program(1.0)
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.7.0"}, {"coremltools-source-dialect", "TorchExport::ATEN"}, {"coremltools-version", "9.0"}})]
{
    func main<ios17>(tensor<fp32, [1, 1001, 60]> features) {
            tensor<int32, [3]> transpose_perm_0 = const()[name = tensor<string, []>("transpose_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> features_to_fp16_dtype_0 = const()[name = tensor<string, []>("features_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
            tensor<int32, [6]> pad_pad_0 = const()[name = tensor<string, []>("pad_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 2])];
            tensor<string, []> pad_mode_0 = const()[name = tensor<string, []>("pad_mode_0"), val = tensor<string, []>("reflect")];
            tensor<fp16, []> const_3_to_fp16 = const()[name = tensor<string, []>("const_3_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 1001, 60]> features_to_fp16 = cast(dtype = features_to_fp16_dtype_0, x = features)[name = tensor<string, []>("cast_5")];
            tensor<fp16, [1, 60, 1001]> transpose_cast_fp16 = transpose(perm = transpose_perm_0, x = features_to_fp16)[name = tensor<string, []>("transpose_3")];
            tensor<fp16, [1, 60, 1005]> pad_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = pad_mode_0, pad = pad_pad_0, x = transpose_cast_fp16)[name = tensor<string, []>("pad_cast_fp16")];
            tensor<string, []> conv1d_pad_type_0 = const()[name = tensor<string, []>("conv1d_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_strides_0 = const()[name = tensor<string, []>("conv1d_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_pad_0 = const()[name = tensor<string, []>("conv1d_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_dilations_0 = const()[name = tensor<string, []>("conv1d_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_groups_0 = const()[name = tensor<string, []>("conv1d_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 60, 5]> p_embedding_model_blocks_0_conv_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_0_conv_conv_weight_to_fp16"), val = tensor<fp16, [1024, 60, 5]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
            tensor<fp16, [1024]> p_embedding_model_blocks_0_conv_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_0_conv_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(614528)))];
            tensor<fp16, [1, 1024, 1001]> conv1d_cast_fp16 = conv(bias = p_embedding_model_blocks_0_conv_conv_bias_to_fp16, dilations = conv1d_dilations_0, groups = conv1d_groups_0, pad = conv1d_pad_0, pad_type = conv1d_pad_type_0, strides = conv1d_strides_0, weight = p_embedding_model_blocks_0_conv_conv_weight_to_fp16, x = pad_cast_fp16)[name = tensor<string, []>("conv1d_cast_fp16")];
            tensor<fp16, [1, 1024, 1001]> relu_cast_fp16 = relu(x = conv1d_cast_fp16)[name = tensor<string, []>("relu_cast_fp16")];
            tensor<fp16, [1024]> b_embedding_model_blocks_0_norm_norm_running_mean_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_0_norm_norm_running_mean_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(616640)))];
            tensor<fp16, [1024]> b_embedding_model_blocks_0_norm_norm_running_var_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_0_norm_norm_running_var_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(618752)))];
            tensor<fp16, [1024]> p_embedding_model_blocks_0_norm_norm_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_0_norm_norm_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(620864)))];
            tensor<fp16, [1024]> p_embedding_model_blocks_0_norm_norm_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_0_norm_norm_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(622976)))];
            tensor<fp16, []> const_5_to_fp16 = const()[name = tensor<string, []>("const_5_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1024, 1001]> _native_batch_norm_legit_no_training_cast_fp16 = batch_norm(beta = p_embedding_model_blocks_0_norm_norm_bias_to_fp16, epsilon = const_5_to_fp16, gamma = p_embedding_model_blocks_0_norm_norm_weight_to_fp16, mean = b_embedding_model_blocks_0_norm_norm_running_mean_to_fp16, variance = b_embedding_model_blocks_0_norm_norm_running_var_to_fp16, x = relu_cast_fp16)[name = tensor<string, []>("_native_batch_norm_legit_no_training_cast_fp16")];
            tensor<string, []> conv1d_1_pad_type_0 = const()[name = tensor<string, []>("conv1d_1_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_1_strides_0 = const()[name = tensor<string, []>("conv1d_1_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_1_pad_0 = const()[name = tensor<string, []>("conv1d_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_1_dilations_0 = const()[name = tensor<string, []>("conv1d_1_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_1_groups_0 = const()[name = tensor<string, []>("conv1d_1_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 1024, 1]> p_embedding_model_blocks_1_tdnn1_conv_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_tdnn1_conv_conv_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(625088)))];
            tensor<fp16, [1024]> p_embedding_model_blocks_1_tdnn1_conv_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_tdnn1_conv_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2722304)))];
            tensor<fp16, [1, 1024, 1001]> conv1d_1_cast_fp16 = conv(bias = p_embedding_model_blocks_1_tdnn1_conv_conv_bias_to_fp16, dilations = conv1d_1_dilations_0, groups = conv1d_1_groups_0, pad = conv1d_1_pad_0, pad_type = conv1d_1_pad_type_0, strides = conv1d_1_strides_0, weight = p_embedding_model_blocks_1_tdnn1_conv_conv_weight_to_fp16, x = _native_batch_norm_legit_no_training_cast_fp16)[name = tensor<string, []>("conv1d_1_cast_fp16")];
            tensor<fp16, [1, 1024, 1001]> relu_1_cast_fp16 = relu(x = conv1d_1_cast_fp16)[name = tensor<string, []>("relu_1_cast_fp16")];
            tensor<fp16, [1024]> b_embedding_model_blocks_1_tdnn1_norm_norm_running_mean_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_1_tdnn1_norm_norm_running_mean_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2724416)))];
            tensor<fp16, [1024]> b_embedding_model_blocks_1_tdnn1_norm_norm_running_var_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_1_tdnn1_norm_norm_running_var_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2726528)))];
            tensor<fp16, [1024]> p_embedding_model_blocks_1_tdnn1_norm_norm_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_tdnn1_norm_norm_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2728640)))];
            tensor<fp16, [1024]> p_embedding_model_blocks_1_tdnn1_norm_norm_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_tdnn1_norm_norm_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2730752)))];
            tensor<fp16, []> const_10_to_fp16 = const()[name = tensor<string, []>("const_10_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1024, 1001]> _native_batch_norm_legit_no_training_1_cast_fp16 = batch_norm(beta = p_embedding_model_blocks_1_tdnn1_norm_norm_bias_to_fp16, epsilon = const_10_to_fp16, gamma = p_embedding_model_blocks_1_tdnn1_norm_norm_weight_to_fp16, mean = b_embedding_model_blocks_1_tdnn1_norm_norm_running_mean_to_fp16, variance = b_embedding_model_blocks_1_tdnn1_norm_norm_running_var_to_fp16, x = relu_1_cast_fp16)[name = tensor<string, []>("_native_batch_norm_legit_no_training_1_cast_fp16")];
            tensor<int32, [8]> tile_0 = const()[name = tensor<string, []>("tile_0"), val = tensor<int32, [8]>([128, 128, 128, 128, 128, 128, 128, 128])];
            tensor<int32, []> split_axis_0 = const()[name = tensor<string, []>("split_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 128, 1001]> split_cast_fp16_0, tensor<fp16, [1, 128, 1001]> split_cast_fp16_1, tensor<fp16, [1, 128, 1001]> split_cast_fp16_2, tensor<fp16, [1, 128, 1001]> split_cast_fp16_3, tensor<fp16, [1, 128, 1001]> split_cast_fp16_4, tensor<fp16, [1, 128, 1001]> split_cast_fp16_5, tensor<fp16, [1, 128, 1001]> split_cast_fp16_6, tensor<fp16, [1, 128, 1001]> split_cast_fp16_7 = split(axis = split_axis_0, split_sizes = tile_0, x = _native_batch_norm_legit_no_training_1_cast_fp16)[name = tensor<string, []>("split_cast_fp16")];
            tensor<int32, [6]> pad_2_pad_0 = const()[name = tensor<string, []>("pad_2_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 2])];
            tensor<string, []> pad_2_mode_0 = const()[name = tensor<string, []>("pad_2_mode_0"), val = tensor<string, []>("reflect")];
            tensor<fp16, []> const_25_to_fp16 = const()[name = tensor<string, []>("const_25_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 1005]> pad_2_cast_fp16 = pad(constant_val = const_25_to_fp16, mode = pad_2_mode_0, pad = pad_2_pad_0, x = split_cast_fp16_1)[name = tensor<string, []>("pad_2_cast_fp16")];
            tensor<string, []> conv1d_2_pad_type_0 = const()[name = tensor<string, []>("conv1d_2_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_2_dilations_0 = const()[name = tensor<string, []>("conv1d_2_dilations_0"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1]> conv1d_2_strides_0 = const()[name = tensor<string, []>("conv1d_2_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_2_pad_0 = const()[name = tensor<string, []>("conv1d_2_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, []> conv1d_2_groups_0 = const()[name = tensor<string, []>("conv1d_2_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [128, 128, 3]> p_embedding_model_blocks_1_res2net_block_blocks_0_conv_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_res2net_block_blocks_0_conv_conv_weight_to_fp16"), val = tensor<fp16, [128, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2732864)))];
            tensor<fp16, [128]> p_embedding_model_blocks_1_res2net_block_blocks_0_conv_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_res2net_block_blocks_0_conv_conv_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2831232)))];
            tensor<fp16, [1, 128, 1001]> conv1d_2_cast_fp16 = conv(bias = p_embedding_model_blocks_1_res2net_block_blocks_0_conv_conv_bias_to_fp16, dilations = conv1d_2_dilations_0, groups = conv1d_2_groups_0, pad = conv1d_2_pad_0, pad_type = conv1d_2_pad_type_0, strides = conv1d_2_strides_0, weight = p_embedding_model_blocks_1_res2net_block_blocks_0_conv_conv_weight_to_fp16, x = pad_2_cast_fp16)[name = tensor<string, []>("conv1d_2_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> relu_2_cast_fp16 = relu(x = conv1d_2_cast_fp16)[name = tensor<string, []>("relu_2_cast_fp16")];
            tensor<fp16, [128]> b_embedding_model_blocks_1_res2net_block_blocks_0_norm_norm_running_mean_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_1_res2net_block_blocks_0_norm_norm_running_mean_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2831552)))];
            tensor<fp16, [128]> b_embedding_model_blocks_1_res2net_block_blocks_0_norm_norm_running_var_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_1_res2net_block_blocks_0_norm_norm_running_var_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2831872)))];
            tensor<fp16, [128]> p_embedding_model_blocks_1_res2net_block_blocks_0_norm_norm_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_res2net_block_blocks_0_norm_norm_weight_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2832192)))];
            tensor<fp16, [128]> p_embedding_model_blocks_1_res2net_block_blocks_0_norm_norm_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_res2net_block_blocks_0_norm_norm_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2832512)))];
            tensor<fp16, []> const_30_to_fp16 = const()[name = tensor<string, []>("const_30_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 128, 1001]> _native_batch_norm_legit_no_training_2_cast_fp16 = batch_norm(beta = p_embedding_model_blocks_1_res2net_block_blocks_0_norm_norm_bias_to_fp16, epsilon = const_30_to_fp16, gamma = p_embedding_model_blocks_1_res2net_block_blocks_0_norm_norm_weight_to_fp16, mean = b_embedding_model_blocks_1_res2net_block_blocks_0_norm_norm_running_mean_to_fp16, variance = b_embedding_model_blocks_1_res2net_block_blocks_0_norm_norm_running_var_to_fp16, x = relu_2_cast_fp16)[name = tensor<string, []>("_native_batch_norm_legit_no_training_2_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> add_cast_fp16 = add(x = split_cast_fp16_2, y = _native_batch_norm_legit_no_training_2_cast_fp16)[name = tensor<string, []>("add_cast_fp16")];
            tensor<int32, [6]> pad_3_pad_0 = const()[name = tensor<string, []>("pad_3_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 2])];
            tensor<string, []> pad_3_mode_0 = const()[name = tensor<string, []>("pad_3_mode_0"), val = tensor<string, []>("reflect")];
            tensor<fp16, []> const_33_to_fp16 = const()[name = tensor<string, []>("const_33_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 1005]> pad_3_cast_fp16 = pad(constant_val = const_33_to_fp16, mode = pad_3_mode_0, pad = pad_3_pad_0, x = add_cast_fp16)[name = tensor<string, []>("pad_3_cast_fp16")];
            tensor<string, []> conv1d_3_pad_type_0 = const()[name = tensor<string, []>("conv1d_3_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_3_dilations_0 = const()[name = tensor<string, []>("conv1d_3_dilations_0"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1]> conv1d_3_strides_0 = const()[name = tensor<string, []>("conv1d_3_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_3_pad_0 = const()[name = tensor<string, []>("conv1d_3_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, []> conv1d_3_groups_0 = const()[name = tensor<string, []>("conv1d_3_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [128, 128, 3]> p_embedding_model_blocks_1_res2net_block_blocks_1_conv_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_res2net_block_blocks_1_conv_conv_weight_to_fp16"), val = tensor<fp16, [128, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2832832)))];
            tensor<fp16, [128]> p_embedding_model_blocks_1_res2net_block_blocks_1_conv_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_res2net_block_blocks_1_conv_conv_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2931200)))];
            tensor<fp16, [1, 128, 1001]> conv1d_3_cast_fp16 = conv(bias = p_embedding_model_blocks_1_res2net_block_blocks_1_conv_conv_bias_to_fp16, dilations = conv1d_3_dilations_0, groups = conv1d_3_groups_0, pad = conv1d_3_pad_0, pad_type = conv1d_3_pad_type_0, strides = conv1d_3_strides_0, weight = p_embedding_model_blocks_1_res2net_block_blocks_1_conv_conv_weight_to_fp16, x = pad_3_cast_fp16)[name = tensor<string, []>("conv1d_3_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> relu_3_cast_fp16 = relu(x = conv1d_3_cast_fp16)[name = tensor<string, []>("relu_3_cast_fp16")];
            tensor<fp16, [128]> b_embedding_model_blocks_1_res2net_block_blocks_1_norm_norm_running_mean_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_1_res2net_block_blocks_1_norm_norm_running_mean_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2931520)))];
            tensor<fp16, [128]> b_embedding_model_blocks_1_res2net_block_blocks_1_norm_norm_running_var_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_1_res2net_block_blocks_1_norm_norm_running_var_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2931840)))];
            tensor<fp16, [128]> p_embedding_model_blocks_1_res2net_block_blocks_1_norm_norm_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_res2net_block_blocks_1_norm_norm_weight_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2932160)))];
            tensor<fp16, [128]> p_embedding_model_blocks_1_res2net_block_blocks_1_norm_norm_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_res2net_block_blocks_1_norm_norm_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2932480)))];
            tensor<fp16, []> const_38_to_fp16 = const()[name = tensor<string, []>("const_38_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 128, 1001]> _native_batch_norm_legit_no_training_3_cast_fp16 = batch_norm(beta = p_embedding_model_blocks_1_res2net_block_blocks_1_norm_norm_bias_to_fp16, epsilon = const_38_to_fp16, gamma = p_embedding_model_blocks_1_res2net_block_blocks_1_norm_norm_weight_to_fp16, mean = b_embedding_model_blocks_1_res2net_block_blocks_1_norm_norm_running_mean_to_fp16, variance = b_embedding_model_blocks_1_res2net_block_blocks_1_norm_norm_running_var_to_fp16, x = relu_3_cast_fp16)[name = tensor<string, []>("_native_batch_norm_legit_no_training_3_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> add_1_cast_fp16 = add(x = split_cast_fp16_3, y = _native_batch_norm_legit_no_training_3_cast_fp16)[name = tensor<string, []>("add_1_cast_fp16")];
            tensor<int32, [6]> pad_4_pad_0 = const()[name = tensor<string, []>("pad_4_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 2])];
            tensor<string, []> pad_4_mode_0 = const()[name = tensor<string, []>("pad_4_mode_0"), val = tensor<string, []>("reflect")];
            tensor<fp16, []> const_41_to_fp16 = const()[name = tensor<string, []>("const_41_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 1005]> pad_4_cast_fp16 = pad(constant_val = const_41_to_fp16, mode = pad_4_mode_0, pad = pad_4_pad_0, x = add_1_cast_fp16)[name = tensor<string, []>("pad_4_cast_fp16")];
            tensor<string, []> conv1d_4_pad_type_0 = const()[name = tensor<string, []>("conv1d_4_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_4_dilations_0 = const()[name = tensor<string, []>("conv1d_4_dilations_0"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1]> conv1d_4_strides_0 = const()[name = tensor<string, []>("conv1d_4_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_4_pad_0 = const()[name = tensor<string, []>("conv1d_4_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, []> conv1d_4_groups_0 = const()[name = tensor<string, []>("conv1d_4_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [128, 128, 3]> p_embedding_model_blocks_1_res2net_block_blocks_2_conv_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_res2net_block_blocks_2_conv_conv_weight_to_fp16"), val = tensor<fp16, [128, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2932800)))];
            tensor<fp16, [128]> p_embedding_model_blocks_1_res2net_block_blocks_2_conv_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_res2net_block_blocks_2_conv_conv_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3031168)))];
            tensor<fp16, [1, 128, 1001]> conv1d_4_cast_fp16 = conv(bias = p_embedding_model_blocks_1_res2net_block_blocks_2_conv_conv_bias_to_fp16, dilations = conv1d_4_dilations_0, groups = conv1d_4_groups_0, pad = conv1d_4_pad_0, pad_type = conv1d_4_pad_type_0, strides = conv1d_4_strides_0, weight = p_embedding_model_blocks_1_res2net_block_blocks_2_conv_conv_weight_to_fp16, x = pad_4_cast_fp16)[name = tensor<string, []>("conv1d_4_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> relu_4_cast_fp16 = relu(x = conv1d_4_cast_fp16)[name = tensor<string, []>("relu_4_cast_fp16")];
            tensor<fp16, [128]> b_embedding_model_blocks_1_res2net_block_blocks_2_norm_norm_running_mean_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_1_res2net_block_blocks_2_norm_norm_running_mean_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3031488)))];
            tensor<fp16, [128]> b_embedding_model_blocks_1_res2net_block_blocks_2_norm_norm_running_var_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_1_res2net_block_blocks_2_norm_norm_running_var_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3031808)))];
