*   XLA:GPU reductions are deterministic by default (reductions within
        `jit_compile=True` are now deterministic).
    *   XLA:GPU works with Horovod (OSS contribution by Trent Lo from NVidia)
    *   XLA:CPU and XLA:GPU can compile tf.unique and tf.where when shapes are
        provably correct at compile time.
*   `tf.saved_model.save`:

- **Authentication**: NTLM, Kerberos, SPNEGO unified subsystem
- **Network Layer**: Transport abstraction, NetBIOS name resolution, session management

### Contributing

We welcome contributions! Please:
1. Fork the repository
2. Create a feature branch
3. Make your changes with appropriate tests
4. Run `mvn clean test` to ensure all tests pass
5. Submit a pull request

### License

    .converter("dateField", new DateConverter("yyyy-MM-dd"));

BeanUtil.copyBeanToBean(source, dest, options);
```

## 🤝 Contributing

We welcome contributions! Please see our contributing guidelines for details.

### Development Workflow
1. **Fork** the repository on GitHub
2. **Create** your feature branch: `git checkout -b feature/amazing-feature`

    int processed = dataService.getCount(sessionId);
    System.out.println("Processed: " + processed + " URLs");
    Thread.sleep(5000);
}
```

## Contributing

We welcome contributions to Fess Crawler! Please follow these guidelines:

1. **Fork** the repository
2. **Create** a feature branch (`git checkout -b feature/amazing-feature`)
3. **Commit** your changes (`git commit -m 'Add amazing feature'`)