class_docstr: Receives bert contextual embeddings of a text, extracts all the
  possible mentions in that text. 
class_docstr_tok: ["Receives","bert","contextual","embeddings","of","a","text",",","extracts","all","the","possible","mentions","in","that","text","."]
docstr: Args:
      features (int): the number of featues in the input embeddings
      config (Config): the configuration of the current session
docstr_tok: ["Args",":","features","(","int",")",":","the","number","of","featues","in","the","input","embeddings","config","(","Config",")",":","the","configuration","of","the","current","session"]
code: def __init__(self, features: int, config: Config):
      """
      Args:
          features (int): the number of featues in the input embeddings
          config (Config): the configuration of the current session
      """
      super().__init__()
      self.attn = torch.nn.Linear(in_features=features, out_features=1)
      self.dropout = torch.nn.Dropout(config.dropout_rate)
  
file_path: stanza/stanza/models/coref/word_encoder.py
imports: from typing import Tuple
  import torch
  from stanza.models.coref.config import Config
  from stanza.models.coref.const import Doc
label_desc: Use image node_id 1 to create a new WordEncoder object from inherited base classes: torch.nn with example: obj = WordEncoder(features, config)