code: def testLogitNormalMeanLogProbApprox(self):
      loc, scale = [-1.5, 0.0, 1.5], 0.4
      dist = ln_lib.LogitNormal(
          loc=loc, scale=scale, validate_args=True
      )
      x = dist.sample(int(10e3), seed=test_util.test_seed())
      y = tf.constant([0.0, 0.1, 0.5, 1.0], dist.dtype)[:, tf.newaxis]
      samples = bernoulli.Bernoulli(probs=x).log_prob(
          y[..., tf.newaxis]
      )
      [samples_, mean_approx_, mean_approx_default_] = self.evaluate(
          [
              samples,
              dist.mean_log_prob_approx(y),
              dist.mean_log_prob_approx(),
          ]
      )
      self.assertAllMeansClose(
          samples_, mean_approx_, axis=1, atol=1e-4, rtol=0.02
      )
      self.assertAllMeansClose(
          samples_[-1, :],
          mean_approx_default_,
          axis=0,
          atol=1e-4,
          rtol=0.02,
      )
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import bernoulli
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import logitnormal
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 2 for calling the LogitNormalTest obj's underlying member method code with example usage: obj.testLogitNormalMeanLogProbApprox() without return types