ABSL_EXCLUSIVE_LOCKS_REQUIRED(mu_) {
    auto it = cache_.find(key);
    if (it == cache_.end()) {
      return false;
    }
    lru_list_.erase(it->second.lru_iterator);
    if (timer_seconds_() - it->second.timestamp > max_age_) {
      cache_.erase(it);
      return false;
    }
    *value = it->second.value;
    lru_list_.push_front(it->first);
    it->second.lru_iterator = lru_list_.begin();
    return true;

// 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;

// A TF_AbstractTensor is an input to an operation. E.g. it could be a union
// type of eager and graph tensors. It is also the result of executing an
// operation.

  virtual ImmediateExecutionContext* GetContext() const = 0;

  // Following two methods are used to support custom device.
  // Return true if the inputs contain custom device tensor handle. It means
  // that the argument need to be handled by a custom device.
  virtual bool HasCustomDeviceInput() const = 0;

  virtual const tensorflow::OpDef* OpDef() const = 0;

// caller is responsible for freeing it (see TF_CloseWritableFile).
TF_CAPI_EXPORT extern void TF_NewWritableFile(const char* filename,
                                              TF_WritableFileHandle** handle,
                                              TF_Status* status);

// Closes the given handle and frees its memory. If there was a problem closing
// the file, it is indicated by status. Memory is freed in any case.

  // clients from directly destroying this object since it may manage its own
  // lifetime through ref counting. Thus clients MUST call Release() in order to
  // destroy an instance of this class.
  virtual void Release() = 0;

  // Creates an operation builder and ties it to this context.
  // The returned object can be used for setting operation's attributes,

  // Helper for constructing a resource handle and wrapping it in a `Variable`
  // object.
  static Variable* Create(TFE_Context* context, TF_DataType type,
                          const int64_t* dims, const int num_dims,
                          const char* device, TF_Status* status);
  // Dereferences the backing buffer for the variable. Note that since this can
  // fail (it runs operations), it must be called explicitly and the resulting

                   const char* device_type);

// Create a ServerDef with the given `job_name` and add `num_tasks` tasks in it.
tensorflow::ServerDef GetServerDef(const tensorflow::string& job_name,
                                   int num_tasks);

// Create a ServerDef with job name "localhost" and add `num_tasks` tasks in it.
tensorflow::ServerDef GetServerDef(int num_tasks);

// and `underlying_devices` is
//   {"/job:localhost/replica:0/task:0/device:GPU:0",
//    "/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.
//

// just nice to know that it compiles.
void* create(TF_OpKernelConstruction* ctx) {
  TF_DataType type;
  TF_Status* s = TF_NewStatus();
  TF_OpKernelConstruction_GetAttrType(ctx, "foobar", &type, s);
  TF_DeleteStatus(s);
  return NULL;
}

// A compute function. This will never actually get called in this test, it's
// just nice to know that it compiles.

  // Release any underlying resources, including the interface object.
  //
  // WARNING: The destructor of this class is marked as protected to disallow
  // clients from directly destroying this object since it may manage it's own
  // lifetime through ref counting. Thus this must be allocated on the heap and
  // clients MUST call Release() in order to destroy an instance of this class.
  virtual void Release() = 0;