class_docstr: A small encoder module that reduces the dimensionality
  and normalizes word embeddings
  
  Arguments
  ---------
  word_emb_dim: int
      the dimension of the original word embeddings
  word_emb_enc_dim: int
      the dimension of the encoded word embeddings
  norm: torch.nn.Module
      the normalization to be used (
          e.g. speechbrain.nnet.normalization.LayerNorm)
  norm_type: str
      the type of normalization to be used
docstr: Computes the forward pass of the embedding
  
  Arguments
  ---------
  emb: torch.Tensor
      the original word embeddings
  
  Returns
  -------
  emb_enc: torch.Tensor
      encoded word embeddings
docstr_tok: ["Computes","the","forward","pass","of","the","embedding","Arguments","--","--","--","--","-","emb",":","torch.Tensor","the","original","word","embeddings","Returns","--","--","--","-","emb_enc",":","torch.Tensor","encoded","word","embeddings"]
code: def forward(self, emb):
      """Computes the forward pass of the embedding
  
      Arguments
      ---------
      emb: torch.Tensor
          the original word embeddings
  
      Returns
      -------
      emb_enc: torch.Tensor
          encoded word embeddings
      """
      if self.norm is not None:
          x = self.norm(emb)
      x = self.lin(x)
      x = self.activation(x)
      return x
  
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 WordEmbeddingEncoder obj's underlying member method code with example usage: obj.forward(emb) and returns: x