code: def decode_bbox(offsets, anchors):
      dx, dy, dw, dh = np.rollaxis(offsets, 1)
      widths, heights = (
          anchors[:, 2] - anchors[:, 0],
          anchors[:, 3] - anchors[:, 1],
      )
      cx, cy = (
          anchors[:, 0] + 0.5 * widths,
          anchors[:, 1] + 0.5 * heights,
      )
      pred_cx, pred_cy = dx * widths + cx, dy * heights + cy
      pred_w, pred_h = np.exp(dw) * widths, np.exp(dh) * heights
      pred_x1, pred_y1 = pred_cx - 0.5 * pred_w, pred_cy - 0.5 * pred_h
      pred_x2, pred_y2 = pred_cx + 0.5 * pred_w, pred_cy + 0.5 * pred_h
      return np.stack(
          [pred_x1, pred_y1, pred_x2, pred_y2], axis=1, dtype=np.float32
      )
  
imports: import math
  from tinygrad.helpers import flatten, get_child
  import tinygrad.nn
  from extra.models.resnet import ResNet
  import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: decode_bbox(offsets, anchors) and returns: np