How to use the duecredit.entries.Doi function in duecredit

def inject(injector):
    #http://nipy.org/nipype/about.html
    injector.add('nipype', None, Doi('10.3389/fninf.2011.00013'),
                    description='Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python',
                    tags=['implementation'])

    #http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/
    injector.add('nipype.interfaces', 'fsl', Doi('10.1016/j.neuroimage.2004.07.051'),
                    description='Advances in functional and structural MR image analysis and implementation as FSL',
                    tags=['implementation'])
    injector.add('nipype.interfaces', 'fsl', Doi('10.1016/j.neuroimage.2008.10.055'),
                    description='Bayesian analysis of neuroimaging data in FSL',
                    tags=['implementation'])
    injector.add('nipype.interfaces', 'fsl', Doi('10.1016/j.neuroimage.2011.09.015'),
                    description='FSL.',
                    tags=['implementation'])

author={Wold, H.},
            title={Nonlinear estimation by iterative least squares procedures.},
            booktitle={Research Papers in Statistics},
            publisher={Wiley}
            year={1966},
            editor={David, F.},
            pages={411--444},
            }
        """), description="Principal Component Analysis using the NIPALS algorithm.",
        tags=['edu'])

    injector.add('mdp.nodes', 'FastICANode.train', Doi('10.1109/72.761722'),
                 description="Independent Component Analysis using the FastICA algorithm",
                 tags=['implementation'])

    injector.add('mdp.nodes', 'CuBICANode.train', Doi('10.1109/TSP.2004.826173'),
                 description='Independent Component Analysis using the CuBICA algorithm.',
                 tags=['implementation'])
    
    injector.add('mdp.nodes', 'NIPALSNode.train', BibTeX("""
        @conference{ZieheMuller1998,
            author={Ziehe, Andreas and Muller, Klaus-Robert},
            title={TDSEP an efficient algorithm for blind separation using time structure.},
            booktitle={Proc. 8th Int. Conf. Artificial Neural Networks},
            year={1998},
            editor={Niklasson, L, Boden, M, and Ziemke, T},
            publisher={ICANN}
            }
        """), description='Independent Component Analysis using the TDSEP algorithm',
        tags=['edu'])

tags=['implementation'])
    
    injector.add('mdp.nodes', 'NIPALSNode.train', BibTeX("""
        @conference{ZieheMuller1998,
            author={Ziehe, Andreas and Muller, Klaus-Robert},
            title={TDSEP an efficient algorithm for blind separation using time structure.},
            booktitle={Proc. 8th Int. Conf. Artificial Neural Networks},
            year={1998},
            editor={Niklasson, L, Boden, M, and Ziemke, T},
            publisher={ICANN}
            }
        """), description='Independent Component Analysis using the TDSEP algorithm',
        tags=['edu'])


    injector.add('mdp.nodes', 'JADENode.train', Doi('10.1049/ip-f-2.1993.0054'),
                    description='Independent Component Analysis using the JADE algorithm',
                    tags=['implementation'])
    injector.add('mdp.nodes', 'JADENode.train', Doi('10.1162/089976699300016863'),
                    description='Independent Component Analysis using the JADE algorithm',
                    tags=['implementation'])

    injector.add('mdp.nodes', 'SFANode.train', Doi('10.1162/089976602317318938'),
                    description='Slow Feature Analysis',
                    tags=['implementation'])

    injector.add('mdp.nodes', 'SFA2Node.train', Doi('10.1162/089976602317318938'),
                    description='Slow Feature Analysis (via the space of inhomogeneous polynomials)',
                    tags=['implementation'])
    

    injector.add('mdp.nodes', 'ISFANode.train', Doi('10.1007/978-3-540-30110-3_94'),

Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss,
        Ron and Dubourg, Vincent and others},
        journal={The Journal of Machine Learning Research},
        volume={12},
        pages={2825--2830},
        year={2011},
        publisher={JMLR.org}
        }
    """), description="Machine Learning library")

    # sklearn.cluster.affinity_propagation_
    injector.add('sklearn.cluster.affinity_propagation_', None, Doi('10.1126/science.1136800'),
                 description="Affinity propagation clustering algorithm", tags=['implementation'])

    # sklearn.cluster.bicluster
    injector.add('sklearn.cluster.bicluster', 'SpectralCoclustering._fit', Doi('10.1101/gr.648603'),
                 description="Spectral Coclustering algorithm", tags=['implementation'])
    injector.add('sklearn.cluster.bicluster', 'SpectralBiclustering._fit', Doi('10.1101/gr.648603'),
                 description="Spectral Biclustering algorithm", tags=['implementation'])

