En este ejemplo, no declaramos ningún modelo Pydantic. De hecho, el cuerpo del request ni siquiera se <abbr title="converted from some plain format, like bytes, into Python objects">parse</abbr> como JSON, se lee directamente como `bytes`, y la función `magic_data_reader()` sería la encargada de parsearlo de alguna manera.

  }

  /*
   * AbstractCollection fails the removeAll(null) test when the subject
   * collection is empty, but we'd still like to test removeAll(null) when we
   * can. We split the test into empty and non-empty cases. This allows us to
   * suppress only the former.
   */

  @CollectionFeature.Require(SUPPORTS_REMOVE)
  @CollectionSize.Require(ZERO)

    public static List<AvPair> decode(final byte[] data) throws CIFSException {
        final List<AvPair> pairs = new LinkedList<>();
        int pos = 0;
        boolean foundEnd = false;
        while (pos + 4 <= data.length) {
            final int avId = SMBUtil.readInt2(data, pos);
            final int avLen = SMBUtil.readInt2(data, pos + 2);
            pos += 4;

            if (avId == AvPair.MsvAvEOL) {
                if (avLen != 0) {

    }

    override fun windowUpdate(
      streamId: Int,
      windowSizeIncrement: Long,
    ) {
      check(type == -1)
      this.type = Http2.TYPE_WINDOW_UPDATE
      this.streamId = streamId
      this.windowSizeIncrement = windowSizeIncrement
    }

    override fun priority(
      streamId: Int,
      streamDependency: Int,
      weight: Int,
      exclusive: Boolean,

### Async Context Manager { #async-context-manager }

If you check, the function is decorated with an `@asynccontextmanager`.

That converts the function into something called an "**async context manager**".

{* ../../docs_src/events/tutorial003.py hl[1,13] *}

  void resizeEntries(int newCapacity) {
    this.entries = Arrays.copyOf(requireEntries(), newCapacity);
    this.keys = Arrays.copyOf(requireKeys(), newCapacity);
    this.values = Arrays.copyOf(requireValues(), newCapacity);
  }

  @CanIgnoreReturnValue
  private int resizeTable(int oldMask, int newCapacity, int targetHash, int targetEntryIndex) {
    Object newTable = CompactHashing.createTable(newCapacity);

`tf` instead of `tensorflow`, following the common practice of importing
TensorFlow via the command `import tensorflow as tf`.

The primary function of this module is to import all of the public TensorFlow
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.

Note that the file `__init__.py` in the TensorFlow source code tree is actually

        input.equals(".%2e", ignoreCase = true) ||
        input.equals("%2e%2e", ignoreCase = true)

    /**
     * Cuts this string up into alternating parameter names and values. This divides a query string
     * like `subject=math&easy&problem=5-2=3` into the list `["subject", "math", "easy", null,
     * "problem", "5-2=3"]`. Note that values may be null and may contain '=' characters.
     */

   * Array.newInstance(type, len))}.
   *
   * @param type the type of the elements
   * @return a newly-allocated array into which all the elements of this fluent iterable have been
   *     copied
   */
  @GwtIncompatible // Array.newArray(Class, int)
  public final E[] toArray(Class<@NonNull E> type) {
    return Iterables.<E>toArray(getDelegate(), type);
  }

  /**

   * Array.newInstance(type, len))}.
   *
   * @param type the type of the elements
   * @return a newly-allocated array into which all the elements of this fluent iterable have been
   *     copied
   */
  @GwtIncompatible // Array.newArray(Class, int)
  public final E[] toArray(Class<@NonNull E> type) {
    return Iterables.<E>toArray(getDelegate(), type);
  }

  /**