docstr: Test whether it is possible to mix both `4bit` and `fp32` weights when using `keep_in_fp32_modules` correctly.
  `flan-t5-small` uses `T5DenseGatedActDense` whereas `t5-small` uses `T5DenseReluDense`. We need to test
  both cases.
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code: def test_inference_without_keep_in_fp32(self):
      r"""
      Test whether it is possible to mix both `4bit` and `fp32` weights when using `keep_in_fp32_modules` correctly.
      `flan-t5-small` uses `T5DenseGatedActDense` whereas `t5-small` uses `T5DenseReluDense`. We need to test
      both cases.
      """
      from transformers import T5ForConditionalGeneration
  
      modules = T5ForConditionalGeneration._keep_in_fp32_modules
      T5ForConditionalGeneration._keep_in_fp32_modules = None
  
      # test with `t5-small`
      model = T5ForConditionalGeneration.from_pretrained(
          self.model_name, load_in_4bit=True, device_map="auto"
      )
      encoded_input = self.tokenizer(
          self.input_text, return_tensors="pt"
      ).to(0)
      _ = model.generate(**encoded_input)
  
      # test with `flan-t5-small`
      model = T5ForConditionalGeneration.from_pretrained(
          self.dense_act_model_name,
          load_in_4bit=True,
          device_map="auto",
      )
      encoded_input = self.tokenizer(
          self.input_text, return_tensors="pt"
      ).to(0)
      _ = model.generate(**encoded_input)
      T5ForConditionalGeneration._keep_in_fp32_modules = modules
  
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_bitsandbytes_available, is_torch_available, require_accelerate, require_bitsandbytes, require_torch, require_torch_gpu, require_torch_multi_gpu, slow, torch_device
label_desc: Use image node_id 3 for calling the Bnb4BitT5Test obj's underlying member method code with example usage: obj.test_inference_without_keep_in_fp32() without return types