Disable to_timestamp('366', 'DD') test case

$ bin/spark-shell --conf spark.sql.ansi.enabled=true
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
22/03/12 00:59:31 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Spark context Web UI available at http://172.16.0.31:4040
Spark context available as 'sc' (master = local[*], app id = local-1647075572229).
Spark session available as 'spark'.
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 3.3.0-SNAPSHOT
      /_/

Using Scala version 2.12.15 (OpenJDK 64-Bit Server VM, Java 1.8.0_322)
Type in expressions to have them evaluated.
Type :help for more information.

scala> sql("select to_timestamp('366', 'DD')").show
java.time.format.DateTimeParseException: Text '366' could not be parsed, unparsed text found at index 2. If necessary set spark.sql.ansi.enabled to false to bypass this error.

$ bin/spark-shell --conf spark.sql.ansi.enabled=true
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
22/03/12 01:00:07 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Spark context Web UI available at http://172.16.0.31:4040
Spark context available as 'sc' (master = local[*], app id = local-1647075607932).
Spark session available as 'spark'.
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 3.3.0-SNAPSHOT
      /_/

Using Scala version 2.12.15 (OpenJDK 64-Bit Server VM, Java 11.0.12)
Type in expressions to have them evaluated.
Type :help for more information.

scala> sql("select to_timestamp('366', 'DD')").show
java.time.DateTimeException: Invalid date 'DayOfYear 366' as '1970' is not a leap year. If necessary set spark.sql.ansi.enabled to false to bypass this error.