32, [2]> var_2729_split_sizes_0 = const()[name = tensor<string, []>("op_2729_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_2729_axis_0 = const()[name = tensor<string, []>("op_2729_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_2729_0, tensor<fp32, [1, 128, 1]> var_2729_1 = split(axis = var_2729_axis_0, split_sizes = var_2729_split_sizes_0, x = h_227)[name = tensor<string, []>("op_2729")];
            tensor<fp32, []> var_2731_promoted = const()[name = tensor<string, []>("op_2731_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_2732 = add(x = var_2729_0, y = var_2731_promoted)[name = tensor<string, []>("op_2732")];
            tensor<fp32, [1, 128, 51200]> var_2733 = instance_norm(epsilon = var_516, x = input_277)[name = tensor<string, []>("op_2733")];
            tensor<fp32, [1, 128, 51200]> var_2734 = mul(x = var_2732, y = var_2733)[name = tensor<string, []>("op_2734")];
            tensor<fp32, [1, 128, 51200]> xt_139 = add(x = var_2734, y = var_2729_1)[name = tensor<string, []>("xt_139")];
            tensor<fp32, [1, 128, 1]> var_2736 = const()[name = tensor<string, []>("op_2736"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(214369024)))];
            tensor<fp32, [1, 128, 51200]> var_2739 = mul(x = generator_resblocks_5_alpha1_2, y = xt_139)[name = tensor<string, []>("op_2739")];
            tensor<fp32, [1, 128, 51200]> var_2740 = sin(x = var_2739)[name = tensor<string, []>("op_2740")];
            tensor<fp32, []> var_532_promoted_48 = const()[name = tensor<string, []>("op_532_promoted_48"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 51200]> var_2741 = pow(x = var_2740, y = var_532_promoted_48)[name = tensor<string, []>("op_2741")];
            tensor<fp32, [1, 128, 51200]> var_2742 = mul(x = var_2736, y = var_2741)[name = tensor<string, []>("op_2742")];
            tensor<fp32, [1, 128, 51200]> input_279 = add(x = xt_139, y = var_2742)[name = tensor<string, []>("input_279")];
            tensor<fp32, [128, 128, 11]> weight_133 = const()[name = tensor<string, []>("weight_133"), val = tensor<fp32, [128, 128, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(214369600)))];
            tensor<string, []> input_281_pad_type_0 = const()[name = tensor<string, []>("input_281_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_281_pad_0 = const()[name = tensor<string, []>("input_281_pad_0"), val = tensor<int32, [2]>([25, 25])];
            tensor<int32, [1]> input_281_dilations_0 = const()[name = tensor<string, []>("input_281_dilations_0"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> input_281_strides_0 = const()[name = tensor<string, []>("input_281_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_281_groups_0 = const()[name = tensor<string, []>("input_281_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 51200]> input_281 = conv(bias = generator_resblocks_5_convs1_2_bias, dilations = input_281_dilations_0, groups = input_281_groups_0, pad = input_281_pad_0, pad_type = input_281_pad_type_0, strides = input_281_strides_0, weight = weight_133, x = input_279)[name = tensor<string, []>("input_281")];
            tensor<fp32, [1, 256]> h_229 = linear(bias = generator_resblocks_5_adain2_2_fc_bias, weight = generator_resblocks_5_adain2_2_fc_weight, x = s)[name = tensor<string, []>("linear_58")];
            tensor<int32, [3]> var_2757 = const()[name = tensor<string, []>("op_2757"), val = tensor<int32, [3]>([1, 256, 1])];
            tensor<fp32, [1, 256, 1]> h_231 = reshape(shape = var_2757, x = h_229)[name = tensor<string, []>("h_231")];
            tensor<int32, [2]> var_2759_split_sizes_0 = const()[name = tensor<string, []>("op_2759_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            tensor<int32, []> var_2759_axis_0 = const()[name = tensor<string, []>("op_2759_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 1]> var_2759_0, tensor<fp32, [1, 128, 1]> var_2759_1 = split(axis = var_2759_axis_0, split_sizes = var_2759_split_sizes_0, x = h_231)[name = tensor<string, []>("op_2759")];
            tensor<fp32, []> var_2761_promoted = const()[name = tensor<string, []>("op_2761_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 128, 1]> var_2762 = add(x = var_2759_0, y = var_2761_promoted)[name = tensor<string, []>("op_2762")];
            tensor<fp32, [1, 128, 51200]> var_2763 = instance_norm(epsilon = var_516, x = input_281)[name = tensor<string, []>("op_2763")];
            tensor<fp32, [1, 128, 51200]> var_2764 = mul(x = var_2762, y = var_2763)[name = tensor<string, []>("op_2764")];
            tensor<fp32, [1, 128, 51200]> xt_141 = add(x = var_2764, y = var_2759_1)[name = tensor<string, []>("xt_141")];
            tensor<fp32, [1, 128, 1]> var_2766 = const()[name = tensor<string, []>("op_2766"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(215090560)))];
            tensor<fp32, [1, 128, 51200]> var_2769 = mul(x = generator_resblocks_5_alpha2_2, y = xt_141)[name = tensor<string, []>("op_2769")];
            tensor<fp32, [1, 128, 51200]> var_2770 = sin(x = var_2769)[name = tensor<string, []>("op_2770")];
            tensor<fp32, []> var_532_promoted_49 = const()[name = tensor<string, []>("op_532_promoted_49"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 51200]> var_2771 = pow(x = var_2770, y = var_532_promoted_49)[name = tensor<string, []>("op_2771")];
            tensor<fp32, [1, 128, 51200]> var_2772 = mul(x = var_2766, y = var_2771)[name = tensor<string, []>("op_2772")];
            tensor<fp32, [1, 128, 51200]> input_283 = add(x = xt_141, y = var_2772)[name = tensor<string, []>("input_283")];
            tensor<fp32, [128, 128, 11]> weight_135 = const()[name = tensor<string, []>("weight_135"), val = tensor<fp32, [128, 128, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(215091136)))];
            tensor<string, []> xt_143_pad_type_0 = const()[name = tensor<string, []>("xt_143_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_143_pad_0 = const()[name = tensor<string, []>("xt_143_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> xt_143_strides_0 = const()[name = tensor<string, []>("xt_143_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_143_dilations_0 = const()[name = tensor<string, []>("xt_143_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_143_groups_0 = const()[name = tensor<string, []>("xt_143_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 128, 51200]> xt_143 = conv(bias = generator_resblocks_5_convs2_2_bias, dilations = xt_143_dilations_0, groups = xt_143_groups_0, pad = xt_143_pad_0, pad_type = xt_143_pad_type_0, strides = xt_143_strides_0, weight = weight_135, x = input_283)[name = tensor<string, []>("xt_143")];
            tensor<fp32, [1, 128, 51200]> var_2781 = add(x = xt_143, y = input_277)[name = tensor<string, []>("op_2781")];
            tensor<fp32, [1, 128, 51200]> xs_11 = add(x = xs_9, y = var_2781)[name = tensor<string, []>("xs_11")];
            tensor<fp32, []> _inversed_x_17_y_0 = const()[name = tensor<string, []>("_inversed_x_17_y_0"), val = tensor<fp32, []>(0x1.555556p-2)];
            tensor<fp32, [1, 128, 51200]> _inversed_x_17 = mul(x = xs_11, y = _inversed_x_17_y_0)[name = tensor<string, []>("_inversed_x_17")];
            tensor<fp32, [1, 128, 1]> var_2785 = const()[name = tensor<string, []>("op_2785"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(215812096)))];
            tensor<fp32, [1, 128, 51200]> var_2788 = mul(x = generator_alphas_2, y = _inversed_x_17)[name = tensor<string, []>("op_2788")];
            tensor<fp32, [1, 128, 51200]> var_2789 = sin(x = var_2788)[name = tensor<string, []>("op_2789")];
            tensor<fp32, []> var_532_promoted_50 = const()[name = tensor<string, []>("op_532_promoted_50"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 128, 51200]> var_2790 = pow(x = var_2789, y = var_532_promoted_50)[name = tensor<string, []>("op_2790")];
            tensor<fp32, [1, 128, 51200]> var_2791 = mul(x = var_2785, y = var_2790)[name = tensor<string, []>("op_2791")];
            tensor<fp32, [1, 128, 51200]> input_309 = add(x = _inversed_x_17, y = var_2791)[name = tensor<string, []>("input_309")];
            tensor<string, []> input_285_pad_type_0 = const()[name = tensor<string, []>("input_285_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_285_pad_0 = const()[name = tensor<string, []>("input_285_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> input_285_strides_0 = const()[name = tensor<string, []>("input_285_strides_0"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1]> input_285_dilations_0 = const()[name = tensor<string, []>("input_285_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_285_groups_0 = const()[name = tensor<string, []>("input_285_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 153600]> input_285 = conv(bias = generator_noise_convs_2_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 = generator_noise_convs_2_weight, x = input_95)[name = tensor<string, []>("input_285")];
            tensor<fp32, [1, 128]> h_233 = linear(bias = generator_noise_res_2_adain1_0_fc_bias, weight = generator_noise_res_2_adain1_0_fc_weight, x = s)[name = tensor<string, []>("linear_59")];
            tensor<int32, [3]> var_2878 = const()[name = tensor<string, []>("op_2878"), val = tensor<int32, [3]>([1, 128, 1])];
            tensor<fp32, [1, 128, 1]> h_235 = reshape(shape = var_2878, x = h_233)[name = tensor<string, []>("h_235")];
            tensor<int32, [2]> var_2880_split_sizes_0 = const()[name = tensor<string, []>("op_2880_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            tensor<int32, []> var_2880_axis_0 = const()[name = tensor<string, []>("op_2880_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 1]> var_2880_0, tensor<fp32, [1, 64, 1]> var_2880_1 = split(axis = var_2880_axis_0, split_sizes = var_2880_split_sizes_0, x = h_235)[name = tensor<string, []>("op_2880")];
            tensor<fp32, []> var_2882_promoted = const()[name = tensor<string, []>("op_2882_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 64, 1]> var_2883 = add(x = var_2880_0, y = var_2882_promoted)[name = tensor<string, []>("op_2883")];
            tensor<fp32, [1, 64, 153600]> var_2884 = instance_norm(epsilon = var_516, x = input_285)[name = tensor<string, []>("op_2884")];
            tensor<fp32, [1, 64, 153600]> var_2885 = mul(x = var_2883, y = var_2884)[name = tensor<string, []>("op_2885")];
            tensor<fp32, [1, 64, 153600]> xt_145 = add(x = var_2885, y = var_2880_1)[name = tensor<string, []>("xt_145")];
            tensor<fp32, [1, 64, 1]> var_2887 = const()[name = tensor<string, []>("op_2887"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(215812672)))];
            tensor<fp32, [1, 64, 153600]> var_2890 = mul(x = generator_noise_res_2_alpha1_0, y = xt_145)[name = tensor<string, []>("op_2890")];
            tensor<fp32, [1, 64, 153600]> var_2891 = sin(x = var_2890)[name = tensor<string, []>("op_2891")];
            tensor<fp32, []> var_532_promoted_51 = const()[name = tensor<string, []>("op_532_promoted_51"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 64, 153600]> var_2892 = pow(x = var_2891, y = var_532_promoted_51)[name = tensor<string, []>("op_2892")];
            tensor<fp32, [1, 64, 153600]> var_2893 = mul(x = var_2887, y = var_2892)[name = tensor<string, []>("op_2893")];
            tensor<fp32, [1, 64, 153600]> input_287 = add(x = xt_145, y = var_2893)[name = tensor<string, []>("input_287")];
            tensor<fp32, [64, 64, 7]> weight_139 = const()[name = tensor<string, []>("weight_139"), val = tensor<fp32, [64, 64, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(215812992)))];
            tensor<string, []> input_289_pad_type_0 = const()[name = tensor<string, []>("input_289_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_289_pad_0 = const()[name = tensor<string, []>("input_289_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> input_289_strides_0 = const()[name = tensor<string, []>("input_289_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_289_dilations_0 = const()[name = tensor<string, []>("input_289_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_289_groups_0 = const()[name = tensor<string, []>("input_289_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 153600]> input_289 = conv(bias = generator_noise_res_2_convs1_0_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 = weight_139, x = input_287)[name = tensor<string, []>("input_289")];
            tensor<fp32, [1, 128]> h_237 = linear(bias = generator_noise_res_2_adain2_0_fc_bias, weight = generator_noise_res_2_adain2_0_fc_weight, x = s)[name = tensor<string, []>("linear_60")];
            tensor<int32, [3]> var_2908 = const()[name = tensor<string, []>("op_2908"), val = tensor<int32, [3]>([1, 128, 1])];
            tensor<fp32, [1, 128, 1]> h_239 = reshape(shape = var_2908, x = h_237)[name = tensor<string, []>("h_239")];
            tensor<int32, [2]> var_2910_split_sizes_0 = const()[name = tensor<string, []>("op_2910_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            tensor<int32, []> var_2910_axis_0 = const()[name = tensor<string, []>("op_2910_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 1]> var_2910_0, tensor<fp32, [1, 64, 1]> var_2910_1 = split(axis = var_2910_axis_0, split_sizes = var_2910_split_sizes_0, x = h_239)[name = tensor<string, []>("op_2910")];
            tensor<fp32, []> var_2912_promoted = const()[name = tensor<string, []>("op_2912_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 64, 1]> var_2913 = add(x = var_2910_0, y = var_2912_promoted)[name = tensor<string, []>("op_2913")];
            tensor<fp32, [1, 64, 153600]> var_2914 = instance_norm(epsilon = var_516, x = input_289)[name = tensor<string, []>("op_2914")];
            tensor<fp32, [1, 64, 153600]> var_2915 = mul(x = var_2913, y = var_2914)[name = tensor<string, []>("op_2915")];
            tensor<fp32, [1, 64, 153600]> xt_147 = add(x = var_2915, y = var_2910_1)[name = tensor<string, []>("xt_147")];
            tensor<fp32, [1, 64, 1]> var_2917 = const()[name = tensor<string, []>("op_2917"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(215927744)))];
            tensor<fp32, [1, 64, 153600]> var_2920 = mul(x = generator_noise_res_2_alpha2_0, y = xt_147)[name = tensor<string, []>("op_2920")];
            tensor<fp32, [1, 64, 153600]> var_2921 = sin(x = var_2920)[name = tensor<string, []>("op_2921")];
            tensor<fp32, []> var_532_promoted_52 = const()[name = tensor<string, []>("op_532_promoted_52"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 64, 153600]> var_2922 = pow(x = var_2921, y = var_532_promoted_52)[name = tensor<string, []>("op_2922")];
            tensor<fp32, [1, 64, 153600]> var_2923 = mul(x = var_2917, y = var_2922)[name = tensor<string, []>("op_2923")];
            tensor<fp32, [1, 64, 153600]> input_291 = add(x = xt_147, y = var_2923)[name = tensor<string, []>("input_291")];
            tensor<fp32, [64, 64, 7]> weight_141 = const()[name = tensor<string, []>("weight_141"), val = tensor<fp32, [64, 64, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(215928064)))];
            tensor<string, []> xt_149_pad_type_0 = const()[name = tensor<string, []>("xt_149_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_149_pad_0 = const()[name = tensor<string, []>("xt_149_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> xt_149_strides_0 = const()[name = tensor<string, []>("xt_149_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_149_dilations_0 = const()[name = tensor<string, []>("xt_149_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_149_groups_0 = const()[name = tensor<string, []>("xt_149_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 153600]> xt_149 = conv(bias = generator_noise_res_2_convs2_0_bias, dilations = xt_149_dilations_0, groups = xt_149_groups_0, pad = xt_149_pad_0, pad_type = xt_149_pad_type_0, strides = xt_149_strides_0, weight = weight_141, x = input_291)[name = tensor<string, []>("xt_149")];
            tensor<fp32, [1, 64, 153600]> input_293 = add(x = xt_149, y = input_285)[name = tensor<string, []>("input_293")];
            tensor<fp32, [1, 128]> h_241 = linear(bias = generator_noise_res_2_adain1_1_fc_bias, weight = generator_noise_res_2_adain1_1_fc_weight, x = s)[name = tensor<string, []>("linear_61")];
            tensor<int32, [3]> var_2939 = const()[name = tensor<string, []>("op_2939"), val = tensor<int32, [3]>([1, 128, 1])];
            tensor<fp32, [1, 128, 1]> h_243 = reshape(shape = var_2939, x = h_241)[name = tensor<string, []>("h_243")];
            tensor<int32, [2]> var_2941_split_sizes_0 = const()[name = tensor<string, []>("op_2941_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            tensor<int32, []> var_2941_axis_0 = const()[name = tensor<string, []>("op_2941_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 1]> var_2941_0, tensor<fp32, [1, 64, 1]> var_2941_1 = split(axis = var_2941_axis_0, split_sizes = var_2941_split_sizes_0, x = h_243)[name = tensor<string, []>("op_2941")];
            tensor<fp32, []> var_2943_promoted = const()[name = tensor<string, []>("op_2943_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 64, 1]> var_2944 = add(x = var_2941_0, y = var_2943_promoted)[name = tensor<string, []>("op_2944")];
            tensor<fp32, [1, 64, 153600]> var_2945 = instance_norm(epsilon = var_516, x = input_293)[name = tensor<string, []>("op_2945")];
            tensor<fp32, [1, 64, 153600]> var_2946 = mul(x = var_2944, y = var_2945)[name = tensor<string, []>("op_2946")];
            tensor<fp32, [1, 64, 153600]> xt_151 = add(x = var_2946, y = var_2941_1)[name = tensor<string, []>("xt_151")];
            tensor<fp32, [1, 64, 1]> var_2948 = const()[name = tensor<string, []>("op_2948"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(216042816)))];
            tensor<fp32, [1, 64, 153600]> var_2951 = mul(x = generator_noise_res_2_alpha1_1, y = xt_151)[name = tensor<string, []>("op_2951")];
            tensor<fp32, [1, 64, 153600]> var_2952 = sin(x = var_2951)[name = tensor<string, []>("op_2952")];
            tensor<fp32, []> var_532_promoted_53 = const()[name = tensor<string, []>("op_532_promoted_53"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 64, 153600]> var_2953 = pow(x = var_2952, y = var_532_promoted_53)[name = tensor<string, []>("op_2953")];
            tensor<fp32, [1, 64, 153600]> var_2954 = mul(x = var_2948, y = var_2953)[name = tensor<string, []>("op_2954")];
            tensor<fp32, [1, 64, 153600]> input_295 = add(x = xt_151, y = var_2954)[name = tensor<string, []>("input_295")];
            tensor<fp32, [64, 64, 7]> weight_143 = const()[name = tensor<string, []>("weight_143"), val = tensor<fp32, [64, 64, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(216043136)))];
            tensor<string, []> input_297_pad_type_0 = const()[name = tensor<string, []>("input_297_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_297_pad_0 = const()[name = tensor<string, []>("input_297_pad_0"), val = tensor<int32, [2]>([9, 9])];
            tensor<int32, [1]> input_297_dilations_0 = const()[name = tensor<string, []>("input_297_dilations_0"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> input_297_strides_0 = const()[name = tensor<string, []>("input_297_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_297_groups_0 = const()[name = tensor<string, []>("input_297_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 153600]> input_297 = conv(bias = generator_noise_res_2_convs1_1_bias, 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 = weight_143, x = input_295)[name = tensor<string, []>("input_297")];
            tensor<fp32, [1, 128]> h_245 = linear(bias = generator_noise_res_2_adain2_1_fc_bias, weight = generator_noise_res_2_adain2_1_fc_weight, x = s)[name = tensor<string, []>("linear_62")];
            tensor<int32, [3]> var_2969 = const()[name = tensor<string, []>("op_2969"), val = tensor<int32, [3]>([1, 128, 1])];
            tensor<fp32, [1, 128, 1]> h_247 = reshape(shape = var_2969, x = h_245)[name = tensor<string, []>("h_247")];
            tensor<int32, [2]> var_2971_split_sizes_0 = const()[name = tensor<string, []>("op_2971_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            tensor<int32, []> var_2971_axis_0 = const()[name = tensor<string, []>("op_2971_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 1]> var_2971_0, tensor<fp32, [1, 64, 1]> var_2971_1 = split(axis = var_2971_axis_0, split_sizes = var_2971_split_sizes_0, x = h_247)[name = tensor<string, []>("op_2971")];
            tensor<fp32, []> var_2973_promoted = const()[name = tensor<string, []>("op_2973_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 64, 1]> var_2974 = add(x = var_2971_0, y = var_2973_promoted)[name = tensor<string, []>("op_2974")];
            tensor<fp32, [1, 64, 153600]> var_2975 = instance_norm(epsilon = var_516, x = input_297)[name = tensor<string, []>("op_2975")];
            tensor<fp32, [1, 64, 153600]> var_2976 = mul(x = var_2974, y = var_2975)[name = tensor<string, []>("op_2976")];
            tensor<fp32, [1, 64, 153600]> xt_153 = add(x = var_2976, y = var_2971_1)[name = tensor<string, []>("xt_153")];
            tensor<fp32, [1, 64, 1]> var_2978 = const()[name = tensor<string, []>("op_2978"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(216157888)))];
            tensor<fp32, [1, 64, 153600]> var_2981 = mul(x = generator_noise_res_2_alpha2_1, y = xt_153)[name = tensor<string, []>("op_2981")];
            tensor<fp32, [1, 64, 153600]> var_2982 = sin(x = var_2981)[name = tensor<string, []>("op_2982")];
            tensor<fp32, []> var_532_promoted_54 = const()[name = tensor<string, []>("op_532_promoted_54"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 64, 153600]> var_2983 = pow(x = var_2982, y = var_532_promoted_54)[name = tensor<string, []>("op_2983")];
            tensor<fp32, [1, 64, 153600]> var_2984 = mul(x = var_2978, y = var_2983)[name = tensor<string, []>("op_2984")];
            tensor<fp32, [1, 64, 153600]> input_299 = add(x = xt_153, y = var_2984)[name = tensor<string, []>("input_299")];
            tensor<fp32, [64, 64, 7]> weight_145 = const()[name = tensor<string, []>("weight_145"), val = tensor<fp32, [64, 64, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(216158208)))];
            tensor<string, []> xt_155_pad_type_0 = const()[name = tensor<string, []>("xt_155_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_155_pad_0 = const()[name = tensor<string, []>("xt_155_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> xt_155_strides_0 = const()[name = tensor<string, []>("xt_155_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_155_dilations_0 = const()[name = tensor<string, []>("xt_155_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_155_groups_0 = const()[name = tensor<string, []>("xt_155_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 153600]> xt_155 = conv(bias = generator_noise_res_2_convs2_1_bias, dilations = xt_155_dilations_0, groups = xt_155_groups_0, pad = xt_155_pad_0, pad_type = xt_155_pad_type_0, strides = xt_155_strides_0, weight = weight_145, x = input_299)[name = tensor<string, []>("xt_155")];
            tensor<fp32, [1, 64, 153600]> input_301 = add(x = xt_155, y = input_293)[name = tensor<string, []>("input_301")];
            tensor<fp32, [1, 128]> h_249 = linear(bias = generator_noise_res_2_adain1_2_fc_bias, weight = generator_noise_res_2_adain1_2_fc_weight, x = s)[name = tensor<string, []>("linear_63")];
            tensor<int32, [3]> var_3000 = const()[name = tensor<string, []>("op_3000"), val = tensor<int32, [3]>([1, 128, 1])];
            tensor<fp32, [1, 128, 1]> h_251 = reshape(shape = var_3000, x = h_249)[name = tensor<string, []>("h_251")];
