```

A string será verificada para se tornar uma URL válida e documentada no esquema JSON/1OpenAPI como tal.

## Atributos como listas de submodelos

Você também pode usar modelos Pydantic como subtipos de `list`, `set`, etc:

```Python hl_lines="20"
{!../../docs_src/body_nested_models/tutorial006.py!}
```

Isso vai esperar(converter, validar, documentar, etc) um corpo JSON tal qual:

```JSON hl_lines="11"

    * Como as estruturas de dados do Pydantic são apenas instâncias de classes que você define, a auto completação, _linting_, _mypy_ e a sua intuição devem funcionar corretamente com seus dados validados.
* Valida **estruturas complexas**:
    * Use modelos hierárquicos do Pydantic, `List` e `Dict` do `typing` do Python, etc.

* Valida **estructuras complejas**:
    * Usa modelos jerárquicos de modelos de Pydantic, `typing` de Python,  `List` y `Dict`, etc.

			}
		})
	}
}

// Tests validate volume name.
func TestIsValidVolname(t *testing.T) {
	testCases := []struct {
		volName    string
		shouldPass bool
	}{
		// Cases which should pass the test.
		// passing in valid bucket names.
		{"lol", true},
		{"1-this-is-valid", true},
		{"1-this-too-is-valid-1", true},
		{"this.works.too.1", true},
		{"1234567", true},
		{"123", true},

		Example: `  # Validate bookinfo-gateway.yaml
  istioctl validate -f samples/bookinfo/networking/bookinfo-gateway.yaml

  # Validate bookinfo-gateway.yaml with shorthand syntax
  istioctl v -f samples/bookinfo/networking/bookinfo-gateway.yaml

  # Validate all yaml files under samples/bookinfo/networking directory
  istioctl validate -f samples/bookinfo/networking

        SimpleProblemCollector result = validate("invalid-coordinate-ids-pom.xml");

        assertViolations(result, 0, 2, 0);

        assertEquals(
                "'groupId' with value 'o/a/m' does not match a valid coordinate id pattern.",
                result.getErrors().get(0));

        assertEquals(
                "'artifactId' with value 'm$-do$' does not match a valid coordinate id pattern.",

* Valide les **structures complexes**:
    * Utilise les modèles hiérarchique de Pydantic, le `typage` Python pour les `Lists`, `Dict`, etc.
    * Et les validateurs permettent aux schémas de données complexes d'être clairement et facilement définis, validés et documentés sous forme d'un schéma JSON.

	DeleteReplicationType
	MetadataReplicationType
	HealReplicationType
	ExistingObjectReplicationType
	ResyncReplicationType
	AllReplicationType
)

// Valid returns true if replication type is set
func (t Type) Valid() bool {
	return t > 0
}

// IsDataReplication returns true if content being replicated
func (t Type) IsDataReplication() bool {
	switch t {

	}

	// normalize the name to letters, digits, _ or $
	valid := true
	cleaned := strings.Map(func(r rune) rune {
		switch {
		case unicode.IsLetter(r):
			return 'a'
		case unicode.IsDigit(r):
			return '0'
		case r == '_', r == '$':
			return r
		default:
			valid = false
			return -1
		}
	}, name)

	if valid {
		// check for simple name or quoted name

		{bucketName: "------", objName: "obj", PartID: 1, expectedError: fmt.Errorf("%s", "Bucket name invalid: ------")},
		{
			bucketName: "$this-is-not-valid-too", objName: "obj", PartID: 1,
			expectedError: fmt.Errorf("%s", "Bucket name invalid: $this-is-not-valid-too"),
		},
		{bucketName: "a", objName: "obj", PartID: 1, expectedError: fmt.Errorf("%s", "Bucket name invalid: a")},
		// Test case - 5.