Hi,

I am attempting to utilize the continuous zero-inflated exponential likelihood distribution using pm.DensityDist with:

```
def logp_zero_inflated_exponential(value, psi, mu):
return tt.switch(tt.eq(value, 0), tt.log(psi), tt.log1p(-psi) - mu * value)
psi = pm.Beta('psi', 1, 1)
z = pm.HalfNormal('Z', sigma=500)
a = pm.HalfNormal('α', sigma=1000) # weak informative prior (we know it should be positive)
b = pm.Normal('β', mu=0, sigma=1000)
ET_I_est = f_ET_I(SM1, SM2, a, b, dt)
mu = z*np.abs(dsm) - ET_I_est
Y_obs = pm.DensityDist('Y_obs', logp_zero_inflated_exponential, observed={'value': y_obs, 'psi': psi, 'mu': mu})
```

This is working fine with the version 3, however, when I use it with

`Y_obs = pm.DensityDist('Y_obs', logp_zero_inflated_exponential, observed=np.array([y_obs, psi, mu]))`

in the version 5, I got the following error:

```
860 Y_obs = pm.DensityDist('Y_obs', logp_zero_inflated_exponential,
861 observed={'value': y_obs, 'psi': psi, 'mu': mu})
862 elif pmv == 5:
863 Y_obs = pm.DensityDist('Y_obs', logp_zero_inflated_exponential,
--> 864 observed=np.array([y_obs, psi, mu]))
865 # sampling or fit
866 rng = 321
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (3,) + inhomogeneous part.
```

I might be missing something with the Version 5 API. If you can provide me with instructions on how to use the Version 5 API correctly, that would be really helpful. If it works, I won’t need to use pymc3. By the way, the conda-forge pymc3 library installed on macOS works just fine with the mkl-service library. I’m not sure why the conda-forge pymc3 library for Linux comes with a numpy version that is not 1.22.

Additional information:

```
I use different logp_zero_inflated_exponential function for pymc3 (pmv == 3) and pymc5:
if pmv == 3:
def logp_zero_inflated_exponential(value, psi, mu):
return tt.switch(tt.eq(value, 0), tt.log(psi), tt.log1p(-psi) - mu * value)
if pmv == 5:
def logp_zero_inflated_exponential(value, psi, mu):
if value == 0:
return pm.math.log(psi)
else:
return pm.math.log1p(-psi) - mu * value
```

Thank you for your help.