            tensor<int32, [2]> var_3002_split_sizes_0 = const()[name = tensor<string, []>("op_3002_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            tensor<int32, []> var_3002_axis_0 = const()[name = tensor<string, []>("op_3002_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 1]> var_3002_0, tensor<fp32, [1, 64, 1]> var_3002_1 = split(axis = var_3002_axis_0, split_sizes = var_3002_split_sizes_0, x = h_251)[name = tensor<string, []>("op_3002")];
            tensor<fp32, []> var_3004_promoted = const()[name = tensor<string, []>("op_3004_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 64, 1]> var_3005 = add(x = var_3002_0, y = var_3004_promoted)[name = tensor<string, []>("op_3005")];
            tensor<fp32, [1, 64, 153600]> var_3006 = instance_norm(epsilon = var_516, x = input_301)[name = tensor<string, []>("op_3006")];
            tensor<fp32, [1, 64, 153600]> var_3007 = mul(x = var_3005, y = var_3006)[name = tensor<string, []>("op_3007")];
            tensor<fp32, [1, 64, 153600]> xt_157 = add(x = var_3007, y = var_3002_1)[name = tensor<string, []>("xt_157")];
            tensor<fp32, [1, 64, 1]> var_3009 = const()[name = tensor<string, []>("op_3009"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(216272960)))];
            tensor<fp32, [1, 64, 153600]> var_3012 = mul(x = generator_noise_res_2_alpha1_2, y = xt_157)[name = tensor<string, []>("op_3012")];
            tensor<fp32, [1, 64, 153600]> var_3013 = sin(x = var_3012)[name = tensor<string, []>("op_3013")];
            tensor<fp32, []> var_532_promoted_55 = const()[name = tensor<string, []>("op_532_promoted_55"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 64, 153600]> var_3014 = pow(x = var_3013, y = var_532_promoted_55)[name = tensor<string, []>("op_3014")];
            tensor<fp32, [1, 64, 153600]> var_3015 = mul(x = var_3009, y = var_3014)[name = tensor<string, []>("op_3015")];
            tensor<fp32, [1, 64, 153600]> input_303 = add(x = xt_157, y = var_3015)[name = tensor<string, []>("input_303")];
            tensor<fp32, [64, 64, 7]> weight_147 = const()[name = tensor<string, []>("weight_147"), val = tensor<fp32, [64, 64, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(216273280)))];
            tensor<string, []> input_305_pad_type_0 = const()[name = tensor<string, []>("input_305_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_305_pad_0 = const()[name = tensor<string, []>("input_305_pad_0"), val = tensor<int32, [2]>([15, 15])];
            tensor<int32, [1]> input_305_dilations_0 = const()[name = tensor<string, []>("input_305_dilations_0"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> input_305_strides_0 = const()[name = tensor<string, []>("input_305_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_305_groups_0 = const()[name = tensor<string, []>("input_305_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 153600]> input_305 = conv(bias = generator_noise_res_2_convs1_2_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 = weight_147, x = input_303)[name = tensor<string, []>("input_305")];
            tensor<fp32, [1, 128]> h_253 = linear(bias = generator_noise_res_2_adain2_2_fc_bias, weight = generator_noise_res_2_adain2_2_fc_weight, x = s)[name = tensor<string, []>("linear_64")];
            tensor<int32, [3]> var_3030 = const()[name = tensor<string, []>("op_3030"), val = tensor<int32, [3]>([1, 128, 1])];
            tensor<fp32, [1, 128, 1]> h_255 = reshape(shape = var_3030, x = h_253)[name = tensor<string, []>("h_255")];
            tensor<int32, [2]> var_3032_split_sizes_0 = const()[name = tensor<string, []>("op_3032_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            tensor<int32, []> var_3032_axis_0 = const()[name = tensor<string, []>("op_3032_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 1]> var_3032_0, tensor<fp32, [1, 64, 1]> var_3032_1 = split(axis = var_3032_axis_0, split_sizes = var_3032_split_sizes_0, x = h_255)[name = tensor<string, []>("op_3032")];
            tensor<fp32, []> var_3034_promoted = const()[name = tensor<string, []>("op_3034_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 64, 1]> var_3035 = add(x = var_3032_0, y = var_3034_promoted)[name = tensor<string, []>("op_3035")];
            tensor<fp32, [1, 64, 153600]> var_3036 = instance_norm(epsilon = var_516, x = input_305)[name = tensor<string, []>("op_3036")];
            tensor<fp32, [1, 64, 153600]> var_3037 = mul(x = var_3035, y = var_3036)[name = tensor<string, []>("op_3037")];
            tensor<fp32, [1, 64, 153600]> xt_159 = add(x = var_3037, y = var_3032_1)[name = tensor<string, []>("xt_159")];
            tensor<fp32, [1, 64, 1]> var_3039 = const()[name = tensor<string, []>("op_3039"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(216388032)))];
            tensor<fp32, [1, 64, 153600]> var_3042 = mul(x = generator_noise_res_2_alpha2_2, y = xt_159)[name = tensor<string, []>("op_3042")];
            tensor<fp32, [1, 64, 153600]> var_3043 = sin(x = var_3042)[name = tensor<string, []>("op_3043")];
            tensor<fp32, []> var_532_promoted_56 = const()[name = tensor<string, []>("op_532_promoted_56"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 64, 153600]> var_3044 = pow(x = var_3043, y = var_532_promoted_56)[name = tensor<string, []>("op_3044")];
            tensor<fp32, [1, 64, 153600]> var_3045 = mul(x = var_3039, y = var_3044)[name = tensor<string, []>("op_3045")];
            tensor<fp32, [1, 64, 153600]> input_307 = add(x = xt_159, y = var_3045)[name = tensor<string, []>("input_307")];
            tensor<fp32, [64, 64, 7]> weight_149 = const()[name = tensor<string, []>("weight_149"), val = tensor<fp32, [64, 64, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(216388352)))];
            tensor<string, []> xt_161_pad_type_0 = const()[name = tensor<string, []>("xt_161_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_161_pad_0 = const()[name = tensor<string, []>("xt_161_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> xt_161_strides_0 = const()[name = tensor<string, []>("xt_161_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_161_dilations_0 = const()[name = tensor<string, []>("xt_161_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_161_groups_0 = const()[name = tensor<string, []>("xt_161_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 153600]> xt_161 = conv(bias = generator_noise_res_2_convs2_2_bias, dilations = xt_161_dilations_0, groups = xt_161_groups_0, pad = xt_161_pad_0, pad_type = xt_161_pad_type_0, strides = xt_161_strides_0, weight = weight_149, x = input_307)[name = tensor<string, []>("xt_161")];
            tensor<fp32, [1, 64, 153600]> x_source_5 = add(x = xt_161, y = input_301)[name = tensor<string, []>("x_source_5")];
            tensor<fp32, [128, 64, 6]> var_3055 = const()[name = tensor<string, []>("op_3055"), val = tensor<fp32, [128, 64, 6]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(216503104)))];
            tensor<string, []> conv_transpose_2_pad_type_0 = const()[name = tensor<string, []>("conv_transpose_2_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> conv_transpose_2_pad_0 = const()[name = tensor<string, []>("conv_transpose_2_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_transpose_2_strides_0 = const()[name = tensor<string, []>("conv_transpose_2_strides_0"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> conv_transpose_2_dilations_0 = const()[name = tensor<string, []>("conv_transpose_2_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv_transpose_2_groups_0 = const()[name = tensor<string, []>("conv_transpose_2_groups_0"), val = tensor<int32, []>(1)];
            tensor<int32, [3]> conv_transpose_2_has_output_shape_output_shape_0 = const()[name = tensor<string, []>("conv_transpose_2_has_output_shape_output_shape_0"), val = tensor<int32, [3]>([1, 64, 153603])];
            tensor<fp32, [1, 64, 153603]> conv_transpose_2_has_output_shape = conv_transpose(bias = generator_ups_2_bias, dilations = conv_transpose_2_dilations_0, groups = conv_transpose_2_groups_0, output_shape = conv_transpose_2_has_output_shape_output_shape_0, pad = conv_transpose_2_pad_0, pad_type = conv_transpose_2_pad_type_0, strides = conv_transpose_2_strides_0, weight = var_3055, x = input_309)[name = tensor<string, []>("conv_transpose_2_has_output_shape")];
            tensor<int32, [3]> x_19_begin_0 = const()[name = tensor<string, []>("x_19_begin_0"), val = tensor<int32, [3]>([0, 0, 2])];
            tensor<int32, [3]> x_19_end_0 = const()[name = tensor<string, []>("x_19_end_0"), val = tensor<int32, [3]>([0, 0, 153602])];
            tensor<bool, [3]> x_19_begin_mask_0 = const()[name = tensor<string, []>("x_19_begin_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<bool, [3]> x_19_end_mask_0 = const()[name = tensor<string, []>("x_19_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp32, [1, 64, 153600]> x_19 = slice_by_index(begin = x_19_begin_0, begin_mask = x_19_begin_mask_0, end = x_19_end_0, end_mask = x_19_end_mask_0, x = conv_transpose_2_has_output_shape)[name = tensor<string, []>("x_19")];
            tensor<fp32, [1, 64, 153600]> input_311 = add(x = x_19, y = x_source_5)[name = tensor<string, []>("input_311")];
            tensor<fp32, [1, 128]> h_257 = linear(bias = generator_resblocks_6_adain1_0_fc_bias, weight = generator_resblocks_6_adain1_0_fc_weight, x = s)[name = tensor<string, []>("linear_65")];
            tensor<int32, [3]> var_3141 = const()[name = tensor<string, []>("op_3141"), val = tensor<int32, [3]>([1, 128, 1])];
            tensor<fp32, [1, 128, 1]> h_259 = reshape(shape = var_3141, x = h_257)[name = tensor<string, []>("h_259")];
            tensor<int32, [2]> var_3143_split_sizes_0 = const()[name = tensor<string, []>("op_3143_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            tensor<int32, []> var_3143_axis_0 = const()[name = tensor<string, []>("op_3143_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 1]> var_3143_0, tensor<fp32, [1, 64, 1]> var_3143_1 = split(axis = var_3143_axis_0, split_sizes = var_3143_split_sizes_0, x = h_259)[name = tensor<string, []>("op_3143")];
            tensor<fp32, []> var_3145_promoted = const()[name = tensor<string, []>("op_3145_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 64, 1]> var_3146 = add(x = var_3143_0, y = var_3145_promoted)[name = tensor<string, []>("op_3146")];
            tensor<fp32, [1, 64, 153600]> var_3147 = instance_norm(epsilon = var_516, x = input_311)[name = tensor<string, []>("op_3147")];
            tensor<fp32, [1, 64, 153600]> var_3148 = mul(x = var_3146, y = var_3147)[name = tensor<string, []>("op_3148")];
            tensor<fp32, [1, 64, 153600]> xt_163 = add(x = var_3148, y = var_3143_1)[name = tensor<string, []>("xt_163")];
            tensor<fp32, [1, 64, 1]> var_3150 = const()[name = tensor<string, []>("op_3150"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(216699776)))];
            tensor<fp32, [1, 64, 153600]> var_3153 = mul(x = generator_resblocks_6_alpha1_0, y = xt_163)[name = tensor<string, []>("op_3153")];
            tensor<fp32, [1, 64, 153600]> var_3154 = sin(x = var_3153)[name = tensor<string, []>("op_3154")];
            tensor<fp32, []> var_532_promoted_57 = const()[name = tensor<string, []>("op_532_promoted_57"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 64, 153600]> var_3155 = pow(x = var_3154, y = var_532_promoted_57)[name = tensor<string, []>("op_3155")];
            tensor<fp32, [1, 64, 153600]> var_3156 = mul(x = var_3150, y = var_3155)[name = tensor<string, []>("op_3156")];
            tensor<fp32, [1, 64, 153600]> input_313 = add(x = xt_163, y = var_3156)[name = tensor<string, []>("input_313")];
            tensor<fp32, [64, 64, 3]> weight_151 = const()[name = tensor<string, []>("weight_151"), val = tensor<fp32, [64, 64, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(216700096)))];
            tensor<string, []> input_315_pad_type_0 = const()[name = tensor<string, []>("input_315_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_315_pad_0 = const()[name = tensor<string, []>("input_315_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> input_315_strides_0 = const()[name = tensor<string, []>("input_315_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_315_dilations_0 = const()[name = tensor<string, []>("input_315_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_315_groups_0 = const()[name = tensor<string, []>("input_315_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 153600]> input_315 = conv(bias = generator_resblocks_6_convs1_0_bias, dilations = input_315_dilations_0, groups = input_315_groups_0, pad = input_315_pad_0, pad_type = input_315_pad_type_0, strides = input_315_strides_0, weight = weight_151, x = input_313)[name = tensor<string, []>("input_315")];
            tensor<fp32, [1, 128]> h_261 = linear(bias = generator_resblocks_6_adain2_0_fc_bias, weight = generator_resblocks_6_adain2_0_fc_weight, x = s)[name = tensor<string, []>("linear_66")];
            tensor<int32, [3]> var_3171 = const()[name = tensor<string, []>("op_3171"), val = tensor<int32, [3]>([1, 128, 1])];
            tensor<fp32, [1, 128, 1]> h_263 = reshape(shape = var_3171, x = h_261)[name = tensor<string, []>("h_263")];
            tensor<int32, [2]> var_3173_split_sizes_0 = const()[name = tensor<string, []>("op_3173_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            tensor<int32, []> var_3173_axis_0 = const()[name = tensor<string, []>("op_3173_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 1]> var_3173_0, tensor<fp32, [1, 64, 1]> var_3173_1 = split(axis = var_3173_axis_0, split_sizes = var_3173_split_sizes_0, x = h_263)[name = tensor<string, []>("op_3173")];
            tensor<fp32, []> var_3175_promoted = const()[name = tensor<string, []>("op_3175_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 64, 1]> var_3176 = add(x = var_3173_0, y = var_3175_promoted)[name = tensor<string, []>("op_3176")];
            tensor<fp32, [1, 64, 153600]> var_3177 = instance_norm(epsilon = var_516, x = input_315)[name = tensor<string, []>("op_3177")];
            tensor<fp32, [1, 64, 153600]> var_3178 = mul(x = var_3176, y = var_3177)[name = tensor<string, []>("op_3178")];
            tensor<fp32, [1, 64, 153600]> xt_165 = add(x = var_3178, y = var_3173_1)[name = tensor<string, []>("xt_165")];
            tensor<fp32, [1, 64, 1]> var_3180 = const()[name = tensor<string, []>("op_3180"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(216749312)))];
            tensor<fp32, [1, 64, 153600]> var_3183 = mul(x = generator_resblocks_6_alpha2_0, y = xt_165)[name = tensor<string, []>("op_3183")];
            tensor<fp32, [1, 64, 153600]> var_3184 = sin(x = var_3183)[name = tensor<string, []>("op_3184")];
            tensor<fp32, []> var_532_promoted_58 = const()[name = tensor<string, []>("op_532_promoted_58"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 64, 153600]> var_3185 = pow(x = var_3184, y = var_532_promoted_58)[name = tensor<string, []>("op_3185")];
            tensor<fp32, [1, 64, 153600]> var_3186 = mul(x = var_3180, y = var_3185)[name = tensor<string, []>("op_3186")];
            tensor<fp32, [1, 64, 153600]> input_317 = add(x = xt_165, y = var_3186)[name = tensor<string, []>("input_317")];
            tensor<fp32, [64, 64, 3]> weight_153 = const()[name = tensor<string, []>("weight_153"), val = tensor<fp32, [64, 64, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(216749632)))];
            tensor<string, []> xt_167_pad_type_0 = const()[name = tensor<string, []>("xt_167_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_167_pad_0 = const()[name = tensor<string, []>("xt_167_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> xt_167_strides_0 = const()[name = tensor<string, []>("xt_167_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_167_dilations_0 = const()[name = tensor<string, []>("xt_167_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_167_groups_0 = const()[name = tensor<string, []>("xt_167_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 153600]> xt_167 = conv(bias = generator_resblocks_6_convs2_0_bias, dilations = xt_167_dilations_0, groups = xt_167_groups_0, pad = xt_167_pad_0, pad_type = xt_167_pad_type_0, strides = xt_167_strides_0, weight = weight_153, x = input_317)[name = tensor<string, []>("xt_167")];
            tensor<fp32, [1, 64, 153600]> input_319 = add(x = xt_167, y = input_311)[name = tensor<string, []>("input_319")];
            tensor<fp32, [1, 128]> h_265 = linear(bias = generator_resblocks_6_adain1_1_fc_bias, weight = generator_resblocks_6_adain1_1_fc_weight, x = s)[name = tensor<string, []>("linear_67")];
            tensor<int32, [3]> var_3202 = const()[name = tensor<string, []>("op_3202"), val = tensor<int32, [3]>([1, 128, 1])];
            tensor<fp32, [1, 128, 1]> h_267 = reshape(shape = var_3202, x = h_265)[name = tensor<string, []>("h_267")];
            tensor<int32, [2]> var_3204_split_sizes_0 = const()[name = tensor<string, []>("op_3204_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            tensor<int32, []> var_3204_axis_0 = const()[name = tensor<string, []>("op_3204_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 1]> var_3204_0, tensor<fp32, [1, 64, 1]> var_3204_1 = split(axis = var_3204_axis_0, split_sizes = var_3204_split_sizes_0, x = h_267)[name = tensor<string, []>("op_3204")];
            tensor<fp32, []> var_3206_promoted = const()[name = tensor<string, []>("op_3206_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 64, 1]> var_3207 = add(x = var_3204_0, y = var_3206_promoted)[name = tensor<string, []>("op_3207")];
            tensor<fp32, [1, 64, 153600]> var_3208 = instance_norm(epsilon = var_516, x = input_319)[name = tensor<string, []>("op_3208")];
            tensor<fp32, [1, 64, 153600]> var_3209 = mul(x = var_3207, y = var_3208)[name = tensor<string, []>("op_3209")];
            tensor<fp32, [1, 64, 153600]> xt_169 = add(x = var_3209, y = var_3204_1)[name = tensor<string, []>("xt_169")];
            tensor<fp32, [1, 64, 1]> var_3211 = const()[name = tensor<string, []>("op_3211"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(216798848)))];
            tensor<fp32, [1, 64, 153600]> var_3214 = mul(x = generator_resblocks_6_alpha1_1, y = xt_169)[name = tensor<string, []>("op_3214")];
            tensor<fp32, [1, 64, 153600]> var_3215 = sin(x = var_3214)[name = tensor<string, []>("op_3215")];
            tensor<fp32, []> var_532_promoted_59 = const()[name = tensor<string, []>("op_532_promoted_59"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 64, 153600]> var_3216 = pow(x = var_3215, y = var_532_promoted_59)[name = tensor<string, []>("op_3216")];
            tensor<fp32, [1, 64, 153600]> var_3217 = mul(x = var_3211, y = var_3216)[name = tensor<string, []>("op_3217")];
            tensor<fp32, [1, 64, 153600]> input_321 = add(x = xt_169, y = var_3217)[name = tensor<string, []>("input_321")];
            tensor<fp32, [64, 64, 3]> weight_155 = const()[name = tensor<string, []>("weight_155"), val = tensor<fp32, [64, 64, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(216799168)))];
            tensor<string, []> input_323_pad_type_0 = const()[name = tensor<string, []>("input_323_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_323_pad_0 = const()[name = tensor<string, []>("input_323_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> input_323_dilations_0 = const()[name = tensor<string, []>("input_323_dilations_0"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> input_323_strides_0 = const()[name = tensor<string, []>("input_323_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_323_groups_0 = const()[name = tensor<string, []>("input_323_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 153600]> input_323 = conv(bias = generator_resblocks_6_convs1_1_bias, 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 = weight_155, x = input_321)[name = tensor<string, []>("input_323")];
            tensor<fp32, [1, 128]> h_269 = linear(bias = generator_resblocks_6_adain2_1_fc_bias, weight = generator_resblocks_6_adain2_1_fc_weight, x = s)[name = tensor<string, []>("linear_68")];
            tensor<int32, [3]> var_3232 = const()[name = tensor<string, []>("op_3232"), val = tensor<int32, [3]>([1, 128, 1])];
            tensor<fp32, [1, 128, 1]> h_271 = reshape(shape = var_3232, x = h_269)[name = tensor<string, []>("h_271")];
            tensor<int32, [2]> var_3234_split_sizes_0 = const()[name = tensor<string, []>("op_3234_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            tensor<int32, []> var_3234_axis_0 = const()[name = tensor<string, []>("op_3234_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 1]> var_3234_0, tensor<fp32, [1, 64, 1]> var_3234_1 = split(axis = var_3234_axis_0, split_sizes = var_3234_split_sizes_0, x = h_271)[name = tensor<string, []>("op_3234")];
            tensor<fp32, []> var_3236_promoted = const()[name = tensor<string, []>("op_3236_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 64, 1]> var_3237 = add(x = var_3234_0, y = var_3236_promoted)[name = tensor<string, []>("op_3237")];
            tensor<fp32, [1, 64, 153600]> var_3238 = instance_norm(epsilon = var_516, x = input_323)[name = tensor<string, []>("op_3238")];
            tensor<fp32, [1, 64, 153600]> var_3239 = mul(x = var_3237, y = var_3238)[name = tensor<string, []>("op_3239")];
            tensor<fp32, [1, 64, 153600]> xt_171 = add(x = var_3239, y = var_3234_1)[name = tensor<string, []>("xt_171")];
            tensor<fp32, [1, 64, 1]> var_3241 = const()[name = tensor<string, []>("op_3241"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(216848384)))];
            tensor<fp32, [1, 64, 153600]> var_3244 = mul(x = generator_resblocks_6_alpha2_1, y = xt_171)[name = tensor<string, []>("op_3244")];
            tensor<fp32, [1, 64, 153600]> var_3245 = sin(x = var_3244)[name = tensor<string, []>("op_3245")];
            tensor<fp32, []> var_532_promoted_60 = const()[name = tensor<string, []>("op_532_promoted_60"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 64, 153600]> var_3246 = pow(x = var_3245, y = var_532_promoted_60)[name = tensor<string, []>("op_3246")];
            tensor<fp32, [1, 64, 153600]> var_3247 = mul(x = var_3241, y = var_3246)[name = tensor<string, []>("op_3247")];
            tensor<fp32, [1, 64, 153600]> input_325 = add(x = xt_171, y = var_3247)[name = tensor<string, []>("input_325")];
            tensor<fp32, [64, 64, 3]> weight_157 = const()[name = tensor<string, []>("weight_157"), val = tensor<fp32, [64, 64, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(216848704)))];
            tensor<string, []> xt_173_pad_type_0 = const()[name = tensor<string, []>("xt_173_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_173_pad_0 = const()[name = tensor<string, []>("xt_173_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> xt_173_strides_0 = const()[name = tensor<string, []>("xt_173_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_173_dilations_0 = const()[name = tensor<string, []>("xt_173_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_173_groups_0 = const()[name = tensor<string, []>("xt_173_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 153600]> xt_173 = conv(bias = generator_resblocks_6_convs2_1_bias, dilations = xt_173_dilations_0, groups = xt_173_groups_0, pad = xt_173_pad_0, pad_type = xt_173_pad_type_0, strides = xt_173_strides_0, weight = weight_157, x = input_325)[name = tensor<string, []>("xt_173")];
            tensor<fp32, [1, 64, 153600]> input_327 = add(x = xt_173, y = input_319)[name = tensor<string, []>("input_327")];
            tensor<fp32, [1, 128]> h_273 = linear(bias = generator_resblocks_6_adain1_2_fc_bias, weight = generator_resblocks_6_adain1_2_fc_weight, x = s)[name = tensor<string, []>("linear_69")];
            tensor<int32, [3]> var_3263 = const()[name = tensor<string, []>("op_3263"), val = tensor<int32, [3]>([1, 128, 1])];
            tensor<fp32, [1, 128, 1]> h_275 = reshape(shape = var_3263, x = h_273)[name = tensor<string, []>("h_275")];
