class_docstr: Batch together multiple py policies and act as a single batch.
  
  The policies should only access shared python variables using
  shared mutex locks (from the threading module).
class_docstr_tok: ["Batch","together","multiple","py","policies","and","act","as","a","single","batch",".","The","policies","should","only","access","shared","python","variables","using","shared","mutex","locks","(","from","the","threading","module",")","."]
code: def _execute(self, fn, iterable):
      if (
          self._parallel_execution
      ):  # pytype: disable=attribute-error  # trace-all-classes
          return self._pool.map(
              fn, iterable
          )  # pytype: disable=attribute-error  # trace-all-classes
      else:
          return [fn(x) for x in iterable]
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from multiprocessing import dummy
  from typing import Sequence, Optional
  import gin
  from tf_agents.policies import py_policy
  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 nest_utils
label_desc: Use image node_id 4 for calling the BatchedPyPolicy obj's underlying member method code with example usage: obj._execute(fn, iterable) and returns: self, unknown