code: def test_layer_norm_grad(
      self, M, N, axis, eps, elementwise_affine, gc, dc
  ):
      op = core.CreateOperator(
          "LayerNorm",
          ["X", "gamma", "beta"] if elementwise_affine else ["X"],
          ["Y", "mean", "std"],
          axis=axis,
          epsilon=eps,
          elementwise_affine=elementwise_affine,
      )
  
      X = np.arange(M * N).astype(np.float32)
      np.random.shuffle(X)
      X = X.reshape((M, N))
      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]
  
      for i in range(len(inputs)):
          self.assertGradientChecks(gc, op, inputs, i, [0])
  
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 3 for calling the TestLayerNormOp obj's underlying member method code with example usage: obj.test_layer_norm_grad(M, N, axis, eps, elementwise_affine, gc, dc) without return types