(args)
return rv
Return True (all True), False (any False) or None.

    Examples
    ========

    >>> from sympy.core.logic import fuzzy_and
    >>> from sympy import Dummy

    If you had a list of objects to test the commutivity of
    and you want the fuzzy_and logic applied, passing an
    iterator will allow the commutativity to only be computed
    as many times as necessary. With this list, False can be
    returned after analyzing the first symbol:

    >>> syms = [Dummy(commutative=False), Dummy()]
    >>> fuzzy_and(s.is_commutative for s in syms)
    False

    That False would require less work than if a list of pre-computed
    items was sent:

    >>> fuzzy_and([s.is_commutative for s in syms])
    False
Return True (all True), False (any False) or None.
Return True ( all True ) False ( any False ) or None .
def fuzzy_and(args):
    """Return True (all True), False (any False) or None.

    Examples
    ========

    >>> from sympy.core.logic import fuzzy_and
    >>> from sympy import Dummy

    If you had a list of objects to test the commutivity of
    and you want the fuzzy_and logic applied, passing an
    iterator will allow the commutativity to only be computed
    as many times as necessary. With this list, False can be
    returned after analyzing the first symbol:

    >>> syms = [Dummy(commutative=False), Dummy()]
    >>> fuzzy_and(s.is_commutative for s in syms)
    False

    That False would require less work than if a list of pre-computed
    items was sent:

    >>> fuzzy_and([s.is_commutative for s in syms])
    False
    """

    rv = True
    for ai in args:
        ai = fuzzy_bool(ai)
        if ai is False:
            return False
        if rv:  # this will stop updating if a None is ever trapped
            rv = ai
    return rv
def fuzzy_and ( args ) : rv = True for ai in args : ai = fuzzy_bool ( ai ) if ai is False : return False if rv : # this will stop updating if a None is ever trapped rv = ai return rv