*/
    boolean isIncludesDependencies();

    /**
     * Types of path (class-path, module-path, …) where the dependency can be placed.
     * For most deterministic builds, the array length should be 1. In such case,
     * the dependency will be unconditionally placed on the specified type of path
     * and no heuristic rule will be involved.
     *

            assertEquals(0, response.writeBytesWireFormat(dst, 50));
            assertEquals(0, response.writeBytesWireFormat(dst, 99));
        }

        @Test
        @DisplayName("Should return 0 with empty array")
        void testWriteBytesWireFormatWithEmptyArray() {
            // Given
            byte[] dst = new byte[0];

            // When
            int result = response.writeBytesWireFormat(dst, 0);

        /** The number of sub-authorities in the SID */
        public byte sub_authority_count;
        /** The identifier authority value (6 bytes) */
        public byte[] identifier_authority;
        /** The array of sub-authority values */
        public int[] sub_authority;

        @Override
        public void encode(NdrBuffer _dst) throws NdrException {
            _dst.align(4);

from your test's `tearDown()`.

### API

#### MockResponse

Mock responses default to an empty response body and a `200` status code.
You can set a custom body with a string, input stream or byte array. Also
add headers with a fluent builder API.

### Java
```java
MockResponse response = new MockResponse.Builder()
    .addHeader("Content-Type", "application/json; charset=utf-8")

Notice how `Offer` has a list of `Item`s, which in turn have an optional list of `Image`s

///

## Bodies of pure lists { #bodies-of-pure-lists }

If the top level value of the JSON body you expect is a JSON `array` (a Python `list`), you can declare the type in the parameter of the function, the same as in Pydantic models:

```Python
images: list[Image]
```

as in:

///

## Тела с чистыми списками элементов { #bodies-of-pure-lists }

Если верхний уровень значения тела JSON-объекта представляет собой JSON `array` (в Python — `list`), вы можете объявить тип в параметре функции, так же как в моделях Pydantic:

```Python
images: list[Image]
```

например так:

     * Extracts strings from a document using the specified XPath expression.
     *
     * @param document the DOM document to extract strings from
     * @param path the XPath expression to evaluate
     * @return an array of strings extracted from the document
     */
    protected String[] getStringsByXPath(final Document document, final String path) {
        try {

* Um einen JSON-Body zu übergeben, übergeben Sie ein Python-Objekt (z. B. ein <abbr title="Dictionary – Zuordnungstabelle: In anderen Sprachen auch Hash, Map, Objekt, Assoziatives Array genannt">`dict`</abbr>) an den Parameter `json`.
* Wenn Sie *Formulardaten* anstelle von JSON senden müssen, verwenden Sie stattdessen den `data`-Parameter.
* Um *Header* zu übergeben, verwenden Sie ein `dict` im `headers`-Parameter.

     */
    public String getDesignJspFileName(final String fileName) {
        return designJspFileNameMap.get(fileName);
    }

    /**
     * Gets an array of design JSP file names.
     *
     * @return An array of pairs of keys and file names.
     */
    @SuppressWarnings("unchecked")
    public Pair<String, String>[] getDesignJspFileNames() {

   *
   * <p>Implementations should be collections of pure functions (i.e. stateless).
   */
  interface Strategy extends Serializable {

    /**
     * Sets {@code numHashFunctions} bits of the given bit array, by hashing a user element.
     *
     * <p>Returns whether any bits changed as a result of this operation.
     */
    <T extends @Nullable Object> boolean put(
        @ParametricNullness T object,