address lint again

diff --git a/tensorflow/contrib/tensor_forest/python/tensor_forest.py b/tensorflow/contrib/tensor_forest/python/tensor_forest.py
index ba1755e..6f62cd1 100644 (file)
--- a/tensorflow/contrib/tensor_forest/python/tensor_forest.py
+++ b/tensorflow/contrib/tensor_forest/python/tensor_forest.py
@@ -354,7 +354,7 @@ class ForestVariables(object):
         if tree_stats is not None:
           kwargs.update(dict(tree_stat=tree_stats[i]))
         self.variables.append(tree_variables_class(
-                params, i, training, **kwargs))
+            params, i, training, **kwargs))
 
   def __setitem__(self, t, val):
     self.variables[t] = val

diff --git a/tensorflow/contrib/tensor_forest/python/tensor_forest_test.py b/tensorflow/contrib/tensor_forest/python/tensor_forest_test.py
index 7c5883d..1c9c818 100644 (file)
--- a/tensorflow/contrib/tensor_forest/python/tensor_forest_test.py
+++ b/tensorflow/contrib/tensor_forest/python/tensor_forest_test.py
@@ -126,23 +126,25 @@ class TensorForestTest(test_util.TensorFlowTestCase):
         max_nodes=1000,
         split_after_samples=25).fill()
     tree_weight = {'decisionTree':
-                    {'nodes':
+                       {'nodes':
                         [{'binaryNode':
-                            {'rightChildId': 2,
-                             'leftChildId': 1,
-                             'inequalityLeftChildTest':
-                             {'featureId': {'id': '0'},
-                             'threshold': {'floatValue': 0}}}},
+                          {'rightChildId': 2,
+                           'leftChildId': 1,
+                           'inequalityLeftChildTest':
+                           {'featureId': {'id': '0'},
+                            'threshold': {'floatValue': 0}}}},
                          {'leaf': {'vector':
-                            {'value': [{'floatValue': 0.0},
-                                       {'floatValue': 1.0}]}},
-                            'nodeId': 1},
-                        {'leaf': {'vector':
-                            {'value': [{'floatValue': 0.0},
-                                       {'floatValue': 1.0}]}},
-                            'nodeId': 2}]}}
-    restored_tree_param = ParseDict(tree_weight, _tree_proto.Model()).SerializeToString()
-    graph_builder = tensor_forest.RandomForestGraphs(hparams, [restored_tree_param])
+                                   {'value': [{'floatValue': 0.0},
+                                              {'floatValue': 1.0}]}},
+                          'nodeId': 1},
+                         {'leaf': {'vector':
+                                   {'value': [{'floatValue': 0.0},
+                                              {'floatValue': 1.0}]}},
+                          'nodeId': 2}]}}
+    restored_tree_param = ParseDict(tree_weight,
+                                    _tree_proto.Model()).SerializeToString()
+    graph_builder = tensor_forest.RandomForestGraphs(hparams,
+                                                     [restored_tree_param])
     probs, paths, var = graph_builder.inference_graph(input_data)
     self.assertTrue(isinstance(probs, ops.Tensor))
     self.assertTrue(isinstance(paths, ops.Tensor))