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: Load the embedding backend.
code: def warm_up(self):
      """
      Load the embedding backend.
      """
      if not hasattr(self, "embedding_backend"):
          self.embedding_backend = _SentenceTransformersEmbeddingBackendFactory.get_embedding_backend(
              model=self.model,
              device=self.device,
              use_auth_token=self.token,
          )
  
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 4 for calling the SentenceTransformersDocumentEmbedder obj's underlying member method code with example usage: obj.warm_up() without return types