code: def _dtw_region(
      x=None,
      y=None,
      dist="square",
      region=None,
      precomputed_cost=None,
      return_cost=False,
      return_accumulated=False,
      return_path=False,
  ):
      """Dynamic Time Warping (DTW) distance with a constraint region.
  
      This version of DTW allows users to provide their own constraint region.
      If not provided, no constraint region is used, which is exactly the
      classic DTW.
  
      Options
      -------
      region : None or array-like, shape = (2, n_timestamps_1) (default = None)
          Constraint region. If None, no constraint region is used. Otherwise,
          the first row consists of the starting indices (included) and the
          second row consists of the ending indices (excluded) of the valid rows
          for each column.
  
      """
      (
          x,
          y,
          precomputed_cost,
          n_timestamps_1,
          n_timestamps_2,
      ) = _check_input_dtw(
          x, y, precomputed_cost, dist, method="region"
      )
      cost_mat = _input_to_cost(
          x, y, dist, precomputed_cost, region=region
      )
      acc_cost_mat = accumulated_cost_matrix(cost_mat, region)
      dtw_dist = acc_cost_mat[-1, -1]
      if dist == "square":
          dtw_dist = sqrt(dtw_dist)
  
      res = _return_results(
          dtw_dist,
          cost_mat,
          acc_cost_mat,
          return_cost,
          return_accumulated,
          return_path,
      )
      return res
  
imports: import numpy
  from math import ceil, log2, sqrt
  from numba import njit, prange
  from sklearn.utils import check_array
label_desc: Use image node_id 16 for calling a global function with example usage: _dtw_region(x, y, dist, region, precomputed_cost, return_cost, return_accumulated, return_path) and returns: res