TF_Status* status);

TF_CAPI_EXPORT extern void TF_AllocateTempForVariableInfo(
    TF_OpKernelContext* ctx, TF_VariableInfo* var_info, TF_Status* status);

TF_CAPI_EXPORT extern TF_Tensor* TF_GetTensorFromVariableInfo(
    TF_VariableInfo* var_info, TF_Status* status);

TF_CAPI_EXPORT extern void TF_DeleteVariableInfo(TF_VariableInfo* var_info);

  ~ModularWritableFile() override { ops_->cleanup(file_.get()); }

  Status Append(StringPiece data) override;
  Status Close() override;
  Status Flush() override;
  Status Sync() override;
  Status Name(StringPiece* result) const override;
  Status Tell(int64_t* position) override;

 private:
  std::string filename_;
  std::unique_ptr<TF_WritableFile> file_;

  virtual tensorflow::DataType DataType() const = 0;

  // Returns the status of the tensor handle. If it is a tfrt::TensorHandle,
  // the tensor handle can be an error and return non-OK status.
  virtual tensorflow::Status TensorHandleStatus() const;

  // Returns tensor shape. If tensor has unknown rank, shape remains untouched.
  virtual tensorflow::Status Shape(
      tensorflow::PartialTensorShape* shape) const = 0;

  virtual Status AddDevices(std::vector<std::unique_ptr<Device>> devices) = 0;

  // Block until all pending nodes are finished.
  virtual Status AsyncWait() = 0;

  // Add a function (serialized FunctionDef protocol buffer) so that it can
  // be executed as an op. Return error if the function with the same name
  // already exists.
  virtual Status AddFunctionDef(const FunctionDef& fdef) = 0;

  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);
  TFE_Context* eager_ctx =
      TF_ExecutionContextGetTFEContext(wrap(ctx), status.get());
  TF_RETURN_IF_ERROR(StatusFromTF_Status(status.get()));
  TFE_TensorHandle* input_eager =
      TestScalarTensorHandle<T, datatype>(eager_ctx, value);
  *tensor =
      unwrap(TF_CreateAbstractTensorFromEagerTensor(input_eager, status.get()));

  // Returns the device ID which created the handle.
  virtual int DeviceId(Status* status) const = 0;
  // Returns a tensor for the handle. If tensor is remote, it will be copied.
  virtual AbstractTensorInterface* Resolve(Status* status) = 0;

  std::string DebugString() const override;

  // Returns a Boolean hint indicating whether callers should prefer
  // `SummarizeValue` to resolving this handle and formatting the tensor.

      ::tensorflow::Set_TF_Status_from_Status(C_STATUS, _s);      \
      TF_OpKernelContext_Failure(CTX, C_STATUS);                  \
      TF_DeleteStatus(C_STATUS);                                  \
      return;                                                     \
    }                                                             \
  } while (0)

#define TF_CLEANUP_AND_RETURN_IF_ERROR(C_STATUS, BUFFER, __VA_ARGS__) \

#include "tensorflow/core/platform/status.h"
#include "tensorflow/core/protobuf/rewriter_config.pb.h"

namespace tensorflow {
namespace grappler {

// Plugin initialization function that a device plugin
// must define.
typedef void (*TFInitGraphPluginFn)(TP_OptimizerRegistrationParams* const,
                                    TF_Status* const);

// Registers Graph optimizers.
Status InitGraphPlugin(void* dso_handle);

  void GetTensor(const string& name,
                 std::unique_ptr<tensorflow::Tensor>* out_tensor,
                 TF_Status* out_status) const;

 private:
  // Uses "v2_reader_" to build "var name -> shape" and "var name -> data type"
  // maps; both owned by caller.
  // REQUIRES: "v2_reader_ != nullptr && v2_reader_.status().ok()".
  std::pair<std::unique_ptr<TensorSliceReader::VarToShapeMap>,

                                           TF_Status* status);

void RecordMutation(TF_Graph* graph, const TF_Operation& op,
                    const char* mutation_type)
    TF_EXCLUSIVE_LOCKS_REQUIRED(graph->mu);

bool ExtendSessionGraphHelper(TF_Session* session, TF_Status* status)
    TF_LOCKS_EXCLUDED(session->graph->mu, session->mu);