docs: def forward(self, x, boxes):
          """
          Arguments:
              x (Tensor): the mask logits
              boxes (list[BoxList]): bounding boxes that are used as
                  reference, one for ech image
  
          Returns:
              results (list[BoxList]): one BoxList for each image, containing
                  the extra field mask
          """
          mask_prob = x.sigmoid()
  
          # select masks coresponding to the predicted classes
          num_masks = x.shape[0]
          labels = [bbox.get_field("labels") for bbox in boxes]
          labels = torch.cat(labels)
          index = torch.arange(num_masks, device=labels.device)
          mask_prob = mask_prob[index, labels][:, None]
  
          boxes_per_image = [len(box) for box in boxes]
          mask_prob = mask_prob.split(boxes_per_image, dim=0)
  
          if self.masker:
              mask_prob = self.masker(mask_prob, boxes)
  
          results = []
          for prob, box in zip(mask_prob, boxes):
              bbox = BoxList(box.bbox, box.size, mode="xyxy")
              for field in box.fields():
                  bbox.add_field(field, box.get_field(field))
              bbox.add_field("mask", prob)
              results.append(bbox)
  
          return results