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 numpy
  import numba
  from pynndescent.optimal_transport import allocate_graph_structures, initialize_graph_structures, initialize_supply, initialize_cost, network_simplex_core, total_cost, ProblemStatus, sinkhorn_transport_plan
label_desc: Use image node_id 3 for calling a global function with example usage: standardised_euclidean(x, y, sigma) and returns: np