One of the GATv2Conv hyperparameters, heads, describes the number of multi-head attentions . Each "head" denotes an independent attention computation, resulting in a separate node embedding. GATv2Conv implements subsequent embeddings by averaging or concatenation when there are multiple heads (by default, heads = 1). Xue and co-workers proposed to reduce the size of such concatenated embeddings by using an additional dense layer (DL) before passing them to the subsequent GATv2Conv layer (GATv2Conv + DL). Unfortunately, after testing the NN architecture proposed by the previous authors via 10-fold cross-validation on METLIN SMRT, we obtained only three converged folds out of ten. In our hands, the high number of hidden layers of GATv2Conv during the hyperparameter optimization step tended to produce non-converged NNs.