using TFE_MonitoringGauge::TFE_MonitoringGauge;
};

struct TFE_MonitoringBuckets {
  explicit TFE_MonitoringBuckets(
      std::function<std::unique_ptr<tensorflow::monitoring::Buckets>(void)>
          fn) {
    create_buckets = fn;
  }

  std::function<std::unique_ptr<tensorflow::monitoring::Buckets>(void)>
      create_buckets;
};

struct TFE_MonitoringSamplerCell {

      TFE_DEVICE_PLACEMENT_SILENT};
  // If true, use TFRT backend
  bool use_tfrt = false;
  // Whether to run elementary eager ops wrapped in a call op.
  bool run_eager_op_as_function = false;
  // Whether to rewrite jit_compile functions.
  bool jit_compile_rewrite = false;
};

TF_CAPI_EXPORT extern const char* TF_GraphDebugString(TF_Graph* graph,
                                                      size_t* len);

// Returns the function content in a human-readable format, with length set in
// `len`. The format is subject to change in the future.
// The returned string is heap-allocated, and caller should call free() on it.
//

#endif  // !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD)
#include "tensorflow/core/common_runtime/graph_constructor.h"
#include "tensorflow/core/common_runtime/shape_refiner.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/graph/graph.h"
#include "tensorflow/core/graph/node_builder.h"

  // Same as `AddFunctionDef`, but additionally saves the `stack_traces` under
  // the key of the function definition name (to be retrieved during function
  // instantiation).
  virtual Status AddFunctionDefWithStackTraces(
      const FunctionDef& fdef, const StackTracesMap& stack_traces) = 0;

  // Find and return a added function by its name.

using GradientFunctionFactory =
    std::function<GradientFunction*(const ForwardOperation& op)>;

// Map from op name to a `GradientFunctionFactory`.
class GradientRegistry {
 public:
  Status Register(const string& op,
                  GradientFunctionFactory gradient_function_factory);
  Status Lookup(const ForwardOperation& op,
                std::unique_ptr<GradientFunction>* gradient_function) const;

 private:

  std::vector<std::unique_ptr<DeviceThread>> device_threads_;
  // A cancellation manager to use if the caller does not provide one. When ops
  // are executed asynchronously this must outlive the queued op, so it can't be
  // function-local to Execute.
  mutable std::unique_ptr<CancellationManager> default_cancellation_manager_;
};

// Contains a tuple of tensors, one on each of the `underlying_devices_` of the
// ParallelDevice.

      std::unique_ptr<const TF_WritableFileOps> writable_file_ops,
      std::unique_ptr<const TF_ReadOnlyMemoryRegionOps>
          read_only_memory_region_ops,
      std::function<void*(size_t)> plugin_memory_allocate,
      std::function<void(void*)> plugin_memory_free)
      : filesystem_(std::move(filesystem)),
        ops_(std::move(filesystem_ops)),
        random_access_file_ops_(std::move(random_access_file_ops)),

/// assume that before any function using path arguments is invoked, the path is
/// made canonical by calling the function provided by `translate_name` or a
/// default implementation of that (supplied by core TensorFlow).
///
/// The only time the pointer to the `TF_*` structures from section 1 is not
/// marked `const` in these functions is when these function are either

#define TENSORFLOW_C_EAGER_GRADIENTS_INTERNAL_H_

#include "tensorflow/c/eager/gradients.h"

namespace tensorflow {
namespace gradients {
namespace internal {

// Helper functions which delegate to `AbstractOperation`, update
// the state of the ForwardOperation and call the tape as appropriate.
// These APIs are mainly to facilitate testing and are subject to change.