code: def finetune(
      sess,
      dataset,
      steps=-1,
      model_name="124M",
      model_dir="models",
      combine=50000,
      batch_size=1,
      learning_rate=0.0001,
      accumulate_gradients=5,
      restore_from="latest",
      run_name="run1",
      checkpoint_dir="checkpoint",
      sample_every=100,
      sample_length=1023,
      sample_num=1,
      multi_gpu=False,
      save_every=1000,
      print_every=1,
      max_checkpoints=1,
      use_memory_saving_gradients=False,
      only_train_transformer_layers=False,
      optimizer="adam",
      overwrite=False,
      reuse=False,
  ):
      """Finetunes the model on the given dataset.
  
      Adapted from https://github.com/nshepperd/gpt-2/blob/finetuning/train.py.
      See that file for parameter definitions.
      """
  
      # assert model_name not in ['774M', '1558M'] or multi_gpu, "Currently, a modern single GPU cannot finetune the 774M GPT-2 model or larger."
  
      SAMPLE_DIR = "samples"
  
      checkpoint_path = os.path.join(checkpoint_dir, run_name)
  
      def maketree(path):
          try:
              os.makedirs(path)
          except:
              pass
  
      maketree(checkpoint_path)
      files = [f for f in os.listdir(checkpoint_path)]
      for file in ["hparams.json", "encoder.json", "vocab.bpe"]:
          try:
              shutil.copyfile(
                  os.path.join(model_dir, model_name, file),
                  os.path.join(checkpoint_path, file),
              )
          except FileNotFoundError as fnf_error:
              print(
                  "You need to download the GPT-2 model first via download_gpt2()"
              )
              raise (fnf_error)
  
      enc = encoder.get_encoder(checkpoint_path)
      hparams = model.default_hparams()
      with open(os.path.join(checkpoint_path, "hparams.json")) as f:
          hparams.override_from_dict(json.load(f))
  
      if sample_length > hparams.n_ctx:
          raise ValueError(
              "Can't get samples longer than window size: %s"
              % hparams.n_ctx
          )
  
      if model_name not in ["117M", "124M"]:
          print(
              "For larger models, the recommended finetune() parameters are:"
          )
          print("\tuse_memory_saving_gradients = True")
          print("\tonly_train_transformer_layers = True")
          print("\taccumulate_gradients = 1\n")
  
      context = tf.compat.v1.placeholder(tf.int32, [batch_size, None])
      gpus = []
  
      if multi_gpu:
          gpus = get_available_gpus()
  
      output = model.model(
          hparams=hparams, X=context, gpus=gpus, reuse=reuse
      )
      loss = tf.reduce_mean(
          input_tensor=tf.nn.sparse_softmax_cross_entropy_with_logits(
              labels=context[:, 1:], logits=output["logits"][:, :-1]
          )
      )
  
      tf_sample = sample.sample_sequence(
          hparams=hparams,
          length=sample_length,
          context=context,
          batch_size=batch_size,
          temperature=1.0,
          top_k=40,
      )
  
      all_vars = [
          v
          for v in tf.compat.v1.trainable_variables()
          if "model" in v.name
      ]
      train_vars = (
          [v for v in all_vars if "/h" in v.name]
          if only_train_transformer_layers
          else all_vars
      )
  
      if optimizer == "adam":
          opt = tf.compat.v1.train.AdamOptimizer(
              learning_rate=learning_rate
          )
      elif optimizer == "sgd":
          opt = tf.compat.v1.train.GradientDescentOptimizer(
              learning_rate=learning_rate
          )
  
      if tf.__version__ >= "2.0.0" and use_memory_saving_gradients:
          exit(
              "Memory saving gradients are not implemented for Tensorflow 2 yet."
          )
  
      if accumulate_gradients > 1:
          if use_memory_saving_gradients:
              exit(
                  "Memory saving gradients are not implemented for gradient accumulation yet."
              )
          opt = AccumulatingOptimizer(opt=opt, var_list=train_vars)
          opt_reset = opt.reset()
          opt_compute = opt.compute_gradients(loss)
          opt_apply = opt.apply_gradients()
          summary_loss = tf.compat.v1.summary.scalar("loss", opt_apply)
      else:
          if use_memory_saving_gradients:
              opt_grads = memory_saving_gradients.gradients(
                  loss, train_vars
              )
          else:
              opt_grads = tf.gradients(ys=loss, xs=train_vars)
          opt_grads = list(zip(opt_grads, train_vars))
          opt_apply = opt.apply_gradients(opt_grads)
          summary_loss = tf.compat.v1.summary.scalar("loss", loss)
  
      summary_log = tf.compat.v1.summary.FileWriter(checkpoint_path)
  