            tensor<fp16, [128]> p_embedding_model_blocks_1_res2net_block_blocks_2_norm_norm_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_res2net_block_blocks_2_norm_norm_weight_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3032128)))];
            tensor<fp16, [128]> p_embedding_model_blocks_1_res2net_block_blocks_2_norm_norm_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_res2net_block_blocks_2_norm_norm_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3032448)))];
            tensor<fp16, []> const_46_to_fp16 = const()[name = tensor<string, []>("const_46_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 128, 1001]> _native_batch_norm_legit_no_training_4_cast_fp16 = batch_norm(beta = p_embedding_model_blocks_1_res2net_block_blocks_2_norm_norm_bias_to_fp16, epsilon = const_46_to_fp16, gamma = p_embedding_model_blocks_1_res2net_block_blocks_2_norm_norm_weight_to_fp16, mean = b_embedding_model_blocks_1_res2net_block_blocks_2_norm_norm_running_mean_to_fp16, variance = b_embedding_model_blocks_1_res2net_block_blocks_2_norm_norm_running_var_to_fp16, x = relu_4_cast_fp16)[name = tensor<string, []>("_native_batch_norm_legit_no_training_4_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> add_2_cast_fp16 = add(x = split_cast_fp16_4, y = _native_batch_norm_legit_no_training_4_cast_fp16)[name = tensor<string, []>("add_2_cast_fp16")];
            tensor<int32, [6]> pad_5_pad_0 = const()[name = tensor<string, []>("pad_5_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 2])];
            tensor<string, []> pad_5_mode_0 = const()[name = tensor<string, []>("pad_5_mode_0"), val = tensor<string, []>("reflect")];
            tensor<fp16, []> const_49_to_fp16 = const()[name = tensor<string, []>("const_49_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 1005]> pad_5_cast_fp16 = pad(constant_val = const_49_to_fp16, mode = pad_5_mode_0, pad = pad_5_pad_0, x = add_2_cast_fp16)[name = tensor<string, []>("pad_5_cast_fp16")];
            tensor<string, []> conv1d_5_pad_type_0 = const()[name = tensor<string, []>("conv1d_5_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_5_dilations_0 = const()[name = tensor<string, []>("conv1d_5_dilations_0"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1]> conv1d_5_strides_0 = const()[name = tensor<string, []>("conv1d_5_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_5_pad_0 = const()[name = tensor<string, []>("conv1d_5_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, []> conv1d_5_groups_0 = const()[name = tensor<string, []>("conv1d_5_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [128, 128, 3]> p_embedding_model_blocks_1_res2net_block_blocks_3_conv_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_res2net_block_blocks_3_conv_conv_weight_to_fp16"), val = tensor<fp16, [128, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3032768)))];
            tensor<fp16, [128]> p_embedding_model_blocks_1_res2net_block_blocks_3_conv_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_res2net_block_blocks_3_conv_conv_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3131136)))];
            tensor<fp16, [1, 128, 1001]> conv1d_5_cast_fp16 = conv(bias = p_embedding_model_blocks_1_res2net_block_blocks_3_conv_conv_bias_to_fp16, dilations = conv1d_5_dilations_0, groups = conv1d_5_groups_0, pad = conv1d_5_pad_0, pad_type = conv1d_5_pad_type_0, strides = conv1d_5_strides_0, weight = p_embedding_model_blocks_1_res2net_block_blocks_3_conv_conv_weight_to_fp16, x = pad_5_cast_fp16)[name = tensor<string, []>("conv1d_5_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> relu_5_cast_fp16 = relu(x = conv1d_5_cast_fp16)[name = tensor<string, []>("relu_5_cast_fp16")];
            tensor<fp16, [128]> b_embedding_model_blocks_1_res2net_block_blocks_3_norm_norm_running_mean_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_1_res2net_block_blocks_3_norm_norm_running_mean_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3131456)))];
            tensor<fp16, [128]> b_embedding_model_blocks_1_res2net_block_blocks_3_norm_norm_running_var_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_1_res2net_block_blocks_3_norm_norm_running_var_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3131776)))];
            tensor<fp16, [128]> p_embedding_model_blocks_1_res2net_block_blocks_3_norm_norm_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_res2net_block_blocks_3_norm_norm_weight_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3132096)))];
            tensor<fp16, [128]> p_embedding_model_blocks_1_res2net_block_blocks_3_norm_norm_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_res2net_block_blocks_3_norm_norm_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3132416)))];
            tensor<fp16, []> const_54_to_fp16 = const()[name = tensor<string, []>("const_54_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 128, 1001]> _native_batch_norm_legit_no_training_5_cast_fp16 = batch_norm(beta = p_embedding_model_blocks_1_res2net_block_blocks_3_norm_norm_bias_to_fp16, epsilon = const_54_to_fp16, gamma = p_embedding_model_blocks_1_res2net_block_blocks_3_norm_norm_weight_to_fp16, mean = b_embedding_model_blocks_1_res2net_block_blocks_3_norm_norm_running_mean_to_fp16, variance = b_embedding_model_blocks_1_res2net_block_blocks_3_norm_norm_running_var_to_fp16, x = relu_5_cast_fp16)[name = tensor<string, []>("_native_batch_norm_legit_no_training_5_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> add_3_cast_fp16 = add(x = split_cast_fp16_5, y = _native_batch_norm_legit_no_training_5_cast_fp16)[name = tensor<string, []>("add_3_cast_fp16")];
            tensor<int32, [6]> pad_6_pad_0 = const()[name = tensor<string, []>("pad_6_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 2])];
            tensor<string, []> pad_6_mode_0 = const()[name = tensor<string, []>("pad_6_mode_0"), val = tensor<string, []>("reflect")];
            tensor<fp16, []> const_57_to_fp16 = const()[name = tensor<string, []>("const_57_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 1005]> pad_6_cast_fp16 = pad(constant_val = const_57_to_fp16, mode = pad_6_mode_0, pad = pad_6_pad_0, x = add_3_cast_fp16)[name = tensor<string, []>("pad_6_cast_fp16")];
            tensor<string, []> conv1d_6_pad_type_0 = const()[name = tensor<string, []>("conv1d_6_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_6_dilations_0 = const()[name = tensor<string, []>("conv1d_6_dilations_0"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1]> conv1d_6_strides_0 = const()[name = tensor<string, []>("conv1d_6_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_6_pad_0 = const()[name = tensor<string, []>("conv1d_6_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, []> conv1d_6_groups_0 = const()[name = tensor<string, []>("conv1d_6_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [128, 128, 3]> p_embedding_model_blocks_1_res2net_block_blocks_4_conv_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_res2net_block_blocks_4_conv_conv_weight_to_fp16"), val = tensor<fp16, [128, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3132736)))];
            tensor<fp16, [128]> p_embedding_model_blocks_1_res2net_block_blocks_4_conv_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_res2net_block_blocks_4_conv_conv_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3231104)))];
            tensor<fp16, [1, 128, 1001]> conv1d_6_cast_fp16 = conv(bias = p_embedding_model_blocks_1_res2net_block_blocks_4_conv_conv_bias_to_fp16, dilations = conv1d_6_dilations_0, groups = conv1d_6_groups_0, pad = conv1d_6_pad_0, pad_type = conv1d_6_pad_type_0, strides = conv1d_6_strides_0, weight = p_embedding_model_blocks_1_res2net_block_blocks_4_conv_conv_weight_to_fp16, x = pad_6_cast_fp16)[name = tensor<string, []>("conv1d_6_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> relu_6_cast_fp16 = relu(x = conv1d_6_cast_fp16)[name = tensor<string, []>("relu_6_cast_fp16")];
            tensor<fp16, [128]> b_embedding_model_blocks_1_res2net_block_blocks_4_norm_norm_running_mean_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_1_res2net_block_blocks_4_norm_norm_running_mean_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3231424)))];
            tensor<fp16, [128]> b_embedding_model_blocks_1_res2net_block_blocks_4_norm_norm_running_var_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_1_res2net_block_blocks_4_norm_norm_running_var_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3231744)))];
            tensor<fp16, [128]> p_embedding_model_blocks_1_res2net_block_blocks_4_norm_norm_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_res2net_block_blocks_4_norm_norm_weight_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3232064)))];
            tensor<fp16, [128]> p_embedding_model_blocks_1_res2net_block_blocks_4_norm_norm_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_res2net_block_blocks_4_norm_norm_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3232384)))];
            tensor<fp16, []> const_62_to_fp16 = const()[name = tensor<string, []>("const_62_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 128, 1001]> _native_batch_norm_legit_no_training_6_cast_fp16 = batch_norm(beta = p_embedding_model_blocks_1_res2net_block_blocks_4_norm_norm_bias_to_fp16, epsilon = const_62_to_fp16, gamma = p_embedding_model_blocks_1_res2net_block_blocks_4_norm_norm_weight_to_fp16, mean = b_embedding_model_blocks_1_res2net_block_blocks_4_norm_norm_running_mean_to_fp16, variance = b_embedding_model_blocks_1_res2net_block_blocks_4_norm_norm_running_var_to_fp16, x = relu_6_cast_fp16)[name = tensor<string, []>("_native_batch_norm_legit_no_training_6_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> add_4_cast_fp16 = add(x = split_cast_fp16_6, y = _native_batch_norm_legit_no_training_6_cast_fp16)[name = tensor<string, []>("add_4_cast_fp16")];
            tensor<int32, [6]> pad_7_pad_0 = const()[name = tensor<string, []>("pad_7_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 2])];
            tensor<string, []> pad_7_mode_0 = const()[name = tensor<string, []>("pad_7_mode_0"), val = tensor<string, []>("reflect")];
            tensor<fp16, []> const_65_to_fp16 = const()[name = tensor<string, []>("const_65_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 1005]> pad_7_cast_fp16 = pad(constant_val = const_65_to_fp16, mode = pad_7_mode_0, pad = pad_7_pad_0, x = add_4_cast_fp16)[name = tensor<string, []>("pad_7_cast_fp16")];
            tensor<string, []> conv1d_7_pad_type_0 = const()[name = tensor<string, []>("conv1d_7_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_7_dilations_0 = const()[name = tensor<string, []>("conv1d_7_dilations_0"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1]> conv1d_7_strides_0 = const()[name = tensor<string, []>("conv1d_7_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_7_pad_0 = const()[name = tensor<string, []>("conv1d_7_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, []> conv1d_7_groups_0 = const()[name = tensor<string, []>("conv1d_7_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [128, 128, 3]> p_embedding_model_blocks_1_res2net_block_blocks_5_conv_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_res2net_block_blocks_5_conv_conv_weight_to_fp16"), val = tensor<fp16, [128, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3232704)))];
            tensor<fp16, [128]> p_embedding_model_blocks_1_res2net_block_blocks_5_conv_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_res2net_block_blocks_5_conv_conv_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3331072)))];
            tensor<fp16, [1, 128, 1001]> conv1d_7_cast_fp16 = conv(bias = p_embedding_model_blocks_1_res2net_block_blocks_5_conv_conv_bias_to_fp16, dilations = conv1d_7_dilations_0, groups = conv1d_7_groups_0, pad = conv1d_7_pad_0, pad_type = conv1d_7_pad_type_0, strides = conv1d_7_strides_0, weight = p_embedding_model_blocks_1_res2net_block_blocks_5_conv_conv_weight_to_fp16, x = pad_7_cast_fp16)[name = tensor<string, []>("conv1d_7_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> relu_7_cast_fp16 = relu(x = conv1d_7_cast_fp16)[name = tensor<string, []>("relu_7_cast_fp16")];
            tensor<fp16, [128]> b_embedding_model_blocks_1_res2net_block_blocks_5_norm_norm_running_mean_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_1_res2net_block_blocks_5_norm_norm_running_mean_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3331392)))];
            tensor<fp16, [128]> b_embedding_model_blocks_1_res2net_block_blocks_5_norm_norm_running_var_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_1_res2net_block_blocks_5_norm_norm_running_var_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3331712)))];
            tensor<fp16, [128]> p_embedding_model_blocks_1_res2net_block_blocks_5_norm_norm_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_res2net_block_blocks_5_norm_norm_weight_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3332032)))];
            tensor<fp16, [128]> p_embedding_model_blocks_1_res2net_block_blocks_5_norm_norm_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_res2net_block_blocks_5_norm_norm_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3332352)))];
            tensor<fp16, []> const_70_to_fp16 = const()[name = tensor<string, []>("const_70_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 128, 1001]> _native_batch_norm_legit_no_training_7_cast_fp16 = batch_norm(beta = p_embedding_model_blocks_1_res2net_block_blocks_5_norm_norm_bias_to_fp16, epsilon = const_70_to_fp16, gamma = p_embedding_model_blocks_1_res2net_block_blocks_5_norm_norm_weight_to_fp16, mean = b_embedding_model_blocks_1_res2net_block_blocks_5_norm_norm_running_mean_to_fp16, variance = b_embedding_model_blocks_1_res2net_block_blocks_5_norm_norm_running_var_to_fp16, x = relu_7_cast_fp16)[name = tensor<string, []>("_native_batch_norm_legit_no_training_7_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> add_5_cast_fp16 = add(x = split_cast_fp16_7, y = _native_batch_norm_legit_no_training_7_cast_fp16)[name = tensor<string, []>("add_5_cast_fp16")];
            tensor<int32, [6]> pad_8_pad_0 = const()[name = tensor<string, []>("pad_8_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 2])];
            tensor<string, []> pad_8_mode_0 = const()[name = tensor<string, []>("pad_8_mode_0"), val = tensor<string, []>("reflect")];
            tensor<fp16, []> const_73_to_fp16 = const()[name = tensor<string, []>("const_73_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 1005]> pad_8_cast_fp16 = pad(constant_val = const_73_to_fp16, mode = pad_8_mode_0, pad = pad_8_pad_0, x = add_5_cast_fp16)[name = tensor<string, []>("pad_8_cast_fp16")];
            tensor<string, []> conv1d_8_pad_type_0 = const()[name = tensor<string, []>("conv1d_8_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_8_dilations_0 = const()[name = tensor<string, []>("conv1d_8_dilations_0"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1]> conv1d_8_strides_0 = const()[name = tensor<string, []>("conv1d_8_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_8_pad_0 = const()[name = tensor<string, []>("conv1d_8_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, []> conv1d_8_groups_0 = const()[name = tensor<string, []>("conv1d_8_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [128, 128, 3]> p_embedding_model_blocks_1_res2net_block_blocks_6_conv_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_res2net_block_blocks_6_conv_conv_weight_to_fp16"), val = tensor<fp16, [128, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3332672)))];
            tensor<fp16, [128]> p_embedding_model_blocks_1_res2net_block_blocks_6_conv_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_res2net_block_blocks_6_conv_conv_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3431040)))];
            tensor<fp16, [1, 128, 1001]> conv1d_8_cast_fp16 = conv(bias = p_embedding_model_blocks_1_res2net_block_blocks_6_conv_conv_bias_to_fp16, dilations = conv1d_8_dilations_0, groups = conv1d_8_groups_0, pad = conv1d_8_pad_0, pad_type = conv1d_8_pad_type_0, strides = conv1d_8_strides_0, weight = p_embedding_model_blocks_1_res2net_block_blocks_6_conv_conv_weight_to_fp16, x = pad_8_cast_fp16)[name = tensor<string, []>("conv1d_8_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> relu_8_cast_fp16 = relu(x = conv1d_8_cast_fp16)[name = tensor<string, []>("relu_8_cast_fp16")];
            tensor<fp16, [128]> b_embedding_model_blocks_1_res2net_block_blocks_6_norm_norm_running_mean_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_1_res2net_block_blocks_6_norm_norm_running_mean_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3431360)))];
            tensor<fp16, [128]> b_embedding_model_blocks_1_res2net_block_blocks_6_norm_norm_running_var_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_1_res2net_block_blocks_6_norm_norm_running_var_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3431680)))];
            tensor<fp16, [128]> p_embedding_model_blocks_1_res2net_block_blocks_6_norm_norm_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_res2net_block_blocks_6_norm_norm_weight_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3432000)))];
            tensor<fp16, [128]> p_embedding_model_blocks_1_res2net_block_blocks_6_norm_norm_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_res2net_block_blocks_6_norm_norm_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3432320)))];
            tensor<fp16, []> const_78_to_fp16 = const()[name = tensor<string, []>("const_78_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 128, 1001]> _native_batch_norm_legit_no_training_8_cast_fp16 = batch_norm(beta = p_embedding_model_blocks_1_res2net_block_blocks_6_norm_norm_bias_to_fp16, epsilon = const_78_to_fp16, gamma = p_embedding_model_blocks_1_res2net_block_blocks_6_norm_norm_weight_to_fp16, mean = b_embedding_model_blocks_1_res2net_block_blocks_6_norm_norm_running_mean_to_fp16, variance = b_embedding_model_blocks_1_res2net_block_blocks_6_norm_norm_running_var_to_fp16, x = relu_8_cast_fp16)[name = tensor<string, []>("_native_batch_norm_legit_no_training_8_cast_fp16")];
            tensor<int32, []> const_80 = const()[name = tensor<string, []>("const_80"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_interleave_0 = const()[name = tensor<string, []>("cat_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 1024, 1001]> cat_cast_fp16 = concat(axis = const_80, interleave = cat_interleave_0, values = (split_cast_fp16_0, _native_batch_norm_legit_no_training_2_cast_fp16, _native_batch_norm_legit_no_training_3_cast_fp16, _native_batch_norm_legit_no_training_4_cast_fp16, _native_batch_norm_legit_no_training_5_cast_fp16, _native_batch_norm_legit_no_training_6_cast_fp16, _native_batch_norm_legit_no_training_7_cast_fp16, _native_batch_norm_legit_no_training_8_cast_fp16))[name = tensor<string, []>("cat_cast_fp16")];
            tensor<string, []> conv1d_9_pad_type_0 = const()[name = tensor<string, []>("conv1d_9_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_9_strides_0 = const()[name = tensor<string, []>("conv1d_9_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_9_pad_0 = const()[name = tensor<string, []>("conv1d_9_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_9_dilations_0 = const()[name = tensor<string, []>("conv1d_9_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_9_groups_0 = const()[name = tensor<string, []>("conv1d_9_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 1024, 1]> p_embedding_model_blocks_1_tdnn2_conv_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_tdnn2_conv_conv_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3432640)))];
