code: def test_validate_data_df_inputs():
      """Test if validate_data properly preprocesses dataframes to ndarray."""
      for (X, Y, Z), types in generate_data_combinations():
          mitigator = get_instance(fake_mixin=True)
          X, Y, Z = mitigator._validate_input(
              pd.DataFrame(X), pd.DataFrame(Y), pd.DataFrame(Z)
          )
          for x in (X, Y, Z):
              check_2dnp(x)
  
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 24 for calling a global function with example usage: test_validate_data_df_inputs() without return types