code: def __init__(
      self,
      epochs=None,
      batch_size=None,
      optimizer=None,
      learning_rate=None,
      init_param=None,
      reveal_every_epoch=True,
      reveal_loss_freq=3,
      early_stop=None,
      tol=None,
  ):
      self.epochs = epochs
      self.batch_size = batch_size
      self.optimizer = optimizer
      self.lr = learning_rate
      self.init_param = init_param
      self.reveal_every_epoch = reveal_every_epoch
      self.reveal_loss_freq = reveal_loss_freq
      self.early_stop = early_stop
      self.tol = tol
  
      self.w = None
      self.start_epoch = 0
      self.end_epoch = -1
      self.is_converged = False
      self.header = None
      self.converge_func = None
      if early_stop is not None:
          self.converge_func = converge_func_factory(
              self.early_stop, self.tol
          )
  
imports: import logging
  import torch
  from fate.arch import Context, dataframe
  from fate.arch.dataframe import DataFrame
  from fate.arch.protocol.mpc.nn.sshe.linr_layer import SSHELinearRegressionLayer, SSHELinearRegressionLossLayer, SSHEOptimizerSGD
  from fate.ml.abc.module import Module, HeteroModule
  from fate.ml.utils import predict_tools
  from fate.ml.utils._convergence import converge_func_factory
  from fate.ml.utils._model_param import get_initialize_func
  from fate.ml.utils._model_param import serialize_param, deserialize_param
label_desc: Use image node_id 1 to create a new SSHELREstimator object from inherited base classes: HeteroModule with example: obj = SSHELREstimator(epochs, batch_size, optimizer, learning_rate, init_param, reveal_every_epoch, reveal_loss_freq, early_stop, tol)