map sigbuild-57469-python3.10 57469-python3.10
        # TF 2.12
        map sigbuild-r2.12 2.12-python3.9
        map sigbuild-r2.12-python3.8 2.12-python3.8
        map sigbuild-r2.12-python3.9 2.12-python3.9
        map sigbuild-r2.12-python3.10 2.12-python3.10
        map sigbuild-r2.12-python3.11 2.12-python3.11
        # TF 2.12 + Clang (containers are the same, but env vars in configs.bzl are different)

        this.time_mid = S(hex_to_bin(arr, 9, 4));
        this.time_hi_and_version = S(hex_to_bin(arr, 14, 4));
        this.clock_seq_hi_and_reserved = B(hex_to_bin(arr, 19, 2));
        this.clock_seq_low = B(hex_to_bin(arr, 21, 2));
        this.node = new byte[6];
        this.node[0] = B(hex_to_bin(arr, 24, 2));
        this.node[1] = B(hex_to_bin(arr, 26, 2));
        this.node[2] = B(hex_to_bin(arr, 28, 2));

        time_mid = S(hex_to_bin(arr, 9, 4));
        time_hi_and_version = S(hex_to_bin(arr, 14, 4));
        clock_seq_hi_and_reserved = B(hex_to_bin(arr, 19, 2));
        clock_seq_low = B(hex_to_bin(arr, 21, 2));
        node = new byte[6];
        node[0] = B(hex_to_bin(arr, 24, 2));
        node[1] = B(hex_to_bin(arr, 26, 2));
        node[2] = B(hex_to_bin(arr, 28, 2));
        node[3] = B(hex_to_bin(arr, 30, 2));

    testSortDescending(new long[] {1, 3, 1}, 0, 1, new long[] {1, 3, 1});
    testSortDescending(
        new long[] {GREATEST - 1, 1, GREATEST - 2, 2},
        1,
        3,
        new long[] {GREATEST - 1, GREATEST - 2, 1, 2});
  }

  public void testDivide() {
    assertThat(UnsignedLongs.divide(14, 5)).isEqualTo(2);
    assertThat(UnsignedLongs.divide(0, 50)).isEqualTo(0);

            final byte[] clientChallenge) {
        try {
            final byte[] hash = new byte[16];
            final byte[] response = new byte[24];
            // The next 2-1/2 lines of this should be placed with nTOWFv1 in place of password
            final MD4 md4 = new MD4();
            md4.update(password.getBytes(SmbConstants.UNI_ENCODING));
            HMACT64 hmac = new HMACT64(md4.digest());

 * If you use masked losses with Keras the loss values may be different in TensorFlow `2.12` compared to previous versions.
 * In cases where the mask was previously ignored, you will now get an error if you pass a mask with an incompatible shape.

### Major Features and Improvements

`tf.keras`: