<default>
      <key>package</key>
      <value>org.apache.maven.artifact.repository.metadata</value>
    </default>
  </defaults>
  <classes>
    <class rootElement="true" xml.tagName="metadata" java.clone="deep">
      <name>Metadata</name>
      <version>1.0.0+</version>
      <fields>
        <field xml.attribute="true" xml.tagName="modelVersion">
          <name>modelVersion</name>
          <version>1.1.0+</version>

    ]]>
  </description>
  <defaults>
    <default>
      <key>package</key>
      <value>org.apache.maven.settings</value>
    </default>
  </defaults>
  <classes>
    <class java.clone="deep">
      <name>TrackableBase</name>
      <version>1.0.0+</version>
      <description>
        Common base class that contains code to track the source for this instance.
      </description>
      <codeSegments>

///

/// tip | "Tipp"

Falls Sie sich fragen, was „AlexNet“, „ResNet“ und „LeNet“ ist, das sind Namen von <abbr title="Genau genommen, Deep-Learning-Modellarchitekturen">Modellen</abbr> für maschinelles Lernen.

///

### Deklarieren Sie einen *Pfad-Parameter*

    assertThat(request.requestLine).isEqualTo(
      "GET /a/deep/path?key=foo%20bar HTTP/1.1",
    )
    val requestUrl = request.requestUrl
    assertThat(requestUrl!!.scheme).isEqualTo("http")
    assertThat(requestUrl.host).isEqualTo(server.hostName)
    assertThat(requestUrl.port).isEqualTo(server.port)
    assertThat(requestUrl.encodedPath).isEqualTo("/a/deep/path")

    assertThat(request.requestLine).isEqualTo(
      "GET /a/deep/path?key=foo%20bar HTTP/1.1",
    )
    val requestUrl = request.requestUrl
    assertThat(requestUrl!!.scheme).isEqualTo("http")
    assertThat(requestUrl.host).isEqualTo(server.hostName)
    assertThat(requestUrl.port).isEqualTo(server.port)
    assertThat(requestUrl.encodedPath).isEqualTo("/a/deep/path")

///

/// tip | "Dica"



///

	Se você está se perguntando, "AlexNet", "ResNet", e "LeNet" são apenas nomes de <abbr title="técnicamente, modelos de arquitetura de Deep Learning">modelos</abbr> de Machine Learning (aprendizado de máquina).

### Declare um *parâmetro de rota*

Logo, crie um *parâmetro de rota* com anotações de tipo usando a classe enum que você criou (`ModelName`):

In particular, an easy answer to "I just want to have mTLS everywhere, then I can think about adopting the rest of service mesh" was desired.

Other documents include:
* Deep dive on [Peer Authentication implementation](peer-authentication.md)
* Deep dive on [lifecycle](ztunnel-cni-lifecycle.md) when running on Kubernetes

## Goals

Ztunnel should:

    @Test
    public void resolveRootNotExist () throws CIFSException, URISyntaxException {
        DfsReferralData ref = doResolve("\\doesnotexist\\", null, false);
        assertNull(ref);

        ref = doResolve("\\deep\\doesnotexist\\", null, false);
        assertNull(ref);
    }


    @Test
    public void resolveNonDfs () throws CIFSException {
        CIFSContext context = getContext();

///

/// tip | "豆知識"

"AlexNet"、"ResNet"そして"LeNet"は機械学習<abbr title="Technically, Deep Learning model architectures">モデル</abbr>の名前です。

///

### *パスパラメータ*の宣言

次に、作成したenumクラスである`ModelName`を使用した型アノテーションをもつ*パスパラメータ*を作成します:

```Python hl_lines="16"

* **Machine Learning**: Normalmente exige muita multiplicação de matrizes e vetores. Pense numa grande folha de papel com números e multiplicando todos eles juntos e ao mesmo tempo.