code: def __init__(
      self,
      epochs=None,
      batch_size=None,
      optimizer=None,
      learning_rate_scheduler=None,
      init_param=None,
      floating_point_precision=23,
  ):
      self.epochs = epochs
      self.batch_size = batch_size
      self.optimizer = optimizer
      self.lr_scheduler = learning_rate_scheduler
      self.init_param = init_param
      self.floating_point_precision = floating_point_precision
      self._fixpoint_precision = 2**floating_point_precision
  
      self.w = None
      self.start_epoch = 0
      self.end_epoch = -1
      self.is_converged = False
      self.header = None
  
imports: import logging
  import torch
  from fate.arch import Context, dataframe
  from fate.ml.abc.module import HeteroModule
  from fate.ml.utils import predict_tools
  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 CoordinatedLinREstimatorGuest object from inherited base classes: HeteroModule with example: obj = CoordinatedLinREstimatorGuest(epochs, batch_size, optimizer, learning_rate_scheduler, init_param, floating_point_precision)