            tensor<fp16, [1024]> p_embedding_model_blocks_1_tdnn2_conv_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_tdnn2_conv_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5529856)))];
            tensor<fp16, [1, 1024, 1001]> conv1d_9_cast_fp16 = conv(bias = p_embedding_model_blocks_1_tdnn2_conv_conv_bias_to_fp16, dilations = conv1d_9_dilations_0, groups = conv1d_9_groups_0, pad = conv1d_9_pad_0, pad_type = conv1d_9_pad_type_0, strides = conv1d_9_strides_0, weight = p_embedding_model_blocks_1_tdnn2_conv_conv_weight_to_fp16, x = cat_cast_fp16)[name = tensor<string, []>("conv1d_9_cast_fp16")];
            tensor<fp16, [1, 1024, 1001]> relu_9_cast_fp16 = relu(x = conv1d_9_cast_fp16)[name = tensor<string, []>("relu_9_cast_fp16")];
            tensor<fp16, [1024]> b_embedding_model_blocks_1_tdnn2_norm_norm_running_mean_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_1_tdnn2_norm_norm_running_mean_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5531968)))];
            tensor<fp16, [1024]> b_embedding_model_blocks_1_tdnn2_norm_norm_running_var_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_1_tdnn2_norm_norm_running_var_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5534080)))];
            tensor<fp16, [1024]> p_embedding_model_blocks_1_tdnn2_norm_norm_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_tdnn2_norm_norm_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5536192)))];
            tensor<fp16, [1024]> p_embedding_model_blocks_1_tdnn2_norm_norm_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_tdnn2_norm_norm_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5538304)))];
            tensor<fp16, []> const_84_to_fp16 = const()[name = tensor<string, []>("const_84_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1024, 1001]> _native_batch_norm_legit_no_training_9_cast_fp16 = batch_norm(beta = p_embedding_model_blocks_1_tdnn2_norm_norm_bias_to_fp16, epsilon = const_84_to_fp16, gamma = p_embedding_model_blocks_1_tdnn2_norm_norm_weight_to_fp16, mean = b_embedding_model_blocks_1_tdnn2_norm_norm_running_mean_to_fp16, variance = b_embedding_model_blocks_1_tdnn2_norm_norm_running_var_to_fp16, x = relu_9_cast_fp16)[name = tensor<string, []>("_native_batch_norm_legit_no_training_9_cast_fp16")];
            tensor<int32, [1]> mean_axes_0 = const()[name = tensor<string, []>("mean_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<bool, []> mean_keep_dims_0 = const()[name = tensor<string, []>("mean_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1024, 1]> mean_cast_fp16 = reduce_mean(axes = mean_axes_0, keep_dims = mean_keep_dims_0, x = _native_batch_norm_legit_no_training_9_cast_fp16)[name = tensor<string, []>("mean_cast_fp16")];
            tensor<string, []> conv1d_10_pad_type_0 = const()[name = tensor<string, []>("conv1d_10_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_10_strides_0 = const()[name = tensor<string, []>("conv1d_10_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_10_pad_0 = const()[name = tensor<string, []>("conv1d_10_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_10_dilations_0 = const()[name = tensor<string, []>("conv1d_10_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_10_groups_0 = const()[name = tensor<string, []>("conv1d_10_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [128, 1024, 1]> p_embedding_model_blocks_1_se_block_conv1_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_se_block_conv1_conv_weight_to_fp16"), val = tensor<fp16, [128, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5540416)))];
            tensor<fp16, [128]> p_embedding_model_blocks_1_se_block_conv1_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_se_block_conv1_conv_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5802624)))];
            tensor<fp16, [1, 128, 1]> conv1d_10_cast_fp16 = conv(bias = p_embedding_model_blocks_1_se_block_conv1_conv_bias_to_fp16, dilations = conv1d_10_dilations_0, groups = conv1d_10_groups_0, pad = conv1d_10_pad_0, pad_type = conv1d_10_pad_type_0, strides = conv1d_10_strides_0, weight = p_embedding_model_blocks_1_se_block_conv1_conv_weight_to_fp16, x = mean_cast_fp16)[name = tensor<string, []>("conv1d_10_cast_fp16")];
            tensor<fp16, [1, 128, 1]> relu_10_cast_fp16 = relu(x = conv1d_10_cast_fp16)[name = tensor<string, []>("relu_10_cast_fp16")];
            tensor<string, []> conv1d_11_pad_type_0 = const()[name = tensor<string, []>("conv1d_11_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_11_strides_0 = const()[name = tensor<string, []>("conv1d_11_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_11_pad_0 = const()[name = tensor<string, []>("conv1d_11_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_11_dilations_0 = const()[name = tensor<string, []>("conv1d_11_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_11_groups_0 = const()[name = tensor<string, []>("conv1d_11_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 128, 1]> p_embedding_model_blocks_1_se_block_conv2_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_se_block_conv2_conv_weight_to_fp16"), val = tensor<fp16, [1024, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5802944)))];
            tensor<fp16, [1024]> p_embedding_model_blocks_1_se_block_conv2_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_1_se_block_conv2_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6065152)))];
            tensor<fp16, [1, 1024, 1]> conv1d_11_cast_fp16 = conv(bias = p_embedding_model_blocks_1_se_block_conv2_conv_bias_to_fp16, dilations = conv1d_11_dilations_0, groups = conv1d_11_groups_0, pad = conv1d_11_pad_0, pad_type = conv1d_11_pad_type_0, strides = conv1d_11_strides_0, weight = p_embedding_model_blocks_1_se_block_conv2_conv_weight_to_fp16, x = relu_10_cast_fp16)[name = tensor<string, []>("conv1d_11_cast_fp16")];
            tensor<fp16, [1, 1024, 1]> sigmoid_cast_fp16 = sigmoid(x = conv1d_11_cast_fp16)[name = tensor<string, []>("sigmoid_cast_fp16")];
            tensor<fp16, [1, 1024, 1001]> mul_cast_fp16 = mul(x = sigmoid_cast_fp16, y = _native_batch_norm_legit_no_training_9_cast_fp16)[name = tensor<string, []>("mul_cast_fp16")];
            tensor<fp16, [1, 1024, 1001]> add_6_cast_fp16 = add(x = mul_cast_fp16, y = _native_batch_norm_legit_no_training_cast_fp16)[name = tensor<string, []>("add_6_cast_fp16")];
            tensor<string, []> conv1d_12_pad_type_0 = const()[name = tensor<string, []>("conv1d_12_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_12_strides_0 = const()[name = tensor<string, []>("conv1d_12_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_12_pad_0 = const()[name = tensor<string, []>("conv1d_12_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_12_dilations_0 = const()[name = tensor<string, []>("conv1d_12_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_12_groups_0 = const()[name = tensor<string, []>("conv1d_12_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 1024, 1]> p_embedding_model_blocks_2_tdnn1_conv_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_tdnn1_conv_conv_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6067264)))];
            tensor<fp16, [1024]> p_embedding_model_blocks_2_tdnn1_conv_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_tdnn1_conv_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8164480)))];
            tensor<fp16, [1, 1024, 1001]> conv1d_12_cast_fp16 = conv(bias = p_embedding_model_blocks_2_tdnn1_conv_conv_bias_to_fp16, dilations = conv1d_12_dilations_0, groups = conv1d_12_groups_0, pad = conv1d_12_pad_0, pad_type = conv1d_12_pad_type_0, strides = conv1d_12_strides_0, weight = p_embedding_model_blocks_2_tdnn1_conv_conv_weight_to_fp16, x = add_6_cast_fp16)[name = tensor<string, []>("conv1d_12_cast_fp16")];
            tensor<fp16, [1, 1024, 1001]> relu_11_cast_fp16 = relu(x = conv1d_12_cast_fp16)[name = tensor<string, []>("relu_11_cast_fp16")];
            tensor<fp16, [1024]> b_embedding_model_blocks_2_tdnn1_norm_norm_running_mean_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_2_tdnn1_norm_norm_running_mean_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8166592)))];
            tensor<fp16, [1024]> b_embedding_model_blocks_2_tdnn1_norm_norm_running_var_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_2_tdnn1_norm_norm_running_var_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8168704)))];
            tensor<fp16, [1024]> p_embedding_model_blocks_2_tdnn1_norm_norm_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_tdnn1_norm_norm_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8170816)))];
            tensor<fp16, [1024]> p_embedding_model_blocks_2_tdnn1_norm_norm_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_tdnn1_norm_norm_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8172928)))];
            tensor<fp16, []> const_95_to_fp16 = const()[name = tensor<string, []>("const_95_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1024, 1001]> _native_batch_norm_legit_no_training_10_cast_fp16 = batch_norm(beta = p_embedding_model_blocks_2_tdnn1_norm_norm_bias_to_fp16, epsilon = const_95_to_fp16, gamma = p_embedding_model_blocks_2_tdnn1_norm_norm_weight_to_fp16, mean = b_embedding_model_blocks_2_tdnn1_norm_norm_running_mean_to_fp16, variance = b_embedding_model_blocks_2_tdnn1_norm_norm_running_var_to_fp16, x = relu_11_cast_fp16)[name = tensor<string, []>("_native_batch_norm_legit_no_training_10_cast_fp16")];
            tensor<int32, [8]> tile_1 = const()[name = tensor<string, []>("tile_1"), val = tensor<int32, [8]>([128, 128, 128, 128, 128, 128, 128, 128])];
            tensor<int32, []> split_1_axis_0 = const()[name = tensor<string, []>("split_1_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 128, 1001]> split_1_cast_fp16_0, tensor<fp16, [1, 128, 1001]> split_1_cast_fp16_1, tensor<fp16, [1, 128, 1001]> split_1_cast_fp16_2, tensor<fp16, [1, 128, 1001]> split_1_cast_fp16_3, tensor<fp16, [1, 128, 1001]> split_1_cast_fp16_4, tensor<fp16, [1, 128, 1001]> split_1_cast_fp16_5, tensor<fp16, [1, 128, 1001]> split_1_cast_fp16_6, tensor<fp16, [1, 128, 1001]> split_1_cast_fp16_7 = split(axis = split_1_axis_0, split_sizes = tile_1, x = _native_batch_norm_legit_no_training_10_cast_fp16)[name = tensor<string, []>("split_1_cast_fp16")];
            tensor<int32, [6]> pad_13_pad_0 = const()[name = tensor<string, []>("pad_13_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 3, 3])];
            tensor<string, []> pad_13_mode_0 = const()[name = tensor<string, []>("pad_13_mode_0"), val = tensor<string, []>("reflect")];
            tensor<fp16, []> const_110_to_fp16 = const()[name = tensor<string, []>("const_110_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 1007]> pad_13_cast_fp16 = pad(constant_val = const_110_to_fp16, mode = pad_13_mode_0, pad = pad_13_pad_0, x = split_1_cast_fp16_1)[name = tensor<string, []>("pad_13_cast_fp16")];
            tensor<string, []> conv1d_13_pad_type_0 = const()[name = tensor<string, []>("conv1d_13_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_13_dilations_0 = const()[name = tensor<string, []>("conv1d_13_dilations_0"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> conv1d_13_strides_0 = const()[name = tensor<string, []>("conv1d_13_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_13_pad_0 = const()[name = tensor<string, []>("conv1d_13_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, []> conv1d_13_groups_0 = const()[name = tensor<string, []>("conv1d_13_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [128, 128, 3]> p_embedding_model_blocks_2_res2net_block_blocks_0_conv_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_res2net_block_blocks_0_conv_conv_weight_to_fp16"), val = tensor<fp16, [128, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8175040)))];
            tensor<fp16, [128]> p_embedding_model_blocks_2_res2net_block_blocks_0_conv_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_res2net_block_blocks_0_conv_conv_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8273408)))];
            tensor<fp16, [1, 128, 1001]> conv1d_13_cast_fp16 = conv(bias = p_embedding_model_blocks_2_res2net_block_blocks_0_conv_conv_bias_to_fp16, dilations = conv1d_13_dilations_0, groups = conv1d_13_groups_0, pad = conv1d_13_pad_0, pad_type = conv1d_13_pad_type_0, strides = conv1d_13_strides_0, weight = p_embedding_model_blocks_2_res2net_block_blocks_0_conv_conv_weight_to_fp16, x = pad_13_cast_fp16)[name = tensor<string, []>("conv1d_13_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> relu_12_cast_fp16 = relu(x = conv1d_13_cast_fp16)[name = tensor<string, []>("relu_12_cast_fp16")];
            tensor<fp16, [128]> b_embedding_model_blocks_2_res2net_block_blocks_0_norm_norm_running_mean_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_2_res2net_block_blocks_0_norm_norm_running_mean_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8273728)))];
            tensor<fp16, [128]> b_embedding_model_blocks_2_res2net_block_blocks_0_norm_norm_running_var_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_2_res2net_block_blocks_0_norm_norm_running_var_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8274048)))];
            tensor<fp16, [128]> p_embedding_model_blocks_2_res2net_block_blocks_0_norm_norm_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_res2net_block_blocks_0_norm_norm_weight_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8274368)))];
            tensor<fp16, [128]> p_embedding_model_blocks_2_res2net_block_blocks_0_norm_norm_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_res2net_block_blocks_0_norm_norm_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8274688)))];
            tensor<fp16, []> const_115_to_fp16 = const()[name = tensor<string, []>("const_115_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 128, 1001]> _native_batch_norm_legit_no_training_11_cast_fp16 = batch_norm(beta = p_embedding_model_blocks_2_res2net_block_blocks_0_norm_norm_bias_to_fp16, epsilon = const_115_to_fp16, gamma = p_embedding_model_blocks_2_res2net_block_blocks_0_norm_norm_weight_to_fp16, mean = b_embedding_model_blocks_2_res2net_block_blocks_0_norm_norm_running_mean_to_fp16, variance = b_embedding_model_blocks_2_res2net_block_blocks_0_norm_norm_running_var_to_fp16, x = relu_12_cast_fp16)[name = tensor<string, []>("_native_batch_norm_legit_no_training_11_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> add_7_cast_fp16 = add(x = split_1_cast_fp16_2, y = _native_batch_norm_legit_no_training_11_cast_fp16)[name = tensor<string, []>("add_7_cast_fp16")];
            tensor<int32, [6]> pad_14_pad_0 = const()[name = tensor<string, []>("pad_14_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 3, 3])];
            tensor<string, []> pad_14_mode_0 = const()[name = tensor<string, []>("pad_14_mode_0"), val = tensor<string, []>("reflect")];
            tensor<fp16, []> const_118_to_fp16 = const()[name = tensor<string, []>("const_118_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 1007]> pad_14_cast_fp16 = pad(constant_val = const_118_to_fp16, mode = pad_14_mode_0, pad = pad_14_pad_0, x = add_7_cast_fp16)[name = tensor<string, []>("pad_14_cast_fp16")];
            tensor<string, []> conv1d_14_pad_type_0 = const()[name = tensor<string, []>("conv1d_14_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_14_dilations_0 = const()[name = tensor<string, []>("conv1d_14_dilations_0"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> conv1d_14_strides_0 = const()[name = tensor<string, []>("conv1d_14_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_14_pad_0 = const()[name = tensor<string, []>("conv1d_14_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, []> conv1d_14_groups_0 = const()[name = tensor<string, []>("conv1d_14_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [128, 128, 3]> p_embedding_model_blocks_2_res2net_block_blocks_1_conv_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_res2net_block_blocks_1_conv_conv_weight_to_fp16"), val = tensor<fp16, [128, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8275008)))];
            tensor<fp16, [128]> p_embedding_model_blocks_2_res2net_block_blocks_1_conv_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_res2net_block_blocks_1_conv_conv_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8373376)))];
            tensor<fp16, [1, 128, 1001]> conv1d_14_cast_fp16 = conv(bias = p_embedding_model_blocks_2_res2net_block_blocks_1_conv_conv_bias_to_fp16, dilations = conv1d_14_dilations_0, groups = conv1d_14_groups_0, pad = conv1d_14_pad_0, pad_type = conv1d_14_pad_type_0, strides = conv1d_14_strides_0, weight = p_embedding_model_blocks_2_res2net_block_blocks_1_conv_conv_weight_to_fp16, x = pad_14_cast_fp16)[name = tensor<string, []>("conv1d_14_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> relu_13_cast_fp16 = relu(x = conv1d_14_cast_fp16)[name = tensor<string, []>("relu_13_cast_fp16")];
            tensor<fp16, [128]> b_embedding_model_blocks_2_res2net_block_blocks_1_norm_norm_running_mean_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_2_res2net_block_blocks_1_norm_norm_running_mean_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8373696)))];
            tensor<fp16, [128]> b_embedding_model_blocks_2_res2net_block_blocks_1_norm_norm_running_var_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_2_res2net_block_blocks_1_norm_norm_running_var_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8374016)))];
            tensor<fp16, [128]> p_embedding_model_blocks_2_res2net_block_blocks_1_norm_norm_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_res2net_block_blocks_1_norm_norm_weight_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8374336)))];
            tensor<fp16, [128]> p_embedding_model_blocks_2_res2net_block_blocks_1_norm_norm_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_res2net_block_blocks_1_norm_norm_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8374656)))];
            tensor<fp16, []> const_123_to_fp16 = const()[name = tensor<string, []>("const_123_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 128, 1001]> _native_batch_norm_legit_no_training_12_cast_fp16 = batch_norm(beta = p_embedding_model_blocks_2_res2net_block_blocks_1_norm_norm_bias_to_fp16, epsilon = const_123_to_fp16, gamma = p_embedding_model_blocks_2_res2net_block_blocks_1_norm_norm_weight_to_fp16, mean = b_embedding_model_blocks_2_res2net_block_blocks_1_norm_norm_running_mean_to_fp16, variance = b_embedding_model_blocks_2_res2net_block_blocks_1_norm_norm_running_var_to_fp16, x = relu_13_cast_fp16)[name = tensor<string, []>("_native_batch_norm_legit_no_training_12_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> add_8_cast_fp16 = add(x = split_1_cast_fp16_3, y = _native_batch_norm_legit_no_training_12_cast_fp16)[name = tensor<string, []>("add_8_cast_fp16")];
            tensor<int32, [6]> pad_15_pad_0 = const()[name = tensor<string, []>("pad_15_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 3, 3])];
            tensor<string, []> pad_15_mode_0 = const()[name = tensor<string, []>("pad_15_mode_0"), val = tensor<string, []>("reflect")];
