code: def get_loss(self, feat, inputs):
      index = inputs["index"]
      mask = inputs["index_mask"]
      target = inputs["reid"]
      target = paddle.masked_select(target, mask > 0)
      target = paddle.unsqueeze(target, 1)
  
      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])
      feat = F.normalize(feat)
      feat = self.emb_scale * feat
      logit = self.classifier(feat)
      target.stop_gradient = True
      loss = self.reid_loss(logit, target)
      valid = 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
  
      return loss
  
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 5 for calling the FairMOTEmbeddingHead obj's underlying member method code with example usage: obj.get_loss(feat, inputs) and returns: loss