return profilingDataFound
	}

	data, err := getProfileData()
	if err != nil {
		reqInfo := (&logger.ReqInfo{}).AppendTags("peerAddress", thisAddr.String())
		ctx := logger.SetReqInfo(ctx, reqInfo)
		bugLogIf(ctx, err)
		return profilingDataFound
	}

	profilingDataFound = true

	// Send profiling data to zip as file
	for typ, data := range data {

 * must be identical across languages. Also beware that {@link Charset} definitions may occasionally
 * change between Java releases.
 *
 * <p><b>Warning:</b> Chunks of data that are put into the {@link Hasher} are not delimited. The
 * resulting {@link HashCode} is dependent only on the bytes inserted, and the order in which they

Many packages that simplify it a lot have to make many compromises with the data model, database, and available features. And some of these packages that simplify things too much actually have security flaws underneath.

---

**FastAPI** doesn't make any compromise with any database, data model or tool.

It gives you all the flexibility to choose the ones that fit your project the best.

	if len(results) != 2 {
		t.Errorf("failed to return delete data, got %v", results)
	}

	var count int64
	DB.Model(&User{}).Where("name IN ?", []string{users[0].Name, users[1].Name, users[2].Name}).Count(&count)
	if count != 1 {
		t.Errorf("failed to delete data, current count %v", count)
	}
}

func TestDeleteReturning(t *testing.T) {

	callbackTypeAfterDelete  callbackType = "AfterDelete"
	callbackTypeAfterFind    callbackType = "AfterFind"
)

// ErrUnsupportedDataType unsupported data type
var ErrUnsupportedDataType = errors.New("unsupported data type")

type Schema struct {
	Name                      string
	ModelType                 reflect.Type
	Table                     string
	PrioritizedPrimaryField   *Field

    val data = Buffer().writeUtf8(content)
    val source: Source =
      object : Source {
        var closed = false

        override fun close() {
          closed = true
        }

        override fun read(
          sink: Buffer,
          byteCount: Long,
        ): Long {
          check(!closed)
          return data.read(sink, byteCount)
        }

Declare the **header parameters** that you need in a **Pydantic model**, and then declare the parameter as `Header`:

{* ../../docs_src/header_param_models/tutorial001_an_py310.py hl[9:14,18] *}

**FastAPI** will **extract** the data for **each field** from the **headers** in the request and give you the Pydantic model you defined.

## Check the Docs { #check-the-docs }

You can see the required headers in the docs UI at `/docs`:

     * Creates a new SitemapsResponseProcessor instance.
     */
    public SitemapsResponseProcessor() {
        super();
    }

    /**
     * Processes the given response data, extracting URLs from sitemaps.
     * @param responseData The response data.
     */
    @Override
    public void process(final ResponseData responseData) {
        final SitemapsHelper sitemapsHelper = crawlerContainer.getComponent("sitemapsHelper");

    /** Info map key for data crawling execution time. */
    public static final String DATA_CRAWLING_EXEC_TIME = "DataCrawlExecTime";

    /** Info map key for data indexing execution time. */
    public static final String DATA_INDEX_EXEC_TIME = "DataIndexExecTime";

    /** Info map key for data index size. */
    public static final String DATA_INDEX_SIZE = "DataIndexSize";

 */
package jcifs.smb;

import jcifs.CIFSException;

/**
 * Output stream for writing to SMB named pipes.
 *
 * This class provides a stream-based interface for writing
 * data to SMB named pipes over the network.
 *
 * @author mbechler
 */
public class SmbPipeOutputStream extends SmbFileOutputStream {

    private final SmbPipeHandleImpl handle;

    /**
     * @param handle