I want to create a mixture of multivariate (2D) normals, which I am achieving via the `pymc.Mixture`

class. I can get the model to sample, and sometimes I get good results, other times I have issues with label switching (the label switching behavior is seemingly random).

To address the label switching issue, I am trying to apply an ordered transformation to the mixture weights. However, this is creating errors upon initialization. Below is the code with the transformation

```
coords = {"axis": ["x1", "x2"], "components": [1, 2]}
# create the model
with pm.Model(coords=coords) as model:
weights = pm.Dirichlet("w", [1, 1], dims="components", transform=pm.distributions.transforms.ordered, initval=[0.2, 0.8])
components = []
for i in range(2):
chol, corr, stds = pm.LKJCholeskyCov(
"chol{}".format(i), n=2, eta=2.0, sd_dist=pm.Exponential.dist(1.0, shape=2)
)
mu = pm.Normal("mu{}".format(i), 0.0, sigma=4.0, dims="axis")
components.append(pm.MvNormal.dist(mu, chol=chol))
obs = pm.Mixture("obs", w=weights, comp_dists=components, observed=sample)
```

When I try to sample the model I get the following error:

```
SamplingError: Initial evaluation of model at starting point failed!
Starting values:
{'w_ordered__': array([ 1.13887493, -0.77774487]), 'chol0_cholesky-cov-packed__': array([-0.40933986, 0.89455485, -0.64019363]), 'mu0': array([-0.68340835, 0.93668752]), 'chol1_cholesky-cov-packed__': array([ 0.5663125 , 0.15188474, -0.20971411]), 'mu1': array([-0.16076354, -0.39587404])}
Logp initial evaluation results:
{'w': -inf, 'chol0': -4.54, 'mu0': -4.65, 'chol1': -1.8, 'mu1': -4.62, 'obs': -inf}
You can call `model.debug()` for more details.
```

For some reason applying the ordered transform to the `weights`

variable is creating infinity values… but removing that transform there is no issue (except the label switching).

Can someone explain what is wrong?