class_docstr: Computes a kernel using the transportation distance as the distance. 
docstr: Constructor. 
docstr_tok: ["Constructor","."]
code: def __init__(
      self,
      trans_dist_computer,
      alphas,
      betas,
      powers=DFLT_KERN_DIST_POWERS,
  ):
      """Constructor."""
      self.trans_dist_computer = trans_dist_computer
      self.num_dists = len(betas)
      self.num_dist_types = len(
          self.trans_dist_computer.dflt_dist_type.split("-")
      )
      groups = self._get_groups(self.num_dists, self.num_dist_types)
      # Call super constructor
      super(DistSumNNKernel, self).__init__(
          self.compute_dists, alphas, groups, betas, powers
      )
  
imports: import numpy
  from .None import neural_network
  from ..gp.kernel import ExpSumOfDistsKernel, SumOfExpSumOfDistsKernel
  from ..utils.oper_utils import opt_transport
label_desc: Use image node_id 1 to create a new DistSumNNKernel object from inherited base classes: SumOfExpSumOfDistsKernel with example: obj = DistSumNNKernel(trans_dist_computer, alphas, betas, powers)