//       versioning guarantees yet.
//   * `void* ext` is a free-form field that can be populated by
//     a plugin in `TP_*` structs or potential future extension points .
//
// Example usage:
//
//   /* Sample TensorFlow code below, exact implementation might differ. */
//   // Version checking uses `struct_size`. It should be set both by core
//   // and the plugin.
//   TP_OptimizerRegistrationParams params{

                                const char* label1, const char* label2);

// -----------------------------------------------------------------------------
// Monitoring Sampler APIs.
// These APIs de-templated monitoring Sampler for swig.

typedef struct TFE_MonitoringSamplerCell TFE_MonitoringSamplerCell;

// Atomically add the value of the cell.
TF_CAPI_EXPORT extern void TFE_MonitoringSamplerCellAdd(

#include "tensorflow/core/common_runtime/eager/attr_builder.h"

namespace tensorflow {
namespace gradients {

// =============== Experimental C++ API for computing gradients ===============

// Sample gradient function:
//
// class AddGradientFunction : public GradientFunction {
//  public:
//   Status Compute(Context* ctx,
//                  absl::Span<AbstractTensorHandle* const> grad_inputs,

namespace parallel_device {

// Allocate a parallel device named `device_name` which forwards operations to
// `underlying_devices`, maintaining "parallel tensors" with components placed
// on each underlying device.
//
// For example if `device_name` is
//   "/job:localhost/replica:0/task:0/device:CUSTOM:0"
// and `underlying_devices` is
//   {"/job:localhost/replica:0/task:0/device:GPU:0",
//    "/job:localhost/replica:0/task:0/device:GPU:1"}

// * unsigned char is used for booleans (instead of the 'bool' type).
//   In C++ bool is a keyword while in C99 bool is a macro defined
//   in stdbool.h. It is possible for the two to be inconsistent.
//   For example, neither the C99 nor the C++11 standard force a byte
//   size on the bool type, so the macro defined in stdbool.h could
//   be inconsistent with the bool keyword in C++. Thus, the use

      std::unique_ptr<tensorflow::monitoring::Buckets> buckets,
      const char* description, LabelDesc&&... label) {
    sampler = absl::WrapUnique(tensorflow::monitoring::Sampler<NumLabels>::New(
        {name, description, label...}, std::move(buckets)));
  }

  std::unique_ptr<tensorflow::monitoring::Sampler<NumLabels>> sampler;
};

struct TFE_MonitoringSampler0 : TFE_MonitoringSampler<0> {

// destructor. The easiest way to use MakeCleanup is with a lambda argument,
// capturing the return value in an 'auto' local variable. Most users will not
// need more sophisticated syntax than that.
//
// Example:
//   void func() {
//     FILE* fp = fopen("data.txt", "r");
//     if (fp == nullptr) return;
//     auto fp_cleaner = gtl::MakeCleanup([fp] { fclose(fp); });
//     // No matter what, fclose(fp) will happen.

  // The value returned may be different from the one set by SetDeviceName, but
  // it will be compatible with it: the name will be updated by device placement
  // logic to refer to the specific device chosen.
  //
  // Example: If one calls `op->SetDeviceName("/device:GPU")`, the value
  // returned by DeviceName should be "/device:GPU:*" until a particular GPU is
  // chosen for the operation by the device placement logic in the

// This function will block till the operation that produces `h` has
// completed. The memory returned might alias the internal memory used by
// TensorFlow. Hence, callers should not mutate this memory (for example by
// modifying the memory region pointed to by TF_TensorData() on the returned
// TF_Tensor).
TF_CAPI_EXPORT extern TF_Tensor* TFE_TensorHandleResolve(TFE_TensorHandle* h,

/// TensorFlow but can be used by each filesystem plugin to represent internal
/// data.
///
/// We prefer to have these structures instead of passing `void*` into
/// method signatures to have some type of type safety: for example, operations
/// that are only valid on random access files have a `TF_RandomAccessFile`
/// argument.
///
/// Lifetime: The wrapper data structures are owned by core TensorFlow. The data