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#ifndef TENSORFLOW_PYTHON_CLIENT_TF_SESSION_HELPER_H_
#define TENSORFLOW_PYTHON_CLIENT_TF_SESSION_HELPER_H_
#include <Python.h>
#include "numpy/arrayobject.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/lib/gtl/inlined_vector.h"
#include "tensorflow/core/public/status.h"
#include "tensorflow/core/public/tensor_c_api.h"
namespace tensorflow {
// Container types for the various arguments and temporary values used
// in the wrapper.
// A FeedVector is a vector of tensor name and numpy array pairs. The
// name is a borrowed C string.
typedef tensorflow::gtl::InlinedVector<std::pair<const char*, PyArrayObject*>,
8> FeedVector;
// A NameVector is a vector of tensor or operation names, as borrowed
// C strings.
typedef tensorflow::gtl::InlinedVector<const char*, 8> NameVector;
// A PyObjectVector is a vector of borrowed pointers to PyObjects.
typedef tensorflow::gtl::InlinedVector<PyObject*, 8> PyObjectVector;
// Safe containers for (an) owned PyObject(s). On destruction, the
// reference count of the contained object will be decremented.
inline void Py_DECREF_wrapper(PyObject* o) { Py_DECREF(o); }
typedef void (*Py_DECREF_wrapper_type)(PyObject*);
typedef std::unique_ptr<PyObject, Py_DECREF_wrapper_type> Safe_PyObjectPtr;
typedef std::vector<Safe_PyObjectPtr> Safe_PyObjectVector;
Safe_PyObjectPtr make_safe(PyObject* o);
// Run the graph associated with the session starting with the
// supplied inputs[]. Regardless of success of failure, inputs[] are
// stolen by the implementation (i.e. the implementation will
// eventually call Py_DECREF on each array input).
//
// On success, the tensors corresponding to output_names[0,noutputs-1]
// are placed in out_values[], and these outputs[] become the property
// of the caller (the caller must eventually call Py_DECREF on them).
//
// On failure, out_status contains a tensorflow::Status with an error
// message.
void TF_Run_wrapper(TF_Session* session, const FeedVector& inputs,
const NameVector& output_names,
const NameVector& target_nodes, Status* out_status,
PyObjectVector* out_values);
} // namespace tensorflow
#endif // TENSORFLOW_PYTHON_CLIENT_TF_SESSION_HELPER_H_
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