class_docstr: Replay buffer for off policy learning.
  
  :param buffer_size: The maximum buffer size.
  :param seed: The random seed used for initializing the uniform random sampling in the buffer.
class_docstr_tok: ["Replay","buffer","for","off","policy","learning",".",":","param","buffer_size",":","The","maximum","buffer","size",".",":","param","seed",":","The","random","seed","used","for","initializing","the","uniform","random","sampling","in","the","buffer","."]
docstr: implementation of :class:`~maze.train.trainers.common.replay_buffer.replay_buffer.BaseReplayBuffer`
          
docstr_tok: ["implementation","of",":","class",":","`","~maze.train.trainers.common.replay_buffer.replay_buffer.BaseReplayBuffer","`"]
code: def __len__(self) -> int:
      """implementation of :class:`~maze.train.trainers.common.replay_buffer.replay_buffer.BaseReplayBuffer`"""
      return self._fill_state
  
file_path: maze/maze/train/trainers/common/replay_buffer/uniform_replay_buffer.py
imports: from typing import Union, List
  import numpy
  from maze.core.annotations import override
  from maze.core.trajectory_recording.records.structured_spaces_record import StructuredSpacesRecord
  from maze.core.trajectory_recording.records.trajectory_record import SpacesTrajectoryRecord
  from maze.train.trainers.common.replay_buffer.replay_buffer import BaseReplayBuffer
label_desc: Use image node_id 5 for calling the UniformReplayBuffer obj's underlying member method code with example usage: obj.__len__() and returns: self