class_docstr: A distance between neural networks based on solving a transportation problem. 
docstr: Returns the layer cost matrix from the graphs and label_mismatch_penalty 
docstr_tok: ["Returns","the","layer","cost","matrix","from","the","graphs","and","label_mismatch_penalty"]
code: def get_mislabel_cost_matrix(self, x1, x2):
      """Returns the layer cost matrix from the graphs and label_mismatch_penalty"""
      label_idxs_1 = [
          self.all_layer_labels.index(elem) for elem in x1.layer_labels
      ]
      label_idxs_2 = [
          self.all_layer_labels.index(elem) for elem in x2.layer_labels
      ]
      return self.label_mismatch_penalty[
          np.ix_(label_idxs_1, label_idxs_2)
      ]
  
imports: import numpy
  from .None import neural_network
  from ..gp.kernel import ExpSumOfDistsKernel, SumOfExpSumOfDistsKernel
  from ..utils.oper_utils import opt_transport
label_desc: Use image node_id 3 for calling the OTMANNDistanceComputer obj's underlying member method code with example usage: obj.get_mislabel_cost_matrix(x1, x2) and returns: self