code: def fit(self, ctx: Context, train_data, validate_data=None) -> None:
      # original_label = train_data.label
      train_data_binarized_label = train_data.label.get_dummies()
      label_count = train_data_binarized_label.shape[1]
      ctx.arbiter.put("label_count", label_count)
      ctx.hosts.put("label_count", label_count)
      encryptor = ctx.arbiter("encryptor").get()
      labels = [
          int(label_name.split("_")[1])
          for label_name in train_data_binarized_label.columns
      ]
      if self.labels is None:
          self.labels = sorted(labels)
      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")
              # optimizer = copy.deepcopy(self.optimizer)
              if not warm_start:
                  optimizer = Optimizer(
                      self.optimizer_param["method"],
                      self.optimizer_param["penalty"],
                      self.optimizer_param["alpha"],
                      self.optimizer_param["optimizer_params"],
                  )
                  lr_scheduler = LRScheduler(
                      self.learning_rate_param["method"],
                      self.learning_rate_param["scheduler_params"],
                  )
                  single_estimator = CoordinatedLREstimatorGuest(
                      epochs=self.epochs,
                      batch_size=self.batch_size,
                      optimizer=optimizer,
                      learning_rate_scheduler=lr_scheduler,
                      init_param=self.init_param,
                      floating_point_precision=self.floating_point_precision,
                  )
              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()
              class_train_data.label = train_data_binarized_label[
                  train_data_binarized_label.columns[i]
              ]
              single_estimator.fit_single_model(
                  class_ctx,
                  encryptor,
                  class_train_data,
                  class_validate_data,
              )
              self.estimator[i] = single_estimator
  
      else:
          if self.estimator is None:
              optimizer = Optimizer(
                  self.optimizer_param["method"],
                  self.optimizer_param["penalty"],
                  self.optimizer_param["alpha"],
                  self.optimizer_param["optimizer_params"],
              )
              lr_scheduler = LRScheduler(
                  self.learning_rate_param["method"],
                  self.learning_rate_param["scheduler_params"],
              )
              single_estimator = CoordinatedLREstimatorGuest(
                  epochs=self.epochs,
                  batch_size=self.batch_size,
                  optimizer=optimizer,
                  learning_rate_scheduler=lr_scheduler,
                  init_param=self.init_param,
                  floating_point_precision=self.floating_point_precision,
              )
          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, encryptor, train_data_fit, validate_data_fit
          )
          self.estimator = single_estimator
  
imports: import logging
  import numpy
  import torch
  from fate.arch import Context, dataframe
  from fate.ml.abc.module import HeteroModule
  from fate.ml.utils import predict_tools
  from fate.ml.utils._model_param import check_overflow, deserialize_param, initialize_param, serialize_param
  from fate.ml.utils._optimizer import LRScheduler, Optimizer
label_desc: Use image node_id 4 for calling the CoordinatedLRModuleGuest obj's underlying member method code with example usage: obj.fit(ctx, train_data, validate_data) without return types