def METHOD_NAME(self):
    data_list = []
    for cat in self.categories:
        paths = glob.glob(osp.join(self.raw_dir, f'{cat}*.tri'))
        paths = [path[:-4] for path in paths]
        paths = sorted(paths, key=lambda e: (len(e), e))
        for path in paths:
            pos = read_txt_array(f'{path}.vert')
            face = read_txt_array(f'{path}.tri', dtype=torch.long)
            face = face - face.min()  # Ensure zero-based index.
            data = Data(pos=pos, face=face.t().contiguous())
            if self.pre_filter is not None and not self.pre_filter(data):
                continue
            if self.pre_transform is not None:
                data = self.pre_transform(data)
            data_list.append(data)
    torch.save(self.collate(data_list), self.processed_paths[0])