private ImmutableNetwork(Network<N, E> network) {
    super(
        NetworkBuilder.from(network), getNodeConnections(network), getEdgeToReferenceNode(network));
  }

  /** Returns an immutable copy of {@code network}. */
  public static <N, E> ImmutableNetwork<N, E> copyOf(Network<N, E> network) {
    return (network instanceof ImmutableNetwork)
        ? (ImmutableNetwork<N, E>) network

 * subtype of {@code Network} that provides methods for adding and removing nodes and edges. If you
 * do not need to mutate a network (e.g. if you write a method than runs a read-only algorithm on
 * the network), you should use the non-mutating {@link Network} interface, or an {@link
 * ImmutableNetwork}.
 *
 * <p>You can create an immutable copy of an existing {@code Network} using {@link
 * ImmutableNetwork#copyOf(Network)}:

   * network}.
   *
   * <p>The "queryable" properties are those that are exposed through the {@link Network} interface,
   * such as {@link Network#isDirected()}. Other properties, such as {@link
   * #expectedNodeCount(int)}, are not set in the new builder.
   */
  public static <N, E> NetworkBuilder<N, E> from(Network<N, E> network) {
    return new NetworkBuilder<N, E>(network.isDirected())

  final MapIteratorCache<N, NetworkConnections<N, E>> nodeConnections;

  // We could make this a Map<E, EndpointPair<N>>. It would make incidentNodes(edge) slightly
  // faster, but also make Networks consume 5 to 20+% (increasing with average degree) more memory.
  final MapIteratorCache<E, N> edgeToReferenceNode; // referenceNode == source if directed

deploy ML-powered applications.

TensorFlow was originally developed by researchers and engineers working within
the Machine Intelligence team at Google Brain to conduct research in machine
learning and neural networks. However, the framework is versatile enough to be
used in other areas as well.

TensorFlow provides stable [Python](https://www.tensorflow.org/api_docs/python)
and [C++](https://www.tensorflow.org/api_docs/cc) APIs, as well as a

**This release introduces fast fallback to better support mixed IPv4+IPv6 networks.** Fast fallback
is what we're calling our implementation of Happy Eyeballs, [RFC 8305][rfc_8305]. With this
feature OkHttp will attempt both IPv6 and IPv4 connections concurrently, keeping whichever connects
first. Fast fallback gives IPv6 connections a 250 ms head start so IPv6 is preferred on networks
where it's available.

{* ../../docs_src/openapi_callbacks/tutorial001_py310.py hl[7:11,34:51] *}

/// tip

The `callback_url` query parameter uses a Pydantic [Url](https://docs.pydantic.dev/latest/api/networks/) type.

///

The only new thing is the `callbacks=invoices_callback_router.routes` as an argument to the *path operation decorator*. We'll see what that is next.

這部分很正常，多數程式碼你應該已經很熟悉了：

{* ../../docs_src/openapi_callbacks/tutorial001_py310.py hl[7:11,34:51] *}

/// tip

`callback_url` 查詢參數使用的是 Pydantic 的 [Url](https://docs.pydantic.dev/latest/api/networks/) 型別。

///

唯一新的地方是在「路徑操作裝飾器」中加入參數 `callbacks=invoices_callback_router.routes`。我們接下來會看到那是什麼。

## 文件化回呼 { #documenting-the-callback }

實際的回呼程式碼會高度依賴你的 API 應用本身。

而且很可能每個應用都差很多。

它可能就只有一兩行，例如：

这部分代码很常规，您对绝大多数代码应该都比较熟悉了：

{* ../../docs_src/openapi_callbacks/tutorial001_py310.py hl[7:11,34:51] *}

/// tip | 提示

`callback_url` 查询参数使用 Pydantic 的 [Url](https://docs.pydantic.dev/latest/api/networks/) 类型。

///

此处唯一比较新的内容是*路径操作装饰器*中的 `callbacks=invoices_callback_router.routes` 参数，下文介绍。

## 存档回调 { #documenting-the-callback }

实际的回调代码高度依赖于您自己的 API 应用。

并且可能每个应用都各不相同。

回调代码可能只有一两行，比如：

この部分はとても普通で、ほとんどのコードはすでに見覚えがあるはずです:

{* ../../docs_src/openapi_callbacks/tutorial001_py310.py hl[7:11,34:51] *}

/// tip | 豆知識

`callback_url` クエリパラメータは、Pydantic の [Url](https://docs.pydantic.dev/latest/api/networks/) 型を使用します。

///

唯一の新しい点は、*path operation デコレータ*の引数として `callbacks=invoices_callback_router.routes` を渡すことです。これが何かは次で見ます。

## コールバックのドキュメント化 { #documenting-the-callback }

実際のコールバックのコードは、あなた自身の API アプリに大きく依存します。