builder.opts().WithName("A"));
    Node* tanh0 = ops::UnaryOp("Tanh", call, builder.opts().WithName("tanh0"));
    Node* tanh1 = ops::UnaryOp("Tanh", tanh0, builder.opts().WithName("tanh1"));
    ops::UnaryOp("Tanh", tanh1, builder.opts().WithName("tanh2"));
    TF_EXPECT_OK(GraphDefBuilderToGraph(builder, graph.get()));
  }

}

// Complex hyperbolic tangent
//
// DESCRIPTION:
//
// tanh z = (sinh 2x  +  i sin 2y) / (cosh 2x + cos 2y) .
//
// ACCURACY:
//
//                      Relative error:
// arithmetic   domain     # trials      peak         rms
//    IEEE      -10,+10     30000       1.7e-14     2.4e-16

// Tanh returns the hyperbolic tangent of x.
func Tanh(x complex128) complex128 {
	switch re, im := real(x), imag(x); {

var SinNoVec = sin
var SinhNoVec = sinh
var TanhNoVec = tanh
var Log1pNovec = log1p
var AtanhNovec = atanh
var AcosNovec = acos
var AcoshNovec = acosh
var AsinNovec = asin
var AsinhNovec = asinh
var ErfNovec = erf
var ErfcNovec = erfc
var AtanNovec = atan
var Atan2Novec = atan2
var CbrtNovec = cbrt
var LogNovec = log
var TanNovec = tan
var ExpNovec = exp
var Expm1Novec = expm1
var PowNovec = pow

  // CHECK-NEXT: [[r2:%.*]] = tfrt_fallback_async.executeop {{.*}} "tf.Tanh"([[r1]]) {T = f32}
  %4 = "tf.Tanh"(%3) {T = f32, _output_shapes = ["tfshape$dim { size: 3 } dim { size: 3 }"], device = "/device:CPU:0"} : (tensor<3x3xf32>) -> tensor<3x3xf32>
  // CHECK-NOT: tf.Identity

		if f := TanNovec(vfsinSC[i]); !alike(sinSC[i], f) {
			t.Errorf("Tan(%g) = %g, want %g", vfsinSC[i], f, sinSC[i])
		}
	}
}

func TestTanhNovec(t *testing.T) {
	if !HasVX {
		t.Skipf("no vector support")
	}
	for i := 0; i < len(vf); i++ {
		if f := TanhNoVec(vf[i]); !veryclose(tanh[i], f) {
			t.Errorf("Tanh(%g) = %g, want %g", vf[i], f, tanh[i])
		}
	}
	for i := 0; i < len(vftanhSC); i++ {

  res = tf.raw_ops.Add(x=res, y=bias)
  if act == 'RELU':
    return tf.raw_ops.Relu(features=res)
  elif act == 'RELU6':
    return tf.raw_ops.Relu6(features=res)
  elif act == 'TANH':
    return tf.raw_ops.Tanh(x=res)
  else:
    return res

```

Besides defining new ops, composition can be specified for an existing op
for portability. The following code defines the semantics of `AddNOp`:

```python
@Composite('AddNOp')

        break;
      case COSH:
        y = Cosh(scope_, x);
        break;
      case TANH:
        y = Tanh(scope_, x);
        break;
      case ASINH:
        y = Asinh(scope_, x);
        break;
      case ACOSH:
        y = Acosh(scope_, x);
        break;
      case ATANH:
        y = Atanh(scope_, x);
        break;
      case SIGMOID:
        y = Sigmoid(scope_, x);
        break;

    %9, %9, %9, %9) {
      asymmetric_quantize_inputs = false,
      cell_clip = 1.000000e+01 : f32,
      effective_hidden_scale_intermediate = tensor<0x!quant.calibrated<f32<0.0:1.0>>>,
      fused_activation_function = "TANH",
      input_to_cell_intermediate = tensor<0xf32>,
      input_to_forget_intermediate = tensor<0xf32>,
      input_to_input_intermediate = tensor<0xf32>,
      input_to_output_intermediate = tensor<0xf32>,

	for i := 0; i < len(vf); i++ {
		if f := Tanh(vf[i]); !veryclose(tanh[i], f) {
			t.Errorf("Tanh(%g) = %g, want %g", vf[i], f, tanh[i])
		}
	}
	for i := 0; i < len(vftanhSC); i++ {
		if f := Tanh(vftanhSC[i]); !alike(tanhSC[i], f) {
			t.Errorf("Tanh(%g) = %g, want %g", vftanhSC[i], f, tanhSC[i])
		}
	}
}

func TestTrunc(t *testing.T) {
	for i := 0; i < len(vf); i++ {

"tfl.lstm"(%arg0, %arg1, %arg2, %arg3, %arg4, %arg5, %arg6, %arg7, %arg8, %cst, %cst, %cst, %arg9, %arg10, %arg11, %arg12, %arg13, %arg14, %arg19, %arg20, %arg15, %arg16, %arg17, %arg18) ({}) {cell_clip = 1.000000e+01 : f32, fused_activation_function = "TANH", input_to_input_intermediate = tensor<0x!quant.uniform<i16:f32, 0.0049890000373125076>>, input_to_forget_intermediate = tensor<0x!quant.uniform<i16:f32, 0.0078849997371435165>>, input_to_cell_intermediate = tensor<0x!quant.uniform<i16:f32, 0.0087630003690719604>>,...