code: def download(cls, architecture, path="./"):
      if architecture in cls.thirdparty_map:
          weight_file = download_file(
              cls.thirdparty_map[architecture], directory=path
          )
          return weight_file
  
      elif cls.sanity_check(architecture):
          output_filename = path + "imagenet_{}.h5".format(architecture)
          if os.path.exists(output_filename) == False:
              model = cls.architecture_map[architecture]()
              model.save(output_filename)
              print(
                  "Keras model {} is saved in [{}]".format(
                      architecture, output_filename
                  )
              )
              K.clear_session()
              del model
              return output_filename
  
          else:
              print(
                  "File [{}] existed, skip download.".format(
                      output_filename
                  )
              )
              return output_filename
  
      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 2 for calling the keras_extractor obj's underlying member method code with example usage: obj.download(cls, architecture, path) and returns: weight_file, None, output_filename, output_filename