diff --git a/backends/cadence/aot/utils.py b/backends/cadence/aot/utils.py
index b710f7d4e57..c412ce7aa61 100644
--- a/backends/cadence/aot/utils.py
+++ b/backends/cadence/aot/utils.py
@@ -78,6 +78,76 @@ def get_conv2d_output_size(
     return torch.Size((in_size[0], out_channels, hout, wout))
 
 
+# Get the output size of a transposed 1D convolution given the input size and parameters
+def get_conv_transpose1d_output_size(
+    in_size: torch.Size,
+    kernel_size: List[int],
+    out_channels: int,
+    stride: Tuple[int],
+    padding: Tuple[int],
+    dilation: Tuple[int],
+    output_padding: Tuple[int],
+    channel_last: bool = False,
+) -> torch.Size:
+    assert len(in_size) == 3
+    if channel_last:
+        N, L, C = in_size
+    else:
+        N, C, L = in_size
+
+    # Reference: https://pytorch.org/docs/stable/generated/torch.nn.ConvTranspose1d.html
+    lout = (
+        (L - 1) * stride[0]
+        - 2 * padding[0]
+        + dilation[0] * (kernel_size[0] - 1)
+        + output_padding[0]
+        + 1
+    )
+
+    if channel_last:
+        return torch.Size((in_size[0], lout, out_channels))
+    else:
+        return torch.Size((in_size[0], out_channels, lout))
+
+
+def get_conv_transpose2d_output_size(
+    in_size: torch.Size,
+    kernel_size: List[int],
+    out_channels: int,
+    stride: Tuple[int],
+    padding: Tuple[int],
+    dilation: Tuple[int],
+    output_padding: Tuple[int],
+    channel_last: bool = False,
+) -> torch.Size:
+    assert len(in_size) == 4
+    if channel_last:
+        N, H, W, C = in_size
+    else:
+        N, C, H, W = in_size
+
+    # Reference: https://pytorch.org/docs/stable/generated/torch.nn.ConvTranspose2d.html
+    hout = (
+        (H - 1) * stride[0]
+        - 2 * padding[0]
+        + dilation[0] * (kernel_size[0] - 1)
+        + output_padding[0]
+        + 1
+    )
+    wout = (
+        (W - 1) * stride[1]
+        - 2 * padding[1]
+        + dilation[1] * (kernel_size[1] - 1)
+        + output_padding[1]
+        + 1
+    )
+
+    if channel_last:
+        return torch.Size((in_size[0], hout, wout, out_channels))
+    else:
+        return torch.Size((in_size[0], out_channels, hout, wout))
+
+
 # Return the overload packet for the edge op
 def get_edge_overload_packet(edge_op: EdgeOpOverload) -> EdgeOpOverloadPacket:
     edge_op_namespace, edge_op_name = (