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)

ret=$?

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

  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) {

	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
	}
}

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

	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))

      : 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) {}

/// 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.

	# Iterate down a (possible) chain of symlinks
	while [ -L "$TARGET_FILE" ]; do
		TARGET_FILE=$(env readlink $TARGET_FILE)
		cd $(dirname $TARGET_FILE)
		TARGET_FILE=$(basename $TARGET_FILE)
	done

	# Compute the canonicalized name by finding the physical path
	# for the directory we're in and appending the target file.
	PHYS_DIR=$(pwd -P)
	RESULT=$PHYS_DIR/$TARGET_FILE
	echo $RESULT
}

## FIXME:

```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。