code: def assign(
      predictions,
      targets,
      img_w,
      img_h,
      distance_cost_weight=3.0,
      cls_cost_weight=1.0,
  ):
      """
      computes dynamicly matching based on the cost, including cls cost and lane similarity cost
      Args:
          predictions (Tensor): predictions predicted by each stage, shape: (num_priors, 78)
          targets (Tensor): lane targets, shape: (num_targets, 78)
      return:
          matched_row_inds (Tensor): matched predictions, shape: (num_targets)
          matched_col_inds (Tensor): matched targets, shape: (num_targets)
      """
      predictions = predictions.detach().clone()
      predictions[:, 3] *= img_w - 1
      predictions[:, 6:] *= img_w - 1
  
      targets = targets.detach().clone()
      distances_score = distance_cost(predictions, targets, img_w)
      distances_score = (
          1 - distances_score / paddle.max(x=distances_score) + 0.01
      )
  
      cls_score = focal_cost(
          predictions[:, :2], targets[:, 1].cast("int64")
      )
  
      num_priors = predictions.shape[0]
      num_targets = targets.shape[0]
      target_start_xys = targets[:, 2:4]
      target_start_xys[..., 0] *= img_h - 1
      prediction_start_xys = predictions[:, 2:4]
      prediction_start_xys[..., 0] *= img_h - 1
      start_xys_score = cdist_paddle(
          prediction_start_xys, target_start_xys, p=2
      ).reshape([num_priors, num_targets])
  
      start_xys_score = (
          1 - start_xys_score / paddle.max(x=start_xys_score) + 0.01
      )
  
      target_thetas = targets[:, 4].unsqueeze(axis=-1)
      theta_score = (
          cdist_paddle(
              predictions[:, 4].unsqueeze(axis=-1), target_thetas, p=1
          ).reshape([num_priors, num_targets])
          * 180
      )
      theta_score = 1 - theta_score / paddle.max(x=theta_score) + 0.01
  
      cost = (
          -((distances_score * start_xys_score * theta_score) ** 2)
          * distance_cost_weight
          + cls_score * cls_cost_weight
      )
      iou = line_iou(
          predictions[..., 6:], targets[..., 6:], img_w, aligned=False
      )
  
      matched_row_inds, matched_col_inds = dynamic_k_assign(cost, iou)
      return matched_row_inds, matched_col_inds
  
imports: import paddle
  import paddle.nn.functional
  from ppdet.modeling.losses.clrnet_line_iou_loss import line_iou
label_desc: Use image node_id 5 for calling a global function with example usage: assign(predictions, targets, img_w, img_h, distance_cost_weight, cls_cost_weight) and returns: matched_row_inds,  matched_col_inds