message: fix coding style issues in new rnn tests
diff: @@ -1248,18 +1248,18 @@ class BackendNNOpsTest(test.TestCase, parameterized.TestCase):
  expected_outputs[1, -mask_last_num_timesteps:] = \
  expected_outputs[1, -(mask_last_num_timesteps + 1)]
  - expected_state = initial_state_vals.copy()
  + expected_last_state = initial_state_vals.copy()
  # first state should be incremented for every timestep (no masking)
  - expected_state[0] += num_timesteps
  + expected_last_state[0] += num_timesteps
  # second state should not be incremented for last two timesteps
  - expected_state[1] += (num_timesteps - mask_last_num_timesteps)
  + expected_last_state[1] += (num_timesteps - mask_last_num_timesteps)
  # verify same expected output for `unroll=true/false`
  inputs = keras.backend.variable(inputs_vals)
  initial_states = [keras.backend.variable(initial_state_vals)]
  mask = keras.backend.variable(mask_vals)
  for unroll in [True, False]:
  - last_output, outputs, last_states = keras.backend.rnn(
  + _, outputs, last_states = keras.backend.rnn(
  step_function,
  inputs,
  initial_states,
  @@ -1267,10 +1267,9 @@ class BackendNNOpsTest(test.TestCase, parameterized.TestCase):
  unroll=unroll,
  input_length=num_timesteps if unroll else None)
  - self.assertAllClose(
  - keras.backend.eval(outputs), expected_outputs)
  - self.assertAllClose(
  - keras.backend.eval(last_states[0]), expected_state)
  + self.assertAllClose(keras.backend.eval(outputs), expected_outputs)
  + self.assertAllClose(keras.backend.eval(
  + last_states[0]), expected_last_state)
  def test_rnn_output_num_dim_larger_than_2_masking(self):
  num_samples = 3
  @@ -1298,7 +1297,7 @@ class BackendNNOpsTest(test.TestCase, parameterized.TestCase):
  initial_states = [keras.backend.variable(initial_state_vals)]
  mask = keras.backend.variable(mask_vals)
  for unroll in [True, False]:
  - last_output, outputs, last_states = keras.backend.rnn(
  + _, outputs, _ = keras.backend.rnn(
  step_function,
  inputs,
  initial_states,
  @@ -1328,7 +1327,7 @@ class BackendNNOpsTest(test.TestCase, parameterized.TestCase):
  initial_states = [keras.backend.variable(initial_state_vals)]
  mask = keras.backend.variable(mask_vals)
  for unroll in [True, False]:
  - last_output, outputs, last_states = keras.backend.rnn(
  + _, _, last_states = keras.backend.rnn(
  step_function,
  inputs,
  initial_states,
  @@ -1336,10 +1335,8 @@ class BackendNNOpsTest(test.TestCase, parameterized.TestCase):
  unroll=unroll,
  input_length=num_timesteps if unroll else None)
  - # not updated last timestep:
  self.assertAllClose(
  - keras.backend.eval(last_states[0]),
  - expected_last_state)
  + keras.backend.eval(last_states[0]), expected_last_state)
  def test_normalize_batch_in_training(self):
  val = np.random.random((10, 3, 10, 10))
  