designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and demonstrate...

#    -X prop       set non-standard JAVA system property
#   --prop=val
#   --prop val     set fess property (i.e.  -Des.<prop>=<val>)

CDPATH=""
SCRIPT="$0"

# SCRIPT may be an arbitrarily deep series of symlinks. Loop until we have the concrete path.
while [ -h "$SCRIPT" ] ; do
  ls=`ls -ld "$SCRIPT"`
  # Drop everything prior to ->
  link=`expr "$ls" : '.*-> \(.*\)$'`
  if expr "$link" : '/.*' > /dev/null; then

///

/// 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:

{* ../../docs_src/path_params/tutorial005.py hl[16] *}

                "'\\server\\share\\path', 'server', 'share', 'path'",
                "'\\server\\share\\path\\file.txt', 'server', 'share', 'path\\file.txt'",
                "'\\server.domain.com\\share\\deep\\path\\structure', 'server.domain.com', 'share', 'deep\\path\\structure'" })
        @DisplayName("Should split DFS paths correctly")
        void testDfsPathSplit(String node, String expectedServer, String expectedShare, String expectedPath) {

    }
}
```

### Benutzerdefiniertes OpenAPI-*Pfadoperation*-Schema

Das Dictionary in `openapi_extra` wird mit dem automatisch generierten OpenAPI-Schema für die *Pfadoperation* zusammengeführt (mittels Deep Merge).

Sie können dem automatisch generierten Schema also zusätzliche Daten hinzufügen.

    ]]></description>

  <defaults>
    <default>
      <key>package</key>
      <value>org.apache.maven.toolchain.model</value>
    </default>
  </defaults>

  <classes>
    <class java.clone="deep">
      <name>TrackableBase</name>
      <version>1.1.0+</version>
      <description>
        Common base class that contains code to track the source for this instance.
      </description>
      <codeSegments>

{* ../../docs_src/path_params/tutorial005.py hl[18,21,23] *}
Вы отправите клиенту такой JSON-ответ:

```JSON
{
  "model_name": "alexnet",
  "message": "Deep Learning FTW!"
}
```

## Path-параметры, содержащие пути

Предположим, что есть *операция пути* с путем `/files/{file_path}`.

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

        // This is complex due to the class structure. A better approach would be
        // to refactor SmbFile to be more testable (e.g., dependency injection).
        // For now, we test methods that don't require deep mocks.
        return new SmbFile(url, auth);
    }

      return;
    } finally {
      function = null;
    }

    /*
     * If set()/setValue() throws an Error, we let it propagate. Why? The most likely Error is a
     * StackOverflowError (from deep transform(..., directExecutor()) nesting), and calling
     * setException(stackOverflowError) would fail:
     *
     * - If the stack overflowed before set()/setValue() could even store the result in the output