code: def decode_box_outputs(pred_boxes, anchor_boxes):
      """Transforms relative regression coordinates to absolute positions.
  
      Network predictions are normalized and relative to a given anchor; this
      reverses the transformation and outputs absolute coordinates for the input
      image.
  
      Args:
        pred_boxes: predicted box regression targets.
        anchor_boxes: anchors on all feature levels.
      Returns:
        outputs: bounding boxes.
      """
      anchor_boxes = tf.cast(anchor_boxes, pred_boxes.dtype)
      ycenter_a = (anchor_boxes[..., 0] + anchor_boxes[..., 2]) / 2
      xcenter_a = (anchor_boxes[..., 1] + anchor_boxes[..., 3]) / 2
      ha = anchor_boxes[..., 2] - anchor_boxes[..., 0]
      wa = anchor_boxes[..., 3] - anchor_boxes[..., 1]
      ty, tx, th, tw = tf.unstack(pred_boxes, num=4, axis=-1)
  
      w = tf.math.exp(tw) * wa
      h = tf.math.exp(th) * ha
      ycenter = ty * ha + ycenter_a
      xcenter = tx * wa + xcenter_a
      ymin = ycenter - h / 2.0
      xmin = xcenter - w / 2.0
      ymax = ycenter + h / 2.0
      xmax = xcenter + w / 2.0
      return tf.stack([ymin, xmin, ymax, xmax], axis=-1)
  
imports: import collections
  import numpy
  import tensorflow
  import utils
  from .anchors_utils import argmax_matcher
  from .anchors_utils import box_list
  from .anchors_utils import faster_rcnn_box_coder
  from .anchors_utils import region_similarity_calculator
  from .anchors_utils import target_assigner
label_desc: Use image node_id 1 for calling a global function with example usage: decode_box_outputs(pred_boxes, anchor_boxes) and returns: tf