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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 match(to_match, values, na_sentinel=-1):
 """ Compute locations of to_match into values Parameters to_match : array-like values to find positions of values : array-like Unique set of values na_sentinel : int, default -1 Value to mark "not found" Examples -------- Returns ------- match : ndarray of integers """

    values = com.asarray_tuplesafe(values)
    htable, _, values, dtype, ndtype = _get_hashtable_algo(values)
    to_match, _, _ = _ensure_data(to_match, dtype)
    table = htable(min(len(to_match), 1000000))
    table.map_locations(values)
    result = table.lookup(to_match)

    if na_sentinel != -1:

        # replace but return a numpy array
        # use a Series because it handles dtype conversions properly
        from pandas import Series
        result = Series(result.ravel()).replace(-1, na_sentinel)
        result = result.values.reshape(result.shape)

    return result