code: def testNormalization(self, num_outer_dims):
      means_var, variance_var = self._tensor_normalizer.variables
      self.evaluate(
          [
              tf.compat.v1.assign(means_var, [10.0] * 3),
              tf.compat.v1.assign(variance_var, [0.1] * 3),
          ]
      )
  
      vector = [9.0, 10.0, 11.0]
      # Above, the estimated mean was set to 10, and variance to 0.1. Thus the
      # estimated stddev is sqrt(0.1) = 0.3162.
      # The middle sample falls on the mean, so should be normalized to 0.0. Each
      # of the other samples is 1 away from the mean. 1 / 0.3162 = 3.162
      expected = [-3.1622776601, 0.0, 3.1622776601]
      for _ in range(num_outer_dims - 1):
          vector = [vector] * 2
          expected = [expected] * 2
      tensor = tf.constant(vector)
  
      norm_obs = self._tensor_normalizer.normalize(
          tensor, variance_epsilon=0.0
      )
      self.assertAllClose(
          expected, self.evaluate(norm_obs), atol=0.0001
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tf_agents.specs import tensor_spec
  from tf_agents.utils import tensor_normalizer
label_desc: Use image node_id 5 for calling the EMATensorNormalizerTest obj's underlying member method code with example usage: obj.testNormalization(num_outer_dims) without return types