var = list() --> var = []

tensorflow/contrib/distributions/python/ops/bijectors/batch_normalization.py

@@ -266,7 +266,7 @@ class BatchNormalization(bijector.Bijector):
     else:
       # At training-time, ildj is computed from the mean and log-variance across
       # the current minibatch.
-      _, v = nn.moments(y, axes=reduction_axes, keep_dims=True)
+      _, v = nn.moments(y, axes=reduction_axes, keepdims=True)
       log_variance = math_ops.log(v + self.batchnorm.epsilon)
 
     # `gamma` and `log Var(y)` reductions over event_dims.

tensorflow/contrib/kinesis/python/kernel_tests/kinesis_test.py

@@ -110,7 +110,7 @@ class KinesisDatasetTest(test.TestCase):
     init_batch_op = iterator.make_initializer(batch_dataset)
     get_next = iterator.get_next()
 
-    data = list()
+    data = []
     with self.cached_session() as sess:
       # Basic test: read from shard 0 of stream 2.
       sess.run(

tensorflow/examples/tutorials/word2vec/word2vec_basic.py

@@ -84,7 +84,7 @@ def word2vec_basic(log_dir):
     dictionary = dict()
     for word, _ in count:
       dictionary[word] = len(dictionary)
-    data = list()
+    data = []
     unk_count = 0
     for word in words:
       index = dictionary.get(word, 0)

tensorflow/examples/udacity/5_word2vec.ipynb

@@ -254,7 +254,7 @@
         "  dictionary = dict()\n",
         "  for word, _ in count:\n",
         "    dictionary[word] = len(dictionary)\n",
-        "  data = list()\n",
+        "  data = []\n",
         "  unk_count = 0\n",
         "  for word in words:\n",
         "    if word in dictionary:\n",

tensorflow/examples/udacity/6_lstm.ipynb

@@ -554,7 +554,7 @@
         "    return output_gate * tf.tanh(state), state\n",
         "\n",
         "  # Input data.\n",
-        "  train_data = list()\n",
+        "  train_data = []\n",
         "  for _ in range(num_unrollings + 1):\n",
         "    train_data.append(\n",
         "      tf.placeholder(tf.float32, shape=[batch_size,vocabulary_size]))\n",
@@ -562,7 +562,7 @@
         "  train_labels = train_data[1:]  # labels are inputs shifted by one time step.\n",
         "\n",
         "  # Unrolled LSTM loop.\n",
-        "  outputs = list()\n",
+        "  outputs = []\n",
         "  output = saved_output\n",
         "  state = saved_state\n",
         "  for i in train_inputs:\n",

tensorflow/python/eager/def_function.py

@@ -582,8 +582,8 @@ class Function(object):
       concrete_functions.extend(
           self._stateless_fn._function_cache.all_values())
     # pylint: enable=protected-access
-    deduplicated_concrete_functions = list()
-    seen_signatures = list()
+    deduplicated_concrete_functions = []
+    seen_signatures = []
     # We are using a list so that:
     #  - the returned collection is deterministic, and
     #  - we can use a custom equality operator (is_same_structure).

tensorflow/python/kernel_tests/fifo_queue_test.py

@@ -1247,7 +1247,7 @@ class FIFOQueueTest(test.TestCase):
   def testSelectQueue(self):
     with self.cached_session():
       num_queues = 10
-      qlist = list()
+      qlist = []
       for _ in xrange(num_queues):
         qlist.append(data_flow_ops.FIFOQueue(10, dtypes_lib.float32))
       # Enqueue/Dequeue into a dynamically selected queue

tensorflow/python/kernel_tests/padding_fifo_queue_test.py

@@ -1360,7 +1360,7 @@ class PaddingFIFOQueueTest(test.TestCase):
   def testSelectQueue(self):
     with self.cached_session():
       num_queues = 10
-      qlist = list()
+      qlist = []
       for _ in xrange(num_queues):
         qlist.append(
             data_flow_ops.PaddingFIFOQueue(10, dtypes_lib.float32, ((),)))

tensorflow/python/kernel_tests/random/random_shuffle_queue_test.py

@@ -1197,7 +1197,7 @@ class RandomShuffleQueueTest(test.TestCase):
   def testSelectQueue(self):
     with self.cached_session():
       num_queues = 10
-      qlist = list()
+      qlist = []
       for _ in xrange(num_queues):
         qlist.append(
             data_flow_ops.RandomShuffleQueue(10, 0, dtypes_lib.float32))

tensorflow/python/tpu/tpu_embedding.py

@@ -669,8 +669,7 @@ class TPUEmbedding(object):
       Arguments for `enqueue_tpu_embedding_sparse_tensor_batch()`.
     """
 
-    sample_idcs, embedding_idcs, aggregation_weights, table_ids = (
-        list(), list(), list(), list())
+    sample_idcs, embedding_idcs, aggregation_weights, table_ids = [], [], [], []
     for table_id, table in enumerate(self._table_to_features_dict):
       features = self._table_to_features_dict[table]
       for feature in features: