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: A workaround to get current device (which is assumed to be the
  device of the first parameter of one of the submodules) 
docstr_tok: ["A","workaround","to","get","current","device","(","which","is","assumed","to","be","the","device","of","the","first","parameter","of","one","of","the","submodules",")"]
code: def device(self) -> torch.device:
      """A workaround to get current device (which is assumed to be the
      device of the first parameter of one of the submodules)"""
      return next(self.attn.parameters()).device
  
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 2 for calling the WordEncoder obj's underlying member method code with example usage: obj.device() and returns: next