code: def forward(self, image, targets=None):
      x = self.feature_extractor(image)
  
      # Feature Map 38x38x1024, 19x19x512, 10x10x512, 5x5x256, 3x3x256, 1x1x256
      detection_features = torch.jit.annotate(List[Tensor], [])  # [x]
      detection_features.append(x)
      for layer in self.additional_blocks:
          x = layer(x)
          detection_features.append(x)
  
      # Feature Map 38x38x4, 19x19x6, 10x10x6, 5x5x6, 3x3x4, 1x1x4
      locs, confs = self.bbox_view(
          detection_features, self.loc, self.conf
      )
  
      # For SSD 300, shall return nbatch x 8732 x {nlabels, nlocs} results
      # 38x38x4 + 19x19x6 + 10x10x6 + 5x5x6 + 3x3x4 + 1x1x4 = 8732
  
      if self.training:
          if targets is None:
              raise ValueError(
                  "In training mode, targets should be passed"
              )
          # bboxes_out (Tensor 8732 x 4), labels_out (Tensor 8732)
          bboxes_out = targets["boxes"]
          bboxes_out = bboxes_out.transpose(1, 2).contiguous()
          # print(bboxes_out.is_contiguous())
          labels_out = targets["labels"]
          # print(labels_out.is_contiguous())
  
          # ploc, plabel, gloc, glabel
          loss = self.compute_loss(locs, confs, bboxes_out, labels_out)
          return {"total_losses": loss}
  
      # 将预测回归参数叠加到default box上得到最终预测box，并执行非极大值抑制虑除重叠框
      # results = self.encoder.decode_batch(locs, confs)
      results = self.postprocess(locs, confs)
      return results
  
imports: import torch
  from torch import nn, Tensor
  from torch.jit.annotations import List
  from .res50_backbone import resnet50
  from .utils import dboxes300_coco, Encoder, PostProcess
label_desc: Use image node_id 5 for calling the SSD300 obj's underlying member method code with example usage: obj.forward(image, targets) and returns: results, dict