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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 isin(self, values, level=None):
 """ Return a boolean array where the index values are in `values`. Compute boolean array of whether each index value is found in the passed set of values. The length of the returned boolean array matches the length of the index. Parameters values : set or list-like Sought values. .. versionadded:: 0.18.1 Support for values as a set. level : str or int, optional Name or position of the index level to use (if the index is a `MultiIndex`). Returns ------- is_contained : ndarray NumPy array of boolean values. See Also -------- Series.isin : Same for Series. DataFrame.isin : Same method for DataFrames. Notes ----- In the case of `MultiIndex` you must either specify `values` as a list-like object containing tuples that are the same length as the number of levels, or specify `level`. Otherwise it will raise a ``ValueError``. If `level` is specified: - if it is the name of one *and only one* index level, use that level; - otherwise it should be a number indicating level position. Examples -------- Int64Index([1, 2, 3], dtype='int64') Check whether each index value in a list of values. array([ True, False, False]) MultiIndex(levels=[[1, 2, 3], ['blue', 'green', 'red']], codes=[[0, 1, 2], [2, 0, 1]], names=['number', 'color']) Check whether the strings in the 'color' level of the MultiIndex are in a list of colors. array([ True, False, False]) To check across the levels of a MultiIndex, pass a list of tuples: array([ True, False, False]) For a DatetimeIndex, string values in `values` are converted to Timestamps. DatetimeIndex(['2000-03-11', '2000-03-12', '2000-03-13'], dtype='datetime64[ns]', freq=None) array([ True, False, False]) """

        if level is not None:
            self._validate_index_level(level)
        return algos.isin(self, values)