code: def decode(bboxes, deltas):
      """
      Args:
          boxes: numpy array of bounding boxes of shape: (num_boxes, 4) following
              the encoding (x_min, y_min, x_max, y_max).
          deltas: numpy array of bounding box deltas, one for each bounding box.
              Its shape is (num_boxes, 4), where the deltas are encoded as
              (dx, dy, dw, dh).
  
      Returns:
          bboxes: bounding boxes transformed to (x1, y1, x2, y2) coordinates. It
              has the same shape as bboxes.
      """
      widths, heights, ctr_x, ctr_y = get_bbox_properties(bboxes)
  
      # The dx, dy deltas are relative while the dw, dh deltas are "log relative"
      # d[:, x::y] is used for having a `(num_boxes, 1)` shape instead of
      # `(num_boxes,)`
  
      # Split deltas columns into flat array
      dx = deltas[:, 0]
      dy = deltas[:, 1]
      dw = deltas[:, 2]
      dh = deltas[:, 3]
  
      # We get the center of the real box as center anchor + relative width
      pred_ctr_x = dx * widths + ctr_x
      pred_ctr_y = dy * heights + ctr_y
  
      # New width and height using exp
      pred_w = np.exp(dw) * widths
      pred_h = np.exp(dh) * heights
  
      # Calculate (x_min, y_min, x_max, y_max) and pack them together.
      pred_boxes = np.column_stack(
          (
              pred_ctr_x - 0.5 * pred_w,
              pred_ctr_y - 0.5 * pred_h,
              pred_ctr_x + 0.5 * pred_w - 1.0,
              pred_ctr_y + 0.5 * pred_h - 1.0,
          )
      )
  
      return pred_boxes
  
imports: import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: decode(bboxes, deltas) and returns: pred_boxes