class_docstr: Base class for py_policy instances of TF policies in Eager mode.
  
  Handles adding and removing batch dimensions from the actions and time_steps.
  Note if you have a tf_policy you should directly use the PyTFEagerPolicy class
  instead of this Base.
class_docstr_tok: ["Base","class","for","py_policy","instances","of","TF","policies","in","Eager","mode",".","Handles","adding","and","removing","batch","dimensions","from","the","actions","and","time_steps",".","Note","if","you","have","a","tf_policy","you","should","directly","use","the","PyTFEagerPolicy","class","instead","of","this","Base","."]
code: def _get_initial_state(self, batch_size):
      if batch_size is None:
          batch_size = 0
      return self._policy.get_initial_state(batch_size=batch_size)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  from typing import Optional, Text
  from absl import logging
  import gin
  import tensorflow
  from tf_agents.policies import policy_saver
  from tf_agents.policies import py_policy
  from tf_agents.policies import tf_policy
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  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 3 for calling the PyTFEagerPolicyBase obj's underlying member method code with example usage: obj._get_initial_state(batch_size) and returns: self