def test_str(self):
arg_spec = function_utils.SimpleArgSpec(
args=[], varargs=[], keywords=[], defaults=[])
self.assertEqual('()', str(arg_spec))
arg_spec = function_utils.SimpleArgSpec(
args=[1, 2, 3], varargs=[], keywords=[], defaults=[])
self.assertEqual('(args=[1, 2, 3])', str(arg_spec))
arg_spec = function_utils.SimpleArgSpec(
args=[1], varargs=[2., True], keywords={'a': 'b'}, defaults={'x': 3})
self.assertEqual(
"(args=[1], varargs=[2.0, True], kwargs={'a': 'b'}, defaults={'x': 3})",
str(arg_spec))
def test_get_defun_argspec_with_untyped_non_eager_defun(self):
# In a non-eager function with no input signature, the same restrictions as
# in a typed eager function apply.
fn = tf.function(lambda x, y, *z: None)
self.assertEqual(
function_utils.get_argspec(fn),
function_utils.SimpleArgSpec(
args=['x', 'y'], varargs='z', keywords=None, defaults=None))
def test_get_defun_argspec_with_typed_non_eager_defun(self):
# In a non-eager function with a defined input signature, **kwargs or
# default values are not allowed, but *args are, and the input signature may
# overlap with *args.
fn = tf.function(lambda x, y, *z: None, (
tf.TensorSpec(None, tf.int32),
tf.TensorSpec(None, tf.bool),
tf.TensorSpec(None, tf.float32),
tf.TensorSpec(None, tf.float32),
))
self.assertEqual(
function_utils.get_argspec(fn),
function_utils.SimpleArgSpec(
args=['x', 'y'], varargs='z', keywords=None, defaults=None))