code: def test_domain_constraint_fn(self):
      dist = beta.Beta(concentration0=5.0, concentration1=3.0)
      xs = self.evaluate(dist.sample([100], seed=test_util.test_seed()))
  
      bij = sfwii.ScalarFunctionWithInferredInverse(
          dist.cdf,
          domain_constraint_fn=dist.experimental_default_event_space_bijector(),
      )
      self.assertAllClose(xs, bij.inverse(bij.forward(xs)))
  
imports: from absl.testing import parameterized
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import chain
  from tensorflow_probability.python.bijectors import power
  from tensorflow_probability.python.bijectors import scale
  from tensorflow_probability.python.bijectors import shift
  from tensorflow_probability.python.distributions import beta
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import student_t
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.experimental.bijectors import scalar_function_with_inferred_inverse
  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 3 for calling the ScalarFunctionWithInferredInverseTests obj's underlying member method code with example usage: obj.test_domain_constraint_fn() without return types