code: def generate_n_pairs(
      context_len=32,
      max_steps=10,
      size_objective_set=100,
      min_len=1026,
      trim=True,
      data_file="data/tokenized_stories_train_wikitext103.jbl",
      igf_data_file="igf_context_pairs.jbl",
  ):
      """
      Collecting *n* pairs for training the secondary learner
      Args:
          context_len: The maximum total input sequence length after tokenization. Sequences longer
                      than this will be truncated, sequences shorter will be padded
          max_steps: To calculate training epochs of secondary learner
          size_objective_set: size of objective data set used to create (X,IG(X)) pairs which is the training data for secondary learner
          min_len: The minimum length of the article to be used as objective set
          trim: If True truncate the context if it exceeds context length
          data_file: Tokenized data set split for training and evaluation of model
          igf_data_file: file to store (I,IG(X)) paired data set to train secondary learner
  
      Returns:
          Data stored in igf_data_file
  
      """
      # generates same data everytime
      set_seed(3)
      # generate train_data and objective_set
      train_data, objective_set = generate_datasets(
          context_len,
          data_file,
          number=size_objective_set,
          min_len=1026,
          trim=True,
      )
      # keeps model same across runs
      set_seed(4)
      # model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
      # can we train on GPU?
      device = torch.device(
          "cuda:0" if torch.cuda.is_available() else "cpu"
      )
  
      # load pretrained model
      model = load_gpt2("gpt2").to(device)
      print("computing perplexity on objective set")
      orig_perp = compute_perplexity(
          model, objective_set, context_len
      ).item()
      print("perplexity on objective set:", orig_perp)
  
      # collect igf pairs and save to file demo.jbl
      collect_objective_set(
          model,
          orig_perp,
          context_len,
          train_data,
          objective_set,
          max_steps,
          device,
          igf_data_file,
      )
  
      # clean up, delete model and data we don't need anymore
      del model, train_data, objective_set
      torch.cuda.empty_cache()
  
file_path: transformers/examples/research_projects/information-gain-filtration/run_clm_igf.py
imports: import argparse
  import random
  import joblib
  import numpy
  import torch
  from igf.igf import SecondaryLearner, collect_objective_set, compute_perplexity, generate_datasets, load_gpt2, recopy_gpt2, set_seed, train_secondary_learner
  from torch.utils.data import DataLoader, RandomSampler
  from transformers import GPT2LMHeadModel
label_desc: Use image node_id 1 for calling a global function with example usage: generate_n_pairs(context_len, max_steps, size_objective_set, min_len, trim, data_file, igf_data_file) without return types