public EditForm() {
        super();
    }

    /**
     * The unique identifier of the bad word entry being edited.
     * This is a required field for identifying which bad word to update.
     */
    @Required
    @Size(max = 1000)
    public String id;

    /**
     * The username of the user who last updated this bad word entry.
     * Used for audit trail purposes to track who made changes.
     */
    @Size(max = 1000)

     *   <li>If this field's value == null because of unsafe publication, we know that it isn't the
     *       object associated with our thread, because if it was the publication wouldn't have been
     *       unsafe and we'd have seen our thread as the value. This state is also why a new
     *       ThreadConfinedTaskQueue object must be created for each inline execution, because

labels.suggest_word_configuration=Suggest Word
labels.suggest_word_title_details=Suggest Word
labels.suggest_word_type=Word Type
labels.suggest_word_number=Number of Words
labels.suggest_word_type_all=All
labels.suggest_word_type_document=Document
labels.suggest_word_type_query=Query
labels.elevate_word_configuration=Elevate Word
labels.elevate_word_title_details=Elevate Word
labels.elevate_word_link_list=List

* In der OpenAPI *Pfadoperation* ein **JSON-Schema** für die Response hinzuzufügen.
    * Dieses wird von der **automatischen Dokumentation** verwendet.
    * Es wird auch von automatisch Client-Code-generierenden Tools verwendet.

Aber am wichtigsten:

* Es wird die Ausgabedaten auf das **limitieren und filtern**, was im Rückgabetyp definiert ist.

* Sie nimmt jeden **Request** entgegen, der an Ihre Anwendung gesendet wird.
* Sie kann dann etwas mit diesem **Request** tun oder beliebigen Code ausführen.

file: org.apache.spark.rdd.RDD[String] = s3a://testbucket/testdata MapPartitionsRDD[1] at textFile at <console>:24

scala> val counts = file.flatMap(line => line.split(" ")).map(word => (word, 1)).reduceByKey(_ + _)
counts: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[4] at reduceByKey at <console>:25

scala> counts.saveAsTextFile("s3a://testbucket/wordcount")
```

</p>

<p>
Reads of memory locations larger than a single machine word
are encouraged but not required to meet the same semantics
as word-sized memory locations,
observing a single allowed write <i>w</i>.
For performance reasons,
implementations may instead treat larger operations
as a set of individual machine-word-sized operations
in an unspecified order.
This means that races on multiword data structures

            String formattedLeft = CollectionUtils.join("\n", keptAccessors.keySet());
            throw new RuntimeException("The following accessors were upgraded, but didn't match any removed/changed method:\n\n" + formattedLeft);
        }

        // Find accessors that were removed but shouldn't be
        Map<AccessorKey, ReplacedAccessor> removedAccessors = new HashMap<>(oldAccessorsOfUpgradedProperties);

        writeInt2(andxOffset, dst, start + ANDX_OFFSET_OFFSET);

        andx.useUnicode = useUnicode;
        if (andx instanceof AndXServerMessageBlock) {

            /*
             * A word about communicating header info to andx smbs
             *
             * This is where we recursively invoke the provided andx smb
             * object to write it's parameter words and bytes to our outgoing

 * <ul>
 *   <li>{@code ELEVATE_WORD_SETTINGD_KEY} - The key for elevate word settings.</li>
 *   <li>{@code ELEVATE_WORD_BOOST} - The key for elevate word boost value.</li>
 *   <li>{@code ELEVATE_WORD_READING} - The key for elevate word readings.</li>
 *   <li>{@code ELEVATE_WORD_FIELDS} - The key for elevate word fields.</li>
 *   <li>{@code ELEVATE_WORD_TAGS} - The key for elevate word tags.</li>