make([]uint, n) copy(newLimbs, x.limbs) x.limbs = newLimbs return x } extraLimbs := x.limbs[len(x.limbs):n] clear(extraLimbs) x.limbs = x.limbs[:n] return x } // reset returns a zero nat of n limbs, reusing x's storage if n <= cap(x.limbs). func (x *Nat) reset(n int) *Nat { if cap(x.limbs) < n { x.limbs = make([]uint, n) return x } // Clear both the returned limbs and the previously used ones. clear(x.limbs[:max(n, len(x.limbs))]) x.limbs = x.limbs[:n] return x } // resetToBytes assigns x = b, where b...

ciphertext, data); err != nil { // We sometimes decrypt and authenticate concurrently, so we overwrite // dst in the event of a tag mismatch. To be consistent across platforms // and to avoid releasing unauthenticated plaintext, we clear the buffer // in the event of an error. clear(out) return nil, err } return ret, nil } // sliceForAppend takes a slice and a requested number of bytes. It returns a // slice with the contents of the given slice followed by that many bytes and a // second slice that aliases...

@asynccontextmanager
async def lifespan(app: FastAPI):
    # Load the ML model
    ml_models["answer_to_everything"] = fake_answer_to_everything_ml_model
    yield
    # Clean up the ML models and release the resources
    ml_models.clear()


app = FastAPI(lifespan=lifespan)


@app.get("/predict")
async def predict(x: float):
    result = ml_models["answer_to_everything"](x)

    *   Add tf.keras.layers.AbstractRNNCell as the preferred implementation of
        RNN cell for TF v2. User can use it to implement RNN cell with custom
        behavior.
    *   Adding `clear_losses` API to be able to clear losses at the end of
        forward pass in a custom training loop in eager.
    *   Add support for passing list of lists to the `metrics` param in Keras
        `compile`.