code: def test_init_is_valid_for_large_observations(self):
      num_timesteps = 20
      observed_time_series = self._build_tensor(
          -1e8 + 1e6 * np.random.randn(num_timesteps)
      )
      model = self._build_model(observed_time_series)
      surrogate_posterior = fitting.build_factored_surrogate_posterior(
          model=model
      )
      variational_loss = (
          csiszar_divergence.monte_carlo_variational_loss(
              target_log_prob_fn=model.joint_distribution(
                  observed_time_series
              ).log_prob,
              surrogate_posterior=surrogate_posterior,
          )
      )
      self.evaluate(tf1.global_variables_initializer())
      loss_ = self.evaluate(variational_loss)
      self.assertTrue(np.isfinite(loss_))
  
      # When this test was written, the variational loss with default
      # initialization and seed was 431.5 nats. Larger finite initial losses are
      # not 'incorrect' as such, but if your change makes the next line fail,
      # you have probably done something questionable.
      self.assertLessEqual(loss_, 10000)
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.sts import fitting
  from tensorflow_probability.python.sts.components import local_linear_trend
  from tensorflow_probability.python.sts.components import seasonal
  from tensorflow_probability.python.sts.components import sum
  from tensorflow_probability.python.sts.internal import missing_values_util
  from tensorflow_probability.python.vi import csiszar_divergence
  from tensorflow_probability.python.vi import optimization
label_desc: Use image node_id 5 for calling the _VariationalInferenceTests obj's underlying member method code with example usage: obj.test_init_is_valid_for_large_observations() without return types