code: def _test_dropout_discontiguous(
      self, cls, device, memory_format=torch.contiguous_format
  ):
      # In this test, we verify that dropout preserves the layout and data for different memory formats.
      # We check whether, we get same values for the output of dropout, when the probability
      # of dropout is 0 or very close to 0.
      # Reference: https://github.com/pytorch/pytorch/issues/47176
      close_to_zero_p = 1e-10  # Should be almost zero but not zero, as for p=0 different path is taken
      for p in [0, close_to_zero_p]:
          inp = torch.ones(2, 3, 3, 3, device=device)
          inp_discontiguous = torch.empty(
              2, 3, 3, 6, device=device, memory_format=memory_format
          )[..., ::2]
          inp_discontiguous.copy_(inp)
          mod = cls(p=p)
          out = mod(inp_discontiguous)
          if p != 0:  # Zero will keep strides as is based on input.
              # When prob == 0, input stride (54, 18, 6, 2) -> output stride (54, 18, 6, 2)
              # When prob != 0, input stride (54, 18, 6, 2) -> output stride (27, 9, 3, 1)
              self.assertTrue(
                  out.is_contiguous(memory_format=memory_format)
              )
          self.assertEqual(inp_discontiguous, out)
  
imports: from itertools import product
  import unittest
  import random
  import itertools
  import torch
  from torch.testing._internal.common_utils import run_tests, set_default_dtype, instantiate_parametrized_tests, TEST_PRIVATEUSE1
  from torch.testing._internal.common_cuda import TEST_CUDA
  from torch.testing._internal.common_nn import NNTestCase, freeze_rng_state
  from torch.testing._internal.common_device_type import instantiate_device_type_tests, expectedFailureXLA
  import torch.nn.functional
  import torch.nn
label_desc: Use image node_id 2 for calling the TestDropoutNNDeviceType obj's underlying member method code with example usage: obj._test_dropout_discontiguous(cls, device, memory_format) without return types