code: def _coqa_tokenize(inp: str) -> Sequence[str]:
      """Normalize English text and tokenize into words based on spaces.
  
      Adapted from official evaluation tokenization at
      https://stanfordnlp.github.io/coqa/.
  
      Args:
        inp: string.
  
      Returns:
        Tokenization of normalized text as List[str]
      """
  
      def remove_articles(text):
          regex = re.compile(r"\b(a|an|the)\b", re.UNICODE)
          return re.sub(regex, " ", text)
  
      def normalize_whitespace(text):
          return " ".join(text.split())
  
      def remove_punc(text):
          exclude = set(string.punctuation)
          return "".join(ch for ch in text if ch not in exclude)
  
      return normalize_whitespace(
          remove_articles(remove_punc(inp.lower()))
      ).split()
  
imports: import collections
  import itertools
  import re
  import string
  from typing import Any, Dict, Mapping, Optional, Sequence, Tuple, Union
  from absl import logging
  import editdistance
  import flax
  import jax.numpy
  import numpy
  import sacrebleu
  import scipy.stats
  import seqio
  import sklearn.metrics
  from t5.evaluation import qa_utils
  import tensorflow.compat.v2
  from rouge_score import rouge_scorer
  from rouge_score import scoring
label_desc: Use image node_id 22 for calling a global function with example usage: _coqa_tokenize(inp) and returns: normalize_whitespace, re, str, str