code: def assign_priors(
      gt_boxes: Tensor,
      gt_labels: Tensor,
      corner_form_priors: Tensor,
      iou_threshold: float,
      background_id: Optional[int] = 0,
      *args,
      **kwargs,
  ) -> Tuple[Tensor, Tensor]:
      """
      Assign ground truth boxes and targets to priors (or anchors)
  
      Args:
          gt_boxes (Tensor): Ground-truth boxes tensor of shape (num_targets, 4)
          gt_labels (Tensor): Ground-truth labels of shape (num_targets)
          corner_form_priors (Tensor): Priors in corner form and has shape (num_priors, 4)
          iou_threshold (float): Overlap between priors and gt_boxes.
          background_id (int): Background class index. Default: 0
  
      Returns:
          boxes (Tensor): Boxes mapped to priors and has shape (num_priors, 4)
          labels (Tensor): Labels for mapped boxes and has shape (num_priors)
      """
  
      if gt_labels.nelement() == 0:
          # Images may not have any labels
          dev = corner_form_priors.device
          gt_boxes = torch.zeros(
              (1, 4), dtype=torch.float32, device=dev
          )
          gt_labels = torch.zeros(1, dtype=torch.int64, device=dev)
  
      ious = box_iou(
          gt_boxes.unsqueeze(0), corner_form_priors.unsqueeze(1)
      )
  
      # size: num_priors
      best_target_per_prior, best_target_per_prior_index = ious.max(1)
      # size: num_targets
      best_prior_per_target, best_prior_per_target_index = ious.max(0)
  
      for target_index, prior_index in enumerate(
          best_prior_per_target_index
      ):
          best_target_per_prior_index[prior_index] = target_index
      # 2.0 is used to make sure every target has a prior assigned
      best_target_per_prior.index_fill_(
          0, best_prior_per_target_index, 2
      )
      # size: num_priors
      labels = gt_labels[best_target_per_prior_index]
      labels[best_target_per_prior < iou_threshold] = background_id
      boxes = gt_boxes[best_target_per_prior_index]
      return boxes, labels
  
imports: import math
  from typing import Optional, Tuple
  import torch
  from torch import Tensor
label_desc: Use image node_id 1 for calling a global function with example usage: assign_priors(gt_boxes, gt_labels, corner_form_priors, iou_threshold, background_id) and returns: boxes,  labels