// RUN: tf-opt %s -tfl-prepare-quantize-dynamic-range="enable-float16-quantization" -tfl-quantize="enable-dynamic-range-quantization=true" | FileCheck --check-prefix=CHECK %s

// CHECK-LABEL: QuantizeUnidirectionalLstm
func.func @QuantizeUnidirectionalLstm(%arg0: tensor<1x2x3xf32>) -> (tensor<1x2x3xf32>) {
  %1 = "tfl.pseudo_const"() {value = dense<[[0.1]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>

// to the decoded value.
func (z *Float) GobDecode(buf []byte) error {
	if len(buf) == 0 {
		// Other side sent a nil or default value.
		*z = Float{}
		return nil
	}
	if len(buf) < 6 {
		return errors.New("Float.GobDecode: buffer too small")
	}

	if buf[0] != floatGobVersion {
		return fmt.Errorf("Float.GobDecode: encoding version %d not supported", buf[0])
	}

	oldPrec := z.prec

      // If the tensor is a weight, it should have type FLOAT16.
      // If the tensor is a bias, it should have type FLOAT16.
      // If the tensor is an input or output it should have type FLOAT32.
      // The input to dequantize should be FLOAT16, and all other tensors should
      // be FLOAT32.
      if (i == dequant_input_idx) {
        EXPECT_EQ(quant_tensor->type(), TensorType_FLOAT16);

//
// The `input_type`, `output_type` and `inference_type` can be float32 / qint8 /
// int8 / int16.
//
// Returns a partially quantized model if `fully_quantize` is false. Returns a
// non-OK status if the quantization fails.
//
// When `verify_numeric` is true, the model will have it's original float ops
// and NumericVerify ops to compare output values from the quantized and float
// ops.
//

    "'h_scale' type: 'float'} attr : { name: 'max_classes_per_detection' "
    "type: 'int'} attr : { name: 'max_detections' type: 'int'} attr : { "
    "name: 'nms_iou_threshold' type: 'float'} attr : { name: "
    "'nms_score_threshold' type: 'float'} attr : { name: 'num_classes' type: "
    "'int'} attr : { name: 'w_scale' type: 'float'} attr : { name: 'x_scale' "
    "type: 'float'} attr : { name: 'y_scale' type: 'float'} attr { name: "

}

func (v *Int) Set(value int64) {
	v.i.Store(value)
}

// Float is a 64-bit float variable that satisfies the [Var] interface.
type Float struct {
	f atomic.Uint64
}

func (v *Float) Value() float64 {
	return math.Float64frombits(v.f.Load())
}

func (v *Float) String() string {
	return string(v.appendJSON(nil))
}

func (v *Float) appendJSON(b []byte) []byte {

	"unsafe"
)

type Struct struct {
	Int8       int8
	Int16      int16
	Int32      int32
	Int64      int64
	Uint8      uint8
	Uint16     uint16
	Uint32     uint32
	Uint64     uint64
	Float32    float32
	Float64    float64
	Complex64  complex64
	Complex128 complex128
	Array      [4]uint8
	Bool       bool
	BoolArray  [4]bool
}

type T struct {
	Int     int
	Uint    uint
	Uintptr uintptr

			return []string{"-mabi=64", "-mhard-float"}
		} else if gomips64 == "softfloat" {
			return []string{"-mabi=64", "-msoft-float"}
		}
	case "mips", "mipsle":
		if gomips == "hardfloat" {
			return []string{"-mabi=32", "-mfp32", "-mhard-float", "-mno-odd-spreg"}
		} else if gomips == "softfloat" {
			return []string{"-mabi=32", "-msoft-float"}
		}
	case "loong64":

		{`package e0; const _ = float32( 1e-200)`, `float32(1e-200)`, `float32`, `0`},
		{`package e1; const _ = float32(-1e-200)`, `float32(-1e-200)`, `float32`, `0`},
		{`package e2; const _ = float64( 1e-2000)`, `float64(1e-2000)`, `float64`, `0`},
		{`package e3; const _ = float64(-1e-2000)`, `float64(-1e-2000)`, `float64`, `0`},

			return
		}

		x.mode = novalue
		if check.recordTypes() {
			check.recordBuiltinType(call.Fun, makeSig(nil, map_, key))
		}

	case _Imag, _Real:
		// imag(complexT) floatT
		// real(complexT) floatT

		// convert or check untyped argument
		if isUntyped(x.typ) {
			if x.mode == constant_ {
				// an untyped constant number can always be considered
				// as a complex constant