Revert test_rnnrelu_sym to origin

tests/python/unittest/test_operator.py

@@ -258,23 +258,17 @@ def test_rnntanh_bidirectional():
 @with_seed()
 @assert_raises_cudnn_not_satisfied(min_version='5.1.10')
 def test_rnnrelu_sym():
-    if default_context().device_type == 'gpu':
-        print("Skip test `rnn_relu_sym` on gpu. This is tracked by https://github.com/apache/incubator-mxnet/issues/16548")
-        return
-    Ts = [1, 5]
-    Ns = [1, 32]
-    Is = [32, 128, 512]
-    Hs = [32, 128, 512]
-    for T, N, I, H in itertools.product(Ts, Ns, Is, Hs):
-        fused = mx.rnn.FusedRNNCell(H, num_layers=3, mode='rnn_relu', get_next_state=True, prefix='')
-        stack = mx.rnn.SequentialRNNCell()
-        stack.add(mx.rnn.RNNCell(H, activation='relu', prefix='l0_'))
-        stack.add(mx.rnn.RNNCell(H, activation='relu', prefix='l1_'))
-        stack.add(mx.rnn.RNNCell(H, activation='relu', prefix='l2_'))
+    T, N, I, H = 5, 32, 200, 200
 
-        check_rnn_consistency(fused, stack, T, N, I, H, 'write')
-        check_rnn_consistency(fused, stack, T, N, I, H, 'add')
-        check_rnn_consistency(fused, stack, T, N, I, H, 'null')
+    fused = mx.rnn.FusedRNNCell(H, num_layers=3, mode='rnn_relu', get_next_state=True, prefix='')
+    stack = mx.rnn.SequentialRNNCell()
+    stack.add(mx.rnn.RNNCell(H, activation='relu', prefix='l0_'))
+    stack.add(mx.rnn.RNNCell(H, activation='relu', prefix='l1_'))
+    stack.add(mx.rnn.RNNCell(H, activation='relu', prefix='l2_'))
+
+    check_rnn_consistency(fused, stack, T, N, I, H, 'write')
+    check_rnn_consistency(fused, stack, T, N, I, H, 'add')
+    check_rnn_consistency(fused, stack, T, N, I, H, 'null')
 
 @with_seed()
 @assert_raises_cudnn_not_satisfied(min_version='5.1.10')