benchmarks.
    *   `image.resize` in 2.0 now supports gradients for the new resize kernels.
    *   `image.resize` now considers proper pixel centers and has new kernels
        (incl. anti-aliasing).
    *   Renamed `tf.image` functions to remove duplicate "image" where it is
        redundant.
    *   Variadic reduce is supported on CPU Variadic reduce is supported on CPU

    ml_models.clear()


app = FastAPI(lifespan=lifespan)


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

**Note**: This is the recommended way going forward, instead of using `startup` and `shutdown` events.

iteration Result = Composite W = 41 B = 16 # End of iteration Result = Composite W = 41 B = 14 # End of iteration Result = Composite W = 41 B = 2 # End of iteration Result = Composite W = 41 B = 3a # End of iteration Result = Composite W = 55 B = 40 # End of iteration Result = Composite W = 55 B = 7 # End of iteration Result = Composite W = 55 B = 23 # End of iteration Result = Composite W = 55 B = 2e # End of iteration Result = Composite W = 55 B = 2a # W below is composite, but it is one of the...