code: def test_one2many_df2df(model):
      """
      if a one-to-many model is applied to a DataFrame, the output should have
      prefix in column names to indicate the input columns they correspond.
      """
      df = pd.DataFrame(
          np.arange(300).reshape(100, 3),
          index=pd.date_range(start="2017-1-1", periods=100, freq="D"),
          columns=["A", "B", "C"],
      )
      if isinstance(model, _TrainableModel):
          result = model.fit_predict(df)
      else:
          result = model.predict(df)
      n_cols = round(len(result.columns) / 3)
      assert all([col[:2] == "A_" for col in result.columns[:n_cols]])
      assert all([col[2:4] != "A_" for col in result.columns[:n_cols]])
      assert all(
          [
              col[:2] == "B_"
              for col in result.columns[n_cols : 2 * n_cols]
          ]
      )
      assert all(
          [
              col[2:4] != "B_"
              for col in result.columns[n_cols : 2 * n_cols]
          ]
      )
      assert all(
          [col[:2] == "C_" for col in result.columns[2 * n_cols :]]
      )
      assert all(
          [col[2:4] != "C_" for col in result.columns[2 * n_cols :]]
      )
  
file_path: adtk/tests/test_series_name.py
imports: import sys
  import numpy
  import pandas
  import pytest
  from sklearn.cluster import KMeans
  from sklearn.linear_model import LinearRegression
  from sklearn.neighbors import LocalOutlierFactor
  import adtk.detector
  import adtk.transformer
  from adtk._base import _TrainableModel
  from adtk._detector_base import _NonTrainableUnivariateDetector, _TrainableMultivariateDetector, _TrainableUnivariateDetector
label_desc: Use image node_id 7 for calling a global function with example usage: test_one2many_df2df(model) without return types