def __init__(self,
               diag_bijector=None,
               diag_shift=1e-5,
               validate_args=False,
               name="scale_tril"):
    """Instantiates the `ScaleTriL` bijector.

    Args:
      diag_bijector: `Bijector` instance, used to transform the output diagonal
        to be positive.
        Default value: `None` (i.e., `tfb.Softplus()`).
      diag_shift: Float value broadcastable and added to all diagonal entries
        after applying the `diag_bijector`. Setting a positive
        value forces the output diagonal entries to be positive, but
        prevents inverting the transformation for matrices with
        diagonal entries less than this value.
        Default value: `1e-5`.
      validate_args: Python `bool` indicating whether arguments should be
        checked for correctness.
        Default value: `False` (i.e., arguments are not validated).
      name: Python `str` name given to ops managed by this object.
        Default value: `scale_tril`.
    """
    with tf.name_scope(name) as name:
      if diag_bijector is None:
        diag_bijector = softplus.Softplus(validate_args=validate_args)

      if diag_shift is not None:
        diag_shift = tf.convert_to_tensor(
            value=diag_shift, dtype=diag_bijector.dtype, name="diag_shift")
        diag_bijector = chain.Chain([
            affine_scalar.AffineScalar(shift=diag_shift),
            diag_bijector
        ])

      super(ScaleTriL, self).__init__(
          [transform_diagonal.TransformDiagonal(diag_bijector=diag_bijector),
           fill_triangular.FillTriangular()],
          validate_args=validate_args,
          name=name)
def __init__ ( self , diag_bijector = None , diag_shift = 1e-5 , validate_args = False , name = "scale_tril" ) : with tf . name_scope ( name ) as name : if diag_bijector is None : diag_bijector = softplus . Softplus ( validate_args = validate_args ) if diag_shift is not None : diag_shift = tf . convert_to_tensor ( value = diag_shift , dtype = diag_bijector . dtype , name = "diag_shift" ) diag_bijector = chain . Chain ( [ affine_scalar . AffineScalar ( shift = diag_shift ) , diag_bijector ] ) super ( ScaleTriL , self ) . __init__ ( [ transform_diagonal . TransformDiagonal ( diag_bijector = diag_bijector ) , fill_triangular . FillTriangular ( ) ] , validate_args = validate_args , name = name )
Instantiates the `ScaleTriL` bijector.

    Args:
      diag_bijector: `Bijector` instance, used to transform the output diagonal
        to be positive.
        Default value: `None` (i.e., `tfb.Softplus()`).
      diag_shift: Float value broadcastable and added to all diagonal entries
        after applying the `diag_bijector`. Setting a positive
        value forces the output diagonal entries to be positive, but
        prevents inverting the transformation for matrices with
        diagonal entries less than this value.
        Default value: `1e-5`.
      validate_args: Python `bool` indicating whether arguments should be
        checked for correctness.
        Default value: `False` (i.e., arguments are not validated).
      name: Python `str` name given to ops managed by this object.
        Default value: `scale_tril`.