I’m trying to build a spatially correlated Bayesian model using PyMC. By querying the API, I found that there is a `pymc.ICAR`

function in version 5.10.4. However, when I tried to run the sample code provided in the API, it didn’t work. I want to ask if there are any issues with the function? Here is the sample code I used:

```
W = np.array([
[0,1,0,1],
[1,0,1,0],
[0,1,0,1],
[1,0,1,0]
])
with pm.Model():
sigma = pm.Exponential('sigma', 1)
phi = pm.ICAR('phi', W=W, sigma=sigma)
mu = phi
print(pm.draw(phi))
```

I encountered the following error:

```
NotImplementedError: Cannot sample from ICAR prior
During handling of the above exception, another exception occurred:
NotImplementedError Traceback (most recent call last)
/usr/local/lib/python3.10/dist-packages/pymc/distributions/multivariate.py in rng_fn(cls, rng, size, W, node1, node2, N, sigma, zero_sum_stdev)
2323 @classmethod
2324 def rng_fn(cls, rng, size, W, node1, node2, N, sigma, zero_sum_stdev):
-> 2325 raise NotImplementedError("Cannot sample from ICAR prior")
2326
2327
NotImplementedError: Cannot sample from ICAR prior
Apply node that caused the error: icar_rv{1, (2, 1, 1, 0, 0, 0), floatX, True}(RandomGeneratorSharedVariable(<Generator(PCG64) at 0x7F9DDC07F140>), [], 11, [[0 1 0 1] ... [1 0 1 0]], [1 2 3 3], [0 1 0 2], 4, sigma, 0.001)
Toposort index: 1
Inputs types: [RandomGeneratorType, TensorType(int64, shape=(0,)), TensorType(int64, shape=()), TensorType(int64, shape=(4, 4)), TensorType(int64, shape=(4,)), TensorType(int64, shape=(4,)), TensorType(int64, shape=()), TensorType(float64, shape=()), TensorType(float64, shape=())]
Inputs shapes: ['No shapes', (0,), (), (4, 4), (4,), (4,), (), (), ()]
Inputs strides: ['No strides', (0,), (), (32, 8), (8,), (8,), (), (), ()]
Inputs values: [Generator(PCG64) at 0x7F9DDC07F140, array([], dtype=int64), array(11), 'not shown', array([1, 2, 3, 3]), array([0, 1, 0, 2]), array(4), array(2.22426998), array(0.001)]
Outputs clients: [['output'], ['output']]
HINT: Re-running with most PyTensor optimizations disabled could provide a back-trace showing when this node was created. This can be done by setting the PyTensor flag 'optimizer=fast_compile'. If that does not work, PyTensor optimizations can be disabled with 'optimizer=None'.
HINT: Use the PyTensor flag `exception_verbosity=high` for a debug print-out and storage map footprint of this Apply node.
```