code: def twe_distance(
      x: np.ndarray,
      y: np.ndarray,
      window: Union[float, None] = None,
      itakura_max_slope: Union[float, None] = None,
      bounding_matrix: np.ndarray = None,
      lmbda: float = 1.0,
      nu: float = 0.001,
      p: int = 2,
      **kwargs: Any,
  ) -> float:
      """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
      -------
      float
          Dtw distance between x and y.
  
      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
  
      Examples
      --------
      >>> import numpy as np
      >>> from sktime.distances import twe_distance
      >>> x_1d = np.array([1, 2, 3, 4])  # 1d array
      >>> y_1d = np.array([5, 6, 7, 8])  # 1d array
      >>> twe_distance(x_1d, y_1d)
      28.0
  
      >>> x_2d = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])  # 2d array
      >>> y_2d = np.array([[9, 10, 11, 12], [13, 14, 15, 16]])  # 2d array
      >>> twe_distance(x_2d, y_2d)
      78.37353236814714
  
      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(x, y, metric="twe", **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 9 for calling a global function with example usage: twe_distance(x, y, window, itakura_max_slope, bounding_matrix, lmbda, nu, p) and returns: distance