diff --git a/tensorflow/contrib/layers/kernels/sparse_feature_cross_kernel.cc b/tensorflow/contrib/layers/kernels/sparse_feature_cross_kernel.cc
index 219473153bd..72df272af89 100644
--- a/tensorflow/contrib/layers/kernels/sparse_feature_cross_kernel.cc
+++ b/tensorflow/contrib/layers/kernels/sparse_feature_cross_kernel.cc
@@ -41,13 +41,7 @@ class ColumnInterface {
   virtual int64 FeatureCount(int64 batch) const = 0;
 
   // Returns the fingerprint of nth feature from the specified batch.
-  InternalType Feature(int64 batch, int64 n) const {
-    InternalType not_used = InternalType();
-    return DoFeature(batch, n, not_used);
-  }
-
-  virtual InternalType DoFeature(int64 batch, int64 n,
-                                 InternalType not_used) const = 0;
+  virtual InternalType Feature(int64 batch, int64 n) const = 0;
 
   virtual ~ColumnInterface() {}
 };
@@ -68,26 +62,7 @@ class SparseTensorColumn : public ColumnInterface<InternalType> {
     return feature_counts_[batch];
   }
 
-  // InternalType is int64 only when using HashCrosser.
-  int64 DoFeature(int64 batch, int64 n, int64 not_used) const {
-    const int64 start = feature_start_indices_[batch];
-    if (DT_STRING == values_.dtype())
-      return Fingerprint64(values_.vec<string>().data()[start + n]);
-    return values_.vec<int64>().data()[start + n];
-  }
-
-  // InternalType is string or StringPiece when using StringCrosser.
-  string DoFeature(int64 batch, int64 n, string not_used) const {
-    const int64 start = feature_start_indices_[batch];
-    if (DT_STRING == values_.dtype())
-      return values_.vec<string>().data()[start + n];
-    return std::to_string(values_.vec<int64>().data()[start + n]);
-  }
-
-  StringPiece DoFeature(int64 batch, int64 n, StringPiece not_used) const {
-    const int64 start = feature_start_indices_[batch];
-    return values_.vec<string>().data()[start + n];
-  }
+  InternalType Feature(int64 batch, int64 n) const override;
 
   ~SparseTensorColumn() override {}
 
@@ -97,6 +72,31 @@ class SparseTensorColumn : public ColumnInterface<InternalType> {
   std::vector<int64> feature_start_indices_;
 };
 
+// InternalType is int64 only when using HashCrosser.
+template <>
+int64 SparseTensorColumn<int64>::Feature(int64 batch, int64 n) const {
+  const int64 start = feature_start_indices_[batch];
+  if (DT_STRING == values_.dtype())
+    return Fingerprint64(values_.vec<string>().data()[start + n]);
+  return values_.vec<int64>().data()[start + n];
+}
+
+// InternalType is string or StringPiece when using StringCrosser.
+template <>
+string SparseTensorColumn<string>::Feature(int64 batch, int64 n) const {
+  const int64 start = feature_start_indices_[batch];
+  if (DT_STRING == values_.dtype())
+    return values_.vec<string>().data()[start + n];
+  return std::to_string(values_.vec<int64>().data()[start + n]);
+}
+
+template <>
+StringPiece SparseTensorColumn<StringPiece>::Feature(int64 batch,
+                                                     int64 n) const {
+  const int64 start = feature_start_indices_[batch];
+  return values_.vec<string>().data()[start + n];
+}
+
 // A column that is backed by a dense tensor.
 template <typename InternalType>
 class DenseTensorColumn : public ColumnInterface<InternalType> {
@@ -105,22 +105,7 @@ class DenseTensorColumn : public ColumnInterface<InternalType> {
 
