class_docstr: Normalizes mean & variance based on full history of tensor values.
docstr: Reset the count, mean and variance to its initial state.
docstr_tok: ["Reset","the","count",",","mean","and","variance","to","its","initial","state","."]
code: def reset(self):
      """Reset the count, mean and variance to its initial state."""
      reset_ops = []
      for i in range(len(self._count)):
          reset_ops.extend(
              [
                  self._count[i].assign(
                      _EPS * tf.ones_like(self._count[i])
                  ),
                  self._avg[i].assign(tf.zeros_like(self._avg[i])),
                  self._m2[i].assign(tf.zeros_like(self._m2[i])),
                  self._m2_carry[i].assign(
                      tf.zeros_like(self._m2_carry[i])
                  ),
              ]
          )
  
      return reset_ops
  
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 5 for calling the StreamingTensorNormalizer obj's underlying member method code with example usage: obj.reset() and returns: reset_ops