code: def _apply(self, fn):
      # models that were serialized using torch versions older than 1.4.0 lack the _flat_weights_names attribute
      # check if this is the case and if so, set it
      for child_module in self.children():
          if isinstance(child_module, torch.nn.RNNBase) and not hasattr(
              child_module, "_flat_weights_names"
          ):
              _flat_weights_names = []
  
              num_direction = (
                  2 if child_module.__dict__["bidirectional"] else 1
              )
              for layer in range(child_module.__dict__["num_layers"]):
                  for direction in range(num_direction):
                      suffix = "_reverse" if direction == 1 else ""
                      param_names = [
                          "weight_ih_l{}{}",
                          "weight_hh_l{}{}",
                      ]
                      if child_module.__dict__["bias"]:
                          param_names += [
                              "bias_ih_l{}{}",
                              "bias_hh_l{}{}",
                          ]
                      param_names = [
                          x.format(layer, suffix) for x in param_names
                      ]
                      _flat_weights_names.extend(param_names)
  
              child_module._flat_weights_names = _flat_weights_names
  
          child_module._apply(fn)
  
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 4 for calling the DocumentRNNEmbeddings obj's underlying member method code with example usage: obj._apply(fn) without return types