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diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorAssign.h b/unsupported/Eigen/CXX11/src/Tensor/TensorAssign.h
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+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorAssign.h
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+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// 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_CXX11_TENSOR_TENSOR_ASSIGN_H
+#define EIGEN_CXX11_TENSOR_TENSOR_ASSIGN_H
+
+namespace Eigen {
+
+/** \class TensorAssign
+  * \ingroup CXX11_Tensor_Module
+  *
+  * \brief The tensor assignment class.
+  *
+  * This class is represents the assignment of the values resulting from the evaluation of
+  * the rhs expression to the memory locations denoted by the lhs expression.
+  */
+namespace internal {
+template<typename LhsXprType, typename RhsXprType>
+struct traits<TensorAssignOp<LhsXprType, RhsXprType> >
+{
+  typedef typename LhsXprType::Scalar Scalar;
+  typedef typename traits<LhsXprType>::StorageKind StorageKind;
+  typedef typename promote_index_type<typename traits<LhsXprType>::Index,
+                                      typename traits<RhsXprType>::Index>::type Index;
+  typedef typename LhsXprType::Nested LhsNested;
+  typedef typename RhsXprType::Nested RhsNested;
+  typedef typename remove_reference<LhsNested>::type _LhsNested;
+  typedef typename remove_reference<RhsNested>::type _RhsNested;
+  static const std::size_t NumDimensions = internal::traits<LhsXprType>::NumDimensions;
+  static const int Layout = internal::traits<LhsXprType>::Layout;
+
+  enum {
+    Flags = 0
+  };
+};
+
+template<typename LhsXprType, typename RhsXprType>
+struct eval<TensorAssignOp<LhsXprType, RhsXprType>, Eigen::Dense>
+{
+  typedef const TensorAssignOp<LhsXprType, RhsXprType>& type;
+};
+
+template<typename LhsXprType, typename RhsXprType>
+struct nested<TensorAssignOp<LhsXprType, RhsXprType>, 1, typename eval<TensorAssignOp<LhsXprType, RhsXprType> >::type>
+{
+  typedef TensorAssignOp<LhsXprType, RhsXprType> type;
+};
+
+}  // end namespace internal
+
+
+
+template<typename LhsXprType, typename RhsXprType>
+class TensorAssignOp : public TensorBase<TensorAssignOp<LhsXprType, RhsXprType> >
+{
+  public:
+  typedef typename Eigen::internal::traits<TensorAssignOp>::Scalar Scalar;
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+  typedef typename LhsXprType::CoeffReturnType CoeffReturnType;
+  typedef typename Eigen::internal::nested<TensorAssignOp>::type Nested;
+  typedef typename Eigen::internal::traits<TensorAssignOp>::StorageKind StorageKind;
+  typedef typename Eigen::internal::traits<TensorAssignOp>::Index Index;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorAssignOp(LhsXprType& lhs, const RhsXprType& rhs)
+      : m_lhs_xpr(lhs), m_rhs_xpr(rhs) {}
+
+    /** \returns the nested expressions */
+    EIGEN_DEVICE_FUNC
+    typename internal::remove_all<typename LhsXprType::Nested>::type&
+    lhsExpression() const { return *((typename internal::remove_all<typename LhsXprType::Nested>::type*)&m_lhs_xpr); }
+
+    EIGEN_DEVICE_FUNC
+    const typename internal::remove_all<typename RhsXprType::Nested>::type&
+    rhsExpression() const { return m_rhs_xpr; }
+
+  protected:
+    typename internal::remove_all<typename LhsXprType::Nested>::type& m_lhs_xpr;
+    const typename internal::remove_all<typename RhsXprType::Nested>::type& m_rhs_xpr;
+};
+
+
+template<typename LeftArgType, typename RightArgType, typename Device>
+struct TensorEvaluator<const TensorAssignOp<LeftArgType, RightArgType>, Device>
+{
+  typedef TensorAssignOp<LeftArgType, RightArgType> XprType;
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
+  typedef typename TensorEvaluator<RightArgType, Device>::Dimensions Dimensions;
+  static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
+
+  enum {
+    IsAligned = TensorEvaluator<LeftArgType, Device>::IsAligned & TensorEvaluator<RightArgType, Device>::IsAligned,
+    PacketAccess = TensorEvaluator<LeftArgType, Device>::PacketAccess & TensorEvaluator<RightArgType, Device>::PacketAccess,
+    Layout = TensorEvaluator<LeftArgType, Device>::Layout,
+    RawAccess = TensorEvaluator<LeftArgType, Device>::RawAccess
+  };
+
+  EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const Device& device) :
+      m_leftImpl(op.lhsExpression(), device),
+      m_rightImpl(op.rhsExpression(), device)
+  {
+    EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<LeftArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<RightArgType, Device>::Layout)), YOU_MADE_A_PROGRAMMING_MISTAKE);
+  }
+
+  EIGEN_DEVICE_FUNC const Dimensions& dimensions() const
+  {
+    // The dimensions of the lhs and the rhs tensors should be equal to prevent
+    // overflows and ensure the result is fully initialized.
