def METHOD_NAME(A, create_using=None, edge_attribute="weight"):
    r"""Creates a new bipartite graph from a biadjacency matrix given as a
    SciPy sparse array.
    Parameters
    ----------
    A: scipy sparse array
      A biadjacency matrix representation of a graph
    create_using: NetworkX graph
       Use specified graph for result.  The default is Graph()
    edge_attribute: string
       Name of edge attribute to store matrix numeric value. The data will
       have the same type as the matrix entry (int, float, (real,imag)).
    Notes
    -----
    The nodes are labeled with the attribute `bipartite` set to an integer
    0 or 1 representing membership in part 0 or part 1 of the bipartite graph.
    If `create_using` is an instance of :class:`networkx.MultiGraph` or
    :class:`networkx.MultiDiGraph` and the entries of `A` are of
    type :class:`int`, then this function returns a multigraph (of the same
    type as `create_using`) with parallel edges. In this case, `edge_attribute`
    will be ignored.
    See Also
    --------
    biadjacency_matrix
    from_numpy_array
    References
    ----------
    [1] https://en.wikipedia.org/wiki/Adjacency_matrix#Adjacency_matrix_of_a_bipartite_graph
    """
    G = nx.empty_graph(0, create_using)
    n, m = A.shape
    # Make sure we get even the isolated nodes of the graph.
    G.add_nodes_from(range(n), bipartite=0)
    G.add_nodes_from(range(n, n + m), bipartite=1)
    # Create an iterable over (u, v, w) triples and for each triple, add an
    # edge from u to v with weight w.
    triples = ((u, n + v, d) for (u, v, d) in _generate_weighted_edges(A))
    # If the entries in the adjacency matrix are integers and the graph is a
    # multigraph, then create parallel edges, each with weight 1, for each
    # entry in the adjacency matrix. Otherwise, create one edge for each
    # positive entry in the adjacency matrix and set the weight of that edge to
    # be the entry in the matrix.
    if A.dtype.kind in ("i", "u") and G.is_multigraph():
        chain = itertools.chain.from_iterable
        triples = chain(((u, v, 1) for d in range(w)) for (u, v, w) in triples)
    G.add_weighted_edges_from(triples, weight=edge_attribute)
    return G