unique_tensor_ptr;

TF_Tensor* BoolTensor(int32_t v);

// Create a tensor with values of type TF_INT8 provided by `values`.
TF_Tensor* Int8Tensor(const int64_t* dims, int num_dims, const char* values);

// Create a tensor with values of type TF_INT32 provided by `values`.
TF_Tensor* Int32Tensor(const int64_t* dims, int num_dims,
                       const int32_t* values);

// Fills data with values [start,end) with given step size.
void Range(vector<int32_t>* data, int32_t start, int32_t end,
           int32_t step = 1) {
  for (int32_t i = start; i < end; i += step) {
    (*data)[i] = i;
  }
}

// Fills out_dims with the dimensions of the given tensor.
void GetDims(const TF_Tensor* t, int64_t* out_dims) {
  int num_dims = TF_NumDims(t);
  for (int i = 0; i < num_dims; i++) {

  int64_t num_values = 1;
  for (int i = 0; i < num_dims; ++i) {
    num_values *= dims[i];
  }
  TF_Tensor* t =
      TF_AllocateTensor(TF_INT32, dims, num_dims, sizeof(int32_t) * num_values);
  memcpy(TF_TensorData(t), values, sizeof(int32_t) * num_values);
  return t;
}

TF_Tensor* Int32Tensor(const std::vector<int32_t>& values) {
  int64_t dims = values.size();
  return Int32Tensor(&dims, 1, values.data());
}

namespace tensorflow {
namespace {

void TestHandleFromDLPack(TF_Status* status, TFE_Context* ctx,
                          std::vector<int64_t> shape,
                          std::vector<int64_t> strides) {
  size_t num_elements = 1;
  for (int i = 0; i < static_cast<int32_t>(shape.size()); ++i) {
    num_elements *= shape[i];
  }
  std::vector<float> data(num_elements);
  for (size_t j = 0; j < num_elements; ++j) {

}

std::unique_ptr<ParallelTensor> ParallelDevice::DeviceIDs(
    TFE_Context* context, TF_Status* status) const {
  std::vector<int32_t> ids;
  ids.reserve(num_underlying_devices());
  for (int i = 0; i < num_underlying_devices(); ++i) {
    ids.push_back(i);
  }
  return ScalarsFromSequence<int32_t>(ids, context, status);
}

absl::optional<std::vector<std::unique_ptr<ParallelTensor>>>

  // Get a list of the names of functions that have been registered.
  virtual std::vector<string> ListFunctionNames() = 0;

  struct CacheStats {
    int64_t kernel_cache_size;
    int64_t device_cache_size;
    std::map<std::string, int64_t> func_kernel_cache_entries;
    int64_t local_rendezvous_cache_active_size;
  };
  virtual CacheStats GetCacheStats() = 0;

  TapeVSpace vspace(ctx);
  std::vector<int64_t> target_tensor_ids = MakeTensorIDList(targets);
  std::vector<int64_t> source_tensor_ids = MakeTensorIDList(sources);
  tensorflow::gtl::FlatSet<int64_t> sources_set(source_tensor_ids.begin(),
                                                source_tensor_ids.end());
  std::unordered_map<int64_t, TapeTensor> sources_that_are_targets;

interface rpc
{
	/* base types */

	typedef unsigned char uint8_t;
	typedef unsigned short uint16_t;
	typedef unsigned int uint32_t;

	/* dce */

	typedef struct {
	    uint32_t time_low;
	    uint16_t time_mid;
	    uint16_t time_hi_and_version;
	    uint8_t clock_seq_hi_and_reserved;
	    uint8_t clock_seq_low;
	    uint8_t node[6];
	} uuid_t;

	/* win32 stuff */

	typedef struct {
		uint32_t type;

interface rpc
{
	/* base types */

	typedef unsigned char uint8_t;
	typedef unsigned short uint16_t;
	typedef unsigned int uint32_t;

	/* dce */

	typedef struct {
	    uint32_t time_low;
	    uint16_t time_mid;
	    uint16_t time_hi_and_version;
	    uint8_t clock_seq_hi_and_reserved;
	    uint8_t clock_seq_low;
	    uint8_t node[6];
	} uuid_t;

	/* win32 stuff */

	typedef struct {
		uint32_t type;

	version(0.0)
]
interface lsarpc
{
	import "../rpc.idl";

	typedef struct {
		uint32_t length;
		uint16_t impersonation_level;
		uint8_t context_mode;
		uint8_t effective_only;
	} LsarQosInfo;

	typedef struct {
		uint32_t length;
		uint8_t *root_directory;
		unicode_string *object_name;
		uint32_t attributes;
		uint32_t security_descriptor;
		LsarQosInfo *security_quality_of_service;