code: def fit(self, X, C):
          
          X, C = _check_fit_input(X, C)
          self.nclasses = C.shape[1]
          ncombs = int( self.nclasses * (self.nclasses - 1) / 2 )
          self.classifiers = [ deepcopy(self.base_classifier) for c in range(ncombs) ]
          self.classes_compared = [None for i in range(ncombs)]
          if self.weigh_by_cost_diff:
              V = C
          else:
              V = self._calculate_v(C)
  
          V = np.asfortranarray(V)
          Parallel(n_jobs=self.njobs, verbose=0, require="sharedmem")\
              (  delayed(self._fit)(i, j, V, X) for i in range(self.nclasses - 1) for j in range(i + 1, self.nclasses) )
          self.classes_compared = np.array(self.classes_compared)
          return self
docs: Fit one classifier comparing each pair of classes
          
          Parameters
          ----------
          X : array (n_samples, n_features)
              The data on which to fit a cost-sensitive classifier.
          C : array (n_samples, n_classes)
              The cost of predicting each label for each observation (more means worse).