~/.android/avd/*
            ~/.android/adb*
            # Added the actual system image path
            ${{ env.ANDROID_HOME }}/system-images/android-${{ matrix.api-level }}

      - name: Create AVD and generate snapshot for caching
        if: steps.avd-cache.outputs.cache-hit != 'true'
        uses: reactivecircus/android-emulator-runner@v2
        with:
          api-level: ${{ matrix.api-level }}

```java
public static final MediaType JSON = MediaType.get("application/json");

OkHttpClient client = new OkHttpClient();

String post(String url, String json) throws IOException {
  RequestBody body = RequestBody.create(json, JSON);
  Request request = new Request.Builder()
      .url(url)
      .post(body)
      .build();
  try (Response response = client.newCall(request).execute()) {
    return response.body().string();
  }
}

    /**
     * Sets up the mocks and the NtlmHttpURLConnection instance before each test.
     * @throws IOException
     */
    @BeforeEach
    void setUp() throws IOException {
        // Create a real URL instead of mocking to avoid protocol issues
        mockUrl = new URL("http://test.example.com/path");

        // Basic setup for mocks to avoid NullPointerExceptions

///

## 匯入 Pydantic 的 `BaseModel` { #import-pydantics-basemodel }

首先，從 `pydantic` 匯入 `BaseModel`：

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

## 建立你的資料模型 { #create-your-data-model }

接著，你將資料模型宣告為繼承自 `BaseModel` 的類別。

對所有屬性使用標準的 Python 型別：

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

就和宣告查詢參數時一樣，當模型屬性有預設值時，它就不是必填；否則就是必填。使用 `None` 可使其成為選填。

		}
	}
	return extractedSignedHeaders, ErrNone
}

// Trim leading and trailing spaces and replace sequential spaces with one space, following Trimall()
// in http://docs.aws.amazon.com/general/latest/gr/sigv4-create-canonical-request.html
func signV4TrimAll(input string) string {
	// Compress adjacent spaces (a space is determined by
	// unicode.IsSpace() internally here) to one space and return

    suite.addTest(testsForSetUpTearDown());
    return suite;
  }

  private abstract static class WrappedHashMapGenerator extends TestStringMapGenerator {
    @Override
    protected final Map<String, String> create(Entry<String, String>[] entries) {
      HashMap<String, String> map = new HashMap<>();
      for (Entry<String, String> entry : entries) {
        map.put(entry.getKey(), entry.getValue());
      }
      return wrap(map);

    return requireNonNull(values[cellRowIndices[index]][cellColumnIndices[index]]);
  }

  @Override
  @J2ktIncompatible
  @GwtIncompatible
    Object writeReplace() {
    return SerializedForm.create(this, cellRowIndices, cellColumnIndices);
  }

        CountDownLatch startLatch = new CountDownLatch(1);
        CountDownLatch doneLatch = new CountDownLatch(threadCount);
        ExecutorService executor = Executors.newFixedThreadPool(threadCount);

        // Create test data
        byte[] testData = new byte[64];
        for (int i = 0; i < testData.length; i++) {
            testData[i] = (byte) i;
        }

        // Run concurrent updates

## Pydantics `BaseModel` importieren { #import-pydantics-basemodel }

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] *}

#### Pydantic's `.model_dump()` { #pydantics-model-dump }

`user_in` is a Pydantic model of class `UserIn`.

Pydantic models have a `.model_dump()` method that returns a `dict` with the model's data.

So, if we create a Pydantic object `user_in` like:

```Python
user_in = UserIn(username="john", password="secret", email="******@****.***")
```

and then we call:

```Python
user_dict = user_in.model_dump()
```