Hi all,

I was playing with additive Gaussian processes similar to documentation here:

https://docs.pymc.io/Gaussian_Processes.html?highlight=gaussian%20processes

but I am having issues while sampling from the conditional distribution for f1_star and f2_star.

My notebook can be found here:

```
with pm.Model() as model:
sigma1 = pm.HalfCauchy("η1", beta=5)
sigma2 = pm.HalfCauchy("η2", beta=5)
ls1 = pm.HalfCauchy('ls1', beta=10)
ls2 = pm.HalfCauchy('ls2', beta=10)
cov1 = sigma1 * pm.gp.cov.ExpQuad(2, ls=ls1, active_dims=[0])
cov2 = sigma2 * pm.gp.cov.ExpQuad(2, ls=ls2, active_dims=[1])
gp1 = pm.gp.Marginal(cov1)
gp2 = pm.gp.Marginal(cov2)
gp = gp1 + gp2
sigma = pm.HalfCauchy("sigma", beta=3)
f = gp.marginal_likelihood("f", X_2D_train, y, sigma)
trace = pm.sample(1000, chains=4)
with model:
f_star = gp.conditional("f_star", X_2D_star)
```

TypeError: For compute_test_value, one input test value does not have the requested type.

The error when converting the test value to that variable type:

Wrong number of dimensions: expected 1, got 2 with shape (1000, 1000).

X_2D_star has shape (1000,2). So, I think I am using additive GP wrong. Can someone please help me with this?

Thanks,

Anurag