code: def __init__(
      self,
      epochs=None,
      batch_size=None,
      optimizer_param=None,
      learning_rate_param=None,
      init_param=None,
      floating_point_precision=23,
  ):
      self.epochs = epochs
      self.optimizer_param = optimizer_param
      self.learning_rate_param = learning_rate_param
      self.batch_size = batch_size
      self.init_param = init_param or {}
      self.init_param["fit_intercept"] = False
      self.floating_point_precision = 23
  
      self.estimator = None
  
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 1 to create a new CoordinatedLinRModuleHost object from inherited base classes: HeteroModule with example: obj = CoordinatedLinRModuleHost(epochs, batch_size, optimizer_param, learning_rate_param, init_param, floating_point_precision)