            tensor<fp16, []> const_126_to_fp16 = const()[name = tensor<string, []>("const_126_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 1007]> pad_15_cast_fp16 = pad(constant_val = const_126_to_fp16, mode = pad_15_mode_0, pad = pad_15_pad_0, x = add_8_cast_fp16)[name = tensor<string, []>("pad_15_cast_fp16")];
            tensor<string, []> conv1d_15_pad_type_0 = const()[name = tensor<string, []>("conv1d_15_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_15_dilations_0 = const()[name = tensor<string, []>("conv1d_15_dilations_0"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> conv1d_15_strides_0 = const()[name = tensor<string, []>("conv1d_15_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_15_pad_0 = const()[name = tensor<string, []>("conv1d_15_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, []> conv1d_15_groups_0 = const()[name = tensor<string, []>("conv1d_15_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [128, 128, 3]> p_embedding_model_blocks_2_res2net_block_blocks_2_conv_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_res2net_block_blocks_2_conv_conv_weight_to_fp16"), val = tensor<fp16, [128, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8374976)))];
            tensor<fp16, [128]> p_embedding_model_blocks_2_res2net_block_blocks_2_conv_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_res2net_block_blocks_2_conv_conv_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8473344)))];
            tensor<fp16, [1, 128, 1001]> conv1d_15_cast_fp16 = conv(bias = p_embedding_model_blocks_2_res2net_block_blocks_2_conv_conv_bias_to_fp16, dilations = conv1d_15_dilations_0, groups = conv1d_15_groups_0, pad = conv1d_15_pad_0, pad_type = conv1d_15_pad_type_0, strides = conv1d_15_strides_0, weight = p_embedding_model_blocks_2_res2net_block_blocks_2_conv_conv_weight_to_fp16, x = pad_15_cast_fp16)[name = tensor<string, []>("conv1d_15_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> relu_14_cast_fp16 = relu(x = conv1d_15_cast_fp16)[name = tensor<string, []>("relu_14_cast_fp16")];
            tensor<fp16, [128]> b_embedding_model_blocks_2_res2net_block_blocks_2_norm_norm_running_mean_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_2_res2net_block_blocks_2_norm_norm_running_mean_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8473664)))];
            tensor<fp16, [128]> b_embedding_model_blocks_2_res2net_block_blocks_2_norm_norm_running_var_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_2_res2net_block_blocks_2_norm_norm_running_var_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8473984)))];
            tensor<fp16, [128]> p_embedding_model_blocks_2_res2net_block_blocks_2_norm_norm_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_res2net_block_blocks_2_norm_norm_weight_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8474304)))];
            tensor<fp16, [128]> p_embedding_model_blocks_2_res2net_block_blocks_2_norm_norm_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_res2net_block_blocks_2_norm_norm_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8474624)))];
            tensor<fp16, []> const_131_to_fp16 = const()[name = tensor<string, []>("const_131_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 128, 1001]> _native_batch_norm_legit_no_training_13_cast_fp16 = batch_norm(beta = p_embedding_model_blocks_2_res2net_block_blocks_2_norm_norm_bias_to_fp16, epsilon = const_131_to_fp16, gamma = p_embedding_model_blocks_2_res2net_block_blocks_2_norm_norm_weight_to_fp16, mean = b_embedding_model_blocks_2_res2net_block_blocks_2_norm_norm_running_mean_to_fp16, variance = b_embedding_model_blocks_2_res2net_block_blocks_2_norm_norm_running_var_to_fp16, x = relu_14_cast_fp16)[name = tensor<string, []>("_native_batch_norm_legit_no_training_13_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> add_9_cast_fp16 = add(x = split_1_cast_fp16_4, y = _native_batch_norm_legit_no_training_13_cast_fp16)[name = tensor<string, []>("add_9_cast_fp16")];
            tensor<int32, [6]> pad_16_pad_0 = const()[name = tensor<string, []>("pad_16_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 3, 3])];
            tensor<string, []> pad_16_mode_0 = const()[name = tensor<string, []>("pad_16_mode_0"), val = tensor<string, []>("reflect")];
            tensor<fp16, []> const_134_to_fp16 = const()[name = tensor<string, []>("const_134_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 1007]> pad_16_cast_fp16 = pad(constant_val = const_134_to_fp16, mode = pad_16_mode_0, pad = pad_16_pad_0, x = add_9_cast_fp16)[name = tensor<string, []>("pad_16_cast_fp16")];
            tensor<string, []> conv1d_16_pad_type_0 = const()[name = tensor<string, []>("conv1d_16_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_16_dilations_0 = const()[name = tensor<string, []>("conv1d_16_dilations_0"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> conv1d_16_strides_0 = const()[name = tensor<string, []>("conv1d_16_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_16_pad_0 = const()[name = tensor<string, []>("conv1d_16_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, []> conv1d_16_groups_0 = const()[name = tensor<string, []>("conv1d_16_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [128, 128, 3]> p_embedding_model_blocks_2_res2net_block_blocks_3_conv_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_res2net_block_blocks_3_conv_conv_weight_to_fp16"), val = tensor<fp16, [128, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8474944)))];
            tensor<fp16, [128]> p_embedding_model_blocks_2_res2net_block_blocks_3_conv_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_res2net_block_blocks_3_conv_conv_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8573312)))];
            tensor<fp16, [1, 128, 1001]> conv1d_16_cast_fp16 = conv(bias = p_embedding_model_blocks_2_res2net_block_blocks_3_conv_conv_bias_to_fp16, dilations = conv1d_16_dilations_0, groups = conv1d_16_groups_0, pad = conv1d_16_pad_0, pad_type = conv1d_16_pad_type_0, strides = conv1d_16_strides_0, weight = p_embedding_model_blocks_2_res2net_block_blocks_3_conv_conv_weight_to_fp16, x = pad_16_cast_fp16)[name = tensor<string, []>("conv1d_16_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> relu_15_cast_fp16 = relu(x = conv1d_16_cast_fp16)[name = tensor<string, []>("relu_15_cast_fp16")];
            tensor<fp16, [128]> b_embedding_model_blocks_2_res2net_block_blocks_3_norm_norm_running_mean_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_2_res2net_block_blocks_3_norm_norm_running_mean_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8573632)))];
            tensor<fp16, [128]> b_embedding_model_blocks_2_res2net_block_blocks_3_norm_norm_running_var_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_2_res2net_block_blocks_3_norm_norm_running_var_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8573952)))];
            tensor<fp16, [128]> p_embedding_model_blocks_2_res2net_block_blocks_3_norm_norm_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_res2net_block_blocks_3_norm_norm_weight_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8574272)))];
            tensor<fp16, [128]> p_embedding_model_blocks_2_res2net_block_blocks_3_norm_norm_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_res2net_block_blocks_3_norm_norm_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8574592)))];
            tensor<fp16, []> const_139_to_fp16 = const()[name = tensor<string, []>("const_139_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 128, 1001]> _native_batch_norm_legit_no_training_14_cast_fp16 = batch_norm(beta = p_embedding_model_blocks_2_res2net_block_blocks_3_norm_norm_bias_to_fp16, epsilon = const_139_to_fp16, gamma = p_embedding_model_blocks_2_res2net_block_blocks_3_norm_norm_weight_to_fp16, mean = b_embedding_model_blocks_2_res2net_block_blocks_3_norm_norm_running_mean_to_fp16, variance = b_embedding_model_blocks_2_res2net_block_blocks_3_norm_norm_running_var_to_fp16, x = relu_15_cast_fp16)[name = tensor<string, []>("_native_batch_norm_legit_no_training_14_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> add_10_cast_fp16 = add(x = split_1_cast_fp16_5, y = _native_batch_norm_legit_no_training_14_cast_fp16)[name = tensor<string, []>("add_10_cast_fp16")];
            tensor<int32, [6]> pad_17_pad_0 = const()[name = tensor<string, []>("pad_17_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 3, 3])];
            tensor<string, []> pad_17_mode_0 = const()[name = tensor<string, []>("pad_17_mode_0"), val = tensor<string, []>("reflect")];
            tensor<fp16, []> const_142_to_fp16 = const()[name = tensor<string, []>("const_142_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 1007]> pad_17_cast_fp16 = pad(constant_val = const_142_to_fp16, mode = pad_17_mode_0, pad = pad_17_pad_0, x = add_10_cast_fp16)[name = tensor<string, []>("pad_17_cast_fp16")];
            tensor<string, []> conv1d_17_pad_type_0 = const()[name = tensor<string, []>("conv1d_17_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_17_dilations_0 = const()[name = tensor<string, []>("conv1d_17_dilations_0"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> conv1d_17_strides_0 = const()[name = tensor<string, []>("conv1d_17_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_17_pad_0 = const()[name = tensor<string, []>("conv1d_17_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, []> conv1d_17_groups_0 = const()[name = tensor<string, []>("conv1d_17_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [128, 128, 3]> p_embedding_model_blocks_2_res2net_block_blocks_4_conv_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_res2net_block_blocks_4_conv_conv_weight_to_fp16"), val = tensor<fp16, [128, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8574912)))];
            tensor<fp16, [128]> p_embedding_model_blocks_2_res2net_block_blocks_4_conv_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_res2net_block_blocks_4_conv_conv_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8673280)))];
            tensor<fp16, [1, 128, 1001]> conv1d_17_cast_fp16 = conv(bias = p_embedding_model_blocks_2_res2net_block_blocks_4_conv_conv_bias_to_fp16, dilations = conv1d_17_dilations_0, groups = conv1d_17_groups_0, pad = conv1d_17_pad_0, pad_type = conv1d_17_pad_type_0, strides = conv1d_17_strides_0, weight = p_embedding_model_blocks_2_res2net_block_blocks_4_conv_conv_weight_to_fp16, x = pad_17_cast_fp16)[name = tensor<string, []>("conv1d_17_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> relu_16_cast_fp16 = relu(x = conv1d_17_cast_fp16)[name = tensor<string, []>("relu_16_cast_fp16")];
            tensor<fp16, [128]> b_embedding_model_blocks_2_res2net_block_blocks_4_norm_norm_running_mean_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_2_res2net_block_blocks_4_norm_norm_running_mean_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8673600)))];
            tensor<fp16, [128]> b_embedding_model_blocks_2_res2net_block_blocks_4_norm_norm_running_var_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_2_res2net_block_blocks_4_norm_norm_running_var_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8673920)))];
            tensor<fp16, [128]> p_embedding_model_blocks_2_res2net_block_blocks_4_norm_norm_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_res2net_block_blocks_4_norm_norm_weight_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8674240)))];
            tensor<fp16, [128]> p_embedding_model_blocks_2_res2net_block_blocks_4_norm_norm_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_res2net_block_blocks_4_norm_norm_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8674560)))];
            tensor<fp16, []> const_147_to_fp16 = const()[name = tensor<string, []>("const_147_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 128, 1001]> _native_batch_norm_legit_no_training_15_cast_fp16 = batch_norm(beta = p_embedding_model_blocks_2_res2net_block_blocks_4_norm_norm_bias_to_fp16, epsilon = const_147_to_fp16, gamma = p_embedding_model_blocks_2_res2net_block_blocks_4_norm_norm_weight_to_fp16, mean = b_embedding_model_blocks_2_res2net_block_blocks_4_norm_norm_running_mean_to_fp16, variance = b_embedding_model_blocks_2_res2net_block_blocks_4_norm_norm_running_var_to_fp16, x = relu_16_cast_fp16)[name = tensor<string, []>("_native_batch_norm_legit_no_training_15_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> add_11_cast_fp16 = add(x = split_1_cast_fp16_6, y = _native_batch_norm_legit_no_training_15_cast_fp16)[name = tensor<string, []>("add_11_cast_fp16")];
            tensor<int32, [6]> pad_18_pad_0 = const()[name = tensor<string, []>("pad_18_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 3, 3])];
            tensor<string, []> pad_18_mode_0 = const()[name = tensor<string, []>("pad_18_mode_0"), val = tensor<string, []>("reflect")];
            tensor<fp16, []> const_150_to_fp16 = const()[name = tensor<string, []>("const_150_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 1007]> pad_18_cast_fp16 = pad(constant_val = const_150_to_fp16, mode = pad_18_mode_0, pad = pad_18_pad_0, x = add_11_cast_fp16)[name = tensor<string, []>("pad_18_cast_fp16")];
            tensor<string, []> conv1d_18_pad_type_0 = const()[name = tensor<string, []>("conv1d_18_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_18_dilations_0 = const()[name = tensor<string, []>("conv1d_18_dilations_0"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> conv1d_18_strides_0 = const()[name = tensor<string, []>("conv1d_18_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_18_pad_0 = const()[name = tensor<string, []>("conv1d_18_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, []> conv1d_18_groups_0 = const()[name = tensor<string, []>("conv1d_18_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [128, 128, 3]> p_embedding_model_blocks_2_res2net_block_blocks_5_conv_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_res2net_block_blocks_5_conv_conv_weight_to_fp16"), val = tensor<fp16, [128, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8674880)))];
            tensor<fp16, [128]> p_embedding_model_blocks_2_res2net_block_blocks_5_conv_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_res2net_block_blocks_5_conv_conv_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8773248)))];
            tensor<fp16, [1, 128, 1001]> conv1d_18_cast_fp16 = conv(bias = p_embedding_model_blocks_2_res2net_block_blocks_5_conv_conv_bias_to_fp16, dilations = conv1d_18_dilations_0, groups = conv1d_18_groups_0, pad = conv1d_18_pad_0, pad_type = conv1d_18_pad_type_0, strides = conv1d_18_strides_0, weight = p_embedding_model_blocks_2_res2net_block_blocks_5_conv_conv_weight_to_fp16, x = pad_18_cast_fp16)[name = tensor<string, []>("conv1d_18_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> relu_17_cast_fp16 = relu(x = conv1d_18_cast_fp16)[name = tensor<string, []>("relu_17_cast_fp16")];
            tensor<fp16, [128]> b_embedding_model_blocks_2_res2net_block_blocks_5_norm_norm_running_mean_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_2_res2net_block_blocks_5_norm_norm_running_mean_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8773568)))];
            tensor<fp16, [128]> b_embedding_model_blocks_2_res2net_block_blocks_5_norm_norm_running_var_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_2_res2net_block_blocks_5_norm_norm_running_var_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8773888)))];
            tensor<fp16, [128]> p_embedding_model_blocks_2_res2net_block_blocks_5_norm_norm_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_res2net_block_blocks_5_norm_norm_weight_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8774208)))];
            tensor<fp16, [128]> p_embedding_model_blocks_2_res2net_block_blocks_5_norm_norm_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_res2net_block_blocks_5_norm_norm_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8774528)))];
            tensor<fp16, []> const_155_to_fp16 = const()[name = tensor<string, []>("const_155_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 128, 1001]> _native_batch_norm_legit_no_training_16_cast_fp16 = batch_norm(beta = p_embedding_model_blocks_2_res2net_block_blocks_5_norm_norm_bias_to_fp16, epsilon = const_155_to_fp16, gamma = p_embedding_model_blocks_2_res2net_block_blocks_5_norm_norm_weight_to_fp16, mean = b_embedding_model_blocks_2_res2net_block_blocks_5_norm_norm_running_mean_to_fp16, variance = b_embedding_model_blocks_2_res2net_block_blocks_5_norm_norm_running_var_to_fp16, x = relu_17_cast_fp16)[name = tensor<string, []>("_native_batch_norm_legit_no_training_16_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> add_12_cast_fp16 = add(x = split_1_cast_fp16_7, y = _native_batch_norm_legit_no_training_16_cast_fp16)[name = tensor<string, []>("add_12_cast_fp16")];
            tensor<int32, [6]> pad_19_pad_0 = const()[name = tensor<string, []>("pad_19_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 3, 3])];
            tensor<string, []> pad_19_mode_0 = const()[name = tensor<string, []>("pad_19_mode_0"), val = tensor<string, []>("reflect")];
            tensor<fp16, []> const_158_to_fp16 = const()[name = tensor<string, []>("const_158_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 1007]> pad_19_cast_fp16 = pad(constant_val = const_158_to_fp16, mode = pad_19_mode_0, pad = pad_19_pad_0, x = add_12_cast_fp16)[name = tensor<string, []>("pad_19_cast_fp16")];
            tensor<string, []> conv1d_19_pad_type_0 = const()[name = tensor<string, []>("conv1d_19_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_19_dilations_0 = const()[name = tensor<string, []>("conv1d_19_dilations_0"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> conv1d_19_strides_0 = const()[name = tensor<string, []>("conv1d_19_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_19_pad_0 = const()[name = tensor<string, []>("conv1d_19_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, []> conv1d_19_groups_0 = const()[name = tensor<string, []>("conv1d_19_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [128, 128, 3]> p_embedding_model_blocks_2_res2net_block_blocks_6_conv_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_res2net_block_blocks_6_conv_conv_weight_to_fp16"), val = tensor<fp16, [128, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8774848)))];
            tensor<fp16, [128]> p_embedding_model_blocks_2_res2net_block_blocks_6_conv_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_res2net_block_blocks_6_conv_conv_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8873216)))];
            tensor<fp16, [1, 128, 1001]> conv1d_19_cast_fp16 = conv(bias = p_embedding_model_blocks_2_res2net_block_blocks_6_conv_conv_bias_to_fp16, dilations = conv1d_19_dilations_0, groups = conv1d_19_groups_0, pad = conv1d_19_pad_0, pad_type = conv1d_19_pad_type_0, strides = conv1d_19_strides_0, weight = p_embedding_model_blocks_2_res2net_block_blocks_6_conv_conv_weight_to_fp16, x = pad_19_cast_fp16)[name = tensor<string, []>("conv1d_19_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> relu_18_cast_fp16 = relu(x = conv1d_19_cast_fp16)[name = tensor<string, []>("relu_18_cast_fp16")];
            tensor<fp16, [128]> b_embedding_model_blocks_2_res2net_block_blocks_6_norm_norm_running_mean_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_2_res2net_block_blocks_6_norm_norm_running_mean_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8873536)))];
