diff --git a/backends/xnnpack/_passes/fuse_batch_norm.py b/backends/xnnpack/_passes/fuse_batch_norm.py
index 114f06dc6a6..a51920ed5ad 100644
--- a/backends/xnnpack/_passes/fuse_batch_norm.py
+++ b/backends/xnnpack/_passes/fuse_batch_norm.py
@@ -115,8 +115,14 @@ def can_fuse(
             return False
 
         # Check the rank of the convolutution input - only Conv1d and 2d are supported.
-        if is_conv and len(input_node.args[0].meta["val"].shape) not in (3, 4):
-            return False
+        if is_conv:
+            conv_input = input_node.args[0]
+            if (
+                not isinstance(conv_input, torch.fx.Node)
+                or "val" not in conv_input.meta
+                or len(conv_input.meta["val"].shape) not in (3, 4)
+            ):
+                return False
 
         return True
 
diff --git a/backends/xnnpack/test/passes/test_batch_norm_fusion.py b/backends/xnnpack/test/passes/test_batch_norm_fusion.py
index e5196b6a6a0..4e4cd065fa5 100644
--- a/backends/xnnpack/test/passes/test_batch_norm_fusion.py
+++ b/backends/xnnpack/test/passes/test_batch_norm_fusion.py
@@ -60,7 +60,7 @@ def __init__(
             self,
             in_features: int,
             out_features: int,
-            kernel_size: Tuple[int, int],
+            kernel_size: Tuple[int, int, int],
         ):
             super().__init__()
             op = torch.nn.Conv3d