class_docstr: Simple implementation of Hash Layers and the Ladder model from the following papers:
  
  - https://arxiv.org/abs/2106.04426
  - https://arxiv.org/abs/2106.04279
docstr: Compute and return the loss for the given batch.
docstr_tok: ["Compute","and","return","the","loss","for","the","given","batch","."]
code: def compute_loss(self, batch, return_output=False):
      """
      Compute and return the loss for the given batch.
      """
      if return_output:
          loss, model_output = super().compute_loss(
              batch, return_output
          )
      else:
          loss = super().compute_loss(batch, return_output)
  
      if self.opt["hash_layer"] != -1:
          loss = loss + self.dummy_loss()
  
      if return_output:
          return (loss, model_output)
      else:
          return loss
  
imports: from __future__ import annotations
  import torch
  from typing import Dict, Optional, Tuple
  import torch.nn
  from parlai.agents.transformer.modules import TransformerDecoder, TransformerGeneratorModel
  from parlai.agents.transformer.transformer import TransformerGeneratorAgent
  from parlai.core.opt import Opt
  from parlai.core.params import ParlaiParser
  import torch.nn.functional
label_desc: Use image node_id 4 for calling the HashLadderAgent obj's underlying member method code with example usage: obj.compute_loss(batch, return_output) and returns: loss,  model_output, loss