code: def _should_update(self):
      wait_for_full_episode = self.ap.algorithm.act_for_full_episodes
      steps = self.ap.algorithm.num_consecutive_playing_steps
  
      if steps.__class__ == EnvironmentEpisodes:
          should_update = (
              self.current_episode - self.last_training_phase_step
          ) >= steps.num_steps
          should_update = (
              should_update and self.call_memory("length") > 0
          )
  
      elif steps.__class__ == EnvironmentSteps:
          should_update = (
              self.total_steps_counter - self.last_training_phase_step
          ) >= steps.num_steps
          should_update = (
              should_update and self.call_memory("num_transitions") > 0
          )
  
          if wait_for_full_episode:
              should_update = (
                  should_update
                  and self.current_episode_buffer.is_complete
              )
      else:
          raise ValueError(
              "The num_consecutive_playing_steps parameter should be either "
              "EnvironmentSteps or Episodes. Instead it is {}".format(
                  steps.__class__
              )
          )
  
      return should_update
  
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 24 for calling the Agent obj's underlying member method code with example usage: obj._should_update() and returns: should_update