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                name = Column(String)

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            )

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    a :class:`.GenericFunction` class is so that a function
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    as additional methods.

    Subclasses of :class:`.GenericFunction` are automatically
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        from sqlalchemy.sql.functions import GenericFunction
        from sqlalchemy.types import DateTime

        class as_utc(GenericFunction):
            type = DateTime

        print select([func.as_utc()])

    User-defined generic functions can be organized into
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        class as_utc(GenericFunction):
            type = DateTime
            package = "time"

    The above function would be available from :data:`.func`
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    A final option is to allow the function to be accessed
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    The ``identifier`` attribute will override the name used to
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        class GeoBuffer(GenericFunction):
            type = Geometry
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    The return type of this function is the same as the sort expression,
    or if the arguments are an array, an :class:`.types.ARRAY` of the sort
    expression's type.

    .. versionadded:: 1.1

    TN)rlrmrnror�rrrrr��sr�c@seZdZdZdZdS)�percentile_disca�implement the ``percentile_disc`` ordered-set aggregate function.

    This function must be used with the :meth:`.FunctionElement.within_group`
    modifier to supply a sort expression to operate upon.

    The return type of this function is the same as the sort expression,
    or if the arguments are an array, an :class:`.types.ARRAY` of the sort
    expression's type.

    .. versionadded:: 1.1

    TN)rlrmrnror�rrrrr��sr�c@seZdZdZe��ZdS)�ranka!Implement the ``rank`` hypothetical-set aggregate function.

    This function must be used with the :meth:`.FunctionElement.within_group`
    modifier to supply a sort expression to operate upon.

    The return type of this function is :class:`.Integer`.

    .. versionadded:: 1.1

    N)rlrmrnrorr�rcrrrrr��s
r�c@seZdZdZe��ZdS)�
dense_ranka'Implement the ``dense_rank`` hypothetical-set aggregate function.

    This function must be used with the :meth:`.FunctionElement.within_group`
    modifier to supply a sort expression to operate upon.

    The return type of this function is :class:`.Integer`.

    .. versionadded:: 1.1

    N)rlrmrnrorr�rcrrrrr��s
r�c@seZdZdZe��ZdS)�percent_ranka)Implement the ``percent_rank`` hypothetical-set aggregate function.

    This function must be used with the :meth:`.FunctionElement.within_group`
    modifier to supply a sort expression to operate upon.

    The return type of this function is :class:`.Numeric`.

    .. versionadded:: 1.1

    N)rlrmrnror�Numericrcrrrrr��s
r�c@seZdZdZe��ZdS)�	cume_dista&Implement the ``cume_dist`` hypothetical-set aggregate function.

    This function must be used with the :meth:`.FunctionElement.within_group`
    modifier to supply a sort expression to operate upon.

    The return type of this function is :class:`.Numeric`.

    .. versionadded:: 1.1

    N)rlrmrnrorr�rcrrrrr�s
r�c@seZdZdZdZdS)�cubeaNImplement the ``CUBE`` grouping operation.

    This function is used as part of the GROUP BY of a statement,
    e.g. :meth:`.Select.group_by`::

        stmt = select(
            [func.sum(table.c.value), table.c.col_1, table.c.col_2]
            ).group_by(func.cube(table.c.col_1, table.c.col_2))

    .. versionadded:: 1.2

    TN)rlrmrnror;rrrrr�sr�c@seZdZdZdZdS)�rollupaNImplement the ``ROLLUP`` grouping operation.

    This function is used as part of the GROUP BY of a statement,
    e.g. :meth:`.Select.group_by`::

        stmt = select(
            [func.sum(table.c.value), table.c.col_1, table.c.col_2]
        ).group_by(func.rollup(table.c.col_1, table.c.col_2))

    .. versionadded:: 1.2

    TN)rlrmrnror;rrrrr� sr�c@seZdZdZdZdS)�
grouping_setsa%Implement the ``GROUPING SETS`` grouping operation.

    This function is used as part of the GROUP BY of a statement,
    e.g. :meth:`.Select.group_by`::

        stmt = select(
            [func.sum(table.c.value), table.c.col_1, table.c.col_2]
        ).group_by(func.grouping_sets(table.c.col_1, table.c.col_2))

    In order to group by multiple sets, use the :func:`.tuple_` construct::

        from sqlalchemy import tuple_

        stmt = select(
            [
                func.sum(table.c.value),
                table.c.col_1, table.c.col_2,
                table.c.col_3]
        ).group_by(
            func.grouping_sets(
                tuple_(table.c.col_1, table.c.col_2),
                tuple_(table.c.value, table.c.col_3),
            )
        )


    .. versionadded:: 1.2

    TN)rlrmrnror;rrrrr�0sr�N)r$)Rro�rrrrrr��baserr�elementsr	r
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selectablerrrZvisitorsrrrr&r'�symbolr)r3r4rP�objectryr��modifierr�r��with_metaclassr�r�r�r�r�r�r�r�r�r�r�r�r�r�r�r�r�r�r�r�r�r�r�r�r�r�r�r�r�r�r�r�r�r�rrrr�<module>
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