code: def test_batch_layer_norm_fusion(self):
      for has_weight in [True, False]:
          for has_bias in [True, False]:
              counters.clear()
              module = MyModule3("cuda", has_weight, has_bias).to(
                  "cuda"
              )
              input = [torch.randn(2, 5, 50, device="cuda")]
              traced = torch.compile(module)
              ref = module(*input)
              res = traced(*input)
              self.compare_pred(module, traced, input)
              self.assertEqual(
                  counters["inductor"]["group_fusion"],
                  0,
              )
              self.assertEqual(counters["inductor"]["batch_fusion"], 2)
              self.assertEqual(
                  counters["inductor"]["scmerge_split_removed"],
                  3,
              )
              self.assertEqual(
                  counters["inductor"]["scmerge_cat_removed"],
                  3,
              )
              ref.sum().backward()
              res.sum().backward()
              self.compare_parameters(
                  module, traced, rtol=1e-8, atol=1e-8
              )
              self.compare_gradients(
                  module, traced, rtol=1e-8, atol=1e-8
              )
              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 7 for calling the TestGroupBatchFusion obj's underlying member method code with example usage: obj.test_batch_layer_norm_fusion() without return types