// inputs, passes it to an instance of the specified reduction op and then
// returns the result.
template <typename ReductionOp>
LogicalResult MatchBinaryReduceFunction(mlir::Region& function) {
  Block& body = function.front();
  if (body.getNumArguments() != 2) return failure();

  mhlo::ReturnOp return_op = dyn_cast<mhlo::ReturnOp>(body.back());

}

// Check if the specified region is a binary reduction function that takes 2
// inputs and returns the second input. Functions like this are used by update
// scatter like ops.
template <>
LogicalResult MatchBinaryReduceFunction<void>(mlir::Region& function) {
  Block& body = function.front();
  if (body.getNumArguments() != 2) return failure();

  mhlo::ReturnOp return_op = dyn_cast<mhlo::ReturnOp>(body.back());

          !updates_type.hasStaticShape()) {
        return failure();
      }

      // Match the scatter computation against computations supported by TF.
      if (failed(MatchBinaryReduceFunction<BinaryOp>(
              scatter_op.getUpdateComputation()))) {
        return failure();
      }

      auto scatter_dimension_numbers = scatter_op.getScatterDimensionNumbers();

      mhlo::ReduceOp reduce_op, OpAdaptor adaptor,
      ConversionPatternRewriter& rewriter) const final {
    if (failed(MatchReduceOpOperand(reduce_op))) return failure();

    if (failed(MatchBinaryReduceFunction<BinaryOp>(reduce_op.getBody())))
      return failure();

    auto operand = reduce_op.getInputs()[0];

    // Get reduction dimension.
    DenseIntElementsAttr dimension = reduce_op.getDimensions();

class RewriteQuantizedReduceWindowOpWithMax
    : public OpRewritePattern<stablehlo::ReduceWindowOp> {
 public:
  using OpRewritePattern<stablehlo::ReduceWindowOp>::OpRewritePattern;

  LogicalResult MatchBinaryReduceFunction(Region& function) const {
    Block& body = function.front();
    if (body.getNumArguments() != 2) return failure();

    auto return_op = dyn_cast<stablehlo::ReturnOp>(body.back());