code: def forward(
      self,
      distances,
      gt_instances,
      gt_kernel_instances,
      training_masks,
      gt_distances,
      reduce=True,
  ):
      selected_training_masks = []
      for i in range(distances.shape[0]):
          selected_training_masks.append(
              self.select_single(
                  distances[i, :, :, :],
                  gt_instances[i, :, :],
                  gt_kernel_instances[i, :, :],
                  training_masks[i, :, :],
              )
          )
      selected_training_masks = paddle.cast(
          paddle.concat(selected_training_masks, 0), "float32"
      )
  
      loss = self.forward_single(
          distances, gt_distances, selected_training_masks, self.beta
      )
      loss = self.loss_weight * loss
  
      with paddle.no_grad():
          batch_size = distances.shape[0]
          false_num = selected_training_masks.reshape((batch_size, -1))
          false_num = false_num.sum(axis=-1)
          total_num = paddle.cast(
              training_masks.reshape((batch_size, -1)), "float32"
          )
          total_num = total_num.sum(axis=-1)
          iou_text = (total_num - false_num) / (total_num + 1e-6)
  
      if reduce:
          loss = paddle.mean(loss)
  
      return loss, iou_text
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  from paddle import nn
  import paddle.nn.functional
  import numpy
label_desc: Use image node_id 4 for calling the SmoothL1Loss obj's underlying member method code with example usage: obj.forward(distances, gt_instances, gt_kernel_instances, training_masks, gt_distances, reduce) and returns: loss,  iou_text