class_docstr: Multi-class Lovasz-Softmax loss.
  
  Args:
      ignore_index (int64): Specifies a target value that is ignored and does not contribute to the input gradient. Default ``255``.
      classes (str|list): 'all' for all, 'present' for classes present in labels, or a list of classes to average.
class_docstr_tok: ["Multi-class","Lovasz-Softmax","loss",".","Args",":","ignore_index","(","int64",")",":","Specifies","a","target","value","that","is","ignored","and","does","not","contribute","to","the","input","gradient",".","Default","``","255","``",".","classes","(","str|list",")",":","'all","'","for","all",",","'present","'","for","classes","present","in","labels",",","or","a","list","of","classes","to","average","."]
code: def __init__(self, ignore_index=255, classes="present"):
      super(LovaszSoftmaxLoss, self).__init__()
      self.ignore_index = ignore_index
      self.classes = classes
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import paddle
  from paddle import nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
label_desc: Use image node_id 1 to create a new LovaszSoftmaxLoss object from inherited base classes: nn with example: obj = LovaszSoftmaxLoss(ignore_index, classes)