    # sklearn.cluster.birch
    injector.add('sklearn.cluster.birch', 'Birch._fit', Doi('10.1145/233269.233324'),
                 description="BIRCH clustering algorithm", tags=['implementation'])
    injector.add('sklearn.cluster.birch', 'Birch._fit', Url('https://code.google.com/p/jbirch/'),
                 description="Java implementation of BIRCH clustering algorithm", tags=['another-implementation'])

    # sklearn.cluster.dbscan_
    injector.add('sklearn.cluster.dbscan_', 'dbscan',
                 BibTeX("""@inproceedings{ester1996density,
  title={A density-based algorithm for discovering clusters in large spatial databases with noise.},
  author={Ester, Martin and Kriegel, Hans-Peter and Sander, J{\"o}rg and Xu, Xiaowei},
  booktitle={Kdd},

def inject(injector):
    #http://martinos.org/mne/stable/cite.html
    injector.add('mne', None, Doi('10.1016/j.neuroimage.2013.10.027'),
                    description='MNE software for processing MEG and EEG data.',
                    tags=['implementation'])
    injector.add('mne', None, Doi('10.3389/fnins.2013.00267'),
                    description='MEG and EEG data analysis with MNE-Python.',
                    tags=['implementation'])

description="Java implementation of BIRCH clustering algorithm", tags=['another-implementation'])

    # sklearn.cluster.dbscan_
    injector.add('sklearn.cluster.dbscan_', 'dbscan',
                 BibTeX("""@inproceedings{ester1996density,
  title={A density-based algorithm for discovering clusters in large spatial databases with noise.},
  author={Ester, Martin and Kriegel, Hans-Peter and Sander, J{\"o}rg and Xu, Xiaowei},
  booktitle={Kdd},
  volume={96},
  number={34},
  pages={226--231},
  year={1996}
}"""), description="dbscan clustering algorithm", tags=['implementation'])

    # sklearn.cluster.mean_shift_
    injector.add('sklearn.cluster.mean_shift_', 'mean_shift', Doi('10.1109/34.1000236'),
                 description="Mean shift clustering algorithm", tags=['implementation'])

    # sklearn.cluster.spectral
    injector.add('sklearn.cluster.spectral', 'discretize', Doi('10.1109/ICCV.2003.1238361'),
                 description="Multiclass spectral clustering", tags=['reference'])
    injector.add('sklearn.cluster.spectral', 'spectral_clustering', Doi('10.1109/34.868688'),
                 description="Spectral clustering", tags=['implementation'])
    injector.add('sklearn.cluster.spectral', 'spectral_clustering', Doi('10.1007/s11222-007-9033-z'),
                 description="Spectral clustering", tags=['implementation'])

    # sklearn.ensemble.forest and tree
    Breiman_2001 = Doi("10.1023/A:1010933404324")
    Breiman_1984 = BibTeX("""@BOOK{breiman-friedman-olshen-stone-1984,
  author        = {L. Breiman and J. Friedman and R. Olshen and C. Stone},
  title         = {{Classification and Regression Trees}},
  publisher     = {Wadsworth and Brooks},

def inject(injector):
    injector.add('nibabel', None,
                 Doi('10.5281/zenodo.60847'),
                 cite_module=True,
                 description="I/O library to access to common neuroimaging file formats",
                 tags=['implementation'])

def get_bibtex_rendering(entry):
    if isinstance(entry, Doi):
        return BibTeX(import_doi(entry.doi))
    elif isinstance(entry, BibTeX):
        return entry
    else:
        raise ValueError("Have no clue how to get bibtex out of %s" % entry)

def inject(injector):
    #http://nipy.org/nipype/about.html
    injector.add('nipype', None, Doi('10.3389/fninf.2011.00013'),
                    description='Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python',
                    tags=['implementation'])

    #http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/
    injector.add('nipype.interfaces', 'fsl', Doi('10.1016/j.neuroimage.2004.07.051'),
                    description='Advances in functional and structural MR image analysis and implementation as FSL',
                    tags=['implementation'])
    injector.add('nipype.interfaces', 'fsl', Doi('10.1016/j.neuroimage.2008.10.055'),
                    description='Bayesian analysis of neuroimaging data in FSL',
                    tags=['implementation'])
    injector.add('nipype.interfaces', 'fsl', Doi('10.1016/j.neuroimage.2011.09.015'),
                    description='FSL.',
                    tags=['implementation'])

def inject(injector):
    #http://scikit-image.org
    injector.add('skimage', None, Doi('10.7717/peerj.453'),
                    description='scikit-image: Image processing in Python.',
                    tags=['implementation'])