docstr: Given a response from the environment, distill the observation from it and store it for later use.
  The response should be a dictionary containing the performed action, the new observation and measurements,
  the reward, a game over flag and any additional information necessary.
  
  :param env_response: result of call from environment.step(action)
  :return: a boolean value which determines if the agent has decided to terminate the episode after seeing the
           given observation
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code: def observe(self, env_response: EnvResponse) -> bool:
      """
      Given a response from the environment, distill the observation from it and store it for later use.
      The response should be a dictionary containing the performed action, the new observation and measurements,
      the reward, a game over flag and any additional information necessary.
  
      :param env_response: result of call from environment.step(action)
      :return: a boolean value which determines if the agent has decided to terminate the episode after seeing the
               given observation
      """
  
      # filter the env_response
      filtered_env_response = self.input_filter.filter(env_response)[0]
  
      # inject agent collected statistics, if required
      if self.ap.algorithm.use_accumulated_reward_as_measurement:
          if "measurements" in filtered_env_response.next_state:
              filtered_env_response.next_state[
                  "measurements"
              ] = np.append(
                  filtered_env_response.next_state["measurements"],
                  self.total_shaped_reward_in_current_episode,
              )
          else:
              filtered_env_response.next_state[
                  "measurements"
              ] = np.array(
                  [self.total_shaped_reward_in_current_episode]
              )
  
      # if we are in the first step in the episode, then we don't have a a next state and a reward and thus no
      # transition yet, and therefore we don't need to store anything in the memory.
      # also we did not reach the goal yet.
      if self.current_episode_steps_counter == 0:
          # initialize the current state
          self.curr_state = filtered_env_response.next_state
          return env_response.game_over
      else:
          transition = Transition(
              state=copy.copy(self.curr_state),
              action=self.last_action_info.action,
              reward=filtered_env_response.reward,
              next_state=filtered_env_response.next_state,
              game_over=filtered_env_response.game_over,
              info=filtered_env_response.info,
          )
  
          # now that we have formed a basic transition - the next state progresses to be the current state
          self.curr_state = filtered_env_response.next_state
  
          # make agent specific changes to the transition if needed
          transition = (
              self.update_transition_before_adding_to_replay_buffer(
                  transition
              )
          )
  
          # add action info to transition
          if type(self.parent).__name__ == "CompositeAgent":
              transition.add_info(self.parent.last_action_info.__dict__)
          else:
              transition.add_info(self.last_action_info.__dict__)
  
          self.total_reward_in_current_episode += env_response.reward
          self.reward.add_sample(env_response.reward)
  
          return self.observe_transition(transition)
  
imports: import copy
  import random
  from collections import OrderedDict
  from typing import Dict, List, Union, Tuple
  import numpy
  from six.moves import range
  from rl_coach.agents.agent_interface import AgentInterface
  from rl_coach.architectures.network_wrapper import NetworkWrapper
  from rl_coach.base_parameters import AgentParameters, Device, DeviceType, DistributedTaskParameters, Frameworks
  from rl_coach.core_types import RunPhase, PredictionType, EnvironmentEpisodes, ActionType, Batch, Episode, StateType
  from rl_coach.core_types import Transition, ActionInfo, TrainingSteps, EnvironmentSteps, EnvResponse
  from rl_coach.logger import screen, Logger, EpisodeLogger
  from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplay
  from rl_coach.saver import SaverCollection
  from rl_coach.spaces import SpacesDefinition, VectorObservationSpace, GoalsSpace, AttentionActionSpace
  from rl_coach.utils import Signal, force_list
  from rl_coach.utils import dynamic_import_and_instantiate_module_from_params
  from rl_coach.memories.backend.memory_impl import get_memory_backend
  from rl_coach.core_types import TimeTypes
  from rl_coach.off_policy_evaluators.ope_manager import OpeManager
  from rl_coach.core_types import PickledReplayBuffer, CsvDataset
label_desc: Use image node_id 32 for calling the Agent obj's underlying member method code with example usage: obj.observe(env_response) and returns: env_response, self