diff --git a/model_conditioning_examples/weight_clustering.py b/model_conditioning_examples/weight_clustering.py
index 6672d53..e966336 100644
--- a/model_conditioning_examples/weight_clustering.py
+++ b/model_conditioning_examples/weight_clustering.py
@@ -1,4 +1,4 @@
-#  SPDX-FileCopyrightText: Copyright 2021 Arm Limited and/or its affiliates <open-source-office@arm.com>
+#  SPDX-FileCopyrightText:  Copyright 2021, 2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
 #  SPDX-License-Identifier: Apache-2.0
 #
 #  Licensed under the Apache License, Version 2.0 (the "License");
@@ -13,22 +13,29 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 """
-This script will provide you with a short example of how to perform clustering of weights (weight sharing) in
-TensorFlow using the TensorFlow Model Optimization Toolkit.
+This script will provide you with a short example of how to perform
+clustering of weights (weight sharing) in TensorFlow
+using the TensorFlow Model Optimization Toolkit.
 
-The output from this example will be a TensorFlow Lite model file where weights in each layer have been 'clustered' into
-16 clusters during training - quantization has then been applied on top of this.
+The output from this example will be a TensorFlow Lite model file
+where weights in each layer have been 'clustered' into 16 clusters
+during training - quantization has then been applied on top of this.
 
-By clustering the model we can improve compression of the model file. This can be essential for deploying certain
-models on systems with limited resources - such as embedded systems using an Arm Ethos NPU.
+By clustering the model we can improve compression of the model file.
+This can be essential for deploying certain models on systems with
+limited resources - such as embedded systems using an Arm Ethos NPU.
 
-After performing clustering we do post-training quantization to quantize the model and then generate a TensorFlow Lite file.
+After performing clustering we do post-training quantization
+to quantize the model and then generate a TensorFlow Lite file.
 
-If you are targetting an Arm Ethos-U55 NPU then the output TensorFlow Lite file will also need to be passed through the Vela
+If you are targeting an Arm Ethos-U55 NPU then the output
+TensorFlow Lite file will also need to be passed through the Vela
 compiler for further optimizations before it can be used.
 
-For more information on using Vela see: https://git.mlplatform.org/ml/ethos-u/ethos-u-vela.git/about/
-For more information on clustering see: https://www.tensorflow.org/model_optimization/guide/clustering
+For more information on using Vela see:
+    https://git.mlplatform.org/ml/ethos-u/ethos-u-vela.git/about/
+For more information on clustering see:
+    https://www.tensorflow.org/model_optimization/guide/clustering
 """
 import pathlib
 
@@ -42,39 +49,52 @@
 def prepare_for_clustering(keras_model):
     """Prepares a Keras model for clustering."""
 
-    # Choose the number of clusters to use and how to initialize them. Using more clusters will generally
-    # reduce accuracy so you will need to find the optimal number for your use-case.
+    # Choose the number of clusters to use and how to initialize them.
+    # Using more clusters will generally reduce accuracy,
+    # so you will need to find the optimal number for your use-case.
     number_of_clusters = 16
     cluster_centroids_init = tfmot.clustering.keras.CentroidInitialization.LINEAR
 
-    # Apply the clustering wrapper to the whole model so weights in every layer will get clustered. You may find that
-    # to avoid too much accuracy loss only certain non-critical layers in your model should be clustered.
-    clustering_ready_model = tfmot.clustering.keras.cluster_weights(keras_model,
-                                                                    number_of_clusters=number_of_clusters,
-                                                                    cluster_centroids_init=cluster_centroids_init)
+    # Apply the clustering wrapper to the whole model so weights in
+    # every layer will get clustered. You may find that to avoid
+    # too much accuracy loss only certain non-critical layers in
+    # your model should be clustered.
+    clustering_ready_model = tfmot.clustering.keras.cluster_weights(
+        keras_model,
+        number_of_clusters=number_of_clusters,
+        cluster_centroids_init=cluster_centroids_init
+    )
 
     # We must recompile the model after making it ready for clustering.
-    clustering_ready_model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
-                                   loss=tf.keras.losses.sparse_categorical_crossentropy,
-                                   metrics=['accuracy'])
+    clustering_ready_model.compile(
+        optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
+        loss=tf.keras.losses.sparse_categorical_crossentropy,
+        metrics=['accuracy']
+    )
 
     return clustering_ready_model
 
 
 def main():
+    """
+    Run weight clustering
+    """
     x_train, y_train, x_test, y_test = get_data()
     model = create_model()
 
     # Compile and train the model first.
-    # In general it is easier to do clustering as a fine-tuning step after the model is fully trained.
-    model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
-                  loss=tf.keras.losses.sparse_categorical_crossentropy,
-                  metrics=['accuracy'])
+    # In general, it is easier to do clustering as a
+    # fine-tuning step after the model is fully trained.
+    model.compile(
+        optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
+        loss=tf.keras.losses.sparse_categorical_crossentropy,
+        metrics=['accuracy']
+    )
 
     model.fit(x=x_train, y=y_train, batch_size=128, epochs=5, verbose=1, shuffle=True)
 
     # Test the trained model accuracy.
-    test_loss, test_acc = model.evaluate(x_test, y_test)
+    test_loss, test_acc = model.evaluate(x_test, y_test)  # pylint: disable=unused-variable
     print(f"Test accuracy before clustering: {test_acc:.3f}")
 
     # Prepare the model for clustering.
@@ -88,19 +108,26 @@
     # Remove all variables that clustering only needed in the training phase.
     model_for_export = tfmot.clustering.keras.strip_clustering(clustered_model)
 
-    # Apply post-training quantization on top of the clustering and save the resulting TensorFlow Lite model to file.
+    # Apply post-training quantization on top of the clustering
+    # and save the resulting TensorFlow Lite model to file.
     tflite_model = post_training_quantize(model_for_export, x_train)
 
     tflite_models_dir = pathlib.Path('./conditioned_models/')
     tflite_models_dir.mkdir(exist_ok=True, parents=True)
 
-    clustered_quant_model_save_path = tflite_models_dir / 'clustered_post_training_quant_model.tflite'
+    clustered_quant_model_save_path = \
+        tflite_models_dir / 'clustered_post_training_quant_model.tflite'
     with open(clustered_quant_model_save_path, 'wb') as f:
         f.write(tflite_model)
 
-    # Test the clustered quantized model accuracy. Save time by only testing a subset of the whole data.
+    # Test the clustered quantized model accuracy.
+    # Save time by only testing a subset of the whole data.
     num_test_samples = 1000
-    evaluate_tflite_model(clustered_quant_model_save_path, x_test[0:num_test_samples], y_test[0:num_test_samples])
+    evaluate_tflite_model(
+        clustered_quant_model_save_path,
+        x_test[0:num_test_samples],
+        y_test[0:num_test_samples]
+    )
 
 
 if __name__ == "__main__":