class_docstr: Issue #411:
  
  A common use case is to have embeddings and ref_emb be augmented versions of each other.
  For most losses right now you have to create labels to indicate
  which embeddings correspond with which ref_emb.
  A wrapper that does this for the user would be nice.
  
      loss_fn = SelfSupervisedLoss(TripletMarginLoss())
      loss = loss_fn(embeddings, ref_emb1, ref_emb2, ...)
  
  where ref_embk = kth augmentation of embeddings.
code: def check_loss_support(cls, loss_name):
      if loss_name not in cls.supported_losses():
          raise Exception(
              f"SelfSupervisedLoss not supported for {loss_name}"
          )
  
imports: import torch
  from .base_loss_wrapper import BaseLossWrapper
label_desc: Use image node_id 3 for calling the SelfSupervisedLoss obj's underlying member method code with example usage: obj.check_loss_support(cls, loss_name) without return types