class_docstr: A component for computing Document embeddings using Sentence Transformers models.
  The embedding of each Document is stored in the `embedding` field of the Document.
  
  Usage example:
  ```python
  from haystack import Document
  from haystack.components.embedders import SentenceTransformersDocumentEmbedder
  doc = Document(content="I love pizza!")
  doc_embedder = SentenceTransformersDocumentEmbedder()
  doc_embedder.warm_up()
  
  result = doc_embedder.run([doc])
  print(result['documents'][0].embedding)
  
  # [-0.07804739475250244, 0.1498992145061493, ...]
  ```
docstr: Serialize this component to a dictionary.
code: def to_dict(self) -> Dict[str, Any]:
      """
      Serialize this component to a dictionary.
      """
      return default_to_dict(
          self,
          model=self.model,
          device=self.device,
          token=self.token
          if not isinstance(self.token, str)
          else None,  # don't serialize valid tokens
          prefix=self.prefix,
          suffix=self.suffix,
          batch_size=self.batch_size,
          progress_bar=self.progress_bar,
          normalize_embeddings=self.normalize_embeddings,
          meta_fields_to_embed=self.meta_fields_to_embed,
          embedding_separator=self.embedding_separator,
      )
  
file_path: haystack/haystack/components/embedders/sentence_transformers_document_embedder.py
imports: from typing import List, Optional, Union, Dict, Any
  from haystack import component, Document, default_to_dict
  from haystack.components.embedders.backends.sentence_transformers_backend import _SentenceTransformersEmbeddingBackendFactory
label_desc: Use image node_id 3 for calling the SentenceTransformersDocumentEmbedder obj's underlying member method code with example usage: obj.to_dict() and returns: default_to_dict