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: Determines the type of normalizer
  
  Arguments
  ---------
  norm: str
      the normalization type: "batch", "layer" or "instance
  dim: int
      the dimensionality of the inputs
docstr_tok: ["Determines","the","type","of","normalizer","Arguments","--","--","--","--","-","norm",":","str","the","normalization","type",":","``","batch","''",",","``","layer","''","or","``","instance","dim",":","int","the","dimensionality","of","the","inputs"]
code: def _get_norm(self, norm, dim):
      """Determines the type of normalizer
  
      Arguments
      ---------
      norm: str
          the normalization type: "batch", "layer" or "instance
      dim: int
          the dimensionality of the inputs
      """
      norm_cls = self.NORMS.get(norm)
      if not norm_cls:
          raise ValueError(f"Invalid norm: {norm}")
      return norm_cls(input_size=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 2 for calling the WordEmbeddingEncoder obj's underlying member method code with example usage: obj._get_norm(norm, dim) and returns: norm_cls