code: def test_issue100806(self):
      class Model(torch.nn.Module):
          def __init__(self):
              super().__init__()
              self.linear1 = torch.nn.Linear(10, 20)
              self.linear2 = torch.nn.Linear(20, 30)
              self.relu = torch.nn.ReLU()
  
          def forward(self, x):
              x = self.linear1(x)
              x = self.linear2(x)
              x = torch.cat((x, x), dim=1)
              x = x.view(-1, 2, 30)
              x = x[:, 1, :]
              x = self.relu(x)
              return x
  
      device = "cuda"
      batch_size = 2
      x = torch.randn(batch_size, 10).to(device)
      func = Model().to(device)
  
      with torch.no_grad():
          func.train(False)
          jit_func = torch.compile(func)
  
          res1 = func(x)
          res2 = jit_func(x)
          self.assertEqual(res1, res2)
  
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 34 for calling the CudaReproTests obj's underlying member method code with example usage: obj.test_issue100806() and returns: x