code: def fit(self, ctx: Context, train_data, validate_data=None) -> None:
      encryptor = ctx.arbiter("encryptor").get()
      label_count = ctx.guest("label_count").get()
      if label_count > 2 or self.ovr:
          self.ovr = True
          self.label_count = label_count
          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
          ):
              # optimizer = copy.deepcopy(self.optimizer)
              # lr_scheduler = copy.deepcopy(self.lr_scheduler)
              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 = CoordinatedLREstimatorHost(
                      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[i]
                  single_estimator.epochs = self.epochs
                  single_estimator.batch_size = self.batch_size
              single_estimator.fit_single_model(
                  class_ctx, encryptor, train_data, 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 = CoordinatedLREstimatorHost(
                  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
          single_estimator.fit_single_model(
              ctx, encryptor, train_data, validate_data
          )
          self.estimator = single_estimator
  
imports: import logging
  import torch
  from fate.arch import Context
  from fate.arch.dataframe import DataLoader
  from fate.ml.abc.module import HeteroModule
  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 CoordinatedLRModuleHost obj's underlying member method code with example usage: obj.fit(ctx, train_data, validate_data) without return types