code: def test_batch_generated_text(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_sentences = [
          "Hello what is",
          "Running a quick test with the",
      ]
      inputs = tokenizer(
          input_sentences,
          return_tensors="pt",
          padding=True,
          truncation=True,
      )
      generated_ids = model.generate(
          inputs["input_ids"].to(torch_device),
          attention_mask=inputs["attention_mask"],
          max_length=20,
      )
      generated_text = tokenizer.batch_decode(
          generated_ids, skip_special_tokens=True
      )
  
      # these generations match those of the PyTorch model
      EXPECTED_GENERATIONS = [
          "Hello what is the best way to get the data from the server? I have tried",
          "Running a quick test with the following command:\nsudo apt-get install python3\nsudo apt-get install python2",
      ]
  
      self.assertListEqual(generated_text, EXPECTED_GENERATIONS)
  
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 17 for calling the BloomModelTest obj's underlying member method code with example usage: obj.test_batch_generated_text() without return types