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

def setup_printer(config):
    score_cols = config["training"]["scores"]
    score_widths = [max(len(col), 6) for col in score_cols]
    loss_cols = ["Loss {}".format(pipe) for pipe in config["nlp"]["pipeline"]]
    loss_widths = [max(len(col), 8) for col in loss_cols]
    table_header = ["#"] + loss_cols + score_cols + ["Score"]
    table_header = [col.upper() for col in table_header]
    table_widths = [6] + loss_widths + score_widths + [6]
    table_aligns = ["r" for _ in table_widths]

    msg.row(table_header, widths=table_widths)
    msg.row(["-" * width for width in table_widths])

    def print_row(info):
        losses = [
            "{0:.2f}".format(info["losses"].get(col, 0.0))
            for col in config["nlp"]["pipeline"]
        ]
        scores = [
            "{0:.2f}".format(info["other_scores"].get(col, 0.0))
            for col in config["training"]["scores"]
        ]
        data = [info["step"]] + losses + scores + ["{0:.2f}".format(info["score"])]
        msg.row(data, widths=table_widths, aligns=table_aligns)

    return print_row

else:
            nlp = spacy.load(model)
    msg.good(f"Loaded model '{model}'")
    train_data, labels = format_data(srsly.read_jsonl(train_path))
    eval_data, _ = format_data(srsly.read_jsonl(eval_path))
    textcat = nlp.create_pipe("textcat")
    for label in labels:
        textcat.add_label(label)
    nlp.add_pipe(textcat)
    cfg = {"textcat": {"exclusive_classes": True}}
    optimizer = nlp.begin_training(component_cfg=cfg)
    batch_size = spacy.util.compounding(1.0, 16.0, 1.001)
    best_acc = 0
    best_model = None
    row_widths = (2, 8, 8)
    msg.row(("#", "L", "F"), widths=row_widths)
    for i in range(n_iter):
        random.shuffle(train_data)
        losses = {}
        data = tqdm.tqdm(train_data, leave=False)
        for batch in spacy.util.minibatch(data, size=batch_size):
            texts, annots = zip(*batch)
            nlp.update(texts, annots, drop=0.2, losses=losses)
        with nlp.use_params(optimizer.averages):
            sc = nlp.evaluate(eval_data)
            if sc.textcat_score > best_acc:
                best_acc = sc.textcat_score
                if output:
                    best_model = nlp.to_bytes()
        acc = f"{sc.textcat_score:.3f}"
        msg.row((i + 1, f"{losses['textcat']:.2f}", acc), widths=row_widths)
    msg.text(f"Best F-Score: {best_acc:.3f}")

"the labels '{}'".format(", ".join(textcat_labels))
            )
        else:
            msg.fail(
                "Unsupported textcat configuration. Use `spacy debug-data` "
                "for more information."
            )

    # fmt: off
    row_head, output_stats = _configure_training_output(pipeline, use_gpu, has_beam_widths)
    row_widths = [len(w) for w in row_head]
    row_settings = {"widths": row_widths, "aligns": tuple(["r" for i in row_head]), "spacing": 2}
    # fmt: on
    print("")
    msg.row(row_head, **row_settings)
    msg.row(["-" * width for width in row_settings["widths"]], **row_settings)
    try:
        iter_since_best = 0
        best_score = 0.0
        for i in range(n_iter):
            train_docs = corpus.train_docs(
                nlp,
                noise_level=noise_level,
                orth_variant_level=orth_variant_level,
                gold_preproc=gold_preproc,
                max_length=0,
                ignore_misaligned=True,
            )
            if raw_text:
                random.shuffle(raw_text)
                raw_batches = util.minibatch(
                    (nlp.make_doc(rt["text"]) for rt in raw_text), size=8

def print_row(info):
        losses = [
            "{0:.2f}".format(info["losses"].get(col, 0.0))
            for col in config["nlp"]["pipeline"]
        ]
        scores = [
            "{0:.2f}".format(info["other_scores"].get(col, 0.0))
            for col in config["training"]["scores"]
        ]
        data = [info["step"]] + losses + scores + ["{0:.2f}".format(info["score"])]
        msg.row(data, widths=table_widths, aligns=table_aligns)

def setup_printer(config):
    score_cols = config["training"]["scores"]
    score_widths = [max(len(col), 6) for col in score_cols]
    loss_cols = ["Loss {}".format(pipe) for pipe in config["nlp"]["pipeline"]]
    loss_widths = [max(len(col), 8) for col in loss_cols]
    table_header = ["#"] + loss_cols + score_cols + ["Score"]
    table_header = [col.upper() for col in table_header]
    table_widths = [6] + loss_widths + score_widths + [6]
    table_aligns = ["r" for _ in table_widths]

