code: def input_dim(use_word_emb, embedding_dim, word_emb_enc_dim):
      """Computes the input dimension (intended for hparam files)
  
      Arguments
      ---------
      use_word_emb: bool
          whether to use word embeddings
  
      embedding_dim: int
          the embedding dimension
  
      word_emb_enc_dim: int
          the dimension of encoded word embeddings
  
      Returns
      -------
      input_dim: int
          the input dimension
      """
      return embedding_dim + use_word_emb * word_emb_enc_dim
  
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 1 for calling a global function with example usage: input_dim(use_word_emb, embedding_dim, word_emb_enc_dim) and returns: unknown