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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 _frequency_order_transform(sets):
 """Transform tokens to integers according to global frequency order. This step replaces all original tokens in the sets with integers, and helps to speed up subsequent prefix filtering and similarity computation. See Section 4.3.2 in the paper "A Primitive Operator for Similarity Joins in Data Cleaning" by Chaudhuri et al.. Args: sets (list):
 a list of sets, each entry is an iterable representing a set. Returns: sets (list):
 a list of sets, each entry is a sorted Numpy array with integer tokens replacing the tokens in the original set. order (dict):
 a dictionary that maps token to its integer representation in the frequency order. """

    logging.debug("Applying frequency order transform on tokens...")
    counts = reversed(Counter(token for s in sets for token in s).most_common())
    order = dict((token, i) for i, (token, _) in enumerate(counts))
    sets = [np.sort([order[token] for token in s]) for s in sets]
    logging.debug("Done applying frequency order.")
    return sets, order