class_docstr: Class that stores the results of a ActivityNet detection evaluation.
  
  Args:
      samples: the :class:`fiftyone.core.collections.SampleCollection` used
      config: the :class:`ActivityNetEvaluationConfig` used
      eval_key: the evaluation key
      matches: a list of
          ``(gt_label, pred_label, iou, pred_confidence, gt_id, pred_id)``
          matches. Either label can be ``None`` to indicate an unmatched
          segment
      precision: an array of precision values of shape
          ``num_iou_threshs x num_classes x num_recall``
      recall: an array of recall values
      classwise_AP: an array of average precision values of shape
          ``num_iou_threshs x num_classes``
      iou_threshs: an array of IoU thresholds
      classes: the list of possible classes
      thresholds (None): an optional array of decision thresholds of shape
          ``num_iou_threshs x num_classes x num_recall``
      missing (None): a missing label string. Any unmatched segments are
          given this label for evaluation purposes
      backend (None): a :class:`ActivityNetEvaluation` backend
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code: def _get_class_index(self, label):
      inds = np.where(self.classes == label)[0]
      if inds.size == 0:
          raise ValueError("Class '%s' not found" % label)
  
      return inds[0]
  
imports: import logging
  from collections import defaultdict
  import numpy
  import eta.core.utils
  import fiftyone.core.plots
  import fiftyone.utils.iou
  from .detection import DetectionEvaluation, DetectionEvaluationConfig, DetectionResults
label_desc: Use image node_id 5 for calling the ActivityNetDetectionResults obj's underlying member method code with example usage: obj._get_class_index(label) and returns: inds