   int64 FeatureCount(int64 batch) const override { return tensor_.dim_size(1); }
 
-  // InternalType is int64 only when using HashCrosser.
-  int64 DoFeature(int64 batch, int64 n, int64 not_used) const {
-    if (DT_STRING == tensor_.dtype())
-      return Fingerprint64(tensor_.matrix<string>()(batch, n));
-    return tensor_.matrix<int64>()(batch, n);
-  }
-
-  // Internal type is string or StringPiece when using StringCrosser.
-  string DoFeature(int64 batch, int64 n, string not_used) const {
-    if (DT_STRING == tensor_.dtype()) return tensor_.matrix<string>()(batch, n);
-    return std::to_string(tensor_.matrix<int64>()(batch, n));
-  }
-
-  StringPiece DoFeature(int64 batch, int64 n, StringPiece not_used) const {
-    return tensor_.matrix<string>()(batch, n);
-  }
+  InternalType Feature(int64 batch, int64 n) const override;
 
   ~DenseTensorColumn() override {}
 
@@ -128,6 +113,27 @@ class DenseTensorColumn : public ColumnInterface<InternalType> {
   const Tensor& tensor_;
 };
 
+// InternalType is int64 only when using HashCrosser.
+template <>
+int64 DenseTensorColumn<int64>::Feature(int64 batch, int64 n) const {
+  if (DT_STRING == tensor_.dtype())
+    return Fingerprint64(tensor_.matrix<string>()(batch, n));
+  return tensor_.matrix<int64>()(batch, n);
+}
+
+// Internal type is string or StringPiece when using StringCrosser.
+template <>
+string DenseTensorColumn<string>::Feature(int64 batch, int64 n) const {
+  if (DT_STRING == tensor_.dtype()) return tensor_.matrix<string>()(batch, n);
+  return std::to_string(tensor_.matrix<int64>()(batch, n));
+}
+
+template <>
+StringPiece DenseTensorColumn<StringPiece>::Feature(int64 batch,
+                                                    int64 n) const {
+  return tensor_.matrix<string>()(batch, n);
+}
+
 // Updates Output tensors with sparse crosses.
 template <typename OutType>
 class OutputUpdater {
diff --git a/tensorflow/core/kernels/sparse_cross_op.cc b/tensorflow/core/kernels/sparse_cross_op.cc
index ed93caad331..c7bf250fad7 100644
--- a/tensorflow/core/kernels/sparse_cross_op.cc
+++ b/tensorflow/core/kernels/sparse_cross_op.cc
@@ -41,13 +41,7 @@ class ColumnInterface {
   virtual int64 FeatureCount(int64 batch) const = 0;
 
   // Returns the fingerprint of nth feature from the specified batch.
-  InternalType Feature(int64 batch, int64 n) const {
-    InternalType not_used = InternalType();
-    return DoFeature(batch, n, not_used);
-  }
-
-  virtual InternalType DoFeature(int64 batch, int64 n,
-                                 InternalType not_used) const = 0;
+  virtual InternalType Feature(int64 batch, int64 n) const = 0;
 
   virtual ~ColumnInterface() {}
 };
@@ -68,26 +62,7 @@ class SparseTensorColumn : public ColumnInterface<InternalType> {
     return feature_counts_[batch];
   }
 
-  // InternalType is int64 only when using HashCrosser.
-  int64 DoFeature(int64 batch, int64 n, int64 not_used) const {
-    const int64 start = feature_start_indices_[batch];
-    if (DT_STRING == values_.dtype())
-      return Fingerprint64(values_.vec<string>().data()[start + n]);
-    return values_.vec<int64>().data()[start + n];
-  }
-
-  // InternalType is string or StringPiece when using StringCrosser.
-  string DoFeature(int64 batch, int64 n, string not_used) const {
-    const int64 start = feature_start_indices_[batch];
-    if (DT_STRING == values_.dtype())
-      return values_.vec<string>().data()[start + n];
-    return std::to_string(values_.vec<int64>().data()[start + n]);
-  }
-
-  StringPiece DoFeature(int64 batch, int64 n, StringPiece not_used) const {
-    const int64 start = feature_start_indices_[batch];
-    return values_.vec<string>().data()[start + n];
-  }
+  InternalType Feature(int64 batch, int64 n) const override;
 
