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 between two time series.
  
  Parameters
  ----------
  x: np.ndarray (2d array)
      First time series.
  y: np.ndarray (2d array)
      Second time series.
  kwargs: dict
      kwargs for the distance computation.
  
  Returns
  -------
  float
      Distance between x and y.
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code: def distance(
      self, x: np.ndarray, y: np.ndarray, **kwargs: dict
  ) -> float:
      """Compute the distance between two time series.
  
      Parameters
      ----------
      x: np.ndarray (2d array)
          First time series.
      y: np.ndarray (2d array)
          Second time series.
      kwargs: dict
          kwargs for the distance computation.
  
      Returns
      -------
      float
          Distance between x and y.
      """
      dist_callable = self.distance_factory(x, y, **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 1 for calling the NumbaDistance obj's underlying member method code with example usage: obj.distance(x, y) and returns: dist_callable