code: def test_tensor_sharing_with_forward(self):
      module = self.Msm1(self.Msm()).cuda()
      replica = dp.replicate(module, {0, 1})
      x = torch.ones(2, 2, requires_grad=True).cuda()
      first_forward = module(x)
      first_forward.sum().backward()
      with torch.no_grad():
          for p in module.parameters():
              # Use .data here to avoid version counter bump.
              # The graph created by the following forward will be wrong but
              # we never backward through them so it's fine
              p.data -= 1.0 * p.grad
      second_forward = module(x)
  
      # replica which is on the same GPU has a shallow copy of the original
      # params and buffers
      r0_forward = replica[0](x)
      self.assertEqual(second_forward, r0_forward)
  
      # replica which is on a different GPU has a deep copy of the original
      # params and buffers
      x1 = torch.ones(2, 2, requires_grad=True).cuda(device=1)
      r1_forward = replica[1](x1)
      self.assertEqual(first_forward, r1_forward)
  
file_path: pytorch/test/jit/test_data_parallel.py
imports: import os
  import sys
  import unittest
  import torch
  import torch.nn
  import torch.nn.parallel
  from torch.testing._internal.jit_utils import JitTestCase, RUN_CUDA_MULTI_GPU
label_desc: Use image node_id 6 for calling the TestDataParallel obj's underlying member method code with example usage: obj.test_tensor_sharing_with_forward() without return types