class_docstr: Euclidean distance between two time series.
class_docstr_tok: ["Euclidean","distance","between","two","time","series","."]
docstr: Create a no_python compiled euclidean distance callable.
  
  Series should be shape (d, m), where d is the number of dimensions, m the series
  length. Requires equal length series.
  
  Parameters
  ----------
  x: np.ndarray (1d or 2d array)
      First time series.
  y: np.ndarray (1d or 2d array)
      Second times eries.
  kwargs: Any
      Extra kwargs. For euclidean there are none however, this is kept for
      consistency.
  
  Returns
  -------
  Callable[[np.ndarray, np.ndarray], float]
      No_python compiled euclidean distance callable.
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code: def _distance_factory(
      self, x: np.ndarray, y: np.ndarray, **kwargs: Any
  ) -> DistanceCallable:
      """Create a no_python compiled euclidean distance callable.
  
      Series should be shape (d, m), where d is the number of dimensions, m the series
      length. Requires equal length series.
  
      Parameters
      ----------
      x: np.ndarray (1d or 2d array)
          First time series.
      y: np.ndarray (1d or 2d array)
          Second times eries.
      kwargs: Any
          Extra kwargs. For euclidean there are none however, this is kept for
          consistency.
  
      Returns
      -------
      Callable[[np.ndarray, np.ndarray], float]
          No_python compiled euclidean distance callable.
      """
      from sktime.distances._euclidean_numba import (
          _numba_euclidean_distance,
      )
  
      return _numba_euclidean_distance
  
imports: from typing import Any
  import numpy
  from sktime.distances.base import DistanceCallable, NumbaDistance
label_desc: Use image node_id 1 for calling the _EuclideanDistance obj's underlying member method code with example usage: obj._distance_factory(x, y) and returns: _numba_euclidean_distance