code: def test_for_features_fingerprinting_collisions():
      """Tests that features fingerprints are unique."""
      m1 = np.asarray(
          [[0.5, 3.1, 3.0], [1.1, 1.2, 1.3], [4.7, 0.3, 2.7]]
      )
  
      m2 = np.asarray([[0, 0, 0], [1, 2, 3], [0, 0, 1]])
  
      dense_features = [
          Features(
              m1, FEATURE_TYPE_SENTENCE, TEXT, "CountVectorsFeaturizer"
          ),
          Features(
              m2, FEATURE_TYPE_SENTENCE, TEXT, "CountVectorsFeaturizer"
          ),
          Features(
              m1, FEATURE_TYPE_SEQUENCE, TEXT, "CountVectorsFeaturizer"
          ),
          Features(m1, FEATURE_TYPE_SEQUENCE, TEXT, "RegexFeaturizer"),
          Features(
              m1,
              FEATURE_TYPE_SENTENCE,
              INTENT,
              "CountVectorsFeaturizer",
          ),
      ]
      dense_fingerprints = {f.fingerprint() for f in dense_features}
      assert len(dense_fingerprints) == len(dense_features)
  
      sparse_features = [
          Features(
              scipy.sparse.coo_matrix(m1),
              FEATURE_TYPE_SENTENCE,
              TEXT,
              "CountVectorsFeaturizer",
          ),
          Features(
              scipy.sparse.coo_matrix(m2),
              FEATURE_TYPE_SENTENCE,
              TEXT,
              "CountVectorsFeaturizer",
          ),
          Features(
              scipy.sparse.coo_matrix(m1),
              FEATURE_TYPE_SEQUENCE,
              TEXT,
              "CountVectorsFeaturizer",
          ),
          Features(
              scipy.sparse.coo_matrix(m1),
              FEATURE_TYPE_SEQUENCE,
              TEXT,
              "RegexFeaturizer",
          ),
          Features(
              scipy.sparse.coo_matrix(m1),
              FEATURE_TYPE_SENTENCE,
              INTENT,
              "CountVectorsFeaturizer",
          ),
      ]
      sparse_fingerprints = {f.fingerprint() for f in sparse_features}
      assert len(sparse_fingerprints) == len(sparse_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 6 for calling a global function with example usage: test_for_features_fingerprinting_collisions() without return types