code: def test_layer_norm_op_pytorch_cuda(self, X, eps, elementwise_affine):
      axis = np.random.randint(0, len(X.shape))
  
      if elementwise_affine:
          gamma = np.random.randn(*X.shape[axis:]).astype(np.float32)
          beta = np.random.randn(*X.shape[axis:]).astype(np.float32)
          (
              expected_norm,
              expected_mean,
              expected_std,
          ) = _layer_norm_with_affine_ref(axis, eps, X, gamma, beta)
          (
              actual_norm,
              actual_mean,
              actual_std,
          ) = torch.ops._caffe2.LayerNorm(
              torch.tensor(X).cuda(),
              torch.tensor(gamma).cuda(),
              torch.tensor(beta).cuda(),
              axis,
              eps,
              True,
          )
      else:
          expected_norm, expected_mean, expected_std = _layer_norm_ref(
              axis, eps, X
          )
          (
              actual_norm,
              actual_mean,
              actual_std,
          ) = torch.ops._caffe2.LayerNorm(
              torch.tensor(X).cuda(), None, None, axis, eps
          )
  
      assert_allclose(expected_norm, actual_norm, rtol=1e-4, atol=1e-4)
      assert_allclose(expected_mean, actual_mean)
      assert_allclose(expected_std, actual_std)
  
imports: from caffe2.python import brew, core, workspace
  from caffe2.python.model_helper import ModelHelper
  from functools import partial
  from hypothesis import given, settings
  from typing import Optional, Tuple
  import caffe2.python.hypothesis_test_util
  import caffe2.python.serialized_test.serialized_test_util
  import hypothesis.strategies
  import numpy
  import torch
  import unittest
  from ._utils import assert_allclose
label_desc: Use image node_id 7 for calling the TestLayerNormOp obj's underlying member method code with example usage: obj.test_layer_norm_op_pytorch_cuda(X, eps, elementwise_affine) without return types