## Dépendances facultatives

Utilisées par Pydantic:

* <a href="https://github.com/JoshData/python-email-validator" target="_blank"><code>email-validator</code></a> - pour la validation des adresses email.

Utilisées par Starlette :

想了解更多訊息，可以參考 <a href="https://fastapi.tiangolo.com/benchmarks/" class="internal-link" target="_blank">測試結果</a>。

## 可選的依賴套件

用於 Pydantic：

- <a href="https://github.com/JoshData/python-email-validator" target="_blank"><code>email-validator</code></a> - 用於電子郵件驗證。
- <a href="https://docs.pydantic.dev/latest/usage/pydantic_settings/" target="_blank"><code>pydantic-settings</code></a> - 用於設定管理。

		return nil, err
	}
	return &req, nil
}

// validate a RestoreObjectRequest as per AWS S3 spec https://docs.aws.amazon.com/AmazonS3/latest/API/API_RestoreObject.html
func (r *RestoreObjectRequest) validate(ctx context.Context, objAPI ObjectLayer) error {
	if r.Type != SelectRestoreRequest && !r.SelectParameters.IsEmpty() {

* Deleting federation-push.sh ([#27400](https://github.com/kubernetes/kubernetes/pull/27400), [@nikhiljindal](https://github.com/nikhiljindal))
* Validate-cluster finishes shortly after at most ALLOWED_NOTREADY_NODE… ([#26778](https://github.com/kubernetes/kubernetes/pull/26778), [@gmarek](https://github.com/gmarek))

	// Add future tracking capabilities
	// Be sure that they are included in toHealingDisk
}

// loadHealingTracker will load the healing tracker from the supplied disk.
// The disk ID will be validated against the loaded one.
func loadHealingTracker(ctx context.Context, disk StorageAPI) (*healingTracker, error) {
	if disk == nil {
		return nil, errors.New("loadHealingTracker: nil drive given")
	}

	if group == "" {
		return updatedAt, errInvalidArgument
	}

	cache := store.lock()
	defer store.unlock()

	// Validate that all members exist.
	for _, member := range members {
		u, ok := cache.iamUsersMap[member]
		if !ok {
			return updatedAt, errNoSuchUser
		}
		cr := u.Credentials

## **4. Run Sample Applications**

After installing Hive, Hadoop and Spark successfully, we can now proceed to run some sample applications to see if they are configured appropriately.  We can use Spark Pi and Spark WordCount programs to validate our Spark installation. We can also explore how to run Spark jobs from the command line and Spark shell.

### **4.1 Spark Pi**

func TestScanRune(t *testing.T) {
	for n, test := range scanTests {
		buf := strings.NewReader(test)
		s := NewScanner(buf)
		s.Split(ScanRunes)
		var i, runeCount int
		var expect rune
		// Use a string range loop to validate the sequence of runes.
		for i, expect = range test {
			if !s.Scan() {
				break
			}
			runeCount++
			got, _ := utf8.DecodeRune(s.Bytes())
			if got != expect {

            } else {
              return Integer.compare(m1.getParameterTypes().length, m2.getParameterTypes().length);
            }
          }
        });
  }

  /** Validates that the given method's signature meets all of our assumptions. */
  private static void validateMethod(Method method) {
    String desc = method.toString();

    assertTrue(desc, isAnyEnter(method) || isWaitFor(method));

	// This call should never be over the network, this is always
	// a cached value - caller should make sure to use this
	// function on a fresh disk or make sure to look at the error
	// from a different networked call to validate the GetDiskID()
	return *client.diskID.Load(), nil
}

func (client *storageRESTClient) SetDiskID(id string) {
	client.diskID.Store(&id)
}