typo

README.md

@@ -82,15 +82,15 @@ from torch_spline_conv import SplineConv
 src = torch.rand((4, 2), dtype=torch.float)  # 4 nodes with 2 features each
 edge_index = torch.tensor([[0, 1, 1, 2, 2, 3], [1, 0, 2, 1, 3, 2]])  # 6 edges
 pseudo = torch.rand((6, 2), dtype=torch.float)  # two-dimensional edge attributes
-weight = torch.rand((25, 2, 4), dtype=torch.float)  # 25 trainable parameters for in_channels x out_channels
-kernel_size = torch.tensor([5, 5])  # 5 trainable parameters in each edge dimension
+weight = torch.rand((25, 2, 4), dtype=torch.float)  # 25 parameters for in_channels x out_channels
+kernel_size = torch.tensor([5, 5])  # 5 parameters in each edge dimension
 is_open_spline = torch.tensor([1, 1], dtype=torch.uint8)  # only use open B-splines
 degree = 1  # B-spline degree of 1
 root_weight = torch.rand((2, 4), dtype=torch.float)  # separately weight root nodes
 bias = None  # do not apply an additional bias
 
 out = SplineConv.apply(src, edge_index, pseudo, weight, kernel_size,
-                          is_open_spline, degree, root_weight, bias)
+                       is_open_spline, degree, root_weight, bias)
 
 print(out.size())
 torch.Size([4, 4])  # 4 nodes with 4 features each