class_docstr: Batch together multiple py environments and act as a single batch.
  
  The environments should only access shared python variables using
  shared mutex locks (from the threading module).
class_docstr_tok: ["Batch","together","multiple","py","environments","and","act","as","a","single","batch",".","The","environments","should","only","access","shared","python","variables","using","shared","mutex","locks","(","from","the","threading","module",")","."]
code: def action_spec(self) -> types.NestedArraySpec:
      return self._action_spec
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from multiprocessing import dummy
  from multiprocessing import pool
  from typing import Sequence, Optional
  import gin
  import tensorflow
  from tf_agents.environments import py_environment
  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 7 for calling the BatchedPyEnvironment obj's underlying member method code with example usage: obj.action_spec() and returns: self