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diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Attention.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Attention.h
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+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 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_NEURAL_NETWORKS_ATTENTION_H
+#define EIGEN_CXX11_NEURAL_NETWORKS_ATTENTION_H
+
+namespace Eigen {
+
+/** ExtractGlimpses
+  * \ingroup CXX11_NeuralNetworks_Module
+  *
+  * \brief Extract glimpses from an input tensor.
+  *
+  * The input parameter is expected to be a col-major tensor with a rank of 4 (depth, x, y, and batch).
+  * The width and height parameters specify the extension of the returned glimpses.
+  * The offsets parameter specifies the x, y locations of the center of the glimpses relative to the center of the input image. The vector is expected to contain one IndexPair for each image in the batch dimension.
+  * The normalized boolean indicates if incoming coordinates are normalized so that 0.0 and 1.0 correspond to the minimum and maximum of each height and width dimension.
+  * The centered boolean indicates if incoming coordinates are centered relative to the image, in which case -1.0 and 1.0 correspond to minimum and maximum of each dimension while 0.0 corresponds to the center.
+  *
+  * The result can be assigned to a tensor of rank equal to that of the input. The result will be laid out in col-major order (depth, x, y, batch).
+  * The dimensions of the result will be equal to the dimensions of the input except for width and height which will be equal to the requested glimpse size.
+  */
+namespace {
+template <typename Index>
+struct GlimpseExtractionOp {
+  GlimpseExtractionOp(const Index width, const Index height,
+                      const std::vector<IndexPair<float> >& offsets,
+                      const bool normalized,
+                      const bool centered,
+                      const bool uniform_noise) :
+      width_(width), height_(height), offsets_(offsets),
+      normalized_(normalized), centered_(centered), uniform_noise_(uniform_noise) { }
+
+  template <typename Input>
+  DSizes<Index, 4> dimensions(const Input& input) const {
+    typedef typename internal::traits<Input>::Index IndexType;
+    typedef TensorRef<Tensor<typename internal::traits<Input>::Scalar, 4,
+                             internal::traits<Input>::Layout, IndexType> > Ref;
+    Ref in(input);
+
+    DSizes<Index, 4> dims = in.dimensions();
+
+    dims[0] = in.dimension(0);
+    dims[1] = width_;
+    dims[2] = height_;
+    dims[3] = in.dimension(3);
+    return dims;
+  }
+
+  template <typename Input, typename Output, typename Device>
+  EIGEN_DEVICE_FUNC
+  void eval(const Input& input, Output& output, const Device& device) const
+  {
+    typedef typename internal::traits<Input>::Index IndexType;
+    typedef TensorRef<Tensor<typename internal::traits<Input>::Scalar, 4,
+                             internal::traits<Input>::Layout, IndexType> > Ref;
+    Ref in(input);
+
+    const Index num_channels = in.dimension(0);
+    const Index input_width = in.dimension(1);
+    const Index input_height = in.dimension(2);
+    const Index batch_size = in.dimension(3);
+    eigen_assert(input_width > 0);
+    eigen_assert(input_height > 0);
+
+    for (Index i = 0; i < batch_size; ++i) {
+      float x = offsets_[i].first, y = offsets_[i].second;
+
+      // Un-normalize coordinates back to pixel space if normalized.
+      if (normalized_) {
+        x *= input_width;
+        y *= input_height;
+      }
+      // Un-center if coordinates are centered on the image center.
+      if (centered_) {
+        x /= 2.0f;
+        y /= 2.0f;
+        x += input_width / 2.0f;
+        y += input_height / 2.0f;
+      }
+      // Remove half of the glimpse window.
+      x -= width_ / 2.0f;
+      y -= height_ / 2.0f;
+
+      const Index offset_x = (Index) x;
+      const Index offset_y = (Index) y;
+      Index glimpse_width = width_;
+      Index glimpse_height = height_;
+      bool partial_overlap = false;
+      DSizes<Index, 3> slice_offset(0, offset_x, offset_y);
+      DSizes<Index, 3> slice_extent(num_channels, width_, height_);
+      DSizes<Index, 3> base_offset(0, 0, 0);
+
+      if (offset_x < 0) {
+        slice_offset[1] = 0;
+        glimpse_width = (std::max<Index>)(0, width_ + offset_x);
+        slice_extent[1] = glimpse_width;
+        base_offset[1] = width_ - glimpse_width;
+        partial_overlap = true;
+      } else if (offset_x + width_ >= input_width) {
+        glimpse_width = (std::max<Index>)(0, input_width - offset_x);
+        slice_extent[1] = glimpse_width;
+        partial_overlap = true;
+      }
+      if (offset_y < 0) {
+        slice_offset[2] = 0;
+        glimpse_height = (std::max<Index>)(0, height_ + offset_y);
+        slice_extent[2] = glimpse_height;
+        base_offset[2] = height_ - glimpse_height;
+        partial_overlap = true;
+      } else if (offset_y + height_ >= input_height) {
+        glimpse_height = (std::max<Index>)(0, input_height - offset_y);
+        slice_extent[2] = glimpse_height;
+        partial_overlap = true;
+      }
+      slice_extent[1] = std::min<Index>(input_width, slice_extent[1]);
+      slice_extent[2] = std::min<Index>(input_height, slice_extent[2]);
+
+      if (partial_overlap) {
+        if (uniform_noise_) {
+          // Initialize the glimpse with uniform noise.
