code: def test_prefix_lm_generation(self):
      model = GPTSanJapaneseForConditionalGeneration.from_pretrained(
          "Tanrei/GPTSAN-japanese"
      )
      tokenizer = GPTSanJapaneseTokenizer.from_pretrained(
          "Tanrei/GPTSAN-japanese"
      )
      model.to(torch_device)
  
      # set deterministically
      generation_config = GenerationConfig.from_pretrained(
          "Tanrei/GPTSAN-japanese"
      )
      generation_config.top_k = 1
  
      prefix_text_1 = "武田信玄"
      prefix_text_2 = "織田信長"
      input_text_1 = "は、"
      input_text_2 = "が、"
      input_tok_1 = tokenizer(
          input_text_1, prefix_text=prefix_text_1, return_tensors="pt"
      )
      input_tok_2 = tokenizer(
          input_text_2, prefix_text=prefix_text_2, return_tensors="pt"
      )
      input_tok_3 = tokenizer(
          [
              [prefix_text_1, input_text_1],
              [prefix_text_2, input_text_2],
          ],
          return_tensors="pt",
      )
  
      output1 = model.generate(
          input_ids=input_tok_1.input_ids.to(torch_device),
          token_type_ids=input_tok_1.token_type_ids.to(torch_device),
          max_new_tokens=20,
          generation_config=generation_config,
      )
  
      output2 = model.generate(
          input_ids=input_tok_2.input_ids.to(torch_device),
          token_type_ids=input_tok_2.token_type_ids.to(torch_device),
          max_new_tokens=20,
          generation_config=generation_config,
      )
  
      output3 = model.generate(
          input_ids=input_tok_3.input_ids.to(torch_device),
          token_type_ids=input_tok_3.token_type_ids.to(torch_device),
          attention_mask=input_tok_3.attention_mask.to(torch_device),
          max_new_tokens=20,
          generation_config=generation_config,
      )
  
      out1_sentence = tokenizer.decode(output1[0])
      out2_sentence = tokenizer.decode(output2[0])
      batch_out_sentence = tokenizer.batch_decode(output3)
  
      expected_output_sentence = [
          "武田信玄は、武田氏の祖である武田信虎を、その子・武田信友を擁して",
          "織田信長が、織田信長の妻・お市の方を妻として迎えたという逸話が残",
      ]
      self.assertListEqual(expected_output_sentence, batch_out_sentence)
      self.assertListEqual(
          batch_out_sentence, [out1_sentence, out2_sentence]
      )
  
imports: import unittest
  import numpy
  from transformers import GPTSanJapaneseConfig, GPTSanJapaneseForConditionalGeneration, GPTSanJapaneseModel, GPTSanJapaneseTokenizer, is_torch_available
  from transformers.generation import GenerationConfig
  from transformers.testing_utils import require_torch, slow, tooslow, torch_device
  from ...generation.test_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 9 for calling the GPTSanJapaneseForConditionalGenerationTest obj's underlying member method code with example usage: obj.test_prefix_lm_generation() without return types