docstr: This TestCase checks the behaviour that occurs during distributed training or evaluation,
  when the batch size does not evenly divide the dataset size.
docstr_tok: ["This","TestCase","checks","the","behaviour","that","occurs","during","distributed","training","or","evaluation",",","when","the","batch","size","does","not","evenly","divide","the","dataset","size","."]
code: def test_distributed_data_loop(self):
      """
      This TestCase checks the behaviour that occurs during distributed training or evaluation,
      when the batch size does not evenly divide the dataset size.
      """
      print(
          f"Found {torch.cuda.device_count()} devices, using 2 devices only"
      )
      cmd = [
          "torchrun",
          f"--nproc_per_node={torch.cuda.device_count()}",
          self.data_loop_file_path,
      ]
      with patch_environment(
          omp_num_threads=1, cuda_visible_devices="0,1"
      ):
          execute_subprocess_async(cmd, env=os.environ.copy())
  
file_path: accelerate/tests/test_multigpu.py
imports: import inspect
  import os
  import unittest
  import torch
  import accelerate
  from accelerate import Accelerator
  from accelerate.big_modeling import dispatch_model
  from accelerate.test_utils import assert_exception, execute_subprocess_async, require_multi_gpu
  from accelerate.utils import patch_environment
label_desc: Use image node_id 5 for calling the MultiGPUTester obj's underlying member method code with example usage: obj.test_distributed_data_loop() without return types