code: def _predict_proba(self, X, **kwargs):
      X = self.preprocess(X, **kwargs)
  
      self.model._enable_progress_bar = False
      if self.problem_type == REGRESSION:
          return self.model.predict(X, as_pandas=False)
  
      y_pred_proba = self.model.predict_proba(X, as_pandas=False)
      return self._convert_proba_to_unified_form(y_pred_proba)
  
imports: import logging
  import os
  from typing import Dict, Optional
  import pandas
  from autogluon.common.features.types import R_CATEGORY, R_FLOAT, R_INT, R_OBJECT, S_IMAGE_PATH, S_TEXT, S_TEXT_AS_CATEGORY, S_TEXT_NGRAM, S_TEXT_SPECIAL
  from autogluon.common.utils.resource_utils import ResourceManager
  from autogluon.common.utils.try_import import try_import_autogluon_multimodal
  from autogluon.core.constants import BINARY, MULTICLASS, REGRESSION
  from autogluon.core.models import AbstractModel
label_desc: Use image node_id 8 for calling the MultiModalPredictorModel obj's underlying member method code with example usage: obj._predict_proba(X) and returns: self, self