class_docstr: The network wrapper contains multiple copies of the same network, each one with a different set of weights which is
  updating in a different time scale. The network wrapper will always contain an online network.
  It will contain an additional slow updating target network if it was requested by the user,
  and it will contain a global network shared between different workers, if Coach is run in a single-node
  multi-process distributed mode. The network wrapper contains functionality for managing these networks and syncing
  between them.
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docstr: Collect all of network's savers for global or online network
  Note: global, online, and target network are all copies fo the same network which parameters that are
      updated at different rates. So we only need to save one of the networks; the one that holds the most
      recent parameters. target network is created for some agents and used for stabilizing training by
      updating parameters from online network at a slower rate. As a result, target network never contains
      the most recent set of parameters. In single-worker training, no global network is created and online
      network contains the most recent parameters. In vertical distributed training with more than one worker,
      global network is updated by all workers and contains the most recent parameters.
      Therefore preference is given to global network if it exists, otherwise online network is used
      for saving.
  :param parent_path_suffix: path suffix of the parent of the network wrapper
      (e.g. could be name of level manager plus name of agent)
  :return: collection of all checkpoint objects
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code: def collect_savers(self, parent_path_suffix: str) -> SaverCollection:
      """
      Collect all of network's savers for global or online network
      Note: global, online, and target network are all copies fo the same network which parameters that are
          updated at different rates. So we only need to save one of the networks; the one that holds the most
          recent parameters. target network is created for some agents and used for stabilizing training by
          updating parameters from online network at a slower rate. As a result, target network never contains
          the most recent set of parameters. In single-worker training, no global network is created and online
          network contains the most recent parameters. In vertical distributed training with more than one worker,
          global network is updated by all workers and contains the most recent parameters.
          Therefore preference is given to global network if it exists, otherwise online network is used
          for saving.
      :param parent_path_suffix: path suffix of the parent of the network wrapper
          (e.g. could be name of level manager plus name of agent)
      :return: collection of all checkpoint objects
      """
      if self.global_network:
          savers = self.global_network.collect_savers(
              parent_path_suffix
          )
      else:
          savers = self.online_network.collect_savers(
              parent_path_suffix
          )
      return savers
  
imports: from typing import List, Tuple
  from rl_coach.base_parameters import Frameworks, AgentParameters
  from rl_coach.logger import failed_imports
  from rl_coach.saver import SaverCollection
  from rl_coach.spaces import SpacesDefinition
  from rl_coach.utils import force_list
label_desc: Use image node_id 13 for calling the NetworkWrapper obj's underlying member method code with example usage: obj.collect_savers(parent_path_suffix) and returns: savers