//    "/job:localhost/replica:0/task:0/device:GPU:1"}
// Then executing an operation on CUSTOM:0 will execute it on GPU:0 and GPU:1.
//
// Implicit copies onto `device_name` are allowed, replicating the value once
// per device in `underlying_devices`. Implicit copies off of the device throw
// an error.
//
// All component tensors must have the same dtype. Currently they must also have

// environment, a traced representation etc.
class AbstractContext {
 protected:
  enum AbstractContextKind { kGraph, kMlir, kEager, kTfrt, kTape, kOpHandler };
  explicit AbstractContext(AbstractContextKind kind) : kind_(kind) {}
  virtual ~AbstractContext() {}

 public:
  AbstractContextKind getKind() const { return kind_; }

// placed on each underlying device.
class ParallelDevice {
 public:
  // Eager async execution is only supported when remote eager is not in use
  // (b/157523095).
  explicit ParallelDevice(const std::vector<std::string>& devices,
                          bool is_async = false, int in_flight_nodes_limit = 0);

  ~ParallelDevice();

namespace tensorflow {
namespace tracing {
namespace graph {
using tensorflow::AbstractFunction;
// Thin wrapper around a FunctionDef.
class GraphFunction : public AbstractFunction {
 public:
  explicit GraphFunction(FunctionDef fdef);
  ~GraphFunction() override;

  // GraphFunction maybe stay alive for the duration of the returned
  // FunctionDef.
  Status GetFunctionDef(const FunctionDef** fdef) override;

// destroyed. Cleanup<F> is the return type of gtl::MakeCleanup(F).
template <typename F>
class Cleanup {
 public:
  Cleanup() : released_(true), f_() {}

  template <typename G>
  explicit Cleanup(G&& f)          // NOLINT
      : f_(std::forward<G>(f)) {}  // NOLINT(build/c++11)

  Cleanup(Cleanup&& src)  // NOLINT
      : released_(src.is_released()), f_(src.release()) {}

  using TFE_MonitoringGauge::TFE_MonitoringGauge;
};
struct TFE_MonitoringBoolGauge2 : TFE_MonitoringGauge<bool, 2> {
  using TFE_MonitoringGauge::TFE_MonitoringGauge;
};

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

// tracing or immediate execution mode.
class AbstractOperation {
 protected:
  enum AbstractOperationKind {
    kGraph,
    kMlir,
    kEager,
    kTfrt,
    kTape,
    kOpHandler
  };
  explicit AbstractOperation(AbstractOperationKind kind) : kind_(kind) {}
  virtual ~AbstractOperation() {}

 public:
  AbstractOperationKind getKind() const { return kind_; }

// LINT.IfChange
// Note: Keep in sync with internal copy of enum in eager/context.h.
typedef enum TFE_ContextDevicePlacementPolicy {
  // Running operations with input tensors on the wrong device will fail.
  TFE_DEVICE_PLACEMENT_EXPLICIT = 0,
  // Copy the tensor to the right device but log a warning.
  TFE_DEVICE_PLACEMENT_WARN = 1,
  // Silently copy the tensor, which has a performance cost since the operation

#include "tensorflow/c/eager/abstract_context.h"
#include "tensorflow/c/experimental/gradients/tape/tape_operation.h"

namespace tensorflow {
namespace gradients {
class TapeContext : public AbstractContext {
 public:
  explicit TapeContext(AbstractContext*, Tape*, const GradientRegistry&);
  void Release() override;
  TapeOperation* CreateOperation() override;
  Status RegisterFunction(AbstractFunction*) override;

// representation between various supported formats e.g. FunctionDef and Mlir
// function.
class AbstractFunction : public core::RefCounted {
 protected:
  enum AbstractFunctionKind { kGraph, kMlir };
  explicit AbstractFunction(AbstractFunctionKind kind) : kind_(kind) {}

 public:
  // Returns which subclass is this instance of.
  AbstractFunctionKind getKind() const { return kind_; }