code: def minkowski_distance(x, y, p=2):
      """Compute the L**p distance between two arrays.
  
      The last dimensions of `x` and `y` must be the same length.  Any
      other dimensions must be compatible for broadcasting.
  
      Parameters
      ----------
      x : (..., K) array_like
          Input array.
      y : (..., K) array_like
          Input array.
      p : float, 1 <= p <= infinity
          Which Minkowski p-norm to use.
  
      Returns
      -------
      dist : ndarray
          Distance between the input arrays.
  
      Examples
      --------
      >>> from scipy.spatial import minkowski_distance
      >>> minkowski_distance([[0, 0], [0, 0]], [[1, 1], [0, 1]])
      array([ 1.41421356,  1.        ])
  
      """
      x = np.asarray(x)
      y = np.asarray(y)
      if p == np.inf or p == 1:
          return minkowski_distance_p(x, y, p)
      else:
          return minkowski_distance_p(x, y, p) ** (1.0 / p)
  
imports: import numpy
  from ._ckdtree import cKDTree, cKDTreeNode
label_desc: Use image node_id 2 for calling a global function with example usage: minkowski_distance(x, y, p) and returns: minkowski_distance_p, unknown