code: def erp_alignment_path(
      x: np.ndarray,
      y: np.ndarray,
      return_cost_matrix: bool = False,
      window: Union[float, None] = None,
      itakura_max_slope: Union[float, None] = None,
      bounding_matrix: Union[np.ndarray, None] = None,
      g: float = 0.0,
      **kwargs: Any,
  ) -> AlignmentPathReturn:
      """Compute the Edit distance for real penalty (ERP) alignment path.
  
      ERP, first proposed in [1]_, attempts align time series
      by better considering how indexes are carried forward through the cost matrix.
      Usually in the dtw cost matrix, if an alignment can't be found the previous value
      is carried forward. Erp instead proposes the idea of gaps or sequences of points
      that have no matches. These gaps are then punished based on their distance from 'g'.
  
      Parameters
      ----------
      x: np.ndarray (1d or 2d array)
          First time series.
      y: np.ndarray (1d or 2d array)
          Second time series.
      return_cost_matrix: bool, defaults = False
          Boolean that when true will also return the cost matrix.
      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.
      g: float, defaults = 0.
          The reference value to penalise gaps.
      kwargs: Any
          Extra kwargs.
  
      Returns
      -------
      list[tuple]
          List of tuples containing the erp alignment path.
      float
          Erp distance between x and y.
      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 3 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 the metric type cannot be determined
          If g is not a float.
          If both window and itakura_max_slope are set
  
      References
      ----------
      .. [1] Lei Chen and Raymond Ng. 2004. On the marriage of Lp-norms and edit distance.
      In Proceedings of the Thirtieth international conference on Very large data bases
       - Volume 30 (VLDB '04). VLDB Endowment, 792–803.
      """
      format_kwargs = {
          "window": window,
          "itakura_max_slope": itakura_max_slope,
          "bounding_matrix": bounding_matrix,
          "g": g,
      }
      format_kwargs = {**format_kwargs, **kwargs}
  
      return distance_alignment_path(
          x,
          y,
          metric="erp",
          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 17 for calling a global function with example usage: erp_alignment_path(x, y, return_cost_matrix, window, itakura_max_slope, bounding_matrix, g) and returns: distance_alignment_path