class_docstr: A component for embedding strings using Sentence Transformers models.
  
  Usage example:
  ```python
  from haystack.components.embedders import SentenceTransformersTextEmbedder
  
  text_to_embed = "I love pizza!"
  
  text_embedder = SentenceTransformersTextEmbedder()
  text_embedder.warm_up()
  
  print(text_embedder.run(text_to_embed))
  
  # {'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_text_embedder.py
imports: from typing import List, Optional, Union, Dict, Any
  from haystack import component, default_to_dict
  from haystack.components.embedders.backends.sentence_transformers_backend import _SentenceTransformersEmbeddingBackendFactory
label_desc: Use image node_id 4 for calling the SentenceTransformersTextEmbedder obj's underlying member method code with example usage: obj.warm_up() without return types