code: def test_rewrap_tensor_input_to_pytorch(self, dtype):
      class M(nn.Module):
          def __init__(self, eltwise_fn):
              super().__init__()
              self.conv1 = nn.Conv2d(32, 32, 3, padding=1, bias=True)
              self.conv2 = nn.Conv2d(32, 32, 3, padding=1, bias=True)
              self.eltwise = eltwise_fn
              self.adaptive_avg_pool_2d = nn.AdaptiveAvgPool2d((5, 7))
  
          def forward(self, x, y):
              x = self.conv1(x)
              x = self.eltwise(x)
              x = self.conv2(x)
              x = self.eltwise(x)
              x = torch.add(x, y)
              x = self.adaptive_avg_pool_2d(x)
              return x
  
      eltwise_fn_name = "relu"
      eltwise_fn = get_eltwise_fn(eltwise_fn_name)
      m = M(eltwise_fn)
      m = m.to(memory_format=torch.channels_last)
      x = torch.rand(1, 32, 28, 28).to(
          memory_format=torch.channels_last
      )
      y = torch.rand(1, 32, 28, 28).to(
          memory_format=torch.channels_last
      )
      # Simply test if the output is accurate
      # The output of the second partition is input to adaptive_avg_pool2d, which is
      # unsupported by LLGA, so it must be handled by PyTorch, which should receive
      # correct strides info of the channels-last tensor.
      graph, _ = self.checkTrace(m, [x, y], dtype)
  
imports: import sys
  import torch
  import unittest
  import itertools
  import torch.nn
  from functools import wraps
  from concurrent import futures
  import torch.nn.functional
  import torch.fx.experimental.optimization
  from torch.testing._internal.jit_utils import JitTestCase
  from torch.testing._internal.common_utils import run_tests, TEST_SCIPY, IS_WINDOWS, IS_MACOS
  from torch.testing._internal.common_device_type import instantiate_device_type_tests, onlyCPU, dtypes
label_desc: Use image node_id 12 for calling the TestFusionPattern obj's underlying member method code with example usage: obj.test_rewrap_tensor_input_to_pytorch(dtype) and returns: x