// 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

```

////

### Partial updates recap

In summary, to apply partial updates you would:

* (Optionally) use `PATCH` instead of `PUT`.
* Retrieve the stored data.
* Put that data in a Pydantic model.
* Generate a `dict` without default values from the input model (using `exclude_unset`).

          fetch-depth: 0
      - name: Update jdks.yaml
        uses: gradle/update-jdks-action@main
      - name: Create Pull Request
        uses: peter-evans/create-pull-request@v7
        with:
          signoff: true
          sign-commits: true
          commit-message: "Update jdks.yaml"
          add-paths: .teamcity/jdks.yaml
          title: "Update jdks.yaml"

# 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

////

//// tab | Python 3.8+

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

////

### Pydantics `update`-Parameter verwenden

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.

/// info

	// Check if we are in DCOS environment, return
	// deployment guide for update procedures.
	if IsDCOS() {
		return mesosDeploymentDoc
	}

	// Check if we are in kubernetes environment, return
	// deployment guide for update procedures.
	if IsKubernetes() {
		return kubernetesDeploymentDoc
	}

	// Check if we are docker environment, return docker update command
	if IsDocker() {
		// Construct release tag name.

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:

//// tab | 🐍 3️⃣.1️⃣0️⃣ & 🔛

```Python hl_lines="32"
{!> ../../docs_src/body_updates/tutorial002_py310.py!}
```

////

### ⚙️ Pydantic `update` 🔢

🔜, 👆 💪 ✍ 📁 ♻ 🏷 ⚙️ `.copy()`, & 🚶‍♀️ `update` 🔢 ⏮️ `dict` ⚗ 💽 ℹ.

💖 `stored_item_model.copy(update=update_data)`:

//// tab | 🐍 3️⃣.6️⃣ & 🔛

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

////

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

////

### Использование параметра `update` в Pydantic

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

Например, `stored_item_model.copy(update=update_data)`:

//// tab | Python 3.10+

```Python hl_lines="33"