How to use the pyperformance.benchmarks.bm_deltablue.OrderedCollection function in pyperformance

def projection_test(n):
    """
    This test constructs a two sets of variables related to each
    other by a simple linear transformation (scale and offset). The
    time is measured to change a variable on either side of the
    mapping and to change the scale and offset factors.
    """
    global planner
    planner = Planner()
    scale = Variable("scale", 10)
    offset = Variable("offset", 1000)
    src = None

    dests = OrderedCollection()

    for i in range(n):
        src = Variable("src%s" % i, i)
        dst = Variable("dst%s" % i, i)
        dests.append(dst)
        StayConstraint(src, Strength.NORMAL)
        ScaleConstraint(src, scale, offset, dst, Strength.REQUIRED)

    change(src, 17)

    if dst.value != 1170:
        print("Projection 1 failed")

    change(dst, 1050)

    if src.value != 5:

v = Variable(name)

        if prev is not None:
            EqualityConstraint(prev, v, Strength.REQUIRED)

        if i == 0:
            first = v

        if i == n:
            last = v

        prev = v

    StayConstraint(last, Strength.STRONG_DEFAULT)
    edit = EditConstraint(first, Strength.PREFERRED)
    edits = OrderedCollection()
    edits.append(edit)
    plan = planner.extract_plan_from_constraints(edits)

    for i in range(100):
        first.value = i
        plan.execute()

        if last.value != i:
            print("Chain test failed.")

def remove_propagate_from(self, out):
        out.determined_by = None
        out.walk_strength = Strength.WEAKEST
        out.stay = True
        unsatisfied = OrderedCollection()
        todo = OrderedCollection()
        todo.append(out)

        while len(todo):
            v = todo.pop(0)

            for c in v.constraints:
                if not c.is_satisfied():
                    unsatisfied.append(c)

            determining = v.determined_by

            for c in v.constraints:
                if c != determining and c.is_satisfied():
                    c.recalculate()
                    todo.append(c.output())

def extract_plan_from_constraints(self, constraints):
        sources = OrderedCollection()

        for c in constraints:
            if c.is_input() and c.is_satisfied():
                sources.append(c)

        return self.make_plan(sources)

def __init__(self):
        super(Plan, self).__init__()
        self.v = OrderedCollection()

def change(v, new_value):
    global planner
    edit = EditConstraint(v, Strength.PREFERRED)
    edits = OrderedCollection()
    edits.append(edit)

    plan = planner.extract_plan_from_constraints(edits)

    for i in range(10):
        v.value = new_value
        plan.execute()

    edit.destroy_constraint()

def remove_propagate_from(self, out):
        out.determined_by = None
        out.walk_strength = Strength.WEAKEST
        out.stay = True
        unsatisfied = OrderedCollection()
        todo = OrderedCollection()
        todo.append(out)

        while len(todo):
            v = todo.pop(0)

            for c in v.constraints:
                if not c.is_satisfied():
                    unsatisfied.append(c)

            determining = v.determined_by

            for c in v.constraints:
                if c != determining and c.is_satisfied():
                    c.recalculate()
                    todo.append(c.output())

def add_propagate(self, c, mark):
        todo = OrderedCollection()
        todo.append(c)

        while len(todo):
            d = todo.pop(0)

            if d.output().mark == mark:
                self.incremental_remove(c)
                return False

            d.recalculate()
            self.add_constraints_consuming_to(d.output(), todo)

        return True