class_docstr: Start every episode with action 1 (FIRE) + another action (2).
  
  In some environments (e.g., BeamRider, Breakout, Tennis) nothing
  happens until the player presses the FIRE button. This wrapper can
  be helpful in those environments, but it is not necessary.
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code: def reset(self) -> np.ndarray:
      observation = self.env.reset()
      # The following code is from https://github.com/openai/gym/...
      # ...blob/master/gym/wrappers/atari_preprocessing.py
      action_meanings = self.env.unwrapped.get_action_meanings()
      if action_meanings[1] == "FIRE" and len(action_meanings) >= 3:
          self.env.step(1)
          observation, _, _, _ = self.env.step(2)
      return observation
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  from typing import Any, Text
  import gym
  import numpy
  from tf_agents.environments import py_environment
  from tf_agents.environments import wrappers
  from tf_agents.trajectories import time_step
label_desc: Use image node_id 1 for calling the FireOnReset obj's underlying member method code with example usage: obj.reset() and returns: observation