code: def random_powerlaw_tree_sequence(n, gamma=3, seed=None, tries=100):
      """Returns a degree sequence for a tree with a power law 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 power law distribution until
      the sequence makes a tree (by checking, for example, that the number of
      edges is one smaller than the number of nodes).
  
      """
      # get trial sequence
      z = nx.utils.powerlaw_sequence(n, exponent=gamma, seed=seed)
      # round to integer values in the range [0,n]
      zseq = [min(n, max(round(s), 0)) for s in z]
  
      # another sequence to swap values from
      z = nx.utils.powerlaw_sequence(tries, exponent=gamma, seed=seed)
      # round to integer values in the range [0,n]
      swap = [min(n, max(round(s), 0)) for s in z]
  
      for deg in swap:
          # If this degree sequence can be the degree sequence of a tree, return
          # it. It can be a tree if the number of edges is one fewer than the
          # number of nodes, or in other words, `n - sum(zseq) / 2 == 1`. We
          # use an equivalent condition below that avoids floating point
          # operations.
          if 2 * n - sum(zseq) == 2:
              return zseq
          index = seed.randint(0, n - 1)
          zseq[index] = swap.pop()
  
      raise nx.NetworkXError(
          f"Exceeded max ({tries}) attempts for a valid tree sequence."
      )
  
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 17 for calling a global function with example usage: random_powerlaw_tree_sequence(n, gamma, seed, tries) and returns: zseq