class_docstr: Abstract base class for Python RL environments.
  
  Observations and valid actions are described with `ArraySpec`s, defined in
  the `specs` module.
  
  If the environment can run multiple steps at the same time and take a batched
  set of actions and return a batched set of observations, it should overwrite
  the property batched to True.
  
  Environments are assumed to auto reset once they reach the end of the episode,
  if prefer that the base class handle auto_reset set `handle_auto_reset=True`.
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docstr: Whether the environment is batched or not.
  
  If the environment supports batched observations and actions, then overwrite
  this property to True.
  
  A batched environment takes in a batched set of actions and returns a
  batched set of observations. This means for all numpy arrays in the input
  and output nested structures, the first dimension is the batch size.
  
  When batched, the left-most dimension is not part of the action_spec
  or the observation_spec and corresponds to the batch dimension.
  
  When batched and handle_auto_reset, it checks `np.all(steps.is_last())`.
  
  Returns:
    A boolean indicating whether the environment is batched or not.
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code: def batched(self) -> bool:
      """Whether the environment is batched or not.
  
      If the environment supports batched observations and actions, then overwrite
      this property to True.
  
      A batched environment takes in a batched set of actions and returns a
      batched set of observations. This means for all numpy arrays in the input
      and output nested structures, the first dimension is the batch size.
  
      When batched, the left-most dimension is not part of the action_spec
      or the observation_spec and corresponds to the batch dimension.
  
      When batched and handle_auto_reset, it checks `np.all(steps.is_last())`.
  
      Returns:
        A boolean indicating whether the environment is batched or not.
      """
      return False
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import abc
  from typing import Any, Optional, Text
  import numpy
  import six
  from tf_agents.specs import array_spec
  from tf_agents.trajectories import time_step
  from tf_agents.typing import types
  from tf_agents.utils import common
label_desc: Use image node_id 2 for calling the PyEnvironment obj's underlying member method code with example usage: obj.batched() and returns: False