code: def test_transform_fn_identity(self):
      shape = [5, 20, 1]
      loc = 2.0 * np.random.uniform(size=shape).astype(self.dtype)
      scale = 3.0 + np.random.uniform(size=[20]).astype(self.dtype)
      model = normal.Normal(loc, scale, validate_args=True)
      observations = [2.0, 3.0, 4.0]
      shared_seed = test_util.test_seed(sampler_type="stateless")
  
      expected_ucb = (
          upper_confidence_bound.ParallelUpperConfidenceBound(
              predictive_distribution=model,
              observations=observations,
              exploration=1.0,
              num_samples=int(1e4),
              seed=shared_seed,
          )()
      )
  
      actual_ucb = upper_confidence_bound.ParallelUpperConfidenceBound(
          predictive_distribution=model,
          observations=observations,
          exploration=1.0,
          transform_fn=lambda x: x,
          num_samples=int(1e4),
          seed=shared_seed,
      )()
      self.assertAllClose(
          self.evaluate(actual_ucb),
          self.evaluate(expected_ucb),
          rtol=8e-3,
      )
  
file_path: probability/tensorflow_probability/python/experimental/bayesopt/acquisition/upper_confidence_bound_test.py
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import gaussian_process_regression_model
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.experimental.bayesopt.acquisition import upper_confidence_bound
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.math.psd_kernels import exponentiated_quadratic
label_desc: Use image node_id 4 for calling the _UpperConfidenceBoundTest obj's underlying member method code with example usage: obj.test_transform_fn_identity() without return types