code: def generate(
      sess,
      run_name="run1",
      checkpoint_dir="checkpoint",
      model_name=None,
      model_dir="models",
      sample_dir="samples",
      return_as_list=False,
      truncate=None,
      destination_path=None,
      sample_delim="=" * 20 + "\n",
      prefix=None,
      seed=None,
      nsamples=1,
      batch_size=1,
      length=1023,
      temperature=0.7,
      top_k=0,
      top_p=0.0,
      include_prefix=True,
  ):
      """Generates text from a model loaded into memory.
  
      Adapted from https://github.com/openai/gpt-2/blob/master/src/interactive_conditional_samples.py
      """
  
      if batch_size is None:
          batch_size = 1
      assert nsamples % batch_size == 0
  
      if nsamples == 1:
          sample_delim = ""
  
      if prefix == "":
          prefix = None
  
      if model_name:
          checkpoint_path = os.path.join(model_dir, model_name)
      else:
          checkpoint_path = os.path.join(checkpoint_dir, run_name)
  
      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 prefix:
          context = tf.compat.v1.placeholder(
              tf.int32, [batch_size, None]
          )
          context_tokens = enc.encode(prefix)
  
      np.random.seed(seed)
      tf.compat.v1.set_random_seed(seed)
  
      output = sample.sample_sequence(
          hparams=hparams,
          length=min(
              length, length - (len(context_tokens) if prefix else 0)
          ),
          start_token=enc.encoder["<|endoftext|>"]
          if not prefix
          else None,
          context=context if prefix else None,
          batch_size=batch_size,
          temperature=temperature,
          top_k=top_k,
          top_p=top_p,
      )[:, 1:]
  
      if destination_path:
          f = codecs.open(destination_path, "w", "utf-8")
      generated = 0
      gen_texts = []
      while generated < nsamples:
          if not prefix:
              out = sess.run(output)
          else:
              out = sess.run(
                  output,
                  feed_dict={context: batch_size * [context_tokens]},
              )
          for i in range(batch_size):
              generated += 1
              gen_text = enc.decode(out[i])
              if prefix:
                  gen_text = enc.decode(context_tokens[:1]) + gen_text
              if truncate:
                  truncate_esc = re.escape(truncate)
                  if prefix and not include_prefix:
                      prefix_esc = re.escape(prefix)
                      pattern = "(?:{})(.*?)(?:{})".format(
                          prefix_esc, truncate_esc
                      )
                  else:
                      pattern = "(.*?)(?:{})".format(truncate_esc)
  
                  trunc_text = re.search(pattern, gen_text, re.S)
                  if trunc_text:
                      gen_text = trunc_text.group(1)
              gen_text = gen_text.lstrip("\n")
              if destination_path:
                  f.write("{}\n{}".format(gen_text, sample_delim))
              if not return_as_list and not destination_path:
                  print("{}\n{}".format(gen_text, sample_delim), end="")
              gen_texts.append(gen_text)
  
      if destination_path:
          f.close()
  
      if return_as_list:
          return gen_texts
  
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 8 for calling a global function with example usage: generate(sess, run_name, checkpoint_dir, model_name, model_dir, sample_dir, return_as_list, truncate, destination_path, sample_delim, prefix, seed, nsamples, batch_size, length, temperature, top_k, top_p, include_prefix) and returns: gen_texts