code: def njit_lcss_accumulated_matrix(s1, s2, eps, mask):
      """Compute the longest common subsequence similarity 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
          Matching threshold.
      mask : array-like, shape=(sz1, sz2)
          Mask. Unconsidered cells must have infinite values.
  
      Returns
      -------
      acc_cost_mat : array-like, shape=(sz1 + 1, sz2 + 1)
          Accumulated cost matrix.
      """
      l1 = s1.shape[0]
      l2 = s2.shape[0]
      acc_cost_mat = numpy.full((l1 + 1, l2 + 1), 0)
  
      for i in range(1, l1 + 1):
          for j in range(1, l2 + 1):
              if numpy.isfinite(mask[i - 1, j - 1]):
                  if (
                      numpy.sqrt(
                          _njit_local_squared_dist(s1[i - 1], s2[j - 1])
                      )
                      <= eps
                  ):
                      acc_cost_mat[i][j] = (
                          1 + acc_cost_mat[i - 1][j - 1]
                      )
                  else:
                      acc_cost_mat[i][j] = max(
                          acc_cost_mat[i][j - 1], acc_cost_mat[i - 1][j]
                      )
  
      return acc_cost_mat
  
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 37 for calling a global function with example usage: njit_lcss_accumulated_matrix(s1, s2, eps, mask) and returns: acc_cost_mat