code: def download(cls, architecture, path="./"):
      if cls.sanity_check(architecture):
          architecture_file = path + "imagenet_{}.pth".format(
              architecture
          )
          if not os.path.exists(architecture_file):
              kwargs = {}
              if architecture == "inception_v3":
                  kwargs["transform_input"] = False
              model = models.__dict__[architecture](
                  pretrained=True, **kwargs
              )
              torch.save(model, architecture_file)
              print(
                  "PyTorch pretrained model is saved as [{}].".format(
                      architecture_file
                  )
              )
          else:
              print("File [{}] existed!".format(architecture_file))
  
          return architecture_file
  
      else:
          return None
  
file_path: MMdnn/mmdnn/conversion/examples/pytorch/extractor.py
imports: from __future__ import absolute_import
  import os
  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 torch
  import torchvision.models
label_desc: Use image node_id 2 for calling the pytorch_extractor obj's underlying member method code with example usage: obj.download(cls, architecture, path) and returns: architecture_file, None