parameters: (self, event_ndims=0, validate_args=False, name="absolute_value")
docstring: Instantiates the `AbsoluteValue` bijector.
  
      Args:
        event_ndims: Python scalar indicating the number of dimensions associated
          with a particular draw from the distribution.  Currently only zero is
          supported.
        validate_args: Python `bool` indicating whether arguments should be
          checked for correctness, in particular whether inputs to `inverse` and
          `inverse_log_det_jacobian` are non-negative.
        name: Python `str` name given to ops managed by this object.
  
      Raises:
        ValueError:  If `event_ndims` is not zero.
docstring_summary: Instantiates the `AbsoluteValue` bijector.
docstring_tokens: Instantiates the AbsoluteValue bijector .
function: def __init__(self, event_ndims=0, validate_args=False, name="absolute_value"):
      """Instantiates the `AbsoluteValue` bijector.
  
      Args:
        event_ndims: Python scalar indicating the number of dimensions associated
          with a particular draw from the distribution.  Currently only zero is
          supported.
        validate_args: Python `bool` indicating whether arguments should be
          checked for correctness, in particular whether inputs to `inverse` and
          `inverse_log_det_jacobian` are non-negative.
        name: Python `str` name given to ops managed by this object.
  
      Raises:
        ValueError:  If `event_ndims` is not zero.
      """
      self._graph_parents = []
      self._name = name
  
      event_ndims = ops.convert_to_tensor(event_ndims, name="event_ndims")
      event_ndims_const = tensor_util.constant_value(event_ndims)
      if event_ndims_const is not None and event_ndims_const not in (0,):
        raise ValueError("event_ndims(%s) was not 0" % event_ndims_const)
      else:
        if validate_args:
          event_ndims = control_flow_ops.with_dependencies(
              [check_ops.assert_equal(
                  event_ndims, 0, message="event_ndims was not 0")],
              event_ndims)
  
      with self._name_scope("init"):
        super(AbsoluteValue, self).__init__(
            event_ndims=event_ndims,
            validate_args=validate_args,
            name=name)
function_tokens: def __init__ ( self , event_ndims = 0 , validate_args = False , name = "absolute_value" ) : self . _graph_parents = [ ] self . _name = name event_ndims = ops . convert_to_tensor ( event_ndims , name = "event_ndims" ) event_ndims_const = tensor_util . constant_value ( event_ndims ) if event_ndims_const is not None and event_ndims_const not in ( 0 , ) : raise ValueError ( "event_ndims(%s) was not 0" % event_ndims_const ) else : if validate_args : event_ndims = control_flow_ops . with_dependencies ( [ check_ops . assert_equal ( event_ndims , 0 , message = "event_ndims was not 0" ) ] , event_ndims ) with self . _name_scope ( "init" ) : super ( AbsoluteValue , self ) . __init__ ( event_ndims = event_ndims , validate_args = validate_args , name = name )