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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 save_features_and_arrays(features, arrays, prefix, compressed=False, link_features=False, overwrite=False):
 """ Saves NumPy arrays of processed data, along with the features that correspond to each row, to files for later use. Two files will be saved, both starting with `prefix`: prefix.features : a file of features. If GFF features were provided, this will be in GFF format, if BED features were provided it will be in BED format, and so on. prefix.npz : A NumPy .npz file. Parameters arrays : dict of NumPy arrays Rows in each array should correspond to `features`. This dictionary is passed to np.savez features : iterable of Feature-like objects This is usually the same features that were used to create the array in the first place. link_features : bool If True, then assume that `features` is either a pybedtools.BedTool pointing to a file, or a filename. In this case, instead of making a copy, a symlink will be created to the original features. This helps save disk space. prefix : str Path to where data will be saved. compressed : bool If True, saves arrays using np.savez_compressed rather than np.savez. This will save disk space, but will be slower when accessing the data later. """


    if link_features:
        if isinstance(features, pybedtools.BedTool):
            assert isinstance(features.fn, basestring)
            features_filename = features.fn
        else:
            assert isinstance(features, basestring)
            features_filename = features

        if overwrite:
            force_flag = '-f'
        else:
            force_flag = ''

        cmds = [
            'ln', '-s', force_flag, os.path.abspath(features_filename), prefix + '.features']
        os.system(' '.join(cmds))
    else:
        pybedtools.BedTool(features).saveas(prefix + '.features')

    if compressed:
        np.savez_compressed(
            prefix,
            **arrays)
    else:
        np.savez(prefix, **arrays)