I’m following the Pymc repo of chapter 14 of McElreath’s Statistical Rethiking.

In cell 61 (Code 14.39), we have the following model and I don’t understand where rhosq is being used:

```
with pm.Model() as m14_8:
a = pm.Exponential("a", 1.0)
b = pm.Exponential("b", 1.0)
g = pm.Exponential("g", 1.0)
etasq = pm.Exponential("etasq", 2.0)
ls_inv = pm.HalfNormal("ls_inv", 2.0)
rhosq = pm.Deterministic("rhosq", 0.5 * ls_inv**2)
# Implementation with PyMC's GP module:
cov = etasq * pm.gp.cov.ExpQuad(input_dim=1, ls_inv=ls_inv)
gp = pm.gp.Latent(cov_func=cov)
k = gp.prior("k", X=Dmat)
lam = (a * P**b / g) * at.exp(k[society])
T = pm.Poisson("total_tools", lam, observed=total_tools)
trace_14_8 = pm.sample(4000, tune=2000, target_accept=0.99, random_seed=RANDOM_SEED)
```

I’m new to Gaussian Processes and have gone through to understand the ExpQuad and Latent syntax. However, I’m not following - rhosq doesn’t appear to be used anywhere after its definition. I see that it’s dependent on ls_inv, and ls_inv is used to define the covariance function.

Note that in the latest Pymc version, you would need to replace `from aesara import tensor as at `

with `import pytensor.tensor`