return errors::Unimplemented("No custom device pinning implementation.");
  }

 private:
  TFE_Context* context_;
  TFE_CustomDevice device_;
  void* info_;
  string name_;
};

// An adapter which wraps the shape/data produced by C custom devices and uses
// it to implement custom device methods.
class CAPICustomDeviceTensorHandle
    : public tensorflow::CustomDeviceTensorHandle {
 public:

Les modèles, bien qu'ils soient généralement livrés avec une configuration spécifique, sont conçus pour être flexibles et personnalisables. Cela vous permet de les modifier et de les adapter aux exigences de votre projet, ce qui en fait un excellent point de départ. 🏁

public final class ParseResponseWithMoshi {
  private final OkHttpClient client = new OkHttpClient();
  private final Moshi moshi = new Moshi.Builder().build();
  private final JsonAdapter<Gist> gistJsonAdapter = moshi.adapter(Gist.class);

  public void run() throws Exception {
    Request request = new Request.Builder()
        .url("https://api.github.com/gists/c2a7c39532239ff261be")
        .build();

  private static final Moshi MOSHI = new Moshi.Builder().build();
  private static final JsonAdapter<List<Contributor>> CONTRIBUTORS_JSON_ADAPTER = MOSHI.adapter(
      Types.newParameterizedType(List.class, Contributor.class));

  static class Contributor {
    String login;
    int contributions;
  }

  public static void main(String... args) throws Exception {

      .addInterceptor(new GzipRequestInterceptor())
      .build();
  private final Moshi moshi = new Moshi.Builder().build();
  private final JsonAdapter<Map<String, String>> mapJsonAdapter = moshi.adapter(
      Types.newParameterizedType(Map.class, String.class, String.class));

  public void run() throws Exception {
    Map<String, String> requestBody = new LinkedHashMap<>();

 * code changes, you can use <a
 * href="https://www.javadoc.io/doc/com.github.ben-manes.caffeine/guava/latest/com.github.benmanes.caffeine.guava/com/github/benmanes/caffeine/guava/CaffeinatedGuava.html">its
 * {@code CaffeinatedGuava} adapter class</a>, which lets you build a Guava {@code Cache} or a Guava
 * {@code LoadingCache} backed by a Guava {@code CacheLoader}.
 *
 * <p>Caffeine's API for asynchronous operations uses {@code CompletableFuture}: <a

Vous pouvez utiliser [`bump-pydantic`](https://github.com/pydantic/bump-pydantic) de la même équipe Pydantic.

Cet outil vous aidera à modifier automatiquement la majeure partie du code à adapter.

Après cela, vous pouvez exécuter les tests et vérifier que tout fonctionne. Si c'est le cas, vous avez terminé. 😎

## Pydantic v1 dans v2 { #pydantic-v1-in-v2 }

      }
    };
  }

  /**
   * Adapts an {@code Iterator} to the {@code Enumeration} interface.
   *
   * <p>The {@code Iterable} equivalent of this method is either {@link Collections#enumeration} (if
   * you have a {@link Collection}), or {@code Iterators.asEnumeration(collection.iterator())}.
   */
  // This is an adapter for cases in which users do need an Enumeration for whatever reason.

      }
    };
  }

  /**
   * Adapts an {@code Iterator} to the {@code Enumeration} interface.
   *
   * <p>The {@code Iterable} equivalent of this method is either {@link Collections#enumeration} (if
   * you have a {@link Collection}), or {@code Iterators.asEnumeration(collection.iterator())}.
   */
  // This is an adapter for cases in which users do need an Enumeration for whatever reason.

 * code changes, you can use <a
 * href="https://www.javadoc.io/doc/com.github.ben-manes.caffeine/guava/latest/com.github.benmanes.caffeine.guava/com/github/benmanes/caffeine/guava/CaffeinatedGuava.html">its
 * {@code CaffeinatedGuava} adapter class</a>, which lets you build a Guava {@code Cache} or a Guava
 * {@code LoadingCache} backed by a Guava {@code CacheLoader}.
 *
 * <p>Caffeine's API for asynchronous operations uses {@code CompletableFuture}: <a