code: def test_torch_batch_process_all_slots_checkpoint_same_number_of_times(
      mock_report_progress_to_master: unittest.mock.MagicMock,
      mock_synchronize_and_checkpoint: unittest.mock.MagicMock,
      mock_initialize_default_inference_context: unittest.mock.MagicMock,
      data_length: int,
      batch_size: int,
      checkpoint_interval: int,
      rank: int,
      num_slots: int,
  ) -> None:
      with test_util.set_mock_cluster_info(
          DEFAULT_ADDRS, rank, num_slots
      ):
          mock_initialize_default_inference_context.return_value = (
              core._dummy_init(distributed=_get_dist_context(rank=rank))
          )
          index_dataset = IndexData(data_length)
  
          MyProcessorCLS = unittest.mock.Mock()
          my_processor_instance = unittest.mock.Mock()
          MyProcessorCLS.return_value = my_processor_instance
  
          experimental.torch_batch_process(
              dataset=index_dataset,
              batch_processor_cls=MyProcessorCLS,
              batch_size=batch_size,
              checkpoint_interval=checkpoint_interval,
          )
  
          # Regardless of a worker's rank, _synchronize_and_checkpoint should be called the same
          # amount of time
          # Otherwise, _synchronize_and_checkpoint will hang forever waiting for the workers.
          # Note that rank is not part of the calculation of times_iterate
          times_iterate = math.ceil(
              math.ceil(data_length / batch_size / num_slots)
              / checkpoint_interval
          )
          assert (
              mock_synchronize_and_checkpoint.call_count
              == times_iterate
          )
          # on_checkpoint_start should be called the same number of times as
          # _synchronize_and_checkpoint
          assert (
              my_processor_instance.on_checkpoint_start.call_count
              == times_iterate
          )
  
imports: import math
  import unittest.mock
  from typing import Any, Dict, List, Optional
  import pytest
  import torch
  from torch.utils import data
  from determined import core, pytorch
  from determined.pytorch import experimental
  from tests.launch import test_util
label_desc: Use image node_id 4 for calling a global function with example usage: test_torch_batch_process_all_slots_checkpoint_same_number_of_times(mock_report_progress_to_master, mock_synchronize_and_checkpoint, mock_initialize_default_inference_context, data_length, batch_size, checkpoint_interval, rank, num_slots) without return types