code: def test_updated_state_matches_initial_computation(
      self, num_outputs, num_features, num_flips, batch_shape, use_xla
  ):
      rng = test_util.test_np_rng()
      initial_nonzeros = rng.randint(
          low=0, high=2, size=batch_shape + [num_features]
      ).astype(bool)
      flip_idxs = rng.choice(
          num_features, size=num_flips, replace=False
      ).astype(np.int32)
      if batch_shape:
          should_flip = rng.randint(
              low=0, high=2, size=[num_flips] + batch_shape
          ).astype(bool)
      else:
          should_flip = np.array([True] * num_flips)
  
      nonzeros = initial_nonzeros.copy()
      for i in range(num_flips):
          nonzeros[..., flip_idxs[i]] = (
              nonzeros[..., flip_idxs[i]] != should_flip[i]
          )
  
      design_matrix, _, targets = self._random_regression_task(
          num_outputs=num_outputs,
          num_features=num_features,
          batch_shape=batch_shape,
          seed=test_util.test_seed(),
      )
      sampler = spike_and_slab.SpikeSlabSampler(
          design_matrix=design_matrix, nonzero_prior_prob=0.3
      )
  
      @tf.function(autograph=False, jit_compile=use_xla)
      def _do_flips():
          state = sampler._initialize_sampler_state(
              targets=targets,
              nonzeros=initial_nonzeros,
              observation_noise_variance=1.0,
          )
  
          def _do_flip(state, i):
              new_state = sampler._flip_feature(
                  state, tf.gather(flip_idxs, i)
              )
              return mcmc_util.choose(
                  tf.gather(should_flip, i), new_state, state
              )
  
          return tf.foldl(
              _do_flip, elems=tf.range(num_flips), initializer=state
          )
  
      self.assertAllCloseNested(
          sampler._initialize_sampler_state(targets, nonzeros, 1.0),
          _do_flips(),
          atol=num_outputs * 2e-4,
          rtol=num_outputs * 2e-4,
      )
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import inverse_gamma
  from tensorflow_probability.python.distributions import normal_conjugate_posteriors
  from tensorflow_probability.python.experimental.sts_gibbs import spike_and_slab
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.mcmc.internal import util
label_desc: Use image node_id 7 for calling the SpikeAndSlabTest obj's underlying member method code with example usage: obj.test_updated_state_matches_initial_computation(num_outputs, num_features, num_flips, batch_shape, use_xla) and returns: tf, mcmc_util