code: def test_threshold_optimization_different_input_lengths(
      data_X_y_sf, constraints
  ):
      n = len(X_ex)
      expected_exception_messages = {
          "inconsistent": "Found input variables with inconsistent numbers of samples",
          "empty": "Found array with 0 sample",
      }
      for permutation in [(0, 1), (1, 0)]:
          with pytest.raises(
              ValueError,
              match=expected_exception_messages["inconsistent"].format(
                  "X, sensitive_features, and y"
              ),
          ):
              adjusted_predictor = ThresholdOptimizer(
                  estimator=ExamplePredictor(scores_ex),
                  constraints=constraints,
                  predict_method="predict",
              )
              adjusted_predictor.fit(
                  data_X_y_sf.X[: n - permutation[0]],
                  data_X_y_sf.y[: n - permutation[1]],
                  sensitive_features=data_X_y_sf.sensitive_features,
              )
  
      # try providing empty lists in all combinations
      for permutation in [(0, n, "inconsistent"), (n, 0, "empty")]:
          adjusted_predictor = ThresholdOptimizer(
              estimator=ExamplePredictor(scores_ex),
              constraints=constraints,
              predict_method="predict",
          )
          with pytest.raises(
              ValueError,
              match=expected_exception_messages[permutation[2]],
          ):
              adjusted_predictor.fit(
                  data_X_y_sf.X[: n - permutation[0]],
                  data_X_y_sf.y[: n - permutation[1]],
                  sensitive_features=data_X_y_sf.sensitive_features,
              )
  
imports: from copy import deepcopy
  from test.unit.input_convertors import _map_into_single_column
  import numpy
  import pandas
  import pytest
  from sklearn.base import BaseEstimator, ClassifierMixin
  from sklearn.datasets import make_classification
  from fairlearn.postprocessing import ThresholdOptimizer
  from fairlearn.postprocessing._threshold_optimizer import BASE_ESTIMATOR_NONE_ERROR_MESSAGE, NOT_SUPPORTED_CONSTRAINTS_ERROR_MESSAGE
  from fairlearn.postprocessing._tradeoff_curve_utilities import DEGENERATE_LABELS_ERROR_MESSAGE
  from fairlearn.utils._input_validation import _LABELS_NOT_0_1_ERROR_MESSAGE, _MESSAGE_SENSITIVE_FEATURES_NONE, _MESSAGE_Y_NONE
  from .conftest import ExamplePredictor, X_ex, _get_predictions_by_sensitive_feature, candidate_A_transforms, candidate_X_transforms, candidate_Y_transforms, degenerate_labels_ex, is_invalid_transformation, labels_ex, scores_ex, sensitive_feature_names_ex1, sensitive_features_ex1
label_desc: Use image node_id 7 for calling a global function with example usage: test_threshold_optimization_different_input_lengths(data_X_y_sf, constraints) without return types