}

force_reload() {
    restart
}

rh_status() {
    # run checks to determine if the service is running or use generic status
    status -p $pidfile $prog
}

rh_status_q() {
    rh_status >/dev/null 2>&1
}


case "$1" in
    start)
        rh_status_q && exit 0
        $1
        ;;
    stop)
        rh_status_q || exit 0
        $1
        ;;
    restart)

      ::tensorflow::Set_TF_Status_from_Status(C_STATUS, _s);      \
      TF_OpKernelContext_Failure(CTX, C_STATUS);                  \
      TF_DeleteStatus(C_STATUS);                                  \
      return;                                                     \
    }                                                             \
  } while (0)

#define TF_CLEANUP_AND_RETURN_IF_ERROR(C_STATUS, BUFFER, __VA_ARGS__) \

		}
		if repStatus := rinfos.ReplicationStatus(); repStatus != test.expectedReplicationStatus {
			t.Errorf("Test%d (%s): ReplicationStatus  got %s , want %s", i+1, test.name, repStatus, test.expectedReplicationStatus)
		}
		if action := rinfos.Action(); action != test.expectedAction {

Shinsuke Sugaya <******@****.***> 1477533782 +0900

                            TF_Status* s);
// `op_name` must outlive `op`.
void TF_AbstractOpSetOpName(TF_AbstractOp* op, const char* const op_name,
                            TF_Status* s);
// `attr_name` must outlive `op`.
void TF_AbstractOpSetAttrType(TF_AbstractOp* op, const char* const attr_name,
                              TF_DataType value, TF_Status* s);

void TF_DeleteAbstractTensor(TF_AbstractTensor*);

TF_Operation* Placeholder(TF_Graph* graph, TF_Status* s,
                          const char* name = "feed",
                          TF_DataType dtype = TF_INT32,
                          const std::vector<int64_t>& dims = {});

TF_Operation* Const(TF_Tensor* t, TF_Graph* graph, TF_Status* s,
                    const char* name = "const");

TF_Operation* ScalarConst(bool v, TF_Graph* graph, TF_Status* s,

                                                 TF_Status* s) {
  TFE_Context* c_ctx = TFE_NewContext(options, s);
  if (TF_GetCode(s) != TF_OK) {
    return nullptr;
  }
  return wrap(static_cast<AbstractContext*>(unwrap(c_ctx)));
}

TF_AbstractTensor* TF_CreateAbstractTensorFromEagerTensor(TFE_TensorHandle* t,
                                                          TF_Status* s) {

#include "tensorflow/c/eager/c_api.h"
#include "tensorflow/c/eager/c_api_experimental.h"
#include "tensorflow/c/tf_status.h"

void RegisterLoggingDevice(TFE_Context* context, const char* name,
                           bool strict_scope_placement, bool* arrived_flag,
                           bool* executed_flag, TF_Status* status);
void AllocateLoggingDevice(const char* name, bool* arrived_flag,

void TF_SetTracingImplementation(const char* name, TF_Status* s) {
  tsl::Set_TF_Status_from_Status(s, SetDefaultTracingEngine(name));
}

// Creates a new TensorFlow function, it is an execution context attached to a
// given tracing context.
TF_ExecutionContext* TF_CreateFunction(const char* fn_name, TF_Status* s) {
  return wrap(CreateTracingExecutionContext(fn_name, s));
}

TF_CAPI_EXPORT extern void* TFE_GetDLDevice(TFE_TensorHandle* h,
                                            TF_Status* status);

// Converts eager tensor handle to DLPack (DLManagedTensor*), and return the
// void* for further PyCapsule construction.
TF_CAPI_EXPORT extern void* TFE_HandleToDLPack(TFE_TensorHandle* h,
                                               TF_Status* status);

// Converts DLPack (DLManagedTensor*) to eager tensor handle.