class_docstr: Normalizes mean & variance based on full history of tensor values.
docstr: Returns a tuple of nested TF Variables owned by this normalizer.
docstr_tok: ["Returns","a","tuple","of","nested","TF","Variables","owned","by","this","normalizer","."]
code: def variables(self):
      """Returns a tuple of nested TF Variables owned by this normalizer."""
      return (
          tf.nest.pack_sequence_as(self._tensor_spec, self._count),
          tf.nest.pack_sequence_as(self._tensor_spec, self._avg),
          tf.nest.pack_sequence_as(self._tensor_spec, self._m2),
          tf.nest.pack_sequence_as(self._tensor_spec, self._m2_carry),
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import abc
  from typing import Tuple
  import six
  import tensorflow
  from tf_agents.specs import tensor_spec
  from tf_agents.typing import types
  from tf_agents.utils import common
  from tf_agents.utils import nest_utils
label_desc: Use image node_id 2 for calling the StreamingTensorNormalizer obj's underlying member method code with example usage: obj.variables() and returns: tf,  tf,  tf,  tf