code: def fit(self, X, y, sample_weight=None):
          
          random_state = check_random_state(self.random_state)
  
          x_pairs, y_pairs = self._get_survival_pairs(X, y, random_state)
  
          self.C = self.alpha
          return super().fit(x_pairs, y_pairs, sample_weight=sample_weight)
docs: Build a survival support vector machine model from training data.
  
          Parameters
          ----------
          X : array-like, shape = (n_samples, n_features)
              Data matrix.
  
          y : structured array, shape = (n_samples,)
              A structured array containing the binary event indicator
              as first field, and time of event or time of censoring as
              second field.
  
          sample_weight : array-like, shape = (n_samples,), optional
              Array of weights that are assigned to individual
              samples. If not provided,
              then each sample is given unit weight.
  
          Returns
          -------
          self