code: def test_layer_norm_op(self, X, eps, elementwise_affine, gc, dc):
      axis = np.random.randint(0, len(X.shape))
  
      op = core.CreateOperator(
          "LayerNorm",
          ["X", "gamma", "beta"] if elementwise_affine else ["X"],
          ["Y", "mean", "std"],
          axis=axis,
          epsilon=eps,
          elementwise_affine=elementwise_affine,
      )
  
      if elementwise_affine:
          ref = partial(_layer_norm_with_affine_ref, axis, eps)
      else:
          ref = partial(_layer_norm_ref, axis, eps)
  
      if elementwise_affine:
          gamma = np.random.randn(*X.shape[axis:]).astype(np.float32)
          beta = np.random.randn(*X.shape[axis:]).astype(np.float32)
          inputs = [X, gamma, beta]
      else:
          inputs = [X]
  
      self.assertReferenceChecks(
          device_option=gc,
          op=op,
          inputs=inputs,
          reference=ref,
      )
      self.assertDeviceChecks(
          device_options=dc,
          op=op,
          inputs=inputs,
          outputs_to_check=[0, 1, 2],
      )
  
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 2 for calling the TestLayerNormOp obj's underlying member method code with example usage: obj.test_layer_norm_op(X, eps, elementwise_affine, gc, dc) without return types