(self, normalization, adj, cuda)
| 46 | self.degree_p = None |
| 47 | |
| 48 | def _preprocess_adj(self, normalization, adj, cuda): |
| 49 | adj_normalizer = fetch_normalization(normalization) |
| 50 | r_adj = adj_normalizer(adj) |
| 51 | r_adj = sparse_mx_to_torch_sparse_tensor(r_adj).float() |
| 52 | if cuda: |
| 53 | r_adj = r_adj.cuda() |
| 54 | return r_adj |
| 55 | |
| 56 | def _preprocess_fea(self, fea, cuda): |
| 57 | if cuda: |
no test coverage detected