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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 evaluate(self, verbose=False, decode=True, passes=None, num_threads=1, apply_experimental=True):
 """Evaluates by creating a Series containing evaluated data and index. See `LazyResult` Returns ------- Series Series with evaluated data and index. Examples -------- array([False, True, True]) """

        # TODO: work on masking (branch masking) ~ evaluate the mask first and use on both index and data;
        # TODO right now the filter gets computed twice, once for index and once for the data
        evaluated_data = super(Series, self).evaluate(verbose, decode, passes, num_threads, apply_experimental)
        evaluated_index = self.index.evaluate(verbose, decode, passes, num_threads, apply_experimental)

        return Series(evaluated_data, evaluated_index, self.dtype, self.name)