code: def get_random_forest_classifier():
      from cuml.ensemble import (
          RandomForestClassifier as cuMLRandomForestClassifier,
      )
  
      class RandomForestClassifier(cuMLRandomForestClassifier):
          def fit(self, X, y, *args, **kwargs):
              super().fit(X, y, *args, **kwargs)
              self.classes_ = self.classes_.astype(int)
              return self
  
          def predict(self, X, *args, **kwargs):
              X = super().predict(X, *args, **kwargs)
              try:
                  X = X.to_output("numpy")
              except AttributeError:
                  pass
              return X.astype(int)
  
      return RandomForestClassifier
  
file_path: pycaret/pycaret/internal/cuml_wrappers.py
imports: from sklearn.linear_model._ridge import _RidgeClassifierMixin
  from sklearn.preprocessing import LabelEncoder
  from pycaret.utils._dependencies import _check_soft_dependencies
label_desc: Use image node_id 5 for calling a global function with example usage: get_random_forest_classifier() and returns: RandomForestClassifier, self, X