code: def test_split_between_processes_list():
      state = AcceleratorState()
      data = list(range(0, 2 * state.num_processes))
      with state.split_between_processes(data) as results:
          assert (
              len(results) == 2
          ), f"Each process did not have two items. Process index: {state.process_index}; Length: {len(results)}"
  
      data = list(range(0, (3 * state.num_processes) - 1))
      with state.split_between_processes(
          data, apply_padding=True
      ) as results:
          if state.is_last_process:
              # Test that the last process gets the extra item(s)
              num_samples_per_device = math.ceil(
                  len(data) / state.num_processes
              )
              assert (
                  len(results) == num_samples_per_device
              ), f"Last process did not get the extra item(s). Process index: {state.process_index}; Length: {len(results)}"
      state.wait_for_everyone()
  
imports: import contextlib
  import io
  import math
  import time
  from copy import deepcopy
  from pathlib import Path
  import numpy
  import torch
  from torch.utils.data import DataLoader, Dataset
  from accelerate import Accelerator
  from accelerate.data_loader import SeedableRandomSampler, prepare_data_loader
  from accelerate.state import AcceleratorState
  from accelerate.test_utils import RegressionDataset, are_the_same_tensors
  from accelerate.utils import DistributedType, gather, is_bf16_available, is_ipex_available, is_npu_available, is_xpu_available, set_seed, synchronize_rng_states
label_desc: Use image node_id 13 for calling a global function with example usage: test_split_between_processes_list() without return types