code: def _njit_lcss(s1, s2, eps, mask):
      """Compute the longest common subsequence score between two time series.
  
      Parameters
      ----------
      s1 : array-like, shape=(sz1, d)
          First time series.
      s2 : array-like, shape=(sz2, d)
          Second time series.
      eps : float (default: 1.)
          Maximum matching distance threshold.
      mask : array-like, shape=(sz1, sz2)
          Mask. Unconsidered cells must have infinite values.
  
      Returns
      -------
      lcss_score : float
          Longest Common Subsquence score between both time series.
      """
      l1 = s1.shape[0]
      l2 = s2.shape[0]
      acc_cost_mat = njit_lcss_accumulated_matrix(s1, s2, eps, mask)
  
      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 39 for calling a global function with example usage: _njit_lcss(s1, s2, eps, mask) and returns: unknown