code: def from_model(cls, model):
      lr = SSHELogisticRegression(
          epochs=model["meta"]["epochs"],
          batch_size=model["meta"]["batch_size"],
          learning_rate=model["meta"]["learning_rate"],
          threshold=model["meta"]["threshold"],
          init_param=model["meta"]["init_param"],
          reveal_every_epoch=model["meta"]["reveal_every_epoch"],
          reveal_loss_freq=model["meta"]["reveal_loss_freq"],
          tol=model["meta"]["tol"],
          early_stop=model["meta"]["early_stop"],
      )
      lr.ovr = model["meta"]["ovr"]
      lr.labels = model["meta"]["labels"]
      lr.label_count = model["meta"]["label_count"]
  
      all_estimator = model["data"]["estimator"]
      lr.estimator = {}
      if lr.ovr:
          for label, d in all_estimator.items():
              estimator = SSHELREstimator(
                  epochs=model["meta"]["epochs"],
                  batch_size=model["meta"]["batch_size"],
                  init_param=model["meta"]["init_param"],
                  reveal_every_epoch=model["meta"][
                      "reveal_every_epoch"
                  ],
                  reveal_loss_freq=model["meta"]["reveal_loss_freq"],
                  tol=model["meta"]["tol"],
                  early_stop=model["meta"]["early_stop"],
              )
              estimator.restore(d)
              lr.estimator[int(label)] = estimator
      else:
          estimator = SSHELREstimator(
              epochs=model["meta"]["epochs"],
              batch_size=model["meta"]["batch_size"],
              init_param=model["meta"]["init_param"],
              reveal_every_epoch=model["meta"]["reveal_every_epoch"],
              reveal_loss_freq=model["meta"]["reveal_loss_freq"],
              tol=model["meta"]["tol"],
              early_stop=model["meta"]["early_stop"],
          )
          estimator.restore(all_estimator)
          lr.estimator = estimator
  
      return lr
  
imports: import logging
  import torch
  from fate.arch import Context, dataframe
  from fate.arch.dataframe import DataFrame
  from fate.arch.protocol.mpc.nn.sshe.lr_layer import SSHELogisticRegressionLayer, SSHELogisticRegressionLossLayer, 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 6 for calling the SSHELogisticRegression obj's underlying member method code with example usage: obj.from_model(cls, model) and returns: lr