class User(BaseModel):
    id: int
    name: str = "John Doe"
    signup_ts: Union[datetime, None] = None
    friends: List[int] = []


external_data = {
    "id": "123",
    "signup_ts": "2017-06-01 12:22",
    "friends": [1, "2", b"3"],
}
user = User(**external_data)
print(user)
# > User id=123 name='John Doe' signup_ts=datetime.datetime(2017, 6, 1, 12, 22) friends=[1, 2, 3]
print(user.id)

  auto collective_executor_handle = context->GetCollectiveExecutorHandle();
  tensorflow::Notification done;
  collective_executor_handle->get()->remote_access()->CheckPeerHealth(
      task, timeout_in_ms, [&done, status](const Status& s) {
        status->status = s;
        done.Notify();
      });
  done.WaitForNotification();
}

TF_ShapeAndTypeList* TF_NewShapeAndTypeList(int num_items) {

ret=$?

if [ $ret != 0 ] ; then
  for f in `find ./target -type f | grep surefire-reports | grep -v /TEST-` ; do
    cat $f
  done
fi

BASE="$1"
find "${BASE}" -type l | \
  while read l ; do
    if [[ "$(readlink "$l")" == \.\./* ]]; then
      CANONICAL="$(readlink "$l")";
      rm "$l";
      ln -s "${CANONICAL}" "$l"
    fi

	select {
	case <-writer.doneCh:
		return fmt.Errorf("messageWriter is done")
	default:
		writer.finBytesScanned = bytesScanned
		writer.finBytesProcessed = bytesProcessed
		close(writer.payloadCh)
		// Wait until the `start` go-routine is done.
		<-writer.doneCh
		return nil
	}
}

	for i := 0; i < resyncParallelRoutines; i++ {
		wg.Add(1)
		workers[i] = make(chan ReplicateObjectInfo, 100)
		i := i
		go func(ctx context.Context, idx int) {
			defer wg.Done()
			for roi := range workers[idx] {
				select {
				case <-ctx.Done():
					return
				case <-s.resyncCancelCh:
				default:
				}
				traceFn := s.trace(tgt.ResetID, fmt.Sprintf("%s/%s (%s)", opts.bucket, roi.Name, roi.VersionID))

/// info | Informação

No Pydantic v1, o método que era chamado `.dict()` e foi depreciado (mas ainda suportado) no Pydantic v2. Agora, deve-se usar o método `.model_dump()`.

Os exemplos aqui usam `.dict()` para compatibilidade com o Pydantic v1, mas você deve usar `.model_dump()` a partir do Pydantic v2.

///

Isso gera um `dict` com apenas os dados definidos ao criar o modelo `item`, excluindo os valores padrão.

      : device_(device),
        tensors_(std::move(tensors)),
        shape_(std::vector<int64_t>(shape.begin(), shape.end())),
        dtype_(dtype) {}
  ParallelTensor(const ParallelDevice& device,
                 std::vector<TensorHandlePtr> tensors, const TF_DataType dtype)
      : device_(device),
        tensors_(std::move(tensors)),
        shape_(absl::nullopt),
        dtype_(dtype) {}

```JSON
{
    "item": {
        "name": "Foo",
        "description": "The pretender",
        "price": 42.0,
        "tax": 3.2
    },
    "user": {
        "username": "dave",
        "full_name": "Dave Grohl"
    }
}
```

/// note

请注意，即使 `item` 的声明方式与之前相同，但现在它被期望通过 `item` 键内嵌在请求体中。

///

**FastAPI** 将自动对请求中的数据进行转换，因此 `item` 参数将接收指定的内容，`user` 参数也是如此。

```JSON
{
    "item": {
        "name": "Foo",
        "description": "The pretender",
        "price": 42.0,
        "tax": 3.2
    },
    "user": {
        "username": "dave",
        "full_name": "Dave Grohl"
    }
}
```

/// note | "備考"

以前と同じように`item`が宣言されていたにもかかわらず、`item`はキー`item`を持つボディの内部にあることが期待されていることに注意してください。

///