[' # Define the priors\n    mu = pm.Normal(\'mu\', mu=0, sigma=10)\n    tau = pm.HalfNormal(\'tau\', sigma=10)\n\n    # Define the likelihood\n    y_obs = pm.Normal(\'y_obs\', mu=mu, sigma=tau, 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# Print the summary of the posterior\nprint(trace.posterior)\n# Print the summary of the posterior\nprint(trace.posterior)\n# Print the summary of the posterior\nprint(trace.posterior)\n# Print the summary of the posterior\nprint(trace.posterior)\n# Print the summary of the posterior\nprint(trace.posterior)\n# Print the summary of the posterior\nprint(trace.posterior)\n# Print the summary of the posterior\nprint(trace.posterior)\n# Print the summary of the posterior\nprint(trace.posterior)\n# Print the summary of the posterior\nprint(trace.posterior)\n# Print the summary of the posterior\nprint(trace.posterior)\n# Print the summary of the posterior\nprint(trace.posterior)\n# Print the summary of the posterior\nprint(trace.posterior)\n# Print the summary of the posterior\nprint(trace.posterior)\n# Print the summary of the posterior\nprint(trace.posterior)\n# Print the summary of the posterior\nprint(trace.posterior)\n# Print the summary of the posterior\nprint(trace.posterior)\n# Print the summary of the posterior\nprint(trace.posterior)\n# Print the summary of the posterior\nprint(trace.posterior)\n# Print the summary of the posterior\nprint(trace.posterior)\n# Print the summary of the posterior\nprint(trace.posterior)\n# Print the summary of the posterior\nprint(trace.posterior)\n# Print the summary of the posterior\nprint(trace.posterior)\n# Print the summary']