code: def predict_proba(self, X):
      predictions = self.get_all_predictions(X)
  
      # If this is just a single dictionary we're getting predictions from:
      if X.shape[0] == 1:
          # predictions is just a dictionary where all the values are the predicted values from one of our subpredictors. we'll want that as a list
          predicted_vals = list(predictions.values())
          predicted_vals = self.get_predictions_by_class(predicted_vals)
  
          if self.ensemble_method == "median":
              return [
                  np.median(class_preds)
                  for class_preds in predicted_vals
              ]
          elif (
              self.ensemble_method == "average"
              or self.ensemble_method == "mean"
              or self.ensemble_method == "avg"
          ):
              return [
                  np.average(class_preds)
                  for class_preds in predicted_vals
              ]
          elif self.ensemble_method == "max":
              return [
                  np.max(class_preds) for class_preds in predicted_vals
              ]
          elif self.ensemble_method == "min":
              return [
                  np.min(class_preds) for class_preds in predicted_vals
              ]
  
      else:
          classed_predictions = predictions.apply(
              self.get_predictions_by_class, axis=1
          )
  
          if self.ensemble_method == "median":
              return classed_predictions.apply(np.median, axis=1)
          elif (
              self.ensemble_method == "average"
              or self.ensemble_method == "mean"
              or self.ensemble_method == "avg"
          ):
              return classed_predictions.apply(np.average, axis=1)
          elif self.ensemble_method == "max":
              return classed_predictions.apply(np.max, axis=1)
          elif self.ensemble_method == "min":
              return classed_predictions.apply(np.min, axis=1)
  
file_path: auto_ml/auto_ml/utils_ensembling.py
imports: import os
  import numpy
  import pandas
  import pathos
  from sklearn.base import BaseEstimator, TransformerMixin
label_desc: Use image node_id 6 for calling the Ensembler obj's underlying member method code with example usage: obj.predict_proba(X) and returns: unknown, classed_predictions, unknown, classed_predictions, unknown, classed_predictions, unknown, classed_predictions