return op_->SetAttrTypeList(attr_name, values, num_values);
}
Status SetAttrBoolList(AbstractOperation* op_, const char* attr_name,
                       const unsigned char* values, int num_values,
                       ForwardOperation* forward_op_) {
  std::unique_ptr<bool[]> b(new bool[num_values]);
  for (int i = 0; i < num_values; ++i) {
    b[i] = values[i];
  }

void TF_GetNodesToPreserveListSize(const TF_GrapplerItem* item, int* num_values,
                                   size_t* storage_size, TF_Status* status) {
  TF_SetStatus(status, TF_OK, "");
  const std::unordered_set<std::string>& nodes =
      reinterpret_cast<const tensorflow::grappler::GrapplerItem*>(item)
          ->NodesToPreserve();
  *num_values = nodes.size();
  *storage_size = 0;

        assertEquals(1, restrictions.size(), CHECK_NUM_RESTRICTIONS);

        VersionRange version = VersionRange.createFromVersion("1.0");
        assertSame(version, VersionRange.createFromVersion("1.0")); // same instance from version cache
        restrictions = version.getRestrictions();
        assertEquals(0, restrictions.size(), CHECK_NUM_RESTRICTIONS);

        assertFalse(

TF_ShapeAndTypeList* TF_NewShapeAndTypeList(int num_items) {
  TF_ShapeAndTypeList* result = new TF_ShapeAndTypeList;
  result->num_items = num_items;
  result->items = (num_items == 0) ? nullptr : new TF_ShapeAndType[num_items]();
  return result;
}

void TF_ShapeAndTypeListSetShape(TF_ShapeAndTypeList* shape_list, int index,
                                 const int64_t* dims, int num_dims) {

// or removed during the graph transformation. This includes feed and fetch
// nodes, keep_ops, init_ops. Fills in `num_values` and `storage_size`, they
// will be used in `TF_GetNodesToPreserveList`.
TF_CAPI_EXPORT extern void TF_GetNodesToPreserveListSize(
    const TF_GrapplerItem* item, int* num_values, size_t* storage_size,
    TF_Status* status);

// Get a set of node names that must be preserved. They can not be transformed

                           ArraySlice<const bool>(b.get(), num_values));

    return absl::OkStatus();
  }
  Status SetAttrShapeList(const char* attr_name, const int64_t** dims,
                          const int* num_dims, int num_values) override {
    std::vector<PartialTensorShape> shapes;
    shapes.reserve(num_values);
    for (int i = 0; i < num_values; ++i) {
      if (num_dims[i] < 0) {
        shapes.emplace_back();

    }

    @Test
    public void searchTestWith1Word() throws Exception {
        String query = "java";
        Map<String, String> params = new HashMap<>();
        params.put("q", query);
        params.put("num", "100");
        String response = checkMethodBase(new HashMap<>()).params(params).get("/api/v1/documents").asString();
        assertTrue(JsonPath.from(response).getInt("record_count") > 0);

	}

	// prepend asterisks
	return append([]byte("***"), data...), nil
}

type Num int64

func (i *Num) Scan(src interface{}) error {
	switch s := src.(type) {
	case []byte:
		n, _ := strconv.Atoi(string(s))
		*i = Num(n)
	case int64:
		*i = Num(s)
	default:
		return errors.New("Cannot scan NamedInt from " + reflect.ValueOf(src).String())
	}
	return nil
}

  }
  return server_def;
}

tensorflow::ServerDef GetServerDef(int num_tasks) {
  return GetServerDef("localhost", num_tasks);
}

tensorflow::ServerDef GetMultiClientServerDef(const std::string& job_name,
                                              int num_tasks,
                                              int num_virtual_gpus) {
  tensorflow::ServerDef server_def;
  server_def.set_protocol("grpc");

    return -1;
  }

  int num_dims = -1;
  status->status = tensorflow::unwrap(h)->NumDims(&num_dims);
  return num_dims;
}

int64_t TFE_TensorHandleNumElements(TFE_TensorHandle* h, TF_Status* status) {
  if (h == nullptr) {
    status->status = tensorflow::errors::InvalidArgument("Invalid handle");
    return -1;
  }

  int64_t num_elements = -1;