// NewFromFunc allocates a new cached value instance and initializes it with an
// update function, making it ready for use.
func NewFromFunc[T any](ttl time.Duration, opts Opts, update func(ctx context.Context) (T, error)) *Cache[T] {
	return &Cache[T]{
		ttl:      ttl,
		updateFn: update,
		opts:     opts,
	}
}

// InitOnce initializes the cache with a TTL and an update function. It is
// guaranteed to be called only once.

../mkzip ../../zoneinfo.zip
cd ../..

files="update.bash zoneinfo.zip"
modified=true
if git diff --quiet $files; then
	modified=false
fi

if [ "$1" = "-work" ]; then
	echo Left workspace behind in work/.
	shift
else
	rm -rf work
fi

if ! $modified; then
	echo No updates needed.
	exit 0
fi

echo Updated for $CODE/$DATA: $files

commitmsg="lib/time: update to $CODE/$DATA

### Using Pydantic's `update` parameter { #using-pydantics-update-parameter }

Now, you can create a copy of the existing model using `.model_copy()`, and pass the `update` parameter with a `dict` containing the data to update.

Like `stored_item_model.model_copy(update=update_data)`:

{* ../../docs_src/body_updates/tutorial002_py310.py hl[33] *}

### Partial updates recap { #partial-updates-recap }

{* ../../docs_src/body_updates/tutorial002_py310.py hl[32] *}

### Pydantics `update`-Parameter verwenden { #using-pydantics-update-parameter }

Jetzt können Sie eine Kopie des existierenden Modells mittels `.model_copy()` erstellen, wobei Sie dem `update`-Parameter ein `dict` mit den zu ändernden Daten übergeben.

Wie in `stored_item_model.model_copy(update=update_data)`:

{* ../../docs_src/body_updates/tutorial002_py310.py hl[33] *}

{* ../../docs_src/body_updates/tutorial002_py310.py hl[32] *}

### Uso del parámetro `update` de Pydantic { #using-pydantics-update-parameter }

Ahora, puedes crear una copia del modelo existente usando `.model_copy()`, y pasar el parámetro `update` con un `dict` que contenga los datos a actualizar.

/// info | Información

{* ../../docs_src/body_updates/tutorial002_py310.py hl[32] *}

### Использование параметра `update` в Pydantic { #using-pydantics-update-parameter }

Теперь можно создать копию существующей модели, используя `.model_copy()`, и передать параметр `update` с `dict`, содержащим данные для обновления.

/// info | Информация

{* ../../docs_src/body_updates/tutorial002_py310.py hl[32] *}

### Usando o parâmetro `update` do Pydantic { #using-pydantics-update-parameter }

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

        with:
          signoff: true
          sign-commits: true
          commit-message: "Update jdks.yaml"
          add-paths: .teamcity/jdks.yaml
          title: "Update jdks.yaml"
          body: "This PR updates JDK to the latest versions available in `.teamcity/jdks.yaml`."
          delete-branch: true
          branch: "devprod/update-jdks"
          branch-suffix: timestamp
          labels: |
            in:building-gradle

        try (MappingUpdater updater = new MappingUpdater(item)) {
            reload(updater);
        }
    }

    /**
     * Reloads the character mapping items from the dictionary file.
     *
     * @param updater the mapping updater to use for writing changes, or null for read-only reload
     */
    protected void reload(final MappingUpdater updater) {

# This Workflow updates tensorflow/tools/toolchains/remote_config/configs.bzl
# to reference the most recent versions of the SIG Build Docker images.
name: Update RBE Configs
on:
  workflow_dispatch:

permissions:
  contents: read

jobs:
  rbe:
    name: Update RBE Configs
    runs-on: ubuntu-latest