Hi All,

I’m encountering the following error on what I think is a rather simple model on simulated data:

```
ValueError: dimensions ('ys',) must have the same length as the number of data dimensions, ndim=2
```

Strangely (to me), the error occurs just as sampling finishes.

I uploaded a complete notebook example replicating the error.

I am modelling the support of multiple political parties (\pi) over time as a Multivariate Gaussian Random Walk. This latent support \pi is used to draw opinion poll results y from a multinomial distribution. \pi is first softmaxed (\pi^*) to ensure that we can use it as the p parameter of the multinomial.

An excerpt of the code that defines the model:

```
coords = {
"ys": ys
}
with pm.Model(coords=coords) as model:
pi = pm.MvGaussianRandomWalk(
name="pi",
mu=np.zeros(D),
chol=L, # We use the actual grand-truth covariance here to simply things
init_dist=pm.MvNormal.dist(mu=[0.3191,0.3947,0.2862], chol=L),
shape=(NUM_TS, D)
)
pi_star = pm.Deterministic("pi_star", pm.math.exp(pi)/pm.math.exp(pi).sum(1,keepdims=True))
y = pm.Multinomial('y', p=pi_star[ts], n=ns, dims="ys", observed=ys)
pm.model_to_graphviz(model)
```

In this example, `ys`

is of shape (20, 3)

Thanks for any help you can provide, I’m new to PyMC and so these errors aren’t always obvious to me.