Você também pode declarar um corpo como um `dict` com chaves de algum tipo e valores de outro tipo.

Sem ter que saber de antemão quais são os nomes de campos/atributos válidos (como seria o caso dos modelos Pydantic).

Isso seria útil se você deseja receber chaves que ainda não conhece.

---

Outro caso útil é quando você deseja ter chaves de outro tipo, por exemplo, `int`.

É isso que vamos ver aqui.

SubstringMatcher TypeSafeDiagnosingMa TypeSafeMatcher constant-values.html Overview Package Class Use Tree Deprecated Index Help PREV NEXT FRAMES NO FRAMES All Classes Constant Field Values Contents Overview Package Class Use Tree Deprecated Index Help PREV NEXT FRAMES NO FRAMES All Classes deprecated-list.html Overview Package Class Use Tree Deprecated Index Help PREV NEXT FRAMES NO FRAMES All Classes Deprecated API Contents Deprecated Methods Deprecated Methods org.hamcrest.Matcher._dont_implement_Matcher___i...

import "context"

const (
	healthDrivesOfflineCount = "drives_offline_count"
	healthDrivesOnlineCount  = "drives_online_count"
	healthDrivesCount        = "drives_count"
)

var (
	healthDrivesOfflineCountMD = NewGaugeMD(healthDrivesOfflineCount,
		"Count of offline drives in the cluster")
	healthDrivesOnlineCountMD = NewGaugeMD(healthDrivesOnlineCount,
		"Count of online drives in the cluster")

    opcode = b0 and B0_MASK_OPCODE
    isFinalFrame = b0 and B0_FLAG_FIN != 0
    isControlFrame = b0 and OPCODE_FLAG_CONTROL != 0

    // Control frames must be final frames (cannot contain continuations).
    if (isControlFrame && !isFinalFrame) {
      throw ProtocolException("Control frames must be final.")
    }

    val reservedFlag1 = b0 and B0_FLAG_RSV1 != 0
    when (opcode) {
      OPCODE_TEXT, OPCODE_BINARY -> {

- We limited the number of drives to 16 for erasure set because, erasure code shards more than 16 can become chatty and do not have any performance advantages. Additionally since 16 drive erasure set gives you tolerance of 8 drives per object by default which is plenty in any practical scenario.

			return
		}
		sz := d.sizeRecursive(path.Key())
		leaves = append(leaves, struct {
			objects uint64
			path    dataUsageHash
		}{objects: sz.Objects, path: path})
		for ch := range e.Children {
			add(dataUsageHash(ch))
		}
	}

	// Add path recursively.
	add(path)
	sort.Slice(leaves, func(i, j int) bool {
		return leaves[i].objects < leaves[j].objects
	})
	for remove > 0 && len(leaves) > 0 {

	// prefers setCounts to be sorted for optimal behavior.
	if divisibleSize < setCounts[len(setCounts)-1] {
		return divisibleSize
	}

	// Figure out largest value of total_drives_in_erasure_set which results
	// in least number of total_drives/total_drives_erasure_set ratio.
	prevD := divisibleSize / setCounts[0]
	for _, cnt := range setCounts {
		if divisibleSize%cnt == 0 {
			d := divisibleSize / cnt
			if d <= prevD {

| `minio_cluster_health_drives_offline_count`        | `gauge` | Count of offline drives in the cluster         |        |
| `minio_cluster_health_drives_online_count`         | `gauge` | Count of online drives in the cluster          |        |
| `minio_cluster_health_drives_count`                | `gauge` | Count of all drives in the cluster             |        |

  private val pongQueue = ArrayDeque<ByteString>()

  /** Outgoing messages and close frames in the order they should be written. */
  private val messageAndCloseQueue = ArrayDeque<Any>()

  /** The total size in bytes of enqueued but not yet transmitted messages. */
  private var queueSize = 0L

  /** True if we've enqueued a close frame. No further message frames will be enqueued. */
  private var enqueuedClose = false

		DistributionAlgo string `json:"distributionAlgo"`
	} `json:"xl"`
	Info DiskInfo `json:"-"`
}

func (f *formatErasureV3) Drives() (drives int) {
	for _, set := range f.Erasure.Sets {
		drives += len(set)
	}
	return drives
}

func (f *formatErasureV3) Clone() *formatErasureV3 {
	b, err := json.Marshal(f)
	if err != nil {
		panic(err)
	}
	var dst formatErasureV3