code: def fit(self, ctx: Context, train_data, validate_data=None) -> None:
      encryptor = ctx.arbiter("encryptor").get()
      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"],
          )
          estimator = CoordinatedLinREstimatorHost(
              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,
          )
          self.estimator = estimator
  
      self.estimator.fit_model(
          ctx, encryptor, train_data, validate_data
      )
  
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 deserialize_param, initialize_param, serialize_param
  from fate.ml.utils._optimizer import LRScheduler, Optimizer
label_desc: Use image node_id 4 for calling the CoordinatedLinRModuleHost obj's underlying member method code with example usage: obj.fit(ctx, train_data, validate_data) without return types