diff --git a/src/brevitas_examples/common/learned_round/learned_round_optimizer.py b/src/brevitas_examples/common/learned_round/learned_round_optimizer.py
index 662f9142a..c9927d23b 100644
--- a/src/brevitas_examples/common/learned_round/learned_round_optimizer.py
+++ b/src/brevitas_examples/common/learned_round/learned_round_optimizer.py
@@ -723,7 +723,7 @@ def skip_full_execution(self, block, next_block, block_forward, cache):
         # Finally (!), we compute the quantized input of the next block
         block.eval()
         if torch.cuda.is_available():
-          block.cuda()
+            block.cuda()
         next_quant_input = []
         pbar = tqdm(range(len(cache)), desc='', leave=False)
         with torch.no_grad():