std::tuple<StringRef, StringRef, StringRef> GetResourceKey(Operation* op) {
  StringRef device;
  if (auto attr = op->getAttrOfType<StringAttr>("device")) {
    device = attr.getValue();
  }

  StringRef container;
  if (auto attr = op->getAttrOfType<StringAttr>("container")) {
    container = attr.getValue();
  }

  StringRef shared_name;
  if (auto attr = op->getAttrOfType<StringAttr>("shared_name")) {
    shared_name = attr.getValue();
  }

               std::vector<Output>* grad_outputs) {
  auto x = op.input(0);
  auto a = op.input(1);  // [Rank(x), 2]
  // Takes a slice of a. The 1st column. [Rank(x), 1].
  auto size = Stack(scope, {Rank(scope, x), 1});
  auto pad_before = Slice(scope, a, {0, 0}, size);
  // Make it a 1-D tensor.
  auto begin = Reshape(scope, pad_before, {-1});

    Scope scope = Scope::NewRootScope().ExitOnError();
    auto a = ops::_Arg(scope.WithOpName("A"), DT_INT32, 0);
    auto b = ops::_Arg(scope.WithOpName("B"), DT_INT32, 1);
    if (mul) {
      auto c = ops::Mul(scope.WithOpName("C"), a, b);
      auto d = ops::_Retval(scope.WithOpName("D"), c, 0);
      TF_RETURN_IF_ERROR(scope.ToGraph(graph.get()));
    } else {
      auto c = ops::Add(scope.WithOpName("C"), a, b);

    if (auto while_op = llvm::dyn_cast<TF::WhileOp>(use.getOwner())) {
      auto body = while_op.body_function();
      assert(body);
      auto type_from_body = GetElementTypeFromAccess(
          body.getArgument(use.getOperandNumber()), module, infer_from_op);
      if (type_from_body.has_value()) return type_from_body;
    } else if (auto if_op = llvm::dyn_cast<TF::IfOp>(use.getOwner())) {

    // Create Uniform Quantized constant for the filter.
    auto subtract_op =
        cast<stablehlo::SubtractOp>(*op.getResult().user_begin());
    auto other_conv_op = cast<stablehlo::ConvolutionOp>(
        subtract_op.getOperand(1).getDefiningOp());
    auto combined_scale_multiply_op =
        cast<stablehlo::MulOp>(*subtract_op.getResult().user_begin());

    auto scale_combined_broadcast_in_dim_op = cast<stablehlo::BroadcastInDimOp>(

  OpBuilder builder(func);

  func.walk([&](SparseOpInterface sparse_op) {
    const auto& sparse_operands = sparse_op.GetSparseOperands();
    std::vector<std::vector<int>> supported_block_size;
    for (int operand : sparse_operands) {
      auto* op = sparse_op.getOperation();
      auto value = op->getOperand(operand);

      auto* inst = value.getDefiningOp();
      if (!inst) {
        continue;
      }

  OperationState op_state(loc, op_name);

  for (auto input_num : op.inputs) {
    if (input_num == -1) {
      assert(optional_arg_marker != nullptr);
      op_state.addOperands({optional_arg_marker});
    } else {
      op_state.addOperands({vals_map.at(input_num)});
    }
  }

  for (auto output_num : op.outputs) {
    auto& tensor = *tensors.at(output_num);
    auto type_or_err = tfl::GetTensorType(tensor, builder);

    SmallVector<Value, 4> dequantized_inputs;
    for (auto& input : op->getOpOperands()) {
      auto input_type = input.get().getType();
      if (IsQI8Type(input_type) || IsQUI8Type(input_type) ||
          IsQI32Type(input_type)) {
        auto dequantized_input_type =
            mlir::quant::QuantizedType::castToExpressedType(input_type);
        builder->setInsertionPoint(op);
        auto dequantize_op = builder->create<TFL::DequantizeOp>(

  auto attr = mlir::dyn_cast<ArrayAttr>(named_attr.getValue());
  if (!attr) {
    return op->emitError() << "'" << kTfSavedModelIndexPathAttr
                           << "' attribute should be an ArrayAttr";
  }
  for (auto element : attr) {
    if (mlir::isa<StringAttr>(element)) {
      continue;
    }
    if (auto integer = mlir::dyn_cast<IntegerAttr>(element)) {

    auto symRef = callee.get<SymbolRefAttr>();
    return setCalleeAttr(cast<FlatSymbolRefAttr>(symRef));
  }
  // Indirect call, callee Value is the first operand.
  return setOperand(0, callee.get<Value>());
}

LogicalResult ConstantTensorOp::verify() {
  ConstantTensorOp op = *this;
  auto input_type = op.getArg().getType();
  auto output_type = op.getOut().getType();