code: def forward(
      self,
      neck_feat,
      inputs,
      bboxes=None,
      bbox_inds=None,
      topk_clses=None,
  ):
      reid_feat = self.reid(neck_feat)
      if self.training:
          if self.num_classes == 1:
              loss = self.get_loss(reid_feat, inputs)
          else:
              loss = self.get_mc_loss(reid_feat, inputs)
          return loss
      else:
          assert bboxes is not None and bbox_inds is not None
          reid_feat = F.normalize(reid_feat)
          embedding = paddle.transpose(reid_feat, [0, 2, 3, 1])
          embedding = paddle.reshape(embedding, [-1, self.ch_emb])
          # embedding shape: [bs * h * w, ch_emb]
  
          if self.num_classes == 1:
              pred_dets = bboxes
              pred_embs = paddle.gather(embedding, bbox_inds)
          else:
              pred_dets, pred_embs = self.process_by_class(
                  bboxes, embedding, bbox_inds, topk_clses
              )
          return pred_dets, pred_embs
  
file_path: PaddleDetection/ppdet/modeling/reid/fairmot_embedding_head.py
imports: import numpy
  import math
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle.nn.initializer import KaimingUniform, Uniform
  from ppdet.core.workspace import register
  from ppdet.modeling.heads.centernet_head import ConvLayer
label_desc: Use image node_id 4 for calling the FairMOTEmbeddingHead obj's underlying member method code with example usage: obj.forward(neck_feat, inputs, bboxes, bbox_inds, topk_clses) and returns: loss, pred_dets,  pred_embs