code: def __call__(self, x, y, classifier_labels=None, **kwargs):
      if classifier_labels is None:
          classifier_labels = -torch.ones_like(y).to(y.device)
  
      # turn no-class provided label (-1) into positive label (1)
      classifier_labels_ce = torch.abs(classifier_labels)
      if not self.train_ce_on_positive_examples:
          # only train CE on no-class labels
          classifier_labels_ce = classifier_labels.eq(-1)
  
      if self.train_ct_on_positive_examples:
          # no-class (-1 to 0), positive (1 to 1), negative (0 to 1)
          classifier_labels_ct = torch.clamp(
              classifier_labels + 1, max=1
          )
      else:
          # no-class (-1 to 0), positive (1 to 0), negative (0 to 1)
          classifier_labels_ct = torch.abs(
              torch.abs(classifier_labels) - 1
          )
      classifier_labels_ct = classifier_labels_ct.bool()
  
      ce_loss = super().__call__(x, y, **kwargs)
      # multiply with classifier labels to not train with negative feedback (0)
      ce_loss *= classifier_labels_ce
  
      # compute the contrastive loss part for the negative labels
      # first, get the positives as the top predictions != target
      preds = torch.topk(x, k=self.num_pos_predictions + 1, axis=-1)
      y_rep = y.unsqueeze(-1).repeat(1, self.num_pos_predictions + 1)
      logits = preds.values - (preds.indices == y_rep) * 1e10
  
      # if the positive is not in the first k predictions, mask out
      # the final (k+1)'s logit
      prediction_mask = torch.cat(
          (
              torch.zeros_like(logits)[:, :-1],
              torch.abs(
                  (preds.indices == y_rep).sum(-1).unsqueeze(1) - 1
              ),
          ),
          1,
      )
      logits -= prediction_mask * 1e10
  
      # Sample from the categorical distribution of the top-k predictions
      # (with the label masked out).
      preds_dist = Categorical(logits=logits)
      idx_sample = preds_dist.sample()
      sample_preds_values = preds.values[
          torch.arange(x.shape[0]), idx_sample
      ]
  
      if self.detach_positives_during_ct:
          sample_preds_values = sample_preds_values.detach()
  
      # concatenate the logits of the preds with the actual label's logits
      x_target = x[torch.arange(x.shape[0]), y]
      x_ct = torch.cat(
          [x_target.unsqueeze(1), sample_preds_values.unsqueeze(1)], -1
      )
      # get the y's for the x_ct (the correct label is index 0 if
      # the target is positive and index 1 if the target is negative)
      y_ct = (
          torch.abs(torch.abs(classifier_labels) - 1)
          .type(y.dtype)
          .to(x_ct.device)
      )
      # y_ct = (torch.ones(y.shape) * ).type(y.dtype).to(x_ct.device)
      # compute the contrastive loss as cross entropy loss between x_ct, y_ct
      ct_loss = super().__call__(x_ct, y_ct, **kwargs)
      ct_loss *= classifier_labels_ct
  
      # remove loss from ignore index
      notnull = y.ne(self.ignore_index)
      ce_loss *= notnull
      ct_loss *= notnull
  
      loss = ce_loss + self.ct_loss_weight * ct_loss
  
      return (
          loss,
          ce_loss,
          ct_loss,
          classifier_labels_ce,
          classifier_labels_ct,
      )
  
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 2 for calling the ContrastiveCrossEntropyLoss obj's underlying member method code with example usage: obj.__call__(x, y, classifier_labels) and returns: loss,  ce_loss,  ct_loss,  classifier_labels_ce,  classifier_labels_ct