code: def compute_weighted_value_loss(
      eltwise_loss, weights, batch_accumulator="mean"
  ):
      """Compute a loss for value prediction problem.
  
      Args:
          eltwise_loss (Variable): Element-wise loss per example
          weights (ndarray): Weights for y, t.
          batch_accumulator (str): 'mean' will divide loss by batchsize
      Returns:
          (Variable) scalar loss
      """
      batch_size = eltwise_loss.shape[0]
      assert batch_accumulator in ("mean", "sum")
      assert eltwise_loss.ndim == 3
      # eltwise_loss is (batchsize, n , n') array of losses
      # weights is an array of shape (batch_size)
      # apply weights per example in batch
      loss_sum = torch.matmul(eltwise_loss.mean(2).sum(1), weights)
      if batch_accumulator == "mean":
          loss = loss_sum / batch_size
      elif batch_accumulator == "sum":
          loss = loss_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 5 for calling a global function with example usage: compute_weighted_value_loss(eltwise_loss, weights, batch_accumulator) and returns: loss