namespace internal {
struct ImmediateExecutionOperationDeleter {
  void operator()(ImmediateExecutionOperation* p) const {
    if (p != nullptr) {
      p->Release();
    }
  }
};
}  // namespace internal

using ImmediateOpPtr =
    std::unique_ptr<ImmediateExecutionOperation,
                    internal::ImmediateExecutionOperationDeleter>;

}  // namespace tensorflow

// APIs for Counter without label.
typedef struct TFE_MonitoringCounter0 TFE_MonitoringCounter0;
// Returns a new Counter metric object. The caller should manage lifetime of
// the object. Using duplicate metric name will crash the program with fatal
// error.
TF_CAPI_EXPORT extern TFE_MonitoringCounter0* TFE_MonitoringNewCounter0(
    const char* name, TF_Status* status, const char* description);

};

namespace internal {
struct AbstractContextDeleter {
  void operator()(AbstractContext* p) const {
    if (p != nullptr) {
      p->Release();
    }
  }
};
}  // namespace internal

using AbstractContextPtr =
    std::unique_ptr<AbstractContext, internal::AbstractContextDeleter>;

}  // namespace tensorflow

//
// `device_name` must not name an existing physical or custom device (see
// the documentation for TFE_RegisterCustomDevice for more information).
//
// Tensors may be copied on or off the device explicitly using
// TPUReplicatedInput and TPUReplicatedOutput respectively. For example, with
// two component devices, running `x = TPUReplicatedInput(inputs=[a, b])` on the
// parallel device creates a parallel tensor `x` with `a` on the first of

#include "tensorflow/core/framework/cancellation.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/types.h"

namespace tensorflow {
namespace parallel_device {

using TensorHandlePtr = tensorflow::Safe_TFE_TensorHandlePtr;

class ParallelTensor;
class DeviceThread;

// Forwards operations to `devices`, maintaining ParallelTensor with components
// placed on each underlying device.

                                                TF_Status* status);

// This function creates a new TF_Session (which is created on success) using
// `session_options`, and then initializes state (restoring tensors and other
// assets) using `run_options`.
//
// Any NULL and non-NULL value combinations for (`run_options, `meta_graph_def`)
// are valid.
//

// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

// It's going to be hard to include a whole real JVM to test this.
// So we'll simulate a really easy JVM using just the parts we need.

// This is the relevant part of jni.h.

// On Android NDK16, jobject is defined like this in C and C++
typedef void* jobject;

typedef jobject jclass;
typedef jobject jthrowable;

  // Returns a shared reference to the wrapped function.
  virtual absl::StatusOr<core::RefCountPtr<FunctionRecord>>
  GetFunctionRecord() = 0;

 private:
  const AbstractFunctionKind kind_;
};

using AbstractFunctionPtr =
    tensorflow::core::IntrusivePtr<tensorflow::AbstractFunction>;

}  // namespace tensorflow

// produced this tensor. A value of -1 means that the tensor was directly
// watched and not the result of any operation in the tape.
using TensorTape = std::unordered_map<int64_t, int64_t>;

// Map from operation-id to tape entry.
template <typename BackwardFunction, typename TapeTensor>
using OpTape =
    std::unordered_map<int64_t, OpTapeEntry<BackwardFunction, TapeTensor>>;

  using TFE_MonitoringGauge::TFE_MonitoringGauge;
};
struct TFE_MonitoringStringGauge1 : TFE_MonitoringGauge<tensorflow::string, 1> {
  using TFE_MonitoringGauge::TFE_MonitoringGauge;
};
struct TFE_MonitoringStringGauge2 : TFE_MonitoringGauge<tensorflow::string, 2> {
  using TFE_MonitoringGauge::TFE_MonitoringGauge;
};
struct TFE_MonitoringStringGauge3 : TFE_MonitoringGauge<tensorflow::string, 3> {