code: def test_group_linear_fusion(self):
      z = 10
      for has_bias in [True, False]:
          counters.clear()
          module = MyModule(z, has_bias).to("cuda")
          input = [torch.randn(z, z, device="cuda")]
          traced = torch.compile(module)
          ref = module(*input)
          res = traced(*input)
          self.compare_pred(module, traced, input)
          self.assertEqual(
              counters["inductor"]["group_fusion"],
              2,
          )
          self.assertEqual(
              counters["inductor"]["batch_fusion"],
              0,
          )
          ref.sum().backward()
          res.sum().backward()
          self.compare_parameters(module, traced)
          self.compare_gradients(module, traced)
          self.assertEqual(
              counters["inductor"]["group_fusion"],
              4,
          )
          self.assertEqual(
              counters["inductor"]["batch_fusion"],
              3,
          )
          counters.clear()
  
imports: import functools
  import unittest
  import torch
  import torch._inductor
  from torch._dynamo.test_case import run_tests, TestCase
  from torch._dynamo.utils import counters
  from torch.testing._internal.inductor_utils import HAS_CUDA
label_desc: Use image node_id 5 for calling the TestGroupBatchFusion obj's underlying member method code with example usage: obj.test_group_linear_fusion() without return types