* streams.
 *
 * <p><i>Performance notes:</i> Unless otherwise noted, all of the iterables produced in this class
 * are <i>lazy</i>, which means that their iterators only advance the backing iteration when
 * absolutely necessary.
 *
 * <p>See the Guava User Guide article on <a href=
 * "https://github.com/google/guava/wiki/CollectionUtilitiesExplained#iterables">{@code
 * Iterables}</a>.
 *

 *         .nullsLast();
 * }
 *
 * Note that each chaining method returns a new ordering instance which is backed by the previous
 * instance, but has the chance to act on values <i>before</i> handing off to that backing instance.
 * As a result, it usually helps to read chained ordering expressions <i>backwards</i>. For example,
 * when {@code compare} is called on the above ordering:
 *
 * <ol>

    bytesIn.writeByte(0x3F) // Dynamic table size update (size = 55).
    bytesIn.writeByte(0x18)
    hpackReader!!.readHeaders()
    assertThat(hpackReader!!.headerCount).isEqualTo(1)
  }

  /** Header table backing array is initially 8 long, let's ensure it grows.  */
  @Test
  fun dynamicallyGrowsBeyond64Entries() {
    // Lots of headers need more room!
    hpackReader = Hpack.Reader(bytesIn, 16384, 4096)

  // If the set has this many elements, it will "max out" the table size
  private static final int CUTOFF = (int) (MAX_TABLE_SIZE * DESIRED_LOAD_FACTOR);

  /**
   * Returns an array size suitable for the backing array of a hash table that uses open addressing
   * with linear probing in its implementation. The returned size is the smallest power of two that

### Internal Storage Layer
* upgrade to etcd3 prior to upgrading to 1.6 **OR** explicitly specify `--storage-backend=etcd2 --storage-media-type=application/json` when starting the apiserver

### Node Components
* **Kubelet with the Docker-CRI implementation**
  * The Docker-CRI implementation is enabled by default.
  * It is not compatible with containers created by older Kubelets. It is

## Pasos Previos Antes de Comenzar y Contenedores { #previous-steps-before-starting-and-containers }

Si estás usando contenedores (por ejemplo, Docker, Kubernetes), entonces hay dos enfoques principales que puedes usar.

### Múltiples Contenedores { #multiple-containers }

    return result.toString();
  }

  /**
   * Returns an {@code OutputStream} that encodes bytes using this encoding into the specified
   * {@code Writer}. When the returned {@code OutputStream} is closed, so is the backing {@code
   * Writer}.
   */
  @J2ktIncompatible
  @GwtIncompatible // Writer,OutputStream
  public abstract OutputStream encodingStream(Writer writer);

  /**

Se você executar **múltiplos processos por contêiner**, deve garantir que o número de processos iniciados não **consuma mais memória** do que o disponível.

## Passos anteriores antes de iniciar e contêineres { #previous-steps-before-starting-and-containers }

Se você estiver usando contêineres (por exemplo, Docker, Kubernetes), existem duas abordagens principais que você pode usar.

### Contêineres Múltiplos { #multiple-containers }

            // XML schema and this will not change anytime soon. We do not want to build effective models based on
            // models without a version starting with 3.4.
            validateStringNotEmpty("modelVersion", problems, Severity.ERROR, Version.V20, m.getModelVersion(), m);

            validateModelVersion(problems, m.getModelVersion(), m, "4.0.0");

    write_action_env_to_bazelrc('HIP_PLATFORM', environ_cp.get('HIP_PLATFORM'))

  if is_windows():
    print('\nWARNING: Cannot build with CUDA support on Windows.\n'
          'Starting in TF 2.11, CUDA build is not supported for Windows. '
          'For using TensorFlow GPU on Windows, you will need to build/install '
          'TensorFlow in WSL2.\n')
    environ_cp['TF_NEED_CUDA'] = '0'
  else: