old_contents: import chainer
  
  
  class DetectionLink(chainer.Link):
      """A chainer.Link for object detection.
  
      This is an abstract class for object detection links.
      All object detectors should inherit this class.
      """
  
      def predict(self, img):
          """Detect objects in an image.
  
          This method detects objects in an image.
          This method returns a tuple of three arrays,
          :obj:`(bbox, label, score)`.
  
          Args:
              img (~numpy.ndarray): An array of shape :math:`(3, height, width)`.
                  This is in BGR format and the range of its value is
                  :math:`[0, 255]`.
  
          Returns:
              (~numpy.ndarray, ~numpy.ndarray, ~numpy.ndarray):
              This method returns a tuple of three arrays,
              :obj:`(bbox, label, score)`.
  
              * **bbox**: A float array of shape :math:`(R, 4)`, where \
                   :math:`R` is the number of bounding boxes in the image. \
                  Elements are organized by :obj:`(x_min, y_min, x_max, y_max)` \
                  in the second axis.
              * **label** : An integer array of shape :math:`(R,)`. \
                  Each value indicates the class of the bounding box.
              * **score** : A float array of shape :math:`(R,)`. \
                  Each value indicates how confident the prediction is.
          """
  
          raise NotImplementedError
  
new_contents: import chainer
  
  
  class DetectionLink(chainer.Link):
      """A chainer.Link for object detection.
  
      This is an abstract class for object detection links.
      All object detectors should inherit this class.
      """
  
      def predict(self, img):
          """Detect objects in an image.
  
          This method detects objects in an image.
          This method returns a tuple of three arrays,
          :obj:`(bbox, label, score)`.
  
          Args:
              img (~numpy.ndarray): An array of shape :math:`(3, height, width)`.
                  This is in BGR format and the range of its value is
                  :math:`[0, 255]`.
  
          Returns:
              (~numpy.ndarray, ~numpy.ndarray, ~numpy.ndarray):
              This method returns a tuple of three arrays,
              :obj:`(bbox, label, score)`.
  
              * **bbox**: A float array of shape :math:`(R, 4)`, where \
                   :math:`R` is the number of bounding boxes in the image. \
                  Each bouding box is organized by \
                  :obj:`(x_min, y_min, x_max, y_max)` \
                  in the second axis.
              * **label** : An integer array of shape :math:`(R,)`. \
                  Each value indicates the class of the bounding box.
              * **score** : A float array of shape :math:`(R,)`. \
                  Each value indicates how confident the prediction is.
          """
  
          raise NotImplementedError
  
subject: update doc