message: In lite_mlir_test, ensure that we are testing against Keras LSTM v2.
diff: @@ -34,6 +34,8 @@ from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_spec
  from tensorflow.python.framework import test_util
  +from tensorflow.python.keras.layers import recurrent
  +from tensorflow.python.keras.layers import recurrent_v2
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import math_ops
  @@ -459,9 +461,9 @@ class FromConcreteFunctionTest(TFLiteMLIRTest):
  class FromKerasModelTest(TFLiteMLIRTest, parameterized.TestCase):
  - @parameterized.named_parameters(('LSTM', keras.layers.LSTM),
  - ('SimpleRNN', keras.layers.SimpleRNN),
  - ('GRU', keras.layers.GRU))
  + @parameterized.named_parameters(('LSTM', recurrent_v2.LSTM),
  + ('SimpleRNN', recurrent.SimpleRNN),
  + ('GRU', recurrent_v2.GRU))
  @test_util.run_v2_only
  def testKerasRNN(self, rnn_layer):
  # This test case is similar to `FromConcreteFunctionTest.testKerasLSTM`
  @@ -471,7 +473,13 @@ class FromKerasModelTest(TFLiteMLIRTest, parameterized.TestCase):
  input_data = constant_op.constant(
  np.array(np.random.random_sample((1, 10, 10)), dtype=np.float32))
  - model = keras.models.Sequential([rnn_layer(units=10, input_shape=(10, 10))])
  + # We need to instruct keras to not use `cudnn` kernels explicitly here.
  + # Otherwise the frozen graph will contain ops that we can't convert (e.g.
  + # `StatefulPartitionedCall`, `CudnnRNN`).
  + # TODO(b/142360481): Remove this when the freezing issue is resolved.
  + rnn_obj = rnn_layer(units=10, input_shape=(10, 10))
  + rnn_obj.could_use_cudnn = False
  + model = keras.models.Sequential([rnn_obj])
  # Convert model.
  converter = lite.TFLiteConverterV2.from_keras_model(model)
  