TF_AbstractTensor* TF_AddFunctionParameter(TF_ExecutionContext* func,
                                           TF_DataType dtype, TF_Shape shape,
                                           TF_Status* s);

// Create an operation suitable to use with the provided context. The operation
// requires its type (e.g. "AddV2") to be set independently.
TF_AbstractOp* TF_NewAbstractOp(TF_ExecutionContext* ctx);
void TF_DeleteAbstractOp(TF_AbstractOp*);

    int64_t init_timeout_in_ms, int retries, TF_Status* status,
    bool clear_existing_contexts);

// Checks whether a remote worker is alive or not. This will return true even if
// the context doesn't exist on the remote worker.
TF_CAPI_EXPORT extern bool TFE_ContextCheckAlive(TFE_Context* ctx,
                                                 const char* worker_name,

                   const char* device_type);

// Create a ServerDef with the given `job_name` and add `num_tasks` tasks in it.
tensorflow::ServerDef GetServerDef(const tensorflow::string& job_name,
                                   int num_tasks);

// Create a ServerDef with job name "localhost" and add `num_tasks` tasks in it.
tensorflow::ServerDef GetServerDef(int num_tasks);

  //
  // Note that creating this resource-dtype handle can fail, so `Create` is a
  // separate static method which returns a status.
  Variable(TFE_TensorHandle* handle, TF_DataType type)
      : handle_(handle), type_(type) {}

  // Helper for constructing a resource handle and wrapping it in a `Variable`
  // object.
  static Variable* Create(TFE_Context* context, TF_DataType type,

class DeviceThread;

// Forwards operations to `devices`, maintaining ParallelTensor with components
// placed on each underlying device.
class ParallelDevice {
 public:
  // Eager async execution is only supported when remote eager is not in use
  // (b/157523095).
  explicit ParallelDevice(const std::vector<std::string>& devices,
                          bool is_async = false, int in_flight_nodes_limit = 0);

  ~ParallelDevice();

                                              TF_Output* x, int nx,
                                              TF_Output* dx, TF_Status* status,
                                              TF_Output* dy);

// Create a TF_Function from a TF_Graph
//
// Params:
//  fn_body - the graph whose operations (or subset of whose operations) will be
//            converted to TF_Function.

    TF_DataType data_type, void* data, size_t len, TF_Status* status);

// Specify the server_def that enables collective ops.
// This is different to the above function in that it doesn't create remote
// contexts, and remotely executing ops is not possible. It just enables
// communication for collective ops.
TF_CAPI_EXPORT extern void TFE_EnableCollectiveOps(TFE_Context* ctx,

	for(i=0; i<nthread; i++)
		pthread_create(&thread_id[i], 0, addThread, &max);
	for(i=0; i<nthread; i++)
		pthread_join(thread_id[i], 0);		
}

static void*
goDummyCallbackThread(void* p)
{
	int i, max;

	max = *(int*)p;
	for(i=0; i<max; i++)
		goDummy();
	return NULL;
}

int
callGoInCThread(int max)
{
	pthread_t thread;

using OpTape =
    std::unordered_map<int64_t, OpTapeEntry<BackwardFunction, TapeTensor>>;

// Operations the tape needs to perform on tensors to do backpropagation. Named
// "vspace" because a subset of these are related to a vector space, such as
// adding gradients, getting zeroes, etc. Currently cannot be implemented
// without using tensorflow python code, hence left unspecified here.
//

  // Set up distributed execution environment on local and remote tasks.
  // When `reset_context` is true, initialize new cluster context state based
  // on cluster configurations provided in `server_def`; otherwise, update
  // existing context state with the provided `server_def`. Contexts created
  // on remote tasks will be considered stale and garbage collected after
  // `keep_alive_secs` of inactivity.