            tensor<int32, [2]> var_3265_split_sizes_0 = const()[name = tensor<string, []>("op_3265_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            tensor<int32, []> var_3265_axis_0 = const()[name = tensor<string, []>("op_3265_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 1]> var_3265_0, tensor<fp32, [1, 64, 1]> var_3265_1 = split(axis = var_3265_axis_0, split_sizes = var_3265_split_sizes_0, x = h_275)[name = tensor<string, []>("op_3265")];
            tensor<fp32, []> var_3267_promoted = const()[name = tensor<string, []>("op_3267_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 64, 1]> var_3268 = add(x = var_3265_0, y = var_3267_promoted)[name = tensor<string, []>("op_3268")];
            tensor<fp32, [1, 64, 153600]> var_3269 = instance_norm(epsilon = var_516, x = input_327)[name = tensor<string, []>("op_3269")];
            tensor<fp32, [1, 64, 153600]> var_3270 = mul(x = var_3268, y = var_3269)[name = tensor<string, []>("op_3270")];
            tensor<fp32, [1, 64, 153600]> xt_175 = add(x = var_3270, y = var_3265_1)[name = tensor<string, []>("xt_175")];
            tensor<fp32, [1, 64, 1]> var_3272 = const()[name = tensor<string, []>("op_3272"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(216897920)))];
            tensor<fp32, [1, 64, 153600]> var_3275 = mul(x = generator_resblocks_6_alpha1_2, y = xt_175)[name = tensor<string, []>("op_3275")];
            tensor<fp32, [1, 64, 153600]> var_3276 = sin(x = var_3275)[name = tensor<string, []>("op_3276")];
            tensor<fp32, []> var_532_promoted_61 = const()[name = tensor<string, []>("op_532_promoted_61"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 64, 153600]> var_3277 = pow(x = var_3276, y = var_532_promoted_61)[name = tensor<string, []>("op_3277")];
            tensor<fp32, [1, 64, 153600]> var_3278 = mul(x = var_3272, y = var_3277)[name = tensor<string, []>("op_3278")];
            tensor<fp32, [1, 64, 153600]> input_329 = add(x = xt_175, y = var_3278)[name = tensor<string, []>("input_329")];
            tensor<fp32, [64, 64, 3]> weight_159 = const()[name = tensor<string, []>("weight_159"), val = tensor<fp32, [64, 64, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(216898240)))];
            tensor<string, []> input_331_pad_type_0 = const()[name = tensor<string, []>("input_331_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_331_pad_0 = const()[name = tensor<string, []>("input_331_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> input_331_dilations_0 = const()[name = tensor<string, []>("input_331_dilations_0"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> input_331_strides_0 = const()[name = tensor<string, []>("input_331_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_331_groups_0 = const()[name = tensor<string, []>("input_331_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 153600]> input_331 = conv(bias = generator_resblocks_6_convs1_2_bias, dilations = input_331_dilations_0, groups = input_331_groups_0, pad = input_331_pad_0, pad_type = input_331_pad_type_0, strides = input_331_strides_0, weight = weight_159, x = input_329)[name = tensor<string, []>("input_331")];
            tensor<fp32, [1, 128]> h_277 = linear(bias = generator_resblocks_6_adain2_2_fc_bias, weight = generator_resblocks_6_adain2_2_fc_weight, x = s)[name = tensor<string, []>("linear_70")];
            tensor<int32, [3]> var_3293 = const()[name = tensor<string, []>("op_3293"), val = tensor<int32, [3]>([1, 128, 1])];
            tensor<fp32, [1, 128, 1]> h_279 = reshape(shape = var_3293, x = h_277)[name = tensor<string, []>("h_279")];
            tensor<int32, [2]> var_3295_split_sizes_0 = const()[name = tensor<string, []>("op_3295_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            tensor<int32, []> var_3295_axis_0 = const()[name = tensor<string, []>("op_3295_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 1]> var_3295_0, tensor<fp32, [1, 64, 1]> var_3295_1 = split(axis = var_3295_axis_0, split_sizes = var_3295_split_sizes_0, x = h_279)[name = tensor<string, []>("op_3295")];
            tensor<fp32, []> var_3297_promoted = const()[name = tensor<string, []>("op_3297_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 64, 1]> var_3298 = add(x = var_3295_0, y = var_3297_promoted)[name = tensor<string, []>("op_3298")];
            tensor<fp32, [1, 64, 153600]> var_3299 = instance_norm(epsilon = var_516, x = input_331)[name = tensor<string, []>("op_3299")];
            tensor<fp32, [1, 64, 153600]> var_3300 = mul(x = var_3298, y = var_3299)[name = tensor<string, []>("op_3300")];
            tensor<fp32, [1, 64, 153600]> xt_177 = add(x = var_3300, y = var_3295_1)[name = tensor<string, []>("xt_177")];
            tensor<fp32, [1, 64, 1]> var_3302 = const()[name = tensor<string, []>("op_3302"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(216947456)))];
            tensor<fp32, [1, 64, 153600]> var_3305 = mul(x = generator_resblocks_6_alpha2_2, y = xt_177)[name = tensor<string, []>("op_3305")];
            tensor<fp32, [1, 64, 153600]> var_3306 = sin(x = var_3305)[name = tensor<string, []>("op_3306")];
            tensor<fp32, []> var_532_promoted_62 = const()[name = tensor<string, []>("op_532_promoted_62"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 64, 153600]> var_3307 = pow(x = var_3306, y = var_532_promoted_62)[name = tensor<string, []>("op_3307")];
            tensor<fp32, [1, 64, 153600]> var_3308 = mul(x = var_3302, y = var_3307)[name = tensor<string, []>("op_3308")];
            tensor<fp32, [1, 64, 153600]> input_333 = add(x = xt_177, y = var_3308)[name = tensor<string, []>("input_333")];
            tensor<fp32, [64, 64, 3]> weight_161 = const()[name = tensor<string, []>("weight_161"), val = tensor<fp32, [64, 64, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(216947776)))];
            tensor<string, []> xt_179_pad_type_0 = const()[name = tensor<string, []>("xt_179_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_179_pad_0 = const()[name = tensor<string, []>("xt_179_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> xt_179_strides_0 = const()[name = tensor<string, []>("xt_179_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_179_dilations_0 = const()[name = tensor<string, []>("xt_179_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_179_groups_0 = const()[name = tensor<string, []>("xt_179_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 153600]> xt_179 = conv(bias = generator_resblocks_6_convs2_2_bias, dilations = xt_179_dilations_0, groups = xt_179_groups_0, pad = xt_179_pad_0, pad_type = xt_179_pad_type_0, strides = xt_179_strides_0, weight = weight_161, x = input_333)[name = tensor<string, []>("xt_179")];
            tensor<fp32, [1, 64, 153600]> xs_13 = add(x = xt_179, y = input_327)[name = tensor<string, []>("xs_13")];
            tensor<fp32, [1, 128]> h_281 = linear(bias = generator_resblocks_7_adain1_0_fc_bias, weight = generator_resblocks_7_adain1_0_fc_weight, x = s)[name = tensor<string, []>("linear_71")];
            tensor<int32, [3]> var_3396 = const()[name = tensor<string, []>("op_3396"), val = tensor<int32, [3]>([1, 128, 1])];
            tensor<fp32, [1, 128, 1]> h_283 = reshape(shape = var_3396, x = h_281)[name = tensor<string, []>("h_283")];
            tensor<int32, [2]> var_3398_split_sizes_0 = const()[name = tensor<string, []>("op_3398_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            tensor<int32, []> var_3398_axis_0 = const()[name = tensor<string, []>("op_3398_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 1]> var_3398_0, tensor<fp32, [1, 64, 1]> var_3398_1 = split(axis = var_3398_axis_0, split_sizes = var_3398_split_sizes_0, x = h_283)[name = tensor<string, []>("op_3398")];
            tensor<fp32, []> var_3400_promoted = const()[name = tensor<string, []>("op_3400_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 64, 1]> var_3401 = add(x = var_3398_0, y = var_3400_promoted)[name = tensor<string, []>("op_3401")];
            tensor<fp32, [1, 64, 153600]> var_3403 = mul(x = var_3401, y = var_3147)[name = tensor<string, []>("op_3403")];
            tensor<fp32, [1, 64, 153600]> xt_181 = add(x = var_3403, y = var_3398_1)[name = tensor<string, []>("xt_181")];
            tensor<fp32, [1, 64, 1]> var_3405 = const()[name = tensor<string, []>("op_3405"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(216996992)))];
            tensor<fp32, [1, 64, 153600]> var_3408 = mul(x = generator_resblocks_7_alpha1_0, y = xt_181)[name = tensor<string, []>("op_3408")];
            tensor<fp32, [1, 64, 153600]> var_3409 = sin(x = var_3408)[name = tensor<string, []>("op_3409")];
            tensor<fp32, []> var_532_promoted_63 = const()[name = tensor<string, []>("op_532_promoted_63"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 64, 153600]> var_3410 = pow(x = var_3409, y = var_532_promoted_63)[name = tensor<string, []>("op_3410")];
            tensor<fp32, [1, 64, 153600]> var_3411 = mul(x = var_3405, y = var_3410)[name = tensor<string, []>("op_3411")];
            tensor<fp32, [1, 64, 153600]> input_335 = add(x = xt_181, y = var_3411)[name = tensor<string, []>("input_335")];
            tensor<fp32, [64, 64, 7]> weight_163 = const()[name = tensor<string, []>("weight_163"), val = tensor<fp32, [64, 64, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(216997312)))];
            tensor<string, []> input_337_pad_type_0 = const()[name = tensor<string, []>("input_337_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_337_pad_0 = const()[name = tensor<string, []>("input_337_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> input_337_strides_0 = const()[name = tensor<string, []>("input_337_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_337_dilations_0 = const()[name = tensor<string, []>("input_337_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_337_groups_0 = const()[name = tensor<string, []>("input_337_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 153600]> input_337 = conv(bias = generator_resblocks_7_convs1_0_bias, dilations = input_337_dilations_0, groups = input_337_groups_0, pad = input_337_pad_0, pad_type = input_337_pad_type_0, strides = input_337_strides_0, weight = weight_163, x = input_335)[name = tensor<string, []>("input_337")];
            tensor<fp32, [1, 128]> h_285 = linear(bias = generator_resblocks_7_adain2_0_fc_bias, weight = generator_resblocks_7_adain2_0_fc_weight, x = s)[name = tensor<string, []>("linear_72")];
            tensor<int32, [3]> var_3426 = const()[name = tensor<string, []>("op_3426"), val = tensor<int32, [3]>([1, 128, 1])];
            tensor<fp32, [1, 128, 1]> h_287 = reshape(shape = var_3426, x = h_285)[name = tensor<string, []>("h_287")];
            tensor<int32, [2]> var_3428_split_sizes_0 = const()[name = tensor<string, []>("op_3428_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            tensor<int32, []> var_3428_axis_0 = const()[name = tensor<string, []>("op_3428_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 1]> var_3428_0, tensor<fp32, [1, 64, 1]> var_3428_1 = split(axis = var_3428_axis_0, split_sizes = var_3428_split_sizes_0, x = h_287)[name = tensor<string, []>("op_3428")];
            tensor<fp32, []> var_3430_promoted = const()[name = tensor<string, []>("op_3430_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 64, 1]> var_3431 = add(x = var_3428_0, y = var_3430_promoted)[name = tensor<string, []>("op_3431")];
            tensor<fp32, [1, 64, 153600]> var_3432 = instance_norm(epsilon = var_516, x = input_337)[name = tensor<string, []>("op_3432")];
            tensor<fp32, [1, 64, 153600]> var_3433 = mul(x = var_3431, y = var_3432)[name = tensor<string, []>("op_3433")];
            tensor<fp32, [1, 64, 153600]> xt_183 = add(x = var_3433, y = var_3428_1)[name = tensor<string, []>("xt_183")];
            tensor<fp32, [1, 64, 1]> var_3435 = const()[name = tensor<string, []>("op_3435"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(217112064)))];
            tensor<fp32, [1, 64, 153600]> var_3438 = mul(x = generator_resblocks_7_alpha2_0, y = xt_183)[name = tensor<string, []>("op_3438")];
            tensor<fp32, [1, 64, 153600]> var_3439 = sin(x = var_3438)[name = tensor<string, []>("op_3439")];
            tensor<fp32, []> var_532_promoted_64 = const()[name = tensor<string, []>("op_532_promoted_64"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 64, 153600]> var_3440 = pow(x = var_3439, y = var_532_promoted_64)[name = tensor<string, []>("op_3440")];
            tensor<fp32, [1, 64, 153600]> var_3441 = mul(x = var_3435, y = var_3440)[name = tensor<string, []>("op_3441")];
            tensor<fp32, [1, 64, 153600]> input_339 = add(x = xt_183, y = var_3441)[name = tensor<string, []>("input_339")];
            tensor<fp32, [64, 64, 7]> weight_165 = const()[name = tensor<string, []>("weight_165"), val = tensor<fp32, [64, 64, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(217112384)))];
            tensor<string, []> xt_185_pad_type_0 = const()[name = tensor<string, []>("xt_185_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_185_pad_0 = const()[name = tensor<string, []>("xt_185_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> xt_185_strides_0 = const()[name = tensor<string, []>("xt_185_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_185_dilations_0 = const()[name = tensor<string, []>("xt_185_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_185_groups_0 = const()[name = tensor<string, []>("xt_185_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 153600]> xt_185 = conv(bias = generator_resblocks_7_convs2_0_bias, dilations = xt_185_dilations_0, groups = xt_185_groups_0, pad = xt_185_pad_0, pad_type = xt_185_pad_type_0, strides = xt_185_strides_0, weight = weight_165, x = input_339)[name = tensor<string, []>("xt_185")];
            tensor<fp32, [1, 64, 153600]> input_341 = add(x = xt_185, y = input_311)[name = tensor<string, []>("input_341")];
            tensor<fp32, [1, 128]> h_289 = linear(bias = generator_resblocks_7_adain1_1_fc_bias, weight = generator_resblocks_7_adain1_1_fc_weight, x = s)[name = tensor<string, []>("linear_73")];
            tensor<int32, [3]> var_3457 = const()[name = tensor<string, []>("op_3457"), val = tensor<int32, [3]>([1, 128, 1])];
            tensor<fp32, [1, 128, 1]> h_291 = reshape(shape = var_3457, x = h_289)[name = tensor<string, []>("h_291")];
            tensor<int32, [2]> var_3459_split_sizes_0 = const()[name = tensor<string, []>("op_3459_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            tensor<int32, []> var_3459_axis_0 = const()[name = tensor<string, []>("op_3459_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 1]> var_3459_0, tensor<fp32, [1, 64, 1]> var_3459_1 = split(axis = var_3459_axis_0, split_sizes = var_3459_split_sizes_0, x = h_291)[name = tensor<string, []>("op_3459")];
            tensor<fp32, []> var_3461_promoted = const()[name = tensor<string, []>("op_3461_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 64, 1]> var_3462 = add(x = var_3459_0, y = var_3461_promoted)[name = tensor<string, []>("op_3462")];
            tensor<fp32, [1, 64, 153600]> var_3463 = instance_norm(epsilon = var_516, x = input_341)[name = tensor<string, []>("op_3463")];
            tensor<fp32, [1, 64, 153600]> var_3464 = mul(x = var_3462, y = var_3463)[name = tensor<string, []>("op_3464")];
            tensor<fp32, [1, 64, 153600]> xt_187 = add(x = var_3464, y = var_3459_1)[name = tensor<string, []>("xt_187")];
            tensor<fp32, [1, 64, 1]> var_3466 = const()[name = tensor<string, []>("op_3466"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(217227136)))];
            tensor<fp32, [1, 64, 153600]> var_3469 = mul(x = generator_resblocks_7_alpha1_1, y = xt_187)[name = tensor<string, []>("op_3469")];
            tensor<fp32, [1, 64, 153600]> var_3470 = sin(x = var_3469)[name = tensor<string, []>("op_3470")];
            tensor<fp32, []> var_532_promoted_65 = const()[name = tensor<string, []>("op_532_promoted_65"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 64, 153600]> var_3471 = pow(x = var_3470, y = var_532_promoted_65)[name = tensor<string, []>("op_3471")];
            tensor<fp32, [1, 64, 153600]> var_3472 = mul(x = var_3466, y = var_3471)[name = tensor<string, []>("op_3472")];
            tensor<fp32, [1, 64, 153600]> input_343 = add(x = xt_187, y = var_3472)[name = tensor<string, []>("input_343")];
            tensor<fp32, [64, 64, 7]> weight_167 = const()[name = tensor<string, []>("weight_167"), val = tensor<fp32, [64, 64, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(217227456)))];
            tensor<string, []> input_345_pad_type_0 = const()[name = tensor<string, []>("input_345_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_345_pad_0 = const()[name = tensor<string, []>("input_345_pad_0"), val = tensor<int32, [2]>([9, 9])];
            tensor<int32, [1]> input_345_dilations_0 = const()[name = tensor<string, []>("input_345_dilations_0"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> input_345_strides_0 = const()[name = tensor<string, []>("input_345_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_345_groups_0 = const()[name = tensor<string, []>("input_345_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 153600]> input_345 = conv(bias = generator_resblocks_7_convs1_1_bias, dilations = input_345_dilations_0, groups = input_345_groups_0, pad = input_345_pad_0, pad_type = input_345_pad_type_0, strides = input_345_strides_0, weight = weight_167, x = input_343)[name = tensor<string, []>("input_345")];
            tensor<fp32, [1, 128]> h_293 = linear(bias = generator_resblocks_7_adain2_1_fc_bias, weight = generator_resblocks_7_adain2_1_fc_weight, x = s)[name = tensor<string, []>("linear_74")];
            tensor<int32, [3]> var_3487 = const()[name = tensor<string, []>("op_3487"), val = tensor<int32, [3]>([1, 128, 1])];
            tensor<fp32, [1, 128, 1]> h_295 = reshape(shape = var_3487, x = h_293)[name = tensor<string, []>("h_295")];
            tensor<int32, [2]> var_3489_split_sizes_0 = const()[name = tensor<string, []>("op_3489_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            tensor<int32, []> var_3489_axis_0 = const()[name = tensor<string, []>("op_3489_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 1]> var_3489_0, tensor<fp32, [1, 64, 1]> var_3489_1 = split(axis = var_3489_axis_0, split_sizes = var_3489_split_sizes_0, x = h_295)[name = tensor<string, []>("op_3489")];
            tensor<fp32, []> var_3491_promoted = const()[name = tensor<string, []>("op_3491_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 64, 1]> var_3492 = add(x = var_3489_0, y = var_3491_promoted)[name = tensor<string, []>("op_3492")];
            tensor<fp32, [1, 64, 153600]> var_3493 = instance_norm(epsilon = var_516, x = input_345)[name = tensor<string, []>("op_3493")];
            tensor<fp32, [1, 64, 153600]> var_3494 = mul(x = var_3492, y = var_3493)[name = tensor<string, []>("op_3494")];
            tensor<fp32, [1, 64, 153600]> xt_189 = add(x = var_3494, y = var_3489_1)[name = tensor<string, []>("xt_189")];
            tensor<fp32, [1, 64, 1]> var_3496 = const()[name = tensor<string, []>("op_3496"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(217342208)))];
            tensor<fp32, [1, 64, 153600]> var_3499 = mul(x = generator_resblocks_7_alpha2_1, y = xt_189)[name = tensor<string, []>("op_3499")];
            tensor<fp32, [1, 64, 153600]> var_3500 = sin(x = var_3499)[name = tensor<string, []>("op_3500")];
            tensor<fp32, []> var_532_promoted_66 = const()[name = tensor<string, []>("op_532_promoted_66"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 64, 153600]> var_3501 = pow(x = var_3500, y = var_532_promoted_66)[name = tensor<string, []>("op_3501")];
            tensor<fp32, [1, 64, 153600]> var_3502 = mul(x = var_3496, y = var_3501)[name = tensor<string, []>("op_3502")];
            tensor<fp32, [1, 64, 153600]> input_347 = add(x = xt_189, y = var_3502)[name = tensor<string, []>("input_347")];
            tensor<fp32, [64, 64, 7]> weight_169 = const()[name = tensor<string, []>("weight_169"), val = tensor<fp32, [64, 64, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(217342528)))];
            tensor<string, []> xt_191_pad_type_0 = const()[name = tensor<string, []>("xt_191_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_191_pad_0 = const()[name = tensor<string, []>("xt_191_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> xt_191_strides_0 = const()[name = tensor<string, []>("xt_191_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_191_dilations_0 = const()[name = tensor<string, []>("xt_191_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_191_groups_0 = const()[name = tensor<string, []>("xt_191_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 153600]> xt_191 = conv(bias = generator_resblocks_7_convs2_1_bias, dilations = xt_191_dilations_0, groups = xt_191_groups_0, pad = xt_191_pad_0, pad_type = xt_191_pad_type_0, strides = xt_191_strides_0, weight = weight_169, x = input_347)[name = tensor<string, []>("xt_191")];
            tensor<fp32, [1, 64, 153600]> input_349 = add(x = xt_191, y = input_341)[name = tensor<string, []>("input_349")];
            tensor<fp32, [1, 128]> h_297 = linear(bias = generator_resblocks_7_adain1_2_fc_bias, weight = generator_resblocks_7_adain1_2_fc_weight, x = s)[name = tensor<string, []>("linear_75")];
            tensor<int32, [3]> var_3518 = const()[name = tensor<string, []>("op_3518"), val = tensor<int32, [3]>([1, 128, 1])];
            tensor<fp32, [1, 128, 1]> h_299 = reshape(shape = var_3518, x = h_297)[name = tensor<string, []>("h_299")];
            tensor<int32, [2]> var_3520_split_sizes_0 = const()[name = tensor<string, []>("op_3520_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            tensor<int32, []> var_3520_axis_0 = const()[name = tensor<string, []>("op_3520_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 1]> var_3520_0, tensor<fp32, [1, 64, 1]> var_3520_1 = split(axis = var_3520_axis_0, split_sizes = var_3520_split_sizes_0, x = h_299)[name = tensor<string, []>("op_3520")];
            tensor<fp32, []> var_3522_promoted = const()[name = tensor<string, []>("op_3522_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 64, 1]> var_3523 = add(x = var_3520_0, y = var_3522_promoted)[name = tensor<string, []>("op_3523")];
            tensor<fp32, [1, 64, 153600]> var_3524 = instance_norm(epsilon = var_516, x = input_349)[name = tensor<string, []>("op_3524")];
            tensor<fp32, [1, 64, 153600]> var_3525 = mul(x = var_3523, y = var_3524)[name = tensor<string, []>("op_3525")];
            tensor<fp32, [1, 64, 153600]> xt_193 = add(x = var_3525, y = var_3520_1)[name = tensor<string, []>("xt_193")];
            tensor<fp32, [1, 64, 1]> var_3527 = const()[name = tensor<string, []>("op_3527"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(217457280)))];
            tensor<fp32, [1, 64, 153600]> var_3530 = mul(x = generator_resblocks_7_alpha1_2, y = xt_193)[name = tensor<string, []>("op_3530")];
            tensor<fp32, [1, 64, 153600]> var_3531 = sin(x = var_3530)[name = tensor<string, []>("op_3531")];
            tensor<fp32, []> var_532_promoted_67 = const()[name = tensor<string, []>("op_532_promoted_67"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 64, 153600]> var_3532 = pow(x = var_3531, y = var_532_promoted_67)[name = tensor<string, []>("op_3532")];
            tensor<fp32, [1, 64, 153600]> var_3533 = mul(x = var_3527, y = var_3532)[name = tensor<string, []>("op_3533")];
            tensor<fp32, [1, 64, 153600]> input_351 = add(x = xt_193, y = var_3533)[name = tensor<string, []>("input_351")];
            tensor<fp32, [64, 64, 7]> weight_171 = const()[name = tensor<string, []>("weight_171"), val = tensor<fp32, [64, 64, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(217457600)))];
