code: def finetune(
      model,
      train_dataset,
      test_dataset,
      context_len=32,
      max_steps=1000,
      batch_size=16,
      threshold=1.0,
      recopy_model=recopy_gpt2,
      secondary_learner=None,
      eval_interval=10,
      finetuned_model_name="gpt2_finetuned.pt",
  ):
      """
      fine-tune with IGF if secondary_learner is not None, else standard fine-tuning
  
      Args:
          model: pre-trained GPT-2 model
          train_dataset: Data set to train GPT-2 model
          test_dataset: Evaluate GPT-2 model
          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
          batch_size: Batch size to train GPT-2 model
          threshold: The threshold value used by secondary learner to filter the train_data and allow only"
                      informative data as input to the model
          recopy_model: Reset the model to the original pretrained GPT-2 weights after each iteration
          secondary_learner: Selection of IGF as fine-tuning method if not None
          eval_interval: number of batches after which decay the selectivity of our secondary learner filter from
                          1 standard deviation above average to 1 below average
          fine-tuned_model_name: name of the final final-tuned GPT-2 model
  
      Returns:
          Fine-tuned GPT-2 model
  
      """
  
      device = torch.device(
          "cuda:0" if torch.cuda.is_available() else "cpu"
      )
      train_sampler = RandomSampler(train_dataset)
      train_dataloader = DataLoader(
          train_dataset, sampler=train_sampler
      )
  
      num_train_epochs = max_steps // (len(train_dataset)) + 1
      global_step = 0
      context = torch.zeros(
          (1, context_len), dtype=torch.long, device=device
      )
      model, lm_optimizer, lm_scheduler = recopy_model(
          model, device, max_steps
      )
  
      model.train()
      if secondary_learner is not None:
          secondary_learner.to(device)
          secondary_learner.eval()
      contexts = []
      examples = 0
  
      observed_qs = []
      test_perps = []
  
      # Compute the performance of the transformer model at the beginning
      real_perp = compute_perplexity(model, test_dataset, context_len)
      test_perps.append(real_perp)
      print("Test perplexity, step", global_step, ":", real_perp)
      for epoch in range(int(num_train_epochs)):
          for step, example in enumerate(train_dataloader):
              torch.cuda.empty_cache()
              start = random.randint(
                  0, example.size(2) - context_len - 1
              )
              context[0, :] = example[0, 0, start : start + context_len]
              lm_optimizer.zero_grad()
              outputs = model(context, labels=context)
              do_backprop = True
  
              if secondary_learner is not None:
                  predicted_q = secondary_learner.forward(
                      torch.tensor(
                          context, dtype=torch.long, device=device
                      ).unsqueeze(0)
                  )[0].item()
                  observed_qs.append(float(predicted_q))
  
                  # Here we implement the simple non-constant threshold for the predicted IG(X) value
                  # We will decay the selectivity of our secondary learner filter from
                  # 1 standard deviation above average to 1 below average after 10 batches.
  
                  if global_step == 10:
                      threshold = -1
                  if predicted_q < threshold:
                      do_backprop = False
  
              # If we passed the filter, add the context to the batch!
              if do_backprop:
                  contexts.append(np.array(context.cpu()))
                  lm_loss = outputs[0]
                  lm_loss.backward()
                  examples += 1
  
              del outputs
  
              # Once the batch is filled with enough contexts, backprop on the batch.
              if examples == batch_size:
                  torch.cuda.empty_cache()
                  examples = 0
                  # Do LM backprop
                  torch.nn.utils.clip_grad_norm_(
                      model.parameters(), 3.0
                  )
                  lm_optimizer.step()
                  lm_scheduler.step()  # Update learning rate schedule
                  global_step += 1
                  # Compute the performance of the transformer model at this batch
                  if global_step % eval_interval == 0:
                      real_perp = compute_perplexity(
                          model, test_dataset, context_len
                      )
                      test_perps.append(real_perp)
  
                      print(
                          "Test perplexity, step",
                          global_step,
                          ":",
                          real_perp,
                      )
              # Break out of the loop after 60 batches
              if max_steps > 0 and global_step > 60:
                  break
          if max_steps > 0 and global_step > 60:
              break
  
      # save finetuned transformer model
      torch.save(model.state_dict(), finetuned_model_name)
      torch.cuda.empty_cache()
      # Do some cleaning up so we can reinitialize for the next run of this function
      del lm_optimizer
      del lm_scheduler
      return model
  
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 3 for calling a global function with example usage: finetune(model, train_dataset, test_dataset, context_len, max_steps, batch_size, threshold, recopy_model, secondary_learner, eval_interval, finetuned_model_name) and returns: model