losses will now use `SUM_OVER_BATCH_SIZE` reduction as default.
    *   `model.add_loss(symbolic_tensor)` should work in ambient eager.
    *   Update metric name to always reflect what the user has given in compile.
        Affects following cases
    *   When name is given as 'accuracy'/'crossentropy'
    *   When an aliased function name is used eg. 'mse'
    *   Removing the `weighted` prefix from weighted metric names.

1))], weirdLimbs51[rand.Intn(len(weirdLimbs51))], weirdLimbs51[rand.Intn(len(weirdLimbs51))], weirdLimbs51[rand.Intn(len(weirdLimbs51))], } } func (Element) Generate(rand *mathrand.Rand, size int) reflect.Value { if rand.Intn(2) == 0 { return reflect.ValueOf(generateWeirdFieldEl(rand)) } return reflect.ValueOf(generateFieldElement(rand)) } // isInBounds returns whether the element is within the expected bit size bounds // after a light reduction. func isInBounds(x *Element) bool { return bits.Len64(x.l0)...

pkg os/exec, var ErrWaitDelay error #50436
pkg path/filepath, func IsLocal(string) bool #56219
pkg path/filepath, var SkipAll error #47209
pkg reflect, method (Value) Comparable() bool #46746
pkg reflect, method (Value) Equal(Value) bool #46746
pkg reflect, method (Value) Grow(int) #48000
pkg reflect, method (Value) SetZero() #52376
pkg regexp/syntax, const ErrLarge ErrorCode #56041
pkg regexp/syntax, const ErrLarge = "expression too large" #56041