diff --git a/experimental/torch_xla2/torch_xla2/ops/jaten.py b/experimental/torch_xla2/torch_xla2/ops/jaten.py
index 659901b74dc..05f440cae98 100644
--- a/experimental/torch_xla2/torch_xla2/ops/jaten.py
+++ b/experimental/torch_xla2/torch_xla2/ops/jaten.py
@@ -4844,22 +4844,17 @@ def _aten_lu_unpack(LU_data, LU_pivots, unpack_data=True, unpack_pivots=True):
     tile_shape[-1] = 1
     tile_shape[-2] = 1
     P = jnp.tile(identity2d, tile_shape)
-    #print("debug: start permutation matrix:", P)
 
     # closure to be called for each input 2D matrix.
     def _lu_unpack_2d(p, pivot):
-      jax.debug.print("unpack2d: {} {} {}", p , pivot, pivot.size)
       _pivot = pivot - 1           # pivots are offset by 1 in jax
       indices = jnp.array([*range(n)], dtype=jnp.int32)
       def update_indices(i, _indices):
-        #jax.debug.print("fori <<: {} {} {} {}", i, _indices, _pivot, p)
         tmp = _indices[i]
         _indices = _indices.at[i].set(_indices[_pivot[i]])
         _indices = _indices.at[_pivot[i]].set(tmp)
-        #jax.debug.print("fori >>: {} {} {} {}", i, _indices, _pivot, p)
         return _indices
       indices = jax.lax.fori_loop(0, _pivot.size, update_indices, indices)
-      #jax.debug.print("indices {}", indices)
       p = p[jnp.array(indices)]
       p = jnp.transpose(p)
       return p
@@ -4888,12 +4883,9 @@ def update_indices(i, _indices):
 
       # reshape result back to P's shape
       newRetshape = (*P.shape[:-2], unpackedP.shape[-2], unpackedP.shape[-1])
-      #print("newshape: {} {}", newRetshape, unpackedP.shape, dim)
       P = unpackedP.reshape(newRetshape)
-    #print("permutation after: ", P)
   else:
     # emulate pytroch behavior: return empty tensors
     P = torch.empty(torch.Size([0]))
 
-  #print("debug output:", P, L, U)
   return P, L, U