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        This would create a :class:`.BinaryExpression` that is equivalent to::

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            class Venue(Base):
                __tablename__ = 'venue'
                id = Column(Integer, primary_key=True)
                name = Column(String)

                descendants = relationship(
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                    viewonly=True,
                    order_by=name
                )

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        functions to create join conditions.

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        >>> print(select([func.count(table.c.id)]))
        SELECT count(sometable.id) FROM sometable

    Any name can be given to :data:`.func`. If the function name is unknown to
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        >>> print(func.current_timestamp())
        CURRENT_TIMESTAMP

    To call functions which are present in dot-separated packages,
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        >>> print(func.stats.yield_curve(5, 10))
        stats.yield_curve(:yield_curve_1, :yield_curve_2)

    SQLAlchemy can be made aware of the return type of functions to enable
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        >>> print(func.my_string(u'hi', type_=Unicode) + ' ' +
        ...       func.my_string(u'there', type_=Unicode))
        my_string(:my_string_1) || :my_string_2 || my_string(:my_string_3)

    The object returned by a :data:`.func` call is usually an instance of
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    Functions which are interpreted as "generic" functions know how to
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    .. note::

        The :data:`.func` construct has only limited support for calling
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        parameterization concerns.

        See the section :ref:`stored_procedures` for details on how to use
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        func.mypackage.some_function(col1, col2)


    .. seealso::

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    by name from the :data:`.func` attribute.    Note that
    calling any name from :data:`.func` has the effect that
    a new :class:`.Function` instance is created automatically,
    given that name.  The primary use case for defining
    a :class:`.GenericFunction` class is so that a function
    of a particular name may be given a fixed return type.
    It can also include custom argument parsing schemes as well
    as additional methods.

    Subclasses of :class:`.GenericFunction` are automatically
    registered under the name of the class.  For
    example, a user-defined function ``as_utc()`` would
    be available immediately::

        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
    packages by specifying the "package" attribute when defining
    :class:`.GenericFunction`.   Third party libraries
    containing many functions may want to use this in order
    to avoid name conflicts with other systems.   For example,
    if our ``as_utc()`` function were part of a package
    "time"::

        class as_utc(GenericFunction):
            type = DateTime
            package = "time"

    The above function would be available from :data:`.func`
    using the package name ``time``::

        print(select([func.time.as_utc()]))

    A final option is to allow the function to be accessed
    from one name in :data:`.func` but to render as a different name.
    The ``identifier`` attribute will override the name used to
    access the function as loaded from :data:`.func`, but will retain
    the usage of ``name`` as the rendered name::

        class GeoBuffer(GenericFunction):
            type = Geometry
            package = "geo"
            name = "ST_Buffer"
            identifier = "buffer"

    The above function will render as follows::

        >>> print(func.geo.buffer())
        ST_Buffer()

    The name will be rendered as is, however without quoting unless the name
    contains special characters that require quoting.  To force quoting
    on or off for the name, use the :class:`.sqlalchemy.sql.quoted_name`
    construct::

        from sqlalchemy.sql import quoted_name

        class GeoBuffer(GenericFunction):
            type = Geometry
            package = "geo"
            name = quoted_name("ST_Buffer", True)
            identifier = "buffer"

    The above function will render as::

        >>> print(func.geo.buffer())
        "ST_Buffer"()

    .. versionadded:: 1.3.13  The :class:`.quoted_name` construct is now
       recognized for quoting when used with the "name" attribute of the
       object, so that quoting can be forced on or off for the function
       name.


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        from sqlalchemy import func
        from sqlalchemy import select
        from sqlalchemy import table, column

        my_table = table('some_table', column('id'))

        stmt = select([func.count()]).select_from(my_table)

    Executing ``stmt`` would emit::

        SELECT count(*) AS count_1
        FROM some_table


    Nc�l��|�td��}tt|��j|fi|��dS)N�*)rr�rrV)rI�
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    .. versionadded:: 1.1

    .. seealso::

        :func:`_postgresql.array_agg` - PostgreSQL-specific version that
        returns :class:`_postgresql.ARRAY`, which has PG-specific operators
        added.

    c�,��d�|D��}|�dtj��}d|vr=t|��}t	|tj��r||d<n||��|d<||d<tt|��j|i|��dS)Nc�,�g|]}t|����Sr%r)rGrHs  r'rJz&array_agg.__init__.<locals>.<listcomp>�s!��3�3�3��!�!�$�$�3�3�3r&�_default_array_typer�r�)r�rr�r
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    N)r�r�r�r�r%r&r'rGrG�s������	�	�	�	r&rGc��eZdZdZdZdS)�percentile_conta�Implement the ``percentile_cont`` ordered-set aggregate function.

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    modifier to supply a sort expression to operate upon.

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    or if the arguments are an array, an :class:`_types.ARRAY` of the sort
    expression's type.

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    TN�r�r�r�r�rDr%r&r'rIrI��"��������"���r&rIc��eZdZdZdZdS)�percentile_disca�Implement the ``percentile_disc`` ordered-set aggregate function.

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    modifier to supply a sort expression to operate upon.

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    or if the arguments are an array, an :class:`_types.ARRAY` of the sort
    expression's type.

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    TNrJr%r&r'rMrMrKr&rMc�0�eZdZdZej��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�r�r�r�r�rr�r�r%r&r'rOrO�*������	�	��8����D�D�Dr&rOc�0�eZdZdZej��ZdS)�
dense_ranka'Implement the ``dense_rank`` hypothetical-set aggregate function.

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    modifier to supply a sort expression to operate upon.

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

    .. versionadded:: 1.1

    NrPr%r&r'rSrS$rQr&rSc�0�eZdZdZej��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�r�r�r�r�r�Numericr�r%r&r'rUrU3rQr&rUc�0�eZdZdZej��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

    NrVr%r&r'rYrYBrQr&rYc��eZdZdZdZdS)�cubeaYImplement the ``CUBE`` grouping operation.

    This function is used as part of the GROUP BY of a statement,
    e.g. :meth:`_expression.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�r�r�r�r�rNr%r&r'r[r[Q����������I�I�Ir&r[c��eZdZdZdZdS)�rollupaYImplement the ``ROLLUP`` grouping operation.

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

    TNr\r%r&r'r_r_ar]r&r_c��eZdZdZdZdS)�
grouping_setsa0Implement the ``GROUPING SETS`` grouping operation.

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

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