// Removes relevant ops in topological order.
  for (auto& op : vars) RecursiveRemove(op, erase_list, dead_ops);

  // Erases the ops.
  for (auto op : erase_list) op->erase();
}

void RemoveVariablesInSessionInitializerPass::runOnOperation() {
  ModuleOp module_op = getOperation();

  for (auto init_func_op : GetInitializerFunctions(module_op)) {
    if (!init_func_op) return;

  if (options.has_chain) result.types.push_back(chain_type);
  result.types.append(num_results, tensor_type);

  llvm::SmallVector<mlir::Attribute, 4> op_attr_array;
  for (const auto &key_value : op_attrs) {
    auto key = key_value.getName();
    auto value = key_value.getValue();
    op_attr_array.push_back(builder.getArrayAttr({key, value}));
  }

  result.attributes.push_back(

  Value resource;
  if (auto read = llvm::dyn_cast<TF::ReadVariableOp>(op)) {
    resource = read.getResource();
  } else if (auto write = llvm::dyn_cast<TF::AssignVariableOp>(op)) {
    resource = write.getResource();
  }
  const TF::ResourceDataflowState* state =
      solver.lookupState<TF::ResourceDataflowState>(resource);
  if (!state) {
    return nullptr;
  }
  auto ops = state->getValue().ops;

                                PatternRewriter &rewriter) const override {
    auto const_type = mlir::dyn_cast<ShapedType>(op.getType());
    if (!const_type || !const_type.getElementType().isSignlessInteger(8)) {
      return failure();
    }

    auto attr = mlir::cast<ElementsAttr>(op.getValue());
    for (mlir::APInt v : attr.getValues<mlir::APInt>()) {
      auto v_int = static_cast<int8_t>(*(v.getRawData()));
      if (v_int > 7 || v_int < -8) {

  GlobalTensorUsesMap global_tensor_uses;

  SymbolTable symbol_table(module);
  for (auto func : module.getOps<func::FuncOp>()) {
    for (size_t i = 0, e = func.getNumArguments(); i < e; i++) {
      auto sym =
          func.getArgAttrOfType<SymbolRefAttr>(i, "tf_saved_model.bound_input");
      if (!sym) {
        continue;
      }
      auto global_tensor = symbol_table.lookup<GlobalTensorOp>(

                                  func::CallOp to_graph) {
  int64_t total_size_transferred = 0;
  for (auto input : to_graph.getOperands()) {
    Operation* input_op = input.getDefiningOp();
    if (input_op && input_op == from_graph.getOperation()) {
      auto input_type =
          mlir::dyn_cast_or_null<RankedTensorType>(input.getType());

    if (!HasTfSavedModelSemantics(module)) {
      return;
    }

    auto init_op = GetSessionInitializerOp(module);
    if (init_op == nullptr || init_op.getInitializers().empty()) {
      return;
    }

    SymbolTable symbol_table(module);
    for (auto initializer : init_op.getInitializers()) {
      auto func = symbol_table.lookup<func::FuncOp>(
          mlir::cast<FlatSymbolRefAttr>(initializer).getValue());

  if (element_type.isF16()) {
    auto floatType = mlir::FloatType::getF16(element_type.getContext());
    auto floatAttr = mlir::FloatAttr::get(floatType, static_cast<float>(value));
    std::vector<Attribute> floatValues({floatAttr});
    return DenseElementsAttr::get(shaped_type, floatValues);
  } else if (element_type.isBF16()) {
    auto floatType = mlir::FloatType::getBF16(element_type.getContext());

    flatbuffers::FlatBufferBuilder* builder,
    std::map<std::string, uint8_t>* hardware_names) {
  uint8_t index = 0;
  for (auto& func : *funcs) {
    func.walk([&hardware_names, &index](mlir::Operation* op) {
      auto device_name = GetDeviceName(op);
      if (!device_name.has_value()) return;

      auto iter = hardware_names->find(*device_name);
      if (iter == hardware_names->end()) {

                                     mlir::TF::RuntimeDevices* devices) {
  auto devices_attr = op->getAttr(kDevicesAttr);
  if (!devices_attr) return mlir::success();

  if (auto array_attr = mlir::dyn_cast<mlir::ArrayAttr>(devices_attr)) {
    return GetDevicesFromOp(op, array_attr, devices);

  } else if (auto dict_attr =
                 mlir::dyn_cast<mlir::DictionaryAttr>(devices_attr)) {