class_docstr: This class computes the loss for SparseRCNN.
  The process happens in two steps:
      1) we compute hungarian assignment between ground truth boxes and the outputs of the model
      2) we supervise each pair of matched ground-truth / prediction (supervise class and box)
class_docstr_tok: ["This","class","computes","the","loss","for","SparseRCNN",".","The","process","happens","in","two","steps",":","1",")","we","compute","hungarian","assignment","between","ground","truth","boxes","and","the","outputs","of","the","model","2",")","we","supervise","each","pair","of","matched","ground-truth","\/","prediction","(","supervise","class","and","box",")"]
code: def _get_tgt_permutation_idx(self, indices):
      # permute targets following indices
      batch_idx = paddle.concat(
          [
              paddle.full_like(tgt, i)
              for i, (_, tgt) in enumerate(indices)
          ]
      )
      tgt_idx = paddle.concat([tgt for (_, tgt) in indices])
      return batch_idx, tgt_idx
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from scipy.optimize import linear_sum_assignment
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle.metric import accuracy
  from ppdet.core.workspace import register
  from ppdet.modeling.losses.iou_loss import GIoULoss
label_desc: Use image node_id 5 for calling the SparseRCNNLoss obj's underlying member method code with example usage: obj._get_tgt_permutation_idx(indices) and returns: batch_idx,  tgt_idx