code: def manhattan_grad(x, y):
      r"""Manhattan, taxicab, or l1 distance with gradient.
  
      ..math::
          D(x, y) = \sum_i |x_i - y_i|
      """
      result = 0.0
      grad = np.zeros(x.shape)
      for i in range(x.shape[0]):
          result += np.abs(x[i] - y[i])
          grad[i] = np.sign(x[i] - y[i])
      return result, grad
  
imports: import numba
  import numpy
  import scipy.stats
  from sklearn.metrics import pairwise_distances
label_desc: Use image node_id 7 for calling a global function with example usage: manhattan_grad(x, y) and returns: result,  grad