            tensor<fp16, [128]> b_embedding_model_blocks_2_res2net_block_blocks_6_norm_norm_running_var_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_2_res2net_block_blocks_6_norm_norm_running_var_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8873856)))];
            tensor<fp16, [128]> p_embedding_model_blocks_2_res2net_block_blocks_6_norm_norm_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_res2net_block_blocks_6_norm_norm_weight_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8874176)))];
            tensor<fp16, [128]> p_embedding_model_blocks_2_res2net_block_blocks_6_norm_norm_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_res2net_block_blocks_6_norm_norm_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8874496)))];
            tensor<fp16, []> const_163_to_fp16 = const()[name = tensor<string, []>("const_163_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 128, 1001]> _native_batch_norm_legit_no_training_17_cast_fp16 = batch_norm(beta = p_embedding_model_blocks_2_res2net_block_blocks_6_norm_norm_bias_to_fp16, epsilon = const_163_to_fp16, gamma = p_embedding_model_blocks_2_res2net_block_blocks_6_norm_norm_weight_to_fp16, mean = b_embedding_model_blocks_2_res2net_block_blocks_6_norm_norm_running_mean_to_fp16, variance = b_embedding_model_blocks_2_res2net_block_blocks_6_norm_norm_running_var_to_fp16, x = relu_18_cast_fp16)[name = tensor<string, []>("_native_batch_norm_legit_no_training_17_cast_fp16")];
            tensor<int32, []> const_165 = const()[name = tensor<string, []>("const_165"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_1_interleave_0 = const()[name = tensor<string, []>("cat_1_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 1024, 1001]> cat_1_cast_fp16 = concat(axis = const_165, interleave = cat_1_interleave_0, values = (split_1_cast_fp16_0, _native_batch_norm_legit_no_training_11_cast_fp16, _native_batch_norm_legit_no_training_12_cast_fp16, _native_batch_norm_legit_no_training_13_cast_fp16, _native_batch_norm_legit_no_training_14_cast_fp16, _native_batch_norm_legit_no_training_15_cast_fp16, _native_batch_norm_legit_no_training_16_cast_fp16, _native_batch_norm_legit_no_training_17_cast_fp16))[name = tensor<string, []>("cat_1_cast_fp16")];
            tensor<string, []> conv1d_20_pad_type_0 = const()[name = tensor<string, []>("conv1d_20_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_20_strides_0 = const()[name = tensor<string, []>("conv1d_20_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_20_pad_0 = const()[name = tensor<string, []>("conv1d_20_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_20_dilations_0 = const()[name = tensor<string, []>("conv1d_20_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_20_groups_0 = const()[name = tensor<string, []>("conv1d_20_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 1024, 1]> p_embedding_model_blocks_2_tdnn2_conv_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_tdnn2_conv_conv_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8874816)))];
            tensor<fp16, [1024]> p_embedding_model_blocks_2_tdnn2_conv_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_tdnn2_conv_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10972032)))];
            tensor<fp16, [1, 1024, 1001]> conv1d_20_cast_fp16 = conv(bias = p_embedding_model_blocks_2_tdnn2_conv_conv_bias_to_fp16, dilations = conv1d_20_dilations_0, groups = conv1d_20_groups_0, pad = conv1d_20_pad_0, pad_type = conv1d_20_pad_type_0, strides = conv1d_20_strides_0, weight = p_embedding_model_blocks_2_tdnn2_conv_conv_weight_to_fp16, x = cat_1_cast_fp16)[name = tensor<string, []>("conv1d_20_cast_fp16")];
            tensor<fp16, [1, 1024, 1001]> relu_19_cast_fp16 = relu(x = conv1d_20_cast_fp16)[name = tensor<string, []>("relu_19_cast_fp16")];
            tensor<fp16, [1024]> b_embedding_model_blocks_2_tdnn2_norm_norm_running_mean_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_2_tdnn2_norm_norm_running_mean_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10974144)))];
            tensor<fp16, [1024]> b_embedding_model_blocks_2_tdnn2_norm_norm_running_var_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_2_tdnn2_norm_norm_running_var_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10976256)))];
            tensor<fp16, [1024]> p_embedding_model_blocks_2_tdnn2_norm_norm_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_tdnn2_norm_norm_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10978368)))];
            tensor<fp16, [1024]> p_embedding_model_blocks_2_tdnn2_norm_norm_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_tdnn2_norm_norm_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10980480)))];
            tensor<fp16, []> const_169_to_fp16 = const()[name = tensor<string, []>("const_169_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1024, 1001]> _native_batch_norm_legit_no_training_18_cast_fp16 = batch_norm(beta = p_embedding_model_blocks_2_tdnn2_norm_norm_bias_to_fp16, epsilon = const_169_to_fp16, gamma = p_embedding_model_blocks_2_tdnn2_norm_norm_weight_to_fp16, mean = b_embedding_model_blocks_2_tdnn2_norm_norm_running_mean_to_fp16, variance = b_embedding_model_blocks_2_tdnn2_norm_norm_running_var_to_fp16, x = relu_19_cast_fp16)[name = tensor<string, []>("_native_batch_norm_legit_no_training_18_cast_fp16")];
            tensor<int32, [1]> mean_1_axes_0 = const()[name = tensor<string, []>("mean_1_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<bool, []> mean_1_keep_dims_0 = const()[name = tensor<string, []>("mean_1_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1024, 1]> mean_1_cast_fp16 = reduce_mean(axes = mean_1_axes_0, keep_dims = mean_1_keep_dims_0, x = _native_batch_norm_legit_no_training_18_cast_fp16)[name = tensor<string, []>("mean_1_cast_fp16")];
            tensor<string, []> conv1d_21_pad_type_0 = const()[name = tensor<string, []>("conv1d_21_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_21_strides_0 = const()[name = tensor<string, []>("conv1d_21_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_21_pad_0 = const()[name = tensor<string, []>("conv1d_21_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_21_dilations_0 = const()[name = tensor<string, []>("conv1d_21_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_21_groups_0 = const()[name = tensor<string, []>("conv1d_21_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [128, 1024, 1]> p_embedding_model_blocks_2_se_block_conv1_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_se_block_conv1_conv_weight_to_fp16"), val = tensor<fp16, [128, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10982592)))];
            tensor<fp16, [128]> p_embedding_model_blocks_2_se_block_conv1_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_se_block_conv1_conv_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11244800)))];
            tensor<fp16, [1, 128, 1]> conv1d_21_cast_fp16 = conv(bias = p_embedding_model_blocks_2_se_block_conv1_conv_bias_to_fp16, dilations = conv1d_21_dilations_0, groups = conv1d_21_groups_0, pad = conv1d_21_pad_0, pad_type = conv1d_21_pad_type_0, strides = conv1d_21_strides_0, weight = p_embedding_model_blocks_2_se_block_conv1_conv_weight_to_fp16, x = mean_1_cast_fp16)[name = tensor<string, []>("conv1d_21_cast_fp16")];
            tensor<fp16, [1, 128, 1]> relu_20_cast_fp16 = relu(x = conv1d_21_cast_fp16)[name = tensor<string, []>("relu_20_cast_fp16")];
            tensor<string, []> conv1d_22_pad_type_0 = const()[name = tensor<string, []>("conv1d_22_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_22_strides_0 = const()[name = tensor<string, []>("conv1d_22_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_22_pad_0 = const()[name = tensor<string, []>("conv1d_22_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_22_dilations_0 = const()[name = tensor<string, []>("conv1d_22_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_22_groups_0 = const()[name = tensor<string, []>("conv1d_22_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 128, 1]> p_embedding_model_blocks_2_se_block_conv2_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_se_block_conv2_conv_weight_to_fp16"), val = tensor<fp16, [1024, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11245120)))];
            tensor<fp16, [1024]> p_embedding_model_blocks_2_se_block_conv2_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_2_se_block_conv2_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11507328)))];
            tensor<fp16, [1, 1024, 1]> conv1d_22_cast_fp16 = conv(bias = p_embedding_model_blocks_2_se_block_conv2_conv_bias_to_fp16, dilations = conv1d_22_dilations_0, groups = conv1d_22_groups_0, pad = conv1d_22_pad_0, pad_type = conv1d_22_pad_type_0, strides = conv1d_22_strides_0, weight = p_embedding_model_blocks_2_se_block_conv2_conv_weight_to_fp16, x = relu_20_cast_fp16)[name = tensor<string, []>("conv1d_22_cast_fp16")];
            tensor<fp16, [1, 1024, 1]> sigmoid_1_cast_fp16 = sigmoid(x = conv1d_22_cast_fp16)[name = tensor<string, []>("sigmoid_1_cast_fp16")];
            tensor<fp16, [1, 1024, 1001]> mul_1_1_cast_fp16 = mul(x = sigmoid_1_cast_fp16, y = _native_batch_norm_legit_no_training_18_cast_fp16)[name = tensor<string, []>("mul_1_1_cast_fp16")];
            tensor<fp16, [1, 1024, 1001]> add_13_cast_fp16 = add(x = mul_1_1_cast_fp16, y = add_6_cast_fp16)[name = tensor<string, []>("add_13_cast_fp16")];
            tensor<string, []> conv1d_23_pad_type_0 = const()[name = tensor<string, []>("conv1d_23_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_23_strides_0 = const()[name = tensor<string, []>("conv1d_23_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_23_pad_0 = const()[name = tensor<string, []>("conv1d_23_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_23_dilations_0 = const()[name = tensor<string, []>("conv1d_23_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_23_groups_0 = const()[name = tensor<string, []>("conv1d_23_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 1024, 1]> p_embedding_model_blocks_3_tdnn1_conv_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_tdnn1_conv_conv_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11509440)))];
            tensor<fp16, [1024]> p_embedding_model_blocks_3_tdnn1_conv_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_tdnn1_conv_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13606656)))];
            tensor<fp16, [1, 1024, 1001]> conv1d_23_cast_fp16 = conv(bias = p_embedding_model_blocks_3_tdnn1_conv_conv_bias_to_fp16, dilations = conv1d_23_dilations_0, groups = conv1d_23_groups_0, pad = conv1d_23_pad_0, pad_type = conv1d_23_pad_type_0, strides = conv1d_23_strides_0, weight = p_embedding_model_blocks_3_tdnn1_conv_conv_weight_to_fp16, x = add_13_cast_fp16)[name = tensor<string, []>("conv1d_23_cast_fp16")];
            tensor<fp16, [1, 1024, 1001]> relu_21_cast_fp16 = relu(x = conv1d_23_cast_fp16)[name = tensor<string, []>("relu_21_cast_fp16")];
            tensor<fp16, [1024]> b_embedding_model_blocks_3_tdnn1_norm_norm_running_mean_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_3_tdnn1_norm_norm_running_mean_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13608768)))];
            tensor<fp16, [1024]> b_embedding_model_blocks_3_tdnn1_norm_norm_running_var_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_3_tdnn1_norm_norm_running_var_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13610880)))];
            tensor<fp16, [1024]> p_embedding_model_blocks_3_tdnn1_norm_norm_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_tdnn1_norm_norm_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13612992)))];
            tensor<fp16, [1024]> p_embedding_model_blocks_3_tdnn1_norm_norm_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_tdnn1_norm_norm_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13615104)))];
            tensor<fp16, []> const_180_to_fp16 = const()[name = tensor<string, []>("const_180_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1024, 1001]> _native_batch_norm_legit_no_training_19_cast_fp16 = batch_norm(beta = p_embedding_model_blocks_3_tdnn1_norm_norm_bias_to_fp16, epsilon = const_180_to_fp16, gamma = p_embedding_model_blocks_3_tdnn1_norm_norm_weight_to_fp16, mean = b_embedding_model_blocks_3_tdnn1_norm_norm_running_mean_to_fp16, variance = b_embedding_model_blocks_3_tdnn1_norm_norm_running_var_to_fp16, x = relu_21_cast_fp16)[name = tensor<string, []>("_native_batch_norm_legit_no_training_19_cast_fp16")];
            tensor<int32, [8]> tile_2 = const()[name = tensor<string, []>("tile_2"), val = tensor<int32, [8]>([128, 128, 128, 128, 128, 128, 128, 128])];
            tensor<int32, []> split_2_axis_0 = const()[name = tensor<string, []>("split_2_axis_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1, 128, 1001]> split_2_cast_fp16_0, tensor<fp16, [1, 128, 1001]> split_2_cast_fp16_1, tensor<fp16, [1, 128, 1001]> split_2_cast_fp16_2, tensor<fp16, [1, 128, 1001]> split_2_cast_fp16_3, tensor<fp16, [1, 128, 1001]> split_2_cast_fp16_4, tensor<fp16, [1, 128, 1001]> split_2_cast_fp16_5, tensor<fp16, [1, 128, 1001]> split_2_cast_fp16_6, tensor<fp16, [1, 128, 1001]> split_2_cast_fp16_7 = split(axis = split_2_axis_0, split_sizes = tile_2, x = _native_batch_norm_legit_no_training_19_cast_fp16)[name = tensor<string, []>("split_2_cast_fp16")];
            tensor<int32, [6]> pad_24_pad_0 = const()[name = tensor<string, []>("pad_24_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> pad_24_mode_0 = const()[name = tensor<string, []>("pad_24_mode_0"), val = tensor<string, []>("reflect")];
            tensor<fp16, []> const_195_to_fp16 = const()[name = tensor<string, []>("const_195_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 1009]> pad_24_cast_fp16 = pad(constant_val = const_195_to_fp16, mode = pad_24_mode_0, pad = pad_24_pad_0, x = split_2_cast_fp16_1)[name = tensor<string, []>("pad_24_cast_fp16")];
            tensor<string, []> conv1d_24_pad_type_0 = const()[name = tensor<string, []>("conv1d_24_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_24_dilations_0 = const()[name = tensor<string, []>("conv1d_24_dilations_0"), val = tensor<int32, [1]>([4])];
            tensor<int32, [1]> conv1d_24_strides_0 = const()[name = tensor<string, []>("conv1d_24_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_24_pad_0 = const()[name = tensor<string, []>("conv1d_24_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, []> conv1d_24_groups_0 = const()[name = tensor<string, []>("conv1d_24_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [128, 128, 3]> p_embedding_model_blocks_3_res2net_block_blocks_0_conv_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_res2net_block_blocks_0_conv_conv_weight_to_fp16"), val = tensor<fp16, [128, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13617216)))];
            tensor<fp16, [128]> p_embedding_model_blocks_3_res2net_block_blocks_0_conv_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_res2net_block_blocks_0_conv_conv_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13715584)))];
            tensor<fp16, [1, 128, 1001]> conv1d_24_cast_fp16 = conv(bias = p_embedding_model_blocks_3_res2net_block_blocks_0_conv_conv_bias_to_fp16, dilations = conv1d_24_dilations_0, groups = conv1d_24_groups_0, pad = conv1d_24_pad_0, pad_type = conv1d_24_pad_type_0, strides = conv1d_24_strides_0, weight = p_embedding_model_blocks_3_res2net_block_blocks_0_conv_conv_weight_to_fp16, x = pad_24_cast_fp16)[name = tensor<string, []>("conv1d_24_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> relu_22_cast_fp16 = relu(x = conv1d_24_cast_fp16)[name = tensor<string, []>("relu_22_cast_fp16")];
            tensor<fp16, [128]> b_embedding_model_blocks_3_res2net_block_blocks_0_norm_norm_running_mean_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_3_res2net_block_blocks_0_norm_norm_running_mean_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13715904)))];
            tensor<fp16, [128]> b_embedding_model_blocks_3_res2net_block_blocks_0_norm_norm_running_var_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_3_res2net_block_blocks_0_norm_norm_running_var_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13716224)))];
            tensor<fp16, [128]> p_embedding_model_blocks_3_res2net_block_blocks_0_norm_norm_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_res2net_block_blocks_0_norm_norm_weight_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13716544)))];
            tensor<fp16, [128]> p_embedding_model_blocks_3_res2net_block_blocks_0_norm_norm_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_res2net_block_blocks_0_norm_norm_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13716864)))];
            tensor<fp16, []> const_200_to_fp16 = const()[name = tensor<string, []>("const_200_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 128, 1001]> _native_batch_norm_legit_no_training_20_cast_fp16 = batch_norm(beta = p_embedding_model_blocks_3_res2net_block_blocks_0_norm_norm_bias_to_fp16, epsilon = const_200_to_fp16, gamma = p_embedding_model_blocks_3_res2net_block_blocks_0_norm_norm_weight_to_fp16, mean = b_embedding_model_blocks_3_res2net_block_blocks_0_norm_norm_running_mean_to_fp16, variance = b_embedding_model_blocks_3_res2net_block_blocks_0_norm_norm_running_var_to_fp16, x = relu_22_cast_fp16)[name = tensor<string, []>("_native_batch_norm_legit_no_training_20_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> add_14_cast_fp16 = add(x = split_2_cast_fp16_2, y = _native_batch_norm_legit_no_training_20_cast_fp16)[name = tensor<string, []>("add_14_cast_fp16")];
            tensor<int32, [6]> pad_25_pad_0 = const()[name = tensor<string, []>("pad_25_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> pad_25_mode_0 = const()[name = tensor<string, []>("pad_25_mode_0"), val = tensor<string, []>("reflect")];
            tensor<fp16, []> const_203_to_fp16 = const()[name = tensor<string, []>("const_203_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 1009]> pad_25_cast_fp16 = pad(constant_val = const_203_to_fp16, mode = pad_25_mode_0, pad = pad_25_pad_0, x = add_14_cast_fp16)[name = tensor<string, []>("pad_25_cast_fp16")];
            tensor<string, []> conv1d_25_pad_type_0 = const()[name = tensor<string, []>("conv1d_25_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_25_dilations_0 = const()[name = tensor<string, []>("conv1d_25_dilations_0"), val = tensor<int32, [1]>([4])];
            tensor<int32, [1]> conv1d_25_strides_0 = const()[name = tensor<string, []>("conv1d_25_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_25_pad_0 = const()[name = tensor<string, []>("conv1d_25_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, []> conv1d_25_groups_0 = const()[name = tensor<string, []>("conv1d_25_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [128, 128, 3]> p_embedding_model_blocks_3_res2net_block_blocks_1_conv_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_res2net_block_blocks_1_conv_conv_weight_to_fp16"), val = tensor<fp16, [128, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13717184)))];
            tensor<fp16, [128]> p_embedding_model_blocks_3_res2net_block_blocks_1_conv_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_res2net_block_blocks_1_conv_conv_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13815552)))];
