subprocess.CalledProcessError: Command '['ninja', '-v']' returned non-zero exit status 1.

RuntimeError: Error building extension 'torch_ext':
...
... ...site-packages/torchdrug/utils/extension/torch_ext.cpp:1:
/usr/include/features.h:424:12: fatal error: sys/cdefs.h: No such file or directory
 424 | # include <sys/cdefs.h>
 | ^~~~~~~~~~~~~
compilation terminated.
ninja: build stopped: subcommand failed.

NotImplementedError: Could not run 'aten::view' with arguments from the 'SparseCPU' backend

...
# graph
graph_construction_model = layers.GraphConstruction(node_layers=[geometry.AlphaCarbonNode()], 
 edge_layers=[geometry.SpatialEdge(radius=10.0, min_distance=5),
 geometry.KNNEdge(k=10, min_distance=5),
 geometry.SequentialEdge(max_distance=2)],
 edge_feature="gearnet")
# model
gearnet_edge = models.GearNet(input_dim=21, hidden_dims=[512, 512, 512, 512, 512, 512],
 num_relation=7, edge_input_dim=59, num_angle_bin=8,
 batch_norm=True, concat_hidden=True, short_cut=True, readout="sum")
pthfile = 'models/mc_gearnet_edge.pth'
net = torch.load(pthfile, map_location=torch.device(device))
#print('torch succesfully load model')
gearnet_edge.load_state_dict(net)
gearnet_edge.eval()
print('successfully load gearnet')
def get_subdataset_rep(pdbs: list, proteins: list, subroot: str):
 for idx in range(0, len(pdbs), bs): # reformulate to batches
 pdb_batch = pdbs[idx : min(len(pdbs), idx + bs)]
 protein_batch = proteins[idx : min(len(pdbs), idx + bs)]
 # protein
 _protein = data.Protein.pack(protein_batch)
 _protein.view = "residue"
 print(_protein)
 final_protein = graph_construction_model(_protein)
 print(final_protein)
 # output
 with torch.no_grad():
 output = gearnet_edge(final_protein, final_protein.node_feature.float(), all_loss=None, metric=None)
 print(output['graph_feature'].shape, output['node_feature'].shape)
 counter = 0
 for idx in range(len(final_protein.num_residues)): # idx: protein/graph id in this batch
 this_graph_feature = output['graph_feature'][idx]
 this_node_feature = output['node_feature'][counter : counter + final_protein.num_residues[idx], :]
 print(this_graph_feature.shape, this_node_feature.shape)
 torch.save((this_graph_feature, this_node_feature), f"{subroot}/{os.path.splitext(pdb_batch[idx])[0].split('/')[-1]}.pt")
 counter += final_protein.num_residues[idx]
 
 break
# get representations
if args.task not in ['FC', 'fc']:
 for root in roots:
 pdbs = [os.path.join(root, i) for i in os.listdir(root)]
 proteins = []
 for pdb_file in pdbs:
 try:
 protein = data.Protein.from_pdb(pdb_file, atom_feature="position", bond_feature="length", residue_feature="symbol")
 protein.view = "residue"
 proteins.append(protein)
 except:
 error_fn = os.path.basename(root) + '_' if args.task in ['EC', 'ec', 'GO', 'go'] else ''
 with open(f"{error_path}/{args.task}_{error_fn}error.txt", "a") as f:
 f.write(os.path.splitext(pdb_file)[0].split('/')[-1] + '\n')
 f.close()
 
 if len(proteins) == bs: # for debug
 break
 
 subroot = os.path.join(output_dir, root.split('/')[-1]) if args.task in ['EC', 'ec', 'GO', 'go'] else output_dir
 get_subdataset_rep(pdbs, proteins, subroot)
 break
else:
 transform = transforms.Compose([transforms.ProteinView(view='residue')])
 dataset = Fold3D(root, transform=transform) #, atom_feature=None, bond_feature=None
 
 split_sets = dataset.split() # train_set, valid_set, test_fold_set
 print('There are', len(split_sets), 'sets in total.')
 for split_set in split_sets:
 print(split_set.indices)
 this_slice = slice(list(split_set.indices)[0], (list(split_set.indices)[-1] + 1))
 this_pdbs, this_datas = dataset.pdb_files[this_slice], dataset.data[this_slice]
 #for fn, protein in zip(this_pdbs, this_datas):
 # print(fn, protein)
 # break
 get_subdataset_rep(this_pdbs, this_datas, os.path.join(output_dir, this_pdbs[0].split('/')[0]))