class_docstr: Multi-Class Confidence Distribution
class_docstr_tok: ["Multi-Class","Confidence","Distribution"]
docstr: add information about multi class confidence distribution to report
  
  Args:
      *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
      notes(str,Optional): text to explain the block
code: def __init__(
      self, visual_result_tuple: tuple, max_num_classes=9, notes=None
  ) -> None:
      """
      add information about multi class confidence distribution to report
  
      Args:
          *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
          notes(str,Optional): text to explain the block
      """
      super(MultiClassConfidenceDistribution, self).__init__(
          visual_result_tuple, max_num_classes, notes
      )
      self._visual_result_tuple = visual_result_tuple
      self._max_num_classes = max_num_classes
      if not (notes is None):
          self._notes = notes
      else:
          self._notes = (
              "This section shows multi-class confidence "
              "distribution result."
          )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from xai.formatter.contents.base import Content
  from xai.formatter.writer.base import Writer
label_desc: Use image node_id 1 to create a new MultiClassConfidenceDistribution object from inherited base classes: Content with example: obj = MultiClassConfidenceDistribution(visual_result_tuple, max_num_classes, notes)