class_docstr: This class implements a wrapper to torch.nn.ModuleList with a forward()
  method to forward all the layers sequentially.
  For some pretrained model with the SpeechBrain older implementation of
  Sequential class, user can use this class to load those pretrained models
  
  Arguments
  ---------
  *layers : torch class
      Torch objects to be put in a ModuleList.
docstr: Appends module to the layers list.
docstr_tok: ["Appends","module","to","the","layers","list","."]
code: def extend(self, modules):
      """Appends module to the layers list."""
      self.layers.extend(modules)
  
imports: import torch
  import inspect
  import logging
  import operator
  import functools
  from speechbrain.nnet.linear import Linear
  from speechbrain.utils.callchains import lengths_arg_exists
label_desc: Use image node_id 4 for calling the ModuleList obj's underlying member method code with example usage: obj.extend(modules) without return types