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","."]
docstr: Initializes the class.
  
  See `tfp.distributions.LambertWDistribution` for details.
  
  Args:
    loc: location parameter `loc` of the Normal distribution(s). This
      coincides with the location parameter of the resulting LambertWNormal.
    scale: scale parameter `scale` of the Normal distribution(s).
    tailweight: Tail parameter `delta` of the distribution(s). If `None`, it
      defaults to 0, which implies LambertWNormal == Normal.
    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.
    allow_nan_stats: Python `bool`, default `True`. When `True`,
      statistics (e.g., mean, mode, variance) use the value '`NaN`' to
      indicate the result is undefined. When `False`, an exception is raised
      if one or more of the statistic's batch members are undefined.
    name: A name for the operation (optional).
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code: def __init__(
      self,
      loc,
      scale,
      tailweight=None,
      validate_args=False,
      allow_nan_stats=True,
      name="LambertWNormal",
  ):
      """Initializes the class.
  
      See `tfp.distributions.LambertWDistribution` for details.
  
      Args:
        loc: location parameter `loc` of the Normal distribution(s). This
          coincides with the location parameter of the resulting LambertWNormal.
        scale: scale parameter `scale` of the Normal distribution(s).
        tailweight: Tail parameter `delta` of the distribution(s). If `None`, it
          defaults to 0, which implies LambertWNormal == Normal.
        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.
        allow_nan_stats: Python `bool`, default `True`. When `True`,
          statistics (e.g., mean, mode, variance) use the value '`NaN`' to
          indicate the result is undefined. When `False`, an exception is raised
          if one or more of the statistic's batch members are undefined.
        name: A name for the operation (optional).
      """
      parameters = dict(locals())
      with tf.name_scope(name) as name:
          dtype = dtype_util.common_dtype(
              [tailweight, loc, scale], tf.float32
          )
          super(LambertWNormal, self).__init__(
              distribution=normal.Normal(loc=loc, scale=scale),
              shift=loc,
              scale=scale,
              tailweight=tailweight,
              validate_args=validate_args,
              allow_nan_stats=allow_nan_stats,
              name=name,
          )
          self._parameters = parameters
          self._loc = tensor_util.convert_nonref_to_tensor(
              loc, name="loc", dtype=dtype
          )
          dtype_util.assert_same_float_dtype(
              (self.tailweight, self.loc, self.scale)
          )
  
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 1 to create a new LambertWNormal object from inherited base classes: LambertWDistribution with example: obj = LambertWNormal(loc, scale, tailweight, validate_args, allow_nan_stats, name)