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 = eltwise_loss.mean(2).sum()
      else:
          # mean over (batch_size, N_prime), then sum over N
          loss = eltwise_loss.mean((0, 2)).sum()
  
      return loss
  
imports: import numpy
  import torch
  from torch import nn
  from pfrl.action_value import QuantileDiscreteActionValue
  from pfrl.agents import dqn
  from pfrl.nn import Recurrent
  from pfrl.utils.recurrent import one_step_forward, pack_and_forward
label_desc: Use image node_id 4 for calling a global function with example usage: compute_value_loss(eltwise_loss, batch_accumulator) and returns: loss