diff --git a/clip/clip.py b/clip/clip.py
index 257511e..f7a5da5 100644
--- a/clip/clip.py
+++ b/clip/clip.py
@@ -145,6 +145,14 @@ def load(name: str, device: Union[str, torch.device] = "cuda" if torch.cuda.is_a
     device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[])
     device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1]
 
+    def _node_get(node: torch._C.Node, key: str):
+        """Gets attributes of a node which is polymorphic over return type.
+        
+        From https://github.com/pytorch/pytorch/pull/82628
+        """
+        sel = node.kindOf(key)
+        return getattr(node, sel)(key)
+
     def patch_device(module):
         try:
             graphs = [module.graph] if hasattr(module, "graph") else []
@@ -156,7 +164,7 @@ def load(name: str, device: Union[str, torch.device] = "cuda" if torch.cuda.is_a
 
         for graph in graphs:
             for node in graph.findAllNodes("prim::Constant"):
-                if "value" in node.attributeNames() and str(node["value"]).startswith("cuda"):
+                if "value" in node.attributeNames() and str(_node_get(node, "value")).startswith("cuda"):
                     node.copyAttributes(device_node)
 
     model.apply(patch_device)
@@ -182,7 +190,7 @@ def load(name: str, device: Union[str, torch.device] = "cuda" if torch.cuda.is_a
                 for node in graph.findAllNodes("aten::to"):
                     inputs = list(node.inputs())
                     for i in [1, 2]:  # dtype can be the second or third argument to aten::to()
-                        if inputs[i].node()["value"] == 5:
+                        if _node_get(inputs[i].node(), "value") == 5:
                             inputs[i].node().copyAttributes(float_node)
 
         model.apply(patch_float)