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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, text, k=1, threshold=0.0, on_unicode_error='strict'):
 """ Given a string, get a list of labels and a list of corresponding probabilities. k controls the number of returned labels. A choice of 5, will return the 5 most probable labels. By default this returns only the most likely label and probability. threshold filters the returned labels by a threshold on probability. A choice of 0.5 will return labels with at least 0.5 probability. k and threshold will be applied together to determine the returned labels. This function assumes to be given a single line of text. We split words on whitespace (space, newline, tab, vertical tab) and the control characters carriage return, formfeed and the null character. If the model is not supervised, this function will throw a ValueError. If given a list of strings, it will return a list of results as usually received for a single line of text. """


        def check(entry):
            if entry.find('\n') != -1:
                raise ValueError(
                    "predict processes one line at a time (remove \'\\n\')"
                )
            entry += "\n"
            return entry

        if type(text) == list:
            text = [check(entry) for entry in text]
            predictions = self.f.multilinePredict(text, k, threshold, on_unicode_error)
            dt = np.dtype([('probability', 'float64'), ('label', 'object')])
            result_as_pair = np.array(predictions, dtype=dt)

            return result_as_pair['label'].tolist(), result_as_pair['probability']
        else:
            text = check(text)
            predictions = self.f.predict(text, k, threshold, on_unicode_error)
            probs, labels = zip(*predictions)

            return labels, np.array(probs, copy=False)