def test_ones_like_explicit_dtype(self, t):
"""Test that the ones like function creates the correct
shape and type tensor."""
res = fn.ones_like(t, dtype=np.float16)
if isinstance(t, (list, tuple)):
t = onp.asarray(t)
assert res.shape == t.shape
assert fn.get_interface(res) == fn.get_interface(t)
assert fn.allclose(res, np.ones(t.shape))
# if tensorflow or pytorch, extract view of underlying data
if hasattr(res, "numpy"):
res = res.numpy()
t = t.numpy()
assert onp.asarray(res).dtype.type is np.float16
def test_where(t):
"""Test that the where function works as expected"""
res = fn.where(t < 0, 100 * fn.ones_like(t), t)
expected = np.array([[[1, 2], [3, 4], [100, 1]], [[5, 6], [0, 100], [2,
1]]])
assert fn.allclose(res, expected)