def METHOD_NAME(  # type: ignore[override]
    self,
    get_examples: Callable[[], Iterable[Example]],
    *,
    nlp: Optional[Language] = None,
    labels: Optional[Iterable[str]] = None,
):
    """Initialize the pipe for training, using a representative set
    of data examples.
    get_examples (Callable[[], Iterable[Example]]): Function that
        returns a representative sample of gold-standard Example objects.
    nlp (Language): The current nlp object the component is part of.
    labels: The labels to add to the component, typically generated by the
        `init labels` command. If no labels are provided, the get_examples
        callback is used to extract the labels from the data.
    DOCS: https://spacy.io/api/textcategorizer#initialize
    """
    validate_get_examples(get_examples, "MultiLabel_TextCategorizer.initialize")
    if labels is None:
        for example in get_examples():
            for cat in example.y.cats:
                self.add_label(cat)
    else:
        for label in labels:
            self.add_label(label)
    subbatch = list(islice(get_examples(), 10))
    self._validate_categories(subbatch)
    doc_sample = [eg.reference for eg in subbatch]
    label_sample, _ = self._examples_to_truth(subbatch)
    self._require_labels()
    assert len(doc_sample) > 0, Errors.E923.format(name=self.name)
    assert len(label_sample) > 0, Errors.E923.format(name=self.name)
    self.model.METHOD_NAME(X=doc_sample, Y=label_sample)