code: def from_model(cls, model) -> "CoordinatedLRModuleArbiter":
      lr = CoordinatedLRModuleArbiter(
          epochs=model["meta"]["epochs"],
          early_stop=model["meta"]["early_stop"],
          tol=model["meta"]["tol"],
          batch_size=model["meta"]["batch_size"],
          optimizer_param=model["meta"]["optimizer_param"],
          learning_rate_param=model["meta"]["learning_rate_param"],
      )
      all_estimator = model["data"]["estimator"]
      lr.estimator = {}
      if lr.ovr:
          for label, d in all_estimator.items():
              estimator = CoordinatedLREstimatorArbiter(
                  epochs=model["meta"]["epochs"],
                  batch_size=model["meta"]["batch_size"],
              )
              estimator.restore(d)
              lr.estimator[int(label)] = estimator
      else:
          estimator = CoordinatedLREstimatorArbiter()
          estimator.restore(all_estimator)
          lr.estimator = estimator
      return lr
  
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._convergence import converge_func_factory
  from fate.ml.utils._optimizer import LRScheduler, Optimizer, separate
label_desc: Use image node_id 6 for calling the CoordinatedLRModuleArbiter obj's underlying member method code with example usage: obj.from_model(cls, model) and returns: lr