now served with tensorflow/serving, it will accept requests using 'inputs'
    and 'outputs'. Starting at 1.2, such a model will accept the keys specified
    during export. Therefore, inference requests using 'inputs' and 'outputs'
    may start to fail. To fix this, either update any inference clients to send
    requests with the actual input and output keys used by the trainer code, or

Sebastián Ramírez <******@****.***> 1731896744 +0100

Sebastián Ramírez <******@****.***> 1731896744 +0100

Sebastián Ramírez <******@****.***> 1731896744 +0100

**FastAPI** **extraerá** los datos de **cada campo** de los **form data** en el request y te dará el modelo de Pydantic que definiste.

## Revisa la Documentación

Puedes verificarlo en la interfaz de documentación en `/docs`:

<div class="screenshot">
<img src="/img/tutorial/request-form-models/image01.png">
</div>

## Prohibir Campos de Formulario Extra

Sebastián Ramírez <******@****.***> 1731896744 +0100

Sebastián Ramírez <******@****.***> 1731896744 +0100

Sebastián Ramírez <******@****.***> 1731896744 +0100

 * [RFC 2817][1]. The HTTP CONNECT request that creates this tunnel connection is special: it
 * does not participate in any [interceptors][Interceptor] or [event listeners][EventListener]. It
 * doesn't include the motivating request's HTTP headers or even its full URL; only the target
 * server's hostname is sent to the proxy.
 *
 * Prior to sending any CONNECT request OkHttp always calls the proxy authenticator so that it may

  public void run() throws Exception {
    for (int i = 0; i < 5; i++) {
      System.out.println("    Request: " + i);

      Request request = new Request.Builder()
          .url("https://api.github.com/search/repositories?q=http")
          .build();

      OkHttpClient clientForCall;
      if (i == 2) {
        // Force this request's response to be written to the cache. This way, subsequent responses
        // can be read from the cache.