            tensor<fp16, [1, 128, 1001]> conv1d_25_cast_fp16 = conv(bias = p_embedding_model_blocks_3_res2net_block_blocks_1_conv_conv_bias_to_fp16, dilations = conv1d_25_dilations_0, groups = conv1d_25_groups_0, pad = conv1d_25_pad_0, pad_type = conv1d_25_pad_type_0, strides = conv1d_25_strides_0, weight = p_embedding_model_blocks_3_res2net_block_blocks_1_conv_conv_weight_to_fp16, x = pad_25_cast_fp16)[name = tensor<string, []>("conv1d_25_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> relu_23_cast_fp16 = relu(x = conv1d_25_cast_fp16)[name = tensor<string, []>("relu_23_cast_fp16")];
            tensor<fp16, [128]> b_embedding_model_blocks_3_res2net_block_blocks_1_norm_norm_running_mean_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_3_res2net_block_blocks_1_norm_norm_running_mean_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13815872)))];
            tensor<fp16, [128]> b_embedding_model_blocks_3_res2net_block_blocks_1_norm_norm_running_var_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_3_res2net_block_blocks_1_norm_norm_running_var_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13816192)))];
            tensor<fp16, [128]> p_embedding_model_blocks_3_res2net_block_blocks_1_norm_norm_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_res2net_block_blocks_1_norm_norm_weight_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13816512)))];
            tensor<fp16, [128]> p_embedding_model_blocks_3_res2net_block_blocks_1_norm_norm_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_res2net_block_blocks_1_norm_norm_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13816832)))];
            tensor<fp16, []> const_208_to_fp16 = const()[name = tensor<string, []>("const_208_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 128, 1001]> _native_batch_norm_legit_no_training_21_cast_fp16 = batch_norm(beta = p_embedding_model_blocks_3_res2net_block_blocks_1_norm_norm_bias_to_fp16, epsilon = const_208_to_fp16, gamma = p_embedding_model_blocks_3_res2net_block_blocks_1_norm_norm_weight_to_fp16, mean = b_embedding_model_blocks_3_res2net_block_blocks_1_norm_norm_running_mean_to_fp16, variance = b_embedding_model_blocks_3_res2net_block_blocks_1_norm_norm_running_var_to_fp16, x = relu_23_cast_fp16)[name = tensor<string, []>("_native_batch_norm_legit_no_training_21_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> add_15_cast_fp16 = add(x = split_2_cast_fp16_3, y = _native_batch_norm_legit_no_training_21_cast_fp16)[name = tensor<string, []>("add_15_cast_fp16")];
            tensor<int32, [6]> pad_26_pad_0 = const()[name = tensor<string, []>("pad_26_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> pad_26_mode_0 = const()[name = tensor<string, []>("pad_26_mode_0"), val = tensor<string, []>("reflect")];
            tensor<fp16, []> const_211_to_fp16 = const()[name = tensor<string, []>("const_211_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 1009]> pad_26_cast_fp16 = pad(constant_val = const_211_to_fp16, mode = pad_26_mode_0, pad = pad_26_pad_0, x = add_15_cast_fp16)[name = tensor<string, []>("pad_26_cast_fp16")];
            tensor<string, []> conv1d_26_pad_type_0 = const()[name = tensor<string, []>("conv1d_26_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_26_dilations_0 = const()[name = tensor<string, []>("conv1d_26_dilations_0"), val = tensor<int32, [1]>([4])];
            tensor<int32, [1]> conv1d_26_strides_0 = const()[name = tensor<string, []>("conv1d_26_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_26_pad_0 = const()[name = tensor<string, []>("conv1d_26_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, []> conv1d_26_groups_0 = const()[name = tensor<string, []>("conv1d_26_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [128, 128, 3]> p_embedding_model_blocks_3_res2net_block_blocks_2_conv_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_res2net_block_blocks_2_conv_conv_weight_to_fp16"), val = tensor<fp16, [128, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13817152)))];
            tensor<fp16, [128]> p_embedding_model_blocks_3_res2net_block_blocks_2_conv_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_res2net_block_blocks_2_conv_conv_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13915520)))];
            tensor<fp16, [1, 128, 1001]> conv1d_26_cast_fp16 = conv(bias = p_embedding_model_blocks_3_res2net_block_blocks_2_conv_conv_bias_to_fp16, dilations = conv1d_26_dilations_0, groups = conv1d_26_groups_0, pad = conv1d_26_pad_0, pad_type = conv1d_26_pad_type_0, strides = conv1d_26_strides_0, weight = p_embedding_model_blocks_3_res2net_block_blocks_2_conv_conv_weight_to_fp16, x = pad_26_cast_fp16)[name = tensor<string, []>("conv1d_26_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> relu_24_cast_fp16 = relu(x = conv1d_26_cast_fp16)[name = tensor<string, []>("relu_24_cast_fp16")];
            tensor<fp16, [128]> b_embedding_model_blocks_3_res2net_block_blocks_2_norm_norm_running_mean_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_3_res2net_block_blocks_2_norm_norm_running_mean_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13915840)))];
            tensor<fp16, [128]> b_embedding_model_blocks_3_res2net_block_blocks_2_norm_norm_running_var_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_3_res2net_block_blocks_2_norm_norm_running_var_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13916160)))];
            tensor<fp16, [128]> p_embedding_model_blocks_3_res2net_block_blocks_2_norm_norm_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_res2net_block_blocks_2_norm_norm_weight_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13916480)))];
            tensor<fp16, [128]> p_embedding_model_blocks_3_res2net_block_blocks_2_norm_norm_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_res2net_block_blocks_2_norm_norm_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13916800)))];
            tensor<fp16, []> const_216_to_fp16 = const()[name = tensor<string, []>("const_216_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 128, 1001]> _native_batch_norm_legit_no_training_22_cast_fp16 = batch_norm(beta = p_embedding_model_blocks_3_res2net_block_blocks_2_norm_norm_bias_to_fp16, epsilon = const_216_to_fp16, gamma = p_embedding_model_blocks_3_res2net_block_blocks_2_norm_norm_weight_to_fp16, mean = b_embedding_model_blocks_3_res2net_block_blocks_2_norm_norm_running_mean_to_fp16, variance = b_embedding_model_blocks_3_res2net_block_blocks_2_norm_norm_running_var_to_fp16, x = relu_24_cast_fp16)[name = tensor<string, []>("_native_batch_norm_legit_no_training_22_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> add_16_cast_fp16 = add(x = split_2_cast_fp16_4, y = _native_batch_norm_legit_no_training_22_cast_fp16)[name = tensor<string, []>("add_16_cast_fp16")];
            tensor<int32, [6]> pad_27_pad_0 = const()[name = tensor<string, []>("pad_27_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> pad_27_mode_0 = const()[name = tensor<string, []>("pad_27_mode_0"), val = tensor<string, []>("reflect")];
            tensor<fp16, []> const_219_to_fp16 = const()[name = tensor<string, []>("const_219_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 1009]> pad_27_cast_fp16 = pad(constant_val = const_219_to_fp16, mode = pad_27_mode_0, pad = pad_27_pad_0, x = add_16_cast_fp16)[name = tensor<string, []>("pad_27_cast_fp16")];
            tensor<string, []> conv1d_27_pad_type_0 = const()[name = tensor<string, []>("conv1d_27_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_27_dilations_0 = const()[name = tensor<string, []>("conv1d_27_dilations_0"), val = tensor<int32, [1]>([4])];
            tensor<int32, [1]> conv1d_27_strides_0 = const()[name = tensor<string, []>("conv1d_27_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_27_pad_0 = const()[name = tensor<string, []>("conv1d_27_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, []> conv1d_27_groups_0 = const()[name = tensor<string, []>("conv1d_27_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [128, 128, 3]> p_embedding_model_blocks_3_res2net_block_blocks_3_conv_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_res2net_block_blocks_3_conv_conv_weight_to_fp16"), val = tensor<fp16, [128, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13917120)))];
            tensor<fp16, [128]> p_embedding_model_blocks_3_res2net_block_blocks_3_conv_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_res2net_block_blocks_3_conv_conv_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14015488)))];
            tensor<fp16, [1, 128, 1001]> conv1d_27_cast_fp16 = conv(bias = p_embedding_model_blocks_3_res2net_block_blocks_3_conv_conv_bias_to_fp16, dilations = conv1d_27_dilations_0, groups = conv1d_27_groups_0, pad = conv1d_27_pad_0, pad_type = conv1d_27_pad_type_0, strides = conv1d_27_strides_0, weight = p_embedding_model_blocks_3_res2net_block_blocks_3_conv_conv_weight_to_fp16, x = pad_27_cast_fp16)[name = tensor<string, []>("conv1d_27_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> relu_25_cast_fp16 = relu(x = conv1d_27_cast_fp16)[name = tensor<string, []>("relu_25_cast_fp16")];
            tensor<fp16, [128]> b_embedding_model_blocks_3_res2net_block_blocks_3_norm_norm_running_mean_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_3_res2net_block_blocks_3_norm_norm_running_mean_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14015808)))];
            tensor<fp16, [128]> b_embedding_model_blocks_3_res2net_block_blocks_3_norm_norm_running_var_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_3_res2net_block_blocks_3_norm_norm_running_var_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14016128)))];
            tensor<fp16, [128]> p_embedding_model_blocks_3_res2net_block_blocks_3_norm_norm_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_res2net_block_blocks_3_norm_norm_weight_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14016448)))];
            tensor<fp16, [128]> p_embedding_model_blocks_3_res2net_block_blocks_3_norm_norm_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_res2net_block_blocks_3_norm_norm_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14016768)))];
            tensor<fp16, []> const_224_to_fp16 = const()[name = tensor<string, []>("const_224_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 128, 1001]> _native_batch_norm_legit_no_training_23_cast_fp16 = batch_norm(beta = p_embedding_model_blocks_3_res2net_block_blocks_3_norm_norm_bias_to_fp16, epsilon = const_224_to_fp16, gamma = p_embedding_model_blocks_3_res2net_block_blocks_3_norm_norm_weight_to_fp16, mean = b_embedding_model_blocks_3_res2net_block_blocks_3_norm_norm_running_mean_to_fp16, variance = b_embedding_model_blocks_3_res2net_block_blocks_3_norm_norm_running_var_to_fp16, x = relu_25_cast_fp16)[name = tensor<string, []>("_native_batch_norm_legit_no_training_23_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> add_17_cast_fp16 = add(x = split_2_cast_fp16_5, y = _native_batch_norm_legit_no_training_23_cast_fp16)[name = tensor<string, []>("add_17_cast_fp16")];
            tensor<int32, [6]> pad_28_pad_0 = const()[name = tensor<string, []>("pad_28_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> pad_28_mode_0 = const()[name = tensor<string, []>("pad_28_mode_0"), val = tensor<string, []>("reflect")];
            tensor<fp16, []> const_227_to_fp16 = const()[name = tensor<string, []>("const_227_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 1009]> pad_28_cast_fp16 = pad(constant_val = const_227_to_fp16, mode = pad_28_mode_0, pad = pad_28_pad_0, x = add_17_cast_fp16)[name = tensor<string, []>("pad_28_cast_fp16")];
            tensor<string, []> conv1d_28_pad_type_0 = const()[name = tensor<string, []>("conv1d_28_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_28_dilations_0 = const()[name = tensor<string, []>("conv1d_28_dilations_0"), val = tensor<int32, [1]>([4])];
            tensor<int32, [1]> conv1d_28_strides_0 = const()[name = tensor<string, []>("conv1d_28_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_28_pad_0 = const()[name = tensor<string, []>("conv1d_28_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, []> conv1d_28_groups_0 = const()[name = tensor<string, []>("conv1d_28_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [128, 128, 3]> p_embedding_model_blocks_3_res2net_block_blocks_4_conv_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_res2net_block_blocks_4_conv_conv_weight_to_fp16"), val = tensor<fp16, [128, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14017088)))];
            tensor<fp16, [128]> p_embedding_model_blocks_3_res2net_block_blocks_4_conv_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_res2net_block_blocks_4_conv_conv_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14115456)))];
            tensor<fp16, [1, 128, 1001]> conv1d_28_cast_fp16 = conv(bias = p_embedding_model_blocks_3_res2net_block_blocks_4_conv_conv_bias_to_fp16, dilations = conv1d_28_dilations_0, groups = conv1d_28_groups_0, pad = conv1d_28_pad_0, pad_type = conv1d_28_pad_type_0, strides = conv1d_28_strides_0, weight = p_embedding_model_blocks_3_res2net_block_blocks_4_conv_conv_weight_to_fp16, x = pad_28_cast_fp16)[name = tensor<string, []>("conv1d_28_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> relu_26_cast_fp16 = relu(x = conv1d_28_cast_fp16)[name = tensor<string, []>("relu_26_cast_fp16")];
            tensor<fp16, [128]> b_embedding_model_blocks_3_res2net_block_blocks_4_norm_norm_running_mean_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_3_res2net_block_blocks_4_norm_norm_running_mean_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14115776)))];
            tensor<fp16, [128]> b_embedding_model_blocks_3_res2net_block_blocks_4_norm_norm_running_var_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_3_res2net_block_blocks_4_norm_norm_running_var_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14116096)))];
            tensor<fp16, [128]> p_embedding_model_blocks_3_res2net_block_blocks_4_norm_norm_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_res2net_block_blocks_4_norm_norm_weight_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14116416)))];
            tensor<fp16, [128]> p_embedding_model_blocks_3_res2net_block_blocks_4_norm_norm_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_res2net_block_blocks_4_norm_norm_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14116736)))];
            tensor<fp16, []> const_232_to_fp16 = const()[name = tensor<string, []>("const_232_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 128, 1001]> _native_batch_norm_legit_no_training_24_cast_fp16 = batch_norm(beta = p_embedding_model_blocks_3_res2net_block_blocks_4_norm_norm_bias_to_fp16, epsilon = const_232_to_fp16, gamma = p_embedding_model_blocks_3_res2net_block_blocks_4_norm_norm_weight_to_fp16, mean = b_embedding_model_blocks_3_res2net_block_blocks_4_norm_norm_running_mean_to_fp16, variance = b_embedding_model_blocks_3_res2net_block_blocks_4_norm_norm_running_var_to_fp16, x = relu_26_cast_fp16)[name = tensor<string, []>("_native_batch_norm_legit_no_training_24_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> add_18_cast_fp16 = add(x = split_2_cast_fp16_6, y = _native_batch_norm_legit_no_training_24_cast_fp16)[name = tensor<string, []>("add_18_cast_fp16")];
            tensor<int32, [6]> pad_29_pad_0 = const()[name = tensor<string, []>("pad_29_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> pad_29_mode_0 = const()[name = tensor<string, []>("pad_29_mode_0"), val = tensor<string, []>("reflect")];
            tensor<fp16, []> const_235_to_fp16 = const()[name = tensor<string, []>("const_235_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 1009]> pad_29_cast_fp16 = pad(constant_val = const_235_to_fp16, mode = pad_29_mode_0, pad = pad_29_pad_0, x = add_18_cast_fp16)[name = tensor<string, []>("pad_29_cast_fp16")];
            tensor<string, []> conv1d_29_pad_type_0 = const()[name = tensor<string, []>("conv1d_29_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_29_dilations_0 = const()[name = tensor<string, []>("conv1d_29_dilations_0"), val = tensor<int32, [1]>([4])];
            tensor<int32, [1]> conv1d_29_strides_0 = const()[name = tensor<string, []>("conv1d_29_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_29_pad_0 = const()[name = tensor<string, []>("conv1d_29_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, []> conv1d_29_groups_0 = const()[name = tensor<string, []>("conv1d_29_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [128, 128, 3]> p_embedding_model_blocks_3_res2net_block_blocks_5_conv_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_res2net_block_blocks_5_conv_conv_weight_to_fp16"), val = tensor<fp16, [128, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14117056)))];
            tensor<fp16, [128]> p_embedding_model_blocks_3_res2net_block_blocks_5_conv_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_res2net_block_blocks_5_conv_conv_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14215424)))];
            tensor<fp16, [1, 128, 1001]> conv1d_29_cast_fp16 = conv(bias = p_embedding_model_blocks_3_res2net_block_blocks_5_conv_conv_bias_to_fp16, dilations = conv1d_29_dilations_0, groups = conv1d_29_groups_0, pad = conv1d_29_pad_0, pad_type = conv1d_29_pad_type_0, strides = conv1d_29_strides_0, weight = p_embedding_model_blocks_3_res2net_block_blocks_5_conv_conv_weight_to_fp16, x = pad_29_cast_fp16)[name = tensor<string, []>("conv1d_29_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> relu_27_cast_fp16 = relu(x = conv1d_29_cast_fp16)[name = tensor<string, []>("relu_27_cast_fp16")];
            tensor<fp16, [128]> b_embedding_model_blocks_3_res2net_block_blocks_5_norm_norm_running_mean_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_3_res2net_block_blocks_5_norm_norm_running_mean_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14215744)))];
            tensor<fp16, [128]> b_embedding_model_blocks_3_res2net_block_blocks_5_norm_norm_running_var_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_3_res2net_block_blocks_5_norm_norm_running_var_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14216064)))];
            tensor<fp16, [128]> p_embedding_model_blocks_3_res2net_block_blocks_5_norm_norm_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_res2net_block_blocks_5_norm_norm_weight_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14216384)))];
            tensor<fp16, [128]> p_embedding_model_blocks_3_res2net_block_blocks_5_norm_norm_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_res2net_block_blocks_5_norm_norm_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14216704)))];
            tensor<fp16, []> const_240_to_fp16 = const()[name = tensor<string, []>("const_240_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 128, 1001]> _native_batch_norm_legit_no_training_25_cast_fp16 = batch_norm(beta = p_embedding_model_blocks_3_res2net_block_blocks_5_norm_norm_bias_to_fp16, epsilon = const_240_to_fp16, gamma = p_embedding_model_blocks_3_res2net_block_blocks_5_norm_norm_weight_to_fp16, mean = b_embedding_model_blocks_3_res2net_block_blocks_5_norm_norm_running_mean_to_fp16, variance = b_embedding_model_blocks_3_res2net_block_blocks_5_norm_norm_running_var_to_fp16, x = relu_27_cast_fp16)[name = tensor<string, []>("_native_batch_norm_legit_no_training_25_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> add_19_cast_fp16 = add(x = split_2_cast_fp16_7, y = _native_batch_norm_legit_no_training_25_cast_fp16)[name = tensor<string, []>("add_19_cast_fp16")];
