Clean up JAX random seed handling in Time Series example notebook.

PiperOrigin-RevId: 493035184

Author: ursk

tensorflow_probability/examples/jupyter_notebooks/Structural_Time_Series_Modeling_Case_Studies_Atmospheric_CO2_and_Electricity_Demand_JAX.ipynb

@@ -477,19 +477,19 @@
         "num_variational_steps = 200 # @param { isTemplate: true}\n",
         "num_variational_steps = int(num_variational_steps)\n",
         "\n",
-        "seed = tfp.random.sanitize_seed(jax.random.PRNGKey(42), salt='fit_stateless') \n",
-        "init_seed, fit_seed, sample_seed = tfp.random.split_seed(seed, n=3) \n",
-        "initial_parameters = init_fn(init_seed) \n",
-        "jd = co2_model.joint_distribution(co2_by_month_training_data) \n",
+        "seed = jax.random.PRNGKey(42)\n",
+        "init_seed, fit_seed, sample_seed = jax.random.split(seed, 3)\n",
+        "initial_parameters = init_fn(init_seed)\n",
+        "jd = co2_model.joint_distribution(co2_by_month_training_data)\n",
         "\n",
         "# Build and optimize the variational loss function.\n",
-        "optimized_parameters, elbo_loss_curve = tfp.vi.fit_surrogate_posterior_stateless( \n",
-        "  target_log_prob_fn=jd.log_prob, \n",
-        "  initial_parameters=initial_parameters, \n",
-        "  build_surrogate_posterior_fn=build_surrogate_fn, \n",
+        "optimized_parameters, elbo_loss_curve = tfp.vi.fit_surrogate_posterior_stateless(\n",
+        "  target_log_prob_fn=jd.log_prob,\n",
+        "  initial_parameters=initial_parameters,\n",
+        "  build_surrogate_posterior_fn=build_surrogate_fn,\n",
         "  optimizer=optax.adam(0.1), \n",
         "  num_steps=num_variational_steps,\n",
-        "  seed=fit_seed) \n",
+        "  seed=fit_seed)\n",
         "plt.plot(elbo_loss_curve)\n",
         "plt.show()\n",
         "\n",