code: def _dtw_sakoechiba(
      x=None,
      y=None,
      dist="square",
      window_size=0.1,
      precomputed_cost=None,
      return_cost=False,
      return_accumulated=False,
      return_path=False,
  ):
      """Dynamic Time Warping (DTW) distance with Sakoe-Chiba band constraint.
  
      The Sakoe-Chiba constraint region is a band centered around the main
      diagonal. Any cell whose distance to the diagonale is lower than a
      given value is valid for the path.
  
      Options
      -------
      window_size : float or int (default = 0.1)
          The window size above and below the diagonal.
          If float, `window_size` must be between 0 and
          1, and the actual window size will be computed as
          ``ceil(window_size * max((n_timestamps_1, n_timestamps_2) - 1))``.
          If int, `window_size` must be the largest temporal shift allowed.
          Each cell whose distance with the diagonal is lower than or equal to
          `window_size` becomes a valid cell for the path.
  
      References
      ----------
      .. [1] H. Sakoe and S. Chiba, "Dynamic programming algorithm optimization
             for spoken word recognition". IEEE Transactions on Acoustics,
             Speech, and Signal Processing, 26(1), 43-49 (1978).
  
  
      """
      (
          x,
          y,
          precomputed_cost,
          n_timestamps_1,
          n_timestamps_2,
      ) = _check_input_dtw(
          x, y, precomputed_cost, dist, method="sakoechiba"
      )
      region = sakoe_chiba_band(
          n_timestamps_1, n_timestamps_2, window_size
      )
      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 19 for calling a global function with example usage: _dtw_sakoechiba(x, y, dist, window_size, precomputed_cost, return_cost, return_accumulated, return_path) and returns: res