code: def get_label_names():
      """Returns labels as a list of strings with indices matching training data.
  
      Reference:
  
      - [Reuters Dataset](https://martin-thoma.com/nlp-reuters/)
      """
      return (
          "cocoa",
          "grain",
          "veg-oil",
          "earn",
          "acq",
          "wheat",
          "copper",
          "housing",
          "money-supply",
          "coffee",
          "sugar",
          "trade",
          "reserves",
          "ship",
          "cotton",
          "carcass",
          "crude",
          "nat-gas",
          "cpi",
          "money-fx",
          "interest",
          "gnp",
          "meal-feed",
          "alum",
          "oilseed",
          "gold",
          "tin",
          "strategic-metal",
          "livestock",
          "retail",
          "ipi",
          "iron-steel",
          "rubber",
          "heat",
          "jobs",
          "lei",
          "bop",
          "zinc",
          "orange",
          "pet-chem",
          "dlr",
          "gas",
          "silver",
          "wpi",
          "hog",
          "lead",
      )
  
file_path: keras/keras/datasets/reuters.py
imports: import json
  import numpy
  from keras.api_export import keras_export
  from keras.utils.file_utils import get_file
  from keras.utils.python_utils import remove_long_seq
label_desc: Use image node_id 3 for calling a global function with example usage: get_label_names() and returns: str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str,  str