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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 sg_producer_func(func):
 r"""Decorates a function `func` as sg_producer_func. Args: func: A function to decorate. """

    @wraps(func)
    def wrapper(**kwargs):
        r"""Manages arguments of `tf.sg_opt`.

        Args:
          **kwargs:
            source: A source queue list to enqueue
            dtypes: Input data types of each tensor
            out_dtypes: Output data types of each tensor ( If None, same as dtypes )
            capacity: Queue capacity. Default is 32.
            num_threads: Number of threads. Default is 1.
        """

        # default option
        opt = tf.sg_opt(kwargs) + tf.sg_opt(dtypes=[tf.sg_floatx], capacity=32, num_threads=1)

        # source queue list check
        assert opt.source is not None, 'source is mandatory.'
        if type(opt.source) is not list and type(opt.source) is not tuple:
            opt.source = [opt.source]
        if type(opt.dtypes) is not list and type(opt.dtypes) is not tuple:
            opt.dtypes = [opt.dtypes]
        # default out_dtypes
        if opt.out_dtypes is None:
            opt.out_dtypes = opt.dtypes
        if type(opt.out_dtypes) is not list and type(opt.out_dtypes) is not tuple:
            opt.out_dtypes = [opt.out_dtypes]
        assert len(opt.source) == len(opt.dtypes), 'Source and dtypes should have same length.'

        # enqueue function
        def enqueue_func(sess, op):
            # read data from source queue
            data = func(sess.run(opt.source))
            # create feeder dict
            feed_dict = {}
            for ph, col in zip(placeholders, data):
                feed_dict[ph] = col
            # run session
            sess.run(op, feed_dict=feed_dict)

        # create place holder list
        placeholders = []
        for dtype in opt.dtypes:
            placeholders.append(tf.placeholder(dtype=dtype))

        # create FIFO queue
        queue = tf.FIFOQueue(opt.capacity, dtypes=opt.out_dtypes)

        # enqueue operation
        enqueue_op = queue.enqueue(placeholders)

        # create queue runner
        runner = _FuncQueueRunner(enqueue_func, queue, [enqueue_op] * opt.num_threads)

        # register to global collection
        tf.train.add_queue_runner(runner)

        # return de-queue operation
        return queue.dequeue()

    return wrapper