code: def true_log_prob_fn(w, x, data):
      return (
          normal.Normal(0.0, 1.0).log_prob(w)
          + sample_dist_lib.Sample(
              normal.Normal(w, 1.0), (test_lib.NUM_DEVICES, 1)
          ).log_prob(x)
          + independent.Independent(normal.Normal(x, 1.0), 2).log_prob(
              data
          )
      )
  
imports: from absl.testing import parameterized
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import scale
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import joint_distribution_coroutine
  from tensorflow_probability.python.distributions import log_prob_ratio
  from tensorflow_probability.python.distributions import lognormal
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import sample
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.experimental.distribute import joint_distribution
  from tensorflow_probability.python.experimental.distribute import sharded
  from tensorflow_probability.python.internal import distribute_test_lib
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 1 for calling a global function with example usage: true_log_prob_fn(w, x, data) and returns: unknown