class_docstr: Contextual word embeddings using word-level LM, as proposed in Peters et al., 2018.
  ELMo word vectors can be constructed by combining layers in different ways.
  Default is to concatene the top 3 layers in the LM.
code: def use_layers_average(self, x):
      return torch.mean(torch.stack(x), 0)
  
imports: import logging
  import re
  from pathlib import Path
  from typing import List, Optional, Union
  import torch
  from deprecated.sphinx import deprecated
  import flair
  from flair.data import Sentence, Token
  from flair.embeddings.document import DocumentEmbeddings
  from flair.embeddings.token import StackedEmbeddings, TokenEmbeddings
  from flair.file_utils import cached_path
  from flair.nn import LockedDropout, WordDropout
label_desc: Use image node_id 5 for calling the ELMoEmbeddings obj's underlying member method code with example usage: obj.use_layers_average(x) and returns: torch