code: def twe_alignment_path(
      x: np.ndarray,
      y: np.ndarray,
      return_cost_matrix: bool = False,
      window: float = None,
      itakura_max_slope: float = None,
      bounding_matrix: np.ndarray = None,
      lmbda: float = 1.0,
      nu: float = 0.001,
      p: int = 2,
      **kwargs: Any,
  ) -> AlignmentPathReturn:
      """Time Warp Edit (TWE) distance between two time series.
  
      The Time Warp Edit (TWE) distance is a distance measure for discrete time series
      matching with time 'elasticity'. In comparison to other distance measures, (e.g.
      DTW (Dynamic Time Warping) or LCS (Longest Common Subsequence Problem)), TWE is a
      metric. Its computational time complexity is O(n^2), but can be drastically reduced
      in some specific situation by using a corridor to reduce the search space. Its
      memory space complexity can be reduced to O(n). It was first proposed in [1].
  
      Parameters
      ----------
      x: np.ndarray (1d or 2d array)
          First time series.
      y: np.ndarray (1d or 2d array)
          Second time series.
      window: float, defaults = None
          Float that is the radius of the sakoe chiba window (if using Sakoe-Chiba
          lower bounding). Value must be between 0. and 1.
      itakura_max_slope: float, defaults = None
          Gradient of the slope for itakura parallelogram (if using Itakura
          Parallelogram lower bounding). Value must be between 0. and 1.
      bounding_matrix: np.ndarray (2d of size mxn where m is len(x) and n is len(y)),
                                      defaults = None
          Custom bounding matrix to use. If defined then other lower_bounding params
          are ignored. The matrix should be structure so that indexes considered in
          bound should be the value 0. and indexes outside the bounding matrix should be
          infinity.
      lmbda: float, defaults = 1.0
          A constant penalty that punishes the editing efforts. Must be >= 1.0.
      nu: float, defaults = 0.001
          A non-negative constant which characterizes the stiffness of the elastic
          twe measure. Must be > 0.
      p: int, defaults = 2
          Order of the p-norm for local cost.
      kwargs: Any
          Extra kwargs.
  
      Returns
      -------
      list[tuple]
          List of tuples containing the twe alignment path.
      float
          Twe distance between x and y. The value returned will be between 0.0 and 1.0,
          where 0.0 means the two time series are exactly the same and 1.0 means they
          are complete opposites.
      np.ndarray (of shape (len(x), len(y)).
          Optional return only given if return_cost_matrix = True.
          Cost matrix used to compute the distance.
  
      Raises
      ------
      ValueError
          If the sakoe_chiba_window_radius is not a float.
          If the itakura_max_slope is not a float.
          If the value of x or y provided is not a numpy array.
          If the value of x or y has more than 2 dimensions.
          If a metric string provided, and is not a defined valid string.
          If a metric object (instance of class) is provided and doesn't inherit from
          NumbaDistance.
          If a resolved metric is not no_python compiled.
          If the metric type cannot be determined
          If both window and itakura_max_slope are set
  
      References
      ----------
      .. [1] Marteau, P.; F. (2009). "Time Warp Edit Distance with Stiffness Adjustment
      for Time Series Matching". IEEE Transactions on Pattern Analysis and Machine
      Intelligence. 31 (2): 306–318.
      """
      format_kwargs = {
          "window": window,
          "itakura_max_slope": itakura_max_slope,
          "bounding_matrix": bounding_matrix,
          "lmbda": lmbda,
          "nu": nu,
          "p": p,
      }
      format_kwargs = {**format_kwargs, **kwargs}
  
      return distance_alignment_path(
          x,
          y,
          metric="twe",
          return_cost_matrix=return_cost_matrix,
          **format_kwargs,
      )
  
imports: from typing import Any, Callable, Union
  import numpy
  from sktime.distances._ddtw import _DdtwDistance
  from sktime.distances._dtw import _DtwDistance
  from sktime.distances._edr import _EdrDistance
  from sktime.distances._erp import _ErpDistance
  from sktime.distances._euclidean import _EuclideanDistance
  from sktime.distances._lcss import _LcssDistance
  from sktime.distances._msm import _MsmDistance
  from sktime.distances._resolve_metric import _resolve_dist_instance, _resolve_metric_to_factory
  from sktime.distances._squared import _SquaredDistance
  from sktime.distances._twe import _TweDistance
  from sktime.distances._wddtw import _WddtwDistance
  from sktime.distances._wdtw import _WdtwDistance
  from sktime.distances.base import AlignmentPathReturn, DistanceAlignmentPathCallable, DistanceCallable, MetricInfo, NumbaDistance
label_desc: Use image node_id 20 for calling a global function with example usage: twe_alignment_path(x, y, return_cost_matrix, window, itakura_max_slope, bounding_matrix, lmbda, nu, p) and returns: distance_alignment_path