def METHOD_NAME():
    """
    Converts PyTorch/TorchScript file to ONNX files, for dynamic batchsize and explicit batchsize
    """
    args = get_args()
    print("Converting PyTorch model to ONNX...")
    output_dir_path = Path(args.output_dir).absolute()
    output_dir_path.mkdir(parents=True, exist_ok=True)
    model_path = Path(args.model).absolute()
    output_path = Path(args.output_dir, args.output_name).absolute()
    dynamic_bs_output_path = Path(
        args.output_dir, args.dynamic_bs_output_name).absolute()
    print("Loading PyTorch model...")
    assert Path(model_path).is_file(
    ), "Cannot find the model file {:}!".format(model_path)
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    model = torch.jit.load(model_path, map_location=device)
    model.eval()
    batchsize = 1
    input_channels = 1
    output_channels = 3
    depth, height, width = ROI_SHAPE
    dummy_input = torch.rand(
        [batchsize, input_channels, height, width, depth]).float().to(device)
    dummy_output = torch.rand(
        [batchsize, output_channels, height, width, depth]).float().to(device)
    # using opset version 12
    torch.onnx.export(model, dummy_input, output_path, opset_version=12,
                      do_constant_folding=False, input_names=['input'], output_names=['output'],
                      example_outputs=dummy_output)
    torch.onnx.export(model, dummy_input, dynamic_bs_output_path, opset_version=12,
                      do_constant_folding=False, input_names=['input'], output_names=['output'],
                      dynamic_axes={"input": {0: "batch_size"},
                                    "output": {0: "batch_size"}},
                      example_outputs=dummy_output)
    print("Successfully exported model:\n  {}\nand\n  {}".format(
        output_path, dynamic_bs_output_path))