code: def forward(self, features, proposals, targets=None):
          
          if self.training:
              with torch.no_grad():
                  proposals = self.loss_evaluator.subsample(proposals, targets)
  
          x = self.feature_extractor(features, proposals)
          kp_logits = self.predictor(x)
  
          if not self.training:
              result = self.post_processor(kp_logits, proposals)
              return x, result, {}
  
          loss_kp = self.loss_evaluator(proposals, kp_logits)
  
          return x, proposals, dict(loss_kp=loss_kp)
docs: 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 original proposals
                  are returned. During testing, the predicted boxlists are returned
                  with the `mask` field set
              losses (dict[Tensor]): During training, returns the losses for the
                  head. During testing, returns an empty dict.