code: def standardised_euclidean_grad(x, y, sigma=_mock_ones):
      r"""Euclidean distance standardised against a vector of standard
      deviations per coordinate with gradient.
  
      ..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]
      d = np.sqrt(result)
      grad = (x - y) / (1e-6 + d * sigma)
      return d, grad
  
imports: import numba
  import numpy
  import scipy.stats
  from sklearn.metrics import pairwise_distances
label_desc: Use image node_id 5 for calling a global function with example usage: standardised_euclidean_grad(x, y, sigma) and returns: d,  grad