class_docstr: Abstract class to define a numba compatible distance metric.
class_docstr_tok: ["Abstract","class","to","define","a","numba","compatible","distance","metric","."]
docstr: Compute the distance alignment path between two time series.
  
  Parameters
  ----------
  x: np.ndarray (2d array)
      First time series.
  y: np.ndarray (2d array)
      Second time series.
  return_cost_matrix: bool, defaults = False
      Boolean that when true will also return the cost matrix
  kwargs: dict
      kwargs for the distance computation.
  
  Returns
  -------
  list[tuple]
      List of tuples that is the path through the matrix
  float
      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.
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code: def distance_alignment_path(
      self,
      x: np.ndarray,
      y: np.ndarray,
      return_cost_matrix: bool = False,
      **kwargs: dict,
  ) -> float:
      """Compute the distance alignment path between two time series.
  
      Parameters
      ----------
      x: np.ndarray (2d array)
          First time series.
      y: np.ndarray (2d array)
          Second time series.
      return_cost_matrix: bool, defaults = False
          Boolean that when true will also return the cost matrix
      kwargs: dict
          kwargs for the distance computation.
  
      Returns
      -------
      list[tuple]
          List of tuples that is the path through the matrix
      float
          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.
      """
      dist_callable = self.distance_alignment_path_factory(
          x, y, return_cost_matrix=return_cost_matrix, **kwargs
      )
      return dist_callable(x, y)
  
file_path: sktime/sktime/distances/base/_base.py
imports: from abc import ABC, abstractmethod
  from typing import Callable, NamedTuple, Set
  import numpy
  from sktime.distances.base._types import AlignmentPathReturn, DistanceAlignmentPathCallable, DistanceCallable
label_desc: Use image node_id 2 for calling the NumbaDistance obj's underlying member method code with example usage: obj.distance_alignment_path(x, y, return_cost_matrix) and returns: dist_callable