Add C0326 bad-whitespace error to pylint sanity check.

PiperOrigin-RevId: 183689499

1 files changed, 7 insertions, 12 deletions

diff --git a/tensorflow/examples/learn/text_classification.py b/tensorflow/examples/learn/text_classification.py
index eb117c39a1..e4e61862b0 100644
--- a/tensorflow/examples/learn/text_classification.py
+++ b/tensorflow/examples/learn/text_classification.py
@@ -34,8 +34,7 @@ MAX_LABEL = 15
 WORDS_FEATURE = 'words'  # Name of the input words feature.
 
 
-def estimator_spec_for_softmax_classification(
-    logits, labels, mode):
+def estimator_spec_for_softmax_classification(logits, labels, mode):
   """Returns EstimatorSpec instance for softmax classification."""
   predicted_classes = tf.argmax(logits, 1)
   if mode == tf.estimator.ModeKeys.PREDICT:
@@ -53,8 +52,8 @@ def estimator_spec_for_softmax_classification(
     return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
 
   eval_metric_ops = {
-      'accuracy': tf.metrics.accuracy(
-          labels=labels, predictions=predicted_classes)
+      'accuracy':
+          tf.metrics.accuracy(labels=labels, predictions=predicted_classes)
   }
   return tf.estimator.EstimatorSpec(
       mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
@@ -67,8 +66,7 @@ def bag_of_words_model(features, labels, mode):
   bow_embedding_column = tf.feature_column.embedding_column(
       bow_column, dimension=EMBEDDING_SIZE)
   bow = tf.feature_column.input_layer(
-      features,
-      feature_columns=[bow_embedding_column])
+      features, feature_columns=[bow_embedding_column])
   logits = tf.layers.dense(bow, MAX_LABEL, activation=None)
 
   return estimator_spec_for_softmax_classification(
@@ -110,9 +108,9 @@ def main(unused_argv):
   # Prepare training and testing data
   dbpedia = tf.contrib.learn.datasets.load_dataset(
       'dbpedia', test_with_fake_data=FLAGS.test_with_fake_data)
-  x_train = pandas.Series(dbpedia.train.data[:,1])
+  x_train = pandas.Series(dbpedia.train.data[:, 1])
   y_train = pandas.Series(dbpedia.train.target)
-  x_test = pandas.Series(dbpedia.test.data[:,1])
+  x_test = pandas.Series(dbpedia.test.data[:, 1])
   y_test = pandas.Series(dbpedia.test.target)
 
   # Process vocabulary
@@ -152,10 +150,7 @@ def main(unused_argv):
 
   # Predict.
   test_input_fn = tf.estimator.inputs.numpy_input_fn(
-      x={WORDS_FEATURE: x_test},
-      y=y_test,
-      num_epochs=1,
-      shuffle=False)
+      x={WORDS_FEATURE: x_test}, y=y_test, num_epochs=1, shuffle=False)
   predictions = classifier.predict(input_fn=test_input_fn)
   y_predicted = np.array(list(p['class'] for p in predictions))
   y_predicted = y_predicted.reshape(np.array(y_test).shape)