code: def random_lobster(n, p1, p2, seed=None):
      """Returns a random lobster graph.
  
      A lobster is a tree that reduces to a caterpillar when pruning all
      leaf nodes. A caterpillar is a tree that reduces to a path graph
      when pruning all leaf nodes; setting `p2` to zero produces a caterpillar.
  
      This implementation iterates on the probabilities `p1` and `p2` to add
      edges at levels 1 and 2, respectively. Graphs are therefore constructed
      iteratively with uniform randomness at each level rather than being selected
      uniformly at random from the set of all possible lobsters.
  
      Parameters
      ----------
      n : int
          The expected number of nodes in the backbone
      p1 : float
          Probability of adding an edge to the backbone
      p2 : float
          Probability of adding an edge one level beyond backbone
      seed : integer, random_state, or None (default)
          Indicator of random number generation state.
          See :ref:`Randomness<randomness>`.
  
      Raises
      ------
      NetworkXError
          If `p1` or `p2` parameters are >= 1 because the while loops would never finish.
      """
      p1, p2 = abs(p1), abs(p2)
      if any(p >= 1 for p in [p1, p2]):
          raise nx.NetworkXError(
              "Probability values for `p1` and `p2` must both be < 1."
          )
  
      # a necessary ingredient in any self-respecting graph library
      llen = int(2 * seed.random() * n + 0.5)
      L = path_graph(llen)
      # build caterpillar: add edges to path graph with probability p1
      current_node = llen - 1
      for n in range(llen):
          while seed.random() < p1:  # add fuzzy caterpillar parts
              current_node += 1
              L.add_edge(n, current_node)
              cat_node = current_node
              while seed.random() < p2:  # add crunchy lobster bits
                  current_node += 1
                  L.add_edge(cat_node, current_node)
      return L
  
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 14 for calling a global function with example usage: random_lobster(n, p1, p2, seed) and returns: L