            tensor<string, []> input_353_pad_type_0 = const()[name = tensor<string, []>("input_353_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_353_pad_0 = const()[name = tensor<string, []>("input_353_pad_0"), val = tensor<int32, [2]>([15, 15])];
            tensor<int32, [1]> input_353_dilations_0 = const()[name = tensor<string, []>("input_353_dilations_0"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> input_353_strides_0 = const()[name = tensor<string, []>("input_353_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_353_groups_0 = const()[name = tensor<string, []>("input_353_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 153600]> input_353 = conv(bias = generator_resblocks_7_convs1_2_bias, dilations = input_353_dilations_0, groups = input_353_groups_0, pad = input_353_pad_0, pad_type = input_353_pad_type_0, strides = input_353_strides_0, weight = weight_171, x = input_351)[name = tensor<string, []>("input_353")];
            tensor<fp32, [1, 128]> h_301 = linear(bias = generator_resblocks_7_adain2_2_fc_bias, weight = generator_resblocks_7_adain2_2_fc_weight, x = s)[name = tensor<string, []>("linear_76")];
            tensor<int32, [3]> var_3548 = const()[name = tensor<string, []>("op_3548"), val = tensor<int32, [3]>([1, 128, 1])];
            tensor<fp32, [1, 128, 1]> h_303 = reshape(shape = var_3548, x = h_301)[name = tensor<string, []>("h_303")];
            tensor<int32, [2]> var_3550_split_sizes_0 = const()[name = tensor<string, []>("op_3550_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            tensor<int32, []> var_3550_axis_0 = const()[name = tensor<string, []>("op_3550_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 1]> var_3550_0, tensor<fp32, [1, 64, 1]> var_3550_1 = split(axis = var_3550_axis_0, split_sizes = var_3550_split_sizes_0, x = h_303)[name = tensor<string, []>("op_3550")];
            tensor<fp32, []> var_3552_promoted = const()[name = tensor<string, []>("op_3552_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 64, 1]> var_3553 = add(x = var_3550_0, y = var_3552_promoted)[name = tensor<string, []>("op_3553")];
            tensor<fp32, [1, 64, 153600]> var_3554 = instance_norm(epsilon = var_516, x = input_353)[name = tensor<string, []>("op_3554")];
            tensor<fp32, [1, 64, 153600]> var_3555 = mul(x = var_3553, y = var_3554)[name = tensor<string, []>("op_3555")];
            tensor<fp32, [1, 64, 153600]> xt_195 = add(x = var_3555, y = var_3550_1)[name = tensor<string, []>("xt_195")];
            tensor<fp32, [1, 64, 1]> var_3557 = const()[name = tensor<string, []>("op_3557"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(217572352)))];
            tensor<fp32, [1, 64, 153600]> var_3560 = mul(x = generator_resblocks_7_alpha2_2, y = xt_195)[name = tensor<string, []>("op_3560")];
            tensor<fp32, [1, 64, 153600]> var_3561 = sin(x = var_3560)[name = tensor<string, []>("op_3561")];
            tensor<fp32, []> var_532_promoted_68 = const()[name = tensor<string, []>("op_532_promoted_68"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 64, 153600]> var_3562 = pow(x = var_3561, y = var_532_promoted_68)[name = tensor<string, []>("op_3562")];
            tensor<fp32, [1, 64, 153600]> var_3563 = mul(x = var_3557, y = var_3562)[name = tensor<string, []>("op_3563")];
            tensor<fp32, [1, 64, 153600]> input_355 = add(x = xt_195, y = var_3563)[name = tensor<string, []>("input_355")];
            tensor<fp32, [64, 64, 7]> weight_173 = const()[name = tensor<string, []>("weight_173"), val = tensor<fp32, [64, 64, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(217572672)))];
            tensor<string, []> xt_197_pad_type_0 = const()[name = tensor<string, []>("xt_197_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_197_pad_0 = const()[name = tensor<string, []>("xt_197_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> xt_197_strides_0 = const()[name = tensor<string, []>("xt_197_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_197_dilations_0 = const()[name = tensor<string, []>("xt_197_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_197_groups_0 = const()[name = tensor<string, []>("xt_197_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 153600]> xt_197 = conv(bias = generator_resblocks_7_convs2_2_bias, dilations = xt_197_dilations_0, groups = xt_197_groups_0, pad = xt_197_pad_0, pad_type = xt_197_pad_type_0, strides = xt_197_strides_0, weight = weight_173, x = input_355)[name = tensor<string, []>("xt_197")];
            tensor<fp32, [1, 64, 153600]> var_3572 = add(x = xt_197, y = input_349)[name = tensor<string, []>("op_3572")];
            tensor<fp32, [1, 64, 153600]> xs_15 = add(x = xs_13, y = var_3572)[name = tensor<string, []>("xs_15")];
            tensor<fp32, [1, 128]> h_305 = linear(bias = generator_resblocks_8_adain1_0_fc_bias, weight = generator_resblocks_8_adain1_0_fc_weight, x = s)[name = tensor<string, []>("linear_77")];
            tensor<int32, [3]> var_3652 = const()[name = tensor<string, []>("op_3652"), val = tensor<int32, [3]>([1, 128, 1])];
            tensor<fp32, [1, 128, 1]> h_307 = reshape(shape = var_3652, x = h_305)[name = tensor<string, []>("h_307")];
            tensor<int32, [2]> var_3654_split_sizes_0 = const()[name = tensor<string, []>("op_3654_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            tensor<int32, []> var_3654_axis_0 = const()[name = tensor<string, []>("op_3654_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 1]> var_3654_0, tensor<fp32, [1, 64, 1]> var_3654_1 = split(axis = var_3654_axis_0, split_sizes = var_3654_split_sizes_0, x = h_307)[name = tensor<string, []>("op_3654")];
            tensor<fp32, []> var_3656_promoted = const()[name = tensor<string, []>("op_3656_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 64, 1]> var_3657 = add(x = var_3654_0, y = var_3656_promoted)[name = tensor<string, []>("op_3657")];
            tensor<fp32, [1, 64, 153600]> var_3659 = mul(x = var_3657, y = var_3147)[name = tensor<string, []>("op_3659")];
            tensor<fp32, [1, 64, 153600]> xt_199 = add(x = var_3659, y = var_3654_1)[name = tensor<string, []>("xt_199")];
            tensor<fp32, [1, 64, 1]> var_3661 = const()[name = tensor<string, []>("op_3661"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(217687424)))];
            tensor<fp32, [1, 64, 153600]> var_3664 = mul(x = generator_resblocks_8_alpha1_0, y = xt_199)[name = tensor<string, []>("op_3664")];
            tensor<fp32, [1, 64, 153600]> var_3665 = sin(x = var_3664)[name = tensor<string, []>("op_3665")];
            tensor<fp32, []> var_532_promoted_69 = const()[name = tensor<string, []>("op_532_promoted_69"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 64, 153600]> var_3666 = pow(x = var_3665, y = var_532_promoted_69)[name = tensor<string, []>("op_3666")];
            tensor<fp32, [1, 64, 153600]> var_3667 = mul(x = var_3661, y = var_3666)[name = tensor<string, []>("op_3667")];
            tensor<fp32, [1, 64, 153600]> input_357 = add(x = xt_199, y = var_3667)[name = tensor<string, []>("input_357")];
            tensor<fp32, [64, 64, 11]> weight_175 = const()[name = tensor<string, []>("weight_175"), val = tensor<fp32, [64, 64, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(217687744)))];
            tensor<string, []> input_359_pad_type_0 = const()[name = tensor<string, []>("input_359_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_359_pad_0 = const()[name = tensor<string, []>("input_359_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> input_359_strides_0 = const()[name = tensor<string, []>("input_359_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_359_dilations_0 = const()[name = tensor<string, []>("input_359_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_359_groups_0 = const()[name = tensor<string, []>("input_359_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 153600]> input_359 = conv(bias = generator_resblocks_8_convs1_0_bias, dilations = input_359_dilations_0, groups = input_359_groups_0, pad = input_359_pad_0, pad_type = input_359_pad_type_0, strides = input_359_strides_0, weight = weight_175, x = input_357)[name = tensor<string, []>("input_359")];
            tensor<fp32, [1, 128]> h_309 = linear(bias = generator_resblocks_8_adain2_0_fc_bias, weight = generator_resblocks_8_adain2_0_fc_weight, x = s)[name = tensor<string, []>("linear_78")];
            tensor<int32, [3]> var_3682 = const()[name = tensor<string, []>("op_3682"), val = tensor<int32, [3]>([1, 128, 1])];
            tensor<fp32, [1, 128, 1]> h_311 = reshape(shape = var_3682, x = h_309)[name = tensor<string, []>("h_311")];
            tensor<int32, [2]> var_3684_split_sizes_0 = const()[name = tensor<string, []>("op_3684_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            tensor<int32, []> var_3684_axis_0 = const()[name = tensor<string, []>("op_3684_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 1]> var_3684_0, tensor<fp32, [1, 64, 1]> var_3684_1 = split(axis = var_3684_axis_0, split_sizes = var_3684_split_sizes_0, x = h_311)[name = tensor<string, []>("op_3684")];
            tensor<fp32, []> var_3686_promoted = const()[name = tensor<string, []>("op_3686_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 64, 1]> var_3687 = add(x = var_3684_0, y = var_3686_promoted)[name = tensor<string, []>("op_3687")];
            tensor<fp32, [1, 64, 153600]> var_3688 = instance_norm(epsilon = var_516, x = input_359)[name = tensor<string, []>("op_3688")];
            tensor<fp32, [1, 64, 153600]> var_3689 = mul(x = var_3687, y = var_3688)[name = tensor<string, []>("op_3689")];
            tensor<fp32, [1, 64, 153600]> xt_201 = add(x = var_3689, y = var_3684_1)[name = tensor<string, []>("xt_201")];
            tensor<fp32, [1, 64, 1]> var_3691 = const()[name = tensor<string, []>("op_3691"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(217868032)))];
            tensor<fp32, [1, 64, 153600]> var_3694 = mul(x = generator_resblocks_8_alpha2_0, y = xt_201)[name = tensor<string, []>("op_3694")];
            tensor<fp32, [1, 64, 153600]> var_3695 = sin(x = var_3694)[name = tensor<string, []>("op_3695")];
            tensor<fp32, []> var_532_promoted_70 = const()[name = tensor<string, []>("op_532_promoted_70"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 64, 153600]> var_3696 = pow(x = var_3695, y = var_532_promoted_70)[name = tensor<string, []>("op_3696")];
            tensor<fp32, [1, 64, 153600]> var_3697 = mul(x = var_3691, y = var_3696)[name = tensor<string, []>("op_3697")];
            tensor<fp32, [1, 64, 153600]> input_361 = add(x = xt_201, y = var_3697)[name = tensor<string, []>("input_361")];
            tensor<fp32, [64, 64, 11]> weight_177 = const()[name = tensor<string, []>("weight_177"), val = tensor<fp32, [64, 64, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(217868352)))];
            tensor<string, []> xt_203_pad_type_0 = const()[name = tensor<string, []>("xt_203_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_203_pad_0 = const()[name = tensor<string, []>("xt_203_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> xt_203_strides_0 = const()[name = tensor<string, []>("xt_203_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_203_dilations_0 = const()[name = tensor<string, []>("xt_203_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_203_groups_0 = const()[name = tensor<string, []>("xt_203_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 153600]> xt_203 = conv(bias = generator_resblocks_8_convs2_0_bias, dilations = xt_203_dilations_0, groups = xt_203_groups_0, pad = xt_203_pad_0, pad_type = xt_203_pad_type_0, strides = xt_203_strides_0, weight = weight_177, x = input_361)[name = tensor<string, []>("xt_203")];
            tensor<fp32, [1, 64, 153600]> input_363 = add(x = xt_203, y = input_311)[name = tensor<string, []>("input_363")];
            tensor<fp32, [1, 128]> h_313 = linear(bias = generator_resblocks_8_adain1_1_fc_bias, weight = generator_resblocks_8_adain1_1_fc_weight, x = s)[name = tensor<string, []>("linear_79")];
            tensor<int32, [3]> var_3713 = const()[name = tensor<string, []>("op_3713"), val = tensor<int32, [3]>([1, 128, 1])];
            tensor<fp32, [1, 128, 1]> h_315 = reshape(shape = var_3713, x = h_313)[name = tensor<string, []>("h_315")];
            tensor<int32, [2]> var_3715_split_sizes_0 = const()[name = tensor<string, []>("op_3715_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            tensor<int32, []> var_3715_axis_0 = const()[name = tensor<string, []>("op_3715_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 1]> var_3715_0, tensor<fp32, [1, 64, 1]> var_3715_1 = split(axis = var_3715_axis_0, split_sizes = var_3715_split_sizes_0, x = h_315)[name = tensor<string, []>("op_3715")];
            tensor<fp32, []> var_3717_promoted = const()[name = tensor<string, []>("op_3717_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 64, 1]> var_3718 = add(x = var_3715_0, y = var_3717_promoted)[name = tensor<string, []>("op_3718")];
            tensor<fp32, [1, 64, 153600]> var_3719 = instance_norm(epsilon = var_516, x = input_363)[name = tensor<string, []>("op_3719")];
            tensor<fp32, [1, 64, 153600]> var_3720 = mul(x = var_3718, y = var_3719)[name = tensor<string, []>("op_3720")];
            tensor<fp32, [1, 64, 153600]> xt_205 = add(x = var_3720, y = var_3715_1)[name = tensor<string, []>("xt_205")];
            tensor<fp32, [1, 64, 1]> var_3722 = const()[name = tensor<string, []>("op_3722"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218048640)))];
            tensor<fp32, [1, 64, 153600]> var_3725 = mul(x = generator_resblocks_8_alpha1_1, y = xt_205)[name = tensor<string, []>("op_3725")];
            tensor<fp32, [1, 64, 153600]> var_3726 = sin(x = var_3725)[name = tensor<string, []>("op_3726")];
            tensor<fp32, []> var_532_promoted_71 = const()[name = tensor<string, []>("op_532_promoted_71"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 64, 153600]> var_3727 = pow(x = var_3726, y = var_532_promoted_71)[name = tensor<string, []>("op_3727")];
            tensor<fp32, [1, 64, 153600]> var_3728 = mul(x = var_3722, y = var_3727)[name = tensor<string, []>("op_3728")];
            tensor<fp32, [1, 64, 153600]> input_365 = add(x = xt_205, y = var_3728)[name = tensor<string, []>("input_365")];
            tensor<fp32, [64, 64, 11]> weight_179 = const()[name = tensor<string, []>("weight_179"), val = tensor<fp32, [64, 64, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218048960)))];
            tensor<string, []> input_367_pad_type_0 = const()[name = tensor<string, []>("input_367_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_367_pad_0 = const()[name = tensor<string, []>("input_367_pad_0"), val = tensor<int32, [2]>([15, 15])];
            tensor<int32, [1]> input_367_dilations_0 = const()[name = tensor<string, []>("input_367_dilations_0"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> input_367_strides_0 = const()[name = tensor<string, []>("input_367_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_367_groups_0 = const()[name = tensor<string, []>("input_367_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 153600]> input_367 = conv(bias = generator_resblocks_8_convs1_1_bias, dilations = input_367_dilations_0, groups = input_367_groups_0, pad = input_367_pad_0, pad_type = input_367_pad_type_0, strides = input_367_strides_0, weight = weight_179, x = input_365)[name = tensor<string, []>("input_367")];
            tensor<fp32, [1, 128]> h_317 = linear(bias = generator_resblocks_8_adain2_1_fc_bias, weight = generator_resblocks_8_adain2_1_fc_weight, x = s)[name = tensor<string, []>("linear_80")];
            tensor<int32, [3]> var_3743 = const()[name = tensor<string, []>("op_3743"), val = tensor<int32, [3]>([1, 128, 1])];
            tensor<fp32, [1, 128, 1]> h_319 = reshape(shape = var_3743, x = h_317)[name = tensor<string, []>("h_319")];
            tensor<int32, [2]> var_3745_split_sizes_0 = const()[name = tensor<string, []>("op_3745_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            tensor<int32, []> var_3745_axis_0 = const()[name = tensor<string, []>("op_3745_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 1]> var_3745_0, tensor<fp32, [1, 64, 1]> var_3745_1 = split(axis = var_3745_axis_0, split_sizes = var_3745_split_sizes_0, x = h_319)[name = tensor<string, []>("op_3745")];
            tensor<fp32, []> var_3747_promoted = const()[name = tensor<string, []>("op_3747_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 64, 1]> var_3748 = add(x = var_3745_0, y = var_3747_promoted)[name = tensor<string, []>("op_3748")];
            tensor<fp32, [1, 64, 153600]> var_3749 = instance_norm(epsilon = var_516, x = input_367)[name = tensor<string, []>("op_3749")];
            tensor<fp32, [1, 64, 153600]> var_3750 = mul(x = var_3748, y = var_3749)[name = tensor<string, []>("op_3750")];
            tensor<fp32, [1, 64, 153600]> xt_207 = add(x = var_3750, y = var_3745_1)[name = tensor<string, []>("xt_207")];
            tensor<fp32, [1, 64, 1]> var_3752 = const()[name = tensor<string, []>("op_3752"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218229248)))];
            tensor<fp32, [1, 64, 153600]> var_3755 = mul(x = generator_resblocks_8_alpha2_1, y = xt_207)[name = tensor<string, []>("op_3755")];
            tensor<fp32, [1, 64, 153600]> var_3756 = sin(x = var_3755)[name = tensor<string, []>("op_3756")];
            tensor<fp32, []> var_532_promoted_72 = const()[name = tensor<string, []>("op_532_promoted_72"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 64, 153600]> var_3757 = pow(x = var_3756, y = var_532_promoted_72)[name = tensor<string, []>("op_3757")];
            tensor<fp32, [1, 64, 153600]> var_3758 = mul(x = var_3752, y = var_3757)[name = tensor<string, []>("op_3758")];
            tensor<fp32, [1, 64, 153600]> input_369 = add(x = xt_207, y = var_3758)[name = tensor<string, []>("input_369")];
            tensor<fp32, [64, 64, 11]> weight_181 = const()[name = tensor<string, []>("weight_181"), val = tensor<fp32, [64, 64, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218229568)))];
            tensor<string, []> xt_209_pad_type_0 = const()[name = tensor<string, []>("xt_209_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_209_pad_0 = const()[name = tensor<string, []>("xt_209_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> xt_209_strides_0 = const()[name = tensor<string, []>("xt_209_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_209_dilations_0 = const()[name = tensor<string, []>("xt_209_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_209_groups_0 = const()[name = tensor<string, []>("xt_209_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 153600]> xt_209 = conv(bias = generator_resblocks_8_convs2_1_bias, dilations = xt_209_dilations_0, groups = xt_209_groups_0, pad = xt_209_pad_0, pad_type = xt_209_pad_type_0, strides = xt_209_strides_0, weight = weight_181, x = input_369)[name = tensor<string, []>("xt_209")];
            tensor<fp32, [1, 64, 153600]> input_371 = add(x = xt_209, y = input_363)[name = tensor<string, []>("input_371")];
            tensor<fp32, [1, 128]> h_321 = linear(bias = generator_resblocks_8_adain1_2_fc_bias, weight = generator_resblocks_8_adain1_2_fc_weight, x = s)[name = tensor<string, []>("linear_81")];
            tensor<int32, [3]> var_3774 = const()[name = tensor<string, []>("op_3774"), val = tensor<int32, [3]>([1, 128, 1])];
            tensor<fp32, [1, 128, 1]> h_323 = reshape(shape = var_3774, x = h_321)[name = tensor<string, []>("h_323")];
            tensor<int32, [2]> var_3776_split_sizes_0 = const()[name = tensor<string, []>("op_3776_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            tensor<int32, []> var_3776_axis_0 = const()[name = tensor<string, []>("op_3776_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 1]> var_3776_0, tensor<fp32, [1, 64, 1]> var_3776_1 = split(axis = var_3776_axis_0, split_sizes = var_3776_split_sizes_0, x = h_323)[name = tensor<string, []>("op_3776")];
            tensor<fp32, []> var_3778_promoted = const()[name = tensor<string, []>("op_3778_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 64, 1]> var_3779 = add(x = var_3776_0, y = var_3778_promoted)[name = tensor<string, []>("op_3779")];
            tensor<fp32, [1, 64, 153600]> var_3780 = instance_norm(epsilon = var_516, x = input_371)[name = tensor<string, []>("op_3780")];
            tensor<fp32, [1, 64, 153600]> var_3781 = mul(x = var_3779, y = var_3780)[name = tensor<string, []>("op_3781")];
            tensor<fp32, [1, 64, 153600]> xt_211 = add(x = var_3781, y = var_3776_1)[name = tensor<string, []>("xt_211")];
            tensor<fp32, [1, 64, 1]> var_3783 = const()[name = tensor<string, []>("op_3783"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218409856)))];
            tensor<fp32, [1, 64, 153600]> var_3786 = mul(x = generator_resblocks_8_alpha1_2, y = xt_211)[name = tensor<string, []>("op_3786")];
            tensor<fp32, [1, 64, 153600]> var_3787 = sin(x = var_3786)[name = tensor<string, []>("op_3787")];
            tensor<fp32, []> var_532_promoted_73 = const()[name = tensor<string, []>("op_532_promoted_73"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 64, 153600]> var_3788 = pow(x = var_3787, y = var_532_promoted_73)[name = tensor<string, []>("op_3788")];
            tensor<fp32, [1, 64, 153600]> var_3789 = mul(x = var_3783, y = var_3788)[name = tensor<string, []>("op_3789")];
            tensor<fp32, [1, 64, 153600]> input_373 = add(x = xt_211, y = var_3789)[name = tensor<string, []>("input_373")];
            tensor<fp32, [64, 64, 11]> weight_183 = const()[name = tensor<string, []>("weight_183"), val = tensor<fp32, [64, 64, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218410176)))];
            tensor<string, []> input_375_pad_type_0 = const()[name = tensor<string, []>("input_375_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_375_pad_0 = const()[name = tensor<string, []>("input_375_pad_0"), val = tensor<int32, [2]>([25, 25])];
            tensor<int32, [1]> input_375_dilations_0 = const()[name = tensor<string, []>("input_375_dilations_0"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> input_375_strides_0 = const()[name = tensor<string, []>("input_375_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_375_groups_0 = const()[name = tensor<string, []>("input_375_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 153600]> input_375 = conv(bias = generator_resblocks_8_convs1_2_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 = weight_183, x = input_373)[name = tensor<string, []>("input_375")];
            tensor<fp32, [1, 128]> h_325 = linear(bias = generator_resblocks_8_adain2_2_fc_bias, weight = generator_resblocks_8_adain2_2_fc_weight, x = s)[name = tensor<string, []>("linear_82")];
            tensor<int32, [3]> var_3804 = const()[name = tensor<string, []>("op_3804"), val = tensor<int32, [3]>([1, 128, 1])];
            tensor<fp32, [1, 128, 1]> h_327 = reshape(shape = var_3804, x = h_325)[name = tensor<string, []>("h_327")];
            tensor<int32, [2]> var_3806_split_sizes_0 = const()[name = tensor<string, []>("op_3806_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            tensor<int32, []> var_3806_axis_0 = const()[name = tensor<string, []>("op_3806_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 1]> var_3806_0, tensor<fp32, [1, 64, 1]> var_3806_1 = split(axis = var_3806_axis_0, split_sizes = var_3806_split_sizes_0, x = h_327)[name = tensor<string, []>("op_3806")];
            tensor<fp32, []> var_3808_promoted = const()[name = tensor<string, []>("op_3808_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 64, 1]> var_3809 = add(x = var_3806_0, y = var_3808_promoted)[name = tensor<string, []>("op_3809")];
