int adjust = size() - 1;
      for (int i = 0; i < axes.size(); i++) {
        adjust *= 31;
        adjust = ~~adjust;
        // in GWT, we have to deal with integer overflow carefully
      }
      int hash = 1;
      for (Set<E> axis : axes) {
        hash = 31 * hash + (size() / axis.size() * axis.hashCode());

        hash = ~~hash;
      }
      hash += adjust;

      int adjust = size() - 1;
      for (int i = 0; i < axes.size(); i++) {
        adjust *= 31;
        adjust = ~~adjust;
        // in GWT, we have to deal with integer overflow carefully
      }
      int hash = 1;
      for (Set<E> axis : axes) {
        hash = 31 * hash + (size() / axis.size() * axis.hashCode());

        hash = ~~hash;
      }
      hash += adjust;

   *
   * @param lists the lists to choose elements from, in the order that the elements chosen from
   *     those lists should appear in the resulting lists
   * @param <B> any common base class shared by all axes (often just {@link Object})
   * @return the Cartesian product, as an immutable list containing immutable lists
   * @throws IllegalArgumentException if the size of the cartesian product would be greater than

   *
   * @param lists the lists to choose elements from, in the order that the elements chosen from
   *     those lists should appear in the resulting lists
   * @param <B> any common base class shared by all axes (often just {@link Object})
   * @return the Cartesian product, as an immutable list containing immutable lists
   * @throws IllegalArgumentException if the size of the cartesian product would be greater than

`tf.reduce_join`: `reduction_indices` becomes `axis` * `tf.reduce_logsumexp`:
`reduction_indices` becomes `axis` * `tf.reduce_max`: `reduction_indices`
becomes `axis` * `tf.reduce_mean`: `reduction_indices` becomes `axis` *
`tf.reduce_min`: `reduction_indices` becomes `axis` * `tf.reduce_prod`:
`reduction_indices` becomes `axis` * `tf.reduce_sum`: `reduction_indices`

                return "AES";
            }

            @Override
            public String getAppCipherKey() {
                return "1234567890123456"; // 16 bytes for AES
            }
        });

        // Create test cipher - use proper constructor with simple implementation
        InvertibleCryptographer cookieCipher = new InvertibleCryptographer("AES", "1234567890123456", null) {
            @Override

                case FessConfig.search_engine_heartbeat_interval:
                    return "10000";
                case FessConfig.APP_CIPHER_ALGORITHM:
                    return "aes";
                case FessConfig.APP_CIPHER_KEY:
                    return "___change__me___";
                case FessConfig.APP_ENCRYPT_PROPERTY_PATTERN:
                    return ".*password|.*key|.*token|.*secret";

- `@Secured` annotation with role array (`"admin-user"`, `"admin-user-view"`)
- Role-based query filtering via `RoleQueryHelper`
- Authentication: Local (UserService), LDAP, OIDC, SAML, SPNEGO, Entra ID
- Security features: AES encryption, SHA256 digest, LDAP injection prevention, password policy, rate limiting

## Naming Conventions

| Element | Convention | Example |
|---------|------------|---------|

    private OneWayCryptographer oneWayCryptographer;

    @Override
    protected void setUp(TestInfo testInfo) throws Exception {
        super.setUp(testInfo);

        // Create InvertibleCryptographer with AES
        invertibleCryptographer = InvertibleCryptographer.createAesCipher("1234567890123456");

        // Create OneWayCryptographer with SHA256
        oneWayCryptographer = OneWayCryptographer.createSha256Cryptographer();

> **NOTE:** Do **NOT** run `gradle build` on the local development environment,
> even if you have Gradle or Develocity build caching enabled for the project.
> The Gradle Build Tool repository is massive, and it will take ages to build on
> a local machine without necessary parallelization and caching.
> The full test suites are executed on the CI instance for multiple configurations,