The norm value is calculated as `sqrt(scale) / matrix norm`.
Finally, the result is returned as `input_tensor * norm value`.
This layer can be used instead of LayerNorm when a scaled version of the norm is required.
Instead of performing the scaling operation (`scale / norm`) in a lambda-like layer, we are defining it within this layer to make prototyping more efficient.
References
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.. [1] Lukasz Maziarka et al. "Molecule Attention Transformer" Graph Representation Learning workshop and Machine Learning and the Physical Sciences workshop at NeurIPS 2019. 2020. https://arxiv.org/abs/2002.08264
