code: def fit(self, X, y):
          
          # fit random hidden layer and compute the hidden layer activations
          self.hidden_activations_ = self.hidden_layer.fit_transform(X)
  
          # solve the regression from hidden activations to outputs
          self._fit_regression(as_float_array(y, copy=True))
  
          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.