code: def test_batching_strategy(
      step_lr, optimizer, get_dataset, tokenizer, get_model, train
  ):
      kwargs = {"batching_strategy": "packing"}
  
      get_dataset.return_value = get_fake_dataset()
  
      main(**kwargs)
  
      assert train.call_count == 1
  
      args, kwargs = train.call_args
      train_dataloader, eval_dataloader = args[1:3]
      assert isinstance(train_dataloader.batch_sampler, BatchSampler)
      assert isinstance(eval_dataloader.batch_sampler, BatchSampler)
  
      kwargs["batching_strategy"] = "padding"
      train.reset_mock()
      main(**kwargs)
  
      assert train.call_count == 1
  
      args, kwargs = train.call_args
      train_dataloader, eval_dataloader = args[1:3]
      assert isinstance(
          train_dataloader.batch_sampler, LengthBasedBatchSampler
      )
      assert isinstance(
          eval_dataloader.batch_sampler, LengthBasedBatchSampler
      )
  
      kwargs["batching_strategy"] = "none"
  
      with pytest.raises(ValueError):
          main(**kwargs)
  
file_path: llama-recipes/tests/test_finetuning.py
imports: import pytest
  from pytest import approx
  from unittest.mock import patch
  from torch.nn import Linear
  from torch.optim import AdamW
  from torch.utils.data.dataloader import DataLoader
  from torch.utils.data.sampler import BatchSampler
  from llama_recipes.finetuning import main
  from llama_recipes.data.sampler import LengthBasedBatchSampler
label_desc: Use image node_id 6 for calling a global function with example usage: test_batching_strategy(step_lr, optimizer, get_dataset, tokenizer, get_model, train) without return types