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diff --git a/unsupported/Eigen/src/AutoDiff/AutoDiffJacobian.h b/unsupported/Eigen/src/AutoDiff/AutoDiffJacobian.h
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+++ b/unsupported/Eigen/src/AutoDiff/AutoDiffJacobian.h
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+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_AUTODIFF_JACOBIAN_H
+#define EIGEN_AUTODIFF_JACOBIAN_H
+
+namespace Eigen
+{
+
+template<typename Functor> class AutoDiffJacobian : public Functor
+{
+public:
+  AutoDiffJacobian() : Functor() {}
+  AutoDiffJacobian(const Functor& f) : Functor(f) {}
+
+  // forward constructors
+  template<typename T0>
+  AutoDiffJacobian(const T0& a0) : Functor(a0) {}
+  template<typename T0, typename T1>
+  AutoDiffJacobian(const T0& a0, const T1& a1) : Functor(a0, a1) {}
+  template<typename T0, typename T1, typename T2>
+  AutoDiffJacobian(const T0& a0, const T1& a1, const T2& a2) : Functor(a0, a1, a2) {}
+
+  enum {
+    InputsAtCompileTime = Functor::InputsAtCompileTime,
+    ValuesAtCompileTime = Functor::ValuesAtCompileTime
+  };
+
+  typedef typename Functor::InputType InputType;
+  typedef typename Functor::ValueType ValueType;
+  typedef typename Functor::JacobianType JacobianType;
+  typedef typename JacobianType::Scalar Scalar;
+  typedef typename JacobianType::Index Index;
+
+  typedef Matrix<Scalar,InputsAtCompileTime,1> DerivativeType;
+  typedef AutoDiffScalar<DerivativeType> ActiveScalar;
+
+
+  typedef Matrix<ActiveScalar, InputsAtCompileTime, 1> ActiveInput;
+  typedef Matrix<ActiveScalar, ValuesAtCompileTime, 1> ActiveValue;
+
+  void operator() (const InputType& x, ValueType* v, JacobianType* _jac=0) const
+  {
+    eigen_assert(v!=0);
+    if (!_jac)
+    {
+      Functor::operator()(x, v);
+      return;
+    }
+
+    JacobianType& jac = *_jac;
+
+    ActiveInput ax = x.template cast<ActiveScalar>();
+    ActiveValue av(jac.rows());
+
+    if(InputsAtCompileTime==Dynamic)
+      for (Index j=0; j<jac.rows(); j++)
+        av[j].derivatives().resize(this->inputs());
+
+    for (Index i=0; i<jac.cols(); i++)
+      ax[i].derivatives() = DerivativeType::Unit(this->inputs(),i);
+
+    Functor::operator()(ax, &av);
+
+    for (Index i=0; i<jac.rows(); i++)
+    {
+      (*v)[i] = av[i].value();
+      jac.row(i) = av[i].derivatives();
+    }
+  }
+protected:
+
+};
+
+}
+
+#endif // EIGEN_AUTODIFF_JACOBIAN_H