parameters: (self,
                 power=0.,
                 event_ndims=0,
                 validate_args=False,
                 name="power_transform")
docstring: Instantiates the `PowerTransform` bijector.
  
      Args:
        power: Python `float` scalar indicating the transform power, i.e.,
          `Y = g(X) = (1 + X * c)**(1 / c)` where `c` is the `power`.
        event_ndims: Python scalar indicating the number of dimensions associated
          with a particular draw from the distribution.
        validate_args: Python `bool` indicating whether arguments should be
          checked for correctness.
        name: Python `str` name given to ops managed by this object.
  
      Raises:
        ValueError: if `power < 0` or is not known statically.
docstring_summary: Instantiates the `PowerTransform` bijector.
docstring_tokens: Instantiates the PowerTransform bijector .
function: def __init__(self,
                 power=0.,
                 event_ndims=0,
                 validate_args=False,
                 name="power_transform"):
      """Instantiates the `PowerTransform` bijector.
  
      Args:
        power: Python `float` scalar indicating the transform power, i.e.,
          `Y = g(X) = (1 + X * c)**(1 / c)` where `c` is the `power`.
        event_ndims: Python scalar indicating the number of dimensions associated
          with a particular draw from the distribution.
        validate_args: Python `bool` indicating whether arguments should be
          checked for correctness.
        name: Python `str` name given to ops managed by this object.
  
      Raises:
        ValueError: if `power < 0` or is not known statically.
      """
      self._graph_parents = []
      self._name = name
      self._validate_args = validate_args
      with self._name_scope("init", values=[power]):
        power = tensor_util.constant_value(
            ops.convert_to_tensor(power, name="power"))
      if power is None or power < 0:
        raise ValueError("`power` must be a non-negative TF constant.")
      self._power = power
      super(PowerTransform, self).__init__(
          event_ndims=event_ndims,
          validate_args=validate_args,
          name=name)
function_tokens: def __init__ ( self , power = 0. , event_ndims = 0 , validate_args = False , name = "power_transform" ) : self . _graph_parents = [ ] self . _name = name self . _validate_args = validate_args with self . _name_scope ( "init" , values = [ power ] ) : power = tensor_util . constant_value ( ops . convert_to_tensor ( power , name = "power" ) ) if power is None or power < 0 : raise ValueError ( "`power` must be a non-negative TF constant." ) self . _power = power super ( PowerTransform , self ) . __init__ ( event_ndims = event_ndims , validate_args = validate_args , name = name )