Por ejemplo, si tu código carga un modelo de Machine Learning con **1 GB de tamaño**, cuando ejecutas un proceso con tu API, consumirá al menos 1 GB de RAM. Y si inicias **4 procesos** (4 workers), cada uno consumirá 1 GB de RAM. Así que, en total, tu API consumirá **4 GB de RAM**.

Y si tu servidor remoto o máquina virtual solo tiene 3 GB de RAM, intentar cargar más de 4 GB de RAM causará problemas. 🚨

     */
    public UnreleasedVersionInfo unreleasedInfo(Version version) {
        return unreleased.get(version);
    }

    public void forPreviousUnreleased(Consumer<UnreleasedVersionInfo> consumer) {
        List<UnreleasedVersionInfo> collect = filterSupportedVersions(
            getUnreleased().stream().filter(version -> version.equals(currentVersion) == false).collect(Collectors.toList())

     *
     * @param src the source bean object
     * @param dest the destination bean object
     * @param option a consumer function to configure copy options
     */
    protected static void copyBeanToBean(final Object src, final Object dest, final Consumer<CopyOptions> option) {
        BeanUtil.copyBeanToBean(src, dest, option);
    }

    /**
     * Copies properties from a map to a bean object.

    // Note that this may be be an encoded "end of data" header.
    return DerHeader(tagClass, tag, constructed, length)
  }

  /**
   * Consume a header and execute [block], which should consume the entire value described by the
   * header. It is an error to not consume a full value in [block].
   */
  internal inline fun <T> read(
    name: String?,
    block: (DerHeader) -> T,
  ): T {

import java.io.PrintStream;
import java.util.function.Consumer;

public class LoggingOutputStream extends FilterOutputStream {

    static final byte[] LINE_SEP = System.lineSeparator().getBytes();

    final EolBaos buf;
    final Consumer<String> consumer;

    public LoggingOutputStream(Consumer<String> consumer) {
        this(new EolBaos(), consumer);
    }

### Memória do servidor { #server-memory }

Por exemplo, se seu código carrega um modelo de Aprendizado de Máquina com **1 GB de tamanho**, quando você executa um processo com sua API, ele consumirá pelo menos 1 GB de RAM. E se você iniciar **4 processos** (4 trabalhadores), cada um consumirá 1 GB de RAM. Então, no total, sua API consumirá **4 GB de RAM**.

        String sessionId = "test_session_buffer";
        List<String> cmdList = Arrays.asList("echo", "hello world");
        Consumer<ProcessBuilder> pbCall = pb -> {
            pb.redirectErrorStream(true);
        };

        List<String> outputs = new ArrayList<>();
        Consumer<String> outputCallback = output -> outputs.add(output);

        try {

And if your remote server or virtual machine only has 3 GB of RAM, trying to load more than 4 GB of RAM will cause problems. 🚨

### Multiple Processes - An Example { #multiple-processes-an-example }

    }

    /**
     * Processes thumbnail generation with a consumer function.
     * @param id The document ID.
     * @param consumer The consumer function to process thumbnail and config ID.
     * @return True if processing was successful, false otherwise.
     */
    protected boolean process(final String id, final BiPredicate<String, String> consumer) {
        final FessConfig fessConfig = ComponentUtil.getFessConfig();

    private static class StreamGobbler implements Runnable {
        private final InputStream inputStream;
        private final Consumer<String> consumer;

        private StreamGobbler(InputStream inputStream, Consumer<String> consumer) {
            this.inputStream = inputStream;
            this.consumer = consumer;
        }

        @Override
        public void run() {