Prevent test value computation from complaining about subtensor creation

tests/tensor/test_subtensor.py

@@ -2183,6 +2183,19 @@ class TestInferShape(utt.InferShapeTester):
             AdvancedSubtensor,
         )
 
+        admat.tag.test_value = admat_val
+        aivec.tag.test_value = aivec_val
+        bivec.tag.test_value = bivec_val
+
+        # Make sure it doesn't complain about test values
+        with theano.change_flags(compute_test_value="raise"):
+            self._compile_and_check(
+                [admat, aivec],
+                [admat[1:3, aivec]],
+                [admat_val, aivec_val],
+                AdvancedSubtensor,
+            )
+
     def test_AdvancedSubtensor_bool(self):
         n = dmatrix()
         n_val = np.arange(6).reshape((2, 3))

theano/tensor/subtensor.py

@@ -2321,24 +2321,28 @@ class AdvancedSubtensor(Op):
         # `Subtensor` calls, so we create a fake symbolic shape tuple and
         # identify the broadcast dimensions from the shape result of this
         # entire subtensor operation.
-        fake_shape = tuple(
-            theano.tensor.tensor(dtype="int64", broadcastable=()) if not bcast else 1
-            for bcast in x.broadcastable
-        )
+        with theano.change_flags(compute_test_value="off"):
+            fake_shape = tuple(
+                theano.tensor.tensor(dtype="int64", broadcastable=())
+                if not bcast
+                else 1
+                for bcast in x.broadcastable
+            )
 
-        bcast_index = tuple(
-            chain.from_iterable(
-                theano.tensor.basic.nonzero(idx)
-                if getattr(idx, "ndim", 0) > 0 and getattr(idx, "dtype", None) == "bool"
-                else (idx,)
-                for idx in index
+            bcast_index = tuple(
+                chain.from_iterable(
+                    theano.tensor.basic.nonzero(idx)
+                    if getattr(idx, "ndim", 0) > 0
+                    and getattr(idx, "dtype", None) == "bool"
+                    else (idx,)
+                    for idx in index
+                )
             )
-        )
 
-        bcast = [
-            getattr(i, "value", i) == 1
-            for i in indexed_result_shape(fake_shape, bcast_index)
-        ]
+            bcast = [
+                getattr(i, "value", i) == 1
+                for i in indexed_result_shape(fake_shape, bcast_index)
+            ]
 
         return gof.Apply(
             self,