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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 drop_duplicates(self, keep='first', inplace=False):
 """ Return Series with duplicate values removed. Parameters keep : {'first', 'last', ``False``}, default 'first' - 'first' : Drop duplicates except for the first occurrence. - 'last' : Drop duplicates except for the last occurrence. - ``False`` : Drop all duplicates. inplace : bool, default ``False`` If ``True``, performs operation inplace and returns None. Returns ------- Series Series with duplicates dropped. See Also -------- Index.drop_duplicates : Equivalent method on Index. DataFrame.drop_duplicates : Equivalent method on DataFrame. Series.duplicated : Related method on Series, indicating duplicate Series values. Examples -------- Generate an Series with duplicated entries. 0 lama 1 cow 2 lama 3 beetle 4 lama 5 hippo Name: animal, dtype: object With the 'keep' parameter, the selection behaviour of duplicated values can be changed. The value 'first' keeps the first occurrence for each set of duplicated entries. The default value of keep is 'first'. 0 lama 1 cow 3 beetle 5 hippo Name: animal, dtype: object The value 'last' for parameter 'keep' keeps the last occurrence for each set of duplicated entries. 1 cow 3 beetle 4 lama 5 hippo Name: animal, dtype: object The value ``False`` for parameter 'keep' discards all sets of duplicated entries. Setting the value of 'inplace' to ``True`` performs the operation inplace and returns ``None``. 1 cow 3 beetle 5 hippo Name: animal, dtype: object """

        return super().drop_duplicates(keep=keep, inplace=inplace)