class_docstr: Dummy Blackjack-like environment that increments `state` by `action`.
  
  The environment resets when state becomes greater or equal than final_state.
  Actions are 1 or 2.
  A reward of 1 for all non restart states.
class_docstr_tok: ["Dummy","Blackjack-like","environment","that","increments","`","state","`","by","`","action","`",".","The","environment","resets","when","state","becomes","greater","or","equal","than","final_state",".","Actions","are","1","or","2",".","A","reward","of","1","for","all","non","restart","states","."]
code: def action_spec(self):
      return self._action_spec
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from typing import Any
  import numpy
  import tensorflow
  from tf_agents import specs
  from tf_agents.environments import py_environment
  from tf_agents.policies import py_policy
  from tf_agents.policies import tf_policy
  from tf_agents.replay_buffers import tf_uniform_replay_buffer
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import policy_step
  from tf_agents.trajectories import time_step
  from tf_agents.trajectories import trajectory
  from tf_agents.utils import common
label_desc: Use image node_id 5 for calling the PyEnvironmentMock obj's underlying member method code with example usage: obj.action_spec() and returns: self