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: Applies the computation pipeline.
docstr_tok: ["Applies","the","computation","pipeline","."]
code: def forward(self, x):
      """Applies the computation pipeline."""
      for layer in self.layers:
          x = layer(x)
          if isinstance(x, tuple):
              x = x[0]
      return x
  
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 2 for calling the ModuleList obj's underlying member method code with example usage: obj.forward(x) and returns: x