code: def euclidean_distance(
      x: np.ndarray, y: np.ndarray, **kwargs: Any
  ) -> float:
      r"""Compute the Euclidean distance between two time series.
  
      Euclidean distance is supported for 1d, 2d and 3d arrays.
  
      The Euclidean distance between two time series of length m is the square root of
      the squared distance and is defined as:
  
      .. math::
          ed(x, y) = \sqrt{\sum_{i=1}^{n} (x_i - y_i)^2}
  
      Parameters
      ----------
      x: np.ndarray (1d or 2d array)
          First time series.
      y: np.ndarray (1d or 2d array)
          Second time series.
      kwargs: Any
          Extra kwargs. For euclidean there are none however, this is kept for
          consistency.
  
      Returns
      -------
      float
          Euclidean distance between x and y.
  
      Raises
      ------
      ValueError
          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.
  
      Examples
      --------
      >>> import numpy as np
      >>> from sktime.distances import euclidean_distance
      >>> x_1d = np.array([1, 2, 3, 4])  # 1d array
      >>> y_1d = np.array([5, 6, 7, 8])  # 1d array
      >>> euclidean_distance(x_1d, y_1d)
      8.0
  
      >>> 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
      >>> euclidean_distance(x_2d, y_2d)
      22.627416997969522
      """
      return distance(x, y, metric="euclidean", **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 11 for calling a global function with example usage: euclidean_distance(x, y) and returns: distance