code: def test_embed_batch(self, mock_check_valid_model):
      texts = ["text 1", "text 2", "text 3", "text 4", "text 5"]
  
      with patch(
          "huggingface_hub.InferenceClient.feature_extraction"
      ) as mock_embedding_patch:
          mock_embedding_patch.side_effect = mock_embedding_generation
  
          embedder = HuggingFaceTEIDocumentEmbedder(
              model="BAAI/bge-small-en-v1.5",
              url="https://some_embedding_model.com",
              token="fake-api-token",
          )
          embeddings = embedder._embed_batch(
              texts_to_embed=texts, batch_size=2
          )
  
          assert mock_embedding_patch.call_count == 3
  
      assert isinstance(embeddings, list)
      assert len(embeddings) == len(texts)
      for embedding in embeddings:
          assert isinstance(embedding, list)
          assert len(embedding) == 384
          assert all(isinstance(x, float) for x in embedding)
  
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 9 for calling the TestHuggingFaceTEIDocumentEmbedder obj's underlying member method code with example usage: obj.test_embed_batch(mock_check_valid_model) without return types