code: def _df_loss(pred_dist, target):
      target_left = paddle.cast(target, "int64")
      target_right = target_left + 1
      weight_left = target_right.astype("float32") - target
      weight_right = 1 - weight_left
      loss_left = (
          F.cross_entropy(pred_dist, target_left, reduction="none")
          * weight_left
      )
      loss_right = (
          F.cross_entropy(pred_dist, target_right, reduction="none")
          * weight_right
      )
      return (loss_left + loss_right).mean(-1, keepdim=True)
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register
  from ..losses import ProbIoULoss
  from ..initializer import bias_init_with_prob, constant_, normal_, vector_
  from ppdet.modeling.backbones.cspresnet import ConvBNLayer
  from ppdet.modeling.ops import get_static_shape, get_act_fn, anchor_generator
  from ppdet.modeling.layers import MultiClassNMS
label_desc: Use image node_id 12 for calling the PPYOLOERHead obj's underlying member method code with example usage: obj._df_loss(pred_dist, target) and returns: unknown