license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted...

>>> hello.numpy()
b'Hello, TensorFlow!'
```

For more examples, see the
[TensorFlow Tutorials](https://www.tensorflow.org/tutorials/).

## Contribution guidelines

**If you want to contribute to TensorFlow, be sure to review the
[Contribution Guidelines](CONTRIBUTING.md). This project adheres to TensorFlow's
[Code of Conduct](CODE_OF_CONDUCT.md). By participating, you are expected to
uphold this code.**

    // the `keys` and `values` arrays do not need to change. But because the "short hash" now has a
    // different number of bits, we must rewrite each element of `entries` so that its contribution
    // to the full hashcode reflects the change, and so that its `next` link corresponds to the new
    // linked list of entries with the new short hash.

    // the `keys` and `values` arrays do not need to change. But because the "short hash" now has a
    // different number of bits, we must rewrite each element of `entries` so that its contribution
    // to the full hashcode reflects the change, and so that its `next` link corresponds to the new
    // linked list of entries with the new short hash.

            The roles the contributor plays in the project. Each role is described by a
            {@code role} element, the body of which is a role name. This can also be used to
            describe the contribution.
          </description>
          <association>
            <type>String</type>
            <multiplicity>*</multiplicity>
          </association>
        </field>
        <field>

    *   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`:

 * specific language governing permissions and limitations
 * under the License.
 * ====================================================================
 *
 * This software consists of voluntary contributions made by many
 * individuals on behalf of the Apache Software Foundation.  For more
 * information on the Apache Software Foundation, please see
 * <http://www.apache.org/>.
 */

package okhttp3.internal.http

- **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'`)