['μ = pm.Normal(\'μ\', mu=0, sd=10)\n    σ = pm.HalfNormal(\'σ\', sd=10)\n    \n    # Define the growth model\n    growth_model = pm.Normal(\'growth_model\', mu=μ, sd=σ, shape=len(X))\n    \n    # Define the likelihood\n    likelihood = pm.Normal(\'likelihood\', mu=growth_model[X.astype(int) - 1], sd=0.1, observed=y)\n    \n    # Sample from the posterior\n    trace = pm.sample(1000, tune=1000, chains=4, return_inferencedata=True, idata_kwargs={"log_likelihood": True})\n    \n# Visualize the results\naz.plot_trace(trace)\nplt.show()\n\n']