code: def fit(
      self,
      ctx: Context,
      train_data: DataFrame,
      validate_data: DataFrame = None,
  ) -> None:
      # check data, must be fate Dataframe
      assert isinstance(
          train_data, DataFrame
      ), "train_data must be a fate DataFrame"
      if validate_data is not None:
          assert isinstance(
              validate_data, DataFrame
          ), "validate_data must be a fate DataFrame"
  
      self.train_set = self._make_dataset(train_data)
      if not self.train_set.has_label():
          raise RuntimeError("train data must have label column")
      self.train_feature_num = self.train_set.features.shape[1]
      unique_label_set = set(self.train_set.get_classes())
  
      if validate_data is not None:
          self.validate_set = self._make_dataset(validate_data)
          if not self.validate_set.has_label():
              raise RuntimeError("validate data must have label column")
          self.validate_feature_num = self.validate_set.features.shape[
              1
          ]
          assert (
              self.train_feature_num == self.validate_feature_num
          ), "train and validate feature num not match: {} vs {}".format(
              self.train_feature_num, self.validate_feature_num
          )
          unique_label_set = unique_label_set.union(
              set(self.validate_set.get_classes())
          )
  
      self._check_labels(unique_label_set, validate_data is not None)
  
      if self.batch_size is None:
          self.batch_size = len(self.train_set)
  
      # prepare loss function
      loss_fn = functools.partial(
          homo_lr_loss, dim=len(unique_label_set)
      )
      optimizer_params = self.optimizer_param["optimizer_params"]
      opt_method = self.optimizer_param["method"]
      if self.optimizer_param["penalty"] == "l2":
          self.l2 = self.optimizer_param["alpha"]
          optimizer_params["weight_decay"] = self.l2
      elif self.optimizer_param["penalty"] == "l1":
          self.l1 = self.optimizer_param["alpha"]
  
      # initialize model
      if self.model is None:
          fit_intercept = self.init_param["fit_intercept"]
          self.model = HomoLRModel(
              self.train_feature_num,
              label_num=len(unique_label_set),
              l1=self.l1,
              bias=fit_intercept,
          )
          # init model here
          init_model(
              self.model,
              method=self.init_param["method"],
              fill_val=self.init_param["fill_val"],
          )
          logger.info("model initialized")
          logger.info(
              "model parameters are {}".format(
                  list(self.model.parameters())
              )
          )
      else:
          logger.info("model is loaded, warm start training")
      logger.info("model structure is {}".format(self.model))
  
      self.optimizer = optimizer_factory(
          self.model.parameters(), opt_method, optimizer_params
      )
      self.lr_scheduler = lr_scheduler_factory(
          self.optimizer,
          self.learning_rate_param["method"],
          self.learning_rate_param["scheduler_params"],
      )
  
      if self.optimizer_state_dict is not None:
          optimizer_state_dict = {
              "state": {
                  k: t.tensor(v)
                  for k, v in self.optimizer_state_dict["state"].items()
              },
              "param_groups": self.optimizer_state_dict["param_groups"],
          }
          self.optimizer.load_state_dict(optimizer_state_dict)
          logger.info("load warmstart optimizer state dict")
  
      # training
      fed_arg = FedAVGArguments()
      train_arg = TrainingArguments(
          num_train_epochs=self.max_iter,
          per_device_train_batch_size=self.batch_size,
          per_device_eval_batch_size=self.batch_size,
      )
      self.trainer = FedAVGClient(
          ctx,
          model=self.model,
          loss_fn=loss_fn,
          optimizer=self.optimizer,
          train_set=self.train_set,
          val_set=self.validate_set,
          training_args=train_arg,
          fed_args=fed_arg,
          data_collator=default_data_collator,
          scheduler=self.lr_scheduler,
      )
      if self.local_mode:  # for debugging
          self.trainer.set_local_mode()
      self.trainer.train()
  
      logger.info("homo lr fit done")
  
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 5 for calling the HomoLRClient obj's underlying member method code with example usage: obj.fit(ctx, train_data, validate_data) without return types