code: def test_simple_numeric_imputation(imputation_method):
      """Assert that missing values are imputed."""
      data = pycaret.datasets.get_data("juice")
      data.loc[100, "WeekofPurchase"] = np.nan
      pc = pycaret.classification.setup(
          data=data,
          imputation_type="simple",
          numeric_iterative_imputer=imputation_method,
      )
      X, _ = pc.pipeline.transform(pc.X, pc.y)
      assert X.isna().sum().sum() == 0
  
imports: import io
  import numpy
  import pandas
  import pytest
  from imblearn.over_sampling import ADASYN
  from scipy.sparse import csr_matrix
  from sklearn.decomposition import PCA
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import StandardScaler
  import pycaret.classification
  import pycaret.datasets
  import pycaret.regression
label_desc: Use image node_id 19 for calling a global function with example usage: test_simple_numeric_imputation(imputation_method) without return types