code: def test_layernorm(
      self, model, width, same_inputs, is_ntk, proj_into_2d, layer_norm
  ):
      is_conv = "conv" in model
      # Check for duplicate / incorrectly-shaped NN configs / wrong backend.
      if is_conv:
          test_utils.skip_test(self)
      elif proj_into_2d != PROJECTIONS[0] or layer_norm not in (
          "C",
          "NC",
      ):
          raise absltest.SkipTest(
              "FC models do not have these parameters."
          )
  
      W_std, b_std = 2.0**0.5, 0.5**0.5
      filter_shape = FILTER_SHAPES[0]
      padding = PADDINGS[0]
      strides = STRIDES[0]
      phi = stax.Relu()
      use_pooling, is_res = False, False
      parameterization = "ntk"
      pool_type = "AVG"
      use_dropout = False
  
      net = _get_net(
          W_std,
          b_std,
          filter_shape,
          is_conv,
          use_pooling,
          is_res,
          padding,
          phi,
          strides,
          width,
          is_ntk,
          proj_into_2d,
          pool_type,
          layer_norm,
          parameterization,
          1,
          use_dropout,
      )
      _check_agreement_with_empirical(
          self, net, same_inputs, use_dropout, is_ntk, 0.07
      )
  
imports: import functools
  import itertools
  import random
  from absl.testing import absltest
  from jax import config
  from jax import default_backend
  from jax import jit
  from jax import random
  from jax.example_libraries import stax
  import jax.numpy
  import neural_tangents
  from neural_tangents import stax
  from neural_tangents._src.empirical import _DEFAULT_TESTING_NTK_IMPLEMENTATION
  from tests import test_utils
  import numpy
label_desc: Use image node_id 3 for calling the StaxTest obj's underlying member method code with example usage: obj.test_layernorm(model, width, same_inputs, is_ntk, proj_into_2d, layer_norm) without return types