code: def test_input_channels_last(self):
      m = torch.nn.Sequential(
          torch.nn.Conv2d(3, 3, 1, 1),
          ToTuple(),
      ).cuda()
      inp = (
          torch.randn([2, 3, 16, 16])
          .to(memory_format=torch.channels_last)
          .cuda()
      )
  
      self.common(
          m,
          (inp,),
          check_lowp=False,
      )
  
      @torch._dynamo.optimize()
      def foo(m, inp):
          return m(inp)
  
      self.assertTrue(
          foo(m, inp)[0].is_contiguous(
              memory_format=torch.channels_last
          )
      )
  
imports: import math
  import sys
  import unittest
  import torch
  import torch._dynamo.config
  import torch.backends.cuda
  import torch.nn.functional
  from torch import nn
  from torch._dynamo.debug_utils import same_two_models
  from torch._dynamo.testing import rand_strided
  from torch._dynamo.utils import same
  from torch._inductor import config
  from torch._inductor.compile_fx import compile_fx_inner
  from torch.fx.experimental.proxy_tensor import make_fx
  from torch.testing._internal.common_cuda import PLATFORM_SUPPORTS_FLASH_ATTENTION
  from torch.testing._internal.common_utils import DeterministicGuard, freeze_rng_state, IS_FBCODE, skipIfRocm, TEST_WITH_ASAN
label_desc: Use image node_id 2 for calling the CudaReproTests obj's underlying member method code with example usage: obj.test_input_channels_last() and returns: m