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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 memory_usage(self, index=True, deep=False):
 """ Return the memory usage of the Series. The memory usage can optionally include the contribution of the index and of elements of `object` dtype. Parameters index : bool, default True Specifies whether to include the memory usage of the Series index. deep : bool, default False If True, introspect the data deeply by interrogating `object` dtypes for system-level memory consumption, and include it in the returned value. Returns ------- int Bytes of memory consumed. See Also -------- numpy.ndarray.nbytes : Total bytes consumed by the elements of the array. DataFrame.memory_usage : Bytes consumed by a DataFrame. Examples -------- 104 Not including the index gives the size of the rest of the data, which is necessarily smaller: 24 The memory footprint of `object` values is ignored by default: array(['a', 'b'], dtype=object) 96 212 """

        v = super().memory_usage(deep=deep)
        if index:
            v += self.index.memory_usage(deep=deep)
        return v