Up to now, you have been using common data types, like:

* `int`
* `float`
* `str`
* `bool`

But you can also use more complex data types.

And you will still have the same features as seen up to now:

* Great editor support.
* Data conversion from incoming requests.
* Data conversion for response data.
* Data validation.
* Automatic annotation and documentation.

* `Decimal`:
    * `Decimal` estándar de Python.
    * En requests y responses, manejado igual que un `float`.
* Puedes revisar todos los tipos de datos válidos de Pydantic aquí: <a href="https://docs.pydantic.dev/latest/usage/types/types/" class="external-link" target="_blank">Tipos de datos de Pydantic</a>.

## Ejemplo { #example }

Aquí tienes un ejemplo de una *path operation* con parámetros usando algunos de los tipos anteriores.

    * 生成的模式将指定这个 `str` 是 `binary` "格式"。
* `Decimal`:
    * 标准的 Python `Decimal`。
    * 在请求和响应中被当做 `float` 一样处理。
* 您可以在这里检查所有有效的pydantic数据类型: <a href="https://docs.pydantic.dev/latest/concepts/types/" class="external-link" target="_blank">Pydantic data types</a>.

## 例子

下面是一个*路径操作*的示例，其中的参数使用了上面的一些类型。

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

注意，函数内的参数有原生的数据类型，你可以，例如，执行正常的日期操作，如:

    * Стандартний Пайтонівський `Decimal`.
    * У запитах і відповідях це буде оброблено так само, як і `float`.
* Ви можете перевірити всі дійсні типи даних Pydantic тут: <a href="https://docs.pydantic.dev/latest/concepts/types/" class="external-link" target="_blank">типи даних Pydantic</a>.

## Приклад

Ось приклад *path operation* з параметрами, використовуючи деякі з вищезазначених типів.

import java.util.Set;

/**
 * Contains static utility methods pertaining to primitive types and their corresponding wrapper
 * types.
 *
 * @author Kevin Bourrillion
 * @since 1.0
 */
@GwtCompatible
public final class Primitives {
  private Primitives() {}

  /** A map from primitive types to their corresponding wrapper types. */
  // It's a constant, and we can't use ImmutableMap here without creating a circular dependency.

  public void testNewArrayType_upperBoundedWildcard() {
    Type wildcard = Types.subtypeOf(Number.class);
    assertEquals(Types.subtypeOf(Number[].class), Types.newArrayType(wildcard));
  }

  public void testNewArrayType_lowerBoundedWildcard() {
    Type wildcard = Types.supertypeOf(Number.class);
    assertEquals(Types.supertypeOf(Number[].class), Types.newArrayType(wildcard));
  }

  public void testNewArrayType_serializable() {

     */
    <T> T newInstance(Object... args);

    /**
     * Returns the {@link ConstructorDesc} for the given parameter types.
     *
     * @param paramTypes
     *            The array of parameter types for the constructor
     * @return The {@link ConstructorDesc} for the given parameter types
     */
    ConstructorDesc getConstructorDesc(Class<?>... paramTypes);

    /**

@Target({FIELD, METHOD, TYPE})
@Retention(RUNTIME)
@Documented
public @interface Typed {
    /**
     * Specifies the types that should be considered for dependency injection.
     * <p>
     * When specified, only the listed types will be available for injection,
     * even if the class implements or extends other types. If empty, the
     * default behavior is to make all supertypes available for injection.
     * <p>
     * Example:

flask >=1.1.2,<4.0.0
strawberry-graphql >=0.200.0,< 1.0.0
anyio[trio] >=3.2.1,<5.0.0
PyJWT==2.9.0
pyyaml >=5.3.1,<7.0.0
pwdlib[argon2] >=0.2.1
inline-snapshot>=0.21.1
pytest-codspeed==4.2.0
# types
types-ujson ==5.10.0.20240515

  @CanIgnoreReturnValue
  Type[] resolveTypesInPlace(Type[] types) {
    for (int i = 0; i < types.length; i++) {
      types[i] = resolveType(types[i]);
    }
    return types;
  }

  private Type[] resolveTypes(Type[] types) {
    Type[] result = new Type[types.length];
    for (int i = 0; i < types.length; i++) {
      result[i] = resolveType(types[i]);
    }
    return result;
  }