class_docstr: Args:
      in_channels (int): the channel number of input to CenterNetHead.
      num_classes (int): the number of classes, 1 (MOT17 dataset) by default.
      head_planes (int): the channel number in all head, 256 by default.
      task (str): the type of task for regression, 'tracking' by default.
      loss_weight (dict): the weight of each loss.
      add_ltrb_amodal (bool): whether to add ltrb_amodal branch, False by default.
class_docstr_tok: ["Args",":","in_channels","(","int",")",":","the","channel","number","of","input","to","CenterNetHead",".","num_classes","(","int",")",":","the","number","of","classes",",","1","(","MOT17","dataset",")","by","default",".","head_planes","(","int",")",":","the","channel","number","in","all","head",",","256","by","default",".","task","(","str",")",":","the","type","of","task","for","regression",",","'tracking","'","by","default",".","loss_weight","(","dict",")",":","the","weight","of","each","loss",".","add_ltrb_amodal","(","bool",")",":","whether","to","add","ltrb_amodal","branch",",","False","by","default","."]
code: def get_loss(self, inputs, weights, head_outs):
      index = inputs["index"].unsqueeze(2)
      mask = inputs["index_mask"].unsqueeze(2)
      batch_inds = list()
      for i in range(head_outs["tracking"].shape[0]):
          batch_ind = paddle.full(
              shape=[1, index.shape[1], 1], fill_value=i, dtype="int64"
          )
          batch_inds.append(batch_ind)
      batch_inds = paddle.concat(batch_inds, axis=0)
      index = paddle.concat(x=[batch_inds, index], axis=2)
  
      # 1.tracking head loss: L1 loss
      tracking = head_outs["tracking"].transpose([0, 2, 3, 1])
      tracking_target = inputs["tracking"]
      bs, _, _, c = tracking.shape
      tracking = tracking.reshape([bs, -1, c])
      pos_tracking = paddle.gather_nd(tracking, index=index)
      tracking_mask = paddle.cast(
          paddle.expand_as(mask, pos_tracking), dtype=pos_tracking.dtype
      )
      pos_num = tracking_mask.sum()
      tracking_mask.stop_gradient = True
      tracking_target.stop_gradient = True
      tracking_loss = F.l1_loss(
          pos_tracking * tracking_mask,
          tracking_target * tracking_mask,
          reduction="sum",
      )
      tracking_loss = tracking_loss / (pos_num + 1e-4)
  
      # 2.ltrb_amodal head loss(optinal): L1 loss
      if self.add_ltrb_amodal and "ltrb_amodal" in self.loss_weight:
          ltrb_amodal = head_outs["ltrb_amodal"].transpose([0, 2, 3, 1])
          ltrb_amodal_target = inputs["ltrb_amodal"]
          bs, _, _, c = ltrb_amodal.shape
          ltrb_amodal = ltrb_amodal.reshape([bs, -1, c])
          pos_ltrb_amodal = paddle.gather_nd(ltrb_amodal, index=index)
          ltrb_amodal_mask = paddle.cast(
              paddle.expand_as(mask, pos_ltrb_amodal),
              dtype=pos_ltrb_amodal.dtype,
          )
          pos_num = ltrb_amodal_mask.sum()
          ltrb_amodal_mask.stop_gradient = True
          ltrb_amodal_target.stop_gradient = True
          ltrb_amodal_loss = F.l1_loss(
              pos_ltrb_amodal * ltrb_amodal_mask,
              ltrb_amodal_target * ltrb_amodal_mask,
              reduction="sum",
          )
          ltrb_amodal_loss = ltrb_amodal_loss / (pos_num + 1e-4)
  
      losses = {
          "tracking_loss": tracking_loss,
      }
      plugin_loss = weights["tracking"] * tracking_loss
  
      if self.add_ltrb_amodal and "ltrb_amodal" in self.loss_weight:
          losses.update({"ltrb_amodal_loss": ltrb_amodal_loss})
          plugin_loss += weights["ltrb_amodal"] * ltrb_amodal_loss
      losses.update({"plugin_loss": plugin_loss})
      return losses
  
file_path: PaddleDetection/ppdet/modeling/heads/centertrack_head.py
imports: import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register
  from .centernet_head import ConvLayer
  from ..keypoint_utils import get_affine_transform
label_desc: Use image node_id 4 for calling the CenterTrackHead obj's underlying member method code with example usage: obj.get_loss(inputs, weights, head_outs) and returns: losses