if err := row.Scan(&age); err != nil {
		t.Fatalf("Failed to scan age, got %v", err)
	}

	if age != 20 {
		t.Errorf("Scan with Row, age expects: %v, got %v", user2.Age, age)
	}
}

func TestRows(t *testing.T) {
	user1 := User{Name: "RowsUser1", Age: 1}
	user2 := User{Name: "RowsUser2", Age: 10}
	user3 := User{Name: "RowsUser3", Age: 20}
	DB.Save(&user1).Save(&user2).Save(&user3)

        assertEquals(totalentries, enumEx.totalentries);
        assertEquals(0x15, enumEx.getOpnum());
    }

    @Test
    void testNetrDfsEnumEx_EncodeInDecodeOut() throws NdrException {
        String dfsName = "test_dfs";
        int level = 1;
        int prefmaxlen = 100;
        netdfs.DfsEnumStruct info = new netdfs.DfsEnumStruct();
        info.level = 1;
        info.e = new netdfs.DfsEnumArray1();

pkg syscall (windows-amd64), type RawSockaddrAny struct, Pad [96]int8
pkg testing, func MainStart(func(string, string) (bool, error), []InternalTest, []InternalBenchmark, []InternalExample) *M
pkg testing, func MainStart(testDeps, []InternalTest, []InternalBenchmark, []InternalExample) *M
pkg testing, func RegisterCover(Cover)
pkg text/scanner, const GoTokens = 1012
pkg text/template/parse, type DotNode bool

	c.Assert(errorResponse.Code, code)
	c.Assert(errorResponse.Message, description)
	c.Assert(response.StatusCode, statusCode)
}

func runAllTests(suite *TestSuiteCommon, c *check) {
	suite.SetUpSuite(c)
	suite.TestCors(c)
	suite.TestObjectDir(c)
	suite.TestBucketPolicy(c)
	suite.TestDeleteBucket(c)
	suite.TestDeleteBucketNotEmpty(c)
	suite.TestDeleteMultipleObjects(c)
	suite.TestDeleteObject(c)
	suite.TestNonExistentBucket(c)

  {
    // Load the library.
    TF_Status* status = TF_NewStatus();
    string lib_path = tensorflow::GetDataDependencyFilepath(
        tensorflow::io::JoinPath("tensorflow", "c", "test_op1.so"));
    TF_Library* lib = TF_LoadLibrary(lib_path.c_str(), status);
    TF_Code code = TF_GetCode(status);
    string status_msg(TF_Message(status));
    TF_DeleteStatus(status);

        ML authoring is generally discouraged.
    *   Add support for unsigned 16-bit integer tensor types in cast op.
    *   Experimental support for lowering `list_ops.tensor_list_set_item` with
        `DynamicUpdateSlice`.
    *   Enabled a new MLIR-based dynamic range quantization backend by default
        *   The new backend is used for post-training int8 dynamic range