class_docstr: Sequential model that can take ``lengths`` in the forward method.
  
  This is useful for Sequential models that include RNNs where it is
  important to avoid padding, or for some feature normalization layers.
  
  Unfortunately, this module is not jit-able because the compiler doesn't
  know ahead of time if the length will be passed, and some layers don't
  accept the length parameter.
code: def __init__(self, *args, **kwargs):
      self.takes_lengths = []
      super().__init__(*args, **kwargs)
  
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 LengthsCapableSequential object from inherited base classes: Sequential with example: obj = LengthsCapableSequential()