class_docstr: Metric util function mixin for object detection.
class_docstr_tok: ["Metric","util","function","mixin","for","object","detection","."]
docstr: Get a single result from group_class_detection_label and return a matrix of IoUs.
docstr_tok: ["Get","a","single","result","from","group_class_detection_label","and","return","a","matrix","of","IoUs","."]
code: def calc_pairwise_ious(self, detections, labels) -> np.ndarray:
      """Get a single result from group_class_detection_label and return a matrix of IoUs."""
      return compute_pairwise_ious(detections, labels, jaccard_iou)
  
file_path: deepchecks/deepchecks/vision/metrics_utils/metric_mixin.py
imports: import typing
  from abc import abstractmethod
  import numpy
  from deepchecks.vision.metrics_utils.iou_utils import compute_pairwise_ious, group_class_detection_label, jaccard_iou
label_desc: Use image node_id 4 for calling the ObjectDetectionMetricMixin obj's underlying member method code with example usage: obj.calc_pairwise_ious(detections, labels) and returns: compute_pairwise_ious