code: def compute_value_loss(eltwise_loss, batch_accumulator="mean"):
      """Compute a loss for value prediction problem.
  
      Args:
          eltwise_loss (Variable): Element-wise loss per example
          batch_accumulator (str): 'mean' or 'sum'. 'mean' will use the mean of
              the loss values in a batch. 'sum' will use the sum.
      Returns:
          (Variable) scalar loss
      """
      assert batch_accumulator in ("mean", "sum")
      assert eltwise_loss.ndim == 3
  
      if batch_accumulator == "sum":
          # mean over N_prime, then sum over (batch_size, N)
          loss = F.sum(F.mean(eltwise_loss, axis=2))
      else:
          # mean over (batch_size, N_prime), then sum over N
          loss = F.sum(F.mean(eltwise_loss, axis=(0, 2)))
  
      return loss
  
imports: import chainer
  from chainer import cuda
  import chainer.functions
  import chainer.links
  from chainerrl.action_value import QuantileDiscreteActionValue
  from chainerrl.agents import dqn
  from chainerrl.links import StatelessRecurrentChainList
label_desc: Use image node_id 5 for calling a global function with example usage: compute_value_loss(eltwise_loss, batch_accumulator) and returns: loss