code: def test_hf_batch_streamer():
      tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
      streamer = TextIteratorStreamerBatch(
          tokenizer=tokenizer, batch_size=2, skip_special_tokens=True
      )
  
      input1 = "hello world"
      input2 = "good day"
  
      for inputs in zip(
          tokenizer(input1)["input_ids"], tokenizer(input2)["input_ids"]
      ):
          streamer.put(torch.tensor(inputs))
  
      streamer.end()
  
      output1 = ""
      output2 = ""
  
      for data in streamer:
          assert len(data) == 2
          output1 += data[0]
          output2 += data[1]
  
      assert output1 == input1
      assert output2 == input2
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_hf_batch_streamer() without return types