};

  bool need_to_set_input_nodes_quantization_params = false;
  for (const BlockArgument arg : func.getArguments()) {
    auto shaped = mlir::dyn_cast<ShapedType>(arg.getType());
    if (shaped && mlir::isa<FloatType>(shaped.getElementType()) &&
        !has_quantize_op(arg)) {
      need_to_set_input_nodes_quantization_params = true;
      break;
    }
  }

  if (!need_to_set_input_nodes_quantization_params) {

      Operation* op, QuantizedMultipliers* input_multipliers,
      QuantizedMultipliers* output_multipliers, QuantizedRanges* output_ranges);

  // A set of parameters are required to build the signatures.
  FloatType f32_;
  IntegerType i8_, i32_;
  int64_t i8_min_, i8_max_, i32_min_, i32_max_;
  quant::AnyQuantizedType any_, qi8_, qi8n_, qi32_;

 private:
  // Maps the kernel names to all the available kernels.

      auto op = this->getOperation();
      const auto result_type =
          op->getResult(index).getType().template cast<ShapedType>();
      if (!result_type.getElementType().template isa<FloatType>()) return {};
      Builder builder(op->getContext());
      const IntegerType storage_type = builder.getIntegerType(BitWidth);
      const double scale = static_cast<double>(ScaleMantissa) *

            .Case<IntegerType>([&](Type) {
              dtype =
                  absl::StrCat("i", operand_or_result.getIntOrFloatBitWidth());
            })
            .Case<FloatType>([&](Type) {
              dtype =
                  absl::StrCat("f", operand_or_result.getIntOrFloatBitWidth());
            })
            .Case<UniformQuantizedType>([&](Type) {

// Checks if the element value has a float type.
def IsFloatElementsAttr : ElementsAttrBase<
  CPred<"$_self.isa<ElementsAttr>() && "
        "getElementTypeOrSelf($_self.cast<ElementsAttr>().getType()).isa<FloatType>()">,
        "float constant tensor">;

// Checks if the boolean value is false.
def IsFalseBoolAttr : AttrConstraint<
  CPred<"!$_self.cast<BoolAttr>().getValue()">>;

// Checks if the value has only one user.

			},
		},
		{name: "numbers",
			obj:    objs(math.MaxFloat64, math.MaxFloat64, math.MaxFloat32, math.MaxFloat32, math.MaxFloat64, math.MaxFloat64, int64(1)),
			schema: schemas(numberType, numberType, floatType, floatType, doubleType, doubleType, doubleType),
			expectCost: map[string]int64{
				ValsEqualThemselvesAndDataLiteral("self.val1", "self.val2", fmt.Sprintf("%f", math.MaxFloat64)): 11,

// An IntType represents a signed integer type.
type IntType struct {
	BasicType
}

// A UintType represents an unsigned integer type.
type UintType struct {
	BasicType
}

// A FloatType represents a floating point type.
type FloatType struct {
	BasicType
}

// A ComplexType represents a complex floating point type.
type ComplexType struct {
	BasicType
}

// A BoolType represents a boolean type.

  };

  bool need_to_set_input_nodes_quantization_params = false;
  for (const BlockArgument arg : func.getArguments()) {
    auto shaped = mlir::dyn_cast<ShapedType>(arg.getType());
    if (shaped && mlir::isa<FloatType>(shaped.getElementType()) &&
        !has_quantize_op(arg)) {
      need_to_set_input_nodes_quantization_params = true;
      break;
    }
  }

  if (!need_to_set_input_nodes_quantization_params) {

      return;
    }

    // Get types
    TensorType old_result_type =
        mlir::dyn_cast<TensorType>(op.getResult().getType());
    FloatType quantized_type = FloatType::getF16(op.getContext());
    ShapedType new_result_type = old_result_type.clone(quantized_type);

    // Insert CastOp if it does not exist yet. Otherwise, just rewire without
    // creating a CastOp.

  using ConvertReduceOpToTfOp::ConvertReduceOpToTfOp;

  LogicalResult MatchInitValue(Value init_value) const override {
    auto type = mlir::cast<ShapedType>(init_value.getType()).getElementType();
    if (mlir::isa<FloatType>(type)) {
      float const_value;
      if (failed(GetConstantSplatValue<float>(init_value, const_value)) ||
          const_value != 1.0)
        return failure();