code: def standardised_euclidean(x, y, sigma=_mock_ones):
      r"""Euclidean distance standardised against a vector of standard
      deviations per coordinate.
  
      ..math::
          D(x, y) = \sqrt{\sum_i \frac{(x_i - y_i)**2}{v_i}}
      """
      result = 0.0
      for i in range(x.shape[0]):
          result += ((x[i] - y[i]) ** 2) / sigma[i]
  
      return np.sqrt(result)
  
imports: import numba
  import numpy
  import scipy.stats
  from sklearn.metrics import pairwise_distances
label_desc: Use image node_id 4 for calling a global function with example usage: standardised_euclidean(x, y, sigma) and returns: np