class_docstr: A replay buffer that stores episodes of transitions. The additional structure allows performing various
  calculations of total return and other values that depend on the sequential behavior of the transitions
  in the episode.
class_docstr_tok: ["A","replay","buffer","that","stores","episodes","of","transitions",".","The","additional","structure","allows","performing","various","calculations","of","total","return","and","other","values","that","depend","on","the","sequential","behavior","of","the","transitions","in","the","episode","."]
docstr: Restore the replay buffer contents from a pickle file.
  The pickle file is assumed to include a list of transitions.
  :param file_path: The path to a pickle file to restore
code: def load_pickled(self, file_path: str) -> None:
      """
      Restore the replay buffer contents from a pickle file.
      The pickle file is assumed to include a list of transitions.
      :param file_path: The path to a pickle file to restore
      """
      self.assert_not_frozen()
  
      with open(file_path, "rb") as file:
          episodes = pickle.load(file)
          num_transitions = sum([len(e.transitions) for e in episodes])
          if num_transitions > self.max_size[1]:
              screen.warning(
                  "Warning! The number of transition to load into the replay buffer ({}) is "
                  "bigger than the max size of the replay buffer ({}). The excessive transitions will "
                  "not be stored.".format(
                      num_transitions, self.max_size[1]
                  )
              )
  
          progress_bar = ProgressBar(len(episodes))
          for episode_idx, episode in enumerate(episodes):
              self.store_episode(episode)
  
              # print progress
              progress_bar.update(episode_idx)
  
          progress_bar.close()
  
imports: import ast
  import pickle
  from copy import deepcopy
  import math
  import pandas
  from typing import List, Tuple, Union
  import numpy
  import random
  from rl_coach.core_types import Transition, Episode
  from rl_coach.filters.filter import InputFilter
  from rl_coach.logger import screen
  from rl_coach.memories.memory import Memory, MemoryGranularity, MemoryParameters
  from rl_coach.utils import ReaderWriterLock, ProgressBar
  from rl_coach.core_types import CsvDataset
label_desc: Use image node_id 34 for calling the EpisodicExperienceReplay obj's underlying member method code with example usage: obj.load_pickled(file_path) without return types