def METHOD_NAME(doc: Document):
    # Preprocess for visualizations
    df: pd.DataFrame = generate_rows(100)
    df['time'] = pd.to_datetime(df['timestamps'], unit='s')
    cudf_df = cudf.from_pandas(df)
    cux_df = cux.DataFrame.from_dataframe(cudf_df)
    chart_map = charts.choropleth(
        x='zips',
        elevation_column='humans',
        elevation_aggregate_fn='mean',
        color_column='amounts',
        color_aggregate_fn='mean',
        mapbox_api_key=MAPBOX_API_KEY,
        geoJSONSource=zip_codes_geojson_url,
        title='ATM Withdrawals Across the US',
    )
    chart_humans = charts.bokeh.bar('humans')
    slider_time = charts.date_range_slider(
        'time',
        title='Timeframe',
    )
    overall_amounts = charts.number(
        x='amounts',
        aggregate_fn='sum',
        format='${value:,.1f}',
        title='Total Withdrawals',
    )
    overall_humans = charts.number(
        x='humans',
        aggregate_fn='sum',
        format='{value:,.0f}',
        title='Humans Detected',
    )
    d = cux_df.dashboard(
        [chart_map],
        layout_array=[[1]],
        sidebar=[slider_time, overall_amounts, overall_humans],
        theme=cux.themes.rapids,
        title='Map of ATMs',
    )
    # run the dashboard as a webapp:
    d._dashboard.generate_dashboard(
        d.title, d._charts, d._theme
    ).server_doc(doc)