#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)

#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);

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.

}

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,

*   `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.

- github.com/certifi/gocertifi: [2c3bb06](https://github.com/certifi/gocertifi/tree/2c3bb06)
- github.com/checkpoint-restore/go-criu/v5: [v5.0.0](https://github.com/checkpoint-restore/go-criu/v5/tree/v5.0.0)
- github.com/cncf/udpa/go: [5459f2c](https://github.com/cncf/udpa/go/tree/5459f2c)

message NodeConfigStatus {
  // Assigned reports the checkpointed config the node will try to use.
  // When Node.Spec.ConfigSource is updated, the node checkpoints the associated
  // config payload to local disk, along with a record indicating intended
  // config. The node refers to this record to choose its config checkpoint, and
  // reports this record in Assigned. Assigned only updates in the status after

- Added a new feature gate `CheckpointRestore` to enable support to checkpoint containers. If enabled it is possible to checkpoint a container using the newly kubelet API (/checkpoint/{podNamespace}/{podName}/{containerName}). ([#104907](https://github.com/kubernetes/kubernetes/pull/104907), [@adrianreber](https://github.com/adrianreber)) [SIG Node and Testing]

- Empty values are no longer assigned to undefined resources (CPU or memory) when storing the resources allocated to the pod in checkpoint. ([#117615](https://github.com/kubernetes/kubernetes/pull/117615), [@aheng-ch](https://github.com/aheng-ch))
- Fixed CEL estimated cost of `replace()` to handle a zero length replacement string correctly.