// This method will actually merge the information contained in the
// types, it is capable of refining:
//   tensor<!tf_type.variant<tensor<?x8xf32>>>
// and:
//   tensor<!tf_type.variant<tensor<10x?xf32>>>
// into:
//   tensor<!tf_type.variant<tensor<10x8xf32>>>
//
// In case of inconsistencies (rank disagreement for example), it returns `lhs`.
Type TypeMeet(Type lhs, Type rhs) {

     * </pre>
     * @param property The property name for the message. (NotNull)
     * @param modType The parameter modType for message. (NotNull)
     * @param value The parameter value for message. (NotNull)
     * @return this. (NotNull)
     */
    public FessMessages addConstraintsModCheckMessage(String property, String modType, String value) {

	handle = Handle(r0)
	if handle == 0 {
		err = errnoErr(e1)
	}
	return
}

func createWellKnownSid(sidType WELL_KNOWN_SID_TYPE, domainSid *SID, sid *SID, sizeSid *uint32) (err error) {
	r1, _, e1 := syscall.Syscall6(procCreateWellKnownSid.Addr(), 4, uintptr(sidType), uintptr(unsafe.Pointer(domainSid)), uintptr(unsafe.Pointer(sid)), uintptr(unsafe.Pointer(sizeSid)), 0, 0)
	if r1 == 0 {
		err = errnoErr(e1)
	}

        }
        node->AddAttr("shape", index_it->second->second.shape);
        DataType dtype = index_it->second->second.imported_dtype;
        // Uses the existing output type if it isn't specified by the user.
        if (dtype == DT_INVALID) {
          dtype = node->output_type(0);
        }
        node->AddAttr("dtype", dtype);
      }
    }
    if (!node_added_to_shape_refiner) {

    return tflite::TensorType_UINT8;
  } else if (auto complex_type = mlir::dyn_cast<mlir::ComplexType>(type)) {
    auto ftype = complex_type.getElementType();
    if (ftype.isF32()) {
      return tflite::TensorType_COMPLEX64;
    }
    if (ftype.isF64()) {
      return tflite::TensorType_COMPLEX128;
    }
    return Status(absl::StatusCode::kInvalidArgument, "Unsupported type");

          dtype == tensorflow::DT_UINT16 || dtype == tensorflow::DT_INT16 ||
          dtype == tensorflow::DT_UINT8 || dtype == tensorflow::DT_INT8 ||
          dtype == tensorflow::DT_HALF || dtype == tensorflow::DT_BFLOAT16 ||
          dtype == tensorflow::DT_FLOAT || dtype == tensorflow::DT_DOUBLE ||
          dtype == tensorflow::DT_COMPLEX64 ||
          dtype == tensorflow::DT_COMPLEX128 || dtype == tensorflow::DT_BOOL) {
        return {};

#include "tensorflow/compiler/mlir/tensorflow/ir/tf_op_interfaces.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops_a_m.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_types.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/dynamic_shape_utils.h"
#include "tensorflow/core/framework/kernel_shape_util.h"

namespace mlir {
namespace TFL {