// Only changing meaning of fields requires a version bump.
// bucketMetadataFormat refers to the format.
// bucketMetadataVersion can be used to track a rolling upgrade of a field.
type BucketMetadata struct {
	Name                        string
	Created                     time.Time
	LockEnabled                 bool // legacy not used anymore.
	PolicyConfigJSON            []byte

        ComponentLookupException e =
                assertThrows(ComponentLookupException.class, () -> container.lookup(MySingletonBean2.class));
        assertTrue(e.getMessage().matches("[\\s\\S]*: Can not set .* field .* to [\\s\\S]*"));

        MySingletonBean bean = container.lookup(MySingletonBean.class);
        assertNotNull(bean);
        assertNotNull(bean.anotherBean);

Nesse caso, você pode simplesmente trocar as `dataclasses` padrão por `pydantic.dataclasses`, que é um substituto direto:

{* ../../docs_src/dataclasses_/tutorial003_py310.py hl[1,4,7:10,13:16,22:24,27] *}

1. Ainda importamos `field` das `dataclasses` padrão.

2. `pydantic.dataclasses` é um substituto direto para `dataclasses`.

3. A dataclass `Author` inclui uma lista de dataclasses `Item`.

{* ../../docs_src/dataclasses_/tutorial003_py310.py hl[1,4,7:10,13:16,22:24,27] *}

1. Todavía importamos `field` de los `dataclasses` estándar.

2. `pydantic.dataclasses` es un reemplazo directo para `dataclasses`.

3. El dataclass `Author` incluye una lista de dataclasses `Item`.

For example, as in the `Image` model we have a `url` field, we can declare it to be an instance of Pydantic's `HttpUrl` instead of a `str`:

{* ../../docs_src/body_nested_models/tutorial005_py310.py hl[2,8] *}

        // SMB_COM_LOGOFF_ANDX = 0x74
        assertEquals((byte) 0x74, msg.command, "Command should be SMB_COM_LOGOFF_ANDX");
    }

    @Test
    @DisplayName("constructor with mock andx properly sets andx field")
    void constructorWithMockAndx() {
        // Create a mock ServerMessageBlock
        ServerMessageBlock mockAndx = mock(ServerMessageBlock.class);

{* ../../docs_src/cookie_param_models/tutorial001_an_py310.py hl[9:12,16] *}

**FastAPI** will **extract** the data for **each field** from the **cookies** received in the request and give you the Pydantic model you defined.

## Check the Docs { #check-the-docs }

You can see the defined cookies in the docs UI at `/docs`:

<div class="screenshot">

        // Call the decode method
        ndrSmall.decode(mockNdrBuffer);

        // Verify that dec_ndr_small was called
        verify(mockNdrBuffer, times(1)).dec_ndr_small();
        // Verify that the value field was updated correctly
        assertEquals(decodedValue, ndrSmall.value, "Value should be updated after decoding.");
        verifyNoMoreInteractions(mockNdrBuffer);
    }

    public interface ContentTransformer {
        /**
         * Interpolate the value read from the xpp3 document
         *
         * @param source The source value
         * @param fieldName A description of the field being interpolated. The implementation may use this to
         *         log stuff.
         * @return The interpolated value.
         */
        String transform(String source, String fieldName);
    }

		}
		_ = stmt.Schema.ParseIndexes()
		field := stmt.Schema.LookUpField(fieldName)
		if field == nil {
			t.Fatalf("%v: failed to find column %q", utils.FileWithLineNum(), fieldName)
		}
		if field.Unique != unique {
			t.Fatalf("%v: %q column %q unique should be %v but got %v", utils.FileWithLineNum(), stmt.Schema.Table, fieldName, unique, field.Unique)
		}
		if field.UniqueIndex != uniqueIndex {