code: def sinkhorn_distance_batch(
      x, y, cost=_dummy_cost, regularization=1.0
  ):
      dim_x = x.shape[0]
      dim_y = y.shape[0]
  
      batch_size = y.shape[1]
  
      u = np.full((dim_x, batch_size), 1.0 / dim_x, dtype=cost.dtype)
      v = np.full((dim_y, batch_size), 1.0 / dim_y, dtype=cost.dtype)
  
      K = K_from_cost(cost, regularization)
      u, v = sinkhorn_iterations_batch(
          x,
          y,
          u,
          v,
          K,
      )
  
      i_dim = K.shape[0]
      j_dim = K.shape[1]
      result = np.zeros(batch_size)
      for i in range(i_dim):
          for j in range(j_dim):
              K_times_cost = K[i, j] * cost[i, j]
              for batch in range(batch_size):
                  result[batch] += (
                      u[i, batch] * K_times_cost * v[j, batch]
                  )
  
      return result
  
imports: import numpy
  import numba
  from collections import namedtuple
  from enum import Enum, IntEnum
label_desc: Use image node_id 27 for calling a global function with example usage: sinkhorn_distance_batch(x, y, cost, regularization) and returns: result