code: def testHalfNormalMeanAndMode(self):
      scale = np.array([11.0, 12.0, 13.0])
  
      halfnorm = half_normal.HalfNormal(scale=scale, validate_args=True)
      expected_mean = scale * np.sqrt(2.0) / np.sqrt(np.pi)
  
      self.assertAllEqual((3,), self.evaluate(halfnorm.mean()).shape)
      self.assertAllEqual(expected_mean, self.evaluate(halfnorm.mean()))
  
      self.assertAllEqual((3,), self.evaluate(halfnorm.mode()).shape)
      self.assertAllEqual(
          [0.0, 0.0, 0.0], self.evaluate(halfnorm.mode())
      )
  
imports: from absl.testing import parameterized
  import numpy
  from scipy import stats
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import half_normal
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 16 for calling the HalfNormalTest obj's underlying member method code with example usage: obj.testHalfNormalMeanAndMode() without return types