diff --git a/experimental/torch_xla2/torch_xla2/ops/jaten.py b/experimental/torch_xla2/torch_xla2/ops/jaten.py
index e9ce412379e..4181f11e3cf 100644
--- a/experimental/torch_xla2/torch_xla2/ops/jaten.py
+++ b/experimental/torch_xla2/torch_xla2/ops/jaten.py
@@ -4074,8 +4074,9 @@ def _aten__fft_c2r(self, dim, normalization, last_dim_size):
   return jnp.fft.irfftn(self, norm=norm, axes=dim, s=s)
 
 
+@op(torch.ops.aten.max_unpool2d)
 @op(torch.ops.aten.max_unpool3d)
-def _aten_max_unpool3d(input, indices, output_size, stride=None, padding=0):
+def _aten_max_unpoolxd(input, indices, output_size, stride=None, padding=0):
     if output_size is None:
       raise ValueError("output_size value is not set correctly. It cannot be None or empty.")
 
@@ -4091,21 +4092,3 @@ def _aten_max_unpool3d(input, indices, output_size, stride=None, padding=0):
 
     return output
 
-@op(torch.ops.aten.max_unpool2d)
-def _aten_max_unpool2d(input, indices, output_size, stride=None, padding=0):
-    if output_size is None:
-        raise ValueError("output_size value is not set correctly. It cannot be None or empty.")
-
-    output_size = [input.shape[0], input.shape[1]] + output_size
-
-    output = jnp.zeros(output_size, dtype=input.dtype)
-
-    for idx in np.ndindex(input.shape):
-        max_index = indices[idx]
-        spatial_dims = output_size[2:]
-        unpooled_spatial_idx = np.unravel_index(max_index, spatial_dims)
-        full_idx = idx[:2] + unpooled_spatial_idx
-        output = output.at[full_idx].set(input[idx])
-
-    return output
-