///

## Motivation { #motivation }

Let's start with a simple example:

{* ../../docs_src/python_types/tutorial001_py39.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()`.

   *
   * <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>

// boundary of the block containing the last newline.
//
// Note that the GNU manual says that numeric values should be encoded in octal
// format. However, the GNU tar utility itself outputs these values in decimal.
// As such, this library treats values as being encoded in decimal.
func readGNUSparseMap1x0(r io.Reader) (sparseDatas, error) {
	var (
		cntNewline int64
		buf        bytes.Buffer

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

labels.docId=Document-ID
labels.endTime=Eindtijd
labels.hq=hq
labels.inputs=Invoer
labels.jobLogging=Loggen
labels.jobName=Naam
labels.jobStatus=Status
labels.labelTypeIds=Labels
labels.lang=Taal
labels.outputs=Uitvoer
labels.pos=Woordsoort
labels.purgeJobLogDay=Vorige taaklogboeken verwijderen
labels.purgeUserInfoDay=Vorige gebruikerslogboeken verwijderen
labels.reading=Lezen
labels.roleTypeIds=Rol-ID

    'outputs'. If a model was exported with different names before 1.2, and is
    now served with tensorflow/serving, it will accept requests using 'inputs'
    and 'outputs'. Starting at 1.2, such a model will accept the keys specified
    during export. Therefore, inference requests using 'inputs' and 'outputs'

   * 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
   * creation time, while Android may always output it as an interface name, even if it was supplied
   * as a numeric ID.
   *

labels.docId=ID do documento
labels.endTime=Hora de término
labels.hq=hq
labels.inputs=Entradas
labels.jobLogging=Logging
labels.jobName=Nome
labels.jobStatus=Estado
labels.labelTypeIds=Rótulos
labels.lang=Idioma
labels.outputs=Saídas
labels.pos=Parte da fala
labels.purgeJobLogDay=Excluir logs de trabalho anteriores
labels.purgeUserInfoDay=Excluir logs de usuário anteriores
labels.reading=Leitura
labels.roleTypeIds=ID da função

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

labels.docId=ID Dokumen
labels.endTime=Waktu Selesai
labels.hq=hq
labels.inputs=Input
labels.jobLogging=Logging
labels.jobName=Nama
labels.jobStatus=Status
labels.labelTypeIds=Label
labels.lang=Bahasa
labels.outputs=Output
labels.pos=Kelas Kata
labels.purgeJobLogDay=Hapus log job lama
labels.purgeUserInfoDay=Hapus log pengguna lama
labels.reading=Pembacaan
labels.roleTypeIds=ID Peran
labels.scriptData=Script