{"PSSSaltLengthEqualsHash", Const, 2},
		{"PrecomputedValues", Type, 0},
		{"PrecomputedValues.CRTValues", Field, 0},
		{"PrecomputedValues.Dp", Field, 0},
		{"PrecomputedValues.Dq", Field, 0},
		{"PrecomputedValues.Qinv", Field, 0},
		{"PrivateKey", Type, 0},
		{"PrivateKey.D", Field, 0},
		{"PrivateKey.Precomputed", Field, 0},
		{"PrivateKey.Primes", Field, 0},
		{"PrivateKey.PublicKey", Field, 0},
		{"PublicKey", Type, 0},

  );

  TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<0>;
}

def TF_InvOp : TF_Op<"Inv", [Pure, TF_SameOperandsAndResultTypeResolveRef]> {
  let summary = "Computes the reciprocal of x element-wise.";

  let description = [{
I.e., \\(y = 1 / x\\).
  }];

  let arguments = (ins

`tf.sparse_reduce_sum_sparse`: `reduction_axes` becomes `axis` *
`tf.sparse_split`: `split_dim` becomes `axis` * `tf.listdiff` has been renamed
to `tf.setdiff1d` to match NumPy naming. * `tf.inv` has been renamed to be
`tf.reciprocal` (component-wise reciprocal) to avoid confusion with `np.inv`
which is matrix inversion * tf.round now uses banker's rounding (round to even)
semantics to match NumPy. * `tf.split` now takes arguments in a reversed order