code: def store_hypergraph_adjacency(hypergraph_adjacency, hdf5_file_name):
      
     
      assert(hypergraph_adjacency.__class__ == scipy.sparse.csr.csr_matrix)
      
      byte_counts = hypergraph_adjacency.data.nbytes + hypergraph_adjacency.indices.nbytes + hypergraph_adjacency.indptr.nbytes
      FILTERS = get_compression_filter(byte_counts)
  
      with tables.open_file(hdf5_file_name, 'r+') as fileh:
          for par in ('data', 'indices', 'indptr', 'shape'):
              try:
                  n = getattr(fileh.root.consensus_group, par)
                  n._f_remove()
              except AttributeError:
                  pass
  
              array = np.array(getattr(hypergraph_adjacency, par))
  
              atom = tables.Atom.from_dtype(array.dtype)
              ds = fileh.create_carray(fileh.root.consensus_group, par, atom, 
                                       array.shape, filters = FILTERS)
  
              ds[:] = array
docs: Write an hypergraph adjacency to disk to disk in an HDF5 data structure.
      
      Parameters
      ----------
      hypergraph_adjacency : compressed sparse row matrix
      
      hdf5_file_name : file handle or string