class_docstr: Normalizes mean & variance based on full history of tensor values.
docstr: Create all variables needed for the normalizer.
docstr_tok: ["Create","all","variables","needed","for","the","normalizer","."]
code: def _create_variables(self):
      """Create all variables needed for the normalizer."""
      self._count = [
          create_variable(
              "count_%d" % i,
              _EPS,
              spec.shape,
              spec.dtype,
              trainable=False,
          )
          for i, spec in enumerate(self._flat_variable_spec)
      ]
      self._avg = [
          create_variable(
              "avg_%d" % i, 0, spec.shape, spec.dtype, trainable=False
          )
          for i, spec in enumerate(self._flat_variable_spec)
      ]
      self._m2 = [
          create_variable(
              "m2_%d" % i, 0, spec.shape, spec.dtype, trainable=False
          )
          for i, spec in enumerate(self._flat_variable_spec)
      ]
      self._m2_carry = [
          create_variable(
              "m2_carry_%d" % i,
              0,
              spec.shape,
              spec.dtype,
              trainable=False,
          )
          for i, spec in enumerate(self._flat_variable_spec)
      ]
  
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 1 for calling the StreamingTensorNormalizer obj's underlying member method code with example usage: obj._create_variables() without return types