#include <memory>

#include "tensorflow/core/lib/monitoring/cell_reader.h"

struct TFE_MonitoringCounterReader {
  explicit TFE_MonitoringCounterReader(const char* name) {
    counter = std::make_unique<
        ::tensorflow::monitoring::testing::CellReader<int64_t>>(name);
  }
  template <typename... LabelType>
  int64_t Read(const LabelType&... labels);

// Deletes the eager Executor without waiting for enqueued nodes. Please call
// TFE_ExecutorWaitForAllPendingNodes before calling this API if you want to
// make sure all nodes are finished.
TF_CAPI_EXPORT extern void TFE_DeleteExecutor(TFE_Executor*);

// Returns true if the executor is in async mode.
TF_CAPI_EXPORT extern bool TFE_ExecutorIsAsync(TFE_Executor*);

//
// Questions left to address:
// * Might at some point need a way for callers to provide their own Env.
// * Maybe add TF_TensorShape that encapsulates dimension info.
//
// Design decisions made:
// * Backing store for tensor memory has an associated deallocation
//   function.  This deallocation function will point to client code
//   for tensors populated by the client.  So the client can do things

  TF_DataType datatype_enum(
      static_cast<TF_DataType>(DataTypeToEnum<DataType>().value));
  for (int device_index = 0; device_index < num_underlying_devices();
       ++device_index) {
    auto device_value = absl::make_unique<DataType>();
    *device_value = values[device_index];
    std::unique_ptr<TF_Tensor, decltype(&TF_DeleteTensor)> tensor(
        TF_NewTensor(
            datatype_enum, /*dims=*/nullptr, /*num_dims=*/0,

  }
  if (!ShouldRecord(input_tensor_id, input_dtypes)) {
    return absl::OkStatus();
  }

  // We may need to allocate zero inputs for trainable dtypes we don't have JVPs
  // for. Make sure they get cleaned up.
  std::vector<Gradient*> new_zeros;
  auto delete_new_zeros = gtl::MakeCleanup([&new_zeros, this] {
    for (Gradient* tensor : new_zeros) {
      this->vspace_.DeleteGradient(tensor);
    }

//
// Very similar to TF_OperationDescription with some differences:
// (1) TF_Output or TFE_TensorHandle* as arguments to TF_AddInput,
//     TF_AddInputList
// (2) TF_ColocateWith, TF_AddControlInput etc. do not make sense.
// (3) Implementation detail: Avoid use of NodeBuilder/NodeDefBuilder since
//     the additional sanity checks there seem unnecessary;
typedef struct TFE_Op TFE_Op;

namespace internal {
struct AbstractTensorHandleDeleter {
  void operator()(AbstractTensorHandle* p) const {
    if (p != nullptr) {
      p->Unref();
    }
  }
};
}  // namespace internal

// TODO(b/185908092): Make AbstractTensorHandlePtr an IntrusivePtr.
using AbstractTensorHandlePtr =
    std::unique_ptr<AbstractTensorHandle,
                    internal::AbstractTensorHandleDeleter>;

}  // namespace tensorflow

                         absl::Span<AbstractTensorHandle*> grad_inputs) = 0;
  virtual ~GradientFunction() {}
};

// Metadata from the forward operation that is made available to the
// gradient registerer to instantiate a GradientFunction.
struct ForwardOperation {
 public:
  string op_name;
  std::vector<AbstractTensorHandle*> inputs;
  std::vector<AbstractTensorHandle*> outputs;

typedef struct TF_Filesystem_Option_Value {
  int type_tag;    // type of values in the values union
  int num_values;  // number of values
  TF_Filesystem_Option_Value_Union*
      values;  // owned (plugins must make a copy if storing this)
} TF_Filesystem_Option_Value;

typedef enum TF_Filesystem_Option_Type {
  TF_Filesystem_Option_Type_Int = 0,
  TF_Filesystem_Option_Type_Real,
  TF_Filesystem_Option_Type_Buffer,