probably silently use `float32` in TensorFlow2, and a warning will be
        issued that starts with Layer "layer-name" is casting an input tensor
        from dtype float64 to the layer's dtype of float32. To fix, either set
        the default dtype to float64 with
        `tf.keras.backend.set_floatx('float64')`, or pass `dtype='float64'` to

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

- Fixes a `cannot convert int64 to float64` error seen using server-side apply with certain CRDs in 1.19.10+ ([#103105](https://github.com/kubernetes/kubernetes/pull/103105), [@liggitt](https://github.com/liggitt)) [SIG API Machinery]

 } // 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,...