code: def predict(self, X):
          
          if not self.fitted_:
              raise ValueError("ELMRegressor not fitted")
  
          # compute hidden layer activations
          self.hidden_activations_ = self.hidden_layer.transform(X)
  
          # compute output predictions for new hidden activations
          predictions = self._get_predictions()
  
          return predictions
docs: Predict values using the model
  
          Parameters
          ----------
          X : {array-like, sparse matrix} of shape [n_samples, n_features]
  
          Returns
          -------
          C : numpy array of shape [n_samples, n_outputs]
              Predicted values.