diff --git a/internal/ceres/gradient_checking_cost_function.h b/internal/ceres/gradient_checking_cost_function.h
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+// Ceres Solver - A fast non-linear least squares minimizer
+// Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
+// http://code.google.com/p/ceres-solver/
+//
+// Redistribution and use in source and binary forms, with or without
+// modification, are permitted provided that the following conditions are met:
+//
+// * Redistributions of source code must retain the above copyright notice,
+//   this list of conditions and the following disclaimer.
+// * Redistributions in binary form must reproduce the above copyright notice,
+//   this list of conditions and the following disclaimer in the documentation
+//   and/or other materials provided with the distribution.
+// * Neither the name of Google Inc. nor the names of its contributors may be
+//   used to endorse or promote products derived from this software without
+//   specific prior written permission.
+//
+// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+// POSSIBILITY OF SUCH DAMAGE.
+//
+// Author: keir@google.com (Keir Mierle)
+
+#ifndef CERES_INTERNAL_GRADIENT_CHECKING_COST_FUNCTION_H_
+#define CERES_INTERNAL_GRADIENT_CHECKING_COST_FUNCTION_H_
+
+#include <string>
+
+#include "ceres/cost_function.h"
+
+namespace ceres {
+namespace internal {
+
+class ProblemImpl;
+
+// Creates a CostFunction that checks the jacobians that cost_function computes
+// with finite differences. Bad results are logged; required precision is
+// controlled by relative_precision and the numeric differentiation step size is
+// controlled with relative_step_size. See solver.h for a better explanation of
+// relative_step_size. Caller owns result.
+//
+// The condition enforced is that
+//
+//    (J_actual(i, j) - J_numeric(i, j))
+//   ------------------------------------  <  relative_precision
+//   max(J_actual(i, j), J_numeric(i, j))
+//
+// where J_actual(i, j) is the jacobian as computed by the supplied cost
+// function (by the user) and J_numeric is the jacobian as computed by finite
+// differences.
+//
+// Note: This is quite inefficient and is intended only for debugging.
+CostFunction* CreateGradientCheckingCostFunction(
+    const CostFunction* cost_function,
+    double relative_step_size,
+    double relative_precision,
+    const string& extra_info);
+
+// Create a new ProblemImpl object from the input problem_impl, where
+// each CostFunctions in problem_impl are wrapped inside a
+// GradientCheckingCostFunctions. This gives us a ProblemImpl object
+// which checks its derivatives against estimates from numeric
+// differentiation everytime a ResidualBlock is evaluated.
+//
+// relative_step_size and relative_precision are parameters to control
+// the numeric differentiation and the relative tolerance between the
+// jacobian computed by the CostFunctions in problem_impl and
+// jacobians obtained by numerically differentiating them. For more
+// details see the documentation for
+// CreateGradientCheckingCostFunction above.
+ProblemImpl* CreateGradientCheckingProblemImpl(ProblemImpl* problem_impl,
+                                               double relative_step_size,
+                                               double relative_precision);
+
+}  // namespace internal
+}  // namespace ceres
+
+#endif  // CERES_INTERNAL_GRADIENT_CHECKING_COST_FUNCTION_H_