import torch n_nodes = 10 n_edges = 20 batch_size = 2 sparse_list = [] for i in range(batch_size): indices = torch.randint(0, n_nodes, (2, n_edges)) values = torch.randn(n_edges) size = (n_nodes, n_nodes) sparse_list.append(torch.sparse_coo_tensor(indices, values, size...
python3.11/site-packages/torch/_functorch/eager_transforms.py", line 1312, in wrapper_fn results = vmap(push_jvp, randomness=randomness)(basis) File "/home/cwtan/anaconda3/envs/florch/lib/python3.11/site-packages/torch/_functorch/apis.py", line 200, in wrapped return vmap_impl( File "...