class_docstr: Implements a location-scale heavy-tail Lambert W x Normal distribution.
class_docstr_tok: ["Implements","a","location-scale","heavy-tail","Lambert","W","x","Normal","distribution","."]
code: def _mean(self):
      tailweight = tf.convert_to_tensor(self.tailweight)
      loc = tf.convert_to_tensor(self.loc)
      mean = loc * tf.ones(self.batch_shape, dtype=self.dtype)
      if self.allow_nan_stats:
          return tf.where(
              tailweight < 1.0,
              mean,
              dtype_util.as_numpy_dtype(self.dtype)(np.nan),
          )
      else:
          return distribution_util.with_dependencies(
              [
                  assert_util.assert_less(
                      tf.ones([], dtype=self.dtype),
                      tailweight,
                      message="mean not defined for components of tailweight >= 1",
                  ),
              ],
              mean,
          )
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.bijectors import lambertw_transform
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import distribution_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import tensor_util
label_desc: Use image node_id 4 for calling the LambertWNormal obj's underlying member method code with example usage: obj._mean() and returns: tf, distribution_util