class_docstr: Wraps model that uses index to retrieve image data for making
  predictions.
class_docstr_tok: ["Wraps","model","that","uses","index","to","retrieve","image","data","for","making","predictions","."]
docstr: Predict the class probabilities for the provided data.
  
  :param X: Data to predict the probabilities for.
  :type X: pandas.DataFrame
  :return: Predicted class probabilities.
  :rtype: list[list]
docstr_tok: ["Predict","the","class","probabilities","for","the","provided","data",".",":","param","X",":","Data","to","predict","the","probabilities","for",".",":","type","X",":","pandas.DataFrame",":","return",":","Predicted","class","probabilities",".",":","rtype",":","list","[","list","]"]
code: def predict_proba(self, X):
      """Predict the class probabilities for the provided data.
  
      :param X: Data to predict the probabilities for.
      :type X: pandas.DataFrame
      :return: Predicted class probabilities.
      :rtype: list[list]
      """
      index = X.index
      pred_proba = self.index_predictions(index, self.predict_proba)
      return pred_proba
  
imports: import json
  from typing import Any, List, Optional
  import jsonschema
  import numpy
  import pandas
  from ml_wrappers import wrap_model
  from erroranalysis._internal.error_analyzer import ModelAnalyzer
  from erroranalysis._internal.error_report import as_error_report
  from responsibleai._tools.shared.state_directory_management import DirectoryManager
  from responsibleai.managers.error_analysis_manager import ErrorAnalysisManager
  from responsibleai.managers.error_analysis_manager import as_error_config
  from responsibleai_vision.common.constants import MLFlowSchemaLiterals, ModelTask
  from responsibleai_vision.utils.image_reader import get_base64_string_from_path, is_automl_image_model
  from responsibleai_vision.utils.image_utils import get_images
label_desc: Use image node_id 4 for calling the WrappedIndexPredictorModel obj's underlying member method code with example usage: obj.predict_proba(X) and returns: pred_proba