code: def test_linear_cpu_input(self):
      class Model(nn.Module):
          def __init__(self):
              super().__init__()
              self.linear = nn.Linear(4, 4)
  
          def forward(self, data):
              data = data.to("cuda")
              return self.linear(data)
  
      mod = Model().cuda().eval()
      with torch.no_grad():
          self.common(mod, (torch.randn(4, 4),))
  
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 31 for calling the CudaReproTests obj's underlying member method code with example usage: obj.test_linear_cpu_input() and returns: self