code: def dtw_limited_warping_length(s1, s2, max_length, be=None):
      r"""Compute Dynamic Time Warping (DTW) similarity measure between
      (possibly multidimensional) time series under an upper bound constraint on
      the resulting path length and return the similarity cost.
  
      DTW is computed as the Euclidean distance between aligned time series,
      i.e., if :math:`\pi` is the optimal alignment path:
  
      .. math::
  
          DTW(X, Y) = \sqrt{\sum_{(i, j) \in \pi} \|X_{i} - Y_{j}\|^2}
  
      Note that this formula is still valid for the multivariate case.
  
      It is not required that both time series share the same size, but they must
      be the same dimension. DTW was originally presented in [1]_.
      This constrained-length variant was introduced in [2]_.
      Both bariants are
      discussed in more details in our :ref:`dedicated user-guide page <dtw>`
  
      Parameters
      ----------
      s1 : array-like, shape=(sz1, d) or (sz1,)
          A time series. If shape is (sz1,), the time series is assumed to be univariate.
      s2 : array-like, shape=(sz2, d) or (sz2,)
          Another time series. If shape is (sz2,), the time series is assumed to be univariate.
      max_length : int
          Maximum allowed warping path length.
          If greater than len(s1) + len(s2), then it is equivalent to
          unconstrained DTW.
          If lower than max(len(s1), len(s2)), no path can be found and a
          ValueError is raised.
      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
      -------
      float
          Similarity score
  
      Examples
      --------
      >>> dtw_limited_warping_length([1, 2, 3], [1., 2., 2., 3.], 5)
      0.0
      >>> dtw_limited_warping_length([1, 2, 3], [1., 2., 2., 3., 4.], 5)
      1.0
  
      See Also
      --------
      dtw : Get the similarity score for DTW
      dtw_path_limited_warping_length : Get both the warping path and the
          similarity score for DTW with limited warping path length
  
      References
      ----------
      .. [1] H. Sakoe, S. Chiba, "Dynamic programming algorithm optimization for
             spoken word recognition," IEEE Transactions on Acoustics, Speech and
             Signal Processing, vol. 26(1), pp. 43--49, 1978.
      .. [2] Z. Zhang, R. Tavenard, A. Bailly, X. Tang, P. Tang, T. Corpetti
             Dynamic time warping under limited warping path length.
             Information Sciences, vol. 393, pp. 91--107, 2017.
      """
      be = instantiate_backend(be, s1, s2)
      s1 = to_time_series(s1, remove_nans=True, be=be)
      s2 = to_time_series(s2, remove_nans=True, be=be)
  
      if max_length < max(s1.shape[0], s2.shape[0]):
          raise ValueError(
              "Cannot find a path of length {} to align given "
              "time series.".format(max_length)
          )
  
      accumulated_costs = _limited_warping_length_cost(
          s1, s2, max_length, be=be
      )
      idx_pair = (s1.shape[0] - 1, s2.shape[0] - 1)
      optimal_cost = min(accumulated_costs[idx_pair].values())
      return be.sqrt(optimal_cost)
  
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 16 for calling a global function with example usage: dtw_limited_warping_length(s1, s2, max_length, be) and returns: be