code: def testIncrementalVariance(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)
      # It fails when run more than 383 iterations.
      for i in range(383):
          self.evaluate(t_normalizer.update(tensor + 100 * i))
  
          # Get new mean and variance, and make sure they changed.
          _, new_variance = self.evaluate(
              t_normalizer._get_mean_var_estimates()
          )
          new_array = np.concatenate(
              [np_array + 100 * j for j in range(i + 1)], axis=0
          )
  
          self.assertAllClose(new_array.var(axis=0), new_variance[0])
  
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 9 for calling the StreamingTensorNormalizerTest obj's underlying member method code with example usage: obj.testIncrementalVariance(dtype) without return types