code: def drnn_layer(self, cell, inputs, rate, hidden=None):
      n_steps = len(inputs)
      batch_size = inputs[0].size(0)
      hidden_size = cell.hidden_size
  
      inputs, dilated_steps = self._pad_inputs(inputs, n_steps, rate)
      dilated_inputs = self._prepare_inputs(inputs, rate)
  
      if hidden is None:
          dilated_outputs, hidden = self._apply_cell(
              dilated_inputs, cell, batch_size, rate, hidden_size
          )
      else:
          hidden = self._prepare_inputs(hidden, rate)
          dilated_outputs, hidden = self._apply_cell(
              dilated_inputs,
              cell,
              batch_size,
              rate,
              hidden_size,
              hidden=hidden,
          )
  
      splitted_outputs = self._split_outputs(dilated_outputs, rate)
      outputs = self._unpad_outputs(splitted_outputs, n_steps)
  
      return outputs, hidden
  
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 3 for calling the DRNN obj's underlying member method code with example usage: obj.drnn_layer(cell, inputs, rate, hidden) and returns: outputs,  hidden