Removing underscore prefixes from hidden generated Python functions.

PiperOrigin-RevId: 187386941

1 files changed, 5 insertions, 5 deletions

diff --git a/tensorflow/python/grappler/layout_optimizer_test.py b/tensorflow/python/grappler/layout_optimizer_test.py
index 0f51501740..5a84b16a23 100644
--- a/tensorflow/python/grappler/layout_optimizer_test.py
+++ b/tensorflow/python/grappler/layout_optimizer_test.py
@@ -321,7 +321,7 @@ class LayoutOptimizerTest(test.TestCase):
       conv = _two_layer_model(x)
       dim = array_ops.placeholder(dtype='int32')
       sizes = constant_op.constant([50, 10, 4], shape=[3])
-      split = gen_array_ops._split_v(
+      split = gen_array_ops.split_v(
           value=conv, size_splits=sizes, axis=dim, num_split=3)
       output = math_ops.reduce_sum(split[0])
 
@@ -896,7 +896,7 @@ class LayoutOptimizerTest(test.TestCase):
       add = math_ops.add(conv, conv)
       mean = math_ops.reduce_mean(conv)
       condition = math_ops.less(conv, mean)
-      select = gen_math_ops._select(condition, conv, add)
+      select = gen_math_ops.select(condition, conv, add)
       output = array_ops.identity(select)
 
       with session.Session(config=_get_config(False)) as sess:
@@ -926,7 +926,7 @@ class LayoutOptimizerTest(test.TestCase):
       conv = _two_layer_model(x)
       add = math_ops.add(conv, conv)
       condition = array_ops.placeholder(dtype='bool')
-      select = gen_math_ops._select(condition, conv, add)
+      select = gen_math_ops.select(condition, conv, add)
       output = array_ops.identity(select)
 
       condition_val = np.zeros((1, 7, 7, 64))
@@ -957,7 +957,7 @@ class LayoutOptimizerTest(test.TestCase):
       conv = _two_layer_model(x)
       add = math_ops.add(conv, conv)
       condition = constant_op.constant(True)
-      select = gen_math_ops._select(condition, conv, add)
+      select = gen_math_ops.select(condition, conv, add)
       output = array_ops.identity(select)
 
       with session.Session(config=_get_config(False)) as sess:
@@ -1023,7 +1023,7 @@ class LayoutOptimizerTest(test.TestCase):
       conv = _two_layer_model(x)
       ksize = constant_op.constant([1, 2, 3, 1], shape=[4])
       strides = array_ops.placeholder(dtype='int32', shape=[4])
-      max_pool = gen_nn_ops._max_pool_v2(conv, ksize, strides, 'VALID')
+      max_pool = gen_nn_ops.max_pool_v2(conv, ksize, strides, 'VALID')
       output = array_ops.identity(max_pool)
 
       strides_val = [1, 3, 2, 1]