code: def test_combine_with_existing_dense_features_shape_mismatch():
      existing_features = Features(
          np.array([[1, 0, 2, 3], [2, 0, 0, 1]]),
          FEATURE_TYPE_SEQUENCE,
          TEXT,
          "test",
      )
      new_features = Features(
          np.array([[0, 1]]), FEATURE_TYPE_SEQUENCE, TEXT, "origin"
      )
  
      with pytest.raises(ValueError):
          existing_features.combine_with_features(new_features)
  
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 3 for calling a global function with example usage: test_combine_with_existing_dense_features_shape_mismatch() without return types