['μ = pm.Normal(\'μ\', mu=0, sd=10)\n    σ = pm.HalfNormal(\'σ\', sd=10)\n    hospital_effects = pm.Normal(\'hospital_effects\', mu=μ, sd=σ, shape=N)\n    likelihood = pm.Poisson(\'likelihood\', mu=hospital_effects, observed=r)\n    \n    trace = pm.sample(1000, tune=1000, chains=4, return_inferencedata=True, idata_kwargs={"log_likelihood": True})\naz.plot_trace(trace, var_names=[\'μ\', \'σ\', \'hospital_effects\'])\nplt.show()\n\n# Output: The code defines a Bayesian model using PyMC3 to analyze the mortality rates in 12 hospitals performing cardiac surgery on babies. It assumes a normal distribution for the hospital effects with a shared mean (`μ`) and standard deviation (`σ`). The observed data (`r`) is modeled using a Poisson distribution, where the rate parameter is the hospital effects. The model is sampled using the No-U-Turn Sampler (NUTS) with 4 chains and 1000 iterations for tuning. The trace plots for the parameters are generated to assess convergence and the posterior distributions.\n\n']