### Bug Fixes and Other Changes

*   Add TensorFlow Quantizer to TensorFlow pip package.

*   `tf.sparse.segment_sum` `tf.sparse.segment_mean` `tf.sparse.segment_sqrt_n` `SparseSegmentSum/Mean/SqrtN[WithNumSegments]`

    *   Added `sparse_gradient` option (default=false) that makes the gradient of these functions/ops sparse (`IndexedSlices`) instead of dense (`Tensor`), using new `SparseSegmentSum/Mean/SqrtNGradV2` ops.

"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...

        * Before this, the return content was first passed through `jsonable_encoder` to ensure it was a "jsonable" object, like a `dict`, instead of an arbitrary object with attributes (like an ORM model). That's why you should make sure to update your Pydantic models for objects with attributes to use `orm_mode = True`.
        * If you don't have a `response_model`, the return object will still be passed through `jsonable_encoder` first.