code: def __init__(
      self,
      capacity=None,
      alpha=0.6,
      beta0=0.4,
      betasteps=2e5,
      eps=1e-8,
      normalize_by_max=True,
      default_priority_func=None,
      uniform_ratio=0,
      wait_priority_after_sampling=True,
      return_sample_weights=True,
      error_min=None,
      error_max=None,
  ):
      self.current_episode = collections.defaultdict(list)
      self.episodic_memory = PrioritizedBuffer(
          capacity=None,
          wait_priority_after_sampling=wait_priority_after_sampling,
      )
      self.memory = RandomAccessQueue(maxlen=capacity)
      self.capacity_left = capacity
      self.default_priority_func = default_priority_func
      self.uniform_ratio = uniform_ratio
      self.return_sample_weights = return_sample_weights
      PriorityWeightError.__init__(
          self,
          alpha,
          beta0,
          betasteps,
          eps,
          normalize_by_max,
          error_min=error_min,
          error_max=error_max,
      )
  
file_path: pfrl/pfrl/replay_buffers/prioritized_episodic.py
imports: import collections
  from pfrl.collections.prioritized import PrioritizedBuffer
  from pfrl.collections.random_access_queue import RandomAccessQueue
  from pfrl.replay_buffer import random_subseq
  from pfrl.replay_buffers import EpisodicReplayBuffer, PriorityWeightError
label_desc: Use image node_id 1 to create a new PrioritizedEpisodicReplayBuffer object from inherited base classes: EpisodicReplayBuffer, PriorityWeightError with example: obj = PrioritizedEpisodicReplayBuffer(capacity, alpha, beta0, betasteps, eps, normalize_by_max, default_priority_func, uniform_ratio, wait_priority_after_sampling, return_sample_weights, error_min, error_max)