code: def test_instance_slicing(
      monkeypatch,
      reader_class,
      world_size: Optional[int],
      num_workers: Optional[int],
      max_instances: Optional[int],
  ):
      """
      Ensure that the intances read by each worker are always unique and the total
      adds up to `max_instances`.
      """
      results: List[Set[int]] = []
  
      minimum_expected_result_size = max_instances or TOTAL_INSTANCES
      maximum_expected_result_size = max_instances or TOTAL_INSTANCES
  
      if world_size is not None and num_workers is not None:
          minimum_expected_result_size //= world_size
          minimum_expected_result_size //= num_workers
          maximum_expected_result_size = (
              minimum_expected_result_size + 1
          )
          for global_rank in range(world_size):
              monkeypatch.setattr(
                  common_util, "is_distributed", lambda: True
              )
              monkeypatch.setattr(dist, "get_rank", lambda: global_rank)
              monkeypatch.setattr(
                  dist, "get_world_size", lambda: world_size
              )
              for worker_id in range(num_workers):
                  reader = reader_class(max_instances=max_instances)
                  reader._set_worker_info(
                      WorkerInfo(num_workers, worker_id)
                  )
                  result = set(
                      x["index"].label for x in reader.read("the-path-doesnt-matter")  # type: ignore
                  )
                  results.append(result)
      elif world_size is not None:
          minimum_expected_result_size //= world_size
          maximum_expected_result_size = (
              minimum_expected_result_size + 1
          )
          for global_rank in range(world_size):
              monkeypatch.setattr(
                  common_util, "is_distributed", lambda: True
              )
              monkeypatch.setattr(dist, "get_rank", lambda: global_rank)
              monkeypatch.setattr(
                  dist, "get_world_size", lambda: world_size
              )
              reader = reader_class(max_instances=max_instances)
              result = set(
                  x["index"].label for x in reader.read("the-path-doesnt-matter")  # type: ignore
              )
              results.append(result)
      elif num_workers is not None:
          minimum_expected_result_size //= num_workers
          maximum_expected_result_size = (
              minimum_expected_result_size + 1
          )
          for worker_id in range(num_workers):
              reader = reader_class(max_instances=max_instances)
              reader._set_worker_info(
                  WorkerInfo(num_workers, worker_id)
              )
              result = set(
                  x["index"].label for x in reader.read("the-path-doesnt-matter")  # type: ignore
              )
              results.append(result)
      else:
          reader = reader_class(max_instances=max_instances)
          result = set(
              x["index"].label for x in reader.read("the-path-doesnt-matter")  # type: ignore
          )
          results.append(result)
  
      # We need to check that all of the result sets are mutually exclusive and that they're
      # union has size `max_instances`.
      # Checking that they're mutually exclusive is equivalent to checking that the sum
      # of the size of each set is equal to the size of the union.
  
      union: Set[int] = set()
      total: int = 0
      for result in results:
          union |= result
          total += len(result)
          # Also make sure the size of the set is within the expected bounds.
          assert minimum_expected_result_size <= len(result)
          assert len(result) <= maximum_expected_result_size
  
      assert len(union) == total == (max_instances or TOTAL_INSTANCES)
  
imports: from itertools import islice
  from typing import Optional, List, Set
  import pytest
  import torch.distributed
  from allennlp.common import util
  from allennlp.data import Instance
  from allennlp.data.dataset_readers import DatasetReader, WorkerInfo
  from allennlp.data.fields import LabelField
label_desc: Use image node_id 1 for calling a global function with example usage: test_instance_slicing(monkeypatch, reader_class, world_size, num_workers, max_instances) without return types