code: def test_generation_pre_attn_layer_norm(self):
      model_id = "facebook/opt-125m"
  
      EXPECTED_OUTPUTS = [
          "Today is a beautiful day and I want to",
          "In the city of New York, the city",
          "Paris is the capital of France and the capital",
          "Computers and mobile phones have taken over the",
      ]
  
      predicted_outputs = []
      tokenizer = GPT2Tokenizer.from_pretrained(model_id)
      model = TFOPTForCausalLM.from_pretrained(model_id)
  
      for prompt in self.prompts:
          input_ids = tokenizer(prompt, return_tensors="tf").input_ids
  
          generated_ids = model.generate(input_ids, max_length=10)
  
          generated_string = tokenizer.batch_decode(
              generated_ids, skip_special_tokens=True
          )
          predicted_outputs += generated_string
  
      self.assertListEqual(predicted_outputs, EXPECTED_OUTPUTS)
  
imports: from __future__ import annotations
  import unittest
  import numpy
  from transformers import OPTConfig, is_tf_available
  from transformers.testing_utils import require_sentencepiece, require_tf, slow
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 2 for calling the TFOPTGenerationTest obj's underlying member method code with example usage: obj.test_generation_pre_attn_layer_norm() without return types