remove extra outdated comments + fixed bug in case return_steps =1, and n_steps =1 or -1

theano/scan.py

@@ -716,14 +716,6 @@ def scan(fn, sequences=[], outputs_info=[], non_sequences=[],
     # make the compilation as fast as possible by not applying any optimization
     # or conversion to C [ note this region is not important for performance
     # so we can do stuff as unoptimal as we wish ]
-    '''
-    Why did I use gof.graph.inputs to pick the inputs here ??
-    
-    dummy_f = function(filter(lambda x: isinstance(x,gof.Variable) and \
-            not isinstance(x,SharedVariable) and not isinstance(x,gof.Constant), \
-            reversed(gof.graph.inputs(dummy_args))), outputs, updates = updates, mode = \
-                 compile.mode.Mode(linker = 'py', optimizer = None) )
-    '''
     if n_fixed_steps in [-1,1]:
         ''' We do have a special case here, namely is so might happen that
         whatever we have in dummy_args is not sufficient to compile the 
@@ -872,22 +864,8 @@ def scan(fn, sequences=[], outputs_info=[], non_sequences=[],
     else:
         # If we do not actually need scan
         for pos, inner_out in enumerate(inner_fn_outs):
-            # check if we are suppose to return just the last step
-            # we treat this case differently because the tensor we return
-            # in this case is different (it has one dimension less)
-            if return_steps.has_key(pos):
-                if return_steps[pos] != 1:
-                    # if we return more then one step, we need to add 
-                    # one more dimension to our output and make it 
-                    # unbroadcastable
-                    inner_fn_outs[pos] = tensor.unbroadcast(
-                            tensor.shape_padleft(inner_out),0)
-            else:
-                # same if we do not have any information about how many
-                # steps we should return (to read return everything in this
-                # case
-                inner_fn_outs[pos] = tensor.unbroadcast( 
-                        tensor.shape_padleft(inner_out),0)
+            if isinstance(inner_out.type, tensor.TensorType):
+                inner_fn_outs[pos] = tensor.unbroadcast( tensor.shape_padleft(inner_out),0)
         values = inner_fn_outs