}
  Status Optimize(Cluster* cluster, const GrapplerItem& item,
                  GraphDef* optimized_graph_def) override;

  ~CGraphOptimizer() override {
    if (optimizer_.destroy_func != nullptr) {
      (*optimizer_.destroy_func)(c_optimizer_);
    }
  }

 private:
  TP_Optimizer optimizer_;
  std::string device_type_;
  void* c_optimizer_;
};

  ///
  /// NOTE: TensorFlow does not unload DSOs. Thus, the only way a filesystem
  /// won't be registered anymore is if this function gets called by core
  /// TensorFlow and the `TF_Filesystem*` object is destroyed. However, due to
  /// registration being done in a static instance of `Env`, the destructor of
  /// `FileSystem` is never called (see
  /// https://github.com/tensorflow/tensorflow/issues/27535). In turn, this

                        TF_Buffer*, TF_Status*);

  // [Optional]
  // Destroy function for optimizer. If Create function is provided, destroy
  // function is must.
  void (*destroy_func)(void*);
} TP_Optimizer;

#define TP_OPTIMIZER_STRUCT_SIZE TF_OFFSET_OF_END(TP_Optimizer, destroy_func)

typedef struct TP_OptimizerRegistrationParams {
  size_t struct_size;

#
#   Important: because the container is persistent, you cannot change TFCI
#   variables in between script executions. To forcibly remove the
#   container and start fresh, run "docker rm -f tf". Removing the container
#   destroys some temporary bazel data and causes longer builds.
#
#   You will need the NVIDIA Container Toolkit for GPU testing:
#   https://github.com/NVIDIA/nvidia-container-toolkit
#

                                                      unsigned char enable);

TF_CAPI_EXPORT extern void TFE_ContextOptionsSetDevicePlacementPolicy(
    TFE_ContextOptions*, TFE_ContextDevicePlacementPolicy);

// Destroy an options object.
TF_CAPI_EXPORT extern void TFE_DeleteContextOptions(TFE_ContextOptions*);

// "Context" under which operations/functions are executed. It encapsulates
// things like the available devices, resource manager etc.

  // Initialize the step resource container for a training step. This is used
  // in current TF runtime. For tfrt, it is used by fallback op handler.
  virtual void StartStep() = 0;
  // Destroy the step resource container for a training step.
  virtual void EndStep() = 0;

  // Return the Eager Executor for current thread. Please note that Eager
  // Executor is only used in current TF but not in TFRT.

    *   Make `num_parallel_calls` of `tf.data.Dataset.interleave` and
        `tf.data.Dataset.map` work in Eager mode.
*   Toolchains
    *   Fixed OpenSSL compatibility by avoiding `EVP_MD_CTX_destroy`.
    *   Added bounds checking to printing deprecation warnings.
    *   Upgraded CUDA dependency to 10.0
    *   To build with Android NDK r14b, add "#include <linux/compiler.h>" to

  params->optimizer->struct_size = TP_OPTIMIZER_STRUCT_SIZE;
  params->optimizer->create_func = nullptr;
  params->optimizer->optimize_func = optimize_func;
  params->optimizer->destroy_func = nullptr;
}

TEST(Grappler, SuccessfulRegistration) {
  auto plugin_init = [](TP_OptimizerRegistrationParams* const params,
                        TF_Status* const status) -> void {

TF_CAPI_EXPORT extern void TF_SetConfig(TF_SessionOptions* options,
                                        const void* proto, size_t proto_len,
                                        TF_Status* status);

// Destroy an options object.
TF_CAPI_EXPORT extern void TF_DeleteSessionOptions(TF_SessionOptions*);

// TODO(jeff,sanjay):
// - export functions to set Config fields