How to use the fitlins.interfaces.utils.MergeAll function in fitlins

def __init__(self, fields=None):
        super(MergeAll, self).__init__()
        if not fields:
            raise ValueError("Fields must be a non-empty list")

        self._fields = fields
        add_traits(self.inputs, fields)

self._results["effect_maps"] = effect_maps
        self._results["variance_maps"] = variance_maps
        self._results["stat_maps"] = stat_maps
        self._results["zscore_maps"] = zscore_maps
        self._results["pvalue_maps"] = pvalue_maps
        self._results["contrast_metadata"] = contrast_metadata

    def get_stim_labels(self):
        # Iterate through all weight specifications to get a list of stimulus
        # column labels.
        weights = _flatten([x["weights"] for x in self.inputs.contrast_info])
        return list(set(_flatten([x.keys() for x in weights])))


class AFNIMergeAll(MergeAll):
    def _list_outputs(self):
        outputs = self._outputs().get()
        for key in self._fields:
            val = getattr(self.inputs, key)
            outputs[key] = [elem for sublist in val for elem in sublist]

        return outputs


def get_afni_design_matrix(design, contrasts, stim_labels, t_r):
    """Add appropriate metadata to the design matrix and write to file for
    calling 3dREMLfit.  For a description of the target format see
    https://docs.google.com/document/d/1zpujpZYuleB7HuIFjb2vC4sYXG5M97hJ655ceAj4vE0/edit

    Parameters
    ----------

niu.Select(index=ix),
            name='select_{}_contrasts'.format(level),
            run_without_submitting=True)

        # Squash the results of MapNodes that may have generated multiple maps
        # into single lists.
        # Do the same with corresponding metadata - interface will complain if shapes mismatch
        if estimator == "afni":
            collate = pe.Node(
                    AFNIMergeAll(['effect_maps', 'variance_maps', 'stat_maps', 'zscore_maps',
                                  'pvalue_maps', 'contrast_metadata']),
                name='collate_{}'.format(level),
                run_without_submitting=True)
        else:
            collate = pe.Node(
                    MergeAll(['effect_maps', 'variance_maps', 'stat_maps', 'zscore_maps',
                              'pvalue_maps', 'contrast_metadata']),
                 name='collate_{}'.format(level),
                 run_without_submitting=True)

        #
        # Plotting
        #

        plot_contrasts = pe.MapNode(
            GlassBrainPlot(image_type='png'),
            iterfield='data',
            name='plot_{}_contrasts'.format(level))

        #
        # Derivatives
        #