code: def fit(self, X, y):
          
          self.classes_ = np.unique(y)
  
          y_bin = self.binarizer.fit_transform(y)
  
          super(ELMClassifier, self).fit(X, y_bin)
  
          return self
docs: Fit the model using X, y as training data.
  
          Parameters
          ----------
          X : {array-like, sparse matrix} of shape [n_samples, n_features]
              Training vectors, where n_samples is the number of samples
              and n_features is the number of features.
  
          y : array-like of shape [n_samples, n_outputs]
              Target values (class labels in classification, real numbers in
              regression)
  
          Returns
          -------
          self : object
  
              Returns an instance of self.