def visualize_topics_over_time(
        self,
        topics_over_time: pd.DataFrame,
        top_n_topics: int = None,
        topics: List[int] = None,
        normalize_frequency: bool = False,
        custom_labels: bool = False,
        title: str = "<b>Topics over Time</b>",
        width: int = 1250,
        height: int = 450,
    ) -> go.Figure:
        """Visualize topics over time.

        Arguments:
            topics_over_time: The topics you would like to be visualized with the
                              corresponding topic representation
            top_n_topics: To visualize the most frequent topics instead of all
            topics: Select which topics you would like to be visualized
            normalize_frequency: Whether to normalize each topic's frequency individually
            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.

        Returns:
            A plotly.graph_objects.Figure including all traces

        Examples:
        To visualize the topics over time, simply run:

        ```python
        topics_over_time = topic_model.topics_over_time(docs, timestamps)
        topic_model.visualize_topics_over_time(topics_over_time)
        ```

        Or if you want to save the resulting figure:

        ```python
        fig = topic_model.visualize_topics_over_time(topics_over_time)
        fig.write_html("path/to/file.html")
        ```
        """
        check_is_fitted(self)
        return plotting.visualize_topics_over_time(
            self,
            topics_over_time=topics_over_time,
            top_n_topics=top_n_topics,
            topics=topics,
            normalize_frequency=normalize_frequency,
            custom_labels=custom_labels,
            title=title,
            width=width,
            height=height,
        )
Visualize topics over time.

        Arguments:
            topics_over_time: The topics you would like to be visualized with the
                              corresponding topic representation
            top_n_topics: To visualize the most frequent topics instead of all
            topics: Select which topics you would like to be visualized
            normalize_frequency: Whether to normalize each topic's frequency individually
            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.

        Returns:
            A plotly.graph_objects.Figure including all traces

        Examples:
        To visualize the topics over time, simply run:

        ```python
        topics_over_time = topic_model.topics_over_time(docs, timestamps)
        topic_model.visualize_topics_over_time(topics_over_time)
        ```

        Or if you want to save the resulting figure:

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