#ifndef TENSORFLOW_KERNELS_BIAS_OP_H_
#define TENSORFLOW_KERNELS_BIAS_OP_H_
// Functor definition for BiasOp, must be compilable by nvcc.

#include "tensorflow/core/framework/tensor_types.h"
#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"

namespace tensorflow {
namespace functor {

// Functor used by BiasOp to do the computations.
template <typename Device, typename T, int Dims>
struct Bias {
  // Add "bias" to "input", broadcasting it on all dimensions but the last one.
  void operator()(const Device& d, typename TTypes<T, Dims>::ConstTensor input,
                  typename TTypes<T>::ConstVec bias,
                  typename TTypes<T, Dims>::Tensor output) {
    const int bias_size = bias.dimension(0);
    const int rest_size = input.size() / bias_size;

    Eigen::DSizes<int, 2> rest_by_bias(rest_size, bias_size);
#if !defined(EIGEN_HAS_INDEX_LIST)
    Eigen::DSizes<int, 2> rest_by_one(rest_size, 1);
    Eigen::DSizes<int, 2> one_by_bias(1, bias_size);
#else
    Eigen::IndexList<int, Eigen::type2index<1> > rest_by_one;
    rest_by_one.set(0, rest_size);
    Eigen::IndexList<Eigen::type2index<1>, int> one_by_bias;
    one_by_bias.set(1, bias_size);
#endif

    output.reshape(rest_by_bias).device(d) =
        input.reshape(rest_by_bias) +
        bias.reshape(one_by_bias).broadcast(rest_by_one);
  }
};

}  // namespace functor
}  // namespace tensorflow

#endif  // TENSORFLOW_KERNELS_BIAS_OP_H_