code: def to_params(self) -> Dict[str, Any]:
      return {
          "pooling": self.pooling,
          "fine_tune_mode": self.fine_tune_mode,
          "embeddings": self.embeddings.save_embeddings(False),
      }
  
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 DocumentPoolEmbeddings obj's underlying member method code with example usage: obj.to_params() and returns: dict