class_docstr: The Attentional RNN encoder-decoder model
  
  Arguments
  ---------
  enc: torch.nn.Module
      the encoder module
  encoder_emb: torch.nn.Module
      the encoder_embedding_module
  emb: torch.nn.Module
      the embedding module
  dec: torch.nn.Module
      the decoder module
  lin: torch.nn.Module
      the linear module
  out: torch.nn.Module
      the output layer (typically log_softmax)
  use_word_emb: bool
      whether or not to use word embedding
  bos_token: int
      the index of teh Beginning-of-Sentence token
  word_emb_enc: nn.Module
      a module to encode word embeddings
  
  
  Returns
  -------
  result: tuple
      a (p_seq, char_lens) tuple
docstr: Concatenate character embeddings with word embeddeings,
  possibly encoding the word embeddings if an encoder
  is provided
  
  Arguments
  ---------
  emb_char: torch.Tensor
      the character embedding tensor
  word_emb: torch.Tensor
      the word embedding tensor
  
  Returns
  -------
  result: torch.Tensor
      the concatenation of the tensor
docstr_tok: ["Concatenate","character","embeddings","with","word","embeddeings",",","possibly","encoding","the","word","embeddings","if","an","encoder","is","provided","Arguments","--","--","--","--","-","emb_char",":","torch.Tensor","the","character","embedding","tensor","word_emb",":","torch.Tensor","the","word","embedding","tensor","Returns","--","--","--","-","result",":","torch.Tensor","the","concatenation","of","the","tensor"]
code: def _apply_word_emb(self, emb_char, word_emb):
      """Concatenate character embeddings with word embeddeings,
      possibly encoding the word embeddings if an encoder
      is provided
  
      Arguments
      ---------
      emb_char: torch.Tensor
          the character embedding tensor
      word_emb: torch.Tensor
          the word embedding tensor
  
      Returns
      -------
      result: torch.Tensor
          the concatenation of the tensor"""
      word_emb_enc = (
          self.word_emb_enc(word_emb)
          if self.word_emb_enc is not None
          else word_emb
      )
      return torch.cat([emb_char, word_emb_enc], dim=-1)
  
imports: from speechbrain.lobes.models.transformer.Transformer import TransformerInterface, get_lookahead_mask, get_key_padding_mask
  import torch
  from torch import nn
  from speechbrain.nnet.linear import Linear
  from speechbrain.nnet import normalization
label_desc: Use image node_id 3 for calling the AttentionSeq2Seq obj's underlying member method code with example usage: obj._apply_word_emb(emb_char, word_emb) and returns: torch