code: def forward(self, inputs, hidden=None):
      if self.batch_first:
          inputs = inputs.transpose(0, 1)
      outputs = []
      for i, (cell, dilation) in enumerate(
          zip(self.cells, self.dilations)
      ):
          if hidden is None:
              inputs, _ = self.drnn_layer(cell, inputs, dilation)
          else:
              inputs, hidden[i] = self.drnn_layer(
                  cell, inputs, dilation, hidden[i]
              )
  
          outputs.append(inputs[-dilation:])
  
      if self.batch_first:
          inputs = inputs.transpose(0, 1)
      return inputs, outputs
  
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 2 for calling the DRNN obj's underlying member method code with example usage: obj.forward(inputs, hidden) and returns: inputs,  outputs