class_docstr: CharLSTM aims to generate character-level embeddings for tokens.
  It summarizes the information of characters in each token to an embedding using a LSTM layer.
  
  Args:
      n_char (int):
          The number of characters.
      n_embed (int):
          The size of each embedding vector as input to LSTM.
      n_out (int):
          The size of each output vector.
      pad_index (int):
          The index of the padding token in the vocabulary. Default: 0.
class_docstr_tok: ["CharLSTM","aims","to","generate","character-level","embeddings","for","tokens",".","It","summarizes","the","information","of","characters","in","each","token","to","an","embedding","using","a","LSTM","layer",".","Args",":","n_char","(","int",")",":","The","number","of","characters",".","n_embed","(","int",")",":","The","size","of","each","embedding","vector","as","input","to","LSTM",".","n_out","(","int",")",":","The","size","of","each","output","vector",".","pad_index","(","int",")",":","The","index","of","the","padding","token","in","the","vocabulary",".","Default",":","0","."]
code: def __repr__(self):
      s = f"{self.n_chars}, {self.n_embed}, "
      s += f"n_out={self.n_out}, "
      s += f"pad_index={self.pad_index}"
  
      return f"{self.__class__.__name__}({s})"
  
imports: import torch
  import torch.nn
  from torch.nn.utils.rnn import pack_padded_sequence, PackedSequence
  from torch.nn.modules.rnn import apply_permutation
label_desc: Use image node_id 2 for calling the CharLSTM obj's underlying member method code with example usage: obj.__repr__() and returns: self, str, s, str