* This class provides methods for managing a queue of URLs to be crawled,
 * including adding, deleting, and retrieving URLs from the queue.
 * It uses a {@link MemoryDataHelper} to store the URL queue data in memory.
 *
 * <p>
 * The class is responsible for:
 * </p>
 * <ul>
 *   <li>Updating session IDs for URL queues.</li>
 *   <li>Adding new URLs to the queue.</li>

        final UniAddress[] addrs = new UniAddress[iaddrs.length];
        for (int ii = 0; ii < iaddrs.length; ii++) {
            addrs[ii] = new UniAddress(iaddrs[ii]);
        }
        return addrs;
    }

    private static UniAddress[] wrapNetbiosAddresses(final NetbiosAddress[] addr) {
        final UniAddress[] addrs = new UniAddress[addr.length];

   
     * If you have above configuration and using _**Windows**_ platform -
       * Try adding the CUDA, CUPTI, and cuDNN installation directories to the %PATH% environment variable.
       * Refer [windows setup guide](https://www.tensorflow.org/install/gpu#windows_setup).
     * If you have above configuration and using _**Ubuntu/Linux**_ platform -
       * Try adding the CUDA, CUPTI, and cuDNN installation directories to the $LD_LIBRARY_PATH environment variable.

import org.opensearch.core.common.Strings;
import org.opensearch.transport.client.Client;

/**
 * The BadWordSettings class manages the settings related to bad words.
 * It allows adding, deleting, and retrieving bad words from the settings.
 * It also supports loading default bad words from a file.
 */
public class BadWordSettings {

 * (or {@link FluentFuture#transform(com.google.common.base.Function, Executor)
 * FluentFuture.transform}), but you will often find it easier to use a framework. Frameworks
 * automate the process, often adding features like monitoring, debugging, and cancellation.
 * Examples of frameworks include:
 *
 * <ul>
 *   <li><a href="https://dagger.dev/producers.html">Dagger Producers</a>
 * </ul>
 *

3. Make focused commits
4. Add tests
5. Format code (`mvn formatter:format && mvn license:format`)
6. Run tests (`mvn test`)
7. Open Pull Request

---

## Common Development Tasks

### Adding a Protocol Client

1. **Implement `CrawlerClient`**:
```java
public class MyClient extends AbstractCrawlerClient {
    @Override
    public ResponseData execute(RequestData request) { /* ... */ }

    missing_paths = list_missing(language)
    for path in missing_paths:
        print(f"Adding lang: {language} path: {path}")
        translate_page(language=language, en_path=path)
        print(f"Done adding: {path}")
    print("Done adding all missing paths")


@app.command()
def update_and_add(language: Annotated[str, typer.Option(envvar="LANGUAGE")]) -> None:

 * (or {@link FluentFuture#transform(com.google.common.base.Function, Executor)
 * FluentFuture.transform}), but you will often find it easier to use a framework. Frameworks
 * automate the process, often adding features like monitoring, debugging, and cancellation.
 * Examples of frameworks include:
 *
 * <ul>
 *   <li><a href="https://dagger.dev/producers.html">Dagger Producers</a>
 * </ul>
 *

 * (or {@link FluentFuture#transform(com.google.common.base.Function, Executor)
 * FluentFuture.transform}), but you will often find it easier to use a framework. Frameworks
 * automate the process, often adding features like monitoring, debugging, and cancellation.
 * Examples of frameworks include:
 *
 * <ul>
 *   <li><a href="https://dagger.dev/producers.html">Dagger Producers</a>
 * </ul>
 *

Primeiro, você precisa importar `BaseModel` do `pydantic`:

{* ../../docs_src/body/tutorial001_py310.py hl[2] *}

## Crie seu modelo de dados { #create-your-data-model }

Então você declara seu modelo de dados como uma classe que herda `BaseModel`.

Utilize os tipos Python padrão para todos os atributos:

{* ../../docs_src/body/tutorial001_py310.py hl[5:9] *}