docstr: abstract forward function
  
  input: shape (batch, max_num_ch, T)
  num_mic: shape (batch, ), the number of channels for each input.
           Zero for fixed geometry configuration.
docstr_tok: ["abstract","forward","function","input",":","shape","(","batch",",","max_num_ch",",","T",")","num_mic",":","shape","(","batch",",",")",",","the","number","of","channels","for","each","input",".","Zero","for","fixed","geometry","configuration","."]
code: def forward(self, input, num_mic):
      """abstract forward function
  
      input: shape (batch, max_num_ch, T)
      num_mic: shape (batch, ), the number of channels for each input.
               Zero for fixed geometry configuration.
      """
      pass
  
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from espnet2.enh.layers import dprnn
label_desc: Use image node_id 6 for calling the FaSNet_base obj's underlying member method code with example usage: obj.forward(input, num_mic) without return types