      loss = cross_entropy_loss(logits, labels, get_sequence_lengths(labels))
      return loss, logits
    grad_fn = jax.value_and_grad(loss_fn, has_aux=True)
-@@ -354,7 +358,7 @@ def decode(model, inputs, rng):
+@@ -354,7 +359,7 @@ def decode(model, inputs, rng):
    init_decoder_inputs = jnp.tile(init_decoder_input,
                                   (inputs.shape[0], get_max_output_len(), 1))
    with nn.stochastic(rng):

+            metrics['accuracy'] * 100)
         decode_batch(optimizer.target, 5)
   return optimizer.target

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