diff --git a/metrics/aggregation.py b/metrics/aggregation.py
index 57f75d5..a3c4e79 100644
--- a/metrics/aggregation.py
+++ b/metrics/aggregation.py
@@ -50,7 +50,8 @@ def stable_mean_dist_reduce_fn(state: torch.Tensor) -> torch.Tensor:
     value=state[:, 0],
     weight=state[:, 1],
   )
-  return torch.stack([mean, weight_sum])
+  merged_accumulated_mean = torch.stack([mean, weight_sum])
+  return merged_accumulated_mean
 
 
 class StableMean(torchmetrics.Metric):
@@ -94,4 +95,5 @@ def compute(self) -> torch.Tensor:
     """
     Compute and return the accumulated mean.
     """
-    return self.mean_and_weight_sum[0]
+    accumulated_mean = self.mean_and_weight_sum[0]
+    return accumulated_mean
diff --git a/metrics/auroc.py b/metrics/auroc.py
index 6979c20..d04e27d 100644
--- a/metrics/auroc.py
+++ b/metrics/auroc.py
@@ -159,4 +159,6 @@ def compute(self) -> torch.Tensor:
     )
 
     # Compute auroc with the weight set to 1/2 when positive & negative have identical scores.
-    return auroc_le - (auroc_le - auroc_lt) / 2.0
+    auroc = auroc_le - (auroc_le - auroc_lt) / 2.0
+    return auroc
+  
\ No newline at end of file
diff --git a/metrics/rce.py b/metrics/rce.py
index b6ada1d..be5dfa4 100644
--- a/metrics/rce.py
+++ b/metrics/rce.py
@@ -21,7 +21,8 @@ def _smooth(
     label_smoothing: smoothing constant.
   Returns: Smoothed values.
   """
-  return value * (1.0 - label_smoothing) + 0.5 * label_smoothing
+  smoothed_values = value * (1.0 - label_smoothing) + 0.5 * label_smoothing
+  return smoothed_values
 
 
 def _binary_cross_entropy_with_clipping(
@@ -178,7 +179,8 @@ def compute(self) -> torch.Tensor:
 
     pred_ce = self.binary_cross_entropy.compute()
 
-    return (1.0 - (pred_ce / baseline_ce)) * 100
+    rce = (1.0 - (pred_ce / baseline_ce)) * 100
+    return rce
 
   def reset(self):
     """