These types that have internal types are called "**generic**" types. And it's possible to declare them, even with their internal types.

To declare those types and the internal types, you can use the standard Python module `typing`. It exists specifically to support these type hints.

#### Newer versions of Python { #newer-versions-of-python }

    @Override
    public ArtifactType get(String typeId) {
        Type type = typeRegistry.require(typeId);
        if (type instanceof ArtifactType artifactType) {
            return artifactType;
        }
        return new DefaultType(
                type.id(),
                type.getLanguage(),
                type.getExtension(),
                type.getClassifier(),
                type.isIncludesDependencies(),

      val length = buf.readShort().toInt() and 0xffff

      if (type == TYPE_A || type == TYPE_AAAA) {
        val bytes = ByteArray(length)
        buf.read(bytes)
        result.add(InetAddress.getByAddress(bytes))
      } else {
        buf.skip(length.toLong())
      }
    }

    return result
  }

  @Throws(EOFException::class)
  private fun skipName(source: Buffer) {
    // 0 - 63 bytes

typedef struct TF_AttrMetadata {
  // A boolean: 1 if the attribute value is a list, 0 otherwise.
  unsigned char is_list;

  // Length of the list if is_list is true. Undefined otherwise.
  int64_t list_size;

  // Type of elements of the list if is_list != 0.
  // Type of the single value stored in the attribute if is_list == 0.
  TF_AttrType type;

  // Total size the attribute value.

    return BodyModel


def get_model_fields(model: type[BaseModel]) -> list[ModelField]:
    model_fields: list[ModelField] = []
    for name, field_info in model.model_fields.items():
        type_ = field_info.annotation
        if lenient_issubclass(type_, (BaseModel, dict)) or is_dataclass(type_):
            model_config = None
        else:

    TF_Status* status);

// Information about the shape of a Tensor and its type.
struct TF_ShapeAndType {
  // Number of dimensions. -1 indicates unknown rank.
  int num_dims;
  // Array of dimensions. -1 indicates unknown dim.
  int64_t* dims;
  // The data type. May be 0 to denote unknown type.
  TF_DataType dtype;
};

typedef struct TF_ShapeAndType TF_ShapeAndType;

// A list of TF_ShapeAndType elements..

// Header Test テスト

import okhttp3.dnsoverhttps.DnsRecordCodec.TYPE_A
import okhttp3.dnsoverhttps.DnsRecordCodec.TYPE_AAAA
import okhttp3.dnsoverhttps.DnsRecordCodec.decodeAnswers
import okio.ByteString.Companion.decodeHex
import org.junit.jupiter.api.Test

class DnsRecordCodecTest {
  @Test
  fun testGoogleDotComEncoding() {
    val encoded = encodeQuery("google.com", TYPE_A)

* Sie können alle gültigen Pydantic-Datentypen hier überprüfen: <a href="https://docs.pydantic.dev/latest/usage/types/types/" class="external-link" target="_blank">Pydantic-Datentypen</a>.

## Beispiel { #example }

Hier ist ein Beispiel für eine *Pfadoperation* mit Parametern, die einige der oben genannten Typen verwenden.

{* ../../docs_src/extra_data_types/tutorial001_an_py310.py hl[1,3,12:16] *}

#### Types génériques

Les types qui peuvent contenir des paramètres de types entre crochets, comme :

* `List`
* `Tuple`
* `Set`
* `Dict`
* `Optional`
* ...et d'autres.

sont appelés des **types génériques** ou **Generics**.

### Classes en tant que types

Vous pouvez aussi déclarer une classe comme type d'une variable.