m.mu.Unlock()
		}
	}()
}

// updateCacheEntry will update non-transient state.
func (m *metacacheManager) updateCacheEntry(update metacache) (metacache, error) {
	m.mu.RLock()
	if meta, ok := m.trash[update.id]; ok {
		m.mu.RUnlock()
		return meta, nil
	}

	b, ok := m.buckets[update.bucket]
	m.mu.RUnlock()
	if ok {
		return b.updateCacheEntry(update)
	}

	// We should have either a trashed bucket or this

                boolean update;

                if (!policy.isEnabled()) {
                    update = false;

                    if (getLogger().isDebugEnabled()) {
                        getLogger()
                                .debug("Skipping update check for " + metadata.getKey() + " (" + file

    }

    @Override
    protected void update(byte b) {
      checkNotDone();
      digest.update(b);
    }

    @Override
    protected void update(byte[] b, int off, int len) {
      checkNotDone();
      digest.update(b, off, len);
    }

    @Override
    protected void update(ByteBuffer bytes) {
      checkNotDone();
      digest.update(bytes);
    }

//// tab | Python 3.8+

```Python hl_lines="34"
{!> ../../../docs_src/body_updates/tutorial002.py!}
```

////

### Usando o parâmetro `update` do Pydantic

Agora, você pode criar uma cópia do modelo existente usando `.model_copy()`, e passar o parâmetro `update` com um `dict` contendo os dados para atualizar.

/// info | Informação

        }
    }

    protected byte[] engineDigest() {
        byte[] digest = md5.digest();
        md5.update(opad);
        return md5.digest(digest);
    }

    protected int engineDigest(byte[] buf, int offset, int len) {
        byte[] digest = md5.digest();
        md5.update(opad);
        md5.update(digest);
        try {
            return md5.digest(buf, offset, len);
        } catch (Exception ex) {

        byte[] digest = this.md5.digest();
        this.md5.update(this.opad);
        return this.md5.digest(digest);
    }


    @Override
    protected int engineDigest ( byte[] buf, int offset, int len ) {
        byte[] digest = this.md5.digest();
        this.md5.update(this.opad);
        this.md5.update(digest);
        try {
            return this.md5.digest(buf, offset, len);
        }

        *,
        default_factory: Union[Callable[[], Any], None] = _Unset,
        annotation: Optional[Any] = None,
        alias: Optional[str] = None,
        alias_priority: Union[int, None] = _Unset,
        # TODO: update when deprecating Pydantic v1, import these types
        # validation_alias: str | AliasPath | AliasChoices | None
        validation_alias: Union[str, None] = None,
        serialization_alias: Union[str, None] = None,

		} else {
			globalHealConfig.Update(healCfg)
		}
	case config.BatchSubSys:
		batchCfg, err := batch.LookupConfig(s[config.BatchSubSys][config.Default])
		if err != nil {
			errs = append(errs, fmt.Errorf("Unable to apply batch config: %w", err))
		} else {
			globalBatchConfig.Update(batchCfg)
		}
	case config.ScannerSubSys:

* 🔧 Update sponsors, remove Fern. PR [#10729](https://github.com/tiangolo/fastapi/pull/10729) by [@tiangolo](https://github.com/tiangolo).
* 🔧 Update sponsors, add Codacy. PR [#10677](https://github.com/tiangolo/fastapi/pull/10677) by [@tiangolo](https://github.com/tiangolo).
* 🔧 Update sponsors, add Reflex. PR [#10676](https://github.com/tiangolo/fastapi/pull/10676) by [@tiangolo](https://github.com/tiangolo).

これを使うことで、デフォルト値を省略して、設定された（リクエストで送られた）データのみを含む`dict`を生成することができます:

```Python hl_lines="34"
{!../../docs_src/body_updates/tutorial002.py!}
```

### Pydanticの`update`パラメータ

ここで、`.copy()`を用いて既存のモデルのコピーを作成し、`update`パラメータに更新するデータを含む`dict`を渡すことができます。

`stored_item_model.copy(update=update_data)`のように:

```Python hl_lines="35"
{!../../docs_src/body_updates/tutorial002.py!}
```

### 部分的更新のまとめ

まとめると、部分的な更新を適用するには、次のようにします: