code: def test_lb_keogh():
      for be in backends:
          for array_type in array_types:
              ts1 = cast([1, 2, 3, 2, 1], array_type)
              env_low, env_up = tslearn.metrics.lb_envelope(
                  ts1, radius=1, be=be
              )
              np.testing.assert_allclose(
                  env_low, np.array([[1.0], [1.0], [2.0], [1.0], [1.0]])
              )
              np.testing.assert_allclose(
                  env_up, np.array([[2.0], [3.0], [3.0], [3.0], [2.0]])
              )
              backend = instantiate_backend(be, array_type)
              assert backend.belongs_to_backend(env_low)
              assert backend.belongs_to_backend(env_up)
  
imports: import platform
  import sys
  import numpy
  import pytest
  import tslearn.clustering
  import tslearn.metrics
  from scipy.spatial.distance import cdist
  from tslearn.backend.backend import Backend, cast, instantiate_backend
  from tslearn.metrics.dtw_variants import dtw_path
  from tslearn.utils import to_time_series
label_desc: Use image node_id 14 for calling a global function with example usage: test_lb_keogh() without return types