code: def test_transform_fn_identity(self):
      shape = [5, 20]
      df = self.dtype(50.0)
      loc = 2.0 * np.random.uniform(size=shape).astype(self.dtype)
      scale = 3.0 + np.random.uniform(size=[20]).astype(self.dtype)
      observations = np.array([2.0, 3.0, 4.0]).astype(self.dtype)
      model = student_t.StudentT(
          df,
          loc[..., tf.newaxis],
          scale[..., tf.newaxis],
          validate_args=True,
      )
      shared_seed = test_util.test_seed(sampler_type="stateless")
      expected_ei = expected_improvement.ParallelExpectedImprovement(
          predictive_distribution=model,
          observations=observations,
          exploration=1.0,
          num_samples=30,
          seed=shared_seed,
      )()
      actual_ei = expected_improvement.ParallelExpectedImprovement(
          predictive_distribution=model,
          observations=observations,
          exploration=1.0,
          transform_fn=lambda x: x,
          num_samples=30,
          seed=shared_seed,
      )()
      self.assertAllClose(
          self.evaluate(actual_ei), self.evaluate(expected_ei)
      )
  
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.distributions import student_t
  from tensorflow_probability.python.distributions import student_t_process_regression_model
  from tensorflow_probability.python.experimental.bayesopt.acquisition import expected_improvement
  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 7 for calling the _ExpectedImprovementTest obj's underlying member method code with example usage: obj.test_transform_fn_identity() without return types