code: def inference(cls, architecture, files, path, image_path):
      if architecture in cls.thirdparty_map:
          model = keras.models.load_model(files)
  
      elif cls.sanity_check(architecture):
          model = cls.architecture_map[architecture]()
  
      else:
          model = None
  
      if model:
          import numpy as np
  
          func = TestKit.preprocess_func["keras"][architecture]
          img = func(image_path)
          img = np.expand_dims(img, axis=0)
          predict = model.predict(img)
          predict = np.squeeze(predict)
          K.clear_session()
          del model
          return predict
  
      else:
          return None
  
file_path: MMdnn/mmdnn/conversion/examples/keras/extractor.py
imports: from __future__ import absolute_import
  import os
  import keras
  from keras import backend
  from mmdnn.conversion.examples.imagenet_test import TestKit
  from mmdnn.conversion.examples.extractor import base_extractor
  from mmdnn.conversion.common.utils import download_file
label_desc: Use image node_id 3 for calling the keras_extractor obj's underlying member method code with example usage: obj.inference(cls, architecture, files, path, image_path) and returns: predict, None