// Creates a new eager Executor. Nodes in one executor are guaranteed to be
// executed in sequence. Assigning nodes to different executors allows executing
// nodes in parallel.
// in_flight_nodes_limit: when is_async is true, this value controls the
// maximum number of in flight async nodes. Enqueuing of additional async ops
// after the limit is reached blocks until some inflight nodes finishes.

typedef enum TF_TriState {
  TF_TriState_Default = 0,
  TF_TriState_Off,
  TF_TriState_On,
} TF_TriState;

// TF_GrapplerItem represents a combination of a graph, one of more fetch nodes,
// and potentially a set of nodes to feed.
typedef struct TF_GrapplerItem TF_GrapplerItem;

// Flags indicating whether existing optimizers should be turned off.
// It's optional for plugin to set functions to return true/false. If not

// Set the prefix to be prepended to the names of nodes in `graph_def` that will
// be imported into `graph`. `prefix` is copied and has no lifetime
// requirements.
TF_CAPI_EXPORT extern void TF_ImportGraphDefOptionsSetPrefix(
    TF_ImportGraphDefOptions* opts, const char* prefix);

// Set the execution device for nodes in `graph_def`.
// Only applies to nodes where a device was not already explicitly specified.

  // Eager async execution is only supported when remote eager is not in use
  // (b/157523095).
  explicit ParallelDevice(const std::vector<std::string>& devices,
                          bool is_async = false, int in_flight_nodes_limit = 0);

  ~ParallelDevice();

  // Helper to copy a tensor handle from another device once for each component
  // of the ParallelDevice.
  //

    TF_ShapeAndTypeList** shape_list_array, int num_items);

// Infer shapes for the given `op`. The arguments mimic the arguments of the
// `shape_inference::InferenceContext` constructor. Note the following:
//   - The inputs of the `op` are not used for shape inference. So, it is
//     OK to not have the inputs properly set in `op`. See `input_tensors`

    ids.push_back(i);
  }
  const int64_t op_id = next_op_id_++;
  std::vector<TapeTensor> tensors;
  tensors.reserve(output_tensors.size());
  for (const TapeTensor& o : output_tensors) {
    // Note: the tensor can have already been watched and hence be in the tape,
    // so we cannot check that we're inserting it here.
    tensor_tape_[o.GetID()] = op_id;
    tensor_usage_[o.GetID()] = 1;
    tensors.push_back(o);

    TF_Status* status);

// Controls how to act when we try to run an operation on a given device but
// some input tensors are not on that device.
// LINT.IfChange
// Note: Keep in sync with internal copy of enum in eager/context.h.
typedef enum TFE_ContextDevicePlacementPolicy {
  // Running operations with input tensors on the wrong device will fail.
  TFE_DEVICE_PLACEMENT_EXPLICIT = 0,

struct TF_Session {
  TF_Session(tensorflow::Session* s, TF_Graph* g);

  tensorflow::Session* session;
  TF_Graph* const graph;

  tensorflow::mutex mu TF_ACQUIRED_AFTER(TF_Graph::mu);
  int last_num_graph_nodes;

  // If true, TF_SessionRun and similar methods will call
  // ExtendSessionGraphHelper before running the graph (this is the default
  // public behavior). Can be set to false if the caller needs to call

  // will take ownership and maintain devices' lifetime.
  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.

struct TFE_Executor {
  explicit TFE_Executor(bool async, bool enable_streaming_enqueue,
                        int in_flight_nodes_limit)
      : owned_executor(new tensorflow::EagerExecutor(
            async, enable_streaming_enqueue, in_flight_nodes_limit)) {}

  explicit TFE_Executor(tensorflow::EagerExecutor* executor)
      : owned_executor(nullptr), unowned_executor(executor) {}