tensorflow::DataType dtype, void* data,
                               TFE_CustomDeviceTensorHandleMethods methods)
      : tensorflow::CustomDeviceTensorHandle(context, device, dtype),
        data_(data),
        methods_(methods) {}

  ~CAPICustomDeviceTensorHandle() override { methods_.deallocator(data_); }
  void* DevicePointer() const override { return data_; }
  absl::Status NumDims(int* num_dims) const override {

    <<: *minio-common
    hostname: minio1
    volumes:
      - data1-1:/data1
      - data1-2:/data2

  minio2:
    <<: *minio-common
    hostname: minio2
    volumes:
      - data2-1:/data1
      - data2-2:/data2

  minio3:
    <<: *minio-common
    hostname: minio3
    volumes:
      - data3-1:/data1
      - data3-2:/data2

  minio4:
    <<: *minio-common
    hostname: minio4
    volumes:

    volumes:
      - data2-1:/data1
      - data2-2:/data2
      - data2-3:/data3

  minio3:
    <<: *minio-common
    hostname: minio3
    volumes:
      - data3-1:/data1
      - data3-2:/data2
      - data3-3:/data3

  minio4:
    <<: *minio-common
    hostname: minio4
    volumes:
      - data4-1:/data1
      - data4-2:/data2
      - data4-3:/data3

  nginx:

      "accessResultData": {
        "properties": {
          "transformerName": {
            "type": "keyword"
          },
          "encoding": {
            "type": "keyword"
          },
          "data": {
            "type": "binary"
          }
        }
      },
      "contentLength": {
        "type": "long"
      },
      "lastModified": {
        "type": "long"
      },
      "ruleId": {

)

// initDataScanner will start the scanner in the background.
func initDataScanner(ctx context.Context, objAPI ObjectLayer) {
	go func() {
		r := rand.New(rand.NewSource(time.Now().UnixNano()))
		// Run the data scanner in a loop
		for {
			runDataScanner(ctx, objAPI)
			duration := time.Duration(r.Float64() * float64(scannerCycle.Load()))
			if duration < time.Second {

# Extra Data Types

Up to now, you have been using common data types, like:

* `int`
* `float`
* `str`
* `bool`

But you can also use more complex data types.

And you will still have the same features as seen up to now:

* Great editor support.
* Data conversion from incoming requests.
* Data conversion for response data.
* Data validation.
* Automatic annotation and documentation.

## Other data types

		hashPathCutSet = dataUsageRoot + string(filepath.Separator)
	}
}

// hashPath calculates a hash of the provided string.
func hashPath(data string) dataUsageHash {
	if data != dataUsageRoot {
		data = strings.Trim(data, hashPathCutSet)
	}
	return dataUsageHash(path.Clean(data))
}

//msgp:ignore dataUsageHashMap
type dataUsageHashMap map[string]struct{}

// DecodeMsg implements msgp.Decodable

    * In Requests und Responses wird es wie ein `float` behandelt.
* Sie können alle gültigen Pydantic-Datentypen hier überprüfen: <a href="https://docs.pydantic.dev/latest/usage/types/types/" class="external-link" target="_blank">Pydantic data types</a>.

## Beispiel

Hier ist ein Beispiel für eine *Pfadoperation* mit Parametern, die einige der oben genannten Typen verwenden.

//// tab | Python 3.10+

```Python hl_lines="1  3  12-16"

* `Decimal`:
    * 标准的 Python `Decimal`。
    * 在请求和响应中被当做 `float` 一样处理。
* 您可以在这里检查所有有效的pydantic数据类型: <a href="https://docs.pydantic.dev/latest/concepts/types/" class="external-link" target="_blank">Pydantic data types</a>.

## 例子

下面是一个*路径操作*的示例，其中的参数使用了上面的一些类型。

//// tab | Python 3.10+

```Python hl_lines="1  3  12-16"
{!> ../../docs_src/extra_data_types/tutorial001_an_py310.py!}
```

////

* `Decimal`:
    * Pythonの標準的な`Decimal`です。
    * リクエストやレスポンスでは`float`と同じように扱います。

* Pydanticの全ての有効な型はこちらで確認できます: <a href="https://docs.pydantic.dev/latest/concepts/types/" class="external-link" target="_blank">Pydantic data types</a>。
## 例

ここでは、上記の型のいくつかを使用したパラメータを持つ*path operation*の例を示します。

```Python hl_lines="1 2 12-16"
{!../../docs_src/extra_data_types/tutorial001.py!}
```