code: def adjacency_matrix(G, nodelist=None, dtype=None, weight="weight"):
      """Returns adjacency matrix of G.
  
      Parameters
      ----------
      G : graph
         A NetworkX graph
  
      nodelist : list, optional
         The rows and columns are ordered according to the nodes in nodelist.
         If nodelist is None, then the ordering is produced by G.nodes().
  
      dtype : NumPy data-type, optional
          The desired data-type for 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.
  
      Returns
      -------
      A : SciPy sparse array
        Adjacency matrix representation of G.
  
      Notes
      -----
      For directed graphs, entry i,j corresponds to an edge from i to j.
  
      If you want a pure Python adjacency matrix representation try
      networkx.convert.to_dict_of_dicts which will return a
      dictionary-of-dictionaries format that can be addressed as a
      sparse matrix.
  
      For MultiGraph/MultiDiGraph with parallel edges the weights are summed.
      See `to_numpy_array` for other options.
  
      The convention used for self-loop edges in graphs is to assign the
      diagonal matrix entry value to the edge weight attribute
      (or the number 1 if the edge has no weight attribute).  If the
      alternate convention of doubling the edge weight is desired the
      resulting SciPy sparse array can be modified as follows:
  
      >>> G = nx.Graph([(1, 1)])
      >>> A = nx.adjacency_matrix(G)
      >>> print(A.todense())
      [[1]]
      >>> A.setdiag(A.diagonal() * 2)
      >>> print(A.todense())
      [[2]]
  
      See Also
      --------
      to_numpy_array
      to_scipy_sparse_array
      to_dict_of_dicts
      adjacency_spectrum
      """
      return nx.to_scipy_sparse_array(
          G, nodelist=nodelist, dtype=dtype, weight=weight
      )
  
label_desc: Use image node_id 2 for calling a global function with example usage: adjacency_matrix(G, nodelist, dtype, weight) and returns: nx