code: def __init__(
      self,
      epochs=None,
      batch_size=None,
      optimizer_param=None,
      learning_rate_param=None,
      init_param=None,
      threshold=0.5,
      floating_point_precision=23,
  ):
      self.epochs = epochs
      self.batch_size = batch_size
      self.learning_rate_param = learning_rate_param
      self.optimizer_param = optimizer_param
      self.init_param = init_param
      self.threshold = threshold
      self.floating_point_precision = floating_point_precision
  
      self.estimator = None
      self.ovr = False
      self.labels = None
  
imports: import logging
  import numpy
  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 check_overflow, 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 CoordinatedLRModuleGuest object from inherited base classes: HeteroModule with example: obj = CoordinatedLRModuleGuest(epochs, batch_size, optimizer_param, learning_rate_param, init_param, threshold, floating_point_precision)