docstr: Test the generation quality of the quantized model and see that we are matching the expected output.
  Given that we are operating on small numbers + the testing model is relatively small, we might not get
  the same output across GPUs. So we'll generate few tokens (5-10) and check their output.
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code: def check_inference_correctness(self, model):
      """
      Test the generation quality of the quantized model and see that we are matching the expected output.
      Given that we are operating on small numbers + the testing model is relatively small, we might not get
      the same output across GPUs. So we'll generate few tokens (5-10) and check their output.
      """
      # Check that inference pass works on the model
      encoded_input = self.tokenizer(
          self.input_text, return_tensors="pt"
      )
  
      # Get the generation
      output_sequences = model.generate(
          input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10
      )
  
      # Check the exactness of the result
      self.assertIn(
          self.tokenizer.decode(
              output_sequences[0], skip_special_tokens=True
          ),
          self.EXPECTED_OUTPUTS,
      )
  
imports: import tempfile
  import unittest
  import torch
  from parameterized import parameterized
  from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
  from transformers.testing_utils import slow
  from optimum.gptq import GPTQQuantizer, load_quantized_model
  from optimum.gptq.data import get_dataset
  from optimum.utils.import_utils import is_auto_gptq_available
  from optimum.utils.testing_utils import require_accelerate, require_auto_gptq, require_torch_gpu
label_desc: Use image node_id 5 for calling the GPTQTest obj's underlying member method code with example usage: obj.check_inference_correctness(model) without return types