class_docstr: An implementation of the DQN agent.
class_docstr_tok: ["An","implementation","of","the","DQN","agent","."]
docstr: Restores the agent from a checkpoint.
  
  Restores the agent's Python objects to those specified in bundle_dictionary,
  and restores the TensorFlow objects to those specified in the
  checkpoint_dir. If the checkpoint_dir does not exist, will not reset the
    agent's state.
  
  Args:
    checkpoint_dir: str, path to the checkpoint saved by tf.Save.
    iteration_number: int, checkpoint version, used when restoring the replay
      buffer.
    bundle_dictionary: dict, containing additional Python objects owned by
      the agent.
  
  Returns:
    bool, True if unbundling was successful.
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code: def unbundle(
      self, checkpoint_dir, iteration_number, bundle_dictionary
  ):
      """Restores the agent from a checkpoint.
  
      Restores the agent's Python objects to those specified in bundle_dictionary,
      and restores the TensorFlow objects to those specified in the
      checkpoint_dir. If the checkpoint_dir does not exist, will not reset the
        agent's state.
  
      Args:
        checkpoint_dir: str, path to the checkpoint saved by tf.Save.
        iteration_number: int, checkpoint version, used when restoring the replay
          buffer.
        bundle_dictionary: dict, containing additional Python objects owned by
          the agent.
  
      Returns:
        bool, True if unbundling was successful.
      """
      try:
          # self._replay.load() will throw a NotFoundError if it does not find all
          # the necessary files.
          self._replay.load(checkpoint_dir, iteration_number)
      except tf.errors.NotFoundError:
          if not self.allow_partial_reload:
              # If we don't allow partial reloads, we will return False.
              return False
          logging.warning("Unable to reload replay buffer!")
      if bundle_dictionary is not None:
          for key in self.__dict__:
              if key in bundle_dictionary:
                  self.__dict__[key] = bundle_dictionary[key]
      elif not self.allow_partial_reload:
          return False
      else:
          logging.warning("Unable to reload the agent's parameters!")
      # Restore the agent's TensorFlow graph.
      self._saver.restore(
          self._sess,
          os.path.join(
              checkpoint_dir, "tf_ckpt-{}".format(iteration_number)
          ),
      )
      return True
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import os
  import random
  from absl import logging
  from dopamine.discrete_domains import atari_lib
  from dopamine.replay_memory import circular_replay_buffer
  import gin.tf
  import numpy
  import tensorflow
label_desc: Use image node_id 17 for calling the DQNAgent obj's underlying member method code with example usage: obj.unbundle(checkpoint_dir, iteration_number, bundle_dictionary) and returns: True, False, False