code: def dual_barabasi_albert_graph(
      n, m1, m2, p, seed=None, initial_graph=None
  ):
      """Returns a random graph using dual Barabási–Albert preferential attachment
  
      A graph of $n$ nodes is grown by attaching new nodes each with either $m_1$
      edges (with probability $p$) or $m_2$ edges (with probability $1-p$) that
      are preferentially attached to existing nodes with high degree.
  
      Parameters
      ----------
      n : int
          Number of nodes
      m1 : int
          Number of edges to link each new node to existing nodes with probability $p$
      m2 : int
          Number of edges to link each new node to existing nodes with probability $1-p$
      p : float
          The probability of attaching $m_1$ edges (as opposed to $m_2$ edges)
      seed : integer, random_state, or None (default)
          Indicator of random number generation state.
          See :ref:`Randomness<randomness>`.
      initial_graph : Graph or None (default)
          Initial network for Barabási–Albert algorithm.
          A copy of `initial_graph` is used.
          It should be connected for most use cases.
          If None, starts from an star graph on max(m1, m2) + 1 nodes.
  
      Returns
      -------
      G : Graph
  
      Raises
      ------
      NetworkXError
          If `m1` and `m2` do not satisfy ``1 <= m1,m2 < n``, or
          `p` does not satisfy ``0 <= p <= 1``, or
          the initial graph number of nodes m0 does not satisfy m1, m2 <= m0 <= n.
  
      References
      ----------
      .. [1] N. Moshiri "The dual-Barabasi-Albert model", arXiv:1810.10538.
      """
  
      if m1 < 1 or m1 >= n:
          raise nx.NetworkXError(
              f"Dual Barabási–Albert must have m1 >= 1 and m1 < n, m1 = {m1}, n = {n}"
          )
      if m2 < 1 or m2 >= n:
          raise nx.NetworkXError(
              f"Dual Barabási–Albert must have m2 >= 1 and m2 < n, m2 = {m2}, n = {n}"
          )
      if p < 0 or p > 1:
          raise nx.NetworkXError(
              f"Dual Barabási–Albert network must have 0 <= p <= 1, p = {p}"
          )
  
      # For simplicity, if p == 0 or 1, just return BA
      if p == 1:
          return barabasi_albert_graph(n, m1, seed)
      elif p == 0:
          return barabasi_albert_graph(n, m2, seed)
  
      if initial_graph is None:
          # Default initial graph : empty graph on max(m1, m2) nodes
          G = star_graph(max(m1, m2))
      else:
          if len(initial_graph) < max(m1, m2) or len(initial_graph) > n:
              raise nx.NetworkXError(
                  f"Barabási–Albert initial graph must have between "
                  f"max(m1, m2) = {max(m1, m2)} and n = {n} nodes"
              )
          G = initial_graph.copy()
  
      # Target nodes for new edges
      targets = list(G)
      # List of existing nodes, with nodes repeated once for each adjacent edge
      repeated_nodes = [n for n, d in G.degree() for _ in range(d)]
      # Start adding the remaining nodes.
      source = len(G)
      while source < n:
          # Pick which m to use (m1 or m2)
          if seed.random() < p:
              m = m1
          else:
              m = m2
          # Now choose m unique nodes from the existing nodes
          # Pick uniformly from repeated_nodes (preferential attachment)
          targets = _random_subset(repeated_nodes, m, seed)
          # Add edges to m nodes from the source.
          G.add_edges_from(zip([source] * m, targets))
          # Add one node to the list for each new edge just created.
          repeated_nodes.extend(targets)
          # And the new node "source" has m edges to add to the list.
          repeated_nodes.extend([source] * m)
  
          source += 1
      return G
  
imports: import itertools
  import math
  from collections import defaultdict
  import networkx
  from networkx.utils import py_random_state
  from .classic import complete_graph, empty_graph, path_graph, star_graph
  from .degree_seq import degree_sequence_tree
label_desc: Use image node_id 11 for calling a global function with example usage: dual_barabasi_albert_graph(n, m1, m2, p, seed, initial_graph) and returns: G, barabasi_albert_graph, barabasi_albert_graph