code: def test_linear(
      self,
      get,
      s,
      depth,
      same_inputs,
      b_std,
      W_std,
      parameterization,
  ):
      if parameterization == "standard":
          width = 2**9 // s
      elif parameterization == "ntk":
          if s != 2**9:
              raise absltest.SkipTest(
                  '"ntk" parameterization does not depend on "s".'
              )
          width = 2**10
      else:
          raise ValueError(parameterization)
  
      layers = []
      for i in range(depth + 1):
          s_in = 1 if i == 0 else s
          s_out = 1 if (i == depth and get == "ntk") else s
          out_dim = (
              1 if (i == depth and get == "ntk") else width * (i + 1)
          )
          layers += [
              stax.Dense(
                  out_dim,
                  W_std=W_std / (i + 1),
                  b_std=b_std if b_std is None else b_std / (i + 1),
                  parameterization=parameterization,
                  s=(s_in, s_out),
              )
          ]
  
      net = stax.serial(*layers)
      net = net, (BATCH_SIZE, 3), -1, 1
      _check_agreement_with_empirical(
          self,
          net,
          same_inputs,
          False,
          get == "ntk",
          rtol=0.02,
          atol=10,
      )
  
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 1 for calling the ParameterizationTest obj's underlying member method code with example usage: obj.test_linear(get, s, depth, same_inputs, b_std, W_std, parameterization) without return types