code: def download(cls, architecture, path="./"):
      if cls.sanity_check(architecture):
          reset_parser()
  
          DATA_DIM = (
              3
              * paddle_extractor._image_size
              * paddle_extractor._image_size
          )  # Use 3 * 331 * 331 or 3 * 299 * 299 for Inception-ResNet-v2.
          CLASS_DIM = paddle_extractor.class_dim_map[architecture]
  
          image = paddle.layer.data(
              name="image", type=paddle.data_type.dense_vector(DATA_DIM)
          )
          if "resnet" in architecture:
              from mmdnn.conversion.examples.paddle.models import resnet
  
              depth = int(architecture.strip("resnet"))
              out = resnet.resnet_imagenet(
                  image, class_dim=CLASS_DIM, depth=depth
              )
          elif architecture == "vgg16":
              from mmdnn.conversion.examples.paddle.models import vgg
  
              out = vgg.vgg16(image, class_dim=CLASS_DIM)
          else:
              print("Not support for {} yet.", architecture)
              return None
          architecture_file = path + architecture + ".bin"
          paddle_extractor.dump_v2_config(out, architecture_file, True)
  
          weight_file = download_file(
              cls.architecture_map[architecture]["params"],
              directory=path,
              local_fname=architecture + ".tar.gz",
          )
          if not weight_file:
              return None
  
          print(
              "MXNet Model {} saved as [{}] and [{}].".format(
                  architecture, architecture_file, weight_file
              )
          )
          return (architecture_file, weight_file)
  
      else:
          return None
  
file_path: MMdnn/mmdnn/conversion/examples/paddle/extractor.py
imports: from __future__ import absolute_import
  from mmdnn.conversion.examples.imagenet_test import TestKit
  from mmdnn.conversion.examples.extractor import base_extractor
  from mmdnn.conversion.common.utils import download_file
  import paddle.v2
  import gzip
  from paddle.trainer_config_helpers.config_parser_utils import reset_parser
label_desc: Use image node_id 2 for calling the paddle_extractor obj's underlying member method code with example usage: obj.download(cls, architecture, path) and returns: architecture_file,  weight_file, None, None, None