['μ = pm.Normal(\'μ\', mu=0, sd=10)\n    σ = pm.HalfNormal(\'σ\', sd=10)\n    \n    # Define the growth model\n    growth = pm.Normal(\'growth\', mu=μ, sd=σ, observed=y)\n    \n    # Define priors for the missing data\n    missing_data_prior = pm.Normal(\'missing_data\', mu=μ, sd=σ)\n    \n    # Define the likelihood for the missing data\n    missing_data_likelihood = pm.Normal(\'missing_data_likelihood\', mu=missing_data_prior, sd=σ, observed=np.isnan(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# Visualize the posterior\naz.plot_trace(trace)\nplt.show()\n\n']