#ifdef __cplusplus
extern "C" {
#endif  // __cplusplus

// An opaque type that corresponds to a SignatureDefFunction loaded from a
// SavedModel.
typedef struct TF_SignatureDefFunction TF_SignatureDefFunction;

// Returns FunctionMetadata associated with `func`. Metadata's lifetime is
// bound to `func`, which is bound to the TF_SavedModel it was loaded from.

static void plugin_memory_free(void* ptr) { free(ptr); }

// SECTION 1. Implementation for `TF_RandomAccessFile`
// ----------------------------------------------------------------------------
namespace tf_random_access_file {

typedef struct PosixFile {
  const char* filename;
  int fd;
} PosixFile;

static void Cleanup(TF_RandomAccessFile* file) {
  auto posix_file = static_cast<PosixFile*>(file->plugin_file);
  close(posix_file->fd);

#endif  // __cplusplus

// An opaque type that corresponds to a Function loaded from a SavedModel.
// TODO(bmzhao): Work together w/srbs@ to make sure this composes w/the
// C++ Unified Eager/Graph API's AbstractFunction
typedef struct TF_ConcreteFunction TF_ConcreteFunction;

// Returns FunctionMetadata associated with `func`. Metadata's lifetime is
// bound to `func`, which is bound to the TF_SavedModel it was loaded from.

  PartialTensorShape shape;

  // If the argument is a resource variable, the type and shape of the
  // variable's value.
  DataType handle_type = DT_INVALID;
  PartialTensorShape handle_shape;
};
typedef std::unordered_map<string, std::vector<InferredShape>> GraphShapeInfo;

// Infer shapes for all Tensors in a graph, and save them in a map.  The vector
// for a Node contains the information about each of its outputs.

// --------------------------------------------------------------------------
// TF_DataType holds the type for a scalar value.  E.g., one slot in a tensor.
// The enum values here are identical to corresponding values in types.proto.
typedef enum TF_DataType {
  TF_FLOAT = 1,
  TF_DOUBLE = 2,
  TF_INT32 = 3,  // Int32 tensors are always in 'host' memory.
  TF_UINT8 = 4,
  TF_INT16 = 5,
  TF_INT8 = 6,
  TF_STRING = 7,

/// GradFunc is the signature for all gradient functions in GradOpRegistry.
/// Implementations should add operations to compute the gradient outputs of
/// 'op' (returned in 'grad_outputs') using 'scope' and 'grad_inputs'.
typedef Status (*GradFunc)(const Scope& scope, const Operation& op,
                           const std::vector<Output>& grad_inputs,
                           std::vector<Output>* grad_outputs);

template <typename T>
Output Const(const Scope& scope, const Input::Initializer& val) {
  auto orig_const_output = Const(scope, val);
  if (!scope.ok()) return Output();

  typedef typename Input::Initializer::RealType<T>::type DstT;

  if (val.tensor.dtype() == DataTypeToEnum<DstT>::v()) {
    return orig_const_output;
  }
  if (val.tensor.NumElements() == 0) {

/** CUnit error codes.
 *  If codes are added or removed, be sure to make a change to the
 *  error messages in CUError.c/get_error_desc().
 *  @see CU_set_error()
 *  @see CU_get_error()
 *  @see CU_get_error_msg()
 */
typedef enum {
  /* basic errors */
  CUE_SUCCESS           = 0,  /**< No error condition. */
  CUE_NOMEMORY          = 1,  /**< Memory allocation failed. */

  /* Test Registry Level Errors */

};

// DumpStatsToStdout printfs to stdout stats in a multi-line human-friendly
// form.
void DumpStatsToStdout(const Stats& stats);

// BenchmarkFn is the signature of the function generated by tfcompile.
typedef std::function<void()> BenchmarkFn;

// Benchmark runs a benchmark of the function `fn`, collecting stats in `stats`.
// Use `options` to configure benchmarking options.

#include "tensorflow/compiler/mlir/tensorflow/ir/tf_traits.h"
#define GET_ATTRDEF_CLASSES
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops_attrdefs.h.inc"

namespace mlir {
namespace TFL {

typedef TFLDialect TensorFlowLiteDialect;

// The Control type is a token-like value that models control dependencies
class ControlType : public Type::TypeBase<ControlType, Type, TypeStorage> {
 public:
  using Base::Base;