TF_CAPI_EXPORT extern void TF_CreateDir(const char* dirname, TF_Status* status);

// Deletes the specified directory. Typical status codes are:
//  * TF_OK - successfully deleted the directory
//  * TF_FAILED_PRECONDITION - the directory is not empty
TF_CAPI_EXPORT extern void TF_DeleteDir(const char* dirname, TF_Status* status);

// Deletes the specified directory and all subdirectories and files underneath

//   * a non-null value pointing to the added operation is returned --
//     this value is valid until the underlying graph is deleted.
// Otherwise:
//   * *status is set to a non-OK value,
//   * the graph is not modified,
//   * a null value is returned.
// In either case, it deletes `desc`.
TF_CAPI_EXPORT extern TF_Operation* TF_FinishOperation(
    TF_OperationDescription* desc, TF_Status* status);

// that produces `handle` has completed.
TF_CAPI_EXPORT extern TFE_TensorDebugInfo* TFE_TensorHandleTensorDebugInfo(
    TFE_TensorHandle* h, TF_Status* status);

// Deletes `debug_info`.
TF_CAPI_EXPORT extern void TFE_DeleteTensorDebugInfo(
    TFE_TensorDebugInfo* debug_info);

// Returns the number of dimensions used to represent the tensor on its device.

              delete reinterpret_cast<DataType*>(data);
            },
            nullptr),
        TF_DeleteTensor);
    // TODO(allenl): Here and when executing regular operations, we could hold
    // on to one TFE_Op per device and just call TFE_ResetOp to avoid parsing
    // device names repeatedly.
    std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> const_op(

	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();

    TFE_Context* context,
    const std::array<TFE_TensorHandle*, num_replicas>& components,
    const char* device, TF_Status* status) {
  std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> op(
      TFE_NewOp(context, "TPUReplicatedInput", status), TFE_DeleteOp);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  TFE_OpSetAttrInt(op.get(), "N", num_replicas);
  for (int i = 0; i < num_replicas; ++i) {

//
// WARNING: Since the underlying object could be ref-counted a user of this
// interface cannot destruct the underlying operation object. Instead, call
// TFE_DeleteOp who calls Release() on the operation pointer and deletes
// the TFE_Op structure.
typedef struct TFE_Op TFE_Op;

namespace tensorflow {

DEFINE_CONVERSION_FUNCTIONS(tensorflow::ImmediateExecutionOperation, TFE_Op);

//
// WARNING: Since the underlying object could be ref-counted a user of this
// interface cannot destruct the underlying handle object. Instead, call
// TFE_DeleteTensorHandle who calls Release() on the handle pointer and deletes
// the TFE_TensorHandle structure.
typedef struct TFE_TensorHandle TFE_TensorHandle;

namespace tensorflow {

DEFINE_CONVERSION_FUNCTIONS(tensorflow::ImmediateExecutionTensorHandle,

//
// WARNING: Since the underlying object could be ref-counted a user of this
// interface cannot destruct the underlying context object. Instead, call
// TFE_DeleteContext who calls Release() on the context pointer and deletes
// the TFE_Context structure.
typedef struct TFE_Context TFE_Context;

namespace tensorflow {

DEFINE_CONVERSION_FUNCTIONS(tensorflow::ImmediateExecutionContext, TFE_Context);

}  // namespace tensorflow

};

// If `persistent_tape` is true, op_tape is not changed and none of the
// backwards functions are deleted.
// If `persistent_tape` is false, op_tape is cleared and backwards functions
// not needed for gradient computation are deleted. Backwards functions that
// are needed, are copied and returned in BackpropInitialState.
template <typename BackwardFunction, typename TapeTensor>