code: def test_pipeline():
      kernel = mf.ImputationKernel(X_train, datasets=1)
  
      pipe = Pipeline(
          [
              ("impute", kernel),
              ("scaler", StandardScaler()),
          ]
      )
  
      # The pipeline can be used as any other estimator
      # and avoids leaking the test set into the train set
      X_train_t = pipe.fit_transform(
          X_train, y_train, impute__iterations=2
      )
      X_test_t = pipe.transform(X_test)
  
      assert not np.any(np.isnan(X_train_t))
      assert not np.any(np.isnan(X_test_t))
  
imports: import numpy
  from sklearn.preprocessing import StandardScaler
  from sklearn.datasets import make_classification
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import Pipeline
  import miceforest
label_desc: Use image node_id 1 for calling a global function with example usage: test_pipeline() without return types