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.
docstr: Add a layer to the list of layers, inferring shape if necessary.
          
docstr_tok: ["Add","a","layer","to","the","list","of","layers",",","inferring","shape","if","necessary","."]
code: def append(self, *args, **kwargs):
      """Add a layer to the list of layers, inferring shape if necessary."""
      # Add lengths arg inference here.
      super().append(*args, **kwargs)
      latest_forward_method = list(self.values())[-1].forward
      self.takes_lengths.append(
          lengths_arg_exists(latest_forward_method)
      )
  
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 LengthsCapableSequential obj's underlying member method code with example usage: obj.append() without return types