code: def _check_labels(self, label_set, has_validate=False):
      dataset_descrb = (
          "train dataset"
          if not has_validate
          else "train and validate dataset"
      )
      if not self.ovr and len(label_set) > 2:
          raise ValueError(
              "please set ovr=True to enable multi-label classification, multiple labels found in {}: {}".format(
                  dataset_descrb, label_set
              )
          )
      if not self.ovr and len(label_set) == 2:
          # 0, 1 is required
          if 0 not in label_set or 1 not in label_set:
              # ask for label 0, 1 when running binary classification
              raise ValueError(
                  "when doing binary classification, lables must be 0, 1, but found in {}'s label set is {}".format(
                      label_set, dataset_descrb
                  )
              )
      if self.ovr:
          if max(label_set) > self.label_num - 1:
              # make sure labels start from 0 and not the label indices not
              # exceed the label num parameter
              raise ValueError(
                  "when doing multi-label classification, labels must start from 0 and not exceed the label num parameter, \
                                   but {}'s label set is {}, while label num is {}".format(
                      label_set, dataset_descrb, self.label_num
                  )
              )
  
imports: import torch.nn
  from fate.arch.dataframe import DataFrame
  from fate.ml.abc.module import HomoModule
  from fate.ml.utils.model_io import ModelIO
  from fate.arch import Context
  import logging
  import torch
  from fate.ml.nn.homo.fedavg import FedAVGClient, TrainingArguments, FedAVGArguments
  from transformers import default_data_collator
  import functools
  import tempfile
  from fate.ml.utils.predict_tools import array_to_predict_df
  from fate.ml.utils.predict_tools import MULTI, BINARY
  from fate.ml.nn.dataset.table import TableDataset
  from fate.ml.utils._optimizer import optimizer_factory, lr_scheduler_factory
label_desc: Use image node_id 4 for calling the HomoLRClient obj's underlying member method code with example usage: obj._check_labels(label_set, has_validate) without return types