class_docstr: A wrapper that applies exponential moving averaging to action outputs.
docstr: Creates zero state tuple with wrapped initial state and smoothing vars.
  
  Args:
    batch_size: The batch shape.
  
  Returns:
    A tuple of (wrapped_policy_initial_state, initial_smoothing_state)
docstr_tok: ["Creates","zero","state","tuple","with","wrapped","initial","state","and","smoothing","vars",".","Args",":","batch_size",":","The","batch","shape",".","Returns",":","A","tuple","of","(","wrapped_policy_initial_state",",","initial_smoothing_state",")"]
code: def _get_initial_state(self, batch_size):
      """Creates zero state tuple with wrapped initial state and smoothing vars.
  
      Args:
        batch_size: The batch shape.
  
      Returns:
        A tuple of (wrapped_policy_initial_state, initial_smoothing_state)
      """
      wrapped_initial_state = self._wrapped_policy.get_initial_state(
          batch_size
      )
      initial_smoothing_state = super(
          TemporalActionSmoothing, self
      )._get_initial_state(batch_size)[1]
      return (wrapped_initial_state, initial_smoothing_state)
  
file_path: agents/tf_agents/policies/temporal_action_smoothing.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from typing import Optional, Text
  import tensorflow
  from tf_agents.policies import tf_policy
  from tf_agents.trajectories import policy_step
label_desc: Use image node_id 2 for calling the TemporalActionSmoothing obj's underlying member method code with example usage: obj._get_initial_state(batch_size) and returns: wrapped_initial_state,  initial_smoothing_state