How to use the wasabi.msg.table function in wasabi

nlp = spacy.load(model)
    texts = (eg["text"] for eg in srsly.read_jsonl(data))
    n_docs = 0
    n_words = 0
    start_time = timer()
    for doc in nlp.pipe(texts):
        n_docs += 1
        n_words += len(doc)
    end_time = timer()
    wps = int(n_words / (end_time - start_time))
    result = [
        ("Docs", f"{n_docs:,}"),
        ("Words", f"{n_words:,}"),
        ("Words/s", f"{wps:,}"),
    ]
    msg.table(result, widths=(7, 12), aligns=("l", "r"))

def evaluate_model(model, eval_path):
    """
    Evaluate a trained model on Prodigy annotations and print the accuracy.
    """
    with msg.loading(f"Loading model '{model}'..."):
        nlp = spacy.load(model)
    data, _ = format_data(srsly.read_jsonl(eval_path))
    sc = nlp.evaluate(data)
    result = [("F-Score", f"{sc.textcat_score:.3f}")]
    msg.table(result)

end = timer()
    nwords = sum(len(doc_gold[0]) for doc_gold in dev_docs)
    results = {
        "Time": "%.2f s" % (end - begin),
        "Words": nwords,
        "Words/s": "%.0f" % (nwords / (end - begin)),
        "TOK": "%.2f" % scorer.token_acc,
        "POS": "%.2f" % scorer.tags_acc,
        "UAS": "%.2f" % scorer.uas,
        "LAS": "%.2f" % scorer.las,
        "NER P": "%.2f" % scorer.ents_p,
        "NER R": "%.2f" % scorer.ents_r,
        "NER F": "%.2f" % scorer.ents_f,
        "Textcat": "%.2f" % scorer.textcat_score,
    }
    msg.table(results, title="Results")

    if displacy_path:
        docs, golds = zip(*dev_docs)
        render_deps = "parser" in nlp.meta.get("pipeline", [])
        render_ents = "ner" in nlp.meta.get("pipeline", [])
        render_parses(
            docs,
            displacy_path,
            model_name=model,
            limit=displacy_limit,
            deps=render_deps,
            ents=render_ents,
        )
        msg.good("Generated {} parses as HTML".format(displacy_limit), displacy_path)
    if return_scores:
        return scorer.scores

)
    na_models = [m for m in incompat_models if m not in current_compat]
    update_models = [m for m in incompat_models if m in current_compat]
    spacy_dir = Path(__file__).parent.parent

    msg.divider("Installed models (spaCy v{})".format(about.__version__))
    msg.info("spaCy installation: {}".format(path2str(spacy_dir)))

    if model_links or model_pkgs:
        header = ("TYPE", "NAME", "MODEL", "VERSION", "")
        rows = []
        for name, data in model_pkgs.items():
            rows.append(get_model_row(current_compat, name, data, msg))
        for name, data in model_links.items():
            rows.append(get_model_row(current_compat, name, data, msg, "link"))
        msg.table(rows, header=header)
    else:
        msg.text("No models found in your current environment.", exits=0)
    if update_models:
        msg.divider("Install updates")
        msg.text("Use the following commands to update the model packages:")
        cmd = "python -m spacy download {}"
        print("\n".join([cmd.format(pkg) for pkg in update_models]) + "\n")
    if na_models:
        msg.text(
            "The following models are not available for spaCy "
            "v{}: {}".format(about.__version__, ", ".join(na_models))
        )
    if incompat_links:
        msg.text(
            "You may also want to overwrite the incompatible links using the "
            "`python -m spacy link` command with `--force`, or remove them "

def evaluate_model(model, eval_path):
    """
    Evaluate a trained model on Prodigy annotations and print the accuracy.
    """
    with msg.loading(f"Loading model '{model}'..."):
        nlp = spacy.load(model)
    data, _ = format_data(srsly.read_jsonl(eval_path))
    sc = nlp.evaluate(data)
    result = [
        ("Precision", f"{sc.ents_p:.3f}"),
        ("Recall", f"{sc.ents_r:.3f}"),
        ("F-Score", f"{sc.ents_f:.3f}"),
    ]
    msg.table(result)

nlp = spacy.load(model)
    texts = (eg["text"] for eg in srsly.read_jsonl(data))
    n_docs = 0
    n_words = 0
    start_time = timer()
    for doc in nlp.pipe(texts):
        n_docs += 1
        n_words += len(doc)
    end_time = timer()
    wps = int(n_words / (end_time - start_time))
    result = [
        ("Docs", f"{n_docs:,}"),
        ("Words", f"{n_words:,}"),
        ("Words/s", f"{wps:,}"),
    ]
    msg.table(result, widths=(7, 12), aligns=("l", "r"))

else:
                msg.table(model_meta, title=title)
        return meta
    data = {
        "spaCy version": about.__version__,
        "Location": path2str(Path(__file__).parent.parent),
        "Platform": platform.platform(),
        "Python version": platform.python_version(),
        "Models": list_models(),
    }
    if not silent:
        title = "Info about spaCy"
        if markdown:
            print_markdown(data, title=title)
        else:
            msg.table(data, title=title)
    return data

msg.fail("Can't find model meta.json", meta_path, exits=1)
        meta = srsly.read_json(meta_path)
        if model_path.resolve() != model_path:
            meta["link"] = path2str(model_path)
            meta["source"] = path2str(model_path.resolve())
        else:
            meta["source"] = path2str(model_path)
        if not silent:
            title = "Info about model '{}'".format(model)
            model_meta = {
                k: v for k, v in meta.items() if k not in ("accuracy", "speed")
            }
            if markdown:
                print_markdown(model_meta, title=title)
            else:
                msg.table(model_meta, title=title)
        return meta
    data = {
        "spaCy version": about.__version__,
        "Location": path2str(Path(__file__).parent.parent),
        "Platform": platform.platform(),
        "Python version": platform.python_version(),
        "Models": list_models(),
    }
    if not silent:
        title = "Info about spaCy"
        if markdown:
            print_markdown(data, title=title)
        else:
            msg.table(data, title=title)
    return data