def analyze_pipes(
    nlp: "Language", *, keys: List[str] = DEFAULT_KEYS
) -> Dict[str, Dict[str, Union[List[str], Dict]]]:
    """Print a formatted summary for the current nlp object's pipeline. Shows
    a table with the pipeline components and why they assign and require, as
    well as any problems if available.

    nlp (Language): The nlp object.
    keys (List[str]): The meta keys to show in the table.
    RETURNS (dict): A dict with "summary" and "problems".
    """
    result: Dict[str, Dict[str, Union[List[str], Dict]]] = {
        "summary": {},
        "problems": {},
    }
    all_attrs: Set[str] = set()
    for i, name in enumerate(nlp.pipe_names):
        meta = nlp.get_pipe_meta(name)
        all_attrs.update(meta.assigns)
        all_attrs.update(meta.requires)
        result["summary"][name] = {key: getattr(meta, key, None) for key in keys}
        prev_pipes = nlp.pipeline[:i]
        requires = {annot: False for annot in meta.requires}
        if requires:
            for prev_name, prev_pipe in prev_pipes:
                prev_meta = nlp.get_pipe_meta(prev_name)
                for annot in prev_meta.assigns:
                    requires[annot] = True
        result["problems"][name] = [
            annot for annot, fulfilled in requires.items() if not fulfilled
        ]
    result["attrs"] = {attr: get_attr_info(nlp, attr) for attr in all_attrs}
    return result
Print a formatted summary for the current nlp object's pipeline. Shows
    a table with the pipeline components and why they assign and require, as
    well as any problems if available.

    nlp (Language): The nlp object.
    keys (List[str]): The meta keys to show in the table.
    RETURNS (dict): A dict with "summary" and "problems".
    
spacy/pipe_analysis.py