code: def test_layer_norm_with_empty_batch(
      self, N, elementwise_affine, gc, dc
  ):
      X = np.random.randn(0, N).astype(np.float32)
      gamma = np.random.rand(N).astype(np.float32)
      beta = np.random.rand(N).astype(np.float32)
  
      op = core.CreateOperator(
          "LayerNorm",
          ["X", "gamma", "beta"] if elementwise_affine else ["X"],
          ["Y", "mean", "sigma"],
          elementwise_affine=elementwise_affine,
      )
  
      def ref(X, gamma=None, beta=None):
          Y = np.zeros_like(X)
          axis = 1
          mean = np.zeros(X.shape[:axis] + (1,), dtype=X.dtype)
          sigma = np.zeros(X.shape[:axis] + (1,), dtype=X.dtype)
          return Y, mean, sigma
  
      inputs = [X, gamma, beta] if elementwise_affine else [X]
      self.assertReferenceChecks(gc, op, inputs, ref)
      self.assertDeviceChecks(dc, op, inputs, [0, 1])
      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 10 for calling the TestLayerNormOp obj's underlying member method code with example usage: obj.test_layer_norm_with_empty_batch(N, elementwise_affine, gc, dc) and returns: Y,  mean,  sigma