code: def test_batch_generation(self):
      # Marked as @tooslow due to GPU OOM
      model = GPTJForCausalLM.from_pretrained(
          "EleutherAI/gpt-j-6B",
          revision="float16",
          torch_dtype=torch.float16,
      )
      model.to(torch_device)
      tokenizer = AutoTokenizer.from_pretrained(
          "EleutherAI/gpt-j-6B", revision="float16"
      )
  
      tokenizer.padding_side = "left"
  
      # Define PAD Token = EOS Token = 50256
      tokenizer.pad_token = tokenizer.eos_token
      model.config.pad_token_id = model.config.eos_token_id
  
      # use different length sentences to test batching
      sentences = [
          "Hello, my dog is a little",
          "Today, I",
      ]
  
      inputs = tokenizer(sentences, return_tensors="pt", padding=True)
      input_ids = inputs["input_ids"].to(torch_device)
      token_type_ids = torch.cat(
          [
              input_ids.new_full(
                  (input_ids.shape[0], input_ids.shape[1] - 1), 0
              ),
              input_ids.new_full((input_ids.shape[0], 1), 500),
          ],
          dim=-1,
      )
  
      outputs = model.generate(
          input_ids=input_ids,
          attention_mask=inputs["attention_mask"].to(torch_device),
      )
  
      outputs_tt = model.generate(
          input_ids=input_ids,
          attention_mask=inputs["attention_mask"].to(torch_device),
          token_type_ids=token_type_ids,
      )
  
      inputs_non_padded = tokenizer(
          sentences[0], return_tensors="pt"
      ).input_ids.to(torch_device)
      output_non_padded = model.generate(input_ids=inputs_non_padded)
  
      num_paddings = (
          inputs_non_padded.shape[-1]
          - inputs["attention_mask"][-1].long().sum().cpu().item()
      )
      inputs_padded = tokenizer(
          sentences[1], return_tensors="pt"
      ).input_ids.to(torch_device)
      output_padded = model.generate(
          input_ids=inputs_padded,
          max_length=model.config.max_length - num_paddings,
      )
  
      batch_out_sentence = tokenizer.batch_decode(
          outputs, skip_special_tokens=True
      )
      batch_out_sentence_tt = tokenizer.batch_decode(
          outputs_tt, skip_special_tokens=True
      )
      non_padded_sentence = tokenizer.decode(
          output_non_padded[0], skip_special_tokens=True
      )
      padded_sentence = tokenizer.decode(
          output_padded[0], skip_special_tokens=True
      )
  
      expected_output_sentence = [
          "Hello, my dog is a little over a year old and has been diagnosed with a heart murmur",
          "Today, I’m going to talk about the most important thing in the",
      ]
      self.assertListEqual(expected_output_sentence, batch_out_sentence)
      self.assertTrue(
          batch_out_sentence_tt != batch_out_sentence
      )  # token_type_ids should change output
      self.assertListEqual(
          expected_output_sentence,
          [non_padded_sentence, padded_sentence],
      )
  
imports: import datetime
  import unittest
  from transformers import GPTJConfig, is_torch_available
  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, floats_tensor, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 13 for calling the GPTJModelTest obj's underlying member method code with example usage: obj.test_batch_generation() without return types