code: def train_model(
      ctx,
      role,
      train_data,
      validate_data,
      train_output_data,
      output_model,
      epochs,
      batch_size,
      learning_rate,
      tol,
      early_stop,
      init_param,
      reveal_every_epoch,
      reveal_loss_freq,
      threshold,
      input_model,
  ):
      if input_model is not None:
          logger.info(f"warm start model provided")
          model = input_model.read()
          module = SSHELogisticRegression.from_model(model)
          module.set_epochs(epochs)
          module.set_batch_size(batch_size)
  
      else:
          module = SSHELogisticRegression(
              epochs=epochs,
              batch_size=batch_size,
              tol=tol,
              early_stop=early_stop,
              learning_rate=learning_rate,
              init_param=init_param,
              threshold=threshold,
              reveal_every_epoch=reveal_every_epoch,
              reveal_loss_freq=reveal_loss_freq,
          )
      # optimizer = optimizer_factory(optimizer_param)
      logger.info(f"sshe lr guest start train")
      sub_ctx = ctx.sub_ctx("train")
      train_data = train_data.read()
  
      if validate_data is not None:
          logger.info(f"validate data provided")
          validate_data = validate_data.read()
  
      module.fit(sub_ctx, train_data, validate_data)
      model = module.get_model()
      output_model.write(model, metadata={})
  
      sub_ctx = ctx.sub_ctx("predict")
  
      predict_df = module.predict(sub_ctx, train_data)
  
      if role.is_guest:
          predict_result = tools.add_dataset_type(
              predict_df, consts.TRAIN_SET
          )
      if validate_data is not None:
          sub_ctx = ctx.sub_ctx("validate_predict")
          predict_df = module.predict(sub_ctx, validate_data)
          if role.is_guest:
              validate_predict_result = tools.add_dataset_type(
                  predict_df, consts.VALIDATE_SET
              )
              predict_result = DataFrame.vstack(
                  [predict_result, validate_predict_result]
              )
      if role.is_guest:
          train_output_data.write(predict_result)
  
file_path: FATE/python/fate/components/components/sshe_lr.py
imports: import logging
  from fate.arch import Context
  from fate.arch.dataframe import DataFrame
  from fate.components.components.utils import consts, tools
  from fate.components.core import GUEST, HOST, Role, cpn, params
  from fate.ml.glm.hetero.sshe import SSHELogisticRegression
label_desc: Use image node_id 5 for calling a global function with example usage: train_model(ctx, role, train_data, validate_data, train_output_data, output_model, epochs, batch_size, learning_rate, tol, early_stop, init_param, reveal_every_epoch, reveal_loss_freq, threshold, input_model) without return types