if (op->getOperands().size() != 2)
      return rewriter.notifyMatchFailure(
          op, "only match for the case where operands is of size 2");
    auto keys = op.getInputs()[0];
    auto indices = op.getInputs()[1];
    auto keys_ty = mlir::dyn_cast_or_null<ShapedType>(keys.getType());
    auto indices_ty = mlir::dyn_cast_or_null<ShapedType>(indices.getType());
    if (!keys_ty || !keys_ty.hasStaticShape() ||

  constexpr int kValues = 0;
  constexpr int kRowSplits = 1;

  if (func.getFunctionType().getInputs().size() !=
      func.getFunctionType().getResults().size()) {
    return func.emitError() << "Mismatched number of inputs and outputs.";
  }

  int row_splits = func.getFunctionType().getInputs().size() - kRowSplits;
  if (row_splits == 0) {
    auto input_values = GetInputType(func, kValues);

                   dyn_cast_or_null<TF::TPUReplicatedInputOp>(op)) {
      // TODO(b/267700110): Handle multiple input/output cases.
      rewriter.replaceOp(replicated_input_op, replicated_input_op.getInputs());
    } else if (auto replicated_output_op =
                   dyn_cast_or_null<TF::TPUReplicatedOutputOp>(op)) {
      // TODO(b/267700110): Handle multiple input/output cases.

    // this is a forced/hard-coded constraint.
    auto qbarrier = rewriter.create<QuantizeCastOp>(op.getLoc(), quantizedType,
                                                    op.getInputs());
    rewriter.replaceOpWithNewOp<DequantizeCastOp>(op, converter.input_type,
                                                  qbarrier.getResult());

    return false;
  }
};

class ConstFakeQuantRewrite

    llvm::SmallVectorImpl<std::string>* var_handle_shared_names) {
  Block& block = function.front();
  auto func_type = function.getFunctionType();

  auto func_arg_types = llvm::to_vector<4>(func_type.getInputs());
  llvm::SmallDenseMap<llvm::StringRef, int> var_arg_index_by_name;
  for (auto var_handle_op :
       llvm::make_early_inc_range(block.getOps<TF::VarHandleOp>())) {

            task.setTitle("Gradle API " + version);

            Javadocs javadocs = extension.getJavadocs();

            // TODO: This should be part of Javadoc task
            task.getInputs().file(javadocs.getJavadocCss())
                .withPropertyName("stylesheetFile")
                .withPathSensitivity(PathSensitivity.NAME_ONLY);

    // constants and the tf.FakeQuantWithMinMaxVarsOp.
    FetchAttrType min_value, max_value;
    if (!fetch_min_max_(tf_op, min_value, max_value)) {
      return failure();
    }

    Value input = tf_op.getInputs();
    int quant_dim = -1;
    auto input_type = mlir::cast<ShapedType>(input.getType());
    if (PerAxis) {
      if (!input_type.hasRank()) {

  LogicalResult matchAndRewrite(stablehlo::ReduceWindowOp reduce_op,
                                PatternRewriter& rewriter) const override {
    MLIRContext* ctx = reduce_op.getContext();
    ValueRange inputs = reduce_op.getInputs();
    // Only handle binary reduce ops for now
    if (inputs.size() != 1) return failure();
    Value reduce_input = inputs[0];

    RankedTensorType reduce_type =

  FunctionType func_type = func.getFunctionType();
  if (llvm::ArrayRef(return_types) == func_type.getResults()) return;

  auto updated_type =
      FunctionType::get(func.getContext(), func_type.getInputs(), return_types);
  func.setType(updated_type);
}

// TODO(jpienaar): Remove when recursive side-effect modeling is added.
bool IsSideEffectFree(func::FuncOp func) {
  return !func.getBody()

        }
      } else {
        assert(input_sharding_type == xla::OpSharding::OTHER);
        if (partitioned_input.getInputs().size() != num_cores_per_replica)
          return tiled_sharding_mismatched(
              partitioned_input.getInputs().size());

        for (int i = 0; i < sharding.tile_assignment_devices_size(); ++i) {
          const int assigned_logical_device =