code: def _njit_dtw(s1, s2, mask):
      """Compute the dynamic time warping score between two time series.
  
      Parameters
      ----------
      s1 : array-like, shape=(sz1, d)
          First time series.
      s2 : array-like, shape=(sz2, d)
          Second time series.
      mask : array-like, shape=(sz1, sz2)
          Mask. Unconsidered cells must have infinite values.
  
      Returns
      -------
      dtw_score : float
          Dynamic Time Warping score between both time series.
  
      """
      cum_sum = njit_accumulated_matrix(s1, s2, mask)
      return numpy.sqrt(cum_sum[-1, -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 5 for calling a global function with example usage: _njit_dtw(s1, s2, mask) and returns: numpy