code: def fit_pairwise_model(self, X, y, weights, rng, configuration):
      base_model = sklearn.ensemble.RandomForestClassifier(
          random_state=rng,
          n_estimators=self.n_estimators,
          bootstrap=True
          if configuration["bootstrap"] == "True"
          else False,
          min_samples_split=configuration["min_samples_split"],
          min_samples_leaf=configuration["min_samples_leaf"],
          max_features=int(configuration["max_features"]),
          max_depth=configuration["max_depth"],
      )
      base_model.fit(X, y, sample_weight=weights)
      return base_model
  
imports: import typing
  import copy
  import itertools
  import numpy
  import pandas
  import scipy.stats
  import sklearn.ensemble
label_desc: Use image node_id 2 for calling the OVORF obj's underlying member method code with example usage: obj.fit_pairwise_model(X, y, weights, rng, configuration) and returns: base_model