    return predictions
  
  
-@@ -372,15 +377,22 @@ def decode_batch(model, batch_size):
+@@ -372,15 +376,22 @@ def decode_batch(model, batch_size):
  
  def train_model():
    """Train for a fixed number of steps and decode during training."""

       hidden_size: int, the number of hidden dimensions in the encoder and
         decoder LSTMs.
     Returns:
@@ -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], 
-        teacher_force=teacher_force)
+        sample_probability=sample_probability)
 
     return logits, predictions
 
@@ -323,14 +326,16 @@ def compute_metrics(logits, labels):
 
 
 @jax.jit

         decode_batch(optimizer.target, 5)
   return optimizer.target
 
@@ -390,4 +401,4 @@ def main(_):
 
 
 if __name__ == '__main__':
