code: def _compute_conditional_weights_mean(
      nonzeros, weights_posterior_precision, x_transpose_y
  ):
      indices = tf.where(nonzeros)[:, 0]
      conditional_posterior_precision_chol = tf.linalg.cholesky(
          tf.gather(
              tf.gather(weights_posterior_precision, indices),
              indices,
              axis=1,
          )
      )
      return tf.linalg.cholesky_solve(
          conditional_posterior_precision_chol,
          tf.gather(x_transpose_y, indices)[..., tf.newaxis],
      )[..., 0]
  
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 dynamic_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 a global function with example usage: _compute_conditional_weights_mean(nonzeros, weights_posterior_precision, x_transpose_y) and returns: tf