  When manipulating three-dimensional data, it is possible to ensure that
rotational and translational symmetries are respected by applying so-called
SE(3)-equivariant models. Protein structure prediction is a prominent example
of a task which displays these symmetries. Recent work in this area has
successfully made use of an SE(3)-equivariant model, applying an iterative
SE(3)-equivariant attention mechanism. Motivated by this application, we
implement an iterative version of the SE(3)-Transformer, an SE(3)-equivariant
attention-based model for graph data. We address the additional complications
which arise when applying the SE(3)-Transformer in an iterative fashion,
compare the iterative and single-pass versions on a toy problem, and consider
why an iterative model may be beneficial in some problem settings. We make the
code for our implementation available to the community.
