Updated documentation

tensor.py

@@ -520,12 +520,14 @@ class MaxAndArgmax(Op):
         max[0] = numpy.max(x, axis)
         max_idx[0] = numpy.argmax(x, axis)
     def grad(self, (x, axis), (g_max, g_max_idx)):
+        # @warning: This only works if axis is 0, else the max is
+        # broadcasted wrong in the call to eq.
+        # @note: This function should work correctly for L{vector}s.
 #        (x, y), (gz, gw)
 #        gz*dz/dx + gw*dw/dx, gz*dz/dy + gw*dw/dy
 #        gMax * dMax/dx + gArgMax * dArgMax/dx, gMax * dMax/daxis + gArgMax * dArgMax/daxis
 #       g_max has one less dimension than x, so you need to complete g_max to x's shape
 #        when axis=0 the broadcasting mechanism does it automatically
-        # This only works if axis is 0, else the max is broadcasted wrong in the call to eq
         assert axis.data == 0
         g_x = eq(max(x, axis), x) * g_max
         return g_x, None