```
# our model
with pm.Model() as marginal_gp_model:
# Specify the covariance function.
length_scale = pm.HalfCauchy("length_scale", 0.1, shape=(11,))
# width_scale = pm.HalfCauchy("width_scale", 1)
cov_func = pm.gp.cov.Matern52(11, ls=length_scale)
# Specify the GP. The default mean function is `Zero`.
gp = pm.gp.MarginalApprox(cov_func=cov_func, approx="FITC")
Xu = pm.gp.util.kmeans_inducing_points(20, train_X.values)
# The scale of the noise term can be provided,
sigma = pm.HalfCauchy("sigma",beta= 0.01)
y_ = gp.marginal_likelihood("y_", X=train_X.values, Xu=Xu, y=train_Y.values, sigma=sigma)
```

I am getting the error here,

```
with marginal_gp_model:
f_pred = gp.conditional("f_pred", test_X.values, given={"X": train_X.values, "y": train_Y.values})
pred_samples = pm.sample_posterior_predictive(trace, random_seed=42, model=marginal_gp_model, var_names=["f_pred"])
```