code: def process_data():
      from bokeh.sampledata.gapminder import (
          fertility,
          life_expectancy,
          population,
          regions,
      )
  
      # Make the column names ints not strings for handling
      columns = list(fertility.columns)
      years = [int(year) for year in columns]
      rename_dict = dict(zip(columns, years))
  
      fertility = fertility.rename(columns=rename_dict)
      life_expectancy = life_expectancy.rename(columns=rename_dict)
      population = population.rename(columns=rename_dict)
      regions = regions.rename(columns=rename_dict)
  
      regions_list = list(regions.Group.unique())
  
      # Turn population into bubble sizes. Use min_size and factor to tweak.
      scale_factor = 200
      population_size = np.sqrt(population / np.pi) / scale_factor
      min_size = 3
      population_size = population_size.where(
          population_size >= min_size
      ).fillna(min_size)
  
      return (
          fertility,
          life_expectancy,
          population_size,
          regions,
          years,
          regions_list,
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: process_data() and returns: fertility,  life_expectancy,  population_size,  regions,  years,  regions_list