code: def decode_landm(pre, priors, variances):
      """
      Decode landm from predictions using priors to undo
      the encoding we did for offset regression at train time.
  
      Args:
          pre (`torch.FloatTensor`):
              landm predictions for loc layers, Shape: [num_priors,10].
          priors (`torch.FloatTensor`):
              Prior boxes in center-offset form. Shape: [num_priors,4].
          variances (`list[float]`):
              Variances of priorboxes.
      Return:
          (`torch.FloatTensor`)
              decoded landm predictions.
      """
      landms = torch.cat(
          (
              priors[:, :2] + pre[:, :2] * variances[0] * priors[:, 2:],
              priors[:, :2]
              + pre[:, 2:4] * variances[0] * priors[:, 2:],
              priors[:, :2]
              + pre[:, 4:6] * variances[0] * priors[:, 2:],
              priors[:, :2]
              + pre[:, 6:8] * variances[0] * priors[:, 2:],
              priors[:, :2]
              + pre[:, 8:10] * variances[0] * priors[:, 2:],
          ),
          dim=1,
      )
      return landms
  
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 6 for calling a global function with example usage: decode_landm(pre, priors, variances) and returns: landms