code: def fit(
      self, ctx: Context, train_data: DataFrame, validate_data=None
  ):
      if len(ctx.hosts) > 1:
          raise ValueError(
              f"SSHE LR only support single-host case. Please check configuration."
          )
      if ctx.is_on_guest:
          train_data_binarized_label = train_data.label.get_dummies()
          label_count = train_data_binarized_label.shape[1]
          ctx.hosts.put("label_count", label_count)
          labels = [
              int(label_name.split("_")[1])
              for label_name in train_data_binarized_label.columns
          ]
          if self.labels is None:
              self.labels = sorted(labels)
      else:
          self.init_param["fit_intercept"] = False
          label_count = ctx.guest.get("label_count")
      self.label_count = label_count
      if label_count > 2 or self.ovr:
          logger.info(f"OVR data provided, will train OVR models.")
          self.ovr = True
          warm_start = True
          if self.estimator is None:
              self.estimator = {}
              warm_start = False
          for i, class_ctx in ctx.sub_ctx("class").ctxs_range(
              label_count
          ):
              logger.info(f"start train for {i}th class")
              if not warm_start:
                  single_estimator = SSHELREstimator(
                      epochs=self.epochs,
                      batch_size=self.batch_size,
                      learning_rate=self.learning_rate,
                      init_param=self.init_param,
                      reveal_every_epoch=self.reveal_every_epoch,
                      reveal_loss_freq=self.reveal_loss_freq,
                      early_stop=self.early_stop,
                      tol=self.tol,
                  )
              else:
                  # warm start
                  logger.info(
                      "estimator is not none, will train with warm start"
                  )
                  # single_estimator = self.estimator[self.labels.index(labels[i])]
                  single_estimator = self.estimator[i]
                  single_estimator.epochs = self.epochs
                  single_estimator.batch_size = self.batch_size
              class_train_data = train_data.copy()
              class_validate_data = validate_data
              if validate_data:
                  class_validate_data = validate_data.copy()
              if ctx.is_on_guest:
                  class_train_data.label = train_data_binarized_label[
                      train_data_binarized_label.columns[i]
                  ]
              single_estimator.fit_single_model(
                  class_ctx, class_train_data, class_validate_data
              )
  
              self.estimator[i] = single_estimator
  
      else:
          if self.estimator is None:
              single_estimator = SSHELREstimator(
                  epochs=self.epochs,
                  batch_size=self.batch_size,
                  learning_rate=self.learning_rate,
                  init_param=self.init_param,
                  reveal_every_epoch=self.reveal_every_epoch,
                  reveal_loss_freq=self.reveal_loss_freq,
                  early_stop=self.early_stop,
                  tol=self.tol,
              )
          else:
              logger.info(
                  "estimator is not none, will train with warm start"
              )
              single_estimator = self.estimator
              single_estimator.epochs = self.epochs
              single_estimator.batch_size = self.batch_size
          train_data_fit = train_data.copy()
          validate_data_fit = validate_data
          if validate_data:
              validate_data_fit = validate_data.copy()
          single_estimator.fit_single_model(
              ctx, train_data_fit, validate_data_fit
          )
          self.estimator = single_estimator
  
imports: import logging
  import torch
  from fate.arch import Context, dataframe
  from fate.arch.dataframe import DataFrame
  from fate.arch.protocol.mpc.nn.sshe.lr_layer import SSHELogisticRegressionLayer, SSHELogisticRegressionLossLayer, SSHEOptimizerSGD
  from fate.ml.abc.module import Module, HeteroModule
  from fate.ml.utils import predict_tools
  from fate.ml.utils._convergence import converge_func_factory
  from fate.ml.utils._model_param import get_initialize_func
  from fate.ml.utils._model_param import serialize_param, deserialize_param
label_desc: Use image node_id 4 for calling the SSHELogisticRegression obj's underlying member method code with example usage: obj.fit(ctx, train_data, validate_data) without return types