* Para passar um corpo JSON, passe um objeto Python (por exemplo, um `dict`) para o parâmetro `json`.
* Se você precisar enviar *Dados de Formulário* em vez de JSON, use o parâmetro `data`.
* Para passar *headers*, use um `dict` no parâmetro `headers`.
* Para *cookies*, um `dict` no parâmetro `cookies`.

        }

        // Test that getAddress returns null for our dummy implementation
        assertNull(loc.getAddress());
    }

    @Test
    @DisplayName("Test DFS referral data")
    void testDfsReferralData() throws Exception {
        String url = "smb://server/share/path/";
        DummySmbResourceLocator loc = new DummySmbResourceLocator(url);

        // Initially no DFS referral

/// tip | Tipp

Wenn Sie Binärdaten streamen möchten, zum Beispiel Video oder Audio, sehen Sie sich den erweiterten Leitfaden an: [Daten streamen](../advanced/stream-data.md).

///

## JSON Lines mit FastAPI streamen { #stream-json-lines-with-fastapi }

    * As connecting to an email server and sending an email tends to be "slow" (several seconds), you can return the response right away and send the email notification in the background.
* Processing data:
    * For example, let's say you receive a file that must go through a slow process, you can return a response of "Accepted" (HTTP 202) and process the file in the background.

## Using `BackgroundTasks` { #using-backgroundtasks }

該 dataclass 會自動轉換為 Pydantic 的 dataclass。

如此一來，其結構描述（schema）會顯示在 API 文件介面中：

<img src="/img/tutorial/dataclasses/image01.png">

## 巢狀資料結構中的 Dataclasses { #dataclasses-in-nested-data-structures }

你也可以將 `dataclasses` 與其他型別註記結合，建立巢狀的資料結構。

在某些情況下，你可能仍需要使用 Pydantic 版本的 `dataclasses`。例如，當自動產生的 API 文件出現錯誤時。

這種情況下，你可以把標準的 `dataclasses` 直接換成 `pydantic.dataclasses`，它是可直接替換（drop-in replacement）的：

  private val perMessageDeflate: Boolean,
  private val noContextTakeover: Boolean,
) : Closeable {
  private var closed = false
  private var receivedCloseFrame = false

  // Stateful data about the current frame.
  private var opcode = 0
  private var frameLength = 0L
  private var isFinalFrame = false
  private var isControlFrame = false
  private var readingCompressedMessage = false

  }

  /**
   * Creates a {@code CompactLinkedHashMap} instance, with a high enough "initial capacity" that it
   * <i>should</i> hold {@code expectedSize} elements without rebuilding internal data structures.
   *
   * @param expectedSize the number of elements you expect to add to the returned set
   * @return a new, empty {@code CompactLinkedHashMap} with enough capacity to hold {@code

import com.google.errorprone.annotations.Immutable;
import com.google.j2objc.annotations.WeakOuter;
import java.util.Map;
import org.jspecify.annotations.Nullable;

/** A {@code RegularImmutableTable} optimized for dense data. */
@GwtCompatible
@Immutable(containerOf = {"R", "C", "V"})
final class DenseImmutableTable<R, C, V> extends RegularImmutableTable<R, C, V> {
  private final ImmutableMap<R, Integer> rowKeyToIndex;

import org.bouncycastle.asn1.DLSequence;

/**
 * Utility class for ASN.1 parsing and manipulation operations.
 *
 * This class provides helper methods for working with ASN.1
 * encoded data structures used in Kerberos PAC processing.
 */
public final class ASN1Util {

    private ASN1Util() {
    }

    /**
     * Casts an object to the specified type with type checking

Zuerst müssen Sie `BaseModel` von `pydantic` importieren:

{* ../../docs_src/body/tutorial001_py310.py hl[2] *}

## Ihr Datenmodell erstellen { #create-your-data-model }

Dann deklarieren Sie Ihr Datenmodell als eine Klasse, die von `BaseModel` erbt.

Verwenden Sie Standard-Python-Typen für alle Attribute:

{* ../../docs_src/body/tutorial001_py310.py hl[5:9] *}