code: def test_builder_eval(self):
      def np_layer_norm(x, axes, gamma, beta, epsilon=1e-5):
          normalized_shape = x.shape[-len(axes) :]
          gamma = (
              np.ones(shape=normalized_shape)
              if gamma is None
              else gamma
          )
          beta = (
              np.zeros(shape=normalized_shape) if beta is None else beta
          )
          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
  
      x_val = random_gen(
          shape=(1, 3, 4, 4), rand_min=-100.0, rand_max=100.0
      )
      g = random_gen(shape=(4, 4), rand_min=1.0, rand_max=2.0)
      b = random_gen(shape=(4, 4), rand_min=0.0, rand_max=1.0)
      res = mb.layer_norm(x=x_val, axes=[-2, -1], gamma=g, beta=b)
      ref = np_layer_norm(x=x_val, axes=[-2, -1], gamma=g, beta=b)
      assert is_close(ref, res.val)
  
file_path: turicreate/src/external/coremltools_wrap/coremltools/coremltools/converters/mil/mil/ops/tests/test_normalization.py
imports: from coremltools.converters.mil import testing_reqs
  from coremltools.converters.mil.testing_reqs import *
  from .testing_utils import run_compare_builder
label_desc: Use image node_id 2 for calling the TestNormalizationLayerNorm obj's underlying member method code with example usage: obj.test_builder_eval() and returns: unknown