Use unique input names in numba_funcify_Scan

aesara/link/numba/dispatch/scan.py

@@ -54,7 +54,7 @@ def numba_funcify_Scan(op, node, **kwargs):
     p_outer_in_nit_sot = p_outer_in_shared + n_shared_outs
     p_outer_in_non_seqs = p_outer_in_nit_sot + n_nit_sot
 
-    input_names = [n.auto_name for n in node.inputs[1:]]
+    input_names = [f"{n.auto_name}_{i}" for i, n in enumerate(node.inputs[1:])]
     outer_in_seqs_names = input_names[:n_seqs]
     outer_in_mit_mot_names = input_names[p_in_mit_mot : p_in_mit_mot + n_mit_mot]
     outer_in_mit_sot_names = input_names[p_in_mit_sot : p_in_mit_sot + n_mit_sot]

tests/link/numba/test_scan.py

@@ -179,3 +179,22 @@ def power_of_2(previous_power, max_value):
         np.array(45).astype(config.floatX),
     ]
     compare_numba_and_py(out_fg, test_input_vals)
+
+
+def test_scan_multiple_none_output():
+    A = at.dvector("A")
+
+    def power_step(prior_result, x):
+        return prior_result * x, prior_result * x * x, prior_result * x * x * x
+
+    result, _ = scan(
+        power_step,
+        non_sequences=[A],
+        outputs_info=[at.ones_like(A), None, None],
+        n_steps=3,
+    )
+
+    out_fg = FunctionGraph([A], result)
+    test_input_vals = (np.array([1.0, 2.0]),)
+
+    compare_numba_and_py(out_fg, test_input_vals)