fmt.Printf("%.2f", math.Copysign(3.2, -1))
	// Output: -3.20
}

func ExampleCos() {
	fmt.Printf("%.2f", math.Cos(math.Pi/2))
	// Output: 0.00
}

func ExampleCosh() {
	fmt.Printf("%.2f", math.Cosh(0))
	// Output: 1.00
}

func ExampleSin() {
	fmt.Printf("%.2f", math.Sin(math.Pi))
	// Output: 0.00
}

func ExampleSincos() {
	sin, cos := math.Sincos(0)
	fmt.Printf("%.2f, %.2f", sin, cos)

TEXT ·coshTrampolineSetup(SB), NOSPLIT, $0
	MOVB   ·hasVX(SB), R1
	CMPBEQ R1, $1, vectorimpl                // vectorfacility = 1, vector supported
	MOVD   $·coshvectorfacility+0x00(SB), R1
	MOVD   $·cosh(SB), R2
	MOVD   R2, 0(R1)
	BR     ·cosh(SB)

vectorimpl:
	MOVD $·coshvectorfacility+0x00(SB), R1
	MOVD $·coshAsm(SB), R2
	MOVD R2, 0(R1)
	BR   ·coshAsm(SB)

GLOBL ·coshvectorfacility+0x00(SB), NOPTR, $8

Status SinhGrad(const Scope& scope, const Operation& op,
                const std::vector<Output>& grad_inputs,
                std::vector<Output>* grad_outputs) {
  // y = sinh(x)
  // dy/dx = cosh(x)
  auto dydx = Cosh(scope, op.input(0));
  // grad(x) = grad(y) * conj(dy/dx)
  grad_outputs->push_back(
      Mul(scope, grad_inputs[0], ConjugateHelper(scope, dydx)));
  return scope.status();
}

        TypeID::get<TF::ComplexAbsOp>(),
        TypeID::get<TF::ConjugateTransposeOp>(),
        TypeID::get<TF::ConcatV2Op>(),
        TypeID::get<TF::ConvOp>(),
        TypeID::get<TF::CoshOp>(),
        TypeID::get<TF::CrossOp>(),
        TypeID::get<TF::CumulativeLogsumexpOp>(),
        TypeID::get<TF::DataFormatDimMapOp>(),
        TypeID::get<TF::DataFormatVecPermuteOp>(),

      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",
            "Log1p", "Invert", "LogicalNot", "Ndtri", "Neg", "Rint", "Round",

INFER_RETURN_TYPE_COMPONENTS_FROM_OPERANDS(CollectiveReduceOp);
INFER_RETURN_TYPE_COMPONENTS_FROM_OPERANDS(ConjOp);
INFER_RETURN_TYPE_COMPONENTS_FROM_OPERANDS(CosOp);
INFER_RETURN_TYPE_COMPONENTS_FROM_OPERANDS(CoshOp);
INFER_RETURN_TYPE_COMPONENTS_FROM_OPERANDS(CrossOp);
INFER_RETURN_TYPE_COMPONENTS_FROM_OPERANDS(DataFormatDimMapOp);
INFER_RETURN_TYPE_COMPONENTS_FROM_OPERANDS(DataFormatVecPermuteOp);

		{"Asinh", Func, 0},
		{"Atan", Func, 0},
		{"Atan2", Func, 0},
		{"Atanh", Func, 0},
		{"Cbrt", Func, 0},
		{"Ceil", Func, 0},
		{"Copysign", Func, 0},
		{"Cos", Func, 0},
		{"Cosh", Func, 0},
		{"Dim", Func, 0},
		{"E", Const, 0},
		{"Erf", Func, 0},
		{"Erfc", Func, 0},
		{"Erfcinv", Func, 10},
		{"Erfinv", Func, 10},
		{"Exp", Func, 0},
		{"Exp2", Func, 0},

*   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`

    static bool isCompatibleReturnTypes(TypeRange inferred, TypeRange actual) {
      return ArraysAreCastCompatible(inferred, actual);
    }
  }];
}

def TF_CoshOp : TF_Op<"Cosh", [Pure, TF_SameOperandsAndResultTypeResolveRef]> {
  let summary = "Computes hyperbolic cosine of x element-wise.";

  let description = [{
Given an input tensor, this function computes hyperbolic cosine of every

pkg math, func Atanh(float64) float64
pkg math, func Cbrt(float64) float64
pkg math, func Ceil(float64) float64
pkg math, func Copysign(float64, float64) float64
pkg math, func Cos(float64) float64
pkg math, func Cosh(float64) float64
pkg math, func Dim(float64, float64) float64
pkg math, func Erf(float64) float64
pkg math, func Erfc(float64) float64
pkg math, func Exp(float64) float64
pkg math, func Exp2(float64) float64