code: def test_functional_call_with_gradient(self, functional_call):
      module = MockModule()
      x = torch.rand((1, 1))
      weight = torch.tensor([[1.0]], requires_grad=True)
      bias = torch.tensor([0.0], requires_grad=True)
      buffer = torch.tensor([0.0])
      parameters = {
          "l1.weight": weight,
          "l1.bias": bias,
          "buffer": buffer,
      }
      res = functional_call(module, parameters, x)
      # Check that a backward step calculates the gradient of the supplied parameters
      res.backward()
      self.assertIsNotNone(weight.grad)
      self.assertIsNotNone(bias.grad)
      self.assertIsNone(buffer.grad)
      # Gradient was not calculated for the module stated and buffers
      self.assertIsNone(module.l1.weight.grad)
      self.assertIsNone(module.l1.bias.grad)
      self.assertIsNone(module.buffer.grad)
  
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 7 for calling the TestStatelessFunctionalAPI obj's underlying member method code with example usage: obj.test_functional_call_with_gradient(functional_call) without return types