def METHOD_NAME(self, item):
    if item["name"] == "clara_pt_self_supervised_learning_segmentation":  # test the byow model
        default_model_file = os.path.join("ssl_models_2gpu", "best_metric_model.pt")
        pretrained_weights = load_from_mmar(
            item=item["name"],
            mmar_dir="./",
            map_location="cpu",
            api=True,
            model_file=default_model_file,
            weights_only=True,
        )
        pretrained_weights = {k.split(".", 1)[1]: v for k, v in pretrained_weights["state_dict"].items()}
        sys.path.append(os.path.join(f"{item['name']}", "custom"))  # custom model folder
        from vit_network import ViTAutoEnc  # pylint: disable=E0401
        model = ViTAutoEnc(
            in_channels=1,
            img_size=(96, 96, 96),
            patch_size=(16, 16, 16),
            pos_embed="conv",
            hidden_size=768,
            mlp_dim=3072,
        )
        _, loaded, not_loaded = copy_model_state(model, pretrained_weights)
        self.assertTrue(len(loaded) > 0 and len(not_loaded) == 0)
        return
    if item["name"] == "clara_pt_fed_learning_brain_tumor_mri_segmentation":
        default_model_file = os.path.join("models", "server", "best_FL_global_model.pt")
    else:
        default_model_file = None
    pretrained_model = load_from_mmar(
        item=item["name"], mmar_dir="./", map_location="cpu", api=True, model_file=default_model_file
    )
    self.assertTrue(isinstance(pretrained_model, torch.nn.Module))