Is it possible to generate a matrix comprised of N draws from a multivariate normal from within the `pm.Model()`

context?

Here is what I’ve tried

```
import pymc3 as pm
N = 10
Sigma = np.eye(2)
with pm.Model() as model:
X = pm.MvNormal('X', mu=np.zeros(2), cov = Sigma, shape = N)
betas = pm.Normal('betas', 0, 1, shape = 2)
y = pm.Deterministic('y', pm.math.dot(X,betas))
prior_pred = pm.sample_prior_predictive(1)
```

However, I receive the following error:

`ValueError: operands could not be broadcast together with shapes (10,) (2,)`

Clearly some sort of shape error, but when I try `X=pm.MvNormal('X',mu=np.zeros(2), cov=Sigma, shape=(N,2))`

I get

`ValueError: 1-dimensional argument does not have enough dimensions for all core dimensions ('i_0_0', 'i_0_1')`

What’s the problem here? How can I generate what I want from within the model context?