Fix the db to tfrecods tests

bob/learn/tensorflow/test/test_db_to_tfrecords.py

@@ -3,14 +3,15 @@ import shutil
 import pkg_resources
 import tempfile
 from click.testing import CliRunner
-import bob.io.base
-from bob.learn.tensorflow.script.db_to_tfrecords import db_to_tfrecords, describe_tf_record
+from bob.io.base import create_directories_safe
+from bob.learn.tensorflow.script.db_to_tfrecords import (
+    db_to_tfrecords, describe_tf_record)
 from bob.learn.tensorflow.utils import load_mnist, create_mnist_tfrecord
 
 regenerate_reference = False
 
 dummy_config = pkg_resources.resource_filename(
-    'bob.learn.tensorflow', 'test/data/tfrecord_config.py')
+    'bob.learn.tensorflow', 'test/data/db_to_tfrecords_config.py')
 
 
 def test_db_to_tfrecords():
@@ -52,20 +53,21 @@ def test_db_to_tfrecords_size_estimate():
 
 def test_tfrecord_counter():
     tfrecord_train = "./tf-train-test/train_mnist.tfrecord"
-    shape = (3136,) # I'm saving the thing as float
+    shape = (3136,)  # I'm saving the thing as float
     batch_size = 1000
 
     try:
-        train_data, train_labels, validation_data, validation_labels = load_mnist()
-        bob.io.base.create_directories_safe(os.path.dirname(tfrecord_train))
+        train_data, train_labels, validation_data, validation_labels = \
+            load_mnist()
+        create_directories_safe(os.path.dirname(tfrecord_train))
         create_mnist_tfrecord(
             tfrecord_train, train_data, train_labels, n_samples=6000)
 
-        n_samples, n_labels = describe_tf_record(os.path.dirname(tfrecord_train), shape, batch_size)
+        n_samples, n_labels = describe_tf_record(
+            os.path.dirname(tfrecord_train), shape, batch_size)
 
         assert n_samples == 6000
         assert n_labels == 10
 
     finally:
         shutil.rmtree(os.path.dirname(tfrecord_train))
-