code: def number_normalizer(tokens):
      """Map all numeric tokens to a placeholder.
  
      For many applications, tokens that begin with a number are not directly
      useful, but the fact that such a token exists can be relevant.  By applying
      this form of dimensionality reduction, some methods may perform better.
      """
      return (
          "#NUMBER" if token[0].isdigit() else token for token in tokens
      )
  
file_path: scikit-learn/examples/bicluster/plot_bicluster_newsgroups.py
imports: import operator
  from collections import defaultdict
  from time import time
  import numpy
  from sklearn.cluster import MiniBatchKMeans, SpectralCoclustering
  from sklearn.datasets import fetch_20newsgroups
  from sklearn.feature_extraction.text import TfidfVectorizer
  from sklearn.metrics.cluster import v_measure_score
label_desc: Use image node_id 1 for calling a global function with example usage: number_normalizer(tokens) and returns: unknown