code: def from_params(
      cls, params: Dict[str, Any]
  ) -> "DocumentPoolEmbeddings":
      embeddings = cast(
          StackedEmbeddings, load_embeddings(params.pop("embeddings"))
      ).embeddings
      return cls(embeddings=embeddings, **params)
  
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 DocumentPoolEmbeddings obj's underlying member method code with example usage: obj.from_params(cls, params) and returns: cls