code: def dtw_subsequence_path(subseq, longseq, be=None):
      r"""Compute sub-sequence Dynamic Time Warping (DTW) similarity measure
      between a (possibly multidimensional) query and a long time series and
      return both the path and the similarity.
  
      DTW is computed as the Euclidean distance between aligned time series,
      i.e., if :math:`\pi` is the alignment path:
  
      .. math::
  
          DTW(X, Y) = \sqrt{\sum_{(i, j) \in \pi} \|X_{i} - Y_{j}\|^2}
  
      Compared to traditional DTW, here, border constraints on admissible paths
      :math:`\pi` are relaxed such that :math:`\pi_0 = (0, ?)` and
      :math:`\pi_L = (N-1, ?)` where :math:`L` is the length of the considered
      path and :math:`N` is the length of the subsequence time series.
  
      It is not required that both time series share the same size, but they must
      be the same dimension. This implementation finds the best matching starting
      and ending positions for `subseq` inside `longseq`.
  
      Parameters
      ----------
      subseq : array-like, shape=(sz1, d) or (sz1,)
          A query time series.
          If shape is (sz1,), the time series is assumed to be univariate.
      longseq : array-like, shape=(sz2, d) or (sz2,)
          A reference (supposed to be longer than `subseq`) time series.
          If shape is (sz2,), the time series is assumed to be univariate.
      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
      -------
      list of integer pairs
          Matching path represented as a list of index pairs. In each pair, the
          first index corresponds to `subseq` and the second one corresponds to
          `longseq`.
      float
          Similarity score
  
      Examples
      --------
      >>> path, dist = dtw_subsequence_path([2., 3.], [1., 2., 2., 3., 4.])
      >>> path
      [(0, 2), (1, 3)]
      >>> dist
      0.0
  
      See Also
      --------
      dtw : Get the similarity score for DTW
      subsequence_cost_matrix: Calculate the required cost matrix
      subsequence_path: Calculate a matching path manually
      """
      be = instantiate_backend(be, subseq, longseq)
      subseq = to_time_series(subseq, be=be)
      longseq = to_time_series(longseq, be=be)
      acc_cost_mat = subsequence_cost_matrix(
          subseq=subseq, longseq=longseq, be=be
      )
      global_optimal_match = be.argmin(acc_cost_mat[-1, :])
      path = subsequence_path(acc_cost_mat, global_optimal_match, be=be)
      return path, be.sqrt(acc_cost_mat[-1, :][global_optimal_match])
  
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 25 for calling a global function with example usage: dtw_subsequence_path(subseq, longseq, be) and returns: path,  be