code: def weighted_minkowski(x, y, w=_mock_ones, p=2):
      r"""A weighted version of Minkowski distance.
  
      ..math::
          D(x, y) = \left(\sum_i w_i |x_i - y_i|^p\right)^{\frac{1}{p}}
  
      If weights w_i are inverse standard deviations of data in each dimension
      then this represented a standardised Minkowski distance (and is
      equivalent to standardised Euclidean distance for p=1).
      """
      result = 0.0
      for i in range(x.shape[0]):
          result += w[i] * 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 14 for calling a global function with example usage: weighted_minkowski(x, y, w, p) and returns: unknown