code: def run_sshe_lr(ctx: "Context"):
      from fate.ml.glm.hetero.sshe import SSHELogisticRegression
      from fate.arch import dataframe
  
      ctx.mpc.init()
      args, _ = HfArgumentParser(
          SSHEArguments
      ).parse_args_into_dataclasses(return_remaining_strings=True)
      inst = SSHELogisticRegression(
          epochs=5,
          batch_size=300,
          tol=0.01,
          early_stop="diff",
          learning_rate=args.lr,
          init_param={
              "method": "random_uniform",
              "fit_intercept": True,
              "random_state": 1,
          },
          reveal_every_epoch=False,
          reveal_loss_freq=2,
          threshold=0.5,
      )
      if ctx.is_on_guest:
          kwargs = {
              "sample_id_name": None,
              "match_id_name": "id",
              "delimiter": ",",
              "label_name": "y",
              "label_type": "int32",
              "dtype": "float32",
          }
          input_data = dataframe.CSVReader(**kwargs).to_frame(
              ctx, args.guest_data
          )
      else:
          kwargs = {
              "sample_id_name": None,
              "match_id_name": "id",
              "delimiter": ",",
              "dtype": "float32",
          }
          input_data = dataframe.CSVReader(**kwargs).to_frame(
              ctx, args.host_data
          )
      inst.fit(ctx, train_data=input_data)
      logger.info(f"model: {pprint.pformat(inst.get_model())}")
  
file_path: FATE/examples/launchers/sshe_lr_launcher.py
imports: import logging
  import pprint
  import typing
  from dataclasses import dataclass, field
  from fate.arch.launchers.argparser import HfArgumentParser
  from fate.arch.launchers.multiprocess_launcher import launch
label_desc: Use image node_id 1 for calling a global function with example usage: run_sshe_lr(ctx) without return types