code: def test_layer_norm_grad_op(self, X, gc, dc):
      axis = np.random.randint(0, len(X.shape))
      epsilon = 1e-4
      op = core.CreateOperator(
          "LayerNormGradient",
          ["gout", "out", "mean", "stdev", "in"],
          ["gin"],
          axis=axis,
          epsilon=epsilon,
      )
  
      norm, mean, stdev = _layer_norm_ref(axis, epsilon, X)
      gout = norm
  
      self.assertReferenceChecks(
          device_option=gc,
          op=op,
          inputs=[gout, norm, mean, stdev, X],
          reference=partial(_layer_norm_grad_ref, axis),
      )
      self.assertDeviceChecks(
          device_options=dc,
          op=op,
          inputs=[gout, norm, mean, stdev, X],
          outputs_to_check=[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 1 for calling the TestLayerNormOp obj's underlying member method code with example usage: obj.test_layer_norm_grad_op(X, gc, dc) without return types