code: def generate_brokeh_figure():
      sampledata.download()
  
      from bokeh.sampledata.unemployment import data as unemployment
      from bokeh.sampledata.us_counties import data as counties
  
      palette2 = tuple(reversed(palettes.Viridis6))
  
      cnts = {
          code: county
          for code, county in counties.items()
          if county["state"] == "tx"
      }
  
      county_xs = [county["lons"] for county in cnts.values()]
      county_ys = [county["lats"] for county in cnts.values()]
  
      county_names = [county["name"] for county in cnts.values()]
      county_rates = [unemployment[county_id] for county_id in cnts]
      color_mapper = models.LogColorMapper(palette=palette2)
  
      chart_data = dict(
          x=county_xs,
          y=county_ys,
          name=county_names,
          rate=county_rates,
      )
  
      TOOLS = "pan,wheel_zoom,reset,hover,save"
  
      bokeh_figure = plotting.figure(
          title="Texas Unemployment, 2009",
          tools=TOOLS,
          x_axis_location=None,
          y_axis_location=None,
          tooltips=[
              ("Name", "@name"),
              ("Unemployment rate", "@rate%"),
              ("(Long, Lat)", "($x, $y)"),
          ],
      )
      bokeh_figure.grid.grid_line_color = None
      bokeh_figure.hover.point_policy = "follow_mouse"
  
      bokeh_figure.patches(
          "x",
          "y",
          source=chart_data,
          fill_color={"field": "rate", "transform": color_mapper},
          fill_alpha=0.7,
          line_color="white",
          line_width=0.5,
      )
  
      return bokeh_figure
  
file_path: neptune-client/tests/e2e/plot_utils.py
imports: import altair
  import matplotlib.pyplot
  import numpy
  import plotly.express
  import seaborn
  from bokeh import models, palettes, plotting, sampledata
  from PIL import Image
  from vega_datasets import data
label_desc: Use image node_id 4 for calling a global function with example usage: generate_brokeh_figure() and returns: bokeh_figure