class_docstr: EmbLoss, regularization on embeddings
class_docstr_tok: ["EmbLoss",",","regularization","on","embeddings"]
code: def forward(self, *embeddings, require_pow=False):
      if require_pow:
          emb_loss = torch.zeros(1).to(embeddings[-1].device)
          for embedding in embeddings:
              emb_loss += torch.pow(
                  input=torch.norm(embedding, p=self.norm),
                  exponent=self.norm,
              )
          emb_loss /= embeddings[-1].shape[0]
          emb_loss /= self.norm
          return emb_loss
      else:
          emb_loss = torch.zeros(1).to(embeddings[-1].device)
          for embedding in embeddings:
              emb_loss += torch.norm(embedding, p=self.norm)
          emb_loss /= embeddings[-1].shape[0]
          return emb_loss
  
file_path: RecBole/recbole/model/loss.py
imports: import torch
  import torch.nn
label_desc: Use image node_id 2 for calling the EmbLoss obj's underlying member method code with example usage: obj.forward() and returns: emb_loss, emb_loss