+          typedef typename internal::remove_const<
+            typename internal::traits<Input>::Scalar>::type Scalar;
+          TensorFixedSize<Scalar, Sizes<> > mini;
+          mini.device(device) = input.template chip<3>(i).minimum();
+          TensorFixedSize<float, Sizes<> > range;
+          range.device(device) =
+              (input.template chip<3>(i).maximum() - mini).template cast<float>();
+
+          DSizes<Index, 3> glimpse_size(num_channels, width_, height_);
+          TensorMap<Tensor<float, 3> > tmp(NULL, glimpse_size);
+          output.template chip<3>(i).device(device) =
+              mini.reshape(Sizes<1,1,1>()).broadcast(glimpse_size) +
+              (tmp.random() * range.reshape(Sizes<1,1,1>()).broadcast(glimpse_size)).template cast<Scalar>();
+        } else {
+          // Initialize the glimpse with white noise: compute the mean and sigma
+          // of each channel, and use them to shape the gaussian.
+          DSizes<Index, 2> glimpse_size(width_, height_);
+          DSizes<Index, 2> input_size(input_width, input_height);
+          typedef typename internal::remove_const<
+            typename internal::traits<Input>::Scalar>::type Scalar;
+
+          for (int j = 0; j < num_channels; ++j) {
+            TensorFixedSize<Scalar, Sizes<> > mean;
+            mean.device(device) = input.template chip<3>(i).template chip<0>(j).template cast<float>().mean();
+            TensorFixedSize<float, Sizes<> > sigma;
+            sigma.device(device) =
+                (input.template chip<3>(i).template chip<0>(j).template cast<float>() - mean.reshape(Sizes<1,1>()).broadcast(input_size)).square().mean().sqrt();
+            TensorFixedSize<Scalar, Sizes<> > mini;
+            mini.device(device) = input.template chip<3>(i).template chip<0>(j).minimum();
+            TensorFixedSize<float, Sizes<> > maxi;
+            maxi.device(device) = input.template chip<3>(i).template chip<0>(j).maximum();
+
+            TensorMap<Tensor<float, 2> > tmp(NULL, glimpse_size);
+            output.template chip<3>(i).template chip<0>(j).device(device) =
+                (mean.reshape(Sizes<1,1>()).broadcast(glimpse_size) +
+                 (tmp.random(internal::NormalRandomGenerator<float>()) * sigma.reshape(Sizes<1,1>()).broadcast(glimpse_size)).template cast<Scalar>()).cwiseMin(maxi.reshape(Sizes<1,1>()).broadcast(glimpse_size)).cwiseMax(mini.reshape(Sizes<1,1>()).broadcast(glimpse_size));
+          }
+        }
+
+        // Copy the part of the glimpse that cover the input image if any.
+        if (glimpse_width == 0 || glimpse_height == 0) {
+          continue;
+        }
+        output.template chip<3>(i).slice(base_offset, slice_extent).device(device) = input.template chip<3>(i).slice(slice_offset, slice_extent);
+      } else {
+        output.template chip<3>(i).device(device) = input.template chip<3>(i).slice(slice_offset, slice_extent);
+      }
+    }
+  }
+
+ private:
+  const Index width_;
+  const Index height_;
+  const std::vector<IndexPair<float> > offsets_;
+  const bool normalized_;
+  const bool centered_;
+  const bool uniform_noise_;
+};
+}
+
+
+template <typename Input>
+EIGEN_ALWAYS_INLINE
+static const TensorCustomUnaryOp<const GlimpseExtractionOp<typename internal::traits<Input>::Index>, const Input>
+ExtractGlimpses(const Input& input,
+                const typename internal::traits<Input>::Index width,
+                const typename internal::traits<Input>::Index height,
+                const std::vector<IndexPair<float> >& offsets,
+                const bool normalized = true, const bool centered = true,
+                const bool uniform_noise = true)
+{
+  EIGEN_STATIC_ASSERT(internal::traits<Input>::Layout == ColMajor, YOU_MADE_A_PROGRAMMING_MISTAKE);
+  EIGEN_STATIC_ASSERT(internal::traits<Input>::NumDimensions == 4, YOU_MADE_A_PROGRAMMING_MISTAKE);
+
+  typedef typename internal::traits<Input>::Index Index;
+  const GlimpseExtractionOp<Index> op(width, height, offsets, normalized,
+                                      centered, uniform_noise);
+  return input.customOp(op);
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_CXX11_NEURAL_NETWORKS_ATTENTION_H