* `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.

      }
    }

    // Write the payload.
    sink.writeAll(content)
  }

  /**
   * Execute [block] with a new namespace for type hints. Type hints from the enclosing type are no
   * longer usable by the current type's members.
   */
  fun <T> withTypeHint(block: () -> T): T {
    typeHintStack.add(null)
    try {
      return block()
    } finally {
      typeHintStack.removeAt(typeHintStack.size - 1)

Ich habe seit mehreren Jahren APIs mit komplexen Anforderungen (maschinelles Lernen, verteilte Systeme, asynchrone Jobs, NoSQL-Datenbanken, usw.) erstellt und leitete mehrere Entwicklerteams.

Dabei musste ich viele Alternativen untersuchen, testen und nutzen.

Die Geschichte von **FastAPI** ist zu einem großen Teil die Geschichte seiner Vorgänger.

   * ClosingFuture} will be equivalent to this one.
   *
   * <p>If the function throws an exception, that exception is used as the result of the derived
   * {@code ClosingFuture}.
   *
   * <p>Example usage:
   *
   * {@snippet :
   * ClosingFuture<List<Row>> rowsFuture =
   *     queryFuture.transform((closer, result) -> result.getRows(), executor);
   * }
   *

import org.codelibs.core.exception.ClUnsupportedOperationException;

/**
 * An {@link Iterator} that iterates over multiple {@link Iterator}s as if they were a single {@link Iterator}.
 * <p>
 * Usage example:
 * </p>
 *
 * <pre>
 * import static org.codelibs.core.collection.MultiIterator.*;
 *
 * List&lt;String&gt; list = ...;
 * Set&lt;String&gt; set = ...;
 * Map&lt;String, Object&gt; map = ...;

import com.google.common.annotations.GwtCompatible;
import java.lang.annotation.Retention;
import java.lang.annotation.Target;

/**
 * Annotates a "top-level" type-variable usage that takes its nullness from the type argument
 * supplied by the user of the class. For example, {@code Multiset.Entry.getElement()} returns
 * {@code @ParametricNullness E}, which means:
 *
 * <ul>

import com.google.common.annotations.GwtCompatible;
import java.lang.annotation.Retention;
import java.lang.annotation.Target;

/**
 * Annotates a "top-level" type-variable usage that takes its nullness from the type argument
 * supplied by the user of the class. For example, {@code Multiset.Entry.getElement()} returns
 * {@code @ParametricNullness E}, which means:
 *
 * <ul>

import com.google.common.annotations.GwtCompatible;
import java.lang.annotation.Retention;
import java.lang.annotation.Target;

/**
 * Annotates a "top-level" type-variable usage that takes its nullness from the type argument
 * supplied by the user of the class. For example, {@code Multiset.Entry.getElement()} returns
 * {@code @ParametricNullness E}, which means:
 *
 * <ul>

import com.google.common.annotations.GwtCompatible;
import java.lang.annotation.Retention;
import java.lang.annotation.Target;

/**
 * Annotates a "top-level" type-variable usage that takes its nullness from the type argument
 * supplied by the user of the class. For example, {@code Multiset.Entry.getElement()} returns
 * {@code @ParametricNullness E}, which means:
 *
 * <ul>

Angesichts der Einfachheit von Flask schien es eine gute Ergänzung zum Erstellen von APIs zu sein. Als Nächstes musste ein „Django REST Framework“ für Flask gefunden werden.

/// check | Inspirierte **FastAPI**

Ein Mikroframework zu sein. Es einfach zu machen, die benötigten Tools und Teile zu kombinieren.