code: def _pad_inputs(self, inputs, n_steps, rate):
      iseven = (n_steps % rate) == 0
  
      if not iseven:
          dilated_steps = n_steps // rate + 1
  
          zeros_ = torch.zeros(
              dilated_steps * rate - inputs.size(0),
              inputs.size(1),
              inputs.size(2),
              dtype=inputs.dtype,
              device=inputs.device,
          )
          inputs = torch.cat((inputs, zeros_))
      else:
          dilated_steps = n_steps // rate
  
      return inputs, dilated_steps
  
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 7 for calling the DRNN obj's underlying member method code with example usage: obj._pad_inputs(inputs, n_steps, rate) and returns: inputs,  dilated_steps