}
  return outputs;
}

void TFOutputsFromOutputs(const std::vector<tensorflow::Output>& outputs,
                          TF_Output* tf_outputs) {
  for (int i = 0; i < outputs.size(); i++) {
    tf_outputs[i].oper = ToOperation(outputs[i].node());
    tf_outputs[i].index = outputs[i].index();
  }
}
#endif  // !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD)

 */
@Deprecated(since = "4.0.0")
public interface ModelWriter {

    /**
     * Writes the supplied model to the specified file. Any non-existing parent directories of the output file will be
     * created automatically.
     *
     * @param output The file to serialize the model to, must not be {@code null}.
     * @param options The options to use for serialization, may be {@code null} to use the default values.

</div>

### Modelo para Salida { #model-for-output }

Pero si usas el mismo modelo como salida, como aquí:

{* ../../docs_src/separate_openapi_schemas/tutorial001_py310.py hl[19] *}

...entonces, porque `description` tiene un valor por defecto, si **no devuelves nada** para ese campo, aún tendrá ese **valor por defecto**.

### Modelo para Datos de Response de Salida { #model-for-output-response-data }

</div>

### Modelo para Saída { #model-for-output }

Mas se você usar o mesmo modelo como saída, como aqui:

{* ../../docs_src/separate_openapi_schemas/tutorial001_py310.py hl[19] *}

... então, como `description` tem um valor padrão, se você **não retornar nada** para esse campo, ele ainda terá o **valor padrão**.

### Modelo para Dados de Resposta de Saída { #model-for-output-response-data }

      value: |
        <details>

        ```console
        # On Linux:
        $ cat /etc/os-release
        # paste output here
        $ uname -a
        # paste output here

        # On Windows:
        C:\> wmic os get Caption, Version, BuildNumber, OSArchitecture
        # paste output here
        ```

        </details>

  - type: textarea
    id: installer
    attributes:
      label: Install tools

        byte[] outBuf = new byte[output.length];
        Smb2IoctlResponse resp = new Smb2IoctlResponse(config, outBuf);

        resp.decode(packet, 0);
        assertEquals(output.length, resp.getOutputLength());
        assertArrayEquals(output, outBuf, "Output should be copied into provided buffer");
        // No Decodable output when buffer is provided
        assertNull(resp.getOutputData());

	JSONArgs    json.WriterArgs `xml:"JSON"`
	unmarshaled bool
	format      string
}

// IsEmpty - returns whether output serialization is empty or not.
func (output *OutputSerialization) IsEmpty() bool {
	return !output.unmarshaled
}

// UnmarshalXML - decodes XML data.
func (output *OutputSerialization) UnmarshalXML(d *xml.Decoder, start xml.StartElement) error {
	// Make subtype to avoid recursive UnmarshalXML().

     * The error's stacktrace will be output when this error level is enabled.
     *
     * @param content
     * @param error
     */
    void debug(CharSequence content, Throwable error);

    /**
     * Send an exception to the user in the <b>debug</b> error level.<br>
     * The stack trace for this exception will be output when this error level is enabled.
     *
     * @param error
     */

  }

  void Run(const std::vector<std::pair<TF_Operation*, TF_Tensor*>>& inputs,
           TF_Operation* output, int32_t expected_result) {
    Run(inputs, {{output, 0}}, {expected_result});
  }

  // Run the host graph, which now contains a function and check that
  // outputs are as expected.
  // 'T' stands for 'tensor' since the outputs are tensors, not scalars.
  void RunT(const std::vector<std::pair<TF_Operation*, TF_Tensor*>>& inputs,

      ListenableFuture<I> input,
      AsyncFunction<? super I, ? extends O> function,
      Executor executor) {
    AsyncTransformFuture<I, O> output = new AsyncTransformFuture<>(input, function);
    input.addListener(output, rejectionPropagatingExecutor(executor, output));
    return output;
  }

  static <I extends @Nullable Object, O extends @Nullable Object> ListenableFuture<O> create(