Do equivalent fix in Scan.grad() on other new input sequence.

theano/scan_module/scan_op.py

@@ -1581,7 +1581,15 @@ class Scan(PureOp):
             if not isinstance(x.type, DisconnectedType):
                 outer_inp_seqs.append(x[::-1])
 
-        outer_inp_seqs += [x[::-1] for x in self.outer_mitsot_outs(outs)]
+        if hasattr(inputs[0].tag, 'test_value'):
+            for x in self.outer_mitsot_outs(outs):
+                if hasattr(x[::-1][:inputs[0]].tag, 'test_value'):
+                    assert x[::-1][:inputs[0]].tag.test_value.shape[0] == inputs[0].tag.test_value
+            for x in self.outer_sitsot_outs(outs):
+                if hasattr(x[::-1][:-1].tag, 'test_value'):
+                    assert x[::-1][:-1].tag.test_value.shape[0] == inputs[0].tag.test_value
+        outer_inp_seqs += [x[::-1][:inputs[0]]
+                           for x in self.outer_mitsot_outs(outs)]
         outer_inp_seqs += [x[::-1][:-1] for x in self.outer_sitsot_outs(outs)]
         outer_inp_seqs += [x[::-1] for x in self.outer_nitsot_outs(outs)]
 

theano/scan_module/tests/test_scan.py

@@ -1141,7 +1141,7 @@ class T_Scan(unittest.TestCase):
             go_backwards=False)
         gX, gY = tensor.grad(values[1].sum(), [x, y])
         f = theano.function([c, x, y], [gX, gY],
-                           allow_input_downcast=True)
+                            allow_input_downcast=True)
         # Check for runtime errors
         f(numpy.int32(0), numpy.float32(1.), numpy.float32(.5))