// mime package in MinIO, which correctly encodes strings with fixed B/Q encodings,
			// avoiding S3’s heuristic-driven issues.
			//
			// For MinIO developers, decode S3 metadata with mime.WordDecoder, validate outputs,
			// report encoding bugs to AWS, and use ASCII-only metadata to ensure reliable S3 API
			// compatibility.
			if needsMimeEncoding(v) {

labels.docId=Document ID
labels.endTime=End Time
labels.hq=hq
labels.inputs=Inputs
labels.jobLogging=Logging
labels.jobName=Name
labels.jobStatus=Status
labels.labelTypeIds=Labels
labels.lang=Language
labels.outputs=Outputs
labels.pos=Part-of-speech
labels.purgeJobLogDay=Delete old job logs
labels.purgeUserInfoDay=Delete old user logs
labels.reading=Reading
labels.roleTypeIds=Role IDs
labels.scriptData=Script

```

Use one of the following approaches to view job output:

View output in the Scala shell:

```
scala> counts.count()
364
```

To view the output from MinIO exit the Scala shell. View WordCount job status:

```
hadoop fs -ls s3a://testbucket/wordcount
```

The output should be similar to the following:

```
Found 3 items

   *
   * <p>If the mapped elements contain duplicates (according to {@link Object#equals}), the first
   * occurrence in encounter order appears in the resulting multiset, with count equal to the sum of
   * the outputs of {@code countFunction.applyAsInt(t)} for each {@code t} mapped to that element.
   *
   * @since 22.0
   */
  public static <T extends @Nullable Object, E>
      Collector<T, ?, ImmutableMultiset<E>> toImmutableMultiset(

labels.docId=Document ID
labels.endTime=End Time
labels.hq=hq
labels.inputs=Inputs
labels.jobLogging=Logging
labels.jobName=Name
labels.jobStatus=Status
labels.labelTypeIds=Labels
labels.lang=Language
labels.outputs=Outputs
labels.pos=Part-of-speech
labels.purgeJobLogDay=Delete old job logs
labels.purgeUserInfoDay=Delete old user logs
labels.reading=Reading
labels.roleTypeIds=Role IDs
labels.scriptData=Script

///

## Motivation { #motivation }

Let's start with a simple example:

{* ../../docs_src/python_types/tutorial001.py *}

Calling this program outputs:

```
John Doe
```

The function does the following:

* Takes a `first_name` and `last_name`.
* Converts the first letter of each one to upper case with `title()`.

labels.dictId=字典ID
labels.docId=文檔ID
labels.endTime=結束時間
labels.hq=hq
labels.inputs=輸入
labels.jobLogging=日誌記錄
labels.jobName=名稱
labels.jobStatus=狀態
labels.labelTypeIds=標籤
labels.lang=語言
labels.outputs=輸出
labels.pos=詞性
labels.purgeJobLogDay=刪除以前的作業日誌
labels.purgeUserInfoDay=刪除以前的使用者日誌
labels.reading=讀音
labels.roleTypeIds=角色ID
labels.scriptData=腳本
labels.scriptResult=結果
labels.scriptType=執行方法

   *
   * <p>If the mapped elements contain duplicates (according to {@code comparator}), the first
   * occurrence in encounter order appears in the resulting multiset, with count equal to the sum of
   * the outputs of {@code countFunction.applyAsInt(t)} for each {@code t} mapped to that element.
   *
   * @since 22.0
   */
  public static <T extends @Nullable Object, E>

   * addresses, the output follows <a href="http://tools.ietf.org/html/rfc5952">RFC 5952</a> section
   * 4. The main difference is that this method uses "::" for zero compression, while Java's version
   * uses the uncompressed form (except on Android, where the zero compression is also done). The
   * other difference is that this method outputs any scope ID in the format that it was provided at

labels.docId=Dokument-ID
labels.endTime=Endzeit
labels.hq=hq
labels.inputs=Eingaben
labels.jobLogging=Protokollierung
labels.jobName=Name
labels.jobStatus=Status
labels.labelTypeIds=Labels
labels.lang=Sprache
labels.outputs=Ausgaben
labels.pos=Wortart
labels.purgeJobLogDay=Alte Jobprotokolle löschen
labels.purgeUserInfoDay=Alte Benutzerprotokolle löschen
labels.reading=Lesen
labels.roleTypeIds=Rollen-IDs
labels.scriptData=Skript