class_docstr: This class computes an assignment between the targets and the predictions of the network
  
  For efficiency reasons, the targets don't include the no_object. Because of this, in general,
  there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions,
  while the others are un-matched (and thus treated as non-objects).
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code: def forward(self, sample_dict, net_out_dict):
      return self.memory_efficient_forward(sample_dict, net_out_dict)
  
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 3 for calling the HungarianMatcher obj's underlying member method code with example usage: obj.forward(sample_dict, net_out_dict) and returns: self