code: def calculate_zipf_coefficient(sample_ids, tokenizer):
      """The Zipfian coefficient (R-squared) can be used to compare the distribution in a given
      text to a theoretically perfect exponential curve.
      """
      cnt = Counter()
      for sample_id in sample_ids:
          cnt.update(sample_id)
  
      xs = np.arange(1, min(len(cnt), len(tokenizer.vocab)) + 1)
      ys = np.array(
          sorted(cnt.values(), key=operator.neg)[: len(tokenizer.vocab)]
      )
      _, _, r, _, _ = stats.linregress(np.log(xs), np.log(ys))
      return r**2
  
file_path: gluon-nlp/scripts/generation/calculate_metrics.py
imports: import os
  import re
  import argparse
  from nltk.translate.bleu_score import sentence_bleu
  from collections import Counter
  import operator
  import numpy
  from scipy import stats
  import random
  from tqdm import tqdm
  from functools import partial
  from multiprocessing.pool import Pool
  from gluonnlp.models.gpt2 import get_pretrained_gpt2
label_desc: Use image node_id 4 for calling a global function with example usage: calculate_zipf_coefficient(sample_ids, tokenizer) and returns: unknown