Add C0301 line-too-long error to pylint sanity check.

PiperOrigin-RevId: 183467186

1 files changed, 13 insertions, 19 deletions

diff --git a/tensorflow/tools/dist_test/python/mnist_replica.py b/tensorflow/tools/dist_test/python/mnist_replica.py
index e40ecb43f9..a2d12442c4 100644
--- a/tensorflow/tools/dist_test/python/mnist_replica.py
+++ b/tensorflow/tools/dist_test/python/mnist_replica.py
@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-
 """Distributed MNIST training and validation, with model replicas.
 
 A simple softmax model with one hidden layer is defined. The parameters
@@ -32,7 +31,6 @@ perform forward computation and gradient calculation in parallel, which
 should lead to increased training speed for the simple model.
 """
 
-
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
@@ -45,7 +43,6 @@ import time
 import tensorflow as tf
 from tensorflow.examples.tutorials.mnist import input_data
 
-
 flags = tf.app.flags
 flags.DEFINE_string("data_dir", "/tmp/mnist-data",
                     "Directory for storing mnist data")
@@ -56,8 +53,7 @@ flags.DEFINE_integer("task_index", None,
                      "Worker task index, should be >= 0. task_index=0 is "
                      "the master worker task the performs the variable "
                      "initialization ")
-flags.DEFINE_integer("num_gpus", 1,
-                     "Total number of gpus for each machine."
+flags.DEFINE_integer("num_gpus", 1, "Total number of gpus for each machine."
                      "If you don't use GPU, please set it to '0'")
 flags.DEFINE_integer("replicas_to_aggregate", None,
                      "Number of replicas to aggregate before parameter update"
@@ -69,24 +65,24 @@ flags.DEFINE_integer("train_steps", 200,
                      "Number of (global) training steps to perform")
 flags.DEFINE_integer("batch_size", 100, "Training batch size")
 flags.DEFINE_float("learning_rate", 0.01, "Learning rate")
-flags.DEFINE_boolean("sync_replicas", False,
-                     "Use the sync_replicas (synchronized replicas) mode, "
-                     "wherein the parameter updates from workers are aggregated "
-                     "before applied to avoid stale gradients")
+flags.DEFINE_boolean(
+    "sync_replicas", False,
+    "Use the sync_replicas (synchronized replicas) mode, "
+    "wherein the parameter updates from workers are aggregated "
+    "before applied to avoid stale gradients")
 flags.DEFINE_boolean(
     "existing_servers", False, "Whether servers already exists. If True, "
     "will use the worker hosts via their GRPC URLs (one client process "
     "per worker host). Otherwise, will create an in-process TensorFlow "
     "server.")
-flags.DEFINE_string("ps_hosts","localhost:2222",
+flags.DEFINE_string("ps_hosts", "localhost:2222",
                     "Comma-separated list of hostname:port pairs")
 flags.DEFINE_string("worker_hosts", "localhost:2223,localhost:2224",
                     "Comma-separated list of hostname:port pairs")
-flags.DEFINE_string("job_name", None,"job name: worker or ps")
+flags.DEFINE_string("job_name", None, "job name: worker or ps")
 
 FLAGS = flags.FLAGS
 
-
 IMAGE_PIXELS = 28
 
 
@@ -97,7 +93,7 @@ def main(unused_argv):
 
   if FLAGS.job_name is None or FLAGS.job_name == "":
     raise ValueError("Must specify an explicit `job_name`")
-  if FLAGS.task_index is None or FLAGS.task_index =="":
+  if FLAGS.task_index is None or FLAGS.task_index == "":
     raise ValueError("Must specify an explicit `task_index`")
 
   print("job name = %s" % FLAGS.job_name)
@@ -110,9 +106,7 @@ def main(unused_argv):
   # Get the number of workers.
   num_workers = len(worker_spec)
 
-  cluster = tf.train.ClusterSpec({
-      "ps": ps_spec,
-      "worker": worker_spec})
+  cluster = tf.train.ClusterSpec({"ps": ps_spec, "worker": worker_spec})
 
   if not FLAGS.existing_servers:
     # Not using existing servers. Create an in-process server.
@@ -217,7 +211,8 @@ def main(unused_argv):
     sess_config = tf.ConfigProto(
         allow_soft_placement=True,
         log_device_placement=False,
-        device_filters=["/job:ps", "/job:worker/task:%d" % FLAGS.task_index])
+        device_filters=["/job:ps",
+                        "/job:worker/task:%d" % FLAGS.task_index])
 
     # The chief worker (task_index==0) session will prepare the session,
     # while the remaining workers will wait for the preparation to complete.
@@ -231,8 +226,7 @@ def main(unused_argv):
       server_grpc_url = "grpc://" + worker_spec[FLAGS.task_index]
       print("Using existing server at: %s" % server_grpc_url)
 
-      sess = sv.prepare_or_wait_for_session(server_grpc_url,
-                                            config=sess_config)
+      sess = sv.prepare_or_wait_for_session(server_grpc_url, config=sess_config)
     else:
       sess = sv.prepare_or_wait_for_session(server.target, config=sess_config)