def METHOD_NAME(
    predictions: Union[List[Label], List[Dict[str, Label]]],
    filepaths: Optional[List[str]] = None,
    label_field: Optional[str] = "predictions",
    wait: Optional[bool] = False,
    **kwargs,
) -> Optional[Session]:
    """Visualizes predictions from a model with a FiftyOne Output in the :ref:`FiftyOne App <fiftyone:fiftyone-app>`.
    This method can be used in all of the following environments:
    -   **Local Python shell**: The App will launch in a new tab in your
        default web browser.
    -   **Remote Python shell**: Pass the ``remote=True`` option to this method
        and then follow the instructions printed to your remote shell to open
        the App in your browser on your local machine.
    -   **Jupyter notebook**: The App will launch in the output of your current
        cell.
    -   **Google Colab**: The App will launch in the output of your current
        cell.
    -   **Python script**: Pass the ``wait=True`` option to block execution of
        your script until the App is closed.
    See :ref:`this page <fiftyone:environments>` for more information about
    using the FiftyOne App in different environments.
    Args:
        predictions: Can be either a list of FiftyOne labels that will be
            matched with the corresponding ``filepaths``, or a list of
            dictionaries with "filepath" and "predictions" keys that contains
            the filepaths and predictions.
        filepaths: A list of filepaths to images or videos corresponding to the
            provided ``predictions``.
        label_field: The name of the label field in which to store the
            predictions in the FiftyOne dataset.
        wait: Whether to block execution until the FiftyOne App is closed.
        **kwargs: Optional keyword arguments for
            :meth:`fiftyone:fiftyone.core.session.launch_app`.
    Returns:
        a :class:`fiftyone:fiftyone.core.session.Session`
    """
    if flash._IS_TESTING:
        return None
    # Flatten list if batches were used
    if all(isinstance(fl, list) for fl in predictions):
        predictions = list(chain.from_iterable(predictions))
    if all(isinstance(fl, dict) for fl in predictions):
        filepaths = [lab["filepath"] for lab in predictions]
        labels = [lab["predictions"] for lab in predictions]
    else:
        labels = predictions
    if filepaths is None:
        raise ValueError("The `filepaths` argument is required if filepaths are not provided in `labels`.")
    dataset = fo.Dataset()
    if filepaths:
        dataset.add_samples([fo.Sample(filepath=fp, **{label_field: lb}) for fp, lb in zip(filepaths, labels)])
    session = fo.launch_app(dataset, **kwargs)
    if wait:
        session.wait()
    return session