['μ_σ = pm.Normal(\'μ_σ\', mu=0, sd=10)\n    σ_σ = pm.HalfNormal(\'σ_σ\', sd=10)\n    \n    μ = pm.Normal(\'μ\', mu=μ_σ, sd=σ_σ, shape=len(y))\n    \n    obs = pm.Normal(\'obs\', mu=μ, sd=sigma, observed=y)\n    \n    idata = pm.sample(1000, tune=1000, chains=4, return_inferencedata=True, idata_kwargs={"log_likelihood": True})\n\naz.plot_trace(idata)\naz.plot_posterior(idata, var_names=[\'μ_σ\', \'σ_σ\'], ref_val=0)\nplt.show()\n\n']