<name>executionPoint</name>
          <required>false</required>
          <version>2.0.0+</version>
          <type>String</type>
          <defaultValue><![CDATA[]]></defaultValue>
          <description><![CDATA[If specified, identifies this phase as a dynamic phase to decorate the specified phase id, e.g. {@code after} or {@code before}.]]></description>
        </field>
        <field xml.attribute="true">

# 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

  TFE_DeleteContextOptions(opts);

  TFE_TensorHandle* m = TestMatrixTensorHandle(ctx);
  TF_Tensor* m_data = TFE_TensorHandleResolve(m, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  float* m_float = static_cast<float*>(TF_TensorData(m_data));
  TF_DeviceList* devices = TFE_ContextListDevices(ctx, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

objects to a different cluster or the public cloud by setting up transition rules in the bucket lifecycle configuration. This feature enables applications to optimize storage costs by moving less frequently accessed data to a cheaper storage without compromising accessibility of data.

To transition objects in a bucket to a destination bucket on a different cluster, applications need to specify a transition tier defined on MinIO instead of storage class while setting up the ILM lifecycle...

     * <origin><![CDATA[
     * <distributionManagement>
     * <repository>
     * <id>some-repo</id>
     * <url>scp://host/path</url>
     * </repository>
     * <snapshotRepository>
     * <id>some-snap-repo</id>
     * <url>scp://host/snapshot-path</url>
     * </snapshotRepository>
     * </distributionManagement>
     * ]]></origin>
     * <usage><![CDATA[

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

  xml.namespace="http://maven.apache.org/TOOLCHAINS/${version}"
  xml.schemaLocation="http://maven.apache.org/xsd/toolchains-${version}.xsd">
  <id>toolchains</id>
  <name>MavenToolchains</name>
  <description><![CDATA[
    This is a reference for the Maven Toolchains descriptor.
    <p>The default location for the toolchains file is {@code ~/.m2/toolchains.xml}

* `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!}
```

////

}

// asmPCData assembles a PCDATA pseudo-op.
// PCDATA $2, $705
func (p *Parser) asmPCData(operands [][]lex.Token) {
	if len(operands) != 2 {
		p.errorf("expect two operands for PCDATA")
		return
	}

	// Operand 0 must be an immediate constant.
	key := p.address(operands[0])
	if !p.validImmediate("PCDATA", &key) {
		return
	}