def METHOD_NAME():
    if not os.path.exists(DATA_DIR):
        return []
    # Path to the directory with videos
    files_dir = os.path.join(DATA_DIR, "videos/")
    # Path to a .csv with labels. First column is video name, second column is label
    path_to_labels = os.path.join(DATA_DIR, "labels.csv")
    # Create output directory
    output_path = os.path.join(files_dir, "video_frames/")
    os.makedirs(output_path, exist_ok=True)
    # Read data
    df = pd.read_csv(path_to_labels)
    video2label = dict(zip(df.iloc[:, 0], df.iloc[:, 1]))
    # Convert video to image frames and save them
    vid2frames = VideoToFrames()
    video_faces = {}
    for video_id in df.iloc[:, 0]:
        path = os.path.join(files_dir, video_id)
        image_paths = vid2frames.video_to_frames(
            video_path=path, output_path=output_path
        )
        # If there are available images
        if len(image_paths) > 0:
            video_faces[os.path.split(path)[-1]] = image_paths
    output_df = pd.DataFrame(
        [(key, var) for (key, L) in video_faces.items() for var in L],
        columns=["video_id", "image_id"],
    )
    output_df["label"] = output_df.video_id.map(video2label)
    # Save .csv file with labels
    output_df.to_csv(os.path.join(output_path, "labels.csv"), index=False)
    # Create .zip archive to upload to DAI
    shutil.make_archive(
        base_name=os.path.join(files_dir, "video_frames"),
        format="zip",
        root_dir=output_path,
        base_dir=output_path,
    )
    zip_archive_path = os.path.join(files_dir, "video_frames.zip")
    return zip_archive_path