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 (`torch.FloatTensor`):
              location predictions for loc layers, Shape: [num_priors,4].
          priors (`torch.FloatTensor`):
              Prior boxes in center-offset form. Shape: [num_priors,4].
          variances (`list[float]`):
              Variances of priorboxes.
      Return:
          (`torch.FloatTensor`)
              decoded bounding box predictions.
      """
  
      boxes = torch.cat(
          (
              priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:],
              priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1]),
          ),
          1,
      )
      boxes[:, :2] -= boxes[:, 2:] / 2
      boxes[:, 2:] += boxes[:, :2]
      return boxes
  
file_path: towhee/towhee/models/retina_face/utils.py
imports: from typing import Dict
  from collections import OrderedDict
  import torch
  from torch import nn
label_desc: Use image node_id 5 for calling a global function with example usage: decode(loc, priors, variances) and returns: boxes