for i := 0; i < len(vf); i++ {
		a := 1 + Abs(vf[i])
		if f := Acosh(a); !veryclose(acosh[i], f) {
			t.Errorf("Acosh(%g) = %g, want %g", a, f, acosh[i])
		}
	}
	for i := 0; i < len(vfacoshSC); i++ {
		if f := Acosh(vfacoshSC[i]); !alike(acoshSC[i], f) {
			t.Errorf("Acosh(%g) = %g, want %g", vfacoshSC[i], f, acoshSC[i])
		}
	}
}

func TestAsin(t *testing.T) {

// Method :
//	Based on
//	        acosh(x) = log [ x + sqrt(x*x-1) ]
//	we have
//	        acosh(x) := log(x)+ln2,	if x is large; else
//	        acosh(x) := log(2x-1/(sqrt(x*x-1)+x)) if x>2; else
//	        acosh(x) := log1p(t+sqrt(2.0*t+t*t)); where t=x-1.
//
// Special cases:
//	acosh(x) is NaN with signal if x<1.
//	acosh(NaN) is NaN without signal.
//

// Acosh returns the inverse hyperbolic cosine of x.
//

}

TEST_F(CWiseUnaryGradTest, Acosh) {
  auto x_fn = [this](const int i) { return RV({1.5, 2, 2.5}); };
  TestCWiseGrad<float, float>(ACOSH, x_fn);
}

TEST_F(CWiseUnaryGradTest, Acosh_Complex) {
  auto x_fn = [this](const int i) {
    return CRV({{1, 0.5}, {0.5, 1}, {0.5, -1}, {1, 1.5}});
  };
  TestCWiseGrad<complex64, complex64>(ACOSH, x_fn);
}

TEST_F(CWiseUnaryGradTest, Atanh) {

package math_test

import (
	"fmt"
	"math"
)

func ExampleAcos() {
	fmt.Printf("%.2f", math.Acos(1))
	// Output: 0.00
}

func ExampleAcosh() {
	fmt.Printf("%.2f", math.Acosh(1))
	// Output: 0.00
}

func ExampleAsin() {
	fmt.Printf("%.2f", math.Asin(0))
	// Output: 0.00
}

func ExampleAsinh() {
	fmt.Printf("%.2f", math.Asinh(0))
	// Output: 0.00
}

  // end, which would not be thread safe.

  static auto* ops = [] {
    llvm::SmallDenseSet<mlir::TypeID, 512>* ops_set = new llvm::SmallDenseSet<
        mlir::TypeID, 512>{
        TypeID::get<TF::AcoshOp>(),
        TypeID::get<TF::AcosOp>(),
        TypeID::get<TF::AddNOp>(),
        TypeID::get<TF::AddV2Op>(),
        TypeID::get<TF::AngleOp>(),
        TypeID::get<TF::AdjustContrastv2Op>(),

                 std::vector<Output>* grad_outputs) {
  // y = acosh(x)
  // dy/dx = 1 / sinh(y)
  auto dydx = Reciprocal(scope, Sinh(scope, op.output(0)));
  // grad(x) = grad(y) * conj(dy/dx)
  grad_outputs->push_back(
      Mul(scope, grad_inputs[0], ConjugateHelper(scope, dydx)));
  return scope.status();
}
REGISTER_GRADIENT_OP("Acosh", AcoshGrad);

Status AtanhGrad(const Scope& scope, const Operation& op,

  static absl::flat_hash_map<string, std::vector<string>>* result =
      new absl::flat_hash_map<string, std::vector<string>>{
          // Unary
          {"PW",
           {"ComplexAbs", "Angle", "Conj", "Abs", "Acos", "Acosh", "Asin",
            "Atan", "Atanh", "Ceil", "Cos", "Cosh", "Sin", "Exp", "Expm1",
            "Floor", "IsFinite", "IsInf", "IsNan", "Inv", "Reciprocal", "Log",

}  // namespace

INFER_RETURN_TYPE_COMPONENTS_FROM_OPERANDS(AbsOp);
INFER_RETURN_TYPE_COMPONENTS_FROM_OPERANDS(AcosOp);
INFER_RETURN_TYPE_COMPONENTS_FROM_OPERANDS(AcoshOp);
INFER_RETURN_TYPE_COMPONENTS_FROM_OPERANDS(AsinOp);
INFER_RETURN_TYPE_COMPONENTS_FROM_OPERANDS(AsinhOp);
INFER_RETURN_TYPE_COMPONENTS_FROM_OPERANDS(AtanOp);
INFER_RETURN_TYPE_COMPONENTS_FROM_OPERANDS(AtanhOp);

  %0, %1, %2 = "tf.IfRegion"(%arg0) ({
     %t0 = "tf.Abs"(%arg1) : (tensor<2xf32>) -> tensor<2xf32>
     %t1 = "tf.Acos"(%arg1) : (tensor<2xf32>) -> tensor<2xf32>
     %t2 = "tf.Acosh"(%arg1) : (tensor<2xf32>) -> tensor<2xf32>
    "tf.Yield"(%t0, %t1, %t2) : (tensor<2xf32>, tensor<2xf32>, tensor<2xf32>) -> ()
    }, {
     %e0 = "tf.Neg"(%arg1) : (tensor<2xf32>) -> tensor<2xf32>

*   Enable JIT-compiled i64-indexed kernels on GPU for large tensors with more than 2**32 elements.
    *   Unary GPU kernels: Abs, Atanh, Acos, Acosh, Asin, Asinh, Atan, Cos, Cosh, Sin, Sinh, Tan, Tanh.
    *   Binary GPU kernels: AddV2, Sub, Div, DivNoNan, Mul, MulNoNan, FloorDiv, Equal, NotEqual, Greater, GreaterEqual, LessEqual, Less.

* `tf.lite`