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 __init__(self, final_state=3):
      self._state = np.int32(0)
      self._action_spec = specs.BoundedArraySpec(
          [], np.int32, minimum=1, maximum=2, name="action"
      )
      self._observation_spec = specs.ArraySpec(
          [], np.int32, name="observation"
      )
      self._final_state = final_state
      super(PyEnvironmentMock, self).__init__()
  
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 1 to create a new PyEnvironmentMock object from inherited base classes: py_environment with example: obj = PyEnvironmentMock(final_state)