code: def get_log_scale_df(corpus, y_category, x_category):
      term_coord_df = corpus.get_term_freq_df("")
  
      # Log scale term counts (with a smoothing constant) as the initial coordinates
      coord_columns = []
      for category in [y_category, x_category]:
          col_name = category + "_coord"
          term_coord_df[col_name] = np.log(
              term_coord_df[category] + 1e-6
          ) / np.log(2)
          coord_columns.append(col_name)
  
      # Scale these coordinates to between 0 and 1
      min_offset = term_coord_df[coord_columns].min(axis=0).min()
      for coord_column in coord_columns:
          term_coord_df[coord_column] -= min_offset
      max_offset = term_coord_df[coord_columns].max(axis=0).max()
      for coord_column in coord_columns:
          term_coord_df[coord_column] /= max_offset
      return term_coord_df
  
label_desc: Use image node_id 1 for calling a global function with example usage: get_log_scale_df(corpus, y_category, x_category) and returns: term_coord_df