code: def encode(matched, priors, variances):
      """Encode the variances from the priorbox layers into the ground truth boxes
      we have matched (based on jaccard overlap) with the prior boxes.
      Args:
          matched: (tensor) Coords of ground truth for each prior in point-form
              Shape: [num_priors, 4].
          priors: (tensor) Prior boxes in center-offset form
              Shape: [num_priors,4].
          variances: (list[float]) Variances of priorboxes
      Return:
          encoded boxes (tensor), Shape: [num_priors, 4]
      """
  
      # dist b/t match center and prior's center
      g_cxcy = (matched[:, :2] + matched[:, 2:]) / 2 - priors[:, :2]
      # encode variance
      g_cxcy /= variances[0] * priors[:, 2:]
      # match wh / prior wh
      g_wh = (matched[:, 2:] - matched[:, :2]) / priors[:, 2:]
      g_wh = np.log(g_wh) / variances[1]
  
      # return target for smooth_l1_loss
      return np.concatenate([g_cxcy, g_wh], 1)
  
imports: import math
  import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: encode(matched, priors, variances) and returns: np