If your application is **simple**, this will probably **not be a problem**, and you might not need to specify hard memory limits. But if you are **using a lot of memory** (for example with **machine learning** models), you should check how much memory you are consuming and adjust the **number of containers** that runs in **each machine** (and maybe add more machines to your cluster).

- github.com/antlr/antlr4/runtime/Go/antlr/v4: [8188dc5](https://github.com/antlr/antlr4/runtime/Go/antlr/v4/tree/8188dc5)
- github.com/google/gnostic-models: [v0.6.8](https://github.com/google/gnostic-models/tree/v0.6.8)
- github.com/googleapis/enterprise-certificate-proxy: [v0.2.3](https://github.com/googleapis/enterprise-certificate-proxy/tree/v0.2.3)

	// utilization to be optimal, instead of blindly throttling the way we are
	// doing below. However the changes that are needed here are a bit involved,
	// further discussion advised. Remove this comment and remove the worker model
	// for this function in future.
	maxWorkers := runtime.GOMAXPROCS(0) / 2
	ng := WithNPeersThrottled(len(sys.peerClients), maxWorkers)
	for idx, client := range sys.peerClients {
		if client == nil {
			continue

//    the SavedModel.
// - `graph` must be a graph newly allocated with TF_NewGraph().
//
// If successful, populates `graph` with the contents of the Graph and
// `meta_graph_def` with the MetaGraphDef of the loaded model.
TF_CAPI_EXPORT extern TF_Session* TF_LoadSessionFromSavedModel(
    const TF_SessionOptions* session_options, const TF_Buffer* run_options,
    const char* export_dir, const char* const* tags, int tags_len,

- Removed all support for _classic_ dynamic resource allocation (DRA). The `DRAControlPlaneController` feature gate, formerly alpha, is no longer available. Kubernetes now only uses the _structured parameters_ model (also alpha) for allocating dynamic resources to Pods.
  

* Delete the federation namespace from control plane instead of individual objects ([#51768](https://github.com/kubernetes/kubernetes/pull/51768), [@shashidharatd](https://github.com/shashidharatd))