code: def minkowski(x, y, p=2):
      r"""Minkowski distance.
  
      ..math::
          D(x, y) = \left(\sum_i |x_i - y_i|^p\right)^{\frac{1}{p}}
  
      This is a general distance. For p=1 it is equivalent to
      manhattan distance, for p=2 it is Euclidean distance, and
      for p=infinity it is Chebyshev distance. In general it is better
      to use the more specialised functions for those distances.
      """
      result = 0.0
      for i in range(x.shape[0]):
          result += (np.abs(x[i] - y[i])) ** p
  
      return result ** (1.0 / p)
  
imports: import numba
  import numpy
  import scipy.stats
  from sklearn.metrics import pairwise_distances
label_desc: Use image node_id 10 for calling a global function with example usage: minkowski(x, y, p) and returns: unknown