code: def get_word_index(path="imdb_word_index.json"):
      """Retrieves a dict mapping words to their index in the IMDB dataset.
  
      Args:
          path: where to cache the data (relative to `~/.keras/dataset`).
  
      Returns:
          The word index dictionary. Keys are word strings, values are their
          index.
  
      Example:
  
      ```python
      # Use the default parameters to keras.datasets.imdb.load_data
      start_char = 1
      oov_char = 2
      index_from = 3
      # Retrieve the training sequences.
      (x_train, _), _ = keras.datasets.imdb.load_data(
          start_char=start_char, oov_char=oov_char, index_from=index_from
      )
      # Retrieve the word index file mapping words to indices
      word_index = keras.datasets.imdb.get_word_index()
      # Reverse the word index to obtain a dict mapping indices to words
      # And add `index_from` to indices to sync with `x_train`
      inverted_word_index = dict(
          (i + index_from, word) for (word, i) in word_index.items()
      )
      # Update `inverted_word_index` to include `start_char` and `oov_char`
      inverted_word_index[start_char] = "[START]"
      inverted_word_index[oov_char] = "[OOV]"
      # Decode the first sequence in the dataset
      decoded_sequence = " ".join(inverted_word_index[i] for i in x_train[0])
      ```
      """
      origin_folder = (
          "https://storage.googleapis.com/tensorflow/tf-keras-datasets/"
      )
      path = get_file(
          fname=path,
          origin=origin_folder + "imdb_word_index.json",
          file_hash="bfafd718b763782e994055a2d397834f",
      )
      with open(path) as f:
          return json.load(f)
  
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 2 for calling a global function with example usage: get_word_index(path) and returns: json