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.
code: def check_inference_correctness(self, model):
      r"""
      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"
      )
  
      # Check the exactness of the results
      output_parallel = model.generate(
          input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10
      )
  
      # Get the generation
      output_text = self.tokenizer.decode(
          output_parallel[0], skip_special_tokens=True
      )
      self.assertEqual(output_text, self.EXPECTED_OUTPUT)
  
imports: import gc
  import tempfile
  import unittest
  import torch
  import torch.nn
  from accelerate import Accelerator, init_empty_weights
  from accelerate.test_utils import require_bnb, require_cuda, require_huggingface_suite, require_multi_gpu, slow
  from accelerate.utils.bnb import load_and_quantize_model
  from accelerate.utils.dataclasses import BnbQuantizationConfig
label_desc: Use image node_id 6 for calling the MixedInt8EmptyModelTest obj's underlying member method code with example usage: obj.check_inference_correctness(model) without return types