            tensor<fp32, [1, 64, 153600]> var_3810 = instance_norm(epsilon = var_516, x = input_375)[name = tensor<string, []>("op_3810")];
            tensor<fp32, [1, 64, 153600]> var_3811 = mul(x = var_3809, y = var_3810)[name = tensor<string, []>("op_3811")];
            tensor<fp32, [1, 64, 153600]> xt_213 = add(x = var_3811, y = var_3806_1)[name = tensor<string, []>("xt_213")];
            tensor<fp32, [1, 64, 1]> var_3813 = const()[name = tensor<string, []>("op_3813"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218590464)))];
            tensor<fp32, [1, 64, 153600]> var_3816 = mul(x = generator_resblocks_8_alpha2_2, y = xt_213)[name = tensor<string, []>("op_3816")];
            tensor<fp32, [1, 64, 153600]> var_3817 = sin(x = var_3816)[name = tensor<string, []>("op_3817")];
            tensor<fp32, []> var_532_promoted_74 = const()[name = tensor<string, []>("op_532_promoted_74"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 64, 153600]> var_3818 = pow(x = var_3817, y = var_532_promoted_74)[name = tensor<string, []>("op_3818")];
            tensor<fp32, [1, 64, 153600]> var_3819 = mul(x = var_3813, y = var_3818)[name = tensor<string, []>("op_3819")];
            tensor<fp32, [1, 64, 153600]> input_377 = add(x = xt_213, y = var_3819)[name = tensor<string, []>("input_377")];
            tensor<fp32, [64, 64, 11]> weight_185 = const()[name = tensor<string, []>("weight_185"), val = tensor<fp32, [64, 64, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218590784)))];
            tensor<string, []> xt_215_pad_type_0 = const()[name = tensor<string, []>("xt_215_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_215_pad_0 = const()[name = tensor<string, []>("xt_215_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> xt_215_strides_0 = const()[name = tensor<string, []>("xt_215_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_215_dilations_0 = const()[name = tensor<string, []>("xt_215_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_215_groups_0 = const()[name = tensor<string, []>("xt_215_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 64, 153600]> xt_215 = conv(bias = generator_resblocks_8_convs2_2_bias, dilations = xt_215_dilations_0, groups = xt_215_groups_0, pad = xt_215_pad_0, pad_type = xt_215_pad_type_0, strides = xt_215_strides_0, weight = weight_185, x = input_377)[name = tensor<string, []>("xt_215")];
            tensor<fp32, [1, 64, 153600]> var_3828 = add(x = xt_215, y = input_371)[name = tensor<string, []>("op_3828")];
            tensor<fp32, [1, 64, 153600]> xs_17 = add(x = xs_15, y = var_3828)[name = tensor<string, []>("xs_17")];
            tensor<fp32, []> _inversed_x_21_y_0 = const()[name = tensor<string, []>("_inversed_x_21_y_0"), val = tensor<fp32, []>(0x1.555556p-2)];
            tensor<fp32, [1, 64, 153600]> _inversed_x_21 = mul(x = xs_17, y = _inversed_x_21_y_0)[name = tensor<string, []>("_inversed_x_21")];
            tensor<fp32, [1, 64, 1]> var_3832 = const()[name = tensor<string, []>("op_3832"), val = tensor<fp32, [1, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218771072)))];
            tensor<fp32, [1, 64, 153600]> var_3835 = mul(x = generator_alphas_3, y = _inversed_x_21)[name = tensor<string, []>("op_3835")];
            tensor<fp32, [1, 64, 153600]> var_3836 = sin(x = var_3835)[name = tensor<string, []>("op_3836")];
            tensor<fp32, []> var_532_promoted_75 = const()[name = tensor<string, []>("op_532_promoted_75"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 64, 153600]> var_3837 = pow(x = var_3836, y = var_532_promoted_75)[name = tensor<string, []>("op_3837")];
            tensor<fp32, [1, 64, 153600]> var_3838 = mul(x = var_3832, y = var_3837)[name = tensor<string, []>("op_3838")];
            tensor<fp32, [1, 64, 153600]> input_403 = add(x = _inversed_x_21, y = var_3838)[name = tensor<string, []>("input_403")];
            tensor<string, []> input_379_pad_type_0 = const()[name = tensor<string, []>("input_379_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> input_379_strides_0 = const()[name = tensor<string, []>("input_379_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_379_pad_0 = const()[name = tensor<string, []>("input_379_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_379_dilations_0 = const()[name = tensor<string, []>("input_379_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_379_groups_0 = const()[name = tensor<string, []>("input_379_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 307200]> input_379 = conv(bias = generator_noise_convs_3_bias, dilations = input_379_dilations_0, groups = input_379_groups_0, pad = input_379_pad_0, pad_type = input_379_pad_type_0, strides = input_379_strides_0, weight = generator_noise_convs_3_weight, x = input_95)[name = tensor<string, []>("input_379")];
            tensor<fp32, [1, 64]> h_329 = linear(bias = generator_noise_res_3_adain1_0_fc_bias, weight = generator_noise_res_3_adain1_0_fc_weight, x = s)[name = tensor<string, []>("linear_83")];
            tensor<int32, [3]> var_3925 = const()[name = tensor<string, []>("op_3925"), val = tensor<int32, [3]>([1, 64, 1])];
            tensor<fp32, [1, 64, 1]> h_331 = reshape(shape = var_3925, x = h_329)[name = tensor<string, []>("h_331")];
            tensor<int32, [2]> var_3927_split_sizes_0 = const()[name = tensor<string, []>("op_3927_split_sizes_0"), val = tensor<int32, [2]>([32, 32])];
            tensor<int32, []> var_3927_axis_0 = const()[name = tensor<string, []>("op_3927_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 1]> var_3927_0, tensor<fp32, [1, 32, 1]> var_3927_1 = split(axis = var_3927_axis_0, split_sizes = var_3927_split_sizes_0, x = h_331)[name = tensor<string, []>("op_3927")];
            tensor<fp32, []> var_3929_promoted = const()[name = tensor<string, []>("op_3929_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 32, 1]> var_3930 = add(x = var_3927_0, y = var_3929_promoted)[name = tensor<string, []>("op_3930")];
            tensor<fp32, [1, 32, 307200]> var_3931 = instance_norm(epsilon = var_516, x = input_379)[name = tensor<string, []>("op_3931")];
            tensor<fp32, [1, 32, 307200]> var_3932 = mul(x = var_3930, y = var_3931)[name = tensor<string, []>("op_3932")];
            tensor<fp32, [1, 32, 307200]> xt_217 = add(x = var_3932, y = var_3927_1)[name = tensor<string, []>("xt_217")];
            tensor<fp32, [1, 32, 1]> var_3934 = const()[name = tensor<string, []>("op_3934"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218771392)))];
            tensor<fp32, [1, 32, 307200]> var_3937 = mul(x = generator_noise_res_3_alpha1_0, y = xt_217)[name = tensor<string, []>("op_3937")];
            tensor<fp32, [1, 32, 307200]> var_3938 = sin(x = var_3937)[name = tensor<string, []>("op_3938")];
            tensor<fp32, []> var_532_promoted_76 = const()[name = tensor<string, []>("op_532_promoted_76"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 32, 307200]> var_3939 = pow(x = var_3938, y = var_532_promoted_76)[name = tensor<string, []>("op_3939")];
            tensor<fp32, [1, 32, 307200]> var_3940 = mul(x = var_3934, y = var_3939)[name = tensor<string, []>("op_3940")];
            tensor<fp32, [1, 32, 307200]> input_381 = add(x = xt_217, y = var_3940)[name = tensor<string, []>("input_381")];
            tensor<fp32, [32, 32, 11]> weight_189 = const()[name = tensor<string, []>("weight_189"), val = tensor<fp32, [32, 32, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218771584)))];
            tensor<string, []> input_383_pad_type_0 = const()[name = tensor<string, []>("input_383_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_383_pad_0 = const()[name = tensor<string, []>("input_383_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> input_383_strides_0 = const()[name = tensor<string, []>("input_383_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_383_dilations_0 = const()[name = tensor<string, []>("input_383_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_383_groups_0 = const()[name = tensor<string, []>("input_383_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 307200]> input_383 = conv(bias = generator_noise_res_3_convs1_0_bias, 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 = weight_189, x = input_381)[name = tensor<string, []>("input_383")];
            tensor<fp32, [1, 64]> h_333 = linear(bias = generator_noise_res_3_adain2_0_fc_bias, weight = generator_noise_res_3_adain2_0_fc_weight, x = s)[name = tensor<string, []>("linear_84")];
            tensor<int32, [3]> var_3955 = const()[name = tensor<string, []>("op_3955"), val = tensor<int32, [3]>([1, 64, 1])];
            tensor<fp32, [1, 64, 1]> h_335 = reshape(shape = var_3955, x = h_333)[name = tensor<string, []>("h_335")];
            tensor<int32, [2]> var_3957_split_sizes_0 = const()[name = tensor<string, []>("op_3957_split_sizes_0"), val = tensor<int32, [2]>([32, 32])];
            tensor<int32, []> var_3957_axis_0 = const()[name = tensor<string, []>("op_3957_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 1]> var_3957_0, tensor<fp32, [1, 32, 1]> var_3957_1 = split(axis = var_3957_axis_0, split_sizes = var_3957_split_sizes_0, x = h_335)[name = tensor<string, []>("op_3957")];
            tensor<fp32, []> var_3959_promoted = const()[name = tensor<string, []>("op_3959_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 32, 1]> var_3960 = add(x = var_3957_0, y = var_3959_promoted)[name = tensor<string, []>("op_3960")];
            tensor<fp32, [1, 32, 307200]> var_3961 = instance_norm(epsilon = var_516, x = input_383)[name = tensor<string, []>("op_3961")];
            tensor<fp32, [1, 32, 307200]> var_3962 = mul(x = var_3960, y = var_3961)[name = tensor<string, []>("op_3962")];
            tensor<fp32, [1, 32, 307200]> xt_219 = add(x = var_3962, y = var_3957_1)[name = tensor<string, []>("xt_219")];
            tensor<fp32, [1, 32, 1]> var_3964 = const()[name = tensor<string, []>("op_3964"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218816704)))];
            tensor<fp32, [1, 32, 307200]> var_3967 = mul(x = generator_noise_res_3_alpha2_0, y = xt_219)[name = tensor<string, []>("op_3967")];
            tensor<fp32, [1, 32, 307200]> var_3968 = sin(x = var_3967)[name = tensor<string, []>("op_3968")];
            tensor<fp32, []> var_532_promoted_77 = const()[name = tensor<string, []>("op_532_promoted_77"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 32, 307200]> var_3969 = pow(x = var_3968, y = var_532_promoted_77)[name = tensor<string, []>("op_3969")];
            tensor<fp32, [1, 32, 307200]> var_3970 = mul(x = var_3964, y = var_3969)[name = tensor<string, []>("op_3970")];
            tensor<fp32, [1, 32, 307200]> input_385 = add(x = xt_219, y = var_3970)[name = tensor<string, []>("input_385")];
            tensor<fp32, [32, 32, 11]> weight_191 = const()[name = tensor<string, []>("weight_191"), val = tensor<fp32, [32, 32, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218816896)))];
            tensor<string, []> xt_221_pad_type_0 = const()[name = tensor<string, []>("xt_221_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_221_pad_0 = const()[name = tensor<string, []>("xt_221_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> xt_221_strides_0 = const()[name = tensor<string, []>("xt_221_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_221_dilations_0 = const()[name = tensor<string, []>("xt_221_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_221_groups_0 = const()[name = tensor<string, []>("xt_221_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 307200]> xt_221 = conv(bias = generator_noise_res_3_convs2_0_bias, dilations = xt_221_dilations_0, groups = xt_221_groups_0, pad = xt_221_pad_0, pad_type = xt_221_pad_type_0, strides = xt_221_strides_0, weight = weight_191, x = input_385)[name = tensor<string, []>("xt_221")];
            tensor<fp32, [1, 32, 307200]> input_387 = add(x = xt_221, y = input_379)[name = tensor<string, []>("input_387")];
            tensor<fp32, [1, 64]> h_337 = linear(bias = generator_noise_res_3_adain1_1_fc_bias, weight = generator_noise_res_3_adain1_1_fc_weight, x = s)[name = tensor<string, []>("linear_85")];
            tensor<int32, [3]> var_3986 = const()[name = tensor<string, []>("op_3986"), val = tensor<int32, [3]>([1, 64, 1])];
            tensor<fp32, [1, 64, 1]> h_339 = reshape(shape = var_3986, x = h_337)[name = tensor<string, []>("h_339")];
            tensor<int32, [2]> var_3988_split_sizes_0 = const()[name = tensor<string, []>("op_3988_split_sizes_0"), val = tensor<int32, [2]>([32, 32])];
            tensor<int32, []> var_3988_axis_0 = const()[name = tensor<string, []>("op_3988_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 1]> var_3988_0, tensor<fp32, [1, 32, 1]> var_3988_1 = split(axis = var_3988_axis_0, split_sizes = var_3988_split_sizes_0, x = h_339)[name = tensor<string, []>("op_3988")];
            tensor<fp32, []> var_3990_promoted = const()[name = tensor<string, []>("op_3990_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 32, 1]> var_3991 = add(x = var_3988_0, y = var_3990_promoted)[name = tensor<string, []>("op_3991")];
            tensor<fp32, [1, 32, 307200]> var_3992 = instance_norm(epsilon = var_516, x = input_387)[name = tensor<string, []>("op_3992")];
            tensor<fp32, [1, 32, 307200]> var_3993 = mul(x = var_3991, y = var_3992)[name = tensor<string, []>("op_3993")];
            tensor<fp32, [1, 32, 307200]> xt_223 = add(x = var_3993, y = var_3988_1)[name = tensor<string, []>("xt_223")];
            tensor<fp32, [1, 32, 1]> var_3995 = const()[name = tensor<string, []>("op_3995"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218862016)))];
            tensor<fp32, [1, 32, 307200]> var_3998 = mul(x = generator_noise_res_3_alpha1_1, y = xt_223)[name = tensor<string, []>("op_3998")];
            tensor<fp32, [1, 32, 307200]> var_3999 = sin(x = var_3998)[name = tensor<string, []>("op_3999")];
            tensor<fp32, []> var_532_promoted_78 = const()[name = tensor<string, []>("op_532_promoted_78"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 32, 307200]> var_4000 = pow(x = var_3999, y = var_532_promoted_78)[name = tensor<string, []>("op_4000")];
            tensor<fp32, [1, 32, 307200]> var_4001 = mul(x = var_3995, y = var_4000)[name = tensor<string, []>("op_4001")];
            tensor<fp32, [1, 32, 307200]> input_389 = add(x = xt_223, y = var_4001)[name = tensor<string, []>("input_389")];
            tensor<fp32, [32, 32, 11]> weight_193 = const()[name = tensor<string, []>("weight_193"), val = tensor<fp32, [32, 32, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218862208)))];
            tensor<string, []> input_391_pad_type_0 = const()[name = tensor<string, []>("input_391_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_391_pad_0 = const()[name = tensor<string, []>("input_391_pad_0"), val = tensor<int32, [2]>([15, 15])];
            tensor<int32, [1]> input_391_dilations_0 = const()[name = tensor<string, []>("input_391_dilations_0"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> input_391_strides_0 = const()[name = tensor<string, []>("input_391_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_391_groups_0 = const()[name = tensor<string, []>("input_391_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 307200]> input_391 = conv(bias = generator_noise_res_3_convs1_1_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 = weight_193, x = input_389)[name = tensor<string, []>("input_391")];
            tensor<fp32, [1, 64]> h_341 = linear(bias = generator_noise_res_3_adain2_1_fc_bias, weight = generator_noise_res_3_adain2_1_fc_weight, x = s)[name = tensor<string, []>("linear_86")];
            tensor<int32, [3]> var_4016 = const()[name = tensor<string, []>("op_4016"), val = tensor<int32, [3]>([1, 64, 1])];
            tensor<fp32, [1, 64, 1]> h_343 = reshape(shape = var_4016, x = h_341)[name = tensor<string, []>("h_343")];
            tensor<int32, [2]> var_4018_split_sizes_0 = const()[name = tensor<string, []>("op_4018_split_sizes_0"), val = tensor<int32, [2]>([32, 32])];
            tensor<int32, []> var_4018_axis_0 = const()[name = tensor<string, []>("op_4018_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 1]> var_4018_0, tensor<fp32, [1, 32, 1]> var_4018_1 = split(axis = var_4018_axis_0, split_sizes = var_4018_split_sizes_0, x = h_343)[name = tensor<string, []>("op_4018")];
            tensor<fp32, []> var_4020_promoted = const()[name = tensor<string, []>("op_4020_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 32, 1]> var_4021 = add(x = var_4018_0, y = var_4020_promoted)[name = tensor<string, []>("op_4021")];
            tensor<fp32, [1, 32, 307200]> var_4022 = instance_norm(epsilon = var_516, x = input_391)[name = tensor<string, []>("op_4022")];
            tensor<fp32, [1, 32, 307200]> var_4023 = mul(x = var_4021, y = var_4022)[name = tensor<string, []>("op_4023")];
            tensor<fp32, [1, 32, 307200]> xt_225 = add(x = var_4023, y = var_4018_1)[name = tensor<string, []>("xt_225")];
            tensor<fp32, [1, 32, 1]> var_4025 = const()[name = tensor<string, []>("op_4025"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218907328)))];
            tensor<fp32, [1, 32, 307200]> var_4028 = mul(x = generator_noise_res_3_alpha2_1, y = xt_225)[name = tensor<string, []>("op_4028")];
            tensor<fp32, [1, 32, 307200]> var_4029 = sin(x = var_4028)[name = tensor<string, []>("op_4029")];
            tensor<fp32, []> var_532_promoted_79 = const()[name = tensor<string, []>("op_532_promoted_79"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 32, 307200]> var_4030 = pow(x = var_4029, y = var_532_promoted_79)[name = tensor<string, []>("op_4030")];
            tensor<fp32, [1, 32, 307200]> var_4031 = mul(x = var_4025, y = var_4030)[name = tensor<string, []>("op_4031")];
            tensor<fp32, [1, 32, 307200]> input_393 = add(x = xt_225, y = var_4031)[name = tensor<string, []>("input_393")];
            tensor<fp32, [32, 32, 11]> weight_195 = const()[name = tensor<string, []>("weight_195"), val = tensor<fp32, [32, 32, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218907520)))];
            tensor<string, []> xt_227_pad_type_0 = const()[name = tensor<string, []>("xt_227_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_227_pad_0 = const()[name = tensor<string, []>("xt_227_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> xt_227_strides_0 = const()[name = tensor<string, []>("xt_227_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_227_dilations_0 = const()[name = tensor<string, []>("xt_227_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_227_groups_0 = const()[name = tensor<string, []>("xt_227_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 307200]> xt_227 = conv(bias = generator_noise_res_3_convs2_1_bias, dilations = xt_227_dilations_0, groups = xt_227_groups_0, pad = xt_227_pad_0, pad_type = xt_227_pad_type_0, strides = xt_227_strides_0, weight = weight_195, x = input_393)[name = tensor<string, []>("xt_227")];
            tensor<fp32, [1, 32, 307200]> input_395 = add(x = xt_227, y = input_387)[name = tensor<string, []>("input_395")];
            tensor<fp32, [1, 64]> h_345 = linear(bias = generator_noise_res_3_adain1_2_fc_bias, weight = generator_noise_res_3_adain1_2_fc_weight, x = s)[name = tensor<string, []>("linear_87")];
            tensor<int32, [3]> var_4047 = const()[name = tensor<string, []>("op_4047"), val = tensor<int32, [3]>([1, 64, 1])];
            tensor<fp32, [1, 64, 1]> h_347 = reshape(shape = var_4047, x = h_345)[name = tensor<string, []>("h_347")];
            tensor<int32, [2]> var_4049_split_sizes_0 = const()[name = tensor<string, []>("op_4049_split_sizes_0"), val = tensor<int32, [2]>([32, 32])];
            tensor<int32, []> var_4049_axis_0 = const()[name = tensor<string, []>("op_4049_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 1]> var_4049_0, tensor<fp32, [1, 32, 1]> var_4049_1 = split(axis = var_4049_axis_0, split_sizes = var_4049_split_sizes_0, x = h_347)[name = tensor<string, []>("op_4049")];
            tensor<fp32, []> var_4051_promoted = const()[name = tensor<string, []>("op_4051_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 32, 1]> var_4052 = add(x = var_4049_0, y = var_4051_promoted)[name = tensor<string, []>("op_4052")];
            tensor<fp32, [1, 32, 307200]> var_4053 = instance_norm(epsilon = var_516, x = input_395)[name = tensor<string, []>("op_4053")];
            tensor<fp32, [1, 32, 307200]> var_4054 = mul(x = var_4052, y = var_4053)[name = tensor<string, []>("op_4054")];
            tensor<fp32, [1, 32, 307200]> xt_229 = add(x = var_4054, y = var_4049_1)[name = tensor<string, []>("xt_229")];
            tensor<fp32, [1, 32, 1]> var_4056 = const()[name = tensor<string, []>("op_4056"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218952640)))];
            tensor<fp32, [1, 32, 307200]> var_4059 = mul(x = generator_noise_res_3_alpha1_2, y = xt_229)[name = tensor<string, []>("op_4059")];
            tensor<fp32, [1, 32, 307200]> var_4060 = sin(x = var_4059)[name = tensor<string, []>("op_4060")];
            tensor<fp32, []> var_532_promoted_80 = const()[name = tensor<string, []>("op_532_promoted_80"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 32, 307200]> var_4061 = pow(x = var_4060, y = var_532_promoted_80)[name = tensor<string, []>("op_4061")];
            tensor<fp32, [1, 32, 307200]> var_4062 = mul(x = var_4056, y = var_4061)[name = tensor<string, []>("op_4062")];
            tensor<fp32, [1, 32, 307200]> input_397 = add(x = xt_229, y = var_4062)[name = tensor<string, []>("input_397")];
            tensor<fp32, [32, 32, 11]> weight_197 = const()[name = tensor<string, []>("weight_197"), val = tensor<fp32, [32, 32, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218952832)))];
            tensor<string, []> input_399_pad_type_0 = const()[name = tensor<string, []>("input_399_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_399_pad_0 = const()[name = tensor<string, []>("input_399_pad_0"), val = tensor<int32, [2]>([25, 25])];
            tensor<int32, [1]> input_399_dilations_0 = const()[name = tensor<string, []>("input_399_dilations_0"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> input_399_strides_0 = const()[name = tensor<string, []>("input_399_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_399_groups_0 = const()[name = tensor<string, []>("input_399_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 307200]> input_399 = conv(bias = generator_noise_res_3_convs1_2_bias, dilations = input_399_dilations_0, groups = input_399_groups_0, pad = input_399_pad_0, pad_type = input_399_pad_type_0, strides = input_399_strides_0, weight = weight_197, x = input_397)[name = tensor<string, []>("input_399")];
            tensor<fp32, [1, 64]> h_349 = linear(bias = generator_noise_res_3_adain2_2_fc_bias, weight = generator_noise_res_3_adain2_2_fc_weight, x = s)[name = tensor<string, []>("linear_88")];
            tensor<int32, [3]> var_4077 = const()[name = tensor<string, []>("op_4077"), val = tensor<int32, [3]>([1, 64, 1])];
            tensor<fp32, [1, 64, 1]> h_351 = reshape(shape = var_4077, x = h_349)[name = tensor<string, []>("h_351")];
            tensor<int32, [2]> var_4079_split_sizes_0 = const()[name = tensor<string, []>("op_4079_split_sizes_0"), val = tensor<int32, [2]>([32, 32])];
            tensor<int32, []> var_4079_axis_0 = const()[name = tensor<string, []>("op_4079_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 1]> var_4079_0, tensor<fp32, [1, 32, 1]> var_4079_1 = split(axis = var_4079_axis_0, split_sizes = var_4079_split_sizes_0, x = h_351)[name = tensor<string, []>("op_4079")];
            tensor<fp32, []> var_4081_promoted = const()[name = tensor<string, []>("op_4081_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 32, 1]> var_4082 = add(x = var_4079_0, y = var_4081_promoted)[name = tensor<string, []>("op_4082")];
