code: def loss_labels(self, sample_dict, net_out_dict, indices, num_masks):
      src_logits = net_out_dict["logits"].astype("float32")
  
      idx = self._get_src_permutation_idx(indices)
      target_classes_o = paddle.concat(
          [t[J] for t, (_, J) in zip(sample_dict["gt_ids"], indices)]
      )
      target_classes = paddle.full(
          src_logits.shape[:2], self.num_classes, dtype="int64"
      )
      target_classes[idx] = target_classes_o
  
      loss_ce = F.cross_entropy(
          src_logits.transpose((0, 2, 1)),
          target_classes,
          self.empty_weight,
          axis=1,
      )
      return self.weight_ce * loss_ce
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from scipy.optimize import linear_sum_assignment
  from paddlepanseg.cvlibs import manager, build_info_dict
  from paddlepanseg.core.runner import PanSegRunner
label_desc: Use image node_id 2 for calling the MaskFormerRunner obj's underlying member method code with example usage: obj.loss_labels(sample_dict, net_out_dict, indices, num_masks) and returns: unknown