code: def fit(self, X, C):
          
          X, C = _check_fit_input(X, C)
          C = np.asfortranarray(C)
          self.nclasses = C.shape[1]
          self.regressors = [deepcopy(self.base_regressor) for i in range(self.nclasses)]
          Parallel(n_jobs=self.njobs, verbose=0, require="sharedmem")(delayed(self._fit)(c, X, C) for c in range(self.nclasses))
          return self
docs: Fit one regressor per class
          
          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).