class_docstr: Sums a collection of (weighted) torch.nn losses.
  
  NOTE: this applies all the losses to the same output and does not support
  taking a list of outputs as input.
code: def forward(self, prediction, target):
      for idx, loss in enumerate(self.losses):
          current_loss = loss(prediction, target)
          if idx == 0:
              total_loss = current_loss
          else:
              total_loss = total_loss.add(
                  self.weights[idx], current_loss
              )
      return total_loss
  
file_path: ClassyVision/classy_vision/losses/sum_arbitrary_loss.py
imports: from typing import Any, Dict, List, Optional
  import torch
  from torch import Tensor
  from .None import build_loss, ClassyLoss, register_loss
label_desc: Use image node_id 3 for calling the SumArbitraryLoss obj's underlying member method code with example usage: obj.forward(prediction, target) and returns: total_loss