code: def compute_classifier_loss(self, classifier_scores, batch):
      if classifier_scores.size(1) != batch.label_vec.size(1):
          assert self.generation_model == "bart"
          # ignore start
          classifier_scores = classifier_scores[:, 1:, :]
      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]
  
      # log %classifier examples in the batch
      self.record_local_metric(
          "pct_classifier_exs", AverageMetric.many(classification_idxs)
      )
  
      # if none of the exs 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.
      # Exapand classifier weights to shape classifier_labels and
      # set them to zero for neg examples.
      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",
          pos_weight=self.pos_class_weight * classifier_labels.float(),
      )
  
      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(
          "director_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(
          "director_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(
          "director_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(
          "director_classifier_accuracy",
          AverageMetric.many(
              classifier_accuracy_reshaped, num_target_tokens_reshaped
          ),
      )
  
      # Compute F1 Score.
      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"director_{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(
          "director_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, List
  from abc import abstractmethod
  from parlai.agents.transformer.modules import TransformerDecoderLayer
  from parlai.agents.fid.fid import FidAgent, FidModel
  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
  from parlai.core.torch_agent import Batch, DictionaryAgent
  import parlai.utils.logging
  from projects.blenderbot2.agents.blenderbot2 import BlenderBot2FidAgent, BlenderBot2FidModel, T5BlenderBot2FidModel
  from parlai.agents.fid.fid import WizIntGoldDocRetrieverFiDAgent
  import parlai.agents.transformer.modules.ffn
  from parlai.agents.transformer.modules import LAYER_NORM_EPS
label_desc: Use image node_id 11 for calling the DirectorFidAgent 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