code: def _verify_same_spaces(self):
      
  
      # Pre-conditions: self._envs is initialized.
  
      if self._envs is None:
        raise ValueError("Environments not initialized.")
  
      if not isinstance(self._envs, list):
        tf.logging.warning("Not checking observation and action space "
                           "compatibility across envs, since there is just one.")
        return
  
      # NOTE: We compare string representations of observation_space and
      # action_space because compositional classes like space.Tuple don't return
      # true on object comparison.
  
      if not all(
          str(env.observation_space) == str(self.observation_space)
          for env in self._envs):
        err_str = ("All environments should have the same observation space, but "
                   "don't.")
        tf.logging.error(err_str)
        # Log all observation spaces.
        for i, env in enumerate(self._envs):
          tf.logging.error("Env[%d] has observation space [%s]", i,
                           env.observation_space)
        raise ValueError(err_str)
  
      if not all(
          str(env.action_space) == str(self.action_space) for env in self._envs):
        err_str = "All environments should have the same action space, but don't."
        tf.logging.error(err_str)
        # Log all action spaces.
        for i, env in enumerate(self._envs):
          tf.logging.error("Env[%d] has action space [%s]", i, env.action_space)
        raise ValueError(err_str)
docs: Verifies that all the envs have the same observation and action space.