code: def _mean_or_nan(xs):
      """Return its mean a non-empty sequence, numpy.nan for a empty one."""
      return np.mean(xs) if xs else np.nan
  
imports: import collections
  import itertools
  import random
  import numpy
  import torch
  import torch.nn.functional
  import pfrl
  from pfrl import agent
  from pfrl.utils.batch_states import batch_states
  from pfrl.utils.mode_of_distribution import mode_of_distribution
  from pfrl.utils.recurrent import concatenate_recurrent_states, flatten_sequences_time_first, get_recurrent_state_at, mask_recurrent_state_at, one_step_forward, pack_and_forward
label_desc: Use image node_id 1 for calling a global function with example usage: _mean_or_nan(xs) and returns: unknown