code: def testNormalizeVSNumpy(self, dtype):
      t_normalizer = tensor_normalizer.StreamingTensorNormalizer(
          tensor_spec=tensor_spec.TensorSpec([3], dtype, "obs")
      )
      self.evaluate(tf.compat.v1.global_variables_initializer())
      np_array = np.array(
          [[1.3, 4.2, 7.5], [8.3, 2.2, 9.5], [3.3, 5.2, 6.5]],
          dtype.as_numpy_dtype,
      )
      tensor = tf.constant(np_array, dtype=dtype)
      self.evaluate(t_normalizer.update(tensor))
  
      epsilon = 1e-6
      # Get new mean and variance, and make sure they changed.
      norm_obs = t_normalizer.normalize(
          tensor, variance_epsilon=epsilon
      )
  
      exp_obs = (np_array - np_array.mean(axis=0)) / (
          np_array.std(axis=0) + epsilon
      )
  
      self.assertAllClose(norm_obs, exp_obs)
  
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 StreamingTensorNormalizerTest obj's underlying member method code with example usage: obj.testNormalizeVSNumpy(dtype) without return types