def init_labels_cli(
    # fmt: off
    ctx: typer.Context,  # This is only used to read additional arguments
    config_path: Path = Arg(..., help="Path to config file", exists=True, allow_dash=True),
    output_path: Path = Arg(..., help="Output directory for the labels"),
    code_path: Optional[Path] = Opt(None, "--code", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
    verbose: bool = Opt(False, "--verbose", "-V", "-VV", help="Display more information for debugging purposes"),
    use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU")
    # fmt: on
):
    """Generate JSON files for the labels in the data. This helps speed up the
    training process, since spaCy won't have to preprocess the data to
    extract the labels."""
    if verbose:
        util.logger.setLevel(logging.DEBUG)
    if not output_path.exists():
        output_path.mkdir(parents=True)
    overrides = parse_config_overrides(ctx.args)
    import_code(code_path)
    setup_gpu(use_gpu)
    with show_validation_error(config_path):
        config = util.load_config(config_path, overrides=overrides)
    with show_validation_error(hint_fill=False):
        nlp = init_nlp(config, use_gpu=use_gpu)
    _init_labels(nlp, output_path)
Generate JSON files for the labels in the data. This helps speed up the
    training process, since spaCy won't have to preprocess the data to
    extract the labels.
spacy/cli/init_pipeline.py