docstr: Add embeddings to every sentence in the given list of sentences.
docstr_tok: ["Add","embeddings","to","every","sentence","in","the","given","list","of","sentences","."]
code: def embed(self, sentences: Union[List[Sentence], Sentence]):
      """Add embeddings to every sentence in the given list of sentences."""
      # if only one sentence is passed, convert to list of sentence
      if isinstance(sentences, Sentence):
          sentences = [sentences]
  
      raw_sentences = [s.to_original_text() for s in sentences]
      tfidf_vectors = torch.from_numpy(
          self.vectorizer.transform(raw_sentences).A
      )
  
      for sentence_id, sentence in enumerate(sentences):
          sentence.set_embedding(self.name, tfidf_vectors[sentence_id])
  
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 3 for calling the DocumentTFIDFEmbeddings obj's underlying member method code with example usage: obj.embed(sentences) without return types