scipy/sparse/linalg/_isolve/tests/test_iterative.py:93: in xp_case
xp.arange(np.prod(A_shape), dtype=A.dtype),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
A = tensor([[7.5234+4.5699e-17j, 5.4298-1.0201e-01j, 5.9857-7.2208e-01j,
6.1996-9.8623e-01j, 6.1057+1.5862e-01j, ....1225-8.6625e-01j,
5.4780-2.8882e+00j, 4.0053-1.2447e+00j, 5.7663+5.4772e-17j]],
dtype=torch.complex64)
A_shape = (5, 9, 9)
batch_A = (5,)
batch_b = (0, 1)
case = <rand-cmplx-sym-pd-F-cg>
xp = <module 'scipy._external.array_api_compat.torch' from '/Users/lucascolley/ghq/github.com/scipy/scipy/build-cpu-install/usr/lib/python3.13/site-packages/scipy/_external/array_api_compat/torch/__init__.py'>
scipy/_external/array_api_compat/torch/_aliases.py:620: in arange
return torch.arange(start, stop, step, dtype=dtype, device=device, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
device = None
dtype = torch.complex64
kwargs = {}
start = 0
step = 1
stop = np.int64(405)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <torch.utils._device.DeviceContext object at 0x116e05d30>
func = <built-in method arange of type object at 0x126a7b600>, types = (), args = (0, np.int64(405), 1)
kwargs = {'device': device(type='cpu'), 'dtype': torch.complex64}
def __torch_function__(self, func, types, args=(), kwargs=None):
kwargs = kwargs or {}
if func in _device_constructors() and kwargs.get("device") is None:
kwargs["device"] = self.device
> return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
E NotImplementedError: "arange_cpu" not implemented for 'ComplexFloat'
