code: def test_prepare_texts_to_embed_w_metadata(
      self, mock_check_valid_model
  ):
      documents = [
          Document(
              content=f"document number {i}: content",
              meta={"meta_field": f"meta_value {i}"},
          )
          for i in range(5)
      ]
  
      embedder = HuggingFaceTEIDocumentEmbedder(
          model="sentence-transformers/all-mpnet-base-v2",
          url="https://some_embedding_model.com",
          token="fake-api-token",
          meta_fields_to_embed=["meta_field"],
          embedding_separator=" | ",
      )
  
      prepared_texts = embedder._prepare_texts_to_embed(documents)
  
      assert prepared_texts == [
          "meta_value 0 | document number 0: content",
          "meta_value 1 | document number 1: content",
          "meta_value 2 | document number 2: content",
          "meta_value 3 | document number 3: content",
          "meta_value 4 | document number 4: content",
      ]
  
imports: from unittest.mock import MagicMock, patch
  import numpy
  import pytest
  from huggingface_hub.utils import RepositoryNotFoundError
  from haystack.components.embedders.hugging_face_tei_document_embedder import HuggingFaceTEIDocumentEmbedder
  from haystack.dataclasses import Document
label_desc: Use image node_id 7 for calling the TestHuggingFaceTEIDocumentEmbedder obj's underlying member method code with example usage: obj.test_prepare_texts_to_embed_w_metadata(mock_check_valid_model) without return types