docstr: Zero-padding input according to window/stride size.
docstr_tok: ["Zero-padding","input","according","to","window\/stride","size","."]
code: def pad_input(self, input, window):
      """Zero-padding input according to window/stride size."""
  
      batch_size, nmic, nsample = input.shape
  
      stride = self.stride
  
      # pad the signals at the end for matching the window/stride size
      rest = window - (stride + nsample % window) % window
      if rest > 0:
          pad = torch.zeros(batch_size, nmic, rest).type(input.type())
          input = torch.cat([input, pad], 2)
      pad_aux = torch.zeros(batch_size, nmic, stride).type(input.type())
      input = torch.cat([pad_aux, input, pad_aux], 2)
  
      return input, rest
  
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from espnet2.enh.layers import dprnn
label_desc: Use image node_id 2 for calling the FaSNet_base obj's underlying member method code with example usage: obj.pad_input(input, window) and returns: input,  rest