            tensor<int32, [6]> pad_30_pad_0 = const()[name = tensor<string, []>("pad_30_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            tensor<string, []> pad_30_mode_0 = const()[name = tensor<string, []>("pad_30_mode_0"), val = tensor<string, []>("reflect")];
            tensor<fp16, []> const_243_to_fp16 = const()[name = tensor<string, []>("const_243_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
            tensor<fp16, [1, 128, 1009]> pad_30_cast_fp16 = pad(constant_val = const_243_to_fp16, mode = pad_30_mode_0, pad = pad_30_pad_0, x = add_19_cast_fp16)[name = tensor<string, []>("pad_30_cast_fp16")];
            tensor<string, []> conv1d_30_pad_type_0 = const()[name = tensor<string, []>("conv1d_30_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_30_dilations_0 = const()[name = tensor<string, []>("conv1d_30_dilations_0"), val = tensor<int32, [1]>([4])];
            tensor<int32, [1]> conv1d_30_strides_0 = const()[name = tensor<string, []>("conv1d_30_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_30_pad_0 = const()[name = tensor<string, []>("conv1d_30_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, []> conv1d_30_groups_0 = const()[name = tensor<string, []>("conv1d_30_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [128, 128, 3]> p_embedding_model_blocks_3_res2net_block_blocks_6_conv_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_res2net_block_blocks_6_conv_conv_weight_to_fp16"), val = tensor<fp16, [128, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14217024)))];
            tensor<fp16, [128]> p_embedding_model_blocks_3_res2net_block_blocks_6_conv_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_res2net_block_blocks_6_conv_conv_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14315392)))];
            tensor<fp16, [1, 128, 1001]> conv1d_30_cast_fp16 = conv(bias = p_embedding_model_blocks_3_res2net_block_blocks_6_conv_conv_bias_to_fp16, dilations = conv1d_30_dilations_0, groups = conv1d_30_groups_0, pad = conv1d_30_pad_0, pad_type = conv1d_30_pad_type_0, strides = conv1d_30_strides_0, weight = p_embedding_model_blocks_3_res2net_block_blocks_6_conv_conv_weight_to_fp16, x = pad_30_cast_fp16)[name = tensor<string, []>("conv1d_30_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> relu_28_cast_fp16 = relu(x = conv1d_30_cast_fp16)[name = tensor<string, []>("relu_28_cast_fp16")];
            tensor<fp16, [128]> b_embedding_model_blocks_3_res2net_block_blocks_6_norm_norm_running_mean_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_3_res2net_block_blocks_6_norm_norm_running_mean_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14315712)))];
            tensor<fp16, [128]> b_embedding_model_blocks_3_res2net_block_blocks_6_norm_norm_running_var_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_3_res2net_block_blocks_6_norm_norm_running_var_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14316032)))];
            tensor<fp16, [128]> p_embedding_model_blocks_3_res2net_block_blocks_6_norm_norm_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_res2net_block_blocks_6_norm_norm_weight_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14316352)))];
            tensor<fp16, [128]> p_embedding_model_blocks_3_res2net_block_blocks_6_norm_norm_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_res2net_block_blocks_6_norm_norm_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14316672)))];
            tensor<fp16, []> const_248_to_fp16 = const()[name = tensor<string, []>("const_248_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 128, 1001]> _native_batch_norm_legit_no_training_26_cast_fp16 = batch_norm(beta = p_embedding_model_blocks_3_res2net_block_blocks_6_norm_norm_bias_to_fp16, epsilon = const_248_to_fp16, gamma = p_embedding_model_blocks_3_res2net_block_blocks_6_norm_norm_weight_to_fp16, mean = b_embedding_model_blocks_3_res2net_block_blocks_6_norm_norm_running_mean_to_fp16, variance = b_embedding_model_blocks_3_res2net_block_blocks_6_norm_norm_running_var_to_fp16, x = relu_28_cast_fp16)[name = tensor<string, []>("_native_batch_norm_legit_no_training_26_cast_fp16")];
            tensor<int32, []> const_250 = const()[name = tensor<string, []>("const_250"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_2_interleave_0 = const()[name = tensor<string, []>("cat_2_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 1024, 1001]> cat_2_cast_fp16 = concat(axis = const_250, interleave = cat_2_interleave_0, values = (split_2_cast_fp16_0, _native_batch_norm_legit_no_training_20_cast_fp16, _native_batch_norm_legit_no_training_21_cast_fp16, _native_batch_norm_legit_no_training_22_cast_fp16, _native_batch_norm_legit_no_training_23_cast_fp16, _native_batch_norm_legit_no_training_24_cast_fp16, _native_batch_norm_legit_no_training_25_cast_fp16, _native_batch_norm_legit_no_training_26_cast_fp16))[name = tensor<string, []>("cat_2_cast_fp16")];
            tensor<string, []> conv1d_31_pad_type_0 = const()[name = tensor<string, []>("conv1d_31_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_31_strides_0 = const()[name = tensor<string, []>("conv1d_31_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_31_pad_0 = const()[name = tensor<string, []>("conv1d_31_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_31_dilations_0 = const()[name = tensor<string, []>("conv1d_31_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_31_groups_0 = const()[name = tensor<string, []>("conv1d_31_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 1024, 1]> p_embedding_model_blocks_3_tdnn2_conv_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_tdnn2_conv_conv_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14316992)))];
            tensor<fp16, [1024]> p_embedding_model_blocks_3_tdnn2_conv_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_tdnn2_conv_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16414208)))];
            tensor<fp16, [1, 1024, 1001]> conv1d_31_cast_fp16 = conv(bias = p_embedding_model_blocks_3_tdnn2_conv_conv_bias_to_fp16, dilations = conv1d_31_dilations_0, groups = conv1d_31_groups_0, pad = conv1d_31_pad_0, pad_type = conv1d_31_pad_type_0, strides = conv1d_31_strides_0, weight = p_embedding_model_blocks_3_tdnn2_conv_conv_weight_to_fp16, x = cat_2_cast_fp16)[name = tensor<string, []>("conv1d_31_cast_fp16")];
            tensor<fp16, [1, 1024, 1001]> relu_29_cast_fp16 = relu(x = conv1d_31_cast_fp16)[name = tensor<string, []>("relu_29_cast_fp16")];
            tensor<fp16, [1024]> b_embedding_model_blocks_3_tdnn2_norm_norm_running_mean_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_3_tdnn2_norm_norm_running_mean_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16416320)))];
            tensor<fp16, [1024]> b_embedding_model_blocks_3_tdnn2_norm_norm_running_var_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_blocks_3_tdnn2_norm_norm_running_var_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16418432)))];
            tensor<fp16, [1024]> p_embedding_model_blocks_3_tdnn2_norm_norm_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_tdnn2_norm_norm_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16420544)))];
            tensor<fp16, [1024]> p_embedding_model_blocks_3_tdnn2_norm_norm_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_tdnn2_norm_norm_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16422656)))];
            tensor<fp16, []> const_254_to_fp16 = const()[name = tensor<string, []>("const_254_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 1024, 1001]> _native_batch_norm_legit_no_training_27_cast_fp16 = batch_norm(beta = p_embedding_model_blocks_3_tdnn2_norm_norm_bias_to_fp16, epsilon = const_254_to_fp16, gamma = p_embedding_model_blocks_3_tdnn2_norm_norm_weight_to_fp16, mean = b_embedding_model_blocks_3_tdnn2_norm_norm_running_mean_to_fp16, variance = b_embedding_model_blocks_3_tdnn2_norm_norm_running_var_to_fp16, x = relu_29_cast_fp16)[name = tensor<string, []>("_native_batch_norm_legit_no_training_27_cast_fp16")];
            tensor<int32, [1]> mean_2_axes_0 = const()[name = tensor<string, []>("mean_2_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<bool, []> mean_2_keep_dims_0 = const()[name = tensor<string, []>("mean_2_keep_dims_0"), val = tensor<bool, []>(true)];
            tensor<fp16, [1, 1024, 1]> mean_2_cast_fp16 = reduce_mean(axes = mean_2_axes_0, keep_dims = mean_2_keep_dims_0, x = _native_batch_norm_legit_no_training_27_cast_fp16)[name = tensor<string, []>("mean_2_cast_fp16")];
            tensor<string, []> conv1d_32_pad_type_0 = const()[name = tensor<string, []>("conv1d_32_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_32_strides_0 = const()[name = tensor<string, []>("conv1d_32_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_32_pad_0 = const()[name = tensor<string, []>("conv1d_32_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_32_dilations_0 = const()[name = tensor<string, []>("conv1d_32_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_32_groups_0 = const()[name = tensor<string, []>("conv1d_32_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [128, 1024, 1]> p_embedding_model_blocks_3_se_block_conv1_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_se_block_conv1_conv_weight_to_fp16"), val = tensor<fp16, [128, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16424768)))];
            tensor<fp16, [128]> p_embedding_model_blocks_3_se_block_conv1_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_se_block_conv1_conv_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16686976)))];
            tensor<fp16, [1, 128, 1]> conv1d_32_cast_fp16 = conv(bias = p_embedding_model_blocks_3_se_block_conv1_conv_bias_to_fp16, dilations = conv1d_32_dilations_0, groups = conv1d_32_groups_0, pad = conv1d_32_pad_0, pad_type = conv1d_32_pad_type_0, strides = conv1d_32_strides_0, weight = p_embedding_model_blocks_3_se_block_conv1_conv_weight_to_fp16, x = mean_2_cast_fp16)[name = tensor<string, []>("conv1d_32_cast_fp16")];
            tensor<fp16, [1, 128, 1]> relu_30_cast_fp16 = relu(x = conv1d_32_cast_fp16)[name = tensor<string, []>("relu_30_cast_fp16")];
            tensor<string, []> conv1d_33_pad_type_0 = const()[name = tensor<string, []>("conv1d_33_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_33_strides_0 = const()[name = tensor<string, []>("conv1d_33_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_33_pad_0 = const()[name = tensor<string, []>("conv1d_33_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_33_dilations_0 = const()[name = tensor<string, []>("conv1d_33_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_33_groups_0 = const()[name = tensor<string, []>("conv1d_33_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [1024, 128, 1]> p_embedding_model_blocks_3_se_block_conv2_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_se_block_conv2_conv_weight_to_fp16"), val = tensor<fp16, [1024, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16687296)))];
            tensor<fp16, [1024]> p_embedding_model_blocks_3_se_block_conv2_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_blocks_3_se_block_conv2_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16949504)))];
            tensor<fp16, [1, 1024, 1]> conv1d_33_cast_fp16 = conv(bias = p_embedding_model_blocks_3_se_block_conv2_conv_bias_to_fp16, dilations = conv1d_33_dilations_0, groups = conv1d_33_groups_0, pad = conv1d_33_pad_0, pad_type = conv1d_33_pad_type_0, strides = conv1d_33_strides_0, weight = p_embedding_model_blocks_3_se_block_conv2_conv_weight_to_fp16, x = relu_30_cast_fp16)[name = tensor<string, []>("conv1d_33_cast_fp16")];
            tensor<fp16, [1, 1024, 1]> sigmoid_2_cast_fp16 = sigmoid(x = conv1d_33_cast_fp16)[name = tensor<string, []>("sigmoid_2_cast_fp16")];
            tensor<fp16, [1, 1024, 1001]> mul_2_1_cast_fp16 = mul(x = sigmoid_2_cast_fp16, y = _native_batch_norm_legit_no_training_27_cast_fp16)[name = tensor<string, []>("mul_2_1_cast_fp16")];
            tensor<fp16, [1, 1024, 1001]> add_20_cast_fp16 = add(x = mul_2_1_cast_fp16, y = add_13_cast_fp16)[name = tensor<string, []>("add_20_cast_fp16")];
            tensor<int32, []> const_262 = const()[name = tensor<string, []>("const_262"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_3_interleave_0 = const()[name = tensor<string, []>("cat_3_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 3072, 1001]> cat_3_cast_fp16 = concat(axis = const_262, interleave = cat_3_interleave_0, values = (add_6_cast_fp16, add_13_cast_fp16, add_20_cast_fp16))[name = tensor<string, []>("cat_3_cast_fp16")];
            tensor<string, []> conv1d_34_pad_type_0 = const()[name = tensor<string, []>("conv1d_34_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_34_strides_0 = const()[name = tensor<string, []>("conv1d_34_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_34_pad_0 = const()[name = tensor<string, []>("conv1d_34_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_34_dilations_0 = const()[name = tensor<string, []>("conv1d_34_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_34_groups_0 = const()[name = tensor<string, []>("conv1d_34_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [3072, 3072, 1]> p_embedding_model_mfa_conv_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_mfa_conv_conv_weight_to_fp16"), val = tensor<fp16, [3072, 3072, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16951616)))];
            tensor<fp16, [3072]> p_embedding_model_mfa_conv_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_mfa_conv_conv_bias_to_fp16"), val = tensor<fp16, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(35826048)))];
            tensor<fp16, [1, 3072, 1001]> conv1d_34_cast_fp16 = conv(bias = p_embedding_model_mfa_conv_conv_bias_to_fp16, dilations = conv1d_34_dilations_0, groups = conv1d_34_groups_0, pad = conv1d_34_pad_0, pad_type = conv1d_34_pad_type_0, strides = conv1d_34_strides_0, weight = p_embedding_model_mfa_conv_conv_weight_to_fp16, x = cat_3_cast_fp16)[name = tensor<string, []>("conv1d_34_cast_fp16")];
            tensor<fp16, [1, 3072, 1001]> relu_31_cast_fp16 = relu(x = conv1d_34_cast_fp16)[name = tensor<string, []>("relu_31_cast_fp16")];
            tensor<fp16, [3072]> b_embedding_model_mfa_norm_norm_running_mean_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_mfa_norm_norm_running_mean_to_fp16"), val = tensor<fp16, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(35832256)))];
            tensor<fp16, [3072]> b_embedding_model_mfa_norm_norm_running_var_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_mfa_norm_norm_running_var_to_fp16"), val = tensor<fp16, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(35838464)))];
            tensor<fp16, [3072]> p_embedding_model_mfa_norm_norm_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_mfa_norm_norm_weight_to_fp16"), val = tensor<fp16, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(35844672)))];
            tensor<fp16, [3072]> p_embedding_model_mfa_norm_norm_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_mfa_norm_norm_bias_to_fp16"), val = tensor<fp16, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(35850880)))];
            tensor<fp16, []> const_266_to_fp16 = const()[name = tensor<string, []>("const_266_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 3072, 1001]> _native_batch_norm_legit_no_training_28_cast_fp16 = batch_norm(beta = p_embedding_model_mfa_norm_norm_bias_to_fp16, epsilon = const_266_to_fp16, gamma = p_embedding_model_mfa_norm_norm_weight_to_fp16, mean = b_embedding_model_mfa_norm_norm_running_mean_to_fp16, variance = b_embedding_model_mfa_norm_norm_running_var_to_fp16, x = relu_31_cast_fp16)[name = tensor<string, []>("_native_batch_norm_legit_no_training_28_cast_fp16")];
            tensor<fp16, [1, 1, 1001]> div_to_fp16 = const()[name = tensor<string, []>("div_to_fp16"), val = tensor<fp16, [1, 1, 1001]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(35857088)))];
            tensor<fp16, [1, 3072, 1001]> mul_4_1_cast_fp16 = mul(x = div_to_fp16, y = _native_batch_norm_legit_no_training_28_cast_fp16)[name = tensor<string, []>("mul_4_1_cast_fp16")];
            tensor<int32, [1]> sum_2_axes_0 = const()[name = tensor<string, []>("sum_2_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<bool, []> sum_2_keep_dims_0 = const()[name = tensor<string, []>("sum_2_keep_dims_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 3072]> sum_2_cast_fp16 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul_4_1_cast_fp16)[name = tensor<string, []>("sum_2_cast_fp16")];
            tensor<int32, [1]> unsqueeze_2_axes_0 = const()[name = tensor<string, []>("unsqueeze_2_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<fp16, [1, 3072, 1]> unsqueeze_2_cast_fp16 = expand_dims(axes = unsqueeze_2_axes_0, x = sum_2_cast_fp16)[name = tensor<string, []>("unsqueeze_2_cast_fp16")];
            tensor<fp16, [1, 3072, 1001]> sub_cast_fp16 = sub(x = _native_batch_norm_legit_no_training_28_cast_fp16, y = unsqueeze_2_cast_fp16)[name = tensor<string, []>("sub_cast_fp16")];
            tensor<fp16, []> const_285_promoted_to_fp16 = const()[name = tensor<string, []>("const_285_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 3072, 1001]> pow_1_cast_fp16 = pow(x = sub_cast_fp16, y = const_285_promoted_to_fp16)[name = tensor<string, []>("pow_1_cast_fp16")];
            tensor<fp16, [1, 3072, 1001]> mul_5_1_cast_fp16 = mul(x = div_to_fp16, y = pow_1_cast_fp16)[name = tensor<string, []>("mul_5_1_cast_fp16")];
            tensor<int32, [1]> sum_3_axes_0 = const()[name = tensor<string, []>("sum_3_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<bool, []> sum_3_keep_dims_0 = const()[name = tensor<string, []>("sum_3_keep_dims_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 3072]> sum_3_cast_fp16 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = mul_5_1_cast_fp16)[name = tensor<string, []>("sum_3_cast_fp16")];
            tensor<fp16, []> const_287_to_fp16 = const()[name = tensor<string, []>("const_287_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, []> const_288_to_fp16 = const()[name = tensor<string, []>("const_288_to_fp16"), val = tensor<fp16, []>(inf)];
            tensor<fp16, [1, 3072]> clip_0_cast_fp16 = clip(alpha = const_287_to_fp16, beta = const_288_to_fp16, x = sum_3_cast_fp16)[name = tensor<string, []>("clip_0_cast_fp16")];
            tensor<fp16, [1, 3072]> sqrt_cast_fp16 = sqrt(x = clip_0_cast_fp16)[name = tensor<string, []>("sqrt_cast_fp16")];
            tensor<int32, [3]> const_290 = const()[name = tensor<string, []>("const_290"), val = tensor<int32, [3]>([1, 1, 1001])];
            tensor<fp16, [1, 3072, 1001]> repeat_cast_fp16 = tile(reps = const_290, x = unsqueeze_2_cast_fp16)[name = tensor<string, []>("repeat_cast_fp16")];
            tensor<int32, [1]> unsqueeze_4_axes_0 = const()[name = tensor<string, []>("unsqueeze_4_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<fp16, [1, 3072, 1]> unsqueeze_4_cast_fp16 = expand_dims(axes = unsqueeze_4_axes_0, x = sqrt_cast_fp16)[name = tensor<string, []>("unsqueeze_4_cast_fp16")];
            tensor<int32, [3]> const_292 = const()[name = tensor<string, []>("const_292"), val = tensor<int32, [3]>([1, 1, 1001])];
            tensor<fp16, [1, 3072, 1001]> repeat_1_cast_fp16 = tile(reps = const_292, x = unsqueeze_4_cast_fp16)[name = tensor<string, []>("repeat_1_cast_fp16")];
