code: def test_one_many_consistent():
      """Checks that using transform_one or transform_many produces the same result."""
  
      product = (
          compose.Select("ordinal_date")
          + compose.Select("gallup", "ipsos")
          * compose.Select("morning_consult")
          + compose.Select("rasmussen")
          * compose.Select("you_gov", "five_thirty_eight")
      )
      X = pd.read_csv(datasets.TrumpApproval().path)
  
      one_outputs = []
      for x, _ in stream.iter_pandas(X):
          one_outputs.append(product.transform_one(x))
      one_outputs = pd.DataFrame(one_outputs)
  
      many_outputs = product.transform_many(X)
  
      # check_dtype=False to avoid int/float comparison
      pd.testing.assert_frame_equal(
          many_outputs[one_outputs.columns],
          one_outputs,
          check_dtype=False,
      )
  
file_path: river/river/compose/test_product.py
imports: from __future__ import annotations
  import numpy
  import pandas
  from sklearn import datasets
  from sklearn import linear_model
  from river import compat, compose, datasets, preprocessing, stream
label_desc: Use image node_id 10 for calling a global function with example usage: test_one_many_consistent() without return types