            tensor<fp32, [1, 32, 307200]> var_4083 = instance_norm(epsilon = var_516, x = input_399)[name = tensor<string, []>("op_4083")];
            tensor<fp32, [1, 32, 307200]> var_4084 = mul(x = var_4082, y = var_4083)[name = tensor<string, []>("op_4084")];
            tensor<fp32, [1, 32, 307200]> xt_231 = add(x = var_4084, y = var_4079_1)[name = tensor<string, []>("xt_231")];
            tensor<fp32, [1, 32, 1]> var_4086 = const()[name = tensor<string, []>("op_4086"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218997952)))];
            tensor<fp32, [1, 32, 307200]> var_4089 = mul(x = generator_noise_res_3_alpha2_2, y = xt_231)[name = tensor<string, []>("op_4089")];
            tensor<fp32, [1, 32, 307200]> var_4090 = sin(x = var_4089)[name = tensor<string, []>("op_4090")];
            tensor<fp32, []> var_532_promoted_81 = const()[name = tensor<string, []>("op_532_promoted_81"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 32, 307200]> var_4091 = pow(x = var_4090, y = var_532_promoted_81)[name = tensor<string, []>("op_4091")];
            tensor<fp32, [1, 32, 307200]> var_4092 = mul(x = var_4086, y = var_4091)[name = tensor<string, []>("op_4092")];
            tensor<fp32, [1, 32, 307200]> input_401 = add(x = xt_231, y = var_4092)[name = tensor<string, []>("input_401")];
            tensor<fp32, [32, 32, 11]> weight_199 = const()[name = tensor<string, []>("weight_199"), val = tensor<fp32, [32, 32, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218998144)))];
            tensor<string, []> xt_233_pad_type_0 = const()[name = tensor<string, []>("xt_233_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_233_pad_0 = const()[name = tensor<string, []>("xt_233_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> xt_233_strides_0 = const()[name = tensor<string, []>("xt_233_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_233_dilations_0 = const()[name = tensor<string, []>("xt_233_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_233_groups_0 = const()[name = tensor<string, []>("xt_233_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 307200]> xt_233 = conv(bias = generator_noise_res_3_convs2_2_bias, dilations = xt_233_dilations_0, groups = xt_233_groups_0, pad = xt_233_pad_0, pad_type = xt_233_pad_type_0, strides = xt_233_strides_0, weight = weight_199, x = input_401)[name = tensor<string, []>("xt_233")];
            tensor<fp32, [1, 32, 307200]> x_source = add(x = xt_233, y = input_395)[name = tensor<string, []>("x_source")];
            tensor<fp32, [64, 32, 4]> var_4102 = const()[name = tensor<string, []>("op_4102"), val = tensor<fp32, [64, 32, 4]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219043264)))];
            tensor<string, []> x_23_pad_type_0 = const()[name = tensor<string, []>("x_23_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> x_23_pad_0 = const()[name = tensor<string, []>("x_23_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> x_23_strides_0 = const()[name = tensor<string, []>("x_23_strides_0"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1]> x_23_dilations_0 = const()[name = tensor<string, []>("x_23_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_23_groups_0 = const()[name = tensor<string, []>("x_23_groups_0"), val = tensor<int32, []>(1)];
            tensor<int32, [3]> x_23_has_output_shape_output_shape_0 = const()[name = tensor<string, []>("x_23_has_output_shape_output_shape_0"), val = tensor<int32, [3]>([1, 32, 307200])];
            tensor<fp32, [1, 32, 307200]> x_23_has_output_shape = conv_transpose(bias = generator_ups_3_bias, dilations = x_23_dilations_0, groups = x_23_groups_0, output_shape = x_23_has_output_shape_output_shape_0, pad = x_23_pad_0, pad_type = x_23_pad_type_0, strides = x_23_strides_0, weight = var_4102, x = input_403)[name = tensor<string, []>("x_23_has_output_shape")];
            tensor<fp32, [1, 32, 307200]> input_405 = add(x = x_23_has_output_shape, y = x_source)[name = tensor<string, []>("input_405")];
            tensor<fp32, [1, 64]> h_353 = linear(bias = generator_resblocks_9_adain1_0_fc_bias, weight = generator_resblocks_9_adain1_0_fc_weight, x = s)[name = tensor<string, []>("linear_89")];
            tensor<int32, [3]> var_4188 = const()[name = tensor<string, []>("op_4188"), val = tensor<int32, [3]>([1, 64, 1])];
            tensor<fp32, [1, 64, 1]> h_355 = reshape(shape = var_4188, x = h_353)[name = tensor<string, []>("h_355")];
            tensor<int32, [2]> var_4190_split_sizes_0 = const()[name = tensor<string, []>("op_4190_split_sizes_0"), val = tensor<int32, [2]>([32, 32])];
            tensor<int32, []> var_4190_axis_0 = const()[name = tensor<string, []>("op_4190_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 1]> var_4190_0, tensor<fp32, [1, 32, 1]> var_4190_1 = split(axis = var_4190_axis_0, split_sizes = var_4190_split_sizes_0, x = h_355)[name = tensor<string, []>("op_4190")];
            tensor<fp32, []> var_4192_promoted = const()[name = tensor<string, []>("op_4192_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 32, 1]> var_4193 = add(x = var_4190_0, y = var_4192_promoted)[name = tensor<string, []>("op_4193")];
            tensor<fp32, [1, 32, 307200]> var_4194 = instance_norm(epsilon = var_516, x = input_405)[name = tensor<string, []>("op_4194")];
            tensor<fp32, [1, 32, 307200]> var_4195 = mul(x = var_4193, y = var_4194)[name = tensor<string, []>("op_4195")];
            tensor<fp32, [1, 32, 307200]> xt_235 = add(x = var_4195, y = var_4190_1)[name = tensor<string, []>("xt_235")];
            tensor<fp32, [1, 32, 1]> var_4197 = const()[name = tensor<string, []>("op_4197"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219076096)))];
            tensor<fp32, [1, 32, 307200]> var_4200 = mul(x = generator_resblocks_9_alpha1_0, y = xt_235)[name = tensor<string, []>("op_4200")];
            tensor<fp32, [1, 32, 307200]> var_4201 = sin(x = var_4200)[name = tensor<string, []>("op_4201")];
            tensor<fp32, []> var_532_promoted_82 = const()[name = tensor<string, []>("op_532_promoted_82"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 32, 307200]> var_4202 = pow(x = var_4201, y = var_532_promoted_82)[name = tensor<string, []>("op_4202")];
            tensor<fp32, [1, 32, 307200]> var_4203 = mul(x = var_4197, y = var_4202)[name = tensor<string, []>("op_4203")];
            tensor<fp32, [1, 32, 307200]> input_407 = add(x = xt_235, y = var_4203)[name = tensor<string, []>("input_407")];
            tensor<fp32, [32, 32, 3]> weight_201 = const()[name = tensor<string, []>("weight_201"), val = tensor<fp32, [32, 32, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219076288)))];
            tensor<string, []> input_409_pad_type_0 = const()[name = tensor<string, []>("input_409_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_409_pad_0 = const()[name = tensor<string, []>("input_409_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> input_409_strides_0 = const()[name = tensor<string, []>("input_409_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_409_dilations_0 = const()[name = tensor<string, []>("input_409_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_409_groups_0 = const()[name = tensor<string, []>("input_409_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 307200]> input_409 = conv(bias = generator_resblocks_9_convs1_0_bias, dilations = input_409_dilations_0, groups = input_409_groups_0, pad = input_409_pad_0, pad_type = input_409_pad_type_0, strides = input_409_strides_0, weight = weight_201, x = input_407)[name = tensor<string, []>("input_409")];
            tensor<fp32, [1, 64]> h_357 = linear(bias = generator_resblocks_9_adain2_0_fc_bias, weight = generator_resblocks_9_adain2_0_fc_weight, x = s)[name = tensor<string, []>("linear_90")];
            tensor<int32, [3]> var_4218 = const()[name = tensor<string, []>("op_4218"), val = tensor<int32, [3]>([1, 64, 1])];
            tensor<fp32, [1, 64, 1]> h_359 = reshape(shape = var_4218, x = h_357)[name = tensor<string, []>("h_359")];
            tensor<int32, [2]> var_4220_split_sizes_0 = const()[name = tensor<string, []>("op_4220_split_sizes_0"), val = tensor<int32, [2]>([32, 32])];
            tensor<int32, []> var_4220_axis_0 = const()[name = tensor<string, []>("op_4220_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 1]> var_4220_0, tensor<fp32, [1, 32, 1]> var_4220_1 = split(axis = var_4220_axis_0, split_sizes = var_4220_split_sizes_0, x = h_359)[name = tensor<string, []>("op_4220")];
            tensor<fp32, []> var_4222_promoted = const()[name = tensor<string, []>("op_4222_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 32, 1]> var_4223 = add(x = var_4220_0, y = var_4222_promoted)[name = tensor<string, []>("op_4223")];
            tensor<fp32, [1, 32, 307200]> var_4224 = instance_norm(epsilon = var_516, x = input_409)[name = tensor<string, []>("op_4224")];
            tensor<fp32, [1, 32, 307200]> var_4225 = mul(x = var_4223, y = var_4224)[name = tensor<string, []>("op_4225")];
            tensor<fp32, [1, 32, 307200]> xt_237 = add(x = var_4225, y = var_4220_1)[name = tensor<string, []>("xt_237")];
            tensor<fp32, [1, 32, 1]> var_4227 = const()[name = tensor<string, []>("op_4227"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219088640)))];
            tensor<fp32, [1, 32, 307200]> var_4230 = mul(x = generator_resblocks_9_alpha2_0, y = xt_237)[name = tensor<string, []>("op_4230")];
            tensor<fp32, [1, 32, 307200]> var_4231 = sin(x = var_4230)[name = tensor<string, []>("op_4231")];
            tensor<fp32, []> var_532_promoted_83 = const()[name = tensor<string, []>("op_532_promoted_83"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 32, 307200]> var_4232 = pow(x = var_4231, y = var_532_promoted_83)[name = tensor<string, []>("op_4232")];
            tensor<fp32, [1, 32, 307200]> var_4233 = mul(x = var_4227, y = var_4232)[name = tensor<string, []>("op_4233")];
            tensor<fp32, [1, 32, 307200]> input_411 = add(x = xt_237, y = var_4233)[name = tensor<string, []>("input_411")];
            tensor<fp32, [32, 32, 3]> weight_203 = const()[name = tensor<string, []>("weight_203"), val = tensor<fp32, [32, 32, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219088832)))];
            tensor<string, []> xt_239_pad_type_0 = const()[name = tensor<string, []>("xt_239_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_239_pad_0 = const()[name = tensor<string, []>("xt_239_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> xt_239_strides_0 = const()[name = tensor<string, []>("xt_239_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_239_dilations_0 = const()[name = tensor<string, []>("xt_239_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_239_groups_0 = const()[name = tensor<string, []>("xt_239_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 307200]> xt_239 = conv(bias = generator_resblocks_9_convs2_0_bias, dilations = xt_239_dilations_0, groups = xt_239_groups_0, pad = xt_239_pad_0, pad_type = xt_239_pad_type_0, strides = xt_239_strides_0, weight = weight_203, x = input_411)[name = tensor<string, []>("xt_239")];
            tensor<fp32, [1, 32, 307200]> input_413 = add(x = xt_239, y = input_405)[name = tensor<string, []>("input_413")];
            tensor<fp32, [1, 64]> h_361 = linear(bias = generator_resblocks_9_adain1_1_fc_bias, weight = generator_resblocks_9_adain1_1_fc_weight, x = s)[name = tensor<string, []>("linear_91")];
            tensor<int32, [3]> var_4249 = const()[name = tensor<string, []>("op_4249"), val = tensor<int32, [3]>([1, 64, 1])];
            tensor<fp32, [1, 64, 1]> h_363 = reshape(shape = var_4249, x = h_361)[name = tensor<string, []>("h_363")];
            tensor<int32, [2]> var_4251_split_sizes_0 = const()[name = tensor<string, []>("op_4251_split_sizes_0"), val = tensor<int32, [2]>([32, 32])];
            tensor<int32, []> var_4251_axis_0 = const()[name = tensor<string, []>("op_4251_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 1]> var_4251_0, tensor<fp32, [1, 32, 1]> var_4251_1 = split(axis = var_4251_axis_0, split_sizes = var_4251_split_sizes_0, x = h_363)[name = tensor<string, []>("op_4251")];
            tensor<fp32, []> var_4253_promoted = const()[name = tensor<string, []>("op_4253_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 32, 1]> var_4254 = add(x = var_4251_0, y = var_4253_promoted)[name = tensor<string, []>("op_4254")];
            tensor<fp32, [1, 32, 307200]> var_4255 = instance_norm(epsilon = var_516, x = input_413)[name = tensor<string, []>("op_4255")];
            tensor<fp32, [1, 32, 307200]> var_4256 = mul(x = var_4254, y = var_4255)[name = tensor<string, []>("op_4256")];
            tensor<fp32, [1, 32, 307200]> xt_241 = add(x = var_4256, y = var_4251_1)[name = tensor<string, []>("xt_241")];
            tensor<fp32, [1, 32, 1]> var_4258 = const()[name = tensor<string, []>("op_4258"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219101184)))];
            tensor<fp32, [1, 32, 307200]> var_4261 = mul(x = generator_resblocks_9_alpha1_1, y = xt_241)[name = tensor<string, []>("op_4261")];
            tensor<fp32, [1, 32, 307200]> var_4262 = sin(x = var_4261)[name = tensor<string, []>("op_4262")];
            tensor<fp32, []> var_532_promoted_84 = const()[name = tensor<string, []>("op_532_promoted_84"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 32, 307200]> var_4263 = pow(x = var_4262, y = var_532_promoted_84)[name = tensor<string, []>("op_4263")];
            tensor<fp32, [1, 32, 307200]> var_4264 = mul(x = var_4258, y = var_4263)[name = tensor<string, []>("op_4264")];
            tensor<fp32, [1, 32, 307200]> input_415 = add(x = xt_241, y = var_4264)[name = tensor<string, []>("input_415")];
            tensor<fp32, [32, 32, 3]> weight_205 = const()[name = tensor<string, []>("weight_205"), val = tensor<fp32, [32, 32, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219101376)))];
            tensor<string, []> input_417_pad_type_0 = const()[name = tensor<string, []>("input_417_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_417_pad_0 = const()[name = tensor<string, []>("input_417_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> input_417_dilations_0 = const()[name = tensor<string, []>("input_417_dilations_0"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> input_417_strides_0 = const()[name = tensor<string, []>("input_417_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_417_groups_0 = const()[name = tensor<string, []>("input_417_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 307200]> input_417 = conv(bias = generator_resblocks_9_convs1_1_bias, dilations = input_417_dilations_0, groups = input_417_groups_0, pad = input_417_pad_0, pad_type = input_417_pad_type_0, strides = input_417_strides_0, weight = weight_205, x = input_415)[name = tensor<string, []>("input_417")];
            tensor<fp32, [1, 64]> h_365 = linear(bias = generator_resblocks_9_adain2_1_fc_bias, weight = generator_resblocks_9_adain2_1_fc_weight, x = s)[name = tensor<string, []>("linear_92")];
            tensor<int32, [3]> var_4279 = const()[name = tensor<string, []>("op_4279"), val = tensor<int32, [3]>([1, 64, 1])];
            tensor<fp32, [1, 64, 1]> h_367 = reshape(shape = var_4279, x = h_365)[name = tensor<string, []>("h_367")];
            tensor<int32, [2]> var_4281_split_sizes_0 = const()[name = tensor<string, []>("op_4281_split_sizes_0"), val = tensor<int32, [2]>([32, 32])];
            tensor<int32, []> var_4281_axis_0 = const()[name = tensor<string, []>("op_4281_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 1]> var_4281_0, tensor<fp32, [1, 32, 1]> var_4281_1 = split(axis = var_4281_axis_0, split_sizes = var_4281_split_sizes_0, x = h_367)[name = tensor<string, []>("op_4281")];
            tensor<fp32, []> var_4283_promoted = const()[name = tensor<string, []>("op_4283_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 32, 1]> var_4284 = add(x = var_4281_0, y = var_4283_promoted)[name = tensor<string, []>("op_4284")];
            tensor<fp32, [1, 32, 307200]> var_4285 = instance_norm(epsilon = var_516, x = input_417)[name = tensor<string, []>("op_4285")];
            tensor<fp32, [1, 32, 307200]> var_4286 = mul(x = var_4284, y = var_4285)[name = tensor<string, []>("op_4286")];
            tensor<fp32, [1, 32, 307200]> xt_243 = add(x = var_4286, y = var_4281_1)[name = tensor<string, []>("xt_243")];
            tensor<fp32, [1, 32, 1]> var_4288 = const()[name = tensor<string, []>("op_4288"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219113728)))];
            tensor<fp32, [1, 32, 307200]> var_4291 = mul(x = generator_resblocks_9_alpha2_1, y = xt_243)[name = tensor<string, []>("op_4291")];
            tensor<fp32, [1, 32, 307200]> var_4292 = sin(x = var_4291)[name = tensor<string, []>("op_4292")];
            tensor<fp32, []> var_532_promoted_85 = const()[name = tensor<string, []>("op_532_promoted_85"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 32, 307200]> var_4293 = pow(x = var_4292, y = var_532_promoted_85)[name = tensor<string, []>("op_4293")];
            tensor<fp32, [1, 32, 307200]> var_4294 = mul(x = var_4288, y = var_4293)[name = tensor<string, []>("op_4294")];
            tensor<fp32, [1, 32, 307200]> input_419 = add(x = xt_243, y = var_4294)[name = tensor<string, []>("input_419")];
            tensor<fp32, [32, 32, 3]> weight_207 = const()[name = tensor<string, []>("weight_207"), val = tensor<fp32, [32, 32, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219113920)))];
            tensor<string, []> xt_245_pad_type_0 = const()[name = tensor<string, []>("xt_245_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_245_pad_0 = const()[name = tensor<string, []>("xt_245_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> xt_245_strides_0 = const()[name = tensor<string, []>("xt_245_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_245_dilations_0 = const()[name = tensor<string, []>("xt_245_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_245_groups_0 = const()[name = tensor<string, []>("xt_245_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 307200]> xt_245 = conv(bias = generator_resblocks_9_convs2_1_bias, dilations = xt_245_dilations_0, groups = xt_245_groups_0, pad = xt_245_pad_0, pad_type = xt_245_pad_type_0, strides = xt_245_strides_0, weight = weight_207, x = input_419)[name = tensor<string, []>("xt_245")];
            tensor<fp32, [1, 32, 307200]> input_421 = add(x = xt_245, y = input_413)[name = tensor<string, []>("input_421")];
            tensor<fp32, [1, 64]> h_369 = linear(bias = generator_resblocks_9_adain1_2_fc_bias, weight = generator_resblocks_9_adain1_2_fc_weight, x = s)[name = tensor<string, []>("linear_93")];
            tensor<int32, [3]> var_4310 = const()[name = tensor<string, []>("op_4310"), val = tensor<int32, [3]>([1, 64, 1])];
            tensor<fp32, [1, 64, 1]> h_371 = reshape(shape = var_4310, x = h_369)[name = tensor<string, []>("h_371")];
            tensor<int32, [2]> var_4312_split_sizes_0 = const()[name = tensor<string, []>("op_4312_split_sizes_0"), val = tensor<int32, [2]>([32, 32])];
            tensor<int32, []> var_4312_axis_0 = const()[name = tensor<string, []>("op_4312_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 1]> var_4312_0, tensor<fp32, [1, 32, 1]> var_4312_1 = split(axis = var_4312_axis_0, split_sizes = var_4312_split_sizes_0, x = h_371)[name = tensor<string, []>("op_4312")];
            tensor<fp32, []> var_4314_promoted = const()[name = tensor<string, []>("op_4314_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 32, 1]> var_4315 = add(x = var_4312_0, y = var_4314_promoted)[name = tensor<string, []>("op_4315")];
            tensor<fp32, [1, 32, 307200]> var_4316 = instance_norm(epsilon = var_516, x = input_421)[name = tensor<string, []>("op_4316")];
            tensor<fp32, [1, 32, 307200]> var_4317 = mul(x = var_4315, y = var_4316)[name = tensor<string, []>("op_4317")];
            tensor<fp32, [1, 32, 307200]> xt_247 = add(x = var_4317, y = var_4312_1)[name = tensor<string, []>("xt_247")];
            tensor<fp32, [1, 32, 1]> var_4319 = const()[name = tensor<string, []>("op_4319"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219126272)))];
            tensor<fp32, [1, 32, 307200]> var_4322 = mul(x = generator_resblocks_9_alpha1_2, y = xt_247)[name = tensor<string, []>("op_4322")];
            tensor<fp32, [1, 32, 307200]> var_4323 = sin(x = var_4322)[name = tensor<string, []>("op_4323")];
            tensor<fp32, []> var_532_promoted_86 = const()[name = tensor<string, []>("op_532_promoted_86"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 32, 307200]> var_4324 = pow(x = var_4323, y = var_532_promoted_86)[name = tensor<string, []>("op_4324")];
            tensor<fp32, [1, 32, 307200]> var_4325 = mul(x = var_4319, y = var_4324)[name = tensor<string, []>("op_4325")];
            tensor<fp32, [1, 32, 307200]> input_423 = add(x = xt_247, y = var_4325)[name = tensor<string, []>("input_423")];
            tensor<fp32, [32, 32, 3]> weight_209 = const()[name = tensor<string, []>("weight_209"), val = tensor<fp32, [32, 32, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219126464)))];
            tensor<string, []> input_425_pad_type_0 = const()[name = tensor<string, []>("input_425_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_425_pad_0 = const()[name = tensor<string, []>("input_425_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> input_425_dilations_0 = const()[name = tensor<string, []>("input_425_dilations_0"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> input_425_strides_0 = const()[name = tensor<string, []>("input_425_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_425_groups_0 = const()[name = tensor<string, []>("input_425_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 307200]> input_425 = conv(bias = generator_resblocks_9_convs1_2_bias, dilations = input_425_dilations_0, groups = input_425_groups_0, pad = input_425_pad_0, pad_type = input_425_pad_type_0, strides = input_425_strides_0, weight = weight_209, x = input_423)[name = tensor<string, []>("input_425")];
            tensor<fp32, [1, 64]> h_373 = linear(bias = generator_resblocks_9_adain2_2_fc_bias, weight = generator_resblocks_9_adain2_2_fc_weight, x = s)[name = tensor<string, []>("linear_94")];
            tensor<int32, [3]> var_4340 = const()[name = tensor<string, []>("op_4340"), val = tensor<int32, [3]>([1, 64, 1])];
            tensor<fp32, [1, 64, 1]> h_375 = reshape(shape = var_4340, x = h_373)[name = tensor<string, []>("h_375")];
            tensor<int32, [2]> var_4342_split_sizes_0 = const()[name = tensor<string, []>("op_4342_split_sizes_0"), val = tensor<int32, [2]>([32, 32])];
            tensor<int32, []> var_4342_axis_0 = const()[name = tensor<string, []>("op_4342_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 1]> var_4342_0, tensor<fp32, [1, 32, 1]> var_4342_1 = split(axis = var_4342_axis_0, split_sizes = var_4342_split_sizes_0, x = h_375)[name = tensor<string, []>("op_4342")];
            tensor<fp32, []> var_4344_promoted = const()[name = tensor<string, []>("op_4344_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 32, 1]> var_4345 = add(x = var_4342_0, y = var_4344_promoted)[name = tensor<string, []>("op_4345")];
            tensor<fp32, [1, 32, 307200]> var_4346 = instance_norm(epsilon = var_516, x = input_425)[name = tensor<string, []>("op_4346")];
            tensor<fp32, [1, 32, 307200]> var_4347 = mul(x = var_4345, y = var_4346)[name = tensor<string, []>("op_4347")];
            tensor<fp32, [1, 32, 307200]> xt_249 = add(x = var_4347, y = var_4342_1)[name = tensor<string, []>("xt_249")];
            tensor<fp32, [1, 32, 1]> var_4349 = const()[name = tensor<string, []>("op_4349"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219138816)))];
            tensor<fp32, [1, 32, 307200]> var_4352 = mul(x = generator_resblocks_9_alpha2_2, y = xt_249)[name = tensor<string, []>("op_4352")];
