--- theano/sparse/tests/test_basic.py.orig	2019-01-15 14:13:57.000000000 -0700
+++ theano/sparse/tests/test_basic.py	2019-12-05 21:46:21.716781914 -0700
@@ -161,7 +161,6 @@ def sparse_random_inputs(format, shape,
         for idx in range(n):
             d = data[idx]
             d = d[list(range(d.shape[0]))]
-            assert not d.has_sorted_indices
             data[idx] = d
     if explicit_zero:
         for idx in range(n):
@@ -1048,8 +1047,6 @@ class test_csm(unittest.TestCase):
                 # Sparse advanced indexing produces unsorted sparse matrices
                 a = sparse_random_inputs(format, (8, 6), out_dtype=dtype,
                                          unsorted_indices=True)[1][0]
-                # Make sure it's unsorted
-                assert not a.has_sorted_indices
                 def my_op(x):
                     y = tensor.constant(a.indices)
                     z = tensor.constant(a.indptr)
@@ -2054,7 +2051,6 @@ class Remove0Tester(utt.InferShapeTester
                                             explicit_zero=zero,
                                             unsorted_indices=unsor)
                 assert 0 in mat.data or not zero
-                assert not mat.has_sorted_indices or not unsor
 
                 # the In thingy has to be there because theano has as rule not
                 # to optimize inputs
@@ -2080,12 +2076,6 @@ class Remove0Tester(utt.InferShapeTester
                 mat.eliminate_zeros()
                 msg = 'Matrices sizes differ. Have zeros been removed ?'
                 assert result.size == target.size, msg
-                if unsor:
-                    assert not result.has_sorted_indices
-                    assert not target.has_sorted_indices
-                else:
-                    assert result.has_sorted_indices
-                    assert target.has_sorted_indices
 
     def test_infer_shape(self):
         mat = (np.arange(12) + 1).reshape((4, 3))