diff --git a/experimental/torch_xla2/test/test_ops.py b/experimental/torch_xla2/test/test_ops.py
index b242636c5ca..3b4e7d98cc0 100644
--- a/experimental/torch_xla2/test/test_ops.py
+++ b/experimental/torch_xla2/test/test_ops.py
@@ -31,7 +31,6 @@
     "linalg.lu_solve",
     "linalg.matrix_norm",
     "linalg.matrix_power",
-    "linalg.tensorsolve",
     "masked.median",
     "max_pool2d_with_indices_backward",
     "nn.functional.adaptive_avg_pool3d",
diff --git a/experimental/torch_xla2/torch_xla2/ops/jtorch.py b/experimental/torch_xla2/torch_xla2/ops/jtorch.py
index e82621bec3b..b6f4b1ca9d3 100644
--- a/experimental/torch_xla2/torch_xla2/ops/jtorch.py
+++ b/experimental/torch_xla2/torch_xla2/ops/jtorch.py
@@ -376,3 +376,25 @@ def lu_solve(b, LU_data, LU_pivots, **kwargs):
   _pivots = LU_pivots - 1
   x = jax.scipy.linalg.lu_solve((LU_data, _pivots), b)
   return x
+
+@register_function(torch.linalg.tensorsolve)
+def linalg_tensorsolve(A, b, dims=None):
+  # examples:
+  # A = torch.randn(2, 3, 6), b = torch.randn(3, 2)
+  # A = torch.randn(2, 3, 6), b = torch.randn(2, 3) -> torch.Size([3, 6])
+  # A = torch.randn(9, 2, 6, 3) b = torch.randn(6, 3) -> torch.Size([6, 3])
+  # A = torch.randn(9, 2, 3, 6) b = torch.randn(6, 3) -> torch.Size([3, 6])
+  # A = torch.randn(18, 6, 3) b = torch.randn(18) -> torch.Size([6, 3])
+  # A = torch.randn(3, 8, 4, 6) b = torch.randn(4, 6) -> torch.Size([4,6])
+  # A = torch.randn(3, 8, 1, 2, 2, 6) b = torch.randn(3, 4, 2) -> torch.Size([2, 2, 6])
+
+  # torch allows b to be shaped differently.
+  # especially when axes are moved using dims.
+  # ValueError: After moving axes to end, leading shape of a must match shape of b. got a.shape=(3, 2, 6), b.shape=(2, 3)
+  # So we are handling the moveaxis and forcing b's shape to match what jax expects
+  if dims is not None:
+    A = jnp.moveaxis(A, dims, len(dims) * (A.ndim - 1,))
+    dims = None
+  if A.shape[:b.ndim] != b.shape:
+    b = jnp.reshape(b, A.shape[:b.ndim])
+  return jnp.linalg.tensorsolve(A, b, axes=dims)