docstr: TearDown function needs to be called at the end of each test to free the GPU memory and cache, also to
  avoid unexpected behaviors. Please see: https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530/27
docstr_tok: ["TearDown","function","needs","to","be","called","at","the","end","of","each","test","to","free","the","GPU","memory","and","cache",",","also","to","avoid","unexpected","behaviors",".","Please","see",":","https",":","\/\/discuss.pytorch.org\/t\/how-can-we-release-gpu-memory-cache\/14530\/27"]
code: def tearDown(self):
      r"""
      TearDown function needs to be called at the end of each test to free the GPU memory and cache, also to
      avoid unexpected behaviors. Please see: https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530/27
      """
      gc.collect()
      torch.cuda.empty_cache()
  
imports: import gc
  import importlib.metadata
  import tempfile
  import unittest
  from packaging import version
  from transformers import AutoConfig, AutoModel, AutoModelForCausalLM, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification, AutoTokenizer, BitsAndBytesConfig, pipeline
  from transformers.testing_utils import is_accelerate_available, is_torch_available, require_accelerate, require_bitsandbytes, require_torch, require_torch_gpu, require_torch_multi_gpu, slow
label_desc: Use image node_id 2 for calling the MixedInt8T5Test obj's underlying member method code with example usage: obj.tearDown() without return types