human
Complete the below
@register_canonicalize
@gof.local_optimizer([T.add])
def local_IncSubtensor_serialize(node):

    ''''When using Subtensor, gradient graphs can be ugly.
    If we ask for grad(f(a[0]), a), we are going to get something like
    IncSubtensor(Elemwise{second}(a, 0), g(f(a[0])), [0])
    This might be ugly, but at least it\'s as fast as you could want.
    If we ask for grad(f(a[0], a[1], a[2]), a), it\'s much worse...
    Elemwise{Add}
    IncSubtensor(Elemwise{second}(a, 0), g(f(a[0])), [0])
    IncSubtensor(Elemwise{second}(a, 0), g(f(a[1])), [1])
    IncSubtensor(Elemwise{second}(a, 0), g(f(a[2])), [2])
    This is much worse because this time we have to produce 3 matrices
    the size of \'a\', just so we can add them together.
    This Op rearranges IncSubtensor\'s that all work on the same
    initial argument (here, Elemwise{second}(a,0)) into a chain.  The
    advantage of the chain structure is that each one can be optimized
    later in the pipeline to operate inplace.
    Ideally, the op will do something like this:
    #  add(x, incsubtensor(b, c), incsubtensor(b, d))
    #  -> incsubtensor(incsubtensor(add(x,b,b), c), d)'
    '''
assistant
#Here is the Python code you requested.
    def movable(i):
        return (i.owner and isinstance(i.owner.op, (IncSubtensor, AdvancedIncSubtensor1, AdvancedIncSubtensor)) and (i.type == o_type) and (len(i.clients) == 1) and (not i.owner.op.set_instead_of_inc))
    if (node.op == T.add):
        o_type = node.outputs[0].type
        movable_inputs = [i for i in node.inputs if movable(i)]
        if movable_inputs:
            new_inputs = ([i for i in node.inputs if (not movable(i))] + [mi.owner.inputs[0] for mi in movable_inputs])
            if (len(new_inputs) == 0):
                new_add = new_inputs[0]
            else:
                new_add = T.add(*new_inputs)
                copy_stack_trace(node.outputs[0], new_add)
            tip = new_add
            for mi in movable_inputs:
                assert (tip.type == o_type)
                assert (tip.type == mi.owner.inputs[0].type)
                tip = mi.owner.op(tip, *mi.owner.inputs[1:])
                copy_stack_trace((node.outputs + mi.owner.outputs), tip)
            return [tip]