from pydantic.v1 import BaseModel


class Item(BaseModel):
    name: str
    description: str | None = None


class ItemV2(BaseModelV2):
    title: str
    summary: str | None = None


app = FastAPI()


@app.post("/items/", response_model=ItemV2)
def create_item(item: Item):
    return {"title": item.name, "summary": item.description}
```

is an int, which is safe as (x+y-1)/y should always fit, regardless // of the integer size. func divRoundUp(x, y int) int { return int((int64(x) + int64(y) - 1) / int64(y)) } func Key[Hash fips140.Hash](h func() Hash, password string, salt []byte, iter, keyLength int) ([]byte, error) { setServiceIndicator(salt, keyLength) if keyLength <= 0 { return nil, errors.New("pkbdf2: keyLength must be larger than 0") } prf := hmac.New(h, []byte(password)) hmac.MarkAsUsedInKDF(prf) hashLen := prf.Size() numBlocks...

// is an int, which is safe as (x+y-1)/y should always fit, regardless // of the integer size. func divRoundUp(x, y int) int { return int((int64(x) + int64(y) - 1) / int64(y)) } func Key[Hash hash.Hash](h func() Hash, password string, salt []byte, iter, keyLength int) ([]byte, error) { setServiceIndicator(salt, keyLength) if keyLength <= 0 { return nil, errors.New("pkbdf2: keyLength must be larger than 0") } prf := hmac.New(h, []byte(password)) hmac.MarkAsUsedInKDF(prf) hashLen := prf.Size() numBlocks...

    provides class `ndarray`, which mimics the `ndarray` class in NumPy, and
    wraps an immutable `tf.Tensor` under the hood. A subset of NumPy functions
    (e.g. `numpy.add`) are provided. Their inter-operation with TF facilities is
    seamless in most cases. See
    [tensorflow/python/ops/numpy_ops/README.md](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/numpy_ops/README.md)