diff --git a/src/base_tester.py b/src/base_tester.py
index 55ecf52..e71270c 100644
--- a/src/base_tester.py
+++ b/src/base_tester.py
@@ -39,6 +39,7 @@ def __init__(
             train_loader (torch.utils.data.DataLoader): Training dataloader.
             val_loader (torch.utils.data.DataLoader): Validation dataloader.
         """
+        _ = kwargs
         self._run_name = run_name
         self._model = model
         assert model_ckpt_path is not None, "No model checkpoint path provided."
@@ -54,7 +55,9 @@ def _visualize(
         batch: Union[Tuple, List, torch.Tensor],
         epoch: int,
     ) -> None:
-        pass
+        _ = epoch
+        _ = batch
+        raise NotImplementedError("You must implement the _visualize method.")
 
     @to_cuda
     def _test_iteration(
@@ -79,6 +82,7 @@ def test(self, visualize_every: int = 0, **kwargs):
             visualize_every (int, optional): Visualize the model predictions every n batches.
             Defaults to 0 (no visualization).
         """
+        _ = kwargs
         test_loss: MeanMetric = MeanMetric()
         test_metrics: Dict[str, MeanMetric] = defaultdict(MeanMetric)
         self._model.eval()
diff --git a/src/base_trainer.py b/src/base_trainer.py
index 1658c0e..bffae53 100644
--- a/src/base_trainer.py
+++ b/src/base_trainer.py
@@ -49,6 +49,7 @@ def __init__(
             train_loader (torch.utils.data.DataLoader): Training dataloader.
             val_loader (torch.utils.data.DataLoader): Validation dataloader.
         """
+        _ = kwargs
         self._run_name = run_name
         self._model = model
         self._opt = opt
@@ -98,6 +99,8 @@ def _train_val_iteration(
             torch.Tensor: The loss for the batch.
             Dict[str, torch.Tensor]: The loss components for the batch.
         """
+        _ = epoch
+        _ = validation
         # TODO: You'll most likely want to override this method.
         x, y = batch
         y_hat = self._model(x)
@@ -433,6 +436,8 @@ def _terminator(self, sig, frame):
         """
         Handles the SIGINT signal (Ctrl+C) and stops the training loop.
         """
+        _ = sig
+        _ = frame
         if (
             project_conf.SIGINT_BEHAVIOR
             == project_conf.TerminationBehavior.WAIT_FOR_EPOCH_END
diff --git a/utils/training.py b/utils/training.py
index f2c9b80..53815c2 100644
--- a/utils/training.py
+++ b/utils/training.py
@@ -15,13 +15,14 @@
 import torch
 
 import conf.project as project_conf
-from utils import to_cuda_
 
 
 def visualize_model_predictions(
     model: torch.nn.Module, batch: Union[Tuple, List, torch.Tensor], step: int
 ) -> None:
-    x, y = to_cuda_(batch)  # type: ignore
+    _ = model
+    _ = step
+    _ = batch
     if not project_conf.HEADLESS:
         raise NotImplementedError("Visualization is not implemented.")
     if project_conf.USE_WANDB: