const int batch_size = 4;
  const int num_dims = 2;
  int64_t* dims = new int64_t[num_dims];
  int64_t num_elements = 1;
  dims[0] = batch_size;
  dims[1] = 1;
  for (int64_t i = 0; i < num_dims; ++i) {
    num_elements *= dims[i];
  }
  TF_Tensor* t = TF_AllocateTensor(TF_STRING, dims, num_dims,
                                   sizeof(TF_TString) * num_elements);
  delete[] dims;

        new ThreadPoolExecutor(
            NUM_THREADS,
            NUM_THREADS,
            Long.MAX_VALUE,
            TimeUnit.SECONDS,
            new ArrayBlockingQueue<Runnable>(1000));
    executorService.prestartAllCoreThreads();
    final AtomicInteger integer = new AtomicInteger();
    // Execute a bunch of tasks to ensure that our threads are allocated and hot
    for (int i = 0; i < NUM_THREADS * 10; i++) {

                          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) {
    data[j] = j;
  }
  DLManagedTensor dlm_in = {};
  DLTensor* dltensor_in = &dlm_in.dl_tensor;

      reinterpret_cast<NamedParallelDevice*>(device_info);
  std::vector<MaybeParallelTensorUnowned> typed_inputs;
  int num_inputs = TFE_OpGetFlatInputCount(original_op, status);
  if (TF_GetCode(status) != TF_OK) return;
  typed_inputs.reserve(num_inputs);
  for (int i = 0; i < num_inputs; ++i) {
    TFE_TensorHandle* input = TFE_OpGetFlatInput(original_op, i, status);
    if (TF_GetCode(status) != TF_OK) return;

                                                 const TF_DataType* values,
                                                 int num_values);
TF_CAPI_EXPORT extern void TFE_OpSetAttrShapeList(
    TFE_Op* op, const char* attr_name, const int64_t** dims,
    const int* num_dims, int num_values, TF_Status* out_status);
TF_CAPI_EXPORT extern void TFE_OpSetAttrFunctionList(TFE_Op* op,

}

// ARM64RegisterExtension constructs an ARM64 register with extension or arrangement.
func ARM64RegisterExtension(a *obj.Addr, ext string, reg, num int16, isAmount, isIndex bool) error {
	Rnum := (reg & 31) + int16(num<<5)
	if isAmount {
		if num < 0 || num > 7 {
			return errors.New("index shift amount is out of range")
		}
	}
	if reg <= arm64.REG_R31 && reg >= arm64.REG_R0 {
		if !isAmount {

      if ((num & nextPowerOfTwo) != 0) {
        nextPowerOfTwo <<= 1;
        bits++;
      }
      // Get rid of the 2s in num.
      int tz = Long.numberOfTrailingZeros(num);
      long normalizedNum = num >> tz;
      shift += tz;
      // Adjust floor(log2(num)) + 1.
      int normalizedBits = bits - tz;
      // If it won't fit in a long, then we store off the intermediate product.

        final Map<String, Object> res = JsonPath.from(response).getMap("response.setting");
        assertTrue(res.containsKey("total_words_num"));
        assertTrue(res.containsKey("document_words_num"));
        assertTrue(res.containsKey("query_words_num"));
        assertEquals(new Integer(0), JsonPath.from(response).get("response.status"));
    }

    @Override
    protected void tearDown() {

        // artificial restriction and require that when num_opers=-1, such
        // nodes must have a single output.
        if (node->num_outputs() != 1) {
          return InvalidArgument(
              "When `num_opers` is set to -1, nodes referenced in `inputs` "
              "must have a single output. Node ",
              node->name(), " has ", node->num_outputs(),

// b) ConfigProto.gpu_options.allow_growth is set to `gpu_memory_allow_growth`.
// c) ConfigProto.device_count is set to `num_cpu_devices`.
TF_CAPI_EXPORT extern TF_Buffer* TF_CreateConfig(
    unsigned char enable_xla_compilation, unsigned char gpu_memory_allow_growth,
    unsigned int num_cpu_devices);

// Create a serialized tensorflow.RunOptions proto, where RunOptions.trace_level