BUG: get_tau_sigma does not support list of variables

[tvwenger]

Describe the issue:

The function get_tau_sigma in distributions/continuous.py does not properly handle lists of Variables. When a list is passed, the function attempts to convert it to a numpy array before checking for positivity (N.B. the positivity check does not occur if a Variable is passed).

Reproduceable code example:

import pymc as pm
print(pm.__version__)
import pytensor.tensor as pt
import numpy as np

data = np.random.normal(size=100)

with pm.Model() as model:
    mu_1 = pm.Normal('mu_1', mu=0.0, sigma=1.0)
    mu_2 = pm.Normal('mu_2', mu=0.0, sigma=1.0)
    mu = [mu_1, mu_2]
    
    sigma_1 = pm.HalfNormal('sigma_1', sigma=1.0)
    sigma_2 = pm.HalfNormal('sigma_2', sigma=1.0)
    sigma = [sigma_1, sigma_2]
    # the following works:
    # sigma = pt.as_tensor_variable([sigma_1, sigma_2])
    
    w = pm.Dirichlet('w', a=np.ones(2))
    _ = pm.NormalMixture('data', w=w, mu=mu, sigma=sigma, observed=data)

Error message:

5.9.1+3.gec4407d3
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[6], line 18
     15 sigma = [sigma_1, sigma_2]
     17 w = pm.Dirichlet('w', a=np.ones(2))
---> 18 _ = pm.NormalMixture('data', w=w, mu=mu, sigma=sigma, observed=data)

File ~/science/python/pymc/pymc/distributions/mixture.py:552, in NormalMixture.__new__(cls, name, w, mu, sigma, tau, comp_shape, **kwargs)
    551 def __new__(cls, name, w, mu, sigma=None, tau=None, comp_shape=(), **kwargs):
--> 552     _, sigma = get_tau_sigma(tau=tau, sigma=sigma)
    554     return Mixture(name, w, Normal.dist(mu, sigma=sigma, size=comp_shape), **kwargs)

File ~/science/python/pymc/pymc/distributions/continuous.py:242, in get_tau_sigma(tau, sigma)
    240 else:
    241     sigma_ = np.asarray(sigma)
--> 242     if np.any(sigma_ <= 0):
    243         raise ValueError("sigma must be positive")
    244     tau = sigma_**-2.0

File ~/miniconda3/envs/pymc/lib/python3.11/site-packages/pytensor/tensor/variable.py:72, in _tensor_py_operators.__bool__(self)
     70     return True
     71 else:
---> 72     raise TypeError("Variables do not support boolean operations.")

TypeError: Variables do not support boolean operations.

PyMC version information:

5.9.1+3.gec4407d3

Context for the issue:

Since the distributions that use get_tau_sigma (e.g., NormalMixture) state that sigma and tau should be tensor_like, and since tensor_like includes lists and other iterables, this is a bug.

[ricardoV94]

Should be solved by #7114

Maybe not, the floatX comes after the problem is done