code: def _run_call_with_mock_module(
      self, module, functional_call, device="cpu", prefix=""
  ):
      x = torch.rand((1, 1)).to(device)
      weight = torch.tensor([[1.0]], device=device)
      bias = torch.tensor([0.0], device=device)
      buffer = torch.tensor([0.0], device=device)
      if prefix != "":
          parameters = {
              f"{prefix}.l1.weight": weight,
              f"{prefix}.l1.bias": bias,
              f"{prefix}.buffer": buffer,
          }
      else:
          parameters = {
              "l1.weight": weight,
              "l1.bias": bias,
              "buffer": buffer,
          }
      to_check = module
      if prefix != "":
          to_check = getattr(module, prefix)
      prev_weight = to_check.l1.weight.clone()
      prev_buffer = to_check.buffer.clone()
      # the parameters represent an identity function contrary to the
      # existing params in module. So here we expect the result to be the
      # same as the input if the weight swapping went well.
      res = functional_call(module, parameters, x)
      self.assertEqual(x, res)
      # check that the weight remain unmodified
      cur_weight = to_check.l1.weight
      cur_buffer = to_check.buffer
      self.assertEqual(cur_weight, prev_weight)
      self.assertEqual(cur_buffer, prev_buffer)
  
imports: import contextlib
  import os
  import re
  import subprocess
  import sys
  import unittest
  import torch
  import torch.nn.utils.stateless
  from torch.testing._internal.common_cuda import TEST_MULTIGPU
  from torch.testing._internal.common_utils import run_tests, TestCase, parametrize, instantiate_parametrized_tests, subtest
label_desc: Use image node_id 1 for calling the TestStatelessFunctionalAPI obj's underlying member method code with example usage: obj._run_call_with_mock_module(module, functional_call, device, prefix) without return types