code: def test_chol_symmetric_increment_batch(self):
      matrix = samplers.normal([7, 5, 5], seed=test_util.test_seed())
      psd_matrix = self.evaluate(
          tf.matmul(matrix, matrix, adjoint_b=True) + tf.eye(5)
      )
      chol = tf.linalg.cholesky(psd_matrix)
      increment = self.evaluate(
          0.1 * samplers.normal([7, 5], seed=test_util.test_seed())
      )
      for idx in range(5):
          expected_result = _naive_symmetric_increment(
              psd_matrix, idx, increment
          )
          chol_result = spike_and_slab._symmetric_increment_chol(
              chol, idx, increment
          )
          result = tf.matmul(chol_result, chol_result, adjoint_b=True)
          self.assertAllClose(expected_result, result)
  
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 1 for calling the SpikeAndSlabTest obj's underlying member method code with example usage: obj.test_chol_symmetric_increment_batch() without return types