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 __init__(
      self, losses: List[ClassyLoss], weights: Optional[Tensor] = None
  ) -> None:
      super().__init__()
      if weights is None:
          weights = torch.ones((len(losses)))
  
      self.losses = losses
      self.weights = weights
  
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 1 to create a new SumArbitraryLoss object from inherited base classes: ClassyLoss with example: obj = SumArbitraryLoss(losses, weights)