parameters: (boxes,
                          scores,
                          max_output_size,
                          iou_threshold=0.5,
                          name=None)
docstring: Greedily selects a subset of bounding boxes in descending order of score.
  
    Prunes away boxes that have high intersection-over-union (IOU) overlap
    with previously selected boxes.  Bounding boxes are supplied as
    [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any
    diagonal pair of box corners and the coordinates can be provided as normalized
    (i.e., lying in the interval [0, 1]) or absolute.  Note that this algorithm
    is agnostic to where the origin is in the coordinate system.  Note that this
    algorithm is invariant to orthogonal transformations and translations
    of the coordinate system; thus translating or reflections of the coordinate
    system result in the same boxes being selected by the algorithm.
    The output of this operation is a set of integers indexing into the input
    collection of bounding boxes representing the selected boxes.  The bounding
    box coordinates corresponding to the selected indices can then be obtained
    using the `tf.gather operation`.  For example:
      selected_indices = tf.image.non_max_suppression(
          boxes, scores, max_output_size, iou_threshold)
      selected_boxes = tf.gather(boxes, selected_indices)
  
    Args:
      boxes: A 2-D float `Tensor` of shape `[num_boxes, 4]`.
      scores: A 1-D float `Tensor` of shape `[num_boxes]` representing a single
        score corresponding to each box (each row of boxes).
      max_output_size: A scalar integer `Tensor` representing the maximum number
        of boxes to be selected by non max suppression.
      iou_threshold: A float representing the threshold for deciding whether boxes
        overlap too much with respect to IOU.
      name: A name for the operation (optional).
  
    Returns:
      selected_indices: A 1-D integer `Tensor` of shape `[M]` representing the
        selected indices from the boxes tensor, where `M <= max_output_size`.
docstring_summary: Greedily selects a subset of bounding boxes in descending order of score.
docstring_tokens: Greedily selects a subset of bounding boxes in descending order of score .
function: def non_max_suppression(boxes,
                          scores,
                          max_output_size,
                          iou_threshold=0.5,
                          name=None):
    """Greedily selects a subset of bounding boxes in descending order of score.
  
    Prunes away boxes that have high intersection-over-union (IOU) overlap
    with previously selected boxes.  Bounding boxes are supplied as
    [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any
    diagonal pair of box corners and the coordinates can be provided as normalized
    (i.e., lying in the interval [0, 1]) or absolute.  Note that this algorithm
    is agnostic to where the origin is in the coordinate system.  Note that this
    algorithm is invariant to orthogonal transformations and translations
    of the coordinate system; thus translating or reflections of the coordinate
    system result in the same boxes being selected by the algorithm.
    The output of this operation is a set of integers indexing into the input
    collection of bounding boxes representing the selected boxes.  The bounding
    box coordinates corresponding to the selected indices can then be obtained
    using the `tf.gather operation`.  For example:
      selected_indices = tf.image.non_max_suppression(
          boxes, scores, max_output_size, iou_threshold)
      selected_boxes = tf.gather(boxes, selected_indices)
  
    Args:
      boxes: A 2-D float `Tensor` of shape `[num_boxes, 4]`.
      scores: A 1-D float `Tensor` of shape `[num_boxes]` representing a single
        score corresponding to each box (each row of boxes).
      max_output_size: A scalar integer `Tensor` representing the maximum number
        of boxes to be selected by non max suppression.
      iou_threshold: A float representing the threshold for deciding whether boxes
        overlap too much with respect to IOU.
      name: A name for the operation (optional).
  
    Returns:
      selected_indices: A 1-D integer `Tensor` of shape `[M]` representing the
        selected indices from the boxes tensor, where `M <= max_output_size`.
    """
    with ops.name_scope(name, 'non_max_suppression'):
      iou_threshold = ops.convert_to_tensor(iou_threshold, name='iou_threshold')
      # pylint: disable=protected-access
      return gen_image_ops._non_max_suppression_v2(boxes, scores, max_output_size,
                                                   iou_threshold)
function_tokens: def non_max_suppression ( boxes , scores , max_output_size , iou_threshold = 0.5 , name = None ) : with ops . name_scope ( name , 'non_max_suppression' ) : iou_threshold = ops . convert_to_tensor ( iou_threshold , name = 'iou_threshold' ) # pylint: disable=protected-access return gen_image_ops . _non_max_suppression_v2 ( boxes , scores , max_output_size , iou_threshold )