works with variables

torch_spline_conv/functions/spline_conv.py

 import torch
+from torch.autograd import Variable as Var
 
 from .degree import node_degree
 from .utils import spline_basis, spline_weighting
@@ -31,12 +32,14 @@ def spline_conv(x,
 
     # Perform the real convolution => Convert |E| x M_out to N x M_out output.
     row = edge_index[0].unsqueeze(-1).expand(e, m_out)
-    zero = x.new(n, m_out).fill_(0)
-    output = zero.scatter_add_(0, row, output)
+    row = row if torch.is_tensor(x) else Var(row)
+    zero = x.new(n, m_out) if torch.is_tensor(x) else Var(x.data.new(n, m_out))
+    output = zero.fill_(0).scatter_add_(0, row, output)
 
     # Normalize output by node degree.
-    degree = node_degree(edge_index, n, out=x.new())
-    output /= degree.unsqueeze(-1).clamp_(min=1)
+    degree = x.new() if torch.is_tensor(x) else x.data.new()
+    degree = node_degree(edge_index, n, out=degree).unsqueeze(-1).clamp_(min=1)
+    output /= degree if torch.is_tensor(x) else Var(degree)
 
     # Weight root node separately (if wished).
     if root_weight is not None: