code: def compute_losses(self, net_out, data):
      # Check if 'gt_ids' and 'gt_masks' exist and convert the values to tensor
      if "gt_ids" not in data:
          raise ValueError(
              "Sample dict does not contain the key 'gt_ids'."
          )
      if "gt_masks" not in data:
          raise ValueError(
              "Sample dict does not contain the key 'gt_masks'."
          )
      nonzero_len_idcs = [
          i
          for i, gt_ids in enumerate(data["gt_ids"])
          if len(gt_ids) > 0
      ]
      nonzero_len_idcs = paddle.to_tensor(
          nonzero_len_idcs, dtype="int64"
      )
      # If there are no gt targets, return zero loss.
      if len(nonzero_len_idcs) == 0:
          return (data["logits"] - data["logits"]).mean()
      # XXX: Inplace modification
      data["gt_ids"] = [
          paddle.to_tensor(gt_ids, dtype="int64")
          for i, gt_ids in enumerate(data["gt_ids"])
          if i in nonzero_len_idcs
      ]
      data["gt_masks"] = [
          paddle.to_tensor(gt_masks, dtype="float32")
          for i, gt_masks in enumerate(data["gt_masks"])
          if i in nonzero_len_idcs
      ]
      if len(nonzero_len_idcs) < net_out["logits"].shape[0]:
          net_out["logits"] = paddle.index_select(
              net_out["logits"], nonzero_len_idcs, axis=0
          )
          net_out["masks"] = paddle.index_select(
              net_out["masks"], nonzero_len_idcs, axis=0
          )
  
      # Retrieve the matching between the outputs and the targets
      indices = self.matcher(data, net_out)
  
      # Compute the average number of target boxes across all nodes, for normalization purposes
      num_masks = sum(len(gt_ids) for gt_ids in data["gt_ids"])
      num_masks = paddle.to_tensor([num_masks], dtype="float32")
      n_ranks = paddle.distributed.get_world_size()
      if n_ranks > 1:
          paddle.distributed.all_reduce(num_masks)
      num_masks = int(paddle.clip(num_masks / n_ranks, min=1))
  
      l1 = self.loss_labels(data, net_out, indices, num_masks)
      l2 = self.loss_masks(data, net_out, indices, num_masks)
      loss = l1 + l2
  
      # Calculate auxiliary losses
      if "aux_masks" in net_out and "aux_logits" in net_out:
          for i, (aux_logit, aux_mask) in enumerate(
              zip(net_out["aux_logits"], net_out["aux_masks"])
          ):
              aux_dict = build_info_dict(
                  _type_="net_out", logits=aux_logit, masks=aux_mask
              )
              if len(nonzero_len_idcs) < aux_dict["logits"].shape[0]:
                  aux_dict["logits"] = paddle.index_select(
                      aux_dict["logits"], nonzero_len_idcs, axis=0
                  )
                  aux_dict["masks"] = paddle.index_select(
                      aux_dict["masks"], nonzero_len_idcs, axis=0
                  )
              indices = self.matcher(data, aux_dict)
              l1 = self.loss_labels(data, aux_dict, indices, num_masks)
              l2 = self.loss_masks(data, aux_dict, indices, num_masks)
              loss += l1 + l2
  
      return [loss]
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from scipy.optimize import linear_sum_assignment
  from paddlepanseg.cvlibs import manager, build_info_dict
  from paddlepanseg.core.runner import PanSegRunner
label_desc: Use image node_id 6 for calling the MaskFormerRunner obj's underlying member method code with example usage: obj.compute_losses(net_out, data) and returns: list, unknown