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 _mode(self):
      # Mode always exists (for any tail parameter) and equals the location / mean
      # independent of the tail parameter.
      loc = tf.convert_to_tensor(self.loc)
      return tf.broadcast_to(loc, self.batch_shape)
  
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 6 for calling the LambertWNormal obj's underlying member method code with example usage: obj._mode() and returns: tf