forward path for data of type `tf.float32` but not for data of type
        `tf.float64` (for which there was no GPU implementation). In this
        current release, GPU support for other floating-point types
        (`tf.float16`, `tf.float64`, `tf.complex64`, and `tf.complex128`) has
        been added for this op. If you were relying on the determinism of the

pkg hash/maphash, type Hash struct
pkg hash/maphash, type Seed struct
pkg log, const Lmsgprefix = 64
pkg log, const Lmsgprefix ideal-int
pkg math, func FMA(float64, float64, float64) float64
pkg math/bits, func Rem(uint, uint, uint) uint
pkg math/bits, func Rem32(uint32, uint32, uint32) uint32
pkg math/bits, func Rem64(uint64, uint64, uint64) uint64

 } // quickCheckConfig returns a quick.Config that scales the max count by the // given factor if the -short flag is not set. func quickCheckConfig(slowScale int) *quick.Config { cfg := new(quick.Config) if !testing.Short() { cfg.MaxCountScale = float64(slowScale) } return cfg } func generateFieldElement(rand *mathrand.Rand) Element { const maskLow52Bits = (1 << 52) - 1 return Element{ rand.Uint64() & maskLow52Bits, rand.Uint64() & maskLow52Bits, rand.Uint64() & maskLow52Bits, rand.Uint64() & maskLow52Bits,...

 } // quickCheckConfig returns a quick.Config that scales the max count by the // given factor if the -short flag is not set. func quickCheckConfig(slowScale int) *quick.Config { cfg := new(quick.Config) if !testing.Short() { cfg.MaxCountScale = float64(slowScale) } return cfg } func generateFieldElement(rand *mathrand.Rand) Element { const maskLow52Bits = (1 << 52) - 1 return Element{ rand.Uint64() & maskLow52Bits, rand.Uint64() & maskLow52Bits, rand.Uint64() & maskLow52Bits, rand.Uint64() & maskLow52Bits,...

For example:

```Python
from contextlib import asynccontextmanager

from fastapi import FastAPI


def fake_answer_to_everything_ml_model(x: float):
    return x * 42


ml_models = {}


@asynccontextmanager
async def lifespan(app: FastAPI):
    # Load the ML model