I would like to scale the output of a beta distribution and include this as a component of a mixture model with pymc v4.1.7. I tried passing in a transform via `pm.Beta.dist(a, b, transform=...)`

but this is not supported. I also tried just scaling the dist via `d = 10 * pm.Beta.dist(a, b)`

but this leads to “Component dist must be a distribution created via the `.dist()`

API, got <class ‘aesara.tensor.var.TensorVariable’>”

Full example below that mocks up what I would like to do:

```
with pm.Model() as model:
mu_r = pm.TruncatedNormal("mu_r", mu=2.16, sigma=0.5, lower=0)
ln_std_r = pm.TruncatedNormal("ln_std_r", -6, 5, lower=-10, upper=-1)
std_r = pm.Deterministic("std_r", pm.math.exp(ln_std_r))
a = pm.TruncatedNormal("a", mu=2., sigma=2, lower=0, initval=2.)
b = pm.TruncatedNormal("b", mu=5., sigma=2, lower=0, initval=5.)
w = pm.Dirichlet('w', a=np.array([1, 1]))
dist1 = pm.TruncatedNormal.dist(mu_r, std_r, lower=0)
dist2 = 10 * pm.Beta.dist(alpha, beta)
r = pm.Mixture(
'r',
w=w,
comp_dists=[dist1, dist2],
shape=len(data)
)
```

Thanks for reading!