docs: def forward(self, features, proposals, targets=None):
          """
          Arguments:
              features (list[Tensor]): feature-maps from possibly several levels
              proposals (list[BoxList]): proposal boxes
              targets (list[BoxList], optional): the ground-truth targets.
  
          Returns:
              x (Tensor): the result of the feature extractor
              proposals (list[BoxList]): during training, the subsampled proposals
                  are returned. During testing, the predicted boxlists are returned
              losses (dict[Tensor]): During training, returns the losses for the
                  head. During testing, returns an empty dict.
          """
  
          if self.training:
              # Faster R-CNN subsamples during training the proposals with a fixed
              # positive / negative ratio
              with torch.no_grad():
                  proposals = self.loss_evaluator.subsample(proposals, targets)
  
          # extract features that will be fed to the final classifier. The
          # feature_extractor generally corresponds to the pooler + heads
          x = self.feature_extractor(features, proposals)
          # final classifier that converts the features into predictions
          class_logits, box_regression = self.predictor(x)
  
          if not self.training:
              result = self.post_processor((class_logits, box_regression), proposals)
              return x, result, {}
  
          loss_classifier, loss_box_reg = self.loss_evaluator(
              [class_logits], [box_regression]
          )
          return (
              x,
              proposals,
              dict(loss_classifier=loss_classifier, loss_box_reg=loss_box_reg),
          )