code: def from_params(
      cls, params: Dict[str, Any]
  ) -> "DocumentRNNEmbeddings":
      stacked_embeddings = load_embeddings(params["embeddings"])
      assert isinstance(stacked_embeddings, StackedEmbeddings)
      return cls(
          embeddings=stacked_embeddings.embeddings,
          hidden_size=params["hidden_size"],
          rnn_layers=params["rnn_layers"],
          reproject_words=params["reproject_words"],
          reproject_words_dimension=params["reproject_words_dimension"],
          bidirectional=params["bidirectional"],
          dropout=params["dropout"],
          word_dropout=params["word_dropout"],
          locked_dropout=params["locked_dropout"],
          rnn_type=params["rnn_type"],
          fine_tune=params["fine_tune"],
      )
  
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 6 for calling the DocumentRNNEmbeddings obj's underlying member method code with example usage: obj.from_params(cls, params) and returns: cls