code: def test_inverse_mills_ratio(self):
      x = tf.random.stateless_normal(
          [int(1e2)],
          dtype=self.dtype,
          seed=test_util.test_seed(sampler_type="stateless"),
      )
      actual = inverse_mills_ratio(x)
      normal_dist = normal.Normal(self.dtype(0.0), 1.0)
      expected = tf.math.exp(
          normal_dist.log_prob(x) - normal_dist.log_cdf(x)
      )
      actual_, expected_ = self.evaluate([actual, expected])
      self.assertAllClose(actual_, expected_)
      self.assertDTypeEqual(actual_, self.dtype)
  
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 max_value_entropy_search
  from tensorflow_probability.python.experimental.bayesopt.acquisition.max_value_entropy_search import fit_max_value_distribution
  from tensorflow_probability.python.experimental.bayesopt.acquisition.max_value_entropy_search import inverse_mills_ratio
  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 1 for calling the _MaxValueEntropySearchTest obj's underlying member method code with example usage: obj.test_inverse_mills_ratio() without return types