code: def test_conv_with_as_strided(self):
      class Model(nn.Module):
          def __init__(self, groups):
              super().__init__()
              self.kv = torch.nn.Conv2d(
                  256,
                  384,
                  kernel_size=(1, 1),
                  stride=(1, 1),
                  bias=False,
                  groups=groups,
              )
  
          def forward(self, x):
              convolution = self.kv(x)
              constant_pad_nd = torch.ops.aten.constant_pad_nd.default(
                  convolution, [2, 2, 2, 2], 0.0
              )
              # as_strided inputs are depend on input's size and stide.
              as_strided = torch.ops.aten.as_strided.default(
                  constant_pad_nd,
                  [8, 384, 2, 20, 12],
                  [153600, 400, 160, 1, 20],
              )
              as_strided_1 = torch.ops.aten.as_strided.default(
                  as_strided,
                  [8, 384, 2, 2, 12, 12],
                  [153600, 400, 160, 8, 20, 1],
              )
              clone = torch.ops.aten.clone.default(
                  as_strided_1, memory_format=torch.contiguous_format
              )
              return clone
  
      @torch.compile()
      def foo(mod, inp):
          return mod(inp)
  
      with torch.no_grad():
          x = torch.randn(8, 256, 16, 16).to(self.device)
          for groups in [1, 2]:
              mod = Model(groups).to(self.device).eval()
              mod_eager = mod(x)
              self.assertEqual(foo(mod, x), mod_eager)
  
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 13 for calling the OptimizeForInferenceTemplate obj's underlying member method code with example usage: obj.test_conv_with_as_strided() and returns: mod, clone