class_docstr: ActivityNet-style evaluation config.
  
  Args:
      pred_field: the name of the field containing the predicted
          :class:`fiftyone.core.labels.TemporalDetection` instances
      gt_field: the name of the field containing the ground truth
          :class:`fiftyone.core.labels.TemporalDetection` instances
      iou (None): the IoU threshold to use to determine matches
      classwise (None): whether to only match segments with the same class
          label (True) or allow matches between classes (False)
      compute_mAP (False): whether to perform the necessary computations so
          that mAP and PR curves can be generated
      iou_threshs (None): a list of IoU thresholds to use when computing mAP
          and PR curves. Only applicable when ``compute_mAP`` is True
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code: def method(self):
      return "activitynet"
  
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 2 for calling the ActivityNetEvaluationConfig obj's underlying member method code with example usage: obj.method() and returns: str