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:

    volumes:
      - site1-data2-1:/data1
      - site1-data2-2:/data2

  site1-minio3:
    <<: *minio-common
    hostname: site1-minio3
    volumes:
      - site1-data3-1:/data1
      - site1-data3-2:/data2

  site1-minio4:
    <<: *minio-common
    hostname: site1-minio4
    volumes:
      - site1-data4-1:/data1
      - site1-data4-2:/data2

  site1-nginx:
    image: nginx:1.19.2-alpine

    volumes:
      - site2-data2-1:/data1
      - site2-data2-2:/data2

  site2-minio3:
    <<: *minio-common
    hostname: site2-minio3
    volumes:
      - site2-data3-1:/data1
      - site2-data3-2:/data2

  site2-minio4:
    <<: *minio-common
    hostname: site2-minio4
    volumes:
      - site2-data4-1:/data1
      - site2-data4-2:/data2

  site2-nginx:
    image: nginx:1.19.2-alpine

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

  },
  "tags": {
    "objectLocation": {
      "name": "hosts",
      "poolId": 1,
      "setId": 1,
      "drives": [
        "/mnt/data1",
        "/mnt/data2",
        "/mnt/data3",
        "/mnt/data4"
      ]
    }
  },
  "accessKey": "minioadmin"
}
```

### Kafka Target

Assuming that you already have Apache Kafka configured and running.

```

)

// 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