code: def testMeanMode(self, delta, mean_exists):
      loc = 2.0
      scale = 3.0
      lwn = lambertw_f.LambertWNormal(
          loc=loc, scale=scale, tailweight=delta
      )
      self.assertAllClose(lwn.mode(), loc)
      if mean_exists:
          self.assertAllClose(lwn.mean(), loc)
      else:
          self.assertAllClose(lwn.mean(), np.nan)
  
imports: import math
  from absl.testing import parameterized
  import numpy
  from scipy import stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import lambertw_transform
  from tensorflow_probability.python.distributions import lambertw_f
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import student_t
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.internal import tf_keras
label_desc: Use image node_id 8 for calling the LambertWNormalTest obj's underlying member method code with example usage: obj.testMeanMode(delta, mean_exists) without return types