class_docstr: the Mem-LSTM of SkiM
  
  args:
      hidden_size: int, dimension of the hidden state.
      dropout: float, dropout ratio. Default is 0.
      bidirectional: bool, whether the LSTM layers are bidirectional.
          Default is False.
      mem_type: 'hc', 'h', 'c' or 'id'.
          It controls whether the hidden (or cell) state of
          SegLSTM will be processed by MemLSTM.
          In 'id' mode, both the hidden and cell states will
          be identically returned.
      norm_type: gLN, cLN. cLN is for causal implementation.
class_docstr_tok: ["the","Mem-LSTM","of","SkiM","args",":","hidden_size",":","int",",","dimension","of","the","hidden","state",".","dropout",":","float",",","dropout","ratio",".","Default","is","0.","bidirectional",":","bool",",","whether","the","LSTM","layers","are","bidirectional",".","Default","is","False",".","mem_type",":","'hc","'",",","'","h","'",",","'","c","'","or","'id","'",".","It","controls","whether","the","hidden","(","or","cell",")","state","of","SegLSTM","will","be","processed","by","MemLSTM",".","In","'id","'","mode",",","both","the","hidden","and","cell","states","will","be","identically","returned",".","norm_type",":","gLN",",","cLN",".","cLN","is","for","causal","implementation","."]
code: def extra_repr(self) -> str:
      return f"Mem_type: {self.mem_type}, bidirectional: {self.bidirectional}"
  
file_path: espnet/espnet2/enh/layers/skim.py
imports: import torch
  import torch.nn
  from espnet2.enh.layers.dprnn import SingleRNN, merge_feature, split_feature
  from espnet2.enh.layers.tcn import choose_norm
label_desc: Use image node_id 2 for calling the MemLSTM obj's underlying member method code with example usage: obj.extra_repr() and returns: str, self, str, self