code: def testStacklessLogitBetaTargetConservation(self):
      if not tf.executing_eagerly():
          return
  
      def mk_logit_beta():
          beta = beta_lib.Beta(concentration0=1.0, concentration1=2.0)
          return invert.Invert(sigmoid.Sigmoid())(beta)
  
      self.evaluate(
          assert_univariate_target_conservation(
              self, mk_logit_beta, step_size=0.02, stackless=True
          )
      )
  
imports: import itertools
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import chain
  from tensorflow_probability.python.bijectors import invert
  from tensorflow_probability.python.bijectors import scale_matvec_tril
  from tensorflow_probability.python.bijectors import shift
  from tensorflow_probability.python.bijectors import sigmoid
  from tensorflow_probability.python.distributions import beta
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.distributions import wishart
  from tensorflow_probability.python.distributions.internal import statistical_testing
  from tensorflow_probability.python.experimental.auto_batching import instructions
  from tensorflow_probability.python.experimental.mcmc import nuts_autobatching
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.mcmc import sample
label_desc: Use image node_id 11 for calling the NutsTest obj's underlying member method code with example usage: obj.testStacklessLogitBetaTargetConservation() and returns: invert