code: def test_batch_generation(self):
      path_560m = "bigscience/bloom-560m"
      model = BloomForCausalLM.from_pretrained(
          path_560m, use_cache=True, revision="gs555750"
      ).to(torch_device)
      model = model.eval()
      tokenizer = BloomTokenizerFast.from_pretrained(
          path_560m, padding_side="left"
      )
  
      input_sentence = [
          "I enjoy walking with my cute dog",
          "I enjoy walking with my cute dog",
      ]
  
      inputs = tokenizer.batch_encode_plus(
          input_sentence, return_tensors="pt", padding=True
      )
      input_ids = inputs["input_ids"].to(torch_device)
      attention_mask = inputs["attention_mask"]
      greedy_output = model.generate(
          input_ids,
          attention_mask=attention_mask,
          max_length=50,
          do_sample=False,
      )
  
      self.assertEqual(
          tokenizer.decode(greedy_output[0], skip_special_tokens=True),
          tokenizer.decode(greedy_output[1], skip_special_tokens=True),
      )
  
imports: import math
  import unittest
  from transformers import BloomConfig, is_torch_available
  from transformers.testing_utils import require_torch, require_torch_accelerator, slow, torch_device
  from ...generation.test_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 15 for calling the BloomModelTest obj's underlying member method code with example usage: obj.test_batch_generation() without return types