class_docstr: Heavy tail transformation for Lambert W x F distributions.
  
  This bijector defines the transformation z = u * exp(0.5 * delta * u**2)
  and its inverse, where it is assumed that `u` is already an appropriately
  shifted & scaled input. It is the basis of the location-scale heavy-tail
  Lambert W x F distributions / transformations.
  The effect of this transformation is that it adds heavy tails to input.
  
  Attributes:
    tailweight: Tail parameter `delta` of the transformation(s).
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docstr: Construct heavy-tail Lambert W bijector.
  
  Args:
    tailweight: Floating point tensor; specifies the excess tail behaviors of
      the distribution(s). Must contain only non-negative values.
    validate_args: Python `bool`, default `False`. When `True` distribution
      parameters are checked for validity despite possibly degrading runtime
      performance. When `False` invalid inputs may silently render incorrect
      outputs.
    name: Python `str` name prefixed to Ops created by this class.
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code: def __init__(
      self, tailweight, validate_args=False, name="HeavyTailOnly"
  ):
      """Construct heavy-tail Lambert W bijector.
  
      Args:
        tailweight: Floating point tensor; specifies the excess tail behaviors of
          the distribution(s). Must contain only non-negative values.
        validate_args: Python `bool`, default `False`. When `True` distribution
          parameters are checked for validity despite possibly degrading runtime
          performance. When `False` invalid inputs may silently render incorrect
          outputs.
        name: Python `str` name prefixed to Ops created by this class.
      """
      parameters = dict(locals())
      with tf.name_scope(name) as name:
          dtype = dtype_util.common_dtype([tailweight], tf.float32)
          self._tailweight = tensor_util.convert_nonref_to_tensor(
              tailweight, name="tailweight", dtype=dtype
          )
  
          super(_HeavyTailOnly, self).__init__(
              validate_args=validate_args,
              forward_min_event_ndims=0,
              parameters=parameters,
              name=name,
          )
  
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector
  from tensorflow_probability.python.bijectors import chain
  from tensorflow_probability.python.bijectors import invert
  from tensorflow_probability.python.bijectors import scale
  from tensorflow_probability.python.bijectors import shift
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.math import special
label_desc: Use image node_id 1 to create a new _HeavyTailOnly object from inherited base classes: bijector, bijector with example: obj = _HeavyTailOnly(tailweight, validate_args, name)