From f6e7205516eeb54d3e85b5d283c2d7a387ec81bc Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Wed, 23 Mar 2016 13:13:09 -0800
Subject: Add dependency for contrib kernels. Change: 117967381

---
 .gitmodules                                                | 2 +-
 tensorflow/contrib/BUILD                                   | 4 ++--
 tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py | 8 +++++---
 3 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/.gitmodules b/.gitmodules
index 1b17ea57b9..0edca21239 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -1,3 +1,3 @@
 [submodule "google/protobuf"]
 	path = google/protobuf
-	url = https://github.com/google/protobuf.git
+	url = https://github.googlesource.com/google/protobuf.git
diff --git a/tensorflow/contrib/BUILD b/tensorflow/contrib/BUILD
index 708cfddefc..ece038a572 100644
--- a/tensorflow/contrib/BUILD
+++ b/tensorflow/contrib/BUILD
@@ -11,7 +11,7 @@ py_library(
     name = "contrib_py",
     srcs = glob(["**/*.py"]),
     srcs_version = "PY2AND3",
-    visibility = ["//tensorflow:internal"],
+    visibility = ["//visibility:public"],
     deps = [
         "//tensorflow/contrib/ctc:ctc_py",
         "//tensorflow/contrib/distributions:distributions_py",
@@ -28,7 +28,7 @@ cc_library(
     name = "contrib_kernels",
     visibility = ["//visibility:public"],
     deps = [
-        "//tensorflow/contrib/linear_optimizer/kernels:sdca_ops",
+        "//tensorflow/contrib/linear_optimizer:sdca_op_kernels",
     ],
 )
 
diff --git a/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py b/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py
index a54b631ef2..06c986beb3 100644
--- a/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py
+++ b/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py
@@ -224,14 +224,16 @@ class SdcaModel(object):
         fv = array_ops.reshape(st_i.values, [-1])
         # TODO(sibyl-Aix6ihai): This does not work if examples have empty features.
         result += math_ops.segment_sum(
-            math_ops.mul(
-                array_ops.gather(sv, fi), fv), array_ops.reshape(ei, [-1]))
+            math_ops.mul(array_ops.gather(sv, fi), fv), ei)
       dense_features = self._convert_n_to_tensor(examples['dense_features'])
       dense_variables = self._convert_n_to_tensor(self._variables[
           'dense_features_weights'])
+
       for i in range(len(dense_variables)):
         result += dense_features[i] * dense_variables[i]
-    return result
+
+    # Reshaping to allow shape inference at graph construction time.
+    return array_ops.reshape(result, [-1])
 
   def predictions(self, examples):
     """Add operations to compute predictions by the model.
-- 
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