module attributes {tf.versions = {producer = 179 : i32}} {
  func.func @main(%arg0: tensor<f32>) -> tensor<f32> {
    %0 = "stablehlo.uniform_quantize"(%arg0) : (tensor<f32>) -> tensor<!quant.uniform<ui8:f32, 34.0:16>>
    %1 = "stablehlo.uniform_dequantize"(%0) : (tensor<!quant.uniform<ui8:f32, 34.0:16>>) -> tensor<f32>
    func.return %1 : tensor<f32>
  }
}

// CHECK-LABEL: HloModule main
// CHECK:       ENTRY %main.{{[0-9]+}} ([[ARG0:.*]]: f32[]) -> (f32[]) {

  let arguments = (ins
     TFL_TensorOf<[I8, I16, I32, I64, UI8, UI16, UI32, UI64, F32, F64]>:$input,
     TFL_TensorOf<[I32]>:$dilations,
     TFL_0DTensorOf<[I8, I16, I32, I64, UI8, UI16, UI32, UI64, F32, F64]>:$padding_value
  );

  let results = (outs TFL_TensorOf<[I8, I16, I32, I64, UI8, UI16, UI32, UI64, F32, F64]>:$output);
}

def TFL_AddOp : TFL_Op<"add", [

// `tfl.quantize` or `tfl.dequantize` ops. ui8, i8 and i16 are supported.
bool IsSupportedByTfliteQuantizeOrDequantizeOps(IntegerType storage_type) {
  if (storage_type.getWidth() == 8 ||
      (storage_type.isSigned() && storage_type.getWidth() == 16)) {
    return true;
  }
  LLVM_DEBUG(llvm::dbgs()
             << "Uniform quantize / dequantize op only supports ui8, i8 or "

    <I16>  6703  </I16>
    <I16Neg>  -6703  </I16Neg>
    <I32>  266703  </I32>
    <I32Neg>  -266703  </I32Neg>
    <I64>  266703  </I64>
    <I64Neg>  -266703  </I64Neg>
    <UI>   266703   </UI>
    <UI8>  112  </UI8>
    <UI16>  6703  </UI16>
    <UI32>  266703  </UI32>
    <UI64>  266703  </UI64>
    <F32>  266.703  </F32>
    <F32Neg>  -266.703  </F32Neg>
    <F64>  266.703  </F64>
    <F64Neg>  -266.703  </F64Neg>

bool IsI32F32UniformQuantizedPerAxisType(Type type);

// Determines whether the storage type of a quantized type is supported by
// `tfl.quantize` or `tfl.dequantize` ops. ui8, i8 and i16 are supported.
bool IsSupportedByTfliteQuantizeOrDequantizeOps(IntegerType storage_type);

// Returns true if a type is quantized tensor type.
bool IsQuantizedTensorType(Type type);

    return true;

  // Complex<F<32>> is allowed.
  if (mlir::isa<ComplexType>(elemType) &&
      mlir::cast<ComplexType>(elemType).getElementType().isF32())
    return true;

  // QUINT8 and UI8 are allowed.
  if (mlir::isa<TF::Quint8Type>(elemType) ||
      (elemType.isInteger(8) && mlir::cast<IntegerType>(elemType).isUnsigned()))
    return true;

  return false;
}

      {"F32", "32-bit float"},
      {"I32", "32-bit signless integer"},
      {"I64", "64-bit signless integer"},
      {"QI16", "QI16 type"},
      {"I8", "8-bit signless integer"},
      {"UI8", "8-bit unsigned integer"},
      {"QI8", "QI8 type"},
      {"QUI8", "QUI8 type"},
      {"TFL_Quint8", "TFLite quint8 type"},
  });

  return *entries;
}

	return "structKey"
}

func TestEmptyMapWithInterfaceKey(t *testing.T) {
	var (
		b    bool
		i    int
		i8   int8
		i16  int16
		i32  int32
		i64  int64
		ui   uint
		ui8  uint8
		ui16 uint16
		ui32 uint32
		ui64 uint64
		uipt uintptr
		f32  float32
		f64  float64
		c64  complex64
		c128 complex128
		a    [4]string
		s    string
		p    *int

include "stablehlo/dialect/ChloOps.td"
include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.td"
include "mhlo/IR/hlo_ops.td"

def SignedIntTensor : TensorOf<[I1, I8, I16, I32, I64]>;
def UnsignedIntTensor : TensorOf<[UI8, UI16, UI32, UI64]>;

// IEEE compliant floating point tensors.
def IEEEFloatTensor : TensorOf<[F16, F32, F64]>;

//===----------------------------------------------------------------------===//
// BatchNorm op patterns.

      (Arith_ConstantOp ConstantAttr<RankedF32ElementsAttr<[]>, "1.0f">),
      (Arith_ConstantOp ConstantAttr<RankedF32ElementsAttr<[]>, "0.0f">),
      ConstantAttr<I32Attr, "-1">),
    StaticShapeTensorOf<[F32, I8, UI8]>:$filter,
    $bias,
    TFL_AF_None,
    TFL_FCWO_Default,
    $keep_num_dims,
    $asymmetric_quantize_inputs),
  (TFL_ReshapeOp
  (TFL_EmbeddingLookupOp