code: def _split_outputs(self, dilated_outputs, rate):
      batchsize = dilated_outputs.size(1) // rate
  
      blocks = [
          dilated_outputs[:, i * batchsize : (i + 1) * batchsize, :]
          for i in range(rate)
      ]
  
      interleaved = torch.stack((blocks)).transpose(1, 0).contiguous()
      interleaved = interleaved.view(
          dilated_outputs.size(0) * rate,
          batchsize,
          dilated_outputs.size(2),
      )
      return interleaved
  
imports: from typing import List, Optional
  import torch
  import torch.nn
  from ..losses.pytorch import MAE
  from ..common._base_recurrent import BaseRecurrent
  from ..common._modules import MLP
label_desc: Use image node_id 6 for calling the DRNN obj's underlying member method code with example usage: obj._split_outputs(dilated_outputs, rate) and returns: interleaved