convert size to a list

torch_scatter/scatter.py

@@ -11,7 +11,7 @@ def scatter_sum(src: torch.Tensor, index: torch.Tensor, dim: int = -1,
                 dim_size: Optional[int] = None) -> torch.Tensor:
     index = broadcast(index, src, dim)
     if out is None:
-        size = src.size()
+        size = list(src.size())
         if dim_size is not None:
             size[dim] = dim_size
         elif index.numel() == 0:
@@ -57,18 +57,18 @@ def scatter_mean(src: torch.Tensor, index: torch.Tensor, dim: int = -1,
 
 
 @torch.jit.script
-def scatter_min(src: torch.Tensor, index: torch.Tensor, dim: int = -1,
-                out: Optional[torch.Tensor] = None,
-                dim_size: Optional[int] = None
-                ) -> Tuple[torch.Tensor, torch.Tensor]:
+def scatter_min(
+        src: torch.Tensor, index: torch.Tensor, dim: int = -1,
+        out: Optional[torch.Tensor] = None,
+        dim_size: Optional[int] = None) -> Tuple[torch.Tensor, torch.Tensor]:
     return torch.ops.torch_scatter.scatter_min(src, index, dim, out, dim_size)
 
 
 @torch.jit.script
-def scatter_max(src: torch.Tensor, index: torch.Tensor, dim: int = -1,
-                out: Optional[torch.Tensor] = None,
-                dim_size: Optional[int] = None
-                ) -> Tuple[torch.Tensor, torch.Tensor]:
+def scatter_max(
+        src: torch.Tensor, index: torch.Tensor, dim: int = -1,
+        out: Optional[torch.Tensor] = None,
+        dim_size: Optional[int] = None) -> Tuple[torch.Tensor, torch.Tensor]:
     return torch.ops.torch_scatter.scatter_max(src, index, dim, out, dim_size)