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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def file_items(container, iterable):
 """ Files away objects into the appropriate attributes of the container. """


    # container._value = copy(iterable)

    container.nodes = set()
    container.variables = set()
    container.deterministics = set()
    container.stochastics = set()
    container.potentials = set()
    container.observed_stochastics = set()

    # containers needs to be a list to hold unhashable items.
    container.containers = []

    i = -1

    for item in iterable:

        # If this is a dictionary, switch from key to item.
        if isinstance(iterable, (dict, dict_proxy_type)):
            key = item
            item = iterable[key]
        # Item counter
        else:
            i += 1

        # If the item isn't iterable, file it away.
        if isinstance(item, Variable):
            container.variables.add(item)
            if isinstance(item, StochasticBase):
                if item.observed or not getattr(item, 'mask', None) is None:
                    container.observed_stochastics.add(item)
                if not item.observed:
                    container.stochastics.add(item)
            elif isinstance(item, DeterministicBase):
                container.deterministics.add(item)
        elif isinstance(item, PotentialBase):
            container.potentials.add(item)

        elif isinstance(item, ContainerBase):
            container.assimilate(item)
            container.containers.append(item)

        # Wrap internal containers
        elif hasattr(item, '__iter__'):

            # If this is a non-object-valued ndarray, don't container-ize it.
            if isinstance(item, ndarray):
                if item.dtype != dtype('object'):
                    continue

            # If the item is iterable, wrap it in a container. Replace the item
            # with the wrapped version.
            try:
                new_container = Container(item)
            except:
                continue

            # Update all of container's variables, potentials, etc. with the new wrapped
            # iterable's. This process recursively unpacks nested iterables.
            container.assimilate(new_container)

            if isinstance(container, dict):
                container.replace(key, new_container)
            elif isinstance(container, tuple):
                return container[:i] + (new_container,) + container[i + 1:]
            else:
                container.replace(item, new_container, i)

    container.nodes = container.potentials | container.variables

    # 'Freeze' markov blanket, moral neighbors, coparents of all constituent stochastics
    # for future use
    for attr in ['moral_neighbors', 'markov_blanket', 'coparents']:
        setattr(container, attr, {})
    for s in container.stochastics:
        for attr in ['moral_neighbors', 'markov_blanket', 'coparents']:
            getattr(container, attr)[s] = getattr(s, attr)