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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 values(cls, dataset, dimension, expanded, flat):
 """ Returns a single concatenated array of all subpaths separated by NaN values. If expanded keyword is False an array of arrays is returned. """

        if not dataset.data:
            return np.array([])
        values = []
        ds = cls._inner_dataset_template(dataset)
        for d in dataset.data:
            ds.data = d
            dvals = ds.interface.values(ds, dimension, expanded, flat)
            if not len(dvals):
                continue
            elif expanded:
                values.append(dvals)
                values.append([np.NaN])
            else:
                values.append(dvals)
        if not values:
            return np.array([])
        elif expanded:
            return np.concatenate(values[:-1])
        else:
            return np.concatenate(values)