code: def distance_cost(predictions, targets, img_w):
      """
      repeat predictions and targets to generate all combinations
      use the abs distance as the new distance cost
      """
      num_priors = predictions.shape[0]
      num_targets = targets.shape[0]
      predictions = paddle.repeat_interleave(
          predictions, num_targets, axis=0
      )[..., 6:]
      targets = paddle.concat(x=num_priors * [targets])[..., 6:]
      invalid_masks = (targets < 0) | (targets >= img_w)
      lengths = (~invalid_masks).sum(axis=1)
      distances = paddle.abs(x=targets - predictions)
      distances[invalid_masks] = 0.0
      distances = distances.sum(axis=1) / (
          lengths.cast("float32") + 1e-09
      )
      distances = distances.reshape([num_priors, num_targets])
      return distances
  
imports: import paddle
  import paddle.nn.functional
  from ppdet.modeling.losses.clrnet_line_iou_loss import line_iou
label_desc: Use image node_id 1 for calling a global function with example usage: distance_cost(predictions, targets, img_w) and returns: distances