class_docstr: Random Forest model (scikit-learn): https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
code: def _predict_proba(self, X, **kwargs):
      X = self.preprocess(X, **kwargs)
  
      if self.problem_type == REGRESSION:
          return self.model.predict(X)
      elif self.problem_type == SOFTCLASS:
          return self.model.predict(X)
      elif self.problem_type == QUANTILE:
          return self.model.predict(
              X, quantile_levels=self.quantile_levels
          )
  
      y_pred_proba = self.model.predict_proba(X)
      return self._convert_proba_to_unified_form(y_pred_proba)
  
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 11 for calling the RFModel obj's underlying member method code with example usage: obj._predict_proba(X) and returns: self, self, self, self