code: def compute_loss(self, batch, return_output=False):
      if batch.label_vec is None:
          raise ValueError("Cannot compute loss without a label.")
      model_output = self._model_output(batch)
      scores, preds, *_ = model_output
      score_view = scores.reshape(-1, scores.size(-1))
      (loss, ce_loss, ct_loss, ce_mask, ct_mask) = self.criterion(
          score_view,
          batch.label_vec.view(-1),
          batch.classifier_label.repeat(1, scores.shape[1])
          .view(-1)
          .to(batch.label_vec.device),
      )
  
      def loss_reshape(loss):
          return loss.view(scores.shape[:-1]).sum(dim=1)
  
      loss = loss_reshape(loss)
      ce_loss = loss_reshape(ce_loss)
      ct_loss = loss_reshape(ct_loss)
      notnull = batch.label_vec.ne(self.NULL_IDX)
      ce_mask = torch.logical_and(
          notnull, ce_mask.view(-1, batch.label_vec.size(-1))
      )
      ct_mask = torch.logical_and(
          notnull, ct_mask.view(-1, batch.label_vec.size(-1))
      )
      # number of tokens in each examples for cross entropy or cringe loss.
      metric_notnull = torch.logical_or(ce_mask, ct_mask)
      target_tokens = metric_notnull.long().sum(dim=-1)
      ce_target_tokens = ce_mask.long().sum(dim=-1)
      ct_target_tokens = ct_mask.long().sum(dim=-1)
  
      correct = ((batch.label_vec == preds) * metric_notnull).sum(
          dim=-1
      )
  
      pos_labels = (torch.abs(batch.classifier_label) == 1).view(-1)
      neg_labels = (torch.abs(batch.classifier_label) == 0).view(-1)
      correct_pos = torch.where(pos_labels, correct, -1)
      correct_neg = torch.where(neg_labels, correct, -1)
  
      # record loss
      self.record_local_metric(
          "loss", AverageMetric.many(loss, target_tokens)
      )
      self.record_local_metric(
          "ce_loss",
          [
              metric if ce_token_cnt > 0 else None
              for ce_token_cnt, metric in zip(
                  ce_target_tokens,
                  AverageMetric.many(ce_loss, target_tokens),
              )
          ],  # type: ignore
      )
      self.record_local_metric(
          "ct_loss",
          [
              metric if ct_token_cnt > 0 else None
              for ct_token_cnt, metric in zip(
                  ct_target_tokens,
                  AverageMetric.many(ct_loss, target_tokens),
              )
          ],  # type: ignore
      )
      # token-wise accuracy
      self.record_local_metric(
          "token_acc",
          [
              metric if per_target_token > 0 else None
              for per_target_token, metric in zip(
                  target_tokens,
                  AverageMetric.many(correct, target_tokens),
              )
          ],  # type: ignore
      )
      self.record_local_metric(
          "token_acc_pos",
          [
              metric if metric >= 0 else None
              for metric in AverageMetric.many(
                  correct_pos, target_tokens
              )
          ],
      )
      self.record_local_metric(
          "token_acc_neg",
          [
              metric if metric >= 0 else None
              for metric in AverageMetric.many(
                  correct_neg, target_tokens
              )
          ],
      )
      # perplexity
      self.record_local_metric(
          "ppl_debug",
          [
              metric if per_target_token > 0 else None
              for per_target_token, metric in zip(
                  target_tokens,
                  PPLMetric.many(ce_loss + ct_loss, target_tokens),
              )
          ],  # type: ignore
      )
      self.record_local_metric(
          "ppl_ce",
          [
              metric if ce_token_cnt > 0 else None
              for ce_token_cnt, metric in zip(
                  ce_target_tokens,
                  PPLMetric.many(ce_loss, ce_target_tokens),
              )
          ],  # type: ignore
      )
      self.record_local_metric(
          "ppl_pos",
          [
              metric if pos_label else None
              for pos_label, metric in zip(
                  pos_labels, PPLMetric.many(ce_loss, target_tokens)
              )
          ],
      )
      self.record_local_metric(
          "ppl_ct",
          [
              metric if neg_label else None
              for neg_label, metric in zip(
                  neg_labels, PPLMetric.many(ct_loss, target_tokens)
              )
          ],
      )
  
      # record sample size
      self.record_local_metric(
          "ce_target_tokens", AverageMetric.many(ce_target_tokens)
      )
      self.record_local_metric(
          "ct_target_tokens", AverageMetric.many(ct_target_tokens)
      )
      self.record_local_metric(
          "total_target_tokens", AverageMetric.many(target_tokens)
      )
  
      # actually do backwards loss
      loss = loss.sum()
      loss /= target_tokens.sum()  # average loss per token
      if return_output:
          return (loss, model_output)
      else:
          return loss
  
imports: import torch
  from torch.nn import CrossEntropyLoss
  from torch.distributions.categorical import Categorical
  from typing import Optional, Dict, Union, Tuple, Any
  from parlai.core.message import Message
  from parlai.core.opt import Opt
  from parlai.core.params import ParlaiParser
  from parlai.agents.transformer.transformer import TransformerGeneratorAgent
  from parlai.agents.fid.fid import FidAgent
  from parlai.core.torch_generator_agent import PPLMetric, TorchGeneratorAgent
  from parlai.core.metrics import AverageMetric
  from parlai.agents.fid.fid import WizIntGoldDocRetrieverFiDAgent
  from projects.blenderbot2.agents.blenderbot2 import BlenderBot2FidAgent, BlenderBot2FidModel, T5BlenderBot2FidModel
  from projects.seeker.agents.seeker import ComboFidAgent
label_desc: Use image node_id 7 for calling the ContrastiveTorchGeneratorAgent obj's underlying member method code with example usage: obj.compute_loss(batch, return_output) and returns: loss, loss,  model_output, loss