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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 predict(self, X):
 """Returns the predictions for ``X``. Under the hood this method simply goes through the outputs of ``iter_predict`` and returns the final one. Arguments: X (array-like or sparse matrix of shape (n_samples, n_features)):
 The input samples. Sparse matrices are accepted only if they are supported by the weak model. Returns: array of shape (n_samples,) containing the predicted values. """

        return collections.deque(self.iter_predict(X), maxlen=1).pop()