absl::MutexLock lock(&mu_);
    return DeleteLocked(key);
  }

  /// Look up the entry with key `key` and copy it to `value` if found. Returns
  /// true if an entry was found for `key`, and its timestamp is not more than
  /// max_age_ seconds in the past.
  bool Lookup(const std::string& key, T* value) {
    if (max_age_ == 0) {
      return false;
    }
    absl::MutexLock lock(&mu_);
    return LookupLocked(key, value);
  }

//     plugin or core implementation:
//     * Struct that should be filled by the plugin: `TP_OptimizerConfigs`,
//       `TP_Optimizer`, `TP_OptimizerRegistrationParams`
//     * Struct that should be filled by the proper: `TF_GrapplerItem`,
//       `TF_GraphProperties`, `TF_FunctionLibraryDefinition`
//   * We use `struct_size` for version checking. It should be set both by
//     core and the plugin.

//
// The custom device defines copy operations for moving TensorHandles on and
// off, and an execution operation for named operations. Often execution will
// simply wrap op execution on one or more physical devices.
//
// device_info is an opaque caller-defined type stored with the custom device
// which is passed to the functions referenced in the TFE_CustomDevice struct

// -----------------------------------------------------------------------------
// Core APIs
// -----------------------------------------------------------------------------

// A TF_ExecutionContext stores knowledge about how to execute an operation.
// E.g. it could know whether we're in eager mode or graph mode, keeps track
// of gradient tapes, etc.
typedef struct TF_ExecutionContext TF_ExecutionContext;

// the same shape, although this requirement may be relaxed in the future.
//
// `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

#include "tensorflow/c/tensor_interface.h"
#include "tensorflow/core/framework/cancellation.h"
#include "tensorflow/core/framework/device_attributes.pb.h"
#include "tensorflow/core/framework/op_def.pb.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/platform/casts.h"
#include "tensorflow/core/platform/status.h"
#include "tensorflow/core/util/managed_stack_trace.h"

struct TFE_Op;

namespace tensorflow {

#include <fstream>
#include <string>

class TempFile : public std::fstream {
 public:
  // We should specify openmode each time we call TempFile.
  TempFile(const std::string& temp_file_name, std::ios::openmode mode);
  TempFile(TempFile&& rhs);
  ~TempFile() override;
  const std::string getName() const;
  bool truncate();

 private:
  const std::string name_;
};

#ifndef TENSORFLOW_C_EAGER_TFE_MONITORING_READER_INTERNAL_H_
#define TENSORFLOW_C_EAGER_TFE_MONITORING_READER_INTERNAL_H_

#include <memory>

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

struct TFE_MonitoringCounterReader {
  explicit TFE_MonitoringCounterReader(const char* name) {
    counter = std::make_unique<

 * parameter specified by `input_index`.
 *
 * I.e. if y = <output of the forward model> and w = inputs[input_index],
 * this will calculate dy/dw numerically.
 *
 * `use_function` indicates whether to use graph mode(true) or eager(false).
 *
 * `numerical_grad` is the pointer to the AbstractTensorHandle* which will
 * hold the numerical gradient data at the end of the function.
 */

#include "tensorflow/c/eager/parallel_device/parallel_device.h"
#include "tensorflow/c/eager/parallel_device/parallel_device_lib.h"
#include "tensorflow/core/platform/test.h"

namespace tensorflow {
namespace parallel_device {

// A helper for performing common operations on variables. A much more
// restricted stand-in for tf.Variable in Python.
class Variable {
 public:
  // Construct a Variable from a resource-dtype TFE_TensorHandle and an