code: def test_reduce_raises_if_combining_different_origins_or_attributes(
      differ: Text,
  ):
      # create features accordingly
      arbitrary_fixed_type = FEATURE_TYPE_SENTENCE
      features_list = []
      for idx in range(2):
          first_dim = 1
          arbitrary_matrix_matching_type = np.full(
              shape=(first_dim, 1), fill_value=1
          )
          config = dict(
              features=arbitrary_matrix_matching_type,
              attribute="fixed-attribute"
              if differ != "attribute"
              else f"attr-{idx}",
              feature_type=arbitrary_fixed_type,
              origin="fixed-origin"
              if differ != "origin"
              else f"origin-{idx}",
          )
          feat = Features(**config)
          features_list.append(feat)
  
      # reduce!
      if differ == "attribute":
          message = (
              "Expected all Features to describe the same attribute"
          )
          expected_origin = ["origin"]
      else:
          message = "Expected 'origin-1' to be the origin of the 0-th"
          expected_origin = ["origin-1"]
      with pytest.raises(ValueError, match=message):
          Features.reduce(
              features_list, expected_origins=expected_origin
          )
  
imports: import itertools
  from typing import Optional, Text, List, Dict, Tuple, Any
  import numpy
  import pytest
  import scipy.sparse
  from rasa.shared.nlu.training_data.features import Features
  from rasa.shared.nlu.constants import FEATURE_TYPE_SENTENCE, FEATURE_TYPE_SEQUENCE, TEXT, INTENT
label_desc: Use image node_id 13 for calling a global function with example usage: test_reduce_raises_if_combining_different_origins_or_attributes(differ) without return types