docstr: Text generation test for Mixtral + AWQ + fused
docstr_tok: ["Text","generation","test","for","Mixtral","+","AWQ","+","fused"]
code: def test_generation_mixtral_fused(self):
      """
      Text generation test for Mixtral + AWQ + fused
      """
      quantization_config = AwqConfig(
          bits=4, fuse_max_seq_len=1024, do_fuse=True
      )
      model = AutoModelForCausalLM.from_pretrained(
          self.mixtral_model_name,
          quantization_config=quantization_config,
          device_map="auto",
          revision=self.mixtral_model_revision,
      )
  
      tokenizer = AutoTokenizer.from_pretrained(self.mixtral_model_name)
      tokenizer.pad_token = tokenizer.eos_token
  
      inputs = tokenizer(
          [self.prompt, self.prompt], return_tensors="pt", padding=True
      ).to(torch_device)
  
      outputs = model.generate(**inputs, max_new_tokens=12)
      self.assertEqual(
          tokenizer.decode(outputs[0], skip_special_tokens=True),
          self.EXPECTED_GENERATION_MIXTRAL,
      )
  
imports: import gc
  import tempfile
  import unittest
  from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, AwqConfig, OPTForCausalLM
  from transformers.testing_utils import require_accelerate, require_auto_awq, require_torch_gpu, require_torch_multi_gpu, slow, torch_device
  from transformers.utils import is_accelerate_available, is_torch_available
label_desc: Use image node_id 9 for calling the AwqFusedTest obj's underlying member method code with example usage: obj.test_generation_mixtral_fused() without return types