code: def euclidean_grad(x, y):
      r"""Standard euclidean distance and its gradient.
  
      ..math::
          D(x, y) = \sqrt{\sum_i (x_i - y_i)^2}
          \frac{dD(x, y)}{dx} = (x_i - y_i)/D(x,y)
      """
      result = 0.0
      for i in range(x.shape[0]):
          result += (x[i] - y[i]) ** 2
      d = np.sqrt(result)
      grad = (x - y) / (1e-6 + d)
      return d, grad
  
imports: import numba
  import numpy
  import scipy.stats
  from sklearn.metrics import pairwise_distances
label_desc: Use image node_id 3 for calling a global function with example usage: euclidean_grad(x, y) and returns: d,  grad