inferring the shape like that doesn't work

theano/tensor/basic.py

@@ -7202,7 +7202,7 @@ class MakeSlice(Op):
         
         return Apply(self,
                      map(as_int_none_variable,[slc.start, slc.stop, slc.step]),
-                     [Slice()])
+                     [slicetype()])
 
     def perform(self, node, inp, out_):
         out, = out_
@@ -7210,10 +7210,10 @@ class MakeSlice(Op):
 
     def __str__(self):
         return self.__class__.__name__    
-make_slice = MakeSlice
+make_slice = MakeSlice()
  
 
-class Slice(gof.Type):
+class SliceType(gof.Type):
 
     def filter(self, x, strict=False, allow_downcast=None):
         if isinstance(x, slice):
@@ -7223,7 +7223,7 @@ class Slice(gof.Type):
 
     def __str__(self):
         return "slice"
-    
+
 
 
 class NoneTypeT(gof.Type):
@@ -7236,7 +7236,8 @@ class NoneTypeT(gof.Type):
 
     def __str__(self):
         return "None"
-    
+slicetype = SliceType()
+
 NoneConst = Constant(NoneTypeT(), None, name = 'None')
     
 def adv_broadcastable(a, idx):
@@ -7251,7 +7252,7 @@ def adv_broadcastable(a, idx):
             
         if v is NoneConst:
             return None
-        if isinstance(v.type, Slice): 
+        if isinstance(v.type, SliceType): 
             return slice(None,None)
  
         return numpy.zeros( (1,)* v.ndim, int)
@@ -7296,18 +7297,6 @@ class AdvancedSubtensor(Op):
         return self.make_node(eval_points[0], *inputs[1:]).outputs
 
     def infer_shape(self, node, ishapes):
-        # Really special case
-        if len(ishapes) == 3:
-            xshp, ind1shp, ind2shp = ishapes
-            if len(xshp) == 2 and len(ind1shp) == 1 and len(ind2shp) == 1:
-                # if the graph is correct, we can assume ind1shp[0] and
-                # ind2shp[0] will have the same value.
-                # Try to return the one closest to the graph input.
-                if node.inputs[2].owner is None:
-                    return [ind2shp]
-                else:
-                    return [ind1shp]
-        # Default case, we don't know
         return node.fgraph.shape_feature.default_infer_shape(node, ishapes)
 
     def perform(self, node, inputs, out_):