code: def extract_model(args):
      if args.framework == "caffe":
          from mmdnn.conversion.examples.caffe.extractor import (
              caffe_extractor,
          )
  
          extractor = caffe_extractor()
  
      elif args.framework == "keras":
          from mmdnn.conversion.examples.keras.extractor import (
              keras_extractor,
          )
  
          extractor = keras_extractor()
  
      elif args.framework == "tensorflow" or args.framework == "tf":
          from mmdnn.conversion.examples.tensorflow.extractor import (
              tensorflow_extractor,
          )
  
          extractor = tensorflow_extractor()
  
      elif args.framework == "mxnet":
          from mmdnn.conversion.examples.mxnet.extractor import (
              mxnet_extractor,
          )
  
          extractor = mxnet_extractor()
  
      elif args.framework == "cntk":
          from mmdnn.conversion.examples.cntk.extractor import (
              cntk_extractor,
          )
  
          extractor = cntk_extractor()
  
      elif args.framework == "pytorch":
          from mmdnn.conversion.examples.pytorch.extractor import (
              pytorch_extractor,
          )
  
          extractor = pytorch_extractor()
  
      elif args.framework == "darknet":
          from mmdnn.conversion.examples.darknet.extractor import (
              darknet_extractor,
          )
  
          extractor = darknet_extractor()
  
      elif args.framework == "coreml":
          from mmdnn.conversion.examples.coreml.extractor import (
              coreml_extractor,
          )
  
          extractor = coreml_extractor()
  
      else:
          raise ValueError(
              "Unknown framework [{}].".format(args.framework)
          )
  
      files = extractor.download(args.network, args.path)
  
      if files and args.image:
          predict = extractor.inference(
              args.network, files, args.path, args.image
          )
          if type(predict) == list:
              print(predict)
  
          else:
              if predict.ndim == 1:
                  if predict.shape[0] == 1001:
                      offset = 1
                  else:
                      offset = 0
                  top_indices = predict.argsort()[-5:][::-1]
                  predict = [(i, predict[i]) for i in top_indices]
                  predict = generate_label(predict, args.label, offset)
  
                  for line in predict:
                      print(line)
  
              else:
                  print(predict.shape)
                  print(predict)
  
label_desc: Use image node_id 2 for calling a global function with example usage: extract_model(args) without return types