code: def test_pool(
      self,
      width,
      same_inputs,
      is_ntk,
      pool_type,
      padding,
      filter_shape,
      strides,
      normalize_edges,
  ):
      use_dropout = False
      # Check for duplicate / incorrectly-shaped NN configs / wrong backend.
      test_utils.skip_test(self)
      if pool_type == "SUM" and normalize_edges:
          raise absltest.SkipTest(
              "normalize_edges not applicable to SumPool."
          )
  
      net = _get_net_pool(
          width,
          is_ntk,
          pool_type,
          padding,
          filter_shape,
          strides,
          normalize_edges,
      )
      _check_agreement_with_empirical(
          self, net, same_inputs, use_dropout, is_ntk
      )
  
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 4 for calling the StaxTest obj's underlying member method code with example usage: obj.test_pool(width, same_inputs, is_ntk, pool_type, padding, filter_shape, strides, normalize_edges) without return types