diff --git a/python/tvm/relay/frontend/tensorflow.py b/python/tvm/relay/frontend/tensorflow.py
index 0de00517e9a8a..50221c7baf288 100644
--- a/python/tvm/relay/frontend/tensorflow.py
+++ b/python/tvm/relay/frontend/tensorflow.py
@@ -19,6 +19,7 @@
 """TF: Tensorflow frontend."""
 import warnings
 from collections import defaultdict
+from collections import deque
 
 # Numpy support
 import numpy as np
@@ -1778,7 +1779,6 @@ def _impl(inputs, attr, params, mod):
         s0 = list(s0.asnumpy().reshape([-1]))
         s1 = list(s1.asnumpy().reshape([-1]))
         s0_size, s1_size = len(s0), len(s1)
-        from collections import deque
 
         out = deque([])
         for i in range(1, min(s0_size, s1_size) + 1):
diff --git a/tests/python/frontend/tensorflow/test_forward.py b/tests/python/frontend/tensorflow/test_forward.py
index 2e4237b34ab14..56e3799850138 100644
--- a/tests/python/frontend/tensorflow/test_forward.py
+++ b/tests/python/frontend/tensorflow/test_forward.py
@@ -1725,9 +1725,7 @@ def test_read_variable_op(target, dev):
 
         # Now convert the variables to constant and run inference on the converted graph
         final_graph_def = tf.graph_util.convert_variables_to_constants(
-            sess,
-            sess.graph.as_graph_def(add_shapes=True),
-            out_node,
+            sess, sess.graph.as_graph_def(add_shapes=True), out_node,
         )
 
         tvm_output = run_tvm_graph(
@@ -2228,20 +2226,10 @@ def _test_sparse_segment_variant(
 )
 @pytest.mark.parametrize("use_dyn", [True, False])
 @pytest.mark.parametrize(
-    "tf_op",
-    [
-        tf.sparse.segment_sum,
-        tf.sparse.segment_sqrt_n,
-        tf.sparse.segment_mean,
-    ],
+    "tf_op", [tf.sparse.segment_sum, tf.sparse.segment_sqrt_n, tf.sparse.segment_mean,],
 )
 def test_forward_sparse_segment_sum_variants(
-    tf_op,
-    data_np,
-    indices_np,
-    segment_ids_np,
-    num_segments,
-    use_dyn,
+    tf_op, data_np, indices_np, segment_ids_np, num_segments, use_dyn,
 ):
     """sparse segment sum variants tests"""
     _test_sparse_segment_variant(tf_op, data_np, indices_np, segment_ids_np, num_segments, use_dyn)
@@ -2269,10 +2257,7 @@ def _test_math_segment_sum(data_np, segment_ids_np, use_dyn=False):
 
         _ = tf.math.segment_sum(data, segment_ids, name="segment_sum")
         compare_tf_with_tvm(
-            [data_np, segment_ids_np],
-            [data.name, segment_ids.name],
-            ["segment_sum:0"],
-            mode="vm",
+            [data_np, segment_ids_np], [data.name, segment_ids.name], ["segment_sum:0"], mode="vm",
         )
 
 
@@ -2287,18 +2272,12 @@ def _test_math_segment_sum(data_np, segment_ids_np, use_dyn=False):
             np.array([[1, 2, 3, 4], [-1, -2, -3, -4], [5, 6, 7, 8]], dtype=np.float64),
             np.array([0, 0, 1], dtype=np.int32),
         ),
-        (
-            np.random.random((6, 4, 5)),
-            np.array([0, 0, 1, 2, 2, 3], dtype=np.int64),
-        ),
+        (np.random.random((6, 4, 5)), np.array([0, 0, 1, 2, 2, 3], dtype=np.int64),),
         (
             np.array([[[1, 7]], [[3, 8]], [[2, 9]]], dtype=np.float32),
             np.array([0, 0, 1], dtype=np.int32),
         ),
-        (
-            np.random.random((9, 4, 5, 7)),
-            np.array([0, 0, 0, 1, 2, 3, 4, 4, 5], dtype=np.int64),
-        ),
+        (np.random.random((9, 4, 5, 7)), np.array([0, 0, 0, 1, 2, 3, 4, 4, 5], dtype=np.int64),),
     ],
 )
 @pytest.mark.parametrize("use_dyn", [True, False])
@@ -2961,9 +2940,7 @@ def test_forward_multi_output():
 
         with tf.Session() as sess:
             final_graph_def = tf.graph_util.convert_variables_to_constants(
-                sess,
-                sess.graph.as_graph_def(add_shapes=True),
-                out_node,
+                sess, sess.graph.as_graph_def(add_shapes=True), out_node,
             )
             tf_output = run_tf_graph(sess, in_data, in_name, out_name)
             tvm_output = run_tvm_graph(
@@ -3438,11 +3415,7 @@ def _get_tensorflow_output():
                 sess.run([variables.global_variables_initializer()])
                 res = sess.run(
                     [g, out_m0, out_m1],
-                    {
-                        x.name: np.array([[1.0, 1.0]]),
-                        m0.name: in_state_c,
-                        m1.name: in_state_h,
-                    },
+                    {x.name: np.array([[1.0, 1.0]]), m0.name: in_state_c, m1.name: in_state_h,},
                 )
             graph_def = sess.graph.as_graph_def(add_shapes=True)
             final_graph_def = graph_util.convert_variables_to_constants(
@@ -3620,7 +3593,7 @@ def test_forward_logical():
 
 
 #######################################################################
-# Where, Select
+# Where, Select, SelectV2
 # -------------
 def test_forward_where():
     """ Where: return elements depending on conditions"""
@@ -4843,9 +4816,7 @@ def _test_identityn(data_np_list):
         output = tf.identity_n(data_tensors)
         output_names = [out.name for out in output]
         compare_tf_with_tvm(
-            data_np_list,
-            data_tensors_name,
-            output_names,
+            data_np_list, data_tensors_name, output_names,
         )
 
 
@@ -4867,12 +4838,7 @@ def _test_identityn(data_np_list):
                 np.array([True, False, True]),
             ]
         ),
-        (
-            [
-                np.array([]),
-                np.array([[]]),
-            ]
-        ),
+        ([np.array([]), np.array([[]]),]),
     ],
 )
 def test_forward_identityn(data_np_list):