    msg.row(table_header, widths=table_widths)
    msg.row(["-" * width for width in table_widths])

    def print_row(info):
        losses = [
            "{0:.2f}".format(info["losses"].get(col, 0.0))
            for col in config["nlp"]["pipeline"]
        ]
        scores = [
            "{0:.2f}".format(info["other_scores"].get(col, 0.0))
            for col in config["training"]["scores"]
        ]
        data = [info["step"]] + losses + scores + ["{0:.2f}".format(info["score"])]
        msg.row(data, widths=table_widths, aligns=table_aligns)

    return print_row

ner.add_label(label)
    nlp.add_pipe(ner)
    t2v_cfg = {
        "embed_rows": 10000,
        "token_vector_width": 128,
        "conv_depth": 8,
        "nr_feature_tokens": 3,
    }
    optimizer = nlp.begin_training(component_cfg={"ner": t2v_cfg} if tok2vec else {})
    if tok2vec:
        _load_pretrained_tok2vec(nlp, Path(tok2vec))
    batch_size = spacy.util.compounding(1.0, 16.0, 1.001)
    best_acc = 0
    best_model = None
    row_widths = (2, 8, 8, 8, 8)
    msg.row(("#", "L", "P", "R", "F"), widths=row_widths)
    for i in range(n_iter):
        random.shuffle(train_data)
        losses = {}
        data = tqdm.tqdm(train_data, leave=False)
        for batch in spacy.util.minibatch(data, size=batch_size):
            texts, annots = zip(*batch)
            nlp.update(texts, annots, drop=0.2, losses=losses)
        with nlp.use_params(optimizer.averages):
            sc = nlp.evaluate(eval_data)
            if sc.ents_f > best_acc:
                best_acc = sc.ents_f
                if output:
                    best_model = nlp.to_bytes()
        acc = (f"{sc.ents_p:.3f}", f"{sc.ents_r:.3f}", f"{sc.ents_f:.3f}")
        msg.row((i + 1, f"{losses['ner']:.2f}", *acc), widths=row_widths)
    msg.text(f"Best F-Score: {best_acc:.3f}")

losses,
                        scorer.scores,
                        output_stats,
                        beam_width=beam_width if has_beam_widths else None,
                        cpu_wps=cpu_wps,
                        gpu_wps=gpu_wps,
                    )
                    if i == 0 and "textcat" in pipeline:
                        textcats_per_cat = scorer.scores.get("textcats_per_cat", {})
                        for cat, cat_score in textcats_per_cat.items():
                            if cat_score.get("roc_auc_score", 0) < 0:
                                msg.warn(
                                    "Textcat ROC AUC score is undefined due to "
                                    "only one value in label '{}'.".format(cat)
                                )
                    msg.row(progress, **row_settings)
                # Early stopping
                if n_early_stopping is not None:
                    current_score = _score_for_model(meta)
                    if current_score < best_score:
                        iter_since_best += 1
                    else:
                        iter_since_best = 0
                        best_score = current_score
                    if iter_since_best >= n_early_stopping:
                        msg.text(
                            "Early stopping, best iteration "
                            "is: {}".format(i - iter_since_best)
                        )
                        msg.text(
                            "Best score = {}; Final iteration "
                            "score = {}".format(best_score, current_score)

"Textcat evaluation score: F1-score macro-averaged across "
                "the labels '{}'".format(", ".join(textcat_labels))
            )
        else:
            msg.fail(
                "Unsupported textcat configuration. Use `spacy debug-data` "
                "for more information."
            )

    # fmt: off
    row_head, output_stats = _configure_training_output(pipeline, use_gpu, has_beam_widths)
    row_widths = [len(w) for w in row_head]
    row_settings = {"widths": row_widths, "aligns": tuple(["r" for i in row_head]), "spacing": 2}
    # fmt: on
    print("")
    msg.row(row_head, **row_settings)
    msg.row(["-" * width for width in row_settings["widths"]], **row_settings)
    try:
        iter_since_best = 0
        best_score = 0.0
        for i in range(n_iter):
            train_docs = corpus.train_docs(
                nlp,
                noise_level=noise_level,
                orth_variant_level=orth_variant_level,
                gold_preproc=gold_preproc,
                max_length=0,
                ignore_misaligned=True,
            )
            if raw_text:
                random.shuffle(raw_text)
                raw_batches = util.minibatch(