code: def _mean_or_nan(xs: Sequence[float]) -> float:
      """Return its mean a non-empty sequence, numpy.nan for a empty one."""
      return typing.cast(float, np.mean(xs)) if xs else np.nan
  
imports: import collections
  import copy
  import ctypes
  import multiprocessing
  import multiprocessing.synchronize
  import os
  import time
  import typing
  from logging import Logger, getLogger
  from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple
  import numpy
  import torch
  import torch.nn.functional
  import pfrl
  from pfrl import agent
  from pfrl.action_value import ActionValue
  from pfrl.explorer import Explorer
  from pfrl.replay_buffer import AbstractEpisodicReplayBuffer, ReplayUpdater, batch_experiences, batch_recurrent_experiences
  from pfrl.replay_buffers import PrioritizedReplayBuffer
  from pfrl.utils.batch_states import batch_states
  from pfrl.utils.contexts import evaluating
  from pfrl.utils.copy_param import synchronize_parameters
  from pfrl.utils.recurrent import get_recurrent_state_at, mask_recurrent_state_at, one_step_forward, pack_and_forward, recurrent_state_as_numpy
label_desc: Use image node_id 1 for calling a global function with example usage: _mean_or_nan(xs) and returns: unknown