diff --git a/intermediate_source/dqn_with_rnn_tutorial.py b/intermediate_source/dqn_with_rnn_tutorial.py
index bcc484f0a0..462415dcc7 100644
--- a/intermediate_source/dqn_with_rnn_tutorial.py
+++ b/intermediate_source/dqn_with_rnn_tutorial.py
@@ -342,7 +342,9 @@
 # will return a new instance of the LSTM (with shared weights) that will
 # assume that the input data is sequential in nature.
 #
-policy = Seq(feature, lstm.set_recurrent_mode(True), mlp, qval)
+from torchrl.modules import set_recurrent_mode
+
+policy = Seq(feature, lstm, mlp, qval)
 
 ######################################################################
 # Because we still have a couple of uninitialized parameters we should
@@ -389,7 +391,10 @@
 #   For the sake of efficiency, we're only running a few thousands iterations
 #   here. In a real setting, the total number of frames should be set to 1M.
 #
-collector = SyncDataCollector(env, stoch_policy, frames_per_batch=50, total_frames=200, device=device)
+
+collector = SyncDataCollector(
+    env, stoch_policy, frames_per_batch=50, total_frames=200, device=device
+)
 rb = TensorDictReplayBuffer(
     storage=LazyMemmapStorage(20_000), batch_size=4, prefetch=10
 )
@@ -422,7 +427,8 @@
     rb.extend(data.unsqueeze(0).to_tensordict().cpu())
     for _ in range(utd):
         s = rb.sample().to(device, non_blocking=True)
-        loss_vals = loss_fn(s)
+        with set_recurrent_mode(True):
+            loss_vals = loss_fn(s)
         loss_vals["loss"].backward()
         optim.step()
         optim.zero_grad()
@@ -464,5 +470,5 @@
 #
 # Further Reading
 # ---------------
-# 
+#
 # - The TorchRL documentation can be found `here <https://pytorch.org/rl/>`_.