def all(a, axis=None, out=None, keepdims=False):
arr = asanyarray(a)<EOL>try:<EOL><INDENT>return arr.all(axis=axis, out=out, keepdims=keepdims)<EOL><DEDENT>except TypeError:<EOL><INDENT>return arr.all(axis=axis, out=out)<EOL><DEDENT>
Test whether all array elements along a given axis evaluate to True.

Parameters
----------
a : array_like
    Input array or object that can be converted to an array.
axis : None or int or tuple of ints, optional
    Axis or axes along which a logical AND reduction is performed.
    The default (`axis` = `None`) is to perform a logical AND over all
    the dimensions of the input array. `axis` may be negative, in
    which case it counts from the last to the first axis.

    .. versionadded:: 1.7.0

    If this is a tuple of ints, a reduction is performed on multiple
    axes, instead of a single axis or all the axes as before.
out : ndarray, optional
    Alternate output array in which to place the result.
    It must have the same shape as the expected output and its
    type is preserved (e.g., if ``dtype(out)`` is float, the result
    will consist of 0.0's and 1.0's).  See `doc.ufuncs` (Section
    "Output arguments") for more details.
keepdims : bool, optional
    If this is set to True, the axes which are reduced are left
    in the result as dimensions with size one. With this option,
    the result will broadcast correctly against the original `arr`.

Returns
-------
all : ndarray, bool
    A new boolean or array is returned unless `out` is specified,
    in which case a reference to `out` is returned.

See Also
--------
ndarray.all : equivalent method

any : Test whether any element along a given axis evaluates to True.

Notes
-----
Not a Number (NaN), positive infinity and negative infinity
evaluate to `True` because these are not equal to zero.

Examples
--------
>>> np.all([[True,False],[True,True]])
False

>>> np.all([[True,False],[True,True]], axis=0)
array([ True, False], dtype=bool)

>>> np.all([-1, 4, 5])
True

>>> np.all([1.0, np.nan])
True

>>> o=np.array([False])
>>> z=np.all([-1, 4, 5], out=o)
>>> id(z), id(o), z                             # doctest: +SKIP
(28293632, 28293632, array([ True], dtype=bool))