*   Extends `tf.keras.layers.Lambda` layers to support multi-argument lambdas,
    and keyword arguments when calling the layer.
*   Functional models now get constructed if *any* tensor in a layer call's
    arguments/keyword arguments comes from a keras input. Previously the
    functional api would only work if all of the elements in the first argument
    to the layer came from a keras input.

v1 != v { return false } // Test aliasing the second argument and the receiver. v1 = x if out := f(&v1, &x, &v1); out != &v1 || v1 != v { return false } // Test aliasing both arguments and the receiver. v1 = x if out := f(&v1, &v1, &v1); out != &v1 || v1 != v { return false } // Ensure the arguments were not modified. return x == x1 && y == y1 } } // TestAliasing checks that receivers and arguments can alias each other without // leading to incorrect results. That is, it ensures that it's safe to...

v1 != v { return false } // Test aliasing the second argument and the receiver. v1 = x if out := f(&v1, &x, &v1); out != &v1 || v1 != v { return false } // Test aliasing both arguments and the receiver. v1 = x if out := f(&v1, &v1, &v1); out != &v1 || v1 != v { return false } // Ensure the arguments were not modified. return x == x1 && y == y1 } } // TestAliasing checks that receivers and arguments can alias each other without // leading to incorrect results. That is, it ensures that it's safe to...

with `Annotated` would **not be affected** at all.

If you call those functions in **other places in your code**, the actual **default values** will be kept, your editor will help you notice missing **required arguments**, Python will require you to pass required arguments at **runtime**, you will be able to **use the same functions** for different things and with different libraries (e.g. **Typer** will soon support `Annotated` too, then you could use the same function for an API and a CLI),...