code: def test_combine_with_existing_sparse_features():
      existing_features = Features(
          scipy.sparse.csr_matrix([[1, 0, 2, 3], [2, 0, 0, 1]]),
          FEATURE_TYPE_SEQUENCE,
          TEXT,
          "test",
      )
      fingerprint = existing_features.fingerprint()
      new_features = Features(
          scipy.sparse.csr_matrix([[1, 0], [0, 1]]),
          FEATURE_TYPE_SEQUENCE,
          TEXT,
          "origin",
      )
      expected_features = [[1, 0, 2, 3, 1, 0], [2, 0, 0, 1, 0, 1]]
  
      existing_features.combine_with_features(new_features)
      actual_features = existing_features.features.toarray()
  
      assert np.all(expected_features == actual_features)
      # check that combining features changes fingerprint
      assert fingerprint != existing_features.fingerprint()
  
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 4 for calling a global function with example usage: test_combine_with_existing_sparse_features() without return types