class_docstr: Wrapper that trains a separate LGBM Booster for each quantile level.
class_docstr_tok: ["Wrapper","that","trains","a","separate","LGBM","Booster","for","each","quantile","level","."]
code: def current_iteration(self):
      return int(
          np.ceil(
              np.mean(
                  [
                      model.current_iteration()
                      for model in self.model_dict.values()
                  ]
              )
          )
      )
  
imports: import copy
  import os
  import time
  from typing import List, Optional
  import numpy
  import pandas
  from pandas import DataFrame, Series
  from autogluon.common.utils.try_import import try_import_lightgbm
  from autogluon.core.constants import BINARY, MULTICLASS, QUANTILE, REGRESSION, SOFTCLASS
  from autogluon.core.utils.exceptions import TimeLimitExceeded
label_desc: Use image node_id 5 for calling the QuantileBooster obj's underlying member method code with example usage: obj.current_iteration() and returns: int