code: def predict(self, images):
      results = np.zeros(
          (images.shape[0], self.outshape[1], self.outshape[2])
      )
      images = ((images - self.min) / (self.max - self.min)).mean(
          axis=3
      )
      for i in range(self.n_classes):
          results[
              (self.quantiles[i] < images)
              & (images <= self.quantiles[i + 1])
          ] = BDD_IDS[i]
      return results
  
imports: import os
  import pickle
  import shutil
  import time
  import numpy
  import wandb
  from PIL import Image
label_desc: Use image node_id 3 for calling the ExampleSegmentationModel obj's underlying member method code with example usage: obj.predict(images) and returns: results