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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def staged_predict(self, X):
 """Predict hazard at each stage for X. This method allows monitoring (i.e. determine error on testing set) after each stage. Parameters X : array-like, shape = (n_samples, n_features) The input samples. Returns ------- y : generator of array of shape = (n_samples,) The predicted value of the input samples. """

        check_is_fitted(self, 'estimators_')

        # if dropout wasn't used during training, proceed as usual,
        # otherwise consider scaling factor of individual trees
        if not hasattr(self, "scale_"):
            for y in self._staged_decision_function(X):
                yield self._scale_prediction(y.ravel())
        else:
            for y in self._dropout_staged_decision_function(X):
                yield self._scale_prediction(y.ravel())