TypeError: 'NoneType' object is not callable

[putuoka]

class FairSlipLoaderBase(BaseMmcLoader):
    """
    SLIP models via https://github.com/facebookresearch/SLIP
    """
    def __init__(
        self,
        id,
        architecture,
    ):
        self.architecture = architecture
        self.publisher = 'facebookresearch'
        self.id = id
        self.modalities = (TEXT, IMAGE)
    def _napm_install(self):
        logger.debug('using napm to "install" facebookresearch/SLIP')
        url = "https://github.com/facebookresearch/SLIP"
        napm.pseudoinstall_git_repo(url, env_name='mmc', add_install_dir_to_path=True)
        napm.populate_pythonpaths('mmc')
        from SLIP.models import (
            SLIP_VITS16,
            SLIP_VITB16, 
            SLIP_VITL16
            )

    def load(self, device=DEVICE):
        """
        Returns the MMC associated with this loader.
        """
        self._napm_install()

        model_factory = model_factory_from_id(self.id)
        logger.debug(f"model_factory: {model_factory}")
        ckpt_url = url_from_id(self.id)
        ckpt = fetch_weights(
            url=ckpt_url, 
            namespace='fair_slip', 
            device=device,
            )
        d_args = vars(ckpt['args'])
        kwargs = {k:d_args[k] for k in ('ssl_emb_dim', 'ssl_mlp_dim') if k in d_args}
        logger.debug(kwargs)
        fix_param_names(ckpt)
        model = model_factory(**kwargs)
        model.load_state_dict(ckpt['state_dict'], strict=True)
        model = model.eval().to(device)

        from SLIP.tokenizer import SimpleTokenizer
        tokenizer = SimpleTokenizer()

        def preprocess_image_extended(*args, **kwargs):
            x = val_transform(*args, **kwargs)
            if x.ndim == 3:
                logger.debug("adding batch dimension")
                x = x.unsqueeze(0)
            return x.to(device)
        #logger.debug(model)
        mmc = MultiModalComparator(name=str(self), device=device)
        mmc.register_modality(modality=TEXT, projector=model.encode_text, preprocessor=tokenizer)
        mmc.register_modality(modality=IMAGE, projector=model.encode_image, preprocessor= preprocess_image_extended)
        mmc._model = model
        return mmc