code: def __setstate__(self, d):
      # re-initialize language model with constructor arguments
      language_model = DocumentRNNEmbeddings(
          embeddings=d["embeddings"],
          hidden_size=d["hidden_size"],
          rnn_layers=d["rnn_layers"],
          reproject_words=d["reproject_words"],
          reproject_words_dimension=d["reproject_words_dimension"],
          bidirectional=d["bidirectional"],
          dropout=d["dropout"],
          word_dropout=d["word_dropout"],
          locked_dropout=d["locked_dropout"],
          rnn_type=d["rnn_type"],
          fine_tune=d["fine_tune"],
      )
  
      # special handling for deserializing language models
      if "state_dict" in d:
          language_model.load_state_dict(d["state_dict"])
  
      # copy over state dictionary to self
      for key in language_model.__dict__:
          self.__dict__[key] = language_model.__dict__[key]
  
      # set the language model to eval() by default (this is necessary since FlairEmbeddings "protect" the LM
      # in their "self.train()" method)
      self.eval()
  
imports: import logging
  from typing import Any, Dict, List, Optional, Union, cast
  import torch
  from sklearn.feature_extraction.text import TfidfVectorizer
  from torch.nn import RNNBase
  from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
  import flair
  from flair.data import Sentence
  from flair.embeddings.base import DocumentEmbeddings, load_embeddings, register_embeddings
  from flair.embeddings.token import FlairEmbeddings, StackedEmbeddings, TokenEmbeddings
  from flair.embeddings.transformer import TransformerEmbeddings, TransformerOnnxDocumentEmbeddings
  from flair.nn import LockedDropout, WordDropout
label_desc: Use image node_id 7 for calling the DocumentRNNEmbeddings obj's underlying member method code with example usage: obj.__setstate__(d) without return types