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

        // Test without timeout
        FsctlPipeWaitRequest request1 = new FsctlPipeWaitRequest(pipeName);
        byte[] buffer1 = new byte[200];
        int encoded1 = request1.encode(buffer1, 0);
        assertEquals(request1.size(), encoded1);

        // Test with timeout
        FsctlPipeWaitRequest request2 = new FsctlPipeWaitRequest(pipeName, 5000L);
        byte[] buffer2 = new byte[200];

**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 { #check-the-docs }

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 { #forbid-extra-form-fields }

        VersionRequest requestB = new VersionRequest();
        requestB.addRepository(newTestRepository());
        Artifact artifactB =
                new DefaultArtifact("org.apache.maven.its", "dep-mng5324", "classifierB", "jar", "07.20.3-SNAPSHOT");
        requestB.setArtifact(artifactB);

        VersionResult resultB = versionResolver.resolveVersion(session, requestB);

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

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

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

  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.

    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

Valentyn <******@****.***> 1740060824 -0500