#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)
// variables.
class CheckpointReader {
 public:

  inline const cpp_impl *unwrap(const wrapper *w) {                            \
    return reinterpret_cast<const cpp_impl *>(w);                              \
  }                                                                            \
                                                                               \
  inline wrapper *wrap(cpp_impl *i) { return reinterpret_cast<wrapper *>(i); } \

using tensorflow::AbstractFunction;
// Thin wrapper around a FunctionDef.
class GraphFunction : public AbstractFunction {
 public:
  explicit GraphFunction(FunctionDef fdef);
  ~GraphFunction() override;

  // GraphFunction maybe stay alive for the duration of the returned
  // FunctionDef.
  Status GetFunctionDef(const FunctionDef** fdef) override;

  // Returns a shared reference to the wrapped function.

# Make sure code inside the TensorFlow codebase can use tf2.enabled() at import.
_os.environ["TF2_BEHAVIOR"] = "1"
from tensorflow.python import tf2 as _tf2
_tf2.enable()

# API IMPORTS PLACEHOLDER

# WRAPPER_PLACEHOLDER

# Make sure directory containing top level submodules is in
# the __path__ so that "from tensorflow.foo import bar" works.
# We're using bitwise, but there's nothing special about that.

from tensorflow.python.util.lazy_loader import LazyLoader as _LazyLoader
from tensorflow.python.util.lazy_loader import KerasLazyLoader as _KerasLazyLoader

# API IMPORTS PLACEHOLDER

# WRAPPER_PLACEHOLDER

if "dev" in __version__:   # pylint: disable=undefined-variable
  _logging.warning("""

  TensorFlow's `tf-nightly` package will soon be updated to TensorFlow 2.0.

};

// TODO(srbs): Figure out if we can avoid declaring this in the public header.
// Wrapper for a tensor output of an operation executing under a tape.
//
// `GetID` returns a unique id for the wrapped tensor which is used to maintain
// a map (`tensorflow::eager::TensorTape`) from the wrapped tensor to the id of
// the op that produced it (or -1 if this tensor was watched using

using Model = std::function<Status(AbstractContext*,
                                   absl::Span<AbstractTensorHandle* const>,
                                   absl::Span<AbstractTensorHandle*>)>;

// Runs `model` maybe wrapped in a function call op. This can be thought as
// being equivalent to the following python code.
//
// if use_function:
//   outputs = tf.function(model)(inputs)
// else:
//   outputs = model(inputs)

  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  // Add a config_proto attr, to trigger grappler graph rewrites in the current
  // eager runtime.
  if (enable_grappler) {
    tensorflow::ConfigProto config;
    // Do not skip grappler optimization even for small graphs.
    config.mutable_graph_options()
        ->mutable_rewrite_options()
        ->set_min_graph_nodes(-1);

/// method signatures to have some type of type safety: for example, operations
/// that are only valid on random access files have a `TF_RandomAccessFile`
/// argument.
///
/// Lifetime: The wrapper data structures are owned by core TensorFlow. The data
/// pointed to by the `void*` members is always owned by the plugin. The plugin
/// will provide functions to call to allocate and deallocate this data (see

    ([CVE-2022-23580](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-23580))
*   Fixes multiple `CHECK`-failures during Grappler's `IsSimplifiableReshape`
    ([CVE-2022-23581](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-23581))
*   Fixes multiple `CHECK`-failures during Grappler's `SafeToRemoveIdentity`
    ([CVE-2022-23579](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-23579))