variables from your model are also going to be untrusted. That means that if
your code interacts with the filesystem, network, etc. and uses checkpointed
variables as part of those interactions (ex: using a string variable to build a
filesystem path), a maliciously created checkpoint might be able to change the
targets of those operations, which could result in arbitrary
read/write/executions.

#include "tensorflow/core/util/tensor_bundle/tensor_bundle.h"
#include "tensorflow/core/util/tensor_slice_reader.h"

namespace tensorflow {
namespace checkpoint {

class TensorSliceReader;

// A wrapper around BundleReader (for V2 checkpoints) and
// checkpoint::TensorSliceReader (for V1), that is more easily SWIG wrapped for
// other languages.
//
// The class currently only interacts with single-slice (i.e., non-partitioned)

*   `tf.train.Checkpoint`:
    *   Now accepts a `root` argument in the initialization, which generates a
        checkpoint with a root object. This allows users to create a
        `Checkpoint` object that is compatible with Keras `model.save_weights()`
        and `model.load_weights`. The checkpoint is also compatible with the
        checkpoint saved in the `variables/` folder in the SavedModel.

More information [here](https://github.com/docker/docker/issues/17720)
  * Docker daemon restarts can fail. Docker checkpoints have to deleted between
restarts. More information [here](https://github.com/kubernetes/kubernetes/issues/20995)
  * Pod IP allocation-related issues. Deleting the docker checkpoint prior to
restarting the daemon alleviates this issue, but hasn’t been verified to

#include "tensorflow/core/util/saved_tensor_slice_util.h"
#include "tensorflow/core/util/tensor_bundle/naming.h"
#include "tensorflow/core/util/tensor_bundle/tensor_bundle.h"

namespace tensorflow {
namespace checkpoint {

class TensorSliceReader;

CheckpointReader::CheckpointReader(const string& filename, TF_Status* status)
    : reader_(nullptr),
      v2_reader_(nullptr),
      var_to_shape_map_(nullptr),

                                                      const char* errMsg);

// TF_NewCheckpointReader() return the CheckpointReader that can be use to
// investigate or load the variable from the checkpoint file
typedef struct TF_CheckpointReader TF_CheckpointReader;
TF_CAPI_EXPORT extern TF_CheckpointReader* TF_NewCheckpointReader(
    const char* filename, TF_Status* status);

}

void TF_MakeInternalErrorStatus(TF_Status* status, const char* errMsg) {
  status->status = tensorflow::errors::Internal(errMsg);
}

struct TF_CheckpointReader : public tensorflow::checkpoint::CheckpointReader {
  using tensorflow::checkpoint::CheckpointReader::CheckpointReader;
  std::vector<std::string> variable_list;
};

TF_CheckpointReader* TF_NewCheckpointReader(const char* filename,

* kubeadm: clean up exited containers and network checkpoints ([#43836](https://github.com/kubernetes/kubernetes/pull/43836), [@yujuhong](https://github.com/yujuhong))

* Implement API usage metrics for gce storage ([#40338](https://github.com/kubernetes/kubernetes/pull/40338), [@gnufied](https://github.com/gnufied))
* kubeadm: clean up exited containers and network checkpoints ([#43836](https://github.com/kubernetes/kubernetes/pull/43836), [@yujuhong](https://github.com/yujuhong))

  - [Changelog since v1.32.1](#changelog-since-v1321)
  - [Important Security Information](#important-security-information-2)
    - [CVE-2025-0426: Node Denial of Service via Kubelet Checkpoint API](#cve-2025-0426-node-denial-of-service-via-kubelet-checkpoint-api)
  - [Changes by Kind](#changes-by-kind-9)
    - [Feature](#feature-6)
    - [Bug or Regression](#bug-or-regression-9)
    - [Other (Cleanup or Flake)](#other-cleanup-or-flake-3)