code: def weight_reduce_loss(
      loss, weight=None, reduction="mean", avg_factor=None
  ):
      """Apply element-wise weight and reduce loss.
  
      Args:
          loss (Tensor): Element-wise loss.
          weight (Tensor): Element-wise weights.
          reduction (str): Same as built-in losses of PyTorch.
          avg_factor (float): Avarage factor when computing the mean of losses.
  
      Returns:
          Tensor: Processed loss values.
      """
      # if weight is specified, apply element-wise weight
      if weight is not None:
          loss = loss * weight
  
      # if avg_factor is not specified, just reduce the loss
      if avg_factor is None:
          loss = reduce_loss(loss, reduction)
      else:
          # if reduction is mean, then average the loss by avg_factor
          if reduction == "mean":
              loss = loss.sum() / avg_factor
          # if reduction is 'none', then do nothing, otherwise raise an error
          elif reduction != "none":
              raise ValueError(
                  'avg_factor can not be used with reduction="sum"'
              )
      return loss
  
imports: import functools
  import torch.nn.functional
label_desc: Use image node_id 2 for calling a global function with example usage: weight_reduce_loss(loss, weight, reduction, avg_factor) and returns: loss