not supported when used with `tf.distribute.Strategy` outside of
        built-in training loops like `tf.keras` `compile`/`fit`.
    *   Wraps losses passed to the `compile` API (strings and v1 losses) which
        are not instances of v2 `Loss` class in `LossWrapper` class. => All
        losses will now use `SUM_OVER_BATCH_SIZE` reduction as default.

"ringDecodeAndDecompr": "ringDecodeAndDecompr", "ringDecodeAndDecompr": "ringDecodeAndDecompr", } func main() { inputFile := flag.String("input", "", "") outputFile := flag.String("output", "", "") flag.Parse() fset := token.NewFileSet() f, err := parser.ParseFile(fset, *inputFile, nil, parser.SkipObjectResolution|parser.ParseComments) if err != nil { log.Fatal(err) } cmap := ast.NewCommentMap(fset, f, f.Comments) // Drop header comments. cmap[ast.Node(f)] = nil // Remove top-level consts used across the main...

* Improved **correctness** in corner cases.
* **Safer** types.
* Better **performance** and **less energy** consumption.
* Better **extensibility**.
* etc.

...all this while keeping the **same Python API**. In most of the cases, for simple models, you can simply upgrade the Pydantic version and get all the benefits. 🚀

- Kubeadm: switch control-plane static...