I have the following model where I use the horseshoe prior for regularization purposes:

```
with pm.Model() as re_horse:
# Level 2 intercept
v_2 = pm.Normal(name="v_2", mu=0.0, sigma=1.0, shape=())
# Level 2 error term
eps_2 = pm.Normal(name="eps_2", mu=0.0, sigma=1.0, shape=(n_matches, 1))
# Random intercept
gamma = pm.Deterministic(name="gamma", var=v_2 + eps_2)
lam_1 = pm.HalfCauchy(name="lam_1", beta=1.0, shape=n_vars_1) # lam allows parameter to escape shrinkage
lam_2 = pm.HalfCauchy(name="lam_2", beta=1.0, shape=(n_vars_2, 1))
tau_1 = pm.InverseGamma(name="tau_1", alpha=3.0, beta=1.0, shape=()) # shrinkage param, large tau means low shrinkage
tau_2 = pm.InverseGamma(name="tau_2", alpha=3.0, beta=1.0, shape=())
# Level 2 variables
K_2 = pm.Normal(name="K_2", mu=0.0, sigma=tau_2**2 * lam_2**2, shape=(n_vars_2, 1))
# Level 1 intercept
v_1 = pm.Deterministic(name="v_1", var=pm.math.dot(X_2, K_2) + gamma)
# Level 1 variables
K_1 = pm.Normal(name="K_1", mu=0.0, sigma=tau_1**2 * lam_1**2, shape=n_vars_1)
# Model error
eps_1 = pm.Gamma(name="eps_1", alpha=9.0, beta=4.0, shape=())
# Model mean
y_hat = pm.Deterministic(name="y_hat", var=pm.math.dot(X_1, K_1) + v_1)
# Likelihood
y_like = pm.Normal(name="y_like", mu=y_hat, sigma=eps_1, observed=y)
with re_horse:
step = pm.NUTS(target_accept=0.9)
trace_re_horse = pm.sample(draws=20000, chains=2, tune=20000, init="auto", return_inferencedata=True,
random_seed=SEED, cores=1, step=step)
```

However, I cannot get rid of the sampling issues reported below:

There were 4210 divergences after tuning. Increase `target_accept`

or reparameterize.

The chain reached the maximum tree depth. Increase max_treedepth, increase target_accept or reparameterize.

There were 10420 divergences after tuning. Increase `target_accept`

or reparameterize.

The chain reached the maximum tree depth. Increase max_treedepth, increase target_accept or reparameterize.

The rhat statistic is larger than 1.4 for some parameters. The sampler did not converge.

The estimated number of effective samples is smaller than 200 for some parameters.

I already tried to increase the number of tuning steps from 2000 to 20000, and the same to the number of iterations without success.

Moreover I tried different (hyper-)parameter combinations e.g. for alpha and beta, and increased the target_accept to 0.9. Also without success.

How can I fix the problem?

Many thanks in advance!