code: def compute_classifier_loss(self, classifier_scores, batch):
      bsz = batch.batchsize
      device = classifier_scores.device
  
      classifier_losses = torch.zeros((bsz,), device=device)
      num_target_tokens = torch.zeros(
          (bsz,), device=device, dtype=torch.long
      )
  
      # idxs of all the classification exs in the batch.
      classification_idxs = batch.is_ltr[:, 0]
  
      self.record_local_metric(
          "pct_classifier_exs", AverageMetric.many(classification_idxs)
      )
  
      # if none of the exs in the batch are classifier examples,
      # return zero classifier loss.
      if not torch.any(classification_idxs):
          return classifier_losses, num_target_tokens
  
      classifier_scores = classifier_scores[classification_idxs]
  
      # Select the classifier scores for next tokens
      target_tokens = batch.label_vec[classification_idxs]
      next_tokens = target_tokens
  
      next_token_scores = classifier_scores.gather(
          -1, next_tokens.unsqueeze(-1)
      ).squeeze(-1)
  
      classifier_labels = batch.classifier_label[classification_idxs]
  
      # 1/0 (pos/neg) labels for each next token given the context.
      classifier_labels = classifier_labels.expand_as(
          next_token_scores
      ).float()
  
      # Compute BCE loss based on classifier/attribute labels for the next tokens.
      classifier_losses = F.binary_cross_entropy_with_logits(
          next_token_scores,
          classifier_labels,
          reduction="none",
      )
  
      notnull = target_tokens.ne(self.NULL_IDX)
      classifier_losses *= notnull
  
      num_target_tokens = notnull.long().sum(dim=-1)
  
      non_target_indices = torch.ones_like(
          classifier_scores, dtype=torch.bool
      )
      non_target_indices.scatter_(-1, next_tokens.unsqueeze(-1), False)
  
      normalized_classifier_scores = (
          torch.sigmoid(classifier_scores) - 0.5
      ) * notnull.unsqueeze(dim=-1)
  
      normalized_non_target_classifier_scores = (
          normalized_classifier_scores[non_target_indices].reshape(
              *classifier_scores.shape[:-1], -1
          )
      )
  
      normalized_non_target_classifier_scores_squared = (
          normalized_non_target_classifier_scores**2
      )
      normalized_non_target_classifier_scores_mean = (
          normalized_non_target_classifier_scores.mean(dim=-1)
      )
      normalized_non_target_classifier_var = (
          normalized_non_target_classifier_scores.var(dim=-1)
      )
  
      (
          normalized_non_target_classifier_scores_mean_reshaped,
          num_target_tokens_reshaped,
      ) = self._reshape_to_record_metrics(
          batch,
          normalized_non_target_classifier_scores_mean.mean(-1),
          num_target_tokens,
          classification_idxs,
      )
      self.record_local_metric(
          "classifier_score_mean",
          AverageMetric.many(
              normalized_non_target_classifier_scores_mean_reshaped,
              num_target_tokens_reshaped,
          ),
      )
  
      (
          normalized_non_target_classifier_var_reshaped,
          num_target_tokens_reshaped,
      ) = self._reshape_to_record_metrics(
          batch,
          normalized_non_target_classifier_var.mean(-1),
          num_target_tokens,
          classification_idxs,
      )
      self.record_local_metric(
          "classifier_score_var",
          AverageMetric.many(
              normalized_non_target_classifier_var_reshaped,
              num_target_tokens_reshaped,
          ),
      )
  
      # Explicitly force the score for non-target tokens to 0.5.
      # This is done as << 0.5 indicates negative attributes and
      # >> 0.5 indicates positive attributes.
      if self.explicit_classifier_norm:
          classifier_losses += (
              self.explicit_classifier_norm_coeff
              * normalized_non_target_classifier_scores_squared.mean(
                  dim=-1
              )
          )
  
      classifier_losses = classifier_losses.sum(dim=1)
  
      (
          classifier_losses_reshaped,
          num_target_tokens_reshaped,
      ) = self._reshape_to_record_metrics(
          batch,
          classifier_losses,
          num_target_tokens,
          classification_idxs,
      )
      self.record_local_metric(
          "classifier_loss",
          AverageMetric.many(
              classifier_losses_reshaped, num_target_tokens_reshaped
          ),
      )
  
      classifier_predictions = (
          torch.sigmoid(next_token_scores) > 0.5
      ).long()
  
      classifier_accuracy = classifier_labels == classifier_predictions
      classifier_accuracy *= notnull
      classifier_accuracy = classifier_accuracy.sum(-1)
      (
          classifier_accuracy_reshaped,
          num_target_tokens_reshaped,
      ) = self._reshape_to_record_metrics(
          batch,
          classifier_accuracy,
          num_target_tokens,
          classification_idxs,
      )
      self.record_local_metric(
          "classifier_accuracy",
          AverageMetric.many(
              classifier_accuracy_reshaped, num_target_tokens_reshaped
          ),
      )
  
      f1s = {}
      for class_name, positive_class in (("neg", 0), ("pos", 1)):
          positives = classifier_labels == positive_class
          negatives = classifier_labels != positive_class
          trues = classifier_predictions == classifier_labels
          falses = classifier_predictions != classifier_labels
  
          true_positives = ((positives & trues) * notnull).sum(-1)
          false_positives = ((negatives & falses) * notnull).sum(-1)
          false_negatives = ((positives & falses) * notnull).sum(-1)
  
          classifier_f1 = (2 * true_positives) / (
              2 * true_positives + false_positives + false_negatives
          )
          classifier_f1[true_positives == 0] = 0
  
          (classifier_f1_reshaped, _) = self._reshape_to_record_metrics(
              batch,
              classifier_f1,
              num_target_tokens,
              classification_idxs,
          )
  
          f1s[class_name] = classifier_f1_reshaped
  
          batch_positives = (
              batch.classifier_label[:, 0] == positive_class
          )
          # We use (classification_idxs & (batch_positives > 0) to indicate that we only consider the exs
          # that are ltr classification examples and classifier_labels == positive_class.
          self.record_local_metric(
              f"{class_name}_classifier_f1",
              AverageMetric.many(
                  classifier_f1_reshaped,
                  (classification_idxs & (batch_positives > 0)).int(),
              ),
          )
  
      avg_classifier_f1_reshaped = sum(f1s.values())
      # We use classification_idxs.int()  to indicate that we only consider the exs that are
      # ltr classification examples.
      self.record_local_metric(
          f"classifier_f1",
          AverageMetric.many(
              avg_classifier_f1_reshaped, classification_idxs.int()
          ),
      )
      return classifier_losses_reshaped, num_target_tokens_reshaped
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from typing import Optional, Any, Dict, Tuple, Union
  from parlai.agents.transformer.modules import TransformerGeneratorModel
  from parlai.agents.transformer.transformer import TransformerGeneratorAgent
  from parlai.core.dict import DictionaryAgent
  from parlai.core.message import Message
  from parlai.core.metrics import AverageMetric
  from parlai.core.opt import Opt
  from parlai.core.params import ParlaiParser
  from parlai.core.torch_generator_agent import PPLMetric
  import parlai.utils.logging
label_desc: Use image node_id 10 for calling the DirectorAgent obj's underlying member method code with example usage: obj.compute_classifier_loss(classifier_scores, batch) and returns: classifier_losses_reshaped,  num_target_tokens_reshaped, classifier_losses,  num_target_tokens