code: def test_normalize_embeddings() -> None:
      """Tests the correct computation of util.normalize_embeddings"""
      embedding_size = 100
      a = torch.tensor(np.random.randn(50, embedding_size))
      a_norm = util.normalize_embeddings(a)
  
      for embedding in a_norm:
          assert len(embedding) == embedding_size
          emb_norm = torch.norm(embedding)
          assert abs(emb_norm.item() - 1) < 0.0001
  
file_path: sentence-transformers/tests/test_util.py
imports: import numpy
  import sklearn
  import torch
  from sentence_transformers import SentenceTransformer, util
label_desc: Use image node_id 1 for calling a global function with example usage: test_normalize_embeddings() without return types