class_docstr: Random Forest model (scikit-learn): https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
code: def _get_model_type(self):
      if self.problem_type == QUANTILE:
          from .rf_quantile import RandomForestQuantileRegressor
  
          return RandomForestQuantileRegressor
      if self.params_aux.get("use_daal", False):
          # Disabled by default because OOB score does not yet work properly
          try:
              # FIXME: sklearnex OOB score is broken, returns biased predictions. Without this optimization, can't compute Efficient OOF.
              #  Refer to https://github.com/intel/scikit-learn-intelex/issues/933
              #  Current workaround: Forcibly set oob_score=True during fit to compute OOB during train time.
              #  Downsides:
              #    1. Slows down training slightly by forcing computation of OOB even if OOB is not needed (such as in medium_quality)
              #    2. Makes computing the correct pred_time_val difficult, as the time is instead added to the fit_time,
              #       and we would need to waste extra time to compute the proper pred_time_val post-fit.
              #       Therefore with sklearnex enabled, pred_time_val is incorrect.
              from sklearnex.ensemble import (
                  RandomForestClassifier,
                  RandomForestRegressor,
              )
  
              logger.log(15, "\tUsing sklearnex RF backend...")
              self._daal = True
          except:
              from sklearn.ensemble import (
                  RandomForestClassifier,
                  RandomForestRegressor,
              )
  
              self._daal = False
      else:
          from sklearn.ensemble import (
              RandomForestClassifier,
              RandomForestRegressor,
          )
  
          self._daal = False
      if self.problem_type in [REGRESSION, SOFTCLASS]:
          return RandomForestRegressor
      else:
          return RandomForestClassifier
  
imports: import logging
  import math
  import os
  import pickle
  import sys
  import time
  import numpy
  from autogluon.common.features.types import R_BOOL, R_CATEGORY, R_FLOAT, R_INT
  from autogluon.common.utils.resource_utils import ResourceManager
  from autogluon.core.constants import MULTICLASS, QUANTILE, REGRESSION, SOFTCLASS
  from autogluon.core.models import AbstractModel
  from autogluon.core.utils.exceptions import NotEnoughMemoryError, TimeLimitExceeded
  from autogluon.core.utils.utils import normalize_pred_probas
  from autogluon.features.generators import LabelEncoderFeatureGenerator
  from .compilers.native import RFNativeCompiler
  from .compilers.onnx import RFOnnxCompiler
label_desc: Use image node_id 2 for calling the RFModel obj's underlying member method code with example usage: obj._get_model_type() and returns: RandomForestQuantileRegressor, RandomForestRegressor, RandomForestClassifier