Replace some use of broadcastable with shape in tests.tensor.test_subtensor

tests/tensor/test_subtensor.py

@@ -465,7 +465,7 @@ class TestSubtensor(utt.OptimizationTestMixin):
     def test_ok_row(self):
         n = self.shared(np.arange(6, dtype=self.dtype).reshape((2, 3)))
         t = n[1]
-        assert not any(n.type.broadcastable)
+        assert not any(s == 1 for s in n.type.shape)
         assert isinstance(t.owner.op, Subtensor)
         tval = self.eval_output_and_check(t)
         assert tval.shape == (3,)
@@ -475,7 +475,7 @@ class TestSubtensor(utt.OptimizationTestMixin):
         n = self.shared(np.arange(6, dtype=self.dtype).reshape((2, 3)))
         t = n[:, 0]
         assert isinstance(t.owner.op, Subtensor)
-        assert not any(n.type.broadcastable)
+        assert not any(s == 1 for s in n.type.shape)
         tval = self.eval_output_and_check(t)
         assert tval.shape == (2,)
         assert np.all(tval == [0, 3])
@@ -1773,15 +1773,17 @@ class TestAdvancedSubtensor:
     def test_index_into_vec_w_matrix(self):
         a = self.v[self.ix2]
         assert a.dtype == self.v.dtype, (a.dtype, self.v.dtype)
-        assert a.broadcastable == self.ix2.broadcastable, (
-            a.broadcastable,
-            self.ix2.broadcastable,
+        assert a.type.ndim == self.ix2.type.ndim
+        assert all(
+            s1 == s2
+            for s1, s2 in zip(a.type.shape, self.ix2.type.shape)
+            if s1 == 1 or s2 == 1
         )
 
     def test_index_into_mat_w_row(self):
         a = self.m[self.ixr]
         assert a.dtype == self.m.dtype, (a.dtype, self.m.dtype)
-        assert a.broadcastable == (True, False, False)
+        assert a.type.shape == (1, None, None)
 
     def test_index_w_int_and_vec(self):
         # like test_ok_list, but with a single index on the first one
@@ -2447,7 +2449,7 @@ class TestInferShape(utt.InferShapeTester):
         )
 
         abs_res = n[~isinf(n)]
-        assert abs_res.broadcastable == (False,)
+        assert abs_res.type.shape == (None,)
 
 
 @config.change_flags(compute_test_value="raise")
@@ -2468,9 +2470,7 @@ def idx_as_tensor(x):
 def bcast_shape_tuple(x):
     if not hasattr(x, "shape"):
         return x
-    return tuple(
-        s if not bcast else 1 for s, bcast in zip(tuple(x.shape), x.broadcastable)
-    )
+    return tuple(s if ss != 1 else 1 for s, ss in zip(tuple(x.shape), x.type.shape))
 
 
 test_idx = np.ix_(np.array([True, True]), np.array([True]), np.array([True, True]))