code: def test_print(type: Text, is_sparse: bool):
      first_dim = 1 if type == FEATURE_TYPE_SEQUENCE else 3
      matrix = np.full(shape=(first_dim, 2), fill_value=1)
      if is_sparse:
          matrix = scipy.sparse.coo_matrix(matrix)
      feat = Features(
          features=matrix,
          attribute="fixed-attribute",
          feature_type=type,
          origin="origin--doesn't-matter-here",
      )
      assert repr(feat)
      assert str(feat)
  
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 1 for calling a global function with example usage: test_print(type, is_sparse) without return types