code: def forward(self, a: int):
      values = torch.tensor(
          [4.0, 1.0, 1.0, 16.0],
      )
      if a == 0:
          return torch.gradient(
              values,
              spacing=torch.scalar_tensor(2.0, dtype=torch.float64),
          )
      elif a == 1:
          return torch.gradient(
              values, spacing=[torch.tensor(1.0).item()]
          )
  
imports: import functools
  import os
  from io import BytesIO
  import shutil
  import sys
  import torch
  from torch.jit.mobile import _load_for_lite_interpreter, _export_operator_list
label_desc: Use image node_id 1 for calling the ModelWithScalarList obj's underlying member method code with example usage: obj.forward(a) and returns: torch, torch