docstr: Create all the networks of the agent.
  The network creation will be done after setting the environment parameters for the agent, since they are needed
  for creating the network.
  
  :return: A list containing all the networks
docstr_tok: ["Create","all","the","networks","of","the","agent",".","The","network","creation","will","be","done","after","setting","the","environment","parameters","for","the","agent",",","since","they","are","needed","for","creating","the","network",".",":","return",":","A","list","containing","all","the","networks"]
code: def create_networks(self) -> Dict[str, NetworkWrapper]:
      """
      Create all the networks of the agent.
      The network creation will be done after setting the environment parameters for the agent, since they are needed
      for creating the network.
  
      :return: A list containing all the networks
      """
      networks = {}
      for network_name in sorted(self.ap.network_wrappers.keys()):
          networks[network_name] = NetworkWrapper(
              name=network_name,
              agent_parameters=self.ap,
              has_target=self.ap.network_wrappers[
                  network_name
              ].create_target_network,
              has_global=self.has_global,
              spaces=self.spaces,
              replicated_device=self.replicated_device,
              worker_device=self.worker_device,
          )
  
          if self.ap.visualization.print_networks_summary:
              screen.print(networks[network_name])
  
      return networks
  
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 10 for calling the Agent obj's underlying member method code with example usage: obj.create_networks() and returns: networks