message: Update tests for TF v2.
diff: @@ -23,6 +23,7 @@ import copy
  import numpy as np
  from tensorflow.python import keras
  +from tensorflow.python.eager import context
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import test_util as tf_test_util
  from tensorflow.python.platform import test
  @@ -396,7 +397,6 @@ class BidirectionalTest(test.TestCase):
  model.compile(loss='mse', optimizer='sgd')
  model.fit(x, y, epochs=1, batch_size=1)
  - @tf_test_util.run_v1_only('b/120545219')
  def test_Bidirectional_merged_value(self):
  rnn = keras.layers.LSTM
  samples = 2
  @@ -424,10 +424,10 @@ class BidirectionalTest(test.TestCase):
  rnn(units, return_sequences=True), merge_mode=merge_mode)
  f_merged = keras.backend.function([inputs], _to_list(layer(inputs)))
  f_forward = keras.backend.function([inputs],
  - [layer.forward_layer.call(inputs)])
  + [layer.forward_layer(inputs)])
  f_backward = keras.backend.function(
  [inputs],
  - [keras.backend.reverse(layer.backward_layer.call(inputs), 1)])
  + [keras.backend.reverse(layer.backward_layer(inputs), 1)])
  y_merged = f_merged(x)
  y_expected = _to_list(merge_func(f_forward(x)[0], f_backward(x)[0]))
  @@ -441,9 +441,9 @@ class BidirectionalTest(test.TestCase):
  rnn(units, return_state=True), merge_mode=merge_mode)
  f_merged = keras.backend.function([inputs], layer(inputs))
  f_forward = keras.backend.function([inputs],
  - layer.forward_layer.call(inputs))
  + layer.forward_layer(inputs))
  f_backward = keras.backend.function([inputs],
  - layer.backward_layer.call(inputs))
  + layer.backward_layer(inputs))
  n_states = len(layer.layer.states)
  y_merged = f_merged(x)
  @@ -527,8 +527,10 @@ class BidirectionalTest(test.TestCase):
  layer.trainable = True
  assert len(layer.trainable_weights) == 6
  - @tf_test_util.run_v1_only('b/120545219')
  def test_Bidirectional_updates(self):
  + if context.executing_eagerly():
  + self.skipTest('layer.updates is only available in graph mode.')
  +
  with self.cached_session():
  x = keras.layers.Input(shape=(3, 2))
  x_reachable_update = x * x
  