code: def test_fit_transform(pipeline):
      """Assert that the pipeline can be fit-transformed normally."""
      data = get_data("juice", verbose=False)
      pipeline.steps.append(("test", "passthrough"))
      X, y = pipeline.fit_transform(data.iloc[:, :-1], data.iloc[:, -1])
      assert isinstance(X, pd.DataFrame) and isinstance(y, pd.Series)
  
file_path: pycaret/tests/test_pipeline.py
imports: import pandas
  import pytest
  from sklearn.preprocessing import LabelEncoder
  import pycaret.classification
  import pycaret.regression
  from pycaret.datasets import get_data
label_desc: Use image node_id 5 for calling a global function with example usage: test_fit_transform(pipeline) without return types