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.

.idea
.ipynb_checkpoints
.mypy_cache
.vscode
__pycache__
.pytest_cache
htmlcov
dist
site
.coverage
coverage.xml
.netlify
test.db
log.txt
Pipfile.lock
env3.*
env
docs_build
site_build
venv
docs.zip
archive.zip

# vim temporary files
*~
.*.sw?
.cache

# macOS

local_settings.py
db.sqlite3

# Flask stuff:
instance/
.webassets-cache

# Scrapy stuff:
.scrapy

# Sphinx documentation
docs/_build/

# PyBuilder
target/

# Jupyter Notebook
.ipynb_checkpoints

# pyenv
.python-version

# celery beat schedule file
celerybeat-schedule

# SageMath parsed files
*.sage.py

# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/

#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)

.DS_Store
.ipynb_checkpoints
node_modules
/.bazelrc.user
/.tf_configure.bazelrc
/xla_configure.bazelrc
/bazel-*
/bazel_pip
/tools/python_bin_path.sh
/tensorflow/tools/git/gen
/pip_test
/_python_build
*.pyc
__pycache__
*.swp
.vscode/
cmake_build/
tensorflow/contrib/cmake/_build/
.idea/**
/build/
[Bb]uild/
/build_output/
/tensorflow/core/util/version_info.cc
/tensorflow/python/framework/fast_tensor_util.cpp

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

*   `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.

See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#include "tensorflow/c/checkpoint_reader.h"

#include <unordered_set>
#include <utility>

#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/platform/status.h"
#include "tensorflow/core/platform/stringpiece.h"

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

        "@local_tsl//tsl/platform:status",
        "@local_tsl//tsl/platform:test",
        "@local_tsl//tsl/platform:test_main",
    ],
)

tf_cuda_library(
    name = "checkpoint_reader",
    srcs = ["checkpoint_reader.cc"],
    hdrs = ["checkpoint_reader.h"],
    visibility = ["//visibility:public"],
    deps = [
        ":tf_status",
        ":tf_status_helper",
        "//tensorflow/core:framework",