code: def fit(self, data, args):
      params = self.configure(data, args)
  
      if data.learning_task == LearningTask.REGRESSION:
          self.model = RandomForestRegressor(
              max_depth=params["max_depth"],
              n_estimators=params["ntrees"],
              n_jobs=params["njobs"],
          )
      else:
          self.model = RandomForestClassifier(
              max_depth=params["max_depth"],
              n_estimators=params["ntrees"],
              n_jobs=params["njobs"],
          )
  
      with Timer() as t:
          self.model.fit(data.X_train, data.y_train.astype("|i4"))
  
      return t.interval
  
imports: from abc import ABC, abstractmethod
  import lightgbm
  import numpy
  from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
  import xgboost
  from benchmarks.timer import Timer
  from benchmarks.datasets import LearningTask
label_desc: Use image node_id 2 for calling the RandomForestAlgorithm obj's underlying member method code with example usage: obj.fit(data, args) and returns: t