        hidden_size: int, the number of hidden dimensions in the encoder and
          decoder LSTMs.
      Returns:
-@@ -252,8 +255,7 @@ class Seq2seq(nn.Module):
-     """
+@@ -253,7 +256,7 @@ class Seq2seq(nn.Module):
      init_decoder_state = Encoder(encoder_inputs, eos_id=eos_id,
          hidden_size=hidden_size)
--    logits, predictions = Decoder(init_decoder_state, decoder_inputs[:, :-1], 
+     logits, predictions = Decoder(init_decoder_state, decoder_inputs[:, :-1], 
 -        teacher_force=teacher_force)
-+    logits, predictions = Decoder(init_decoder_state, decoder_inputs[:, :-1])
++        sample_probability=sample_probability)
  
      return logits, predictions
  
-@@ -323,14 +325,16 @@ def compute_metrics(logits, labels):
+@@ -323,14 +326,16 @@ def compute_metrics(logits, labels):
  
  
  @jax.jit

   return predictions
 
 
@@ -372,15 +376,22 @@ def decode_batch(model, batch_size):
 
 def train_model():
   """Train for a fixed number of steps and decode during training."""

+            metrics['accuracy'] * 100)
         decode_batch(optimizer.target, 5)
   return optimizer.target
 
diff --git a/examples/seq2seq/train_test.py b/examples/seq2seq/train_test.py
index b638d55..26fc7b8 100644
--- a/examples/seq2seq/train_test.py
+++ b/examples/seq2seq/train_test.py
@@ -101,7 +101,7 @@ class TrainTest(absltest.TestCase):
       model = train.create_model()
       optimizer = train.create_optimizer(model, 0.003)
       optimizer, train_metrics = train.train_step(
-          optimizer, batch, nn.make_rng())
+          optimizer, batch, 0.5, nn.make_rng())
 
     self.assertLessEqual(train_metrics['loss'], 5)
     self.assertGreaterEqual(train_metrics['accuracy'], 0)
@@ -113,3 +113,4 @@ class TrainTest(absltest.TestCase):
 
 if __name__ == '__main__':
   absltest.main()
+
