code: def _njit_return_lcss_path(s1, s2, eps, mask, acc_cost_mat, sz1, sz2):
      """Return the Longest Common Subsequence (LCSS) path.
  
      Parameters
      ----------
      s1 : array-like, shape=(sz1, d)
          A time series.
      s2 : array-like, shape=(sz2, d)
          Another time series.
      eps : float
          Maximum matching distance threshold.
      mask : array-like, shape=(sz1, sz2)
          Mask. Unconsidered cells must have infinite values.
      acc_cost_mat : array-like, shape=(sz1 + 1, sz2 + 1)
          Accumulated cost matrix.
      sz1 : int
          Length of the first time series.
      sz2 : int
          Length of the second time series.
  
      Returns
      -------
      path : list of integer pairs
          Matching path represented as a list of index pairs. In each pair, the
          first index corresponds to s1 and the second one corresponds to s2.
      """
      i, j = (sz1, sz2)
      path = []
  
      while i > 0 and j > 0:
          if numpy.isfinite(mask[i - 1, j - 1]):
              if (
                  numpy.sqrt(
                      _njit_local_squared_dist(s1[i - 1], s2[j - 1])
                  )
                  <= eps
              ):
                  path.append((i - 1, j - 1))
                  i, j = (i - 1, j - 1)
              elif acc_cost_mat[i - 1][j] > acc_cost_mat[i][j - 1]:
                  i = i - 1
              else:
                  j = j - 1
      return path[::-1]
  
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 42 for calling a global function with example usage: _njit_return_lcss_path(s1, s2, eps, mask, acc_cost_mat, sz1, sz2) and returns: path