static ArgType CreateInput(const OpDef::ArgDef& arg_def);
  static ArgType CreateInputRef(const OpDef::ArgDef& arg_def);
  static ArgType CreateOutput(const OpDef::ArgDef& arg_def);

  const tensorflow::DataType data_type() const { return data_type_; }
  const string type_attr_name() const { return type_attr_name_; }
  const bool is_read_only() const { return kind_ == kInput; }
  const bool is_list() const { return is_list_; }

 private:

  DataType data_type() const { return data_type_; }
  Status status() const { return status_; }
  const Tensor& tensor() const { return tensor_; }

 private:
  Status status_;
  Output output_ = Output(Operation(nullptr), 0);
  Tensor tensor_;
  const std::string node_name_ = "";
  int32 index_ = 0;
  DataType data_type_ = DT_INVALID;
};

}

ArgType ArgType::CreateOutput(const OpDef::ArgDef& arg_def) {
  return ArgType(arg_def, kOutput);
}

ArgType::ArgType(const OpDef::ArgDef& arg_def, Kind kind)
    : kind_(kind), data_type_(arg_def.type()) {
  if (!arg_def.type_attr().empty()) {
    type_attr_name_ = arg_def.type_attr();
  }
  if (!arg_def.type_list_attr().empty()) {
    type_attr_name_ = arg_def.type_list_attr();
  }

  is_list_ =

import java.io.Serializable


@ToolingModelContract(subTypes = [
    DataType.NullType::class,
    DataType.UnitType::class,
    DataType.ConstantType::class,
    DataType.IntDataType::class,
    DataType.LongDataType::class,
    DataType.StringDataType::class,
    DataType.BooleanDataType::class,
    DataClass::class
])
sealed interface DataType : Serializable {

    @ToolingModelContract(subTypes = [

    }
    data_types.push_back(data_type);
  }
  n->ClearAttr(type_attr_name);
  n->AddAttr(type_attr_name, data_types);

  return data_types;
}

// Add edges from lifted outside compilation argument nodes to `n` in Graph `g`.
void AddEdgesFromOutsideCompilationNodes(
    const int original_arg_count, const int arg_to_input_edge_offset,
    const std::vector<DataType>& data_types,

                      llvm::SmallVectorImpl<DataType>& data_types) {
  data_types.clear();
  std::vector<std::string> input_dtypes_vector;
  TF_RETURN_IF_ERROR(ParseNodeDataTypes(data_types_str, input_dtypes_vector));
  data_types.resize(input_dtypes_vector.size(), DT_INVALID);
  for (auto data_type : llvm::enumerate(input_dtypes_vector)) {
    if (!DataType_Parse(data_type.value(), &data_types[data_type.index()]))

  enum OpType { RESIZE_NEAREST, RESIZE_BILINEAR, RESIZE_BICUBIC };

  template <typename T>
  Tensor MakeData(const TensorShape& data_shape) {
    DataType data_type = DataTypeToEnum<T>::v();
    Tensor data(data_type, data_shape);
    auto data_flat = data.flat<T>();
    for (int i = 0; i < data_flat.size(); ++i) {
      data_flat(i) = T(i);
    }
    return data;
  }

  template <typename T>

Klaus Post <******@****.***> 1709924898 +0100

  DataType data_type = DT_INT32;
  std::initializer_list<int64_t> dims = {2, 3, 4, 5};
  Tensor tensor(data_type, TensorShape(dims));
  for (int i = 0; i < 2 * 3 * 4 * 5; ++i) {
    tensor.flat<int32>()(i) = i;
  }

  NodeDef node;
  auto builder = NodeDefBuilder("some_node", "Const")
                     .Attr("dtype", data_type)
                     .Attr("value", tensor);

  }
  if (inp.node()) {
    return NodeBuilder::NodeOut(inp.node(), inp.index());
  }
  if (!inp.node_name().empty()) {
    return NodeBuilder::NodeOut(inp.node_name(), inp.index(), inp.data_type());
  }
  auto transformed = Input{
      Const(scope.NewSubScope("Const"), Input::Initializer(inp.tensor()))};
  return NodeBuilder::NodeOut{transformed.node(), transformed.index()};
}