            tensor<fp32, [1, 32, 307200]> var_4353 = sin(x = var_4352)[name = tensor<string, []>("op_4353")];
            tensor<fp32, []> var_532_promoted_87 = const()[name = tensor<string, []>("op_532_promoted_87"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 32, 307200]> var_4354 = pow(x = var_4353, y = var_532_promoted_87)[name = tensor<string, []>("op_4354")];
            tensor<fp32, [1, 32, 307200]> var_4355 = mul(x = var_4349, y = var_4354)[name = tensor<string, []>("op_4355")];
            tensor<fp32, [1, 32, 307200]> input_427 = add(x = xt_249, y = var_4355)[name = tensor<string, []>("input_427")];
            tensor<fp32, [32, 32, 3]> weight_211 = const()[name = tensor<string, []>("weight_211"), val = tensor<fp32, [32, 32, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219139008)))];
            tensor<string, []> xt_251_pad_type_0 = const()[name = tensor<string, []>("xt_251_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_251_pad_0 = const()[name = tensor<string, []>("xt_251_pad_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [1]> xt_251_strides_0 = const()[name = tensor<string, []>("xt_251_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_251_dilations_0 = const()[name = tensor<string, []>("xt_251_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_251_groups_0 = const()[name = tensor<string, []>("xt_251_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 307200]> xt_251 = conv(bias = generator_resblocks_9_convs2_2_bias, dilations = xt_251_dilations_0, groups = xt_251_groups_0, pad = xt_251_pad_0, pad_type = xt_251_pad_type_0, strides = xt_251_strides_0, weight = weight_211, x = input_427)[name = tensor<string, []>("xt_251")];
            tensor<fp32, [1, 32, 307200]> xs_19 = add(x = xt_251, y = input_421)[name = tensor<string, []>("xs_19")];
            tensor<fp32, [1, 64]> h_377 = linear(bias = generator_resblocks_10_adain1_0_fc_bias, weight = generator_resblocks_10_adain1_0_fc_weight, x = s)[name = tensor<string, []>("linear_95")];
            tensor<int32, [3]> var_4443 = const()[name = tensor<string, []>("op_4443"), val = tensor<int32, [3]>([1, 64, 1])];
            tensor<fp32, [1, 64, 1]> h_379 = reshape(shape = var_4443, x = h_377)[name = tensor<string, []>("h_379")];
            tensor<int32, [2]> var_4445_split_sizes_0 = const()[name = tensor<string, []>("op_4445_split_sizes_0"), val = tensor<int32, [2]>([32, 32])];
            tensor<int32, []> var_4445_axis_0 = const()[name = tensor<string, []>("op_4445_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 1]> var_4445_0, tensor<fp32, [1, 32, 1]> var_4445_1 = split(axis = var_4445_axis_0, split_sizes = var_4445_split_sizes_0, x = h_379)[name = tensor<string, []>("op_4445")];
            tensor<fp32, []> var_4447_promoted = const()[name = tensor<string, []>("op_4447_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 32, 1]> var_4448 = add(x = var_4445_0, y = var_4447_promoted)[name = tensor<string, []>("op_4448")];
            tensor<fp32, [1, 32, 307200]> var_4450 = mul(x = var_4448, y = var_4194)[name = tensor<string, []>("op_4450")];
            tensor<fp32, [1, 32, 307200]> xt_253 = add(x = var_4450, y = var_4445_1)[name = tensor<string, []>("xt_253")];
            tensor<fp32, [1, 32, 1]> var_4452 = const()[name = tensor<string, []>("op_4452"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219151360)))];
            tensor<fp32, [1, 32, 307200]> var_4455 = mul(x = generator_resblocks_10_alpha1_0, y = xt_253)[name = tensor<string, []>("op_4455")];
            tensor<fp32, [1, 32, 307200]> var_4456 = sin(x = var_4455)[name = tensor<string, []>("op_4456")];
            tensor<fp32, []> var_532_promoted_88 = const()[name = tensor<string, []>("op_532_promoted_88"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 32, 307200]> var_4457 = pow(x = var_4456, y = var_532_promoted_88)[name = tensor<string, []>("op_4457")];
            tensor<fp32, [1, 32, 307200]> var_4458 = mul(x = var_4452, y = var_4457)[name = tensor<string, []>("op_4458")];
            tensor<fp32, [1, 32, 307200]> input_429 = add(x = xt_253, y = var_4458)[name = tensor<string, []>("input_429")];
            tensor<fp32, [32, 32, 7]> weight_213 = const()[name = tensor<string, []>("weight_213"), val = tensor<fp32, [32, 32, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219151552)))];
            tensor<string, []> input_431_pad_type_0 = const()[name = tensor<string, []>("input_431_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_431_pad_0 = const()[name = tensor<string, []>("input_431_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> input_431_strides_0 = const()[name = tensor<string, []>("input_431_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_431_dilations_0 = const()[name = tensor<string, []>("input_431_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_431_groups_0 = const()[name = tensor<string, []>("input_431_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 307200]> input_431 = conv(bias = generator_resblocks_10_convs1_0_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 = weight_213, x = input_429)[name = tensor<string, []>("input_431")];
            tensor<fp32, [1, 64]> h_381 = linear(bias = generator_resblocks_10_adain2_0_fc_bias, weight = generator_resblocks_10_adain2_0_fc_weight, x = s)[name = tensor<string, []>("linear_96")];
            tensor<int32, [3]> var_4473 = const()[name = tensor<string, []>("op_4473"), val = tensor<int32, [3]>([1, 64, 1])];
            tensor<fp32, [1, 64, 1]> h_383 = reshape(shape = var_4473, x = h_381)[name = tensor<string, []>("h_383")];
            tensor<int32, [2]> var_4475_split_sizes_0 = const()[name = tensor<string, []>("op_4475_split_sizes_0"), val = tensor<int32, [2]>([32, 32])];
            tensor<int32, []> var_4475_axis_0 = const()[name = tensor<string, []>("op_4475_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 1]> var_4475_0, tensor<fp32, [1, 32, 1]> var_4475_1 = split(axis = var_4475_axis_0, split_sizes = var_4475_split_sizes_0, x = h_383)[name = tensor<string, []>("op_4475")];
            tensor<fp32, []> var_4477_promoted = const()[name = tensor<string, []>("op_4477_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 32, 1]> var_4478 = add(x = var_4475_0, y = var_4477_promoted)[name = tensor<string, []>("op_4478")];
            tensor<fp32, [1, 32, 307200]> var_4479 = instance_norm(epsilon = var_516, x = input_431)[name = tensor<string, []>("op_4479")];
            tensor<fp32, [1, 32, 307200]> var_4480 = mul(x = var_4478, y = var_4479)[name = tensor<string, []>("op_4480")];
            tensor<fp32, [1, 32, 307200]> xt_255 = add(x = var_4480, y = var_4475_1)[name = tensor<string, []>("xt_255")];
            tensor<fp32, [1, 32, 1]> var_4482 = const()[name = tensor<string, []>("op_4482"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219180288)))];
            tensor<fp32, [1, 32, 307200]> var_4485 = mul(x = generator_resblocks_10_alpha2_0, y = xt_255)[name = tensor<string, []>("op_4485")];
            tensor<fp32, [1, 32, 307200]> var_4486 = sin(x = var_4485)[name = tensor<string, []>("op_4486")];
            tensor<fp32, []> var_532_promoted_89 = const()[name = tensor<string, []>("op_532_promoted_89"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 32, 307200]> var_4487 = pow(x = var_4486, y = var_532_promoted_89)[name = tensor<string, []>("op_4487")];
            tensor<fp32, [1, 32, 307200]> var_4488 = mul(x = var_4482, y = var_4487)[name = tensor<string, []>("op_4488")];
            tensor<fp32, [1, 32, 307200]> input_433 = add(x = xt_255, y = var_4488)[name = tensor<string, []>("input_433")];
            tensor<fp32, [32, 32, 7]> weight_215 = const()[name = tensor<string, []>("weight_215"), val = tensor<fp32, [32, 32, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219180480)))];
            tensor<string, []> xt_257_pad_type_0 = const()[name = tensor<string, []>("xt_257_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_257_pad_0 = const()[name = tensor<string, []>("xt_257_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> xt_257_strides_0 = const()[name = tensor<string, []>("xt_257_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_257_dilations_0 = const()[name = tensor<string, []>("xt_257_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_257_groups_0 = const()[name = tensor<string, []>("xt_257_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 307200]> xt_257 = conv(bias = generator_resblocks_10_convs2_0_bias, dilations = xt_257_dilations_0, groups = xt_257_groups_0, pad = xt_257_pad_0, pad_type = xt_257_pad_type_0, strides = xt_257_strides_0, weight = weight_215, x = input_433)[name = tensor<string, []>("xt_257")];
            tensor<fp32, [1, 32, 307200]> input_435 = add(x = xt_257, y = input_405)[name = tensor<string, []>("input_435")];
            tensor<fp32, [1, 64]> h_385 = linear(bias = generator_resblocks_10_adain1_1_fc_bias, weight = generator_resblocks_10_adain1_1_fc_weight, x = s)[name = tensor<string, []>("linear_97")];
            tensor<int32, [3]> var_4504 = const()[name = tensor<string, []>("op_4504"), val = tensor<int32, [3]>([1, 64, 1])];
            tensor<fp32, [1, 64, 1]> h_387 = reshape(shape = var_4504, x = h_385)[name = tensor<string, []>("h_387")];
            tensor<int32, [2]> var_4506_split_sizes_0 = const()[name = tensor<string, []>("op_4506_split_sizes_0"), val = tensor<int32, [2]>([32, 32])];
            tensor<int32, []> var_4506_axis_0 = const()[name = tensor<string, []>("op_4506_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 1]> var_4506_0, tensor<fp32, [1, 32, 1]> var_4506_1 = split(axis = var_4506_axis_0, split_sizes = var_4506_split_sizes_0, x = h_387)[name = tensor<string, []>("op_4506")];
            tensor<fp32, []> var_4508_promoted = const()[name = tensor<string, []>("op_4508_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 32, 1]> var_4509 = add(x = var_4506_0, y = var_4508_promoted)[name = tensor<string, []>("op_4509")];
            tensor<fp32, [1, 32, 307200]> var_4510 = instance_norm(epsilon = var_516, x = input_435)[name = tensor<string, []>("op_4510")];
            tensor<fp32, [1, 32, 307200]> var_4511 = mul(x = var_4509, y = var_4510)[name = tensor<string, []>("op_4511")];
            tensor<fp32, [1, 32, 307200]> xt_259 = add(x = var_4511, y = var_4506_1)[name = tensor<string, []>("xt_259")];
            tensor<fp32, [1, 32, 1]> var_4513 = const()[name = tensor<string, []>("op_4513"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219209216)))];
            tensor<fp32, [1, 32, 307200]> var_4516 = mul(x = generator_resblocks_10_alpha1_1, y = xt_259)[name = tensor<string, []>("op_4516")];
            tensor<fp32, [1, 32, 307200]> var_4517 = sin(x = var_4516)[name = tensor<string, []>("op_4517")];
            tensor<fp32, []> var_532_promoted_90 = const()[name = tensor<string, []>("op_532_promoted_90"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 32, 307200]> var_4518 = pow(x = var_4517, y = var_532_promoted_90)[name = tensor<string, []>("op_4518")];
            tensor<fp32, [1, 32, 307200]> var_4519 = mul(x = var_4513, y = var_4518)[name = tensor<string, []>("op_4519")];
            tensor<fp32, [1, 32, 307200]> input_437 = add(x = xt_259, y = var_4519)[name = tensor<string, []>("input_437")];
            tensor<fp32, [32, 32, 7]> weight_217 = const()[name = tensor<string, []>("weight_217"), val = tensor<fp32, [32, 32, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219209408)))];
            tensor<string, []> input_439_pad_type_0 = const()[name = tensor<string, []>("input_439_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_439_pad_0 = const()[name = tensor<string, []>("input_439_pad_0"), val = tensor<int32, [2]>([9, 9])];
            tensor<int32, [1]> input_439_dilations_0 = const()[name = tensor<string, []>("input_439_dilations_0"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> input_439_strides_0 = const()[name = tensor<string, []>("input_439_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_439_groups_0 = const()[name = tensor<string, []>("input_439_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 307200]> input_439 = conv(bias = generator_resblocks_10_convs1_1_bias, dilations = input_439_dilations_0, groups = input_439_groups_0, pad = input_439_pad_0, pad_type = input_439_pad_type_0, strides = input_439_strides_0, weight = weight_217, x = input_437)[name = tensor<string, []>("input_439")];
            tensor<fp32, [1, 64]> h_389 = linear(bias = generator_resblocks_10_adain2_1_fc_bias, weight = generator_resblocks_10_adain2_1_fc_weight, x = s)[name = tensor<string, []>("linear_98")];
            tensor<int32, [3]> var_4534 = const()[name = tensor<string, []>("op_4534"), val = tensor<int32, [3]>([1, 64, 1])];
            tensor<fp32, [1, 64, 1]> h_391 = reshape(shape = var_4534, x = h_389)[name = tensor<string, []>("h_391")];
            tensor<int32, [2]> var_4536_split_sizes_0 = const()[name = tensor<string, []>("op_4536_split_sizes_0"), val = tensor<int32, [2]>([32, 32])];
            tensor<int32, []> var_4536_axis_0 = const()[name = tensor<string, []>("op_4536_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 1]> var_4536_0, tensor<fp32, [1, 32, 1]> var_4536_1 = split(axis = var_4536_axis_0, split_sizes = var_4536_split_sizes_0, x = h_391)[name = tensor<string, []>("op_4536")];
            tensor<fp32, []> var_4538_promoted = const()[name = tensor<string, []>("op_4538_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 32, 1]> var_4539 = add(x = var_4536_0, y = var_4538_promoted)[name = tensor<string, []>("op_4539")];
            tensor<fp32, [1, 32, 307200]> var_4540 = instance_norm(epsilon = var_516, x = input_439)[name = tensor<string, []>("op_4540")];
            tensor<fp32, [1, 32, 307200]> var_4541 = mul(x = var_4539, y = var_4540)[name = tensor<string, []>("op_4541")];
            tensor<fp32, [1, 32, 307200]> xt_261 = add(x = var_4541, y = var_4536_1)[name = tensor<string, []>("xt_261")];
            tensor<fp32, [1, 32, 1]> var_4543 = const()[name = tensor<string, []>("op_4543"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219238144)))];
            tensor<fp32, [1, 32, 307200]> var_4546 = mul(x = generator_resblocks_10_alpha2_1, y = xt_261)[name = tensor<string, []>("op_4546")];
            tensor<fp32, [1, 32, 307200]> var_4547 = sin(x = var_4546)[name = tensor<string, []>("op_4547")];
            tensor<fp32, []> var_532_promoted_91 = const()[name = tensor<string, []>("op_532_promoted_91"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 32, 307200]> var_4548 = pow(x = var_4547, y = var_532_promoted_91)[name = tensor<string, []>("op_4548")];
            tensor<fp32, [1, 32, 307200]> var_4549 = mul(x = var_4543, y = var_4548)[name = tensor<string, []>("op_4549")];
            tensor<fp32, [1, 32, 307200]> input_441 = add(x = xt_261, y = var_4549)[name = tensor<string, []>("input_441")];
            tensor<fp32, [32, 32, 7]> weight_219 = const()[name = tensor<string, []>("weight_219"), val = tensor<fp32, [32, 32, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219238336)))];
            tensor<string, []> xt_263_pad_type_0 = const()[name = tensor<string, []>("xt_263_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_263_pad_0 = const()[name = tensor<string, []>("xt_263_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> xt_263_strides_0 = const()[name = tensor<string, []>("xt_263_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_263_dilations_0 = const()[name = tensor<string, []>("xt_263_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_263_groups_0 = const()[name = tensor<string, []>("xt_263_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 307200]> xt_263 = conv(bias = generator_resblocks_10_convs2_1_bias, dilations = xt_263_dilations_0, groups = xt_263_groups_0, pad = xt_263_pad_0, pad_type = xt_263_pad_type_0, strides = xt_263_strides_0, weight = weight_219, x = input_441)[name = tensor<string, []>("xt_263")];
            tensor<fp32, [1, 32, 307200]> input_443 = add(x = xt_263, y = input_435)[name = tensor<string, []>("input_443")];
            tensor<fp32, [1, 64]> h_393 = linear(bias = generator_resblocks_10_adain1_2_fc_bias, weight = generator_resblocks_10_adain1_2_fc_weight, x = s)[name = tensor<string, []>("linear_99")];
            tensor<int32, [3]> var_4565 = const()[name = tensor<string, []>("op_4565"), val = tensor<int32, [3]>([1, 64, 1])];
            tensor<fp32, [1, 64, 1]> h_395 = reshape(shape = var_4565, x = h_393)[name = tensor<string, []>("h_395")];
            tensor<int32, [2]> var_4567_split_sizes_0 = const()[name = tensor<string, []>("op_4567_split_sizes_0"), val = tensor<int32, [2]>([32, 32])];
            tensor<int32, []> var_4567_axis_0 = const()[name = tensor<string, []>("op_4567_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 1]> var_4567_0, tensor<fp32, [1, 32, 1]> var_4567_1 = split(axis = var_4567_axis_0, split_sizes = var_4567_split_sizes_0, x = h_395)[name = tensor<string, []>("op_4567")];
            tensor<fp32, []> var_4569_promoted = const()[name = tensor<string, []>("op_4569_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 32, 1]> var_4570 = add(x = var_4567_0, y = var_4569_promoted)[name = tensor<string, []>("op_4570")];
            tensor<fp32, [1, 32, 307200]> var_4571 = instance_norm(epsilon = var_516, x = input_443)[name = tensor<string, []>("op_4571")];
            tensor<fp32, [1, 32, 307200]> var_4572 = mul(x = var_4570, y = var_4571)[name = tensor<string, []>("op_4572")];
            tensor<fp32, [1, 32, 307200]> xt_265 = add(x = var_4572, y = var_4567_1)[name = tensor<string, []>("xt_265")];
            tensor<fp32, [1, 32, 1]> var_4574 = const()[name = tensor<string, []>("op_4574"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219267072)))];
            tensor<fp32, [1, 32, 307200]> var_4577 = mul(x = generator_resblocks_10_alpha1_2, y = xt_265)[name = tensor<string, []>("op_4577")];
            tensor<fp32, [1, 32, 307200]> var_4578 = sin(x = var_4577)[name = tensor<string, []>("op_4578")];
            tensor<fp32, []> var_532_promoted_92 = const()[name = tensor<string, []>("op_532_promoted_92"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 32, 307200]> var_4579 = pow(x = var_4578, y = var_532_promoted_92)[name = tensor<string, []>("op_4579")];
            tensor<fp32, [1, 32, 307200]> var_4580 = mul(x = var_4574, y = var_4579)[name = tensor<string, []>("op_4580")];
            tensor<fp32, [1, 32, 307200]> input_445 = add(x = xt_265, y = var_4580)[name = tensor<string, []>("input_445")];
            tensor<fp32, [32, 32, 7]> weight_221 = const()[name = tensor<string, []>("weight_221"), val = tensor<fp32, [32, 32, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219267264)))];
            tensor<string, []> input_447_pad_type_0 = const()[name = tensor<string, []>("input_447_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_447_pad_0 = const()[name = tensor<string, []>("input_447_pad_0"), val = tensor<int32, [2]>([15, 15])];
            tensor<int32, [1]> input_447_dilations_0 = const()[name = tensor<string, []>("input_447_dilations_0"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> input_447_strides_0 = const()[name = tensor<string, []>("input_447_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_447_groups_0 = const()[name = tensor<string, []>("input_447_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 307200]> input_447 = conv(bias = generator_resblocks_10_convs1_2_bias, dilations = input_447_dilations_0, groups = input_447_groups_0, pad = input_447_pad_0, pad_type = input_447_pad_type_0, strides = input_447_strides_0, weight = weight_221, x = input_445)[name = tensor<string, []>("input_447")];
            tensor<fp32, [1, 64]> h_397 = linear(bias = generator_resblocks_10_adain2_2_fc_bias, weight = generator_resblocks_10_adain2_2_fc_weight, x = s)[name = tensor<string, []>("linear_100")];
            tensor<int32, [3]> var_4595 = const()[name = tensor<string, []>("op_4595"), val = tensor<int32, [3]>([1, 64, 1])];
            tensor<fp32, [1, 64, 1]> h_399 = reshape(shape = var_4595, x = h_397)[name = tensor<string, []>("h_399")];
            tensor<int32, [2]> var_4597_split_sizes_0 = const()[name = tensor<string, []>("op_4597_split_sizes_0"), val = tensor<int32, [2]>([32, 32])];
            tensor<int32, []> var_4597_axis_0 = const()[name = tensor<string, []>("op_4597_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 1]> var_4597_0, tensor<fp32, [1, 32, 1]> var_4597_1 = split(axis = var_4597_axis_0, split_sizes = var_4597_split_sizes_0, x = h_399)[name = tensor<string, []>("op_4597")];
            tensor<fp32, []> var_4599_promoted = const()[name = tensor<string, []>("op_4599_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 32, 1]> var_4600 = add(x = var_4597_0, y = var_4599_promoted)[name = tensor<string, []>("op_4600")];
            tensor<fp32, [1, 32, 307200]> var_4601 = instance_norm(epsilon = var_516, x = input_447)[name = tensor<string, []>("op_4601")];
            tensor<fp32, [1, 32, 307200]> var_4602 = mul(x = var_4600, y = var_4601)[name = tensor<string, []>("op_4602")];
            tensor<fp32, [1, 32, 307200]> xt_267 = add(x = var_4602, y = var_4597_1)[name = tensor<string, []>("xt_267")];
            tensor<fp32, [1, 32, 1]> var_4604 = const()[name = tensor<string, []>("op_4604"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219296000)))];
            tensor<fp32, [1, 32, 307200]> var_4607 = mul(x = generator_resblocks_10_alpha2_2, y = xt_267)[name = tensor<string, []>("op_4607")];
            tensor<fp32, [1, 32, 307200]> var_4608 = sin(x = var_4607)[name = tensor<string, []>("op_4608")];
            tensor<fp32, []> var_532_promoted_93 = const()[name = tensor<string, []>("op_532_promoted_93"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 32, 307200]> var_4609 = pow(x = var_4608, y = var_532_promoted_93)[name = tensor<string, []>("op_4609")];
            tensor<fp32, [1, 32, 307200]> var_4610 = mul(x = var_4604, y = var_4609)[name = tensor<string, []>("op_4610")];
            tensor<fp32, [1, 32, 307200]> input_449 = add(x = xt_267, y = var_4610)[name = tensor<string, []>("input_449")];
            tensor<fp32, [32, 32, 7]> weight_223 = const()[name = tensor<string, []>("weight_223"), val = tensor<fp32, [32, 32, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219296192)))];
            tensor<string, []> xt_269_pad_type_0 = const()[name = tensor<string, []>("xt_269_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_269_pad_0 = const()[name = tensor<string, []>("xt_269_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> xt_269_strides_0 = const()[name = tensor<string, []>("xt_269_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_269_dilations_0 = const()[name = tensor<string, []>("xt_269_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_269_groups_0 = const()[name = tensor<string, []>("xt_269_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 307200]> xt_269 = conv(bias = generator_resblocks_10_convs2_2_bias, dilations = xt_269_dilations_0, groups = xt_269_groups_0, pad = xt_269_pad_0, pad_type = xt_269_pad_type_0, strides = xt_269_strides_0, weight = weight_223, x = input_449)[name = tensor<string, []>("xt_269")];
            tensor<fp32, [1, 32, 307200]> var_4619 = add(x = xt_269, y = input_443)[name = tensor<string, []>("op_4619")];
            tensor<fp32, [1, 32, 307200]> xs_21 = add(x = xs_19, y = var_4619)[name = tensor<string, []>("xs_21")];
            tensor<fp32, [1, 64]> h_401 = linear(bias = generator_resblocks_11_adain1_0_fc_bias, weight = generator_resblocks_11_adain1_0_fc_weight, x = s)[name = tensor<string, []>("linear_101")];
            tensor<int32, [3]> var_4699 = const()[name = tensor<string, []>("op_4699"), val = tensor<int32, [3]>([1, 64, 1])];
