code: def _test_fused_layer_norm(
      self,
      batch_size,
      contiguous,
      elementwise_affine,
      mixed_fused,
      dtype,
      memory_efficient,
      fwd_thresholds=dict(rtol=None, atol=None),
      bwd_thresholds=dict(rtol=None, atol=None),
  ):
      normalized_shape = [32, 16]
  
      if not mixed_fused:
          module_cpu_ = FusedLayerNorm(
              normalized_shape=normalized_shape,
              elementwise_affine=elementwise_affine,
              memory_efficient=memory_efficient,
          ).cpu()
          module_cuda_ = FusedLayerNorm(
              normalized_shape=normalized_shape,
              elementwise_affine=elementwise_affine,
              memory_efficient=memory_efficient,
          ).to(device="cuda", dtype=dtype)
      else:
          assert elementwise_affine
          module_cpu_ = MixedFusedLayerNorm(
              normalized_shape=normalized_shape,
              memory_efficient=memory_efficient,
          ).cpu()
          module_cuda_ = MixedFusedLayerNorm(
              normalized_shape=normalized_shape,
              memory_efficient=memory_efficient,
          ).to(device="cuda", dtype=dtype)
  
      torch.cuda.manual_seed(42)
      if contiguous:
          input_shape = [batch_size] + normalized_shape
          input_ = torch.randn(
              input_shape, device="cpu"
          ).requires_grad_(True)
          input_cuda_ = (
              input_.to(device="cuda", dtype=dtype)
              .detach()
              .requires_grad_(True)
          )
          self.assertTrue(input_.is_contiguous())
          self.assertTrue(input_cuda_.is_contiguous())
      else:
          input_shape = [batch_size] + normalized_shape
          input_shape = [batch_size * 3] + [
              normalized_shape[0] * 5,
              normalized_shape[1] * 3,
          ]
          input_src_ = torch.randn(input_shape, device="cpu")
          input_ = (
              input_src_[::3, ::5, ::3].detach().requires_grad_(True)
          )
          input_cuda_ = (
              input_src_.to(device="cuda", dtype=dtype)[::3, ::5, ::3]
              .detach()
              .requires_grad_(True)
          )
          # make sure that tensors are NOT contiguous.
          self.assertFalse(input_.is_contiguous())
          self.assertFalse(input_cuda_.is_contiguous())
      out_cpu_ = module_cpu_(input_)
      gO = torch.rand_like(out_cpu_)
      out_cpu_.backward(gO)
      out_cuda_ = module_cuda_(input_cuda_)
  
      gO = gO.to(device="cuda", dtype=dtype)
      out_cuda_.backward(gO)
      self.assertFalse(out_cpu_.is_cuda)
      self.assertTrue(out_cuda_.is_cuda)
      torch.testing.assert_close(
          out_cpu_.to(device="cuda", dtype=dtype),
          out_cuda_,
          **fwd_thresholds,
      )
      torch.testing.assert_close(
          input_.grad.to(device="cuda", dtype=dtype),
          input_cuda_.grad,
          **bwd_thresholds,
      )
  
imports: import torch
  from apex.normalization import FusedLayerNorm
  from apex.normalization import FusedRMSNorm
  from apex.normalization import MixedFusedLayerNorm
  from apex.normalization import MixedFusedRMSNorm
  from torch.testing._internal import common_utils
  from torch.testing._internal.common_device_type import instantiate_device_type_tests
  from itertools import product
label_desc: Use image node_id 1 for calling the TestFusedLayerNorm obj's underlying member method code with example usage: obj._test_fused_layer_norm(batch_size, contiguous, elementwise_affine, mixed_fused, dtype, memory_efficient, fwd_thresholds, bwd_thresholds) without return types