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.

    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.

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")

			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 {

// Supported Storage Class format is "Scheme:Number of parity drives".
// Currently only supported scheme is "EC".
func parseStorageClass(storageClassEnv string) (sc StorageClass, err error) {
	s := strings.Split(storageClassEnv, ":")

	// only two elements allowed in the string - "scheme" and "number of parity drives"
	if len(s) > 2 {

  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

If you have traditional spinning (hdd) drives, some applications with high concurrency might require MinIO cluster to be tuned such that to avoid random I/O on the drives. The way to convert high concurrent I/O into a sequential I/O is by reducing the number of concurrent operations allowed per cluster. This allows MinIO cluster to be operationally resilient to such workloads, while also making sure the drives are at optimal efficiency and responsive.

			// all remaining cases imply corrupt data/metadata
			driveState = madmin.DriveStateCorrupt
		}

		result.Before.Drives = append(result.Before.Drives, madmin.HealDriveInfo{
			UUID:     "",
			Endpoint: storageEndpoints[i].String(),
			State:    driveState,
		})
		result.After.Drives = append(result.After.Drives, madmin.HealDriveInfo{
			UUID:     "",
			Endpoint: storageEndpoints[i].String(),
			State:    driveState,
		})

		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