return err
	}
	failed := false
	for res := range results {
		if res.Err != nil {
			failed = true
			batchLogIf(ctx, res.Err)
			break
		}
		result := res.Item
		sseKMS := crypto.S3KMS.IsEncrypted(result.UserDefined)
		sseS3 := crypto.S3.IsEncrypted(result.UserDefined)
		if !sseKMS && !sseS3 { // neither sse-s3 nor sse-kms disallowed
			continue
		}
		wk.Take()

      private var canRemove = false

      override fun hasNext(): Boolean {
        if (nextUrl != null) return true

        canRemove = false // Prevent delegate.remove() on the wrong item!
        while (delegate.hasNext()) {
          try {
            delegate.next().use { snapshot ->
              val metadata = snapshot.getSource(ENTRY_METADATA).buffer()

	// The usual case is caught by Push, below, but be safe.
	for nesting := 0; nesting < 100; {
		tok := in.Stack.Next()
		switch tok {
		case '#':
			if !in.beginningOfLine {
				in.Error("'#' must be first item on line")
			}
			in.beginningOfLine = in.hash()
			in.text = "#"
			return '#'

		case scanner.Ident:
			// Is it a macro name?
			name := in.Stack.Text()
			macro := in.macros[name]

labels.file_auth_parameters=Parameters
labels.file_auth_file_crawling_config=File System Config
labels.file_auth_scheme_samba=Samba
labels.file_auth_scheme_ftp=FTP
labels.pagination_page_guide_msg={0}/{1} ({2} items)
labels.list_could_not_find_crud_table=No data.
labels.scheduledjob_configuration=Job Scheduler
labels.scheduledjob_title_details=Jobs
labels.scheduledjob_name=Name
labels.scheduledjob_target=Target

Para respostas grandes, retornar uma `Response` diretamente é muito mais rápido que retornar um dicionário.

///

## 深くネストされたモデル

深くネストされた任意のモデルを定義することができます:

```Python hl_lines="9 14 20 23 27"
{!../../docs_src/body_nested_models/tutorial007.py!}
```

/// info | "情報"

`Offer`は`Item`のリストであり、オプションの`Image`のリストを持っていることに注目してください。

///

## 純粋なリストのボディ

期待するJSONボディのトップレベルの値がJSON`array`（Pythonの`list`）であれば、Pydanticモデルと同じように、関数のパラメータで型を宣言することができます:

```Python
images: List[Image]
```

		TypeMeta:      metav1.TypeMeta{},
		LabelSelector: strings.Join(kv, ","),
		FieldSelector: "spec.nodeName=" + node,
	})
	if err != nil {
		return types.NamespacedName{}, err
	}
	pods := slices.Reference(podsr.Items)
	if len(pods) > 0 {
		// We need to pass in a sorter, and the one used by `kubectl logs` is good enough.
		sortBy := func(pods []*corev1.Pod) sort.Interface { return podutils.ByLogging(pods) }
		sort.Sort(sortBy(pods))

    // This tests that it.remove() above actually changed the order. It
    // indicates that the value 40 was stored in forgetMeNot, so it is
    // returned in the last call to it.next(). Without it, 30 should be the last
    // item returned by the iterator.
    Integer lastItem = 0;
    for (Integer tmp : mmHeap) {
      lastItem = tmp;
    }
    assertEquals((Integer) 30, lastItem);
  }

  /**

/// note

👀 👈 `response_model` 🔢 "👨‍🎨" 👩‍🔬 (`get`, `post`, ♒️). 🚫 👆 *➡ 🛠️ 🔢*, 💖 🌐 🔢 & 💪.

///

`response_model` 📨 🎏 🆎 👆 🔜 📣 Pydantic 🏷 🏑,, ⚫️ 💪 Pydantic 🏷, ✋️ ⚫️ 💪, ✅ `list` Pydantic 🏷, 💖 `List[Item]`.

FastAPI 🔜 ⚙️ 👉 `response_model` 🌐 💽 🧾, 🔬, ♒️. & **🗜 & ⛽ 🔢 📊** 🚮 🆎 📄.

/// tip

🚥 👆 ✔️ ⚠ 🆎 ✅ 👆 👨‍🎨, ✍, ♒️, 👆 💪 📣 🔢 📨 🆎 `Any`.

///

`response_model` nimmt denselben Typ entgegen, den Sie auch für ein Pydantic-Modellfeld deklarieren würden, also etwa ein Pydantic-Modell, aber es kann auch z. B. eine `list`e von Pydantic-Modellen sein, wie etwa `List[Item]`.

FastAPI wird dieses `response_model` nehmen, um die Daten zu dokumentieren, validieren, usw. und auch, um **die Ausgabedaten** entsprechend der Typdeklaration **zu konvertieren und filtern**.

/// tip | "Tipp"