diff --git a/dlib/svm/cross_validate_graph_labeling_trainer_abstract.h b/dlib/svm/cross_validate_graph_labeling_trainer_abstract.h
index 139597f9c..d075f9c52 100644
--- a/dlib/svm/cross_validate_graph_labeling_trainer_abstract.h
+++ b/dlib/svm/cross_validate_graph_labeling_trainer_abstract.h
@@ -1,2 +1,82 @@
+// Copyright (C) 2012  Davis E. King (davis@dlib.net)
+// License: Boost Software License   See LICENSE.txt for the full license.
+#undef DLIB_CROSS_VALIDATE_GRAPh_LABELING_TRAINER_ABSTRACT_H__
+#ifdef DLIB_CROSS_VALIDATE_GRAPh_LABELING_TRAINER_ABSTRACT_H__
+
+#include "../array/array_kernel_abstract.h"
+#include <vector>
+#include "../matrix/matrix_abstract.h"
+
+namespace dlib
+{
+
+// ----------------------------------------------------------------------------------------
+
+    template <
+        typename graph_labeler,
+        typename graph_type
+        >
+    matrix<double,1,2> test_graph_labeling_function (
+        const graph_labeler& labeler,
+        const dlib::array<graph_type>& samples,
+        const std::vector<std::vector<node_label> >& labels
+    );
+    /*!
+        requires
+            - is_graph_labeling_problem(samples,labels) == true
+            - graph_labeler == an object with an interface compatible with the
+              dlib::graph_labeler object.
+            - the following must be a valid expression: labeler(samples[0]);
+        ensures
+            - This function tests the accuracy of the given graph labeler against
+              the sample graphs and their associated labels.  In particular, this
+              function returns a matrix R such that:
+                - R(0) == The fraction of nodes which are supposed to have a label of
+                  true that are labeled as such by the labeler.
+                - R(1) == The fraction of nodes which are supposed to have a label of
+                  false that are labeled as such by the labeler.
+              Therefore, if R is [1,1] then the labeler makes perfect predictions while
+              an R of [0,0] indicates that it gets everything wrong.
+    !*/
+
+// ----------------------------------------------------------------------------------------
+
+    template <
+        typename trainer_type,
+        typename graph_type
+        >
+    matrix<double,1,2> cross_validate_graph_labeling_trainer (
+        const trainer_type& trainer,
+        const dlib::array<graph_type>& samples,
+        const std::vector<std::vector<node_label> >& labels,
+        const long folds
+    );
+    /*!
+        requires
+            - is_graph_labeling_problem(samples,labels) == true
+            - 1 < folds <= samples.size()
+            - trainer_type == an object which trains some kind of graph labeler object
+              (e.g. structural_graph_labeling_trainer)
+        ensures
+            - performs k-fold cross validation by using the given trainer to solve the
+              given graph labeling problem for the given number of folds.  Each fold 
+              is tested using the output of the trainer and the average classification 
+              accuracy from all folds is returned.  In particular, this function returns 
+              a matrix R such that:
+                - R(0) == The fraction of nodes which are supposed to have a label of
+                  true that are labeled as such by the learned labeler.
+                - R(1) == The fraction of nodes which are supposed to have a label of
+                  false that are labeled as such by the learned labeler.
+              Therefore, if R is [1,1] then the labeler makes perfect predictions while
+              an R of [0,0] indicates that it gets everything wrong.
+            - The number of folds used is given by the folds argument.
+    !*/
+
+// ----------------------------------------------------------------------------------------
+
+}
+
+#endif // DLIB_CROSS_VALIDATE_GRAPh_LABELING_TRAINER_ABSTRACT_H__
+