class_docstr: Exposes a Python policy as wrapper over a TF Policy.
class_docstr_tok: ["Exposes","a","Python","policy","as","wrapper","over","a","TF","Policy","."]
code: def _get_initial_state(self, batch_size):
      if not self._built:
          self.initialize(batch_size)
  
      if batch_size not in [self._batch_size, self._batch_size or 1]:
          raise ValueError(
              "`batch_size` argument is different from the batch size provided "
              "previously. Expected {}, but saw {}.".format(
                  self._batch_size, batch_size
              )
          )
      return self.session.run(self._tf_initial_state)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from typing import Optional, Text
  from absl import logging
  import tensorflow
  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 policy_step
  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
  from tf_agents.utils import session_utils
label_desc: Use image node_id 7 for calling the PyTFPolicy obj's underlying member method code with example usage: obj._get_initial_state(batch_size) and returns: self