code: def test_fit_transform(testCase):
      """Test fit_transform the transformer."""
      df = pd.DataFrame(
          np.array(testCase["df"]).T,
          pd.date_range(
              start="2017-1-1", periods=len(testCase["df"][0]), freq="D"
          ),
      )
      model = testCase["model"](**testCase["params"])
      if isinstance(model, _TrainableModel):
          t = model.fit_transform(df)
      else:
          t = model.transform(df)
      if not isinstance(testCase["t"], dict):
          t_true = pd.Series(testCase["t"], index=df.index)
          pd.testing.assert_series_equal(t, t_true, check_dtype=False)
      else:
          t_true = pd.DataFrame(testCase["t"], index=df.index)
          pd.testing.assert_frame_equal(t, t_true, check_dtype=False)
  
file_path: adtk/tests/test_transformerhd.py
imports: import numpy
  import pandas
  import pytest
  from sklearn.linear_model import LinearRegression
  import adtk.transformer
  from adtk._base import _TrainableModel
label_desc: Use image node_id 1 for calling a global function with example usage: test_fit_transform(testCase) without return types