code: def distance(
      x: np.ndarray,
      y: np.ndarray,
      metric: Union[
          str,
          Callable[
              [np.ndarray, np.ndarray, dict],
              Callable[[np.ndarray, np.ndarray], float],
          ],
          Callable[[np.ndarray, np.ndarray], float],
          NumbaDistance,
      ],
      **kwargs: Any,
  ) -> float:
      """Compute the distance between two time series.
  
      First the distance metric is 'resolved'. This means the metric that is passed
      is resolved to a callable. The callable is then called with x and y and the
      value is then returned.
  
      Parameters
      ----------
      x: np.ndarray (1d or 2d array)
          First time series.
      y: np.ndarray (1d or 2d array)
          Second time series.
      metric: str or Callable
          The distance metric to use.
          If a string is given, the value must be one of the following strings:
          'euclidean', 'squared', 'dtw', 'ddtw', 'wdtw', 'wddtw', 'lcss', 'edr', 'erp',
          'msm'
  
          If callable then it has to be a distance factory or numba distance callable.
          If you want to pass custom kwargs to the distance at runtime, use a distance
          factory as it constructs the distance using the kwargs before distance
          computation.
          A distance callable takes the form (must be no_python compiled):
          Callable[[np.ndarray, np.ndarray], float]
  
          A distance factory takes the form (must return a no_python callable):
          Callable[[np.ndarray, np.ndarray, bool, dict], Callable[[np.ndarray,
          np.ndarray], float]].
      kwargs: Any
          Arguments for metric. Refer to each metrics documentation for a list of
          possible arguments.
  
      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 distance
      >>> x_1d = np.array([1, 2, 3, 4])  # 1d array
      >>> y_1d = np.array([5, 6, 7, 8])  # 1d array
      >>> distance(x_1d, y_1d, metric='dtw')
      58.0
  
      >>> 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
      >>> distance(x_2d, y_2d, metric='dtw')
      512.0
  
      >>> 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
      >>> distance(x_2d, y_2d, metric='dtw', window=0.5)
      512.0
  
      Returns
      -------
      float
          Distance between the x and y.
      """
      from sktime.distances._numba_utils import to_numba_timeseries
  
      _x = to_numba_timeseries(x)
      _y = to_numba_timeseries(y)
  
      _metric_callable = _resolve_metric_to_factory(
          metric, _x, _y, _METRIC_INFOS, **kwargs
      )
  
      return _metric_callable(_x, _y)
  
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 21 for calling a global function with example usage: distance(x, y, metric) and returns: _metric_callable