{"Copy", Func, 21},
		{"DeleteFunc", Func, 21},
		{"Equal", Func, 21},
		{"EqualFunc", Func, 21},
	},
	"math": {
		{"Abs", Func, 0},
		{"Acos", Func, 0},
		{"Acosh", Func, 0},
		{"Asin", Func, 0},
		{"Asinh", Func, 0},
		{"Atan", Func, 0},
		{"Atan2", Func, 0},
		{"Atanh", Func, 0},
		{"Cbrt", Func, 0},
		{"Ceil", Func, 0},
		{"Copysign", Func, 0},
		{"Cos", Func, 0},

	const src = `
package p

import (
	. "os"
	_ "io"
	"math"
	"path/filepath"
	snort "sort"
)

// avoid imported and not used errors
var (
	_ = Open // os.Open
	_ = math.Sin
	_ = filepath.Abs
	_ = snort.Ints
)
`

	var tests = []struct {
		path string // path string enclosed in "'s
		want string
	}{
		{`"os"`, "."},
		{`"io"`, "_"},
		{`"math"`, "math"},

	const src = `
package p

import (
	. "os"
	_ "io"
	"math"
	"path/filepath"
	snort "sort"
)

// avoid imported and not used errors
var (
	_ = Open // os.Open
	_ = math.Sin
	_ = filepath.Abs
	_ = snort.Ints
)
`

	var tests = []struct {
		path string // path string enclosed in "'s
		want string
	}{
		{`"os"`, "."},
		{`"io"`, "_"},
		{`"math"`, "math"},

  auto _min = _o->min;
  auto _max = _o->max;
  auto _num_bits = _o->num_bits;
  auto _narrow_range = _o->narrow_range;
  return tflite::CreateFakeQuantOptions(
      _fbb,
      _min,
      _max,
      _num_bits,
      _narrow_range);
}

    OpBuilder<(ins "Value":$cond, "Value":$x, "Value":$y),
    [{
    BuildSelectV2Op(&$_builder, $_state, cond, x, y);
  }]>];

  let hasOptions = 1;
}

def TFL_SinOp: TFL_Op<"sin", [
    Pure,
    TF_SameOperandsAndResultTypeResolveRef]> {
  let summary = "Sine operator";

  let description = [{
    Computes element-wise Sine of input
  }];

	_mo  = 784
	_mr  = 795
	_ms  = 799
	_mul = 806
	_my  = 817
	_nb  = 839
	_ne  = 849
	_nl  = 871
	_no  = 879
	_pa  = 925
	_pl  = 947
	_pt  = 960
	_ro  = 988
	_ru  = 994
	_sh  = 1031
	_si  = 1036
	_sk  = 1042
	_sl  = 1046
	_sq  = 1073
	_sr  = 1074
	_sv  = 1092
	_sw  = 1093
	_ta  = 1104
	_te  = 1121
	_th  = 1131
	_tl  = 1146
	_tn  = 1152
	_tr  = 1162
	_uk  = 1198
	_ur  = 1204

*   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`

FB2B          ; mapped                 ; 05E9 05C2     # 1.1  HEBREW LETTER SHIN WITH SIN DOT
FB2C          ; mapped                 ; 05E9 05BC 05C1 #1.1  HEBREW LETTER SHIN WITH DAGESH AND SHIN DOT
FB2D          ; mapped                 ; 05E9 05BC 05C2 #1.1  HEBREW LETTER SHIN WITH DAGESH AND SIN DOT
FB2E          ; mapped                 ; 05D0 05B7     # 1.1  HEBREW LETTER ALEF WITH PATAH

// Dm+1, ..., Dn]. Then
//   output[segment_ids[SI_i0, SI_i1, ..., SI_im], D_im+1, ..., D_in] =
//      <ReductionOp> over data[SI_i0, SI_i1, ..., SI_im, D_im+1, ..., D_in]
// where SI_iN is in the range of [0, SIN) and D_iN is in the range of [0, DN).
//
// The op will be translated to XLA HLO scatter with the following parameters:
// * Update window dims is [segment_id_rank, data_rank).
// * Inserted window dims is {0}.

    }, {
     %e0 = "tf.Neg"(%arg1) : (tensor<2xf32>) -> tensor<2xf32>
     %e1 = "tf.Relu"(%arg1) : (tensor<2xf32>) -> tensor<2xf32>
     %e2 = "tf.Sin"(%arg1) : (tensor<2xf32>) -> tensor<2xf32>
     "tf.Yield"(%e0, %e1, %e2) : (tensor<2xf32>, tensor<2xf32>, tensor<2xf32>) -> ()
    }) { is_stateless = false} : (tensor<i1>) -> (tensor<2xf32>, tensor<2xf32>, tensor<2xf32>)