docstr: Add information about multi class confidence distribution to report
  
  Args:
      notes(str): Explain the block
      visual_result_tuple(tuple): (visual_result_header, visual_result_dict)
         - visual_result_header(str): a header for confusion_matrix,
                                      can be split or round number.
         - visual_result_dict(dict): key-value
             - key(str): the predicted class
             - value(dit): result dict
                  - `gt` (:list of :str): ground truth class label for all samples
                  - `values` (:list of :float): probability for all samples
      max_num_classes(int, Optional): maximum number of classes
                              displayed for each graph, default 9
code: def draw_multi_class_confidence_distribution(
      self, notes: str, *, visual_result_tuple: tuple, max_num_classes=9
  ):
      """
      Add information about multi class confidence distribution to report
  
      Args:
          notes(str): Explain the block
          visual_result_tuple(tuple): (visual_result_header, visual_result_dict)
             - visual_result_header(str): a header for confusion_matrix,
                                          can be split or round number.
             - visual_result_dict(dict): key-value
                 - key(str): the predicted class
                 - value(dit): result dict
                      - `gt` (:list of :str): ground truth class label for all samples
                      - `values` (:list of :float): probability for all samples
          max_num_classes(int, Optional): maximum number of classes
                                  displayed for each graph, default 9
      """
      import operator
      from xai.graphs import graph_generator
  
      # -- Draw Content --
      if not (notes is None):
          self.html.article[-1].items.append(
              self.html.add_paragraph(text=notes)
          )
  
      top_classes = list()
      for eval_name, eval_vis_result in visual_result_tuple:
          predicted_class_count = dict()
          label = ""
          for label in eval_vis_result.keys():
              data = eval_vis_result[label]
              num_sample = len(data["gt"])
              predicted_class_count[label] = num_sample
  
              sorted_class_size = sorted(
                  predicted_class_count.items(),
                  key=operator.itemgetter(1),
              )[::-1]
              top_classes = [a for (a, _) in sorted_class_size]
  
          images = dict()
          for class_label in top_classes:
              data = eval_vis_result[class_label]
              num_sample = len(data["gt"])
              if num_sample > 0:
                  image_path = "%s/%s_%s_conf_dist.png" % (
                      self.figure_path,
                      label,
                      class_label,
                  )
                  sw_image_path = (
                      graph_generator.ResultProbabilityForMultiClass(
                          image_path, data, "Predicted as %s" % label
                      ).draw()
                  )
                  images[class_label] = sw_image_path
              if len(images) >= max_num_classes:
                  break
          self.html.article[-1].items.append(
              self.html.add_grid_image(srcs=images)
          )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import copy
  import os
  import tempfile
  import warnings
  import shutil
  from typing import Tuple, Dict, List
  import numpy
  from xai import MODE
  from xai.data.explorer import CategoricalStats, NumericalStats, TextStats, DatetimeStats
  from xai.formatter.contents import Header, Title, SectionTitle
  from xai.formatter.hypertext_markup.publisher import CustomHtml, Div
  from xai.formatter.report.section import OverviewSection, DetailSection
  from xai.formatter.writer import Writer
label_desc: Use image node_id 34 for calling the HtmlWriter obj's underlying member method code with example usage: obj.draw_multi_class_confidence_distribution(notes) without return types