            tensor<fp32, [1, 64, 1]> h_403 = reshape(shape = var_4699, x = h_401)[name = tensor<string, []>("h_403")];
            tensor<int32, [2]> var_4701_split_sizes_0 = const()[name = tensor<string, []>("op_4701_split_sizes_0"), val = tensor<int32, [2]>([32, 32])];
            tensor<int32, []> var_4701_axis_0 = const()[name = tensor<string, []>("op_4701_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 1]> var_4701_0, tensor<fp32, [1, 32, 1]> var_4701_1 = split(axis = var_4701_axis_0, split_sizes = var_4701_split_sizes_0, x = h_403)[name = tensor<string, []>("op_4701")];
            tensor<fp32, []> var_4703_promoted = const()[name = tensor<string, []>("op_4703_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 32, 1]> var_4704 = add(x = var_4701_0, y = var_4703_promoted)[name = tensor<string, []>("op_4704")];
            tensor<fp32, [1, 32, 307200]> var_4706 = mul(x = var_4704, y = var_4194)[name = tensor<string, []>("op_4706")];
            tensor<fp32, [1, 32, 307200]> xt_271 = add(x = var_4706, y = var_4701_1)[name = tensor<string, []>("xt_271")];
            tensor<fp32, [1, 32, 1]> var_4708 = const()[name = tensor<string, []>("op_4708"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219324928)))];
            tensor<fp32, [1, 32, 307200]> var_4711 = mul(x = generator_resblocks_11_alpha1_0, y = xt_271)[name = tensor<string, []>("op_4711")];
            tensor<fp32, [1, 32, 307200]> var_4712 = sin(x = var_4711)[name = tensor<string, []>("op_4712")];
            tensor<fp32, []> var_532_promoted_94 = const()[name = tensor<string, []>("op_532_promoted_94"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 32, 307200]> var_4713 = pow(x = var_4712, y = var_532_promoted_94)[name = tensor<string, []>("op_4713")];
            tensor<fp32, [1, 32, 307200]> var_4714 = mul(x = var_4708, y = var_4713)[name = tensor<string, []>("op_4714")];
            tensor<fp32, [1, 32, 307200]> input_451 = add(x = xt_271, y = var_4714)[name = tensor<string, []>("input_451")];
            tensor<fp32, [32, 32, 11]> weight_225 = const()[name = tensor<string, []>("weight_225"), val = tensor<fp32, [32, 32, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219325120)))];
            tensor<string, []> input_453_pad_type_0 = const()[name = tensor<string, []>("input_453_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_453_pad_0 = const()[name = tensor<string, []>("input_453_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> input_453_strides_0 = const()[name = tensor<string, []>("input_453_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> input_453_dilations_0 = const()[name = tensor<string, []>("input_453_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_453_groups_0 = const()[name = tensor<string, []>("input_453_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 307200]> input_453 = conv(bias = generator_resblocks_11_convs1_0_bias, dilations = input_453_dilations_0, groups = input_453_groups_0, pad = input_453_pad_0, pad_type = input_453_pad_type_0, strides = input_453_strides_0, weight = weight_225, x = input_451)[name = tensor<string, []>("input_453")];
            tensor<fp32, [1, 64]> h_405 = linear(bias = generator_resblocks_11_adain2_0_fc_bias, weight = generator_resblocks_11_adain2_0_fc_weight, x = s)[name = tensor<string, []>("linear_102")];
            tensor<int32, [3]> var_4729 = const()[name = tensor<string, []>("op_4729"), val = tensor<int32, [3]>([1, 64, 1])];
            tensor<fp32, [1, 64, 1]> h_407 = reshape(shape = var_4729, x = h_405)[name = tensor<string, []>("h_407")];
            tensor<int32, [2]> var_4731_split_sizes_0 = const()[name = tensor<string, []>("op_4731_split_sizes_0"), val = tensor<int32, [2]>([32, 32])];
            tensor<int32, []> var_4731_axis_0 = const()[name = tensor<string, []>("op_4731_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 1]> var_4731_0, tensor<fp32, [1, 32, 1]> var_4731_1 = split(axis = var_4731_axis_0, split_sizes = var_4731_split_sizes_0, x = h_407)[name = tensor<string, []>("op_4731")];
            tensor<fp32, []> var_4733_promoted = const()[name = tensor<string, []>("op_4733_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 32, 1]> var_4734 = add(x = var_4731_0, y = var_4733_promoted)[name = tensor<string, []>("op_4734")];
            tensor<fp32, [1, 32, 307200]> var_4735 = instance_norm(epsilon = var_516, x = input_453)[name = tensor<string, []>("op_4735")];
            tensor<fp32, [1, 32, 307200]> var_4736 = mul(x = var_4734, y = var_4735)[name = tensor<string, []>("op_4736")];
            tensor<fp32, [1, 32, 307200]> xt_273 = add(x = var_4736, y = var_4731_1)[name = tensor<string, []>("xt_273")];
            tensor<fp32, [1, 32, 1]> var_4738 = const()[name = tensor<string, []>("op_4738"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219370240)))];
            tensor<fp32, [1, 32, 307200]> var_4741 = mul(x = generator_resblocks_11_alpha2_0, y = xt_273)[name = tensor<string, []>("op_4741")];
            tensor<fp32, [1, 32, 307200]> var_4742 = sin(x = var_4741)[name = tensor<string, []>("op_4742")];
            tensor<fp32, []> var_532_promoted_95 = const()[name = tensor<string, []>("op_532_promoted_95"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 32, 307200]> var_4743 = pow(x = var_4742, y = var_532_promoted_95)[name = tensor<string, []>("op_4743")];
            tensor<fp32, [1, 32, 307200]> var_4744 = mul(x = var_4738, y = var_4743)[name = tensor<string, []>("op_4744")];
            tensor<fp32, [1, 32, 307200]> input_455 = add(x = xt_273, y = var_4744)[name = tensor<string, []>("input_455")];
            tensor<fp32, [32, 32, 11]> weight_227 = const()[name = tensor<string, []>("weight_227"), val = tensor<fp32, [32, 32, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219370432)))];
            tensor<string, []> xt_275_pad_type_0 = const()[name = tensor<string, []>("xt_275_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_275_pad_0 = const()[name = tensor<string, []>("xt_275_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> xt_275_strides_0 = const()[name = tensor<string, []>("xt_275_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_275_dilations_0 = const()[name = tensor<string, []>("xt_275_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_275_groups_0 = const()[name = tensor<string, []>("xt_275_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 307200]> xt_275 = conv(bias = generator_resblocks_11_convs2_0_bias, dilations = xt_275_dilations_0, groups = xt_275_groups_0, pad = xt_275_pad_0, pad_type = xt_275_pad_type_0, strides = xt_275_strides_0, weight = weight_227, x = input_455)[name = tensor<string, []>("xt_275")];
            tensor<fp32, [1, 32, 307200]> input_457 = add(x = xt_275, y = input_405)[name = tensor<string, []>("input_457")];
            tensor<fp32, [1, 64]> h_409 = linear(bias = generator_resblocks_11_adain1_1_fc_bias, weight = generator_resblocks_11_adain1_1_fc_weight, x = s)[name = tensor<string, []>("linear_103")];
            tensor<int32, [3]> var_4760 = const()[name = tensor<string, []>("op_4760"), val = tensor<int32, [3]>([1, 64, 1])];
            tensor<fp32, [1, 64, 1]> h_411 = reshape(shape = var_4760, x = h_409)[name = tensor<string, []>("h_411")];
            tensor<int32, [2]> var_4762_split_sizes_0 = const()[name = tensor<string, []>("op_4762_split_sizes_0"), val = tensor<int32, [2]>([32, 32])];
            tensor<int32, []> var_4762_axis_0 = const()[name = tensor<string, []>("op_4762_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 1]> var_4762_0, tensor<fp32, [1, 32, 1]> var_4762_1 = split(axis = var_4762_axis_0, split_sizes = var_4762_split_sizes_0, x = h_411)[name = tensor<string, []>("op_4762")];
            tensor<fp32, []> var_4764_promoted = const()[name = tensor<string, []>("op_4764_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 32, 1]> var_4765 = add(x = var_4762_0, y = var_4764_promoted)[name = tensor<string, []>("op_4765")];
            tensor<fp32, [1, 32, 307200]> var_4766 = instance_norm(epsilon = var_516, x = input_457)[name = tensor<string, []>("op_4766")];
            tensor<fp32, [1, 32, 307200]> var_4767 = mul(x = var_4765, y = var_4766)[name = tensor<string, []>("op_4767")];
            tensor<fp32, [1, 32, 307200]> xt_277 = add(x = var_4767, y = var_4762_1)[name = tensor<string, []>("xt_277")];
            tensor<fp32, [1, 32, 1]> var_4769 = const()[name = tensor<string, []>("op_4769"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219415552)))];
            tensor<fp32, [1, 32, 307200]> var_4772 = mul(x = generator_resblocks_11_alpha1_1, y = xt_277)[name = tensor<string, []>("op_4772")];
            tensor<fp32, [1, 32, 307200]> var_4773 = sin(x = var_4772)[name = tensor<string, []>("op_4773")];
            tensor<fp32, []> var_532_promoted_96 = const()[name = tensor<string, []>("op_532_promoted_96"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 32, 307200]> var_4774 = pow(x = var_4773, y = var_532_promoted_96)[name = tensor<string, []>("op_4774")];
            tensor<fp32, [1, 32, 307200]> var_4775 = mul(x = var_4769, y = var_4774)[name = tensor<string, []>("op_4775")];
            tensor<fp32, [1, 32, 307200]> input_459 = add(x = xt_277, y = var_4775)[name = tensor<string, []>("input_459")];
            tensor<fp32, [32, 32, 11]> weight_229 = const()[name = tensor<string, []>("weight_229"), val = tensor<fp32, [32, 32, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219415744)))];
            tensor<string, []> input_461_pad_type_0 = const()[name = tensor<string, []>("input_461_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_461_pad_0 = const()[name = tensor<string, []>("input_461_pad_0"), val = tensor<int32, [2]>([15, 15])];
            tensor<int32, [1]> input_461_dilations_0 = const()[name = tensor<string, []>("input_461_dilations_0"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> input_461_strides_0 = const()[name = tensor<string, []>("input_461_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_461_groups_0 = const()[name = tensor<string, []>("input_461_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 307200]> input_461 = conv(bias = generator_resblocks_11_convs1_1_bias, dilations = input_461_dilations_0, groups = input_461_groups_0, pad = input_461_pad_0, pad_type = input_461_pad_type_0, strides = input_461_strides_0, weight = weight_229, x = input_459)[name = tensor<string, []>("input_461")];
            tensor<fp32, [1, 64]> h_413 = linear(bias = generator_resblocks_11_adain2_1_fc_bias, weight = generator_resblocks_11_adain2_1_fc_weight, x = s)[name = tensor<string, []>("linear_104")];
            tensor<int32, [3]> var_4790 = const()[name = tensor<string, []>("op_4790"), val = tensor<int32, [3]>([1, 64, 1])];
            tensor<fp32, [1, 64, 1]> h_415 = reshape(shape = var_4790, x = h_413)[name = tensor<string, []>("h_415")];
            tensor<int32, [2]> var_4792_split_sizes_0 = const()[name = tensor<string, []>("op_4792_split_sizes_0"), val = tensor<int32, [2]>([32, 32])];
            tensor<int32, []> var_4792_axis_0 = const()[name = tensor<string, []>("op_4792_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 1]> var_4792_0, tensor<fp32, [1, 32, 1]> var_4792_1 = split(axis = var_4792_axis_0, split_sizes = var_4792_split_sizes_0, x = h_415)[name = tensor<string, []>("op_4792")];
            tensor<fp32, []> var_4794_promoted = const()[name = tensor<string, []>("op_4794_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 32, 1]> var_4795 = add(x = var_4792_0, y = var_4794_promoted)[name = tensor<string, []>("op_4795")];
            tensor<fp32, [1, 32, 307200]> var_4796 = instance_norm(epsilon = var_516, x = input_461)[name = tensor<string, []>("op_4796")];
            tensor<fp32, [1, 32, 307200]> var_4797 = mul(x = var_4795, y = var_4796)[name = tensor<string, []>("op_4797")];
            tensor<fp32, [1, 32, 307200]> xt_279 = add(x = var_4797, y = var_4792_1)[name = tensor<string, []>("xt_279")];
            tensor<fp32, [1, 32, 1]> var_4799 = const()[name = tensor<string, []>("op_4799"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219460864)))];
            tensor<fp32, [1, 32, 307200]> var_4802 = mul(x = generator_resblocks_11_alpha2_1, y = xt_279)[name = tensor<string, []>("op_4802")];
            tensor<fp32, [1, 32, 307200]> var_4803 = sin(x = var_4802)[name = tensor<string, []>("op_4803")];
            tensor<fp32, []> var_532_promoted_97 = const()[name = tensor<string, []>("op_532_promoted_97"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 32, 307200]> var_4804 = pow(x = var_4803, y = var_532_promoted_97)[name = tensor<string, []>("op_4804")];
            tensor<fp32, [1, 32, 307200]> var_4805 = mul(x = var_4799, y = var_4804)[name = tensor<string, []>("op_4805")];
            tensor<fp32, [1, 32, 307200]> input_463 = add(x = xt_279, y = var_4805)[name = tensor<string, []>("input_463")];
            tensor<fp32, [32, 32, 11]> weight_231 = const()[name = tensor<string, []>("weight_231"), val = tensor<fp32, [32, 32, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219461056)))];
            tensor<string, []> xt_281_pad_type_0 = const()[name = tensor<string, []>("xt_281_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_281_pad_0 = const()[name = tensor<string, []>("xt_281_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> xt_281_strides_0 = const()[name = tensor<string, []>("xt_281_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_281_dilations_0 = const()[name = tensor<string, []>("xt_281_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_281_groups_0 = const()[name = tensor<string, []>("xt_281_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 307200]> xt_281 = conv(bias = generator_resblocks_11_convs2_1_bias, dilations = xt_281_dilations_0, groups = xt_281_groups_0, pad = xt_281_pad_0, pad_type = xt_281_pad_type_0, strides = xt_281_strides_0, weight = weight_231, x = input_463)[name = tensor<string, []>("xt_281")];
            tensor<fp32, [1, 32, 307200]> input_465 = add(x = xt_281, y = input_457)[name = tensor<string, []>("input_465")];
            tensor<fp32, [1, 64]> h_417 = linear(bias = generator_resblocks_11_adain1_2_fc_bias, weight = generator_resblocks_11_adain1_2_fc_weight, x = s)[name = tensor<string, []>("linear_105")];
            tensor<int32, [3]> var_4821 = const()[name = tensor<string, []>("op_4821"), val = tensor<int32, [3]>([1, 64, 1])];
            tensor<fp32, [1, 64, 1]> h_419 = reshape(shape = var_4821, x = h_417)[name = tensor<string, []>("h_419")];
            tensor<int32, [2]> var_4823_split_sizes_0 = const()[name = tensor<string, []>("op_4823_split_sizes_0"), val = tensor<int32, [2]>([32, 32])];
            tensor<int32, []> var_4823_axis_0 = const()[name = tensor<string, []>("op_4823_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 1]> var_4823_0, tensor<fp32, [1, 32, 1]> var_4823_1 = split(axis = var_4823_axis_0, split_sizes = var_4823_split_sizes_0, x = h_419)[name = tensor<string, []>("op_4823")];
            tensor<fp32, []> var_4825_promoted = const()[name = tensor<string, []>("op_4825_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 32, 1]> var_4826 = add(x = var_4823_0, y = var_4825_promoted)[name = tensor<string, []>("op_4826")];
            tensor<fp32, [1, 32, 307200]> var_4827 = instance_norm(epsilon = var_516, x = input_465)[name = tensor<string, []>("op_4827")];
            tensor<fp32, [1, 32, 307200]> var_4828 = mul(x = var_4826, y = var_4827)[name = tensor<string, []>("op_4828")];
            tensor<fp32, [1, 32, 307200]> xt_283 = add(x = var_4828, y = var_4823_1)[name = tensor<string, []>("xt_283")];
            tensor<fp32, [1, 32, 1]> var_4830 = const()[name = tensor<string, []>("op_4830"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219506176)))];
            tensor<fp32, [1, 32, 307200]> var_4833 = mul(x = generator_resblocks_11_alpha1_2, y = xt_283)[name = tensor<string, []>("op_4833")];
            tensor<fp32, [1, 32, 307200]> var_4834 = sin(x = var_4833)[name = tensor<string, []>("op_4834")];
            tensor<fp32, []> var_532_promoted_98 = const()[name = tensor<string, []>("op_532_promoted_98"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 32, 307200]> var_4835 = pow(x = var_4834, y = var_532_promoted_98)[name = tensor<string, []>("op_4835")];
            tensor<fp32, [1, 32, 307200]> var_4836 = mul(x = var_4830, y = var_4835)[name = tensor<string, []>("op_4836")];
            tensor<fp32, [1, 32, 307200]> input_467 = add(x = xt_283, y = var_4836)[name = tensor<string, []>("input_467")];
            tensor<fp32, [32, 32, 11]> weight_233 = const()[name = tensor<string, []>("weight_233"), val = tensor<fp32, [32, 32, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219506368)))];
            tensor<string, []> input_469_pad_type_0 = const()[name = tensor<string, []>("input_469_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> input_469_pad_0 = const()[name = tensor<string, []>("input_469_pad_0"), val = tensor<int32, [2]>([25, 25])];
            tensor<int32, [1]> input_469_dilations_0 = const()[name = tensor<string, []>("input_469_dilations_0"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> input_469_strides_0 = const()[name = tensor<string, []>("input_469_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> input_469_groups_0 = const()[name = tensor<string, []>("input_469_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 307200]> input_469 = conv(bias = generator_resblocks_11_convs1_2_bias, dilations = input_469_dilations_0, groups = input_469_groups_0, pad = input_469_pad_0, pad_type = input_469_pad_type_0, strides = input_469_strides_0, weight = weight_233, x = input_467)[name = tensor<string, []>("input_469")];
            tensor<fp32, [1, 64]> h_421 = linear(bias = generator_resblocks_11_adain2_2_fc_bias, weight = generator_resblocks_11_adain2_2_fc_weight, x = s)[name = tensor<string, []>("linear_106")];
            tensor<int32, [3]> var_4851 = const()[name = tensor<string, []>("op_4851"), val = tensor<int32, [3]>([1, 64, 1])];
            tensor<fp32, [1, 64, 1]> h = reshape(shape = var_4851, x = h_421)[name = tensor<string, []>("h")];
            tensor<int32, [2]> var_4853_split_sizes_0 = const()[name = tensor<string, []>("op_4853_split_sizes_0"), val = tensor<int32, [2]>([32, 32])];
            tensor<int32, []> var_4853_axis_0 = const()[name = tensor<string, []>("op_4853_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 1]> var_4853_0, tensor<fp32, [1, 32, 1]> var_4853_1 = split(axis = var_4853_axis_0, split_sizes = var_4853_split_sizes_0, x = h)[name = tensor<string, []>("op_4853")];
            tensor<fp32, []> var_4855_promoted = const()[name = tensor<string, []>("op_4855_promoted"), val = tensor<fp32, []>(0x1p+0)];
            tensor<fp32, [1, 32, 1]> var_4856 = add(x = var_4853_0, y = var_4855_promoted)[name = tensor<string, []>("op_4856")];
            tensor<fp32, [1, 32, 307200]> var_4857 = instance_norm(epsilon = var_516, x = input_469)[name = tensor<string, []>("op_4857")];
            tensor<fp32, [1, 32, 307200]> var_4858 = mul(x = var_4856, y = var_4857)[name = tensor<string, []>("op_4858")];
            tensor<fp32, [1, 32, 307200]> xt_285 = add(x = var_4858, y = var_4853_1)[name = tensor<string, []>("xt_285")];
            tensor<fp32, [1, 32, 1]> var_4860 = const()[name = tensor<string, []>("op_4860"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219551488)))];
            tensor<fp32, [1, 32, 307200]> var_4863 = mul(x = generator_resblocks_11_alpha2_2, y = xt_285)[name = tensor<string, []>("op_4863")];
            tensor<fp32, [1, 32, 307200]> var_4864 = sin(x = var_4863)[name = tensor<string, []>("op_4864")];
            tensor<fp32, []> var_532_promoted_99 = const()[name = tensor<string, []>("op_532_promoted_99"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 32, 307200]> var_4865 = pow(x = var_4864, y = var_532_promoted_99)[name = tensor<string, []>("op_4865")];
            tensor<fp32, [1, 32, 307200]> var_4866 = mul(x = var_4860, y = var_4865)[name = tensor<string, []>("op_4866")];
            tensor<fp32, [1, 32, 307200]> input_471 = add(x = xt_285, y = var_4866)[name = tensor<string, []>("input_471")];
            tensor<fp32, [32, 32, 11]> weight_235 = const()[name = tensor<string, []>("weight_235"), val = tensor<fp32, [32, 32, 11]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219551680)))];
            tensor<string, []> xt_pad_type_0 = const()[name = tensor<string, []>("xt_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> xt_pad_0 = const()[name = tensor<string, []>("xt_pad_0"), val = tensor<int32, [2]>([5, 5])];
            tensor<int32, [1]> xt_strides_0 = const()[name = tensor<string, []>("xt_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> xt_dilations_0 = const()[name = tensor<string, []>("xt_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> xt_groups_0 = const()[name = tensor<string, []>("xt_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 32, 307200]> xt = conv(bias = generator_resblocks_11_convs2_2_bias, dilations = xt_dilations_0, groups = xt_groups_0, pad = xt_pad_0, pad_type = xt_pad_type_0, strides = xt_strides_0, weight = weight_235, x = input_471)[name = tensor<string, []>("xt")];
            tensor<fp32, [1, 32, 307200]> var_4875 = add(x = xt, y = input_465)[name = tensor<string, []>("op_4875")];
            tensor<fp32, [1, 32, 307200]> xs = add(x = xs_21, y = var_4875)[name = tensor<string, []>("xs")];
            tensor<fp32, []> _inversed_x_25_y_0 = const()[name = tensor<string, []>("_inversed_x_25_y_0"), val = tensor<fp32, []>(0x1.555556p-2)];
            tensor<fp32, [1, 32, 307200]> _inversed_x_25 = mul(x = xs, y = _inversed_x_25_y_0)[name = tensor<string, []>("_inversed_x_25")];
            tensor<fp32, [1, 32, 1]> var_4879 = const()[name = tensor<string, []>("op_4879"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219596800)))];
            tensor<fp32, [1, 32, 307200]> var_4882 = mul(x = generator_alphas_4, y = _inversed_x_25)[name = tensor<string, []>("op_4882")];
            tensor<fp32, [1, 32, 307200]> var_4883 = sin(x = var_4882)[name = tensor<string, []>("op_4883")];
            tensor<fp32, []> var_532_promoted_100 = const()[name = tensor<string, []>("op_532_promoted_100"), val = tensor<fp32, []>(0x1p+1)];
            tensor<fp32, [1, 32, 307200]> var_4884 = pow(x = var_4883, y = var_532_promoted_100)[name = tensor<string, []>("op_4884")];
            tensor<fp32, [1, 32, 307200]> var_4885 = mul(x = var_4879, y = var_4884)[name = tensor<string, []>("op_4885")];
            tensor<fp32, [1, 32, 307200]> input = add(x = _inversed_x_25, y = var_4885)[name = tensor<string, []>("input")];
            tensor<fp32, [1, 32, 7]> weight_237 = const()[name = tensor<string, []>("weight_237"), val = tensor<fp32, [1, 32, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219596992)))];
            tensor<string, []> x_pad_type_0 = const()[name = tensor<string, []>("x_pad_type_0"), val = tensor<string, []>("custom")];
            tensor<int32, [2]> x_pad_0 = const()[name = tensor<string, []>("x_pad_0"), val = tensor<int32, [2]>([3, 3])];
            tensor<int32, [1]> x_strides_0 = const()[name = tensor<string, []>("x_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> x_dilations_0 = const()[name = tensor<string, []>("x_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> x_groups_0 = const()[name = tensor<string, []>("x_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp32, [1, 1, 307200]> x = conv(bias = generator_conv_post_bias, dilations = x_dilations_0, groups = x_groups_0, pad = x_pad_0, pad_type = x_pad_type_0, strides = x_strides_0, weight = weight_237, x = input)[name = tensor<string, []>("x")];
            tensor<fp32, [1, 1, 307200]> waveform = tanh(x = x)[name = tensor<string, []>("op_4894")];
        } -> (waveform);
}