How to use the msticpy.common.utility.export function in msticpy

@export
def risky_cmd_line(
    events: pd.DataFrame,
    log_type: str,
    detection_rules: str = os.path.join(
        os.path.join(os.path.dirname(os.path.dirname(__file__)), _DETECTIONS_DEF_DIR),
        "cmd_line_rules.json",
    ),
    cmd_field: str = "Command",
) -> dict:
    """
    Detect patterns of risky commands in syslog messages.

    Risky patterns are defined in a json format file.

    Parameters
    ----------

@export
@lru_cache(maxsize=1024)
def token_count(value: str, delimiter: str = " ") -> int:
    """
    Return count of delimiter-separated tokens pd.Series column.

    Parameters
    ----------
    value : str
        Data to process
    delimiter : str, optional
        Delimiter used to split the column string.
        (the default is ' ')

    Returns
    -------
    int

@export
def entity_distance(ip_src: IpAddress, ip_dest: IpAddress) -> float:
    """
    Return distance between two IP Entities.

    Parameters
    ----------
    ip_src : IpAddress
        Source/Origin IpAddress Entity
    ip_dest : IpAddress
        Destination IpAddress Entity

    Returns
    -------
    float
        Distance in kilometers.

@export
def load_kql_magic():
    """Load KqlMagic if not loaded."""
    # KqlMagic
    if not _KQL_LOADER():
        raise EnvironmentError("Kqlmagic did not load correctly.")

@export
def create_host_record(
    syslog_df: pd.DataFrame, heartbeat_df: pd.DataFrame, az_net_df: pd.DataFrame = None
) -> Host:
    """
    Generate host_entity record for selected computer.

    Parameters
    ----------
    syslog_df : pd.DataFrame
        A dataframe of all syslog events for the host in the time window requried
    heartbeat_df : pd.DataFrame
        A dataframe of heartbeat data for the host
    az_net_df : pd.DataFrame
        Option dataframe of Azure network data for the host

    Returns

@export
def get_items_from_gzip(binary: bytes) -> Tuple[str, Dict[str, bytes]]:
    """
    Return decompressed gzip contents.

    Parameters
    ----------
    binary : bytes
        byte array of gz file

    Returns
    -------
    Tuple[str, bytes]
        File type + decompressed file

    """
    archive_file = gzip.decompress(binary)

@export
def replace_query_params(query_name: str, *args, **kwargs) -> str:
    """
    Return the parameterized query for query_name.

    Parameters
    ----------
    query_name : str
        The query to use

    Other Parameters
    ----------------
    args : Tuple[QueryParamProvider]
        objects that implement QueryParamProvider
        (from which query parameters can be extracted).
    provs : Iterable[QueryParamProvider]
        this should be a collection of objects that

@export
@lru_cache(maxsize=1024)
def crc32_hash(value: str) -> int:
    """
    Return the CRC32 hash of the input column.

    Parameters
    ----------
    value : str
        Data to process

    Returns
    -------
    int
        CRC32 hash

    """

@export  # noqa: C901, MC0001
def display_timeline_values(
    data: pd.DataFrame,
    y: str,
    time_column: str = "TimeGenerated",
    source_columns: list = None,
    **kwargs,
) -> figure:
    """
    Display a timeline of events.

    Parameters
    ----------
    data : pd.DataFrame
        DataFrame as a single data set or grouped into individual
        plot series using the `group_by` parameter
    time_column : str, optional

@export  # noqa: C901, MC0001
def plot_cluster(
    db_cluster: DBSCAN,
    data: pd.DataFrame,
    x_predict: np.ndarray,
    plot_label: str = None,
    plot_features: Tuple[int, int] = (0, 1),
    verbose: bool = False,
    cut_off: int = 3,
    xlabel: str = None,
    ylabel: str = None,
):
    """
    Plot clustered data as scatter chart.

    Parameters
    ----------