code: def forward(
      self,
      identify_feats,
      targets,
      loss_confs=None,
      loss_boxes=None,
      bboxes=None,
      boxes_idx=None,
      nms_keep_idx=None,
  ):
      assert self.num_classes == 1, "JDE only support sindle class MOT."
      assert len(identify_feats) == self.anchor_levels
      ide_outs = []
      for feat, ide_head in zip(identify_feats, self.identify_outputs):
          ide_outs.append(ide_head(feat))
  
      if self.training:
          assert (
              len(loss_confs) == len(loss_boxes) == self.anchor_levels
          )
          loss_ides = self.emb_loss(
              ide_outs, targets, self.emb_scale, self.classifier
          )
          jde_losses = self.jde_loss(
              loss_confs,
              loss_boxes,
              loss_ides,
              self.loss_params_cls,
              self.loss_params_reg,
              self.loss_params_ide,
              targets,
          )
          return jde_losses
      else:
          assert bboxes is not None
          assert boxes_idx is not None
          assert nms_keep_idx is not None
  
          emb_outs = self.get_emb_outs(ide_outs)
          emb_valid = paddle.gather_nd(emb_outs, boxes_idx)
          pred_embs = paddle.gather_nd(emb_valid, nms_keep_idx)
  
          input_shape = targets["image"].shape[2:]
          # input_shape: [h, w], before data transforms, set in model config
          im_shape = targets["im_shape"][0].numpy()
          # im_shape: [new_h, new_w], after data transforms
          scale_factor = targets["scale_factor"][0].numpy()
          bboxes[:, 2:] = self.scale_coords(
              bboxes[:, 2:], input_shape, im_shape, scale_factor
          )
          # cls_ids, scores, tlwhs
          pred_dets = bboxes
          return pred_dets, pred_embs
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import ParamAttr
  from paddle.regularizer import L2Decay
  from ppdet.core.workspace import register
  from paddle.nn.initializer import Normal, Constant
label_desc: Use image node_id 2 for calling the JDEEmbeddingHead obj's underlying member method code with example usage: obj.forward(identify_feats, targets, loss_confs, loss_boxes, bboxes, boxes_idx, nms_keep_idx) and returns: jde_losses, pred_dets,  pred_embs