   ~SparseTensorColumn() override {}
 
@@ -97,6 +72,31 @@ class SparseTensorColumn : public ColumnInterface<InternalType> {
   std::vector<int64> feature_start_indices_;
 };
 
+// InternalType is int64 only when using HashCrosser.
+template <>
+int64 SparseTensorColumn<int64>::Feature(int64 batch, int64 n) const {
+  const int64 start = feature_start_indices_[batch];
+  if (DT_STRING == values_.dtype())
+    return Fingerprint64(values_.vec<string>().data()[start + n]);
+  return values_.vec<int64>().data()[start + n];
+}
+
+// InternalType is string or StringPiece when using StringCrosser.
+template <>
+string SparseTensorColumn<string>::Feature(int64 batch, int64 n) const {
+  const int64 start = feature_start_indices_[batch];
+  if (DT_STRING == values_.dtype())
+    return values_.vec<string>().data()[start + n];
+  return std::to_string(values_.vec<int64>().data()[start + n]);
+}
+
+template <>
+StringPiece SparseTensorColumn<StringPiece>::Feature(int64 batch,
+                                                     int64 n) const {
+  const int64 start = feature_start_indices_[batch];
+  return values_.vec<string>().data()[start + n];
+}
+
 // A column that is backed by a dense tensor.
 template <typename InternalType>
 class DenseTensorColumn : public ColumnInterface<InternalType> {
@@ -105,22 +105,7 @@ class DenseTensorColumn : public ColumnInterface<InternalType> {
 
   int64 FeatureCount(int64 batch) const override { return tensor_.dim_size(1); }
 
-  // InternalType is int64 only when using HashCrosser.
-  int64 DoFeature(int64 batch, int64 n, int64 not_used) const {
-    if (DT_STRING == tensor_.dtype())
-      return Fingerprint64(tensor_.matrix<string>()(batch, n));
-    return tensor_.matrix<int64>()(batch, n);
-  }
-
-  // Internal type is string or StringPiece when using StringCrosser.
-  string DoFeature(int64 batch, int64 n, string not_used) const {
-    if (DT_STRING == tensor_.dtype()) return tensor_.matrix<string>()(batch, n);
-    return std::to_string(tensor_.matrix<int64>()(batch, n));
-  }
-
-  StringPiece DoFeature(int64 batch, int64 n, StringPiece not_used) const {
-    return tensor_.matrix<string>()(batch, n);
-  }
+  InternalType Feature(int64 batch, int64 n) const override;
 
   ~DenseTensorColumn() override {}
 
@@ -128,6 +113,27 @@ class DenseTensorColumn : public ColumnInterface<InternalType> {
   const Tensor& tensor_;
 };
 
+// InternalType is int64 only when using HashCrosser.
+template <>
+int64 DenseTensorColumn<int64>::Feature(int64 batch, int64 n) const {
+  if (DT_STRING == tensor_.dtype())
+    return Fingerprint64(tensor_.matrix<string>()(batch, n));
+  return tensor_.matrix<int64>()(batch, n);
+}
+
+// Internal type is string or StringPiece when using StringCrosser.
+template <>
+string DenseTensorColumn<string>::Feature(int64 batch, int64 n) const {
+  if (DT_STRING == tensor_.dtype()) return tensor_.matrix<string>()(batch, n);
+  return std::to_string(tensor_.matrix<int64>()(batch, n));
+}
+
+template <>
+StringPiece DenseTensorColumn<StringPiece>::Feature(int64 batch,
+                                                    int64 n) const {
+  return tensor_.matrix<string>()(batch, n);
+}
+
 // Updates Output tensors with sparse crosses.
 template <typename OutType>
 class OutputUpdater {