code: def test_redundant_clone_for_layout_convert(self):
      class Model(torch.nn.Module):
          def __init__(self):
              super().__init__()
              self.conv = nn.Conv2d(
                  3, 128, kernel_size=3, padding=1, stride=1, bias=False
              )
  
          def forward(self, x):
              y = x + 1
              return self.conv(x), y
  
          @staticmethod
          def get_example_inputs():
              return (torch.rand(2, 3, 5, 5).to(self.device),)
  
      mod = Model().eval().to(self.device)
      inp = mod.get_example_inputs()
      with torch.no_grad():
          expected_outputs = mod(*inp)
  
      num_same_stride = 0
      num_diff_stride = 0
  
      def debug_inductor_force_stride_order(
          orig_fn, input_tensor, stride
      ):
          nonlocal num_same_stride, num_diff_stride
          input_tensor.realize()
          if tuple(input_tensor.get_stride()) == tuple(stride):
              num_same_stride += 1
          else:
              num_diff_stride += 1
          return orig_fn(input_tensor, stride)
  
      with override_lowering(
          prims.inductor_force_stride_order.default,
          debug_inductor_force_stride_order,
      ):
          opt_mod = torch.compile(mod)
          with torch.no_grad():
              actual_outputs = opt_mod(*inp)
  
      self.assertEqual(len(actual_outputs), len(expected_outputs))
      self.assertEqual(2, len(actual_outputs))
      for i, actual, expected in zip(
          itertools.count(), actual_outputs, expected_outputs
      ):
          self.assertTrue(
              torch.allclose(expected, actual, atol=1e-4, rtol=1e-4),
              f"{i}th output: expected {expected}, actual {actual}",
          )
  
      if self.device == "cpu":
          # CPU use different convolution implementation, skip the checks below
          return
  
      self.assertTrue(
          actual_outputs[0].is_contiguous(
              memory_format=torch.contiguous_format
          )
      )
      self.assertTrue(
          actual_outputs[1].is_contiguous(
              memory_format=torch.contiguous_format
          )
      )
  
      # we don't change the stride of y returned by forward. So there will
      # be no extra copy
      self.assertTrue(
          num_same_stride == 1, f"num_same_stride is {num_same_stride}"
      )
      # we changed the stride of self.conv(x) returned by forward. So there
      # may be an extra copy
      self.assertTrue(
          num_diff_stride == 1, f"num_diff_stride is {num_diff_stride}"
      )
  
imports: import contextlib
  import functools
  import importlib
  import itertools
  import os
  import sys
  import unittest
  import weakref
  import torch
  from torch import nn
  from torch._inductor import config
  from torch._inductor.utils import override_lowering, run_and_get_code
  from torch.testing import FileCheck
  from torch.testing._internal.common_cuda import SM80OrLater
  from torch.testing._internal.common_utils import IS_CI, IS_WINDOWS, TEST_WITH_ASAN, TEST_WITH_ROCM, TestCase
  from inductor.test_torchinductor import check_model, check_model_cuda, copy_tests
  from torch.testing._internal.inductor_utils import HAS_CPU, HAS_CUDA
label_desc: Use image node_id 17 for calling the OptimizeForInferenceTemplate obj's underlying member method code with example usage: obj.test_redundant_clone_for_layout_convert() and returns: orig_fn, self,  y, torch