code: def train(
      ctx: Context,
      role: Role,
      train_data: cpn.dataframe_input(roles=[GUEST, HOST]),
      validate_data: cpn.dataframe_input(
          roles=[GUEST, HOST], optional=True
      ),
      learning_rate_scheduler: cpn.parameter(
          type=params.lr_scheduler_param(),
          default=params.LRSchedulerParam(
              method="linear", scheduler_params={"start_factor": 1.0}
          ),
          desc="learning rate scheduler, "
          "select method from {'step', 'linear', 'constant'}"
          "for list of configurable arguments, "
          "refer to torch.optim.lr_scheduler",
      ),
      epochs: cpn.parameter(
          type=params.conint(gt=0), default=20, desc="max iteration num"
      ),
      batch_size: cpn.parameter(
          type=params.conint(ge=10),
          default=None,
          desc="batch size, None means full batch, otherwise should be no less than 10, default None",
      ),
      optimizer: cpn.parameter(
          type=params.optimizer_param(),
          default=params.OptimizerParam(
              method="sgd",
              penalty="l2",
              alpha=1.0,
              optimizer_params={"lr": 1e-2, "weight_decay": 0},
          ),
      ),
      floating_point_precision: cpn.parameter(
          type=params.conint(ge=0),
          default=23,
          desc="floating point precision, ",
      ),
      tol: cpn.parameter(type=params.confloat(ge=0), default=1e-4),
      early_stop: cpn.parameter(
          type=params.string_choice(["weight_diff", "diff", "abs"]),
          default="diff",
          desc="early stopping criterion, choose from {weight_diff, diff, abs, val_metrics}",
      ),
      he_param: cpn.parameter(
          type=params.he_param(),
          default=params.HEParam(kind="paillier", key_length=1024),
          desc="homomorphic encryption param",
      ),
      init_param: cpn.parameter(
          type=params.init_param(),
          default=params.InitParam(
              method="random_uniform",
              fit_intercept=True,
              random_state=None,
          ),
          desc="Model param init setting.",
      ),
      threshold: cpn.parameter(
          type=params.confloat(ge=0.0, le=1.0),
          default=0.5,
          desc="predict threshold for binary data",
      ),
      train_output_data: cpn.dataframe_output(roles=[GUEST, HOST]),
      output_model: cpn.json_model_output(roles=[GUEST, HOST, ARBITER]),
      warm_start_model: cpn.json_model_input(
          roles=[GUEST, HOST, ARBITER], optional=True
      ),
  ):
      logger.info(f"enter coordinated lr train")
      # temp code start
      optimizer = optimizer.dict()
      learning_rate_scheduler = learning_rate_scheduler.dict()
      init_param = init_param.dict()
      ctx.cipher.set_phe(ctx.device, he_param.dict())
  
      if role.is_guest:
          train_guest(
              ctx,
              train_data,
              validate_data,
              train_output_data,
              output_model,
              epochs,
              batch_size,
              optimizer,
              learning_rate_scheduler,
              init_param,
              threshold,
              floating_point_precision,
              warm_start_model,
          )
      elif role.is_host:
          train_host(
              ctx,
              train_data,
              validate_data,
              train_output_data,
              output_model,
              epochs,
              batch_size,
              optimizer,
              learning_rate_scheduler,
              init_param,
              floating_point_precision,
              warm_start_model,
          )
      elif role.is_arbiter:
          train_arbiter(
              ctx,
              epochs,
              early_stop,
              tol,
              batch_size,
              optimizer,
              learning_rate_scheduler,
              output_model,
              warm_start_model,
          )
  
file_path: FATE/python/fate/components/components/coordinated_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 ARBITER, GUEST, HOST, Role, cpn, params
  from fate.ml.glm import CoordinatedLRModuleGuest, CoordinatedLRModuleHost, CoordinatedLRModuleArbiter
label_desc: Use image node_id 2 for calling a global function with example usage: train(ctx, role, train_data, validate_data, learning_rate_scheduler, epochs, batch_size, optimizer, floating_point_precision, tol, early_stop, he_param, init_param, threshold, train_output_data, output_model, warm_start_model) without return types