code: def forward(self, inputs, targets, anchors):
      np = len(inputs)
      gt_targets = [targets["target{}".format(i)] for i in range(np)]
      gt_box = targets["gt_bbox"]
      yolo_losses = dict()
      self.distill_pairs.clear()
      for x, t, anchor, downsample in zip(
          inputs, gt_targets, anchors, self.downsample
      ):
          yolo_loss = self.yolov3_loss(
              x.astype("float32"),
              t,
              gt_box,
              anchor,
              downsample,
              self.scale_x_y,
          )
          for k, v in yolo_loss.items():
              if k in yolo_losses:
                  yolo_losses[k] += v
              else:
                  yolo_losses[k] = v
  
      loss = 0
      for k, v in yolo_losses.items():
          loss += v
  
      yolo_losses["loss"] = loss
      return yolo_losses
  
file_path: PaddleDetection/ppdet/modeling/losses/yolo_loss.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register
  from ..bbox_utils import decode_yolo, xywh2xyxy, batch_iou_similarity
label_desc: Use image node_id 5 for calling the YOLOv3Loss obj's underlying member method code with example usage: obj.forward(inputs, targets, anchors) and returns: yolo_losses