std::optional<ConstBytesAttr> CustomOptions(MLIRContext* context,
                                            mlir::Operation* op) {
  if (auto reserve =
          llvm::dyn_cast_or_null<mlir::TF::TensorListReserveOp>(op)) {
    tflite::TensorType tflite_type =
        tflite::ConvertTypeToTensorType(reserve.getElementDtype());

    return CreateListReserveOptions(context, tflite_type);
  }

  llvm::SmallVector<mlir::APInt> ret;
  size_t read_size = sizeof(T);
  int bytes_len = bytes.size();
  assert(bytes_len % read_size == 0);

  int elem_count = bytes_len / read_size;
  ret.reserve(elem_count);

  const char* data_ptr = reinterpret_cast<const char*>(bytes.data());
  for (int i = 0; i < elem_count; i++) {
    T val = llvm::support::endian::readNext<T, llvm::endianness::native,

                                              device_->parsed_name().id));

    std::vector<xla::PjRtBuffer*> executable_args;
    executable_args.reserve(result->input_mapping.size());
    absl::flat_hash_set<int> non_donatable_input_indices;
    TF_EXPECT_OK(PreparePjRtExecutableArguments(
        /*num_missing_prefix_ctx_inputs=*/0, result->input_mapping, inputs,

    const mlir::Value& original_source, mlir::OpBuilder* builder,
    llvm::SmallVectorImpl<mlir::Value>* tiled_inputs) {
  llvm::SmallVector<mlir::TF::SplitOp, 4> split_ops_for_tiled_input;
  split_ops_for_tiled_input.reserve(
      input_sharding.tile_assignment_devices_size());

  // Creates a tree of split nodes for sharding tiled inputs. Splits nodes
  // are created such that input data is sharded in row major order.

		}, nil
	}
}

// FakeReservePlugin is a test reserve plugin.
type FakeReservePlugin struct {
	Status *framework.Status
}

// Name returns name of the plugin.
func (pl *FakeReservePlugin) Name() string {
	return "FakeReserve"
}

// Reserve invoked at the Reserve extension point.
func (pl *FakeReservePlugin) Reserve(_ context.Context, _ *framework.CycleState, _ *v1.Pod, _ string) *framework.Status {

    }
    return FoldAdaptor(operation);
  }

  // Gets a list of ElementsAttr behind each constant operand.
  llvm::SmallVector<ElementsAttr> OperandData() {
    llvm::SmallVector<ElementsAttr> res;
    res.reserve(operation_->getNumOperands());
    for (auto opr : operation_->getOperands()) {
      auto op = llvm::dyn_cast<stablehlo::ConstantOp>(opr.getDefiningOp());
      res.push_back(op.getValue());
    }
    return res;
  }

                                                 ShapedType type) {
  // Extract to a vector of StringRefs for converting.
  auto arr = input_tensor.flat<tstring>();
  std::vector<mlir::StringRef> string_refs;
  string_refs.reserve(arr.size());
  for (int i = 0; i < arr.size(); i++) {
    const auto& val = arr(i);
    string_refs.push_back({val.data(), val.size()});
  }

  return ElementsAttr(DenseStringElementsAttr::get(type, string_refs));

    absl::flat_hash_set<int> null_indices;
    {
      AbstractContextPtr func_ctx(BuildFunction(fn_name));
      std::vector<AbstractTensorHandle*> func_inputs;
      func_inputs.reserve(inputs.size());
      TF_RETURN_IF_ERROR(
          CreateParamsForInputs(func_ctx.get(), inputs, &func_inputs));
      std::vector<AbstractTensorHandle*> model_outputs;
      model_outputs.resize(outputs.size());

	ScoreExtensionNormalize     = "ScoreExtensionNormalize"
	PreBind                     = "PreBind"
	Bind                        = "Bind"
	PostBind                    = "PostBind"
	Reserve                     = "Reserve"
	Unreserve                   = "Unreserve"
	Permit                      = "Permit"
)

// All the histogram based metrics have 1ms as size for the smallest bucket.
var (

  quant::QuantizedType convertFakeQuantAttrsToType(ConstFakeQuantPerAxis fqOp,
                                                   Type expressedType) const {
    SmallVector<double, 4> min, max;
    min.reserve(fqOp.getMin().size());
    max.reserve(fqOp.getMax().size());
    for (auto m : fqOp.getMin())
      min.push_back(cast<FloatAttr>(m).getValueAsDouble());
    for (auto m : fqOp.getMax())