virtual std::string run() { return "Callback::run"; }
};

class Caller {
private:
	Callback *callback_;
public:
	Caller(): callback_(0) { }
	~Caller() { delCallback(); }
	void delCallback() { delete callback_; callback_ = 0; }
	void setCallback(Callback *cb) { delCallback(); callback_ = cb; }
	std::string call();

// of these entries could not be deleted.
//
// Typical status codes:
//  * TF_OK - dirname exists and we were able to delete everything underneath
//  * TF_NOT_FOUND - dirname doesn't exist
//  * TF_PERMISSION_DENIED - dirname or some descendant is not writable
//  * TF_UNIMPLEMENTED - some underlying functions (like Delete) are not
//    implemented
TF_CAPI_EXPORT extern void TF_DeleteRecursively(const char* dirname,

        TF_NewTensor(
            datatype_enum, /*dims=*/nullptr, /*num_dims=*/0,
            device_value.release(), sizeof(DataType),
            [](void* data, size_t, void* arg) {
              delete reinterpret_cast<DataType*>(data);
            },
            nullptr),
        TF_DeleteTensor);
    // TODO(allenl): Here and when executing regular operations, we could hold

// Retrieves the full name of the device (e.g. /job:worker/replica:0/...)
// The return value will be a pointer to a null terminated string. The caller
// must not modify or delete the string. It will be deallocated upon a call to
// TF_DeleteDeviceList.
//
// If index is out of bounds, an error code will be set in the status object,
// and a null pointer will be returned.

  std::unique_ptr<TensorSliceReader::VarToShapeMap> var_to_shape_map_;
  std::unique_ptr<TensorSliceReader::VarToDataTypeMap> var_to_data_type_map_;

  CheckpointReader(const CheckpointReader&) = delete;
  void operator=(const CheckpointReader&) = delete;
};

}  // namespace checkpoint
}  // namespace tensorflow

  if (op_id == -1) {
    // Do not delete watched tensors.
    return;
  }
  tensor_tape_.erase(tensor_op_it);
  auto op_it = op_tape_.find(op_id);
  CHECK(op_it != op_tape_.end());
  for (const auto& output : op_it->second.output_tensor_info) {
    if (tensor_usage_.find(output.GetID()) != tensor_usage_.end()) {
      // Found a usage for an output, so cannot delete the op.
      return;
    }
  }

  // TF_Graph may only / must be deleted when
  //   sessions.size() == 0 && delete_requested == true
  //
  // TODO(b/74949947): mutations currently trigger a warning instead of a bad
  // status, this should be reverted when possible.
  tensorflow::gtl::FlatMap<TF_Session*, tensorflow::string> sessions
      TF_GUARDED_BY(mu);
  bool delete_requested TF_GUARDED_BY(mu);  // set true by TF_DeleteGraph

      return;
    }
    absl::MutexLock lock(&mu_);
    InsertLocked(key, value);
  }

  // Delete the entry with key `key`. Return true if the entry was found for
  // `key`, false if the entry was not found. In both cases, there is no entry
  // with key `key` existed after the call.
  bool Delete(const std::string& key) {
    absl::MutexLock lock(&mu_);
    return DeleteLocked(key);
  }

typedef struct TF_GraphProperties TF_GraphProperties;

// Create GraphProperties. The item must outlive the properties.
TF_CAPI_EXPORT extern TF_GraphProperties* TF_NewGraphProperties(
    const TF_GrapplerItem* item);

// Delete GraphProperties.
TF_CAPI_EXPORT extern void TF_DeleteGraphProperties(
    TF_GraphProperties* graph_properties);

// Infer tensor shapes through abstract interpretation.

  void (*execute)(const TFE_Op* op, int* num_outputs,
                  TFE_TensorHandle** outputs, TF_Status* s, void* device_info);

  // Method to delete a device.
  void (*delete_device)(void* device_info);

  // Implements TFE_CreatePackedTensorHandle when one of `handles` is on this
  // custom device.
  //
  // Many devices will want to simply return an "unimplemented" status