code: def to_params(self):
      # serialize the language models and the constructor arguments (but nothing else)
      model_state = {
          "embeddings": self.embeddings.save_embeddings(False),
          "hidden_size": self.rnn.hidden_size,
          "rnn_layers": self.rnn.num_layers,
          "reproject_words": self.reproject_words,
          "reproject_words_dimension": self.embeddings_dimension,
          "bidirectional": self.bidirectional,
          "dropout": self.dropout.p
          if self.dropout is not None
          else 0.0,
          "word_dropout": self.word_dropout.p
          if self.word_dropout is not None
          else 0.0,
          "locked_dropout": self.locked_dropout.p
          if self.locked_dropout is not None
          else 0.0,
          "rnn_type": self.rnn_type,
          "fine_tune": not self.static_embeddings,
      }
  
      return model_state
  
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 5 for calling the DocumentRNNEmbeddings obj's underlying member method code with example usage: obj.to_params() and returns: model_state