class_docstr: The Maskformer loss implemeted with PaddlePaddle.
  
  Args:
      num_classes(int): The number of classes that you want this network to classify. Default:150.
      eos_coef(float): The weight coefficient of the last class. Default: 0.1.
      losses(Tuple): The category of losses that you want to compute. Default: ("labels", 'masks').
      ignore_index(int): The ignored label when we calculate the loss. Default:255.
class_docstr_tok: ["The","Maskformer","loss","implemeted","with","PaddlePaddle",".","Args",":","num_classes","(","int",")",":","The","number","of","classes","that","you","want","this","network","to","classify",".","Default:150.","eos_coef","(","float",")",":","The","weight","coefficient","of","the","last","class",".","Default",":","0.1.","losses","(","Tuple",")",":","The","category","of","losses","that","you","want","to","compute",".","Default",":","(","``","labels","''",",","'masks","'",")",".","ignore_index","(","int",")",":","The","ignored","label","when","we","calculate","the","loss",".","Default:255","."]
code: def _get_src_permutation_idx(self, indices):
      batch_idx = paddle.concat(
          [
              paddle.full_like(src, i)
              for i, (src, _) in enumerate(indices)
          ]
      )
      src_idx = paddle.concat([src for (src, _) in indices])
  
      return batch_idx, src_idx
  
imports: import copy
  import numpy
  from scipy.optimize import linear_sum_assignment
  import paddle
  import paddle.nn
  import paddle.distributed
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
label_desc: Use image node_id 4 for calling the MaskFormerLoss obj's underlying member method code with example usage: obj._get_src_permutation_idx(indices) and returns: batch_idx,  src_idx