code: def test_results_are_consistent_with_batch_size(tmp_path: Path):
      """
      Test that find_label_issues works with large memmap arrays and different batch sizes
      """
  
      # Create dummy versions of pred_probs and labels
      # write to the pytest tmp_path so that the files are deleted after the test
      pred_probs_file = tmp_path / "pred_probs.npy"
      labels_file = tmp_path / "labels.npy"
      np.save(pred_probs_file, np.random.rand(100, 2, 5, 5))
      np.save(labels_file, np.random.randint(0, 2, (100, 5, 5)))
  
      # Load the numpy arrays from disk
      pred_probs = np.load(pred_probs_file, mmap_mode="r")
      pred_labels = np.load(labels_file, mmap_mode="r")
  
      # Test with different batch sizes
      batch_sizes = [1, 50, 100]
      issues_list = []
      for batch_size in batch_sizes:
          issues = find_label_issues(
              pred_labels,
              pred_probs,
              n_jobs=None,
              batch_size=batch_size,
          )
          issues_list.append(issues)
  
      # Verify that the results are identical regardless of the batch size
      for i in range(len(batch_sizes) - 1):
          assert np.array_equal(issues_list[i], issues_list[i + 1])
  
imports: import numpy
  import random
  import pytest
  from unittest import mock
  import matplotlib.pyplot
  from pathlib import Path
  from cleanlab.internal.multilabel_scorer import softmin
  from cleanlab.internal.segmentation_utils import _check_input, _get_valid_optional_params, _get_summary_optional_params
  from cleanlab.segmentation.filter import find_label_issues
  from cleanlab.segmentation.rank import get_label_quality_scores, issues_from_scores, _get_label_quality_per_image
  from cleanlab.segmentation.summary import display_issues, common_label_issues, filter_by_class, _generate_colormap
label_desc: Use image node_id 3 for calling a global function with example usage: test_results_are_consistent_with_batch_size(tmp_path) without return types