code: def get_mc_loss(self, feat, inputs):
      # feat.shape = [bs, ch_emb, h, w]
      assert "cls_id_map" in inputs and "cls_tr_ids" in inputs
      index = inputs["index"]
      mask = inputs["index_mask"]
      cls_id_map = inputs["cls_id_map"]  # [bs, h, w]
      cls_tr_ids = inputs["cls_tr_ids"]  # [bs, num_classes, h, w]
  
      feat = paddle.transpose(feat, perm=[0, 2, 3, 1])
      feat_n, feat_h, feat_w, feat_c = feat.shape
      feat = paddle.reshape(feat, shape=[feat_n, -1, feat_c])
  
      index = paddle.unsqueeze(index, 2)
      batch_inds = list()
      for i in range(feat_n):
          batch_ind = paddle.full(
              shape=[1, index.shape[1], 1], fill_value=i, dtype="int64"
          )
          batch_inds.append(batch_ind)
      batch_inds = paddle.concat(batch_inds, axis=0)
      index = paddle.concat(x=[batch_inds, index], axis=2)
      feat = paddle.gather_nd(feat, index=index)
  
      mask = paddle.unsqueeze(mask, axis=2)
      mask = paddle.expand_as(mask, feat)
      mask.stop_gradient = True
      feat = paddle.masked_select(feat, mask > 0)
      feat = paddle.reshape(feat, shape=[-1, feat_c])
  
      reid_losses = 0
      for cls_id, id_num in self.num_identities_dict.items():
          # target
          cur_cls_tr_ids = paddle.reshape(
              cls_tr_ids[:, cls_id, :, :], shape=[feat_n, -1]
          )  # [bs, h*w]
          cls_id_target = paddle.gather_nd(cur_cls_tr_ids, index=index)
          mask = inputs["index_mask"]
          cls_id_target = paddle.masked_select(cls_id_target, mask > 0)
          cls_id_target.stop_gradient = True
  
          # feat
          cls_id_feat = self.emb_scale_dict[str(cls_id)] * F.normalize(
              feat
          )
          cls_id_pred = self.classifiers[str(cls_id)](cls_id_feat)
  
          loss = self.reid_loss(cls_id_pred, cls_id_target)
          valid = cls_id_target != self.reid_loss.ignore_index
          valid.stop_gradient = True
          count = paddle.sum((paddle.cast(valid, dtype=np.int32)))
          count.stop_gradient = True
          if count > 0:
              loss = loss / count
          reid_losses += loss
  
      return reid_losses
  
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 6 for calling the FairMOTEmbeddingHead obj's underlying member method code with example usage: obj.get_mc_loss(feat, inputs) and returns: reid_losses