email_file.write(content)


@app.post("/send-notification/{email}")
async def send_notification(email: str, background_tasks: BackgroundTasks):
    background_tasks.add_task(write_notification, email, message="some notification")

    entries.add(entry(null, v3()));

    resetContainer(getSubjectGenerator().create(entries.toArray()));
    assertTrue(
        "A Map should equal any other Map containing the same entries,"
            + " even if some keys are null.",
        getMap().equals(newHashMap(entries)));
  }

  @CollectionSize.Require(absent = CollectionSize.ZERO)
  public void testEquals_otherContainsNullKey() {

      for (Object o : elements) {
        Entry<?, ?> entry = (Entry<?, ?>) o;
        // These come from Entry<K, Collection<V>>> objects somewhere.
        @SuppressWarnings("unchecked")
        K key = (K) entry.getKey();
        keySet.add(key);
        for (Object v : (Collection<?>) entry.getValue()) {
          // These come from Entry<K, Collection<V>>> objects somewhere.
          @SuppressWarnings("unchecked")

fake_adoptium_osx.tar jdk-1.0.2.jdk/Contents/Home/bin/java...

fake_openjdk_osx.tar ./jdk-1.0.2.jdk/Contents/Home/bin/java...

</div>

你可以發送如下的請求：

```json
{
    "description": "Some data",
    "data": "aGVsbG8="
}
```

/// tip

`aGVsbG8=` 是 `hello` 的 base64 編碼。

///

接著 Pydantic 會將該 base64 字串解碼，並在模型的 `data` 欄位中提供原始位元組。

你會收到類似以下的回應：

```json
{
  "description": "Some data",
  "content": "hello"
}
```

## Pydantic `bytes` 用於輸出資料 { #pydantic-bytes-for-output-data }

            LOGGER.quiet("  Runtime java.home     : " + BuildParams.getRuntimeJavaHome());
            LOGGER.quiet("  Gradle JDK Version    : " + gradleJvmImplementationVersion + " (" + gradleJvmVendorDetails + ")");
            LOGGER.quiet("  Gradle java.home      : " + gradleJvm.getJavaHome());
        } else {

from fastapi import FastAPI
from fastapi.responses import JSONResponse

app = FastAPI()


@app.post("/cookie/")
def create_cookie():
    content = {"message": "Come to the dark side, we have cookies"}
    response = JSONResponse(content=content)
    response.set_cookie(key="fakesession", value="fake-cookie-session-value")

        assertTrue(options.directory().isPresent(), "--directory option should be present");
        assertEquals("/some/path", options.directory().get(), "--directory should be /some/path");
    }

    @Test
    void testGoalsParsing() throws ParseException {
        String[] args = {"apply", "--plugins"};
        CommonsCliUpgradeOptions options = CommonsCliUpgradeOptions.parse(args);

In this example, we didn't declare any Pydantic model. In fact, the request body is not even <dfn title="converted from some plain format, like bytes, into Python objects">parsed</dfn> as JSON, it is read directly as `bytes`, and the function `magic_data_reader()` would be in charge of parsing it in some way.

Nevertheless, we can declare the expected schema for the request body.