How to use the tedana.due.due function in tedana

@due.dcite(Doi('10.1002/mrm.20900'),
           description='STE method of combining data across echoes using just '
                       'SNR/signal and TE.')
def _combine_ste(data, tes):
    """
    Combine data across echoes using SNR/signal and TE.

    Parameters
    ----------
    data : (M x E x T) array_like
        Masked data.
    tes : (1 x E) array_like
        Echo times in milliseconds.

    Returns
    -------
    combined : (M x T) :obj:`numpy.ndarray`

@due.dcite(BibTeX("""
    @inproceedings{minka2001automatic,
      title={Automatic choice of dimensionality for PCA},
      author={Minka, Thomas P},
      booktitle={Advances in neural information processing systems},
      pages={598--604},
      year={2001}
    }
    """),
           description='Introduces method for choosing PCA dimensionality '
           'automatically')
def run_mlepca(data):
    """
    Run Singular Value Decomposition (SVD) on input data,
    automatically select components on MLE variance cut-off.

    Parameters

@due.dcite(BibTeX(
    """
    @inproceedings{minka2001automatic,
      title={Automatic choice of dimensionality for PCA},
      author={Minka, Thomas P},
      booktitle={Advances in neural information processing systems},
      pages={598--604},
      year={2001}
    }
    """),
    description='Introduces method for choosing PCA dimensionality '
                'automatically')
def run_mlepca(data):
    """
    Run Singular Value Decomposition (SVD) on input data.

    Parameters

@due.dcite(BibTeX('@article{sorensen1948method,'
                  'author={S{\\o}rensen, Thorvald},'
                  'title={A method of establishing groups of equal amplitude '
                  'in plant sociology based on similarity of species and its '
                  'application to analyses of the vegetation on Danish commons},'
                  'year = {1948},'
                  'publisher = {Wiley Online Library},'
                  'journal = {Biol. Skr.},'
                  'volume={5},'
                  'pages={1--34}}'),
           description='Introduction of Sorenson-Dice index by Sorenson in 1948.')
def dice(arr1, arr2):
    """
    Compute Dice's similarity index between two numpy arrays. Arrays will be
    binarized before comparison.

    Parameters

@due.dcite(Doi('10.1002/(SICI)1522-2594(199907)42:1<87::AID-MRM13>3.0.CO;2-O'),
           description='T2* method of combining data across echoes using '
                       'monoexponential equation.')
def _combine_t2s(data, tes, ft2s):
    """
    Combine data across echoes using weighted averaging according to voxel-
    (and sometimes volume-) wise estimates of T2*.

    Parameters
    ----------
    data : (M x E x T) array_like
        Masked data.
    tes : (1 x E) array_like
        Echo times in milliseconds.
    ft2s : (M [x T] X 1) array_like
        Either voxel-wise or voxel- and volume-wise estimates of T2*.

@due.dcite(BibTeX(
    """
    @inproceedings{minka2001automatic,
      title={Automatic choice of dimensionality for PCA},
      author={Minka, Thomas P},
      booktitle={Advances in neural information processing systems},
      pages={598--604},
      year={2001}
    }
    """),
    description='Introduces method for choosing PCA dimensionality '
                'automatically')
def run_mlepca(data):
    """
    Run Singular Value Decomposition (SVD) on input data.

    Parameters

__maintainer__,
    __email__,
    __status__,
    __url__,
    __packagename__,
    __description__,
    __longdesc__
)

import warnings

# cmp is not used, so ignore nipype-generated warnings
warnings.filterwarnings('ignore', r'cmp not installed')

# Citation for the algorithm.
due.cite(Doi('10.1016/j.neuroimage.2011.12.028'),
         description='Introduces MEICA and tedana.',
         version=__version__, path='tedana', cite_module=True)
due.cite(Doi('10.1073/pnas.1301725110'),
         description='Improves MEICA and tedana.',
         version=__version__, path='tedana', cite_module=True)

# Citation for package version.
due.cite(Doi('10.5281/zenodo.1250562'),
         description='The tedana package version 0.0.1',
         version='0.0.1', path='tedana', cite_module=True)

import warnings

# cmp is not used, so ignore nipype-generated warnings
warnings.filterwarnings('ignore', r'cmp not installed')

# Citation for the algorithm.
due.cite(Doi('10.1016/j.neuroimage.2011.12.028'),
         description='Introduces MEICA and tedana.',
         version=__version__, path='tedana', cite_module=True)
due.cite(Doi('10.1073/pnas.1301725110'),
         description='Improves MEICA and tedana.',
         version=__version__, path='tedana', cite_module=True)

# Citation for package version.
due.cite(Doi('10.5281/zenodo.1250562'),
         description='The tedana package version 0.0.1',
         version='0.0.1', path='tedana', cite_module=True)

@due.dcite(BibTeX('@article{dice1945measures,'
                  'author={Dice, Lee R},'
                  'title={Measures of the amount of ecologic association between species},'
                  'year = {1945},'
                  'publisher = {Wiley Online Library},'
                  'journal = {Ecology},'
                  'volume={26},'
                  'number={3},'
                  'pages={297--302}}'),
           description='Introduction of Sorenson-Dice index by Dice in 1945.')
@due.dcite(BibTeX('@article{sorensen1948method,'
                  'author={S{\\o}rensen, Thorvald},'
                  'title={A method of establishing groups of equal amplitude '
                  'in plant sociology based on similarity of species and its '
                  'application to analyses of the vegetation on Danish commons},'
                  'year = {1948},'
                  'publisher = {Wiley Online Library},'