docstr: Gets a response from the environment.
  Processes this information for later use. For example, create a transition and store it in memory.
  The action info (a class containing any info the agent wants to store regarding its action decision process) is
  stored by the agent itself when deciding on the action.
  :param env_response: a EnvResponse containing the response from the environment
  :return: a done signal which is based on the agent knowledge. This can be different from the done signal from
           the environment. For example, an agent can decide to finish the episode each time it gets some
           intrinsic reward
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code: def observe(self, env_response: EnvResponse) -> bool:
      """
      Gets a response from the environment.
      Processes this information for later use. For example, create a transition and store it in memory.
      The action info (a class containing any info the agent wants to store regarding its action decision process) is
      stored by the agent itself when deciding on the action.
      :param env_response: a EnvResponse containing the response from the environment
      :return: a done signal which is based on the agent knowledge. This can be different from the done signal from
               the environment. For example, an agent can decide to finish the episode each time it gets some
               intrinsic reward
      """
      raise NotImplementedError("")
  
imports: from typing import Union, List, Dict
  import numpy
  from rl_coach.core_types import EnvResponse, ActionInfo, RunPhase, PredictionType, ActionType, Transition
  from rl_coach.saver import SaverCollection
label_desc: Use image node_id 9 for calling the AgentInterface obj's underlying member method code with example usage: obj.observe(env_response) without return types