code: def check_leave_one_group_out_results(
      sample_data, estimator, domain_adaptation=False
  ):
      x, y, groups = sample_data
      results = cross_validation.leave_one_group_out(
          x, y, groups, estimator, domain_adaptation
      )
  
      # Check if all keys are present in the result dictionary
      assert "Target" in results
      assert "Num_samples" in results
      assert "Accuracy" in results
  
      # Check if the length of 'Target', 'Num_samples', and 'Accuracy' lists is the same
      assert (
          len(results["Target"])
          == len(results["Num_samples"])
          == len(results["Accuracy"])
      )
  
      # Check if the last element of 'Target' is "Average"
      assert results["Target"] == ["A", "B", "Average"]
  
      # Check if the number of samples is correct for each target group
      assert results["Num_samples"] == [4, 4, 8]
  
      # Check if the accuracy scores are within the expected range
      for accuracy in results["Accuracy"]:
          assert 0 <= accuracy <= 1
  
file_path: pykale/tests/evaluate/test_cross_validation.py
imports: import numpy
  import pytest
  from sklearn.dummy import DummyClassifier
  from kale.evaluate import cross_validation
  from kale.pipeline.multi_domain_adapter import CoIRLS
label_desc: Use image node_id 2 for calling a global function with example usage: check_leave_one_group_out_results(sample_data, estimator, domain_adaptation) without return types