`tf.mixed_precision. experimental.LossScale` in the constructor, and no
        longer has a `LossScale` associated with it. Instead, `Model.compile`
        will automatically wrap the optimizer with a `LossScaleOptimizer` using
        dynamic loss scaling if `Policy.name` is "mixed_float16".
    *   `tf.keras.mixed_precision.LossScaleOptimizer`'s constructor takes in

For example, if you have an app like this:

```Python
from fastapi import FastAPI
from pydantic import BaseModel

class Item(BaseModel):

SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR // BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, // WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE // OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, // EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. // // See FIPS 197 for specification, and see Daemen and Rijmen's Rijndael...