code: def test_lcss_path():
      for be in backends:
          for array_type in array_types:
              path, sim = tslearn.metrics.lcss_path(
                  cast([1.0, 2.0, 3.0], array_type),
                  cast([1.0, 2.0, 2.0, 3.0], array_type),
                  be=be,
              )
              np.testing.assert_equal(sim, 1.0)
              assert isinstance(sim, float)
              np.testing.assert_equal(path, [(0, 1), (1, 2), (2, 3)])
              assert isinstance(path, list)
  
              path, sim = tslearn.metrics.lcss_path(
                  cast([1.0, 2.0, 3.0], array_type),
                  cast([1.0, 2.0, 2.0, 4.0], array_type),
                  be=be,
              )
              np.testing.assert_equal(sim, 1.0)
              np.testing.assert_equal(path, [(0, 1), (1, 2), (2, 3)])
  
              path, sim = tslearn.metrics.lcss_path(
                  cast([1.0, 2.0, 3.0], array_type),
                  cast([-2.0, 5.0, 7.0], array_type),
                  eps=3,
                  be=be,
              )
              np.testing.assert_equal(round(sim, 2), 0.67)
              np.testing.assert_equal(path, [(0, 0), (2, 1)])
  
              path, sim = tslearn.metrics.lcss_path(
                  cast([1.0, 2.0, 3.0], array_type),
                  cast([1.0, 2.0, 2.0, 2.0, 3.0], array_type),
                  eps=0,
                  be=be,
              )
              np.testing.assert_equal(sim, 1.0)
              np.testing.assert_equal(path, [(0, 0), (1, 3), (2, 4)])
  
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 5 for calling a global function with example usage: test_lcss_path() without return types