diff --git a/src/brevitas/quant/fixed_point.py b/src/brevitas/quant/fixed_point.py
index bb3d7893e..a29a299f3 100644
--- a/src/brevitas/quant/fixed_point.py
+++ b/src/brevitas/quant/fixed_point.py
@@ -108,8 +108,8 @@ class Int4WeightPerTensorFixedPointDecoupled(WeightPerTensorFloatDecoupledL2Para
 class Int8WeightNormL2PerChannelFixedPoint(WeightNormPerChannelFloatDecoupled):
     """
     Experimental 8-bit narrow signed integer quantizer with learned per-channel scaling factors
-    and L2 weight normalization based on `Quantized Neural Networks for Low-Precision Accumulation 
-    with Guaranteed Overflow Avoidance` by I. Colbert, A. Pappalardo, and J. Petri-Koenig 
+    and L2 weight normalization based on `Quantized Neural Networks for Low-Precision Accumulation
+    with Guaranteed Overflow Avoidance` by I. Colbert, A. Pappalardo, and J. Petri-Koenig
     (https://arxiv.org/abs/2301.13376). The quantizer learns scaling factors in the float domain and
     learns vector parameter g in the log domain with the half-way rounding function. Suitable for
     retraining from floating-point depthwise separable weights.