code: def random_powerlaw_tree(n, gamma=3, seed=None, tries=100):
      """Returns a tree with a power law degree distribution.
  
      Parameters
      ----------
      n : int
          The number of nodes.
      gamma : float
          Exponent of the power law.
      seed : integer, random_state, or None (default)
          Indicator of random number generation state.
          See :ref:`Randomness<randomness>`.
      tries : int
          Number of attempts to adjust the sequence to make it a tree.
  
      Raises
      ------
      NetworkXError
          If no valid sequence is found within the maximum number of
          attempts.
  
      Notes
      -----
      A trial power law degree sequence is chosen and then elements are
      swapped with new elements from a powerlaw distribution until the
      sequence makes a tree (by checking, for example, that the number of
      edges is one smaller than the number of nodes).
  
      """
      # This call may raise a NetworkXError if the number of tries is succeeded.
      seq = random_powerlaw_tree_sequence(
          n, gamma=gamma, seed=seed, tries=tries
      )
      G = degree_sequence_tree(seq)
      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 16 for calling a global function with example usage: random_powerlaw_tree(n, gamma, seed, tries) and returns: G