code: def _lcss(s1, s2, eps, mask, be=None):
      """Compute the longest common subsequence score between two time series.
  
      Parameters
      ----------
      s1 : array-like, shape=(sz1, d) or (sz1,)
          First time series. If shape is (sz1,), the time series is assumed to be univariate.
      s2 : array-like, shape=(sz2, d) or (sz2,)
          Second time series. If shape is (sz2,), the time series is assumed to be univariate.
      eps : float (default: 1.)
          Maximum matching distance threshold.
      mask : array-like, shape=(sz1, sz2)
          Mask. Unconsidered cells must have infinite values.
      be : Backend object or string or None
          Backend. If `be` is an instance of the class `NumPyBackend` or the string `"numpy"`,
          the NumPy backend is used.
          If `be` is an instance of the class `PyTorchBackend` or the string `"pytorch"`,
          the PyTorch backend is used.
          If `be` is `None`, the backend is determined by the input arrays.
          See our :ref:`dedicated user-guide page <backend>` for more information.
  
      Returns
      -------
      lcss_score : float
          Longest Common Subsquence score between both time series.
      """
      be = instantiate_backend(be, s1, s2)
      s1 = be.array(s1)
      s2 = be.array(s2)
      l1 = be.shape(s1)[0]
      l2 = be.shape(s2)[0]
      acc_cost_mat = lcss_accumulated_matrix(s1, s2, eps, mask, be=be)
      return float(acc_cost_mat[-1][-1]) / min([l1, l2])
  
imports: import warnings
  import numpy
  from numba import njit, prange
  from tslearn.backend import instantiate_backend
  from tslearn.utils import to_time_series
  from .utils import _cdist_generic
label_desc: Use image node_id 40 for calling a global function with example usage: _lcss(s1, s2, eps, mask, be) and returns: unknown