code: def _return_path_limited_warping_length(
      accum_costs, target_indices, optimal_length, be=None
  ):
      """Return the optimal path under an upper bound constraint on
      the path length.
  
      Parameters
      ----------
      accum_costs : dict
          Accumulated scores. This dict associates (i, j) pairs (keys) to
          dictionaries with desired length as key and associated score as value.
      target_indices : tuple (a pair of integers)
          Target indices.
      optimal_length : int
          Optimal length.
      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
      -------
      path : list of integer pairs
          Matching path represented as a list of index pairs. In each pair, the
          first index corresponds to a first time series s1 and the second one
          corresponds to a second time series s2.
      """
      be = instantiate_backend(be)
      path = [target_indices]
      cur_length = optimal_length
      while path[-1] != (0, 0):
          i, j = path[-1]
          if i == 0:
              path.append((0, j - 1))
          elif j == 0:
              path.append((i - 1, 0))
          else:
              arr = be.array(
                  [
                      accum_costs[i - 1, j - 1].get(
                          cur_length - 1, be.inf
                      ),
                      accum_costs[i - 1, j].get(cur_length - 1, be.inf),
                      accum_costs[i, j - 1].get(cur_length - 1, be.inf),
                  ]
              )
              argmin = be.argmin(arr)
              if argmin == 0:
                  path.append((i - 1, j - 1))
              elif argmin == 1:
                  path.append((i - 1, j))
              else:
                  path.append((i, j - 1))
              cur_length -= 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 17 for calling a global function with example usage: _return_path_limited_warping_length(accum_costs, target_indices, optimal_length, be) and returns: path