code: def _np_layer_norm(x, axes, gamma=None, beta=None, epsilon=1e-5):
      rank = len(x.shape)
      axes = [axis + rank if axis < 0 else axis for axis in axes]
      normalized_shape = [
          x.shape[i] if i in axes else 1 for i in range(rank)
      ]
      gamma = (
          np.ones(shape=normalized_shape)
          if gamma is None
          else np.reshape(gamma, normalized_shape)
      )
      beta = (
          np.zeros(shape=normalized_shape)
          if beta is None
          else np.reshape(beta, normalized_shape)
      )
      num = x - np.mean(x, axis=tuple(axes), keepdims=True)
      dem = np.sqrt(
          np.sum(np.square(num), axis=tuple(axes), keepdims=True)
          / np.prod(normalized_shape)
          + epsilon
      )
      return num / dem * gamma + beta
  
imports: import itertools
  import platform
  import numpy
  import pytest
  import coremltools
  from coremltools._deps import _HAS_TF_2, _HAS_TORCH, MSG_TF2_NOT_FOUND, MSG_TORCH_NOT_FOUND
  from coremltools.converters.mil.mil import Builder
  from coremltools.converters.mil.mil import Function, get_new_symbol, types
  from coremltools.converters.mil.mil.ops.tests.iOS14 import backends
  from coremltools.converters.mil.mil.ops.tests.testing_utils import UNK_SYM, construct_inputs_from_placeholders, run_compare_builder
  from coremltools.converters.mil.testing_reqs import compute_units
  from coremltools.converters.mil.testing_utils import random_gen
label_desc: Use image node_id 2 for calling the TestNormalizationLayerNorm obj's underlying member method code with example usage: obj._np_layer_norm(x, axes, gamma, beta, epsilon) and returns: unknown