class_docstr: Random Forest model (scikit-learn): https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
docstr: X should be the same X passed to `.fit`
code: def predict_proba_oof(self, X, normalize=None, **kwargs):
      """X should be the same X passed to `.fit`"""
      y_oof_pred_proba = self._predict_proba_oof(X=X, **kwargs)
      if normalize is None:
          normalize = self.normalize_pred_probas
      if normalize:
          y_oof_pred_proba = normalize_pred_probas(
              y_oof_pred_proba, self.problem_type
          )
      y_oof_pred_proba = y_oof_pred_proba.astype(np.float32)
      return y_oof_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 12 for calling the RFModel obj's underlying member method code with example usage: obj.predict_proba_oof(X, normalize) and returns: y_oof_pred_proba