code: def printCCGDerivation(tree):
      # Get the leaves and initial categories
      leafcats = tree.pos()
      leafstr = ""
      catstr = ""
  
      # Construct a string with both the leaf word and corresponding
      # category aligned.
      for leaf, cat in leafcats:
          str_cat = "%s" % cat
          nextlen = 2 + max(len(leaf), len(str_cat))
          lcatlen = (nextlen - len(str_cat)) // 2
          rcatlen = lcatlen + (nextlen - len(str_cat)) % 2
          catstr += " " * lcatlen + str_cat + " " * rcatlen
          lleaflen = (nextlen - len(leaf)) // 2
          rleaflen = lleaflen + (nextlen - len(leaf)) % 2
          leafstr += " " * lleaflen + leaf + " " * rleaflen
      print(leafstr.rstrip())
      print(catstr.rstrip())
  
      # Display the derivation steps
      printCCGTree(0, tree)
  
imports: import itertools
  from nltk.ccg.combinator import *
  from nltk.ccg.combinator import BackwardApplication, BackwardBx, BackwardComposition, BackwardSx, BackwardT, ForwardApplication, ForwardComposition, ForwardSubstitution, ForwardT
  from nltk.ccg.lexicon import Token, fromstring
  from nltk.ccg.logic import *
  from nltk.parse import ParserI
  from nltk.parse.chart import AbstractChartRule, Chart, EdgeI
  from nltk.sem.logic import *
  from nltk.tree import Tree
label_desc: Use image node_id 2 for calling a global function with example usage: printCCGDerivation(tree) without return types