      saver = tf.compat.v1.train.Saver(
          var_list=all_vars, max_to_keep=max_checkpoints
      )
      sess.run(tf.compat.v1.global_variables_initializer())
  
      if restore_from == "latest":
          ckpt = tf.train.latest_checkpoint(checkpoint_path)
          if ckpt is None:
              # Get fresh GPT weights if new run.
              ckpt = tf.train.latest_checkpoint(
                  os.path.join(model_dir, model_name)
              )
      elif restore_from == "fresh":
          ckpt = tf.train.latest_checkpoint(
              os.path.join(model_dir, model_name)
          )
      else:
          ckpt = tf.train.latest_checkpoint(restore_from)
      print("Loading checkpoint", ckpt)
      saver.restore(sess, ckpt)
  
      print("Loading dataset...")
      chunks = load_dataset(enc, dataset, combine)
      data_sampler = Sampler(chunks)
      print("dataset has", data_sampler.total_size, "tokens")
      print("Training...")
  
      counter = 1
      counter_path = os.path.join(checkpoint_path, "counter")
      if os.path.exists(counter_path) and restore_from == "latest":
          # Load the step number if we're resuming a run
          # Add 1 so we don't immediately try to save again
          with open(counter_path, "r") as fp:
              counter = int(fp.read()) + 1
      counter_base = counter
  
      def save():
          maketree(checkpoint_path)
          print(
              "Saving",
              os.path.join(checkpoint_path, "model-{}").format(
                  counter - 1
              ),
          )
          saver.save(
              sess,
              os.path.join(checkpoint_path, "model"),
              global_step=counter - 1,
          )
          with open(counter_path, "w") as fp:
              fp.write(str(counter - 1) + "\n")
  
      def generate_samples():
          context_tokens = data_sampler.sample(1)
          all_text = []
          index = 0
          while index < sample_num:
              out = sess.run(
                  tf_sample,
                  feed_dict={context: batch_size * [context_tokens]},
              )
              for i in range(min(sample_num - index, batch_size)):
                  text = enc.decode(out[i])
                  text = "======== SAMPLE {} ========\n{}\n".format(
                      index + 1, text
                  )
                  all_text.append(text)
                  index += 1
          print(text)
          maketree(os.path.join(SAMPLE_DIR, run_name))
          with codecs.open(
              os.path.join(SAMPLE_DIR, run_name, "samples-{}").format(
                  counter
              ),
              "w",
              "utf8",
          ) as fp:
              fp.write("\n".join(all_text))
  
      def sample_batch():
          return [data_sampler.sample(1024) for _ in range(batch_size)]
  
      if overwrite and restore_from == "latest":
          for file in files:
              if file.startswith("model") or file.startswith("events"):
                  os.remove(os.path.join(checkpoint_path, file))
          save()
  
      avg_loss = (0.0, 0.0)
      start_time = time.time()
  
      if steps:
          steps = int(steps)
  
      try:
          while True:
              if steps > 0 and counter == (counter_base + steps):
                  save()
                  return
              if (counter - 1) % save_every == 0 and counter > 1:
                  save()
              if (counter - 1) % sample_every == 0 and counter > 1:
                  generate_samples()
  
              if accumulate_gradients > 1:
                  sess.run(opt_reset)
                  for _ in range(accumulate_gradients):
                      sess.run(
                          opt_compute,
                          feed_dict={context: sample_batch()},
                      )
                  (v_loss, v_summary) = sess.run(
                      (opt_apply, summary_loss)
                  )
              else:
                  (_, v_loss, v_summary) = sess.run(
                      (opt_apply, loss, summary_loss),
                      feed_dict={context: sample_batch()},
                  )
  
              summary_log.add_summary(v_summary, counter)
  
              if counter % print_every == 0:
                  avg_loss = (
                      avg_loss[0] * 0.99 + v_loss,
                      avg_loss[1] * 0.99 + 1.0,
                  )
  
                  print(
                      "[{counter} | {time:2.2f}] loss={loss:2.2f} avg={avg:2.2f}".format(
                          counter=counter,
                          time=time.time() - start_time,
                          loss=v_loss,
                          avg=avg_loss[0] / avg_loss[1],
                      )
                  )
  
              counter += 1
      except KeyboardInterrupt:
          print("interrupted")
          save()
  
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 6 for calling a global function with example usage: finetune(sess, dataset, steps, model_name, model_dir, combine, batch_size, learning_rate, accumulate_gradients, restore_from, run_name, checkpoint_dir, sample_every, sample_length, sample_num, multi_gpu, save_every, print_every, max_checkpoints, use_memory_saving_gradients, only_train_transformer_layers, optimizer, overwrite, reuse) and returns: unknown