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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 transform(self, X):
 """Performs predictions blending using the trained weights. Args: X (array-like):
 Predictions of different models. Returns: dict with blended predictions (key is 'y_pred'). """

        assert np.shape(X)[0] == len(self._weights), (
            'BlendingOptimizer: Number of models to blend its predictions and weights does not match: '
            'n_models={}, weights_len={}'.format(np.shape(X)[0], len(self._weights)))
        blended_predictions = np.average(np.power(X, self._power),
                                         weights=self._weights,
                                         axis=0) ** (1.0 / self._power)

        return {'y_pred': blended_predictions}