def get_oof_pred_proba(self, X, normalize=None, **kwargs):
    'X should be the same X passed to `.fit`'
    y_oof_pred_proba = self._get_oof_pred_proba(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
X should be the same X passed to `.fit`
def get_oof_pred_proba(self, X, normalize=None, **kwargs):
    
    y_oof_pred_proba = self._get_oof_pred_proba(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