code: def test_batch_generation(self):
      model = self.model
      tokenizer = self.tokenizer
  
      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 am",
      ]
  
      inputs = tokenizer(sentences, return_tensors="pt", padding=True)
      input_ids = inputs["input_ids"].to(torch_device)
  
      outputs = model.generate(
          input_ids=input_ids,
          attention_mask=inputs["attention_mask"].to(torch_device),
      )
  
      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
      )
      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 bit of a kitty. She is a very sweet and loving",
          "Today, I am going to talk about the best way to get a job in the",
      ]
      self.assertListEqual(expected_output_sentence, batch_out_sentence)
      self.assertListEqual(
          expected_output_sentence,
          [non_padded_sentence, padded_sentence],
      )
  
imports: import unittest
  from transformers import GPTNeoConfig, is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  from transformers.utils import cached_property
  from ...generation.test_generation_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
label_desc: Use image node_id 5 for calling the GPTNeoModelLanguageGenerationTest obj's underlying member method code with example usage: obj.test_batch_generation() without return types