code: def test_dynamic_cache_batched(self):
      tokenizer = AutoTokenizer.from_pretrained(
          "meta-llama/Llama-2-7b-hf", padding_side="left"
      )
      tokenizer.pad_token = tokenizer.eos_token
      model = AutoModelForCausalLM.from_pretrained(
          "meta-llama/Llama-2-7b-hf",
          device_map="auto",
          torch_dtype=torch.float16,
      )
      inputs = tokenizer(
          ["A sequence: 1, 2, 3, 4, 5", "A sequence: A, B, C"],
          padding=True,
          return_tensors="pt",
      ).to(model.device)
  
      gen_out = model.generate(
          **inputs,
          do_sample=False,
          max_new_tokens=10,
          past_key_values=DynamicCache(),
      )
      decoded = tokenizer.batch_decode(
          gen_out, skip_special_tokens=True
      )
      expected_text = [
          "A sequence: 1, 2, 3, 4, 5, 6, 7, 8,",
          "A sequence: A, B, C, D, E, F, G, H",
      ]
      self.assertListEqual(decoded, expected_text)
  
file_path: transformers/tests/test_cache_utils.py
imports: import unittest
  from transformers import set_seed
  from transformers.testing_utils import is_torch_available, require_auto_gptq, require_torch, require_torch_gpu, slow
label_desc: Use image node_id 2 for calling the CacheIntegrationTest obj's underlying member method code with example usage: obj.test_dynamic_cache_batched() without return types