code: def bninception(num_classes=1000, pretrained='imagenet'):
      r
      model = BNInception(num_classes=num_classes)
      if pretrained is not None:
          settings = pretrained_settings['bninception'][pretrained]
          assert num_classes == settings['num_classes'], \
              "num_classes should be {}, but is {}".format(settings['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']
      return model
docs: r"""BNInception model architecture from <https://arxiv.org/pdf/1502.03167.pdf>`_ paper.