diff --git a/tutorials/torch_example.py b/tutorials/torch_example.py
index bdc0cd4f..59148778 100755
--- a/tutorials/torch_example.py
+++ b/tutorials/torch_example.py
@@ -1,3 +1,10 @@
+"""
+Toy example for hyperparameter optimization / NAS in Propulate, using a simple convolutional network trained on the
+MNIST dataset.
+
+This script was tested on a single compute node with 4 GPUs. Note that you need to adapt ``GPUS_PER_NODE`` (see ll. 25).
+and
+"""
 import logging
 import random
 from typing import Union, Dict, Tuple
@@ -15,8 +22,12 @@
 from propulate.utils import get_default_propagator, set_logger_config
 
 
-GPUS_PER_NODE: int = 4
+GPUS_PER_NODE: int = 4  # This example script was tested on a single node with 4 GPUs.
+NUM_WORKERS: int = (
+    2  # Set this to the recommended number of workers in the PyTorch dataloader.
+)
 log_path = "torch_ckpts"
+log = logging.getLogger(__name__)  # Get logger instance.
 
 
 class Net(LightningModule):
@@ -39,11 +50,11 @@ def __init__(
         activation: torch.nn.modules.activation
             The activation function to use.
         lr: float
-            learning rate
+            The learning rate.
         loss_fn: torch.nn.modules.loss
             The loss function.
         """
-        super(Net, self).__init__()
+        super().__init__()
 
         self.lr = lr  # Set learning rate.
         self.loss_fn = loss_fn  # Set the loss function used for training the model.
@@ -180,11 +191,11 @@ def get_data_loaders(batch_size: int) -> Tuple[DataLoader, DataLoader]:
         The validation dataloader.
     """
     data_transform = Compose([ToTensor(), Normalize((0.1307,), (0.3081,))])
-    num_workers = 2
+    num_workers = NUM_WORKERS
     # num_workers = int(int(os.getenv("SLURM_CPUS_ON_NODE")) / MPI.COMM_WORLD.size)
     # Alternatively, use "SLURM_CPUS_PER_GPU".
     # Only set if the --cpus-per-gpu option is specified.
-    print(f"Use {num_workers} workers in dataloader.")
+    log.info(f"Use {num_workers} workers in dataloader.")
 
     if MPI.COMM_WORLD.rank == 0:  # Only root downloads data.
         train_loader = DataLoader(
@@ -229,7 +240,7 @@ def ind_loss(params: Dict[str, Union[int, float, str]]) -> float:
 
     Parameters
     ----------
-    param s: dict[str, int | float | str]]
+    params : Dict[str, int | float | str]
         The hyperparameters to be optimized evolutionarily.
 
     Returns