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.
code: def __init__(self, *layers):
      super().__init__()
      self.layers = torch.nn.ModuleList(layers)
  
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 1 to create a new ModuleList object from inherited base classes: torch.nn with example: obj = ModuleList()