[' # Define priors for the GP parameters\n    sigma = pm.HalfNormal("sigma", sigma=10)\n    rho = pm.HalfNormal("rho", sigma=10)\n    eta = pm.HalfNormal("eta", sigma=10)\n    \n    # Define the GP model\n    gp = pm.gp.Latent(cov_func=pm.gp.cov.Matern52(1, ls=rho, sigma=sigma))\n    \n    # Define the likelihood\n    y_obs = pm.Poisson("y_obs", mu=gp(x), observed=y)\n    \n    # Sample the posterior\n    trace = pm.sample(1000, tune=1000, chains=4, return_inferencedata=True, idata_kwargs={"log_likelihood": True})\n\n# Extract the posterior samples\nposterior_samples = trace.posterior\n\n# Plot the posterior samples\naz.plot_trace(posterior_samples)\nplt.show()\n\n# Plot the posterior predictive samples\naz.plot_posterior_predictive_glm(trace, y_obs="y_obs", hdi_prob=0.95, num_pp_samples=1000)\nplt.show()\n\n# Print the summary of the posterior samples\nprint(az.summary(posterior_samples, round_to=3))']