code: def decode(loc, priors, variances):
      """Decode locations from predictions using priors to undo
      the encoding we did for offset regression at train time.
      Args:
          loc (tensor): location predictions for loc layers,
              Shape: [num_priors,4]
          priors (tensor): Prior boxes in center-offset form.
              Shape: [num_priors,4].
          variances: (list[float]) Variances of priorboxes
      Return:
          decoded bounding box predictions
      """
  
      boxes = np.concatenate(
          (
              priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:],
              priors[:, 2:] * np.exp(loc[:, 2:] * variances[1]),
          ),
          1,
      )
      boxes[:, :2] -= boxes[:, 2:] / 2
      boxes[:, 2:] += boxes[:, :2]
      return boxes
  
imports: import math
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: decode(loc, priors, variances) and returns: boxes