def visualize_topics(
        self,
        topics: List[int] = None,
        top_n_topics: int = None,
        use_ctfidf: bool = False,
        custom_labels: bool = False,
        title: str = "<b>Intertopic Distance Map</b>",
        width: int = 650,
        height: int = 650,
    ) -> go.Figure:
        """Visualize topics, their sizes, and their corresponding words.

        This visualization is highly inspired by LDAvis, a great visualization
        technique typically reserved for LDA.

        Arguments:
            topics: A selection of topics to visualize
                    Not to be confused with the topics that you get from `.fit_transform`.
                    For example, if you want to visualize only topics 1 through 5:
                    `topics = [1, 2, 3, 4, 5]`.
            top_n_topics: Only select the top n most frequent topics
            use_ctfidf: Whether to use c-TF-IDF representations instead of the embeddings from the embedding model.
            custom_labels: Whether to use custom topic labels that were defined using
                           `topic_model.set_topic_labels`.
            title: Title of the plot.
            width: The width of the figure.
            height: The height of the figure.

        Examples:
        To visualize the topics simply run:

        ```python
        topic_model.visualize_topics()
        ```

        Or if you want to save the resulting figure:

        ```python
        fig = topic_model.visualize_topics()
        fig.write_html("path/to/file.html")
        ```
        """
        check_is_fitted(self)
        return plotting.visualize_topics(
            self,
            topics=topics,
            top_n_topics=top_n_topics,
            use_ctfidf=use_ctfidf,
            custom_labels=custom_labels,
            title=title,
            width=width,
            height=height,
        )
Visualize topics, their sizes, and their corresponding words.

        This visualization is highly inspired by LDAvis, a great visualization
        technique typically reserved for LDA.

        Arguments:
            topics: A selection of topics to visualize
                    Not to be confused with the topics that you get from `.fit_transform`.
                    For example, if you want to visualize only topics 1 through 5:
                    `topics = [1, 2, 3, 4, 5]`.
            top_n_topics: Only select the top n most frequent topics
            use_ctfidf: Whether to use c-TF-IDF representations instead of the embeddings from the embedding model.
            custom_labels: Whether to use custom topic labels that were defined using
                           `topic_model.set_topic_labels`.
            title: Title of the plot.
            width: The width of the figure.
            height: The height of the figure.

        Examples:
        To visualize the topics simply run:

        ```python
        topic_model.visualize_topics()
        ```

        Or if you want to save the resulting figure:

        ```python
        fig = topic_model.visualize_topics()
        fig.write_html("path/to/file.html")
        ```
        
bertopic/_bertopic.py