code: def test_embedding(device):
      from speechbrain.nnet.embedding import Embedding
  
      # create one hot vector and consider blank as zero vector
      embedding_dim = 39
      blank_id = 39
      size_dict = 40
      emb = Embedding(
          num_embeddings=size_dict,
          consider_as_one_hot=True,
          blank_id=blank_id,
      ).to(device)
      inputs = torch.Tensor([10, 5, 2, 0, 39]).to(device).long()
      output = emb(inputs)
      assert output.shape == (5, 39)
  
      # use standard embedding layer
      embedding_dim = 128
      emb = Embedding(
          num_embeddings=size_dict, embedding_dim=embedding_dim
      ).to(device)
      inputs = torch.randint(0, 40, (5, 10), device=device)
      output = emb(inputs)
      assert output.shape == (5, 10, 128)
  
      assert torch.jit.trace(emb, inputs)
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_embedding(device) without return types