            tensor<int32, []> const_293 = const()[name = tensor<string, []>("const_293"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_4_interleave_0 = const()[name = tensor<string, []>("cat_4_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 9216, 1001]> cat_4_cast_fp16 = concat(axis = const_293, interleave = cat_4_interleave_0, values = (_native_batch_norm_legit_no_training_28_cast_fp16, repeat_cast_fp16, repeat_1_cast_fp16))[name = tensor<string, []>("cat_4_cast_fp16")];
            tensor<string, []> conv1d_35_pad_type_0 = const()[name = tensor<string, []>("conv1d_35_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_35_strides_0 = const()[name = tensor<string, []>("conv1d_35_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_35_pad_0 = const()[name = tensor<string, []>("conv1d_35_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_35_dilations_0 = const()[name = tensor<string, []>("conv1d_35_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_35_groups_0 = const()[name = tensor<string, []>("conv1d_35_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [128, 9216, 1]> p_embedding_model_asp_tdnn_conv_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_asp_tdnn_conv_conv_weight_to_fp16"), val = tensor<fp16, [128, 9216, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(35859200)))];
            tensor<fp16, [128]> p_embedding_model_asp_tdnn_conv_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_asp_tdnn_conv_conv_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38218560)))];
            tensor<fp16, [1, 128, 1001]> conv1d_35_cast_fp16 = conv(bias = p_embedding_model_asp_tdnn_conv_conv_bias_to_fp16, dilations = conv1d_35_dilations_0, groups = conv1d_35_groups_0, pad = conv1d_35_pad_0, pad_type = conv1d_35_pad_type_0, strides = conv1d_35_strides_0, weight = p_embedding_model_asp_tdnn_conv_conv_weight_to_fp16, x = cat_4_cast_fp16)[name = tensor<string, []>("conv1d_35_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> relu_32_cast_fp16 = relu(x = conv1d_35_cast_fp16)[name = tensor<string, []>("relu_32_cast_fp16")];
            tensor<fp16, [128]> b_embedding_model_asp_tdnn_norm_norm_running_mean_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_asp_tdnn_norm_norm_running_mean_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38218880)))];
            tensor<fp16, [128]> b_embedding_model_asp_tdnn_norm_norm_running_var_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_asp_tdnn_norm_norm_running_var_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38219200)))];
            tensor<fp16, [128]> p_embedding_model_asp_tdnn_norm_norm_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_asp_tdnn_norm_norm_weight_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38219520)))];
            tensor<fp16, [128]> p_embedding_model_asp_tdnn_norm_norm_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_asp_tdnn_norm_norm_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38219840)))];
            tensor<fp16, []> const_297_to_fp16 = const()[name = tensor<string, []>("const_297_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 128, 1001]> _native_batch_norm_legit_no_training_29_cast_fp16 = batch_norm(beta = p_embedding_model_asp_tdnn_norm_norm_bias_to_fp16, epsilon = const_297_to_fp16, gamma = p_embedding_model_asp_tdnn_norm_norm_weight_to_fp16, mean = b_embedding_model_asp_tdnn_norm_norm_running_mean_to_fp16, variance = b_embedding_model_asp_tdnn_norm_norm_running_var_to_fp16, x = relu_32_cast_fp16)[name = tensor<string, []>("_native_batch_norm_legit_no_training_29_cast_fp16")];
            tensor<fp16, [1, 128, 1001]> tanh_cast_fp16 = tanh(x = _native_batch_norm_legit_no_training_29_cast_fp16)[name = tensor<string, []>("tanh_cast_fp16")];
            tensor<string, []> conv1d_36_pad_type_0 = const()[name = tensor<string, []>("conv1d_36_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_36_strides_0 = const()[name = tensor<string, []>("conv1d_36_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_36_pad_0 = const()[name = tensor<string, []>("conv1d_36_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_36_dilations_0 = const()[name = tensor<string, []>("conv1d_36_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_36_groups_0 = const()[name = tensor<string, []>("conv1d_36_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [3072, 128, 1]> p_embedding_model_asp_conv_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_asp_conv_conv_weight_to_fp16"), val = tensor<fp16, [3072, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38220160)))];
            tensor<fp16, [3072]> p_embedding_model_asp_conv_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_asp_conv_conv_bias_to_fp16"), val = tensor<fp16, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39006656)))];
            tensor<fp16, [1, 3072, 1001]> conv1d_36_cast_fp16 = conv(bias = p_embedding_model_asp_conv_conv_bias_to_fp16, dilations = conv1d_36_dilations_0, groups = conv1d_36_groups_0, pad = conv1d_36_pad_0, pad_type = conv1d_36_pad_type_0, strides = conv1d_36_strides_0, weight = p_embedding_model_asp_conv_conv_weight_to_fp16, x = tanh_cast_fp16)[name = tensor<string, []>("conv1d_36_cast_fp16")];
            tensor<int32, []> const_303 = const()[name = tensor<string, []>("const_303"), val = tensor<int32, []>(2)];
            tensor<fp16, [1, 3072, 1001]> softmax_cast_fp16 = softmax(axis = const_303, x = conv1d_36_cast_fp16)[name = tensor<string, []>("softmax_cast_fp16")];
            tensor<fp16, [1, 3072, 1001]> mul_6_cast_fp16 = mul(x = softmax_cast_fp16, y = _native_batch_norm_legit_no_training_28_cast_fp16)[name = tensor<string, []>("mul_6_cast_fp16")];
            tensor<int32, [1]> sum_4_axes_0 = const()[name = tensor<string, []>("sum_4_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<bool, []> sum_4_keep_dims_0 = const()[name = tensor<string, []>("sum_4_keep_dims_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 3072]> sum_4_cast_fp16 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_6_cast_fp16)[name = tensor<string, []>("sum_4_cast_fp16")];
            tensor<int32, [1]> unsqueeze_5_axes_0 = const()[name = tensor<string, []>("unsqueeze_5_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<fp16, [1, 3072, 1]> unsqueeze_5_cast_fp16 = expand_dims(axes = unsqueeze_5_axes_0, x = sum_4_cast_fp16)[name = tensor<string, []>("unsqueeze_5_cast_fp16")];
            tensor<fp16, [1, 3072, 1001]> sub_1_cast_fp16 = sub(x = _native_batch_norm_legit_no_training_28_cast_fp16, y = unsqueeze_5_cast_fp16)[name = tensor<string, []>("sub_1_cast_fp16")];
            tensor<fp16, []> const_306_promoted_to_fp16 = const()[name = tensor<string, []>("const_306_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
            tensor<fp16, [1, 3072, 1001]> pow_2_cast_fp16 = pow(x = sub_1_cast_fp16, y = const_306_promoted_to_fp16)[name = tensor<string, []>("pow_2_cast_fp16")];
            tensor<fp16, [1, 3072, 1001]> mul_7_cast_fp16 = mul(x = softmax_cast_fp16, y = pow_2_cast_fp16)[name = tensor<string, []>("mul_7_cast_fp16")];
            tensor<int32, [1]> sum_5_axes_0 = const()[name = tensor<string, []>("sum_5_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<bool, []> sum_5_keep_dims_0 = const()[name = tensor<string, []>("sum_5_keep_dims_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 3072]> sum_5_cast_fp16 = reduce_sum(axes = sum_5_axes_0, keep_dims = sum_5_keep_dims_0, x = mul_7_cast_fp16)[name = tensor<string, []>("sum_5_cast_fp16")];
            tensor<fp16, []> const_308_to_fp16 = const()[name = tensor<string, []>("const_308_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
            tensor<fp16, []> const_309_to_fp16 = const()[name = tensor<string, []>("const_309_to_fp16"), val = tensor<fp16, []>(inf)];
            tensor<fp16, [1, 3072]> clip_1_cast_fp16 = clip(alpha = const_308_to_fp16, beta = const_309_to_fp16, x = sum_5_cast_fp16)[name = tensor<string, []>("clip_1_cast_fp16")];
            tensor<fp16, [1, 3072]> sqrt_1_cast_fp16 = sqrt(x = clip_1_cast_fp16)[name = tensor<string, []>("sqrt_1_cast_fp16")];
            tensor<int32, []> const_310 = const()[name = tensor<string, []>("const_310"), val = tensor<int32, []>(1)];
            tensor<bool, []> cat_5_interleave_0 = const()[name = tensor<string, []>("cat_5_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 6144]> cat_5_cast_fp16 = concat(axis = const_310, interleave = cat_5_interleave_0, values = (sum_4_cast_fp16, sqrt_1_cast_fp16))[name = tensor<string, []>("cat_5_cast_fp16")];
            tensor<int32, [1]> unsqueeze_6_axes_0 = const()[name = tensor<string, []>("unsqueeze_6_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<fp16, [1, 6144, 1]> unsqueeze_6_cast_fp16 = expand_dims(axes = unsqueeze_6_axes_0, x = cat_5_cast_fp16)[name = tensor<string, []>("unsqueeze_6_cast_fp16")];
            tensor<fp16, [6144]> b_embedding_model_asp_bn_norm_running_mean_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_asp_bn_norm_running_mean_to_fp16"), val = tensor<fp16, [6144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39012864)))];
            tensor<fp16, [6144]> b_embedding_model_asp_bn_norm_running_var_to_fp16 = const()[name = tensor<string, []>("b_embedding_model_asp_bn_norm_running_var_to_fp16"), val = tensor<fp16, [6144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39025216)))];
            tensor<fp16, [6144]> p_embedding_model_asp_bn_norm_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_asp_bn_norm_weight_to_fp16"), val = tensor<fp16, [6144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39037568)))];
            tensor<fp16, [6144]> p_embedding_model_asp_bn_norm_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_asp_bn_norm_bias_to_fp16"), val = tensor<fp16, [6144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39049920)))];
            tensor<fp16, []> const_313_to_fp16 = const()[name = tensor<string, []>("const_313_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 6144, 1]> _native_batch_norm_legit_no_training_30_cast_fp16 = batch_norm(beta = p_embedding_model_asp_bn_norm_bias_to_fp16, epsilon = const_313_to_fp16, gamma = p_embedding_model_asp_bn_norm_weight_to_fp16, mean = b_embedding_model_asp_bn_norm_running_mean_to_fp16, variance = b_embedding_model_asp_bn_norm_running_var_to_fp16, x = unsqueeze_6_cast_fp16)[name = tensor<string, []>("_native_batch_norm_legit_no_training_30_cast_fp16")];
            tensor<string, []> conv1d_37_pad_type_0 = const()[name = tensor<string, []>("conv1d_37_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv1d_37_strides_0 = const()[name = tensor<string, []>("conv1d_37_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv1d_37_pad_0 = const()[name = tensor<string, []>("conv1d_37_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv1d_37_dilations_0 = const()[name = tensor<string, []>("conv1d_37_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv1d_37_groups_0 = const()[name = tensor<string, []>("conv1d_37_groups_0"), val = tensor<int32, []>(1)];
            tensor<fp16, [256, 6144, 1]> p_embedding_model_fc_conv_weight_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_fc_conv_weight_to_fp16"), val = tensor<fp16, [256, 6144, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39062272)))];
            tensor<fp16, [256]> p_embedding_model_fc_conv_bias_to_fp16 = const()[name = tensor<string, []>("p_embedding_model_fc_conv_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42208064)))];
            tensor<fp16, [1, 256, 1]> conv1d_37_cast_fp16 = conv(bias = p_embedding_model_fc_conv_bias_to_fp16, dilations = conv1d_37_dilations_0, groups = conv1d_37_groups_0, pad = conv1d_37_pad_0, pad_type = conv1d_37_pad_type_0, strides = conv1d_37_strides_0, weight = p_embedding_model_fc_conv_weight_to_fp16, x = _native_batch_norm_legit_no_training_30_cast_fp16)[name = tensor<string, []>("conv1d_37_cast_fp16")];
            tensor<fp32, []> leaky_relu_alpha_0 = const()[name = tensor<string, []>("leaky_relu_alpha_0"), val = tensor<fp32, []>(0x1.47ae14p-7)];
            tensor<fp16, [1, 256, 1]> leaky_relu_cast_fp16 = leaky_relu(alpha = leaky_relu_alpha_0, x = conv1d_37_cast_fp16)[name = tensor<string, []>("leaky_relu_cast_fp16")];
            tensor<fp16, [256]> b_classifier_mod_norm_norm_running_mean_to_fp16 = const()[name = tensor<string, []>("b_classifier_mod_norm_norm_running_mean_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42208640)))];
            tensor<fp16, [256]> b_classifier_mod_norm_norm_running_var_to_fp16 = const()[name = tensor<string, []>("b_classifier_mod_norm_norm_running_var_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42209216)))];
            tensor<fp16, [256]> p_classifier_mod_norm_norm_weight_to_fp16 = const()[name = tensor<string, []>("p_classifier_mod_norm_norm_weight_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42209792)))];
            tensor<fp16, [256]> p_classifier_mod_norm_norm_bias_to_fp16 = const()[name = tensor<string, []>("p_classifier_mod_norm_norm_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42210368)))];
            tensor<fp16, []> const_322_to_fp16 = const()[name = tensor<string, []>("const_322_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 256, 1]> _native_batch_norm_legit_no_training_31_cast_fp16 = batch_norm(beta = p_classifier_mod_norm_norm_bias_to_fp16, epsilon = const_322_to_fp16, gamma = p_classifier_mod_norm_norm_weight_to_fp16, mean = b_classifier_mod_norm_norm_running_mean_to_fp16, variance = b_classifier_mod_norm_norm_running_var_to_fp16, x = leaky_relu_cast_fp16)[name = tensor<string, []>("_native_batch_norm_legit_no_training_31_cast_fp16")];
            tensor<int32, [3]> transpose_3_perm_0 = const()[name = tensor<string, []>("transpose_3_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
            tensor<fp16, [512, 256]> p_classifier_mod_dnn_block_0_linear_w_weight_to_fp16 = const()[name = tensor<string, []>("p_classifier_mod_dnn_block_0_linear_w_weight_to_fp16"), val = tensor<fp16, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42210944)))];
            tensor<fp16, [512]> p_classifier_mod_dnn_block_0_linear_w_bias_to_fp16 = const()[name = tensor<string, []>("p_classifier_mod_dnn_block_0_linear_w_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42473152)))];
            tensor<fp16, [1, 1, 256]> transpose_3_cast_fp16 = transpose(perm = transpose_3_perm_0, x = _native_batch_norm_legit_no_training_31_cast_fp16)[name = tensor<string, []>("transpose_2")];
            tensor<fp16, [1, 1, 512]> linear_0_cast_fp16 = linear(bias = p_classifier_mod_dnn_block_0_linear_w_bias_to_fp16, weight = p_classifier_mod_dnn_block_0_linear_w_weight_to_fp16, x = transpose_3_cast_fp16)[name = tensor<string, []>("linear_0_cast_fp16")];
            tensor<fp32, []> leaky_relu_1_alpha_0 = const()[name = tensor<string, []>("leaky_relu_1_alpha_0"), val = tensor<fp32, []>(0x1.47ae14p-7)];
            tensor<fp16, [1, 1, 512]> leaky_relu_1_cast_fp16 = leaky_relu(alpha = leaky_relu_1_alpha_0, x = linear_0_cast_fp16)[name = tensor<string, []>("leaky_relu_1_cast_fp16")];
            tensor<int32, [3]> transpose_4_perm_0 = const()[name = tensor<string, []>("transpose_4_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
            tensor<fp16, [512]> b_classifier_mod_dnn_block_0_norm_norm_running_mean_to_fp16 = const()[name = tensor<string, []>("b_classifier_mod_dnn_block_0_norm_norm_running_mean_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42474240)))];
            tensor<fp16, [512]> b_classifier_mod_dnn_block_0_norm_norm_running_var_to_fp16 = const()[name = tensor<string, []>("b_classifier_mod_dnn_block_0_norm_norm_running_var_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42475328)))];
            tensor<fp16, [512]> p_classifier_mod_dnn_block_0_norm_norm_weight_to_fp16 = const()[name = tensor<string, []>("p_classifier_mod_dnn_block_0_norm_norm_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42476416)))];
            tensor<fp16, [512]> p_classifier_mod_dnn_block_0_norm_norm_bias_to_fp16 = const()[name = tensor<string, []>("p_classifier_mod_dnn_block_0_norm_norm_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42477504)))];
            tensor<fp16, []> const_329_to_fp16 = const()[name = tensor<string, []>("const_329_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
            tensor<fp16, [1, 512, 1]> transpose_4_cast_fp16 = transpose(perm = transpose_4_perm_0, x = leaky_relu_1_cast_fp16)[name = tensor<string, []>("transpose_1")];
            tensor<fp16, [1, 512, 1]> _native_batch_norm_legit_no_training_32_cast_fp16 = batch_norm(beta = p_classifier_mod_dnn_block_0_norm_norm_bias_to_fp16, epsilon = const_329_to_fp16, gamma = p_classifier_mod_dnn_block_0_norm_norm_weight_to_fp16, mean = b_classifier_mod_dnn_block_0_norm_norm_running_mean_to_fp16, variance = b_classifier_mod_dnn_block_0_norm_norm_running_var_to_fp16, x = transpose_4_cast_fp16)[name = tensor<string, []>("_native_batch_norm_legit_no_training_32_cast_fp16")];
            tensor<int32, [3]> transpose_5_perm_0 = const()[name = tensor<string, []>("transpose_5_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
            tensor<fp16, [107, 512]> p_classifier_mod_out_w_weight_to_fp16 = const()[name = tensor<string, []>("p_classifier_mod_out_w_weight_to_fp16"), val = tensor<fp16, [107, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42478592)))];
            tensor<fp16, [107]> p_classifier_mod_out_w_bias_to_fp16 = const()[name = tensor<string, []>("p_classifier_mod_out_w_bias_to_fp16"), val = tensor<fp16, [107]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42588224)))];
            tensor<fp16, [1, 1, 512]> transpose_5_cast_fp16 = transpose(perm = transpose_5_perm_0, x = _native_batch_norm_legit_no_training_32_cast_fp16)[name = tensor<string, []>("transpose_0")];
            tensor<fp16, [1, 1, 107]> linear_1_cast_fp16 = linear(bias = p_classifier_mod_out_w_bias_to_fp16, weight = p_classifier_mod_out_w_weight_to_fp16, x = transpose_5_cast_fp16)[name = tensor<string, []>("linear_1_cast_fp16")];
            tensor<int32, [2]> const_333 = const()[name = tensor<string, []>("const_333"), val = tensor<int32, [2]>([1, 107])];
            tensor<fp16, [1, 107]> view_cast_fp16 = reshape(shape = const_333, x = linear_1_cast_fp16)[name = tensor<string, []>("view_cast_fp16")];
            tensor<int32, []> const_334 = const()[name = tensor<string, []>("const_334"), val = tensor<int32, []>(-1)];
            tensor<fp16, [1, 107]> log_softmax_softmax_cast_fp16 = softmax(axis = const_334, x = view_cast_fp16)[name = tensor<string, []>("log_softmax_softmax_cast_fp16")];
            tensor<fp32, []> log_softmax_epsilon_0 = const()[name = tensor<string, []>("log_softmax_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
            tensor<fp16, [1, 107]> log_softmax_cast_fp16 = log(epsilon = log_softmax_epsilon_0, x = log_softmax_softmax_cast_fp16)[name = tensor<string, []>("log_softmax_cast_fp16")];
            tensor<int32, [3]> const_336 = const()[name = tensor<string, []>("const_336"), val = tensor<int32, [3]>([1, 1, 107])];
            tensor<fp16, [1, 1, 107]> view_1_cast_fp16 = reshape(shape = const_336, x = log_softmax_cast_fp16)[name = tensor<string, []>("view_1_cast_fp16")];
            tensor<int32, [1]> squeeze_axes_0 = const()[name = tensor<string, []>("squeeze_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 107]> language_logits = squeeze(axes = squeeze_axes_0, x = view_1_cast_fp16)[name = tensor<string, []>("squeeze_cast_fp16")];
        } -> (language_logits);
}