code: def sinkhorn_distance(
      x, y, M=_mock_identity, cost=_mock_cost, maxiter=64
  ):  # pragma: no cover
      p = (x / x.sum()).astype(np.float32)
      q = (y / y.sum()).astype(np.float32)
  
      u = np.ones(p.shape, dtype=np.float32)
      v = np.ones(q.shape, dtype=np.float32)
  
      for n in range(maxiter):
          t = M @ v
          u[t > 0] = p[t > 0] / t[t > 0]
          t = M.T @ u
          v[t > 0] = q[t > 0] / t[t > 0]
  
      pi = np.diag(v) @ M @ np.diag(u)
      result = 0.0
      for i in range(pi.shape[0]):
          for j in range(pi.shape[1]):
              if pi[i, j] > 0:
                  result += pi[i, j] * cost[i, j]
  
      return result
  
imports: import numba
  import numpy
  import scipy.stats
  from sklearn.metrics import pairwise_distances
label_desc: Use image node_id 46 for calling a global function with example usage: sinkhorn_distance(x, y, M, cost, maxiter) and returns: result