code: def biadjacency_matrix(
      G,
      row_order,
      column_order=None,
      dtype=None,
      weight="weight",
      format="csr",
  ):
      r"""Returns the biadjacency matrix of the bipartite graph G.
  
      Let `G = (U, V, E)` be a bipartite graph with node sets
      `U = u_{1},...,u_{r}` and `V = v_{1},...,v_{s}`. The biadjacency
      matrix [1]_ is the `r` x `s` matrix `B` in which `b_{i,j} = 1`
      if, and only if, `(u_i, v_j) \in E`. If the parameter `weight` is
      not `None` and matches the name of an edge attribute, its value is
      used instead of 1.
  
      Parameters
      ----------
      G : graph
         A NetworkX graph
  
      row_order : list of nodes
         The rows of the matrix are ordered according to the list of nodes.
  
      column_order : list, optional
         The columns of the matrix are ordered according to the list of nodes.
         If column_order is None, then the ordering of columns is arbitrary.
  
      dtype : NumPy data-type, optional
          A valid NumPy dtype used to initialize the array. If None, then the
          NumPy default is used.
  
      weight : string or None, optional (default='weight')
         The edge data key used to provide each value in the matrix.
         If None, then each edge has weight 1.
  
      format : str in {'bsr', 'csr', 'csc', 'coo', 'lil', 'dia', 'dok'}
          The type of the matrix to be returned (default 'csr').  For
          some algorithms different implementations of sparse matrices
          can perform better.  See [2]_ for details.
  
      Returns
      -------
      M : SciPy sparse array
          Biadjacency matrix representation of the bipartite graph G.
  
      Notes
      -----
      No attempt is made to check that the input graph is bipartite.
  
      For directed bipartite graphs only successors are considered as neighbors.
      To obtain an adjacency matrix with ones (or weight values) for both
      predecessors and successors you have to generate two biadjacency matrices
      where the rows of one of them are the columns of the other, and then add
      one to the transpose of the other.
  
      See Also
      --------
      adjacency_matrix
      from_biadjacency_matrix
  
      References
      ----------
      .. [1] https://en.wikipedia.org/wiki/Adjacency_matrix#Adjacency_matrix_of_a_bipartite_graph
      .. [2] Scipy Dev. References, "Sparse Matrices",
         https://docs.scipy.org/doc/scipy/reference/sparse.html
      """
      import scipy as sp
  
      nlen = len(row_order)
      if nlen == 0:
          raise nx.NetworkXError("row_order is empty list")
      if len(row_order) != len(set(row_order)):
          msg = "Ambiguous ordering: `row_order` contained duplicates."
          raise nx.NetworkXError(msg)
      if column_order is None:
          column_order = list(set(G) - set(row_order))
      mlen = len(column_order)
      if len(column_order) != len(set(column_order)):
          msg = (
              "Ambiguous ordering: `column_order` contained duplicates."
          )
          raise nx.NetworkXError(msg)
  
      row_index = dict(zip(row_order, itertools.count()))
      col_index = dict(zip(column_order, itertools.count()))
  
      if G.number_of_edges() == 0:
          row, col, data = [], [], []
      else:
          row, col, data = zip(
              *(
                  (row_index[u], col_index[v], d.get(weight, 1))
                  for u, v, d in G.edges(row_order, data=True)
                  if u in row_index and v in col_index
              )
          )
      A = sp.sparse.coo_array(
          (data, (row, col)), shape=(nlen, mlen), dtype=dtype
      )
      try:
          return A.asformat(format)
      except ValueError as err:
          raise nx.NetworkXError(
              f"Unknown sparse array format: {format}"
          ) from err
  
imports: import itertools
  import networkx
  from networkx.convert_matrix import _generate_weighted_edges
label_desc: Use image node_id 1 for calling a global function with example usage: biadjacency_matrix(G, row_order, column_order, dtype, weight, format) and returns: A