code: def test_share_params_batch_normalization(self):
      # A's params and persistent values are all shared with B and C
  
      model_a = L.BatchNormalization(3)
  
      arrays = async_.share_params_as_shared_arrays(model_a)
      assert isinstance(arrays, dict)
      assert set(arrays.keys()) == {
          "/gamma",
          "/beta",
          "/avg_mean",
          "/avg_var",
          "/N",
      }
  
      model_b = L.BatchNormalization(3)
      model_c = L.BatchNormalization(3)
  
      async_.set_shared_params(model_b, arrays)
      async_.set_shared_params(model_c, arrays)
  
      # Pointers to parameters must be the same
      _assert_same_pointers_to_param_data(model_a, model_b)
      _assert_same_pointers_to_param_data(model_a, model_c)
      # Pointers to gradients must be different
      _assert_different_pointers_to_param_grad(model_a, model_b)
      _assert_different_pointers_to_param_grad(model_a, model_c)
      _assert_different_pointers_to_param_grad(model_b, model_c)
      # Pointers to persistent values must be the same
      _assert_same_pointers_to_persistent_values(model_a, model_b)
      _assert_same_pointers_to_persistent_values(model_a, model_c)
  
      # Check if N is shared correctly among links
      assert model_a.N == 0
      assert model_b.N == 0
      assert model_c.N == 0
      test_input = np.random.normal(size=(2, 3)).astype(np.float32)
      model_a(test_input, finetune=True)
      assert model_a.N == 1
      assert model_b.N == 1
      assert model_c.N == 1
      model_c(test_input, finetune=True)
      assert model_a.N == 2
      assert model_b.N == 2
      assert model_c.N == 2
  
imports: import multiprocessing
  import os
  import signal
  import sys
  import unittest
  import warnings
  import chainer
  import chainer.links
  from chainer import optimizers
  import copy
  import numpy
  import chainerrl
  from chainerrl.misc import async_
label_desc: Use image node_id 3 for calling the TestAsync obj's underlying member method code with example usage: obj.test_share_params_batch_normalization() without return types