diff --git a/tensornetwork/backends/pytorch/pytorch_backend.py b/tensornetwork/backends/pytorch/pytorch_backend.py
index e0b1dc743..2f48e973e 100644
--- a/tensornetwork/backends/pytorch/pytorch_backend.py
+++ b/tensornetwork/backends/pytorch/pytorch_backend.py
@@ -410,45 +410,16 @@ def trace(self, tensor: Tensor, offset: int = 0, axis1: int = -2,
     axis1 and axis2 are used to determine the 2-D sub-array whose diagonal is
     summed.
 
-    In the PyTorch backend the trace is always over the main diagonal of the
-    last two entries.
-
     Args:
       tensor: A tensor.
       offset: Offset of the diagonal from the main diagonal.
-              This argument is not supported  by the PyTorch
-              backend and an error will be raised if they are
-              specified.
       axis1, axis2: Axis to be used as the first/second axis of the 2D
                     sub-arrays from which the diagonals should be taken.
-                    Defaults to first/second axis.
-                    These arguments are not supported by the PyTorch
-                    backend and an error will be raised if they are
-                    specified.
+                    Defaults to second-last/last axis.
     Returns:
       array_of_diagonals: The batched summed diagonals.
     """
-    if offset != 0:
-      errstr = (f"offset = {offset} must be 0 (the default)"
-                f"with PyTorch backend.")
-      raise NotImplementedError(errstr)
-    if axis1 == axis2:
-      raise ValueError(f"axis1 = {axis1} cannot equal axis2 = {axis2}")
-    N = len(tensor.shape)
-    if N > 25:
-      raise ValueError(f"Currently only tensors with ndim <= 25 can be traced"
-                       f"in the PyTorch backend (yours was {N})")
-
-    if axis1 < 0:
-      axis1 = N+axis1
-    if axis2 < 0:
-      axis2 = N+axis2
-
-    inds = list(map(chr, range(98, 98+N)))
-    indsout = [i for n, i in enumerate(inds) if n not in (axis1, axis2)]
-    inds[axis1] = 'a'
-    inds[axis2] = 'a'
-    return torchlib.einsum(''.join(inds) + '->' +''.join(indsout), tensor)
+    return torchlib.sum(torchlib.diagonal(tensor, offset=offset, dim1=axis1, dim2=axis2), dim=-1)
 
   def abs(self, tensor: Tensor) -> Tensor:
     """
diff --git a/tensornetwork/backends/pytorch/pytorch_backend_test.py b/tensornetwork/backends/pytorch/pytorch_backend_test.py
index d5fbade5f..cbedf9952 100644
--- a/tensornetwork/backends/pytorch/pytorch_backend_test.py
+++ b/tensornetwork/backends/pytorch/pytorch_backend_test.py
@@ -621,27 +621,15 @@ def test_trace(dtype, offset, axis1, axis2):
   shape = (5, 5, 5, 5)
   backend = pytorch_backend.PyTorchBackend()
   array = backend.randn(shape, dtype=dtype, seed=10)
-  if offset != 0:
-    with pytest.raises(NotImplementedError):
-      actual = backend.trace(array, offset=offset, axis1=axis1, axis2=axis2)
-
-  elif axis1 == axis2:
-    with pytest.raises(ValueError):
+  if axis1 == axis2:
+    with pytest.raises(RuntimeError):
       actual = backend.trace(array, offset=offset, axis1=axis1, axis2=axis2)
   else:
     actual = backend.trace(array, offset=offset, axis1=axis1, axis2=axis2)
-    expected = np.trace(array, axis1=axis1, axis2=axis2)
+    expected = np.trace(array, offset=offset, axis1=axis1, axis2=axis2)
     np.testing.assert_allclose(actual, expected, atol=1e-6, rtol=1e-6)
 
 
-def test_trace_raises():
-  shape = tuple([1] * 30)
-  backend = pytorch_backend.PyTorchBackend()
-  array = backend.randn(shape, seed=10)
-  with pytest.raises(ValueError):
-    _ = backend.trace(array)
-
-
 @pytest.mark.parametrize("pivot_axis", [-1, 1, 2])
 @pytest.mark.parametrize("dtype", torch_randn_dtypes)
 def test_pivot(dtype, pivot_axis):