bool IsI8ToF32Cast(stablehlo::ConvertOp convert_op) {
  const bool is_i8_operand =
      convert_op.getOperand().getType().getElementType().isInteger(/*width=*/8);
  const bool is_f32_result =
      mlir::isa<Float32Type>(convert_op.getResult().getType().getElementType());
  return is_i8_operand && is_f32_result;
}

// Tests whether a `stablehlo::ConvertOp` is a i8 -> i32 cast.

        loc, tensorflow::GetTypeFromTFTensorShape({}, rewriter.getI1Type()),
        size_diff, scalar_zero);

    // Build the argument/result types for if branch function.
    auto input_shape = rewriter.create<TF::ShapeOp>(
        loc, tensorflow::GetTypeFromTFTensorShape({-1}, shape_dtype),
        input_handle);

    Type branch_args_type[] = {input_handle.getType(), input_shape.getType(),

  "TFL::IsBroadcastableElementsAttrAndType($0.getType(), $1.getType())">>;

def OperandsBroadcastToOutputType : Constraint<CPred<
  "TFL::OperandsBroadcastToOutputType($0.getType(), $1.getType(), "
                                     "$2.getType())">>;

def IsTailOfShape : Constraint<CPred<
  "TFL::IsTailOfShape($0.getType(), $1.getType())">>;

def IsReducedTailOfShape : Constraint<CPred<

bool IsResource(Value value) {
  return mlir::isa<TF::ResourceType>(getElementTypeOrSelf(value.getType()));
}

// Get the type of the data contained in a resource. Returns null if there is
// no single type in the resource.
Type GetResourceSubtype(Value value) {
  auto resource_type =
      mlir::dyn_cast<TF::ResourceType>(getElementTypeOrSelf(value.getType()));
  auto subtypes = resource_type.getSubtypes();

    auto input_type = mlir::dyn_cast_or_null<RankedTensorType>(input.getType());
    if (!input_type) return failure();
    auto fft_len = rfft_op.getFftLength();
    auto fft_len_type = mlir::dyn_cast_or_null<ShapedType>(fft_len.getType());
    if (!fft_len_type) return failure();

    auto output_type =
        mlir::dyn_cast_or_null<RankedTensorType>(rfft_op.getResult().getType());
    if (!output_type) return failure();

    auto loc = op.getLoc();
    auto result_ty = mlir::cast<ShapedType>(op.getType());

    auto input = op.getImages();
    auto input_ty = mlir::cast<ShapedType>(input.getType());
    auto input_element_ty = input_ty.getElementType();
    auto out_size = op.getSize();
    auto out_size_ty = mlir::cast<ShapedType>(out_size.getType());
    auto out_size_element_ty = out_size_ty.getElementType();

    new_quantized_func.setType(
        FunctionType::get(getContext(), TypeRange{ValueRange{args}},
                          new_quantized_func.getResultTypes()));
    for (auto [partitioned_call_arg, new_quantized_func_arg] :
         llvm::zip_equal(args, new_quantized_func.getArguments())) {
      new_quantized_func_arg.setType(partitioned_call_arg.getType());
    }

  llvm::SmallVector<Type, 4> operand_types;
  operand_types.reserve(inputs.size());
  for (Value v : inputs) operand_types.emplace_back(v.getType());
  llvm::SmallVector<Type, 4> output_types;
  output_types.reserve(outputs.size());
  for (Value v : outputs) output_types.emplace_back(v.getType());

  auto func_type = builder->getFunctionType(operand_types, output_types);

  FuncOp outlined_func =

        // using convention mapping.
        return Provider.class.isAssignableFrom(property.getType()) || SupportsConvention.class.isAssignableFrom(property.getType());
    }

    private static boolean isLazyAttachProperty(PropertyMetadata property) {

        return this.isExists;
    }


    @Override
    public int getType () throws SmbException {
        try {
            int t = this.fileLocator.getType();
            if ( t == TYPE_SHARE ) {
                try ( SmbTreeHandle th = ensureTreeConnected() ) {
                    this.fileLocator.updateType(th.getTreeType());