code: def _dtw_itakura(
      x=None,
      y=None,
      dist="square",
      max_slope=2.0,
      precomputed_cost=None,
      return_cost=False,
      return_accumulated=False,
      return_path=False,
  ):
      """Dynamic Time Warping distance with Itakura parallelogram constraint.
  
      The Itakura constraint region is a parallelogram. Contrary to the
      Sakoe-Chiba band, whose width is constant, the Itakura parallelogram
      has a varying width, allowing for larger time shifts in the middle than
      at the first and last time points.
  
      Options
      -------
      max_slope : float (default = 2.)
          Maximum slope of the parallelogram.
  
      References
      ----------
      .. [1] F. Itakura, "Minimum prediction residual principle applied to
             speech recognition". IEEE Transactions on Acoustics,
             Speech, and Signal Processing, 23(1), 67–72 (1975).
  
      """
      (
          x,
          y,
          precomputed_cost,
          n_timestamps_1,
          n_timestamps_2,
      ) = _check_input_dtw(
          x, y, precomputed_cost, dist, method="itakura"
      )
      region = itakura_parallelogram(
          n_timestamps_1, n_timestamps_2, max_slope
      )
      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 22 for calling a global function with example usage: _dtw_itakura(x, y, dist, max_slope, precomputed_cost, return_cost, return_accumulated, return_path) and returns: res