code: def predict(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())
          if self.ensemble_method == "median":
              return np.median(predicted_vals)
          elif (
              self.ensemble_method == "average"
              or self.ensemble_method == "mean"
              or self.ensemble_method == "avg"
          ):
              return np.average(predicted_vals)
          elif self.ensemble_method == "max":
              return np.max(predicted_vals)
          elif self.ensemble_method == "min":
              return np.min(predicted_vals)
  
      else:
          if self.ensemble_method == "median":
              return predictions.apply(np.median, axis=1).values
          elif (
              self.ensemble_method == "average"
              or self.ensemble_method == "mean"
              or self.ensemble_method == "avg"
          ):
              return predictions.apply(np.average, axis=1).values
          elif self.ensemble_method == "max":
              return predictions.apply(np.max, axis=1).values
          elif self.ensemble_method == "min":
              return predictions.apply(np.min, axis=1).values
  
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 4 for calling the Ensembler obj's underlying member method code with example usage: obj.predict(X) and returns: np, predictions, np, predictions, np, predictions, np, predictions