class_docstr: KNearestNeighbors model (scikit-learn): https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html
class_docstr_tok: ["KNearestNeighbors","model","(","scikit-learn",")",":","https",":","\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.neighbors.KNeighborsClassifier.html"]
docstr: X should be the same X passed to `.fit`
docstr_tok: ["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 time
  from typing import Dict, Union
  import numpy
  from autogluon.common.features.types import R_FLOAT, R_INT, S_BOOL
  from autogluon.common.utils.resource_utils import ResourceManager
  from autogluon.core.constants import BINARY, MULTICLASS, REGRESSION
  from autogluon.core.models import AbstractModel
  from autogluon.core.utils.exceptions import NotEnoughMemoryError
  from autogluon.core.utils.utils import normalize_pred_probas
label_desc: Use image node_id 12 for calling the KNNModel obj's underlying member method code with example usage: obj.predict_proba_oof(X, normalize) and returns: y_oof_pred_proba