code: def _add_embeddings_internal(
      self, sentences: List[Sentence]
  ) -> List[Sentence]:
      sentence_batches = [
          sentences[i * self.batch_size : (i + 1) * self.batch_size]
          for i in range(
              (len(sentences) + self.batch_size - 1) // self.batch_size
          )
      ]
  
      for batch in sentence_batches:
          self._add_embeddings_to_sentences(batch)
  
      return sentences
  
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 2 for calling the SentenceTransformerDocumentEmbeddings obj's underlying member method code with example usage: obj._add_embeddings_internal(sentences) and returns: sentences