code: def test_validate_data_ambiguous_rows():
      """Test if an ambiguous number of rows are caught."""
      for (X, Y, Z), types in generate_data_combinations():
          X = X[:5, :]
          mitigator = get_instance(fake_mixin=True)
          with pytest.raises(ValueError) as exc:
              mitigator._validate_input(
                  X.tolist(), Y.tolist(), Z.tolist()
              )
              assert str(
                  exc.value
              ) == "Input data has an ambiguous number of rows: {}, {}, {}.".format(
                  X.shape[0], Y.shape[0], Z.shape[0]
              )
  
imports: import sys
  import pytest
  import numpy
  import pandas
  from sklearn.utils import estimator_checks
  from fairlearn.adversarial import AdversarialFairnessClassifier, AdversarialFairnessRegressor
  from fairlearn.adversarial._adversarial_mitigation import _AdversarialFairness
  from fairlearn.adversarial._preprocessor import FloatTransformer
  from fairlearn.adversarial._constants import _TYPE_COMPLIANCE_ERROR
  from .helper import get_instance, generate_data_combinations, cols, Bin2d, Bin1d, Cat, Cont2d, Cont1d, BCE, CCE, MSE, Keyword_CATEGORY, Keyword_BINARY, Keyword_CONTINUOUS, Keyword_CLASSIFICATION, Keyword_AUTO, KeywordToClass
label_desc: Use image node_id 27 for calling a global function with example usage: test_validate_data_ambiguous_rows() without return types