code: def _compute_explained_variance(transitions):
      """Compute 1 - Var[return - v]/Var[return].
  
      This function computes the fraction of variance that value predictions can
      explain about returns.
      """
      t = np.array([tr["v_teacher"] for tr in transitions])
      y = np.array([tr["v_pred"] for tr in transitions])
      vart = np.var(t)
      if vart == 0:
          return np.nan
      else:
          return float(1 - np.var(t - y) / vart)
  
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 9 for calling a global function with example usage: _compute_explained_variance(transitions) and returns: np, float