code: def encode_dataset(
      file_path,
      model_dir="models",
      out_path="text_encoded.npz",
      model_name="124M",
      combine=50000,
  ):
      """Preencodes a text document into chunks and compresses it,
      saving time when generated.
  
      Adapted from https://github.com/nshepperd/gpt-2/blob/finetuning/encode.py
      """
  
      model_path = os.path.join(model_dir, model_name)
      enc = encoder.get_encoder(model_path)
      print("Reading files")
      chunks = load_dataset(enc, file_path, combine)
      print("Writing", out_path)
      np.savez_compressed(out_path, *chunks)
  
imports: import tarfile
  import os
  import json
  import requests
  import sys
  import shutil
  import re
  from tqdm import tqdm, trange
  import numpy
  import tensorflow
  from tensorflow.core.protobuf import rewriter_config_pb2
  from tensorflow.python.client import device_lib
  import time
  from datetime import datetime
  import csv
  import argparse
  from sys import exit
  import codecs
  from gpt_2_simple.src import model, sample, encoder, memory_saving_gradients
  from gpt_2_simple.src.load_dataset import load_dataset, Sampler
  from gpt_2_simple.src.accumulate import AccumulatingOptimizer
label_desc: Use image node_id 19 for calling a global function with example usage: encode_dataset(file_path, model_dir, out_path, model_name, combine) without return types