code: def compute_value_loss(
      y, t, clip_delta=True, batch_accumulator="mean"
  ):
      """Compute a loss for value prediction problem.
  
      Args:
          y (Variable or ndarray): Predicted values.
          t (Variable or ndarray): Target values.
          clip_delta (bool): Use the Huber loss function if set True.
          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")
      y = F.reshape(y, (-1, 1))
      t = F.reshape(t, (-1, 1))
      if clip_delta:
          loss_sum = F.sum(F.huber_loss(y, t, delta=1.0))
          if batch_accumulator == "mean":
              loss = loss_sum / y.shape[0]
          elif batch_accumulator == "sum":
              loss = loss_sum
      else:
          loss_mean = F.mean_squared_error(y, t) / 2
          if batch_accumulator == "mean":
              loss = loss_mean
          elif batch_accumulator == "sum":
              loss = loss_mean * y.shape[0]
      return loss
  
imports: import copy
  from logging import getLogger
  import chainer
  from chainer import cuda
  import chainer.functions
  from chainerrl import agent
  from chainerrl.misc.batch_states import batch_states
  from chainerrl.misc.copy_param import synchronize_parameters
  from chainerrl.replay_buffer import batch_experiences
  from chainerrl.replay_buffer import batch_recurrent_experiences
  from chainerrl.replay_buffer import ReplayUpdater
label_desc: Use image node_id 1 for calling a global function with example usage: compute_value_loss(y, t, clip_delta, batch_accumulator) and returns: loss