+    // TODO: use left impl instead if right impl dimensions are known at compile time.
+    return m_rightImpl.dimensions();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar*) {
+    eigen_assert(dimensions_match(m_leftImpl.dimensions(), m_rightImpl.dimensions()));
+    m_leftImpl.evalSubExprsIfNeeded(NULL);
+    // If the lhs provides raw access to its storage area (i.e. if m_leftImpl.data() returns a non
+    // null value), attempt to evaluate the rhs expression in place. Returns true iff in place
+    // evaluation isn't supported and the caller still needs to manually assign the values generated
+    // by the rhs to the lhs.
+    return m_rightImpl.evalSubExprsIfNeeded(m_leftImpl.data());
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
+    m_leftImpl.cleanup();
+    m_rightImpl.cleanup();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalScalar(Index i) {
+    m_leftImpl.coeffRef(i) = m_rightImpl.coeff(i);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalPacket(Index i) {
+    const int LhsStoreMode = TensorEvaluator<LeftArgType, Device>::IsAligned ? Aligned : Unaligned;
+    const int RhsLoadMode = TensorEvaluator<RightArgType, Device>::IsAligned ? Aligned : Unaligned;
+    m_leftImpl.template writePacket<LhsStoreMode>(i, m_rightImpl.template packet<RhsLoadMode>(i));
+  }
+  EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
+  {
+    return m_leftImpl.coeff(index);
+  }
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const
+  {
+    return m_leftImpl.template packet<LoadMode>(index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
+  costPerCoeff(bool vectorized) const {
+    // We assume that evalPacket or evalScalar is called to perform the
+    // assignment and account for the cost of the write here, but reduce left
+    // cost by one load because we are using m_leftImpl.coeffRef.
+    TensorOpCost left = m_leftImpl.costPerCoeff(vectorized);
+    return m_rightImpl.costPerCoeff(vectorized) +
+           TensorOpCost(
+               numext::maxi(0.0, left.bytes_loaded() - sizeof(CoeffReturnType)),
+               left.bytes_stored(), left.compute_cycles()) +
+           TensorOpCost(0, sizeof(CoeffReturnType), 0, vectorized, PacketSize);
+  }
+
+  /// required by sycl in order to extract the accessor
+  const TensorEvaluator<LeftArgType, Device>& left_impl() const { return m_leftImpl; }
+  /// required by sycl in order to extract the accessor
+  const TensorEvaluator<RightArgType, Device>& right_impl() const { return m_rightImpl; }
+
+  EIGEN_DEVICE_FUNC CoeffReturnType* data() const { return m_leftImpl.data(); }
+
+ private:
+  TensorEvaluator<LeftArgType, Device> m_leftImpl;
+  TensorEvaluator<RightArgType, Device> m_rightImpl;
+};
+
+}
+
+
+#endif // EIGEN_CXX11_TENSOR_TENSOR_ASSIGN_H