diff --git a/examples/04_training/01_train_dynedge.py b/examples/04_training/01_train_dynedge.py
index 3e31fc126..b8a8e82b7 100644
--- a/examples/04_training/01_train_dynedge.py
+++ b/examples/04_training/01_train_dynedge.py
@@ -82,13 +82,16 @@ def main(
         wandb_logger.experiment.config.update(config)
 
     # Define graph/data representation, here the KNNGraph is used.
-    # The KNNGraph is a graph representation which uses the KNNEdges edge definition with 8 neighbours as default.
-    # The graph representation is defined by the detector, in this case the Prometheus detector.
+    # The KNNGraph is a graph representation, which uses the
+    # KNNEdges edge definition with 8 neighbours as default.
+    # The graph representation is defined by the detector,
+    # in this case the Prometheus detector.
     # The standard node definition is used, which is NodesAsPulses.
     graph_definition = KNNGraph(detector=Prometheus())
 
     # Use GraphNetDataModule to load in data and create dataloaders
-    # The input here depends on the dataset being used, in this case the Prometheus dataset.
+    # The input here depends on the dataset being used,
+    # in this case the Prometheus dataset.
     dm = GraphNeTDataModule(
         dataset_reference=config["dataset_reference"],
         dataset_args={
@@ -114,16 +117,18 @@ def main(
 
     # Building model
 
-    # Define architecture of the backbone, in this example we use the DynEdge architecture
-    # described in detail in the Jinst paper: https://iopscience.iop.org/article/10.1088/1748-0221/17/11/P11003
+    # Define architecture of the backbone, in this example
+    # the DynEdge architecture is used.
+    # https://iopscience.iop.org/article/10.1088/1748-0221/17/11/P11003
     backbone = DynEdge(
         nb_inputs=graph_definition.nb_outputs,
         global_pooling_schemes=["min", "max", "mean", "sum"],
     )
     # Define the task.
-    # In this case we are performing energy reconstruction, with a LogCoshLoss function.
-    # The target and prediction are transformed using the log10 function. When infering
-    # the prediction is transformed back to the original scale using 10^x.
+    # Here an energy reconstruction, with a LogCoshLoss function.
+    # The target and prediction are transformed using the log10 function.
+    # When infering the prediction is transformed back to the
+    # original scale using 10^x.
     task = EnergyReconstruction(
         hidden_size=backbone.nb_outputs,
         target_labels=config["target"],
@@ -131,8 +136,9 @@ def main(
         transform_prediction_and_target=lambda x: torch.log10(x),
         transform_inference=lambda x: torch.pow(10, x),
     )
-    # Define the full model, which includes the backbone, task(s) along with typical
-    # machine learning options such as learning rate optimizer and scheduler.
+    # Define the full model, which includes the backbone, task(s),
+    # along with typical machine learning options such as
+    # learning rate optimizers and schedulers.
     model = StandardModel(
         graph_definition=graph_definition,
         backbone=backbone,