declaration-no-important\n\n// Contextual backgrounds\n\n@mixin bg-variant($parent, $color, $ignore-warning: false) {\n  #{$parent} {\n    background-color: $color !important;\n  }\n  a#{$parent},\n  button#{$parent} {\n    @include hover-focus() {\n      background-color: darken($color, 10%) !important;\n    }\n  }\n  @include deprecate(\"The `bg-variant` mixin\", \"v4.4.0\", \"v5\", $ignore-warning);\n}\n\n@mixin bg-gradient-variant($parent, $color, $ignore-warning: false) {\n  #{$parent} {\n    background:...

### 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.

    /** The key of the configuration. e.g. cpu */
    String CRAWLER_HOTTHREAD_TYPE = "crawler.hotthread.type";

    /** The key of the configuration. e.g. resourceName,X-Parsed-By,Content-Encoding.*,Content-Type.*,X-TIKA.*,X-FESS.* */
    String CRAWLER_METADATA_CONTENT_EXCLUDES = "crawler.metadata.content.excludes";

    /** The key of the configuration. e.g. title=title:string<br>

"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...

- Fix bug in JSON path parser where an error occurs when a range is empty ([#95933](https://github.com/kubernetes/kubernetes/pull/95933), [@brianpursley](https://github.com/brianpursley)) [SIG API Machinery]

        * 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.