"timeout": "0s",
                      "retry_policy": {
                        "retry_on": "connect-failure,refused-stream,unavailable,cancelled,retriable-status-codes",
                        "num_retries": 2,
                        "retry_host_predicate": [
                          {
                            "name": "envoy.retry_host_predicates.previous_hosts"
                          }
                        ],

  ASSERT_EQ(errors::OK, s.code()) << s.message();
  auto num_elem_numerical = TF_TensorElementCount(numerical_tensor);
  ASSERT_EQ(num_elem_numerical, num_grad);

  float* dnumerical = new float[num_elem_numerical]{0};
  memcpy(&dnumerical[0], TF_TensorData(numerical_tensor),
         TF_TensorByteSize(numerical_tensor));

  for (int j = 0; j < num_grad; j++) {
    ASSERT_NEAR(dnumerical[j], expected_grad[j], abs_error);
  }

    public void testPerformance() throws Exception {
        final int num = 100;
        long start = System.currentTimeMillis();
        for (int i = 0; i < num; i++) {
            System.out.println("test" + i);
        }
        final long sysout = System.currentTimeMillis() - start;
        start = System.currentTimeMillis();
        for (int i = 0; i < num; i++) {
            logger.fatal("test" + i);
        }

                                           const int64_t* dims, int num_dims);
// `dims` and `num_dims` must point to arrays of length `num_shapes`.
// Set `num_dims[i]` to -1 to represent "unknown rank".  Otherwise,
// `dims[i]` points to an array of length `num_dims[i]`.  `dims[i][j]`
// must be >= -1, with -1 meaning "unknown dimension".
TF_CAPI_EXPORT extern void TF_SetAttrShapeList(TF_OperationDescription* desc,

                return pageSize;
            }

            final String num = request.getParameter("num");
            if (StringUtil.isBlank(num)) {
                pageSize = fessConfig.getPagingSearchPageSizeAsInteger();
            } else {
                try {
                    pageSize = Integer.parseInt(num);

    std::vector<TFE_TensorHandle*> result;
    result.push_back(nullptr);
    int num_retvals = 1;
    TFE_Execute(op, result.data(), &num_retvals, status);
    TFE_DeleteOp(op);
    ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status);
    ASSERT_EQ(num_retvals, 1);

    TF_Tensor* r = TFE_TensorHandleResolve(result[0], status);

        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++) {

  // TensorFlow, so we need to use `plugin_memory_free_` here.
  char** children = nullptr;
  const int num_children =
      ops_->get_children(filesystem_.get(), translated_name.c_str(), &children,
                         plugin_status.get());
  if (num_children >= 0) {
    for (int i = 0; i < num_children; i++) {
      result->push_back(std::string(children[i]));
      plugin_memory_free_(children[i]);
    }

    checkHashCode(Sets.cartesianProduct(set(1), set(2, num)));
    checkHashCode(Sets.cartesianProduct(set(1, num), set(2, num - 1)));
    checkHashCode(Sets.cartesianProduct(set(1, num), set(2, num - 1), set(3, num + 1)));

    // a bigger one
    checkHashCode(
        Sets.cartesianProduct(set(1, num, num + 1), set(2), set(3, num + 2), set(4, 5, 6, 7, 8)));
  }

  public void testPowerSetEmpty() {

        if (o == null) {
            return null;
        } else if (o instanceof Boolean) {
            return (Boolean) o;
        } else if (o instanceof Number) {
            final int num = ((Number) o).intValue();
            return num != 0;
        } else if (o instanceof String) {
            final String s = (String) o;
            if ("true".equalsIgnoreCase(s)) {
                return Boolean.TRUE;