code: def forward(self, bboxes_in, scores_in):
      # 通过预测的boxes回归参数得到最终预测坐标, 将预测目标score通过softmax处理
      bboxes, probs = self.scale_back_batch(bboxes_in, scores_in)
  
      outputs = torch.jit.annotate(
          List[Tuple[Tensor, Tensor, Tensor]], []
      )
      # 遍历一个batch中的每张image数据
      # bboxes: [batch, 8732, 4]
      for bbox, prob in zip(
          bboxes.split(1, 0), probs.split(1, 0)
      ):  # split_size, split_dim
          # bbox: [1, 8732, 4]
          bbox = bbox.squeeze(0)
          prob = prob.squeeze(0)
          outputs.append(
              self.decode_single_new(
                  bbox, prob, self.criteria, self.max_output
              )
          )
      return outputs
  
imports: from math import sqrt
  import itertools
  import torch
  import torch.nn.functional
  from torch.jit.annotations import Tuple, List
  from torch import nn, Tensor
  import numpy
label_desc: Use image node_id 4 for calling the PostProcess obj's underlying member method code with example usage: obj.forward(bboxes_in, scores_in) and returns: outputs