code: def _decode(
      loc: torch.Tensor, priors: torch.Tensor, variances: List[float]
  ) -> torch.Tensor:
      """Decode locations from predictions using priors to undo the encoding we did for offset regression at train
      time.
  
      Args:
          loc:location predictions for loc layers. Shape: [num_priors,4].
          priors: Prior boxes in center-offset form. Shape: [num_priors,4].
          variances: (list[float]) Variances of priorboxes.
  
      Return:
          Tensor containing decoded bounding box predictions.
      """
      boxes = torch.cat(
          (
              priors[:, 0:2]
              + loc[:, 0:2] * variances[0] * priors[:, 2:4],
              priors[:, 2:4] * torch.exp(loc[:, 2:4] * variances[1]),
              priors[:, 0:2]
              + loc[:, 4:6] * variances[0] * priors[:, 2:4],
              priors[:, 0:2]
              + loc[:, 6:8] * variances[0] * priors[:, 2:4],
              priors[:, 0:2]
              + loc[:, 8:10] * variances[0] * priors[:, 2:4],
              priors[:, 0:2]
              + loc[:, 10:12] * variances[0] * priors[:, 2:4],
              priors[:, 0:2]
              + loc[:, 12:14] * variances[0] * priors[:, 2:4],
          ),
          1,
      )
      # prepare final output
      tmp = boxes[:, 0:2] - boxes[:, 2:4] / 2
      return torch.cat((tmp, boxes[:, 2:4] + tmp, boxes[:, 4:]), dim=-1)
  
imports: import math
  from enum import Enum
  from typing import Dict, List, Optional, Tuple
  import torch
  import torch.nn.functional
  from torch import nn
  from kornia.geometry.bbox import nms
  from kornia.utils.helpers import map_location_to_cpu
label_desc: Use image node_id 1 for calling a global function with example usage: _decode(loc, priors, variances) and returns: torch