code: def test_distributed_packing(
      dist,
      is_initialized,
      fsdp,
      setup,
      step_lr,
      optimizer,
      get_model,
      tokenizer,
      train,
      setup_tokenizer,
  ):
      import os
      from llama_recipes.finetuning import main
  
      setup_tokenizer(tokenizer)
  
      rank = 0
      os.environ["LOCAL_RANK"] = f"{rank}"
      os.environ["RANK"] = f"{rank}"
      os.environ["WORLD_SIZE"] = "2"
      os.environ["MASTER_ADDR"] = "localhost"
      os.environ["MASTER_PORT"] = "12345"
  
      kwargs = {
          "model_name": "meta-llama/Llama-2-7b-hf",
          "batch_size_training": 8,
          "val_batch_size": 1,
          "use_peft": False,
          "dataset": "samsum_dataset",
          "batching_strategy": "packing",
          "enable_fsdp": True,
      }
  
      is_initialized.return_value = True
      dist.get_rank.return_value = rank
      dist.get_world_size.return_value = 2
  
      main(**kwargs)
  
      assert train.call_count == 1
  
      args, kwargs = train.call_args
      train_dataloader = args[1]
      eval_dataloader = args[2]
  
      assert len(train_dataloader) == 96 // 2
      assert len(eval_dataloader) == 42 // 2
  
imports: import pytest
  from unittest.mock import patch
label_desc: Use image node_id 2 for calling a global function with example usage: test_distributed_packing(dist, is_initialized, fsdp, setup, step_lr, optimizer, get_model, tokenizer, train, setup_tokenizer) without return types