class_docstr: Binary Class Evaluation Metric Result
class_docstr_tok: ["Binary","Class","Evaluation","Metric","Result"]
docstr: add information about metric results for binary-class evaluation
  
  Args:
      metric_tuple(tuple): (evaluation_header, evaluation_metric_dict)
          - evaluation_header(str): a header for current evaluation, can be split or round number.
          - evaluation_metric_dict(dict): key-value pair for metric
              - key: metric name
              - value: metric value
      aggregated(bool): whether to aggregate multiple result tables into one
                          default True
      notes(str): text to explain the block
code: def __init__(
      self, metric_tuple: tuple, aggregated=True, notes=None
  ) -> None:
      """
      add information about metric results for binary-class evaluation
  
      Args:
          metric_tuple(tuple): (evaluation_header, evaluation_metric_dict)
              - evaluation_header(str): a header for current evaluation, can be split or round number.
              - evaluation_metric_dict(dict): key-value pair for metric
                  - key: metric name
                  - value: metric value
          aggregated(bool): whether to aggregate multiple result tables into one
                              default True
          notes(str): text to explain the block
      """
      super(BinaryClassEvaluationMetricResult, self).__init__(
          metric_tuple, notes
      )
      self._metric_tuple = metric_tuple
      self._aggregated = aggregated
      if not (notes is None):
          self._notes = notes
      else:
          self._notes = (
              "This section shows binary-class evaluation metric."
          )
  
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 BinaryClassEvaluationMetricResult object from inherited base classes: Content with example: obj = BinaryClassEvaluationMetricResult(metric_tuple, aggregated, notes)