code: def polynet(num_classes=1000, pretrained='imagenet'):
      
      if pretrained:
          settings = pretrained_settings['polynet'][pretrained]
          assert num_classes == settings['num_classes'], \
              'num_classes should be {}, but is {}'.format(
                  settings['num_classes'], num_classes)
          model = PolyNet(num_classes=num_classes)
          model.load_state_dict(model_zoo.load_url(settings['url']))
          model.input_space = settings['input_space']
          model.input_size = settings['input_size']
          model.input_range = settings['input_range']
          model.mean = settings['mean']
          model.std = settings['std']
      else:
          model = PolyNet(num_classes=num_classes)
      return model
docs: PolyNet architecture from the paper
      'PolyNet: A Pursuit of Structural Diversity in Very Deep Networks'
      https://arxiv.org/abs/1611.05725