code: def test_smoke(Mitigator, constraints, Estimator, prefit):
      # This test case ensures that input validation doesn't remove metadata from the input
      # matrix X, as described at https://github.com/fairlearn/fairlearn/issues/312
      np.random.seed(0)
      n = 100
      X0 = np.random.normal(size=n)
      X1 = np.random.choice([1, 2, 3], size=n)
      Y = np.random.choice([0, 1], size=n)
      A = np.random.choice([0, 1], size=n)
      df = pd.DataFrame({"X0": X0, "X1": X1})
      # Set X1 as categorical
      df["X1"] = df["X1"].astype("category")
  
      estimator = Estimator()
      if prefit:
          estimator.fit(df, Y)
  
      mitigator = Mitigator(
          estimator=estimator,
          constraints=constraints,
          prefit=prefit,
          predict_method="predict",
      )
      mitigator.fit(df, Y, sensitive_features=A)
      mitigator.predict(df, sensitive_features=A)
  
file_path: fairlearn/test/unit/postprocessing/test_smoke.py
imports: import platform
  import numpy
  import pandas
  import pytest
  from sklearn.linear_model import LogisticRegression
  from sklearn.svm import SVC
  from sklearn.tree import DecisionTreeClassifier
  from fairlearn.postprocessing import ThresholdOptimizer
label_desc: Use image node_id 1 for calling a global function with example usage: test_smoke(Mitigator, constraints, Estimator, prefit) without return types