diff --git a/backends/cadence/aot/replace_ops.py b/backends/cadence/aot/replace_ops.py
index 72d7fc164c4..5cf36bdc605 100644
--- a/backends/cadence/aot/replace_ops.py
+++ b/backends/cadence/aot/replace_ops.py
@@ -1458,17 +1458,7 @@ def maybe_remove_or_replace(self, node: torch.fx.Node) -> bool:
         # kernel_width for conv1d, and X = kernel_height * kernel_width for
         # conv2d. We extract X as the kernel_size for im2row.
         kernel_size = list(weight_shape[1:-1] if channel_last else weight_shape[2:])
-        # If the transposed_convolution op was quantized, we need the input tensor's
-        # zero_point for im2row. Otherwise in_zero_point defaults to a zero
-        # tensor. For quantized ops, the input_zero_point is at args[8] as an int,
-        # and we need to convert it to a tensor since transposed_im2row expects
-        # a Tensor for in_zero_point (unlike im2row which expects an int).
         assert isinstance(in_tensor, torch.fx.Node)
-        in_zero_point = (
-            torch.tensor(node.args[8], dtype=torch.int32)
-            if quantized_op
-            else torch.tensor(0, dtype=torch.int32)
-        )
 
         # Cast to appropriate types
         stride = cast(Sequence[int], stride)
@@ -1493,6 +1483,20 @@ def maybe_remove_or_replace(self, node: torch.fx.Node) -> bool:
 
         graph = node.graph
 
+        # If the transposed_convolution op was quantized, we need the input tensor's
+        # zero_point for im2row. Otherwise in_zero_point defaults to a zero tensor.
+        # We create the tensor as a graph node using aten.full to avoid
+        # DataDependentOutputException during FakeTensor shape propagation.
+        in_zero_point_val = node.args[8] if quantized_op else 0
+        in_zero_point = graph.call_function(
+            exir_ops.edge.aten.full.default,
+            args=([1], in_zero_point_val),
+            kwargs={"dtype": torch.int32},
+        )
+        # Insert the node before the current node
+        node.prepend(in_zero_point)
+        in_zero_point.meta = node.meta
+
         # Create a transposed_im2row node with the input. This will create a 2d
         # matrix of shape [out_height*out_weight, X*in_channels]. X is as
         # defined in the comment above.